  The law introduced by Karlsson in spectroscopy of low-redshift quasars
involves the Lyman spectrum of hydrogen atoms. Thus, it appears necessary to
study the concepts introduced by a standard spectroscopy of quasars, studied
here, with those deducted from $\Lambda$-CDM.A visible absorption of a sharp
and saturated spectral line in a gas requires a long path without perturbations
as collisions or cosmological redshift. Spectra of absorbed, saturated lines of
quasars obeying Karlsson's law mainly result from interactions of natural,
thermal light radiated by quasar with relatively cold, low presure atomic
hydrogen. These lines are produced by three processes: a) A conventional
absorption in a relatively cold gas produces a set of lines; b) These lines are
multiplied by absorption after fundamental 3K or 4K redshifts, where K is
Karlsson's constant: Spectra show that redshifts 3K (or 4K) exactly bring
absorbed Lyman beta (or gamma) line on Lyman alpha: redshift almost disappears,
and gas lines are intensely absorbed in the absence of alpha absorption; c)
Redshifts occur in regions where light at alpha frequency is poorly absorbed
due to permanent redshift, except when excitation of hydrogen to 2P level is
sufficient for a superradiant flash emission at alpha frequency. This causes an
intense absorption of high radiance rays from quasar, so the absorption of a
line at current Lyman alpha frequency. Lightning and pumping produce relaxation
oscillations that write many absorption lines. Redshifts by H atoms in 2P
levels are due to parametric interactions composed of Impulsive Stimulated
Raman Scatterings (ISRS): excited hydrogen atoms catalyze energy exchanges
between observed ray and background cold thermal radiation, in agreement with
thermodynamics. Description of Universe becomes much simpler, but less
marvelous.


  Linear-optics gates, the enabling tool of photonic quantum information
processing, depend on indistinguishable photons, as they harness quantum
interference to achieve nonlinear operations. Traditionally, meeting this
criterion involves generating pure identical photons, a task that remains a
significant challenge in the field. Yet, the required indistinguishability is
linked to the spatial exchange symmetry of the multiphoton wavefunction and
does not strictly necessitate identical photons. Here, we show that the ideal
operation of two-photon gates, particularly fusion gates, can be recovered from
distinguishable photons by ensuring the exchange symmetry of the input photonic
state. To this end, we introduce a temporal quantum eraser between a pair of
modally-impure single-photon sources, which heralds the symmetry of the
generated two-photon state. We demonstrate this mechanism in two relevant
platforms: parametric photon pair generation and single-photon extraction by a
single quantum emitter. The ability to lift the requirement for identical
photons bears considerable potential in linear-optics quantum information
processing.


  Several aspects of the time-dependent Schrodinger equation are discussed in
the context of Quantum Information Theory.


  The state-of-the-art dimensionality reduction approaches largely rely on
complicated optimization procedures. On the other hand, closed-form approaches
requiring merely eigen-decomposition do not have enough sophistication and
nonlinearity. In this paper, we propose a novel nonlinear dimensionality
reduction method -- Inverse Kernel Decomposition (IKD) -- based on an
eigen-decomposition of the sample covariance matrix of data. The method is
inspired by Gaussian process latent variable models (GPLVMs) and has comparable
performance with GPLVMs. To deal with very noisy data with weak correlations,
we propose two solutions -- blockwise and geodesic -- to make use of locally
correlated data points and provide better and numerically more stable latent
estimations. We use synthetic datasets and four real-world datasets to show
that IKD is a better dimensionality reduction method than other
eigen-decomposition-based methods, and achieves comparable performance against
optimization-based methods with faster running speeds. Open-source IKD
implementation in Python can be accessed at this
\url{https://github.com/JerrySoybean/ikd}.


  Rarita-Schwinger (RS) quantum free field is reexamined in the context of
deformation quantization. It is found out that the subsidiary condition does
not introduce any change either in the Wigner function or in other aspects of
the deformation quantization formalism, in relation to the Dirac field case.
This happens because the vector structure of the RS field imposes constraints
on the space of wave function solutions and not on the operator structure. The
RS propagator was also calculated within this formalism.


  The Terahertz band, spanning from 0.1~THz to 10~THz, is envisioned as a key
technology to realize ultra-high data rates in the 6G and beyond mobile
communication systems, due to its abundant bandwidth resource. However, to
realize THz communications, one substantial step is to fully understand the THz
channels, which relies on extensive channel measurements. In this paper, using
a correlation-based time domain channel sounder, measurement campaigns are
conducted in a laboratory at 140~GHz and 220~GHz. In the data post-processing
procedures, the time drift of clock signals is corrected using a linear
interpolation/extrapolation method. Based on the measured results, the main
objects that provide significant once-scattering clusters are found, based on
which the scattering losses are calculated and analyzed. Furthermore, the
channel characteristics, including path loss, shadow fading, K-factor, etc. are
calculated and compared to 3GPP standard values. The propagation analysis and
channel characteristics are helpful to study channel modeling and guide system
design for THz communications.


  High luminosity in DAFNE needs very high electron and positron currents
stored. A full energy (510 MeV) injection system composed by a full energy
electron and positron linac and an accumulator-damping ring is presently used.
The electron and positron beams, alternatively accelerated by the linac, are
injected and stacked in the accumulator with high efficiency thanks to its
large acceptance and short damping time. The damped beams are extracted and
transferred to the main ring through a long transfer line that has been built
inside already existing buildings. The refill time of the collider is limited
by the transfer line set-up change between the two different beams modes. In
this paper a transfer line modification is proposed in order to reduce the
switch time. A possible injection scheme for the main rings is also described.


  Co-part segmentation is an important problem in computer vision for its rich
applications. We propose an unsupervised learning approach for co-part
segmentation from images. For the training stage, we leverage motion
information embedded in videos and explicitly extract latent representations to
segment meaningful object parts. More importantly, we introduce a dual
procedure of part-assembly to form a closed loop with part-segmentation,
enabling an effective self-supervision. We demonstrate the effectiveness of our
approach with a host of extensive experiments, ranging from human bodies,
hands, quadruped, and robot arms. We show that our approach can achieve
meaningful and compact part segmentation, outperforming state-of-the-art
approaches on diverse benchmarks.


  The forward-backward splitting method (FBS) for minimizing a nonsmooth
composite function can be interpreted as a (variable-metric) gradient method
over a continuously differentiable function which we call forward-backward
envelope (FBE). This allows to extend algorithms for smooth unconstrained
optimization and apply them to nonsmooth (possibly constrained) problems. Since
the FBE and its gradient can be computed by simply evaluating forward-backward
steps, the resulting methods rely on the very same black-box oracle as FBS. We
propose an algorithmic scheme that enjoys the same global convergence
properties of FBS when the problem is convex, or when the objective function
possesses the Kurdyka-{\L}ojasiewicz property at its critical points. Moreover,
when using quasi-Newton directions the proposed method achieves superlinear
convergence provided that usual second-order sufficiency conditions on the FBE
hold at the limit point of the generated sequence. Such conditions translate
into milder requirements on the original function involving generalized
second-order differentiability. We show that BFGS fits our framework and that
the limited-memory variant L-BFGS is well suited for large-scale problems,
greatly outperforming FBS or its accelerated version in practice. The analysis
of superlinear convergence is based on an extension of the Dennis and Mor\'e
theorem for the proposed algorithmic scheme.


  We describe a method to probe the spectral fluctuations of a transition over
broad ranges of frequencies and timescales with the high spectral resolution of
Fourier spectroscopy, and a temporal resolution as high as the excited state
lifetime, even in the limit of very low photocounting rates. The method derives
from a simple relation between the fluorescence spectral dynamics of a single
radiating dipole and its fluorescence intensity correlations at the outputs of
a continuously scanning Michelson interferometer. These findings define an
approach to investigate the fast fluorescence spectral dynamics of single
molecules and other faint light sources beyond the time-resolution capabilities
of standard spectroscopy experiments.


  Controlling thermal transport in insulators and semiconductors is crucial for
many technological fields such as thermoelectrics and thermal insulation, for
which a low thermal conductivity ($\kappa$) is desirable. A major obstacle for
realizing low $\kappa$ materials is Rayleigh's law, which implies that acoustic
phonons, which carry most of the heat, are insensitive to scattering by point
defects at low energy. We demonstrate, with large scale simulations on tens of
millions of atoms, that isotropic long-range spatial correlations in the defect
distribution can dramatically reduce phonon lifetimes of important
low-frequency heat-carrying modes, leading to a large reduction of $\kappa$ --
potentially an order of magnitude at room temperature. We propose a general and
quantitative framework for controlling thermal transport in complex functional
materials through structural spatial correlations, and we establish the optimal
functional form of spatial correlations that minimize $\kappa$. We end by
briefly discussing experimental realizations of various correlated structures.


  The events of recent years have highlighted the importance of telemedicine
solutions which could potentially allow remote treatment and diagnosis.
Relatedly, Computational Paralinguistics, a unique subfield of Speech
Processing, aims to extract information about the speaker and form an important
part of telemedicine applications. In this work, we focus on two paralinguistic
problems: mask detection and breathing state prediction. Solutions developed
for these tasks could be invaluable and have the potential to help monitor and
limit the spread of a virus like COVID-19. The current state-of-the-art methods
proposed for these tasks are ensembles based on deep neural networks like
ResNets in conjunction with feature engineering. Although these ensembles can
achieve high accuracy, they also have a large footprint and require substantial
computational power reducing portability to devices with limited resources.
These drawbacks also mean that the previously proposed solutions are infeasible
to be used in a telemedicine system due to their size and speed. On the other
hand, employing lighter feature-engineered systems can be laborious and add
further complexity making them difficult to create a deployable system quickly.
This work proposes an ensemble-based automatic feature selection method to
enable the development of fast and memory-efficient systems. In particular, we
propose an output-gradient-based method to discover essential features using
large, well-performing ensembles before training a smaller one. In our
experiments, we observed considerable (25-32%) reductions in inference times
using neural network ensembles based on output-gradient-based features. Our
method offers a simple way to increase the speed of the system and enable
real-time usage while maintaining competitive results with larger-footprint
ensemble using all spectral features.


  We show that there is a complete connected 2-dimensional Riemannian manifold
with discontinuous isoperimetric profile, answering a question of Nardulli and
Pansu.


  Substantial evidence in the last few decades suggests that outflows from
supermassive black holes (SMBH) may play a significant role in the evolution of
galaxies.Large-scale outflows known as warm absorbers (WA) and fast disk winds
known as ultra-fast outflows (UFO) are commonly found in the spectra of many
Seyfert galaxies and quasars, and a correlation has been suggested between
them. Recent detections of low ionization and low column density outflows, but
with a high velocity comparable to UFOs, challenge such initial possible
correlations. Observations of UFOs in AGN indicate that their energetics may be
enough to have an impact on the interstellar medium (ISM). However,
observational evidence of the interaction between the inner high-ionization
outflow and the ISM is still missing. We present here the spectral analysis of
12 XMM-Newton/EPIC archival observations of the quasar PG 1114+445, aimed at
studying the complex outflowing nature of its absorbers. Our analysis revealed
the presence of three absorbing structures. We find a WA with velocity
$v\sim530$ km s$^{-1}$, ionization $\log\xi/\text{erg cm s}^{-1}\sim0.35,$ and
column density $\log N_\text{H}/\text{cm}^{-2}\sim22$, and a UFO with
$v_\text{out}\sim0.145c$, $\log\xi/\text{erg cm s}^{-1}\sim4$, and $\log
N_\text{H}/\text{cm}^{-2}\sim23$. We also find an additional absorber in the
soft X-rays ($E<2$ keV) with velocity comparable to that of the UFO
($v_\text{out}\sim0.120c$), but ionization ($\log\xi/\text{erg cm
s}^{-1}\sim0.5$) and column density ($\log N_\text{H}/\text{cm}^{-2}\sim21.5$)
comparable with those of the WA. The ionization, velocity, and variability of
the three absorbers indicate an origin in a multiphase and multiscale outflow,
consistent with entrainment of the clumpy ISM by an inner UFO moving at
$\sim15\%$ the speed of light, producing an entrained ultra-fast outflow
(E-UFO).


  Variational quantum algorithms (VQAs) are promising methods that leverage
noisy quantum computers and classical computing techniques for practical
applications. In VQAs, the classical optimizers such as gradient-based
optimizers are utilized to adjust the parameters of the quantum circuit so that
the objective function is minimized. However, they often suffer from the
so-called vanishing gradient or barren plateau issue. On the other hand, the
normalized gradient descent (NGD) method, which employs the normalized gradient
vector to update the parameters, has been successfully utilized in several
optimization problems. Here, we study the performance of the NGD methods in the
optimization of VQAs for the first time. Our goal is two-fold. The first is to
examine the effectiveness of NGD and its variants for overcoming the vanishing
gradient problems. The second is to propose a new NGD that can attain the
faster convergence than the ordinary NGD. We performed numerical simulations of
these gradient-based optimizers in the context of quantum chemistry where VQAs
are used to find the ground state of a given Hamiltonian. The results show the
effective convergence property of the NGD methods in VQAs, compared to the
relevant optimizers without normalization. Moreover, we make use of some
normalized gradient vectors at the past iteration steps to propose the novel
historical NGD that has a theoretical guarantee to accelerate the convergence
speed, which is observed in the numerical experiments as well.


  The problem of computation of parameters of m-ary channel is considered. It
is demonstrated that although the problem is ill-posed, it is possible turning
of the parameters of the system and transform the problem to well-posed one.


  We use a T-duality invariant action to investigate the behaviour of a string
in non-geometric backgrounds, where there is a non-trivial global $O(D,D)$
patching or monodromy. This action leads to a set of Dirac brackets describing
the dynamics of the doubled string, with these brackets determined only by the
monodromy. This allows for a simple derivation of non-commutativity and
non-associativity in backgrounds which are (even locally) non-geometric. We
focus here on the example of the three-torus with H-flux, finding
non-commutativity but not non-associativity, and also comment on the relation
to the exotic $5^2_2$ brane, which shares the same monodromy.


  Accurate and quick identification of high-impedance faults is critical for
the reliable operation of distribution systems. Unlike other faults in power
grids, HIFs are very difficult to detect by conventional overcurrent relays due
to the low fault current. Although HIFs can be affected by various factors, the
voltage current characteristics can substantially imply how the system responds
to the disturbance and thus provides opportunities to effectively localize
HIFs. In this work, we propose a data-driven approach for the identification of
HIF events. To tackle the nonlinearity of the voltage current trajectory,
first, we formulate optimization problems to approximate the trajectory with
piecewise functions. Then we collect the function features of all segments as
inputs and use the support vector machine approach to efficiently identify HIFs
at different locations. Numerical studies on the IEEE 123-node test feeder
demonstrate the validity and accuracy of the proposed approach for real-time
HIF identification.


  Recently, diffusion models were applied to a wide range of image analysis
tasks. We build on a method for image-to-image translation using denoising
diffusion implicit models and include a regression problem and a segmentation
problem for guiding the image generation to the desired output. The main
advantage of our approach is that the guidance during the denoising process is
done by an external gradient. Consequently, the diffusion model does not need
to be retrained for the different tasks on the same dataset. We apply our
method to simulate the aging process on facial photos using a regression task,
as well as on a brain magnetic resonance (MR) imaging dataset for the
simulation of brain tumor growth. Furthermore, we use a segmentation model to
inpaint tumors at the desired location in healthy slices of brain MR images. We
achieve convincing results for all problems.


  Despite of many experimental and theoretical efforts, the pairing symmetry of
superconductivity in Sr$_2$RuO$_4$ remains undecided. The accidentally
degenerate $d_{x^2-y^2}+ig$ is consistent with most current experiments and
seems to be one of the most probable candidates, but we still lack a
satisfactory theoretical mechanism for its appearance. Here we construct a
phenomenological model combining realistic electronic band structures and all
symmetry-allowed multipole fluctuations as potential pairing glues, and make a
systematic survey of major pairing states within the Eliashberg framework. Our
calculations show that $d_{x^2-y^2}+ig$ can arise naturally from the interplay
of antiferromagnetic, ferromagnetic, and electric multipole fluctuations whose
coexistence is manifested in previous experiments and calculations. Our work
provides a physically reasonable basis supporting the possibility of
$d_{x^2-y^2}+ig$ pairing in superconducting Sr$_2$RuO$_4$.


  The recent observations of Be and B in metal poor stars has led to a
reassessment of the origin of the light elements in the early Galaxy. At low it
is metallicity ([O/H] < -1.75), it is necessary to introduce a production
mechanism which is independent of the interstellar metallicity (primary). At
higher metallicities, existing data might indicate that secondary production is
dominant. In this paper, we focus on the secondary process, related to the
standard Galactic cosmic rays, and we examine the cosmic ray energy
requirements for both present and past epochs. We find the power input to
maintain the present-day Galactic cosmic ray flux is about 1.5e41 erg/s = 5e50
erg/century. This implies that, if supernovae are the sites of cosmic ray
acceleration, the fraction of explosion energy going to accelerated particles
is about 30%, a value which we obtain consistently both from considering the
present cosmic ray flux and confinement and from the present 9Be and 6Li
abundances. Using the abundances of 9Be (and 6Li) in metal-poor halo stars, we
extend the analysis to show the effect of the interstellar gas mass on the
standard galactic cosmic ray energetic constraints on models of Li, Be, and B
evolution. The efficiency of the beryllium production per erg may be enhanced
in the past by a factor of about 10; thus the energetic requirement by itself
cannot be used to rule out a secondary origin of light elements. Only a clear
and undisputable observational determination of the O-Fe relation in the halo
will discriminate between the two processes. (abridged)


  We prove the logarithmic divergence of equivariant analytic torsion for
one-parameter degenerations of projective algebraic manifolds, when the
coefficient vector bundle is given by a Nakano semi-positive vector bundle
twisted by the relative canonical bundle.


  We show that based on the general solution, given by Corrigan, Olive, Fairlie
and Nuyts, in the region outside the monopole's core; the equations of motion
in the Higgs vacuum (i.e. outside the monopole's core) will not allow
asymptotically non-singular extended non-trivial non-Dyonic (including, also,
all static) solutions of the 't Hooft-Polyakov monopole. In other words, unless
the monopole's magnetic charge is shielded (by some mechanism), the Dirac
string is inevitable asymptotically, in the region outside the monopole's core,
for all non-Dyonic solutions that are admissible by the equations of motion.
That we show that the non-dyonic solutions (based on Corrigan et al) will
include all "admissible" static solutions and their gauge transform might be
interpreted as that all admissible dyonic solutions (based on Corrigan et al)
are composite solutions.


  A review will be presented on the algebraic extension of the standard Theory
of Relativity (GR) to the pseudo-complex formulation (pc-GR). The pc-GR
predicts the existence of a dark energy outside and inside the mass
distribution, corresponding to a modification of the GR- metric. The structure
of the emission profile of an accretion disc changes also inside a star.
Discussed are the consequences of the dark energy for cosmological models,
permitting different outcomes on the evolution of the universe.


  We give a new proof of the generalized Minkowski identities relating the
higher degree mean curvatures of orientable closed hypersurfaces immersed in a
given constant sectional curvature manifold. Our methods rely on a fundamental
differential system of Riemannian geometry introduced by the author. We develop
the notion of position vector field, which lies at the core of the Minkowski
identities.


  growclusters for R is a package that estimates a partition structure for
multivariate data. It does this by implementing a hierarchical version of
k-means clustering that accounts for possible known dependencies in a
collection of datasets, where each set draws its cluster means from a single,
global partition. Each component data set in the collection corresponds to a
known group in the data. This paper focuses on R Shiny applications that
implement the clustering methodology and simulate data sets with known group
structures. These Shiny applications implement novel ways of visualizing the
results of the clustering. These visualizations include scatterplots of
individual data sets in the context of the entire collection and cluster
distributions versus component (or sub-domain) datasets. Data obtained from a
collection of 2000-2013 articles from the Bureau of Labor Statistics (BLS)
Monthly Labor Review (MLR) will be used to illustrate the R-Shiny applications.
Here, the known grouping in the collection is the year of publication.


  We give a general description of the interplay that can occur between local
and global anomalies, in terms of (co)bordism. Mathematically, such an
interplay is encoded in the non-canonical splitting of short exact sequences
known to classify invertible field theories. We study various examples of the
phenomenon in 2, 4, and 6 dimensions. We also describe how this understanding
of anomaly interplay provides a rigorous bordism-based version of an old method
for calculating global anomalies (starting from local anomalies in a related
theory) due to Elitzur and Nair.


  Software flaw detection using multimodal deep learning models has been
demonstrated as a very competitive approach on benchmark problems. In this
work, we demonstrate that even better performance can be achieved using neural
architecture search (NAS) combined with multimodal learning models. We adapt a
NAS framework aimed at investigating image classification to the problem of
software flaw detection and demonstrate improved results on the Juliet Test
Suite, a popular benchmarking data set for measuring performance of machine
learning models in this problem domain.


  We consider the effect of a momentum kick on the ground state of a
non-interacting two-dimensional Fermi gas subject to Rashba spin-orbit
coupling. Although the total momentum is a constant of motion, the gas does not
obey the rules of Galilean relativity. Upon imprinting a small overall velocity
to the non-interacting gas, we find that the Fermi sea is deformed in a
non-trivial way. We also consider a weakly repulsive Fermi gas, and find, from
its Hartree shift, that the total ground state of the system may change into a
deformed, finite momentum ground state as the repulsion is increased beyond a
critical value, without the need of any external Zeeman fields. We also discuss
possible experimental signatures of these effects.


  The process of massive star ($M\geq8~M_\odot$) formation is still poorly
understood. Observations of massive young stellar objects (MYSOs) are
challenging due to their rarity, short formation timescale, large distances,
and high circumstellar extinction. Here, we present the results of a
spectroscopic analysis of a population of MYSOs in the Large Magellanic Cloud
(LMC). We took advantage of the spectral resolution and wavelength coverage of
X-shooter (300-2500 nm), mounted on the European Southern Observatory Very
Large Telescope, to detect characteristic spectral features in a dozen MYSO
candidates near 30 Doradus, the largest starburst region in the Local Group
hosting the most massive stars known. The X-shooter spectra are strongly
contaminated by nebular emission. We used a scaling method to subtract the
nebular contamination from our objects. We detect H$\alpha,\beta$, [O I] 630.0
nm, Ca II infrared triplet, [Fe II] 1643.5 nm, fluorescent Fe II 1687.8 nm,
H$_2$ 2121.8 nm, Br$\gamma$, and CO bandhead emission in the spectra of
multiple candidates. This leads to the spectroscopic confirmation of 10
candidates as bona fide MYSOs. We compare our observations with photometric
observations from the literature and find all MYSOs to have a strong
near-infrared excess. We compute lower limits to the brightness and luminosity
of the MYSO candidates, confirming the near-infrared excess and the massive
nature of the objects. No clear correlation is seen between the Br$\gamma$
luminosity and metallicity. Combining our sample with other LMC samples results
in a combined detection rate of disk features such as fluorescent Fe II and CO
bandheads which is consistent with the Galactic rate (40\%). Most of our MYSOs
show outflow features.


  The academic evaluation of the publication record of researchers is relevant
for identifying talented candidates for promotion and funding. A key tool for
this is the use of the indexes provided by Web of Science and SCOPUS, costly
databases that sometimes exceed the possibilities of academic institutions in
many parts of the world. We show here how the data in one of the bases can be
used to infer the main index of the other one. Methods of data analysis used in
Machine Learning allow us to select just a few of the hundreds of variables in
a database, which later are used in a panel regression, yielding a good
approximation to the main index in the other database. Since the information of
SCOPUS can be freely scraped from the Web, this approach allows to infer for
free the Impact Factor of publications, the main index used in research
assessments around the globe.


  We study several different aspects of the energy equipartition principle for
water waves. We prove a virial identity that implies that the potential energy
is equal, on average, to a modified version of the kinetic energy. This is an
exact identity for the complete nonlinear water wave problem, which is valid
for arbitrary solutions. As an application, we obtain non-perturbative results
justifying the formation of bubbles for the free-surface Rayleigh-Taylor
instability, for any non-zero initial data. We also derive exact virial
identities involving higher order energies. The fact that such exact identities
are valid for nonlinear equations is new and general: as explained in a
companion paper, similar identities can be derived for many other nonlinear
equations. We illustrate this result by an explicit computation for standing
waves. As side results, we prove trace inequalities for harmonic functions in
Lipschitz domains which are optimal with respect to the dependence in the
Lipschitz norm of the graph.


  We consider the renormalization group(RG) improved inflaton potential in
unitarized Higgs inflation where the original Higgs inflation is unitarized by
the addition of a real singlet scalar of sigma-model type. The sigma field
coupling to the Higgs, which is introduced to reproduce a large non-minimal
coupling of the Higgs below the sigma scalar threshold, also improves the
Standard Model vacuum stability due to the RG running. Furthermore, the same
sigma field coupling determines the reheating temperature or the number of
efoldings. Considering the uncertainties in the number of efoldings in the
model, we show that the loop-corrected spectral index and tensor-to-scalar
ratio are consistent with nine-year WMAP and new Planck data within 1sigma.


  The optical light-curves of GRB afterglows 990123 and 021211 exhibit a steep
decay at 100--600 seconds after the burst, the decay becoming slower after
about 10 minutes. We investigate two scenarios for the fast decaying early
optical emission of these GRB afterglows. In the reverse-forward shock
scenario, this emission arises in the reverse shock crossing the GRB ejecta,
the mitigation of the light-curve decay occurring when the forward shock
emission overtakes that from the reverse shock. Both a homogeneous and
wind-like circumburst medium are considered. In the wind-bubble scenario, the
steeply decaying, early optical emission arises from the forward shock
interacting with a 1/r^2 bubble, with a negligible contribution from the
reverse shock, the slower decay starting when the blast wave reaches the bubble
termination shock and enters a homogeneous region of the circumburst medium.
  We determine the shock microphysical parameters, ejecta kinetic energy, and
circumburst density which accommodate the radio and optical measurements of the
GRB afterglows 990123 and 021211. We find that, for a homogeneous medium, the
radio and optical emissions of the afterglow 990123 can be accommodated by the
reverse-forward shock scenario if the microphysical parameters behind the two
shocks differ substantially. A wind-like circumburst medium also allows the
reverse-forward shocks scenario to account for the radio and optical properties
of the afterglows 990123 and 021211, but the required wind densities are at
least 10 times smaller than those of Galactic Wolf-Rayet stars. The wind-bubble
scenario requires a variation of the microphysical parameters when the
afterglow fireball reaches the wind termination shock, which seems a contrived
feature.


  Proteins are macromolecules that perform essential functions in all living
organisms. Designing novel proteins with specific structures and desired
functions has been a long-standing challenge in the field of bioengineering.
Existing approaches generate both protein sequence and structure using either
autoregressive models or diffusion models, both of which suffer from high
inference costs. In this paper, we propose a new approach capable of protein
sequence and structure co-design, which iteratively translates both protein
sequence and structure into the desired state from random initialization, based
on context features given a priori. Our model consists of a trigonometry-aware
encoder that reasons geometrical constraints and interactions from context
features, and a roto-translation equivariant decoder that translates protein
sequence and structure interdependently. Notably, all protein amino acids are
updated in one shot in each translation step, which significantly accelerates
the inference process. Experimental results across multiple tasks show that our
model outperforms previous state-of-the-art baselines by a large margin, and is
able to design proteins of high fidelity as regards both sequence and
structure, with running time orders of magnitude less than sampling-based
methods.


  Multilingual translation supports multiple translation directions by
projecting all languages in a shared space, but the translation quality is
undermined by the difference between languages in the text-only modality,
especially when the number of languages is large. To bridge this gap, we
introduce visual context as the universal language-independent representation
to facilitate multilingual translation. In this paper, we propose a framework
to leverage the multimodal prompt to guide the Multimodal Multilingual neural
Machine Translation (m3P), which aligns the representations of different
languages with the same meaning and generates the conditional vision-language
memory for translation. We construct a multilingual multimodal instruction
dataset (InstrMulti102) to support 102 languages. Our method aims to minimize
the representation distance of different languages by regarding the image as a
central language. Experimental results show that m3P outperforms previous
text-only baselines and multilingual multimodal methods by a large margin.
Furthermore, the probing experiments validate the effectiveness of our method
in enhancing translation under the low-resource and massively multilingual
scenario.


  The goal of the present paper is to study the viscoelastic wave equation with
the time delay \[ |u_t|^\rho u_{tt}-\Delta u-\Delta u_{tt}+\int_0^tg(t-s)\Delta
u(s)ds+\mu_1u_t(x,t)+\mu_2 u_t(x,t-\tau)=b|u|^{p-2}u\] under initial boundary
value conditions, where $\rho,~b,~\mu_1$ are positive constants, $\mu_2$ is a
real number, $\tau>0$ represents the time delay. By using the multiplier method
together with some properties of the convex functions, the explicit and general
stability results of energy are proved under the general assumption on the
relaxation function $g$. This work generalizes and improves earlier results on
the stability of the viscoelastic equations with the time delay in the
literature.


  The Quantum Approximate Optimization Algorithm (QAOA) is an algorithm
originally proposed to find approximate solutions to Combinatorial Optimization
problems on quantum computers. However, the algorithm has also attracted
interest for sampling purposes since it was theoretically demonstrated under
reasonable complexity assumptions that one layer of the algorithm already
engineers a probability distribution beyond what can be simulated by classical
computers. In this regard, a recent study has shown as well that, in universal
Ising models, this global probability distribution resembles pure but
thermal-like distributions at a temperature that depends on internal
correlations of the spin model. In this work, through an interferometric
interpretation of the algorithm, we extend the theoretical derivation of the
amplitudes of the eigenstates, and the Boltzmann distributions generated by
single-layer QAOA. We also review the implications that this behavior has from
both a practical and fundamental perspective.


  The degenerate Whittaker vector of the superconformal algebra can be
represented in terms of Jack superpolynomials. However, in this representation
the norm of the Whittaker vector involves a scalar product with respect to
which the Jack superpolynomials are not orthogonal. In this note, we point out
that this defect can be cured at c=3/2 by means of a trick specific to the
supersymmetric case. At c=3/2, we thus end up with a closed-form expression for
the norm of the degenerate super-Whittaker vector. Granting the super-version
of the AGT conjecture, this closed-form expression should be equal to the
Z_2-symmetric SU(2) pure-gauge instanton partition function -- the
corresponding equality taking the form of a rather nontrivial combinatorial
identity.


  We propose and study a holographic dual of the type IIB superstring theory of
AdS_5 x S^5 in terms of the N=4 superconformal Yang-Mills theory on dS_4. We
use the bulk to boundary formalism to evaluate the boundary correlation
functions and verify that it agrees with the expected result in dS conformal
field theory. The gauge theory is expected to be UV finite and enjoy exact
SL(2,Z) strong-weak duality. As the string theory Green-Schwarz sigma model
carries an infinite number of classically conserved charges, it also suggest
that the superconformal Yang-Mills theory is integrable and deserves further
studies.


  Two topics of high current interest in the field of unconventional
superconductivity are non-centrosymmetric superconductors and multiband
superconductivity. Half-Heusler superconductors such as YPtBi exemplify both.
In this paper, we study bulk and surface states in nodal superconducting phases
of the half-Heusler compounds, belonging to the $A_1$ ($s+p$-like) and $T_2$
($k_zk_x+ik_zk_y$-like) irreducible representations of the point group. These
two phases preserve and break time-reversal symmetry, respectively. For the
$A_1$ case, we find that flat surface bands persist in the multiband system. In
addition, the system has dispersive surface bands with zero-energy crossings
forming Fermi arcs, which are protected by mirror symmetries. For the $T_2$
case, there is an interesting coexistence of point and line nodes, known from
the single-band case, with Bogoliubov Fermi surfaces (two-dimensional nodes).
There are no flat-band surface states, as expected, but dispersive surface
bands with Fermi arcs exist. If these arcs do not lie in high-symmetry planes,
they are split by the antisymmetric spin-orbit coupling so that their number is
doubled compared to the inversion-symmetric case.


  A broad class of optimization problems can be cast in composite form, that
is, considering the minimization of the composition of a lower semicontinuous
function with a differentiable mapping. This paper investigates the versatile
template of composite optimization without any convexity assumptions. First-
and second-order optimality conditions are discussed. We highlight the
difficulties that stem from the lack of convexity when dealing with necessary
conditions in a Lagrangian framework and when considering error bounds.
Building upon these characterizations, a local convergence analysis is
delineated for a recently developed augmented Lagrangian method, deriving rates
of convergence in the fully nonconvex setting.


  It is shown that accounting for the relativistic structure of the deuteron
allows to explain the ratio of the Drell-Yan pair production cross-section at
the low Bjorken $x$ off the deuteron and the proton. Thus, the sea quark
distributions in the nucleon should be studied with accounting for the effects
of the relativistic structure of the deuteron. The suggested approach reduces
theoretical uncertainty in extracting the ratio $\bar u/\bar d$ from the data
and it is important for the clarification of the nature of the sea quark
asymmetry in the nucleon.


  We review the science case for studying CMB polarization. We then discuss the
main issues related to the analysis of forth-coming polarized CMB data, such as
those expected from balloon-borne (e.g. BOOMERanG) and satellite (e.g. Planck)
experiments.


  Propensity score (PS) matching to estimate causal effects of exposure is
biased when unmeasured spatial confounding exists. Some exposures are
continuous yet dependent on a binary variable (e.g., level of a contaminant
(continuous) within a specified radius from residence (binary)). Further,
unmeasured spatial confounding may vary by spatial patterns for both continuous
and binary attributes of exposure. We propose a new generalized propensity
score (GPS) matching method for such settings, referred to as conditional GPS
(CGPS)-based spatial matching (CGPSsm). A motivating example is to investigate
the association between proximity to refineries with high petroleum production
and refining (PPR) and stroke prevalence in the southeastern United States.
CGPSsm matches exposed observational units (e.g., exposed participants) to
unexposed units by their spatial proximity and GPS integrated with spatial
information. GPS is estimated by separately estimating PS for the binary status
(exposed vs. unexposed) and CGPS on the binary status. CGPSsm maintains the
salient benefits of PS matching and spatial analysis: straightforward
assessments of covariate balance and adjustment for unmeasured spatial
confounding. Simulations showed that CGPSsm can adjust for unmeasured spatial
confounding. Using our example, we found positive association between PPR and
stroke prevalence. Our R package, CGPSspatialmatch, has been made publicly
available.


  We establish two-weight norm inequalities for singular integral operators
defined on spaces of homogeneous type. We do so first when the weights satisfy
a double bump condition and then when the weights satisfy separated logarithmic
bump conditions. Our results generalize recent work on the Euclidean case, but
our proofs are simpler even in this setting. The other interesting feature of
our approach is that we are able to prove the separated bump results (which
always imply the corresponding double bump results) as a consequence of the
double bump theorem.


  We numerically investigate the effect of feedback from the ionizing radiation
heating from massive stars on the evolution of giant molecular clouds (GMCs)
and their star formation efficiency (SFE). We find that the star-forming
regions within the GMCs are invariably formed by gravitational contraction.
After an initial period of contraction, the collapsing clouds begin forming
stars, whose feedback evaporates part of the clouds' mass, opposing the
continuing accretion from the infalling gas. The competition of accretion
against dense gas consumption by star formation (SF) and evaporation by the
feedback, regulates the clouds' mass and energy balance, as well as their SFE.
We find that, in the presence of feedback, the clouds attain levels of the SFE
that are consistent at all times with observational determinations for regions
of comparable SF rates (SFRs). However, we observe that the dense gas mass is
larger in general in the presence of feedback, while the total (dense gas +
stars) is nearly insensitive to the presence of feedback, suggesting that the
total mass is determined by the accretion, while the feedback inhibits mainly
the conversion of dense gas to stars. The factor by which the SFE is reduced
upon the inclusion of feedback is a decreasing function of the cloud's mass,
for clouds of size ~ 10 pc. This naturally explains the larger observed SFEs of
massive-star forming regions. We also find that the clouds may attain a
pseudo-virialized state, with a value of the virial mass very similar to the
actual cloud mass. However, this state differs from true virialization in that
the clouds are the center of a large-scale collapse, continuously accreting
mass, rather than being equilibrium entities.


  The Receiver Operating Characteristic (ROC) curve and the Area Under the
Curve (AUC) of the ROC curve are widely used to compare the performance of
diagnostic and prognostic assays. The ROC curve has the advantage that it is
independent of disease prevalence. However, in this note we remind readers that
the performance of an assay upon translation to the clinic is critically
dependent upon that very same prevalence. Without an understanding of
prevalence in the test population, even robust bioassays with excellent ROC
characteristics may perform poorly in the clinic. Instead, simple plots of
candidate assay performance as a function of prevalence rate give a more
realistic understanding of the likely real-world performance and a greater
understanding of the likely impact of variation in that prevalence on
translational performance in the clinic.


  When testing multiple hypotheses, a suitable error rate should be controlled
even in exploratory trials. Conventional methods to control the False Discovery
Rate (FDR) assume that all p-values are available at the time point of test
decision. In platform trials, however, treatment arms enter and leave the trial
at any time during its conduct. Therefore, the number of treatments and
hypothesis tests is not fixed in advance and hypotheses are not tested at once,
but sequentially. Recently, for such a setting the concept of online control of
the FDR was introduced. We investigate the LOND procedure to control the online
FDR in platform trials and propose an extension to allow for interim analyses
with the option of early stopping for efficacy or futility for individual
hypotheses. The power depends sensitively on the prior distribution of effect
sizes, e.g., whether true alternatives are uniformly distributed over time or
not. We consider the choice of design parameters for the LOND procedure to
maximize the overall power and compare the OBrien-Fleming group-sequential
design with the Pocock approach. Finally we investigate the impact on error
rates by including both concurrent and non-concurrent control data.


  SketchySGD improves upon existing stochastic gradient methods in machine
learning by using randomized low-rank approximations to the subsampled Hessian
and by introducing an automated stepsize that works well across a wide range of
convex machine learning problems. We show theoretically that SketchySGD with a
fixed stepsize converges linearly to a small ball around the optimum. Further,
in the ill-conditioned setting we show SketchySGD converges at a faster rate
than SGD for least-squares problems. We validate this improvement empirically
with ridge regression experiments on real data. Numerical experiments on both
ridge and logistic regression problems with dense and sparse data, show that
SketchySGD equipped with its default hyperparameters can achieve comparable or
better results than popular stochastic gradient methods, even when they have
been tuned to yield their best performance. In particular, SketchySGD is able
to solve an ill-conditioned logistic regression problem with a data matrix that
takes more than $840$GB RAM to store, while its competitors, even when tuned,
are unable to make any progress. SketchySGD's ability to work out-of-the box
with its default hyperparameters and excel on ill-conditioned problems is an
advantage over other stochastic gradient methods, most of which require careful
hyperparameter tuning (especially of the learning rate) to obtain good
performance and degrade in the presence of ill-conditioning.


  Monaural speech enhancement has been widely studied using real networks in
the time-frequency (TF) domain. However, the input and the target are naturally
complex-valued in the TF domain, a fully complex network is highly desirable
for effectively learning the feature representation and modelling the sequence
in the complex domain. Moreover, phase, an important factor for perceptual
quality of speech, has been proved learnable together with magnitude from noisy
speech using complex masking or complex spectral mapping. Many recent studies
focus on either complex masking or complex spectral mapping, ignoring their
performance boundaries. To address above issues, we propose a fully complex
dual-path dual-decoder conformer network (D2Former) using joint complex masking
and complex spectral mapping for monaural speech enhancement. In D2Former, we
extend the conformer network into the complex domain and form a dual-path
complex TF self-attention architecture for effectively modelling the
complex-valued TF sequence. We further boost the TF feature representation in
the encoder and the decoders using a dual-path learning structure by exploiting
complex dilated convolutions on time dependency and complex feedforward
sequential memory networks (CFSMN) for frequency recurrence. In addition, we
improve the performance boundaries of complex masking and complex spectral
mapping by combining the strengths of the two training targets into a
joint-learning framework. As a consequence, D2Former takes fully advantages of
the complex-valued operations, the dual-path processing, and the joint-training
targets. Compared to the previous models, D2Former achieves state-of-the-art
results on the VoiceBank+Demand benchmark with the smallest model size of 0.87M
parameters.


  We examine the conditions for superconformal invariance and the specific form
of the K\"ahler potential for a two-dimensional lagrangian model with $N=2$
supersymmetry and superpotential $gX^k$. Away from the superconformal point we
study the renormalization group flow induced by a particular class of K\"ahler
potentials. We find trajectories which, in the infrared, reach the fixed point
with a central charge whose value is that of the $N=2$, $A_{k-1}$ minimal
model.


  Artificial gauge fields are a unique way of manipulating the motional state
of cold atoms. Here we propose the use of artificial gauge fields -- obtained
e.g. via lattice shaking -- to perform primary noise thermometry of cold atoms
in optical lattices - not requiring any form of prior calibration. The proposed
thermometric scheme relies on fundamental fluctuation-dissipation relations,
connecting the global response to the variation of the applied gauge field and
the fluctuation of quantities related to the momentum distribution (such as the
average kinetic energy or the average current). We demonstrate gauge-field
thermometry for several physical situations, including free fermions and
strongly interacting bosons. The proposed approach is extremely robust to
quantum fluctuations - even in the vicinity of a quantum phase transition -
when it relies on the thermal fluctuations of an emerging classical field,
associated with the onset of Bose condensation or chiral order.


  We introduce language-driven image generation, the task of generating an
image visualizing the semantic contents of a word embedding, e.g., given the
word embedding of grasshopper, we generate a natural image of a grasshopper. We
implement a simple method based on two mapping functions. The first takes as
input a word embedding (as produced, e.g., by the word2vec toolkit) and maps it
onto a high-level visual space (e.g., the space defined by one of the top
layers of a Convolutional Neural Network). The second function maps this
abstract visual representation to pixel space, in order to generate the target
image. Several user studies suggest that the current system produces images
that capture general visual properties of the concepts encoded in the word
embedding, such as color or typical environment, and are sufficient to
discriminate between general categories of objects.


  We investigate the finite temperature quark anti-quark problem in a compact
space $S^{n-1}\times S^1$ by considering static strings in global $AdS_{n+1}$
space with $n\ge 3$. For high temperatures we work in the black hole metric
where two possible solutions show up : the big black hole and the small black
hole. Using the AdS/CFT correspondence, we calculate the quark anti-quark
potential (free energy) as a function of the distance. We show that this
potential can be intrepeted as confining for the $AdS$ space and deconfining
for the big black hole. We find for the small black hole a confining limit for
the potential but this solution is instable following the Hawking-Page
criteria. Our results for the free energy reinforce the Witten interpretation
of the confinement/deconfinement transition as the dual of the well-known
Hawking-Page transition.


  Complex systems often involve random fluctuations for which self-similar
properties in space and time play an important role. Fractional Brownian
motions, characterized by a single scaling exponent, the Hurst exponent $H$,
provide a convenient tool to construct synthetic signals that capture the
statistical properties of many processes in the physical sciences and beyond.
However, in certain strongly interacting systems, e.g., turbulent flows, stock
market indices, or cardiac interbeats, multiscale interactions lead to
significant deviations from self-similarity and may therefore require a more
elaborate description. In the context of turbulence, the Kolmogorov-Oboukhov
model (K62) describes anomalous scaling, albeit explicit constructions of a
turbulent signal by this model are not available yet. Here, we derive an
explicit formula for the joint multipoint probability density function of a
multifractal field. To this end, we consider a scale mixture of fractional
Ornstein-Uhlenbeck processes and introduce a fluctuating length scale in the
corresponding covariance function. In deriving the complete statistical
properties of the field, we are able to systematically model synthetic
multifractal phenomena. We conclude by giving a brief outlook on potential
applications which range from specific tailoring or stochastic interpolation of
wind fields to the modeling of financial data or non-Gaussian features in
geophysical or geospatial settings.


  We study housing markets as introduced by Shapley and Scarf (1974). We
investigate the computational complexity of various questions regarding the
situation of an agent $a$ in a housing market $H$: we show that it is
$\mathsf{NP}$-hard to find an allocation in the core of $H$ where (i) $a$
receives a certain house, (ii) $a$ does not receive a certain house, or (iii)
$a$ receives a house other than her own. We prove that the core of housing
markets respects improvement in the following sense: given an allocation in the
core of $H$ where agent $a$ receives a house $h$, if the value of the house
owned by $a$ increases, then the resulting housing market admits an allocation
in its core in which $a$ receives either $h$, or a house that $a$ prefers to
$h$; moreover, such an allocation can be found efficiently. We further show an
analogous result in the Stable Roommates setting by proving that stable
matchings in a one-sided market also respect improvement.


  The computational complexity of the Maximum Likelihood decoding algorithm in
[1], [2] for orthogonal space-time block codes is smaller than specified.


  Online education platforms enable teachers to share a large number of
educational resources such as questions to form exercises and quizzes for
students. With large volumes of available questions, it is important to have an
automated way to quantify their properties and intelligently select them for
students, enabling effective and personalized learning experiences. In this
work, we propose a framework for mining insights from educational questions at
scale. We utilize the state-of-the-art Bayesian deep learning method, in
particular partial variational auto-encoders (p-VAE), to analyze real students'
answers to a large collection of questions. Based on p-VAE, we propose two
novel metrics that quantify question quality and difficulty, respectively, and
a personalized strategy to adaptively select questions for students. We apply
our proposed framework to a real-world dataset with tens of thousands of
questions and tens of millions of answers from an online education platform.
Our framework not only demonstrates promising results in terms of statistical
metrics but also obtains highly consistent results with domain experts'
evaluation.


  A recent paper by Owens (2018) presents a statistical analysis of the
properties of interplanetary coronal mass ejections (ICMEs) and variations in
their compositions and ion charge-state signatures using data from the Advanced
Composition Explorer (ACE) spacecraft. However, the article contains several
serious flaws, which we will discuss here.


  The interrelations between the two definitions of momentum operator, via the
canonical energy-momentum tensorial operator and as translation operator (on
the operator space), are studied in quantum field theory. These definitions
give rise to similar but, generally, different momentum operators, each of them
having its own place in the theory. Some speculations on the relations between
quantum field theory and quantum mechanics are presented.


  As machine learning algorithms enter applications in industrial settings,
there is increased interest in controlling their cpu-time during testing. The
cpu-time consists of the running time of the algorithm and the extraction time
of the features. The latter can vary drastically when the feature set is
diverse. In this paper, we propose an algorithm, the Greedy Miser, that
incorporates the feature extraction cost during training to explicitly minimize
the cpu-time during testing. The algorithm is a straightforward extension of
stage-wise regression and is equally suitable for regression or multi-class
classification. Compared to prior work, it is significantly more cost-effective
and scales to larger data sets.


  Emotion recognition based on physiological signals is a hot topic and has a
wide range of applications, like safe driving, health care and creating a
secure society. This paper introduces a physiological dataset PAFEW, which is
obtained using movie clips from the Acted Facial Expressions in the Wild (AFEW)
dataset as stimuli. To establish a baseline, we use the electrodermal activity
(EDA) signals in this dataset and extract 6 features from each signal series
corresponding to each movie clip to recognize 7 emotions, i.e., Anger, Disgust,
Fear, Happy, Surprise, Sad and Neutral. Overall, 24 observers participated in
our collection of the training set, including 19 observers who participated in
only one session watching 80 videos from 7 classes and 5 observers who
participated multiple times and watched all the videos. All videos were
presented in an order balanced fashion. Leave-one-observer-out was employed in
this classification task. We report the classification accuracy of our
baseline, a three-layer network, on this initial training set while training
with signals from all participants, only single participants and only multiple
participants. We also investigate the recognition accuracy of grouping the
dataset by arousal or valence, which achieves 68.66% and 72.72% separately.
Finally, we provide a two-step network. The first step is to classify the
features into high/low arousal or positive/negative valence by a network. Then
the arousal/valence middle output of the first step is concatenated with
feature sets as input of the second step for emotion recognition. We found that
adding arousal or valence information can help to improve the classification
accuracy. In addition, the information of positive/negative valence boosts the
classification accuracy to a higher degree on this dataset.


  This work examines under what circumstances adaptivity for truncated SVD
estimation can be achieved by an early stopping rule based on the smoothed
residuals $ \| ( A A^{\top} )^{\alpha / 2} ( Y - A \hat{\mu}^{( m )}) \|^{2} $.
Lower and upper bounds for the risk are derived, which show that moderate
smoothing of the residuals can be used to adapt over classes of signals with
varying smoothness, while oversmoothing yields suboptimal convergence rates.
The theoretical results are illustrated by Monte-Carlo simulations.


  We obtain vacuum solutions in the presence of a cosmological constant in the
context of the Weyl geometrical scalar-tensor theory. We investigate the limit
when $\omega$ goes to infinity and show by working out the solutions that in
this limit there are some cases in which the scalar field tends to a constant
(with the implicit consequence of the geometry becoming Riemannian), although
the solutions do not reduce to the corresponding Einstein solutions. We have
also extended a previous result, known in the literature, by showing that in
the case of vacuum with cosmological constant the field equations of the Weyl
geometrical scalar-tensor theory are formally identical to Brans-Dicke field
equations, even though these theories are not physically equivalent.


  In this paper we present our work on a case study between Statistical Machien
Transaltion (SMT) and Rule-Based Machine Translation (RBMT) systems on
English-Indian langugae and Indian to Indian langugae perspective. Main
objective of our study is to make a five way performance compariosn; such as,
a) SMT and RBMT b) SMT on English-Indian langugae c) RBMT on English-Indian
langugae d) SMT on Indian to Indian langugae perspective e) RBMT on Indian to
Indian langugae perspective. Through a detailed analysis we describe the Rule
Based and the Statistical Machine Translation system developments and its
evaluations. Through a detailed error analysis, we point out the relative
strengths and weaknesses of both systems. The observations based on our study
are: a) SMT systems outperforms RBMT b) In the case of SMT, English to Indian
language MT systmes performs better than Indian to English langugae MT systems
c) In the case of RBMT, English to Indian langugae MT systems perofrms better
than Indian to Englsih Language MT systems d) SMT systems performs better for
Indian to Indian language MT systems compared to RBMT. Effectively, we shall
see that even with a small amount of training corpus a statistical machine
translation system has many advantages for high quality domain specific machine
translation over that of a rule-based counterpart.


  We show that the existence of a computationally efficient calibration
algorithm, with a low weak calibration rate, would imply the existence of an
efficient algorithm for computing approximate Nash equilibria - thus implying
the unlikely conclusion that every problem in PPAD is solvable in polynomial
time.


  A field-theoretical space-time position operator can be properly introduced
for the Dirac field, it plays the role of a generalized Noether charge
associated with a local symmetry, and its second-quantized form shows that
quantum fields possess zero-point time, which implies that a zero-point
fluctuation of energy must be accompanied with a zero-point fluctuation of
time, and then in agreement with the time-energy uncertainty principle. In
practice, some possible physical effects owing to zero-point time remain to be
found.


  This paper concerns about the stability for the acoustic wave motion with
boundary frictions and random forces. We will show there exists a unique
invariant measure for the stochastic evolution equation associated with this
acoustic wave motion, and the invariant measure possesses the property of
strong mixing. This result is new with respect to the literature on two
accounts: (i) stochasticity is accounted for in the acoustic wave model; (ii)
the controllability of the dynamical system modeling the acoustic wave motion
implies the mixing.


  An increasing amount of information is generated from the rapidly increasing
number of sensor networks and smart devices. A wide variety of sources generate
and publish information in different formats, thus highlighting
interoperability as one of the key prerequisites for the success of Internet of
Things (IoT). The BT Hypercat Data Hub provides a focal point for the sharing
and consumption of available datasets from a wide range of sources. In this
work, we propose a semantic enrichment of the BT Hypercat Data Hub, using
well-accepted Semantic Web standards and tools. We propose an ontology that
captures the semantics of the imported data and present the BT SPARQL Endpoint
by means of a mapping between SPARQL and SQL queries. Furthermore, federated
SPARQL queries allow queries over multiple hub-based and external data sources.
Finally, we provide two use cases in order to illustrate the advantages
afforded by our semantic approach.


  We prove that the class of finite two-graphs has the extension property for
partial automorphisms (EPPA, or Hrushovski property), thereby answering a
question of Macpherson. In other words, we show that the class of graphs has
the extension property for switching automorphisms. We present a short,
self-contained, purely combinatorial proof which also proves EPPA for the class
of integer valued antipodal metric spaces of diameter 3, answering a question
of Aranda et al.
  The class of two-graphs is an important new example which behaves differently
from all the other known classes with EPPA: Two-graphs do not have the
amalgamation property with automorphisms (APA), their Ramsey expansion has to
add a graph, it is not known if they have coherent EPPA and even EPPA itself
cannot be proved using the Herwig--Lascar theorem.


  We study the following problem. Given a multiset $M$ of non-negative
integers, decide whether there exist and, in the positive case, compute two
non-trivial multisets whose Minkowski sum is equal to $M$. The Minkowski sum of
two multisets A and B is a multiset containing all possible sums of any element
of A and any element of B. This problem was proved to be NP-complete when
multisets are replaced by sets. This version of the problem is strictly related
to the factorization of boolean polynomials that turns out to be NP-complete as
well. When multisets are considered, the problem is equivalent to the
factorization of polynomials with non-negative integer coefficients. The
computational complexity of both these problems is still unknown.
  The main contribution of this paper is a heuristic technique for decomposing
multisets of non-negative integers. Experimental results show that our
heuristic decomposes multisets of hundreds of elements within seconds
independently of the magnitude of numbers belonging to the multisets. Our
heuristic can be used also for factoring polynomials in N[x]. We show that,
when the degree of the polynomials gets larger, our technique is much faster
than the state-of-the-art algorithms implemented in commercial software like
Mathematica and MatLab.


  We present the first demonstration of shot-noise limited supercontinuum-based
spectral domain optical coherence tomography (SD-OCT) with axial resolution of
5.9 $\mu$m at a center wavelength of 1370 nm. Current supercontinuum-based
SD-OCT systems cannot be operated in the shot-noise limited detection regime
because of severe pulse-to-pulse relative intensity noise of the supercontinuum
source. To overcome this disadvantage we have developed a low-noise
supercontinuum source based on an all-normal dispersion (ANDi) fiber, pumped by
a femtosecond laser. The noise performance of our 90 MHz ANDi supercontinuum
source is compared to that of two commercial sources operating at 80 and 320
MHz repetition rate. We show that the low noise of the ANDi supercontinuum
source improves the OCT images significantly in terms of both higher contrast,
better sensitivity, and improved penetration. From SD-OCT imaging of skin,
retina, and multi-layer stacks we conclude that supercontinuum-based SD-OCT can
enter the domain of shot-noise limited detection.


  We develop the connection between large deviation theory and more applied
approaches to stochastic hybrid systems by highlighting a common underlying
Hamiltonian structure. A stochastic hybrid system involves the coupling between
a piecewise deterministic dynamical system in $R^d$ and a time-homogeneous
Markov chain on some discrete space $\Gamma$. We assume that the Markov chain
on $\Gamma$ is ergodic, and that the discrete dynamics is much faster than the
piecewise deterministic dynamics (separation of time-scales). Using the
Perron-Frobenius theorem and the calculus-of-variations, we evaluate the rate
function of a large deviation principle in terms of a classical action, whose
Hamiltonian is given by the Perron eigenvalue of a $|\Gamma|$-dimensional
linear equation. The corresponding linear operator depends on the transition
rates of the Markov chain and the nonlinear functions of the piecewise
deterministic system. The resulting Hamiltonian is identical to one derived
using path-integrals and WKB methods. We illustrate the theory by considering
the example of stochastic ion channels.


  We apply the generalized uncertainty principle to the thermodynamics of a
small black hole. Here we have a black hole system with the UV cutoff. It is
shown that the minimal length induced by the GUP interrupts the
Gross-Perry-Yaffe phase transition for a small black hole. In order to see
whether the black hole remnant takes place a transition to a large black hole,
we introduce a black hole in a cavity (IR system). However, we fail to show the
phase transition of the remnant to the large black hole.


  We present a molecular line scan in the Hubble Deep Field North (HDF-N) that
covers the entire 3mm window (79-115 GHz) using the IRAM Plateau de Bure
Interferometer. Our CO redshift coverage spans z<0.45, 1<z<1.9 and all z>2. We
reach a CO detection limit that is deep enough to detect essentially all z>1 CO
lines reported in the literature so far. We have developed and applied
different line searching algorithms, resulting in the discovery of 17 line
candidates. We estimate that the rate of false positive line detections is
~2/17. We identify optical/NIR counterparts from the deep ancillary database of
the HDF-N for seven of these candidates and investigate their available SEDs.
Two secure CO detections in our scan are identified with star-forming galaxies
at z=1.784 and at z=2.047. These galaxies have colors consistent with the `BzK'
color selection and they show relatively bright CO emission compared with
galaxies of similar dust continuum luminosity. We also detect two spectral
lines in the submillimeter galaxy HDF850.1 at z=5.183. We consider an
additional 9 line candidates as high quality. Our observations also provide a
deep 3mm continuum map (1-sigma noise level = 8.6 $\mu$Jy/beam). Via a stacking
approach, we find that optical/MIR bright galaxies contribute only to <50% of
the SFR density at 1<z<3, unless high dust temperatures are invoked. The
present study represents a first, fundamental step towards an unbiased census
of molecular gas in `normal' galaxies at high-z, a crucial goal of
extragalactic astronomy in the ALMA era.


  Embedding real-world networks presents challenges because it is not clear how
to identify their latent geometries. Embedding some disassortative networks,
such as scale-free networks, to the Euclidean space has been shown to incur
distortions. Embedding scale-free networks to hyperbolic spaces offer an
exciting alternative but incurs distortions when embedding assortative networks
with latent geometries not hyperbolic. We propose an inductive model that
leverages both the expressiveness of GCNs and trivial bundle to learn inductive
node representations for networks with or without node features. A trivial
bundle is a simple case of fiber bundles,a space that is globally a product
space of its base space and fiber. The coordinates of base space and those of
fiber can be used to express the assortative and disassortative factors in
generating edges. Therefore, the model has the ability to learn embeddings that
can express those factors. In practice, it reduces errors for link prediction
and node classification when compared to the Euclidean and hyperbolic GCNs.


  Previous works on stochastic gradient descent (SGD) often focus on its
success. In this work, we construct worst-case optimization problems
illustrating that, when not in the regimes that the previous works often
assume, SGD can exhibit many strange and potentially undesirable behaviors.
Specifically, we construct landscapes and data distributions such that (1) SGD
converges to local maxima, (2) SGD escapes saddle points arbitrarily slowly,
(3) SGD prefers sharp minima over flat ones, and (4) AMSGrad converges to local
maxima. We also realize results in a minimal neural network-like example. Our
results highlight the importance of simultaneously analyzing the minibatch
sampling, discrete-time updates rules, and realistic landscapes to understand
the role of SGD in deep learning.


  We investigate the effect of chemically patterned surfaces on the morphology
of diblock copolymers below the order-disorder transition. Profiles for
lamellar phases in contact with one surface, or confined between two surfaces
are obtained in the weak segregation limit using a Ginzburg-Landau expansion of
the free energy, and treating it with mean-field theory. The periodically
patterned surface induces a tilt of the lamellae in order to match the surface
periodicity. The lamellae relax from the constrained periodicity close to the
surface to the bulk periodicity far from it. The phases we investigate are a
generalization to the mixed (perpendicular and parallel to the surface)
lamellar phases occurring when the two surfaces are homogeneous. A special case
when the surface pattern has a period equal to the bulk lamellar period showing
``T-junction'' morphology is examined. Our analytic calculation agrees with
previous computer simulations and self consistent field theories.


  We study numerically the thermal conductivity coefficient $\kappa$ as a
function of system length $L$ for several different quasi one dimensional
models: classical gases of hard spheres with both longitudinal and transverse
degrees of freedom. We introduce a model that is ergodic and highly chaotic but
also conserves energy and momentum, and is very useful because it shows scaling
even at small system sizes. We find that $\kappa \sim L^\alpha$ over more than
two decades, with $\alpha$ very close to the analytical prediction of 1/3.


  We present a phenomenological model for baryogenesis based on particle
creation in the Friedman-Robertson-Walker (FRW) spacetime. This study is a
continuation of our proposal that Hawking-like radiation in FRW space-time
explains several physical aspects of the early Universe including inflation. In
this model we study a coupling between the FRW space-time, in the form of the
derivative of the Ricci scalar, and the $B-L$ current, $J^{\mu} _{B-L}$, which
leads to a different chemical potential between baryons and anti-baryons
resulting in an excess of baryons over anti-baryons with the right order of
magnitude. In this model the generation of baryon asymmetry, in principle,
occurs over the entire history of the Universe starting from the beginning of
the radiation phase. However, in practice, almost the entire contribution to
the baryon asymmetry only comes from the very beginning of the Universe and is
negligible thereafter. There is a free parameter in our model which can be
interpreted as defining the boundary between the unknown quantum gravity regime
and the inflation/baryogenesis regime covered by our model. When this parameter
is adjusted to give the observed value of baryon asymmetry we get a higher than
usual energy scale for our inflation model which however may be in line with
the GUT scale for inflation in view of the BICEP2 and Planck results. In
addition our model provides the correct temperature for the CMB photons at the
time of decoupling.


  This is the second in a series of papers that construct minimal surfaces by
gluing singly periodic Karcher--Scherk saddle towers along their wings. This
paper aims to construct singly periodic minimal surfaces with Scherk ends. As
in the first paper, we prescribe phase differences between saddle towers, and
obtain many new examples without any horizontal reflection plane. This
construction is not very different from previous ones, hence we will only
provide sketched proofs.
  We will however study the embeddedness with great care. Previously,
embeddedness can not be determined in the presence of "parallel" Scherk ends,
as it was not clear if they bend towards or away from each other. In a recent
study, the bending was completely ignored and embeddedness was falsely claimed.
We correct this mistake by carefully analysing slight bendings, thus identify
scenarios where the constructed surfaces are indeed embedded.


  We report the experimental observation of rectified momentum transport for a
Bose-Einstein Condensate kicked at the Talbot time (quantum resonance) by an
optical standing wave. Atoms are initially prepared in a superposition of the 0
and -2*hbar*kl momentum states using an optical pi/2 pulse. By changing the
relative phase of the superposed states, a momentum current in either direction
along the standing wave may be produced. We offer an interpretation based on
matter wave interference, showing that the observed effect is uniquely quantum.


  In this article, we report the recent progress on decoherence dynamics of
electrons in quantum dot quantum computing systems using the exact master
equation we derived recently based on the Feynman-Vernon influence functional
approach. The exact master equation is valid for general nanostructure systems
coupled to multi-reservoirs with arbitrary spectral densities, temperatures and
biases. We take the double quantum dot charge qubit system as a specific
example, and discuss in details the decoherence dynamics of the charge qubit
under coherence controls. The decoherence dynamics risen from the entanglement
between the system and the environment is mainly non-Markovian. We further
discuss the decoherence of the double-dot charge qubit induced by quantum point
contact (QPC) measurement where the master equation is re-derived using the
Keldysh non-equilibrium Green function technique due to the non-linear coupling
between the charge qubit and the QPC. The non-Markovian decoherence dynamics in
the measurement processes is extensively discussed as well.


  In this paper, continuous piecewise quadratic finite element wavelets are
constructed on general polygons in $\mathbb{R}^2$. The wavelets are stable in
$H^s$ for $|s|<\frac{3}{2}$ and have two vanishing moments. Each wavelet is a
linear combination of 11 or 13 nodal basis functions. Numerically computed
condition numbers for $s \in \{-1,0,1\}$ are provided for the unit square.


  The generalized Tur\'an problem $ext(n,T,F)$ is to determine the maximal
number of copies of a graph $T$ that can exist in an $F$-free graph on $n$
vertices. Recently, Gerbner and Palmer noted that the solution to the
generalized Tur\'an problem is often the original Tur\'an graph. They gave the
name "$F$-Tur\'an-good" to graphs $T$ for which, for large enough $n$, the
solution to the generalized Tur\'an problem is realized by a Tur\'an graph.
They prove that the path graph on two edges, $P_2$, is $K_{r+1}$-Tur\'an-good
for all $r \ge 3$, but they conjecture that the same result should hold for all
$P_\ell$. In this paper, using arguments based in flag algebras, we prove that
the path on three edges, $P_3$, is also $K_{r+1}$-Tur\'an-good for all $r \ge
3$.


  Induced effects by direct exposure to ionizing radiation (IR) are a central
issue in many fields like radiation protection, clinic diagnosis and
oncological therapies. Direct irradiation at certain doses induce cell death,
but similar effects can also occur in cells no directly exposed to IR, a
mechanism known as bystander effect. Non-IR (radiofrequency waves) can induce
the death of cells loaded with MNPs in a focused oncological therapy known as
magnetic hyperthermia. Indirect mechanisms are also able to induce the death of
unloaded MNPs cells. Using in vitro cell models, we found that colocalization
of the MNPs at the lysosomes and the non-increase of the temperature induces
bystander effect under non-IR. Our results provide a landscape in which
bystander effects are a more general mechanism, up to now only observed and
clinically used in the field of radiotherapy.


  A systematic method of summing the corrections to the renormalon residue
arising from higher order renormalons is discussed.


  We report the use of plasma-enhanced atomic layer deposition (PEALD) to
fabricate single-electron transistors (SETs) featuring ultra-thin (~1 nm)
tunnel-transparent SiO2 in Ni-SiO2-Ni tunnel junctions. We show that as a
result of the O2 plasma steps in PEALD of SiO2, the top surface of the
underlying Ni electrode is oxidized. Additionally, the bottom surface of the
upper Ni layer is also oxidized where it is in contact with the deposited SiO2,
most likely as a result of oxygen-containing species on the surface of the
SiO2. Due to the presence of these surface parasitic layers of NiO, which
exhibit features typical of thermally activated transport, the resistance of
Ni-SiO2-Ni tunnel junctions is drastically increased. Moreover, the transport
mechanism is changed from quantum tunneling through the dielectric barrier to
one consistent with thermally activated resistors in series with tunnel
junctions. The reduction of NiO to Ni is therefore required to restore the
metal-insulator-metal (MIM) structure of the junctions. Rapid thermal annealing
in a forming gas ambient at elevated temperatures is presented as a technique
to reduce both parasitic oxide layers. This method is of great interest for
devices that rely on MIM tunnel junctions with ultra-thin barriers. Using this
technique, we successfully fabricated MIM SETs with minimal trace of parasitic
NiO component. We demonstrate that the properties of the tunnel barrier in
nanoscale tunnel junctions can be evaluated by electrical characterization of
SETs.


  In 1967, Langlands conjectured a natural correspondence between automorphic
representations and Galois representations, over number fields as well as over
function fields. In 1983, Drinfeld discovered a geometric analog of the
Langlands correspondence in the function field case, which extends the
geometric class field theory of Lang and Rosenlicht. The so called
Drinfeld-Langlands correspondence is a conjectural duality between two moduli
spaces that are naturally associated to an algebraic curve X and a reductive
group G. When X is projective and G is the full linear group GL(n), a large
part of this correspondence has recently been established by E. Frenkel, D.
Gaitsgory et K. Vilonen.


  This paper introduces a method for efficiently inferring a high-dimensional
distributed quantity from a few observations. The quantity of interest (QoI) is
approximated in a basis (dictionary) learned from a training set. The
coefficients associated with the approximation of the QoI in the basis are
determined by minimizing the misfit with the observations. To obtain a
probabilistic estimate of the quantity of interest, a Bayesian approach is
employed. The QoI is treated as a random field endowed with a hierarchical
prior distribution so that closed-form expressions can be obtained for the
posterior distribution. The main contribution of the present work lies in the
derivation of \emph{a representation basis consistent with the observation
chain} used to infer the associated coefficients. The resulting dictionary is
then tailored to be both observable by the sensors and accurate in
approximating the posterior mean. An algorithm for deriving such an observable
dictionary is presented. The method is illustrated with the estimation of the
velocity field of an open cavity flow from a handful of wall-mounted point
sensors. Comparison with standard estimation approaches relying on Principal
Component Analysis and K-SVD dictionaries is provided and illustrates the
superior performance of the present approach.


  In the present work, we construct the Tsallis holographic quintessence model
of dark energy in $f(R, T)$ gravity with Hubble horizon as IR cut-off. In a
flat FRW background, the correspondence among the energy density of the
quintessence model with the Tsallis holographic density permits the
reconstruction of the dynamics and the potentials for the quintessence field.
The suggested Hubble horizon infrared cut-off for the THDE density acts for two
specific cases: (i) THDE 1 and (ii) THDE 2. We have reconstructed the Tsallis
holographic quintessence model in the region $\omega_{\Lambda} > -1$ for the
EoS parameter for both the cases. In addition, the quintessence phase of the
THDE models is analyzed with swampland conjecture to describe the accelerated
expansion of the Universe.


  Applications of deep learning for audio effects often focus on modeling
analog effects or learning to control effects to emulate a trained audio
engineer. However, deep learning approaches also have the potential to expand
creativity through neural audio effects that enable new sound transformations.
While recent work demonstrated that neural networks with random weights produce
compelling audio effects, control of these effects is limited and unintuitive.
To address this, we introduce a method for the steerable discovery of neural
audio effects. This method enables the design of effects using example
recordings provided by the user. We demonstrate how this method produces an
effect similar to the target effect, along with interesting inaccuracies, while
also providing perceptually relevant controls.


  We study the complex-temperature phase diagram of the square-lattice Ising
model for nonzero external magnetic field $H$, i.e. for $0 \le \mu \le \infty$,
where $\mu=e^{-2\beta H}$. We also carry out a similar analysis for $-\infty
\le \mu \le 0$. The results for the interval $-1 \le \mu \le 1$ provide a new
way of continuously connecting the two known exact solutions of this model,
viz., at $\mu=1$ (Onsager, Yang) and $\mu=-1$ (Lee and Yang). Our methods
include calculations of complex-temperature zeros of the partition function and
analysis of low-temperature series expansions. For real nonzero $H$, the inner
branch of a lima\c{c}on bounding the FM phase breaks and forms two
complex-conjugate arcs. We study the singularities and associated exponents of
thermodynamic functions at the endpoints of these arcs. For $\mu < 0$, there
are two line segments of singularities on the negative and positive $u$ axis,
and we carry out a similar study of the behavior at the inner endpoints of
these arcs, which constitute the nearest singularities to the origin in this
case. Finally, we also determine the exact complex-temperature phase diagrams
at $\mu=-1$ on the honeycomb and triangular lattices and discuss the relation
between these and the corresponding zero-field phase diagrams.


  Meta-learning aims to solve unseen tasks with few labelled instances.
Nevertheless, despite its effectiveness for quick learning in existing
optimization-based methods, it has several flaws. Inconsequential connections
are frequently seen during meta-training, which results in an
over-parameterized neural network. Because of this, meta-testing observes
unnecessary computations and extra memory overhead. To overcome such flaws. We
propose a novel meta-learning method called Meta-LTH that includes
indispensible (necessary) connections. We applied the lottery ticket hypothesis
technique known as magnitude pruning to generate these crucial connections that
can effectively solve few-shot learning problem. We aim to perform two things:
(a) to find a sub-network capable of more adaptive meta-learning and (b) to
learn new low-level features of unseen tasks and recombine those features with
the already learned features during the meta-test phase. Experimental results
show that our proposed Met-LTH method outperformed existing first-order MAML
algorithm for three different classification datasets. Our method improves the
classification accuracy by approximately 2% (20-way 1-shot task setting) for
omniglot dataset.


  Many multi-agent systems in nature are comprised of agents that interact
with, and respond to, the dynamics of their environment. In this paper, we
approach the study of such agent-environment interactions through the study of
passively compliant vehicles coupled to their environment via simple
nonholonomic constraints. We first consider a single passively compliant
Chaplygin beanie atop a platform having translational compliance, introduce the
reduced equations for the system using the notion of nonholonomic momentum, and
provide proof for its stability under arbitrary deformations of the elastic
element modeling its compliance. We then direct our focus to results concerning
the frequency response and control of passive Chaplygin beanies under actuation
of the platform, discuss rich dynamical features arising from periodic
actuation, and develop rules by which control can be exerted to collect and
disperse multiple passive vehicles. We then discuss how the latter of these
results clarifies the extent to which stable behavior can be excited in the
system through exogenous control.


  Positive-unlabeled learning (PU learning) in hyperspectral remote sensing
imagery (HSI) is aimed at learning a binary classifier from positive and
unlabeled data, which has broad prospects in various earth vision applications.
However, when PU learning meets limited labeled HSI, the unlabeled data may
dominate the optimization process, which makes the neural networks overfit the
unlabeled data. In this paper, a Taylor variational loss is proposed for HSI PU
learning, which reduces the weight of the gradient of the unlabeled data by
Taylor series expansion to enable the network to find a balance between
overfitting and underfitting. In addition, the self-calibrated optimization
strategy is designed to stabilize the training process. Experiments on 7
benchmark datasets (21 tasks in total) validate the effectiveness of the
proposed method. Code is at: https://github.com/Hengwei-Zhao96/T-HOneCls.


  With the advent of automated machine learning, automated hyperparameter
optimization methods are by now routinely used in data mining. However, this
progress is not yet matched by equal progress on automatic analyses that yield
information beyond performance-optimizing hyperparameter settings. In this
work, we aim to answer the following two questions: Given an algorithm, what
are generally its most important hyperparameters, and what are typically good
values for these? We present methodology and a framework to answer these
questions based on meta-learning across many datasets. We apply this
methodology using the experimental meta-data available on OpenML to determine
the most important hyperparameters of support vector machines, random forests
and Adaboost, and to infer priors for all their hyperparameters. The results,
obtained fully automatically, provide a quantitative basis to focus efforts in
both manual algorithm design and in automated hyperparameter optimization. The
conducted experiments confirm that the hyperparameters selected by the proposed
method are indeed the most important ones and that the obtained priors also
lead to statistically significant improvements in hyperparameter optimization.


  The intermediate-state interaction and structure of amplitudes of complicated
processes in medium (decays, reactions and the $n\bar{n}$ transitions) are
studied. It is proposed to use the branching ratio of channels of free-space
hadron-nucleon interaction as a test in the construction and verification of
the models. The corresponding formulas for the processes in the medium are
obtained. The connection between particle self-energy and amplitudes of
subprocesses are analyzed as well.


  We elaborate on the computation of the pressure of thermal quantum
electrodynamics, with massless electrons, to the fifth ($e^5$) order. The
calculation is performed within the Feynman gauge and the imaginary-time
formalism is employed. For the $e^4$ calculation, the method of Sudakov
decomposition is used to evaluate some ultraviolet finite integrals which have
a collinear singularity. For the $e^5$ contribution, we give an alternative
derivation and extend the discussion to massive electrons and nonzero chemical
potential. Comments are made on expected similarities and differences for
prospective three-loop calculations in QCD.


  We use house prices (HP) and house price indices (HPI) as a proxy to income
distribution. Specifically, we analyze sale prices in the 1970-2010 window of
over 116,000 single-family homes in Hamilton County, Ohio, including Cincinnati
metro area of about 2.2 million people. We also analyze HPI, published by
Federal Housing Finance Agency (FHFA), for nearly 18,000 US ZIP codes that
cover a period of over 40 years starting in 1980's. If HP can be viewed as a
first derivative of income, HPI can be viewed as its second derivative. We use
generalized beta (GB) family of functions to fit distributions of HP and HPI
since GB naturally arises from the models of economic exchange described by
stochastic differential equations. Our main finding is that HP and multi-year
HPI exhibit a negative Dragon King (nDK) behavior, wherein power-law
distribution tail gives way to an abrupt decay to a finite upper limit value,
which is similar to our recent findings for realized volatility of S\&P500
index in the US stock market. This type of tail behavior is best fitted by a
modified GB (mGB) distribution. Tails of single-year HPI appear to show more
consistency with power-law behavior, which is better described by a GB Prime
(GB2) distribution. We supplement full distribution fits by mGB and GB2 with
direct linear fits (LF) of the tails. Our numerical procedure relies on
evaluation of confidence intervals (CI) of the fits, as well as of p-values
that give the likelihood that data come from the fitted distributions.


  3D articulated objects are inherently challenging for manipulation due to the
varied geometries and intricate functionalities associated with articulated
objects.Point-level affordance, which predicts the per-point actionable score
and thus proposes the best point to interact with, has demonstrated excellent
performance and generalization capabilities in articulated object manipulation.
However, a significant challenge remains: while previous works use perfect
point cloud generated in simulation, the models cannot directly apply to the
noisy point cloud in the real-world. To tackle this challenge, we leverage the
property of real-world scanned point cloud that, the point cloud becomes less
noisy when the camera is closer to the object. Therefore, we propose a novel
coarse-to-fine affordance learning pipeline to mitigate the effect of point
cloud noise in two stages. In the first stage, we learn the affordance on the
noisy far point cloud which includes the whole object to propose the
approximated place to manipulate. Then, we move the camera in front of the
approximated place, scan a less noisy point cloud containing precise local
geometries for manipulation, and learn affordance on such point cloud to
propose fine-grained final actions. The proposed method is thoroughly evaluated
both using large-scale simulated noisy point clouds mimicking real-world scans,
and in the real world scenarios, with superiority over existing methods,
demonstrating the effectiveness in tackling the noisy real-world point cloud
problem.


  A finite element cochain complex on Cartesian meshes of any dimension based
on the H1-inner product is introduced. It yields H1-conforming finite element
spaces with exterior derivatives in H1. We use a tensor product construction to
obtain L2-stable projectors into these spaces which commute with the exterior
derivative. The finite element complex is generalized to a family of arbitrary
order.


  On the basis of density functional theory (DFT) calculations thermoelectric
properties are derived for Sb-doped skutterudites MPt$_4$Ge$_{12-x}$Sb$_x$ with
M=Ba,La,Th. It is predicted that the originally very small absolute values of
Seebeck coefficients $|S|$ of the undoped compounds is increased by factors of
10 or more for suitable dopings. The optimal dopings correspond to a "magic"
valence electron number for which all electronic states up to a (pseudo)gap are
filled. The theoretical findings are corroborated by measurements of $S$ for
LaPt$_4$Ge$_{12-x}$Sb$_x$ skutterudites. DFT derived vibrational rattling-like
modes for LaPt$_4$Ge$_{12}$ indicate a small value for the lattice thermal
conductivity which in combination with a large value of $S^2$ makes the
La-based skutterudites appear as promising thermoelectric materials.


  A simple scheme for all-optical time interpolation using spectral
interferometry is put forward that is in principle capable of single-shot
measurements. In this method, the arrival time of optical timing pulses is
encoded into the spectrum of a time-stretched supercontinuum via cross-phase
modulation. The proof-of-concept test setup points toward femtosecond-level
absolute timing capabilities with only minor additions to modern optical
clockwork.


  These are a set of lectures presented at the CERN-La Plata-Santiago de
Compostella School of Physics, La Plata, May 1997. A brief introduction to the
non-perturbative structure of string theory is presented. Various
non-perturbative dualities in ten and six dimensions as well as D-branes are
discussed.


  We introduce small cap square function estimates for parabola and cone, and
prove the sharp estimates. More precisely, we study the inequalities of form \[
\|f\|_p\le C_{\alpha,p}(R)
\Big\|(\sum_{\gamma\in\Gamma_\alpha(R^{-1})}|f_\gamma|^2)^{1/2}\Big\|_p, \]
where $\Gamma_\alpha(R^{-1})$ is the set of small caps of width $R^{-\alpha}$.
We find the sharp constant $C_{\alpha,p}(R)$.


  We introduce the asymptotic spectrum of graphs and apply the theory of
asymptotic spectra of Strassen (J. Reine Angew. Math. 1988) to obtain a new
dual characterisation of the Shannon capacity of graphs. Elements in the
asymptotic spectrum of graphs include the Lov\'asz theta number, the fractional
clique cover number, the complement of the fractional orthogonal rank and the
fractional Haemers bounds.