Abstract:
  We present a many-body expansion (MBE) formulation and implementation for
efficient computation of analytical energy gradients from OSV-MP2 theory based
on our earlier work (Zhou et al. J. Chem. Theory Comput. 2020, 16, 196-210).
The third-order MBE(3) expansion of OSV-MP2 wavefunction was developed to adopt
the orbital-specific clustering and long-range termination schemes, which
avoids term-by-term differentiations of the MBE energy bodies. We achieve
better efficiency by exploiting the algorithmic sparsity that allows to prune
out insignificant fitting integrals and OSV relaxations. With these
approximations, the present implementation is benchmarked on a range of
molecules that show an economic scaling in the linear and quadratic regimes for
computing MBE(3)-OSV-MP2 amplitude and gradient equations, respectively, and
yields normal accuracy comparable to the original OSV-MP2 results. The
MPI-3-based parallelism through shared memory one-sided communication is
further developed for improving parallel scalability and memory accessibility
by sorting the MBE(3) orbital clusters into independent tasks that are
distributed on multiple processes across many nodes, supporting both global and
local data locations in which selected MBE(3)-OSV-MP2 intermediates of
different sizes are distinguished and accordingly placed. The accuracy and
efficiency level of our MBE(3)-OSV-MP2 analytical gradient implementation is
finally illustrated in two applications: we show that the subtle coordination
structure differences of mechanically interlocked Cu-catenane complexes can be
distinguished when tuning ligand lengths; and the porphycene molecular dynamics
reveals the emergence of the vibrational signature arising from softened N-H
stretching associated with hydrogen transfer, using an MP2 level of electron
correlation and classical nuclei for the first time.


Abstract:
  We provide a brief review of many aspects of the planetary physics of hot
Jupiters. Our aim is to cover most of the major areas of current study while
providing the reader with additional references for more detailed follow-up. We
first discuss giant planet formation and subsequent orbital evolution via
disk-driven torques or dynamical interactions. More than one formation pathway
is needed to understand the population. Next, we examine our current
understanding of the evolutionary history and current interior structure of the
planets, where we focus on bulk composition as well as viable models to explain
the inflated radii of the population. Finally we discuss aspects of their
atmospheres in the context of observations and 1D and 3D models, including
atmospheric structure and escape, spectroscopic signatures, and complex
atmospheric circulation. The major opacity sources in these atmospheres,
including alkali metals, water vapor, and others, are discussed. We discuss
physics that control the 3D atmospheric circulation and day-to-night
temperature structures. We conclude by suggesting important future work for
still-open questions.


Abstract:
  Manual fact-checking does not scale well to serve the needs of the internet.
This issue is further compounded in non-English contexts. In this paper, we
discuss claim matching as a possible solution to scale fact-checking. We define
claim matching as the task of identifying pairs of textual messages containing
claims that can be served with one fact-check. We construct a novel dataset of
WhatsApp tipline and public group messages alongside fact-checked claims that
are first annotated for containing "claim-like statements" and then matched
with potentially similar items and annotated for claim matching. Our dataset
contains content in high-resource (English, Hindi) and lower-resource (Bengali,
Malayalam, Tamil) languages. We train our own embedding model using knowledge
distillation and a high-quality "teacher" model in order to address the
imbalance in embedding quality between the low- and high-resource languages in
our dataset. We provide evaluations on the performance of our solution and
compare with baselines and existing state-of-the-art multilingual embedding
models, namely LASER and LaBSE. We demonstrate that our performance exceeds
LASER and LaBSE in all settings. We release our annotated datasets, codebooks,
and trained embedding model to allow for further research.


Abstract:
  We show that if $(\ell_{\phi_0}, \ell_{\phi_1})$ is a couple of suitable
Orlicz sequence spaces then the corresponding Rochberg derived spaces of all
orders associated to the complex interpolation method are Fenchel-Orlicz
spaces. In particular, the induced twisted sums have the $(C[0, 1],
\mathbb{C})$-extension property.


Abstract:
  The thermodynamic structure of protoplanetary discs is determined by dust
opacities, which depend on the size of the dust grains and their chemical
composition. In the inner regions, the grain sizes are regulated by the level
of turbulence (e.g. $\alpha$ viscosity) and by the dust fragmentation velocity
that represents the maximal velocity that grains can have at a collision before
they fragment. Here, we perform self-consistently calculated 2D hydrodynamical
simulations that consider a full grain size distribution of dust grains with a
transition in the dust fragmentation velocity at the water-ice line. This
approach accounts for the results of previous particle collision laboratory
experiments, in which silicate particles typically have a lower dust
fragmentation velocity than water-ice particles. Furthermore, we probe the
effects of variations in the water abundance, the dust-to-gas ratio, and the
turbulence parameter on the disc structure. For the discs with a transition in
the dust fragmentation velocity at the water-ice line, we find a narrow but
striking zone of planetary outward migration, including for low viscosities. In
addition, we find a bump in the radial pressure gradient profile that tends to
be located slightly inside the ice line. Both of these features are present for
all tested disc parameters. Thus, we conclude that the ice line can function
both as a migration trap, which can extend the growth times of planets before
they migrate to the inner edge of the protoplanetary disc, and as a pressure
trap, where planetesimal formation can be initiated or enhanced.


Abstract:
  In this work we consider strange stars formed by quark matter in the
color-flavor-locked (CFL) phase of color superconductivity. The CFL phase is
described by a Nambu-Jona-Lasinio model with four-fermion vector and diquark
interaction channels. The effect of the color superconducting medium on the
gluons are incorporated into the model by including the gluon self-energy in
the thermodynamic potential. We construct parametrizations of the model by
varying the vector coupling $G_V$ and comparing the results to the data on
tidal deformability from the GW170817 event, the observational data on maximum
masses from massive pulsars such as the MSP J0740+6620, and the mass/radius
fits to NICER data for PSR J003+0451. Our results points out to windows for the
$G_V$ parameter space of the model, with and without gluon effects included,
that are compatible with all these astrophysical constraints, namely,
$0.21<G_V/G_S<0.4$, and $0.02<G_V/G_S<0.1$, respectively. We also observe a
strong correlation between the tidal deformabilites of the GW170817 event and
$G_V$. Our results indicate that strange stars cannot be ruled out in
collisions of compact binaries from the structural point of view.


Abstract:
  Understanding the electron pairing in hole-doped cuprate superconductors has
been a challenge, in particular because the "normal" state from which it
evolves is unprecedented. Now, after three and a half decades of research,
involving a wide range of experimental characterizations, it is possible to
delineate a clear and consistent cuprate story. It starts with doping holes
into a charge-transfer insulator, resulting in in-gap states. These states
exhibit a pseudogap resulting from the competition between antiferromagnetic
superexchange $J$ between nearest-neighbor Cu atoms (a real-space interaction)
and the kinetic energy of the doped holes, which, in the absence of
interactions, would lead to extended Bloch-wave states whose occupancy is
characterized in reciprocal space. To develop some degree of coherence on
cooling, the spin and charge correlations must self-organize in a cooperative
fashion. A specific example of resulting emergent order is that of spin and
charge stripes, as observed in La$_{2-x}$Ba$_x$CuO$_4$. While stripe order
frustrates bulk superconductivity, it nevertheless develops pairing and
superconducting order of an unusual character. The antiphase order of the spin
stripes decouples them from the charge stripes, which can be viewed as
hole-doped, two-leg, spin-$\frac12$ ladders. To achieve superconducting order,
the pair correlations in neighboring ladders must develop phase order. In the
presence of spin stripe order, antiphase Josephson coupling can lead to
pair-density-wave superconductivity. Alternatively, in-phase superconductivity
requires that the spin stripes have an energy gap, which empirically limits the
coherent superconducting gap. Hence, superconducting order in the cuprates
involves a compromise between the pairing scale, which is maximized at
$x\sim\frac18$, and phase coherence, which is optimized at $x\sim0.2$.


Abstract:
  A deconstructed finite temperature gauge theory has the Euclidean time
direction kept discrete and finite. The ultraviolet behavior is that of one
dimension less than at zero temperature. One can add to the action a gauge
invariant term dependent on Polyakov loops, without worrying about nonlocality.
The deformation of {\" U}nsal and Yaffe is best analyzed in this framework. It
is shown that turning it on causes a dramatic change in the Hilbert space on
which the transfer matrix in the temperature direction acts. The undeformed
Hilbert space is everywhere a gauge singlet while the deformed one includes all
states transforming locally, anywhere, under any SU(N)/Z(N) irreducible
representation. Implications of this in the context of large N reduction are
discussed.


Abstract:
  The paper presents a numerical analysis of the class of mathematical models
of linear fractional oscillators, which is the Cauchy problem for a
differential equation with derivatives of fractional orders in the sense of
Gerasimov-Caputo. A method based on an explicit nonlocal finite-difference
scheme (ENFDS) and the Adams-Bashfort-Moulton (ABM) method is considered a
numerical analysis tool. An analysis of the errors of the methods is carried
out, and it is shown that the ABM method is more accurate and converges faster
to an exact solution than the ENFDS method.


Abstract:
  The ability of a planet to maintain surface water, key to life as we know it,
depends on solar and planetary energy. As a star ages, it delivers more energy
to a planet. As a planet ages it produces less internal heat, which leads to
cooling. For the Earth, interior cooling connects to plate tectonics - the
surface manifestation of convection within the Earth's interior. This process
cycles volatiles (CO2 and water) between surface and interior reservoirs, which
affects planetary climate. Cycling rates depend on the efficiency of plate
tectonic cooling. That efficiency remains debated and multiple hypotheses have
been put forth. Geological proxy data allow us to validate these hypotheses
accounting for model and data uncertainty. Multiple models pass the validation
test. Those models define a distribution for terrestrial exoplanets akin to
Earth, accounting for variations in tectonic efficiency. Feeding this
distribution into climate models indicates that the time at which habitable
conditions are established can vary by billions of years. Planets of the same
absolute age and orbital distance can reside and not reside within the classic
habitable zone due to differences in plate tectonic cooling efficiencies. The
full model population allows a probability distribution to be constructed for
the the time at which habitable conditions are established. The distribution
indicates that Earth-like exoplanets, of the same age, can be at different
evolutionary stages. It also indicates that planets around stars whose early
evolution is unfavorable for life can become habitable later in their energetic
histories.


Abstract:
  A systematic study is performed to measure the ion backflow fraction of the
GEM detectors. The effects of different voltage configurations and Ar/CO_2 gas
mixtures, in ratios of 70:30, 80:20 and 90:10, on positive ion fraction are
investigated in detail. Moreover, a comparative study is performed between
single and quadruple GEM detectors.The ion current with detector effective gain
is measured with various field configurations and with three proportions of gas
mixtures. The ion backflow fraction for the GEM is substantially reduced with
the lower drift field. A minimum ion backflow fraction of 18 % is achieved in
the single GEM detector with Ar/CO_2 80:20 gas mixture, however, a minimum ion
backflow fraction of 3.5 %, 3.0%, and 3.8 % are obtained for a drift field of
0.1kV/cm with Ar/CO_2 70:30, 80:20 and 90:10 gas mixtures, respectively for
quadrupole GEM detector. Similar values of effective gain and ion backflow
fraction have been found by calculating the current from pulse height spectrum
method, obtained in the Multi Channel Analyser.


Abstract:
  Motivated by Yabe's classification of symmetric $2$-generated axial algebras
of Monster type, we introduce a large class of algebras of Monster type
$(\alpha, \frac{1}{2})$, generalising Yabe's $\mathrm{III}(\alpha,\frac{1}{2},
\delta)$ family. Our algebras bear a striking similarity with Jordan spin
factor algebras with the difference being that we asymmetrically split the
identity as a sum of two idempotents. We investigate the properties of this
algebra, including the existence of a Frobenius form and ideals. In the
$2$-generated case, where our algebra is isomorphic to one of Yabe's examples,
we use our new viewpoint to identify the axet, that is, the closure of the two
generating axes.


Abstract:
  Fokker-Planck equations represent a suitable description of the finite-time
behavior for a large class of particle systems as the size of the population
tends to infinity. Recently, the theory of graph limits have been introduced in
the classical mean-field framework to account for heterogeneous interactions
among particles. In many instances, such network heterogeneity is preserved in
the limit which turns from being a single Fokker-Planck equation (also known as
McKean-Vlasov) to an infinite system of non-linear partial differential
equations (PDE) coupled by means of a graphon. While appealing from an applied
viewpoint, few rigorous results exist on the graphon particle system. This note
addresses such limit system focusing on the relation between initial conditions
and interaction network: if the system initial datum and the graphon degrees
satisfy a suitable condition, a significantly simpler representation of the
solution is available.
  Interesting conclusions can be drawn from this result: substantially
different graphons can give rise to exactly the same particle behavior, e.g.,
if the initial condition is independent of the graphon, the particle behavior
is indistinguishable from the well-known mean-field limit as long as the
graphon has constant degree; step-kernels, a building block of graph limit
theory, can be used to approximate the graphon particle system with a finite
system of Fokker-Planck equations and an explicit rate of convergence.


Abstract:
  A plane graph is called a rectangular graph if each of its edges can be
oriented either horizontally or vertically, each of its interior regions is a
four-sided region and all interior regions can be fitted in a rectangular
enclosure. Only planar graphs can be dualized. If the dual of a plane graph is
a rectangular graph, then the plane graph is a rectangularly dualizable graph.
  In 1985, Ko\'zmi\'nski and Kinnen presented a necessary and sufficient
condition for the existence of a rectangularly dualizable graph for a separable
connected plane graph. In this paper, we present a counter example for which
the conditions given by them for separable connected plane graphs fail and
hence, we derive a necessary and sufficient condition for a plane graph to be a
rectangularly dualizable graph.


Abstract:
  Deep learning compiler frameworks are gaining ground as a more portable
back-end for deep learning applications on increasingly diverse hardware.
However, they face the daunting challenge of matching performance offered by
hand-tuned target-specific libraries. While auto-tuning frameworks with
statistical cost models can provide dynamic and efficient code optimization,
they suffer from large space exploration and cost model training overheads.
This paper proposes MetaTune, a meta-learning based cost model that more
quickly and accurately predicts the performance of optimized codes with
pre-trained model parameters. MetaTune encodes convolution kernel codes as
structurally similar graphs to facilitate meta-learning, meta-trains a GNN
model with a very small input data set, and then predicts optimization
parameters for unseen convolution operations with varying sizes and structures
during compilation. The resulting framework with MetaTune provides 8 to 13%
better inference time on average for four CNN models with comparable or lower
optimization time while outperforming transfer learning by 10% in
cross-platform cases.


Abstract:
  3D shape reconstruction from a single image has been a long-standing problem
in computer vision. Recent advances have led to 3D representation learning,
wherein pixel-aligned 3D reconstruction methods show impressive performance.
However, it is normally hard to exploit meaningful local image features to
describe 3D point samplings from the aligned pixels when large variations of
occlusions, views, and appearances exist. In this paper, we study a general
kernel to encode local image features with considering geometric relationships
of point samplings from the underlying surfaces. The kernel is derived from the
proposed spatial pattern, in a way the kernel points are obtained as the 2D
projections of a number of 3D pattern points around a sampling. Supported by
the spatial pattern, the 2D kernel encodes geometric information that is
essential for 3D reconstruction tasks, while traditional 2D kernels mainly
consider appearance information. Furthermore, to enable the network to discover
more adaptive spatial patterns for further capturing non-local contextual
information, the spatial pattern is devised to be deformable. Experimental
results on both synthetic datasets and real datasets demonstrate the
superiority of the proposed method.


Abstract:
  This paper is concerned with the existence and regularity of mininizers as
well as of corresponding multipliers to an optimal control problem governed by
semilinear elliptic equations, in which mixed pointwise control-state
constraints are considered in a quite general form and the controls act
simultaneously in the domain and on the boundary. Under standing assumptions,
the minimizers and the corresponding multipliers do exist. Furthermore, by
applying the bootstrapping technique and establishing some calculation tools
for functions in Sobolev spaces of fractional order, the optimal solutions and
the associated Lagrange multipliers are shown to be Lipschitz continuous.


Abstract:
  Using ultraviolet (UV) light curves we constrain the circumstellar
environments of 1080 Type Ia supernovae (SNe Ia) within $z<0.5$ from archival
Galaxy Evolution Explorer (GALEX) observations. All SNe Ia are required to have
pre- and post-explosion GALEX observations to ensure adequate subtraction of
the host-galaxy flux. Using the late-time GALEX observations we look for the UV
excess expected from any interaction between the SN ejecta and circumstellar
material (CSM). Four SNe Ia are detected near maximum light and we compare the
GALEX photometry to archival data, but we find none of our targets show
convincing evidence of CSM interaction. A recent Hubble Space Telescope (HST)
survey estimates that $\sim6\%$ of SNe Ia may interact with distant CSM, but
statistical inferences are complicated by the small sample size and selection
effects. By injecting model light curves into our data and then recovering
them, we constrain a broad range of CSM interactions based on the CSM
interaction start time and the maximum luminosity. Combining our GALEX
non-detections with the HST results, we constrain occurrence of late-onset CSM
interaction among SNe Ia with moderate CSM interaction, similar to that
observed in PTF11kx, to $f_\text{CSM}\lesssim5.1\%$ between $0-500$ days after
discovery and $\lesssim2.7\%$ between $500-1000$ days after discovery at $90\%$
confidence. For weaker CSM interactions similar to SN 2015cp, we obtain limits
of $\lesssim16\%$ and $\lesssim4.8\%$, respectively, for the same time ranges.


Abstract:
  The symmetries of asymptotically flat spacetimes in general relativity are
given by the Bondi-Metzner-Sachs (BMS) group, though there are proposed
generalizations of its symmetry algebra. Associated with each symmetry is a
charge and a flux, and the values of these charges and their changes can
characterize a spacetime. The charges of the BMS group are relativistic angular
momentum and supermomentum (which includes 4-momentum); the extensions of the
BMS algebra also include generalizations of angular momentum called "super
angular momentum." Several different formalisms have been used to define
angular momentum, and they produce nonequivalent expressions for the charge. It
was shown recently that these definitions can be summarized in a two-parameter
family of angular momenta, which we investigate in this paper. We find that
requiring that the angular momentum vanishes in flat spacetime restricts the
two parameters to be equal. If we do not require that the angular momentum
agrees with a common Hamiltonian definition, then we are left with a
one-parameter family of angular momenta that includes the definitions from the
several different formalisms. We then also propose a similar two-parameter
family of super angular momentum. We examine the effect of the free parameters
on the values of the angular momentum and super angular momentum from
nonprecessing binary-black-hole mergers. The definitions of angular momentum
differ at a high post-Newtonian order for these systems, but only when the
system is radiating gravitational waves. The different super-angular-momentum
definitions occur at lower orders, and there is a difference in the change of
super angular momentum even after the gravitational waves pass, which arises
because of the gravitational-wave memory effect. We estimate the size of these
effects using numerical-relativity surrogate waveforms and find they are small
but resolvable.


Abstract:
  We comment on a recent paper regarding the derivation of the magnetic field
components of a solenoid in analytical form by proposing a different and
simpler method


Abstract:
  We take a geometrical viewpoint and present a unifying view on supervised
deep learning with the Bregman divergence loss function - this entails frequent
classification and prediction tasks. Motivated by simulations we suggest that
there is principally no implicit bias of vanilla stochastic gradient descent
training of deep models towards "simpler" functions. Instead, we show that good
generalization may be instigated by bounded spectral products over layers
leading to a novel geometric regularizer. It is revealed that in deep enough
models such a regularizer enables both, extreme accuracy and generalization, to
be reached. We associate popular regularization techniques like weight decay,
drop out, batch normalization, and early stopping with this perspective. Backed
up by theory we further demonstrate that "generalization by design" is
practically possible and that good generalization may be encoded into the
structure of the network. We design two such easy-to-use structural
regularizers that insert an additional \textit{generalization layer} into a
model architecture, one with a skip connection and another one with drop-out.
We verify our theoretical results in experiments on various feedforward and
convolutional architectures, including ResNets, and datasets (MNIST, CIFAR10,
synthetic data). We believe this work opens up new avenues of research towards
better generalizing architectures.


Abstract:
  The assumption that voters' preferences share some common structure is a
standard way to circumvent NP-hardness results in social choice problems. While
the Kemeny ranking problem is NP-hard in the general case, it is known to
become easy if the preferences are 1-dimensional Euclidean. In this note, we
prove that the Kemeny ranking problem is NP-hard for d-dimensional Euclidean
preferences with d>=2. We note that this result also holds for the Slater
ranking problem.


Abstract:
  The long lifetime and the evolving nature of industrial products make them
subject to technical debt at different levels. Despite multiple years of
research on technical debt management, our industrial experience shows that
introducing systematic technical debt management in a large-scale company is
very challenging. To identify the challenges, we provide a conceptual framework
for holistic debt management across the product development value stream, which
takes multiple categories of debt and their interplays into account.We use this
framework to identify multiple challenges that are still open to be explored by
the research community. Due to the practical nature of technical debt
management, we believe this paper can guide the research community on the needs
of industry for the effective application of technical debt management in
practice.


Abstract:
  Short-orbit gas giant planet formation/evolution mechanisms are still not
well understood. One promising pathway to discriminate between mechanisms is to
constrain the occurrence rate of these peculiar exoplanets at the earliest
stage of the system's life. However, a major limitation when studying newly
born stars is stellar activity. This cocktail of phenomena triggered by fast
rotation, strong magnetic fields and complex internal dynamics, especially
present in very young stars, compromises our ability to detect exoplanets. In
this paper, we investigated the limitations of such detections in the context
of already acquired data solely using radial velocity data acquired with a
non-stabilised spectrograph. We employed two strategies: Doppler Imaging and
Gaussian Processes and could confidently detect Hot Jupiters with
semi-amplitude of 100 $m.s^{-1}$ buried in the stellar activity. We also showed
the advantages of the Gaussian Process approach in this case. This study serves
as a proof of concept to identify potential candidates for follow-up
observations or even discover such planets in legacy datasets available to the
community.


Abstract:
  For a given linear program (LP) a permutation of its variables that sends
feasible points to feasible points and preserves the objective function value
of each of its feasible points is a symmetry of the LP. The set of all
symmetries of an LP, denoted by $G^{\rm LP}$, is the symmetry group of the LP.
Margot [F. Margot, 50 Years of Integer Programming 1958-2008 (2010), 647-686]
described a method for computing a subgroup of the symmetry group $G^{\rm LP}$
of an LP. This method computes $G^{\rm LP}$ when the LP has only non-redundant
inequalities and its feasible set satisfies no equality constraints. However,
when the feasible set of the LP satisfies equality constraints this method
finds only a subgroup of $G^{\rm LP}$ and can miss symmetries. We develop a
method for finding the symmetry group of a feasible LP whose feasible set
satisfies equality constraints. We apply this method to find and exploit the
previously unexploited symmetries of an orthogonal array defining integer
linear program (ILP) within the branch-and-bound (B\&B) with isomorphism
pruning algorithm [F. Margot, Symmetric ILP: Coloring and small integers,
Discrete Optimization 4 (1) (2007), 40-62]. Our method reduced the running time
for finding all OD-equivalence classes of OA$(160,8,2,4)$ and OA$(176,8,2,4)$
by factors of $1/(2.16)$ and $1/(1.36)$ compared to the fastest known method
[D. A. Bulutoglu and K. J. Ryan, Integer programming for classifying orthogonal
arrays, Australasian Journal of Combinatorics 70 (3) (2018), 362-385]. These
were the two bottleneck cases that could not have been solved until the B\&B
with isomorphism pruning algorithm was applied.


Abstract:
  We consider the Hartle-Hawking wavefunction of the universe defined as a
Euclidean path integral that satisfies the "no-boundary proposal." We focus on
the simplest minisuperspace model that comprises a single scale factor degree
of freedom and a positive cosmological constant. The model can be seen as a
non-linear $\sigma$-model with a line-segment base. We reduce the path integral
over the lapse function to an integral over the proper length of the base and
use diffeomorphism-invariant measures for the ghosts and the scale factor. As a
result, the gauge-fixed path integral is independent of the gauge. However, we
point out that all field redefinitions of the scale factor degree of freedom
yield different choices of gauge-invariant path-integral measures. For each
prescription, we compute the wavefunction at the semi-classical level and find
a different result. We resolve in each case the ambiguity in the form of the
Wheeler-DeWitt equation at this level of approximation. By imposing that the
Hamiltonians associated with these possibly distinct quantum theories are
Hermitian, we determine the inner products of the corresponding Hilbert spaces
and find that they lead to a universal norm, at least semi-classically. Quantum
predictions are thus independent of the prescription at this level of
approximation. Finally, all wavefunctions of the Hilbert spaces of the
minisuperspace model we consider turn out to be non-normalizable, including the
no-boundary states.


Abstract:
  In group testing, the goal is to identify a subset of defective items within
a larger set of items based on tests whose outcomes indicate whether at least
one defective item is present. This problem is relevant in areas such as
medical testing, DNA sequencing, communication protocols, and many more. In
this paper, we study (i) a sparsity-constrained version of the problem, in
which the testing procedure is subjected to one of the following two
constraints: items are finitely divisible and thus may participate in at most
$\gamma$ tests; or tests are size-constrained to pool no more than $\rho$ items
per test; and (ii) a noisy version of the problem, where each test outcome is
independently flipped with some constant probability. Under each of these
settings, considering the for-each recovery guarantee with asymptotically
vanishing error probability, we introduce a fast splitting algorithm and
establish its near-optimality not only in terms of the number of tests, but
also in terms of the decoding time. While the most basic formulations of our
algorithms require $\Omega(n)$ storage for each algorithm, we also provide
low-storage variants based on hashing, with similar recovery guarantees.


Abstract:
  In the paper, we prove the local Gan-Gross-Prasad conjecture for special
orthogonal groups over archimedean local fields for generic local L-parameters.
The non-archimedean counterpart of the conjecture for generic parameters was
proved by C. Moeglin and J.-L. Waldspurger in [MW12] based on that for tempered
parameters, which was proved by Waldspurger in [Wal09]. Over archimedean local
fields, part of the conjecture, the Multiplicity One in Vogan L-packets, was
proved by Zhilin Luo for tempered L-parameters, and the other part of the
conjecture for tempered L-parameters is our ongoing work joint with Zhilin Luo.


Abstract:
  The size of rings (also called cyclic polymers) in bidisperse blends of
chemically identical rings is analyzed by computer simulations. Data of
entangled ring blends and blends of interpenetrating rings are compared and it
is shown that the compression of entangled rings can be explained by the
changes in the penetrable fraction of the minimal surface bounded by the ring.
Corrections for small rings can be approximated by a concatenation probability
$1-P_{OO}$ that a ring entraps at least one other ring. Both results are in
line with a previous work [1] to explain the compression of entangled rings in
monodisperse melts. Bond-bond correlations in melts of interpenetrating rings
lead to similar corrections for ring sizes as reported previously [2] for
monodisperse linear melts. For entangled rings, bond-bond correlations show an
anti-correlation peak at a curvilinear distance of about ten segments that
coincides with a horizontal tangent in the normalized mean square internal
distances along the ring. Both observations become independent of melt
molecular weight for sufficiently large degrees of polymerization and such
behavior is not found in samples with entanglements switched off. In
consequence, the length scale of topological interactions (entanglement length)
in a melt of entangled rings must be considered as constant in contrast to a
recent proposal by Sakaue [3].


Abstract:
  Image registration is a fundamental building block for various applications
in medical image analysis. To better explore the correlation between the fixed
and moving images and improve registration performance, we propose a novel deep
learning network, Co-Attention guided Registration Network (CAR-Net). CAR-Net
employs a co-attention block to learn a new representation of the inputs, which
drives the registration of the fixed and moving images. Experiments on UK
Biobank cardiac cine-magnetic resonance image data demonstrate that CAR-Net
obtains higher registration accuracy and smoother deformation fields than
state-of-the-art unsupervised registration methods, while achieving comparable
or better registration performance than corresponding weakly-supervised
variants. In addition, our approach can provide critical structural information
of the input fixed and moving images simultaneously in a completely
unsupervised manner.


Abstract:
  We perform a complete next-to-leading order calculation of the non-Abelian
electric field correlator in a SU($N_c$) plasma, which encodes properties of
the plasma relevant for heavy particle bound state formation and dissociation
and is different from the correlator for the heavy quark diffusion coefficient.
The calculation is carried out in the real-time formalism of thermal field
theory and includes both vacuum and finite temperature contributions. By
working in the $R_\xi$ gauge, we explicitly show the results are gauge
independent, infrared and collinear safe. The renormalization group equation of
this electric field correlator is determined by that of the strong coupling
constant. Our next-to-leading order calculation can be directly applied to any
dipole singlet-adjoint transition of heavy particle pairs. For example, it can
be used to describe dissociation and (re)generation of heavy quarkonia inside
the quark-gluon plasma well below the melting temperature, as well as heavy
dark matter pairs (or charged co-annihilating partners) in the early universe.


Abstract:
  Reward engineering and designing an incentive reward function are non-trivial
tasks to train agents in complex environments. Furthermore, an inaccurate
reward function may lead to a biased behaviour which is far from an efficient
and optimised behaviour. In this paper, we focus on training a single agent to
score goals with binary success/failure reward function in Half Field Offense
domain. As the major advantage of this research, the agent has no presumption
about the environment which means it only follows the original formulation of
reinforcement learning agents. The main challenge of using such a reward
function is the high sparsity of positive reward signals. To address this
problem, we use a simple prediction-based exploration strategy (called Curious
Exploration) along with a Return-based Memory Restoration (RMR) technique which
tends to remember more valuable memories. The proposed method can be utilized
to train agents in environments with fairly complex state and action spaces.
Our experimental results show that many recent solutions including our baseline
method fail to learn and perform in complex soccer domain. However, the
proposed method can converge easily to the nearly optimal behaviour. The video
presenting the performance of our trained agent is available at
http://bit.ly/HFO_Binary_Reward.


Abstract:
  We consider a model-agnostic solution to the problem of Multi-Domain Learning
(MDL) for multi-modal applications. Many existing MDL techniques are
model-dependent solutions which explicitly require nontrivial architectural
changes to construct domain-specific modules. Thus, properly applying these MDL
techniques for new problems with well-established models, e.g. U-Net for
semantic segmentation, may demand various low-level implementation efforts. In
this paper, given emerging multi-modal data (e.g., various structural
neuroimaging modalities), we aim to enable MDL purely algorithmically so that
widely used neural networks can trivially achieve MDL in a model-independent
manner. To this end, we consider a weighted loss function and extend it to an
effective procedure by employing techniques from the recently active area of
learning-to-learn (meta-learning). Specifically, we take inner-loop gradient
steps to dynamically estimate posterior distributions over the hyperparameters
of our loss function. Thus, our method is model-agnostic, requiring no
additional model parameters and no network architecture changes; instead, only
a few efficient algorithmic modifications are needed to improve performance in
MDL. We demonstrate our solution to a fitting problem in medical imaging,
specifically, in the automatic segmentation of white matter hyperintensity
(WMH). We look at two neuroimaging modalities (T1-MR and FLAIR) with
complementary information fitting for our problem.


Abstract:
  We display an application of the notions of kernelization and data reduction
from parameterized complexity to proof complexity: Specifically, we show that
the existence of data reduction rules for a parameterized problem having (a). a
small-length reduction chain, and (b). small-size (extended) Frege proofs
certifying the soundness of reduction steps implies the existence of
subexponential size (extended) Frege proofs for propositional formalizations of
the given problem.
  We apply our result to infer the existence of subexponential Frege and
extended Frege proofs for a variety of problems. Improving earlier results of
Aisenberg et al. (ICALP 2015), we show that propositional formulas expressing
(a stronger form of) the Kneser-Lov\'asz Theorem have polynomial size Frege
proofs for each constant value of the parameter k. Previously only
quasipolynomial bounds were known (and only for the ordinary Kneser-Lov\'asz
Theorem).
  Another notable application of our framework is to impossibility results in
computational social choice: we show that, for any fixed number of agents,
propositional translations of the Arrow and Gibbard-Satterthwaite impossibility
theorems have subexponential size Frege proofs.


Abstract:
  We present a rapid-scanning approach to fluorescence-detected two-dimensional
electronic spectroscopy that combines acousto-optic phase-modulation with
digital lock-in detection. The approach shifts the signal detection window to
suppress 1/f laser noise and enables interferometric tracking of the time
delays to allow for correction of spectral phase distortions and accurate
phasing of the data. This use of digital lock-in detection enables acquisition
of linear and nonlinear signals of interest in a single measurement. We
demonstrate the method on a laser dye, measuring the linear fluorescence
excitation spectrum, as well as rephasing, non-rephasing and absorptive
fluorescence-detected two-dimensional electronic spectra.


Abstract:
  We present a novel approach for unsupervised activity segmentation, which
uses video frame clustering as a pretext task and simultaneously performs
representation learning and online clustering. This is in contrast with prior
works where representation learning and clustering are often performed
sequentially. We leverage temporal information in videos by employing temporal
optimal transport. In particular, we incorporate a temporal regularization term
which preserves the temporal order of the activity into the standard optimal
transport module for computing pseudo-label cluster assignments. The temporal
optimal transport module enables our approach to learn effective
representations for unsupervised activity segmentation. Furthermore, previous
methods require storing learned features for the entire dataset before
clustering them in an offline manner, whereas our approach processes one
mini-batch at a time in an online manner. Extensive evaluations on three public
datasets, i.e. 50-Salads, YouTube Instructions, and Breakfast, and our dataset,
i.e., Desktop Assembly, show that our approach performs on par or better than
previous methods for unsupervised activity segmentation, despite having
significantly less memory constraints.


Abstract:
  This discussion applies quantitative finance methods and economic arguments
to cryptocurrencies in general and bitcoin in particular -- as there are about
$10,000$ cryptocurrencies, we focus (unless otherwise specified) on the most
discussed crypto of those that claim to hew to the original protocol (Nakamoto
2009) and the one with, by far, the largest market capitalization.
  In its current version, in spite of the hype, bitcoin failed to satisfy the
notion of "currency without government" (it proved to not even be a currency at
all), can be neither a short nor long term store of value (its expected value
is no higher than $0$), cannot operate as a reliable inflation hedge, and,
worst of all, does not constitute, not even remotely, a safe haven for one's
investments, a shield against government tyranny, or a tail protection vehicle
for catastrophic episodes.
  Furthermore, bitcoin promoters appear to conflate the success of a payment
mechanism (as a decentralized mode of exchange), which so far has failed, with
the speculative variations in the price of a zero-sum maximally fragile asset
with massive negative externalities.
  Going through monetary history, we show how a true numeraire must be one of
minimum variance with respect to an arbitrary basket of goods and services, how
gold and silver lost their inflation hedge status during the Hunt brothers
squeeze in the late 1970s and what would be required from a true inflation
hedged store of value.


Abstract:
  It is conjectured that the sum $$ S_r(n)=\sum_{k=1}^{n}
\frac{k}{k+r}\binom{n}{k} $$ for positive integers $r,n$ is never integral.
This has been shown for $r\le 22$. In this note we study the problem in the
``$n$ aspect" showing that the set of $n$ such that $S_r(n)\in {\mathbb Z}$ for
some $r\ge 1$ has asymptotic density $0$. Our principal tools are some deep
results on the distribution of primes in short intervals.


Abstract:
  In communication systems, there are many tasks, like modulation recognition,
which rely on Deep Neural Networks (DNNs) models. However, these models have
been shown to be susceptible to adversarial perturbations, namely imperceptible
additive noise crafted to induce misclassification. This raises questions about
the security but also the general trust in model predictions. We propose to use
adversarial training, which consists of fine-tuning the model with adversarial
perturbations, to increase the robustness of automatic modulation recognition
(AMC) models. We show that current state-of-the-art models benefit from
adversarial training, which mitigates the robustness issues for some families
of modulations. We use adversarial perturbations to visualize the features
learned, and we found that in robust models the signal symbols are shifted
towards the nearest classes in constellation space, like maximum likelihood
methods. This confirms that robust models not only are more secure, but also
more interpretable, building their decisions on signal statistics that are
relevant to modulation recognition.


Abstract:
  In April 2021, the European Commission published a proposed regulation on AI.
It intends to create a uniform legal framework for AI within the European Union
(EU). In this chapter, we analyze and assess the proposal. We show that the
proposed regulation is actually not needed due to existing regulations. We also
argue that the proposal clearly poses the risk of overregulation. As a
consequence, this would make the use or development of AI applications in
safety-critical application areas, such as in healthcare, almost impossible in
the EU. This would also likely further strengthen Chinese and US corporations
in their technology leadership. Our assessment is based on the oral evidence we
gave in May 2021 to the joint session of the European Union affairs committees
of the German federal parliament and the French National Assembly.


Abstract:
  In this paper, we initiate the study of global stability for anisotropic
systems of quasilinear wave equations. Equations of this kind arise naturally
in the study of crystal optics, and they exhibit birefringence. We introduce a
physical space strategy based on bilinear energy estimates that allows us to
prove decay for the nonlinear problem. This uses decay for the homogeneous wave
equation as a black box. The proof also requires us to interface this strategy
with the vector field method and take advantage of the scaling vector field. A
careful analysis of the spacetime geometry of the interaction between waves is
necessary in the proof.


Abstract:
  This study aims to review and get information on the different mobile game
applications and the possibility of being a supplementary tool for learning to
enhance and empower the e-Learning aspect of a Technical Education and Skills
Development Authority. It is reviewed to help in the development of the
eLearning department of TESDA. The study aims to facilitate the reorganization
of the E-learning program of TESDA by using a method called Game Based
Learning. To accomplish this target, different applications will be reviewed to
give an idea on how game-based learning is being used as a supplementary
learning tool for different subject areas. This paper will focus on TESDA
language Skills institute and Technical-Vocational program since there are a
lot of reviews for main subject areas but only few in this field. The database
to be used in the methodology is Google Scholar, inclusion and exclusion
criteria used is that article should be within the span of five years and
should be game based learning mobile applications. Will be selecting 4
applications as a representation since it is an informational review. No need
to indicate the other applications as this is just a basis for future studies.
Mobile games have such a powerful instructional potential, by which teaching
can be revolutionized, which output is acquiring the praise and judgment of
academic professionals and educators. After this review, future studies can be
made to support this study like reviewing from other databases like Web of
Science and Scopus to support the foundation made on this review. The review
will open further studies to help TESDA administrator in using mobile games as
a supplementary learning tool for their e-learning.


Abstract:
  We present a margin-free finite mixture model which allows us to
simultaneously classify objects into known classes and to identify possible new
object types using a set of continuous attributes. This application is
motivated by the needs of identifying and possibly detecting new types of a
particular kind of stars known as variable stars. We first suitably transform
the physical attributes of the stars onto the simplex to achieve scale
invariance while maintaining their dependence structure. This allows us to
compare data collected by different sky surveys which can have different
scales. The model hence combines a mixture of Dirichlet mixtures to represent
the known classes with the semi-supervised classification strategy of Vatanen
et al. (2012) for outlier detection. In line with previous work on
semiparametric model-based clustering, the single Dirichlet distributions can
be seen as providing the baseline pattern of the data. These are then combined
to effectively model the complex distributions of the attributes for the
different classes. The model is estimated using a hierarchical two-step
procedure which combines a suitably adapted version of the
Expectation-Maximization (EM) algorithm with Bayes' rule. We validate our model
on a reliable sample of periodic variable stars available in the literature
(Dubath et al., 2011) achieving an overall classification accuracy of 71.95%, a
sensitivity of 86.11% and a specificity of 99.79% for new class detection.


Abstract:
  Sharding, i.e. splitting the miners or validators to form and run several
subchains in parallel, is known as one of the main solutions to the scalability
problem of blockchains. The drawback is that as the number of miners expanding
each subchain becomes small, it becomes vulnerable to security attacks. To
solve this problem, a framework, named as \textit{Polyshard}, has been proposed
in which each validator verifies a coded combination of the blocks introduced
by different subchains, thus helping to protect the security of all subchains.
In this paper, we introduce an attack on Polyshard, called \textit{the
discrepancy} attack, which is the result of malicious nodes controlling a few
subchains and dispersing different blocks to different nodes. We show that this
attack undermines the security of Polyshard and is undetectable in its current
setting.


Abstract:
  We present multi-band Hubble Space Telescope imaging that spans rest-frame
near-ultraviolet through near-infrared wavelengths (0.3-1.1 $\mu$m) for 12
compact starburst galaxies at z=0.4-0.8. These massive galaxies (M_stellar ~
10^11 M_Sun) are driving very fast outflows ($v_{max}$=1000-3000 km/s), and
their light profiles are dominated by an extremely compact starburst component
(half-light radius ~ 100 pc). Our goal is to constrain the physical mechanisms
responsible for launching these fast outflows by measuring the physical
conditions within the central kiloparsec. Based on our stellar population
analysis, the central component typically contributes $\approx$25% of the total
stellar mass and the central escape velocities $v_{esc,central}\approx900$ km/s
are a factor of two smaller than the observed outflow velocities. This requires
physical mechanisms that can accelerate gas to speeds significantly beyond the
central escape velocities, and it makes clear that these fast outflows are
capable of traveling into the circumgalactic medium, and potentially beyond. We
find central stellar densities comparable to theoretical estimates of the
Eddington limit, and we estimate $\Sigma_1$ surface densities within the
central kpc comparable to those of compact massive galaxies at $0.5<z<3.0$.
Relative to "red nuggets" and "blue nuggets" at $z\sim2$, we find significantly
smaller $r_e$ values at a given stellar mass, which we attribute to the
dominance of a young stellar component in our sample and the better physical
resolution for rest-frame optical observations at $z\sim0.6$ versus $z\sim2$.
We compare to theoretical scenarios involving major mergers and violent disc
instability, and we speculate that our galaxies are progenitors of power-law
ellipticals in the local universe with prominent stellar cusps.


Abstract:
  Internet of Everything (IoE) applications such as haptics, human-computer
interaction, and extended reality, using the sixth-generation (6G) of wireless
systems have diverse requirements in terms of latency, reliability, data rate,
and user-defined performance metrics. Therefore, enabling IoE applications over
6G requires a new framework that can be used to manage, operate, and optimize
the 6G wireless system and its underlying IoE services. Such a new framework
for 6G can be based on digital twins. Digital twins use a virtual
representation of the 6G physical system along with the associated algorithms
(e.g., machine learning, optimization), communication technologies (e.g.,
millimeter-wave and terahertz communication), computing systems (e.g., edge
computing and cloud computing), as well as privacy and security-related
technologists (e.g., blockchain). First, we present the key design requirements
for enabling 6G through the use of a digital twin. Next, the architectural
components and trends such as edge-based twins, cloud-based-twins, and
edge-cloud-based twins are presented. Furthermore, we provide a comparative
description of various twins. Finally, we outline and recommend guidelines for
several future research directions.


Abstract:
  As the real-time digital counterpart of a physical system or process, digital
twins are utilized for system simulation and optimization. Neural networks are
one way to build a digital twins model by using data especially when a
physics-based model is not accurate or even not available. However, for a newly
designed system, it takes time to accumulate enough data for neural network
model and only an approximate physics-based model is available. To take
advantage of both models, this paper proposed a model that combines the
physics-based model and the neural network model to improve the prediction
accuracy for the whole life cycle of a system. The proposed hybrid model
(PhysiNet) was able to automatically combine the models and boost their
prediction performance. Experiments showed that the PhysiNet outperformed both
the physics-based model and the neural network model.


Abstract:
  Oblique plane microscopy (OPM) enables high speed, volumetric fluorescence
imaging through a single-objective geometry. While these advantages have
positioned OPM as a valuable tool to probe biological questions in animal
models, its potential for in vivo human imaging is largely unexplored due to
its typical use with exogenous fluorescent dyes. Here we introduce a
scattering-contrast oblique plane microscope (sOPM) and demonstrate label-free
imaging of blood cells flowing through human capillaries in vivo. The sOPM
illuminates a capillary bed in the ventral tongue with an oblique light sheet,
and images side- and back- scattered signal from blood cells. By synchronizing
sOPM with a conventional capillaroscope, we acquire paired widefield and axial
images of blood cells flowing through a capillary loop. The widefield
capillaroscope image provides absorption contrast and confirms the presence of
red blood cells (RBCs), while the sOPM image may aid in determining whether
optical absorption gaps (OAGs) between RBCs have cellular or acellular
composition. Further, we demonstrate consequential differences between
fluorescence and scattering versions of OPM by imaging the same polystyrene
beads sequentially with each technique. Lastly, we substantiate in vivo
observations by imaging isolated red blood cells, white blood cells, and
platelets in vitro using 3D agar phantoms. These results demonstrate a
promising new avenue towards in vivo blood analysis.


Abstract:
  Estimating the gradients for binary variables is a task that arises
frequently in various domains, such as training discrete latent variable
models. What has been commonly used is a REINFORCE based Monte Carlo estimation
method that uses either independent samples or pairs of negatively correlated
samples. To better utilize more than two samples, we propose ARMS, an
Antithetic REINFORCE-based Multi-Sample gradient estimator. ARMS uses a copula
to generate any number of mutually antithetic samples. It is unbiased, has low
variance, and generalizes both DisARM, which we show to be ARMS with two
samples, and the leave-one-out REINFORCE (LOORF) estimator, which is ARMS with
uncorrelated samples. We evaluate ARMS on several datasets for training
generative models, and our experimental results show that it outperforms
competing methods. We also develop a version of ARMS for optimizing the
multi-sample variational bound, and show that it outperforms both VIMCO and
DisARM. The code is publicly available.


Abstract:
  Existing physical model-based imaging methods for ultrasound elasticity
reconstruction utilize fixed variational regularizers that may not be
appropriate for the application of interest or may not capture complex spatial
prior information about the underlying tissues. On the other hand, end-to-end
learning-based methods count solely on the training data, not taking advantage
of the governing physical laws of the imaging system. Integrating
learning-based priors with physical forward models for ultrasound elasticity
imaging, we present a joint reconstruction framework which guarantees that
learning driven reconstructions are consistent with the underlying physics. For
solving the elasticity inverse problem as a regularized optimization problem,
we propose a plug-and-play (PnP) reconstruction approach in which each
iteration of the elasticity image estimation process involves separate updates
incorporating data fidelity and learning-based regularization. In this
methodology, the data fidelity term is developed using a statistical linear
algebraic model of quasi-static equilibrium equation revealing the relationship
of the observed displacement fields \cmmnt{measured deformation data} to the
unobserved elastic modulus. The regularizer comprises a convolutional neural
network (CNN) based denoiser that captures the learned prior structure of the
underlying tissues. Preliminary simulation results demonstrate the robustness
and effectiveness of the proposed approach with limited training datasets and
noisy displacement measurements.


Abstract:
  Geometric feature extraction is a crucial component of point cloud
registration pipelines. Recent work has demonstrated how supervised learning
can be leveraged to learn better and more compact 3D features. However, those
approaches' reliance on ground-truth annotation limits their scalability. We
propose BYOC: a self-supervised approach that learns visual and geometric
features from RGB-D video without relying on ground-truth pose or
correspondence. Our key observation is that randomly-initialized CNNs readily
provide us with good correspondences; allowing us to bootstrap the learning of
both visual and geometric features. Our approach combines classic ideas from
point cloud registration with more recent representation learning approaches.
We evaluate our approach on indoor scene datasets and find that our method
outperforms traditional and learned descriptors, while being competitive with
current state-of-the-art supervised approaches.


Abstract:
  The ignition of Quantum Cascade Lasers can occur from a state of oscillating
fie ld domains. Here, the interplay between lasing and the kinetics of
traveling dom ain boundaries provides complex oscillation scenarios. We analyze
our numerical findings in detail for a device operating at terahertz
frequencies and manifest chaotic evolution by positive Lyapunov exponents. This
shows that these importan t devices can exhibit chaotic behavior even without
periodic driving, which need s to be taken into account in their design.


Abstract:
  With the unprecedented reliance on cloud computing as the backbone for
storing today's big data, we argue in this paper that the role of the cloud
should be reshaped from being a passive virtual market to become an active
platform for monetizing the big data through Artificial Intelligence (AI)
services. The objective is to enable the cloud to be an active platform that
can help big data service providers reach a wider set of customers and cloud
users (i.e., data consumers) to be exposed to a larger and richer variety of
data to run their data analytic tasks. To achieve this vision, we propose a
novel game theoretical model, which consists of a mix of cooperative and
competitive strategies. The players of the game are the big data service
providers, cloud computing platform, and cloud users. The strategies of the
players are modeled using the two-sided market theory that takes into
consideration the network effects among involved parties, while integrating the
externalities between the cloud resources and consumer demands into the design
of the game. Simulations conducted using Amazon and google clustered data show
that the proposed model improves the total surplus of all the involved parties
in terms of cloud resources provision and monetary profits compared to the
current merchant model.


Abstract:
  In this paper, we propose SACHER (soft actor-critic (SAC) with hindsight
experience replay (HER)), which constitutes a class of deep reinforcement
learning (DRL) algorithms. SAC is known as an off-policy model-free DRL
algorithm based on the maximum entropy framework, which outperforms earlier DRL
algorithms in terms of exploration, robustness and learning performance.
However, in SAC, maximizing the entropy-augmented objective may degrade the
optimality of learning outcomes. HER is known as a sample-efficient replay
method that enhances the performance of off-policy DRL algorithms by allowing
the agent to learn from both failures and successes. We apply HER to SAC and
propose SACHER to improve the learning performance of SAC. More precisely,
SACHER achieves the desired optimal outcomes faster and more accurately than
SAC, since HER improves the sample efficiency of SAC. We apply SACHER to the
navigation and control problem of unmanned aerial vehicles (UAVs), where SACHER
generates the optimal navigation path of the UAV under various obstacles in
operation. Specifically, we show the effectiveness of SACHER in terms of the
tracking error and cumulative reward in UAV operation by comparing them with
those of state-of-the-art DRL algorithms, SAC and DDPG. Note that SACHER in UAV
navigation and control problems can be applied to arbitrary models of UAVs.


Abstract:
  Most of today's security solutions, such as security information and event
management (SIEM) and signature based IDS, require the operator to evaluate
potential attack vectors and update detection signatures and rules in a timely
manner. However, today's sophisticated and tailored advanced persistent threats
(APT), malware, ransomware and rootkits, can be so complex and diverse, and
often use zero day exploits, that a pure signature-based blacklisting approach
would not be sufficient to detect them. Therefore, we could observe a major
paradigm shift towards anomaly-based detection mechanisms, which try to
establish a system behavior baseline -- either based on netflow data or system
logging data -- and report any deviations from this baseline. While these
approaches look promising, they usually suffer from scalability issues. As the
amount of log data generated during IT operations is exponentially growing,
high-performance analysis methods are required that can handle this huge amount
of data in real-time. In this paper, we demonstrate how high-performance
bioinformatics tools can be applied to tackle this issue. We investigate their
application to log data for outlier detection to timely reveal anomalous system
behavior that points to cyber attacks. Finally, we assess the detection
capability and run-time performance of the proposed approach.


Abstract:
  We show in ZF that: (i) Every subcompact metrizable space is completely
metrizable, and every completely metrizable space is countably subcompact. (ii)
A metrizable space X=(X,T) is countably compact iff it is countably subcompact
relative to T. (iii) For every metric space X=(X,d), X is compact iff it is
subcompact relative to T. We also show: (iv) The negation of each of the
statements, (a) every countably subcompact metrizable space is completely
metrizable, (b) every countably subcompact metrizable space is subcompact, (c)
every complete metrizable space is subcompact is relatively consistent with ZF.


Abstract:
  We consider an ergodic harvesting problem with model ambiguity that arises
from biology. To account for the ambiguity, the problem is constructed as a
stochastic game with two players: the decision-maker (DM) chooses the `best'
harvesting policy and an adverse player chooses the `worst' probability
measure. The main result is establishing an optimal strategy (also referred to
as a control) of the DM and showing that it is a threshold policy. The optimal
threshold and the optimal payoff are obtained by solving a free-boundary
problem emerging from the Hamilton--Jacobi--Bellman (HJB) equation. As part of
the proof, we fix a gap that appeared in the HJB analysis of [Alvarez and
Hening, {\em Stochastic Process. Appl.}, 2019], a paper that analyzed the
risk-neutral version of the ergodic harvesting problem. Finally, we study the
dependence of the optimal threshold and the optimal payoff on the ambiguity
parameter and show that if the ambiguity goes to 0, the problem converges, to
the risk-neutral problem.


Abstract:
  We present the application of simultaneous diagonalization and minimum energy
(SDME) high-order finite element modal bases for simulation of transient
non-linear elastodynamic problem, including impact cases with neo-hookean
hyperelastic materials. The bases are constructed using procedures for
simultaneous diagonalization of the internal modes and Schur complement of the
boundary modes from the standard nodal and modal bases, constructed using
Lagrange and Jacobi polynomials, respectively. The implementation of these
bases in a high-order finite element code is straightforward, since the
procedure is applied only to the one-dimensional expansion bases. Non-linear
transient structural problems with large deformation, hyperelastic materials
and impact are solved using the obtained bases with explicit and implicit time
integration procedures. Iterative solutions based on preconditioned conjugate
gradient methods are considered. The performance of the proposed bases in terms
of the number of iterations of pre-conditioned conjugate gradient methods and
computational time are compared with the standard nodal and modal bases. Our
numerical tests obtained speedups up to 41 using the considered bases when
compared to the standard ones.


Abstract:
  Recently, machine learning has been used to substitute parts of conventional
computational fluid dynamics, e.g. the cell-face reconstruction in
finite-volume solvers or the curvature computation in the Volume-of-Fluid (VOF)
method. The latter showed improvements in terms of accuracy for coarsely
resolved interfaces, however at the expense of convergence and symmetry. In
this work, a combined approach is proposed, adressing the aforementioned
shortcomings. We focus on interface reconstruction (IR) in the level-set
method, i.e. the computation of the volume fraction and apertures. The combined
model consists of a classification neural network, that chooses between the
conventional (linear) IR and the neural network IR depending on the local
interface resolution. The proposed approach improves accuracy for coarsely
resolved interfaces and recovers the conventional IR for high resolutions,
yielding first order overall convergence. Symmetry is preserved by mirroring
and rotating the input level-set grid and subsequently averaging the
predictions. The combined model is implemented into a CFD solver and
demonstrated for two-phase flows. Furthermore, we provide details of floating
point symmetric implementation and computational efficiency.


Abstract:
  Natural Language Processing (NLP) techniques can be applied to help with the
diagnosis of medical conditions such as depression, using a collection of a
person's utterances. Depression is a serious medical illness that can have
adverse effects on how one feels, thinks, and acts, which can lead to emotional
and physical problems. Due to the sensitive nature of such data, privacy
measures need to be taken for handling and training models with such data. In
this work, we study the effects that the application of Differential Privacy
(DP) has, in both a centralized and a Federated Learning (FL) setup, on
training contextualized language models (BERT, ALBERT, RoBERTa and DistilBERT).
We offer insights on how to privately train NLP models and what architectures
and setups provide more desirable privacy utility trade-offs. We envisage this
work to be used in future healthcare and mental health studies to keep medical
history private. Therefore, we provide an open-source implementation of this
work.


Abstract:
  High temperature superconducting materials have been known since the
pioneering work of Bednorz and Mueller in 1986. While the microscopic mechanism
responsible for high Tc superconductivity is still debated, most materials
showing high Tc contain highly electronic polarizable ions, suggesting that the
mechanism driving high Tc superconductivity can be related to the ion
electronic polarizability in high Tc materials. Here we show that a free charge
carrier polarizes the ions surrounding it and the total electrical potential
generated by the charge carrier itself and the polarized ions becomes
attractive in some regions of space. Our results on bulk FeSe, monolayer FeSe
on SrTiO3 and La2CuO4 are in excellent agreement with the experiments. The fact
that the electronic polarizability explains correctly and quantitatively the
superconductivity parameters: Tc, gap and paring energies of both pnictides and
cuprates with similar polarizability parameters, suggests that the same model
may be applicable to other material systems within these groups as well as
other high Tc groups.


Abstract:
  NASA's Double Asteroid Redirection Test (DART) mission will impact its target
asteroid, Dimorphos, at an oblique angle that will not be known prior to the
impact. We computed iSALE-3D simulations of DART-like impacts on asteroid
surfaces at different impact angles and found that the the vertical momentum
transfer efficiency, $\beta$, is similar for different impact angles, however,
the imparted momentum is reduced as the impact angle decreases. It is expected
that the momentum imparted from a 45$^\circ$ impact is reduced by up to 50\%
compared to a vertical impact. The direction of the ejected momentum is not
normal to the surface, however it is observed to `straighten up' with crater
growth. iSALE-2D simulations of vertical impacts provide context for the
iSALE-3D simulation results and show that the ejection angle varies with both
target properties and with crater growth. While the ejection angle is
relatively insensitive to the target porosity, it varies by up to 30$^\circ$
with target coefficient of internal friction. The simulation results presented
in this paper can help constrain target properties from the DART crater ejecta
cone, which will be imaged by the LICIACube. The results presented here
represent the basis for an empirical scaling relationship for oblique impacts
and can be used as a framework to determine an analytical approximation of the
vertical component of the ejecta momentum, $\beta-1$, given known target
properties.


Abstract:
  A relativistic density-functional theory based on a Fock-space effective
quantum-electrodynamics (QED) Hamiltonian using the Coulomb or Coulomb-Breit
two-particle interaction is developed. This effective QED theory properly
includes the effects of vacuum polarization through the creation of
electron-positron pairs but does not include explicitly the photon degrees of
freedom. It is thus a more tractable alternative to full QED for atomic and
molecular calculations. Using the constrained-search formalism, a Kohn-Sham
scheme is formulated in a quite similar way to non-relativistic
density-functional theory, and some exact properties of the involved density
functionals are studied, namely charge-conjugation symmetry and uniform
coordinate scaling. The usual no-pair Kohn-Sham scheme is obtained as a
well-defined approximation to this relativistic density-functional theory.


Abstract:
  We present an investigation of the magnetic activity and flare
characteristics of the sub-giant stars mostly from F and G spectral types and
compare the results with the main-sequence (MS) stars. The light curve of 352
stars on the sub-giant branch (SGB) from the Kepler mission is analyzed in
order to infer stability, relative coverage and contrast of the magnetic
structures and also flare properties using three flare indexes. The results
show that: (i) Relative coverage and contrast of the magnetic features along
with rate, power and magnitude of flares increase on the SGB due to the
deepening of the convective zone and more vigorous magnetic field production
(ii) Magnetic activity of the F and G-type stars on the SGB does not show
dependency to the rotation rate and does not obey the saturation regime. This
is the opposite of what we saw for the main sequence, in which the G-, K- and
M-type stars show clear dependency to the Rossby number; (iii) The positive
relationship between the magnetic features stability and their relative
coverage and contrast remains true on the SGB, though it has lower dependency
coefficient in comparison with the MS; (iv) Magnetic proxies and flare indexes
of the SGB stars increase with increasing the relative mass of the convective
zone.


Abstract:
  Few-shot learning (FSL) is the task of learning to recognize previously
unseen categories of images from a small number of training examples. This is a
challenging task, as the available examples may not be enough to unambiguously
determine which visual features are most characteristic of the considered
categories. To alleviate this issue, we propose a method that additionally
takes into account the names of the image classes. While the use of class names
has already been explored in previous work, our approach differs in two key
aspects. First, while previous work has aimed to directly predict visual
prototypes from word embeddings, we found that better results can be obtained
by treating visual and text-based prototypes separately. Second, we propose a
simple strategy for learning class name embeddings using the BERT language
model, which we found to substantially outperform the GloVe vectors that were
used in previous work. We furthermore propose a strategy for dealing with the
high dimensionality of these vectors, inspired by models for aligning
cross-lingual word embeddings. We provide experiments on miniImageNet, CUB and
tieredImageNet, showing that our approach consistently improves the
state-of-the-art in metric-based FSL.


Abstract:
  The phenomenon of low-temperature superconductivity is intimately associated
with the condensation of weakly bound, very extended, strongly overlapping
Cooper pairs, and systematic experimental studies of the associated mean square
radius (coherence length) have been made. While the extension of BCS theory to
the atomic nucleus has been successful beyond expectation, to our knowledge, no
measurement of the nuclear coherence length (expected to be much larger than
nuclear dimensions) has been reported in the literature. Recent studies of
Cooper pair transfer across a Josephson-like junction, transiently established
in a heavy ion collision between superfluid nuclei, have likely changed the
situation, providing the experimental input for a quantitative estimate of the
nuclear coherence length, as well as the basis for a nuclear analogue of the
(ac) Josephson effect.


Abstract:
  The extreme intensities obtainable with lasers such as Gemini allow
non-linear QED phenomena to be investigated according to our calculations.
Electron-positron pair production from a pure vacuum target, which has yet to
be observed experimentally, is possibly the most iconic process. Beyond
pair-production our campaign will allow the experimental investigation of
currently unexplored extreme radiation regimes, like the quantum radiation
dominated regime (where quantum and self-field effects become important) and
non-linear Compton scattering. This is the first experiment in a multi-part
campaign proposed by a major international collaboration to investigate
non-linear QED. This proposal is for the first experiment in a series of 3 to
achieve our most high-profile experimental goal of pair production in vacuum,
but each experiment is designed to have its own tangible high-profile outcome.


Abstract:
  We introduce SHARE: a System for Hierarchical Assistive Recipe Editing to
assist home cooks with dietary restrictions -- a population under-served by
existing cooking resources. Our hierarchical recipe editor makes necessary
substitutions to a recipe's ingredients list and re-writes the directions to
make use of the new ingredients. We introduce the novel RecipePairs dataset of
84K pairs of similar recipes in which one recipe satisfies one of seven dietary
constraints, allowing for supervised training of such recipe editing models.
Experiments on this dataset demonstrate that our system produces convincing,
coherent recipes that are appropriate for a target dietary constraint (contain
no prohibited ingredients). We show that this is a challenging task that cannot
be adequately solved with human-written ingredient substitution rules or
straightforward adaptation of state-of-the-art models for recipe generation. We
further demonstrate through human evaluations and real-world cooking trials
that recipes edited by our system can be easily followed by home cooks to
create delicious and satisfactory dishes.


Abstract:
  The center-of-gravity rule is tested for heavy and light-quark mesons. In the
heavy-meson sector, the rule is excellently satisfied. In the light-quark
sector, the rule suggests that the $a_0(980)$ could be the spin-partner of
$a_2(1320)$, $a_1(1260)$, and $b_1(1235)$; $f_0(500)$ the spin-partner of
$f_2(1270)$, $f_1(1285)$, and $h_1(1170)$; and $f_0(980)$ the spin-partner of
$f_2'(1525)$, $f_1(1420)$, and $h_1(1415)$. From the decay and the production
of light scalar mesons we find a consistent mixing angle $\theta^{\rm
s}=(14\pm4)^\circ$. We conclude that $f_0(980)$ is likely "octet-like" in SU(3)
with a slightly larger $s\bar s$ content and $f_0(500)$ is SU(3) "singlet-like"
with a larger $n\bar n$ component. The $a_0(1450)$, $K^*_0(1430)$, $f_0(1500)$
and $f_0(1370)$ are suggested as nonet of radial excitations. The scalar
glueball is discussed as part of the wave function of scalar isoscalar mesons
and not as additional "intruder". It seems not to cause supernumerosity.


Abstract:
  Crack detection is of great significance for monitoring the integrity and
well-being of the infrastructure such as bridges and underground pipelines,
which are harsh environments for people to access. In recent years, computer
vision techniques have been applied in detecting cracks in concrete structures.
However, they suffer from variances in light conditions and shadows, lacking
robustness and resulting in many false positives. To address the uncertainty in
vision, human inspectors actively touch the surface of the structures, guided
by vision, which has not been explored in autonomous crack detection. In this
paper, we propose a novel approach to detect and reconstruct cracks in concrete
structures using vision-guided active tactile perception. Given an RGB-D image
of a structure, the rough profile of the crack in the structure surface will
first be segmented with a fine-tuned Deep Convolutional Neural Networks, and a
set of contact points are generated to guide the collection of tactile images
by a camera-based optical tactile sensor. When contacts are made, a pixel-wise
mask of the crack can be obtained from the tactile images and therefore the
profile of the crack can be refined by aligning the RGB-D image and the tactile
images. Extensive experiment results have shown that the proposed method
improves the effectiveness and robustness of crack detection and reconstruction
significantly, compared to crack detection with vision only, and has the
potential to enable robots to help humans with the inspection and repair of the
concrete infrastructure.


Abstract:
  We present a catalog of high-velocity CIV $\lambda$ 1548,1551 mini-Broad
Absorption Lines (mini-BALs) in the archives of the VLT-UVES and Keck-HIRES
spectrographs. We identify high-velocity CIV mini-BALs based on smooth rounded
BAL-like profiles with velocity blueshifts $<$ $-$4000 km/s and widths in the
range 70 $\lesssim$ FWHM(1548) $\lesssim$ 2000 km/s (for $\lambda$1548 alone).
We find 105 mini-BALs in 44 quasars from a total sample of 638 quasars. The
fraction of quasars with at least one mini-BAL meeting our criteria is roughly
$\sim9$% after correcting for incomplete velocity coverage. However, the
numbers of systems rise sharply at lower velocities and narrower FWHMs,
suggesting that many outflow lines are missed by our study. All of the systems
are highly ionized based on the strong presence of NV and OVI and/or the
absence of SiII and CII when within the wavelength coverage. Two of the
mini-BAL systems in our catalog, plus three others at smaller velocity shifts,
have PV $\lambda$1118,1128 absorption indicating highly saturated CIV
absorption and total hydrogen column densities $\gtrsim 10^{22}$ cm$^{-3}$.
Most of the mini-BALs are confirmed to have optical depths $\gtrsim$1 with
partial covering of the quasar continuum source. The covering fractions are as
small as 0.06 in CIV and 0.03 in SiIV , corresponding to outflow absorbing
structures $<0.002$ pc across. When multiple lines are measured, the lines of
less abundant ions tend to have narrower profiles and smaller covering
fractions indicative of inhomogeneous absorbers where higher column densities
occur in smaller clumps. This picture might extend to BAL outflows if the
broader and generally deeper BALs form in either the largest clumps or
collections of many mini-BAL-like clumps that blend together in observed quasar
spectra.


Abstract:
  Thouless pumping is a fundamental instance of quantized transport, which is
topologically protected. Although its theoretical importance, the adiabaticity
condition is an obstacle for further practical applications. Here, focusing on
the Rice-Mele model, we provide a family of finite-frequency examples that
ensure both the absence of excitations and the perfect quantization of the
pumped charge at the end of each cycle. This family, which contains an
adiabatic protocol as a limiting case, is obtained through a mapping onto the
zero curvature representation of the Euclidean sinh-Gordon equation.


Abstract:
  The consumer Internet of Things (IoT) have developed in recent years. Mass
IoT devices are constructed to build a huge communications network. But these
devices are insecure in reality, it means that the communications network are
exposed by the attacker. Moreover, the IoT communication network also faces
with variety of sudden errors. Therefore, it easily leads to that is vulnerable
with the threat of attacker and system failure. The severe situation of IoT
communication network motivates the development of new techniques to
automatically detect multi-anomaly. In this paper, we propose SS-VTCN, a
semi-supervised network for IoT multiple anomaly detection that works well
effectively for IoT communication network. SS-VTCN is designed to capture the
normal patterns of the IoT traffic data based on the distribution whether it is
labeled or not by learning their representations with key techniques such as
Variational Autoencoders and Temporal Convolutional Network. This network can
use the encode data to predict preliminary result, and reconstruct input data
to determine anomalies by the representations. Extensive evaluation experiments
based on a benchmark dataset and a real consumer smart home dataset demonstrate
that SS-VTCN is more suitable than supervised and unsupervised method with
better performance when compared other state-of-art semi-supervised method.


Abstract:
  The aim of this article is to introduce the concept of graded $2$-absorbing
coprimary submodules as a generalization of graded strongly $2$-absorbing
second submodules, and explore some properties of this class. A non-zero graded
$R$-submodule $N$ of a graded $R$-module $M$ is called a graded $2$-absorbing
coprimary $R$-submodule if whenever $x, y$ are homogeneous elements of $R$ and
$K$ is a graded $R$-submodule of $M$ such that $xyN\subseteq K$, then either
$x$ or $y$ is in the graded radical of $(K :_{R} N)$ or $xy\in Ann_{R}(N)$.
Several results have been achieved.


Abstract:
  A small number of anomalously bright boulders on the near-Earth, rubble-pile
asteroid (101955) Bennu were recently identified as eucritic material
originating from asteroid (4) Vesta. Building on this discovery, we explored
the global presence of exogenic boulders on Bennu. Our analysis focused on
boulders larger than 1 m that show the characteristic 1-micron pyroxene
absorption band in the four-color MapCam data from the OSIRIS-REx mission. We
confirm the presence of exogenic boulders similar to eucrites and find that
mixtures of eucrites with carbonaceous material is also a possible composition
for some boulders. Some of the exogenic boulders have spectral properties
similar to those of ordinary chondrite (OC) meteorites, although the laboratory
spectra of these meteorites have a higher albedo than those measured on Bennu,
which could be explained by either a grain size effect, the presence of impact
melt, or optical mixing with carbonaceous material owing to dust coating. Our
Monte Carlo simulations predict that the median amount of OC mass added to the
parent body of Bennu is 0.055% and 0.037% of the volume of a 100- and
200-km-diameter parent body, respectively. If Bennu was a uniformly mixed
byproduct of parent body and S-type projectiles, the equivalent mass of OC
material would be a sphere with a diameter of 36 to 40 m (or a volume of 24,200
to 33,600 m3). The total amount of OC material in the interior of Bennu
estimated from the MapCam data is slightly higher (91,000-150,000 m3).


Abstract:
  This is the second paper treating the double coset problem for classical
groups. Let $G$ be an algebraic group over an algebraically closed field $K$.
The double coset problem consists of classifying the pairs $H,J$ of closed
connected subgroups of $G$ with finitely many $(H,J)$-double cosets in $G$. The
critical setup occurs when one of $H,J$, say $H$, is reductive, and $J$ is a
parabolic subgroup. Assume that $G$ is a classical group, $H$ is simple and $J$
is a maximal parabolic $P_k$, the stabilizer of a totally singular $k$-space.
Then most candidates have $k=1$ or $k=2$. The case $k=1$ was solved in a
previous paper and here we deal with $k=2$. We solve this case by determining
all faithful irreducible self-dual $H$-modules $V$, such that $H$ has finitely
may orbits on totally singular $2$-spaces of $V$.


Abstract:
  We consider the moduli space of vector bundles of rank $n$ and degree $ng$
over a fixed Riemann surface of genus $g\geq 2$. We use the explicit
parametrization in terms of the Tyurin data. In the moduli space there is a
"non-abelian" Theta divisor, consisting of bundles with $h^1\geq 1$. On the
complement of this divisor we construct a non-abelian Cauchy kernel explicitly
in terms of the Tyurin data. With the additional datum of a non-special
divisor, we can construct a reference flat holomorphic connection which is also
dependent holomorphically on the moduli of the bundle. This allows us to
identify the bundle of Higgs fields, i.e. the cotangent bundle of the moduli
space, with the affine bundle of holomorphic connections and provide a
monodromy map into the ${\rm GL}_n$ character variety. We show that the Goldman
symplectic structure on the character variety pulls back along this map to the
complex canonical symplectic structure on the cotangent bundle and hence also
on the space of affine connections. The pull-back of the Liouville one-form to
the affine bundle of connections is then shown to be a logarithmic form with
poles along the non-abelian theta divisor and residue given by $h^1$.


Abstract:
  Non-intrusive load monitoring (NILM) as the process of extracting the usage
pattern of appliances from the aggregated power signal is among successful
approaches aiding residential energy management. In recent years, high volume
datasets on power profiles have become available, which has helped make
classification methods employed for the NILM purpose more effective and more
accurate. However, the presence of multi-mode appliances and appliances with
close power values have remained influential in worsening the computational
complexity and diminishing the accuracy of these algorithms. To tackle these
challenges, we propose an event-based classification process, in the first
phase of which the $K$-nearest neighbors method, as a fast classification
technique, is employed to extract power signals of appliances with exclusive
non-overlapping power values. Then, two deep learning models, which consider
the water consumption of some appliances as a novel signature in the network,
are utilized to distinguish between appliances with overlapping power values.
In addition to power disaggregation, the proposed process as well extracts the
water consumption profiles of specific appliances. To illustrate the proposed
process and validate its efficiency, seven appliances of the AMPds are
considered, with the numerical classification results showing marked
improvement with respect to the existing classification-based NILM techniques.


Abstract:
  Majorana bound states are zero-energy states predicted to emerge in
topological superconductors and intense efforts seeking a definitive proof of
their observation are still ongoing. A standard route to realize them involves
antagonistic orders: a superconductor in proximity to a ferromagnet. Here we
show this issue can be resolved using antiferromagnetic rather than
ferromagnetic order. We propose to use a chain of antiferromagnetic skyrmions,
in an otherwise collinear antiferromagnet, coupled to a bulk conventional
superconductor as a novel platform capable of supporting Majorana bound states
that are robust against disorder. Crucially, the collinear antiferromagnetic
region neither suppresses superconductivity nor induces topological
superconductivity, thus allowing for Majorana bound states localized at the
ends of the chain. Our model introduces a new class of systems where
topological superconductivity can be induced by editing antiferromagnetic
textures rather than locally tuning material parameters, opening avenues for
the conclusive observation of Majorana bound states.


Abstract:
  We describe the use of machine learning algorithms to select high-quality
measurements for the Mu2e experiment. This technique is important for
experiments with backgrounds that arise due to measurement errors. The
algorithms use multiple pieces of ancillary information that are sensitive to
measurement quality to separate high-quality and low-quality measurements.


Abstract:
  Family history is a major risk factor for many types of cancer. Mendelian
risk prediction models translate family histories into cancer risk predictions
based on knowledge of cancer susceptibility genes. These models are widely used
in clinical practice to help identify high-risk individuals. Mendelian models
leverage the entire family history, but they rely on many assumptions about
cancer susceptibility genes that are either unrealistic or challenging to
validate due to low mutation prevalence. Training more flexible models, such as
neural networks, on large databases of pedigrees can potentially lead to
accuracy gains. In this paper, we develop a framework to apply neural networks
to family history data and investigate their ability to learn inherited
susceptibility to cancer. While there is an extensive literature on neural
networks and their state-of-the-art performance in many tasks, there is little
work applying them to family history data. We propose adaptations of
fully-connected neural networks and convolutional neural networks to pedigrees.
In data simulated under Mendelian inheritance, we demonstrate that our proposed
neural network models are able to achieve nearly optimal prediction
performance. Moreover, when the observed family history includes misreported
cancer diagnoses, neural networks are able to outperform the Mendelian BRCAPRO
model embedding the correct inheritance laws. Using a large dataset of over
200,000 family histories, the Risk Service cohort, we train prediction models
for future risk of breast cancer. We validate the models using data from the
Cancer Genetics Network.


Abstract:
  Controlling growth via cell division is crucial in the development of higher
organisms, and yet the mechanisms through which this is achieved, e.g., in
epithelial tissue, is not yet fully understood. We show that by coupling the
cell cycle oscillator governing cell division to signals that encode
inter-cellular contacts, this phenomenon can be seen as a collective dynamical
transition in a system of coupled oscillators in lattices with changing degree
of disorder. As the distribution of cellular morphological characteristics
become more homogeneous over the course of development, the contact-induced
signals to the cells increase beyond a critical value to trigger coordinated
cessation of oscillations, eventually leading to growth arrest. Our results
suggest that the global phenomenon of growth rate reduction as a tissue
approaches its appropriate size is causally related to the increasingly regular
geometry of local cell-cell contact interfaces.


Abstract:
  Latent class models with covariates are widely used for psychological,
social, and educational researches. Yet the fundamental identifiability issue
of these models has not been fully addressed. Among the previous researches on
the identifiability of latent class models containing covariates, Huang and
Bandeen-Roche (2004, Psychometrika, 69:5-32) studied the local identifiability
conditions. However, motivated by recent advances in the identifiability of
restricted latent class models, particularly the Cognitive Diagnosis Models
(CDMs), we show in this work that the conditions in Huang and Bandeen-Roche
(2004) are only necessary but not sufficient to determine the local
identifiability of the model parameters. To address the open identifiability
issue for latent class models with covariates, this work establishes conditions
to ensure the global identifiability of the model parameters in both strict and
generic sense. Moreover, our results extend to polytomous-response CDMs with
covariates, which generalizes the existing identifiability results for CDMs.


Abstract:
  Inflationary model driven by a scalar field whose potential has a step in the
second derivative with respect to the field is considered. For the best fit
potential parameter values, the 3-point function and the non-Gaussianity
associated with the featured model is calculated. We study the shape and scale
dependence of the 3-point function. The distinctive feature of this model is
its characteristic ringing behaviour of $f_{NL}$. We can see that the
oscillations in $f_{NL}$ in this model last for a much longer range of k
values, as compared to the previously studied models. In that sense, this model
is potentially distinguishable from models with other features in the
potential.


Abstract:
  Agents operating in unstructured environments often produce negative side
effects (NSE), which are difficult to identify at design time. While the agent
can learn to mitigate the side effects from human feedback, such feedback is
often expensive and the rate of learning is sensitive to the agent's state
representation. We examine how humans can assist an agent, beyond providing
feedback, and exploit their broader scope of knowledge to mitigate the impacts
of NSE. We formulate this problem as a human-agent team with decoupled
objectives. The agent optimizes its assigned task, during which its actions may
produce NSE. The human shapes the environment through minor reconfiguration
actions so as to mitigate the impacts of the agent's side effects, without
affecting the agent's ability to complete its assigned task. We present an
algorithm to solve this problem and analyze its theoretical properties. Through
experiments with human subjects, we assess the willingness of users to perform
minor environment modifications to mitigate the impacts of NSE. Empirical
evaluation of our approach shows that the proposed framework can successfully
mitigate NSE, without affecting the agent's ability to complete its assigned
task.


Abstract:
  We find the quantum power emitted and distribution in $3+1$-dimensions of
relativistic acceleration radiation using a single perfectly reflecting mirror
via Lorentz invariance demonstrating close analogies to point charge radiation
in classical electrodynamics.


Abstract:
  The minimum height of vertex and edge partition trees are well-studied graph
parameters known as, for instance, vertex and edge ranking number. While they
are NP-hard to determine in general, linear-time algorithms exist for trees.
Motivated by a correspondence with Dasgupta's objective for hierarchical
clustering we consider the total rather than maximum depth of vertices as an
alternative objective for minimization. For vertex partition trees this leads
to a new parameter with a natural interpretation as a measure of robustness
against vertex removal.
  As tools for the study of this family of parameters we show that they have
similar recursive expressions and prove a binary tree rotation lemma. The new
parameter is related to trivially perfect graph completion and therefore
intractable like the other three are known to be. We give polynomial-time
algorithms for both total-depth variants on caterpillars and on trees with a
bounded number of leaf neighbors. For general trees, we obtain a
2-approximation algorithm.