title:
Electron impact excitation of N IV: calculations with the DARC code and
  a comparison with ICFT results

abstract:
  There have been discussions in the recent literature regarding the accuracy
of the available electron impact excitation rates (equivalently effective
collision strengths $\Upsilon$) for transitions in Be-like ions. In the present
paper we demonstrate, once again, that earlier results for $\Upsilon$ are
indeed overestimated (by up to four orders of magnitude), for over 40\% of
transitions and over a wide range of temperatures. To do this we have performed
two sets of calculations for N~IV, with two different model sizes consisting of
166 and 238 fine-structure energy levels. As in our previous work, for the
determination of atomic structure the GRASP (General-purpose Relativistic
Atomic Structure Package) is adopted and for the scattering calculations (the
standard and parallelised versions of) the Dirac Atomic R-matrix Code ({\sc
darc}) are employed. Calculations for collision strengths and effective
collision strengths have been performed over a wide range of energy (up to
45~Ryd) and temperature (up to 2.0$\times$10$^6$~K), useful for applications in
a variety of plasmas. Corresponding results for energy levels, lifetimes and
A-values for all E1, E2, M1 and M2 transitions among 238 levels of N~IV are
also reported.


title: Old and New Scaling Laws in Quantum Quench

abstract:
  The response of a many body system to a time dependent coupling which passes
through or approaches a critical point displays universal scaling behavior. In
some regimes, scaling laws have been known since the 1970's. Recently
holographic techniques have been used to understand the origins of such
scaling. Along the way, new scaling behaviors in other regimes have been found
in holographic models, which have later been shown to hold in a generic field
theory regardless of holography. This contribution summarizes recent work on
these various scaling regimes.


title: Robustness of Inflation to Inhomogeneous Initial Conditions

abstract:
  We consider the effects of inhomogeneous initial conditions in both the
scalar field profile and the extrinsic curvature on different inflationary
models. In particular, we compare the robustness of small field inflation to
that of large field inflation, using numerical simulations with Einstein
gravity in 3+1 dimensions. We find that small field inflation can fail in the
presence of subdominant gradient energies, suggesting that it is much less
robust to inhomogeneities than large field inflation, which withstands dominant
gradient energies. However, we also show that small field inflation can be
successful even if some regions of spacetime start out in the region of the
potential that does not support inflation. In the large field case, we confirm
previous results that inflation is robust if the inflaton occupies the
inflationary part of the potential. Furthermore, we show that increasing
initial scalar gradients will not form sufficiently massive inflation-ending
black holes if the initial hypersurface is approximately flat. Finally, we
consider the large field case with a varying extrinsic curvature K, such that
some regions are initially collapsing. We find that this may again lead to
local black holes, but overall the spacetime remains inflationary if the
spacetime is open, which confirms previous theoretical studies.


title:
Plasma streams in the Hermean dayside magnetosphere: solar wind
  injection through the reconnection region

abstract:
  The aim of this research is to simulate the interaction of the solar wind
with the magnetic field of Mercury and to study the particle fluxes between the
magnetosheath and the planet surface. We simulate the magnetosphere structure
using the open source MHD code PLUTO in spherical geometry with a multipolar
expansion of the Hermean magnetic field (Anderson, B. J. et al, 2012). We
perform two simulations with realistic solar wind parameters to study the
properties of a plasma stream originated in the reconnection region between the
interplanetary and the Hermean magnetic field. The plasma precipitates along
the open magnetic field lines to the planet surface showing a fast expansion,
rarefaction and cooling. The plasma stream is correlated with a flattening of
the magnetic field observed by MESSENGER due to the adjacency of the
reconnection region where the solar wind is injected to the inner
magnetosphere.


title:
Mn-doping induced ferromagnetism and enhanced superconductivity in
  Bi_4-x Mn_x O_4 S_3 (0.075 < = x < = 0.15)

abstract:
  We demonstrate that Mn-doping in the layered sulfides Bi_4O_4S_3 leads to
stable Bi_4-x Mn_x O_4 S_3 compounds that exhibit both long-range
ferromagnetism and enhanced superconductivity for 0.075 < = x < = 0.15, with a
possible record superconducting transition temperature (T_c) = 15 K among all
BiS_2-based superconductors. We conjecture that the coexistence of
superconductivity and ferromagnetism may be attributed to Mn-doping in the
spacer Bi2O2 layers away from the superconducting BiS_2 layers, whereas the
enhancement of T_c may be due to excess electron transfer to BiS_2 from the
Mn4+/Mn3+-substitutions in Bi_2O_2. This notion is empirically corroborated by
the increased electron-carrier densities upon Mn doping, and by further studies
of the Bi_4-x A_x O_4 S_3 compounds (A = Co, Ni; x = 0.1, 0.125), where the T_c
values remain comparable to that of the undoped Bi_4O_4S_3 system (= 4.5 K) due
to lack of 4+ valences in either Co or Ni ions for excess electron transfer to
the BiS_2 layers. These findings therefore shed new light on feasible pathways
to enhance the T_c values of BiS_2-based superconductors.


title:
Topology Abstraction Service for IP VPNs: Core Network Partitioning for
  Resource Sharing

abstract:
  VPN service providers (VSP) and IP-VPN customers have traditionally
maintained service demarcation boundaries between their routing and signaling
entities. This has resulted in the VPNs viewing the VSP network as an opaque
entity and therefore limiting any meaningful interaction between the VSP and
the VPNs. A key challenge is to expose each VPN to information about available
network resources through an abstraction (TA) [1] which is both accurate and
fair. In [2] we proposed three decentralized schemes assuming that all the
border nodes performing the abstraction have access to the entire core network
topology. This assumption likely leads to over- or under-subscription. In this
paper we develop centralized schemes to partition the core network capacities,
and assign each partition to a specific VPN for applying the decentralized
abstraction schemes presented in [2]. First, we present two schemes based on
the maximum concurrent flow and the maximum multicommodity flow (MMCF)
formulations. We then propose approaches to address the fairness concerns that
arise when MMCF formulation is used. We present results based on extensive
simulations on several topologies, and provide a comparative evaluation of the
different schemes in terms of abstraction efficiency, fairness to VPNs and call
performance characteristics achieved.


title: Velocity fields of a bed-load layer under a turbulent liquid flow

abstract:
  The transport of sediments by a fluid flow is commonly found in nature and in
industry. In nature, it is found in rivers, oceans, deserts, and other
environments. In industry, it is found in petroleum pipelines conveying grains,
in sewer systems, and in dredging lines, for example. This study investigates
experimentally the transport of the grains of a granular bed sheared by a
turbulent liquid flow. In our experiments, fully developed turbulent water
flows were imposed over a flat granular bed of known granulometry. Under the
tested conditions, the grains were transported as bed load, i.e., they became
entrained by rolling and sliding over each other, forming a moving granular
layer. The present experiments were performed close to incipient bed load, a
case for which experimental data on grains velocities are scarce. Distinct from
previous experiments, an entrance length assured that the water stream over the
loose bed was fully developed. At different water flow rates, the moving layer
was filmed using a high-speed camera, and the grains' displacements and
velocities were determined by post-processing the images with a numerical code
developed in the course of this study. The bed-load transport rate was
estimated and correlated to the water flow conditions.


title: Search for gamma-ray emission from star-forming galaxies with Fermi LAT

abstract:
  Recent studies have found a positive correlation between the star-formation
rate of galaxies and their gamma-ray luminosity. Galaxies with a high
star-formation rate are expected to produce a large amount of high-energy
cosmic rays, which emit gamma-rays when interacting with the interstellar
medium and radiation fields. We search for gamma-ray emission from a sample of
galaxies within and beyond the Local Group with data from the LAT instrument
onboard the Fermi satellite. We exclude recently detected galaxies (NGC 253,
M82, NGC 4945, NGC 1068, NGC 2146, Arp 220) and use seven years of cumulative
Pass 8 data from the LAT in the 100 MeV to 100 GeV range. No new detections are
seen in the data and upper limits for the gamma- ray fluxes are calculated. The
correlation between gamma-ray luminosity and infrared luminosity for galaxies
obtained using our new upper limits is in agreement with a previously published
correlation, but the new upper limits imply that some galaxies are not as
efficient gamma-ray emitters as previously thought.


title:
Generalization of ERM in Stochastic Convex Optimization: The Dimension
  Strikes Back

abstract:
  In stochastic convex optimization the goal is to minimize a convex function
$F(x) \doteq {\mathbf E}_{{\mathbf f}\sim D}[{\mathbf f}(x)]$ over a convex set
$\cal K \subset {\mathbb R}^d$ where $D$ is some unknown distribution and each
$f(\cdot)$ in the support of $D$ is convex over $\cal K$. The optimization is
commonly based on i.i.d.~samples $f^1,f^2,\ldots,f^n$ from $D$. A standard
approach to such problems is empirical risk minimization (ERM) that optimizes
$F_S(x) \doteq \frac{1}{n}\sum_{i\leq n} f^i(x)$. Here we consider the question
of how many samples are necessary for ERM to succeed and the closely related
question of uniform convergence of $F_S$ to $F$ over $\cal K$. We demonstrate
that in the standard $\ell_p/\ell_q$ setting of Lipschitz-bounded functions
over a $\cal K$ of bounded radius, ERM requires sample size that scales
linearly with the dimension $d$. This nearly matches standard upper bounds and
improves on $\Omega(\log d)$ dependence proved for $\ell_2/\ell_2$ setting by
Shalev-Shwartz et al. (2009). In stark contrast, these problems can be solved
using dimension-independent number of samples for $\ell_2/\ell_2$ setting and
$\log d$ dependence for $\ell_1/\ell_\infty$ setting using other approaches. We
further show that our lower bound applies even if the functions in the support
of $D$ are smooth and efficiently computable and even if an $\ell_1$
regularization term is added. Finally, we demonstrate that for a more general
class of bounded-range (but not Lipschitz-bounded) stochastic convex programs
an infinite gap appears already in dimension 2.


title: A Productivity Checker for Logic Programming

abstract:
  Automated analysis of recursive derivations in logic programming is known to
be a hard problem. Both termination and non-termination are undecidable
problems in Turing-complete languages. However, some declarative languages
offer a practical work-around for this problem, by making a clear distinction
between whether a program is meant to be understood inductively or
coinductively. For programs meant to be understood inductively, termination
must be guaranteed, whereas for programs meant to be understood coinductively,
productive non-termination (or "productivity") must be ensured. In practice,
such classification helps to better understand and implement some
non-terminating computations.
  Logic programming was one of the first declarative languages to make this
distinction: in the 1980's, Lloyd and van Emden's "computations at infinity"
captured the big-step operational semantics of derivations that produce
infinite terms as answers. In modern terms, computations at infinity describe
"global productivity" of computations in logic programming. Most programming
languages featuring coinduction also provide an observational, or small-step,
notion of productivity as a computational counterpart to global productivity.
This kind of productivity is ensured by checking that finite initial fragments
of infinite computations can always be observed to produce finite portions of
their infinite answer terms.
  In this paper we introduce a notion of observational productivity for logic
programming as an algorithmic approximation of global productivity, give an
effective procedure for semi-deciding observational productivity, and offer an
implemented automated observational productivity checker for logic programs.


title:
Visual Analysis of Nonlinear Dynamical Systems: Chaos, Fractals,
  Self-Similarity and the Limits of Prediction

abstract:
  Nearly all nontrivial real-world systems are nonlinear dynamical systems.
Chaos describes certain nonlinear dynamical systems that have a very sensitive
dependence on initial conditions. Chaotic systems are always deterministic and
may be very simple, yet they produce completely unpredictable and divergent
behavior. Systems of nonlinear equations are difficult to solve analytically,
and scientists have relied heavily on visual and qualitative approaches to
discover and analyze the dynamics of nonlinearity. Indeed, few fields have
drawn as heavily from visualization methods for their seminal innovations: from
strange attractors, to bifurcation diagrams, to cobweb plots, to phase diagrams
and embedding. Although the social sciences are increasingly studying these
types of systems, seminal concepts remain murky or loosely adopted. This
article has three aims. First, it argues for several visualization methods to
critically analyze and understand the behavior of nonlinear dynamical systems.
Second, it uses these visualizations to introduce the foundations of nonlinear
dynamics, chaos, fractals, self-similarity and the limits of prediction.
Finally, it presents Pynamical, an open-source Python package to easily
visualize and explore nonlinear dynamical systems' behavior.


title:
Slow modes in the Hermean magnetosphere: effect of the solar wind
  hydrodynamic parameters and IMF orientation

abstract:
  The aim of this study is to simulate the slow mode structures in the Hermean
magnetosphere. We use a single fluid MHD model and a multipolar expansion of
the Northward displaced Hermean magnetic field, to perform simulations with
different solar wind parameter to foreseen the most favorable configuration for
the formation of slow modes, attending to the solar wind density, velocity,
temperature and the interplanetary magnetic field orientation. If the
interplanetary magnetic field is aligned with the Mercury-Sun direction, the
magnetic axis of Mercury in the Northward direction or the planet orbital
plane, slow mode structures are observed nearby the South pole. If the
orientation is in the Sun-Mercury or Northward directions, slow mode structures
are observed nearby the North pole, but smaller compared with the structures
near the South pole. Increase the density or the solar wind velocity avoids the
formation of slow modes structures, not observed for a dynamic pressure larger
than $6.25 \cdot 10^{-9}$ Pa in the case of a Northward interplanetary magnetic
field orientation, due to the enhancement of the bow shock compression. If the
solar wind temperature increases, the slow mode structures are wider because
the sonic Mach number is smaller and the bow shock is less compressed.


title: A Note on Markov Normalized Magnetic Eigenmaps

abstract:
  We note that building a magnetic Laplacian from the Markov transition matrix,
rather than the graph adjacency matrix, yields several benefits for the
magnetic eigenmaps algorithm. The two largest benefits are that the embedding
becomes more stable as a function of the rotation parameter g, and the
principal eigenvector of the magnetic Laplacian now converges to the page rank
of the network as a function of diffusion time. We show empirically that this
normalization improves the phase and real/imaginary embeddings of the
low-frequency eigenvectors of the magnetic Laplacian.


title: Imaginary Multiquadratic Fields of Class Number 1

abstract:
  In this paper, we present a complete classification of all imaginary
$n$-quadratic fields of class number 1.


title:
Anisotropic Hanle lineshape via anisotropic magnetothermoelectric
  phenomena

abstract:
  We observe anisotropic Hanle lineshape with unequal in-plane and out-of-plane
non-local signals for spin precession measurements carried out on lateral
metallic spin valves with transparent interfaces. The conventional
interpretation for this anisotropy corresponds to unequal spin relaxation times
for in-plane and out-of-plane spin orientations as for the case of 2D materials
like graphene, but it is unexpected in a polycrystalline metallic channel.
Systematic measurements as a function of temperature and channel length,
combined with both analytical and numerical thermoelectric transport models,
demonstrate that the anisotropy in the Hanle lineshape is magneto-thermal in
origin, caused by the anisotropic modulation of the Peltier and Seebeck
coefficients of the ferromagnetic electrodes. Our results call for the
consideration of such magnetothermoelectric effects in the study of anisotropic
spin relaxation.


title:
The Formation of Supermassive Black Holes from Population III.1 Seeds.
  I. Cosmic Formation Histories and Clustering Properties

abstract:
  We calculate cosmic distributions in space and time of the formation sites of
the first, "Pop III.1" stars, exploring a model in which these are the
progenitors of all supermassive black holes (SMBHs), seen in the centers of
most large galaxies. Pop III.1 stars are defined to form from primordial
composition gas in dark matter minihalos with $\sim10^6\:M_\odot$ that are
isolated from neighboring astrophysical sources by a given isolation distance,
$d_{\rm{iso}}$. We assume Pop III.1 sources are seeds of SMBHs, based on
protostellar support by dark matter annihilation heating that allows them to
accrete a large fraction of their minihalo gas, i.e., $\sim10^5\:M_\odot$.
Exploring $d_{\rm{iso}}$ from $10 - 100\:\rm{kpc}$ (proper distances), we
predict the redshift evolution of Pop III.1 source and SMBH remnant number
densities. The local, $z=0$ density of SMBHs constrains $d_{\rm{iso}}\lesssim
100\:\rm{kpc}$ (i.e., $3\:\rm{Mpc}$ comoving distance at $z\simeq30$). In our
simulated ($\sim60\:\rm{Mpc}$)$^3$ comoving volume, Pop III.1 stars start
forming just after $z=40$. Their formation is largely complete by $z\simeq25$
to $20$ for $d_{\rm{iso}}=100$ to $50\:\rm{kpc}$. We follow source evolution to
$z=10$, by which point most SMBHs reside in halos with $\gtrsim10^8\:M_\odot$.
Over this period, there is relatively limited merging of SMBHs for these values
of $d_{\rm{iso}}$. We also predict SMBH clustering properties at $z=10$:
feedback suppression of neighboring sources leads to relatively flat angular
correlation functions.


title:
A Geometric Approach to Aggregate Flexibility Modeling of
  Thermostatically Controlled Loads

abstract:
  Coordinated aggregation of a large population of thermostatically controlled
loads (TCLs) presents a great potential to provide various ancillary services
to the grid. One of the key challenges of integrating TCLs into system level
operation and control is developing a simple and portable model to accurately
capture their aggregate flexibility. In this paper, we propose a geometric
approach to model the aggregate flexibility of TCLs. We show that the set of
admissible power profiles of an individual TCL is a polytope, and their
aggregate flexibility is the Minkowski sum of the individual polytopes. In
order to represent their aggregate flexibility in an intuitive way and achieve
a tractable approximation, we develop optimization-based algorithms to
approximate the polytopes by the homothets of a given convex set. As a special
application, this set is chosen as a \emph{virtual battery model} and the
corresponding optimal approximations are solved efficiently by equivalent
linear programming problems. Numerical results show that our algorithms yield
significant improvement in characterizing the aggregate flexibility over
existing modeling methods. We also conduct case studies to demonstrate the
efficacy of our approaches by coordinating TCLs to track a frequency regulation
signal from the Pennsylvania-New Jersey-Maryland (PJM) Interconnection.


title: Stability of Equilibria in Modified-Gradient Systems

abstract:
  Motivated by questions in biology, we investigate the stability of equilibria
of the dynamical system $\mathbf{x}^{\prime}=P(t)\nabla f(x)$ which arise as
critical points of $f$, under the assumption that $P(t)$ is positive
semi-definite. It is shown that the condition
$\int^{\infty}\lambda_{1}(P(t))~dt=\infty$, where $\lambda_{1}(P(t))$ is the
smallest eigenvalue of $P(t)$, plays a key role in guaranteeing uniform
asymptotic stability and in providing information on the basis of attraction of
those equilibria.


title:
Constraints on the merging of the transition lines at the tricritical
  point in a wing-structure phase diagram

abstract:
  We consider the phase diagram of a ferromagnetic system driven to a quantum
phase transition with a tuning parameter $p$. Before being suppressed, the
transition becomes of the first order at a tricritical point, from which wings
emerge under application of the magnetic field $H$ in the $T$-$p$-$H$ phase
diagram. We show that the edge of the wings merge with tangent slopes at the
tricritical point.


title:
Binary Optimization via Mathematical Programming with Equilibrium
  Constraints

abstract:
  Binary optimization is a central problem in mathematical optimization and its
applications are abundant. To solve this problem, we propose a new class of
continuous optimization techniques which is based on Mathematical Programming
with Equilibrium Constraints (MPECs). We first reformulate the binary program
as an equivalent augmented biconvex optimization problem with a bilinear
equality constraint, then we propose two penalization/regularization methods
(exact penalty and alternating direction) to solve it. The resulting algorithms
seek desirable solutions to the original problem via solving a sequence of
linear programming convex relaxation subproblems. In addition, we prove that
both the penalty function and augmented Lagrangian function, induced by adding
the complementarity constraint to the objectives, are exact, i.e., they have
the same local and global minima with those of the original binary program when
the penalty parameter is over some threshold. The convergence of both
algorithms can be guaranteed, since they essentially reduce to block coordinate
descent in the literature. Finally, we demonstrate the effectiveness and
versatility of our methods on several important problems, including graph
bisection, constrained image segmentation, dense subgraph discovery, modularity
clustering and Markov random fields. Extensive experiments show that our
methods outperform existing popular techniques, such as iterative hard
thresholding, linear programming relaxation and semidefinite programming
relaxation.


title: Regularization for Unsupervised Deep Neural Nets

abstract:
  Unsupervised neural networks, such as restricted Boltzmann machines (RBMs)
and deep belief networks (DBNs), are powerful tools for feature selection and
pattern recognition tasks. We demonstrate that overfitting occurs in such
models just as in deep feedforward neural networks, and discuss possible
regularization methods to reduce overfitting. We also propose a "partial"
approach to improve the efficiency of Dropout/DropConnect in this scenario, and
discuss the theoretical justification of these methods from model convergence
and likelihood bounds. Finally, we compare the performance of these methods
based on their likelihood and classification error rates for various pattern
recognition data sets.


title: Ordering of the $0d_{5/2}$ and $1s_{1/2}$ proton levels in light nuclei

abstract:
  A survey of the available single-proton data in $A\leq17$ nuclei, along with
calculations using a Woods-Saxon potential, show that the ordering of the
$0d_{5/2}$ and $1s_{1/2}$ proton orbitals are determined primarily by the
proximity of the $s$-state proton energy to the Coulomb barrier. This is
analogous to the dependence of the corresponding neutron orbitals in proximity
to the neutron threshold, that was previously discussed.


title: TerpreT: A Probabilistic Programming Language for Program Induction

abstract:
  We study machine learning formulations of inductive program synthesis; given
input-output examples, we try to synthesize source code that maps inputs to
corresponding outputs. Our aims are to develop new machine learning approaches
based on neural networks and graphical models, and to understand the
capabilities of machine learning techniques relative to traditional
alternatives, such as those based on constraint solving from the programming
languages community.
  Our key contribution is the proposal of TerpreT, a domain-specific language
for expressing program synthesis problems. TerpreT is similar to a
probabilistic programming language: a model is composed of a specification of a
program representation (declarations of random variables) and an interpreter
describing how programs map inputs to outputs (a model connecting unknowns to
observations). The inference task is to observe a set of input-output examples
and infer the underlying program. TerpreT has two main benefits. First, it
enables rapid exploration of a range of domains, program representations, and
interpreter models. Second, it separates the model specification from the
inference algorithm, allowing like-to-like comparisons between different
approaches to inference. From a single TerpreT specification we automatically
perform inference using four different back-ends. These are based on gradient
descent, linear program (LP) relaxations for graphical models, discrete
satisfiability solving, and the Sketch program synthesis system.
  We illustrate the value of TerpreT by developing several interpreter models
and performing an empirical comparison between alternative inference
algorithms. Our key empirical finding is that constraint solvers dominate the
gradient descent and LP-based formulations. We conclude with suggestions for
the machine learning community to make progress on program synthesis.


title: Phenomenology of Dihadron Fragmentation Function

abstract:
  We report on the phenomenological results obtained through Dihadron
Fragmentation Functions related processes. In 2015, an update on the fitting
techniques for the Dihadron Fragmentation Functions has led to an improved
extraction of the transversity PDF and, as a consequence, the nucleon tensor
charge. We discuss the impact of the determination of the latter on search for
physics Beyond the Standard Model, focusing on the error treatment. We also
comment on the future of the extraction of the subleading-twist PDF $e(x)$ from
JLab soon-to-be-released Beam Spin Asymmetry data.


title:
Sparsity Constrained Minimization via Mathematical Programming with
  Equilibrium Constraints

abstract:
  Sparsity constrained minimization captures a wide spectrum of applications in
both machine learning and signal processing. This class of problems is
difficult to solve since it is NP-hard and existing solutions are primarily
based on Iterative Hard Thresholding (IHT). In this paper, we consider a class
of continuous optimization techniques based on Mathematical Programs with
Equilibrium Constraints (MPECs) to solve general sparsity constrained problems.
Specifically, we reformulate the problem as an equivalent biconvex MPEC, which
we can solve using an exact penalty method or an alternating direction method.
We elaborate on the merits of both proposed methods and analyze their
convergence properties. Finally, we demonstrate the effectiveness and
versatility of our methods on several important problems, including feature
selection, segmented regression, MRF optimization, trend filtering and impulse
noise removal. Extensive experiments show that our MPEC-based methods
outperform state-of-the-art techniques, especially those based on IHT.


title:
Parallel Non-divergent Flow Accumulation For Trillion Cell Digital
  Elevation Models On Desktops Or Clusters

abstract:
  Continent-scale datasets challenge hydrological algorithms for processing
digital elevation models. Flow accumulation is an important input for many such
algorithms; here, I parallelize its calculation. The new algorithm works on one
or many cores, or multiple machines, and can take advantage of large memories
or cope with small ones. Unlike previous parallel algorithms, the new algorithm
guarantees a fixed number of memory access and communication events per raster
cell. In testing, the new algorithm ran faster and used fewer resources than
previous algorithms, exhibiting ~30% strong and weak scaling efficiencies up to
48 cores and linear scaling across datasets ranging over three orders of
magnitude. The largest dataset tested had two trillion (2*10^12) cells. With 48
cores, processing required 24 minutes wall-time (14.5 compute-hours). This test
is three orders of magnitude larger than any previously performed in the
literature. Complete, well-commented source code and correctness tests are
available on Github.


title: New Suns in the Cosmos III: multifractal signature analysis

abstract:
  In present paper, we investigate the multifractality signatures in hourly
time series extracted from CoRoT spacecraft database. Our analysis is intended
to highlight the possibility that astrophysical time series can be members of a
particular class of complex and dynamic processes which require several
photometric variability diagnostics to characterize their structural and
topological properties. To achieve this goal, we search for contributions due
to nonlinear temporal correlation and effects caused by heavier tails than the
Gaussian distribution, using a detrending moving average algorithm for
one-dimensional multifractal signals (MFDMA). We observe that the correlation
structure is the main source of multifractality, while heavy-tailed
distribution plays a minor role in generating the multifractal effects. Our
work also reveals that rotation period of stars is inherently scaled by degree
of multifractality. As a result, analyzing the multifractal degree of referred
series, we uncover an evolution of multifractality from shorter to larger
periods.


title: HVC Interneuron Properties from Statistical Data Assimilation

abstract:
  Data assimilation (DA) solves the inverse problem of inferring initial
conditions given data and a model. Here we use biophysically motivated
Hodgkin-Huxley (HH) models of avian HVCI neurons, experimentally obtained
recordings of these neurons, and our data assimilation algorithm to infer the
full set of parameters and a minimal set of ionic currents precisely
reproducing the observed waveform information. We find many distinct validated
sets of parameters selected by our DA method and choice of model. We conclude
exploring variations on the inverse problem applied to neurons producing
accurate or inaccurate results; by manipulating data presented to the
algorithm, varying sample rate and waveform; and by manipulating the model by
adding and subtracting ionic currents.


title: Natural Language Processing using Hadoop and KOSHIK

abstract:
  Natural language processing, as a data analytics related technology, is used
widely in many research areas such as artificial intelligence, human language
processing, and translation. At present, due to explosive growth of data, there
are many challenges for natural language processing. Hadoop is one of the
platforms that can process the large amount of data required for natural
language processing. KOSHIK is one of the natural language processing
architectures, and utilizes Hadoop and contains language processing components
such as Stanford CoreNLP and OpenNLP. This study describes how to build a
KOSHIK platform with the relevant tools, and provides the steps to analyze wiki
data. Finally, it evaluates and discusses the advantages and disadvantages of
the KOSHIK architecture, and gives recommendations on improving the processing
performance.


title:
On the Equivalence of Module Categories over a Group-Theoretical Fusion
  Category

abstract:
  We give a necessary and sufficient condition in terms of group cohomology for
two indecomposable module categories over a group-theoretical fusion category
${\mathcal C}$ to be equivalent. This concludes the classification of such
module categories.


title:
Pseudo-spectral methods for the Laplace-Beltrami equation and the Hodge
  decomposition on surfaces of genus one

abstract:
  The inversion of the Laplace-Beltrami operator and the computation of the
Hodge decomposition of a tangential vector field on smooth surfaces arise as
computational tasks in many areas of science, from computer graphics to machine
learning to com- putational physics. Here, we present a high-order accurate
pseudo-spectral approach, applicable to closed surfaces of genus one in three
dimensional space, with a view toward applications in plasma physics and fluid
dynamics.


title: Self-contained NoSQL Resources for Cross-Domain RDF

abstract:
  Cross-domain knowledge bases such as DBpedia, Freebase and YAGO have emerged
as encyclopedic hubs in the Web of Linked Data. Despite enabling several
practical applications in the Semantic Web, the large-scale, schema-free nature
of such graphs often precludes research groups from employing them widely as
evaluation test cases for entity resolution and instance-based ontology
alignment applications. Although the ground-truth linkages between the three
knowledge bases above are available, they are not amenable to resource-limited
applications. One reason is that the ground-truth files are not self-contained,
meaning that a researcher must usually perform a series of expensive joins
(typically in MapReduce) to obtain usable information sets. In this paper, we
upload several publicly licensed data resources to the public cloud and use
simple Hadoop clusters to compile, and make accessible, three cross-domain
self-contained test cases involving linked instances from DBpedia, Freebase and
YAGO. Self-containment is enabled by virtue of a simple NoSQL JSON-like
serialization format. Potential applications for these resources, particularly
related to testing transfer learning research hypotheses, are also briefly
described.


title:
Chalcogenide glass planar MIR couplers for future chip based Bracewell
  interferometers

abstract:
  Photonic integrated circuits are established as the technique of choice for a
number of astronomical processing functions due to their compactness, high
level of integration, low losses, and stability. Temperature control,
mechanical vibration and acoustic noise become controllable for such a device
enabling much more complex processing than can realistically be considered with
bulk optics. To date the benefits have mainly been at wavelengths around 1550
nm but in the important Mid-Infrared region, standard photonic chips absorb
light strongly. Chalcogenide glasses are well known for their transparency to
beyond 10000 nm, and the first results from coupler devices intended for use in
an interferometric nuller for exoplanetary observation in the Mid-Infrared L
band (3800-4200 nm) are presented here showing that suitable performance can be
obtained both theoretically and experimentally for the first fabricated devices
operating at 4000 nm.


title:
Distributed Space-Time Coding Techniques with Dynamic Buffers for
  Cooperative DS-CDMA Systems

abstract:
  In this work, we propose a dynamic buffer-aided distributed space-time coding
(DSTC) scheme for cooperative direct-sequence code-division multiple access
systems. We first devise a relay selection algorithm that can automatically
select the optimum set of relays among both the source-relay phase and the
relay-destination phase for DSTC transmission according to the
signal-to-interference-plus-noise ratio (SINR) criterion. Multiple relays
equipped with dynamic buffers are introduced in the network, which allows the
relays to store data received from the sources and wait until the most
appropriate time for transmission. The proposed technique improves the quality
of the transmission with an acceptable delay as the buffer size is adjustable.
Simulation results show that the proposed dynamic buffer-aided DSTC scheme and
algorithm outperforms prior art.


title: Hurwitz matrices of doubly infinite series

abstract:
  This paper aims at extending the criterion that the quasi-stability of a
polynomial is equivalent to the total nonnegativity of its Hurwitz matrix. We
give a complete description of functions generating doubly infinite series with
totally nonnegative Hurwitz and Hurwitz-type matrices (in a Hurwitz-type matrix
odd and even rows come from two distinct power series). The corresponding
result for singly infinite series is known: it is based on a certain
factorization of Hurwitz-type matrices, which is absent in the doubly infinite
case. A necessary condition for total nonnegativity of generalized Hurwitz
matrices follows as an application.


title:
Optical dependence of electrically-detected magnetic resonance in
  lightly-doped Si:P devices

abstract:
  Electrically-detected magnetic resonance (EDMR) provides a highly sensitive
method for reading out the state of donor spins in silicon. The technique
relies on a spin-dependent recombination (SDR) process involving dopant spins
that are coupled to interfacial defect spins near the Si/SiO$_2$ interface. To
prevent ionization of the donors, the experiments are performed at cryogenic
temperatures and the mobile charge carriers needed are generated via optical
excitation. The influence of this optical excitation on the SDR process and the
resulting EDMR signal is still not well understood. Here, we use EDMR to
characterize changes to both phosphorus and defect spin readout as a function
of optical excitation using: a 980 nm laser with energy just above the silicon
band edge at cryogenic temperatures; a 405 nm laser to generate hot
surface-carriers; and a broadband white light source. EDMR signals are observed
from the phosphorus donor and two distinct defect species in all the
experiments. With near-infrared excitation, we find that the EDMR signal
primarily arises from donor-defect pairs, while at higher photon energies there
are significant additional contributions from defect-defect pairs. The optical
penetration depth into silicon is also known to be strongly wavelength
dependent at cryogenic temperatures. The energy of the optical excitation is
observed to strongly modulate the kinetics of the SDR process. Careful tuning
of the optical photon energy could therefore be used to control both the subset
of spin pairs contributing to the EDMR signal as well as the dynamics of the
SDR process.


title: Experience: Type alignment on DBpedia and Freebase

abstract:
  Linked Open Data exhibits growth in both volume and variety of published
data. Due to this variety, instances of many different types (e.g. Person) can
be found in published datasets. Type alignment is the problem of automatically
matching types (in a possibly many-many fashion) between two such datasets.
Type alignment is an important preprocessing step in instance matching.
Instance matching concerns identifying pairs of instances referring to the same
underlying entity. By performing type alignment a priori, only instances
conforming to aligned types are processed together, leading to significant
savings. This article describes a type alignment experience with two
large-scale cross-domain RDF knowledge graphs, DBpedia and Freebase, that
contain hundreds, or even thousands, of unique types. Specifically, we present
a MapReduce-based type alignment algorithm and show that there are at least
three reasonable ways of evaluating type alignment within the larger context of
instance matching. We comment on the consistency of those results, and note
some general observations for researchers evaluating similar algorithms on
cross-domain graphs.


title:
On the origin of field-like spin-orbit torques in heavy
  metal-ferromagnet-oxide thin film heterostructures

abstract:
  We report measurements of the thickness and temperature (T) dependencies of
current-induced spin-orbit torques, especially the field-like (FL) component,
in various heavy metal (HM)/normal metal (NM) spacer/ferromagnet (FM)/Oxide
(MgO and HfOx/MgO) heterostructures. The FL torque in these samples originates
from spin current generated by the spin Hall effect (SHE) in the HM. For a FM
layer sufficiently thin that a substantial portion of this spin current can
reach the FM/Oxide interface, T-dependent spin scattering there can yield a
strong FL torque that is, in some cases, opposite in sign to that exerted at
the NM/FM interface.


title: Thermodynamic geometry of minimum-dissipation driven barrier crossing

abstract:
  We explore the thermodynamic geometry of a simple system that models the
bistable dynamics of nucleic acid hairpins in single molecule force-extension
experiments. Near equilibrium, optimal (minimum-dissipation) driving protocols
are governed by a generalized linear response friction coefficient. Our
analysis and simulations demonstrate that the friction coefficient of the
driving protocols is sharply peaked at the interface between metastable
regions, which leads to minimum-dissipation protocols that drive rapidly within
a metastable basin, but then linger longest at the interface, giving thermal
fluctuations maximal time to kick the system over the barrier. Intuitively, the
same principle applies generically in free energy estimation (both in steered
molecular dynamics simulations and in single-molecule experiments), provides a
design principle for the construction of thermodynamically efficient coupling
between stochastic objects, and makes a prediction regarding the construction
of evolved biomolecular motors.


title:
Every bounded self-ajoint operator is a real linear combination of $4$
  orthoprojections

abstract:
  We prove that every bounded self-adjoint operator in Hilbert space is a real
linear combination of $4$ orthoprojections. Also we show that operators of the
form identity minus compact positive operator can not be decomposed in a real
linear combination of $3$ orthoprojections. Using ideas applied in infinite
dimensional space, we find $n\times n$ matrices that are not real linear
combinations of $3$ orthoprojections for every $n\ge 76$.


title:
Measuring the velocity field from type Ia supernovae in an LSST-like sky
  survey

abstract:
  With the upcoming sky survey with the Large Synoptic Survey Telescope a great
sample of type Ia supernovae will be observed, allowing for a precise mapping
of the velocity structure of the universe. Since the source of peculiar
velocities is variations in the density field, cosmological parameters related
to the matter distribution can subsequently be extracted from the velocity
power spectrum.
  One way to quantify this is through the angular power spectrum of radial
peculiar velocities on spheres at different redshifts. We investigate how well
this observable can be measured, despite the problems caused by areas with no
information. To obtain a realistic distribution of supernovae, we create mock
supernova catalogs by using a semi-analytical code for galaxy formation on the
merger trees extracted from N-body simulations. We measure the cosmic variance
in the velocity power spectrum by repeating the procedure many times for
differently located observers, and vary different aspects of the analysis, such
as the observer environment, to see how this affects the measurements.
  Our results confirm the findings from earlier studies regarding the precision
with which the angular velocity power spectrum can be determined in the near
future. This level of precision has been found to imply, that the angular
velocity power spectrum from type Ia supernovae is competitive in its potential
to measure parameters such as $\sigma_8$. This makes the peculiar velocity
power spectrum from type Ia supernovae a promising new observable, which
deserves further attention.


title:
High-energy mid-infrared sub-cycle pulse synthesis from a parametric
  amplifier

abstract:
  High-energy, carrier-envelope phase (CEP)-stable, sub-cycle, mid-infrared
(mid-IR) pulses can provide unique opportunities of exploring phase-sensitive
strong-field light-matter interactions in atoms, molecules, and solids. In the
mid-IR wavelength, the ponderomotive energy of laser pulses is dramatically
increased (versus the visible/near-infrared) and, therefore, the Keldysh
parameter is much smaller than unity even at relatively modest laser
intensities. This enables to study the sub-cycle electron dynamics in solids
via high-harmonic generation (HHG) without damage. One can also control the
electron emissions from nano-devices in the sub-cycle time scale. These efforts
are opening a great opportunity towards petahertz electronics. Here, we present
a high-energy, sub-cycle pulse synthesizer based on a mid-IR optical parametric
amplifier (OPA), pumped by CEP-stable, 2.1 um femtosecond pulses, and its
application to HHG in solids. The signal and idler combined spectrum spans from
2.5 to 9.0 um, which covers the whole midwave-infrared (MWIR) region. We
coherently synthesize the passively CEP-stable few-cycle signal and idler
pulses to generate 33 uJ, 0.88-cycle (12.4 fs), multi-GW pulses centered at
~4.2 um, which is further energy scalable. The in-line synthesis of the
CEP-stable sub-cycle pulse is realized through the type-I collinear OPA with
minimal temporal walk-off. The MWIR sub-cycle pulse is used for driving HHG in
thin silicon samples, producing harmonics up to ~19th order with a continuous
spectral coverage due to the isolated emission by the sub-cycle driver. Our
demonstration offers an energy scalable and technically simple platform of
laser sources generating CEP-stable sub-cycle pulses in the whole MWIR region
for investigating isolated phase-sensitive strong-field interactions in solids
and gases.


title: Giant strain control of magnetoelectric effect in Ta/Fe/MgO

abstract:
  The exploration of electric field controlled magnetism has come under
scrutiny for its intriguing magnetoelectric phenomenon as well as technological
advances in spintronics. Herein, the tremendous effect of an epitaxial strain
on voltage-controlled perpendicular magnetic anisotropy (VPMA) is demonstrated
in a transition-metal/ferromagnet/MgO (TM/FM/MgO) heterostructure from
first-principles electronic structure computation. By tuning the epitaxial
strain in Ta/Fe/MgO as a model system of TM/FM/MgO, we find distinctly
different behaviours of VPMA from V- to {\Lambda}-shape trends with a
substantially large magnetoelectric coefficient, up to an order of 1000 fJ/Vm.
We further reveal that the VPMA modulation under strain is mainly governed by
the inherently large spin-orbit coupling of Ta 5d-Fe 3d hybridized orbitals at
the TM/FM interface, although the Fe 3d-O 2p hybridization at the FM/MgO is
partly responsible in determining the PMA of Ta/Fe/MgO. These results suggest
that the control of epitaxial strain enables the engineering of VPMA, and
provides physical insights for the divergent behaviors of VPMA and
magnetoelectric coefficients found in TM/FM/MgO experiments.


title:
The complete set of infinite volume ground states for Kitaev's abelian
  quantum double models

abstract:
  We study the set of infinite volume ground states of Kitaev's quantum double
model on $\mathbb{Z}^2$ for an arbitrary finite abelian group $G$. It is known
that these models have a unique frustration-free ground state. Here we drop the
requirement of frustration freeness, and classify the full set of ground
states. We show that the ground state space decomposes into $|G|^2$ different
charged sectors, corresponding to the different types of abelian anyons (also
known as superselection sectors). In particular, all pure ground states are
equivalent to ground states that can be interpreted as describing a single
excitation. Our proof proceeds by showing that each ground state can be
obtained as the weak$^*$ limit of finite volume ground states of the quantum
double model with suitable boundary terms. The boundary terms allow for states
which represent a pair of excitations, with one excitation in the bulk and one
pinned to the boundary, to be included in the ground state space.


title:
Assessing exchange-correlation functional performance for structure and
  property predictions of oxyfluoride compounds from first principles

abstract:
  Motivated by the resurgence of electronic and optical property design in
ordered fluoride and oxyfluoride compounds, we present a density functional
theory (DFT) study on 19 materials with structures, ranging from simple to
complex, and variable oxygen-to-fluorine ratios. We focus on understanding the
accuracy of the exchange-correlation potentials ($V_{xc}$) to DFT for the
prediction of structural, electronic, and lattice dynamical properties at four
different levels of theory, \emph{i.e.}, the local density approximation (LDA),
generalized gradient approximation (GGA), metaGGA, and hybrid functional level
which includes fractions of exact exchange. We investigate in detail the
metaGGA functionals MS2 [Sun \emph{et al}., Phys.\ Rev.\ Lett., \textbf{111},
106401 (2013)] and SCAN [Sun \emph{et al}., Phys.\ Rev.\ Lett., \textbf{115},
036402 (2015)], and show that although the metaGGAs show improvements over the
LDA and GGA functionals in describing the electronic structure and phonon
frequencies, the static structural properties of fluorides and oxyfluorides are
often more accurately predicted by the GGA-level functional PBEsol. Results
from LDA calculations are unsatisfactory for any compound regardless of oxygen
concentration. PBEsol or HSE06 gives good performance in all oxide or all
fluoride compounds. For the oxyfluorides, PBEsol is consistently more accurate
for structural properties across all oxygen concentrations, however, we stress
the need for detailed property assessment with various functionals for
oxyfluorides with variable composition. The "best" functional is anticipated to
be more strongly dependent on the property of interest. Our study provides
useful insights in selecting an $V_{xc}$ that achieves the best performance
comprise, enabling more accurate predictive design of functional fluoride-based
materials with density functional theory.


title:
Application of Microgrids in Addressing Distribution Network Net-Load
  Ramping

abstract:
  In spite of all advantages of solar energy, its deployment will significantly
change the typical electric load profile, thus necessitating a change in
traditional distribution grid management practices. In particular, the net load
ramping, created as a result of simultaneous solar generation drop and load
increase at early evening hours, is one of the major operational issues that
needs to be carefully addressed. In this paper, microgrids are utilized to
offer a viable and localized solution to this challenge while removing the need
for costly investments by the electric utility. In this regard, first the
microgrid ramping capability is determined via a min-max optimization, and
second, the microgrid optimal scheduling model is developed to coordinate the
microgrid net load with the distribution grid net load for addressing the
ramping issue. Numerical simulations on a test distribution feeder with one
microgrid exhibit the effectiveness of the proposed model.


title:
Leveraging Microgrids for Capturing Uncertain Distribution Network Net
  Load Ramping

abstract:
  In this paper, a flexibility-oriented microgrid optimal scheduling model is
proposed to mitigate distribution network net load variability caused by large
penetration distributed solar generation. The distributed solar generation
variability, which is caused by increasing adoption of this technology by
end-use consumers, is mainly addressed by electric utilities using grid
reinforcement. Microgrids, however, provide viable and local solutions to this
pressing challenge. The proposed model, which is developed using mixed-integer
programming and employs robust optimization, not only can efficiently capture
distribution network net load variations, mainly in terms of ramping, but also
accounts for possible uncertainties in forecasting. Numerical simulations on a
test distribution feeder with one microgrid and several consumers/prosumers
indicate the effectiveness of the proposed model.


title: The Conway Polynomial and Amphicheiral Knots

abstract:
  According to work of Hartley and Kawauchi in 1979 and 1980, the Conway
Polynomial of all negative amphicheiral knots and strongly positive
amphicheiral knots factors as $\phi(z)\phi(-z)$ for some $\phi(z)\in\mathbb
Z[z]$. Moreover, a 2012 example due to Ermotti, Hongler and Weber shows that
this is not true for general amphicheiral knots. On the other hand, in 2006 the
first author made a conjecture equivalent to saying that the Conway polynomial
of all amphicheiral knots splits as $\phi(z)\phi(-z)$ in the ring $\mathbb
Z_4[z]$. In this paper, we establish this conjecture for all periodically
amphicheiral knots built from braids, where the period preserves the braid
structure. We also give counterexamples to conjectures on the leading
coefficient of the Conway polynomial of an amphicheiral knot due to Stoimenow.


title: Cosmological surveys with multi-object spectrographs

abstract:
  Multi-object spectroscopy has been a key technique contributing to the
current era of 'precision cosmology'. From the first exploratory surveys of the
large-scale structure and evolution of the universe to the current generation
of superbly detailed maps spanning a wide range of redshifts, multi-object
spectroscopy has been a fundamentally important tool for mapping the rich
structure of the cosmic web and extracting cosmological information of
increasing variety and precision. This will continue to be true for the
foreseeable future, as we seek to map the evolving geometry and structure of
the universe over the full extent of cosmic history in order to obtain the most
precise and comprehensive measurements of cosmological parameters. Here I
briefly summarize the contributions that multi-object spectroscopy has made to
cosmology so far, then review the major surveys and instruments currently in
play and their prospects for pushing back the cosmological frontier. Finally, I
examine some of the next generation of instruments and surveys to explore how
the field will develop in coming years, with a particular focus on specialised
multi-object spectrographs for cosmology and the capabilities of multi-object
spectrographs on the new generation of extremely large telescopes.


title: Quantifying minimal non-collinearity among random points

abstract:
  Let $\varphi_{n,K}$ denote the largest angle in all the triangles with
vertices among the $n$ points selected at random in a compact convex subset $K$
of $\mathbb{R}^d$ with nonempty interior, where $d\ge2$. It is shown that the
distribution of the random variable
$\lambda_d(K)\,\frac{n^3}{3!}\,(\pi-\varphi_{n,K})^{d-1}$, where $\lambda_d(K)$
is a certain positive real number which depends only on the dimension $d$ and
the shape of $K$, converges to the standard exponential distribution as
$n\to\infty$. By using the Steiner symmetrization, it is also shown that
$\lambda_d(K)$ -- which is referred to in the paper as the elongation of $K$ --
attains its minimum if and only if $K$ is a ball $B^{(d)}$ in $\mathbb{R}^d$.
Finally, the asymptotics of $\lambda_d(B^{(d)})$ for large $d$ is determined.


title:
An Improved Algorithm for Diameter-Optimally Augmenting Paths in a
  Metric Space

abstract:
  Let $P$ be a path graph of $n$ vertices embedded in a metric space. We
consider the problem of adding a new edge to $P$ such that the diameter of the
resulting graph is minimized. Previously (in ICALP 2015) the problem was solved
in $O(n\log^3 n)$ time. In this paper, based on new observations and different
algorithmic techniques, we present an $O(n\log n)$ time algorithm.


title:
Dimensionality determination: a thresholding double ridge ratio
  criterion

abstract:
  Popularly used eigendecomposition-based criteria such as BIC type, ratio
estimation and principal component-based criterion often underdetermine model
dimensionality for regressions or the number of factors for factor models. This
longstanding problem is caused by the existence of one or two dominating
eigenvalues compared to other nonzero eigenvalues. To alleviate this
difficulty, we propose a thresholding double ridge ratio criterion such that
the true dimension can be better identified and is less underdetermined. Unlike
all existing eigendecomposition-based criteria, this criterion can define
consistent estimate without requiring the uniqueness of minimum and can then
handle possible multiple local minima scenarios. This generic strategy would be
readily applied to other dimensionality or order determination problems. In
this paper, we systematically investigate, for general sufficient dimension
reduction theory, the dimensionality determination with fixed and divergent
dimensions; for local alternative models that converge to its limiting model
with fewer projected covariates, discuss when the number of projected
covariates can be consistently estimated, when cannot; and for ultra-high
dimensional factor models, study the estimation consistency for the number of
common factors. Numerical studies are conducted to examine the finite sample
performance of the method.


title: Some Remarks on Quasinearly Subharmonic Functions

abstract:
  We prove some basic properties of quasinearly subharmonic functions and
quasinearly subharmonic functions in the narrow sense.


title: Entanglement as an axiomatic foundation for statistical mechanics

abstract:
  We propose four information-theoretic axioms for the foundations of
statistical mechanics in general physical theories. The axioms---Causality,
Purity Preservation, Pure Sharpness, and Purification---identify a class of
theories where every mixed state can be modelled as the marginal of a pure
entangled state and where every unsharp measurement can be modelled as a sharp
measurement on a composite system. This class of theories, called sharp
theories with purification, includes quantum theory both with complex and real
amplitudes, as well as a suitable extension of classical probability theory
where classical systems can be entangled with other, non-classical systems.
Theories satisfying our axioms support well-behaved notions of majorization,
entropy, and Gibbs states, allowing for an information-theoretic derivation of
Landauer's principle. We conjecture that every theory admitting a sensible
thermodynamics must be extendable to a sharp theory with purification.


title: Microcanonical thermodynamics in general physical theories

abstract:
  Microcanonical thermodynamics studies the operations that can be performed on
systems with well-defined energy. So far, this approach has been applied to
classical and quantum systems. Here we extend it to arbitrary physical
theories, proposing two requirements for the development of a general
microcanonical framework. We then formulate three resource theories,
corresponding to three different sets of basic operations: i) random reversible
operations, resulting from reversible dynamics with fluctuating parameters, ii)
noisy operations, generated by the interaction with ancillas in the
microcanonical state, and iii) unital operations, defined as the operations
that preserve the microcanonical state. We focus our attention on a class of
physical theories, called sharp theories with purification, where these three
sets of operations exhibit remarkable properties. Firstly, each set is
contained into the next. Secondly, the convertibility of states by unital
operations is completely characterised by a majorisation criterion. Thirdly,
the three sets are equivalent in terms of state convertibility if and only if
the dynamics allowed by theory satisfy a suitable condition, which we call
unrestricted reversibility. Under this condition, we derive a duality between
the resource theory of microcanonical thermodynamics and the resource theory of
pure bipartite entanglement.

categories: quant-ph cond-mat.stat-mech cs.IT math.IT


title: Thermodynamics and the structure of quantum theory

abstract:
  Despite its enormous empirical success, the formalism of quantum theory still
raises fundamental questions: why is nature described in terms of complex
Hilbert spaces, and what modifications of it could we reasonably expect to find
in some regimes of physics? Here we address these questions by studying how
compatibility with thermodynamics constrains the structure of quantum theory.
We employ two postulates that any probabilistic theory with reasonable
thermodynamic behavior should arguably satisfy. In the framework of generalized
probabilistic theories, we show that these postulates already imply important
aspects of quantum theory, like self-duality and analogues of projective
measurements, subspaces and eigenvalues. However, they may still admit a class
of theories beyond quantum mechanics. Using a thought experiment by von
Neumann, we show that these theories admit a consistent thermodynamic notion of
entropy, and prove that the second law holds for projective measurements and
mixing procedures. Furthermore, we study additional entropy-like quantities
based on measurement probabilities and convex decomposition probabilities, and
uncover a relation between one of these quantities and Sorkin's notion of
higher-order interference.


title:
Ionization at a solid-water interface in an applied electric field:
  Charge regulation

abstract:
  We investigate ionization at a solid-water interface in applied electric
field. We attach an electrode to a dielectric film bearing silanol or carboxyl
groups with an areal density $\Gamma_0$, where the degree of dissociation
$\alpha$ is determined by the proton density in water close to the film. We
show how $\alpha$ depends on the density $n_0$ of NaOH in water and the surface
charge density $\sigma_m$ on the electrode. For $\sigma_m>0$, the protons are
expelled away from the film, leading to an increase in $\alpha$. In particular,
in the range $0<\sigma_m<e\Gamma_0$, self-regulation occurs to realize $\alpha
\cong \sigma_m/e\Gamma_0 $ for $n_0\ll n_c$, where $n_c$ is $0.01$ mol$/$L for
silica surfaces and is $2\times 10^{-5}$ mol$/$L for carboxyl-bearing surfaces.
We also examine the charge regulation with decreasing the cell thickness $H$
below the Debye length $\kappa^{-1}$, where a crossover occurs at the
Gouy-Chapman length. In particular, when $\sigma_m \sim e\Gamma_0$ and $H\ll
\kappa^{-1}$, the surface charges remain only partially screened by ions,
leading to an electric field in the interior.


title:
Resonant enhancement in nanostructured thermoelectric performance via
  electronic thermal conductivity engineering

abstract:
  The use of an asymmetric broadening in the transport distribution, a
characteristic of resonant structures, is proposed as a route to engineer a
decrease in electronic thermal conductivity thereby enhancing the electronic
figure of merit in nanostructured thermoelectrics. Using toy models, we first
demonstrate that a decrease in thermal conductivity resulting from such an
asymmetric broadening may indeed lead to an electronic figure of merit well in
excess of 1000 in an idealized situation and in excess of 10 in a realistic
situation. We then substantiate with realistic resonant structures designed
using graphene nano-ribbons by employing a tight binding framework with edge
correction that match density functional theory calculations under the local
density approximation. The calculated figure of merit exceeding 10 in such
realistic structures further reinforces the concept and sets a promising
direction to use nano-ribbon structures to engineer a favorable decrease in the
electronic thermal conductivity.


title:
Time-Variable Aluminum Absorption in the Polar AR Ursae Majoris, and an
  Updated Estimate for the Mass of the White Dwarf

abstract:
  We present spectra of the extreme polar AR Ursae Majoris (AR UMa) which
display a clear Al I absorption doublet, alongside spectra taken less than a
year earlier in which that feature is not present. Re-examination of earlier
SDSS spectra indicates that the Al I absorption doublet was also present
$\approx$8 years before our first non-detection. We conclude that this
absorbing material is unlikely to be on the surface of either the white dwarf
(WD) or the donor star. We suggest that this Al I absorption feature arises in
circumstellar material, perhaps produced by the evaporation of asteroids as
they approach the hot WD. The presence of any remaining reservoir of rocky
material in AR UMa might help to constrain the prior evolution of this unusual
binary system. We also apply spectral decomposition to find the stellar
parameters of the M dwarf companion, and attempt to dynamically measure the
mass of the WD in AR UMa by considering both the radial velocity curves of the
H$_\beta$ emission line and the Na I absorption line. Thereby we infer a mass
range for the WD in AR UMa of 0.91 $M_{\odot}$ $<$ $M_{\mathrm{WD}}$ $<$ 1.24
$M_{\odot}$.


title:
Fast, Small and Exact: Infinite-order Language Modelling with Compressed
  Suffix Trees

abstract:
  Efficient methods for storing and querying are critical for scaling
high-order n-gram language models to large corpora. We propose a language model
based on compressed suffix trees, a representation that is highly compact and
can be easily held in memory, while supporting queries needed in computing
language model probabilities on-the-fly. We present several optimisations which
improve query runtimes up to 2500x, despite only incurring a modest increase in
construction time and memory usage. For large corpora and high Markov orders,
our method is highly competitive with the state-of-the-art KenLM package. It
imposes much lower memory requirements, often by orders of magnitude, and has
runtimes that are either similar (for training) or comparable (for querying).


title:
Distributed Charging Control in Broadband Wireless Power Transfer
  Networks

abstract:
  Wireless power transfer (WPT) technology provides a cost-effective solution
to achieve sustainable energy supply in wireless networks, where WPT-enabled
energy nodes (ENs) can charge wireless devices (WDs) remotely without
interruption to the use. However, in a heterogeneous WPT network with
distributed ENs and WDs, some WDs may quickly deplete their batteries due to
the lack of timely wireless power supply by the ENs, thus resulting in short
network operating lifetime. In this paper, we exploit frequency diversity in a
broadband WPT network and study the distributed charging control by ENs to
maximize network lifetime. In particular, we propose a practical voting-based
distributed charging control framework where each WD simply estimates the
broadband channel, casts its vote(s) for some strong sub-channel(s) and sends
to the ENs along with its battery state information, based on which the ENs
independently allocate their transmit power over the sub-channels without the
need of centralized control. Under this framework, we aim to design
lifetime-maximizing power allocation and efficient voting-based feedback
methods. Towards this end, we first derive the general expression of the
expected lifetime of a WPT network and draw the general design principles for
lifetime-maximizing charging control. Based on the analysis, we then propose a
distributed charging control protocol with voting-based feedback, where the
power allocated to sub-channels at each EN is a function of the weighted sum
vote received from all WDs. Besides, the number of votes cast by a WD and the
weight of each vote are related to its current battery state. Simulation
results show that the proposed distributed charging control protocol could
significantly increase the network lifetime under stringent transmit power
constraint in a broadband WPT network.


title:
Operations- and Uncertainty-Aware Installation of FACTS Devices in a
  Large Transmission System

abstract:
  Decentralized electricity markets and more integration of renewables demand
expansion of the existing transmission infrastructure to accommodate inflected
variabilities in power flows. However, such expansion is severely limited in
many countries because of political and environmental issues. Furthermore, high
renewables integration requires additional reactive power support, which forces
the transmission system operators to utilize the existing grid creatively,
e.g., take advantage of new technologies, such as flexible alternating current
transmission system (FACTS) devices. We formulate, analyze and solve the
challenging investment planning problem of installation in an existing
large-scale transmission grid multiple FACTS devices of two types (series
capacitors and static VAR compensators.) We account for details of AC character
of the power flows, probabilistic modeling of multiple-load scenarios, FACTS
devices flexibility in terms of their adjustments within the capacity
constraints, and long term practical tradeoffs between capital vs operational
expenditures (CAPEX vs OPEX). It is demonstrated that proper installation of
the devices allows to do both - extend or improve feasibility domain for the
system and also decrease long term power generation cost (make cheaper
generation available). Nonlinear, nonconvex, and multiple-scenario-aware
optimization is resolved through an efficient heuristic algorithm consisting of
a sequence of quadratic programmings solved by CPLEX combined with exact AC PF
resolution for each scenario for maintaining feasible operational states during
iterations. Efficiency and scalability of the approach is illustrated on the
IEEE 30-bus model and the 2736-bus Polish model from Matpower.


title: Unbiased Learning-to-Rank with Biased Feedback

abstract:
  Implicit feedback (e.g., clicks, dwell times, etc.) is an abundant source of
data in human-interactive systems. While implicit feedback has many advantages
(e.g., it is inexpensive to collect, user centric, and timely), its inherent
biases are a key obstacle to its effective use. For example, position bias in
search rankings strongly influences how many clicks a result receives, so that
directly using click data as a training signal in Learning-to-Rank (LTR)
methods yields sub-optimal results. To overcome this bias problem, we present a
counterfactual inference framework that provides the theoretical basis for
unbiased LTR via Empirical Risk Minimization despite biased data. Using this
framework, we derive a Propensity-Weighted Ranking SVM for discriminative
learning from implicit feedback, where click models take the role of the
propensity estimator. In contrast to most conventional approaches to de-bias
the data using click models, this allows training of ranking functions even in
settings where queries do not repeat. Beyond the theoretical support, we show
empirically that the proposed learning method is highly effective in dealing
with biases, that it is robust to noise and propensity model misspecification,
and that it scales efficiently. We also demonstrate the real-world
applicability of our approach on an operational search engine, where it
substantially improves retrieval performance.


title:
Numerical Simulations of Turbulent, Molecular Clouds Regulated by
  Radiation Feedback Forces I: Star Formation Rate and Efficiency

abstract:
  Radiation feedback from stellar clusters is expected to play a key role in
setting the rate and efficiency of star formation in giant molecular clouds
(GMCs). To investigate how radiation forces influence realistic turbulent
systems, we have conducted a series of numerical simulations employing the {\it
Hyperion} radiation hydrodynamics solver, considering the regime that is
optically thick to ultraviolet (UV) and optically thin to infrared (IR)
radiation. Our model clouds cover initial surface densities between
$\Sigma_{\rm cl,0} \sim 10-300~M_{\odot}~{\rm pc^{-2}}$, with varying initial
turbulence. We follow them through turbulent, self-gravitating collapse,
formation of star clusters, and cloud dispersal by stellar radiation. All our
models display a lognormal distribution of gas surface density $\Sigma$; for an
initial virial parameter $\alpha_{\rm vir,0} = 2$, the lognormal standard
deviation is $\sigma_{\rm ln \Sigma} = 1-1.5$ and the star formation rate
coefficient $\varepsilon_{\rm ff,\bar\rho} = 0.3-0.5$, both of which are
sensitive to turbulence but not radiation feedback. The net star formation
efficiency $\varepsilon_\mathrm{final}$ increases with $\Sigma_{\rm cl,0}$ and
decreases with $\alpha_{\rm vir,0}$. We interpret these results via a simple
conceptual framework, whereby steady star formation increases the radiation
force, such that local gas patches at successively higher $\Sigma$ become
unbound. Based on this formalism (with fixed $\sigma_{\rm ln \Sigma}$), we
provide an analytic upper bound on $\varepsilon_\mathrm{final}$, which is in
good agreement with our numerical results. The final star formation efficiency
depends on the distribution of Eddington ratios in the cloud and is strongly
increased by turbulent compression of gas.


title:
Calculation of 1/m^{2}_{b} corrections to
  {\Lambda}_b\rightarrow{\Lambda}_c decay widths in the Bethe-Salpeter equation
  approach

abstract:
  The matrix element of the weak transition {\Lambda}_b\rightarrow{\Lambda}_c
can be expressed in terms of six form factors. {\Lambda}_Q(Q = b;c) can be
regarded as composed of a heavy quark Q(Q = b;c) and a diquark which is made up
of the remaining two light quarks. In this picture, we express these six form
factors in terms of Bethe-Salpeter wave functions to second order in the 1/m_Q
expansion. With the kernel containing both the scalar confinement and the
one-gluon-exchange terms we calculate the form factors and the decay widths of
the semileptonic decay {\Lambda}_b\rightarrow{\Lambda}_clv as well as
nonleptonic decays {\Lambda}_b\rightarrow{\Lambda}_cP(V) numerically. We also
add QCD corrections since they are comparable with 1/m_Q corrections.


title:
Stein Variational Gradient Descent: A General Purpose Bayesian Inference
  Algorithm

abstract:
  We propose a general purpose variational inference algorithm that forms a
natural counterpart of gradient descent for optimization. Our method
iteratively transports a set of particles to match the target distribution, by
applying a form of functional gradient descent that minimizes the KL
divergence. Empirical studies are performed on various real world models and
datasets, on which our method is competitive with existing state-of-the-art
methods. The derivation of our method is based on a new theoretical result that
connects the derivative of KL divergence under smooth transforms with Stein's
identity and a recently proposed kernelized Stein discrepancy, which is of
independent interest.


title: Refining Approximating Betweenness Centrality Based on Samplings

abstract:
  Betweenness Centrality (BC) is an important measure used widely in complex
network analysis, such as social network, web page search, etc. Computing the
exact BC values is highly time consuming. Currently the fastest exact BC
determining algorithm is given by Brandes, taking $O(nm)$ time for unweighted
graphs and $O(nm+n^2\log n)$ time for weighted graphs, where $n$ is the number
of vertices and $m$ is the number of edges in the graph. Due to the extreme
difficulty of reducing the time complexity of exact BC determining problem,
many researchers have considered the possibility of any satisfactory BC
approximation algorithms, especially those based on samplings. Bader et al.
give the currently best BC approximation algorithm, with a high probability to
successfully estimate the BC of one vertex within a factor of $1/\varepsilon$
using $\varepsilon t$ samples, where $t$ is the ratio between $n^2$ and the BC
value of the vertex. However, some of the algorithmic parameters in Bader's
work are not yet tightly bounded, leaving some space to improve their
algorithm. In this project, we revisit Bader's algorithm, give more tight
bounds on the estimations, and improve the performance of the approximation
algorithm, i.e., fewer samples and lower factors. On the other hand, Riondato
proposes a non-adaptive BC approximation method with samplings on shortest
paths. We investigate the possibility to give an adaptive algorithm with
samplings on vertices. With rigorous reasoning, we show that this algorithm can
also give bounded approximation factors and number of samplings. To evaluate
our results, we also conduct extensive experiments using real-world datasets,
and find that both our algorithms can achieve acceptable performance. We verify
that our work can improve the current BC approximation algorithm with better
parameters, i.e., higher successful probabilities, lower factors and fewer
samples.


title:
Homological mirror symmetry for open Riemann surfaces from pair-of-pants
  decompositions

abstract:
  Given a punctured Riemann surface with a pair-of-pants decomposition, we
compute its wrapped Fukaya category in a suitable model by reconstructing it
from those of various pairs of pants. The pieces are glued together in the
sense that the restrictions of the wrapped Floer complexes from two adjacent
pairs of pants to their adjoining cylindrical piece agree. The
$A_\infty$-structures are given by those in the pairs of pants. The category of
singularities of the mirror Landau-Ginzburg model can also be constructed in
the same way from local affine pieces that are mirrors of the pairs of pants.


title: Conditional Reliability in Uncertain Graphs

abstract:
  Network reliability is a well-studied problem that requires to measure the
probability that a target node is reachable from a source node in a
probabilistic (or uncertain) graph, i.e., a graph where every edge is assigned
a probability of existence. Many approaches and problem variants have been
considered in the literature, all assuming that edge-existence probabilities
are fixed. Nevertheless, in real-world graphs, edge probabilities typically
depend on external conditions. In metabolic networks a protein can be converted
into another protein with some probability depending on the presence of certain
enzymes. In social influence networks the probability that a tweet of some user
will be re-tweeted by her followers depends on whether the tweet contains
specific hashtags. In transportation networks the probability that a network
segment will work properly or not might depend on external conditions such as
weather or time of the day. In this paper we overcome this limitation and focus
on conditional reliability, that is assessing reliability when edge-existence
probabilities depend on a set of conditions. In particular, we study the
problem of determining the k conditions that maximize the reliability between
two nodes. We deeply characterize our problem and show that, even employing
polynomial-time reliability-estimation methods, it is NP-hard, does not admit
any PTAS, and the underlying objective function is non-submodular. We then
devise a practical method that targets both accuracy and efficiency. We also
study natural generalizations of the problem with multiple source and target
nodes. An extensive empirical evaluation on several large, real-life graphs
demonstrates effectiveness and scalability of the proposed methods.


title: The Gromov boundary of the ray graph

abstract:
  The ray graph is a Gromov hyperbolic graph on which the mapping class group
of the plane minus a Cantor set acts by isometries. We give a description of
the Gromov boundary of the ray graph in terms of cliques of long rays on the
plane minus a Cantor set. As a consequence, we prove that the Gromov boundary
of the ray graph is homeomorphic to a quotient of a subset of the circle.
  This version contains some updates and corrections.


title: Seshadri constants on surfaces with Picard number 1

abstract:
  Let $X$ be a smooth projective surface with Picard number 1. Let $L$ be the
ample generator of the N\'eron-Severi group of $X$. Given an integer $r\ge 2$,
we prove lower bounds for the Seshadri constant of $L$ at $r$ general points in
$X$.


title:
A class of multi-marginal c-cyclically monotone sets with explicit
  c-splitting potentials

abstract:
  Multi-marginal optimal transport plans are concentrated on c-splitting sets.
It is known that, similar to the two-marginal case, c-splitting sets are
c-cyclically monotone. Within a suitable framework, the converse implication
was very recently established by Griessler. However, for an arbitrary cost c,
given a multi-marginal c-cyclically monotone set, the question whether there
exists an analogous explicit construction to the one from the two-marginal case
of c-splitting potentials is still open. When the margins are one-dimensional
and the cost belongs to a certain class, Carlier proved that the two-marginal
projections of a c-splitting set are monotone. For arbitrary products of sets
equipped with cost functions which are sums of two-marginal costs, we show that
the two-marginal monotonicity condition is a sufficient condition which does
give rise to an explicit construction of c-splitting potentials. Our condition
is, in principle, easier to verify than the one of multi-marginal c-cyclic
monotonicity. Various examples illustrate our results. We show that, in
general, our condition is sufficient; however, it is not necessary. On the
other hand, we conclude that when the margins are one-dimensional equipped with
classical cost functions, our condition is a characterization of c-splitting
sets and extends classical convex analysis.


title: A Geometrical Approach to Topic Model Estimation

abstract:
  In the probabilistic topic models, the quantity of interest---a low-rank
matrix consisting of topic vectors---is hidden in the text corpus matrix,
masked by noise, and the Singular Value Decomposition (SVD) is a potentially
useful tool for learning such a low-rank matrix. However, the connection
between this low-rank matrix and the singular vectors of the text corpus matrix
are usually complicated and hard to spell out, so how to use SVD for learning
topic models faces challenges. In this paper, we overcome the challenge by
revealing a surprising insight: there is a low-dimensional simplex structure
which can be viewed as a bridge between the low-rank matrix of interest and the
SVD of the text corpus matrix, and allows us to conveniently reconstruct the
former using the latter. Such an insight motivates a new SVD approach to
learning topic models, which we analyze with delicate random matrix theory and
derive the rate of convergence. We support our methods and theory numerically,
using both simulated data and real data.


title:
Expanded Comment on: Optical Properties of Fluid Hydrogen at the
  Transition to a Conducting State

abstract:
  We comment on a recent paper published by McWilliams et al claiming that
high-pressure/high-temperature hydrogen is a semi-conductor or semi-metal, in
conflict with all earlier measurements on this system which show that it is
metallic. We point out problems and inconsistencies and propose an alternate
interpretation such that their results can be compatible with the observation
of metallic hydrogen.


title:
Faint warm debris disks around nearby bright stars explored by AKARI and
  IRSF

abstract:
  Context: Debris disks are important observational clues for understanding
planetary-system formation process. In particular, faint warm debris disks may
be related to late planet formation near 1 AU. A systematic search of faint
warm debris disks is necessary to reveal terrestrial planet formation. Aims:
Faint warm debris disks show excess emission that peaks at mid-IR wavelengths.
Thus we explore debris disks using the AKARI mid-IR all-sky point source
catalog (PSC), a product of the second generation unbiased IR all-sky survey.
Methods : We investigate IR excess emission for 678 isolated main-sequence
stars for which there are 18 micron detections in the AKARI mid-IR all-sky
catalog by comparing their fluxes with the predicted fluxes of the photospheres
based on optical to near-IR fluxes and model spectra. The near-IR fluxes are
first taken from the 2MASS PSC. However, 286 stars with Ks<4.5 in our sample
have large flux errors in the 2MASS photometry due to saturation. Thus we have
measured accurate J, H, and Ks band fluxes, applying neutral density (ND)
filters for Simultaneous InfraRed Imager for Unbiased Survey (SIRIUS) on IRSF,
the \phi 1.4 m near-IR telescope in South Africa, and improved the flux
accuracy from 14% to 1.8% on average. Results: We identified 53 debris-disk
candidates including eight new detections from our sample of 678 main-sequence
stars. The detection rate of debris disks for this work is ~8%, which is
comparable with those in previous works by Spitzer and Herschel. Conclusion:
The importance of this study is the detection of faint warm debris disks around
nearby field stars. At least nine objects have a large amount of dust for their
ages, which cannot be explained by the conventional steady-state collisional
cascade model.


title: Lecture Notes on Randomized Linear Algebra

abstract:
  These are lecture notes that are based on the lectures from a class I taught
on the topic of Randomized Linear Algebra (RLA) at UC Berkeley during the Fall
2013 semester.


title: Exact probability distribution functions for Parrondo's games

abstract:
  We consider discrete time Brownian ratchet models: Parrondo's games. Using
the Fourier transform, we calculate the exact probability distribution
functions for both the capital dependent and history dependent Parrondo's
games. We find that in some cases there are oscillations near the maximum of
the probability distribution, and after many rounds there are two limiting
distributions, for the odd and even total number of rounds of gambling. We
assume that the solution of the aforementioned models can be applied to
portfolio optimization.


title:
Cohomological invariants for G-Galois algebras and self-dual normal
  bases

abstract:
  We define degree two cohomological invariants for G-Galois algebras over
fields of characteristic not 2, and use them to give necessary conditions for
the existence of a self--dual normal basis. In some cases, we show that these
conditions are also sufficient.


title: Layered Synthesis of Latent Gaussian Trees

abstract:
  A new synthesis scheme is proposed to generate a random vector with
prescribed joint density that induces a (latent) Gaussian tree structure. The
quality of synthesis is shown by vanishing total variation distance between the
synthesized and desired statistics. The proposed layered and successive
synthesis scheme relies on the learned structure of tree to use sufficient
number of common random variables to synthesize the desired density. We
characterize the achievable rate region for the rate tuples of multi-layer
latent Gaussian tree, through which the number of bits needed to synthesize
such Gaussian joint density are determined. The random sources used in our
algorithm are the latent variables at the top layer of tree, the additive
independent Gaussian noises, and the Bernoulli sign inputs that capture the
ambiguity of correlation signs between the variables. We have shown that such
ambiguity can further help in reducing the synthesis rates for the underlying
Gaussian trees.


title: Authorship clustering using multi-headed recurrent neural networks

abstract:
  A recurrent neural network that has been trained to separately model the
language of several documents by unknown authors is used to measure similarity
between the documents. It is able to find clues of common authorship even when
the documents are very short and about disparate topics. While it is easy to
make statistically significant predictions regarding authorship, it is
difficult to group documents into definite clusters with high accuracy.


title: Absence of dipolar ordering in Co doped CuO

abstract:
  Polycrystalline CuO samples with Co doping were prepared by solid state
method with flowing oxygen condition and examined their structural and
multiferroic properties. Structural studies have confirmed single phase
monoclinic crystal structure of all samples, however, in Co doped samples a
decrease in volume with an increase in monoclinic distortion is found. For
pristine sample, temperature dependent magnetization has confirmed two
antiferromagnetic (AFM) transitions at 213 K and 230 K and frequency
independent dielectric peaks at these AFM transitions suggesting the
ferroelectric nature. Magnetization of the Co doped samples has showed a
marginal increase in ordering temperature of the high-temperature AFM
transition and decrease in low temperature AFM ordering temperature. Further,
doped samples have shown giant dielectric constant with no signature of
ferroelectricity. The x-ray photoelectric spectroscopy study has revealed
multiple valance states for both Co and Cu in the doped samples that
simultaneously explain the giant dielectric constant and suppression of
ferroelectric order.


title: On the conjugacy class of the Fibonacci dynamical system

abstract:
  We characterize the symbolical dynamical systems which are topologically
isomorphic to the Fibonacci dynmaical system. We prove that there are
infinitely many injective primitive substitutions generating a dynamical system
in the Fibonacci conjugacy class. In this class there are infinitely many
dynamical systems not generated by a substitution. An example is the system
generated by doubling the 0's in the infinite Fibonacci word.


title: A Novel Implementation for Automated Health Monitoring System

abstract:
  The main concept of the proposed work is derived from Wireless Body Area
network (WBAN). The proposed work employs Raspberry Pi kit as a personal server
which logs the health data and it can be accessed by any PDA within the LAN
range. In this paper, two vital parameters namely Temperature sensor and Heart
beat sensor have been considered.


title: SenTion: A framework for Sensing Facial Expressions

abstract:
  Facial expressions are an integral part of human cognition and communication,
and can be applied in various real life applications. A vital precursor to
accurate expression recognition is feature extraction. In this paper, we
propose SenTion: A framework for sensing facial expressions. We propose a novel
person independent and scale invariant method of extracting Inter Vector Angles
(IVA) as geometric features, which proves to be robust and reliable across
databases. SenTion employs a novel framework of combining geometric (IVA's) and
appearance based features (Histogram of Gradients) to create a hybrid model,
that achieves state of the art recognition accuracy. We evaluate the
performance of SenTion on two famous face expression data set, namely: CK+ and
JAFFE; and subsequently evaluate the viability of facial expression systems by
a user study. Extensive experiments showed that SenTion framework yielded
dramatic improvements in facial expression recognition and could be employed in
real-world applications with low resolution imaging and minimal computational
resources in real-time, achieving 15-18 fps on a 2.4 GHz CPU with no GPU.


title:
Incommensurate spiral magnetic order on anisotropic triangular lattice:
  Dynamical mean field study in a spin-rotating frame

abstract:
  We study the ground-state magnetism of the half-filled Hubbard model on the
anisotropic triangular lattice, where two out of three bonds have hopping $t$
and the third one has $t^\prime$ in a unit triangle. Working in a spin-rotating
frame and using the density matrix renormalization group method as an impurity
solver, we provide a proper description of incommensurate magnetizations at
zero temperature in the framework of the dynamical mean-field theory (DMFT). It
is shown that the incommensurate spiral magnetic order for $t^\prime/t\gtrsim
0.7$ survives the dynamical fluctuations of itinerant electrons in the Hubbard
interaction range from the strong-coupling (localized-spin) limit down to the
insulator-to-metal transition. We also find that the magnetic moment reduction
from the localized-spin limit is pronounced in the vicinity of the transition
between the commensurate N\'eel and incommensurate spiral phases at
$t^\prime/t\sim 0.7$. When the anisotropy parameter $t^\prime/t$ increases from
the N\'eel-to-spiral transition, the magnitude of the magnetic moment
immediately reaches a maximum and then rapidly decreases in the range of larger
$t^\prime/t$ including the isotropic triangular lattice point $t^\prime/t=1$.
This work gives a solid foundation for further extension of the study including
nonlocal correlation effects neglected at the standard DMFT level.


title: Computing the Fr\'echet Derivative of the Polar Decomposition

abstract:
  We derive iterative methods for computing the Fr\'{e}chet derivative of the
map which sends a full-rank matrix $A$ to the factor $U$ in its polar
decomposition $A=UH$, where $U$ has orthonormal columns and $H$ is Hermitian
positive definite. The methods apply to square matrices as well as rectangular
matrices having more rows than columns. Our derivation relies on a novel
identity that relates the Fr\'{e}chet derivative of the polar decomposition to
the matrix sign function $\mathrm{sign}(X) = X (X^2)^{-1/2}$ applied to a
certain block matrix $X$.


title:
Persistence analysis of the age-structured population model on several
  patches

abstract:
  We consider a system of nonlinear partial differential equations that
describes an age-structured population living in changing environment on $N$
patches. We prove existence and uniqueness of solution and analyze large time
behavior of the system in time-independent case, for periodically changing and
for irregularly varying environment. Under the assumption that every patch can
be reached from every other patch, directly or through several intermediary
patches, and that net reproductive operator has spectral radius larger than
one, we prove that population is persistent on all patches. If the spectral
radius is less or equal one, extinction on all patches is imminent.


title: Dynamic Network Surgery for Efficient DNNs

abstract:
  Deep learning has become a ubiquitous technology to improve machine
intelligence. However, most of the existing deep models are structurally very
complex, making them difficult to be deployed on the mobile platforms with
limited computational power. In this paper, we propose a novel network
compression method called dynamic network surgery, which can remarkably reduce
the network complexity by making on-the-fly connection pruning. Unlike the
previous methods which accomplish this task in a greedy way, we properly
incorporate connection splicing into the whole process to avoid incorrect
pruning and make it as a continual network maintenance. The effectiveness of
our method is proved with experiments. Without any accuracy loss, our method
can efficiently compress the number of parameters in LeNet-5 and AlexNet by a
factor of $\bm{108}\times$ and $\bm{17.7}\times$ respectively, proving that it
outperforms the recent pruning method by considerable margins. Code and some
models are available at https://github.com/yiwenguo/Dynamic-Network-Surgery.


title:
Exciton transfer dynamics and quantum diffusion in a lattice of two
  level systems: Interplay between transport and coherent population transfer
  dynamics

abstract:
  We study excitation transfer dynamics in a lattice of two level systems
characterized by dynamic disorder. The diagonal and off-diagonal energy
disorders arise from the coupling of system and bath. We consider both the same
and the independent bath limits. In case of independent bath all diagonal and
off-diagonal bath coupling elements fluctuate independently of each other and
the dynamics is complicated. We obtain the time dependent population
distribution by solving quantum stochastic Liouville equation (QSLE) derived by
Kubo. The main result of our study is both the population transfer dynamics and
the mean square displacement of the exciton behave the similar way in the same
and independent bath cases in the Markovian limit. However, these two baths can
give rise to markedly different behavior in the non-Markovian limit where
coherent transport becomes important. There are also several additional new
results as follows. (i) Exciton migration remains coherent all the time for an
average, non-zero off-diagonal coupling value J for the same bath case while it
becomes incoherent for independent bath case in the Markovian limit. (ii) An
oscillatory behavior of the population transfer dynamics supports the coherent
mode of transfer of exciton. (iii) Agreement with available analytical
expression of mean squared displacement is good in the Markovian limit for
independent bath case with off-diagonal fluctuation but only qualitative in the
non-Markovian limit for which no complete analytical solution is available.
(iv) Transition from coherent to incoherent transport is observed in the
independent bath case with diagonal fluctuation when the bath is made
progressively more Markovian. (v) The correlation time of the bath plays a
unique role in dictating the diffusive spread that is not anticipated in a
Markovian treatment.


title:
Exchange anisotropy as mechanism for spin-stripe formation in frustrated
  spin chains

abstract:
  We investigate the spin-stripe mechanism responsible for the peculiar
nanometer modulation of the incommensurate magnetic order that emerges between
the vector-chiral and the spin-density-wave phase in the frustrated zigzag
spin-1/2 chain compound $\beta$-TeVO$_4$. A combination of magnetic-torque,
neutron-diffraction and spherical-neutron-polarimetry measurements is employed
to determine the complex magnetic structures of all three ordered phases. Based
on these results, we develop a simple phenomenological model, which exposes the
exchange anisotropy as the key ingredient for the spin-stripe formation in
frustrated spin systems.


title: Complex of injective words revisited

abstract:
  We give a simple proof that (a generalization of) the complex of injective
words has vanishing homology in all except the top degree.