pwc-archive/papers-with-abstracts huggingface dataset:
Indirect Inference for Locally Stationary Models
Date: 2020-05-11 00:00:00

We propose the use of indirect inference estimation to conduct inference in
complex locally stationary models. We develop a local indirect inference
algorithm and establish the asymptotic properties of the proposed estimator.
Due to the nonparametric nature of locally stationary models, the resulting
indirect inference estimator exhibits nonparametric rates of convergence. We
validate our methodology with simulation studies in the confines of a locally
stationary moving average model and a new locally stationary multiplicative
stochastic volatility model. Using this indirect inference methodology and the
new locally stationary volatility model, we obtain evidence of non-linear,
time-varying volatility trends for monthly returns on several Fama-French
portfolios.
Opportunistic NOMA for Uplink Short-Message Delivery with a Delay Constraint
Date: 2020-02-20 00:00:00

In this paper, we study the application of opportunistic non-orthogonal
multiple access (NOMA) mode to short-message transmissions with user's power
control under a finite power budget. It is shown that opportunistic NOMA mode,
which can transmit multiple packets per slot, can dramatically lower the
session error probability when W packets are to be transmitted within a session
consisting of Ws slots, where Ws >= W and the slot length is equivalent to the
packet length, compared to orthogonal multiple access (OMA) where at most one
packet can be transmitted in each slot. From this, opportunistic NOMA mode can
be seen as an attractive approach for uplink transmissions. We derive an
upper-bound on the session error probability as a closed-form expression and
also obtain a closed-form for the NOMA factor that shows the minimum possible
ratio of the session error probability of opportunistic NOMA to that of OMA.
Simulation results also confirm that opportunistic NOMA mode has a much lower
session error probability than OMA.
Automatic Retrosynthetic Pathway Planning Using Template-free Models
Date: 2019-05-21 00:00:00
Tasks: Prediction, Retrosynthesis

We present an attention-based Transformer model for automatic retrosynthesis
route planning. Our approach starts from reactants prediction of single-step
organic reactions for given products, followed by Monte Carlo tree search-based
automatic retrosynthetic pathway prediction. Trained on two datasets from the
United States patent literature, our models achieved a top-1 prediction
accuracy of over 54.6% and 63.0% with more than 95% and 99.6% validity rate of
SMILES, respectively, which is the best up to now to our knowledge. We also
demonstrate the application potential of our model by successfully performing
multi-step retrosynthetic route planning for four case products, i.e.,
antiseizure drug Rufinamide, a novel allosteric activator, an inhibitor of
human acute-myeloid-leukemia cells and a complex intermediate of drug
candidate. Further, by using heuristics Monte Carlo tree search, we achieved
automatic retrosynthetic pathway searching and successfully reproduced
published synthesis pathways. In summary, our model has achieved the
state-of-the-art performance on single-step retrosynthetic prediction and
provides a novel strategy for automatic retrosynthetic pathway planning.
Intelligent Reflecting Surface Assisted Wireless Communication: Modeling and Channel Estimation
Date: 2019-12-13 00:00:00

The recently completed 5G new radio standard is a result of several
cutting-edge technologies, including massive multiple-input multiple-output
(MIMO), millimeter (mm)-Wave communication and network densification. However,
these technologies face two main practical limitations 1) the lack of control
over the wireless channel, and 2) the high power consumption of the wireless
interface. To address the need for green and sustainable future cellular
networks, the concept of reconfiguring wireless propagation environments using
Intelligent Reflecting Surfaces (IRS)s has emerged. An IRS comprises of a large
number of low-cost passive antennas that can smartly reflect the impinging
electromagnetic waves for performance enhancement. This paper looks at the
evolution of the reflective radio concept towards IRSs, outlines the
IRS-assisted multi-user multiple-input single-output (MISO) communication model
and discusses how it differentiates from the conventional multi-antenna
communication models. We propose a minimum mean squared error (MMSE) based
channel estimation protocol for the design and analysis of IRS-assisted
systems. Performance evaluation results at 2.5 GHz operating frequency are
provided to illustrate the efficiency of the proposed system.
A partial order and cluster-similarity metric on rooted phylogenetic trees
Date: 2019-11-25 00:00:00

Metrics on rooted phylogenetic trees are integral to a number of areas of
phylogenetic analysis. Cluster-similarity metrics have recently been introduced
in order to limit skew in the distribution of distances, and to ensure that
trees in the neighbourhood of each other have similar hierarchies. In the
present paper we introduce a new cluster-similarity metric on rooted
phylogenetic tree space that has an associated local operation, allowing for
easy calculation of neighbourhoods, a trait that is desirable for MCMC
calculations. The metric is defined by the distance on the Hasse diagram
induced by a partial order on the set of rooted phylogenetic trees, itself
based on the notion of a hierarchy-preserving map between trees. The partial
order we introduce is a refinement of the well-known refinement order on
hierarchies. Both the partial order and the hierarchy-preserving maps may also
be of independent interest.
The first order statistics of backscatter from the fractal branching vasculature
Date: 2019-09-19 00:00:00

The issue of speckle statistics from ultrasound images of soft tissues such
as the liver has a long and rich history. A number of theoretical
distributions, some related to random scatterers or fades in optics and radar,
have been formulated for pulse-echo interference patterns. This work proposes
an alternative framework in which the dominant echoes are presumed to result
from Born scattering from fluid filled vessels that permeate the tissue
parenchyma. These are modeled as a branching, fractal, self-similar, multi
scale collection of cylindrical scatterers governed by a power law distribution
relating the number of branches at each radius. A deterministic accounting of
the echo envelopes across the scales from small to large is undertaken, leading
to a closed form theoretical formula for the histogram of the envelope of the
echoes. The normalized histogram is found to be related to the classical Burr
distribution, with the key power law parameter directly related to that of the
number density of vessels vs. diameter, frequently reported in the range of 2
to 4. Examples are given from liver scans to demonstrate the applicability of
the theory.
Flexible Cancer-Associated Chromatin Configuration (CACC) Might Be the Fundamental Reason Why Cancer Is So Difficult to Cure
Authors: Gao-De Li
Date: 2019-06-06 00:00:00
Tasks: Vocal Bursts Type Prediction

We once proposed that cell-type-associated chromatin configurations determine cell types and that cancer cell type is determined by cancer-associated chromatin configuration (CACC). In this paper, we hypothesize that flexible cell-type-associated chromatin configuration is associated with cell potency and has an advantage over inflexible one in regulating genome related activities, such as DNA replication, DNA transcription, DNA repair, and DNA mutagenesis. The reason why cancer is so difficult to treat is because CACC is flexible, which enables cancer cells not only to produce heterogeneous subclones through limited cell differentiation, but also to maximally and efficiently use genome related resources to survive environmental changes. Therefore, to beat cancer, more efforts should be made to restrict the flexibility of CACC or to change CACC so that cancer cells can be turned back to normal or become less malignant.
Role of non-linear data processing on speech recognition task in the framework of reservoir computing
Date: 2019-12-19 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

The reservoir computing neural network architecture is widely used to test
hardware systems for neuromorphic computing. One of the preferred tasks for
bench-marking such devices is automatic speech recognition. However, this task
requires acoustic transformations from sound waveforms with varying amplitudes
to frequency domain maps that can be seen as feature extraction techniques.
Depending on the conversion method, these may obscure the contribution of the
neuromorphic hardware to the overall speech recognition performance. Here, we
quantify and separate the contributions of the acoustic transformations and the
neuromorphic hardware to the speech recognition success rate. We show that the
non-linearity in the acoustic transformation plays a critical role in feature
extraction. We compute the gain in word success rate provided by a reservoir
computing device compared to the acoustic transformation only, and show that it
is an appropriate benchmark for comparing different hardware. Finally, we
experimentally and numerically quantify the impact of the different acoustic
transformations for neuromorphic hardware based on magnetic nano-oscillators.
Double Generalized Linear Model Reveals Those with High Intelligence are More Similar in Cortical Thickness
Date: 2019-11-22 00:00:00

Most studies indicate that intelligence (g) is positively correlated with
cortical thickness. However, the interindividual variability of cortical
thickness has not been taken into account. In this study, we aimed to identify
the association between intelligence and cortical thickness in adolescents from
both the group's mean and dispersion point of view, utilizing the structural
brain imaging from the Adolescent Brain and Cognitive Development (ABCD)
Consortium, the largest cohort in early adolescents around 10 years old. The
mean and dispersion parameters of cortical thickness and their association with
intelligence were estimated using double generalized linear models(DGLM). We
found that for the mean model part, the thickness of the frontal lobe like
superior frontal gyrus was negatively related to intelligence, while the
surface area was most positively associated with intelligence in the frontal
lobe. In the dispersion part, intelligence was negatively correlated with the
dispersion of cortical thickness in widespread areas, but not with the surface
area. These results suggested that people with higher IQ are more similar in
cortical thickness, which may be related to less differentiation or
heterogeneity in cortical columns.
Smart Sustainable Agriculture (SSA) Solution Underpinned by Internet of Things (IoT) and Artificial Intelligence (AI)
Date: 2019-05-30 00:00:00
Tasks: Management

The Internet of Things (IoT) and Artificial Intelligence (AI) have been
employed in agriculture over a long period of time, alongside other advanced
computing technologies. However, increased attention is currently being paid to
the use of such smart technologies. Agriculture has provided an important
source of food for human beings over many thousands of years, including the
development of appropriate farming methods for different types of crops. The
emergence of new advanced IoT technologies has the potential to monitor the
agricultural environment to ensure high-quality products. However, there
remains a lack of research and development in relation to Smart Sustainable
Agriculture (SSA), accompanied by complex obstacles arising from the
fragmentation of agricultural processes, i.e. the control and operation of
IoT/AI machines; data sharing and management; interoperability; and large
amounts of data analysis and storage. This study firstly, explores existing
IoT/AI technologies adopted for SSA and secondly, identifies IoT/AI technical
architecture capable of underpinning the development of SSA platforms. As well
as contributing to the current body of knowledge, this research reviews
research and development within SSA and provides an IoT/AI architecture to
establish a Smart, Sustainable Agriculture platform as a solution.
CompRRAE: RRAM-based Convolutional Neural Network Accelerator with Reduced Computations through a Runtime Activation Estimation
Date: 2019-04-25 00:00:00

Recently Resistive-RAM (RRAM) crossbar has been used in the design of the
accelerator of convolutional neural networks (CNNs) to solve the memory wall
issue. However, the intensive multiply-accumulate computations (MACs) executed
at the crossbars during the inference phase are still the bottleneck for the
further improvement of energy efficiency and throughput. In this work, we
explore several methods to reduce the computations for the RRAM-based CNN
accelerators. First, the output sparsity resulting from the widely employed
Rectified Linear Unit is exploited, and a significant portion of computations
are bypassed through an early detection of the negative output activations.
Second, an adaptive approximation is proposed to terminate the MAC early when
the sum of the partial results of the remaining computations is considered to
be within a certain range of the intermediate accumulated result and thus has
an insignificant contribution to the inference. In order to determine these
redundant computations, a novel runtime estimation on the maximum and minimum
values of each output activation is developed and used during the MAC
operation. Experimental results show that around 70% of the computations can be
reduced during the inference with a negligible accuracy loss smaller than 0.2%.
As a result, the energy efficiency and the throughput are improved by over 2.9
and 2.8 times, respectively, compared with the state-of-the-art RRAM-based
accelerators.
Learning from Others in the Financial Market
Date: 2019-08-17 00:00:00

Prediction problems in finance go beyond estimating the unknown parameters of
a model (e.g. of expected returns). This is because such a model would have to
include parameters governing the market participants' propensity to change
their opinions on the validity of that model. This leads to a well--known
circular situation characteristic of financial markets, where participants
collectively create the future they wish to estimate. In this paper, we
introduce a framework for organizing multiple expectation models and study the
conditions under which they are adopted by a majority of market participants.
Parametric context adaptive Laplace distribution for multimedia compression
Date: 2019-10-14 00:00:00
Tasks: Data Compression, Quantization

Data compression often subtracts prediction and encodes the difference
(residue) e.g. assuming Laplace distribution, for example for images, videos,
audio, or numerical data. Its performance is strongly dependent on the proper
choice of width (scale parameter) of this parametric distribution, can be
improved if optimizing it based on local situation like context. For example in
popular LOCO-I \cite{loco} (JPEG-LS) lossless image compressor there is used 3
dimensional context quantized into 365 discrete possibilities treated
independently. This article discusses inexpensive approaches for exploiting
their dependencies with autoregressive ARCH-like context dependent models for
parameters of parametric distribution for residue, also evolving in time for
adaptive case. For example tested such 4 or 11 parameter models turned out to
provide similar performance as 365 parameter LOCO-I model for 48 tested images.
Beside smaller headers, such reduction of number of parameters can lead to
better generalization. In contrast to context quantization approaches,
parameterized models also allow to directly use higher dimensional contexts,
for example using information from all 3 color channels, further pixels, some
additional region classifiers, or from interleaving multi-scale scanning - for
which there is proposed Haar upscale scan combining advantages of Haar wavelets
with possibility of scanning exploiting local contexts.
On the Equilibrium Uniqueness in Cournot Competition with Demand Uncertainty
Authors: Stefanos Leonardos, Costis Melolidakis
Date: 2019-06-09 00:00:00

We revisit the linear Cournot model with uncertain demand that is studied in Lagerl\"of (2006)* and provide sufficient conditions for equilibrium uniqueness that complement the existing results. We show that if the distribution of the demand intercept has the decreasing mean residual demand (DMRD) or the increasing generalized failure rate (IGFR) property, then uniqueness of equilibrium is guaranteed. The DMRD condition implies log-concavity of the expected profits per unit of output without additional assumptions on the existence or the shape of the density of the demand intercept and, hence, answers in the affirmative the conjecture of Lagerl\"of (2006)* that such conditions may not be necessary. *Johan Lagerl\"of, Equilibrium uniqueness in a Cournot model with demand uncertainty. The B.E. Journal in Theoretical Economics, Vol. 6: Iss 1. (Topics), Article 19:1--6, 2006.
A Top-Down Approach for the Multiple Exercises and Valuation of Employee Stock Options
Authors: Tim Leung, Yang Zhou
Date: 2019-06-09 00:00:00

We propose a new framework to value employee stock options (ESOs) that captures multiple exercises of different quantities over time. We also model the ESO holder's job termination risk and incorporate its impact on the payoffs of both vested and unvested ESOs. Numerical methods based on Fourier transform and finite differences are developed and implemented to solve the associated systems of PDEs. In addition, we introduce a new valuation method based on maturity randomization that yields analytic formulae for vested and unvested ESO costs. We examine the cost impact of job termination risk, exercise intensity, and various contractual features.
Machine learning with kernels for portfolio valuation and risk management
Authors: Lotfi Boudabsa, Damir Filipovic
Date: 2019-06-09 00:00:00
Tasks: BIG-bench Machine Learning, Management

We introduce a simulation method for dynamic portfolio valuation and risk management building on machine learning with kernels. We learn the dynamic value process of a portfolio from a finite sample of its cumulative cash flow. The learned value process is given in closed form thanks to a suitable choice of the kernel. We show asymptotic consistency and derive finite sample error bounds under conditions that are suitable for finance applications. Numerical experiments show good results in large dimensions for a moderate training sample size.
Evolutionary Theory in Pacific Island Archaeology
Date: 2019-06-16 00:00:00

Evolution is the principal theoretical framework in Pacific Island
archaeology. Progressive cultural evolutionary stages, particularly the
chiefdom, provided the earliest framework. These ideas were increasingly linked
to islands as relatively isolated laboratories of cultural evolution and
diversification, albeit their populations phylogenetically linked in a series
of ancestral and descendant societies. With more sophisticated environmental
data, much research has investigated cultural adaptations and their homologous
or analogous origins. Most recently, Darwinian mechanisms of transmission,
selection, and drift comprise an important approach. The history of evolution
in Pacific archaeology suggests it will continue as a useful theoretical
framework, perhaps more so than in other world regions.
Selection of Waveform Parameters Using Machine Learning for 5G and Beyond
Date: 2019-06-12 00:00:00
Tasks: BIG-bench Machine Learning, Dataset Generation

Flexibility is one of the essential requirements in future cellular
communications technologies. Providing customized communications solutions for
each user and service type cannot be possible without the flexibility in 5G and
beyond. Different optimizations need to be done for the flexibility related
structures of 5G and beyond systems. In this paper, a novel machine learning
(ML) based selection mechanism for the configurable waveform parameters is
designed from the flexibility perspective. Moreover, a simulation based dataset
generation methodology is proposed for ML systems. Results of computer
simulations are presented using the generated dataset.
Parallel scalable simulations of biological neural networks using TensorFlow: A beginner's guide
Authors: Rishika Mohanta, Collins Assisi
Date: 2019-06-10 00:00:00

Biological neural networks are often modeled as systems of coupled, nonlinear, ordinary or partial differential equations. The number of differential equations used to model a network increases with the size of the network and the level of detail used to model individual neurons and synapses. As one scales up the size of the simulation, it becomes essential to utilize powerful computing platforms. While many tools exist that solve these equations numerically, they are often platform-specific. Further, there is a high barrier of entry to developing flexible platform-independent general-purpose code that supports hardware acceleration on modern computing architectures such as GPUs/TPUs and Distributed Platforms. TensorFlow is a Python-based open-source package designed for machine learning algorithms. However, it is also a scalable environment for a variety of computations, including solving differential equations using iterative algorithms such as Runge-Kutta methods. In this article and the accompanying tutorials, we present a simple exposition of numerical methods to solve ordinary differential equations using Python and TensorFlow. The tutorials consist of a series of Python notebooks that, over the course of five sessions, will lead novice programmers from writing programs to integrate simple one-dimensional ordinary differential equations using Python to solving a large system (1000's of differential equations) of coupled conductance-based neurons using a highly parallelized and scalable framework. Embedded with the tutorial is a physiologically realistic implementation of a network in the insect olfactory system. This system, consisting of multiple neuron and synapse types, can serve as a template to simulate other networks.
Superpixel Tensor Pooling for Visual Tracking using Multiple Midlevel Visual Cues Fusion
Date: 2019-10-13 00:00:00
Tasks: Superpixels, Visual Tracking

In this paper, we propose a method called superpixel tensor pooling tracker
which can fuse multiple midlevel cues captured by superpixels into sparse
pooled tensor features. Our method first adopts the superpixel method to
generate different patches (superpixels) from the target template or
candidates. Then for each superpixel, it encodes different midlevel cues
including HSI color, RGB color, and spatial coordinates into a histogram matrix
to construct a new feature space. Next, these matrices are formed to a third
order tensor. After that, the tensor is pooled into the sparse representation.
Then the incremental positive and negative subspaces learning is performed. Our
method has both good characteristics of midlevel cues and sparse representation
hence is more robust to large appearance variations and can capture compact and
informative appearance of the target object. To validate the proposed method,
we compare it with state-of-the-art methods on 24 sequences with multiple
visual tracking challenges. Experiment results demonstrate that our method
outperforms them significantly.