title:
Phase Transitions and a Model Order Selection Criterion for Spectral
  Graph Clustering

abstract:
  One of the longstanding open problems in spectral graph clustering (SGC) is
the so-called model order selection problem: automated selection of the correct
number of clusters. This is equivalent to the problem of finding the number of
connected components or communities in an undirected graph. We propose
automated model order selection (AMOS), a solution to the SGC model selection
problem under a random interconnection model (RIM) using a novel selection
criterion that is based on an asymptotic phase transition analysis. AMOS can
more generally be applied to discovering hidden block diagonal structure in
symmetric non-negative matrices. Numerical experiments on simulated graphs
validate the phase transition analysis, and real-world network data is used to
validate the performance of the proposed model selection procedure.


title: Towards a Privacy Research Roadmap for the Computing Community

abstract:
  Great advances in computing and communication technology are bringing many
benefits to society, with transformative changes and financial opportunities
being created in health care, transportation, education, law enforcement,
national security, commerce, and social interactions. Many of these benefits,
however, involve the use of sensitive personal data, and thereby raise concerns
about privacy. Failure to address these concerns can lead to a loss of trust in
the private and public institutions that handle personal data, and can stifle
the independent thought and expression that is needed for our democracy to
flourish.
  This report, sponsored by the Computing Community Consortium (CCC), suggests
a roadmap for privacy research over the next decade, aimed at enabling society
to appropriately control threats to privacy while enjoying the benefits of
information technology and data science. We hope that it will be useful to the
agencies of the Federal Networking and Information Technology Research and
Development (NITRD) Program as they develop a joint National Privacy Research
Strategy over the coming months. The report synthesizes input drawn from the
privacy and computing communities submitted to both the CCC and NITRD, as well
as past reports on the topic.


title: Friendly frogs, stable marriage, and the magic of invariance

abstract:
  We introduce a two-player game involving two tokens located at points of a
fixed set. The players take turns to move a token to an unoccupied point in
such a way that the distance between the two tokens is decreased. Optimal
strategies for this game and its variants are intimately tied to Gale-Shapley
stable marriage. We focus particularly on the case of random infinite sets,
where we use invariance, ergodicity, mass transport, and deletion-tolerance to
determine game outcomes.


title: Highly symmetric hypertopes

abstract:
  We study incidence geometries that are thin and residually connected. These
geometries generalise abstract polytopes. In this generalised setting, guided
by the ideas from the polytopes theory, we introduce the concept of chirality,
a property of orderly asymmetry occurring frequently in nature as a natural
phenomenon. The main result in this paper is that automorphism groups of
regular and chiral thin residually connected geometries need to be $C$-groups
in the regular case and $C^+$-groups in the chiral case.


title:
Phase retrieval in the general setting of continuous frames for Banach
  spaces

abstract:
  We develop a novel and unifying setting for phase retrieval problems that
works in Banach spaces and for continuous frames and consider the questions of
uniqueness and stability of the reconstruction from phaseless measurements. Our
main result states that also in this framework, the problem of phase retrieval
is never uniformly stable in infinite dimensions. On the other hand, we show
weak stability of the problem. This complements recent work [9], where it has
been shown that phase retrieval is always unstable for the setting of discrete
frames in Hilbert spaces. In particular, our result implies that the stability
properties cannot be improved by oversampling the underlying discrete frame.
  We generalize the notion of complement property (CP) to the setting of
continuous frames for Banach spaces (over $\mathbb{K}=\mathbb{R}$ or
$\mathbb{K}=\mathbb{C}$) and verify that it is a necessary condition for
uniqueness of the phase retrieval problem; when $\mathbb{K}=\mathbb{R}$ the CP
is also sufficient for uniqueness. In our general setting, we also prove a
conjecture posed by Bandeira et al. [5], which was originally formulated for
finite-dimensional spaces: for the case $\mathbb{K}=\mathbb{C}$ the strong
complement property (SCP) is a necessary condition for stability. To prove our
main result, we show that the SCP can never hold for frames of
infinite-dimensional Banach spaces.


title: Probabilistic Consequences of Some Polynomial Recurrences

abstract:
  In this paper, we consider sequences of polynomials that satisfy
differential--difference recurrences. Our interest is motivated by the fact
that polynomials satisfying such recurrences frequently appear as generating
polynomials of integer valued random variables that are of interest in discrete
mathematics. It is, therefore, of interest to understand the properties of such
polynomials and their probabilistic consequences. As an illustration we analyze
probabilistic properties of tree--like tableaux, combinatorial objects that are
connected to asymmetric exclusion processes. In particular, we show that the
number of diagonal boxes in symmetric tree--like tableaux is asymptotically
normal and that the number of occupied corners in a random tree--like tableau
is asymptotically Poisson. This extends earlier results of Aval, Boussicault,
Nadeau, and Laborde Zubieta, respectively.


title: Young stellar structures in four nearby galaxies

abstract:
  A cluster finding method was developed and applied in four Local Group
Galaxies (SMC, M31, M33 and NGC 6822). The aim is to study the young stellar
population of these galaxies by identifying stellar structures in small and
large scales. Also our aim is to assess the potential of using the observations
of ESA's space mission Gaia for the study of nearby galaxies resolved in stars.
The detection method used is a Hierarchical technique based on a modified
friends of friends algorithm. The identified clusters are classified in five
distinct categories according to their size. The data for our study were used
from two ground based surveys, the Local Group Galaxy Survey and the Maggelanic
Clouds Spectroscopic Survey. Relatively young main sequence stars were selected
from the stellar catalogs and were used by the detection algorithm. Multiple
young stellar structures were identified in all galaxies with size varying from
very small scales of a few pc up to scales larger than 1 kpc. The same cluster
finding method was used in six spiral galaxies observed with the Hubble Space
Telescope in a previous study. The average size in each category of the
identified structures in the Local Group galaxies presents values consistent
with the identified structures in the relatively distant spiral galaxies. Most
of the structures consist of stars within the observational limits of Gaia's
instruments. It is expected that Gaia's observations will contribute
significantly on the study of the young stellar population of nearby galaxies.


title:
Current-induced Pinwheel Oscillations in Perpendicular Magnetic
  Anisotropy Spin Valve Nanopillars

abstract:
  Nanopillar spin valve devices are typically comprised of two ferromagnetic
layers: a reference layer and a free layer whose magnetic orientation can be
changed by both an external magnetic field and through the introduction of
spin-polarized electric current. Here we report the continuous repeated
switching behavior of both the reference and free layers of a perpendicular
spin valve made of Co/Pd and Co/Ni multilayers that arises for sufficiently
large DC currents. This periodic switching of the two layers produces an
oscillating signal in the MHz regime but is only observed for one sign of the
applied current. The observed behavior agrees well with micromagnetic
simulations.


title:
Wide bandwidth and high resolution planar filter array based on
  DBR-metasurface-DBR structures

abstract:
  We propose and experimentally demonstrate a planar array of optical bandpass
filters composed of low loss dielectric metasurface layers sandwiched between
two distributed Bragg reflectors (DBRs). The two DBRs form a Fabry-P\'erot
resonator whose center wavelength is controlled by the design of the
transmissive metasurface layer which functions as a phase shifting element. We
demonstrate an array of bandpass filters with spatially varying center
wavelengths covering a wide range of operation wavelengths of 250 nm around
{\lambda} = 1550 nm ({\Delta}{\lambda}/{\lambda} = 16%). The center wavelengths
of each filter are independently controlled only by changing the in-plane
geometry of the sandwiched metasurfaces, and the experimentally measured
quality factors are larger than 700. The demonstrated filter array can be
directly integrated on top of photodetector arrays to realize on-chip
high-resolution spectrometers with free-space coupling.


title: Hardware-oriented Approximation of Convolutional Neural Networks

abstract:
  High computational complexity hinders the widespread usage of Convolutional
Neural Networks (CNNs), especially in mobile devices. Hardware accelerators are
arguably the most promising approach for reducing both execution time and power
consumption. One of the most important steps in accelerator development is
hardware-oriented model approximation. In this paper we present Ristretto, a
model approximation framework that analyzes a given CNN with respect to
numerical resolution used in representing weights and outputs of convolutional
and fully connected layers. Ristretto can condense models by using fixed point
arithmetic and representation instead of floating point. Moreover, Ristretto
fine-tunes the resulting fixed point network. Given a maximum error tolerance
of 1%, Ristretto can successfully condense CaffeNet and SqueezeNet to 8-bit.
The code for Ristretto is available.


title: Using Deep Learning for Image-Based Plant Disease Detection

abstract:
  Crop diseases are a major threat to food security, but their rapid
identification remains difficult in many parts of the world due to the lack of
the necessary infrastructure. The combination of increasing global smartphone
penetration and recent advances in computer vision made possible by deep
learning has paved the way for smartphone-assisted disease diagnosis. Using a
public dataset of 54,306 images of diseased and healthy plant leaves collected
under controlled conditions, we train a deep convolutional neural network to
identify 14 crop species and 26 diseases (or absence thereof). The trained
model achieves an accuracy of 99.35% on a held-out test set, demonstrating the
feasibility of this approach. When testing the model on a set of images
collected from trusted online sources - i.e. taken under conditions different
from the images used for training - the model still achieves an accuracy of
31.4%. While this accuracy is much higher than the one based on random
selection (2.6%), a more diverse set of training data is needed to improve the
general accuracy. Overall, the approach of training deep learning models on
increasingly large and publicly available image datasets presents a clear path
towards smartphone-assisted crop disease diagnosis on a massive global scale.


title: Selfadjoint and $m$ sectorial extensions of Sturm-Liouville operators

abstract:
  The self-adjoint and $m$-sectorial extensions of coercive Sturm-Liouville
operators are characterised, under minimal smoothness conditions on the
coefficients of the differential expression.


title: Learning Simple Auctions

abstract:
  We present a general framework for proving polynomial sample complexity
bounds for the problem of learning from samples the best auction in a class of
"simple" auctions. Our framework captures all of the most prominent examples of
"simple" auctions, including anonymous and non-anonymous item and bundle
pricings, with either a single or multiple buyers. The technique we propose is
to break the analysis of auctions into two natural pieces. First, one shows
that the set of allocation rules have large amounts of structure; second,
fixing an allocation on a sample, one shows that the set of auctions agreeing
with this allocation on that sample have revenue functions with low
dimensionality. Our results effectively imply that whenever it's possible to
compute a near-optimal simple auction with a known prior, it is also possible
to compute such an auction with an unknown prior (given a polynomial number of
samples).


title: A boundary quotient diagram for right LCM semigroups

abstract:
  We propose a boundary quotient diagram for right LCM semigroups with property
(AR) that generalizes the boundary quotient diagram for the $ax+b$-semigroup
over the natural numbers. Our approach focuses on two important subsemigroups:
the core subsemigroup and the semigroup of core irreducible elements. The
diagram is then employed to unify several case studies on KMS-states, and we
end with a discussion on $K$-theoretical aspects of the diagram motivated by
recent findings for integral dynamics.


title: Pressure type metrics on spaces of metric graphs

abstract:
  In this note, we consider two Riemannian metrics on a moduli space of metric
graphs. Each of them could be thought of as an analogue of the Weil-Petersson
metric on the moduli space of metric graphs. We discuss and compare geometric
features of these two metrics with the "classic" Weil-Petersson metric in
Teichm\"uller theory. This paper is motivated by Pollicott and Sharp's earlier
work.


title:
Evidence for Planck-scale resonant particle production during inflation
  from the CMB power spectrum

abstract:
  The power spectrum of the cosmic microwave background from both the {\it
Planck} and {\it WMAP} data exhibits a slight dip for multipoles in the range
of $l= 10-30$. We show that such a dip could be the result of the resonant
creation of massive particles that couple to the inflaton field. For our
best-fit models, the epoch of resonant particle creation reenters the horizon
at a wave number of $k_* \sim 0.0011 \pm 0.0004 $ ($h$ Mpc$^{-1}$). The
amplitude and location of this feature corresponds to the creation of a number
of degenerate fermion species of mass $\sim (8-11) /\lambda^{3/2} $ $m_{pl}$
during inflation where $\lambda \sim (1.0 \pm 0.5) N^{-2/5}$ is the coupling
constant between the inflaton field and the created fermion species, while $N$
is the number of degenerate species. Although the evidence is of marginal
statistical significance, this could constitute new observational hints of
unexplored physics beyond the Planck scale


title: Adaptive Caching Networks with Optimality Guarantees

abstract:
  We study the problem of optimal content placement over a network of caches, a
problem naturally arising in several networking applications, including ICNs,
CDNs, and P2P systems. Given a demand of content request rates and paths
followed, we wish to determine the content placement that maximizes the
expected caching gain, i.e., the reduction of routing costs due to intermediate
caching. The offline version of this problem is NP-hard and, in general, the
demand and topology may be a priori unknown. Hence, a distributed, adaptive,
constant approximation content placement algorithm is desired. We show that
path replication, a simple algorithm frequently encountered in literature, can
be arbitrarily suboptimal when combined with traditional eviction policies,
like LRU, LFU, or FIFO. We propose a distributed, adaptive algorithm that
performs stochastic gradient ascent on a concave relaxation of the expected
caching gain, and constructs a probabilistic content placement within 1-1/e
factor from the optimal, in expectation. Motivated by our analysis, we also
propose a novel greedy eviction policy to be used with path replication, and
show through numerical evaluations that both algorithms significantly
outperform path replication with traditional eviction policies over a broad
array of network topologies.


title:
The tropicalization of the moduli space of curves II: Topology and
  applications

abstract:
  We study the topology of the tropical moduli space parametrizing stable
tropical curves of genus g with n marked points in which the bounded edges have
total length 1, and prove that it is highly connected. Using the identification
of this space with the dual complex of the boundary in the moduli space of
stable algebraic curves, we give a simple expression for the top weight
cohomology of M_{1,n} as a representation of the symmetric group and describe
an explicit dual basis in homology consisting of abelian cycles for the pure
mapping class group.


title:
Sensitivity to Majorana neutrinos in $\Delta L=2$ decays of $B_c$ meson
  at LHCb

abstract:
  The possible existence of Majorana neutrinos can be tested through the study
of processes where the total lepton number $L$ is violated by two units
($\Delta L=2$). In this work, the production of an on-shell Majorana neutrino
with a mass around $\sim$ 0.2 GeV to a few GeV is studied in $\Delta L= 2$
decays of the $B_c$ meson. We focus on the same-sign di-muon channels:
three-body $B_c^- \to \pi^+\mu^-\mu^-$ and four-body $B_c^- \to J/\psi
\pi^+\mu^-\mu^-$ and their experimental sensitivity at the LHCb. In both
channels, we find that sensitivities on the branching fraction of the order
$\lesssim 10^{-7} \ (10^{-8})$ might be accessible at the LHC run 2 (future LHC
run 3), allowing us to set additional and complementary constraints on the
parameter space associated with the mass and mixings of the Majorana neutrino.
In particular, bounds can be obtained on the mixing $|V_{\mu N}|^2 \sim
\mathcal{O}(10^{-5}-10^{-4})$ that are similar or better than the ones obtained
from heavy meson $\Delta L=2$ decays: $D_{(s)}^- \to \pi^+\mu^-\mu^-$ and $B^-
\to \pi^+\mu^-\mu^- \ (D^0\pi^+\mu^-\mu^-)$.


title: Incentives for Truthful Peer Grading

abstract:
  Peer grading systems work well only if users have incentives to grade
truthfully. An example of non-truthful grading, that we observed in classrooms,
consists in students assigning the maximum grade to all submissions. With a
naive grading scheme, such as averaging the assigned grades, all students would
receive the maximum grade. In this paper, we develop three grading schemes that
provide incentives for truthful peer grading. In the first scheme, the
instructor grades a fraction p of the submissions, and penalizes students whose
grade deviates from the instructor grade. We provide lower bounds on p to
ensure truthfulness, and conclude that these schemes work only for moderate
class sizes, up to a few hundred students. To overcome this limitation, we
propose a hierarchical extension of this supervised scheme, and we show that it
can handle classes of any size with bounded (and little) instructor work, and
is therefore applicable to Massive Open Online Courses (MOOCs). Finally, we
propose unsupervised incentive schemes, in which the student incentive is based
on statistical properties of the grade distribution, without any grading
required by the instructor. We show that the proposed unsupervised schemes
provide incentives to truthful grading, at the price of being possibly unfair
to individual students.


title:
Kinetic study of radiation-reaction-limited particle acceleration during
  the relaxation of unstable force-free equilibria

abstract:
  Many powerful and variable gamma-ray sources, including pulsar wind nebulae,
active galactic nuclei and gamma-ray bursts, seem capable of accelerating
particles to gamma-ray emitting energies efficiently over very short time
scales. These are likely due to rapid dissipation of electromagnetic energy in
a highly magnetized, relativistic plasma. In order to understand the generic
features of such processes, we have investigated simple models based on
relaxation of unstable force-free magnetostatic equilibria. In this work, we
make the connection between the corresponding plasma dynamics and the expected
radiation signal, using 2D particle-in-cell simulations that self-consistently
include synchrotron radiation reaction. We focus on the lowest order unstable
force-free equilibrium in a 2D periodic box. We find that rapid variability,
with modest apparent radiation efficiency as perceived by a fixed observer, can
be produced during the evolution of the instability. The "flares" are
accompanied by an increased polarization degree in the high energy band, with
rapid variation in the polarization angle. Furthermore, the separation between
the acceleration sites and the synchrotron radiation sites for the highest
energy particles facilitates acceleration beyond the synchrotron radiation
reaction limit. We also discuss the dynamical consequences of radiation
reaction, and some astrophysical applications of this model. Our current
simulations with numerically tractable parameters are not yet able to reproduce
the most dramatic gamma-ray flares, e.g., from Crab Nebula. Higher
magnetization studies are promising and will be carried out in the future.


title: Observational Constraints on Planet Nine: Cassini Range Observations

abstract:
  We significantly constrain the sky position, distance, and mass of a possible
additional, distant planet in the solar system by examining its influence on
the distance between Earth and the Cassini Spacecraft. Our preferred region is
approximately centered on (RA, Dec) = ($40\arcdeg$, $-15\arcdeg$), extending
approximately 20 degrees in all directions.


title: Adjoint twisted Alexander polynomials of genus one two-bridge knots

abstract:
  We give explicit formulas for the adjoint twisted Alexander polynomial and
the nonabelian Reidemeister torsion of genus one two-bridge knots.


title:
Cascade and locally dissipative realizations of linear quantum systems
  for pure Gaussian state covariance assignment

abstract:
  This paper presents two realizations of linear quantum systems for covariance
assignment corresponding to pure Gaussian states. The first one is called a
cascade realization; given any covariance matrix corresponding to a pure
Gaussian state, we can construct a cascaded quantum system generating that
state. The second one is called a locally dissipative realization; given a
covariance matrix corresponding to a pure Gaussian state, if it satisfies
certain conditions, we can construct a linear quantum system that has only
local interactions with its environment and achieves the assigned covariance
matrix. Both realizations are illustrated by examples from quantum optics.


title: A Primer on Cellular Network Analysis Using Stochastic Geometry

abstract:
  This tutorial is intended as an accessible but rigorous first reference for
someone interested in learning how to model and analyze cellular network
performance using stochastic geometry. In particular, we focus on computing the
signal-to-interference-plus-noise ratio (SINR) distribution, which can be
characterized by the coverage probability (the SINR CCDF) or the outage
probability (its CDF). We model base stations (BSs) in the network as a
realization of a homogeneous Poisson point process of density $\lambda$, and
compute the SINR for three main cases: the downlink, uplink, and finally the
multi-tier downlink, which is characterized by having $k$ tiers of BSs each
with a unique density $\lambda_i$ and transmit power $p_i$. These three
baseline results have been extensively extended to many different scenarios,
and we conclude with a brief summary of some of those extensions.


title: Desiree - a Refinement Calculus for Requirements Engineering

abstract:
  The requirements elicited from stakeholders suffer from various afflictions,
including informality, incompleteness, ambiguity, vagueness, inconsistencies,
and more. It is the task of requirements engineering (RE) processes to derive
from these an eligible (formal, complete enough, unambiguous, consistent,
measurable, satisfiable, modifiable and traceable) requirements specification
that truly captures stakeholder needs.
  We propose Desiree, a refinement calculus for systematically transforming
stakeholder require-ments into an eligible specification. The core of the
calculus is a rich set of requirements operators that iteratively transform
stakeholder requirements by strengthening or weakening them, thereby reducing
incompleteness, removing ambiguities and vagueness, eliminating unattainability
and conflicts, turning them into an eligible specification. The framework also
includes an ontology for modeling and classifying requirements, a
description-based language for representing requirements, as well as a
systematic method for applying the concepts and operators. In addition, we
define the semantics of the requirements concepts and operators, and develop a
graphical modeling tool in support of the entire framework.
  To evaluate our proposal, we have conducted a series of empirical
evaluations, including an ontology evaluation by classifying a large public
requirements set, a language evaluation by rewriting the large set of
requirements using our description-based syntax, a method evaluation through a
realistic case study, and an evaluation of the entire framework through three
controlled experiments. The results of our evaluations show that our ontology,
language, and method are adequate in capturing requirements in practice, and
offer strong evidence that with sufficient training, our framework indeed helps
people conduct more effective requirements engineering.


title: The resource theory under conditioned thermal operations

abstract:
  The "thermal operations" framework developed in past works is used to model
the evolution of microscopic quantum systems in contact with thermal baths.
Here we extend this model to bipartite devices with one part acting as a
control external to the system--bath setup. We define the operations of such
hybrid devices as conditioned thermal operations. We examine the resource under
these operations, which we call conditional athermality. In the quasiclassical
limit, we quantify this resource and find the conditions for its conversion
between different forms.


title: Estimating an NBA player's impact on his team's chances of winning

abstract:
  Traditional NBA player evaluation metrics are based on scoring differential
or some pace-adjusted linear combination of box score statistics like points,
rebounds, assists, etc. These measures treat performances with the outcome of
the game still in question (e.g. tie score with five minutes left) in exactly
the same way as they treat performances with the outcome virtually decided
(e.g. when one team leads by 30 points with one minute left). Because they
ignore the context in which players perform, these measures can result in
misleading estimates of how players help their teams win. We instead use a win
probability framework for evaluating the impact NBA players have on their
teams' chances of winning. We propose a Bayesian linear regression model to
estimate an individual player's impact, after controlling for the other players
on the court. We introduce several posterior summaries to derive rank-orderings
of players within their team and across the league. This allows us to identify
highly paid players with low impact relative to their teammates, as well as
players whose high impact is not captured by existing metrics.


title: Evoking complex neuronal networks by stimulating a single neuron

abstract:
  The dynamical responses of complex neuronal networks to external stimulus
injected on a \emph{single} neuron are investigated. Stimulating the
largest-degree neuron in the network, it is found that as the intensity of the
stimulus increases, the network will be transiting from the resting to firing
states and then restoring to the resting state, showing a bounded firing region
in the parameter space. Furthermore, it is found that as the coupling strength
decreases, the firing region is gradually expanded and, at the weak couplings,
separated into disconnected subregions. By a simplified network model, we
conduct a detail analysis on the bifurcation diagram of the network dynamics in
the two-dimensional parameter space spanned by stimulating intensity and
coupling strength, and, by introducing a new coefficient named effective
stimulus, explore the mechanisms of the modified firing region. It is revealed
that the coupling strength and stimulating intensity are equally important in
evoking the network, but with different mechanisms. Specifically, the effective
stimuli are \emph{shifted up} globally with the increase of the stimulating
intensity, while are \emph{drawn closer} with the increase of the coupling
strength. The dynamical responses of small-world and random complex networks to
external stimulus injected on the largest-degree neuron are also investigated,
which confirm the generality of the observed phenomena.


title:
Line-of-sight velocity and metallicity measurements of the Palomar 5
  tidal stream

abstract:
  We present Subaru/FOCAS and Keck/DEIMOS medium-resolution spectroscopy of a
tidally disrupting Milky Way (MW) globular cluster Palomar 5 and its tidal
stream. The observed fields are located to cover an angular extent of $\sim
17$\arcdeg along the stream, providing an opportunity to investigate a trend in
line-of-sight velocities ($V_{\rm los}$) along the stream, which is essential
to constrain its orbit and underlying gravitational potential of the Milky
Way's dark matter halo. A spectral fitting technique is applied to the observed
spectra to obtain stellar parameters and metallicities ([Fe/H]) of the target
stars. The 19 stars most likely belonging to the central Pal 5 cluster have a
mean $V_{\rm los}$ of $-58.1\pm 0.7$ km s$^{-1}$ and metallicity
[Fe/H]$=-1.35\pm 0.06$ dex, both of which are in good agreement with those
derived in previous high-resolution spectroscopic studies. Assuming that the
stream stars have the same [Fe/H] as the progenitor cluster, the derived [Fe/H]
and $V_{\rm los}$ values are used to estimate the possible $V_{\rm los}$ range
of the member stars at each location along the stream. Because of the heavy
contamination of the field MW stars, estimated $V_{\rm los}$ range depends on
prior assumptions about the stream's $V_{\rm los}$, which highlights the
importance of more definitely identifying the member stars using proper motion
and chemical abundances to obtain an unbiased information of $V_{\rm los}$ in
the outer part of the Pal 5 stream. The models for the gravitational potential
of the MW's dark matter halo that are compatible with the estimated $V_{\rm
los}$ range are discussed.


title:
Geodesic period integrals of eigenfunctions on Riemannian surfaces and
  the Gauss-Bonnet Theorem

abstract:
  We use the Gauss-Bonnet theorem and the triangle comparison theorems of Rauch
and Toponogov to show that on compact Riemann surfaces of negative curvature
period integrals of eigenfunctions $e_\lambda$ over geodesics go to zero at the
rate of $O((\log\lambda)^{-1/2})$ if $\lambda$ are their frequencies. As
discussed in \cite{CSPer}, no such result is possible in the constant curvature
case if the curvature is $\ge0$. Notwithstanding, we also show that these
bounds for period integrals are valid provided that integrals of the curvature
over all geodesic balls of radius $r\le 1$ are pinched from above by $-\delta
r^N$ for some fixed $N$ and $\delta>0$. This allows, for instance, the
curvature to be nonpositive and to vanish of finite order at a finite number of
isolated points. Naturally, the above results also hold for the appropriate
type of quasi-modes.


title:
Suppression of DC term in Fresnel digital holography by sequence
  subtraction of holograms

abstract:
  An experimental method for suppression of DC term in the reconstructed images
from Fresnel digital holograms is presented. In this method, two holograms for
the same object are captured sequentially and subtracted. Since these two
holograms are captured at different moments, they are slightly different from
each other for fluctuations of noises. The DC term is suppressed in the image
reconstructed from the subtraction hologram, while the two virtual and real
images are successfully reconstructed. This method can be potentially used for
the improvement of image quality reconstructed from Fresnel digital holograms.


title:
Finite current stationary states of random walks on one-dimensional
  lattices with aperiodic disorder

abstract:
  Stationary states of random walks with finite induced drift velocity on
one-dimensional lattices with aperiodic disorder are investigated by scaling
analysis. Three aperiodic sequences, the Thue-Morse (TM), the paperfolding
(PF), and the Rudin-Shapiro (RS) sequences, are used to construct the aperiodic
disorder. These are binary sequences, composed of two symbols A and B, and the
ratio of the number of As to that of Bs converges to unity in the infinite
sequence length limit, but their effects on diffusional behavior are different.
For the TM model, the stationary distribution is extended, as in the case
without current, and the drift velocity is independent of the system size. For
the PF model and the RS model, as the system size increases, the hierarchical
and fractal structure and the localized structure, respectively, are broken by
a finite current and changed to an extended distribution if the system size
becomes larger than a certain threshold value. Correspondingly, the drift
velocity is saturated in a large system while in a small system it decreases as
the system size increases.


title: Scalar-on-Image Regression via the Soft-Thresholded Gaussian Process

abstract:
  The focus of this work is on spatial variable selection for scalar-on-image
regression. We propose a new class of Bayesian nonparametric models,
soft-thresholded Gaussian processes and develop the efficient posterior
computation algorithms. Theoretically, soft-thresholded Gaussian processes
provide large prior support for the spatially varying coefficients that enjoy
piecewise smoothness, sparsity and continuity, characterizing the important
features of imaging data. Also, under some mild regularity conditions, the
soft-thresholded Gaussian process leads to the posterior consistency for both
parameter estimation and variable selection for scalar-on-image regression,
even when the number of true predictors is larger than the sample size. The
proposed method is illustrated via simulations, compared numerically with
existing alternatives and applied to Electroencephalography (EEG) study of
alcoholism.


title:
Application of the Second-Order Statistics for Estimation of the Pure
  Spectra of Individual Components from the Visible Hyperspectral Images of
  Their Mixture

abstract:
  The second-order statistics (SOS) can be applied in estimation of the pure
spectra of chemical components from the spectrum of their mixture, when SOS
seems to be good at estimation of spectral patterns, but their peak directions
are opposite in some cases. In this paper, one method for judgment of the peak
direction of the pure spectra was proposed, where the base line of the pure
spectra was drawn by using their histograms and the peak directions were chosen
so as to make all of the pure spectra located upwards over the base line.
Results of the SOS analysis on the visible hyperspectral images of the mixture
composed of two or three chemical components showed that the present method
offered the reasonable shape and direction of the pure spectra of its
components.


title:
3D Nanostructures on Ge/Si(100) Wetting Layers: Hillocks and Pre-Quantum
  Dots

abstract:
  The annealing of sub-critical Ge wetting layers (WL<3.5 ML) initiates the
formation of 3D nanostructures, whose shape and orientation is determined by
the WL thickness and thus directly related to the strain energy. The emergence
of these nanostructures, hillocks and pre-quantum dots, is studied by scanning
tunneling microscopy. A wetting layer deposited at 350 C is initially rough on
the nanometer length-scale and undergoes a progressive transformation and
smoothening until for T>460 C vacancy lines and the 2xn reconstruction are
observed. The metastable Ge wetting layer (WL) then collapses to form 3D
nanostructures whose morphology is controlled by the WL thickness: firstly,
hillocks, with a wedding cake-type structure where the step edges run parallel
to the <110> direction, are formed for thin wetting layers, while {105}-faceted
structures, so-called pre-quantum dots (p-QDs), are formed from thicker layers.
The wetting layer thickness and thus the misfit strain energy controls the type
of structure. The crossover thickness between hillock and p-QDs regime is
between 1.6 and 2.1 ML. The hillocks have larger lateral dimensions and volumes
than p-QDs, and the p-QDs are exceptionally small quantum dots with a lower
limit of 10 nm in width. Our work opens a new pathway to the control of
nanostructure morphology and size in the elastically strained Ge/Si system.
Keywords: Quantum dots, Stranski-Krastanov, epitaxy, strained layers, scanning
tunneling microscopy


title: Multibody minimum-energy trajectory with applications to protein folding

abstract:
  This work addresses the optimal control of multibody systems being actuated
with control forces in order to find a dynamically feasible minimum-energy
trajectory of the system. The optimal control problem and its constraints are
integrated in a discrete version of the equation of motion allowing the
minimization of system energy with respect to a discrete state and control
trajectory. The work is centred on a specific type of open-chain multibody
system, with strong local propensity, where the overall system kinematics is
described essentially by the torsion around the links that connect rigid
bodies. The coupling between the rigid body motion, and the optimal
conformation is described as an elastic band of replicas of the original system
with different conformations. The band forces are used to control system's
motion directly, reflecting the influence of the system energy field on its
conformation, using for that the Nudged-Elastic Band method. Here the equation
of motion of the multibody grid are solved by using the augmented Lagrangean
method. In this context, if a feasible minimum-energy trajectory of the
original system exists it is a stationary state of the extended system. This
approach is applied to the folding of a single chain protein.


title:
Privacy-Preserving Human Activity Recognition from Extreme Low
  Resolution

abstract:
  Privacy protection from surreptitious video recordings is an important
societal challenge. We desire a computer vision system (e.g., a robot) that can
recognize human activities and assist our daily life, yet ensure that it is not
recording video that may invade our privacy. This paper presents a fundamental
approach to address such contradicting objectives: human activity recognition
while only using extreme low-resolution (e.g., 16x12) anonymized videos. We
introduce the paradigm of inverse super resolution (ISR), the concept of
learning the optimal set of image transformations to generate multiple
low-resolution (LR) training videos from a single video. Our ISR learns
different types of sub-pixel transformations optimized for the activity
classification, allowing the classifier to best take advantage of existing
high-resolution videos (e.g., YouTube videos) by creating multiple LR training
videos tailored for the problem. We experimentally confirm that the paradigm of
inverse super resolution is able to benefit activity recognition from extreme
low-resolution videos.


title: Frontiers in Nuclear Astrophysics

abstract:
  The synthesis of nuclei in diverse cosmic scenarios is reviewed, with a
summary of the basic concepts involved before a discussion of the current
status in each case is made. We review the physics of the early universe, the
proton to neutron ratio influence in the observed helium abundance, reaction
networks, the formation of elements up to beryllium, the inhomogeneous Big Bang
model, and the Big Bang nucleosynthesis constraints on cosmological models.
Attention is paid to element production in stars, together with the details of
the pp chain, the pp reaction, $^3$He formation and destruction, electron
capture on $^7$Be, the importance of $^8$B formation and its relation to solar
neutrinos, and neutrino oscillations. Nucleosynthesis in massive stars is also
reviewed, with focus on the CNO cycle and its hot companion cycle, the
rp-process, triple-$\alpha$ capture, and red giants and AGB stars. The stellar
burning of carbon, neon, oxygen, and silicon is presented in a separate
section, as well as the slow and rapid nucleon capture processes and the
importance of medium modifications due to electrons also for pycnonuclear
reactions. The nucleosynthesis in cataclysmic events such as in novae, X-ray
bursters and in core-collapse supernovae, the role of neutrinos, and the
supernova radioactivity and light-curve is further discussed, as well as the
structure of neutron stars and its equation of state. A brief review of the
element composition found in cosmic rays is made in the end.


title:
Black-body radiation shift of atomic energy-levels:The $ (Z
  \alpha)^2\alpha T^2/m $ correction

abstract:
  The next-to-leading order black-body radiation(BBR) shift to atomic
energy-levels, namely $ (Z\alpha)^2\alpha T^2/m $ correction, was studied by
using the nonrelativistic quantum electrodynamics(NRQED). We also estimate the
one-loop contribution of quadrupole and the two-loop contributions of BBR-shift
of the thermal(real) photon. These corrections have not been investigated
before. The order of magnitude BBR-shift indicates the one-loop contribution of
quadrupole is stronger than the previous result. And the two-loop contribution
of BBR-shift of the thermal(real) photon is tiny, but this next-to-leading
order BBR-shift may be as significant as the leading order in the
multi-electron atoms or cold ones.


title:
From sample to knowledge: Towards an integrated approach for
  neuroscience discovery

abstract:
  Imaging methods used in modern neuroscience experiments are quickly producing
large amounts of data capable of providing increasing amounts of knowledge
about neuroanatomy and function. A great deal of information in these datasets
is relatively unexplored and untapped. One of the bottlenecks in knowledge
extraction is that often there is no feedback loop between the knowledge
produced (e.g., graph, density estimate, or other statistic) and the earlier
stages of the pipeline (e.g., acquisition). We thus advocate for the
development of sample-to-knowledge discovery pipelines that one can use to
optimize acquisition and processing steps with a particular end goal (i.e.,
piece of knowledge) in mind. We therefore propose that optimization takes place
not just within each processing stage but also between adjacent (and
non-adjacent) steps of the pipeline. Furthermore, we explore the existing
categories of knowledge representation and models to motivate the types of
experiments and analysis needed to achieve the ultimate goal. To illustrate
this approach, we provide an experimental paradigm to answer questions about
large-scale synaptic distributions through a multimodal approach combining
X-ray microtomography and electron microscopy.


title: Efficient Classification of Multi-Labelled Text Streams by Clashing

abstract:
  We present a method for the classification of multi-labelled text documents
explicitly designed for data stream applications that require to process a
virtually infinite sequence of data using constant memory and constant
processing time. Our method is composed of an online procedure used to
efficiently map text into a low-dimensional feature space and a partition of
this space into a set of regions for which the system extracts and keeps
statistics used to predict multi-label text annotations. Documents are fed into
the system as a sequence of words, mapped to a region of the partition, and
annotated using the statistics computed from the labelled instances colliding
in the same region. This approach is referred to as clashing. We illustrate the
method in real-world text data, comparing the results with those obtained using
other text classifiers. In addition, we provide an analysis about the effect of
the representation space dimensionality on the predictive performance of the
system. Our results show that the online embedding indeed approximates the
geometry of the full corpus-wise TF and TF-IDF space. The model obtains
competitive F measures with respect to the most accurate methods, using
significantly fewer computational resources. In addition, the method achieves a
higher macro-averaged F measure than methods with similar running time.
Furthermore, the system is able to learn faster than the other methods from
partially labelled streams.


title:
Hydrogenation of Penta-Graphene Leads to Unexpected Large Improvement in
  Thermal Conductivity

abstract:
  Penta-graphene (PG) has been identified as a novel 2D material with an
intrinsic bandgap, which makes it especially promising for electronics
applications. In this work, we use first-principles lattice dynamics and
iterative solution of the phonon Boltzmann transport equation (BTE) to
determine the thermal conductivity of PG and its more stable derivative -
hydrogenated penta-graphene (HPG). As a comparison, we also studied the effect
of hydrogenation on graphene thermal conductivity. In contrast to hydrogenation
of graphene, which leads to a dramatic decrease in thermal conductivity (from
3590 to 1328 W/mK - a 63% reduction), HPG shows a notable increase in thermal
conductivity (615 W/mK), which is 76% higher than that of PG (350 W/mK). The
high thermal conductivity of HPG makes it more thermally conductive than most
other semi-conducting 2D materials, such as the transition metal chalcogenides.
Our detailed analyses show that the primary reason for the counter-intuitive
hydrogenation-induced thermal conductivity enhancement is the weaker bond
anharmonicity in HPG than PG. This leads to weaker phonon scattering after
hydrogenation, despite the increase in the phonon scattering phase space. The
high thermal conductivity of HPG may inspire intensive research around HPG and
other derivatives of PG as potential materials for future nanoelectronic
devices. The fundamental physics understood from this study may open up a new
strategy to engineer thermal transport properties of other 2D materials by
controlling bond anharmonicity via functionalization.


title:
Effects of realistic tensor force on nuclear quadrupole deformation
  around the shore of the island of inversion

abstract:
  The M3Y-type semi-realistic interaction is applied to deformed nuclei for the
first time. The constrained Hartree-Fock calculations assuming axial symmetry
are implemented for the $N=20$ isotones $^{30}$Ne, $^{32}$Mg, $^{34}$Si and the
$N=28$ isotones $^{40}$Mg, $^{42}$Si, $^{44}$S with the M3Y-P6 interaction. The
results match the experimental data well. Effects of the realistic tensor force
on the nuclear quadrupole deformation are investigated in relation to the loss
of the $N=20$ and $28$ magic numbers. The tensor force is confirmed to favor
the deformation for the $N=28$ nuclei owing to the closure of the $jj$-shell
(\textit{i.e.}, $n0f_{7/2}$), while favoring the sphericity for the $N=20$
nuclei owing to the $\ell s$-closure of $N=20$.


title:
Numerical solution to the Bloch equations, paramagnetic solutions under
  wideband continuous radio frequency irradiation in a pulsed magnetic field

abstract:
  A novel NMR experimental scheme, called wide band continuous wave NMR, is
presented in this article.


title: Distributed Index Coding

abstract:
  In this paper, we study the capacity region of the general distributed index
coding. In contrast to the traditional centralized index coding where a single
server contains all $n$ messages requested by the receivers, in the distributed
index coding there are $2^n-1$ servers, each containing a unique non-empty
subset $J$ of the messages and each is connected to all receivers via a
noiseless independent broadcast link with an arbitrary capacity $C_J \ge 0$.
First, we generalize the existing polymatroidal outer bound on the capacity
region of the centralized problem to the distributed case. Next, building upon
the existing centralized composite coding scheme, we propose three distributed
composite coding schemes and derive the corresponding inner bounds on the
capacity region. We present a number of interesting numerical examples, which
highlight the subtleties and challenges of dealing with the distributed index
coding, even for very small problem sizes of $n=3$ and $n=4$.


title:
A Statistical Study of the Average Iron Charge Distributions inside
  Magnetic Clouds for Solar Cycle 23

abstract:
  Magnetic clouds (MCs) are the interplanetary counterpart of coronal magnetic
flux ropes. They can provide valuable information to reveal the flux rope
characteristics at their eruption stage in the corona, which are unable to be
explored in situ at present. In this paper, we make a comprehensive survey of
the average iron charge state (<Q>Fe) distributions inside 96 MCs for solar
cycle 23 using ACE (Advanced Composition Explorer) data. As the <Q>Fe in the
solar wind are typically around 9+ to 11+, the Fe charge state is defined as
high when the <Q>Fe is larger than 12+, which implies the existence of a
considerable amount of Fe ions with high charge states (e.g., \geq 16+). The
statistical results show that the <Q>Fe distributions of 92 (~ 96%) MCs can be
classified into four groups with different characteristics. In group A (11
MCs), the <Q>Fe shows a bimodal distribution with both peaks higher than 12+.
Group B (4 MCs) presents a unimodal distribution of <Q>Fe with its peak higher
than 12+. In groups C (29 MCs) and D (48 MCs), the <Q>Fe remains higher and
lower than 12+ throughout ACE passage through the MC, respectively. Possible
explanations to these distributions are discussed.


title:
Shifted genus expanded $\cal{W}_{\infty}$ algebra and shifted Hurwtiz
  numbers

abstract:
  We construct the shifted genus expanded $\cal{W}_{\infty}$ algebra, which is
isomorphic to the central subalgebra $\cal{A}_{\infty}$ of infinite symmetric
group algebra and to the shifted Schur symmetrical function algebra
$\Lambda^\ast$ defined by A. Y. Okounkov and G. I. Olshanskii. As an
application, we get some differential equations for the generating functions of
the shifted Hurwitz numbers, thus we can express the generating functions in
terms of the shifted genus expanded cut-and-join operators.


title: The spin dependent odderon in the diquark model

abstract:
  In this short note, we report a di-quark model calculation for the spin
dependent odderon and demonstrate that the asymmetrical color source
distribution in the transverse plane of a transversely polarized hadron plays
an essential role in yielding the spin dependent odderon. This calculation
confirms the earlier finding that the spin dependent odderon is closely related
to the parton orbital angular momentum.


title: The ultimate physical limits to reversibility

abstract:
  We argue that if, in order to reverse an object's dynamics, we need to be
able to keep track of it with enough precision, then there is an upper bound on
the size of the object whose dynamics we can reverse - even using all the
available resources of the universe. Using a bound from the holographic
principle and treating the universe as the ultimate quantum computer, we show
that if we want to reverse the dynamics of an object which has evolved since
the beginning of time, its radius cannot exceed ten microns. If this
computation is performed irreversibly, the object must be much smaller. This
places a lower bound on the size of the smallest possible quantum measurement
device.


title: Disfluency Detection using a Bidirectional LSTM

abstract:
  We introduce a new approach for disfluency detection using a Bidirectional
Long-Short Term Memory neural network (BLSTM). In addition to the word
sequence, the model takes as input pattern match features that were developed
to reduce sensitivity to vocabulary size in training, which lead to improved
performance over the word sequence alone. The BLSTM takes advantage of explicit
repair states in addition to the standard reparandum states. The final output
leverages integer linear programming to incorporate constraints of disfluency
structure. In experiments on the Switchboard corpus, the model achieves
state-of-the-art performance for both the standard disfluency detection task
and the correction detection task. Analysis shows that the model has better
detection of non-repetition disfluencies, which tend to be much harder to
detect.


title: An Analysis of General Fuzzy Logic and Fuzzy Reasoning Method

abstract:
  In this article, we describe the fuzzy logic, fuzzy language and algorithms
as the basis of fuzzy reasoning, one of the intelligent information processing
method, and then describe the general fuzzy reasoning method.


title:
Automatic Parallelization: Executing Sequential Programs on a Task-Based
  Parallel Runtime

abstract:
  There are billions of lines of sequential code inside nowadays' software
which do not benefit from the parallelism available in modern multicore
architectures. Automatically parallelizing sequential code, to promote an
efficient use of the available parallelism, has been a research goal for some
time now. This work proposes a new approach for achieving such goal. We created
a new parallelizing compiler that analyses the read and write instructions, and
control-flow modifications in programs to identify a set of dependencies
between the instructions in the program. Afterwards, the compiler, based on the
generated dependencies graph, rewrites and organizes the program in a
task-oriented structure. Parallel tasks are composed by instructions that
cannot be executed in parallel. A work-stealing-based parallel runtime is
responsible for scheduling and managing the granularity of the generated tasks.
Furthermore, a compile-time granularity control mechanism also avoids creating
unnecessary data-structures. This work focuses on the Java language, but the
techniques are general enough to be applied to other programming languages. We
have evaluated our approach on 8 benchmark programs against OoOJava, achieving
higher speedups. In some cases, values were close to those of a manual
parallelization. The resulting parallel code also has the advantage of being
readable and easily configured to improve further its performance manually.


title: Recommendations for web service composition by mining usage logs

abstract:
  Web service composition has been one of the most researched topics of the
past decade. Novel methods of web service composition are being proposed in the
literature include Semantics-based composition, WSDLbased composition. Although
these methods provide promising results for composition, search and discovery
of web service based on QoS parameter of network and semantics or ontology
associated with WSDL, they do not address composition based on usage of web
service. Web Service usage logs capture time series data of web service
invocation by business objects, which innately captures patterns or workflows
associated with business operations. Web service composition based on such
patterns and workflows can greatly streamline the business operations. In this
research work, we try to explore and implement methods of mining web service
usage logs. Main objectives include Identifying usage association of services.
Linking one service invocation with other, Evaluation of the causal
relationship between associations of services


title: Group-like expansions and invariants of string links

abstract:
  In this article, we define and study the total Milnor invariant and the
infinitesimal Morita-Milnor homomorphism as punctured disk analogues of the
total Johnson map and the infinitesimal Morita homomorphism studied by Kawazumi
and Massuyeau in the case of surface of positive genus with one boundary
component.


title: DIRA: A Framework Of Data Integration Using Data Quality

abstract:
  Data integration is the process of collecting data from different data
sources and providing user with unified view of answers that meet his
requirements. The quality of query answers can be improved by identifying the
quality of data sources according to some quality measures and retrieving data
from only significant ones. Query answers that returned from significant data
sources can be ranked according to quality requirements that specified in user
query and proposed queries types to return only top-k query answers. In this
paper, Data integration framework called data integration to return ranked
alternatives (DIRA) will be introduced depending on data quality assessment
module that will use data sources quality to choose the significant ones and
ranking algorithm to return top-k query answers according to different queries
types.


title:
DSRS: Estimation and Forecasting of Journal Influence in the Science and
  Technology Domain via a Lightweight Quantitative Approach

abstract:
  The evaluation of journals based on their influence is of interest for
numerous reasons. Various methods of computing a score have been proposed for
measuring the scientific influence of scholarly journals. Typically the
computation of any of these scores involves compiling the citation information
pertaining to the journal under consideration. This involves significant
overhead since the article citation information of not only the journal under
consideration but also that of other journals for the recent few years need to
be stored. Our work is motivated by the idea of developing a computationally
lightweight approach that does not require any data storage, yet yields a score
which is useful for measuring the importance of journals. In this paper, a
regression analysis based method is proposed to calculate Journal Influence
Score. Proposed model is validated using historical data from the SCImago
portal. The results show that the error is small between rankings obtained
using the proposed method and the SCImago Journal Rank, thus proving that the
proposed approach is a feasible and effective method of calculating scientific
impact of journals.


title:
Integrals of logarithmic forms on semi-algebraic sets and a generalized
  Cauchy formula Part II: generalized Cauchy formula

abstract:
  This paper is the continuation of the paper arXiv:1509.06950, which is Part I
under the same title. In this paper, we prove a generalized Cauchy formula for
the integrals of logarithmic forms on products of projective lines, and give an
application to the construction of Hodge realization of mixed Tate motives.


title:
PSNR and Jitter Analysis of Routing Protocols for Video Streaming in
  Sparse MANET Networks, using NS2 and the Evalvid Framework

abstract:
  Advances in multimedia and ad-hoc networking have urged a wealth of research
in multimedia delivery over ad-hoc networks. This comes as no surprise, as
those networks are versatile and beneficial to a plethora of applications where
the use of fully wired network has proved intricate if not impossible, such as
prompt formation of networks during conferences, disaster relief in case of
flood and earthquake, and also in war activities. It this paper, we aim to
investigate the combined impact of network sparsity and network node density on
the Peak Signal Noise to Ratio (PSNR) and jitter performance of proactive and
reactive routing protocols in ad-hoc networks. We also shed light onto the
combined effect of mobility and sparsity on the performance of these protocols.
We validate our results through the use of an integrated Simulator-Evaluator
environment consisting of the Network Simulator NS2, and the Video Evaluation
Framework Evalvid.


title:
Distributional limits of positive, ergodic stationary processes and
  infinite ergodic transformations

abstract:
  In this note we identify the distributional limits of non-negative, ergodic
stationary processes, showing that all are possible.
  Consequences for infinite ergodic theory are also explored and new examples
of distributionally stable- and $\alpha$-rationally ergodic tranformations are
presented.


title: Offline software for the DAMPE experiment

abstract:
  A software system has been developed for the DArk Matter Particle Explorer
(DAMPE) mission, a satellite-based experiment. The DAMPE software is mainly
written in C++ and steered using Python script. This article presents an
overview of the DAMPE offline software, including the major architecture design
and specific implementation for simulation, calibration and reconstruction. The
whole system has been successfully applied to DAMPE data analysis, based on
which some results from simulation and beam test experiments are obtained and
presented.


title:
Algorithms and Identities for B$\acute{e}$zier curves via Post Quantum
  Blossom

abstract:
  In this paper, a new analogue of blossom based on post quantum calculus is
introduced. The post quantum blossom has been adapted for developing identities
and algorithms for Bernstein bases and B$\acute{e}$zier curves. By applying the
post quantum blossom, various new identities and formulae expressing the
monomials in terms of the post quantun Bernstein basis functions and a post
quantun variant of Marsden's identity are investigated. For each post quantum
B$\acute{e}$zier curves of degree $m,$ a collection of $m!$ new, affine
invariant, recursive evaluation algorithms are derived.


title: Leveraging Network Dynamics for Improved Link Prediction

abstract:
  The aim of link prediction is to forecast connections that are most likely to
occur in the future, based on examples of previously observed links. A key
insight is that it is useful to explicitly model network dynamics, how
frequently links are created or destroyed when doing link prediction. In this
paper, we introduce a new supervised link prediction framework, RPM (Rate
Prediction Model). In addition to network similarity measures, RPM uses the
predicted rate of link modifications, modeled using time series data; it is
implemented in Spark-ML and trained with the original link distribution, rather
than a small balanced subset. We compare the use of this network dynamics model
to directly creating time series of network similarity measures. Our
experiments show that RPM, which leverages predicted rates, outperforms the use
of network similarity measures, either individually or within a time series.


title: Fuzzy Logic Trajectory Tracking Controller for a Tanker

abstract:
  This paper proposes a fuzzy logic controller for design of autopilot of a
ship. Triangular membership functions have been use for fuzzification and the
centroid method for defuzzification. A nonlinear mathematical model of an oil
tanker has been considered whose parameters vary with the depth of water. The
performance of proposed controller has been tested under both course changing
and trajectory keeping mode of operations. It has been demonstrated that the
performance is robust in shallow as well as deep waters.


title:
Design of Three-Phase Hybrid Active Power Filter for Compensating the
  Harmonic Currents of Three-Phase System

abstract:
  Power quality standards (IEEE-519) require to limit the total harmonic
distortion within satisfactory range caused by power electronic based devices.
Our work deals with the design of hybrid active filter to reduce current
perturbations produced by power electronics based devices. The Instantaneous
Active and Reactive Power Method (pq) is used to perform the identification of
disturbing currents. The pq algorithm creates a reference current, whereas,
this reference current is tracked by the current of the voltage source
converter. The currents of the voltage source converter are controlled by
hysteresis controller. Simulation results showed that the hybrid active filter
can compensate the harmonic currents effectively and improve power quality.


title: One-Level density for holomorphic cusp forms of arbitrary level

abstract:
  In 2000 Iwaniec, Luo, and Sarnak proved for certain families of $L$-functions
associated to holomorphic newforms of square-free level that, under the
Generalized Riemann Hypothesis, as the conductors tend to infinity the
one-level density of their zeros matches the one-level density of eigenvalues
of large random matrices from certain classical compact groups in the
appropriate scaling limit. We remove the square-free restriction by obtaining a
trace formula for arbitrary level by using a basis developed by Blomer and
Mili\'cevi\'c, which is of use for other problems as well.


title:
Geometric Feature-Based Facial Expression Recognition in Image Sequences
  Using Multi-Class AdaBoost and Support Vector Machines

abstract:
  Facial expressions are widely used in the behavioral interpretation of
emotions, cognitive science, and social interactions. In this paper, we present
a novel method for fully automatic facial expression recognition in facial
image sequences. As the facial expression evolves over time facial landmarks
are automatically tracked in consecutive video frames, using displacements
based on elastic bunch graph matching displacement estimation. Feature vectors
from individual landmarks, as well as pairs of landmarks tracking results are
extracted, and normalized, with respect to the first frame in the sequence. The
prototypical expression sequence for each class of facial expression is formed,
by taking the median of the landmark tracking results from the training facial
expression sequences. Multi-class AdaBoost with dynamic time warping similarity
distance between the feature vector of input facial expression and prototypical
facial expression, is used as a weak classifier to select the subset of
discriminative feature vectors. Finally, two methods for facial expression
recognition are presented, either by using multi-class AdaBoost with dynamic
time warping, or by using support vector machine on the boosted feature
vectors. The results on the Cohn-Kanade (CK+) facial expression database show a
recognition accuracy of 95.17% and 97.35% using multi-class AdaBoost and
support vector machines, respectively.


title: Fast Failure Recovery for Main-Memory DBMSs on Multicores

abstract:
  Main-memory database management systems (DBMS) can achieve excellent
performance when processing massive volume of on-line transactions on modern
multi-core machines. But existing durability schemes, namely, tuple-level and
transaction-level logging-and-recovery mechanisms, either degrade the
performance of transaction processing or slow down the process of failure
recovery. In this paper, we show that, by exploiting application semantics, it
is possible to achieve speedy failure recovery without introducing any costly
logging overhead to the execution of concurrent transactions. We propose
PACMAN, a parallel database recovery mechanism that is specifically designed
for lightweight, coarse-grained transaction-level logging. PACMAN leverages a
combination of static and dynamic analyses to parallelize the log recovery: at
compile time, PACMAN decomposes stored procedures by carefully analyzing
dependencies within and across programs; at recovery time, PACMAN exploits the
availability of the runtime parameter values to attain an execution schedule
with a high degree of parallelism. As such, recovery performance is remarkably
increased. We evaluated PACMAN in a fully-fledged main-memory DBMS running on a
40-core machine. Compared to several state-of-the-art database recovery
mechanisms, PACMAN can significantly reduce recovery time without compromising
the efficiency of transaction processing.


title: Recurrent Attentional Networks for Saliency Detection

abstract:
  Convolutional-deconvolution networks can be adopted to perform end-to-end
saliency detection. But, they do not work well with objects of multiple scales.
To overcome such a limitation, in this work, we propose a recurrent attentional
convolutional-deconvolution network (RACDNN). Using spatial transformer and
recurrent network units, RACDNN is able to iteratively attend to selected image
sub-regions to perform saliency refinement progressively. Besides tackling the
scale problem, RACDNN can also learn context-aware features from past
iterations to enhance saliency refinement in future iterations. Experiments on
several challenging saliency detection datasets validate the effectiveness of
RACDNN, and show that RACDNN outperforms state-of-the-art saliency detection
methods.


title: Efficient Parallel Algorithms for k-Center Clustering

abstract:
  The k-center problem is one of several classic NP-hard clustering questions.
For contemporary massive data sets, RAM-based algorithms become impractical.
And although there exist good sequential algorithms for k-center, they are not
easily parallelizable.
  In this paper, we design and implement parallel approximation algorithms for
this problem. We observe that Gonzalez's greedy algorithm can be efficiently
parallelized in several MapReduce rounds; in practice, we find that two rounds
are sufficient, leading to a 4-approximation. We contrast this with an existing
parallel algorithm for k-center that runs in a constant number of rounds, and
offers a 10-approximation. In depth runtime analysis reveals that this scheme
is often slow, and that its sampling procedure only runs if k is sufficiently
small, relative to the input size. To trade off runtime for approximation
guarantee, we parameterize this sampling algorithm, and find in our experiments
that the algorithm is not only faster, but sometimes more effective. Yet the
parallel version of Gonzalez is about 100 times faster than both its sequential
version and the parallel sampling algorithm, barely compromising solution
quality.


title:
Phase transition and entropy inequality of noncommutative black holes in
  a new extended phase space

abstract:
  We analyze the thermodynamics of the noncommutative high-dimensional
Schwarzschild-Tangherlini AdS black hole with the non-Gaussian smeared matter
distribution by regarding a noncommutative parameter as an independent
thermodynamic variable named as the noncommutative pressure. In the new
extended phase space that includes this noncommutative pressure and its
conjugate variable, we reveal that the noncommutative pressure and the original
thermodynamic pressure related to the negative cosmological constant make the
opposite effects in the phase transition of the noncommutative black hole, i.e.
the former dominates the UV regime while the latter does the IR regime,
respectively. In addition, by means of the reverse isoperimetric inequality, we
indicate that only the black hole with the Gaussian smeared matter distribution
holds the maximum entropy for a given thermodynamic volume among the
noncommutative black holes with various matter distributions.


title: Perturbations of crossed product C*-algebras by amenable groups

abstract:
  We study uniform perturbations of crossed product C$^*$-algebras by amenable
groups. Given a unital inclusion of C$^*$-algebras $C\subseteq D$ and
sufficiently close separable intermediate C$^*$-subalgebras $A$, $B$ for this
inclusion with a conditional expectation from $D$ onto $B$, if $A=C\rtimes G$
with $G$ discrete amenable, then $A$ and $B$ are isomorphic. Furthermore, if
$C\subseteq D$ is irreducible, then $A=B$.


title: Nonexistence of large nuclei in the liquid drop model

abstract:
  We give a simplified proof of the nonexistence of large nuclei in the liquid
drop model and provide an explicit bound. Our bound is within a factor of 2.3
of the conjectured value and seems to be the first quantitative result.


title: Defect Tolerant Monolayer Transition Metal Dichalcogenides

abstract:
  Localized electronic states formed inside the band gap of a semiconductor due
to crystal defects can be detrimental to the material's optoelectronic
properties. Semiconductors with lower tendency to form defect induced deep gap
states are termed defect tolerant. Here we provide a systematic first
principles investigation of defect tolerance in 29 monolayer transition metal
dichalcogenides (TMDs) of interest for nanoscale optoelectronics. We find that
the TMDs based on group VI and X metals form deep gap states upon creation of a
chalcogen (S, Se, Te) vacancy while the TMDs based on group IV metals form only
shallow defect levels and are thus predicted to be defect tolerant.
Interestingly, all the defect sensitive TMDs have valence and conduction bands
with very similar orbital composition. This indicates a bonding/anti-bonding
nature of the gap which in turn suggests that dangling bonds will fall inside
the gap. These ideas are made quantitative by introducing a descriptor that
measures the degree of similarity of the conduction and valence band manifolds.
Finally, the study is generalized to non-polar nanoribbons of the TMDs where we
find that only the defect sensitive materials form edge states within the band
gap.


title: Precision Ephemerides For Gravitational Wave Searches: II. Cyg X-2

abstract:
  Accreting neutron stars in low-mass X-ray binaries (LMXBs) are candidate
high-frequency persistent gravitational wave sources. These may be detectable
with next generation interferometers such as Advanced LIGO/VIRGO within this
decade. However, the search sensitivity is expected to be limited principally
by the uncertainty in the binary system parameters. We combine new optical
spectroscopy of Cyg X-2 obtained with the Liverpool Telescope (LT) with
available historical radial velocity data, which gives us improved orbital
parameter uncertainties based on a 44-year baseline. We obtained an improvement
of a factor of 2.6 in the orbital period precision and a factor of 2 in the
epoch of inferior conjunction T_0. The updated orbital parameters imply a mass
function of 0.65 +/- 0.01 M_sun, leading to a primary mass (M_1) of 1.67 +/-
0.22 M_sun (for i=62.5 +/- 4 deg). In addition, we estimate the likely orbital
parameter precision through to the expected Advanced LIGO and VIRGO detector
observing period and quantify the corresponding improvement in sensitivity via
the required number of templates.


title: Hippo: A Fast, yet Scalable, Database Indexing Approach

abstract:
  Even though existing database indexes (e.g., B+-Tree) speed up the query
execution, they suffer from two main drawbacks: (1) A database index usually
yields 5% to 15% additional storage overhead which results in non-ignorable
dollar cost in big data scenarios especially when deployed on modern storage
devices like Solid State Disk (SSD) or Non-Volatile Memory (NVM). (2)
Maintaining a database index incurs high latency because the DBMS has to find
and update those index pages affected by the underlying table changes. This
paper proposes Hippo a fast, yet scalable, database indexing approach. Hippo
only stores the pointers of disk pages along with light weight histogram-based
summaries. The proposed structure significantly shrinks index storage and
maintenance overhead without compromising much on query execution performance.
Experiments, based on real Hippo implementation inside PostgreSQL 9.5, using
the TPC-H benchmark show that Hippo achieves up to two orders of magnitude less
storage space and up to three orders of magnitude less maintenance overhead
than traditional database indexes, i.e., B+-Tree. Furthermore, the experiments
also show that Hippo achieves comparable query execution performance to that of
the B+-Tree for various selectivity factors.


title: Growing Wikipedia Across Languages via Recommendation

abstract:
  The different Wikipedia language editions vary dramatically in how
comprehensive they are. As a result, most language editions contain only a
small fraction of the sum of information that exists across all Wikipedias. In
this paper, we present an approach to filling gaps in article coverage across
different Wikipedia editions. Our main contribution is an end-to-end system for
recommending articles for creation that exist in one language but are missing
in another. The system involves identifying missing articles, ranking the
missing articles according to their importance, and recommending important
missing articles to editors based on their interests. We empirically validate
our models in a controlled experiment involving 12,000 French Wikipedia
editors. We find that personalizing recommendations increases editor engagement
by a factor of two. Moreover, recommending articles increases their chance of
being created by a factor of 3.2. Finally, articles created as a result of our
recommendations are of comparable quality to organically created articles.
Overall, our system leads to more engaged editors and faster growth of
Wikipedia with no effect on its quality.


title: The Network Behind the Cosmic Web

abstract:
  The concept of the cosmic web, viewing the Universe as a set of discrete
galaxies held together by gravity, is deeply engrained in cosmology. Yet,
little is known about the most effective construction and the characteristics
of the underlying network. Here we explore seven network construction
algorithms that use various galaxy properties, from their location, to their
size and relative velocity, to assign a network to galaxy distributions
provided by both simulations and observations. We find that a model relying
only on spatial proximity offers the best correlations between the physical
characteristics of the connected galaxies. We show that the properties of the
networks generated from simulations and observations are identical, unveiling a
deep universality of the cosmic web.


title:
Neutrino Induced 4He Break-up Reaction -- Application of the Maximum
  Entropy Method in Calculating Nuclear Strength Function

abstract:
  The maximum entropy method is examined as a new tool for solving the
ill-posed inversion problem involved in the Lorentz integral transformation
(LIT) method. As an example, we apply the method to the spin-dipole strength
function of 4He. We show that the method can be successfully used for inversion
of LIT, provided the LIT function is available with a sufficient accuracy.


title: Bialgebra and Hopf algebra structures on free Rota-Baxter algebras

abstract:
  In this paper, we obtain a canonical factorization of basis elements in free
Rota-Baxter algebras built on bracketed words. This canonical factorization is
applied to give a coalgebra structure on the free Rota-Baxter algebras.
Together with the Rota-Baxter algebra multiplication, this coproduct gives a
bialgebra structure on the free Rota-Baxter algebra of rooted forests. When the
weight of the Rota-Baxter algebra is zero, we further obtain a Hopf algebra
structure.


title: CRAFT Objects from Images

abstract:
  Object detection is a fundamental problem in image understanding. One popular
solution is the R-CNN framework and its fast versions. They decompose the
object detection problem into two cascaded easier tasks: 1) generating object
proposals from images, 2) classifying proposals into various object categories.
Despite that we are handling with two relatively easier tasks, they are not
solved perfectly and there's still room for improvement. In this paper, we push
the "divide and conquer" solution even further by dividing each task into two
sub-tasks. We call the proposed method "CRAFT" (Cascade Region-proposal-network
And FasT-rcnn), which tackles each task with a carefully designed network
cascade. We show that the cascade structure helps in both tasks: in proposal
generation, it provides more compact and better localized object proposals; in
object classification, it reduces false positives (mainly between ambiguous
categories) by capturing both inter- and intra-category variances. CRAFT
achieves consistent and considerable improvement over the state-of-the-art on
object detection benchmarks like PASCAL VOC 07/12 and ILSVRC.


title: Disease dynamics on a network game: a little empathy goes a long way

abstract:
  Individuals change their behavior during an epidemic in response to whether
they and/or those they interact with are healthy or sick. Healthy individuals
are concerned about contracting a disease from their sick contacts and may
utilize protective measures. Sick individuals may be concerned with spreading
the disease to their healthy contacts and adopt preemptive measures. Yet, in
practice both protective and preemptive changes in behavior come with costs.
This paper proposes a stochastic network disease game model that captures the
self-interests of individuals during the spread of a
susceptible-infected-susceptible (SIS) disease where individuals react to
current risk of disease spread, and their reactions together with the current
state of the disease stochastically determine the next stage of the disease. We
show that there is a critical level of concern, i.e., empathy, by the sick
individuals above which disease is eradicated fast. Furthermore, we find that
if the network and disease parameters are above the epidemic threshold, the
risk averse behavior by the healthy individuals cannot eradicate the disease
without the preemptive measures of the sick individuals. This imbalance in the
role played by the response of the infected versus the susceptible individuals
in disease eradication affords critical policy insights.


title:
Four dimensional static and related critical spaces with harmonic
  curvature

abstract:
  In this article we study any 4-dimensional Riemannian manifold $(M,g)$ with
harmonic curvature which admits a smooth nonzero solution $f$ to the following
equation \begin{eqnarray} \label{0002bx} \nabla df = f(Rc -\frac{R}{n-1} g) + x
Rc+ y(R) g. \end{eqnarray} where $Rc$ is the Ricci tensor of $g$, $x$ is a
constant and $y(R)$ a function of the scalar curvature $R$. We show that a
neighborhood of any point in some open dense subset of $M$ is locally isometric
to one of the following five types; {\rm (i)} $ \mathbb{S}^2(\frac{R}{6})
\times \mathbb{S}^2(\frac{R}{3})$ with $R>0$, {\rm (ii)} $
\mathbb{H}^2(\frac{R}{6}) \times \mathbb{H}^2(\frac{R}{3}) $ with $R<0$, where
$\mathbb{S}^2(k) $ and $\mathbb{H}^2(k) $ are the two-dimensional Riemannian
manifold with constant sectional curvature $k>0$ and $k<0$, respectively, {\rm
(iii)} the static spaces in Example 3 below, {\rm (iv)} conformally flat static
spaces described in Kobayashi's \cite{Ko}, and {\rm (v)} a Ricci flat metric.
  We then get a number of Corollaries, including the classification of the
following four dimensional spaces with harmonic curvature; static spaces,
Miao-Tam critical metrics and $V$-static spaces.
  The proof is based on the argument from a preceding study of gradient Ricci
solitons \cite{Ki}. Some Codazzi-tensor properties of Ricci tensor, which come
from the harmonicity of curvature, are effectively used.


title:
Charge-Transfer Matrix Elements by FMO-LCMO Approach: Hole Transfer in
  DNA with Parameter Tuned Range-Separated DFT

abstract:
  A scheme for computing charge-transfer matrix elements with the linear
combination of fragment molecular orbitals and the 'nonempirically tuned
range-separated' density functional is presented. It takes account of the
self-consistent orbital relaxation induced by environmental Coulomb field and
the exchange interaction in fragment pairs at low computational scaling along
the system size. The accuracy was confirmed numerically on benchmark systems of
imidazole and furane homo-dimer cations. Applications to hole transfers in DNA
nucleobase pairs and in a $\pi$-stack adenine octomer highlight the effects of
orbital relaxation.


title:
Separating Sets of Strings by Finding Matching Patterns is Almost Always
  Hard

abstract:
  We study the complexity of the problem of searching for a set of patterns
that separate two given sets of strings. This problem has applications in a
wide variety of areas, most notably in data mining, computational biology, and
in understanding the complexity of genetic algorithms. We show that the basic
problem of finding a small set of patterns that match one set of strings but do
not match any string in a second set is difficult (NP-complete, W[2]-hard when
parameterized by the size of the pattern set, and APX-hard). We then perform a
detailed parameterized analysis of the problem, separating tractable and
intractable variants. In particular we show that parameterizing by the size of
pattern set and the number of strings, and the size of the alphabet and the
number of strings give FPT results, amongst others.


title:
Complexes Detection in Biological Networks via Diversified Dense
  Subgraphs Mining

abstract:
  Protein-protein interaction (PPI) networks, providing a comprehensive
landscape of protein interacting patterns, enable us to explore biological
processes and cellular components at multiple resolutions. For a biological
process, a number of proteins need to work together to perform the job.
Proteins densely interact with each other, forming large molecular machines or
cellular building blocks. Identification of such densely interconnected
clusters or protein complexes from PPI networks enables us to obtain a better
understanding of the hierarchy and organization of biological processes and
cellular components. Most existing methods apply efficient graph clustering
algorithms on PPI networks, often failing to detect possible densely connected
subgraphs and overlapped subgraphs. Besides clustering-based methods, dense
subgraph enumeration methods have also been used, which aim to find all densely
connected protein sets. However, such methods are not practically tractable
even on a small yeast PPI network, due to high computational complexity. In
this paper, we introduce a novel approximate algorithm to efficiently enumerate
putative protein complexes from biological networks. The key insight of our
algorithm is that we do not need to enumerate all dense subgraphs. Instead we
only need to find a small subset of subgraphs that cover as many proteins as
possible. The problem is formulated as finding a diverse set of dense
subgraphs, where we develop highly effective pruning techniques to guarantee
efficiency. To handle large networks, we take a divide-and-conquer approach to
speed up the algorithm in a distributed manner. By comparing with existing
clustering and dense subgraph-based algorithms on several human and yeast PPI
networks, we demonstrate that our method can detect more putative protein
complexes and achieves better prediction accuracy.


title:
A fresh approach to classical Eisenstein series and the newer
  Hilbert-Eisenstein series

abstract:
  This paper is concerned with new results for the circular Eisenstein series
$\varepsilon_r(z)$ as well as with a novel approach to Hilbert-Eisenstein
series $\mathfrak h_r(z)$, introduced by Michael Hauss in 1995. The latter turn
out to be the product of the hyperbolic sinh--function with an explicit closed
form linear combination of digamma functions. The results, which include
differentiability properties and integral representations, are established by
independent and different argumentations. Highlights are new results on the
Butzer--Flocke--Hauss Omega function, one basis for the study of
Hilbert-Eisenstein series, which have been the subject of several recent
papers.


title:
Radio Resource Allocation for Device-to-Device Underlay Communication
  Using Hypergraph Theory

abstract:
  Device-to-Device (D2D) communication has been recognized as a promising
technique to offload the traffic for the evolved Node B (eNB). However, the D2D
transmission as an underlay causes severe interference to both the cellular and
other D2D links, which imposes a great technical challenge to radio resource
allocation. Conventional graph based resource allocation methods typically
consider the interference between two user equipments (UEs), but they cannot
model the interference from multiple UEs to completely characterize the
interference. In this paper, we study channel allocation using hypergraph
theory to coordinate the interference between D2D pairs and cellular UEs, where
an arbitrary number of D2D pairs are allowed to share the uplink channels with
the cellular UEs. Hypergraph coloring is used to model the cumulative
interference from multiple D2D pairs, and thus, eliminate the mutual
interference. Simulation results show that the system capacity is significantly
improved using the proposed hypergraph method in comparison to the conventional
graph based one.


title: Thesis: Multiple Kernel Learning for Object Categorization

abstract:
  Object Categorization is a challenging problem, especially when the images
have clutter background, occlusions or different lighting conditions. In the
past, many descriptors have been proposed which aid object categorization even
in such adverse conditions. Each descriptor has its own merits and de-merits.
Some descriptors are invariant to transformations while the others are more
discriminative. Past research has shown that, employing multiple descriptors
rather than any single descriptor leads to better recognition. The problem of
learning the optimal combination of the available descriptors for a particular
classification task is studied. Multiple Kernel Learning (MKL) framework has
been developed for learning an optimal combination of descriptors for object
categorization. Existing MKL formulations often employ block l-1 norm
regularization which is equivalent to selecting a single kernel from a library
of kernels. Since essentially a single descriptor is selected, the existing
formulations maybe sub- optimal for object categorization. A MKL formulation
based on block l-infinity norm regularization has been developed, which chooses
an optimal combination of kernels as opposed to selecting a single kernel. A
Composite Multiple Kernel Learning(CKL) formulation based on mixed l-infinity
and l-1 norm regularization has been developed. These formulations end in
Second Order Cone Programs(SOCP). Other efficient alter- native algorithms for
these formulation have been implemented. Empirical results on benchmark
datasets show significant improvement using these new MKL formulations.


title:
The Univariate Flagging Algorithm (UFA): a Fully-Automated Approach for
  Identifying Optimal Thresholds in Data

abstract:
  In many data classification problems, there is no linear relationship between
an explanatory and the dependent variables. Instead, there may be ranges of the
input variable for which the observed outcome is signficantly more or less
likely. This paper describes an algorithm for automatic detection of such
thresholds, called the Univariate Flagging Algorithm (UFA). The algorithm
searches for a separation that optimizes the difference between separated areas
while providing the maximum support. We evaluate its performance using three
examples and demonstrate that thresholds identified by the algorithm align well
with visual inspection and subject matter expertise. We also introduce two
classification approaches that use UFA and show that the performance attained
on unseen test data is equal to or better than that of more traditional
classifiers. We demonstrate that the proposed algorithm is robust against
missing data and noise, is scalable, and is easy to interpret and visualize. It
is also well suited for problems where incidence of the target is low.


title:
Attributes as Semantic Units between Natural Language and Visual
  Recognition

abstract:
  Impressive progress has been made in the fields of computer vision and
natural language processing. However, it remains a challenge to find the best
point of interaction for these very different modalities. In this chapter we
discuss how attributes allow us to exchange information between the two
modalities and in this way lead to an interaction on a semantic level.
Specifically we discuss how attributes allow using knowledge mined from
language resources for recognizing novel visual categories, how we can generate
sentence description about images and video, how we can ground natural language
in visual content, and finally, how we can answer natural language questions
about images.


title:
Accurate, Fast and Lightweight Clustering of de novo Transcriptomes
  using Fragment Equivalence Classes

abstract:
  Motivation: De novo transcriptome assembly of non-model organisms is the
first major step for many RNA-seq analysis tasks. Current methods for de novo
assembly often report a large number of contiguous sequences (contigs), which
may be fractured and incomplete sequences instead of full-length transcripts.
Dealing with a large number of such contigs can slow and complicate downstream
analysis.
  Results :We present a method for clustering contigs from de novo
transcriptome assemblies based upon the relationships exposed by multi-mapping
sequencing fragments. Specifically, we cast the problem of clustering contigs
as one of clustering a sparse graph that is induced by equivalence classes of
fragments that map to subsets of the transcriptome. Leveraging recent
developments in efficient read mapping and transcript quantification, we have
developed RapClust, a tool implementing this approach that is capable of
accurately clustering most large de novo transcriptomes in a matter of minutes,
while simultaneously providing accurate estimates of expression for the
resulting clusters. We compare RapClust against a number of tools commonly used
for de novo transcriptome clustering. Using de novo assemblies of organisms for
which reference genomes are available, we assess the accuracy of these
different methods in terms of the quality of the resulting clusterings, and the
concordance of differential expression tests with those based on ground truth
clusters. We find that RapClust produces clusters of comparable or better
quality than existing state-of-the-art approaches, and does so substantially
faster. RapClust also confers a large benefit in terms of space usage, as it
produces only succinct intermediate files - usually on the order of a few
megabytes - even when processing hundreds of millions of reads.


title:
Pattern-transition, microstructure and dynamics in two-dimensional
  vibrofluidized granular bed

abstract:
  Experiments are conducted in a two-dimensional mono-layer vibrofluidized bed
of glass beads, with a goal to understand the transition scenario and the
underlying microstructure and dynamics in different patterned-states. At small
shaking accelerations ($\Gamma=A\omega^2/g <1$), the particles remain attached
with the base of the vibrating container -- this is known as the solid bed
(SB). With increasing $\Gamma$ (at large enough shaking amplitude $A/d$) and/or
with increasing $A/d$ (at large enough $\Gamma$), the sequence of
transitions/bifurcations unfolds as follows: SB to BB ("bouncing bed") to LS
("Leidenfrost state") to "2-roll Convection" to "1-roll Convection" and finally
to a gas-like state. For a given length of the container, the coarsening of
multiple convection rolls leading to the genesis of a "single-roll" structure
(dubbed the {\it multi-roll transition}), and its subsequent transition to a
granular-gas are two novel findings of this work. We show that the critical
shaking intensity ($\Gamma_{BB}^{LS}$) for "$BB\to LS$"-transition has a
power-law dependence on the particle loading ($F=h_0/d$, where $h_0$ is the
number of particle layers at rest and $d$ is the particle diameter) and the
shaking amplitude ($A/d$). The characteristics of $BB$ and $LS$ states are
studied by calculating (i) the coarse-grained density and temperature profiles
and (ii) the pair correlation function. It is shown that while the contact
network of particles in the BB represents a hexagonal-packed structure, the
contact network within the LS resembles a liquid-like state. An unsteadiness of
the LS has been uncovered wherein the interface (between the floating-cluster
and the dilute gas underneath) and the top of the bed are found to oscillate
sinusoidally, with its oscillation frequency closely matching with the
frequency of external shaking.


title: Rank 3 rigid representations of projective fundamental groups

abstract:
  Let X be a smooth complex projective variety with basepoint x. We prove that
every rigid integral irreducible representation $\pi_1(X,x)\to SL (3,{\mathbb
C})$ is of geometric origin, i.e., it comes from some family of smooth
projective varieties. This partially generalizes an earlier result by K.
Corlette and the second author in the rank 2 case and answers one of their
questions.


title: Towards a Theory of Nonassociative Gravity

abstract:
  Violating the strong constraint of double field theory, non-geometric fluxes
were argued to give rise to noncommutative/nonassociative structures. We derive
in a rather pedestrian physicist way a differential geometry on the simplest
nonassociative (phase-)space arising for a constant non-geometric R-flux. This
provides a complementary presentation to the quasi-Hopf representation
categorial one delivered by Barnes, Schenkel, Szabo in
arXiv:1409.6331+1507.02792. As there, the notions of tensors, covariant
derivative, torsion and curvature find a star-generalization. We continue the
construction with the introduction of a star-metric and its star-inverse where,
due to the nonassociativity, we encounter major deviations from the familiar
structure. Comments on the Levi-Civita connection, a star-Einstein-Hilbert
action and the relation to string theory are included, as well.