  We assess the potential of using presupernova neutrino signals at the
Jiangmen Underground Neutrino Observatory (JUNO) to probe the yet-unknown
neutrino mass hierarchy. Using models for stars of 12, 15, 20, and 25 solar
masses, we find that if the electron antineutrino signals from such a star can
be predicted precisely and the star is within ~440-880 pc, the number of events
of electron antineutrino captures on protons detected within one day of its
explosion allows to determine the hierarchy at the > ~95% confidence level. For
determination at this level using such signals from Betelgeuse, which is at a
distance of ~222 pc, the uncertainty in the predicted number of signals needs
to be < ~14-30%. In view of more realistic uncertainties, we discuss and
advocate a model-independent determination using both electron neutrino and
antineutrino signals from Betelgeuse. This method is feasible if the cosmogenic
background for neutrino-electron scattering events can be reduced by a factor
of ~2.5-10 from the current estimate. Such reduction might be achieved by using
coincidence of the background events, the exploration of which for JUNO is
highly desirable.


  Productivity issues such as lengthy compilation and limited code reuse have
restricted usage of field-programmable gate arrays (FPGAs), despite significant
technical advantages. Recent work into overlays -- virtual coarse-grained
architectures implemented atop FPGAs -- has aimed to address these concerns
through abstraction, but have mostly focused on pipelined applications with
minimal control requirements. Although research has introduced overlays for
finite-state machines, those architectures suffer from limited scalability and
flexibility, which we address with a new overlay architecture using memory
decomposition on transitional logic. Although our overlay provides modest
average improvements of 15% to 29% fewer lookup tables for individual
finite-state machines, for the more common usage of an overlay supporting
different finite-state machines, our overlay achieves a 77% to 99% reduction in
lookup tables. In addition, our overlay reduces compilation time to tenths of a
second to enable rapid iterative-development methodologies.


  We propose a novel diffusion-based image generation method called the
observation-guided diffusion probabilistic model (OGDM), which effectively
addresses the tradeoff between quality control and fast sampling. Our approach
reestablishes the training objective by integrating the guidance of the
observation process with the Markov chain in a principled way. This is achieved
by introducing an additional loss term derived from the observation based on a
conditional discriminator on noise level, which employs a Bernoulli
distribution indicating whether its input lies on the (noisy) real manifold or
not. This strategy allows us to optimize the more accurate negative
log-likelihood induced in the inference stage especially when the number of
function evaluations is limited. The proposed training scheme is also
advantageous even when incorporated only into the fine-tuning process, and it
is compatible with various fast inference strategies since our method yields
better denoising networks using the exactly the same inference procedure
without incurring extra computational cost. We demonstrate the effectiveness of
our training algorithm using diverse inference techniques on strong diffusion
model baselines. Our implementation is available at
https://github.com/Junoh-Kang/OGDM_edm.


  In this paper, we present a distributed resource allocation mechanism in
cognitive radio networks, based on a new coopeti-tion methodology, which
combines advantages of nodes competition and cooperation. We postulate that
this new method allows for fully distributed resource management between
cognitive radio devices. The presented framework is generic, however, we
consider it for the application in OFDMA networks. Coopetition takes the best
from cooperative and competitive problem formulation and provides the
opportunity to control the balance between fairness and spectral efficiency
(SE) of resource allocation. Simulation results confirm that coopetition allows
for efficient resource utilization, and may be used practically in wireless
cognitive networks.


  The confrontation between percolation processes and superconducting
fluctuations to account for the observed enhanced in-plane electrical
conductivity above but near $T_c$ in cuprates is revisited. The cuprates
studied here, La$_{1.85}$Sr$_{0.15}$CuO$_4$,
Bi$_2$Sr$_2$CaCu$_2$O$_{8+\delta}$, and Tl$_2$Ba$_2$Ca$_2$Cu$_3$O$_{10}$, have
a different number of superconducting CuO$_2$ layers per unit-cell length and
different Josephson coupling between them, and are optimally-doped to minimize
$T_c$-inhomogeneities. The excellent chemical and structural quality of these
samples also contribute to minimize the effect of extrinsic
$T_c$-inhomogeneities, a crucial aspect when analyzing the possible presence of
intrinsic percolative processes. Our analyses also cover the so-called high
reduced-temperature region, up to the resistivity rounding onset
$\varepsilon_{onset}$. By using the simplest form of the effective-medium
theory, we show that possible emergent percolation processes alone cannot
account for the measured enhanced conductivity. In contrast, these measurements
can be quantitatively explained using the Gaussian-Ginzburg-Landau (GGL)
approach for the effect of superconducting fluctuations in layered
superconductors, extended to $\varepsilon_{onset}$ by including a total energy
cutoff, which takes into account the limits imposed by the Heisenberg
uncertainty principle to the shrinkage of the superconducting wavefunction. Our
analysis confirms the adequacy of this cutoff, and that the effective
periodicity length is controlled by the relative Josephson coupling between
superconducting layers. These conclusions are reinforced by analyzing one of
the recent works that allegedly discards the superconducting fluctuations
scenario while supporting a percolative scenario for the enhanced conductivity
above $T_c$ in cuprates.


  In this paper, a supervised clustering based-heuristic is proposed for the
real-time implementation of approximate solutions to stochastic nonlinear model
predictive control frameworks. The key idea is to update on-line a low
cardinality set of uncertainty vectors to be used in the expression of the
stochastic cost and constraints. These vectors are the centers of uncertainty
clusters that are built using the optimal control sequences, cost and
constraints indicators as supervision labels. The use of a moving clustering
data buffer which accumulates recent past computations enables to reduce the
computational burden per sampling period while making available at each period
a relevant amount of samples for the clustering task. A relevant example is
given to illustrate the contribution and the associated algorithms.


  Transformer-based methods have demonstrated superior performance for
monocular 3D object detection recently, which aims at predicting 3D attributes
from a single 2D image. Most existing transformer-based methods leverage both
visual and depth representations to explore valuable query points on objects,
and the quality of the learned query points has a great impact on detection
accuracy. Unfortunately, existing unsupervised attention mechanisms in
transformers are prone to generate low-quality query features due to inaccurate
receptive fields, especially on hard objects. To tackle this problem, this
paper proposes a novel "Supervised Scale-aware Deformable Attention" (SSDA) for
monocular 3D object detection. Specifically, SSDA presets several masks with
different scales and utilizes depth and visual features to adaptively learn a
scale-aware filter for object query augmentation. Imposing the scale awareness,
SSDA could well predict the accurate receptive field of an object query to
support robust query feature generation. Aside from this, SSDA is assigned with
a Weighted Scale Matching (WSM) loss to supervise scale prediction, which
presents more confident results as compared to the unsupervised attention
mechanisms. Extensive experiments on the KITTI and Waymo Open datasets
demonstrate that SSDA significantly improves the detection accuracy, especially
on moderate and hard objects, yielding state-of-the-art performance as compared
to the existing approaches. Our code will be made publicly available at
https://github.com/mikasa3lili/SSD-MonoDETR.


  Data quality is a key element for building and optimizing good learning
models. Despite many attempts to characterize data quality, there is still a
need for rigorous formalization and an efficient measure of the quality from
available observations. Indeed, without a clear understanding of the training
and testing processes, it is hard to evaluate the intrinsic performance of a
model. Besides, tools allowing to measure data quality specific to machine
learning are still lacking. In this paper, we introduce and explain a novel
metric to measure data quality. This metric is based on the correlated
evolution between the classification performance and the deterioration of data.
The proposed method has the major advantage of being model-independent.
Furthermore, we provide an interpretation of each criterion and examples of
assessment levels. We confirm the utility of the proposed metric with intensive
numerical experiments and detail some illustrative cases with controlled and
interpretable qualities.


  The credit cards' fraud transactions detection is the important problem in
machine learning field. To detect the credit cards's fraud transactions help
reduce the significant loss of the credit cards' holders and the banks. To
detect the credit cards' fraud transactions, data scientists normally employ
the unsupervised learning techniques and supervised learning techniques. In
this paper, we employ the graph p-Laplacian based semi-supervised learning
methods combined with the undersampling techniques such as Cluster Centroids to
solve the credit cards' fraud transactions detection problem. Experimental
results show that the graph p-Laplacian semi-supervised learning methods
outperform the current state of the art graph Laplacian based semi-supervised
learning method (p=2).


  We study the regularity at the positions of the (fixed) nuclei of solutions
to (non-relativistic) multiconfiguration equations (including Hartree--Fock) of
Coulomb systems. We prove the following: Let {phi_1,...,phi_M} be any solution
to the rank--M multiconfiguration equations for a molecule with L fixed nuclei
at R_1,...,R_L in R^3. Then, for any j in {1,...,M} and k in {1,...,L}, there
exists a neighbourhood U_{j,k} in R^3 of R_k, and functions phi^{(1)}_{j,k},
phi^{(2)}_{j,k}, real analytic in U_{j,k}, such that phi_j(x) =
phi^{(1)}_{j,k}(x) + |x - R_k| phi^{(2)}_{j,k}(x), x in U_{j,k} A similar
result holds for the corresponding electron density. The proof uses the
Kustaanheimo--Stiefel transformation, as applied earlier by the authors to the
study of the eigenfunctions of the Schr"odinger operator of atoms and molecules
near two-particle coalescence points.


  High-entropy oxides (HEOs), as multi-component ceramics with high
configurational entropy, have been of recent interest due to their attractive
properties including photocatalytic activity for H2 production and CO2
conversion. However, the photocatalytic activity of HEOs is still limited to
ultraviolet light. In this study, to achieve visible-light-driven
photocatalysis, 10 different heterojunctions were simultaneously introduced in
the Ti-Zr-Nb-Ta-W-O system. The oxide, which was synthesized by a high-pressure
torsion method and oxidation, successfully produced oxygen from water under
visible light without co-catalyst addition. The photocatalytic performance was
attributed to high visible-light absorption, narrow bandgap, appropriate band
structure, presence of multiple heterojunctions and accordingly easy
electron-hole separation and slow recombination. These results not only show
the potential of high-entropy oxides as new visible-light-active
photocatalysts, but also introduce the multiple-heterojunction introduction as
a strategy to achieve photocatalysis under visible light.


  We present a general convex relaxation approach to study a wide class of
Unbalanced Optimal Transport problems for finite non-negative measures with
possibly different masses. These are obtained as the lower semicontinuous and
convex envelope of a cost for non-negative Dirac masses. New general
primal-dual formulations, optimality conditions, and metric-topological
properties are carefully studied and discussed.


  We report on X-ray observations of the young, energetic radio pulsar PSR
J1105-6107 obtained using the Rossi X-ray Timing Explorer. Our timing analysis
of Proportional Counter Array data finds no evidence for X-ray pulsations from
this source. We set a new upper limit on the pulsed X-ray flux from the pulsar
of 18% in the 2-10 keV band, for a duty cycle of 0.5. Our improved upper limit
supports the hypothesis that the X-ray emission seen by the ASCA X-ray
satellite in the direction of PSR J1105-6107 is the result of a pulsar-powered
synchrotron nebula.


  We calculate the flux of $\gamma$-rays emitted from primordial black holes
(PBHs) which are formed by a ``blue'' power-law spectrum of density
fluctuations in the early universe. Gamma-ray emission from such PBHs may
contribute significantly to the observed extragalactic diffuse $\gamma$-ray
background (DGB). Using the observed DGB flux from the imaging Compton
Telescope (COMPTEL) and the Energetic Gamma Ray Experiment Telescope (EGRET) as
the upper limit of $\gamma$-ray flux from PBHs, we derive the upper limit on
the spectral index $n$ of the density fluctuations. The range of initial PBH
masses which can contribute to the DGB is $2\times 10^{13}\g - 5\times
10^{14}\g$, corresponding to a cosmic reheating temperature of $7\times
10^{7}\gev - 4\times 10^{8} \gev$. In this range, we find the upper limit to be
$n \lsim 1.23-1.25$. This limit is stronger than those derived from the energy
density in PBHs or PBH relics and matches the value of $n$ required to explain
the cosmic microwave background anisotropy.


  Drawing on ideas from game theory and quantum physics, we investigate
nonlocal correlations from the point of view of equilibria in games of
incomplete information. These equilibria can be classified in decreasing power
as communication equilibria, belief-invariant equilibria, and correlated
equilibria, all of which contain the familiar Nash equilibria. The notion of
belief-invariant equilibrium appeared in game theory in the 90s. However, the
class of non-signalling correlations associated to belief-invariance arose
naturally already in the 80s in the foundations of quantum mechanics. In the
present work, we explain and unify these two origins of the idea and study the
above classes of equilibria, together with quantum correlated equilibria, using
tools from quantum information but the language of (algorithmic) game theory.
We present a general framework of belief-invariant communication equilibria,
which contains correlated equilibria and quantum correlated equilibria as
special cases. Our framework also contains the theory of Bell inequalities and
their violations due to non-locality, which is a question of intense interest
in the foundations of quantum mechanics, and it was indeed the original
motivation for the aforementioned studies. Moreover, in our framework we can
also model quantum games where players have conflicting interests, a recent
developing topic in physics. We then use our framework to show new results
related to the social welfare of equilibria. Namely, we exhibit a game where
belief-invariance is socially better than any correlated equilibrium, and a
game where all non-belief-invariant communication equilibria have a suboptimal
social welfare. We also show that optimal social welfare can sometimes be
achieved by quantum mechanical correlations, which do not need an informed
mediator to be implemented, and go beyond the classical shared randomness
approach.


  With the increasing demands for privacy protection, many privacy-preserving
machine learning systems were proposed in recent years. However, most of them
cannot be put into production due to their slow training and inference speed
caused by the heavy cost of homomorphic encryption and secure multiparty
computation(MPC) methods. To circumvent this, I proposed a privacy definition
which is suitable for large amount of data in machine learning tasks. Based on
that, I showed that random transformations like linear transformation and
random permutation can well protect privacy. Merging random transformations and
arithmetic sharing together, I designed a framework for private machine
learning with high efficiency and low computation cost.


  Quantum chromodynamics (QCD) claims that the major source of the nucleon
invariant mass is not the Higgs mechanism but the trace anomaly in QCD energy
momentum tensor. Although experimental and theoretical results support such
conclusion, a direct demonstration is still absent. We present the first
Lattice QCD calculation of the quark and gluon trace anomaly contributions to
the hadron masses, using the overlap fermion on the 2+1 flavor dynamical Domain
wall quark ensemble at $m_{\pi}=340$ MeV and lattice spacing $a=$0.1105 fm. The
result shows that the gluon trace anomaly contributes to most of the nucleon
mass, and the contribution in the pion state is smaller than that in others
nearly by a factor $\sim$10 since the gluon trace anomaly density inside pion
is different from the other hadrons and the magnitude is much smaller. The
gluon trace anomaly coefficient $\beta/g^3=-0.056(6)$ we obtained is consistent
with its regularization independent leading order value
$(-11+\frac{2N_f}{3})/(4\pi)^2$ perfectly.


  If a neutral inclusion is inserted in a matrix containing a uniform applied
electric field, it does not disturb the field outside the inclusion. The well
known Hashin coated sphere is an example of a neutral coated inclusion. In this
paper, we consider the problem of constructing neutral inclusions from
nonlinear materials. In particular, we discuss assemblages of coated spheres
and the two-dimensional analogous problem of assemblages of coated disks.


  We formulate a theoretical model of the shear failure of a thin film tethered
to a rigid substrate. The interface between film and substrate is modeled as a
cohesive layer with randomly fluctuating shear strength/fracture energy. We
demonstrate that, on scales large compared with the film thickness, the
internal shear stresses acting on the interface can be approximated by a
second-order gradient of the shear displacement across the interface. The model
is used to study one-dimensional shear cracks, for which we evaluate the
stress-dependent probability of nucleation of a critical crack. This is used to
determine the interfacial shear strength as a function of film geometry and
statistical properties of the interface.


  We show that the tetrad field whose metric gives the Reissner-Nordstr\"om
anti-de Sitter black holes gives the correct value of energy in M{\o}ller
tetrad theory of gravitation.


  We extend the approach of Aizenman, Sims and Starr for the SK-type models to
their spherical versions. Such an extension has already been performed for
diluted spin glasses. The factorization property of the optimal structures
found by Guerra for the SK model, which holds for diluted models as well, is
verified also in the case of spherical systems, with the due modifications.
Hence we show that there are some common structural features in various mean
field spin models. These similarities seem to be quite paradigmatic, and we
summarize the various techniques typically used to prove the structural
analogies and to tackle the computation of the free energy per spin in the
thermodynamic limit.


  We consider collective effects in pp-collisions at the LHC energies related
to presence of the large orbital angular momentum in the initial state and role
of this orbital momentum in the elliptic flow behavior.


  Two significant progresses have been made in the past years on our
understanding of hot accretion flows. One is that only a small fraction of
accretion flow available at the outer boundary can finally falls onto the black
hole while most of them is lost in outflow. Another one is that electrons may
directly receive a large fraction of the viscously dissipated energy in the
accretion flow, i.e, $\delta\sim 0.1-0.5$. The radiative efficiency of hot
accretion flow when these two progresses are taken into account has not been
systematically studied and is the subject of the present paper. We consider two
regimes of hot accretion model. One is the advection dominated accretion flows
(ADAFs) which lie on low accretion rate regime, $\la 10\alpha^2\ledd/c^2$;
another being the luminous hot accretion flows (LHAFs) which lie above this
accretion rate. For the latter, we assume that the accretion flow will has a
two-phase structure above a certain accretion rate, and a simplification is
adopted in our calculation of the dynamics. Our results indicate that the
radiative efficiency of hot accretion flow increases with the accretion rate
and is highly enhanced by the direct viscous heating to electrons compared to
the previous case of $\delta\ll 1$. When the accretion rate is high, the
radiative efficiency of hot accretion flow is comparable to that of the
standard thin disk. Fitting formulae of radiative efficiency as a function of
accretion rate for various $\delta$ values are presented.


  Nearest and higher neighbor distances as well as bond length distributions
(static and thermal) of the In_xGa_(1-x)As (0<x<1) semiconductor alloys have
been obtained from high real-space resolution atomic pair distribution
functions (PDFs). Using this structural information, we modeled the local
atomic displacements in In_xGa_(1-x)As alloys. From a supercell model based on
the Kirkwood potential, we obtained 3-D As and (In,Ga) ensemble averaged
probability distributions. This clearly shows that As atom displacements are
highly directional and can be represented as a combination of <100> and <111>
displacements. Examination of the Kirkwood model indicates that the standard
deviation (sigma) of the static disorder on the (In,Ga) sublattice is around
60% of the value on the As sublattice and the (In,Ga) atomic displacements are
much more isotropic than those on the As sublattice. The single crystal diffuse
scattering calculated from the Kirkwood model shows that atomic displacements
are most strongly correlated along <110> directions.


  We investigate the properties of multi-particle states in Deformed Special
Relativity (DSR). Starting from the Lagrangian formalism with an energy
dependent metric, the conserved Noether current can be derived which is
additive in the usual way. The integrated Noether current had previously been
discarded as a conserved quantity, because it was correctly realized that it
does no longer obey the DSR transformations. We identify the reason for this
mismatch in the fact that DSR depends only on the extensive quantity of total
four-momentum instead of the energy-momentum densities as would be appropriate
for a field theory. We argue that the reason for the failure of DSR to
reproduce the standard transformation behavior in the well established limits
is due to the missing sensitivity to the volume inside which energy is
accumulated. We show that the soccer-ball problem is absent if one formulates
DSR instead for the field densities. As a consequence, estimates for predicted
effects have to be corrected by many orders of magnitude. Further, we derive
that the modified quantum field theory implies a locality bound.


  Thermal radiation from hot polar caps is examined in radio pulsars with
drifting subpulses. It is argued that if these subpulses correspond to sparking
discharges of the inner acceleration region right above the polar cap surface
then a simple relationship between the observed subpulse drift rate in radio
and thermal X-ray luminosity from the polar cap heated by sparks should exist.
This relationship is derived and tested in pulsars for which an appropriate
good quality data is available.


  The satellite Planetary Transits and Oscillations of stars (PLATO), due to be
launched late 2026, will provide us with an unprecedented sample of light
curves of solar-type stars that will exhibit both solar-type oscillations and
signatures of activity-induced brightness modulations. Solar-type pulsators
only have moderate levels of activity because high levels of activity inhibit
oscillations. This means that these targets represent a specific challenge for
starspot modelling. In order to assess the possibilities that PLATO will soon
open, we wish to characterise the morphology of active regions at the surface
of stars for which we also have a detection of solar-like acoustic
oscillations. In this context, we report the results of an ensemble starspot
modelling analysis of the Sun and ten solar-type pulsators observed by the
Kepler satellite. We implement a Bayesian starspot modelling approach based on
a continuous-grid model, accounting for the combined starspot and facular
contribution to activity-induced brightness modulations. From our analysis, we
find that several stars of our sample exhibit clear signatures of stable
longitudinal active nests while sharing activity levels and convection versus
rotation regimes similar to the solar regime. By searching for modulations in
the reconstructed starspot coverage, we found significant periodicities that we
identify as possible signatures of cyclic modulations similar to the
quasi-biennal oscillation or the Rieger cycle. We can infer the corresponding
intensity of the magnetic field at the bottom of the convective envelope based
on the hypothesis that internal magneto-Rossby waves acting on the tachocline
cause these modulations.


  We study the Loschmidt echo (LE) in a central spin model in which a central
spin is globally coupled to an environment (E) which is subjected to a small
and sudden quench at $t=0$ so that its state at $t=0^+$, remains the same as
the ground state of the initial environmental Hamiltonian before the quench;
this leads to a non-equilibrium situation. This state now evolves with two
Hamiltonians, the final Hamiltonian following the quench and its modified
version which incorporates an additional term arising due to the coupling of
the central spin to the environment. Using a generic short-time scaling of the
decay rate, we establish that in the early time limit, the rate of decay of the
LE (or the overlap between two states generated from the initial state evolving
through two channels) close to the quantum critical point (QCP) of E is
independent of the quenching. We do also study the temporal evolution of the LE
and establish the presence of a crossover to a situation where the quenching
becomes irrelevant. In the limit of large quench amplitude the non-equilibrium
initial condition is found to result in a drastic increase in decoherence at
large times, even far away from a QCP. These generic results are verified
analytically as well as numerically, choosing E to be a transverse Ising chain
where the transverse field is suddenly quenched.


  We examine the capabilities of the DUNE experiment as a probe of the neutrino
mixing paradigm. Taking the current status of neutrino oscillations and the
design specifications of DUNE, we determine the experiment's potential to probe
the structure of neutrino mixing and CP violation. We focus on the poorly
determined parameters $\theta_{23}$ and $\delta_{CP}$ and consider both two and
seven years of run. We take various benchmarks as our true values, such as the
current preferred values of $\theta_{23}$ and $\delta_{CP}$, as well as several
theory-motivated choices. We determine quantitatively DUNE's potential to
perform a precision measurement of $\theta_{23}$, as well as to test the CP
violation hypothesis in a model-independent way. We find that, after running
for seven years, DUNE will make a substantial step in the precise determination
of these parameters, bringing to quantitative test the predictions of various
theories of neutrino mixing.


  We consider the evolutionary symmetric $p$-Laplacian with safety $1$. By
symmetric we mean that the full gradient of $p$-Laplacian is replaced by its
symmetric part, which causes breakdown of the Uhlenbeck structure. We derive
the interior regularity of time derivatives of its local weak solution. To
circumvent the space-time growth mismatch, we devise a new local regularity
technique of iterations in Nikolskii-Bochner spaces. It is interesting by
itself, as it may be modified to provide new regularity results for the
full-gradient $p$-Laplacian case with lower-order dependencies. Finally, having
the regularity result for time derivatives, we obtain respective regularity of
the main part. The Appendix on Nikolskii-Bochner spaces, that includes theorems
on their embeddings and interpolations, may be of independent interest.


  Contrastive speaker embedding assumes that the contrast between the positive
and negative pairs of speech segments is attributed to speaker identity only.
However, this assumption is incorrect because speech signals contain not only
speaker identity but also linguistic content. In this paper, we propose a
contrastive learning framework with sequential disentanglement to remove
linguistic content by incorporating a disentangled sequential variational
autoencoder (DSVAE) into the conventional SimCLR framework. The DSVAE aims to
disentangle speaker factors from content factors in an embedding space so that
only the speaker factors are used for constructing a contrastive loss
objective. Because content factors have been removed from the contrastive
learning, the resulting speaker embeddings will be content-invariant.
Experimental results on VoxCeleb1-test show that the proposed method
consistently outperforms SimCLR. This suggests that applying sequential
disentanglement is beneficial to learning speaker-discriminative embeddings.


  The object of the present paper is to introduce and investigate two new
general subclasses ${{S}^{*}}C(\alpha ,\beta ;\gamma )$ and $T{{S}^{*}}C(\alpha
,\beta ;\gamma )~~(\alpha, \beta \in [0,1),~\gamma \in [0,1])$ of the analytic
functions. Here, we give sufficient conditions as well as necessary and
sufficient conditions for the functions belonging to the classes.


  We propose a general scheme for solving convex and non-convex optimization
problems on manifolds. The central idea is that, by adding a multiple of the
squared retraction distance to the objective function in question, we
"convexify" the objective function and solve a series of convex sub-problems in
the optimization procedure. One of the key challenges for optimization on
manifolds is the difficulty of verifying the complexity of the objective
function, e.g., whether the objective function is convex or non-convex, and the
degree of non-convexity. Our proposed algorithm adapts to the level of
complexity in the objective function. We show that when the objective function
is convex, the algorithm provably converges to the optimum and leads to
accelerated convergence. When the objective function is non-convex, the
algorithm will converge to a stationary point. Our proposed method unifies
insights from Nesterov's original idea for accelerating gradient descent
algorithms with recent developments in optimization algorithms in Euclidean
space. We demonstrate the utility of our algorithms on several manifold
optimization tasks such as estimating intrinsic and extrinsic Fr\'echet means
on spheres and low-rank matrix factorization with Grassmann manifolds applied
to the Netflix rating data set.


  E-Learning uses electronic media, information & communication technologies to
provide education to the masses. E-learning deliver hypertext, text, audio,
images, animation and videos using desktop standalone computer, local area
network based intranet and internet based contents. While producing an
e-learning content or course-ware, a major decision making factor is whether to
use audio for the benefit of the end users. Generally, three types of audio can
be used in e-learning: narration, music and sound effect. This paper shows that
the use of proper audio based on contents type and subject can make the content
more interesting as well as help the end users to better understand the
contents.


  As new technologies are invented, their commercial viability needs to be
carefully examined along with their technical merits and demerits. The posit
data format, proposed as a drop-in replacement for IEEE 754 float format, is
one such invention that requires extensive theoretical and experimental study
to identify products that can benefit from the advantages of posits for
specific market segments. In this paper, we present an extensive empirical
study of posit-based arithmetic vis-\`a-vis IEEE 754 compliant arithmetic for
the optical flow estimation method called Lucas-Kanade (LuKa). First, we use
SoftPosit and SoftFloat format emulators to perform an empirical error analysis
of the LuKa method. Our study shows that the average error in LuKa with
SoftPosit is an order of magnitude lower than LuKa with SoftFloat. We then
present the integration of the hardware implementation of a posit adder and
multiplier in a RISC-V open-source platform. We make several recommendations,
along with the analysis of LuKa in the RISC-V context, for future generation
platforms incorporating posit arithmetic units.


  Replacing U for Ca in semiconducting CaB$_6$ at the few at.% level induces
metallic behaviour and Kondo-type phenomena at low temperatures, a rather
unusual feature for U impurities in metallic hosts. For
Ca$_{0.992}$U$_{0.008}$B$_6$, the resistance minimum occurs at $T$ = 17 K. The
subsequent characteristic logarithmic increase of the resistivity with
decreasing temperature merges into the expected $T^2$ dependence below 0.8 K.
Data of the low-temperature specific heat and the magnetization are analyzed by
employing a simple resonance-level model. Analogous measurements on LaB$_6$
with a small amount of U revealed no traces of Kondo behavior, above 0.4 K.


  We propose to measure the spectrum of magnetic excitation in magnetic
materials using motion of vortex lattice driven by both ac and dc current in
superconductors. When the motion of vortex lattice is resonant with oscillation
of magnetic moments, the voltage decreases at a given current. From transport
measurement, one can obtain frequency of the magnetic excitation with the wave
number determined by vortex lattice constant. By changing the lattice constant
through applied magnetic fields, one can obtains the spectrum of the magnetic
excitation up to a wave vector of order $10\rm{\ nm^{-1}}$.


  Bongartz and Gabriel gave a classification of maximal rigid representations
for quivers of type $A$ with linear orientation and counted the number of
isomorphism classes. In this paper, we give a formula on the number of
isomorphism classes of a kind of maximal rigid representations for continuous
quivers of type $A$ introduced by Igusa, Rock and Todorov.


  Ranking and selection (R&S) is a popular model for studying discrete-event
dynamic systems. It aims to select the best design (the design with the largest
mean performance) from a finite set, where the mean of each design is unknown
and has to be learned by samples. Great research efforts have been devoted to
this problem in the literature for developing procedures with superior
empirical performance and showing their optimality. In these efforts, myopic
procedures were popular. They select the best design using a 'naive' mechanism
of iteratively and myopically improving an approximation of the objective
measure. Although they are based on simple heuristics and lack theoretical
support, they turned out highly effective, and often achieved competitive
empirical performance compared to procedures that were proposed later and shown
to be asymptotically optimal. In this paper, we theoretically analyze these
myopic procedures and prove that they also satisfy the optimality conditions of
R&S, just like some other popular R&S methods. It explains the good performance
of myopic procedures in various numerical tests, and provides good insight into
the structure and theoretical development of efficient R&S procedures.


  This paper proposes an online voltage control strategy of distributed energy
resources (DERs), based on the projected Newton method (PNM), for unbalanced
distribution networks. The optimal Volt/VAr control (VVC) problem is formulated
as an optimization program, with the goal of maintaining the voltage profile
across the network by coordinating the VAr outputs of DERs. To overcome the
slow convergence rate of conventional gradient-based methods, a PNM-based
solution algorithm is developed to solve this VVC problem. It utilizes a
non-diagonal symmetric positive definite matrix, developed from the Hessian
matrix of the objective, to scale the gradient, and thus a fast convergence
performance can be expected in this Newton-like algorithm. Moreover, taking
advantage of the instantaneous feedback of voltage measurements, the online
implementation of the PNM-based VVC is further designed to deal with fast
system variations. In this online PNM-based VVC scheme, each bus agent
communicates the instantaneous voltage measurements to the central agent, and
the central agent communicates the VAr output commands of DERs back to each bus
agent. The fast convergence performance of PNM results in a stronger capability
to track the system variations in real time. Finally, numerical case studies
are performed to validate the effectiveness, superiority, and scalability of
the proposed method.


  Cognitive distraction and measurement noise are two distinct factors that
significantly impact the performance of humans and engineering systems.
Cognitive distraction occurs when an individual's attention is diverted from a
task, while measurement noise refers to the random variation that can occur in
system measurements. Although humans and engineering systems employ different
methods to overcome these obstacles, the ultimate goal is to achieve optimal
performance. An intriguing question arises: what are the similarities and
differences between using the term "noise" in engineering and cognitive
psychology? Additionally, it is worthwhile to explore whether the human brain
and engineering control systems use similar or different approaches to
attenuate noise. While this article does not provide a definitive answer, it
emphasizes the importance of addressing this question and encourages further
investigation.


  After performing a reassessment of the known dynamical asteroid families in
the inner main belt, we report a newly discovered ancient asteroid family with
an estimated age of $4.3\pm1.7$ billion years. Additionally, we report the most
comprehensive list of planetesimals, which are asteroids that survived since
the planet forming days of the solar system.


  The nuclear track detector CR39 was calibrated with different ions of
different energies. Due to the low detection threshold (Z/beta~6e) and the good
charge resolution (sigma_Z ~ 0.2e for 6e < Z/beta <83e with 2 measurements),
the detector was used for different purposes: (i) fragmentation of high and
medium energy ions; (ii) search for magnetic monopoles, nuclearites,
strangelets and Q-balls in the cosmic radiation.


  We study soliton dynamics in a system of two linearly coupled discrete
nonlinear Schr\"odinger equations, which describe the dynamics of a
two-component Bose gas, coupled by an electromagnetic field, and confined in a
strong optical lattice. When the nonlinear coupling strengths are equal, we use
a unitary transformation to remove the linear coupling terms, and show that the
existing soliton solutions oscillate from one species to the other. When the
nonlinear coupling strengths are different, the soliton dynamics is numerically
investigated and the findings are compared to the results of an effective
two-mode model. The case of two linearly coupled Ablowitz-Ladik equations is
also investigated.


  We point out that neutrino events observed at Kamiokande and IMB from SN1987A
disfavor the neutrino oscillation parameters preferred by the LSND experiment.
For $\Delta m^2 > 0$ (the light side), the electron neutrinos from the
neutronization burst would be lost, while the first event at Kamiokande is
quite likely to be due to an electron neutrino. For $\Delta m^2 < 0$ (the dark
side), the average energy of the dominantly $\bar{\nu}_e$ events is already
lower than the theoretical expectations, which would get aggravated by a
complete conversion from $\bar{\nu}_\mu$ to $\bar{\nu}_e$. If taken seriously,
the LSND data are disfavored independent of the existence of a sterile
neutrino. A possible remedy is CPT violation, which allows different mass
spectra for neutrinos and anti-neutrinos and hence can accommodate atmospheric,
solar and LSND data without a sterile neutrino. If this is the case, Mini-BooNE
must run in $\bar{\nu}$ rather than the planned $\nu$ mode to test the LSND
signal. We speculate on a possible origin of CPT violation.


  Proof of Stake (PoS) protocols rely on voting mechanisms to reach consensus
on the current state. If an enhanced majority of staking nodes, also called
validators, agree on a proposed block, then this block is appended to the
blockchain. Yet, these protocols remain vulnerable to faults caused by
validators who abstain either accidentally or maliciously. To protect against
such faults while retaining the PoS selection and reward allocation schemes, we
study weighted voting in validator committees. We formalize the block creation
process and introduce validators' voting profiles which we update by a
multiplicative weights algorithm relative to validators' voting behavior and
aggregate blockchain rewards. Using this framework, we leverage weighted
majority voting rules that optimize collective decision making to show, both
numerically and analytically, that the consensus mechanism is more robust if
validators' votes are appropriately scaled. We raise potential issues and
limitations of weighted voting in trustless, decentralized networks and relate
our results to the design of current PoS protocols.


  When Morris and Thorne first proposed that wormholes might be actual physical
structures suitable for interstellar travel, they needed to pay close attention
to certain traversability conditions such as low tidal forces, which placed
severe constraints on the wormhole geometry. Even more problematical was the
need for "exotic matter" resulting from the unavoidable violation of the null
energy condition required to hold a wormhole open. The purpose of this paper is
to overcome these problems by starting with the charged wormhole model of Kim
and Lee and assuming a noncommutative-geometry background: the violation of the
null energy condition can be attributed to the latter, while the electric
charge allows the reduction of the tidal forces to acceptable levels without
invoking the trivial zero-tidal-force assumption.


  Portfolio optimization aims at constructing a realistic portfolio with
significant out-of-sample performance, which is typically measured by the
out-of-sample Sharpe ratio. However, due to in-sample optimism, it is
inappropriate to use the in-sample estimated covariance to evaluate the
out-of-sample Sharpe, especially in the high dimensional settings. In this
paper, we propose a novel method to estimate the out-of-sample Sharpe ratio
using only in-sample data, based on random matrix theory. Furthermore,
portfolio managers can use the estimated out-of-sample Sharpe as a criterion to
decide the best tuning for constructing their portfolios. Specifically, we
consider the classical framework of Markowits mean-variance portfolio
optimization with known mean vector and the high dimensional regime of $p/n \to
c \in (0,\infty)$, where $p$ is the portfolio dimension and $n$ is the number
of samples or time points. We propose to correct the sample covariance by a
regularization matrix and provide a consistent estimator of its Sharpe ratio.
The new estimator works well under either of three conditions: (1) bounded
covariance spectrum, (2) arbitrary number of diverging spikes when $c < 1$, and
(3) fixed number of diverging spikes when $c \ge 1$. We can also extend the
results to construct global minimum variance portfolio and correct
out-of-sample efficient frontier. We demonstrate the effectiveness of our
approach through comprehensive simulations and real data experiments. Our
results highlight the potential of this methodology as a powerful tool for
portfolio optimization in high dimensional settings.


  We introduce a data-driven learning framework that assimilates two powerful
ideas: ideal large eddy simulation (LES) from turbulence closure modeling and
neural stochastic differential equations (SDE) for stochastic modeling. The
ideal LES models the LES flow by treating each full-order trajectory as a
random realization of the underlying dynamics, as such, the effect of
small-scales is marginalized to obtain the deterministic evolution of the LES
state. However, ideal LES is analytically intractable. In our work, we use a
latent neural SDE to model the evolution of the stochastic process and an
encoder-decoder pair for transforming between the latent space and the desired
ideal flow field. This stands in sharp contrast to other types of neural
parameterization of closure models where each trajectory is treated as a
deterministic realization of the dynamics. We show the effectiveness of our
approach (niLES - neural ideal LES) on a challenging chaotic dynamical system:
Kolmogorov flow at a Reynolds number of 20,000. Compared to competing methods,
our method can handle non-uniform geometries using unstructured meshes
seamlessly. In particular, niLES leads to trajectories with more accurate
statistics and enhances stability, particularly for long-horizon rollouts.


  A generalized augmented link of a knot $K$ is a link obtained by adding
trivial components to $K$ that bound $n$-punctured disks. In this paper we
consider that $K$ is given by a positive braid with at least one full twist. We
characterize when the generalized augmented links of $K$ have hyperbolic
geometry.


  Gene regulatory networks (GRNs) control cellular function and decision making
during tissue development and homeostasis. Mathematical tools based on
dynamical systems theory are often used to model these networks, but the size
and complexity of these models mean that their behaviour is not always
intuitive and the underlying mechanisms can be difficult to decipher. For this
reason, methods that simplify and aid exploration of complex networks are
necessary. To this end we develop a broadly applicable form of the Zwanzig-Mori
projection. By first converting a thermodynamic state ensemble model of gene
regulation into mass action reactions we derive a general method that produces
a set of time evolution equations for a subset of components of a network. The
influence of the rest of the network, the bulk, is captured by memory functions
that describe how the subnetwork reacts to its own past state via components in
the bulk. These memory functions provide probes of near-steady state dynamics,
revealing information not easily accessible otherwise. We illustrate the method
on a simple cross-repressive transcriptional motif to show that memory
functions not only simplify the analysis of the subnetwork but also have a
natural interpretation. We then apply the approach to a GRN from the vertebrate
neural tube, a well characterised developmental transcriptional network
composed of four interacting transcription factors. The memory functions reveal
the function of specific links within the neural tube network and identify
features of the regulatory structure that specifically increase the robustness
of the network to initial conditions. Taken together, the study provides
evidence that Zwanzig-Mori projections offer powerful and effective tools for
simplifying and exploring the behaviour of GRNs.


  The IceCube evidence for cosmic neutrinos has inspired a large number of
hypothesis on their origin, mainly due to the poor precision on the measurement
of the direction of showering events. A North/South asymmetry in the present
data set suggests the presence of a possible Galactic component. This could be
originated either by single point-like sources or from an extended Galactic
region. Expected fluxes derived from these hypotheses are presented. Some
values have been constrained from the present available upper limits from the
ANTARES neutrino telescope.


  Let $\mathcal{P}$ be an $n$-point subset of Euclidean space and $d\geq 3$ be
an integer. In this paper we study the following question: What is the smallest
(normalized) relative change of the volume of subsets of $\mathcal{P}$ when it
is projected into $\RR^d$. We prove that there exists a linear mapping
$f:\mathcal{P} \mapsto \RR^d$ that relatively preserves the volume of all
subsets of size up to $\lfloor d/2\rfloor$ within at most a factor of
$O(n^{2/d}\sqrt{\log n \log\log n})$.


  Warm inflation dynamics is fundamentally based on a system-reservoir
configuration in which the dynamics is dictated by a fluctuation-dissipation
relation. Recent work by Cerioni et. al. (arXiv:0804.0163) examined dissipative
dynamics with no associated fluctuation component. Their results, which are
heavily dependent on initial conditions, are at odds with results in the warm
inflation literature, especially the spectral indices. The inconsistency of
their formalism is outlined here and it is shown how this would lead to their
erroneous conclusions. It is then shown how, following the correct dynamical
equations of warm inflation, the results in the literature are correct.


  Fabry-P\'erot (FP) cavities are fundamental and ubiquitous optical elements
frequently used in various sensing applications. Here, we introduce a general
theoretical framework to study arbitrary light-cavity mode interactions for
planar FPs and show how optical aberrations, intrinsic to the interrogating
beam or due to imperfect cavities, reduce optical sensitivity by exciting
higher-order spatial modes in the cavity. We find that particular Zernike
aberrations play a dominant role in sensitivity degradation, and that the
general loss of sensitivity can be significantly recovered by appropriate
wavefront correction or mode filtering. We then demonstrate our theoretical
findings also experimentally and show that in practice the sensitivity of
realistic planar FP sensors can be improved up to three-fold by a synergistic
combination of adaptive optics and passive mode filtering.


  It is shown that correlations of dichotomic functions can not conform to
results from Quantum Mechanics. Also, it is seen that the assumptions attendant
to optical tests of Bell's Inequalities actually are consistent with classical
physics so that in conclusion, Bell's Theorems do not preclude hidden variable
interpretations of Quantum Mechanics.


  The power spectrum of cosmic microwave background (CMB) lensing will be
measured to sub-percent precision with upcoming surveys, enabling tight
constraints on the sum of neutrino masses and other cosmological parameters.
Measuring the lensing power spectrum involves the estimation of the connected
trispectrum of the four-point function of the CMB map, which requires the
subtraction of a large Gaussian disconnected noise bias. This reconstruction
noise bias receives contributions both from CMB and foreground fluctuations as
well as instrument noise (both detector and atmospheric noise for ground-based
surveys). The debiasing procedure therefore relies on the quality of
simulations of the instrument noise which may be expensive or inaccurate. We
propose a new estimator that makes use of at least four splits of the CMB maps
with independent instrument noise. This estimator makes the CMB lensing power
spectrum completely insensitive to any assumptions made in modeling or
simulating the instrument noise. We show that this estimator, in many practical
situations, leads to no substantial loss in signal-to-noise. We provide an
efficient algorithm for its computation that scales with the number of splits
$m$ as $\mathcal{O}(m^2)$ as opposed to a naive $\mathcal{O}(m^4)$ expectation.


  It is demonstrated that in the (2+1)-dimensional topologically massive gauge
theories an agreement of the Pauli-Villars regularization scheme with the other
schemes can be achieved by employing pairs of auxiliary fermions with the
opposite sign masses. This approach does not introduce additional violation of
discrete (P and T) symmetries. Although it breaks the local gauge symmetry only
in the regulator fields' sector, its trace disappears completely after removing
the regularization as a result of superrenormalizability of the model. It is
shown also that analogous extension of the Pauli-Villars regularization in the
vector particle sector can be used to agree the arbitrary covariant gauge
results with the Landau ones. The source of ambiguities in the covariant gauges
is studied in detail. It is demonstrated that in gauges that are softer in the
infrared region (e.g. Coulomb or axial) nonphysical ambiguities inherent to the
covariant gauges do not arise.


  Accurate characterization of subsurface geophysical properties and detection
of the fault system are essential for geothermal energy exploration and
production. The Soda Lake geothermal field is in western Nevada with a complex
fault system. Previous seismic characterization only produced a low-resolution,
smooth velocity model along with a simple, conceptual fault model. Using
optimized correlation-based full-waveform inversion, wavefield-separation-based
reverse-time migration, and automatic fault detection techniques, we present 3D
seismic characterization for the Soda Lake geothermal field using 3D surface
seismic data acquired with Vibroseis sources. We obtain 3D high-resolution
velocity, density, and acoustic impedance models, 3D seismic images with
different grid spacings, and a high-resolution fault system. Consistency check
between the constructed faults and currently active injection and production
geothermal wells verifies that our seismic inversion and imaging results and
detected faults are reliable. These results can provide valuable information
for optimizing well placement and geothermal energy production at the Soda Lake
geothermal field.


  We construct a new class of non-supersymmetric ten-dimensional type II flux
vacua, by studying first order differential equations which are deformations of
the $\mathcal{N}=1$ supersymmetry conditions. We do so within the context of
Generalised Complex Geometry, where there is a natural interpretation of the
$\mathcal{N}=1$ supersymmetry conditions in terms of calibration conditions for
probe D-branes, called D-string, domain-wall or space-filling branes, depending
on them wrapping two, three or four non-compact dimensions. We focus on the
class of non-supersymmetric vacua violating the D-string calibration condition,
and write down their general equations of motion in the language of pure
spinors. We solve them for a subclass of vacua, where the deformation of the
calibration condition is dictated by the foliated geometry of the internal
space. We also construct backgrounds violating both the domain-wall and
D-string calibration conditions, generalising the one-parameter DWSB class of
backgrounds introduced in L\"ust et al. We present several explicit solutions
with SU$(2)$ and SU$(3)$ structures, and we investigate briefly their
associated low energy effective theories.


  Electrical charging of metal surfaces due to photoelectric generation of
carriers is of concern in trapped ion quantum computation systems, due to the
high sensitivity of the ions' motional quantum states to deformation of the
trapping potential. The charging induced by typical laser frequencies involved
in doppler cooling and quantum control is studied here, with microfabricated
surface electrode traps made of aluminum, copper, and gold, operated at 6 K
with a single Sr$^+$ ion trapped 100 $\mu$m above the trap surface. The lasers
used are at 370, 405, 460, and 674 nm, and the typical photon flux at the trap
is 10$^{14}$ photons/cm$^2$/sec. Charging is detected by monitoring the ion's
micromotion signal, which is related to the number of charges created on the
trap. A wavelength and material dependence of the charging behavior is
observed: lasers at lower wavelengths cause more charging, and aluminum
exhibits more charging than copper or gold. We describe the charging dynamic
based on a rate equation approach.


  We give a computational approach to theorem proving in homological algebra.
This approach is based on computations in the free abelian category of an
additive category $\mathbf{A}$. We show that the free abelian category is
amenable to explicit computations whenever we can decide homotopy equations in
$\mathbf{A}$. As some consequences of our investigations, we recover Dowker's
explicit formula for the connecting homomorphism $\partial$ in the snake lemma,
we find a universal sense in which $\partial$ is unique, and we give a refined
version of the 5-lemma.


  We show that an angular analysis of B --> V1 V2 decays yields numerous tests
for new physics in the decay amplitudes. Unlike direct CP asymmetries, many of
these new-physics observables are nonzero even if the strong phase differences
vanish. For certain observables, neither time-dependent measurements nor
tagging is necessary. Should a signal for new physics be found, one can place a
lower limit on the size of the new-physics parameters, as well as on their
effect on the measurement of the phase of B0--Bbar0 mixing.


  The ability to choose an appropriate camera view among multiple cameras plays
a vital role in TV shows delivery. But it is hard to figure out the statistical
pattern and apply intelligent processing due to the lack of high-quality
training data. To solve this issue, we first collect a novel benchmark on this
setting with four diverse scenarios including concerts, sports games, gala
shows, and contests, where each scenario contains 6 synchronized tracks
recorded by different cameras. It contains 88-hour raw videos that contribute
to the 14-hour edited videos. Based on this benchmark, we further propose a new
approach temporal and contextual transformer that utilizes clues from
historical shots and other views to make shot transition decisions and predict
which view to be used. Extensive experiments show that our method outperforms
existing methods on the proposed multi-camera editing benchmark.


  Let $F$ be a non-Archimedean locally compact field. Let $\sigma$ and $\tau$
be finite-dimensional semisimple representations of the Weil-Deligne group of
$F$. We give strong upper and lower bounds for the Artin and Swan exponents of
$\sigma\otimes\tau$ in terms of those of $\sigma$ and $\tau$. We give a
different lower bound in terms of $\sigma\otimes\check\sigma$ and
$\tau\otimes\check\tau$. Using the Langlands correspondence, we obtain the
bounds for Rankin-Selberg exponents.


  The industrial Internet of Things (IIoT) and network slicing (NS) paradigms
have been envisioned as key enablers for flexible and intelligent manufacturing
in the industry 4.0, where a myriad of interconnected machines, sensors, and
devices of diversified quality of service (QoS) requirements coexist. To
optimize network resource usage, stakeholders in the IIoT network are
encouraged to take pragmatic steps towards resource sharing. However, resource
sharing is only attractive if the entities involved are able to settle on a
fair exchange of resource for remuneration in a win-win situation. In this
paper, we design an economic model that analyzes the multilateral strategic
trading interactions between sliced tenants in IIoT networks. We formulate the
resource pricing and purchasing problem of the seller and buyer tenants as a
cooperative Stackelberg game. Particularly, the cooperative game enforces
collaboration among the buyer tenants by coalition formation in order to
strengthen their position in resource price negotiations as opposed to acting
individually, while the Stackelberg game determines the optimal policy
optimization of the seller tenants and buyer tenant coalitions. To achieve a
Stackelberg equilibrium (SE), a multi-agent deep reinforcement learning (MADRL)
method is developed to make flexible pricing and purchasing decisions without
prior knowledge of the environment. Simulation results and analysis prove that
the proposed method achieves convergence and is superior to other baselines, in
terms of utility maximization.


  We suggest that classicalization can cure non-local quantum field theories
from acausal divergences in scattering amplitudes, restoring unitarity and
causality. In particular, in "trans-non-local" limit, the formation of
non-perturbative classical configurations, called {\it classicalons}, in
scatterings like $\phi\phi\rightarrow \phi\phi$, can avoid typical acausal
divergences.


  Two-dimensional quantum cromodynamics in the light-front frame is studied
following hamiltonian methods. The theory is quantized using the path integral
formalism and an effective theory similar to the Nambu-Jona Lasinio model is
obtained. Confinement in two dimensions is derived analyzing directly the
constraints in the path integral.


  In this study, we propose a new concept, the gammachirp envelope distortion
index (GEDI), based on the signal-to-distortion ratio in the auditory envelope,
SDRenv to predict the intelligibility of speech enhanced by nonlinear
algorithms. The objective of GEDI is to calculate the distortion between
enhanced and clean-speech representations in the domain of a temporal envelope
extracted by the gammachirp auditory filterbank and modulation filterbank. We
also extend GEDI with multi-resolution analysis (mr-GEDI) to predict the speech
intelligibility of sounds under non-stationary noise conditions. We evaluate
GEDI in terms of speech intelligibility predictions of speech sounds enhanced
by a classic spectral subtraction and a Wiener filtering method. The
predictions are compared with human results for various signal-to-noise ratio
conditions with additive pink and babble noises. The results showed that
mr-GEDI predicted the intelligibility curves better than short-time objective
intelligibility (STOI) measure, extended-STOI (ESTOI) measure, and hearing-aid
speech perception index (HASPI) under pink-noise conditions, and better than
HASPI under babble-noise conditions. The mr-GEDI method does not present an
overestimation tendency and is considered a more conservative approach than
STOI and ESTOI. Therefore, the evaluation with mr-GEDI may provide additional
information in the development of speech enhancement algorithms.


  Some linear integro-differential operators have old and classical
representations as the Dirichlet-to-Neumann operators for linear elliptic
equations, such as the 1/2-Laplacian or the generator of the boundary process
of a reflected diffusion. In this work, we make some extensions of this theory
to the case of a \emph{nonlinear} Dirichlet-to-Neumann mapping that is
constructed using a solution to a \emph{fully nonlinear} elliptic equation in a
given domain, mapping Dirichlet data to its normal derivative of the resulting
solution. Here we begin the process of giving detailed information about the
L\'evy measures that will result from the integro-differential representation
of the Dirichlet-to-Neumann mapping. We provide new results about both linear
and nonlinear Dirichlet-to-Neumann mappings. Information about the L\'evy
measures is important if one hopes to use recent advancements of the
integro-differential theory to study problems involving Dirichlet-to-Neumann
mappings.


  We show a method for constructing a polynomial interpolating roots'
multiplicities of another polynomial, that does not use companion matrices.
This leads to a modification to Guersenzvaig--Szechtman square-free
decomposition algorithm that is more efficient both in theory and in practice.


  By means of N-body simulations we study the global dynamics of triplets of
galaxies, considering initial conditions starting from `maximum expansion' and
in virial equilibrium. Unlike previous studies we treat galaxies
self-consistently. We do not consider the influence of a primordial common halo
of dark matter. Our results indicate that a low number of triple mergers are
expected at the present epoch (~10%) for collapsing triplets. Initially
virialised conditions yield ~5% of triple mergers in ~10 Gyr of evolution;
hence, the 3-galaxy problem has stable states. No overmerging problem for these
small groups of galaxies is found. Their geometrical properties, as reflected
by the Agekyan-Anosova map, do not show an excess of extreme hierarchical
triplets. The median velocity dispersion of observed compact triplets (~100
km/s) is not well reproduced in our models at the present epoch. However, about
10% of simulated triplets starting from maximum expansion have dynamical
properties very similar to them. Our median values agree, however, very well
with new data on triples. We find that the median mass estimators performs
somewhat better than the virial mass estimate. The VME underestimates mass by
\~35%. We conclude that Karachentsev's compact triplets probably represent the
most advanced stage of gravitational clustering of initially diffuse triplets.


  Quantum oscillations of metallic systems at low temperatures is one of the
key rules to experimentally access their electronic properties, such as energy
spectrum, scattering mechanisms, geometry of Fermi surface and many other
features. The importance of these knowledge is enormous, since from these a
thorough understanding of anomalous Hall effect, thermopower and Nernst
coefficients, just to name a few, is possible; and from those knowledge, a
plenty of applications arise as emerging technologies. In this direction, the
present contribution focus on a complete description of quantum capacitance
oscillations of monolayer and bilayer graphenes under crossed electric and
magnetic fields. We found a closed theoretical expression for the quantum
capacitance and highlight their amplitude, period and phase - important
parameters to access the electronic properties of graphenes. These results open
doors for further experimental studies.


  Besides the cuprate superconductors lead-doped barium bismuthate still
attracts intense interest concerning its relatively high critical temperature
and the nature of its superconductivity in vicinity to a charge density wave
ordered topological insulator. Here we present bilayers of barium bismuthate
and barium leadate and find two-dimensional superconductivity at their
interfaces well described by current-voltage characteristics compatible with a
Berezinskii-Kosterlitz-Thouless transition. Magneto-transport studies
independently prove the two-dimensional character of the superconducting
properties in these bilayers. The particular dependence of the superconducting
transition temperature on the thickness of the barium leadate top layer
suggests the formation of the superconducting state to be strain-induced
through the interface.


  Biological systems, unlike physical or chemical systems, are characterized by
the very inhomogeneous distribution of their components. The immune system, in
particular, is notable for self-organizing its structure. Classically, the
dynamics of natural systems have been described using differential equations.
But, differential equation models fail to account for the emergence of
large-scale inhomogeneities and for the influence of inhomogeneity on the
overall dynamics of biological systems. Here, we show that a microscopic
simulation methodology enables us to model the emergence of large-scale objects
and to extend the scope of mathematical modeling in biology. We take a simple
example from immunology and illustrate that the methods of classical
differential equations and microscopic simulation generate contradictory
results. Microscopic simulations generate a more faithful approximation of the
reality of the immune system.


  Accurately determining salient regions of an image is challenging when
labeled data is scarce. DINO-based self-supervised approaches have recently
leveraged meaningful image semantics captured by patch-wise features for
locating foreground objects. Recent methods have also incorporated intuitive
priors and demonstrated value in unsupervised methods for object partitioning.
In this paper, we propose SEMPART, which jointly infers coarse and fine
bi-partitions over an image's DINO-based semantic graph. Furthermore, SEMPART
preserves fine boundary details using graph-driven regularization and
successfully distills the coarse mask semantics into the fine mask. Our salient
object detection and single object localization findings suggest that SEMPART
produces high-quality masks rapidly without additional post-processing and
benefits from co-optimizing the coarse and fine branches.


  This paper improves two existing theorems of interest to neo-logicist
philosophers of mathematics. The first is a classification theorem due to Fine
for equivalence relations between concepts definable in a well-behaved
second-order logic. The improved theorem states that if an equivalence relation
$E$ is defined without non-logical vocabulary, then the bicardinal slice of any
equivalence class---those equinumerous elements of the equivalence class with
equinumerous complements---can have one of only three profiles. The
improvements to Fine's theorem allow for an analysis of the well-behaved models
had by an abstraction principle, and this in turn leads to an improvement of
Walsh and Ebels-Duggan's relative categoricity theorem.


  We study a large class of supersymmetric solutions in four-dimensional $N=5$
gauged supergravity with $SO(5)$ gauge group. There is only one $N=5$
supersymmetric $AdS_4$ vacuum preserving the full $SO(5)$ symmetry dual to an
$N=5$ SCFT in three dimensions. We give a number of domain walls interpolating
between this $AdS_4$ fixed point and singular geometries in the IR with $SO(4)$
and $SO(3)$ symmetries. These solutions describe RG flows from the $N=5$ SCFT
to non-conformal field theories driven by mass deformations. The $SO(4)$
solutions are precisely in agreement with the previously known mass
deformations within the dual $N=5$ SCFT. We also find supersymmetric Janus
solutions describing two-dimensional conformal defects in the $N=5$ SCFT with
$N=(4,1)$ and $N=(1,1)$ supersymmetries on the defects. Finally, we study
supersymmetric solutions of the form $AdS_2\times \Sigma^2$, with
$\Sigma^2=S^2,H^2$ being a Riemann surface, corresponding to near horizon
geometries of $AdS_4$ black holes. We consider both magnetic and dyonic
solutions and find that there exists a class of magnetic $AdS_2\times H^2$
solutions with $SO(2)$ symmetry. It is rather remarkable that a complete
analytic solution interpolating between $AdS_4$ and $AdS_2\times H^2$ with a
running scalar can be obtained. The solution corresponds to a twisted
compactification of $N=5$ SCFT to superconformal quantum mechanics. We also
show that no purely magnetic or dyonic black holes with $AdS_2\times \Sigma^2$
horizon from $SO(2)\times SO(2)$ twist exist in $N=5$, $SO(5)$ gauged
supergravity.


  Neural networks need big annotated datasets for training. However, manual
annotation can be too expensive or even unfeasible for certain tasks, like
multi-person 2D pose estimation with severe occlusions. A remedy for this is
synthetic data with perfect ground truth. Here we explore two variations of
synthetic data for this challenging problem; a dataset with purely synthetic
humans and a real dataset augmented with synthetic humans. We then study which
approach better generalizes to real data, as well as the influence of virtual
humans in the training loss. Using the augmented dataset, without considering
synthetic humans in the loss, leads to the best results. We observe that not
all synthetic samples are equally informative for training, while the
informative samples are different for each training stage. To exploit this
observation, we employ an adversarial student-teacher framework; the teacher
improves the student by providing the hardest samples for its current state as
a challenge. Experiments show that the student-teacher framework outperforms
normal training on the purely synthetic dataset.


  The work is devoted to the study of the processes of formation and analysis
of the parameters of a functional nanostructure -- a superconducting spin
valve, which is a multilayer structure consisting of ferromagnetic cobalt
nanofilms separated by niobium superconductor films. The studies were carried
out using molecular dynamics simulations. The atomic structure of individual
nanolayers of the system is considered.


  In-context system identification aims at constructing meta-models to describe
classes of systems, differently from traditional approaches that model single
systems. This paradigm facilitates the leveraging of knowledge acquired from
observing the behaviour of different, yet related dynamics. This paper
discusses the role of meta-model adaptation. Through numerical examples, we
demonstrate how meta-model adaptation can enhance predictive performance in
three realistic scenarios: tailoring the meta-model to describe a specific
system rather than a class; extending the meta-model to capture the behaviour
of systems beyond the initial training class; and recalibrating the model for
new prediction tasks. Results highlight the effectiveness of meta-model
adaptation to achieve a more robust and versatile meta-learning framework for
system identification.


  In this paper, we propose a method to learn a minimizing geodesic within a
data manifold. Along the learned geodesic, our method can generate high-quality
interpolations between two given data samples. Specifically, we use an
autoencoder network to map data samples into latent space and perform
interpolation via an interpolation network. We add prior geometric information
to regularize our autoencoder for the convexity of representations so that for
any given interpolation approach, the generated interpolations remain within
the distribution of the data manifold. Before the learning of a geodesic, a
proper Riemannianmetric should be defined. Therefore, we induce a Riemannian
metric by the canonical metric in the Euclidean space which the data manifold
is isometrically immersed in. Based on this defined Riemannian metric, we
introduce a constant speed loss and a minimizing geodesic loss to regularize
the interpolation network to generate uniform interpolation along the learned
geodesic on the manifold. We provide a theoretical analysis of our model and
use image translation as an example to demonstrate the effectiveness of our
method.


  We extend recently proposed variational coupled-cluster method to describe
excitation states of quantum antiferromagnetic bipartite lattices. We reproduce
the spin-wave excitations (i.e., magnons with spin $\pm 1$). In addition, we
obtain a new, spin-zero excitation (magnon-density waves) which has been
missing in all existing spin-wave theories. Within our approximation, this
magnon-density-wave excitation has a nonzero energy gap in a cubic lattice and
is gapless in a square lattice, similar to those charge-density-wave
excitations (plasmons) in quantum electron gases.


  Cavity-mediated magnon-magnon coupling can lead to a transfer of spin-wave
excitations between two spatially separated magnetic samples. We enunciate how
the application of a two-photon parametric drive to the cavity can lead to
stark amplification in this transfer efficiency. The recurrent multiphoton
absorption by the cavity opens up an infinite ladder of accessible energy
levels, which can induce higher-order transitions within the magnon Fock space.
This is reflected in a heightened spin-current response from one of the
magnetic samples when the neighboring sample is coherently pumped. The
enhancement induced by the parametric drive can be considerably high within the
stable dynamical region. Specifically, near the periphery of the stability
boundary, the spin current is amplified by several orders of magnitude. Such
striking enhancement factors are attributed to the emergence of parametrically
induced strong coherences precipitated by a long-lived mode. While
contextualized in magnonics, the generality of the principle would allow
applications to energy transfer between systems contained in parametric
cavities.


  We investigate the ground state properties of a gas of interacting particles
confined in an external potential in three dimensions and subject to rotation
around an axis of symmetry. We consider the so-called Gross-Pitaevskii (GP)
limit of a dilute gas. Analyzing both the absolute and the bosonic ground state
of the system we show, in particular, their different behavior for a certain
range of parameters. This parameter range is determined by the question whether
the rotational symmetry in the minimizer of the GP functional is broken or not.
For the absolute ground state, we prove that in the GP limit a modified GP
functional depending on density matrices correctly describes the energy and
reduced density matrices, independent of symmetry breaking. For the bosonic
ground state this holds true if and only if the symmetry is unbroken.


  Deep neural networks have become popular in many supervised learning tasks,
but they may suffer from overfitting when the training dataset is limited. To
mitigate this, many researchers use data augmentation, which is a widely used
and effective method for increasing the variety of datasets. However, the
randomness introduced by data augmentation causes inevitable inconsistency
between training and inference, which leads to poor improvement. In this paper,
we propose a consistency regularization framework based on data augmentation,
called CR-Aug, which forces the output distributions of different sub models
generated by data augmentation to be consistent with each other. Specifically,
CR-Aug evaluates the discrepancy between the output distributions of two
augmented versions of each sample, and it utilizes a stop-gradient operation to
minimize the consistency loss. We implement CR-Aug to image and audio
classification tasks and conduct extensive experiments to verify its
effectiveness in improving the generalization ability of classifiers. Our
CR-Aug framework is ready-to-use, it can be easily adapted to many
state-of-the-art network architectures. Our empirical results show that CR-Aug
outperforms baseline methods by a significant margin.


  The circadian clock can adapt itself to external cues, but the molecular
mechanisms and regulatory networks governing circadian oscillations' transient
adjustments are still largely unknown. Here we consider the specific case of
circadian oscillations transiently responding to a temperature change. Using a
framework motivated by Floquet theory, we model the mRNA expression level of
the fat body from Drosophila melanogaster following a step change from 25C to
18C. Using the method we infer the adaptation rates of individual genes as they
adapt to the new temperature. To deal with heteroskedastic noise and outliers
present in the expression data we employ quantile regression and wild bootstrap
for significance testing. Model selection with finite-sample corrected Akaike
Information Criterion (AICc) is performed additionally for robust inference. We
identify several genes with fast transition rates as potential sources of
temperature-mediated responses in the circadian system of fruit flies, and the
constructed network suggests that the proteasome may play important roles in
governing these responses.


  Some quantum information processing protocols necessitate quantum operations
that are invariant under complex conjugation. In this study, we analyze the
non-local resources necessary for implementing conjugation-symmetric
measurements on multipartite quantum networks. We derive conditions under which
a given multipartite conjugation can have locally implementable symmetric
measurements. In particular, a family of numbers called the ``magic-basis
spectrum'' comprehensively characterizes the local measurability of a given
2-qubit conjugation, as well as any other properties that are invariant under
local unitary transformations. We also explore the non-local resources required
for optimal measurements on known quantum sensor networks by using their
conjugation symmetries as a guide.


  Cooperation and defection are social traits whose evolutionary origin is
still unresolved. Recent behavioral experiments with humans suggested that
strategy changes are driven mainly by the individuals' expectations and not by
imitation. This work theoretically analyzes and numerically explores an
aspiration-driven strategy updating in a well-mixed population playing games.
The payoffs of the game matrix and the aspiration are condensed into just two
parameters that allow a comprehensive description of the dynamics. We find
continuous and abrupt transitions in the cooperation density with excellent
agreement between theory and the Gillespie simulations. Under strong selection,
the system can display several levels of steady cooperation or get trapped into
absorbing states. These states are still relevant for experiments even when
irrational choices are made due to their prolonged relaxation times. Finally,
we show that for the particular case of the Prisoner Dilemma, where defection
is the dominant strategy under imitation mechanisms, the self-evaluation update
instead favors cooperation nonlinearly with the level of aspiration. Thus, our
work provides insights into the distinct role between imitation and
self-evaluation with no learning dynamics.


  Using on-demand coherence conversion via optical locking in conventional
stimulated (three-pulse) photon echoes, ultralong, ultraefficient photon
storage has been observed in a rare-earth Pr3+ doped Y2SiO5, where storage time
is several orders of magnitude longer and retrieval efficiency is enhanced by
fifty times. Compared with the same method applied to two-pulse photon echoes
for a rephasing halt, use of spectral grating in the stimulated photon echoes
offers a phase-independent coherence conversion between optical and spin
states. Eventually an ultralong spin population decay-dependent photon storage
protocol is achieved by applying optical locking to a spectral grating in
conventional stimulated photon echoes. The present breakthrough in both photon
storage time and retrieval efficiency opens a door to long-distance quantum
communications utilizing quantum repeaters.


  DNA is a leading candidate as the next archival storage media due to its
density, durability and sustainability. To read (and write) data DNA storage
exploits technology that has been developed over decades to sequence naturally
occurring DNA in the life sciences. To achieve higher accuracy for previously
unseen, biological DNA, sequencing relies on extending and training deep
machine learning models known as basecallers. This growth in model complexity
requires substantial resources, both computational and data sets. It also
eliminates the possibility of a compact read head for DNA as a storage medium.
  We argue that we need to depart from blindly using sequencing models from the
life sciences for DNA data storage. The difference is striking: for life
science applications we have no control over the DNA, however, in the case of
DNA data storage, we control how it is written, as well as the particular write
head. More specifically, data-carrying DNA can be modulated and embedded with
alignment markers and error correcting codes to guarantee higher fidelity and
to carry out some of the work that the machine learning models perform.
  In this paper, we study accuracy trade-offs between deep model size and error
correcting codes. We show that, starting with a model size of 107MB, the
reduced accuracy from model compression can be compensated by using simple
error correcting codes in the DNA sequences. In our experiments, we show that a
substantial reduction in the size of the model does not incur an undue penalty
for the error correcting codes used, therefore paving the way for portable
data-carrying DNA read head. Crucially, we show that through the joint use of
model compression and error correcting codes, we achieve a higher read accuracy
than without compression and error correction codes.


  The development of X-ray Free Electron Lasers (XFELs) has opened numerous
opportunities to probe atomic structure and ultrafast dynamics of various
materials. Single Particle Imaging (SPI) with XFELs enables the investigation
of biological particles in their natural physiological states with unparalleled
temporal resolution, while circumventing the need for cryogenic conditions or
crystallization. However, reconstructing real-space structures from
reciprocal-space x-ray diffraction data is highly challenging due to the
absence of phase and orientation information, which is further complicated by
weak scattering signals and considerable fluctuations in the number of photons
per pulse. In this work, we present an end-to-end, self-supervised machine
learning approach to recover particle orientations and estimate reciprocal
space intensities from diffraction images only. Our method demonstrates great
robustness under demanding experimental conditions with significantly enhanced
reconstruction capabilities compared with conventional algorithms, and
signifies a paradigm shift in SPI as currently practiced at XFELs.


  In this review paper we discuss the development of the minimal self in
humans, the behavioural measures indicating the presence of different aspects
of the minimal self, namely, body ownership and sense of agency, and also
discuss robotics research investigating and developing these concepts in
artificial agents. We investigate possible avenues for expanding the research
in robotics to further explore the development of an artificial minimal self.


  We show that if in the particle beam there are linear correlations between
energy of particles and their transverse positions and momenta (linear beam
dispersions), then the transverse projected emittances always can be reduced by
letting the beam to pass through magnetostatic system with specially chosen
nonzero lattice dispersions. The maximum possible reduction of the transverse
projected emittances occurs when all beam dispersions are zeroed, and the
values of the lattice dispersions required for that are completely defined by
the values of the beam dispersions and the beam rms energy spread and are
independent from any other second-order central beam moments. Besides that, we
prove that, alternatively, one can also use the lattice dispersions to remove
linear correlations between longitudinal positions of particles and their
transverse coordinates (linear beam tilts), but in this situation solution for
the lattice dispersions is nonunique and the reduction of the transverse
projected emittances is not guaranteed.


  Neural models have shown several state-of-the-art performances on Semantic
Role Labeling (SRL). However, the neural models require an immense amount of
semantic-role corpora and are thus not well suited for low-resource languages
or domains. The paper proposes a semi-supervised semantic role labeling method
that outperforms the state-of-the-art in limited SRL training corpora. The
method is based on explicitly enforcing syntactic constraints by augmenting the
training objective with a syntactic-inconsistency loss component and uses
SRL-unlabeled instances to train a joint-objective LSTM. On CoNLL-2012 English
section, the proposed semi-supervised training with 1%, 10% SRL-labeled data
and varying amounts of SRL-unlabeled data achieves +1.58, +0.78 F1,
respectively, over the pre-trained models that were trained on SOTA
architecture with ELMo on the same SRL-labeled data. Additionally, by using the
syntactic-inconsistency loss on inference time, the proposed model achieves
+3.67, +2.1 F1 over pre-trained model on 1%, 10% SRL-labeled data,
respectively.


  Three linearly independent Hermitian invariants for the nonstationary
generalized singular oscillator (SO) are constructed and their complex linear
combination is diagonalized. The constructed family of eigenstates contains as
subsets all previously obtained solutions for the SO and includes all Robertson
and Schr\"odinger intelligent states for the three invariants. It is shown that
the constructed analogues of the SU(1,1) group-related coherent states for the
SO minimize the Robertson and Schr\"odinger relations for the three invariants
and for every pair of them simultaneously. The squeezing properties of the new
states are briefly discussed.


  Optical non-linearities at the single-photon level are key ingredients for
future photonic quantum technologies. Prime candidates for the realization of
strong photon-photon interactions necessary for implementing quantum
information processing tasks as well as for studying strongly correlated
photons in an integrated photonic device setting are quantum dots embedded in
photonic crystal nanocavities. Here, we report strong quantum correlations
between photons on picosecond timescales. We observe (a) photon antibunching
upon resonant excitation of the lowest-energy polariton state, proving that the
first cavity photon blocks the subsequent injection events, and (b) photon
bunching when the laser field is in two-photon resonance with the polariton
eigenstates of the second Jaynes-Cummings manifold, demonstrating that two
photons at this color are more likely to be injected into the cavity jointly,
than they would otherwise. Together,these results demonstrate unprecedented
strong single-photon non-linearities, paving the way for realizing a
single-photon transistor or a quantum optical Josephson interferometer.


  Current tabling systems suffer from an increase in space complexity, time
complexity or both when dealing with sequences due to the use of data
structures for tabled subgoals and answers and the need to copy terms into and
from the table area. This symptom can be seen in not only B-Prolog, which uses
hash tables, but also systems that use tries such as XSB and YAP. In this
paper, we apply hash-consing to tabling structured data in B-Prolog. While
hash-consing can reduce the space consumption when sharing is effective, it
does not change the time complexity. We enhance hash-consing with two
techniques, called input sharing and hash code memoization, for reducing the
time complexity by avoiding computing hash codes for certain terms. The
improved system is able to eliminate the extra linear factor in the old system
for processing sequences, thus significantly enhancing the scalability of
applications such as language parsing and bio-sequence analysis applications.
We confirm this improvement with experimental results.


  Analytical expressions for the flow field as well as for the effective slip
length of a shear flow over a surface with periodic rectangular grooves are
derived. The primary fluid is in the Cassie state with the grooves being filled
with a secondary immiscible fluid. The coupling of both fluids is reflected in
a locally varying slip distribution along the fluid-fluid interface, which
models the effect of the secondary fluid on the outer flow. The obtained
closed-form analytical expressions for the flow field and effective slip length
of the primary fluid explicitly contain the influence of the viscosities of the
two fluids as well as the magnitude of the local slip, which is a function of
the surface geometry. They agree well with results from numerical computations
of the full geometry. The analytical expressions allow investigating the
influence of the viscous stresses inside the secondary fluid for arbitrary
geometries of the rectangular grooves. For classic superhydrophobic surfaces,
the deviations in the effective slip length compared to the case of inviscid
gas flow are are pointed out. Another important finding with respect to an
accurate modeling of flow over microstructured surfaces is that the local slip
length of a grooved surface is anisotropic.


  Variational Quantum algorithms, especially Quantum Approximate Optimization
and Variational Quantum Eigensolver (VQE) have established their potential to
provide computational advantage in the realm of combinatorial optimization.
However, these algorithms suffer from classically intractable gradients
limiting the scalability. This work addresses the scalability challenge for VQE
by proposing a classical gradient computation method which utilizes the
parameter shift rule but computes the expected values from the circuits using a
tensor ring approximation. The parametrized gates from the circuit transform
the tensor ring by contracting the matrix along the free edges of the tensor
ring. While the single qubit gates do not alter the ring structure, the state
transformations from the two qubit rotations are evaluated by truncating the
singular values thereby preserving the structure of the tensor ring and
reducing the computational complexity. This variation of the Matrix product
state approximation grows linearly in number of qubits and the number of two
qubit gates as opposed to the exponential growth in the classical simulations,
allowing for a faster evaluation of the gradients on classical simulators.


  Leptophobic Z' gauge bosons can appear in models with an E_6 gauge symmetry.
We show that flavour-changing neutral currents can be generated in some of
these models due to the mixing of the ordinary d_R, s_R and b_R quark fields
with the exotic h_R. Because the Z' does not couple to charged leptons, the
constraints on the flavour-changing couplings U^{Z'}_{db} and U^{Z'}_{sb} are
relatively weak. Indeed, B_q--Bbar_q mixing (q=d,s) can be dominated by Z'
exchange, which will affect CP-violating rate asymmetries in B decays. Rare
hadronic B decays can also be affected, while decays involving charged leptons
will be unchanged.


  We study the problem of optimal subset selection from a set of correlated
random variables. In particular, we consider the associated combinatorial
optimization problem of maximizing the determinant of a symmetric positive
definite matrix that characterizes the chosen subset. This problem arises in
many domains, such as experimental designs, regression modeling, and
environmental statistics. We establish an efficient polynomial-time algorithm
using Determinantal Point Process for approximating the optimal solution to the
problem. We demonstrate the advantages of our methods by presenting
computational results for both synthetic and real data sets.


  Motivated by the development of deep convolution neural networks (DCNNs),
tremendous progress has been gained in the field of aircraft detection. These
DCNNs based detectors mainly belong to top-down approaches, which first
enumerate massive potential locations of objects with the form of rectangular
regions, and then identify whether they are objects or not. Compared with these
top-down approaches, this paper shows that aircraft detection via bottom-up
approach still performs competitively in the era of deep learning. We present a
novel one-stage and anchor-free aircraft detection model in a bottom-up manner,
which formulates the task as detection of two intersecting line segments inside
each target and grouping of them without any rectangular region classification.
This model is named as X-LineNet. With simple post-processing, X-LineNet can
simultaneously provide multiple representation forms of the detection result:
the horizontal bounding box, the rotating bounding box, and the pentagonal
mask. The pentagonal mask is a more accurate representation form which has less
redundancy and can better represent aircraft than that of rectangular box.
Experiments show that X-LineNet outperforms state-of-the-art one-stage object
detectors and is competitive compared with advanced two-stage detectors on both
UCAS-AOD and NWPU VHR-10 open dataset in the field of aircraft detection.


  In this paper, we study the pair production and their decays of the Higges in
the neutrinophilic Higgs two doublet model. The pair production occurs through
W and Z gauge bosons fusion process. In the neutrinophilic model, the vacuum
expectation value (VEV) of the second Higgs doublet is small and is
proportional to the neutrino mass. The smallness of VEV is associated with the
approximate global U(1) symmetry which is slightly broken. Therefore, there is
a suppression factor for the U(1) charge breaking process. The second Higgs
doublet has U(1) charge and its single production from the gauge boson fusion
violates the U(1) charge conservation and is suppressed strongly to occur. In
contrast to the single production, the pair production of the Higgses conserves
U(1) charge and the approximate symmetry does not forbid it. To search for the
pair productions in collider experiment,we study the production cross section
of a pair of the charged Higgs and neutral Higgs bosons in e+ e- collision with
center of energy from 600 (GeV) to 2000 (GeV). The total cross section varies
from 10^{-4}(fb) to 10^{-3}(fb) for degenerate (200 GeV) charged and neutral
Higgses mass case. The background process to the signal is gauge bosons pair W
+ Z production and their decays. We show the signal over background ratio is
about 2% ~ 3% by combining the cross section ratio with ratios of branching
fractions.


  We propose a fully convolutional one-stage object detector (FCOS) to solve
object detection in a per-pixel prediction fashion, analogue to semantic
segmentation. Almost all state-of-the-art object detectors such as RetinaNet,
SSD, YOLOv3, and Faster R-CNN rely on pre-defined anchor boxes. In contrast,
our proposed detector FCOS is anchor box free, as well as proposal free. By
eliminating the predefined set of anchor boxes, FCOS completely avoids the
complicated computation related to anchor boxes such as calculating overlapping
during training. More importantly, we also avoid all hyper-parameters related
to anchor boxes, which are often very sensitive to the final detection
performance. With the only post-processing non-maximum suppression (NMS), FCOS
with ResNeXt-64x4d-101 achieves 44.7% in AP with single-model and single-scale
testing, surpassing previous one-stage detectors with the advantage of being
much simpler. For the first time, we demonstrate a much simpler and flexible
detection framework achieving improved detection accuracy. We hope that the
proposed FCOS framework can serve as a simple and strong alternative for many
other instance-level tasks. Code is available at:Code is available at:
https://tinyurl.com/FCOSv1