title: The non-Gaussian distribution of galaxies gravitational fields

abstract:
  We perform a theoretical analysis of the observational dependence between
angular momentum of the galaxy clusters and their mass (richness), based on the
method introduced in our previous paper. For that we obtain the distribution
function of astronomical objects (like galaxies and/or smooth halos of
different kinds) gravitational fields due to their tidal interaction. Within
the statistical method of Chandrasekhar we are able to show that the
distribution function is determined by the form of interaction between objects
and for multipole (tidal) interaction it is never Gaussian. Our calculation
permits to demonstrate how the alignment of galaxies angular momenta depend on
the cluster richness. The specific form of the corresponding dependence is due
to assumptions made about cluster morphology. Our approach also predicts the
time evolution of stellar objects angular momenta within CDM and $\Lambda$CDM
models. Namely, we have shown that angular momentum of galaxies increases with
time.


title: Revisiting the Futamura Projections: A Diagrammatic Approach

abstract:
  The advent of language implementation tools such as PyPy and Truffle/Graal
have reinvigorated and broadened interest in topics related to automatic
compiler generation and optimization. Given this broader interest, we revisit
the Futamura Projections using a novel diagram scheme. Through these diagrams
we emphasize the recurring patterns in the Futamura Projections while
addressing their complexity and abstract nature. We anticipate that this
approach will improve the accessibility of the Futamura Projections and help
foster analysis of those new tools through the lens of partial evaluation.


title: On rank-width of even-hole-free graphs

abstract:
  We present a class of (diamond, even hole)-free graphs with no clique cutset
that has unbounded rank-width. In general, even-hole-free graphs have unbounded
rank-width, because chordal graphs are even-hole-free. A.A. da Silva, A. Silva
and C. Linhares-Sales (2010) showed that planar even-hole-free graphs have
bounded rank-width, and N.K. Le (2016) showed that even-hole-free graphs with
no star cutset have bounded rank-width. A natural question is to ask, whether
even-hole-free graphs with no clique cutsets have bounded rank-width. Our
result gives a negative answer. Hence we cannot apply Courcelle and Makowsky's
meta-theorem which would provide efficient algorithms for a large number of
problems, including the maximum independent set problem, whose complexity
remains open for (diamond, even hole)-free graphs.


title: On the Validity of Dark Matter Effective Theory

abstract:
  An effective theory of dark matter offers an attractive framework for global
analyses of dark matter. In the light of global fits we test the validity of
the link between the non-relativistic dark matter annihilation, or the
predicted relic density, and LHC signatures. Specifically, we study how well
the effective theory describes the main features of simple models with
s-channel and t-channel mediators coupling to the Standard Model at tree level
or through one-loop diagrams. Our results indicate that global dark matter
analyses in terms of effective Lagrangians are highly non-trivial to interpret
in term of actual models.


title: The Bethe ansatz for the six-vertex and XXZ models: an exposition

abstract:
  In this paper, we review a few known facts on the coordinate Bethe ansatz. We
present a detailed construction of the Bethe ansatz vector $\psi$ and energy
$\Lambda$, which satisfy $V \psi = \Lambda \psi$, where $V$ is the the transfer
matrix of the six-vertex model on a finite square lattice with periodic
boundary conditions for weights $a= b=1$ and $c > 0$. We also show that the
same vector $\psi$ satisfies $H \psi = E \psi$, where $H$ is the Hamiltonian of
the XXZ model (which is the model for which the Bethe ansatz was first
developed), with a value $E$ computed explicitly.
  Variants of this approach have become central techniques for the study of
exactly solvable statistical mechanics models in both the physics and
mathematics communities. Our aim in this paper is to provide a
pedagogically-minded exposition of this construction, aimed at a mathematical
audience. It also provides the opportunity to introduce the notation and
framework which will be used in a subsequent paper by the authors that amounts
to proving that the random cluster model on $\mathbb{Z}^2$ with cluster weight
$q >4$ exhibits a first-order phase transition.


title:
The evolution of superluminous supernova LSQ14mo and its interacting
  host galaxy system

abstract:
  We present and analyse an extensive dataset of the superluminous supernova
(SLSN) LSQ14mo (z = 0.256), consisting of a multi-colour lightcurve from -30 d
to +70 d in the rest-frame and a series of 6 spectra from PESSTO covering -7 d
to +50 d. This is among the densest spectroscopic coverage, and
best-constrained rising lightcurve, for a fast-declining hydrogen-poor SLSN.
The bolometric lightcurve can be reproduced with a millisecond magnetar model
with ~ 4 M_sol ejecta mass, and the temperature and velocity evolution is also
suggestive of a magnetar as the power source. Spectral modelling indicates that
the SN ejected ~ 6 M_sol of CO-rich material with a kinetic energy of ~ 7 x
10^51 erg, and suggests a partially thermalised additional source of luminosity
between -2 d and +22 d. This may be due to interaction with a shell of material
originating from pre-explosion mass loss. We further present a detailed
analysis of the host galaxy system of LSQ14mo. PESSTO and GROND imaging show
three spatially resolved bright regions, and we used the VLT and FORS2 to
obtain a deep (five-hour exposure) spectra of the SN position and the three
star-forming regions, which are at a similar redshift. The FORS spectrum at
+300 days shows no trace of SN emission lines and we place limits on the
strength of [O I] from comparisons with other Ic SNe. The deep spectra provides
a unique chance to investigate spatial variations in the host star-formation
activity and metallicity. The specific star-formation rate is similar in all
three components, as is the presence of a young stellar population. However,
the position of LSQ14mo exhibits a lower metallicity, with 12 + log(O/H) = 8.2
in both the R23 and N2 scales (corresponding to ~ 0.3 Z_sol). We propose that
the three bright regions in the host system are interacting, which thus
triggers star-formation and forms young stellar populations.


title: Future Gravitational Wave Detectors Based on Atom Interferometry

abstract:
  We present the perspective of using atom interferometry for gravitational
wave (GW) detection in the mHz to about 10 Hz frequency band. We focus on
light-pulse atom interferometers which have been subject to intense
developments in the last 25 years. We calculate the effect of the GW on the
atom interferometer and present in details the atomic gradiometer configuration
which has retained more attention recently. The principle of such a detector is
to use free falling atoms to measure the phase of a laser, which is modified by
the GW. We highlight the potential benefits of using atom interferometry
compared to optical interferometry as well as the challenges which remain for
the realization of an atom interferometry based GW detector. We present some of
the important noise sources which are expected in such detectors and strategies
to cirucumvent them. Experimental techniques related to cold atom
interferometers are briefly explained. We finally present the current progress
and projects in this rapidly evolving field.


title:
Strong vibration nonlinearity in semiconductor-based nanomechanical
  systems

abstract:
  We study the effect of the electron-phonon coupling on vibrational eigenmodes
of nano- and micro-mechanical systems made of semiconductors with equivalent
energy valleys. We show that the coupling can lead to a strong mode
nonlinearity. The mechanism is the lifting of the valley degeneracy by the
strain. The redistribution of the electrons between the valleys is controlled
by a large ratio of the electron-phonon coupling constant to the electron
chemical potential or temperature. We find the quartic in the strain terms in
the electron free energy, which determine the amplitude dependence of the mode
frequencies. This dependence is calculated for silicon micro-systems. It is
significantly different for different modes and the crystal orientation, and
can vary nonmonotonously with the electron density and temperature.


title: Capacity and Trainability in Recurrent Neural Networks

abstract:
  Two potential bottlenecks on the expressiveness of recurrent neural networks
(RNNs) are their ability to store information about the task in their
parameters, and to store information about the input history in their units. We
show experimentally that all common RNN architectures achieve nearly the same
per-task and per-unit capacity bounds with careful training, for a variety of
tasks and stacking depths. They can store an amount of task information which
is linear in the number of parameters, and is approximately 5 bits per
parameter. They can additionally store approximately one real number from their
input history per hidden unit. We further find that for several tasks it is the
per-task parameter capacity bound that determines performance. These results
suggest that many previous results comparing RNN architectures are driven
primarily by differences in training effectiveness, rather than differences in
capacity. Supporting this observation, we compare training difficulty for
several architectures, and show that vanilla RNNs are far more difficult to
train, yet have slightly higher capacity. Finally, we propose two novel RNN
architectures, one of which is easier to train than the LSTM or GRU for deeply
stacked architectures.


title: Batch and PIR Codes and Their Connections to Locally Repairable Codes

abstract:
  In this survey, two related families of codes are discussed: batch codes and
codes for private information retrieval. These two families can be viewed as
natural generalizations of locally repairable codes, which were extensively
studied in the context of coding for fault tolerance in distributed data
storage systems. Bounds on the parameters of the codes, as well as basic
constructions, are presented. Connections between different code families are
discussed.


title:
Stub Wireless Multi-hop Networks using Self-configurable Wi-Fi Basic
  Service Set Cascading

abstract:
  The increasing trend in wireless Internet access has been boosted by IEEE
802.11. However, the application scenarios are still limited by its short radio
range. Stub Wireless Multi-hop Networks (WMNs) are a robust, flexible, and
cost-effective solution to the problem. Yet, typically they are formed by
single radio mesh nodes and suffer from hidden node, unfairness, and
scalability problems.
  We propose a simple multi-radio, multi-channel WMN solution, named Wi-Fi
network Infrastructure eXtension - Dual-Radio (WiFIX-DR), to overcome these
problems. WiFIX-DR reuses IEEE 802.11 built-in mechanisms and beacons to form a
Stub WMN as a set of self-configurable interconnected Basic Service Sets
(BSSs). Experimental results show the improved scalability enabled by the
proposed solution when compared to single-radio WMNs.


title: New Bell inequalities for three-qubit pure states

abstract:
  We introduce a set of Bell inequalities for a three-qubit system. Each
inequality within this set is violated by all generalized GHZ states. More
entangled a generalized GHZ state is, more will be the violation. This
establishes a relation between nonlocality and entanglement for this class of
states. Certain inequalities within this set are violated by pure biseparable
states. We also provide numerical evidence that at least one of these Bell
inequalities is violated by a pure genuinely entangled state. These Bell
inequalities can distinguish between separable, biseparable and genuinely
entangled pure three-qubit states. We also generalize this set to n-qubit
systems and may be suitable to characterize the entanglement of n-qubit pure
states.


title: Nucleon spin-averaged forward virtual Compton tensor at large $Q^2$

abstract:
  The nucleon spin-averaged forward virtual Compton tensor determines important
physical quantities such as electromagnetically-induced mass differences of
nucleons, and two-photon exchange contributions in hydrogen spectroscopy. It
depends on two kinematic variables: $\nu$, the virtual photon energy in the
proton rest frame, and $Q^2$, the photon's invariant four-momentum squared.
Using the operator product expansion, we calculate the tensor's large-$Q^2$
behavior for $\nu=0$, including for the first time the full spin-2 contribution
and correcting a previous result in the literature for the spin-0 contribution.
Implications for the proton radius puzzle are discussed.


title: Effects of Scalar-Tensor-Vector Gravity on relativistic jets

abstract:
  Scalar-Tensor-Vector Gravity (STVG) is a theory that does not require dark
components to describe astrophysical data. We aim at constraining the free
parameters of STVG based on recent observations of the jet in M87. We derive
the equations of motion for particles in STVG-Kerr spacetime, we develop a
numerical code that integrates such equations, and apply it to the jet of M87.
We find that STVG deviates from GR and we set new upper limits for the free
parameters of the former. We conclude that STVG is not contradicted by the
observational data of M87, and may help to explain jet formation.


title:
Detecting gravitational decoherence with clocks: Limits on temporal
  resolution from a classical channel model of gravity

abstract:
  The notion of time is given a different footing in Quantum Mechanics and
General Relativity, treated as a parameter in the former and being an observer
dependent property in the later. From a operational point of view time is
simply the correlation between a system and a clock, where an idealized clock
can be modelled as a two level systems. We investigate the dynamics of clocks
interacting gravitationally by treating the gravitational interaction as a
classical information channel. In particular, we focus on the decoherence rates
and temporal resolution of arrays of $N$ clocks showing how the minimum
dephasing rate scales with $N$, and the spatial configuration. Furthermore, we
consider the gravitational redshift between a clock and massive particle and
show that a classical channel model of gravity predicts a finite dephasing rate
from the non-local interaction. In our model we obtain a fundamental limitation
in time accuracy that is intrinsic to each clock.


title:
Topological nanocolloids with facile electric switching of plasmonic
  properties

abstract:
  Combining topology and plasmonics paradigms in nanocolloidal systems may
enable new means of pre-engineering desired composite material properties. Here
we design and realize orientationally ordered assemblies of noble metal
nanoparticles with genus-one topology and unusual long-range ordering mediated
by their interactions with the surrounding nematic fluid host. Facile electric
switching of these composites is reminiscent to that of pristine liquid
crystals (LCs), but provides a means of reconfiguring the nanoparticle assembly
and thus also the ensuing composite medium's optical properties. Our findings
may lead to formation of new molecular-colloidal soft matter phases with
unusual optical properties as well as optical metamaterials.


title:
Less is More: Learning Prominent and Diverse Topics for Data
  Summarization

abstract:
  Statistical topic models efficiently facilitate the exploration of
large-scale data sets. Many models have been developed and broadly used to
summarize the semantic structure in news, science, social media, and digital
humanities. However, a common and practical objective in data exploration tasks
is not to enumerate all existing topics, but to quickly extract representative
ones that broadly cover the content of the corpus, i.e., a few topics that
serve as a good summary of the data. Most existing topic models fit exactly the
same number of topics as a user specifies, which have imposed an unnecessary
burden to the users who have limited prior knowledge. We instead propose new
models that are able to learn fewer but more representative topics for the
purpose of data summarization. We propose a reinforced random walk that allows
prominent topics to absorb tokens from similar and smaller topics, thus
enhances the diversity among the top topics extracted. With this reinforced
random walk as a general process embedded in classical topic models, we obtain
\textit{diverse topic models} that are able to extract the most prominent and
diverse topics from data. The inference procedures of these diverse topic
models remain as simple and efficient as the classical models. Experimental
results demonstrate that the diverse topic models not only discover topics that
better summarize the data, but also require minimal prior knowledge of the
users.


title:
Density profile of dark matter haloes and galaxies in the Horizon-AGN
  simulation: the impact of AGN feedback

abstract:
  Using a suite of three large cosmological hydrodynamical simulations,
Horizon-AGN, Horizon-noAGN (no AGN feedback) and Horizon-DM (no baryons), we
investigate how a typical sub-grid model for AGN feedback affects the evolution
of the inner density profiles of massive dark matter haloes and galaxies. Based
on direct object-to-object comparisons, we find that the integrated inner mass
and density slope differences between objects formed in these three simulations
(hereafter, H_AGN, H_noAGN and H_DM) significantly evolve with time. More
specifically, at high redshift (z~5), the mean central density profiles of
H_AGN and H_noAGN dark matter haloes tend to be much steeper than their H_DM
counterparts owing to the rapidly growing baryonic component and ensuing
adiabatic contraction. By z~1.5, these mean halo density profiles in H_AGN have
flattened, pummelled by powerful AGN activity ("quasar mode"): the integrated
inner mass difference gaps with H_noAGN haloes have widened, and those with
H_DM haloes have narrowed. Fast forward 9.5 billion years, down to z=0, and the
trend reverses: H_AGN halo mean density profiles drift back to a more cusped
shape as AGN feedback efficiency dwindles ("radio mode"), and the gaps in
integrated central mass difference with H_noAGN and H_DM close and broaden
respectively. On the galaxy side, the story differs noticeably. Averaged
stellar profile central densities and inner slopes are monotonically reduced by
AGN activity as a function of cosmic time, resulting in better agreement with
local observations. As both dark matter and stellar inner density profiles
respond quite sensitively to the presence of a central AGN, there is hope that
future observational determinations of these quantities can be used constrain
AGN feedback models.


title:
A Grism Design Review and the as-built performance of the silicon grisms
  for JWST-NIRCAM

abstract:
  Grisms are dispersive transmission optics that find their most frequent use
in instruments that combine imaging and spectroscopy. This application is
particularly popular in the infrared where imagers frequently have a cold pupil
in their optical path that is a suitable location for a dispersive element. In
particular, several recent and planned space experiments make use of grisms in
slit-less spectrographs capable of multi-object spectroscopy. We present an
astronomer-oriented general purpose introduction to grisms and their use in
current and future astronomical instruments. We present a simple, step-by-step
procedure for adding a grism spectroscopy capability to an existing imager
design. This procedure serves as an introduction to a discussion of the device
performance requirements for grisms, focusing in particular on the problems of
lithographically patterned silicon devices, the most effective grism technology
for the 1.1-8 micron range. We begin by summarizing the manufacturing process
of monolithic silicon gratings. We follow this with a report in detail on the
as-built performance of parts constructed for a significant new space
application, the NIRCam instrument on JWST and compare these measurements to
the requirements.


title: Remarks on coloured triply graded link invariants

abstract:
  We explain how existing results (such as categorical sl(n) actions,
associated braid group actions and infinite twists) can be used to define a
triply graded link invariant which categorifies the HOMFLY polynomial of links
coloured by arbitrary partitions. The construction uses a categorified HOMFLY
clasp defined via cabling and infinite twists. We briefly discuss differentials
and speculate on related structures.


title:
Bounded, efficient and multiply robust estimation of average treatment
  effects using instrumental variables

abstract:
  Instrumental variables (IVs) are widely used for estimating causal effects in
the presence of unmeasured confounding. Under the standard IV model, however,
the average treatment effect (ATE) is only partially identifiable. To address
this, we propose novel assumptions that allow for identification of the ATE.
Our identification assumptions are clearly separated from model assumptions
needed for estimation, so that researchers are not required to commit to a
specific observed data model in establishing identification. We then construct
multiple estimators that are consistent under three different observed data
models, and multiply robust estimators that are consistent in the union of
these observed data models. We pay special attention to the case of binary
outcomes, for which we obtain bounded estimators of the ATE that are guaranteed
to lie between -1 and 1. Our approaches are illustrated with simulations and a
data analysis evaluating the causal effect of education on earnings.


title: Choquet integral in decision analysis - lessons from the axiomatization

abstract:
  The Choquet integral is a powerful aggregation operator which lists many
well-known models as its special cases. We look at these special cases and
provide their axiomatic analysis. In cases where an axiomatization has been
previously given in the literature, we connect the existing results with the
framework that we have developed. Next we turn to the question of learning,
which is especially important for the practical applications of the model. So
far, learning of the Choquet integral has been mostly confined to the learning
of the capacity. Such an approach requires making a powerful assumption that
all dimensions (e.g. criteria) are evaluated on the same scale, which is rarely
justified in practice. Too often categorical data is given arbitrary numerical
labels (e.g. AHP), and numerical data is considered cardinally and ordinally
commensurate, sometimes after a simple normalization. Such approaches clearly
lack scientific rigour, and yet they are commonly seen in all kinds of
applications. We discuss the pros and cons of making such an assumption and
look at the consequences which axiomatization uniqueness results have for the
learning problems. Finally, we review some of the applications of the Choquet
integral in decision analysis. Apart from MCDA, which is the main area of
interest for our results, we also discuss how the model can be interpreted in
the social choice context. We look in detail at the state-dependent utility,
and show how comonotonicity, central to the previous axiomatizations, actually
implies state-independency in the Choquet integral model. We also discuss the
conditions required to have a meaningful state-dependent utility representation
and show the novelty of our results compared to the previous methods of
building state-dependent models.


title: A Symplectic Instanton Homology via Traceless Character Varieties

abstract:
  Since its inception, Floer homology has been an important tool in
low-dimensional topology. Floer theoretic invariants of $3$-manifolds tend to
be either gauge theoretic or symplecto-geometric in nature, and there is a
general philosophy that each gauge theoretic Floer homology should have a
corresponding symplectic Floer homology and vice-versa. In this article, we
construct a Lagrangian Floer invariant for any closed, oriented $3$-manifold
$Y$ (called the symplectic instanton homology of $Y$ and denoted
$\mathrm{SI}(Y)$) which is conjecturally equivalent to a Floer homology defined
using a certain variant of Yang-Mills gauge theory. The crucial ingredient for
defining $\mathrm{SI}(Y)$ is the use of traceless character varieties in the
symplectic setting, which allow us to avoid the debilitating technical hurdles
present when one attempts to define a symplectic version of instanton Floer
homologies. Furthermore, by studying the effect of Dehn surgeries on traceless
character varieties, we establish a surgery exact triangle using work of Seidel
that relates the geometry of Lefschetz fibrations with exact triangles in
Lagrangian Floer theory.


title: Proportional Justified Representation

abstract:
  The goal of multi-winner elections is to choose a fixed-size committee based
on voters' preferences. An important concern in this setting is representation:
large groups of voters with cohesive preferences should be adequately
represented by the election winners. Recently, Aziz et al. (2015a;2017)
proposed two axioms that aim to capture this idea: justified representation
(JR) and its strengthening extended justified representation (EJR). In this
paper, we extend the work of Aziz et al. in several directions. First, we
answer an open question of Aziz et al., by showing that Reweighted Approval
Voting satisfies JR for $k=3, 4, 5$, but fails it for $k\ge 6$. Second, we
observe that EJR is incompatible with the Perfect Representation criterion,
which is important for many applications of multi-winner voting, and propose a
relaxation of EJR, which we call Proportional Justified Representation (PJR).
PJR is more demanding than JR, but, unlike EJR, it is compatible with perfect
representation, and a committee that provides PJR can be computed in polynomial
time if the committee size divides the number of voters. Moreover, just like
EJR, PJR can be used to characterize the classic PAV rule in the class of
weighted PAV rules. On the other hand, we show that EJR provides stronger
guarantees with respect to average voter satisfaction than PJR does.


title:
Bending Strain Engineering of Spin Transport in Quantum Spin Hall
  Systems: Topological Nano-mechanospintronics

abstract:
  Quantum spin Hall (QSH) system can exhibit exotic spin transport phenomena,
mediated by its topological edge states. Here a novel concept of bending strain
engineering to tune the spin transport properties of a QSH system is
demonstrated by both model and first-principles calculations. Interestingly, we
discover that bending strain can be used to mitigate the spin conservation of a
QSH system to generate a non-zero spin current (SC), meanwhile the preservation
of time reversal symmetry renders its edge states topologically protected to
transport robust SC without back scattering. This novel physics mechanism can
be applied to effectively tune the SC and spin Hall current in a QSH system by
control of its bending curvature. Furthermore, the realization of QSH systems
with controllable curvature can be achieved by the concept of "topological
nanomechnical architecture". Taking Bi/Cl/Si(111) as a material example, we
demonstrate that the relative spin orientations between two edge states of a
Bi/Cl/Si(111) film can indeed be tuned dramatically by its self-bending
behaviors induced by the pre-designed inherent strain. Therefore, this concept
of "bending strain engineering of spins" via topological nanomechanical
architecture affords a promising route towards the realization of topological
nano-mechanospintronics.


title:
Influence of the Surface of a Nanocrystal on its Electronic and Phononic
  Properties

abstract:
  Over the past thirty years, it has been consistently observed that surface
engineering of colloidal nanocrystals (NC) is key to their performance
parameters. In the case of lead chalcogenide NCs, for example, replacing thiols
with halide anion surface termination has been shown to increase power
conversion efficiency in NC-based solar cells. To gain insight into the origins
of these improvements, we perform ab initio molecular dynamics (AIMD) on
experimentally-relevant sized lead sulfide (PbS) NCs constructed with thiol or
Cl, Br, and I anion surfaces. The surface of both the thiol- and
halide-terminated NCs exhibit low and high-energy phonon modes with large
thermal displacements not present in bulk PbS; however, halide anion surface
termination reduces the overlap of the electronic wavefunctions with these
vibration modes. These findings suggest that electron-phonon interactions will
be reduced in the halide terminated NCs, a conclusion that is supported by
analyzing the time-dependent evolution of the electronic energies and
wavefunctions extracted from the AIMD. This work explains why electron-phonon
interactions are crucial to charge carrier dynamics in NCs and how surface
engineering can be applied to systematically control their electronic and
phononic properties. Furthermore, we propose that the computationally efficient
approach of gauging electron-phonon interaction implemented here can be used to
guide the design of application-specific surface terminations for arbitrary
nanomaterials.


title: Gaussian Polynomials and Restricted Partition Functions with Constraints

abstract:
  We derive an explicit formula for a restricted partition function P_n^m(s)
with constraints making use of known expression for a restricted partition
function W_m(s) without constraints


title:
Learning a Discriminative Filter Bank within a CNN for Fine-grained
  Recognition

abstract:
  Compared to earlier multistage frameworks using CNN features, recent
end-to-end deep approaches for fine-grained recognition essentially enhance the
mid-level learning capability of CNNs. Previous approaches achieve this by
introducing an auxiliary network to infuse localization information into the
main classification network, or a sophisticated feature encoding method to
capture higher order feature statistics. We show that mid-level representation
learning can be enhanced within the CNN framework, by learning a bank of
convolutional filters that capture class-specific discriminative patches
without extra part or bounding box annotations. Such a filter bank is well
structured, properly initialized and discriminatively learned through a novel
asymmetric multi-stream architecture with convolutional filter supervision and
a non-random layer initialization. Experimental results show that our approach
achieves state-of-the-art on three publicly available fine-grained recognition
datasets (CUB-200-2011, Stanford Cars and FGVC-Aircraft). Ablation studies and
visualizations are provided to understand our approach.


title: Trimmed Conformal Prediction for High-Dimensional Models

abstract:
  In regression, conformal prediction is a general methodology to construct
prediction intervals in a distribution-free manner. Although conformal
prediction guarantees strong statistical property for predictive inference, its
inherent computational challenge has attracted the attention of researchers in
the community. In this paper, we propose a new framework, called Trimmed
Conformal Prediction (TCP), based on two stage procedure, a trimming step and a
prediction step. The idea is to use a preliminary trimming step to
substantially reduce the range of possible values for the prediction interval,
and then applying conformal prediction becomes far more efficient. As is the
case of conformal prediction, TCP can be applied to any regression method, and
further offers both statistical accuracy and computational gains. For a
specific example, we also show how TCP can be implemented in the sparse
regression setting. The experiments on both synthetic and real data validate
the empirical performance of TCP.


title:
Neural Network Models for Software Development Effort Estimation: A
  Comparative Study

abstract:
  Software development effort estimation (SDEE) is one of the main tasks in
software project management. It is crucial for a project manager to efficiently
predict the effort or cost of a software project in a bidding process, since
overestimation will lead to bidding loss and underestimation will cause the
company to lose money. Several SDEE models exist; machine learning models,
especially neural network models, are among the most prominent in the field. In
this study, four different neural network models: Multilayer Perceptron,
General Regression Neural Network, Radial Basis Function Neural Network, and
Cascade Correlation Neural Network are compared with each other based on: (1)
predictive accuracy centered on the Mean Absolute Error criterion, (2) whether
such a model tends to overestimate or underestimate, and (3) how each model
classifies the importance of its inputs. Industrial datasets from the
International Software Benchmarking Standards Group (ISBSG) are used to train
and validate the four models. The main ISBSG dataset was filtered and then
divided into five datasets based on the productivity value of each project.
Results show that the four models tend to overestimate in 80percent of the
datasets, and the significance of the model inputs varies based on the selected
model. Furthermore, the Cascade Correlation Neural Network outperforms the
other three models in the majority of the datasets constructed on the Mean
Absolute Residual criterion.


title: A Concurrent Global-local Numerical Method for Multiscale PDEs

abstract:
  We present a new hybrid numerical method for multiscale partial differential
equations, which simultaneously captures the global macroscopic information and
resolves the local microscopic events over regions of relatively small size.
The method couples concurrently the microscopic coefficients in the region of
interest with the homogenized coefficients elsewhere. The cost of the method is
comparable to the heterogeneous multiscale method, while being able to recover
microscopic information of the solution. The convergence of the method is
proved for problems with bounded and measurable coefficients, while the rate of
convergence is established for problems with rapidly oscillating periodic or
almost-periodic coefficients. Numerical results are reported to show the
efficiency and accuracy of the proposed method.


title:
ALMA Reveals Potential Localized Dust Enrichment from Massive Star
  Clusters in II Zw 40

abstract:
  We present subarcsecond images of submillimeter CO and continuum emission
from a local galaxy forming massive star clusters: the blue compact dwarf
galaxy II Zw 40. At $\sim$0.4" resolution (20 pc), the CO(3-2), CO(1-0), 3mm
and 870${\mu}$m continuum maps illustrate star formation on the scales of
individual molecular clouds. Dust contributes about a third of the 870${\mu}$m
continuum emission, with free-free accounting for the rest. On these scales,
there is not a good correspondence between gas, dust, and free-free emission.
Dust continuum is enhanced toward the star-forming region as compared to the CO
emission. We suggest that an unexpectedly low and spatially variable
gas-to-dust ratio is the result of massive clusters of the starburst.


title: Model Atmospheres for X-ray Bursting Neutron Stars

abstract:
  The hydrogen and helium accreted by X-ray bursting neutron stars is
periodically consumed in runaway thermonuclear reactions that cause the entire
surface to glow brightly in X-rays for a few seconds. With models of the
emission, the mass and radius of the neutron star can be inferred from the
observations. By simultaneously probing neutron star masses and radii, X-ray
bursts are one of the strongest diagnostics of the nature of matter at
extremely high densities. Accurate determinations of these parameters are
difficult, however, due to the highly non-ideal nature of the atmospheres where
X-ray bursts occur. Observations from X-ray telescopes such as RXTE and NuStar
can potentially place strong constraints on nuclear matter once uncertainties
in atmosphere models have been reduced. Here we discuss current progress on
modeling atmospheres of X-ray bursting neutron stars and some of the challenges
still to be overcome.


title:
High Gas Surface Densities yet Low UV Attenuation in z $\sim$ 1 Disc
  Galaxies

abstract:
  The gas in galaxies is both the fuel for star formation and a medium that
attenuates the light of the young stars. We study the relations between UV
attenuation, spectral slope, star formation rates, and molecular gas surface
densities in a sample of 28 z$\sim$1 and a reference sample of 32 z$\sim$0
galaxies that are detected in CO, far-infrared, and rest frame UV. The samples
are dominated by disc-like galaxies close to the main SFR--mass relation. We
find that the location of the z$\sim$1 galaxies on the IRX-$\beta$ plane is
correlated with their gas-depletion time-scale $\tau_{dep}$ and can predict
$\tau_{dep}$ with a standard deviation of 0.16 dex. We use IRX-$\beta$ to
estimate the mean total gas column densities at the locations of star formation
in the galaxies, and compare them to the mean molecular gas surface densities
as measured from CO. We confirm previous results regarding high $N_H/A_V$ in
z$\sim$1 galaxies. We estimate an increase in the gas filling factor by a
factor of 4--6 from z$\sim$0 to z$\sim$1 and a corresponding increase of factor
3--2 in the mean column densities of the star forming clouds. After accounting
for the filling factor, the z$\sim$1 and the z$\sim$0 samples exhibit similar
attenuation properties. These indicate to similar porous geometries to the
molecular clouds in star-forming disc galaxies at 0<z<1.


title:
Gravitational instantons of type $D_k$ and a generalization of the
  Gibbons-Hawking Ansatz

abstract:
  We describe a quaternionic-based Ansatz generalizing the Gibbons-Hawking
Ansatz to a class of hyperk\"ahler metrics with hidden symmetries. We then
apply it to obtain explicit expressions for gravitational instanton metrics of
type $D_k$.


title: Neural Combinatorial Optimization with Reinforcement Learning

abstract:
  This paper presents a framework to tackle combinatorial optimization problems
using neural networks and reinforcement learning. We focus on the traveling
salesman problem (TSP) and train a recurrent network that, given a set of city
coordinates, predicts a distribution over different city permutations. Using
negative tour length as the reward signal, we optimize the parameters of the
recurrent network using a policy gradient method. We compare learning the
network parameters on a set of training graphs against learning them on
individual test graphs. Despite the computational expense, without much
engineering and heuristic designing, Neural Combinatorial Optimization achieves
close to optimal results on 2D Euclidean graphs with up to 100 nodes. Applied
to the KnapSack, another NP-hard problem, the same method obtains optimal
solutions for instances with up to 200 items.


title:
The stability of fixed points for a Kuramoto model with Hebbian
  interactions

abstract:
  We consider a variation of the Kuramoto model with dynamic coupling, where
the coupling strengths are allowed to evolve in response to the phase
difference between the oscillators, a model first considered by Ha, Noh and
Park. In particular we study the stability of fixed points for this model. We
demonstrate a somewhat surprising fact: namely that the fixed points of this
model, as well as their stability, can be completely expressed in terms of the
fixed points and stability of the analogous classical Kuramoto problem where
the coupling strengths are fixed to a constant (the same for all edges). In
particular for the "all-to-all" network, where the underlying graph is the
complete graph, the problem reduces to the problem of understanding the fixed
points and stability of the all-to-all Kuramoto model with equal edge weights,
a problem that has been completely solved.


title: Photographic home styles in Congress: a computer vision approach

abstract:
  While members of Congress now routinely communicate with constituents using
images on a variety of internet platforms, little is known about how images are
used as a means of strategic political communication. This is due primarily to
computational limitations which have prevented large-scale, systematic analyses
of image features. New developments in computer vision, however, are bringing
the systematic study of images within reach. Here, we develop a framework for
understanding visual political communication by extending Fenno's analysis of
home style (Fenno 1978) to images and introduce "photographic" home styles.
Using approximately 192,000 photographs collected from MCs Facebook profiles,
we build machine learning software with convolutional neural networks and
conduct an image manipulation experiment to explore how the race of people that
MCs pose with shape photographic home styles. We find evidence that electoral
pressures shape photographic home styles and demonstrate that Democratic and
Republican members of Congress use images in very different ways.


title:
Application of time-reversal-based processing techniques to enhance
  detection of GPR targets

abstract:
  In this paper we analyze the performance of time-reversal (TR) techniques in
conjunction with various Ground Penetrating Radar (GPR) pre-processing methods
aimed at improving detection of subsurface targets. TR techniques were first
developed for ultrasound applications and, by exploiting the invariance of the
wave equation under time reversal, can yield features such as superresolution
and statistical stability. The TR method was examined here using both synthetic
and actual GPR field data under four different pre-processing strategies on the
raw data, namely: mean background removal, eigenvalue background removal, a
sliding-window space-frequency technique, and a noise-robust spatial
differentiator along the scan direction. Depending on the acquisition mode, it
was possible to determine with good precision the position and depth of the
studied targets as well as, in some cases, to differentiate the targets from
nearby clutter such as localized geological anomalies. The proposed methodology
has the potential to be useful when applied to GPR data for the detection of
buried targets.


title:
Proposal of Real Time Predictive Maintenance Platform with 3D Printer
  for Business Vehicles

abstract:
  This paper proposes a maintenance platform for business vehicles which
detects failure sign using IoT data on the move, orders to create repair parts
by 3D printers and to deliver them to the destination. Recently, IoT and 3D
printer technologies have been progressed and application cases to
manufacturing and maintenance have been increased. Especially in air flight
industry, various sensing data are collected during flight by IoT technologies
and parts are created by 3D printers. And IoT platforms which improve
development/operation of IoT applications also have been appeared. However,
existing IoT platforms mainly targets to visualize "things" statuses by batch
processing of collected sensing data, and 3 factors of real-time, automatic
orders of repair parts and parts stock cost are insufficient to accelerate
businesses. This paper targets maintenance of business vehicles such as
airplane or high-speed bus. We propose a maintenance platform with real-time
analysis, automatic orders of repair parts and minimum stock cost of parts. The
proposed platform collects data via closed VPN, analyzes stream data and
predicts failures in real-time by online machine learning framework Jubatus,
coordinates ERP or SCM via in memory DB to order repair parts and also
distributes repair parts data to 3D printers to create repair parts near the
destination.


title: Three-dimensional microfabrication through a multimode optical fiber

abstract:
  Additive manufacturing, also known as 3D printing, is an advanced
manufacturing technique that allows the fabrication of arbitrary macroscopic
and microscopic objects. All 3D printing systems require large optical elements
or nozzles in proximity to the built structure. This prevents their use in
applications in which there is no direct access to the area where the objects
have to be printed. Here, we demonstrate three-dimensional microfabrication
based on two-photon polymerization (TPP) with sub diffraction-limited
resolution through an ultra-thin, 50 mm long printing nozzle of 560 micrometers
in diameter. Using wavefront shaping, femtosecond infrared pulses are focused
and scanned through a multimode optical fiber (MMF) inside a photoresist that
polymerizes via two-photon absorption. We show the construction of arbitrary 3D
structures of 500 nm resolution on the other side of the fiber. To our
knowledge, this is the first demonstration of microfabrication through a
multimode optical fiber. Our work represents a new area which we refer to as
endofabrication.


title: Vector-Valued Optimal Mass Transport

abstract:
  We introduce the problem of transporting vector-valued distributions. In
this, a salient feature is that mass may flow between vectorial entries as well
as across space (discrete or continuous). The theory relies on a first step
taken to define an appropriate notion of optimal transport on a graph. The
corresponding distance between distributions is readily computable via convex
optimization and provides a suitable generalization of Wasserstein-type
metrics. Building on this, we define Wasserstein-type metrics on vector-valued
distributions supported on continuous spaces as well as graphs. Motivation for
developing vector-valued mass transport is provided by applications such as
multi-color image processing, polarimetric radar, as well as network problems
where resources may be vectorial.


title: Towards Spectral Geometry for Causal Sets

abstract:
  We show that the Feynman propagator (or the d'Alembertian) of a causal set
contains the complete information about the causal set. Intuitively, this is
because the Feynman propagator, being a correlator that decays with distance,
provides a measure for the invariant distance between pairs of events. Further,
we show that even the spectra alone (of the self-adjoint and anti-self-adjoint
parts) of the propagator(s) and d'Alembertian already carry large amounts of
geometric information about their causal set. This geometric information is
basis independent and also gauge invariant in the sense that it is relabeling
invariant (which is analogue to diffeomorphism invariance). We provide
numerical evidence that the associated spectral distance between causal sets
can serve as a measure for the geometric similarity between causal sets.


title:
Contextualizing Geometric Data Analysis and Related Data Analytics: A
  Virtual Microscope for Big Data Analytics

abstract:
  The relevance and importance of contextualizing data analytics is described.
Qualitative characteristics might form the context of quantitative analysis.
Topics that are at issue include: contrast, baselining, secondary data sources,
supplementary data sources, dynamic and heterogeneous data. In geometric data
analysis, especially with the Correspondence Analysis platform, various case
studies are both experimented with, and are reviewed. In such aspects as
paradigms followed, and technical implementation, implicitly and explicitly, an
important point made is the major relevance of such work for both burgeoning
analytical needs and for new analytical areas including Big Data analytics, and
so on. For the general reader, it is aimed to display and describe, first of
all, the analytical outcomes that are subject to analysis here, and then
proceed to detail the more quantitative outcomes that fully support the
analytics carried out.


title:
UV-sensitive superconducting nanowire single photon detectors for
  integration in an ion trap

abstract:
  We demonstrate superconducting nanowire single photon detectors with 76 +/- 4
% system detection efficiency at a wavelength of 315 nm and an operating
temperature of 3.2 K, with a background count rate below 1 count per second at
saturated detection efficiency. We propose integrating these detectors into
planar surface electrode radio-frequency Paul traps for use in trapped ion
quantum information processing. We operate detectors integrated into test ion
trap structures at 3.8 K both with and without typical radio-frequency trapping
electric fields. The trapping fields reduce system detection efficiency by 9 %,
but do not increase background count rates.

categories: quant-ph cond-mat.supr-con physics.ins-det physics.optics


title: The optimal rate for resolving a near-polytomy in a phylogeny

abstract:
  The reconstruction of phylogenetic trees from discrete character data
typically relies on models that assume the characters evolve under a
continuous-time Markov process operating at some overall rate $\lambda$. When
$\lambda$ is too high or too low, it becomes difficult to distinguish a short
interior edge from a polytomy (the tree that results from collapsing the edge).
In this note, we investigate the rate that maximizes the expected
log-likelihood ratio (i.e. the Kullback--Leibler separation) between the
four-leaf unresolved (star) tree and a four-leaf binary tree with interior edge
length $\epsilon$. For a simple two-state model, we show that as $\epsilon$
converges to $0$ the optimal rate also converges to zero when the four pendant
edges have equal length. However, when the four pendant branches have unequal
length, two local optima can arise, and it is possible for the globally optimal
rate to converge to a non-zero constant as $\epsilon \rightarrow 0$. Moreover,
in the setting where the four pendant branches have equal lengths and either
(i) we replace the two-state model by an infinite-state model or (ii) we retain
the two-state model and replace the Kullback--Leibler separation by Euclidean
distance as the maximization goal, then the optimal rate also converges to a
non-zero constant.


title:
A High Efficiency Aluminum-Ion Battery Using an AlCl3-Urea Ionic Liquid
  Analogue Electrolyte

abstract:
  In recent years, impressive advances in harvesting renewable energy have led
to pressing demand for the complimentary energy storage technology. Here, a
high coulombic efficiency (~ 99.7%) Al battery is developed using
earth-abundant aluminum as the anode, graphite as the cathode, and a cheap
ionic liquid analogue electrolyte made from a mixture of AlCl3 and urea in 1.3
: 1 molar ratio. The battery displays discharge voltage plateaus around 1.9 V
and 1.5 V (average discharge = 1.73 V) and yielded a specific cathode capacity
of ~73 mAh g-1 at a current density of 100 mA g-1 (~ 1.4 C). High coulombic
efficiency over a range of charge-discharge rates and stability over ~150-200
cycles was easily demonstrated. In-situ Raman spectroscopy clearly showed
chloroaluminate anion intercalation/deintercalation of graphite in the cathode
side during charge/discharge and suggested the formation of a stage 2 graphite
intercalation compound when fully charged. Raman spectroscopy and nuclear
magnetic resonance suggested the existence of AlCl4-, Al2Cl7- anions, and
[AlCl2. (urea)n]+ cations in the urea/AlCl3 electrolyte when an excess of AlCl3
was present. Aluminum deposition therefore proceeded through two pathways, one
involving Al2Cl7- anions and the other involving [AlCl2.(urea)n]+ cations. This
battery is a promising prospect for a future high performance, low cost energy
storage device.


title: Inverse obstacle scattering with non-over-determined data

abstract:
  It is proved that the scattering amplitude $A(\beta, \alpha_0, k_0)$, known
for all $\beta\in S^2$, where $S^2$ is the unit sphere in $\mathbb{R}^3$, and
fixed $\alpha_0\in S^2$ and $k_0>0$, determines uniquely the surface $S$ of the
obstacle $D$ and the boundary condition on $S$. The boundary condition on $S$
is assumed to be the Dirichlet, or Neumann, or the impedance one. The
uniqueness theorem for the solution of multidimensional inverse scattering
problems with non-over-determined data was not known for many decades. A
detailed proof of such a theorem is given in this paper for inverse scattering
by obstacles for the first time. It follows from our results that the
scattering solution vanishing on the boundary $S$ of the obstacle cannot have
closed surfaces of zeros in the exterior of the obstacle different from $S$. To
have a uniqueness theorem for inverse scattering problems with
non-over-determined data is of principal interest because these are the minimal
scattering data that allow one to uniquely recover the scatterer.


title: "Orphan" $\gamma$-ray Flares and Stationary Sheaths of Blazar Jets

abstract:
  Blazars exhibit flares across the entire electromagnetic spectrum. Many
$\gamma$-ray flares are highly correlated with flares detected at longer
wavelengths; however, a small subset appears to occur in isolation, with little
or no correlated variability at longer wavelengths. These "orphan" $\gamma$-ray
flares challenge current models of blazar variability, most of which are unable
to reproduce this type of behavior. Macdonald et al. have developed the Ring of
Fire model to explain the origin of orphan $\gamma$-ray flares from within
blazar jets. In this model, electrons contained within a blob of plasma moving
relativistically along the spine of the jet inverse-Compton scatter synchrotron
photons emanating off of a ring of shocked sheath plasma that enshrouds the jet
spine. As the blob propagates through the ring, the scattering of the ring
photons by the blob electrons creates an orphan $\gamma$-ray flare. This model
was successfully applied to modeling a prominent orphan $\gamma$-ray flare
observed in the blazar PKS 1510$-$089. To further support the plausibility of
this model, Macdonald et al. presented a stacked radio map of PKS 1510$-$089
containing the polarimetric signature of a sheath of plasma surrounding the
spine of the jet. In this paper, we extend our modeling and stacking techniques
to a larger sample of blazars: 3C 273, 4C 71$.$01, 3C 279, 1055$+$018, CTA 102,
and 3C 345, the majority of which have exhibited orphan $\gamma$-ray flares. We
find that the model can successfully reproduce these flares, while our stacked
maps reveal the existence of jet sheaths within these blazars.


title: Faraday Conversion in Turbulent Blazar Jets

abstract:
  Low ($\lesssim 1\%$) levels of circular polarization (CP) detected at radio
frequencies in the relativistic jets of some blazars can provide insight into
the underlying nature of the jet plasma. CP can be produced through linear
birefringence, in which initially linearly polarized emission produced in one
region of the jet is altered by Faraday rotation as it propagates through other
regions of the jet with varying magnetic field orientation. Marscher has begun
a study of jets with such magnetic geometries using the Turbulent Extreme
Multi-Zone (TEMZ) model, in which turbulent plasma crossing a standing shock in
the jet is represented by a collection of thousands of individual plasma cells,
each with distinct magnetic field orientations. Here we develop a radiative
transfer scheme that allows the numerical TEMZ code to produce simulated images
of the time-dependent linearly and circularly polarized intensity at different
radio frequencies. In this initial study, we produce synthetic polarized
emission maps that highlight the linear and circular polarization expected
within the model.


title: Inverse problems for parabolic equations 3

abstract:
  Let $u_t-a(t)u_{xx}=f(x, t)$ in $0\leq x \leq \pi,\,\,t\geq 0.$ Assume that
$u(0,t)=u_1(t)$, $u(\pi,t)=u_2(t)$, $u(x,0)=h(x)$, and the extra data
$u_x(0,t)=g(t)$ are known. The inverse problem is: {\it
  How does one determine the unknown $a(t)$?}
  The function $a(t)>a_0>0$ is assumed continuous and bounded. This question is
answered and a method for recovery of $a(t)$ is proposed. There are several
papers in which sufficient conditions are given for the uniqueness and
existence of $a(t)$, but apparently there was no method proposed for
calculating of $a$. The method given in this paper for proving the uniqueness
and existence of the solution to inverse problem is new and it allows one to
calculate the unknown coefficient $a(t)$.


title: Efficient Likelihood Bayesian Constrained Local Model

abstract:
  The constrained local model (CLM) proposes a paradigm that the locations of a
set of local landmark detectors are constrained to lie in a subspace, spanned
by a shape point distribution model (PDM). Fitting the model to an object
involves two steps. A response map, which represents the likelihood of the
location of a landmark, is first computed for each landmark using local-texture
detectors. Then, an optimal PDM is determined by jointly maximizing all the
response maps simultaneously, with a global shape constraint. This global
optimization can be considered as a Bayesian inference problem, where the
posterior distribution of the shape parameters, as well as the pose parameters,
can be inferred using maximum a posteriori (MAP). In this paper, we present a
cascaded face-alignment approach, which employs random-forest regressors to
estimate the positions of each landmark, as a likelihood term, efficiently in
the CLM model. Interpretation from CLM framework, this algorithm is named as an
efficient likelihood Bayesian constrained local model (elBCLM). Furthermore, in
each stage of the regressors, the PDM non-rigid parameters of previous stage
can work as shape clues for training each stage regressors. Experimental
results on benchmarks show our approach achieve about 3 to 5 times speed-up
compared with CLM models and improve around 10% on fitting quality compare with
the same setting regression models.


title: Low-dimensional Data Embedding via Robust Ranking

abstract:
  We describe a new method called t-ETE for finding a low-dimensional embedding
of a set of objects in Euclidean space. We formulate the embedding problem as a
joint ranking problem over a set of triplets, where each triplet captures the
relative similarities between three objects in the set. By exploiting recent
advances in robust ranking, t-ETE produces high-quality embeddings even in the
presence of a significant amount of noise and better preserves local scale than
known methods, such as t-STE and t-SNE. In particular, our method produces
significantly better results than t-SNE on signature datasets while also being
faster to compute.


title: Machine Learning for Dental Image Analysis

abstract:
  In order to study the application of artificial intelligence (AI) to dental
imaging, we applied AI technology to classify a set of panoramic radiographs
using (a) a convolutional neural network (CNN) which is a form of an artificial
neural network (ANN), (b) representative image cognition algorithms that
implement scale-invariant feature transform (SIFT), and (c) histogram of
oriented gradients (HOG).


title: Distribution of the nodal sets of eigenfunctions on analytic manifolds

abstract:
  The nodal set of the Laplacian eigenfunction has co-dimension one and has
finite hypersurface measure on a compact Riemannian manifold. In this paper, we
investigate the distribution of the nodal sets of eigenfunctions, when the
metric on the manifold is analytic. We prove that if the eigenfunctions are
equidistributed at a small scale, then the weak limits of the hypersurface
volume form of their nodal sets are comparable to the volume form on the
manifold.
  In particular, on the negatively curved manifolds with analytic metric and on
the tori, we show that in any eigenbasis, there is a full density subsequence
of eigenfunctions such that the weak limits of the hypersurface volume form of
their nodal sets are comparable to the volume form on the manifold.


title:
Attend in groups: a weakly-supervised deep learning framework for
  learning from web data

abstract:
  Large-scale datasets have driven the rapid development of deep neural
networks for visual recognition. However, annotating a massive dataset is
expensive and time-consuming. Web images and their labels are, in comparison,
much easier to obtain, but direct training on such automatically harvested
images can lead to unsatisfactory performance, because the noisy labels of Web
images adversely affect the learned recognition models. To address this
drawback we propose an end-to-end weakly-supervised deep learning framework
which is robust to the label noise in Web images. The proposed framework relies
on two unified strategies -- random grouping and attention -- to effectively
reduce the negative impact of noisy web image annotations. Specifically, random
grouping stacks multiple images into a single training instance and thus
increases the labeling accuracy at the instance level. Attention, on the other
hand, suppresses the noisy signals from both incorrectly labeled images and
less discriminative image regions. By conducting intensive experiments on two
challenging datasets, including a newly collected fine-grained dataset with Web
images of different car models, the superior performance of the proposed
methods over competitive baselines is clearly demonstrated.


title: Semantic Facial Expression Editing using Autoencoded Flow

abstract:
  High-level manipulation of facial expressions in images --- such as changing
a smile to a neutral expression --- is challenging because facial expression
changes are highly non-linear, and vary depending on the appearance of the
face. We present a fully automatic approach to editing faces that combines the
advantages of flow-based face manipulation with the more recent generative
capabilities of Variational Autoencoders (VAEs). During training, our model
learns to encode the flow from one expression to another over a low-dimensional
latent space. At test time, expression editing can be done simply using latent
vector arithmetic. We evaluate our methods on two applications: 1) single-image
facial expression editing, and 2) facial expression interpolation between two
images. We demonstrate that our method generates images of higher perceptual
quality than previous VAE and flow-based methods.


title:
Large deviations for stochastic heat equations with memory driven by
  Levy-type noise

abstract:
  For a heat equation with memory driven by a L\'evy-type noise we establish
the existence of a unique solution. The main part of the article focuses on the
Freidlin-Wentzell large deviation principle of the solutions of heat equation
with memory driven by a L\'evy-type noise. For this purpose, we exploit the
recently introduced weak convergence approach.


title:
Scaling of the minimal step-step distance with the step-bunch size:
  Theoretical predictions and experimental findings (a mini-review)

abstract:
  We review the studies on the scaling of the minimal step-step distance lmin
in the bunch with the bunch size N, l_min~N^(-{\gamma}). We build our
retrospective around the different values of the exponent {\gamma} obtained
from models and experiments. It was mainly studied in the context of the
electromigration driven instability on Si(111) vicinal surfaces. In this
context the full scaling relation is given in general form as l_min ~
(A/l_cFN^2)^[1/(n+1)], where A is the magnitude of the step-step repulsions
with range n, F is the electromigration force acting on the charged surface
atoms and l_c is a length-scale, characteristic for the regime of step
bunching, diffusion-limited (DL) or attachment-detachment limited (KL).


title:
Performance Limits of Energy Detection Systems with Massive Receiver
  Arrays

abstract:
  Energy detection (ED) is an attractive technique for symbol detection at
receivers equipped with a large number of antennas, for example in millimeter
wave communication systems. This paper investigates the performance bounds of
ED with pulse amplitude modulation (PAM) in large antenna arrays under single
stream transmission and fast fading assumptions. The analysis leverages
information-theoretic tools and semi-numerical approach to provide bounds on
the information rate, which are shown to be tight in the low and high
signal-to-noise ratio (SNR) regimes, respectively. For a fixed constellation
size, the impact of the number of antennas and SNR on the achievable
information rate is investigated. Based on the results, heuristics are provided
for the choice of the cardinality of the adaptive modulation scheme as a
function of the SNR and the number of antennas.


title: A generalization of Marstrand's theorem and a geometric application

abstract:
  In this paper we prove two general results related to Marstrand's projection
theorem in a quite general formulation over separable metric spaces under a
suitable transversality hypothesis (the "projections" are in principle only
continuous) - the result is flexible enough to, in particular, recover most of
the classical Marstrand-like theorems. Our proofs use simpler tools than many
classical works in the subject, where some techniques from harmonic analysis or
special geometrical structures on the spaces are used. Also we show a geometric
application of our results.


title:
Reconnection and particle acceleration in interacting flux ropes I.
  Magnetohydrodynamics and test particles in 2.5D

abstract:
  Magnetic reconnection and non-thermal particle distributions associated with
current-driven instabilities are investigated by means of resistive
magnetohydrodynamics (MHD) simulations combined with relativistic test particle
methods. We propose a system with two parallel, repelling current channels in
an initially force-free equilibrium, as a simplified representation of flux
ropes in a stellar magnetosphere. The current channels undergo a rotation and
separation on Alfv\'enic timescales, forming secondary islands and (up to
tearing unstable) current sheets in which non-thermal energy distributions are
expected to develop. Using the recently developed particle module of our
open-source grid-adaptive MPI-AMRVAC software, we simulate MHD evolution
combined with test particle treatments in MHD snapshots. We explore under which
plasma-$\beta$ conditions the fastest reconnection occurs in two-and-a-half
dimensional (2.5D) scenarios and in these settings test particles are evolved.
We quantify energy distributions, acceleration mechanisms, relativistic
corrections to the particle equations of motion and effects of resistivity in
magnetically dominated proton-electron plasmas. Due to large resistive electric
fields and indefinite acceleration of particles in the infinitely long current
channels, hard energy spectra are found in 2.5D configurations. Solutions to
these numerical artifacts are proposed for both 2.5D setups and future 3D work.
We discuss the magnetohydrodynamics of an additional kink instability in 3D
setups and the expected effects on energy distributions. The obtained results
hold as a proof-of-principle for test particle approaches in MHD simulations,
relevant to explore less idealised scenarios like solar flares and more exotic
astrophysical phenomena, like black hole flares, magnetar magnetospheres and
pulsar wind nebulae.


title: Sequential Person Recognition in Photo Albums with a Recurrent Network

abstract:
  Recognizing the identities of people in everyday photos is still a very
challenging problem for machine vision, due to non-frontal faces, changes in
clothing, location, lighting and similar. Recent studies have shown that rich
relational information between people in the same photo can help in recognizing
their identities. In this work, we propose to model the relational information
between people as a sequence prediction task. At the core of our work is a
novel recurrent network architecture, in which relational information between
instances' labels and appearance are modeled jointly. In addition to relational
cues, scene context is incorporated in our sequence prediction model with no
additional cost. In this sense, our approach is a unified framework for
modeling both contextual cues and visual appearance of person instances. Our
model is trained end-to-end with a sequence of annotated instances in a photo
as inputs, and a sequence of corresponding labels as targets. We demonstrate
that this simple but elegant formulation achieves state-of-the-art performance
on the newly released People In Photo Albums (PIPA) dataset.


title: Cauchy MDS Array Codes With Efficient Decoding Method

abstract:
  Array codes have been widely used in communication and storage systems. To
reduce computational complexity, one important property of the array codes is
that only XOR operation is used in the encoding and decoding process. In this
work, we present a novel family of maximal-distance separable (MDS) array codes
based on Cauchy matrix, which can correct up to any number of failures. We also
propose an efficient decoding method for the new codes to recover the failures.
We show that the encoding/decoding complexities of the proposed approach are
lower than those of existing Cauchy MDS array codes, such as Rabin-Like codes
and CRS codes. Thus, the proposed MDS array codes are attractive for
distributed storage systems.


title:
High-Resolution Image Inpainting using Multi-Scale Neural Patch
  Synthesis

abstract:
  Recent advances in deep learning have shown exciting promise in filling large
holes in natural images with semantically plausible and context aware details,
impacting fundamental image manipulation tasks such as object removal. While
these learning-based methods are significantly more effective in capturing
high-level features than prior techniques, they can only handle very
low-resolution inputs due to memory limitations and difficulty in training.
Even for slightly larger images, the inpainted regions would appear blurry and
unpleasant boundaries become visible. We propose a multi-scale neural patch
synthesis approach based on joint optimization of image content and texture
constraints, which not only preserves contextual structures but also produces
high-frequency details by matching and adapting patches with the most similar
mid-layer feature correlations of a deep classification network. We evaluate
our method on the ImageNet and Paris Streetview datasets and achieved
state-of-the-art inpainting accuracy. We show our approach produces sharper and
more coherent results than prior methods, especially for high-resolution
images.


title: Geometrothermodynamic model for the evolution of the Universe

abstract:
  Using the formalism of geometrothermodynamics to derive a fundamental
thermodynamic equation, we construct a cosmological model in the framework of
relativistic cosmology. In a first step, we describe a system without
thermodynamic interaction, and show it to be equivalent to the standard
$\Lambda$CDM paradigm. The second step includes thermodynamic interaction and
produces a model consistent with the main features of inflation. With the
proposed fundamental equation we are thus able to describe all the known epochs
in the evolution of our Universe, starting from the inflationary phase.


title:
Model theory and metric convergence I: Metastability and dominated
  convergence

abstract:
  We study Tao's finitary viewpoint of convergence in metric spaces, as
captured by the notion of metastability. We adopt the perspective of continuous
model theory. We show that, in essence, metastable convergence with a given
rate is the only formulation of metric convergence that can be captured by a
theory in continuous first-order logic, a result we call the Uniform
Metastability Principle. Philosophically, this principle amounts to the
following meta-theorem: "If a classical statement about convergence in metric
structures is refined to a statement about metastable convergence with some
rate, then the validity of the original statement implies the validity of its
metastable version." As an instance of this phenomenon, we formulate an
abstract version of Tao's Metastable Dominated Convergence Theorem as a
statement about axiomatizable classes of metric structures, and show that it is
a direct consequence of the Uniform Metastability Principle.


title:
Nonparametric Regression with Adaptive Truncation via a Convex
  Hierarchical Penalty

abstract:
  We consider the problem of non-parametric regression with a potentially large
number of covariates. We propose a convex, penalized estimation framework that
is particularly well-suited for high-dimensional sparse additive models. The
proposed approach combines appealing features of finite basis representation
and smoothing penalties for non-parametric estimation. In particular, in the
case of additive models, a finite basis representation provides a parsimonious
representation for fitted functions but is not adaptive when component
functions posses different levels of complexity. On the other hand, a smoothing
spline type penalty on the component functions is adaptive but does not offer a
parsimonious representation of the estimated function. The proposed approach
simultaneously achieves parsimony and adaptivity in a computationally efficient
framework. We demonstrate these properties through empirical studies on both
real and simulated datasets. We show that our estimator converges at the
minimax rate for functions within a hierarchical class. We further establish
minimax rates for a large class of sparse additive models. The proposed method
is implemented using an efficient algorithm that scales similarly to the Lasso
with the number of covariates and samples size.


title:
Ladders of compactly generated triangulated categories and preprojective
  algebras

abstract:
  In this paper we characterize when a recollement of compactly generated
triangulated categories admits a ladder of some height going either upwards or
downwards. As an application, we show that the derived category of the
preprojective algebra of Dynkin type $\mathbb{A}_n$ admits a periodic infinite
ladder, where the one outer term in the recollement is the derived category of
a differential graded algebra.


title:
On a role of corotation radius in the low $T/W$ dynamical instability of
  differentially rotating stars

abstract:
  We investigate the nature of so-called low $T/W$ dynamical instability in a
differentially rotating star by focusing on the role played by the corotation
radius of the unstable oscillation modes. An one dimensional model of linear
perturbation, which neglects dependence of variables on the coordinate along
the rotational axis of the star, is solved to obtain stable and unstable
eigenmodes. A linear eigenmode having a corotation radius, at which azimuthal
pattern speed of the mode coincides with the stellar angular velocity, is
categorized to either a complex (growing or damping) mode or a purely real mode
belonging to a continuous spectrum of frequency. We compute canonical angular
momentum and its flux to study eigenmodes with corotation radius. In a
dynamically unstable mode, sound wave transports its angular momentum in such a
way that the absolute value of the angular momentum is increased on both sides
of the corotation radius. We further evaluate growth of amplitude of reflected
sound wave incident to a corotation point and find that the over-reflection of
the wave and the trapping of it between the corotation radius and the surface
of the star may qualitatively explain dependences of eigenfrequencies on the
stellar differential rotation. The results suggest that the low $T/W$
instability may be caused by over-reflection of sound waves trapped mainly
between the surface of the star and a corotation radius.


title:
Scale-Chiral Symmetry, Proton Mass and Sound Velocity in Compact-Star
  Matter

abstract:
  With a light dilaton $\sigma$ and the light-quark vector mesons
$V=(\rho,\omega)$ incorporated into an effective scale-invariant hidden local
symmetric Lagrangian, scale-chiral symmetry -- hidden in QCD -- arises at a
high density, $n_{1/2}$, as an "emergent" symmetry, a phenomenon absent in
standard chiral perturbative approaches but highly relevant for massive compact
stars. What takes place as the density increases beyond $n_{1/2}\sim 2n_0$ in
compressed baryonic matter is (1) a topology change from skyrmions to
half-skyrmions, (2) parity doubling in the nucleon structure, (3) the maximum
neutron star mass $M\simeq 2.01 M_{\odot}$ and the radius $R\simeq 12.0$ km and
(4) the sound velocity $v_s^2/c^2\simeq 1/3$ due to the "vector manifestation
(VM)" fixed point of $\rho$ and a "walking" dilaton condensate, which is
intricately connected to the source of the proton mass.


title:
The Surface Brightness--Color Relations Based on Eclipsing Binary Stars:
  Toward Precision Better than 1% in Angular Diameter Predictions

abstract:
  In this study we investigate the calibration of surface brightness--color
(SBC) relations based solely on eclipsing binary stars. We selected a sample of
35 detached eclipsing binaries with trigonometric parallaxes from Gaia DR1 or
Hipparcos, whose absolute dimensions are known with an accuracy better than 3%
and that lie within 0.3 kpc from the Sun. For the purpose of this study, we
used mostly homogeneous optical and near-infrared photometry based on the
Tycho-2 and 2MASS catalogs. We derived geometric angular diameters for all
stars in our sample with a precision better than 10%, and for 11 of them with a
precision better than 2%. The precision of individual angular diameters of the
eclipsing binary components is currently limited by the precision of the
geometric distances ($\sim$5% on average). However, by using a subsample of
systems with the best agreement between their geometric and photometric
distances, we derived the precise SBC relations based only on eclipsing binary
stars. These relations have precisions that are comparable to the best
available SBC relations based on interferometric angular diameters, and they
are fully consistent with them. With very precise Gaia parallaxes becoming
available in the near future, angular diameters with a precision better than 1%
will be abundant. At that point, the main uncertainty in the total error budget
of the SBC relations will come from transformations between different
photometric systems, disentangling of component magnitudes, and for hot OB
stars, the main uncertainty will come from the interstellar extinction
determination. We argue that all these issues can be overcome with modern
high-quality data and conclude that a precision better than 1% is entirely
feasible.


title:
Ramanujan Cayley graphs of the generalized quaternion groups and the
  Hardy-Littlewood conjecture

abstract:
  In this article, we investigate the bound of the valency of the Cayley graphs
of the generalized quaternion groups which guarantees to be Ramanujan. As is
the cases of the cyclic and dihedral groups in our previous studies, we show
that the determination of the bound in a special setting is related to the
classical Hardy-Littlewood conjecture for primes represented by a quadratic
polynomial.


title:
Modeling Relationships in Referential Expressions with Compositional
  Modular Networks

abstract:
  People often refer to entities in an image in terms of their relationships
with other entities. For example, "the black cat sitting under the table"
refers to both a "black cat" entity and its relationship with another "table"
entity. Understanding these relationships is essential for interpreting and
grounding such natural language expressions. Most prior work focuses on either
grounding entire referential expressions holistically to one region, or
localizing relationships based on a fixed set of categories. In this paper we
instead present a modular deep architecture capable of analyzing referential
expressions into their component parts, identifying entities and relationships
mentioned in the input expression and grounding them all in the scene. We call
this approach Compositional Modular Networks (CMNs): a novel architecture that
learns linguistic analysis and visual inference end-to-end. Our approach is
built around two types of neural modules that inspect local regions and
pairwise interactions between regions. We evaluate CMNs on multiple referential
expression datasets, outperforming state-of-the-art approaches on all tasks.


title: Laser ablation production of Ba, Ca, Dy, Er, La, Lu, and Yb ions

abstract:
  We use a pulsed nitrogen laser to produce atomic ions by laser ablation,
measuring the relative ion yield for several elements, including some that have
only recently been proposed for use in cold trapped ion experiments. For
barium, we monitor the ion yield as a function of the number of applied
ablation pulses for different substrates. We also investigate the ion
production as a function of the pulse energy, and the efficiency of loading an
ion trap as a function of radiofrequency voltage.


title:
Generalised Poisson-Dirichlet Distributions and the Negative Binomial
  Point Process

abstract:
  When $S=(S_t)_{t\ge 0}$ is an $\alpha$-stable subordinator, the sequence of
ordered jumps of $S$, up till time $1$, omitting the $r$ largest of them, and
taken as proportions of their sum $^{(r)}S_t$, defines a 2-parameter
distribution on the infinite dimensional simplex, $\nabla_{\infty}$, which we
call the $\mathrm{PD}_\alpha^{(r)}$ distribution. When $r=0$ it reduces to the
$\mathrm{PD}_\alpha$ distribution introduced by Kingman in 1975. We observe a
serendipitous connection between $\mathrm{PD}_\alpha^{(r)}$ and the negative
binomial point process of Gregoire (1984), which we exploit to analyse in
detail a size-biased version of $\mathrm{PD}_\alpha^{(r)}$. As a consequence we
derive a stick-breaking representation for the process and a useful form for
its distribution. This program produces a large new class of distributions
available for a variety of modelling purposes.


title: Decoding from Pooled Data: Sharp Information-Theoretic Bounds

abstract:
  Consider a population consisting of n individuals, each of whom has one of d
types (e.g. their blood type, in which case d=4). We are allowed to query this
database by specifying a subset of the population, and in response we observe a
noiseless histogram (a d-dimensional vector of counts) of types of the pooled
individuals. This measurement model arises in practical situations such as
pooling of genetic data and may also be motivated by privacy considerations. We
are interested in the number of queries one needs to unambiguously determine
the type of each individual. In this paper, we study this information-theoretic
question under the random, dense setting where in each query, a random subset
of individuals of size proportional to n is chosen. This makes the problem a
particular example of a random constraint satisfaction problem (CSP) with a
"planted" solution. We establish almost matching upper and lower bounds on the
minimum number of queries m such that there is no solution other than the
planted one with probability tending to 1 as n tends to infinity. Our proof
relies on the computation of the exact "annealed free energy" of this model in
the thermodynamic limit, which corresponds to the exponential rate of decay of
the expected number of solution to this planted CSP. As a by-product of the
analysis, we show an identity of independent interest relating the Gaussian
integral over the space of Eulerian flows of a graph to its spanning tree
polynomial.


title: Ground test of satellite constellation based quantum communication

abstract:
  Satellite based quantum communication has been proven as a feasible way to
achieve global scale quantum communication network. Very recently, a
low-Earth-orbit (LEO) satellite has been launched for this purpose. However,
with a single satellite, it takes an inefficient 3-day period to provide the
worldwide connectivity. On the other hand, similar to how the Iridium system
functions in classic communication, satellite constellation (SC) composed of
many quantum satellites, could provide global real-time quantum communication.
In such a SC, most of the satellites will work in sunlight. Unfortunately, none
of previous ground testing experiments could be implemented at daytime. During
daytime, the bright sunlight background prohibits quantum communication in
transmission over long distances. In this letter, by choosing a working
wavelength of 1550 nm and developing free-space single-mode fibre coupling
technology and ultralow noise up-conversion single photon detectors, we
overcome the noise due to sunlight and demonstrate a 53-km free space quantum
key distribution (QKD) in the daytime through a 48-dB loss channel. Our system
not only shows the feasibility of satellite based quantum communication in
daylight, but also has the ability to naturally adapt to ground fibre optics,
representing an essential step towards a SC-based global quantum network.


title: Anticipating Moves to Prevent Botnet Generated DDoS Flooding Attacks

abstract:
  Volumetric Distributed Denial of Service (DDoS) attacks have been a recurrent
issue on the Internet. These attacks generate a flooding of fake network
traffic to interfere with targeted servers or network links. Despite many
efforts to detect and mitigate them, attackers have played a game always
circumventing countermeasures. Today, there is an increase in the number of
infected devices, even more with the advent of the Internet of Things and
flexible communication technologies. Leveraging device-to-device short range
wireless communications and others, infected devices can coordinate
sophisticated botnets, which can be employed to intensify DDoS attacks. The new
generation of botnets is even harder to detect because of their adaptive and
dynamic behavior yielded by infected mobile portable devices. Additionally,
because there can be a large number of geographically distributed devices,
botnets increase DDoS traffic significantly. In face of their new behavior and
the increasing volume of DDoS traffic, novel and intelligent-driven approaches
are required. Specifically, we advocate for {\em anticipating} trends of DDoS
attacks in the early stages as much as possible. This work provides an overview
of approaches that can be employed to anticipate trends of DDoS attacks
generated by botnets in their early stages and brings an insightful discussion
about the advantages of each kind of approach and open issues.


title: On the optimal control problem for a class of monotone bilinear systems

abstract:
  We consider a class of monotone systems in which the control signal
multiplies the state. Among other applications, such bilinear systems can be
used to model the evolutionary dynamics of HIV in the presence of combination
drug therapy. For this class of systems, we formulate an infinite horizon
optimal control problem, prove that the optimal control signal is constant over
time, and show that it can be computed by solving a finite-dimensional
non-smooth convex optimization problem. We provide an explicit expression for
the subdifferential set of the objective function and use a subgradient
algorithm to design the optimal controller. We further extend our results to
characterize the optimal robust controller for systems with uncertain dynamics
and show that computing the robust controller is no harder than computing the
nominal controller. We illustrate our results with an example motivated by
combination drug therapy.


title: Gowers norms for the Thue-Morse and Rudin-Shapiro sequences

abstract:
  We estimate Gowers uniformity norms for some classical automatic sequences,
such as the Thue-Morse and Rudin-Shapiro sequences. The methods can also be
extended to other automatic sequences. As an application, we asymptotically
count arithmetic progressions in the set of integers $\leq N$ where the
Thue-Morse (resp. Rudin-Shapiro) sequence takes the value $+1$.


title: Alternating maps on Hatcher-Thurston graphs

abstract:
  Let $S_{1}$ and $S_{2}$ be connected orientable surfaces of genus $g_{1},
g_{2} \geq 3$, $n_{1},n_{2} \geq 0$ punctures, and empty boundary. Let also
$\varphi: \mathcal{HT}(S_{1}) \rightarrow \mathcal{HT}(S_{2})$ be an
edge-preserving alternating map between their Hatcher-Thurston graphs. We prove
that $g_{1} \leq g_{2}$ and that there is also a multicurve of cardinality
$g_{2} - g_{1}$ contained in every element of the image. We also prove that if
$n_{1} = 0$ and $g_{1} = g_{2}$, then the map $\widetilde{\varphi}$ obtained by
filling the punctures of $S_{2}$, is induced by a homeomorphism of $S_{1}$.


title:
Digraphs with Distinguishable Dynamics under the Multi-Agent Agreement
  Protocol

abstract:
  In this work, the ability to distinguish digraphs from the output response of
some observing agents in a multi-agent network under the agreement protocol has
been studied. Given a fixed observation point, it is desired to find sufficient
graphical conditions under which the failure of a set of edges in the network
information flow digraph is distinguishable from another set. When the latter
is empty, this corresponds to the detectability of the former link set given
the response of the observing agent. In developing the results, a powerful
extension of the all-minors matrix tree theorem in algebraic graph theory is
proved which relates the minors of the transformed Laplacian of a directed
graph to the number and length of the shortest paths between its vertices. The
results reveal an intricate relationship between the ability to distinguish the
responses of a healthy and a faulty multi-agent network and the inter-nodal
paths in their information flow digraphs. The results have direct implications
for the operation and design of multi-agent systems subject to multiple link
losses. Simulations and examples are presented to illustrate the analytic
findings.


title:
Buddy-RAM: Improving the Performance and Efficiency of Bulk Bitwise
  Operations Using DRAM

abstract:
  Bitwise operations are an important component of modern day programming. Many
widely-used data structures (e.g., bitmap indices in databases) rely on fast
bitwise operations on large bit vectors to achieve high performance.
Unfortunately, in existing systems, regardless of the underlying architecture
(e.g., CPU, GPU, FPGA), the throughput of such bulk bitwise operations is
limited by the available memory bandwidth.
  We propose Buddy, a new mechanism that exploits the analog operation of DRAM
to perform bulk bitwise operations completely inside the DRAM chip. Buddy
consists of two components. First, simultaneous activation of three DRAM rows
that are connected to the same set of sense amplifiers enables us to perform
bitwise AND and OR operations. Second, the inverters present in each sense
amplifier enables us to perform bitwise NOT operations, with modest changes to
the DRAM array. These two components make Buddy functionally complete. Our
implementation of Buddy largely exploits the existing DRAM structure and
interface, and incurs low overhead (1% of DRAM chip area).
  Our evaluations based on SPICE simulations show that, across seven
commonly-used bitwise operations, Buddy provides between 10.9X---25.6X
improvement in raw throughput and 25.1X---59.5X reduction in energy
consumption. We evaluate three real-world data-intensive applications that
exploit bitwise operations: 1) bitmap indices, 2) BitWeaving, and 3)
bitvector-based implementation of sets. Our evaluations show that Buddy
significantly outperforms the state-of-the-art.


title:
Probing Spontaneous Wave-Function Collapse with Entangled Levitating
  Nanospheres

abstract:
  Wave function collapse models are considered as the modified theories of
standard quantum mechanics at the macroscopic level. By introducing nonlinear
stochastic terms in the Schr\"odinger equation, these models make predictions,
differently from those of standard quantum mechanics, that it is fundamentally
impossible to prepare macroscopic systems in macroscopic superpositions. The
validity of these models can only be examined by experiments and hence
efficient protocols for this kind of experiments are highly needed. Here we
provide a protocol that is able to probe the postulated collapse effect by
means of the entanglement of the center-of-mass motion of two nanospheres
optically trapped in a Fabry-P\'erot cavity. We show that the collapse noise
results in large reduction of the steady-state entanglement and the
entanglement, with and without the collapse effect, shows distinguishable
scalings with certain system parameters, which can be used to unambiguously
determine the effect of these models.


title:
Experimental Test of Irreducible Four-Qubit Greenberger-Horne-Zeilinger
  Paradox

abstract:
  Bell's theorem shows a profound contradiction between local realism and
quantum mechanics on the level of statistical predictions. It does not involve
directly Einstein-Podolsky-Rosen (EPR) correlations. The paradox of
Greenberger-Horne-Zeilinger (GHZ) disproves directly the concept of EPR
elements of reality, based on the EPR correlations, in an all-versus-nothing
way. A three-qubit experimental demonstration of the GHZ paradox was achieved
nearly twenty years ago, and followed by demonstrations for more qubits. Still,
the GHZ contradictions underlying the tests can be reduced to three-qubit one.
We show an irreducible four-qubit GHZ paradox, and report its experimental
demonstration. The reducibility loophole is closed. The bound of a
three-setting per party Bell-GHZ inequality is violated by $7\sigma$. The
fidelity of the GHZ state was around $81\%$, and an entanglement witness
reveals a violation of the separability threshold by $19\sigma$.


title: Kitaev's exact solution approximated

abstract:
  For 25 years condensed matter physicists have searched for a material that
realizes a macroscopic quantum state of matter, the quantum spin liquid. Recent
experiments show that the necessary interaction may be found in a family of
hexagonal ruthenium based materials.


title: Finite-size effect of \eta-deformed AdS_5 x S^5 at strong coupling

abstract:
  We compute Luscher corrections for a giant magnon in the \eta-deformed
(AdS_5\times S^5)_{\eta} using the su(2|2)_q-invariant S-matrix at strong
coupling and compare with the finite-size effect of the corresponding string
state, derived previously. We find that these two results match and confirm
that the su(2|2)_q-invariant S-matrix is describing world-sheet excitations of
the \eta-deformed background.


title:
The exponential map of the group of area-preserving diffeomorphisms of a
  surface with boundary

abstract:
  We prove that the Riemannian exponential map of the right-invariant $L^2$
metric on the group of volume-preserving diffeomorphisms of a two-dimensional
manifold with a nonempty boundary is a nonlinear Fredholm map of index zero.


title: A Case Study in Astronomical 3-D Printing: The Mysterious Eta Carinae

abstract:
  3-D printing moves beyond interactive 3-D graphics and provides an excellent
tool for both visual and tactile learners, since 3-D printing can now easily
communicate complex geometries and full color information. Some limitations of
interactive 3-D graphics are also alleviated by 3-D printable models, including
issues of limited software support, portability, accessibility, and
sustainability. We describe the motivations, methods, and results of our work
on using 3-D printing (1) to visualize and understand the Eta Car Homunculus
nebula and central binary system and (2) for astronomy outreach and education,
specifically, with visually impaired students. One new result we present is the
ability to 3-D print full-color models of Eta Car's colliding stellar winds. We
also demonstrate how 3-D printing has helped us communicate our improved
understanding of the detailed structure of Eta Car's Homunculus nebula and
central binary colliding stellar winds, and their links to each other. Attached
to this article are full-color 3-D printable files of both a red-blue
Homunculus model and the Eta Car colliding stellar winds at orbital phase
1.045. 3-D printing could prove to be vital to how astronomer's reach out and
share their work with each other, the public, and new audiences.


title: Asteroid-Comet Continuum Objects in the Solar System

abstract:
  In this review presented at the Royal Society meeting, "Cometary Science
After Rosetta", I present an overview of studies of small solar system objects
that exhibit properties of both asteroids and comets (with a focus on so-called
active asteroids). Sometimes referred to as "transition objects", these bodies
are perhaps more appropriately described as "continuum objects", to reflect the
notion that rather than necessarily representing actual transitional
evolutionary states between asteroids and comets, they simply belong to the
general population of small solar system bodies that happen to exhibit a
continuous range of observational, physical, and dynamical properties.
Continuum objects are intriguing because they possess many of the properties
that make classical comets interesting to study (e.g., relatively primitive
compositions, ejection of surface and subsurface material into space where it
can be more easily studied, and orbital properties that allow us to sample
material from distant parts of the solar system that would otherwise be
inaccessible), while allowing us to study regions of the solar system that are
not sampled by classical comets.


title: Holographic conductivity of 1+1 dimensional systems in soft wall model

abstract:
  We study the optical conductivity of 1+1 dimensional systems using soft wall
model in the bottom up approach of AdS/CFT (anti-de Sitter/conformal field
theory) duality. We find the numerical results for optical conductivity and
investigate the system using holographic model in the probe limit. The
dependence of conductivity on chemical potential is also investigated. Further,
we extend the soft wall model as a `no-wall' model by eliminating the dilaton
background and study the response of the system in a simplified approach.