pwc-archive/papers-with-abstracts huggingface dataset:
Tradeoff between Ergodic Rate and Delivery Latency in Fog Radio Access Networks
Date: 2020-02-13 00:00:00

Wireless content caching has recently been considered as an efficient way in
fog radio access networks (FRANs) to alleviate the heavy burden on
capacity-limited fronthaul links and reduce delivery latency. In this paper, an
advanced minimal delay association policy is proposed to minimize latency while
guaranteeing spectral efficiency in F-RANs. By utilizing stochastic geometry
and queueing theory, closed-form expressions of successful delivery
probability, average ergodic rate, and average delivery latency are derived,
where both the traditional association policy based on accessing the base
station with maximal received power and the proposed minimal delay association
policy are concerned. Impacts of key operating parameters on the aforementioned
performance metrics are exploited. It is shown that the proposed association
policy has a better delivery latency than the traditional association policy.
Increasing the cache size of fog-computing based access points (F-APs) can more
significantly reduce average delivery latency, compared with increasing the
density of F-APs. Meanwhile, the latter comes at the expense of decreasing
average ergodic rate. This implies the deployment of large cache size at F-APs
rather than high density of F-APs can promote performance effectively in
F-RANs.
Augmenting an Assisted Living Lab with Non-Intrusive Load Monitoring
Date: 2020-02-13 00:00:00
Tasks: Non-Intrusive Load Monitoring

The need for reducing our energy consumption footprint and the increasing
number of electric devices in today's homes is calling for new solutions that
allow users to efficiently manage their energy consumption. Real-time feedback
at device level would be of a significant benefit for this application. In
addition, the aging population and their wish to be more autonomous have
motivated the use of this same real-time data to indirectly monitor the
household's occupants for their safety. By breaking down aggregate power
consumption into its components, Non-Intrusive Load Monitoring provides
information on individual appliances and their current state of operation.
Since no additional metering equipment is required, residents are not
confronted with intrusion into their familiar environment. Our work aims to
depict an architecture supporting non-intrusive measurement with a smart
electricity meter and the handling of these data using an open-source platform
that allows to visualize and process real-time data about the total energy
consumed. As a case study, we describe a series of measurements from common
household devices and show how abnormal behavior can be detected.
Target spiking patterns enable efficient and biologically plausible learning for complex temporal tasks
Authors: Paolo Muratore, Cristiano Capone, Pier Stanislao Paolucci
Date: 2020-02-13 00:00:00

Recurrent spiking neural networks (RSNN) in the human brain learn to perform a wide range of perceptual, cognitive and motor tasks very efficiently in terms of energy consumption and requires very few examples. This motivates the search for biologically inspired learning rules for RSNNs to improve our understanding of brain computation and the efficiency of artificial intelligence. Several spiking models and learning rules have been proposed, but it remains a challenge to design RSNNs whose learning relies on biologically plausible mechanisms and are capable of solving complex temporal tasks. In this paper, we derive a learning rule, local to the synapse, from a simple mathematical principle, the maximization of the likelihood for the network to solve a specific task. We propose a novel target-based learning scheme in which the learning rule derived from likelihood maximization is used to mimic a specific spiking pattern that encodes the solution to complex temporal tasks. This method makes the learning extremely rapid and precise, outperforming state of the art algorithms for RSNNs. We demonstrate the capacity of our model to tackle several problems like learning multidimensional trajectories and solving the classical temporal XOR benchmark. Finally, we show that an online approximation of the gradient ascent, in addition to guaranteeing complete locality in time and space, allows learning after very few presentations of the target output. Our model can be applied to different types of biological neurons. The analytically derived plasticity learning rule is specific to each neuron model and can produce a theoretical prediction for experimental validation.
ACCORDION: Clustering and Selecting Relevant Data for Guided Network Extension and Query Answering
Authors: Yasmine Ahmed, Cheryl Telmer, Natasa Miskov-Zivanov
Date: 2020-02-13 00:00:00
Tasks: Clustering

Querying new information from knowledge sources, in general, and published literature, in particular, aims to provide precise and quick answers to questions raised about a system under study. In this paper, we present ACCORDION (Automated Clustering Conditional On Relating Data of Interactions tO a Network), a novel tool and a methodology to enable efficient answering of biological questions by automatically assembling new, or expanding existing models using published literature. Our approach integrates information extraction and clustering with simulation and formal analysis to allow for an automated iterative process that includes assembling, testing and selecting the most relevant models, given a set of desired system properties. We applied our methodology to a model of the circuitry that con-trols T cell differentiation. To evaluate our approach, we compare the model that we obtained, using our automated model extension approach, with the previously published manually extended T cell differentiation model. Besides demonstrating automated and rapid reconstruction of a model that was previously built manually, ACCORDION can assemble multiple models that satisfy desired properties. As such, it replaces large number of tedious or even imprac-tical manual experiments and guides alternative hypotheses and interventions in biological systems.
Risk Sensitive Rendezvous Algorithm for Heterogeneous Agents in Urban Environments
Date: 2020-02-13 00:00:00

Demand for fast and inexpensive parcel deliveries in urban environments has
risen considerably in recent years. A framework is envisioned to enforce
efficient last mile delivery in urban environments by leveraging a network of
ride-sharing vehicles, where Unmanned Aerial Systems (UASs) drop packages on
said vehicles which then cover the majority of the distance to finally be
picked up by another UAS for delivery. This approach presents many engineering
challenges, including the safe rendezvous of both agents: the UAS and the
human-operated ground vehicle. In this paper, we introduce a framework to
minimize the risk of failure, while allowing for optimal usage of the
controlled agent. We formulate a compact fast planner to drive a UAS to a
passive ground vehicle with inexact behavior, while providing intuitive and
meaningful procedures to guarantee safety with minimal sacrifice of optimality.
The resulting algorithm is shown to be fast and implementable in real-time via
numerical tests.
Economic complexity of prefectures in Japan
Date: 2020-08-31 00:00:00

Every nation prioritizes the inclusive economic growth and development of all
regions. However, we observe that economic activities are clustered in space,
which results in a disparity in per-capita income among different regions. A
complexity-based method was proposed by Hidalgo and Hausmann [PNAS 106,
10570-10575 (2009)] to explain the large gaps in per-capita income across
countries. Although there have been extensive studies on countries' economic
complexity using international export data, studies on economic complexity at
the regional level are relatively less studied. Here, we study the industrial
sector complexity of prefectures in Japan based on the basic information of
more than one million firms. We aggregate the data as a bipartite network of
prefectures and industrial sectors. We decompose the bipartite network as a
prefecture-prefecture network and sector-sector network, which reveals the
relationships among them. Similarities among the prefectures and among the
sectors are measured using a metric. From these similarity matrices, we cluster
the prefectures and sectors using the minimal spanning tree technique.The
computed economic complexity index from the structure of the bipartite network
shows a high correlation with macroeconomic indicators, such as per-capita
gross prefectural product and prefectural income per person. We argue that this
index reflects the present economic performance and hidden potential of the
prefectures for future growth.
Consistency-aware multi-channel speech enhancement using deep neural networks
Date: 2020-02-14 00:00:00
Tasks: Speech Enhancement

This paper proposes a deep neural network (DNN)-based multi-channel speech
enhancement system in which a DNN is trained to maximize the quality of the
enhanced time-domain signal. DNN-based multi-channel speech enhancement is
often conducted in the time-frequency (T-F) domain because spatial filtering
can be efficiently implemented in the T-F domain. In such a case, ordinary
objective functions are computed on the estimated T-F mask or spectrogram.
However, the estimated spectrogram is often inconsistent, and its amplitude and
phase may change when the spectrogram is converted back to the time-domain.
That is, the objective function does not evaluate the enhanced time-domain
signal properly. To address this problem, we propose to use an objective
function defined on the reconstructed time-domain signal. Specifically, speech
enhancement is conducted by multi-channel Wiener filtering in the T-F domain,
and its result is converted back to the time-domain. We propose two objective
functions computed on the reconstructed signal where the first one is defined
in the time-domain, and the other one is defined in the T-F domain. Our
experiment demonstrates the effectiveness of the proposed system comparing to
T-F masking and mask-based beamforming.
A Sequence Matching Network for Polyphonic Sound Event Localization and Detection
Date: 2020-02-14 00:00:00
Tasks: Direction of Arrival Estimation, Event Detection, Sound Event Detection, Sound Event Localization and Detection, Transfer Learning

Polyphonic sound event detection and direction-of-arrival estimation require
different input features from audio signals. While sound event detection mainly
relies on time-frequency patterns, direction-of-arrival estimation relies on
magnitude or phase differences between microphones. Previous approaches use the
same input features for sound event detection and direction-of-arrival
estimation, and train the two tasks jointly or in a two-stage transfer-learning
manner. We propose a two-step approach that decouples the learning of the sound
event detection and directional-of-arrival estimation systems. In the first
step, we detect the sound events and estimate the directions-of-arrival
separately to optimize the performance of each system. In the second step, we
train a deep neural network to match the two output sequences of the event
detector and the direction-of-arrival estimator. This modular and hierarchical
approach allows the flexibility in the system design, and increase the
performance of the whole sound event localization and detection system. The
experimental results using the DCASE 2019 sound event localization and
detection dataset show an improved performance compared to the previous
state-of-the-art solutions.
Sampling Policy Design for Tracking Time-Varying Graph Signals with Adaptive Budget Allocation
Authors: Xuan Xie, Hui Feng, Bo Hu
Date: 2020-02-14 00:00:00

There have been many works that focus on the sampling set design for a static graph signal, but few for time-varying graph signals (GS). In this paper, we concentrate on how to select vertices to sample and how to allocate the sampling budget for a time-varying GS to achieve a minimal tracking error for the long-term. In the Kalman Filter (KF) framework, the problem of sampling policy design and budget allocation is formulated as an infinite horizon sequential decision process, in which the optimal sampling policy is obtained by Dynamic Programming (DP). Since the optimal policy is intractable, an approximate algorithm is proposed by truncating the infinite horizon. By introducing a new tool for analyzing the convexity or concavity of composite functions, we prove that the truncated problem is convex. Finally, we demonstrate the performance of the proposed approach through numerical experiments.
Realizing a Low-Power Head-Mounted Phase-Only Holographic Display by Light-Weight Compression
Date: 2020-02-14 00:00:00
Tasks: Decoder, Quantization

Head-mounted holographic displays (HMHD) are projected to be the first
commercial realization of holographic video display systems. HMHDs use liquid
crystal on silicon (LCoS) spatial light modulators (SLM), which are best suited
to display phase-only holograms (POH). The performance/watt requirement of a
monochrome, 60 fps Full HD, 2-eye, POH HMHD system is about 10 TFLOPS/W, which
is orders of magnitude higher than that is achievable by commercially available
mobile processors. To mitigate this compute power constraint, display-ready
POHs shall be generated on a nearby server and sent to the HMHD in compressed
form over a wireless link. This paper discusses design of a feasible HMHD-based
augmented reality system, focusing on compression requirements and per-pixel
rate-distortion trade-off for transmission of display-ready POH from the server
to HMHD. Since the decoder in the HMHD needs to operate on low power, only
coding methods that have low-power decoder implementation are considered.
Effects of 2D phase unwrapping and flat quantization on compression performance
are also reported. We next propose a versatile PCM-POH codec with progressive
quantization that can adapt to SLM-dynamic-range and available bitrate, and
features per-pixel rate-distortion control to achieve acceptable POH quality at
target rates of 60-200 Mbit/s that can be reliably achieved by current wireless
technologies. Our results demonstrate feasibility of realizing a low-power,
quality-ensured, multi-user, interactive HMHD augmented reality system with
commercially available components using the proposed adaptive compression of
display-ready POH with light-weight decoding.
DNA Torsion-based Model of Cell Fate Phase Transitions
Date: 2020-02-14 00:00:00

All stem cell fate transitions, including the metabolic reprogramming of stem
cells and the somatic reprogramming of fibroblasts into pluripotent stem cells,
can be understood from a unified theoretical model of cell fates. Each cell
fate transition can be regarded as a phase transition in DNA supercoiling.
However, there has been a dearth of quantitative biophysical models to explain
and predict the behaviors of these phase transitions. The generalized Ising
model is proposed to define such phase transitions. The model predicts that,
apart from temperature-induced phase transitions, there exists DNA torsion
frequency-induced phase transitions. Major transitions in epigenetic states,
from stem cell activation to differentiation and reprogramming, can be
explained by such torsion frequency-induced phase transitions, with important
implications for regenerative medicine and medical diagnostics in the future.
Sound Event Localization based on Sound Intensity Vector Refined By DNN-Based Denoising and Source Separation
Date: 2020-02-14 00:00:00
Tasks: Denoising, Sound Event Localization and Detection

We propose a direction-of-arrival (DOA) estimation method for Sound Event
Localization and Detection (SELD). Direct estimation of DOA using a deep neural
network (DNN), i.e. completely-datadriven approach, achieves high accuracy.
However, there is a gap in the accuracy between DOA estimation for single and
overlapping sources because they cannot incorporate physical knowledge.
Meanwhile, although the accuracy of physics-based approaches is inferior to
DNN-based approaches, it is robust for overlapping source. In this study, we
consider a combination of physics-based and DNN-based approaches; the sound
intensity vectors (IVs) for physics-based DOA estimation is refined based on
DNN-based denoising and source separation. This method enables the accurate DOA
estimation for both single and overlapping sources using a spherical microphone
array. Experimental results show that the proposed method achieves
state-of-the-art DOA estimation accuracy on an open dataset of the SELD.
Enabling Technologies For 6g Future Wireless Communications: Opportunities And Challenges
Date: 2020-02-14 00:00:00

5G wireless communications technology is being launched, with many smart
applications being integrated. However, 5G specifications merge the
requirements of new emerging technologies forcefully. These include data rate,
capacity, latency, reliability, resources sharing, and energy/bit. To meet
these challenging demands, research is focusing on 6G wireless communications
enabling different technologies and emerging new applications. In this report,
the latest research work on 6G technologies and applications is summarized, and
the associated research challenges are discussed.
Three-Dimensional Single-Shot Ptychography
Date: 2020-02-14 00:00:00
Tasks: Object

Here we introduce three-dimensional single-shot ptychography (3DSSP). 3DSSP
leverages an additional constraint unique to the single-shot geometry to
deconvolve multiple 2D planes of a 3D object. Numeric simulations and analytic
calculations demonstrate that 3DSSP reconstructs multiple planes in an extended
3D object with a minimum separation consistent with the depth of field for a
conventional microscope. We experimentally demonstrate 3DSSP by reconstructing
orthogonal hair strands axially separated by 5 mm. Three-dimensional
single-shot ptychography provides a pathway towards volumetric imaging of
dynamically evolving systems on ultrafast timescales.
Structural modeling of 2019-novel coronavirus (nCoV) spike protein reveals a proteolytically-sensitive activation loop as a distinguishing feature compared to SARS-CoV and related SARS-like coronaviruses
Date: 2020-02-14 00:00:00

The 2019 novel coronavirus (2019-nCoV) is currently causing a widespread
outbreak centered on Hubei province, China and is a major public health
concern. Taxonomically 2019-nCoV is closely related to SARS-CoV and
SARS-related bat coronaviruses, and it appears to share a common receptor with
SARS-CoV (ACE-2). Here, we perform structural modeling of the 2019-nCoV spike
glycoprotein. Our data provide support for the similar receptor utilization
between 2019-nCoV and SARS-CoV, despite a relatively low amino acid similarity
in the receptor binding module. Compared to SARS-CoV, we identify an extended
structural loop containing basic amino acids at the interface of the receptor
binding (S1) and fusion (S2) domains, which we predict to be
proteolytically-sensitive. We suggest this loop confers fusion activation and
entry properties more in line with MERS-CoV and other coronaviruses, and that
the presence of this structural loop in 2019-nCoV may affect virus stability
and transmission.
Detection of arbitrage opportunities in multi-asset derivatives markets
Authors: Antonis Papapantoleon, Paulo Yanez Sarmiento
Date: 2020-02-14 00:00:00

We are interested in the existence of equivalent martingale measures and the detection of arbitrage opportunities in markets where several multi-asset derivatives are traded simultaneously. More specifically, we consider a financial market with multiple traded assets whose marginal risk-neutral distributions are known, and assume that several derivatives written on these assets are traded simultaneously. In this setting, there is a bijection between the existence of an equivalent martingale measure and the existence of a copula that couples these marginals. Using this bijection and recent results on improved Fr\'echet-Hoeffding bounds in the presence of additional information on functionals of a copula by Lux and Papapantoleon [18], we can extend the results of Tavin [33] on the detection of arbitrage opportunities to the general multi-dimensional case. More specifically, we derive sufficient conditions for the absence of arbitrage and formulate an optimization problem for the detection of a possible arbitrage opportunity. This problem can be solved efficiently using numerical optimization routines. The most interesting practical outcome is the following: we can construct a financial market where each multi-asset derivative is traded within its own no-arbitrage interval, and yet when considered together an arbitrage opportunity may arise.
A Comparison of Pooling Methods on LSTM Models for Rare Acoustic Event Classification
Date: 2020-02-14 00:00:00
Tasks: Benchmarking, Classification, Event Detection, Position

Acoustic event classification (AEC) and acoustic event detection (AED) refer
to the task of detecting whether specific target events occur in audios. As
long short-term memory (LSTM) leads to state-of-the-art results in various
speech related tasks, it is employed as a popular solution for AEC as well.
This paper focuses on investigating the dynamics of LSTM model on AEC tasks. It
includes a detailed analysis on LSTM memory retaining, and a benchmarking of
nine different pooling methods on LSTM models using 1.7M generated mixture
clips of multiple events with different signal-to-noise ratios. This paper
focuses on understanding: 1) utterance-level classification accuracy; 2)
sensitivity to event position within an utterance. The analysis is done on the
dataset for the detection of rare sound events from DCASE 2017 Challenge. We
find max pooling on the prediction level to perform the best among the nine
pooling approaches in terms of classification accuracy and insensitivity to
event position within an utterance. To authors' best knowledge, this is the
first kind of such work focused on LSTM dynamics for AEC tasks.
Optimal Participation of Price-Maker Battery Energy Storage Systems (BESSs) in Energy, Reserve and Pay as Performance Regulation Markets
Date: 2020-02-15 00:00:00

Motivated by the need of assessing the optimal allocation of battery energy
storage services across various markets and the corresponding impact on market
operations, an optimization framework is proposed in this work to coordinate
the operation of an independent utility-scale price-maker battery energy
storage system (BESS) in the energy, spinning reserve and performance-based
regulation markets. The entire problem is formulated as a bi-level optimization
process, where the structure of all markets is modeled considering the joint
operation limits. The strategic bidding behavior of a price-maker BESS in a pay
as performance regulation market is investigated. Additionally, a specific
approach is introduced for modeling automatic generation control (AGC) signals
in the optimization. Although the formulated problem is non-linear, it is
converted to mixed-integer linear programming (MILP) to find the optimum
solution. The proposed framework is evaluated using test case scenarios created
from real-world market data. Case study results show the impact of BESS's
price-making behavior on the joint operation of energy, reserve, and regulation
markets.