  We explore the possibility of quantum states of matter that are compressible
but have vanishing DC conductivity in the absence of disorder. We show that the
composite Fermi liquid emerging from strong interaction in a generic Chern band
has zero Drude weight, in stark contrast to normal Fermi liquids. Our work
establishes the absence of Drude weight as the defining property of the
composite Fermi liquid phase, which distinguishes it from the Fermi liquid or
other types of non-Fermi liquids. Our findings point to a possibly wide class
of gapless quantum phases with unexpected transport and optical properties.


  We discuss the advantages of searchable, collaborative, language-independent
databases of mathematical results, indexed by "fingerprints" of small and
canonical data. Our motivating example is Neil Sloane's massively influential
On-Line Encyclopedia of Integer Sequences. We hope to encourage the greater
mathematical community to search for the appropriate fingerprints within each
discipline, and to compile fingerprint databases of results wherever possible.
The benefits of these databases are broad - advancing the state of knowledge,
enhancing experimental mathematics, enabling researchers to discover unexpected
connections between areas, and even improving the refereeing process for
journal publication.


  We prove that sufficiently many intertwining operators of type $F_4$ unitary
affine VOAs satisfy polynomial energy bounds. This finishes the Wassermann type
analysis of intertwining operators for all WZW-models.


  Let $f\in W^{3,1}_{\mathrm{loc}}(\Omega)$ be a function defined on a
connected open subset $\Omega\subseteq\mathbb R^2$. We will show that its graph
is contained in a quadratic surface if and only if $f$ is a weak solution to a
certain system of \nth{3} order partial differential equations unless the
Hessian determinant of $f$ is non-positive on the whole $\Omega$. Moreover, we
will prove that the system is in some sense the simplest possible in a wide
class of differential equations, which will lead to the classification of all
polynomial partial differential equations satisfied by parametrizations of
generic quadratic surfaces. Although we will mainly use the tools of linear and
commutative algebra, the theorem itself is also somehow related to holomorphic
functions.


  The ground state of an impurity immersed in a Fermi sea changes from a
polaron to a molecule as the interaction strength is increased.
  We show here that the coupling between these two states is strongly
suppressed due to a combination of phase space effects and Fermi statistics,
and that it vanishes much faster than the energy difference between the two
states, thereby confirming the first order nature of the polaron-molecule
transition. In the regime where each state is metastable, we find quasiparticle
lifetimes which are much longer than what is expected for a usual Fermi liquid.
Our analysis indicates that the decay rates are sufficiently slow to be
experimentally observable.


  The color gauge-invariant transverse momentum dependent (TMD) quark
correlators contain process dependent gauge links in the bilocal matrix
elements. In this paper, we split these process dependent correlators into
universal TMD correlators, which in turn can be parametrized in universal TMD
distribution functions. The process dependence is contained in gluonic pole
factors, of which the value is determined by the gauge link. The operator
structures of the universal TMD correlators are identified using transverse
moments. In this paper, specific results for double transverse weighting of
quark TMDs are given. In particular, we show that for a spin 1/2 target one has
three universal time-reversal even leading `pretzelocity distributions', two of
which involve double gluonic pole matrix elements and come with process
dependent gluonic pole factors. We generalize the results for single and double
weighting to TMD correlators of any specific rank, illustrating it for
unpolarized, spin 1/2 and spin 1 targets.


  This note analytically investigates non-reciprocal wave dispersion in locally
resonant acoustic metamaterials. Dispersion relations associated with
space-time varying modulations of inertial and stiffness parameters of the base
material and the resonant components are derived. It is shown that the
resultant dispersion bias onsets intriguing features culminating in a break-up
of both acoustic and optic propagation modes and one-way local resonance band
gaps. The derived band structures are validated using the full transient
displacement response of a finite metamaterial. A mathematical framework is
presented to characterize power flow in the modulated acoustic metamaterials to
quantify energy transmission patterns associated with the non-reciprocal
response. Since local resonance band gaps are size-independent and frequency
tunable, the outcome enables the synthesis of a new class of sub-wavelength
low-frequency one-way wave guides.


  Drying suspensions often leave behind complex patterns of particulates, as
might be seen in the coffee stains on a table. Here we consider the dynamics of
periodic band or uniform solid film formation on a vertical plate suspended
partially in a drying colloidal solution. Direct observations allow us to
visualize the dynamics of the band and film deposition, and the transition in
between when the colloidal concentration is varied. A minimal theory of the
liquid meniscus motion along the plate reveals the dynamics of the banding and
its transition to the filming as a function of the ratio of deposition and
evaporation rates. We also provide a complementary multiphase model of colloids
dissolved in the liquid, which couples the inhomogeneous evaporation at the
evolving meniscus to the fluid and particulate flows and the transition from a
dilute suspension to a porous plug. This allows us to determine the
concentration dependence of the bandwidth and the deposition rate. Together,
our findings allow for the control of drying-induced patterning as a function
of the colloidal concentration and evaporation rate.


  The process of alternately row scaling and column scaling a positive $n
\times n$ matrix $A$ converges to a doubly stochastic positive $n \times n$
matrix $S(A)$, often called the \emph{Sinkhorn limit} of $A$. The main result
in this paper is the computation of exact formulae for the Sinkhorn limits of
certain symmetric positive $3\times 3$ matrices.


  The intermarket analysis, in particular the lead-lag relationship, plays an
important role within financial markets. Therefore a mathematical approach to
be able to find interrelations between the price development of two different
financial underlyings is developed in this paper. Computing the differences of
the relative positions of relevant local extrema of two charts, i.e., the local
phase shifts of these underlyings, gives us an empirical distribution on the
unit circle. With the aid of directional statistics such angular distributions
are studied for many pairs of markets. It is shown that there are several very
strongly correlated underlyings in the field of foreign exchange, commodities
and indexes. In some cases one of the two underlyings is significantly ahead
with respect to the relevant local extrema, i.e., there is a phase shift
unequal to zero between these two underlyings.


  We prove a no-go theorem for a class of hidden variables theories that
satisfy parameter independence. Specifically, we show that, assuming two
conditions, there are no non-trivial hidden variables models of the quantum
predictions for product measurements on two systems in any maximally entangled
state in a Hilbert space of dimension at least 3x3. The two conditions are
parameter independence and a condition that we call conditional parameter
independence. The result is analogous to the recent no-go theorems based on
Leggett's inequalities and their generalisations.


  Here we explore the possibility of extending to the Solar System scenario the
combined residual approach employed for the deterination of the Lense-Thirring
effect in the Earth-LAGEOS satellites system. After calculating the secular
gravitoelectric advance of the mean anomaly, we propose several linear
combinations of the residuals of the nodes, the perihelia and the mean
anomalies of some inner planets in order to measure the gravitoelectric
advance, the Lense-Thirring effect and the solar quadrupole mass moment. The
obtainable accuracies are comparable to those of new complex and expensive
missions involving the launch of one or more spacecrafts. They are 10^-4 for
the PPN parameter beta, 10^-5 for gamma, 10^-9 for the solar J2, 20-30% for the
Lense-Thirring effect. In the case of the solar gravitomagnetic field, it would
be the first attempt to measure its effect on the planetary motions. However,
the improvements in the ephemerides of Mercury thanks to the Messenger and
BepiColombo missions will map into an increasing accuracy of the proposed
tests.


  We propose Fuzzy Jaccard Index (FUJI) -- a scale-invariant score for
assessment of the similarity between two ranked/ordered lists. FUJI improves
upon the Jaccard index by incorporating a membership function which takes into
account the particular ranks, thus producing both more stable and more accurate
similarity estimates. We provide theoretical insights into the properties of
the FUJI score as well as propose an efficient algorithm for computing it. We
also present empirical evidence of its performance on different synthetic
scenarios. Finally, we demonstrate its utility in a typical machine learning
setting -- comparing feature ranking lists relevant to a given machine learning
task. In real-life, and in particular high-dimensional domains, where only a
small percentage of the whole feature space might be relevant, a robust and
confident feature ranking leads to interpretable findings as well as efficient
computation and good predictive performance. In such cases, FUJI correctly
distinguishes between existing feature ranking approaches, while being more
robust and efficient than the benchmark similarity scores.


  A model for the formation of supermassive primordial black holes in galactic
nuclei with the simultaneous suppression of the formation of intermediate-mass
black holes is presented. A bimodal mass function for black holes formed
through phase transitions in a model with a "Mexican hat" potential has been
found. The classical motion of the phase of a complex scalar field during
inflation has been taken into account. Possible observational manifestations of
primordial black holes in galaxies and constraints on their number are
discussed.


  Control Lyapunov functions (CLFs) and control barrier functions (CBFs) are
widely used tools for synthesizing controllers subject to stability and safety
constraints. Paired with online optimization, they provide stabilizing control
actions that satisfy input constraints and avoid unsafe regions of state-space.
Designing CLFs and CBFs with rigorous performance guarantees is computationally
challenging. To certify existence of control actions, current techniques not
only design a CLF/CBF, but also a nominal controller. This can make the
synthesis task more expensive, and performance estimation more conservative. In
this work, we characterize polynomial CLFs/CBFs using sum-of-squares
conditions, which can be directly certified using convex optimization. This
yields a CLF and CBF synthesis technique that does not rely on a nominal
controller. We then present algorithms for iteratively enlarging estimates of
the stabilizable and safe regions. We demonstrate our algorithms on a 2D toy
system, a pendulum and a quadrotor.


  We study nematic equilibria on three-dimensional square wells, with emphasis
on Well Order Reconstruction Solutions (WORS) as a function of the well size,
characterized by $\lambda$, and the well height denoted by $\epsilon$. The WORS
are distinctive equilibria reported in [10] for square domains, without taking
the third dimension into account, which have two mutually perpendicular defect
lines running along the square diagonals, intersecting at the square centre. We
prove the existence of WORS on three-dimensional wells for arbitrary well
heights, with (i) natural boundary conditions and (ii) realistic surface
energies on the top and bottom well surfaces, along with Dirichlet conditions
on the lateral surfaces. Moreover, the WORS is globally stable for $\lambda$
small enough in both cases and unstable as $\lambda$ increases. We numerically
compute novel mixed 3D solutions for large $\lambda$ and $\epsilon$ followed by
a numerical investigation of the effects of surface anchoring on the WORS,
exemplifying the relevance of the WORS solution in a 3D context.


  Let $\mathsf{PreOrd}(\mathbb C)$ be the category of internal preorders in an
exact category $\mathbb C$. We show that the pair $(\mathsf{Eq}(\mathbb C),
\mathsf{ParOrd}(\mathbb C))$ is a pretorsion theory in $\mathsf{PreOrd}(\mathbb
C)$, where $\mathsf{Eq}(\mathbb C)$ and $\mathsf{ParOrd}(\mathbb C)$) are the
full subcategories of internal equivalence relations and of internal partial
orders in $\mathbb C$, respectively. We observe that $\mathsf{ParOrd}(\mathbb
C)$ is a reflective subcategory of $\mathsf{PreOrd}(\mathbb C)$ such that each
component of the unit of the adjunction is a pullback-stable regular
epimorphism. The reflector $F:\mathsf{PreOrd}(\mathbb C)\to
\mathsf{ParOrd}(\mathbb C)$ turns out to have stable units in the sense of
Cassidy, H\'ebert and Kelly, thus inducing an admissible categorical Galois
structure. In particular, when $\mathbb C$ is the category $\mathsf{Set}$ of
sets, we show that this reflection induces a monotone-light factorization
system (in the sense of Carboni, Janelidze, Kelly and Par\'e) in
$\mathsf{PreOrd}(\mathsf{Set})$. A topological interpretation of our results in
the category of Alexandroff-discrete spaces is also given, via the well-known
isomorphism between this latter category and $\mathsf{PreOrd}(\mathsf{Set})$.


  Indirectly resolving the line-emitting gas regions in distant Active Galactic
Nuclei (AGN) requires both high-resolution photometry and spectroscopy (i.e.
through reverberation mapping). Emission in AGN originates on widely different
scales; the broad-line region (BLR) has a typical radius less than a few
parsec, the narrow-line region (NLR) extends out to hundreds of parsecs. But
emission also appears on large scales from heated nebulae in the host galaxies
(tenths of kpc).
  We propose a novel, data-driven method based on correlations between
emission-line fluxes to identify which of the emission lines are produced in
the same kind of emission-line regions. We test the method on Seyfert galaxies
from the Sloan Digital Sky Survey (SDSS) Data Release 7 (DR7) and Galaxy Zoo
project.
  We demonstrate the usefulness of the method on Seyfert-1s and Seyfert-2
objects, showing similar narrow-line regions (NLRs). Preliminary results from
comparing Seyfert-2s in spiral and elliptical galaxy hosts suggest that the
presence of particular emission lines in the NLR depends both on host
morphology and eventual radio-loudness. Finally, we explore an apparent linear
relation between the final correlation coefficient obtained from the method and
time lags as measured in reverberation mapping for Zw229-015.


  We present theoretical analysis on why and how rechargeable metal-air type
batteries can be made significantly safer and more practical by utilizing
CO2/oxalate conversions instead of O2/peroxide or O2/hydroxide ones, in the
positive electrode. Metal-air batteries, such as the Li-air one, may have very
large energy densities, comparable to that of gasoline, theoretically allowing
for long range all-electric vehicles. There are, however, still significant
challenges, especially related to the safety of their underlying chemistries,
the robustness of their recharging and the need of supplying high purity O2
from air to the battery. We point out that the CO2/oxalate reversible
electrochemical conversion is a viable alternative of the O2-based ones,
allowing for similarly high energy density and almost identical voltage, while
being much safer through the elimination of aggressive oxidant peroxides and
the use of thermally stable, non-oxidative and environmentally benign oxalates
instead.


  Given a parametric polynomial ideal I, the algorithm DISPGB, introduced by
the author in 2002, builds up a binary tree describing a dichotomic discussion
of the different reduced Groebner bases depending on the values of the
parameters, whose set of terminal vertices form a Comprehensive Groebner System
(CGS). It is relevant to obtain CGS's having further properties in order to
make them more useful for the applications. In this paper the interest is
focused on obtaining a canonical CGS. We define the objective, show the
difficulties and formulate a natural conjecture. If the conjecture is true then
such a canonical CGS will exist and can be computed. We also give an algorithm
to transform our original CGS in this direction and show its utility in
applications.


  It is known that the volume function for hyperbolic manifolds of dimension
$\geq 3$ is finite-to-one. We show that the number of nonhomeomorphic
hyperbolic 4-manifolds with the same volume can be made arbitrarily large. This
is done by constructing a sequence of finite-sided finite-volume polyhedra with
side-pairings that yield manifolds. In fact, we show that arbitrarily many
nonhomeomorphic hyperbolic 4-manifolds may share a fundamental polyhedron. As a
by-product of our examples, we also show in a constructive way that the set of
volumes of hyperbolic 4-manifolds contains the set of even integral multiples
of $4\pi^2/3$. This is ``half'' the set of possible values for volumes, which
is the integral multiples of $4\pi^2/3$ due to the Gauss-Bonnet formula.


  This paper explores the current state-of-the-art of object reconstruction
from multiple orthographic drawings using deep neural networks. It proposes two
algorithms to extract multiple views from a single image. The paper proposes a
system based on pixel-aligned implicit functions (PIFu) and develops an
advanced sampling strategy to generate signed distance samples. It also
compares this approach to depth map regression from multiple views.
Additionally, the paper uses a novel dataset for vehicle reconstruction from
the racing game Assetto Corsa, which features higher quality models than the
commonly used ShapeNET dataset. The trained neural network generalizes well to
real-world inputs and creates plausible and detailed reconstructions.


  I review the results of a recent dispersion--theoretical analysis of the
nucleon electromagnetic form factors and comment on the strangeness form
factors. The need for a better data basis at low, intermediate and large
momentum transfer is stressed.


  The recent realization that Sweet-Parker current sheets are violently
unstable to the secondary tearing (plasmoid) instability implies that such
current sheets cannot occur in real systems. This suggests that, in order to
understand the onset of magnetic reconnection, one needs to consider the growth
of the tearing instability in a current layer as it is being formed. Such an
analysis is performed here in the context of nonlinear resistive MHD for a
generic time-dependent equilibrium representing a gradually forming current
sheet. It is shown that two onset regimes, single-island and multi-island, are
possible, depending on the rate of current sheet formation. A simple model is
used to compute the criterion for transition between these two regimes, as well
as the reconnection onset time and the current sheet parameters at that moment.
For typical solar corona parameters this model yields results consistent with
observations.


  We introduce a very general extension of the monomorphism category as studied
by Ringel and Schmidmeier which in particular covers generalised species over
locally bounded quivers. We prove that analogues of the kernel and cokernel
functor send almost split sequences over the path algebra and the preprojective
algebra to split or almost split sequences in the monomorphism category. We
derive this from a general result on preservation of almost split morphisms
under adjoint functors whose counit is a monomorphism. Despite of its
generality, our monomorphism categories still allow for explicit computations
as in the case of Ringel and Schmidmeier.


  In this work, we discover a phenomenon of community bias amplification in
graph representation learning, which refers to the exacerbation of performance
bias between different classes by graph representation learning. We conduct an
in-depth theoretical study of this phenomenon from a novel spectral
perspective. Our analysis suggests that structural bias between communities
results in varying local convergence speeds for node embeddings. This
phenomenon leads to bias amplification in the classification results of
downstream tasks. Based on the theoretical insights, we propose random graph
coarsening, which is proved to be effective in dealing with the above issue.
Finally, we propose a novel graph contrastive learning model called Random
Graph Coarsening Contrastive Learning (RGCCL), which utilizes random coarsening
as data augmentation and mitigates community bias by contrasting the coarsened
graph with the original graph. Extensive experiments on various datasets
demonstrate the advantage of our method when dealing with community bias
amplification.


  Two-sample tests are important areas aiming to determine whether two
collections of observations follow the same distribution or not. We propose
two-sample tests based on integral probability metric (IPM) for
high-dimensional samples supported on a low-dimensional manifold. We
characterize the properties of proposed tests with respect to the number of
samples $n$ and the structure of the manifold with intrinsic dimension $d$.
When an atlas is given, we propose two-step test to identify the difference
between general distributions, which achieves the type-II risk in the order of
$n^{-1/\max\{d,2\}}$. When an atlas is not given, we propose H\"older IPM test
that applies for data distributions with $(s,\beta)$-H\"older densities, which
achieves the type-II risk in the order of $n^{-(s+\beta)/d}$. To mitigate the
heavy computation burden of evaluating the H\"older IPM, we approximate the
H\"older function class using neural networks. Based on the approximation
theory of neural networks, we show that the neural network IPM test has the
type-II risk in the order of $n^{-(s+\beta)/d}$, which is in the same order of
the type-II risk as the H\"older IPM test. Our proposed tests are adaptive to
low-dimensional geometric structure because their performance crucially depends
on the intrinsic dimension instead of the data dimension.


  The paper addresses the problem of detecting attacks on distributed estimator
networks that aim to intentionally bias process estimates produced by the
network. It provides a sufficient condition, in terms of the feasibility of
certain linear matrix inequalities, which guarantees distributed input attack
detection using an $H_\infty$ approach.


  Incorporating solid-state quantum emitters into optical fiber networks
enables the long-distance transmission of quantum information and the remote
connection of distributed quantum nodes. However, interfacing quantum emitters
with fiber optics encounters several challenges, including low coupling
efficiency and stability. Here, we demonstrate a highly efficient
fiber-interfacing photonic device that directly launches single photons from
quantum dots into a standard FC/PC-connectorized single-mode fiber (SMF28).
Optimally designed photonic structures based on hole gratings produce an
ultra-narrow directional beam that matches the small numerical aperture of a
single-mode fiber. A pick-and-place technique selectively integrates a single
miniaturized device into the core of the fiber. Our approach realizes a
plug-and-play single-photon device that does not require any optical alignment
and thus guarantees long-term stability. The results thus represent a major
step toward practical and reliable quantum lights across a fiber network.


  We obtain sufficient criteria for simplicity of systems, that is, rings $R$
that are equipped with a family of additive subgroups $R_s$, for $s \in S$,
where $S$ is a semigroup, satisfying $R = \sum_{s \in S} R_s$ and $R_s R_t
\subseteq R_{st}$, for $s,t \in S$. These criteria are specialized to obtain
sufficient criteria for simplicity of, what we call, s-unital epsilon-strong
systems, that is systems where $S$ is an inverse semigroup, $R$ is coherent, in
the sense that for all $s,t \in S$ with $s \leq t$, the inclusion $R_s
\subseteq R_t$ holds, and for each $s \in S$, the $R_s
R_{s^*}$-$R_{s^*}R_s$-bimodule $R_s$ is s-unital. As an aplication of this, we
obtain generalizations of recent criteria for simplicity of skew inverse
semigroup rings, by Beuter, Goncalves, \"{O}inert and Royer, and then, in turn,
for Steinberg algebras, over non-commutative rings, by Brown, Farthing, Sims,
Steinberg, Clark and Edie-Michel.


  New SUSY thought is presented. Maximal stop mixing needed for 125 GeV Higgs
is linked to the tachyonic stop at the UV boundary. Large $\mu$ does not mean
the severe fine tuning if Higgs comes out as a pseudo-Goldstone boson. The
small mass of the pseudo-Goldstone Higgs is overcome with extra vector-like
fermions needed to explain the Higgs to di-photon rates.


  Adversarial examples, which are usually generated for specific inputs with a
specific model, are ubiquitous for neural networks. In this paper we unveil a
surprising property of adversarial noises when they are put together, i.e.,
adversarial noises crafted by one-step gradient methods are linearly separable
if equipped with the corresponding labels. We theoretically prove this property
for a two-layer network with randomly initialized entries and the neural
tangent kernel setup where the parameters are not far from initialization. The
proof idea is to show the label information can be efficiently backpropagated
to the input while keeping the linear separability. Our theory and experimental
evidence further show that the linear classifier trained with the adversarial
noises of the training data can well classify the adversarial noises of the
test data, indicating that adversarial noises actually inject a distributional
perturbation to the original data distribution. Furthermore, we empirically
demonstrate that the adversarial noises may become less linearly separable when
the above conditions are compromised while they are still much easier to
classify than original features.


  Asteroseismology of DAV stars (ZZ Ceti variables) can provide valuable clues
about the origin, structure and evolution of DA (atmospheres rich in H) white
dwarfs. Recently, a new DAV star, WD J191643.83+393849.7, has been discovered
in the field of the Kepler spacecraft. It is expected that further monitoring
of this star in the next years will enable astronomers to obtain the best
lightcurve of a pulsating DA white dwarf ever recorded, and thus to know with
unprecedented precision the hidden details of the internal structure of this
star. In this paper, we perform a first asteroseismological analysis of WD
J191643.83+393849.7 on the basis of fully evolutionary DA white-dwarf models.
Specifically, we employ a complete set of evolutionary DA white-dwarf
structures covering a wide range of effective temperatures, stellar masses, and
H envelope thicknesses. These models have been obtained on the basis of a
complete treatment of the evolutionary history of progenitors stars. We compute
g-mode adiabatic pulsation periods for this set of models and compare them with
the pulsation periods exhibited by WD J191643.83+393849.7. Based on a tentative
estimation of the mean period spacing of the star, we find that the stellar
mass should be substantially large ($\gtrsim 0.80 M_{\odot}$), in agreement
with the spectroscopically derived stellar mass. Also, from period-to-period
fits we find an asteroseismological model characterised by a low effective
temperature, rather high stellar mass and a thin H envelope. The possibility
that this rather massive pulsating white dwarf can be further monitored with
Kepler with a high degree of detail turns the star WD J191643.83+393849.7 into
a promising and unique object to study the physics of crystallization and
carbon/oxygen phase diagrams at high densities. ALL THIS ANALYSIS IS OUTDATED
AND INVALID


  This review summarizes the state of the art in searches for supersymmetry at
colliders on the eve of the LHC era. Supersymmetry is unique among extensions
of the standard model in being motivated by naturalness, dark matter, and force
unification, both with and without gravity. At the same time, weak-scale
supersymmetry encompasses a wide range of experimental signals that are also
found in many other frameworks. We recall the motivations for supersymmetry and
review the various models and their distinctive features. We then
comprehensively summarize searches for neutral and charged Higgs bosons and
standard model superpartners at the high energy frontier, considering both
canonical and non-canonical supersymmetric models, and including results from
LEP, HERA, and the Tevatron.


  In this paper we develop a co-induction operation which transforms an
invariant random subgroup of a group into an invariant random subgroup of a
larger group.
  We use this operation to construct new continuum size families of non-atomic,
weakly mixing invariant random subgroups of certain classes of wreath products,
HNN-extensions and free products with amalgamation. By use of small
cancellation theory, we also construct a new continuum size family of
non-atomic invariant random subgroups of $\mathbb{F}_2$ which are all invariant
and weakly mixing with respect to the action of $\text{Aut}(\mathbb{F}_2)$.
  Moreover, for amenable groups $\Gamma\leq \Delta$, we obtain that the
standard co-induction operation from the space of weak equivalence classes of
$\Gamma$ to the space of weak equivalence classes of $\Delta$ is continuous if
and only if $[\Delta :\Gamma]<\infty$ or $\text{core}_\Delta(\Gamma)$ is
trivial. For general groups we obtain that the co-induction operation is not
continuous when $[\Delta:\Gamma]=\infty$. This answers a question raised by
Burton and Kechris. Independently such an answer was also obtained, using a
different method, by Bernshteyn.


  Today's online platforms rely heavily on recommendation systems to serve
content to their users; social media is a prime example. In turn,
recommendation systems largely depend on artificial intelligence algorithms to
decide who gets to see what. While the content social media platforms deliver
is as varied as the users who engage with them, it has been shown that
platforms can contribute to serious harm to individuals, groups and societies.
Studies have suggested that these negative impacts range from worsening an
individual's mental health to driving society-wide polarisation capable of
putting democracies at risk. To better safeguard people from these harms, the
European Union's Digital Services Act (DSA) requires platforms, especially
those with large numbers of users, to make their algorithmic systems more
transparent and follow due diligence obligations. These requirements constitute
an important legislative step towards mitigating the systemic risks posed by
online platforms. However, the DSA lacks concrete guidelines to operationalise
a viable audit process that would allow auditors to hold these platforms
accountable. This void could foster the spread of 'audit-washing', that is,
platforms exploiting audits to legitimise their practices and neglect
responsibility.
  To fill this gap, we propose a risk-scenario-based audit process. We explain
in detail what audits and assessments of recommender systems according to the
DSA should look like. Our approach also considers the evolving nature of
platforms and emphasises the observability of their recommender systems'
components. The resulting audit facilitates internal (among audits of the same
system at different moments in time) and external comparability (among audits
of different platforms) while also affording the evaluation of mitigation
measures implemented by the platforms themselves.


  Let G be a group, Fin the family of its finite subgroups, and E(G,Fin) the
classifying space. Let L^1 be the algebra of trace-class operators in an
infinite dimensional, separable Hilbert space over the complex numbers.
Consider the rational assembly map in homotopy algebraic K-theory
H_p^G(E(G,Fin),KH(L^1))\otimes\Q \to KH_p(L^1[G])\otimes\Q. The rational
KH-isomorphism conjecture predicts that the map above is an isomorphism; it
follows from a theorem of Yu (see arXiv:1106.3796, arXiv:1202.4999) that it is
always injective. In the current article we prove the following. Theorem:
Assume that the map above is surjective. Let n\equiv p+1\mod 2. Then: i) The
assembly map for the trivial family H_n^G(E(G,{1}),K(\Z)) \to K_n(\Z[G]) is
rationally injective. ii) For every number field F, the assembly map
H_n^G(E(G,Fin),K(F)) \to K_n(F[G]) is rationally injective. We remark that the
K-theory Novikov conjecture asserts that part i) of the theorem above holds for
all G, and that part ii) is equivalent to the rational injectivity part of the
K-theory Farrell-Jones conjecture for number fields. The idea of the proof of
the Theorem is to use an algebraic, equivariant version of Karoubi's
multiplicative K-theory, which we introduce in this article.


  We generalize the notion of saturated order to infinite partial orders and
give both a set-theoretic and an algebraic characterization of such orders. We
then study the proof theoretic strength of the equivalence of these
characterizations in the context of reverse mathematics, showing that depending
on one's choice of definitions it is either provable in $\mathsf{RCA}_0$ or
equivalent to $\mathsf{ACA}_0$.


  Wave propagation is a common occurrence in all of physics. A linear
approximation provides a simpler way to describe various fields related to
observable phenomena in laboratory physics as well as astronomy and cosmology,
allowing us to probe gravitation through its effect on the trajectories of
particles associated with those fields. This paper proposes a unified framework
to describe the wave propagation of a set of interacting tensor fields that
obey coupled homogeneous linear second-order partial differential equations for
arbitrary curved spacetimes, both Lorentzian and metric-affine. We use JWKB
Ans\"atze for all fields, written in terms of a perturbation parameter
proportional to a representative wavelength among them, deriving a set of
hierarchical algebraic and differential equations that link the fields' phases
and different order amplitudes. This allows us to reobtain the well-known laws
of geometrical optics and beyond geometrical optics in a generalized form,
showing that these laws are independent of the rank of the fields involved.
This is true as long as what we refer to as the kinetic tensor of a given field
satisfies a set of diagonality conditions, which further imply a handful of
simplifications on the transport equations obtained in the subleading orders of
the JWKB Ans\"atze. We explore these results in several notable examples in
Lorentzian and metric-affine spacetimes, illustrating the reach of our
derivations in general relativity, reduced Horndeski theories, spacetimes with
completely antisymmetric torsion and Weyl spacetimes. The formalism presented
herein lays the groundwork for the study of rays associated with different
types of waves in curved spacetimes and provides the tools to compute
modifications to their brightness evolution laws, consequential distance
duality relations, and beyond geometrical optics phenomena.


  The quantum system under periodical modulation is the simplest path to
understand the quantum non-equilibrium system, because it can be well described
by the effective static Floquet Hamiltonian. Under the stroboscopic
measurement, the initial phase is usually irrelevant. However, if two
uncorrelated parameters are modulated, their relative phase can not be gauged
out, so that the physics can be dramatically changed. Here, we simultaneously
modulate the frequency of the lattice laser and the Rabi frequency in an
optical lattice clock (OLC) system. Thanks to ultra-high precision and
ultra-stability of OLC, the relative phase could be fine-tuned. As a smoking
gun, we observed the interference between two Floquet channels. Finally, by
experimentally detecting the eigen-energies, we demonstrate the relation
between effective Floquet Hamiltonian and 1-D topological insulator with high
winding number. Our experiment not only provides a direction for detecting the
phase effect, but also paves a way in simulating quantum topological phase in
OLC platform.


  We present a method that learns neural shadow fields which are neural scene
representations that are only learnt from the shadows present in the scene.
While traditional shape-from-shadow (SfS) algorithms reconstruct geometry from
shadows, they assume a fixed scanning setup and fail to generalize to complex
scenes. Neural rendering algorithms, on the other hand, rely on photometric
consistency between RGB images, but largely ignore physical cues such as
shadows, which have been shown to provide valuable information about the scene.
We observe that shadows are a powerful cue that can constrain neural scene
representations to learn SfS, and even outperform NeRF to reconstruct otherwise
hidden geometry. We propose a graphics-inspired differentiable approach to
render accurate shadows with volumetric rendering, predicting a shadow map that
can be compared to the ground truth shadow. Even with just binary shadow maps,
we show that neural rendering can localize the object and estimate coarse
geometry. Our approach reveals that sparse cues in images can be used to
estimate geometry using differentiable volumetric rendering. Moreover, our
framework is highly generalizable and can work alongside existing 3D
reconstruction techniques that otherwise only use photometric consistency.


  Strongly anisotropic critical systems are considered in a $d$-dimensional
film geometry. Such systems involve two (or more) distinct correlation lengths
$\xi_\beta$ and $\xi_\alpha$ that scale as nontrivial powers of each other,
i.e.\ $\xi_\alpha\sim\xi_\beta^\theta$ with anisotropy index $\theta\ne 1$.
Thus two fundamental orientations, perpendicular ($\perp$) and parallel ($\|$),
for which the surface normal is oriented along an $\alpha$- and
$\beta$-direction, respectively, must be distinguished. The confinement of
critical fluctuations caused by the film's boundary planes is shown to induce
effective forces $\mathcal{F}_C$ that decay as
$\mathcal{F}_C\propto-(\partial/\partial
L)\Delta_{\perp,\|}\,L^{-\zeta_{\perp,\|}}$ as the film thickness $L$ becomes
large, where the proportionality constants involve nonuniversal amplitudes. The
decay exponents $\zeta_{\perp,\|}$ and the Casimir amplitudes
$\Delta_{\perp,\|}$ are universal but depend on the type of orientation. To
corroborate these findings, $n$-vector models with an $m$-axial bulk Lifshitz
point are investigated by means of RG methods below the upper critical
dimension $d^*(m)=4+m/2$ under various boundary conditions (BC). The exponents
$\zeta_{\perp,\|}$ are determined, and explicit results to one- or two-loop
order are presented for several Casimir amplitudes
$\Delta^{\mathrm{BC}}_{\perp,\|}$. The large-$n$ limits of the Casimir
amplitudes $\Delta_{\|}^{\mathrm{BC}}/n$ for periodic and Dirichlet BC are
shown to be proportional to their critical-point analogues at dimension
$d-m/2$. The limiting values
$\Delta^{\mathrm{PBC}}_{\|,\perp,\infty}=\lim_{n\to\infty}\Delta_{\|,\perp}^{\mathrm{PBC}}/n$
are determined exactly for the uniaxial cases $(d,m)=(3,1)$ under periodic BC.
Unlike $\Delta^{\mathrm{PBC}}_{\|,\infty}$,
$\Delta^{\mathrm{PBC}}_{\perp,\infty}$ is positive, so that the corresponding
Casimir force is repulsive.


  Short-range electron-electron interactions are incorporated into the network
model of the integer quantum Hall effect. In the presence of interactions, the
electrons, propagating along one link, experience exchange scattering off the
Friedel oscillations of the density matrix of electrons on the neighboring
links. As a result, the energy dependence of the transmission, ${\cal
T}(\epsilon)$, of the node, connecting the two links, develops an anomaly at
the Fermi level, $\epsilon=\epsilon_F$. We show that this interaction-induced
anomaly in ${\cal T}(\epsilon)$ translates into the anomalous behavior of the
Hall conductivity, $\sigma_{xy}(\nu)$, where $\nu$ is the filling factor (we
assume that the electrons are {\em spinless}). At low temperatures, $T \to 0$,
the evolution of the quantized $\sigma_{xy}$ with decreasing $\nu$ proceeds as
$1\to 2 \to 0$, in apparent violation of the semicircle relation. The anomaly
in ${\cal T}(\epsilon)$ also affects the temperature dependence of the peak in
the diagonal conductivity, $\sigma_{xx}(\nu, T)$. In particular, unlike the
case of noninteracting electrons,the maximum value of $\sigma_{xx}$ stays at
$\sigma_{xx} = 0.5$ within a wide temperature interval.


  The evolution of effective force chains percolating through a compressed
granular system is investigated. We performed experiments by compressing an
ensemble of spherical particles in a cylindrical container monitoring the
macroscopic constitutive behavior and the acoustic signals emitted by
microscopic rearrangements of particles. As a novel approach, we applied the
continuous damage model of fiber bundles to describe the evolution of the array
of force chains during the loading process. The model provides a nonlinear
constitutive behavior in good quantitative agreement with the experimental
results. For a system of hard particles the model predicts a universal power
law divergence of stress when approaching a critical deformation. The amplitude
distribution of acoustic signals was found experimentally to follow a power law
with exponent $\delta = 1.15 \pm 0.05$ which is in a good agreement with the
analytic solution of the model.


  Uncertainty quantification for full-waveform inversion provides a
probabilistic characterization of the ill-conditioning of the problem,
comprising the sensitivity of the solution with respect to the starting model
and data noise. This analysis allows to assess the confidence in the candidate
solution and how it is reflected in the tasks that are typically performed
after imaging (e.g., stratigraphic segmentation following reservoir
characterization). Classically, uncertainty comes in the form of a probability
distribution formulated from Bayesian principles, from which we seek to obtain
samples. A popular solution involves Monte Carlo sampling. Here, we propose
instead an approach characterized by training a deep network that "pushes
forward" Gaussian random inputs into the model space (representing, for
example, density or velocity) as if they were sampled from the actual posterior
distribution. Such network is designed to solve a variational optimization
problem based on the Kullback-Leibler divergence between the posterior and the
network output distributions. This work is fundamentally rooted in recent
developments for invertible networks. Special invertible architectures, besides
being computational advantageous with respect to traditional networks, do also
enable analytic computation of the output density function. Therefore, after
training, these networks can be readily used as a new prior for a related
inversion problem. This stands in stark contrast with Monte-Carlo methods,
which only produce samples. We validate these ideas with an application to
angle-versus-ray parameter analysis for reservoir characterization.


  It is shown that previous arguments leading to the equality $z=d$ ($d$ being
the spatial dimensionality) for the dynamical exponent describing the Bose
glass to superfluid transition may break down, as apparently seen in recent
simulations (Ref. \cite{Baranger}). The key observation is that the major
contribution to the compressibility, which remains finite through the
transition and was predicted to scale as $\kappa \sim |\delta|^{(d-z)\nu}$
(where $\delta$ is the deviation from criticality and $\nu$ is the correlation
length exponent) comes from the analytic part, not the singular part of the
free energy, and therefore is not restricted by any conventional scaling
hypothesis.


  The quality control of fetal sonographic (FS) images is essential for the
correct biometric measurements and fetal anomaly diagnosis. However, quality
control requires professional sonographers to perform and is often
labor-intensive. To solve this problem, we propose an automatic image quality
assessment scheme based on multi-task learning to assist in FS image quality
control. An essential criterion for FS image quality control is that all the
essential anatomical structures in the section should appear full and
remarkable with a clear boundary. Therefore, our scheme aims to identify those
essential anatomical structures to judge whether an FS image is the standard
image, which is achieved by three convolutional neural networks. The Feature
Extraction Network aims to extract deep level features of FS images. Based on
the extracted features, the Class Prediction Network determines whether the
structure meets the standard and Region Proposal Network identifies its
position. The scheme has been applied to three types of fetal sections, which
are the head, abdominal, and heart. The experimental results show that our
method can make a quality assessment of an FS image within less a second. Also,
our method achieves competitive performance in both the detection and
classification compared with state-of-the-art methods.


  In online advertising, a set of potential advertisements can be ranked by a
certain auction system where usually the top-1 advertisement would be selected
and displayed at an advertising space. In this paper, we show a selection bias
issue that is present in an auction system. We analyze that the selection bias
destroy truthfulness of the auction, which implies that the buyers
(advertisers) on the auction can not maximize their profits. Although selection
bias is well known in the field of statistics and there are lot of studies for
it, our main contribution is to combine the theoretical analysis of the bias
with the auction mechanism. In our experiment using online A/B testing, we
evaluate the selection bias on an auction system whose ranking score is the
function of predicted CTR (click through rate) of advertisement. The experiment
showed that the selection bias is drastically reduced by using a multi-task
learning which learns the data for all advertisements.


  Suffix Array (SA) is a cardinal data structure in many pattern matching
applications, including data compression, plagiarism detection and sequence
alignment. However, as the volumes of data increase abruptly, the construction
of SA is not amenable to the current large-scale data processing frameworks
anymore due to its intrinsic proliferation of suffixes during the construction.
That is, ameliorating the performance by just adding the resources to the
frameworks becomes less cost- effective, even having the severe diminishing
returns. At issue now is whether we can permit SA construction to be more
scalable and efficient for the everlasting accretion of data by creating a
radical shift in perspective. Regarding TeraSort [1] as our baseline, we first
demonstrate the fragile scalability of TeraSort and investigate what causes it
through the experiments on the sequence alignment of a grouper (i.e., the SA
construc- tion used in bioinformatics). As such, we propose a scheme that
amalgamates the distributed key-value store system into MapReduce to leverage
the in-memory queries about suffixes. Rather than handling the communication of
suffixes, MapReduce is in charge of the communication of their indexes, which
means better capacity for more data. It significantly abates the required disk
space for constructing SA and better utilizes the memory, which in turn
improves the scalability radically. We also examine the efficiency of our
scheme in terms of memory and show it outperforms TeraSort. At last, our scheme
can complete the pair- end sequencing and alignment with two input files
without any degradation on scalability, and can accommodate the suffixes of
nearly 6.7 TB in a small cluster composed of 16 nodes and Gigabit Ethernet
without any compression.


  We find exact solutions to the Dirac equation in D-dimensional de Sitter
spacetime. Using these solutions we analytically calculate the de Sitter
quasinormal (QN) frequencies of the Dirac field. For the massive Dirac field
this computation is similar to that previously published for massive fields of
half-integer spin moving in four dimensions. However to calculate the QN
frequencies of the massless Dirac field we must use distinct methods in odd and
even dimensions, therefore the computation is different from that already known
for other massless fields of integer spin.


  In this paper, we introduce a new multiple-parameters (multi-index) extension
of the Wright function that arises from an eigenvalue problem for a case of
hyper-Bessel operator involving Caputo fractional derivatives. We show that by
giving particular values to the parameters involved in this special function,
this leads to some known special functions (as the classical Wright function,
the $\alpha$-Mittag-Leffer function, the Tricomi function, etc.) that on their
turn appear as cases of the so-called multi-index Mittag-Leffer functions. As
an application, we mention that this new generalization Wright function is an
isochronous solution of a nonlinear fractional partial differential equation.


  Recently RuB$_2$ was shown to be a possible two-gap, type-I superconductor.
Temperature dependent heat capacity measurements revealed a two-gap
superconducting ground state while magnetic field dependent magnetization
measurements indicated surprising type-I superconductivity with a very low
experimental critical field ($H_c$) $\sim$ 120 Oe. In this paper, we report
direct spectroscopic evidence of two superconducting energy gaps in RuB$_2$. We
have measured scanning tunnelling spectra exhibiting signature of two gaps on
different grains of polycrystalline RuB$_2$, possibly originating from multiple
bands. Analysis of the temperature dependent tunnelling spectra revealed that
the gaps from different bands evolve differently with temperature before
disappearing simultaneously at a single $T_c$. Interestingly, our experiments
also reveal that the gaps in quasiparticle density of states survive up to
magnetic fields much higher than the bulk $H_c$ and they evolve smoothly with
field, unlike what is expected for a type-I superconductor, indicating the
existence of a ``mixed state".


  Graphene nanoribbons with sub-nanometer widths are extremely interesting for
nanoscale electronics and devices as they combine the unusual transport
properties of graphene with the opening of a band gap due to quantum
confinement in the lateral dimension. Strong research efforts are presently
paid to grow such nanoribbons. Here we show the synthesis of 6- and 7-armchair
graphene nanoribbons, with widths of 0.61 and 0.74 nm, and excitonic gaps of
1.83 and 2.18 eV, by high-temperature vacuum annealing of ferrocene molecules
inside single-walled carbon nanotubes. The encapsulation of the so-obtained
graphene nanoribbons is proved by atomic resolution electron microscopy, while
their assignment is provided by a combination of an extensive
wavelength-dependent Raman scattering characterization and quantum-chemical
calculations. These findings enable a facile and scalable approach leading to
the controlled growth and detailed analysis of well-defined sub-nanometer
graphene nanoribbons.


  With the electrification in freight transportation, the availability of
fast-charging facilities becomes essential to facilitate en-route charging for
freight electric vehicles. Most studies focus on planning charging facilities
based on mathematical modeling and hypothetical scenarios. This study aims to
develop a data-driven integrated framework for fast-charging facility planning.
By leveraging the highway traffic data, we extracted, analyzed, and compared
spatial and temporal flow patterns of general traffic and freight traffic.
Furthermore, graph theory-based network evaluation methods are employed to
identify traffic nodes within the highway network that play a significant role
in accommodating charging infrastructure. A candidate selection method is
proposed to obtain potential deployment locations for charging stations and
to-go chargers. Based on this, we present a multi-period bi-objective
optimization model to provide optimal solutions for the placement of charging
facilities, with the objectives of minimizing investment cost and maximizing
demand coverage. The case study on the Amsterdam highway network shows how
existing traffic data can be used to generate more realistic charging demand
scenarios and how it can be integrated and evaluated within the optimization
framework for facility planning. The study also shows that the proposed model
can leverage the potential of early investment in improving the charging demand
coverage.


  In two experiments (total N=693) we explored whether people are willing to
consider paintings made by AI-driven robots as art, and robots as artists.
Across the two experiments, we manipulated three factors: (i) agent type
(AI-driven robot v. human agent), (ii) behavior type (intentional creation of a
painting v. accidental creation), and (iii) object type (abstract v.
representational painting). We found that people judge robot paintings and
human painting as art to roughly the same extent. However, people are much less
willing to consider robots as artists than humans, which is partially explained
by the fact that they are less disposed to attribute artistic intentions to
robots.


  In this work we introduce the obstacle-mass constraint problem for a
multidimensional scalar hyperbolic conservation law. We prove existence of an
entropy solution to this problem by a penalization/viscosity method. The mass
constraint introduces a nonlocal Lagrange multiplier in the penalized equation,
giving rise to a nonlocal parabolic problem. We determine conditions on the
initial data and on the obstacle function which ensure global in time existence
of solution. These are not smoothness conditions, but relate to the propagation
of the support of the initial data.


  We propose a new scheme for quantum secret sharing (QSS) that uses a
modulated high-dimensional time-bin entanglement. By modulating the relative
phase randomly by {0,pi}, a sender with the entanglement source can randomly
change the sign of the correlation of the measurement outcomes obtained by two
distant recipients. The two recipients must cooperate if they are to obtain the
sign of the correlation, which is used as a secret key. We show that our scheme
is secure against intercept-and-resend (I-R) and beam splitting attacks by an
outside eavesdropper thanks to the non-orthogonality of high-dimensional
time-bin entangled states. We also show that a cheating attempt based on an I-R
attack by one of the recipients can be detected by changing the dimension of
the time bin entanglement randomly and inserting two "vacant" slots between the
packets. Then, cheating attempts can be detected by monitoring the count rate
in the vacant slots. The proposed scheme has better experimental feasibility
than previously proposed entanglement-based QSS schemes.


  An inverse problem framework for constructing reaction systems with
prescribed properties is presented. Kinetic transformations are defined and
analysed as a part of the framework, allowing an arbitrary polynomial ordinary
differential equation to be mapped to the one that can be represented as a
reaction network. The framework is used for construction of specific two- and
three-dimensional bistable reaction systems undergoing a supercritical
homoclinic bifurcation, and the topology of their phase spaces is discussed.


  We report finite-volume simulations of the phonon Boltzmann transport
equation (BTE) for heat conduction across the heterogeneous interfaces in SiGe
superlattices. The diffuse mismatch model incorporating phonon dispersion and
polarization is implemented over a wide range of Knudsen numbers. The results
indicate that the thermal conductivity of a Si/Ge superlattice is much lower
than that of the constitutive bulk materials for superlattice periods in the
submicron regime. We report results for effective thermal conductivity of
various material volume fractions and superlattice periods. Details of the
non-equilibrium energy exchange between optical and acoustic phonons that
originate from the mismatch of phonon spectra in silicon and germanium are
delineated for the first time. Conditions are identified for which this effect
can produce significantly more thermal resistance than that due to boundary
scattering of phonons.


  We investigate a simplified model of dark matter where a Majorana fermion
$\chi$ coannihilates with a colored scalar top partner $\tilde{t}$. We explore
the cosmological history, with particular emphasis on the most relevant
low-energy parameters: the mass splitting between the dark matter and the
coannihilator, and the Yukawa coupling $y_\chi$ that connects these fields to
the Standard Model top quarks. We also allow a free quartic coupling
$\lambda_h$ between a pair of Higgs bosons and $\tilde{t}$ pairs. We pay
special attention to the case where the values take on those expected where
$\tilde{t}$ corresponds to the superpartner of the right-handed top, and $\chi$
is a bino. Direct detection, indirect detection, and colliders are
complementary probes of this simple model.


  The aim of this short review is to give an overview to non-specialists of
recent arguments from fundamental physics in favor and disfavor of quantum
corrections to black hole horizons. I will mainly discuss the black hole
information paradox, its possible resolutions and shortly address its relevance
or irrelevance to astronomy.


  We show that Feldman and Gefen's comment revolves around a trivial issue -
the relative magnitudes for the two crossover scales in the problem.


  We present SCUBA-2 850um submillimetre (submm) observations of the fields of
10 dusty, luminous galaxies at z ~ 1.7 - 4.6, detected at 12um and/or 22um by
the WISE all-sky survey, but faint or undetected at 3.4um and 4.6um; dubbed
hot, dust-obscured galaxies (Hot DOGs). The six detected targets all have total
infrared luminosities greater than 10^13 L_sun, with one greater than 10^14
L_sun. Their spectral energy distributions (SEDs) are very blue from
mid-infrared to submm wavelengths and not well fitted by standard AGN SED
templates, without adding extra dust extinction to fit the WISE 3.4um and 4.6um
data. The SCUBA-2 850um observations confirm that the Hot DOGs have less cold
and/or more warm dust emission than standard AGN templates, and limit an
underlying extended spiral or ULIRG-type galaxy to contribute less than about
2% or 55% of the typical total Hot DOG IR luminosity, respectively. The two
most distant and luminous targets have similar observed submm to mid-infrared
ratios to the rest, and thus appear to have even hotter SEDs. The number of
serendipitous submm galaxies (SMGs) detected in the 1.5-arcmin-radius SCUBA-2
850um maps indicates there is a significant over-density of serendipitous
sources around Hot DOGs. These submm observations confirm that the
WISE-selected ultra-luminous galaxies have very blue mid-infrared to submm
SEDs, suggesting that they contain very powerful AGN, and are apparently
located in unusual arcmin-scale overdensities of very luminous dusty galaxies.


  The Indiana University Cyclotron Facility (IUCF) is in the process of
building the Midwest Proton Radiation Institute (MPRI). This involves
refurbishing the 200MeV cyclotron and building new facilities for the purpose
of providing clinical treatment of human cancer tumors. First patients are
expected in the Spring of 2003. This paper presents the design and
implementation to date of the controls, hardware and software, for both
accelerator and treatment areas. Particular attention is placed on issues of
personnel safety and control system security, development of inexpensive VMEbus
boards with emphasis on the use of MicroChip PIC processors, beam diagnostics
and monitoring and the use of commercial robots and vision systems for patient
positioning.


  Vertical Federated Learning (VFL) has emerged as a critical approach in
machine learning to address privacy concerns associated with centralized data
storage and processing. VFL facilitates collaboration among multiple entities
with distinct feature sets on the same user population, enabling the joint
training of predictive models without direct data sharing. A key aspect of VFL
is the fair and accurate evaluation of each entity's contribution to the
learning process. This is crucial for maintaining trust among participating
entities, ensuring equitable resource sharing, and fostering a sustainable
collaboration framework. This paper provides a thorough review of contribution
evaluation in VFL. We categorize the vast array of contribution evaluation
techniques along the VFL lifecycle, granularity of evaluation, privacy
considerations, and core computational methods. We also explore various tasks
in VFL that involving contribution evaluation and analyze their required
evaluation properties and relation to the VFL lifecycle phases. Finally, we
present a vision for the future challenges of contribution evaluation in VFL.
By providing a structured analysis of the current landscape and potential
advancements, this paper aims to guide researchers and practitioners in the
design and implementation of more effective, efficient, and privacy-centric VFL
solutions. Relevant literature and open-source resources have been compiled and
are being continuously updated at the GitHub repository:
\url{https://github.com/cuiyuebing/VFL_CE}.


  We use masses, radii, and luminosities of the detached eclipsing binary
OGLEGC17 derived from photometric and spectroscopic observations to calculate
the age and distance of the globular cluster omega Cen. Age versus turnoff mass
and age versus luminosity relations from Girardi et al. (2000) yield two
independent estimates of the age, 9.1<t<16.7 Gyr and 12.9<t<18.5 Gyr. The
distance and distance modulus derived by use of the infrared versus surface
brightness relation are d =5385+-300 pc and (m-M)_V=14.06+-0.11. Distances
derived from our infrared surface brightness versus color relation and the Teff
versus B-V color relation of Sekiguchi & Fukugita (2000) disagree by about 10
per cent. Major improvements in the accuracy in estimated age and distance can
be made with better measurements of the masses of the components of OGLEGC17.


  Common artefacts such as baseline drift, rescaling, and noise critically
limit the performance of machine learningbased automated ECG analysis and
interpretation. This study proposes Derived Peak (DP) encoding, a
non-parametric method that generates signed spikes corresponding to zero
crossings of the signals first and second-order time derivatives. Notably, DP
encoding is invariant to shift and scaling artefacts, and its implementation is
further simplified by the absence of userdefined parameters. DP encoding was
used to encode the 12-lead ECG data from the PTB-XL dataset (n=18,869
participants) and was fed to 1D-ResNet-18 models trained to identify myocardial
infarction, conductive deficits and ST-segment abnormalities. Robustness to
artefacts was assessed by corrupting ECG data with sinusoidal baseline drift,
shift, rescaling and noise, before encoding. The addition of these artefacts
resulted in a significant drop in accuracy for seven other methods from prior
art, while DP encoding maintained a baseline AUC of 0.88 under drift, shift and
rescaling. DP achieved superior performance to unencoded inputs in the presence
of shift (AUC under 1mV shift: 0.91 vs 0.62), and rescaling artefacts (AUC 0.91
vs 0.79). Thus, DP encoding is a simple method by which robustness to common
ECG artefacts may be improved for automated ECG analysis and interpretation.


  GAUGE INVARIANCE: The Sachs-Wolfe formula describing the Cosmic Microwave
Background (CMB) temperature anisotropies is one of the most important
relations in cosmology. Despite its importance, the gauge invariance of this
formula has only been discussed at first order. Here we discuss the subtle
issue of second-order gauge transformations on the CMB. By introducing two
rules (needed to handle the subtle issues), we prove the gauge invariance of
the second-order Sachs-Wolfe formula and provide several compact expressions
which can be useful for the study of gauge transformations on cosmology. Our
results go beyond a simple technicality: we discuss from a physical point of
view several aspects that improve our understanding of the CMB. We also
elucidate how crucial it is to understand gauge transformations on the CMB in
order to avoid errors and/or misconceptions as occurred in the past. THE RIVER
FRAME: we introduce a cosmological frame which we call the river frame. In this
frame, photons and any object can be thought as fishes swimming in the river
and relations are easily expressed in either the metric or the covariant
formalism then ensuring a transparent geometric meaning. Finally, our results
show that the river frame is useful to make perturbative and non-perturbative
analysis. In particular, it was already used to obtain the fully nonlinear
generalization of the Sachs-Wolfe formula and is used here to describe
second-order perturbations.


  Wilson loop averages of pure gauge QCD at large N on a sphere are calculated
by means of Makeenko-Migdal loop equation.


  The dependence of star formation efficiency (SFE) on galactic structures,
especially whether the SFE in the bar region is lower than those in the other
regions, has recently been debated. We report the SFEs of 18 nearby gas-rich
massive star-forming barred galaxies with a large apparent bar major axis
($\geqq 75^{\prime\prime}$). We statistically measure the SFE by distinguishing
the center, bar-end, and bar regions for the first time. The molecular gas
surface density is derived from archival CO(1-0) and/or CO(2-1) data by
assuming a constant CO-to-H$_2$ conversion factor ($\alpha_{\rm CO}$), and the
star formation rate surface density is derived from a linear combination of
far-ultraviolet and mid-infrared intensities. The angular resolution is
$15^{\prime\prime}$, which corresponds to $0.3 - 1.8~\rm kpc$. We find that the
ratio of the SFE in the bar to that in the disk was systematically lower than
unity (typically $0.6-0.8$), which means that the star formation in the bar is
systematically suppressed. Our results are inconsistent with similar recent
statistical studies that reported that SFE tends to be independent of galactic
structures. This inconsistency can be attributed to the differences in the
definition of the bar region, spatial resolution, $\alpha_{\rm CO}$, and sample
galaxies. Furthermore, we find a negative correlation between SFE and velocity
width of the CO spectrum, which is consistent with the idea that the large
dynamical effects, such as strong shocks, large shear, and fast cloud-cloud
collisions caused by the noncircular motion of the bar, result in a low SFE.


  We use deep narrowband Ca H&K ($F395N$) imaging taken with the Hubble Space
Telescope (HST) to construct the metallicity distribution function (MDF) of
Local Group (LG) ultra-faint dwarf (UFD) galaxy Eridanus II (Eri II). When
combined with archival $F475W$ and $F814W$ data, we measure metallicities for
60 resolved red giant branch stars as faint as $m_{F475W}\sim24$ mag, a factor
of $\sim4$x more stars than current spectroscopic MDF determinations. We find
that Eri II has a mean metallicity of [Fe/H]$=$-2.50$^{+0.07}_{-0.07}$ and a
dispersion of $\sigma_{\mbox{[Fe/H]}}=0.42^{+0.06}_{-0.06}$, which are
consistent with spectroscopic MDFs, though more precisely constrained owing to
a larger sample. We identify a handful of extremely metal-poor star candidates
(EMP; [Fe/H] $< -3$) that are marginally bright enough for spectroscopic follow
up. Eri II's MDF appears well-described by a leaky box chemical evolution
model. We also compute an updated orbital history for Eri II using Gaia eDR3
proper motions, and find that it is likely on first infall into the Milky Way.
Our findings suggest that Eri II underwent an evolutionary history similar to
that of an isolated galaxy. Compared to MDFs for select cosmological
simulations of similar mass galaxies, we find that Eri II has a lower fraction
of stars with [Fe/H] $< -3$, though such comparisons should currently be
treated with caution due to a paucity of simulations, selection effects, and
known limitations of Ca H&K for EMPs. This study demonstrates the power of deep
HST CaHK imaging for measuring the MDFs of UFDs.


  We show that in a holomorphic family of compact complex connected manifolds
parametrized by an irreducible complex space $S$, assuming that on a dense
Zariski open set $S^{*}$ in $S$ the fibres satisfy the
$\partial\bar\partial-$lemma, the algebraic dimension of each fibre in this
family is at least equal to the minimal algebraic dimension of the fibres in
$S^{*}$. For instance, if each fibre in $S^{*}$ are Moishezon, then all fibres
are Moishezon.


  To study the effect of metallicity on the mass-loss of AGB stars, we have
conducted mid-infrared photometric measurements of AGB stars in the Sagittarius
and Fornax Dwarf Spheroidal Galaxies ([Fe/H]=-1.1 and -1.3) with the 10-micron
camera of VISIR at the VLT. These observations combined with previous
near-infrared photometric measurements allow us to estimate mass-loss rates in
these galaxies. We show here that the observed AGB display dust-driven
mass-loss. Dust mass-loss rate are found to be in the range
0.2$\times10^{-10}-1.3\times10^{-8}$ M$_{\odot}$yr$^{-1}$ for the observed AGB
stars in SgrD and around 5$\times10^{-11}$ M$_{\odot}$yr$^{-1}$ for the
observed star in Fornax.


  As a useful and elegant tool of extreme value theory, the study of point
processes on a metric space is important and necessary for the analyses of
heavy-tailed functional data. This paper focuses on the definition and
properties of such point processes. A complete convergence result for a
regularly varying iid sequence in a metric space is proved as an example of the
application in extreme value theory.


  We present abundance measurements of elements O, C, Si and Fe for three
gas-rich galaxies at z~5 using observations from the Very Large Telescope (VLT)
and the Keck telescope in order to better constrain the early chemical
enrichment of gas-rich galaxies. These galaxies show strong Lyman-{\alpha}
absorption in the spectra of background quasars, with neutral hydrogen column
densities log N${_H{}_I}$(cm$^{-2}$)=20.10$\pm$0.15, 20.10$\pm$0.15, and
20.80$\pm$0.15. Using the undepleted element O, we find the metallicities [O/H]
to be in the range of -2.51 to -2.05 dex. Our study has doubled the existing
sample of measurements of undepleted elements at z>4.5. Combining our
measurements with those from the literature, we find that the
N${_H{}_I}$-weighted mean metallicity of z~5 absorbers is consistent with the
prediction based on z<4.5 DLAs. Thus, we find no significant evidence of a
sudden drop in metallicity at z>4.7 as reported by some prior studies. We also
determine the extent of dust depletion using a combination of both the volatile
element O and the refractory elements Si and/or Fe. Some of the absorbers show
evidence of depletion of elements on dust grains, e.g. low [Si/O] or [Fe/O].
The relative abundances of these absorbers along with other z~5 absorbers from
the literature show some peculiarities, e.g. low [C/O] in several absorbers and
high [Si/O] in one absorber. Finally, we find that the metallicity vs. velocity
dispersion relation of z~5 absorbers may be different from that of
lower-redshift absorbers.


  We answer, by counterexample, several open questions concerning algebras of
operators on a Hilbert space. The answers add further weight to the thesis
that, for many purposes, such algebras ought to be studied in the framework of
operator spaces, as opposed to that of Banach spaces and Banach algebras. In
particular, the `nonselfadjoint analogue' of a W*-algebra resides naturally in
the category of dual operator spaces, as opposed to dual Banach spaces. We also
show that an automatic w*-continuity result in the preceding paper of the
authors is sharp.


  Intermittent computing requires custom programming models to ensure the
correct execution of applications despite power failures. However, existing
programming models lead to programs that are hardware-dependent and not
reusable. This paper aims at virtualizing intermittent computing to remedy
these problems. We introduce PureVM, a virtual machine that abstracts a
transiently powered computer, and PureLANG, a continuation-passing-style
programming language to develop programs that run on PureVM. This
virtualization, for the first time, paves the way for portable and reusable
transiently-powered applications.


  The extension of the topological classification of band insulators to
topological semimetals gave way to the topology classes of Dirac, Weyl, and
nodal line semimetals with their unique Fermi arc and drum head boundary modes.
Similarly, there are several suggestions to employ the classification of
topological superconductors for topological nodal superconductors with Majorana
boundary modes. Here, we show that the surface 1H termination of the transition
metal dichalcogenide compound 4Hb-TaS$_2$, in which 1T-TaS$_2$ and 1H-TaS$_2$
layers are interleaved, has the phenomenology of a topological nodal point
superconductor. We find in scanning tunneling spectroscopy a residual density
of states within the superconducting gap. An exponentially decaying bound mode
is imaged within the superconducting gap along the boundaries of the exposed 1H
layer characteristic of a gapless Majorana edge mode. The anisotropic nature of
the localization length of the edge mode aims towards topological nodal
superconductivity. A zero-bias conductance peak is further imaged within fairly
isotropic vortex cores. All our observations are accommodated by a theoretical
model of a two-dimensional nodal Weyl-like superconducting state, which ensues
from inter-orbital Cooper pairing. The observation of an intrinsic topological
nodal superconductivity in a layered material will pave the way for further
studies of Majorana edge modes and its applications in quantum information
processing.


  The peculiar defect-related photon emission processes in 2D hexagonal boron
nitride (hBN) have become a topic of intense research due to their potential
applications in quantum information and sensing technologies. Recent efforts
have focused on activating and modulating the defect energy levels in hBN by
methods that can be integrated on a chip, and understanding the underlying
physical mechanism. Here, we report on exotic single photon and enhanced
deep-level emissions in 2D hBN strain superlattice, which is fabricated by
transferring multilayer hBN onto hexagonal close-packed silica spheres on
silica substrate. We realize effective activation of the single photon
emissions (SPEs) in the multilayer hBN at the positions that are in contact
with the apex of the SiO2 spheres. At these points, the local tensile strain
induced blue-shift of the SPE is found to be up to 12 nm. Furthermore, high
spatial resolution cathodoluminescence measurments show remarkable
strain-enhanced deep-level (DL) emissions in the multilayer hBN with the
emission intensity distribution following the periodic hexagonal pattern of the
strain superlattice. The maximum DL emission enhancement is up to 350% with a
energy redshift of 6 nm. Our results provide a simple on-chip compatible method
for activating and tuning the defect-related photon emissions in multilayer
hBN, demonstrating the potential of hBN strain superlattice as a building block
for future on-chip quantum nanophotonic devices.


  In contrast to traditional videos, the imaging in virtual reality (VR) is
360{\deg}, and it consumes larger bandwidth to transmit video contents. To
reduce bandwidth consumption, tile-based streaming has been proposed to deliver
the focused part of the video, instead of the whole one. On the other hand, the
techniques to alleviate cybersickness, which is akin to motion sickness and
happens when using digital displays, have not been jointly explored with the
tile selection in VR. In this paper, we investigate Tile Selection with
Cybersickness Control (TSCC) in an adaptive 360{\deg} video streaming system
with cybersickness alleviation. We propose an m-competitive online algorithm
with Cybersickness Indicator (CI) and Video Loss Indicator (VLI) to evaluate
instant cybersickness and the total loss of video quality. Moreover, the
algorithm exploits Sickness Migration Indicator (SMI) to evaluate the
cybersickness accumulated over time and the increase of optical flow to improve
the tile quality assignment. Simulations with a real network dataset show that
our algorithm outperforms the baselines regarding video quality and
cybersickness accumulation.


  The Cosmological General Relativity (CGR) of Carmeli, a 5-dimensional (5-D)
theory of time, space and velocity, predicts the existence of an acceleration
a_0 = c / tau due to the expansion of the universe, where c is the speed of
light in vacuum, tau = 1 / h is the Hubble-Carmeli time constant, where h is
the Hubble constant at zero distance and no gravity.
  The Carmeli force on a particle of mass m is F_c = m a_0, a fifth force in
nature.
  In CGR, the effective mass density rho_eff = rho - rho_c, where rho is the
matter density and rho_c is the critical mass density which we identify with
the vacuum mass density rho_vac = -rho_c.
  The fields resulting from the weak field solution of the Einstein field
equations in 5-D CGR and the Carmeli force are used to hypothesize the
production of a pair of particles.
  The mass of each particle is found to be m = tau c^3 / 4 G, where G is
Newton's constant.
  The vacuum mass density derived from the physics is rho_vac = -rho_c = -3 /
(8 pi G tau^2).
  The cosmic microwave background (CMB) black body radiation at the temperature
T_o = 2.72548 K which fills that volume is found to have a relationship to the
ionization energy of the Hydrogen atom. Define the radiation energy
epsilon_gamma = (1 - g) m c^2 / N_gamma, where (1-g) is the fraction of the
initial energy m c^2 which converts to photons, g is a function of the baryon
density parameter Omega_b and N_gamma is the total number of photons in the CMB
radiation field. We make the connection with the ionization energy of the first
quantum level of the Hydrogen atom by the hypothesis epsilon_gamma = [(1 - g) m
c^2] / N_gamma = alpha^2 mu c^2 / 2, where alpha is the fine-structure constant
and mu = m_p f / (1 + f), where f= m_e / m_p with m_e the electron mass and m_p
the proton mass.


  We consider a scalar potential with two minima, one of which is arbitrarily
deep, such as could be the case for the Higgs potential in the Standard Model.
A recent calculation within the thin-wall approximation [arXiv:2205.10240]
concludes that regions in which the scalar field takes values beyond the top of
the potential barrier are forced by gravity to collapse, while they remain
hidden behind a black hole horizon. We show that the thin-wall approximation is
not applicable to this problem. We clarify the issue through numerical and
analytical solutions to the field equations of the gravity-scalar system. We
find that regions around the deeper minimum expand, and would thereby engulf
the Universe in post-inflationary cosmology. We also show that black holes with
Higgs hair are unstable. Even though the physics of the true vacuum is
different, our final conclusion replicates the earlier `Higgstory' paper
[arXiv:1505.04825].


  In commutative invariant theory, a classical result due to Auslander says
that if $R = \Bbbk[x_1, \dots, x_n]$ and $G$ is a finite subgroup of
$\text{Aut}_{\text{gr}}(R) \cong \text{GL}(n,\Bbbk)$ which contains no
reflections, then there is a natural graded isomorphism $R \hspace{1pt} \#
\hspace{1pt} G \cong \text{End}_{R^G}(R)$. In this paper, we show that a
version of Auslander's Theorem holds if we replace $R$ by an Artin-Schelter
regular algebra $A$ of global dimension 2, and $G$ by a finite subgroup of
$\text{Aut}_{\text{gr}}(A)$ which contains no quasi-reflections. This extends
work of Chan-Kirkman-Walton-Zhang. As part of the proof, we classify all such
pairs $(A,G)$, up to conjugation of $G$ by an element of
$\text{Aut}_{\text{gr}}(A)$. In all but one case, we also write down explicit
presentations for the invariant rings $A^G$, and show that they are isomorphic
to factors of AS regular algebras.


  Deep learning algorithms provide a new paradigm to study high-dimensional
dynamical behaviors, such as those in fusion plasma systems. Development of
novel model reduction methods, coupled with detection of abnormal modes with
plasma physics, opens a unique opportunity for building efficient models to
identify plasma instabilities for real-time control. Our Fusion Transfer
Learning (FTL) model demonstrates success in reconstructing nonlinear kink mode
structures by learning from a limited amount of nonlinear simulation data. The
knowledge transfer process leverages a pre-trained neural encoder-decoder
network, initially trained on linear simulations, to effectively capture
nonlinear dynamics. The low-dimensional embeddings extract the coherent
structures of interest, while preserving the inherent dynamics of the complex
system. Experimental results highlight FTL's capacity to capture transitional
behaviors and dynamical features in plasma dynamics -- a task often challenging
for conventional methods. The model developed in this study is generalizable
and can be extended broadly through transfer learning to address various
magnetohydrodynamics (MHD) modes.


  The semiclassical quantization rule is derived for a system with a
spherically symmetric potential $V(r) \sim r^{\nu}$ $(-2<\nu <\infty)$ and an
Aharonov-Bohm magnetic flux. Numerical results are presented and compared with
known results for models with $\nu = -1,0,2,\infty$. It is shown that the
results provided by our method are in good agreement with previous results. One
expects that the semiclassical quantization rule shown in this paper will
provide a good approximation for all principle quantum number even the rule is
derived in the large principal quantum number limit $n \gg 1$. We also discuss
the power parameter $\nu $ dependence of the energy spectra pattern in this
paper.


  A case-based reasoning (CBR) system solves a new problem by retrieving
`cases' that are similar to the given problem. If such a system can achieve
high accuracy, it is appealing owing to its simplicity, interpretability, and
scalability. In this paper, we demonstrate that such a system is achievable for
reasoning in knowledge-bases (KBs). Our approach predicts attributes for an
entity by gathering reasoning paths from similar entities in the KB. Our
probabilistic model estimates the likelihood that a path is effective at
answering a query about the given entity. The parameters of our model can be
efficiently computed using simple path statistics and require no iterative
optimization. Our model is non-parametric, growing dynamically as new entities
and relations are added to the KB. On several benchmark datasets our approach
significantly outperforms other rule learning approaches and performs
comparably to state-of-the-art embedding-based approaches. Furthermore, we
demonstrate the effectiveness of our model in an "open-world" setting where new
entities arrive in an online fashion, significantly outperforming
state-of-the-art approaches and nearly matching the best offline method. Code
available at https://github.com/ameyagodbole/Prob-CBR


  Over the years, researchers have studied the complexity of several decision
versions of Nash equilibrium in (symmetric) two-player games (bimatrix games).
To the best of our knowledge, the last remaining open problem of this sort is
the following; it was stated by Papadimitriou in 2007: find a non-symmetric
Nash equilibrium (NE) in a symmetric game. We show that this problem is
NP-complete and the problem of counting the number of non-symmetric NE in a
symmetric game is #P-complete.
  In 2005, Kannan and Theobald defined the "rank of a bimatrix game"
represented by matrices (A, B) to be rank(A+B) and asked whether a NE can be
computed in rank 1 games in polynomial time. Observe that the rank 0 case is
precisely the zero sum case, for which a polynomial time algorithm follows from
von Neumann's reduction of such games to linear programming. In 2011, Adsul et.
al. obtained an algorithm for rank 1 games; however, it does not solve the case
of symmetric rank 1 games. We resolve this problem.


  We calculate the spectrum of gravitational waves generated during inflation
in open $(\Omega _0<1)$ inflationary models. In such models an initial epoch of
old inflation solves the horizon and flatness problems, and during this first
epoch of inflation the quantum state of the graviton field rapidly approaches
the Bunch-Davies vacuum. Then old inflation ends by the nucleation of a single
bubble, inside of which there is a shortened epoch of slow-roll inflation
giving $\Omega _0<1$ today. In this paper we re-express the Bunch-Davies vacuum
for the graviton field in terms of the hyperbolic modes inside the bubble and
propagate these modes forward in time into the present era. We derive the
expression for the contribution from these gravity waves to the cosmic
microwave background anisotropy including the effect of a finite energy
difference across the bubble wall.


  Data movement between main memory and the CPU is a major bottleneck in
parallel data-intensive applications. In response, researchers have proposed
using compilers and intermediate representations (IRs) that apply optimizations
such as loop fusion under existing high-level APIs such as NumPy and
TensorFlow. Even though these techniques generally do not require changes to
user applications, they require intrusive changes to the library itself: often,
library developers must rewrite each function using a new IR. In this paper, we
propose a new technique called split annotations (SAs) that enables key data
movement optimizations over unmodified library functions. SAs only require
developers to annotate functions and implement an API that specifies how to
partition data in the library. The annotation and API describe how to enable
cross-function data pipelining and parallelization, while respecting each
function's correctness constraints. We implement a parallel runtime for SAs in
a system called Mozart. We show that Mozart can accelerate workloads in
libraries such as Intel MKL and Pandas by up to 15x, with no library
modifications. Mozart also provides performance gains competitive with
solutions that require rewriting libraries, and can sometimes outperform these
systems by up to 2x by leveraging existing hand-optimized code.


  We have performed a comprehensive, first-principles high-throughput study of
the magnetic properties of halide double perovskites, $Cs_2BB^\prime Cl_6$,
with magnetic ions occupying one or both B and B$^\prime$ sites. Our findings
indicate a general tendency for these materials to exhibit antiferromagnetic
ordering with low N\'eel temperatures. At the same time, we reveal a few
potential candidates that predicted to be ferromagnetic with relatively high
Curie temperatures. Achieving ferromagnetic coupling might be feasible via
simultaneously alloying at B and B$^\prime$ sites with magnetic 3d and
non-magnetic 5d ions. With this approach, we discover that $Cs_2HgCrCl_6$,
$Cs_2AgNiCl_6$ and $Cs_2AuNiCl_6$ have high Curie temperatures relative to
their peers, with the latter two exhibiting half metallic behaviour. Further,
this study illuminates the underpinning mechanism of magnetic exchange
interactions in halide double perovskites, enabling a deeper understanding of
their magnetic behaviour. Our findings, especially the discovery of the
compounds with robust half-metallic properties and high Curie temperatures
holds promise for potential applications in the field of spintronics.


  Recurrent networks are trained to memorize their input better, often in the
hopes that such training will increase the ability of the network to predict.
We show that networks designed to memorize input can be arbitrarily bad at
prediction. We also find, for several types of inputs, that one-node networks
optimized for prediction are nearly at upper bounds on predictive capacity
given by Wiener filters, and are roughly equivalent in performance to randomly
generated five-node networks. Our results suggest that maximizing memory
capacity leads to very different networks than maximizing predictive capacity,
and that optimizing recurrent weights can decrease reservoir size by half an
order of magnitude.


  We explore the possible effect of cluster environments on the structure and
star formation histories of galaxies by analysing the projected phase-space
(PPS) of intermediate-redshift cluster (0.4<z<0.8). HST I-band imaging data
from the ESO Distant Cluster Survey (EDisCS) allow us to measure deviations of
the galaxies' light distributions from symmetric and smooth profiles using two
parameters, Ares ('asymmetry') and RFF (residual flux fraction or 'roughness').
Combining these structural parameters with age-sensitive spectral indicators
like Hdelta, Hgamma and Dn4000, we establish that in all environments younger
star-forming galaxies of all morphologies are 'rougher' and more asymmetric
than older, more quiescent ones. Combining a subset of the EDisCS clusters we
construct a stacked PPS diagram and find a significant correlation between the
position of the galaxies on the PPS and their stellar ages, irrespective of
their morphology. We also observe an increasing fraction of galaxies with older
stellar populations towards the cluster core, while the galaxies' structural
parameters (Ares and RFF) do not seem to segregate strongly with PPS. These
results may imply that, transformation happens on a longer timescale as they
accumulate and age in the cluster cores.


  Step by step completion of a left-to-right tiling of a rectangular floor with
tiles of a single shape starts from one edge of the floor, considers the
possible ways of inserting a tile at the leftmost uncovered square, passes
through a sequence of rugged shapes of the front line between covered and
uncovered regions of the floor, and finishes with a straight front line at the
opposite edge. We count the tilings by mapping the front shapes to nodes in a
digraph, then counting closed walks on that digraph with the transfer matrix
method.
  Generating functions are detailed for tiles of shape 1 x 3, 1 x 4 and 2 x 3
and modestly wide floors. Equivalent results are shown for the 3-dimensional
analog of filling bricks of shape 1x 1 x 2, 1 x 1 x 3, 1 x 1 x 4, 1 x 2 x 2 or
1 x 2 x 3 into rectangular containers of small cross sections.


  A strictly empirical review is given of presently available data on the
evolution of galaxy morphology. From HST observations of distant galaxies and
ground-based observations of nearby ones observed at the same rest-frame
wavelength it is found that late-type (Sbc-Sc) galaxies evolve more rapidly
with increasing redshift than do early-type (E-Sa-Sab) galaxies. Furthermore
the fraction of peculiar objects, that cannot be shoehorned into the Hubble
tuning fork classification scheme, increases rapidly with redshift.
Unexpectedly it is found that, over a wide range of densities, the fraction of
barred galaxies is independent of environment. However, this fraction of barred
galaxies appears to decline rapidly with increasing redshift.


  Digital audio effects are widely used by audio engineers to alter the
acoustic and temporal qualities of audio data. However, these effects can have
a large number of parameters which can make them difficult to learn for
beginners and hamper creativity for professionals. Recently, there have been a
number of efforts to employ progress in deep learning to acquire the low-level
parameter configurations of audio effects by minimising an objective function
between an input and reference track, commonly referred to as style transfer.
However, current approaches use inflexible black-box techniques or require that
the effects under consideration are implemented in an auto-differentiation
framework. In this work, we propose a deep learning approach to audio
production style matching which can be used with effects implemented in some of
the most widely used frameworks, requiring only that the parameters under
consideration have a continuous domain. Further, our method includes style
matching for various classes of effects, many of which are difficult or
impossible to be approximated closely using differentiable functions. We show
that our audio embedding approach creates logical encodings of timbral
information, which can be used for a number of downstream tasks. Further, we
perform a listening test which demonstrates that our approach is able to
convincingly style match a multi-band compressor effect.


  A numerical study of the string tension and of the masses of the lowest-lying
glueballs is performed in SU($N$) gauge theories for $2 \le N \le 8$ in D=3+1
and $2 \le N \le 6$ in D=2+1. It is shown that for the string tension a smooth
$N \to \infty$ limit exists that depends only on the 't Hooft coupling $\lambda
= g^2 N$. An extrapolation of the masses of the lightest glueballs to $N =
\infty$ using a power series in $1/N^2$ shows that the leading correction to
the infinite $N$ value accounts for finite $N$ effects for $N$ at least as
small as 3 and all the way down to N=2 in many cases. $k$-string tension ratios
and possible issues connected with correlation functions at large $N$ are also
discussed.


  The deconfining transition in non-Abelian gauge theory is known to occur by a
condensation of Wilson lines. By expanding around an appropriate Wilson line
background, it is possible at large $N$ to analytically continue the confining
phase to arbitrarily high temperatures, reaching a weak coupling confinement
regime. This is used to study the high temperature partition function of an
$SU(N)$ electric flux tube. It is found that the partition function corresponds
to that of a string theory with a number of world-sheet fields that diverges at
short distance.


  Animal movement studies have become ubiquitous in animal ecology for
estimation of space use and analysis of movement behavior. In these studies,
animal movement data are primarily collected at regular time intervals. We
propose an irregular sampling design which could lead to greater efficiency and
information gain in animal movement studies. Our novel sampling design, called
lattice and random intermediate point (LARI), combines samples at regular and
random time intervals. We compare the LARI sampling design to regular sampling
designs in an example with common black carpenter ant location data, an example
with guppy location data, and a simulation study of movement with a point of
attraction. We modify a general stochastic differential equation model to allow
for irregular time intervals and use this framework to compare sampling
designs. When parameters are estimated reasonably well, regular sampling
results in greater precision and accuracy in prediction of missing data.
However, in each of the data and simulation examples explored in this paper,
LARI sampling results in more accurate and precise parameter estimation, and
thus better prediction of missing data as well. This result suggests that
researchers might gain greater insight into underlying animal movement
processes by choosing LARI sampling over regular sampling.


  We investigate the effect of magnetic field and charge noise on the
generation of entanglement between two Heisenberg exchange-coupled electron
spins in a double quantum dot. We focus on exchange-driven evolution that would
ideally take an initial unentangled tensor product state to a
maximally-entangled state in the absence of noise. The presence of noise
obviously adversely affects the attainment of maximal entanglement, which we
study quantitatively and exactly. To quantify the effects of noise, we
calculate two-qubit coherence times and entanglement fidelity, both of which
can be extracted from simulations or measurements of the return probability as
a function of interaction time, i.e., the time period during which the exchange
coupling remains effective between the two spins. We perform these calculations
for a broad range of noise strengths that includes the regime of recent
experiments. We find that the two types of noise reduce the amount of
entanglement in qualitatively distinct ways and that, although charge noise
generally leads to faster decoherence, the relative importance of the two types
of noise in entanglement creation depends sensitively on the strength of the
exchange coupling. Our results can be used to determine the level of noise
suppression needed to reach quantum error correction thresholds. We provide
quantitative guidance for the requisite noise constraints necessary to
eventually reach the $>99\%$ fidelity consistent with the quantum error
correction threshold.


  The interplay between topological defects (branes) and black holes has been a
subject of recent study, motivated in part by interest in brane-world
scenarios. In this paper we analyze in detail the description of a black hole
bound to a domain wall (a two-brane in four dimensions), for which an exact
description in the limit of zero wall thickness has been given recently. We
show how to smooth this singular solution with a thick domain wall. We also
show that charged extremal black holes of a size (roughly) smaller than the
brane thickness expel the wall, thereby extending the phenomenon of flux
expulsion. Finally, we analyze the process of black hole nucleation {\it on} a
domain wall, and argue that it is preferred over a previously studied mechanism
of black hole nucleation {\it away} from the wall.