title:
On the Computational Consequences of Cost Function Design in Nonlinear
  Optimal Control

abstract:
  Optimal control is an essential tool for stabilizing complex nonlinear
system. However, despite the extensive impacts of methods such as receding
horizon control, dynamic programming and reinforcement learning, the design of
cost functions for a particular system often remains a heuristic-driven process
of trial and error. In this paper we seek to gain insights into how the choice
of cost function interacts with the underlying structure of the control system
and impacts the amount of computation required to obtain a stabilizing
controller. We treat the cost design problem as a two-step process where the
designer specifies outputs for the system that are to be penalized and then
modulates the relative weighting of the inputs and the outputs in the cost. We
then bound the length of the prediction horizon $T>0$ that is required for
receding horizon control methods to stabilize the system as a concrete way of
characterizing the computational difficulty of stabilizing the system using the
chosen cost function. Drawing on insights from the `cheap control' literature,
we investigate cases where the chosen outputs lead to minimumphase and
non-minimumphase input-output dynamics. When the system is minimumphase, the
prediction horizon needed to ensure stability can be made arbitrarily small by
making the penalty on the control small enough. This indicates that choices of
cost function which implicitly induce minimumphase behavior lead to an optimal
control problem from which it is `easy' to obtain a stabilizing controller.
Using these insights, we investigate empirically how the choice of cost
function affects the ability of modern reinforcement learning algorithms to
learn a stabilizing controller. Taken together, the results in this paper
indicate that cost functions which induce non-minimum phase behavior lead to
inherent computational difficulties.


title:
An End-to-End Integrated Computation and Communication Architecture for
  Goal-oriented Networking: A Perspective on Live Surveillance Video

abstract:
  Real-time video surveillance has become a crucial technology for smart
cities, made possible through the large-scale deployment of mobile and fixed
video cameras. In this paper, we propose situation-aware streaming, for
real-time identification of important events from live-feeds at the source
rather than a cloud based analysis. For this, we first identify the frames
containing a specific situation and assign them a high scale-of-importance
(SI). The identification is made at the source using a tiny neural network
(having a small number of hidden layers), which incurs a small computational
resource, albeit at the cost of accuracy. The frames with a high SI value are
then streamed with a certain required Signal-to-Noise-Ratio (SNR) to retain the
frame quality, while the remaining ones are transmitted with a small SNR. The
received frames are then analyzed using a deep neural network (with many hidden
layers) to extract the situation accurately. We show that the proposed scheme
is able to reduce the required power consumption of the transmitter by 38.5%
for 2160p (UHD) video, while achieving a classification accuracy of 97.5%, for
the given situation.


title:
Tunnel Barrier to Spin Filter: Electronic Transport Characteristics of
  Transition Metal Atom Encapsulated in Smallest Cadmium Telluride Cage

abstract:
  We report first principles theory-based comparative electronic transport
studies performed for an atomic chain of Au, bare Cd9Te9 cage-like cluster and
single transition metal (TM) (Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ru, Rh, Pd)
atom encapsulated within the Cd9Te9 using Au(111) as electrodes. The bare
cluster is semiconducting and acts as a tunnel barrier up to a particular
applied bias and beyond that, the device has a linear current-voltage
relationship. Several TM (Ti, V, Cr, Mn, Fe) encapsulated in the cage show
half-metallic behavior and spin filtering effect in the I-V characteristics of
the device. A detailed qualitative and quantitative analysis of I-V
characteristics for metallic, semiconducting, and half-metallic nanostructures
has been carried out.


title:
Bulk reconstruction from a scalar CFT at the boundary by the smearing
  with the flow equation

abstract:
  We explain our proposal for an alternative bulk reconstruction of AdS/CFT
correspondences from a scalar field by the flow method. By smearing and then
normalizing a primary field in a $d$ dimensional CFT, we construct a bulk
field, through which a $d+1$ dimensional AdS space emerges.


title: Genus spectra of abelian $p$-groups

abstract:
  The {\it genus spetrum} of a finite group $G$ is the set of integers $g\geq
2$ such that $G$ can acts faithfully on the orientable closed surface of genus
$g$ by orientation-preserving homeomorphisms. In this paper, we propose an
efficient method for determining the genus spectra for abelian $p$-groups.


title:
A generalized ACK structure and the denseness of norm attaining
  operators

abstract:
  Inspired by the recent work of Cascales et al., we introduce a generalized
concept of ACK structure on Banach spaces. Using this property, which we call
by the quasi-ACK structure, we are able to extend known universal properties on
range spaces concerning the density of norm attaining operators. We provide
sufficient conditions for quasi-ACK structure of spaces and results on the
stability of quasi-ACK structure. As a consequence, we present new examples
satisfying the (Lindenstrauss) property B$^k$, which have not been known
previously. We also prove that property B$^k$ is stable under injective tensor
products in certain cases. Moreover, ACK structure of some Banach spaces of
vector-valued holomorphic functions is also discussed, leading to new examples
of universal BPB range spaces for certain operator ideals.


title:
Snowmass2021 Cosmic Frontier White Paper: Enabling Flagship Dark Energy
  Experiments to Reach their Full Potential

abstract:
  A new generation of powerful dark energy experiments will open new vistas for
cosmology in the next decade. However, these projects cannot reach their utmost
potential without data from other telescopes. This white paper focuses in
particular on the compelling benefits of ground-based spectroscopic and
photometric observations to complement the Vera C. Rubin Observatory, as well
as smaller programs in aid of a DESI-2 experiment and CMB-S4. These additional
data sets will both improve dark energy constraints from these flagship
projects beyond what would possible on their own and open completely new
windows into fundamental physics. For example, additional photometry and
single-object spectroscopy will provide necessary follow-up information for
supernova and strong lensing cosmology, while highly-multiplexed spectroscopy
both from smaller facilities over wide fields and from larger facilities over
narrower regions of sky will yield more accurate photometric redshift estimates
for weak lensing and galaxy clustering measurements from the Rubin Observatory,
provide critical spectroscopic host galaxy redshifts for supernova Hubble
diagrams, provide improved understanding of limiting astrophysical systematic
effects, and enable new measurements that probe the nature of gravity. A common
thread is that access to complementary data from a range of
telescopes/instruments would have a substantial impact on the rate of advance
of dark energy science in the coming years.


title:
Exploring Mechanisms of Hydration and Carbonation of MgO and Mg(OH)2 in
  Reactive Magnesium Oxide-based Cements

abstract:
  Reactive magnesium oxide (MgO)-based cement (RMC) can play a key role in
carbon capture processes. However, knowledge on the driving forces that control
the degree of carbonation and hydration and rate of reactions in this system
remains limited. In this work, density functional theory-based simulations are
used to investigate the physical nature of the reactions taking place during
the fabrication of RMCs under ambient conditions. Parametric indicators such as
adsorption energies, charge transfer, electron localization function,
adsorption/dissociation energy barriers and the mechanisms of interaction of
H2O and CO2 molecules with MgO and brucite (Mg(OH)2) clusters are considered.
The following hydration and carbonation interactions relevant to RMCs are
evaluated i) carbonation of MgO, ii) hydration of MgO, carbonation of hydrated
MgO, iii) carbonation of Mg(OH)2, iv) hydration of Mg(OH)2 and v) hydration of
carbonated Mg(OH)2. A comparison of the energy barriers and reaction pathways
of these mechanisms shows that the carbonation of MgO is hindered by presence
of H2O molecules, while the carbonation of Mg(OH)2 is hindered by the formation
of initial carbonate and hydrate layers as well as presence of excessed H2O
molecules. To compare these finding to bulk mineral surfaces, the interactions
of the CO2 and H2O molecules with the MgO(001) and Mg(OH)2 (001) surfaces are
studied. Therefore, this work presents deep insights into the physical nature
of the reactions and the mechanisms involved in hydrated magnesium carbonates
production that can be beneficial for its development.


title: Towards Security-Optimized Placement of ADS-B Sensors

abstract:
  Automatic Dependent Surveillance Broadcast (ADS-B) sensors deployed on the
ground are central to observing aerial movements of aircraft. Their
unsystematic placement, however, results in over-densification of sensor
coverage in some areas and insufficient sensor coverage in other areas. ADS-B
sensor coverage has so far been recognized and analyzed as an availability
problem; it was tackled by sensor placement optimization techniques that aim
for covering large enough areas. In this paper, we demonstrate that the
unsystematic placement of ADS-B sensors leads to a security problem, since the
realization and possible deployment of protective mechanisms is closely linked
to aspects of redundancy in ADS-B sensor coverage. In particular, we model
ADS-B sensor coverage as a multi-dimensional security problem. We then use
multi-objective optimization techniques to tackle this problem and derive
security-optimized near-optimal placement solutions. Our results show how the
location of sensors play a significant role in reducing the success rate of
attackers by providing a sufficient number of sensors within a specific
geographical area to verify location claims and reducing the exposure to
jamming attacks.


title:
Applications of the dynamical generator coordinate method to quadrupole
  excitations

abstract:
  We apply the dynamical generator coordinate method (DGCM) with a conjugate
momentum to a nuclear collective excitation. To this end, we first discuss how
to construct a numerically workable scheme of the DGCM for a general one-body
operator. We then apply the DGCM to the quadrupole vibration of $^{16}$O using
the Gogny D1S interaction. We show that both the ground state energy and the
excitation energies are lowered as compared to the conventional GCM with the
same number of basis functions. We also compute the sum rule values for the
quadrupole and monopole operators, and show that the DGCM yields more
consistent results than the conventional GCM to the values from the double
commutator. These results imply that the conjugate momentum is an important and
relevant degree of freedom in collective motions.


title:
Global Mapping of Surface Composition on an Exo-Earth Using Sparse
  Modeling

abstract:
  The time series of light reflected from exoplanets by future direct imaging
can provide spatial information with respect to the planetary surface. We apply
sparse modeling to the retrieval method that disentangles the spatial and
spectral information from multi-band reflected light curves termed as
spin-orbit unmixing. We use the $\ell_1$-norm and the Total Squared Variation
norm as regularization terms for the surface distribution. Applying our
technique to a toy model of cloudless Earth, we show that our method can infer
sparse and continuous surface distributions and also unmixed spectra without
prior knowledge of the planet surface. We also apply the technique to the real
Earth data as observed by DSCOVR/EPIC. We determined the representative
components that can be interpreted as cloud and ocean. Additionally, we found
two components that resembled the distribution of land. One of the components
captures the Sahara Desert, and the other roughly corresponds to vegetation
although their spectra are still contaminated by clouds. Sparse modeling
significantly improves the geographic retrieval, in particular, of cloud and
leads to higher resolutions for other components when compared with spin-orbit
unmixing using Tikhonov regularization.


title: On $n$-universal quadratic forms over dyadic local fields

abstract:
  Let $ n \ge 2$ be an integer. We give necessary and sufficient conditions for
an integral quadratic form over dyadic local fields to be $ n $-universal by
using invariants from Beli's theory of bases of norm generators. Also, we
provide a minimal set for testing $ n $-universal quadratic forms over dyadic
local fields, as an analogue of Bhargava and Hanke's 290-theorem (or Conway and
Schneeberger's 15-theorem) on universal quadratic forms with integer
coefficients.


title: Parton energy loss at LHC tests for a strongly coupled medium

abstract:
  We construct a measure of transverse momentum loss of jets in nuclear
collisions at LHC directly using measurements of jet cross sections in PbPb and
pp collisions. The proposal is shown to be equivalent to R_AA and is equally
straightforward to construct. Using data from the ATLAS collaboration at two
different collision energies, we show that the proposed measure has small
statistical uncertainties. We argue that systematic errors can be easily
improved over our estimates by the experimental collaboration to such an extent
that it directly probes whether the jet-medium interaction is due to a strongly
interacting medium or a weakly interacting plasma. We argue that the current
data may marginally favour a strongly interacting medium. On the other hand,
assuming that the medium is weakly interacting, we are able to provide
estimates of the jet quenching parameter qhat which are in rough agreement with
previously reported estimates.


title:
Anti-chiral order and spin reorientation transitions of triangle-based
  antiferromagnets

abstract:
  We show that triangle-based antiferromagnets with "anti-chiral" order display
a non-trivial dependence of the spin orientation with an in-plane field. The
spins evolve from rotating in the opposite sense to the field at very low
fields to rotating in the same sense as the field above some critical field
scale. In the latter regime the system displays first order transitions at
which the spin angles jump, and these first order lines terminate in critical
points in the Ising universality class. Application to Mn$_3$Sn is discussed.


title:
The COVMis-Stance dataset: Stance Detection on Twitter for COVID-19
  Misinformation

abstract:
  During the COVID-19 pandemic, large amounts of COVID-19 misinformation are
spreading on social media. We are interested in the stance of Twitter users
towards COVID-19 misinformation. However, due to the relative recent nature of
the pandemic, only a few stance detection datasets fit our task. We have
constructed a new stance dataset consisting of 2631 tweets annotated with the
stance towards COVID-19 misinformation. In contexts with limited labeled data,
we fine-tune our models by leveraging the MNLI dataset and two existing stance
detection datasets (RumourEval and COVIDLies), and evaluate the model
performance on our dataset. Our experimental results show that the model
performs the best when fine-tuned sequentially on the MNLI dataset and the
combination of the undersampled RumourEval and COVIDLies datasets. Our code and
dataset are publicly available at
https://github.com/yanfangh/covid-rumor-stance


title: Compute- and Data-Intensive Networks: The Key to the Metaverse

abstract:
  The worlds of computing, communication, and storage have for a long time been
treated separately, and even the recent trends of cloud computing, distributed
computing, and mobile edge computing have not fundamentally changed the role of
networks, still designed to move data between end users and pre-determined
computation nodes, without true optimization of the end-to-end
compute-communication process. However, the emergence of Metaverse
applications, where users consume multimedia experiences that result from the
real-time combination of distributed live sources and stored digital assets,
has changed the requirements for, and possibilities of, systems that provide
distributed caching, computation, and communication. We argue that the
real-time interactive nature and high demands on data storage, streaming rates,
and processing power of Metaverse applications will accelerate the merging of
the cloud into the network, leading to highly-distributed tightly-integrated
compute- and data-intensive networks becoming universal compute platforms for
next-generation digital experiences. In this paper, we first describe the
requirements of Metaverse applications and associated supporting
infrastructure, including relevant use cases. We then outline a comprehensive
cloud network flow mathematical framework, designed for the end-to-end
optimization and control of such systems, and show numerical results
illustrating its promising role for the efficient operation of Metaverse-ready
networks.


title: Micro-Behavior Encoding for Session-based Recommendation

abstract:
  Session-based Recommendation (SR) aims to predict the next item for
recommendation based on previously recorded sessions of user interaction. The
majority of existing approaches to SR focus on modeling the transition patterns
of items. In such models, the so-called micro-behaviors describing how the user
locates an item and carries out various activities on it (e.g., click,
add-to-cart, and read-comments), are simply ignored. A few recent studies have
tried to incorporate the sequential patterns of micro-behaviors into SR models.
However, those sequential models still cannot effectively capture all the
inherent interdependencies between micro-behavior operations. In this work, we
aim to investigate the effects of the micro-behavior information in SR
systematically. Specifically, we identify two different patterns of
micro-behaviors: "sequential patterns" and "dyadic relational patterns". To
build a unified model of user micro-behaviors, we first devise a multigraph to
aggregate the sequential patterns from different items via a graph neural
network, and then utilize an extended self-attention network to exploit the
pair-wise relational patterns of micro-behaviors. Extensive experiments on
three public real-world datasets show the superiority of the proposed approach
over the state-of-theart baselines and confirm the usefulness of these two
different micro-behavior patterns for SR.


title: Ordinal Optimization Through Multi-objective Reformulation

abstract:
  We analyze combinatorial optimization problems with ordinal, i.e.,
non-additive, objective functions that assign categories (like good, medium and
bad) rather than cost coefficients to the elements of feasible solutions. We
review different optimality concepts for ordinal optimization problems and
discuss their similarities and differences. We then focus on two prevalent
optimality concepts that are shown to be equivalent. Our main result is a
bijective linear transformation that transforms ordinal optimization problems
to associated standard multi-objective optimization problems with binary cost
coefficients. Since this transformation preserves all properties of the
underlying problem, problem-specific solution methods remain applicable. A
prominent example is dynamic programming and Bellman's principle of optimality,
that can be applied, e.g., to ordinal shortest path and ordinal knapsack
problems. We extend our results to multi-objective optimization problems that
combine ordinal and real-valued objective functions.


title: Bimodal Distributed Binarized Neural Networks

abstract:
  Binary Neural Networks (BNNs) are an extremely promising method to reduce
deep neural networks' complexity and power consumption massively. Binarization
techniques, however, suffer from ineligible performance degradation compared to
their full-precision counterparts.
  Prior work mainly focused on strategies for sign function approximation
during forward and backward phases to reduce the quantization error during the
binarization process. In this work, we propose a Bi-Modal Distributed
binarization method (\methodname{}). That imposes bi-modal distribution of the
network weights by kurtosis regularization. The proposed method consists of a
training scheme that we call Weight Distribution Mimicking (WDM), which
efficiently imitates the full-precision network weight distribution to their
binary counterpart. Preserving this distribution during binarization-aware
training creates robust and informative binary feature maps and significantly
reduces the generalization error of the BNN. Extensive evaluations on CIFAR-10
and ImageNet demonstrate the superiority of our method over current
state-of-the-art schemes. Our source code, experimental settings, training
logs, and binary models are available at
\url{https://github.com/BlueAnon/BD-BNN}.


title: Aerial Computing: A New Computing Paradigm, Applications, and Challenges

abstract:
  In existing computing systems, such as edge computing and cloud computing,
several emerging applications and practical scenarios are mostly unavailable or
only partially implemented. To overcome the limitations that restrict such
applications, the development of a comprehensive computing paradigm has
garnered attention in both academia and industry. However, a gap exists in the
literature owing to the scarce research, and a comprehensive computing paradigm
is yet to be systematically designed and reviewed. This study introduces a
novel concept, called aerial computing, via the amalgamation of aerial radio
access networks and edge computing, which attempts to bridge the gap.
Specifically, first, we propose a novel comprehensive computing architecture
that is composed of low-altitude computing, high-altitude computing, and
satellite computing platforms, along with conventional computing systems. We
determine that aerial computing offers several desirable attributes: global
computing service, better mobility, higher scalability and availability, and
simultaneity. Second, we comprehensively discuss key technologies that
facilitate aerial computing, including energy refilling, edge computing,
network softwarization, frequency spectrum, multi-access techniques, artificial
intelligence, and big data. In addition, we discuss vertical domain
applications (e.g., smart cities, smart vehicles, smart factories, and smart
grids) supported by aerial computing. Finally, we highlight several challenges
that need to be addressed and their possible solutions.


title:
Octupole correlations in collective excitations of neutron-rich
  $N\approx56$ nuclei

abstract:
  Octupole correlations in the low-energy collective states of neutron-rich
nuclei near the neutron number $N=56$ are studied within the interacting boson
model (IBM) based on the nuclear density functional theory. The constrained
self-consistent mean-field (SCMF) calculations using a universal energy density
functional and a pairing interaction provide the potential energy surfaces in
terms of the axially-symmetric quadrupole and octupole deformations for the
even-even nuclei $^{86-94}$Se, $^{88-96}$Kr, $^{90-98}$Sr, $^{92-100}$Zr, and
$^{94-102}$Mo. Spectroscopic properties are computed in terms of the IBM that
consists of the monopole $s$, quadrupole $d$, and octupole $f$ bosons. Strength
parameters for the boson Hamiltonian are determined by mapping the SCMF energy
surface onto the expectation value of the Hamiltonian in the boson condensate
state. At the SCMF level, no octupole deformed ground-state shape is found,
while the energy surface is generally soft in the octupole deformation at
$N\approx56$. The predicted negative-parity yrast bands with the bandhead state
$3^-_1$ weakly depend on $N$ and become lowest in energy at $N=56$ in each of
the considered isotopic chains. The model further predicts finite electric
octupole transition rates between the lowest negative-parity and the
positive-parity ground-state bands.


title: Fact Checking with Insufficient Evidence

abstract:
  Automating the fact checking (FC) process relies on information obtained from
external sources. In this work, we posit that it is crucial for FC models to
make veracity predictions only when there is sufficient evidence and otherwise
indicate when it is not enough. To this end, we are the first to study what
information FC models consider sufficient by introducing a novel task and
advancing it with three main contributions. First, we conduct an in-depth
empirical analysis of the task with a new fluency-preserving method for
omitting information from the evidence at the constituent and sentence level.
We identify when models consider the remaining evidence (in)sufficient for FC,
based on three trained models with different Transformer architectures and
three FC datasets. Second, we ask annotators whether the omitted evidence was
important for FC, resulting in a novel diagnostic dataset, SufficientFacts, for
FC with omitted evidence. We find that models are least successful in detecting
missing evidence when adverbial modifiers are omitted (21% accuracy), whereas
it is easiest for omitted date modifiers (63% accuracy). Finally, we propose a
novel data augmentation strategy for contrastive self-learning of missing
evidence by employing the proposed omission method combined with tri-training.
It improves performance for Evidence Sufficiency Prediction by up to 17.8 F1
score, which in turn improves FC performance by up to 2.6 F1 score.


title:
Learning Video Salient Object Detection Progressively from Unlabeled
  Videos

abstract:
  Recent deep learning-based video salient object detection (VSOD) has achieved
some breakthrough, but these methods rely on expensive annotated videos with
pixel-wise annotations, weak annotations, or part of the pixel-wise
annotations. In this paper, based on the similarities and the differences
between VSOD and image salient object detection (SOD), we propose a novel VSOD
method via a progressive framework that locates and segments salient objects in
sequence without utilizing any video annotation. To use the knowledge learned
in the SOD dataset for VSOD efficiently, we introduce dynamic saliency to
compensate for the lack of motion information of SOD during the locating
process but retain the same fine segmenting process. Specifically, an algorithm
for generating spatiotemporal location labels, which consists of generating
high-saliency location labels and tracking salient objects in adjacent frames,
is proposed. Based on these location labels, a two-stream locating network that
introduces an optical flow branch for video salient object locating is
presented. Although our method does not require labeled video at all, the
experimental results on five public benchmarks of DAVIS, FBMS, ViSal, VOS, and
DAVSOD demonstrate that our proposed method is competitive with fully
supervised methods and outperforms the state-of-the-art weakly and unsupervised
methods.


title: An extension of Batanin's approach to globular algebras

abstract:
  In earlier work, Batanin has shown that an important class of definitions of
higher categories could be apprehended together simply as monads over globular
sets. This allowed him to generalize the notion of polygraph, initially
introduced by Street and Burroni for strict categories, to all algebraic
globular higher categories. In this work, we refine this perspective and
introduce new constructions and properties for this class of higher categories.
In particular, we define the notion of cellular extension and its associated
free construction, from which we obtain another definition of polygraphs and
the adjunction between globular algebras and polygraphs. We moreover introduce
two criteria allowing one to use most of the constructions of this article
without having to describe explicitly the underlying globular monad.


title: LatentGAN Autoencoder: Learning Disentangled Latent Distribution

abstract:
  In autoencoder, the encoder generally approximates the latent distribution
over the dataset, and the decoder generates samples using this learned latent
distribution. There is very little control over the latent vector as using the
random latent vector for generation will lead to trivial outputs. This work
tries to address this issue by using the LatentGAN generator to directly learn
to approximate the latent distribution of the autoencoder and show meaningful
results on MNIST, 3D Chair, and CelebA datasets, an additional
information-theoretic constrain is used which successfully learns to control
autoencoder latent distribution. With this, our model also achieves an error
rate of 2.38 on MNIST unsupervised image classification, which is better as
compared to InfoGAN and AAE.


title: ELECRec: Training Sequential Recommenders as Discriminators

abstract:
  Sequential recommendation is often considered as a generative task, i.e.,
training a sequential encoder to generate the next item of a user's interests
based on her historical interacted items. Despite their prevalence, these
methods usually require training with more meaningful samples to be effective,
which otherwise will lead to a poorly trained model. In this work, we propose
to train the sequential recommenders as discriminators rather than generators.
Instead of predicting the next item, our method trains a discriminator to
distinguish if a sampled item is a 'real' target item or not. A generator, as
an auxiliary model, is trained jointly with the discriminator to sample
plausible alternative next items and will be thrown out after training. The
trained discriminator is considered as the final SR model and denoted as
\modelname. Experiments conducted on four datasets demonstrate the
effectiveness and efficiency of the proposed approach.


title:
Mean value theorems for the Apostol-Vu double zeta-function and its
  application

abstract:
  In this paper, we obtain mean square values of the double zeta-functions of
the Apostol-Vu type and the Mordell-Tornheim type.


title: RL4ReAl: Reinforcement Learning for Register Allocation

abstract:
  We propose a novel solution for the Register Allocation problem, leveraging
multi-agent hierarchical Reinforcement Learning. We formalize the constraints
that precisely define the problem for a given instruction-set architecture,
while ensuring that the generated code preserves semantic correctness. We also
develop a gRPC based framework providing a modular and efficient compiler
interface for training and inference. Experimental results match or outperform
the LLVM register allocators, targeting Intel x86 and ARM AArch64.


title: Double lines in the quintic del Pezzo fourfold

abstract:
  Let $Y$ be the del Pezzo $4$-fold defined by the linear section
$\textrm{Gr}(2,5)$ by $\mathbb{P}^7$. In this paper, we classify the type of
normal bundles of lines in $Y$ and describe its parameter space. As a
corollary, we obtain the desigularized model of the moduli space of stable maps
in $Y$. Also we compute the intersection Poincar\'e polynomial of the stable
maps space.


title: A novel spectral method for the subdiffusion equation

abstract:
  In this paper, we design and analyze a novel spectral method for the
subdiffusion equation. As it has been known, the solutions of this equation are
usually singular near the initial time. Consequently, direct application of the
traditional high-order numerical methods is inefficient. We try to overcome
this difficulty in a novel approach by combining variable transformation
techniques with spectral methods. The idea is to first use suitable variable
transformation to re-scale the underlying equation, then construct spectral
methods for the re-scaled equation. We establish a new variational framework
based on the $\psi$-fractional Sobolev spaces. This allows us to prove the
well-posedness of the associated variational problem. The proposed spectral
method is based on the variational problem and generalized Jacobi polynomials
to approximate the re-scaled fractional differential equation. Our theoretical
and numerical investigation show that the proposed method is exponentially
convergent for general right hand side functions, even though the exact
solution has very limited regularity. Implementation details are also provided,
along with a series of numerical examples to show the efficiency of the
proposed method.


title:
Existence, uniqueness and approximation of solutions to Carath\'eodory
  delay differential equations

abstract:
  In this paper we address the existence, uniqueness and approximation of
solutions of delay differential equations (DDEs) with Carath\'eodory type
right-hand side functions. We provide construction of randomized Euler scheme
for DDEs and investigate its error. We also report results of numerical
experiments.


title:
Systematic KMTNet Planetary Anomaly Search. IV. Complete Statistical
  Sample of 2019 Prime-Field Microlensing Planets

abstract:
  We report the complete statistical planetary sample from the prime fields
($\Gamma \geq 2~{\rm hr}^{-1}$) of the 2019 Korea Microlensing Telescope
Network (KMTNet) microlensing survey. We develop the optimized KMTNet
AnomalyFinder algorithm and apply it to the 2019 KMTNet prime fields. We find a
total of 14 homogeneously selected planets and report the analysis of three
planetary events, KMT-2019-BLG-(1042,1552,2974). The planet-host mass ratios,
$q$, for the three planetary events are $6.34 \times 10^{-4}, 4.89 \times
10^{-3}$ and $6.18 \times 10^{-4}$, respectively. A Bayesian analysis indicates
the three planets are all cold giant planets beyond the snow line of their host
stars. The 14 planets are basically uniform in $\log q$ over the range $-5.0 <
\log q < -1.5$. This result suggests that the planets below $q_{\rm break} =
1.7 \times 10^{-4}$ proposed by the MOA-II survey may be more common than
previously believed. This work is an early component of a large project to
determine the KMTNet mass-ratio function, and the whole sample of 2016--2019
KMTNet events should contain about 120 planets.


title: A sum-bracket theorem for simple Lie algebras

abstract:
  Let $\mathfrak{g}$ be an algebra over $K$ with a bilinear operation
$[\cdot,\cdot]:\mathfrak{g}\times\mathfrak{g}\rightarrow\mathfrak{g}$ not
necessarily associative. For $A\subseteq\mathfrak{g}$, let $A^{k}$ be the set
of elements of $\mathfrak{g}$ written combining $k$ elements of $A$ via $+$ and
$[\cdot,\cdot]$.
  We show a "sum-bracket theorem" for simple Lie algebras over $K$ of the form
$\mathfrak{g}=\mathfrak{sl}_{n},\mathfrak{so}_{n},\mathfrak{sp}_{2n},\mathfrak{e}_{6},\mathfrak{e}_{7},\mathfrak{e}_{8},\mathfrak{f}_{4},\mathfrak{g}_{2}$:
namely, if $\mathrm{char}(K)$ is not too small, we have exponential growth of
the form $|A^{k}|\geq|A|^{1+\varepsilon}$ for all generating symmetric sets $A$
whose size is not too close to that of a subfield of $K$. Over $\mathbb{F}_{p}$
in particular, we have a diameter bound in line with analogous bounds for
groups of Lie type, matching the best qualitative estimates of [BDH21].
  As an independent intermediate result, we prove also a dimensional estimate
for linear affine subspaces $V$ of $\mathfrak{g}$, i.e. an estimate of the form
$|A\cap V|\leq|A^{k}|^{\dim(V)/\dim(\mathfrak{g})}$. This estimate is valid for
all simple algebras. Moreover, $k$ is especially small for a large class of
algebras: these include associative algebras, Lie algebras, Lie superalgebras,
and Mal'cev algebras with $\mathrm{char}(K)\neq 2$.


title: Mixing detection on Bitcoin transactions using statistical patterns

abstract:
  Cryptocurrencies gained lots of attention mainly because of the anonymous way
of online payment, which they suggested. Meanwhile, Bitcoin and other major
cryptocurrencies have experienced severe deanonymization attacks. To address
these attacks, Bitcoin contributors introduced services called mixers or
tumblers. Mixing or laundry services aim to return anonymity back to the
network. In this research, we tackle the problem of losing the footprint of
money in Bitcoin and other cryptocurrencies networks caused by the usage of
mixing services. We devise methods to track transactions and addresses of these
services and the addresses of dirty and cleaned money. Because of the lack of
labeled data, we had to transact with these services and prepare labeled data.
Using this data, we found reliable patterns and developed an integrated
algorithm to detect mixing transactions, mixing addresses, sender addresses,
and receiver addresses in the Bitcoin network.


title: Discriminants of Theta-Representations

abstract:
  Tevelev has given a remarkable explicit formula for the discriminant of a
complex simple Lie algebra, which can be defined as the equation of the dual
hypersurface of the minimal nilpotent orbit, or of the so-called adjoint
variety. In this paper we extend this formula to the setting of graded Lie
algebras, and express the equation of the corresponding dual hypersurfaces in
terms of the reflections in the little Weyl groups, the associated complex
reflection groups. This explains for example why the codegree of the
Grassmannian $G(4, 8)$ is equal to the number of roots of $\mathfrak{e}_7$ .


title: Geodesics in first-passage percolation cross any pattern

abstract:
  In first-passage percolation, one places nonnegative i.i.d. random variables
(T (e)) on the edges of Z d. A geodesic is an optimal path for the passage
times T (e). Consider a local property of the time environment. We call it a
pattern. We investigate the number of times a geodesic crosses a translate of
this pattern. Under mild conditions, we show that, apart from an event with
exponentially small probability, this number is linear in the distance between
the extremities of the geodesic.


title:
Towards Digital Twin-enabled DevOps for CPS providing Architecture-Based
  Service Adaptation & Verification at Runtime

abstract:
  Industrial Product-Service Systems (IPSS) denote a service-oriented (SO) way
of providing access to CPS capabilities. The design of such systems bears high
risk due to uncertainty in requirements related to service function and
behavior, operation environments, and evolving customer needs. Such risks and
uncertainties are well known in the IT sector, where DevOps principles ensure
continuous system improvement through reliable and frequent delivery processes.
A modular and SO system architecture complements these processes to facilitate
IT system adaptation and evolution. This work proposes a method to use and
extend the Digital Twins (DTs) of IPSS assets for enabling the continuous
optimization of CPS service delivery and the latter's adaptation to changing
needs and environments. This reduces uncertainty during design and operations
by assuring IPSS integrity and availability, especially for design and service
adaptations at CPS runtime. The method builds on transferring IT DevOps
principles to DT-enabled CPS IPSS. The chosen design approach integrates,
reuses, and aligns the DT processing and communication resources with DevOps
requirements derived from literature. We use these requirements to propose a
DT-enabled self-adaptive CPS model, which guides the realization of DT-enabled
DevOps in CPS IPSS. We further propose detailed design models for
operation-critical DTs that integrate CPS closed-loop control and
architecture-based CPS adaptation. This integrated approach enables the
implementation of A/B testing as a use case and central concept to enable CPS
IPSS service adaptation and reconfiguration. The self-adaptive CPS model and DT
design concept have been validated in an evaluation environment for
operation-critical CPS IPSS. The demonstrator achieved sub-millisecond cycle
times during service A/B testing at runtime without causing CPS operation
interferences and downtime.


title:
A Complementary Joint Training Approach Using Unpaired Speech and Text
  for Low-Resource Automatic Speech Recognition

abstract:
  Unpaired data has shown to be beneficial for low-resource automatic speech
recognition~(ASR), which can be involved in the design of hybrid models with
multi-task training or language model dependent pre-training. In this work, we
leverage unpaired data to train a general sequence-to-sequence model. Unpaired
speech and text are used in the form of data pairs by generating the
corresponding missing parts in prior to model training. Inspired by the
complementarity of speech-PseudoLabel pair and SynthesizedAudio-text pair in
both acoustic features and linguistic features, we propose a complementary
joint training~(CJT) method that trains a model alternatively with two data
pairs. Furthermore, label masking for pseudo-labels and gradient restriction
for synthesized audio are proposed to further cope with the deviations from
real data, termed as CJT++. Experimental results show that compared to
speech-only training, the proposed basic CJT achieves great performance
improvements on clean/other test sets, and the CJT++ re-training yields further
performance enhancements. It is also apparent that the proposed method
outperforms the wav2vec2.0 model with the same model size and beam size,
particularly in extreme low-resource cases.


title: Morse-Rad\'o Theory for Minimal Surfaces

abstract:
  For a class of functions (called Rad\'o functions) that arise naturally in
minimal surface theory, we bound the number of interior critical points
(counting multiplicity) in terms of the boundary data and the Euler
characteristic.


title: Revisiting Pushchino RRAT search using neural network

abstract:
  The search for rotating radio transients (RRAT) at declination from -9o to
+42o was carried out in the semi-annual monitoring data obtained on the Large
Phased Array (LPA) radio telescope at the frequency of 111 MHz. A neural
network was used to search for candidates. 4 new RRATs were detected, having
dispersion measures (DM) 5-16 pc/cm3. A comparison with an earlier RRAT search
conducted using the same data shows that the neural network reduced the amount
of interference by 80 times, down to 1.3% of the initial amount of
interferences. The loss of real pulsar pulses does not exceed 6% of their total
number.


title:
Efficiency of Penrose process in spacetime of axially symmetric
  magnetized Reissner-Nordstr\"{o}m black hole

abstract:
  In this paper, we investigate the Penrose process in the purlieus of the
axially symmetric magnetised Reissner-Nordstr\"{o}m black hole for both neutral
and charged particles. We start with the study of the geometry of the black
hole and find the regions where the $g_{tt}$ component of the metric tensor is
positive (i.e. $g_{tt}>0$). It is interestingly found that the condition
$g_{tt}>0$ is fulfilled not only close to the event horizon known as the
ergosphere but also far away from the event horizon in the silhouette of
potential wells. We also show that as the dimensionless magnetic field $B$
increases the silhouette of potential wells for which $g_{tt}>0$ grows
correspondingly and eventually merges with ergoregion when $B\gtrsim 1.6$.
Finally, we investigate the efficiency of the Penrose process for the axially
symmetric magnetized black hole case and bring out the effect of the magnetic
field on it. Further, we also compare our results with the one for Kerr black
hole. We show that when the charge $Q$ of the black hole is kept constant, the
efficiency of the energy extraction process for the case of {a neutral particle
(i.e. $q/m=0$) first increases and then begins to decrease with rise in the
value of $B$ field, in contrast to Kerr black hole where it always increases as
the rotation parameter grows. However, for the case of a charged particle (i.e.
$q\neq 0$) the efficiency always increases with the rise in the $B$ field and
can go over $100\%$, when both $B$ and $q/m$ are large enough (say $B\approx1$
and $q/m>2.2$)}. It is worth noting that the existence of regions away from the
horizon where $g_{tt}>0$ also favours the energy-extraction process away from
the effect of the black hole. However, the energy extraction from these regions
is pure consequence of the magnetic field.


title:
A Survey on Dropout Methods and Experimental Verification in
  Recommendation

abstract:
  Overfitting is a common problem in machine learning, which means the model
too closely fits the training data while performing poorly in the test data.
Among various methods of coping with overfitting, dropout is one of the
representative ways. From randomly dropping neurons to dropping neural
structures, dropout has achieved great success in improving model performances.
Although various dropout methods have been designed and widely applied in past
years, their effectiveness, application scenarios, and contributions have not
been comprehensively summarized and empirically compared by far. It is the
right time to make a comprehensive survey.
  In this paper, we systematically review previous dropout methods and classify
them into three major categories according to the stage where dropout operation
is performed. Specifically, more than seventy dropout methods published in top
AI conferences or journals (e.g., TKDE, KDD, TheWebConf, SIGIR) are involved.
The designed taxonomy is easy to understand and capable of including new
dropout methods. Then, we further discuss their application scenarios,
connections, and contributions. To verify the effectiveness of distinct dropout
methods, extensive experiments are conducted on recommendation scenarios with
abundant heterogeneous information. Finally, we propose some open problems and
potential research directions about dropout that worth to be further explored.


title:
A Generative Deep Learning Approach to Stochastic Downscaling of
  Precipitation Forecasts

abstract:
  Despite continuous improvements, precipitation forecasts are still not as
accurate and reliable as those of other meteorological variables. A major
contributing factor to this is that several key processes affecting
precipitation distribution and intensity occur below the resolved scale of
global weather models. Generative adversarial networks (GANs) have been
demonstrated by the computer vision community to be successful at
super-resolution problems, i.e., learning to add fine-scale structure to coarse
images. Leinonen et al. (2020) previously applied a GAN to produce ensembles of
reconstructed high-resolution atmospheric fields, given coarsened input data.
In this paper, we demonstrate this approach can be extended to the more
challenging problem of increasing the accuracy and resolution of comparatively
low-resolution input from a weather forecasting model, using high-resolution
radar measurements as a "ground truth". The neural network must learn to add
resolution and structure whilst accounting for non-negligible forecast error.
We show that GANs and VAE-GANs can match the statistical properties of
state-of-the-art pointwise post-processing methods whilst creating
high-resolution, spatially coherent precipitation maps. Our model compares
favourably to the best existing downscaling methods in both pixel-wise and
pooled CRPS scores, power spectrum information and rank histograms (used to
assess calibration). We test our models and show that they perform in a range
of scenarios, including heavy rainfall.


title:
To reduce soil salinity: the role of irrigation and water management in
  global arid regions across development phases

abstract:
  Reducing soil salinization of croplands with optimized irrigation and water
management is essential to achieve land degradation neutralization. The
effectiveness and sustainability of various irrigation and water management
measures to reduce basin-scale salinization remain uncertain. Here we use
remote sensing to estimate soil salinity of arid croplands from 1984 to 2018.
We then use Bayesian network analysis to compare the spatial-temporal response
of salinity to water management, including various irrigation and drainage
methods, in ten large arid river basins: Nile, Tigris-Euphrates, Indus, Tarim,
Amu, Ili, Syr, Junggar, Colorado, and San Joaquin. Managers in basins at more
advanced phases of development implemented drip and groundwater irrigation,
which effectively controlled salinity by lowering groundwater levels. For the
remaining basins where conventional flood irrigation is used, economic
development and policies are crucial to establishing a virtuous circle of
improving irrigation systems, reducing salinity, and increasing agricultural
incomes necessary to achieve LDN.


title:
$\textit{latent}$-GLAT: Glancing at Latent Variables for Parallel Text
  Generation

abstract:
  Recently, parallel text generation has received widespread attention due to
its success in generation efficiency. Although many advanced techniques are
proposed to improve its generation quality, they still need the help of an
autoregressive model for training to overcome the one-to-many multi-modal
phenomenon in the dataset, limiting their applications. In this paper, we
propose $\textit{latent}$-GLAT, which employs the discrete latent variables to
capture word categorical information and invoke an advanced curriculum learning
technique, alleviating the multi-modality problem. Experiment results show that
our method outperforms strong baselines without the help of an autoregressive
model, which further broadens the application scenarios of the parallel
decoding paradigm.


title:
Practical Bounds of Kullback-Leibler Divergence Using Maximum Mean
  Discrepancy

abstract:
  Estimating Kullback Leibler (KL) divergence from data samples is a strenuous
task, with existing approaches either imposing restrictive assumptions on the
domain knowledge of the underlying model or requiring density approximation
like space partitioning. The use of kernel maximum mean discrepancy (MMD)
yields an alternative non-parametric approach to compare two populations by
finding the maximum mean difference generated by measurable functions in the
unit ball in a reproducing kernel Hilbert space (RKHS). Predicated on the
universal approximation of universal kernels, we propose two corresponding
classes of functions in a RKHS that can bound the KL divergence from below and
above, and derive the RKHS representations of these bounds. This allows us to
develop asymptotically consistent estimates for the derived bounds. We evaluate
the proposed bounds as mutual information proxies on an image dataset, and
demonstrate that these bounds can stably track variations in mutual
information.


title: Built-in scordatura in U-DHOST

abstract:
  Modified gravity theories can accommodate exact solutions, for which the
metric has the same form as the one in general relativity, i.e., stealth
solutions. One problem with these stealth solutions is that perturbations
around them exhibit strong coupling when the solutions are realized in
degenerate higher-order scalar-tensor theories. We show that the strong
coupling problem can be circumvented in the framework of the so-called U-DHOST
theories, in which the degeneracy is partially broken in such a way that
higher-derivative terms are degenerate only in the unitary gauge. There is an
apparent Ostrogradsky mode in U-DHOST theories, but it does not propagate as it
satisfies a three-dimensional elliptic differential equation on a spacelike
hypersurface. We also clarify how this nonpropagating mode, i.e., the "shadowy"
mode shows up at the nonlinear level.


title:
Learning to Reduce Information Bottleneck for Object Detection in Aerial
  Images

abstract:
  Object detection in aerial images is a fundamental research topic in the
domain of geoscience and remote sensing. However, advanced progresses on this
topic are mainly focused on the designment of backbone networks or header
networks, but surprisingly ignored the neck ones. In this letter, we first
analyse the importance of the neck network in object detection frameworks from
the theory of information bottleneck. Then, to alleviate the information loss
problem in the current neck network, we propose a global semantic network,
which acts as a bridge from the backbone to the head network in a bidirectional
global convolution manner. Compared to the existing neck networks, our method
has advantages of capturing rich detailed information and less computational
costs. Moreover, we further propose a fusion refinement module, which is used
for feature fusion with rich details from different scales. To demonstrate the
effectiveness and efficiency of our method, experiments are carried out on two
challenging datasets (i.e., DOTA and HRSC2016). Results in terms of accuracy
and computational complexity both can verify the superiority of our method.


title: Optical Spectral Variations of a Large Sample of Fermi Blazars

abstract:
  We have investigated the optical spectral behavior of a large sample of Fermi
blazars (40 FSRQs and 13 BL Lacs), and found two new universal optical spectral
behaviors. In the low state the optical spectrum gradually becomes softer
(steeper) or harder (flatter) but more and more slowly when the brightness
increases, and then tends to stable in the high state, which are briefly named
the redder-stable-when-brighter (RSWB) and bluer-stable-when-brighter (BSWB)
behaviors, respectively. 34 FSRQs and 7 BL Lacs exhibit clear RSWB behavior,
and 2 FSRQs and 5 BL Lacs show distinct BSWB behavior, which mean that FSRQs
favor more RSWB than BSWB behavior, while BL Lacs have no clear preference
among both behaviors. We have put forward a unified nonlinear formula to
quantitatively characterize the optical spectral behaviors of FSRQs and BL
Lacs, which can fit both kinds of behaviors very well. We argue that the RSWB
and BSWB behaviors originate from the same mechanism, and they are the
universal optical spectral behaviors for blazars. The frequently observed
redder-when-brighter (RWB) and bluer-when-brighter (BWB) trends can be
considered to be the approximations of the behaviors of RSWB and BSWB,
respectively. The rarely observed stable-when-brighter (SWB) trend can also be
viewed as an approximation or a special case of the RSWB or BSWB behavior. We
have developed a model with two constant-spectral-index components which can
not only explain well both two kinds of optical spectral behaviors, but also
successfully interpret the differential behaviors between FSRQs and BL Lacs.


title: DT2I: Dense Text-to-Image Generation from Region Descriptions

abstract:
  Despite astonishing progress, generating realistic images of complex scenes
remains a challenging problem. Recently, layout-to-image synthesis approaches
have attracted much interest by conditioning the generator on a list of
bounding boxes and corresponding class labels. However, previous approaches are
very restrictive because the set of labels is fixed a priori. Meanwhile,
text-to-image synthesis methods have substantially improved and provide a
flexible way for conditional image generation. In this work, we introduce dense
text-to-image (DT2I) synthesis as a new task to pave the way toward more
intuitive image generation. Furthermore, we propose DTC-GAN, a novel method to
generate images from semantically rich region descriptions, and a multi-modal
region feature matching loss to encourage semantic image-text matching. Our
results demonstrate the capability of our approach to generate plausible images
of complex scenes using region captions.


title: Schottky's forgotten step to the Ising model

abstract:
  A longstanding problem in natural science and later in physics was the
understanding of the existence of ferromagnetism and its disappearance under
heating to high temperatures. Although a qualitative description was possible
by the Curie-Weiss theory it was obvious that a microscopic model was necessary
to explain the tendency of the elementary magnetons to prefer parallel ordering
at low temperatures. Such a model was proposed in 1922 by W. Schottky within
the old Bohr-Sommerfeld quantum mechanics and claimed to explain the high
values of the Curie temperatures of certain ferromagnets. Based on this idea
Ising formulated a new model for ferromagnetism in solids. Simultaneously the
old quantum mechanics was replaced by new concepts of Heisenberg and
Schr\"odinger and the discovery of spin. Thus Schottky's idea was outperformed
and finally replaced in 1928 by Heisenberg exchange interaction. This led to a
reformulation of Ising's model by Pauli at the Solvay conference in 1930.
Nevertheless one might consider Schottky's idea as a forerunner of this
development explaining and asserting that the main point is the Coulomb energy
leading to the essential interaction of neighboring elementary magnets.


title:
Tracking interfacial changes of graphene/Ge(110) during in-vacuum
  annealing

abstract:
  Graphene quality indicators obtained by Raman spectroscopy have been
correlated to the structural changes of the graphene/Germanium interface as a
function of in-vacuum thermal annealing. Specifically, it is found that
graphene becomes markedly defected at 650 {\deg}C. By combining scanning
tunneling microscopy, x-Ray Photoelectron Spectroscopy and Near Edge x-ray
Absorption Fine Structure Spectroscopy, we conclude that these defects are due
to the release of H_{2} gas trapped at the graphene/Germanium interface. The
H_{2} gas was produced following the transition from the as-grown
hydrogen-termination of the Ge(110) surface to the emergence of surface
reconstructions in the substrate. Interestingly, a complete self-healing
process is observed in graphene upon annealing to 800 {\deg}C. The subtle
interplay revealed between the microscopic changes occurring at the
graphene/Germanium interface and graphene's defect density is valuable for
advancing graphene growth, controlled 2D-3D heterogeneous materials interfacing
and integrated fabrication technology on semiconductors.


title:
Macroeconomic Dynamics in a finite world: the Thermodynamic Potential
  Approach

abstract:
  This paper presents a conceptual model describing the medium and long-term
co-evolution of natural and socio-economic subsystems of Earth. An economy is
viewed as an out-of-equilibrium dissipative structure that can only be
maintained with a flow of energy and matter. The distinctive approach
emphasized here consists in capturing the economic impact of natural ecosystems
being depleted and destroyed by human activities via a pinch of thermodynamic
potentials. This viewpoint allows: (i) the full-blown integration of a limited
quantity of primary resources into a non-linear macrodynamics that is
stock-flow consistent both in terms of matter-energy as well as economic
transactions; (ii) the inclusion of natural and forced recycling; (iii) the
inclusion of a friction term which reflects the impossibility of producing
goods and services in high metabolising intensity without exuding energy and
matter wastes; (iv) the computation of the anthropically produced entropy as a
function of intensity and friction. Analysis and numerical computations confirm
the role played by intensity and friction as key factors for sustainability.
Our approach is flexible enough to allow for various economic models to be
embedded into our thermodynamic framework.


title:
The Husimi function of a semiconfined harmonic oscillator model with a
  position-dependent effective mass

abstract:
  The phase space representation for a semiconfined harmonic oscillator model
with a position-dependent effective mass is constructed. We have found the
Husimi distribution function for the stationary states of the oscillator model
under consideration for both cases without and with the applied external
homogeneous field. The obtained function is expressed through the double sum of
the parabolic cylinder function. Different special cases and the limit
relations are discussed, too.


title: On the Relevance of Bandwidth Extension for Speaker Verification

abstract:
  In this paper, we consider the effect of a bandwidth extension of narrow-band
speech signals (0.3-3.4 kHz) to 0.3-8 kHz on speaker verification. Using
covariance matrix based verification systems together with detection error
trade-off curves, we compare the performance between systems operating on
narrow-band, wide-band (0-8 kHz), and bandwidth-extended speech. The
experiments were conducted using different short-time spectral
parameterizations derived from microphone and ISDN speech databases. The
studied bandwidth-extension algorithm did not introduce artifacts that affected
the speaker verification task, and we achieved improvements between 1 and 10
percent (depending on the model order) over the verification system designed
for narrow-band speech when mel-frequency cepstral coefficients for the
short-time spectral parameterization were used.


title: Automating Reinforcement Learning with Example-based Resets

abstract:
  Deep reinforcement learning has enabled robots to learn motor skills from
environmental interactions with minimal to no prior knowledge. However,
existing reinforcement learning algorithms assume an episodic setting, in which
the agent resets to a fixed initial state distribution at the end of each
episode, to successfully train the agents from repeated trials. Such reset
mechanism, while trivial for simulated tasks, can be challenging to provide for
real-world robotics tasks. Resets in robotic systems often require extensive
human supervision and task-specific workarounds, which contradicts the goal of
autonomous robot learning. In this paper, we propose an extension to
conventional reinforcement learning towards greater autonomy by introducing an
additional agent that learns to reset in a self-supervised manner. The reset
agent preemptively triggers a reset to prevent manual resets and implicitly
imposes a curriculum for the forward agent. We apply our method to learn from
scratch on a suite of simulated and real-world continuous control tasks and
demonstrate that the reset agent successfully learns to reduce manual resets
whilst also allowing the forward policy to improve gradually over time.


title:
Cr doping-induced ferromagnetism in the spin-glass Cd1-xMnxTe studied by
  x-ray magnetic circular dichroism

abstract:
  The prototypical diluted magnetic semiconductor Cd1-xMnxTe is a spin glass
(x<0.6) or an antiferromagnet (x>0.6), but becomes ferromagnetic upon doping
with a small amount of Cr atoms substituting for Mn. In order to investigate
the origin of the ferromagnetism in Cd1-x-yMnxCryTe, we have studied its
element specific magnetic properties by x-ray absorption spectroscopy (XAS) and
x-ray magnetic circular dichroism (XMCD) at the Cr and Mn L2,3 edges. Thin
films were grown by molecular beam epitaxy with a fixed Mn content of x = 0.2
and varying Cr content in the range of y = 0 - 0.04. Measured XAS and XMCD
spectra indicate that both Cr and Mn atoms are divalent and that the
ferromagnetic or superparamagnetic components of Cr and Mn are aligned in the
same directions. The magnetization of Mn increases with increasing Cr content.
These results can be explained if ferromagnetic interaction exists between
neighboring Mn and Cr ions although interaction between Mn atoms is largely
antiferromagnetic. We conclude that each ferromagnetic or superparamagnetic
cluster consists of ferromagnetically coupled several Cr and a much larger
number of Mn ions.


title:
$O(\alpha_s)$ perturbative and nonperturbative corrections to polarized
  semileptonic $\Lambda_b$ decay distributions

abstract:
  In this paper we calculate first order radiative QCD corrections to the decay
process $b\to cW^-(\to\ell^-\bar\nu_\ell)$ of a polarized bottom quark. Taking
into account both the bottom and charm quark masses, the analytical expressions
given in this paper allow for a detailed analysis of the differential decay
rate of the polarized quark in dependence on the polar and azimuthal angles of
the lepton pair and the angle of the polarization vector of the quark relative
to the momentum direction of the $W$ boson. The results are given for different
polarization states of the charged lepton. In addition, we calculated
nonperturbative corrections and estimated their contribution.


title: Giant-cavity-based quantum sensors with enhanced performance

abstract:
  Recent progresses have revealed that quantum systems with multiple
position-dependent couplings, e.g., giant atoms, can exhibit some
unconventional phenomena, such as non-exponential decay etc. However, their
potential applications are still open questions. In this paper, we propose a
giant-cavity-based quantum sensor for the first time, whose performance can be
greatly enhanced compared to traditional cavity-based sensors. In our proposal,
two cavities couple to a dissipative reservoir at multiple points while they
couple to a gain reservoir in a single-point way. To detecting a unknown
parameter using this sensor, a waveguide is coupled to one of the cavities
where detecting fields can pass through for homodyne detection. We find that
multiple position-dependent couplings can induce an inherent non-reciprocal
coupling between the cavities, which can enhance the performance of sensors.
Output noise in our scheme can be reduced to the shot noise level, which is
about one order magnitude lower than the results in Ref. [Nature
Communications, 2018, 9, 4320.]. Besides, the signal-to-noise ratio per photon
is also enhanced by about one order of magnitude. These results show that the
multiple-point-coupling structure is beneficial to nowadays quantum devices.


title: Iitaka conjecture for anticanonical divisors in positive characteristics

abstract:
  Given a separable fibration over an algebraically closed field of positive
characteristic, we study an Iitaka-type inequality for the anticanonical
divisors. We conclude that it holds when the source of the fibration is a
threefold or when the target is a curve. We then give counterexamples in
characteristics $2$ and $3$ for fibrations with "very singular" fibres,
constructed from Tango-Raynaud surfaces.


title:
Less is More: A Call to Focus on Simpler Models in Genetic Programming
  for Interpretable Machine Learning

abstract:
  Interpretability can be critical for the safe and responsible use of machine
learning models in high-stakes applications. So far, evolutionary computation
(EC), in particular in the form of genetic programming (GP), represents a key
enabler for the discovery of interpretable machine learning (IML) models. In
this short paper, we argue that research in GP for IML needs to focus on
searching in the space of low-complexity models, by investigating new kinds of
search strategies and recombination methods. Moreover, based on our experience
of bringing research into clinical practice, we believe that research should
strive to design better ways of modeling and pursuing interpretability, for the
obtained solutions to ultimately be most useful.


title:
Moving structures in ultraviolet bright points: observations from Solar
  Orbiter/EUI

abstract:
  Moving structures have been detected in coronal bright points and in a solar
flare in active regions, which were bi-directional, symmetrical, simultaneous,
and quasi-periodic (Ning & Guo 2014; Ning 2016; Li et al. 2016a). They could be
regarded as observational evidence of plasma outflows via magnetic
reconnection. In this article, we explored pairs of moving structures in
fifteen ultraviolet bright points (UBPs), which were observed in the quiet Sun
or inside a small active region on 19 November 2020, and measured by the High
Resolution (HRI) Telescopes of the Extreme Ultraviolet Imager (EUI) on board
the Solar Orbiter (SolO) in two passbands, HRIEUV 174 {\AA} and HRILy{\alpha}
1216 {\AA}. Moving structures observed in ten UBPs as starting from their
bright cores and propagating toward two ends, are interpreted as diverging
motions of bi-directional moving structures. These moving structures are also
characterized by simultaneity and symmetry and in the case of seven UBPs they
exhibit quasi-periodicity. They could be generated by outflows after magnetic
reconnections. Moving structures seen in another five UBPs as originating from
double ends and moving closer, and merging together, are manifested as
converging motions, which might be caused by inflows through the magnetic
reconnection, or might be interpreted as upflows driven by the chromospheric
evaporation.


title: Graded Lie Algebras, Compactified Jacobians and Arithmetic Statistics

abstract:
  A simply laced Dynkin diagram gives rise to a family of curves over
$\mathbb{Q}$ and a coregular representation, using deformations of simple
singularities and Vinberg theory respectively. Thorne has conjectured and
partially proven a strong link between the arithmetic of these curves and the
rational orbits of these representations. In this paper, we complete Thorne's
picture and show that $2$-Selmer elements of the Jacobians of the smooth curves
in each family can be parametrised by integral orbits of the corresponding
representation. Using geometry-of-numbers techniques, we deduce statistical
results on the arithmetic of these curves. We prove these results in a uniform
manner. This recovers and generalises results of Bhargava, Gross, Ho, Shankar,
Shankar and Wang. The main innovations are: an analysis of torsors on affine
spaces using results of Colliot-Th\'el\`ene and the Grothendieck--Serre
conjecture, a study of geometric properties of compactified Jacobians using the
Bialynicki-Birula decomposition, and a general construction of integral orbit
representatives.


title:
Engineering of spontaneous emission in free space via conditional
  measurements

abstract:
  We study the collective spontaneous emission of three identical two-level
atoms initially prepared in the excited states by measuring Glauber's
third-order photon correlation function. Assuming two atoms at sub-wavelength
distance from each other such that they are subject to the dipole-dipole
interaction while the third one is located several wavelengths away, we observe
super- and subradiant decay alike, depending on the direction of observation.
Unlike the case where no remote atom is introduced or no conditional
measurements are performed, the spontaneous emission behavior of the
conditioned three-atom system differs strongly from the single-atom and the
canonical two-atom configuration. The conditional measurements associated with
the three-photon correlation function in combination with the dipole-dipole
interaction between the adjacent atoms lead to quantum interference among the
different decay channels allowing to engineer the spontaneous emission in space
and time.


title: On representation formulas for optimal control: A Lagrangian perspective

abstract:
  In this paper, we study representation formulas for finite-horizon optimal
control problems with or without state constraints, unifying two different
viewpoints: the Lagrangian and dynamic programming (DP) frameworks. In a recent
work [1], the generalized Lax formula is obtained via DP for optimal control
problems with state constraints and nonlinear systems. We revisit the formula
from the Lagrangian perspective to provide a unified framework for
understanding and implementing the nontrivial representation of the value
function. Our simple derivation makes direct use of the Lagrangian formula from
the theory of Hamilton-Jacobi (HJ) equations. We also discuss a rigorous way to
construct an optimal control using a $\delta$-net, as well as a numerical
scheme for controller synthesis via convex optimization.


title:
First Identification of New X-Ray Spectra of Mo39+, Mo40+, W43+, W44+
  and W45+ on EAST

abstract:
  New high-resolution x-ray spectra of Mo39+, Mo40+, W43+, W44+ and W45+ have
been carefully confirmed for the first time by use of the x-ray imaging crystal
spectrometer (XCS) in Experimental Advanced Superconducting Tokamak (EAST)
under various combined auxiliary heating plasmas conditions. Wavelength of
these new x-ray spectra is ranged from 3.895 {\AA} to 3.986 {\AA}. When core
electron temperature (Te0) reaches 6.0 keV, Mo39+ and Mo40+ lines of 3.9727,
3.9294 and 3.9480 {\AA} can be effectively detected on XCS for EAST; meanwhile,
line-integrated brightness of these spectral lines of Mo39+ and Mo40+ is very
considerable when electron temperature reaches 12.9 keV. Multi-components
spectral lines for W43+, W44+ and W45+ have also been identified when Te0
reaches 6 keV. Parts of spectral lines, such as Zn-1, Cu-2, Cu-4a, Cu-4d and
Cu-5 lines of tungsten, are first observed experimentally. When electron
temperature reaches 12.9 keV, line-integrated intensity for part of these
spectral lines of W43+, W44+ and W45+ are considerable. These experimental
results and theoretical predictions from FAC and FLYCHK codes are in good
general agreement. These new spectral lines, obtained on XCS for EAST, are
vital for deeply uncovering the mechanisms of ion and electron thermal, high-Z
impurity and momentum (anomalous) transport to achieve the advanced
steady-state operation scenarios for ITER and CFETR.


title:
Linear differential operators with distribution coefficients of various
  singularity orders

abstract:
  In this paper, the linear differential expression of order $n \ge 2$ with
distribution coefficients of various singularity orders is considered. We
obtain the associated matrix for the regularization of this expression.
Furthermore, we present the new statements of inverse spectral problems that
consist in the recovery of differential operators with distribution
coefficients from the Weyl matrix on the half-line and on a finite interval.
The uniqueness theorems for these inverse problems are proved by developing the
method of spectral mappings.


title:
Escaping the maze: a statistical sub-grid model for cloud-scale density
  structures in the interstellar medium

abstract:
  The interstellar medium (ISM) is a turbulent, highly structured multi-phase
medium. State-of-the-art cosmological simulations of the formation of galactic
discs usually lack the resolution to accurately resolve those multi-phase
structures. However, small-scale density structures play an important role in
the life cycle of the ISM, and determine the fraction of cold, dense gas, the
amount of star formation and the amount of radiation and momentum leakage from
cloud-embedded sources. Here, we derive a $statistical\, model$ to calculate
the unresolved small-scale ISM density structure from coarse-grained,
volume-averaged quantities such as the $gas\, clumping\, factor$,
$\mathcal{C}$, and mean density $\left<\rho\right>_V$. Assuming that the
large-scale ISM density is statistically isotropic, we derive a relation
between the three-dimensional clumping factor, $\mathcal{C}_\rho$, and the
clumping factor of the $4\pi$ column density distribution on the cloud surface,
$\mathcal{C}_\Sigma$, and find $\mathcal{C}_\Sigma=\mathcal{C}_\rho^{2/3}$.
Applying our model to calculate the covering fraction, i.e., the $4\pi$ sky
distribution of optically thick sight-lines around sources inside interstellar
gas clouds, we demonstrate that small-scale density structures lead to
significant differences at fixed physical ISM density. Our model predicts that
gas clumping increases the covering fraction by up to 30 per cent at low ISM
densities compared to a uniform medium. On the other hand, at larger ISM
densities, gas clumping suppresses the covering fraction and leads to increased
scatter such that covering fractions can span a range from 20 to 100 per cent
at fixed ISM density. All data and example code is publicly available at
GitHub.

categories: astro-ph.GA astro-ph.CO physics.comp-ph physics.data-an physics.space-ph


title:
An efficient real-time target tracking algorithm using adaptive feature
  fusion

abstract:
  Visual-based target tracking is easily influenced by multiple factors, such
as background clutter, targets fast-moving, illumination variation, object
shape change, occlusion, etc. These factors influence the tracking accuracy of
a target tracking task. To address this issue, an efficient real-time target
tracking method based on a low-dimension adaptive feature fusion is proposed to
allow us the simultaneous implementation of the high-accuracy and real-time
target tracking. First, the adaptive fusion of a histogram of oriented gradient
(HOG) feature and color feature is utilized to improve the tracking accuracy.
Second, a convolution dimension reduction method applies to the fusion between
the HOG feature and color feature to reduce the over-fitting caused by their
high-dimension fusions. Third, an average correlation energy estimation method
is used to extract the relative confidence adaptive coefficients to ensure
tracking accuracy. We experimentally confirm the proposed method on an OTB100
data set. Compared with nine popular target tracking algorithms, the proposed
algorithm gains the highest tracking accuracy and success tracking rate.
Compared with the traditional Sum of Template and Pixel-wise LEarners (STAPLE)
algorithm, the proposed algorithm can obtain a higher success rate and
accuracy, improving by 0.023 and 0.019, respectively. The experimental results
also demonstrate that the proposed algorithm can reach the real-time target
tracking with 50 fps. The proposed method paves a more promising way for
real-time target tracking tasks under a complex environment, such as appearance
deformation, illumination change, motion blur, background, similarity, scale
change, and occlusion.


title:
Compact molecular gas emission in local LIRGs among low- and high-z
  galaxies

abstract:
  We present new CO(2-1) observations of a representative sample of 24 local
(z$<$0.02) luminous infrared galaxies (LIRGs) obtained at high spatial
resolution ($<$100 pc) from ALMA. We derive the effective radii of the CO(2-1)
and the 1.3 mm continuum emissions using the curve-of-growth method. LIRGs show
an extremely compact cold molecular gas distribution (median R$_{CO}$ $\sim$0.7
kpc), which is a factor 2 smaller than the ionized gas, and 3.5 times smaller
than the stellar size. The molecular size of LIRGs is similar to that of
early-type galaxies (R$_{CO}\sim$1 kpc) and about a factor of 6 more compact
than local Spirals of similar stellar mass. Only the CO emission in low-z
ULIRGs is more compact than these local LIRGs by a factor of 2. Compared to
high-z (1$<$z$<$6) systems, the stellar sizes and masses of local LIRGs are
similar to those of high-z MS star-forming galaxies (SFG) and about a factor of
2-3 lower than sub-mm galaxies (SMG). The molecular sizes of high-z MS SFGs and
SMGs are larger than those derived for LIRGs by a factor of $\sim$3 and
$\sim$8, respectively. These results indicate that while low-z LIRGs and high-z
MS-SFGs have similar stellar masses and sizes, the regions of current star
formation (traced by the ionized gas) and of potential star-formation (traced
by the molecular gas) are substantially smaller in LIRGs, and constrained to
the central kpc region. High-z galaxies represent a wider population but their
star-forming regions are more extended, even covering the overall size of the
host galaxy. High-z galaxies have larger fractions of gas than low-z LIRGs, and
therefore the formation of stars could be induced by interactions and mergers
in extended disks or filaments with large enough molecular gas surface density
involving physical mechanisms similar to those identified in the central kpc of
LIRGs.


title:
Modeling a labyrinthine acoustic metamaterial through an
  inertia-augmented relaxed micromorphic approach

abstract:
  We present an inertia-augmented relaxed micromorphic model that enriches the
relaxed micromorphic model previously introduced by the authors via a term
$\text{Curl}\dot{P}$ in the kinetic energy density. This enriched model allows
us to obtain a good overall fitting of the dispersion curves while introducing
the new possibility of describing modes with negative group velocity that are
known to trigger negative refraction effects. The inertia-augmented model also
allows for more freedom on the values of the asymptotes corresponding to the
cut-offs. In the previous version of the relaxed micromorphic model, the
asymptote of one curve (pressure or shear) is always bounded by the cut-off of
the following curve of the same type. This constraint does not hold anymore in
the enhanced version of the model. While the obtained curves' fitting is of
good quality overall, a perfect quantitative agreement must still be reached
for very small wavelengths that are close to the size of the unit cell.


title: Large-Scale Shill Bidder Detection in E-commerce

abstract:
  User feedback is one of the most effective methods to build and maintain
trust in electronic commerce platforms. Unfortunately, dishonest sellers often
bend over backward to manipulate users' feedback or place phony bids in order
to increase their own sales and harm competitors. The black market of user
feedback, supported by a plethora of shill bidders, prospers on top of
legitimate electronic commerce. In this paper, we investigate the ecosystem of
shill bidders based on large-scale data by analyzing hundreds of millions of
users who performed billions of transactions, and we propose a
machine-learning-based method for identifying communities of users that
methodically provide dishonest feedback. Our results show that (1) shill
bidders can be identified with high precision based on their transaction and
feedback statistics; and (2) in contrast to legitimate buyers and sellers,
shill bidders form cliques to support each other.


title:
HyperBox: A Supervised Approach for Hypernym Discovery using Box
  Embeddings

abstract:
  Hypernymy plays a fundamental role in many AI tasks like taxonomy learning,
ontology learning, etc. This has motivated the development of many automatic
identification methods for extracting this relation, most of which rely on word
distribution. We present a novel model HyperBox to learn box embeddings for
hypernym discovery. Given an input term, HyperBox retrieves its suitable
hypernym from a target corpus. For this task, we use the dataset published for
SemEval 2018 Shared Task on Hypernym Discovery. We compare the performance of
our model on two specific domains of knowledge: medical and music.
Experimentally, we show that our model outperforms existing methods on the
majority of the evaluation metrics. Moreover, our model generalize well over
unseen hypernymy pairs using only a small set of training data.


title: Parameter Filter-based Event-triggered Learning

abstract:
  Model-based algorithms are deeply rooted in modern control and systems
theory. However, they usually come with a critical assumption - access to an
accurate model of the system. In practice, models are far from perfect. Even
precisely tuned estimates of unknown parameters will deteriorate over time.
Therefore, it is essential to detect the change to avoid suboptimal or even
dangerous behavior of a control system. We propose to combine statistical tests
with dedicated parameter filters that track unknown system parameters from
state data. These filters yield point estimates of the unknown parameters and,
further, an inherent notion of uncertainty. When the point estimate leaves the
confidence region, we trigger active learning experiments. We update models
only after enforcing a sufficiently small uncertainty in the filter. Thus,
models are only updated when necessary and statistically significant while
ensuring guaranteed improvement, which we call event-triggered learning. We
validate the proposed method in numerical simulations of a DC motor in
combination with model predictive control.


title:
Characterization of pixelated silicon detectors for daily quality
  assurance measurements in proton therapy

abstract:
  The advanced imaging and delivery techniques in proton therapy allow
conformal high-dose irradiation of the target volume with high accuracy using
pencil beam scanning or beam shaping apertures. These irradiation methods
increasingly include small radiation fields with large dose gradients, which
require detector systems with high spatial resolution for quality assurance. In
addition the measurement of all success parameters for daily quality assurance
with only one proton field and one simple detector system would save a lot of
time in clinical usage. Based on their good spatial resolution and high rate
compatibility, pixelated silicon detectors could meet the new requirements. To
assess their applicability in proton therapy, ATLAS pixelated silicon detectors
are used to measure the lateral beam profile with high spatial resolution.
Furthermore, a dose dependent detector calibration is presented to allow the
measurement of the requested output constancy. A strategy to verify the proton
energy during the daily quality assurance is under study. Promising results
from proof-of-principle measurements at the West German Proton Therapy Centre
in Essen, Germany, have been obtained.


title:
Role of nucleon effective mass and symmetry energy on the neutrino mean
  free path in neutron star

abstract:
  The Korea-IBS-Daegu-SKKU energy density functional (KIDS-EDF) models, derived
from the universal Skyrme functional, have been successfully and widely applied
in describing the properties of finite nuclei and infinite nuclear matter. In
the present work, we extend the applications of the KIDS-EDF models to
investigate the implications of the nucleon effective mass and nuclear symmetry
energy obtained from the KIDS-EDF models on the properties of neutron star (NS)
and neutrino interaction with the NS constituents matter in the linear response
approximation (LRA). We then analyze the total differential cross-section of
neutrino, neutrino mean free path (NMFP), and the NS mass-radius (M-R)
relations. We find that the NS M-R relations predictions for all KIDS-EDF
models are in excellent agreement with the recent observations as well as the
NICER result. Remarkable prediction results on the NMFPs are given by the
KIDS0-m*77 and KIDS0-m*99 models with $M_n^* /M \lesssim 1$ which are quite
higher in comparison with those obtained for the KIDS0, KIDS-A, and KIDS-B
models with $M_n^*/M \gtrsim 1$. For the KIDS0, KIDS-A, and KIDS-B models, we
obtain the $\lambda \lesssim R_{\textrm{NS}}$, indicating that these models
support the slow NS cooling and neutrino trapping in NS. On the contrary, both
KIDS0-m*77 and KIDS0-m*99 models support faster NS cooling and a small
possibility of neutrino trapping within NS, predicting $\lambda \gtrsim
R_{\textrm{NS}}$. More interestingly the NMFP decreases as the density and
neutrino energy increase, which is consistent with those obtained in the
Brussels-Montreal Skyrme (BSk17 and BSk18) models at saturation density.


title:
Modeling COVID-19 optimal testing strategies in long-term care
  facilities: An optimization-based approach

abstract:
  Long-term care facilities have been widely affected by the COVID-19 pandemic.
Retirement homes are particularly vulnerable due to the higher mortality risk
of infected elderly individuals. Once an outbreak occurs, suppressing the
spread of the virus in retirement homes is challenging because the residents
are in contact with each other, and isolation measures cannot be widely
enforced. Regular testing strategies, on the other hand, have been shown to
effectively prevent outbreaks in retirement homes. However, high frequency
testing may consume substantial staff working time, which results in a
trade-off between the time invested in testing, and the time spent providing
essential care to residents. Thus, developing an optimal testing strategy is
crucial to proactively detect infections while guaranteeing efficient use of
limited staff time in these facilities. Although numerous efforts have been
made to prevent the virus from spreading in long-term care facilities, this is
the first study to develop testing strategies based on formal optimization
methods. This paper proposes two novel optimization models for testing
schedules. The models aim to minimize the risk of infection in retirement
homes, considering the trade-off between the probability of infection and staff
workload. We employ a probabilistic approach in conjunction with the
optimization models, to compute the risk of infection, including contact rates,
incidence status, and the probability of infection of the residents. To solve
the models, we propose an enhanced local search algorithm by leveraging the
symmetry property of the optimal solution. We perform several experiments with
realistically sized instances and show that the proposed approach can derive
optimal testing strategies.


title: Verifiable Quantum Advantage without Structure

abstract:
  We show the following hold, unconditionally unless otherwise stated, relative
to a random oracle with probability 1:
  - There are NP search problems solvable by BQP machines but not BPP machines.
  - There exist functions that are one-way, and even collision resistant,
against classical adversaries but are easily inverted quantumly. Similar
separations hold for digital signatures and CPA-secure public key encryption
(the latter requiring the assumption of a classically CPA-secure encryption
scheme). Interestingly, the separation does not necessarily extend to the case
of other cryptographic objects such as PRGs.
  - There are unconditional publicly verifiable proofs of quantumness with the
minimal rounds of interaction: for uniform adversaries, the proofs are
non-interactive, whereas for non-uniform adversaries the proofs are two message
public coin.
  - Our results do not appear to contradict the Aaronson-Ambanis conjecture.
Assuming this conjecture, there exist publicly verifiable certifiable
randomness, again with the minimal rounds of interaction.
  By replacing the random oracle with a concrete cryptographic hash function
such as SHA2, we obtain plausible Minicrypt instantiations of the above
results. Previous analogous results all required substantial structure, either
in terms of highly structured oracles and/or algebraic assumptions in
Cryptomania and beyond.


title: Persistent Kernels for Iterative Memory-bound GPU Applications

abstract:
  Iterative memory-bound solvers commonly occur in HPC codes. Typical GPU
implementations have a loop on the host side that invokes the GPU kernel as
much as time/algorithm steps there are. The termination of each kernel
implicitly acts as the barrier required after advancing the solution every time
step. We propose a scheme for running memory-bound iterative GPU kernels:
PERsistent KernelS (PERKS). In this scheme the time loop is moved inside a
persistent kernel, and device-wide barriers are used for synchronization. We
then reduce the traffic to device memory by caching a subset of the output in
each time step in registers and shared memory to be used as input for the
following time step. PERKS can be generalized to any iterative solver: they are
largely independent of the solver's implementation. We explain the design
principle of PERKS and demonstrate the effectiveness of PERKS for a wide range
of iterative 2D/3D stencil benchmarks (geometric mean speedup of $2.29$x in
small domains and $1.53$x in large domains), and a Krylov subspace solver
(geometric mean speedup of $4.67$x in smaller SpMV datasets from SuiteSparse
and $1.39$x in larger SpMV datasets, for conjugate gradient).


title: Free cyclic actions on surfaces and the Borsuk-Ulam theorem

abstract:
  Let $M$ and $N$ be topological spaces, let $G$ be a group, and let $\tau
\colon\thinspace G \times M \to M$ be a proper free action of $G$. In this
paper, we define a Borsuk-Ulam-type property for homotopy classes of maps from
$M$ to $N$ with respect to the pair $(G,\tau)$ that generalises the classical
antipodal Borsuk-Ulam theorem of maps from the $n$-sphere $\mathbb{S}^n$ to
$\mathbb{R}^n$. In the cases where $M$ is a finite pathwise-connected
CW-complex, $G$ is a finite, non-trivial Abelian group, $\tau$ is a proper free
cellular action, and $N$ is either $\mathbb{R}^2$ or a compact surface without
boundary different of $\mathbb{S}^2$ and $\mathbb{RP}^2$, we give an algebraic
criterion involving braid groups to decide whether a free homotopy class $\beta
\in [M,N]$ has the Borsuk-Ulam property. As an application of this criterion,
we consider the case where $M$ is a compact surface without boundary equipped
with a free action $\tau$ of the finite cyclic group $\mathbb{Z}_n$. In terms
of the orientability of the orbit space $M_\tau$ of $M$ by the action $\tau$,
the value of $n$ modulo $4$ and a certain algebraic condition involving the
first homology group of $M_\tau$, we are able to determine if the single
homotopy class of maps from $M$ to $\mathbb{R}^2$ possesses the Borsuk-Ulam
property with respect to $(\mathbb{Z}_n,\tau)$. Finally, we give some examples
of surfaces on which the symmetric group acts, and for these cases, we obtain
some partial results regarding the Borsuk-Ulam property for maps whose target
is $\mathbb{R}^2$.


title: Inverse Problems Are Solvable on Real Number Signal Processing Hardware

abstract:
  Inverse problems are used to model numerous tasks in imaging sciences, in
particular, they encompass any task to reconstruct data from measurements.
Thus, the algorithmic solvability of inverse problems is of significant
importance. The study of this question is inherently related to the underlying
computing model and hardware, since the admissible operations of any
implemented algorithm are defined by the computing model and the hardware.
Turing machines provide the fundamental model of today's digital computers.
However, it has been shown that Turing machines are incapable of solving finite
dimensional inverse problems for any given accuracy. This stimulates the
question of how powerful the computing model must be to enable the general
solution of finite dimensional inverse problems. This paper investigates the
general computation framework of Blum-Shub-Smale (BSS) machines which allows
the processing and storage of arbitrary real values. Although a corresponding
real world computing device does not exist at the moment, research and
development towards real number computing hardware, usually referred to by the
term "neuromorphic computing", has increased in recent years. In this work, we
show that real number computing in the framework of BSS machines does enable
the algorithmic solvability of finite dimensional inverse problems. Our results
emphasize the influence of the considered computing model in questions of
algorithmic solvability of inverse problems.


title:
Design considerations for a hierarchical semantic compositional
  framework for medical natural language understanding

abstract:
  Medical natural language processing (NLP) systems are a key enabling
technology for transforming Big Data from clinical report repositories to
information used to support disease models and validate intervention methods.
However, current medical NLP systems fall considerably short when faced with
the task of logically interpreting clinical text. In this paper, we describe a
framework inspired by mechanisms of human cognition in an attempt to jump the
NLP performance curve. The design centers about a hierarchical semantic
compositional model (HSCM) which provides an internal substrate for guiding the
interpretation process. The paper describes insights from four key cognitive
aspects including semantic memory, semantic composition, semantic activation,
and hierarchical predictive coding. We discuss the design of a generative
semantic model and an associated semantic parser used to transform a free-text
sentence into a logical representation of its meaning. The paper discusses
supportive and antagonistic arguments for the key features of the architecture
as a long-term foundational framework.


title: Theoretical analysis of the extended cyclic reduction algorithm

abstract:
  The extended cyclic reduction algorithm developed by Swarztrauber in 1974 was
used to solve the block-tridiagonal linear system. The paper fills in the gap
of theoretical results concerning the zeros of matrix polynomial $B_{i}^{(r)}$
with respect to a tridiagonal matrix which are computed by Newton's method in
the extended cyclic reduction algorithm. Meanwhile, the forward error analysis
of the extended cyclic reduction algorithm for solving the block-tridiagonal
system is studied. To achieve the two aims, the critical point is to find out
that the zeros of matrix polynomial $B_{i}^{(r)}$ are eigenvalues of a
principal submatrix of the coefficient matrix.


title: Mirror symmetry on levels of non-abelian Landau--Ginzburg orbifolds

abstract:
  We consider the Berglund-H\"ubsch-Henningson-Takahashi duality of
Landau-Ginzburg orbifolds with a symmetry group generated by some diagonal
symmetries and some permutations of variables. We study the orbifold Euler
characteristics, the orbifold zeta functions and the orbifold E-functions of
such dual pairs. We conjecture that we get a mirror symmetry between these
invariants even on each level, where we call level the conjugacy class of a
permutation. We support this conjecture by giving partial results for each of
these invariants.


title:
Spread Spurious Attribute: Improving Worst-group Accuracy with Spurious
  Attribute Estimation

abstract:
  The paradigm of worst-group loss minimization has shown its promise in
avoiding to learn spurious correlations, but requires costly additional
supervision on spurious attributes. To resolve this, recent works focus on
developing weaker forms of supervision -- e.g., hyperparameters discovered with
a small number of validation samples with spurious attribute annotation -- but
none of the methods retain comparable performance to methods using full
supervision on the spurious attribute. In this paper, instead of searching for
weaker supervisions, we ask: Given access to a fixed number of samples with
spurious attribute annotations, what is the best achievable worst-group loss if
we "fully exploit" them? To this end, we propose a pseudo-attribute-based
algorithm, coined Spread Spurious Attribute (SSA), for improving the
worst-group accuracy. In particular, we leverage samples both with and without
spurious attribute annotations to train a model to predict the spurious
attribute, then use the pseudo-attribute predicted by the trained model as
supervision on the spurious attribute to train a new robust model having
minimal worst-group loss. Our experiments on various benchmark datasets show
that our algorithm consistently outperforms the baseline methods using the same
number of validation samples with spurious attribute annotations. We also
demonstrate that the proposed SSA can achieve comparable performances to
methods using full (100%) spurious attribute supervision, by using a much
smaller number of annotated samples -- from 0.6% and up to 1.5%, depending on
the dataset.