pwc-archive/papers-with-abstracts huggingface dataset:
Interactive Machine Comprehension with Dynamic Knowledge Graphs
Authors: Xingdi Yuan
Date: 2021-08-31 00:00:00
Proceeding: EMNLP 2021 11
Tasks: Knowledge Graphs, Machine Reading Comprehension, Reading Comprehension

Interactive machine reading comprehension (iMRC) is machine comprehension tasks where knowledge sources are partially observable. An agent must interact with an environment sequentially to gather necessary knowledge in order to answer a question. We hypothesize that graph representations are good inductive biases, which can serve as an agent's memory mechanism in iMRC tasks. We explore four different categories of graphs that can capture text information at various levels. We describe methods that dynamically build and update these graphs during information gathering, as well as neural models to encode graph representations in RL agents. Extensive experiments on iSQuAD suggest that graph representations can result in significant performance improvements for RL agents.
Sentence Bottleneck Autoencoders from Transformer Language Models
Authors: Ivan Montero, Nikolaos Pappas, Noah A. Smith
Date: 2021-08-31 00:00:00
Proceeding: EMNLP 2021 11
Tasks: Decoder, Denoising, Language Modeling, Language Modelling, Masked Language Modeling, Representation Learning, Sentence, Sentence Classification, Style Transfer, text similarity

Representation learning for text via pretraining a language model on a large corpus has become a standard starting point for building NLP systems. This approach stands in contrast to autoencoders, also trained on raw text, but with the objective of learning to encode each input as a vector that allows full reconstruction. Autoencoders are attractive because of their latent space structure and generative properties. We therefore explore the construction of a sentence-level autoencoder from a pretrained, frozen transformer language model. We adapt the masked language modeling objective as a generative, denoising one, while only training a sentence bottleneck and a single-layer modified transformer decoder. We demonstrate that the sentence representations discovered by our model achieve better quality than previous methods that extract representations from pretrained transformers on text similarity tasks, style transfer (an example of controlled generation), and single-sentence classification tasks in the GLUE benchmark, while using fewer parameters than large pretrained models.
Mean absorption estimation from room impulse responses using virtually supervised learning
Authors: Cédric Foy, Antoine Deleforge, Diego Di Carlo
Date: 2021-09-01 00:00:00
Tasks: Room Impulse Response (RIR)

In the context of building acoustics and the acoustic diagnosis of an existing room, this paper introduces and investigates a new approach to estimate mean absorption coefficients solely from a room impulse response (RIR). This inverse problem is tackled via virtually-supervised learning, namely, the RIR-to-absorption mapping is implicitly learned by regression on a simulated dataset using artificial neural networks. We focus on simple models based on well-understood architectures. The critical choices of geometric, acoustic and simulation parameters used to train the models are extensively discussed and studied, while keeping in mind conditions that are representative of the field of building acoustics. Estimation errors from the learned neural models are compared to those obtained with classical formulas that require knowledge of the room's geometry and reverberation times. Extensive comparisons made on a variety of simulated test sets highlight different conditions under which the learned models can overcome the well-known limitations of the diffuse sound field hypothesis underlying these formulas. Results obtained on real RIRs measured in an acoustically configurable room show that at 1~kHz and above, the proposed approach performs comparably to classical models when reverberation times can be reliably estimated, and continues to work even when they cannot.
Bayesian data combination model with Gaussian process latent variable model for mixed observed variables under NMAR missingness
Authors: Masaki Mitsuhiro, Takahiro Hoshino
Date: 2021-09-01 00:00:00
Tasks: model, valid

In the analysis of observational data in social sciences and businesses, it is difficult to obtain a "(quasi) single-source dataset" in which the variables of interest are simultaneously observed. Instead, multiple-source datasets are typically acquired for different individuals or units. Various methods have been proposed to investigate the relationship between the variables in each dataset, e.g., matching and latent variable modeling. It is necessary to utilize these datasets as a single-source dataset with missing variables. Existing methods assume that the datasets to be integrated are acquired from the same population or that the sampling depends on covariates. This assumption is referred to as missing at random (MAR) in terms of missingness. However, as will been shown in application studies, it is likely that this assumption does not hold in actual data analysis and the results obtained may be biased. We propose a data fusion method that does not assume that datasets are homogenous. We use a Gaussian process latent variable model for non-MAR missing data. This model assumes that the variables of concern and the probability of being missing depend on latent variables. A simulation study and real-world data analysis show that the proposed method with a missing-data mechanism and the latent Gaussian process yields valid estimates, whereas an existing method provides severely biased estimates. This is the first study in which non-random assignment to datasets is considered and resolved under resonable assumptions in data fusion problem.
The emergence of a concept in shallow neural networks
Authors: Elena Agliari, Francesco Alemanno, Adriano Barra, Giordano De Marzo
Date: 2021-09-01 00:00:00

We consider restricted Boltzmann machine (RBMs) trained over an unstructured dataset made of blurred copies of definite but unavailable ``archetypes'' and we show that there exists a critical sample size beyond which the RBM can learn archetypes, namely the machine can successfully play as a generative model or as a classifier, according to the operational routine. In general, assessing a critical sample size (possibly in relation to the quality of the dataset) is still an open problem in machine learning. Here, restricting to the random theory, where shallow networks suffice and the grand-mother cell scenario is correct, we leverage the formal equivalence between RBMs and Hopfield networks, to obtain a phase diagram for both the neural architectures which highlights regions, in the space of the control parameters (i.e., number of archetypes, number of neurons, size and quality of the training set), where learning can be accomplished. Our investigations are led by analytical methods based on the statistical-mechanics of disordered systems and results are further corroborated by extensive Monte Carlo simulations.
Hypergraph-of-Entity: A General Model for Entity-Oriented Search
Authors: José Devezas, Sérgio Nunes
Date: 2021-09-01 00:00:00
Tasks: Entity Retrieval, Information Retrieval, Retrieval

The hypergraph-of-entity was conceptually proposed as a general model for entity-oriented search. However, only the performance for ad hoc document retrieval had been assessed. We continue this line of research by also evaluating ad hoc entity retrieval, and entity list completion. We also attempt to scale the model, so that it can support the complete INEX 2009 Wikipedia collection. We do this by indexing the top keywords for each document, reducing complexity by partially lowering the number of nodes and, indirectly, the number of hyperedges linking terms to entities. This enables us to compare the effectiveness of the hypergraph-of-entity with the results obtained by the participants of the INEX tracks for the considered tasks. We find this to be a viable model that is, to our knowledge, the first attempt at a generalization in information retrieval, in particular by supporting a universal ranking function for multiple entity-oriented search tasks.
Shallow pooling for sparse labels
Authors: Negar Arabzadeh, Alexandra Vtyurina, Xinyi Yan, Charles L. A. Clarke
Date: 2021-08-31 00:00:00
Tasks: Passage Ranking

Recent years have seen enormous gains in core IR tasks, including document and passage ranking. Datasets and leaderboards, and in particular the MS MARCO datasets, illustrate the dramatic improvements achieved by modern neural rankers. When compared with traditional test collections, the MS MARCO datasets employ substantially more queries with substantially fewer known relevant items per query. Given the sparsity of these relevance labels, the MS MARCO leaderboards track improvements with mean reciprocal rank (MRR). In essence, a relevant item is treated as the "right answer", with rankers scored on their ability to place this item high in the ranking. In working with these sparse labels, we have observed that the top items returned by a ranker often appear superior to judged relevant items. To test this observation, we employed crowdsourced workers to make preference judgments between the top item returned by a modern neural ranking stack and a judged relevant item. The results support our observation. If we imagine a perfect ranker under MRR, with a score of 1 on all queries, our preference judgments indicate that a searcher would prefer the top result from a modern neural ranking stack more frequently than the top result from the imaginary perfect ranker, making our neural ranker "better than perfect". To understand the implications for the leaderboard, we pooled the top document from available runs near the top of the passage ranking leaderboard for over 500 queries. We employed crowdsourced workers to make preference judgments over these pools and re-evaluated the runs. Our results support our concerns that current MS MARCO datasets may no longer be able to recognize genuine improvements in rankers. In future, if rankers are measured against a single "right answer", this answer should be the best answer or most preferred answer, and maintained with ongoing judgments.
V2X Communication Between Connected and Automated Vehicles (CAVs) and Unmanned Aerial Vehicles (UAVs)
Authors: Ozgenur Kavas-Torris, Sukru Yaren Gelbal, Mustafa Ridvan Cantas, Bilin Aksun-Guvenc, Levent Guvenc
Date: 2021-09-01 00:00:00
Tasks: Autonomous Vehicles, Management

Connectivity between ground vehicles can be utilized and expanded to include aerial vehicles for coordinated missions. Using Vehicle-to-Everything (V2X) communication technologies, a communication link can be established between Connected and Autonomous vehicles (CAVs) and Unmanned Aerial vehicles (UAVs). Hardware implementation and testing of a ground to air communication link is crucial for real-life applications. Two different communication links were established, Dedicated Short Range communication (DSRC) and 4G internet based WebSocket communication. Both links were tested separately both for stationary and dynamic test cases. One step further, both links were used together for a real-life use case scenario called Quick Clear demonstration. The aim was to first send ground vehicle location information from the CAV to the UAV through DSRC communication. On the UAV side, the connection between the DSRC modem and Raspberry Pi companion computer was established through User Datagram Protocol (UDP) to get the CAV location information to the companion computer. Raspberry Pi handles 2 different connection, it first connects to a traffic contingency management system (CMP) through Transmission Control Protocol (TCP) to send CAV and UAV location information to the CMP. Secondly, Raspberry Pi uses a WebSocket communication to connect to a web server to send photos taken by an on-board camera the UAV has. Quick Clear demo was conducted both for stationary test and dynamic flight tests. The results show that this communication structure can be utilized for real-life scenarios.
Linear Fractional Transformation modeling of multibody dynamics around parameter-dependent equilibrium
Authors: Ervan Kassarian, Francesco Sanfedino, Daniel Alazard, Charles-Antoine Chevrier, Johan Montel
Date: 2021-09-01 00:00:00
Tasks: Form

This paper proposes a new Linear Fractional Transformation (LFT) modeling approach for uncertain Linear Parameter Varying (LPV) multibody systems with parameter-dependent equilibrium. Traditional multibody approaches, which consist in building the nonlinear model of the whole structure and linearizing it around equilibrium after a numerical trimming, do not allow to isolate parametric variations with the LFT form. Although additional techniques, such as polynomial fitting or symbolic linearization, can provide an LFT model, they may be time-consuming or miss worst-case configurations. The proposed approach relies on the trimming and linearization of the equations at the substructure level, before assembly of the multibody structure, which allows to only perform operations that preserve the LFT form throughout the linearization process. Since the physical origin of the parameters is retained, the linearized LFT-LPV model of the structure exactly covers all plants, in a single parametric model, without introducing conservatism or fitting errors. An application to the LFT-LPV modeling of a robotic arm is proposed; in its nominal configuration, the model obtained with the proposed approach matches the model provided by the software Simscape Multibody, but it is enhanced with parametric variations with the LFT form; a robust LPV synthesis is performed using Matlab robust control toolbox to illustrate the capacity of the proposed approach for control design.
Robust Orbital Stabilization: A Floquet Theory-based Approach
Authors: Christian Fredrik Sætre, Anton S. Shiriaev, Leonid B. Freidovich, Sergei V. Gusev, Leonid M. Fridman
Date: 2020-11-27 00:00:00

The design of robust orbitally stabilizing feedback is considered. From a known orbitally stabilizing controller for a nominal, disturbance-free system, a robustifying feedback extension is designed utilizing the sliding-mode control (SMC) methodology. The main contribution of the paper is to provide a constructive procedure for designing the time-invariant switching function used in the SMC synthesis. More specifically, its zero-level set (the sliding manifold) is designed using a real Floquet-Lyapunov transformation to locally correspond to an invariant subspace of the Monodromy matrix of a transverse linearization. This ensures asymptotic stability of the periodic orbit when the system is confined to the sliding manifold, despite any system uncertainties and external disturbances satisfying a matching condition. The challenging task of oscillation control of the underactuated Cart-Pendulum system subject to both matched and unmatched disturbances/uncertainties demonstrates the efficacy of the proposed scheme.
Analytic estimation of the MMC sub-module capacitor voltage ripple for balanced and unbalanced AC grid conditions
Authors: Daniel Westerman Spier, Eduardo Prieto-Araujo, Oriol Gomis-Bellmunt, Joaquim López Mestre
Date: 2021-09-01 00:00:00

In this paper, a mathematical expression to define the maximum and minimum voltage ripples of the modular multilevel converter (MMC) sub-module (SM) capacitors is proposed. Using the arm averaged model of the MMC, the instantaneous power for the upper and lower arms of the converter is obtained, giving the basis to describe the instantaneous energy of the arms. To calculate the SM capacitors peak voltage values, it is required to obtain an analytic expression for the maximum and minimum energy levels. Due to the presence of terms with different magnitudes, frequencies and phases, finding it can be quite challenging. To overcome this issue, the instantaneous arm energy expression is modified using mathematical assumptions in order to join the different components into a single term which can analytically describes the maximum energy point. Then, this expression is used to calculate the peak values of the arm capacitor voltages. By employing the same principles from the arm level, the final analytical expression for the SM capacitors maximum and minimum voltages is found. Simulation results are carried out in order to validate the accuracy of the proposed analysis for different power delivery conditions.
A Resilience-Oriented Centralised-to-Decentralised Framework for Networked Microgrids Management
Authors: Pudong Ge, Fei Teng, Charalambos Konstantinou, Shiyan Hu
Date: 2021-09-01 00:00:00
Tasks: Management

This paper proposes a cyber-physical cooperative mitigation framework to enhance power systems resilience under extreme events, e.g., earthquakes and hurricanes. Extreme events can simultaneously damage the physical-layer electric power infrastructure and the cyber-layer communication facilities. Microgrid (MG) has been widely recognised as an effective physical-layer response to such events, however, the mitigation strategy in the cyber lay is yet to be fully investigated. Therefore, this paper proposes a resilience-oriented centralised-to-decentralised framework to maintain the power supply of critical loads such as hospitals, data centers, etc., under extreme events. For the resilient control, controller-to-controller (C2C) wireless network is utilised to form the emergency regional communication when centralised base station being compromised. Owing to the limited reliable bandwidth that reserved as a backup, the inevitable delays are dynamically minimised and used to guide the design of a discrete-time distributed control algorithm to maintain post-event power supply. The effectiveness of the cooperative cyber-physical mitigation framework is demonstrated through extensive simulations in MATLAB/Simulink.
Comparison between autosar platforms with functional safety for automotive software architectures
Authors: Youssef Elkharaz, Saad Motahhir, Abdelaziz Elghzizal
Date: 2021-08-31 00:00:00

In the next Vehicle generations, connected and highly developed driving cars will have an important impact on the networking architecture and the interconnection between ECUs(Electronic Control Unit). The automotive industry begins to develop new and efficient strategies to improve the performance of the global system. AUTOSAR organization as part of this industry tries to present plenary solutions especially software architectures for new technologies in this field. Thus, in this paper, we present the aspects of new E/E architectures with upcoming technologies. We discuss a new solution presented by AUTOSAR organization to implement new software requirements for next generation cars. This solution aims to provide a safe environment for the features that require complex data processing and to communicate with AUTOSAR and non AUTOSAR Platforms. We summarize a comprehensive comparison between AUTOSAR adaptive and AUTOSAR classic in terms of functionality and application area. We provide functional Safety preliminaries for the global E/E architectures.
Noise can lead to exponential epidemic spreading despite $R_0$ below one
Authors: Johannes Pausch, Rosalba Garcia-Millan, Gunnar Pruessner
Date: 2021-09-01 00:00:00

Branching processes are widely used to model evolutionary and population dynamics as well as the spread of infectious diseases. To characterize the dynamics of their growth or spread, the basic reproduction number $R_0$ has received considerable attention. In the context of infectious diseases, it is usually defined as the expected number of secondary cases produced by an infectious case in a completely susceptible population. Typically $R_0>1$ indicates that an outbreak is expected to continue and to grow exponentially, while $R_0<1$ usually indicates that an outbreak is expected to terminate after some time. In this work, we show that fluctuations of the dynamics in time can lead to a continuation of outbreaks even when the expected number of secondary cases from a single case is below $1$. Such fluctuations are usually neglected in modelling of infectious diseases by a set of ordinary differential equations, such as the classic SIR model. We showcase three examples: 1) extinction following an Ornstein-Uhlenbeck process, 2) extinction switching randomly between two values and 3) mixing of two populations with different $R_0$ values. We corroborate our analytical findings with computer simulations.
Regulatory Feedback Effects on Tissue Growth Dynamics in a Two-Stage Cell Lineage Model
Authors: Mao-Xiang Wang, Arthur Lander, Pik-Yin Lai
Date: 2021-09-01 00:00:00

Identifying the mechanism of intercellular feedback regulation is critical for the basic understanding of tissue growth control in organisms. In this paper, we analyze a tissue growth model consisting of a single lineage of two cell types regulated by negative feedback signalling molecules that undergo spatial diffusion. By deriving the fixed points for the uniform steady states and carrying out linear stability analysis, phase diagrams are obtained analytically for arbitrary parameters of the model. Two different generic growth modes are found: blow-up growth and final-state controlled growth which are governed by the non-trivial fixed point and the trivial fixed point respectively, and can be sensitively switched by varying the negative feedback regulation on the proliferation of the stem cells. Analytic expressions for the characteristic time scales for these two growth modes are also derived. Remarkably, the trivial and non-trivial uniform steady states can coexist and a sharp transition occurs in the bistable regime as the relevant parameters are varied. Furthermore, the bi-stable growth properties allows for the external control to switch between these two growth modes. In addition, the condition for an early accelerated growth followed by a retarded growth can be derived. These analytical results are further verified by numerical simulations and provide insights on the growth behavior of the tissue. Our results are also discussed in the light of possible realistic biological experiments and tissue growth control strategy. Furthermore, by external feedback control of the concentration of regulatory molecules, it is possible to achieve a desired growth mode, as demonstrated with an analysis of boosted growth, catch-up growth and the design for the target of a linear growth dynamic.
How to Detect Network Dependence in Latent Factor Models? A Bias-Corrected CD Test
Authors: M. Hashem Pesaran, Yimeng Xie
Date: 2021-09-01 00:00:00
Tasks: valid

In a recent paper Juodis and Reese (2022) (JR) show that the application of the CD test proposed by Pesaran (2004) to residuals from panels with latent factors results in over-rejection. They propose a randomized test statistic to correct for over-rejection, and add a screening component to achieve power. This paper considers the same problem but from a different perspective, and shows that the standard CD test remains valid if the latent factors are weak in the sense the strength is less than half. In the case where latent factors are strong, we propose a bias-corrected version, CD*, which is shown to be asymptotically standard normal under the null of error cross-sectional independence and have power against network type alternatives. This result is shown to hold for pure latent factor models as well as for panel regression models with latent factors. The case where the errors are serially correlated is also considered. Small sample properties of the CD* test are investigated by Monte Carlo experiments and are shown to have the correct size for strong and weak factors as well as for Gaussian and non-Gaussian errors. In contrast, it is found that JR's test tends to over-reject in the case of panels with non-Gaussian errors, and has low power against spatial network alternatives. In an empirical application, using the CD* test, it is shown that there remains spatial error dependence in a panel data model for real house price changes across 377 Metropolitan Statistical Areas in the U.S., even after the effects of latent factors are filtered out.