title: Modular and Didactic Compiler Design with XML Inter-Phases Communication

abstract:
  In Compiler Design courses, students learn how a program written in high
level programming language and designed for humans understanding is
systematically converted into low level assembly language understood by
machines, through different representations. This article presents the design,
educative characteristics and possibilities of a modular and didactic compiler
for a Pascal-like programming mini-language that is super-set of Niklaus
Wirth's PL/0. The main feature is that it implements the compiling phases in
such a way that the information delivered to each next one may be reflected as
an XML document, which can be studied separately. It is also shown that its
design is suitable for being included as learning tool into compiler design
courses. It is possible to implement a compiler in a high-level language like
Python.


title: A New Rate Splitting Strategy for Uplink CR-NOMA Systems

abstract:
  In uplink non-orthogonal multiple access (NOMA) channels, the existing
cooperative successive interference cancellation (SIC) and power control (PC)
schemes lack the capability of achieving the full capacity region, which
restricts the outage performance of uplink NOMA users. For the uplink cognitive
radio inspired NOMA system, we propose a new rate splitting (RS) strategy to
maximize the achievable rate of the secondary user without deteriorating the
primary user's outage performance. Based on the interference threshold and its
own channel gain, the secondary user adaptively conducts RS, transmit power
allocation and SIC, which utilizes the transmit power efficiently. The
closed-form expression for the outage probability is derived for the secondary
user. Numerical results show that the proposed RS scheme achieves the best
outage performance for the secondary user among the existing cooperative SIC
and PC schemes.


title: Pulse Profiles and Polarization of Terzan 5 Pulsars

abstract:
  Terzan 5 is a rich globular cluster within the galactic bulge that contains
39 known millisecond pulsars, the largest known population of any globular
cluster. The Terzan 5 pulsars are faint, so that individual observations of
most of the pulsars have too little signal-to-noise (S/N) to measure reliable
flux density or polarization information. We combined over 5.2\,days of
archival data, at each of 1.5\,GHz and 2.0\,GHz, taken with the Green Bank
Telescope over the past 11\,years. We created high S/N profiles for 32 of the
pulsars and determined precise rotation measures (RMs) for 28 of them. We used
the RMs, and the known pulsar positions and dispersion measures (DMs), to map
the projected parallel component of the Galactic magnetic field toward the
cluster. The $\langle B_{||}\rangle$ shows a rough gradient of
$\sim$6\,nG/arcsec ($\sim$160\,nG/parsec), or fractionally, a change of
$\sim$20$\%$ in the right ascension direction across the cluster, implying
Galactic magnetic field variability at sub-parsec scales. We also measured
average flux densities $S_\nu$ for the pulsars, ranging from $\sim$10\,$\mu$Jy
to $\sim$2\,mJy, and an average spectral index $\alpha = -1.35$, where $S_\nu
\propto \nu^{\alpha})$. This spectral index is flatter than most known pulsars,
likely a selection effect due to the high frequencies used in pulsar searches
to mitigate dispersion and scattering. The inferred pulsar luminosity function
is roughly power-law, with slope $(d\log N)/(d\log L) = -1$ at the
high-luminosity end. At the low-luminosity end, there are incompleteness
effects implying that Terzan 5 contains many more pulsars to be found.


title:
Systematic conformation-to-phenotype mapping via limited deep-sequencing
  of proteins

abstract:
  Non-native conformations drive protein misfolding diseases, complicate
bioengineering efforts, and fuel molecular evolution. No current experimental
technique is well-suited for elucidating them and their phenotypic effects.
Especially intractable are the transient conformations populated by
intrinsically disordered proteins. We describe an approach to systematically
discover, stabilize, and purify native and non-native conformations, generated
in vitro or in vivo, and directly link conformations to molecular, organismal,
or evolutionary phenotypes. This approach involves high-throughput disulfide
scanning (HTDS) of the entire protein. To reveal which disulfides trap which
chromatographically resolvable conformers, we devised a limited deep-sequencing
method for proteins that identifies the exact sequence positions of both
cysteines simultaneously within each polypeptide molecule. HTDS of the abundant
E. coli periplasmic chaperone HdeA revealed distinct classes of disordered
hydrophobic conformers with variable cytotoxicity depending on where the
backbone was cross-linked. HTDS can bridge conformational and phenotypic
landscapes for many proteins that function in disulfide-permissive
environments.


title:
Neural Texture Extraction and Distribution for Controllable Person Image
  Synthesis

abstract:
  We deal with the controllable person image synthesis task which aims to
re-render a human from a reference image with explicit control over body pose
and appearance. Observing that person images are highly structured, we propose
to generate desired images by extracting and distributing semantic entities of
reference images. To achieve this goal, a neural texture extraction and
distribution operation based on double attention is described. This operation
first extracts semantic neural textures from reference feature maps. Then, it
distributes the extracted neural textures according to the spatial
distributions learned from target poses. Our model is trained to predict human
images in arbitrary poses, which encourages it to extract disentangled and
expressive neural textures representing the appearance of different semantic
entities. The disentangled representation further enables explicit appearance
control. Neural textures of different reference images can be fused to control
the appearance of the interested areas. Experimental comparisons show the
superiority of the proposed model. Code is available at
https://github.com/RenYurui/Neural-Texture-Extraction-Distribution.


title:
Realistic Detection and Early Warning of Binary Neutron Stars with
  Decihertz Gravitational-wave Observatories

abstract:
  We investigated the detection and localization of binary neutron star (BNS)
populations with decihertz gravitational-wave observatories in a realistic
detecting strategy, including real-time observations and early warnings.
Assuming 4 years' operation of B-DECIGO, we found that the detected BNSs can be
divided into three categories: (a) sources that merge within 1 year, which
could be localized with an uncertainty of $\Delta\Omega \sim 10^{0}$ deg$^2$;
(b) sources that merge in 1-4 years, which take up three quarters of the total
events and yield the most precise angular resolution with $\Delta \Omega\sim
10^{-2}$ deg$^2$ and time-of-merger accuracy with $\Delta t_c\sim 10^{-1}$ s;
and (c) sources that do not merge during the 4-yr mission window, which enable
possible early warnings, with $\Delta \Omega\sim 10^{-1}$ deg$^2$ and $\Delta
t_c\sim 10^{0}$ s. Furthermore, we compared the pros and cons of B-DECIGO with
the Einstein Telescope, and explored the prospects of detections using 3 other
decihertz observatories and 4 BNS population models. In realistic observing
scenarios, we found that decihertz detectors could even provide early-warning
alerts to a source decades before its merger while their localizations are
still more accurate than ground-based facilities. Finally we found a decrease
of events when considering the confusion noise, but this could be partially
solved by a proper noise subtraction.


title:
Bertrand and Mannheim curves of framed curves in the 4-dimensional
  Euclidean space

abstract:
  A Bertrand curve in the 4-dimensional Euclidean space is a space curve whose
first normal line is the same as the first normal line of another curve. On the
other hand, a Mannheim curve in the 4-dimensional Euclidean space is a space
curve whose first normal line is the same as the second or third normal line of
another curve. By definitions, another curve is a parallel curve with respect
to the direction of the first normal vector. As smooth curves with singular
points, we consider framed curves in the Euclidean space. Then we define and
investigate Bertrand and Mannheim curves of framed curves. We give necessary
and sufficient conditions of Bertrand and Mannheim curves of both regular and
framed curves. It is well-known that the Bertrand curves of regular curves do
not exist under a condition. However, even if regular curves, Bertrand curves
exist as framed curves.


title:
Sub-Phases in the Superconducting State of CeIrIn$_5$ Revealed by Low
  Temperature $c$-axis Heat Transport

abstract:
  Low-temperature (down to $\sim$ 50 mK) thermal conductivity measurements with
the heat flow direction along the inter-plane tetragonal $c$-axis, $\kappa_c$,
were used to study the superconducting state of heavy fermion CeIrIn$_5$.
Measurements were performed in the magnetic fields both parallel to the heat
flow direction, $H\parallel c$, and transverse to it, $H \parallel a$.
Inter-plane heat conductivity in $H \parallel c$ configuration shows negligible
initial increase with magnetic field and a rapid rise on approaching $H_{c2}$
from below, similar to the expectations for the superconducting gap without
line nodes. This observation is in stark contrast to monotonic increase found
in the previous in-plane heat transport measurements. In the configuration with
the magnetic field breaking the tetragonal symmetry of the lattice, $H
\parallel a$, $\kappa_c$ reveals non-monotonic evolution with temperature and
magnetic field suggesting sub-phase boundary in the superconducting state. The
characteristic temperature $T_{kink} \sim$ 0.07~K of the sub-boundary is well
within the domain of bulk superconductivity $T_c \sim$ 0.4~K and $H_{c2}\sim$
1.0~T. These results are consistent with a superconducting gap with an
equatorial line node and polar point nodes, a gap symmetry of the D$_{4h}$
point group, for which magnetic field along the tetragonal plane breaks the
degeneracy of the multi-component order parameter and induces a phase
transition with nodal topology change.


title: A Unified Cascaded Encoder ASR Model for Dynamic Model Sizes

abstract:
  In this paper, we propose a dynamic cascaded encoder Automatic Speech
Recognition (ASR) model, which unifies models for different deployment
scenarios. Moreover, the model can significantly reduce model size and power
consumption without loss of quality. Namely, with the dynamic cascaded encoder
model, we explore three techniques to maximally boost the performance of each
model size: 1) Use separate decoders for each sub-model while sharing the
encoders; 2) Use funnel-pooling to improve the encoder efficiency; 3) Balance
the size of causal and non-causal encoders to improve quality and fit
deployment constraints. Overall, the proposed large-medium model has 30%
smaller size and reduces power consumption by 33%, compared to the baseline
cascaded encoder model. The triple-size model that unifies the large, medium,
and small models achieves 37% total size reduction with minimal quality loss,
while substantially reducing the engineering efforts of having separate models.


title: A Study on the Power Parameter in Power Prior Bayesian Analysis

abstract:
  The power prior and its variations have been proven to be a useful class of
informative priors in Bayesian inference due to their flexibility in
incorporating the historical information by raising the likelihood of the
historical data to a fractional power {\delta}. The derivation of the marginal
likelihood based on the original power prior,and its variation, the normalized
power prior, introduces a scaling factor C({\delta}) in the form of a prior
predictive distribution with powered likelihood. In this paper, we show that
the scaling factor might be infinite for some positive {\delta} with
conventionally used initial priors, which would change the admissible set of
the power parameter. This result seems to have been almost completely ignored
in the literature. We then illustrate that such a phenomenon may jeopardize the
posterior inference under the power priors when the initial prior of the model
parameters is improper. The main findings of this paper suggest that special
attention should be paid when the suggested level of borrowing is close to 0,
while the actual optimum might be below the suggested value. We use a normal
linear model as an example for illustrative purposes.


title:
Representation theoretic interpretation and interpolation properties of
  inhomogeneous spin $q$-Whittaker polynomials

abstract:
  We establish new properties of inhomogeneous spin $q$-Whittaker polynomials,
which are symmetric polynomials generalizing $t=0$ Macdonald polynomials. We
show that these polynomials are defined in terms of a vertex model, whose
weights come not from an $R$-matrix, as is often the case, but from other
intertwining operators of $U'_q(\hat{\mathfrak{sl}}_2)$-modules. Using this
construction, we are able to prove a Cauchy-type identity for inhomogeneous
spin $q$-Whittaker polynomials in full generality. Moreover, we are able to
characterize spin $q$-Whittaker polynomials in terms of vanishing at certain
points, and we find interpolation analogues of $q$-Whittaker and elementary
symmetric polynomials.


title: Pseudo-differentialoperators on Orlicz modulation spaces

abstract:
  We deduce continuity properties for pseudo-differential operators with
symbols in quasi-Banach Orlicz modulation spaces when rely on other
quasi-Banach Orlicz modulation spaces. In particular we extend certain results
in \cite{GH1,GH2,Toft2,Toft10,Toft16}.


title:
ENO-Based High-Order Data-Bounded and Constrained Positivity-Preserving
  Interpolation

abstract:
  A number of key scientific computing applications that are based upon
tensor-product grid constructions, such as numerical weather prediction (NWP)
and combustion simulations, require property-preserving interpolation.
Essentially Non-Oscillatory (ENO) interpolation is a classic example of such
interpolation schemes. In the aforementioned application areas, property
preservation often manifests itself as a requirement for either data
boundedness or positivity preservation. For example, in NWP, one may have to
interpolate between the grid on which the dynamics is calculated to a grid on
which the physics is calculated (and back). Interpolating density or other key
physical quantities without accounting for property preservation may lead to
negative values that are nonphysical and result in inaccurate representations
and/or interpretations of the physical data. Property-preserving interpolation
is straightforward when used in the context of low-order numerical simulation
methods. High-order property-preserving interpolation is, however, nontrivial,
especially in the case where the interpolation points are not equispaced. In
this paper, we demonstrate that it is possible to construct high-order
interpolation methods that ensure either data boundedness or constrained
positivity preservation. A novel feature of the algorithm is that the
positivity-preserving interpolant is constrained; that is, the amount by which
it exceeds the data values may be strictly controlled. The algorithm we have
developed comes with theoretical estimates that provide sufficient conditions
for data boundedness and constrained positivity preservation. We demonstrate
the application of our algorithm on a collection of 1D and 2D numerical
examples, and show that in all cases property preservation is respected.


title:
Core localized alpha-channeling via low frequency Alfven mode generation
  in reversed shear scenarios

abstract:
  A novel channel for fuel ions heating in tokamak core plasma is proposed and
analyzed using nonlinear gyrokinetic theory. The channel is achieved via
spontaneous decay of reversed shear Alfven eigenmode (RSAE) into low frequency
Alfven modes (LFAM), which then heat fuel ions via collisionless ion Landau
damping. The conditions for RSAE spontaneous decay are investigated, and the
saturation level and the consequent fuel ion heating rate are also derived. The
channel is expected to be crucial for future reactors operating under reversed
shear configurations, where fusion alpha particles are generated in the tokamak
core where the magnetic shear is typically reversed, and there is a dense RSAE
spectrum due to the small alpha particle characteristic dimensionless orbits.


title:
Remarkable correspondence of Sagittarius A* submillimeter variability
  with a stellar-wind-fed accretion flow model

abstract:
  We compare the 230 GHz near-horizon emission from Sagittarius A* to
simulations representing three classes of accretion flows. Using the structure
function to capture the variability statistics of the light curve, we find a
noticeable discrepancy between the observations and models based on torus-fed
accretion disks, whether those disks bring in a small or large amount of net
magnetic flux. On the other hand, the simulations that are fed more
realistically by stellar winds match the observed structure function very well.
We describe the differences between models, arguing that feeding by stellar
winds may be a critical component in constructing theoretical models for
accretion in the Galactic Center.


title:
Self-Supervised Traffic Advisors: Distributed, Multi-view Traffic
  Prediction for Smart Cities

abstract:
  Connected and Autonomous Vehicles (CAVs) are becoming more widely deployed,
but it is unclear how to best deploy smart infrastructure to maximize their
capabilities. One key challenge is to ensure CAVs can reliably perceive other
agents, especially occluded ones. A further challenge is the desire for smart
infrastructure to be autonomous and readily scalable to wide-area deployments,
similar to modern traffic lights. The present work proposes the Self-Supervised
Traffic Advisor (SSTA), an infrastructure edge device concept that leverages
self-supervised video prediction in concert with a communication and
co-training framework to enable autonomously predicting traffic throughout a
smart city. An SSTA is a statically-mounted camera that overlooks an
intersection or area of complex traffic flow that predicts traffic flow as
future video frames and learns to communicate with neighboring SSTAs to enable
predicting traffic before it appears in the Field of View (FOV). The proposed
framework aims at three goals: (1) inter-device communication to enable
high-quality predictions, (2) scalability to an arbitrary number of devices,
and (3) lifelong online learning to ensure adaptability to changing
circumstances. Finally, an SSTA can broadcast its future predicted video frames
directly as information for CAVs to run their own post-processing for the
purpose of control.


title: On blow up for a class of radial Hartree type equations

abstract:
  We study a class of Hartree type equations and prove a quantitative blow up
rate for their blow up solutions. This is an analogue of the result by Merle
and Rapha\"el on 3d NLS.


title: Task-Driven Data Augmentation for Vision-Based Robotic Control

abstract:
  Today's robots often interface data-driven perception and planning models
with classical model-based controllers. For example, drones often use computer
vision models to estimate navigation waypoints that are tracked by model
predictive control (MPC). Often, such learned perception/planning models
produce erroneous waypoint predictions on out-of-distribution (OoD) or even
adversarial visual inputs, which increase control cost. However, today's
methods to train robust perception models are largely task-agnostic - they
augment a dataset using random image transformations or adversarial examples
targeted at the vision model in isolation. As such, they often introduce pixel
perturbations that are ultimately benign for control, while missing those that
are most adversarial. In contrast to prior work that synthesizes adversarial
examples for single-step vision tasks, our key contribution is to efficiently
synthesize adversarial scenarios for multi-step, model-based control. To do so,
we leverage differentiable MPC methods to calculate the sensitivity of a
model-based controller to errors in state estimation, which in turn guides how
we synthesize adversarial inputs. We show that re-training vision models on
these adversarial datasets improves control performance on OoD test scenarios
by up to 28.2% compared to standard task-agnostic data augmentation. Our system
is tested on examples of robotic navigation and vision-based control of an
autonomous air vehicle.


title:
Oxygen Octahedral Tilt Controlled Topological Hall Effect in Epitaxial
  and Freestanding SrRuO3/SrIrO3 Heterostructures

abstract:
  The fabrication technique of freestanding oxide flakes by epitaxial lift-off
has made significant contributions to the multifunctional oxide thin film
research. Several highly impactful work have recently demonstrated the
robustness of freestanding oxide flakes retaining their desirable properties
after detachment from the substrate by dissolving away the water-soluble
Sr3Al2O6 buffer layer. Firstly, in epitaxial SrRuO3/SrIrO3 systems showing
Hall-humps reminiescence of Topological Hall Effect, first-principle
calculations revealed that octahedral tilts can modify the sign of
Dzyaloshinskii-Moriya interaction and avoid cancellation in a symmetric
trilayer structure. Secondly, freestanding flakes of the trilayer is also able
to retain the Hall-humps across a wide temperature range. The behaviour of
humps' peak field does not vary with field direction rotation away from surface
normal, consistent to a micromagnetic simulation result of N\'eel-type magnetic
Skyrmions. The layer-resolved octahedral tilts in the freestanding
heterostructures also crucially controls the occurrence of Hall-humps,
consistent to the insight from the epitaxial ones. This work offers a new
perspective to understanding Hall-humps in perovskite oxides, as well as
demonstrates the fabrication of oxide heterostructure membranes with high
interfacial quality.


title: Efficient Cluster-Based k-Nearest-Neighbor Machine Translation

abstract:
  k-Nearest-Neighbor Machine Translation (kNN-MT) has been recently proposed as
a non-parametric solution for domain adaptation in neural machine translation
(NMT). It aims to alleviate the performance degradation of advanced MT systems
in translating out-of-domain sentences by coordinating with an additional
token-level feature-based retrieval module constructed from in-domain data.
Previous studies have already demonstrated that non-parametric NMT is even
superior to models fine-tuned on out-of-domain data. In spite of this success,
kNN retrieval is at the expense of high latency, in particular for large
datastores. To make it practical, in this paper, we explore a more efficient
kNN-MT and propose to use clustering to improve the retrieval efficiency.
Concretely, we first propose a cluster-based Compact Network for feature
reduction in a contrastive learning manner to compress context features into
90+% lower dimensional vectors. We then suggest a cluster-based pruning
solution to filter out 10%-40% redundant nodes in large datastores while
retaining translation quality. Our proposed methods achieve better or
comparable performance while reducing up to 57% inference latency against the
advanced non-parametric MT model on several machine translation benchmarks.
Experimental results indicate that the proposed methods maintain the most
useful information of the original datastore and the Compact Network shows good
generalization on unseen domains.


title:
ALMA Survey of Orion Planck Galactic Cold Clumps (ALMASOP): How do dense
  core properties affect the multiplicity of protostars?

abstract:
  During the transition phase from prestellar to protostellar cloud core, one
or several protostars can form within a single gas core. The detailed physical
processes of this transition, however, still remain unclear. We present 1.3 mm
dust continuum and molecular line observations with the Atacama Large
Millimeter/submillimeter Array (ALMA) toward 43 protostellar cores in the Orion
molecular cloud complex ({\lambda}Orionis, Orion B, and Orion A) with an
angular resolution of \sim 0. 35 (\sim 140 au). In total, we detect 13
binary/multiple systems. We derive an overall multiplicity frequency (MF) of
28% \pm 4% and a companion star fraction (CSF) of 51% \pm 6%, over a separation
range of 300-8900 au. The median separation of companions is about 2100 au. The
occurrence of stellar multiplicity may depend on the physical characteristics
of the dense cores. Notably, those containing binary/multiple systems tend to
show higher gas density and Mach number than cores forming single stars. The
integral-shaped filament (ISF) of Orion A GMC, which has the highest gas
density and hosts high-mass star formation in its central region (Orion Nebula
Cluster or ONC), shows the highest MF and CSF among Orion GMCs. In contrast,
the {\lambda} Orionis Giant Molecular Cloud (GMC) has a lower MF and CSF than
the Orion B and Orion A GMCs, indicating that feedback from Hii regions may
suppress the formation of multiple systems. We also find that the protostars
comprising binary/multiple systems are usually at different evolutionary
stages.


title: Moir\'e disorder effect in twisted bilayer graphene

abstract:
  We theoretically study the electronic structure of magic-angle twisted
bilayer graphene with disordered moir\'e patterns. By using an extended
continuum model incorporating non-uniform lattice distortion, we find that the
local density of states of the flat band is hardly broadened, but splits into
upper and lower subbands in most places. The spatial dependence of the
splitting energy is almost exclusively determined by the local value of the
effective vector potential induced by heterostrain, whereas the variation of
local twist angle and local moir\'e period give relatively minor effects on the
electronic structure. We explain the exclusive dependence on the local vector
potential by a pseudo Landau level picture for the magic-angle flat band, and
we obtain an analytic expression of the splitting energy as a function of the
strain amplitude.


title: Floquet Quantum Thermal Transistor

abstract:
  We apply periodic control to realize a quantum thermal transistor, which we
term as the Floquet Quantum thermal Transistor. Periodic modulation allows us
to control the heat flows and achieve large amplification factors even for
fixed bath temperatures. Importantly, this transistor effect persists in the
cut-off region, where traditional quantum thermal transistors operating in
absence of periodic modulation, fail to act as viable heat modulation devices.


title:
An Ensemble Learning Based Approach to Multi-label Power Text
  Classification for Fault-type Recognition

abstract:
  With the rapid development of ICT Custom Services (ICT CS) in power
industries, the deployed power ICT CS systems mainly rely on the experience of
customer service staff for fault type recognition, questioning, and answering,
which makes it difficult and inefficient to precisely resolve the problems
issued by users. To resolve this problem, in this paper, firstly, a multi-label
fault text classification ensemble approach called BR-GBDT is proposed by
combining Binary Relevance and Gradient Boosting Decision Tree for assisted
fault type diagnosis and improving the accuracy of fault type recognition.
Second, for the problem that there is lack of the training set for power ICT
multi-label text classification, an automatic approach is presented to
construct the training set from the historical fault text data stored in power
ICT CS systems. The extensive experiments were made based on the power ICT CS
training set and some general-purpose benchmark training datasets. The
experiment results show that our approach outperforms the well known ensemble
learning based approaches BR+LR and ML-KNN for fault text classification,
efficiently handling the multi-label classification of ICT custom service text
data for fault type recognition.


title:
Dynamic Neural Textures: Generating Talking-Face Videos with
  Continuously Controllable Expressions

abstract:
  Recently, talking-face video generation has received considerable attention.
So far most methods generate results with neutral expressions or expressions
that are implicitly determined by neural networks in an uncontrollable way. In
this paper, we propose a method to generate talking-face videos with
continuously controllable expressions in real-time. Our method is based on an
important observation: In contrast to facial geometry of moderate resolution,
most expression information lies in textures. Then we make use of neural
textures to generate high-quality talking face videos and design a novel neural
network that can generate neural textures for image frames (which we called
dynamic neural textures) based on the input expression and continuous intensity
expression coding (CIEC). Our method uses 3DMM as a 3D model to sample the
dynamic neural texture. The 3DMM does not cover the teeth area, so we propose a
teeth submodule to complete the details in teeth. Results and an ablation study
show the effectiveness of our method in generating high-quality talking-face
videos with continuously controllable expressions. We also set up four baseline
methods by combining existing representative methods and compare them with our
method. Experimental results including a user study show that our method has
the best performance.


title:
A branch and bound technique for finding the minimal solutions of the
  linear optimization problems subjected to Lukasiewicz

abstract:
  In this paper, an optimization model with a linear objective function subject
to a system of fuzzy relation equations (FRE) is studied where the feasible
region is defined by the Lukasiewicz t-norm. Since the finding of all minimal
solutions is an NP-hard problem, designing an efficient solution procedure for
solving such problems is not a trivial job. Firstly, the feasible domain is
characterized and then the problem is solved with a modified branch-and-bound
solution technique based on a new solution set that includes the minimal
solutions. After presenting our solution procedure, a concrete example is
included for illustration purposes.


title:
The Convergence Properties of Infeasible Inexact Proximal Alternating
  Linearized Minimization

abstract:
  The proximal alternating linearized minimization method (PALM) suits well for
solving block-structured optimization problems, which are ubiquitous in real
applications. In the cases where subproblems do not have closed-form solutions,
e.g., due to complex constraints, infeasible subsolvers are indispensable,
giving rise to an infeasible inexact PALM (PALM-I). Numerous efforts have been
devoted to analyzing feasible PALM, while little attention has been paid to
PALM-I. The usage of PALM-I thus lacks theoretical guarantee. The essential
difficulty of analyses consists in the objective value nonmonotonicity induced
by the infeasibility. We study in the present work the convergence properties
of PALM-I. In particular, we construct a surrogate sequence to surmount the
nonmonotonicity issue and devise an implementable inexact criterion. Based upon
these, we manage to establish the stationarity of any accumulation point and,
moreover, show the iterate convergence and the asymptotic convergence rates
under the assumption of the Lojasiewicz property. The prominent advantages of
PALM-I on CPU time are illustrated via numerical experiments on problems
arising from quantum physics and 3D anisotropic frictional contact.


title: ViViD++: Vision for Visibility Dataset

abstract:
  In this paper, we present a dataset capturing diverse visual data formats
that target varying luminance conditions. While RGB cameras provide nourishing
and intuitive information, changes in lighting conditions potentially result in
catastrophic failure for robotic applications based on vision sensors.
Approaches overcoming illumination problems have included developing more
robust algorithms or other types of visual sensors, such as thermal and event
cameras. Despite the alternative sensors' potential, there still are few
datasets with alternative vision sensors. Thus, we provided a dataset recorded
from alternative vision sensors, by handheld or mounted on a car, repeatedly in
the same space but in different conditions. We aim to acquire visible
information from co-aligned alternative vision sensors. Our sensor system
collects data more independently from visible light intensity by measuring the
amount of infrared dissipation, depth by structured reflection, and
instantaneous temporal changes in luminance. We provide these measurements
along with inertial sensors and ground-truth for developing robust visual SLAM
under poor illumination. The full dataset is available at:
https://visibilitydataset.github.io/


title: COAP: Compositional Articulated Occupancy of People

abstract:
  We present a novel neural implicit representation for articulated human
bodies. Compared to explicit template meshes, neural implicit body
representations provide an efficient mechanism for modeling interactions with
the environment, which is essential for human motion reconstruction and
synthesis in 3D scenes. However, existing neural implicit bodies suffer from
either poor generalization on highly articulated poses or slow inference time.
In this work, we observe that prior knowledge about the human body's shape and
kinematic structure can be leveraged to improve generalization and efficiency.
We decompose the full-body geometry into local body parts and employ a
part-aware encoder-decoder architecture to learn neural articulated occupancy
that models complex deformations locally. Our local shape encoder represents
the body deformation of not only the corresponding body part but also the
neighboring body parts. The decoder incorporates the geometric constraints of
local body shape which significantly improves pose generalization. We
demonstrate that our model is suitable for resolving self-intersections and
collisions with 3D environments. Quantitative and qualitative experiments show
that our method largely outperforms existing solutions in terms of both
efficiency and accuracy. The code and models are available at
https://neuralbodies.github.io/COAP/index.html


title:
A Universality-Individuality Integration Model for Dialog Act
  Classification

abstract:
  Dialog Act (DA) reveals the general intent of the speaker utterance in a
conversation. Accurately predicting DAs can greatly facilitate the development
of dialog agents. Although researchers have done extensive research on dialog
act classification, the feature information of classification has not been
fully considered. This paper suggests that word cues, part-of-speech cues and
statistical cues can complement each other to improve the basis for
recognition. In addition, the different types of the three lead to the
diversity of their distribution forms, which hinders the mining of feature
information. To solve this problem, we propose a novel model based on
universality and individuality strategies, called Universality-Individuality
Integration Model (UIIM). UIIM not only deepens the connection between the
clues by learning universality, but also utilizes the learning of individuality
to capture the characteristics of the clues themselves. Experiments were made
over two most popular benchmark data sets SwDA and MRDA for dialogue act
classification, and the results show that extracting the universalities and
individualities between cues can more fully excavate the hidden information in
the utterance, and improve the accuracy of automatic dialogue act recognition.


title:
Entanglement statistics of polymers in a lattice tube and unknotting of
  4-plats

abstract:
  From polymer models, it has been conjectured that the exponential growth rate
of the number of lattice polygons with knot-type $K$ is the same as that for
unknot polygons, and that the entropic critical exponent increases by one for
each prime knot factor in the knot decomposition of $K$. Here we prove this
conjecture for any knot or non-split link embeddable in $\mathbb{T}^*$, the
$\infty \times 2\times 1$ tubular sublattice of the simple cubic lattice. This
is the first model for which the conjecture has been proved. For the proof, we
establish upper and lower bounds relating the asymptotics of the number of
$n$-edge polygons with fixed knot or link-type $K$ in $\mathbb{T}^*$ (as
$n\to\infty$) to that of the number of $n$-edge unknot polygons. For the upper
bounds, we prove that polygons of any knot or non-split link type $K$ can be
unknotted by $f_K$ braid insertions, where $f_K$ is the number of prime factors
of $K$. As part of the proof, we show that a 4-plat diagram of a 2-bridge link
can always be unknotted by the insertion of a 4-braid diagram whose crossing
number is bounded by the minimal crossing number of the link. For the lower
bounds, we prove a pattern theorem for unknots using information from exact
transfer-matrix calculations for polygons in the tube. Thus, unknot polygons
can be transformed into any fixed knot or non-split link type by inserting one
pattern for each prime factor.

categories: math.GT cond-mat.soft cond-mat.stat-mech math.CO


title:
Calibrating Class Weights with Multi-Modal Information for Partial Video
  Domain Adaptation

abstract:
  Assuming the source label space subsumes the target one, Partial Video Domain
Adaptation (PVDA) is a more general and practical scenario for cross-domain
video classification problems. The key challenge of PVDA is to mitigate the
negative transfer caused by the source-only outlier classes. To tackle this
challenge, a crucial step is to aggregate target predictions to assign class
weights by up-weighing target classes and down-weighing outlier classes.
However, the incorrect predictions of class weights can mislead the network and
lead to negative transfer. Previous works improve the class weight accuracy by
utilizing temporal features and attention mechanisms, but these methods may
fall short when trying to generate accurate class weight when domain shifts are
significant, as in most real-world scenarios. To deal with these challenges, we
propose the Multi-modality Cluster-calibrated partial Adversarial Network
(MCAN). MCAN enhances video feature extraction with multi-modal features from
multiple temporal scales to form more robust overall features. It utilizes a
novel class weight calibration method to alleviate the negative transfer caused
by incorrect class weights. The calibration method tries to identify and weigh
correct and incorrect predictions using distributional information implied by
unsupervised clustering. Extensive experiments are conducted on prevailing PVDA
benchmarks, and the proposed MCAN achieves significant improvements when
compared to state-of-the-art PVDA methods.


title: Layer-adapted meshes for weak boundary layers

abstract:
  We propose a new class of layer-adapted meshes for weak boundary layers.
Especially for fourth-order problems these meshes are extremely useful.


title:
Deep Learning Model with GA based Feature Selection and Context
  Integration

abstract:
  Deep learning models have been very successful in computer vision and image
processing applications. Since its inception, Many top-performing methods for
image segmentation are based on deep CNN models. However, deep CNN models fail
to integrate global and local context alongside visual features despite having
complex multi-layer architectures. We propose a novel three-layered deep
learning model that assiminlate or learns independently global and local
contextual information alongside visual features. The novelty of the proposed
model is that One-vs-All binary class-based learners are introduced to learn
Genetic Algorithm (GA) optimized features in the visual layer, followed by the
contextual layer that learns global and local contexts of an image, and finally
the third layer integrates all the information optimally to obtain the final
class label. Stanford Background and CamVid benchmark image parsing datasets
were used for our model evaluation, and our model shows promising results. The
empirical analysis reveals that optimized visual features with global and local
contextual information play a significant role to improve accuracy and produce
stable predictions comparable to state-of-the-art deep CNN models.


title:
Spectral Switch Anomalies in Sagnac Interferometer with respect to
  Galilean Frame

abstract:
  Sagnac interferometer experiments are used widely in many measurements. When
we use a white light source in this interferometer set-up we found the
rotational and radial changes resulting in a Doppler shift in white light
spectrum in terms of spectral switch amplitude shift. Detailed theoretical
analysis are shown to support our claims.


title:
Full geometric control of hidden colour information in diffraction
  gratings under angled white light illumination

abstract:
  When gratings are illuminated by white light, they produce an angular
distribution of wavelengths dependent on the diffraction order and grating
periodicity. Previous studies of gratings mostly focused on highly ordered
structures with at least one geometric parameter (height, periodicity,
orientation, angle of incidence) kept constant. However, the combined variation
of these geometric parameters to hide multiple sets of colour information in
gratings has not been widely explored. In this work, we offer a quantitative
discussion of these geometric parameters and 2 design approaches for hiding
multiple sets of information by a versatile fabrication method (two-photon
polymerization lithography). The first approach hides 1 set of colour
information by decoupling the effects of grating height and periodicity under
normal and oblique incidences. The second approach hides up to 5 sets of colour
information by arranging gratings in sectors around a semicircle. Different
colour images are distinctly revealed in sequence with minimal crosstalk under
oblique incidence and varying sample rotation angles. To suppress the
crosstalk, we find that a minimum angular separation of 10{\deg} between the
sectors is required. Our work can potentially be used to create optical
illusions and anti-counterfeiting watermarks in printed artworks.


title:
6G-enabled Edge AI for Metaverse: Challenges, Methods, and Future
  Research Directions

abstract:
  6G-enabled edge intelligence opens up a new era of Internet of Everything and
makes it possible to interconnect people-devices-cloud anytime, anywhere. More
and more next-generation wireless network smart service applications are
changing our way of life and improving our quality of life. As the hottest new
form of next-generation Internet applications, Metaverse is striving to connect
billions of users and create a shared world where virtual and reality merge.
However, limited by resources, computing power, and sensory devices, Metaverse
is still far from realizing its full vision of immersion, materialization, and
interoperability. To this end, this survey aims to realize this vision through
the organic integration of 6G-enabled edge AI and Metaverse. Specifically, we
first introduce three new types of edge-Metaverse architectures that use
6G-enabled edge AI to solve resource and computing constraints in Metaverse.
Then we summarize technical challenges that these architectures face in
Metaverse and the existing solutions. Furthermore, we explore how the
edge-Metaverse architecture technology helps Metaverse to interact and share
digital data. Finally, we discuss future research directions to realize the
true vision of Metaverse with 6G-enabled edge AI.


title: Search for the Efficient Entanglement Witness Operator

abstract:
  Entanglement detection problem is one of the important problem in quantum
information theory. Gurvit showed that this problem is NP complete and thus
this may be the possible reason that only one criterion is not sufficient to
detect all entangled states. There are some powerful entanglement detection
criterion such as partial transposition criterion, realignment criterion that
it may not be possible to implement them successfully in the experiment. But
this situation can be avoided if the entanglement is detected through the
construction of witness operator method. The construction of witness operator
to detect entanglement is advantageous in two ways: (i) the entanglement can be
detected experimentally if we have prior partial information about the state
which is under investigation and (ii) It can detect both negative partial
transpose entangled states (NPTES) and positive partial transpose entangled
states (PPTES). In this work, we take an analytical approach to construct a
witness operator. To achieve this task, we first construct a linear map using
partial transposition and realignment operation. Then we find some conditions
on the parameters of the map for which the map represent a positive map.
Further, we have constructed a Choi matrix corresponding to the map and have
shown that the constructed map is not completely positive. We then construct an
operator and show that it can detect both NPTES and PPTES. Finally, we prove
its efficiency by detecting several bipartite bound entangled states which were
previously undetected by some well-known separability criteria. We also
compared the detection power of our witness operator with three well-known
powerful entanglement detection criteria and find that our witness operator
detect more entangled states than these criterion.


title: Form Factors for Meson-Kink Scattering

abstract:
  We calculate the leading quantum corrections to the meson form factors of
nonrelativistic kinks, at momentum transfer much higher than the meson mass. We
consider general scalar theories which need not be integrable. Our approach is
much simpler than previous approaches, avoiding any need for counterterms,
ordering ambiguities and nonlinear canonical transformations at any order in
perturbation theory.


title: Chirality dependence of thermoelectric response in a thermal QCD medium

abstract:
  The lifting of the degeneracy between L- and R-modes of massless flavors in a
weakly magnetized thermal QCD medium leads to a novel phenomenon of chirality
dependence of the thermoelectric tensor, whose diagonal and non-diagonal
elements are the Seebeck and Hall-type Nernst coefficient, respectively. Both
coefficients in L-mode have been found to be greater than their counterparts in
R-mode, however the disparity is more pronounced in the Nernst coefficient.
Another noteworthy observation is the impact of the dimensionality of
temperature (T) profile on the Seebeck coefficient, wherein we find that the
coefficient magnitude is significantly enhanced (one order of magnitude) in the
2-D setup, compared to a 1-D T profile. Further, the chiral dependent
quasifermion masses constrain the range of magnetic field (B) and T in a manner
so as to enforce the weak magnetic field (eB << T^2 ) condition.


title:
Global Strong/Classical Solutions to the One-dimensional Compressible
  Quantum Navier-Stokes Equations with Large Initial Data

abstract:
  We are concerned with the global existence and singular limit of
strong/classical solutions to the Cauchy problem of the one-dimensional
barotropic compressible quantum Navier-Stokes equations, which consists of the
compressible Navier-Stokes equations with a linearly density-dependent
viscosity and a nonlinear third-order differential operator known as the
quantum Bohm potential. The pressure $p(\rho)=\rho^\gamma$ is considered with
$\gamma\geq1$ being a constant. We focus on the case when the viscosity
constant $\nu$ and the Planck constant $\varepsilon$ are not equal. Under some
suitable assumptions on $\nu,\varepsilon, \gamma$ and the initial data, we
proved the global existence and large-time behavior of strong and classical
solutions away from vacuum to the compressible quantum Navier-Stokes equations
with large initial data. This extends the previous results on the global strong
large-amplitude solutions of the compressible quantum Navier-Stokes equations
to the case $\nu\neq\varepsilon$. Moreover, the vanishing dispersion limit for
the classical solutions of the quantum Navier-Stokes equations is also
established with certain convergence rates. Some new mathematical techniques
and useful estimates are developed to deducing the uniform-in-time positive
lower and upper bounds on the specific volume.


title:
Tricritical fluctuations and elastic properties of the Ising
  antiferromagnet UIrSi$_3$

abstract:
  Elastic constants, thermal expansion, magnetostriction and heat capacity
measurements were performed with and without applied magnetic field on a single
crystal of UIrSi$_3$. The elastic properties were interpreted within the theory
of the strain-exchange effect. The exchange-striction model together with the
Ising model for the behavior of magnetic localized 5$f$ electrons and itinerant
electrons of uranium correctly reproduces the main features of our
magneto-acoustic experiments in UIrSi$_3$. Data on thermal expansion and
magnetostriction confirm the conclusion that the dominant contribution of the
measured temperature and field change in the speed of sound comes from the
change in the elastic modulus itself. Based on the analysis of heat capacity
measurements in the magnetic field, we explain the significant anomalies at the
second-order branch of the phase transition boundary as a manifestation of the
tricritical fluctuations, dominating the region below the tricritical point.
The outcome confirms the 3D Ising model, at zero and low magnetic fields, with
a crossover to the mean-field tricritical behavior at fields close to the
tricritical point, where tricritical fluctuations dominate the temperature
evolution of the given property.


title:
Optimal Sensor Placement for Hybrid Source Localization Using Fused
  TOA-RSS-AOA Measurements

abstract:
  Source localization techniques incorporating hybrid measurements improve the
reliability and accuracy of the location estimate. Given a set of hybrid
sensors that can collect combined time of arrival (TOA), received signal
strength (RSS) and angle of arrival (AOA) measurements, the localization
accuracy can be enhanced further by optimally designing the placements of the
hybrid sensors. In this paper, we present an optimal sensor placement
methodology, which is based on the principle of majorization-minimization (MM),
for hybrid localization technique. We first derive the Cramer-Rao lower bound
(CRLB) of the hybrid measurement model, and formulate the design problem using
the A-optimal criterion. Next, we introduce an auxiliary variable to
reformulate the design problem into an equivalent saddle-point problem, and
then construct simple surrogate functions (having closed form solutions) over
both primal and dual variables. The application of MM in this paper is distinct
from the conventional MM (that is usually developed only over the primal
variable), and we believe that the MM framework developed in this paper can be
employed to solve many optimization problems. The main advantage of our method
over most of the existing state-of-the-art algorithms (which are mostly
analytical in nature) is its ability to work for both uncorrelated and
correlated noise in the measurements. We also discuss the extension of the
proposed algorithm for the optimal placement designs based on D and E optimal
criteria. Finally, the performance of the proposed method is studied under
different noise conditions and different design parameters.


title:
Realization of broadband index-near-zero modes in nonreciprocal
  magneto-optical heterostructures

abstract:
  Epsilon-near-zero (ENZ) metamaterial with the relative permittivity
approaching zero has been a hot research subject in the past decades. The wave
in the ENZ region has infinite phase velocity ($v=1/\sqrt{\varepsilon\mu}$),
whereas it cannot efficiently travel into the other devices or air due to the
impedance mismatch or near-zero group velocity. In this paper, we demonstrate
that the tunable index-near-zero (INZ) modes with vanishing wavenumbers ($k=0$)
and nonzero group velocities ($v_\mathrm{g} \neq 0$) can be achieved in
nonreciprocal magneto-optical systems. This kind of INZ modes has been
experimentally demonstrated in the photonic crystals at Dirac point frequencies
and that impedance-matching effect has been observed as well. Our theoretical
analysis reveals that the INZ modes exhibit tunability when changing the
parameter of the one-way (nonreciprocal) waveguides. Moreover, owing to the
zero-phase-shift characteristic and decreasing $v_\mathrm{g}$ of the INZ modes,
several perfect optical buffers (POBs) are proposed in the microwave and
terahertz regimes. The theoretical results are further verified by the
numerical simulations performed by the finite element method. Our findings may
open the new avenues for research in the areas of ultra -strong or -fast
nonlinearity, perfect cloaking, high-resolution holographic imaging and
wireless communications.


title: S-type stars discovered in Medium-Resolution Spectra of LAMOST DR9

abstract:
  In this paper, we report on 606 S-type stars identified from Data Release 9
of the LAMOST medium-resolution spectroscopic (MRS) survey, and 539 of them are
reported for the first time. The discovery of these stars is a three-step
process, i.e., selecting with the ZrO band indices greater than 0.25, excluding
non-S-type stars with the iterative Support Vector Machine method, and finally
retaining stars with absolute bolometric magnitude larger than -7.1. The 606
stars are consistent with the distribution of known S-type stars in the
color-magnitude diagram. We estimated the C/Os using the [C/Fe] and [O/Fe]
provided by APOGEE and the MARCS model for S-type stars, respectively, and the
results of the two methods show that C/Os of all stars are larger than 0.5.
Both the locations on the color-magnitude diagram and C/Os further verify the
nature of our S-type sample. Investigating the effect of TiO and atmospheric
parameters on ZrO with the sample, we found that log g has a more significant
impact on ZrO than Teff and [Fe/H], and both TiO and log g may negatively
correlate with ZrO. According to the criterion of Tian et al. (2020), a total
of 238 binary candidates were found by the zero-point-calibrated radial
velocities from the officially released catalog of LAMOST MRS and the catalog
of Zhang et al. (2021). A catalog of these 606 S-type stars is available from
the following link https://doi.org/10.12149/101097.


title: Probing for Constituency Structure in Neural Language Models

abstract:
  In this paper, we investigate to which extent contextual neural language
models (LMs) implicitly learn syntactic structure. More concretely, we focus on
constituent structure as represented in the Penn Treebank (PTB). Using standard
probing techniques based on diagnostic classifiers, we assess the accuracy of
representing constituents of different categories within the neuron activations
of a LM such as RoBERTa. In order to make sure that our probe focuses on
syntactic knowledge and not on implicit semantic generalizations, we also
experiment on a PTB version that is obtained by randomly replacing constituents
with each other while keeping syntactic structure, i.e., a semantically
ill-formed but syntactically well-formed version of the PTB. We find that 4
pretrained transfomer LMs obtain high performance on our probing tasks even on
manipulated data, suggesting that semantic and syntactic knowledge in their
representations can be separated and that constituency information is in fact
learned by the LM. Moreover, we show that a complete constituency tree can be
linearly separated from LM representations.


title:
Well-posedness for the 1D cubic nonlinear Schr\"odinger equation in
  $L^p$, $p>2$

abstract:
  In this paper, local well-posedness is shown for the one dimensional cubic
nonlinear Schr\"odinger equation in $L^p$-spaces for $2<p<4$, which generalizes
a classical result for $p=2$ by Y. Tsutsumi and recent work for $1<p<2$ by Y.
Zhou. As a consequence, a local theory of solutions is established for a class
of data which decay more slowly than square integrable functions. Regularity
properties of the local solutions in the $L^p$-based Sobolev spaces and
Stricharz spaces are also proved.


title:
Discovery of post-mass-transfer helium-burning red giants using
  asteroseismology

abstract:
  A star expands to become a red giant when it has fused all the hydrogen in
its core into helium. If the star is in a binary system, its envelope can
overflow onto its companion or be ejected into space, leaving a hot core and
potentially forming a subdwarf-B star. However, most red giants that have
partially transferred envelopes in this way remain cool on the surface and are
almost indistinguishable from those that have not. Among $\sim$7000
helium-burning red giants observed by NASA's Kepler mission, we use
asteroseismology to identify two classes of stars that must have undergone
dramatic mass loss, presumably due to stripping in binary interactions. The
first class comprises about 7 underluminous stars with smaller helium-burning
cores than their single-star counterparts. Theoretical models show that these
small cores imply the stars had much larger masses when ascending the red giant
branch. The second class consists of 32 red giants with masses down to 0.5
M$_\odot$, whose implied ages would exceed the age of the universe had no mass
loss occurred. The numbers are consistent with binary statistics, and our
results open up new possibilities to study the evolution of post-mass-transfer
binary systems.


title: First-Order Bilevel Topology Optimization for Fast Mechanical Design

abstract:
  Topology Optimization (TO), which maximizes structural robustness under
material weight constraints, is becoming an essential step for the automatic
design of mechanical parts. However, existing TO algorithms use the Finite
Element Analysis (FEA) that requires massive computational resources. We
present a novel TO algorithm that incurs a much lower iterative cost. Unlike
conventional methods that require exact inversions of large FEA system matrices
at every iteration, we reformulate the problem as a bilevel optimization that
can be solved using a first-order algorithm and only inverts the system matrix
approximately. As a result, our method incurs a low iterative cost, and users
can preview the TO results interactively for fast design updates. Theoretical
convergence analysis and numerical experiments are conducted to verify our
effectiveness. We further discuss extensions to use high-performance
preconditioners and fine-grained parallelism on the Graphics Processing Unit
(GPU).


title:
Deuteron electromagnetic form factors, structure functions and tensor
  polarization observables in the framework of the hard-wall AdS/QCD model

abstract:
  We study the electromagnetic form factors, structure functions and tensor
polarization observables of the deuteron in the framework of the hard-wall
AdS/QCD model. We find profile function for the bulk twist $\tau=6$ vector
field, which describes deuteron on the boundary and fix the infrared boundary
cut-off of AdS space in accordance with the mass spectrum of the deuteron
field. We obtain the deuteron charge monopole, quadrupole and magnetic dipole
form factors, structure functions and tensor polarization observables from the
bulk Lagrangians for the deuteron and photon fields interactions. We plot the
momentum transfer dependence of the form factors, structure functions and
tensor polarization observables and compare our numerical results with ones in
the soft-wall model and experimental data.


title:
Selecting Regularization Parameters for nuclear norm type minimization
  problems

abstract:
  The reconstruction of low-rank matrix from its noisy observation finds its
usage in many applications. It can be reformulated into a constrained nuclear
norm minimization problem, where the bound $\eta$ of the constraint is
explicitly given or can be estimated by the probability distribution of the
noise. When the Lagrangian method is applied to find the minimizer, the
solution can be obtained by the singular value thresholding operator where the
thresholding parameter $\lambda$ is related to the Lagrangian multiplier. In
this paper, we first show that the Frobenius norm of the discrepancy between
the minimizer and the observed matrix is a strictly increasing function of
$\lambda$. From that we derive a closed-form solution for $\lambda$ in terms of
$\eta$. The result can be used to solve the constrained nuclear-norm-type
minimization problem when $\eta$ is given. For the unconstrained
nuclear-norm-type regularized problems, our result allows us to automatically
choose a suitable regularization parameter by using the discrepancy principle.
The regularization parameters obtained are comparable to (and sometimes better
than) those obtained by Stein's unbiased risk estimator (SURE) approach while
the cost of solving the minimization problem can be reduced by 11--18 times.
Numerical experiments with both synthetic data and real MRI data are performed
to validate the proposed approach.


title: Safe Stochastic Model Predictive Control

abstract:
  Combining efficient and safe control for safety-critical systems is
challenging. Robust methods may be overly conservative, whereas probabilistic
controllers require a trade-off between efficiency and safety. In this work, we
propose a safety algorithm that is compatible with any stochastic Model
Predictive Control method for linear systems with additive uncertainty and
polytopic constraints. This safety algorithm allows to use the optimistic
control inputs of stochastic Model Predictive Control as long as a safe backup
planner can ensure safety with respect to satisfying hard constraints subject
to bounded uncertainty. Besides ensuring safe behavior, the proposed stochastic
Model Predictive Control algorithm guarantees recursive feasibility and
input-to-state stability of the system origin. The benefits of the safe
stochastic Model Predictive Control algorithm are demonstrated in a numerical
simulation, highlighting the advantages compared to purely robust or stochastic
predictive controllers.


title:
Rate Splitting Multiple Access Aided Mobile Edge Computing in Cognitive
  Radio Networks

abstract:
  In this paper, we investigate rate splitting multiple access (RSMA) aided
mobile edge computing (MEC) in a cognitive radio network. We propose a RSMA
scheme that enables the secondary user to offload tasks to the MEC server
utilizing dynamic rate splitting without deteriorating the primary user's
offloading. The expressions for the optimal rate splitting parameters that
maximize the achievable rate for the secondary user and successful computation
probability of the proposed RSMA scheme are derived in closed-form. We
formulate a problem to maximize successful computation probability by jointly
optimizing task offloading ratio and task offloading time and obtain the
optimal solutions in closed-form. Simulation results clarify that the proposed
RSMA scheme achieves a higher successful computation probability than the
existing non-orthogonal multiple access scheme.


title: An analogue of the Blaschke-Santal\'{o} inequality for billiard dynamics

abstract:
  The Blaschke-Santal\'{o} inequality is a classical inequality in convex
geometry concerning the volume of a convex body and that of its dual. In this
work we investigate an analogue of this inequality in the context of billiard
dynamical system: we replace the volume with the length of the shortest closed
billiard trajectory. We define a quantity called the "billiard product" of a
convex body K, which is analogous to the volume product studied in the
Blaschke-Santal\'{o} inequality. In the planar case, we derive an explicit
expression for the billiard product in terms of the diameter of the body. We
also investigate upper bounds for this quantity in the class of polygons with a
fixed number of vertices.


title: Bouncing universe in Gauss-Bonnet gravity

abstract:
  In this paper, a bouncing cosmological scenario is studied in the background
of a flat FLRW model with a specific parametrized hyperbolic form of scale
factor $ a $ in terms of $ t $, where $ \lambda $ is taken as the model
parameter. This model is discussed in $ f(R,G) $ formalism having structured as
$ f(R,G)=R+F(G) $, where $ R $ is Ricci scalar and $ G $ is the Gauss-Bonnet
invariant. The proposed functional form of the Hubble parameter is considered
in such a way that it satisfies the bouncing criteria of the model, which is
free from initial singularity. The physical consequences of the model are
discussed. In this model, one can see that this model crosses the Quintom line
$ \omega=-1 $ in the neighbourhood of bouncing point $ t \simeq 17.4 $, which
is a very strong criterion for a successful bouncing dark energy model.
Finally, we find that all the essential features of bouncing model are
satisfied successfully.


title: 5G Features and Standards for Vehicle Data Exploitation

abstract:
  Cars capture and generate huge volumes of data in real-time about the driving
dynamics, the environment, and the driver and passengers' activities. Due to
the proliferation of cooperative, connected and automated mobility (CCAM), the
value of data from vehicles is getting strategic, not just for the automotive
industry, but also for many diverse stakeholders including small and
medium-sized enterprises (SMEs) and start-ups. 5G can enable car-captured data
to feed innovative applications and services deployed in the cloud ensuring
lower latency and higher throughput than previous cellular technologies. This
paper identifies and discusses the relevance of the main 5G features that can
contribute to a scalable, flexible, reliable and secure data pipeline, pointing
to the standards and technical reports that specify their implementation.


title:
A New Robot Arm Calibration Method Based on Cubic Interpolated Beetle
  Antennae Search Approach

abstract:
  Industrial robot arms are extensively important for intelligent
manufacturing. An industrial robot arm commonly enjoys its high repetitive
positioning accuracy while suffering from its low absolute positioning
accuracy, which greatly restricts its application in high-precision
manufacture, like automobile manufacture. Aiming at addressing this hot issue,
this work proposes a novel robot arm calibration method based on cubic
interpolated beetle antennae search (CIBAS). This study has three ideas: a)
developing a novel CIBAS algorithm, which can effectively addresses the issue
of local optimum in a Beetle Antennae Search algorithm; b) utilizing a particle
filter to reduce the influence of non-Gaussian noises; and c) proposing a new
calibration method incorporating CIBAS algorithm and particle filter to
searching the optimal kinematic parameters. Experimental results on an ABB
IRB120 industrial robot arm demonstrate that the proposed method achieves much
higher calibration accuracy than several state-of-the-art calibration methods.


title: Defensive Patches for Robust Recognition in the Physical World

abstract:
  To operate in real-world high-stakes environments, deep learning systems have
to endure noises that have been continuously thwarting their robustness.
Data-end defense, which improves robustness by operations on input data instead
of modifying models, has attracted intensive attention due to its feasibility
in practice. However, previous data-end defenses show low generalization
against diverse noises and weak transferability across multiple models.
Motivated by the fact that robust recognition depends on both local and global
features, we propose a defensive patch generation framework to address these
problems by helping models better exploit these features. For the
generalization against diverse noises, we inject class-specific identifiable
patterns into a confined local patch prior, so that defensive patches could
preserve more recognizable features towards specific classes, leading models
for better recognition under noises. For the transferability across multiple
models, we guide the defensive patches to capture more global feature
correlations within a class, so that they could activate model-shared global
perceptions and transfer better among models. Our defensive patches show great
potentials to improve application robustness in practice by simply sticking
them around target objects. Extensive experiments show that we outperform
others by large margins (improve 20+\% accuracy for both adversarial and
corruption robustness on average in the digital and physical world). Our codes
are available at https://github.com/nlsde-safety-team/DefensivePatch


title:
Context-based Deep Learning Architecture with Optimal Integration Layer
  for Image Parsing

abstract:
  Deep learning models have been efficient lately on image parsing tasks.
However, deep learning models are not fully capable of exploiting visual and
contextual information simultaneously. The proposed three-layer context-based
deep architecture is capable of integrating context explicitly with visual
information. The novel idea here is to have a visual layer to learn visual
characteristics from binary class-based learners, a contextual layer to learn
context, and then an integration layer to learn from both via genetic
algorithm-based optimal fusion to produce a final decision. The experimental
outcomes when evaluated on benchmark datasets are promising. Further analysis
shows that optimized network weights can improve performance and make stable
predictions.


title:
Arithmetical Complexity of the Language of Generic Limit Sets of
  Cellular Automata

abstract:
  The generic limit set of a dynamical system is the smallest set that attracts
most of the space in a topological sense: it is the smallest closed set with a
comeager basin of attraction. Introduced by Milnor, it has been studied in the
context of one-dimensional cellular automata by Djenaoui and Guillon,
Delacourt, and T\"orm\"a. In this article we present complexity bounds on
realizations of generic limit sets of cellular automata with prescribed
properties. We show that generic limit sets have a $\Pi^0_2$ language if they
are inclusion-minimal, a $\Sigma^0_1$ language if the cellular automaton has
equicontinuous points, and that these bounds are tight. We also prove that many
chain mixing $\Pi^0_2$ subshifts and all chain mixing $\Delta^0_2$ subshifts
are realizable as generic limit sets. As a corollary, we characterize the
minimal subshifts that occur as generic limit sets.


title:
Sapinet: A sparse event-based spatiotemporal oscillator for learning in
  the wild

abstract:
  We introduce Sapinet -- a spike timing (event)-based multilayer neural
network for \textit{learning in the wild} -- that is: one-shot online learning
of multiple inputs without catastrophic forgetting, and without the need for
data-specific hyperparameter retuning. Key features of Sapinet include data
regularization, model scaling, data classification, and denoising. The model
also supports stimulus similarity mapping. We propose a systematic method to
tune the network for performance. We studied the model performance on different
levels of odor similarity, gaussian and impulse noise. Sapinet achieved high
classification accuracies on standard machine olfaction datasets without the
requirement of fine tuning for a specific dataset.


title:
An Advancing Ensemble with Diversified Algorithms for Robot Arm
  Calibration

abstract:
  Recently, industrial robots plays a significant role in intelligent
manufacturing. Hence, it is an urgent issue to ensure the robot with the high
positioning precision. To address this hot issue, a novel calibration method
based on an powerful ensemble with various algorithms is proposed. This paper
has two ideas: a) developing eight calibration methods to identify the
kinematic parameter errors; 2) establishing an effective ensemble to search
calibrated kinematic parameters. Enough experimental results show that this
ensemble can achieve: 1) higher calibration accuracy for the robot; 2) model
diversity; 3) strong generalization ability.


title:
A Novel Quadratic Interpolated Beetle Antennae Search for Manipulator
  Calibration

abstract:
  Over the past decades, industrial manipulators play a vital role in in
various fields, like aircraft manufacturing and automobile manufacturing.
However, an industrial manipulator without calibration suffers from its low
absolute positioning accuracy, which extensively restricts its application in
high-precision intelligent manufacture. Recent manipulator calibration methods
are developed to address this issue, while they frequently encounter long-tail
convergence and low calibration accuracy. To address this thorny issue, this
work proposes a novel manipulator calibration method incorporating an extended
Kalman filter with a Quadratic Interpolated Beetle Antennae Search algorithm.
This paper has three-fold ideas: a) proposing a new Quadratic Interpolated
Beetle Antennae Search algorithm to deal with the issue of local optimum and
low convergence rate in a Beetle Antennae Search algorithm; b) adopting an
extended Kalman filter algorithm to suppress non-Gaussian noises and c)
developing a new manipulator calibration method incorporating an extended
Kalman filter with a Quadratic Interpolated Beetle Antennae Search algorithm to
calibrating a manipulator. Extensively experimental results on an ABB IRB120
industrial manipulator demonstrate that the proposed method achieves much
higher calibration accuracy than several state-of-the-art calibration methods.


title:
Wi-Fi Based Passive Human Motion Sensing for In-Home Healthcare
  Applications

abstract:
  This paper introduces a Wi-Fi signal based passive wireless sensing system
that has the capability to detect diverse indoor human movements, from whole
body motions to limb movements and including breathing movements of the chest.
The real time signal processing used for human body motion sensing and software
defined radio demo system are described and verified in practical experiments
scenarios, which include detection of through-wall human body movement, hand
gesture or tremor, and even respiration. The experiment results offer potential
for promising healthcare applications using Wi-Fi passive sensing in the home
to monitor daily activities, to gather health data and detect emergency
situations.


title:
Product Inequalities for Multivariate Gaussian, Gamma, and Positively
  Upper Orthant Dependent Distributions

abstract:
  The Gaussian product inequality is an important conjecture concerning the
moments of Gaussian random vectors. While all attempts to prove the Gaussian
product inequality in full generality have been unsuccessful to date, numerous
partial results have been derived in recent decades and we provide here further
results on the problem. Most importantly, we establish a strong version of the
Gaussian product inequality for multivariate gamma distributions in the case of
nonnegative correlations, thereby extending a result recently derived by Genest
and Ouimet [5]. Further, we show that the Gaussian product inequality holds
with nonnegative exponents for all random vectors with positive components
whenever the underlying vector is positively upper orthant dependent. Finally,
we show that the Gaussian product inequality with negative exponents follows
directly from the Gaussian correlation inequality.


title: Research on Intellectual Property Resource Profile and Evolution Law

abstract:
  In the era of big data, intellectual property-oriented scientific and
technological resources show the trend of large data scale, high information
density and low value density, which brings severe challenges to the effective
use of intellectual property resources, and the demand for mining hidden
information in intellectual property is increasing. This makes intellectual
property-oriented science and technology resource portraits and analysis of
evolution become the current research hotspot. This paper sorts out the
construction method of intellectual property resource intellectual portrait and
its pre-work property entity extraction and entity completion from the aspects
of algorithm classification and general process, and directions for improvement
of future methods.


title:
Quantum Monte Carlo study of superconductivity in rhombohedral trilayer
  graphene under an electric field

abstract:
  By using the constrained-phase quantum Monte Carlo method, we performed a
systematic study of the ground state of the half-filled Hubbard model for a
trilayer honeycomb lattice. We analyse the effect of the perpendicular electric
field on the electronic structure, magnetic property and pairing correlations.
It is found that antiferromagnetic fluctuations emerge with the perpendicular
electric field, and the electronic correlation drives a $d+id$ superconducting
pairing to be dominant over other pairing patterns among various electric
fields and interaction strengths. We also found that the $d+id$ pairing
correlation is greatly enhanced as the on-site Coulomb interaction is
increased. Our intensive numerical results may unveil the nature of the
recently observed superconductivity in rhombohedral trilayer graphene under an
electric field.


title:
On Validating Attack Trees with Attack Effects: An Approach from
  Barwise-Seligman's Channel Theory

abstract:
  In security analysis, attack trees are a major tool for showing the
structural decomposition of attacks and for supporting the evaluation of the
quantitative properties (called attributes) of the attacks. However, the
validities of decompositions are not established by attack trees themselves,
and fallacious decisions about security may be made when the attack trees are
inaccurate. This paper enriches attack trees with effects of attacks, with a
formal system focusing on refinement scenarios. Relationships among effects
indicate relationships among attacks and it allows for a systematic evaluation
of attack decompositions. To describe effects this paper applies
Barwise-Seligman's channel theory. Infomorphisms, in particular, play a
significant role to connect effects with distinct granularities. As a result,
the consistency of a decomposition is formally defined and a condition for it
is stated. This framework is applied to a case study of a vehicular network
system. As an application of the idea of consistency, possible degrees of
mitigation for attacks in attack trees are discussed.


title: Some recent developments in nonleptonic $B$ decays

abstract:
  I report on three recent topics from the field of two-body nonleptonic decays
of $B_{(s)}$ mesons. The computation of two-loop NNLO corrections to the
leading penguin amplitudes in QCD factorisation, puzzles that have emerged in
colour-allowed tree-level decays to heavy-light final states, and a combination
of QCD factorisation with $SU(3)$ flavour symmetry to estimate the size of
weak-annihilation amplitudes.


title: The $B_c$ decay rate in the Standard Model

abstract:
  The Standard Model decay rate of the $B_c$ meson is discussed together with a
novel approach based on the usage of experimental data in combination with an
operator product expansion. In the new method differences of $B,\,D$ and $B_c$
meson decay rates are considered, for which the free-quark contributions drop
out, leading to a reduction of the theory prediction.


title:
Assessing IT Architecture Evolution using Enriched Enterprise
  Architecture Models

abstract:
  Enterprise Architecture (EA) help companies to keep the evolution of their IT
architecture aligned with their business evolution using a set of complementary
models ranging from vision to infrastructure. In this paper, we explore how
such models can be exploited to best drive this co-evolution by helping in
validating different change management strategies. Considering state of the art
techniques in service oriented and Cloud architecture, we propose to enrich and
improve the quality of the information about the applications and software
components on various qualities (e.g. performance, scalability, security,
technical debt) in order to include them in the assessment process already at
the Enterprise Architecture level rather than discovering them later in the
change implementation phase. Our work is experimented on the LabNaf EA
framework and is illustrated on a partial case study taken from an industrial
project.


title:
Global existence of strong solutions with large oscillations and vacuum
  to the compressible nematic liquid crystal flows in bounded domains with
  boundaries

abstract:
  We investigate compressible nematic liquid crystal flows in three-dimensional
(3D) bounded domains with slip boundary condition for velocity and Neumann
boundary condition for orientation field. By applying piecewise-estimate method
and delicate analysis based on the effective viscous flux and vorticity, we
derive the global existence and uniqueness of strong solutions provided that
the initial total energy is suitably small. In particular, our result shows
that blow up mechanism for local strong solutions obtained by Huang-Wang-Wen
(Arch. Ration. Mech. Anal. 204:285--311, 2012) cannot occur. Moreover, it is
also an extension of the work of Li-Xu-Zhang (J. Math. Fluid Mech.
20:2105--2145, 2018), where the global strong solutions for 3D Cauchy problem
were established.


title:
Do You Really Mean That? Content Driven Audio-Visual Deepfake Dataset
  and Multimodal Method for Temporal Forgery Localization

abstract:
  Due to its high societal impact, deepfake detection is getting active
attention in the computer vision community. Most deepfake detection methods
rely on identity, facial attribute and adversarial perturbation based
spatio-temporal modifications at the whole video or random locations, while
keeping the meaning of the content intact. However, a sophisticated deepfake
may contain only a small segment of video/audio manipulation, through which the
meaning of the content can be, for example, completely inverted from sentiment
perspective. To address this gap, we introduce a content driven audio-visual
deepfake dataset, termed as Localized Audio Visual DeepFake (LAV-DF),
explicitly designed for the task of learning temporal forgery localization.
Specifically, the content driven audio-visual manipulations are performed at
strategic locations in order to change the sentiment polarity of the whole
video. Our baseline method for benchmarking the proposed dataset is a 3DCNN
model, termed as Boundary Aware Temporal Forgery Detection (BA-TFD), which is
guided via contrastive, boundary matching and frame classification loss
functions. Our extensive quantitative analysis demonstrates the strong
performance of the proposed method for both task of temporal forgery
localization and deepfake detection.


title:
Computational Design of Alloy Nanostructures for Optical Sensing: The
  Limits of Tuning Hydrogen Sensitivity via Composition and Geometry

abstract:
  Pd nanoalloys have shown great potential as hysteresis-free, reliable
hydrogen sensors. Here, a multi-scale modeling approach is employed to
determine optimal conditions for optical hydrogen sensing using the Pd-Au-H
system. Changes in hydrogen pressure translate to changes in hydrogen content
and eventually the optical spectrum. At the single particle level, the shift of
the plasmon peak position with hydrogen concentration (i.e. sensitivity) is
approximately constant at 180 nm/$c_\text{H}$ for nanodisk diameters >~100 nm.
For smaller particles, the sensitivity is negative and increases with
decreasing diameter, due to the emergence of a second peak originating from
coupling between a localized surface plasmon and interband transitions. In
addition to tracking peak position, the onset of extinction as well as
extinction at fixed wavelengths is considered. Invariably, the results suggest
that there is an upper bound for the sensitivity that cannot be overcome by
engineering composition and/or geometry. While the alloy composition has a
limited impact on sensitivity, it can strongly affect H uptake and consequently
the detection limit. It is shown that the latter could be substantially
improved via the formation of an ordered phase (L1$_2$-Pd$_3$Au), which can be
synthesized at higher H partial pressures.


title:
Performance Analysis of Wireless Network Aided by Discrete-Phase-Shifter
  IRS

abstract:
  Discrete phase shifters of intelligent reflecting surface (IRS) generates
phase quantization error (QE) and degrades the receive performance at the
receiver. To make an analysis of the performance loss caused by IRS with phase
QE, based on the law of large numbers, the closed-form expressions of
signal-to-noise ratio (SNR) performance loss (PL), achievable rate (AR), and
bit error rate (BER) are successively derived under line-of-sight (LoS)
channels and Rayleigh channels. Moreover, based on the Taylor series expansion,
the approximate simple closed form of PL of IRS with approximate QE is also
given. The simulation results show that the performance losses of SNR and AR
decrease as the number of quantization bits increase, while they gradually
increase with the number of IRS phase shifter elements increase. Regardless of
LoS channels or Rayleigh channels, when the number of quantization bits is
larger than or equal to 3, the performance losses of SNR and AR are less than
0.23dB and 0.08bits/s/Hz, respectively, and the BER performance degradation is
trivial. In particular, the performance loss difference between IRS with QE and
IRS with approximate QE is negligible when the number of quantization bits is
not less than 2.


title:
Terahertz lattice and charge dynamics in ferroelectric semiconductor
  Sn$_x$Pb$_{1-x}$Te

abstract:
  The symmetry breaking induced by the ferroelectric transition often triggers
the emergence of topological electronic states such as Weyl fermions in
ferroelectric-like metals/semimetals. Such strong coupling between the lattice
deformation and electronic states is therefore essentially important for the
control of novel topological phases. Here, we study the terahertz lattice and
charge dynamics in ferroelectric semiconductor SnxPb1-xTe thin films hosting
versatile topological phases by means of the terahertz time-domain
spectroscopy. With lowering the temperature, the resonant frequency of
transverse optical phonon shows the significant softening and upturn. This
temperature anomaly of lattice dynamics directly indicates the displacive-type
ferroelectric transition. The resulting phase diagram suggests the enhancement
of ferroelectricity in the films due to compressive strain compared with the
bulk crystals. The soft phonon induces the large DC and terahertz dielectric
constant even in metallic state. Furthermore, we find that the Born effective
charge of soft phonon mode is enhanced at around the compositions showing the
band gap closing associated with the topological transition.


title: The non-ideal finite Larmor radius effect in the solar atmosphere

abstract:
  The dynamics of the partially ionized solar atmosphere is controlled by the
frequent collision and charge exchange between the predominant neutral Hydrogen
atoms and charged ions. At signal frequencies below or of the order of either
of the collision or charge exchange frequencies the magnetic stress is {\it
felt} by both the charged and neutral particles simultaneously. The resulting
neutral-mass loading of the ions leads to the rescaling of the effective
ion-cyclotron frequency-it becomes the Hall frequency, and the resultant
effective Larmor radius becomes of the order of few kms. Thus the finite Larmor
radius (FLR) effect which manifests as the ion and neutral pressure stress
tensors operates over macroscopic scales. Whereas parallel and perpendicular
(with respect to the magnetic field) viscous momentum transport competes with
the Ohm and Hall diffusion of the magnetic field in the
photosphere-chromosphre, the gyroviscous effect becomes important only in the
transition region between the chromosphere and corona, where it competes with
the ambipolar diffusion. The wave propagation in the gyroviscous effect
dominated medium depends on the plasma $\beta$ (a ratio of the thermal and
magnetic energies). The abundance of free energy makes gyro waves unstable with
the onset condition exactly opposite of the Hall instability. However, the
maximum growth rate is identical to the Hall instability. For a flow gradient
$\sim 0.1 \,\mbox{s}^{-1}$ the instability growth time is one minute. Thus, the
transition region may become subject to this fast growing, gyroviscous
instability.


title: Approximation of Lipschitz Functions using Deep Spline Neural Networks

abstract:
  Lipschitz-constrained neural networks have many applications in machine
learning. Since designing and training expressive Lipschitz-constrained
networks is very challenging, there is a need for improved methods and a better
theoretical understanding. Unfortunately, it turns out that ReLU networks have
provable disadvantages in this setting. Hence, we propose to use learnable
spline activation functions with at least 3 linear regions instead. We prove
that this choice is optimal among all component-wise $1$-Lipschitz activation
functions in the sense that no other weight constrained architecture can
approximate a larger class of functions. Additionally, this choice is at least
as expressive as the recently introduced non component-wise Groupsort
activation function for spectral-norm-constrained weights. Previously published
numerical results support our theoretical findings.


title: The lepton flavor violating decays of vector mesons in the MRSSM

abstract:
  Recently several new bounds of the lepton flavor violating decays of the
neutral vector mesons $J/\psi$ and $\Upsilon(nS)$ are reported from the
experiments. In the work, we analyze these processes in the scenario of the
minimal R-symmetric supersymmetric standard model by means of the effective
Lagrangian method. The predicted branching ratios are affected by the mass
insertion parameters $\delta^{ij}$ and the contributions from different parts
are comparable. Taking account of the constraints on the mass insertion
parameters from the experiments, the branching ratios for the most promising
processes $\Upsilon(nS)\rightarrow l\tau$ are predicted to be ten orders of
magnitude smaller than the present experimental bounds.


title: Anti-polar state in BiFeO3/NdFeO3 superlattices

abstract:
  Antiferroelectrics are promising materials for high energy density capacitors
and the search for environmentally-friendly and efficient systems is actively
pursued. An elegant strategy to create and design new (anti)ferroic system
relies on the use of nanoscale superlattices. We report here the use of such
strategy and the fabrication of nanoscale BiFeO3/NdFeO3 superlattices and in
depth characterization using high resolution X-ray diffraction and Transmission
Electron Microscopy. The structural analysis at atomic scale demonstrates that
such superlattices host anti-polar ordering most likely described by an
antiferroelectric-like Pbnm symmetry. Temperature dependence of anti-polar
state and structural transition further hint that the stability of the
anti-polar state is controlled by the BiFeO3 layer thickness within the
stacking and, in a more moderate way, by interlayer strain. Discovery of such
polar arrangement in superlattices and the possible generalization to the whole
rare-earth family pave the way to new platforms for energy storage application
as well as nano-electronic devices.


title:
An Improved Numerical Method for Three-dimensional Hyperbolic Lagrangian
  Coherent Structures using Differential Algebra

abstract:
  In dynamical systems, it is advantageous to be able to identify separate
regions of qualitatively different flow. Lagrangian Coherent Structures have
been introduced to obtain separatrices between regions of qualitatively
different flow in three-dimensional dynamical systems with arbitrary
time-dependence. However, the numerical method to compute them requires
obtaining derivatives associated with the system, often performed through the
approximation of divided differences, which leads to significant numerical
error and numerical noise. In this paper, we introduce a novel method for the
numerical calculation of hyperbolic Lagrangian Coherent Structures using
Differential Algebra called DA-LCS. As a form of automatic forward
differentiation, it allows direct computation of the Taylor expansion of the
flow, its derivatives, and its eigenvectors, with all derivatives obtained
algebraically and to machine precision. It does so without a priori information
about the system, such as variational equations or explicit derivatives. We
demonstrate that this provides significant improvements in the accuracy of the
Lagrangian Coherent Structures identified compared to finite-differencing
methods in a series of test cases drawn from the literature. We also show how
DA-LCS uncovers additional dynamical behaviour in a real-world example drawn
from astrodynamics.


title: Ada-Detector: Adaptive Frontier Detector for Rapid Exploration

abstract:
  In this paper, we propose an efficient frontier detector method based on
adaptive Rapidly-exploring Random Tree (RRT) for autonomous robot exploration.
Robots can achieve real-time incremental frontier detection when they are
exploring unknown environments. First, our detector adaptively adjusts the
sampling space of RRT by sensing the surrounding environment structure. The
adaptive sampling space can greatly improve the successful sampling rate of RRT
(the ratio of the number of samples successfully added to the RRT tree to the
number of sampling attempts) according to the environment structure and control
the expansion bias of the RRT. Second, by generating non-uniform distributed
samples, our method also solves the over-sampling problem of RRT in the sliding
windows, where uniform random sampling causes over-sampling in the overlap area
between two adjacent sliding windows. In this way, our detector is more
inclined to sample in the latest explored area, which improves the efficiency
of frontier detection and achieves incremental detection. We validated our
method in three simulated benchmark scenarios. The experimental comparison
shows that we reduce the frontier detection runtime by about 40% compared with
the SOTA method, DSV Planner.


title:
Photonic quadrupole topological insulator using orbital-induced
  synthetic flux

abstract:
  The rich physical properties of multiatomic molecules and crystalline
structures are determined, to a significant extent, by the underlying geometry
and connectivity of atomic orbitals. This orbital degree of freedom has also
been used effectively to introduce structural diversity in a few synthetic
materials including polariton lattices nonlinear photonic lattices and
ultracold atoms in optical lattices. In particular, the mixing of orbitals with
distinct parity representations, such as $s$ and $p$ orbitals, has been shown
to be especially useful for generating systems that require alternating phase
patterns, as with the sign of couplings within a lattice. Here we show that by
further breaking the symmetries of such mixed-orbital lattices, it is possible
to generate synthetic magnetic flux threading the lattice. This capability
allows the generation of multipole higher-order topological phases in synthetic
bosonic platforms, in which $\pi$ flux threading each plaquette of the lattice
is required, and which to date have only been implemented using tailored
connectivity patterns. We use this insight to experimentally demonstrate a
quadrupole photonic topological insulator in a two-dimensional lattice of
waveguides that leverage modes with both $s$ and $p$ orbital-type
representations. We confirm the nontrivial quadrupole topology of the system by
observing the presence of protected zero-dimensional states, which are
spatially confined to the corners, and by confirming that these states sit at
the band gap. Our approach is also applicable to a broader range of
time-reversal-invariant synthetic materials that do not allow for tailored
connectivity, e.g. with nanoscale geometries, and in which synthetic fluxes are
essential.


title: Can Question Rewriting Help Conversational Question Answering?

abstract:
  Question rewriting (QR) is a subtask of conversational question answering
(CQA) aiming to ease the challenges of understanding dependencies among
dialogue history by reformulating questions in a self-contained form. Despite
seeming plausible, little evidence is available to justify QR as a mitigation
method for CQA. To verify the effectiveness of QR in CQA, we investigate a
reinforcement learning approach that integrates QR and CQA tasks and does not
require corresponding QR datasets for targeted CQA. We find, however, that the
RL method is on par with the end-to-end baseline. We provide an analysis of the
failure and describe the difficulty of exploiting QR for CQA.


title:
CowClip: Reducing CTR Prediction Model Training Time from 12 hours to 10
  minutes on 1 GPU

abstract:
  The click-through rate (CTR) prediction task is to predict whether a user
will click on the recommended item. As mind-boggling amounts of data are
produced online daily, accelerating CTR prediction model training is critical
to ensuring an up-to-date model and reducing the training cost. One approach to
increase the training speed is to apply large batch training. However, as shown
in computer vision and natural language processing tasks, training with a large
batch easily suffers from the loss of accuracy. Our experiments show that
previous scaling rules fail in the training of CTR prediction neural networks.
To tackle this problem, we first theoretically show that different frequencies
of ids make it challenging to scale hyperparameters when scaling the batch
size. To stabilize the training process in a large batch size setting, we
develop the adaptive Column-wise Clipping (CowClip). It enables an easy and
effective scaling rule for the embeddings, which keeps the learning rate
unchanged and scales the L2 loss. We conduct extensive experiments with four
CTR prediction networks on two real-world datasets and successfully scaled 128
times the original batch size without accuracy loss. In particular, for CTR
prediction model DeepFM training on the Criteo dataset, our optimization
framework enlarges the batch size from 1K to 128K with over 0.1% AUC
improvement and reduces training time from 12 hours to 10 minutes on a single
V100 GPU. Our code locates at https://github.com/bytedance/LargeBatchCTR.