pwc-archive/papers-with-abstracts huggingface dataset:
Individualized Time-Series Segmentation for Mining Mobile Phone User Behavior
Authors: Sarker Iqbal H., Colman Alan, Kabir MA, Han Jun
Date: 2018-11-15 00:00:00
Tasks: Segmentation, Time Series, Time Series Analysis

Mobile phones can record individual's daily behavioral data as a time-series.
In this paper, we present an effective time-series segmentation technique that
extracts optimal time segments of individual's similar behavioral
characteristics utilizing their mobile phone data. One of the determinants of
an individual's behavior is the various activities undertaken at various
times-of-the-day and days-of-the-week. In many cases, such behavior will follow
temporal patterns. Currently, researchers use either equal or unequal
interval-based segmentation of time for mining mobile phone users' behavior.
Most of them take into account static temporal coverage of 24-h-a-day and few
of them take into account the number of incidences in time-series data.
However, such segmentations do not necessarily map to the patterns of
individual user activity and subsequent behavior because of not taking into
account the diverse behaviors of individuals over time-of-the-week. Therefore,
we propose a behavior-oriented time segmentation (BOTS) technique that takes
into account not only the temporal coverage of the week but also the number of
incidences of diverse behaviors dynamically for producing similar behavioral
time segments over the week utilizing time-series data. Experiments on the real
mobile phone datasets show that our proposed segmentation technique better
captures the user's dominant behavior at various times-of-the-day and
days-of-the-week enabling the generation of high confidence temporal rules in
order to mine individual mobile phone users' behavior.
Representation Results for Law Invariant Recursive Dynamic Deviation Measures and Risk Sharing
Date: 2018-12-11 00:00:00

In this paper we analyze a dynamic recursive extension of the (static) notion
of a deviation measure and its properties. We study distribution invariant
deviation measures and show that the only dynamic deviation measure which is
law invariant and recursive is the variance. We also solve the problem of
optimal risk-sharing generalizing classical risk-sharing results for variance
through a dynamic inf-convolution problem involving a transformation of the
original dynamic deviation measures.
Lee-Carter method for forecasting mortality for Peruvian Population
Date: 2018-11-23 00:00:00

In this article, we have modeled mortality rates of Peruvian female and male
populations during the period of 1950-2017 using the Lee-Carter (LC) model. The
stochastic mortality model was introduced by Lee and Carter (1992) and has been
used by many authors for fitting and forecasting the human mortality rates. The
Singular Value Decomposition (SVD) approach is used for estimation of the
parameters of the LC model. Utilizing the best fitted auto regressive
integrated moving average (ARIMA) model we forecast the values of the time
dependent parameter of the LC model for the next thirty years. The forecasted
values of life expectancy at different age group with $95\%$ confidence
intervals are also reported for the next thirty years. In this research we use
the data, obtained from the Peruvian National Institute of Statistics (INEI).
A Voice Controlled E-Commerce Web Application
Authors: Kandhari Mandeep Singh, Zulkernine Farhana, Isah Haruna
Date: 2018-11-16 00:00:00
Tasks: Medical Diagnosis, speech-recognition, Speech Recognition, Speech-to-Text

Automatic voice-controlled systems have changed the way humans interact with
a computer. Voice or speech recognition systems allow a user to make a
hands-free request to the computer, which in turn processes the request and
serves the user with appropriate responses. After years of research and
developments in machine learning and artificial intelligence, today
voice-controlled technologies have become more efficient and are widely applied
in many domains to enable and improve human-to-human and human-to-computer
interactions. The state-of-the-art e-commerce applications with the help of web
technologies offer interactive and user-friendly interfaces. However, there are
some instances where people, especially with visual disabilities, are not able
to fully experience the serviceability of such applications. A voice-controlled
system embedded in a web application can enhance user experience and can
provide voice as a means to control the functionality of e-commerce websites.
In this paper, we propose a taxonomy of speech recognition systems (SRS) and
present a voice-controlled commodity purchase e-commerce application using IBM
Watson speech-to-text to demonstrate its usability. The prototype can be
extended to other application scenarios such as government service kiosks and
enable analytics of the converted text data for scenarios such as medical
diagnosis at the clinics.
MIMO Antenna Elements Effect on Chassis Modes
Date: 2018-11-23 00:00:00
Tasks: Blocking

In this paper, a 4-element printed multiple-input-multiple-output (MIMO) loop
antenna having a bandwidth of 400 MHz is proposed. The effect of the MIMO
antenna on the chassis modes system is analyzed via the theory of
characteristic modes (TCM), and it is shown that chassis CM are not enough for
the full analysis. A defected-ground-structure (DGS) is also proposed to
enhance the isolation between the antenna elements by blocking the coupling
modes.
Streaming Generalized Cross Entropy
Date: 2020-01-14 00:00:00

We propose a new method to combine adaptive processes with a class of entropy
estimators for the case of streams of data. Starting from a first estimation
obtained from a batch of initial data, model parameters are estimated at each
step by combining the prior knowledge with the new observation (or a block of
observations). This allows to extend the maximum entropy technique to a
dynamical setting, also distinguishing between entropic contributions of the
signal and the error. Furthermore, it provides a suitable approximation of
standard GME problems when the exacted solutions are hard to evaluate. We test
this method by performing numerical simulations at various sample sizes and
batch dimensions. Moreover, we extend this analysis exploring intermediate
cases between streaming GCE and standard GCE, i.e. considering blocks of
observations of different sizes to update the estimates, and incorporating
collinearity effects as well. The role of time in the balance between entropic
contributions of signal and errors is further explored considering a variation
of the Streaming GCE algorithm, namely, Weighted Streaming GCE. Finally, we
discuss the results: in particular, we highlight the main characteristics of
this method, the range of application, and future perspectives.
Overview of Current Type I/II Kinase Inhibitors
Date: 2018-11-23 00:00:00
Tasks: Drug Design, Vocal Bursts Type Prediction

Research on kinase-targeting drugs has made great strides over the last 30
years and is attracting greater attention for the treatment of yet more
kinase-related diseases. Currently, 42 kinase drugs have been approved by the
FDA, most of which (39) are Type I/II inhibitors. Notwithstanding these
advances, it is desirable to target additional kinases for drug development as
more than 200 diseases, particularly cancers, are directly associated with
aberrant kinase regulation and signaling. Here, we review the extant Type I/II
drugs systematically to obtain insights into the binding pocket
characteristics, the associated features of Type I/II drugs, and the mechanism
of action to facilitate future kinase drug design and discovery. We conclude by
summarizing the main successes and limitations of targeting kinase for the
development of drugs.
A probabilistic population code based on neural samples
Date: 2018-11-24 00:00:00
Proceeding: NeurIPS 2018 12

Sensory processing is often characterized as implementing probabilistic
inference: networks of neurons compute posterior beliefs over unobserved causes
given the sensory inputs. How these beliefs are computed and represented by
neural responses is much-debated (Fiser et al. 2010, Pouget et al. 2013). A
central debate concerns the question of whether neural responses represent
samples of latent variables (Hoyer & Hyvarinnen 2003) or parameters of their
distributions (Ma et al. 2006) with efforts being made to distinguish between
them (Grabska-Barwinska et al. 2013). A separate debate addresses the question
of whether neural responses are proportionally related to the encoded
probabilities (Barlow 1969), or proportional to the logarithm of those
probabilities (Jazayeri & Movshon 2006, Ma et al. 2006, Beck et al. 2012).
Here, we show that these alternatives - contrary to common assumptions - are
not mutually exclusive and that the very same system can be compatible with all
of them. As a central analytical result, we show that modeling neural responses
in area V1 as samples from a posterior distribution over latents in a linear
Gaussian model of the image implies that those neural responses form a linear
Probabilistic Population Code (PPC, Ma et al. 2006). In particular, the
posterior distribution over some experimenter-defined variable like
"orientation" is part of the exponential family with sufficient statistics that
are linear in the neural sampling-based firing rates.
Novelty and Coverage in context-based information filtering
Authors: Dumitrescu Alexandra, Santini Simone
Date: 2018-11-24 00:00:00
Tasks: Information Retrieval, Retrieval

We present a collection of algorithms to filter a stream of documents in such
a way that the filtered documents will cover as well as possible the interest
of a person, keeping in mind that, at any given time, the offered documents
should not only be relevant, but should also be diversified, in the sense not
only of avoiding nearly identical documents, but also of covering as well as
possible all the interests of the person. We use a modification of the WEBSOM
algorithm, with limited architectural adaptation, to create a user model (which
we call the "user context" or simply the "context") based on a network of units
laid out in the word space and trained using a collection of documents
representative of the context.
  We introduce the concepts of novelty and coverage. Novelty is related to, but
not identical to, the homonymous information retrieval concept: a document is
novel it it belongs to a semantic area of interest to a person for which no
documents have been seen in the recent past. A group of documents has coverage
to the extent to which it is a good representation of all the interests of a
person.
  In order to increase coverage, we introduce an "interest" (or "urgency")
factor for each unit of the user model, modulated by the scores of the incoming
documents: the interest of a unit is decreased drastically when a document
arrives that belongs to its semantic area and slowly recovers its initial value
if no documents from that semantic area are displayed.
  Our tests show that these algorithms can effectively increase the coverage of
the documents that are shown to the user without overly affecting precision.
Identification of Treatment Effects under Limited Exogenous Variation
Authors: Whitney K. Newey, Sami Stouli
Date: 2018-11-24 00:00:00

Multidimensional heterogeneity and endogeneity are important features of a wide class of econometric models. With control variables to correct for endogeneity, nonparametric identification of treatment effects requires strong support conditions. To alleviate this requirement, we consider varying coefficients specifications for the conditional expectation function of the outcome given a treatment and control variables. This function is expressed as a linear combination of either known functions of the treatment, with unknown coefficients varying with the controls, or known functions of the controls, with unknown coefficients varying with the treatment. We use this modeling approach to give necessary and sufficient conditions for identification of average treatment effects. A sufficient condition for identification is conditional nonsingularity, that the second moment matrix of the known functions given the variable in the varying coefficients is nonsingular with probability one. For known treatment functions with sufficient variation, we find that triangular models with discrete instrument cannot identify average treatment effects when the number of support points for the instrument is less than the number of coefficients. For known functions of the controls, we find that average treatment effects can be identified in general nonseparable triangular models with binary or discrete instruments. We extend our analysis to flexible models of increasing dimension and relate conditional nonsingularity to the full support condition of Imbens and Newey (2009), thereby embedding semi- and non-parametric identification into a common framework.
An adaptive treatment recommendation and outcome prediction model for metastatic melanoma
Date: 2018-11-24 00:00:00

Melanoma is a type of skin cancer developed from melanocytes. It is one of
the most lethal types of cancer, accounting for approximately 75% of skin
cancer deaths. Late stage melanoma is very difficult to treat, since the cancer
cells are deranged, may be genetically linked and can be unresponsive to
therapy. Therefore, determining how to effectively make use of different
treatment regimens is of vital importance to survival. In this analysis, we
propose an adaptive treatment recommendation system based on a hybrid
cluster-classification (CC) structure. Our proposed system consists of two
parts,1) distribution based clustering and 2) classification. Our
recommendation system can help to identify high-risk melanoma patients and
suggest the best approach to treatment, which enables clinicians and patients
to make decisions on the basis of real-world data. Our data came from the
Canadian Melanoma Research Network (CMRN) database, a pan-Canadian multi-year
observational database, which is part of Global Melanoma Registry Network
(GMRN). Training/testing sets are generated based on data from different
sources, leading to cross cohort analysis tasks. Experimental results show that
our proposed system achieves very promising results with an overall accuracy of
up to 80%.
Subband Beamforming in Coherent Hybrid Massive MIMO Using Eigenbeams
Date: 2018-11-24 00:00:00
Tasks: All

Hybrid Massive MIMO reduces implementation complexity but only supports
beamforming coefficients that are common across all subbands. However, in macro
cellular where the channel has limited degrees of freedom, the long-term
component of the channel can be decomposed into a set of subband-independent
beamforming basis functions referred to as eigenbeams. A Coherent Hybrid
Massive MIMO system can form arbitrary linear combinations of the eigenbeams at
every subband to mimic Digital Massive MIMO beamforming as observed across all
locations in the cell.
Validation of Biometric Identification of Dairy Cows based on Udder NIR Images
Date: 2018-11-25 00:00:00

Identifying dairy cows with infections such as mastitis or cows on
medications is an extremely important task and legally required by the FDA$'$s
Pasteurized Milk Ordinance. The milk produced by these dairy cows cannot be
allowed to mix with the milk from healthy cows or it risks contaminating the
entire bulk tank or milk truck. Ear tags, ankle bands, RFID tags and even iris
patterns are some of the identification methods currently used in the dairy
farms. In this work we propose the use of NIR images of cow$'$s mammary glands
as a novel biometric identification modality. Two datasets, containing 302
samples from 151 cows has been collected and various machine learning
techniques applied to demonstrate the viability of the proposed biometric
modality. The results suggest promising identification accuracy for samples
collected over consecutive days
Retirement spending and biological age
Date: 2018-11-25 00:00:00

We solve a lifecycle model in which the consumer's chronological age does not
move in lockstep with calendar time. Instead, biological age increases at a
stochastic non-linear rate in time like a broken clock that might occasionally
move backwards. In other words, biological age could actually decline. Our
paper is inspired by the growing body of medical literature that has identified
biomarkers which indicate how people age at different rates. This offers better
estimates of expected remaining lifetime and future mortality rates. It isn't
farfetched to argue that in the not-too-distant future personal age will be
more closely associated with biological vs. calendar age. Thus, after
introducing our stochastic mortality model we derive optimal consumption rates
in a classic Yaari (1965) framework adjusted to our proper clock time. In
addition to the normative implications of having access to biological age, our
positive objective is to partially explain the cross-sectional heterogeneity in
retirement spending rates at any given chronological age. In sum, we argue that
neither biological nor chronological age alone is a sufficient statistic for
making economic decisions. Rather, both ages are required to behave rationally.
The most probable neural circuit exhibits low-dimensional sustained activity
Date: 2018-11-25 00:00:00

Cortical neurons whose activity is recorded in behavioral experiments has
been classified into several types such as stimulus-related neurons,
delay-period neurons, and reward-related neurons. Moreover, the population
activity of neurons during a reaching task can be described by dynamics in
low-dimensional space. These results suggest that the low-dimensional dynamics
of a few types of neurons emerge in the cerebral cortex that is trained to
perform a task. This study investigates a simple neural circuit emerges with a
few types of neurons. Assuming an infinite number of neurons in the circuit
with connection weights drawn from a Gaussian distribution, we model the
dynamics of neurons by using a kernel function. Given that the system is
infinitely large, almost all capable circuits approximate the most probable
circuit for the task. We simulate two delayed response tasks and a
motor-pattern generation task. The model network exhibits low-dimensional
dynamics in all tasks. Considering that the connection weights are drawn from a
Gaussian distribution, the most probable circuit is the circuit with the
smallest connection weight; that is, the simplest circuit with the simplest
dynamics. Finally, we relate the dynamics to algorithmic information theory.
Free Energy of ATP hydrolysis manipulates the cellular calcium signals
Date: 2018-11-25 00:00:00

In living cells, oscillation of the concentration of cytosolic Ca2+ is an
important and pervasive signal for the intercellular and intracellular
information conduction. To generate the oscillation, the hydrolysis of ATP is
always needed. Many recent studies show that both ATP molecules themselves and
the free energy by ATP hydrolysis play significant role in the biochemical
process involving ATP hydrolysis. To verify the prediction, we consider the
role of ATP molecules and their hydrolysis in a classic one-pool model of Ca2+
oscillation. Our results show that the available Gibbs free energy of ATP
hydrolysis {\DeltaG}, which measures the "distance" of a reaction to its
equilibrium state, is another important regulatory factor of the oscillation
system besides the concentration of ATP. Furthermore, our model suggest a
rudimental prediction of how the oscillation system changes in an aging cell,
such as the decrease of the amplitude and the increase of the least {\DeltaG}
required for the oscillation.
SparseCast: Hybrid Digital-Analog Wireless Image Transmission Exploiting Frequency Domain Sparsity
Date: 2018-11-25 00:00:00
Tasks: compressed sensing

A hybrid digital-analog wireless image transmission scheme, called
SparseCast, is introduced, which provides graceful degradation with channel
quality. SparseCast achieves improved end-to-end reconstruction quality while
reducing the bandwidth requirement by exploiting frequency domain sparsity
through compressed sensing. The proposed algorithm produces a linear
relationship between the channel signal-to-noise ratio (CSNR) and peak
signal-to-noise ratio (PSNR), without requiring the channel state knowledge at
the transmitter. This is particularly attractive when transmitting to multiple
receivers or over unknown time-varying channels, as the receiver PSNR depends
on the experienced channel quality, and is not bottlenecked by the worst
channel. SparseCast is benchmarked against two alternative algorithms: SoftCast
and BCS-SPL. Our findings show that the proposed algorithm outperforms SoftCast
by approximately 3.5 dB and BCS-SPL by 15.2 dB.
Save Our Spectrum: Contact-Free Human Sensing Using Single Carrier Radio
Date: 2018-11-26 00:00:00
Tasks: Gesture Recognition

Recent research has demonstrated new capabilities in radio frequency (RF)
sensing that apply to health care, smart home, and security applications.
However, previous work in RF sensing requires heavy utilization of the radio
spectrum, for example, transmitting thousands of WiFi packets per second. In
this paper, we present a device-free human sensing system based on received
signal strength (RSS) measurements from a low-cost single carrier narrowband
radio transceiver. We test and validate its performance in three different
applications: real-time heart rate monitoring, gesture recognition, and human
speed estimation. The challenges in these applications stem from the very low
signal-to-noise ratio and the use of a single-dimensional measurement of the
channel. We apply a combination of linear and non-linear filtering, and
time-frequency analysis, and develop new estimators to address the challenges
in the particular applications. Our experimental results indicate that RF
sensing based on single-carrier magnitude measurements performs nearly as well
as the state-of-the-art while utilizing three orders of magnitude less
bandwidth.
A Variable Rate Fronthaul Scheme for Cloud Radio Access Networks (C-RAN)
Date: 2019-04-18 00:00:00
Tasks: Blocking

Cloud Radio Access Network (C-RAN) is considered as one of the most promising
candidate for Fifth Generation (5G) wireless communication system. However,
C-RAN imposes stringent requirements on fronthaul in terms of capacity which in
turn becomes a bottleneck for deployment of cloud-RAN in dense networks.
Compressed CPRI and functional split are two primary solutions to overcome
this. However, this makes Remote Radio Unit (RRU) more complex and the
computational resources in RRU are localized and thus cannot be shared with
other RRUs when not in use. In this paper, we introduce the concept of Variable
Rate Fronthaul (VRF) for C-RAN. This scheme operates on a CPRI type of
interface (e.g., one that transmits I/Q data samples) with the novelty of
dynamically changing the cell bandwidth, and consequently the fronthaul data
rates, depending on the cell load, with the support of a Software Defined
Network (SDN) controller. This allows for a more efficient transport of C-RAN
cells data over a shared backhaul. We first propose a mathematical analysis of
the VRF performance using a queuing theory approach based on the Markov model.
We then provide the results of our simulation framework both for validation and
in support of the mathematical analysis. Our results show that the proposed VRF
scheme provides significantly lower blocking probability over a shared backhaul
than standard CPRI.