pwc-archive/papers-with-abstracts huggingface dataset:
Replay Spoofing Countermeasure Using Autoencoder and Siamese Network on ASVspoof 2019 Challenge
Date: 2019-10-29 00:00:00
Tasks: Speaker Verification, Speech Synthesis, Voice Conversion

Automatic Speaker Verification (ASV) is the process of identifying a person
based on the voice presented to a system. Different synthetic approaches allow
spoofing to deceive ASV systems (ASVs), whether using techniques to imitate a
voice or recunstruct the features. Attackers try to beat up the ASVs using four
general techniques; impersonation, speech synthesis, voice conversion, and
replay. The last technique is considered as a common and high potential tool
for spoofing purposes since replay attacks are more accessible and require no
technical knowledge from adversaries. In this study, we introduce a novel
replay spoofing countermeasure for ASVs. Accordingly, we used the Constant Q
Cepstral Coefficient (CQCC) features fed into an autoencoder to attain more
informative features and to consider the noise information of spoofed
utterances for discrimination purpose. Finally, different configurations of the
Siamese network were used for the first time in this context for
classification. The experiments performed on ASVspoof challenge 2019 dataset
using Equal Error Rate (EER) and Tandem Detection Cost Function (t-DCF) as
evaluation metrics show that the proposed system improved the results over the
baseline by 10.73% and 0.2344 in terms of EER and t-DCF, respectively.
Roles of repertoire diversity in robustness of humoral immune response
Authors: Alexander Mozeika, Franca Fraternali, Deborah Dunn-Walters, Anthony C. C. Coolen
Date: 2019-10-29 00:00:00
Tasks: Diversity, Specificity

The adaptive immune system relies on diversity of its repertoire of receptors to protect the organism from a great variety of pathogens. Since the initial repertoire is the result of random gene rearrangement, binding of receptors is not limited to pathogen-associated antigens but also includes self antigens. There is a fine balance between having a diverse repertoire, protecting from many different pathogens, and yet reducing its self-reactivity as far as possible to avoid damage to self. In the ageing immune system this balance is altered, manifesting in reduced specificity of response to pathogens or vaccination on a background of higher self-reactivity. To answer the question whether age-related changes of repertoire in the diversity and self/non-self affinity balance of antibodies could explain the reduced efficacy of the humoral response in older people, we construct a minimal mathematical model of the humoral immune response. The principle of least damage allows us, for a given repertoire of antibodies, to resolve a tension between the necessity to neutralise target antigens as quickly as possible and the requirement to limit the damage to self antigens leading to an optimal dynamics of immune response. The model predicts slowing down of immune response for repertoires with reduced diversity and increased self-reactivity.
Assessment of climate change effects on mountain ecosystems through a cross-site analysis in the Alps and Apennines
Date: 2019-10-29 00:00:00

Mountain ecosystems are sensitive indicators of climate change. Long-term
studies may be extremely useful in assessing the responses of high-elevation
ecosystems to climate change and other anthropogenic drivers. Mountain research
sites within the LTER (Long-Term Ecosystem Research) network are representative
of various types of ecosystems and span a wide bioclimatic and elevational
range. Here, we present a synthesis and a review of the main results from
long-term ecological studies in mountain ecosystems at 20 LTER sites in Italy,
Switzerland and Austria. We analyzed a set of key climate parameters, such as
temperature and snow cover duration, in relation to vascular species
composition, plant traits, abundance patterns, pedoclimate, nutrient dynamics
in soils and water, phenology and composition of freshwater biota. The overall
results highlight the rapid response of mountain ecosystems to climate change.
As temperatures increased, vegetation cover in alpine and subalpine summits
increased as well. Years with limited snow cover duration caused an increase in
soil temperature and microbial biomass during the growing season. Effects on
freshwater ecosystems were observed, in terms of increases in solutes,
decreases in nitrates and changes in plankton phenology and benthos
communities. This work highlights the importance of comparing and integrating
long-term ecological data collected in different ecosystems, for a more
comprehensive overview of the ecological effects of climate change.
Nevertheless, there is a need for i) adopting co-located monitoring site
networks to improve our ability to obtain sound results from cross-site
analysis, ii) carrying out further studies, with fine spatial and temporal
resolutions to improve understanding of responses to extreme events, and iii)
increasing comparability and standardizing protocols across networks to clarify
local from global patterns.
Persuasion with Coarse Communication
Authors: Yunus C. Aybas, Eray Turkel
Date: 2019-10-29 00:00:00

In many real-world scenarios, experts must convey complex information with limited message capacity. This paper explores how the availability of messages influences an expert's persuasive ability. We develop a geometric representation of the expert's payoff with limited message capacity and identify bounds on the value of an additional signal for the sender. In a special class of games, the marginal value of a signal increases as the receiver becomes more difficult to persuade. Moreover, we show that access to an additional signal does not necessarily translate into more information transmitted in equilibrium, and the receiver might prefer coarser communication. This suggests that regulations on communication capacity have the potential to shift the balance of power from the expert to the decision-maker, ultimately improving welfare. Finally, we study the geometric properties of optimal information structures and show that the complexity of the sender's problem can be simplified to a finite algorithm.
A novel fuzzy logic-based metric for audio quality assessment: Objective audio quality assessment
Date: 2019-10-29 00:00:00
Tasks: Audio Quality Assessment

ITU-R BS.1387 states a method for objective assessment of perceived audio
quality. This Recommendation, known also as PEAQ (Perceptual Evaluation of
Audio Quality) is based on a psychoacoustic model of the human ear and was
standardized by the International Telecommunications Union as an alternative to
subjective tests, which are expensive and time-consuming processes. PEAQ
combines various physiological and psycho-acoustical properties of the human
ear to give a measure of the quality difference between a reference audio and a
test audio. The reference audio signal could be considered as a distortion-free
source, whereas the test signal is a distorted version of the reference, which
may have audible artifacts because of compression. The algorithm computes the
Model Output Variables (MOVs) which are mapped to a single quality measure,
Objective Difference Grade (ODG), using a three-layer perceptron artificial
neural network. The ODG estimates the perceived distortion between both audio
signals. In this paper we propose a new metric of low computational complexity
called FQI (Fuzzy Quality Index) which is based on Fuzzy Logic reasoning and
has been incorporated into the existing PEAQ model to improve its overall
performance. Results show that the modified version slightly outperforms PEAQ.
Single-molecule stretching shows glycosylation sets tension in the hyaluronan-aggrecan bottlebrush
Authors: Sarah N. Innes-Gold, John P. Berezney, Omar A. Saleh
Date: 2019-10-29 00:00:00

Large bottlebrush complexes formed from the polysaccharide hyaluronan (HA) and the proteoglycan aggrecan contribute to cartilage compression resistance and are necessary for healthy joint function. A variety of mechanical forces act on these complexes in the cartilage extracellular matrix, motivating the need for a quantitative description which links their structure and mechanical response. Studies using electron microscopy have imaged the HA-aggrecan brush but require adsorption to a surface, dramatically altering the complex from its native conformation. We use magnetic tweezers force spectroscopy to measure changes in extension and mechanical response of an HA chain as aggrecan monomers bind and form a bottlebrush. This technique directly measures changes undergone by a single complex with time and under varying solution conditions. Upon addition of aggrecan, we find a large swelling effect manifests when the HA chain is under very low external tension (i.e. stretching forces less than ~1 pN). We use models of force-extension behavior to show that repulsion between the aggrecans induces an internal tension in the HA chain. Through reference to theories of bottlebrush polymer behavior, we demonstrate that the experimental values of internal tension are consistent with a polydisperse aggrecan population, likely caused by varying degrees of glycosylation. By enzymatically deglycosylating aggrecan, we show that aggrecan glycosylation is the structural feature which causes HA stiffening. We then construct a simple stochastic binding model to show that variable glycosylation leads to a wide distribution of internal tensions in HA, causing variations in the mechanics at much longer length-scales. Our results provide a mechanistic picture of how flexibility and size of HA and aggrecan lead to the brush architecture and mechanical properties of this important component of cartilage.
Disclosure Games with Large Evidence Spaces
Date: 2019-10-30 00:00:00

We study a disclosure game with a large evidence space. There is an unknown binary state. A sender observes a sequence of binary signals about the state and discloses a left truncation of the sequence to a receiver in order to convince him that the state is good. We focus on truth-leaning equilibria (cf. Hart et al. (2017)), where the sender discloses truthfully when doing so is optimal, and the receiver takes off-path disclosure at face value. In equilibrium, seemingly sub-optimal truncations are disclosed, and the disclosure contains the longest truncation that yields the maximal difference between the number of good and bad signals. We also study a general framework of disclosure games which is compatible with large evidence spaces, a wide range of disclosure technologies, and finitely many states. We characterize the unique equilibrium value function of the sender and propose a method to construct equilibria for a broad class of games.
Exploiting Intelligent Reflecting Surfaces in NOMA Networks: Joint Beamforming Optimization
Authors: Xidong Mu, Yuanwei Liu, Li Guo, Jiaru Lin, Naofal Al-Dhahir
Date: 2019-10-30 00:00:00
Tasks: Quantization

This paper investigates a downlink multiple-input single-output intelligent reflecting surface (IRS) aided non-orthogonal multiple access (NOMA) system, where a base station (BS) serves multiple users with the aid of IRSs. Our goal is to maximize the sum rate of all users by jointly optimizing the active beamforming at the BS and the passive beamforming at the IRS, subject to successive interference cancellation decoding rate conditions and IRS reflecting elements constraints. In term of the characteristics of reflection amplitudes and phase shifts, we consider ideal and non-ideal IRS assumptions. To tackle the formulated non-convex problems, we propose efficient algorithms by invoking alternating optimization, which design the active beamforming and passive beamforming alternately. For the ideal IRS scenario, the two subproblems are solved by invoking the successive convex approximation technique. For the non-ideal IRS scenario, constant modulus IRS elements are further divided into continuous phase shifts and discrete phase shifts. To tackle the passive beamforming problem with continuous phase shifts, a novel algorithm is developed by utilizing the sequential rank-one constraint relaxation approach, which is guaranteed to find a locally optimal rank-one solution. Then, a quantization-based scheme is proposed for discrete phase shifts. Finally, numerical results illustrate that: i) the system sum rate can be significantly improved by deploying the IRS with the proposed algorithms; ii) 3-bit phase shifters are capable of achieving almost the same performance as the ideal IRS; iii) the proposed IRS-aided NOMA systems achieve higher system sum rate than the IRS-aided orthogonal multiple access system.
Achieving Positive Covert Capacity over MIMO AWGN Channels
Date: 2019-10-30 00:00:00

We consider covert communication, i.e., hiding the presence of communication from an adversary for multiple-input multiple-output (MIMO) additive white Gaussian noise (AWGN) channels. We characterize the maximum covert coding rate under a variety of settings, including different regimes where either the number of transmit antennas or the blocklength is scaled up. We show that a non-zero covert capacity can be achieved in the massive MIMO regime in which the number of transmit antennas scales up but under specific conditions. Under such conditions, we show that the covert capacity of MIMO AWGN channels converges the capacity of MIMO AWGN channels. Furthermore, we derive the order-optimal scaling of the number of covert bits in the regime where the covert capacity is zero. We provide an insightful comparative analysis of different cases in which secrecy and energy-undetectability constraints are imposed separately or jointly.
Not even wrong: Reply to Wagg et al
Date: 2019-10-30 00:00:00
Tasks: Diversity

We demonstrate that the issues described in the Wagg et al. (2019) Comment on
our paper (Pillai and Gouhier, 2019) are all due to misunderstandings about the
implications of pairwise effects, the nature of the null baseline in both our
framework and in the Loreau-Hector (LH) partitioning scheme (i.e., the midpoint
of the monocultures), and the impact of nonlinearity on the LH partitioning
results. Specifically, we show that (i) pairwise effects can be computed over
any time horizon and thus do not imply stable coexistence, (ii) the midpoint of
the monocultures corresponds to a neutral community so coexistence was always
part of the LH baseline, and (iii) contrary to what Wagg et al. suggested,
generalized diversity-interaction models do not account for (and may in fact
exacerbate) the problem of nonlinearity in monocultures, which inflates the LH
net biodiversity effect and generates incorrect estimates of selection and
complementarity. Hence, all of our original claims about the triviality
inherent in biodiversity-ecosystem functioning research and the issues with the
LH partitioning scheme hold.
Polarimetric Radar Cross-Sections of Pedestrians at Automotive Radar Frequencies
Date: 2019-10-30 00:00:00

Simulation of radar cross-sections (RCS) of pedestrians at automotive radar
frequencies forms a key tool for software verification test beds for advanced
driver assistance systems. Two commonly used simulation methods are: the
computationally simple scattering center model of dynamic humans; and the
shooting and bouncing ray technique based on geometric optics. The latter
technique is more accurate but due to its computational complexity, it is
usually used only for modeling scattered returns of still human poses. In this
work, we combine the two methods in a linear regression framework to accurately
estimate the scattering coefficients or reflectivies of point scatterers in a
realistic automotive radar signal model which we subsequently use to simulate
range-time, Doppler-time and range-Doppler radar signatures. The simulated
signatures show a normalized mean square error below 10% and a structural
similarity above $81\%$ with respect to measurement results generated with an
automotive radar at 77 GHz.
Time-dependent lead-lag relationships between the VIX and VIX futures markets
Date: 2019-10-30 00:00:00

We utilize the symmetric thermal optimal path (TOPS) method to examine the
dynamic interaction patterns between the VIX and VIX futures markets. We
document that the VIX dominates the VIX futures more in the first few years,
especially before the introduction of VIX options. We further observe that the
TOPS paths show an alternate lead-lag relationship instead of a dominance
between the VIX and VIX futures in most of the time periods. Meanwhile, we find
that the VIX futures have been increasingly more important in the price
discovery since the launch of several VIX ETPs.
Wireless communication for safe UAVs: From Long-Range Deconfliction to short-range collision avoidance
Date: 2020-03-11 00:00:00
Tasks: Collision Avoidance, Management

Small drones are becoming a part of our everyday life. They are used in a
wide variety of commercial applications, and the number of drones in the air is
steadily growing. To ensure the safe operation of drones, traffic management
rules must be designed and implemented by avionics and telecommunication
experts. In this article, we propose to establish a common terminology for
these two communities. We first describe the traffic management architecture
and services. Next, we overview several approaches for defining the inter-drone
separation distances ensuring the safe operation. Moreover, we analyze which
existing technologies can be useful for each of these definitions. Finally, we
present measurement results indicating that our new Wi-Fi-based messaging
scheme is a potentially useful tool for the drone traffic management system.
Indian EmoSpeech Command Dataset: A dataset for emotion based speech recognition in the wild
Date: 2019-10-18 00:00:00
Tasks: Emotion Recognition, Keyword Spotting, speech-recognition, Speech Recognition

Speech emotion analysis is an important task which further enables several
application use cases. The non-verbal sounds within speech utterances also play
a pivotal role in emotion analysis in speech. Due to the widespread use of
smartphones, it becomes viable to analyze speech commands captured using
microphones for emotion understanding by utilizing on-device machine learning
models. The non-verbal information includes the environment background sounds
describing the type of surroundings, current situation and activities being
performed. In this work, we consider both verbal (speech commands) and
non-verbal sounds (background noises) within an utterance for emotion analysis
in real-life scenarios. We create an indigenous dataset for this task namely
"Indian EmoSpeech Command Dataset". It contains keywords with diverse emotions
and background sounds, presented to explore new challenges in audio analysis.
We exhaustively compare with various baseline models for emotion analysis on
speech commands on several performance metrics. We demonstrate that we achieve
a significant average gain of 3.3% in top-one score over a subset of speech
command dataset for keyword spotting.
Sharp images from diffuse beams: factorisation of the discrete delta function
Date: 2019-11-21 00:00:00

Discrete delta functions define the limits of attainable spatial resolution
for all imaging systems. Here we construct broad, multi-dimensional discrete
functions that replicate closely the action of a Dirac delta function under
aperiodic convolution. These arrays spread the energy of a sharp probe beam to
simultaneously sample multiple points across the volume of a large object,
without losing image sharpness. A diffuse point-spread function applied in any
imaging system can reveal the underlying structure of objects less intrusively
and with equal or better signal-to-noise ratio. These multi-dimensional arrays
are related to previously known, but relatively rarely employed,
one-dimensional integer Huffman sequences. Practical point-spread functions can
now be made sufficiently large to span the size of the object under measure.
Such large arrays can be applied to ghost imaging, which has demonstrated
potential to greatly improve signal-to-noise ratios and reduce the total dose
required for tomographic imaging. The discrete arrays built here parallel the
continuum self-adjoint or Hermitian functions that underpin wave theory and
quantum mechanics.
Michael Milken: The Junk Dealer
Authors: Ravi Kashyap
Date: 2019-10-17 00:00:00

We take a closer look at the life and legacy of Micheal Milken. We discuss why Michael Milken, also know as the Junk Bond King, was not just any other King or run-of-the-mill Junk Dealer, but "The Junk Dealer". We find parallels between the three parts to any magic act and what Micheal Milken did, showing that his accomplishments were nothing short of a miracle. His compensation at that time captures to a certain extent the magnitude of the changes he brought about, the eco-system he created for businesses to flourish, the impact he had on the wider economy and also on the future growth and development of American Industry. We emphasize two of his contributions to the financial industry that have grown in importance over the years. One was the impetus given to the Private Equity industry and the use of LBOs. The second was the realization that thorough research was the key to success, financial and otherwise. Perhaps an unintended consequence of the growth in junk bonds and tailored financing was the growth of Silicon valley and technology powerhouses in the California bay area. Investors witnessed that there was a possibility for significant returns and that financial success could be had due to the risk mitigation that Milken demonstrated by investing in portfolios of so called high risk and low profile companies. We point out the current trend in many regions of the world, which is the birth of financial and technology firms and we suggest that finding innovative ways of financing could be the key to the sustained growth of these eco-systems.
Probabilistic performance validation of deep learning-based robust NMPC controllers
Authors: Benjamin Karg, Teodoro Alamo, Sergio Lucia
Date: 2019-10-30 00:00:00
Tasks: Deep Learning, Model Predictive Control, valid

Solving nonlinear model predictive control problems in real time is still an important challenge despite of recent advances in computing hardware, optimization algorithms and tailored implementations. This challenge is even greater when uncertainty is present due to disturbances, unknown parameters or measurement and estimation errors. To enable the application of advanced control schemes to fast systems and on low-cost embedded hardware, we propose to approximate a robust nonlinear model controller using deep learning and to verify its quality using probabilistic validation techniques. We propose a probabilistic validation technique based on finite families, combined with the idea of generalized maximum and constraint backoff to enable statistically valid conclusions related to general performance indicators. The potential of the proposed approach is demonstrated with simulation results of an uncertain nonlinear system.
Analytical Evaluation of Space-Time M-QAM Signaling and Time-Delay Estimation in Multipath Terrestrial Communications
Date: 2019-10-28 00:00:00

This paper presents novel information-theoretic PHY layer performance
improvement for combating the system fading effects in wireless terrestrial
communication applications. Robust space-time system strategies in cooperation
with the high performance unpunctured serially concatenated codes have been
jointly employed. The wireless terrestrial system also uses M-ary quadrature
amplitude modulation (QAM) signaling to develop a well-established system
modeling for multiple-input single-output (MISO) and multiple-input
multiple-output (MIMO) analysis. This research also undertakes the analytical
evaluation of time-delay estimation in the presence of additive white Gaussian
noise (AWGN) in multipath areas. The objective of this investigation is to
develop mathematically-oriented estimations intended for suitable employment in
terrestrial communications. This model considers the problem of overlapping the
terrestrial signals arrival-times estimation from a noisy received waveform in
order to develop a system model for terrestrial TDE.