pwc-archive/papers-with-abstracts huggingface dataset:
Reconfigurable Intelligent Surface Empowered Downlink Non-Orthogonal Multiple Access
Date: 2020-03-22 00:00:00

Power-domain non-orthogonal multiple access (NOMA) has become a promising
technology to exploit the new dimension of the power domain to enhance the
spectral efficiency of wireless networks. However, most existing NOMA schemes
rely on the strong assumption that users' channel gains are quite different,
which may be invalid in practice. To unleash the potential of power-domain
NOMA, we will propose a reconfigurable intelligent surface (RIS)-empowered NOMA
network to introduce desirable channel gain differences among the users by
adjusting the phase shifts at RIS. Our goal is to minimize the total transmit
power by jointly optimizing the beamforming vectors at the base station and the
phase-shift matrix at the RIS. To address the highly coupled optimization
variables, we present an alternating optimization framework to decompose the
non-convex bi-quadratically constrained quadratic problem into two rank-one
constrained matrices optimization problems via matrix lifting. At the same
time, to accurately detect the feasibility of the non-convex rank-one
constraints and improve performance by avoiding early stopping in the
alternating optimization procedure, we equivalently represent the rank-one
constraint as the difference between nuclear norm and spectral norm. A
difference-of-convex (DC) algorithm is further developed to solve the resulting
DC programs via successive convex relaxation, followed by establishing the
convergence of the proposed DC-based alternating optimization method. We
further propose an efficient user ordering scheme with closed-form expressions,
considering both the channel conditions and users' target data rates.
Simulation results will validate the effectiveness of deploying an RIS and the
superiority of the proposed DC-based alternating optimization method in
reducing the total transmit power.
Standard Errors for Panel Data Models with Unknown Clusters
Date: 2020-05-18 00:00:00

This paper develops a new standard-error estimator for linear panel data
models. The proposed estimator is robust to heteroskedasticity, serial
correlation, and cross-sectional correlation of unknown forms. The serial
correlation is controlled by the Newey-West method. To control for
cross-sectional correlations, we propose to use the thresholding method,
without assuming the clusters to be known. We establish the consistency of the
proposed estimator. Monte Carlo simulations show the method works well. An
empirical application is considered.
Metabolic constraints on synaptic learning and memory
Date: 2019-10-16 00:00:00

Dendritic spines, the carriers of long-term memory, occupy a small fraction
of cortical space, and yet they are the major consumers of brain metabolic
energy. What fraction of this energy goes for synaptic plasticity, correlated
with learning and memory? It is estimated here based on neurophysiological and
proteomic data for rat brain that, depending on the level of protein
phosphorylation, the energy cost of synaptic plasticity constitutes a small
fraction of the energy used for fast excitatory synaptic transmission,
typically $4.0-11.2 \%$. Next, this study analyzes a metabolic cost of a new
learning and its memory trace in relation to the cost of prior memories, using
a class of cascade models of synaptic plasticity. It is argued that these
models must contain bidirectional cyclic motifs, related to protein
phosphorylation, to be compatible with basic thermodynamic principles. For most
investigated parameters longer memories generally require proportionally more
energy to store. The exception are the parameters controlling the speed of
molecular transitions (e.g. ATP driven phosphorylation rate), for which memory
lifetime per invested energy can increase progressively for longer memories.
Furthermore, in general, a memory trace decouples dynamically from a
corresponding synaptic metabolic rate such that the energy expended on a new
learning and its memory trace constitutes in most cases only a small fraction
of the baseline energy associated with prior memories. Taken together, these
empirical and theoretical results suggest a metabolic efficiency of
synaptically stored information.
Deciphering neural circuits for Caenorhabditis elegans behavior by computations and perturbations to genome and connectome
Date: 2019-10-16 00:00:00

{\it Caenorhabditis elegans} nematode worms are the only animals with the
known detailed neural connectivity diagram, well characterized genomics, and
relatively simple quantifiable behavioral output. With this in mind, many
researchers view this animal as the best candidate for a systems biology
approach, where one can integrate molecular and cellular knowledge to gain
global understanding of worm's behavior. This work reviews some research in
this direction, emphasizing computational perspective, and points out some
successes and challenges to meet this lofty goal.
Partial Exponential Stability Analysis of Slow-fast Systems via Periodic Averaging
Date: 2019-10-16 00:00:00

This paper presents some new criteria for partial exponential stability of a
slow-fast nonlinear system with a fast scalar variable using periodic averaging
methods. Unlike classical averaging techniques, we construct an averaged system
by averaging over this fast scalar variable instead of the time variable. We
then show that partial exponential stability of the averaged system implies
partial exponential stability of the original one. As some intermediate
results, we also obtain a new converse Lyapunov theorem and some perturbation
theorems for partially exponentially stable systems. We then apply our
established criteria to study remote synchronization of Kuramoto-Sakaguchi
oscillators coupled by a star network with two peripheral nodes. We
analytically show that detuning the natural frequency of the central mediating
oscillator can increase the robustness of the remote synchronization against
phase shifts.
A polynomial-time scheduling approach to minimise idle energy consumption: an application to an industrial furnace
Authors: Ondrej Benedikt, Baran Alikoc, Premysl Sucha, Sergej Celikovsky, Zdenek Hanzalek
Date: 2019-10-11 00:00:00
Tasks: Scheduling

This article presents a novel scheduling approach to minimise the energy consumption of a machine during its idle periods. In the scheduling domain, it is common to model the behaviour of the machine by defining a small set of machine modes, e.g. "on", "off" and "stand-by". Then the transitions between the modes are represented by a static transition graph. In this paper, we argue that this type of model might be too restrictive for some types of machines (e.g. the furnaces). For such machines, we propose to employ the complete time-domain dynamics and integrate it into an idle energy function. This way, the scheduling algorithm can exploit the full knowledge about the machine dynamics with minimised energy consumption encapsulated in this function. In this paper, we study a scheduling problem, where the tasks characterised by release times and deadlines are scheduled in the given order such that the idle energy consumption of the machine is minimised. We show that this problem can be solved in polynomial time whenever the idle energy function is concave. To highlight the practical applicability, we analyse a heat-intensive system employing a steel-hardening furnace. We derive an energy optimal control law, and the corresponding idle energy function, for the bilinear system model approximating the dynamics of the furnace (and possibly other heat-intensive systems). Further, we prove that the idle energy function is, indeed, concave in this case. Therefore, the proposed scheduling algorithm can be used. Numerical experiments show that by using our approach, combining both the optimal control and optimal scheduling, higher energy savings can be achieved, compared to the state-of-the-art scheduling approaches.
Residual Switching Network for Portfolio Optimization
Authors: Jifei Wang, Lingjing Wang
Date: 2019-10-16 00:00:00
Tasks: Portfolio Optimization

This paper studies deep learning methodologies for portfolio optimization in the US equities market. We present a novel residual switching network that can automatically sense changes in market regimes and switch between momentum and reversal predictors accordingly. The residual switching network architecture combines two separate residual networks (ResNets), namely a switching module that learns stock market conditions, and the main module that learns momentum and reversal predictors. We demonstrate that over-fitting noisy financial data can be controlled with stacked residual blocks and further incorporating the attention mechanism can enhance powerful predictive properties. Over the period 2008 to H12017, the residual switching network (Switching-ResNet) strategy verified superior out-of-sample performance with an average annual Sharpe ratio of 2.22, compared with an average annual Sharpe ratio of 0.81 for the ANN-based strategy and 0.69 for the linear model.
Nonrobustness of asymptotic stability of impulsive systems with inputs
Date: 2019-10-16 00:00:00
Tasks: Open-Ended Question Answering

Suitable continuity and boundedness assumptions on the function f defining
the dynamics of a time-varying nonimpulsive system with inputs are known to
make the system inherit stability properties from the zero-input system.
Whether this type of robustness holds or not for impulsive systems was still an
open question. By means of suitable (counter)examples, we show that such
stability robustness with respect to the inclusion of inputs cannot hold in
general, not even for impulsive systems with time-invariant flow and jump maps.
In particular, we show that zero-input global uniform asymptotic stability
(0-GUAS) does not imply converging input converging state (CICS), and that
0-GUAS and uniform bounded-energy input bounded state (UBEBS) do not imply
integral input-to-state stability (iISS). We also comment on available existing
results that, however, show that suitable constraints on the allowed
impulse-time sequences indeed make some of these robustness properties
possible.
When Two are Better than One: Modeling the Mechanisms of Antibody Mixtures
Authors: Tal Einav, Jesse D Bloom
Date: 2019-10-16 00:00:00

It is difficult to predict how antibodies will behave when mixed together, even after each has been independently characterized. Here, we present a statistical mechanical model for the activity of antibody mixtures that accounts for whether pairs of antibodies bind to distinct or overlapping epitopes. This model requires measuring $n$ individual antibodies and their $n(n-1)/2$ pairwise interactions to predict the $2^n$ potential combinations. We apply this model to epidermal growth factor receptor (EGFR) antibodies and find that the activity of antibody mixtures can be predicted without positing synergy at the molecular level. In addition, we demonstrate how the model can be used in reverse, where straightforward experiments measuring the activity of antibody mixtures can be used to infer the molecular interactions between antibodies. Lastly, we generalize this model to analyze engineered multidomain antibodies, where components of different antibodies are tethered together to form novel amalgams, and characterize how well it predicts recently designed influenza antibodies.
A Projection Framework for Testing Shape Restrictions That Form Convex Cones
Authors: Zheng Fang, Juwon Seo
Date: 2019-10-17 00:00:00
Tasks: Form, valid

This paper develops a uniformly valid and asymptotically nonconservative test based on projection for a class of shape restrictions. The key insight we exploit is that these restrictions form convex cones, a simple and yet elegant structure that has been barely harnessed in the literature. Based on a monotonicity property afforded by such a geometric structure, we construct a bootstrap procedure that, unlike many studies in nonstandard settings, dispenses with estimation of local parameter spaces, and the critical values are obtained in a way as simple as computing the test statistic. Moreover, by appealing to strong approximations, our framework accommodates nonparametric regression models as well as distributional/density-related and structural settings. Since the test entails a tuning parameter (due to the nonstandard nature of the problem), we propose a data-driven choice and prove its validity. Monte Carlo simulations confirm that our test works well.
Fighting for Not-So-Religious Souls: The Role of Religious Competition in Secular Conflicts
Authors: Hector Galindo-Silva, Guy Tchuente
Date: 2019-10-17 00:00:00

Many countries embroiled in non-religious civil conflicts have experienced a dramatic increase in religious competition in recent years. This study examines whether increasing competition between religions affects violence in non-religious or secular conflicts. The study focuses on Colombia, a deeply Catholic country that has suffered one of the world's longest-running internal conflicts and, in the last few decades, has witnessed an intense increase in religious competition between the Catholic Church and new non-Catholic churches. The estimation of a dynamic treatment effect model shows that establishing the first non-Catholic church in a municipality substantially increases the probability of conflict-related violence. The effect is larger for violence by guerrilla groups, and is concentrated on municipalities where the establishment of the first non-Catholic church leads to more intense religious competition. Further analysis suggests that the increase in guerrilla violence is associated with an expectation among guerrilla groups that their membership will decline as a consequence of more intense competition with religious groups for followers.
Multi-Talker MVDR Beamforming Based on Extended Complex Gaussian Mixture Model
Date: 2019-10-17 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Speech Enhancement, speech-recognition, Speech Recognition, Speech Separation, Target Speaker Extraction

In this letter, we present a novel multi-talker minimum variance
distortionless response (MVDR) beamforming as the front-end of an automatic
speech recognition (ASR) system in a dinner party scenario. The CHiME-5 dataset
is selected to evaluate our proposal for overlapping multi-talker scenario with
severe noise. A detailed study on beamforming is conducted based on the
proposed extended complex Gaussian mixture model (CGMM) integrated with various
speech separation and speech enhancement masks. Three main changes are made to
adopt the original CGMM-based MVDR for the multi-talker scenario. First, the
number of Gaussian distributions is extended to 3 with an additional inference
speaker model. Second, the mixture coefficients are introduced as a supervisor
to generate more elaborate masks and avoid the permutation problems. Moreover,
we reorganize the MVDR and mask-based speech separation to achieve both noise
reduction and target speaker extraction. With the official baseline ASR
back-end, our front-end algorithm gained an absolute WER reduction of 13.87%
compared with the baseline front-end.
Econometric Models of Network Formation
Date: 2020-01-11 00:00:00
Tasks: Econometrics

This article provides a selective review on the recent literature on
econometric models of network formation. The survey starts with a brief
exposition on basic concepts and tools for the statistical description of
networks. I then offer a review of dyadic models, focussing on statistical
models on pairs of nodes and describe several developments of interest to the
econometrics literature. The article also presents a discussion of non-dyadic
models where link formation might be influenced by the presence or absence of
additional links, which themselves are subject to similar influences. This is
related to the statistical literature on conditionally specified models and the
econometrics of game theoretical models. I close with a (non-exhaustive)
discussion of potential areas for further development.
EvoZip: Efficient Compression of Large Collections of Evolutionary Trees
Date: 2019-10-17 00:00:00
Tasks: Retrieval

Phylogenetic trees represent evolutionary relationships among sets of
organisms. Popular phylogenetic reconstruction approaches typically yield
hundreds to thousands of trees on a common leafset. Storing and sharing such
large collection of trees requires considerable amount of space and bandwidth.
Furthermore, the huge size of phylogenetic tree databases can make search and
retrieval operations time-consuming. Phylogenetic compression techniques are
specialized compression techniques that exploit redundant topological
information to achieve better compression of phylogenetic trees. Here, we
present EvoZip, a new approach for phylogenetic tree compression. On average,
EvoZip achieves 71.6% better compression and takes 80.71% less compression time
and 60.47% less decompression time than TreeZip, the current state-of-the-art
algorithm for phylogenetic tree compression. While EvoZip is based on TreeZip,
it betters TreeZip due to (a) an improved bipartition and support list encoding
scheme, (b) use of Deflate compression algorithm, and (c) use of an efficient
tree reconstruction algorithm. EvoZip is freely available online for use by the
scientific community.
Multi-scale and Context-adaptive Entropy Model for Image Compression
Date: 2019-10-17 00:00:00
Tasks: Image Compression, MS-SSIM, SSIM

We propose an end-to-end trainable image compression framework with a
multi-scale and context-adaptive entropy model, especially for low bitrate
compression. Due to the success of autoregressive priors in probabilistic
generative model, the complementary combination of autoregressive and
hierarchical priors can estimate the distribution of each latent representation
accurately. Based on this combination, we firstly propose a multi-scale masked
convolutional network as our autoregressive model. Secondly, for the
significant computational penalty of generative model, we focus on decoded
representations covered by receptive field, and skip full zero latents in
arithmetic codec. At last, according to the low-rate compression's constraint
in CLIC-2019, we use a method to maximize MS-SSIM by allocating bitrate for
each image.
Currency Based on Time Standard
Date: 2019-10-17 00:00:00

The Total Economic Time Capacity of a Year 525600 minutes is postulated as a
time standard for a new Monetary Minute currency in this evaluation study.
Consequently, the Monetary Minute MonMin is defined as a 1/525600 part of the
Total Economic Time Capacity of a Year. The Value CMonMin of the Monetary
Minute MonMin is equal to a 1/525600 part of the GDP, p.c., expressed in a
specific state currency C. There is described how the Monetary Minutes MonMin
are determined, and how their values CMonMin are calculated based on the GDP
and all the population in specific economies. The Monetary Minutes trace
different aggregate productivity, i.e. exploitation of the total time capacity
of a year for generating of the GDP in economies of different states.
Analysis of Cooperative Hybrid ARQ with Adaptive Modulation and Coding on a Correlated Fading Channel
Date: 2019-11-12 00:00:00

In this study, a cross-layer design which combines adaptive modulation and
coding (AMC) and hybrid automatic repeat request (HARQ) techniques for a
cooperative wireless network is investigated analytically. Previous analyses of
such systems in the literature are confined to the case where the fading
channel is independent at each retransmission, which can be unrealistic unless
the channel is varying very fast. On the other hand, temporal channel
correlation can have a significant impact on the performance of HARQ systems.
In this study, utilizing a Markov channel model which accounts for the temporal
correlation, the performance of non-cooperative and cooperative networks are
investigated in terms of packet loss rate and throughput metrics for Chase
combining HARQ strategy.
Latency Minimization for Intelligent Reflecting Surface Aided Mobile Edge Computing
Authors: Tong Bai, Cunhua Pan, Yansha Deng, Maged Elkashlan, Arumugam Nallanathan, Lajos Hanzo
Date: 2019-10-17 00:00:00
Tasks: Edge-computing

Computation off-loading in mobile edge computing (MEC) systems constitutes an efficient paradigm of supporting resource-intensive applications on mobile devices. However, the benefit of MEC cannot be fully exploited, when the communications link used for off-loading computational tasks is hostile. Fortunately, the propagation-induced impairments may be mitigated by intelligent reflecting surfaces (IRS), which are capable of enhancing both the spectral- and energy-efficiency. Specifically, an IRS comprises an IRS controller and a large number of passive reflecting elements, each of which may impose a phase shift on the incident signal, thus collaboratively improving the propagation environment. In this paper, the beneficial role of IRSs is investigated in MEC systems, where single-antenna devices may opt for off-loading a fraction of their computational tasks to the edge computing node via a multi-antenna access point with the aid of an IRS. Pertinent latency-minimization problems are formulated for both single-device and multi-device scenarios, subject to practical constraints imposed on both the edge computing capability and the IRS phase shift design. To solve this problem, the block coordinate descent (BCD) technique is invoked to decouple the original problem into two subproblems, and then the computing and communications settings are alternatively optimized using low-complexity iterative algorithms. It is demonstrated that our IRS-aided MEC system is capable of significantly outperforming the conventional MEC system operating without IRSs. Quantitatively, about $20~\%$ computational latency reduction is achieved over the conventional MEC system in a single cell of a $300~\rm{m}$ radius and $5$ active devices, relying on a $5$-antenna access point.
Recent applicable delivery approaches of peptide nucleic acids to the target cells
Date: 2019-09-24 00:00:00

Peptide nucleic acids (PNAs) are artificial nucleic acids with a peptide
backbone instead of sugar phosphate backbone of DNA or RNA. Their resistance to
degradation, selectivity and greater binding affinity in comparison to usual
nucleic acids led to consideration of their great potential for different
applications. For example, they can be used in molecular diagnostics and
antisense therapeutics such as antimicrobial agents or gene regulatory tools.
On the other hand, large hydrophilic property of PNA molecules, which inhibit
them to cross cell membranes readily, is an obstacle for their delivery to
considered target cells and a limiting criterion for their applications.
Therefore, PNA delivery technologies have been developing to hurdle this
limitation. For example, addition of lysine residues, charged membrane
penetrating peptide sequences, PNAs conjugated with antibodies or steroids,
cationic liposomes as carriers of PNA conjugates, protective peptides and
technology of photochemical internalization (PCI) as well as by the recent
technology of nanoparticle-based delivery have been employed. In this article
we compared different delivery technologies which can be applicable to PNAs. As
a result nanoparticle-based delivery showed more advantages in comparison to
others and its application is growing fast. Keywords: Target cell delivery,
Peptide nucleic acid, PNA, Delivery.