pwc-archive/papers-with-abstracts huggingface dataset:
Improving Gender Translation Accuracy with Filtered Self-Training
Authors: Prafulla Kumar Choubey, Anna Currey, Prashant Mathur, Georgiana Dinu
Date: 2021-04-15 00:00:00
Tasks: Machine Translation, Sentence, Translation

Targeted evaluations have found that machine translation systems often output incorrect gender, even when the gender is clear from context. Furthermore, these incorrectly gendered translations have the potential to reflect or amplify social biases. We propose a gender-filtered self-training technique to improve gender translation accuracy on unambiguously gendered inputs. This approach uses a source monolingual corpus and an initial model to generate gender-specific pseudo-parallel corpora which are then added to the training data. We filter the gender-specific corpora on the source and target sides to ensure that sentence pairs contain and correctly translate the specified gender. We evaluate our approach on translation from English into five languages, finding that our models improve gender translation accuracy without any cost to generic translation quality. In addition, we show the viability of our approach on several settings, including re-training from scratch, fine-tuning, controlling the balance of the training data, forward translation, and back-translation.
An exploration of asocial and social learning in the evolution of variable-length structures
Authors: Michael O'Neill, Anthony Brabazon
Date: 2021-04-16 00:00:00

We wish to explore the contribution that asocial and social learning might play as a mechanism for self-adaptation in the search for variable-length structures by an evolutionary algorithm. An extremely challenging, yet simple to understand problem landscape is adopted where the probability of randomly finding a solution is approximately one in a trillion. A number of learning mechanisms operating on variable-length structures are implemented and their performance analysed. The social learning setup, which combines forms of both social and asocial learning in combination with evolution is found to be most performant, while the setups exclusively adopting evolution are incapable of finding solutions.
Explorative Data Analysis of Time Series based AlgorithmFeatures of CMA-ES Variants
Authors: Jacob de Nobel, Hao Wang, Thomas Bäck
Date: 2021-04-16 00:00:00
Tasks: Clustering, feature selection, Time Series, Time Series Analysis

In this study, we analyze behaviours of the well-known CMA-ES by extracting the time-series features on its dynamic strategy parameters. An extensive experiment was conducted on twelve CMA-ES variants and 24 test problems taken from the BBOB (Black-Box Optimization Bench-marking) testbed, where we used two different cutoff times to stop those variants. We utilized the tsfresh package for extracting the features and performed the feature selection procedure using the Boruta algorithm, resulting in 32 features to distinguish either CMA-ES variants or the problems. After measuring the number of predefined targets reached by those variants, we contrive to predict those measured values on each test problem using the feature. From our analysis, we saw that the features can classify the CMA-ES variants, or the function groups decently, and show a potential for predicting the performance of those variants. We conducted a hierarchical clustering analysis on the test problems and noticed a drastic change in the clustering outcome when comparing the longer cutoff time to the shorter one, indicating a huge change in search behaviour of the algorithm. In general, we found that with longer time series, the predictive power of the time series features increase.
Evolving and Merging Hebbian Learning Rules: Increasing Generalization by Decreasing the Number of Rules
Authors: Joachim Winther Pedersen, Sebastian Risi
Date: 2021-04-16 00:00:00
Tasks: Clustering

Generalization to out-of-distribution (OOD) circumstances after training remains a challenge for artificial agents. To improve the robustness displayed by plastic Hebbian neural networks, we evolve a set of Hebbian learning rules, where multiple connections are assigned to a single rule. Inspired by the biological phenomenon of the genomic bottleneck, we show that by allowing multiple connections in the network to share the same local learning rule, it is possible to drastically reduce the number of trainable parameters, while obtaining a more robust agent. During evolution, by iteratively using simple K-Means clustering to combine rules, our Evolve and Merge approach is able to reduce the number of trainable parameters from 61,440 to 1,920, while at the same time improving robustness, all without increasing the number of generations used. While optimization of the agents is done on a standard quadruped robot morphology, we evaluate the agents' performances on slight morphology modifications in a total of 30 unseen morphologies. Our results add to the discussion on generalization, overfitting and OOD adaptation. To create agents that can adapt to a wider array of unexpected situations, Hebbian learning combined with a regularising "genomic bottleneck" could be a promising research direction.
Testing for Outliers with Conformal p-values
Authors: Stephen Bates, Emmanuel Candès, Lihua Lei, Yaniv Romano, Matteo Sesia
Date: 2021-04-16 00:00:00
Tasks: Outlier Detection, valid

This paper studies the construction of p-values for nonparametric outlier detection, taking a multiple-testing perspective. The goal is to test whether new independent samples belong to the same distribution as a reference data set or are outliers. We propose a solution based on conformal inference, a broadly applicable framework which yields p-values that are marginally valid but mutually dependent for different test points. We prove these p-values are positively dependent and enable exact false discovery rate control, although in a relatively weak marginal sense. We then introduce a new method to compute p-values that are both valid conditionally on the training data and independent of each other for different test points; this paves the way to stronger type-I error guarantees. Our results depart from classical conformal inference as we leverage concentration inequalities rather than combinatorial arguments to establish our finite-sample guarantees. Furthermore, our techniques also yield a uniform confidence bound for the false positive rate of any outlier detection algorithm, as a function of the threshold applied to its raw statistics. Finally, the relevance of our results is demonstrated by numerical experiments on real and simulated data.
Jointly Modeling and Clustering Tensors in High Dimensions
Authors: Biao Cai, Jingfei Zhang, Will Wei Sun
Date: 2021-04-15 00:00:00
Tasks: Benchmarking, Clustering, Dimensionality Reduction, Vocal Bursts Intensity Prediction

We consider the problem of jointly modeling and clustering populations of tensors by introducing a high-dimensional tensor mixture model with heterogeneous covariances. To effectively tackle the high dimensionality of tensor objects, we employ plausible dimension reduction assumptions that exploit the intrinsic structures of tensors such as low-rankness in the mean and separability in the covariance. In estimation, we develop an efficient high-dimensional expectation-conditional-maximization (HECM) algorithm that breaks the intractable optimization in the M-step into a sequence of much simpler conditional optimization problems, each of which is convex, admits regularization and has closed-form updating formulas. Our theoretical analysis is challenged by both the non-convexity in the EM-type estimation and having access to only the solutions of conditional maximizations in the M-step, leading to the notion of dual non-convexity. We demonstrate that the proposed HECM algorithm, with an appropriate initialization, converges geometrically to a neighborhood that is within statistical precision of the true parameter. The efficacy of our proposed method is demonstrated through comparative numerical experiments and an application to a medical study, where our proposal achieves an improved clustering accuracy over existing benchmarking methods.
Hierarchical Topic Presence Models
Authors: Jason Wang, Robert E. Weiss
Date: 2021-04-16 00:00:00
Tasks: Data Augmentation, Topic Models

Topic models analyze text from a set of documents. Documents are modeled as a mixture of topics, with topics defined as probability distributions on words. Inferences of interest include the most probable topics and characterization of a topic by inspecting the topic's highest probability words. Motivated by a data set of web pages (documents) nested in web sites, we extend the Poisson factor analysis topic model to hierarchical topic presence models for analyzing text from documents nested in known groups. We incorporate an unknown binary topic presence parameter for each topic at the web site and/or the web page level to allow web sites and/or web pages to be sparse mixtures of topics and we propose logistic regression modeling of topic presence conditional on web site covariates. We introduce local topics into the Poisson factor analysis framework, where each web site has a local topic not found in other web sites. Two data augmentation methods, the Chinese table distribution and P\'{o}lya-Gamma augmentation, aid in constructing our sampler. We analyze text from web pages nested in United States local public health department web sites to abstract topical information and understand national patterns in topic presence.
Faithfully Explainable Recommendation via Neural Logic Reasoning
Authors: Yaxin Zhu, Yikun Xian, Zuohui Fu, Gerard de Melo, Yongfeng Zhang
Date: 2021-04-16 00:00:00
Proceeding: NAACL 2021 4
Tasks: Decision Making, Explainable Recommendation, Explanation Generation, Knowledge Graphs, Recommendation Systems

Knowledge graphs (KG) have become increasingly important to endow modern recommender systems with the ability to generate traceable reasoning paths to explain the recommendation process. However, prior research rarely considers the faithfulness of the derived explanations to justify the decision making process. To the best of our knowledge, this is the first work that models and evaluates faithfully explainable recommendation under the framework of KG reasoning. Specifically, we propose neural logic reasoning for explainable recommendation (LOGER) by drawing on interpretable logical rules to guide the path reasoning process for explanation generation. We experiment on three large-scale datasets in the e-commerce domain, demonstrating the effectiveness of our method in delivering high-quality recommendations as well as ascertaining the faithfulness of the derived explanation.
Semi-Supervised Regression with Generative Adverserial Networks for End to End Learning in Autonomous Driving
Authors: Mehdi Rezagholizadeh, Md Akmal Haidar
Date: 2018-11-13 00:00:00
Tasks: Autonomous Driving, regression

This research concerns solving the semi-supervised learning problem with generative adversarial networks for regression. In contrast to classification, where only a limited number of distinct classes is given, the regression task is defined as predicting continuous labels for a given dataset. Semi-supervised learning is of vital importance for the applications where a small number of labeled samples is available, or labeling samples is difficult or expensive to collect. A case in point is autonomous driving in which obtaining sufficient labeled samples covering all driving conditions is costly. In this context, we can take advantage of semisupervised learning techniques with groundbreaking generative models, such as generative adversarial networks. However, almost all proposed GAN-based semisupervised techniques in the literature are focused on solving the classification problem. Hence, developing a GAN-based semi-supervised method for the regression task is still an open problem. To address this problem, we introduce Reg-GAN in two different architectures. In summary, our proposed method is able to predict continuous labels for a training dataset which has only a limited number of labeled samples. Moreover, the application of this technique for solving the end-to-end task in autonomous driving will be presented. We performed several experiments on a publicly available driving dataset to evaluate our proposed method, and the results are very promising. The results show that our approach generates images with high quality, gives smaller label prediction error and leads to a more stable training compared with the state-of-the-art Improved GAN technique (Salimans et al., 2016).
NV-Tree: Reducing Consistency Cost for NVM-based Single Level Systems
Authors: Jun Yang, Qingsong Wei, Cheng Chen, Chundong Wang, and Khai Leong Yong, Data Storage Institute, A-STAR; Bingsheng He, Nanyang Technological University
Date: 2015-04-16 00:00:00
Tasks: CPU

The non-volatile memory (NVM) has DRAM-like performance and disk-like persistency which make it possible to replace both disk and DRAM to build single level systems. To keep data consistency in such systems is non-trivial because memory writes may be reordered by CPU and memory controller. In this paper, we study the consistency cost for an important and common data structure, B+Tree. Although the memory fence and CPU cacheline flush instructions can order memory writes to achieve data consistency, they introduce a significant overhead (more than 10X slower in performance). Based on our quantitative analysis of consistency cost, we propose NV-Tree, a consistent and cache-optimized B+Tree variant with reduced CPU cacheline flush. We implement and evaluate NV-Tree and NV-Store, a key-value store based on NV-Tree, on an NVDIMM server. NVTree outperforms the state-of-art consistent tree structures by up to 12X under write-intensive workloads. NV-Store increases the throughput by up to 4.8X under YCSB workloads compared to Redis.
Condition for Perfect Dimensionality Recovery by Variational Bayesian PCA
Authors: Shinichi Nakajima, Ryota Tomioka, Masashi Sugiyama, S. Derin Babacan
Date: 2015-12-15 00:00:00

Having shown its good performance in many applications, variational Bayesian (VB) learning is known to be one of the best tractable approximations to Bayesian learning. However, its performance was not well understood theoretically. In this paper, we clarify the behavior of VB learning in probabilistic PCA (or fully-observed matrix factorization). More specifically, we establish a necessary and sufficient condition for perfect dimensionality (or rank) recovery in the large-scale limit when the matrix size goes to infinity. Our result theoretically guarantees the performance of VB-PCA. At the same time, it also reveals the conservative nature of VB learning--- it offers a low false positive rate at the expense of low sensitivity. By contrasting with an alternative dimensionality selection method, we characterize VB learning in PCA. In our analysis, we obtain bounds of the noise variance estimator, and a new and simple analytic-form solution for the other parameters, which themselves are useful for implementation of VB-PCA.
An Overview of Digital Twins Application Domains in Smart Energy Grid
Authors: Tudor Cioara, Ionut Anghel, Marcel Antal, Ioan Salomie, Claudia Antal, Arcas Gabriel Ioan
Date: 2021-04-16 00:00:00
Tasks: Cloud Computing, energy management, Management

The Digital Twins offer promising solutions for smart grid challenges related to the optimal operation, management, and control of energy assets, for safe and reliable distribution of energy. These challenges are more pressing nowadays than ever due to the large-scale adoption of distributed renewable resources at the edge of the grid. The digital twins are leveraging technologies such as the Internet of Things, big data analytics, machine learning, and cloud computing, to analyze data from different energy sensors, view and verify the status of physical energy assets and extract useful information to predict and optimize the assets performance. In this paper, we will provide an overview of the Digital Twins application domains in the smart grid while analyzing existing the state of the art literature. We have focused on the following application domains: energy asset modeling, fault and security diagnosis, operational optimization, and business models. Most of the relevant literature approaches found are published in the last three years showing that the domain of Digital Twins application in smart grid is hot and gradually developing. Anyway, there is no unified view on the Digital Twins implementation and integration with energy management processes, thus, much work still needs to be done to understand and automatize the smart grid management.
Welfare Measure for Resource Allocation with Algorithmic Implementation: Beyond Average and Max-Min
Authors: Ezra Tampubolon, Holger Boche
Date: 2021-04-16 00:00:00
Tasks: Fairness

In this work, we propose an axiomatic approach for measuring the performance/welfare of a system consisting of concurrent agents in a resource-driven system. Our approach provides a unifying view on popular system optimality principles, such as the maximal average/total utilities and the max-min fairness. Moreover, it gives rise to other system optimality notions that have not been fully exploited yet, such as the maximal lowest total subgroup utilities. For the axiomatically defined welfare measures, we provide a generic gradient-based method to find an optimal resource allocation and present a theoretical guarantee for its success. Lastly, we demonstrate the power of our approach through the power control application in wireless networks.
Scaling Beyond Bandwidth Limitations: Wireless Control With Stability Guarantees Under Overload
Authors: Fabian Mager, Dominik Baumann, Carsten Herrmann, Sebastian Trimpe, Marco Zimmerling
Date: 2021-04-16 00:00:00
Tasks: Self-Driving Cars

An important class of cyber-physical systems relies on multiple agents that jointly perform a task by coordinating their actions over a wireless network. Examples include self-driving cars in intelligent transportation and production robots in smart manufacturing. However, the scalability of existing control-over-wireless solutions is limited as they cannot resolve overload situations in which the communication demand exceeds the available bandwidth. This paper presents a novel co-design of distributed control and wireless communication that overcomes this limitation by dynamically allocating the available bandwidth to agents with the greatest need to communicate. Experiments on a real cyber-physical testbed with 20 agents, each consisting of a low-power wireless embedded device and a cart-pole system, demonstrate that our solution achieves significantly better control performance under overload than the state of the art. We further prove that our co-design guarantees closed-loop stability for physical systems with stochastic linear time-invariant dynamics.
Iterative Model Predictive Control for Piecewise Systems
Authors: Ugo Rosolia, Aaron D. Ames
Date: 2021-04-16 00:00:00
Tasks: model, Model Predictive Control

In this paper, we present an iterative Model Predictive Control (MPC) design for piecewise nonlinear systems. We consider finite time control tasks where the goal of the controller is to steer the system from a starting configuration to a goal state while minimizing a cost function. First, we present an algorithm that leverages a feasible trajectory that completes the task to construct a control policy which guarantees that state and input constraints are recursively satisfied and that the closed-loop system reaches the goal state in finite time. Utilizing this construction, we present a policy iteration scheme that iteratively generates safe trajectories which have non-decreasing performance. Finally, we test the proposed strategy on a discretized Spring Loaded Inverted Pendulum (SLIP) model with massless legs. We show that our methodology is robust to changes in initial conditions and disturbances acting on the system. Furthermore, we demonstrate the effectiveness of our policy iteration algorithm in a minimum time control task.
Handling Initial Conditions in Vector Fitting for Real Time Modeling of Power System Dynamics
Authors: Tommaso Bradde, Samuel Chevalier, Marco De Stefano, Stefano Grivet-Talocia, Luca Daniel
Date: 2020-11-25 00:00:00

This paper develops a predictive modeling algorithm, denoted as Real-Time Vector Fitting (RTVF), which is capable of approximating the real-time linearized dynamics of multi-input multi-output (MIMO) dynamical systems via rational transfer function matrices. Based on a generalization of the well-known Time-Domain Vector Fitting (TDVF) algorithm, RTVF is suitable for online modeling of dynamical systems which experience both initial-state decay contributions in the measured output signals and concurrently active input signals. These adaptations were specifically contrived to meet the needs currently present in the electrical power systems community, where real-time modeling of low frequency power system dynamics is becoming an increasingly coveted tool by power system operators. After introducing and validating the RTVF scheme on synthetic test cases, this paper presents a series of numerical tests on high-order closed-loop generator systems in the IEEE 39-bus test system.
Data-Driven Model Predictive Control with Stability and Robustness Guarantees
Authors: Julian Berberich, Johannes Köhler, Matthias A. Müller, Frank Allgöwer
Date: 2019-06-11 00:00:00
Tasks: Model Predictive Control

We propose a robust data-driven model predictive control (MPC) scheme to control linear time-invariant (LTI) systems. The scheme uses an implicit model description based on behavioral systems theory and past measured trajectories. In particular, it does not require any prior identification step, but only an initially measured input-output trajectory as well as an upper bound on the order of the unknown system. First, we prove exponential stability of a nominal data-driven MPC scheme with terminal equality constraints in the case of no measurement noise. For bounded additive output measurement noise, we propose a robust modification of the scheme, including a slack variable with regularization in the cost. We prove that the application of this robust MPC scheme in a multi-step fashion leads to practical exponential stability of the closed loop w.r.t. the noise level. The presented results provide the first (theoretical) analysis of closed-loop properties, resulting from a simple, purely data-driven MPC scheme.
A Practical Assessment of the Power Grid Inertia Constant Using PMUs
Authors: Mohammadali Hayerikhiyavi, Aleksandar Dimitrovski
Date: 2021-04-16 00:00:00

Installation of phasor measurement units (PMUs) in a number of substations in the power grid can help assess a set of its values and parameters, in particular those related to the dynamics when disturbances occur in the system. Inertia constant of the power grid is one of the system stability related parameters that is essential for planning of the system spinning reserve. Estimates of the grid inertia constant require precise information of the system frequency at the time of disturbance. In this paper, an improved method for such estimate is presented, which is based on the derived data of frequency variations obtained from PMUs. In addition, a way to obtain the appropriate interval for the lowest error estimate is discussed. The method has been applied to assess the inertia constant of the Iranian power grid, by obtaining the rate of change of frequency after a disturbance in the defined interval, and the rate of specified power imbalance between electrical power input and output.