Abstract:
  Neural natural language generation (NLG) and understanding (NLU) models are
data-hungry and require massive amounts of annotated data to be competitive.
Recent frameworks address this bottleneck with generative models that
synthesize weak labels at scale, where a small amount of training labels are
expert-curated and the rest of the data is automatically annotated. We follow
that approach, by automatically constructing a large-scale weakly-labeled data
with a fine-tuned GPT-2, and employ a semi-supervised framework to jointly
train the NLG and NLU models. The proposed framework adapts the parameter
updates to the models according to the estimated label-quality. On both the E2E
and Weather benchmarks, we show that this weakly supervised training paradigm
is an effective approach under low resource scenarios and outperforming
benchmark systems on both datasets when 100% of training data is used.


Abstract:
  We present Integral Field Spectroscopic (IFS) observations of the nearby
($z\sim0.03$) dual Active Galactic Nuclei (AGN) Mrk 739, whose projected
nuclear separation is $\sim$3.4~kpc, obtained with the Multi Unit Spectroscopic
Explorer (MUSE) at the Very Large Telescope (VLT). We find that the galaxy has
an extended AGN-ionized emission-line region extending up to $\sim 20$ kpc away
from the nuclei, while star-forming regions are more centrally concentrated
within 2 - 3 kpc. We model the kinematics of the ionized gas surrounding the
East nucleus using a circular disk profile, resulting in a peak velocity of
$237^{+26}_{-28}$ km s$^{-1}$ at a distance of $\sim 1.2$ kpc. The enclosed
dynamical mass within 1.2 kpc is $\log M(M_{\odot})=10.20\pm0.06$, $\sim$1,000
times larger than the estimated supermassive black hole (SMBH) mass of Mrk
739E. The morphology and dynamics of the system are consistent with an early
stage of the collision, where the foreground galaxy (Mrk 739W) is a young
star-forming galaxy in an ongoing first passage with its background companion
(Mrk 739E). Since the SMBH in Mrk 739W does not show evidence of being rapidly
accreting, we claim that the northern spiral arms of Mrk 739W are ionized by
the nuclear activity of Mrk 739E.


Abstract:
  The spin-memory effect in the GaAs / InGaAs heterostructures with
$\delta$<Mn> layer in GaAs barrier have been investigated. The effect consists
in spin polarization of Mn atoms due to interaction with photogenerated
spin-polarized holes. The investigation of the effect was carried out by
analyzing the polarization of the probe photoluminescence pulse in the
pump-probe technique. It was shown that the circular polarization degree of
probe pulse generated photoluminescence is strongly affected by the interaction
of hole spins with spins of Mn atoms polarized by the pump pulse. The latter
leads to decrease of circular polarization degree as compared with single pulse
excitation ($\delta P$ effect). The amplitude of $\delta P$-effect is most
strongly affected by the concentration of resident electrons in the quantum
well which is believed to be due the specific compliance with selection rules
for optical transition with the participation of unpolarized resident
electrons. The rest of the sample's parameters including the spatial separation
between $\delta$<Mn> layer and InGaAs quantum well ($d_s$) have a minor effect
on the $\delta P$ value which leads to a paradoxical situation of decreasing
$\delta P$-effect with the decrease of $d_s$. The proposed experimental
technique consisting in creating the significant concentration of resident
electrons in the QW may serve as a reliable photoluminescence method
determining the strength of this effect as well as the Mn spin relaxation time
in a particular nanostructure.


Abstract:
  We discuss the spectral energy distributions and physical properties of six
galaxies whose photometric redshifts suggest they lie beyond a redshift
$z\simeq$9. Each was selected on account of a prominent excess seen in the
Spitzer/IRAC 4.5$\mu$m band which, for a redshift above $z=9.0$, likely
indicates the presence of a rest-frame Balmer break and a stellar component
that formed earlier than a redshift $z\simeq10$. In addition to constraining
the earlier star formation activity on the basis of fits using stellar
population models with BAGPIPES, we have undertaken the necessary, but
challenging, follow-up spectroscopy for each candidate using various
combinations of Keck/MOSFIRE, VLT/X-shooter, Gemini/FLAMINGOS2 and ALMA. Based
on either Lyman-$\alpha$ or [OIII] 88 $\mu$m emission, we determine a
convincing redshift of $z$=8.78 for GN-z-10-3 and a likely redshift of $z$=9.28
for the lensed galaxy MACS0416-JD. For GN-z9-1, we conclude the case remains
promising for a source beyond $z\simeq$9. Together with earlier spectroscopic
data for MACS1149-JD1, our analysis of this enlarged sample provides further
support for a cosmic star formation history extending beyond redshifts
$z\simeq$10. We use our best-fit stellar population models to reconstruct the
past rest-frame UV luminosities of our sources and discuss the implications for
tracing earlier progenitors of such systems with the James Webb Space
Telescope.


Abstract:
  The optimal time for the controllability of linear hyperbolic systems in one
dimensional space with one-side controls has been obtained recently for
time-independent coefficients in our previous works. In this paper, we consider
linear hyperbolic systems with time-varying zero-order terms. We show the
possibility that the optimal time for the null-controllability becomes
significantly larger than the one of the time-invariant setting even when the
zero-order term is indefinitely differentiable. When the analyticity with
respect to time is imposed for the zero-order term, we also establish that the
optimal time is the same as in the time-independent setting.


Abstract:
  Spatiotemporal imaging is common in medical imaging, with applications in
e.g. cardiac diagnostics, surgical guidance and radiotherapy monitoring. In
this paper, we present an unsupervised model that identifies the underlying
dynamics of the system, only based on the sequential images. The model maps the
input to a low-dimensional latent space wherein a linear relationship holds
between a hidden state process and the observed latent process. Knowledge of
the system dynamics enables denoising, imputation of missing values and
extrapolation of future image frames. We use a Variational Auto-Encoder (VAE)
for the dimensionality reduction and a Linear Gaussian State Space Model
(LGSSM) for the latent dynamics. The model, known as a Kalman Variational
Auto-Encoder, is end-to-end trainable and the weights, both in the VAE and
LGSSM, are simultaneously updated by maximizing the evidence lower bound of the
marginal log likelihood. Our experiment, on cardiac ultrasound time series,
shows that the dynamical model provide better reconstructions than a similar
model without dynamics. And also possibility to impute and extrapolate for
missing samples.


Abstract:
  Calculation of dynamical diffraction patterns for X-ray topography and
similar x-ray scattering-imaging techniques require the numerical integration
of the Takagi-Taupin equations. This is usually done with a simple second order
finite difference scheme on a sheared computational grid with two of the axes
aligned with the wave vectors of the incident and scattered beams respectively.
Here we present a finite difference scheme that carries out this integration on
an arbitrary orthogonal grid by implicitly utilizing Fourier interpolation. The
scheme achieves the expected second order convergence and a similar error to
the traditional approach on similarly dense grids but is more computationally
expensive due to the use of FFT operations.


Abstract:
  Real-time video analytics on the edge is challenging as the computationally
constrained resources typically cannot analyse video streams at full fidelity
and frame rate, which results in loss of accuracy. This paper proposes a
Transprecise Object Detector (TOD) which maximises the real-time object
detection accuracy on an edge device by selecting an appropriate Deep Neural
Network (DNN) on the fly with negligible computational overhead. TOD makes two
key contributions over the state of the art: (1) TOD leverages characteristics
of the video stream such as object size and speed of movement to identify
networks with high prediction accuracy for the current frames; (2) it selects
the best-performing network based on projected accuracy and computational
demand using an effective and low-overhead decision mechanism. Experimental
evaluation on a Jetson Nano demonstrates that TOD improves the average object
detection precision by 34.7 % over the YOLOv4-tiny-288 model on average over
the MOT17Det dataset. In the MOT17-05 test dataset, TOD utilises only 45.1 % of
GPU resource and 62.7 % of the GPU board power without losing accuracy,
compared to YOLOv4-416 model. We expect that TOD will maximise the application
of edge devices to real-time object detection, since TOD maximises real-time
object detection accuracy given edge devices according to dynamic input
features without increasing inference latency in practice.


Abstract:
  Quality-Diversity algorithms search for large collections of diverse and
high-performing solutions, rather than just for a single solution like typical
optimisation methods. They are specially adapted for multi-modal problems that
can be solved in many different ways, such as complex reinforcement learning or
robotics tasks. However, these approaches are highly dependent on the choice of
feature descriptors (FDs) quantifying the similarity in behaviour of the
solutions. While FDs usually needs to be hand-designed, recent studies have
proposed ways to define them automatically by using feature extraction
techniques, such as PCA or Auto-Encoders, to learn a representation of the
problem from previously explored solutions. Here, we extend these approaches to
more complex problems which cannot be efficiently explored by relying only on a
single representation but require instead a set of diverse and complementary
representations. We describe MC-AURORA, a Quality-Diversity approach that
optimises simultaneously several collections of solutions, each with a
different set of FDs, which are, in turn, defined automatically by an ensemble
of modular auto-encoders. We show that this approach produces solutions that
are more diverse than those produced by single-representation approaches.


Abstract:
  Estimating 3D hand and object pose from a single image is an extremely
challenging problem: hands and objects are often self-occluded during
interactions, and the 3D annotations are scarce as even humans cannot directly
label the ground-truths from a single image perfectly. To tackle these
challenges, we propose a unified framework for estimating the 3D hand and
object poses with semi-supervised learning. We build a joint learning framework
where we perform explicit contextual reasoning between hand and object
representations by a Transformer. Going beyond limited 3D annotations in a
single image, we leverage the spatial-temporal consistency in large-scale
hand-object videos as a constraint for generating pseudo labels in
semi-supervised learning. Our method not only improves hand pose estimation in
challenging real-world dataset, but also substantially improve the object pose
which has fewer ground-truths per instance. By training with large-scale
diverse videos, our model also generalizes better across multiple out-of-domain
datasets. Project page and code: https://stevenlsw.github.io/Semi-Hand-Object


Abstract:
  Bayesian experimental design (BED) is to answer the question that how to
choose designs that maximize the information gathering. For implicit models,
where the likelihood is intractable but sampling is possible, conventional BED
methods have difficulties in efficiently estimating the posterior distribution
and maximizing the mutual information (MI) between data and parameters. Recent
work proposed the use of gradient ascent to maximize a lower bound on MI to
deal with these issues. However, the approach requires a sampling path to
compute the pathwise gradient of the MI lower bound with respect to the design
variables, and such a pathwise gradient is usually inaccessible for implicit
models. In this paper, we propose a novel approach that leverages recent
advances in stochastic approximate gradient ascent incorporated with a smoothed
variational MI estimator for efficient and robust BED. Without the necessity of
pathwise gradients, our approach allows the design process to be achieved
through a unified procedure with an approximate gradient for implicit models.
Several experiments show that our approach outperforms baseline methods, and
significantly improves the scalability of BED in high-dimensional problems.


Abstract:
  This paper proposes a novel training model based on shape and appearance
features for object segmentation in images and videos. Whereas most such models
rely on two-dimensional appearance templates or a finite set of descriptors,
our appearance-based feature is a one-dimensional function, which is
efficiently coupled with the object's shape by integrating intensities along
the object's iso-contours. Joint PCA training on these shape and appearance
features further exploits shape-appearance correlations and the resulting
training model is incorporated in an active-contour-type energy functional for
recognition-segmentation tasks. Experiments on synthetic and infrared images
demonstrate how this shape and appearance training model improves accuracy
compared to methods based on the Chan-Vese energy.


Abstract:
  Context. The diffusion of volatile species on amorphous solid water ice
affects the chemistry on dust grains in the interstellar medium as well as the
trapping of gases enriching planetary atmospheres or present in cometary
material.
  Aims. The aim of the work is to provide diffusion coefficients of CH$_4$ on
amorphous solid water (ASW), and to understand how they are affected by the ASW
structure.
  Methods. Ice mixtures of H$_2$O and CH$_4$ were grown in different conditions
and the sublimation of CH$_4$ was monitored via infrared spectroscopy or via
the mass loss of a cryogenic quartz crystal microbalance. Diffusion
coefficients were obtained from the experimental data assuming the systems obey
Fick's law of diffusion. Monte Carlo simulations modeled the different
amorphous solid water ice structures investigated and were used to reproduce
and interpret the experimental results.
  Results. Diffusion coefficients of methane on amorphous solid water have been
measured to be between 10$^{-12}$ and 10$^{-13}$ cm$^2$ s$^{-1}$ for
temperatures ranging between 42 K and 60 K. We showed that diffusion can differ
by one order of magnitude depending on the morphology of amorphous solid water.
The porosity within water ice, and the network created by pore coalescence,
enhance the diffusion of species within the pores.The diffusion rates derived
experimentally cannot be used in our Monte Carlo simulations to reproduce the
measurements.
  Conclusions. We conclude that Fick's law can be used to describe diffusion at
the macroscopic scale, while Monte Carlo simulations describe the microscopic
scale where trapping of species in the ices (and their movement) is considered.


Abstract:
  Let $p$ be prime, and $n,m \in \mathbb{N}$. When $K/F$ is a cyclic extension
of degree $p^n$, we determine the $\mathbb{F}_p[\text{Gal}(K/F)]$-module
structure of $K^\times/K^{\times p^m}$. With at most one exception, each
indecomposable summand is cyclic and free over some quotient group of
$\text{Gal}(K/F)$. For fixed values of $m$ and $n$, there are only finitely
many possible isomorphism classes for the non-free indecomposable summand.
  This result seems to be a significant breakthrough in the study of Galois
modules. For one, these Galois modules act as parameterizing spaces for
solutions to certain inverse Galois problems, and therefore this module
computation should provide key insight into absolute Galois groups. More
immediately, however, these results show that Galois cohomology is a context in
which seemingly impossible module decompositions can practically be achieved:
when $m,n>1$ the modular representation theory allows for an infinite number of
indecomposable summands, with no known classification of indecomposable types.
This paper marks the first time a Galois module decomposition has been computed
under these circumstances over an infinite family of modules.


Abstract:
  In this paper, we consider a second-order scalar auxiliary variable (SAV)
Fourier spectral method to solve the nonlinear fractional generalized wave
equation. Unconditional energy conservation or dissipation properties of the
fully discrete scheme are first established. Next, we utilize the
temporal-spatial error splitting argument to obtain unconditional optimal error
estimate of the fully discrete scheme, which overcomes time-step restrictions
caused by strongly nonlinear system, or the restrictions that the nonlinear
term needs to satisfy the assumption of global Lipschitz condition in all
previous works for fractional undamped or damped wave equations. Finally, some
numerical experiments are presented to confirm our theoretical analysis.


Abstract:
  Automatic draping of carbon-fiber prepreg plies for the aerospace industry is
a promising technique for lowering the manufacturing costs and to this end, a
thorough in-process quality control is crucial. In this paper, out-of-plane
defects in the layup are investigated. After draping, air pockets are
occasionally encountered. The question is, if such apparent defects can be
mitigated sufficiently during vacuum debulking. The 3D topology is measured by
means of a structured-light 3D scanner and air pockets are segmented. An
approximate mass-spring ply model is used to study the behavior of the air
pockets during application of vacuum pressure. The model is computationally
fast and will indicate online whether the air pocket will be removed or manual
intervention is required. Upon comparing the model predictions with
experimental data, it is shown that the system is capable of correctly
predicting 13 out of 14 air pockets in a test layup.


Abstract:
  Mixing transformations in quantum field theory are non-trivial, since they
are intimately related to the unitary inequivalence between Fock spaces for
fields with definite mass and fields with definite flavor. Considering the
superposition of two neutral scalar (spin-0) bosonic fields, we investigate
some features of the emerging condensate structure of the flavor vacuum. In
particular, we quantify the flavor vacuum entanglement in terms of the von
Neumann entanglement entropy of the reduced state. Furthermore, in a suitable
limit, we show that the flavor vacuum has a structure akin to the thermal
vacuum of Thermo Field Dynamics, with a temperature dependent on both the
mixing angle and the particle mass difference.


Abstract:
  Let $S(t) =
\frac{1}{\pi}\operatorname{Im}\log\zeta\left(\frac{1}{2}+it\right)$. Using
Soundararajan's resonance method we prove an unconditional lower bound on the
size of the tails of the distribution of $S(t)$. In particular we reproduce the
best unconditional $\Omega$ result for $S(t)$ which is due to Tsang, \[ S(t) =
\Omega_\pm\left(\left(\frac{\log t}{\log \log t}\right)^{1/3}\right), \] and
get a bound on how often large values of $S(t)$ occur. We also give a
probabilistic argument for why this $\Omega$ result may be the best possible
given our current knowledge of the zeros of the zeta function.


Abstract:
  We present a search for radio afterglows from long gamma-ray bursts using the
Australian Square Kilometre Array Pathfinder (ASKAP). Our search used the Rapid
ASKAP Continuum Survey, covering the entire celestial sphere south of
declination $+41^\circ$, and three epochs of the Variables and Slow Transients
Pilot Survey (Phase 1), covering $\sim 5,000$ square degrees per epoch. The
observations we used from these surveys spanned a nine-month period from 2019
April 21 to 2020 January 11. We crossmatched radio sources found in these
surveys with 779 well-localised (to $\leq 15''$) long gamma-ray bursts
occurring after 2004 and determined whether the associations were more likely
afterglow- or host-related through the analysis of optical images. In our
search, we detected one radio afterglow candidate associated with GRB 171205A,
a local low-luminosity gamma-ray burst with a supernova counterpart SN 2017iuk,
in an ASKAP observation 511 days post-burst. We confirmed this detection with
further observations of the radio afterglow using the Australia Telescope
Compact Array at 859 days and 884 days post-burst. Combining this data with
archival data from early-time radio observations, we showed the evolution of
the radio spectral energy distribution alone could reveal clear signatures of a
wind-like circumburst medium for the burst. Finally, we derived semi-analytical
estimates for the microphysical shock parameters of the burst: electron
power-law index $p = 2.84$, normalised wind-density parameter $A_* = 3$,
fractional energy in electrons $\epsilon_{e} = 0.3$, and fractional energy in
magnetic fields $\epsilon_{B} = 0.0002$.


Abstract:
  The scarcity of labeled data often impedes the application of deep learning
to the segmentation of medical images. Semi-supervised learning seeks to
overcome this limitation by exploiting unlabeled examples in the learning
process. In this paper, we present a novel semi-supervised segmentation method
that leverages mutual information (MI) on categorical distributions to achieve
both global representation invariance and local smoothness. In this method, we
maximize the MI for intermediate feature embeddings that are taken from both
the encoder and decoder of a segmentation network. We first propose a global MI
loss constraining the encoder to learn an image representation that is
invariant to geometric transformations. Instead of resorting to
computationally-expensive techniques for estimating the MI on continuous
feature embeddings, we use projection heads to map them to a discrete cluster
assignment where MI can be computed efficiently. Our method also includes a
local MI loss to promote spatial consistency in the feature maps of the decoder
and provide a smoother segmentation. Since mutual information does not require
a strict ordering of clusters in two different assignments, we incorporate a
final consistency regularization loss on the output which helps align the
cluster labels throughout the network. We evaluate the method on four
challenging publicly-available datasets for medical image segmentation.
Experimental results show our method to outperform recently-proposed approaches
for semi-supervised segmentation and provide an accuracy near to full
supervision while training with very few annotated images.


Abstract:
  Since the day of its explosion, SN 1987A (SN87A) was closely monitored with
the aim to study its evolution and to detect its central compact relic. The
detection of neutrinos from the supernova strongly supports the formation of a
neutron star (NS). However, the constant and fruitless search for this object
has led to different hypotheses on its nature. Up to date, the detection in the
ALMA data of a feature somehow compatible with the emission arising from a
proto Pulsar Wind Nebula (PWN) is the only hint of the existence of such
elusive compact object. Here we tackle this 33-years old issue by analyzing
archived observations of SN87A performed Chandra and NuSTAR in different years.
We firmly detect nonthermal emission in the $10-20$ kev energy band, due to
synchrotron radiation. The possible physical mechanism powering such emission
is twofold: diffusive shock acceleration (DSA) or emission arising from an
absorbed PWN. By relating a state-of-the-art magneto-hydrodynamic simulation of
SN87A to the actual data, we reconstruct the absorption pattern of the PWN
embedded in the remnant and surrounded by cold ejecta. We found that, even
though the DSA scenario cannot be firmly excluded, the most likely scenario
that well explains the data is the PWN emission.


Abstract:
  Closed form expressions to calculate the exponential of a general multivector
(MV) in Clifford geometric algebras (GAs) Cl(p,q) are presented for n=p+q=3.
The obtained exponential formulas were applied to find exact GA trigonometric
and hyperbolic functions of MV argument. We have verified that the presented
exact formulas are in accord with series expansion of MV hyperbolic and
trigonometric functions. The exponentials may be applied to solve GA
differential equations, in signal and image processing, automatic control and
robotics.


Abstract:
  While predicting robot grasps with parallel jaw grippers have been well
studied and widely applied in robot manipulation tasks, the study on natural
human grasp generation with a multi-finger hand remains a very challenging
problem. In this paper, we propose to generate human grasps given a 3D object
in the world. Our key observation is that it is crucial to model the
consistency between the hand contact points and object contact regions. That
is, we encourage the prior hand contact points to be close to the object
surface and the object common contact regions to be touched by the hand at the
same time. Based on the hand-object contact consistency, we design novel
objectives in training the human grasp generation model and also a new
self-supervised task which allows the grasp generation network to be adjusted
even during test time. Our experiments show significant improvement in human
grasp generation over state-of-the-art approaches by a large margin. More
interestingly, by optimizing the model during test time with the
self-supervised task, it helps achieve larger gain on unseen and out-of-domain
objects. Project page: https://hwjiang1510.github.io/GraspTTA/


Abstract:
  Haustral folds are colon wall protrusions implicated for high polyp miss rate
during optical colonoscopy procedures. If segmented accurately, haustral folds
can allow for better estimation of missed surface and can also serve as
valuable landmarks for registering pre-treatment virtual (CT) and optical
colonoscopies, to guide navigation towards the anomalies found in pre-treatment
scans. We present a novel generative adversarial network, FoldIt, for
feature-consistent image translation of optical colonoscopy videos to virtual
colonoscopy renderings with haustral fold overlays. A new transitive loss is
introduced in order to leverage ground truth information between haustral fold
annotations and virtual colonoscopy renderings. We demonstrate the
effectiveness of our model on real challenging optical colonoscopy videos as
well as on textured virtual colonoscopy videos with clinician-verified haustral
fold annotations. All code and scripts to reproduce the experiments of this
paper will be made available via our Computational Endoscopy Platform at
https://github.com/nadeemlab/CEP.


Abstract:
  In this paper, the author gives a complete set of simple Yetter-Drinfeld
modules over Suzuki algebra $A_{N\,2n+1}^{\mu\lambda}$ and investigates the
Nichols algebras over those irreducible Yetter-Drinfeld modules. The finite
dimensional Nichols algebras of diagonal type are of Cartan type $A_1$,
$A_1\times A_1$, $A_2$, Super type ${A}_{2}(q;I_2)$ and the Nichols algebra
$\mathfrak{ufo}(8)$. And the involved finite dimensional Nichols algebras of
non-diagonal type are $12$, $4m$ and $m^2$ dimensional. The left three unsolved
cases are set as open problems.


Abstract:
  The study is devoted to geometrically non-linear modelling of viscoplastic
structures with residual stresses. We advocate and develop a special approach
to residual stresses based on the transition between reference configurations.
The finite strain kinematics of the viscoplastic material is modelled by the
multiplicative decomposition of the deformation gradient tensor. Numerical
algorithms originally developed for unstressed materials are extended to
materials with pre-stresses. Owing to the weak invariance of constitutive
equations, the incorporation of pre-stresses happens without additional costs.
Thus, the advocated approach is especially efficient. A novel
experimental/theoretical method for assessment of residual stresses in welded
structures is presented; the method combines advantages of purely experimental
and theoretical approaches. To demonstrate the applicability of the proposed
procedure, we simulate plate welding. As an example we show that the procedure
allows to extrapolate the filed of residual stresses away from the measurement
points. As another example, we address the reduction of weldment-related
residual stresses by mechanical treatment.


Abstract:
  Visual place recognition is one of the essential and challenging problems in
the fields of robotics. In this letter, we for the first time explore the use
of multi-modal fusion of semantic and visual modalities in dynamics-invariant
space to improve place recognition in dynamic environments. We achieve this by
first designing a novel deep learning architecture to generate the static
semantic segmentation and recover the static image directly from the
corresponding dynamic image. We then innovatively leverage the
spatial-pyramid-matching model to encode the static semantic segmentation into
feature vectors. In parallel, the static image is encoded using the popular
Bag-of-words model. On the basis of the above multi-modal features, we finally
measure the similarity between the query image and target landmark by the joint
similarity of their semantic and visual codes. Extensive experiments
demonstrate the effectiveness and robustness of the proposed approach for place
recognition in dynamic environments.


Abstract:
  Many operators have been bullish on the role of millimeter-wave (mmWave)
communications in fifth-generation (5G) mobile broadband because of its
capability of delivering extreme data speeds and capacity. However, mmWave
comes with challenges related to significantly high path loss and
susceptibility to blockage. Particularly, when mmWave communication is applied
to a mobile terminal device, communication can be frequently broken because of
rampant hand blockage. Although a number of mobile phone companies have
suggested configuring multiple sets of antenna modules at different locations
on a mobile phone to circumvent this problem, identifying an optimal antenna
module and a beam pair by simultaneously opening multiple sets of antenna
modules causes the problem of excessive power consumption and device costs. In
this study, a fast antenna and beam switching method termed Fast-ABS is
proposed. In this method, only one antenna module is used for the reception to
predict the best beam of other antenna modules. As such, unmasked antenna
modules and their corresponding beam pairs can be rapidly selected for
switching to avoid the problem of poor quality or disconnection of
communications caused by hand blockage. Thorough analysis and extensive
simulations, which include the derivation of relevant Cram\'{e}r-Rao lower
bounds, show that the performance of Fast-ABS is close to that of an oracle
solution that can instantaneously identify the best beam of other antenna
modules even in complex multipath scenarios. Furthermore, Fast-ABS is
implemented on a software defined radio and integrated into a 5G New Radio
physical layer. Over-the-air experiments reveal that Fast-ABS can achieve
efficient and seamless connectivity despite hand blockage.


Abstract:
  The Yang-Baxter (YB) deformations of Wess-Zumino-Witten (WZW) model on the
Heisenberg Lie group ($H_4$) are examined. We proceed to obtain the
nonequivalent solutions of (modified) classical Yang-Baxter equation ((m)CYBE)
for the $ h_4$ Lie algebra by using its corresponding automorphism
transformation. Then we show that YB deformations of $H_4$ WZW model are split
into ten nonequivalent backgrounds including metric and $B$-field such that
some of the metrics of these backgrounds can be transformed to the metric of
$H_{4}$ WZW model while the antisymmetric $B$-fields are changed. The rest of
the deformed metrics have a different isometric group structure than the
$H_{4}$ WZW model metric. As an interesting result, it is shown that all new
integrable backgrounds of the YB deformed $ H_4$ WZW model are conformally
invariant up to two-loop order. In this way, we obtain the general form of the
dilaton fields satisfying the vanishing beta-function equations of the
corresponding $\sigma$-models.


Abstract:
  We present the statistical methods that have been developed to analyse the
OzDES reverberation mapping sample. To perform this statistical analysis we
have created a suite of customisable simulations that mimic the characteristics
of each source in the OzDES sample. These characteristics include: the
variability in the photometric and spectroscopic lightcurves, the measurement
uncertainties, and the observational cadence. By simulating the sources in the
OzDES sample that contain the CIV emission line, we developed a set of criteria
that rank the reliability of a recovered time lag depending on the agreement
between different recovery methods, the magnitude of the uncertainties, and the
rate at which false positives were found in the simulations. These criteria
were applied to simulated light curves and these results used to estimate the
quality of the resulting Radius-Luminosity relation.We grade the results using
three quality levels (gold, silver and bronze). The input slope of the R-L
relation was recovered within $1\sigma$ for each of the three quality samples,
with the gold standard having the lowest dispersion with a recovered a R-L
relation slope of $0.454\pm 0.016$ with an input slope of 0.47. Future work
will apply these methods to the entire OzDES sample of 771 AGN.


Abstract:
  Optimization for sensor placement has been intensely studied to monitor
complex, large scale systems, whereas one needs to overcome its intractable
nature of the objective function for the optimization. In this study, a fast
algorithm for greedy sensor selection is presented for a linear reduced-ordered
reconstruction under the assumption of correlated noise on the sensor signals.
The presented algorithm accomplishes the maximization of the determinant of the
Fisher information matrix in the linear inverse problem, while this study
firstly shows that the objective function with correlated noise is neither
submodular nor supermodular. Efficient one-rank computations in the greedy
selection procedure are introduced in both of the underdetermined and
oversampled problem. Several numerical experiments show the effectiveness of
the selection algorithm for its accuracy in the estimation of the states of
large dimensional measurement data.


Abstract:
  We study the magnetic moments and transition magnetic moments of $P_c$ and
$P_{cs}$ states in the molecular picture. We first revisit the magnetic moments
of $P_c$ states as the $S$ wave molecular states without coupled channel
effects. The coupled channel effects and the $D$ wave contributions are then
investigated carefully. The coupled channel effects contribute to the change of
$0.1\sim 0.4$ nuclear magneton $\mu_N$ for most cases while the $D$ wave only
induces the variation of less than $0.03 ~\mu_N$. In addition, we obtain the
transition magnetic moments between different $P_c$ states and the related
electromagnetic decay widths of $P_c'\to P_c\gamma$. The magnetic moments of
$P_{cs}$ are much different for the assumption of spin being 1/2 and 3/2. The
study of electromagnetic properties will help us disclose further the structure
of these unconventional states.


Abstract:
  Motivated by questions about $\mathbb{C}_p$-valued Fourier transform on the
locally compact group $(\mathbb{Q}_p^d,+)$, we study invariant norms on the
$p$-adic Schr\"odinger representation of the Heisenberg group. Our main result
is a minimality and rigidity property for norms in a family of invariant norms
parameterized by a Grassmannian. This family is the orbit of the sup norm under
the action of the symplectic group, acting via intertwining operators. We also
prove general fundamental properties of quotients of the universal unitary
completion of cyclic algebraic representations. Combined with the rigidity
property, we are able to show that the completion of the Schr\"odinger
representation in any of the norms in that family satisfies a strong notion of
irreducibility and a strong version of Schur's lemma. Norms that can be formed
as the maximum of a finite number of norms from that family are also studied.
We conclude this paper with a list of open questions.


Abstract:
  We study the photon spectrum line-shape of the $J/\psi \to \gamma\eta_c\to
\gamma X$ decay process in a nonrelativistic effective field theory (EFT)
framework of quantum chromodynamics (QCD). We take into account the finite
width of the $\eta_c$ and include $\mathcal{O}(v^2)$ corrections. We observe
that the photon spectrum line-shape is divergent at large energies due to
polynomially and logarithmically divergent terms, that upon integration over
the photon energies in Dimensional Regularization (DR) produce no contribution
or can be renormalized. We propose to subtract these divergences at the
line-shape level in a manner consistent with the calculation of the width in DR
and $\overline{\hbox{MS}}$ scheme. We analyze CLEO's data with the proposed
subtracted line-shape and find good agreement between the theoretical
prediction and the experimental result.


Abstract:
  In this paper, we examine the impact of cyberattacks in an integrated
transmission and distribution (T&D) power grid model with distributed energy
resource (DER) integration. We adopt the OCTAVE Allegro methodology to identify
critical system assets, enumerate potential threats, analyze, and prioritize
risks for threat scenarios. Based on the analysis, attack strategies and
exploitation scenarios are identified which could lead to system compromise.
Specifically, we investigate the impact of data integrity attacks in
inverted-based solar PV controllers, control signal blocking attacks in
protective switches and breakers, and coordinated monitoring and switching
time-delay attacks.


Abstract:
  We study to what extent the Bitcoin blockchain security permanently depends
on the underlying distribution of cryptocurrency market outcomes. We use daily
blockchain and Bitcoin data for 2014-2019 and employ the ARDL approach. We test
three equilibrium hypotheses: (i) sensitivity of the Bitcoin blockchain to
mining reward; (ii) security outcomes of the Bitcoin blockchain and the
proof-of-work cost; and (iii) the speed of adjustment of the Bitcoin blockchain
security to deviations from the equilibrium path. Our results suggest that the
Bitcoin price and mining rewards are intrinsically linked to Bitcoin security
outcomes. The Bitcoin blockchain security's dependency on mining costs is
geographically differenced - it is more significant for the global mining
leader China than for other world regions. After input or output price shocks,
the Bitcoin blockchain security reverts to its equilibrium security level.


Abstract:
  We use conformal supergravity techniques to study four-derivative corrections
in four-dimensional gauged supergravity. We show that the four-derivative
Lagrangian for the propagating degrees of freedom of the $\mathcal{N}=2$
gravity multiplet is determined by two real dimensionless constants. We
demonstrate that all solutions of the two-derivative equations of motion in the
supergravity theory also solve the four-derivative equations of motion. These
results are then applied to explicitly calculate the regularized on-shell
action for any asymptotically locally AdS$_4$ solution of the two-derivative
equations of motion. The four-derivative terms in the supergravity Lagrangian
modify the entropy and other thermodynamic observables for the black hole
solutions of the theory. We calculate these corrections explicitly and
demonstrate that the quantum statistical relation holds for general stationary
black holes in the presence of the four-derivative corrections. Employing an
embedding of this supergravity model in M-theory we show how to use
supersymmetric localization results in the holographically dual
three-dimensional SCFT to determine the unknown coefficients in the
four-derivative supergravity action. This in turn leads to new detailed results
for the first subleading $N^{\frac{1}{2}}$ correction to the large $N$
partition function of a class of three-dimensional SCFTs on compact Euclidean
manifolds. In addition, we calculate explicitly the first subleading correction
to the Bekenstein-Hawking entropy of asymptotically AdS$_4$ black holes in
M-theory. We also discuss how to add matter multiplets to the supergravity
theory in the presence of four-derivative terms and to generalize some of these
results to six- and higher-derivative supergravity.


Abstract:
  Armchair phosphorene nanoribbons (APNRs) are known to be semiconductors with
an indirect bandgap. Here, we propose to introduce new states in the gap of
APNRs by creating a periodic structure of vacancies (antidots). Based on the
tight-binding model, we show that a periodic array of vacancies or nanopores
leads to the formation of an impurity band inside the gap region. We first
present an analytical expression for the dispersion relation of an impurity
band induced by hybridization of bound states associated with each single
vacancy defect. Then, we increase the size of vacancy defects to include a
bunch of atoms and theoretically investigate the effect of nanopores size and
their spacing on electronic band structure, carrier transmission function, and
thermoelectric properties. Our analysis of the power generation rate and
thermoelectric efficiency of these structures reveals that an ANPR can be used
as a superb thermoelectric power generation module.


Abstract:
  Though Adams and Hardy-Adams inequalities can be extended to general
symmetric spaces of noncompact type fairly straightforwardly by following
closely the systematic approach developed in our early works on real and
complex hyperbolic spaces, higher order Poincar\'e-Sobolev and
Hardy-Sobolev-Maz'ya inequalities are more difficult to establish. The main
purpose of this goal is to establish the Poincar\'e-Sobolev and
Hardy-Sobolev-Maz'ya inequalities on quaternionic hyperbolic spaces and the
Cayley hyperbolic plane. A crucial part of our work is to establish appropriate
factorization theorems on these spaces which are of their independent
interests. To this end, we need to identify and introduce the ``Quaternionic
Geller's operators" and ``Octonionic Geller's operators" which have been absent
on these spaces. Combining the factorization theorems and the Geller type
operators with the Helgason-Fourier analysis on symmetric spaces, the precise
heat and Bessel-Green-Riesz kernel estimates and the Kunze-Stein phenomenon for
connected real simple groups of real rank one with finite center, we succeed to
establish the higher order Poincar\'e-Sobolev and Hardy-Sobolev-Maz'ya
inequalities on quaternionic hyperbolic spaces and the Cayley hyperbolic plane.
The kernel estimates required to prove these inequalities are also sufficient
for us to establish, as a byproduct, the Adams and Hardy-Adams inequalities on
these spaces. This paper, together with our earlier works, completes our study
of the factorization theorems, higher order Poincar\'e-Sobolev,
Hardy-Sobolev-Maz'ya, Adams and Hardy-Adams inequalities on all rank one
symmetric spaces of noncompact type.


Abstract:
  Background: There is growing evidence that social and behavioral determinants
of health (SBDH) play a substantial effect in a wide range of health outcomes.
Electronic health records (EHRs) have been widely employed to conduct
observational studies in the age of artificial intelligence (AI). However,
there has been little research into how to make the most of SBDH information
from EHRs. Methods: A systematic search was conducted in six databases to find
relevant peer-reviewed publications that had recently been published. Relevance
was determined by screening and evaluating the articles. Based on selected
relevant studies, a methodological analysis of AI algorithms leveraging SBDH
information in EHR data was provided. Results: Our synthesis was driven by an
analysis of SBDH categories, the relationship between SBDH and
healthcare-related statuses, and several NLP approaches for extracting SDOH
from clinical literature. Discussion: The associations between SBDH and health
outcomes are complicated and diverse; several pathways may be involved. Using
Natural Language Processing (NLP) technology to support the extraction of SBDH
and other clinical ideas simplifies the identification and extraction of
essential concepts from clinical data, efficiently unlocks unstructured data,
and aids in the resolution of unstructured data-related issues. Conclusion:
Despite known associations between SBDH and disease, SBDH factors are rarely
investigated as interventions to improve patient outcomes. Gaining knowledge
about SBDH and how SBDH data can be collected from EHRs using NLP approaches
and predictive models improves the chances of influencing health policy change
for patient wellness, and ultimately promoting health and health equity.
  Keywords: Social and Behavioral Determinants of Health, Artificial
Intelligence, Electronic Health Records, Natural Language Processing,
Predictive Model


Abstract:
  In this paper we introduce Besov-type spaces with variable smoothness and
integrability. We show that these spaces are characterized by the $\varphi
$-transforms in appropriate sequence spaces and we obtain atomic decompositions
for these spaces. Moreover the Sobolev embeddings for these function spaces are
obtained.


Abstract:
  The professional services industry (legal, accounting, consulting,
architectural services{\ldots}) employs an important share of the active
population in mature countries. However, after decades of undisputed growth,
the sector appears to be at a turning point in certain geographies. This
article therefore proposes a simple framework to help diagnose where growth
opportunities (if any) may lie.When applied to the US economic context, the
model indicates that at a macro-economic national level the sector should
stall, which concurs with the trend observed over the past decade. However, it
also highlights that a few industrial sectors (e.g. the US beverage industry)
still offer pockets of growth for a variety of professional expertises.
Replicating and fine-tuning those findings could be interesting for
practitioners to steer their marketing and business development efforts. On the
other hand, the quantitative framework presented in this study could pave the
way for future research in the academic community.


Abstract:
  Two novel properties, unique for semiconductors: a negative electron affinity
[1-2], and a high p-type surface electrical conductivity [3-4], were discovered
in diamond at the end of the last century. Both properties appear when the
diamond surface is hydrogenated. A natural question arises: is the influence of
the surface hydrogen on diamond limited only to the electrical properties?
Here, we report the first observation of a transparency peak at 1328 cm-1 in IR
absorption of hydrogen-terminated pure (undoped) nanodiamonds. This new optical
property is ascribed to Fano-type destructive interference between zone-center
phonons and free carriers (holes) appearing in the near-surface layer of
hydrogenated nanodiamond. Our work opens the way to exploring the physics of
electron-phonon coupling in undoped diamonds and promises the application of
the H-terminated nanodiamonds as a new optical material with an induced
transparency in IR optical range.


Abstract:
  In this paper homological ideals associated to some Nakayama algebras are
characterized and enumerated via integer specializations of some suitable
Brauer configuration algebras. Besides, it is shown how the number of such
homological ideals can be connected with the categorification process of
Fibonacci numbers defined by Ringel and Fahr.


Abstract:
  Topology optimization offers optimal material layouts, enabling automation in
the design of devices. Given the recent advances in computer technology and
additive manufacturing, topology optimization is increasingly being used to
design complex porous structures, for example, microfluidic devices. For the
flow of fluids in such miniature-sized porous structures, viscous shearing
stress will be significant. But the Darcy model -- the most popular
mathematical model describing the flow of a single-phase incompressible fluid
in rigid porous media -- neglects the internal friction arising from viscous
shearing stress. We will therefore develop a material design framework under
the topology optimization based on the Darcy-Brinkman model -- a mathematical
model for the flow of fluids through porous media that accounts for internal
friction besides the drag considered in the Darcy model. The proposed framework
uses the total rate of mechanical dissipation -- a physical quantity -- for the
objective function. To understand the effect of viscous shearing stress on the
design, we will compare the optimal material layouts provided by the Darcy and
Darcy-Brinkman models under topology optimization. In particular, we show,
using analytical solutions corroborated by numerical simulations, that the
optimal material layouts are identical for the class of problems exhibiting
axisymmetry, for which viscous shearing stress vanishes. These analytical
solutions will be valuable to check the veracity of numerical simulators. For
those problems with dominant viscous shearing stresses (e.g., flow in a
backward-facing step domain), we show, using numerical solutions, that the
optimal material layouts under the Darcy and Darcy-Brinkman models are very
different. Also, the associated solution fields (i.e., velocity and pressure)
are qualitatively different under their respective optimal layouts for these
two models.


Abstract:
  We establish upper bounds for the $L^p$-quantization error, p in (1, 2+d),
induced by the recursive Markovian quantization of a d-dimensional diffusion
discretized via the Euler scheme. We introduce a hybrid recursive quantization
scheme, easier to implement in the high-dimensional framework, and establish
upper bounds to the corresponding $L^p$-quantization error. To take advantage
of these extensions, we propose a time discretization scheme and a recursive
quantization-based discretization scheme associated to a reflected Backward
Stochastic Differential Equation and estimate $L^p$-error bounds induced by the
space approximation. We will explain how to numerically compute the solution of
the reflected BSDE relying on the recursive quantization and compare it to
other types of quantization.


Abstract:
  We present Calyx, a new intermediate language (IL) for compiling high-level
programs into hardware designs. Calyx combines a hardware-like structural
language with a software-like control flow representation with loops and
conditionals. This split representation enables a new class of hardware-focused
optimizations that require both structural and control flow information which
are crucial for high-level programming models for hardware design. The Calyx
compiler lowers control flow constructs using finite-state machines and
generates synthesizable hardware descriptions.
  We have implemented Calyx in an optimizing compiler that translates
high-level programs to hardware. We demonstrate Calyx using two DSL-to-RTL
compilers, a systolic array generator and one for a recent imperative
accelerator language, and compare them to equivalent designs generated using
high-level synthesis (HLS). The systolic arrays are $4.6\times$ faster and
$1.1\times$ larger on average than HLS implementations, and the HLS-like
imperative language compiler is within a few factors of a highly optimized
commercial HLS toolchain. We also describe three optimizations implemented in
the Calyx compiler.


Abstract:
  The k-distance strong shortest path union cover of a graph is the minimum
cardinality among all strong shortest path union cover at distance k of G. In
this paper we determine the 2-strong shortest path union cover for certain
graphs, also we prove that the k-strong shortest path union cover problem, in
general, is NP-complete.


Abstract:
  This paper aims at investigating the influence of space-time curvature on the
uncertainty relation. In particular, relying on previous findings, we assume
the quantum wave function to be confined to a geodesic ball on a given
space-like hypersurface whose radius is a measure of the position uncertainty.
On the other hand, we concurrently work out a viable physical definition of the
momentum operator and its standard deviation in the non-relativistic limit of
the 3+1 formalism. Finally, we evaluate the uncertainty relation which to
second order depends on the Ricci scalar of the effective 3-metric and the
corresponding covariant derivative of the shift vector. For the sake of
illustration, we apply our general result to a number of examples arising in
the context of both general relativity and extended theories of gravity.


Abstract:
  Due to their unique persuasive power, language-capable robots must be able to
both act in line with human moral norms and clearly and appropriately
communicate those norms. These requirements are complicated by the possibility
that humans may ascribe blame differently to humans and robots. In this work,
we explore how robots should communicate in moral advising scenarios, in which
the norms they are expected to follow (in a moral dilemma scenario) may be
different from those their advisees are expected to follow. Our results suggest
that, in fact, both humans and robots are judged more positively when they
provide the advice that favors the common good over an individual's life. These
results raise critical new questions regarding people's moral responses to
robots and the design of autonomous moral agents.


Abstract:
  $\textit{RigL}$, a sparse training algorithm, claims to directly train sparse
networks that match or exceed the performance of existing dense-to-sparse
training techniques (such as pruning) for a fixed parameter count and compute
budget. We implement $\textit{RigL}$ from scratch in Pytorch and reproduce its
performance on CIFAR-10 within 0.1% of the reported value. On both
CIFAR-10/100, the central claim holds -- given a fixed training budget,
$\textit{RigL}$ surpasses existing dynamic-sparse training methods over a range
of target sparsities. By training longer, the performance can match or exceed
iterative pruning, while consuming constant FLOPs throughout training. We also
show that there is little benefit in tuning $\textit{RigL}$'s hyper-parameters
for every sparsity, initialization pair -- the reference choice of
hyperparameters is often close to optimal performance. Going beyond the
original paper, we find that the optimal initialization scheme depends on the
training constraint. While the Erdos-Renyi-Kernel distribution outperforms the
Uniform distribution for a fixed parameter count, for a fixed FLOP count, the
latter performs better. Finally, redistributing layer-wise sparsity while
training can bridge the performance gap between the two initialization schemes,
but increases computational cost.


Abstract:
  Current formulations of nonequilibrium thermodynamics of open chemical
reaction networks only consider chemostats as free-energy sources sustaining
nonequilibrium behaviours. Here, we extend the theory to include incoherent
light as a source of free energy. We do so by relying on a local equilibrium
assumption to derive the chemical potential of photons relative to the system
they interact with. This allows us to identify the thermodynamic potential and
the thermodynamic forces driving light-reacting chemical systems out of
equilibrium. We use this framework to treat two paradigmatic photochemical
mechanisms describing light-induced unimolecular reactions -- namely the
adiabatic and diabatic mechanisms -- and highlight the different thermodynamics
they lead to. Furthermore, using a thermodynamic coarse-graining procedure, we
express our findings in terms of commonly measured experimental quantities such
as quantum yields.


Abstract:
  Combining the $b\to s\mu^+\mu^-$ anomaly and dark matter observables, we
study the capability of LHC to test dark matter, $Z^{\prime}$, and vector-like
quark. We focus on a local $U(1)_{L_\mu-L_\tau}$ model with a vector-like
$SU(2)_L$ doublet quark $Q$ and a complex singlet scalar whose lightest
component $X_I$ is a candidate of dark matter. After imposing relevant
constraints, we find that the $b\to s\mu^+\mu^-$ anomaly and the relic
abundance of dark matter favor $m_{X_I}< 350$ GeV and $m_{Z^{\prime}}< 450$ GeV
for $m_Q<$ 2 TeV and $m_{X_R}<$ 2 TeV (the heavy partner of $m_{X_I}$). The
current searches for jets and missing transverse momentum at the LHC sizably
reduce the mass ranges of the vector-like quark, and $m_Q$ is required to be
larger than 1.7 TeV. Finally, we discuss the possibility of probing these new
particles at the high luminosity LHC via the QCD process $pp \to D\bar{D}$ or
$pp \to U\bar{U}$ followed by the decay $D\to s (b) Z'X_I$ or $U \to u (c) Z'
X_I$ and then $Z'\to\mu^+\mu^-$. Taking a benchmark point of $m_Q$=1.93 TeV,
$m_{Z^\prime}=170$ GeV, and $m_{X_I}=$ 145 GeV, we perform a detailed Monte
Carlo simulation, and find that such benchmark point can be accessible at the
14 TeV LHC with an integrated luminosity 3000 fb$^{-1}$.


Abstract:
  We prove a proposition that connects constant-PAF sequences and the
corresponding Legendre pairs with integer PSD values. We show how to determine
explicitly the complete spectrum of the $(\ell/3)$-rd value of the discrete
Fourier transform for Legendre pairs of lengths $\ell \equiv 0 \, (\mod 3)$.
This is accomplished by two new algorithms based on number-theoretic arguments.
As an application, we prove that Legendre pairs of the open lengths 117, 129,
133, and 147 exist by finding Legendre pairs of these lengths with a multiplier
group of order at least 3. As a consequence, 85, 87, 115, 145, 159, 161, 169,
175, 177, 185, 187, 195 are the twelve integers in the range < 200 for which
the question of existence of Legendre pairs remains unsolved.


Abstract:
  This note is an erratum to the paper "Tautological classes on moduli spaces
of hyper-K\"ahler manifolds." Thorsten Beckman and Mirko Mauri have pointed to
us a gap in the proof of \cite[Theorem 8.2.1]{Duke}. We do not know how to
correct the proof. We can only recover a partial statement. This gap affects
the proof of one of the two main results of \cite{Duke}, we explain how to
correct it.


Abstract:
  The determination of absolute and relative distances of molecular clouds
along the line-of-sight towards the central molecular zone (CMZ) is crucial to
infer its orbital structure, dynamics, and to understand star formation in the
clouds. Recent results by Zoccali et al. 2021 suggest that the G0.253 + 0.016
cloud (the Brick) does not belong to the CMZ. This motivated us to cross check
their results computing the absolute and relative distance to the Brick and
also to other two molecular clouds (the 50 km/s, and the 20 km/s clouds), and
discuss their CMZ membership. We used the colour magnitude diagrams $K_s$ vs.
$H-K_s$ to compare stars detected towards the target clouds with stars detected
towards three reference regions in the nuclear stellar disc (NSD) and the
Galactic bulge. We used red clump (RC) stars to estimate the distance to each
region. We obtained that all the clouds present a double RC feature. Such a
double RC has been reported in previous work for the NSD, but not for the bulge
adjacent to it. We exclude the possibility that the different RC features are
located at significantly different distances. The obtained absolute and
relative distances are compatible with the Galactic centre distance ($\sim8$
kpc).


Abstract:
  We introduce the Python package, FDApy, as an implementation of functional
data. This package provide modules for the analysis of such data. It includes
classes for different dimensional data as well as irregularly sampled
functional data. A simulation toolbox is also provided. It might be used to
simulate different clusters of functional data. Some methodologies to handle
these data are implemented, such as dimension reduction and clustering. New
methods can be easily added. The package is publicly available on the Python
Package Index and Github.


Abstract:
  In MRI, deep neural networks have been proposed to reconstruct diffusion
model parameters. However, the inputs of the networks were designed for a
specific diffusion gradient scheme (i.e., diffusion gradient directions and
numbers) and a specific b-value that are the same as the training data. In this
study, a new deep neural network, referred to as DIFFnet, is developed to
function as a generalized reconstruction tool of the diffusion-weighted signals
for various gradient schemes and b-values. For generalization, diffusion
signals are normalized in a q-space and then projected and quantized, producing
a matrix (Qmatrix) as an input for the network. To demonstrate the validity of
this approach, DIFFnet is evaluated for diffusion tensor imaging (DIFFnetDTI)
and for neurite orientation dispersion and density imaging (DIFFnetNODDI). In
each model, two datasets with different gradient schemes and b-values are
tested. The results demonstrate accurate reconstruction of the diffusion
parameters at substantially reduced processing time (approximately 8.7 times
and 2240 times faster processing time than conventional methods in DTI and
NODDI, respectively; less than 4% mean normalized root-mean-square errors
(NRMSE) in DTI and less than 8% in NODDI). The generalization capability of the
networks was further validated using reduced numbers of diffusion signals from
the datasets. Different from previously proposed deep neural networks, DIFFnet
does not require any specific gradient scheme and b-value for its input. As a
result, it can be adopted as an online reconstruction tool for various complex
diffusion imaging.


Abstract:
  The construction of HNN-extensions of involutive Hom-associative algebras and
involutive Hom-Lie algebras is described. Then, as an application of
HNN-extension, by using the validity of Poincar\'e-Birkhoff-Witt theorem for
involutive Hom-Lie algebras, we provide an embedding theorem.


Abstract:
  The optical binding of many particles has great potential to achieve the
wide-area formation of a "crystal" of small materials. Unlike conventional
optical binding, where the whole assembly of targeted particles is irradiated
with light, if one can indirectly manipulate remote particles using a single
trapped particle through optical binding, the degrees of freedom to create
ordered structures will be greatly enhanced. In this Letter, we theoretically
investigate the dynamics of the assembly of gold nanoparticles that is
manipulated using a single particle trapped by a focused laser. As a result, we
demonstrate that the spin--orbit coupling and angular momentum generation of
light via scattering induce the assembly and rotational motion of particles
through indirect optical force. This result opens the possibility of creating
ordered structures with a wide area and manipulating them, controlling local
properties using scanning laser beams.


Abstract:
  Adaptive charging can charge electric vehicles (EVs) at scale cost
effectively, despite the uncertainty in EV arrivals. We formulate adaptive EV
charging as a feasibility problem that meets all EVs' energy demands before
their deadlines while satisfying constraints in charging rate and total
charging power. We propose an online algorithm, smoothed least-laxity-first
(sLLF), that decides the current charging rates without the knowledge of future
arrivals and demands. We characterize the performance of the sLLF algorithm
analytically and numerically. Numerical experiments with real-world data show
that it has a significantly higher rate of feasible EV charging than several
other existing EV charging algorithms. Resource augmentation framework is
employed to assess the feasibility condition of the algorithm. The assessment
shows that the sLLF algorithm achieves perfect feasibility with only a 0.07
increase in resources.


Abstract:
  Nanoelectromechanical resonators have been successfully used for a variety of
sensing applications. Their extreme resolution comes from their small size at
the cost of low capture area, making the "needle in a haystack" issue acute.
This leads to poor instrument sensitivity and long analysis time. Moreover,
electrical transductions are limited in frequency, which limits the achievable
mechanical bandwidth again limiting throughput. Multiplexing a large number of
high-frequency resonators appears as a solution, but this is complex with
electrical transductions. We propose here a route to solve these issues, with a
multiplexing scheme for very high frequency optomechanical resonators. We
demonstrate the simultaneous frequency measurement of three silicon microdisks
resonators fabricated through a Very Large Scale Integration process. The
readout architecture is simple and does not degrade the sensing resolutions.
This paves the way towards the realization of sensors for multi-parametric
analysis, extremely low limit of detection and response time.


Abstract:
  A surprising and unexpected phenomenon observed during Cassini's Grand Finale
was the spacecraft charging to positive potentials in Saturn's ionosphere.
Here, the ionospheric plasma was depleted of free electrons with negatively
charged ions and dust accumulating up to over 95 % of the negative charge
density. To further understand the spacecraft-plasma interaction, we perform a
three dimensional Particle-In-Cell study of a model Cassini spacecraft immersed
in plasma representative of Saturn's ionosphere. The simulations reveal complex
interaction features such as electron wings and a highly structured wake
containing spacecraft-scale vortices. The results show how a large negative ion
concentration combined with a large negative to positive ion mass ratio is able
to drive the spacecraft to the observed positive potentials. Despite the high
electron depletions, the electron properties are found as a significant
controlling factor for the spacecraft potential together with the magnetic
field orientation which induces a potential gradient directed across Cassini's
asymmetric body. This study reveals the global spacecraft interaction
experienced by Cassini during the Grand Finale and how this is influenced by
the unexpected negative ion and dust populations.


Abstract:
  The Vertical Shear Instability is an axisymmetric effect suggested to drive
turbulence in the magnetically inactive zones of protoplanetary accretion
disks. Here we examine its physical mechanism in analytically tractable
``minimal models" in three settings that include a uniform density fluid, a
stratified atmosphere, and a shearing-box section of a protoplanetary disk.
Each of these analyses show that the vertical shear instability's essence is
similar to the slantwise convective symmetric instability in the mid-latitude
Earth atmosphere, in the presence of vertical shear of the baroclinic jet
stream, as well as mixing in the top layers of the Gulf Stream. We show that in
order to obtain instability the fluid parcels' slope should exceed the slope of
the mean absolute momentum in the disk radial-vertical plane. We provide a
detailed and mutually self-consistent physical explanation from three
perspectives: in terms of angular momentum conservation, as a dynamical
interplay between a fluid's radial and azimuthal vorticity components, and from
an energy perspective involving a generalized Solberg-H{\o}iland Rayleigh
condition. Furthermore, we explain why anelastic dynamics yield oscillatory
unstable modes and isolate the oscillation mechanism from the instability one.


Abstract:
  We propose a novel, efficient, modular and scalable framework for content
based visual media retrieval systems by leveraging the power of Deep Learning
which is flexible to work both for images and videos conjointly and we also
introduce an efficient comparison and filtering metric for retrieval. We put
forward our findings from critical performance tests comparing our method to
the predominant conventional approach to demonstrate the feasibility and
efficiency of the proposed solution with best practices, possible improvements
that may further augment the ability of retrieval architectures.


Abstract:
  Recently, the action growth rate of a variety of four-dimensional regular
magnetic black holes in F frame is obtained in [1]. Here, we study the action
growth rate of a four-dimensional regular electric black hole in P frame that
is the Legendre transformation of F frame. We also investigate the action
growth rates of the Wheeler-De Witt patch for such black hole configurations at
the late time and examine the Lloyd bound on the rate of quantum computation.
We show that although the form of the Lloyd bound formula remains unaltered,
the energy modifies due to a non-vanishing trace of the energy-momentum tensor
and some extra terms may appear in the total growth action. We also investigate
the asymptotic behavior of complexity in two conjectures for static and
rotating regular black holes.


Abstract:
  The research field of adversarial machine learning witnessed a significant
interest in the last few years. A machine learner or model is secure if it can
deliver main objectives with acceptable accuracy, efficiency, etc. while at the
same time, it can resist different types and/or attempts of adversarial
attacks. This paper focuses on studying aspects and research trends in
adversarial machine learning specifically in text analysis and generation. The
paper summarizes main research trends in the field such as GAN algorithms,
models, types of attacks, and defense against those attacks.


Abstract:
  This paper introduces our position on the critical issue of bias that
recently appeared in AI applications. Specifically, we discuss the combination
of current technologies used in AI applications i.e., Machine Learning and
Knowledge Graphs, and point to their involvement in (de)biased applications of
the C4I domain. Although this is a wider problem that currently emerges from
different application domains, bias appears more critical in C4I than in others
due to its security-related nature. While proposing certain actions to be taken
towards debiasing C4I applications, we acknowledge the immature aspect of this
topic within the Knowledge Graph and Semantic Web communities.


Abstract:
  Machine learning has proved to be a useful tool for extracting knowledge from
scientific data in numerous research fields, including astrophysics, genomics,
and molecular dynamics. Often, data sets from these research areas need to be
processed in distributed platforms due to their magnitude. This can be done
using one of the various distributed machine learning libraries available. One
of these libraries is dislib, a distributed machine learning library for Python
especially designed to process large scale data sets on HPC clusters, which
makes dislib an ideal candidate for analyzing scientific data. However,
dislib's main distributed data structure, called Dataset, has some limitations,
including poor performance in certain operations and low flexibility and
usability. In this paper, we propose a novel distributed data structure for
dislib, called ds-array, that addresses dislib's main limitations in data
management. Ds-arrays simplify distributed data management in dislib by
exposing a NumPy-like API, provide more flexibility, and reduce the
computational complexity of some operations. This results in performance
improvements of up to two orders of magnitude over Datasets, while also greatly
improving scalability and usability.


Abstract:
  Derivatives of equations of motion describing the rigid body dynamics are
becoming increasingly relevant for the robotics community and find many
applications in design and control of robotic systems. Controlling robots, and
multibody systems comprising elastic components in particular, not only
requires smooth trajectories but also the time derivatives of the control
forces/torques, hence of the equations of motion (EOM). This paper presents
novel nth order time derivatives of the EOM in both closed and recursive forms.
While the former provides a direct insight into the structure of these
derivatives,the latter leads to their highly efficient implementation for large
degree of freedom robotic system.


Abstract:
  The integer quantum Hall transition (IQHT) is one of the most mysterious
members of the family of Anderson transitions. Since the 1980s, the scaling
behavior near the IQHT has been vigorously studied in experiments and numerical
simulations. Despite all efforts, it is notoriously difficult to pin down the
precise values of critical exponents, which seem to vary with model details and
thus challenge the principle of universality. Recently, M.
Zirnbauer\citep{Zirnbauer2019} [Nucl. Phys. B \textbf{941}, 458 (2019)] has
conjectured a conformal field theory for the transition, in which linear terms
in the beta-functions vanish, leading to a very slow flow in the fixed point's
vicinity which we term marginal scaling. In this work, we provide numerical
evidence for such a scenario by using extensive simulations of various network
models of the IQHT at unprecedented length scales. At criticality, we show that
the finite-size scaling of the disorder averaged longitudinal Landauer
conductance is consistent with its recently predicted fixed-point value and a
third-order expansion of RG beta functions. In the future, our numerical
findings can be checked with analytical results from the conformal field
theory. Away from criticality we describe a mechanism that could account for
the emergence of an \emph{effective} critical exponents $\nu_\mathrm{eff}$,
which is necessarily dependent on the parameters of the model. We further
support this idea by numerical determination of $\nu_\mathrm{eff}$ in suitably
chosen models.


Abstract:
  We expand upon the simulation study of Setodji et al. (2017) which compared
three promising balancing methods when assessing the average treatment effect
on the treated for binary treatments: generalized boosted models (GBM),
covariate-balancing propensity scores (CBPS), and entropy balance (EB). The
study showed that GBM can outperform CBPS and EB when there are likely to be
non-linear associations in both the treatment assignment and outcome models and
CBPS and EB are fine-tuned to obtain balance only on first order moments. We
explore the potential benefit of using higher-order moments in the balancing
conditions for CBPS and EB. Our findings showcase that CBPS and EB should, by
default, include higher order moments and that focusing only on first moments
can result in substantial bias in both CBPS and EB estimated treatment effect
estimates that could be avoided by the use of higher moments.


Abstract:
  Simultaneous Localization and Mapping (SLAM) is one of the key robotics tasks
as it tackles simultaneous mapping of the unknown environment defined by
multiple landmark positions and localization of the unknown pose (i.e.,
attitude and position) of the robot in three-dimensional (3D) space. The true
SLAM problem is modeled on the Lie group of $\mathbb{SLAM}_{n}\left(3\right)$,
and its true dynamics rely on angular and translational velocities. This paper
proposes a novel geometric nonlinear stochastic estimator algorithm for SLAM on
$\mathbb{SLAM}_{n}\left(3\right)$ that precisely mimics the nonlinear motion
dynamics of the true SLAM problem. Unlike existing solutions, the proposed
stochastic filter takes into account unknown constant bias and noise attached
to the velocity measurements. The proposed nonlinear stochastic estimator on
manifold is guaranteed to produce good results provided with the measurements
of angular velocities, translational velocities, landmarks, and inertial
measurement unit (IMU). Simulation and experimental results reflect the ability
of the proposed filter to successfully estimate the six-degrees-of-freedom (6
DoF) robot's pose and landmark positions. Keywords: Simultaneous Localization
and Mapping, nonlinear stochastic observer for SLAM, stochastic differential
equations, pose estimator, position, attitude, Brownian motion process,
inertial measurement unit, landmarks, features, SDE, SO(3), SE(3), SLAM.


Abstract:
  In this paper, thanks to the generalizations of the dual spaces of the
Hardy-amalgam spaces $\mathcal H^{(q,p)}$ and
$\mathcal{H}_{\mathrm{loc}}^{(q,p)}$ for $0<q\leq1$ and $q\leq p<\infty$,
obtained in our earlier paper, we prove that the inclusion of $\mathcal
H^{(1,p)}$ in $(L^1,\ell^p)$ for $1\leq p<\infty$ is strict, and more
generally, the one of $\mathcal H^{(q,p)}$ in
$\mathcal{H}_{\mathrm{loc}}^{(q,p)}$ for $0<q\leq1$ and $q\leq p<\infty$.
Moreover, as other applications, we obtain results of boundedness of
Calder\'on-Zygmund and convolution operators, generalizing those known in the
context of the spaces $\mathcal H^1$ and $BMO(\mathbb{R}^d)$.


Abstract:
  We analyse backward Euler time stepping schemes for the primal DPG
formulation of a class of parabolic problems. Optimal error estimates are shown
in the natural norm and in the $L^2$ norm of the field variable. For the heat
equation the solution of our primal DPG formulation equals the solution of a
standard Galerkin scheme and, thus, optimal error bounds are found in the
literature. In the presence of advection and reaction terms, however, the
latter identity is not valid anymore and the analysis of optimal error bounds
requires to resort to elliptic projection operators. It is essential that these
operators be projections with respect to the spatial part of the PDE, as in
standard Galerkin schemes, and not with respect to the full PDE at a time step,
as done previously.


Abstract:
  Visual attention in Visual Question Answering (VQA) targets at locating the
right image regions regarding the answer prediction, offering a powerful
technique to promote multi-modal understanding. However, recent studies have
pointed out that the highlighted image regions from the visual attention are
often irrelevant to the given question and answer, leading to model confusion
for correct visual reasoning. To tackle this problem, existing methods mostly
resort to aligning the visual attention weights with human attentions.
Nevertheless, gathering such human data is laborious and expensive, making it
burdensome to adapt well-developed models across datasets. To address this
issue, in this paper, we devise a novel visual attention regularization
approach, namely AttReg, for better visual grounding in VQA. Specifically,
AttReg firstly identifies the image regions which are essential for question
answering yet unexpectedly ignored (i.e., assigned with low attention weights)
by the backbone model. And then a mask-guided learning scheme is leveraged to
regularize the visual attention to focus more on these ignored key regions. The
proposed method is very flexible and model-agnostic, which can be integrated
into most visual attention-based VQA models and require no human attention
supervision. Extensive experiments over three benchmark datasets, i.e., VQA-CP
v2, VQA-CP v1, and VQA v2, have been conducted to evaluate the effectiveness of
AttReg. As a by-product, when incorporating AttReg into the strong baseline
LMH, our approach can achieve a new state-of-the-art accuracy of 60.00% with an
absolute performance gain of 7.01% on the VQA-CP v2 benchmark dataset...


Abstract:
  Understanding assembly instruction has the potential to enhance the robot s
task planning ability and enables advanced robotic applications. To recognize
the key components from the 2D assembly instruction image, We mainly focus on
segmenting the speech bubble area, which contains lots of information about
instructions. For this, We applied Cascade Mask R-CNN and developed a
context-aware data augmentation scheme for speech bubble segmentation, which
randomly combines images cuts by considering the context of assembly
instructions. We showed that the proposed augmentation scheme achieves a better
segmentation performance compared to the existing augmentation algorithm by
increasing the diversity of trainable data while considering the distribution
of components locations. Also, we showed that deep learning can be useful to
understand assembly instruction by detecting the essential objects in the
assembly instruction, such as tools and parts.


Abstract:
  We formulate an alternative approach to describing Ehresmann semigroups by
means of left and right \'etale actions of a meet semilattice on a category. We
also characterize the Ehresmann semigroups that arise as the set of all subsets
of a finite category. As applications, we prove that every restriction
semigroup can be nicely embedded into a restriction semigroup constructed from
a category, and we describe when a restriction semigroup can be nicely embedded
into an inverse semigroup.


Abstract:
  We follow up on the idea of Lars Arge to rephrase the Reduce and Apply
procedures of Binary Decision Diagrams (BDDs) as iterative I/O-efficient
algorithms. We identify multiple avenues to simplify and improve the
performance of his proposed algorithms. Furthermore, we extend the technique to
other common BDD operations, many of which are not derivable using Apply
operations alone, and we provide asymptotic improvements for the procedures
that can be derived using Apply.
  These algorithms are implemented in a new BDD package, named Adiar. We see
very promising results when comparing the performance of Adiar with
conventional BDD package that use recursive depth-first algorithms. For
instances larger than 9.5 GiB, our algorithms, in parts using the disk, are
1.47 to 3.69 times slower compared to CUDD and Sylvan, exclusively using main
memory. Yet, our proposed techniques are able to obtain this performance at a
fraction of the main memory needed by conventional BDD packages to function.
Furthermore, with Adiar we are able to manipulate BDDs that outgrow main memory
and so surpass the limits of other BDD packages.


Abstract:
  This paper addresses the problem of optimal control using search trees. We
start by considering multi-armed bandit problems with continuous action spaces
and propose LD-HOO, a limited depth variant of the hierarchical optimistic
optimization (HOO) algorithm. We provide a regret analysis for LD-HOO and show
that, asymptotically, our algorithm exhibits the same cumulative regret as the
original HOO while being faster and more memory efficient. We then propose a
Monte Carlo tree search algorithm based on LD-HOO for optimal control problems
and illustrate the resulting approach's application in several optimal control
problems.


Abstract:
  Trustworthy deployment of ML models requires a proper measure of uncertainty,
especially in safety-critical applications. We focus on uncertainty
quantification (UQ) for classification problems via two avenues -- prediction
sets using conformal prediction and calibration of probabilistic predictors by
post-hoc binning -- since these possess distribution-free guarantees for i.i.d.
data. Two common ways of generalizing beyond the i.i.d. setting include
handling covariate and label shift. Within the context of distribution-free UQ,
the former has already received attention, but not the latter. It is known that
label shift hurts prediction, and we first argue that it also hurts UQ, by
showing degradation in coverage and calibration. Piggybacking on recent
progress in addressing label shift (for better prediction), we examine the
right way to achieve UQ by reweighting the aforementioned conformal and
calibration procedures whenever some unlabeled data from the target
distribution is available. We examine these techniques theoretically in a
distribution-free framework and demonstrate their excellent practical
performance.


Abstract:
  TeraHertz (THz) communications are envisioned as a promising technology,
owing to its unprecedented multi-GHz bandwidth. One fundamental challenge when
moving to new spectrum is to understand the science of radio propagation and
develop an accurate channel model. In this paper, a wideband channel
measurement campaign between 130 GHz and 143 GHz is investigated in a typical
meeting room. Directional antennas are utilized and rotated for resolving the
multi-path components (MPCs) in the angular domain. With careful system
calibration that eliminates system errors and antenna effects, a realistic
power delay profile is developed. Furthermore, a combined MPC clustering and
matching procedure with ray-tracing techniques is proposed to investigate the
cluster behavior and wave propagation of THz signals. In light of the
measurement results, physical parameters and insights in the THz indoor channel
are comprehensively analyzed, including the line-of-sight path loss, power
distributions, temporal and spatial features, and correlations among THz
multi-path characteristics. Finally, a hybrid channel model that combines
ray-tracing and statistical methods is developed for THz indoor communications.
Numerical results demonstrate that the proposed hybrid channel model shows good
agreement with the measurement and outperforms the conventional statistical and
geometric-based stochastic channel model in terms of the temporal-spatial
characteristics.


Abstract:
  In this paper, we introduce a Liouville action for a harmonic diffeomorphism
from a compact Riemann surface to a compact hyperbolic Riemann surface of genus
$g\ge 2$. We derive the variational formula of this Liouville action for
harmonic diffeomorphisms when the source Riemann surfaces vary with a fixed
target Riemann surface.


Abstract:
  Streaming end-to-end automatic speech recognition (ASR) systems are widely
used in everyday applications that require transcribing speech to text in
real-time. Their minimal latency makes them suitable for such tasks. Unlike
their non-streaming counterparts, streaming models are constrained to be causal
with no future context and suffer from higher word error rates (WER). To
improve streaming models, a recent study [1] proposed to distill a
non-streaming teacher model on unsupervised utterances, and then train a
streaming student using the teachers' predictions. However, the performance gap
between teacher and student WERs remains high. In this paper, we aim to close
this gap by using a diversified set of non-streaming teacher models and
combining them using Recognizer Output Voting Error Reduction (ROVER). In
particular, we show that, despite being weaker than RNN-T models, CTC models
are remarkable teachers. Further, by fusing RNN-T and CTC models together, we
build the strongest teachers. The resulting student models drastically improve
upon streaming models of previous work [1]: the WER decreases by 41% on
Spanish, 27% on Portuguese, and 13% on French.


Abstract:
  Vanadium dioxide (VO2) is a phase transition material that undergoes a
reversible insulator-metal phase transition at ~ 68 C. Atmospheric pressure
thermal oxidation (APTO) of vanadium (V) is a simple VO2 synthesis method in
which V thin film is oxidized in open air. For an optimum oxidation duration,
VO2 films are obtained with good phase transition properties. We recently
reported a modified APTO process using a step temperature profile for oxidation
(Thin Solid Films 706, 138003 (2020)). We demonstrated an ultra-low thermal
budget synthesis of VO2 thin films with good electrical and optical phase
transition properties. For a 130 nm room-temperature RF sputtered V thin film,
an optimum oxidation duration of ~ 30 s was obtained. In this work, we study
how the starting V film thickness and deposition temperature affects the
optimum oxidation duration. V thin films of varying thickness (15-212 nm) and
120 nm thick V films with varying deposition temperature (~27-450 C) are
prepared using RF magnetron sputtering. These films are oxidized for different
oxidation durations and characterized using Raman and four-probe measurements
to find the optimum oxidation duration for each deposition condition. We find
that the optimum oxidation duration increases with the increase in V film
thickness and V deposition temperature. We model the effect of V film thickness
and deposition temperature on the optimal oxidation time using a parabolic law
which can be used to obtain the optimal oxidation times for intermediate V
thicknesses/deposition temperatures.


Abstract:
  Because of the explosive growth of face photos as well as their widespread
dissemination and easy accessibility in social media, the security and privacy
of personal identity information becomes an unprecedented challenge. Meanwhile,
the convenience brought by advanced identity-agnostic computer vision
technologies is attractive. Therefore, it is important to use face images while
taking careful consideration in protecting people's identities. Given a face
image, face de-identification, also known as face anonymization, refers to
generating another image with similar appearance and the same background, while
the real identity is hidden. Although extensive efforts have been made,
existing face de-identification techniques are either insufficient in
photo-reality or incapable of well-balancing privacy and utility. In this
paper, we focus on tackling these challenges to improve face de-identification.
We propose IdentityDP, a face anonymization framework that combines a
data-driven deep neural network with a differential privacy (DP) mechanism.
This framework encompasses three stages: facial representations
disentanglement, $\epsilon$-IdentityDP perturbation and image reconstruction.
Our model can effectively obfuscate the identity-related information of faces,
preserve significant visual similarity, and generate high-quality images that
can be used for identity-agnostic computer vision tasks, such as detection,
tracking, etc. Different from the previous methods, we can adjust the balance
of privacy and utility through the privacy budget according to pratical demands
and provide a diversity of results without pre-annotations. Extensive
experiments demonstrate the effectiveness and generalization ability of our
proposed anonymization framework.


Abstract:
  We study the bilinear and higher-order fermion condensates in $4$-dimensional
$SU(N)$ gauge theories with a single Dirac fermion in a general representation.
Augmented with a mixed anomaly between the $0$-form discrete chiral, $1$-form
center, and $0$-form baryon number symmetries (BC anomaly), we sort out
theories that admit higher-order condensates and vanishing fermion bilinears.
Then, the BC anomaly is utilized to prove, in the absence of a topological
quantum field theory, that nonvanishing fermion bilinears are inevitable in
infrared-gapped theories with $2$-index (anti)symmetric fermions. We also
contrast the BC anomaly with the $0$-form anomalies and show that it is the
former anomaly that determines the infrared physics; we argue that the BC
anomaly lurks deep to the infrared while the $0$-form anomalies are just
variations of local terms. We provide evidence of this assertion by studying
the BC anomaly in vector-like theories compactified on a small spacial circle.
These theories are weakly-coupled, under analytical control, and they admit a
dual description in terms of abelian photons that determine the deep infrared
dynamics. We show that the dual photons talk directly to the $1$-form center
symmetry in order to match the BC anomaly, while the $0$-form anomalies are
variations of local terms and are matched by fiat. Finally, we study the fate
of the BC anomaly in the compactified theories when they are held at a finite
temperature. The effective field theory that describes the low-energy physics
is $2$-dimensional. We show that the BC anomaly cascades from $4$ to $2$
dimensions.


Abstract:
  The rapidly expanding nature of the Internet of Things (IoT) networks is
beginning to attract interest across a range of applications, including smart
homes, smart transportation, smart health, and industrial contexts. This
cutting-edge technology enables individuals to track and control their
integrated environment in real-time and remotely via a thousand IoT devices
comprised of sensors and actuators that actively participate in sensing,
processing, storing, and sharing information. Nonetheless, IoT devices are
frequently deployed in hostile environments, wherein adversaries attempt to
capture and breach them in order to seize control of the entire network. One
such example of potentially malicious behaviour is the cloning of IoT devices,
in which an attacker can physically capture the devices, obtain some sensitive
information, duplicate the devices, and intelligently deploy them in desired
locations to conduct various insider attacks. A device cloning attack on IoT
networks is a significant security concern since it allows for selective
forwarding, sink-hole, and black-hole attacks. To address this issue, this
paper provides an efficient scheme for detecting clone node attacks on IoT
networks that makes use of semantic information about IoT devices known as
context information sensed from the deployed environment to locate them
securely. We design a location proof mechanism by combining location proofs and
batch verification of the extended elliptic curve digital signature technique
to accelerate the verification process at selected trusted nodes. We
demonstrate the security of our scheme and its resilience to secure clone node
attack detection by conducting a comprehensive security analysis. The
performance of our proposed scheme provides a high degree of detection accuracy
with minimal detection time and significantly reduces the computation,
communication and storage overhead.


Abstract:
  In 1968, V.I. Oseledets formulated the question of convergence in the
Birkhoff theorem and the multiplicative ergodic theorem for measurable cocycles
over flows under the condition of integrability for each individual t. A.M.
Stepin and the author established (2016) the convergence along subsets of time
of density 1. In this note, we show that moreover the convergence is fulfilled
modulo time subsets of finite measure.


Abstract:
  We formulate both Markov chain Monte Carlo (MCMC) sampling algorithms and
basic statistical physics in terms of elementary symmetries. This perspective
on sampling yields derivations of well-known MCMC algorithms and a new parallel
algorithm that appears to converge more quickly than current state of the art
methods. The symmetry perspective also yields a parsimonious framework for
statistical physics and a practical approach to constructing meaningful notions
of effective temperature and energy directly from time series data. We apply
these latter ideas to Anosov systems.


Abstract:
  We consider a general (beyond $T\bar T$) deformation of the 2D $O(N+1)$
$\sigma$-model by the irrelevant dimension-four operators. The theory deformed
in this most general way is not integrable, and the $S$-matrix loses its
factorization properties. We perform the all-order summation of the leading
infrared logs for the $2 \to 2$ scattering amplitude and provide the exact
result for the $2 \to 2$ $S$-matrix in the leading logarithmic approximation.
These results can provide us with new insights into the properties of the
theories deformed by irrelevant operators more general than the $T\bar T$
deformation.


Abstract:
  Natal kicks and spins are characteristic properties of neutron stars (NSs)
and black holes (BHs). Both offer valuable clues to dynamical processes during
stellar core collapse and explosion. Moreover, they influence the evolution of
stellar multiple systems and the gravitational-wave signals from their inspiral
and merger. Observational evidence of possibly generic spin-kick alignment has
been interpreted as indication that NS spins are either induced with the NS
kicks or inherited from progenitor rotation, which thus might play a
dynamically important role during stellar collapse. Current three-dimensional
supernova simulations suggest that NS kicks are transferred in the first
seconds of the explosion, mainly by anisotropic mass ejection and, on a
secondary level, anisotropic neutrino emission. In contrast, the NS spins are
only determined minutes to hours later by angular momentum associated with
fallback of matter that does not become gravitationally unbound in the
supernova. Here, we propose a novel scenario to explain spin-kick alignment as
a consequence of tangential vortex flows in the fallback matter that is
accreted mostly from the direction of the NS's motion. For this effect the
initial NS kick is crucial, because it produces a growing offset of the NS away
from the explosion center, thus promoting onesided accretion. In this new
scenario conclusions based on traditional concepts are reversed. For example,
pre-kick NS spins are not required, and rapid progenitor-core rotation can
hamper spin-kick alignment. We also discuss implications for natal BH kicks and
the possibility of tossing the BH's spin axis during its formation.


Abstract:
  To reliably model real robot characteristics, interval linear systems of
equations allow to describe families of problems that consider sets of values.
This allows to easily account for typical complexities such as sets of joint
states and design parameter uncertainties. Inner approximations of the
solutions to the interval linear systems can be used to describe the common
capabilities of a robotic manipulator corresponding to the considered sets of
values. In this work, several classes of problems are considered. For each
class, reliable and efficient polytope, n-cube, and n-ball inner approximations
are presented. The interval approaches usually proposed are inefficient because
they are too computationally heavy for certain applications, such as control.
We propose efficient new inner approximation theorems for the considered
classes of problems. This allows for usage with real-time applications as well
as rapid analysis of potential designs. Several applications are presented for
a redundant planar manipulator including locally evaluating the manipulator's
velocity, acceleration, and static force capabilities, and evaluating its
future acceleration capabilities over a given time horizon.