title:
Testing Non-Standard Interactions Between Solar Neutrinos and Quarks
  with Super-Kamiokande

abstract:
  Non-Standard Interactions (NSI) between neutrinos and matter affect the
neutrino flavor oscillations. Due to the high matter density in the core of the
Sun, solar neutrinos are suited to probe these interactions. Using the $277$
kton-yr exposure of Super-Kamiokande to $^{8}$B solar neutrinos, we search for
the presence of NSI. Our data favors the presence of NSI with down quarks at
1.8$\sigma$, and with up quarks at 1.6$\sigma$, with the best fit NSI
parameters being ($\epsilon_{11}^{d},\epsilon_{12}^{d}$) = (-3.3, -3.1) for
$d$-quarks and ($\epsilon_{11}^{u},\epsilon_{12}^{u}$) = (-2.5, -3.1) for
$u$-quarks. After combining with data from the Sudbury Neutrino Observatory and
Borexino, the significance increases by 0.1$\sigma$.


title: Pumping and mixing in active pores

abstract:
  We show both numerically and analytically that a chemically patterned active
pore can act as a micro/nano-pump for fluids, even if it is fore-aft symmetric.
This is possible due to a spontaneous symmetry breaking which occurs when
advection rather than diffusion is the dominant mechanism of solute transport.
We further demonstrate that, for pumping and tuning the flow rate, a
combination of geometrical and chemical inhomogeneities is required. For
certain parameter values, the flow is unsteady, and persistent oscillations
with a tunable frequency appear. Finally, we find that the flow may lose its
axial symmetry and hence promotes mixing in the low Reynolds number regime.


title:
Estimation of speaker age and height from speech signal using bi-encoder
  transformer mixture model

abstract:
  The estimation of speaker characteristics such as age and height is a
challenging task, having numerous applications in voice forensic analysis. In
this work, we propose a bi-encoder transformer mixture model for speaker age
and height estimation. Considering the wide differences in male and female
voice characteristics such as differences in formant and fundamental
frequencies, we propose the use of two separate transformer encoders for the
extraction of specific voice features in the male and female gender, using
wav2vec 2.0 as a common-level feature extractor. This architecture reduces the
interference effects during backpropagation and improves the generalizability
of the model. We perform our experiments on the TIMIT dataset and significantly
outperform the current state-of-the-art results on age estimation.
Specifically, we achieve root mean squared error (RMSE) of 5.54 years and 6.49
years for male and female age estimation, respectively. Further experiment to
evaluate the relative importance of different phonetic types for our task
demonstrate that vowel sounds are the most distinguishing for age estimation.


title:
Broadband high-efficiency SiN-on-Si waveguide photodetectors in a
  visible-spectrum integrated photonic platform

abstract:
  Integrated high-efficiency waveguide photodetectors operating in the visible
spectrum are indispensable to a growing number of photonic-integrated circuit
(PIC) applications. Prior developments are either incompatible with PIC
integration or limited to discrete operating wavelengths. Here, we demonstrate
silicon nitride-on-silicon (SiN-on-Si), broadband, high-efficiency waveguide
photodiodes realized in a visible-spectrum silicon photonic platform. An
external quantum efficiency (EQE) >60% was observed across a record wavelength
bandwidth of >230 nm from violet (~400 nm) to red (~640 nm). An opto-electronic
bandwidth up to 9 GHz and avalanche multiplication were obtained. A
photodetector was integrated with a tunable microring as a proof-of-concept
demonstration of on-chip power monitoring.


title: Recent development on fragmentation, aggregation and percolation

abstract:
  In this article, I have outlined how an accomplished researcher like Robert
Ziff has influenced a new generation of researchers across the globe like
gravity as an action-at-a-distance. In the 80s Ziff made significant
contributions to the kinetics of fragmentation followed by the kinetics of
aggregation. Here, I will discuss fractal and multifractal that emerges in
fragmentation and aggregation processes where the dynamics is governed by
non-trivial conservation laws. I have then discussed my recent works and
results on percolation where I made extensive use of Newman-Ziff fast Monte
Carlo algorithm. To this end, I have defined entropy which paved the way to
define specific heat and show that the critical exponents of percolation obey
Rushbrooke inequality. Besides, we discuss how entropy and order parameter
together can help us to check whether the percolation is accompanied by
order-disorder transition or not. The idea of entropy also help to explain why
encouraging smaller cluster to grow faster than larger clusters makes the
transition explosive.


title:
Autonomous Bikebot Control for Crossing Obstacles with Assistive Leg
  Impulsive Actuation

abstract:
  As a single-track mobile platform, bikebot (i.e., bicycle-based robot) has
attractive navigation capability to pass through narrow, off-road terrain with
high-speed and high-energy efficiency. However, running crossing step-like
obstacles creates challenges for intrinsically unstable, underactuated
bikebots. This paper presents a novel autonomous bikebot control with assistive
leg actuation to navigate crossing obstacles. The proposed design integrates
the external/internal convertible-based control with leg-assisted impulse
control. The leg-terrain interaction generates assistive impulsive torques to
help maintain the navigation and balance capability when running across
obstacles. The control performance is analyzed and guaranteed. The experimental
results confirm that under the control design, the bikebot can smoothly run
crossing multiple step-like obstacles with height more than one third of the
wheel radius. The comparison results demonstrate the superior performance than
those under only the velocity and steering control without leg assistive
impulsive actuation.


title:
Light control over (n, m)-selective aryl bromide like functionalization
  of substrate supported carbon nanotubes

abstract:
  Diazonium reactions with carbon nanotubes form optical $sp^3$ defects that
can be used in optical and electrical circuits. We investigate a direct
on-device reaction supported by confined laser irradiation and present a
technique where an arbitrary carbon nanotube can be preferentially
functionalized within a device by matching the light frequency with its
transition energy. An exemplary reaction was carried out between (9,7) nanotube
and 4-bromobenzenediazonium tetrafluoroborate. The substrate supported
nanotubes of multiple semiconducting chiralities were locally exposed to laser
light while monitoring the reaction kinetics in-situ via Raman spectroscopy.
The chiral selectivity of the reaction was confirmed by resonant Raman
spectroscopy, reporting a 10 meV $E_{22}$ transition energy red-shift only of
the targeted species. We further demonstrated this method on a single tube
(9,7) electroluminescent device and show a 25 meV red-shifted emission of the
ground state $E_{11}$ compared to the emission from the pristine tubes.


title:
Studying the scientific mobility and international collaboration funded
  by the China Scholarship Council

abstract:
  Every year many scholars are funded by the China Scholarship Council (CSC).
The CSC is a funding agency established by the Chinese government with the main
initiative of training Chinese scholars to conduct research abroad and to
promote international collaboration. In this study, we identified these
CSC-funded scholars sponsored by the China Scholarship Council based on the
acknowledgments text indexed by the Web of Science. Bibliometric data of their
publications were collected to track their scientific mobility in different
fields, and to evaluate the performance of the CSC scholarship in promoting
international collaboration by sponsoring the mobility of scholars. Papers
funded by the China Scholarship Council are mainly from the fields of natural
sciences and engineering sciences. There are few CSC-funded papers in the field
of social sciences and humanities. CSC-funded scholars from mainland China have
the United States, Australia, Canada, and some European countries, such as
Germany, the UK, and the Netherlands, as their preferential mobility
destinations across all fields of science. CSC-funded scholars published most
of their papers with international collaboration during the mobility period,
with a decrease in the share of international collaboration after the support
of the scholarship.


title:
On the Modeling and Simulation of Portfolio Allocation Schemes: an
  Approach based on Network Community Detection

abstract:
  We present a study on portfolio investments in financial applications. We
describe a general modeling and simulation framework and study the impact on
the use of different metrics to measure the correlation among assets. In
particular, besides the traditional Pearson's correlation, we employ the
Detrended Cross-Correlation Analysis (DCCA) and Detrended Partial
Cross-Correlation Analysis (DPCCA). Moreover, a novel portfolio allocation
scheme is introduced that treats assets as a complex network and uses
modularity to detect communities of correlated assets. Weights of the
allocation are then distributed among different communities for the sake of
diversification. Simulations compare this novel scheme against Critical Line
Algorithm (CLA), Inverse Variance Portfolio (IVP), the Hierarchical Risk Parity
(HRP). Synthetic times series are generated using the Gaussian model, Geometric
Brownian motion, GARCH, ARFIMA and modified ARFIMA models. Results show that
the proposed scheme outperforms state of the art approaches in many scenarios.
We also validate simulation results via backtesting, whose results confirm the
viability of the proposal.


title:
Constraining the Gravitational-Wave Spectrum from Cosmological
  First-Order Phase Transitions Using Data from LIGO-Virgo First Three
  Observing Runs

abstract:
  We search for a first-order phase transition (PT) gravitational wave (GW)
signal from Advanced LIGO and Advanced Virgo's first three observing runs. Due
to the large theoretical uncertainties, four GW energy spectral shapes from
bubble and sound wave collisions widely adopted in literature are investigated,
separately. Our results indicate that there is no evidence for the existence of
such GW signals, and therefore we give the upper limits on the amplitude of GW
energy spectrum $\Omega_\text{pt}(f_*)$ in the peak frequency range of $f_*\in
[5,500]$ Hz for these four theoretical models, separately. We find that
$\Omega_\text{pt}(f_*\simeq 40\ \text{Hz})<1.3\times10^{-8}$ at $95\%$ credible
level, and roughly $H_*/\beta\lesssim 0.1$ and $\alpha\lesssim 1$ at $68\%$
credible level in the peak frequency range of $20\lesssim f_*\lesssim 100$ Hz
corresponding to the most sensitive frequency band of Advanced LIGO and
Advanced Virgo's first three observing runs, where $H_*$ is the Hubble
parameter when PT happens, $\beta$ is the bubble nucleation rate and $\alpha$
is the ratio of vacuum and relativistic energy density.


title:
On a workflow for efficient computation of the permeability of tight
  sandstones

abstract:
  The paper presents a workflow for fast pore-scale simulation of single-phase
flow in tight reservoirs typically characterized by low, multiscale porosity.
Multiscale porosity implies that the computational domain contains porous
voxels (unresolved porosity) in addition to pure fluid voxels. In this case,
the Stokes-Brinkman equations govern the flow, with the Darcy term needed to
account for the flow in the porous voxels. As the central part of our workflow,
robust and efficient solvers for Stokes and Stokes-Brinkman equations are
presented. The solvers are customized for low-porosity binary and multiclass
images, respectively. Another essential component of the workflow is a
preprocessing module for classifying images with respect to the connectivity of
the multiscale pore space. Particularly, an approximation of the
Stokes-Brinkman problem, namely, the Darcy problem, is investigated for the
images that do not have pure fluid percolation paths. Thorough computational
experiments demonstrate efficiency and robustness of the workflow for
simulations on images from tight reservoirs. Raw files describing the used CT
images are provided as supplementary materials to enable other researchers to
use them.


title: The Combinatorial Multi-Round Ascending Auction

abstract:
  The Combinatorial Multi-Round Auction (CMRA) is a new auction format which
has already been used in several recent European spectrum auctions. We
characterize equilibria in the CMRA that feature auction-specific forms of
truthful bidding, demand expansion, and demand reduction for settings in which
bidders have either decreasing or non-decreasing marginal values. In
particular, we establish sufficient conditions for riskless collusion. Overall,
our results suggest that the CMRA might be an attractive auction design in the
presence of highly complementary goods on sale. We discuss to what extent our
theory is consistent with outcomes data in Danish spectrum auctions and how our
predictions can be tested using bidding data.


title: Controlling the average degree in random power-law networks

abstract:
  We describe a procedure that allows continuously tuning the average degree
$\langle k \rangle$ of uncorrelated networks with power-law degree distribution
$p(k)$. Inn order to do this, we modify the low-$k$ region of $p(k)$, while
preserving the large-$k$ tail up to a cutoff. Then, we use the modified $p(k)$
to obtain the degree sequence required to construct networks through the
configuration model. We analyze the resulting nearest-neighbor degree and local
clustering to verify the absence of $k$-dependencies. Finally, a further
modification is introduced to eliminate the sample fluctuations in the average
degree.


title:
Dynamic Magnetic Crossover at the Origin of the Hidden-Order in van der
  Waals Antiferromagnet CrSBr

abstract:
  The van der Waals material CrSBr stands out as a promising two-dimensional
magnet. Especially, its high magnetic ordering temperature and versatile
magneto-transport properties make CrSBr an important candidate for new devices
in the emergent field of two-dimensional magnetic materials. To date, the
magnetic and structural properties of CrSBr have not been fully elucidated.
Here, we report on the detailed temperature-dependent magnetic and structural
properties of this material, by comprehensively combining neutron scattering,
muon spin relaxation spectroscopy, synchrotron X-ray diffraction, and
magnetization measurements. We evidence that this material undergoes a
transition to an A-type antiferromagnetic state below $T_{\rm N} \approx$ 140
K, with a pronounced two-dimensional character as deduced from the determined
critical exponent of $\beta \approx $ 0.18. In our analysis of the
field-induced metamagnetic transition, we find that the ferromagnetic
correlations within the monolayers persist clearly above the N\'eel temperature
in this material. Furthermore, we unravel the low-temperature (i.e. $T < T_{\rm
N}$) magnetic hidden order within the long-range magnetically ordered state. We
find that it is associated to a slowing down of the magnetic fluctuations,
accompanied by a continuous reorientation of the internal magnetic field. These
take place upon cooling below $T_s$ $\approx$ 100 K, until a spin freezing
process occurs at $T$* $\approx$ 40 K. We argue this complex dynamic behavior
to reflect a magnetic crossover driven by the in-plane uniaxial anisotropy,
which is ultimately caused by the mixed-anion character of the material. Our
findings indicate that the magnetic and structural properties of CrSBr widen
its potential application as a component for spin-based electronic devices.


title: Algebraic degree of series of reciprocal algebraic integers

abstract:
  In this paper, I give sufficient conditions for any linear combination in
$\mathbb{Q}$ of numbers $\sum_{n=1}^{\infty}\frac{b_{1,n}}{\alpha_{1,n}}$,
$\ldots$, $\sum_{n=1}^{\infty}\frac{b_{K,n}}{\alpha_{K,n}}$ to have algebraic
degree greater than an arbitrary fixed integer $D$ when the numbers
$\alpha_{i,n}$ are algebraic integers of sufficiently rapidly increasing
modulus and the $b_{i,n}$ are positive integers that are not too large.


title:
In search of short gamma-ray burst optical counterpart with the Zwicky
  Transient Facility

abstract:
  The Fermi Gamma-ray Burst Monitor (GBM) triggers on-board in response to
$\sim$ 40 short gamma-ray bursts (SGRBs) per year; however, their large
localization regions have made the search for optical counterparts a
challenging endeavour. We have developed and executed an extensive program with
the wide field of view of the Zwicky Transient Facility (ZTF) camera, mounted
on the Palomar 48 inch Oschin telescope (P48), to perform target-of-opportunity
(ToO) observations on 10 Fermi-GBM SGRBs during 2018 and 2020-2021. Bridging
the large sky areas with small field of view optical telescopes in order to
track the evolution of potential candidates, we look for the elusive SGRB
afterglows and kilonovae (KNe) associated with these high-energy events. No
counterpart has yet been found, even though more than 10 ground based
telescopes, part of the Global Relay of Observatories Watching Transients
Happen (GROWTH) network, have taken part in these efforts. The candidate
selection procedure and the follow-up strategy have shown that ZTF is an
efficient instrument for searching for poorly localized SGRBs, retrieving a
reasonable number of candidates to follow-up and showing promising capabilities
as the community approaches the multi-messenger era. Based on the median
limiting magnitude of ZTF, our searches would have been able to retrieve a
GW170817-like event up to $\sim$ 200 Mpc and SGRB afterglows to z = 0.16 or
0.4, depending on the assumed underlying energy model. Future ToOs will expand
the horizon to z = 0.2 and 0.7 respectively.


title:
Pegasus IV: Discovery and Spectroscopic Confirmation of an Ultra-Faint
  Dwarf Galaxy in the Constellation Pegasus

abstract:
  We report the discovery of Pegasus IV, an ultra-faint dwarf galaxy found in
archival data from the Dark Energy Camera processed by the DECam Local Volume
Exploration Survey. Pegasus IV is a compact, ultra-faint stellar system
($r_{1/2} = 41^{+8}_{-6}$ pc; $M_V = -4.25 \pm 0.2$ mag) located at a
heliocentric distance of $90^{+4}_{-6}$ kpc. Based on spectra of seven
non-variable member stars observed with Magellan/IMACS, we confidently resolve
Pegasus IV's velocity dispersion, measuring $\sigma_{v} = 3.3^{+1.7}_{-1.1}
\text{ km s}^{-1}$ (after excluding three velocity outliers); this implies a
mass-to-light ratio of $M_{1/2}/L_{V,1/2} = 167^{+224}_{-99}
M_{\odot}/L_{\odot}$ for the system. From the five stars with the highest
signal-to-noise spectra, we also measure a systemic metallicity of $\rm [Fe/H]
= -2.67^{+0.25}_{-0.29}$ dex, making Pegasus IV one of the most metal-poor
ultra-faint dwarfs. We tentatively resolve a non-zero metallicity dispersion
for the system. These measurements provide strong evidence that Pegasus IV is a
dark-matter-dominated dwarf galaxy, rather than a star cluster. We measure
Pegasus IV's proper motion using data from Gaia Early Data Release 3, finding
($\mu_{\alpha*}, \mu_{\delta}) = (0.33\pm 0.07, -0.21 \pm 0.08) \text{ mas
yr}^{-1}$. When combined with our measured systemic velocity, this proper
motion suggests that Pegasus IV is on an elliptical, retrograde orbit, and is
currently near its orbital apocenter. Lastly, we identify three potential RR
Lyrae variable stars within Pegasus IV, including one candidate member located
more than ten half-light radii away from the system's centroid. The discovery
of yet another ultra-faint dwarf galaxy strongly suggests that the census of
Milky Way satellites is still incomplete, even within 100 kpc.


title:
HD 133729: A blue large-amplitude pulsator in orbit around a
  main-sequence B-type star

abstract:
  Blue large-amplitude pulsators (BLAPs) form a small group of hot objects
pulsating in a fundamental radial mode with periods of the order of 30 minutes.
Proposed evolutionary scenarios explain them as evolved low-mass stars: either
~0.3 M$_\odot$ shell-hydrogen-burning objects with a degenerated helium core,
or more massive (0.5 - 0.8) M$_\odot$ core-helium-burning stars, or ~0.7
M$_\odot$ surviving companions of type Ia supernovae. Therefore, their origin
remains to be established. Using data from Transiting Exoplanet Survey
Satellite, we discovered that HD 133729 is a binary consisting of a late B-type
main-sequence star and a BLAP. The BLAP pulsates with a period of 32.37 min
decreasing at a rate of $(-7.11 \pm 0.33)\times 10^{-11}$. Due to light
dilution by a brighter companion, the observed amplitude of pulsation is much
smaller than in other BLAPs. From available photometry, we derived times of
maximum light, which revealed the binary nature of the star via O-C diagram.
The diagram shows variations with a period of 23.08433 d that we attribute to
the light-travel-time effect in the system. The analysis of these variations
allowed to derive the spectroscopic parameters of the BLAP's orbit around the
center of the mass of the binary. The presence of a hot companion in the system
was confirmed by the analysis of its spectral energy distribution, which was
also used to place the components in the H-R diagram. The obtained position of
the BLAP fully agrees with the location of the other members of the class. With
the estimated V~11 mag and the Gaia distance of less than 0.5 kpc, the BLAP is
the brightest and the nearest of all known BLAPs. It may become a clue object
in the verification of the evolutionary scenarios for this class of variable.
We argue that low-mass progenitors of the BLAP are excluded if the components
are coeval and no mass transfer between the components took place.


title: Learning Program Semantics with Code Representations: An Empirical Study

abstract:
  Program semantics learning is the core and fundamental for various code
intelligent tasks e.g., vulnerability detection, clone detection. A
considerable amount of existing works propose diverse approaches to learn the
program semantics for different tasks and these works have achieved
state-of-the-art performance. However, currently, a comprehensive and
systematic study on evaluating different program representation techniques
across diverse tasks is still missed.
  From this starting point, in this paper, we conduct an empirical study to
evaluate different program representation techniques. Specifically, we
categorize current mainstream code representation techniques into four
categories i.e., Feature-based, Sequence-based, Tree-based, and Graph-based
program representation technique and evaluate its performance on three diverse
and popular code intelligent tasks i.e., {Code Classification}, Vulnerability
Detection, and Clone Detection on the public released benchmark. We further
design three {research questions (RQs)} and conduct a comprehensive analysis to
investigate the performance. By the extensive experimental results, we conclude
that (1) The graph-based representation is superior to the other selected
techniques across these tasks. (2) Compared with the node type information used
in tree-based and graph-based representations, the node textual information is
more critical to learning the program semantics. (3) Different tasks require
the task-specific semantics to achieve their highest performance, however
combining various program semantics from different dimensions such as control
dependency, data dependency can still produce promising results.


title: Hybrid stars and QCD phase transition with a NJL-like model

abstract:
  In this paper, we introduce a self-consistent mean field approximation to
study the QCD phase transition and the structure of hybrid stars within the
framework of NJL model. In our practice, a phenomenological parameter $\alpha$
is introduced, which reflects the weights of "direct" channel and "exchange"
channel under a finite chemical potential. The mass-radius relation is obtained
by solving the Tolman-Oppenheimer-Volkoff equation using a crossover equation
of state (EOS). We calculate the density distribution in a two solar-mass
hybrid star to show the effects of different parameters. We also calculate the
tidal Love number $k_2$ and the deformability $(\Lambda)$. It is found that the
stiffness of the EOS increases with $\alpha $, which allows us to obtain a
hybrid star with a maximum mass of 2.40 solar-mass through our model. The
observation of over 2.06 solar-mass neutron stars may indicates that the chiral
transition may be a crossover on the whole $T-\mu$ plane.


title: Machine learning light hypernuclei

abstract:
  We employ a feed-forward artificial neural network to extrapolate at large
model spaces the results of {\it ab-initio} hypernuclear No-Core Shell Model
calculations for the $\Lambda$ separation energy $B_\Lambda$ of the lightest
hypernuclei, $^3_\Lambda$H, $^4_\Lambda$H and $^4_\Lambda$He, obtained in
computationally accessible harmonic oscillator basis spaces using chiral
nucleon-nucleon, nucleon-nucleon-nucleon and hyperon-nucleon interactions. The
overfitting problem is avoided by enlarging the size of the input dataset and
by introducing a Gaussian noise during the training process of the neural
network. We find that a network with a single hidden layer of eight neurons is
sufficient to correctly extrapolate the value of $\Lambda$ separation energy to
model spaces of size $N_{max}=100$. The results obtained are in good agreement
with the experimental data for the three light hypernuclei considered.


title: A Perspective on Neural Capacity Estimation: Viability and Reliability

abstract:
  Recently, several methods have been proposed for estimating the mutual
information from sample data using deep neural networks and without the
knowledge of closed-form distribution of the data. This class of estimators is
referred to as neural mutual information estimators (NMIE). In this paper, we
investigate the performance of different NMIE proposed in the literature when
applied to the capacity estimation problem. In particular, we study the
performance of mutual information neural estimator (MINE), smoothed mutual
information lower-bound estimator (SMILE), and directed information neural
estimator (DINE). For the NMIE above, capacity estimation relies on two deep
neural networks (DNN): (i) one DNN generates samples from a distribution that
is learned, and (ii) a DNN to estimate the MI between the channel input and the
channel output. We benchmark these NMIE in three scenarios: (i) AWGN channel
capacity estimation and (ii) channels with unknown capacity and continuous
inputs i.e., optical intensity and peak-power constrained AWGN channel (iii)
channels with unknown capacity and a discrete number of mass points i.e.,
Poisson channel. Additionally, we also (iv) consider the extension to the MAC
capacity problem by considering the AWGN and optical MAC models.


title:
A strategic high throughput search for identifying stable Li based half
  Heusler alloys for spintronics applications

abstract:
  In this work, high throughput DFT calculations are performed on the alkali
metal-based half Heusler alloys; LiY$_p$Y$^\prime_{1-p}$S (Y, Y$^\prime$ = V,
Cr, Mn, Fe, Co, Ni and $\mathit{p}$ = 0, 0.25, 0.5, 0.75, 1). Starting with 243
structural replica, systematic filters are designed to select the energetically
and vibrationally favorable compositions by considering the contributions
stemming from the magnetic alignments of the ions. Thereby, 26 dynamically
stable magnetic compositions are identified, of which 10 are found to be
ferromagnetic (FM), 4 antiferromagnetic (AFM) and 12 ferrimagnetic (FiM). 4 FM
and 8 FiM ones are found to show 100 $\%$ spin polarization. Further,
tetragonal distortion is found to be present in 4 FM, 3 FiM and 4 AFM
compositions, which indicates the possibility of easy-axis magnetocrystalline
anisotropy. The ferromagnetic LiFe$_{0.5}$Mn$_{0.5}$S and antiferromagnetic
LiFeS are found to have the most prominent easy-axis magnetocrystalline
anisotropy.


title: Minimizing communication in the multidimensional FFT

abstract:
  We present a parallel algorithm for the fast Fourier transform (FFT) in
higher dimensions. This algorithm generalizes the cyclic-to-cyclic
one-dimensional parallel algorithm to a cyclic-to-cyclic multidimensional
parallel algorithm while retaining the property of needing only a single
all-to-all communication step. This is under the constraint that we use at most
$\sqrt{N}$ processors for an FFT on an array with a total of $N$ elements,
irrespective of the dimension $d$ or shape of the array. The only assumption we
make is that $N$ is sufficiently composite. Our algorithm starts and ends in
the same distribution.
  We present our multidimensional implementation FFTU which utilizes the
sequential FFTW program for its local FFTs, and which can handle any dimension
$d$. We obtain experimental results for $d\leq 5$ using MPI on up to 4096 cores
of the supercomputer Snellius, comparing FFTU with the parallel FFTW program
and with PFFT. These results show that FFTU is competitive with the
state-of-the-art and that it allows to use a larger number of processors, while
keeping communication limited to a single all-to-all operation. For arrays of
size $1024^3$ and $64^5$, FFTU achieves a speedup of a factor 149 and 176,
respectively, on 4096 processors.


title:
Double integral estimates for Besov type spaces and an application to
  Hankel type operators

abstract:
  For $0<p<\infty$, we give a complete description of nonnegative radial weight
functions $\omega$ on the open unit disk $\mathbb{D}$ such that $$
  \int_{\mathbb{D}} |f'(z)|^p (1-|z|^2)^{p-2}\omega(z)dA(z)<\infty
  $$ if and only if $$
\int_{\mathbb{D}}\int_{\mathbb{D}}\frac{|f(z)-f(\zeta)|^p}{|1-\overline{\zeta}z|^{4+\tau+\sigma}}(1-|z|^2)^{\tau}(1-|\zeta|^2)^{\sigma}\omega(\zeta)dA(z)A(\zeta)<\infty,
$$ where $f$ is analytic in $\mathbb{D}$, $\tau$ and $\sigma$ are some real
numbers. As an application, we characterize the boundedness of Hankel type
operators related to Besov type spaces with radial Bekoll\'e-Bonami weights.


title:
A Novel Framework for Assessment of Learning-based Detectors in
  Realistic Conditions with Application to Deepfake Detection

abstract:
  Deep convolutional neural networks have shown remarkable results on multiple
detection tasks. Despite the significant progress, the performance of such
detectors are often assessed in public benchmarks under non-realistic
conditions. Specifically, impact of conventional distortions and processing
operations such as compression, noise, and enhancement are not sufficiently
studied. This paper proposes a rigorous framework to assess performance of
learning-based detectors in more realistic situations. An illustrative example
is shown under deepfake detection context. Inspired by the assessment results,
a data augmentation strategy based on natural image degradation process is
designed, which significantly improves the generalization ability of two
deepfake detectors.


title: Bayesian Nonparametric Adjustment of Confounding

abstract:
  Analysis of observational studies increasingly confronts the challenge of
determining which of a possibly high-dimensional set of available covariates
are required to satisfy the assumption of ignorable treatment assignment for
estimation of causal effects. We propose a Bayesian nonparametric approach that
simultaneously 1) prioritizes inclusion of adjustment variables in accordance
with existing principles of confounder selection; 2) estimates causal effects
in a manner that permits complex relationships among confounders, exposures,
and outcomes; and 3) provides causal estimates that account for uncertainty in
the nature of confounding. The proposal relies on specification of multiple
Bayesian Additive Regression Trees models, linked together with a common prior
distribution that accrues posterior selection probability to covariates on the
basis of association with both the exposure and the outcome of interest. A set
of extensive simulation studies demonstrates that the proposed method performs
well relative to similarly-motivated methodologies in a variety of scenarios.
We deploy the method to investigate the causal effect of emissions from
coal-fired power plants on ambient air pollution concentrations, where the
prospect of confounding due to local and regional meteorological factors
introduces uncertainty around the confounding role of a high-dimensional set of
measured variables. Ultimately, we show that the proposed method produces more
efficient and more consistent results across adjacent years than alternative
methods, lending strength to the evidence of the causal relationship between
SO2 emissions and ambient particulate pollution.


title:
AP-BSN: Self-Supervised Denoising for Real-World Images via Asymmetric
  PD and Blind-Spot Network

abstract:
  Blind-spot network (BSN) and its variants have made significant advances in
self-supervised denoising. Nevertheless, they are still bound to synthetic
noisy inputs due to less practical assumptions like pixel-wise independent
noise. Hence, it is challenging to deal with spatially correlated real-world
noise using self-supervised BSN. Recently, pixel-shuffle downsampling (PD) has
been proposed to remove the spatial correlation of real-world noise. However,
it is not trivial to integrate PD and BSN directly, which prevents the fully
self-supervised denoising model on real-world images. We propose an Asymmetric
PD (AP) to address this issue, which introduces different PD stride factors for
training and inference. We systematically demonstrate that the proposed AP can
resolve inherent trade-offs caused by specific PD stride factors and make BSN
applicable to practical scenarios. To this end, we develop AP-BSN, a
state-of-the-art self-supervised denoising method for real-world sRGB images.
We further propose random-replacing refinement, which significantly improves
the performance of our AP-BSN without any additional parameters. Extensive
studies demonstrate that our method outperforms the other self-supervised and
even unpaired denoising methods by a large margin, without using any additional
knowledge, e.g., noise level, regarding the underlying unknown noise.


title: Asymptotic estimates for concentrated vortex pairs

abstract:
  In [Comm. Math. Phys. 324 (2013), 445--463], Burton-Lopes Filho-Nussenzveig
Lopes studied the existence and stability of slowly traveling vortex pairs as
maximizers of the kinetic energy penalized by the impulse relative to a
prescribed rearrangement class. In this paper, we prove that after a suitable
scaling transformation the maximization problem studied by Burton-Lopes
Filho-Nussenzveig Lopes in fact gives rise to a family of concentrated vortex
pairs approaching a pair of point vortices with equal magnitude and opposite
signs. The key ingredient of the proof is to deduce a uniform bound for the
size of the supports of the scaled maximizers.


title:
Computing the space of differential forms of a plane curve and its
  Cartier-Manin matrix

abstract:
  In this paper, we propose a feasible algorithm to give an explicit basis of
the space of regular differential forms on the nonsingular projective model of
any given plane algebraic curve. The algorithm is demonstrated for concrete
examples, with our implementation over the computer algebra system Magma. As an
application, we also describe the Cartier-Manin matrix of the nonsingular
projective curve with respect to the basis computed by the algorithm.


title: Spontaneous fission of 246Fm

abstract:
  An experiment on the study of the $^{246}$Fm spontaneous fission was
conducted using the SHELS separator. The isotope was synthesized in the
complete fusion reaction of $^{40}$Ar beam ions and $^{208}$Pb target nuclei.
The neutron yields of $^{246}$Fm spontaneous fission ($\overline{\nu} =
3.79\pm0.30$, $\sigma^{2}_{\nu} = 2.1$) were obtained using the SFiNx detector
system. The multiplicity distribution of emitted prompt neutrons was restored
using the Tikhonov method of statistical regularisation ($\overline{\nu}_{r} =
3.79\pm0.20$, $\sigma^{2}_{\nu r} = 2.8$). The spontaneous fission branching
ratio ($b_{SF} = 0.061\pm0.005$) and the half-life ($T_{1/2} =
1.50^{+0.08}_{-0.07}$ s) of the isotope were determined. The experimental data
were compared with scission point model predictions. Excellent convergence was
observed in the average number of neutrons per spontaneous fission process.
However, the forms of the experimental and model prompt neutron multiplicity
distributions differ significantly.


title: The Stability of Wakes of Floating Wind Turbines

abstract:
  Floating offshore wind turbines (FOWTs) are subjected to platform motion
induced by wind and wave loads. The oscillatory movement trigger vortex
instabilities, modifying the wake structure, influencing the flow reaching
downstream wind turbines. In this work, the wake of a FOWT is analysed by means
of numerical simulations and comparison with linear stability theory. Two
simplified models based on the stability of vortices are developed for all
degrees of freedom of turbine motion. In our numerical simulations, the wind
turbine blades are modeled as actuator lines and a spectral-element method with
low dispersion and dissipation is employed to study the evolution of the
perturbations. The turbine motion excites vortex instability modes predicted by
the linear stability of helical vortices. The flow structures that are formed
in the non-linear regime are a consequence of the growth of these modes and
preserve some of the characteristics that can be explained and predicted by the
linear theory. The number of vortices that interact and the growth rate of
disturbances are well predicted by a simple stability model of a
two-dimensional row of vortices. For all types of motion, the highest growth
rate is observed when the frequency of motion is one and a half the frequency
of rotation of the turbine; that induces the out-of-phase vortex pairing
mechanism. For lower frequencies of motion, several vortices coalesce to form
large flow structures, which cause high amplitude of oscillations in the
streamwise velocities, that may increase fatigue or induce high amplitude
motion on downstream turbines.


title: Information-Theoretic Approaches to Differential Privacy

abstract:
  The tutorial studies relationships between differential privacy and various
information-theoretic measures using several selective articles. In particular,
we present how these connections can provide new interpretations for the
privacy guarantee in systems that deploy differential privacy in an
information-theoretic framework. To this end, the tutorial provides an
extensive summary on the existing literature that makes use of
information-theoretic measures and tools such as mutual information, min
entropy, Kullback-Leibler divergence and rate-distortion function for
quantification and characterization of differential privacy in various
settings.


title: On Robust Classification using Contractive Hamiltonian Neural ODEs

abstract:
  Deep neural networks can be fragile and sensitive to small input
perturbations that might cause a significant change in the output. In this
paper, we employ contraction theory to improve the robustness of neural ODEs
(NODEs). A dynamical system is contractive if all solutions with different
initial conditions converge to each other asymptotically. As a consequence,
perturbations in initial conditions become less and less relevant over time.
Since in NODEs, the input data corresponds to the initial condition of
dynamical systems, we show contractivity can mitigate the effect of input
perturbations. More precisely, inspired by NODEs with Hamiltonian dynamics, we
propose a class of contractive Hamiltonian NODEs (CH-NODEs). By properly tuning
a scalar parameter, CH-NODEs ensure contractivity by design and can be trained
using standard backpropagation and gradient descent algorithms. Moreover,
CH-NODEs enjoy built-in guarantees of non-exploding gradients, which ensures a
well-posed training process. Finally, we demonstrate the robustness of CH-NODEs
on the MNIST image classification problem with noisy test datasets.


title: Cogroupoid structures on the circle and the Hodge degeneration

abstract:
  We exhibit the Hodge degeneration from nonabelian Hodge theory as a $2$-fold
delooping filtered filtered loop space $E_2$-groupoid in formal moduli
problems. This is an iterated groupoid object which in degree $1$ recovers the
filtered circle $S^1_{fil}$ of [MRT19]. This exploits a hitherto unstudied
additional piece of structure on the topological circle, that of an
$E_2$-cogroupoid object in the $\infty$-category of spaces. We relate this
cogroupoid structure with the more commonly studied "pinch map" on $S^1$, as
well as the Todd class of the Lie algebroid $\mathbb{T}_{X}$; this is an
invariant of a smooth and proper scheme $X$ that arises, for example, in the
Grothendieck-Riemann Roch theorem. In particular we relate the existence of
non-trivial Todd classes for schemes to the failure of the pinch map to be
formal in the sense of rational homotopy theory. Finally we record some
consequences of this bit of structure at the level of Hochschild cohomology.


title:
A New Approach to Improve Learning-based Deepfake Detection in Realistic
  Conditions

abstract:
  Deep convolutional neural networks have achieved exceptional results on
multiple detection and recognition tasks. However, the performance of such
detectors are often evaluated in public benchmarks under constrained and
non-realistic situations. The impact of conventional distortions and processing
operations found in imaging workflows such as compression, noise, and
enhancement are not sufficiently studied. Currently, only a few researches have
been done to improve the detector robustness to unseen perturbations. This
paper proposes a more effective data augmentation scheme based on real-world
image degradation process. This novel technique is deployed for deepfake
detection tasks and has been evaluated by a more realistic assessment
framework. Extensive experiments show that the proposed data augmentation
scheme improves generalization ability to unpredictable data distortions and
unseen datasets.


title:
High-precision chemical abundances of Galactic building blocks. II.
  Revisiting the chemical distinctness of the Helmi streams

abstract:
  Context: The Helmi streams are kinematic substructures whose progenitor is
likely a dwarf galaxy. Although 20 years have past since their discovery, it is
still unclear whether their members are chemically distinguishable from other
halo stars in the Milky Way. Aim: We aim to precisely characterize the chemical
properties of the Helmi streams. Methods: We analysed high-resolution,
high-signal-to-noise ratio spectra for eleven Helmi streams' stars through a
line-by-line abundance analysis. We compared the derived abundances to
homogenized literature abundances of the other halo stars, including those
belonging to other kinematic substructures, such as Gaia-Enceladus and Sequoia.
Results: Compared to typical halo stars, the Helmi streams' members clearly
show low values of [X/Fe] in elements produced by massive stars, such as Na and
$\alpha$-elements. This tendency is seen down to metallicities of at least
$[\mathrm{Fe/H}]\sim -2.2$, suggesting type~Ia supernovae already started to
contribute to the chemical evolution at this metallicity. We found that the
[$\alpha$/Fe] ratio does not evolve much with metallicity, making the Helmi
streams' stars less distinguishable from Gaia-Enceladus stars at
$[\mathrm{Fe/H}]\gtrsim -1.5$. The almost constant but low value of
[$\alpha$/Fe] might be indicative of quiescent star formation with low
efficiency at the beginning and bursty star formation at later times. We also
found extremely low values of [Y/Fe] at low metallicity, providing further
support that light neutron capture-elements are deficient in Helmi streams.
While Zn is deficient at low metallicity, it shows a large spread at high
metallicity. The origin of the extremely low Y abundances and Zn variations
remain unclear. Conclusion: The Helmi streams' stars are distinguishable from
the majority of the halo stars if homogeneously derived abundances are
compared.


title: Viscous and centrifugal instabilities of massive stars

abstract:
  Massive stars exhibit a variety of instabilities, many of which are poorly
understood. We explore instabilities induced by centrifugal forces and angular
momentum transport in massive rotating stars. First, we derive and numerically
solve linearized oscillation equations for adiabatic radial modes in polytropic
stellar models. In the presence of differential rotation, we show that
centrifugal and Coriolis forces combined with viscous angular momentum
transport can excite stellar pulsation modes, under both low- or high-viscosity
conditions. In the low-viscosity limit, which is common in real stars, we
demonstrate how to compute mode growth/damping rates via a work integral.
Finally, we build realistic rotating $30\,M_\odot$ star models and show that
overstable (growing) radial modes are predicted to exist for most of the star's
life, in the absence of non-adiabatic effects. Peak growth rates are predicted
to occur while the star is crossing the Hertzsprung-Russell gap, though
non-adiabatic damping may dominate over viscous driving, depending on the
effective viscosity produced by convective and/or magnetic torques. Viscous
instability could be a new mechanism to drive massive star pulsations and is
possibly related to instabilities of luminous blue variable stars.


title:
Analysis Method of Strapdown Inertial Navigation Error Distribution
  Based on Covariance Matrix Decomposition

abstract:
  Error distribution analysis is an important assistant technology for the
research of SINS(Strapdown Inertial Navigation System). Error distribution
result can provide the contribution of different errors to final navigation
error, which is helpful for modifying and optimizing SINS. To realize
decomposing the navigation error into parts that caused by each error source,
the SINS error state space model is established and covariance matrix is
decomposed according to error sources. The proposed error distribution analysis
method based on 34-dimension SINS error model can quantitatively analyze the
contribution to the end navigation error of initial errors, IMU(Inertial
Measurement Unit) bias, IMU scale factor errors, mounting errors of gyroscopes
and accelerometers, and IMU stochastic errors. The simulations in static
condition and single axis rotation condition indict that the distribution
result of proposed analysis method accords with the law of error propagation.
After trajectory determined, the corresponding error distribution result will
be calculated with the proposed method. Compared with the Monte-Carlo method
and other method based on covariance matrix, the proposed method uses more
complete error model, considers the interaction effect of error sources and can
be easily realized with less computation.


title: The geometry of Riemannian curvature radii

abstract:
  In this paper we explore the geometric structures associated with curvature
radii of curves with values on a Riemannian manifold $(M, g)$. We show the
existence of sub-Riemannian manifolds naturally associated with the curvature
radii and we investigate their properties. The main character of our
construction is a pair of global vector fields $f_1, f_2$, which encodes
intrinsic information about the geometry of $(M, g)$.


title:
Neural System Level Synthesis: Learning over All Stabilizing Policies
  for Nonlinear Systems

abstract:
  We address the problem of designing stabilizing control policies for
nonlinear systems in discrete-time, while minimizing an arbitrary cost
function. When the system is linear and the cost is convex, the System Level
Synthesis (SLS) approach offers an exact solution based on convex programming.
Beyond this case, a globally optimal solution cannot be found in a tractable
way, in general. In this paper, we develop a parametrization of all and only
the control policies stabilizing a given time-varying nonlinear system in terms
of the combined effect of 1) a strongly stabilizing base controller and 2) a
stable SLS operator to be freely designed. Based on this result, we propose a
Neural SLS (Neur-SLS) approach guaranteeing closed-loop stability during and
after parameter optimization, without requiring any constraints to be
satisfied. We exploit recent Deep Neural Network (DNN) models based on
Recurrent Equilibrium Networks (RENs) to learn over a rich class of nonlinear
stable operators, and demonstrate the effectiveness of the proposed approach in
numerical examples.


title:
Weight distributions of two classes of linear codes based on Gaussian
  period and Weil sums

abstract:
  In this paper, based on the theory of defining sets, two classes of at most
six-weight linear codes over $\mathbb{F}_p$ are constructed. The weight
distributions of the linear codes are determined by means of Gaussian period
and Weil sums. In some case, there is an almost optimal code with respect to
Griesmer bound, which is also an optimal one according to the online code
table. The linear codes can also be employed to get secret sharing schemes.


title: The current orbit of Methone (S/2004 S 1)

abstract:
  The Cassini spacecraft discovered many close-in small satellites in Saturnian
system, and several of them exhibit exotic orbital states due to interactions
with Mimas and the oblateness of the planet. This work is devoted to Methone,
which is currently involved in a 15:14 Mean-Motion Resonance with Mimas. We
give an in deep study the current orbit of Methone by analyzing and identifying
the short, resonant and long-term gravitational perturbations on its orbit. In
addition, we perform numerical integrations of full equations of motion of
ensembles of close-in small bodies orbiting the non-central field of Saturn.
Spectral analyses of the orbits and interpretation of them in dynamical maps
allow us to describe the orbit and the dynamics of Methone in view of resonant
and long-term dynamics.
  We show that the current geometric orbit of Methone is aligned with Mimas'
due to a forced resonant component in eccentricity, leading to simultaneous
oscillations of several critical angles of the expanded disturbing function.
Thus, we explain the simultaneous oscillations of four critical arguments
associated to the resonance.
  The mapping of the Mimas-Methone resonance shows that the domains of the
15:14 Mimas-Methone resonance are dominated by regular motions associated to
the Corotation resonance located at eccentricities lower than $\sim 0.015$ and
osculating semi-major axis in the interval 194,660-194,730 km. Methone is
currently located deeply within this site.


title: Clustering units in neural networks: upstream vs downstream information

abstract:
  It has been hypothesized that some form of "modular" structure in artificial
neural networks should be useful for learning, compositionality, and
generalization. However, defining and quantifying modularity remains an open
problem. We cast the problem of detecting functional modules into the problem
of detecting clusters of similar-functioning units. This begs the question of
what makes two units functionally similar. For this, we consider two broad
families of methods: those that define similarity based on how units respond to
structured variations in inputs ("upstream"), and those based on how variations
in hidden unit activations affect outputs ("downstream"). We conduct an
empirical study quantifying modularity of hidden layer representations of
simple feedforward, fully connected networks, across a range of
hyperparameters. For each model, we quantify pairwise associations between
hidden units in each layer using a variety of both upstream and downstream
measures, then cluster them by maximizing their "modularity score" using
established tools from network science. We find two surprising results: first,
dropout dramatically increased modularity, while other forms of weight
regularization had more modest effects. Second, although we observe that there
is usually good agreement about clusters within both upstream methods and
downstream methods, there is little agreement about the cluster assignments
across these two families of methods. This has important implications for
representation-learning, as it suggests that finding modular representations
that reflect structure in inputs (e.g. disentanglement) may be a distinct goal
from learning modular representations that reflect structure in outputs (e.g.
compositionality).


title:
A Novel Exploration of Diffusion Process based on Multi-types
  Galton-Watson Forests

abstract:
  Diffusion is a commonly used technique for spreading information from point
to point on a graph. The rationale behind diffusion is not clear. And the
multi-types Galton-Watson forest is a random model of population growth without
space or any other resource constraints. In this paper, we use the degenerated
multi-types Galton-Watson forest (MGWF) to interpret the diffusion process and
establish an equivalent relationship between them. With the two-phase setting
of the MGWF, one can interpret the diffusion process and the Google PageRank
system explicitly. It also improves the convergence behaviour of the iterative
diffusion process and Google PageRank system. We validate the proposal by
experiment while providing new research directions.


title: Modeling Tie Duration in ERGM-Based Dynamic Network Models

abstract:
  Krivitsky and Handcock (2014) proposed a Separable Temporal ERGM (STERGM)
framework for modeling social networks, which facilitates separable modeling of
the tie duration distributions and the structural dynamics of tie formation. In
this note, we explore the hazard structures achievable in this framework, with
first- and higher-order Markov assumptions, and propose ways to model a variety
of duration distributions in this framework.


title:
Deep Extragalactic VIsible Legacy Survey (DEVILS): The evolution of the
  stellar mass-size relation of bulges and disks since $z = 1$

abstract:
  We explore the evolution of the stellar mass-size relation of galaxies of
different morphological types and specifically bulge and disk components. We
use a sample of $\sim35,000$ galaxies within a redshift range $0 < z < 1$, and
stellar mass $\log_{10}(\mathrm{M}_*/\mathrm{M}_\odot) \geq 9.5$ volume-limited
sample drawn from the combined DEVILS and HST-COSMOS region for which we
presented a morphological classification into sub-classes of double-component
(BD), pure-disk (pD), elliptical (E), and compact (C) in Paper-I and a
structural decomposition into disk (D), diffuse bulge (dB), and compact bulge
(cB) in Paper-II. We find that compared to disks, ellipticals and bulges follow
steeper $M_*-R_e$ relations, likely indicating distinct evolutionary
mechanisms. Ellipticals and disk structures follow consistently unchanged
slopes of $\sim0.5$ and $\sim0.3$, respectively, at all redshifts. We quantify
that disks follow a redshift independent $M_*-R_e$ slope regardless of the
presence or absence of a bulge component (i.e., BD or pD) suggesting a similar
origin and evolutionary pathway for all disks. Since $z = 1$ compact-bulges
present a steepening relation which do not follow that of Es whilst
diffuse-bulges experience a modest flattening. Overall, we find a close-to-no
variation in the $M_*-R_e$ relations over the last $\sim8$ Gyr suggesting that
despite ongoing although declining star-formation, mass evolution,
morphological transitions and mergers, evolution moves galaxies along their
$M_*-R_e$ trails. This seems to be consistent with an inside-out growth and
evolution picture in which galaxies grow in size as they do in stellar mass.
Besides, minor mergers are likely to be responsible for the growth of Es, at
least in $z < 1$.


title: A Broad Study of Pre-training for Domain Generalization and Adaptation

abstract:
  Deep models must learn robust and transferable representations in order to
perform well on new domains. While domain transfer methods (e.g., domain
adaptation, domain generalization) have been proposed to learn transferable
representations across domains, they are typically applied to ResNet backbones
pre-trained on ImageNet. Thus, existing works pay little attention to the
effects of pre-training on domain transfer tasks. In this paper, we provide a
broad study and in-depth analysis of pre-training for domain adaptation and
generalization, namely: network architectures, size, pre-training loss, and
datasets. We observe that simply using a state-of-the-art backbone outperforms
existing state-of-the-art domain adaptation baselines and set new baselines on
Office-Home and DomainNet improving by 10.7\% and 5.5\%. We hope that this work
can provide more insights for future domain transfer research.


title: Dealing with Logs and Zeros in Regression Models

abstract:
  Log-linear models are prevalent in empirical research. Yet, how to handle
zeros in the dependent variable remains an unsettled issue. This article
clarifies it and addresses the log of zero by developing a new family of
estimators called iterated Ordinary Least Squares (iOLS). This family nests
standard approaches such as log-linear and Poisson regressions, offers several
computational advantages, and corresponds to the correct way to perform the
popular $\log(Y+1)$ transformation. We extend it to the endogenous regressor
setting (i2SLS) and overcome other common issues with Poisson models, such as
controlling for many fixed-effects. We also develop specification tests to help
researchers select between alternative estimators. Finally, our methods are
illustrated through numerical simulations and replications of landmark
publications.


title: A substandard candle: the low-$\nu$ method at few-GeV neutrino energies

abstract:
  As accelerator-neutrino oscillation experiments improve oscillation parameter
constraints, control over systematic uncertainties on the incoming neutrino
flux and interaction models is increasingly important. The intense beams
offered by modern experiments permit a variety of options to constrain the flux
using in situ "standard candle" measurements. These standard candles must use
very well understood processes to avoid introducing bias. One option discussed
in this context is the "low-$\nu$" method, designed to isolate neutrino
interactions where there is low energy-transfer to the nucleus, such that the
cross section is expected to be approximately constant as a function of
neutrino energy. The shape of the low-energy transfer event sample can then be
used to extract the flux shape. Applications of the method at high neutrino
energies (many tens of GeV) are well understood. However, the applicability of
the method at the few-GeV energies of current and future accelerator neutrino
experiments remains unclear due to the presence of nuclear and form-factor
effects.
  In this analysis we examine the prospects for improving constraints on
accelerator neutrino fluxes with the low-$\nu$ method in an
experiment-independent way, using (anti)neutrino interactions on argon and
hydrocarbon targets from the GENIE, NEUT, NuWro and GiBUU event generators. The
results show that flux constraints from the low-$\nu$ method would be severely
dependent on the specific interaction model assumptions used in an analysis for
neutrino energies less than 5 GeV. The spread of model predictions show that a
low-$\nu$ analysis is unlikely to offer much improvement on typical neutrino
flux uncertainties, even with a perfect detector. Notably -- running counter to
the assumption inherent to the low-$\nu$ method -- the model-dependence
increases with decreasing energy transfer for experiments in the few-GeV
region.


title: Extensions of exact and K-mixing dynamical systems

abstract:
  We consider extensions of non-singular maps which are exact, respectively
K-mixing, or at least have a decomposition into positive-measure exact,
respectively K-mixing, components. The fibers of the extension spaces have
countable (finite or infinite) cardinality and the action on them is assumed
surjective or bijective. We call these systems, respectively, fiber-surjective
and fiber-bijective extensions. Technically, they are skew products, though the
point of view we take here is not the one generally associated with skew
products. Our main results are an Exact and a K-mixing Decomposition Theorem.
The latter can be used to show that a large number of periodic Lorentz gases
(the term denoting here general group extensions of Sinai billiards, including
Lorentz tubes and slabs, in any dimension) are K-mixing.


title: Observation of Elastic Orbital Angular Momentum Transfer

abstract:
  Research into the orbital angular momentum carried by helical wave-fronts has
been dominated by the fields of electromagnetism and acoustics, owing to its
practical utility in sensing, communication and tweezing. Despite the huge
research effort across the wave community, only recently has elastic orbital
angular momentum been theoretically shown to exhibit similar properties. Here
we experimentally observe the transfer of elastic orbital angular momentum from
a hollow elastic pipe to a fluid in which the pipe is partially submerged, in
an elastic analogue of Durnin's slit-ring experiment for optical beams. This
transfer is achieved by coupling the dilatational component of guided flexural
waves in the pipe with the pressure field in the fluid; the circumferential
distribution of the normal stress in the pipe acts as a continuous phased
pressure source in the fluid resulting in the generation of Bessel-like
acoustic beams. This demonstration has implications for future research into a
new regime of orbital angular momentum for elastic waves, as well providing a
new method to excite acoustic beams that carry orbital angular momentum that
could create a new paradigm shift for acoustic tweezing.


title: Was that so hard? Estimating human classification difficulty

abstract:
  When doctors are trained to diagnose a specific disease, they learn faster
when presented with cases in order of increasing difficulty. This creates the
need for automatically estimating how difficult it is for doctors to classify a
given case. In this paper, we introduce methods for estimating how hard it is
for a doctor to diagnose a case represented by a medical image, both when
ground truth difficulties are available for training, and when they are not.
Our methods are based on embeddings obtained with deep metric learning.
Additionally, we introduce a practical method for obtaining ground truth human
difficulty for each image case in a dataset using self-assessed certainty. We
apply our methods to two different medical datasets, achieving high Kendall
rank correlation coefficients, showing that we outperform existing methods by a
large margin on our problem and data.


title: Macrosteps dynamics and the growth of crystals and epitaxial layers

abstract:
  Step pattern stability of the vicinal surfaces during growth was analyzed
using various surface kinetic models. It was shown that standard analysis of
the vicinal surfaces provides no indication on the possible step coalescence
and therefore could not be used to elucidate macrostep creation during growth.
A scenario of the instability, leading go macrostep creation was based on the
dynamics of the step train. The critical is step motion at the rear of the
train which leads to double and multiple step creation. The condition is that
the step density ratio in and out of the train lower than 2 prevents double
step formation irrespective of the kinetics. For higher step density ratio low
density of the step promotes single step stability. Fast step kinetics from
lower terrace stabilizes the single steps slow (high barrier) is promoting step
coalescence. The incorporation kinetics from upper terrace role is close to
neutral. The creation of double step creates slow the step in front to
accelerate and catch the previous double step while those behind catch up the
double step creating multistep structure. The multistep are not mobile as the
alimentation leads to emission of single step which moves forward. The final
structure consist of macrosteps and superterraces with the number of single
steps moving forward. Thus the single step motion is essential crystal growth
mode despite the presence of the macrosteps. The macrostep are prone to
creation of the overhangs which results from surface dynamics coupling to
impingement from the mother phase. The angular preferential access of the bulk
material to the macrostep edge, leads to diffusive instability. Therefore it is
expected that harmful influence of the macrosteps by creation of inclusions and
dislocation is stronger during growth from the liquid phase.


title:
Reduction of Register Pushdown Systems with Freshness Property to
  Pushdown Systems in LTL Model Checking

abstract:
  Pushdown systems (PDS) are known as an abstract model of recursive programs,
and model checking methods for PDS have been studied. Register PDS (RPDS) are
PDS augmented by registers to deal with data values from an infinite domain in
a restricted way. A linear temporal logic (LTL) model checking method for RPDS
with regular valuations has been proposed; however, the method requires the
register automata (RA) used for representing a regular valuation to be
backward-deterministic. This paper proposes another approach to the same
problem, in which the model checking problem for RPDS is reduced to that
problem for PDS by constructing a PDS bisimulation equivalent to a given RPDS.
The construction in the proposed method is simpler than the previous model
checking method and does not require RAs deterministic or
backward-deterministic, and the bisimulation equivalence clearly guarantees the
correctness of this reduction. On the other hand, the proposed method requires
every RPDS (and RA) to have the freshness property, in which whenever the RPDS
updates a register with a data value not stored in any register or the stack
top, the value should be fresh. This paper also shows that this model checking
problem with regular valuations defined by general RA is undecidable, and thus
the freshness constraint is essential in the proposed method.


title: On the road to percent accuracy: The Reaction Way

abstract:
  Taking advantage of the unprecedented statistical power of upcoming cosmic
shear surveys will require exquisite knowledge of the matter power spectrum
over a wide range of scales. Analytical methods can achieve such precision only
up to quasi-linear scales. For smaller non-linear scales we must resort to
$N$-body and hydrodynamical simulations, which despite recent technological
advances and improved algorithms remain computationally expensive. Over the
past decade machine learning and the advent of emulators have propelled our
ability to hit the target accuracy with impressive computing efficiency. Yet,
realistically these techniques will be able to produce high-precision
non-linear matter power spectra only for a restricted sub-set of extensions to
the "vanilla" ${\Lambda}$CDM cosmology. I will present a promising alternative
to alleviate these shortcomings that draws strength from the combination of
halo model, perturbation theory and emulators--the reaction framework. I will
show how a power spectrum evolved in the standard cosmology can be readily
adjusted to account for physics beyond ${\Lambda}$CDM, and discuss the accuracy
of the reaction for well-known modifications to gravity, dark energy
parametrizations and massive neutrino cosmologies.


title:
Explainable Landscape Analysis in Automated Algorithm Performance
  Prediction

abstract:
  Predicting the performance of an optimization algorithm on a new problem
instance is crucial in order to select the most appropriate algorithm for
solving that problem instance. For this purpose, recent studies learn a
supervised machine learning (ML) model using a set of problem landscape
features linked to the performance achieved by the optimization algorithm.
However, these models are black-box with the only goal of achieving good
predictive performance, without providing explanations which landscape features
contribute the most to the prediction of the performance achieved by the
optimization algorithm. In this study, we investigate the expressiveness of
problem landscape features utilized by different supervised ML models in
automated algorithm performance prediction. The experimental results point out
that the selection of the supervised ML method is crucial, since different
supervised ML regression models utilize the problem landscape features
differently and there is no common pattern with regard to which landscape
features are the most informative.


title:
Provable Constrained Stochastic Convex Optimization with XOR-Projected
  Gradient Descent

abstract:
  Provably solving stochastic convex optimization problems with constraints is
essential for various problems in science, business, and statistics. Recently
proposed XOR-Stochastic Gradient Descent (XOR-SGD) provides a convergence rate
guarantee solving the constraints-free version of the problem by leveraging
XOR-Sampling. However, the task becomes more difficult when additional equality
and inequality constraints are needed to be satisfied. Here we propose XOR-PGD,
a novel algorithm based on Projected Gradient Descent (PGD) coupled with the
XOR sampler, which is guaranteed to solve the constrained stochastic convex
optimization problem still in linear convergence rate by choosing proper step
size. We show on both synthetic stochastic inventory management and real-world
road network design problems that the rate of constraints satisfaction of the
solutions optimized by XOR-PGD is $10\%$ more than the competing approaches in
a very large searching space. The improved XOR-PGD algorithm is demonstrated to
be more accurate and efficient than both XOR-SGD and SGD coupled with MCMC
based samplers. It is also shown to be more scalable with respect to the number
of samples and processor cores via experiments with large dimensions.


title: Conformal Bootstrap on the Annulus in Liouville CFT

abstract:
  This paper is the first of a series of work on boundary bootstrap in
Liouville conformal field theory (CFT), which focuses on the case of the
annulus with two boundary insertions lying on different boundaries. In the
course of proving the bootstrap formula, we established several properties on
the corresponding annulus conformal blocks: 1) we show that they converge
everywhere on the spectral line and they are continuous with respect to the
spectrum; 2) we relate them to their torus counterparts by rigorously
implementing Cardy's doubling trick for boundary CFT; 3) we extend the
definition of these conformal blocks to the whole Seiberg bound; 4) when there
is one insertion and its weight is $\gamma$, we show that the block degenerates
and the bootstrap formula settles a conjecture of Martinec, which was properly
written in Ang, Remy and Sun (2021). As an intermediate step, we obtain robust
estimates on the boundary-bulk correlator around the spectrum line. As an
application of our bootstrap result, we give an exact formula for the bosonic
LQG partition function of the annulus when $\gamma\in (0,2)$. Our paper serves
as a key ingredient in the recent derivation of the random moduli for the
Brownian annulus by Ang, Remy and Sun (2022).


title: Transverse Mass Scaling of Heavy Mesons at LHC Energies

abstract:
  Transverse mass scaling has been observed for a wide range of particle
species in proton-proton collisions at various energies from the SPS to RHIC
and the LHC. The observed scaling is known to be different for baryons and
mesons, and this work presents a comprehensive study of the transverse mass
scaling of mesons at LHC energies with a focus on heavier mesons. The study
demonstrates patterns in the scaling properties of mesons, which are related to
the particle quark content. In particular, light species and ground-state
quarkonia obey the same scaling, whereas open flavor particles deviate from it
because their spectra are significantly harder. The magnitude of deviation
depends on the flavor of the heaviest quark in the meson. By extending the
transverse mass scaling assumption to the excited bottomonia states, it is
observed that the measured cross-sections of $\Upsilon$(2S) and $\Upsilon$(3S)
are reduced by factors of 1.6 and 2.4 compared to the expectation from the
scaling.


title: Cross-View Panorama Image Synthesis

abstract:
  In this paper, we tackle the problem of synthesizing a ground-view panorama
image conditioned on a top-view aerial image, which is a challenging problem
due to the large gap between the two image domains with different view-points.
Instead of learning cross-view mapping in a feedforward pass, we propose a
novel adversarial feedback GAN framework named PanoGAN with two key components:
an adversarial feedback module and a dual branch discrimination strategy.
First, the aerial image is fed into the generator to produce a target panorama
image and its associated segmentation map in favor of model training with
layout semantics. Second, the feature responses of the discriminator encoded by
our adversarial feedback module are fed back to the generator to refine the
intermediate representations, so that the generation performance is continually
improved through an iterative generation process. Third, to pursue
high-fidelity and semantic consistency of the generated panorama image, we
propose a pixel-segmentation alignment mechanism under the dual branch
discrimiantion strategy to facilitate cooperation between the generator and the
discriminator. Extensive experimental results on two challenging cross-view
image datasets show that PanoGAN enables high-quality panorama image generation
with more convincing details than state-of-the-art approaches. The source code
and trained models are available at \url{https://github.com/sswuai/PanoGAN}.


title:
On well-posedness of quantum fluid systems in the class of dissipative
  solutions

abstract:
  The main objects of the present work are the quantum Navier-Stokes and
quantum Euler systems; for the first one, in particular, we will consider
constant viscosity coefficients. We deal with the concept of dissipative
solutions, for which we will first prove the weak-strong uniqueness principle
and afterwards, we will show the global existence for any finite energy initial
data. Finally, we will prove that both systems admit a semiflow selection in
the class of dissipative solutions.


title: Improving Generalization in Federated Learning by Seeking Flat Minima

abstract:
  Models trained in federated settings often suffer from degraded performances
and fail at generalizing, especially when facing heterogeneous scenarios. In
this work, we investigate such behavior through the lens of geometry of the
loss and Hessian eigenspectrum, linking the model's lack of generalization
capacity to the sharpness of the solution. Motivated by prior studies
connecting the sharpness of the loss surface and the generalization gap, we
show that i) training clients locally with Sharpness-Aware Minimization (SAM)
or its adaptive version (ASAM) and ii) averaging stochastic weights (SWA) on
the server-side can substantially improve generalization in Federated Learning
and help bridging the gap with centralized models. By seeking parameters in
neighborhoods having uniform low loss, the model converges towards flatter
minima and its generalization significantly improves in both homogeneous and
heterogeneous scenarios. Empirical results demonstrate the effectiveness of
those optimizers across a variety of benchmark vision datasets (e.g.
CIFAR10/100, Landmarks-User-160k, IDDA) and tasks (large scale classification,
semantic segmentation, domain generalization).


title: Rendering Layered Materials with Diffuse Interfaces

abstract:
  In this work, we introduce a novel method to render, in real-time, Lambertian
surfaces with a rough dieletric coating. We show that the appearance of such
configurations is faithfully represented with two microfacet lobes accounting
for direct and indirect interactions respectively. We numerically fit these
lobes based on the first order directional statistics (energy, mean and
variance) of light transport using 5D tables and narrow them down to 2D + 1D
with analytical forms and dimension reduction. We demonstrate the quality of
our method by efficiently rendering rough plastics and ceramics, closely
matching ground truth. In addition, we improve a state-of-the-art layered
material model to include Lambertian interfaces.


title: A Real-time Junk Food Recognition System based on Machine Learning

abstract:
  $ $As a result of bad eating habits, humanity may be destroyed. People are
constantly on the lookout for tasty foods, with junk foods being the most
common source. As a consequence, our eating patterns are shifting, and we're
gravitating toward junk food more than ever, which is bad for our health and
increases our risk of acquiring health problems. Machine learning principles
are applied in every aspect of our lives, and one of them is object recognition
via image processing. However, because foods vary in nature, this procedure is
crucial, and traditional methods like ANN, SVM, KNN, PLS etc., will result in a
low accuracy rate. All of these issues were defeated by the Deep Neural
Network. In this work, we created a fresh dataset of 10,000 data points from 20
junk food classifications to try to recognize junk foods. All of the data in
the data set was gathered using the Google search engine, which is thought to
be one-of-a-kind in every way. The goal was achieved using Convolution Neural
Network (CNN) technology, which is well-known for image processing. We achieved
a 98.05\% accuracy rate throughout the research, which was satisfactory. In
addition, we conducted a test based on a real-life event, and the outcome was
extraordinary. Our goal is to advance this research to the next level, so that
it may be applied to a future study. Our ultimate goal is to create a system
that would encourage people to avoid eating junk food and to be
health-conscious. \keywords{ Machine Learning \and junk food \and object
detection \and YOLOv3 \and custom food dataset.}


title: Gain and phase type multipliers for structured feedback robustness

abstract:
  It is known that the stability of a feedback interconnection of two linear
time-invariant systems implies that the graphs of the open-loop systems are
quadratically separated. This separation is defined by an object known as the
multiplier. The theory of integral quadratic constraints shows that the
converse also holds under certain conditions. This paper establishes that if
the feedback is robustly stable against certain structured uncertainty, then
there always exists a multiplier that takes a corresponding form. In
particular, if the feedback is robustly stable to certain gain-type
uncertainty, then there exists a corresponding multiplier that is of
phase-type, i.e., its diagonal blocks are zeros. These results build on the
notion of phases of matrices and systems, which was recently introduced in the
field of control. Similarly, if the feedback is robustly stable to certain
phase-type uncertainty, then there exists a gain-type multiplier, i.e., its
off-diagonal blocks are zeros. The results are meaningfully instructive in the
search for a valid multiplier for establishing robust closed-loop stability,
and cover the well-known small-gain and the recent small-phase theorems.


title:
Anyonic Defect Branes and Conformal Blocks in Twisted Equivariant
  Differential (TED) K-theory

abstract:
  We demonstrate that twisted equivariant differential K-theory of transverse
complex curves accommodates exotic charges of the form expected of
codimension=2 defect branes, such as of D7-branes in IIB/F-theory on A-type
orbifold singularities, but also of their dual 3-brane defects of class-S
theories on M5-branes. These branes have been argued, within F-theory and the
AGT correspondence, to carry special SL(2)-monodromy charges not seen for other
branes, but none of these had previously been identified in the expected brane
charge quantization law given by K-theory.
  Here we observe that it is the subtle (and previously somewhat neglected)
twisting of equivariant K-theory by flat complex line bundles appearing inside
orbi-singularities ("inner local systems") that makes the secondary Chern
character on a punctured plane inside an A-type singularity evaluate to the
twisted holomorphic de Rham cohomology which Feigin, Schechtman & Varchenko
showed realizes sl(2,C)-conformal blocks, here in degree 1 -- in fact it gives
the direct sum of these over all admissible fractional levels. The remaining
higher-degree conformal blocks appear similarly if we assume our previously
discussed "Hypothesis H" about brane charge quantization in M-theory. Since
conformal blocks -- and hence these twisted equivariant secondary Chern
characters -- solve the Knizhnik-Zamolodchikov equation and thus constitute
representations of the braid group of motions of defect branes inside their
transverse space, this provides a concrete first-principles realization of
anyon statistics of -- and hence of topological quantum computation on --
defect branes in string/M-theory.

categories: hep-th math-ph math.AT math.DG math.MP quant-ph


title:
Piecewise discretization of monodromy operators of delay equations on
  adapted meshes

abstract:
  Periodic solutions of delay equations are usually approximated as continuous
piecewise polynomials on meshes adapted to the solutions' profile. In practical
computations this affects the regularity of the (coefficients of the)
linearized system and, in turn, the effectiveness of assessing local stability
by approximating the Floquet multipliers. To overcome this problem when
computing multipliers by collocation, the discretization grid should include
the piecewise adapted mesh of the computed periodic solution. By introducing a
piecewise version of existing pseudospectral techniques, we explain why and
show experimentally that this choice is essential in presence of either strong
mesh adaptation or nontrivial multipliers whose eigenfunctions' profile is
unrelated to that of the periodic solution.


title:
Field-Induced Electron Generation in Water: Solvation Dynamics and
  Many-Body Interactions

abstract:
  The solvated electron represents an elementary quantum system in a liquid
environment. Electrons solvated in water have raised strong interest because of
their prototypical properties, their role in radiation chemistry, and their
relevance for charge separation and transport. Nonequilibrium dynamics of
photogenerated electrons in water occur on ultrafast time scales and include
charge transfer, localization, and energy dissipation processes. We present new
insight in the role of fluctuating electric fields of the liquid for generating
electrons in presence of an external terahertz field and address polaronic
many-body properties of solvated electrons. The article combines a review of
recent results from experiment and theory with a discussion of basic electric
interactions of electrons in water.


title:
SU-NLP at SemEval-2022 Task 11: Complex Named Entity Recognition with
  Entity Linking

abstract:
  This paper describes the system proposed by Sabanc{\i} University Natural
Language Processing Group in the SemEval-2022 MultiCoNER task. We developed an
unsupervised entity linking pipeline that detects potential entity mentions
with the help of Wikipedia and also uses the corresponding Wikipedia context to
help the classifier in finding the named entity type of that mention. Our
results showed that our pipeline improved performance significantly, especially
for complex entities in low-context settings.


title:
X-MEN: Guaranteed XOR-Maximum Entropy Constrained Inverse Reinforcement
  Learning

abstract:
  Inverse Reinforcement Learning (IRL) is a powerful way of learning from
demonstrations. In this paper, we address IRL problems with the availability of
prior knowledge that optimal policies will never violate certain constraints.
Conventional approaches ignoring these constraints need many demonstrations to
converge. We propose XOR-Maximum Entropy Constrained Inverse Reinforcement
Learning (X-MEN), which is guaranteed to converge to the optimal policy in
linear rate w.r.t. the number of learning iterations. X-MEN embeds XOR-sampling
-- a provable sampling approach that transforms the #P complete sampling
problem into queries to NP oracles -- into the framework of maximum entropy
IRL. X-MEN also guarantees the learned policy will never generate trajectories
that violate constraints. Empirical results in navigation demonstrate that
X-MEN converges faster to the optimal policies compared to baseline approaches
and always generates trajectories that satisfy multi-state combinatorial
constraints.


title:
C,T1,T2: A complementary method to detect Multiple Populations with the
  Washington filter system

abstract:
  In this research we test the ability of a three Washington filter
combination, (C-T1)-(T1-T2), compared with that of the traditional C-T1 color
to find multiple populations on two globular clusters: NGC 7099 and NGC 1851,
types I and II Globular clusters, respectively. Our improved photometry and
membership selection, now using Gaia proper motions, finds that the second
population stars are more centrally concentrated than first population stars,
as expected and contrary to our previous findings for NGC 7099. We find that
multiple populations are more easily detected in both clusters using the new
(C-T1)-(T1-T2) color, although C-T1 conserves the best width/error ratio. We
also search for differences of both colors while splitting the red-RGB and the
blue-RGB in NGC 1851, but no significant improvement was found.


title: Spatial ecology, optimal control and game theoretical fishing problems

abstract:
  Of paramount importance in both ecological systems and economic policies are
the problems of harvesting of natural resources. A paradigmatic situation where
this question is raised is that of fishing strategies. Indeed, overfishing is a
well-known problem in the management of live-stocks, as being too greedy may
lead to an overall dramatic depletion of the population we are harvesting. A
closely related topic is that of Nash equilibria in the context of fishing
policies. Namely, two players being in competition for the same pool of
resources, is it possible for them to find an equilibrium situation? The goal
of this paper is to provide a detailed analysis of these two queries
(\emph{i.e} optimal fishing strategies for single-player models and study of
Nash equilibria for multiple players games) by using a basic yet instructive
mathematical model, the logistic-diffusive equation. In this framework, the
underlying model simply reads $-\mu\Delta \theta=\theta(K(x)-\alpha(x)-\theta)$
where $K$ accounts for natural resources, $\theta$ for the density of the
population that is being harvested and $\alpha=\alpha(x)$ encodes either the
single player fishing strategy or, when dealing with Nash equilibria, a
combination of the fishing strategies of both players. This article consists of
two main parts. The first one gives a very fine characterisation of the
optimisers for the single-player game. In the case where two players are
involved, we aim at finding a Nash equilibrium. We prove the existence of Nash
equilibria in several different regimes \textcolor{black}{and investigate
several related qualitative queries}.Our study is completed by a variety of
numerical simulations that illustrate our results and allow us to formulate
open questions and conjectures.


title: Dualities in the complicial model of $\infty$-categories

abstract:
  In this note, we study the connection between Gray tensor product and
suspension. We derive a characterization of weak equivalences as fully faithful
and essentially surjective functors. We construct the $co$ duality, a weak
involution that reverses the direction of even dimensional cells. We conclude
by studying Grothendieck fibrations of complicial sets.


title:
Snowmass 2021 White Paper: Tabletop experiments for infrared quantum
  gravity

abstract:
  Progress in the quantum readout and control of mechanical devices from single
atoms to large masses may enable a first generation of experiments probing the
gravitational interaction in the quantum regime, conceivably within the next
decade. In this Snowmass whitepaper, we briefly outline the possibilities and
challenges facing the realization of these experiments. In particular, we
emphasize the need for detailed theories of modifications to the usual
effective QFT of gravitons in the infrared regime $E/L^3 \ll m_{\rm
Pl}/\ell_{\rm Pl}^3$ in which these experiments operate, and relations to
possible UV completions.


title: Spectral Algorithms Optimally Recover (Censored) Planted Dense Subgraphs

abstract:
  We study spectral algorithms for the planted dense subgraph problem (PDS), as
well as for a censored variant (CPDS) of PDS, where the edge statuses are
missing at random. More precisely, in the PDS model, we consider $n$ vertices
and a random subset of vertices $S^{\star}$ of size $\Omega(n)$, such that two
vertices share an edge with probability $p$ if both of them are in $S^{\star}$,
and all other edges are present with probability $q$, independently. The goal
is to recover $S^{\star}$ from one observation of the network. In the CPDS
model, edge statuses are revealed with probability $\frac{t \log n}{n}$. For
the PDS model, we show that a simple spectral algorithm based on the top two
eigenvectors of the adjacency matrix can recover $S^{\star}$ up to the
information theoretic threshold. Prior work by Hajek, Wu and Xu required a less
efficient SDP based algorithm to recover $S^{\star}$ up to the information
theoretic threshold. For the CDPS model, we obtain the information theoretic
limit for the recovery problem, and further show that a spectral algorithm
based on a special matrix called the signed adjacency matrix recovers
$S^{\star}$ up to the information theoretic threshold.


title:
Electronic structure and open-orbit Fermi surface topology in
  isostructural semimetals NbAs$_2$ and W$_2$As$_3$ with extremely large
  magnetoresistance

abstract:
  In transition-metal dipnictides $TmPn_2$ ($Tm$ = Ta, Nb; $Pn$ = P, As, Sb),
the origin of extremely large magnetoresistance (XMR) is yet to be studied by
the direct visualization of the experimental band structures. Here, using
angle-resolved photoemission spectroscopy, we map out the three-dimensional
electronic structure of NbAs$_2$. The open-orbit topology contributes to a
non-negligible part of the Fermi surfaces (FSs), like that of the isostructural
compound MoAs$_2$, where the open FS is proposed to likely explain the origin
of XMR. We further demonstrate the observation of open characters in the
overall FSs of W$_2$As$_3$, which is also a XMR semimetal with the same space
group of $C$12/$m$1 as $TmPn_2$ family and MoAs$_2$. Our results suggest that
the open-orbit FS topology may be a shared feature between XMR materials with
the space group of $C$12/$m$1, and thus could possibly play a role in
determining the corresponding XMR effect together with the electron-hole
compensation.


title:
A Girl Has A Name, And It's ... Adversarial Authorship Attribution for
  Deobfuscation

abstract:
  Recent advances in natural language processing have enabled powerful
privacy-invasive authorship attribution. To counter authorship attribution,
researchers have proposed a variety of rule-based and learning-based text
obfuscation approaches. However, existing authorship obfuscation approaches do
not consider the adversarial threat model. Specifically, they are not evaluated
against adversarially trained authorship attributors that are aware of
potential obfuscation. To fill this gap, we investigate the problem of
adversarial authorship attribution for deobfuscation. We show that
adversarially trained authorship attributors are able to degrade the
effectiveness of existing obfuscators from 20-30% to 5-10%. We also evaluate
the effectiveness of adversarial training when the attributor makes incorrect
assumptions about whether and which obfuscator was used. While there is a a
clear degradation in attribution accuracy, it is noteworthy that this
degradation is still at or above the attribution accuracy of the attributor
that is not adversarially trained at all. Our results underline the need for
stronger obfuscation approaches that are resistant to deobfuscation


title:
Superradiant Quantum Phase transition for Landau Polaritons with Rashba
  and Zeeman couplings

abstract:
  We develop a theory of cavity quantum electrodynamics for a two-dimensional
electron gas in the presence of Rashba spin-orbit and Zeeman couplings and
perpendicular magnetic field, coupled to a spatially nonuniform quantum photon
field. We show that the superradiant quantum phase transition (SQPT), also
known as photon condensation, can in principle occur through a pure in-plane
Zeeman coupling, but it requires extremely small (unrealistic) quantum well
widths or extremely fine tuning of the effective Land\'e factor which makes two
Landau levels coincide. Landau level crossings can also be induced by the
Rashba spin-orbit coupling and they promote the SQPT which can be obtained for
certain values of the effective Land\'e factor and filling factors.


title:
Cryptographic switching functions for multiplicative watermarking in
  cyber-physical systems

abstract:
  In this paper we present a novel switching function for multiplicative
watermarking systems. The switching function is based on the algebraic
structure of elliptic curves over finite fields. The resulting function allows
for both watermarking generator and remover to define appropriate system
parameters, sharing only limited information, namely a private key. Given the
definition of the switching function, we prove that the resulting watermarking
parameters lead to a stable watermarking scheme.


title:
A Survey on Techniques for Identifying and Resolving Representation Bias
  in Data

abstract:
  The grand goal of data-driven decision-making is to help humans make
decisions, not only easily and at scale but also wisely, accurately, and just.
However, data-driven algorithms are only as good as the data they work with,
while data sets, especially social data, often miss representing minorities.
Representation Bias in data can happen due to various reasons ranging from
historical discrimination to selection and sampling biases in the data
acquisition and preparation methods. One cannot expect AI-based societal
solutions to have equitable outcomes without addressing the representation
bias. This paper surveys the existing literature on representation bias in the
data. It presents a taxonomy to categorize the studied techniques based on
multiple design dimensions and provide a side-by-side comparison of their
properties. There is still a long way to fully address representation bias
issues in data. The authors hope that this survey motivates researchers to
approach these challenges in the future by observing existing work within their
respective domains.


title:
ImageNet Challenging Classification with the Raspberry Pi: An
  Incremental Local Stochastic Gradient Descent Algorithm

abstract:
  With rising powerful, low-cost embedded devices, the edge computing has
become an increasingly popular choice. In this paper, we propose a new
incremental local stochastic gradient descent (SGD) tailored on the Raspberry
Pi to deal with large ImageNet ILSVRC 2010 dataset having 1,261,405 images with
1,000 classes. The local SGD splits the data block into $k$ partitions using
$k$means algorithm and then it learns in the parallel way SGD models in each
data partition to classify the data locally. The incremental local SGD
sequentially loads small data blocks of the training dataset to learn local SGD
models. The numerical test results on Imagenet dataset show that our
incremental local SGD algorithm with the Raspberry Pi 4 is faster and more
accurate than the state-of-the-art linear SVM run on a PC Intel(R) Core i7-4790
CPU, 3.6 GHz, 4 cores.


title:
Sionna: An Open-Source Library for Next-Generation Physical Layer
  Research

abstract:
  Sionna is a GPU-accelerated open-source library for link-level simulations
based on TensorFlow. It enables the rapid prototyping of complex communication
system architectures and provides native support for the integration of neural
networks. Sionna implements a wide breadth of carefully tested state-of-the-art
algorithms that can be used for benchmarking and end-to-end performance
evaluation. This allows researchers to focus on their research, making it more
impactful and reproducible, while saving time implementing components outside
their area of expertise. This white paper provides a brief introduction to
Sionna, explains its design principles and features, as well as future
extensions, such as integrated ray tracing and custom CUDA kernels. We believe
that Sionna is a valuable tool for research on next-generation communication
systems, such as 6G, and we welcome contributions from our community.


title: Class Field Theory and Arithmetic of Abelian Varieties over Local Fields

abstract:
  We use knowledge of local fields to adapt Jonathan Lubin and Michael Rosen's
proof of Mazur's Proposition 4.39. This changes the result about abelian
varieties from only working over local fields with a finite residue field to
working with local fields with an arbitrary perfect residue field of positive
characteristic. We then briefly discuss the Local Class Field Theory
implications of such information