pwc-archive/papers-with-abstracts huggingface dataset:
Quantum tangent kernel
Authors: Norihito Shirai, Kenji Kubo, Kosuke Mitarai, Keisuke Fujii
Date: 2021-11-04 00:00:00
Tasks: BIG-bench Machine Learning, Quantum Machine Learning

Quantum kernel method is one of the key approaches to quantum machine learning, which has the advantages that it does not require optimization and has theoretical simplicity. By virtue of these properties, several experimental demonstrations and discussions of the potential advantages have been developed so far. However, as is the case in classical machine learning, not all quantum machine learning models could be regarded as kernel methods. In this work, we explore a quantum machine learning model with a deep parameterized quantum circuit and aim to go beyond the conventional quantum kernel method. In this case, the representation power and performance are expected to be enhanced, while the training process might be a bottleneck because of the barren plateaus issue. However, we find that parameters of a deep enough quantum circuit do not move much from its initial values during training, allowing first-order expansion with respect to the parameters. This behavior is similar to the neural tangent kernel in the classical literatures, and such a deep variational quantum machine learning can be described by another emergent kernel, quantum tangent kernel. Numerical simulations show that the proposed quantum tangent kernel outperforms the conventional quantum kernel method for an ansatz-generated dataset. This work provides a new direction beyond the conventional quantum kernel method and explores potential power of quantum machine learning with deep parameterized quantum circuits.
Causal Inference with Hidden Mediators
Authors: AmirEmad Ghassami, Alan Yang, Ilya Shpitser, Eric Tchetgen Tchetgen
Date: 2021-11-04 00:00:00
Tasks: Causal Inference

Proximal causal inference was recently proposed as a framework to identify causal effects from observational data in the presence of hidden confounders for which proxies are available. In this paper, we extend the proximal causal inference approach to settings where identification of causal effects hinges upon a set of mediators which are not observed, yet error prone proxies of the hidden mediators are measured. Specifically, (i) We establish causal hidden mediation analysis, which extends classical causal mediation analysis methods for identifying natural direct and indirect effects under no unmeasured confounding to a setting where the mediator of interest is hidden, but proxies of it are available. (ii) We establish hidden front-door criterion, which extends the classical front-door criterion to allow for hidden mediators for which proxies are available. (iii) We show that the identification of a certain causal effect called population intervention indirect effect remains possible with hidden mediators in settings where challenges in (i) and (ii) might co-exist. We view (i)-(iii) as important steps towards the practical application of front-door criteria and mediation analysis as mediators are almost always measured with error and thus, the most one can hope for in practice is that the measurements are at best proxies of mediating mechanisms. We propose identification approaches for the parameters of interest in our considered models. For the estimation aspect, we propose an influence function-based estimation method and provide an analysis for the robustness of the estimators.
Distributed Sparse Feature Selection in Communication-Restricted Networks
Authors: Hanie Barghi, Amir Najafi, Seyed Abolfazl Motahari
Date: 2021-11-02 00:00:00
Tasks: feature selection

This paper aims to propose and theoretically analyze a new distributed scheme for sparse linear regression and feature selection. The primary goal is to learn the few causal features of a high-dimensional dataset based on noisy observations from an unknown sparse linear model. However, the presumed training set which includes $n$ data samples in $\mathbb{R}^p$ is already distributed over a large network with $N$ clients connected through extremely low-bandwidth links. Also, we consider the asymptotic configuration of $1\ll N\ll n\ll p$. In order to infer the causal dimensions from the whole dataset, we propose a simple, yet effective method for information sharing in the network. In this regard, we theoretically show that the true causal features can be reliably recovered with negligible bandwidth usage of $O\left(N\log p\right)$ across the network. This yields a significantly lower communication cost in comparison with the trivial case of transmitting all the samples to a single node (centralized scenario), which requires $O\left(np\right)$ transmissions. Even more sophisticated schemes such as ADMM still have a communication complexity of $O\left(Np\right)$. Surprisingly, our sample complexity bound is proved to be the same (up to a constant factor) as the optimal centralized approach for a fixed performance measure in each node, while that of a na\"{i}ve decentralized technique grows linearly with $N$. Theoretical guarantees in this paper are based on the recent analytic framework of debiased LASSO in Javanmard et al. (2019), and are supported by several computer experiments performed on both synthetic and real-world datasets.
Sequential Movie Genre Prediction using Average Transition Probability with Clustering
Authors: Jihyeon Kim, Jinkyung Kim, Jaeyoung Choi
Date: 2021-11-04 00:00:00
Tasks: Clustering, Sequential Recommendation

In recent movie recommendations, predicting the user's sequential behavior and suggesting the next movie to watch is one of the most important issues. However, capturing such sequential behavior is not easy because each user's short-term or long-term behavior must be taken into account. For this reason, many research results show that the performance of recommending a specific movie is not very high in a sequential recommendation. In this paper, we propose a cluster-based method for classifying users with similar movie purchase patterns and a movie genre prediction algorithm rather than the movie itself considering their short-term and long-term behaviors. The movie genre prediction does not recommend a specific movie, but it predicts the genre for the next movie to watch in consideration of each user's preference for the movie genre based on the genre included in the movie. Through this, it is possible to provide appropriate guidelines for recommending movies including the genre to users who tend to prefer a specific genre. In particular, in this paper, users with similar genre preferences are organized into clusters to recommend genres, and in clusters that do not have relatively specific tendencies, genre prediction is performed by appropriately trimming genres that are not necessary for recommendation in order to improve performance. We evaluate our method on well-known movie datasets, and qualitatively that it captures personalized dynamics and is able to make meaningful recommendations.
MetaSets：Meta-Learning on Point Sets for Generalizable Representations
Authors: Chao Huang, Zhangjie Cao, Yunbo Wang, Jianmin Wang, Mingsheng Long
Date: 2021-06-01 00:00:00
Proceeding: CVPR 2021 6
Tasks: Domain Generalization

Deep learning techniques for point clouds have achieved strong performance on a range of 3D vision tasks. However, it is costly to annotate large-scale point sets, making it critical to learn generalizable representations that can transfer well across different point sets. In this paper, we study a new problem of 3D Domain Generalization (3DDG) with the goal to generalize the model to other unseen domains of point clouds without any access to them in the training process. It is a challenging problem due to the substantial geometry shift from simulated to real data, such that most existing 3D models underperform due to overfitting the complete geometries in the source domain. We propose to tackle this problem via MetaSets, which meta-learns point cloud representations from a group of classification tasks on carefully-designed transformed point sets containing specific geometry priors. The learned representations are more generalizable to various unseen domains of different geometries. We design two benchmarks for Sim-to-Real transfer of 3D point clouds. Experimental results show that MetaSets outperforms existing 3D deep learning methods by large margins.
Generalized Nesterov's Acceleration-incorporated Non-negative and Adaptive Latent Factor Analysis
Authors: Xin Luo, Yue Zhou, ZhiGang Liu, Lun Hu, Mengchu Zhou
Date: 2021-04-06 00:00:00
Proceeding: IEEE Transactions on Services Computing 2021 4
Tasks: Computational Efficiency

A non-negative latent factor (NLF) model with a single latent factor-dependent, non-negative and multiplicative update (SLF-NMU) algorithm is frequently adopted to extract useful knowledge from non-negative data represented by high-dimensional and sparse (HiDS) matrices arising from various service applications. However, its convergence rate is rather slow. To address this issue, this study proposes a Generalized Nesterov's acceleration-incorporated, Non-negative and Adaptive Latent Factor (GNALF) model. It results from a) incorporating a generalized Nesterov's accelerated gradient (NAG) method into an SLF-NMU algorithm, thereby achieving an NAG-incorporated and element-oriented non-negative (NEN) algorithm to perform efficient parameter update; and b) making its regularization and acceleration hyper-parameters self-adaptive via incorporating the principle of a particle swarm optimization algorithm into the training process, thereby implementing a highly adaptive and practical model. Empirical studies on six large sparse matrices from different recommendation service applications show that a GNALF model achieves very high convergence rate without the need of hyper-parameter tuning, making its computational efficiency significantly higher than state-of-the-art models. Meanwhile, such efficiency gain does not result in accuracy loss, since its prediction accuracy is comparable with its peers. Hence, it can better serve practical service applications with real-time demands.
A Supervised Segmentation Network for Hyperspectral Image Classification
Authors: Hao Sun, Xiangtao Zheng, Xiaoqiang Lu
Date: 2021-02-04 00:00:00
Proceeding: IEEE Transactions on Image Processing 2021 2
Tasks: Classification, Diversity, Hyperspectral Image Classification, image-classification, Image Classification

Recently, deep learning has drawn broad attention in the hyperspectral image (HSI) classification task. Many works have focused on elaborately designing various spectral-spatial networks, where convolutional neural network (CNN) is one of the most popular structures. To explore the spatial information for HSI classification, pixels with its adjacent pixels are usually directly cropped from hyperspectral data to form HSI cubes in CNN-based methods. However, the spatial land-cover distributions of cropped HSI cubes are usually complicated. The land-cover label of a cropped HSI cube cannot simply be determined by its center pixel. In addition, the spatial land-cover distribution of a cropped HSI cube is fixed and has less diversity. For CNN-based methods, training with cropped HSI cubes will result in poor generalization to the changes of spatial land-cover distributions. In this paper, an end-to-end fully convolutional segmentation network (FCSN) is proposed to simultaneously identify land-cover labels of all pixels in a HSI cube. First, several experiments are conducted to demonstrate that recent CNN-based methods show the weak generalization capabilities. Second, a fine label style is proposed to label all pixels of HSI cubes to provide detailed spatial land-cover distributions of HSI cubes. Third, a HSI cube generation method is proposed to generate plentiful HSI cubes with fine labels to improve the diversity of spatial land-cover distributions. Finally, a FCSN is proposed to explore spectral-spatial features from finely labeled HSI cubes for HSI classification. Experimental results show that FCSN has the superior generalization capability to the changes of spatial land-cover distributions.
Reinforcement learning based process optimization and strategy development in conventional tunneling
Authors: Georg H. Erharter, Tom F. Hansen, Zhongqiang Liu, Thomas Marcher
Date: 2021-04-17 00:00:00
Proceeding: Automation in Construction 2021 4
Tasks: reinforcement-learning, Reinforcement Learning (RL)

Reinforcement learning (RL) - a branch of machine learning - refers to the process of an agent learning to achieve a certain goal by interaction with its environment. The process of conventional tunneling shows many similarities, where a geotechnician (agent) tries to achieve a breakthrough (goal) by excavating the rockmass (environment) in an optimum way.
In this paper we present a novel RL based framework for strategy development for conventional tunneling. We developed a virtual environment with the goal of a tunnel breakthrough and with a deep Q-network as the agent's architecture. It can choose from different excavation sequences to reach that goal and learns to do so in an economical and safe way by getting feedback from a specially designed reward system. Result analyses show that the optimal policies have great similarities to current practices of sequential tunneling and the framework has the potential to discover new tunneling strategies.
A unified concurrent-composition method to state/event inference and concealment in discrete-event systems
Authors: Kuize Zhang
Date: 2021-11-03 00:00:00

Discrete-event systems usually consist of discrete states and transitions between them caused by spontaneous occurrences of labelled (aka partially-observed) events. Due to the partially-observed feature, fundamental properties therein could be classified into two categories: state/event-inference-based properties (e.g., strong detectability, diagnosability, and predictability) and state-concealment-based properties (e.g., opacity). Intuitively, the former category describes whether one can use observed output sequences to infer the current and subsequent states, past occurrences of faulty events, or future certain occurrences of faulty events; while the latter describes whether one cannot use observed output sequences to infer whether some secret states have been visited (that is, whether the DES can conceal the status that its secret states have been visited). Over the past two decades these properties were studied separately using different methods. In this review article, for labeled finite-state automata, a unified concurrent-composition method is shown to verify all above inference-based properties and concealment-based properties, resulting in a unified mathematical framework for the two categories of properties. In addition, compared with the previous methods in the literature, the concurrent-composition method does not depend on assumptions and is more efficient.
Self-Supervised Radio-Visual Representation Learning for 6G Sensing
Authors: Mohammed Alloulah, Akash Deep Singh, Maximilian Arnold
Date: 2021-11-01 00:00:00
Tasks: Representation Learning, Self-Supervised Learning

In future 6G cellular networks, a joint communication and sensing protocol will allow the network to perceive the environment, opening the door for many new applications atop a unified communication-perception infrastructure. However, interpreting the sparse radio representation of sensing scenes is challenging, which hinders the potential of these emergent systems. We propose to combine radio and vision to automatically learn a radio-only sensing model with minimal human intervention. We want to build a radio sensing model that can feed on millions of uncurated data points. To this end, we leverage recent advances in self-supervised learning and formulate a new label-free radio-visual co-learning scheme, whereby vision trains radio via cross-modal mutual information. We implement and evaluate our scheme according to the common linear classification benchmark, and report qualitative and quantitative performance metrics. In our evaluation, the representation learnt by radio-visual self-supervision works well for a downstream sensing demonstrator, and outperforms its fully-supervised counterpart when less labelled data is used. This indicates that self-supervised learning could be an important enabler for future scalable radio sensing systems.
Effective Resistance for Pandemics: Mobility Network Sparsification for High-Fidelity Epidemic Simulation
Authors: Alexander M. Mercier, Samuel V. Scarpino, Cristopher Moore
Date: 2021-11-03 00:00:00
Tasks: Epidemiology

Network science has increasingly become central to the field of epidemiology and our ability to respond to infectious disease threats. However, many networks derived from modern datasets are not just large, but dense, with a high ratio of edges to nodes. This includes human mobility networks where most locations have a large number of links to many other locations. Simulating large-scale epidemics requires substantial computational resources and in many cases is practically infeasible. One way to reduce the computational cost of simulating epidemics on these networks is sparsification, where a representative subset of edges is selected based on some measure of their importance. We test several sparsification strategies, ranging from naive thresholding to random sampling of edges, on mobility data from the U.S. Following recent work in computer science, we find that the most accurate approach uses the effective resistances of edges, which prioritizes edges that are the only efficient way to travel between their endpoints. The resulting sparse network preserves many aspects of the behavior of an SIR model, including both global quantities, like the epidemic size, and local details of stochastic events, including the probability each node becomes infected and its distribution of arrival times. This holds even when the sparse network preserves fewer than $10\%$ of the edges of the original network. In addition to its practical utility, this method helps illuminate which links of a weighted, undirected network are most important to disease spread.
Barrier States Embedded Iterative Dynamic Game for Robust and Safe Trajectory Optimization
Authors: Hassan Almubarak, Evangelos A. Theodorou, Nader Sadegh
Date: 2021-11-04 00:00:00
Tasks: Decision Making

Considering uncertainties and disturbances is an important, yet challenging, step in successful decision making. The problem becomes more challenging in safety-constrained environments. In this paper, we propose a robust and safe trajectory optimization algorithm through solving a constrained min-max optimal control problem. The proposed method leverages a game theoretic differential dynamic programming approach with barrier states to handle parametric and non-parametric uncertainties in safety-critical control systems. Barrier states are embedded into the differential game's dynamics and cost to portray the constrained environment in a higher dimensional state space and certify the safety of the optimized trajectory. Moreover, to find a convergent optimal solution, we propose to perform line-search in a Stackleberg (leader-follower) game fashion instead of picking a constant learning rate. The proposed algorithm is evaluated on a velocity-constrained inverted pendulum model in a moderate and high parametric uncertainties to show its efficacy in such a comprehensible system. The algorithm is subsequently implemented on a quadrotor in a windy environment in which sinusoidal wind turbulences applied in all directions.
Safe, efficient and socially-compatible decision of automated vehicles: a case study of unsignalized intersection driving
Authors: Daofei Li, Ao Liu, Hao Pan, Wentao Chen
Date: 2021-11-04 00:00:00
Tasks: Decision Making

Safe and smooth interacting with other vehicles is one of the ultimate goals of driving automation. However, recent reports of demonstrative deployments of automated vehicles (AVs) indicate that AVs are still difficult to meet the expectation of other interacting drivers, which leads to several AV accidents involving human-driven vehicles (HVs). This is most likely due to the lack of understanding about the dynamic interaction process, especially about the human drivers. By investigating the causes of 4,300 video clips of traffic accidents, we find that the limited dynamic visual field of drivers is one leading factor in inter-vehicle interaction accidents, especially in those involving trucks. A game-theoretic decision algorithm considering social compatibility is proposed to handle the interaction with a human-driven truck at an unsignalized intersection. Starting from a probabilistic model for the visual field characteristics of truck drivers, social fitness and reciprocal altruism in the decision are incorporated in the game payoff design. Human-in-the-loop experiments are carried out, in which 24 subjects are invited to drive and interact with AVs deployed with the proposed algorithm and two comparison algorithms. Totally 207 cases of intersection interactions are obtained and analyzed, which shows that the proposed decision-making algorithm can not only improve both safety and time efficiency, but also make AV decisions more in line with the expectation of interacting human drivers. These findings can help inform the design of automated driving decision algorithms, to ensure that AVs can be safely and efficiently integrated into the human-dominated traffic.
idSTLPy: A Python Toolbox for Active Perception and Control
Authors: Rafael Rodrigues da Silva, Kunal Yadav, Hai Lin
Date: 2021-11-04 00:00:00
Tasks: Motion Planning

This paper describes a Python toolbox for active perception and control synthesis of probabilistic signal temporal logic (PrSTL) formulas of switched linear systems with additive Gaussian disturbances and measurement noises. We implement a counterexample-guided synthesis strategy that combines Bounded Model Checking, linear programming, and sampling-based motion planning techniques. We illustrate our approach and the toolbox throughout the paper with a motion planning example for a vehicle with noisy localization. The code is available at \url{https://codeocean.com/capsule/0013534/tree}.
Monetarization of the Feasible Operation Region based on a Cost-Optimal Flexibility Disaggregation
Authors: Marcel Sarstedt, Lutz Hofmann
Date: 2021-11-04 00:00:00

Hierarchical multi-(voltage-)level grid control strategies are an appropriate design concept for the coordination of future TSO/DSO- and DSO/DSO-interactions. Hierarchical approaches are based on the aggregation of decentralized ancillary service potentials, represented by converter-coupled, communicable active and reactive power flexibility providing units (FPU) (e.g. wind turbines) at vertical system interfaces. The resulting PQ-polygon made available by the DSO for a potential request of ancillary service flexibilities by the TSO is called feasible operation region (FOR). A monetarization of the FOR is necessary for the implementation as operational degree of freedom within higher-level grid control. This paper presents an approach for the monetarization of the FOR in the context of a hierarchical multi-level flexibility market by a cost structure using metadata from population based optimization methods. Multiple FPU flexibility polygons at a single bus are aggregated for a reduction of the search space dimensions. The main contribution of the proposed method is the cost-optimal disaggregation of a flexibility demand to the single FPUs within the aggregated FPU by a mixed integer linear program (MILP). Therefore, a local flexibility market considering bids for the active and reactive power flexibilities by the FPUs stakeholders is assumed. The approach is applied within a case-study of the Cigr\'e medium voltage system.