pwc-archive/papers-with-abstracts huggingface dataset:
A Diversity-Enhanced and Constraints-Relaxed Augmentation for Low-Resource Classification
Authors: Guang Liu, Hailong Huang, Yuzhao Mao, Weiguo Gao, Xuan Li, Jianping Shen
Date: 2021-09-24 00:00:00
Tasks: Data Augmentation, Diversity, Language Modeling, Language Modelling

Data augmentation (DA) aims to generate constrained and diversified data to improve classifiers in Low-Resource Classification (LRC). Previous studies mostly use a fine-tuned Language Model (LM) to strengthen the constraints but ignore the fact that the potential of diversity could improve the effectiveness of generated data. In LRC, strong constraints but weak diversity in DA result in the poor generalization ability of classifiers. To address this dilemma, we propose a {D}iversity-{E}nhanced and {C}onstraints-\{R}elaxed {A}ugmentation (DECRA). Our DECRA has two essential components on top of a transformer-based backbone model. 1) A k-beta augmentation, an essential component of DECRA, is proposed to enhance the diversity in generating constrained data. It expands the changing scope and improves the degree of complexity of the generated data. 2) A masked language model loss, instead of fine-tuning, is used as a regularization. It relaxes constraints so that the classifier can be trained with more scattered generated data. The combination of these two components generates data that can reach or approach category boundaries and hence help the classifier generalize better. We evaluate our DECRA on three public benchmark datasets under low-resource settings. Extensive experiments demonstrate that our DECRA outperforms state-of-the-art approaches by 3.8% in the overall score.
Detect and Perturb: Neutral Rewriting of Biased and Sensitive Text via Gradient-based Decoding
Authors: Zexue He, Bodhisattwa Prasad Majumder, Julian McAuley
Date: 2021-09-24 00:00:00
Proceeding: Findings (EMNLP) 2021 11
Tasks: Attribute

Written language carries explicit and implicit biases that can distract from meaningful signals. For example, letters of reference may describe male and female candidates differently, or their writing style may indirectly reveal demographic characteristics. At best, such biases distract from the meaningful content of the text; at worst they can lead to unfair outcomes. We investigate the challenge of re-generating input sentences to 'neutralize' sensitive attributes while maintaining the semantic meaning of the original text (e.g. is the candidate qualified?). We propose a gradient-based rewriting framework, Detect and Perturb to Neutralize (DEPEN), that first detects sensitive components and masks them for regeneration, then perturbs the generation model at decoding time under a neutralizing constraint that pushes the (predicted) distribution of sensitive attributes towards a uniform distribution. Our experiments in two different scenarios show that DEPEN can regenerate fluent alternatives that are neutral in the sensitive attribute while maintaining the semantics of other attributes.
Revisiting the Uniform Information Density Hypothesis
Authors: Clara Meister, Tiago Pimentel, Patrick Haller, Lena Jäger, Ryan Cotterell, Roger Levy
Date: 2021-09-23 00:00:00
Proceeding: EMNLP 2021 11
Tasks: Linguistic Acceptability, Sentence

The uniform information density (UID) hypothesis posits a preference among language users for utterances structured such that information is distributed uniformly across a signal. While its implications on language production have been well explored, the hypothesis potentially makes predictions about language comprehension and linguistic acceptability as well. Further, it is unclear how uniformity in a linguistic signal -- or lack thereof -- should be measured, and over which linguistic unit, e.g., the sentence or language level, this uniformity should hold. Here we investigate these facets of the UID hypothesis using reading time and acceptability data. While our reading time results are generally consistent with previous work, they are also consistent with a weakly super-linear effect of surprisal, which would be compatible with UID's predictions. For acceptability judgments, we find clearer evidence that non-uniformity in information density is predictive of lower acceptability. We then explore multiple operationalizations of UID, motivated by different interpretations of the original hypothesis, and analyze the scope over which the pressure towards uniformity is exerted. The explanatory power of a subset of the proposed operationalizations suggests that the strongest trend may be a regression towards a mean surprisal across the language, rather than the phrase, sentence, or document -- a finding that supports a typical interpretation of UID, namely that it is the byproduct of language users maximizing the use of a (hypothetical) communication channel.
iFacetSum: Coreference-based Interactive Faceted Summarization for Multi-Document Exploration
Authors: Eran Hirsch, Alon Eirew, Ori Shapira, Avi Caciularu, Arie Cattan, Ori Ernst, Ramakanth Pasunuru, Hadar Ronen, Mohit Bansal, Ido Dagan
Date: 2021-09-23 00:00:00
Proceeding: EMNLP (ACL) 2021 11

We introduce iFacetSum, a web application for exploring topical document sets. iFacetSum integrates interactive summarization together with faceted search, by providing a novel faceted navigation scheme that yields abstractive summaries for the user's selections. This approach offers both a comprehensive overview as well as concise details regarding subtopics of choice. Fine-grained facets are automatically produced based on cross-document coreference pipelines, rendering generic concepts, entities and statements surfacing in the source texts. We analyze the effectiveness of our application through small-scale user studies, which suggest the usefulness of our approach.
Equivariant representations for molecular Hamiltonians and N-center atomic-scale properties
Authors: Jigyasa Nigam, Michael Willatt, Michele Ceriotti
Date: 2021-09-24 00:00:00

Symmetry considerations are at the core of the major frameworks used to provide an effective mathematical representation of atomic configurations that is then used in machine-learning models to predict the properties associated with each structure. In most cases, the models rely on a description of atom-centered environments, and are suitable to learn atomic properties, or global observables that can be decomposed into atomic contributions. Many quantities that are relevant for quantum mechanical calculations, however -- most notably the single-particle Hamiltonian matrix when written in an atomic-orbital basis -- are not associated with a single center, but with two (or more) atoms in the structure. We discuss a family of structural descriptors that generalize the very successful atom-centered density correlation features to the N-centers case, and show in particular how this construction can be applied to efficiently learn the matrix elements of the (effective) single-particle Hamiltonian written in an atom-centered orbital basis. These N-centers features are fully equivariant -- not only in terms of translations and rotations, but also in terms of permutations of the indices associated with the atoms -- and are suitable to construct symmetry-adapted machine-learning models of new classes of properties of molecules and materials.
Entropic estimation of optimal transport maps
Authors: Aram-Alexandre Pooladian, Jonathan Niles-Weed
Date: 2021-09-24 00:00:00

We develop a computationally tractable method for estimating the optimal map between two distributions over $\mathbb{R}^d$ with rigorous finite-sample guarantees. Leveraging an entropic version of Brenier's theorem, we show that our estimator -- the \emph{barycentric projection} of the optimal entropic plan -- is easy to compute using Sinkhorn's algorithm. As a result, unlike current approaches for map estimation, which are slow to evaluate when the dimension or number of samples is large, our approach is parallelizable and extremely efficient even for massive data sets. Under smoothness assumptions on the optimal map, we show that our estimator enjoys comparable statistical performance to other estimators in the literature, but with much lower computational cost. We showcase the efficacy of our proposed estimator through numerical examples, even ones not explicitly covered by our assumptions. By virtue of Lepski's method, we propose a modified version of our estimator that is adaptive to the smoothness of the underlying optimal transport map. Our proofs are based on a modified duality principle for entropic optimal transport and on a method for approximating optimal entropic plans due to Pal (2019).
Graph-based Approximate Message Passing Iterations
Authors: Cédric Gerbelot, Raphaël Berthier
Date: 2021-09-24 00:00:00

Approximate-message passing (AMP) algorithms have become an important element of high-dimensional statistical inference, mostly due to their adaptability and concentration properties, the state evolution (SE) equations. This is demonstrated by the growing number of new iterations proposed for increasingly complex problems, ranging from multi-layer inference to low-rank matrix estimation with elaborate priors. In this paper, we address the following questions: is there a structure underlying all AMP iterations that unifies them in a common framework? Can we use such a structure to give a modular proof of state evolution equations, adaptable to new AMP iterations without reproducing each time the full argument ? We propose an answer to both questions, showing that AMP instances can be generically indexed by an oriented graph. This enables to give a unified interpretation of these iterations, independent from the problem they solve, and a way of composing them arbitrarily. We then show that all AMP iterations indexed by such a graph admit rigorous SE equations, extending the reach of previous proofs, and proving a number of recent heuristic derivations of those equations. Our proof naturally includes non-separable functions and we show how existing refinements, such as spatial coupling or matrix-valued variables, can be combined with our framework.
Joint Estimation and Inference for Multi-Experiment Networks of High-Dimensional Point Processes
Authors: Xu Wang, Ali Shojaie
Date: 2021-09-23 00:00:00
Tasks: Point Processes

Modern high-dimensional point process data, especially those from neuroscience experiments, often involve observations from multiple conditions and/or experiments. Networks of interactions corresponding to these conditions are expected to share many edges, but also exhibit unique, condition-specific ones. However, the degree of similarity among the networks from different conditions is generally unknown. Existing approaches for multivariate point processes do not take these structures into account and do not provide inference for jointly estimated networks. To address these needs, we propose a joint estimation procedure for networks of high-dimensional point processes that incorporates easy-to-compute weights in order to data-adaptively encourage similarity between the estimated networks. We also propose a powerful hierarchical multiple testing procedure for edges of all estimated networks, which takes into account the data-driven similarity structure of the multi-experiment networks. Compared to conventional multiple testing procedures, our proposed procedure greatly reduces the number of tests and results in improved power, while tightly controlling the family-wise error rate. Unlike existing procedures, our method is also free of assumptions on dependency between tests, offers flexibility on p-values calculated along the hierarchy, and is robust to misspecification of the hierarchical structure. We verify our theoretical results via simulation studies and demonstrate the application of the proposed procedure using neuronal spike train data.
Multi-behavior Graph Contextual Aware Network for Session-based Recommendation
Authors: Qi Shen, Lingfei Wu, Yitong Pang, Yiming Zhang, Zhihua Wei, Fangli Xu, Bo Long
Date: 2021-09-24 00:00:00
Tasks: Session-Based Recommendations

Predicting the next interaction of a short-term sequence is a challenging task in session-based recommendation (SBR).Multi-behavior session recommendation considers session sequence with multiple interaction types, such as click and purchase, to capture more effective user intention representation sufficiently.Despite the superior performance of existing multi-behavior based methods for SBR, there are still several severe limitations:(i) Almost all existing works concentrate on single target type of next behavior and fail to model multiplex behavior sessions uniformly.(ii) Previous methods also ignore the semantic relations between various next behavior and historical behavior sequence, which are significant signals to obtain current latent intention for SBR.(iii) The global cross-session item-item graph established by some existing models may incorporate semantics and context level noise for multi-behavior session-based recommendation. To overcome the limitations (i) and (ii), we propose two novel tasks for SBR, which require the incorporation of both historical behaviors and next behaviors into unified multi-behavior recommendation modeling. To this end, we design a Multi-behavior Graph Contextual Aware Network (MGCNet) for multi-behavior session-based recommendation for the two proposed tasks. Specifically, we build a multi-behavior global item transition graph based on all sessions involving all interaction types. Based on the global graph, MGCNet attaches the global interest representation to final item representation based on local contextual intention to address the limitation (iii). In the end, we utilize the next behavior information explicitly to guide the learning of general interest and current intention for SBR. Experiments on three public benchmark datasets show that MGCNet can outperform state-of-the-art models for multi-behavior session-based recommendation.
Graph Learning Augmented Heterogeneous Graph Neural Network for Social Recommendation
Authors: Yiming Zhang, Lingfei Wu, Qi Shen, Yitong Pang, Zhihua Wei, Fangli Xu, Ethan Chang, Bo Long
Date: 2021-09-24 00:00:00
Tasks: Graph Learning, Graph Neural Network

Social recommendation based on social network has achieved great success in improving the performance of recommendation system. Since social network (user-user relations) and user-item interactions are both naturally represented as graph-structured data, Graph Neural Networks (GNNs) have thus been widely applied for social recommendation. In this work, we propose an end-to-end heterogeneous global graph learning framework, namely Graph Learning Augmented Heterogeneous Graph Neural Network (GL-HGNN) for social recommendation. GL-HGNN aims to learn a heterogeneous global graph that makes full use of user-user relations, user-item interactions and item-item similarities in a unified perspective. To this end, we design a Graph Learner (GL) method to learn and optimize user-user and item-item connections separately. Moreover, we employ a Heterogeneous Graph Neural Network (HGNN) to capture the high-order complex semantic relations from our learned heterogeneous global graph. To scale up the computation of graph learning, we further present the Anchor-based Graph Learner (AGL) to reduce computational complexity. Extensive experiments on four real-world datasets demonstrate the effectiveness of our model.
Evaluating X-vector-based Speaker Anonymization under White-box Assessment
Authors: Pierre Champion, Denis Jouvet, Anthony Larcher
Date: 2021-09-24 00:00:00
Tasks: Speaker anonymization

In the scenario of the Voice Privacy challenge, anonymization is achieved by converting all utterances from a source speaker to match the same target identity; this identity being randomly selected. In this context, an attacker with maximum knowledge about the anonymization system can not infer the target identity. This article proposed to constrain the target selection to a specific identity, i.e., removing the random selection of identity, to evaluate the extreme threat under a whitebox assessment (the attacker has complete knowledge about the system). Targeting a unique identity also allows us to investigate whether some target's identities are better than others to anonymize a given speaker.
Towards a Governance Framework for Brain Data
Authors: Marcello Ienca, Joseph J. Fins, Ralf J. Jox, Fabrice Jotterand, Silja Voeneky, Roberto Andorno, Tonio Ball, Claude Castelluccia, Ricardo Chavarriaga, Hervé Chneiweiss, Agata Ferretti, Orsolya Friedrich, Samia Hurst, Grischa Merkel, Fruzsina Molnar-Gabor, Jean-Marc Rickli, James Scheibner, Effy Vayena, Rafael Yuste, Philipp Kellmeyer
Date: 2021-09-24 00:00:00
Tasks: Ethics

The increasing availability of brain data within and outside the biomedical field, combined with the application of artificial intelligence (AI) to brain data analysis, poses a challenge for ethics and governance. We identify distinctive ethical implications of brain data acquisition and processing, and outline a multi-level governance framework. This framework is aimed at maximizing the benefits of facilitated brain data collection and further processing for science and medicine whilst minimizing risks and preventing harmful use. The framework consists of four primary areas of regulatory intervention: binding regulation, ethics and soft law, responsible innovation, and human rights.
Sequential TOA-Based Moving Target Localization in Multi-Agent Networks
Authors: Qin Shi, Xiaowei Cui, Sihao Zhao, Mingquan Lu
Date: 2021-09-24 00:00:00
Tasks: Position

Localizing moving targets in unknown harsh environments has always been a severe challenge. This letter investigates a novel localization system based on multi-agent networks, where multiple agents serve as mobile anchors broadcasting their time-space information to the targets. We study how the moving target can localize itself using the sequential time of arrival (TOA) of the one-way broadcast signals. An extended two-step weighted least squares (TSWLS) method is proposed to jointly estimate the position and velocity of the target in the presence of agent information uncertainties. We also address the large target clock offset (LTCO) problem for numerical stability. Analytical results reveal that our method reaches the Cramer-Rao lower bound (CRLB) under small noises. Numerical results show that the proposed method performs better than the existing algorithms.
Compositional Verification of Initial-State Opacity for Switched Systems
Authors: Siyuan Liu, Abdalla Swikir, Majid Zamani
Date: 2021-09-24 00:00:00

In this work, we propose a compositional framework for the verification of approximate initial-state opacity for networks of discrete-time switched systems. The proposed approach is based on a notion of approximate initial-state opacity-preserving simulation functions (InitSOPSFs), which characterize how close concrete networks and their finite abstractions are in terms of the satisfaction of approximate initial state opacity. We show that such InitSOPSFs can be obtained compositionally by assuming some small-gain type conditions and composing so-called local InitSOPSFs constructed for each subsystem separately. Additionally, for switched systems satisfying certain stability properties, we provide an approach to construct their finite abstractions together with the corresponding local InitSOPSFs. Finally, the effectiveness of our results is illustrated through an example.
A Scenario-oriented Approach to Multi-period Energy-Reserve Joint Procurement and Pricing
Authors: Jiantao Shi, Ye Guo, Lang Tong, Wenchuan Wu, Hongbin Sun
Date: 2021-09-24 00:00:00

In [1], a single-period co-optimization model of energy and reserve is considered to better illustrate the properties of the co-optimization model and the associated market mechanism. To make the discussion more general, in this paper, the single-period co-optimization model (II) in [1] will be extended into a one-shot multi-period co-optimization model, where the coupling of ramping and reserve in multi-period operations will be considered. Also, the multi-period pricing approach and settlement process associated with the multi-period co-optimization model will be discussed. In addition, in case study, the average system total costs of the proposed multi-period model and the multi-period traditional model are compared, and the effectiveness of some market properties in multi-period operation is tested.
Implementation of Linear Model Predictive Control -- Tutorial
Authors: Michael Fink
Date: 2021-09-24 00:00:00
Tasks: model, Model Predictive Control

This tutorial shows an overview of Model Predictive Control with a linear discrete-time system and constrained states and inputs. The focus is on the implementation of the method under consideration of stability and recursive feasibility. The MATLAB code for the examples and plots is available online.
Online Robust MPC based Emergency Maneuvering System for Autonomous Vehicles
Authors: Vivek Bithar, Punit Tulpule, Shawn Midlam-Mohler
Date: 2021-09-24 00:00:00
Tasks: Autonomous Vehicles, Model Predictive Control, Steering Control

Nonlinear Robust Model Predictive Control (RMPC) provides a very promising solution to the problem of automatic emergency maneuvering, which is capable of handling multiple possibly conflicting objectives of robustness and performance. Even though RMPC gives a suboptimal solution, the key challenge in real-time implementation is that it is computationally very demanding. In this paper a real-time capable robust tube MPC based framework for steering control during emergency obstacle avoidance maneuver is presented. The novelty of this framework lies in the robust integration of path planning and path following tasks of autonomous vehicles. A simulation study showcases the robust performance improvements due to the proposed strategy over a non-robust MPC in different extreme maneuvering scenarios.
Kinematic Control of 2-wheeled Segway
Authors: Palak Purohit, Poojan Modi, Udit Vyas
Date: 2021-09-24 00:00:00

The Segway is a popular self-balancing two-wheeled vehicle. In this paper, we present a control mechanism for the planar Segway problem. The open-loop analysis validates the fact that the system is unstable by default and there is a need to design a closed-loop feedback to establish control for the system. This has been done by implementing a PD controller for the multiple-output system. Since the kinematic equations of the system are non-linear initially, the controller has been designed by linearizing the equations about the equilibrium point. Later, the response of the non-linear system is examined using the designed controller.
FUBOCO: Structure Synthesis of Basic Op-Amps by FUnctional BlOck COmposition
Authors: Inga Abel, Helmut Graeb
Date: 2021-01-19 00:00:00

This paper presents a method to automatically synthesize the structure of an operational amplifier. It is positioned between approaches with fixed design plans and a small search space of structures and approaches with generic structural production rules and a large search space with technically impractical structures. The presented approach develops a hierarchical composition graph based on functional blocks that spans a search space of thousands of technically meaningful structure variants for single-output, fully-differential and complementary operational amplifiers. The search algorithm is a combined heuristic and enumerative process. The evaluation is based on circuit sizing with a library of behavioral equations of functional blocks. Formalizing the knowledge of functional blocks in op-amps for structural synthesis and sizing inherently reduces the search space and lessens the number of created topologies not fulfilling the specifications. Experimental results for the three op-amp classes are presented. An outlook how this method can be extended to multi-stage op-amps is given.