pwc-archive/papers-with-abstracts huggingface dataset:
Towards Creating a Standardized Collection of Simple and Targeted Experiments to Analyze Core Aspects of the Recommender Systems Problem
Authors: Andrea Barraza-Urbina
Date: 2021-10-08 00:00:00
Tasks: Recommendation Systems

Imagine you are a teacher attempting to assess a student's level in a particular subject. If you design a test with only hard questions, and the student fails, this mostly proves that the student does not understand the more advanced material. A more insightful exam would include different types of questions varying in difficulty to truly understand the student's weaknesses and strengths from different perspectives. In the field of Recommender Systems (RS), more often than not, we design evaluations to measure an algorithm's ability to optimize goals in complex scenarios, representative of the real-world challenges the system would most probably face. Nevertheless, this paper posits that testing an algorithm's ability to address both simple and complex tasks/problems would offer a more detailed view of performance to help identify, at a more granular level, the weaknesses and strengths of solutions when facing different scenarios/domains. We believe the RS community would greatly benefit from creating a collection of standardized, simple, and targeted experiments, which, much like a suite of "unit tests", would individually assess an algorithm's ability to tackle core challenges that make up complex RS tasks. What's more, these experiments go beyond traditional pass/fail "unit tests". Running an algorithm against the collection of experiments allows a researcher to empirically analyze in which type of settings an algorithm performs best and to what degree under different metrics. Not only do we defend this position, in this paper, we also offer a proposal of how these simple and targeted experiments could be defined and shared and suggest potential next steps to make this project a reality.
Multi-trends Enhanced Dynamic Micro-video Recommendation
Authors: Yujie Lu, Yingxuan Huang, Shengyu Zhang, Wei Han, Hui Chen, Zhou Zhao, Fei Wu
Date: 2021-10-08 00:00:00
Tasks: Recommendation Systems

The explosively generated micro-videos on content sharing platforms call for recommender systems to permit personalized micro-video discovery with ease. Recent advances in micro-video recommendation have achieved remarkable performance in mining users' current preference based on historical behaviors. However, most of them neglect the dynamic and time-evolving nature of users' preference, and the prediction on future micro-videos with historically mined preference may deteriorate the effectiveness of recommender systems. In this paper, we propose the DMR framework to explicitly model dynamic multi-trends of users' current preference and make predictions based on both the history and future potential trends. We devise the DMR framework, which comprises: 1) the implicit user network module which identifies sequence fragments from other users with similar interests and extracts the sequence fragments that are chronologically behind the identified fragments; 2) the multi-trend routing module which assigns each extracted sequence fragment into a trend group and update the corresponding trend vector; 3) the history-future trend prediction module jointly uses the history preference vectors and future trend vectors to yield the final click-through-rate. We validate the effectiveness of DMR over multiple state-of-the-art micro-video recommenders on two publicly available real-world datasets. Relatively extensive analysis further demonstrate the superiority of modeling dynamic multi-trend for micro-video recommendation.
Optimizing Oil and Gas Acquisitions Using Recommender Systems
Authors: Harsh Kumar, Geneva Allison, Jehil Mehta, Jesse Pisel, Michael Pyrcz
Date: 2021-10-07 00:00:00
Tasks: Decision Making, Recommendation Systems

Well acquisition in the oil and gas industry can often be a hit or miss process, with a poor purchase resulting in substantial loss. Recommender systems suggest items (wells) that users (companies) are likely to buy based on past activity, and applying this system to well acquisition can increase company profits. While traditional recommender systems are impactful enough on their own, they are not optimized. This is because they ignore many of the complexities involved in human decision-making, and frequently make subpar recommendations. Using a preexisting Python implementation of a Factorization Machine results in more accurate recommendations based on a user-level ranking system. We train a Factorization Machine model on oil and gas well data that includes features such as elevation, total depth, and location. The model produces recommendations by using similarities between companies and wells, as well as their interactions. Our model has a hit rate of 0.680, reciprocal rank of 0.469, precision of 0.229, and recall of 0.463. These metrics imply that while our model is able to recommend the correct wells in a general sense, it does not match exact wells to companies via relevance. To improve the model's accuracy, future models should incorporate additional features such as the well's production data and ownership duration as these features will produce more accurate recommendations.
Data-driven Tissue Mechanics with Polyconvex Neural Ordinary Differential Equations
Authors: Vahidullah Tac, Francisco S. Costabal, Adrian Buganza Tepole
Date: 2021-10-03 00:00:00

Data-driven methods are becoming an essential part of computational mechanics due to their unique advantages over traditional material modeling. Deep neural networks are able to learn complex material response without the constraints of closed-form approximations. However, imposing the physics-based mathematical requirements that any material model must comply with is not straightforward for data-driven approaches. In this study, we use a novel class of neural networks, known as neural ordinary differential equations (N-ODEs), to develop data-driven material models that automatically satisfy polyconvexity of the strain energy function with respect to the deformation gradient, a condition needed for the existence of minimizers for boundary value problems in elasticity. We take advantage of the properties of ordinary differential equations to create monotonic functions that approximate the derivatives of the strain energy function with respect to the invariants of the right Cauchy-Green deformation tensor. The monotonicity of the derivatives guarantees the convexity of the energy. The N-ODE material model is able to capture synthetic data generated from closed-form material models, and it outperforms conventional models when tested against experimental data on skin, a highly nonlinear and anisotropic material. We also showcase the use of the N-ODE material model in finite element simulations. The framework is general and can be used to model a large class of materials. Here we focus on hyperelasticity, but polyconvex strain energies are a core building block for other problems in elasticity such as viscous and plastic deformations. We therefore expect our methodology to further enable data-driven methods in computational mechanics
On tolerance of discrete systems with respect to transition perturbations
Authors: Rômulo Meira-Góes, Eunsuk Kang, Stéphane Lafortune, Stavros Tripakis
Date: 2021-10-08 00:00:00

Control systems should enforce a desired property for both expected modeled situations as well as unexpected unmodeled environmental situations. Existing methods focus on designing controllers to enforce the desired property only when the environment behaves as expected. However, these methods lack discussion on how the system behaves when the environment is perturbed. In this paper, we propose an approach for analyzing control systems with respect to their tolerance against environmental perturbations. A control system tolerates certain environmental perturbations when it remains capable of guaranteeing the desired property despite the perturbations. Each controller inherently has a level of tolerance against environmental perturbations. We formally define this notion of tolerance and describe a general technique to compute it, for any given regular property. We also present a more efficient method to compute tolerance with respect to invariance properties. Moreover, we introduce a new controller synthesis problem based on our notion of tolerance. We demonstrate the application of our framework on an autonomous surveillance example.
A Mechanism Design Approach to Allocating Travel Funds
Authors: Michael A. Jones
Date: 2021-10-08 00:00:00

I explain how faculty members could exploit a method to allocate travel funds and how to use game theory to design a method that cannot be manipulated.
Risk aversion in flexible electricity markets
Authors: Thomas Möbius, Iegor Riepin, Felix Müsgens, Adriaan H. van der Weijde
Date: 2021-10-08 00:00:00

Flexibility options, such as demand response, energy storage and interconnection, have the potential to reduce variation in electricity prices between different future scenarios, therefore reducing investment risk. Moreover, investment in flexibility options can lower the need for generation capacity. However, there are complex interactions between different flexibility options. In this paper, we investigate the interactions between flexibility and investment risk in electricity markets. We employ a large-scale stochastic transmission and generation expansion model of the European electricity system. Using this model, we first investigate the effect of risk aversion on the investment decisions. We find that the interplay of parameters leads to (i) more investment in a less emission-intensive energy system if planners are risk averse (hedging against CO2 price uncertainty) and (ii) constant total installed capacity, regardless of the level of risk aversion (planners do not hedge against demand and RES deployment uncertainties). Second, we investigate the individual effects of three flexibility elements on optimal investment levels under different levels of risk aversion: demand response, investment in additional interconnection capacity and investment in additional energy storage. We find that that flexible technologies have a higher value for risk-averse decision-makers, although the effects are nonlinear. Finally, we investigate the interactions between the flexibility elements. We find that risk-averse decision-makers show a strong preference for transmission grid expansion once flexibility is available at low cost levels.
Self-supervised Point Cloud Prediction Using 3D Spatio-temporal Convolutional Networks
Authors: Benedikt Mersch, Xieyuanli Chen, Jens Behley, Cyrill Stachniss
Date: 2021-09-28 00:00:00
Tasks: Collision Avoidance, Decoder, State Estimation

Exploiting past 3D LiDAR scans to predict future point clouds is a promising method for autonomous mobile systems to realize foresighted state estimation, collision avoidance, and planning. In this paper, we address the problem of predicting future 3D LiDAR point clouds given a sequence of past LiDAR scans. Estimating the future scene on the sensor level does not require any preceding steps as in localization or tracking systems and can be trained self-supervised. We propose an end-to-end approach that exploits a 2D range image representation of each 3D LiDAR scan and concatenates a sequence of range images to obtain a 3D tensor. Based on such tensors, we develop an encoder-decoder architecture using 3D convolutions to jointly aggregate spatial and temporal information of the scene and to predict the future 3D point clouds. We evaluate our method on multiple datasets and the experimental results suggest that our method outperforms existing point cloud prediction architectures and generalizes well to new, unseen environments without additional fine-tuning. Our method operates online and is faster than the common LiDAR frame rate of 10 Hz.
Location-based training for multi-channel talker-independent speaker separation
Authors: Hassan Taherian, Ke Tan, DeLiang Wang
Date: 2021-10-08 00:00:00
Tasks: Speaker Separation

Permutation-invariant training (PIT) is a dominant approach for addressing the permutation ambiguity problem in talker-independent speaker separation. Leveraging spatial information afforded by microphone arrays, we propose a new training approach to resolving permutation ambiguities for multi-channel speaker separation. The proposed approach, named location-based training (LBT), assigns speakers on the basis of their spatial locations. This training strategy is easy to apply, and organizes speakers according to their positions in physical space. Specifically, this study investigates azimuth angles and source distances for location-based training. Evaluation results on separating two- and three-speaker mixtures show that azimuth-based training consistently outperforms PIT, and distance-based training further improves the separation performance when speaker azimuths are close. Furthermore, we dynamically select azimuth-based or distance-based training by estimating the azimuths of separated speakers, which further improves separation performance. LBT has a linear training complexity with respect to the number of speakers, as opposed to the factorial complexity of PIT. We further demonstrate the effectiveness of LBT for the separation of four and five concurrent speakers.
A study of the robustness of raw waveform based speaker embeddings under mismatched conditions
Authors: Ge Zhu, Frank Cwitkowitz, Zhiyao Duan
Date: 2021-10-08 00:00:00
Tasks: Speaker Verification

In this paper, we conduct a cross-dataset study on parametric and non-parametric raw-waveform based speaker embeddings through speaker verification experiments. In general, we observe a more significant performance degradation of these raw-waveform systems compared to spectral based systems. We then propose two strategies to improve the performance of raw-waveform based systems on cross-dataset tests. The first strategy is to change the real-valued filters into analytic filters to ensure shift-invariance. The second strategy is to apply variational dropout to non-parametric filters to prevent them from overfitting irrelevant nuance features.
Cross-speaker Emotion Transfer Based on Speaker Condition Layer Normalization and Semi-Supervised Training in Text-To-Speech
Authors: Pengfei Wu, Junjie Pan, Chenchang Xu, Junhui Zhang, Lin Wu, Xiang Yin, Zejun Ma
Date: 2021-10-08 00:00:00
Tasks: Emotion Interpretation, Expressive Speech Synthesis, Speech Synthesis, text-to-speech, Text to Speech

In expressive speech synthesis, there are high requirements for emotion interpretation. However, it is time-consuming to acquire emotional audio corpus for arbitrary speakers due to their deduction ability. In response to this problem, this paper proposes a cross-speaker emotion transfer method that can realize the transfer of emotions from source speaker to target speaker. A set of emotion tokens is firstly defined to represent various categories of emotions. They are trained to be highly correlated with corresponding emotions for controllable synthesis by cross-entropy loss and semi-supervised training strategy. Meanwhile, to eliminate the down-gradation to the timbre similarity from cross-speaker emotion transfer, speaker condition layer normalization is implemented to model speaker characteristics. Experimental results show that the proposed method outperforms the multi-reference based baseline in terms of timbre similarity, stability and emotion perceive evaluations.
A Method for Capturing and Reproducing Directional Reverberation in Six Degrees of Freedom
Authors: Benoit Alary, Vesa Välimäki
Date: 2021-10-08 00:00:00

The reproduction of acoustics is an important aspect of the preservation of cultural heritage. A common approach is to capture an impulse response in a hall and auralize it by convolving an input signal with the measured reverberant response. For immersive applications, it is typical to acquire spatial impulse responses using a spherical microphone array to capture the reverberant sound field. While this allows a listener to freely rotate their head from the captured location during reproduction, delicate considerations must be made to allow a full six degrees of freedom auralization. Furthermore, the computational cost of convolution with a high-order Ambisonics impulse response remains prohibitively expensive for current real-time applications, where most of the resources are dedicated towards rendering graphics. For this reason, simplifications are often made in the reproduction of reverberation, such as using a uniform decay around the listener. However, recent work has highlighted the importance of directional characteristics in the late reverberant sound field and more efficient reproduction methods have been developed. In this article, we propose a framework that extracts directional decay properties from a set of captured spatial impulse responses to characterize a directional feedback delay network. For this purpose, a data set was acquired in the main auditorium of the Finnish National Opera and Ballet in Helsinki from multiple source-listener positions, in order to analyze the anisotropic characteristics of this auditorium and illustrate the proposed reproduction framework.
TRUNet: Transformer-Recurrent-U Network for Multi-channel Reverberant Sound Source Separation
Authors: Ali Aroudi, Stefan Uhlich, Marc Ferras Font
Date: 2021-10-08 00:00:00
Tasks: Diversity

In recent years, many deep learning techniques for single-channel sound source separation have been proposed using recurrent, convolutional and transformer networks. When multiple microphones are available, spatial diversity between speakers and background noise in addition to spectro-temporal diversity can be exploited by using multi-channel filters for sound source separation. Aiming at end-to-end multi-channel source separation, in this paper we propose a transformer-recurrent-U network (TRUNet), which directly estimates multi-channel filters from multi-channel input spectra. TRUNet consists of a spatial processing network with an attention mechanism across microphone channels aiming at capturing the spatial diversity, and a spectro-temporal processing network aiming at capturing spectral and temporal diversities. In addition to multi-channel filters, we also consider estimating single-channel filters from multi-channel input spectra using TRUNet. We train the network on a large reverberant dataset using a combined compressed mean-squared error loss function, which further improves the sound separation performance. We evaluate the network on a realistic and challenging reverberant dataset, generated from measured room impulse responses of an actual microphone array. The experimental results on realistic reverberant sound source separation show that the proposed TRUNet outperforms state-of-the-art single-channel and multi-channel source separation methods.
Joint Scattering for Automatic Chick Call Recognition
Authors: Changhong Wang, Emmanouil Benetos, Shuge Wang, Elisabetta Versace
Date: 2021-10-08 00:00:00

Animal vocalisations contain important information about health, emotional state, and behaviour, thus can be potentially used for animal welfare monitoring. Motivated by the spectro-temporal patterns of chick calls in the time$-$frequency domain, in this paper we propose an automatic system for chick call recognition using the joint time$-$frequency scattering transform (JTFS). Taking full-length recordings as input, the system first extracts chick call candidates by an onset detector and silence removal. After computing their JTFS features, a support vector machine classifier groups each candidate into different chick call types. Evaluating on a dataset comprising 3013 chick calls collected in laboratory conditions, the proposed recognition system using the JTFS features improves the frame- and event-based macro F-measures by 9.5% and 11.7%, respectively, than that of a mel-frequency cepstral coefficients baseline.
Environment Aware Text-to-Speech Synthesis
Authors: Daxin Tan, Guangyan Zhang, Tan Lee
Date: 2021-10-08 00:00:00
Tasks: Attribute, Disentanglement, Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis

This study aims at designing an environment-aware text-to-speech (TTS) system that can generate speech to suit specific acoustic environments. It is also motivated by the desire to leverage massive data of speech audio from heterogeneous sources in TTS system development. The key idea is to model the acoustic environment in speech audio as a factor of data variability and incorporate it as a condition in the process of neural network based speech synthesis. Two embedding extractors are trained with two purposely constructed datasets for characterization and disentanglement of speaker and environment factors in speech. A neural network model is trained to generate speech from extracted speaker and environment embeddings. Objective and subjective evaluation results demonstrate that the proposed TTS system is able to effectively disentangle speaker and environment factors and synthesize speech audio that carries designated speaker characteristics and environment attribute. Audio samples are available online for demonstration https://daxintan-cuhk.github.io/Environment-Aware-TTS/ .
PEAF: Learnable Power Efficient Analog Acoustic Features for Audio Recognition
Authors: Boris Bergsma, Minhao Yang, Milos Cernak
Date: 2021-10-07 00:00:00
Tasks: Action Detection, Activity Detection, Keyword Spotting, Speaker Identification

At the end of Moore's law, new computing paradigms are required to prolong the battery life of wearable and IoT smart audio devices. Theoretical analysis and physical validation have shown that analog signal processing (ASP) can be more power-efficient than its digital counterpart in the realm of low-to-medium signal-to-noise ratio applications. In addition, ASP allows a direct interface with an analog microphone without a power-hungry analog-to-digital converter. Here, we present power-efficient analog acoustic features (PEAF) that are validated by fabricated CMOS chips for running audio recognition. Linear, non-linear, and learnable PEAF variants are evaluated on two speech processing tasks that are demanded in many battery-operated devices: wake word detection (WWD) and keyword spotting (KWS). Compared to digital acoustic features, higher power efficiency with competitive classification accuracy can be obtained. A novel theoretical framework based on information theory is established to analyze the information flow in each individual stage of the feature extraction pipeline. The analysis identifies the information bottleneck and helps improve the KWS accuracy by up to 7%. This work may pave the way to building more power-efficient smart audio devices with best-in-class inference performance.