  Due to the scarcity in the wireless spectrum and limited energy resources
especially in mobile applications, efficient resource allocation strategies are
critical in wireless networks. Motivated by the recent advances in deep
reinforcement learning (DRL), we address multi-agent DRL-based joint dynamic
channel access and power control in a wireless interference network. We first
propose a multi-agent DRL algorithm with centralized training (DRL-CT) to
tackle the joint resource allocation problem. In this case, the training is
performed at the central unit (CU) and after training, the users make
autonomous decisions on their transmission strategies with only local
information. We demonstrate that with limited information exchange and faster
convergence, DRL-CT algorithm can achieve 90% of the performance achieved by
the combination of weighted minimum mean square error (WMMSE) algorithm for
power control and exhaustive search for dynamic channel access. In the second
part of this paper, we consider distributed multi-agent DRL scenario in which
each user conducts its own training and makes its decisions individually,
acting as a DRL agent. Finally, as a compromise between centralized and fully
distributed scenarios, we consider federated DRL (FDRL) to approach the
performance of DRL-CT with the use of a central unit in training while limiting
the information exchange and preserving privacy of the users in the wireless
system. Via simulation results, we show that proposed learning frameworks lead
to efficient adaptive channel access and power control policies in dynamic
environments.


  For random field theory based multiple comparison corrections In brain
imaging, it is often necessary to compute the distribution of the supremum of a
random field. Unfortunately, computing the distribution of the supremum of the
random field is not easy and requires satisfying many distributional
assumptions that may not be true in real data. Thus, there is a need to come up
with a different framework that does not use the traditional statistical
hypothesis testing paradigm that requires to compute p-values. With this as a
motivation, we can use a different approach called the logistic regression that
does not require computing the p-value and still be able to localize the
regions of brain network differences. Unlike other discriminant and
classification techniques that tried to classify preselected feature vectors,
the method here does not require any preselected feature vectors and performs
the classification at each edge level.


  Recently, our proposed recurrent neural network (RNN) based all deep learning
minimum variance distortionless response (ADL-MVDR) beamformer method yielded
superior performance over the conventional MVDR by replacing the matrix
inversion and eigenvalue decomposition with two recurrent neural networks. In
this work, we present a self-attentive RNN beamformer to further improve our
previous RNN-based beamformer by leveraging on the powerful modeling capability
of self-attention. Temporal-spatial self-attention module is proposed to better
learn the beamforming weights from the speech and noise spatial covariance
matrices. The temporal self-attention module could help RNN to learn global
statistics of covariance matrices. The spatial self-attention module is
designed to attend on the cross-channel correlation in the covariance matrices.
Furthermore, a multi-channel input with multi-speaker directional features and
multi-speaker speech separation outputs (MIMO) model is developed to improve
the inference efficiency. The evaluations demonstrate that our proposed MIMO
self-attentive RNN beamformer improves both the automatic speech recognition
(ASR) accuracy and the perceptual estimation of speech quality (PESQ) against
prior arts.


  Let $ \tilde{G} $ be an algebraic group acting on a variety $ \tilde{L} $,
and $ G \subset \tilde{G} $ a subgroup which leaves a subvariety $ L \subset
\tilde{L} $ stable. For a $ G $-orbit $ O_G = G u (u \in L) $ in $ L $, we can
associate an orbit $ O_{\tilde{G}} = \tilde{G} u $ of $ \tilde{G} $ so that we
get a map $ L/G \to \tilde{L}/\tilde{G} $ between orbit spaces, though this map
is usually not injective. In this note, when $ G $ is a symmetric subgroup
arising from an involutive anti-automorphism, we give certain sufficient
conditions for the map $ L/G \to \tilde{L}/\tilde{G} $ to be injective after
the method of Ohta (2008). Our main concern here is to produce examples of
enhanced Lie algebras (or enhanced $ \theta $-representations). We also analyze
an obstruction which prevents the orbit space inclusion.


  We introduce new vanishing subspaces of the homogeneous H\"{o}lder space
$\dot{C}^{0,\omega}(X,Y),$ in the generality of a doubling modulus $\omega$ and
normed spaces $X$ and $Y.$ For many couples $X,Y,$ we show these vanishing
subspaces to completely characterize those H\"older functions that admit
approximations, in the H\"{o}lder seminorm, by smooth, Lipschitz and boundedly
supported functions. Beyond the intrinsic interest of these results, we also
present connections to bi-parameter harmonic analysis on the Euclidean space
and in particular to compactness of the bi-commutator.


  A critical function of an organization is to foster the level of integration
(coordination and cooperation) necessary to achieve its objectives. The need to
coordinate and motivation to cooperate emerges from the myriad dependencies
between an organization's members and their work. Therefore, to reason about
solutions to coordination and cooperation problems requires a robust
representation that includes the underlying dependencies. We find that such a
representation remains missing from formal organizational models, and we
leverage semantics to bridge this gap. Drawing on well-established
organizational research and our extensive fieldwork with one of North America's
largest municipalities, (1) we introduce an ontology, formalized in first-order
logic, that operationalizes concepts like outcome, reward, and epistemic
dependence, and their links to potential integration risks; and (2) present
real-world applications of this ontology to analyze and support integration in
complex government infrastructure projects. Our ontology is implemented and
validated in both Z3 and OWL. Key features of our model include inferable
dependencies, explainable coordination and cooperation risks, and actionable
insights on how dependency structures within an organization can be altered to
mitigate the risks. Conceptualizing real-world challenges like incentive
misalignment, free-riding, and subgoal optimization in terms of dependency
structures, our semantics-based approach represents a novel method for
modelling and enhancing coordination and cooperation. Integrated within a
decision-support system, our model may serve as an impactful aid for
organizational design and effectiveness. More broadly, our approach underscores
the transformative potential of semantics in deriving tangible, real-world
value from existing organization theory.


  Conformal blocks for the WZW model on tori can be represented by vector
valued Weyl anti-symmetric theta functions on the Cartan subalgebra satisfying
vanishing conditions on root hyperplanes. We introduce a quantum version of
these vanishing conditions in the sl(2) case. They are compatible with the qKZB
equations and are obeyed by the hypergeometric solutions as well as by their
critical level counterpart, which are Bethe eigenfunctions of IRF row-to-row
transfer matrices. In the language of IRF models the vanishing conditions turn
out to be equivalent to the sl(2) fusion rules defining restricted models.


  Let $t$ be a permutation (that shall play the role of the {\em text}) on
$[n]$ and a pattern $p$ be a sequence of $m$ distinct integer(s) of $[n]$,
$m\leq n$. The pattern $p$ occurs in $t$ in position $i$ if and only if $p_1...
p_m$ is order-isomorphic to $t_i... t_{i+m-1}$, that is, for all $1 \leq k<
\ell \leq m$, $p_k>p_\ell$ if and only if $t_{i+k-1}>t_{i+\ell-1}$. Searching
for a pattern $p$ in a text $t$ consists in identifying all occurrences of $p$
in $t$. We first present a forward automaton which allows us to search for $p$
in $t$ in $O(m^2\log \log m +n)$ time. We then introduce a Morris-Pratt
automaton representation of the forward automaton which allows us to reduce
this complexity to $O(m\log \log m +n)$ at the price of an additional amortized
constant term by integer of the text. Both automata occupy $O(m)$ space. We
then extend the problem to search for a set of patterns and exhibit a specific
Aho-Corasick like algorithm. Next we present a sub-linear average case search
algorithm running in $O(\frac{m\log m}{\log\log m}+\frac{n\log m}{m\log\log
m})$ time, that we eventually prove to be optimal on average.


  Finding approximate occurrences of a pattern in a text using a full-text
index is a central problem in bioinformatics and has been extensively
researched. Bidirectional indices have opened new possibilities in this regard
allowing the search to start from anywhere within the pattern and extend in
both directions. In particular, use of search schemes (partitioning the pattern
and searching the pieces in certain orders with given bounds on errors) can
yield significant speed-ups. However, finding optimal search schemes is a
difficult combinatorial optimization problem.
  Here for the first time, we propose a mixed integer program (MIP) capable to
solve this optimization problem for Hamming distance with given number of
pieces. Our experiments show that the optimal search schemes found by our MIP
significantly improve the performance of search in bidirectional FM-index upon
previous ad-hoc solutions. For example, approximate matching of 101-bp Illumina
reads (with two errors) becomes 35 times faster than standard backtracking.
Moreover, despite being performed purely in the index, the running time of
search using our optimal schemes (for up to two errors) is comparable to the
best state-of-the-art aligners, which benefit from combining search in index
with in-text verification using dynamic programming. As a result, we anticipate
a full-fledged aligner that employs an intelligent combination of search in the
bidirectional FM-index using our optimal search schemes and in-text
verification using dynamic programming outperforms today's best aligners. The
development of such an aligner, called FAMOUS (Fast Approximate string Matching
using OptimUm search Schemes), is ongoing as our future work.


  Thermal conductivity (\kappa) of a distorted spin diamond-chain system,
Cu_3(CO_3)_2(OH)_2, is studied at low temperatures down to 0.3 K and in
magnetic fields up to 14 T. In zero field, the \kappa(T) curve with heat
current along the chain direction has very small magnitudes and shows a
pronounced three-peak structure. The magnetic fields along and perpendicular to
the chains change the \kappa strongly in a way having good correspondence to
the changes of magnetic specific heat in fields. The data analysis based on the
Debye model for phononic thermal conductivity indicates that the heat transport
is due to phonons and the three-peak structure is caused by two resonant
scattering processes by the magnetic excitations. In particular, the spin
excitations of the chain subsystem are strongly scattering phonons rather than
transporting heat.


  This article is a study guide for ``On the Hausdorff dimension of Furstenberg
sets and orthogonal projections in the plane" by Orponen and Shmerkin. We begin
by introducing Furstenberg set problem and exceptional set of projections and
provide a summary of the proof with the core ideas.


  Equitable list arboricity, introduced by Zhang in 2016, generalizes the
notion of equitable list coloring by requiring the subgraph induced by each
color class to be acyclic (instead of edgeless) in addition to the usual upper
bound on the size of each color class. Graph $G$ is equitably $k$-list
arborable if an equitable, arborable list coloring of $G$ exists for every list
assignment for $G$ that associates with each vertex in $G$ a list of $k$
available colors. Zhang conjectured that any graph $G$ is equitably $k$-list
arborable for each $k$ satisfying $k \geq \lceil (1+\Delta(G))/2 \rceil$. We
verify this conjecture for powers of cycles by applying a new lemma which is a
general tool for extending partial equitable, arborable list colorings. We also
propose a stronger version of Zhang's Conjecture for certain connected graphs:
any connected graph $G$ is equitably $k$-list arborable for each $k$ satisfying
$k \geq \lceil \Delta(G)/2 \rceil$ provided $G$ is neither a cycle nor a
complete graph of odd order. We verify this stronger version of Zhang's
Conjecture for powers of paths, 2-degenerate graphs, and certain other graphs.
We also show that if $G$ is equitably $k$-list arborable it does not
necessarily follow that $G$ is equitably $(k+1)$-list arborable which addresses
a question of Drgas-Burchardt, Furmanczyk, and Sidorowicz (2018).


  We propose new low-fidelity (LoFi) user equipment (UE) scheduling algorithms
for multiuser multiple-input multiple-output (MIMO) wireless communication
systems. The proposed methods rely on an efficient guess-and-check procedure
that, given an objective function, performs paired comparisons between random
subsets of UEs that should be scheduled in certain time slots. The proposed
LoFi scheduling methods are computationally efficient, highly parallelizable,
and gradient-free, which enables the use of almost arbitrary,
non-differentiable objective functions. System simulations in a millimeter-wave
(mmWave) multiuser MIMO scenario demonstrate that the proposed LoFi schedulers
outperform a range of state-of-the-art user scheduling algorithms in terms of
bit error-rate and/or computational complexity.


  In this article, we have studied transformation formulas of zeta function at
odd integers over an arbitrary number field which in turn generalizes
Ramanujan's identity for the Riemann zeta function. The above transformation
leads to a new number field extension of Eisenstein series, which satisfies the
transformation $z \mapsto -1/z$ like an integral weight modular form over
SL$_2(\Z)$. The results provide number of important applications, which are
important in studying the behaviour of odd zeta values as well as Lambert
series in an arbitrary number field.


  With this paper we introduce the concept of apparent structure of a GRB jet,
as opposed to its intrinsic structure. The latter is customarily defined
specifying the functions epsilon(theta) (the energy emitted per jet unit solid
angle) and Gamma(theta) (the Lorentz factor of the emitting material); the
apparent structure is instead defined by us as the isotropic equivalent energy
E_iso(theta_v) as a function of the viewing angle theta_v. We show how to
predict the apparent structure of a jet given its intrinsic structure. We find
that a Gaussian intrinsic structure yields a power law apparent structure: this
opens a new viewpoint on the Gaussian (which can be understood as a proxy for a
realistic narrow, well collimated jet structure) as a possible candidate for a
quasi-universal GRB jet structure. We show that such a model (a) is consistent
with recent constraints on the observed luminosity function of GRBs; (b)
implies fewer orphan afterglows with respect to the standard uniform model; (c)
can break out the progenitor star (in the collapsar scenario) without wasting
an unreasonable amount of energy; (d) is compatible with the explanation of the
Amati correlation as a viewing angle effect; (e) can be very standard in energy
content, and still yield a very wide range of observed isotropic equivalent
energies.


  We propose a scotogenic extension of the Standard Model which can provide a
scalar Dark Matter candidate in the new, theoretically previously unaddressed,
intermediate region ($200\leq M_{DM}\leq 550$ GeV) and also generate light
Dirac neutrino masses. In this framework, the standard model is extended by
three gauge singlet fermions, two singlet scalar fields, and one additional
scalar doublet, all of which are odd under $Z_{2} \times Z_{4}$ discrete
symmetry. These additional symmetries prevent the singlet fermions from
obtaining Majorana mass terms along with providing the stability to the dark
matter candidate. It is known that in the case of the scalar singlet DM model,
the only region which is not yet excluded is a narrow region close to the Higgs
resonance $m_{S} \simeq\frac{m_{h}}{2}$ - others ruled out from different
experimental and theoretical bounds. In the case of the Inert doublet model,
the mass region ($\sim 60$-$80$ GeV) and the high mass region (heavier than $
550 $ GeV) are allowed. This motivates us to explore a parameter range in the
intermediate-mass region $M_{W}\leq M_{DM}\leq 550 $ GeV, which we do in a
scotogenic extension of SM with a scalar doublet and scalar singlets. The dark
matter in our model is a mixture of singlet and doublet scalars. We constrain
the allowed parameter space of the model using Planck bound on present dark
matter relic abundance, neutrino mass, and the latest bound on spin-independent
DM-nucleon scattering cross-section from XENON1T experiment. Our model may
provide a viable DM candidate in the new, previously unexplored mass range
($200\leq M_{DM}\leq 550$ GeV), if this new window for the DM candidate mass is
detected in future experiments, along with explanation of Dirac mass of
neutrinos, since so far there is no strong evidence in support of Majorana
nature of neutrino mass.


  The virtual try-on task is so attractive that it has drawn considerable
attention in the field of computer vision. However, presenting the
three-dimensional (3D) physical characteristic (e.g., pleat and shadow) based
on a 2D image is very challenging. Although there have been several previous
studies on 2D-based virtual try-on work, most 1) required user-specified target
poses that are not user-friendly and may not be the best for the target
clothing, and 2) failed to address some problematic cases, including facial
details, clothing wrinkles and body occlusions. To address these two
challenges, in this paper, we propose an innovative template-free try-on image
synthesis (TF-TIS) network. The TF-TIS first synthesizes the target pose
according to the user-specified in-shop clothing. Afterward, given an in-shop
clothing image, a user image, and a synthesized pose, we propose a novel model
for synthesizing a human try-on image with the target clothing in the best
fitting pose. The qualitative and quantitative experiments both indicate that
the proposed TF-TIS outperforms the state-of-the-art methods, especially for
difficult cases.


  We investigate in isothermal MHD simulations the instability of toroidal
magnetic fields resulting by the action of z-dependent differential rotation on
a given axial field B^0 in a cylindrical enclosure where in particular the
helicity of the resulting nonaxisymmetric flow is of interest. The idea is
probed that helicity H is related to the external field and the differential
rotation as H ~ B^0_i B^0_j Omega_i,j. The observed instability leads to a
nonaxisymmetric solution with dominating mode m=1. With the onset of
instability both kinematic and current helicity are produced which fulfill the
suggested relation. Obviously, differential rotation dOmega/dz only needs an
external axial field B^0_z to produce remarkable amounts of the helicities. Any
regular time-dependency of the helicity could not be found. The resulting axial
alpha-effect is mainly due to the current helicity, the characteristic time
scale between both the values is of order of the rotation time. If the axial
field is switched off then the helicity and the alpha-effect disappear.


  India-based Neutrino Observatory (INO) is a multi-institutional facility,
planned to be built up in South India. The INO facility will host a 51 kton
magnetized Iron CALorimeter (ICAL) detector to study atmospheric muon
neutrinos. Iron plates have been chosen as the target material whereas
Resistive Plate Chambers (RPCs) have been chosen as the active detector element
for the ICAL experiment. Due to the large number of RPCs needed ($\sim$ 28,000
of $2~m \times 2~m$ in size) for ICAL experiment and for the long lifetime of
the experiment, it is necessary to perform a detailed $R\&D$ such that each and
every parameter of the detector performance can be optimized to improve the
physics output. In this paper, we report on the detailed material and
electrical properties studies for various types of glass electrodes available
locally. We also report on the performance studies carried out on the RPCs made
with these electrodes as well as the effect of gas composition and
environmental temperature on the detector performance. We also lay emphasis on
the usage of materials for RPC electrodes and the suitable enviormental
conditions applicable for operating the RPC detector for optimal physics output
at INO-ICAL experiment.


  We discuss the conformal symmetry between Jordan and Einstein frames
considering their relations with the metric and Palatini formalisms for
modified gravity. Appropriate conformal transformations are taken into account
leading to the evident connection between the gravitational actions in the two
mentioned frames and the Hilbert-Einstein action with a cosmological constant.
We show that the apparent differences between Palatini and metric formalisms
strictly depend on the representation while the number of degrees of freedom is
preserved. This means that the dynamical content of both formalism is
identical.


  Federated learning (FL) enables clients to collaborate with a server to train
a machine learning model. To ensure privacy, the server performs secure
aggregation of updates from the clients. Unfortunately, this prevents
verification of the well-formedness (integrity) of the updates as the updates
are masked. Consequently, malformed updates designed to poison the model can be
injected without detection. In this paper, we formalize the problem of ensuring
\textit{both} update privacy and integrity in FL and present a new system,
\textsf{EIFFeL}, that enables secure aggregation of \textit{verified} updates.
\textsf{EIFFeL} is a general framework that can enforce \textit{arbitrary}
integrity checks and remove malformed updates from the aggregate, without
violating privacy. Our empirical evaluation demonstrates the practicality of
\textsf{EIFFeL}. For instance, with $100$ clients and $10\%$ poisoning,
\textsf{EIFFeL} can train an MNIST classification model to the same accuracy as
that of a non-poisoned federated learner in just $2.4s$ per iteration.


  Portability is critical to ensuring high productivity in developing and
maintaining scientific software as the diversity in on-node hardware
architectures increases. While several programming models provide portability
for diverse GPU platforms, they don't make any guarantees about performance
portability. In this work, we explore several programming models -- CUDA, HIP,
Kokkos, RAJA, OpenMP, OpenACC, and SYCL, to study if the performance of these
models is consistently good across NVIDIA and AMD GPUs. We use five proxy
applications from different scientific domains, create implementations where
missing, and use them to present a comprehensive comparative evaluation of the
programming models. We provide a Spack scripting-based methodology to ensure
reproducibility of experiments conducted in this work. Finally, we attempt to
answer the question -- to what extent does each programming model provide
performance portability for heterogeneous systems in real-world usage?


  In this work, we introduce metric learning (ML) to enhance the deep embedding
learning for text-independent speaker verification (SV). Specifically, the deep
speaker embedding network is trained with conventional cross entropy loss and
auxiliary pair-based ML loss function. For the auxiliary ML task, training
samples of a mini-batch are first arranged into pairs, then positive and
negative pairs are selected and weighted through their own and relative
similarities, and finally the auxiliary ML loss is calculated by the similarity
of the selected pairs. To evaluate the proposed method, we conduct experiments
on the Speaker in the Wild (SITW) dataset. The results demonstrate the
effectiveness of the proposed method.


  This paper describes the design, manufacture, and performance of a highly
dexterous, low-profile, 7 Degree-of-Freedom (DOF) robotic arm for CT-guided
percutaneous needle biopsy. Direct CT guidance allows physicians to localize
tumours quickly; however, needle insertion is still performed by hand. This
system is mounted to a fully active gantry superior to the patient's head and
teleoperated by a radiologist. Unlike other similar robots, this robot's fully
serial-link approach uses a unique combination of belt and cable drives for
high-transparency and minimal-backlash, allowing for an expansive working area
and numerous approach angles to targets all while maintaining a small in-bore
cross-section of less than $16cm^2$. Simulations verified the system's
expansive collision free work-space and ability to hit targets across the
entire chest, as required for lung cancer biopsy. Targeting error is on average
$<1mm$ on a teleoperated accuracy task, illustrating the system's sufficient
accuracy to perform biopsy procedures. The system is designed for lung biopsies
due to the large working volume that is required for reaching peripheral lung
lesions, though, with its large working volume and small in-bore
cross-sectional area, the robotic system is effectively a general-purpose
CT-compatible manipulation device for percutaneous procedures. Finally, with
the considerable development time undertaken in designing a precise and
flexible-use system and with the desire to reduce the burden of other
researchers in developing algorithms for image-guided surgery, this system
provides open-access, and to the best of our knowledge, is the first
open-hardware image-guided biopsy robot of its kind.


  The magnetic properties of carbon materials are at present the focus of an
intense research effort in physics, chemistry and materials science due to
their potential applications in spintronics and quantum computations. Although
the presence of spins in open-shell nanographenes has been recently confirmed,
the ability to control magnetic coupling sign has remained elusive, but the
most desirable. Here, we demonstrate an effective approach of engineering
magnetic ground states in atomically precise open-shell bipartite/nonbipartite
nanographenes using combined scanning probe techniques and mean-field Hubbard
model calculations. The magnetic coupling sign between two spins has been
controlled via breaking bipartite lattice symmetry of nanographenes. In
addition, the exchange-interaction strength between two spins has been widely
tuned by finely tailoring their spin density overlap, realizing a large
exchange-interaction strength of 42 meV. Our demonstrated method provides ample
opportunities for designer above-room-temperature magnetic phases and
functionalities in graphene nanomaterials.


  We calculated a grid of evolutionary models for white dwarfs with helium
cores (He-WDs) and investigated the occurrence of hydrogen-shell flashes due to
unstable hydrogen burning via CNO cycling. Our calculations show that such
thermal instabilities are restricted to a certain mass range
(M=0.21...0.30Msun), consistent with earlier studies. Models within this mass
range undergo the more hydrogen shell flashes the less massive they are. This
is caused by the strong dependence of the envelope mass on the white dwarf core
mass. The maximum luminosities from hydrogen burning during the flashes are of
the order of 10^5 Lsun. Because of the development of a pulse-driven convection
zone whose upper boundary temporarily reaches the surface layers, the
envelope's hydrogen content decreases by Delta(X)=0.06 per flash. Our study
further shows that an additional high mass-loss episode during a flash-driven
Roche lobe overflow to the white dwarf's companion does not affect the final
cooling behaviour of the models. Independent of hydrogen shell flashes the
evolution along the final white dwarf cooling branch is determined by hydrogen
burning via pp-reactions down to effective temperatures as low as 8000 K.


  In this paper, three high-accuracy methods for eigenvalues of second order
elliptic operators are proposed by using the nonconforming Crouzeix-Raviart(CR
for short) element and the nonconforming enriched Crouzeix-Raviart(ECR for
short) element. They are based on a crucial full one order superconvergence of
the first order mixed Raviart-Thomas(RT for short) element. The main ingredient
of such a superconvergence analysis is to employ a discrete Helmholtz
decomposition of the difference between the canonical interpolation and the
finite element solution of the RT element. In particular, it allows for some
vital cancellation between terms in one key sum of boundary terms.
Consequently, a full one order superconvergence follows from a special relation
between the CR element and the RT element, and the equivalence between the ECR
element and the RT element for these two nonconforming elements. These
superconvergence results improve those in literature from a half order to a
full one order for the RT element, the CR element and the ECR element. Based on
the aforementioned superconvergence of the RT element, asymptotic expansions of
eigenvalues are established and employed to achieve high accuracy extrapolation
methods for these two nonconforming elements. In contrast to a classic analysis
in literature, the novelty herein is to use not only the canonical
interpolations of these nonconforming elements but also that of the RT element
to analyze such asymptotic expansions. Based on the superconvergence of these
nonconforming elements, asymptotically exact a posteriori error estimators of
eigenvalues are constructed and analyzed for them. Finally, two post-processing
methods are proposed to improve accuracy of approximate eigenvalues by
employing these a posteriori error estimators.Numerical tests are provided to
justify and compare the performance of the aforementioned methods.


  The superradiant stability is investigated for non-extremal
Reissner-Nordstrom black hole. We use an algebraic method to demonstrate that
all non-extremal Reissner-Nordstrom black holes are superradiantly stable
against a charged massive scalar perturbation. This improves the results
obtained before for non-extremal Reissner-Nordstrom black holes.


  Inspired in discussions presented lately regarding Lorentz-violating
interaction terms in \cite{13,6}, we propose here a slightly different version
for the coupling term. We will consider a modified quantum electrodynamics with
violation of Lorentz symmetry defined in a $\left( 2+1\right) $-dimensional
spacetime. We define the Lagrangian density with a Lorentz-violating
interaction, where the the spacetime dimensionality is explicitly taken into
account in its definition. The work encompasses an analysis of this model at
both zero and finite-temperature, where very interesting features are known to
occur due to the spacetime dimensionality. With that in mind we expect that the
spacetime dimensionality may provide new insights about the radiative
generation of higher-derivative terms into the action, implying in a new
Lorentz-violating electrodynamics, as well the nonminimal coupling may provide
interesting implications on the thermodynamical quantities.


  Let k be an algebraically closed field, let K/k be a finitely generated field
extension of transcendence degree 2 with automorphism sigma, and let A be an
N-graded subalgebra of Q = K[t; sigma] with A_n finite dimensional over k for
all n. Then if A is big enough in Q in an appropriate sense, we prove that
GKdim A = 3,4,5 or is infinite, with the exact value depending only on the
geometric properties of sigma. The proof uses techniques in the birational
geometry of surfaces which are of independent interest.


  We investigate roles of Alfvenic waves in the weakly-ionized atmosphere of
hot Jupiters by carrying out non-ideal magnetohydrodynamic (MHD) simulations
with Ohmic diffusion in one-dimensional magnetic flux tubes. Turbulence at the
surface excites Alfven waves and they propagate upward to drive hot (~ 10^4 K)
outflows. The magnetic diffusion plays an important role in the dissipation of
the Alfvenic waves in the weakly ionized atmosphere of hot Jupiters. The
mass-loss rate of the spontaneously driven planetary wind is considerably
reduced, in comparison with that obtained from ideal MHD simulations because
the Alfvenic waves are severely damped at low altitudes in the atmosphere,
whereas the wave heating is still important in the heating of the upper
atmosphere. Dependence on the surface temperature, planetary radius, and
velocity dispersion at the surface is also investigated. We find an inversion
phenomenon of the transmitted wave energy flux; the energy flux carried by
Alfven waves in the upper atmosphere has a nonmonotonic correlation with the
input energy flux from the surface in a certain range of the surface
temperature because the resistivity is determined by the global physical
properties of the atmosphere in a complicated manner. We also point out that
the heating and mass loss are expected only in limited zones if the open
magnetic field is confined in the limited regions.


  It is shown that in general case may be not correct the statements of
[1,2,6-8] that 1) the isochoric heat capacity on the entire thermodynamic
surface, including the metastable region of states and the region defined by
the spinodal, remains positive and finite except for the critical point, and 2)
the isobaric heat capacity becomes negative in the region defined by spinodal.


  Hierarchical Bayesian models can be especially useful in precision medicine
settings, where clinicians are interested in estimating the patient-level
latent variables associated with an individual's current health state and its
trajectory. Such models are often fit using batch Markov Chain Monte Carlo
(MCMC). However, the slow speed of batch MCMC computation makes it difficult to
implement in clinical settings, where immediate latent variable estimates are
often desired in response to new patient data. In this report, we discuss how
importance sampling (IS) can instead be used to obtain fast, in-clinic
estimates of patient-level latent variables. We apply IS to the hierarchical
model proposed in Coley et al (2015) for predicting an individual's underlying
prostate cancer state. We find that latent variable estimates via IS can
typically be obtained in 1-10 seconds per person and have high agreement with
estimates coming from longer-running batch MCMC methods. Alternative options
for out-of-sample fitting and online updating are also discussed.


  In this article, a three-time levels compact scheme is proposed to solve the
partial integro-differential equation governing the option prices under
jump-diffusion models. In the proposed compact scheme, the second derivative
approximation of unknowns is approximated by the value of unknowns and their
first derivative approximations which allow us to obtain a tri-diagonal system
of linear equations for the fully discrete problem. Moreover, consistency and
stability of the proposed compact scheme are proved. Due to the low regularity
of typical initial conditions, the smoothing operator is employed to ensure the
fourth-order convergence rate. Numerical illustrations for pricing European
options under Merton and Kou jump-diffusion models are presented to validate
the theoretical results.


  The supernova remnant (SNR) B0532$-$67.5 in the Large Magellanic Cloud (LMC)
was first diagnosed by its nonthermal radio emission and its SNR nature was
confirmed by diffuse X-ray emission; however, no optical SNR shell is detected.
The OB association LH75, or NGC 2011, is projected within the boundary of this
SNR. We have analyzed the massive star population in and around SNR
B0532$-$67.5: using optical photometric data to construct color-magnitude
diagrams (CMDs), using stellar evolutionary tracks to estimate stellar masses,
and using isochrones to assess the stellar ages. From these analyses, we find a
20-25 Myr population in LH75 and a younger population less than 10 Myr old to
the southwest of LH75. The center of SNR B0532$-$67.5 is located closer to the
core of LH75 than the massive stars to its southwest. We conclude that the SN
progenitor was probably a member of LH75 with an initial mass $\sim$15
$M_\odot$. The SN exploded in an H I cavity excavated by the energy feedback of
LH75. The low density of the ambient medium prohibits the formation of a
visible nebular shell. Despite the low density in the ambient medium, physical
properties of the hot gas within the SNR interior do not differ from SNRs with
a visible shell by more than a factor of 2-3. The large-scale H I map shows
that SNR B0532$-$67.5 is projected in a cavity that appears to be connected
with the much larger cavity of the supergiant shell LMC-4.


  This paper focuses on molecular dynamics (MD) modeling of graphene reinforced
cross-linked epoxy (Gr-Ep) nanocomposite. The goal is to study the influence of
geometry, and concentration of reinforcing nanographene sheet (NGS) on
interfacial properties and elastic constants such as bulk Young's modulus, and
shear modulus of Gr-Ep nanocomposites. The most typical cross-linked
configuration was obtained in order to use in further simulations. The
mechanical properties of this cross-linked structure were determined using MD
simulations and the results were verified with those available in literatures.
Graphene with different aspect ratios and concentrations (1%, 3% and 5%) were
considered in order to construct amorphous unit cells of Gr-Ep nanocomposites.
The Gr-Ep nanocomposites system undergoes NVT (constant number of atoms, volume
and temperature) and NPT (constant number of atoms, pressure and temperature)
ensemble with applied uniform strain field during MD simulation to obtain bulk
Young's modulus and shear modulus. The stress-strain response was also
evaluated for both amorphous and crystalline unit cells of Gr-Ep system under
uni-axial deformation. The cohesive and pullout force vs. displacement response
were determined for graphenes with different size. Hence as primary goal of
this work, a parametric study using MD simulation was conducted for
characterizing interfacial properties and elastic constants with different NGS
aspect rations and volume fractions. The MD simulation results show reasonable
agreement with available published data in the literature.


  The Castelnuovo-Mumford regularity of a graded ring is an important invariant
in computational commutative algebra, and there is increasing interest in
multigraded generalizations. We study connections between two recent
definitions of multigraded regularity with a view towards a better
understanding of the multigraded Hilbert function of fat point schemes in
P^{n_1} x ... x P^{n_k}.


  We develop a scale-invariant truncated L\'evy (STL) process to describe
physical systems characterized by correlated stochastic variables. The STL
process exhibits L\'evy stability for the probability density, and hence shows
scaling properties (as observed in empirical data); it has the advantage that
all moments are finite (and so accounts for the empirical scaling of the
moments). To test the potential utility of the STL process, we analyze
financial data.


  Reproductive success and survival are influenced by wealth in human
populations. Wealth is transmitted to offsprings and strategies of transmission
vary over time and among populations, the main variation being how equally
wealth is transmitted to children. Here we propose a model where we simulate
both the dynamics of wealth in a population and the evolution of a trait that
determines how wealth is transmitted from parents to offspring, in a darwinian
context.


  The design of complex Systems-on-Chips implies to take into account
communication and memory access constraints for the integration of dedicated
hardware accelerator. In this paper, we present a methodology and a tool that
allow the High-Level Synthesis of DSP algorithm, under both I/O timing and
memory constraints. Based on formal models and a generic architecture, this
tool helps the designer to find a reasonable trade-off between both the
required I/O timing behavior and the internal memory access parallelism of the
circuit. The interest of our approach is demonstrated on the case study of a
FFT algorithm.


  We discuss several issues related to a recent proposal for defining classical
spatial averages to be used in the so-called cosmological backreaction problem.
In the large averaging-volume limit all gauge dependence disappears and
different averages can be univocally characterized by the observers associated
with different scalar fields. The relation between such averaging procedure and
the standard one is emphasized and a gauge invariant way to select different
observers is presented. For finite averaging volumes we show that, within our
proposal, a residual gauge dependence is left, but is suppressed by several
effects.


  Complex numbers appear naturally in biology whenever a system can be analyzed
in the frequency domain, such as physiological data from magnetoencephalography
(MEG). For example, the MEG steady state response to a modulated auditory
stimulus generates a complex magnetic field for each MEG channel, equal to the
Fourier transform at the stimulus modulation frequency. The complex nature of
these data sets, often not taken advantage of, is fully exploited here with new
methods. Whole-head, complex magnetic data can be used to estimate complex
neural current sources, and standard methods of source estimation naturally
generalize for complex sources. We show that a general complex neural vector
source is described by its location, magnitude, and direction, but also by a
phase and by an additional perpendicular component. We give natural
interpretations of all the parameters for the complex equivalent-current dipole
by linking them to the underlying neurophysiology. We demonstrate complex
magnetic fields, and their equivalent fully complex current sources, with both
simulations and experimental data.


  We observe that a ball of radius $1$ in the grandfather graph can not be
realized as a ball of radius $1$ in a finite vertex transitive graph. We remark
on when a ball in a finite vertex transitive graph appears as a ball in an
infinite vertex transitive graph.


  The ever-growing popularity of Kinect and inertial sensors has prompted
intensive research efforts on human action recognition. Since human actions can
be characterized by multiple feature representations extracted from Kinect and
inertial sensors, multiview features must be encoded into a unified space
optimal for human action recognition. In this paper, we propose a new
unsupervised feature fusion method termed Multiview Cauchy Estimator Feature
Embedding (MCEFE) for human action recognition. By minimizing empirical risk,
MCEFE integrates the encoded complementary information in multiple views to
find the unified data representation and the projection matrices. To enhance
robustness to outliers, the Cauchy estimator is imposed on the reconstruction
error. Furthermore, ensemble manifold regularization is enforced on the
projection matrices to encode the correlations between different views and
avoid overfitting. Experiments are conducted on the new Chinese Academy of
Sciences - Yunnan University - Multimodal Human Action Database (CAS-YNU-MHAD)
to demonstrate the effectiveness and robustness of MCEFE for human action
recognition.


  In many fields of science and engineering, models with different fidelities
are available. Physical experiments or detailed simulations that accurately
capture the behavior of the system are regarded as high-fidelity models with
low model uncertainty, however, they are expensive to run. On the other hand,
simplified physical experiments or numerical models are seen as low-fidelity
models that are cheaper to evaluate. Although low-fidelity models are often not
suitable for direct use in reliability analysis due to their low accuracy, they
can offer information about the trend of the high-fidelity model thus providing
the opportunity to explore the design space at a low cost. This study presents
a new approach called adaptive multi-fidelity Gaussian process for reliability
analysis (AMGPRA). Contrary to selecting training points and information
sources in two separate stages as done in state-of-the-art mfEGRA method, the
proposed approach finds the optimal training point and information source
simultaneously using the novel collective learning function (CLF). CLF is able
to assess the global impact of a candidate training point from an information
source and it accommodates any learning function that satisfies a certain
profile. In this context, CLF provides a new direction for quantifying the
impact of new training points and can be easily extended with new learning
functions to adapt to different reliability problems. The performance of the
proposed method is demonstrated by three mathematical examples and one
engineering problem concerning the wind reliability of transmission towers. It
is shown that the proposed method achieves similar or higher accuracy with
reduced computational costs compared to state-of-the-art single and
multi-fidelity methods. A key application of AMGPRA is high-fidelity fragility
modeling using complex and costly physics-based computational models.


  Modern search engines are built on a stack of different components, including
query understanding, retrieval, multi-stage ranking, and question answering,
among others. These components are often optimized and deployed independently.
In this paper, we introduce a novel conceptual framework called large search
model, which redefines the conventional search stack by unifying search tasks
with one large language model (LLM). All tasks are formulated as autoregressive
text generation problems, allowing for the customization of tasks through the
use of natural language prompts. This proposed framework capitalizes on the
strong language understanding and reasoning capabilities of LLMs, offering the
potential to enhance search result quality while simultaneously simplifying the
existing cumbersome search stack. To substantiate the feasibility of this
framework, we present a series of proof-of-concept experiments and discuss the
potential challenges associated with implementing this approach within
real-world search systems.


  Several results on Heun's equation are generalized to a certain class of
Fuchsian differential equations. Namely, we obtain integral representations of
solutions and develop Hermite-Krichever Ansatz on them. In particular, we
investigate linear differential equations that produce Painlev\'e equation by
monodromy preserving deformation and obtain solutions of the sixth Painlev\'e
equation which include Hitchin's solution. The relationship with finite-gap
potential is also discussed.


  Systematic studies of the two high-temperature monolayer oxygen structures
that exist on the (110) tungsten surface were performed using low-energy
electron microscopy and diffraction measurements. Our work questions the
commonly accepted interpretation from the literature that striped oxygen
superstructures arise from alternating site-exchanged (S-E) domains. We
postulate that the superstructures originate from a misfit between tungsten and
oxygen lattices while the striped appearance corresponds to a moir\'e pattern.
Moreover, we show that the two structures, indicated as 113- and 337-phases due
to the characteristic directions of the respective moir\'e patterns, differ
considerably in their symmetry properties. This suggests that oxygen atoms in
the two overlayers occupy different adsorption sites on average. In particular,
the 113-phase features rotational domains that retain mirror symmetries with
respect to the [001] and [1-10] directions, whereas the 337-phase is
characterized by the appearance of additional domains due to the breaking of
these symmetries. We propose structural models for both phases that
consistently explain their unusual properties and suggest a universal mechanism
for the thermal evolution of oxygen monolayer adsorbed on W(110).


  Let $q=4$ and $k$ a positive integer. In this short note, we present a class
of permutation polynomials over $\Bbb F_{q^{3k}}$. We also present a
generalization.


  An alternative, the VW transformation, is proposed to replace the Wilson GF
method for calculating molecular vibration frequencies and normal modes. The VW
transformation yields precisely the same eigenmodes and and eigenfrequencies
that are found with the GF method. The transformation proceeds entirely in the
mass-normalized Cartesian coordinates of the individual atoms, with no
transformations to internal coordinates. The VW transformation thus offers an
enormous computational simplification over the GF method, namely the
mathematical apparatus needed to transform to internal coordinates is
eliminated. All need for new researchers to understand the complex matrix
transformations underlying the GF method is thus also removed. The VW
transformation is not a projection method; the internal vibrations remain
dispersed over all 3N atomic coordinates. In the VW method, the 3N x 3N force
constant matrix V is replaced with a new 3N x 3N matrix W. The replacement is
physically transparent. The matrix W has the same internal normal modes and
vibration frequencies that V does. However, unlike V, W is not singular, so the
normal modes and normal mode frequencies can be obtained using conventional
matrix techniques.


  We present a new mechanism, the S-Track, to stabilize the volume modulus S in
heterotic M-theory flux compactifications along with the orbifold-size T
besides complex structure and vector bundle moduli stabilization. The key
dynamical ingredient which makes the volume modulus stabilization possible, is
M5-instantons arising from M5-branes wrapping the whole Calabi-Yau slice. These
are natural in heterotic M-theory where the warping shrinks the Calabi-Yau
volume along S^1/Z_2. Combined with H-flux, open M2-instantons and hidden
sector gaugino condensation it leads to a superpotential W which stabilizes S
similar like a racetrack but without the need for multi gaugino condensation.
Moreover, W contains two competing non-perturbative effects which stabilize T.
We analyze the potential and superpotentials to show that it leads to heterotic
de Sitter vacua with broken supersymmetry through non-vanishing F-terms.


  Renormalization constants (RCs) of overlap quark bilinear operators on
2+1-flavor domain wall fermion configurations are calculated by using the
RI/MOM and RI/SMOM schemes. The scale independent RC for the axial vector
current is computed by using a Ward identity. Then the RCs for the quark field
and the vector, tensor, scalar and pseudoscalar operators are calculated in
both the RI/MOM and RI/SMOM schemes. The RCs are converted to the
$\overline{\rm MS}$ scheme and we compare the numerical results from using the
two intermediate schemes. The lattice size is $48^3\times96$ and the inverse
spacing $1/a = 1.730(4) {\rm~GeV}$.


  Starting from the rigorous excitation equation, the propagation of waves
through a 2D waveguide with the periodically corrugated finite-length insert is
examined in detail. The corrugation profile is chosen to obey the property that
its amplitude is small as compared to the waveguide width, whereas the
sharpness of the asperities is arbitrarily large. With the aid of the method of
mode separation, which was developed earlier for inhomogeneous-in-bulk
waveguide systems [Waves Random Media \textbf{10}, 395 (2000)], the corrugated
segment of the waveguide is shown to serve as the effective scattering barrier
whose width is coincident with the length of the insert and the average height
is controlled by the sharpness of boundary asperities. Due to this barrier, the
mode spectrum of the waveguide can be substantially rarefied and adjusted so as
to reduce the number of extended modes to the value arbitrarily less than that
in the absence of corrugation (up to zero), without changing considerably the
waveguide average width.


  The neural network (NN) becomes one of the most heated type of models in
various signal processing applications. However, NNs are extremely vulnerable
to adversarial examples (AEs). To defend AEs, adversarial training (AT) is
believed to be the most effective method while due to the intensive
computation, AT is limited to be applied in most applications. In this paper,
to resolve the problem, we design a generic and efficient AT improvement
scheme, namely case-aware adversarial training (CAT). Specifically, the
intuition stems from the fact that a very limited part of informative samples
can contribute to most of model performance. Alternatively, if only the most
informative AEs are used in AT, we can lower the computation complexity of AT
significantly as maintaining the defense effect. To achieve this, CAT achieves
two breakthroughs. First, a method to estimate the information degree of
adversarial examples is proposed for AE filtering. Second, to further enrich
the information that the NN can obtain from AEs, CAT involves a weight
estimation and class-level balancing based sampling strategy to increase the
diversity of AT at each iteration. Extensive experiments show that CAT is
faster than vanilla AT by up to 3x while achieving competitive defense effect.


  This paper investigates the downlink channel state information (CSI) sensing
in 5G heterogeneous networks composed of user equipments (UEs) with different
feedback capabilities. We aim to enhance the CSI accuracy of UEs only affording
the low-resolution Type-I codebook. While existing works have demonstrated that
the task can be accomplished by solving a phase retrieval (PR) formulation
based on the feedback of precoding matrix indicator (PMI) and channel quality
indicator (CQI), they need many feedback rounds. In this paper, we propose a
novel CSI sensing scheme that can significantly reduce the feedback overhead.
Our scheme involves a novel parameter dimension reduction design by exploiting
the spatial consistency of wireless channels among nearby UEs, and a
constrained PR (CPR) formulation that characterizes the feasible region of CSI
by the PMI information. To address the computational challenge due to the
non-convexity and the large number of constraints of CPR, we develop a
two-stage algorithm that firstly identifies and removes inactive constraints,
followed by a fast first-order algorithm. The study is further extended to
multi-carrier systems. Extensive tests over DeepMIMO and QuaDriGa datasets
showcase that our designs greatly outperform existing methods and achieve the
high-resolution Type-II codebook performance with a few rounds of feedback.


  We study the effects of Rashba spin-orbit coupling on the spin torque induced
by spin waves, which are the plane wave dynamics of magnetization. The spin
torque is derived from linear response theory, and we calculate the dynamic
spin torque by considering the impurity-ladder-sum vertex corrections. This
dynamic spin torque is divided into three terms: a damping term, a $distortion$
term, and a correction term for the equation of motion. The $distorting$ torque
describes a phenomenon unique to the Rashba spin-orbit coupling system, where
the distorted motion of magnetization precession is subjected to the
anisotropic force from the Rashba coupling. The oscillation mode of the
precession exhibits an elliptical trajectory, and the ellipticity depends on
the strength of the nesting effects, which could be reduced by decreasing the
electron lifetime.


  We present a simple proof on the existence of $L^1$-flat analytic polynomials
with coefficients $0,1$ on the circle and on the real line and we give an
example of a conservative ergodic map and flow whose unitary operators admits a
simple Lebesgue spectrum. Among other results, we obtain an answer to
Bourgain's question on the supremum of $L^1$-norm of such polynomials and to a
question inspired by Lehmer's problem on the supremum of the Mahler measures of
those polynomials.


  The excursion set approach uses the statistics of the density field smoothed
on a wide range of scales, to gain insight into a number of interesting
processes in nonlinear structure formation, such as cluster assembly, merging
and clustering. The approach treats the curve defined by the height of the
overdensity fluctuation field when changing the smoothing scale as a random
walk. The steps of the walks are often assumed to be uncorrelated, so that the
walk heights are a Markov process, even though this assumption is known to be
inaccurate for physically relevant filters. We develop a model in which the
walk steps, rather than heights, are a Markov process, and correlations between
steps arise because of nearest neighbour interactions. This model is a
particular case of a general class, which we call Markov Velocity models. We
show how these can approximate the walks generated by arbitrary power spectra
and filters, and, unlike walks with Markov heights, provide a very good
approximation to physically relevant models. We define a Markov Velocity Monte
Carlo algorithm to generate walks whose first crossing distribution is very
similar to that of TopHat-smoothed LCDM walks. Finally, we demonstrate that
Markov Velocity walks generically exhibit a simple but realistic form of
assembly bias, so we expect them to be useful in the construction of more
realistic merger history trees.


  We consider recently developed Cohomological Field Theory soliton counting
diagram technique for Khovanov and Khovanov-Rozansky invariants [1,2]. Although
the expectation to obtain a new way for computing the invariants has not yet
come true, we demonstrate that soliton counting technique can be totally
formalized at an intermediate stage, at least in particular cases. We present
the corresponding algorithm, based on the approach involving deformed
$\mathcal{R}$-matrix and minimal positive division, developed previously in
[3]. We start from a detailed review of the minimal positive division approach,
comparing it with other methods, including the rigorous mathematical treatment
[4]. Pieces of data obtained within our approach are presented in the
Appendices.


  Hexagonal FeSe thin films were grown on SrTiO3 substrates and the temperature
and thickness dependence of their electronic structures were studied. The
hexagonal FeSe is found to be metallic and electron doped, whose Fermi surface
consists of six elliptical electron pockets. With decreased temperature, parts
of the bands shift downward to high binding energy while some bands shift
upwards to EF. The shifts of these bands begin around 300 K and saturate at low
temperature, indicating a magnetic phase transition temperature of about 300 K.
With increased film thickness, the Fermi surface topology and band structure
show no obvious change except some minor quantum size effect. Our paper reports
the first electronic structure of hexagonal FeSe, and shows that the possible
magnetic transition is driven by large scale electronic structure
reconstruction.


  We present vibrational properties of Franckeite, which is a naturally
occurring van der Waals heterostructure consisting of two different
semiconducting layers. Franckeite is a complex layered crystal composed of
alternating SnS$_2$ like pseudohexagonal and PbS-like pseudotetragonal layers
stacked on top of each other, providing a unique platform to study vibrational
properties and thermal transport across layers with mass density and phonon
mismatches. By using micro-Raman spectroscopy and first-principles Raman
simulations, we found that the PbS-like pseudotetragonal structure is mostly
composed of Pb$_3$SbS$_4$. We also discovered several low-frequency Raman modes
that originate from the intralayer vibrations of the pseudotetragonal layer.
Using density functional theory, we determined all vibrational patterns of
Franckeite, whose signatures are observed in the Raman spectrum. By studying
temperature dependent Raman spectroscopy (300 K - 500 K), we have found
different temperature coefficients for both pseudotetragonal and
pseudohexagonal layers. We believe that our study will help understand the
vibration modes of its complex heterostructure and the thermal properties at
the nanoscale.


  A fundamental assumption in cosmology is that of statistical isotropy - that
the universe, on average, looks the same in every direction in the sky.
Statistical isotropy has recently been tested stringently using Cosmic
Microwave Background (CMB) data, leading to intriguing results on large angular
scales. Here we apply some of the same techniques used in the CMB to the
distribution of galaxies on the sky. Using the multipole vector approach, where
each multipole in the harmonic decomposition of galaxy density field is
described by unit vectors and an amplitude, we lay out the basic formalism of
how to reconstruct the multipole vectors and their statistics out of galaxy
survey catalogs. We apply the algorithm to synthetic galaxy maps, and study the
sensitivity of the multipole vector reconstruction accuracy to the density,
depth, sky coverage, and pixelization of galaxy catalog maps.


  Recently a variational integrator for ideal magnetohydrodynamics in
Lagrangian labeling has been developed. Its built-in frozen-in equation makes
it optimal for studying current sheet formation. We use this scheme to study
the Hahm-Kulsrud-Taylor problem, which considers the response of a 2D plasma
magnetized by a sheared field under sinusoidal boundary forcing. We obtain an
equilibrium solution that preserves the magnetic topology of the initial field
exactly, with a fluid mapping that is non-differentiable. Unlike previous
studies that examine the current density output, we identify a singular current
sheet from the fluid mapping. These results are benchmarked with a constrained
Grad-Shafranov solver. The same signature of current singularity can be found
in other cases with more complex magnetic topologies.


  Web Search Engine Results Pages (SERP) are one of the most well-known and
used web pages. These pages have started as simple ``10 blue links'' pages, but
the information in SERP currently goes way beyond these links. Several features
have been included in these pages to complement organic and sponsored results
and attempt to provide answers to the query instead of just pointing to
websites that might deliver that information. In this work, we analyze the
appearance and evolution of SERP features in the two leading web search
engines, Google Search and Microsoft Bing. Using a sample of SERP from the
Internet Archive, we analyzed the appearance and evolution of these features.
We found that SERP are becoming more diverse in terms of elements, aggregating
content from different verticals and including more features that provide
direct answers.


  Underexposure regions are vital to construct a complete perception of the
surroundings for safe autonomous driving. The availability of thermal cameras
has provided an essential alternate to explore regions where other optical
sensors lack in capturing interpretable signals. A thermal camera captures an
image using the heat difference emitted by objects in the infrared spectrum,
and object detection in thermal images becomes effective for autonomous driving
in challenging conditions. Although object detection in the visible spectrum
domain imaging has matured, thermal object detection lacks effectiveness. A
significant challenge is scarcity of labeled data for the thermal domain which
is desiderata for SOTA artificial intelligence techniques. This work proposes a
domain adaptation framework which employs a style transfer technique for
transfer learning from visible spectrum images to thermal images. The framework
uses a generative adversarial network (GAN) to transfer the low-level features
from the visible spectrum domain to the thermal domain through style
consistency. The efficacy of the proposed method of object detection in thermal
images is evident from the improved results when used styled images from
publicly available thermal image datasets (FLIR ADAS and KAIST Multi-Spectral).


  Possibilities for the measurement of the longitudinal structure function in
diffraction $F_\mathrm{L}^\mathrm{D}$ at the future US Electron Ion Collider
are investigated. The sensitivity to $F_\mathrm{L}^\mathrm{D}$ arises from the
variation of the reduced diffractive cross section with centre-of-mass energy.
Simulations are performed with various sets of beam energy combinations and for
different assumptions on the precision of the diffractive cross section
measurements. Scenarios compatible with current EIC performance expectations
lead to an unprecedented precision on $F_\mathrm{L}^\mathrm{D}$ at the 5-10 %
level in the best measured regions. While scenarios with data at a larger
number of centre-of-mass energies allow the extraction of
$F_\mathrm{L}^\mathrm{D}$ in the widest kinematic domain and with the smallest
uncertainties, even the more conservative assumptions lead to precise
measurements. The ratio $R^\mathrm{D}$ of photoabsorption cross sections for
longitudinally to transversely polarised photons can also be obtained with high
precision using a separate extraction method.


  The most important characteristics of the fragmentation of heterogeneous
solids is that the mass (size) distribution of pieces is described by a power
law functional form. The exponent of the distribution displays a high degree of
universality depending mainly on the dimensionality and on the brittle-ductile
mechanical response of the system. Recently, experiments and computer
simulations have reported an energy dependence of the exponent increasing with
the imparted energy. These novel findings question the phase transition picture
of fragmentation phenomena, and have also practical importance for industrial
applications. Based on large scale computer simulations here we uncover a
robust mechanism which leads to the emergence of energy dependence in
fragmentation processes resolving controversial issues on the problem: studying
the impact induced breakup of plate-like objects with varying thickness in
three dimensions we show that energy dependence occurs when a lower dimensional
fragmenting object is embedded into a higher dimensional space. The reason is
an underlying transition between two distinct fragmentation mechanisms
controlled by the impact velocity at low plate thicknesses, while it is
hindered for three-dimensional bulk systems. The mass distributions of the
subsets of fragments dominated by the two cracking mechanisms proved to have an
astonishing robustness at all plate thicknesses, which implies that the
non-universality of the complete mass distribution is the consequence of
blending the contributions of universal partial processes.


  GM2Calc is a leading tool for calculating precise contributions to $a_\mu$ in
the Minimal Supersymmetric Standard Model. In this proceeding we detail GM2Calc
version 2 where it is extended so it can calculate two-loop contributions to
$a_\mu$ in the Two-Higgs Doublet Model (2HDM), based on the work in Ref. [1].
The 2HDM is a simple model, yet it is one of the few single field extensions of
the Standard Model which is able to explain the muon $g-2$ anomaly. We
demonstrate the powerful and flexible 2HDM capabilities of GM2Calc2, which
include the most precise contributions in the literature and allow the user to
work in their favourite type of the 2HDM as well as use complex and lepton
flavour violating couplings. With its multiple interfaces and input
flexibility, GM2Calc2 is a powerful tool both as a standalone code and as part
of a larger code toolchain.


  We describe a new method of proving a priori bounds for positive
supersolutions and solutions of superlinear elliptic PDE, based on global weak
Harnack inequalities and a quantitative Hopf lemma. Novel results based on the
method include: (i) equations without a boundary condition on the whole
boundary; (ii) equations with nonlinearities which do not have precise growth
at infinity; (iii) systems of inequalities with opposite sign.


  The problem of dimension reduction is of increasing importance in modern data
analysis. In this paper, we consider modeling the collection of points in a
high dimensional space as a union of low dimensional subspaces. In particular
we propose a highly scalable sampling based algorithm that clusters the entire
data via first spectral clustering of a small random sample followed by
classifying or labeling the remaining out of sample points. The key idea is
that this random subset borrows information across the entire data set and that
the problem of clustering points can be replaced with the more efficient and
robust problem of "clustering sub-clusters". We provide theoretical guarantees
for our procedure. The numerical results indicate we outperform other
state-of-the-art subspace clustering algorithms with respect to accuracy and
speed.


  In this paper, we present a new version of the $i$QIST software package,
which is capable of solving various quantum impurity models by using the
hybridization expansion (or strong coupling expansion) continuous-time quantum
Monte Carlo algorithm. In the revised version, the software architecture is
completely redesigned. New basis (intermediate representation or singular value
decomposition representation) for the single-particle and two-particle Green's
functions is introduced. A lot of useful physical observables are added, such
as the charge susceptibility, fidelity susceptibility, Binder cumulant, and
autocorrelation time. Especially, we optimize measurement for the two-particle
Green's functions. Both the particle-hole and particle-particle channels are
supported. In addition, the block structure of the two-particle Green's
functions is exploited to accelerate the calculation. Finally, we fix some
known bugs and limitations. The computational efficiency of the code is greatly
enhanced.


  Excitons are electron-hole (e-h) pair quasiparticles, which may form a
Bose-Einstein condensate (BEC) and collapse into the phase coherent state at
low temperature. However, because of ephemeral strength of pairing, a clear
evidence for BEC in electron-hole system has not yet been observed. Here, we
report electron-hole pair condensation in graphene (Gr)/MoS2 heterointerface at
10K without magnetic field. As a direct indication of e-h pair condensation, we
demonstrate a vanished Hall drag voltage and the resultant divergence of drag
resistance. While strong excitons are formed at Gr/MoS2 heterointerface without
insulating layer, carrier recombination via interlayer tunneling of carriers is
suppressed by the vertical p-Gr/n-MoS2 junction barrier, consequently yielding
high BEC temperature of 10K, ~1000 times higher than that of two-dimensional
electron gas in III-V quantum wells. The observed excitonic transport is mainly
governed by the interfacial properties of the Gr/MoS2 heterostructure, rather
than the intrinsic properties of each layer. Our approach with available
large-area monolayer graphene and MoS2 provides a high feasibility for quantum
dissipationless electronics towards integration.


  Cellular resolutions are a technique for constructing resolutions of monomial
ideals by giving a cell complex labeled by monomials, or more generally, by
monomial modules. This \verb|Macaulay2| package allows us to work with cellular
resolutions in a natural way.


  In Bayesian analysis of multi-way contingency tables, the selection of a
prior distribution for either the log-linear parameters or the cell
probabilities parameters is a major challenge. In this paper, we define a
flexible family of conjugate priors for the wide class of discrete hierarchical
log-linear models, which includes the class of graphical models. These priors
are defined as the Diaconis--Ylvisaker conjugate priors on the log-linear
parameters subject to "baseline constraints" under multinomial sampling. We
also derive the induced prior on the cell probabilities and show that the
induced prior is a generalization of the hyper Dirichlet prior. We show that
this prior has several desirable properties and illustrate its usefulness by
identifying the most probable decomposable, graphical and hierarchical
log-linear models for a six-way contingency table.


  We propose a unified theoretical framework to examine the energy dissipation
properties at all stages of explicit exponential Runge-Kutta (EERK) methods for
gradient flow problems. The main part of the novel framework is to construct
the differential form of EERK method by using the difference coefficients of
method and the so-called discrete orthogonal convolution kernels. As the main
result, we prove that an EERK method can preserve the original energy
dissipation law unconditionally if the associated differentiation matrix is
positive semi-definite. A simple indicator, namely average dissipation rate, is
also introduced for these multi-stage methods to evaluate the overall energy
dissipation rate of an EERK method such that one can choose proper parameters
in some parameterized EERK methods or compare different kinds of EERK methods.
Some existing EERK methods in the literature are evaluated from the perspective
of preserving the original energy dissipation law and the energy dissipation
rate. Some numerical examples are also included to support our theory.


  Meta-reinforcement learning algorithms provide a data-driven way to acquire
policies that quickly adapt to many tasks with varying rewards or dynamics
functions. However, learned meta-policies are often effective only on the exact
task distribution on which they were trained and struggle in the presence of
distribution shift of test-time rewards or transition dynamics. In this work,
we develop a framework for meta-RL algorithms that are able to behave
appropriately under test-time distribution shifts in the space of tasks. Our
framework centers on an adaptive approach to distributional robustness that
trains a population of meta-policies to be robust to varying levels of
distribution shift. When evaluated on a potentially shifted test-time
distribution of tasks, this allows us to choose the meta-policy with the most
appropriate level of robustness, and use it to perform fast adaptation. We
formally show how our framework allows for improved regret under distribution
shift, and empirically show its efficacy on simulated robotics problems under a
wide range of distribution shifts.


  It is proved that the only geodesically complete stationary vacuum solution
of the Einstein equations is the empty Minkowski space, or a quotient of it by
a discrete group of isometries, generalizing a classical result of
Lichnerowicz. In addition, we obtain an apriori bound on the curvature of
stationary vacuum solutions away from the boundary or horizon.


  Edge-on spiral galaxies often have stellar disks with relatively sharp
truncations and warped HI-layers in the outer parts. Warps appear to start
preferentially near the optical boundaries of the disks. Here we make a
comparative study of warps and truncations in edge-on galaxies. The
Garc\'{\i}a-Ruiz et al. (2002) sample with extensive HI-mapping is complemented
with luminosity distributions from the Sloan Digital Sky Survey. The method to
identify truncations has been tested using the sample of edge-on galaxies of
van der Kruit & Searle. Results are: (i.) The majority (17 out of 23) of the
galaxies show evidence for truncations. (ii.) When an HI-warp is present it
starts at 1.1 truncation radii. (iii.) This supplements the rules for warps
formulated by Briggs (1990), if the Holmberg radius is replaced for edge-on
systems with the truncation radius. (iv.) The truncation radius and the onset
of the warps coincide radially with features in the rotation curve and the HI
surface density. The latter is also true for less inclined systems. (v.) Inner
disks are very flat and the onset of the warp just beyond the truncation radius
is abrupt and discontinuous. These findings suggest that the inner flat disk
and the outer warped disk are distinct components with quite different
formation histories, probably involving quite different epochs. The inner disk
forms initially and the warped outer disk forms as a result of much later
infall of gas with a higher angular momentum in a different orientation. In an
appendix the Holmberg radius is discussed. Contrary to what is often assumed
Holmberg radii are not corrected for inclination.


  In this paper we show that the natural density $\mathcal{D}[(U_m)]$ of Ulam
numbers $(U_m)$ satisfies $\mathcal{D}[(U_m)]=0$. That is, we show that for
$(U_m)\subset [1,k]$ then \begin{align}\lim \limits_{k\longrightarrow
\infty}\frac{\left |(U_m)\cap [1,k]\right |}{k}=0.\nonumber \end{align}


  We study the statistics of spike trains of simultaneously recorded grid cells
in freely behaving rats. We evaluate pairwise correlations between these cells
and, using a generalized linear model (kinetic Ising model), study their
functional connectivity. Even when we account for the covariations in firing
rates due to overlapping fields, both the pairwise correlations and functional
connections decay as a function of the shortest distance between the vertices
of the spatial firing pattern of pairs of grid cells, i.e. their phase
difference. The functional connectivity takes positive values between cells
with nearby phases and approaches zero or negative values for larger phase
differences. We also find similar results when, in addition to correlations due
to overlapping fields, we account for correlations due to theta oscillations
and head directional inputs. The inferred connections between neurons can be
both negative and positive regardless of whether the cells share common spatial
firing characteristics, that is, whether they belong to the same modules, or
not. The mean strength of these inferred connections is close to zero, but the
strongest inferred connections are found between cells of the same module.
Taken together, our results suggest that grid cells in the same module do
indeed form a local network of interconnected neurons with a functional
connectivity that supports a role for attractor dynamics in the generation of
the grid pattern.


  The occurrence of sleep passed through the evolutionary sieve and is
widespread in animal species. Sleep is known to be beneficial to cognitive and
mnemonic tasks, while chronic sleep deprivation is detrimental. Despite the
importance of the phenomenon, a complete understanding of its functions and
underlying mechanisms is still lacking. In this paper, we show interesting
effects of deep-sleep-like slow oscillation activity on a simplified
thalamo-cortical model which is trained to encode, retrieve and classify images
of handwritten digits. During slow oscillations,
spike-timing-dependent-plasticity (STDP) produces a differential homeostatic
process. It is characterized by both a specific unsupervised enhancement of
connections among groups of neurons associated to instances of the same class
(digit) and a simultaneous down-regulation of stronger synapses created by the
training. This hierarchical organization of post-sleep internal representations
favours higher performances in retrieval and classification tasks. The
mechanism is based on the interaction between top-down cortico-thalamic
predictions and bottom-up thalamo-cortical projections during deep-sleep-like
slow oscillations. Indeed, when learned patterns are replayed during sleep,
cortico-thalamo-cortical connections favour the activation of other neurons
coding for similar thalamic inputs, promoting their association. Such mechanism
hints at possible applications to artificial learning systems.


  The sources of the majority of the high-energy astrophysical neutrinos
observed with the IceCube neutrino telescope at the South Pole are unknown. So
far, only a gamma-ray blazar was compellingly associated with the emission of
high-energy neutrinos. In addition, several studies suggest that the neutrino
emission from the gamma-ray blazar population only accounts for a small
fraction of the total astrophysical neutrino flux. In this work we probe the
production of high-energy neutrinos in the cores of Active Galactic Nuclei
(AGN), induced by accelerated cosmic rays in the accretion disk region. We
present a likelihood analysis based on eight years of IceCube data, searching
for a cumulative neutrino signal from three AGN samples created for this work.
The neutrino emission is assumed to be proportional to the accretion disk
luminosity estimated from the soft X-ray flux. Next to the observed soft X-ray
flux, the objects for the three samples have been selected based on their radio
emission and infrared color properties. For the largest sample in this search,
an excess of high-energy neutrino events with respect to an isotropic
background of atmospheric and astrophysical neutrinos is found, corresponding
to a post-trial significance of 2.60$\sigma$. Assuming a power-law spectrum,
the best-fit spectral index is $2.03^{+0.14}_{-0.11}$, consistent with
expectations from particle acceleration in astrophysical sources. If
interpreted as a genuine signal with the assumptions of a proportionality of
X-ray and neutrino fluxes and a model for the sub-threshold flux distribution,
this observation implies that at 100 TeV, 27$\%$ - 100$\%$ of the observed
neutrinos arise from particle acceleration in the core of AGN.


  Feature selection problems have been extensively studied for linear
estimation, for instance, Lasso, but less emphasis has been placed on feature
selection for non-linear functions. In this study, we propose a method for
feature selection in high-dimensional non-linear function estimation problems.
The new procedure is based on minimizing the $\ell_0$ norm of the vector of
indicator variables that represent if a feature is selected or not. Our
approach relies on the continuous relaxation of Bernoulli distributions, which
allows our model to learn the parameters of the approximate Bernoulli
distributions via gradient descent. This general framework simultaneously
minimizes a loss function while selecting relevant features. Furthermore, we
provide an information-theoretic justification of incorporating Bernoulli
distribution into our approach and demonstrate the potential of the approach on
synthetic and real-life applications.


  Classification and understanding of quantum phase transitions and critical
phenomena in itinerant electron systems are outstanding questions in quantum
materials research. Recent experiments on heavy fermion systems with
higher-rank multipolar local moments provide a new platform to study such
questions. In particular, experiments on
$\text{Ce}_{3}\text{Pd}_{20}\text{(Si,Ge)}_{6}$ show novel quantum critical
behaviors via two consecutive magnetic field-driven quantum phase transitions.
At each transition, the derivative of the Hall resistivity jumps
discontinuously, which was attributed to sequential Fermi surface
reconstructions. Motivated by this discovery, we consider an effective quantum
impurity model of itinerant electrons coupled to local dipolar, quadrupolar,
and octupolar moments arising from $\text{Ce}^{3+}$ ions. Using renormalization
group analyses, we demonstrate that two-stage multipolar ordering and Fermi
surface reconstruction arise depending on which multipolar moments participate
in the Fermi surface and which other moments are decoupled via Kondo
destruction.


  We calculate the back-reaction of long wavelength cosmological perturbations
on a general relativistic measure of the local expansion rate of the Universe.
Specifically, we consider a cosmological model in which matter is described by
two scalar matter fields, one being the inflaton and the other one representing
a matter field which is used as a clock. We analyze back-reaction in a phase of
inflaton-driven slow-roll inflation, and find that the leading infrared
back-reaction terms contributing to the evolution of the expansion rate do not
vanish when measured at a fixed value of the clock field. We also analyze the
back-reaction of entropy modes in a specific cosmological model with negative
square mass for the entropy field and find that back-reaction can become
significant. Our work provides evidence that, in general, the back-reaction of
infrared fluctuations could be locally observable.


  Here, we optimized the annealing temperature of HZO/TiN thin film
heterostructure via multiscale analysis of remnant polarization,
crystallographic phase, minimum ferroelectric domain size, and average grain
size. We found that the remnant polarization was closely related to the
relative amount of the orthorhombic phase whereas the minimum domain size was
to the relative amount of the monoclinic phase. The minimum domain size was
obtained at the annealing temperature of 500$^\cird$C while the optimum remnant
polarization and capacitance at the annealing temperature of 600$^\circ$C. We
conclude that the minimum domain size is more important than the sheer
magnitude of remnant polarization considering the retention and fatigue of
switchable polarization in nanoscale ferroelectric devices. Our results are
expected to contribute to the development of ultra-low-power logic transistors
and next-generation non-volatile memory devices.


  This note explores the use of Newton polytopes in the study of Lagrangian
fillings of Legendrian submanifolds. In particular, we show that Newton
polytopes associated to augmented values of Reeb chords can distinguish
infinitely many distinct Lagrangian fillings, both for Legendrian links and
higher-dimensional Legendrian spheres. The computations we perform work in
finite characteristic, which significantly simplifies arguments and also allows
us to show that there exist Legendrian links with infinitely many
non-orientable exact Lagrangian fillings.


  By retracing research on coexistent magnitudes (grandeurs coexistantes) by
Cauchy (1841), Peano in "Applicazioni geometriche del calcolo infinitesimale"
(1887) defines the "density" (strict derivative) of a "mass" (a distributive
set function) with respect to a "volume" (a positive distributive set
function), proves its continuity (whenever the strict derivative exists) and
shows the validity of the mass-density paradigm: "mass" is recovered from
"density" by integration with respect to "volume". It is remarkable that
Peano's strict derivative provides a consistent mathematical ground to the
concept of "infinitesimal ratio" between two magnitudes, successfully used
since Kepler. In this way the classical (i.e., pre-Lebesgue) measure theory
reaches a complete and definitive form in Peano's Applicazioni geometriche.
  A primary aim of the present paper is a detailed exposition of Peano's work
of 1887 leading to the concept of strict derivative of distributive set
functions and their use. Moreover, we compare Peano's work and Lebesgue's "La
mesure des grandeurs" (1935): in this memoir Lebesgue, motivated by coexistent
magnitudes of Cauchy, introduces a uniform-derivative of certain additive set
functions, a concept that coincides with Peano's strict derivative. Intriguing
questions are whether Lebesgue was aware of the contributions of Peano and
which role is played by the notions of strict derivative or of
uniform-derivative in today mathematical practice.


  Recovery of support of a sparse vector from simple measurements is a
widely-studied problem, considered under the frameworks of compressed sensing,
1-bit compressed sensing, and more general single index models. We consider
generalizations of this problem: mixtures of linear regressions, and mixtures
of linear classifiers, where the goal is to recover supports of multiple sparse
vectors using only a small number of possibly noisy linear, and 1-bit
measurements respectively. The key challenge is that the measurements from
different vectors are randomly mixed. Both of these problems have also received
attention recently. In mixtures of linear classifiers, the observations
correspond to the side of queried hyperplane a random unknown vector lies in,
whereas in mixtures of linear regressions we observe the projection of a random
unknown vector on the queried hyperplane. The primary step in recovering the
unknown vectors from the mixture is to first identify the support of all the
individual component vectors. In this work, we study the number of measurements
sufficient for recovering the supports of all the component vectors in a
mixture in both these models. We provide algorithms that use a number of
measurements polynomial in $k, \log n$ and quasi-polynomial in $\ell$, to
recover the support of all the $\ell$ unknown vectors in the mixture with high
probability when each individual component is a $k$-sparse $n$-dimensional
vector.


  Type classes are one of Haskell's most popular features and extend its type
system with ad-hoc polymorphism. Since their conception, there were useful
features that could not be offered because of the desire to offer two
correctness properties: coherence and global uniqueness of instances. Coherence
essentially guarantees that program semantics are independent from type-checker
internals. Global uniqueness of instances is relied upon by libraries for
enforcing, for example, that a single order relation is used for all
manipulations of an ordered binary tree.
  The features that could not be offered include explicit dictionary
application and local instances, which would be highly useful in practice. We
propose a new design for offering explicit dictionary application, without
compromising coherence and global uniqueness. We introduce a novel criterion
based on GHC's type argument roles to decide when a dictionary application is
safe with respect to global uniqueness of instances. We preserve coherence by
detecting potential sources of incoherence, and prove it formally. Moreover,
our solution makes it possible to use local dictionaries. In addition to
developing our ideas formally, we have implemented a working prototype in GHC.
  This report contains the full formalisation and coherence proof.


  Recently, many new dwarf spheroidal satellites (dSphs) have been discovered
by the Dark Energy Survey (DES). These dSphs are ideal candidates for probing
for gamma-ray emissions from dark matter (DM) annihilation. However, no
significant signature has been found by the Fermi-LAT dSph observations. In
this work, we reanalyze the Fermi-LAT Pass 8 data from the direction of
Reticulum II, where a slight excess has been reported by some previous studies.
We treat Reticulum II (DES J0335.6-5403) as a spatially extended source, and
and that no significant gamma-ray signature is observed. Based on this result,
we set upper-limits on the DM annihilation cross section.


  The issue of fairness arises when the automatic speech recognition (ASR)
systems do not perform equally well for all subgroups of the population. In any
fairness measurement studies for ASR, the open questions of how to control the
nuisance factors, how to handle unobserved heterogeneity across speakers, and
how to trace the source of any word error rate (WER) gap among different
subgroups are especially important - if not appropriately accounted for,
incorrect conclusions will be drawn. In this paper, we introduce mixed-effects
Poisson regression to better measure and interpret any WER difference among
subgroups of interest. Particularly, the presented method can effectively
address the three problems raised above and is very flexible to use in
practical disparity analyses. We demonstrate the validity of proposed
model-based approach on both synthetic and real-world speech data.


  We analyze the convergence of piecewise collocation methods for computing
periodic solutions of general retarded functional differential equations under
the abstract framework recently developed in [S. Maset, Numer. Math. (2016)
133(3):525-555], [S. Maset, SIAM J. Numer. Anal. (2015) 53(6):2771--2793] and
[S. Maset, SIAM J. Numer. Anal. (2015) 53(6):2794--2821]. We rigorously show
that a reformulation as a boundary value problem requires a proper
infinite-dimensional boundary periodic condition in order to be amenable of
such analysis. In this regard, we also highlight the role of the period acting
as an unknown parameter, which is critical since it is directly linked to the
course of time. Finally, we prove that the finite element method is convergent,
while we limit ourselves to commenting on the infeasibility of this approach as
far as the spectral element method is concerned.


  Stellar feedback in dwarf galaxies remains, to date, poorly explored, yet is
crucial to understanding galaxy evolution in the early Universe. In particular,
pre-supernova feedback has recently been found to play a significant role in
regulating and disrupting star formation in larger spiral galaxies, but it
remains uncertain if it also plays this role in dwarfs. We study the ionised
gas properties and stellar content of individual star-forming regions across
three nearby, low-metallicity, dwarf starburst galaxies (J0921, KKH046, and Leo
P) to investigate how massive stars influence their surroundings and how this
influence changes as a function of environment. To achieve this, we extracted
integrated spectra of 30 HII regions from archival VLT/MUSE integral field
spectroscopic observations of these three dwarf starburst galaxies. We fitted
the HII regions' main emission lines with Gaussian profiles to derive their
oxygen abundances, electron densities, and luminosities, and we used the
Stochastically Lighting Up Galaxies (SLUG) code to derive the stellar mass,
age, and bolometric luminosity of the stellar populations driving the HII
regions. We then quantified two pre-supernova stellar feedback mechanisms,
namely the direct radiation pressure and photoionisation feedback, and explored
how feedback strength varies with HII region properties. Our findings suggest
that stellar feedback has less of an impact on evolved regions, with both the
pressure of the ionised gas and the direct radiation pressure decreasing as a
function of HII region size. We also find that these stellar feedback
mechanisms are dependent on the metallicity of the HII regions. These findings
extend results from stellar feedback studies of more massive star-forming
galaxies to the low-mass, low-metallicity regime.


  We give a detailed and self-contained description of a recently developed
theoretical and numerical method for the simulation of three identical bosonic
alkali-metal atoms near a Feshbach resonance, where the Efimov effect is
induced. The method is based on a direct construction of the off-shell two-body
transition matrix from exact eigenfunctions of the embedded two-body
Hamiltonians, obtained using realistic parameterizations of the interaction
potentials which accurately reproduce the molecular energy levels. The
transition matrix is then inserted into the appropriate three-body integral
equations, which may be efficiently solved on a computer. We focus especially
on the power of our method in including rigorously the effects of multichannel
physics on the three-body problem, which are usually accounted for only by
various approximations. We demonstrate the method for ${}^7$Li, where we
recently showed that a correct inclusion of this multichannel physics resolves
the long-standing disagreement between theory and experiment regarding the
Efimovian three-body parameter. We analyze the Efimovian enhancement of the
three-body recombination rate on both sides of the Feshbach resonance,
revealing strong sensitivity to the spin structure of the model thus indicating
the prevalence of three-body spin-exchange physics. Finally, we discuss an
extension of our methodology to the calculation of three-body bound-state
energies.


  This paper introduces an efficient procedure to localize user-defined points
on the surface of deformable objects and track their positions in 3D space over
time. To cope with a deformable object's infinite number of DOF, we propose a
discretized deformation field, which is estimated during runtime using a
multi-step non-linear solver pipeline. The resulting high-dimensional energy
minimization problem describes the deviation between an offline-defined
reference model and a pre-processed camera image. An additional regularization
term allows for assumptions about the object's hidden areas and increases the
solver's numerical stability. Our approach is capable of solving the
localization problem online in a data-parallel manner, making it ideally
suitable for the perception of non-rigid objects in industrial manufacturing
processes.


  Accurately answering a question about a given image requires combining
observations with general knowledge. While this is effortless for humans,
reasoning with general knowledge remains an algorithmic challenge. To advance
research in this direction a novel `fact-based' visual question answering
(FVQA) task has been introduced recently along with a large set of curated
facts which link two entities, i.e., two possible answers, via a relation.
Given a question-image pair, deep network techniques have been employed to
successively reduce the large set of facts until one of the two entities of the
final remaining fact is predicted as the answer. We observe that a successive
process which considers one fact at a time to form a local decision is
sub-optimal. Instead, we develop an entity graph and use a graph convolutional
network to `reason' about the correct answer by jointly considering all
entities. We show on the challenging FVQA dataset that this leads to an
improvement in accuracy of around 7% compared to the state of the art.


  The archetypal one-qubit noisy channels ---depolarizing, phase-damping and
amplitude-damping channels--- describe both Markovian and non-Markovian
evolution. Simple microscopic models for the depolarizing channel, both
classical and quantum, are considered. Microscopic models which describe phase
damping and amplitude damping channels are briefly reviewed.