  We develop a new method to determine thermal activation rates, such as for
bubble nucleation, topology change, \textsl{etc.}, using 4-dimensional
Euclidean methods. This allows nonperturbative study on the lattice. We then
investigate the strong sphaleron rate in pure-glue QCD at temperatures between
1.3 $\Tc$ and 1000 $T_\mathrm{c}$, making contact with previous results but
extending them down close to the critical temperature. The extension to full
QCD will be straightforward. Limitations of the proposal (the inability to
compute a certain dynamical prefactor, puzzling large-volume behavior, and the
inability to treat temperatures $T < 1.3 \, T_\mathrm{c}$) are also discussed.


  We re-examine the CHSH experiment, which we abstract here as a multi-round
game played between two parties with each party reporting a single binary
outcome at each round. We explore in particular the role that symmetries, and
the spontaneous breaking thereof, play in determining the maximally achievable
correlations between the two parties. We show, with the help of an explicit
statistical model, that the spontaneous breaking of rotational symmetry allows
for stronger correlations than those that can be achieved in its absence. We
then demonstrate that spontaneous symmetry breaking may lead to a violation of
the renowned CHSH inequality. We believe that the ideas presented in this paper
open the door to novel research avenues that have the potential to deepen our
understanding of the quantum formalism and the physical reality that it
describes.


  Histopathology images are crucial to the study of complex diseases such as
cancer. The histologic characteristics of nuclei play a key role in disease
diagnosis, prognosis and analysis. In this work, we propose a sparse
Convolutional Autoencoder (CAE) for fully unsupervised, simultaneous nucleus
detection and feature extraction in histopathology tissue images. Our CAE
detects and encodes nuclei in image patches in tissue images into sparse
feature maps that encode both the location and appearance of nuclei. Our CAE is
the first unsupervised detection network for computer vision applications. The
pretrained nucleus detection and feature extraction modules in our CAE can be
fine-tuned for supervised learning in an end-to-end fashion. We evaluate our
method on four datasets and reduce the errors of state-of-the-art methods up to
42%. We are able to achieve comparable performance with only 5% of the
fully-supervised annotation cost.


  Quantum phase transitions (QPTs), including symmetry breaking and topological
types, always associated with gap closing and opening. We analyze the
topological features of the quantum phase boundary of the XY model in a
transverse magnetic field. Based on the results from graphs in the auxiliary
space, we find that gapless ground states at boundary have different
topological characters. On the other hand, In the framework of Majorana
representation, the Majorana lattice is shown to be two coupled SSH chains. The
analysis of the quantum fidelity for the Majorana eigen vector indicates the
signature of QPT for the gapless state. Furthermore numerical computation shows
that the transition between two types of gapless phases associates with
divergence of second-order derivative of groundstate energy density, which
obeys scaling behavior. It indicates that a continues QPT can occur among
gapless phases. The underlying mechanism of the gapless QPT is also discussed.
The gap closing and opening are not necessary for a QPT.


  Very high-energy gamma-ray measurements of distant blazars can be well
explained by secondary gamma rays emitted by cascades induced by
ultra-high-energy cosmic rays. The secondary gamma rays will enable one to
detect a large number of blazars with future ground based gamma-ray telescopes
such as Cherenkov Telescope Array (CTA). We show that the secondary emission
process will allow CTA to detect 100, 130, 150, 87, and 8 blazars above 30 GeV,
100 GeV, 300 GeV, 1 TeV, and 10 TeV, respectively, up to $z\sim8$ assuming the
intergalactic magnetic field (IGMF) strength $B=10^{-17}$ G and an unbiased all
sky survey with 0.5 hr exposure at each Field of View, where total observing
time is $\sim540$ hr. These numbers will be 79, 96, 110, 63, and 6 up to
$z\sim5$ in the case of $B=10^{-15}$ G. This large statistics of sources will
be a clear evidence of the secondary gamma-ray scenarios and a new key to
studying the IGMF statistically. We also find that a wider and shallower survey
is favored to detect more and higher redshift sources even if we take into
account secondary gamma rays.


  Science and technology are becoming increasingly collaborative. This paper
aims to explore the factors and mechanisms that impact the dynamic changes of
collaborative innovation networks. We consider both collaborative interactions
of organizations and their knowledge element exchanges to reveal how social and
knowledge network embeddedness affects the collaboration dynamics. Knowledge
elements are extracted to present the core concepts of scientific and technical
information, overcoming the limitations of using predefined categorizations
such as IPC when representing the content. Based on multiple collaboration and
knowledge networks, we then conduct a longitudinal analysis and apply a
stochastic actor-oriented model (SAOM) to model network dynamics over different
periods. The influence of network features and structures, individual node
characteristics, and various dimensions of proximity on collaboration dynamics
is tested and analyzed.


  We have theoretically investigated the magnetic response of two-dimensional
(2D) arrays of superconducting and soft magnetic strips, which are regarded as
models of dc magnetic metamaterials. The anisotropy of the macroscopic
permeabilities depends on whether the applied magnetic field is parallel to the
wide surface of the strips ($\mu_{\parallel}$) or perpendicular
($\mu_{\perp}$). For the 2D arrays of superconducting strips,
$0<\mu_{\perp}/\mu_0\ll \mu_{\parallel}/\mu_0\simeq 1$, whereas for the 2D
arrays of soft magnetic strips,
$\mu_{\parallel}/\mu_0\gg\mu_{\perp}/\mu_0\simeq 1$, where $\mu_0$ is the
vacuum permeability. We also demonstrate that strong anisotropy of the
macroscopic permeability can be obtained for hybrid arrays of superconducting
and soft magnetic strips, where $\mu_{\parallel}/\mu_0\gg 1\gg
\mu_{\perp}/\mu_0>0$.


  We propose a novel integrated framework that jointly models complementary
information from resting-state functional MRI (rs-fMRI) connectivity and
diffusion tensor imaging (DTI) tractography to extract biomarkers of brain
connectivity predictive of behavior. Our framework couples a generative model
of the connectomics data with a deep network that predicts behavioral scores.
The generative component is a structurally-regularized Dynamic Dictionary
Learning (sr-DDL) model that decomposes the dynamic rs-fMRI correlation
matrices into a collection of shared basis networks and time varying
subject-specific loadings. We use the DTI tractography to regularize this
matrix factorization and learn anatomically informed functional connectivity
profiles. The deep component of our framework is an LSTM-ANN block, which uses
the temporal evolution of the subject-specific sr-DDL loadings to predict
multidimensional clinical characterizations. Our joint optimization strategy
collectively estimates the basis networks, the subject-specific time-varying
loadings, and the neural network weights. We validate our framework on a
dataset of neurotypical individuals from the Human Connectome Project (HCP)
database to map to cognition and on a separate multi-score prediction task on
individuals diagnosed with Autism Spectrum Disorder (ASD) in a five-fold cross
validation setting. Our hybrid model outperforms several state-of-the-art
approaches at clinical outcome prediction and learns interpretable multimodal
neural signatures of brain organization.


  We explore the phase diagram of spin-orbit Mott insulators on a honeycomb
lattice, within the Kitaev-Heisenberg model extended to its full parameter
space. Zigzag-type magnetic order is found to occupy a large part of the phase
diagram of the model, and its physical origin is explained as due to
interorbital t2g-eg hopping. Magnetic susceptibility and spin wave spectra are
calculated and compared to the experimental data, obtaining thereby the spin
coupling constants in Na2IrO3 and Li2IrO3.


  We present a new technique for constructing solutions of quasilinear systems
of first-order partial differential equations, in particular inhomogeneous
ones. A generalization of the Riemann invariants method to the case of
inhomogeneous hyperbolic and elliptic systems is formulated. The algebraization
of these systems enables us to construct certain classes of solutions for which
the matrix of derivatives of the unknown functions is expressible in terms of
special orthogonal matrices. These solutions can be interpreted as nonlinear
superpositions of $k$ waves (or $k$ modes) in the case of hyperbolic (or
elliptic) systems, respectively. Theoretical considerations are illustrated by
several examples of inhomogeneous hydrodynamic-type equations which allow us to
construct solitonlike solutions (bump and kinks) and multiwave (mode)
solutions.


  We present the composite luminosity function (hereafter LF) of galaxies for
80 Abell clusters studied in our survey of the Northern Hemisphere, using DPOSS
data in the framework of the CRoNaRio collaboration. Our determination of the
LF has been computed with very high accuracy thanks to the use of homogeneous
data both for the clusters and the control fields and to a local estimate of
the background, which takes into account the presence of large-scale structures
and of foreground clusters and groups.
  The global composite LF is quite flat down to M^+5 and it has a slope
alpha=-1.0 +/- 0.2 with minor variations from blue to red filters, and M^* =
-21.9,-22.0,-22.3 mag (H_0=50 km s^-1 Mpc^-1) in g, r and i filters,
respectively (errors are detailed in the text).
  We find a significant difference between rich and poor clusters supporting
the existence of an LF's dependence on the environment.


  The following results are proved:
  (a) In a model obtained by adding aleph_2 Cohen reals, there is always a
c.c.c. complete Boolean algebra without the weak Freese-Nation property.
  (b) Modulo the consistency strength of a supercompact cardinal, the existence
of a c.c.c. complete Boolean algebras without the weak Freese-Nation property
consistent with GCH.
  (c) Under some consequences of the negation of 0^#, the weak Freese-Nation
property of (P(omega),subseteq) is equivalent to the weak Freese-Nation
property of any of C(kappa) or R(kappa) for uncountable kappa.
  (d) Modulo consistency of (aleph_{omega+1},aleph_omega)-->(aleph_1,aleph_0),
it is consistent with GCH that the assertion in (c) does not hold and also that
adding aleph_omega Cohen reals destroys the weak Freese-Nation property of
(P(omega),subseteq)


  We study the planning and acting phase for the problem of multi-agent path
finding (MAPF) in this paper. MAPF is a problem of navigating agents from their
start positions to specified individual goal positions so that agents do not
collide with each other. Specifically we focus on executing MAPF plans with a
group of Crazyflies, small indoor quadcopters . We show how to modify the
existing continuous time conflict-based search algorithm (CCBS) to produce
plans that are suitable for execution with the quadcopters. The acting phase
uses the the Loco positioning system to check if the plan is executed
correctly. Our finding is that the CCBS algorithm allows for extensions that
can produce safe plans for quadcopters, namely cylindrical protection zone
around each quadcopter can be introduced at the planning level.


  Are Dark Matter and Dark Energy the result of uncalculated addition
derivatives? The need to introduce dark matter dark and energy becomes
unnecessary if we consider that, the phenomenon of dark matter and dark energy
is a result of not computing the additional derivatives of the equation of
motion. For this purpose, we use higher derivatives in the form of non-local
variables, known as the Ostrogradsky formalism. As a mathematician,
Ostrogradsky considered the dependence of the Lagrange function on acceleration
and its higher derivatives with respect to time. This is the case that fully
correspond with the real frame of reference, and that can be both inertial and
non-inertial frames. The problem of dark matter and dark energy presented
starting from basic observations to explain the different results in theory and
experiment. The study of galactic motion, especially the rotation curves,
showed that a large amount of dark matter can be found mainly in galactic
halos. The search for dark matter and dark energy has not confirmed with the
experimental discovery of it, so we use Ostrogradsky formalities to explain the
effects described above, so that the need to introduce dark matter and dark
energy disappears.


  X-ray missions with microcalorimeter technology will resolve spectral
features with unprecedented detail. In this work, we improve the H-like
K$\alpha$ energies for elements between 6 $\leq Z \leq$ 30 for the release
version of the spectral simulation code Cloudy to match laboratory energies. We
update the ionization potential ($I_{\rm ion}$) for these elements and add a
fourth-order polynomial to the level energy difference. This brings the release
version of Cloudy into a near-perfect agreement with NIST. The updated energies
are $\sim$ 15-4000 times more precise than that of the current release version
of Cloudy (C17.02). These new changes will be a part of the next update to the
release version, C17.03.


  This paper presents a procedure to determine a complete belief function from
the known values of belief for some of the subsets of the frame of discerment.
The method is based on the principle of minimum commitment and a new principle
called the focusing principle. This additional principle is based on the idea
that belief is specified for the most relevant sets: the focal elements. The
resulting procedure is compared with existing methods of building complete
belief functions: the minimum specificity principle and the least commitment
principle.


  A disk-corona model for fitting low/hard (LH) state of associated steady jet
of black hole X-ray binaries (BHXBs) is proposed based on the large-scale
magnetic field configuration of the coexistence of the Blandford-Znajek (BZ)
and Blandford-Payne (BP) processes,where the magnetic field configuration for
the BP process is determined by the requirement of energy conversion from
Poynting energy flux into kinetic energy flux in the jet. It is found that
corona current is crucial to guarantee the consistency of the jet launching
from accretion disk. The relative importance of the BZ to BP processes in
powering jets from black hole accretion disk is discussed, and the LH state of
several BHXBs is fitted based on our model.In addition, we suggest that
magnetic field configuration could be regarded as the second parameter for
governing the state transition of BHXBs.


  Artificial intelligence is gaining strength and materials science can both
contribute to and profit from it. In a simultaneous progress race, new
materials, systems and processes can be devised and optimized thanks to machine
learning techniques and such progress can be turned into in-novative computing
platforms. Future materials scientists will profit from understanding how
machine learning can boost the conception of advanced materials. This review
covers aspects of computation from the fundamentals to directions taken and
repercussions produced by compu-tation to account for the origins, procedures
and applications of artificial intelligence. Machine learning and its methods
are reviewed to provide basic knowledge on its implementation and its
potential. The materials and systems used to implement artificial intelligence
with electric charges are finding serious competition from other information
carrying and processing agents. The impact these techniques are having on the
inception of new advanced materials is so deep that a new paradigm is
developing where implicit knowledge is being mined to conceive materi-als and
systems for functions instead of finding applications to found materials. How
far this trend can be carried is hard to fathom as exemplified by the power to
discover unheard of mate-rials or physical laws buried in data.


  Astronomical observations have shown that the expansion of the universe is at
present accelerating, in a way consistent with the presence of a positive
cosmological constant. This is a major puzzle, because we do not understand:
why the cosmological constant is so small; why, being so small, it is not
exactly zero; and why it has precisely the value it must have to make the
expansion start accelerating just at the epoch when we are observing the
universe. We present a new model of cosmology, which we call the stryngbohtyk
model, that solves all these problems and predicts exactly the value that the
cosmological constant must have. The predicted value agrees with the observed
one within the measurement error. We show that in the stryngbohtyk model, the
fact the cosmological constant starts being important at the present epoch is
not a coincidence at all, but a necessity implied by our origin in a planet
orbiting a star that formed when the age of the universe was of the same order
as the lifetime of the star.


  We develop techniques for computing the equivariant local mirror symmetry of
curves, i.e. mirror symmetry for O(k)+O(-2-k) over P^1 for k greater than 0. We
also describe related methods for dealing with mirror symmetry of non-nef toric
varieties. The basic tools are equivariant I functions and their Birkhoff
factorization.


  In industrial applications it is quite common to use stochastic volatility
models driven by semi-martingale Markov volatility processes. However, in order
to fit exactly market volatilities, these models are usually extended by adding
a local volatility term. Here, we consider the case of singular Volterra
processes, and we extend them by adding a local-volatility term to their Markov
lift by preserving the stylized results implied by these models on
plain-vanilla options. In particular, we focus on the rough-Heston model, and
we analyze the small time asymptotics of its implied local-volatility function
in order to provide a proper extrapolation scheme to be used in calibration.


  Context: Several observations of the local Universe (LU) point towards the
existence of very prominent structures. The presence of massive galaxy clusters
and local super clusters on the one hand, but also large local voids and
under-densities on the other hand. However, it is highly non trivial to connect
such different observational selected tracers to the underlying dark matter
(DM) distribution. Methods (abridged): We used a 500 Mpc/h large constrained
simulation of the LU with initial conditions based on peculiar velocities
derived from the CosmicFlows-2 catalogue and follow galaxy formation physics
directly in the hydro-dynamical simulations to base the comparison on stellar
masses of galaxies or X-ray luminosity of clusters. We also used the 2668 Mpc/h
large cosmological box from the Magneticum simulations to evaluate the
frequency of finding such anomalies in random patches within simulations.
Results: We demonstrate that haloes and galaxies in our constrained simulation
trace the local DM density field very differently. Thereby, this simulation
reproduces the observed 50% under-density of galaxy clusters and groups within
the sphere of ~100 Mpc when applying the same mass or X-ray luminosity limit
used in the observed cluster sample (CLASSIX), which is consistent with a
~1.5$\sigma$ feature. At the same time, the simulation reproduces the observed
over-density of massive galaxy clusters within the same sphere, which on its
own also corresponds to a ~1.5$\sigma$ feature. Interestingly, we find that
only 44 out of 15635 random realizations (i.e. 0.28%) are matching both
anomalies, making the LU to be a ~3$\sigma$ environment. We finally compared a
mock galaxy catalogue with the observed distribution of galaxies in the LU,
finding also a match to the observed factor of two over-density at ~16 Mpc as
well as the observed 15% under-density at ~40 Mpc distance.


  We consider a scenario in which the discrete weak symmetry between quarks and
leptons is extended to the weak force by introducing exotic partners. We
conjecture that there exists a hidden discrete symmetry $\tilde{P}$, defining a
$Z_2$ group, between standard and exotic quarks and leptons. The unified model
$SU(3)_{q} \times SU(3)_{\tilde{q}} \times SU(2)_{q \tilde{\ell}} \times
SU(2)_{\tilde{q} \ell} \times U(1)_{Y} \times \tilde{P}$ is discussed, where
the unifying discrete symmetry extends over particles and forces. It is shown
that the lighter neutral and charged weak bosons generated upon spontaneous
symmetry breaking have the same properties as those of the standard model.
Cabbibo-Kobayashi-Maskawa unitarity is used to set a mass of order 2.8 TeV for
the nonstandard weak bosons, which do not exhibit quark-lepton universality. A
grand unified theory of $GUT_{q \tilde{\ell}} \times GUT_{\tilde{q} \ell}
\times \tilde{P}$ type, with decay of exotic matter into standard matter and no
decay of the ordinary nucleon, is put forward.


  Under the assumption that jets explode core collapse supernovae in a negative
jet feedback mechanism (JFM), I show that rapidly rotating neutron stars are
likely to be formed when the explosion is very energetic. Under the assumption
that an accretion disk or an accretion belt around the just-formed neutron star
launch jets and that the accreted gas spins-up the just-formed neutron star, I
derive a crude relation between the energy that is stored in the spinning
neutron star and the explosion energy. This relation reads
Espin/Eexp~(Eexp/1e52erg). It shows that within the frame of the JFM explosion
model of core collapse supernovae, spinning neutron stars, such as magnetars,
might have significant energy in super-energetic explosions. The existence of
magnetars, if confirmed, such as in the recent super-energetic supernova
GAIA16apd, further supports the call for a paradigm shift from neutrino-driven
to jet-driven core-collapse supernova mechanisms.


  Using a combination of quantum Monte Carlo simulations in adapted cluster
bases, the finite temperature Lanczos method, and an effective Hamiltonian
approach, we explore the thermodynamic properties of the spin-1/2 Heisenberg
antiferromagnet on the star lattice. We consider various parameter regimes on
this strongly frustrated Archimedean lattice, including the case of homogeneous
couplings as well as the distinct parameter regimes of dominant vs. weak dimer
coupling. For the latter case, we explore the quantum phase diagram in the
presence of inhomogeneous trimer couplings, preserving inversion symmetry. We
compare the efficiency of different cluster decoupling schemes for the quantum
Monte Carlo simulations in terms of the sign problem, contrast the
thermodynamic properties to those of other strongly frustrated quantum magnets,
such as the kagome lattice model, and comment on previous results from
tensor-network calculations regarding a valence bond crystal phase in the
regime of weak dimer coupling. Finally, we relate our results to recently
reported experimental findings on a Cu-based quantum magnetic spin-1/2 compound
with an underlying star lattice structure.


  Studying the possible breaking of various parton model symmetries by the
parton distribution functions of the nucleon can provide important information
for the non-perturbative structure of hadrons and the strong interaction. We
review theoretical calculations for the breaking of flavor symmetry,
quark-antiquark symmetry and charge symmetry in the unpolarized and polarized
nucleons using the meson cloud model. We report an estimation for the total
distribution of strange and antistrange quarks in the nucleon by combining
theoretical calculations of SU(3) flavor symmetry breaking with light antiquark
distributions obtained from global analysis of available experimental data.


  Adaptive Bitrate (ABR) Streaming over the cellular networks has been well
studied in the literature; however, existing ABR algorithms primarily focus on
improving the end-users' Quality of Experience (QoE) while ignoring the
resource consumption aspect of the underlying device. Consequently, proactive
attempts to download video data to maintain the user's QoE often impact the
battery life of the underlying device unless the download attempts are
synchronized with the network's channel condition. In this work, we develop
EnDASH-5G -- a wrapper over the popular DASH-based ABR streaming algorithm,
which establishes this synchronization by utilizing a network-aware video data
download mechanism. EnDASH-5G utilizes a novel throughput prediction mechanism
for 5G mmWave networks by upgrading the existing throughput prediction models
with a transfer learning-based approach leveraging publicly available 5G
datasets. It then exploits deep reinforcement learning to dynamically decide
the playback buffer length and the video bitrate using the predicted
throughput. This ensures that the data download attempts get synchronized with
the underlying network condition, thus saving the device's battery power. From
a thorough evaluation of EnDASH-5G, we observe that it achieves a near $30.5\%$
decrease in the maximum energy consumption than the state-of-the-art Pensieve
ABR algorithm while performing almost at par in QoE.


  We study the thermodynamic properties of a microscopic model of coupled
oscillators that exhibits a dynamical phase transition from a desynchronized to
a synchronized phase. We consider two different configurations for the
thermodynamic forces applied on the oscillators, one resembling the macroscopic
power grids, and one resembling autonomous molecular motors. We characterize
the input and the output power as well as the efficiency at maximum power,
providing analytic expressions for such quantities near the critical coupling
strength. We discuss the role of the quenched disorder in the thermodynamic
force distributions and show that such a disorder may lead to an enhancement of
the efficiency at maximum power.


  It has been suggested that the hot plasma of the solar corona comes primarily
from impulsive heating events, or nanoflares, that occur in the lower
atmosphere, either in the upper part of the ordinary chromosphere or at the
tips of type II spicules. We test this idea with a series of hydrodynamic
simulations. We find that synthetic Fe XII (195) and Fe XIV (274) line profiles
generated from the simulations disagree dramatically with actual observations.
The integrated line intensities are much too faint; the blue shifts are much
too fast; the blue-red asymmetries are much too large; and the emission is
confined to low altitudes. We conclude that chromospheric nanoflares are not a
primary source of hot coronal plasma. Such events may play an important role in
producing the chromosphere and powering its intense radiation, but they do not,
in general, raise the temperature of the plasma to coronal values. Those cases
where coronal temperatures are reached must be relatively uncommon. The
observed profiles of Fe XII and Fe XIV come primarily from plasma that is
heated in the corona itself, either by coronal nanoflares or a quasi-steady
coronal heating process. Chromospheric nanoflares might play a role in
generating waves that provide this coronal heating.


  Based on a newly advanced phenomenological understanding of the high-field
insulator- Hall liquid transition in a composite fermion picture, we extend its
composite boson counterpart to the analysis of the low-field insulator-Hall
liquid transition. We thus achieve a comparative study of these two
transitions. In this way, the similar reflection symmetries in filling factors
in both transitions are understood consistently as due to the symmetry of the
gapful excitations which dominate $\sigma_{xx}$ across the transitions, and the
abrupt change in $\sigma_{xy}$ at the transitions. The substantially different
characteristic energy scales involved in these two transitions can be
attributed to the differences in critical filling factors $\nu_c$ and the
effective masses. The opposite temperature-dependences of the critical
longitudinal resistivities are also well-understood, which can be traced to the
opposite statistical natures of the composite fermion and the composite boson.
We also give a tentative discussion of the zero-temperature dissipative
conductivity. The above results are supported by a recent experiment
(cond-mat/9708239).


  We derive a Belief-Propagation algorithm for counting large loops in a
directed network. We evaluate the distribution of the number of small loops in
a directed random network with given degree sequence. We apply the algorithm to
a few characteristic directed networks of various network sizes and loop
structures and compare the algorithm with exhaustive counting results when
possible. The algorithm is adequate in estimating loop counts for large
directed networks and can be used to compare the loop structure of directed
networks and their randomized counterparts.


  Aims. The aim of this work is to identify and study spectral features in the
quasar 3C 273 usually blended by its strong jet emission. Method. A historic
minimum in the sub-millimetre emission of 3C 273 triggered coordinated
multi-wavelength observations in June 2004. X-ray observations from the
INTEGRAL, XMM-Newton and RXTE satellites are complemented by ground-based
optical, infrared, millimetre and radio observations. The overall spectrum is
used to model the infrared and X-ray spectral components. Results. Three
thermal dust emission components are identified in the infrared. The dust
emission on scales from 1 pc to several kpc is comparable to that of other
quasars, as expected by AGN unification schemes. The observed weakness of the
X-ray emission supports the hypothesis of a synchrotron self-Compton origin for
the jet component. There is a clear soft-excess and we find evidence for a very
broad iron line which could be emitted in a disk around a Kerr black hole.
Other signatures of a Seyfert-like X-ray component are not detected.


  We have characterized 26 Spitzer/IRAC-selected sources from the SMUVS program
that are undetected in the UltraVISTA DR5 H- and/or Ks-band images, covering 94
square arcmin of the COSMOS field which have deep multiwavelength JWST
photometry. We analyzed the JWST/NIRCam imaging from the PRIMER survey and
ancillary HST data to reveal the properties of these galaxies from spectral
energy distribution (SED) fitting. We find that the majority of these galaxies
are detected by NIRCam at <2 micron, with only four remaining as near-infrared
dropouts in the deeper JWST images. Our results indicate that the UltraVISTA
dropouts candidates are primarily located at z>3 and are characterized by high
dust extinctions, with a typical color excess E(B-V) = 0.5 pm 0.3 and stellar
mass log(M*/Msun) = 9.5 pm 1.0. Remarkably, ~75% of these sources show a flux
enhancement between the observed photometry and modeled continuum SED that can
be attributed to Halpha emission in the corresponding NIRCam bands. The derived
(Halpha+ N[II] + S[II]) rest-frame equivalent widths and Halpha star formation
rates (SFRs) span values ~100-2200 A and ~5-375 Msun/yr, respectively. The
location of these sources on the SFR-M* plane indicates that 35% of them are
starbursts, 40% are main-sequence galaxies and the remaining 25% are located in
the star-formation valley. Our sample includes one active galactic nucleus and
six submillimeter sources, as revealed from ancillary X-ray and submillimeter
photometry. The high dust extinctions combined with the flux boosting from
Halpha emission explain why these sources are relatively bright Spitzer
galaxies and yet unidentified in the ultradeep UltraVISTA near-infrared images.


  We have conducted a deep, complete HI survey, using Arecibo/ALFA, of a field
centered on the nearby, isolated galaxy, NGC 2903, which is similar to the
Milky Way in its properties. The field size was 150 kpc x 260 kpc and the final
velocity range spanned from 100 to 1133 km/s. The ALFA beams have been mapped
as a function of azimuth and cleaned from each azimuth-specific cube prior to
forming final cubes. The final HI data are sensitive down to an HI mass of 2 x
10^5 Mo and column density of 2 x 10^{17} cm^{-2} at the 3sigma x 2deltaV
level, where sigma is the rms noise level and deltaV is the velocity
resolution. NGC 2903 is found to have an HI envelope that is larger than
previously known, extending to at least 3 times the optical diameter of the
galaxy. Our search for companions yields one new discovery with an HI mass of
2.6 x 10^6 Mo. The companion is 64 kpc from NGC 2903 in projection, is likely
associated with a small optical galaxy of similar total stellar mass, and is
dark matter dominated, with a total mass >10^8 Mo. In the region surveyed,
there are now two known companions: our new discovery and a previously known
system that is likely a dwarf spheroidal, lacking HI content. If HI constitutes
1% of the total mass in all possible companions, then we should have detected
230 companions, according to LCDM predictions. Consequently, if this number of
dark matter clumps are indeed present, then they contain less than 1% HI
content, possibly existing as very faint dwarf spheroidals or as starless,
gasless dark matter clumps.


  We show that the first Johnson subgroup of the mapping class group of a
surface S of genus greater than one acts ergodically on the moduli space of
representations of the fundamental group of S in SU_2. Our proof relies on a
local description of the latter space around the trivial representation and on
the Taylor expansion of trace functions.


  Non-linear oscillator networks have revealed properties as the remote
synchronization and the quorum sensing. The remote synchronization, defined as
the synchronization of nodes not directly connected by any sequence of
synchronized nodes, was found firstly in networks of amplitude oscillators and
recently in bipartite delayed networks of phase oscillators. The quorum
sensing, a biological information scheme discovered in cell aggregates, has
been investigated in amplitude oscillators coupled by a common medium.
Implications of such findings are important in technology and biology. We show
both of them in non-bipartite, biologically plausible networks of Kuramoto
oscillators. The quorum sensing emerges using the graph edge density, while the
remote synchronization is obtained by means of an oscillator acting as a
pacemaker. In the remote synchronization two distinct groups of well inter and
infra-synchronized nodes, separated by non-synchronized paths, appear clearly.
Our biological interpretation is that the remote synchronization, bypassing the
normal quorum sensing mechanism, is responsible of the pathological cell
proliferation. This approach seems suitable to study the quorum sensing
alterations due to genetic mutation or to the environmental action before the
actual mass replication begins.


  We show that if a group $G$ acts geometrically by type-preserving
automorphisms on a building, then $G$ satisfies the weak Tits alternative,
namely, that $G$ is either virtually abelian or contains a non-abelian free
group.


  We study a system of two Coulombically interacting electrons in an external
harmonic potential in the presence of an on-centre Coulomb impurity. Detailed
results for the dependencies of the reduced von Neumann entropy on the control
parameters of the system are provided for both the ground state and the triplet
S states with the lowest energy. Among other features, it is found that in the
weak confinement regime the entanglement is strongly affected by the presence
of an acceptor impurity.


  In this work we introduce a quantum sorting algorithm with adaptable
requirements of memory and circuit depth, and then use it to develop a new
quantum version of the classical machine learning algorithm known as k-nearest
neighbors (k-NN). Both the efficiency and performance of this new quantum
version of the k-NN algorithm are compared to those of the classical k-NN and
another quantum version proposed by Schuld et al. \cite{Int13}. Results show
that the efficiency of both quantum algorithms is similar to each other and
superior to that of the classical algorithm. On the other hand, the performance
of our proposed quantum k-NN algorithm is superior to the one proposed by
Schuld et al. and similar to that of the classical k-NN.


  This paper gives the most general form of the Adler-Kostant-Symes Theorem,
and many applications of it, both finite and infinite dimensional, the former
yielding algebraic completely integrable (a.c.i.) systems, and the latter
examples in random matrix theory.


  [Abridged] Barium stars are moderately rare, chemically peculiar objects,
which are believed to be the result of the pollution of an otherwise normal
star by material from an evolved companion on the asymptotic giant branch
(AGB). We aim to derive carbon, nitrogen, oxygen, and fluorine abundances for
the first time from the infrared spectra of the barium red giant star HD 123396
to quantitatively test AGB nucleosynthesis models for producing barium stars
via mass accretion. High-resolution and high S/N infrared spectra were obtained
using the Phoenix spectrograph mounted at the Gemini South telescope. The
abundances were obtained through spectrum synthesis of individual atomic and
molecular lines, using the MOOG stellar line analysis program, together with
Kurucz's stellar atmosphere models. The analysis was classical, using 1D
stellar models and spectral synthesis under the assumption of local
thermodynamic equilibrium. We confirm that HD 123396 is a metal-deficient
barium star ([Fe/H] = -1.05), with A(C) = 7.88, A(N) = 6.65, A(O) = 7.93, and
A(Na) = 5.28 on a logarithmic scale where A(H) = 12, leading to [(C+N)/Fe] ~
0.5. The A(CNO) group, as well as the A(Na) abundances, is in excellent
agreement with those previously derived for this star using high resolution
optical data. We also found A(F) = 4.16, which implies [F/O] = 0.39, a value
that is substantially higher than the F abundances measured in globular
clusters of a similar metallicity, noting that there are no F measurements in
field stars of comparable metallicity. The observed abundance pattern of the
light elements (CNO, F and Na) recovered here as well as the heavy elements
(s-process) studied elsewhere suggest that the surface composition of HD 123396
is well fitted by the predicted abundance pattern of a 1.5Msun AGB model star
with Z = 0.001.


  The point-line geometry known as a \textit{partial quadrangle} (introduced by
Cameron in 1975) has the property that for every point/line non-incident pair
$(P,\ell)$, there is at most one line through $P$ concurrent with $\ell$. So in
particular, the well-studied objects known as \textit{generalised quadrangles}
are each partial quadrangles. An \textit{intriguing set} of a generalised
quadrangle is a set of points which induces an equitable partition of size two
of the underlying strongly regular graph. We extend the theory of intriguing
sets of generalised quadrangles by Bamberg, Law and Penttila to partial
quadrangles, which surprisingly gives insight into the structure of hemisystems
and other intriguing sets of generalised quadrangles.


  The wedge product of vectors has been shown to yield the generalised
entanglement measure I-concurrence, wherein the separability of the multiparty
qubit system arises from the parallelism of vectors in the underlying Hilbert
space of the subsystems. Here, we demonstrate the geometrical conditions of the
post-measurement vectors which maximize the entanglement corresponding to the
bi-partitions and can yield non-identical set of maximally entangled states.
The Bell states for the two qubit case, GHZ and GHZ like states with
superposition of four constituents for three qubits, naturally arise as the
maximally entangled states. The geometric conditions for maximally entangled
two qudit systems are derived, leading to the generalised Bell states, where
the reduced density matrices are maximally mixed. We further show that the
reduced density matrix for an arbitrary finite dimensional subsystem of a
general qudit state can be constructed from the overlap of the post-measurement
vectors. Using this approach, we discuss the trade-off between the local
properties namely predictability and coherence with the global property,
entanglement for the non-maximally entangled two qubit state.


  A self-consistent mean-field method is used to study critical wetting
transitions under nonequilibrium conditions by analyzing Kardar-Parisi-Zhang
(KPZ) interfaces in the presence of a bounding substrate. In the case of
positive KPZ nonlinearity a single (Gaussian) regime is found. On the contrary,
interfaces corresponding to negative nonlinearities lead to three different
regimes of critical behavior for the surface order-parameter: (i) a trivial
Gaussian regime, (ii) a weak-fluctuation regime with a trivially located
critical point and nontrivial exponents, and (iii) a highly non-trivial
strong-fluctuation regime, for which we provide a full solution by finding the
zeros of parabolic-cylinder functions. These analytical results are also
verified by solving numerically the self-consistent equation in each case.
Analogies with and differences from equilibrium critical wetting as well as
nonequilibrium complete wetting are also discussed.


  We investigate electron transport under the two impurity Kondo problem with
the Aharonov--Bohm and Aharonov--Casher effects. These interference effects
induce the Ising-coupled Ruderman--Kittel--Kasuya--Yosida (RKKY) interaction.
We discuss the inter- and intra-site spin conductance as well as charge
conductance in the Kondo and the mixed-valence regimes using the slave boson
mean field approximation.


  Social networks are now ubiquitous and most of them contain interactions
involving multiple actors (groups) like author collaborations, teams or emails
in an organizations, etc. Hypergraphs are natural structures to effectively
capture multi-actor interactions which conventional dyadic graphs fail to
capture. In this work the problem of predicting collaborations is addressed
while modeling the collaboration network as a hypergraph network. The problem
of predicting future multi-actor collaboration is mapped to hyperedge
prediction problem. Given that the higher order edge prediction is an
inherently hard problem, in this work we restrict to the task of predicting
edges (collaborations) that have already been observed in past. In this work,
we propose a novel use of hyperincidence temporal tensors to capture time
varying hypergraphs and provides a tensor decomposition based prediction
algorithm. We quantitatively compare the performance of the hypergraphs based
approach with the conventional dyadic graph based approach. Our hypothesis that
hypergraphs preserve the information that simple graphs destroy is corroborated
by experiments using author collaboration network from the DBLP dataset. Our
results demonstrate the strength of hypergraph based approach to predict higher
order collaborations (size>4) which is very difficult using dyadic graph based
approach. Moreover, while predicting collaborations of size>2 hypergraphs in
most cases provide better results with an average increase of approx. 45% in
F-Score for different sizes = {3,4,5,6,7}.


  This paper studies the quantization of the electromagnetic field on a flat
Euclidean background with boundaries. One-loop scaling factors are evaluated
for the one-boundary and two-boundary backgrounds. The mode-by-mode analysis of
Faddeev-Popov quantum amplitudes is performed by using zeta-function
regularization, and is compared with the space-time covariant evaluation of the
same amplitudes. It is shown that a particular gauge condition exists for which
the corresponding operator matrix acting on gauge modes is in diagonal form
from the beginning. Moreover, various relativistic gauge conditions are studied
in detail, to investigate the gauge invariance of the perturbative quantum
theory.


  Atomic scale characterization and manipulation with scanning probe microscopy
rely upon the use of an atomically sharp probe. Here we present automated
methods based on machine learning to automatically detect and recondition the
quality of the probe of a scanning tunneling microscope. As a model system, we
employ these techniques on the technologically relevant hydrogen-terminated
silicon surface, training the network to recognize abnormalities in the
appearance of surface dangling bonds. Of the machine learning methods tested, a
convolutional neural network yielded the greatest accuracy, achieving a
positive identification of degraded tips in 97% of the test cases. By using
multiple points of comparison and majority voting, the accuracy of the method
is improved beyond 99%. The methods described here can easily be generalized to
other material systems and nanoscale imaging techniques.


  In Yang-Mills theory, the charges of the left and right massless Fermions are
independent of each other. We propose a new paradigm where we remove this
freedom and densify the algebraic structure of Yang-Mills theory by integrating
the scalar Higgs field into a new gauge-chiral 1-form which connects Fermions
of opposite chiralities. Using the Bianchi identity, we prove that the
corresponding covariant differential is associative if and only if we gauge a
Lie-Kac super-algebra. In this model, spontaneous symmetry breakdown naturally
occurs along an odd generator of the super-algebra and induces a representation
of the Connes-Lott non commutative differential geometry of the 2-point finite
space.


  With the continuous expansion of the scale of cloud computing applications,
artificial intelligence technologies such as Deep Learning and Reinforcement
Learning have gradually become the key tools to solve the automated task
scheduling of large-scale cloud computing systems. Aiming at the complexity and
real-time requirement of task scheduling in large-scale cloud computing system,
this paper proposes an automatic task scheduling scheme based on deep learning
and reinforcement learning. Firstly, the deep learning technology is used to
monitor and predict the parameters in the cloud computing system in real time
to obtain the system status information. Then, combined with reinforcement
learning algorithm, the task scheduling strategy is dynamically adjusted
according to the real-time system state and task characteristics to achieve the
optimal utilization of system resources and the maximum of task execution
efficiency. This paper verifies the effectiveness and performance advantages of
the proposed scheme in experiments, and proves the potential and application
prospect of deep learning and reinforcement learning in automatic task
scheduling in large-scale cloud computing systems.


  This paper studies subsets of one-sided shift spaces on a finite alphabet.
Such subsets arise in symbolic dynamics, in fractal constructions, and in
number theory. We study a family of decimation operations, which extract
subsequences of symbol sequences in infinite arithmetic progressions, and show
they are closed under composition. We also study a family of $n$-ary
interleaving operations, one for each $n \ge 1$. Given subsets $X_0, X_1, ...,
X_{n-1}$ of the shift space, the $n$-ary interleaving operator produces a set
whose elements combine individual elements ${\bf x}_i$, one from each $X_i$, by
interleaving their symbol sequences cyclically in arithmetic progressions
$(\bmod\,n)$. We determine algebraic relations between decimation and
interleaving operators and the shift operator. We study set-theoretic $n$-fold
closure operations $X \mapsto X^{[n]}$, which interleave decimations of $X$ of
modulus level $n$. A set is $n$-factorizable if $X=X^{[n]}$. The $n$-fold
interleaving operators are closed under composition and are idempotent. To each
$X$ we assign the set $\mathcal{N}(X)$ of all values $n \ge 1$ for which $X=
X^{[n]}$. We characterize the possible sets $\mathcal{N}(X)$ as nonempty sets
of positive integers that form a distributive lattice under the divisibility
partial order and are downward closed under divisibility. We show that all sets
of this type occur. We introduce a class of weakly shift-stable sets and show
that this class is closed under all decimation, interleaving, and shift
operations. This class includes all shift-invariant sets. We study two notions
of entropy for subsets of the full one-sided shift and show that they coincide
for weakly shift-stable $X$, but can be different in general. We give a formula
for entropy of interleavings of weakly shift-stable sets in terms of individual
entropies.


  Although the stellar and substellar populations have been studied in various
young and old open clusters, additional studies in clusters in the age range
from 5 to 100 Myr is crucial (e.g. to give more constrains on initial mass
function variation with improved statistics). Among the open cluster candidates
from recent studies, two clusters are best suited for photometric survey of
very-low mass stars and brown dwarfs, considering their youth and relative
proximity: Alessi 5 (t ~ 40 Myr, d ~ 400 pc) and beta Monocerotis (t ~ 9.1 Myr,
d ~ 400 pc). For both clusters, we performed an optical and near-infrared
photometric survey, and a virtual observatory survey. Our survey is predicted
to be sensitive from the massive B main sequence stars down to brown dwarfs of
30 M_Jup. Here, we present and discuss preliminary results, including the mass
function obtained for Alessi 5, which is surprisingly very similar to the mass
function of the Hyades (t ~ 600 Myr), although they are of very different ages.


This paper has been withdrawn by the authors due to an error in Section 7.


  We study the geometry and topology of two infinite families Y^{p,k} of
Sasaki-Einstein seven-manifolds, that are expected to be AdS_4/CFT_3 dual to
families of N=2 superconformal field theories in three dimensions. These
manifolds, labelled by two positive integers p and k, are Lens space bundles
S^3/Z_p over CP^2 and CP^1 x CP^1, respectively. The corresponding Calabi-Yau
cones are toric. We present their toric diagrams and gauged linear sigma model
charges in terms of p and k, and find that the Y^{p,k} manifolds interpolate
between certain orbifolds of the homogeneous spaces S^7, M^{3,2} and Q^{1,1,1}.


  Many experiments have been performed that use evolutionary algorithms for
learning the topology and connection weights of a neural network that controls
a robot or virtual agent. These experiments are not only performed to better
understand basic biological principles, but also with the hope that with
further progress of the methods, they will become competitive for automatically
creating robot behaviors of interest. However, current methods are limited with
respect to the (Kolmogorov) complexity of evolved behavior. Using the evolution
of robot trajectories as an example, we show that by adding four features,
namely (1) freezing of previously evolved structure, (2) temporal scaffolding,
(3) a homogeneous transfer function for output nodes, and (4) mutations that
create new pathways to outputs, to standard methods for the evolution of neural
networks, we can achieve an approximately linear growth of the complexity of
behavior over thousands of generations. Overall, evolved complexity is up to
two orders of magnitude over that achieved by standard methods in the
experiments reported here, with the major limiting factor for further growth
being the available run time. Thus, the set of methods proposed here promises
to be a useful addition to various current neuroevolution methods.


  We investigate the rest-frame UV morphologies of a large sample of Lyman-a
emitters (LAEs) from z~2 to z~6, selected in a uniform way with 16 different
narrow- and medium-bands over the full COSMOS field. We use 3045 LAEs with HST
coverage in a stacking analysis and find that they have M_UV~-20, below M*_UV
at these redshifts. We also focus our analysis on a subsample of 780 individual
galaxies with i_AB<25 for which GALFIT converges for 429 of them. The
individual median size (re~1 kpc), ellipticities (slightly elongated with
(b/a)~0.45), S\'ersic index (disk-like with n<2) and light concentration
(comparable to that of disk or irregular galaxies, with C~2.7) of LAEs show
mild evolution from z~2 to z~6. LAEs with the highest rest-frame equivalent
widths (EW) are the smallest/most compact (re~0.8 kpc, compared to re~1.5 kpc
for the lower EW LAEs). When stacking our samples in bins of fixed Lya
luminosity and Lya EW we find evidence for redshift evolution in n and C, but
not in galaxy sizes. The evolution seems to be stronger for LAEs with 25<EW<100
{\AA}. When compared to other SFGs, LAEs are found to be smaller at all
redshifts. The difference between the two populations changes with redshift,
from a factor of ~1 at z>5 to SFGs being a factor of ~2-4 larger than LAEs for
z<2. This means that at the highest redshifts, where typical sizes approach
those of LAEs, the fraction of galaxies showing Lya in emission (and with a
high Lya escape fraction) should be much higher, consistent with observations.


  Active particles disturb the fluid around them as force dipoles, or
stresslets, which govern their collective dynamics. Unlike swimming speeds, the
stresslets of active particles are rarely determined due to the lack of a
suitable theoretical framework for arbitrary geometry. We propose a general
method, based on the reciprocal theorem of Stokes flows, to compute stresslets
as integrals of the velocities on the particle's surface, which we illustrate
for spheroidal chemically-active particles. Our method will allow tuning the
stresslet of artificial swimmers and tailoring their collective motion in
complex environments.


  We study collisions of moving nonlinear-Schr\"{o}dinger solitons with a
$\mathcal{PT}$-symmetric dipole embedded into the one-dimensional self-focusing
or defocusing medium. Accurate analytical results are produced for bright
solitons, and, in a more qualitative form, for dark ones. In the former case,
an essential aspect of the approximation is that it must take into regard the
intrinsic chirp of the soliton, thus going beyond the framework of the simplest
quasi-particle description of the soliton's dynamics. Critical velocities
separating reflection and transmission of the incident bright solitons are
found by means of numerical simulations, and in the approximate semi-analytical
form. An exact solution for the dark soliton pinned by the complex
$\mathcal{PT}$-symmetric dipole is produced too.


  Recently a three-dimensional field theory was derived that is consistent with
all the symmetries expected of the worldvolume action for multiple M2-branes.
In this note we examine several physical predictions of this model and show
that they are in agreement with expected M2-brane dynamics. In particular, we
discuss the quantization of the Chern-Simons coefficient, the vacuum moduli
space, a massive deformation leading to fuzzy three-sphere vacua, and a
possible large n limit. In this large n limit, the fuzzy funnel solution
correctly reproduces the mass of an M5-brane.


  If the atmospheric neutrino oscillation amplitude, sin^2 2theta_atm is not
maximal, there is a two fold ambiguity in the neutrino parameter space: sin^2
theta_atm>0.5 or sin^2 theta_atm<0.5. In this article, we study the impact of
this degeneracy, the so-called octant degeneracy, on the T2KK experiment, which
is a proposed extension of the T2K (Tokai-to-Kaimoka) neutrino oscillation
experiment with an additional water cherenkov detector placed in Korea. We find
that the degeneracy between sin^2 theta_atm= 0.40 and 0.60 can be resolved at
the 3sigma level for sin^2 2theta_rct>0.12 (0.08) for the optimal combination
of a 3.0^circ off-axis beam (OAB) at SK (L=295km) and a 0.5^circ OAB at
L=1000km with a far detector of 100kton volume, after 5 years of exposure with
1.0(5.0) time 10^21 POT/year, if the hierarchy is normal. We also study the
influence of the octant degeneracy on the capability of T2KK experiment to
determine the mass hierarchy and the leptonic CP phase. The capability of
rejecting the wrong mass hierarchy grows with increasing sin^2 theta_atm when
the hierarchy is normal, whereas it is rather insensitive to sin^2 theta_atm
for the inverted hierarchy. We also find that the 1sigma allowed region of the
CP phase is not affected significantly even when the octant degeneracy is not
resolved. All our results are obtained for the 22.5 kton Super-Kamiokande as a
near detector and without an anti-neutrino beam.


  Particle in Cell (PIC) simulations are a widely used tool for the
investigation of both laser- and beam-driven plasma acceleration. It is a known
issue that the beam quality can be artificially degraded by numerical Cherenkov
radiation (NCR) resulting primarily from an incorrectly modeled dispersion
relation. Pseudo-spectral solvers featuring infinite order stencils can
strongly reduce NCR, or even suppress it, and are therefore well suited to
correctly model the beam properties. For efficient parallelization of the PIC
algorithm, however, localized solvers are inevitable. Arbitrary order
pseudo-spectral methods provide this needed locality. Yet, these methods can
again be prone to NCR. Here, we show that acceptably low solver orders are
sufficient to correctly model the physics of interest, while allowing for
parallel computation by domain decomposition.


  Context. Stellar activity is currently challenging the detection of young
planets via the radial velocity (RV) technique. Aims. We attempt to
definitively discriminate the nature of the RV variations for the young active
K5 star BD+20 1790, for which visible (VIS) RV measurements show divergent
results on the existence of a substellar companion. Methods. We compare VIS
data with high precision RVs in the near infrared (NIR) range by using the
GIANO - B and IGRINS spectrographs. In addition, we present for the first time
simultaneous VIS-NIR observations obtained with GIARPS (GIANO - B and HARPS -
N) at Telescopio Nazionale Galileo (TNG). Orbital RVs are achromatic, so the RV
amplitude does not change at different wavelengths, while stellar activity
induces wavelength-dependent RV variations, which are significantly reduced in
the NIR range with respect to the VIS. Results. The NIR radial velocity
measurements from GIANO - B and IGRINS show an average amplitude of about one
quarter with respect to previously published VIS data, as expected when the RV
jitter is due to stellar activity. Coeval multi-band photometry surprisingly
shows larger amplitudes in the NIR range, explainable with a mixture of cool
and hot spots in the same active region. Conclusions. In this work, the claimed
massive planet around BD+20 1790 is ruled out by our data. We exploited the
crucial role of multi- wavelength spectroscopy when observing young active
stars: thanks to facilities like GIARPS that provide simultaneous observations,
this method can reach its maximum potential.


  A cellular automaton is a parallel synchronous computing model, which
consists in a juxtaposition of finite automata whose state evolves according to
that of their neighbors. It induces a dynamical system on the set of
configurations, i.e. the infinite sequences of cell states. The limit set of
the cellular automaton is the set of configurations which can be reached
arbitrarily late in the evolution.
  In this paper, we prove that all properties of limit sets of cellular
automata with binary-state cells are undecidable, except surjectivity. This is
a refinement of the classical "Rice Theorem" that Kari proved on cellular
automata with arbitrary state sets.


  In the field of environmental science, it is crucial to have robust
evaluation metrics for large language models to ensure their efficacy and
accuracy. We propose EnviroExam, a comprehensive evaluation method designed to
assess the knowledge of large language models in the field of environmental
science. EnviroExam is based on the curricula of top international
universities, covering undergraduate, master's, and doctoral courses, and
includes 936 questions across 42 core courses. By conducting 0-shot and 5-shot
tests on 31 open-source large language models, EnviroExam reveals the
performance differences among these models in the domain of environmental
science and provides detailed evaluation standards. The results show that 61.3%
of the models passed the 5-shot tests, while 48.39% passed the 0-shot tests. By
introducing the coefficient of variation as an indicator, we evaluate the
performance of mainstream open-source large language models in environmental
science from multiple perspectives, providing effective criteria for selecting
and fine-tuning language models in this field. Future research will involve
constructing more domain-specific test sets using specialized environmental
science textbooks to further enhance the accuracy and specificity of the
evaluation.


  Real-world machine learning systems need to analyze test data that may differ
from training data. In K-way classification, this is crisply formulated as
open-set recognition, core to which is the ability to discriminate open-set
data outside the K closed-set classes. Two conceptually elegant ideas for
open-set discrimination are: 1) discriminatively learning an open-vs-closed
binary discriminator by exploiting some outlier data as the open-set, and 2)
unsupervised learning the closed-set data distribution with a GAN, using its
discriminator as the open-set likelihood function. However, the former
generalizes poorly to diverse open test data due to overfitting to the training
outliers, which are unlikely to exhaustively span the open-world. The latter
does not work well, presumably due to the instable training of GANs. Motivated
by the above, we propose OpenGAN, which addresses the limitation of each
approach by combining them with several technical insights. First, we show that
a carefully selected GAN-discriminator on some real outlier data already
achieves the state-of-the-art. Second, we augment the available set of real
open training examples with adversarially synthesized "fake" data. Third and
most importantly, we build the discriminator over the features computed by the
closed-world K-way networks. This allows OpenGAN to be implemented via a
lightweight discriminator head built on top of an existing K-way network.
Extensive experiments show that OpenGAN significantly outperforms prior
open-set methods.


  The large discrepancy between the amount of baryons that were synthesized in
Big-Bang and that we detect at $z=0$ locked in stars inside galaxies and in
hot/cold gas in galaxies. goup and clusters, is a well known crucial issue for
present day cosmology. It is also thought that this riddle is at the backbone
of how the ordinary matter has formed, inside dark potential wells, the
structures we see today. Then, it is useful to reprint, by means of this
archive, the first (pre- astro-ph era) published work in which the amount of
"luminous" baryons was actually and suitably computed and the disagreement with
the BBN predictions strongly claimed. This because 12 years after the method
and the results are still up-to-date, and above all, people are occasionally
found to know only later works (appeared on astro-ph) that were meant to be
just (ours or other's) follow ups of the present paper.


  Anomalies are by definition rare, thus labeled examples are very limited or
nonexistent, and likely do not cover unforeseen scenarios. Unsupervised
learning methods that don't necessarily encounter anomalies in training would
be immensely useful. Generative vision models can be useful in this regard but
do not sufficiently represent normal and abnormal data distributions. To this
end, we propose constant curvature manifolds for embedding data distributions
in unsupervised visual anomaly detection. Through theoretical and empirical
explorations of manifold shapes, we develop a novel hyperspherical Variational
Auto-Encoder (VAE) via stereographic projections with a gyroplane layer - a
complete equivalent to the Poincar\'e VAE. This approach with manifold
projections is beneficial in terms of model generalization and can yield more
interpretable representations. We present state-of-the-art results on visual
anomaly benchmarks in precision manufacturing and inspection, demonstrating
real-world utility in industrial AI scenarios. We further demonstrate the
approach on the challenging problem of histopathology: our unsupervised
approach effectively detects cancerous brain tissue from noisy whole-slide
images, learning a smooth, latent organization of tissue types that provides an
interpretable decisions tool for medical professionals.


  Temporal Localized States (TLSs) are individually addressable structures
traveling in optical resonators. They can be used as bits of information and to
generate frequency combs with tunable spectral density. We show that a pair of
specially designed nonlinear mirrors, a 1/2 Vertical-Cavity Surface-Emitting
Laser and a Semiconductor Saturable Absorber, coupled in self-imaging
conditions, can lead to the generation of such TLSs. Our results indicate how a
conventional passive mode- locking scheme can be adapted to provide a robust
and simple system emitting TLSs and it paves the way towards the observation of
three dimensions confined states, the so-called light bullets.


  The formalisms of Wyld [2] and Martin, Siggia, and Rose (MSR) [3] address the
closure problem of a statistical treatment of homogeneous isotropic turbulence
(HIT) based on techniques primarily developed for quantum field theory. In the
Wyld formalism, there is a well-known double-counting problem, for which an
{\it ad hoc} solution was suggested by Lee [44]. We show how to implement this
correction in a more natural way from the basic equations of the formalism.
This leads to what we call the "Improved Wyld-Lee Renormalized Perturbation
Theory". MSR had noted that their formalism had more vertex functions than
Wyld's formalism and based on this felt Wyld's formalism was incorrect. However
a careful comparison of both formalisms here shows that the Wyld formalism
follows a different procedure to that of the MSR formalism and so the treatment
of vertex corrections appears in different ways in the two formalisms. Taking
that into account, along with clarifications made to both formalisms, we find
that they are equivalent and we demonstrate this up to fourth order.


  Few-Shot Class-Incremental Learning (FSCIL) models aim to incrementally learn
new classes with scarce samples while preserving knowledge of old ones.
Existing FSCIL methods usually fine-tune the entire backbone, leading to
overfitting and hindering the potential to learn new classes. On the other
hand, recent prompt-based CIL approaches alleviate forgetting by training
prompts with sufficient data in each task. In this work, we propose a novel
framework named Attention-aware Self-adaptive Prompt (ASP). ASP encourages
task-invariant prompts to capture shared knowledge by reducing specific
information from the attention aspect. Additionally, self-adaptive
task-specific prompts in ASP provide specific information and transfer
knowledge from old classes to new classes with an Information Bottleneck
learning objective. In summary, ASP prevents overfitting on base task and does
not require enormous data in few-shot incremental tasks. Extensive experiments
on three benchmark datasets validate that ASP consistently outperforms
state-of-the-art FSCIL and prompt-based CIL methods in terms of both learning
new classes and mitigating forgetting.


  We describe basic periodic trapping configurations for ultracold atoms above
surfaces. The approach is based on a simple wire grid and can be scaled to
provide large arrays of periodically arranged magnetic or magneto-optical
traps. The unit cells of the trap lattices are based on crossed wire segments.
By alternating the current directions in the wires of the grid it can be
distinguished between 3 basic lattice configurations. As a first demonstration,
we used macroscopic wires in a 2 layer configuration to realize the unit cells
of the lattices. With this experimental setup, we observe two of the basic unit
cells and an array of 2x2 magneto optical traps.


  Neural Radiance Fields (NeRF) is a technique for high quality novel view
synthesis from a collection of posed input images. Like most view synthesis
methods, NeRF uses tonemapped low dynamic range (LDR) as input; these images
have been processed by a lossy camera pipeline that smooths detail, clips
highlights, and distorts the simple noise distribution of raw sensor data. We
modify NeRF to instead train directly on linear raw images, preserving the
scene's full dynamic range. By rendering raw output images from the resulting
NeRF, we can perform novel high dynamic range (HDR) view synthesis tasks. In
addition to changing the camera viewpoint, we can manipulate focus, exposure,
and tonemapping after the fact. Although a single raw image appears
significantly more noisy than a postprocessed one, we show that NeRF is highly
robust to the zero-mean distribution of raw noise. When optimized over many
noisy raw inputs (25-200), NeRF produces a scene representation so accurate
that its rendered novel views outperform dedicated single and multi-image deep
raw denoisers run on the same wide baseline input images. As a result, our
method, which we call RawNeRF, can reconstruct scenes from extremely noisy
images captured in near-darkness.


  The compressibility of molecular cloud (MC) turbulence plays a crucial role
in star formation models, because it controls the amplitude and distribution of
density fluctuations. The relation between the compressive ratio (the ratio of
powers in compressive and solenoidal motions) and the statistics of turbulence
has been previously studied systematically only in idealized simulations with
random external forces. In this work, we analyze a simulation of large-scale
turbulence (250 pc) driven by supernova (SN) explosions that has been shown to
yield realistic MC properties. We demonstrate that SN driving results in MC
turbulence with a broad lognormal distribution of the compressive ratio, with a
mean value $\approx 0.3$, lower than the equilibrium value of $\approx 0.5$
found in the inertial range of isothermal simulations with random solenoidal
driving. We also find that the compressibility of the turbulence is not
noticeably affected by gravity, nor are the mean cloud radial (expansion or
contraction) and solid-body rotation velocities. Furthermore, the clouds follow
a general relation between the rms density and the rms Mach number similar to
that of supersonic isothermal turbulence, though with a large scatter, and
their average gas density PDF is described well by a lognormal distribution,
with the addition of a high-density power-law tail when self-gravity is
included.


  We discuss the construction of a nuclear Energy Density Functional (EDF) from
ab initio calculations, and we advocate the need of a methodical approach that
is free from ad hoc assumptions. The equations of state (EoS) of symmetric
nuclear and pure neutron matter are computed using the chiral NNLO$_{\rm sat}$
and the phenomenological AV4$^\prime$+UIX$_{c}$ Hamiltonians as inputs in the
Self-consistent Green's Function (SCGF) and Auxiliary Field Diffusion Monte
Carlo (AFDMC) methods, respectively. We propose a convenient parametrization of
the EoS as a function of the Fermi momentum and fit it on the SCGF and AFDMC
calculations. We apply the ab initio-based EDF to carry out an analysis of the
binding energies and charge radii of different nuclei in the local density
approximation. The NNLO$_{\rm sat}$-based EDF produces encouraging results,
whereas the AV4$^\prime$+UIX$_{c}$-based one is farther from experiment.
Possible explanations of these different behaviors are suggested, and the
importance of gradient and spin-orbit terms is analyzed. Our work paves the way
for a practical and systematic way to merge ab initio nuclear theory and DFT,
while at the same time it sheds light on some of the critical aspects of this
procedure.


  The liquid spinodal, which is the bedrock of water thermodynamics, has long
been discussed alongside the elusive liquid-liquid critical point hidden behind
the limit of homogeneous nucleation. This has inspired numerous scenarios that
attempt to explain water anomalies. Despite recent breakthrough experiments
eliminating several of thous scenarios, we lacked a tool to localize the
spinodal and the liquid-liquid critical point. We constructed a unique equation
of state combining the famous Speedy's equation and the liquid-liquid critical
point to remove that deficit and to review these explanations. For the first
time, the proposed equation of state independently depicts the spinodal in the
presence of the liquid-liquid critical point and demonstrates that the
explanation for water anomalies based on the reentrance of the spinodal is not
valid; this feature (reentering the spinodal) was solely predicted based on the
curved density surface caused by the existence of the second critical point.
However, the critical point alone is not sufficient to explain the shape of the
density surface of water. In the new equation, hydrogen bond cooperativity is
important to force the critical point to exist outside of zero temperature.
Together with mounting evidence for the existence of a compressibility maximum
behind the homogeneous nucleation limit at positive pressure, the findings
practically exclude all explanations for water anomalies except for the
existence of the liquid-liquid critical point at positive pressure. Finally, an
extensive study of heat capacity demonstrated profound disagreement between the
two major experimental heat capacity datasets and identified the more accurate
dataset.


  Twisted compactification of the 6d N=(2,0) theories on a punctured Riemann
surface give a large class of 4d N=1 and N=2 gauge theories, called class S. We
argue that nonperturbative dynamics of class S theories are described by 5d
maximal Super-Yang-Mills (SYM) twisted on the Riemann surface. In a sense,
twisted 5d SYM might be regarded as a "Lagrangian" for class S theories on
R^{1,2} times S^1. First, we show that twisted 5d SYM gives generalized
Hitchin's equations which was proposed recently. Then, we discuss how to
identify chiral operators with quantities in twisted 5d SYM. Mesons, or
holomorphic moment maps, are identified with operators at punctures which are
realized as 3d superconformal theories T_rho[G] coupled to twisted 5d SYM.
"Baryons" are identified qualitatively through a study of 4d N=2 Higgs
branches. We also derive a simple formula for dynamical superpotential vev
which is relevant for BPS domain wall tensions. With these tools, we examine
many examples of 4d N=1 theories with several phases such as confining, Higgs,
and Coulomb phases, and show perfect agreements between field theories and
twisted 5d SYM. Spectral curve is an essential tool to solve generalized
Hitchin's equations, and our results clarify the physical information encoded
in the curve.


  Conformal predictive systems are a recent modification of conformal
predictors that output, in regression problems, probability distributions for
labels of test observations rather than set predictions. The extra information
provided by conformal predictive systems may be useful, e.g., in decision
making problems. Conformal predictive systems inherit the relative
computational inefficiency of conformal predictors. In this paper we discuss
two computationally efficient versions of conformal predictive systems, which
we call split conformal predictive systems and cross-conformal predictive
systems. The main advantage of split conformal predictive systems is their
guaranteed validity, whereas for cross-conformal predictive systems validity
only holds empirically and in the absence of excessive randomization. The main
advantage of cross-conformal predictive systems is their greater predictive
efficiency.


  In this paper, we provide a systematic approach for the design of stabilizing
feedback controllers for nonlinear control systems using the Koopman operator
framework. The Koopman operator approach provides a linear representation for a
nonlinear dynamical system and a bilinear representation for a nonlinear
control system. The problem of feedback stabilization of a nonlinear control
system is then transformed to the stabilization of a bilinear control system.
We propose a control Lyapunov function (CLF)-based approach for the design of
stabilizing feedback controllers for the bilinear system. The search for
finding a CLF for the bilinear control system is formulated as a convex
optimization problem. This leads to a schematic procedure for designing
CLF-based stabilizing feedback controllers for the bilinear system and hence
the original nonlinear system. Another advantage of the proposed controller
design approach outlined in this paper is that it does not require explicit
knowledge of system dynamics. In particular, the bilinear representation of a
nonlinear control system in the Koopman eigenfunction space can be obtained
from time-series data. Simulation results are presented to verify the main
results on the design of stabilizing feedback controllers and the data-driven
aspect of the proposed approach.


  Electromagnetic interactions of the ground and first radial excited states of
$\eta$ and $\eta'$ mesons in the framework of the extended $U(3)\times U(3)$
NJL model are considered. The radial excitations are described with the help of
polynomial form factor of the second order over the inner quark momentum. The
solution of the $U_A(1)$ problem by means of 't Hooft interaction is taken into
account. For diagonalization of the free Lagrangian the $4\times4$ mixing
matrix is used. Two-photon decay widths of the ground $\eta$ and $\eta'$ meson
are found to be in a satisfactory agreement with the experiment. Predictions
for the two-photon decay widths of $\eta(1295)$ and $\eta(1475)$ are given. The
probabilities of eta meson production by two-photon mechanism in $e^+e^-$
collisions and of their photoproduction on electrons are calculated.


  We prove that the stochastic Burgers equation on $\mathbf{R}^{d}$, $d<4$,
forced by gradient noise that is white in time and smooth in space, admits
spacetime-stationary solutions. These solutions are thus the gradients of
solutions to the KPZ equation on $\mathbf{R}^{d}$ with stationary gradients.
The proof works by proving tightness of the time-averaged laws of the solutions
in an appropriate weighted space.


  The presence of minor bodies in exoplanetary systems is in most cases
inferred through infra-red excesses, with the exception of exocomets. Even if
over 35 years have passed since the first detection of exocomets around beta
Pic, only ~ 25 systems are known to show evidence of evaporating bodies, and
most of them have only been observed in spectroscopy. With the appearance of
new high-precision photometric missions designed to search for exoplanets, such
as CHEOPS, a new opportunity to detect exocomets is available. Combining data
from CHEOPS and TESS we investigate the lightcurve of 5 Vul, an A-type star
with detected variability in spectroscopy, to search for non periodic transits
that could indicate the presence of dusty cometary tails in the system. While
we did not find any evidence of minor bodies, the high precision of the data,
along with the combination with previous spectroscopic results and models,
allows for an estimation of the sizes and spatial distribution of the
exocomets.


  It is demonstrated that in simple soliton models essential features of the
electro-magnetic nucleon form factors observed over three orders of magnitude
in momentum transfer $t$ are naturally reproduced. The analysis shows that
three basic ingredients are required: an extended object, partial coupling to
vector mesons, and relativistic recoil corrections. We use for the extended
object the standard skyrmion, one vector meson propagator for both isospin
channels, and the relativistic boost to the Breit frame. Continuation to
timelike $t$ leads to quite stable results for the spectral functions in the
regime from the 2- or 3-pion threshold to about two rho masses. Especially the
onset of the continuous part of the spectral functions at threshold can be
reliably determined and there are strong analogies to the results imposed on
dispersion theoretic approaches by the unitarity constraint.


  The Data for Refugees (D4R) Challenge is a non-profit challenge initiated to
improve the conditions of the Syrian refugees in Turkey by providing a special
database to scientific community for enabling research on urgent problems
concerning refugees, including health, education, unemployment, safety, and
social integration. The collected database is based on anonymised mobile Call
Detail Record (CDR) of phone calls and SMS messages from one million Turk
Telekom customers. It indicates broad activity and mobility patterns of
refugees and citizens in Turkey for one year. The data collection period is
from 1 January 2017 to 31 December 2017. The project is initiated by Turk
Telekom, in partnership with the Turkish Academic and Research Council
(TUBITAK) and Bogazici University, and in collaboration with several academic
and non-governmental organizations, including UNHCR Turkey, UNICEF, and
International Organization for Migration.


  An expression for the curvature of the "covariant" determinant line bundle is
given in even dimensional space-time. The usefulness is guaranteed by its
prediction of the covariant anomaly and Schwinger term. It allows a parallel
derivation of the consistent anomaly and Schwinger term, and their covariant
counterparts, which clarifies the similarities and differences between them. In
particular, it becomes clear that in contrary to the case for anomalies, the
difference between the consistent and covariant Schwinger term can not be
extended to a local form on the space of gauge potentials.


  The electronic spin in quantum dots can be described by central spin models
(CSMs) with a very large number $N_\mathrm{eff} \approx 10^4$ to $10^6$ of bath
spins posing a tremendous challenge to theoretical simulations. Here, a fully
quantum mechanical theory is developed for the limit $N_\mathrm{eff} \to
\infty$ by means of iterated equations of motion (iEoM). We find that the CSM
can be mapped to a four-dimensional impurity coupled to a non-interacting
bosonic bath in this limit. Remarkably, even for infinite bath the CSM does not
become completely classical. The data obtained by the proposed iEoM approach is
tested successfully against data from other, established approaches. Thus, the
iEoM mapping extends the set of theoretical tools which can be used to
understand the spin dynamics in large CSMs.


  Numerous prognostic factors are currently assessed histopathologically in
biopsies of canine mast cell tumors to evaluate clinical behavior. In addition,
PCR analysis of the c-Kit exon 11 mutational status is often performed to
evaluate the potential success of a tyrosine kinase inhibitor therapy. This
project aimed at training deep learning models (DLMs) to identify the c-Kit-11
mutational status of MCTs solely based on morphology without additional
molecular analysis. HE slides of 195 mutated and 173 non-mutated tumors were
stained consecutively in two different laboratories and scanned with three
different slide scanners. This resulted in six different datasets
(stain-scanner variations) of whole slide images. DLMs were trained with single
and mixed datasets and their performances was assessed under scanner and
staining domain shifts. The DLMs correctly classified HE slides according to
their c-Kit 11 mutation status in, on average, 87% of cases for the best-suited
stain-scanner variant. A relevant performance drop could be observed when the
stain-scanner combination of the training and test dataset differed.
Multi-variant datasets improved the average accuracy but did not reach the
maximum accuracy of algorithms trained and tested on the same stain-scanner
variant. In summary, DLM-assisted morphological examination of MCTs can predict
c-Kit-exon 11 mutational status of MCTs with high accuracy. However, the
recognition performance is impeded by a change of scanner or staining protocol.
Larger data sets with higher numbers of scans originating from different
laboratories and scanners may lead to more robust DLMs to identify c-Kit
mutations in HE slides.


  Surface registration is a technique that is used in various areas such as
object recognition and 3D model reconstruction. Problem of surface registration
can be analyzed as an optimization problem of seeking a rigid motion between
two different views. Genetic algorithms can be used for solving this
optimization problem, both for obtaining the robust parameter estimation and
for its fine-tuning. The main drawback of genetic algorithms is that they are
time consuming which makes them unsuitable for online applications. Modern
acquisition systems enable the implementation of the solutions that would
immediately give the information on the rotational angles between the different
views, thus reducing the dimension of the optimization problem. The paper gives
an analysis of the genetic algorithm implemented in the conditions when the
rotation matrix is known and a comparison of these results with results when
this information is not available.


  Direct numerical simulations of homogeneous decaying turbulence with mild
background rotation show the existence of a systematic and significant
imbalance between the non-linear energy cascade to small scales and its
dissipation. By starting the decay from a statistically stationary and fully
developed rotating turbulence state, where the dissipation and the energy flux
are approximately equal, the data shows a growing imbalance between the two
until a maximum is reached when the dissipation is about twice the energy flux.
This dichotomy of behaviours during decay is reminiscent of the non-equilibrium
and the equilibrium regions previously reported for non-rotating turbulence
[P.C. Valente, J.C. Vassilicos, Phys. Rev. Lett. {\bf 108} 214503 (2012)].
Note, however, that for decaying rotating turbulence the classical scaling of
the dissipation rate $\epsilon \propto u'^3/L$ (where $u'$ and $L$ are the root
mean square fluctuating velocity and the integral length scale, respectively)
does not appear to hold during decay, which may be attributed to the effect of
the background rotation on the energy cascade. On the other hand, the maximum
energy flux holds the scaling $\Pi_{max} \propto u'^3/L$ in the initial stage
of the decay until the maximum imbalance is reached.


  The hyperfine field $B_\text{hf}$ and the magnetic properties of the
BaFe$_2$As$_2$ family are studied using the fully relativistic Dirac formalism
for different types of substitution. The study covers electron doped
Ba(Fe$_{1-x}$Co$_x$)$_2$As$_2$ and Ba(Fe$_{1-x}$Ni$_x$)$_2$As$_2$, hole doped
(Ba$_{1-x}$K$_x$)Fe$_2$As$_2$ and also isovalently doped
Ba(Fe$_{1-x}$Ru$_x$)$_2$As$_2$ and BaFe$_2$(As$_{1-x}$P$_x$)$_2$ for a wide
range of the concentration $x$. For the substituted compounds the hyperfine
fields show a very strong dependence on the dopant type and its concentration
$x$. Relativistic contributions were found to have a significantly stronger
impact for the iron pnictides when compared to bulk Fe. As an important
finding, we demonstrate that it is not sensible to relate the hyperfine field
$B_\text{hf}$ to the average magnetic moment $\mu$ of the compound, as it was
done in earlier literature.


  We formulate scalar field theories coupled non-conformally to gravity in a
manifestly frame-independent fashion. Physical quantities such as the $S$
matrix should be invariant under field redefinitions, and hence can be
represented by the geometry of the target space. This elegant geometric
formulation, however, is obscured when considering the coupling to gravity
because of the redundancy associated with the Weyl transformation. The
well-known example is the Higgs inflation, where the target space of the Higgs
fields is flat in the Jordan frame but is curved in the Einstein frame.
Furthermore, one can even show that any geometry of O$(N)$ nonlinear $\sigma$
models can be flattened by an appropriate Weyl transformation. In this Letter,
we extend the notion of the target space by including the conformal mode of the
metric, and show that the extended geometry provides a compact formulation that
is manifestly Weyl-transformation/field-redefinition invariant. We identify the
cutoff scale with the inverse of square root of the extended target space
curvature and confirm that it coincides with that obtained from two-to-two
scattering amplitudes based on our formalism.


  We study power dissipation for systems of multiple quantum wires meeting at a
junction, in terms of a current splitting matrix (M) describing the junction.
We present a unified framework for studying dissipation for wires with either
interacting electrons (i.e., Tomonaga-Luttinger liquid wires with Fermi liquid
leads) or non-interacting electrons. We show that for a given matrix M, the
eigenvalues of M^T M characterize the dissipation, and the eigenvectors
identify the combinations of bias voltages which need to be applied to the
different wires in order to maximize the dissipation associated with the
junction. We use our analysis to propose and study some microscopic models of a
dissipative junction which employ the edge states of a quantum Hall liquid.
These models realize some specific forms of the M-matrix whose entries depends
on the tunneling amplitudes between the different edges.


  Transverse spin and transverse momentum distribution functions of the
constituents of the nucleon are a crucial input for a complete description of
the nucleon. COMPASS measured such for longitudinally and transversely
polarized deuterons and protons. In the following we will focus on recent
results from the 2007 transverse proton data and on the results for unpolarized
deuterons.


  An important characteristic of cyber-physical systems is their capability to
respond, in-time, to events from their physical environment. However, to the
best of our knowledge there exists no benchmark for assessing and comparing the
interrupt handling performance of different software stacks. Hence, we present
a flexible evaluation method for measuring the interrupt latency and throughput
on ARMv8-A based platforms. We define and validate seven test-cases that stress
individual parts of the overall process and combine them to three benchmark
functions that provoke the minimal and maximal interrupt latency, and maximal
interrupt throughput.


  In the hard pomeron theory with the number of colours Nc in the infinity
limit the diffractive amplitude obtained in [3] is compared with the results
found for Nc=3 in [1] and in the dipole approach in [5]. It is shown that the
double pomeron exchange contribution can be substituted by an equivalent triple
pomeron interaction term. After such a substitution the triple pomeron vertices
in [1,3,5] essentially coincide. It is demonstrated that, in any form, the
triple pomeron vertex is conformal invariant. It is also shown that higher
order densities in the dipole approach do not involve 1 to k pomeron verteces
with k>2 but are rather given by a set of pomeron fan diagrams with only a
triple pomeron coupling.


  This paper introduces ``TOPress3D," a 3D topology optimization MATLAB code
for structures subjected to design-dependent pressure loads. With a primary
focus on pedagogical objectives, the code provides an easy learning experience,
making it a valuable tool and practical gateway for newcomers, students, and
researchers towards this topic. TOPress3D uses Darcy's law with a drainage term
to link the given pressure load to design variables, which is converted to
consistent nodal loads. Compliance minimization subjected to volume constraint
optimization problems with pressure loads are solved. Load sensitivities
arising due to design-dependent nature of the loads are evaluated using the
adjoint-variable approach. The method of moving asymptotes is used to update
the design variables. TOPress3D is constituted by six main parts. Each is
described in detail. The code is also tailored to solve different problems. The
robustness and success of the code are demonstrated while designing a few
pressure load-bearing structures. The code is provided in Appendix B and is
available with extensions in the supplementary material and publicly at
\url{https://github.com/PrabhatIn/TOPress3D}.


  We introduce a comprehensive analytical framework to compare between open
access and closed access in two-tier femtocell networks, with regard to uplink
interference and outage. Interference at both the macrocell and femtocell
levels is considered. A stochastic geometric approach is employed as the basis
for our analysis. We further derive sufficient conditions for open access and
closed access to outperform each other in terms of the outage probability,
leading to closed-form expressions to upper and lower bound the difference in
the targeted received power between the two access modes. Simulations are
conducted to validate the accuracy of the analytical model and the correctness
of the bounds.


  We study the representation theory of the cyclotomic Brauer algebra via
truncation to idempotent subalgebras which are isomorphic to a product of
walled and classical Brauer algebras. In particular, we determine the block
structure and decomposition numbers in characteristic zero.


  Composite function minimization captures a wide spectrum of applications in
both computer vision and machine learning. It includes bound constrained
optimization and cardinality regularized optimization as special cases. This
paper proposes and analyzes a new Matrix Splitting Method (MSM) for minimizing
composite functions. It can be viewed as a generalization of the classical
Gauss-Seidel method and the Successive Over-Relaxation method for solving
linear systems in the literature. Incorporating a new Gaussian elimination
procedure, the matrix splitting method achieves state-of-the-art performance.
For convex problems, we establish the global convergence, convergence rate, and
iteration complexity of MSM, while for non-convex problems, we prove its global
convergence. Finally, we validate the performance of our matrix splitting
method on two particular applications: nonnegative matrix factorization and
cardinality regularized sparse coding. Extensive experiments show that our
method outperforms existing composite function minimization techniques in term
of both efficiency and efficacy.


  Recently, the $L_p$ dual Minkowski problem for unbounded closed convex sets
in a pointed closed convex cone was proposed and a weak solution to this
problem was provided. In smooth setting, this problem is equivalent to solving
the Dirichlet problem for a class of Monge-Amp\`ere type equations. In this
paper, we show the existence, regularity and uniqueness of solutions to this
Monge-Amp\`ere type equation in the case $p\geq 1$ by studying variational
properties for a family of Monge-Amp\`ere functionals. Moreover, the existence
and optimal global H\"older regularity in the case $p<1$ and $q\geq n$ is also
be discussed.


  We discuss theoretical predictions for W-pair production and decay at LEP2
and higher energies in a form suitable for comparison with raw data. We present
a practical framework for calculating uncertainties of predictions given by the
KORALW and grc4f Monte Carlo programs. As an example we use observables in the
$s\bar s c\bar c$ decay channel: the total four-quark (four-jet) cross section
and two-quark/jet invariant-mass distribution and cross section, in the case
when the other two may escape detection. Effects of QED bremsstrahlung,
effective couplings, running W and Z widths, Coulomb interaction and the
complete tree level set of diagrams are discussed. We also revisit the question
of technical precision of the new version 1.21 of the KORALW Monte Carlo code
as well as of version 1.2(26) of the grc4f one.
  Finally we find predictions of the two programs to have an overall physical
uncertainty of 2%.
  As a side result we show, on the example of an $s\bar s$ invariant mass
distribution, the strong interplay of spin correlations and detector cut-offs
in the case of four-fermion final states.


  Chemotaxis and auto-chemotaxis play an important role in many essential
biological processes. We present a self-propelling artificial swimmer system
which exhibits chemotaxis as well as negative auto-chemotaxis. Oil droplets in
an aqueous surfactant solution are driven by interfacial Marangoni flows
induced by micellar solubilization of the oil phase. We demonstrate that
chemotaxis along micellar surfactant gradients can guide these swimmers through
a microfluidic maze. Similarly, a depletion of empty micelles in the wake of a
droplet swimmer causes negative autochemotaxis and thereby trail avoidance. We
have studied autochemotaxis quantitatively in a microfluidic device of
bifurcating channels: Branch choices of consecutive swimmers are
anticorrelated, an effect decaying over time due to trail dispersion. We have
modeled this process by a simple one-dimensional diffusion process and
stochastic Langevin dynamics. Our results are consistent with a linear
surfactant gradient force and diffusion constants appropriate for micellar
diffusion, and provide a measure of autochemotactic feedback strength versus
stochastic forces.


  Tactile perception plays an important role in medical simulation and
training, specifically in surgery. The surgeon must feel organic tissue
hardness, evaluate anatomical structures, measure tissue properties, and apply
appropriate force control actions for safe tissue manipulation. Development of
novel cost effective haptic-based simulators and their introduction in the
minimally invasive surgery learning cycle can absorb the learning curve for
residents. Receiving pre-training in a core set of surgical skills can reduce
skill acquisition time and risks. We present the development of a
cost-effective visuo-haptic simulator for the liver tissue, designed to improve
practice-based education in minimally invasive surgery. Such systems can
positively affect the next generations of learners by enhancing their knowledge
in connection with real-life situations while they train in mandatory safe
conditions.