  We present thermoelectric power (TEP) studies under pressure and high
magnetic field in the antiferromagnet CeRh2Si2 at low temperature. Under
magnetic field, large quantum oscillations are observed in the TEP, S(H), in
the antiferromagnetic phase. They suddenly disappear when entering in the
polarized paramagnetic (PPM) state at Hc pointing out an important
reconstruction of the Fermi surface (FS). Under pressure, S/T increases
strongly of at low temperature near the critical pressure Pc, where the AF
order is suppressed, implying the interplay of a FS change and low energy
excitations driven by spin and valence fluctuations. The difference between the
TEP signal in the PPM state above Hc and in the paramagnetic state (PM) above
Pc can be explained by different FS. Band structure calculations at P = 0
stress that in the AF phase the 4f contribution at the Fermi level (EF) is weak
while it is the main contribution in the PM domain. By analogy to previous work
on CeRu2Si2, in the PPM phase of CeRh2Si2 the 4f contribution at EF will drop.


  The presence of a colored scalar that is a weak doublet with fractional
electric charges of $|Q|=2/3$ and $|Q|=5/3$ with mass below 1,TeV can provide
an explanation of the observed branching ratios in $B \to D^{(*)} \tau \bar
\nu$ decays. The required combination of scalar and tensor operators in the
effective Hamiltonian for $b \to c \tau \bar \nu$ is generated through the
$t$-channel exchange. We focus on a scenario with a minimal set of Yukawa
couplings that can address a semitauonic puzzle and show that its resolution
puts a nontrivial bound on the product of the scalar couplings to $\bar \tau b$
and $\bar c \nu$. We also derive additional constraints posed by $Z \to b\bar
b$, muon magnetic moment, lepton flavor violating decays $\mu \to e \gamma$,
$\tau \to \mu \gamma$, $\tau \to e \gamma$, and $\tau$ electric dipole moment.
The minimal set of Yukawa couplings is not only compatible with the mass
generation in an SU(5) unification framework, a natural environment for colored
scalars, but specifies all matter mixing parameters except for one angle in the
up-type quark sector. We accordingly spell out predictions for the proton decay
signatures through gauge boson exchange and show that $p \rightarrow \pi^0 e^+$
is suppressed with respect to $p \rightarrow K^+ \bar{\nu}$ and even $p
\rightarrow K^0 e^+$ in some parts of available parameter space. Impact of the
colored scalar embedding in 45-dimensional representation of SU(5) on
low-energy phenomenology is also presented. Finally, we make predictions for
rare top and charm decays where presence of this scalar can be tested
independently.


  Electron pairing is central to modern concepts of superconductivity, and
provides a basis for the ubiquitous presence of h/2e in the theory and
phenomenology. However, the lack of a consistent real-space picture of
phonon-mediated pairing suggests that the pairing interpretation may be
incomplete or even misleading. Alternatively, the present picture refers back
to an old proposal by Frohlich, whereby superconductivity is based on an
induced electron charge density wave coupled to a one-dimensional static
lattice distortion. In the new picture, dynamic three-dimensional
charge-density waves couple to coherent standing-wave phonons, giving rise to a
mobile superconducting ground state that does not pin on crystalline defects.
Long-range order in the ground state is associated with localized electron
orbitals in a correlated two-phase field, which provides for flux quantization
in units of h/2e on the macroscopic scale larger than the coherence length,
without the need for a macroscopic pair wave function. While much of the
formalism for this picture may carry over from conventional BCS theory, this
picture makes distinct predictions for observable structures on the microscopic
and mesoscopic scales.


  We present the area and coarea formulas for Lipschitz maps, valid for general
volume densities. As applications, we give a short, "euclidean" proof of the
anisotropic Sobolev inequality and describe an anisotropic tube formula for
hypersurfaces in $mathbb{R}^n$. A discussion about the first variation of the
anisotropic area is also included.


  We present Hubble ACS images of thirteen dust reddened Type-1 quasars
selected from the FIRST/2MASS Red Quasar Survey. These quasars have high
intrinsic luminosities after correction for dust obscuration (-23.5 > M_B >
-26.2 from K-magnitude). The images show strong evidence of recent or ongoing
interaction in eleven of the thirteen cases, even before the quasar nucleus is
subtracted. None of the host galaxies are well fit by a simple elliptical
profile. The fraction of quasars showing interaction is significantly higher
than the 30% seen in samples of host galaxies of normal, unobscured quasars.
There is a weak correlation between the amount of dust reddening and the
magnitude of interaction in the host galaxy, measured using the Gini
coefficient and the Concentration index. Although few host galaxy studies of
normal quasars are matched to ours in intrinsic quasar luminosity, no evidence
has been found for a strong dependence of merger activity on host luminosity in
samples of the host galaxies of normal quasars. We thus believe that the high
merger fraction in our sample is related to their obscured nature, with a
significant amount of reddening occurring in the host galaxy. The red quasar
phenomenon seems to have an evolutionary explanation, with the young quasar
spending the early part of its lifetime enshrouded in an interacting galaxy.
This might be further indication of a link between AGN and starburst galaxies.


  In this addendum we provide an existence and uniqueness result for mild
solutions to semilinear stochastic partial differential equations driven by
Wiener processes and Poisson random measures in the framework of the semigroup
approach with locally monotone coefficients, where the semigroup is allowed to
be pseudo-contractive. This improves an earlier paper of the author, where the
equation was only driven by Wiener processes, and where the semigroup was only
allowed to be a semigroup of contractions.


  A new twistorial field formulation of a massless infinite spin particle is
derived. We find a twistorial infinite spin field and derive its helicity
decomposition. The twistorial equations of motion for infinite spin fields in
the cases of integer and half-integer helicities are derived. We show that the
infinite integer-spin field and infinite half-integer-spin field form the
$\mathcal{N}{=}\,1$ infinite spin supermultiplet. The corresponding
supersymmetry transformations are presented. We prove that the supersymmetry
algebra is closed on-shell.


  We investigate critical phenomena in colloids by means of the
renormalization-group based hierarchical reference theory of fluids (HRT). We
focus on three experimentally relevant model systems: namely, the
Asakura-Oosawa model of a colloidal dispersion under the influence of
polymer-induced attractive depletion forces; fluids with competing short-range
attractive and longer-range repulsive interactions; solutions of star-polymers
whose pair potential presents both an attractive well and an ultrasoft
repulsion at shorter distance. Our results show that the ability to tune the
effective interactions between colloidal particles allows one to generate a
variety of crossovers to the asymptotic critical behavior, which are not
observed in atomic fluids.


  We present SSL-HV: Self-Supervised Learning approaches applied to the task of
Handwriting Verification. This task involves determining whether a given pair
of handwritten images originate from the same or different writer distribution.
We have compared the performance of multiple generative, contrastive SSL
approaches against handcrafted feature extractors and supervised learning on
CEDAR AND dataset. We show that ResNet based Variational Auto-Encoder (VAE)
outperforms other generative approaches achieving 76.3% accuracy, while
ResNet-18 fine-tuned using Variance-Invariance-Covariance Regularization
(VICReg) outperforms other contrastive approaches achieving 78% accuracy. Using
a pre-trained VAE and VICReg for the downstream task of writer verification we
observed a relative improvement in accuracy of 6.7% and 9% over ResNet-18
supervised baseline with 10% writer labels.


  In this paper, we present new incremental algorithms for maintaining data
structures that represent all connectivity cuts of size one in directed graphs
(digraphs), and the strongly connected components that result by the removal of
each of those cuts. We give a conditional lower bound that provides evidence
that our algorithms may be tight up to sub-polynomial factors. As an additional
result, with our approach we can also maintain dynamically the
$2$-vertex-connected components of a digraph during any sequence of edge
insertions in a total of $O(mn)$ time. This matches the bounds for the
incremental maintenance of the $2$-edge-connected components of a digraph.


  We reevaluate electron-positron pair production from electromagnetic
interactions of muons in transit through materials. Our approach, through the
use of structure functions for inelastic and elastic scattering and including
hadronic recoil, make the formalism useful for tau pair production at high
energies. Our results for electron-positron pair production agree well with
prior evaluations. Tau pair production, has a significant contribution from
inelastic scattering in addition to the usual coherent scattering with the
nucleus and scattering with atomic electrons.


  We present a Bayesian angular power spectrum and signal map inference engine
which can be adapted to interferometric observations of anisotropies inthe
cosmic microwave background, 21 cm emission line mapping of galactic brightness
fluctuations, or 21 cm absorption line mapping of neutral hydrogen in the dark
ages. The method uses Gibbs sampling to generate a sampled representation of
the angular power spectrum posterior and the posterior of signal maps given a
set of measured visibilities in the uv-plane. We use a mock interferometric CMB
observation to demonstrate the validity of this method in the flat-sky
approximation when adapted to take into account arbitrary coverage of the
uv-plane, mode-mode correlations due to observations on a finite patch, and
heteroschedastic visibility errors. The computational requirements scale as
O(n_p log n_p) where n_p measures the ratio of the size of the detector array
to the inter-detector spacing, meaning that Gibbs sampling is a promising
technique for meeting the data analysis requirements of future cosmology
missions.


  We provide a queueing-theoretic framework for job replication schemes based
on the principle "\emph{replicate a job as soon as the system detects it as a
\emph{straggler}}". This is called job \emph{speculation}. Recent works have
analyzed {replication} on arrival, which we refer to as \emph{replication}.
Replication is motivated by its implementation in Google's BigTable. However,
systems such as Apache Spark and Hadoop MapReduce implement speculative job
execution. The performance and optimization of speculative job execution is not
well understood. To this end, we propose a queueing network model for load
balancing where each server can speculate on the execution time of a job.
Specifically, each job is initially assigned to a single server by a frontend
dispatcher. Then, when its execution begins, the server sets a timeout. If the
job completes before the timeout, it leaves the network, otherwise the job is
terminated and relaunched or resumed at another server where it will complete.
We provide a necessary and sufficient condition for the stability of
speculative queueing networks with heterogeneous servers, general job sizes and
scheduling disciplines. We find that speculation can increase the stability
region of the network when compared with standard load balancing models and
replication schemes. We provide general conditions under which timeouts
increase the size of the stability region and derive a formula for the optimal
speculation time, i.e., the timeout that minimizes the load induced through
speculation. We compare speculation with redundant-$d$ and
redundant-to-idle-queue-$d$ rules under an $S\& X$ model. For light loaded
systems, redundancy schemes provide better response times. However, for
moderate to heavy loadings, redundancy schemes can lose capacity and have
markedly worse response times when compared with a speculative scheme.


  We consider multidimensional systems of PDEs of generalized evolution form
with t-derivatives of arbitrary order on the left-hand side and with the
right-hand side dependent on lower order t-derivatives and arbitrary space
derivatives. For such systems we find an explicit necessary condition for
existence of higher conservation laws in terms of the system's symbol. For
systems that violate this condition we give an effective upper bound on the
order of conservation laws. Using this result, we completely describe
conservation laws for viscous transonic equations, for the Brusselator model,
and the Belousov-Zhabotinskii system. To achieve this, we solve over an
arbitrary field the matrix equations SA=A^tS and SA=-A^tS for a quadratic
matrix A and its transpose A^t, which may be of independent interest.


  We consider a constrained version of the shortest path problem on the
complete graphs whose edges have independent random lengths and costs. We
establish the asymptotic value of the minimum length as a function of the
cost-budget within a wide range.


  Let $\Gamma$ be a graph. Under suitable geometric assumptions on $\Gamma$, we
give several equivalent characterizations of Sobolev and Hardy-Sobolev spaces
on $\Gamma$, in terms of maximal functionals, Haj{\l} asz type functionals or
atomic decompositions. As an application, we study the boundedness of Riesz
transforms on Hardy spaces on $\Gamma$. This gives the discrete counterpart of
the corresponding results on Riemannian manifolds.


  The Earth's turbulent atmosphere results in speckled and blurred images of
astronomical objects when observed by ground based visible and near-infrared
telescopes. Adaptive optics (AO) systems are employed to reduce these
atmospheric effects by using wavefront sensors (WFS) and deformable mirrors.
Some AO systems are not fast enough to correct for strong, fast, high
turbulence wind layers leading to the wind butterfly effect, or wind-driven
halo, reducing contrast capabilities in coronagraphic images. Estimating the
effective wind speed of the atmosphere allows us to calculate the atmospheric
coherence time. This is not only an important parameter to understand for site
characterization but could be used to help remove the wind butterfly in post
processing. Here we present a method for estimating the atmospheric effective
wind speed from spatio-temporal covariance maps generated from pseudo open-loop
(POL) WFS data. POL WFS data is used as it aims to reconstruct the full
wavefront information when operating in closed-loop. The covariance maps show
how different atmospheric turbulent layers traverse the telescope. Our method
successfully recovered the effective wind speed from simulated WFS data
generated with the soapy python library. The simulated atmospheric turbulence
profiles consist of two turbulent layers of ranging strengths and velocities.
The method has also been applied to Gemini Planet Imager (GPI) AO WFS data.
This gives insight into how the effective wind speed can affect the wind-driven
halo seen in the AO image point spread function. In this paper, we will present
results from simulated and GPI WFS data.


  We present X-ray point-source catalogs for a deep 400 ks Chandra ACIS-I
exposure of the SSA22 field. The observations are centred on a z = 3.09
protocluster, which is populated by Lyman break galaxies (LBGs), Lyalpha
emitters (LAEs), and extended Lyalpha-emitting blobs (LABs). The survey reaches
ultimate (3 count) sensitivity limits of 5.7 X 10^-17 and 3.0 X 10^-16 ergs
cm^-2 s^-1 for the 0.5-2 keV and 2-8 keV bands, respectively. These limits make
SSA22 the fourth deepest extragalactic Chandra survey yet conducted, and the
only one focused on a known high redshift structure. In total, we detect 297
X-ray point sources and identify one obvious bright extended X-ray source over
a ~330 arcmin^2 region. In addition to our X-ray catalogs, we provide all
available optical spectroscopic redshifts and near-infrared and mid-infrared
photometry available for our sources. The basic X-ray and infrared properties
of our Chandra sources indicate a variety of source types, although absorbed
active galactic nuclei (AGNs) appear to dominate. In total, we have identified
12 X-ray sources (either via optical spectroscopic redshifts or LAE selection)
at z =3.06-3.12 that are likely to be associated with the SSA22 protocluster.
These sources have X-ray and multiwavelength properties that suggest they are
powered by AGN with 0.5-8 keV luminosities in the range of 10^43-10^45 ergs
s^-1 We have analysed the AGN fraction of sources in the protocluster as a
function of local LAE source density and find suggestive evidence for a
correlation between AGN fraction and local LAE source density (at the 96 per
cent confidence level), implying that supermassive black hole growth at z = 3
is strongest in the highest density regions.


  I review a new (and still tentative) approach to black hole thermodynamics
that seeks to explain black hole entropy in terms of microscopic quantum
gravitational boundary states induced on the black hole horizon.


  In this paper we investigate the primeness of a class of entire functions and
discuss the dynamics of a periodic member f of this class with respect to a
transcendental entire function g that permutes with f. In particular we show
that the Julia sets of f and g are identical.


  The fourth virial coefficient is calculated exactly for a fluid of hard
spheres in odd dimensions up to 11.


  Multimodal learning involves developing models that can integrate information
from various sources like images and texts. In this field, multimodal text
generation is a crucial aspect that involves processing data from multiple
modalities and outputting text. The image-guided story ending generation
(IgSEG) is a particularly significant task, targeting on an understanding of
complex relationships between text and image data with a complete story text
ending. Unfortunately, deep neural networks, which are the backbone of recent
IgSEG models, are vulnerable to adversarial samples. Current adversarial attack
methods mainly focus on single-modality data and do not analyze adversarial
attacks for multimodal text generation tasks that use cross-modal information.
To this end, we propose an iterative adversarial attack method
(Iterative-attack) that fuses image and text modality attacks, allowing for an
attack search for adversarial text and image in an more effective iterative
way. Experimental results demonstrate that the proposed method outperforms
existing single-modal and non-iterative multimodal attack methods, indicating
the potential for improving the adversarial robustness of multimodal text
generation models, such as multimodal machine translation, multimodal question
answering, etc.


  Studying water droplets is a rich lesson in fields of fluid dynamics,
nonlinear systems, and differential equations. Understanding various physical
aspects of raindrops can help us in understanding drop dynamics, rainfall
density estimation, size distributions which can be grant insights in the
fields of meteorology, hydrology, and climate science. This work identifies the
real world significance in developing more accurate atmospheric attenuation
correction algorithms that could potentially overcome the scattering effect of
rain on radio and micro wave communication. This works presents and aims to
compile some historical work which focuses on the influence of surface tension
in the droplet nucleation and formation, raindrop oscillation, burst cycles,
and transformation into parachute-like shapes before fragmentation. The
interplay between surface tension and air resistance in raindrop dynamics is
also explored.


  Deep quantum neural networks may provide a promising way to achieve quantum
learning advantage with noisy intermediate scale quantum devices. Here, we use
deep quantum feedforward neural networks capable of universal quantum
computation to represent the mixed states for open quantum many-body systems
and introduce a variational method with quantum derivatives to solve the master
equation for dynamics and stationary states. Owning to the special structure of
the quantum networks, this approach enjoys a number of notable features,
including the absence of barren plateaus, efficient quantum analogue of the
backpropagation algorithm, resource-saving reuse of hidden qubits, general
applicability independent of dimensionality and entanglement properties, as
well as the convenient implementation of symmetries. As proof-of-principle
demonstrations, we apply this approach to both one-dimensional transverse field
Ising and two-dimensional $J_1-J_2$ models with dissipation, and show that it
can efficiently capture their dynamics and stationary states with a desired
accuracy.


  We give a Jensen operator inequality for strongly convex functions. As a
corollary, we improve operator Holder-McCarthy inequality under suitable
conditions.


  Mapping traversal costs in an environment and planning paths based on this
map are important for autonomous navigation. We present a neurobotic navigation
system that utilizes a Spiking Neural Network Wavefront Planner and E-prop
learning to concurrently map and plan paths in a large and complex environment.
We incorporate a novel method for mapping which, when combined with the Spiking
Wavefront Planner, allows for adaptive planning by selectively considering any
combination of costs. The system is tested on a mobile robot platform in an
outdoor environment with obstacles and varying terrain. Results indicate that
the system is capable of discerning features in the environment using three
measures of cost, (1) energy expenditure by the wheels, (2) time spent in the
presence of obstacles, and (3) terrain slope. In just twelve hours of online
training, E-prop learns and incorporates traversal costs into the path planning
maps by updating the delays in the Spiking Wavefront Planner. On simulated
paths, the Spiking Wavefront Planner plans significantly shorter and lower cost
paths than A* and RRT*. The spiking wavefront planner is compatible with
neuromorphic hardware and could be used for applications requiring low size,
weight, and power.


  We present new optical observations of the supernova SN 1978K, obtained in
2007 and 2014 with the Very Large Telescope. We discover that the supernova has
not faded significantly, even more than three decades after its explosion. The
spectrum exhibits numerous narrow (FWHM $\lesssim600$ km s$^{-1}$) emission
lines, indicating that the supernova blastwave is persistently interacting with
dense circumstellar material (CSM). Evolution of emission lines indicates that
the supernova ejecta is slowly progressing through the reverse shock, and has
not expanded past the outer edge of the circumstellar envelope. We demonstrate
that the CSM is not likely to be spherically distributed, with mass of
$\lesssim$ 1 M$_\odot$. The progenitor mass loss rate is estimated as $\gtrsim
0.01$ M$_\odot$ yr$^{-1}$. The slowly fading late-time light curve and spectra
show striking similarity with SN 1987A, indicating that a rate at which the CSM
is being swept-up by the blastwave is gradually decaying and SN 1978K is
undergoing similar evolution to become a remnant. Due to its proximity (4 Mpc),
SN 1978K serves as the next best example of late-time supernova evolution after
SN 1987A.


  We study the effect of adaptivity on a social model of opinion dynamics and
consensus formation. We analyze how the adaptivity of the network of contacts
between agents to the underlying social dynamics affects the size and
topological properties of groups and the convergence time to the stable final
state. We find that, while on static networks these properties are determined
by percolation phenomena, on adaptive networks the rewiring process leads to
different behaviors: Adaptive rewiring fosters group formation by enhancing
communication between agents of similar opinion, though it also makes possible
the division of clusters. We show how the convergence time is determined by the
characteristic time of link rearrangement. We finally investigate how the
adaptivity yields nontrivial correlations between the internal topology and the
size of the groups of agreeing agents.


  Picard--Vessiot theorem (1910) provides a necessary and sufficient condition
for solvability of linear differential equations of order $n$ by quadratures in
terms of its Galois group. It is based on the differential Galois theory and is
rather involved. J.Liouville in 1839 found an elementary criterium for such
solvability for $n=2$. J.F.Ritt simplified Liouville's theorem (1948). In 1973
M. Rosenlicht proved a similar criterium for arbitrary $n$. Rosenlicht work
relies on the valuation theory and is not elementary. In these notes we show
that the elementary Liouville--Ritt method based on developing solutions in
Puiseux series as functions of a parameter works smoothly for arbitrary $n$ and
proves the same criterium.


  Safety, security, and compliance are essential requirements when aligning
large language models (LLMs). However, many seemingly aligned LLMs are soon
shown to be susceptible to jailbreak attacks. These attacks aim to circumvent
the models' safety guardrails and security mechanisms by introducing jailbreak
prompts into malicious queries. In response to these challenges, this paper
introduces Defensive Prompt Patch (DPP), a novel prompt-based defense mechanism
specifically designed to protect LLMs against such sophisticated jailbreak
strategies. Unlike previous approaches, which have often compromised the
utility of the model for the sake of safety, DPP is designed to achieve a
minimal Attack Success Rate (ASR) while preserving the high utility of LLMs.
Our method uses strategically designed interpretable suffix prompts that
effectively thwart a wide range of standard and adaptive jailbreak techniques.
Empirical results conducted on LLAMA-2-7B-Chat and Mistral-7B-Instruct-v0.2
models demonstrate the robustness and adaptability of DPP, showing significant
reductions in ASR with negligible impact on utility. Our approach not only
outperforms existing defense strategies in balancing safety and functionality,
but also provides a scalable and interpretable solution applicable to various
LLM platforms.


  A generalized approach derived from Blochs equation of motion of nuclear
magnetic moments is presented to model the frequency, magnetic field, spin
density, and temperature dependencies in the electromagnetic permeability
tensor for materials with magnetic resonances. The resulting tensor model
predicts characteristic polarization signatures which can be observed, for
example, in fully polarization-resolved Mueller matrix element spectra measured
across magnetic resonances as a function of frequency, magnetic field, magnetic
moment density, and temperature. When augmented with thermodynamic
considerations and suitable Hamiltonian description of the magnetic eigenvalue
spectrum, important parameters such as zero-frequency magnetization, spectral
amplitude distribution, relaxation time constants, and geometrical orientation
parameters of the magnetic moment density can be obtained from comparing the
generalized model approach to experimental data. We demonstrate our approach by
comparing model calculations with full Mueller matrix element spectra measured
at oblique angle of incidence in the terahertz spectral range, across electron
spin resonance quintuplet transitions observed in wurtzite-structure GaN doped
with iron. Measurements were performed by ellipsometry, using a superconducting
cryostat magnet at magnetic fields of 7.23 T and at temperatures of 20 K and 30
K. We detail the occurrence of linear and circular birefringence and dichroism
associated with each of the zero-field split spin transitions in the S = 5/2
defect system. We derive the spectral dependence of the magnetic susceptibility
function and obtain the temperature and magnetic field dependence of the spin
Hamiltonian. Our model correctly predicts the complexity of the polarization
signatures observed in the 15 independent elements of the normalized Mueller
matrix for both positive and negative magnetic fields.


  We present our preliminary results of the non-perturbative determination of
the valence mass dependent coefficients $b_\mathrm{A}-b_\mathrm{P}$ and
$b_\mathrm{m}$ as well as the ratio $Z_\mathrm{P} Z_\mathrm{m}/ Z_\mathrm{A}$
entering the flavour non-singlet PCAC relation in lattice QCD with $N_f=3$
dynamical flavours. We apply the method proposed in the past for quenched
approximation and $N_f=2$ cases, employing a set of finite-volume ALPHA
configurations with Schr\"odinger functional boundary conditions, generated
with $O(a)$ improved Wilson fermions and the tree-level Symanzik-improved gauge
action for a range of couplings relevant for simulations at lattice spacings of
about $0.09 \,$fm and below.


  The distribution of Ly$\alpha$ emission is an presently accessible method for
studying the state of the intergalactic medium (IGM) into the reionization era.
We carried out deep spectroscopic observations in order to search for
Ly$\alpha$ emission from galaxies with photometric redshifts $z$ = 5.5 - 8.3
selected from the Cosmic Assembly Near-infrared Deep Extragalactic Legacy
Survey (CANDELS). Utilizing data from the Keck/DEIMOS spectrograph, we explore
a wavelength coverage of Ly$\alpha$ emission at $z$ ~ 5 - 7 with four nights of
spectroscopic observations for 118 galaxies, detecting five emission lines with
~ 5$\sigma$ significance: three in the GOODS-N and two in the GOODS-S field. We
constrain the equivalent width (EW) distribution of Ly$\alpha$ emission by
comparing the number of detected objects with the expected number constructed
from detailed simulations of mock emission lines that account for the
observational conditions (e.g., exposure time, wavelength coverage, and sky
emission) and galaxy photometric redshift probability distribution functions.
The Ly$\alpha$ EW distribution is well described by an exponential form,
$\text{dN/dEW}\propto \text{exp(-EW/}W_0)$, characterized by the $e$-folding
scale ($W_0$) of ~ 60 - 100$\AA$ at 0.3 < $z$ < 6. By contrast, our measure of
the Ly$\alpha$ EW distribution at 6.0 < $z$ < 7.0 rejects a Ly$\alpha$ EW
distribution with $W_0$ > 36.4$\AA$ (125.3$\AA$) at 1$\sigma$ (2$\sigma$)
significance. This provides additional evidence that the EW distribution of
Ly$\alpha$ declines at $z$ > 6, suggesting an increasing fraction of neutral
hydrogen in the IGM at that epoch.


  Uncompensated moments in antiferromagnets are responsible for exchange bias
in antiferromagnet/ferromagnet heterostructures; however, they are difficult to
directly detect because any signal they contribute is typically overwhelmed by
the ferromagnetic layer. We use magneto-thermal microscopy to image
uncompensated moments in thin films of FeRh, a room-temperature antiferromagnet
that exhibits a 1st-order phase transition to a ferromagnetic state near
100~$^\circ$C. FeRh provides the unique opportunity to study both uncompensated
moments in the antiferromagnetic phase and the interaction of uncompensated
moments with emergent ferromagnetism within a relatively broad
(10-15~$^\circ$C) temperature range near $T_C$. In the AF phase below $T_C$, we
image both pinned UMs, which cause local vertical exchange bias, and unpinned
UMs, which exhibit an enhanced coercive field that reflects exchange-coupling
to the AF bulk. Near $T_C$, where AF and FM order coexist, we find that the
emergent FM order is exchange-coupled to the bulk N\'eel order. This exchange
coupling leads to the nucleation of unusual configurations in which different
FM domains are pinned parallel, antiparallel, and perpendicular to the applied
magnetic field before suddenly collapsing into a state uniformly parallel to
the field.


  In this paper we extend Beilinson's realization formalism for triangulated
categories and filtered triangulated categories to a pseudofunctorial and
pseudonatural setting. As a consequence we prove an equivariant version of
Beilinson's Theorem: for any algebraic group $G$ over an algebraically closed
field $K$ and for any $G$-variety $X$, there is an equivalence of categories
$D_G^b(X; \overline{\mathbb{Q}}_{\ell}) \simeq
D_G^b(\mathbf{Perv}(X;\overline{\mathbb{Q}}_{\ell}))$ where $\ell$ is an
integer prime coprime to the characteristic of $K$. We also show that the
equivariant analogues of the other non-$D$-module aspects of Beilinson's
Theorem hold in the equivariant case.


  Video game developers use game engines as a tool to manage complex aspects of
game development. While engines play a big role in the success of games, to the
best of our knowledge, they are often developed in isolation, in a
closed-source manner, without architectural discussions, comparison, and
collaboration among projects. In this work in progress, we compare the call
graphs of two open-source engines: Godot 3.4.4 and Urho3D 1.8. While static
analysis tools could provide us with a general picture without precise call
graph paths, the use of a profiler such as Callgrind allows us to also view the
call order and frequency. These graphs give us insight into the engines'
designs. We showed that, by using Callgrind, we can obtain a high-level view of
an engine's architecture, which can be used to understand it. In future work,
we intend to apply both dynamic and static analysis to other open-source
engines to understand architectural patterns and their impact on aspects such
as performance and maintenance.


  Let $T$ be a non-degenerate Calder\'on-Zygmund operator and let
$b:\mathbb{R}^d\to\mathbb{C}$ be locally integrable. Let $1<p\leq q<\infty$ and
let $\mu^p\in A_p$ and $\lambda^q\in A_q,$ where $A_{p}$ denotes the usual
class of Muckenhoupt weights. We show that
  \begin{align*}
  \|[b,T]\|_{L^p_{\mu}\to L^q_{\lambda}}\sim
\|b\|_{\operatorname{BMO}_{\nu}^{\alpha}},\qquad [b,T]\in
\mathcal{K}(L^p_{\mu}, L^q_{\lambda})\quad\mbox{iff}\quad b\in
\operatorname{VMO}_{\nu}^{\alpha},
  \end{align*} where $L^p_\mu=L^p(\mu^p)$ and $\alpha/d = 1/p-1/q,$ , the
symbol $\mathcal{K}$ stands for the class of compact operators between the
given spaces, and the fractional weighted $\operatorname{BMO}_{\nu}^{\alpha}$
and $\operatorname{VMO}_{\nu}^{\alpha}$ spaces are defined through the
following fractional oscillation and Bloom weight
  \begin{align*}
  \mathcal{O}_{\nu}^{\alpha}(b;Q) =
\nu^{-\alpha/d}(Q)\Big(\frac{1}{\nu(Q)}\int_Q |b-\langle
b\rangle_Q|\Big),\qquad \nu
  = \big(\frac{\mu}{\lambda}\big)^{\beta},\quad \beta = (1+\alpha/d)^{-1}.
  \end{align*} The key novelty is dealing with the off-diagonal range $p<q$,
whereas the case $p=q$ was previously studied by Lacey and Li. However, another
novelty in both cases is that our approach allows complex-valued functions $b$,
while other arguments based on the median of $b$ on a set are inherently
real-valued.


  We discuss how mixing between the Standard Model Higgs boson and the radion
of the Randall-Sundrum model can lead to significant shifts in the expected
properties of the Higgs boson. In particular we show that the total and partial
decay widths of the Higgs, as well as the $h\to gg$ branching fraction, can be
substantially altered from their SM expectations, while the remaining branching
fractions are modified less than about 5% for most of the parameter space
volume. Precision measurements of Higgs boson properties at at a Linear
Collider are shown to probe a large region of the Randall-Sundrum model
parameter space.


  The weak gravity conjecture imposes severe constraints on natural inflation.
A trans-Planckian axion decay constant can only be realized if the potential
exhibits an additional (subdominant) modulation with sub-Planckian periodicity.
The resulting wiggles in the axion potential generate a characteristic
modulation in the scalar power spectrum of inflation which is logarithmic in
the angular scale. The compatibility of this modulation is tested against the
most recent Cosmic Microwave Background (CMB) data by Planck and BICEP/Keck.
Intriguingly, we find that the modulation completely resolves the tension of
natural inflation with the CMB. A Bayesian model comparison reveals that
natural inflation with modulations describes all existing data equally well as
the cosmological standard model $\Lambda$CDM. In addition, the bound of a
tensor-to-scalar ratio r > 0.002 correlated with a striking small-scale
suppression of the scalar power spectrum occurs. Future CMB experiments could
directly probe the modulation through their improved sensitivity to smaller
angular scales and possibly the measurement of spectral distortions. They
could, thus, verify a key prediction of the weak gravity conjecture and provide
dramatic new insights into the theory of quantum gravity.


  Context: The success of software crowdsourcing depends on steady tasks supply
and active worker pool. Existing analysis reveals an average task failure ratio
of 15.7% in software crowdsourcing market. Goal: The objective of this study is
to empirically investigate patterns and effect of task diversity in software
crowdsourcing platform in order to improve the success and efficiency of
software crowdsourcing. Method: We propose a conceptual task diversity model,
and develop an approach to measuring and analyzing task diversity.More
specifically, this includes grouping similar tasks, ranking them based on their
competition level and identifying the dominant attributes that distinguish
among these levels, and then studying the impact of task diversity on task
success and worker performance in crowdsourcing platform. The empirical study
is conducted on more than one year's real-world data from TopCoder, the leading
software crowdsourcing platform. Results: We identified that monetary prize and
task complexity are the dominant attributes that differentiate among different
competition levels. Based on these dominant attributes, we found three task
diversity patterns (configurations) from workers behavior perspective:
responsive to prize, responsive to prize and complexity and over responsive to
prize. This study supports that1) responsive to prize configuration provides
highest level of task density and workers' reliability in a platform; 2)
responsive to prize and complexity configuration leads to attracting high level
of trustworthy workers; 3) over responsive to prize configuration results in
highest task stability and the lowest failure ratio in the platform for not
high similar tasks.


  We report Cu- and F-NMR studies on a four-layered high-temperature
superconductor Ba2Ca3Cu4O8F2(0234F(2.0)) with apical fluorine (F-1), an undoped
55 K-superconductor with a nominal Cu2+ valence on average. We reveal that this
compound exhibits the antiferromagnetism (AFM) with a Neel temperature TN=100 K
despite being a Tc= 55 K-superconductor. Through a comparison with a related
tri-layered cuprate Ba2Ca2Cu3O6F2 (0223F(2.0)), it is demonstrated that
electrons are transferred from the inner plane (IP) to the outer plane (OP) in
0234F(2.0) and 0223F(2.0), confirming the self-doped high-temperature
superconductivity (HTSC) having electron and hole doping in a single compound.
Remarlably, uniform mixing of AFM and HTSC takes place in both the
electron-doped OPs and the hole-doped IPs in 0234F(2.0).


  The paper presents the results of molecular dynamics study of the viscosity
of nickel-containing binary metal melts for a wide range of temperatures,
including the region of the equilibrium liquid phase and supercooled melt. It
is shown that the temperature dependencies of the viscosity of binary metal
melts are described by the Kelton's quasi-universal model. Based on the
analysis of the viscosity coefficient of the binary melt composition within the
framework of the Rosenfeld's scale transfor\-mations, it has been established
that to correctly describe the viscosity of binary/multi\-component metal melts
within the framework of entropy models, it is necessary to use a more complex
representation of the excess entropy $S_{ex}$ than in the approximation of pair
correlation entropy $S_2$.


  Functional magnetic resonance imaging recordings in the resting-state (RS)
from the human brain are characterized by spontaneous low-frequency
fluctuations in the blood oxygenation level dependent signal that reveal
functional connectivity (FC) via their spatial synchronicity. This RS study
applied network analysis to compare FC between late-life depression (LLD)
patients and control subjects. Raw cross-correlation matrices (CM) for LLD were
characterized by higher FC. We analyzed the small-world (SW) and modular
organization of these networks consisting of 110 nodes each as well as the
connectivity patterns of individual nodes of the basal ganglia. Topological
network measures showed no significant differences between groups. The
composition of top hubs was similar between LLD and control subjects, however
in the LLD group posterior medial-parietal regions were more highly connected
compared to controls. In LLD, a number of brain regions showed connections with
more distant neighbors leading to an increase of the average Euclidean distance
between connected regions compared to controls. In addition, right caudate
nucleus connectivity was more diffuse in LLD. In summary, LLD was associated
with overall increased FC strength and changes in the average distance between
connected nodes, but did not lead to global changes in SW or modular
organization.


  Unmanned aerial vehicles (UAVs) have been increasingly used in a wide area of
military and civilian applications such as data collection and monitoring. A
reliable network for command and control, communication, and data transfer is
crucial, not only for mission purposes but also for safety concerns. The
already deployed cellular networks are appropriate candidates for UAV
communication given the solid security and wide coverage of these networks.
However, the reliability of such networks needs a comprehensive investigation.
In this paper, we use the long-term evolution (LTE) network as the
infrastructure for drone communication and data transfer, in a rural area. We
study the communication characteristics of an LTE-connected drone during
low-altitude flights, for different altitudes and UAV speeds. We show that, in
such areas, the higher elevation benefits from a better signal quality and
experiences a fewer number of handover processes. Higher speed flights also
slightly degrade the communication performance.


  This paper studies selecting a subset of the system's output to minimize the
state estimation mean square error (MSE). This results in the maximization
problem of a set function defined on possible sensor selections subject to a
cardinality constraint. We consider to solve it approximately by a greedy
search. Since the MSE function is not submodular nor supermodular, the
well-known performance guarantees for the greedy solutions do not hold in the
present case. Thus, we use the quantities---the submodularity ratio and the
curvature---to evaluate the degrees of submodularity and supermodularity of the
objective function. By using the properties of the MSE function, we
approximately compute these quantities and derive a performance guarantee for
the greedy solutions. It is shown that the guarantee is less conservative than
those in the existing results.


  We consider some string invariants at genus two that appear in the analysis
of the $D^8\mathcal{R}^4$ and $D^6\mathcal{R}^5$ interactions in type II string
theory. We conjecture a Poisson equation involving them and the Kawazumi--Zhang
invariant based on their asymptotic expansions around the non--separating node
in the moduli space of genus two Riemann surfaces.


  The first-principles full-potential linearized augmented plane-wave method
based on density functional theory is used to investigate electronic structure
and magnetic properties of hypothetical binary compounds of I$^{A}$ subgroup
elements with nitrogen (LiN, NaN, KN and RbN) in assumed three types of
cristalline structure (rock salt, wurtzite and zinc-blende). We find that, due
to the spin polarized \textit{p} orbitas of N, all four compounds are
half-metallic ferromagnets with wide energy bandgaps (up to 2.0 eV). The
calculated total magnetic moment in all investigated compounds for all three
types of crystal structure is exactly 2.00 $\mu_{\text{B}}$ per formula unit.
The predicted half-metallicity is robust with respect to lattice-constant
contraction. In all the cases ferromagnetic phase is energetically favored with
respect to the paramagnetic one. The mechanism leading to half-metallic
ferromagnetism and synthesis possibilities are discussed.


  We provide an abstract framework for optimal goal-oriented adaptivity for
finite element methods and boundary element methods in the spirit of
[Carstensen et al., Comput. Math. Appl. 67 (2014)]. We prove that this
framework covers standard discretizations of general second-order linear
elliptic PDEs and hence generalizes available results [Mommer & Stevenson, SIAM
J. Numer. Anal. 47 (2009); Becker et al., SIAM J. Numer. Anal. 49 (2011)]
beyond the Poisson equation.


  We study the new boundary condition of the O(n) model proposed by Jacobsen
and Saleur using the matrix model. The spectrum of boundary operators and their
conformal weights are obtained by solving the loop equations. Using the
diagrammatic expansion of the matrix model as well as the loop equations, we
make an explicit correspondence between the new boundary condition of the O(n)
model and the "alternating height" boundary conditions in RSOS model.


  We revisit the well-known problem of multiscaling in substances passively
advected by homogeneous and isotropic turbulent flows or passive scalar
turbulence. To that end we propose a two-parameter continuum hydrodynamic model
for an advected substance concentration $\theta$, parametrised jointly by $y$
and $\overline y$, that characterise the spatial scaling behaviour of the
variances of the advecting stochastic velocity and the stochastic additive
driving force, respectively. We analyse it within a one-loop dynamic
renormalisation group method to calculate the multiscaling exponents of the
equal-time structure functions of $\theta$. We show how the interplay between
the advective velocity and the additive force may lead to simple scaling or
multiscaling.
  In one limit, our results reduce to the well-known results from the Kraichnan
model for passive scalar. Our framework of analysis should be of help for
analytical approaches for the still intractable problem of fluid turbulence
itself.


  The recent detection of variable infrared emission from Sagittarius A*,
combined with its previously observed flare activity in X-rays, provides
compelling evidence that at least a portion of this object's emission is
produced by nonthermal electrons. We show here that acceleration of electrons
by plasma wave turbulence in hot gases near the black hole's event horizon can
account both for Sagittarius A*'s mm and shorter wavelengths emission in the
quiescent state, and for the infrared and X-ray flares, induced either via an
enhancement of the mass accretion rate onto the black hole or by a
reorganization of the magnetic field coupled to the accretion gas. The
acceleration model proposed here produces distinct flare spectra that may be
compared with future coordinated multi-wavelength observations. We further
suggest that the diffusion of high energy electrons away from the acceleration
site toward larger radii might be able to account for the observed
characteristics of Sagittarius A*'s emission at cm and longer wavelengths.


  In target tracking and sensor fusion contexts it is not unusual to deal with
a large number of Gaussian densities that encode the available information
(multiple hypotheses), as in applications where many sensors, affected by
clutter or multimodal noise, take measurements on the same scene. In such cases
reduction procedures must be implemented, with the purpose of limiting the
computational load. In some situations it is required to fuse all available
information into a single hypothesis, and this is usually done by computing the
barycenter of the set. However, such computation strongly depends on the chosen
dissimilarity measure, and most often it must be performed making use of
numerical methods, since in very few cases the barycenter can be computed
analytically. Some issues, like the constraint on the covariance, that must be
symmetric and positive definite, make it hard the numerical computation of the
barycenter of a set of Gaussians. In this work, Fixed-Point Iterations (FPI)
are presented for the computation of barycenters according to several
dissimilarity measures, making up a useful toolbox for fusion/reduction of
Gaussian sets in applications where specific dissimilarity measures are
required.


  Well-Tempered Metadynamics (WTmetaD) is an efficient method to enhance the
reconstruction of the free-energy surface of proteins. WTmetaD guarantees a
faster convergence in the long time limit in comparison with the standard
metadynamics. It still suffers however from the same limitation, i.e. the non
trivial choice of pertinent collective variables (CVs). To circumvent this
problem, we couple WTmetaD with a set of CVs generated from a dihedral
Principal Component Analysis (dPCA) on the Ramachadran dihedral angles
describing the backbone structure of the protein. The dPCA provides a generic
method to extract relevant CVs built from internal coordinates. We illustrate
the robustness of this method in the case of the small and very diffusive
Metenkephalin pentapeptide, and highlight a criterion to limit the number of
CVs necessary to biased the metadynamics simulation. The free-energy landscape
(FEL) of Met-enkephalin built on CVs generated from dPCA is found rugged
compared with the FEL built on CVs extracted from PCA of the Cartesian
coordinates of the atoms.


  The off-shell massless six-point double box and hexagon conformal Feynman
integrals with generic propagator powers are expressed in terms of linear
combinations of multiple hypergeometric series of the generalized Horn type.
These results are derived from 9-fold Mellin-Barnes representations obtained
from their dual conformal Feynman parameter representations. The individual
terms in the presented expressions satisfy the differential equation that
relates the double box in $D$ dimensions to the hexagon in $D+2$ dimensions.


  We present some new problems in spectral optimization. The first one consists
in determining the best domain for the Dirichlet energy (or for the first
eigenvalue) of the {\it metric Laplacian}, and we consider in particular
Riemannian or Finsler manifolds, Carnot-Carath\'eodory spaces, Gaussian spaces.
The second one deals with the optimal shape of a graph when the minimization
cost is of spectral type. The third one is the optimization problem for a
Schr\"odinger potential in suitable classes.


  The LAMOST-\textit{K}2 (L\textit{K}2) project, initiated in 2015, aims to
collect low-resolution spectra of targets in the \textit{K}2 campaigns, similar
to LAMOST-\textit{Kepler} project. By the end of 2018, a total of 126
L\textit{K}2 plates had been observed by LAMOST. After cross-matching the
catalog of the LAMOST data release 6 (DR6) with that of the \textit{K}2
approved targets, we found 160,619 usable spectra of 84,012 objects, most of
which had been observed more than once. The effective temperature, surface
gravity, metallicity, and radial velocity from 129,974 spectra for 70,895
objects are derived through the LAMOST Stellar Parameter Pipeline (LASP). The
internal uncertainties were estimated to be 81 K, 0.15 dex, 0.09 dex and 5
kms$^{-1}$, respectively, when derived from a spectrum with a signal-to-noise
ratio in the $g$ band (SNR$_g$) of 10. These estimates are based on results for
targets with multiple visits. The external accuracies were assessed by
comparing the parameters of targets in common with the APOGEE and GAIA surveys,
for which we generally found linear relationships. A final calibration is
provided, combining external and internal uncertainties for giants and dwarfs,
separately. We foresee that these spectroscopic data will be used widely in
different research fields, especially in combination with \textit{K}2
photometry.


  We present preliminary experimental data that enable us to suggest that heat
transfer in cellular tissue under local strong heating is a more complex
phenomenon than a simple heat diffusion. Namely, we demonstrate that under
local strong heating of a muscle tissue heat transfer in it exhibits
substantial anisotropy unexplained in the context of the standard diffusion
model. The observed temperature dynamics is also characterized by nonlinear
behavior as well as by a certain repeat reversibility. The latter means that
the time variations in the temperature of a cellular tissue undergoing repeated
acts of heating go in the same way at least approximately. We explain the
observed anomalous properties of heat transfer by suggesting the flow of the
interstitial liquid to appear due to nonuniform heating which, in turn, affects
the heat transfer. A possible mechanism responsible for this effect is
discussed.


  Cryptocurrencies are gaining more popularity due to their security, making
counterfeits impossible. However, these digital currencies have been criticized
for creating a large carbon footprint due to their algorithmic complexity and
decentralized system design for proof of work and mining. We hypothesize that
the carbon footprint of cryptocurrency transactions has a higher dependency on
carbon-rich fuel sources than green or renewable fuel sources. We provide a
machine learning framework to model such transactions and correlate them with
the electricity generation patterns to estimate and analyze their carbon cost.


  Cold interstellar gas clouds provide an exciting new method to discover dark
matter. Their immense size makes them uniquely sensitive to interactions from
the heaviest, most rarefied dark matter models. Using gas cloud observations,
we derive constraints on heavy composite dark matter coupled to the Standard
Model through a light dark photon for dark matter up to a thousand solar
masses. We find gas clouds are also sensitive to very large composite dark
matter that interacts with nuclei through a fixed contact interaction cross
section. We also study the contact interaction model and implement multiscatter
and overburden analyses to obtain bounds from experiments like CDMS, CRESST,
DAMA, XQC, and XENON1T.


  Conventional approaches for 3D imaging in or through scattering media are
usually limited to 2D reconstruction of objects at some discontinuous
locations, although the time-consuming iteration, guide-star, or complex system
are implemented. How to quickly visualize dynamic 3D objects behind scattering
media is still an open issue. Here, by using structured light illumination, we
propose a single-shot technique that can quickly acquire continuous 3D surfaces
of objects hidden behind the diffuser. The proposed method can realize the 3D
imaging of single, multiple, and dynamic targets from the speckled structured
light patterns under broad or narrow band light illumination, in which only
once calibration of the imaging setup is needed before conducting the imaging.
Our approach paves the way to quickly visualize dynamic objects behind
scattering media in 3D and multispectral.


  We study equivariant embeddings with small boundary of a given homogeneous
space $G/H$, where $G$ is a connected, linear algebraic group with trivial
Picard group and only trivial characters, and $H \subset G$ is an extension of
a connected Grosshans subgroup by a torus. Under certain maximality conditions,
like completeness, we obtain finiteness of the number of isomorphism classes of
such embeddings, and we provide a combinatorial description the embbeddings and
their morphisms. The latter allows a systematic treatment of examples and basic
statements on the geometry of the equivariant embeddings of a given homogeneous
space $G/H$.


  We use the threshold expansion and non-relativistic effective theory to
determine the bottom quark mass from moments of the $b\bar{b}$ production cross
section at next-to-next-to-leading order in the (resummed) perturbative
expansion, and including a summation of logarithms. For the $\bar{\rm MS}$ mass
$\bar{m}_b$, we find $\bar{m}_b(\bar{m}_b)=(4.26\pm 0.12)$ GeV.


  X-rays spectroscopy experiments at synchrotron facilities were limited for
many years by the maximum input-count rate and the signal-to-background ratio
of germanium fluorescence detectors. These limitations are related to the
germanium semiconductor device, the sensor configuration and its response to
the incident X-ray flux at different energies. In order to understand and
quantify such limitations, physics simulation of the detector response is a
powerful tool to provide guidelines for designing, prototyping and improving
detectors, as well as modelling experimental environments, which reduces time
and cost of development. For this purpose, a first complete and operational
simulation chain based on Allpix Squared framework is presented, customized to
multi-element germanium detectors and combined with three-dimensional
simulations of the electric field and the weighting potential, based on COMSOL
Multiphysics. Based on this simulation chain, a quantification of charge
sharing effect as well as signal-to-background ratio at different beam energies
has been made for a germanium detector equipped with and without collimator. In
addition, two experimental measurements have been performed on the SAMBA
beamline at SOLEIL synchrotron. The experimental data were used to set up the
full simulation chain and good agreements have been observed between data and
simulation.


  Dynamic mechanism design has garnered significant attention from both
computer scientists and economists in recent years. By allowing agents to
interact with the seller over multiple rounds, where agents' reward functions
may change with time and are state-dependent, the framework is able to model a
rich class of real-world problems. In these works, the interaction between
agents and sellers is often assumed to follow a Markov Decision Process (MDP).
We focus on the setting where the reward and transition functions of such an
MDP are not known a priori, and we are attempting to recover the optimal
mechanism using an a priori collected data set. In the setting where the
function approximation is employed to handle large state spaces, with only mild
assumptions on the expressiveness of the function class, we are able to design
a dynamic mechanism using offline reinforcement learning algorithms. Moreover,
learned mechanisms approximately have three key desiderata: efficiency,
individual rationality, and truthfulness. Our algorithm is based on the
pessimism principle and only requires a mild assumption on the coverage of the
offline data set. To the best of our knowledge, our work provides the first
offline RL algorithm for dynamic mechanism design without assuming uniform
coverage.


  We describe a new technique for probing galactic and extragalactic 2D and 3D
magnetic field distribution using gradients of polarized synchrotron emission.
The fluctuations of magnetic field are elongated along the ambient magnetic
field. Therefore, the field variations are maximal perpendicular to the
B-field. This allows tracing B-field with synchrotron polarization gradients
(SPGs). We demonstrate that the Faraday depolarization allows to map 3D B-field
structure by. The depolarization ensures that the polarization gradients sample
the regions close to the observer with the sampling depth controlled by the
frequency of radiation. We also analyze the B-field properties along the
line-of-sight by applying the gradient technique to the wavelength derivative
of synchrotron polarization. This Synchrotron Derivative Polarization Gradients
(SDPGs) technique can recover the 3D vectors of the underlying B-fields. The
new techniques are different from the Faraday tomography as they provide a way
to map the 3D distribution of B-fields components perpendicular to the line of
sight. In addition, we find that the alignment of gradients of polarization
with the synchrotron polarization can be used to separate the contribution of
the foreground from the polarization of cosmological origin. We notice that the
same alignment is also present for the dust polarization.


  Every principal G-bundle is classified up to equivalence by a homotopy class
of maps into the classifying space of G. On the other hand, for every nice
topological space Milnor constructed a strict model of loop space, that is a
group. Moreover the morphisms of topological groups defined on the loop space
of X generate all the bundles over X up to equivalence. In this paper, we show
that the relationship between Milnor's loop space and the classifying space
functor is, in a precise sense, an adjoint pair between based spaces and
topological groups in a homotopical context. This proof leads to a
classification of principal bundles with a fixed structure group. Such a resul
clarifies the deep relation that exists between the theory of bundles, the
classifying space construction and the loop space construction, which are very
important in topological K-theory, group cohomology and homotopy theory.


  Computer-aided design of molecules has the potential to disrupt the field of
drug and material discovery. Machine learning, and deep learning, in
particular, have been topics where the field has been developing at a rapid
pace. Reinforcement learning is a particularly promising approach since it
allows for molecular design without prior knowledge. However, the search space
is vast and efficient exploration is desirable when using reinforcement
learning agents. In this study, we propose an algorithm to aid efficient
exploration. The algorithm is inspired by a concept known in the literature as
curiosity. We show on three benchmarks that a curious agent finds better
performing molecules. This indicates an exciting new research direction for
reinforcement learning agents that can explore the chemical space out of their
own motivation. This has the potential to eventually lead to unexpected new
molecules that no human has thought about so far.


  The chiral theory of mesons of two flavors have been extended to mesons
containing strange flavor. Two new mass relations between vector and
axial-vector mesons have been obtained. In chiral limit, the physical processes
of normal parity and abnormal parity have been studied. Due to the universality
of the coupling of this theory many interesting results have been obtained. In
chiral limit theoretical results are in reasonable agreement with data.


  Material indentation studies, in which a probe is brought into controlled
physical contact with an experimental sample, have long been a primary means by
which scientists characterize the mechanical properties of materials. More
recently, the advent of atomic force microscopy, which operates on the same
fundamental principle, has in turn revolutionized the nanoscale analysis of
soft biomaterials such as cells and tissues. This paper addresses the
inferential problems associated with material indentation and atomic force
microscopy, through a framework for the changepoint analysis of pre- and
post-contact data that is applicable to experiments across a variety of
physical scales. A hierarchical Bayesian model is proposed to account for
experimentally observed changepoint smoothness constraints and measurement
error variability, with efficient Monte Carlo methods developed and employed to
realize inference via posterior sampling for parameters such as Young's
modulus, a key quantifier of material stiffness. These results are the first to
provide the materials science community with rigorous inference procedures and
uncertainty quantification, via optimized and fully automated high-throughput
algorithms, implemented as the publicly available software package BayesCP. To
demonstrate the consistent accuracy and wide applicability of this approach,
results are shown for a variety of data sets from both macro- and
micro-materials experiments--including silicone, neurons, and red blood
cells--conducted by the authors and others.


  The Wightman two-point function for the gravitational field in the linear
approximation (the rank-2 ``massless'' tensor field) on de Sitter space has a
pathological behaviour for large separated points (infrared divergence). This
behaviour can be eliminated in the two-point function for the traceless part of
this field if one chooses the Gupta-Bleuler vacuum. But it is not possible to
do the same for the pure trace part (conformal sector). We briefly discuss the
consequences of this pure trace behaviour for inflationary models.


  We revisit the problem of learning a single neuron with ReLU activation under
Gaussian input with square loss. We particularly focus on the
over-parameterization setting where the student network has $n\ge 2$ neurons.
We prove the global convergence of randomly initialized gradient descent with a
$O\left(T^{-3}\right)$ rate. This is the first global convergence result for
this problem beyond the exact-parameterization setting ($n=1$) in which the
gradient descent enjoys an $\exp(-\Omega(T))$ rate. Perhaps surprisingly, we
further present an $\Omega\left(T^{-3}\right)$ lower bound for randomly
initialized gradient flow in the over-parameterization setting. These two
bounds jointly give an exact characterization of the convergence rate and
imply, for the first time, that over-parameterization can exponentially slow
down the convergence rate. To prove the global convergence, we need to tackle
the interactions among student neurons in the gradient descent dynamics, which
are not present in the exact-parameterization case. We use a three-phase
structure to analyze GD's dynamics. Along the way, we prove gradient descent
automatically balances student neurons, and use this property to deal with the
non-smoothness of the objective function. To prove the convergence rate lower
bound, we construct a novel potential function that characterizes the pairwise
distances between the student neurons (which cannot be done in the
exact-parameterization case). We show this potential function converges slowly,
which implies the slow convergence rate of the loss function.


  Within a multicomponent BCS-like framework we study the coexistence and
competition of various CDW and SC order parameters in the presence of a strong
ferromagnetic background. We find that the competition of unconventional CDW,
also called orbital antiferromagnet, with SC at finite momentum (${\bf
Q}$-triplet pairing) shows unique characteristics like an extreme sensitivity
on the deviations from nesting. We argue that pressure in UGe$_2$ creates
deviations from the nesting and report a phase diagram in qualitative agreement
with the observed behavior of UGe$_2$.


  The habitable zone (HZ) is the region around a star(s) where standing bodies
of water could exist on the surface of a rocky planet. The classical HZ
definition makes a number of assumptions common to the Earth, including
assuming that the most important greenhouse gases for habitable planets are CO2
and H2O, habitable planets orbit main-sequence stars, and that the
carbonate-silicate cycle is a universal process on potentially habitable
planets. Here, we discuss these and other predictions for the habitable zone
and the observations that are needed to test them. We also, for the first time,
argue why A-stars may be interesting HZ prospects. Instead of relying on
unverified extrapolations from our Earth, we argue that future habitability
studies require first principles approaches where temporal, spatial, physical,
chemical, and biological systems are dynamically coupled. We also suggest that
next-generation missions are only the beginning of a much more data-filled era
in the not-too-distant future, when possibly hundreds to thousands of HZ
planets will yield the statistical data we need to go beyond just finding
habitable zone planets to actually determining which ones are most likely to
exhibit life.


  General properties of an Abelian connection in Fedosov deformation
quantization are investigated. Definition and criterion of being a finite
formal series for an Abelian connection are presented. A proof that in
2-dimensional (2-D) case the Abelian connection is an ifinite formal series is
done.


  On December 2021, a new camera box for two-colour simultaneous visible
photometry was successfully installed on the ASTEP telescope at the Concordia
station in Antarctica. The new focal box offers increased capabilities for the
ASTEP+ project. The opto-mechanical design of the camera was described in a
previous paper. Here, we focus on the laboratory tests of each of the two
cameras, the low-temperature behaviour of the focal box in a thermal chamber,
the on-site installation and alignment of the new focal box on the telescope,
the measurement of the turbulence in the tube and the operation of the
telescope equipped with the new focal box. We also describe the data
acquisition and the telescope guiding procedure and provide a first assessment
of the performances reached during the first part of the 2022 observation
campaign. Observations of the WASP19 field, already observed previously with
ASTEP, demonstrates an improvement of the SNR by a factor 1.7, coherent with an
increased number of photon by a factor of 3. The throughput of the two cameras
is assessed both by calculation of the characteristics of the optics and
quantum efficiency of the cameras, and by direct observations on the sky. We
find that the ASTEP+ two-colour transmission curves (with a dichroic separating
the fluxes at 690nm) are similar to those of GAIA in the blue and red channels,
but with a lower transmission in the ASTEP+ red channel leading to a 1.5
magnitude higher B-R value compared to the GAIA B-R value. With this new
setting, the ASTEP+ telescope will ensure the follow-up and the
characterization of a large number of exoplanetary transits in the coming years
in view of the future space missions JWST and Ariel.


  We show that the zero locus of an admissible normal function on a smooth
complex algebraic variety is algebraic.


  We study the Andr\'e-Quillen cohomology with coefficients of an algebra over
an operad. Using resolutions of algebras coming from Koszul duality theory, we
make this cohomology theory explicit and we give a Lie theoretic
interpretation. For which operads is the associated Andr\'e-Quillen cohomology
equal to an Ext-functor ? We give several criterion, based on the cotangent
complex, to characterize this property. We apply it to homotopy algebras, which
gives a new homotopy stable property for algebras over cofibrant operads.


  We demonstrate how a magnon chemical potential is generated in parametric
parallel pumping. We study how a time-periodic magnetic field of this pumping
affects magnon properties of a ferrimagnet in a nonequilibrium steady state. We
show that the magnon distribution function of our nonequilibrium steady state
becomes the Bose distribution function with $\mu=\omega_{\textrm{p}}/2$, where
$\mu$ is the magnon chemical potential and $\omega_{\textrm{p}}$ is the pumping
frequency. This result is distinct from the absence of the magnon chemical
potential in the standard theory and can qualitatively explain its generation
in experiments. We believe our result is a first theoretical demonstration of
the generation of the magnon chemical potential in the parametric parallel
pumping, providing an important step towards a thorough understanding of
properties of nonequilibrium magnons.


  We report a discovery of extended counterrotating gaseous disks in early-type
disk galaxies NGC 2551 and NGC 5631. To find them, we have undertaken complex
spectral observations including integral-field spectroscopy for the central
parts of the galaxies and long-slit deep spectroscopy to probe the external
parts. The line-of-sight velocity fields have been constructed and compared to
the photometric structure of the galaxies. As a result, we have revealed
full-size counterrotating gaseous disks, the one coplanar to the stellar disk
in NGC 2551 and the other inclined to the main stellar disk in NGC 5631. We
suggest that we observe the early stages of minor-merger events which may be
two different stages of the process of lenticular galaxy formation in rather
sparse environments.


  Purpose: To develop and evaluate a transformer-based deep learning model for
the synthesis of nephrographic phase images in CT urography (CTU) examinations
from the unenhanced and urographic phases.
  Materials and Methods: This retrospective study was approved by the local
Institutional Review Board. A dataset of 119 patients (mean $\pm$ SD age, 65
$\pm$ 12 years; 75/44 males/females) with three-phase CT urography studies was
curated for deep learning model development. The three phases for each patient
were aligned with an affine registration algorithm. A custom model, coined
Residual transformer model for Nephrographic phase CT image synthesis (ResNCT),
was developed and implemented with paired inputs of non-contrast and urographic
sets of images trained to produce the nephrographic phase images, that were
compared with the corresponding ground truth nephrographic phase images. The
synthesized images were evaluated with multiple performance metrics, including
peak signal to noise ratio (PSNR), structural similarity index (SSIM),
normalized cross correlation coefficient (NCC), mean absolute error (MAE), and
root mean squared error (RMSE).
  Results: The ResNCT model successfully generated synthetic nephrographic
images from non-contrast and urographic image inputs. With respect to ground
truth nephrographic phase images, the images synthesized by the model achieved
high PSNR (27.8 $\pm$ 2.7 dB), SSIM (0.88 $\pm$ 0.05), and NCC (0.98 $\pm$
0.02), and low MAE (0.02 $\pm$ 0.005) and RMSE (0.042 $\pm$ 0.016).
  Conclusion: The ResNCT model synthesized nephrographic phase CT images with
high similarity to ground truth images. The ResNCT model provides a means of
eliminating the acquisition of the nephrographic phase with a resultant 33%
reduction in radiation dose for CTU examinations.


  Data obtained with the NICMOS instrument on board the Hubble Space Telescope
(HST) have been used to determine the H-band luminosity function (LF) and mass
function (MF) of three stellar fields in the globular cluster M15, located ~7'
from the cluster centre. The data confirm that the cluster MF has a
characteristic mass of ~0.3 Msolar, as obtained by Paresce & De Marchi (2000)
for a stellar field at 4.6' from the centre. By combining the present data with
those published by other authors for various radial distances (near the centre,
at 20" and at 4.6'), we have studied the radial variation of the LF due to the
effects of mass segregation and derived the global mass function (GMF) using
the Michie-King approach. The model that simultaneously best fits the LF at
various locations, the surface brightness profile and the velocity dispersion
profile suggests that the GMF should resemble a segmented power-law with the
following indices: x ~ 0.8 for stars more massive than 0.8 Msolar, x ~ 0.9 for
0.3 - 0.8 Msolar and x ~ -2.2 at smaller masses (Salpeter's IMF would have
x=1.35). The best fitting model also suggests that the cluster mass is ~5.4
10^5 Msolar and that the mass-to-light ratio is on average M/L_V ~ 2.1, with
M/L_V ~ 3.7 in the core. A large amount of mass (~ 44 %) is found in the
cluster core in the form of stellar heavy remnants, which may be sufficient to
explain the mass segregation in M15 without invoking the presence of an
intermediate-mass black hole.


  We derive the dimensional non-perturbative part of the QCD effective action
for scalar and pseudoscalar meson fields by means of chiral and conformal
bosonization. The related structural coupling constants L_5 and L_8 of the
chiral lagrangian are estimated using general relations which are valid in a
variety of chiral bosonization models without explicit reference to model
parameters. The asymptotics for large scalar fields in QCD is elaborated, and
model-independent constraints on dimensional coupling constants of the
effective meson lagrangian are evaluated. We determine also the interaction
between scalar quarkonium and the gluon density and obtain the scalar
glueball-quarkonium potential.


  The sensitivity of interferometers with linear polarizers to the CMB E and B
mode are variant under the rotation of the polarizer frame, while
interferometer with circular polarizers are equally sensitive to E and B mode.
We present analytically and numerically that the diagonal elements of window
functions for CMB E/B power spectra are maximized in interferometric
measurement of linear polarization, when the polarizer frame is in certain
rotation from the associated baseline. We also present the simulated
observation to show that the 1-$\sigma$ errors on E/B mode power spectrum
estimation are variant under the polarizer frame rotation in the case of linear
polarizers, while they are invariant in the case of circular polarizers.
Simulation of the configuration similar to the DASI shows that minimum
1-$\sigma$ error on B mode in interferometer measurement with linear polarizers
is 26% of that in interferometric measurement with circular polarizers. The
simulation also shows that the E/B mixing in interferometer measurement with
linear polarizers can be as low as 23% of that in interferometric measurement
with circular polarizers. It is not always possible to physically align the
polarizer frame with all the associated baselines in the case of an
interferometer array (N$>$2). There exist certain linear combinations of
visibilities, which are equivalent to visibilities of the optimal polarizer
frame rotation. We present the linear combinations, which enables B mode
optimization for an interferometer array (N$>$2).


  In this note, we prove analytic bounds on the equation of state of a
cosmological fluid composed of an arbitrary number of canonical scalars
evolving in a negative multi-exponential potential. Because of the negative
energy, the universe is contracting and eventually undergoes a big crunch. A
contracting universe is a fundamental feature of models of ekpyrosis and cyclic
universes, which are a proposed alternative to cosmic inflation to solve the
flatness and horizon problems. Our analytic bounds set quantitative constraints
on general effective theories of ekpyrosis. When applied to specific top-down
constructions, our bounds can be used to determine whether ekpyrosis could in
principle be realized. We point out some possible sources of tension in
realizing the ekpyrotic universe in controlled constructions of string theory.


  We address the problem of determining optimal sensor precisions for
estimating the states of linear time-varying discrete-time stochastic dynamical
systems, with guaranteed bounds on the estimation errors. This is performed in
the Kalman filtering framework, where the sensor precisions are treated as
variables. They are determined by solving a constrained convex optimization
problem, which guarantees the specified upper bound on the posterior error
variance. Optimal sensor precisions are determined by minimizing the l1 norm,
which promotes sparseness in the solution and indirectly addresses the sensor
selection problem. The theory is applied to realistic flight mechanics and
astrodynamics problems to highlight its engineering value. These examples
demonstrate the application of the presented theory to a) determine redundant
sensing architectures for linear time invariant systems, b) accurately estimate
states with low-cost sensors, and c) optimally schedule sensors for linear
time-varying systems.


  Due to the widespread use of complex machine learning models in real-world
applications, it is becoming critical to explain model predictions. However,
these models are typically black-box deep neural networks, explained post-hoc
via methods with known faithfulness limitations. Generalized Additive Models
(GAMs) are an inherently interpretable class of models that address this
limitation by learning a non-linear shape function for each feature separately,
followed by a linear model on top. However, these models are typically
difficult to train, require numerous parameters, and are difficult to scale. We
propose an entirely new subfamily of GAMs that utilizes basis decomposition of
shape functions. A small number of basis functions are shared among all
features, and are learned jointly for a given task, thus making our model scale
much better to large-scale data with high-dimensional features, especially when
features are sparse. We propose an architecture denoted as the Neural Basis
Model (NBM) which uses a single neural network to learn these bases. On a
variety of tabular and image datasets, we demonstrate that for interpretable
machine learning, NBMs are the state-of-the-art in accuracy, model size, and,
throughput and can easily model all higher-order feature interactions.
  Source code is available at https://github.com/facebookresearch/nbm-spam.


  Commonly, the strategy revision phase in evolutionary games relies on payoff
comparison. Namely, agents compare their payoff with the opponent, assessing
whether changing strategy can be potentially convenient. Even tiny payoff
differences can be crucial in this decision process.
  In this work, we study the dynamics of cooperation in the Public Goods Game,
introducing a threshold $\epsilon$ in the strategy revision phase. In doing so,
payoff differences narrower than $\epsilon$ entail the decision process reduces
to a coin flip.
  Interestingly, with ordinary agents, results show that payoff thresholds curb
the emergence of cooperation. Yet, the latter can be sustained by these
thresholds if the population is composed of conformist agents, which replace
the random-based revision with selecting the strategy of the majority.
  To conclude, agents sensible only to consistent payoff differences may
represent 'real-world' individuals unable to properly appreciate advantages or
disadvantages when facing a dilemma. These agents may be detrimental to the
emergence of cooperation or, on the contrary, supportive when endowed with a
conformist attitude.


  We obtain a sufficient condition for boundary regularity of quasiminimizers
of the p-energy integral in terms of a Wiener type sum of power type. The
exponent in the sum is independent of the dimension and is explicitly expressed
in terms of p and the quasiminimizing constant. We also show by an example that
the exponent is sharp in a certain sense.


  Accumulating evidence suggest that human behavior in trial-and-error learning
tasks based on decisions between discrete actions may involve a combination of
reinforcement learning (RL) and working-memory (WM). While the understanding of
brain activity at stake in this type of tasks often involve the comparison with
non-human primate neurophysiological results, it is not clear whether monkeys
use similar combined RL and WM processes to solve these tasks. Here we analyzed
the behavior of five monkeys with computational models combining RL and WM. Our
model-based analysis approach enables to not only fit trial-by-trial choices
but also transient slowdowns in reaction times, indicative of WM use. We found
that the behavior of the five monkeys was better explained in terms of a
combination of RL and WM despite inter-individual differences. The same
coordination dynamics we used in a previous study in humans best explained the
behavior of some monkeys while the behavior of others showed the opposite
pattern, revealing a possible different dynamics of WM process. We further
analyzed different variants of the tested models to open a discussion on how
the long pretraining in these tasks may have favored particular coordination
dynamics between RL and WM. This points towards either inter-species
differences or protocol differences which could be further tested in humans.


  Assisted by neural networks, reinforcement learning agents have been able to
solve increasingly complex tasks over the last years. The simulation
environment in which the agents interact is an essential component in any
reinforcement learning problem. The environment simulates the dynamics of the
agents' world and hence provides feedback to their actions in terms of state
observations and external rewards. To ease the design and simulation of such
environments this work introduces $\texttt{APES}$, a highly customizable and
open source package in Python to create 2D grid-world environments for
reinforcement learning problems. $\texttt{APES}$ equips agents with algorithms
to simulate any field of vision, it allows the creation and positioning of
items and rewards according to user-defined rules, and supports the interaction
of multiple agents.


  Understanding and developing a correlation measure that can detect general
dependencies is not only imperative to statistics and machine learning, but
also crucial to general scientific discovery in the big data age. In this
paper, we establish a new framework that generalizes distance correlation --- a
correlation measure that was recently proposed and shown to be universally
consistent for dependence testing against all joint distributions of finite
moments --- to the Multiscale Graph Correlation (MGC). By utilizing the
characteristic functions and incorporating the nearest neighbor machinery, we
formalize the population version of local distance correlations, define the
optimal scale in a given dependency, and name the optimal local correlation as
MGC. The new theoretical framework motivates a theoretically sound Sample MGC
and allows a number of desirable properties to be proved, including the
universal consistency, convergence and almost unbiasedness of the sample
version. The advantages of MGC are illustrated via a comprehensive set of
simulations with linear, nonlinear, univariate, multivariate, and noisy
dependencies, where it loses almost no power in monotone dependencies while
achieving better performance in general dependencies, compared to distance
correlation and other popular methods.


  Ontologies and rules are usually loosely coupled in knowledge representation
formalisms. In fact, ontologies use open-world reasoning while the leading
semantics for rules use non-monotonic, closed-world reasoning. One exception is
the tightly-coupled framework of Minimal Knowledge and Negation as Failure
(MKNF), which allows statements about individuals to be jointly derived via
entailment from an ontology and inferences from rules. Nonetheless, the
practical usefulness of MKNF has not always been clear, although recent work
has formalized a general resolution-based method for querying MKNF when rules
are taken to have the well-founded semantics, and the ontology is modeled by a
general oracle. That work leaves open what algorithms should be used to relate
the entailments of the ontology and the inferences of rules. In this paper we
provide such algorithms, and describe the implementation of a query-driven
system, CDF-Rules, for hybrid knowledge bases combining both (non-monotonic)
rules under the well-founded semantics and a (monotonic) ontology, represented
by a CDF Type-1 (ALQ) theory. To appear in Theory and Practice of Logic
Programming (TPLP)


  The electroweak interaction in the Standard Model (SM) is described by a pure
vector-axial-vector structure, though any Lorentz-invariant component could
contribute. In this work, we present the most precise measurement of tensor
currents in the low-energy regime by examining the $\beta$-$\bar{\nu}$
correlation of trapped $^{8}$Li ions with the Beta-decay Paul Trap. We find
$a_{\beta\nu} = -0.3325 \pm 0.0013_{stat} \pm 0.0019_{syst}$ at $1\sigma$ for
the case of coupling to right-handed neutrinos $(C_T=-C_T')$, which is
consistent with the SM prediction.


  We present a measurement of the ratio of the Bs meson lifetime, in the
flavor-specific decay to $D_s^+\pi^-$, to that of the B0 meson. The pp
collision data used correspond to an integrated luminosity of 1/fb, collected
with the LHCb detector, at a center-of-mass energy of 7 TeV. Combining our
measured value of 1.010 +/- 0.010 +/- 0.008 for this ratio with the known
lifetime, we determine the flavor-specific Bs lifetime to be tau(Bs) = 1.535
+/- 0.015 +/- 0.014 ps, where the uncertainties are statistical and systematic,
respectively. This is the most precise measurement to date, and is consistent
with previous measurements and theoretical predictions.


  Training Generative Adversarial Networks (GANs) is notoriously challenging.
We propose and study an architectural modification, self-modulation, which
improves GAN performance across different data sets, architectures, losses,
regularizers, and hyperparameter settings. Intuitively, self-modulation allows
the intermediate feature maps of a generator to change as a function of the
input noise vector. While reminiscent of other conditioning techniques, it
requires no labeled data. In a large-scale empirical study we observe a
relative decrease of $5\%-35\%$ in FID. Furthermore, all else being equal,
adding this modification to the generator leads to improved performance in
$124/144$ ($86\%$) of the studied settings. Self-modulation is a simple
architectural change that requires no additional parameter tuning, which
suggests that it can be applied readily to any GAN.


  The mechanism of reversible photodegradation of 1-substituted
aminoanthraquinones doped into poly(methyl methacrylate) and polystyrene is
investigated. Time-dependent density functional theory is employed to predict
the transition energies and corresponding oscillator strengths of the proposed
reversibly- and irreversibly-damaged dye species. Ultraviolet-visible and
Fourier transform infrared (FTIR) spectroscopy are used to characterize which
species are present. FTIR spectroscopy indicates that both dye and polymer
undergo reversible photodegradation when irradiated with a visible laser. These
findings suggest that photodegradation of 1-substituted aminoanthraquinones
doped in polymers originates from interactions between dyes and photoinduced
thermally-degraded polymers, and the metastable product may recover or further
degrade irreversibly.


  We construct noncomplete orthogonal systems on the ray $[0,\infty)$ that look
like Jacobi polynomials $P_n(x)$ after a shift of degree $n\mapsto n+a$, where
$a$ is a real constant. These systems are solutions of some exotic
Sturm-Liouville problem for hypergeometric differential operators.
  We obtain the explicit spectral decomposition for these problems.


  We study resonant gravitino production in the early Universe in the presence
of SUSY flat directions whose large VEVs break some but not all gauge
symmetries. We find that for a large region of parameter space the gravitino
abundance is several orders of magnitude larger than the cosmological upper
bound. Since flat directions with large VEVs are generically expected in
supersymmetric theories this result further exacerbates the gravitino problem.


  In the total domination game played on a graph $G$, players Dominator and
Staller alternately select vertices of $G$, as long as possible, such that each
vertex chosen increases the number of vertices totally dominated. Dominator
(Staller) wishes to minimize (maximize) the number of vertices selected. The
game total domination number, $\gamma_{\rm tg}(G)$, of $G$ is the number of
vertices chosen when Dominator starts the game and both players play optimally.
If a vertex $v$ of $G$ is declared to be already totally dominated, then we
denote this graph by $G|v$. In this paper the total domination game critical
graphs are introduced as the graphs $G$ for which $\gamma_{\rm tg}(G|v) <
\gamma_{\rm tg}(G)$ holds for every vertex $v$ in $G$. If $\gamma_{\rm tg}(G) =
k$, then $G$ is called $k$-$\gamma_{\rm tg}$-critical. It is proved that the
cycle $C_n$ is $\gamma_{{\rm tg}}$-critical if and only if $n\pmod 6 \in
\{0,1,3\}$ and that the path $P_n$ is $\gamma_{{\rm tg}}$-critical if and only
if $n\pmod 6\in \{2,4\}$. $2$-$\gamma_{\rm tg}$-critical and $3$-$\gamma_{\rm
tg}$-critical graphs are also characterized as well as $3$-$\gamma_{\rm
tg}$-critical joins of graphs.


  We explore the statistical behavior of the light emanating from a coherently
driven Jaynes-Cummings (JC) oscillator operating in the regime of multiphoton
blockade with two monitored output channels causing the loss of coherence at
equal rates. We do so by adopting an operational approach which draws the
particle and wave aspects of the forwards scattered radiation together,
building upon the relationship between quantum optical correlation functions
and conditional measurements. We first derive an analytical expression of the
intensity cross-correlation function at the peak of the two-photon JC resonance
to demonstrate the breakdown of detailed balance. The application of quantum
trajectory theory in parallel with the quantum regression formula subsequently
uncovers various aspects of temporal asymmetry in the quantum fluctuations
characterizing the cascaded process through which a multiphoton resonance is
established and read out. We find that monitoring different quadratures of the
cavity field in conditional homodyne detection affects the times waited between
successive photon counter ``clicks'', which in turn trigger the sampling of the
homodyne current. Despite the fact that the steady-state cavity occupation is
of the order of a photon, monitoring of the developing bimodality also impacts
on the ratio between the emissions directed along the two decoherence channels.


  We study the detail derivation of the multiple D2-brane effective action from
multiple M2-branes in the Bagger-Lambert-Gustavsson (BLG) theory and the
Aharony-Bergman-Jafferis-Maldacena (ABJM) theory by employing the novel Higgs
mechanism. We show explicitly that the high-order F^3 and F^4 terms are
commutator terms, and conjecture that all the high-order terms are commutator
terms. Because the commutator terms can be treated as the covariant derivative
terms, these high-order terms do not contribute to the multiple D2-brane
effective action. Inspired by the derivation of a single D2-brane from a
M2-brane, we consider the curved M2-branes and introduce an auxiliary field.
Integrating out the auxiliary field, we indeed obtain the correct high-order
F^4 terms in the D2-brane effective action from the BLG theory and the ABJM
theory with SU(2)\times SU(2) gauge symmetry, but we can not obtain the correct
high-order F^4 terms from the ABJM theory with U(N)\times U(N) and SU(N)\times
SU(N) gauge symmetries for N > 2. We also briefly comment on the (gauged) BF
membrane theory.


  The first measurements of the W and Z cross sections in proton-proton
collisions at sqrt{s} = 7 TeV using the ATLAS detector at the Large Hadron
Collider (LHC) have been completed. Cross sections in the electron and muon
channels, as well as the combined cross section, are presented for both the W
and the Z boson. The charge asymmetry of W production as a function of the
psuedorapidity of the lepton has also been measured.


  The phenomenon of explosive synchronization, which originates from
hypersensitivity to small perturbation caused by some form of frustration
prevailed in various physical and biological systems, has been shown to lead
events of cascading failure of the power grid to chronic pain or epileptic
seizure in the brain. Furthermore, networks provide a powerful model to
understand and predict the properties of a diverse range of real-world complex
systems. Recently, a multilayer network has been realized as a better suited
framework for the representation of complex systems having multiple types of
interactions among the same set of constituents. This article shows that by
tuning the properties of one layer (network) of a multilayer network, one can
regulate the dynamical behavior of another layer (network). By taking an
example of a multiplex network comprising two different types of networked
Kuramoto oscillators representing two different layers, this article attempts
to provide a glimpse of opportunities and emerging phenomena multiplexing can
induce which is otherwise not possible for a network in isolation. Here we
consider explosive synchronization to demonstrate the potential of multilayer
networks framework. To the end, we discuss several possible extensions of the
model considered here by incorporating real-world properties.


  Every compact oriented Riemann surface with a finite group of self
homeomorphisms can be embedded conformally in Euclidean three space so that the
image group acts conformally. Here we establish necessary and sufficient
conditions on the fixed point structure as given by Harvey's surface kernel map
for the image group to lift to the three sphere. This builds upon topological
work of Ni, Wang and Wang. It gives rise to a discussion of how terms emerge
and are used for equivalent phenomena that are described in the language of
different fields.


  We consider the case, in QCD, of a single jet propagating within a strongly
interacting fluid, of finite extent. Interactions lead to the appearance of a
source of energy-momentum within the fluid. The remnant jet that escapes the
container is analyzed along with portions of the medium excited by the jet. We
study the effect of a static versus a semi-realistic expanding medium, with
jets traveling inward versus outward. We consider the medium response via
recoils in partonic scatterings based on a weakly-coupled description and its
combination with hydrodynamical medium response based on a strongly-coupled
description, followed by incorporation into a jet. The effects of these limits
on the reconstructed energy, momentum and mass of the jet, as a function of the
angle away from the original parton direction are studied. It is demonstrated
that different flow velocity configurations in the medium produce considerable
differences in jet observables. This work highlights the importance of accurate
dynamical modeling of the soft medium as a foundation on which to calculate jet
modification, and casts skepticism on results obtained without such modeling.