pwc-archive/papers-with-abstracts huggingface dataset:
Structural Bias for Aspect Sentiment Triplet Extraction
Authors: Chen Zhang, Lei Ren, Fang Ma, Jingang Wang, Wei Wu, Dawei Song
Date: 2022-09-02 00:00:00
Proceeding: COLING 2022 10
Tasks: Aspect Sentiment Triplet Extraction, Benchmarking, Triplet

Structural bias has recently been exploited for aspect sentiment triplet extraction (ASTE) and led to improved performance. On the other hand, it is recognized that explicitly incorporating structural bias would have a negative impact on efficiency, whereas pretrained language models (PLMs) can already capture implicit structures. Thus, a natural question arises: Is structural bias still a necessity in the context of PLMs? To answer the question, we propose to address the efficiency issues by using an adapter to integrate structural bias in the PLM and using a cheap-to-compute relative position structure in place of the syntactic dependency structure. Benchmarking evaluation is conducted on the SemEval datasets. The results show that our proposed structural adapter is beneficial to PLMs and achieves state-of-the-art performance over a range of strong baselines, yet with a light parameter demand and low latency. Meanwhile, we give rise to the concern that the current evaluation default with data of small scale is under-confident. Consequently, we release a large-scale dataset for ASTE. The results on the new dataset hint that the structural adapter is confidently effective and efficient to a large scale. Overall, we draw the conclusion that structural bias shall still be a necessity even with PLMs.
Random Text Perturbations Work, but not Always
Authors: Zhengxiang Wang
Date: 2022-09-02 00:00:00
Tasks: Classification, Data Augmentation, Text Matching

We present three large-scale experiments on binary text matching classification task both in Chinese and English to evaluate the effectiveness and generalizability of random text perturbations as a data augmentation approach for NLP. It is found that the augmentation can bring both negative and positive effects to the test set performance of three neural classification models, depending on whether the models train on enough original training examples. This remains true no matter whether five random text editing operations, used to augment text, are applied together or separately. Our study demonstrates with strong implication that the effectiveness of random text perturbations is task specific and not generally positive.
In conversation with Artificial Intelligence: aligning language models with human values
Authors: Atoosa Kasirzadeh, Iason Gabriel
Date: 2022-09-01 00:00:00

Large-scale language technologies are increasingly used in various forms of communication with humans across different contexts. One particular use case for these technologies is conversational agents, which output natural language text in response to prompts and queries. This mode of engagement raises a number of social and ethical questions. For example, what does it mean to align conversational agents with human norms or values? Which norms or values should they be aligned with? And how can this be accomplished? In this paper, we propose a number of steps that help answer these questions. We start by developing a philosophical analysis of the building blocks of linguistic communication between conversational agents and human interlocutors. We then use this analysis to identify and formulate ideal norms of conversation that can govern successful linguistic communication between humans and conversational agents. Furthermore, we explore how these norms can be used to align conversational agents with human values across a range of different discursive domains. We conclude by discussing the practical implications of our proposal for the design of conversational agents that are aligned with these norms and values.
Local Optimization Often is Ill-conditioned in Genetic Programming for Symbolic Regression
Authors: Gabriel Kronberger
Date: 2022-09-02 00:00:00
Tasks: regression, Symbolic Regression

Gradient-based local optimization has been shown to improve results of genetic programming (GP) for symbolic regression. Several state-of-the-art GP implementations use iterative nonlinear least squares (NLS) algorithms such as the Levenberg-Marquardt algorithm for local optimization. The effectiveness of NLS algorithms depends on appropriate scaling and conditioning of the optimization problem. This has so far been ignored in symbolic regression and GP literature. In this study we use a singular value decomposition of NLS Jacobian matrices to determine the numeric rank and the condition number. We perform experiments with a GP implementation and six different benchmark datasets. Our results show that rank-deficient and ill-conditioned Jacobian matrices occur frequently and for all datasets. The issue is less extreme when restricting GP tree size and when using many non-linear functions in the function set.
Cooperative coevolutionary Modified Differential Evolution with Distance-based Selection for Large-Scale Optimization Problems in noisy environments through an automatic Random Grouping
Authors: Rui Zhong, Masaharu Munetomo
Date: 2022-09-02 00:00:00

Many optimization problems suffer from noise, and nonlinearity check-based decomposition methods (e.g. Differential Grouping) will completely fail to detect the interactions between variables in multiplicative noisy environments, thus, it is difficult to decompose the large-scale optimization problems (LSOPs) in noisy environments. In this paper, we propose an automatic Random Grouping (aRG), which does not need any explicit hyperparameter specified by users. Simulation experiments and mathematical analysis show that aRG can detect the interactions between variables without the fitness landscape knowledge, and the sub-problems decomposed by aRG have smaller scales, which is easier for EAs to optimize. Based on the cooperative coevolution (CC) framework, we introduce an advanced optimizer named Modified Differential Evolution with Distance-based Selection (MDE-DS) to enhance the search ability in noisy environments. Compared with canonical DE, the parameter self-adaptation, the balance between diversification and intensification, and the distance-based probability selection endow MDE-DS with stronger ability in exploration and exploitation. To evaluate the performance of our proposal, we design $500$-D and $1000$-D problems with various separability in noisy environments based on the CEC2013 LSGO Suite. Numerical experiments show that our proposal has broad prospects to solve LSOPs in noisy environments and can be easily extended to higher-dimensional problems.
An Interpretable and Efficient Infinite-Order Vector Autoregressive Model for High-Dimensional Time Series
Authors: Yao Zheng
Date: 2022-09-02 00:00:00
Tasks: Time Series, Time Series Analysis

As a special infinite-order vector autoregressive (VAR) model, the vector autoregressive moving average (VARMA) model can capture much richer temporal patterns than the widely used finite-order VAR model. However, its practicality has long been hindered by its non-identifiability, computational intractability, and difficulty of interpretation, especially for high-dimensional time series. This paper proposes a novel sparse infinite-order VAR model for high-dimensional time series, which avoids all above drawbacks while inheriting essential temporal patterns of the VARMA model. As another attractive feature, the temporal and cross-sectional structures of the VARMA-type dynamics captured by this model can be interpreted separately, since they are characterized by different sets of parameters. This separation naturally motivates the sparsity assumption on the parameters determining the cross-sectional dependence. As a result, greater statistical efficiency and interpretability can be achieved with little loss of temporal information. We introduce two $\ell_1$-regularized estimation methods for the proposed model, which can be efficiently implemented via block coordinate descent algorithms, and derive the corresponding nonasymptotic error bounds. A consistent model order selection method based on the Bayesian information criteria is also developed. The merit of the proposed approach is supported by simulation studies and a real-world macroeconomic data analysis.
A taxonomy of surprise definitions
Authors: Alireza Modirshanechi, Johanni Brea, Wulfram Gerstner
Date: 2022-09-02 00:00:00
Tasks: Change Point Detection, Decision Making

Surprising events trigger measurable brain activity and influence human behavior by affecting learning, memory, and decision-making. Currently there is, however, no consensus on the definition of surprise. Here we identify 18 mathematical definitions of surprise in a unifying framework. We first propose a technical classification of these definitions into three groups based on their dependence on an agent's belief, show how they relate to each other, and prove under what conditions they are indistinguishable. Going beyond this technical analysis, we propose a taxonomy of surprise definitions and classify them into four conceptual categories based on the quantity they measure: (i) 'prediction surprise' measures a mismatch between a prediction and an observation; (ii) 'change-point detection surprise' measures the probability of a change in the environment; (iii) 'confidence-corrected surprise' explicitly accounts for the effect of confidence; and (iv) 'information gain surprise' measures the belief-update upon a new observation. The taxonomy poses the foundation for principled studies of the functional roles and physiological signatures of surprise in the brain.
Active learning-assisted neutron spectroscopy with log-Gaussian processes
Authors: Mario Teixeira Parente, Georg Brandl, Christian Franz, Uwe Stuhr, Marina Ganeva, Astrid Schneidewind
Date: 2022-09-02 00:00:00
Tasks: Active Learning, Gaussian Processes

Neutron scattering experiments at three-axes spectrometers (TAS) investigate magnetic and lattice excitations by measuring intensity distributions to understand the origins of materials properties. The high demand and limited availability of beam time for TAS experiments however raise the natural question whether we can improve their efficiency and make better use of the experimenter's time. In fact, there are a number of scientific problems that require searching for signals, which may be time consuming and inefficient if done manually due to measurements in uninformative regions. Here, we describe a probabilistic active learning approach that not only runs autonomously, i.e., without human interference, but can also directly provide locations for informative measurements in a mathematically sound and methodologically robust way by exploiting log-Gaussian processes. Ultimately, the resulting benefits can be demonstrated on a real TAS experiment and a benchmark including numerous different excitations.
Analyzing and visualizing polarization and balance with signed networks: the U.S. Congress case study
Authors: Arthur Capozzi, Alfonso Semeraro, Giancarlo Ruffo
Date: 2022-09-01 00:00:00

Signed networks and balance theory provide a natural setting for real-world scenarios that show polarization dynamics, positive/negative relationships, and political partisanship. For example, they have been proven effective in studying the increasing polarization of the votes in the two chambers of the U.S. Congress from World War II on. To provide further insights into this particular case study, we propose the application of a pipeline to analyze and visualize a signed graph's configuration based on the exploitation of the corresponding Laplacian matrix' spectral properties. The overall methodology is comparable with others based on the frustration index, but it has at least two main advantages: first, it requires a much lower computational cost; second, it allows for a quantitative and visual assessment of how arbitrarily small subgraphs (even single nodes) contribute to the overall balance (or unbalance) of the network. The proposed pipeline allows the exploration of polarization dynamics shown by the U.S. Congress from 1945 to 2020 at different resolution scales. In fact, we are able to spot and point out the influence of some (groups of) congressmen in the overall balance, as well as to observe and explore polarization's evolution of both chambers across the years.
Phase I Analysis of Individual Observations with Missing Data
Authors: Mahmoud A. Mahmoud, Nesma A. Saleh and Doaa F. Madbuly
Date: 2013-03-14 00:00:00
Proceeding: Qual. Reliab. Engng. Int. 2013 3
Tasks: Missing Values, regression

The effect of the methods for handling missing values on the performance of Phase I multivariate control charts has not
been investigated. In this paper, we discuss the effect of four imputationmethods:mean substitution, regression, stochastic
regression and the expectation maximization algorithm. Estimates of mean vector and variance covariance matrix from the
treated data set are used to estimate the unknown parameters in the Hotelling’s T2 chart statistic. Based on a Monte Carlo
simulation study, the performance of each of the four methods is investigated in terms of its ability to obtain the nominal
in-control and out-of-control overall probability of a signal. We consider three sample sizes, five levels of the percentage of
missing values and three types of variable numbers. Our simulation results show that the stochastic regression method has
the best overall performance among all the competing methods.
A Wavelet Transform Based Scheme to Extract Speech Pitch and Formant Frequencies
Authors: Seyedamiryousef Hosseini Goki, Mahdieh Ghazvini, Sajad Hamzenejadi
Date: 2022-09-01 00:00:00
Tasks: speech-recognition, Speech Recognition

Pitch and Formant frequencies are important features in speech processing applications. The period of the vocal cord's output for vowels is known as the pitch or the fundamental frequency, and formant frequencies are essentially resonance frequencies of the vocal tract. These features vary among different persons and even words, but they are within a certain frequency range. In practice, just the first three formants are enough for the most of speech processing. Feature extraction and classification are the main components of each speech recognition system. In this article, two wavelet based approaches are proposed to extract the mentioned features with help of the filter bank idea. By comparing the results of the presented feature extraction methods on several speech signals, it was found out that the wavelet transform has a good accuracy compared to the cepstrum method and it has no sensitivity to noise. In addition, several fuzzy based classification techniques for speech processing are reviewed.
The Syncline Model -- Analyzing the Impact of Time Synchronization in Sensor Fusion
Authors: Erling Rennemo Jellum, Torleiv Håland Bryne, Tor Arne Johansen, Milica Orlandíc
Date: 2022-09-02 00:00:00
Tasks: Sensor Fusion

The accuracy of sensor fusion algorithms are limited by either the intrinsic sensor noise, or by the quality of time synchronization of the sensors. While the intrinsic sensor noise only depends on the respective sensors, the error induced by quality of, or lack of, synchronization depends on the dynamics of the vehicles and robotic system and the magnitude of time synchronization errors. To meet their sensor fusion requirements, system designers must consider both which sensor to use and also how to synchronize them. This paper presents the Syncline model, a simple visual model of how time synchronization affects the accuracy of sensor fusion for different mobile robot platform. The model can serve as a simple tool to determine which synchronization mechanisms should be used.
A Universal Framework for Generalized Run Time Assurance with JAX Automatic Differentiation
Authors: Umberto Ravaioli, Kyle Dunlap, Kerianne Hobbs
Date: 2022-09-02 00:00:00
Tasks: Position

With the rise of increasingly complex autonomous systems powered by black box AI models, there is a growing need for Run Time Assurance (RTA) systems that provide online safety filtering to untrusted primary controller output. Currently, research in RTA tends to be ad hoc and inflexible, diminishing collaboration and the pace of innovation. The Safe Autonomy Run Time Assurance Framework presented in this paper provides a standardized interface for RTA modules and a set of universal implementations of constraint-based RTA capable of providing safety assurance given arbitrary dynamical systems and constraints. Built around JAX, this framework leverages automatic differentiation to populate advanced optimization based RTA methods minimizing user effort and error. To validate the feasibility of this framework, a simulation of a multi-agent spacecraft inspection problem is shown with safety constraints on position and velocity.
Robustifying Event-Triggered Control to Measurement Noise
Authors: Koen J. A. Scheres, Romain Postoyan, W. P. Maurice H. Heemels
Date: 2022-09-02 00:00:00

While many event-triggered control strategies are available in the literature, most of them are designed ignoring the presence of measurement noise. As measurement noise is omnipresent in practice and can have detrimental effects, for instance, by inducing Zeno behavior in the closed-loop system and with that the lack of a positive lower bound on the inter-event times, rendering the event-triggered control design practically useless, it is of great importance to address this gap in the literature. To do so, we present a general framework for set stabilization of (distributed) event-triggered control systems affected by additive measurement noise. It is shown that, under general conditions, Zeno-free static as well as dynamic triggering rules can be designed such that the closed-loop system satisfies an input-to-state practical set stability property. We ensure Zeno-freeness by proving the existence of a uniform strictly positive lower-bound on the minimum inter-event time. The general framework is applied to point stabilization and consensus problems as particular cases, where we show that, under similar assumptions as the original work, existing schemes can be redesigned to robustify them to measurement noise. Consequently, using this framework, noise-robust triggering conditions can be designed both from the ground up and by simple redesign of several important existing schemes. Simulation results are provided that illustrate the strengths of this novel approach.
A 0.6V$-$1.8V Compact Temperature Sensor with 0.24°C Resolution, $\pm$1.4°C Inaccuracy and 1.06nJ per Conversion
Authors: Benjamin Zambrano, Esteban Garzón, Sebastiano Strangio, Giuseppe Iannaccone, Marco Lanuzza
Date: 2022-09-02 00:00:00

This paper presents a fully-integrated CMOS temperature sensor for densely-distributed thermal monitoring in systems on chip supporting dynamic voltage and frequency scaling. The sensor front-end exploits a sub-threshold PMOS-based circuit to convert the local temperature into two biasing currents. These are then used to define two oscillation frequencies, whose ratio is proportional to absolute-temperature. Finally, the sensor back-end translates such frequency ratio into the digital temperature code. Thanks to its low-complexity architecture, the proposed design achieves a very compact footprint along with low-power consumption and high accuracy in a wide temperature range. Moreover, thanks to a simple embedded line regulation mechanism, our sensor supports voltage-scalability. The design was prototyped in a 180nm CMOS technology with a 0{\deg}C $-$ 100{\deg}C temperature detection range, a very wide supply voltage operating range from 0.6V up to 1.8V and very small silicon area occupation of just 0.021$mm^2$. Experimental measurements performed on 20 test chips have shown very competitive figures of merit, including a resolution of 0.24{\deg}C, an inaccuracy of $\pm$1.4{\deg}C, a sampling rate of about 1.5kHz and an energy per conversion of 1.06nJ at 30{\deg}C.
Weather-Driven Flexibility Reserve Procurement: A NYISO Offshore Wind Power Case Study
Authors: Zhirui Liang, Robert Mieth, Yury Dvorkin, Miguel A. Ortega-Vazquez
Date: 2022-09-01 00:00:00

The growing penetration of variable renewable energy sources (VRES) requires additional flexibility reserve to ensure reliable power system operations. Current industry practice typically assumes a certain fraction of the VRES power production forecast as flexibility reserve, thus ignoring other relevant information, such as weather conditions. To address this, probability- and risk-based reserve sizing models have been proposed, which use probabilistic VRES power forecasts that mostly rely on historical forecast and actual VRES power data for model training. Hence, these approaches are not suitable for planned or newly installed wind farms, where no or insufficient historical data is available. This paper addresses this caveat. First, we propose a weather-driven probabilistic forecasting method for wind power installations using publicly available weather data. Second, we apply the resulting probabilistic forecasts to a novel risk-based flexibility reserve sizing model that is compatible with the current reserve procuring pipeline used by US ISOs. Finally, we compare the risk-based reserve requirements to industry practice, state-of-the-art reserve procurement methods, and a weather-ignorant benchmark with respect to system cost and security. Our results are obtained from real-world data on a 1819-bus NYISO system model with both on- and projected off-shore wind power installations, which highlight the usefulness of weather information wind power forecasting and demonstrate efficiency gains from risk-aware reserve procurement.
Final-state, Open-loop Control of Parabolic PDEs with Dirichlet Boundary Conditions
Authors: Gilberto O. Corrêa, Marlon M. López-Flores, Alexandre L. Madureira
Date: 2022-09-01 00:00:00

In this paper, a quadratic optimal control problem is considered for second-order parabolic PDEs with homogeneous Dirichlet boundary conditions, in which the "point" control function (depending only on time) constitutes a source term. These problems involve choosing a control function (with or without "peak-value" constraints) to approximately steer the solution of the PDE in question to a desired function at the end of a prescribed (finite) time-interval. To compute approximations to the desired optimal control functions, semi-discrete, Galerkin approximations to the equation involved are introduced and the corresponding (approximating) control problems are tackled. It is shown that the sequences of solutions to both the constrained and unconstrained approximating (finite-dimensional) control problems converge, respectively, to the optimal solutions of the control problems involving the original initial/boundary value problem. The solution to the unconstrained approximating problem can be quite explicitly characterized, with the main numerical step for its computation requiring only the solution of a Lyapunov equation. Whereas approximate solutions to the constrained control problems can be obtained on the basis of Lagrangian duality and piecewise linear multipliers. These points are worked out in detail and illustrated by numerical examples involving the heat equation (HEq).
The path integral formula for the stochastic evolutionary game dynamics in the Moran process
Authors: Chao Wang
Date: 2022-09-02 00:00:00

The Moran process is one of an basic mathematical structure in the evolutionary game theory. In this work, we introduce the formulation of the path integral approach for evolutionary game theory based on the Moran process. We derive the transition probability by the path integral from the initial state to the final state with updating rule of the Moran process. In this framework, the transition probability is the sum of all the evolutionary paths. The path integral formula of the transition probability maybe expected to be a new mathematical tool to explore the stochastic game evolutionary dynamics.