  Nonequilibrium work-Hamiltonian connection for a microstate plays a central
role in diverse branches of statistical thermodynamics (fluctuation theorems,
quantum thermodynamics, stochastic thermodynamics, etc.). We show that the
change in the Hamiltonian for a microstate should be identified with the work
done by it, and not the work done on it. This contradicts the current practice
in the field. The difference represents a contribution whose average gives the
work that is dissipated due to irreversibility. As the latter has been
overlooked, the current identification does not properly account for
irreversibilty. As an example, we show that the corrected version of
Jarzynski's relation can be applied to free expansion, where the original
relation fails. Thus, the correction has far-reaching consequences and requires
reassessment of current applications.


  How the spacetime evolved non-perturbatively in a landscape with multiple
anti-de Sitter (AdS) vacua, which is theoretically well-motivated, has always
been a matter of concern. As a step towards this issue, we perform (3+1)D
numerical relativity simulations for the inhomogeneous universe in an AdS
landscape, and find that large inhomogeneity of scalar field can develop into
not only sphere-like bubbles, but also novel tube-like structures. It is
observed that the bubble or tube wall (across the potential barrier) likely
inflates as a quasi-dS space, while the different regions separated by the
walls are in different AdS vacua and will collapse towards singularity.


  For integers $l \geq 2$, $d \geq 1$ we study (undirected) graphs with
vertices $1, ..., n$ such that the vertices can be partitioned into $l$ parts
such that every vertex has at most $d$ neighbours in its own part. The set of
all such graphs is denoted $\mbP_n(l,d)$. We prove a labelled first-order limit
law, i.e., for every first-order sentence $\varphi$, the proportion of graphs
in $\mbP_n(l,d)$ that satisfy $\varphi$ converges as $n \to \infty$. By
combining this result with a result of Hundack, Pr\"omel and Steger \cite{HPS}
we also prove that if $1 \leq s_1 \leq ... \leq s_l$ are integers, then
$\mb{Forb}(\mcK_{1, s_1, ..., s_l})$ has a labelled first-order limit law,
where $\mb{Forb}(\mcK_{1, s_1, ..., s_l})$ denotes the set of all graphs with
vertices $1, ..., n$, for some $n$, in which there is no subgraph isomorphic to
the complete $(l+1)$-partite graph with parts of sizes $1, s_1, ..., s_l$. In
the course of doing this we also prove that there exists a first-order formula
$\xi$ (depending only on $l$ and $d$) such that the proportion of $\mcG \in
\mbP_n(l,d)$ with the following property approaches 1 as $n \to \infty$: there
is a unique partition of $\{1, ..., n\}$ into $l$ parts such that every vertex
has at most $d$ neighbours in its own part, and this partition, viewed as an
equivalence relation, is defined by $\xi$.


  We consider the Sparse Principal Component Analysis (SPCA) problem under the
well-known spiked covariance model. Recent work has shown that the SPCA problem
can be reformulated as a Mixed Integer Program (MIP) and can be solved to
global optimality, leading to estimators that are known to enjoy optimal
statistical properties. However, current MIP algorithms for SPCA are unable to
scale beyond instances with a thousand features or so. In this paper, we
propose a new estimator for SPCA which can be formulated as a MIP. Different
from earlier work, we make use of the underlying spiked covariance model and
properties of the multivariate Gaussian distribution to arrive at our
estimator. We establish statistical guarantees for our proposed estimator in
terms of estimation error and support recovery. We propose a custom algorithm
to solve the MIP which is significantly more scalable than off-the-shelf
solvers; and demonstrate that our approach can be much more computationally
attractive compared to earlier exact MIP-based approaches for the SPCA problem.
Our numerical experiments on synthetic and real datasets show that our
algorithms can address problems with up to 20000 features in minutes; and
generally result in favorable statistical properties compared to existing
popular approaches for SPCA.


  We define an equivalence relation called A-isotopy between finitely
determined map-germs, which is a strengthened version of A-equivalence. We
consider the number of A-isotopy classes of equidimensional Morin
singularities, and some other well-known low-dimensional singularities. We also
give an application to stable perturbations of simple equi-dimensional
map-germs.


  We show that once the effects of valley splitting and intervalley scattering
are incorporated, renormalization group theory consistently describes the
metallic phase in silicon metal-oxide-semiconductor field-effect transistors
down to the lowest accessible temperatures.


  The temporal decay of mass accretion in young stars is a fundamental tracer
of the early evolution of circumstellar disks. Through population syntheses, we
study how correlated uncertainties between the estimated parameters of young
stars (luminosity, temperature, mass, age) and mass accretion rates Mdot, as
well as observational selection effects, can bias the temporal decay of mass
accretion rates (Mdot t^-eta) inferred from a comparison of measured Mdot with
isochronal ages in young stellar clusters. We find that the presence of
realistic uncertainties reduces the measured value of eta by up to a factor of
3, leading to the inference of shallower decays than the true value. This
suggests a much faster temporal decay of Mdot than generally assumed. When
considering the minimum uncertainties in ages affecting the Orion Nebula
Cluster, the observed value eta~1.4, typical of Galactic star forming regions,
can only be reproduced if the real decay exponent is eta>4. This effect becomes
more severe if one assumes that observational uncertainties are larger, as
required by some fast star formation scenarios. Our analysis shows that while
selection effects due to sample incompleteness do bias eta, they can not alter
this main result and strengthen it in many cases. A remaining uncertainty in
our work is that it applies to the most commonly used and simple relationship
between Mdot, the accretion luminosity and the stellar parameters. We briefly
explore how a more complex interplay between these quantities might change the
results.


  New limits on $\beta^+$EC and ECEC processes in $^{74}$Se have been obtained
using a 600 cm$^3$ HPGe detector and an external source consisting of 1600 g of
a natural selenium powder. For different $\beta^+$EC and ECEC transitions (to
the ground and excited states) obtained limits are on the level $\sim
(0.2-4.8)\times10^{19}$ yr at 90\% C.L. In particular, for the potentially
resonant transition into the 1204.2 keV excited state of $^{74}$Ge a lower
half-life limit of $1.1\times10^{19}$ yr at 90\% C.L. has been obtained.
Possibility to increase the sensitivity of such measurements is discussed.


  The ability of learning disentangled representations represents a major step
for interpretable NLP systems as it allows latent linguistic features to be
controlled. Most approaches to disentanglement rely on continuous variables,
both for images and text. We argue that despite being suitable for image
datasets, continuous variables may not be ideal to model features of textual
data, due to the fact that most generative factors in text are discrete. We
propose a Variational Autoencoder based method which models language features
as discrete variables and encourages independence between variables for
learning disentangled representations. The proposed model outperforms
continuous and discrete baselines on several qualitative and quantitative
benchmarks for disentanglement as well as on a text style transfer downstream
application.


  We present the Sommerfeld enhanced Dark Matter (DM) annihilation into gamma
ray for a class of three-loop radiative neutrino mass models with large
electroweak multiplets where the DM mass is in O(TeV) range. We show that in
this model, the DM annihilation rate becomes more prominent for larger
multiplets and it is already within the reach of currently operating Imaging
Atmospheric Cherenkov telescopes (IACTs), High Energy Stereoscopic System
(H.E.S.S.). Furthermore, Cherenkov Telescope Array (CTA), which will begin
operating in 2030, will improve this sensitivity by a factor of
$\mathcal{O}{(10)}$ and may exclude a large portion of parameter space of this
radiative neutrino mass model with larger electroweak multiplet. This implies
that the only viable option is the model with lowest electroweak multiplets
i.e. singlets of $SU(2)_{L}$ where the DM annihilation rate is not Sommerfeld
enhanced and hence it is not yet constrained by the indirect detection limits
from H.E.S.S. or future CTA.


  Black-box optimization (BBO) can be used to optimize functions whose analytic
form is unknown. A common approach to realising BBO is to learn a surrogate
model which approximates the target black-box function which can then be solved
via white-box optimization methods. In this paper, we present our approach
BOX-QUBO, where the surrogate model is a QUBO matrix. However, unlike in
previous state-of-the-art approaches, this matrix is not trained entirely by
regression, but mostly by classification between 'good' and 'bad' solutions.
This better accounts for the low capacity of the QUBO matrix, resulting in
significantly better solutions overall. We tested our approach against the
state-of-the-art on four domains and in all of them BOX-QUBO showed better
results. A second contribution of this paper is the idea to also solve
white-box problems, i.e. problems which could be directly formulated as QUBO,
by means of black-box optimization in order to reduce the size of the QUBOs to
the information-theoretic minimum. Experiments show that this significantly
improves the results for MAX-k-SAT.


  The 2nd Workshop on Maritime Computer Vision (MaCVi) 2024 addresses maritime
computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface
Vehicles (USV). Three challenges categories are considered: (i) UAV-based
Maritime Object Tracking with Re-identification, (ii) USV-based Maritime
Obstacle Segmentation and Detection, (iii) USV-based Maritime Boat Tracking.
The USV-based Maritime Obstacle Segmentation and Detection features three
sub-challenges, including a new embedded challenge addressing efficicent
inference on real-world embedded devices. This report offers a comprehensive
overview of the findings from the challenges. We provide both statistical and
qualitative analyses, evaluating trends from over 195 submissions. All
datasets, evaluation code, and the leaderboard are available to the public at
https://macvi.org/workshop/macvi24.


  The 6307A emission line in the spectrum of Eta Car (Martin et al. 2006) is
blue-shifted [S III] 6313A emission originating from the outer wind structures
of the massive binary system. We realized the identification while analyzing
multiple forbidden emission lines not normally seen in the spectra of massive
stars. The high spatial and moderate spectral resolutions of HST/STIS resolve
forbidden lines of Fe+, N+, Fe++, S++, Ne++ and Ar++ into spatially and
velocity-resolved rope-like features originating from collisionally-excited
ions photo-ionized by UV photons or collisions. While the [Fe II] emission
extends across a velocity range of +/-500 km/s out to 0."7, more highly ionized
forbidden emissions are systematically blue-shifted (-500 to +200 km/s) and
extend only to 0."4. The [Fe II] defines the outer regions of the massive
primary wind. The [N II], [Fe III] emission define the the outer wind
interaction regions directly photo-ionized by far-UV radiation. Variations in
emission of [S III] 9533A, 9071A and 6313A suggest density ranges of 10^6 to
10^10 cm^-3 for electron temperatures ranging from 8,000 to 13,000K. Mapping
the temporal changes of the emission structure at critical phases of the
5.54-year period will provide important diagnostics of the interacting winds.


  We review results of papers written on the topic of polynomial amoebas with
an emphasis on computational aspects of the topic. The polynomial amoebas have
a lot of applications in various domains of science. Computation of the amoeba
for a given polynomial and describing its properties is in general a problem of
formidable complexity. We describe the main algorithms for computing and
depicting the amoebas and geometrical objects associated with them, such as
contours and spines. We review the latest software packages for computing the
polynomial amoebas and compare their functionality and performance.


  In this paper, we prove that the generator of any bounded analytic semigroup
in $(\theta,1)$-type real interpolation of its domain and underlying Banach
space has maximal $L^1$-regularity, using a duality argument combined with the
result of maximal continuous regularity.
  As an application, we consider maximal $L^1$-regularity of the
Dirichlet-Laplacian and the Stokes operator in inhomogeneous $B^s_{q,1}$-type
Besov spaces on domains of $\mathbb R^n$, $n\geq 2$.


  In previous studies [1, 2, 3] the possible existence dark matter admixed
pulsar have been discussed based on three different dark matter density
profiles, Singular Isothermal Sphere density profile, Universal Rotational
Curve density profile and Navarro-Frenk-White density profile. They have been
used to discuss the pulsars present in our Milky Way galaxy as well as some
satellite dwarf galaxies of Milky Way. In this article we use the Universal
Rotational Curve (URC) dark matter density profile to observe similar effects
on galaxies M31 and M87. These study hold significant importance, as now it can
be concluded that there is a fair possibility of presence of dark matter
admixed pulser in M31 and M87 galaxies as well.


  Using infrared spectroscopy, we investigate bottom gated ABA-stacked trilayer
graphene subject to an additional environment-induced p-type doping. We find
that the Slonczewski-Weiss-McClure tight-binding model and the Kubo formula
reproduce the gate voltage-modulated reflectivity spectra very accurately. This
allows us to determine the charge densities and the potentials of the
{\pi}-band electrons on all graphene layers separately and to extract the
interlayer permittivity due to higher energy bands.


  In this paper various properties of global and local changes of variables as
well as properties of canonical transforms are investigated on modulation and
Wiener amalgam spaces. We establish several relations among localisations of
modulation and Wiener amalgam spaces and, as a consequence, we obtain several
versions of local and global Beurling-Helson type theorems. We also establish a
number of positive results such as local boundedness of canonical transforms on
modulation spaces, properties of homogeneous changes of variables, and local
continuity of Fourier integral operators on Fourier Lebesgue spaces. Finally,
counterparts of these results are discussed for spaces on the torus as well as
for weighted spaces.


  We consider an ultra-weak first order system discretization of the Helmholtz
equation. When employing the optimal test norm, the `ideal' method yields the
best approximation to the pair of the Helmholtz solution and its scaled
gradient w.r.t.~the norm on $L_2(\Omega)\times L_2(\Omega)^d$ from the selected
finite element trial space. On convex polygons, the `practical', implementable
method is shown to be pollution-free essentially whenever the order $\tilde{p}$
of the finite element test space grows proportionally with $\max(\log
\kappa,p^2)$, with $p$ being the order at trial side. Numerical results also on
other domains show a much better accuracy than for the Galerkin method.


  We develop a linearized boundary control method for the inverse boundary
value problem of determining a potential in the acoustic wave equation from the
Neumann-to-Dirichlet map. When the linearization is at the zero potential, we
derive a reconstruction formula based on the boundary control method and prove
that it is of Lipschitz-type stability. When the linearization is at a nonzero
potential, we prove that the problem is of H\"{o}lder-type stability. The
proposed reconstruction formula is implemented and evaluated using several
numerical experiments to validate its feasibility.


  The classical theory of ion beam sputtering predicts the instability of a
flat surface to uniform ion irradiation at any incidence angle. We relax the
assumption of the classical theory that the average surface erosion rate is
determined by a Gaussian response function representing the effect of the
collision cascade and consider the surface dynamics for other
physically-motivated response functions. We show that although instability of
flat surfaces at any beam angle results from all Gaussian and a wide class of
non-Gaussian erosive response functions, there exist classes of modifications
to the response that can have a dramatic effect. In contrast to the classical
theory, these types of response render the flat surface linearly stable, while
imperceptibly modifying the predicted sputter yield vs. incidence angle. We
discuss the possibility that such corrections underlie recent reports of a
``window of stability'' of ion-bombarded surfaces at a range of beam angles for
certain ion and surface types, and describe some characteristic aspects of
pattern evolution near the transition from unstable to stable dynamics. We
point out that careful analysis of the transition regime may provide valuable
tests for the consistency of any theory of pattern formation on ion sputtered
surfaces.


  Severe constraints on parameters of the minimal supersymmetric standard model
follow from a dynamical electroweak symmetry breaking mechanism dominated by
top and stop loops. In particular, the lightest Higgs boson mass is expected to
be smaller than 100 GeV.


  Features that are equivariant to a larger group of symmetries have been shown
to be more discriminative and powerful in recent studies. However, higher-order
equivariant features often come with an exponentially-growing computational
cost. Furthermore, it remains relatively less explored how rotation-equivariant
features can be leveraged to tackle 3D shape alignment tasks. While many past
approaches have been based on either non-equivariant or invariant descriptors
to align 3D shapes, we argue that such tasks may benefit greatly from an
equivariant framework. In this paper, we propose an effective and practical
SE(3) (3D translation and rotation) equivariant network for point cloud
analysis that addresses both problems. First, we present SE(3) separable point
convolution, a novel framework that breaks down the 6D convolution into two
separable convolutional operators alternatively performed in the 3D Euclidean
and SO(3) spaces. This significantly reduces the computational cost without
compromising the performance. Second, we introduce an attention layer to
effectively harness the expressiveness of the equivariant features. While
jointly trained with the network, the attention layer implicitly derives the
intrinsic local frame in the feature space and generates attention vectors that
can be integrated into different alignment tasks. We evaluate our approach
through extensive studies and visual interpretations. The empirical results
demonstrate that our proposed model outperforms strong baselines in a variety
of benchmarks


  We develop a finite temperature theory for the susceptibility and
electrostriction of isotropic substances in which permanent electric dipoles
are restrained from free rotation by elastic forces. All parameters are
constrained by the measured susceptibility and elastic constants. When applied
to polyurethane, the predicted electrostriction is approximately consistent
wiht some of the wide range of data. The saturation of the electrostriction at
high field may be explained qualitatively if the dipoles consist of several
amide groups locked together by crystallization of the hard segments of the
polymer.


  The success of machine learning methods heavily relies on having an
appropriate representation for data at hand. Traditionally, machine learning
approaches relied on user-defined heuristics to extract features encoding
structural information about data. However, recently there has been a surge in
approaches that learn how to encode the data automatically in a low dimensional
space. Exponential family embedding provides a probabilistic framework for
learning low-dimensional representation for various types of high-dimensional
data. Though successful in practice, theoretical underpinnings for exponential
family embeddings have not been established. In this paper, we study the
Gaussian embedding model and develop the first theoretical results for
exponential family embedding models. First, we show that, under mild condition,
the embedding structure can be learned from one observation by leveraging the
parameter sharing between different contexts even though the data are dependent
with each other. Second, we study properties of two algorithms used for
learning the embedding structure and establish convergence results for each of
them. The first algorithm is based on a convex relaxation, while the other
solved the non-convex formulation of the problem directly. Experiments
demonstrate the effectiveness of our approach.


  Light-Front Field Theory (LFFT) is a good candidate to describe bound states.
In LFFT covariance is non-manifest. Burkardt and Langnau claim that, even for
scattering amplitudes, rotational invariance is broken. We will take a
different path of obtaining rules for light-front time-ordered diagrams.
Covariance depends on the choice of a regulator $\alpha$. We need to apply some
regularisation scheme to render physical amplitudes finite. This is done by
applying minus regularisation. In this process all ambiguities related to the
regulator $\alpha$ are removed. Therefore there is equivalence between the
perturbative expansions of covariant and LFFT.


  We present ~2" resolution submillimeter observations of the submillimeter
luminous giant Ly-alpha blob (LAB1) in the SSA 22 protocluster at redshift
z=3.1 with the Submillimeter Array (SMA). Although the expected submillimeter
flux density is 16 mJy at 880 micron, no emission is detected with the 2".4 x
1".9 (18 x 14 kpc) beam at the 3 sigma level of 4.2 mJy beam^{-1} in the SMA
field of view of 35". This is in contrast to the previous lower angular
resolution (15") observations where a bright (17 mJy) unresolved submillimeter
source was detected at 850 micron toward the LAB1 using the Submillimeter
Common-User Bolometer Array on the James Clerk Maxwell Telescope. The SMA
non-detection suggests that the spatial extent of the submillimeter emission of
LAB1 should be larger than 4" (>30 kpc). The most likely interpretation of the
spatially extended submillimeter emission is that starbursts occur throughout
the large area in LAB1. Some part of the submillimeter emission may come from
spatially extended dust expelled from starburst regions by galactic superwind.
The spatial extent of the submillimeter emission of LAB1 is similar to those of
high redshift radio galaxies rather than submillimeter galaxies.


  Visual kinship recognition aims to identify blood relatives from facial
images. Its practical application-- like in law-enforcement, video
surveillance, automatic family album management, and more-- has motivated many
researchers to put forth effort on the topic as of recent. In this paper, we
focus on a new view of visual kinship technology: kin-based face generation.
Specifically, we propose a two-stage kin-face generation model to predict the
appearance of a child given a pair of parents. The first stage includes a deep
generative adversarial autoencoder conditioned on ages and genders to map
between facial appearance and high-level features. The second stage is our
proposed DNA-Net, which serves as a transformation between the deep and genetic
features based on a random selection process to fuse genes of a parent pair to
form the genes of a child. We demonstrate the effectiveness of the proposed
method quantitatively and qualitatively: quantitatively, pre-trained models and
human subjects perform kinship verification on the generated images of
children; qualitatively, we show photo-realistic face images of children that
closely resemble the given pair of parents. In the end, experiments validate
that the proposed model synthesizes convincing kin-faces using both subjective
and objective standards.


  I bring forward some arguments to support the thesis that nature is
fundamentally discrete, and present my own thoughts about the direction in
which one could look for a possible, consistent "theory of everything"
describing gravitation and quantum particles.


  For Hamiltonian flows we establish the existence of periodic orbits on a
sequence of level sets approaching a Bott-nondegenerate symplectic extremum of
the Hamiltonian. As a consequence, we show that a charge on a compact manifold
with a nondegenerate (i.e. symplectic) magnetic field has periodic orbits on a
sequence of energy levels converging to zero.


  The persistent current of interacting electrons in toroidal single-wall
carbon nanotubes is evaluated within Haldane's concept of topological
excitations. The overall pattern of the persistent current corresponds to the
constant interaction model, whereas the fine structure stems from the
electronic exchange correlations.


  The gas-phase metallicity of galaxies is regulated by multiple astrophysical
processes, which makes it a crucial diagnostic of galaxy formation and
evolution. Beyond the fundamental mass-metallicity relation, a debate about the
secondary galaxy property to predict the metallicity of galaxies arises.
Motivated by this, we systematically examine the relationship between gas-phase
metallicity and other galaxy properties, i.e. star formation rate (SFR) and
galaxy size, in addition to stellar mass in both observation and simulation. We
utilize the data from the MaNGA (Mapping Nearby Galaxies at Apache Point
Observatory) survey and the TNG50 simulations. We find that the combination of
$M_*/R_{\rm e}^\beta$ with $\beta\sim 0.6-1$ is in much stronger correlation to
the metallicity than stellar mass alone, regardless of whether the SFR is
included or not, in both observation and simulation. This indicates that galaxy
size plays a more important role in determining gas-phase metallicity of
galaxies than SFR. In addition, the TNG simulation predicts that the SFR,
although being a subdominant role, becomes increasingly important in high-$z$
universe. Finally, we speculate that SFR modulates metallicity on the temporal
dimension, synchronized with time-varying gas inflows, and galaxy size
regulates metallicity on the spatial dimension by affecting the gravitational
potential and the mass loading factor.


  We have performed Compton scattering measurements on as-grown and reduced
single crystals of the electron-doped T'-cuprate Pr1.3-xLa0.7CexCuO4 (x = 0.10)
to investigate the effect of reduction annealing on the electronic state. The
obtained results have revealed that the numbers of electrons in the O2p orbital
in the Zhang-Rice singlet band, the Cu3dx2-y2 orbital, and the Cu3d3z2-r2
orbital are increased by reduction annealing. The increase in the number of
Cu3d3z2-r2 electrons suggests the existence of Cu3d3z2-r2 holes in the as-grown
single crystal. This is spectroscopic evidence of local hole doping into the
Cu3d3z2-r2 orbital around the excess oxygen, as suggested by transport
measurements [T. Adachi et al., J. Phys. Soc. Jpn. 82, 063713 (2013)].


  In the uniformly hyperbolic setting it is well known that the set of all
measures supported on periodic orbits is dense in the convex space of all
invariant measures. In this paper we consider the converse question, in the
non-uniformly hyperbolic setting: assuming that some ergodic measure converges
to a convex combination of hyperbolic ergodic measures, what can we deduce
about the initial measures?
  To every hyperbolic measure $\mu$ whose stable/unstable Oseledets splitting
is dominated we associate canonically a unique class $H(\mu)$ of periodic
orbits for the homoclinic relation, called its \emph{intersection class}. In a
dominated setting, we prove that a measure for which almost every measure in
its ergodic decomposition is hyperbolic with the same index such as the
dominated splitting is accumulated by ergodic measures if, and only if, almost
all such ergodic measures have a common intersection class.
  We provide examples which indicate the importance of the domination
assumption.


  Given a dataset of $n$ i.i.d. samples from an unknown distribution $P$, we
consider the problem of generating a sample from a distribution that is close
to $P$ in total variation distance, under the constraint of differential
privacy (DP). We study the problem when $P$ is a multi-dimensional Gaussian
distribution, under different assumptions on the information available to the
DP mechanism: known covariance, unknown bounded covariance, and unknown
unbounded covariance. We present new DP sampling algorithms, and show that they
achieve near-optimal sample complexity in the first two settings. Moreover,
when $P$ is a product distribution on the binary hypercube, we obtain a pure-DP
algorithm whereas only an approximate-DP algorithm (with slightly worse sample
complexity) was previously known.


  We propose Neuro-Symbolic Visual Dialog (NSVD) -the first method to combine
deep learning and symbolic program execution for multi-round visually-grounded
reasoning. NSVD significantly outperforms existing purely-connectionist methods
on two key challenges inherent to visual dialog: long-distance co-reference
resolution as well as vanishing question-answering performance. We demonstrate
the latter by proposing a more realistic and stricter evaluation scheme in
which we use predicted answers for the full dialog history when calculating
accuracy. We describe two variants of our model and show that using this new
scheme, our best model achieves an accuracy of 99.72% on CLEVR-Dialog -a
relative improvement of more than 10% over the state of the art while only
requiring a fraction of training data. Moreover, we demonstrate that our
neuro-symbolic models have a higher mean first failure round, are more robust
against incomplete dialog histories, and generalise better not only to dialogs
that are up to three times longer than those seen during training but also to
unseen question types and scenes.


  This is a survey on the usage of the module theoretic notion of a
"retractable module" in the study of algebras with actions. We explain how
classical results can be interpreted using module theory and end the paper with
some open questions.


  We present optical spectra and near-infrared imaging of a sample of
serendipitous X-ray sources detected in the field of Chandra observations of
the A2390 cluster of galaxies. The sources have 0.5-7 keV fluxes of 0.6-8 x
10^{-14} erg cm^{-2} s^{-1} and lie around the break in the 2-10 keV source
counts. They therefore are typical of sources dominating the X-ray Background
in that band. Twelve of the fifteen targets for which we have optical spectra
show emission lines, most of which have soft X-ray spectra. Including
photometric redshifts and published spectra we have redshifts for seventeen of
the sources, ranging from z~0.2 up to z~3 with a peak between z=1-2. Ten of our
sources have hard X-ray spectra indicating a spectral slope flatter than that
of a typical unabsorbed quasar. Two hard sources that are gravitationally
lensed by the foreground cluster are obscured quasars, with intrinsic 2-10 keV
luminosities of 0.2-3 x 10^{45} erg/s and absorbing columns of N_H>10^{23}
cm^{-2}. Both were detected by ISO, showing that the absorbed flux emerges in
the far-infrared.


  In a previous paper [R. Andreani, G. Haeser, L. M. Mito, H. Ram\'irez, T. P.
Silveira. First- and second-order optimality conditions for second-order cone
and semidefinite programming under a constant rank condition. Mathematical
Programming, 2023. DOI: 10.1007/s10107-023-01942-8] we introduced a constant
rank constraint qualification for nonlinear semidefinite and second-order cone
programming by considering all faces of the underlying cone. This condition is
independent of Robinson's condition and it implies a strong second-order
necessary optimality condition which depends on a single Lagrange multiplier
instead of the full set of Lagrange multipliers. In this paper we expand on
this result in several directions, namely, we consider the larger class of
$\mathcal{C}^2-$cone reducible constraints and we show that it is not necessary
to consider all faces of the cone; instead a single specific face should be
considered (which turns out to be weaker than Robinson's condition) in order
for the first order necessary optimality condition to hold. This gives rise to
a notion of facial reduction for nonlinear conic programming, that allows
locally redefining the original problem only in terms of this specific face
instead of the whole cone, providing a more robust formulation of the problem
in which Robinson's condition holds. We were also able to prove the strong
second-order necessary optimality condition in this context by considering only
the subfaces of this particular face, which is a new result even in nonlinear
programming.


  Local feature description is a fundamental yet challenging task in 3D
computer vision. This paper proposes a novel descriptor, named Statistic of
Deviation Angles on Subdivided Space (SDASS), of encoding geometrical and
spatial information of local surface on Local Reference Axis (LRA). In terms of
encoding geometrical information, considering that surface normals, which are
usually used for encoding geometrical information of local surface, are
vulnerable to various nuisances (e.g., noise, varying mesh resolutions etc.),
we propose a robust geometrical attribute, called Local Minimum Axis (LMA), to
replace the normals for generating the geometrical feature in our SDASS
descriptor. For encoding spatial information, we use two spatial features for
fully encoding the spatial information of a local surface based on LRA which
usually presents high overall repeatability than Local Reference Axis (LRF).
Besides, an improved LRA is proposed for increasing the robustness of our SDASS
to noise and varying mesh resolutions. The performance of the SDASS descriptor
is rigorously tested on four popular datasets. The results show that our
descriptor has a high descriptiveness and strong robustness, and its
performance outperform existing algorithms by a large margin. Finally, the
proposed descriptor is applied to 3D registration. The accurate result further
confirms the effectiveness of our SDASS method.


  We consider reasoning and minimization in systems of polynomial ordinary
differential equations (ode's). The ring of multivariate polynomials is
employed as a syntax for denoting system behaviours. We endow this set with a
transition system structure based on the concept of Lie-derivative, thus
inducing a notion of L-bisimulation. We prove that two states (variables) are
L-bisimilar if and only if they correspond to the same solution in the ode's
system. We then characterize L-bisimilarity algebraically, in terms of certain
ideals in the polynomial ring that are invariant under Lie-derivation. This
characterization allows us to develop a complete algorithm, based on building
an ascending chain of ideals, for computing the largest L-bisimulation
containing all valid identities that are instances of a user-specified
template. A specific largest L-bisimulation can be used to build a reduced
system of ode's, equivalent to the original one, but minimal among all those
obtainable by linear aggregation of the original equations. A computationally
less demanding approximate reduction and linearization technique is also
proposed.


  Popular sparse estimation methods based on $\ell_1$-relaxation, such as the
Lasso and the Dantzig selector, require the knowledge of the variance of the
noise in order to properly tune the regularization parameter. This constitutes
a major obstacle in applying these methods in several frameworks---such as time
series, random fields, inverse problems---for which the noise is rarely
homoscedastic and its level is hard to know in advance. In this paper, we
propose a new approach to the joint estimation of the conditional mean and the
conditional variance in a high-dimensional (auto-) regression setting. An
attractive feature of the proposed estimator is that it is efficiently
computable even for very large scale problems by solving a second-order cone
program (SOCP). We present theoretical analysis and numerical results assessing
the performance of the proposed procedure.


  The coherent tunneling phenomenon is investigated in rectangular billiards
divided into two domains by a classically unclimbable potential barrier. We
show that by placing a pointlike scatterer inside the billiard, we can control
the occurrence and the rate of the resonance tunneling. The key role of the
avoided crossing is stressed.
  Keywords: chaotic tunneling, quantum billiard, delta potential, diabolical
degeneracy PACS: 3.65.-w, 4.30.Nk, 5.45.+b, 73.40.Gk


  Ray tracing has been typically known as a graphics rendering method capable
of producing highly realistic imagery and visual effects generated by
computers. More recently the performance improvements in Graphics Processing
Units (GPUs) have enabled developers to exploit sufficient computing power to
build a fair amount of ray tracing applications with the ability to run in
real-time. Typically, real-time ray tracing is achieved by utilizing high
performance kernels written in CUDA, OpenCL, and Vulkan which can be invoked by
high-level languages via native bindings; a technique that fragments
application code bases as well as limits portability.
  This paper presents a hardware-accelerated ray tracing rendering engine,
fully written in Java, that can seamlessly harness the performance of
underlying GPUs via the TornadoVM framework. Through this paper, we show the
potential of Java and acceleration frameworks to process in real time a compute
intensive application. Our results indicate that it is possible to enable real
time ray tracing from Java by achieving up to 234, 152, 45 frames-per-second in
720p, 1080p, and 4K resolutions, respectively.


  We consider a class of tautological top intersection products on the moduli
space of stable pairs consisting of semistable vector bundles together with N
sections on a smooth complex projective curve C. We show that when N is large,
these intersection numbers can equally be computed on the Grothendieck Quot
scheme of coherent sheaf quotients of the rank N trivial sheaf on C. The result
has applications to the calculation of the intersection theory of the moduli
space of semistable bundles on C.


  We present a Maple11+GRTensorII based symbolic calculator for instanton
metrics using Newman-Penrose formalism. Gravitational instantons are exact
solutions of Einstein's vacuum field equations with Euclidean signature. The
Newman-Penrose formalism, which supplies a toolbox for studying the exact
solutions of Einstein's field equations, was adopted to the instanton case and
our code translates it for the computational use.


  A technique is presented for the design of printed unit cells in aperiodic
metasurface environments. The method begins with a solved matrix equation
governing electromagnetic scattering from a homogenized metasurface design. The
matrix equation is used to find the local, inhomogeneous electric field
exciting a printed-circuit unit cell geometry. The local field is then
impressed onto the printed circuit geometry and the induced surface current
numerically computed. The computed surface current is sampled at the matrix
equation discretization. The matrix equation is then used to compute the
electric field scattered by the printed-circuit unit cell onto its neighbors
using the sampled current in place of the current of the original homogenized
unit cell. The printed circuit geometry is optimized to scatter the same field
as the homogenized unit cell when excited with the local electric field
computed. Two design examples are provided. Both a finite-sized, wide-angle
reflecting metasurface, and a metasurface reflectarray designed to scan and
collimate an incident cylindrical wave, are realized with printed-circuit unit
cells using the proposed approach. It is shown that the local periodicity
approximation cannot be used to accurately design the unit cells of either
finite-sized metasurface.


  Despite the recent success of domain generalization in medical image
segmentation, voxel-wise annotation for all source domains remains a huge
burden. Semi-supervised domain generalization has been proposed very recently
to combat this challenge by leveraging limited labeled data along with abundant
unlabeled data collected from multiple medical institutions, depending on
precisely harnessing unlabeled data while improving generalization
simultaneously. In this work, we observe that domain shifts between medical
institutions cause disparate feature statistics, which significantly
deteriorates pseudo-label quality due to an unexpected normalization process.
Nevertheless, this phenomenon could be exploited to facilitate unseen domain
generalization. Therefore, we propose 1) multiple statistics-individual
branches to mitigate the impact of domain shifts for reliable pseudo-labels and
2) one statistics-aggregated branch for domain-invariant feature learning.
Furthermore, to simulate unseen domains with statistics difference, we approach
this from two aspects, i.e., a perturbation with histogram matching at image
level and a random batch normalization selection strategy at feature level,
producing diverse statistics to expand the training distribution. Evaluation
results on three medical image datasets demonstrate the effectiveness of our
method compared with recent SOTA methods. The code is available at
https://github.com/qiumuyang/SIAB.


  We report the discovery of a strong magnetic field in the unique pulsating
carbon-atmosphere white dwarf SDSS J142625.71+575218.3. From spectra gathered
at the MMT and Keck telescopes, we infer a surface field of B_s ~1.2 MG, based
on obvious Zeeman components seen in several carbon lines. We also detect the
presence of a Zeeman-splitted He I 4471 line, which is an indicator of the
presence of a non-negligible amount of helium in the atmosphere of this Hot DQ
star. This is important for understanding its pulsations, as nonadabatic theory
reveals that some helium must be present in the envelope mixture for pulsation
modes to be excited in the range of effective temperature where the target star
is found. Out of nearly 200 pulsating white dwarfs known today, this is the
first example of a star with a large detectable magnetic field. We suggest that
SDSS J142625.71+575218.3 is the white dwarf equivalent of a roAp star.


  Bounded-input bounded-output stability condition of linear time invariant
(LTI) distributed-order system over integral interval $(0,1)$ has been
established for the first time. Two cases about weighting function of the
distributed order are investigated, and sufficient and necessary conditions of
stability for these two types of distributed-order systems are derived. Based
on the complex integration analysis, time-domain responses of distributed-order
systems are also given by analytical method, and numerical examples are
presented to illustrate the proposed conditions.


  We demonstrate a 1 GHz prismless femtosecond Ti:sapphire ring laser which
emits 890 mW for 7.6W of pump power over a continuum extending from 585 to 1200
nm at -20 dB below the maximum. A broadband continuum is obtained without
careful mirror dispersion compensation, with the net cavity
group-delay-dispersion having -50 to +100 fs2 oscillations from 700 to 900 nm.
Further broadening is obtained by use of a slightly convex cavity mirror that
increases self-phase modulation. 17% (75%) of the intracavity (output) power is
generated in single-pass through the crystal, outside the cavity bandwidth and
concentrated in the low gain infrared region from 960 to 1200 nm. This laser
seems well suited for optical frequency metrology, possibly allowing easier
stabilization of the carrier-to-envelope offset frequency without use of
photonic fibers.


  The Derksen--Hadas--Makar-Limanov theorem (2001) says that the invariants for
nontrivial actions of the additive group on a polynomial ring have no intruder.
In this paper, we generalize this theorem to the case of stable invariants.


  The Dyson Brownian Motion (DBM) describes the stochastic evolution of $N$
points on the line driven by an applied potential, a Coulombic repulsion and
identical, independent Brownian forcing at each point. We use an explicit tamed
Euler scheme to numerically solve the Dyson Brownian motion and sample the
equilibrium measure for non-quadratic potentials. The Coulomb repulsion is too
singular for the SDE to satisfy the hypotheses of rigorous convergence proofs
for tamed Euler schemes. Nevertheless, in practice the scheme is observed to be
stable for time steps of O(1/N^2) and to relax exponentially fast to the
equilibrium measure with a rate constant of O(1) independent of $N$. Further,
this convergence rate appears to improve with $N$ in accordance with O(1/N)
relaxation of local statistics of the Dyson Brownian motion. This allows us to
use the Dyson Brownian motion to sample $N\times N$ Hermitian matrices from the
invariant ensembles. The computational cost of generating $M$ independent
samples is O(MN^4) with a naive scheme, and O(MN^3\log N) when a fast multipole
method is used to evaluate the Coulomb interaction.


  We present examples of Lax-integrable multi-dimensional systems of partial
differential equations with higher local symmetries. We also consider
Lagrangian deformations of these equations and construct variational bivectors
on them.


  In this work, a theory of color symmetry is presented that extends the ideas
of traditional theories of color symmetry for periodic crystals to apply to
non-periodic crystals. The color symmetries are associated to each of the
crystalline sites and may correspond to different chemical species, various
orientations of magnetic moments and colorings of a non-periodic tiling. In
particular, we study the color symmetries of periodic and non-periodic
structures via Bravais colorings of planar modules that emerge as the ring of
integers in cyclotomic fields with class number one. Using an approach
involving matrices, we arrive at necessary and sufficient conditions for
determining the color symmetry groups and color fixing groups of the Bravais
colorings associated with the modules M_n = Z[exp(2{\pi}i/n)], and list the
findings for M_{15} = Z[exp(2{\pi}i/15)] and M_{16} = Z[exp({\pi}i/8)]. In the
second part of the paper, we discuss magnetic point groups of crystal and
quasicrystal structures and give some examples of structures whose magnetic
point group symmetries are described by Bravais colorings of planar modules.


  (abridged) We run a Faraday structure determination data challenge to
benchmark the currently available algorithms including Faraday synthesis
(previously called RM synthesis in the literature), wavelet, compressive
sampling and $QU$-fitting. The frequency set is similar to POSSUM/GALFACTS with
a 300 MHz bandwidth from 1.1 to 1.4 GHz. We define three figures of merit
motivated by the underlying science: a) an average RM weighted by polarized
intensity, RMwtd, b) the separation $\Delta\phi$ of two Faraday components and
c) the reduced chi-squared. Based on the current test data of signal to noise
ratio of about 32, we find that: (1) When only one Faraday thin component is
present, most methods perform as expected, with occasional failures where two
components are incorrectly found; (2) For two Faraday thin components,
QU-fitting routines perform the best, with errors close to the theoretical ones
for RMwtd, but with significantly higher errors for $\Delta\phi$. All other
methods including standard Faraday synthesis frequently identify only one
component when $\Delta\phi$ is below or near the width of the Faraday point
spread function; (3) No methods, as currently implemented, work well for
Faraday thick components due to the narrow bandwidth; (4) There exist
combinations of two Faraday components which produce a large range of
acceptable fits and hence large uncertainties in the derived single RMs; in
these cases, different RMs lead to the same Q, U behavior, so no method can
recover a unique input model.


  This paper presents enhancements to the MT3 model, a state-of-the-art (SOTA)
token-based multi-instrument automatic music transcription (AMT) model. Despite
SOTA performance, MT3 has the issue of instrument leakage, where transcriptions
are fragmented across different instruments. To mitigate this, we propose
MR-MT3, with enhancements including a memory retention mechanism, prior token
sampling, and token shuffling are proposed. These methods are evaluated on the
Slakh2100 dataset, demonstrating improved onset F1 scores and reduced
instrument leakage. In addition to the conventional multi-instrument
transcription F1 score, new metrics such as the instrument leakage ratio and
the instrument detection F1 score are introduced for a more comprehensive
assessment of transcription quality. The study also explores the issue of
domain overfitting by evaluating MT3 on single-instrument monophonic datasets
such as ComMU and NSynth. The findings, along with the source code, are shared
to facilitate future work aimed at refining token-based multi-instrument AMT
models.


  In this work we solve in closed form the system of difference equations
\begin{equation*}
  x_{n+1}=\dfrac{ay_nx_{n-1}+bx_{n-1}+c}{y_nx_{n-1}},\;
y_{n+1}=\dfrac{ax_ny_{n-1}+by_{n-1}+c}{x_ny_{n-1}},\;n=0,1,..., \end{equation*}
where the initial values $x_{-1}$, $x_0$, $y_{-1}$ and $y_0$ are arbitrary
nonzero real numbers and the parameters $a$, $b$ and $c$ are arbitrary real
numbers with $c\ne 0$. In particular we represent the solutions of some
particular cases of this system in terms of Tribonacci and Padovan numbers and
we prove the global stability of the corresponding positive equilibrium points.
The result obtained here extend those obtained in some recent papers.


  The thermoelectric effect in a quantum dot (QD) attached to two leads in the
presence of microwave fields is studied by using the Keldysh nonequilibrium
Green function technique. When the microwave is applied only on the QD and in
the linear-response regime, the main peaks in the thermoelectric figure of
merit and the thermopower are found to decrease, with the emergence of a set of
photon-induced peaks. Under this condition the microwave field can not generate
heat current or electrical bias voltage. Surprisingly, when the microwave field
is applied only to one (bright) lead and not to the other (dark) lead or the
QD, heat flows mostly from the dark to the bright lead, almost irrespectively
to the direction of the thermal gradient. We attribute this effect to
microwave-induced opening of additional transport channels below the Fermi
energy. The microwave field can change both the magnitude and the sign of the
electrical bias voltage induced by the temperature gradient.


  Deep neural networks have achieved remarkable success in various challenging
tasks. However, the black-box nature of such networks is not acceptable to
critical applications, such as healthcare. In particular, the existence of
adversarial examples and their overgeneralization to irrelevant,
out-of-distribution inputs with high confidence makes it difficult, if not
impossible, to explain decisions by such networks. In this paper, we analyze
the underlying mechanism of generalization of deep neural networks and propose
an ($n$, $k$) consensus algorithm which is insensitive to adversarial examples
and can reliably reject out-of-distribution samples. Furthermore, the consensus
algorithm is able to improve classification accuracy by using multiple trained
deep neural networks. To handle the complexity of deep neural networks, we
cluster linear approximations of individual models and identify highly
correlated clusters among different models to capture feature importance
robustly, resulting in improved interpretability. Motivated by the importance
of building accurate and interpretable prediction models for healthcare, our
experimental results on an ICU dataset show the effectiveness of our algorithm
in enhancing both the prediction accuracy and the interpretability of deep
neural network models on one-year patient mortality prediction. In particular,
while the proposed method maintains similar interpretability as conventional
shallow models such as logistic regression, it improves the prediction accuracy
significantly.


  We study the propagation of a malicious software in a network of mobile
devices, which are moving in a specific city area, and establish time bounds
for the activation of a counter-measure, i.e., an antivirus or a cleaner in
order to prevent pandemic. More precisely, given an initial infected population
(mobile devices), we establish upper bounds on the time needed for a
counter-measure to take effect after infection (response-time), in order to
prevent the rest susceptible devices to get infected. Thus, within a period of
time, we guarantee that not all the susceptible devices in the city get
infected and the infected ones get sanitized. In our work, we first propose a
malware propagation model along with a device mobility model and then,
utilizing these models, we develop a simulator that we use to study the spread
of malware in such networks. Finally, we provide experimental results for the
pandemic prevention taken by our simulator for various response-time intervals.


  The digest of ideology interpreting D-branes on Calabi-Yau manifolds as
objects of the derived category is given.


  This paper presents the use of element-based algebraic multigrid (AMGe)
hierarchies, implemented in the ParELAG (Parallel Element Agglomeration
Algebraic Multigrid Upscaling and Solvers) library, to produce multilevel
preconditioners and solvers for H(curl) and H(div) formulations. ParELAG
constructs hierarchies of compatible nested spaces, forming an exact de Rham
sequence on each level. This allows the application of hybrid smoothers on all
levels and AMS (Auxiliary-space Maxwell Solver) or ADS (Auxiliary-space
Divergence Solver) on the coarsest levels, obtaining complete multigrid cycles.
Numerical results are presented, showing the parallel performance of the
proposed methods. As a part of the exposition, this paper demonstrates some of
the capabilities of ParELAG and outlines some of the components and procedures
within the library.


  This paper introduces an evaluation methodologies for the e-learners'
behaviour that will be a feedback to the decision makers in e-learning system.
Learner's profile plays a crucial role in the evaluation process to improve the
e-learning process performance. The work focuses on the clustering of the
e-learners based on their behaviour into specific categories that represent the
learner's profiles. The learners' classes named as regular, workers, casual,
bad, and absent. The work may answer the question of how to return bad students
to be regular ones. The work presented the use of different fuzzy clustering
techniques as fuzzy c-means and kernelized fuzzy c-means to find the learners'
categories and predict their profiles. The paper presents the main phases as
data description, preparation, features selection, and the experiments design
using different fuzzy clustering models. Analysis of the obtained results and
comparison with the real world behavior of those learners proved that there is
a match with percentage of 78%. Fuzzy clustering reflects the learners'
behavior more than crisp clustering. Comparison between FCM and KFCM proved
that the KFCM is much better than FCM in predicting the learners' behaviour.


  While several paths have emerged in microelectronics and computing as
follow-ons to Turing architectures, and have been implemented using essentially
silicon circuits, very little beyond Moore research has considered: (1) first
biological processes instead of sequential instructions, and, (2) the
implementation of these processes exploiting particle physics interactions.
This last combination enables native spatial-temporal integration and
correlation, but also powerful interference filtering, gating, splitting and
more. These biological functions, their realization by quantum and charge
carrier interactions, allow proposing a novel computing architecture, with
interfaces, information storage, and programmability. The paper presents the
underlying biological processes, the particle physics phenomena which are
exploited, and the proposed architecture, as well as an algebraic design
formalism.


  We study the effect of time-dependent, non-conservative perturbations on the
dynamics along homoclinic orbits to a normally hyperbolic invariant manifold.
We assume that the unperturbed system is Hamiltonian, and the normally
hyperbolic invariant manifold is parametrized via action-angle coordinates. The
homoclinic excursions can be described via the scattering map, which gives the
future asymptotic of an orbit as a function of the past asymptotic. We provide
explicit formulas, in terms of convergent integrals, for the perturbed
scattering map expressed in action-angle coordinates. We illustrate these
formulas in the case of a perturbed rotator-pendulum system.


  Bacteriophages (or phages), which infect bacteria, have two distinct
lifestyles: virulent and temperate. Predicting the lifestyle of phages helps
decipher their interactions with their bacterial hosts, aiding phages'
applications in fields such as phage therapy. Because experimental methods for
annotating the lifestyle of phages cannot keep pace with the fast accumulation
of sequenced phages, computational method for predicting phages' lifestyles has
become an attractive alternative. Despite some promising results, computational
lifestyle prediction remains difficult because of the limited known annotations
and the sheer amount of sequenced phage contigs assembled from metagenomic
data. In particular, most of the existing tools cannot precisely predict
phages' lifestyles for short contigs. In this work, we develop PhaTYP (Phage
TYPe prediction tool) to improve the accuracy of lifestyle prediction on short
contigs. We design two different training tasks, self-supervised and
fine-tuning tasks, to overcome lifestyle prediction difficulties. We rigorously
tested and compared PhaTYP with four state-of-the-art methods: DeePhage,
PHACTS, PhagePred, and BACPHLIP. The experimental results show that PhaTYP
outperforms all these methods and achieves more stable performance on short
contigs. In addition, we demonstrated the utility of PhaTYP for analyzing the
phage lifestyle on human neonates' gut data. This application shows that PhaTYP
is a useful means for studying phages in metagenomic data and helps extend our
understanding of microbial communities.


  Many current paradigms for acoustic event detection (AED) are not adapted to
the organic variability of natural sounds, and/or they assume a limit on the
number of simultaneous sources: often only one source, or one source of each
type, may be active. These aspects are highly undesirable for applications such
as bird population monitoring. We introduce a simple method modelling the
onsets, durations and offsets of acoustic events to avoid intrinsic limits on
polyphony or on inter-event temporal patterns. We evaluate the method in a case
study with over 3000 zebra finch calls. In comparison against a HMM-based
method we find it more accurate at recovering acoustic events, and more robust
for estimating calling rates.


  For any fixed composition $\mu$ of $M+N$ and any fixed $0^M1^N$-sequence
$\mathfrak{s}$, we obtain a new presentation of the super Yangian $Y_{M|N}$
associated to the general linear Lie superalgebra $\mathfrak{gl}_{M|N}$.


  Humans can leverage physical interaction to teach robot arms. As the human
kinesthetically guides the robot through demonstrations, the robot learns the
desired task. While prior works focus on how the robot learns, it is equally
important for the human teacher to understand what their robot is learning.
Visual displays can communicate this information; however, we hypothesize that
visual feedback alone misses out on the physical connection between the human
and robot. In this paper we introduce a novel class of soft haptic displays
that wrap around the robot arm, adding signals without affecting that
interaction. We first design a pneumatic actuation array that remains flexible
in mounting. We then develop single and multi-dimensional versions of this
wrapped haptic display, and explore human perception of the rendered signals
during psychophysic tests and robot learning. We ultimately find that people
accurately distinguish single-dimensional feedback with a Weber fraction of
11.4%, and identify multi-dimensional feedback with 94.5% accuracy. When
physically teaching robot arms, humans leverage the single- and
multi-dimensional feedback to provide better demonstrations than with visual
feedback: our wrapped haptic display decreases teaching time while increasing
demonstration quality. This improvement depends on the location and
distribution of the wrapped haptic display. You can see videos of our device
and experiments here: https://youtu.be/yPcMGeqsjdM


  S-wave pion-nucleon interactions in the linear sigma model, and in
Manohar-Georgi and Gasser-Sainio-Svarc models with finite number of terms in
Lagrangians, as well as in a general phenomenological approach are reviewed.
Subtleties associated with the current algebra theorems and field redefinitions
are discussed. In the first and third models most likely the s-wave pion
condensation in the isospin-symmetric matter does not occur at least up to high
densities, whereas within the second model it may appear already at moderate
densities. In the phenomenological approach two parameterizations of the s-wave
pion-nucleon scattering amplitude and the pion polarization operator used in
the literature are considered. The first parameterization employs the
off-mass-shell amplitude and allows to fulfil the current algebra theorems.
Using it the s-wave pion polarization operator in the isospin-symmetric matter
is reconstructed within the gas approximation. With this pion polarization
operator the s-wave pion condensation in the isospin-symmetric matter does not
occur at least up to high densities. Second parameterization uses the
on-mass-shell pion-nucleon scattering amplitude and does not satisfy the Adler
and Weinberg conditions. With such a parameterization most likely the s-wave
pion condensation in the isospin-symmetric matter may occur already at the
nucleon density $n\simeq (1.4-2.5) n_0$, where $n_0$ is the density of the
atomic nucleus, that should result in observable effects. Both
parameterizations allow to successfully describe the pion atom data.


  Mid-level visual element discovery aims to find clusters of image patches
that are both representative and discriminative. In this work, we study this
problem from the prospective of pattern mining while relying on the recently
popularized Convolutional Neural Networks (CNNs). Specifically, we find that
for an image patch, activations extracted from the first fully-connected layer
of CNNs have two appealing properties which enable its seamless integration
with pattern mining. Patterns are then discovered from a large number of CNN
activations of image patches through the well-known association rule mining.
When we retrieve and visualize image patches with the same pattern,
surprisingly, they are not only visually similar but also semantically
consistent. We apply our approach to scene and object classification tasks, and
demonstrate that our approach outperforms all previous works on mid-level
visual element discovery by a sizeable margin with far fewer elements being
used. Our approach also outperforms or matches recent works using CNN for these
tasks. Source code of the complete system is available online.


  The single-mode spin-boson model exhibits behavior not included in the
rotating wave approximation (RWA) in the ultra and deep-strong coupling
regimes, where counter-rotating contributions become important. We introduce a
symmetric rotating wave approximation that treats rotating and counter-rotating
terms equally, preserves the invariances of the Hamiltonian with respect to its
parameters, and reproduces several qualitative features of the spin-boson
spectrum not present in the original rotating wave approximation both
off-resonance and at deep strong coupling. The symmetric rotating wave
approximation allows for the treatment of certain ultra and deep-strong
coupling regimes with similar accuracy and mathematical simplicity as does the
RWA in the weak coupling regime. Additionally, we symmetrize the generalized
form of the rotating wave approximation to obtain the same qualitative
correspondence with the addition of improved quantitative agreement with the
exact numerical results. The method is readily extended to higher accuracy if
needed. Finally, we introduce the two-photon parity operator for the two-photon
Rabi Hamiltonian and obtain its generalized symmetric rotating wave
approximation. The existence of this operator reveals a parity symmetry similar
to that in the Rabi Hamiltonian as well as another symmetry that is unique to
the two-photon case, providing insight into the mathematical structure of the
two-photon spectrum, significantly simplifying the numerics, and revealing some
interesting dynamical properties.


  Electrical transport in three dimensional topological insulators(TIs) occurs
through spin-momentum locked topological surface states that enclose an
insulating bulk. In the presence of a magnetic field, surface states get
quantized into Landau levels giving rise to chiral edge states that are
naturally spin-polarized due to spin momentum locking. It has been proposed
that p-n junctions of TIs in the quantum Hall regime can manifest unique spin
dependent effects, apart from forming basic building blocks for highly
functional spintronic devices. Here, for the first time we study
electrostatically defined n-p-n junctions of bulk insulating topological
insulator BiSbTe$_{1.25}$Se$_{1.75}$ in the quantum Hall regime. We reveal the
remarkable quantization of longitudinal resistance into plateaus at 3/2 and 2/3
h/e$^2$, apart from several partially developed fractional plateaus.
Theoretical modeling combining the electrostatics of the dual gated TI n-p-n
junction with Landauer Buttiker formalism for transport through a network of
chiral edge states explains our experimental data, while revealing remarkable
differences from p-n junctions of graphene and two-dimensional electron gas
systems. Our work not only opens up a route towards exotic spintronic devices
but also provides a test bed for investigating the unique signatures of quantum
Hall effects in topological insulators.


  Both fuelling of a central black hole and the build-up of bulges are -- at
least theoretically -- facilitated by non-axisymmetric potentials. It is argued
here that the recently established surface brightness division between
bulge-dominated galaxies that dominate the local type 2 AGN luminosity
function, and disk-dominated galaxies that host (at best) weak AGN can be
understood in terms of a simple bar-instability criterion.


  Consider a two-dimensional domain shaped like a wire, not necessarily of
uniform cross section. Let $V$ denote an electric potential driven by a voltage
drop between the conducting surfaces of the wire. We consider the operator
${\mathcal A}_h=-h^2\Delta+iV$ in the semi-classical limit $h\to 0$. We obtain
both the asymptotic behaviour of the left margin of the spectrum, as well as
resolvent estimates on the left side of this margin. We extend here previous
results obtained for potentials for which the set where the current (or $\nabla
V$) is normal to the boundary is discrete, in contrast with the present case
where $V$ is constant along the conducting surfaces.


  A supersymmetric path integral representation is developed for stochastic
processes whose Langevin equation contains any number N of time derivatives,
thus generalizing the Langevin equation with inertia studied by Kramers, where
N=2. The supersymmetric action contains N fermion fields with first-order time
derivatives whose path integral is evaluated for fermionless asymptotic states.


  Oscillatory flows have become an indispensable tool in microfluidics,
inducing inertial effects for displacing and manipulating fluid-borne objects
in a reliable, controllable, and label-free fashion. However, the quantitative
description of such effects has been confined to limit cases and specialized
scenarios. Here we develop an analytical formalism yielding the equation of
motion of density-mismatched spherical particles in arbitrary background flows,
generalizing previous work. Inertial force terms are systematically derived
from the geometry of the flow field together with analytically known Stokes
number dependences. Supported by independent, first-principles direct numerical
simulations, we find that these forces are important even for nearly
density-matched objects such as cells or bacteria, enabling their fast
displacement and separation. Our formalism thus generalizes the Maxey--Riley
equation, encompassing not only particle inertia, but consistently recovering,
in the limit of large Stokes numbers, the Auton modification to added mass as
well as the far-field acoustofluidic secondary radiation force.


  A topological defect in the form of the Abrikosov-Nielsen-Olesen vortex is
considered as a gauge-flux-carrying tube that is impenetrable for quantum
matter. Charged scalar matter field is quantized in the vortex background with
the perfectly reflecting (Dirichlet) boundary condition imposed at the side
surface of the vortex. We show that a current circulating around the vortex and
a magnetic field directed along the vortex are induced in the vacuum, if the
Compton wavelength of the matter field exceeds considerably the transverse size
of the vortex. The vacuum current and magnetic field are periodic in the value
of the gauge flux of the vortex, providing a quantum-field-theoretical
manifestation of the Aharonov-Bohm effect. The total flux of the induced vacuum
magnetic field attains noticeable finite values even for the Compton wavelength
of the matter field exceeding the transverse size of the vortex by just three
orders of magnitude.


  This volume contains the papers accepted at the DATE Friday Workshop on
Heterogeneous Architectures and Design Methods for Embedded Image Systems (HIS
2015), held in Grenoble, France, March 13, 2015. HIS 2015 was co-located with
the Conference on Design, Automation and Test in Europe (DATE).


  We present an end-to-end virtual try-on pipeline, that can fit different
clothes on a personalized 3-D human model, reconstructed using a single RGB
image. Our main idea is to construct an animatable 3-D human model and try-on
different clothes in a 3-D virtual environment. The existing frame by frame
volumetric reconstruction of 3-D human models are highly resource-demanding and
do not allow clothes switching. Moreover, existing virtual fit-on systems also
lack realism due to predominantly being 2-D or not using user's features in the
reconstruction. These shortcomings are due to either the human body or clothing
model being 2-D or not having the user's facial features in the dressed model.
We solve these problems by manipulating a parametric representation of the 3-D
human body model and stitching a head model reconstructed from the actual
image. Fitting the 3-D clothing models on the parameterized human model is also
adjustable to the body shape of the input image. Our reconstruction results, in
comparison with recent existing work, are more visually-pleasing.


  We predict strong non-local effects in the three-terminal hybrid device,
comprising the quantum dot embedded between two conducting leads and third
superconducting reservoir. They result from competition between the ballistic
electron transfer and the crossed Andreev scattering. The non-local voltage
induced in response to the 'driving' current changes the magnitude and sign
upon varying the gate potential and/or coupling to the superconducting lead.
Such effect is robust both in the linear and non-linear regimes, where the
screening and the long-range interactions play significant role. This novel
subgap transport is provided by the Shiba states and can be contrasted with
much weaker non-local effects observed hitherto in the three-terminal 'planar'
junctions.


  Domain generalization (DG) aims to learn a generalizable model from multiple
training domains such that it can perform well on unseen target domains. A
popular strategy is to augment training data to benefit generalization through
methods such as Mixup~\cite{zhang2018mixup}. While the vanilla Mixup can be
directly applied, theoretical and empirical investigations uncover several
shortcomings that limit its performance. Firstly, Mixup cannot effectively
identify the domain and class information that can be used for learning
invariant representations. Secondly, Mixup may introduce synthetic noisy data
points via random interpolation, which lowers its discrimination capability.
Based on the analysis, we propose a simple yet effective enhancement for
Mixup-based DG, namely domain-invariant Feature mIXup (FIX). It learns
domain-invariant representations for Mixup. To further enhance discrimination,
we leverage existing techniques to enlarge margins among classes to further
propose the domain-invariant Feature MIXup with Enhanced Discrimination (FIXED)
approach. We present theoretical insights about guarantees on its
effectiveness. Extensive experiments on seven public datasets across two
modalities including image classification (Digits-DG, PACS, Office-Home) and
time series (DSADS, PAMAP2, UCI-HAR, and USC-HAD) demonstrate that our approach
significantly outperforms nine state-of-the-art related methods, beating the
best performing baseline by 6.5\% on average in terms of test accuracy. Code is
available at:
https://github.com/jindongwang/transferlearning/tree/master/code/deep/fixed.


  We prove that in the family of static, asymptotically flat, spherically
symmetric scalar hairy black holes with the central electric charge, the set of
the charge-to-mass ratios has the exact upper bound $3\sqrt{2}/4\approx1.06$.


  The pairing symmetry in the iron-based superconductor Ba1-xKxFe2As2 may
change from nodeless s-wave near x~0.4 and Tc>30 K, to nodal (either d-wave or
s-wave) at x=1 and Tc<4 K. Theoretical interest has been focused on this
possibility, where in the transition region both order parameters would be
present and time reversal symmetry would be broken. We report specific heat in
magnetic fields down to 0.4 K of three single crystals, free of low temperature
magnetic anomalies, of heavily overdoped Ba1-xKxFe2As2, x= 0.91, 0.88, and
0.81, Tc(mid) ~ 5.6, 7.2 and 13 K and Hc2 ~ 4.5, 6, and 20 T respectively. The
data can be analyzed in a two gap scenario, Delta2/Delta1 ~ 4, with the field
dependence of gamma (=C/T as T->0) showing an S-shape vs H, with the
suppression of the lower gap by 1 T and gamma ~ H**1/2 overall. Although such a
non-linear gamma vs H is consistent with deep minima or nodes in the gap, it is
not clear evidence for one, or both, of the gaps being nodal. Following the
established analysis of the specific heat of d-wave cuprate superconductors
containing line nodes, we present the specific heat/H**1/2 vs T/H**1/2 of these
Ba1-xKxFe2As2 samples which all, due to the absence of magnetic impurities,
convincingly show the scaling for line node behavior for the larger gap. There
is however no clear observation of the nodal behavior C ~ alpha*T**2 in zero
field at low temperatures, with alpha ~ 2 mJ/molK**3 being consistent with the
data. This, with the scaling, leaves the possibility of extreme anisotropy in a
nodeless larger gap, Delta2, such that the scaling works for fields above 0.25
to 0.5 T (0.2 to 0.4 K in temperature units), where this an estimate for the
size of the deep minima in the Delta2 ~ 20-25 K gap. Thus, the change from
nodeless to nodal gaps in Ba1-xKxFe2As2 may be closer to the KFe2As2 endpoint
than x=0.91.


  We study the formation of second harmonic double-resonance cones in
hyperbolic metamaterials. An electric dipole on the surface of the structure
induces second harmonic light to propagate into two distinct volume
plasmon-polariton channels: A signal that propagates within its own peculiar
resonance cone; and a phase-locked signal that is trapped under the pump's
resonance cone. Metamaterial dispersion and birefringence induce a large
angular divergence between the two volume plasmon-polaritons, making these
structures ideal for subwavelength second and higher harmonic imaging
microscopy.


  Topological insulators (TIs) are a class of materials which are insulating in
their bulk form yet, upon introduction of an a boundary or edge, e.g. by
abruptly terminating the material, may exhibit spontaneous current along their
boundary. This property is quantified by topological indices associated with
either the bulk or the edge system. In the field of condensed matter physics,
tight binding (discrete) approximate models, parametrized by hopping
coefficients, have been used successfully to capture the topological behavior
of TIs in many settings. However, whether such tight binding models capture the
same topological features as the underlying continuum models of quantum physics
has been an open question.
  We resolve this question in the context of the archetypal example of
topological behavior in materials, the integer quantum Hall effect. We study a
class of continuum Hamiltonians, $H^\lambda$, which govern electron motion in a
two-dimensional crystal under the influence of a perpendicular magnetic field.
No assumption is made on translation invariance of the crystal. We prove, in
the regime where both the magnetic field strength and depth of the crystal
potential are sufficiently large, $\lambda\gg1$, that the low-lying energy
spectrum and eigenstates (and corresponding large time dynamics) of $H^\lambda$
are well-described by a scale-free discrete Hamiltonian, $H^{\rm TB}$; we show
norm resolvent convergence. The relevant topological index is the Hall
conductivity, which is expressible as a Fredholm index. We prove that for large
$\lambda$ the topological indices of $H^\lambda$ and $H^{\rm TB}$ agree. This
is proved separately for bulk and edge geometries. Our results justify the
principle of using discrete models in the study of topological matter.


  Observations of high energy emission from gamma-ray bursts (GRBs) constrain
the extreme physical conditions associated with these energetic cosmic
explosions. The Large Area Telescope (LAT) onboard the Fermi Gamma-ray Space
Telescope, a pair conversion telescope, observes energetic quanta from 30 MeV
to > 300 GeV. Synergy of the LAT with the Gamma-ray Burst Monitor (GBM)
enlarges the energy coverage to ~ 7.5 decades, very useful for studying the GRB
emission itself. Prompt optical observations and their possible correlations
with photon emission at LAT energies help our understanding of the physical
mechanisms behind these events. The prompt response times and large fields of
of the ROTSE-III telescopes make afterglow observations possible for Fermi
bursts with ~ 1 degree localized errors. As an example, GRB 090902B, was
observed starting ~ 4803 s after the burst. This is the earliest ground-based
optical detection ever made for long-duration bursts sensed by the LAT. The
ROTSE detection classifies the optical afterglow of GRB 090902B as one of the
brightest.


  We prove a Zariski-Nagata purity theorem for the motivic ramification
filtration of a reciprocity sheaf. An important tool in the proof is a
generalization of the Kato-Saito reciprocity map from geometric global class
field theory to all reciprocity sheaves. As a corollary we obtain cut-by-curves
and cut-by-surfaces criteria for various ramification filtrations. In some
cases this reproves known theorems, in some cases we obtain new results.


  A family of angular momentum coherent states on the sphere is constructed
using previous work by Aragone et al [1]. These states depend on a complex
parameter which allows an arbitrary squeezing of the angular momentum
uncertainties. The time evolution of these states is analyzed assuming a rigid
body hamiltonian. The rich scenario of fractional revivals is exhibited with
cloning and many interference effects.


  The blob algebra is a finite-dimensional quotient of the Hecke algebra of
type $B$ which is almost always quasi-hereditary. We construct the
indecomposable tilting modules for the blob algebra over a field of
characteristic $0$ in the doubly critical case. Every indecomposable tilting
module of maximal highest weight is either a projective module or an extension
of a simple module by a projective module. Moreover, every indecomposable
tilting module is a submodule of an indecomposable tilting module of maximal
highest weight. We conclude that the graded Weyl multiplicities of the
indecomposable tilting modules in this case are given by inverse
Kazhdan-Lusztig polynomials of type $\tilde{A}_1$.


  Tracking fast moving objects, which appear as blurred streaks in video
sequences, is a difficult task for standard trackers as the object position
does not overlap in consecutive video frames and texture information of the
objects is blurred. Up-to-date approaches tuned for this task are based on
background subtraction with static background and slow deblurring algorithms.
In this paper, we present a tracking-by-segmentation approach implemented using
state-of-the-art deep learning methods that performs near-realtime tracking on
real-world video sequences. We implemented a physically plausible FMO sequence
generator to be a robust foundation for our training pipeline and demonstrate
the ease of fast generator and network adaptation for different FMO scenarios
in terms of foreground variations.


  This paper presents a hierarchical Bayesian model to reconstruct sparse
images when the observations are obtained from linear transformations and
corrupted by an additive white Gaussian noise. Our hierarchical Bayes model is
well suited to such naturally sparse image applications as it seamlessly
accounts for properties such as sparsity and positivity of the image via
appropriate Bayes priors. We propose a prior that is based on a weighted
mixture of a positive exponential distribution and a mass at zero. The prior
has hyperparameters that are tuned automatically by marginalization over the
hierarchical Bayesian model. To overcome the complexity of the posterior
distribution, a Gibbs sampling strategy is proposed. The Gibbs samples can be
used to estimate the image to be recovered, e.g. by maximizing the estimated
posterior distribution. In our fully Bayesian approach the posteriors of all
the parameters are available. Thus our algorithm provides more information than
other previously proposed sparse reconstruction methods that only give a point
estimate. The performance of our hierarchical Bayesian sparse reconstruction
method is illustrated on synthetic and real data collected from a tobacco virus
sample using a prototype MRFM instrument.


  The dynamics of escape from an attractive state due to random perturbations
is of central interest to many areas in science. Previous studies of escape in
chaotic systems have rather focused on the case of unbounded noise, usually
assumed to have Gaussian distribution. In this paper, we address the problem of
escape induced by bounded noise. We show that the dynamics of escape from an
attractor's basin is equivalent to that of a closed system with an
appropriately chosen "hole". Using this equivalence, we show that there is a
minimum noise amplitude above which escape takes place, and we derive
analytical expressions for the scaling of the escape rate with noise amplitude
near the escape transition. We verify our analytical predictions through
numerical simulations of a two-dimensional map with noise.


  The LP WAN networks use gateways or base stations to communicate with devices
distributed on large distances, up to tens of kilometres. The selection of
optimal gateway locations in wireless networks should allow providing the
complete coverage for a given set of nodes, taking into account the
limitations, such as the number of nodes served per access point or required
redundancy. In this paper, we describe the problem of selecting the base
stations in a network using the concept of $k$-dominating set. In our model, we
include information about the required redundancy and spectral efficiency. We
consider the additional requirements on the resulting connections and provide
the greedy algorithm for solving the problem. The algorithm is evaluated in
randomly generated network topologies and using the coordinates of sample real
smart metering networks.


  The strongly ferromagnetic spin-1 Bose-Einstein condensate (BEC) has recently
been realized with atomic $^{7}$Li. It was predicted that a strong
ferromagnetic interaction can drive the normal gas into a magnetized phase at a
temperature above the superfluid transition, and $^{7}$Li likely satisfies the
criterion. We re-examine this theoretical proposal employing the
two-particle-irreducible (2PI) effective potential, and conclude that there
exists no stable normal magnetized phase for a dilute ferromagnetic Bose gas.
For $^{7}$Li, we predict that the normal gas undergoes a joint first order
transition and jump directly into a state with finite condensate density and
magnetization. We estimate the size of the first order jump, and examine how a
partial spin polarization in the initial sample affects the first order
transition. We propose a qualitative phase diagram at fixed temperature for the
trapped gas.


  The extreme conditions in Neutron Stars make them ideal test facilities for
fundamental interactions. A Neutron Star can capture Dark Matter via
scattering. As a result of the scattering, Dark Matter kinetic energy is
transferred to the star. An observational consequence of this can be the
warming of old neutron stars to near-infrared temperatures. Different
approximations or simplifications have been applied to previous analyses of the
capture process. In this article, we summarise a significantly improved
treatment of Dark Matter capture, which properly accounts for all relevant
physical effects over a wide range of Dark Matter masses. Among them are
gravitational focusing, a fully relativistic scattering treatment, Pauli
blocking, neutron star opacity and multiple scattering effects. This paper
cites general expressions that allow the capture rate to be computed
numerically, and simplified expressions for particular types of interactions or
mass regimes, which greatly increase the efficiency of computation. As a result
of our method, we are able to model the scattering of Dark Matter from any
neutron star constituent as well as the capture of Dark Matter in other compact
objects. Our results are applied to scattering of Dark Matter from neutrons,
protons, leptons and exotic baryons.


  Linguistic landscape is an important field in sociolinguistic research. Eye
tracking technology is a common technology in psychological research. There are
few cases of using eye movement to study linguistic landscape. This paper uses
eye tracking technology to study the actual fixation of the linguistic
landscape and finds that in the two dimensions of fixation time and fixation
times, the fixation of native Chinese speakers to the linguistic landscape is
higher than that of the general landscape. This paper argues that this
phenomenon is due to the higher information density of linguistic landscapes.
At the same time, the article also discusses other possible reasons for this
phenomenon.


  This article describes a methodology for fitting experimental data to the
discrete power-law distribution and provides the results of a detailed
simulation exercise used to calculate accurate cutoff values used to assess the
fit to a power-law distribution when using the maximum likelihood estimation
for the exponent of the distribution. Using massively parallel programming
computing, we were able to accelerate by a factor of 60 the computational time
required for these calculations across a range of parameters and construct a
series of detailed tables containing the test values to be used in a
Kolmogorov-Smirnov goodness-of-fit test, allowing for an accurate assessment of
the power-law fit from empirical data.


  A Dyck path is a lattice path in the plane integer lattice
$\mathbb{Z}\times\mathbb{Z}$ consisting of steps (1,1) and (1,-1), which never
passes below the x-axis. A peak at height k on a Dyck path is a point on the
path with coordinate y=k that is immediately preceded by a (1,1) step and
immediately followed by a (1,-1) step. In this paper we find an explicit
expression to the generating function for the number of Dyck paths starting at
(0,0) and ending at (2n,0) with exactly r peaks at height k. This allows us to
express this function via Chebyshev polynomials of the second kind and
generating function for the Catalan numbers.


  We present new results from studies on diffractive dijet production and
exclusive production of dijet and diphoton obtained by the CDF Collaboration in
proton-antiproton collisions at the Fermilab Tevatron.


  Aerocapture is a maneuver which uses aerodynamic drag to slow down a
spacecraft in a single pass through the atmosphere. All planetary orbiters to
date have used propulsive orbit insertion. Aerocapture is a promising
alternative, especially for small satellite missions and missions to the ice
giants. The large {\Delta}V requirement makes it practically impossible for
small satellites to enter low circular orbits. Aerocapture can enable insertion
of low-cost satellites into circular orbits around Mars and Venus. For ice
giant missions, aerocapture can enable orbit insertion from fast arrival
trajectories which are impractical with propulsive insertion. By utilizing the
atmospheric drag to impart the {\Delta}V, aerocapture can offer significant
propellant mass and cost savings for a wide range of planetary missions. The
present study analyzes the performance benefit offered by aerocapture for a set
of design reference missions and their applications to future Solar System
exploration from Venus to Neptune. The estimated performance benefit for
aerocapture in terms of delivered mass increase are: Venus (92%), Earth (108%),
Mars (17%), and Titan (614%), Uranus (35%), and Neptune (43%). At Uranus and
Neptune, aerocapture is a mission enabling technology for orbit insertion from
fast arrival interplanetary trajectories.


  We study topological aspects of a Dirac fermion coupled with a Higgs field
associated with the lattice model introduced by Benalcazar et al. which shows
the topological quadrupole phase. Using the index theorem, we show that the
index of the Hamiltonian is just given by the winding number of the Higgs
field, implying that a corner state of the lattice model belongs to the same
class of the Jackiw-Rossi states localized at a vortex. We also calculate the
current density of the Dirac fermion with a symmetry breaking term dependent on
time, which is associated with the dipole pump proposed by Benalcazar et al. We
argue that it is indeed a topological current, and the total pumped charge is
given by an integer related with the index.


  Attitudes toward risk underlie virtually every important economic decision an
individual makes. In this experimental study, I examine how introducing a time
delay into the execution of an investment plan influences individuals' risk
preferences. The field experiment proceeded in three stages: a decision stage,
an execution stage and a payout stage. At the outset, in the Decision Stage
(Stage 1), each subject was asked to make an investment plan by splitting a
monetary investment amount between a risky asset and a safe asset. Subjects
were informed that the investment plans they made in the Decision Stage are
binding and will be executed during the Execution Stage (Stage 2). The Payout
Stage (Stage 3) was the payout date. The timing of the Decision Stage and
Payout Stage was the same for each subject, but the timing of the Execution
Stage varied experimentally. I find that individuals who were assigned to
execute their investment plans later (i.e., for whom there was a greater delay
prior to the Execution Stage) invested a greater amount in the risky asset
during the Decision Stage.


  The core of deep metric learning (DML) involves learning visual similarities
in high-dimensional embedding space. One of the main challenges is to
generalize from seen classes of training data to unseen classes of test data.
Recent works have focused on exploiting past embeddings to increase the number
of instances for the seen classes. Such methods achieve performance improvement
via augmentation, while the strong focus on seen classes still remains. This
can be undesirable for DML, where training and test data exhibit entirely
different classes. In this work, we present a novel training strategy for DML
called MemVir. Unlike previous works, MemVir memorizes both embedding features
and class weights to utilize them as additional virtual classes. The
exploitation of virtual classes not only utilizes augmented information for
training but also alleviates a strong focus on seen classes for better
generalization. Moreover, we embed the idea of curriculum learning by slowly
adding virtual classes for a gradual increase in learning difficulty, which
improves the learning stability as well as the final performance. MemVir can be
easily applied to many existing loss functions without any modification.
Extensive experimental results on famous benchmarks demonstrate the superiority
of MemVir over state-of-the-art competitors. Code of MemVir is publicly
available.


  A partition of a positive integer $n$ is a representation of $n$ as a sum of
a finite number of positive integers (called parts). A trapezoidal number is a
positive integer that has a partition whose parts are a decreasing sequence of
consecutive integers, or, more generally, whose parts form a finite arithmetic
progression. This paper reviews the relation between trapezoidal numbers,
partitions, and the set of divisors of a positive integer. There is also a
complete proof of a theorem of Sylvester that produces a stratification of the
partitions of an integer into odd parts and partitions into disjoint
trapezoids.


  This paper considers the multi-input multi-output (MIMO) relay channel where
multiple antennas are employed by each terminal. Compared to single-input
single-output (SISO) relay channels, MIMO relay channels introduce additional
degrees of freedom, making the design and analysis of optimal cooperative
strategies more complex. In this paper, a partial cooperation strategy that
combines transmit-side message splitting and block-Markov encoding is
presented. Lower bounds on capacity that improve on a previously proposed
non-cooperative lower bound are derived for Gaussian MIMO relay channels.


  We present results of molecular dynamics simulations of water confined in a
silica pore. A cylindrical cavity is created inside a vitreous silica cell with
geometry and size similar to the pores of real Vycor glass. The simulations are
performed at different hydration levels. At all hydration levels water adsorbs
strongly on the Vycor surface; a double layer structure is evident at higher
hydrations. At almost full hydration the modifications of the
confinement-induced site-site pair distribution functions are in qualitative
agreement with neutron diffraction experiment. A decrease in the number of
hydrogen bonds between water molecules is observed along the pore radius, due
to the tendency of the molecules close to the substrate to form hydrogen-bonds
with the hydrophilic pore surface. As a consequence we observe a substrate
induced distortion of the H-bond tetrahedral network of water molecules in the
regions close to the surface.


  Phenotypic heterogeneity along the epithelial-mesenchymal (E-M) axis
contributes to cancer metastasis and drug resistance. Recent experimental
efforts have collated detailed time-course data on the emergence and dynamics
of E-M heterogeneity in a cell population. However, it remains unclear how
different possible processes interplay in shaping the dynamics of E-M
heterogeneity: a) intracellular regulatory interaction among biomolecules, b)
cell division and death, and c) stochastic cell-state transition (biochemical
reaction noise and asymmetric cell division). Here, we propose a Cell
Population Balance (Partial Differential Equation (PDE)) based model that
captures the dynamics of cell population density along the E-M phenotypic axis
due to abovementioned multi-scale cellular processes. We demonstrate how
population distribution resulting from intracellular regulatory networks
driving cell-state transition gets impacted by stochastic fluctuations in E-M
regulatory biomolecules, differences in growth rates among cell subpopulations,
and initial population distribution. Further, we reveal that a linear
dependence of the cell growth rate on the population heterogeneity is
sufficient to recapitulate the faster in vivo growth of orthotopic injected
heterogeneous E-M subclones reported before experimentally. Overall, our model
contributes to the combined understanding of intracellular and cell-population
levels dynamics in the emergence of E-M heterogeneity in a cell population.


  We consider the problem of generating adversarial malware by a cyber-attacker
where the attacker's task is to strategically modify certain bytes within
existing binary malware files, so that the modified files are able to evade a
malware detector such as machine learning-based malware classifier. We have
evaluated three recent adversarial malware generation techniques using binary
malware samples drawn from a single, publicly available malware data set and
compared their performances for evading a machine-learning based malware
classifier called MalConv. Our results show that among the compared techniques,
the most effective technique is the one that strategically modifies bytes in a
binary's header. We conclude by discussing the lessons learned and future
research directions on the topic of adversarial malware generation.