pwc-archive/papers-with-abstracts huggingface dataset:
Domain-specific MT for Low-resource Languages: The case of Bambara-French
Authors: Allahsera Auguste Tapo, Michael Leventhal, Sarah Luger, Christopher M. Homan, Marcos Zampieri
Date: 2021-03-31 00:00:00
Tasks: BIG-bench Machine Learning, Machine Translation, Translation

Translating to and from low-resource languages is a challenge for machine translation (MT) systems due to a lack of parallel data. In this paper we address the issue of domain-specific MT for Bambara, an under-resourced Mande language spoken in Mali. We present the first domain-specific parallel dataset for MT of Bambara into and from French. We discuss challenges in working with small quantities of domain-specific data for a low-resource language and we present the results of machine learning experiments on this data.
Cross-validation: what does it estimate and how well does it do it?
Authors: Stephen Bates, Trevor Hastie, Robert Tibshirani
Date: 2021-04-01 00:00:00
Tasks: Prediction

Cross-validation is a widely-used technique to estimate prediction error, but its behavior is complex and not fully understood. Ideally, one would like to think that cross-validation estimates the prediction error for the model at hand, fit to the training data. We prove that this is not the case for the linear model fit by ordinary least squares; rather it estimates the average prediction error of models fit on other unseen training sets drawn from the same population. We further show that this phenomenon occurs for most popular estimates of prediction error, including data splitting, bootstrapping, and Mallow's Cp. Next, the standard confidence intervals for prediction error derived from cross-validation may have coverage far below the desired level. Because each data point is used for both training and testing, there are correlations among the measured accuracies for each fold, and so the usual estimate of variance is too small. We introduce a nested cross-validation scheme to estimate this variance more accurately, and we show empirically that this modification leads to intervals with approximately correct coverage in many examples where traditional cross-validation intervals fail.
Gaussian Process Convolutional Dictionary Learning
Authors: Andrew H. Song, Bahareh Tolooshams, Demba Ba
Date: 2021-03-28 00:00:00
Tasks: Dictionary Learning, Gaussian Processes

Convolutional dictionary learning (CDL), the problem of estimating shift-invariant templates from data, is typically conducted in the absence of a prior/structure on the templates. In data-scarce or low signal-to-noise ratio (SNR) regimes, learned templates overfit the data and lack smoothness, which can affect the predictive performance of downstream tasks. To address this limitation, we propose GPCDL, a convolutional dictionary learning framework that enforces priors on templates using Gaussian Processes (GPs). With the focus on smoothness, we show theoretically that imposing a GP prior is equivalent to Wiener filtering the learned templates, thereby suppressing high-frequency components and promoting smoothness. We show that the algorithm is a simple extension of the classical iteratively reweighted least squares algorithm, independent of the choice of GP kernels. This property allows one to experiment flexibly with different smoothness assumptions. Through simulation, we show that GPCDL learns smooth dictionaries with better accuracy than the unregularized alternative across a range of SNRs. Through an application to neural spiking data, we show that GPCDL learns a more accurate and visually-interpretable smooth dictionary, leading to superior predictive performance compared to non-regularized CDL, as well as parametric alternatives.
Enriched Music Representations with Multiple Cross-modal Contrastive Learning
Authors: Andres Ferraro, Xavier Favory, Konstantinos Drossos, Yuntae Kim, Dmitry Bogdanov
Date: 2021-04-01 00:00:00
Tasks: Contrastive Learning, Genre classification

Modeling various aspects that make a music piece unique is a challenging task, requiring the combination of multiple sources of information. Deep learning is commonly used to obtain representations using various sources of information, such as the audio, interactions between users and songs, or associated genre metadata. Recently, contrastive learning has led to representations that generalize better compared to traditional supervised methods. In this paper, we present a novel approach that combines multiple types of information related to music using cross-modal contrastive learning, allowing us to learn an audio feature from heterogeneous data simultaneously. We align the latent representations obtained from playlists-track interactions, genre metadata, and the tracks' audio, by maximizing the agreement between these modality representations using a contrastive loss. We evaluate our approach in three tasks, namely, genre classification, playlist continuation and automatic tagging. We compare the performances with a baseline audio-based CNN trained to predict these modalities. We also study the importance of including multiple sources of information when training our embedding model. The results suggest that the proposed method outperforms the baseline in all the three downstream tasks and achieves comparable performance to the state-of-the-art.
Balancing Fairness and Efficiency in Traffic Routing via Interpolated Traffic Assignment
Authors: Devansh Jalota, Kiril Solovey, Matthew Tsao, Stephen Zoepf, Marco Pavone
Date: 2021-03-31 00:00:00
Tasks: Fairness

System optimum (SO) routing, wherein the total travel time of all users is minimized, is a holy grail for transportation authorities. However, SO routing may discriminate against users who incur much larger travel times than others to achieve high system efficiency, i.e., low total travel times. To address the inherent unfairness of SO routing, we study the ${\beta}$-fair SO problem whose goal is to minimize the total travel time while guaranteeing a ${\beta\geq 1}$ level of unfairness, which specifies the maximum possible ratio between the travel times of different users with shared origins and destinations. To obtain feasible solutions to the ${\beta}$-fair SO problem while achieving high system efficiency, we develop a new convex program, the Interpolated Traffic Assignment Problem (I-TAP), which interpolates between a fairness-promoting and an efficiency-promoting traffic-assignment objective. We evaluate the efficacy of I-TAP through theoretical bounds on the total system travel time and level of unfairness in terms of its interpolation parameter, as well as present a numerical comparison between I-TAP and a state-of-the-art algorithm on a range of transportation networks. The numerical results indicate that our approach is faster by several orders of magnitude as compared to the benchmark algorithm, while achieving higher system efficiency for all desirable levels of unfairness. We further leverage the structure of I-TAP to develop two pricing mechanisms to collectively enforce the I-TAP solution in the presence of selfish homogeneous and heterogeneous users, respectively, that independently choose routes to minimize their own travel costs. We mention that this is the first study of pricing in the context of fair routing for general road networks (as opposed to, e.g., parallel road networks).
Deep Reinforcement Learning for Resource Allocation in Business Processes
Authors: Kamil Żbikowski, Michał Ostapowicz, Piotr Gawrysiak
Date: 2021-03-29 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Assigning resources in business processes execution is a repetitive task that can be effectively automated. However, different automation methods may give varying results that may not be optimal. Proper resource allocation is crucial as it may lead to significant cost reductions or increased effectiveness that results in increased revenues. In this work, we first propose a novel representation that allows modeling of a multi-process environment with different process-based rewards. These processes can share resources that differ in their eligibility. Then, we use double deep reinforcement learning to look for optimal resource allocation policy. We compare those results with two popular strategies that are widely used in the industry. Learning optimal policy through reinforcement learning requires frequent interactions with the environment, so we also designed and developed a simulation engine that can mimic real-world processes. The results obtained are promising. Deep reinforcement learning based resource allocation achieved significantly better results compared to two commonly used techniques.
Consensus-Based Distributed Estimation in the Presence of Heterogeneous, Time-Invariant Delays
Authors: Mohammadreza Doostmohammadian, Usman A. Khan, Mohammad Pirani, Themistoklis Charalambous
Date: 2021-04-01 00:00:00

Classical distributed estimation scenarios typically assume timely and reliable exchanges of information over the sensor network. This paper, in contrast, considers single time-scale distributed estimation via a sensor network subject to transmission time-delays. The proposed discrete-time networked estimator consists of two steps: (i) consensus on (delayed) a-priori estimates, and (ii) measurement update. The sensors only share their a-priori estimates with their out-neighbors over (possibly) time-delayed transmission links. The delays are assumed to be fixed over time, heterogeneous, and known. We assume distributed observability instead of local observability, which significantly reduces the communication/sensing loads on sensors. Using the notions of augmented matrices and Kronecker product, the convergence of the proposed estimator over strongly-connected networks is proved for a specific upper-bound on the time-delay.
Distributed Video Adaptive Block Compressive Sensing
Authors: Joseph Zammit, Ian J Wassell
Date: 2021-04-01 00:00:00
Tasks: Compressive Sensing, Decoder, Video Frame Interpolation

Video block compressive sensing has been studied for use in resource constrained scenarios, such as wireless sensor networks, but the approach still suffers from low performance and long reconstruction time. Inspired by classical distributed video coding, we design a lightweight encoder with computationally intensive operations, such as video frame interpolation, performed at the decoder. Straying from recent trends in training end-to-end neural networks, we propose two algorithms that leverage convolutional neural network components to reconstruct video with greatly reduced reconstruction time. At the encoder, we leverage temporal correlation between frames and deploy adaptive techniques based on compressive measurements from previous frames. At the decoder, we exploit temporal correlation by using video frame interpolation and temporal differential pulse code modulation. Simulations show that our two proposed algorithms, VAL-VFI and VAL-IDA-VFI reconstruct higher quality video, achieving state-of-the-art performance.
Towards Robust Data-Driven Control Synthesis for Nonlinear Systems with Actuation Uncertainty
Authors: Andrew J. Taylor, Victor D. Dorobantu, Sarah Dean, Benjamin Recht, Yisong Yue, Aaron D. Ames
Date: 2020-11-21 00:00:00

Modern nonlinear control theory seeks to endow systems with properties such as stability and safety, and has been deployed successfully across various domains. Despite this success, model uncertainty remains a significant challenge in ensuring that model-based controllers transfer to real world systems. This paper develops a data-driven approach to robust control synthesis in the presence of model uncertainty using Control Certificate Functions (CCFs), resulting in a convex optimization based controller for achieving properties like stability and safety. An important benefit of our framework is nuanced data-dependent guarantees, which in principle can yield sample-efficient data collection approaches that need not fully determine the input-to-state relationship. This work serves as a starting point for addressing important questions at the intersection of nonlinear control theory and non-parametric learning, both theoretical and in application. We validate the proposed method in simulation with an inverted pendulum in multiple experimental configurations.
Interactive spatial speech recognition maps based on simulated speech recognition experiments
Authors: Marc René Schädler
Date: 2021-04-01 00:00:00
Tasks: speech-recognition, Speech Recognition

In their everyday life, the speech recognition performance of human listeners is influenced by diverse factors, such as the acoustic environment, the talker and listener positions, possibly impaired hearing, and optional hearing devices. Prediction models come closer to considering all required factors simultaneously to predict the individual speech recognition performance in complex acoustic environments. While such predictions may still not be sufficiently accurate for serious applications, they can already be performed and demand an accessible representation. In this contribution, an interactive representation of speech recognition performance is proposed, which focuses on the listeners head orientation and the spatial dimensions of an acoustic scene. A exemplary modeling toolchain, including an acoustic rendering model, a hearing device model, and a listener model, was used to generate a data set for demonstration purposes. Using the spatial speech recognition maps to explore this data set demonstrated the suitability of the approach to observe possibly relevant behavior. The proposed representation provides a suitable target to compare and validate different modeling approaches in ecologically relevant contexts. Eventually, it may serve as a tool to use validated prediction models in the design of spaces and devices which take speech communication into account.
Multi-layered simulation relations for linear stochastic systems
Authors: B. C. van Huijgevoort, S. Haesaert
Date: 2021-04-01 00:00:00

The design of provably correct controllers for continuous-state stochastic systems crucially depends on approximate finite-state abstractions and their accuracy quantification. For this quantification, one generally uses approximate stochastic simulation relations, whose constant precision limits the achievable guarantees on the control design. This limitation especially affects higher dimensional stochastic systems and complex formal specifications. This work allows for variable precision by defining a simulation relation that contains multiple precision layers. For bi-layered simulation relations, we develop a robust dynamic programming approach yielding a lower bound on the satisfaction probability of temporal logic specifications. We illustrate the benefit of bi-layered simulation relations for linear stochastic systems in an example.
Exponential Stability and Tuning for a Class of Mechanical Systems
Authors: Carmen Chan-Zheng, Pablo Borja, Nima Monshizadeh, Jacquelien M. A. Scherpen
Date: 2020-11-30 00:00:00

In this paper, we prove the exponential stability property of a class of mechanical systems represented in the port-Hamiltonian framework. To this end, we propose a Lyapunov candidate function different from the Hamiltonian of the system. Moreover, we study how the proposed analysis can be used to determine the exponential stability and the rate of convergence of some (nonlinear)-mechanical systems stabilized by two passivity-based control techniques, namely, PID passivity-based control and interconnection and damping assignment. We implement the former control approach to stabilize a three degrees-of-freedom robotic arm at the desired equilibrium point to illustrate the mentioned analysis.
Hetero-functional Network Minimum Cost Flow Optimization: A Hydrogen-Natural Gas Network Example
Authors: Wester C. H. Schoonenberg, Amro M. Farid
Date: 2021-04-01 00:00:00

Over the past decades, engineering systems have developed as networks of systems that deliver multiple services across multiple domains. This work aims to develop an optimization program for a dynamic, hetero-functional graph theory-based model of an engineering system. The manuscript first introduces a general approach to define a dynamic system model by integrating the device models in the hetero-functional graph theory structural model. To this end, the work leverages Petri net dynamics and the hetero-functional incidence tensor. The respective Petri net-based models are translated into the quadratic program canonical form to finalize the optimization program. The optimization program is demonstrated through the application of the program to a hydrogen-natural gas infrastructure test case. Four distinct scenarios are optimized to demonstrate potential synergies or cascading network effects of policy across infrastructures. This work develops the first hetero-functional graph theory-based optimization program and demonstrates that the program can be used to optimize flows across a multi-operand network, transform the operands in the network, store operands over time, analyze the behavior for a quadratic cost function, and implement it for a generic, continuous, large flexible engineering systems of arbitrary topology.
On the relationship between data-enabled predictive control and subspace predictive control
Authors: Felix Fiedler, Sergio Lucia
Date: 2020-11-27 00:00:00

Data-enabled predictive control (DeePC) is a recently proposed approach that combines system identification, estimation and control in a single optimization problem, for which only recorded input/output data of the examined system is required. The same premise holds for the subspace predictive control (SPC) method in which a multi-step prediction model is identified from the same data as required for DeePC. This model is then used to formulate a similar optimal control problem. In this work we investigate the relationship between DeePC and SPC. Our primary contribution is to show that SPC is equivalent to DeePC in the deterministic case. We also show the equivalence of both methods in a special case for the non-deterministic formulation. We investigate the advantages and shortcomings of DeePC as opposed to SPC with and without measurement noise and illustrate them with a simulation example.
Intuitive Tasks Planning Using Visuo-Tactile Perception for Human Robot Cooperation
Authors: Sunny Katyara, Fanny Ficuciello, Tao Teng, Fei Chen, Bruno Siciliano, Darwin G. Caldwell
Date: 2021-04-01 00:00:00

Designing robotic tasks for co-manipulation necessitates to exploit not only proprioceptive but also exteroceptive information for improved safety and autonomy. Following such instinct, this research proposes to formulate intuitive robotic tasks following human viewpoint by incorporating visuo-tactile perception. The visual data using depth cameras surveils and determines the object dimensions and human intentions while the tactile sensing ensures to maintain the desired contact to avoid slippage. Experiment performed on robot platform with human assistance under industrial settings validates the performance and applicability of proposed intuitive task formulation.
A Comprehensive Optimization Method for Commercial Building Loads with Renewable Generation and Energy Storage from Utility Rate Structure Perspective
Authors: A S M Jahid Hasan, Jubair Yusuf, Sadrul Ula
Date: 2021-03-17 00:00:00

To accommodate the changes in the nature and pattern of electricity consumption with the available resources, utility companies have introduced a variety of rate structures over the years. This paper develops a comprehensive optimization method that addresses the diversity of utility rate structure of a commercial building. It includes a general set of constraints that can be used for any system with a building load, a renewable source, and a battery energy storage system (BESS). A cost function is formulated for each type of rate structure that can be exercised by a utility on a commercial building. A novel algorithm is developed to apply the optimization model and generate the desired optimal outputs by using the appropriate cost function. The results for several building loads and rate structure types were obtained and compared. A sensitivity analysis was done on the optimization model based on the changes in the rates using historical data. The results exhibit that adding BESS is more effective for buildings with lower load factor and CPP rate structures in comparison to the buildings with flat energy rates. Savings from adding renewables such as solar is primarily influenced by energy charges whereas additional benefits from BESS are dominated by demand charges. These results can help a customer with deciding on the different rate structure options and resource planning of their renewable generation and energy storage. Utilities may also benefit from this work by designing a unified rate structure, considering the increasing renewable penetration and BESS deployment in the grid.
Strategic Hub-Based Platoon Coordination under Uncertain Travel Times
Authors: Alexander Johansson, Ehsan Nekouei, Karl Henrik Johansson, Jonas Mårtensson
Date: 2021-04-01 00:00:00

We study the strategic interaction among vehicles in a non-cooperative platoon coordination game. Vehicles have predefined routes in a transportation network with a set of hubs where vehicles can wait for other vehicles to form platoons. Vehicles decide on their waiting times at hubs and the utility function of each vehicle includes both the benefit from platooning and the cost of waiting. We show that the platoon coordination game is a potential game when the travel times are either deterministic or stochastic, and the vehicles decide on their waiting times at the beginning of their journeys. We also propose two feedback solutions for the coordination problem when the travel times are stochastic and vehicles are allowed to update their strategies along their routes. The solutions are evaluated in a simulation study over the Swedish road network. It is shown that uncertainty in travel times affects the total benefit of platooning drastically and the benefit from platooning in the system increases significantly when utilizing feedback solutions.
A Semidefinite Programming Approach to Discrete-time Infinite Horizon Persistent Monitoring
Authors: Samuel C. Pinto, Sean B. Andersson, Julien M. Hendrickx, Christos G. Cassandras
Date: 2021-04-01 00:00:00

We investigate the problem of persistent monitoring, where a mobile agent has to survey multiple targets in an environment in order to estimate their internal states. These internal states evolve with linear stochastic dynamics and the agent can observe them with a linear observation model. However, the signal to noise ratio is a monotonically decreasing function of the distance between the agent and the target. The goal is to minimize the uncertainty in the state estimates over the infinite horizon. We show that, for a periodic trajectory with fixed cycle length, the problem can be formulated as a set of semidefinite programs. We design a scheme that leverages the spatial configuration of the targets to guide the search over this set of optimization problems in order to provide efficient trajectories. Results are compared to a state of the art approach and we obtain improvements of up to 91% in terms of cost in a simple scenario, with much lower computational time.