Abstract:
  Understanding the implicit bias of training algorithms is of crucial
importance in order to explain the success of overparametrised neural networks.
In this paper, we study the dynamics of stochastic gradient descent over
diagonal linear networks through its continuous time version, namely stochastic
gradient flow. We explicitly characterise the solution chosen by the stochastic
flow and prove that it always enjoys better generalisation properties than that
of gradient flow. Quite surprisingly, we show that the convergence speed of the
training loss controls the magnitude of the biasing effect: the slower the
convergence, the better the bias. To fully complete our analysis, we provide
convergence guarantees for the dynamics. We also give experimental results
which support our theoretical claims. Our findings highlight the fact that
structured noise can induce better generalisation and they help explain the
greater performances observed in practice of stochastic gradient descent over
gradient descent.


Abstract:
  This paper proposes a novel transmission policy for an intelligent reflecting
surface (IRS) assisted wireless powered sensor network (WPSN). An IRS is
deployed to enhance the performance of wireless energy transfer (WET) and
wireless information transfer (WIT) by intelligently adjusting phase shifts of
each reflecting elements. To achieve its self-sustainability, the IRS needs to
collect energy from energy station to support its control circuit operation.
Our proposed policy for the considered WPSN is called IRS assisted
harvest-then-transmit time switching, which is able to schedule the
transmission time slots by switching between energy collection and energy
reflection modes. We study the achievable sum throughput of the proposed
transmission policy and investigate a joint design of the transmission time
slots, the power allocation, as well as the discrete phase shifts of the WET
and WIT. This formulates the problem as a mixed-integer non-linear program,
which is NP-hard and non-convex. We first relax it to one with continuous phase
shifts, and then propose a two-step approach and decompose the original problem
into two sub-problems. We solve the first sub-problem with respect to the phase
shifts of the WIT in terms of closed-form expression. For the second
sub-problem, we consider a special case without the circuit power of each
sensor node, the Lagrange dual method and the KKT conditions are applied to
derive the optimal closed-form transmission time slots, power allocation, and
phase shift of the WET. Then we generalise the case with the circuit power of
each sensor node, which can be solved via employing a semi-definite programming
relaxation. The optimal discrete phase shifts can be obtained by quantizing the
continuous values. Numerical results demonstrate the effectiveness of the
proposed policy and validate the beneficial role of the IRS in comparison to
the benchmark schemes.


Abstract:
  Convolutional neural networks (CNNs) have made great breakthroughs in 2D
computer vision. However, their irregular structure makes it hard to harness
the potential of CNNs directly on meshes. A subdivision surface provides a
hierarchical multi-resolution structure, in which each face in a closed
2-manifold triangle mesh is exactly adjacent to three faces. Motivated by these
two observations, this paper presents SubdivNet, an innovative and versatile
CNN framework for 3D triangle meshes with Loop subdivision sequence
connectivity. Making an analogy between mesh faces and pixels in a 2D image
allows us to present a mesh convolution operator to aggregate local features
from nearby faces. By exploiting face neighborhoods, this convolution can
support standard 2D convolutional network concepts, e.g. variable kernel size,
stride, and dilation. Based on the multi-resolution hierarchy, we make use of
pooling layers which uniformly merge four faces into one and an upsampling
method which splits one face into four. Thereby, many popular 2D CNN
architectures can be easily adapted to process 3D meshes. Meshes with arbitrary
connectivity can be remeshed to have Loop subdivision sequence connectivity via
self-parameterization, making SubdivNet a general approach. Extensive
evaluation and various applications demonstrate SubdivNet's effectiveness and
efficiency.


Abstract:
  Safe navigation of autonomous agents in human centric environments requires
the ability to understand and predict motion of neighboring pedestrians.
However, predicting pedestrian intent is a complex problem. Pedestrian motion
is governed by complex social navigation norms, is dependent on neighbors'
trajectories, and is multimodal in nature. In this work, we propose SCAN, a
Spatial Context Attentive Network that can jointly predict socially-acceptable
multiple future trajectories for all pedestrians in a scene. SCAN encodes the
influence of spatially close neighbors using a novel spatial attention
mechanism in a manner that relies on fewer assumptions, is parameter efficient,
and is more interpretable compared to state-of-the-art spatial attention
approaches. Through experiments on several datasets we demonstrate that our
approach can also quantitatively outperform state of the art trajectory
prediction methods in terms of accuracy of predicted intent.


Abstract:
  For any given neural network architecture a permutation of weights and biases
results in the same functional network. This implies that optimization
algorithms used to `train' or `learn' the network are faced with a very large
number (in the millions even for small networks) of equivalent optimal
solutions in the parameter space. To the best of our knowledge, this
observation is absent in the literature. In order to narrow down the parameter
search space, a novel technique is introduced in order to fix the bias vector
configurations to be monotonically increasing. This is achieved by augmenting a
typical learning problem with inequality constraints on the bias vectors in
each layer. A Moreau-Yosida regularization based algorithm is proposed to
handle these inequality constraints and a theoretical convergence of the this
algorithm is established. Applications of the proposed approach to standard
trigonometric functions and more challenging stiff ordinary differential
equations arising in chemically reacting ows clearly illustrate the benefits of
the proposed approach.


Abstract:
  The sensitivity of Density Functional Theory plus Dynamical Mean Field Theory
calculations to different constructions of the correlated orbitals is
investigated via a detailed comparison of results obtained for the quantum
material NdNiO$_2$ using different Wannier and projector methods to define the
correlation problem. Using the same interaction parameters we find that the
different methods produce different results for the orbital and band basis mass
enhancements and for the orbital occupancies, with differing implications
regarding the importance of multiorbital effects and charge transfer physics.
Using interaction parameters derived from cRPA enhances the difference in
results. For the isostructural cuprate CaCuO$_2$, the different methods give
quantitatively different mass enhancements but still result in the same
qualitative physics.


Abstract:
  We present a bit encoding scheme for a highly efficient and scalable
representation of bosonic Fock number states in the restricted Boltzmann
machine neural network architecture. In contrast to common density matrix
implementations, the complexity of the neural network scales only with the
number of bit-encoded neurons rather than the maximum boson number. Crucially,
in the high occupation regime its information compression efficiency is shown
to surpass even maximally optimized density matrix implementations, where a
projector method is used to access the sparsest Hilbert space representation
available.


Abstract:
  In this paper we study model reduction of linear and bilinear quadratic
stochastic control problems with parameter uncertainties. Specifically, we
consider slow-fast systems with unknown diffusion coefficient and study the
convergence of the slow process in the limit of infinite scale separation. The
aim of our work is two-fold: Firstly, we want to propose a general framework
for averaging and homogenisation of multiscale systems with parametric
uncertainties in the drift or in the diffusion coefficient. Secondly, we want
to use this framework to quantify the uncertainty in the reduced system by
deriving a limit equation that represents a worst-case scenario for any given
(possibly path-dependent) quantity of interest. We do so by reformulating the
slow-fast system as an optimal control problem in which the unknown parameter
plays the role of a control variable that can take values in a closed bounded
set. For systems with unknown diffusion coefficient, the underlying stochastic
control problem admits an interpretation in terms of a stochastic differential
equation driven by a G-Brownian motion. We prove convergence of the slow
process with respect to the nonlinear expectation on the probability space
induced by the G-Brownian motion. The idea here is to formulate the nonlinear
dynamic programming equation of the underlying control problem as a
forward-backward stochastic differential equation in the G-Brownian motion
framework (in brief: G-FBSDE), for which convergence can be proved by standard
means. We illustrate the theoretical findings with two simple numerical
examples, exploiting the connection between fully nonlinear dynamic programming
equations and second-order BSDE (2BSDE): a linear quadratic Gaussian regulator
problem and a bilinear multiplicative triad that is a standard benchmark system
in turbulence and climate modelling.


Abstract:
  A direct measurement protocol allows reconstructing specific elements of the
density matrix of a quantum state without using quantum state tomography.
However, the direct measurement protocols to date are primarily based on weak
or strong measurements with an ancillary pointer, which interacts with the
investigated system to extract information about the specified elements. Here,
we present a direct measurement scheme based on phase-shifting operations which
do not need ancillary pointers. In this method, estimates of at most six
expectation values of projective observables suffice to determine any specific
element of an unknown quantum density matrix. A concrete quantum circuit to
implement this direct measurement protocol for multiqubit states is provided,
which is composed of just single-qubit gates and two multiqubit
controlled-phase gates. This scheme is also extended for the direct measurement
of the density matrix of continuous-variable quantum states. Our method can be
used in quantum information applications where only partial information about
the quantum state needs to be extracted, for example, problems such as
entanglement witnessing, fidelity estimation of quantum systems, and quantum
coherence estimation.


Abstract:
  We study experimentally the dynamics of long waves among turbulent bending
waves in a thin elastic plate set into vibration by a monochromatic forcing at
a frequency $f_0$. This frequency is chosen large compared with the
characteristic frequencies of bending waves. As a consequence, a range of
conservative scales, without energy flux in average, exists for frequencies
$f<f_0$. Within this range, we report a flat power density spectrum for the
orthogonal velocity, corresponding to energy equipartition between modes. Thus,
the average energy per mode $\beta^{-1}$ -- analogous to a temperature -- fully
characterizes the large-scale turbulent wave field. We present an expression
for $\beta$ as a function of the forcing frequency and amplitude, and of the
plate characteristics.


Abstract:
  Computer modeling of human decision making is of large importance for, e.g.,
sustainable transport, urban development, and online recommendation systems. In
this paper we present a model for predicting the behavior of an individual
during a binary game under different amounts of risk, gain, and time pressure.
The model is based on Quantum Decision Theory (QDT), which has been shown to
enable modeling of the irrational and subjective aspects of the decision
making, not accounted for by the classical Cumulative Prospect Theory (CPT).
Experiments on two different datasets show that our QDT-based approach
outperforms both a CPT-based approach and data driven approaches such as
feed-forward neural networks and random forests.


Abstract:
  Entanglement plays a prominent role in the study of condensed matter
many-body systems: Entanglement measures not only quantify the possible use of
these systems in quantum information protocols, but also shed light on their
physics. However, exact analytical results remain scarce, especially for
systems out of equilibrium. In this work we examine a paradigmatic
one-dimensional fermionic system that consists of a uniform tight-binding chain
with an arbitrary scattering region near its center, which is subject to a DC
bias voltage at zero temperature. The system is thus held in a current-carrying
nonequilibrium steady state, which can nevertheless be described by a pure
quantum state. Using a generalization of the Fisher-Hartwig conjecture, we
present an exact calculation of the bipartite entanglement entropy of a
subsystem with its complement, and show that the scaling of entanglement with
the length of the subsystem is highly unusual, containing both a volume-law
linear term and a logarithmic term. The linear term is related to imperfect
transmission due to scattering, and provides a generalization of the
Levitov-Lesovik full counting statistics formula. The logarithmic term arises
from the Fermi discontinuities in the distribution function. Our analysis also
produces an exact expression for the particle-number-resolved entanglement. We
find that although to leading order entanglement equipartition applies, the
first term breaking it grows with the size of the subsystem, a novel behavior
not observed in previously studied systems. We apply our general results to a
concrete model of a tight-binding chain with a single impurity site, and show
that the analytical expressions are in good agreement with numerical
calculations. The analytical results are further generalized to accommodate the
case of multiple scattering regions.


Abstract:
  For any natural $k \leq n$, the rational cohomology ring of the space of
continuous maps $S^{2k-1} \to S^{2n-1}$ (respectively, $S^{4k-1} \to S^{4n-1}$)
equivariant under the Hopf action of the circle (respectively, of the group
$S^3$ of unit quaternions) is naturally isomorphic to that of the Stiefel
manifold $V_k({\mathbb C}^n)$ (respectively, $V_k({\mathbb H}^n)$).


Abstract:
  We present convergence analysis of operator learning in [Chen and Chen 1995]
and [Lu et al. 2020], where continuous operators are approximated by a sum of
products of branch and trunk networks. In this work, we consider the rates of
learning solution operators from both linear and nonlinear advection-diffusion
equations with or without reaction. We find that the convergence rates depend
on the architecture of branch networks as well as the smoothness of inputs and
outputs of solution operators.


Abstract:
  This paper investigates the problem of regulating in real time a linear
dynamical system to the solution trajectory of a time-varying constrained
convex optimization problem. The proposed feedback controller is based on an
adaptation of the saddle-flow dynamics, modified to take into account
projections on constraint sets and output-feedback from the plant. We derive
sufficient conditions on the tunable parameters of the controller (inherently
related to the time-scale separation between plant and controller dynamics) to
guarantee exponential and input-to-state stability of the closed-loop system.
The analysis is tailored to the case of time-varying strongly convex cost
functions and polytopic output constraints. The theoretical results are further
validated in a ramp metering control problem in a network of traffic highways.


Abstract:
  Entropic regularization of policies in Reinforcement Learning (RL) is a
commonly used heuristic to ensure that the learned policy explores the
state-space sufficiently before overfitting to a local optimal policy. The
primary motivation for using entropy is for exploration and disambiguating
optimal policies; however, the theoretical effects are not entirely understood.
In this work, we study the more general regularized RL objective and using
Fenchel duality; we derive the dual problem which takes the form of an
adversarial reward problem. In particular, we find that the optimal policy
found by a regularized objective is precisely an optimal policy of a
reinforcement learning problem under a worst-case adversarial reward. Our
result allows us to reinterpret the popular entropic regularization scheme as a
form of robustification. Furthermore, due to the generality of our results, we
apply to other existing regularization schemes. Our results thus give insights
into the effects of regularization of policies and deepen our understanding of
exploration through robust rewards at large.


Abstract:
  Source code can be parsed into the abstract syntax tree (AST) based on
defined syntax rules. However, in pre-training, little work has considered the
incorporation of tree structure into the learning process. In this paper, we
present TreeBERT, a tree-based pre-trained model for improving programming
language-oriented generation tasks. To utilize tree structure, TreeBERT
represents the AST corresponding to the code as a set of composition paths and
introduces node position embedding. The model is trained by tree masked
language modeling (TMLM) and node order prediction (NOP) with a hybrid
objective. TMLM uses a novel masking strategy designed according to the tree's
characteristics to help the model understand the AST and infer the missing
semantics of the AST. With NOP, TreeBERT extracts the syntactical structure by
learning the order constraints of nodes in AST. We pre-trained TreeBERT on
datasets covering multiple programming languages. On code summarization and
code documentation tasks, TreeBERT outperforms other pre-trained models and
state-of-the-art models designed for these tasks. Furthermore, TreeBERT
performs well when transferred to the pre-trained unseen programming language.


Abstract:
  We show the coexistence of chaotic behaviors (positive metric entropy) and
elliptic behaviors (intregrable KAM island) among analytic, symplectic
diffeomorphism of any closed surface. In particilar this solves a problem by F.
Przytycki (1982).


Abstract:
  We introduce a reweighting technique which allows for a continuous sampling
of temperatures in a single simulation and employ it to compute the temperature
dependence of the QCD topological susceptibility at high temperatures. The
method determines the ratio of susceptibility between any two temperatures
within the explored temperature range. We find that the results from the method
agree with our previous determination and that it is competitive with but not
better than existing methods of determining the temperature derivative of the
susceptibility. The method may also be useful in exploring the temperature
dependence of other thermodynamical observables in QCD in a continuous way.


Abstract:
  We study the behavior of the quasinormal modes (QNMs) of massless and massive
linear waves on Schwarzschild-de Sitter black holes as the black hole mass
tends to 0. Via uniform estimates for a degenerating family of ODEs, we show
that in bounded subsets of the complex plane and for fixed angular momenta, the
QNMs converge to those of the static model of de Sitter space. Detailed
numerics illustrate our results and suggest a number of open problems.


Abstract:
  This paper outlines a perspective on the future of AI, discussing directions
for machines models of human-like intelligence. We explain how developmental
and evolutionary theories of human cognition should further inform artificial
intelligence. We emphasize the role of ecological niches in sculpting
intelligent behavior, and in particular that human intelligence was
fundamentally shaped to adapt to a constantly changing socio-cultural
environment. We argue that a major limit of current work in AI is that it is
missing this perspective, both theoretically and experimentally. Finally, we
discuss the promising approach of developmental artificial intelligence,
modeling infant development through multi-scale interaction between
intrinsically motivated learning, embodiment and a fastly changing
socio-cultural environment. This paper takes the form of an interview of
Pierre-Yves Oudeyer by Mandred Eppe, organized within the context of a KI -
K{\"{u}}nstliche Intelligenz special issue in developmental robotics.


Abstract:
  The most serious threat to ecosystems is the global climate change fueled by
the uncontrolled increase in carbon emissions. In this project, we use mean
field control and mean field game models to analyze and inform the decisions of
electricity producers on how much renewable sources of production ought to be
used in the presence of a carbon tax. The trade-off between higher revenues
from production and the negative externality of carbon emissions is quantified
for each producer who needs to balance in real time reliance on reliable but
polluting (fossil fuel) thermal power stations versus investing in and
depending upon clean production from uncertain wind and solar technologies. We
compare the impacts of these decisions in two different scenarios: 1) the
producers are competitive and hopefully reach a Nash Equilibrium; 2) they
cooperate and reach a Social Optimum. We first prove that both problems have a
unique solution using forward-backward systems of stochastic differential
equations. We then illustrate with numerical experiments the producers'
behavior in each scenario. We further introduce and analyze the impact of a
regulator in control of the carbon tax policy, and we study the resulting
Stackelberg equilibrium with the field of producers.


Abstract:
  Rich functionalities of transition-metal oxides and their interfaces bear an
enormous technological potential. Its realization in practical devices
requires, however, a significant improvement of yet relatively low electron
mobility in oxide materials. Recently, a mobility boost of about two orders of
magnitude has been demonstrated at the spinel/perovskite {\gamma}-Al2O3/SrTiO3
interface compared to the paradigm perovskite/perovskite LaAlO3/SrTiO3. We
explore the fundamental physics behind this phenomenon from direct measurements
of the momentum-resolved electronic structure of this interface using resonant
soft-X-ray angle-resolved photoemission. We find an anomaly in orbital ordering
of the mobile electrons in {\gamma}-Al2O3/SrTiO3 which depopulates electron
states in the top STO layer. This rearrangement of the mobile electron system
pushes the electron density away from the interface that reduces its overlap
with the interfacial defects and weakens the electron-phonon interaction, both
effects contributing to the mobility boost. A crystal-field analysis shows that
the band order alters owing to the symmetry breaking between the spinel
{\gamma}-Al2O3 and perovskite SrTiO3. The band-order engineering exploiting the
fundamental symmetry properties emerges as another route to boost the
performance of oxide devices.


Abstract:
  In this paper, some of the distance sensor, including Kinect, Hokuyo
UTM-30LX, and RPLidar were observed experimentally. Strengths and weaknesses of
each sensor were reviewed so that it can be used as a reference for selecting a
suitable sensor for any particular application. A software application has been
developed in C programming language as a platform for gathering information for
all tested sensors. According to the experiment results, it showed that Hokuyo
UTM-30LX results in random normally distributed error on measuring distance
with average error 21.94 mm and variance 32.11. On the other hand, error
measurement resulted by Kinect and RPLidar strongly depended on measured
distance of the object from the sensors, while measurement error resulted by
Kinect had a negative correlation with the measured distance and the error
resulted by RPLidar sensor had a positive correlation with the measured
distance. The performance of these three sensors for detecting a transparent
object shows that the Kinect sensors can detect the transparent object on its
effective range measurement, Hokuyo UTM-30LX can detect the transparent object
in the distance more than equal to 200 mm, and the RPLidar sensor cannot detect
the transparent object at all tested distance. Lastly, the experiment shows
that the Hokuyo UTM-30LX has the fastest processing time significantly, and the
RPLidar has the slowest processing time significantly, while the processing
time of Kinect sensor was in between. These processing times were not
significantly affected by various tested distance measurement.


Abstract:
  Open source software projects usually acknowledge contributions with text
files, websites, and other idiosyncratic methods. These data sources are hard
to mine, which is why contributorship is most frequently measured through
changes to repositories, such as commits, pushes, or patches. Recently, some
open source projects have taken to recording contributor actions with
standardized systems; this opens up a unique opportunity to understand how
community-generated notions of contributorship map onto codebases as the
measure of contribution. Here, we characterize contributor acknowledgment
models in open source by analyzing thousands of projects that use a model
called All Contributors to acknowledge diverse contributions like outreach,
finance, infrastructure, and community management. We analyze the life cycle of
projects through this model's lens and contrast its representation of
contributorship with the picture given by other methods of acknowledgment,
including GitHub's top committers indicator and contributions derived from
actions taken on the platform. We find that community-generated systems of
contribution acknowledgment make work like idea generation or bug finding more
visible, which generates a more extensive picture of collaboration. Further, we
find that models requiring explicit attribution lead to more clearly defined
boundaries around what is and what is not a contribution.


Abstract:
  Organic semiconductors are indispensable for today's display technologies in
form of organic light emitting diodes (OLEDs) and further optoelectronic
applications. However, organic materials do not reach the same charge carrier
mobility as inorganic semiconductors, limiting the efficiency of devices. To
find or even design new organic semiconductors with higher charge carrier
mobility, computational approaches, in particular multiscale models, are
becoming increasingly important. However, such models are computationally very
costly, especially when large systems and long time scales are required, which
is the case to compute static and dynamic energy disorder, i.e. dominant factor
to determine charge transport. Here we overcome this drawback by integrating
machine learning models into multiscale simulations. This allows us to obtain
unprecedented insight into relevant microscopic materials properties, in
particular static and dynamic disorder contributions for a series of
application-relevant molecules. We find that static disorder and thus the
distribution of shallow traps is highly asymmetrical for many materials,
impacting widely considered Gaussian disorder models. We furthermore analyse
characteristic energy level fluctuation times and compare them to typical
hopping rates to evaluate the importance of dynamic disorder for charge
transport. We hope that our findings will significantly improve the accuracy of
computational methods used to predict application relevant materials properties
of organic semiconductors, and thus make these methods applicable for virtual
materials design.


Abstract:
  This paper presents a lightweight, open-source and high-performance python
package for solving peridynamics problems in solid mechanics. The development
of this solver is motivated by the need for fast analysis tools to achieve the
large number of simulations required for `outer-loop' applications, including
sensitivity analysis, uncertainty quantification and optimisation. Our python
software toolbox utilises the heterogeneous nature of OpenCL so that it can be
executed on any platform with CPU or GPU cores. We illustrate the package use
through a range of industrially motivated examples, which should enable other
researchers to build on and extend the solver for use in their own
applications. Step improvements in execution speed and functionality over
existing techniques are presented. A comparison between this solver and an
existing OpenCL implementation in the literature is presented, tested on
benchmarks with hundreds of thousands to tens of millions of nodes. We
demonstrate the scalability of the solver on the GeForce RTX 2080 TiGPU from
NVIDIA, and the memory-bound limitations are analysed. In all test cases, the
implementation is between 1.4 and 10.0 times faster than a similar existing GPU
implementation in the literature. In particular, this improvement has been
achieved by utilising local memory on the GPU.


Abstract:
  In this paper, we study tractability of $L_2$-approximation of one-periodic
functions from weighted Korobov spaces in the worst-case setting. The
considered weights are of product form. For the algorithms we allow information
from the class $\Lambda^{{\rm all}}$ consisting of all continuous linear
functionals and from the class $\Lambda^{{\rm std}}$, which only consists of
function evaluations.
  We provide necessary and sufficient conditions on the weights of the function
space for quasi-polynomial tractability, uniform weak tractability, weak
tractability and $(\sigma,\tau)$-weak tractability. Together with the already
known results for strong polynomial and polynomial tractability, our findings
provide a complete picture of the weight conditions for all current standard
notions of tractability.


Abstract:
  The paper proposes a new text recognition network for scene-text images. Many
state-of-the-art methods employ the attention mechanism either in the text
encoder or decoder for the text alignment. Although the encoder-based attention
yields promising results, these schemes inherit noticeable limitations. They
perform the feature extraction (FE) and visual attention (VA) sequentially,
which bounds the attention mechanism to rely only on the FE final single-scale
output. Moreover, the utilization of the attention process is limited by only
applying it directly to the single scale feature-maps. To address these issues,
we propose a new multi-scale and encoder-based attention network for text
recognition that performs the multi-scale FE and VA in parallel. The
multi-scale channels also undergo regular fusion with each other to develop the
coordinated knowledge together. Quantitative evaluation and robustness analysis
on the standard benchmarks demonstrate that the proposed network outperforms
the state-of-the-art in most cases.


Abstract:
  The emerging technique of mid-infrared optical coherence tomography (MIR-OCT)
takes advantage of the reduced scattering of MIR light in various materials and
devices, enabling tomographic imaging at deeper penetration depths. Because of
challenges in MIR detection technology, the image acquisition time is however
significantly longer than for tomographic imaging methods in the
visible/near-infrared. Here we demonstrate an alternative approach to MIR
tomography with high-speed imaging capabilities. Through femtosecond
non-degenerate two-photon absorption of MIR light in a conventional Si-based
CCD camera, we achieve wide-field, high-definition tomographic imaging with
chemical selectivity of structured materials and biological samples in mere
seconds.


Abstract:
  A coarse-grained bead-spring-dashpot chain model with the dashpots
representing the presence of internal friction, is solved exactly numerically,
for the case of chains with more than two beads. Using a decoupling procedure
to remove the explicit coupling of a bead's velocity with that of its nearest
neighbors, the governing set of stochastic differential equations are solved
with Brownian dynamics simulations to obtain material functions in oscillatory
and steady simple shear flow. Simulation results for the real and imaginary
components of the complex viscosity have been compared with the results of
previously derived semi-analytical approximations, and the difference in the
predictions are seen to diminish with an increase in the number of beads in the
chain. The inclusion of internal friction results in a non-monotonous variation
of the viscosity with shear rate, with the occurrence of continuous
shear-thickening following an initial shear-thinning regime. The onset of
shear-thickening in the first normal stress coefficient is pushed to lower
shear rates with an increase in the internal friction parameter.


Abstract:
  Organic color centers in single-walled carbon nanotubes have demonstrated
exceptional ability to generate single photons at room temperature in the
telecom range. Combining the color centers with pristine air-suspended tubes
would be desirable for improved performance, but all current synthetic methods
occur in solution which makes them incompatible. Here we demonstrate formation
of color centers in air-suspended nanotubes using vapor-phase reaction.
Functionalization is directly verified on the same nanotubes by
photoluminescence spectroscopy, with unambiguous statistics from more than a
few thousand individual nanotubes. The color centers show a strong
diameter-dependent emission intensity, which can be explained with a
theoretical model for chemical reactivity taking into account strain along the
tube curvature. We are also able to estimate the defect density by comparing
the experiments with simulations based on a one-dimensional diffusion equation,
whereas the analysis of diameter dependent peak energies gives insight to the
nature of the dopant states. Time-resolved measurements show a longer lifetime
for color center emission compared to E$_{11}$ exciton states. Our results
highlight the influence of the tube structure on vapor-phase reactivity and
emission properties, providing guidelines for development of high-performance
near-infrared quantum light sources.


Abstract:
  Volatility clustering is an important characteristic that has a significant
effect on the behavior of stock markets. However, designing robust models for
accurate prediction of future volatilities of stock prices is a very
challenging research problem. We present several volatility models based on
generalized autoregressive conditional heteroscedasticity (GARCH) framework for
modeling the volatility of ten stocks listed in the national stock exchange
(NSE) of India. The stocks are selected from the auto sector and the banking
sector of the Indian economy, and they have a significant impact on the
sectoral index of their respective sectors in the NSE. The historical stock
price records from Jan 1, 2010, to Apr 30, 2021, are scraped from the Yahoo
Finance website using the DataReader API of the Pandas module in the Python
programming language. The GARCH modules are built and fine-tuned on the
training data and then tested on the out-of-sample data to evaluate the
performance of the models. The analysis of the results shows that asymmetric
GARCH models yield more accurate forecasts on the future volatility of stocks.


Abstract:
  In this work, we develop a combined convolutional neural networks (CNNs) and
finite element method (FEM) to examine the effective thermal properties of
composite phase change materials (CPCMs) consisting of paraffin and copper
foam. In this approach, first the CPCM microstructures are modeled using FEM
and next the image dataset with corresponding thermal properties is created.
The image dataset is subsequently used to train and test the CNN performance,
which is then compared with the performance of a popular network architecture
for image classification tasks. The predicted thermal properties are employed
to define the properties of the CPCM material of a battery pack. The heat
generation and electrochemical response of a Li-ion cell during the
charging/discharging is simulated by applying Newman battery model. Thermal
management is achieved by the latent heat of paraffin, with copper foam for
enhancing the thermal conductivity. The multiscale model is finally developed
using FEM to investigate the effectiveness of the thermal management of the
battery pack. In these models the thermal properties estimated by the FEM and
the CNN are employed to define the CPCM materials properties of a battery pack.
Our results confirm that the model developed on the basis of a CNN can evaluate
the effectiveness of the battery packs thermal management system with an
excellent accuracy in comparison with the original FEM models.


Abstract:
  The Einasto profile has been successful in describing the density profiles of
dark matter haloes in $\Lambda$CDM N-body simulations. It has also been able to
describe multiple components in the surface brightness profiles of galaxies.
However, analytically projecting it to calculate quantities under projection is
challenging. In this paper, we will see the development of a highly accurate
analytical approximation for the mass (or counts) enclosed in an infinitely
long cylindrical column for Einasto profiles--also known as the projected mass
(or counts)--using a novel methodology. We will then develop a self-consistent
high-accuracy model for the surface density from the expression for the
projected mass. Both models are quite accurate for a broad family of functions,
with a shape parameter $\alpha$ varying by a factor of 100 in the range $0.05
\lesssim \alpha \lesssim 5.0$, with fractional errors $\sim 10^{-6}$ for
$\alpha \lesssim 0.4$. Profiles with $\alpha \lesssim 0.4$ have been shown to
fit the density profiles of dark matter haloes in N-body simulations as well as
the luminosity profiles of the outer components of massive galaxies. Since the
projected mass and the surface density are used in gravitational lensing, I
will illustrate how these models facilitate (for the first time) analytical
computation of several quantities of interest in lensing due to Einasto
profiles. The models, however, are not limited to lensing and apply to similar
quantities under projection, such as the projected luminosity, the projected
(columnar) number counts and the projected density or the surface brightness.


Abstract:
  We extend the well-known characterizations of convergence in the spaces $l_p$
($1\le p<\infty$) of $p$-summable sequence and $c_0$ of vanishing sequences to
a general characterization of convergence in a Banach space with a Schauder
basis and obtain as instant corollaries characterizations of convergence in an
infinite-dimensional separable Hilbert space and the space $c$ of convergent
sequences.


Abstract:
  In this paper, we study the Ricci flow on a closed manifold and finite time
interval $[0,T)~(T < \infty)$ on which certain integral curvature energies are
finite. We prove that in dimension four, such flow converges to a smooth
Riemannian manifold except for finitely many orbifold singularities. We also
show that in higher dimensions, the same assertions hold for a closed Ricci
flow satisfying another conditions of integral curvature bounds. Moreover, we
show that such flows can be extended over $T$ by an orbifold Ricci flow.


Abstract:
  By studying the commuting graphs of conjugacy classes of the sequence of
Heisenberg groups $H_{2n+1}(p)$ and their limit $H_\infty(p)$ we find
pseudo-random behavior (and the random graph in the limiting case). This makes
a nice case study for transfer of information between finite and infinite
objects. Some of this behavior transfers to the problem of understanding what
makes understanding the character theory of the uni-upper-triangular group (mod
p) "wild". Our investigations in this paper may be seen as a meditation on the
question: is randomness simple or is it complicated?


Abstract:
  In this paper, we present a novel algorithm named synchronous integral
Q-learning, which is based on synchronous policy iteration, to solve the
continuous-time infinite horizon optimal control problems of input-affine
system dynamics. The integral reinforcement is measured as an excitation signal
in this method to estimate the solution to the Hamilton-Jacobi-Bellman
equation. Moreover, the proposed method is completely model-free, i.e. no a
priori knowledge of the system is required. Using policy iteration, the actor
and critic neural networks can simultaneously approximate the optimal value
function and policy. The persistence of excitation condition is required to
guarantee the convergence of the two networks. Unlike in traditional policy
iteration algorithms, the restriction of the initial admissible policy is
relaxed in this method. The effectiveness of the proposed algorithm is verified
through numerical simulations.


Abstract:
  The concept of porous numbers is presented. A number $k$ which is not a
multiple of 10 is called {\it porous} if every number $m$ with sum of digits =
$k$ and $k$ a divisor of both $m$ and digit reversal of $m$ has a zero in its
digits. It is proved that 11, 37, 74, 101 and 121 are the only porous numbers
smaller than 1000.


Abstract:
  We provide a detailed analysis of the Gelfand integral on Fr\'echet spaces,
showing among other things a Vitali theorem, dominated convergence and a Fubini
result. Furthermore, the Gelfand integral commutes with linear operators. The
Skorohod integral is conveniently expressed in terms of a Gelfand integral on
Hida distribution space, which forms our prime motivation and example. We
extend several results of Skorohod integrals to a general class of pathwise
Gelfand integrals. For example, we provide generalizations of the
Hida-Malliavin derivative and extend the integration-by-parts formula in
Malliavin Calculus. A Fubini-result is also shown, based on the commutative
property of Gelfand integrals with linear operators. Finally, our studies give
the motivation for two existing definitions of stochastic Volterra integration
in Hida space.


Abstract:
  Environmental concerns are the chief drive for more innovative recycling
techniques for end-of-life polymeric products. One attractive option is taking
advantage of C and H content of polymeric waste in steelmaking industry. In
this work, we examined the interaction of two high production polymers, i.e.,
polyurethane and polysulfide with molten iron using ab initio molecular
dynamics simulation. We demonstrate that both polymers can be used as
carburizers for molten iron. Additionally, we found that light weight H$_2$ and
CH$_x$ molecules were released as by-products of the polymer-molten iron
interaction. The outcomes of this study will have applications in the
carburization of molten iron during ladle metallurgy and waste plastic
injection in electric arc furnace.


Abstract:
  Analysis of human sketches in deep learning has advanced immensely through
the use of waypoint-sequences rather than raster-graphic representations. We
further aim to model sketches as a sequence of low-dimensional parametric
curves. To this end, we propose an inverse graphics framework capable of
approximating a raster or waypoint based stroke encoded as a point-cloud with a
variable-degree B\'ezier curve. Building on this module, we present
Cloud2Curve, a generative model for scalable high-resolution vector sketches
that can be trained end-to-end using point-cloud data alone. As a consequence,
our model is also capable of deterministic vectorization which can map novel
raster or waypoint based sketches to their corresponding high-resolution
scalable B\'ezier equivalent. We evaluate the generation and vectorization
capabilities of our model on Quick, Draw! and K-MNIST datasets.


Abstract:
  The idea of coded caching was introduced by Maddah-Ali and Niesen who
demonstrated the advantages of coding in caching problems. To capture the
essence of the problem, they introduced the $(N, K)$ canonical cache network in
which $K$ users with independent caches of size $M$ request files from a server
that has $N$ files. Among other results, the caching scheme and lower bounds
proposed by them led to a characterization of the exact rate memory tradeoff
when $M\geq \frac{N}{K}(K-1)$. These lower bounds along with the caching scheme
proposed by Chen et al. led to a characterization of the exact rate memory
tradeoff when $M\leq \frac{1}{K}$. In this paper we focus on small caches where
$M\in \left[0,\frac{N}{K}\right]$ and derive new lower bounds. For the case
when $\big\lceil\frac{K+1}{2}\big\rceil\leq N \leq K$ and $M\in
\big[\frac{1}{K},\frac{N}{K(N-1)}\big]$, our lower bounds demonstrate that the
caching scheme introduced by G{\'o}mez-Vilardeb{\'o} is optimal and thus extend
the characterization of the exact rate memory tradeoff. For the case $1\leq
N\leq \big\lceil\frac{K+1}{2}\big\rceil$, we show that the new lower bounds
improve upon the previously known lower bounds.


Abstract:
  Let $M$ denote the Merimovich's model in which for each infinite cardinal
$\lambda, 2^\lambda=\lambda^{+3}$. We show that in $M$ the following hold:
  (1) Shelah's strong hypothesis fails at all singular cardinals, indeed,
$\forall \lambda (\lambda$ is a singular cardinal $\Rightarrow
pp(\lambda)=\lambda^{+3}).$
  (2) For each singular cardinal $\lambda$ there is an inner model $N$ of $M$
such that $M$ and $N$ have the same bounded subsets of $\lambda,$ $\lambda$ is
a singular cardinal in $N$, $(\lambda^{+i})^N=(\lambda^{+i})^M$, for $i=1,2,3,$
and $N \models 2^\lambda=\lambda^{+}$. Thus it is possible to add many new
fresh subsets to $\lambda$ without adding any new bounded subsets to $\lambda$.


Abstract:
  The machinery of quantum mechanics is fully capable of describing a single
realistic world. Here we discuss the converse: in spite of appearances, and
indeed numerous claims to the contrary, any quantum mechanical model can be
mimicked, up to any finite accuracy, by a completely classical system of
equations. An implication of this observation is that Bell's theorem is not
applicable in the cases considered. This is explained by scrutinising Bell's
assumptions concerning causality, retrocausality, statistical (in-)dependence,
and his fear of `conspiracy' (there is no conspiracy in the language used to
describe the deterministic models). The most crucial mechanism for the counter
intuitive Bell/CHSH violation is the fact that, regardless the settings chosen
by Alice and Bob, the initial state of the system should be a realistic one.
The potential importance of our construction in model building is discussed.


Abstract:
  Crossing symmetry asserts that particles are indistinguishable from
anti-particles traveling back in time. In quantum field theory, this statement
translates to the long-standing conjecture that probabilities for observing the
two scenarios in a scattering experiment are described by one and the same
function. Why could we expect it to be true? In this work we examine this
question in a simplified setup and take steps towards illuminating a possible
physical interpretation of crossing symmetry. To be more concrete, we consider
planar scattering amplitudes involving any number of particles with arbitrary
spins and masses to all loop orders in perturbation theory. We show that by
deformations of the external momenta one can smoothly interpolate between pairs
of crossing channels without encountering singularities or violating mass-shell
conditions and momentum conservation. The analytic continuation can be realized
using two types of moves. The first one makes use of an $i\varepsilon$
prescription for avoiding singularities near the physical kinematics and allows
us to adjust the momenta of the external particles relative to one another
within their lightcones. The second, more violent, step involves a rotation of
subsets of particle momenta via their complexified lightcones from the future
to the past and vice versa. We show that any singularity along such a
deformation would have to correspond to two beams of particles scattering off
each other. For planar Feynman diagrams, these kinds of singularities are
absent because of the particular flow of energies through their propagators. We
prescribe a five-step sequence of such moves that combined together proves
crossing symmetry for planar scattering amplitudes in perturbation theory,
paving a way towards settling this question for more general scattering
processes in quantum field theories.


Abstract:
  The Search for Extraterrestrial Intelligence (SETI) is a research activity
that started in the late 1950s, predating the arrival of "Big History" and
"Astrobiology" by several decades. Many elements first developed as part of the
original SETI narrative are now incorporated in both of these emergent fields.
However, SETI still offers the widest possible perspective, since the topic
naturally leads us to consider not only the future development of our own
society but also the forward trajectories (and past histories) of many other
intelligent extraterrestrial forms. In this paper, I present a provocative view
of Big History, its rapid convergent focus on our own planet and society, its
oversimplified and incomplete view of events in cosmic history, and its limited
appreciation of how poorly we understand some aspects of the physical world.
Astrophysicists are also not spared - in particular those who wish to
understand the nature of the universe in "splendid isolation", only looking
outwards and upwards. SETI can help re-expand all of our horizons but the
discovery of extraterrestrial intelligence may also require its own
practitioners to abandon preconceptions of what constitutes intelligent,
sentient, thinking minds.


Abstract:
  As the only thiol-bearing amino acid, cysteine (Cys) residues in proteins
have the reactive thiol side chain, which is susceptible to a series of
post-translational modifications (PTMs). These PTMs participate in a wide range
of biological activities including the alteration of enzymatic reactions,
protein-protein interactions and protein stability. Here we summarize the
advance of cysteine PTM identification technologies and the features of the
various kinds of the PTMs. We also discuss in silico approaches for the
prediction of the different types of cysteine modified sites, giving directions
for future study.


Abstract:
  We demonstrate two simple theorems about squeezing induced by bilinear
spin-spin interactions that conserve spin parity -- including a vast majority
of quantum spin models implemented by state-of-the-art quantum simulators. In
particular we show that squeezing captures the first form of quantum
correlations which are produced: 1) at equilibrium, by adiabatically turning on
the spin-spin interactions starting from a factorized state aligned with an
external, arbitrary field; 2) away from equilibrium, by evolving unitarily the
same state with the interacting Hamiltonian.


Abstract:
  We report fractional revival phenomena in an ultracold matter wave inside a
ring waveguide. The specific fractional revival times are precisely identified
and corresponding spatial density patterns are depicted. Thorough analyses of
the autocorrelation function and quantum carpet provide clear evidence of their
occurrence. The exhibited theoretical model is in exact conformity of our
numerical results. We also investigate the stability of the condensate and a
variation of revival time with the diameter of the ring.


Abstract:
  We present PhySG, an end-to-end inverse rendering pipeline that includes a
fully differentiable renderer and can reconstruct geometry, materials, and
illumination from scratch from a set of RGB input images. Our framework
represents specular BRDFs and environmental illumination using mixtures of
spherical Gaussians, and represents geometry as a signed distance function
parameterized as a Multi-Layer Perceptron. The use of spherical Gaussians
allows us to efficiently solve for approximate light transport, and our method
works on scenes with challenging non-Lambertian reflectance captured under
natural, static illumination. We demonstrate, with both synthetic and real
data, that our reconstructions not only enable rendering of novel viewpoints,
but also physics-based appearance editing of materials and illumination.


Abstract:
  Explicit stabilized integrators are an efficient alternative to implicit or
semi-implicit methods to avoid the severe timestep restriction faced by
standard explicit integrators applied to stiff diffusion problems. In this
paper, we provide a fully discrete strong convergence analysis of a family of
explicit stabilized methods coupled with finite element methods for a class of
parabolic semilinear deterministic and stochastic partial differential
equations. Numerical experiments including the semilinear stochastic heat
equation with space-time white noise confirm the theoretical findings.


Abstract:
  We assess the value of machine learning as an accelerator for the
parameterisation schemes of operational weather forecasting systems,
specifically the parameterisation of non-orographic gravity wave drag.
Emulators of this scheme can be trained to produce stable and accurate results
up to seasonal forecasting timescales. Generally, more complex networks produce
more accurate emulators. By training on an increased complexity version of the
existing parameterisation scheme we build emulators that produce more accurate
forecasts. {For medium range forecasting we find evidence our emulators are
more accurate} than the version of the parametrisation scheme that is used for
operational predictions. Using the current operational CPU hardware our
emulators have a similar computational cost to the existing scheme, but are
heavily limited by data movement. On GPU hardware our emulators perform ten
times faster than the existing scheme on a CPU.


Abstract:
  We show how uncertainty in the causal structure of field theory is
essentially inevitable when one includes quantum gravity. This includes the
fact that lightcones are ill-defined in such a theory. This effect is small in
the effective field theory regime, where it it independent of the UV completion
of the theory, but grows with energy and represents an unknown uncertainty for
a generic UV completion. We include details of the causality uncertainty which
arises in a particular UV completion, i.e. quadratic gravity. We describe how
the mechanisms uncovered in the effective field theory treatment, and some of
those in quadratic gravity, could be common features of quantum gravity.


Abstract:
  The Lie algebra generated by supertranslation and superrotation vector fields
at null infinity, known as the extended BMS (eBMS) algebra is expected to be a
symmetry algebra of the quantum gravity S matrix. However, the algebra of
commutators of the quantized eBMS charges has been a thorny issue in the
literature. On the one hand, recent developments in celestial holography point
towards a symmetry algebra which is a closed Lie algebra with no central
extension or anomaly, and on the other hand, work of Distler, Flauger and Horn
has shown that when these charges are quantized at null infinity, the
commutator of a supertranslation and a superrotation charge does not close into
a supertranslation but gets deformed by a 2 cocycle term, which is consistent
with the original proposal of Barnich and Troessaert.
  In this paper, we revisit this issue in light of recent developments in the
classical understanding of superrotation charges. We show that, for extended
BMS symmetries, a phase space at null infinity is an extension of hitherto
considered phase spaces which also includes a mode associated to the spin
memory and its conjugate partner. We also show that for holomorphic vector
fields on the celestial plane, quantization of the eBMS charges in the new
phase space leads to an algebra which closes without a 2 cocycle. The
degenerate vacua are labelled by the soft news and a Schwarzian mode which
corresponds to deformations of the celestial metric by superrotations. The
closed eBMS quantum algebra may also lead to a convergence between two
manifestations of asymptotic symmetries, one via asymptotic quantization at
null infinity and the other through celestial holography.


Abstract:
  In this paper, a novel unsupervised low-rank representation model, i.e.,
Auto-weighted Low-Rank Representation (ALRR), is proposed to construct a more
favorable similarity graph (SG) for clustering. In particular, ALRR enhances
the discriminability of SG by capturing the multi-subspace structure and
extracting the salient features simultaneously. Specifically, an auto-weighted
penalty is introduced to learn a similarity graph by highlighting the effective
features, and meanwhile, overshadowing the disturbed features. Consequently,
ALRR obtains a similarity graph that can preserve the intrinsic geometrical
structures within the data by enforcing a smaller similarity on two dissimilar
samples. Moreover, we employ a block-diagonal regularizer to guarantee the
learned graph contains $k$ diagonal blocks. This can facilitate a more
discriminative representation learning for clustering tasks. Extensive
experimental results on synthetic and real databases demonstrate the
superiority of ALRR over other state-of-the-art methods with a margin of
1.8\%$\sim$10.8\%.


Abstract:
  We prove convergence of the 2- and 4-point fermionic observables of the
FK-Ising model on simply connected domains discretised by a planar isoradial
lattice in massive (near-critical) scaling limit. The former is alternatively
known as a (fermionic) martingale observable (MO) for the massive interface,
and in particular encapsulates boundary visit probabilties of the interface.
The latter encodes connection probabilities in the 4-point alternating
(generalised Dobrushin) boundary condition, whose exact convergence is then
further analysed to yield crossing estimates for general boundary conditions.
Notably, we obtain a massive version of the so-called Russo- Seymour-Welsh
(RSW) type estimates on isoradial lattice. These observables satisfy a massive
version of s-holomorphicity [Smi10], and we develop robust techniques to
exploit this condition which do not require any regularity assumption of the
domain or a particular direction of perturbation. Since many other
near-critical observables satisfy the same relation (cf. [BeDu12, CIM21,
Par19]), these strategies are of direct use in the analysis of massive models
in broader setting.


Abstract:
  Driven by the unprecedented high throughput and low latency requirements in
next-generation wireless networks, this paper introduces an artificial
intelligence (AI) enabled framework in which unmanned aerial vehicles (UAVs)
use non-orthogonal multiple access (NOMA) and mobile edge computing (MEC)
techniques to service terrestrial mobile users (MUs). The proposed framework
enables the terrestrial MUs to offload their computational tasks
simultaneously, intelligently, and flexibly, thus enhancing their connectivity
as well as reducing their transmission latency and their energy consumption. To
this end, the fundamentals of this framework are first introduced. Then, a
number of communication and AI techniques are proposed to improve the quality
of experiences of terrestrial MUs. To this end, federated learning and
reinforcement learning are introduced for intelligent task offloading and
computing resource allocation. For each learning technique, motivations,
challenges, and representative results are introduced. Finally, several key
technical challenges and open research issues of the proposed framework are
summarized.


Abstract:
  Let $f$ be a transcendental entire function. It was shown in a previous paper
that the holomorphic flow $\dot z = f(z)$ always has infinitely many
trajectories tending to infinity in finite time. It will be proved here that
such trajectories are in a certain sense rare, although an example will be
given to show that there can be uncountably many. In contrast, for the
classical antiholomorphic flow $\dot z = \bar f(z)$, such trajectories need not
exist at all, although they must if $f$ belongs to the Eremenko-Lyubich class
$\mathcal{B}$. It is also shown that for transcendental entire $f$ in
$\mathcal{B}$ there exists a path tending to infinity on which $f$ and all its
derivatives tend to infinity, thus affirming a conjecture of Rubel for this
class.


Abstract:
  Deep Neural Networks (DNNs) are a critical component for self-driving
vehicles. They achieve impressive performance by reaping information from high
amounts of labeled data. Yet, the full complexity of the real world cannot be
encapsulated in the training data, no matter how big the dataset, and DNNs can
hardly generalize to unseen conditions. Robustness to various image
corruptions, caused by changing weather conditions or sensor degradation and
aging, is crucial for safety when such vehicles are deployed in the real world.
We address this problem through a novel type of layer, dubbed StyleLess, which
enables DNNs to learn robust and informative features that can cope with
varying external conditions. We propose multiple variations of this layer that
can be integrated in most of the architectures and trained jointly with the
main task. We validate our contribution on typical autonomous-driving tasks
(detection, semantic segmentation), showing that in most cases, this approach
improves predictive performance on unseen conditions (fog, rain), while
preserving performance on seen conditions and objects.


Abstract:
  The ice-rich dwarf planet Ceres is the largest object in the main asteroid
belt and is thought to have a brine or mud layer at a depth of tens of
kilometers. Furthermore, recent surface deposits of brine-sourced material
imply shallow feeder structures such as sills or dikes. Inductive sounding of
Ceres can be performed using the solar wind as a source, as was done for the
Moon during Apollo. However, the magnetotelluric method -- measuring both
electric and magnetic fields at the surface -- is not sensitive to plasma
effects that were experienced for Apollo, which used an orbit-to-surface
magnetic transfer function. The highly conductive brine targets are readily
separable from the resistive ice and rock interior, such that the depth to deep
and shallow brines can be assessed simultaneously. The instrumentation will be
tested on the Moon in 2023 and is ready for implementation on a Ceres landed
mission.


Abstract:
  We study the number of planes for four dimensional projective hypersurfaces
which has so-called inductive structure. We also determine transcendental
lattices for cubic fourfolds of this type.


Abstract:
  The delivery of Medical Countermeasures(MCMs) for mass prophylaxis in the
case of a bio-terrorist attack is an active research topic that has interested
the research community over the past decades. The objective of this study is to
design an efficient algorithm for the Receive Reload and Store Problem(RSS) in
which we aim to find feasible routes to deliver MCMs to a target population
considering time, physical, and human resources, and capacity limitations. For
doing this, we adapt the p-median problem to the POD-based emergency response
planning procedures and propose an efficient algorithm solution to perform the
p-median in reasonable computational time. We present RE-PLAN, the Response
PLan Analyzer system that contains some RSS solutions developed at The Center
for Computational Epidemiology and Response Analysis (CeCERA) at the University
of North Texas. Finally, we analyze a study case where we show how the
computational performance of the algorithm can impact the process of decision
making and emergency planning in the short and long terms.


Abstract:
  This short note provides a new and simple proof of the convergence rate for
Peng's law of large numbers under sublinear expectations, which improves the
corresponding results in Song [15] and Fang et al. [3].


Abstract:
  Invented some 65 years ago in a seminal paper by Marguerite Straus-Frank and
Philip Wolfe, the Frank-Wolfe method recently enjoys a remarkable revival,
fuelled by the need of fast and reliable first-order optimization methods in
Data Science and other relevant application areas. This review tries to explain
the success of this approach by illustrating versatility and applicability in a
wide range of contexts, combined with an account on recent progress in
variants, both improving on the speed and efficiency of this surprisingly
simple principle of first-order optimization.


Abstract:
  We present the first determination of the $x$-dependent pion gluon
distribution from lattice QCD using the pseudo-PDF approach. We use lattice
ensembles with 2+1+1 flavors of highly improved staggered quarks (HISQ),
generated by MILC Collaboration, at two lattice spacings $a\approx 0.12$ and
0.15~fm and three pion masses $M_\pi\approx 220$, 310 and 690 MeV. We use
clover fermions for the valence action and momentum smearing to achieve pion
boost momentum up to 2.29 GeV. We find that the dependence of the pion gluon
parton distribution on lattice spacing and pion mass is mild. We compare our
results from the lightest pion mass ensemble with the determination by JAM and
xFitter global fits.


Abstract:
  Light scattering due to the interaction of photons and acoustic waves present
in a dilute inert gas is analyzed through the use of irreversible
thermodynamics. The dispersion relation, which governs the dynamics of the
density fluctuation of the gas allows the establishment of a simple criterion
for the corresponding Rayleigh-Brillouin spectrum to be observed. The criterion
here proposed allows a clear physical interpretation and suggests
generalizations for other interesting physical scenarios.


Abstract:
  We first announce our recent result on adjunction and inversion of
adjunction. Then we clarify the relationship between our inversion of
adjunction and Hacon's inversion of adjunction for log canonical centers of
arbitrary codimension.


Abstract:
  We examine the properties of the low-redshift circumgalactic medium (CGM)
around star-forming and quenched galaxies in the Simba cosmological
hydrodynamic simulations, focusing on comparing HI and metal line absorption to
observations from the COS-Halos and COS-Dwarfs surveys. Halo baryon fractions
are generally $\lesssim 50\%$ of the cosmic fraction due to stellar feedback at
low masses, and jet-mode AGN feedback at high masses. Baryons and metals in the
CGM of quenched galaxies are $\gtrsim 90\%$ hot gas, while the CGM of
star-forming galaxies is more multi-phase. Hot CGM gas has low metallicity,
while warm and cool CGM gas have metallicity close to that of galactic gas.
Equivalent widths, covering fractions and total path absorption of HI and
selected metal lines (MgII, SiIII, CIV and OVI) around a matched sample of
Simba star-forming galaxies are mostly consistent with COS-Halos and COS-Dwarfs
observations to $\lesssim 0.4$~dex, depending on ion and assumed ionising
background. Around matched quenched galaxies, absorption in all ions is lower,
with HI absorption significantly under-predicted. Metal-line absorption is
sensitive to choice of photo-ionising background; assuming recent backgrounds,
Simba matches OVI but under-predicts low ions, while an older background
matches low ions but under-predicts OVI. Simba reproduces the observed
dichotomy of OVI absorption around star forming and quenched galaxies. CGM
metals primarily come from stellar feedback, while jet-mode AGN feedback
reduces absorption particularly for lower ions.


Abstract:
  Fix an odd prime $p$. If $r$ is a positive integer and $f$ a polynomial with
coefficients in $\mathbb{F}_{p^r}$, let $P_{p,r}(f)$ be the proportion of
$\mathbb{P}^1(\mathbb{F}_{p^r})$ that is periodic with respect to $f$. We show
that as $r$ increases, the expected value of $P_{p,r}(f)$, as $f$ ranges over
quadratic polynomials, is less than $22/(\log{\log{p^r}})$. This result follows
from a uniformity theorem on specializations of dynamical systems of rational
functions over residually finite Dedekind domains. The specialization theorem
generalizes previous work by Juul et al. that holds for rings of integers of
number fields. Moreover, under stronger hypotheses, we effectivize this
uniformity theorem by using the machinery of heights over general global
fields; this version of the theorem generalizes previous work of Juul on
polynomial dynamical systems over rings of integers of number fields. From
these theorems we derive effective bounds on image sizes and periodic point
proportions of families of rational functions over finite fields.


Abstract:
  Consider a message processing system whose objective is to produce the most
current information as measured by the quantity known as "age of information".
We have argued in previous papers that if we are allowed to design the message
processing policy ad libitum, we should keep a small buffer and operate
according to a LIFO policy. In this small note we provide an analysis for the
AoI of the P_m system which uses a buffer of size m, a single server, operating
without service preemption and in a LIFO manner for stored messages. Analytical
expressions for the mean (or even distribution) of the AoI in steady-state are
possible but with the aid computer algebra. We explain the the analysis for
m=3.


Abstract:
  It is well known that sets of $p$-capacity zero are removable for bounded
$p$-harmonic functions, but on metric spaces there are examples of removable
sets of positive capacity. In this paper, we show that this can happen even on
unweighted $\mathbf{R}^n$ when $n > p$, although only in very special cases. A
complete characterization of removable singularities for bounded
$\mathcal{A}$-harmonic functions on weighted $\mathbf{R}^n$, $n \ge 1$, is also
given, where the weight is $p$-admissible. The same characterization is also
shown to hold for bounded quasiharmonic functions on weighted $\mathbf{R}^n$,
$n \ge 2$, as well as on unweighted $\mathbf{R}$. For bounded
$\mathcal{A}$-superharmonic functions and bounded quasisuperharmonic functions
on weighted $\mathbf{R}^n$, $n \ge 2$, we show that relatively closed sets are
removable if and only if they have zero capacity.


Abstract:
  Human Activity Recognition (HAR) is one of the key applications of health
monitoring that requires continuous use of wearable devices to track daily
activities. This paper proposes an Adaptive CNN for energy-efficient HAR (AHAR)
suitable for low-power edge devices. Unlike traditional early exit architecture
that makes the exit decision based on classification confidence, AHAR proposes
a novel adaptive architecture that uses an output block predictor to select a
portion of the baseline architecture to use during the inference phase.
Experimental results show that traditional early exit architectures suffer from
performance loss whereas our adaptive architecture provides similar or better
performance as the baseline one while being energy-efficient. We validate our
methodology in classifying locomotion activities from two datasets- Opportunity
and w-HAR. Compared to the fog/cloud computing approaches for the Opportunity
dataset, our baseline and adaptive architecture shows a comparable weighted F1
score of 91.79%, and 91.57%, respectively. For the w-HAR dataset, our baseline
and adaptive architecture outperforms the state-of-the-art works with a
weighted F1 score of 97.55%, and 97.64%, respectively. Evaluation on real
hardware shows that our baseline architecture is significantly energy-efficient
(422.38x less) and memory-efficient (14.29x less) compared to the works on the
Opportunity dataset. For the w-HAR dataset, our baseline architecture requires
2.04x less energy and 2.18x less memory compared to the state-of-the-art work.
Moreover, experimental results show that our adaptive architecture is 12.32%
(Opportunity) and 11.14% (w-HAR) energy-efficient than our baseline while
providing similar (Opportunity) or better (w-HAR) performance with no
significant memory overhead.


Abstract:
  Many Imitation and Reinforcement Learning approaches rely on the availability
of expert-generated demonstrations for learning policies or value functions
from data. Obtaining a reliable distribution of trajectories from motion
planners is non-trivial, since it must broadly cover the space of states likely
to be encountered during execution while also satisfying task-based
constraints. We propose a sampling strategy based on variational inference to
generate distributions of feasible, low-cost trajectories for high-dof motion
planning tasks. This includes a distributed, particle-based motion planning
algorithm which leverages a structured graphical representations for inference
over multi-modal posterior distributions. We also make explicit connections to
both approximate inference for trajectory optimization and entropy-regularized
reinforcement learning.


Abstract:
  This paper is devoted to studying the two-weight extrapolation theory of
Rubio de Francia. We start to establish endpoint extrapolation results
including $A_1$, $A_p$ and $A_\infty$ extrapolation in the context of Banach
function spaces and modular spaces. Furthermore, we present extrapolation for
commutators in weighted Banach function spaces. Beyond that, we give several
applications which can be easily obtained using extrapolation. First, we get
local decay estimates for various operators and commutators. Second,
Coifman-Fefferman inequalities are established and can be used to show some
known sharp $A_1$ inequalities. Third, Muckenhoupt-Wheeden and Sawyer
conjectures are also presented for many operators, which go beyond
Calder\'{o}n-Zygmund operators. Finally, we obtain two-weight inequalities for
Littlewood-Paley operators and Fourier integral operators on weighted Banach
function spaces.


Abstract:
  An instrumentation unit that measures heat flow along the test column of
solid metal samples is described. The study also describes the design of a
datalogger using Arduino Mega Microcontroller. The designed instrumentation
unit incorporates current and voltage sensing unit, ten thermocouple sensors
and amplifiers, microSD card shield, two LCDs, four microcontrolled switching
relays and a 12 V DC water pump motor. The heat flow parameters measured by the
instrumentation unit includes temperature gradient, heater current and heater
voltage. The designed datalogger logs the measured value of temperature,
current and voltage at a time of ten minutes. An area of application for this
study is in the development of a device for measurement of thermal conductivity
of solid metals. It is an improvement over existing heat flow measurement
techniques.


Abstract:
  Quantum machine learning is an emerging field at the intersection of machine
learning and quantum computing. Classical cross entropy plays a central role in
machine learning. We define its quantum generalization, the quantum cross
entropy, prove its lower bounds, and investigate its relation to quantum
fidelity. In the classical case, minimizing cross entropy is equivalent to
maximizing likelihood. In the quantum case, when the quantum cross entropy is
constructed from quantum data undisturbed by quantum measurements, this
relation holds. Classical cross entropy is equal to negative log-likelihood.
When we obtain quantum cross entropy through empirical density matrix based on
measurement outcomes, the quantum cross entropy is lower-bounded by negative
log-likelihood. These two different scenarios illustrate the information loss
when making quantum measurements. We conclude that to achieve the goal of full
quantum machine learning, it is crucial to utilize the deferred measurement
principle.


Abstract:
  Using dominant eigenvectors for background modeling (usually known as
Eigenbackground) is a common technique in the literature. However, its results
suffer from noticeable artifacts. Thus have been many attempts to reduce the
artifacts by making some improvements/enhancement in the Eigenbackground
algorithm.
  In this paper, we show the main problem of the Eigenbackground is in its own
core and in fact, it is not a good idea to use strongest eigenvectors for
modeling the background. Instead, we propose an alternative solution by
exploiting the weakest eigenvectors (which are usually thrown away and treated
as garbage data) for background modeling. MATLAB codes are available at
\url{https://github.com/mamintoosi/Eigenbackground-Revisited}


Abstract:
  This work presents a new way of exploiting non-uniform file popularity in
coded caching networks. Focusing on a fully-connected fully-interfering
wireless setting with multiple cache-enabled transmitters and receivers, we
show how non-uniform file popularity can be used very efficiently to accelerate
the impact of transmitter-side data redundancy on receiver-side coded caching.
This approach is motivated by the recent discovery that, under any realistic
file-size constraint, having content appear in multiple transmitters can in
fact dramatically boost the speed-up factor attributed to coded caching.
  We formulate an optimization problem that exploits file popularity to
optimize the placement of files at the transmitters. We then provide a proof
that reduces significantly the variable search space, and propose a new search
algorithm that solves the problem at hand. We also prove an analytical
performance upper bound, which is in fact met by our algorithm in the regime of
many receivers. Our work reflects the benefits of allocating higher cache
redundancy to more popular files, but also reflects a law of diminishing
returns where for example very popular files may in fact benefit from minimum
redundancy. In the end, this work reveals that in the context of coded caching,
employing multiple transmitters can be a catalyst in fully exploiting file
popularity, as it avoids various asymmetry complications that appear when file
popularity is used to alter the receiver-side cache placement.


Abstract:
  In financial investing, universal portfolios are a means of constructing
portfolios which guarantee a certain level of performance relative to a
baseline, while making no statistical assumptions about the future market data.
They fall under the broad category of regret minimization algorithms. This
document covers an introduction and survey to universal portfolio techniques,
covering some of the basic concepts and proofs in the area. Topics include:
Constant Rebalanced Portfolios, Cover's Algorithm, Incorporating Transaction
Costs, Efficient Computation of Portfolios, Including Side Information, and
Follow The Leader Algorithm.


Abstract:
  We show that every convergent power series with monomial extended Jacobian
ideal is right equivalent to a Thom-Sebastiani polynomial. This solves a
problem posed by Hauser and Schicho. On the combinatorial side, we introduce a
notion of Jacobian semigroup ideal involving a transversal matroid. For any
such ideal we construct a defining Thom-Sebastiani polynomial. On the analytic
side, we show that power series with a quasihomogeneous extended Jacobian ideal
are strongly Euler homogeneous. Due to a Mather-Yau-type theorem, such power
series are determined by their Jacobian ideal up to right equivalence.


Abstract:
  In this extended abstract, we report on ongoing work towards an approximate
multimodal optimization algorithm with asymptotic guarantees. Multimodal
optimization is the problem of finding all local optimal solutions (modes) to a
path optimization problem. This is important to compress path databases, as
contingencies for replanning and as source of symbolic representations.
Following ideas from Morse theory, we define modes as paths invariant under
optimization of a cost functional. We develop a multi-mode estimation algorithm
which approximately finds all modes of a given motion optimization problem and
asymptotically converges. This is made possible by integrating sparse roadmaps
with an existing single-mode optimization algorithm. Initial evaluation results
show the multi-mode estimation algorithm as a promising direction to study path
spaces from a topological point of view.


Abstract:
  For nearly a century since Ising model was proposed in 1925, it is agreed
that there is no phase transition with temperature in the one-dimensional based
on no global spontaneous magnetization in whole temperature region. In this
paper, the exact calculation of local spontaneous magnetization shows that a
diffuse phase transition with temperature occurs in one-dimensional Ising
model. In addition, although diffuse phase transition phenomenon is common in
the systems of heterogeneous-components and grains etc., there is no accurate
prediction of corresponding theoretical models so far, so the present works lay
the theoretical foundation of this kind of phase transition.


Abstract:
  Many polytopes arising in polyhedral combinatorics are linear projections of
higher-dimensional polytopes with significantly fewer facets. Such lifts may
yield compressed representations of polytopes, which are typically used to
construct small-size linear programs. Motivated by algorithmic implications for
the closest vector problem, we study lifts of Voronoi cells of lattices.
  We construct an explicit $d$-dimensional lattice such that every lift of the
respective Voronoi cell has $2^{\Omega(d / \log d)}$ facets. On the positive
side, we show that Voronoi cells of $d$-dimensional root lattices and their
dual lattices have lifts with $O(d)$ and $O(d \log d)$ facets, respectively. We
obtain similar results for spectrahedral lifts.


Abstract:
  Given input-output pairs of an elliptic partial differential equation (PDE)
in three dimensions, we derive the first theoretically-rigorous scheme for
learning the associated Green's function $G$. By exploiting the hierarchical
low-rank structure of $G$, we show that one can construct an approximant to $G$
that converges almost surely and achieves an expected relative error of
$\epsilon$ using at most
$\mathcal{O}(\epsilon^{-6}\log^4(1/\epsilon)/\Gamma_\epsilon)$ input-output
training pairs, for any $0<\epsilon<1$. The quantity $0<\Gamma_\epsilon\leq 1$
characterizes the quality of the training dataset. Along the way, we extend the
randomized singular value decomposition algorithm for learning matrices to
Hilbert--Schmidt operators and characterize the quality of covariance kernels
for PDE learning.


Abstract:
  We present a calculation of the decays
$\eta^{(')}\to\pi^+\pi^-\gamma^{(\ast)}$ at the one-loop level up to and
including next-to-next-to-leading order (NNLO) in large-$N_c$ chiral
perturbation theory. The numerical evaluation of the results is performed
successively at LO, NLO, and NNLO, fitting the relevant low-energy constants to
the available experimental data. We discuss the widths and decay spectra of
$\eta^{(')}\to\pi^+\pi^-\gamma$ as well as $\eta^{(')}\to\pi^+\pi^-l^+l^-$,
with $l=e,\ \mu$.


Abstract:
  The physical conditions leading the sunspot penumbra decay are poorly
understood so far. We investigate the photospheric magnetic and velocity
properties of a sunspot penumbra during the decay phase to advance the current
knowledge of the conditions leading to this process. A penumbral decay was
observed with the CRISP instrument at the Swedish 1m Solar Telescope on 2016
September 4 and 5 in active region NOAA 12585. During these days, full-Stokes
spectropolarimetric scans along the Fe I 630 nm line pair were acquired over
more than one hour. We inverted these observations with the VFISV code in order
to obtain the evolution of the magnetic and velocity properties. We complement
the study with data from instruments onboard the Solar Dynamics Observatory and
Hinode space missions. The studied penumbra disappears progressively in both
time and space. The magnetic flux evolution seems to be linked to the presence
of Moving Magnetic Features (MMFs). Decreasing Stokes V signals are observed.
Evershed flows and horizontal fields were detected even after the disappearance
of the penumbral sector. The analyzed penumbral decay seems to result from the
interaction between opposite polarity fields in type III MMFs and penumbra,
while the presence of overlying canopies rules the evolution in the different
penumbral sectors.


Abstract:
  We present sensitive ALMA observations of TWA 3, a nearby, young ($\sim$10
Myr) hierarchical system composed of three pre-main sequence M3--M4.5 stars.
For the first time, we detected ${}^{12}$CO and ${}^{13}$CO $J$=2-1 emission
from the circumbinary protoplanetary disk around TWA 3A. We jointly fit the
protoplanetary disk velocity field, stellar astrometric positions, and stellar
radial velocities to infer the architecture of the system. The Aa and Ab stars
($0.29\pm0.01\,M_\odot$ and $0.24\pm0.01\,M_\odot$, respectively) comprising
the tight ($P=35$ days) eccentric ($e=0.63\pm0.01$) spectroscopic binary are
coplanar with their circumbinary disk (misalignment $< 6^{\circ}$ with 68%
confidence), similar to other short-period binary systems. From models of the
spectral energy distribution, we found the inner radius of the circumbinary
disk ($r_\mathrm{inner} = 0.50 - 0.75$ au) to be consistent with theoretical
predictions of dynamical truncation $r_\mathrm{cav}/a_\mathrm{inner} \approx
3$. The outer orbit of the tertiary star B ($0.40\pm0.28\,M_\odot$, $a\sim65
\pm 18$ au, $e=0.3\pm0.2$) is not as well constrained as the inner orbit,
however, orbits coplanar with the A system are still preferred (misalignment $
< 20^{\circ}$). To better understand the influence of the B orbit on the TWA 3A
circumbinary disk, we performed SPH simulations of the system and found that
the outer edge of the gas disk ($r_\mathrm{outer}=8.5\pm0.2$ au) is most
consistent with truncation from a coplanar, circular or moderately eccentric
orbit, supporting the preference from the joint orbital fit.


Abstract:
  Multimedia content transmission heavily taxes network resources and puts a
significant burden on wireless systems in terms of capacity and energy
consumption. In this context, device-to-device (D2D) paradigm has a utilitarian
value to alleviate the network burden by utilizing short-range transmissions
with less energy cost. For the realization of proper D2D networking, caching in
devices is essential. Additionally, caching needs to operate efficiently with
the aim of realizing network-wide energy improvements. To this end, we study
multimedia caching in cellular D2D networks and propose an energy prioritized
D2D caching (EPDC) algorithm in this work. We also investigate the optimal
caching policy in that system setting. The impact of device capacity and D2D
transmission range on energy consumption is studied by focusing on different
operation modes such as D2D and base station transmissions. According to the
simulation results, EPDC algorithm has substantial energy-efficiency gains over
the commonly utilized Least Recently Used (LRU) algorithm and content-based
caching algorithms PDC and SXO. For larger D2D transmission ranges, this
improvement becomes more evident.


Abstract:
  We demonstrate a compact two-chip terahertz-emitting vertical-external-cavity
surface-emitting laser (TECSEL) source, which provides 1-THz output based on
intracavity frequency conversion of dual-wavelength emission in a
periodically-poled lithium niobate crystal. The type-I frequency conversion
scheme at room temperature highly benefits from the power-scaling possibilities
in a multichip cavity with intracavity powers in excess of 500 W.


Abstract:
  Location-aware networks will introduce innovative services and applications
for modern convenience, applied ocean sciences, and public safety. In this
paper, we establish a hybrid method for model-based and data-driven inference.
We consider a cooperative localization (CL) scenario where the mobile agents in
a wireless network aim to localize themselves by performing pairwise
observations with other agents and by exchanging location information. A
traditional method for distributed CL in large agent networks is belief
propagation (BP) which is completely model-based and is known to suffer from
providing inconsistent (overconfident) estimates. The proposed approach
addresses these limitations by complementing BP with learned information
provided by a graph neural network (GNN). We demonstrate numerically that our
method can improve estimation accuracy and avoid overconfident beliefs, while
its computational complexity remains comparable to BP. Notably, more consistent
beliefs are obtained by not explicitly addressing overconfidence in the loss
function used for training of the GNN.


Abstract:
  Calculations of the Fourier transform of a constant quantity over an area or
volume defined by polygons (connected vertices) are often useful in modeling
wave scattering, or in fourier-space filtering of real-space vector-based
volumes and area projections. If the system is discretized onto a regular
array, Fast Fourier techniques can speed up the resulting calculations but if
high spatial resolution is required the initial step of discretization can
limit performance; at other times the discretized methods result in
unacceptable artifacts in the resulting transform. An alternative approach is
to calculate the full Fourier integral transform of a polygonal area as a sum
over the vertices, which has previously been derived in the literature using
the divergence theorem to reduce the problem from a 3-dimensional to line
integrals over the perimeter of the polygon surface elements, and converted to
a sum over the straight segments of that contour. We demonstrate a software
implementation of this algorithm and show that it can provide accurate
approximations of the Fourier transform of real shapes with faster convergence
than a block-based (voxel) discretization.


Abstract:
  Since the discovery of hypervelocity stars in 2005, it has been widely
believed that only the disruption of a binary system by a supermassive black
hole at the Galactic center (GC), that is, the so-called Hills mechanism, is
capable of accelerating stars to beyond the Galactic escape velocity. In the
meantime, however, driven by the Gaia space mission, there is mounting evidence
that many of the most extreme high-velocity early-type stars at high Galactic
latitudes do originate in the Galactic disk and not in the GC. Moreover, the
ejection velocities of these extreme disk-runaway stars exceed the predicted
limits of the classical scenarios for the production of runaway stars. Based on
proper motions from the Gaia early data release 3 and on recent and new
spectrophotometric distances, we studied the kinematics of 30 such extreme
disk-runaway stars, allowing us to deduce their spatial origins in and their
ejection velocities from the Galactic disk with unprecedented precision. Only
three stars in the sample have past trajectories that are consistent with an
origin in the GC, most notably S5-HVS1, which is the most extreme object in the
sample by far. All other program stars are shown to be disk runaways with
ejection velocities that sharply contrast at least with classical ejection
scenarios. They include HVS5 and HVS6, which are both gravitationally unbound
to the Milky Way. While most stars originate from within a galactocentric
radius of 15kpc, which corresponds to the observed extent of the spiral arms, a
group of five stars stems from radii of about 21-29kpc. This indicates a
possible link to outer Galactic rings and a potential origin from infalling
satellite galaxies.


Abstract:
  Nonnegative matrix factorization (NMF) based topic modeling methods do not
rely on model- or data-assumptions much. However, they are usually formulated
as difficult optimization problems, which may suffer from bad local minima and
high computational complexity. In this paper, we propose a deep NMF (DNMF)
topic modeling framework to alleviate the aforementioned problems. It first
applies an unsupervised deep learning method to learn latent hierarchical
structures of documents, under the assumption that if we could learn a good
representation of documents by, e.g. a deep model, then the topic word
discovery problem can be boosted. Then, it takes the output of the deep model
to constrain a topic-document distribution for the discovery of the
discriminant topic words, which not only improves the efficacy but also reduces
the computational complexity over conventional unsupervised NMF methods. We
constrain the topic-document distribution in three ways, which takes the
advantages of the three major sub-categories of NMF -- basic NMF, structured
NMF, and constrained NMF respectively. To overcome the weaknesses of deep
neural networks in unsupervised topic modeling, we adopt a non-neural-network
deep model -- multilayer bootstrap network. To our knowledge, this is the first
time that a deep NMF model is used for unsupervised topic modeling. We have
compared the proposed method with a number of representative references
covering major branches of topic modeling on a variety of real-world text
corpora. Experimental results illustrate the effectiveness of the proposed
method under various evaluation metrics.


Abstract:
  In this paper we consider the inverse problem of determining on a compact
Riemannian manifold the metric tensor in the wave equation with Dirichlet data
from measured Neumann boundary observations. This information is enclosed in
the dynamical Dirichlet-to-Neumann map associated to the wave equation. We
prove in dimension $n\geq 2$ that the knowledge of the Dirichlet-to-Neumann map
for the wave equation uniquely determines the metric tensor and we establish
logarithm-type stability.


Abstract:
  Carpooling has the potential to transform itself into a mass transportation
mode by abandoning its adherence to deterministic passenger-driver matching for
door-to-door journeys, and by adopting instead stochastic matching on a network
of fixed meeting points. Stochastic matching is where a passenger sends out a
carpooling request at a meeting point, and then waits for the arrival of a
self-selected driver who is already travelling to the requested meeting point.
Crucially there is no centrally dispatched driver. Moreover, the carpooling is
assured only between the meeting points, so the onus is on the passengers to
travel to/from them by their own means. Thus the success of a stochastic
carpooling service relies on the convergence, with minimal perturbation to
their existing travel patterns, to the meeting points which are highly
frequented by both passengers and drivers. Due to the innovative nature of
stochastic carpooling, existing off-the-shelf workflows are largely
insufficient for this purpose. To fill the gap in the market, we introduce a
novel workflow, comprising of a combination of data science and GIS (Geographic
Information Systems), to analyse driver GPS traces. We implement it for an
operational stochastic carpooling service in south-eastern France, and we
demonstrate that relaxing door-to-door matching reduces passenger waiting
times. Our workflow provides additional key operational indicators, namely the
driver flow maps, the driver flow temporal profiles and the driver
participation rates.


Abstract:
  In a recent study, we developed a method to model the impact of photometric
redshift uncertainty on the two-point correlation function (2PCF). In this
method, we can obtain both the intrinsic clustering strength and the
photometric redshift errors simultaneously by fitting the projected 2PCF with
two integration depths along the line-of-sight. Here we apply this method to
the DESI Legacy Imaging Surveys Data Release 8 (LS DR8), the largest galaxy
sample currently available. We separate galaxies into 20 samples in 8 redshift
bins from $z=0.1$ to $z=1.0$, and a few $\rm z$-band absolute magnitude bins,
with $M_{\rm z} \le -20$. These galaxies are further separated into red and
blue sub-samples according to their $M^{0.5}_{\rm r}-M^{0.5}_{\rm z}$ colors.
We measure the projected 2PCFs for all these galaxy (sub-)samples, and fit them
using our photometric redshift 2PCF model. We find that the photometric
redshift errors are smaller in red sub-samples than the overall population. On
the other hand, there might be some systematic photometric redshift errors in
the blue sub-samples, so that some of the sub-samples show significantly
enhanced 2PCF at large scales. Therefore, focusing only on the red and all
(sub-)samples, we find that the biases of galaxies in these (sub-)samples show
clear color, redshift and luminosity dependencies, in that red brighter
galaxies at higher redshift are more biased than their bluer and low redshift
counterparts. Apart from the best fit set of parameters, $\sigma_{z}$ and $b$,
from this state-of-the-art photometric redshift survey, we obtain high
precision intrinsic clustering measurements for these 40 red and all galaxy
(sub-)samples. These measurements on large and small scales hold important
information regarding the cosmology and galaxy formation, which will be used in
our subsequent probes in this series.


Abstract:
  We estimate the net information exchange between adjacent quantum subsystems
holographically living on the boundary of $AdS$ spacetime. The information
exchange is a real time phenomenon and only after long time interval it may get
saturated. Normally we prepare systems for small time intervals and measure the
information exchange over finite interval only. We find that the information
flow between entangled subsystems gets reduced if systems are in excited state
whereas the ground state allows maximum information flow at any given time.
Especially for $CFT_2$ we exactly show that a rise in the entropy is
detrimental to the information exchange by a quantum dot and vice-versa. We
next observe that there is a reduction in circuit (CV) complexity too in the
presence of excitations for small times.