title: Completely Quantum Neural Networks

abstract:
  Artificial neural networks are at the heart of modern deep learning
algorithms. We describe how to embed and train a general neural network in a
quantum annealer without introducing any classical element in training. To
implement the network on a state-of-the-art quantum annealer, we develop three
crucial ingredients: binary encoding the free parameters of the network,
polynomial approximation of the activation function, and reduction of binary
higher-order polynomials into quadratic ones. Together, these ideas allow
encoding the loss function as an Ising model Hamiltonian. The quantum annealer
then trains the network by finding the ground state. We implement this for an
elementary network and illustrate the advantages of quantum training: its
consistency in finding the global minimum of the loss function and the fact
that the network training converges in a single annealing step, which leads to
short training times while maintaining a high classification performance. Our
approach opens a novel avenue for the quantum training of general machine
learning models.


title: Symmetry-resolved entanglement entropy in critical free-fermion chains

abstract:
  The symmetry-resolved R\'enyi entanglement entropy is the R\'enyi
entanglement entropy of each symmetry sector of a density matrix $\rho$. This
experimentally relevant quantity is known to have rich theoretical connections
to conformal field theory (CFT). For a family of critical free-fermion chains,
we present a rigorous lattice-based derivation of its scaling properties using
the theory of Toeplitz determinants. We consider a class of critical quantum
chains with a microscopic U(1) symmetry; each chain has a low energy
description given by $N$ massless Dirac fermions. For the density matrix,
$\rho_A$, of subsystems of $L$ neighbouring sites we calculate the leading
terms in the large $L$ asymptotic expansion of the symmetry-resolved R\'enyi
entanglement entropies. This follows from a large $L$ expansion of the charged
moments of $\rho_A$; we derive $tr(e^{i \alpha Q_A} \rho_A^n) = a e^{i \alpha
\langle Q_A\rangle} (\sigma L)^{-x}(1+O(L^{-\mu}))$, where $a, x$ and $\mu$ are
universal and $\sigma$ depends only on the $N$ Fermi momenta. We show that the
exponent $x$ corresponds to the expectation from CFT analysis. The error term
$O(L^{-\mu})$ is consistent with but weaker than the field theory prediction
$O(L^{-2\mu})$. However, using further results and conjectures for the relevant
Toeplitz determinant, we find excellent agreement with the expansion over CFT
operators.

categories: quant-ph cond-mat.stat-mech cond-mat.str-el math-ph math.MP


title: String junctions suspended between giants

abstract:
  We construct $(p,q)$ string junction solutions suspended between both sphere
and AdS giant gravitons in $AdS_5\times S^5$. Our results extend easily to more
general half BPS geometries of LLM type. These carry angular momentum in the
directions of the worldvolume of the giant gravitons. We argue that these are
charged under a central extension of the supersymmetry algebra similar to the
one that has appeared in the works of Beisert for the ${\cal N}=4 $ spin chain.
We also argue that they are BPS with respect to this central extension. We show
that apart from some kinematical details, the junctions end up solving the same
minimization problem that appears in the Coulomb branch of ${\cal N}=4 $ SYM.
Their mass and shape is independent of the angular momentum $Q$ that the
junction carries.


title:
Using Bayesian Deep Learning to infer Planet Mass from Gaps in
  Protoplanetary Disks

abstract:
  Planet induced sub-structures, like annular gaps, observed in dust emission
from protoplanetary disks provide a unique probe to characterize unseen young
planets. While deep learning based model has an edge in characterizing the
planet's properties over traditional methods, like customized simulations and
empirical relations, it lacks in its ability to quantify the uncertainty
associated with its predictions. In this paper, we introduce a Bayesian deep
learning network "DPNNet-Bayesian" that can predict planet mass from disk gaps
and provides uncertainties associated with the prediction. A unique feature of
our approach is that it can distinguish between the uncertainty associated with
the deep learning architecture and uncertainty inherent in the input data due
to measurement noise. The model is trained on a data set generated from
disk-planet simulations using the \textsc{fargo3d} hydrodynamics code with a
newly implemented fixed grain size module and improved initial conditions. The
Bayesian framework enables estimating a gauge/confidence interval over the
validity of the prediction when applied to unknown observations. As a
proof-of-concept, we apply DPNNet-Bayesian to dust gaps observed in HL Tau. The
network predicts masses of $ 86.0 \pm 5.5 M_{\Earth} $, $ 43.8 \pm 3.3
M_{\Earth} $, and $ 92.2 \pm 5.1 M_{\Earth} $ respectively, which are
comparable to other studies based on specialized simulations.


title:
Locality of Spontaneous Symmetry Breaking and Universal Spacing
  Distribution of Topological Defects Formed Across a Phase Transition

abstract:
  The crossing of a continuous phase transition results in the formation of
topological defects with a density predicted by the Kibble-Zurek mechanism
(KZM). We characterize the spatial distribution of point-like topological
defects in the resulting nonequilibrium state and model it using a Poisson
point process in arbitrary spatial dimension with KZM density. Numerical
simulations in a one-dimensional $\phi^4$ theory unveil short-distance
defect-defect corrections stemming from the kink excluded volume, while in two
spatial dimensions, our model accurately describes the vortex spacing
distribution in a strongly-coupled superconductor indicating the suppression of
defect-defect spatial correlations.


title:
Chiral Superconductivity in UTe$_2$ via Emergent $C_4$ Symmetry and Spin
  Orbit Coupling

abstract:
  A lot of attention has been drawn to superconductivity in UTe$_2$, with
suggestions of time-reversal symmetry breaking triplet chiral superconducting
order parameter. The chirality of the order parameter has been attributed to an
accidental near degeneracy of two superconducting components belonging to 1D
irreps $B_{2u}$ and $B_{3u}$ of the relevant $D_{2h}$ point group, and it has
been argued that the chiral $B_{2u}+iB_{3u}$ combination is selected by
ferromagnetic fluctuations. In this work we present a possible explanation of
the near-degeneracy as a result of an accidental $C_4$ symmetry of the band
structure, with the superconducting order parameter belonging the 2D $E_u$
irrep of $D_{4h}$ that uniquely descends to the sought after $B_{2u}+iB_{3u}$
combination. We show that the $C_4$ symmetry is emergent at the level of the
interactions using a renormalization group calculation and argue that the
chiral combination of the order parameter is favored when spin-orbit coupling
is added to the model.


title: The LOFAR Two-metre Sky Survey -- V. Second data release

abstract:
  In this data release from the LOFAR Two-metre Sky Survey (LoTSS) we present
120-168MHz images covering 27% of the northern sky. Our coverage is split into
two regions centred at approximately 12h45m +44$^\circ$30' and 1h00m
+28$^\circ$00' and spanning 4178 and 1457 square degrees respectively. The
images were derived from 3,451hrs (7.6PB) of LOFAR High Band Antenna data which
were corrected for the direction-independent instrumental properties as well as
direction-dependent ionospheric distortions during extensive, but fully
automated, data processing. A catalogue of 4,396,228 radio sources is derived
from our total intensity (Stokes I) maps, where the majority of these have
never been detected at radio wavelengths before. At 6" resolution, our full
bandwidth Stokes I continuum maps with a central frequency of 144MHz have: a
median rms sensitivity of 83$\mu$Jy/beam; a flux density scale accuracy of
approximately 10%; an astrometric accuracy of 0.2"; and we estimate the
point-source completeness to be 90% at a peak brightness of 0.8mJy/beam. By
creating three 16MHz bandwidth images across the band we are able to measure
the in-band spectral index of many sources, albeit with an error on the derived
spectral index of +/-0.2 which is a consequence of our flux-density scale
accuracy and small fractional bandwidth. Our circular polarisation (Stokes V)
20" resolution 120-168MHz continuum images have a median rms sensitivity of
95$\mu$Jy/beam, and we estimate a Stokes I to Stokes V leakage of 0.056%. Our
linear polarisation (Stokes Q and Stokes U) image cubes consist of 480 x 97.6
kHz wide planes and have a median rms sensitivity per plane of 10.8mJy/beam at
4' and 2.2mJy/beam at 20"; we estimate the Stokes I to Stokes Q/U leakage to be
approximately 0.2%. Here we characterise and publicly release our Stokes I, Q,
U and V images in addition to the calibrated uv-data.

categories: astro-ph.GA astro-ph.CO astro-ph.HE astro-ph.IM


title:
The Smallest Scale of Hierarchy Survey (SSH). II. Extended star
  formation and bar-like features in the dwarf galaxy NGC 3741: recent merger
  or ongoing gas accretion?

abstract:
  Using Large Binocular Telescope deep imaging data from the Smallest Scale of
Hierarchy Survey (SSH) and archival Hubble Space Telescope data, we reveal the
presence of two elongated stellar features contiguous to a bar-like stellar
structure in the inner regions of the dwarf irregular galaxy NGC 3741. These
structures are dominated by stars younger than a few hundred Myr and
collectively are about twice as extended as the old stellar component. These
properties are very unusual for dwarf galaxies in the nearby Universe and
difficult to explain by hydro-dynamical simulations. From the analysis of
archival 21-cm observations, we find that the young stellar "bar" coincides
with an HI high-density region proposed by previous studies to be a purely
gaseous bar; we furthermore confirm radial motions of a few km/s, compatible
with an inflow/outflow, and derive a steeply-rising rotation curve and high HI
surface density at the center, indicating a very concentrated mass
distribution. We propose that the peculiar properties of the stellar and
gaseous components of NGC 3741 may be explained by a recent merger or ongoing
gas accretion from the intergalactic medium, which caused gas inflows towards
the galaxy center and triggered star formation a few hundred Myr ago. This
event may explain the young and extended stellar features, the bar-like
structure, the very extended HI disc and the central HI spiral arms. The high
central HI density and the steeply rising rotation curve suggest that NGC 3741
may be the progenitor or the descendant of a starburst dwarf.


title: Truncated LinUCB for Stochastic Linear Bandits

abstract:
  This paper considers contextual bandits with a finite number of arms, where
the contexts are independent and identically distributed $d$-dimensional random
vectors, and the expected rewards are linear in both the arm parameters and
contexts. The LinUCB algorithm, which is near minimax optimal for related
linear bandits, is shown to have a cumulative regret that is suboptimal in both
the dimension $d$ and time horizon $T$, due to its over-exploration. A
truncated version of LinUCB is proposed and termed "Tr-LinUCB", which follows
LinUCB up to a truncation time $S$ and performs pure exploitation afterwards.
The Tr-LinUCB algorithm is shown to achieve $O(d\log(T))$ regret if $S =
Cd\log(T)$ for a sufficiently large constant $C$, and a matching lower bound is
established, which shows the rate optimality of Tr-LinUCB in both $d$ and $T$
under a low dimensional regime. Further, if $S = d\log^{\kappa}(T)$ for some
$\kappa>1$, the loss compared to the optimal is a multiplicative $\log\log(T)$
factor, which does not depend on $d$. This insensitivity to overshooting in
choosing the truncation time of Tr-LinUCB is of practical importance.


title:
Fragile topological phase on the triangular kagome lattice and its
  bulk-boundary correspondence

abstract:
  We predict and examine various topological states on a two-dimensional (2D)
triangular kagome lattice (TKL) using the tight-binding (TB) models and theory
of topological quantum chemistry (TQC). Firstly, on the basis of TQC, we
diagnose band structures with fragile topology and calculate Wilson-loop
spectra and Hofstadter butterfly spectra to confirm their non-trivial nature.
Secondly, we examine the bulk-boundary correspondence and find that an
obstructed-atomic-limit (OAL) insulator hosts fractional corner states without
being accompanied by fragile topological band structures, which implies that
the presence of OALs and corner states is not a sufficient condition to fragile
topology. Last but not least, we predict a topological phase transition from a
second-order topological phase to a first-order topological phase that can be
realized in the TKL under the action of a magnetic field.


title: Renormalization Group Flow as Optimal Transport

abstract:
  We establish that Polchinski's equation for exact renormalization group flow
is equivalent to the optimal transport gradient flow of a field-theoretic
relative entropy. This provides a compelling information-theoretic formulation
of the exact renormalization group, expressed in the language of optimal
transport. A striking consequence is that a regularization of the relative
entropy is in fact an RG monotone. We compute this monotone in several
examples. Our results apply more broadly to other exact renormalization group
flow equations, including widely used specializations of Wegner-Morris flow.
Moreover, our optimal transport framework for RG allows us to reformulate RG
flow as a variational problem. This enables new numerical techniques and
establishes a systematic connection between neural network methods and RG flows
of conventional field theories.


title:
Excitation and inhibition imbalance affects dynamical complexity through
  symmetries

abstract:
  From the perfect radial symmetries of radiolarian mineral skeletons to the
broken symmetry of homochirality, the logic of Nature's regularities has
fascinated scientists for centuries. Some of Nature's symmetries are clearly
visible in morphology and physical structure, whereas others are hidden in the
network of interactions among system components. Just as visible symmetries and
asymmetries contribute to the system's beauty, might hidden symmetries
contribute to the system's functional harmony? And if so, how? Here we
demonstrate that the interaction networks of biological systems from cell
signaling to cancer display a form of dynamical reflection symmetry that serves
to expand their dynamical complexity. The expansion proceeds according to
precise rules regarding the lengths of dynamical cycles, made possible by a
peculiar imbalance between excitation and inhibition. To probe the conditions
under which this reflection symmetry evolves, we use a multi-objective genetic
algorithm to produce networks with long dynamical cycles. We find that local
structural motifs that break the reflection symmetry are deleted in the
evolutionary process, providing evidence for symmetry's causal role in
dynamical complexity. Finally, we identify symmetries in real continuous-valued
scRNA-seq data of gene expression in drug-resistant cancer cells. Broadly, our
work reveals a class of hidden symmetries in biology, present in the network of
interactions among system components, and it underscores their functional
importance. Mathematically simple and computational inexpensive, our approach
is applicable to the types of biological data commonly acquired in modern
experiments.


title: Spin Variations of Black Hole Binaries in AGN Disks

abstract:
  The spin-orbit misalignments of stellar-mass black hole binaries (BHB)
provide important constraints on the formation channels of merging BHBs. Here,
we study the spin evolution of a black-hole component in a BHB around a
supermassive BH (SMBH) in an AGN disk. We consider the BH's spin-precession due
to the $J_2$ moment introduced by a circum-BH disk within the warping/breaking
radius of the disk. We find that the BH's spin-orbit misalignment (obliquity)
can be excited via spin-orbit resonance between the BHB's orbital nodal
precession and the BH spin-precession driven by the circum-BH disk. Assuming a
$10^7$M$_{\odot}$ SMBH, this typically occurs at a distance of $10^{2-4}$AU to
the SMBH or $10^{3-5} GM_{\rm SMBH}/c^2$. In many cases, spin-orbit resonance
leads to a high BH obliquity, and a broad distribution of the binary
components' obliquities and effective spin parameters.


title: Lower bounds on the rank and symmetric rank of real tensors

abstract:
  We lower bound the rank of a tensor by a linear combination of the ranks of
three of its unfoldings, using Sylvester's rank inequality. In a similar way,
we lower bound the symmetric rank by a linear combination of the symmetric
ranks of three unfoldings. Lower bounds on the rank and symmetric rank of
tensors are important for finding counterexamples to Comon's conjecture. A real
counterexample to Comon's conjecture is a tensor whose real rank and real
symmetric rank differ. Previously, there was only one real counterexample
known, constructed in a paper of Shitov. We divide the construction into three
steps. The first step involves linear spaces of binary tensors. The second step
considers a linear space involving larger decomposable tensors. The third step
is to verify a conjecture that lower bounds the symmetric rank, on a tensor of
interest. We use the construction to build an order six real tensor whose real
rank and real symmetric rank differ.


title:
Temperature Dependence of Spin and Charge Orders in the Doped
  Two-Dimensional Hubbard Model

abstract:
  Competing and intertwined orders including inhomogeneous patterns of spin and
charge are observed in many correlated electron materials, such as
high-temperature superconductors. Introducing a new development of the
constrained-path auxiliary-field quantum Monte Carlo (AFQMC) method, we study
the interplay between thermal and quantum fluctuations in the two-dimensional
Hubbard model. We obtain an accurate and systematic characterization of the
evolution of the spin and charge correlations as a function of temperature $T$
and how it connects to the ground state, at three representative doping levels
$\delta = 1/5$, $1/8$, and $1/10$. We find increasing short-range commensurate
antiferromagnetic correlations as $T$ is lowered. As the correlation length
grows sufficiently large, a modulated spin-density-wave (SDW) appears. At
$\delta = 1/5$, the SDW saturates and remains short-ranged as $T \rightarrow
0$. In contrast, at $\delta = 1/8$ and $1/10$ this evolves into a ground-state
stripe phase. We study the relation between spin and charge orders and find
that formation of charge order appears to be driven by that of the spin order.
We identify a finite-temperature phase transition below which charge ordering
sets in and discuss the implications of our results for the nature of this
transition.


title:
Think Global, Act Local: Dual-scale Graph Transformer for
  Vision-and-Language Navigation

abstract:
  Following language instructions to navigate in unseen environments is a
challenging problem for autonomous embodied agents. The agent not only needs to
ground languages in visual scenes, but also should explore the environment to
reach its target. In this work, we propose a dual-scale graph transformer
(DUET) for joint long-term action planning and fine-grained cross-modal
understanding. We build a topological map on-the-fly to enable efficient
exploration in global action space. To balance the complexity of large action
space reasoning and fine-grained language grounding, we dynamically combine a
fine-scale encoding over local observations and a coarse-scale encoding on a
global map via graph transformers. The proposed approach, DUET, significantly
outperforms state-of-the-art methods on goal-oriented vision-and-language
navigation (VLN) benchmarks REVERIE and SOON. It also improves the success rate
on the fine-grained VLN benchmark R2R.


title: Revisiting the Cumulative Incidence Function With Competing Risks Data

abstract:
  We consider estimation of the cumulative incidence function (CIF) in the
competing risks Cox model. We study three methods. Methods 1 and 2 are existing
methods while Method 3 is a newly-proposed method. Method 3 is constructed so
that the sum of the CIF's across all event types at the last observed event
time is guaranteed, assuming no ties, to be equal to 1. The performance of the
methods is examined in a simulation study, and the methods are illustrated on a
data example from the field of computer code comprehension. The newly-proposed
Method 3 exhibits performance comparable to that of Methods 1 and 2 in terms of
bias, variance, and confidence interval coverage rates. Thus, with our
newly-proposed estimator, the advantage of having the end-of-study total CIF
equal to 1 is achieved with no price to be paid in terms of performance.


title: Stability of the smectic phase in arrays of parallel quantum wires

abstract:
  Using bosonization, we study a microscopic model of parallel quantum wires
constructed from two dimensional Dirac fermions in the presence of periodic
topological domain walls. The model accounts for the lateral spread of the
wavefunctions $\ell$ in the transverse direction to the wires. The gapless
modes confined to each domain wall are shown to form Luttinger liquids, which
realize a well known smectic non-Fermi liquid fixed point when interwire
Coulomb interactions are taken into account. Perturbative studies on
phenomenological models have shown that the smectic fixed point is unstable
towards a variety of phases such as superconductivity, stripe, smectic and
Fermi liquid phases. Here, we show that the considered microscopic model leads
to a phase diagram with only smectic metal and Fermi liquid phases. The smectic
metal phase is stable in the ideal quantum wire limit $\ell\to0$. For finite
$\ell$, we find a critical Coulomb coupling $\alpha_{c}$ separating the strong
coupling smectic metal from a weak coupling Fermi liquid phase. We conjecture
that the absence of superconductivity should be a generic feature of similar
microscopic models. Finally, we discuss the physical realization of this model
with moire heterostructures.


title:
Oceanic frontal divergence alters phytoplankton competition and
  distribution

abstract:
  Ecological interactions among phytoplankton occur in a moving fluid
environment. Oceanic flows can modulate the competition and coexistence between
phytoplankton populations, which in turn can affect ecosystem function and
biogeochemical cycling. We explore the impact of submesoscale velocity
gradients on phytoplankton ecology using observations, simulations, and theory.
Observations reveal that the relative abundance of Synechoccocus oligotypes
varies on 1-10 km scales at an ocean front with submesoscale velocity gradients
at the same scale. Simulations using an ocean model demonstrate that regions of
divergence in the horizontal flow field can substantially modify ecological
competition and dispersal on timescales of hours to days. Regions of positive
(negative) divergence provide an advantage (disadvantage) to local populations,
resulting in up to ~20% variation in community composition in our model. We
propose that submesoscale divergence is a plausible contributor to observed
taxonomic variability at oceanic fronts, and can lead to regional variability
in community composition.


title: Quantization of the Zigzag Model

abstract:
  The zigzag model is a relativistic integrable $N$-body system describing the
leading high-energy semiclassical dynamics on the worldsheet of long confining
strings in massive adjoint two-dimensional QCD. We discuss quantization of this
model. We demonstrate that to achieve a consistent quantization of the model it
is necessary to account for the non-trivial geometry of phase space. The
resulting Poincar\'e invariant integrable quantum theory is a close cousin of
$T\bar{T}$ deformed models.


title: Statistical Inference for Functional Linear Quantile Regression

abstract:
  We propose inferential tools for functional linear quantile regression where
the conditional quantile of a scalar response is assumed to be a linear
functional of a functional covariate. In contrast to conventional approaches,
we employ kernel convolution to smooth the original loss function. The
coefficient function is estimated under a reproducing kernel Hilbert space
framework. A gradient descent algorithm is designed to minimize the smoothed
loss function with a roughness penalty. With the aid of the Banach fixed-point
theorem, we show the existence and uniqueness of our proposed estimator as the
minimizer of the regularized loss function in an appropriate Hilbert space.
Furthermore, we establish the convergence rate as well as the weak convergence
of our estimator. As far as we know, this is the first weak convergence result
for a functional quantile regression model. Pointwise confidence intervals and
a simultaneous confidence band for the true coefficient function are then
developed based on these theoretical properties. Numerical studies including
both simulations and a data application are conducted to investigate the
performance of our estimator and inference tools in finite sample.


title: The Need for Interpretable Features: Motivation and Taxonomy

abstract:
  Through extensive experience developing and explaining machine learning (ML)
applications for real-world domains, we have learned that ML models are only as
interpretable as their features. Even simple, highly interpretable model types
such as regression models can be difficult or impossible to understand if they
use uninterpretable features. Different users, especially those using ML models
for decision-making in their domains, may require different levels and types of
feature interpretability. Furthermore, based on our experiences, we claim that
the term "interpretable feature" is not specific nor detailed enough to capture
the full extent to which features impact the usefulness of ML explanations. In
this paper, we motivate and discuss three key lessons: 1) more attention should
be given to what we refer to as the interpretable feature space, or the state
of features that are useful to domain experts taking real-world actions, 2) a
formal taxonomy is needed of the feature properties that may be required by
these domain experts (we propose a partial taxonomy in this paper), and 3)
transforms that take data from the model-ready state to an interpretable form
are just as essential as traditional ML transforms that prepare features for
the model.


title: Are All Linear Regions Created Equal?

abstract:
  The number of linear regions has been studied as a proxy of complexity for
ReLU networks. However, the empirical success of network compression techniques
like pruning and knowledge distillation, suggest that in the overparameterized
setting, linear regions density might fail to capture the effective
nonlinearity. In this work, we propose an efficient algorithm for discovering
linear regions and use it to investigate the effectiveness of density in
capturing the nonlinearity of trained VGGs and ResNets on CIFAR-10 and
CIFAR-100. We contrast the results with a more principled nonlinearity measure
based on function variation, highlighting the shortcomings of linear regions
density. Furthermore, interestingly, our measure of nonlinearity clearly
correlates with model-wise deep double descent, connecting reduced test error
with reduced nonlinearity, and increased local similarity of linear regions.


title: Tunable Geometries in Sparse Clifford Circuits

abstract:
  We investigate the emergence of different effective geometries in stochastic
Clifford circuits with sparse coupling. By changing the probability
distribution for choosing two-site gates as a function of distance, we generate
sparse interactions that either decay or grow with distance as a function of a
single tunable parameter. Tuning this parameter reveals three distinct regimes
of geometry for the spreading of correlations and growth of entanglement in the
system. We observe linear geometry for short-range interactions, treelike
geometry on a sparse coupling graph for long-range interactions, and an
intermediate fast scrambling regime at the crossover point between the linear
and treelike geometries. This transition in geometry is revealed in
calculations of the subsystem entanglement entropy and tripartite mutual
information. We also study emergent lightcones that govern these effective
geometries by teleporting a single qubit of information from an input qubit to
an output qubit. These tools help to analyze distinct geometries arising in
dynamics and correlation spreading in quantum many-body systems.


title:
Obtaining Nanocarbon from Local Raw Materials and Studying Its Textural
  and Sorption Properties

abstract:
  In this article studied the influence of various factors on the synthesis of
nano carbons from walnut shells, apricot kernels, methane, natural gas and
propane-butane fractions, and also checked the textural and sorption
characteristics of the obtained nanocarbon. The catalytic activity of a
catalyst containing (CuO)x*(CoO)y*(NiO)z*(Fe2O3)k*(MoO3)m/HSZ prepared based on
"sol-gel" technology for the implementation of processes was studied under
differential reactor conditions. The morphological composition of the samples
was performed by scanning electron microscopy on an LEO EVO 50 HVP instrument
equipped with a part of energy dispersive X-ray microanalysis.
Energy-dispersive X-ray spectra were recorded at a working distance of 10 mm at
20 kV. The microstructure of the samples was investigated by the method of a
glowing electron microscope on a Jem-2000 CX device. Using the BET method, the
specific surface area of the carbon nanotubes was measured. Statistical and
morphological characteristics of material porosity were calculated. The
specific surface area is 168.7 m2/g, the specific pore volume is 0.456 cm3/g,
and the average hole diameter is 2.58 nm. In the case of walnut shells, this
absorption band was observed at 1376 cm-1 and 1064 cm-1. When examining samples
based on apricot kernels in the infrared spectrum at various temperatures in an
inert nitrogen atmosphere, it can be seen that cellulose and lignin are
characterized by a strong absorption band, and the treatment decreases with
increasing temperature.


title:
Analysis of Coronavirus Envelope Protein with Cellular Automata (CA)
  Model

abstract:
  The reason of significantly higher transmissibility of SARS Covid (2019
CoV-2) compared to SARS Covid (2003 CoV) and MERS Covid (2012 MERS) can be
attributed to mutations reported in structural proteins, and the role played by
non-structural proteins (nsps) and accessory proteins (ORFs) for viral
replication, assembly, and shedding. Envelope protein E is one of the four
structural proteins of minimum length. Recent studies have confirmed critical
role played by the envelope protein in the viral life cycle including assembly
of virion exported from infected cell for its transmission. However, the
determinants of the highly complex viral - host interactions of envelope
protein, particularly with host Golgi complex, have not been adequately
characterized. CoV-2 and CoV Envelope proteins of length 75 and 76 amino acids
differ in four amino acid locations. The additional amino acid Gly (G) at
location 70 makes CoV length 76. The amino acid pair EG at location 69-70 of
CoV in place of amino acid R in location 69 of CoV-2, has been identified as a
major determining factor in the current investigation. This paper concentrates
on the design of computational model to compare the structure/function of wild
and mutants of CoV-2 with wild and mutants of CoV in the functionally important
region of the protein chain pair. We hypothesize that differences of CAML model
parameter of CoV-2 and CoV characterize the deviation in structure and function
of envelope proteins in respect of interaction of virus with host Golgi
complex; and this difference gets reflected in the difference of their
transmissibility. The hypothesis has been validated from single point
mutational study on- (i) human HBB beta-globin hemoglobin protein associated
with sickle cell anemia, (ii) mutants of envelope protein of Covid-2 infected
patients reported in recent publications.


title:
Constraining the CP structure of Higgs-fermion couplings with a global
  LHC fit, the electron EDM and baryogenesis

abstract:
  CP violation in the Higgs couplings to fermions is an intriguing, but not yet
extensively explored possibility. We use inclusive and differential LHC Higgs
boson measurements to fit the CP structure of the Higgs Yukawa couplings.
Starting with simple effective models featuring CP violation in a single
Higgs-fermion coupling, we probe well-motivated models with up to nine free
parameters. We also investigate the complementarity of LHC constraints with the
electron electric dipole moment bound, taking into account the possibility of a
modified electron Yukawa coupling, and assess to which extent CP violation in
the Higgs-fermion couplings can contribute to the observed baryon asymmetry of
the universe. Even after including the recent analysis of angular correlations
in $H\to\tau^+\tau^-$ decays, we find that a complex tau Yukawa coupling alone
may be able to account for the observed baryon asymmetry, but with large
uncertainties in the baryogenesis calculation. A combination of complex top and
bottom quark Yukawa couplings yields a result four times larger than the sum of
their separate contributions, but remains insufficient to account for the
observed baryon asymmetry.


title: Probing inter-layer pairing via density imbalance in exciton bilayers

abstract:
  Layered bosonic dipolar fluids have been suggested to host a condensate of
inter-layer molecular bound states. However, its experimental observation has
remained elusive. Motivated by two recent experimental works [Hubert et al.,
Phys. Rev. X 9, 021026 (2019) and D. J. Choksy et al., Phys. Rev. B 103 045126
(2021)], we theoretically study, using numerically exact quantum Monte Carlo
calculations, the experimental signatures of collective inter-layer pairing in
indirect exciton (IX) bilayers. We find that IX energy shifts associated with
each layer evolve non-trivially as a function of density imbalance following a
non-monotonic trend with a jump discontinuity at density balance, identified
with the inter-layer IX molecule gap. This behavior discriminates between the
superfluidity of inter-layer bound pairs and independent dipole condensation in
distinct layers. Considering finite temperature and finite density imbalance
conditions, we find a cascade of Berezinskii-Kosterlitz-Thouless (BKT)
transitions, initially into a pair superfluid and only then, at lower
temperatures, into complete superfluidity of both layers. Our results may
provide a theoretical interpretation of existing experimental observations in
GaAs double quantum well (DQW) bilayer structures. Furthermore, to optimize the
visibility of pairing dynamics in future studies, we present a detailed
analysis of realistic experimental settings in GaAs and transition-metal
dichalcogenide (TMD) bilayer DQW heterostructures.


title:
HI gas playing hide-and-seek around a powerful FRI-type quasar at
  z$\sim$2.1

abstract:
  We present optical spectroscopic and milli-arcsecond scale radio continuum
observations of the quasar M1540-1453 ($z_{em}$ = 2.104$\pm$0.002) that shows
associated HI 21-cm absorption at $z_{abs}$ = 2.1139. At sub-kpc scales, the
powerful radio source with 1.4 GHz luminosity of $5.9\times10^{27}$ WHz$^{-1}$
shows Fanaroff-Riley (FR) class I morphology caused by the interaction with
dense gas within 70 pc from the AGN. Interestingly, while there are indications
for the presence of absorption from low-ionization species like FeII, SiII and
SiIII in the optical spectrum, the expected strong damped Ly$\alpha$ absorption
is not detected at the redshift of the HI 21-cm absorber. In comparison to
typical high-$z$ quasars, the Ly$\alpha$ emission line is much narrower. The
`ghostly' nature of the HI Ly$\alpha$ absorber partially covering the broad
line region of extent 0.05 pc and the detection of widespread HI 21-cm
absorption covering the diffuse radio source (extent $>$425 pc) imply the
presence of a large clumpy HI halo -- which may have been blown by the jet-ISM
interaction. Further observations are needed to confirm the `ghostly' nature of
the Ly$\alpha$ absorber, and obtain a better understanding of the role played
by the jet-ISM interaction in shaping the radio morphology of this powerful
AGN. The study showcases how joint radio and optical analysis can shed light on
gaseous environment and origin of radio morphology in AGN at high redshifts,
when these are still the assembly sites of giant galaxies.


title: ML-based Anomaly Detection in Optical Fiber Monitoring

abstract:
  Secure and reliable data communication in optical networks is critical for
high-speed internet. We propose a data driven approach for the anomaly
detection and faults identification in optical networks to diagnose physical
attacks such as fiber breaks and optical tapping. The proposed methods include
an autoencoder-based anomaly detection and an attention-based bidirectional
gated recurrent unit algorithm for the fiber fault identification and
localization. We verify the efficiency of our methods by experiments under
various attack scenarios using real operational data.


title: Degradation-Reducing Control for Dynamically Reconfigurable Batteries

abstract:
  Cascaded circuits such as modular multilevel con-verters (MMC) offer
attractive qualities in reconfigurable battery applications. In contrast to
conventional hard-wired dc battery packs, the MMC topology loads modules with
ac current, which may lead to additional ageing of batteries. As recent studies
reveal, such ageing of batteries occurs at low-frequency load ripple, and
almost vanishes at high frequencies. State of the art in MMC bat-tery control
focuses on state of charge and temperature balancing of individual modules.
Previous methods to suppress ripple rely on slow feedback loops and low
dynamics, which tends to form low-frequency patterns in the module load that
negatively contribute to their ageing. This paper presents a novel
module-current-oriented high-bandwidth control technique which minimizes
low-frequency components in the module load spectrum. The control method
respects limitations related to module data acquisition and enhanc-es the
feedback bandwidth using observation techniques. We verify the proposed method
experimentally on a laboratory setup and estimate the influence on the battery
cells.


title:
SPT indices emerging from translation invariance in two dimensional
  quantum spin systems

abstract:
  We consider SPT-phases with on-site $G$ (where $G$ is any group) symmetry for
two-dimensional quantum spin systems. We then impose translation invariance in
one direction and observe that on top of the $H^3(G,\mathbb{T})$-valued index
constructed in \cite{ogata2021h3gmathbb}, an additional
$H^2(G,\mathbb{T})$-valued index emerges. We also show that if we impose
translation invariance in two directions, on top of the expected
$H^3(G,\mathbb{T})\oplus H^2(G,\mathbb{T})\oplus H^2(G,\mathbb{T})$ valued
index, an additional $H^1(G,\mathbb{T})$-valued index emerges.

categories: math-ph cond-mat.stat-mech math.MP quant-ph


title:
Ab initio informed evaluation of the radiative capture of protons on
  $^7$Be

abstract:
  The radiative capture of protons by $^7$Be, which is the source of $^8$B that
$\beta$-decays emitting the majority of solar neutrinos measured on earth, has
not yet been measured at astrophysically relevant energies. The recommended
value for its zero-energy S-factor, $S_{17}$(0) =
20.8$\pm$(0.7)exp$\pm$(1.4)theory eV$\cdot$b, relies on theoretical
extrapolations from higher-energy measurements, a process that leads to
significant uncertainty. We performed a set of first-principle (or, ab initio)
calculations of the $^7$Be($p$, $\gamma$)$^8$B reaction to provide an
independent prediction of the low-energy S-factor with quantified
uncertainties. We demonstrate underlying features in the predicted S-factor
allowing the combination of theoretical calculations and measurements to
produce an evaluated S-factor of $S_{17}$(0) = 19.8$\pm$0.3 eV$\cdot$b. We
expect the calculations and uncertainty quantification process described here
to set a new standard for the evaluation of light-ion astrophysical reactions.


title:
Googling for Abortion: Search Engine Mediation of Abortion Accessibility
  in the United States

abstract:
  Among the myriad barriers to abortion access, crisis pregnancy centers (CPCs)
pose an additional difficulty by targeting women with unexpected or "crisis"
pregnancies in order to dissuade them from the procedure. Web search engines
may prove to be another barrier, being in a powerful position to direct their
users to health information, and above all, health services. In this study we
ask, to what degree does Google Search provide quality responses to users
searching for an abortion provider, specifically in terms of directing them to
abortion clinics (ACs) or CPCs. To answer this question, we considered the
scenario of a woman searching for abortion services online, and conducted 10
abortion-related queries from 467 locations across the United States once a
week for 14 weeks. Overall, among Google's location results that feature
businesses alongside a map, 79.4% were ACs, and 6.9% were CPCs. When an AC was
returned, it was the closest known AC location 86.9% of the time. However, when
a CPC appeared in a result set, it was the closest one to the search location
75.9% of the time. Examining correlates of AC results, we found that fewer AC
results were returned for searches from poorer and rural areas, and those with
TRAP laws governing AC facility and clinician requirements. We also observed
that Google's performance on our queries significantly improved following a
major algorithm update. These results have important implications concerning
health access quality and equity, both for individual users and public health
policy.


title:
Virtual walks inspired by a mean field kinetic exchange model of opinion
  dynamics

abstract:
  We propose two different schemes of realizing a virtual walk corresponding to
a kinetic exchange model of opinion dynamics. The walks are either Markovian or
non-Markovian in nature. The opinion dynamics model is characterized by a
parameter $p$ which drives an order disorder transition at a critical value
$p_c$. The distribution $S(X,t)$ of the displacements $X$ from the origin of
the walkers is computed at different times. Below $p_c$, two time scales
associated with a crossover behavior in time are detected, which diverge in a
power law manner at criticality with different exponent values. $S(X,t)$ also
carries the signature of the phase transition as it changes its form at $p_c$.
The walks show the features of a biased random walk below $p_c$, and above
$p_c$, the walks are like unbiased random walks. The bias vanishes in a power
law manner at $p_c$ and the width of the resulting Gaussian function shows a
discontinuity. Some of the features of the walks are argued to be comparable to
the critical quantities associated with the mean field Ising model, to which
class the opinion dynamics model belongs. The results for the Markovian and
non-Markovian walks are almost identical which is justified by considering the
different fluxes. We compare the present results with some earlier similar
studies.


title:
Safe Control with Learned Certificates: A Survey of Neural Lyapunov,
  Barrier, and Contraction methods

abstract:
  Learning-enabled control systems have demonstrated impressive empirical
performance on challenging control problems in robotics, but this performance
comes at the cost of reduced transparency and lack of guarantees on the safety
or stability of the learned controllers. In recent years, new techniques have
emerged to provide these guarantees by learning certificates alongside control
policies -- these certificates provide concise, data-driven proofs that
guarantee the safety and stability of the learned control system. These methods
not only allow the user to verify the safety of a learned controller but also
provide supervision during training, allowing safety and stability requirements
to influence the training process itself. In this paper, we provide a
comprehensive survey of this rapidly developing field of certificate learning.
We hope that this paper will serve as an accessible introduction to the theory
and practice of certificate learning, both to those who wish to apply these
tools to practical robotics problems and to those who wish to dive more deeply
into the theory of learning for control.


title: Single Image Super-Resolution Methods: A Survey

abstract:
  Super-resolution (SR), the process of obtaining high-resolution images from
one or more low-resolution observations of the same scene, has been a very
popular topic of research in the last few decades in both signal processing and
image processing areas. Due to the recent developments in Convolutional Neural
Networks, the popularity of SR algorithms has skyrocketed as the barrier of
entry has been lowered significantly. Recently, this popularity has spread into
video processing areas to the lengths of developing SR models that work in
real-time. In this paper, we compare different SR models that specialize in
single image processing and will take a glance at how they evolved to take on
many different objectives and shapes over the years.


title:
Spectral and Temporal Measurement of Entangled Photon Interaction with a
  Virtual State Mediated Two-Photon Transition

abstract:
  Entangled photon pairs are predicted to linearize and increase the efficiency
of two-photon absorption, allowing continuous wave laser diodes to drive
ultrafast time-resolved spectroscopy and nonlinear processes. Despite a range
of theoretical studies and experimental measurements, inconsistencies persist
about the value of the entanglement enhanced interaction cross section. A
spectrometer is constructed that can temporally and spectrally characterize the
entangled photon state before, during, and after any potential two-photon
excitation event. For the molecule Rhodamine 6G, which has a virtual state
pathway, any entangled two-photon interaction is found to be equal to or lower
than classical, single photon scattering events.


title: When do GANs replicate? On the choice of dataset size

abstract:
  Do GANs replicate training images? Previous studies have shown that GANs do
not seem to replicate training data without significant change in the training
procedure. This leads to a series of research on the exact condition needed for
GANs to overfit to the training data. Although a number of factors has been
theoretically or empirically identified, the effect of dataset size and
complexity on GANs replication is still unknown. With empirical evidence from
BigGAN and StyleGAN2, on datasets CelebA, Flower and LSUN-bedroom, we show that
dataset size and its complexity play an important role in GANs replication and
perceptual quality of the generated images. We further quantify this
relationship, discovering that replication percentage decays exponentially with
respect to dataset size and complexity, with a shared decaying factor across
GAN-dataset combinations. Meanwhile, the perceptual image quality follows a
U-shape trend w.r.t dataset size. This finding leads to a practical tool for
one-shot estimation on minimal dataset size to prevent GAN replication which
can be used to guide datasets construction and selection.


title: A gentle introduction to Quantum Natural Language Processing

abstract:
  The main goal of this master's thesis is to introduce Quantum Natural
Language Processing (QNLP) in a way understandable by both the NLP engineer and
the quantum computing practitioner. QNLP is a recent application of quantum
computing that aims at representing sentences' meaning as vectors encoded into
quantum computers. To achieve this, the distributional meaning of words is
extended by the compositional meaning of sentences (DisCoCat model) : the
vectors representing words' meanings are composed through the syntactic
structure of the sentence. This is done using an algorithm based on tensor
products. We see that this algorithm is inefficient on classical computers but
scales well using quantum circuits. After exposing the practical details of its
implementation, we go through three use-cases.


abstract:
  The noncommutative space of light-like worldlines that is covariant under the
light-like (or null-plane) $\kappa$-deformation of the (3+1) Poincar\'e group
is fully constructed as the quantization of the corresponding Poisson
homogeneous space of null geodesics. This new noncommutative space of geodesics
is five-dimensional, and turns out to be defined as a quadratic algebra that
can be mapped to a non-central extension of the direct sum of two
Heisenberg-Weyl algebras whose noncommutative parameter is just the Planck
scale parameter $\kappa^{-1}$. Moreover, it is shown that the usual time-like
$\kappa$-deformation of the Poincar\'e group does not allow the construction of
the Poisson homogeneous space of light-like worldlines. Therefore, the most
natural choice in order to model the propagation of massless particles on a
quantum Minkowski spacetime seems to be provided by the light-like
$\kappa$-deformation.


title:
From Unstructured Text to Causal Knowledge Graphs: A Transformer-Based
  Approach

abstract:
  Qualitative causal relationships compactly express the direction, dependency,
temporal constraints, and monotonicity constraints of discrete or continuous
interactions in the world. In everyday or academic language, we may express
interactions between quantities (e.g., sleep decreases stress), between
discrete events or entities (e.g., a protein inhibits another protein's
transcription), or between intentional or functional factors (e.g., hospital
patients pray to relieve their pain). Extracting and representing these diverse
causal relations are critical for cognitive systems that operate in domains
spanning from scientific discovery to social science. This paper presents a
transformer-based NLP architecture that jointly extracts knowledge graphs
including (1) variables or factors described in language, (2) qualitative
causal relationships over these variables, (3) qualifiers and magnitudes that
constrain these causal relationships, and (4) word senses to localize each
extracted node within a large ontology. We do not claim that our
transformer-based architecture is itself a cognitive system; however, we
provide evidence of its accurate knowledge graph extraction in real-world
domains and the practicality of its resulting knowledge graphs for cognitive
systems that perform graph-based reasoning. We demonstrate this approach and
include promising results in two use cases, processing textual inputs from
academic publications, news articles, and social media.


title: Modeling of second sound in carbon nanostructures

abstract:
  The study of thermal transport in low-dimensional materials has attracted a
lot of attention recently after discovery of high thermal conductivity of
graphene. Here we study numerically phonon transport in low-dimensional carbon
structures being interested in the hydrodynamic regime revealed through the
observation of second sound. We demonstrate that correct numerical modeling of
such two-dimensional systems requires semi-classical molecular dynamics
simulations of temperature waves that take into account quantum statistics of
thermalized phonons. We reveal that second sound can be attributed to the
maximum group velocity of bending optical oscillations of carbon structures,
and the hydrodynamic effects disappear for $T>200$K, being replaced by
diffusive dynamics of thermal waves. Our numerical results suggest that the
velocity of second sound in such low-dimensional structures is about 6 km/s,
and the hydrodynamic effects are manifested stronger in carbon nanotubes rather
than in carbon nanoribbons.


title:
Development and performance of a HemeLB GPU code for human-scale blood
  flow simulation

abstract:
  In recent years, it has become increasingly common for high performance
computers (HPC) to possess some level of heterogeneous architecture - typically
in the form of GPU accelerators. In some machines these are isolated within a
dedicated partition, whilst in others they are integral to all compute nodes -
often with multiple GPUs per node - and provide the majority of a machine's
compute performance. In light of this trend, it is becoming essential that
codes deployed on HPC are updated to execute on accelerator hardware. In this
paper we introduce a GPU implementation of the 3D blood flow simulation code
HemeLB that has been developed using CUDA C++. We demonstrate how taking
advantage of NVIDIA GPU hardware can achieve significant performance
improvements compared to the equivalent CPU only code on which it has been
built whilst retaining the excellent strong scaling characteristics that have
been repeatedly demonstrated by the CPU version. With HPC positioned on the
brink of the exascale era, we use HemeLB as a motivation to provide a
discussion on some of the challenges that many users will face when deploying
their own applications on upcoming exascale machines.


title:
Puff-like instability in laminar to turbulence supercritical transition
  of round jets

abstract:
  We explore the laminar to turbulence transition of round jets at low Reynolds
number (Re < 1000) using a novel experimental setup and linear stability theory
(LST). The setup has a large domain and a low disturbance environment which
increases the critical Re to approximately 500, permitting the appearance of a
hitherto unknown puff-like instability (PFI). The instability is identified in
the self-similar region of the jet through clean flow visualizations (FV) and
further corroborated by particle image velocimetry (PIV) measurements. For 400<
Re <700, the flow exhibits PFI embedded in a puff-train encapsulated by a
laminar flow analogous to 'puffs' in pipe-flow transition; the latter being
symbolic of the finite-amplitude disturbance transition scenario. The
observation that PFI convects close to the average local velocity with an
inflectional velocity profile further strengthens the analogy. LST predicts
that PFI is effectively a superposition of the helical mode (HM) pair with
azimuthal wavenumbers n=+-1. Hence, PFI can also appear in the
infinitesimal-amplitude supercritical route to transition of linearly unstable
flows.


title:
Discovering Multiple and Diverse Directions for Cognitive Image
  Properties

abstract:
  Recent research has shown that it is possible to find interpretable
directions in the latent spaces of pre-trained GANs. These directions enable
controllable generation and support a variety of semantic editing operations.
While previous work has focused on discovering a single direction that performs
a desired editing operation such as zoom-in, limited work has been done on the
discovery of multiple and diverse directions that can achieve the desired edit.
In this work, we propose a novel framework that discovers multiple and diverse
directions for a given property of interest. In particular, we focus on the
manipulation of cognitive properties such as Memorability, Emotional Valence
and Aesthetics. We show with extensive experiments that our method successfully
manipulates these properties while producing diverse outputs. Our project page
and source code can be found at http://catlab-team.github.io/latentcognitive.


title:
Exploring the Effect of the Number of Hydrogen Atoms on the Properties
  of Lanthanide Hydrides by DMFT

abstract:
  Lanthanide hydrogen-rich materials have long been considered as one of the
candidates with high-temperature superconducting properties in condensed matter
physics, and have attracted great interest. Attempts to investigate the effects
of different compositions of lanthanide hydrogen-rich materials are ongoing,
with predictions and experimental studies in recent years having shown that
substances such as LaH 10 , CeH 9 , and LaH 16 exhibit extremely high
superconducting temperatures between 150-250 GPa. In particular, researchers
have noted that in those materials an increase in the f character at the Fermi
level leads to an increase in the superconducting temperature. Here, we further
elaborate on the effect of the ratios of lanthanide to hydrogen in these
substances with the aim to bring more clarity to the study of superhydrides in
these extreme cases by comparing a variety of lanthanide hydrogen-rich
materials with different ratios using the DMFT method, and provide ideas for
later structural predictions and material property studies.


title:
All Electrical Control and Temperature Dependence of the Spin and Valley
  Hall Effect in Monolayer WSe2 Transistors

abstract:
  Heavy metal-based two-dimensional van der Waals materials have a large,
coupled spin and valley Hall effect (SVHE) that has potential use in
spintronics and valleytronics. Optical measurements of the SVHE have largely
been performed below 30 K and understanding of the SVHE-induced spin/valley
polarizations that can be electrically generated is limited. Here, we study the
SVHE in monolayer p-type tungsten diselenide (WSe2). Kerr rotation (KR)
measurements show the spatial distribution of the SVHE at different
temperatures, its persistence up to 160 K, and that it can be electrically
modulated via gate and drain bias. A spin/valley drift and diffusion model
together with reflection spectra data is used to interpret the KR data and
predict a lower-bound spin/valley lifetime of 4.1 ns below 90 K and 0.26 ns at
160 K. The excess spin and valley per unit length along the edge is calculated
to be 109 per micron at 45 K, which corresponds to a spin/valley polarization
on the edge of 6%. These results are important steps towards practical use of
the SVHE.


title: Resonators with tailored optical path by cascaded-mode conversions

abstract:
  Optical resonators enable the generation, manipulation, and storage of
electromagnetic waves. They are widely used in technology and fundamental
research, in telecommunications, lasers and nonlinear optics, ultra-sensitive
measurements in cavity optomechanics, and the study of light-matter
interactions in the context of cavity QED. The physics underlying their
operation is determined by the constructive interference of electromagnetic
waves at specific frequencies, giving rise to the resonance spectrum. This
mechanism causes the limitations and trade-offs of resonator design, such as
the difficulty of confining waves larger than the resonator and the fixed
relationship between free spectral range, modal linewidth, and the resonator's
refractive index and size. Here, we introduce a new class of optical
resonators, generating resonances by designing the optical path through
transverse mode coupling in a cascaded process created by mode-converting
mirrors. The generalized round-trip phase condition leads to resonator
characteristics that are markedly different from Fabry-Perot resonators and can
be tailored over a wide range, such as the largest resonant wavelength, the
free spectral range, the linewidth, and the quality factor. We confirm the
existence of these modes experimentally in an integrated waveguide cavity with
mode converters coupling two transverse modes into one supermode. The resonance
signature of the cascaded-mode resonator is a spectrum resulting from the
coherent superposition of the coupled transverse modes. We also demonstrate a
transverse mode-independent transmission through the resonator and show that
its engineered spectral properties agree with theoretical predictions.
Cascaded-mode resonators introduce properties not found in traditional
resonators and provide a mechanism to overcome the existing trade-offs in the
design of resonators in various application areas.


title:
The Challenge of Understanding What Users Want: Inconsistent Preferences
  and Engagement Optimization

abstract:
  Online platforms have a wealth of data, run countless experiments and use
industrial-scale algorithms to optimize user experience. Despite this, many
users seem to regret the time they spend on these platforms. One possible
explanation is that incentives are misaligned: platforms are not optimizing for
user happiness. We suggest the problem runs deeper, transcending the specific
incentives of any particular platform, and instead stems from a mistaken
foundational assumption. To understand what users want, platforms look at what
users do. This is a kind of revealed-preference assumption that is ubiquitous
in user models. Yet research has demonstrated, and personal experience affirms,
that we often make choices in the moment that are inconsistent with what we
actually want: we can choose mindlessly or myopically, behaviors that feel
entirely familiar on online platforms.
  In this work, we develop a model of media consumption where users have
inconsistent preferences. We consider what happens when a platform that simply
wants to maximize user utility is only able to observe behavioral data in the
form of user engagement. Our model produces phenomena related to
overconsumption that are familiar from everyday experience, but difficult to
capture in traditional user interaction models. A key ingredient is a
formulation for how platforms determine what to show users: they optimize over
a large set of potential content (the content manifold) parametrized by
underlying features of the content. We show how the relationship between
engagement and utility depends on the structure of the content manifold,
characterizing when engagement optimization leads to good utility outcomes. By
linking these effects to abstractions of platform design choices, our model
thus creates a theoretical framework and vocabulary in which to explore
interactions between design, behavioral science, and social media.


title: Art Creation with Multi-Conditional StyleGANs

abstract:
  Creating meaningful art is often viewed as a uniquely human endeavor. A human
artist needs a combination of unique skills, understanding, and genuine
intention to create artworks that evoke deep feelings and emotions. In this
paper, we introduce a multi-conditional Generative Adversarial Network (GAN)
approach trained on large amounts of human paintings to synthesize
realistic-looking paintings that emulate human art. Our approach is based on
the StyleGAN neural network architecture, but incorporates a custom
multi-conditional control mechanism that provides fine-granular control over
characteristics of the generated paintings, e.g., with regard to the perceived
emotion evoked in a spectator. For better control, we introduce the conditional
truncation trick, which adapts the standard truncation trick for the
conditional setting and diverse datasets. Finally, we develop a diverse set of
evaluation techniques tailored to multi-conditional generation.


title: baker: An R package for Nested Partially-Latent Class Models

abstract:
  This paper describes and illustrates the functionality of the baker R
package. The package estimates a suite of nested partially-latent class models
(NPLCM) for multivariate binary responses that are observed under a
case-control design. The baker package allows researchers to flexibly estimate
population-level class prevalences and posterior probabilities of class
membership for individual cases. Estimation is accomplished by calling a
cross-platform automatic Bayesian inference software JAGS through a wrapper R
function that parses model specifications and data inputs. The baker package
provides many useful features, including data ingestion, exploratory data
analyses, model diagnostics, extensive plotting and visualization options,
catalyzing communications between practitioners and domain scientists. Package
features and workflows are illustrated using simulated and real data sets.
Package URL: https://github.com/zhenkewu/baker


title:
Optimisation of VLEO Satellite Geometries for Drag Minimisation and
  Lifetime Extension

abstract:
  The utilisation of the Very Low Earth Orbit (VLEO) region offers significant
application specific, technological, operational, and cost benefits. However,
attaining sustained and economically viable VLEO flight is challenging,
primarily due to the significant, barely predictable and dynamically changing
drag caused by the residual atmosphere, which leads to a rapid deterioration of
any spacecraft's orbit unless mitigated by a combination of active and passive
techniques. This article addresses one passive method by optimising satellite
shapes in order to achieve a minimisation of the atmospheric drag force and
thus extension of operational lifetime. Contrary to previous investigations in
the field, a constant internal volume is maintained to account for the
placement of satellite instruments and payload inside the structure. Moreover,
the satellite geometry is not varied heuristically but optimised via a
numerical 2D profile optimisation specifically developed for this purpose. From
the resulting optimal satellite profiles, 3D satellite bodies are derived,
which are then verified via the Direct Simulation Monte Carlo method within the
open-source particle code PICLas. In addition, rather unconventional designs,
i.e. ring geometries, which are based on the assumption of fully specular
particle reflections, are proposed and assessed. The optimised satellite
geometries offer pure passive lifetime extensions of up to 46 $\%$ compared to
a GOCE like reference body, while the above-mentioned ring geometries achieve
passive lifetime extensions of more than 3000 $\%$. Finally, the article
presents design recommendations for VLEO satellites in dependence of different
surface properties.


title:
On the Posterior Distribution of a Random Process Conditioned on
  Empirical Frequencies of a Finite Path: the i.i.d and finite Markov chain
  case

abstract:
  We obtain the posterior distribution of a random process conditioned on
observing the empirical frequencies of a finite sample path. We find under a
rather broad assumption on the "dependence structure" of the process, {\em
c.f.} independence or Markovian, the posterior marginal distribution of the
process at a given time index can be identified as certain empirical
distribution computed from the observed empirical frequencies of the sample
path. We show that in both cases of discrete-valued i.i.d. sequence and finite
Markov chain, a certain "conditional symmetry" given by the observation of the
empirical frequencies leads to the desired result on the posterior
distribution. Results for both finite-time observations and its asymptotic
infinite-time limit are connected via the idea of Gibbs conditioning. Finally,
since our results demonstrate a central role of the empirical frequency in
understanding the information content of data, we use the Large Deviations
Principle (LDP) to construct a general notion of "data-driven entropy", from
which one can apply a formalism from the recent study of statistical
thermodynamics to data.


title:
RadioTransformer: A Cascaded Global-Focal Transformer for Visual
  Attention-guided Disease Classification

abstract:
  In this work, we present RadioTransformer, a novel visual attention-driven
transformer framework, that leverages radiologists' gaze patterns and models
their visuo-cognitive behavior for disease diagnosis on chest radiographs.
Domain experts, such as radiologists, rely on visual information for medical
image interpretation. On the other hand, deep neural networks have demonstrated
significant promise in similar tasks even where visual interpretation is
challenging. Eye-gaze tracking has been used to capture the viewing behavior of
domain experts, lending insights into the complexity of visual search. However,
deep learning frameworks, even those that rely on attention mechanisms, do not
leverage this rich domain information. RadioTransformer fills this critical gap
by learning from radiologists' visual search patterns, encoded as 'human visual
attention regions' in a cascaded global-focal transformer framework. The
overall 'global' image characteristics and the more detailed 'local' features
are captured by the proposed global and focal modules, respectively. We
experimentally validate the efficacy of our student-teacher approach for 8
datasets involving different disease classification tasks where eye-gaze data
is not available during the inference phase.


title:
Prune and Tune Ensembles: Low-Cost Ensemble Learning With Sparse
  Independent Subnetworks

abstract:
  Ensemble Learning is an effective method for improving generalization in
machine learning. However, as state-of-the-art neural networks grow larger, the
computational cost associated with training several independent networks
becomes expensive. We introduce a fast, low-cost method for creating diverse
ensembles of neural networks without needing to train multiple models from
scratch. We do this by first training a single parent network. We then create
child networks by cloning the parent and dramatically pruning the parameters of
each child to create an ensemble of members with unique and diverse topologies.
We then briefly train each child network for a small number of epochs, which
now converge significantly faster when compared to training from scratch. We
explore various ways to maximize diversity in the child networks, including the
use of anti-random pruning and one-cycle tuning. This diversity enables "Prune
and Tune" ensembles to achieve results that are competitive with traditional
ensembles at a fraction of the training cost. We benchmark our approach against
state of the art low-cost ensemble methods and display marked improvement in
both accuracy and uncertainty estimation on CIFAR-10 and CIFAR-100.


title:
Adversarially-regularized mixed effects deep learning (ARMED) models for
  improved interpretability, performance, and generalization on clustered data

abstract:
  Natural science datasets frequently violate assumptions of independence.
Samples may be clustered (e.g. by study site, subject, or experimental batch),
leading to spurious associations, poor model fitting, and confounded analyses.
While largely unaddressed in deep learning, this problem has been handled in
the statistics community through mixed effects models, which separate
cluster-invariant fixed effects from cluster-specific random effects. We
propose a general-purpose framework for Adversarially-Regularized Mixed Effects
Deep learning (ARMED) models through non-intrusive additions to existing neural
networks: 1) an adversarial classifier constraining the original model to learn
only cluster-invariant features, 2) a random effects subnetwork capturing
cluster-specific features, and 3) an approach to apply random effects to
clusters unseen during training. We apply ARMED to dense, convolutional, and
autoencoder neural networks on 4 applications including simulated nonlinear
data, dementia prognosis and diagnosis, and live-cell image analysis. Compared
to prior techniques, ARMED models better distinguish confounded from true
associations in simulations and learn more biologically plausible features in
clinical applications. They can also quantify inter-cluster variance and
visualize cluster effects in data. Finally, ARMED improves accuracy on data
from clusters seen during training (up to 28% vs. conventional models) and
generalization to unseen clusters (up to 9% vs. conventional models).


title:
Design and experimental investigation of a vibro-impact self-propelled
  capsule robot with orientation control

abstract:
  This paper presents a novel design and experimental investigation for a
self-propelled capsule robot that can be used for painless colonoscopy during a
retrograde progression from the patient's rectum. The steerable robot is driven
forward and backward via its internal vibration and impact with orientation
control by using an electromagnetic actuator. The actuator contains four sets
of coils and a shaft made by permanent magnet. The shaft can be excited
linearly in a controllable and tilted angle, so guide the progression
orientation of the robot. Two control strategies are studied in this work and
compared via simulation and experiment. Extensive results are presented to
demonstrate the progression efficiency of the robot and its potential for
robotic colonoscopy.


title:
The Long-Term Effects of Early-Life Pollution Exposure: Evidence from
  the London Smog

abstract:
  This paper uses a large UK cohort to investigate the impact of early-life
pollution exposure on individuals' human capital and health outcomes in older
age. We compare individuals who were exposed to the London smog in December
1952 whilst in utero or in infancy to those born after the smog and those born
at the same time but in unaffected areas. We find that those exposed to the
smog have substantially lower fluid intelligence and worse respiratory health,
with some evidence of a reduction in years of schooling.


title:
The Canonical Ensemble Reloaded: The Complex-Stability of Euclidean
  quantum gravity for Black Holes in a Box

abstract:
  We revisit the stability of black hole saddles for the Euclidean path
integral describing the canonical partition function $Z(\beta)$ for gravity
inside a spherical reflecting cavity. The boundary condition at the cavity wall
couples the transverse-traceless (TT) and pure-trace modes that are
traditionally used to describe fluctuations about Euclidean Schwarzschild black
holes in infinite-volume asymptotically flat and asymototically AdS spacetimes.
This coupling obstructs the familiar Gibbons-Hawking-Perry treatment of the
conformal factor problem, as Wick rotation of the pure-trace modes would
require that the TT modes be rotated as well. The coupling also leads to
complex eigenvalues for the \L operator. We nevertheless find that the \L
operator can be diagonalized in the space of coupled modes. This observation
allows the eigenmodes to define a natural generalization of the pure-trace
Wick-rotation recipe used in infinite volume, with the result that a mode with
eigenvalue $\lambda$ is stable when ${\rm Re}\,\lambda > 0$. In any cavity, and
with any cosmological constant $\Lambda \le 0$, we show this recipe to
reproduce the expectation from black hole thermodynamics that large Euclidean
black holes define stable saddles while the saddles defined by small Euclidean
black holes are unstable.


title: Marked graphs and the chromatic symmetric function

abstract:
  The main result of this paper is the introduction of marked graphs and the
marked graph polynomials ($M$-polynomial) associated with them. These
polynomials can be defined via a deletion-contraction operation. These
polynomials are a generalization of the $W$-polynomial introduced by Noble and
Welsh and a specialization of the $\mathbf{V}$-polynomial introduced by
Ellis-Monaghan and Moffatt. In addition, we describe an important
specialization of the $M$-polynomial which we call the $D$-polynomial.
Furthermore, we give an efficient algorithm for computing the chromatic
symmetric function of a graph in the \emph{star-basis} of symmetric functions.
As an application of these tools, we prove that proper trees of diameter at
most 5 can be reconstructed from its chromatic symmetric function.


title: Generative modeling via tensor train sketching

abstract:
  In this paper we introduce a sketching algorithm for constructing a tensor
train representation of a probability density from its samples. Our method
deviates from the standard recursive SVD-based procedure for constructing a
tensor train. Instead we formulate and solve a sequence of small linear systems
for the individual tensor train cores. This approach can avoid the curse of
dimensionality that threatens both the algorithmic and sample complexities of
the recovery problem. Specifically, for Markov models, we prove that the tensor
cores can be recovered with a sample complexity that is constant with respect
to the dimension. Finally, we illustrate the performance of the method with
several numerical experiments.


title: Investigating the effect of binning on causal discovery

abstract:
  Binning (a.k.a. discretization) of numerically continuous measurements is a
wide-spread but controversial practice in data collection, analysis, and
presentation. The consequences of binning have been evaluated for many
different kinds of data analysis methods, however so far the effect of binning
on causal discovery algorithms has not been directly investigated. This paper
reports the results of a simulation study that examined the effect of binning
on the Greedy Equivalence Search (GES) causal discovery algorithm. Our findings
suggest that unbinned continuous data often result in the highest search
performance, but some exceptions are identified. We also found that binned data
are more sensitive to changes in sample size and tuning parameters, and
identified some interactive effects between sample size, binning, and tuning
parameter on performance.


title: Blind Reverberation Time Estimation in Dynamic Acoustic Conditions

abstract:
  The estimation of reverberation time from real-world signals plays a central
role in a wide range of applications. In many scenarios, acoustic conditions
change over time which in turn requires the estimate to be updated
continuously. Previously proposed methods involving deep neural networks were
mostly designed and tested under the assumption of static acoustic conditions.
In this work, we show that these approaches can perform poorly in dynamically
evolving acoustic environments. Motivated by a recent trend towards
data-centric approaches in machine learning, we propose a novel way of
generating training data and demonstrate, using an existing deep neural network
architecture, the considerable improvement in the ability to follow temporal
changes in reverberation time.


title: Black-hole analog in vehicular traffic

abstract:
  We propose a simple black-hole analog in vehicular-traffic dynamics. The
corresponding causal diagram is determined by the propagation of the taillight
flashes emitted by a convoy of cars on a highway. In addition to being a new
black-hole analog, this illustrates how causal diagrams, so common in general
relativity, may be useful in vehicular-traffic dynamics.


title: Let's Handle It: Generalizable Manipulation of Articulated Objects

abstract:
  In this project we present a framework for building generalizable
manipulation controller policies that map from raw input point clouds and
segmentation masks to joint velocities. We took a traditional robotics
approach, using point cloud processing, end-effector trajectory calculation,
inverse kinematics, closed-loop position controllers, and behavior trees. We
demonstrate our framework on four manipulation skills on common household
objects that comprise the SAPIEN ManiSkill Manipulation challenge.


title:
Blueprint of a scalable spin qubit shuttle device for coherent mid-range
  qubit transfer in disordered Si/SiGe/SiO$_2$

abstract:
  Silicon spin qubits stand out due to their very long coherence times,
compatibility with industrial fabrication, and prospect to integrate classical
control electronics. To achieve a truly scalable architecture, a coherent
mid-range link between qubit registers has been suggested to solve the signal
fan-out problem. Here we present a blueprint of such a $\sim 10\,\mu$m long
link, called a spin qubit shuttle, which is based on connecting an array of
gates into a small number of sets. The number of these gate sets and thus the
number of required control signal is independent of the link distance to
coherently shuttle the electron qubit. We discuss two different operation modes
for the spin qubit shuttle: A qubit conveyor, i.e. a potential minimum that
smoothly moves laterally, and a bucket brigade, in which the electron is
transported through a series of tunnel-coupled quantum dots by adiabatic
passage. We find the former approach more promising considering a realistic
Si/SiGe device including potential disorder from the charged defects at the
Si/SiO$_2$ layer, as well as typical charge noise. Focusing on the qubit
transfer fidelity of the conveyor shuttling mode, motional narrowing, the
interplay between orbital and valley excitation and relaxation in presence of
$g$-factors that depend on orbital and valley state of the electron, and
effects from spin-hotspots are discussed in detail. We find that a transfer
fidelity of 99.9 \% is feasible in Si/SiGe at a speed of $\sim$10 m/s, if the
average valley splitting and its inhomogeneity stay within realistic bounds.
Operation at low global magnetic field $\approx 20$ mT and material engineering
towards high valley splitting is favourable to reach high transfer fidelities.


title:
Autoregressive Moving Average and Generalized Autoregresive Moving
  Average in Covid-19 Confirmed Cases in Indonesia

abstract:
  Autoregressive moving average and generalized autoregressive moving average
are often used in statistical modeling. This study uses this method because the
method uses data from the previous period to model the data for the current
period. In addition, the technique is often used in data prediction. The
familiar data used is count data. Count data is the data that most often cause
data not to spread usually. Therefore, time series modeling, one of which is
through arithmetic series, was developed. This study aims to obtain the best
modeling results from positive confirmed cases of Covid 19 in Indonesia. They
were getting the results from the best modeling for positive confirmed cases of
Covid 19 in Indonesia based on the smallest Aikake information criterion value.


title:
Exploiting Correlation to Achieve Faster Learning Rates in Low-Rank
  Preference Bandits

abstract:
  We introduce the \emph{Correlated Preference Bandits} problem with random
utility-based choice models (RUMs), where the goal is to identify the best item
from a given pool of $n$ items through online subsetwise preference feedback.
We investigate whether models with a simple correlation structure, e.g. low
rank, can result in faster learning rates. While we show that the problem can
be impossible to solve for the general `low rank' choice models, faster
learning rates can be attained assuming more structured item correlations. In
particular, we introduce a new class of \emph{Block-Rank} based RUM model,
where the best item is shown to be $(\epsilon,\delta)$-PAC learnable with only
$O(r \epsilon^{-2} \log(n/\delta))$ samples. This improves on the standard
sample complexity bound of $\tilde{O}(n\epsilon^{-2} \log(1/\delta))$ known for
the usual learning algorithms which might not exploit the item-correlations ($r
\ll n$). We complement the above sample complexity with a matching lower bound
(up to logarithmic factors), justifying the tightness of our analysis.
Surprisingly, we also show a lower bound of
$\Omega(n\epsilon^{-2}\log(1/\delta))$ when the learner is forced to play just
duels instead of larger subsetwise queries. Further, we extend the results to a
more general `\emph{noisy Block-Rank}' model, which ensures robustness of our
techniques. Overall, our results justify the advantage of playing subsetwise
queries over pairwise preferences $(k=2)$, we show the latter provably fails to
exploit correlation.


title:
An expectation-maximization algorithm for estimating the parameters of
  the correlated binomial distribution

abstract:
  The correlated binomial (CB) distribution was proposed by Luce\~no
(Computational Statistics $\&$ Data Analysis, 20, 1995, 511-520) as an
alternative to the binomial distribution for the analysis of the data in the
presence of correlations among events. Due to the complexity of the mixture
likelihood of the model, it may be impossible to derive analytical expressions
of the maximum likelihood estimators (MLEs) of the unknown parameters. To
overcome this difficulty, we develop an expectation-maximization algorithm for
computing the MLEs of the CB parameters. Numerical results from simulation
studies and a real-data application showed that the proposed method is very
effective by consistently reaching a global maximum. Finally, our results
should be of interest to senior undergraduate or first-year graduate students
and their lecturers with an emphasis on the interested applications of the EM
algorithm for finding the MLEs of the parameters in discrete mixture models.


title:
Training Characteristic Functions with Reinforcement Learning:
  XAI-methods play Connect Four

abstract:
  One of the goals of Explainable AI (XAI) is to determine which input
components were relevant for a classifier decision. This is commonly know as
saliency attribution. Characteristic functions (from cooperative game theory)
are able to evaluate partial inputs and form the basis for theoretically "fair"
attribution methods like Shapley values. Given only a standard classifier
function, it is unclear how partial input should be realised. Instead, most
XAI-methods for black-box classifiers like neural networks consider
counterfactual inputs that generally lie off-manifold. This makes them hard to
evaluate and easy to manipulate.
  We propose a setup to directly train characteristic functions in the form of
neural networks to play simple two-player games. We apply this to the game of
Connect Four by randomly hiding colour information from our agents during
training. This has three advantages for comparing XAI-methods: It alleviates
the ambiguity about how to realise partial input, makes off-manifold evaluation
unnecessary and allows us to compare the methods by letting them play against
each other.


title:
Drawing Inductor Layout with a Reinforcement Learning Agent: Method and
  Application for VCO Inductors

abstract:
  Design of Voltage-Controlled Oscillator (VCO) inductors is a laborious and
time-consuming task that is conventionally done manually by human experts. In
this paper, we propose a framework for automating the design of VCO inductors,
using Reinforcement Learning (RL). We formulate the problem as a sequential
procedure, where wire segments are drawn one after another, until a complete
inductor is created. We then employ an RL agent to learn to draw inductors that
meet certain target specifications. In light of the need to tweak the target
specifications throughout the circuit design cycle, we also develop a variant
in which the agent can learn to quickly adapt to draw new inductors for
moderately different target specifications. Our empirical results show that the
proposed framework is successful at automatically generating VCO inductors that
meet or exceed the target specification.


title: Entanglement on Orbits

abstract:
  The study of multipartite entanglement is not only interesting but also
important due to its wide application in quantum information processing.
However, the complicated structure of the Hilbert space for many parties makes
multipartite entanglement extremely complicated. It is then worth studying the
structure of the Hilbert space itself. In this work, we provide a way to study
the structure of SLOCC-equivalence and to determine the number free parameters
for SLOCC-equivalent classes. Additionally, two different entanglement
witnesses are introduced. The method matches well the existing results, and can
make predictions for more-qubit systems.


title: Metastable complex vector bundles over complex projective spaces

abstract:
  We apply Weiss calculus to compute the number of topological complex vector
bundles of rank $n-2$ with vanishing Chern classes over $\mathbb{C}P^n$ for
$n>3$, as given by the list $1, 1, 12, 2, 1, 3, 2, 2, 3, 1, 4, 6, 1, 1, 6, 2,
1, 3, 4, 2, 3, 1, 2, 6$, where the $i$-th entry in this list is the number of
such bundles whenever $n$ is congruent to $i$ modulo $24$, starting with $i =
0$. Similarly, the number of rank $n-1$ bundles with vanishing Chern classes
over $\mathbb{C}P^n$ for $n>2$ is $2$ when $n$ is odd and $1$ when $n$ is even.


title:
Optimizing semilinear representations for State-dependent Riccati
  Equation-based feedback control

abstract:
  An optimized variant of the State Dependent Riccati Equations (SDREs)
approach for nonlinear optimal feedback stabilization is presented. The
proposed method is based on the construction of equivalent semilinear
representations associated to the dynamics and their affine combination. The
optimal combination is chosen to minimize the discrepancy between the SDRE
control and the optimal feedback law stemming from the solution of the
corresponding Hamilton Jacobi Bellman (HJB) equation. Numerical experiments
assess effectiveness of the method in terms of stability of the closed-loop
with near-to-optimal performance.


title:
Fission properties of $^{253}$Rf and the stability of neutron-deficient
  Rf isotopes

abstract:
  An analysis of recent experimental data [J. Khuyagbaatar et al., Phys. Rev. C
104, L031303 (2021)] has established the existence of two fissioning states in
$^{253}$Rf: the ground state and a low-lying isomeric state, most likely
involving the same neutron single-particle configurations as in the lighter
isotone $^{251}$No. The ratio of fission half-lives measured in $^{253}$Rf was
used to predict the fission properties of the 1/2$^{+}$ isomeric state in
$^{251}$No and draw conclusions as to the stability against fission of even
lighter Rf systems. This paper focusses again on the fission properties of
$^{253}$Rf and their impact on the stability of other neutron deficient
isotopes, using new and improved data collected from two experiments performed
at the Flerov Laboratory of Nuclear Reactions in Dubna, Russia. Two fission
activities with half-lives of 52.8(4.4)$~\mu$s and 9.9(1.2) ms were measured in
the case of $^{253}$Rf, confirming the results of J. Kkuyagbaatar et al. A
third state, at much higher excitation energy, was also observed through the
detection of its electromagnetic decay to the 52.8$~\mu$s state. This
observation leads to the opposite quantum-configuration assignments for the
fissioning states as compared to the ones established by J. Khuyagbaatar et
al., namely that the higher-spin state has the shortest fission half-life. This
inversion of the ratio of fission hindrances between the low and high-spin
states is corroborated in the isotone $^{251}$No by the non observation of any
substantial fission branch from the low-spin isomer.


title:
Detection of C2, CN and CH radicals in the spectrum of the transiting
  hot Jupiter HAT-P-1b

abstract:
  In this paper we report spectroscopy of the transiting hot Jupiter HAT-P-1b.
The HAT-P-1b is a giant ($R = 1.2 RJ$), low-mean density transiting extrasolar
planet in a visual binary system, composed of two sun-like stars. The host star
HAT-P-1b known as ADS 16402 B is a G0V C dwarf (V = 9.87). We revealed optical
emission of $C_{2}$, $CN$ and $CH$ radicals in the spectrum of the hot Jupiter
HAT-P-1b. We discovered radial pulsation of the hot Jupiter HAT-P-1b with a
period of about 1900 sec.


title:
Nuclei panoptic segmentation and composition regression with multi-task
  deep neural networks

abstract:
  Nuclear segmentation, classification and quantification within Haematoxylin &
Eosin stained histology images enables the extraction of interpretable
cell-based features that can be used in downstream explainable models in
computational pathology. The Colon Nuclei Identification and Counting (CoNIC)
Challenge is held to help drive forward research and innovation for automatic
nuclei recognition in computational pathology. This report describes our
proposed method submitted to the CoNIC challenge. Our method employs a
multi-task learning framework, which performs a panoptic segmentation task and
a regression task. For the panoptic segmentation task, we use encoder-decoder
type deep neural networks predicting a direction map in addition to a
segmentation map in order to separate neighboring nuclei into different
instances


title: Parametric inequalities and Weyl law for the volume spectrum

abstract:
  We show that the Weyl law for the volume spectrum in a compact Riemannian
manifold conjectured by Gromov can be derived from parametric generalizations
of two famous inequalities: isoperimetric inequality and coarea inequality. We
prove two such generalizations in low dimensions and obtain the Weyl law for
1-cycles in 3-manifolds. We also give a new proof of the Almgren isomorphism
theorem.


title: The nature of the Cygnus extreme B-supergiant 2MASS J20395358+4222505

abstract:
  2MASS J20395358+4222505 is an obscured early B supergiant near the massive OB
star association Cyg OB2. Despite its bright infrared magnitude (K$_{s}$=5.82)
it has remained largely ignored because of its dim optical magnitude (B=16.63,
V=13.68). In a previous paper we classified it as a highly reddened,
potentially extremely luminous, early B-type supergiant. We obtained its
spectrum in the U, B and R spectral bands during commissioning observations
with the instrument MEGARA@GTC. It displays a particularly strong H$\alpha$
emission for its spectral type, B1 Ia. The star seems to be in an intermediate
phase between super- and hypergiant, a group that it will probably join in the
near (astronomical) future. We observe a radial velocity difference between
individual observations and determine the stellar parameters, obtaining
T$_{eff}$ = 24000 K, logg$_{c}$= 2.88 $\pm$ 0.15. The rotational velocity found
is large for a B-supergiant, vsini= 110 $\pm$ 25 km s$^{-1}$. The abundance
pattern is consistent with solar, with a mild C underabundance (based on a
single line). Assuming that J20395358+4222505 is at the distance of Cyg OB2 we
derive the radius from infrared photometry, finding R= 41.2 $\pm$ 4.0
R$_{\odot}$, log(L/L$_{\odot}$)= 5.71 $\pm$ 0.04 and a spectroscopic mass of
46.5 $\pm$ 15.0 M$_{\odot}$. The clumped mass-loss rate (clumping factor 10) is
very high for the spectral type, $\dot{M}$ = 2.4x10$^{-6}$ M$_{\odot}$
a$^{-1}$. The high rotational velocity and mass-loss rate place the star at the
hot side of the bi-stability jump. Together with the nearly solar CNO abundance
pattern, they may also point to evolution in a binary system, J20395358+4222505
being the initial secondary.


title:
Nuclear modification of $\Upsilon$ states in pPb collisions at
  $\sqrt{s_\mathrm{NN}}$ = 5.02 TeV

abstract:
  Production cross sections of $\Upsilon$(1S), $\Upsilon$(2S), and
$\Upsilon$(3S) states decaying into \muplusmuminus in proton-lead (pPb)
collisions are reported using data collected by the CMS experiment
at$\sqrt{s_\mathrm{NN}}$ = 5.02 TeV. A comparison is made with corresponding
cross sections obtained with pp data measured at the same collision energy and
scaled by the Pb nucleus mass number. The nuclear modification factor for
$\Upsilon$(1S) is found to be $R_\mathrm{pPb}(\Upsilon(1S))$ = 0.806 $\pm$
0.024 (stat) $\pm$ 0.059 (syst). Similar results for the excited states
indicate a sequential suppression pattern, such that
$R_\mathrm{pPb}(\Upsilon(1S)) \gt R_\mathrm{pPb}(\Upsilon(2S)) \gt
R_\mathrm{pPb}(\Upsilon(3S))$. The suppression is much less pronounced in pPb
than in PbPb collisions, and independent of transverse momentum
$p_\mathrm{T}^\Upsilon$ and center-of-mass rapidity $y_\mathrm{CM}^\Upsilon$ of
the individual $\Upsilon$ state in the studied range $p_\mathrm{T}^\Upsilon \lt
$ 30 GeV$/c$ and $\vert y_\mathrm{CM}^\Upsilon\vert \lt$ 1.93. Models that
incorporate sequential suppression of bottomonia in pPb collisions are in
better agreement with the data than those which only assume initial-state
modifications.


title: Lattice points in slices of prisms

abstract:
  We study the Ehrhart polynomials of certain slices of rectangular prisms.
These polytopes are generalizations of the hypersimplex and are contained in
the larger class of polypositroids introduced by Lam and Postnikov. We give a
combinatorial formula for all the Ehrhart coefficients in terms of the number
of weighted permutations satisfying certain compatibility properties. This
result proves that all these polytopes are Ehrhart positive. Additionally,
inspired by a result due to Early and Kim, we give a combinatorial
interpretation for all the coefficients of the $h^*$-polynomial; this solves
the problem of providing an interpretation for the numerator of the Hilbert
series, also known as the $h$-vector of all algebras of Veronese type. As
corollaries of our results, we obtain an expression for the volumes of these
slices of prisms as weighted combinations of Eulerian numbers; we use this to
provide some generalizations of Laplace's result on the combinatorial
interpretation of the volume of the hypersimplex. We discuss an application
regarding a generalization of the flag Eulerian numbers and certain
refinements, and give a short proof of the Ehrhart positivity of the
independence polytope of all uniform matroids.


title: On Some Algebraic Properties of Hermite--Pad\'e Polynomials

abstract:
  Let $[f_0,\dots,f_m]$ be a tuple of series in nonnegative powers of $1/z$,
$f_j(\infty)\neq0$. It is supposed that the tuple is in "general position". We
give a construction of type I and type II Hermite--Pad\'e polynomials to the
given tuple of degrees $\leq{n}$ and $\leq{mn}$ respectively and the
corresponding $(m+1)$-multi-indexes with the following property. Let $M_1(z)$
and $M_2(z)$ be two $(m+1)\times(m+1)$ polynomial matrices,
$M_1(z),M_2(z)\in\operatorname{GL}(m+1,\mathbb C[z])$, generated by type I and
type II Hermite--Pad\'e polynomials respectively. Then we have
$M_1(z)M_2(z)\equiv I_{m+1}$, where $I_{m+1}$ is the identity
$(m+1)\times(m+1)$-matrix.
  The result is motivated by some novel applications of Hermite--Pad\'e
polynomials to the investigation of monodromy properties of Fuchsian systems of
differential equations.


title: NSR singular vectors from Uglov polynomials

abstract:
  It was conjectured in arXiv:1211.2788 that bosonization of singular vectors
(in Neveu-Schwarz sector) of $\mathcal{N}=1$ super analogue of the Virasoro
algebra can be identified with Uglov symmetric function. In the paper we prove
this conjecture. We also extend this result to the Ramond sector of
$\mathcal{N}=1$ super-Virasoro algebra.