pwc-archive/papers-with-abstracts huggingface dataset:
LAMNER: Code Comment Generation Using Character Language Model and Named Entity Recognition
Authors: Rishab Sharma, Fuxiang Chen, Fatemeh Fard
Date: 2022-04-05 00:00:00
Tasks: Code Comment Generation, Comment Generation, Language Modeling, Language Modelling, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

Code comment generation is the task of generating a high-level natural language description for a given code method or function. Although researchers have been studying multiple ways to generate code comments automatically, previous work mainly considers representing a code token in its entirety semantics form only (e.g., a language model is used to learn the semantics of a code token), and additional code properties such as the tree structure of a code are included as an auxiliary input to the model. There are two limitations: 1) Learning the code token in its entirety form may not be able to capture information succinctly in source code, and 2) The code token does not contain additional syntactic information, inherently important in programming languages. In this paper, we present LAnguage Model and Named Entity Recognition (LAMNER), a code comment generator capable of encoding code constructs effectively and capturing the structural property of a code token. A character-level language model is used to learn the semantic representation to encode a code token. For the structural property of a token, a Named Entity Recognition model is trained to learn the different types of code tokens. These representations are then fed into an encoder-decoder architecture to generate code comments. We evaluate the generated comments from LAMNER and other baselines on a popular Java dataset with four commonly used metrics. Our results show that LAMNER is effective and improves over the best baseline model in BLEU-1, BLEU-2, BLEU-3, BLEU-4, ROUGE-L, METEOR, and CIDEr by 14.34%, 18.98%, 21.55%, 23.00%, 10.52%, 1.44%, and 25.86%, respectively. Additionally, we fused LAMNER's code representation with the baseline models, and the fused models consistently showed improvement over the non-fused models. The human evaluation further shows that LAMNER produces high-quality code comments.
On the Practical Design of Tube-Enhanced Multi-Stage Nonlinear Model Predictive Control
Authors: Sankaranarayanan Subramanian, Yehia Abdelsalam, Sergio Lucia, Sebastian Engell
Date: 2022-04-20 00:00:00
Tasks: Model Predictive Control

Tube-enhanced multi-stage nonlinear model predictive control is a robust control scheme that can handle a wide range of uncertainties with reduced conservatism and manageable computational complexity. In this paper, we elaborate on the flexibility of the approach from an application point of view. We discuss the path to making design decisions to implement the novel scheme systematically. We illustrate the critical steps in the design and implementation of the scheme for an industrial example.
Informative Path Planning in Random Fields via Mixed Integer Programming
Authors: Shamak Dutta, Nils Wilde, Stephen L. Smith
Date: 2022-04-20 00:00:00

We present a new mixed integer formulation for the discrete informative path planning problem in random fields. The objective is to compute a budget constrained path while collecting measurements whose linear estimate results in minimum error over a finite set of prediction locations. The problem is known to be NP-hard. However, we strive to compute optimal solutions by leveraging advances in mixed integer optimization. Our approach is based on expanding the search space so we optimize not only over the collected measurement subset, but also over the class of all linear estimators. This allows us to formulate a mixed integer quadratic program that is convex in the continuous variables. The formulations are general and are not restricted to any covariance structure of the field. In simulations, we demonstrate the effectiveness of our approach over previous branch and bound algorithms.
Congestion Mitigation in Unbalanced Residential Networks with OPF-based Demand Management
Authors: Marta Vanin, Tom Van Acker, Hakan Ergun, Reinhilde D'hulst, Koen Vanthournout, Dirk Van Hertem
Date: 2022-04-20 00:00:00
Tasks: Management

This paper proposes a novel congestion mitigation strategy for low voltage residential feeders in which the rising power demand due to the electrification of the transport and heating systems leads to congestion problems. The strategy is based on requiring residential customers to limit their demand for a certain amount of time in exchange for economic benefits. The main novelty of the method consists of combining a thorough representation of the network physics with advanced constraints that ensure the comfort of residential users, in a scalable manner that suits real systems. The mitigation strategy is presented from a DSO perspective, and takes the form of contracts between users and system operator. The focus on user comfort aims to make the contracts appealing, encouraging users to voluntarily enroll in the proposed mitigation scheme. The presented solution is implemented as a mixed-integer multi-period optimal power flow problem which relies on a linearized three-phase power flow formulation. Calculations on 100 real-life distribution feeders are performed, to analyze the congestion-relieving potential of several possible system operator-user contracts. From a planning perspective, the results can help the system operator define contractual terms that make a specific congestion mitigation scheme effective and viable. From an operational perspective, the same calculations can be used to optimally schedule power reduction on a day-ahead basis.
Estimating probabilistic dynamic origin-destination demands using multi-day traffic data on computational graphs
Authors: Wei Ma, Sean Qian
Date: 2022-04-20 00:00:00
Tasks: Decision Making

System-level decision making in transportation needs to understand day-to-day variation of network flows, which calls for accurate modeling and estimation of probabilistic dynamic travel demand on networks. Most existing studies estimate deterministic dynamic origin-destination (OD) demand, while the day-to-day variation of demand and flow is overlooked. Estimating probabilistic distributions of dynamic OD demand is challenging due to the complexity of the spatio-temporal networks and the computational intensity of the high-dimensional problems. With the availability of massive traffic data and the emergence of advanced computational methods, this paper develops a data-driven framework that solves the probabilistic dynamic origin-destination demand estimation (PDODE) problem using multi-day data. Different statistical distances (e.g., lp-norm, Wasserstein distance, KL divergence, Bhattacharyya distance) are used and compared to measure the gap between the estimated and the observed traffic conditions, and it is found that 2-Wasserstein distance achieves a balanced accuracy in estimating both mean and standard deviation. The proposed framework is cast into the computational graph and a reparametrization trick is developed to estimate the mean and standard deviation of the probabilistic dynamic OD demand simultaneously. We demonstrate the effectiveness and efficiency of the proposed PDODE framework on both small and real-world networks. In particular, it is demonstrated that the proposed PDODE framework can mitigate the overfitting issues by considering the demand variation. Overall, the developed PDODE framework provides a practical tool for public agencies to understand the sources of demand stochasticity, evaluate day-to-day variation of network flow, and make reliable decisions for intelligent transportation systems.
Conditional Value at Risk-Sensitive Solar Hosting Capacity Analysis in Distribution Networks
Authors: Avinash N. Madavan, Nathan Dahlin, Subhonmesh Bose, Lang Tong
Date: 2022-04-19 00:00:00

Solar hosting capacity analysis (HCA) assesses the ability of a distribution network to host distributed solar generation without seriously violating distribution network constraints. In this paper, we consider risk-sensitive HCA that limits the risk of network constraint violations with a collection of scenarios of solar irradiance and nodal power demands, where risk is modeled via the conditional value at risk (CVaR) measure. First, we consider the question of maximizing aggregate installed solar capacities, subject to risk constraints and solve it as a second-order cone program (SOCP) with a standard conic relaxation of the feasible set with power flow equations. Second, we design an incremental algorithm to decide whether a configuration of solar installations has acceptable risk of constraint violations, modeled via CVaR. The algorithm circumvents explicit risk computation by incrementally constructing inner and outer polyhedral approximations of the set of acceptable solar installation configurations from prior such tests conducted. Our numerical examples study the impact of risk parameters, the number of scenarios and the scalability of our framework.
A Maximum Entropy Approach to Defining Cell State
Authors: Ashika-Sita Jayanthy
Date: 2022-04-19 00:00:00

The past few decades have seen great leaps in technologies to analyze cells and tissues. Omics methods in particular now allow us unprecedented access to their the molecular composition where the base-level resolution of transcripts and their numbers can be determined at a single cell level. Existing methods to analyze the resulting data make use of the count data while discarding the information present in the sequences themselves. In this paper we used a maximum entropy approach to develop a method to analyze RNA-seq data using both the sequence and count information. By mapping sequences to vectors of spins and defining an energy function on them, we were able to identify specific states in a biological process using mean energies and their associated Boltzmann-probabilities. This approach opens up new avenues in the quantitative analysis of -omics data and analysis of biological function.
Physics-Informed Bayesian Learning of Electrohydrodynamic Polymer Jet Printing Dynamics
Authors: Athanasios Oikonomou, Theodoros Loutas, Dixia Fan, Alysia Garmulewicz, George Nounesis, Santanu Chaudhuri, Filippos Tourlomousis
Date: 2022-04-16 00:00:00

Calibration of highly dynamic multi-physics manufacturing processes such as electro-hydrodynamics-based additive manufacturing (AM) technologies (E-jet printing) is still performed by labor-intensive trial-and-error practices. These practices have hindered the broad adoption of these technologies, demanding a new paradigm of self-calibrating E-jet printing machines. To address this need, we developed GPJet, an end-to-end physics-informed Bayesian learning framework, and tested it on a virtual E-jet printing machine with in-process jet monitoring capabilities. GPJet consists of three modules: a) the Machine Vision module, b) the Physics-Based Modeling Module, and c) the Machine Learning (ML) module. We demonstrate that the Machine Vision module can extract high-fidelity jet features in real-time from video data using an automated parallelized computer vision workflow. In addition, we show that the Machine Vision module, combined with the Physics-based modeling module, can act as closed-loop sensory feedback to the Machine Learning module of high- and low-fidelity data. Powered by our data-centric approach, we demonstrate that the online ML planner can actively learn the jet process dynamics using video and physics with minimum experimental cost. GPJet brings us one step closer to realizing the vision of intelligent AM machines that can efficiently search complex process-structure-property landscapes and create optimized material solutions for a wide range of applications at a fraction of the cost and speed.
Extracting particle size distribution from laser speckle with a physics-enhanced autocorrelation-based estimator (PEACE)
Authors: Qihang Zhang, Janaka C. Gamekkanda, Ajinkya Pandit, Wenlong Tang, Charles Papageorgiou, Chris Mitchell, Yihui Yang, Michael Schwaerzler, Tolutola Oyetunde, Richard D. Braatz, Allan S. Myerson, George Barbastathis
Date: 2022-04-20 00:00:00
Tasks: BIG-bench Machine Learning

Extracting quantitative information about highly scattering surfaces from an imaging system is challenging because the phase of the scattered light undergoes multiple folds upon propagation, resulting in complex speckle patterns. One specific application is the drying of wet powders in the pharmaceutical industry, where quantifying the particle size distribution (PSD) is of particular interest. A non-invasive and real-time monitoring probe in the drying process is required, but there is no suitable candidate for this purpose. In this report, we develop a theoretical relationship from the PSD to the speckle image and describe a physics-enhanced autocorrelation-based estimator (PEACE) machine learning algorithm for speckle analysis to measure the PSD of a powder surface. This method solves both the forward and inverse problems together and enjoys increased interpretability, since the machine learning approximator is regularized by the physical law.
Who Benefits from Political Connections in Brazilian Municipalities
Authors: Pedro Forquesato
Date: 2022-04-20 00:00:00

A main issue in improving public sector efficiency is to understand to what extent public appointments are based on worker capability, instead of being used to reward political supporters (patronage). I contribute to a recent literature documenting patronage in public sector employment by establishing what type of workers benefit the most from political connections. Under the (empirically supported) assumption that in close elections the result of the election is as good as random, I estimate a causal forest to identify heterogeneity in the conditional average treatment effect of being affiliated to the party of the winning mayor. Contrary to previous literature, for most positions we find positive selection on education, but a negative selection on (estimated) ability. Overall, unemployed workers or low tenure employees that are newly affiliated to the winning candidate's party benefit the most from political connections, suggesting that those are used for patronage.
Parametric Models for DOA Trajectory Localization
Authors: Ruchi Pandey, Santosh Nannuru
Date: 2022-04-20 00:00:00

Directions of arrival (DOA) estimation or localization of sources is an important problem in many applications for which numerous algorithms have been proposed. Most localization methods use block-level processing that combines multiple data snapshots to estimate DOA within a block. The DOAs are assumed to be constant within the block duration. However, these assumptions are often violated due to source motion. In this paper, we propose a signal model that captures the linear variations in DOA within a block. We applied conventional beamforming (CBF) algorithm to this model to estimate linear DOA trajectories. Further, we formulate the proposed signal model as a block sparse model and subsequently derive sparse Bayesian learning (SBL) algorithm. Our simulation results show that this linear parametric DOA model and corresponding algorithms capture the DOA trajectories for moving sources more accurately than traditional signal models and methods.
Bone marrow sparing for cervical cancer radiotherapy on multimodality medical images
Authors: Yuening Wang, Ying Sun, Jie Yuan, Kexin Gan, Hanzi Xu, Han Gao, Xiuming Zhang
Date: 2022-04-20 00:00:00
Tasks: Image Registration, Point cloud reconstruction

Cervical cancer threatens the health of women seriously. Radiotherapy is one of the main therapy methods but with high risk of acute hematologic toxicity. Delineating the bone marrow (BM) for sparing using computer tomography (CT) images to plan before radiotherapy can effectively avoid this risk. Comparing with magnetic resonance (MR) images, CT lacks the ability to express the activity of BM. Thus, in current clinical practice, medical practitioners manually delineate the BM on CT images by corresponding to MR images. However, the time?consuming delineating BM by hand cannot guarantee the accuracy due to the inconsistency of the CT-MR multimodal images. In this study, we propose a multimodal image oriented automatic registration method for pelvic BM sparing, which consists of three-dimensional bone point cloud reconstruction, a local spherical system iteration closest point registration for marking BM on CT images. Experiments on patient dataset reveal that our proposed method can enhance the multimodal image registration accuracy and efficiency for medical practitioners in sparing BM of cervical cancer radiotherapy. The method proposed in this contribution might also provide references for similar studies in other clinical application.
Extraction of Unaliased High-Frequency Micro-Doppler Signature using FMCW radar
Authors: Soorya Peter, Vinod Veera Reddy
Date: 2022-04-20 00:00:00
Tasks: parameter estimation, Vocal Bursts Intensity Prediction

Micro-Doppler signature is a potent feature that has been used for target identification and micro-motion parameter estimation. The extraction of high frequency micro-Doppler signature from frequency modulated continuous wave (FMCW) radar along with the target range and velocity is the problem considered in this article. The severe aliasing of the high micro-Doppler frequency spread is circumvented by the fast time processing in the proposed method. The use of range-Doppler (RD) filtering and empirical mode decomposition (EMD) enables effective out-of-band and in-band noise suppression. Simulation studies and experimental results present the effectiveness of the proposed approach.
Federated Learning for Distributed Energy-Efficient Resource Allocation
Authors: Zelin Ji, Zhijin Qin
Date: 2022-04-20 00:00:00
Tasks: Federated Learning, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In cellular networks, resource allocation is performed in a centralized way, which brings huge computation complexity to the base station (BS) and high transmission overhead. This paper investigates the distributed resource allocation scheme for cellular networks to maximize the energy efficiency of the system in the uplink transmission, while guaranteeing the quality of service (QoS) for cellular users. Particularly, to cope the fast varying channels in wireless communication environment, we propose a robust federated reinforcement learning (FRL_suc) framework to enable local users to perform distributed resource allocation in items of transmit power and channel assignment by the guidance of the local neural network trained at each user. Analysis and numerical results show that the proposed FRL_suc framework can lower the transmission overhead and offload the computation from the central server to the local users, while outperforming the conventional multi-agent reinforcement learning algorithm in terms of EE, and is more robust to channel variations.
Effective Goal-oriented 6G Communications: the Energy-aware Edge Inferencing Case
Authors: Mattia Merluzzi, Miltiadis C. Filippou, Leonardo Gomes Baltar, Emilio Calvanese Strinati
Date: 2022-04-20 00:00:00
Tasks: Edge-computing

Currently, the world experiences an unprecedentedly increasing generation of application data, from sensor measurements to video streams, thanks to the extreme connectivity capability provided by 5G networks. Going beyond 5G technology, such data aim to be ingested by Artificial Intelligence (AI) functions instantiated in the network to facilitate informed decisions, essential for the operation of applications, such as automated driving and factory automation. Nonetheless, while computing platforms hosting Machine Learning (ML) models are ever powerful, their energy footprint is a key impeding factor towards realizing a wireless network as a sustainable intelligent platform. Focusing on a beyond 5G wireless network, overlaid by a Multi-access Edge Computing (MEC) infrastructure with inferencing capabilities, our paper tackles the problem of energy-aware dependable inference by considering inference effectiveness as value of a goal that needs to be accomplished by paying the minimum price in energy consumption. Both MEC-assisted standalone and ensemble inference options are evaluated. It is shown that, for some system scenarios, goal effectiveness above 84% is achieved and sustained even by relaxing communication reliability requirements by one decimal digit, while enjoying a device radio energy consumption reduction of almost 23% at the same time. Also, ensemble inference is shown to improve system-wide energy efficiency and even achieve higher goal effectiveness, as compared to the standalone case for some system parameterizations.
Two Low-complexity DOA Estimators for Massive/Ultra-massive MIMO Receive Array
Authors: YiWen Chen, Xichao Zhan, Feng Shu, Qijuan Jie, Xin Cheng, Zhihong Zhuang, Jiangzhou Wang
Date: 2022-04-20 00:00:00

Eigen-decomposition-based direction finding methods of using large-scale/ultra-large-scale fully-digital receive antenna arrays lead to a high or ultra-high complexity. To address the complexity dilemma, in this paper, three low-complexity estimators are proposed: partitioned subarray auto-correlation combining (PSAC), partitioned subarray cross-correlation combining (PSCC) and power iteration max correlation successive convex approximation (PI-Max-CSCA). Compared with the conventional no-partitioned direction finding method like root multiple signal classification (Root-MUSIC), in the PSAC method, the total set of antennas are equally partitioned into subsets of antennas, called subarrays, each subarray performs independent DOA estimation, and all DOA estimates are coherently combined to give the final estimation. For a better performance, the cross-correlation among sub-arrays is further exploited in the PSCC method to achieve the near-Cramer-Rao lower bound (CRLB) performance with the help of auto-correlation. To further reduce the complexity, in the PI-Max-CSCA method, using a fraction of all subarrays to make an initial coarse direction measurement (ICDM), the power iterative method is adopted to compute the more precise steering vector (SV) by exploiting the total array, and a more accurate DOA value is found using ICDM and SV through the maximum correlation method solved by successive convex approximation. Simulation results show that as the number of antennas goes to large-scale, the proposed three methods can achieve a dramatic complexity reduction over conventional Root-MUISC. Particularly, the PSCC and PI-Max-CSCA can reach the CRLB while the PSAC shows a substantial performance loss.
Constructions of Polyphase Golay Complementary Arrays
Authors: Cheng Du, Yi Jiang
Date: 2022-04-20 00:00:00

Golay complementary matrices (GCM) have recently drawn considerable attentions owing to its potential applications in omnidirectional precoding. In this paper we generalize the GCM to multi-dimensional Golay complementary arrays (GCA) and propose new constructions of GCA pairs and GCA quads. These constructions are facilitated by introducing a set of identities over a commutative ring. We prove that a quaternary GCA pair is feasible if the product of the array sizes in all dimensions is a quaternary Golay number with an additional constraint on the factorization of the product. For the binary GCM quads, we conjecture that the feasible sizes are arbitrary, and verify for sizes within 78 $\times$ 78 and other less densely distributed sizes. For the quaternary GCM quads, all the positive integers within 1000 can be covered for the size in one dimension.