pwc-archive/papers-with-abstracts huggingface dataset:
Interpretable Feature Construction for Time Series Extrinsic Regression
Authors: Dominique Gay, Alexis Bondu, Vincent Lemaire, Marc Boullé
Date: 2021-03-15 00:00:00
Tasks: Benchmarking, regression, Time Series, Time Series Analysis

Supervised learning of time series data has been extensively studied for the case of a categorical target variable. In some application domains, e.g., energy, environment and health monitoring, it occurs that the target variable is numerical and the problem is known as time series extrinsic regression (TSER). In the literature, some well-known time series classifiers have been extended for TSER problems. As first benchmarking studies have focused on predictive performance, very little attention has been given to interpretability. To fill this gap, in this paper, we suggest an extension of a Bayesian method for robust and interpretable feature construction and selection in the context of TSER. Our approach exploits a relational way to tackle with TSER: (i), we build various and simple representations of the time series which are stored in a relational data scheme, then, (ii), a propositionalisation technique (based on classical aggregation / selection functions from the relational data field) is applied to build interpretable features from secondary tables to "flatten" the data; and (iii), the constructed features are filtered out through a Bayesian Maximum A Posteriori approach. The resulting transformed data can be processed with various existing regressors. Experimental validation on various benchmark data sets demonstrates the benefits of the suggested approach.
Collaborative Filtering Approach to Link Prediction
Authors: Yan-Li Lee, Tao Zhou
Date: 2021-03-14 00:00:00
Tasks: Collaborative Filtering, Link Prediction, Prediction, Recommendation Systems

Link prediction is a fundamental challenge in network science. Among various methods, local similarity indices are widely used for their high cost-performance. However, the performance is less robust: for some networks local indices are highly competitive to state-of-the-art algorithms while for some other networks they are very poor. Inspired by techniques developed for recommender systems, we propose an enhancement framework for local indices based on collaborative filtering (CF). Considering the delicate but important difference between personalized recommendation and link prediction, we further propose an improved framework named as self-included collaborative filtering (SCF). The SCF framework significantly improved the accuracy and robustness of well-known local indices. The combination of SCF framework and a simple local index can produce an index with competitive performance and much lower complexity compared with elaborately-designed state-of-the-art algorithms.
TF-Label: a Topological-Folding Labeling Scheme for Reachability Querying in a Large Graph
Authors: Cheng, J., Huang, S., Wu, H., Fu, A.W.
Date: 2013-06-01 00:00:00

Reachability querying is a basic graph operation with numerous important applications in databases, network analysis, computational biology, software engineering, etc. Although many indexes have been proposed to answer reachability queries, most of them are only efficient for handling relatively small graphs. We propose TF-label, an efficient and scalable labeling scheme for processing reachability queries. TF-label is constructed based on a novel topological folding (TF) that recursively folds an input graph into half so as to reduce the label size, thus improving query efficiency. We show that TF-label is efficient to construct and propose efficient algorithms and optimization schemes. Our experiments verify that TF-label is significantly more scalable and efficient than the state-of-the-art methods in both index construction and query processing.
Efficient approximations of the multi-sensor labelled multi-Bernoulli filter
Authors: S. C. J. Robertson, C. E. van Daalen, J. A. du Preez
Date: 2021-03-18 00:00:00

In this paper, we propose two efficient, approximate formulations of the multi-sensor labelled multi-Bernoulli (LMB) filter, which both allow the sensors' measurement updates to be computed in parallel. Our first filter is based on the direct mathematical manipulation of the multi-sensor, multi-object Bayes filter's posterior distribution. Unfortunately, it requires the division of probability distributions and its extension beyond linear Gaussian applications is not obvious. Our second filter is based on geometric average fusion and it approximates the multi-sensor, multi-object Bayes filter's posterior distribution using the geometric average of each sensor's measurement-updated distribution. This filter can be used under non-linear conditions; however, it is not as accurate as our first filter. In both cases, we approximate the LMB filter's measurement update using an existing loopy belief propagation algorithm. Both filters have a constant complexity in the number of sensors, and linear complexity in both number of measurements and objects. This is an improvement on an iterated-corrector LMB (IC-LMB) filter, which has linear complexity in the number of sensors. The proposed filters are of interest when tracking many objects using several sensors, where filter run-time is more important than filter accuracy. We evaluate both filters' performances on simulated data and the results indicate that the filters' loss of accuracy compared to the IC-LMB filter is not significant.
A Subjective Study on Videos at Various Bit Depths
Authors: Alex Mackin, Di Ma, Fan Zhang, David Bull
Date: 2021-03-18 00:00:00

Bit depth adaptation, where the bit depth of a video sequence is reduced before transmission and up-sampled during display, can potentially reduce data rates with limited impact on perceptual quality. In this context, we conducted a subjective study on a UHD video database, BVI-BD, to explore the relationship between bit depth and visual quality. In this work, three bit depth adaptation methods are investigated, including linear scaling, error diffusion, and a novel adaptive Gaussian filtering approach. The results from a subjective experiment indicate that above a critical bit depth, bit depth adaptation has no significant impact on perceptual quality, while reducing the amount information that is required to be transmitted. Below the critical bit depth, advanced adaptation methods can be used to retain `good' visual quality (on average) down to around 2 bits per color channel for the outlined experimental setup - a large reduction compared to the typically used 8 bits per color channel. A selection of image quality metrics were subsequently bench-marked on the subjective data, and analysis indicates that a bespoke quality metric is required for bit depth adaptation.
Constrained Radar Waveform Design for Range Profiling
Authors: Bo Tang, Jun Liu, Hai Wang, Yihua Hu
Date: 2021-03-18 00:00:00
Tasks: Radar waveform design

Range profiling refers to the measurement of target response along the radar slant range. It plays an important role in automatic target recognition. In this paper, we consider the design of transmit waveform to improve the range profiling performance of radar systems. Two design metrics are adopted for the waveform optimization problem: one is to maximize the mutual information between the received signal and the target impulse response (TIR); the other is to minimize the minimum mean-square error for estimating the TIR. In addition, practical constraints on the waveforms are considered, including an energy constraint, a peak-to-average-power-ratio constraint, and a spectral constraint. Based on minorization-maximization, we propose a unified optimization framework to tackle the constrained waveform design problem. Numerical examples show the superiority of the waveforms synthesized by the proposed algorithms.
In-flight actuator failure recovery of a hexrotor via multiple models and extended high-gain observers
Authors: Connor J. Boss, Vaibhav Srivastava
Date: 2021-03-18 00:00:00

We study an in-flight actuator failure recovery problem for a hexrotor UAV. The hexrotor may experience external disturbances and modeling error, which are accounted for in the control design and distinguished from an actuator failure. A failure of any one actuator occurs during flight and must be identified quickly and accurately. This is achieved through the use of a multiple-model, multiple extended high-gain observer (EHGO) based output feedback control strategy. The family of EHGOs are responsible for estimating states, disturbances, and are used to select the appropriate model based on the system dynamics after a failure has occurred. The proposed method is theoretically analyzed and validated through simulations and experiments.
Data-driven inference on optimal input-output properties of polynomial systems with focus on nonlinearity measures
Authors: Tim Martin, Frank Allgöwer
Date: 2021-03-18 00:00:00

In the context of dynamical systems, nonlinearity measures quantify the strength of nonlinearity by means of the distance of their input-output behaviour to a set of linear input-output mappings. In this paper, we establish a framework to determine nonlinearity measures and other optimal input-output properties for nonlinear polynomial systems without explicitly identifying a model but from a finite number of input-state measurements which are subject to noise. To this end, we deduce from data for the unidentified ground-truth system three possible set-membership representations, compare their accuracy, and prove that they are asymptotically consistent with respect to the amount of samples. Moreover, we leverage these representations to compute guaranteed upper bounds on nonlinearity measures and the corresponding optimal linear approximation model via semi-definite programming. Furthermore, we extend the established framework to determine optimal input-output properties described by time domain hard integral quadratic constraints.
Advances in 3D scattering tomography of cloud micro-physics
Authors: Masada Tzabari, Vadim Holodovsky, Omer Shubi, Eitan Eshkol, Yoav Y. Schechner
Date: 2021-03-18 00:00:00
Tasks: Retrieval

We introduce new adjustments and advances in space-borne 3D volumetric scattering-tomography of cloud micro-physics. The micro-physical properties retrieved are the liquid water content and effective radius within a cloud. New adjustments include an advanced perspective polarization imager model, and the assumption of 3D variation of the effective radius. Under these assumptions, we advanced the retrieval to yield results that (compared to the simulated ground-truth) have smaller errors than the prior art. Elements of our advancement include initialization by a parametric horizontally-uniform micro-physical model. The parameters of this initialization are determined by a grid search of the cost function. Furthermore, we added viewpoints corresponding to single-scattering angles, where polarization yields enhanced sensitivity to the droplet micro-physics (i.e., the cloudbow region). In addition, we introduce an optional adjustment, in which optimization of the liquid water content and effective radius are separated to alternating periods. The suggested initialization model and additional advances have been evaluated by retrieval of a set of large-eddy simulation clouds.
Systematic Mapping Study on the Machine Learning Lifecycle
Authors: Yuanhao Xie, Luís Cruz, Petra Heck, Jan S. Rellermeyer
Date: 2021-03-11 00:00:00
Tasks: BIG-bench Machine Learning, Management

The development of artificial intelligence (AI) has made various industries eager to explore the benefits of AI. There is an increasing amount of research surrounding AI, most of which is centred on the development of new AI algorithms and techniques. However, the advent of AI is bringing an increasing set of practical problems related to AI model lifecycle management that need to be investigated. We address this gap by conducting a systematic mapping study on the lifecycle of AI model. Through quantitative research, we provide an overview of the field, identify research opportunities, and provide suggestions for future research. Our study yields 405 publications published from 2005 to 2020, mapped in 5 different main research topics, and 31 sub-topics. We observe that only a minority of publications focus on data management and model production problems, and that more studies should address the AI lifecycle from a holistic perspective.
Optimal Targeting in Fundraising: A Causal Machine-Learning Approach
Authors: Tobias Cagala, Ulrich Glogowsky, Johannes Rincke, Anthony Strittmatter
Date: 2021-03-10 00:00:00
Tasks: BIG-bench Machine Learning

Ineffective fundraising lowers the resources charities can use to provide goods. We combine a field experiment and a causal machine-learning approach to increase a charity's fundraising effectiveness. The approach optimally targets a fundraising instrument to individuals whose expected donations exceed solicitation costs. Our results demonstrate that machine-learning-based optimal targeting allows the charity to substantially increase donations net of fundraising costs relative to uniform benchmarks in which either everybody or no one receives the gift. To that end, it (a) should direct its fundraising efforts to a subset of past donors and (b) never address individuals who were previously asked but never donated. Further, we show that the benefits of machine-learning-based optimal targeting even materialize when the charity only exploits publicly available geospatial information or applies the estimated optimal targeting rule to later fundraising campaigns conducted in similar samples. We conclude that charities not engaging in optimal targeting waste significant resources.
Failure Analysis on Multivariate Time-series Data given Uncertain Labels
Authors: Hao Huang, Shinjae Yoo, and Yunwen Xu
Date: 2019-07-20 00:00:00
Tasks: Time Series, Time Series Analysis

Machine failure analysis and detection is critical to today’s industrial
society. Given a number of failure events on multivariate temporal data, the ability to identify 1) the discriminative patterns prior to the events and 2) the most
relevant features to the failure has practical use for early warning and root cause
analysis. However, since these patterns are not necessarily adjacent to the onset
of failure in time, faulty labels are often with uncertainty, which makes traditional
supervised detection methods inapplicable. To address the label uncertainty and
learn the complicated correlation in multivariate time series, we design Failure
Analysis on Multivariate Time-series Data (MAMT) that jointly selects the most
failure-relevant features and time-instances by a novel dynamic and directional
label diffusion process. Extensive experiments demonstrate that MAMT is more
effective while more efficient than popular baselines
Friendship and Mobility: User Movement In Location-Based Social Networks
Authors: E. Cho, S.A. Myers, J. Leskovec
Date: 2011-08-21 00:00:00

Even though human movement and mobility patterns have a high degree of freedom and variation, they also exhibit structural pat- terns due to geographic and social constraints. Using cell phone location data, as well as data from two online location-based social networks, we aim to understand what basic laws govern human mo- tion and dynamics. We find that humans experience a combination of periodic movement that is geographically limited and seemingly random jumps correlated with their social networks. Short-ranged travel is periodic both spatially and temporally and not effected by the social network structure, while long-distance travel is more in- fluenced by social network ties. We show that social relationships can explain about 10% to 30% of all human movement, while pe- riodic behavior explains 50% to 70%. Based on our findings, we develop a model of human mobility that combines periodic short range movements with travel due to the social network structure. We show that our model reliably predicts the locations and dynam- ics of future human movement and gives an order of magnitude better performance than present models of human mobility.
Variational Autoencoders for Collaborative Filtering
Authors: D. Liang, R.G. Krishnan, M.D. Hoffman
Date: 2018-02-16 00:00:00
Tasks: Bayesian Inference, Collaborative Filtering, Language Modeling, Language Modelling, parameter estimation, Recommendation Systems

We extend variational autoencoders (vaes) to collaborative filtering for implicit feedback. This non-linear probabilistic model enables us to go beyond the limited modeling capacity of linear factor models which still largely dominate collaborative filtering research. We introduce a generative model with multinomial likelihood and use Bayesian inference for parameter estimation. Despite widespread use in language modeling and economics, the multinomial likeli- hood receives less attention in the recommender systems literature. We introduce a different regularization parameter for the learning objective, which proves to be crucial for achieving competitive per- formance. Remarkably, there is an efficient way to tune the parame- ter using annealing. The resulting model and learning algorithm has information-theoretic connections to maximum entropy discrimi- nation and the information bottleneck principle. Empirically, we show that the proposed approach significantly outperforms several state-of-the-art baselines, including two recently-proposed neural network approaches, on several real-world datasets. We also pro- vide extended experiments comparing the multinomial likelihood with other commonly used likelihood functions in the latent factor collaborative filtering literature and show favorable results. Finally, we identify the pros and cons of employing a principled Bayesian inference approach and characterize settings where it provides the most significant improvements.
Location Recommendation in Location-based Social Networks using User Check-in Data
Authors: Hao Wang, Manolis Terrovitis, Nikos Mamoulis
Date: 2013-11-01 00:00:00
Tasks: Recommendation Systems

This paper studies the problem of recommending new venues
to users who participate in location-based social networks
(LBSNs). As an increasingly larger number of users partake
in LBSNs, the recommendation problem in this setting
has attracted significant attention in research and in practical
applications. The detailed information about past user
behavior that is traced by the LBSN differentiates the problem
significantly from its traditional settings. The spatial
nature in the past user behavior and also the information
about the user social interaction with other users, provide a
richer background to build a more accurate and expressive
recommendation model.
Although there have been extensive studies on recommender
systems working with user-item ratings, GPS trajectories,
and other types of data, there are very few approaches
that exploit the unique properties of the LBSN user check-in
data. In this paper, we propose algorithms that create recommendations
based on four factors: a) past user behavior
(visited places), b) the location of each venue, c) the social
relationships among the users, and d) the similarity between
users. The proposed algorithms outperform traditional recommendation
algorithms and other approaches that try to
exploit LBSN information.
To design our recommendation algorithms we study the
properties of two real LBSNs, Brightkite and Gowalla, and
analyze the relation between users and visited locations. An
experimental evaluation using data from these LBSNs shows
that the exploitation of the additional geographical and social
information allows our proposed
Rank-GeoFM: A Ranking based Geographical Factorization Method for Point of Interest Recommendation
Authors: Xutao  Li, Gao Cong, Xiaoli  Li, Tuan Anh Nguyen Pham, Shonali Priyadarsini Krishnaswamy
Date: 2015-08-01 00:00:00
Tasks: Recommendation Systems

With the rapid growth of location-based social networks, Point of Interest (POI) recommendation has become an
important research problem. However, the scarcity of the
check-in data, a type of implicit feedback data, poses a severe challenge for existing POI recommendation methods. Moreover, different types of context information about
POIs are available and how to leverage them becomes another challenge. In this paper, we propose a ranking based
geographical factorization method, called Rank-GeoFM, for
POI recommendation, which addresses the two challenges.
In the proposed model, we consider that the check-in frequency characterizes users’ visiting preference and learn the
factorization by ranking the POIs correctly. In our model, POIs both with and without check-ins will contribute to
learning the ranking and thus the data sparsity problem can
be alleviated. In addition, our model can easily incorporate different types of context information, such as the geographical influence and temporal influence. We propose a
stochastic gradient descent based algorithm to learn the factorization. Experiments on publicly available datasets under
both user-POI setting and user-time-POI setting have been
conducted to test the effectiveness of the proposed method.
Experimental results under both settings show that the proposed method outperforms the state-of-the-art methods significantly in terms of recommendation accuracy.
Point-of-Interest Recommendations: Learning Potential Check-ins from Friends
Authors: Huayu Li, Yong  Ge, Richang Hong, Hengshu  Zhu
Date: 2016-08-01 00:00:00
Tasks: Decision Making, Recommendation Systems

The emergence of Location-based Social Network (LBSN) services provides a wonderful opportunity to build personalized Point-of-Interest (POI) recommender systems. Although a personalized POI recommender system can significantly facilitate users' outdoor activities, it faces many challenging problems, such as the hardness to model user's POI decision making process and the difficulty to address data sparsity and user/location cold-start problem. To cope with these challenges, we define three types of friends (i.e., social friends, location friends, and neighboring friends) in LBSN, and develop a two-step framework to leverage the information of friends to improve POI recommendation accuracy and address cold-start problem. Specifically, we first propose to learn a set of potential locations that each individual's friends have checked-in before and this individual is most interested in. Then we incorporate three types of check-ins (i.e., observed check-ins, potential check-ins and other unobserved check-ins) into matrix factorization model using two different loss functions (i.e., the square error based loss and the ranking error based loss). To evaluate the proposed model, we conduct extensive experiments with many state-of-the-art baseline methods and evaluation metrics on two real-world data sets. The experimental results demonstrate the effectiveness of our methods.