pwc-archive/papers-with-abstracts huggingface dataset:
Passive Crowd Speed Estimation in Adjacent Regions With Minimal WiFi Sensing
Date: 2018-01-31 00:00:00

In this paper, we propose a methodology for estimating the crowd speed using
WiFi devices without relying on people to carry any device. Our approach not
only enables speed estimation in the region where WiFi links are, but also in
the adjacent possibly WiFi-free regions. More specifically, we use a pair of
WiFi links in one region, whose RSSI measurements are then used to estimate the
crowd speed, not only in this region, but also in adjacent WiFi-free regions.
We first prove how the cross-correlation and the probability of crossing the
two links implicitly carry key information about the pedestrian speeds and
develop a mathematical model to relate them to pedestrian speeds. We then
validate our approach with 108 experiments, in both indoor and outdoor, where
up to 10 people walk in two adjacent areas, with variety of speeds per region,
showing that our framework can accurately estimate these speeds with only a
pair of WiFi links in one region. For instance, the NMSE over all experiments
is 0.18. We also evaluate our framework in a museum-type setting and estimate
the popularity of different exhibits. We finally run experiments in an aisle in
Costco, estimating key attributes of buyers' behaviors.
The optimal timing of reintroducing captive populations into the wild
Date: 2017-11-16 00:00:00

We examine a conservation problem in which the recovery of an endangered
species depends on a captive breeding and reintroduction program. The model is
applied to the case of the black-footed ferret (Mustela nigripes), an
endangered species in North America reliant on captive breeding for survival.
The timing of reintroduction is an important concern in these programs as there
is a tradeoff between the duration (and therefore the cost) of the captive
breeding program and the period the population spends in recovery and in the
wild. In this paper, we develop a stylized bioeconomic model to determine the
optimal reintroduction time, in which the objective is to minimize the cost of
reintroduction while providing a viably-sized population in the wild. Our
control variable is the timing of reintroduction, which departs from a large
body of work in bioeconomics that focuses on adjustable controls that directly
affect the target population. Generally, we find it is optimal to reintroduce
ferrets early in a reintroduction program, although this result is contingent
on species interactions and provisioning services.
Models of protein production along the cell cycle: an investigation of possible sources of noise
Authors: Renaud Dessalles, Vincent Fromion, Philippe Robert
Date: 2017-11-17 00:00:00

In this article, we quantitatively study, through stochastic models, the efects of several intracellular phenomena, such as cell volume growth, cell division, gene replication as well as fuctuations of available RNA polymerases and ribosomes. These phenomena are indeed rarely considered in classic models of protein production and no relative quantitative comparison among them has been performed. The parameters for a large and representative class of proteins are determined using experimental measures. The main important and surprising conclusion of our study is to show that despite the signifcant fuctuations of free RNA polymerases and free ribosomes, they bring little variability to protein production contrary to what has been previously proposed in the literature. After verifying the robustness of this quite counter-intuitive result, we discuss its possible origin from a theoretical view, and interpret it as the result of a mean-feld efect.
Simultaneous identification of linear building dynamic model and disturbance using sparsity-promoting optimization
Authors: Tingting Zeng, Jonathan Brooks, Prabir Barooah
Date: 2017-11-17 00:00:00
Tasks: Self-Learning

We propose a method that simultaneously identifies a linear time-invariant model of a building's temperature dynamics and a transformed version of the unmeasured disturbance affecting the building. Our method uses l1-regularization to encourage the identified disturbance to be approximately sparse, which is motivated by the slowly-varying nature of occupancy that determines the disturbance. The proposed method involves solving a convex optimization problem that guarantees the identified black-box model possesses known properties of the plant, especially input-output stability and positive DC gains. These features enable one to use the method as part of a self-learning control system in which the model of the building is updated periodically without requiring human intervention. Results from the application of the method on data from a simulated and real building are provided.
An Equation for Predicting Binding Strengths of Metal Cations to Protein of Human Serum Transferrin
Authors: Huifang Xu, Yifeng Wang
Date: 2017-11-17 00:00:00

Because human serum transferrin (hTF) exists freely in serum, it is a potential target for cancer treatment drugs and in curing iron-overloaded conditions in patients via long-term transfusion therapy. The understanding of the interactions between hTF and metal ions is very important for biological, pharmalogical, toxicological, and other protein engineering purposes. In this paper, a simple linear free energy correlation is proposed to predict the binding strength between hTF protein and metal cations. The stability constants for a family of metal-hTF complexes can be correlated to the non-solvation energies and the radii of cations. The binding strength is determined by both the physical properties (charge and size or ionic radius) and chemical properties (non-solvation energy) of a given cation. The binding strengths of either divalent and trivalent metals can then be predicted systematically.
A systematic framework to discover pattern for web spam classification
Authors: Jelodar Hamed, Wang Yongli, Yuan Chi, Jiang Xiaohui
Date: 2017-11-19 00:00:00
Tasks: Classification, General Classification, Spam detection

Web spam is a big problem for search engine users in World Wide Web. They use
deceptive techniques to achieve high rankings. Although many researchers have
presented the different approach for classification and web spam detection
still it is an open issue in computer science. Analyzing and evaluating these
websites can be an effective step for discovering and categorizing the features
of these websites. There are several methods and algorithms for detecting those
websites, such as decision tree algorithm. In this paper, we present a
systematic framework based on CHAID algorithm and a modified string matching
algorithm (KMP) for extract features and analysis of these websites. We
evaluated our model and other methods with a dataset of Alexa Top 500 Global
Sites and Bing search engine results in 500 queries.
Decoding of neural data using cohomological feature extraction
Date: 2018-09-10 00:00:00

We introduce a novel data-driven approach to discover and decode features in
the neural code coming from large population neural recordings with minimal
assumptions, using cohomological feature extraction. We apply our approach to
neural recordings of mice moving freely in a box, where we find a circular
feature. We then observe that the decoded value corresponds well to the head
direction of the mouse. Thus we capture head direction cells and decode the
head direction from the neural population activity without having to process
the behaviour of the mouse. Interestingly, the decoded values convey more
information about the neural activity than the tracked head direction does,
with differences that have some spatial organization. Finally, we note that the
residual population activity, after the head direction has been accounted for,
retains some low-dimensional structure which is correlated with the speed of
the mouse.
MEC-aware Cell Association for 5G Heterogeneous Networks
Date: 2018-01-30 00:00:00
Tasks: Edge-computing

The need for efficient use of network resources is continuously increasing
with the grow of traffic demand, however, current mobile systems have been
planned and deployed so far with the mere aim of enhancing radio coverage and
capacity. Unfortunately, this approach is not sustainable anymore, as 5G
communication systems will have to cope with huge amounts of traffic,
heterogeneous in terms of latency among other Qualityof- Service (QoS)
requirements. Moreover, the advent of Multiaccess Edge Computing (MEC) brings
up the need to more efficiently plan and dimension network deployment by means
of jointly exploiting the available radio and processing resources. From this
standpoint, advanced cell association of users can play a key role for 5G
systems. Focusing on a Heterogeneous Network (HetNet), this paper proposes a
comparison between state-of-the-art (i.e., radio-only) and MEC-aware cell
association rules, taking the scenario of task offloading in the Uplink (UL) as
an example. Numerical evaluations show that the proposed cell association rule
provides nearly 60% latency reduction, as compared to its standard,
radio-exclusive counterpart.
A Full Duplex Transceiver with Reduced Hardware Complexity
Date: 2017-11-20 00:00:00

For future wireless communication systems, full duplex is seen as a possible
solution to the ever present spectrum shortage. The key aspect to enable
In-Band Full Duplex (IBFD) is sufficient cancellation of the unavoidable
Self-Interference (SI). In this work we evaluate the performance of a low
complexity IBFD transceiver, including the required analog and digital
interference cancellation techniques. The Radio Frequency Self- Interference
Canceler (RFSIC) is based on the isolation of a circulator in combination with
a vector modulator regenerating the interference signal, to destructively
combine it with the received signal. On the digital side, a Digital
Self-Interference Cancellation (DSIC) algorithm based on non-linear adaptive
filtering is used. With the simplified analog front-end of a Software Defined
Radio (SDR) platform, SI cancellation of 90 dB is achieved with the presence of
a received signal.
Linear-Complexity Relaxed Word Mover's Distance with GPU Acceleration
Authors: Atasu Kubilay, Parnell Thomas, Dünner Celestine, Sifalakis Manolis, Pozidis Haralampos, Vasileiadis Vasileios, Vlachos Michail, Berrospi Cesar, Labbi Abdel
Date: 2017-11-20 00:00:00
Tasks: Clustering, GPU

The amount of unstructured text-based data is growing every day. Querying,
clustering, and classifying this big data requires similarity computations
across large sets of documents. Whereas low-complexity similarity metrics are
available, attention has been shifting towards more complex methods that
achieve a higher accuracy. In particular, the Word Mover's Distance (WMD)
method proposed by Kusner et al. is a promising new approach, but its time
complexity grows cubically with the number of unique words in the documents.
The Relaxed Word Mover's Distance (RWMD) method, again proposed by Kusner et
al., reduces the time complexity from qubic to quadratic and results in a
limited loss in accuracy compared with WMD. Our work contributes a
low-complexity implementation of the RWMD that reduces the average time
complexity to linear when operating on large sets of documents. Our
linear-complexity RWMD implementation, henceforth referred to as LC-RWMD, maps
well onto GPUs and can be efficiently distributed across a cluster of GPUs. Our
experiments on real-life datasets demonstrate 1) a performance improvement of
two orders of magnitude with respect to our GPU-based distributed
implementation of the quadratic RWMD, and 2) a performance improvement of three
to four orders of magnitude with respect to our distributed WMD implementation
that uses GPU-based RWMD for pruning.
Information and Arbitrage: Applications of Quantum Groups in Mathematical Finance
Authors: Paul McCloud
Date: 2017-11-20 00:00:00

The relationship between expectation and price is commonly established with two principles: no-arbitrage, which asserts that both maps are positive; and equivalence, which asserts that the maps share the same null events. Constructed from the Arrow-Debreu securities, classical and quantum models of economics are then distinguished by their respective use of classical and quantum logic, following the program of von Neumann. In this essay, the operations and axioms of quantum groups are discovered in the minimal preconditions of stochastic and functional calculus, making this the natural domain for the axiomatic development of mathematical finance. Quantum economics emerges from the twin pillars of the Gelfand-Naimark-Segal construction, implementing the principle of no-arbitrage, and the Radon-Nikodym theorem, implementing the principle of equivalence. Exploiting quantum group duality, a holographic principle that exchanges the roles of state and observable creates two distinct economic models from the same set of elementary valuations. Advocating on the grounds that this contains and extends classical economics, noncommutativity is presented as a modelling resource, with novel applications in the pricing of options and other derivative securities.
A phase field approach to trabecular bone remodeling
Date: 2017-11-20 00:00:00

We introduce a continuous modeling approach which combines elastic responds
of the trabecular bone structure, the concentration of signaling molecules
within the bone and a mechanism how this concentration at the bone surface is
used for local bone formation and resorption. In an abstract setting bone can
be considered as a shape changing structure. For similar problems in materials
science phase field approximations have been established as an efficient
computational tool. We adapt such an approach for trabecular bone remodeling.
It allows for a smooth representation of the trabecular bone structure and
drastically reduces computational costs if compared with traditional micro
finite element approaches. We demonstrate the advantage of the approach within
a minimal model. We quantitatively compare the results with established micro
finite element approaches on simple geometries and consider the bone morphology
within a bone segment obtained from $\mu$CT data of a sheep vertebra with
realistic parameters.
Asymptotic Analysis for Spectral Risk Measures Parameterized by Confidence Level
Date: 2017-11-20 00:00:00

We study the asymptotic behavior of the difference $\Delta \rho ^{X,
Y}_\alpha := \rho _\alpha (X + Y) - \rho _\alpha (X)$ as $\alpha \rightarrow
1$, where $\rho_\alpha $ is a risk measure equipped with a confidence level
parameter $0 < \alpha < 1$, and where $X$ and $Y$ are non-negative random
variables whose tail probability functions are regularly varying. The case
where $\rho _\alpha $ is the value-at-risk (VaR) at $\alpha $, is treated in
Kato (2017). This paper investigates the case where $\rho _\alpha $ is a
spectral risk measure that converges to the worst-case risk measure as $\alpha
\rightarrow 1$. We give the asymptotic behavior of the difference between the
marginal risk contribution and the Euler contribution of $Y$ to the portfolio
$X + Y$. Similarly to Kato (2017), our results depend primarily on the relative
magnitudes of the thicknesses of the tails of $X$ and $Y$. We also conducted a
numerical experiment, finding that when the tail of $X$ is sufficiently thicker
than that of $Y$, $\Delta \rho ^{X, Y}_\alpha $ does not increase monotonically
with $\alpha$ and takes a maximum at a confidence level strictly less than $1$.
Design of Sampling Set for Bandlimited Graph Signal Estimation
Date: 2017-11-20 00:00:00
Tasks: Experimental Design, Quantization, regression

It is of particular interest to reconstruct or estimate bandlimited graph
signals, which are smoothly varying signals defined over graphs, from partial
noisy measurements. However, choosing an optimal subset of nodes to sample is
NP-hard. We formularize the problem as the experimental design of a linear
regression model if we allow multiple measurements on a single node. By
relaxing it to a convex optimization problem, we get the proportion of sample
for each node given the budget of total sample size. Then, we use a
probabilistic quantization to get the number of each node to be sampled.
Moreover, we analyze how the sample size influences whether our object function
is well-defined by perturbation analysis. Finally, we demonstrate the
performance of the proposed approach through various numerical experiments.
The Linear-Noise Approximation and moment-closure approximations for stochastic chemical kinetics
Date: 2017-11-24 00:00:00

This is a short review of two common approximations in stochastic chemical
and biochemical kinetics. It will appear as Chapter 6 in the book "Quantitative
Biology: Theory, Computational Methods and Examples of Models" edited by Brian
Munsky, Lev Tsimring and Bill Hlavacek (to be published in late 2017/2018 by
MIT Press). All chapter references in this article refer to chapters in the
aforementioned book.
A validation strategy for in silico generated aptamers
Date: 2018-05-21 00:00:00

The selection of high-affinity aptamers is of paramount interest for clinical
and technological applications. A novel strategy is proposed to validate the
reliability of the 3D structures of aptamers, produced in silico by using free
software. The procedure consists of three steps: a. the production of a large
set of conformations for each candidate aptamer, b. the rigid docking upon the
receptor, c. the topological and electrical characterization of the products.
Steps a. and b. allow a global binding score of the ligand-receptor complexes
based on the distribution of the "effective affinity", i.e. the sum of the
conformational and the docking energy. Step c. employs a complex network
approach (Proteotronics) to characterize the electrical properties of the
aptamers and the ligand-receptor complexes. The test-bed is represented by a
group of anti- Angiopoietin-2 aptamers. In a previous literature these aptamers
were processed both in vitro and in silico, by using an approach different from
that here presented, and finally tested with a SPS experiment. Computational
expectations and experimental outcomes did not agree, while our results show a
good agreement with the known measurements. The devised procedure is not
aptamer-specific and, integrating structure production with structure
selection, candidates itself as a quite complete theoretical approach for
aptamer selection.
On the Signal Processing Operations in LIGO signals
Date: 2018-03-12 00:00:00

This article analyzes the data for the five gravitational wave (GW) events
detected in Hanford(H1), Livingston(L1) and Virgo(V1) detectors by the LIGO
collaboration. It is shown that GW170814, GW170817, GW151226 and GW170104 are
very weak signals whose amplitude does not rise significantly during the GW
event, and they are indistinguishable from non-stationary detector noise. LIGO
software implements cross-correlation funcion(CCF) of H1/L1 signals with the
template reference signal, in frequency domain, in a matched filter, using 32
second windows. It is shown that this matched filter misfires with high SNR/CCF
peaks, even for very low-amplitude, short bursts of sine wave signals and
additive white gaussian noise(AWGN), all the time. It is shown that this
erratic behaviour of the matched filter, is due to the error in signal
processing operations, such as lack of cyclic prefix necessary to account for
circular convolution. It is also shown that normalized CCF method implemented
in time domain using short windows, does not have false CCF peaks for sine wave
and noise bursts. It is shown that the normalized CCF for GW151226 and
GW170104, when correlating H1/L1 and template, is indistinguishable from
correlating detector noise and the template. It is also shown that the
normalized CCF for GW151226 and GW170104, when correlating H1/L1 and template,
is indistinguishable from correlating H1/L1 and bogus chirp templates which are
frequency modulated(FM) waveforms which differ significantly from ideal
templates. Similar results are shown with LIGO matched filter, which misfires
with high Signal to Noise Ratio(SNR) for bogus chirp templates.
Determination of carbohydrates in infant milk powders by ultra-performance liquid chromatography with evaporative light scattering detector and BEH HILIC column
Date: 2017-11-20 00:00:00

The possibility of carbohydrate separation in BEH HILIC (Ethylene Bridged
Hybride, Hydrophilic Interaction Liquid Chromatography) column was studied by
ultra-performance liquid chromatography (UPLC) with evaporative light
scattering detector (ELSD) and mobile phase containing amine compounds as
modifiers. The chromatography conditions and ELSD parameters were optimized to
separate five typical carbohydrates and applied to analysis of four infant milk
powders. The linear ranges of carbohydrate determination were 20-300mg/L for
fructose and glucose, 20-250mg/L for sucrose and lactose, and 35-180mg/L for
fructo-oligosaccharide. The LODs were 16.4mg/L for fructose and glucose,
17.3mg/L for sucrose, 20.0mg/L for lactose, and 46.7mg/L for
fructo-oligosaccharide. Relative standard deviations (RSDs) ranged between
3.45-4.23%, 1.46-4.17%, 4.14-5.60%, 1.39-4.09%, and 2.49-3.61% for fructose,
glucose, sucrose, lactose, and fructo-oilgosaccharide, respectively and
recoveries ranged between 95.0 and 105.4%
A unified algorithm framework for quality control of sensor data for behavioural clinimetric testing
Date: 2017-11-23 00:00:00

The use of smartphone and wearable sensing technology for objective,
non-invasive and remote clinimetric testing of symptoms has considerable
potential. However, the clinimetric accuracy achievable with such technology is
highly reliant on separating the useful from irrelevant or confounded sensor
data. Monitoring patient symptoms using digital sensors outside of controlled,
clinical lab settings creates a variety of practical challenges, such as
unavoidable and unexpected user behaviours. These behaviours often violate the
assumptions of clinimetric testing protocols, where these protocols are
designed to probe for specific symptoms. Such violations are frequent outside
the lab, and can affect the accuracy of the subsequent data analysis and
scientific conclusions. At the same time, curating sensor data by hand after
the collection process is inherently subjective, laborious and error-prone. To
address these problems, we report on a unified algorithmic framework for
automated sensor data quality control, which can identify those parts of the
sensor data which are sufficiently reliable for further analysis. Algorithms
which are special cases of this framework for different sensor data types (e.g.
accelerometer, digital audio) detect the extent to which the sensor data
adheres to the assumptions of the test protocol for a variety of clinimetric
tests. The approach is general enough to be applied to a large set of
clinimetric tests and we demonstrate its performance on walking, balance and
voice smartphone-based tests, designed to monitor the symptoms of Parkinson's
disease.