pwc-archive/papers-with-abstracts huggingface dataset:
A Perceptually-Motivated Approach for Low-Complexity, Real-Time Enhancement of Fullband Speech
Date: 2020-08-27 00:00:00
Proceeding: Interspeech 2020 8
Tasks: CPU, Speech Enhancement

Over the past few years, speech enhancement methods based on deep learning
have greatly surpassed traditional methods based on spectral subtraction and
spectral estimation. Many of these new techniques operate directly in the the
short-time Fourier transform (STFT) domain, resulting in a high computational
complexity. In this work, we propose PercepNet, an efficient approach that
relies on human perception of speech by focusing on the spectral envelope and
on the periodicity of the speech. We demonstrate high-quality, real-time
enhancement of fullband (48 kHz) speech with less than 5% of a CPU core.
Progress on a perimeter surveillance problem
Authors: Jeremy Avigad, Floris van Doorn
Date: 2020-08-10 00:00:00

We consider a perimeter surveillance problem introduced by Kingston, Beard, and Holt in 2008 and studied by Davis, Humphrey, and Kingston in 2019. In this problem, $n$ drones surveil a finite interval, moving at uniform speed and exchanging information only when they meet another drone. Kingston et al. described a particular online algorithm for coordinating their behavior and asked for an upper bound on how long it can take before the drones are fully synchronized. They divided the algorithm's behavior into two phases, and presented upper bounds on the length of each phase based on conjectured worst-case configurations. Davis et al. presented counterexamples to the conjecture for phase 1. We present sharp upper bounds on phase 2 which show that in this case the conjectured worst case is correct. We also present new lower bounds on phase 1 and the total time to synchronization, and report partial progress towards obtaining an upper bound.
Nonparametric prediction with spatial data
Authors: Abhimanyu Gupta, Javier Hidalgo
Date: 2020-08-10 00:00:00
Tasks: Prediction

We describe a (nonparametric) prediction algorithm for spatial data, based on a canonical factorization of the spectral density function. We provide theoretical results showing that the predictor has desirable asymptotic properties. Finite sample performance is assessed in a Monte Carlo study that also compares our algorithm to a rival nonparametric method based on the infinite AR representation of the dynamics of the data. Finally, we apply our methodology to predict house prices in Los Angeles.
CG-SENSE revisited: Results from the first ISMRM reproducibility challenge
Authors: Oliver Maier, Steven H. Baete, Alexander Fyrdahl, Kerstin Hammernik, Seb Harrevelt, Lars Kasper, Agah Karakuzu, Michael Loecher, Franz Patzig, Ye Tian, Ke Wang, Daniel Gallichan, Martin Uecker, Florian Knoll
Date: 2020-08-10 00:00:00
Tasks: Image Reconstruction, SSIM

Purpose: The aim of this work is to shed light on the issue of reproducibility in MR image reconstruction in the context of a challenge. Participants had to recreate the results of "Advances in sensitivity encoding with arbitrary k-space trajectories" by Pruessmann et al. Methods: The task of the challenge was to reconstruct radially acquired multi-coil k-space data (brain/heart) following the method in the original paper, reproducing its key figures. Results were compared to consolidated reference implementations created after the challenge, accounting for the two most common programming languages used in the submissions (Matlab/Python). Results: Visually, differences between submissions were small. Pixel-wise differences originated from image orientation, assumed field-of-view or resolution. The reference implementations were in good agreement, both visually and in terms of image similarity metrics. Discussion and Conclusion: While the description level of the published algorithm enabled participants to reproduce CG-SENSE in general, details of the implementation varied, e.g., density compensation or Tikhonov regularization. Implicit assumptions about the data lead to further differences, emphasizing the importance of sufficient meta-data accompanying open data sets. Defining reproducibility quantitatively turned out to be non-trivial for this image reconstruction challenge, in the absence of ground-truth results. Typical similarity measures like NMSE of SSIM were misled by image intensity scaling and outlier pixels. Thus, to facilitate reproducibility, researchers are encouraged to publish code and data alongside the original paper. Future methodological papers on MR image reconstruction might benefit from the consolidated reference implementations of CG-SENSE presented here, as a benchmark for methods comparison.
An Assessment of the Radio Frequency Electromagnetic Field Exposure from A Massive MIMO 5G Testbed
Authors: Tian Hong Loh, Fabien Heliot, David Cheadle, Tom Fielder
Date: 2020-08-10 00:00:00

Current radiofrequency electromagnetic field (RF-EMF) exposure limits have become a critical concern for fifth-generation (5G) mobile network deployment. Regulation is not harmonized and in certain countries and regions it goes beyond the guidelines set out by the International Commission on Non-Ionizing Radiation Protection (ICNIRP). Using a massive multiple-input-multiple-output (mMIMO) testbed with beamforming capabilities that is capable of mimicking realistic 5G base station (BS) performance, this paper presents an experimental and statistical assessment of its associated RF-EMF exposure within a real-world indoor environment. The mMIMO testbed has up to 128 channels with user-programmable software defined radio (SDR) capability. It could perform zero-forcing precoding after channel state information (CSI) acquisition for different beamforming scenarios with respect to the associated user terminal antenna setups and positions. With 64 active mMIMO transmit antennas, 8 beamforming scenarios have been considered for single-user (SU) and multi-user (MU) downlink communications at different locations. Using a calibrated triaxial isotropic field-probe, the received channel power heat map for each beamforming scenario was acquired and then converted into an RF-EMF heat map. The relevant RF-EMF statistics was evaluated based on the variations of beam profiles and number of users.
Distributed Personalized Gradient Tracking with Convex Parametric Models
Authors: Ivano Notarnicola, Andrea Simonetto, Francesco Farina, Giuseppe Notarstefano
Date: 2020-08-10 00:00:00
Tasks: Distributed Optimization

We present a distributed optimization algorithm for solving online personalized optimization problems over a network of computing and communicating nodes, each of which linked to a specific user. The local objective functions are assumed to have a composite structure and to consist of a known time-varying (engineering) part and an unknown (user-specific) part. Regarding the unknown part, it is assumed to have a known parametric (e.g., quadratic) structure a priori, whose parameters are to be learned along with the evolution of the algorithm. The algorithm is composed of two intertwined components: (i) a dynamic gradient tracking scheme for finding local solution estimates and (ii) a recursive least squares scheme for estimating the unknown parameters via user's noisy feedback on the local solution estimates. The algorithm is shown to exhibit a bounded regret under suitable assumptions. Finally, a numerical example corroborates the theoretical analysis.
Connected Incomplete Preferences
Date: 2020-08-10 00:00:00
Tasks: ARC

The standard model of choice in economics is the maximization of a complete
and transitive preference relation over a fixed set of alternatives. While
completeness of preferences is usually regarded as a strong assumption,
weakening it requires care to ensure that the resulting model still has enough
structure to yield interesting results. This paper takes a step in this
direction by studying the class of "connected preferences", that is,
preferences that may fail to be complete but have connected maximal domains of
comparability. We offer four new results. Theorem 1 identifies a basic
necessary condition for a continuous preference to be connected in the sense
above, while Theorem 2 provides sufficient conditions. Building on the latter,
Theorem 3 characterizes the maximal domains of comparability. Finally, Theorem
4 presents conditions that ensure that maximal domains are arc-connected.
Latent dynamical variables produce signatures of spatiotemporal criticality in large biological systems
Date: 2020-08-10 00:00:00
Tasks: Hippocampus

Understanding the activity of large populations of neurons is difficult due
to the combinatorial complexity of possible cell-cell interactions. To reduce
the complexity, coarse-graining had been previously applied to experimental
neural recordings, which showed over two decades of scaling in free energy,
activity variance, eigenvalue spectra, and correlation time, hinting that the
mouse hippocampus operates in a critical regime. We model the experiment by
simulating conditionally independent binary neurons coupled to a small number
of long-timescale stochastic fields and then replicating the coarse-graining
procedure and analysis. This reproduces the experimentally-observed scalings,
suggesting that they may arise from coupling the neural population activity to
latent dynamic stimuli. Further, parameter sweeps for our model suggest that
emergence of scaling requires most of the cells in a population to couple to
the latent stimuli, predicting that even the celebrated place cells must also
respond to non-place stimuli.
A Design of Cooperative Overtaking Based on Complex Lane Detection and Collision Risk Estimation
Authors: Junlan Chen, Ke Wang, Huanhuan Bao, Tao Chen
Date: 2020-08-11 00:00:00
Tasks: Lane Detection

Cooperative overtaking is believed to have the capability of improving road safety and traffic efficiency by means of the real-time information exchange between traffic participants, including road infrastructures, nearby vehicles and others. In this paper, we focused on the critical issues of modeling, computation, and analysis of cooperative overtaking and made it playing a key role in the road overtaking area. In detail, for the purpose of extending the awareness of the surrounding environment, the lane markings in front of ego vehicle were detected and modeled with Bezier curve using an onboard camera. While the nearby vehicle positions were obtained through the vehicle-to-vehicle communication scheme making assure of the accuracy of localization. Then, Gaussian-based conflict potential field was proposed to guarantee the overtaking safety, which can quantitatively estimate the oncoming collision danger. To support the proposed method, many experiments were conducted on the human-in-the-loop simulation platform. The results demonstrated that our proposed method achieves better performance, especially in some unpredictable nature road circumstances.
Acoustic effects of medical, cloth, and transparent face masks on speech signals
Date: 2020-08-11 00:00:00

Face masks muffle speech and make communication more difficult, especially
for people with hearing loss. This study examines the acoustic attenuation
caused by different face masks, including medical, cloth, and transparent
masks, using a head-shaped loudspeaker and a live human talker. The results
suggest that all masks attenuate frequencies above 1 kHz, that attenuation is
greatest in front of the talker, and that there is substantial variation
between mask types, especially cloth masks with different materials and weaves.
Transparent masks have poor acoustic performance compared to both medical and
cloth masks. Most masks have little effect on lapel microphones, suggesting
that existing sound reinforcement and assistive listening systems may be
effective for verbal communication with masks.
Investigating the Product Profiles and Structural Relationships of New Levansucrases with Conventional and Non-Conventional Substrates
Authors: Andrea Hill, Salwa Karboune, Tarun Narwani, Alexandre de Brevern
Date: 2020-08-11 00:00:00

The synthesis of complex oligosaccharides is desired for their potential as prebiotics, and their role in the pharmaceutical and food industry. Levansucrase (LS, EC 2.4.1.10), a fructosyl-transferase, can catalyze the synthesis of these compounds. LS acquires a fructosyl residue from a donor molecule and performs a non-Lenoir transfer to an acceptor molecule, via $\beta$-(2$\to$6)-glycosidic linkages. Genome mining was used to uncover new LS enzymes with increased transfructosylating activity and wider acceptor promiscuity, with an initial screening revealing five LS enzymes. The product profiles and activities of these enzymes were examined after their incubation with sucrose. Alternate acceptor molecules were also incubated with the enzymes to study their consumption. LSs from Gluconobacter oxydans and Novosphingobium aromaticivorans synthesized fructooligosaccharides (FOSs) with up to 13 units in length. Alignment of their amino acid sequences and substrate docking with homology models identified structural elements causing differences in their product spectra. Raffinose, over sucrose, was the preferred donor molecule for the LS from Vibrio natriegens, N. aromaticivorans, and Paraburkolderia graminis. The LSs examined were found to have wide acceptor promiscuity, utilizing monosaccharides, disaccharides, and two alcohols to a high degree.
A purely mechanical model with asymmetric features for early morphogenesis of rod-shaped bacteria micro-colony
Authors: Marie Doumic, Sophie Hecht, Diane Peurichard
Date: 2020-08-11 00:00:00
Tasks: Friction

To model the morphogenesis of rod-shaped bacterial micro-colony, several individual-based models have been proposed in the biophysical literature. When studying the shape of micro-colonies, most models present interaction forces such as attraction or filial link. In this article, we propose a model where the bacteria interact only through non-overlapping constraints. We consider the asymmetry of the bacteria, and its influence on the friction with the substrate. Besides, we consider asymmetry in the mass distribution of the bacteria along their length. These two new modelling assumptions allow us to retrieve mechanical behaviours of micro-colony growth without the need of interaction such as attraction. We compare our model to various sets of experiments, discuss our results, and propose several quantifiers to compare model to data in a systematic way.
Unsupervised Learning For Sequence-to-sequence Text-to-speech For Low-resource Languages
Date: 2020-08-11 00:00:00
Tasks: Quantization, text-to-speech, Text to Speech, Unsupervised Pre-training

Recently, sequence-to-sequence models with attention have been successfully
applied in Text-to-speech (TTS). These models can generate near-human speech
with a large accurately-transcribed speech corpus. However, preparing such a
large data-set is both expensive and laborious. To alleviate the problem of
heavy data demand, we propose a novel unsupervised pre-training mechanism in
this paper. Specifically, we first use Vector-quantization
Variational-Autoencoder (VQ-VAE) to ex-tract the unsupervised linguistic units
from large-scale, publicly found, and untranscribed speech. We then pre-train
the sequence-to-sequence TTS model by using the<unsupervised linguistic units,
audio>pairs. Finally, we fine-tune the model with a small amount of<text,
audio>paired data from the target speaker. As a result, both objective and
subjective evaluations show that our proposed method can synthesize more
intelligible and natural speech with the same amount of paired training data.
Besides, we extend our proposed method to the hypothesized low-resource
languages and verify the effectiveness of the method using objective
evaluation.
Epigraphical Relaxation for Minimizing Layered Mixed Norms
Authors: Seisuke Kyochi, Shunsuke Ono, Ivan Selesnick
Date: 2020-08-11 00:00:00
Tasks: Image Restoration

This paper proposes an epigraphical relaxation (ERx) technique for non-proximable mixed norm minimization. Mixed norm regularization methods play a central role in signal reconstruction and processing, where their optimization relies on the fact that the proximity operators of the mixed norms can be computed efficiently. To bring out the power of regularization, sophisticated layered modeling of mixed norms that can capture inherent signal structure is a key ingredient, but the proximity operator of such a mixed norm is often unavailable (non-proximable). Our ERx decouples a layered non-proximable mixed norm into a norm and multiple epigraphical constraints. This enables us to handle a wide range of non-proximable mixed norms in optimization, as long as both the proximal operator of the outermost norm and the projection onto each epigraphical constraint are efficiently computable. Moreover, under mild conditions, we prove that ERx does not change the minimizer of the original problem despite relaxing equality constraints into inequality ones. We also develop new regularizers based on ERx: one is decorrelated structure-tensor total variation for color image restoration, and the other is amplitude-spectrum nuclear norm for low-rank amplitude recovery. We examine the power of these regularizers through experiments, which illustrates the utility of ERx.
EEG-based Auditory Attention Decoding: Towards Neuro-Steered Hearing Devices
Authors: Simon Geirnaert, Servaas Vandecappelle, Emina Alickovic, Alain de Cheveigné, Edmund Lalor, Bernd T. Meyer, Sina Miran, Tom Francart, Alexander Bertrand
Date: 2020-08-11 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Speaker Separation

People suffering from hearing impairment often have difficulties participating in conversations in so-called `cocktail party' scenarios with multiple people talking simultaneously. Although advanced algorithms exist to suppress background noise in these situations, a hearing device also needs information on which of these speakers the user actually aims to attend to. The correct (attended) speaker can then be enhanced using this information, and all other speakers can be treated as background noise. Recent neuroscientific advances have shown that it is possible to determine the focus of auditory attention from non-invasive neurorecording techniques, such as electroencephalography (EEG). Based on these new insights, a multitude of auditory attention decoding (AAD) algorithms have been proposed, which could, combined with the appropriate speaker separation algorithms and miniaturized EEG sensor devices, lead to so-called neuro-steered hearing devices. In this paper, we provide a broad review and a statistically grounded comparative study of EEG-based AAD algorithms and address the main signal processing challenges in this field.
Why Did the x-Vector System Miss a Target Speaker? Impact of Acoustic Mismatch Upon Target Score on VoxCeleb Data
Date: 2020-08-11 00:00:00
Tasks: BIG-bench Machine Learning, Speaker Verification

Modern automatic speaker verification (ASV) relies heavily on machine
learning implemented through deep neural networks. It can be difficult to
interpret the output of these black boxes. In line with interpretative machine
learning, we model the dependency of ASV detection score upon acoustic mismatch
of the enrollment and test utterances. We aim to identify mismatch factors that
explain target speaker misses (false rejections). We use distance in the first-
and second-order statistics of selected acoustic features as the predictors in
a linear mixed effects model, while a standard Kaldi x-vector system forms our
ASV black-box. Our results on the VoxCeleb data reveal the most prominent
mismatch factor to be in F0 mean, followed by mismatches associated with
formant frequencies. Our findings indicate that x-vector systems lack
robustness to intra-speaker variations.
Measuring Energy-saving Technological Change: International Trends and Differences
Authors: Emiko Inoue, Hiroya Taniguchi, Ken Yamada
Date: 2020-08-11 00:00:00

Technological change is essential to balance economic growth and environmental sustainability. This study documents energy-saving technological change to understand the trends and differences therein in OECD countries. We estimate sector-level production functions with factor-augmenting technologies using cross-country and cross-industry panel data and shift-share instruments, thereby measuring energy-saving technological change for each country and sector. Our results show how the levels and growth rates of energy-saving technology vary across countries, sectors, and time. In addition, we evaluate the extent to which factor-augmenting technologies contribute to economic growth and how this contribution differs across countries and sectors.
S-vectors and TESA: Speaker Embeddings and a Speaker Authenticator Based on Transformer Encoder
Authors: N J Metilda Sagaya Mary, S Umesh, Sandesh V Katta
Date: 2020-08-11 00:00:00
Tasks: Sentence, Speaker Verification

One of the most popular speaker embeddings is x-vectors, which are obtained from an architecture that gradually builds a larger temporal context with layers. In this paper, we propose to derive speaker embeddings from Transformer's encoder trained for speaker classification. Self-attention, on which Transformer's encoder is built, attends to all the features over the entire utterance and might be more suitable in capturing the speaker characteristics in an utterance. We refer to the speaker embeddings obtained from the proposed speaker classification model as s-vectors to emphasize that they are obtained from an architecture that heavily relies on self-attention. Through experiments, we demonstrate that s-vectors perform better than x-vectors. In addition to the s-vectors, we also propose a new architecture based on Transformer's encoder for speaker verification as a replacement for speaker verification based on conventional probabilistic linear discriminant analysis (PLDA). This architecture is inspired by the next sentence prediction task of bidirectional encoder representations from Transformers (BERT), and we feed the s-vectors of two utterances to verify whether they belong to the same speaker. We name this architecture the Transformer encoder speaker authenticator (TESA). Our experiments show that the performance of s-vectors with TESA is better than s-vectors with conventional PLDA-based speaker verification.
Driver Assistance for Safe and Comfortable On-Ramp Merging Using Environment Models Extended through V2X Communication and Role-Based Behavior Predictions
Authors: Lucas Eiermann, Florian Wirthmüller, Kay Massow, Gabi Breuel, Ilja Radusch
Date: 2020-08-11 00:00:00
Tasks: Autonomous Vehicles

Modern driver assistance systems as well as autonomous vehicles take their decisions based on local maps of the environment. These maps include, for example, surrounding moving objects perceived by sensors as well as routes and navigation information. Current research in the field of environment mapping is concerned with two major challenges. The first one is the integration of information from different sources e.g. on-board sensors like radar, camera, ultrasound and lidar, offline map data or backend information. The second challenge comprises in finding an abstract representation of this aggregated information with suitable interfaces for different driving functions and traffic situations. To overcome these challenges, an extended environment model is a reasonable choice. In this paper, we show that role-based motion predictions in combination with v2x-extended environment models are able to contribute to increased traffic safety and driving comfort. Thus, we combine the mentioned research areas and show possible improvements, using the example of a threading process at a motorway access road. Furthermore, it is shown that already an average v2x equipment penetration of 80% can lead to a significant improvement of 0.33m/s^2 of the total acceleration and 12m more safety distance compared to non v2x-equipped vehicles during the threading process.