title: RONELDv2: A faster, improved lane tracking method

abstract:
  Lane detection is an integral part of control systems in autonomous vehicles
and lane departure warning systems as lanes are a key component of the
operating environment for road vehicles. In a previous paper, a robust neural
network output enhancement for active lane detection (RONELD) method augmenting
deep learning lane detection models to improve active, or ego, lane accuracy
performance was presented. This paper extends the work by further investigating
the lane tracking methods used to increase robustness of the method to lane
changes and different lane dimensions (e.g. lane marking thickness) and
proposes an improved, lighter weight lane detection method, RONELDv2. It
improves on the previous RONELD method by detecting the lane point variance,
merging lanes to find a more accurate set of lane parameters, and using an
exponential moving average method to calculate more robust lane weights.
Experiments using the proposed improvements show a consistent increase in lane
detection accuracy results across different datasets and deep learning models,
as well as a decrease in computational complexity observed via an up to
two-fold decrease in runtime, which enhances its suitability for real-time use
on autonomous vehicles and lane departure warning systems.


title:
Numerical bifurcation analysis of improved denatured Morris-Lecar neuron
  model

abstract:
  It is well-known that the electrical activities of neurons are induced by a
wide variety of external factors. This work considers the effect of
electromagnetic induction on improved denatured Morris-Lecar neuron model. The
dependence of dynamical behaviour of the original denatured Morris-Lecar model
on parameters is addressed through numerical bifurcation analysis. This allows
us to explore the changes in dynamics of the model qualitatively as parameters
are varied. Then we investigate the effects of external periodic current and
electromagnetic flux on the dynamical properties of the improved denatured
Morris-Lecar neuron model. Different types of dynamical behaviour, ranging from
regular periodic spiking to complex bursting, are found when the multiple
parameters are varied simultaneously. The improved model could be applied to
research where simple models are required for physiological and
pathophysiological responses in neurons.


title:
Spatiotemporal pulse characterization with far-field beamlet
  cross-correlation

abstract:
  We present a novel, straightforward method for spatiotemporal
characterization of ultra-short laser pulses. The method employs far-field
interferometry and inverse Fourier transform spectroscopy, built on the
theoretical basis derived in this paper. It stands out in its simplicity: it
requires few non-standard optical elements and simple analysis algorithms. This
method was used to measure the space-time intensity of our 100 TW class laser
and to test the efficacy of a refractive doublet as a suppressor of pulse front
curvature (PFC). The measured low-order spatiotemporal couplings agreed with
ray-tracing simulations. In addition, we demonstrate a one-shot measurement
technique, derived from our central method, which allows for quick and precise
alignment of the compressor by pulse front tilt (PFT) minimization and for
optimal refractive doublet positioning for the suppression of PFC.


title:
Consensus Learning from Heterogeneous Objectives for One-Class
  Collaborative Filtering

abstract:
  Over the past decades, for One-Class Collaborative Filtering (OCCF), many
learning objectives have been researched based on a variety of underlying
probabilistic models. From our analysis, we observe that models trained with
different OCCF objectives capture distinct aspects of user-item relationships,
which in turn produces complementary recommendations. This paper proposes a
novel OCCF framework, named ConCF, that exploits the complementarity from
heterogeneous objectives throughout the training process, generating a more
generalizable model. ConCF constructs a multi-branch variant of a given target
model by adding auxiliary heads, each of which is trained with heterogeneous
objectives. Then, it generates consensus by consolidating the various views
from the heads, and guides the heads based on the consensus. The heads are
collaboratively evolved based on their complementarity throughout the training,
which again results in generating more accurate consensus iteratively. After
training, we convert the multi-branch architecture back to the original target
model by removing the auxiliary heads, thus there is no extra inference cost
for the deployment. Our extensive experiments on real-world datasets
demonstrate that ConCF significantly improves the generalization of the model
by exploiting the complementarity from heterogeneous objectives.


title: Irreducible magic sets for $n$-qubit systems

abstract:
  Magic sets of observables are minimal structures that capture quantum
state-independent advantage for systems of $n\ge 2$ qubits and are, therefore,
fundamental tools for investigating the interface between classical and quantum
physics. A theorem by Arkhipov (arXiv:1209.3819) states that $n$-qubit magic
sets in which each observable is in exactly two subsets of compatible
observables can be reduced either to the two-qubit magic square or the
three-qubit magic pentagram [N. D. Mermin, Phys. Rev. Lett. 65, 3373 (1990)].
An open question is whether there are magic sets that cannot be reduced to the
square or the pentagram. If they exist, a second key question is whether they
require $n >3$ qubits, since, if this is the case, these magic sets would
capture minimal state independent quantum advantage that is specific for
$n$-qubit systems with specific values of $n$. Here, we answer both questions
affirmatively. We identify magic sets which cannot be reduced to the square or
the pentagram and require $n=3,4,5$, or $6$ qubits. In addition, we prove a
generalized version of Arkhipov's theorem providing an efficient algorithm for,
given a hypergraph, deciding whether or not it can accommodate a magic set, and
solve another open problem, namely, given a magic set, obtaining the tight
bound of its associated noncontextuality inequality.


title: Blind Image Super Resolution with Semantic-Aware Quantized Texture Prior

abstract:
  A key challenge of blind image super resolution is to recover realistic
textures for low-resolution images with unknown degradations. Most recent works
completely rely on the generative ability of GANs, which are difficult to
train. Other methods resort to high-resolution image references that are
usually not available. In this work, we propose a novel framework, denoted as
QuanTexSR, to restore realistic textures with the Quantized Texture Priors
encoded in Vector Quantized GAN. The QuanTexSR generates textures by aligning
the textureless content features to the quantized feature vectors, i.e., a
pretrained feature codebook. Specifically, QuanTexSR formulates the texture
generation as a feature matching problem between textureless features and a
pretrained feature codebook. The final textures are then generated by the
quantized features from the codebook. Since features in the codebook have shown
the ability to generate natural textures in the pretrain stage, QuanTexSR can
generate rich and realistic textures with the pretrained codebook as texture
priors. Moreover, we propose a semantic regularization technique that
regularizes the pre-training of the codebook using clusters of features
extracted from the pretrained VGG19 network. This further improves texture
generation with semantic context. Experiments demonstrate that the proposed
QuanTexSR can generate competitive or better textures than previous approaches.
Code will be made publicly available.


title: Two square weighted zero-sum constants

abstract:
  The constant $D_A(n)$ is defined to be the smallest natural number $k$ such
that any sequence of $k$ elements in $\mathbb Z_n$ has a subsequence whose
$A$-weighted sum is zero. i.e., some linear combination of its terms is zero,
where the coefficients are taken from the set $A$. We determine the value of
$D_A(n)$ and also the value of a related constant $C_A(n)$, when $A$ is the set
of all non-zero squares in $\mathbb Z_n$.


title:
DGSS : Domain Generalized Semantic Segmentation using Iterative Style
  Mining and Latent Representation Alignment

abstract:
  Semantic segmentation algorithms require access to well-annotated datasets
captured under diverse illumination conditions to ensure consistent
performance. However, poor visibility conditions at varying illumination
conditions result in laborious and error-prone labeling. Alternatively, using
synthetic samples to train segmentation algorithms has gained interest with the
drawback of domain gap that results in sub-optimal performance. While current
state-of-the-art (SoTA) have proposed different mechanisms to bridge the domain
gap, they still perform poorly in low illumination conditions with an average
performance drop of - 10.7 mIOU. In this paper, we focus upon single source
domain generalization to overcome the domain gap and propose a two-step
framework wherein we first identify an adversarial style that maximizes the
domain gap between stylized and source images. Subsequently, these stylized
images are used to categorically align features such that features belonging to
the same class are clustered together in latent space, irrespective of domain
gap. Furthermore, to increase intra-class variance while training, we propose a
style mixing mechanism wherein the same objects from different styles are mixed
to construct a new training image. This framework allows us to achieve a domain
generalized semantic segmentation algorithm with consistent performance without
prior information of the target domain while relying on a single source. Based
on extensive experiments, we match SoTA performance on SYNTHIA $\to$
Cityscapes, GTAV $\to$ Cityscapes while setting new SoTA on GTAV $\to$ Dark
Zurich and GTAV $\to$ Night Driving benchmarks without retraining.


title: QuoteR: A Benchmark of Quote Recommendation for Writing

abstract:
  It is very common to use quotations (quotes) to make our writings more
elegant or convincing. To help people find appropriate quotes efficiently, the
task of quote recommendation is presented, aiming to recommend quotes that fit
the current context of writing. There have been various quote recommendation
approaches, but they are evaluated on different unpublished datasets. To
facilitate the research on this task, we build a large and fully open quote
recommendation dataset called QuoteR, which comprises three parts including
English, standard Chinese and classical Chinese. Any part of it is larger than
previous unpublished counterparts. We conduct an extensive evaluation of
existing quote recommendation methods on QuoteR. Furthermore, we propose a new
quote recommendation model that significantly outperforms previous methods on
all three parts of QuoteR. All the code and data of this paper are available at
https://github.com/thunlp/QuoteR.


title: Effective field theory of pairing rotations

abstract:
  Pairing rotations are the low-energy excitations of finite superfluid
systems, connecting systems that differ in their number of Cooper pairs. This
paper presents a model-independent derivation of pairing rotations within an
effective theory that exploits the emergent breaking of U(1) phase symmetries.
The symmetries are realized nonlinearly and the Nambu-Goldstone modes depend
only on time because the system is finite. Semi-magic nuclei exhibit pairing
rotational bands while the pairing spectrum becomes an elliptical paraboloid
for open-shell nuclei. Model-independent relations between double
charge-exchange reactions and alpha particle capture or knockout in open-shell
nuclei are in analogy to the pair transfer reactions in a single superfluid.
Odd semi-magic nuclei are described by coupling a fermion to the superfluid.
The leading-order theories reproduce data for pairing rotational bands within
uncertainty estimates.


title: Prominence Oscillations activated by an EUV wave

abstract:
  Prominence oscillations are one of interesting phenomena in the solar
atmosphere, which can be utilized to infer the embedded magnetic field
magnitude. We present here the transverse oscillations of two different
prominences located at the East solar limb on 2011 February 11 using the
multi-wavebands data of the Atmospheric Imaging Assembly (AIA) on-board the
Solar Dynamics Observatory (SDO) satellite. A prominence eruption was observed
towards the east direction with an average speed of ~275 km/s. The eruption is
fitted with the combination of a linear and an exponential functions of time.
An extreme ultraviolet (EUV) wave event was associated with the prominence
eruption. This EUV wave triggered the oscillations of both prominences on the
East limb. We computed the period of each prominence using the wavelet analysis
method. The oscillation period varies from 14 to 22 min. The magnetic field of
the prominences was derived, which ranges from 14 to 20 G.


title: Gorenstein and duality pair over triangular matrix rings

abstract:
  Let $A$, $B$ be two rings and $T=\left(\begin{smallmatrix} A & M \\ 0 & B
\\\end{smallmatrix}\right)$ with $M$ an $A$-$B$-bimodule. We first construct a
semi-complete duality pair $\mathcal{D}_{T}$ of $T$-modules using duality pairs
in $A$-Mod and $B$-Mod respectively. Then we characterize when a left
$T$-module is Gorenstein $D_{T}$-projective, Gorenstein $D_{T}$-injective or
Gorenstein $D_{T}$-flat. These three class of $T$-modules will induce model
structures on $T$-Mod. Finally we show that the homotopy category of each of
model structures above admits a recollement relative to corresponding stable
categories. Our results give new characterizations to earlier results in this
direction.


title:
Electromagnetic counterparts of binary neutron star mergers leading to a
  strongly magnetized long-lived remnant neutron star

abstract:
  We explore the electromagnetic counterparts that will associate with binary
neutron star mergers for the case that remnant massive neutron stars survive
for $\gtrsim 0.5$ s after the merger. For this study, we employ the outflow
profiles obtained by long-term general-relativistic neutrino-radiation
magneto-hydrodynamics simulations with a mean field dynamo effect. We show that
a synchrotron afterglow with high luminosity can be associated with the merger
event if the magnetic fields of the remnant neutron stars are significantly
amplified by the dynamo effect. We also perform a radiative transfer
calculation for kilonovae and find that for the highly amplified magnetic field
cases, the kilonovae can be bright in the early epoch ($t\leq 0.5\,{\rm d}$),
while it shows rapid declining ($\lesssim 1\,{\rm d}$) emission and
long-lasting ($\sim 10\,{\rm d}$) emission in the optical and near-infrared
wavelength, respectively. All these features have not been found in GW170817,
indicating that the merger remnant neutron star formed in GW170817 might have
collapsed to a black hole within several hundreds ms or magnetic-field
amplification might be a minor effect.


title:
Importance of diagnostic accuracy in big data: False-positive diagnoses
  of type 2 diabetes in health insurance claims data of 70 million Germans

abstract:
  Large data sets comprising diagnoses about chronic conditions are becoming
increasingly available for research purposes. In Germany, it is planned that
aggregated claims data including medical diagnoses from the statutory health
insurance with roughly 70 million insurants will be published on a regular
basis. Validity of the diagnoses in such big data sets can hardly be assessed.
In case the data set comprises prevalence, incidence and mortality, it is
possible to estimate the proportion of false positive diagnoses using
mathematical relations from the illness-death model. We apply the method to
age-specific aggregated claims data from 70 million Germans about type 2
diabetes in Germany stratified by sex and report the findings in terms of the
ratio of false positive diagnoses of type 2 diabetes (FPR) in the data set. The
age-specific FPR for men and women changes with age. In men, the FPR increases
linearly from 1 to 3 per mil in the age 30 to 50. For ages between 50 to 80
years, FPR remains below 4 per mil. After 80 years of age, we have an increase
to about 5 per mil. In women, we find a steep increase from age 30 to 60, the
peak FPR is reached at about 12 per mil between 60 and 70 years of age. After
age 70, the FPR of women drops tremendously. In all age-groups, the FPR is
higher in women than in men. In terms of absolute numbers, we find that there
are 217 thousand people with a false-positive diagnosis in the data set (95%
confidence interval, CI: 204 to 229), the vast majority women (172 thousand,
95% CI: 162 to 180). Our work indicates that possible false positive (and
negative) diagnoses should appropriately be dealt with in claims data, e.g., by
inclusion of age- and sex-specific error terms in statistical models, to avoid
potentially biased or wrong conclusions.


title:
COMPASS: a Creative Support System that Alerts Novelists to the
  Unnoticed Missing Contents

abstract:
  When humans write, they may unintentionally omit some information.
Complementing the omitted information using a computer is helpful in providing
writing support. Recently, in the field of story understanding and generation,
story completion (SC) was proposed to generate the missing parts of an
incomplete story. Although its applicability is limited because it requires
that the user have prior knowledge of the missing part of a story, missing
position prediction (MPP) can be used to compensate for this problem. MPP aims
to predict the position of the missing part, but the prerequisite knowledge
that "one sentence is missing" is still required. In this study, we propose
Variable Number MPP (VN-MPP), a new MPP task that removes this restriction;
that is, the task to predict multiple missing sentences or to judge whether
there are no missing sentences in the first place. We also propose two methods
for this new MPP task. Furthermore, based on the novel task and methods, we
developed a creative writing support system, COMPASS. The results of a user
experiment involving professional creators who write texts in Japanese confirm
the efficacy and utility of the developed system.


title: Use of Set Shaping theory in the development of locally testable codes

abstract:
  In developing locally testable codes, information is added to the coded
message by creating redundancy in the codewords. In this article, we propose an
alternative method in which redundancy is introduced on the message that must
be transmitted before its encoding and not in the codewords. This approach
exploits the Set Shaping Theory whose goal is the study the bijection functions
f(X)=Y that transform a set of strings into a set of equal size made up of
strings of greater length. In this way, this type of function transforms the
independent variable x into the dependent variable y whose emission probability
is conditioned by the previously emitted variables. Thus, if the decoder
decodes a symbol associated with a conditional probability equal to zero, we
detect an error in the message. If the function f used is the one that
minimizes the average information content, we develop a code that can be tested
efficiently. In fact, it is observed, in terms of compression, that the greater
length of the strings is compensated by the fact of having chosen the strings
with less entropy.


title:
Phenomenology of a flavoured multiscalar BGL-like model with three
  generations of massive neutrinos

abstract:
  In this paper, we present several possible anomaly-free implementations of
the Branco-Grimus-Lavoura (BGL) model with two Higgs doublets and one singlet
scalar. The model also includes three generations of massive neutrinos that get
their mass via a type-I seesaw mechanism. A particular anomaly-free
realization, which we dub $\nu$BGL-1 scenario, is subjected to an extensive
phenomenological analysis, from the perspective of flavour physics and collider
phenomenology.


title:
On-chip mechanical exceptional points based on an optomechanical zipper
  cavity

abstract:
  Exceptional points (EPs) represent a distinct type of spectral singularity in
non-Hermitian systems, and intriguing physics concepts have been studied with
optical EPs recently. As a system beyond photonics, the mechanical oscillators
coupling with many physical systems are expected to be further exploited EPs
for mechanical sensing, topology energy transfer, nonreciprocal dynamics etc.
In this study, we demonstrated on-chip mechanical EPs with a silicon
optomechanical zipper cavity, wherein two near-degenerate mechanical breathing
modes are coupled via a single co-localized optical mode. By tailoring the
dissipative and coherent couplings between two mechanical oscillators, the
spectral splitting with 1/2 order response, a distinctive feature of EP, was
observed successfully. Our work provides an integrated platform for
investigating the physics related to mechanical EPs on silicon chips and
suggests their possible applications for ultrasensitive measurements.


title:
Integrating Text Inputs For Training and Adapting RNN Transducer ASR
  Models

abstract:
  Compared to hybrid automatic speech recognition (ASR) systems that use a
modular architecture in which each component can be independently adapted to a
new domain, recent end-to-end (E2E) ASR system are harder to customize due to
their all-neural monolithic construction. In this paper, we propose a novel
text representation and training framework for E2E ASR models. With this
approach, we show that a trained RNN Transducer (RNN-T) model's internal LM
component can be effectively adapted with text-only data. An RNN-T model
trained using both speech and text inputs improves over a baseline model
trained on just speech with close to 13% word error rate (WER) reduction on the
Switchboard and CallHome test sets of the NIST Hub5 2000 evaluation. The
usefulness of the proposed approach is further demonstrated by customizing this
general purpose RNN-T model to three separate datasets. We observe 20-45%
relative word error rate (WER) reduction in these settings with this novel LM
style customization technique using only unpaired text data from the new
domains.


title:
Impact of Interference Subtraction on Grant-Free Multiple Access with
  Massive MIMO

abstract:
  The design of highly scalable multiple access schemes is a main challenge in
the evolution towards future massive machine-type communications, where
reliability and latency constraints must be ensured to a large number of
uncoordinated devices. In this scenario, coded random access (CRA) schemes,
where successive interference cancellation algorithms allow large improvements
with respect to classical random access protocols, have recently attracted an
increasing interest. Impressive performance can be potentially obtained by
combining CRA with massive multiple input multiple output (MIMO). In this paper
we provide an analysis of such schemes focusing on the effects of imperfect
channel estimation on successive interference cancellation. Based on the
analysis we then propose an innovative signal processing algorithm for CRA in
massive MIMO systems.


title: High Dimensional Statistical Estimation under One-bit Quantization

abstract:
  Compared with data with high precision, one-bit (binary) data are preferable
in many applications because of the efficiency in signal storage, processing,
transmission, and enhancement of privacy. In this paper, we study three
fundamental statistical estimation problems, i.e., sparse covariance matrix
estimation, sparse linear regression, and low-rank matrix completion via binary
data arising from an easy-to-implement one-bit quantization process that
contains truncation, dithering and quantization as typical steps. Under both
sub-Gaussian and heavy-tailed regimes, new estimators that handle
high-dimensional scaling are proposed. In sub-Gaussian case, we show that our
estimators achieve minimax rates up to logarithmic factors, hence the
quantization nearly costs nothing from the perspective of statistical learning
rate. In heavy-tailed case, we truncate the data before dithering to achieve a
bias-variance trade-off, which results in estimators embracing convergence
rates that are the square root of the corresponding minimax rates. Experimental
results on synthetic data are reported to support and demonstrate the
statistical properties of our estimators under one-bit quantization.


title: Semantic Soundness for Language Interoperability

abstract:
  Programs are rarely implemented in a single language, and thus questions of
type soundness should address not only the semantics of a single language, but
how it interacts with others. Even between type-safe languages, disparate
features frustrate interoperability, as invariants from one language can easily
be violated in the other. In their seminal 2007 paper, Matthews and Findler
proposed a multi-language construction that augments the interoperating
languages with a pair of boundaries that allow code from one language to be
embedded in the other. While the technique has been widely applied, their
syntactic source-level interoperability doesn't reflect practical
implementations, where behavior of interaction is defined after compilation to
a common target, and any safety must be ensured by target invariants or
inserted target-level "glue code."
  In this paper, we present a framework for the design and verification of
sound language interoperability that follows an
interoperation-after-compilation strategy. Language designers specify what data
can be converted between types of the languages via a relation $\tau_A \sim
\tau_B$ and specify target glue code implementing conversions. Then, by giving
a semantic model of source types as sets of target terms, we can establish
soundness of conversions: i.e., whenever $\tau_A \sim \tau_B$, the
corresponding pair of conversions convert target terms that behave as $\tau_A$
to target terms that behave as $\tau_B$, and vice versa. We can then prove
semantic type soundness for the entire system. We illustrate our framework via
a series of case studies that demonstrate how our semantic
interoperation-after-compilation approach allows us both to account for complex
differences in language semantics and make efficiency trade-offs based on
particularities of compilers or targets.


title:
A Feasibility Study on Real-Time High Resolution Imaging of the Brain
  Using Electrical Impedance Tomography

abstract:
  Objective: The strengths of Electrical Impedance Tomography (EIT) are its
capability of imaging the internal body by using a noninvasive, radiation safe
technique, and the absence of known hazards. In this paper we introduce a novel
idea of using EIT in microelectrodes during Deep Brain Stimulation (DBS)
surgery in order to obtain an image of the electrical conductivities of the
brain tissues surrounding the microelectrodes. DBS is a surgical treatment
involving the implantation of a medical probe inside the brain. For such
application, the EIT reconstruction method has to offer both high quality and
robustness against noise. Methods: A post-processing method for open-domain EIT
is introduced in this paper, which combines linear and nonlinear methods in
order to use the advantages of both with limited drawbacks. The reconstruction
method is a two-steps method, the first solves the inverse problem with a
linear algorithm and the second brings the nonlinear aspects back into the
image. Results: The proposed method is tested on both simulation and phantom
data, and compared to three widely used method for EIT imaging. Resulting
images and errors gives a strong advantage to the proposed solution.
Conclusion: High quality reconstruction from phantom data validates the
efficiency of the novel reconstruction method. Significance: This feasibility
study presents an efficient method for open domain EIT and opens the way to
clinical trials.


title: Lasting effects of static magnetic field on classical Brownian motion

abstract:
  The Bohr-Van Leeuwen theorem states that an external static magnetic field
does not influence the state of a classical equilibrium system: there is no
equilibrium classical magnetism, since the magnetic field does not do work. In
equilibrium situation it restricts the influence of magnetic fields to the
quantum domain. However, experimental biophysics provides reasons to suspect
that static magnetic fields might influence classical systems even in the
long-time limit. Here we consider a classical charged Brownian particle
interacting with an equilibrium bath. We confirm that the Bohr-Van Leeuwen
theorem holds for the long-time (equilibrium) state of the particle. But the
external static, homogeneous magnetic field does influence the long-time state
of the thermal bath, which is described via the Caldeira-Leggett model. In
particular, the magnetic field induces an average angular momentum for the
(uncharged!) bath, which spontaneously separates into two sets rotating in
opposite directions. The effect relates to the bath going slightly out of
equilibrium under the influence of the Brownian particle and persists for
arbitrary long times.


title: Higher order Hermite-Fejer Interpolation on the unit circle

abstract:
  The aim of this paper is to study the approximation of functions using a
higher order Hermite-Fejer interpolation process on the unit circle. The system
of nodes is composed of vertically projected zeros of Jacobi polynomials onto
the unit circle with boundary points at $ \pm1 $. Values of the polynomial and
its first four derivatives are fixed by the interpolation conditions at the
nodes. Convergence of the process is obtained for analytic functions on a
suitable domain, and the rate of convergence is estimated.


title:
Pix2NeRF: Unsupervised Conditional $\pi$-GAN for Single Image to Neural
  Radiance Fields Translation

abstract:
  We propose a pipeline to generate Neural Radiance Fields~(NeRF) of an object
or a scene of a specific class, conditioned on a single input image. This is a
challenging task, as training NeRF requires multiple views of the same scene,
coupled with corresponding poses, which are hard to obtain. Our method is based
on $\pi$-GAN, a generative model for unconditional 3D-aware image synthesis,
which maps random latent codes to radiance fields of a class of objects. We
jointly optimize (1) the $\pi$-GAN objective to utilize its high-fidelity
3D-aware generation and (2) a carefully designed reconstruction objective. The
latter includes an encoder coupled with $\pi$-GAN generator to form an
auto-encoder. Unlike previous few-shot NeRF approaches, our pipeline is
unsupervised, capable of being trained with independent images without 3D,
multi-view, or pose supervision. Applications of our pipeline include 3d avatar
generation, object-centric novel view synthesis with a single input image, and
3d-aware super-resolution, to name a few.


title:
Statistically Efficient Advantage Learning for Offline Reinforcement
  Learning in Infinite Horizons

abstract:
  We consider reinforcement learning (RL) methods in offline domains without
additional online data collection, such as mobile health applications. Most of
existing policy optimization algorithms in the computer science literature are
developed in online settings where data are easy to collect or simulate. Their
generalizations to mobile health applications with a pre-collected offline
dataset remain unknown. The aim of this paper is to develop a novel advantage
learning framework in order to efficiently use pre-collected data for policy
optimization. The proposed method takes an optimal Q-estimator computed by any
existing state-of-the-art RL algorithms as input, and outputs a new policy
whose value is guaranteed to converge at a faster rate than the policy derived
based on the initial Q-estimator. Extensive numerical experiments are conducted
to back up our theoretical findings.


title: Edge Augmentation for Large-Scale Sketch Recognition without Sketches

abstract:
  This work addresses scaling up the sketch classification task into a large
number of categories. Collecting sketches for training is a slow and tedious
process that has so far precluded any attempts to large-scale sketch
recognition. We overcome the lack of training sketch data by exploiting labeled
collections of natural images that are easier to obtain. To bridge the domain
gap we present a novel augmentation technique that is tailored to the task of
learning sketch recognition from a training set of natural images.
Randomization is introduced in the parameters of edge detection and edge
selection. Natural images are translated to a pseudo-novel domain called
"randomized Binary Thin Edges" (rBTE), which is used as a training domain
instead of natural images. The ability to scale up is demonstrated by training
CNN-based sketch recognition of more than 2.5 times larger number of categories
than used previously. For this purpose, a dataset of natural images from 874
categories is constructed by combining a number of popular computer vision
datasets. The categories are selected to be suitable for sketch recognition. To
estimate the performance, a subset of 393 categories with sketches is also
collected.


title: The analytic structure of the fixed charge expansion

abstract:
  We investigate the analytic properties of the fixed charge expansion for a
number of conformal field theories in different space-time dimensions. The
models investigated here are $O(N)$ and $QED_3$. We show that in $d=3-\epsilon$
dimensions the contribution to the $O(N)$ fixed charge $Q$ conformal dimensions
obtained in the double scaling limit of large charge and vanishing $\epsilon$
is non-Borel summable, doubly factorial divergent, and with order $\sqrt{Q}$
optimal truncation order. By using resurgence techniques we show that the
singularities in the Borel plane are related to worldline instantons that were
discovered in the other double scaling limit of large $Q$ and $N$ of Ref. [1].
In $d=4-\epsilon$ dimensions the story changes since in the same large $Q$ and
small $\epsilon$ regime the next order corrections to the scaling dimensions
lead to a convergent series. The resummed series displays a new branch cut
singularity which is relevant for the stability of the $O(N)$ large charge
sector for negative $\epsilon$. Although the $QED_3$ model shares the same
large charge behaviour of the $O(N)$ model, we discover that at leading order
in the large number of matter field expansion the large charge scaling
dimensions are Borel summable, single factorial divergent, and with order $Q$
optimal truncation order.


title: Hydrological post-processing for predicting extreme quantiles

abstract:
  Hydrological post-processing using quantile regression algorithms constitutes
a prime means of estimating the uncertainty of hydrological predictions.
Nonetheless, conventional large-sample theory for quantile regression does not
apply sufficiently far in the tails of the probability distribution of the
dependent variable. To overcome this limitation that could be crucial when the
interest lies on flood events, we here introduce hydrological post-processing
through extremal quantile regression for estimating the extreme quantiles of
hydrological responses. In summary, the new hydrological post-processing method
exploits properties of the Hill's estimator from the extreme value theory to
extrapolate quantile regression's predictions to high quantiles. As a proof of
concept, the new method is here tested in post-processing daily streamflow
simulations provided by three process-based hydrological models for 180 basins
in the contiguous United States (CONUS) and is further compared to conventional
quantile regression. With this large-scale comparison, it is demonstrated that
hydrological post-processing using conventional quantile regression severely
underestimates high quantiles (at the quantile level 0.9999) compared to
hydrological post-processing using extremal quantile regression, although both
methods are equivalent at lower quantiles (at the quantile level 0.9700).
Moreover, it is shown that, in the same context, extremal quantile regression
estimates the high predictive quantiles with efficiency that is, on average,
equivalent in the large-sample study for the three process-based hydrological
models.


title: The $m$-bipartite Ramsey number of the $K_{2,2}$ versus $K_{6,6}$

abstract:
  Given bipartite graphs $G_1, \ldots, G_n$, the bipartite Ramsey number
$BR(G_1,\ldots, G_n)$ is the last integer $b$ such that any complete bipartite
graph $K_{b,b}$ with edges coloured with colours $1,2,\ldots,n$ contains a copy
of some $G_i$ ($1\leq i\leq n$) where all edges of $G_i$ have colour $i$. As
another view of bipartite Ramsey numbers, for given bipartite graphs $G_1,
\ldots, G_n$ and a positive integer $m$, the $m$-bipartite Ramsey number
$BR_m(G_1, \ldots, G_n)$, is defined as the least integer $b$, such that any
complete bipartite graph $K_{m,b}$ with edges coloured with colours
$1,2,\ldots,n$ contains a copy of some $G_i$ ($1\leq i\leq n$) where all edges
of $G_i$ have colour $i$. The size of $BR_m(G_1, G_2)$, where $G_1=K_{2,2}$ and
$G_2\in \{K_{3,3}, K_{4,4}\}$ for each $m$ and the size of $BR_m(K_{3,3},
K_{3,3})$ and $BR_m(K_{2,2}, K_{5,5})$ for special values of $m$, have been
determined in several article up to now. In this article, we compute the size
of $BR_m(K_{2,2}, K_{6,6})$ for some $m\geq 2$.


title:
Hubble Space Telescope Detection of the Nucleus of Comet C/2014
  UN$_{271}$ (Bernardinelli-Bernstein)

abstract:
  We present a high-resolution observation of distant comet C/2014 UN$_{271}$
(Bernardinelli-Bernstein) using the {\it Hubble Space Telescope} on 2022
January 8. The signal of the nucleus was successfully isolated by means of the
nucleus extraction technique, with an apparent $V$-band magnitude measured to
be $21.64 \pm 0.11$, corresponding to an absolute magnitude of $8.62 \pm 0.11$.
The product of the visual geometric albedo with the effective radius squared is
$p_V R_n^2$ = 159$\pm$16 km$^2$. If the ALMA observation by Lellouch et al.
(2022) refers to a bare nucleus, we derive a visual geometric albedo of $0.034
\pm 0.008$ and an effective diameter of $137 \pm 15$ km. If dust contamination
of the ALMA signal is present at the maximum allowed level (24%), we find
nucleus diameter $119 \pm 13$ km and albedo of $0.044 \pm 0.011$. In either
case, we confirm that C/2014 UN$_{271}$ is the largest long-period comet ever
detected. Judging from the measured surface brightness profile of the coma,
whose logarithmic gradient varies azimuthally between $\sim$1 and 1.7 in
consequence of solar radiation pressure, the mass production is consistent with
steady-state production but not with impulsive ejection, as would be produced
by an outburst. Using aperture photometry we estimated an enormous (albeit
uncertain) mass-loss rate of $\sim$10$^3$ kg s$^{-1}$ at a heliocentric
distance of $\sim$20 au.


title: A Systematic Evaluation of Large Language Models of Code

abstract:
  Large language models (LMs) of code have recently shown tremendous promise in
completing code and synthesizing code from natural language descriptions.
However, the current state-of-the-art code LMs (e.g., Codex (Chen et al.,
2021)) are not publicly available, leaving many questions about their model and
data design decisions. We aim to fill in some of these blanks through a
systematic evaluation of the largest existing models: Codex, GPT-J, GPT-Neo,
GPT-NeoX-20B, and CodeParrot, across various programming languages. Although
Codex itself is not open-source, we find that existing open-source models do
achieve close results in some programming languages, although targeted mainly
for natural language modeling. We further identify an important missing piece
in the form of a large open-source model trained exclusively on a multi-lingual
corpus of code. We release a new model, PolyCoder, with 2.7B parameters based
on the GPT-2 architecture, which was trained on 249GB of code across 12
programming languages on a single machine. In the C programming language,
PolyCoder outperforms all models including Codex. Our trained models are
open-source and publicly available at https://github.com/VHellendoorn/Code-LMs,
which enables future research and application in this area.


title:
Unsupervised Domain Adaptive Salient Object Detection Through
  Uncertainty-Aware Pseudo-Label Learning

abstract:
  Recent advances in deep learning significantly boost the performance of
salient object detection (SOD) at the expense of labeling larger-scale
per-pixel annotations. To relieve the burden of labor-intensive labeling, deep
unsupervised SOD methods have been proposed to exploit noisy labels generated
by handcrafted saliency methods. However, it is still difficult to learn
accurate saliency details from rough noisy labels. In this paper, we propose to
learn saliency from synthetic but clean labels, which naturally has higher
pixel-labeling quality without the effort of manual annotations. Specifically,
we first construct a novel synthetic SOD dataset by a simple copy-paste
strategy. Considering the large appearance differences between the synthetic
and real-world scenarios, directly training with synthetic data will lead to
performance degradation on real-world scenarios. To mitigate this problem, we
propose a novel unsupervised domain adaptive SOD method to adapt between these
two domains by uncertainty-aware self-training. Experimental results show that
our proposed method outperforms the existing state-of-the-art deep unsupervised
SOD methods on several benchmark datasets, and is even comparable to
fully-supervised ones.


title: The topological Petersson product

abstract:
  The nondegeneracy of the Petersson inner product on cusp forms, and the fact
that Hecke operators are self-adjoint with respect to the Petersson product,
together imply that the cusp forms have a basis consisting of Hecke eigenforms.
In the literature on topological modular forms, no topological analogue of the
Petersson product is to be found, and it is not known which topological spaces
have the property that their topological cusp forms admit a basis consisting of
eigenforms for the action of Baker's topological Hecke operators. In this note
we define and study a natural topological Petersson product on complexified
topological cusp forms, whose value on a one-point space recovers the classical
Petersson product. We find that the topological Petersson product is usually
degenerate: in particular, if $X$ is a space with nontrivial rational homology
in any positive degree, then the Petersson product on complexified $tcf^*(X)$
is degenerate. Despite $tcf$-cohomology being a stable invariant, the
topological Petersson product is an unstable invariant, vanishing on all
suspensions. Nevertheless, we demonstrate nontriviality of the topological
Petersson product by giving an explicit calculation of the topological
Petersson product on complexified $tcf$-cohomology of the complex projective
plane. We show that, for a compact Kahler manifold $X$, the Petersson product
is nondegenerate on complexified $tcf$-cohomology of $X$ in a range of
Atiyah-Hirzebruch filtrations (essentially one-third of the possibly-nonzero
Atiyah-Hirzebruch filtrations in the $tcf$-cohomology of $X$).


title:
Fixing the rotating-wave approximation for strongly-detuned quantum
  oscillators

abstract:
  Periodically-driven oscillators are commonly described in a frame co-rotating
with the drive and using the rotating-wave approximation (RWA). This
description, however, is known to induce errors for off-resonant driving. Here
we show that the standard quantum description, using creation and annihilation
of particles with the oscillator's natural frequency, necessarily leads to
incorrect results when combined with the RWA. We demonstrate this on the simple
harmonic oscillator and present an alternative operator basis which reconciles
the RWA with off-resonant driving. The approach is also applicable to more
complex models, where it accounts for known discrepancies. As an example, we
demonstrate the advantage of our scheme on the driven quantum Duffing
oscillator.


title:
On the barrier problem of branching random walk in time-inhomogeneous
  random environment

abstract:
  We consider a supercritical branching random walk in time-inhomogeneous
random environment with a random absorption barrier, i.e.,in each generation,
only the individuals born below the barrier can survive and reproduce. Assume
that the random environment is i.i.d..The barrier is set as
$\chi_n+an^{\alpha},$ where $a,\alpha$ are two constants and $\{\chi_n\}$ is a
certain i.i.d. random walk determined by the random environment.We show that
for almost surely given environment (i.e., a sequence of point processes which
is a realization of the random environment), the time-inhomogeneous branching
random walk under the given environment will become extinct (resp., survive
with positive probability) if $\alpha<1/3$ or $\alpha=1/3, a<a_c$ (resp.,
$\alpha>1/3, a>0$ or $\alpha=1/3, a>a_c$), where $a_c$ is a positive constant
determined by the random environment. The rates of extinction when
$\alpha<\frac{1}{3}, a\geq0$ and $\alpha=1/3, a\in(0,a_c)$ are also obtained.
These extend the main results in A\"{\i}d\'{e}kon $\&$ Jaffuel (2011) and
Jaffuel (2012),to the random environment case. The influence caused by the
random environment have been specified.


title:
BioADAPT-MRC: Adversarial Learning-based Domain Adaptation Improves
  Biomedical Machine Reading Comprehension Task

abstract:
  Motivation: Biomedical machine reading comprehension (biomedical-MRC) aims to
comprehend complex biomedical narratives and assist healthcare professionals in
retrieving information from them. The high performance of modern neural
network-based MRC systems depends on high-quality, large-scale, human-annotated
training datasets. In the biomedical domain, a crucial challenge in creating
such datasets is the requirement for domain knowledge, inducing the scarcity of
labeled data and the need for transfer learning from the labeled
general-purpose (source) domain to the biomedical (target) domain. However,
there is a discrepancy in marginal distributions between the general-purpose
and biomedical domains due to the variances in topics. Therefore,
direct-transferring of learned representations from a model trained on a
general-purpose domain to the biomedical domain can hurt the model's
performance.
  Results: We present an adversarial learning-based domain adaptation framework
for the biomedical machine reading comprehension task (BioADAPT-MRC), a neural
network-based method to address the discrepancies in the marginal distributions
between the general and biomedical domain datasets. BioADAPT-MRC relaxes the
need for generating pseudo labels for training a well-performing biomedical-MRC
model. We extensively evaluate the performance of BioADAPT-MRC by comparing it
with the best existing methods on three widely used benchmark biomedical-MRC
datasets -- BioASQ-7b, BioASQ-8b, and BioASQ-9b. Our results suggest that
without using any synthetic or human-annotated data from the biomedical domain,
BioADAPT-MRC can achieve state-of-the-art performance on these datasets.
  Availability: BioADAPT-MRC is freely available as an open-source project
at\\https://github.com/mmahbub/BioADAPT-MRC


title: Steady-state theory of electron drag on polariton condensates

abstract:
  We present a general theory of drag on a condensate due to interactions with
a moving thermal bath of non-condensate particles, adapted from previous theory
of equilibration of a condensate in a trap. This theory can be used to model
the polariton drag effect observed previously, in which an electric current
passing through a polariton condensate gives a measurable momentum transfer to
the condensate, and an effective potential energy shift.


title:
Constructing the $r$-uniform supertrees with the same spectral radius
  and matching energyv

abstract:
  An $r$-uniform supertree is a connected and acyclic hypergraph of which each
edge has $r$ vertices, where $r\geq 3$. We propose the concept of matching
energy for an $r$-uniform hypergraph, which is defined as the sum of the
absolute value of all the eigenvalues of its matching polynomial. With the aid
of the matching polynomial of an $r$-uniform supertree, three pairs of
$r$-uniform supertrees with the same spectral radius and the same matching
energy are constructed, and two infinite families of $r$-uniform supertrees
with the same spectral radius and the same matching energy are characterized.
Some known results about the graphs with the same spectra regarding to their
adjacency matrices can be naturally deduced from our new results.


title: On the chromatic number of some $P_5$-free graphs

abstract:
  Let $G$ be a graph. We say that $G$ is perfectly divisible if for each
induced subgraph $H$ of $G$, $V(H)$ can be partitioned into $A$ and $B$ such
that $H[A]$ is perfect and $\omega(H[B])<\omega(H)$. We use $P_t$ and $C_t$ to
denote a path and a cycle on $t$ vertices, respectively. For two disjoint
graphs $F_1$ and $F_2$, we use $F_1\cup F_2$ to denote the graph with vertex
set $V(F_1)\cup V(F_2)$ and edge set $E(F_1)\cup E(F_2)$, and use $F_1+F_2$ to
denote the graph with vertex set $V(F_1)\cup V(F_2)$ and edge set $E(F_1)\cup
E(F_2)\cup \{xy\;|\; x\in V(F_1)\mbox{ and } y\in V(F_2)\}$. In this paper, we
prove that (i) $(P_5, C_5, K_{2, 3})$-free graphs are perfectly divisible, (ii)
$\chi(G)\le 2\omega^2(G)-\omega(G)-3$ if $G$ is $(P_5, K_{2,3})$-free with
$\omega(G)\ge 2$, (iii) $\chi(G)\le {3\over 2}(\omega^2(G)-\omega(G))$ if $G$
is $(P_5, K_1+2K_2)$-free, and (iv) $\chi(G)\le 3\omega(G)+11$ if $G$ is $(P_5,
K_1+(K_1\cup K_3))$-free.


title:
Extraction of $\alpha_s$ using Borel-Laplace sum rules for tau decay
  data

abstract:
  Double-pinched Borel-Laplace sum rules are applied to ALEPH $\tau$-decay
data. For the leading-twist ($D=0$) Adler function a renormalon-motivated
extension is used, and the 5-loop coefficient is taken to be $d_4=275 \pm 63$.
Two $D=6$ terms appear in the truncated OPE ($D \leq 6$) to enable cancellation
of the corresponding renormalon ambiguities. Two variants of the fixed order
perturbation theory, and the inverse Borel transform, are applied to the
evaluation of the $D=0$ contribution. Truncation index $N_t$ is fixed by the
requirement of local insensitivity of the momenta $a^{(2,0)}$ and $a^{(2,1)}$
under variation of $N_t$. The averaged value of the coupling obtained is
$\alpha_s(m_{\tau}^2)=0.3235^{+0.0138}_{-0.0126}$ [$\alpha_s(M_Z^2)=0.1191 \pm
0.0016$]. The theoretical uncertainties are significantly larger than the
experimental ones.


title:
On the Achievability of Coded Multicasting Fronthaul Delivery in
  Fog-Aided Networks

abstract:
  Fog networks benefit from content caching at edge nodes (ENs) as well as
fronthaul connectivity to the cloud. In previous works, both dedicated and
multicast fronthaul links has been considered under different assumptions with
Normalized Delivery Time (NDT) as a standard to measure the performance. While
dedicated links has been studied extensively in different caching scenarios,
multicast links has been developed under assumption of 2-by-2 fog network where
cloud is connected to two ENs serving two users. The extension to arbitrary
number of ENs and users only provides the lower bound on the achievable
performance. In contrast, this letter proposes an achievable coded multicasting
scheme for a general M-by-K fog network with M ENs and K users. The optimality
of the proposed scheme is evaluated using both comparison with theoretical
lower bound as well as using numerical analysis.


title:
Dirac-Coulomb Operators with Infinite Mass Boundary Conditions in
  Sectors

abstract:
  We investigate the properties of self-adjointness of a two-dimensional Dirac
operator on an infinite sector with infinite mass boundary conditions and in
presence of a Coulomb-type potential with the singularity placed on the vertex.
In the general case, we prove the appropriate Dirac-Hardy inequality and
exploit the Kato-Rellich theory. In the explicit case of a Coulomb potential,
we describe the self-adjoint extensions for all the intensities of the
potential relying on a radial decomposition in partial wave subspaces adapted
to the infinite-mass boundary conditions. Finally, we integrate our results
giving a description of the spectrum of these operators.


title:
Review of Hybrid Load Balancing Algorithms in Cloud Computing
  Environment

abstract:
  In cloud computing environment, load balancing is a key issue which is
required to distribute the dynamic workload over multiple machines to make
certain that no single machine is overloaded. In recent research, many
organizations lose significant part of their revenues in handling the requests
given by the clients over the web servers i.e. unable to balance the load for
web servers which results in loss of data, delay in time and increased costs.
Various static and dynamic algorithms have been proposed and implemented in the
past but this have not been fully efficient for load balancing. This gave room
to hybrid algorithms. Hybrid methods inherit the properties from both static
and dynamic load balancing techniques and attempts at overcoming the limitation
of both algorithms. This paper is a study of various hybrid load balancing
algorithms in cloud computing environment.


title: On solutions of the Diophantine equation $L_n+L_m=3^a$

abstract:
  Let $(L_n)_{n\geq 0}$ be the Lucas sequence given by $L_0 = 2, L_1 = 1$ and
$L_{n+2} = L_{n+1}+L_n$ for $n \geq 0$. In this paper, we are interested in
finding all powers of three which are sums of two Lucas numbers, i.e., we study
the exponential Diophantine equation $L_n + L_m = 3^{a}$ in nonnegative
integers $n, m,$ and $a$. The proof of our main theorem uses lower bounds for
linear forms in logarithms, properties of continued fractions, and a version of
the Baker-Davenport reduction method in Diophantine approximation.


title:
Depth and Stanley depth of powers of the edge ideals of some caterpillar
  and lobster trees

abstract:
  Let $S$ be a ring of polynomials in finitely many variables over a field. In
this paper we give lower bounds for depth and Stanley depth of modules of the
type $S/I^t$ for $t\geq1$, where $I$ is the edge ideal of some caterpillar and
lobster trees. These new bounds are much sharper than the existing bounds for
the classes of ideals we considered.


title:
Kinematic Control of Redundant Robots with Online Handling of Variable
  Generalized Hard Constraints

abstract:
  We present a generalized version of the Saturation in the Null Space (SNS)
algorithm for the task control of redundant robots when hard inequality
constraints are simultaneously present both in the joint and in the Cartesian
space. These hard bounds should never be violated, are treated equally and in a
unified way by the algorithm, and may also be varied, inserted or deleted
online. When a joint/Cartesian bound saturates, the robot redundancy is
exploited to continue fulfilling the primary task. If no feasible solution
exists, an optimal scaling procedure is applied to enforce directional
consistency with the original task. Simulation and experimental results on
different robotic systems demonstrate the efficiency of the approach. The
proposed algorithm can be viewed as a generic platform that is easily
applicable to any robotic application in which robots operate in an
unstructured environment and online handling of joint and Cartesian constraints
is critical.


title:
Domain Knowledge-Based Automated Analog Circuit Design with Deep
  Reinforcement Learning

abstract:
  The design automation of analog circuits is a longstanding challenge in the
integrated circuit field. This paper presents a deep reinforcement learning
method to expedite the design of analog circuits at the pre-layout stage, where
the goal is to find device parameters to fulfill desired circuit
specifications. Our approach is inspired by experienced human designers who
rely on domain knowledge of analog circuit design (e.g., circuit topology and
couplings between circuit specifications) to tackle the problem. Unlike all
prior methods, our method originally incorporates such key domain knowledge
into policy learning with a graph-based policy network, thereby best modeling
the relations between circuit parameters and design targets. Experimental
results on exemplary circuits show it achieves human-level design accuracy
(~99%) with 1.5x efficiency of existing best-performing methods. Our method
also shows better generalization ability to unseen specifications and
optimality in circuit performance optimization. Moreover, it applies to
designing diverse analog circuits across different semiconductor technologies,
breaking the limitations of prior ad-hoc methods in designing one particular
type of analog circuits with conventional semiconductor technology.


title: Ekpyrotic bounce driven by Kalb-Ramond field

abstract:
  We propose an ekpyrotic bounce scenario driven by a second rank antisymmetric
Kalb-Ramond field, where the universe initially contracts through an ekpyrotic
stage having a non-singular bouncing like behaviour, and consequently, it
smoothly transits to an expanding phase. In particular, the KR field has an
interaction with a scalaron field (coming from higher curvature d.o.f) by a
linear coupling. The interaction energy density between the KR and the scalaron
grows faster than $a^{-6}$ during the contraction phase -- which has negligible
effects at the distant past, however as the universe continues to contract,
this interaction energy density gradually grows and plays a significant role to
trigger a non-singular bounce at a minimum value of the scale factor. The
existence of the ekpyrotic phase justifies the resolution of the BKL
instability, where the anisotropic energy density gets diluted compared to that
of the bouncing agent. The bounce being symmetric and ekpyrotic, the energy
density of the bouncing agent rapidly decreases after the bounce during the
expanding phase of the universe, and consequently the standard Big-Bang
cosmology gets recovered. We find that the comoving Hubble radius diverges at
the distant past, which leads to the generation era of the primordial
perturbation modes at the deep contracting phase far away from the bounce. In
effect, the curvature perturbation gets a blue tilted spectrum over the large
scale modes -- not consistent with the Planck data. To circumvent this problem,
we propose an extended scenario where the ekpyrotic phase is preceded by a
quasi-matter dominated pre-ekpyrotic phase, and re-investigate the perturbation
power spectrum. As a result, the primordial curvature perturbation, at scales
that cross the horizon during the pre-ekpyrotic stage, turns out to be nearly
scale invariant that is indeed consistent with the recent Planck data.


title:
Model-free Reinforcement Learning for Content Caching at the Wireless
  Edge via Restless Bandits

abstract:
  An explosive growth in the number of on-demand content requests has imposed
significant pressure on current wireless network infrastructure. To enhance the
perceived user experience, and support latency-sensitive applications, edge
computing has emerged as a promising computing paradigm. The performance of a
wireless edge depends on contents that are cached. In this paper, we consider
the problem of content caching at the wireless edge with unreliable channels to
minimize average content request latency. We formulate this problem as a
restless bandit problem, which is provably hard to solve. We begin by
investigating a discounted counterpart, and prove that it admits an optimal
policy of the threshold-type. We then show that the result also holds for the
average latency problem. Using these structural results, we establish the
indexability of the problem, and employ Whittle index policy to minimize
average latency. Since system parameters such as content request rate are often
unknown, we further develop a model-free reinforcement learning algorithm
dubbed Q-Whittle learning that relies on our index policy. We also derive a
bound on its finite-time convergence rate. Simulation results using real traces
demonstrate that our proposed algorithms yield excellent empirical performance.


title:
Regularized Bilinear Discriminant Analysis for Multivariate Time Series
  Data

abstract:
  In recent years, the methods on matrix-based or bilinear discriminant
analysis (BLDA) have received much attention. Despite their advantages, it has
been reported that the traditional vector-based regularized LDA (RLDA) is still
quite competitive and could outperform BLDA on some benchmark datasets.
Nevertheless, it is also noted that this finding is mainly limited to image
data. In this paper, we propose regularized BLDA (RBLDA) and further explore
the comparison between RLDA and RBLDA on another type of matrix data, namely
multivariate time series (MTS). Unlike image data, MTS typically consists of
multiple variables measured at different time points. Although many methods for
MTS data classification exist within the literature, there is relatively little
work in exploring the matrix data structure of MTS data. Moreover, the existing
BLDA can not be performed when one of its within-class matrices is singular. To
address the two problems, we propose RBLDA for MTS data classification, where
each of the two within-class matrices is regularized via one parameter. We
develop an efficient implementation of RBLDA and an efficient model selection
algorithm with which the cross validation procedure for RBLDA can be performed
efficiently. Experiments on a number of real MTS data sets are conducted to
evaluate the proposed algorithm and compare RBLDA with several closely related
methods, including RLDA and BLDA. The results reveal that RBLDA achieves the
best overall recognition performance and the proposed model selection algorithm
is efficient; Moreover, RBLDA can produce better visualization of MTS data than
RLDA.


title: Efficient Specialized Spreadsheet Parsing for Data Science

abstract:
  Spreadsheets are widely used for data exploration. Since spreadsheet systems
have limited capabilities, users often need to load spreadsheets to other data
science environments to perform advanced analytics. However, current approaches
for spreadsheet loading suffer from either high runtime or memory usage, which
hinders data exploration on commodity systems. To make spreasheet loading
practical on commodity systems, we introduce a novel parser that minimizes
memory usage by tightly coupling decompression and parsing. Furthermore, to
reduce the runtime, we introduce optimized spreadsheet-specific parsing
routines and employ parallelism. To evaluate our approach, we implement a
prototype for loading Excel spreadsheets into R environments. Our evaluation
shows that our novel approach is up to 3x faster while consuming up to 40x less
memory than state-of-the-art approaches.


title:
Percolation of words on the hypercubic lattice with one-dimensional
  long-range interactions

abstract:
  We investigate the problem of percolation of words in a random environment.
To each vertex, we independently assign a letter $0$ or $1$ according to
Bernoulli r.v.'s with parameter $p$. The environment is the resulting graph
obtained from an independent long-range bond percolation configuration on
$\mathbb{Z}^{d-1} \times \mathbb{Z}$, $d\geq 3$, where each edge parallel to
$\mathbb{Z}^{d-1}$ has length one and is open with probability $\epsilon$,
while edges of length $n$ parallel to $\mathbb{Z}$ are open with probability
$p_n$. We prove that if the sum of $p_n$ diverges, then for any $\epsilon$ and
$p$, there is a $K$ such that all words are seen from the origin with
probability close to $1$, even if all connections with length larger than $K$
are suppressed.


title:
Insights into the equation of state of neutron-rich matter since
  GW170817

abstract:
  The historical detection of gravitational waves emitted from the binary
neutron star merger GW170817 has opened the new era of multi-messenger
astronomy. Since then, many other significant discoveries -- both on heaven and
earth -- are providing new clues into the behavior of neutron-rich matter. It
is the goal of this article to illustrate how the remarkable progress made
during the last few years is spearheading the field into the golden age of
neutron-star physics.


title: Equivariant quadratic forms in characteristic 2

abstract:
  Let $G$ be a finite group and $K$ a finite field of characteristic $2$.
Denote by $t$ the $2$-rank of the commutator factor group $G/G'$ and by $s$ the
number of self-dual simple $KG$-modules. Then the Witt group of equivariant
quadratic forms $\WQ (K,G)$ is isomorphic to an elementary abelian $2$-group of
rank $s+t$.


title:
Transient Joule- and (ac) Josephson-like photon emission in one- and
  two- nucleon tunneling processes between superfluid nuclei: blackbody and
  coherent spectral functions

abstract:
  Effective charged neutrons involved in one- and two- nucleon tunneling
processes in heavy ion collisions between superfluid nuclei are expected to
emit photons. Although the centroid, width and integrated energy area
characterizing the associated gamma-strength functions are rather similar, the
corresponding line shapes reflect the thermal equilibrated-like character of
the quasiparticle transfer (1n-channel, blackbody spectral functional
dependence), and the quantal coherent character of the Cooper pair transfer
(2n-channel, Gaussian functional dependence) respectively. The predicted
angular distributions, polarizations and analyzing power provide further
insight into the profoundly different physics to be found at the basis of what
can be considered a transient Joule-like and a (ac) Josephson-like nuclear
processes


title:
One-Dimensional van der Waals Quantum Materials -- State of the Art and
  Perspectives

abstract:
  The advent of graphene and other two-dimensional van der Waals materials,
with their unique electrical, optical, and thermal properties, has resulted in
tremendous progress for fundamental science. Recent developments suggest that
taking one more step down in dimensionality - from monolayer, atomic sheets to
individual atomic chains - can bring exciting prospects as the ultimate limit
in material downscaling is reached while establishing an entirely new field of
one-dimensional quantum materials. Here we review this emerging area of
one-dimensional van der Waals quantum materials and anticipate its future
directions. We focus on quantum effects associated with the charge-density-wave
condensate, strongly-correlated phenomena, topological phases, and other unique
physical characteristics, which are attainable specifically in van der Waals
materials of lower dimensionality. Possibilities for engineering the properties
of quasi-one-dimensional materials via compositional changes, vacancies, and
defects, as well as the prospects of their applications in composites are also
discussed.


title:
Investigation of metamagnetism and crystal-field splitting in
  pseudo-hexagonal CeRh$_3$Si$_2$

abstract:
  CeRh$_3$Si$_2$ has been reported to exhibit metamagnetic transitions below
5~K, a giant crystal field splitting, and anisotropic magnetic properties from
single crystal magnetization and heat capacity measurements. Here we report
results of neutron and x-ray scattering studies of the magnetic structure and
crystal-field excitations to further understand the magnetism of this compound.
Inelastic neutron scattering (INS) and resonant inelastic x-ray scattering
(RIXS) reveal a $J_z$\,=\,1/2 groundstate for Ce when considering the
crystallographic $a$ direction as quantization axis, thus explaining the
anisotropy of the static susceptibility. Furthermore, we find a total splitting
of 78\,meV for the $J$\,=\,5/2 multiplet. The neutron diffraction study in zero
field reveals that on cooling from the paramagnetic state, the system first
orders at $T_{\text{N}_1}=4.7$\,K in a longitudinal spin density wave with
ordered Ce moments along the $b$-axis (i.e. the [0 1 0] crystal direction) and
an incommensurate propagation vector $\textbf{k}=(0,0.43,0$). Below the
lower-temperature transition $T_{\text{N}_2}=4.48$\,K, the propagation vector
locks to the commensurate value $\textbf{k}=(0,0.5,0)$, with a so-called
lock-in transition. Our neutron diffraction study in applied magnetic field
$H\parallel b$-axis shows a change in the commensurate propagation vector and
development of a ferromagnetic component at $H=3$\,kOe, followed by a series of
transitions before the fully field-induced ferromagnetic phase is reached at $H
= 7$\,kOe. This explains the nature of the steps previously reported in
field-dependent magnetization measurements. A very similar behaviour is also
observed for the $H\parallel$ [0 1 1] crystal direction.


title:
Toward Interpretable Semantic Textual Similarity via Optimal
  Transport-based Contrastive Sentence Learning

abstract:
  Recently, finetuning a pretrained language model to capture the similarity
between sentence embeddings has shown the state-of-the-art performance on the
semantic textual similarity (STS) task. However, the absence of an
interpretation method for the sentence similarity makes it difficult to explain
the model output. In this work, we explicitly describe the sentence distance as
the weighted sum of contextualized token distances on the basis of a
transportation problem, and then present the optimal transport-based distance
measure, named RCMD; it identifies and leverages semantically-aligned token
pairs. In the end, we propose CLRCMD, a contrastive learning framework that
optimizes RCMD of sentence pairs, which enhances the quality of sentence
similarity and their interpretation. Extensive experiments demonstrate that our
learning framework outperforms other baselines on both STS and
interpretable-STS benchmarks, indicating that it computes effective sentence
similarity and also provides interpretation consistent with human judgement.
The code and checkpoint are publicly available at
https://github.com/sh0416/clrcmd.


title: Relational Surrogate Loss Learning

abstract:
  Evaluation metrics in machine learning are often hardly taken as loss
functions, as they could be non-differentiable and non-decomposable, e.g.,
average precision and F1 score. This paper aims to address this problem by
revisiting the surrogate loss learning, where a deep neural network is employed
to approximate the evaluation metrics. Instead of pursuing an exact recovery of
the evaluation metric through a deep neural network, we are reminded of the
purpose of the existence of these evaluation metrics, which is to distinguish
whether one model is better or worse than another. In this paper, we show that
directly maintaining the relation of models between surrogate losses and
metrics suffices, and propose a rank correlation-based optimization method to
maximize this relation and learn surrogate losses. Compared to previous works,
our method is much easier to optimize and enjoys significant efficiency and
performance gains. Extensive experiments show that our method achieves
improvements on various tasks including image classification and neural machine
translation, and even outperforms state-of-the-art methods on human pose
estimation and machine reading comprehension tasks. Code is available at:
https://github.com/hunto/ReLoss.


title: Calculating eigenvectors in max-algebra by mutation method

abstract:
  In this article we introduce a new method, called a mutation method, for
calculating max-eigenvectors of a nonnegative irreducible n x n matrix A. The
advantage of using a mutation method lies in shortening a long process of
finding the answer by means of simple and clear-cut method. Our method reduces
to solving max-eigenproblems for simple mutation matrices that have exactly one
positive entry in each row. We also present some ways of recognizing the
irreducibility of matrices. Some instructive examples are indicated.


title:
Global stability of the Pluriclosed flow on compact simply-connected
  simple Lie groups of rank two

abstract:
  We compute the (1,1)-Aeppli cohomology of compact simply-connected simple Lie
groups of rank two. In particular, we verify that they are of dimension one and
generated by the classes of the Bismut flat metrics coming from the Killing
forms. This yields a result on the stability of the pluriclosed flow on these
manifolds. Moreover, we show that for compact simply-connected simple Lie
groups of rank two the Dolbeaut cohomology, as well as the Bott-Chern and the
Aeppli cohomologies, arise from just the left-invariant forms and we computed
the whole Bott-Chern diamonds of SU(3) and Spin(5) when they are equipped with
a left-invariant isotropic complex structure.


title: The Dark Side of Perceptual Manipulations in Virtual Reality

abstract:
  "Virtual-Physical Perceptual Manipulations" (VPPMs) such as redirected
walking and haptics expand the user's capacity to interact with Virtual Reality
(VR) beyond what would ordinarily physically be possible. VPPMs leverage
knowledge of the limits of human perception to effect changes in the user's
physical movements, becoming able to (perceptibly and imperceptibly) nudge
their physical actions to enhance interactivity in VR. We explore the risks
posed by the malicious use of VPPMs. First, we define, conceptualize and
demonstrate the existence of VPPMs. Next, using speculative design workshops,
we explore and characterize the threats/risks posed, proposing mitigations and
preventative recommendations against the malicious use of VPPMs. Finally, we
implement two sample applications to demonstrate how existing VPPMs could be
trivially subverted to create the potential for physical harm. This paper aims
to raise awareness that the current way we apply and publish VPPMs can lead to
malicious exploits of our perceptual vulnerabilities.


title:
Quantitative investigation of the 4$f$ occupation in the quasikagome
  Kondo lattice CeRh$_{1-x}$Pd$_x$Sn

abstract:
  CeRhSn with the Ce atoms forming a quasikagome lattice in the hexagonal plane
has recently been discussed in the context of quantum criticality driven by
magnetic frustration. Furthermore, it has been reported that the successive
substitution of Rh by Pd leads to magnetic order. Here we have investigated the
change of the 4$f$ occupation in the substitution series CeRh$_{1-x}$Pd$_x$Sn
for for $x$ = 0, 0.1, 0.3, 0.5, 0.75 by means of photoelectron spectroscopy
with hard x-rays (HAXPES). The quantitative analysis of the core level spectra
with a combined full multiplet and configuration interaction analysis shows a
smooth decrease of the 4$f^0$ contribution with rising $x$ due to an increase
of the effective 4$f$ binding energy $\varepsilon_{4f}$ and the reduction of
the effective hybridization $V_\text{eff}$. We further compare valence band
data with the calculated partial density of states and find that the Pd 4$d$
states are about 1eV further away from the Ce 4$f$ states at the Fermi energy
than the Rh 4$d$ states. In fact, the effective binding energy
$\varepsilon_{4f}$ of the 4$f$ states in the configuration interaction analysis
of the core level spectra decreases by the same amount.


title: TaSPM: Targeted Sequential Pattern Mining

abstract:
  Sequential pattern mining (SPM) is an important technique of pattern mining,
which has many applications in reality. Although many efficient sequential
pattern mining algorithms have been proposed, there are few studies can focus
on target sequences. Targeted querying sequential patterns can not only reduce
the number of sequences generated by SPM, but also improve the efficiency of
users in performing pattern analysis. The current algorithms available on
targeted sequence querying are based on specific scenarios and cannot be
generalized to other applications. In this paper, we formulate the problem of
targeted sequential pattern mining and propose a generic framework namely
TaSPM, based on the fast CM-SPAM algorithm. What's more, to improve the
efficiency of TaSPM on large-scale datasets and multiple-items-based sequence
datasets, we propose several pruning strategies to reduce meaningless
operations in mining processes. Totally four pruning strategies are designed in
TaSPM, and hence it can terminate unnecessary pattern extensions quickly and
achieve better performance. Finally, we conduct extensive experiments on
different datasets to compare the existing SPM algorithms with TaSPM.
Experiments show that the novel targeted mining algorithm TaSPM can achieve
faster running time and less memory consumption.


title:
Dropout can Simulate Exponential Number of Models for Sample Selection
  Techniques

abstract:
  Following Coteaching, generally in the literature, two models are used in
sample selection based approaches for training with noisy labels. Meanwhile, it
is also well known that Dropout when present in a network trains an ensemble of
sub-networks. We show how to leverage this property of Dropout to train an
exponential number of shared models, by training a single model with Dropout.
We show how we can modify existing two model-based sample selection
methodologies to use an exponential number of shared models. Not only is it
more convenient to use a single model with Dropout, but this approach also
combines the natural benefits of Dropout with that of training an exponential
number of models, leading to improved results.


title: Quantum Monte Carlo study of positron lifetimes in solids

abstract:
  We present an analysis of positron lifetimes in solids with unprecedented
depth and accuracy. Instead of approximating many-body effects as local, we
study wave functions with long-range correlations included. This proves to be
of crucial importance in understanding positron annihilation in metals,
insulators and semiconductors. By using a new quantum Monte Carlo approach for
prediction of positron lifetimes, an improved accuracy compared to previous
computations is obtained for a representative set of materials when compared
with experiment.


title:
Refined Topological Amplitudes from the $\Omega$-Background in String
  Theory

abstract:
  It was recently shown that the $\mathcal{N}$ = 2 string topological
amplitudes in the heterotic weak coupling limit generate a six-dimensional
Melvin space, providing a description of the $\Omega$-background in string
theory, where string propagation can be exactly studied. In this work, we
generalise the analysis to the refined case of the $\Omega$-background with two
independent deformation parameters. The Melvin space is now ten-dimensional and
is extended by an action on the internal K3 compactification manifold of the
heterotic superstring, corresponding to an ${\rm SU(2)}_R$ rotation in the
field theory description. We identify the class of heterotic topological
amplitudes realising this background as the scattering of two anti-self-dual
gravitons and arbitrary numbers of anti-self-dual graviphotons, self-dual
vector fields of the dilaton multiplet, together with self-dual magnetic fluxes
along the K3. In the field theory limit, our result correctly reproduces the
perturbative part of the Nekrasov free energy in the case where both
equivariant parameters are turned on.


title: Quantum tunneling isotope exchange reaction H2 + D- -> HD + H-

abstract:
  The tunneling reaction H$_2$ + D$^-$ $\rightarrow$ HD + H$^-$ was studied in
a recent experimental work at low temperatures (10, 19, and 23~K) by Endres
{\it et al.}, Phys. Rev. A {\bf 95}, 022706 (2017). An upper limit of the rate
coefficient was found to be about 10$^{-18}$ cm$^3$/s. In the present study,
reaction probabilities are determined using the ABC program developed by
Skouteris {\it et al.}, Comput. Phys. Commun. {\bf 133}, 128 (2000). The
probabilities for ortho-H$_2$ and para-H$_2$ in their ground rovibrational
states are obtained numerically at collision energies above 50~meV with the
total angular momentum $J$ = 0 - 15 and extrapolated below 50~meV using a WKB
approach. Thermally averaged rate coefficients for ortho- and para-H$_2$ are
obtained; the largest one, for ortho-H$_2$ is about $3.1\times10^{-20}$
cm$^3$/s, which agrees with the experimental results.


title:
The location of the Fisher zeros and estimates of yT = 1/{\nu} are found
  for the Baxter-Wu model

abstract:
  It is shown that the location of the Fisher zeros of the Baxter-Wu model, for
two series of finite sized clusters, with spherical boundary conditions, is
extremely simple. They lie on the unit circle in the complex Sinh[2\b{eta}J3]
plane. This is the same location as the Fisher zeros of the Ising model with
nearest neighbor interactions, J2, on the square lattice have, with
Brascamp-Kunz boundary conditions. The Baxter-Wu model is an Ising model with
three site interactions, J3, on the triangle lattice. From the leading Fisher
zeros, using finite size scaling, accurate estimates of the critical exponent
1/{\nu} are obtained. Furthermore, using the imaginary parts of the leading
zeros versus the real part of the leading zeros leads to different results
similar the results of Janke and Kenna for the nearest neighbor, Ising model on
the square lattice and extending this behavior to a multi-site interaction
system.


title:
Revisiting the performance of time-dependent density functional theory
  for electronic excitations: Assessment of 43 popular and recently developed
  functionals from rungs one to four

abstract:
  In this paper, the performance of more than 40 popular or recently developed
density functionals is assessed for the calculation of 463 vertical excitation
energies against the large and accurate QuestDB benchmark set. For this
purpose, the Tamm-Dancoff approximation offers a good balance between
performance and accuracy. The functionals $\omega$B97X-D and BMK are found to
offer the best performance overall with a Root-Mean Square Error (RMSE) of 0.28
eV, better than the computationally more demanding CIS(D) wavefunction method
with a RMSE of 0.36 eV. The results also suggest that Jacob's ladder still
holds for TDDFT excitation energies, though hybrid meta-GGAs are not generally
better than hybrid GGAs. Effects of basis set convergence, gauge invariance
correction to meta-GGAs, and nonlocal correlation (VV10) are also studied, and
practical basis set recommendations are provided.

categories: physics.chem-ph cond-mat.other physics.comp-ph quant-ph


title: Opening the Black Box of Learned Image Coders

abstract:
  End-to-end learned lossy image coders, as opposed to hand-crafted image
codecs, have shown increasing superiority in terms of the rate-distortion
performance. However, they are mainly treated as a black-box system and their
interpretability is not well studied. In this paper, we investigate learned
image coders from the perspective of linear transform coding by measuring their
channel response and linearity. For different learned image coder designs, we
show that their end-to-end learned non-linear transforms share similar
properties with linear orthogonal transformations. Our analysis provides
insights into understanding how learned image coders work and could benefit
future design and development.


title: Dynamical double full Bloch beam created with polariton vortices

abstract:
  Vortices provide elementary quanta of rotation in superfluids,
superconductors, and optics. In the latter case, such vortices are relevant to
the encoding of arbitrary quantized angular momentum for both fiber and
free-air communications, data elaboration, gyroscopes, and medical tools, and
play a prominent role as building blocks in the field of structured light.
Differently from the synthetic generation of arbitrary wave shaping, here we
exploit the natural assets of open-dissipative exciton-polariton fluids to set
into ultrafast motion a doubly-charged vortex, which also results in the
time-varying orbital angular momentum of emitted light. The created topology
comprises all the possible polariton pseudospins, with each state appearing
twice in real space where the Bloch sphere is conformally mapped, with the
orthogonality between the parallels and meridians preserved. The density of the
sphere metric lines is described by the Berry curvature which is reshaping in
time, but always keeps a space integral of twice the solid angle. This confirms
that the spiraling motion of the two observable photonic vortex cores is due to
the creation of a dynamical full Bloch beam and its evolving metric.


title:
Single-channel or multi-channel thermal transport? Effect of
  higher-order anharmonic corrections on the predicted phonon thermal transport
  properties of semiconductors

abstract:
  The phonon thermal transport properties of eight ternary intermetallic
semiconductors are investigated by accounting for higher-order four-phonon
scattering, phonon renormalization, and multi-channel thermal transport. The
commonly used lowest-order theory, which accounts only for three-phonon
scattering and without phonon renormalization, fails drastically for considered
materials and underpredicts the thermal conductivity by up to a factor of two.
The thermal conductivity decreases for three compounds and increases for five
compounds with the application of higher-order corrections owing to a
contrasting role of four-phonon scattering and phonon stiffening on the
predicted thermal conductivity. Using the higher-order theory, at a temperature
of 300 K, the lowest obtained thermal conductivity is 0.31 W/m-K for BiCsK2 and
three other compounds (SbCsK2, SbRbNa2, and SbRbK2) have thermal conductivities
lower than 0.5 W/m-K via the particle-like phonon transport channel. The
contribution from the wave-like coherent transport channel is lower than 0.05
W/m-K in all of these ultra-low thermal conductivity compounds. The
higher-order theory is a must for the correct description of thermal transport
physics, failing which the thermal transport is wrongly characterized as
multi-channel transport by the lowest-order theory.


title:
Faster One-Sample Stochastic Conditional Gradient Method for Composite
  Convex Minimization

abstract:
  We propose a stochastic conditional gradient method (CGM) for minimizing
convex finite-sum objectives formed as a sum of smooth and non-smooth terms.
Existing CGM variants for this template either suffer from slow convergence
rates, or require carefully increasing the batch size over the course of the
algorithm's execution, which leads to computing full gradients. In contrast,
the proposed method, equipped with a stochastic average gradient (SAG)
estimator, requires only one sample per iteration. Nevertheless, it guarantees
fast convergence rates on par with more sophisticated variance reduction
techniques. In applications we put special emphasis on problems with a large
number of separable constraints. Such problems are prevalent among semidefinite
programming (SDP) formulations arising in machine learning and theoretical
computer science. We provide numerical experiments on matrix completion,
unsupervised clustering, and sparsest-cut SDPs.


title: Cubic Fourfolds with an Involution

abstract:
  There are three types of involutions on a cubic fourfold; two of
anti-symplectic type, and one symplectic. Here we show that cubics with
involutions exhibit the full range of behaviour in relation to rationality
conjectures. Namely, we show a general cubic fourfold with symplectic
involution has no associated K3 surface and is conjecturely irrational. In
contrast, we show a cubic fourfold with a particular anti-symplectic involution
has an associated K3, and is in fact rational. We show such a cubic is
contained in the intersection of all non-empty Hassett divisors; we call such a
cubic Hassett maximal. We study the algebraic and transcendental lattices for
cubics with an involution both lattice theoretically and geometrically.


title: Non-iterative finite amplitude methods for E1 and M1 giant resonances

abstract:
  The finite amplitude method (FAM) is a very efficient approach for solving
the fully self-consistent random-phase approximation (RPA) equations. We use
FAM to rederive the RPA matrices for general Skyrme-like functionals, calculate
the electric dipole (E1) and the magnetic dipole (M1) giant resonances, and
compare the results with available experimental and evaluated data. For the E1
transitions in heavy nuclei, the calculations reproduce well the resonance
energy of the photoabsorption cross sections. In the case of M1 transitions, we
show that the residual interaction does not affect the transition strength of
double-magic nuclei, which suggests that the spin terms in the Skyrme force
currently neglected in the present computation could improve the agreement
between FAM and experimental data.


title:
Unique Device Identification Based Linkage of Hierarchically Accessible
  Data Domains in Prospective Hospital Data Ecosystems

abstract:
  The electronic health record (EHR) targets the systematized collection of
patient-specific electronically-stored health data. Currently the EHR is an
evolving concept driven by ongoing technical developments and open or unclear
legal issues concerning used medical technologies, data integration from other
domains and unclear access roles. This paper addresses cross-domain data
integration, data fusion and access control using the specific example of a
Unique Device Identification (UDI) expanded hip implant. In fact, the
integration of technical focus data into the hospital information system (HIS)
is discussed and presented based on surgically relevant information. Moreover,
the acquisition of social focus databased on mHealth is approached, which also
covers data integration and networking with therapeutic intervention or acute
diagnostics data. Data integration from heterogeneous domains is covered while
using a data ecosystem with hierarchical access based on a shell embedded role
model, which includes staggered access scenarios.


title: Adversarial robustness of sparse local Lipschitz predictors

abstract:
  This work studies the adversarial robustness of parametric functions composed
of a linear predictor and a non-linear representation map. Our analysis relies
on sparse local Lipschitzness (SLL), an extension of local Lipschitz continuity
that better captures the stability and reduced effective dimensionality of
predictors upon local perturbations. SLL functions preserve a certain degree of
structure, given by the sparsity pattern in the representation map, and include
several popular hypothesis classes, such as piece-wise linear models, Lasso and
its variants, and deep feed-forward ReLU networks. We provide a tighter
robustness certificate on the minimal energy of an adversarial example, as well
as tighter data-dependent non-uniform bounds on the robust generalization error
of these predictors. We instantiate these results for the case of deep neural
networks and provide numerical evidence that supports our results, shedding new
insights into natural regularization strategies to increase the robustness of
these models.


title:
Evolution of Dopant-Concentration-Induced Magnetic Exchange Interaction
  in Topological Insulator Thin Films

abstract:
  Two essential ingredients for the quantum anomalous Hall (QAH) effect, i.e.
topological and magnetic orders, can be combined by doping magnetic ions into a
topological insulator (TI) film. Through this approach, the QAH effect has been
realized in chromium (Cr)- and/or vanadium (V)-doped TI (Bi,Sb)2Te3 thin films.
In this work, we synthesize both V- and Cr-doped Bi2Te3 thin films with
controlled dopant concentration using molecular beam epitaxy (MBE). By
performing magneto-transport measurements, we find that both systems show an
unusual but yet similar ferromagnetic response with respect to magnetic dopant
concentration, specifically the Curie temperature does not increase
monotonically but shows a local maximum at a critical dopant concentration. Our
angle-resolved photoemission spectroscopy (ARPES) measurements show that the
Cr/V doping introduces hole carriers into Bi2Te3, which consequently move the
chemical potential toward the charge neutral point. In addition, the Cr/V
doping also reduces the spin-orbit coupling of Bi2Te3 which drives it from a
nontrivial TI to a trivial semiconductor. The unusual ferromagnetic response
observed in Cr/V-doped Bi2Te3 thin films is attributed to the
dopant-concentration-induced magnetic exchange interaction, which displays the
evolution from the van Vleck-type ferromagnetism in a nontrivial magnetic TI to
the Ruderman-Kittel-Kasuya-Yosida (RKKY)-type ferromagnetism in a trivial
diluted magnetic semiconductor. Our work provides insights into the
ferromagnetic properties of magnetically doped TI thin films and facilitates
the pursuit of high-temperature QAH effect.


title:
Mechanical, optical, and thermoelectric properties of semiconducting
  ZnIn2X4 (X= S, Se, Te) monolayers

abstract:
  Mechanical stability of the ZnIn2X4 monolayers. The ZnIn2S4 and ZnIn2Se4 are
semiconductors with direct band gaps of 3.94 and 2.77 eV, respectively whereas
the ZnIn2Te4 shows an indirect band gap of 1.84 eV at the G0W0 level. The
optical properties achieved from the solution of the Bethe-Salpeter equation
predict the exciton binding energy of the ZnIn2S4, ZnIn2Se4, and ZnIn2Te4
monolayers to be 0.51, 0.41, and 0.34 eV, respectively, suggesting the high
stability of the excitonic states against thermal dissociation. Using the
iterative solutions of the Boltzmann transport equation accelerated by machine
learning interatomic potentials, the room-temperature lattice thermal
conductivity of the ZnIn2S4, ZnIn2Se4, and ZnIn2Te4 monolayers is predicted to
be remarkably low as 5.8, 2.0, and 0.4 W/mK, respectively. Due to the low
lattice thermal conductivity, high thermopower, and large figure of merit, we
propose the ZnIn2Se4 and ZnIn2Te4 monolayers as promising candidates for
thermoelectric energy conversion systems. This study provides an extensive
vision concerning the intrinsic physical properties of the ZnIn2X4 nanosheets
and highlights their characteristics for energy conversion and optoelectronics
applications.


title:
Weight Selection for Pattern Control of Paraboloidal Reflector Antennas
  with Reconfigurable Rim Scattering

abstract:
  It has been recently demonstrated that modifying the rim scattering of a
paraboloidal reflector antenna through the use of reconfigurable elements along
the rim facilitates sidelobe modification including cancelling sidelobes. In
this work we investigate techniques for determining the unit-magnitude weights
(i.e., weights which modify the phase of the scattered signals) to accomplish
sidelobe cancellation at arbitrary angles from the reflector axis.
Specifically, it is shown that despite the large search space and the
non-convexity of the cost function, weights can be found with reasonable
complexity which provide significant cancellation capability. First, the
optimal weights without any magnitude constraints are found. Afterwards,
algorithms are developed for determining the unit-modulus weights with both
quantized and unquantized phases. Further, it is shown that weights can be
obtained that both cancel sidelobes while providing a constant main lobe gain.
A primary finding is that sufficiently deep nulls are possible with essentially
no change in the main lobe with practical (binary or quaternary) phase-only
weights.


title: How much depth information can radar infer and contribute

abstract:
  Since the release of radar data in large scale autonomous driving dataset,
many works have been proposed fusing radar data as an additional guidance
signal into monocular depth estimation models. Although positive performances
are reported, it is still hard to tell how much depth information radar can
infer and contribute in depth estimation models. In this paper, we conduct two
experiments to investigate the intrinsic depth capability of radar data using
state-of-the-art depth estimation models. Our experiments demonstrate that the
estimated depth from only sparse radar input can detect the shape of
surroundings to a certain extent. Furthermore, the monocular depth estimation
model supervised by preprocessed radar only during training can achieve 70%
performance in delta_1 score compared to the baseline model trained with sparse
lidar.


title: RL-PGO: Reinforcement Learning-based Planar Pose-Graph Optimization

abstract:
  The objective of pose SLAM or pose-graph optimization (PGO) is to estimate
the trajectory of a robot given odometric and loop closing constraints.
State-of-the-art iterative approaches typically involve the linearization of a
non-convex objective function and then repeatedly solve a set of normal
equations. Furthermore, these methods may converge to a local minima yielding
sub-optimal results. In this work, we present to the best of our knowledge the
first Deep Reinforcement Learning (DRL) based environment and proposed agent
for 2D pose-graph optimization. We demonstrate that the pose-graph optimization
problem can be modeled as a partially observable Markov Decision Process and
evaluate performance on real-world and synthetic datasets. The proposed agent
outperforms state-of-the-art solver g2o on challenging instances where
traditional nonlinear least-squares techniques may fail or converge to
unsatisfactory solutions. Experimental results indicate that iterative-based
solvers bootstrapped with the proposed approach allow for significantly higher
quality estimations. We believe that reinforcement learning-based PGO is a
promising avenue to further accelerate research towards globally optimal
algorithms. Thus, our work paves the way to new optimization strategies in the
2D pose SLAM domain.


title: Large-$N$ Limit of the Segal--Bargmann Transforms on the Spheres

abstract:
  We study the large-$N$ limit of the Segal--Bargmann transform on
$S^{N-1}(\sqrt N)$, the $(N-1)$-dimensional sphere of radius $\sqrt N$, as a
unitary map from the space of square-integrable functions with respect to the
normalized spherical measure onto the space of holomorphic square-integrable
functions with respect to the normalized quadric measure. In particular, we
give an explicit formulation and describe the geometric models for the limit of
the domain, the limit of the range, and the limit of the transform when $N$
tends to infinity. We show that the limiting transform is still a unitary map
from the limiting domain onto the limiting range.