pwc-archive/papers-with-abstracts huggingface dataset:
Adaptive Bridge between Training and Inference for Dialogue
Authors: Haoran Xu, Hainan Zhang, Yanyan Zou, Hongshen Chen, Zhuoye Ding, Yanyan Lan
Date: 2021-10-22 00:00:00
Tasks: Dialogue Generation, NMT, Response Generation

Although exposure bias has been widely studied in some NLP tasks, it faces its unique challenges in dialogue response generation, the representative one-to-various generation scenario. In real human dialogue, there are many appropriate responses for the same context, not only with different expressions, but also with different topics. Therefore, due to the much bigger gap between various ground-truth responses and the generated synthetic response, exposure bias is more challenging in dialogue generation task. What's more, as MLE encourages the model to only learn the common words among different ground-truth responses, but ignores the interesting and specific parts, exposure bias may further lead to the common response generation problem, such as "I don't know" and "HaHa?" In this paper, we propose a novel adaptive switching mechanism, which learns to automatically transit between ground-truth learning and generated learning regarding the word-level matching score, such as the cosine similarity. Experimental results on both Chinese STC dataset and English Reddit dataset, show that our adaptive method achieves a significant improvement in terms of metric-based evaluation and human evaluation, as compared with the state-of-the-art exposure bias approaches. Further analysis on NMT task also shows that our model can achieve a significant improvement.
Probabilistic Numerical Method of Lines for Time-Dependent Partial Differential Equations
Authors: Nicholas Krämer, Jonathan Schmidt, Philipp Hennig
Date: 2021-10-22 00:00:00
Tasks: Bayesian Inference

This work develops a class of probabilistic algorithms for the numerical solution of nonlinear, time-dependent partial differential equations (PDEs). Current state-of-the-art PDE solvers treat the space- and time-dimensions separately, serially, and with black-box algorithms, which obscures the interactions between spatial and temporal approximation errors and misguides the quantification of the overall error. To fix this issue, we introduce a probabilistic version of a technique called method of lines. The proposed algorithm begins with a Gaussian process interpretation of finite difference methods, which then interacts naturally with filtering-based probabilistic ordinary differential equation (ODE) solvers because they share a common language: Bayesian inference. Joint quantification of space- and time-uncertainty becomes possible without losing the performance benefits of well-tuned ODE solvers. Thereby, we extend the toolbox of probabilistic programs for differential equation simulation to PDEs.
Testing network correlation efficiently via counting trees
Authors: Cheng Mao, Yihong Wu, Jiaming Xu, Sophie H. Yu
Date: 2021-10-22 00:00:00

We propose a new procedure for testing whether two networks are edge-correlated through some latent vertex correspondence. The test statistic is based on counting the co-occurrences of signed trees for a family of non-isomorphic trees. When the two networks are Erd\H{o}s-R\'enyi random graphs $\mathcal{G}(n,q)$ that are either independent or correlated with correlation coefficient $\rho$, our test runs in $n^{2+o(1)}$ time and succeeds with high probability as $n\to\infty$, provided that $n\min\{q,1-q\} \ge n^{-o(1)}$ and $\rho^2>\alpha \approx 0.338$, where $\alpha$ is Otter's constant so that the number of unlabeled trees with $K$ edges grows as $(1/\alpha)^K$. This significantly improves the prior work in terms of statistical accuracy, running time, and graph sparsity.
Optimal randomized classification trees
Authors: Rafael Blanquero, Emilio Carrizosa, Cristina Molero-Río, Dolores Romero Morales
Date: 2021-10-19 00:00:00
Tasks: Classification

Classification and Regression Trees (CARTs) are off-the-shelf techniques in modern Statistics and Machine Learning. CARTs are traditionally built by means of a greedy procedure, sequentially deciding the splitting predictor variable(s) and the associated threshold. This greedy approach trains trees very fast, but, by its nature, their classification accuracy may not be competitive against other state-of-the-art procedures. Moreover, controlling critical issues, such as the misclassification rates in each of the classes, is difficult. To address these shortcomings, optimal decision trees have been recently proposed in the literature, which use discrete decision variables to model the path each observation will follow in the tree. Instead, we propose a new approach based on continuous optimization. Our classifier can be seen as a randomized tree, since at each node of the decision tree a random decision is made. The computational experience reported demonstrates the good performance of our procedure.
An O(1) algorithm for implementing the LFU cache eviction scheme
Authors: Dhruv Matani, Ketan Shah, Anirban Mitra
Date: 2021-10-22 00:00:00

Cache eviction algorithms are used widely in operating systems, databases and other systems that use caches to speed up execution by caching data that is used by the application. There are many policies such as MRU (Most Recently Used), MFU (Most Frequently Used), LRU (Least Recently Used) and LFU (Least Frequently Used) which each have their advantages and drawbacks and are hence used in specific scenarios. By far, the most widely used algorithm is LRU, both for its $O(1)$ speed of operation as well as its close resemblance to the kind of behaviour that is expected by most applications. The LFU algorithm also has behaviour desirable by many real world workloads. However, in many places, the LRU algorithm is is preferred over the LFU algorithm because of its lower run time complexity of $O(1)$ versus $O(\log n)$. We present here an LFU cache eviction algorithm that has a runtime complexity of $O(1)$ for all of its operations, which include insertion, access and deletion(eviction).
MIC: Model-agnostic Integrated Cross-channel Recommenders
Authors: Yujie Lu, Ping Nie, Shengyu Zhang, Ming Zhao, Ruobing Xie, William Yang Wang, Yi Ren
Date: 2021-10-22 00:00:00
Tasks: model, Recommendation Systems, Retrieval, Semantic Similarity, Semantic Textual Similarity

Semantically connecting users and items is a fundamental problem for the matching stage of an industrial recommender system. Recent advances in this topic are based on multi-channel retrieval to efficiently measure users' interest on items from the massive candidate pool. However, existing work are primarily built upon pre-defined retrieval channels, including User-CF (U2U), Item-CF (I2I), and Embedding-based Retrieval (U2I), thus access to the limited correlation between users and items which solely entail from partial information of latent interactions. In this paper, we propose a model-agnostic integrated cross-channel (MIC) approach for the large-scale recommendation, which maximally leverages the inherent multi-channel mutual information to enhance the matching performance. Specifically, MIC robustly models correlation within user-item, user-user, and item-item from latent interactions in a universal schema. For each channel, MIC naturally aligns pairs with semantic similarity and distinguishes them otherwise with more uniform anisotropic representation space. While state-of-the-art methods require specific architectural design, MIC intuitively considers them as a whole by enabling the complete information flow among users and items. Thus MIC can be easily plugged into other retrieval recommender systems. Extensive experiments show that our MIC helps several state-of-the-art models boost their performance on two real-world benchmarks. The satisfactory deployment of the proposed MIC on industrial online services empirically proves its scalability and flexibility.
Wacky Weights in Learned Sparse Representations and the Revenge of Score-at-a-Time Query Evaluation
Authors: Joel Mackenzie, Andrew Trotman, Jimmy Lin
Date: 2021-10-22 00:00:00
Tasks: Retrieval

Recent advances in retrieval models based on learned sparse representations generated by transformers have led us to, once again, consider score-at-a-time query evaluation techniques for the top-k retrieval problem. Previous studies comparing document-at-a-time and score-at-a-time approaches have consistently found that the former approach yields lower mean query latency, although the latter approach has more predictable query latency. In our experiments with four different retrieval models that exploit representational learning with bags of words, we find that transformers generate "wacky weights" that appear to greatly reduce the opportunities for skipping and early exiting optimizations that lie at the core of standard document-at-a-time techniques. As a result, score-at-a-time approaches appear to be more competitive in terms of query evaluation latency than in previous studies. We find that, if an effectiveness loss of up to three percent can be tolerated, a score-at-a-time approach can yield substantial gains in mean query latency while at the same time dramatically reducing tail latency.
Conditional Temporal Neural Processes with Covariance Loss
Authors: Boseon Yoo, Jiwoo Lee, Janghoon Ju, Seijun Chung, Soyeon Kim, Jaesik Choi
Date: 2021-06-22 00:00:00
Proceeding: ICML 2021 6
Tasks: Time Series Forecasting, Traffic Prediction

We introduce a novel loss function, Covariance Loss, which is conceptually equivalent to conditional neural processes and has a form of regularization so that is applicable to many kinds of neural networks. With the proposed loss, mappings from input variables to target variables are highly affected by dependencies of target variables as well as mean activation and mean dependencies of input and target variables. This nature enables the resulting neural networks to become more robust to noisy observations and recapture missing dependencies from prior information. In order to show the validity of the proposed loss, we conduct extensive sets of experiments on real-world datasets with state-of-the-art models and discuss the benefits and drawbacks of the proposed Covariance Loss.
Airport-Airline Coordination with Economic, Environmental and Social Considerations
Authors: Aasheesh Dixit, Patanjal Kumar, Suresh Jakhar
Date: 2021-10-22 00:00:00

In this paper, we examine the effect of various contracts between a socially concerned airport and an environmentally conscious airline regarding their profitability and channel coordination under two distinct settings. First, we consider no government interventions, while in the second, we explore government-imposed taxations to curb emissions. Furthermore, we investigate the impact of passenger greening sensitivity, greening cost, and consumer surplus coefficient on conveyance fees, ticket fare, greening level and the channel welfare. Our analysis shows that the revenue sharing and linear two part tariff contracts coordinate the decentralised airport-airline channel. Our findings also reveal that players greening and social efforts can improve both the welfare and efficiency of the channel simultaneously. Importantly, under government interventions, taxation does help improve the greening level of the channel in both coordinating and non coordinating contracts. However, the greening level in the non-coordinating contracts with taxation is still less than the coordinating contracts even without tax. Finally, we also extended the model to include a duopoly airline market with pricing and greening competition. We analyze the effect of competetiton between airlines on airport utility, airline profit, ticket fare and greening level.
Game Redesign in No-regret Game Playing
Authors: Yuzhe ma, Young Wu, Xiaojin Zhu
Date: 2021-10-18 00:00:00

We study the game redesign problem in which an external designer has the ability to change the payoff function in each round, but incurs a design cost for deviating from the original game. The players apply no-regret learning algorithms to repeatedly play the changed games with limited feedback. The goals of the designer are to (i) incentivize all players to take a specific target action profile frequently; and (ii) incur small cumulative design cost. We present game redesign algorithms with the guarantee that the target action profile is played in T-o(T) rounds while incurring only o(T) cumulative design cost. Game redesign describes both positive and negative applications: a benevolent designer who incentivizes players to take a target action profile with better social welfare compared to the solution of the original game, or a malicious attacker whose target action profile benefits themselves but not the players. Simulations on four classic games confirm the effectiveness of our proposed redesign algorithms.
Model-aided Geometrical Shaping of Dual-polarization 4D Formats in the Nonlinear Fiber Channel
Authors: Gabriele Liga, Bin Chen, Alex Alvarado
Date: 2021-10-22 00:00:00

The geometry of dual-polarization four-dimensional constellations is optimized in the optical fiber channel using a recent nonlinear interference model. A 0.27 bit/4D rate gain and 13\% reach increase are attained compared to polarization-multiplexed formats.
An Economy of Neural Networks: Learning from Heterogeneous Experiences
Authors: Artem Kuriksha
Date: 2021-10-22 00:00:00
Tasks: Sensitivity

This paper proposes a new way to model behavioral agents in dynamic macro-financial environments. Agents are described as neural networks and learn policies from idiosyncratic past experiences. I investigate the feedback between irrationality and past outcomes in an economy with heterogeneous shocks similar to Aiyagari (1994). In the model, the rational expectations assumption is seriously violated because learning of a decision rule for savings is unstable. Agents who fall into learning traps save either excessively or save nothing, which provides a candidate explanation for several empirical puzzles about wealth distribution. Neural network agents have a higher average MPC and exhibit excess sensitivity of consumption. Learning can negatively affect intergenerational mobility.
Towards Using Clothes Style Transfer for Scenario-aware Person Video Generation
Authors: Jingning Xu, Benlai Tang, Mingjie Wang, Siyuan Bian, Wenyi Guo, Xiang Yin, Zejun Ma
Date: 2021-10-14 00:00:00
Tasks: Decoder, Style Transfer, Video Generation

Clothes style transfer for person video generation is a challenging task, due to drastic variations of intra-person appearance and video scenarios. To tackle this problem, most recent AdaIN-based architectures are proposed to extract clothes and scenario features for generation. However, these approaches suffer from being short of fine-grained details and are prone to distort the origin person. To further improve the generation performance, we propose a novel framework with disentangled multi-branch encoders and a shared decoder. Moreover, to pursue the strong video spatio-temporal consistency, an inner-frame discriminator is delicately designed with input being cross-frame difference. Besides, the proposed framework possesses the property of scenario adaptation. Extensive experiments on the TEDXPeople benchmark demonstrate the superiority of our method over state-of-the-art approaches in terms of image quality and video coherence.
Objective Measures of Perceptual Audio Quality Reviewed: An Evaluation of Their Application Domain Dependence
Authors: Matteo Torcoli, Thorsten Kastner, Jürgen Herre
Date: 2021-10-21 00:00:00

Over the past few decades, computational methods have been developed to estimate perceptual audio quality. These methods, also referred to as objective quality measures, are usually developed and intended for a specific application domain. Because of their convenience, they are often used outside their original intended domain, even if it is unclear whether they provide reliable quality estimates in this case. This work studies the correlation of well-known state-of-the-art objective measures with human perceptual scores in two different domains: audio coding and source separation. The following objective measures are considered: fwSNRseg, dLLR, PESQ, PEAQ, POLQA, PEMO-Q, ViSQOLAudio, (SI-)BSSEval, PEASS, LKR-PI, 2f-model, and HAAQI. Additionally, a novel measure (SI-SA2f) is presented, based on the 2f-model and a BSSEval-based signal decomposition. We use perceptual scores from 7 listening tests about audio coding and 7 listening tests about source separation as ground-truth data for the correlation analysis. The results show that one method (2f-model) performs significantly better than the others on both domains and indicate that the dataset for training the method and a robust underlying auditory model are crucial factors towards a universal, domain-independent objective measure.
On the design of regularized explicit predictive controllers from input-output data
Authors: Valentina Breschi, Andrea Sassella, Simone Formentin
Date: 2021-10-22 00:00:00

On the wave of recent advances in data-driven predictive control, we present an explicit predictive controller that can be constructed from a batch of input/output data only. The proposed explicit law is build upon a regularized implicit data-driven predictive control problem, so as to guarantee the uniqueness of the explicit predictive controller. As a side benefit, the use of regularization is shown to improve the capability of the explicit law in coping with noise on the data. The effectiveness of the retrieved explicit law and the repercussions of regularization on noise handling are analyzed on two benchmark simulation case studies, showing the potential of the proposed regularized explicit controller.
Multi-Horizon Planning of Multi-Energy Systems
Authors: Tim Felling, Oliver Levers, Philipp Fortenbacher
Date: 2021-10-22 00:00:00

In order to reach EU's goal of zero emissions in 2050, the energy system will go through a significant transition over the next decades. To substitute fossil energy carriers, renewable energy sources will be mainly integrated in the power system. Thereby, sector coupling will play a major role by making flexibility from other sectors such as heat or transport accessible to the power system. Planning the cost optimal transition requires a whole-system view over multiple horizons and across all sectors. This imposes the need for multi-energy system (MES) models coupled with multi-horizon investment models. This paper presents two multi-horizon planning approaches to determine the cost optimal pathway of a MES. As a major contribution, we propose a new method to incorporate technology-dependent learning cost curves in the planning problem and show that the resulting mixed-integer linear programming problem can be solved faster with a Benders decomposition technique as compared to a closed optimization. As a further contribution, we demonstrate the usefulness of our approach by showing the MES expansion pathway for a small German test system.
An analysis on metric-driven multi-target sensor management: GOSPA versus OSPA
Authors: Ángel F. García-Fernández, Marcel Hernandez, Simon Maskell
Date: 2021-10-22 00:00:00
Tasks: Management

This paper presents an analysis on sensor management using a cost function based on a multi-target metric, in particular, the optimal subpattern-assignment (OSPA) metric, the unnormalised OSPA (UOSPA) metric and the generalised OSPA (GOSPA) metric (\alpha=2). We consider the problem of managing an array of sensors, where each sensor is able to observe a region of the surveillance area, not covered by other sensors, with a given sensing cost. We look at the case in which there are far-away, independent potential targets, at maximum one per sensor region. In this set-up, the optimal action using GOSPA is taken for each sensor independently, as we may expect. On the contrary, as a consequence of the spooky effect at a distance in optimal OSPA/UOSPA estimation, the optimal actions for different sensors using OSPA and UOSPA are entangled.
Reinforcement Learning for Process Control with Application in Semiconductor Manufacturing
Authors: YanRong Li, Juan Du, Wei Jiang
Date: 2021-10-22 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Process control is widely discussed in the manufacturing process, especially for semiconductor manufacturing. Due to unavoidable disturbances in manufacturing, different process controllers are proposed to realize variation reduction. Since reinforcement learning (RL) has shown great advantages in learning actions from interactions with a dynamic system, we introduce RL methods for process control and propose a new controller called RL-based controller. Considering the fact that most existing process controllers mainly rely on a linear model assumption for the process input-output relationship, we first propose theoretical properties of RL-based controllers according to the linear model assumption. Then the performance of RL-based controllers and traditional process controllers (e.g., exponentially weighted moving average (EWMA), general harmonic rule (GHR) controllers) are compared for linear processes. Furthermore, we find that the RL-based controllers have potential advantages to deal with other complicated nonlinear processes that are with and without assumed explicit model formulations. The intensive numerical studies validate the advantages of the proposed RL-based controllers.
Security-Constrained Unit Commitment Con-sidering Locational Frequency Stability in Low-Inertia Power Grids
Authors: Mingjian Tuo, Xingpeng Li
Date: 2021-10-21 00:00:00

With increasing installation of wind and solar generation, conventional synchronous generators in power systems are gradually displaced resulting in a significant reduction in system inertia. Maintaining system frequency within acceptable ranges becomes more critical for the stability of a power system. In this paper, we first study the impact of inter-area oscillations on the system rate-of-change-of-frequency (RoCoF) security; then, the limitations on locational RoCoFs accounting for G-1 contingency stability are derived. By enforcing these frequency related constraints, a location based RoCoF constrained security constrained unit commitment (LRC-SCUC) model is proposed. Furthermore, an effective piecewise linearization (PWL) technique is employed to formulate a RoCoF linearization problem and linearize the nonlinear function representing the location based RoCoF constraints in SCUC. Simulation results reveal that the inclusion of inertia-related constraints can substantially improve the system stability at the cost of higher operation cost. The results also show that deploying virtual inertia techniques not only reduces the total cost, but also improves the system market efficiency.
Optimal Allocation of Virtual Inertia Devices for Enhancing Frequency Stability in Low-Inertia PowerSystems
Authors: Mingjian Tuo, Xingpeng Li
Date: 2021-10-21 00:00:00

As renewable resources gradually replace conventional generation based synchronous machines, the dynamics of the modern grid changes significantly and the system synchronous inertia decreases substantially. This transformation poses severe challenges for power system stability; for instance, it may lead to larger initial rate of change of frequency and increase frequency excursions. However, new opportunities also arise as novelconverter control techniques, so-called grid-forming strategies, show higher efficiency and faster response than conventional synchronous generators. They mainly involve virtual inertia (VI) emulation to mimic the behavior of synchronous machines. In this study, a state-space model for the power system network is developed with VI as a frequency regulation method. A reduced model based-norm algorithm (RMHA) considering the Fiedler mode impact is proposed in this paper to optimize the allocation of VI devices and improve power system frequency stability. Finally, case studies conducted on the IEEE 24-bus system demonstrate the efficacy of the proposed RMHA approach.