  Torsion gravity is a natural extension to Einstein gravity in the presence of
the fermion matter sources. In this paper we adopt Wald's covariant method of
Noether charge to construct the quasi-local energy of the
Einstein-Cartan-fermion system, and find that its explicit expression is
formally independent of the coupling constant between torsion and axial
current. This seemingly topological nature is unexpected and is reminiscent of
similar nature of quantum Hall effect and topological insulator. However, the
coupling dependence does enter when evaluating it on-shell, and thus the
topological nature is pseudo. Based on the expression of the quasi-local
energy, we evaluate it for a particular solution on the entanglement wedge and
find the agreement with the holographic relative entropy obtained before. This
shows that the equivalence of these two quantities in the
Einstein-Cartan-fermion system. Moreover, the quasi-local energy in this case
is not always positive definite so that it provides an example of swampland in
torsion gravity. Based on the covariant Noether charge, we also derive the
nonzero fermion effect on Komar angular momentum. The implication of our
results to the tests of torsion gravity in the future gravitational wave
astronomy is also discussed.


  Quantum physics, despite its observables being intrinsically of a
probabilistic nature, does not have a quantum entropy assigned to them. We
propose a quantum entropy that quantify the randomness of a pure quantum state
via a conjugate pair of observables forming the quantum phase space. The
entropy is dimensionless, it is a relativistic scalar, it is invariant under
coordinate transformation of position and momentum that maintain conjugate
properties, and under CPT transformations; and its minimum is positive due to
the uncertainty principle. We expand the entropy to also include mixed states
and show that the proposed entropy is always larger than von Neumann's entropy.
We conjecture an entropy law whereby that entropy of a closed system never
decreases, implying a time arrow for particles physics.


  We give an elementary, self-contained proof of the theorem, proven
independently in 1958-9 by Crowell and Murasugi, that the genus of an
alternating knot equals half the breadth of its Alexander polynomial, and that
applying Seifert's algorithm to any alternating knot diagram gives a surface of
minimal genus.


  We report the discovery of a new planetary system around the K giant $\eta$
Cet (HIP 5364, HD 6805) based on 118 high-precision optical radial velocities
taken at Lick Observatory since July 2000. Since October 2011 an additional
nine near-infrared Doppler measurements have been taken using the ESO CRIRES
spectrograph (VLT, UT1). The visible data set shows two clear periodicities.
Although we cannot completely rule out that the shorter period is due to
rotational modulation of stellar features, the infrared data show the same
variations as in the optical, which strongly supports that the variations are
caused by two planets. Assuming the mass of $\eta$ Cet to be 1.7 $M_\odot$, the
best edge-on coplanar dynamical fit to the data is consistent with two massive
planets ($m_b\sin i$ = 2.6 $\pm$ 0.2 $M_{\mathrm{Jup}}$, $m_c\sin i$ = 3.3
$\pm$ 0.2 $M_{\mathrm{Jup}}$), with periods of $P_b$ = 407 $\pm$ 3 days and
$P_c$ = 740 $\pm$ 5 days and eccentricities of $e_b$ = 0.12 $\pm$ 0.05 and
$e_c$ = 0.08 $\pm$ 0.03. We tested a wide variety of edge-on coplanar and
inclined planetary configurations for stability, which agree with the derived
radial velocities. We find that in certain coplanar orbital configurations with
moderate $e_b$ eccentricity, the planets can be effectively trapped in an
anti-aligned 2:1 mean motion resonance. A much larger non-resonant stable
region exists in low-eccentricity parameter space, although it appears to be
much farther from the best fit than the 2:1 resonant region. In all other
cases, the system is categorized as unstable or chaotic. Another conclusion
from the coplanar inclined dynamical test is that the planets can be at most a
factor of $\sim$ 1.4 more massive than their suggested minimum masses. This
stability constraint on the inclination excludes the possibility of two brown
dwarfs, and strongly favors a planetary system.


  We study the effect of an external magnetic field on the transport properties
of a quantum dot using a recently developed extension of the functional
renormalization group approach to non-equilibrium situations. We discuss in
particular the interplay and competition of the different energy scales of the
dot and the magnetic field on the stationary non-equilibrium current and
conductance. As rather interesting behavior we find a switching behavior of the
magnetic field for intermediate correlations and bias voltage.


  Recent results have shown the appearance of induced cooperative motions
called dynamic heterogeneity during the isomerization of diluted azobenzene
molecules in a host glass-former. In this paper we raise the issue of the
coupling between these "artificial" heterogeneities and the isomerization
period. How do these induced heterogeneities differ in the saturation regime
and in the linear response regime ? Is there a maximum of the heterogeneous
motion versus isomerization rate and why ? Are the heterogeneity evolution with
the isomerization rate connected with the diffusion or relaxation time
evolution ? We use out of equilibrium molecular dynamics simulations to answer
these questions. We find that the heterogeneity increases in the linear
response regime for large isomerization periods and small perturbations. In
contrast the heterogeneity decreases in the saturation regime, i.e. when the
isomerization half-period ($\tau_{p}/2$) is smaller than the relaxation time of
the material ($\tau_{\alpha}$). This result makes possible a test of the effect
of cooperative motions on the dynamics using the chromophores as Maxwell demons
that destroy or stimulate the cooperative motions. Because the heterogeneities
increase in the linear regime and then decrease in the saturation regime, we
find a maximum for $\tau_{p}/2 \approx \tau_{\alpha}$. The induced excitations
concentration follows a power law evolution versus the isomerization rate and
then saturates. As a consequence the $\alpha$ relaxation time is related to the
excitation concentration with a power law, a result in qualitative agreement
with recent findings in constrained models. This result supports a common
origin for the heterogeneities with constrained models and a similar relation
to the excitation concentration.


  The space of $L^p$ graphons, symmetric measurable functions $w: [0,1]^2 \to
\mathbb{R}$ with finite $p$-norm, features heavily in the study of sparse graph
limit theory. We show that the triangular cut operator $M_{\chi}$ acting on
this space is not continuous with respect to the cut norm. This is achieved by
showing that as $n\to \infty$, the norm of the triangular truncation operator
$\mathcal{T}_n$ on symmetric matrices equipped with the cut norm grows to
infinity as well. Due to the density of symmetric matrices in the space of
$L^p$ graphons, the norm growth of $\mathcal{T}_n$ generalizes to the
unboundedness of $M_{\chi}$. We also show that the norm of $\mathcal{T}_n$
grows to infinity on symmetric matrices equipped with the operator norm.


  We study classically the problem of two relativistic particles with an
invariant Duffing-like potential which reduces to the usual Duffing form in the
nonrelativistic limit. We use a special relativistic generalization (RGEM) of
the geometric method (GEM) developed for the analysis of nonrelativistic
Hamiltonian systems to study the local stability of a relativistic Duffing
oscillator. Poincar'e plots of the simulated motion are consistent with the
RGEM. We found a threshold for the external driving force required for chaotic
behavior in the Minkowski spacetime.


  Let $X$ be a smooth projective variety of dimension $n$ over an algebraically
closed field $k$ of characteristic $p>0$. Let $F_X:X\rightarrow X$ be the
absolute Frobenius morphism, and $\E$ a torsion free sheaf on $X$. We give a
upper bound of instability of truncated symmetric powers
$\mathrm{T}^l(\E)(0\leq l\leq\rk(\E)(p-1))$ in terms of $L_{\max}(\Omg^1_X)$,
$\mathrm{I}(\Omg^1_X)$ and $\mathrm{I}(\E)$ (Theorem \ref{InstabTl}). As an
application, We obtain a upper bound of Frobenius direct image ${F_X}_*(\E)$
and some sufficient conditions of slope semi-stability of ${F_X}_*(\E)$. In
addition, we study the slope (semi)-stability of sheaves of locally exact
(closed) forms $B^i_X$ ($Z^i_X$).


  Calculations of the entanglement entropy of a spatial region in continuum
quantum field theory require boundary conditions on the fields at the
fictitious boundary of the region. These boundary conditions impact the
treatment of the zero modes of the fields and their contribution to the
entanglement entropy. We explore this issue in the simplest example, the c=1
compact-boson conformal field theory in 1+1 dimensions. We consider three
different types of boundary conditions: spatial Neumann, temporal Neumann, and
Dirichlet. We argue that the first two are well motivated, and show that they
lead to the same result for the Renyi entropies as well as the entanglement
entropy, including a constant term that corresponds to the Affleck-Ludwig
boundary entropy. The last set of boundary conditions is less well motivated,
and leads to a different value of the constant term. The two values are related
by a duality transformation on the compact boson. We also verify some of our
results with heat-kernel methods.


  Many-particle continuous-time quantum walks (CTQWs) represent a resource for
several tasks in quantum technology, including quantum search algorithms and
universal quantum computation. In order to design and implement CTQWs in a
realistic scenario, one needs effective simulation tools for Hamiltonians that
take into account static noise and fluctuations in the lattice, i.e.
Hamiltonians containing stochastic terms. To this aim, we suggest a parallel
algorithm based on the Taylor series expansion of the evolution operator, and
compare its performances with those of algorithms based on the exact
diagonalization of the Hamiltonian or a 4-th order Runge-Kutta integration. We
prove that both Taylor-series expansion and Runge-Kutta algorithms are reliable
and have a low computational cost, the Taylor-series expansion showing the
additional advantage of a memory allocation not depending on the precision of
calculation. Both algorithms are also highly parallelizable within the SIMT
paradigm, and are thus suitable for GPGPU computing. In turn, we have
benchmarked 4 NVIDIA GPUs and 3 quad-core Intel CPUs for a 2-particle system
over lattices of increasing dimension, showing that the speedup providend by
GPU computing, with respect to the OPENMP parallelization, lies in the range
between 8x and (more than) 20x, depending on the frequency of post-processing.
GPU-accelerated codes thus allow one to overcome concerns about the execution
time, and make it possible simulations with many interacting particles on large
lattices, with the only limit of the memory available on the device.


  Classical electrodynamics can be based on the conservation laws of electric
charge and magnetic flux. Both laws are independent of the metric and the
linear connection of spacetime. Within the framework of such a premetric
electrodynamics -- provided a local and linear constitutive law of the vacuum
is added -- the propagation of electromagnetic waves in the geometric-optics
limit can be studied. The wave vectors of the wave fronts obey a quartic
extended Fresnel equation. If one forbids birefringence in vacuum, the light
cone emerges and Maxwell-Lorentz vacuum electrodynamics can be recovered. If
minimal coupling of electrodynamics to gravity is assumed, then only the
gravitational potential, i.e., the metric of spacetime, emerges in the
constitutive law. We discuss recent results within this general framework.


  The isospin splitting of the Dirac mass obtained with the relativistic
Brueckner-Hartree-Fock (RBHF) theory is thoroughly investigated. From the
perspective in the full Dirac space, the long-standing controversy between the
momentum-independence approximation (MIA) method and the projection method on
the isospin splitting of the Dirac mass in asymmetric nuclear matter (ANM) is
analyzed in detail. We find that, the \textit{assumption procedure} of the MIA
method, which assumes that the single-particle potentials are momentum
independent, is not a sufficient condition that directly leads to the wrong
sign of the isospin splitting of the Dirac mass, while the \textit{extraction
procedure} of the MIA method, which extracts the single-particle potentials
from the single-particle potential energy, leads to the wrong sign. By
approximately solving the set of equations involved in the \textit{extraction
procedure}, a formal expression of the Dirac mass is obtained. The wrong
isospin splitting of the Dirac mass is mainly caused by that the
\textit{extraction procedure} forcely assumes the momentum dependence of the
single-particle potential energy to be a quadratic form where the strength is
solely determined by the constant scalar potential.


  In the paper, we study the convergence analysis of Tikhonov regularization in
finding a zero of a maximal monotone operator using the notion of R-continuity.
Applications to convex minimization and DC programming are provided.


  Here we describe a story behind the discovery of Kepler-46, which was the
first exoplanetary system detected and characterized from a method known as the
transit timing variations (TTVs). The TTV method relies on the gravitational
interaction between planets orbiting the same star. If transits of at least one
of the planets are detected, precise measurements of its transit times can be
used, at least in principle, to detect and characterize other non-transiting
planets in the system. Kepler-46 was the first case for which this method was
shown to work in practice. Other detections and characterizations followed
(e.g., Kepler-88). The TTV method plays an important role in addressing the
incompleteness of planetary systems detected from transits.


  The XENON100 experiment, situated in the Laboratori Nazionali del Gran Sasso,
aims at the direct detection of dark matter in the form of weakly interacting
massive particles (WIMPs), based on their interactions with xenon nuclei in an
ultra low background dual-phase time projection chamber. This paper describes
the general methods developed for the analysis of the XENON100 data. These
methods have been used in the 100.9 and 224.6 live days science runs from which
results on spin-independent elastic, spin-dependent elastic and inelastic
WIMP-nucleon cross-sections have already been reported.


  Accurate forecasting of electricity consumption is essential to ensure the
performance and stability of the grid, especially as the use of renewable
energy increases. Forecasting electricity is challenging because it depends on
many external factors, such as weather and calendar variables. While
regression-based models are currently effective, the emergence of new
explanatory variables and the need to refine the temporality of the signals to
be forecasted is encouraging the exploration of novel methodologies, in
particular deep learning models. However, Deep Neural Networks (DNNs) struggle
with this task due to the lack of data points and the different types of
explanatory variables (e.g. integer, float, or categorical). In this paper, we
explain why and how we used Automated Deep Learning (AutoDL) to find performing
DNNs for load forecasting. We ended up creating an AutoDL framework called
EnergyDragon by extending the DRAGON package and applying it to load
forecasting. EnergyDragon automatically selects the features embedded in the
DNN training in an innovative way and optimizes the architecture and the
hyperparameters of the networks. We demonstrate on the French load signal that
EnergyDragon can find original DNNs that outperform state-of-the-art load
forecasting methods as well as other AutoDL approaches.


  Monte Carlo computer simulations are used to study the conformational free
energy of a folded polymer confined to a long cylindrical tube. The polymer is
modeled as a hard-sphere chain. Its conformational free energy $F$ is measured
as a function of $\lambda$, the end-to-end distance of the polymer. In the case
of a flexible linear polymer, $F(\lambda)$ is a linear function in the folded
regime with a gradient that scales as $f\equiv |dF/d\lambda| \sim N^0
D^{-1.20\pm 0.01}$ for a tube of diameter $D$ and a polymer of length $N$. This
is close to the prediction $f \sim N^0 D^{-1}$ obtained from simple scaling
arguments. The discrepancy is due in part to finite-size effects associated
with the de-Gennes blob model. A similar discrepancy was observed for the
folding of a single arm of a three-arm star polymer. We also examine
backfolding of a semiflexible polymer of persistence length $P$ in the classic
Odijk regime. In the overlap regime, the derivative scales $f \sim N^0
D^{-1.72\pm 0.02} P^{-0.35\pm 0.01}$, which is close to the prediction $f \sim
N^0 D^{-5/3} P^{-1/3}$ obtained from a scaling argument that treats
interactions between deflection segments at the second virial level. In
addition, the measured free energy cost of forming a hairpin turn is
quantitatively consistent with a recent theoretical calculation. Finally, we
examine the scaling of $F(\lambda)$ for a confined semiflexible chain in the
presence of an S-loop composed of two hairpins. While the predicted scaling of
the free energy gradient is the same as that for a single hairpin, we observe a
scaling of $f \sim D^{-1.91\pm 0.03} P^{-0.36\pm 0.01}$. Thus, the quantitative
discrepancy between this measurement and the predicted scaling is somewhat
greater for S-loops than for single hairpins.


  This paper examines the question of who technology users on the trail are,
what their technological uses and needs are, and what conflicts exist between
different trail users regarding technology use and experience, toward
understanding how experiences of trail users contribute to designers. We argue
that exploring these tensions provide opportunities for design that can be used
to both mitigate conflicts and improve community on the trail.


  We characterize sequences of positive integers $(a_1,a_2,\ldots,a_n)$ for
which the $2\times2$ matrix $\left( \begin{array}{cc} a_n&-1 1&0 \end{array}
\right) \left( \begin{array}{cc} a_{n-1}&-1 1&0 \end{array} \right) \cdots
\left( \begin{array}{cc} a_1&-1 1&0 \end{array} \right) $ is either the
identity matrix $\mathrm Id$, its negative $-\mathrm Id$, or square root of
$-\mathrm Id$. This extends a theorem of Conway and Coxeter that classifies
such solutions subject to a total positivity restriction.


  Networked robotic systems, such as connected vehicle platoons, can improve
the safety and efficiency of transportation networks by allowing for high-speed
coordination. To enable such coordination, these systems rely on networked
communications. This can make them susceptible to cyber attacks. Though
security methods such as encryption or specially designed network topologies
can increase the difficulty of successfully executing such an attack, these
techniques are unable to guarantee secure communication against an attacker.
More troublingly, these security methods are unable to ensure that individual
agents are able to detect attacks that alter the content of specific messages.
To ensure resilient behavior under such attacks, this paper formulates a
networked linear time-varying version of dynamic watermarking in which each
agent generates and adds a private excitation to the input of its corresponding
robotic subsystem. This paper demonstrates that such a method can enable each
agent in a networked robotic system to detect cyber attacks. By altering
measurements sent between vehicles, this paper illustrates that an attacker can
create unstable behavior within a platoon. By utilizing the dynamic
watermarking method proposed in this paper, the attack is detected, allowing
the vehicles in the platoon to gracefully degrade to a non-communicative
control strategy that maintains safety across a variety of scenarios.


  This article presents a probabilistic modeling method utilizing smart meter
data and an innovative agent-based simulator for electric vehicles (EVs). The
aim is to assess the effects of different cost-driven EV charging strategies on
the power distribution network (PDN). We investigate the effects of a 40% EV
adoption on three parts of Frederiksberg's low voltage distribution network
(LVDN), a densely urbanized municipality in Denmark. Our findings indicate that
cable and transformer overloading especially pose a challenge. However, the
impact of EVs varies significantly between each LVDN area and charging
scenario. Across scenarios and LVDNs, the share of cables facing congestion
ranges between 5% and 60%. It is also revealed that time-of-use (ToU)-based and
single-day cost-minimized charging could be beneficial for LVDNs with moderate
EV adoption rates. In contrast, multiple-day optimization will likely lead to
severe congestion, as such strategies concentrate demand on a single day that
would otherwise be distributed over several days, thus raising concerns about
how to prevent it. The broader implications of our research suggest that,
despite initial worries primarily centered on congestion due to unregulated
charging during peak hours, a transition to cost-based smart charging,
propelled by an increasing awareness of time-dependent electricity prices, may
lead to a significant rise in charging synchronization, bringing about
undesirable consequences for the power distribution network (PDN).


  We analyze the so-called pinched weights, that are generally thought to
reduce the violation of quark-hadron duality in Finite-Energy Sum Rules. After
showing how this is not true in general, we explain how to address this
question for the LR correlator and any particular pinched weight taking
advantage of our previous work [1], where the possible high-energy behavior of
the LR spectral function was studied. In particular we show that the use of
pinched weights allows to determine with high accuracy the dimension six and
eight contributions in the operator product expansion,
O6=(-4.3^[+0.9}_{-0.7})*10^-3 GeV^6 and O8=(-7.2^{+4.2}_{-5.3})*10^-3 GeV^8.


  A major challenge in solar and heliospheric physics is understanding how
highly localized regions, far smaller than 1 degree at the Sun, are the source
of solar-wind structures spanning more than 20 degrees near Earth. The Sun's
atmosphere is divided into magnetically open regions, coronal holes, where
solar-wind plasma streams out freely and fills the solar system, and closed
regions, where the plasma is confined to coronal loops. The boundary between
these regions extends outward as the heliospheric current sheet (HCS).
Measurements of plasma composition imply that the solar wind near the HCS, the
so-called slow solar wind, originates in closed regions, presumably by the
processes of field-line opening or interchange reconnection. Mysteriously,
however, slow wind is also often seen far from the HCS. We use high-resolution,
three-dimensional magnetohydrodynamic simulations to calculate the dynamics of
a coronal hole whose geometry includes a narrow corridor flanked by closed
field and which is driven by supergranule-like flows at the coronal-hole
boundary. We find that these dynamics result in the formation of giant arcs of
closed-field plasma that extend far from the HCS and span tens of degrees in
latitude and longitude at Earth, accounting for the slow solar wind
observations.


  In this paper, we investigate the problem of the joint transceiver and offset
design (JTOD) for point-to-point multiple-input-multiple-output (MIMO) and
multiple user multiple-input-single-output (MU-MISO) visible light
communication (VLC) systems. Both uplink and downlink multi-user scenarios are
considered. The shot noise induced by the incoming signals is considered,
leading to a more realistic MIMO VLC channel model. Under key lighting
constraints, we formulate non-convex optimization problems aiming at minimizing
the sum mean squared error. To optimize the transceiver and the offset jointly,
a gradient projection based procedure is resorted to. When only imperfect
channel state information is available, a semidefinite programming (SDP) based
scheme is proposed to obtain robust transceiver and offset. The proposed method
is shown to non-trivially outperform the conventional scaled zero forcing (ZF)
and singular value decomposition (SVD) based equalization methods. The robust
scheme works particularly well when the signal is much stronger than the noise.


  The structure of the 18O nucleus at excitation energies above the alpha decay
threshold was studied using 14C+alpha resonance elastic scattering. A number of
states with large alpha reduced widths have been observed, indicating that the
alpha-cluster degree of freedom plays an important role in this N not equal Z
nucleus. However, the alpha-cluster structure of this nucleus is very different
from the relatively simple pattern of strong alpha-cluster quasi-rotational
bands in the neighboring 16O and 20Ne nuclei. A 0+ state with an alpha reduced
width exceeding the single particle limit was identified at an excitation
energy of 9.9+/-0.3 MeV. We discuss evidence that states of this kind are
common in light nuclei and give possible explanations of this feature.


  The risk estimator called "Direct Eigenvalue Estimator" (DEE) is studied. DEE
was developed for small sample regression. In contrast to many existing model
selection criteria, derivation of DEE requires neither any asymptotic
assumption nor any prior knowledge about the noise variance and the noise
distribution. It was reported that DEE performed well in small sample cases but
DEE performed a little worse than the state-of-the-art ADJ. This seems somewhat
counter-intuitive because DEE was developed for specifically regression problem
by exploiting available information exhaustively, while ADJ was developed for
general setting. In this paper, we point out that the derivation of DEE
includes an inappropriate part in spite that the resultant form of DEE is valid
in a sense. As its result, DEE cannot derive its potential. We introduce a
class of valid risk estimators based on the idea of DEE and show that better
risk estimators (mDEE) can be found in the class. By numerical experiments, we
verify that mDEE often performs better than or at least equally the original
DEE and ADJ.


  The luminescence yield of transition metal dichalcogenide monolayers
frequently suffers from the formation of long-lived dark states, which include
excitons with intervalley charge carriers, spin-forbidden transitions, and a
large center-of-mass momentum located outside the light cone of dispersion
relations. Efficient relaxation from bright exciton states to dark states
suppresses the quantum yield of photon emission. In addition, the radiative
recombination of excitons is heavily influenced by Auger-type exciton-exciton
scattering, which yields another nonradiative relaxation channel at room
temperature. Here, we show that Auger-type scattering is promoted not only
between (bright) excitons but also between excitons and long-lived dark states.
We studied the luminescence dynamics of monolayer WS2 capped with hexagonal BN
over broad time ranges of picoseconds to milliseconds using carefully designed
pump-and-probe techniques. We observed that luminescence quenching associated
with Auger-type scattering occurs on 1-100 microsecond time scales, which thus
correspond to the lifetimes of the relevant dark states. The broad distribution
of the measured lifetimes implies the impact of various types of long-lived
states on the exciton annihilation process.


  We classify Hopf actions of Taft algebras T(n) on path algebras of quivers,
in the setting where the quiver is loopless, finite, and Schurian. As a
corollary, we see that every quiver admitting a faithful Z_n-action (by
directed graph automorphisms) also admits inner faithful actions of a Taft
algebra. Several examples for actions of the Sweedler algebra T(2) and for
actions of T(3) are presented in detail. We then extend the results on Taft
algebra actions on path algebras to actions of the Frobenius-Lusztig kernel
u_q(sl_2), and to actions of the Drinfeld double of T(n).


  In this paper we develop methods to solve two problems related to time series
(TS) analysis using quantum computing: reconstruction and classification. We
formulate the task of reconstructing a given TS from a training set of data as
an unconstrained binary optimization (QUBO) problem, which can be solved by
both quantum annealers and gate-model quantum processors. We accomplish this by
discretizing the TS and converting the reconstruction to a set cover problem,
allowing us to perform a one-versus-all method of reconstruction. Using the
solution to the reconstruction problem, we show how to extend this method to
perform semi-supervised classification of TS data. We present results
indicating our method is competitive with current semi- and unsupervised
classification techniques, but using less data than classical techniques.


  Mean-field molecular dynamics based on path integrals is used to approximate
canonical quantum observables for particle systems consisting of nuclei and
electrons. A computational bottleneck is the sampling from the Gibbs density of
the electron operator, which due to the fermion sign problem has a
computational complexity that scales exponentially with the number of
electrons. In this work we construct an algorithm that approximates the
mean-field Hamiltonian by path integrals for fermions. The algorithm is based
on the determinant of a matrix with components based on Brownian bridges
connecting permuted electron coordinates. The computational work for $n$
electrons is $\mathcal O(n^3)$, which reduces the computational complexity
associated with the fermion sign problem. We analyze a bias resulting from this
approximation and provide a computational error indicator. It remains to
rigorously explain the surprisingly high accuracy.


  For any Kumjian-Pask algebra $KP_R(\Lambda)$ defined over a $k$-graph
$\Lambda$ of a special kind (a "standard $k$-graph"), we obtain an $R$-basis.


  Using a priori estimates we prove that initially nonnegative, smooth and
compactly supported solutions of the $K(m,n)$ equations must lose their
smoothness in finite time.
  Formation of a singularity is a prerequisite for the emergence of compactons.


  This work reveals undiscovered challenges in the performance and
generalizability of deep learning models. We (1) identify spurious shortcuts
and evaluation issues that can inflate performance and (2) propose training and
analysis methods to address them.
  We trained an AI model to classify cancer on a retrospective dataset of
120,112 US exams (3,467 cancers) acquired from 2008 to 2017 and 16,693 UK exams
(5,655 cancers) acquired from 2011 to 2015.
  We evaluated on a screening mammography test set of 11,593 US exams (102
cancers; 7,594 women; age 57.1 \pm 11.0) and 1,880 UK exams (590 cancers; 1,745
women; age 63.3 \pm 7.2). A model trained on images of only view markers (no
breast) achieved a 0.691 AUC. The original model trained on both datasets
achieved a 0.945 AUC on the combined US+UK dataset but paradoxically only 0.838
and 0.892 on the US and UK datasets, respectively. Sampling cancers equally
from both datasets during training mitigated this shortcut. A similar AUC
paradox (0.903) occurred when evaluating diagnostic exams vs screening exams
(0.862 vs 0.861, respectively). Removing diagnostic exams during training
alleviated this bias. Finally, the model did not exhibit the AUC paradox over
scanner models but still exhibited a bias toward Selenia Dimension (SD) over
Hologic Selenia (HS) exams. Analysis showed that this AUC paradox occurred when
a dataset attribute had values with a higher cancer prevalence (dataset bias)
and the model consequently assigned a higher probability to these attribute
values (model bias). Stratification and balancing cancer prevalence can
mitigate shortcuts during evaluation.
  Dataset and model bias can introduce shortcuts and the AUC paradox,
potentially pervasive issues within the healthcare AI space. Our methods can
verify and mitigate shortcuts while providing a clear understanding of
performance.


  A mesoscopic model of a diblock copolymer is used to study the stability of a
uniform lamellar phase under a reciprocating shear flow. Approximate viscosity
contrast between the microphases is allowed through a linear dependence of the
(Newtonian) shear viscosity on monomer composition. We first show that
viscosity contrast does not affect the composition of the base lamellar phase
in an unbounded geometry, and that it only couples weakly to long wavelength
perturbations. A perturbative analysis is then presented to address the
stability of uniform lamellar structures under long wavelength perturbations by
self-consistently solving for the composition and velocity fields of the
perturbations. Stability boundaries are obtained as functions of the physical
parameters of the polymer, the parameters of the flow and the initial
orientation of the lamellae. We find that all orientations are linearly stable
within specific ranges of parameters, but that the perpendicular orientation is
generally stable within a larger range than the parallel orientation. Secondary
instabilities are both of the Eckhaus type (longitudinal) and zig-zag type
(transverse). The former is not expected to lead to re-orientation of the
lamella, whereas in the second case the critical wavenumber is typically found
to be along the perpendicular orientation.


  We compute the generic mode sum that quantifies the effect on the spectrum of
a harmonic field when a spherical shell is inserted into vacuum. This
encompasses a variety of problems including the Weyl spectral problem and the
Casimir effect of quantum electrodynamics. This allows us to resolve several
long-standing controversies regarding the question of universality of the
Casimir self-energy; the resolution comes naturally through the connection to
the Weyl problem. Specifically we demonstrate that in the case of a scalar
field obeying Dirichlet or Neumann boundary conditions on the shell surface the
Casimir self-energy is cutoff-dependent while in the case of the
electromagnetic field perturbed by a conductive shell the Casimir self-energy
is universal. We additionally show that an analog non-relativistic Casimir
effect due to zero-point magnons takes place when a non-magnetic spherical
shell is inserted inside a bulk ferromagnet.


  In the present study, we propose to implement a new framework for estimating
generative models via an adversarial process to extend an existing GAN
framework and develop a white-box controllable image cartoonization, which can
generate high-quality cartooned images/videos from real-world photos and
videos. The learning purposes of our system are based on three distinct
representations: surface representation, structure representation, and texture
representation. The surface representation refers to the smooth surface of the
images. The structure representation relates to the sparse colour blocks and
compresses generic content. The texture representation shows the texture,
curves, and features in cartoon images. Generative Adversarial Network (GAN)
framework decomposes the images into different representations and learns from
them to generate cartoon images. This decomposition makes the framework more
controllable and flexible which allows users to make changes based on the
required output. This approach overcomes any previous system in terms of
maintaining clarity, colours, textures, shapes of images yet showing the
characteristics of cartoon images.


  An active plasmonic switch based on single- and few-layer doped graphene
ribbon array operating in the mid-infrared spectrum is investigated with
theoretical and numerical calculations. It is shown that significant resonance
wavelength shifts and modulation depths can be achieved with a slight variation
of the doping concentration of the graphene ribbon. The few-layer graphene
ribbon array device outperforms the single-layer one in terms of the achievable
modulation depth. Our simulations reveal that, by modulating the Fermi-energy
level between 0.2 eV and 0.25 eV, a four-layer graphene ribbon array device can
achieve a modulation depth and resonance wavelength shift of ~13 dB and 0.94
micron respectively, compared to ~2.8 dB and 1.85 micron for a single-layer
device. Additionally, simple fitting models to predict the modulation depth and
the resonance wavelength shift are proposed. These prospects pave the way
towards ultrafast active graphene-based plasmonic devices for infrared and THz
applications.


  We assess non-Markovianity of a quantum open-system dynamics through the
violation of temporal bell-like inequalities in a controllable Nuclear Magnetic
Resonance system. We investigate experimentally the connections between the
violation of the addressed temporal Bell-like inequality and the
non-divisibility of the effective evolution of our system, which we fully
characterize in a broad range of experimentally controllable regimes. Finally,
we investigate the link between our approach and Leggett-Garg-like inequalities
based on time-translational two-time correlation functions. These results open
up interesting perspectives for the effective, non-tomographic characterization
of dynamical evolution by combining tools of different nature.


  This talk presents results of a comparitive study of iterative algorithms
like minimal residue ($MR$) and conjugate gradient ($CG$, $BiCG\gamma_5$, and
\bicgstab) used for inverting the Dirac matrix $M$. The tests were done on the
Connection Machine CM-5 using $32^3 \times 64$ lattices. The fermion action
used is of Wilson type, both with and without the clover term. The overall
conclusion is that preconditioned over-relaxed $MR$ is the simplest, uses the
least memory, and is comparable in performance to \bicgstab. We find these two
algorithms to be equally robust, $i.e.$ insensitive to the starting solution
and to round-off errors.


  We developed a density functional theory-free approach for crystal structure
prediction via combing graph network (GN) and Bayesian optimization (BO). GN is
adopted to establish the correlation model between crystal structure and
formation enthalpies. BO is to accelerate searching crystal structure with
optimal formation enthalpy. The approach of combining GN and BO for crystal
Structure Searching (GN-BOSS), in principle, can predict crystal structure at
given chemical compositions without additional constraints on cell shapes and
lattice symmetries. The applicability and efficiency of GN-BOSS approach is
then verified via solving the classical Ph-vV challenge. It can correctly
predict the crystal structures of 24 binary compounds from scratch with
averaged computational cost ~ 30 minutes each by only one CPU core. GN-BOSS
approach may open a new avenue to data-driven crystal structural prediction
without using the expensive DFT calculations.


  Photodissociation regions (PDRs) and shocks give rise to conspicuous emission
from rotationally and vibrationally excited molecular hydrogen. This line
emission has now been studied with ISO and from the ground in great detail. A
remarkable discovery has been that toward the Orion outflow and other
shock-excited regions, the H_2 level populations show a very high excitation
component. We suggest that these high-excitation populations may arise from
non-thermal pumping processes, such as H_2 formation and high-velocity
ion-molecule collision in partially dissociative shocks. In PDRs such as NGC
7023 however, formation pumping is always less important than fluorescent
pumping. We furthermore present two HD emission line detections toward Orion
Peak 1. This enables the first comparison of the H_2 and the HD excitation,
which surprisingly turn out to be identical.


  Topological quantum phase transition in electron gas systems is an
enthralling phenomena. This phase transition has a unique property in that it
is associated with a quantum phase transition point, which separates different
regions with gapped phases, characterized by an immutable topological quantity
called the Chern number. In this paper, we study a tight binding model for a
three-dimensional topological insulator $(TI)$ thin film that captures
different topological quantum phase transitions in the entire Brillouin zone.
We investigate the effects of a staggered magnetic field, an off-diagonal
coupling term, and a Rashba spin-orbit coupling $(SOC)$ on the topological
insulator thin film model. We give a lucid exposition of the topological phases
by exploring all the topological properties of this system in the entire
Brillouin zone, as a function of the competing interactions. We find that the
system exhibits quantum spin Hall $(QSH)$ phase, quantum anomalous Hall
$(QAH)$, semi-metallic phase, and an ordinary insulator phase. The transition
from one phase to another is invariably associated with a gap closing point. We
further corroborate our results by solving for the edge states modes of the
system.


  This paper investigates a multiple unmanned aerial vehicles (UAVs) enabled
cooperative secure transmission scheme in the presence of multiple potential
eavesdroppers. Specifically, multiple source UAVs (SUAVs) send confidential
information to multiple legitimate ground users and in the meantime multiple
jamming UAVs (JUAVs) cooperatively transmit interference signals to multiple
eavesdroppers in order to improve the legitimate users' achievable secrecy
rate. By taking into account the limited energy budget of UAV, our goal is to
maximize the system secrecy energy efficiency (SEE), namely the achievable
secrecy rate per energy consumption unit, by jointly optimizing the UAV
trajectory, transmit power and user scheduling under the constraints of UAV
mobility as well as the maximum transmit power. The resulting optimization
problem is shown to be a non-convex and mixed-integer fractional optimization
problem, which is challenging to solve. We decompose the original problem into
three sub-problems, and then an efficient iterative algorithm is proposed by
leveraging the block coordinate descent and Dinkelbach method in combination
with successive convex approximation techniques. Simulation results show that
the proposed scheme outperforms the other benchmarks significantly in terms of
the system SEE.


  Generative design refers to computational design methods that can
automatically conduct design exploration under constraints defined by
designers. Among many approaches, topology optimization-based generative
designs aim to explore diverse topology designs, which cannot be represented by
conventional parametric design approaches. Recently, data-driven topology
optimization research has started to exploit artificial intelligence, such as
deep learning or machine learning, to improve the capability of design
exploration. This study proposes a reinforcement learning (RL) based generative
design process, with reward functions maximizing the diversity of topology
designs. We formulate generative design as a sequential problem of finding
optimal design parameter combinations in accordance with a given reference
design. Proximal Policy Optimization is used as the learning framework, which
is demonstrated in the case study of an automotive wheel design problem. To
reduce the heavy computational burden of the wheel topology optimization
process required by our RL formulation, we approximate the optimization process
with neural networks. With efficient data preprocessing/augmentation and neural
architecture, the neural networks achieve a generalized performance and
symmetricity-reserving characteristics. We show that RL-based generative design
produces a large number of diverse designs within a short inference time by
exploiting GPU in a fully automated manner. It is different from the previous
approach using CPU which takes much more processing time and involving human
intervention.


  Recent progress in sentence embedding, which represents the meaning of a
sentence as a point in a vector space, has achieved high performance on tasks
such as a semantic textual similarity (STS) task. However, sentence
representations as a point in a vector space can express only a part of the
diverse information that sentences have, such as asymmetrical relationships
between sentences. This paper proposes GaussCSE, a Gaussian distribution-based
contrastive learning framework for sentence embedding that can handle
asymmetric relationships between sentences, along with a similarity measure for
identifying inclusion relations. Our experiments show that GaussCSE achieves
the same performance as previous methods in natural language inference tasks,
and is able to estimate the direction of entailment relations, which is
difficult with point representations.


  Although highly successful on cosmological scales, Cold Dark Matter (CDM)
models predict unobserved over-dense `cusps' in dwarf galaxies and overestimate
their formation rate. We consider an ultra-light axion-like scalar boson which
promises to reduce these observational discrepancies at galactic scales. The
model, known as Fuzzy Dark Matter (FDM), avoids cusps, suppresses small-scale
power, and delays galaxy formation via macroscopic quantum pressure. We compare
the substructure and density fluctuations of galactic dark matter haloes
comprised of ultra-light axions to conventional CDM results. Besides
self-gravitating subhaloes, FDM includes non-virialized over-dense wavelets
formed by quantum interference patterns which are an efficient source of
heating to galactic discs. We find that, in the solar neighborhood, wavelet
heating is sufficient to give the oldest disc stars a velocity dispersion of
$\sim 30 \: \mathrm{km} \: \mathrm{s}^{-1}$ within a Hubble time if energy is
not lost from the disc, the velocity dispersion increasing with stellar age as
$\sigma_D \propto t^{0.4}$ in agreement with observations. Furthermore, we
calculate the radius-dependent velocity dispersion and corresponding scale
height caused by the heating of this dynamical substructure in both CDM and FDM
with the determination that these effects will produce a flaring that
terminates the Milky Way disc at $15 - 20 \: \mathrm{kpc}$. Although the source
of thickened discs is not known, the heating due to perturbations caused by
dark substructure cannot exceed the total disc velocity dispersion. Therefore,
this work provides a lower bound on the FDM particle mass of $m_a > 0.6 \times
10^{-22} \mathrm{eV}$. Furthermore, FDM wavelets with this particle mass should
be considered a viable mechanism for producing the observed disc thickening
with time.


  We study the displacement map associated to small one-parameter polynomial
unfoldings of polynomial Hamiltonian vector fields on the plane. Its leading
term, the generating function $M(t)$, has an analytic continuation in the
complex plane and the real zeroes of $M(t)$ correspond to the limit cycles
bifurcating from the periodic orbits of the Hamiltonian flow. We give a
geometric description of the monodromy group of $M(t)$ and use it to formulate
sufficient conditions for $M(t)$ to satisfy a differential equation of Fuchs or
Picard-Fuchs type. As examples, we consider in more detail the Hamiltonian
vector fields $\dot{z}=i\bar{z}-i(z+\bar{z})^3$ and $\dot{z}=iz+\bar{z}^2$,
possessing a rotational symmetry of order two and three, respectively. In both
cases $M(t)$ satisfies a Fuchs-type equation but in the first example $M(t)$ is
always an Abelian integral (that is to say, the corresponding equation is of
Picard-Fuchs type) while in the second one this is not necessarily true. We
derive an explicit formula of $M(t)$ and estimate the number of its real
zeroes.}


  The Horava-Lifshitz gravity, having broken the symmetry of space and time,
includes three objects: the spatial metric $g_{ij}$, the lapse variable $N$,
and the shift variable $N_{i}$. Each of these objects have their own scaling
dimensions. The action of the theory is required to be invariant under this
scaling, and spatial diffeomorphism, and the temporal foliation symmetry.
Noting that action can non-trivially depend on the lapse variable, we suggest
to consider the Horva's approach to quantum gravity in higher dimensions such
that a set of extra spacial coordinates possess a scaling dimension different
from that of rest. In so so doing, we propose a new power counting
renormalizable theory for quantum gravity in its UV point. We show that the IR
point and UV point of the proposal possess the same number of degrees of
freedom in 3, 8 and 27 extra-dimensional space-time geometry.


  The size-segregation of granular materials, a process colloquially known as
the Brazil Nut Effect, has generally been thought to proceed faster the greater
the size difference of the particles. We experimentally investigate sheared
bidisperse granular materials as a function of the size ratio of the two
species, and find that the mixing rate at low confining pressure behaves as
expected from percolation-based arguments. However, we also observe an
anomalous effect for the re-segregation rates, wherein particles of both
dissimilar and similar sizes segregate more slowly than intermediate particle
size ratios. Combined with the fact that increasing the confining pressure
significantly suppresses both mixing and segregation rates of particles of
dissimilar size, we propose that the anomalous behavior may be attributed to a
species-dependent distribution of forces within the system.


  The thermal gradient along indium-arsenide nanowires was engineered by a
combination of fabricated micro- trenches in the supporting substrate and
focused laser irradiation. This allowed local control of thermally activated
oxidation reactions of the nanowire on the scale of the diffraction limit. The
locality of the oxidation was detected by micro-Raman mapping, and the results
were found consistent with numerical simulations of the temperature profile.
Applying the technique to nanowires in electrical devices the locally oxidized
nanowires remained conducting with a lower conductance as expected for an
effectively thinner conducting core.


  Based on the fact that the Hamiltonians of the Coulomb many-particle systems
are always factorized we develop the two different approaches for analytical
solution of the Schr\"{o}dinger equation written for arbitrary few- and
many-particle Coulomb systems. The first approach is the matrix factorization
method. Another method is based on the $D^{+}-$series of representations of the
hyper-radial O(2,1)-algebra. The both these methods allow us to obtain the
closed analytical formulas for the bound state energies in an arbitrary
many-particle Coulomb system.


  A new method of symbolic analysis based on finite discretization of
velocity-curvature space is proposed. A minimum alphabet is introduced in a
natural way, and a number of initial analytic measures are defined that make it
possible to study the structure of discrete mapping dynamics. The proposed
method is tested by application to a system of two unidirectionally coupled
logistic maps. It is shown that this method can be used to reveal and study
changes in the structure of attractors. In the given example, features in the
attractor structure of the driven subsystem are studied upon its escape from
the identical synchronization regime.


  Internal recirculation in a moving droplet plays an important role in several
droplet-based microfluidic devices as it enhances mixing, chemical reaction and
heat transfer. The occurrence of fluid slip at the wall, which becomes
prominent at high shear rates and lower length scales, results in a significant
change in droplet circulation. Using molecular dynamics (MD) simulations, the
presence of circulation in droplets is demonstrated and quantified. Circulation
is shown to vary inversely with slip length, which is a measure of interface
wettability. A simple circulation model is established that captures the effect
of slip on droplet circulation. Scaling parameters for circulation and slip
length are identified from the circulation model which leads to the collapse of
data for droplets with varying aspect ratio (AR) and slip length. The model is
validated using continuum and MD simulations and is shown to be accurate for
droplets with high AR.


  We determine the blocks of the Brauer algebra in characteristic zero. We also
give information on the submodule structure of standard modules for this
algebra.


  In this paper, we study the following nonlinear problem of Kirchhoff type
with pure power nonlinearities: (a+b\ds\int_{\R^3}|D u|^2\right)\Delta
u+V(x)u=|u|^{p-1}u, u\in H^1(\R^3), u>0, $x\in \R^3, where $a,$ $b>0$ are
constants, $2<p<5$ and $V:\R^3\rightarrow\R$. Under certain assumptions on $V$,
we prove that \eqref{0.1} has a positive ground state solution by using a
monotonicity trick and a new version of global compactness lemma. Our main
results can be viewed as a partial extension of a recent result of He and Zou
in [16] concerning the existence of positive solutions to the nonlinear
Kirchhoff problem (\varepsilon^2a+\varepsilon b\ds\int_{\R^3}|D
u|^2\right)\Delta u+V(x)u=f(u), u\in H^1(\R^3), u>0, $x\in \R^3$, where
$\varepsilon>0$ is a parameter, $V(x)$ is a positive continuous potential and
$f(u)\thicksim |u|^{p-1}u$ with $3<p<5$ and satisfies the Ambrosetti-Rabinowitz
type condition. Our main results extend also the arguments used in [7,36],
which deal with Schr\"{o}dinger-Poisson system with pure power nonlinearities,
to the Kirchhoff type problem.


  In this paper, we study a Bahri-Brezis type problem on a compact
3-dimensional asymptotically hyperbolic manifold. Using the celebrated
Algebraic Topological argument of Bahri-Coron, we show the existence of at
least one solution under the assumption that the corresponding degenerate
boundary value problem has a positive first eigenvalue and a positive Green
function.


  In this work we establish and point out connections between the notion of
query-answer causality in databases and database repairs, model-based diagnosis
in its consistency-based and abductive versions, and database updates through
views. The mutual relationships among these areas of data management and
knowledge representation shed light on each of them and help to share notions
and results they have in common. In one way or another, these are all
approaches to uncertainty management, which becomes even more relevant in the
context of big data that have to be made sense of.


  J-PET analysis framework is a flexible, lightweight, ROOT-based software
package which provides the tools to develop reconstruction and calibration
procedures for PET tomography. In this article we present the implementation of
the full data-processing chain in the J-PET framework which is used for the
data analysis of the J-PET tomography scanner. The Framework incorporates
automated handling of PET setup parameters' database as well as high level
tools for building data reconstruction procedures. Each of these components is
briefly discussed.


  We present a programme of spectroscopic observations of galaxies in a sample
of optically-selected clusters taken from the catalogue of Couch et al (1991).
Previous ROSAT observations of these clusters have shown them to have lower
X-ray luminosities, given their optical richness, than might be expected on the
basis of local samples. In the present paper we extend this work by determining
velocity dispersions of a subsample of the clusters. We confirm the dynamical
reality of all but one of the original sample, and find velocity dispersions
comparable with present-day clusters of equivalent comoving space density.
Thus, in the context of the $L_X-\sigma$ relation for present-day clusters,
there is evidence for a higher velocity dispersion at fixed X-ray luminosity.
  A key question is whether the high velocity dispersions are indicative of the
gravitational potential. If they are, the X-ray luminosities measured in Bower
et al., 1994 (Paper I), would then imply an implausibly low efficiency of X-ray
generation. Alternatively, the discrepancy could be explained if the clusters
were systems of lower virial temperature, in which the apparent velocity
dispersion is inflated by an infalling, unrelaxed halo. This might result
either from an increase with redshift in the infall rate for clusters, or from
the preferential selection of clusters embedded in filaments oriented along the
line of sight. Since clusters with similar properties can be found in local
optically selected catalogues, we suggest that the latter explanation is more
likely.


  The goal of continuous sign language recognition(CSLR) research is to apply
CSLR models as a communication tool in real life, and the real-time requirement
of the models is important. In this paper, we address the model real-time
problem through cross-resolution knowledge distillation. In our study, we found
that keeping the frame-level feature scales consistent between the output of
the student network and the teacher network is better than recovering the
frame-level feature sizes for feature distillation. Based on this finding, we
propose a new frame-level feature extractor that keeps the output frame-level
features at the same scale as the output of by the teacher network. We further
combined with the TSCM+2D hybrid convolution proposed in our previous study to
form a new lightweight end-to-end CSLR network-Low resolution input
net(LRINet). It is then used to combine cross-resolution knowledge distillation
and traditional knowledge distillation methods to form a CSLR model based on
cross-resolution knowledge distillation (CRKD). The CRKD uses high-resolution
frames as input to the teacher network for training, locks the weights after
training, and then uses low-resolution frames as input to the student network
LRINet to perform knowledge distillation on frame-level features and
classification features respectively. Experiments on two large-scale continuous
sign language datasets have proved the effectiveness of CRKD. Compared with the
model with high-resolution data as input, the calculation amount, parameter
amount and inference time of the model have been significantly reduced under
the same experimental conditions, while ensuring the accuracy of the model, and
has achieved very competitive results in comparison with other advanced
methods.


  Our goal is to show that the standard model-theoretic concept of types can be
applied in the study of order-invariant properties, i.e., properties definable
in a logic in the presence of an auxiliary order relation, but not actually
dependent on that order relation. This is somewhat surprising since
order-invariant properties are more of a combinatorial rather than a logical
object. We provide two applications of this notion. One is a proof, from the
basic principles, of a theorem by Courcelle stating that over trees,
order-invariant MSO properties are expressible in MSO with counting
quantifiers. The other is an analog of the Feferman-Vaught theorem for
order-invariant properties.


  ML models are increasingly being pushed to mobile devices, for low-latency
inference and offline operation. However, once the models are deployed, it is
hard for ML operators to track their accuracy, which can degrade unpredictably
(e.g., due to data drift). We design the first end-to-end system for
continuously monitoring and adapting models on mobile devices without requiring
feedback from users. Our key observation is that often model degradation is due
to a specific root cause, which may affect a large group of devices. Therefore,
once the system detects a consistent degradation across a large number of
devices, it employs a root cause analysis to determine the origin of the
problem and applies a cause-specific adaptation. We evaluate the system on two
computer vision datasets, and show it consistently boosts accuracy compared to
existing approaches. On a dataset containing photos collected from driving
cars, our system improves the accuracy on average by 15%.


  Kolmogorov-Arnold Networks (KANs) offer an efficient and interpretable
alternative to traditional multi-layer perceptron (MLP) architectures due to
their finite network topology. However, according to the results of Kolmogorov
and Vitushkin, the representation of generic smooth functions by KAN
implementations using analytic functions constrained to a finite number of
cutoff points cannot be exact. Hence, the convergence of KAN throughout the
training process may be limited. This paper explores the relevance of
smoothness in KANs, proposing that smooth, structurally informed KANs can
achieve equivalence to MLPs in specific function classes. By leveraging
inherent structural knowledge, KANs may reduce the data required for training
and mitigate the risk of generating hallucinated predictions, thereby enhancing
model reliability and performance in computational biomedicine.


  The earlier approach is used for description of qubits and geometric phase
parameters, the things critical in the area of topological quantum computing.
The used tool, Geometric (Clifford) Algebra is the most convenient formalism
for that case. Generalization of formal complex plane to an an arbitrary plane
in three dimensions and of usual Hopf fibration to the map generated by an
arbitrary unit value element of even sub-algebra of the three-dimensional
Geometric Algebra are resulting in more profound description of qubits compared
to quantum mechanical Hilbert space formalism.


  The comparison of the SNO charged current result with the solar neutrino
signal measured by Super-Kamiokande has provided, for the first time, the
evidence of a non electron flavour active neutrino component in the solar flux.
We remark here that this evidence can be obtained in a model independent way,
i.e. without any assumpion about solar models, about the energy dependence of
the neutrino oscillation probability and about the presence of sterile
neutrinos. Furthermore, from the 8B neutrino flux obtained by combining SNO and
Super-Kamiokande, one can determine the central solar temperature, T = 1.57 (1
\pm 1%) 10^7 K, and provide an estimate of the beryllium neutrino flux, Phi_Be
= 4.9 (1 \pm 11%) 10^9 cm^{-2} s^{-1}.


  We study the C$^*$-algebra of Wiener-Hopf operators $A_\Omega$ on a cone
$\Omega$ with polyhedral base $P$. As is known, a sequence of symbol maps may
be defined, and their kernels give a filtration by ideals of $A_\Omega$, with
liminary subquotients. One may define $K$-group valued 'index maps' between the
subquotients. These form the $E^1$ term of the Atiyah-Hirzebruch type spectral
sequence induced by the filtration. We show that this $E^1$ term may, as a
complex, be identified with the cellular complex of $P$, considered as CW
complex by taking convex faces as cells. It follows that $A_\Omega$ is
$KK$-contractible, and that $A_\Omega/\mathbb K$ and $S$ are $KK$-equivalent.
Moreover, the isomorphism class of $A_\Omega$ is a complete invariant for the
combinatorial type of $P$.


  We investigate the $\Delta S = 1,2$ effective weak chiral Lagrangian within
the framework of the chiral quark model. Starting from the effective four-quark
operators, we derive the effective weak chiral action by integrating out the
constituent quark fields. Employing the derivative expansion, we obtain the
effective weak chiral Lagrangian to order ${\cal O} (p^4)$. We examine the
contributions of the order ${\cal O} (N_c)$ to the ratio
$g_{\underline{8}}/g_{\underline{27}}$, considering e.g. the quark axial-vector
constant $g_{\rm A}$ different from unity. The low energy constants of the
counterterms are also presented and discussed.


  The coolest known brown dwarfs are our best analogs to extrasolar gas-giant
planets. The prolific detections of such cold substellar objects in the past
two years has spurred intensive followup, but the lack of accurate distances is
a key gap in our understanding. We present a large sample of precise distances
based on homogeneous mid-infrared astrometry that robustly establish absolute
fluxes, luminosities, and temperatures. The coolest brown dwarfs have
temperatures of 400-450 K and masses ~5-20 times that of Jupiter, showing they
bridge the gap between hotter brown dwarfs and gas-giant planets. At these
extremes, spectral energy distributions no longer follow a simple
correspondence with temperature, suggesting an increasing role of other
physical parameters such as surface gravity, vertical mixing, clouds, and
metallicity.


  In this paper, we give an explicit expression for a certain family of ternary
cyclotomic polynomials: specifically $\Phi_{p_{1}p_{2}p_{3}}$, where
$p_{1}<p_{2}<p_{3}$ are odd primes such that $p_{2} \equiv1 \mod p_{1}$ and
$p_{3} \equiv1 \mod {p_{1}p_{2}}$. As an application of the explicit
expressions, we give an exact formula for the number of nonzero terms in the
polynomials in the family, which in turn immediately shows that the density
(number of non-zeros terms / degree) is roughly inversely proportional to
$p_{2}$, when $p_{1}$ is sufficiently large.


  The sequence of nucleotide bases occurring in an organism's DNA is often
regarded as a codescript for its construction. However, information in a DNA
sequence can only be regarded as a codescript relative to an operational
biochemical machine, which the information constrains in such a way as to
direct the process of construction. In reality, any biochemical machine for
which a DNA codescript is efficacious is itself produced through the mechanical
interpretation of an identical or very similar codescript. In these terms the
origin of life can be described as a bootstrap process involving the
simultaneous accumulation of genetic information and the generation of a
machine that interprets it as instructions for its own construction. This
problem is discussed within the theoretical frameworks of thermodynamics,
informatics and self-reproducing automata, paying special attention to the
physico-chemical origin of genetic coding and the conditions, both
thermodynamic and informatic, which a system must fulfil in order for it to
sustain semiosis. The origin of life is equated with biosemiosis


  Decentralised Autonomous Organisations (DAOs) are a new type of digital
organisation that uses blockchain infrastructure (e.g. smart contracts, tokens)
to coordinate a group of people around a shared mission. Like all
organisations, DAOs must attract sources of funding and other resources, and
discover and retain a talented community and workforce. To do this, they must
signal their true quality. Yet the characteristics of the environment that DAOs
operate in (pseudonymous actors, global scale, permissionless entry and exit)
makes this difficult. We apply costly signalling theory to explore the
information asymmetry problem in DAOs and some of the strategies (behaviours
and investments) and institutional solutions (including better signalling
mechanisms) that have evolved to solve this problem.


  In this work a design is proposed for an active, permanent magnet based,
self-propelled magnetic bearing i.e. levitating motor having the following
features : (a) simple winding structure, (b) high load supporting capacity, (c)
no eccentricity sensors, (d) stable confinement in all translational
dimensions, (e) stable confinement in all rotational dimensions and (f) high
efficiency. This design uses an architecture consisting of a helically wound
three-phase stator, and a rotor with the magnets also arranged in a helical
manner. Active control is used to excite the rotor at a torque angle lying in
the second quadrant. This torque angle is independent of the rotor's position
inside the stator cavity hence the control algorithm is similar to that of a
conventional permanent magnet synchronous motor. It is motivated through a
physical argument that the bearing rotor develops a lift force proportional to
the output torque and that it remains stably confined in space. These
assertions are then proved rigorously through a calculation of the magnetic
fields, forces and torques. The stiffness matrix of the system is presented and
a discussion of stable and unstable operating regions is given.


  We discuss the minimal seesaw model for the Dirac CP violating phase of the
lepton mixing matrix. We introduce two right-handed Majorana neutrinos and
obtain several textures of the tri-maximal lepton mixing matrices. Moreover, we
discuss the observed baryon asymmetry of the universe through the leptogenesis
mechanism. As the result, we obtain the specific model which predicts the
negative sign of maximal Dirac CP violating phase and normal hierarchy of
neutrino masses.


  We present a study of the extinction and depletion-derived dust properties of
gamma-ray burst (GRB) absorbers at $1<z<3$ showing the presence of neutral
carbon (\ion{C}{I}). By modelling their parametric extinction laws, we discover
a broad range of dust models characterizing the GRB \ion{C}{I} absorption
systems. In addition to the already well-established correlation between the
amount of \ion{C}{I} and visual extinction, $A_V$, we also observe a
correlation with the total-to-selective reddening, $R_V$. All three quantities
are also found to be connected to the presence and strength of the 2175\,{\AA}
dust extinction feature. While the amount of \ion{C}{I} is found to be
correlated with the SED-derived dust properties, we do not find any evidence
for a connection with the depletion-derived dust content as measured from
[Zn/Fe] and $N$(Fe)$_{\rm dust}$. To reconcile this, we discuss a scenario
where the observed extinction is dominated by the composition of dust particles
confined in the molecular gas-phase of the ISM. We argue that since the
depletion level trace non-carbonaceous dust in the ISM, the observed extinction
in GRB \ion{C}{I} absorbers is primarily produced by carbon-rich dust in the
molecular cloud and is therefore only observable in the extinction curves and
not in the depletion patterns. This also indicates that the 2175\,{\AA} dust
extinction feature is caused by dust and molecules in the cold and molecular
gas-phase. This scenario provides a possible resolution to the discrepancy
between the depletion- and SED-derived amounts of dust in high-$z$ absorbers.


  Summarizing conversations via neural approaches has been gaining research
traction lately, yet it is still challenging to obtain practical solutions.
Examples of such challenges include unstructured information exchange in
dialogues, informal interactions between speakers, and dynamic role changes of
speakers as the dialogue evolves. Many of such challenges result in complex
coreference links. Therefore, in this work, we investigate different approaches
to explicitly incorporate coreference information in neural abstractive
dialogue summarization models to tackle the aforementioned challenges.
Experimental results show that the proposed approaches achieve state-of-the-art
performance, implying it is useful to utilize coreference information in
dialogue summarization. Evaluation results on factual correctness suggest such
coreference-aware models are better at tracing the information flow among
interlocutors and associating accurate status/actions with the corresponding
interlocutors and person mentions.


  Many new and unidentified Galactic sources have recently been revealed by
ongoing hard X-ray surveys. A significant fraction of these have been shown to
be the type of accreting white dwarfs known as cataclysmic variables (CVs).
Follow-up observations are often required to categorize and classify these
sources, and may also identify potentially unique or interesting cases. One
such case is IGR J18007-4146, which is likely a CV based on follow-up Chandra
observations and constraints from optical/IR catalogs. Utilizing simultaneous
XMM-Newton and NuSTAR observations, as well as the available optical/IR data,
we confirm the nature of IGR J18007-4146 as an intermediate polar type CV.
Timing analysis of the XMM data reveals a periodic signal at 424.4 +/- 0.7 s
that we interpret as the spin period of the white dwarf. Modeling the 0.3-78
keV spectrum, we use a thermal bremsstrahlung continuum but require intrinsic
absorption as well as a soft component and strong Fe lines between 6 and 7 keV.
We model the soft component using a single-temperature blackbody with kT = 73
+8/-6 eV. From the X-ray spectrum, we are able to measure the mass of the white
dwarf to be 1.06 +0.19/-0.10 Msun, which means IGR J18007-4146 is more massive
than the average for magnetic CVs.


  In this paper, we propose a new setting for generating product descriptions
from images, augmented by marketing keywords. It leverages the combined power
of visual and textual information to create descriptions that are more tailored
to the unique features of products. For this setting, previous methods utilize
visual and textual encoders to encode the image and keywords and employ a
language model-based decoder to generate the product description. However, the
generated description is often inaccurate and generic since same-category
products have similar copy-writings, and optimizing the overall framework on
large-scale samples makes models concentrate on common words yet ignore the
product features. To alleviate the issue, we present a simple and effective
Multimodal In-Context Tuning approach, named ModICT, which introduces a similar
product sample as the reference and utilizes the in-context learning capability
of language models to produce the description. During training, we keep the
visual encoder and language model frozen, focusing on optimizing the modules
responsible for creating multimodal in-context references and dynamic prompts.
This approach preserves the language generation prowess of large language
models (LLMs), facilitating a substantial increase in description diversity. To
assess the effectiveness of ModICT across various language model scales and
types, we collect data from three distinct product categories within the
E-commerce domain. Extensive experiments demonstrate that ModICT significantly
improves the accuracy (by up to 3.3% on Rouge-L) and diversity (by up to 9.4%
on D-5) of generated results compared to conventional methods. Our findings
underscore the potential of ModICT as a valuable tool for enhancing automatic
generation of product descriptions in a wide range of applications. Code is at:
https://github.com/HITsz-TMG/Multimodal-In-Context-Tuning


  A graph is said to be a segment graph if its vertices can be mapped to line
segments in the plane such that two vertices have an edge between them if and
only if their corresponding line segments intersect. Kratochv\'{i}l and
Kub\v{e}na [``On intersection representations of co-planar graphs'', Discrete
Mathematics, 178(1-3):251-255, 1998] asked the question of whether the
complements of planar graphs are segment graphs. We show here that the
complements of all partial 2-trees are segment graphs.


  Random numbers are indispensable resources for application in modern science
and technology. Therefore, a dedicate entropy source is essential, particularly
cryptographic tasks and modern applications. In this work, we experimentally
demonstrated a scheme to generate random numbers by multiplexing eight tunnel
diodes onto a single circuit. As a result, the data rate of random number
generation was significantly enhanced to eight folds. In comparison to the
original scheme that employed one diode, this multiplexing scheme produced data
with higher entropy. These data were then post-processed with the
Toeplitz-hashing extractor, yielding final outputs that achieved almost full
entropy and satisfied the U.S. National Institute of Standards and Technology
(NIST) Special Publication 800-90B validation. These data also passed the NIST
Special Publication 800-22 statistical randomness examination and had no sign
of patterns detected from an autocorrelation analysis.


  In the well-known complexity class NP are combinatorial problems, whose
optimization counterparts are important for many practical settings. These
problems typically consider full knowledge about the input. In practical
settings, however, uncertainty in the input data is a usual phenomenon, whereby
this is normally not covered in optimization versions of NP problems. One
concept to model the uncertainty in the input data, is recoverable robustness.
The instance of the recoverable robust version of a combinatorial problem P is
split into a base scenario $\sigma_0$ and an uncertainty scenario set
$\textsf{S}$. The base scenario and all members of the uncertainty scenario set
are instances of the original combinatorial problem P. The task is to calculate
a solution $s_0$ for the base scenario $\sigma_0$ and solutions $s$ for all
uncertainty scenarios $\sigma \in \textsf{S}$ such that $s_0$ and $s$ are not
too far away from each other according to a distance measure, so $s_0$ can be
easily adapted to $s$. This paper introduces Hamming Distance Recoverable
Robustness, in which solutions $s_0$ and $s$ have to be calculated, such that
$s_0$ and $s$ may only differ in at most $\kappa$ elements. We survey the
complexity of Hamming distance recoverable robust versions of optimization
problems, typically found in NP for different scenario encodings. The
complexity is primarily situated in the lower levels of the polynomial
hierarchy. The main contribution of the paper is a gadget reduction framework
that shows that the recoverable robust versions of problems in a large class of
combinatorial problems is $\Sigma^P_{3}$-complete. This class includes problems
such as Vertex Cover, Coloring or Subset Sum. Additionally, we expand the
results to $\Sigma^P_{2m+1}$-completeness for multi-stage recoverable robust
problems with $m \in \mathbb{N}$ stages.


  An analysis of experimental data shows that, in addition to phonon--roton
excitations in superfluid helium, there necessarily exist at least one branch
of elementary excitations whose energy spectrum strongly depends on
temperature. On this basis, the Landau superfluidity criterion is generalized
for several branches of elementary excitations, taking into account that the
critical velocity should vanish during the phase transition of liquid helium
from the superfluid state to the normal state. We suppose that a new spectrum
of excitations with a gap, depending on interparticle interaction, corresponds
to the transition of helium to superfluid state. This gap exists only in
superfluid state and disappears at the transition temperature to normal state.
The phonon-roton branch of excitations has no crucial influence on
superfluidity. Rotons, as well as phonons, are not the specific excitations for
the superfluid helium, but exist as universal excitations in liquid state, and
form the continuous branch of excitations in different liquids. This point of
view is confirmed experimentally and numerically.


  We consider the shape optimization of an object in Navier--Stokes flow by
employing a combined phase field and porous medium approach, along with
additional perimeter regularization. By considering integral control and state
constraints, we extend the results of earlier works concerning the existence of
optimal shapes and the derivation of first order optimality conditions. The
control variable is a phase field function that prescribes the shape and
topology of the object, while the state variables are the velocity and the
pressure of the fluid. In our analysis, we cover a multitude of constraints
which include constraints on the center of mass, the volume of the fluid
region, and the drag of the object. Finally, we present numerical results of
the optimization problem that is solved using the variable metric projection
type (VMPT) method proposed by Blank and Rupprecht, where we consider one
example of topology optimization without constraints and one example of
maximizing the lift of the object with a state constraint, as well as a
comparison with earlier results for the drag minimization.


  In this paper we analyze scalar gravitational perturbations on a
Robertson-Walker background in the presence of multiple scalar fields that take
values on a (geometrically non-trivial) field manifold during slow-roll
inflation. For this purpose modified and generalized slow-roll functions are
introduced and their properties examined. These functions make it possible to
estimate to what extent the gravitational potential decouples from the scalar
field perturbations. The correlation function of the gravitational potential is
calculated in an arbitrary state. We argue that using the vacuum state seems a
reasonable assumption for those perturbations that can be observed in the CMBR.
Various aspects are illustrated by examples with multiple scalar fields that
take values on flat and curved manifolds.


  Current methods in training and benchmarking vision models exhibit an
over-reliance on passive, curated datasets. Although models trained on these
datasets have shown strong performance in a wide variety of tasks such as
classification, detection, and segmentation, they fundamentally are unable to
generalize to an ever-evolving world due to constant out-of-distribution shifts
of input data. Therefore, instead of training on fixed datasets, can we
approach learning in a more human-centric and adaptive manner? In this paper,
we introduce Action-Aware Embodied Learning for Perception (ALP), an embodied
learning framework that incorporates action information into representation
learning through a combination of optimizing a reinforcement learning policy
and an inverse dynamics prediction objective. Our method actively explores in
complex 3D environments to both learn generalizable task-agnostic visual
representations as well as collect downstream training data. We show that ALP
outperforms existing baselines in several downstream perception tasks. In
addition, we show that by training on actively collected data more relevant to
the environment and task, our method generalizes more robustly to downstream
tasks compared to models pre-trained on fixed datasets such as ImageNet.


  We study the pairing of Fermi gases near the scattering resonance of the
$\ell\neq 0$ partial wave. Using a model potential which reproduces the actual
two-body low energy scattering amplitude, we have obtained an analytic solution
of the gap equation. We show that the ground state of $\ell=1$ and $\ell=3$
superfluid are orbital ferromagnets with pairing wavefunctions $Y_{11}$ and
$Y_{32}$ respectively. For $\ell=2$, there is a degeneracy between $Y_{22}$ and
a "cyclic state". Dipole energy will orient the angular momentum axis. The gap
function can be determined by the angular dependence of the momentum
distribution of the fermions.


  A structured approach for the Collatz conjecture is presented using just the
odd integers that are, in turn, divided into categories based on the roles they
play such as Starter, Intermediary and Terminal. The expression 4x+1 is used as
a tool to expose all the hidden and significant characteristics of the
conjecture that lead us to its proof. The mixing properties of the iterates are
addressed by showing that the Collatz iterates of half of all the odd integers
that are of the form 4m+3, on the average, increase by three times the value of
the odd integer that was used to start with, while the iterates of those of
4m+1, on the average, decrease by a factor of four. Further, expressions are
provided to generate all the sets of odd integers where the Collatz iterate of
all the integers in each set is an integer of the of form 6m+1 or 6m+5. The
significance of the Collatz net (tree) becomes obvious since it encompasses all
the Collatz trajectories.


  Existing autonomous robot navigation systems allow robots to move from one
point to another in a collision-free manner. However, when facing new
environments, these systems generally require re-tuning by expert roboticists
with a good understanding of the inner workings of the navigation system. In
contrast, even users who are unversed in the details of robot navigation
algorithms can generate desirable navigation behavior in new environments via
teleoperation. In this paper, we introduce APPLD, Adaptive Planner Parameter
Learning from Demonstration, that allows existing navigation systems to be
successfully applied to new complex environments, given only a human
teleoperated demonstration of desirable navigation. APPLD is verified on two
robots running different navigation systems in different environments.
Experimental results show that APPLD can outperform navigation systems with the
default and expert-tuned parameters, and even the human demonstrator
themselves.


  A quarter of DA white dwarfs are metal polluted, yet elements heavier than
helium sink down through the stellar atmosphere on timescales of days. Hence,
these white dwarfs must be currently accreting material containing heavy
elements. Here, we consider whether the scattering of comets or asteroids from
an outer planetary system, following stellar mass loss on the asymptotic giant
branch, can reproduce these observations. We use N-body simulations to
investigate the effects of stellar mass loss on a simple system consisting of a
planetesimal belt whose inner edge is truncated by a planet. Our simulations
find that, starting with a planetesimal belt population fitted to the observed
main sequence evolution, sufficient mass is scattered into the inner planetary
system to explain the inferred heavy element accretion rates. This assumes that
some fraction of the mass scattered into the inner planetary system ends up on
star-grazing orbits, is tidally disrupted and accreted onto the white dwarf.
The simulations also reproduce the observed decrease in accretion rate with
cooling age and predict accretion rates in old (>1Gyr) white dwarfs, in line
with observations. The efficiency we assumed for material scattered into the
inner planetary system to end up on star-grazing orbits is based on a
Solar-like planetary system, since the simulations show that a single planet is
not sufficient. Although the correct level of accretion is reproduced, the
simulations predict a higher fraction of accreting white dwarfs than observed.
This could indicate that evolved planetary systems are less efficient at
scattering bodies onto star-grazing orbits or that dynamical instabilities
post-stellar mass loss cause rapid planetesimal belt depletion for a
significant fraction of systems.


  Optimal control problems of forward-backward stochastic Volterra integral
equations (FBSVIEs in short) are formulated and studied. A general duality
principle is established for linear backward stochastic integral equation and
linear stochastic Fredholm-Volterra integral equation with mean-field. With the
help of such a duality principle, together with some other new delicate and
subtle skills, Pontryagin type maximum principles are proved for two optimal
control problems of FBSVIEs.


  Quantum teleportation is a process in which an unknown quantum state is
transferred between two spatially separated subspaces of a bipartite quantum
system which share an entangled state and communicate classically. In the case
of photonic states, this process is probabilistic due to the impossibility of
performing a two-particle complete Bell state analysis with linear optics. In
order to achieve a deterministic teleportation scheme, harnessing other degrees
of freedom of a single particle, rather than a third particle, has been
proposed. Indeed, this leads to a novel type of deterministic teleportation
scheme, the so-called hybrid teleportation. Here we report the first
realization of photonic hybrid quantum teleportation from spin-to-orbital
angular momentum degrees of freedom. In our scheme, the polarization state of
photon A is transferred to orbital angular momentum of photon B. The teleported
states are visualized in real-time by means of an intensified CCD camera. The
quality of teleported states is verified by performing quantum state
tomography, which confirms an average fidelity higher than 99.4%. We believe
this experiment paves the route towards a novel way of quantum communication in
which encryption and decryption are carried out in naturally different Hilbert
spaces, and therefore may provide means of enhancing security.


  Accurate nuclear reaction rates are needed for primordial nucleosynthesis and
hydrostatic burning in stars. The relevant reactions are extremely difficult to
measure directly in the laboratory at the small astrophysical energies. In
recent years direct reactions have been developed and applied to extract
low-energy astrophysical S-factors. These methods require a combination of new
experimental techniques and theoretical efforts, which are the subject of this
presentation.


  The aim of this paper is the Ore extension of group-cograded Hopf
coquasigroups. This paper first shows a categorical interpretation and some
examples of group-cograded Hopf coquasigroups, and then gives a necessary and
sufficient conditions for the Ore extensions of group-cograded Hopf
coquasigroups to be group-cograded Hopf coquasigroups. Finally, a certain
isomorphism between Ore extensions are considered.


  We present the first deep polarimetric study of Galactic synchrotron emission
at low radio frequencies. Our study is based on 21 observations of the European
Large Area Infrared Space Observatory Survey-North 1 (ELAIS-N1) field using the
Low-Frequency Array (LOFAR) at frequencies from 114.9 to 177.4 MHz. These data
are a part of the LOFAR Two-metre Sky Survey Deep Fields Data Release 1. We
used very low-resolution ($4.3'$) Stokes QU data cubes of this release. We
applied rotation measure (RM) synthesis to decompose the distribution of
polarised structures in Faraday depth, and cross-correlation RM synthesis to
align different observations in Faraday depth. We stacked images of about 150
hours of the ELAIS-N1 observations to produce the deepest Faraday cube at low
radio frequencies to date, tailored to studies of Galactic synchrotron emission
and the intervening magneto-ionic interstellar medium. This Faraday cube covers
$\sim36~{\rm deg^{2}}$ of the sky and has a noise of $27~{\rm \mu
Jy~PSF^{-1}~RMSF^{-1}}$ in polarised intensity. This is an improvement in noise
by a factor of approximately the square root of the number of stacked data
cubes ($\sim\sqrt{20}$), as expected, compared to the one in a single data cube
based on five-to-eight-hour observations. We detect a faint component of
diffuse polarised emission in the stacked cube, which was not detected
previously. Additionally, we verify the reliability of the ionospheric Faraday
rotation corrections estimated from the satellite-based total electron content
measurements to be of $~\sim0.05~{\rm rad~m^{-2}}$. We also demonstrate that
diffuse polarised emission itself can be used to account for the relative
ionospheric Faraday rotation corrections with respect to a reference
observation.


  The one-loop determination of the coefficient $c_\text{SW}$ of the Wilson
quark action has been useful to push the leading cut-off effects for on-shell
quantities to $\mathcal{O}(\alpha^2 a)$ and, in conjunction with
non-perturbative determinations of $c_\text{SW}$, to $\mathcal{O}(a^2)$, as
long as no link-smearing is employed. These days it is common practice to
include some overall link-smearing into the definition of the fermion action.
Unfortunately, in this situation only the tree-level value
$c_\text{SW}^{(0)}=1$ is known, and cut-off effects start at
$\mathcal{O}(\alpha a)$. We present some general techniques for calculating one
loop quantities in lattice perturbation theory which continue to be useful for
smeared-link fermion actions. Specifically, we discuss the application to the
1-loop improvement coefficient $c_\text{SW}^{(1)}$ for overall stout-smeared
Wilson fermions.


  Phenomenological studies on the proton structure function F^2 have been
carried out to investigate the behavior of F^2, i.e. its x and Q^2 dependence,
in the transition from the deep-inelastic scattering region (Q^2 >> 1 GeV^2) to
the photoproduction region (Q^2 approx. 0 GeV^2). An overview of various
results of F^2 at low Q^2 is given. To quantify the behavior of F2 at low Q^2,
the derivatives dln(F^2)/dln(x) for fixed Q^2 and dF^2/dlog(Q^2) for fixed x
haven been determined and compared to expectations within the framework of
perturbative and non-perturbative QCD. Furthermore, the derivatives were
compared to results of the total inclusive diffractive cross-section and
vector-meson production. An interpretation of inclusive ep results is given in
the framework of the GVD/CDP picture with special emphasis on the limit of Q^2
--> 0 for fixed W^2 and x --> for fixed Q^2.


  Forming a reliable judgement of a machine learning (ML) model's
appropriateness for an application ecosystem is critical for its responsible
use, and requires considering a broad range of factors including harms,
benefits, and responsibilities. In practice, however, evaluations of ML models
frequently focus on only a narrow range of decontextualized predictive
behaviours. We examine the evaluation gaps between the idealized breadth of
evaluation concerns and the observed narrow focus of actual evaluations.
Through an empirical study of papers from recent high-profile conferences in
the Computer Vision and Natural Language Processing communities, we demonstrate
a general focus on a handful of evaluation methods. By considering the metrics
and test data distributions used in these methods, we draw attention to which
properties of models are centered in the field, revealing the properties that
are frequently neglected or sidelined during evaluation. By studying these
properties, we demonstrate the machine learning discipline's implicit
assumption of a range of commitments which have normative impacts; these
include commitments to consequentialism, abstractability from context, the
quantifiability of impacts, the limited role of model inputs in evaluation, and
the equivalence of different failure modes. Shedding light on these assumptions
enables us to question their appropriateness for ML system contexts, pointing
the way towards more contextualized evaluation methodologies for robustly
examining the trustworthiness of ML models


  Prompted by X-ray detections from multiple surveys, we investigated the
A-type star HD 63021 and found that it is a double-lined spectroscopic binary
with highly variable emission associated with the primary star. Analysis of our
multi-epoch spectroscopic observations, the majority of which were carried out
on small aperture telescopes, indicates a very short orbital period of just
$2.9$ days, and a mass ratio M$_2$/M$_1$ of $0.23$. The A1 V star is a slow
rotator, with a rotational speed of $\sim34$ km/s. Assuming its mass is $2.3$
M$_{\odot}$, the present-day secondary is an evolved star of $\sim0.5$
M$_{\odot}$ that nearly fills its Roche lobe. This secondary star rotates
comparatively rapidly at $\sim44$ km/s, and we see evidence that it is
chromospherically active. Analysis of a photometric lightcurve from TESS
reveals two strong periods, one at the orbital period for the system and
another at half the orbital period. These findings suggest that HD 63021 is a
close binary system undergoing mass transfer from the secondary star onto the
primary star -- in all ways like an Algol eclipsing binary system, except
without the eclipse. We discuss the system's mass transfer, which is not steady
but seems to run in fits and bursts, and infer the system's basic physical
properties from an orbital parameter study, the Roche lobe geometry, and its
extant X-ray emission.


  Autonomous driving in an unregulated urban crowd is an outstanding challenge,
especially, in the presence of many aggressive, high-speed traffic
participants. This paper presents SUMMIT, a high-fidelity simulator that
facilitates the development and testing of crowd-driving algorithms. SUMMIT
simulates dense, unregulated urban traffic at any worldwide locations as
supported by the OpenStreetMap. The core of SUMMIT is a multi-agent motion
model, GAMMA, that models the behaviours of heterogeneous traffic agents, and a
real-time POMDP planner, Context-POMDP, that serves as a driving expert. SUMMIT
is built as an extension of CARLA and inherits from it the physical and visual
realism for autonomous driving simulation. SUMMIT supports a wide range of
applications, including perception, vehicle control or planning, and end-to-end
learning. We validate the realism of our motion model using its traffic motion
prediction accuracy on various real-world data sets. We also provide several
real-world benchmark scenarios to show that SUMMIT simulates complex, realistic
traffic behaviors, and Context-POMDP drives safely and efficiently in
challenging crowd-driving settings.


  In a Hilbert space setting H, for convex optimization, we analyze the fast
convergence properties as t tends to infinity of the trajectories generated by
a third-order in time evolution system. The function f to minimize is supposed
to be convex, continuously differentiable, with a nonempty set of minimizers.
It enters into the dynamic through its gradient. Based on this new dynamical
system, we improve the results obtained by [Attouch, Chbani, Riahi: Fast convex
optimization via a third-order in time evolution equation, Optimization 2020].
As a main result, when the damping parameter $\alpha$ satisfies $\alpha > 3$,
we show that the convergence of the values at the order 1/t3 as t goes to
infinity, as well as the convergence of the trajectories. We complement these
results by introducing into the dynamic an Hessian driven damping term, which
reduces the oscillations. In the case of a strongly convex function f, we show
an autonomous evolution system of the third order in time with an exponential
rate of convergence. All these results have natural extensions to the case of a
convex lower semicontinuous function with extended real values. Just replace f
with its Moreau envelope.


  Changes of type transitions for the two-component hydrodynamic type systems
are discussed. It is shown that these systems generically assume the Jordan
form (with 2 X 2 Jordan block) on the transition line with hodograph equations
becoming parabolic. Conditions which allow or forbid the transition from
hyperbolic domain to elliptic one are discussed. Hamiltonian systems and their
special subclasses and equations, like dispersionless nonlinear Schroedinger,
dispersionless Boussinesq, one-dimensional isentropic gas dynamics equations
and nonlinear wave equations are studied. Numerical results concerning the
crossing of transition line for the dispersionless Boussinesq equation are
presented too.