pwc-archive/papers-with-abstracts huggingface dataset:
The IDLAB VoxCeleb Speaker Recognition Challenge 2021 System Description
Authors: Jenthe Thienpondt, Brecht Desplanques, Kris Demuynck
Date: 2021-09-09 00:00:00
Tasks: Speaker Recognition, Speaker Verification

This technical report describes the IDLab submission for track 1 and 2 of the VoxCeleb Speaker Recognition Challenge 2021 (VoxSRC-21). This speaker verification competition focuses on short duration test recordings and cross-lingual trials. Currently, both Time Delay Neural Networks (TDNNs) and ResNets achieve state-of-the-art results in speaker verification. We opt to use a system fusion of hybrid architectures in our final submission. An ECAPA-TDNN baseline is enhanced with a 2D convolutional stem to transfer some of the strong characteristics of a ResNet based model to this hybrid CNN-TDNN architecture. Similarly, we incorporate absolute frequency positional information in the SE-ResNet architectures. All models are trained with a special mini-batch data sampling technique which constructs mini-batches with data that is the most challenging for the system on the level of intra-speaker variability. This intra-speaker variability is mainly caused by differences in language and background conditions between the speaker's utterances. The cross-lingual effects on the speaker verification scores are further compensated by introducing a binary cross-linguality trial feature in the logistic regression based system calibration. The final system fusion with two ECAPA CNN-TDNNs and three SE-ResNets enhanced with frequency positional information achieved a third place on the VoxSRC-21 leaderboard for both track 1 and 2 with a minDCF of 0.1291 and 0.1313 respectively.
Multilingual Speech Recognition for Low-Resource Indian Languages using Multi-Task conformer
Authors: Krishna D N
Date: 2021-08-22 00:00:00
Tasks: Decoder, Machine Translation, Multi-Task Learning, Phoneme Recognition, speech-recognition, Speech Recognition, Translation

Transformers have recently become very popular for sequence-to-sequence applications such as machine translation and speech recognition. In this work, we propose a multi-task learning-based transformer model for low-resource multilingual speech recognition for Indian languages. Our proposed model consists of a conformer [1] encoder and two parallel transformer decoders. We use a phoneme decoder (PHN-DEC) for the phoneme recognition task and a grapheme decoder (GRP-DEC) to predict grapheme sequence. We consider the phoneme recognition task as an auxiliary task for our multi-task learning framework. We jointly optimize the network for both phoneme and grapheme recognition tasks using Joint CTC-Attention [2] training. We use a conditional decoding scheme to inject the language information into the model before predicting the grapheme sequence. Our experiments show that our proposed approach can obtain significant improvement over previous approaches [4]. We also show that our conformer-based dual-decoder approach outperforms both the transformer-based dual-decoder approach and single decoder approach. Finally, We compare monolingual ASR models with our proposed multilingual ASR approach.
Tube-Certified Trajectory Tracking for Nonlinear Systems With Robust Control Contraction Metrics
Authors: Pan Zhao, Arun Lakshmanan, Kasey Ackerman, Aditya Gahlawat, Marco Pavone, Naira Hovakimyan
Date: 2021-09-09 00:00:00
Tasks: Motion Planning, valid

This paper presents an approach towards guaranteed trajectory tracking for nonlinear control-affine systems subject to external disturbances based on robust control contraction metrics (CCM) that aims to minimize the $\mathcal L_\infty$ gain from the disturbances to nominal-actual trajectory deviations. The guarantee is in the form of invariant tubes, computed offline and valid for any nominal trajectories, in which the actual states and inputs of the system are guaranteed to stay despite disturbances. Under mild assumptions, we prove that the proposed robust CCM (RCCM) approach yields tighter tubes than an existing approach based on CCM and input-to-state stability analysis. We show how the RCCM-based tracking controller together with tubes can be incorporated into a feedback motion planning framework to plan safe trajectories for robotic systems. Simulation results illustrate the effectiveness of the proposed method and empirically demonstrate reduced conservatism compared to the CCM-based approach.
Learning Performance Bounds for Safety-Critical Systems
Authors: Prithvi Akella, Ugo Rosolia, Aaron D. Ames
Date: 2021-09-09 00:00:00
Tasks: Bayesian Optimization, Translation

As the complexity of control systems increases, the need for systematic methods to guarantee their efficacy grows as well. However, direct testing of these systems is oftentimes costly, difficult, or impractical. As a result, the test and evaluation ideal would be to verify the efficacy of a system simulator and use this verification result to make a statement on true system performance. This paper formalizes that performance translation for a specific class of desired system behaviors. In that vein, our contribution is twofold. First, we detail a variant on existing Bayesian Optimization Algorithms that identifies minimal upper bounds to maximization problems, with some minimum probability. Second, we use this Algorithm to $i)$ lower bound the minimum simulator robustness and $ii)$ upper bound the expected deviance between true and simulated systems. Then, for the specific class of desired behaviors studied, we leverage these bounds to lower bound the minimum true system robustness, without directly testing the true system. Finally, we compare a high-fidelity ROS simulator of a Segway, with a significantly noisier version of itself, and show that our probabilistic verification bounds are indeed satisfied.
Learning-based Moving Horizon Estimation through Differentiable Convex Optimization Layers
Authors: Simon Muntwiler, Kim P. Wabersich, Melanie N. Zeilinger
Date: 2021-09-08 00:00:00
Tasks: State Estimation

To control a dynamical system it is essential to obtain an accurate estimate of the current system state based on uncertain sensor measurements and existing system knowledge. An optimization-based moving horizon estimation (MHE) approach uses a dynamical model of the system, and further allows for integration of physical constraints on system states and uncertainties, to obtain a trajectory of state estimates. In this work, we address the problem of state estimation in the case of constrained linear systems with parametric uncertainty. The proposed approach makes use of differentiable convex optimization layers to formulate an MHE state estimator for systems with uncertain parameters. This formulation allows us to obtain the gradient of a squared and regularized output error, based on sensor measurements and state estimates, with respect to the current belief of the unknown system parameters. The parameters within the MHE problem can then be updated online using stochastic gradient descent (SGD) to improve the performance of the MHE. In a numerical example of estimating temperatures of a group of manufacturing machines, we show the performance of tuning the unknown system parameters and the benefits of integrating physical state constraints in the MHE formulation.
A Generalized Theory of Power
Authors: Louis L. Schar, Dongliang Duan
Date: 2021-09-08 00:00:00

The complex representation of real-valued instantaneous power may be written as the sum of two complex powers, one Hermitian and the other non-Hermitian, or complementary. A virtue of this representation is that it consists of a power triangle rotating around a fixed phasor, thus clarifying what should be meant by the power triangle. The in-phase and quadrature components of complementary power encode for active and non-active power. When instantaneous power is defined for a Thevenin equivalent circuit, these are time-varying real and reactive power components. These claims hold for sinusoidal voltage and current, and for non-sinusoidal voltage and current. Spectral representations of Hermitian, complementary, and instantaneous power show that, frequency-by-frequency, these powers behave exactly as they behave in the single frequency sinusoidal case. Simple hardware diagrams show how instantaneous active and non-active power may be extracted from metered voltage and current, even in certain non-sinusoidal cases.
Rainbow-link: Beam-Alignment-Free and Grant-Free mmW Multiple Access using True-Time-Delay Array
Authors: Ruifu Li, Han Yan, Danijela Cabric
Date: 2021-08-21 00:00:00
Tasks: Scheduling

The millimeter-wave (mmW) communications is a key enabling technology in 5G to provide ultra-high throughput. Current mmW technologies rely on analog phased arrays to realize beamforming gain and overcome high path loss. However, due to a limited number of simultaneous beams that can be created with analog/hybrid phased antenna arrays, the overheads of beam training and beam scheduling become a bottleneck for emerging networks that need to support a large number of users and low latency applications. This paper introduces rainbow-link, a novel multiple access protocol, that can achieve low latency and massive connectivity by exploiting wide bandwidth at mmW frequencies and novel analog true-time-delay array architecture with frequency dependent beamforming capability. In the proposed design, the network infrastructure is equipped with the true-time-delay array to simultaneously steer different frequency resource blocks towards distinct directions covering the entire cell sector. Users or devices, equipped with a narrowband receiver and either a single antenna or small phased antenna array, connect to the network based on their angular positions by selecting frequency resources within their rainbow beam allocation. Rainbow-link is combined with a contention-based grant-free access to eliminate the explicit beam training and user scheduling. The proposed design and analysis show that rainbow-link grant-free access is a potential candidate for latency-critical use cases within massive connectivity. Our results show that, given less than 1e-5 probability of packet loss, a rainbow-link cell, over 1 GHz bandwidth using 64 element antenna array, attains sub-millisecond user-plane latency and Mbps user rates with an approximate 400m line-of-sight coverage and a density of up to 5 active single antenna users per second per meter square.
adabmDCA: Adaptive Boltzmann machine learning for biological sequences
Authors: Anna Paola Muntoni, Andrea Pagnani, Martin Weigt, Francesco Zamponi
Date: 2021-09-09 00:00:00
Tasks: BIG-bench Machine Learning

Boltzmann machines are energy-based models that have been shown to provide an accurate statistical description of domains of evolutionary-related protein and RNA families. They are parametrized in terms of local biases accounting for residue conservation, and pairwise terms to model epistatic coevolution between residues. From the model parameters, it is possible to extract an accurate prediction of the three-dimensional contact map of the target domain. More recently, the accuracy of these models has been also assessed in terms of their ability in predicting mutational effects and generating in silico functional sequences. Our adaptive implementation of Boltzmann machine learning, adabmDCA, can be generally applied to both protein and RNA families and accomplishes several learning set-ups, depending on the complexity of the input data and on the user requirements. The code is fully available at https://github.com/anna-pa-m/adabmDCA. As an example, we have performed the learning of three Boltzmann machines modeling the Kunitz and Beta-lactamase2 protein domains and TPP-riboswitch RNA domain. The models learned by adabmDCA are comparable to those obtained by state-of-the-art techniques for this task, in terms of the quality of the inferred contact map as well as of the synthetically generated sequences. In addition, the code implements both equilibrium and out-of-equilibrium learning, which allows for an accurate and lossless training when the equilibrium one is prohibitive in terms of computational time, and allows for pruning irrelevant parameters using an information-based criterion.
Emergence of robust memory manifolds
Authors: Tankut Can, Kamesh Krishnamurthy
Date: 2021-09-08 00:00:00

The ability to store continuous variables in the state of a biological system (e.g. a neural network) is critical for many behaviours. Most models for implementing such a memory manifold require hand-crafted symmetries in the interactions or precise fine-tuning of parameters. We present a general principle that we refer to as {\it frozen stabilisation} (FS), which allows a family of neural networks to self-organise to a critical state exhibiting multiple memory manifolds without parameter fine-tuning or symmetries. Memory manifolds arising from FS exhibit a wide range of emergent relaxational timescales and can be used as general purpose integrators for inputs aligned with the manifold. Moreover, FS allows robust memory manifolds in small networks, and this is relevant to debates of implementing continuous attractors with a small number of neurons in light of recent experimental discoveries.
Wind Turbine Gearbox Condition Based Monitoring
Authors: Alan Rezazadeh
Date: 2021-08-30 00:00:00

The main objective of this paper is finding effective gearbox condition monitoring methods by using continuously recorded monitoring SCADA (Supervisory Control and Data Accusation) data points. Typically for wind turbine gearbox condition monitoring; temperature readings, high frequency sounds and vibrations in addition to lubricant condition monitoring have been used. However, collection of such data, require shutting down equipment for installation of costly sensors and measuring lubricant quality. Meanwhile, operational data usually collected every 10 minutes, comprised of wind speed, power generated, pitch angle and similar performance parameters can be used for monitoring health of wind turbine components such as blades, gearbox and generator. This paper uses gear rotational speed for monitoring health of gearbox teeth; since gearbox teeth deterioration can be measured by monitoring rotor to generator rotation ratios over extended period of time. As nature of wind is turbulent with rapid fluctuations, a wind turbine may operate in variety of modes within relatively short period of time. Monitoring rotational speed ratio over time, requires consistent operational conditions such as wind speed and torques within the gearbox. This paper also introduces the concept of clustering such as Normal Mixture algorithm for dividing operating datasets into consistent subgroups, which are used for long term monitoring.
Can a conventional optical camera realize turbulence-free imaging?
Authors: Deyang Duan, Yunjie Xia
Date: 2021-09-09 00:00:00

Atmospheric turbulence is a serious problem for traditional optical imaging, especially for satellite and aircraft-to-ground imaging. Here, we report a novel and practical phenomenon in which turbulence-free images can be reconstructed on a conventional optical camera based on the accumulation of sunlight intensity and photon number fluctuation autocorrelation. Different from conventional ghost imaging, this method can obtain turbulence-free images, and its imaging speed is comparable to that of traditional optical imaging. Moreover, by adding photon number fluctuation autocorrelation algorithm software, almost all optical cameras, including mobile phone cameras, can realize this function without changing the structure of the original camera.
Time-frequency analysis of microwave signals based on stimulated Brillouin scattering
Authors: Dong Ma, Pengcheng Zuo, Yang Chen
Date: 2021-09-07 00:00:00

A novel photonic approach to the time-frequency analysis of microwave signals is proposed based on the stimulated Brillouin scattering (SBS)-assisted frequency-to-time mapping (FTTM). Two types of time-frequency analysis links, namely parallel SBS link and time-division SBS link are proposed. The parallel SBS link can be utilized to perform real-time time-frequency analysis of microwave signal, which provides a promising solution for real-time time-frequency analysis, especially when it is combined with the photonic integration technique. A simulation is made to verify its feasibility by analyzing signals in multiple formats. The time-division SBS link has a simpler and reconfigurable structure, which can realize an ultra-high-resolution time-frequency analysis for periodic signals using the time segmentation and accumulation technique. An experiment is performed for the time-division SBS link. The multi-dimensional reconfigurability of the system is experimentally studied. An analysis bandwidth of 3.9 GHz, an analysis frequency up to 20 GHz, and a frequency resolution of 15 MHz are demonstrated, respectively.
Optical Channel Aggregation by Coherent Spectral Superposition with Electro-Optic Modulators
Authors: Arijit Misra, Stefan Preussler, Karanveer Singh, Janosch Meier, Thomas Schneider
Date: 2021-09-03 00:00:00

As the bit rates of routed data streams exceed the throughput of single wavelength-division multiplexing channels, spectral traffic aggregation becomes essential for optical network scaling. Here we propose a scheme for all-optical aggregation of several low bitrate channels to fewer channels with higher spectral efficiency. The method is based on optical vector summation facilitated by coherent spectral superposition. Thereby it does not need any optical nonlinearities and is based on linear signal processing with an electro-optic modulator. Furthermore, optical phase tuning required for vector addition can be easily achieved by a phase tuning of the radio frequency signal driving the modulator. We experimentally demonstrate the aggregation of two 10 Gbaud BPSK signals into one 10 Gbaud QPSK and one 10 Gbaud PAM-4 signal, the aggregation of two 10 Gbaud QPSK signals into 10 Gbaud QAM-16, as well as the aggregation of sinc-shaped Nyquist signals. The presented concept of in-line all-optical aggregation demonstrates considerable improvement in network spectrum utilization and can significantly enhance the operational capacity with reduced complexity. It provides a new way for realizing the flexible optical transmission of advanced modulation format signals, and suits for future dynamically reconfigurable optical networks. Since the method is based on linear signal processing with electro-optic modulator, integration into any integrated photonic platform is straightforward.
Characterization of the frequency response of channel-interleaved photonic ADCs based on the optical time-division demultiplexer
Authors: Na Qian, Linbo Zhang, Jianping Chen, Weiwen Zou
Date: 2021-09-03 00:00:00
Tasks: Quantization

We characterize the frequency response of channel-interleaved photonic analog-to-digital converters (CI-PADCs) theoretically and experimentally. The CI-PADC is composed of a photonic frontend for photonic sampling and an electronic backend for quantization. The photonic frontend includes a photonic sampling pulse generator for directly high-speed sampling and an optical time-division demultiplexer (OTDM) for channel demultiplexing. It is found that the frequency response of the CI-PADC is influenced by both the photonic sampling pulses and the OTDM, of which the combined impact can be characterized through demultiplexed pulse trains. First, the frequency response can be divided into multiple frequency intervals and the range of the frequency interval equals the repetition rate of demultiplexed pulse trains. Second, the analog bandwidth of the CI-PADC is determined by the optical spectral bandwidth of demultiplexed pulse trains which is broadened in the OTDM. Further, the effect of the OTDM is essential for enlarging the analog bandwidth of the CI-PADC employing the photonic sampling pulses with a limited optical spectral bandwidth.
Variable Selection for Causal Inference via Outcome-Adaptive Random Forest
Authors: Daniel Jacob
Date: 2021-09-09 00:00:00
Tasks: Causal Inference, Variable Selection

Estimating a causal effect from observational data can be biased if we do not control for self-selection. This selection is based on confounding variables that affect the treatment assignment and the outcome. Propensity score methods aim to correct for confounding. However, not all covariates are confounders. We propose the outcome-adaptive random forest (OARF) that only includes desirable variables for estimating the propensity score to decrease bias and variance. Our approach works in high-dimensional datasets and if the outcome and propensity score model are non-linear and potentially complicated. The OARF excludes covariates that are not associated with the outcome, even in the presence of a large number of spurious variables. Simulation results suggest that the OARF produces unbiased estimates, has a smaller variance and is superior in variable selection compared to other approaches. The results from two empirical examples, the effect of right heart catheterization on mortality and the effect of maternal smoking during pregnancy on birth weight, show comparable treatment effects to previous findings but tighter confidence intervals and more plausible selected variables.
Some Impossibility Results for Inference With Cluster Dependence with Large Clusters
Authors: Denis Kojevnikov, Kyungchul Song
Date: 2021-09-08 00:00:00

This paper focuses on a setting with observations having a cluster dependence structure and presents two main impossibility results. First, we show that when there is only one large cluster, i.e., the researcher does not have any knowledge on the dependence structure of the observations, it is not possible to consistently discriminate the mean. When within-cluster observations satisfy the uniform central limit theorem, we also show that a sufficient condition for consistent $\sqrt{n}$-discrimination of the mean is that we have at least two large clusters. This result shows some limitations for inference when we lack information on the dependence structure of observations. Our second result provides a necessary and sufficient condition for the cluster structure that the long run variance is consistently estimable. Our result implies that when there is at least one large cluster, the long run variance is not consistently estimable.
Protection of the Rights of Large Families as One of the Key Tasks of the State's Social Policy
Authors: Valery Dolgov, Mattia Masolletti
Date: 2021-09-09 00:00:00

The authors of the article analyze the policy of the Russian government in the field of family support, paying attention to legal programs at the federal and regional levels. The maternity capital program is considered separately, as well as measures aimed at supporting large families.
Optimizing timetable and network reopen plans for public transportation networks during a COVID19-like pandemic
Authors: Yiduo Huang, ZuoJun Max Shen
Date: 2021-09-08 00:00:00
Tasks: ARC

The recovery of the public transportation system is critical for both social re-engagement and economic rebooting after the shutdown during pandemic like COVID-19. In this study, we focus on the integrated optimization of service line reopening plan and timetable design. We model the transit system as a space-time network. In this network, the number of passengers on each vehicle at the same time can be represented by arc flow. We then apply a simplified spatial compartmental model of epidemic (SCME) to each vehicle and platform to model the spread of pandemic in the system as our objective, and calculate the optimal open plan and timetable. We demonstrate that this optimization problem can be decomposed into a simple integer programming and a linear multi-commodity network flow problem using Lagrangian relaxation techniques. Finally, we test the proposed model using real-world data from the Bay Area Rapid Transit (BART) and give some useful suggestions to system managers.