pwc-archive/papers-with-abstracts huggingface dataset:
Synchronising speech segments with musical beats in Mandarin and English singing
Authors: Cong Zhang, Jian Zhu
Date: 2021-06-18 00:00:00

Generating synthesised singing voice with models trained on speech data has many advantages due to the models' flexibility and controllability. However, since the information about the temporal relationship between segments and beats are lacking in speech training data, the synthesised singing may sound off-beat at times. Therefore, the availability of the information on the temporal relationship between speech segments and music beats is crucial. The current study investigated the segment-beat synchronisation in singing data, with hypotheses formed based on the linguistics theories of P-centre and sonority hierarchy. A Mandarin corpus and an English corpus of professional singing data were manually annotated and analysed. The results showed that the presence of musical beats was more dependent on segment duration than sonority. However, the sonority hierarchy and the P-centre theory were highly related to the location of beats. Mandarin and English demonstrated cross-linguistic variations despite exhibiting common patterns.
Field-aware factorization machines for CTR prediction
Authors: Yuchin Juan, Yong Zhuang, Wei-Sheng Chin, Chih-Jen Lin
Date: 2016-09-07 00:00:00
Proceeding: RecSys 2016 9
Tasks: Click-Through Rate Prediction, Prediction, Recommendation Systems

Click-through rate (CTR) prediction plays an important role
in computational advertising. Models based on degree-2
polynomial mappings and factorization machines (FMs) are
widely used for this task. Recently, a variant of FMs, fieldaware factorization machines (FFMs), outperforms existing
models in some world-wide CTR-prediction competitions.
Based on our experiences in winning two of them, in this
paper we establish FFMs as an effective method for classifying large sparse data including those from CTR prediction. First, we propose efficient implementations for training
FFMs. Then we comprehensively analyze FFMs and compare this approach with competing models. Experiments
show that FFMs are very useful for certain classification
problems. Finally, we have released a package of FFMs for
public use.
Factorization Machines
Authors: Steffen Rendle
Date: 2011-01-20 00:00:00
Proceeding: 2010/01/01 2010 1
Tasks: parameter estimation, Recommendation Systems

In this paper, we introduce Factorization Machines
(FM) which are a new model class that combines the advantages
of Support Vector Machines (SVM) with factorization models.
Like SVMs, FMs are a general predictor working with any
real valued feature vector. In contrast to SVMs, FMs model all
interactions between variables using factorized parameters. Thus
they are able to estimate interactions even in problems with huge
sparsity (like recommender systems) where SVMs fail. We show
that the model equation of FMs can be calculated in linear time
and thus FMs can be optimized directly. So unlike nonlinear
SVMs, a transformation in the dual form is not necessary and
the model parameters can be estimated directly without the need
of any support vector in the solution. We show the relationship
to SVMs and the advantages of FMs for parameter estimation
in sparse settings.
On the other hand there are many different factorization models like matrix factorization, parallel factor analysis or specialized
models like SVD++, PITF or FPMC. The drawback of these
models is that they are not applicable for general prediction tasks
but work only with special input data. Furthermore their model
equations and optimization algorithms are derived individually
for each task. We show that FMs can mimic these models just
by specifying the input data (i.e. the feature vectors). This makes
FMs easily applicable even for users without expert knowledge
in factorization models.
Index Terms—factorization machine; sparse data; tensor factorization; support vector machine
Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations
Authors: Hongyan Tang, Junning Liu, Ming Zhao, Xudong Gong
Date: 2020-02-22 00:00:00
Proceeding: RecSys 2020 2
Tasks: Click-Through Rate Prediction, Multi-Task Learning, Recommendation Systems, Representation Learning

Multi-task learning (MTL) has been successfully applied to many recommendation applications. However, MTL models often suffer from performance degeneration with negative transfer due to the complex and competing task correlation in real-world recommender systems. Moreover, through extensive experiments across SOTA MTL models, we have observed an interesting seesaw phenomenon that performance of one task is often improved by hurting the performance of some other tasks. To address these issues, we propose a Progressive Layered Extraction (PLE) model with a novel sharing structure design. PLE separates shared components and task-specific components explicitly and adopts a progressive routing mechanism to extract and separate deeper semantic knowledge gradually, improving efficiency of joint representation learning and information routing across tasks in a general setup. We apply PLE to both complicatedly correlated and normally correlated tasks, ranging from two-task cases to multi-task cases on a real-world Tencent video recommendation dataset with 1 billion samples, and results show that PLE outperforms state-of-the-art MTL models significantly under different task correlations and task-group size. Furthermore, online evaluation of PLE on a large-scale content recommendation platform at Tencent manifests 2.23% increase in view-count and 1.84% increase in watch time compared to SOTA MTL models, which is a significant improvement and demonstrates the effectiveness of PLE. Finally, extensive offline experiments on public benchmark datasets demonstrate that PLE can be applied to a variety of scenarios besides recommendations to eliminate the seesaw phenomenon. PLE now has been deployed to the online video recommender system in Tencent successfully.
Learning deep structured semantic models for web search using clickthrough data
Authors: Po-Sen Huang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Acero, Larry Heck
Date: 2013-10-27 00:00:00
Proceeding: CIKM 2013 10
Tasks: Document Ranking

Latent semantic models, such as LSA, intend to map a query to its
relevant documents at the semantic level where keyword-based
matching often fails. In this study we strive to develop a series of
new latent semantic models with a deep structure that project
queries and documents into a common low-dimensional space
where the relevance of a document given a query is readily
computed as the distance between them. The proposed deep
structured semantic models are discriminatively trained by
maximizing the conditional likelihood of the clicked documents
given a query using the clickthrough data. To make our models
applicable to large-scale Web search applications, we also use a
technique called word hashing, which is shown to effectively
scale up our semantic models to handle large vocabularies which
are common in such tasks. The new models are evaluated on a
Web document ranking task using a real-world data set. Results
show that our best model significantly outperforms other latent
semantic models, which were considered state-of-the-art in the
performance prior to the work presented in this paper.
A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems
Authors: Ali Elkahky, Yang song, Xiaodong He
Date: 2015-05-28 00:00:00
Proceeding: WWW 2015 5
Tasks: News Recommendation, Recommendation Systems

Recent online services rely heavily on automatic personalization to recommend relevant content to a large number of users. This requires systems to scale promptly to accommodate the stream of new users visiting the online services for the first time. In this work, we propose a content-based recommendation system to address both the recommendation quality and the system scalability. We propose to use a rich feature set to represent users, according to their web browsing history and search queries. We use a Deep Learning approach to map users and items to a latent space where the similarity between users and their preferred items is maximized. We extend the model to jointly learn from features of items from different domains and user features by introducing a multi-view Deep Learning model. We show how to make this rich-feature based user representation scalable by reducing the dimension of the inputs and the amount of training data. The rich user feature representation allows the model to learn relevant user behavior patterns and give useful recommendations for users who do not have any interaction with the service, given that they have adequate search and browsing history. The combination of different domains into a single model for learning helps improve the recommendation quality across all the domains, as well as having a more compact and a semantically richer user latent feature vector. We experiment with our approach on three real-world recommendation systems acquired from different sources of Microsoft products: Windows Apps recommendation, News recommendation, and Movie/TV recommendation. Results indicate that our approach is significantly better than the state-of-the-art algorithms (up to 49% enhancement on existing users and 115% enhancement on new users). In addition, experiments on a publicly open data set also indicate the superiority of our method in comparison with transitional generative topic models, for modeling cross-domain recommender systems. Scalability analysis show that our multi-view DNN model can easily scale to encompass millions of users and billions of item entries. Experimental results also confirm that combining features from all domains produces much better performance than building separate models for each domain.
An effective self-supervised framework for learning expressive molecular global representations to drug discovery
Authors: Pengyong Li, Jun Wang, Yixuan Qiao, Hao Chen, Yihuan Yu, Xiaojun Yao, Peng Gao, Guotong Xie, Sen Song
Date: 2021-05-03 00:00:00
Proceeding: Briefings in Bioinformatics 2021 5
Tasks: Drug Discovery, Graph Neural Network

How to produce expressive molecular representations is a fundamental challenge in artificial intelligence-driven drug discovery. Graph neural network (GNN) has emerged as a powerful technique for modeling molecular data. However, previous supervised approaches usually suffer from the scarcity of labeled data and poor generalization capability. Here, we propose a novel molecular pre-training graph-based deep learning framework, named MPG, that learns molecular representations from large-scale unlabeled molecules. In MPG, we proposed a powerful GNN for modelling molecular graph named MolGNet, and designed an effective self-supervised strategy for pre-training the model at both the node and graph-level. After pre-training on 11 million unlabeled molecules, we revealed that MolGNet can capture valuable chemical insights to produce interpretable representation. The pre-trained MolGNet can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of drug discovery tasks, including molecular properties prediction, drug-drug interaction and drug-target interaction, on 14 benchmark datasets. The pre-trained MolGNet in MPG has the potential to become an advanced molecular encoder in the drug discovery pipeline.
How COVID-19 Has Changed Crowdfunding: Evidence From GoFundMe
Authors: Junda Wang, Xupin Zhang, Jiebo Luo
Date: 2021-06-18 00:00:00
Tasks: counterfactual, Sentiment Analysis

While the long-term effects of COVID-19 are yet to be determined, its immediate impact on crowdfunding is nonetheless significant. This study takes a computational approach to more deeply comprehend this change. Using a unique data set of all the campaigns published over the past two years on GoFundMe, we explore the factors that have led to the successful funding of a crowdfunding project. In particular, we study a corpus of crowdfunded projects, analyzing cover images and other variables commonly present on crowdfunding sites. Furthermore, we construct a classifier and a regression model to assess the significance of features based on XGBoost. In addition, we employ counterfactual analysis to investigate the causality between features and the success of crowdfunding. More importantly, sentiment analysis and the paired sample t-test are performed to examine the differences in crowdfunding campaigns before and after the COVID-19 outbreak that started in March 2020. First, we note that there is significant racial disparity in crowdfunding success. Second, we find that sad emotion expressed through the campaign's description became significant after the COVID-19 outbreak. Considering all these factors, our findings shed light on the impact of COVID-19 on crowdfunding campaigns.
ICINet: ICI-Aware Neural Network Based Channel Estimation for Rapidly Time-Varying OFDM Systems
Authors: Yi Sun, Hong Shen, Zhenguo Du, Lan Peng, Chunming Zhao
Date: 2021-06-18 00:00:00

A novel intercarrier interference (ICI)-aware orthogonal frequency division multiplexing (OFDM) channel estimation network ICINet is presented for rapidly time-varying channels. ICINet consists of two components: a preprocessing deep neural subnetwork (PreDNN) and a cascaded residual learning-based neural subnetwork (CasResNet). By fully taking into account the impact of ICI, the proposed PreDNN first refines the initial channel estimates in a subcarrier-wise fashion. In addition, the CasResNet is designed to further enhance the estimation accuracy. The proposed cascaded network is compatible with any pilot patterns and robust against mismatched system configurations. Simulation results verify the superiority of ICINet over existing networks in terms of better performance and much less complexity.
Comparative assessment of typical controlrealizations of grid forming converters based ontheir voltage source behaviour
Authors: Kanakesh Vatta Kkuni, Sibin Mohan, Guangya Yang, Wilsun Xu
Date: 2021-06-18 00:00:00

The converter control functions to provide the capabilities similar to synchronous generators are referred to as grid forming converters (GFC). Identical to a synchronous machine, a grid forming converter is expected to behave as a voltage source behind an impedance beyond the control bandwidth. However, GFC's realization has been different, with some utilizes inner current and voltage controllers while others do not. This paper studies the impact of the inner loop on the grid forming converter's ability to behave as a voltage source behind an impedance. Three of the most popular GFC structures, 1) GFC with cascaded voltage and current control, 2) with inner current control, 3) with no inner loop, are chosen for the comparison. The analysis revealed that MW level GFC with inner loops could potentially go unstable under weak power system. Additionally, the GFC with cascaded control can only operate stably within a narrow range of network impedances. Furthermore, it is also shown that slow response behavior based on cascaded inner loop can impact on dynamic reactive and active power-sharing.
Exergy-based modeling framework for hybrid and electric ground vehicles
Authors: Federico Dettù, Gabriele Pozzato, Denise M. Rizzo, Simona Onori
Date: 2021-03-15 00:00:00
Tasks: Management

Exergy, or availability, is a thermodynamic concept representing the useful work that can be extracted from a system evolving from a given state to a reference state. It is also a system metric, formulated from the first and the second law of thermodynamics, encompassing the interactions between subsystems and the resulting entropy generation. In this paper, an exergy-based analysis for ground vehicles is proposed. The study, a first to the authors' knowledge, defines a comprehensive vehicle and powertrain-level modeling framework to quantify exergy transfer and destruction phenomena for the vehicle's longitudinal dynamics and its energy storage and conversion devices (namely, electrochemical energy storage, electric motor, and ICE). To show the capabilities of the proposed model in quantifying, locating, and ranking the sources of exergy losses, two case studies based on an electric vehicle and a parallel hybrid electric vehicle are analyzed considering a real-world driving cycle. This modeling framework can serve as a tool for the future development of ground vehicles management strategies aimed at minimizing exergy losses rather than fuel consumption.
Networks obtained by Implicit-Explicit Method: Discrete-time distributed median solver
Authors: Jin Gyu Lee
Date: 2021-06-18 00:00:00

In the purpose of making the consensus algorithm robust to outliers, consensus on the median value has recently attracted some attention. It has its applicability in for instance constructing a resilient distributed state estimator. Meanwhile, most of the existing works consider continuous-time algorithms and uses high-gain and discontinuous vector fields. This issues a problem of the need for smaller time steps and yielding chattering when discretizing by explicit method for its practical use. Thus, in this paper, we highlight that these issues vanish when we utilize instead Implicit-Explicit Method, for a broader class of networks designed by the blended dynamics approach. In particular, for undirected and connected graphs, we propose a discrete-time distributed median solver that does not suffer from chattering. We also verify by simulation that it has a smaller iteration number required to arrive at a steady-state.
Intrusion Detection and Localization for Networked Embedded Control Systems
Authors: Vuk Lesi, Marcio Juliato, Shabbir Ahmed, Christopher Gutierrez, Qian Wang, Manoj Sastry
Date: 2021-06-17 00:00:00
Tasks: Intrusion Detection, State Estimation

Closed-loop control systems employ continuous sensing and actuation to maintain controlled variables within preset bounds and achieve the desired system output. Intentional disturbances in the system, such as in the case of cyberattacks, can compromise reachability of control goals, and in several cases jeopardize safety. The increasing connectivity and exposure of networked control to external networks has enabled attackers to compromise these systems by exploiting security vulnerabilities. Attacks against safety-critical control loops can not only drive the system over a trajectory different from the desired, but also cause fatal consequences to humans. In this paper we present a physics-based Intrusion Detection System (IDS) aimed at increasing the security in control systems. In addition to conventional process state estimation for intrusion detection, since the controller cannot be trusted, we introduce a controller state estimator. Additionally, we make our detector context-aware by utilizing sensor measurements from other control loops, which allows to distinguish and characterize disturbances from attacks. We introduce adaptive thresholding and adaptive filtering as means to achieve context-awareness. Together, these methodologies allow detection and localization of attacks in closed-loop controls. Finally, we demonstrate feasibility of the approach by mounting a series of attacks against a networked Direct Current (DC) motor closed-loop speed control deployed on an ECU testbed, as well as on a simulated automated lane keeping system. Among other application domains, this set of approaches is key to support security in automotive systems, and ultimately increase road and passenger safety.
Decentralized Data-Enabled Predictive Control for Power System Oscillation Damping
Authors: Linbin Huang, Jeremy Coulson, John Lygeros, Florian Dörfler
Date: 2019-11-27 00:00:00

We employ a novel data-enabled predictive control (DeePC) algorithm in voltage source converter (VSC) based high-voltage DC (HVDC) stations to perform safe and optimal wide-area control for power system oscillation damping. Conventional optimal wide-area control is model-based. However, in practice detailed and accurate parametric power system models are rarely available. In contrast, the DeePC algorithm uses only input/output data measured from the unknown system to predict the future trajectories and calculate the optimal control policy. We showcase that the DeePC algorithm can effectively attenuate inter-area oscillations even in the presence of measurement noise, communication delays, nonlinear loads and uncertain load fluctuations. We investigate the performance under different matrix structures as data-driven predictors. Furthermore, we derive a novel Min-Max DeePC algorithm to be applied independently in multiple VSC-HVDC stations to mitigate inter-area oscillations, which enables decentralized and robust optimal wide-area control. Further, we discuss how to relieve the computational burden of the Min-Max DeePC by reducing the dimension of prediction uncertainty and how to leverage disturbance feedback to reduce the conservativeness of robustification. We illustrate our results with high-fidelity, nonlinear, and noisy simulations of a four-area test system.
On Use of the Moore-Penrose Pseudoinverse for Evaluating the RGA of Non-Square Systems
Authors: Rafal Qasim Al Yousuf, Jeffrey Uhlmann
Date: 2021-06-17 00:00:00

A recently-derived alternative method for computing the relative gain array (RGA) for singular and/or non-square systems has been proposed that provably guarantees unit invariance. This property is not offered by the conventional method that uses the Moore-Penrose (MP) pseudoinverse. In this paper we note that the absence of the scale-invariance property by the conventional MP-RGA does not necessarily imply a practical disadvantage in real-world applications. In other words, while it is true that performance of a controller should not depend on the choice of units on its input and output variables, this does not {\em necessarily} imply that the resulting MP-RGA measures of component interaction lead to different controller-design input-output pairings. In this paper we consider the application of the MP-RGA to a realistic system (a Sakai fractional distillation system) to assess whether or not the choice of unit, which in this case relates to temperature, affects the choice of input-output pairings determined by the resulting RGA matrix. Our results show that it does, thus confirming that unit-sensitivity of the MP-RGA undermines its rigorous use for MIMO controller design.
Radio interference reduction in interstellar communications: methods and observations
Authors: William J. Crilly Jr
Date: 2021-06-18 00:00:00

The discovery of interstellar communication signals is complicated by the presence of radio interference. Consequently, interstellar communication signals are hypothesized to have properties that favor discovery in high levels of local planetary radio interference. A hypothesized type of interstellar signal, delta-t delta-f polarized pulse pairs, has properties that are similar to infrequent elements of random noise, while dissimilar from many types of known radio interference. Discovery of delta-t delta-f polarized pulse pairs is aided by the use of interference-filtered receiver systems that are designed to indicate anomalous presence of delta-t delta-f polarized pulse pairs, when pointing a radio telescope to celestial coordinates of a hypothetical transmitter. Observations reported in previous work (ref. arXiv:2105.03727) indicate an anomalous celestial pointing direction having coordinates 5.25 +- 0.15 hours Right Ascension and -7.6 +- 1 degrees Declination. Augmented interference reduction mechanisms used in the current work are described, together with reports of follow-up radio telescope beam transit measurements during 40 days. Conclusions and further work are proposed.