  We explore by molecular dynamic simulations the thermodynamical behavior of
an anomalous fluid confined inside rigid and flexible nanopores. The fluid is
modeled by a two length scale potential. In the bulk this system exhibits the
density and diffusion anomalous behavior observed in liquid water. We show that
the anomalous fluid confined inside rigid and flexible nanopores forms layers.
As the volume of the nanopore is decreased the rigid surface exhibits three
consecutive first order phase transitions associated with the change in the
number of layers. These phase transitions are not present for flexible
confinement. Our results indicate that the nature of confinement is relevant
for the properties of the confined liquid what suggests that confinement in
carbon nanotubes should be quite different from confinement in biological
channels.


  We propose an approach to probe coherent spin-state dynamics of molecules
using circularly polarized hard x-ray pulses. For the dynamically aligned
nitric oxide molecules in a coherent superposition spin-orbit coupled
electronic state that can be prepared through stimulated Raman scattering, we
demonstrate the capability of ultrafast x-ray diffraction to not only reveal
the quantum beating of the coherent spin-state wave packet, but also image the
spatial spin density of the molecule. With circularly polarized ultrafast x-ray
diffraction signal, we show that the electronic density matrix can be
retrieved. The spatio-temporal resolving power of ultrafast x-ray diffraction
paves the way for tracking transient spatial wave function in molecular
dynamics involving spin degree of freedom.


  3D representation disentanglement aims to identify, decompose, and manipulate
the underlying explanatory factors of 3D data, which helps AI fundamentally
understand our 3D world. This task is currently under-explored and poses great
challenges: (i) the 3D representations are complex and in general contains much
more information than 2D image; (ii) many 3D representations are not well
suited for gradient-based optimization, let alone disentanglement. To address
these challenges, we use NeRF as a differentiable 3D representation, and
introduce a self-supervised Navigation to identify interpretable semantic
directions in the latent space. To our best knowledge, this novel method,
dubbed NaviNeRF, is the first work to achieve fine-grained 3D disentanglement
without any priors or supervisions. Specifically, NaviNeRF is built upon the
generative NeRF pipeline, and equipped with an Outer Navigation Branch and an
Inner Refinement Branch. They are complementary -- the outer navigation is to
identify global-view semantic directions, and the inner refinement dedicates to
fine-grained attributes. A synergistic loss is further devised to coordinate
two branches. Extensive experiments demonstrate that NaviNeRF has a superior
fine-grained 3D disentanglement ability than the previous 3D-aware models. Its
performance is also comparable to editing-oriented models relying on semantic
or geometry priors.


  The nearby radio galaxy Centaurus A is poorly studied at high frequencies
with conventional radio telescopes because of its very large angular size, but
is one of a very few extragalactic objects to be detected and resolved by the
Wilkinson Microwave Anisotropy Probe (WMAP). We have used the five-year WMAP
data for Cen A to constrain the high-frequency radio spectra of the 10-degree
giant lobes and to search for spectral changes as a function of position along
the lobes. We show that the high-frequency radio spectra of the northern and
southern giant lobes are significantly different: the spectrum of the southern
lobe steepens monotonically (and is steeper further from the active nucleus)
whereas the spectrum of the northern lobe remains consistent with a power law.
The inferred differences in the northern and southern giant lobes may be the
result of real differences in their high-energy particle acceleration
histories, perhaps due to the influence of the northern middle lobe, an
intermediate-scale feature which has no detectable southern counterpart. In
light of these results, we discuss the prospects for Fermi Gamma-ray Space
Telescope detections of inverse-Compton emission from the giant lobes and the
lobes' possible role in the production of the ultra-high energy cosmic rays
(UHECR) detected by the Pierre Auger Observatory. We show that the possibility
of a Fermi detection depends sensitively on the physical conditions in the
giant lobes, with the northern lobe more likely to be detected, and that any
emission observed by Fermi is likely to be dominated by photons at the soft end
of the Fermi energy band. On the other hand we argue that the estimated
conditions in the giant lobes imply that UHECRs can be accelerated there, with
a potentially detectable gamma-ray signature at TeV energies.


  In a transformation method the numerical solution of a given boundary value
problem is obtained by solving one or more related initial value problems. This
paper is concerned with the application of the iterative transformation method
to the Sakiadis problem. This method is an extension of the Toepfer's
non-iterative algorithm developed as a simple way to solve the celebrated
Blasius problem. As shown by this author [Appl. Anal., 66 (1997) pp. 89-100]
the method provides a simple numerical test for the existence and uniqueness of
solutions. Here we show how the method can be applied to problems with a
homogeneous boundary conditions at infinity and in particular we solve the
Sakiadis problem of boundary layer theory. Moreover, we show how to couple our
method with Newton's root-finder. The obtained numerical results compare well
with those available in literature. The main aim here is that any method
developed for the Blasius, or the Sakiadis, problem might be extended to more
challenging or interesting problems. In this context, the iterative
transformation method has been recently applied to compute the normal and
reverse flow solutions of Stewartson for the Falkner-Skan model [Comput. &
Fluids, 73 (2013) pp. 202-209].


  We analyze the properties and stability of two-color discrete localized modes
in arrays of channel waveguides where tunable quadratic nonlinearity is
introduced as a nonlinear defect by periodic poling of a single waveguide in
the array. We show that, depending on the value of the phase mismatch and the
input power, such two-color defect modes can be realized in three different
localized states. We also study resonant light scattering in the arrays with
the defect waveguide.


  We introduce DeepMorph, an information embedding technique for vector
drawings. Provided a vector drawing, such as a Scalable Vector Graphics (SVG)
file, our method embeds bitstrings in the image by perturbing the drawing
primitives (lines, circles, etc.). This results in a morphed image that can be
decoded to recover the original bitstring. The use-case is similar to that of
the well-known QR code, but our solution provides creatives with artistic
freedom to transfer digital information via drawings of their own design. The
method comprises two neural networks, which are trained jointly: an encoder
network that transforms a bitstring into a perturbation of the drawing
primitives, and a decoder network that recovers the bitstring from an image of
the morphed drawing. To enable end-to-end training via back propagation, we
introduce a soft rasterizer, which is differentiable with respect to
perturbations of the drawing primitives. In order to add robustness towards
real-world image capture conditions, image corruptions are injected between the
soft rasterizer and the decoder. Further, the addition of an object detection
and camera pose estimation system enables decoding of drawings in complex
scenes as well as use of the drawings as markers for use in augmented reality
applications. We demonstrate that our method reliably recovers bitstrings from
real-world photos of printed drawings, thereby providing a novel solution for
creatives to transfer digital information via artistic imagery.


  Recently [3] predicted the existence of an intriguing new phenomenon. It was
shown that if temperature is suddenly raised at the surface of a sphere the
temperature in the interior initially decreases. The authors of [3] gave a
thorough analysis explaining the physics leading to this remarkable effect.
They linked the existence of the phenomenon to the subtle thermomechanical
coupling between displacement and temperature in a sphere that is able to
expand freely and showed that the effect is expected to be largest close to the
glasstransition temperature of the perturbed material. The prediction was based
on the assumption of quasi-elasticity where the sample is much smaller than the
wavelength of acoustic waves at frequencies of relevance. Being in the inertia
free limit they could ignore the acceleration term in the thermoviscoelastic
equations of motion. Here we give the first empirical proof of existence of the
effect by performing molecular dynamics simulations of a supercooled
Kob-Andersen binary mixture of Lennard-Jones particles forming a droplet
consisting of 500.000 particles. We show that the phenomenon is real even when
inertia cannot be disregarded.


  In many applications, robots can benefit from object-level understanding of
their environments, including the ability to distinguish object instances and
re-identify previously seen instances. Object re-identification is challenging
across different viewpoints and in scenes with significant appearance variation
arising from weather or lighting changes. Most works on object
re-identification focus on specific classes; approaches that address general
object re-identification require foreground segmentation and have limited
consideration of challenges such as occlusions, outdoor scenes, and
illumination changes. To address this problem, we introduce CODa Re-ID: an
in-the-wild object re-identification dataset containing 1,037,814 observations
of 557 objects of 8 classes under diverse lighting conditions and viewpoints.
Further, we propose CLOVER, a representation learning method for object
observations that can distinguish between static object instances. Our results
show that CLOVER achieves superior performance in static object
re-identification under varying lighting conditions and viewpoint changes, and
can generalize to unseen instances and classes.


  We consider the problem of maximizing revenue when selling 2 items to a
single buyer with known valuation distributions. Hart and Nisan showed that
selling each item separately using the optimal Myerson's price, gains at least
half of the revenue attainable by optimal auction for two items. We show that
in case the items have different revenues when sold separately the bound can be
tightened.


  Ensuring solution feasibility is a key challenge in developing Deep Neural
Network (DNN) schemes for solving constrained optimization problems, due to
inherent DNN prediction errors. In this paper, we propose a ``preventive
learning'' framework to guarantee DNN solution feasibility for problems with
convex constraints and general objective functions without post-processing,
upon satisfying a mild condition on constraint calibration. Without loss of
generality, we focus on problems with only inequality constraints. We
systematically calibrate inequality constraints used in DNN training, thereby
anticipating prediction errors and ensuring the resulting solutions remain
feasible. We characterize the calibration magnitudes and the DNN size
sufficient for ensuring universal feasibility. We propose a new
Adversarial-Sample Aware training algorithm to improve DNN's optimality
performance without sacrificing feasibility guarantee. Overall, the framework
provides two DNNs. The first one from characterizing the sufficient DNN size
can guarantee universal feasibility while the other from the proposed training
algorithm further improves optimality and maintains DNN's universal feasibility
simultaneously. We apply the framework to develop DeepOPF+ for solving
essential DC optimal power flow problems in grid operation. Simulation results
over IEEE test cases show that it outperforms existing strong DNN baselines in
ensuring 100% feasibility and attaining consistent optimality loss ($<$0.19%)
and speedup (up to $\times$228) in both light-load and heavy-load regimes, as
compared to a state-of-the-art solver. We also apply our framework to a
non-convex problem and show its performance advantage over existing schemes.


  We consider the process of atomic ionization driven by a laser pulse with
varying ellipticity. We study distribution of the momenta of the
photoelectrons, ionized by a strong laser field, emitted in the direction
perpendicular to the polarization plane (transverse distribution). We show,
that with changing laser pulse ellipticity, the transverse distribution evolves
from the singular cusp-like distribution for the close to linear polarization
to the smooth gaussian-like structure for the close to circular polarization.
In the latter case, when the ellipticity parameter $\epsilon\to 1$ the
strong-field approximation formula for the transverse momentum distribution
becomes quantitatively correct.


  Bloxorz is an online puzzle game where players move a 1 by 1 by 2 block by
tilting it on a subset of the two dimensional grid. Bloxorz features switches
that open and close trapdoors. The puzzle is to move the block from its initial
position to an upright position on the destination square. We show that the
problem of deciding whether a given Bloxorz level is solvable is
PSPACE-complete and that this remains so even when all trapdoors are initially
closed or all trapdoors are initially open. We also answer an open question of
Viglietta, showing that 2-buttons are sufficient for PSPACE-hardness of general
puzzle games. We also examine the hardness of some variants of Bloxorz,
including variants where the block is a 1 by 1 by 1 cube, and variants with
single-use tiles.


  The backreaction of D-branes on closed string moduli is studied in perturbed
conformal field theory. To this end we analyse the divergences in the modular
integral of the annulus diagram. By the Fischler-Susskind mechanism, these
divergences lead to additional terms in the bulk renormalisation group
equations. We derive explicit expressions for these backreaction terms, and
follow the resulting renormalisation group flow in several examples, finding
agreement with geometric expectations.


  Next-generation surveys like the Legacy Survey of Space and Time (LSST) on
the Vera C. Rubin Observatory will generate orders of magnitude more
discoveries of transients and variable stars than previous surveys. To prepare
for this data deluge, we developed the Photometric LSST Astronomical
Time-series Classification Challenge (PLAsTiCC), a competition which aimed to
catalyze the development of robust classifiers under LSST-like conditions of a
non-representative training set for a large photometric test set of imbalanced
classes. Over 1,000 teams participated in PLAsTiCC, which was hosted in the
Kaggle data science competition platform between Sep 28, 2018 and Dec 17, 2018,
ultimately identifying three winners in February 2019. Participants produced
classifiers employing a diverse set of machine learning techniques including
hybrid combinations and ensemble averages of a range of approaches, among them
boosted decision trees, neural networks, and multi-layer perceptrons. The
strong performance of the top three classifiers on Type Ia supernovae and
kilonovae represent a major improvement over the current state-of-the-art
within astronomy. This paper summarizes the most promising methods and
evaluates their results in detail, highlighting future directions both for
classifier development and simulation needs for a next generation PLAsTiCC data
set.


  This paper presents a review of deep learning (DL) based medical image
registration methods. We summarized the latest developments and applications of
DL-based registration methods in the medical field. These methods were
classified into seven categories according to their methods, functions and
popularity. A detailed review of each category was presented, highlighting
important contributions and identifying specific challenges. A short assessment
was presented following the detailed review of each category to summarize its
achievements and future potentials. We provided a comprehensive comparison
among DL-based methods for lung and brain deformable registration using
benchmark datasets. Lastly, we analyzed the statistics of all the cited works
from various aspects, revealing the popularity and future trend of development
in medical image registration using deep learning.


  This paper introduces a rate-based nonlinear neural network in which
excitatory (E) neurons receive feedforward excitation from sensory (S) neurons,
and inhibit each other through disynaptic pathways mediated by inhibitory (I)
interneurons. Correlation-based plasticity of disynaptic inhibition serves to
incompletely decorrelate E neuron activity, pushing the E neurons to learn
distinct sensory features. The plasticity equations additionally contain
"extra" terms fostering competition between excitatory synapses converging onto
the same postsynaptic neuron and inhibitory synapses diverging from the same
presynaptic neuron. The parameters of competition between S$\to$E connections
can be adjusted to make learned features look more like "parts" or "wholes."
The parameters of competition between I-E connections can be adjusted to set
the typical decorrelatedness and sparsity of E neuron activity. Numerical
simulations of unsupervised learning show that relatively few I neurons can be
sufficient for achieving good decorrelation, and increasing the number of I
neurons makes decorrelation more complete. Excitatory and inhibitory inputs to
active E neurons are approximately balanced as a result of learning.


  We investigate the behavior of a threshold model for the spreading of fads
and similar phenomena in society. The model is giving the fad dynamics and is
intended to be confined to an underlying network structure. We investigate the
whole parameter space of the fad dynamics on three types of network models. The
dynamics we discover is rich and highly dependent on the underlying network
structure. For some range of the parameter space, for all types of substrate
networks, there are a great variety of sizes and life-lengths of the fads --
what one see in real-world social and economical systems.


  The possible identification of Her X-1 with a strange star (Li et al. 1995)
is shown to be incorrect.


  The model of local electron pairs and itinerant fermions coupled via charge
exchange mechanism, which mutually induces superconductivity in both subsystems
is studied for anisotropic pairing symmetry. The phase diagram is presented and
the phase fluctuations effects are analyzed within the Kosterlitz-Thouless
scenario.


  We discuss the failure dynamics of the Fiber Bundle Model, especially in the
equal-load-sharing scheme. We also highlight the "Critical" aspects of their
dynamics in comparison with those in standard thermodynamic systems undergoing
phase transitions.


  We develop an analytical framework to study experimental design in two-sided
marketplaces. Many of these experiments exhibit interference, where an
intervention applied to one market participant influences the behavior of
another participant. This interference leads to biased estimates of the
treatment effect of the intervention. We develop a stochastic market model and
associated mean field limit to capture dynamics in such experiments, and use
our model to investigate how the performance of different designs and
estimators is affected by marketplace interference effects. Platforms typically
use two common experimental designs: demand-side ("customer") randomization
(CR) and supply-side ("listing") randomization (LR), along with their
associated estimators. We show that good experimental design depends on market
balance: in highly demand-constrained markets, CR is unbiased, while LR is
biased; conversely, in highly supply-constrained markets, LR is unbiased, while
CR is biased. We also introduce and study a novel experimental design based on
two-sided randomization (TSR) where both customers and listings are randomized
to treatment and control. We show that appropriate choices of TSR designs can
be unbiased in both extremes of market balance, while yielding relatively low
bias in intermediate regimes of market balance.


  The development of biologically-plausible learning algorithms is important
for understanding learning in the brain, but most of them fail to scale-up to
real-world tasks, limiting their potential as explanations for learning by real
brains. As such, it is important to explore learning algorithms that come with
strong theoretical guarantees and can match the performance of backpropagation
(BP) on complex tasks. One such algorithm is Difference Target Propagation
(DTP), a biologically-plausible learning algorithm whose close relation with
Gauss-Newton (GN) optimization has been recently established. However, the
conditions under which this connection rigorously holds preclude layer-wise
training of the feedback pathway synaptic weights (which is more biologically
plausible). Moreover, good alignment between DTP weight updates and loss
gradients is only loosely guaranteed and under very specific conditions for the
architecture being trained. In this paper, we propose a novel feedback weight
training scheme that ensures both that DTP approximates BP and that layer-wise
feedback weight training can be restored without sacrificing any theoretical
guarantees. Our theory is corroborated by experimental results and we report
the best performance ever achieved by DTP on CIFAR-10 and ImageNet 32$\times$32


  In this letter, a realistic coaxial feed is integrated into the design of
cascaded cylindrical metasurfaces. This is in contrast to the fictitious
current source that is often reported in literature. The S-matrix of the
coaxial feed is obtained by way of the mode-matching technique, which is
subsequently combined with the S-matrix of the cascaded cylindrical
metasurfaces to account for the interaction between the feed and metasurfaces.
The integration of a realistic feed into the design process enables practical
cylindrical-metasurface-based devices.


  Large-eddy simulations (LES) are performed to study the flow past a 6:1
prolate spheroid placed at an angle of incidence of $\alpha = 10^{\circ}$. The
diameter-based Reynolds number ($Re = U_{\infty}D/\nu$) is set to a value of
$5\times10^{3}$ and four values of diameter-based Froude numbers ($Fr =
U_\infty/ND$) are analyzed: $Fr = \infty, 6, 1.9,$ and $1$. Visualizations of
coefficient of pressure ($C_p$) and friction ($C_f$) contours reveal asymmetry
in the $Fr = \infty$ and $6$ flows while, at $Fr = 1$ and $Fr = 1.9$, the flow
over body does not have any visible asymmetry. This finding is further
corroborated through the analysis of force coefficients on the body. The
changes in the pressure coefficients ($C_p$), friction coefficients ($C_f$),
and drag coefficients ($C_d$) with the Froude number are described in detail
for $\alpha = 10^{\circ}$. We also present the analyses of forces on the body
at $\alpha = 0^{\circ}$ angle of incidence for comparison with the $\alpha =
10^{\circ}$ cases.


  In this article we study the gradient flow of the M\"obius energy introduced
by O'Hara in 1991. We will show a fundamental $\varepsilon$-regularity result
that allows us to bound the infinity norm of all derivatives for some time if
the energy is small on a certain scale. This result enables us to characterize
the formation of a singularity in terms of concentrations of energy and allows
us to construct a blow-up profile at a possible singularity. This solves one of
the open problems listed by Zheng-Xu He. Ruling out blow-ups for planar curves,
we will prove that the flow transforms every planar curve into a round circle.


  There has been an intense search in recent years for long-lived
spin-polarized carriers for spintronic and quantum-computing devices. Here we
report that spin polarized quasi-particles in superconducting aluminum layers
have surprisingly long spin-lifetimes, nearly a million times longer than in
their normal state. The lifetime is determined from the suppression of the
aluminum's superconductivity resulting from the accumulation of spin polarized
carriers in the aluminum layer using tunnel spin injectors. A Hanle effect,
observed in the presence of small in-plane orthogonal fields, is shown to be
quantitatively consistent with the presence of long-lived spin polarized
quasi-particles. Our experiments show that the superconducting state can be
significantly modified by small electric currents, much smaller than the
critical current, which is potentially useful for devices involving
superconducting qubits.


  We present simulation results for lattice QCD with light pions. For the quark
fields we apply chirally symmetric lattice Dirac operators, in particular the
overlap hypercube operator, along with the standard overlap operator for
comparison. This allows us to simulate at very low pion masses. The results are
related to Random Matrix Theory and to Chiral Perturbation Theory in order to
extract information about the pion decay constant, the scalar condensate and
the topological susceptibility.


  The enumeration of k-Dyck paths ending at level j after m up-steps, where the
last step is an up-step, is given as a sum, improving on a previous formula
given by Deng and Mansour.


  By defining a prepotential function for the stationary Schr\"odinger equation
we derive an inversion formula for the space variable $x$ as a function of the
wave-function $\psi$. The resulting equation is a Legendre transform that
relates $x$, the prepotential ${\cal F}$, and the probability density. We
invert the Schr\"odinger equation to a third-order differential equation for
${\cal F}$ and observe that the inversion procedure implies a $x$-$\psi$
duality. This phenomenon is related to a modular symmetry due to the
superposition of the solutions of the Schr\"odinger equation. We propose that
in quantum mechanics the space coordinate can be interpreted as a macroscopic
variable of a statistical system with $\hbar$ playing the role of a scaling
parameter. We show that the scaling property of the space coordinate with
respect to $\tau=\partial_{\psi}^2{\cal F}$ is determined by the
``beta-function''. We propose that the quantization of the inversion formula is
a natural way to quantize geometry. The formalism is extended to higher
dimensions and to the Klein-Gordon equation.


  We study electric field driven deracemization in an achiral liquid crystal
through the formation and coarsening of chiral domains. It is proposed that
deracemization in this system is a curvature-driven process. We test this
prediction using the exact result for the distribution of hull-enclosed areas
in two-dimensional coarsening in non-conserved scalar order parameter dynamics
recently obtained [J.J. Arenzon et al., Phys. Rev. Lett. 98, 061116 (2007)].
The experimental data are in very good agreement with the theory. We thus
demonstrate that deracemization in such bent-core liquid crystals belongs to
the Allen-Cahn universality class, and that the exact formula, which gives us
the statistics of domain sizes during coarsening, can also be used as a strict
test for this dynamic universality class.


  The cosmological evolution of neutrino energy densities driven by cosmon-type
field equations is introduced assuming that active and sterile neutrinos are
intrinsically connected by cosmon fields through the {\em seesaw} mechanism.
Interpreting sterile neutrinos as dark matter adiabatically coupled with dark
energy results in a natural decoupling of (active) mass varying neutrino
(MaVaN) equations. Identifying the dimensionless scale of the {\em seesaw}
mechanism, $m/M$, with a power of the cosmological scale factor, $a$, allows
for embedding the resulting masses into the generalized Chaplygin gas (GCG)
scenario for the dark sector. Without additional assumptions, our findings
establish a precise connection among three distinct frameworks: the cosmon
field dynamics for MaVaN's, the {\em seesaw} mechanism for dynamical mass
generation and the GCG scenario. Our results also corroborate with previous
assertions that mass varying particles can be the right responsible for the
stability issue and for the cosmic acceleration of the universe.


  The stellar initial mass function (IMF) seems to be variable and not
universal, as argued in the literature in the last three decades. Several
relations among the low-mass end of the IMF slope and other stellar population,
photometric or kinematic parameters of massive early-type galaxies (ETGs) have
been proposed, but a consolidated agreement on a factual cause of the observed
variations has not been reached yet. We investigate the relations between the
IMF and other stellar population parameters in NGC 3311, the central galaxy of
the Hydra I cluster. NGC 3311 is characterized by old and metal-rich stars,
like other massive ETGs, but has unusual increasing stellar velocity dispersion
and [$\alpha/$Fe] profiles. We use spatially resolved MUSE observations to
obtain stellar population properties using Bayesian full-spectrum fitting in
the central part of NGC 3311 to compare the IMF slope against other stellar
parameters with the goal of assessing their relations/dependencies. For NGC
3311, we unambiguously invalidate the previously observed direct correlation
between the IMF slope and the local stellar velocity dispersion, confirming
some doubts already raised in the literature. This relation may arise as a
spatial coincidence only, between the region with the largest stellar velocity
dispersion, with that where the oldest, $\textit{in situ}$ population is found
and dominates. We also show robust evidence that the proposed IMF-metallicity
relation is contaminated by the degeneracy between these two parameters. The
tightest correlations we found are those between stellar age and IMF and
between galactocentric radius and IMF. The variation of the IMF is not due to
kinematical, dynamical, or global properties in NGC 3311. We speculate that IMF
might be dwarf-dominated in the "red-nuggets" formed at high redshifts that
ended up being the central cores of today's giant ellipticals. [Abridged]


  We study the effect of a strong magnetic field on the properties of neutron
stars with a quark-hadron phase transition. It is shown that the magnetic field
prevents the appearance of a quark phase, enhances the leptonic fraction,
decreases the baryonic density extension of the mixed phase and stiffens the
total equation of state, including both the stellar matter and the magnetic
field contributions. Two parametrisations of a density dependent static
magnetic field, increasing, respectively, fast and slowly with the density and
reaching $2-4\times 10^{18}$G in the center of the star, are considered. The
compact stars with strong magnetic fields have maximum mass configurations with
larger masses and radius and smaller quark fractions. The parametrisation of
the magnetic field with density has a strong influence on the star properties.


  In this paper we deal with the long time existence for the Cauchy problem
associated to some asymptotic models for long wave, small amplitude gravity
surface waves. We generalize some of the results that can be found in the
literature devoted to the study of Boussinesq systems by implementing an energy
method on spectrally localized equations. In particular, we obtain better
results in terms of the regularity level required to solve the initial value
problem on large time scales.


  NICMOS 2 observations are crucial for constraining distances to most of the
existing sample of z > 1 SNe Ia. Unlike the conventional calibration programs,
these observations involve long exposure times and low count rates. Reciprocity
failure is known to exist in HgCdTe devices and a correction for this effect
has already been implemented for high and medium count-rates. However
observations at faint count-rates rely on extrapolations. Here instead, we
provide a new zeropoint calibration directly applicable to faint sources. This
is obtained via inter-calibration of NIC2 F110W/F160W with WFC3 in the low
count-rate regime using z ~ 1 elliptical galaxies as tertiary calibrators.
These objects have relatively simple near-IR SEDs, uniform colors, and their
extended nature gives superior signal-to-noise at the same count rate than
would stars. The use of extended objects also allows greater tolerances on PSF
profiles. We find ST magnitude zeropoints (after the installation of the NICMOS
cooling system, NCS) of 25.296 +- 0.022 for F110W and 25.803 +- 0.023 for
F160W, both in agreement with the calibration extrapolated from count-rates
1,000 times larger (25.262 and 25.799). Before the installation of the NCS, we
find 24.843 +- 0.025 for F110W and 25.498 +- 0.021 for F160W, also in agreement
with the high-count-rate calibration (24.815 and 25.470). We also check the
standard bandpasses of WFC3 and NICMOS 2 using a range of stars and galaxies at
different colors and find mild tension for WFC3, limiting the accuracy of the
zeropoints. To avoid human bias, our cross-calibration was "blinded" in that
the fitted zeropoint differences were hidden until the analysis was finalized.


  A common use of Markov Chains is the simulation of the seismic cycle in a
fault, i.e. as a renewal model for the repetition of its characteristic
earthquakes. This representation is consistent with Reid's elastic rebound
theory. Here it is proved that in {\it any} one-way Markov cycle, the
aperiodicity of the corresponding distribution of cycle lengths is always lower
than one. This fact concurs with observations of large earthquakes in faults
all over the world.


  We propose a novel cooperative joint sensing-communication (JSC) unmanned
aerial vehicle (UAV) network that can achieve downward-looking detection and
transmit detection data simultaneously using the same time and frequency
resources by exploiting the beam sharing scheme. The UAV network consists of a
UAV that works as a fusion center (FCUAV) and multiple subordinate UAVs (SU).
All UAVs fly at the fixed height. FCUAV integrates the sensing data of network
and carries out downward-looking detection. SUs carry out downward-looking
detection and transmit the sensing data to FCUAV. To achieve the beam sharing
scheme, each UAV is equipped with a novel JSC antenna array that is composed of
both the sensing subarray (SenA) and the communication subarray (ComA) in order
to generate the sensing beam (SenB) and the communication beam (ComB) for
detection and communication, respectively. SenB and ComB of each UAV share a
total amount of radio power. Because of the spatial orthogonality of
communication and sensing, SenB and ComB can be easily formed orthogonally. The
upper bound of average cooperative sensing area (UB-ACSA) is defined as the
metric to measure the sensing performance, which is related to the mutual
sensing interference and the communication capacity. Numerical simulations
prove the validity of the theoretical expressions for UB-ACSA of the network.
The optimal number of UAVs and the optimal SenB power are identified under the
total power constraint.


  Monitoring indoor activities of daily living (ADLs) of a person is neither an
easy nor an accurate process. It is subjected to dependency on sensor type,
power supply stability, and connectivity stability without mentioning artifacts
introduced by the person himself. Multiple challenges have to be overcome in
this field, such as; monitoring the precise spatial location of the person, and
estimating vital signs like an individuals average temperature. Privacy is
another domain of the problem to be thought of with care. Identifying the
persons posture without a camera is another challenge. Posture identification
assists in the persons fall detection. Thermal imaging could be a proper
solution for most of the mentioned challenges. It provides monitoring both the
persons average temperature and spatial location while maintaining privacy. In
this research, we propose an IoT system for monitoring an indoor ADL using
thermal sensor array (TSA). Three classes of ADLs are introduced, which are
daily activity, sleeping activity and no-activity respectively. Estimating
person average temperature using TSAs is introduced as well in this paper.
Results have shown that the three activity classes can be identified as well as
the persons average temperature during day and night. The persons spatial
location can be determined while his/her privacy is maintained as well.


  We compute the partition function of five-dimensional abelian gauge theory on
a five-torus T5 with a general flat metric using the Dirac method of quantizing
with constraints. We compare this with the partition function of a single
fivebrane compactified on S1 times T5, which is obtained from the six-torus
calculation of Dolan and Nappi. The radius R1 of the circle S1 is set to the
dimensionful gauge coupling constant g^2= 4\pi^2 R1. We find the two partition
functions are equal only in the limit where R1 is small relative to T5, a limit
which removes the Kaluza-Klein modes from the 6d sum. This suggests the 6d
N=(2,0) tensor theory on a circle is an ultraviolet completion of the 5d gauge
theory, rather than an exact quantum equivalence.


  This paper introduces a new in-context learning (ICL) mechanism called
In-Image Learning (I$^2$L) that combines demonstration examples, visual cues,
and chain-of-thought reasoning into an aggregated image to enhance the
capabilities of Large Multimodal Models (e.g., GPT-4V) in multimodal reasoning
tasks. Unlike previous approaches that rely on converting images to text or
incorporating visual input into language models, I$^2$L consolidates all
information into an aggregated image and leverages image processing,
understanding, and reasoning abilities. This has several advantages: it reduces
inaccurate textual descriptions of complex images, provides flexibility in
positioning demonstration examples, and avoids multiple input images and
lengthy prompts. We also introduce I$^2$L-Hybrid, a method that combines the
strengths of I$^2$L with other ICL methods. Specifically, it uses an automatic
strategy to select the most suitable method (I$^2$L or another certain ICL
method) for a specific task instance. We conduct extensive experiments to
assess the effectiveness of I$^2$L and I$^2$L-Hybrid on MathVista, which covers
a variety of complex multimodal reasoning tasks. Additionally, we investigate
the influence of image resolution, the number of demonstration examples in a
single image, and the positions of these demonstrations in the aggregated image
on the effectiveness of I$^2$L. Our code is publicly available at
https://github.com/AGI-Edgerunners/IIL.


  Direct numerical simulations are used to study the interaction of a stream of
small heavy inertial particles with the laminar and turbulent wakes of an
immobile sphere facing an incompressible uniform inflow. Particles that do not
collide with the obstacle but move past it, are found to form preferential
concentrations both in the sphere boundary layer and in its wake. In the
laminar case, the upstream diverging flow pattern is responsible for particle
clustering on a cylinder that extends far downstream the sphere. The interior
of this surface contains no particles and can be seen as a shadow of the large
obstacle. Such concentration profiles are also present in the case of turbulent
wakes but show a finite extension. The sphere shadow is followed by a region
around the axis of symmetry where the concentration is higher than the average.
It originates from a resonant centrifugal expulsion of particles from shed
vortices. The consequence of this concentration mechanism on monodisperse
inter-particle collisions is also briefly discussed. They are enhanced by both
the increased concentration and the presence of large velocity differences
between particles in the wake.


  We derive one unified formula for Ricci curvature tensor on arbitrary warped
product manifold by introducing a new notation for the lift vector and the
Levi-Civita connection.This formula is helpful to further consider Ricci flow
(RF) and hyperbolic geometric flow (HGF) and evolution equations on warped
product manifold. We characterize the behavior of warping function under RF and
under HGF. Simultaneously, we give some simple examples to illustrate the
existence of such warping function solution. In addition, we also gain the
evolution equations for metrics and Ricci curvature on a general warped product
manifold and specific warped product manifold whose second factor manifold is
of Einstein metric.


  Determining the spectral density of a molecular system immersed in a
proteomic scaffold and in contact to a solvent is a fundamental challenge in
the coarse-grained description of, e.g., electron and energy transfer dynamics.
Once the spectral density is characterized, all the time scales are captured
and no artificial separation between fast and slow processes need be invoked.
Based on the fluorescence Stokes shift function, we utilize a simple and robust
strategy to extract the spectral density of a number of molecular complexes
from available experimental data. Specifically, we show that experimental data
for dye molecules in several solvents, amino acid proteins in water, and some
photochemical systems (e.g., rhodopsin and green fluorescence proteins), are
well described by a three-parameter family of sub-Ohmic spectral densities that
are characterized by a fast initial Gaussian-like decay followed by a slow
algebraic-like decay rate at long times.


  We address the computation of ground-state properties of chemical systems and
realistic materials within the auxiliary-field quantum Monte Carlo method. The
phase constraint to control the fermion phase problem requires the random walks
in Slater determinant space to be open-ended with branching. This in turn makes
it necessary to use back-propagation (BP) to compute averages and correlation
functions of operators that do not commute with the Hamiltonian. Several BP
schemes are investigated and their optimization with respect to the phaseless
constraint is considered. We propose a modified BP method for the computation
of observables in electronic systems, discuss its numerical stability and
computational complexity, and assess its performance by computing ground-state
properties for several substances, including constituents of the primordial
terrestrial atmosphere and small organic molecules.


  We show that Josephson vortices in a quasi-1D atomic Bose Josephson junction
can be controllably manipulated by imposing a difference of chemical potentials
on the atomic BEC waveguides forming the junction. This effect, which has its
origin in the Berry phase structure of a vortex, turns out to be very robust in
the whole range of the parameters where such vortices can exist. We also
propose that a Josephson vortex can be created by the phase imprinting
technique and can be identified by a specific tangential feature in the
interference picture produced by expanding clouds released from the waveguides.


  Accurately determining the outcome of an election is a complex task with many
potential sources of error, ranging from software glitches in voting machines
to procedural lapses to outright fraud. Risk-limiting audits (RLA) are
statistically principled "incremental" hand counts that provide statistical
assurance that reported outcomes accurately reflect the validly cast votes. We
present a suite of tools for conducting RLAs using confidence sequences --
sequences of confidence sets which uniformly capture an electoral parameter of
interest from the start of an audit to the point of an exhaustive recount with
high probability. Adopting the SHANGRLA framework, we design nonnegative
martingales which yield computationally and statistically efficient confidence
sequences and RLAs for a wide variety of election types.


  This study examines the presence of the day-of-the-week effect on daily
returns of biotechnology stocks over a 16-year period from January 2002 to
December 2015. Using daily returns from the NASDAQ Biotechnology Index (NBI),
we find that the stock returns were the lowest on Mondays, and compared to the
Mondays the stock returns were significantly higher on Wednesdays, Thursdays,
and Fridays. Moreover, the results from using the asymmetric GARCH processes
reveal that momentum and small-firm effect were positively associated with the
market risk-adjusted returns of the biotechnology stocks during this period.
The findings of our study suggest that active portfolio managers need to
consider the day of the week, momentum, and small-firm effect when making
trading decisions for biotechnology stocks.


  In this paper, we use modeling and prediction tool MuMMI (Multiple Metrics
Modeling Infrastructure) and ten machine learning methods to model and predict
performance and power and compare their prediction error rates. We use a
fault-tolerant linear algebra code and a fault-tolerant heat distribution code
to conduct our modeling and prediction study on the Cray XC40 Theta and IBM
BG/Q Mira at Argonne National Laboratory and the Intel Haswell cluster Shepard
at Sandia National Laboratories. Our experiment results show that the
prediction error rates in performance and power using MuMMI are less than 10%
for most cases. Based on the models for runtime, node power, CPU power, and
memory power, we identify the most significant performance counters for
potential optimization efforts associated with the application characteristics
and the target architectures, and we predict theoretical outcomes of the
potential optimizations. When we compare the prediction accuracy using MuMMI
with that using 10 machine learning methods, we observe that MuMMI not only
results in more accurate prediction in both performance and power but also
presents how performance counters impact the performance and power models. This
provides some insights about how to fine-tune the applications and/or systems
for energy efficiency.


  On the base of the theory of time and space with the fractional dimensions a
possibility for information transferring with arbitrary velocities is
demonstrated


  We prove several results on coloring squares of planar graphs without
4-cycles. First, we show that if $G$ is such a graph, then $G^2$ is
$(\Delta(G)+72)$-degenerate. This implies an upper bound of $\Delta(G)+73$ on
the chromatic number of $G^2$ as well as on several variants of the chromatic
number such as the list-chromatic number, paint number, Alon--Tarsi number, and
correspondence chromatic number. We also show that if $\Delta(G)$ is
sufficiently large, then the upper bounds on each of these parameters of $G^2$
can all be lowered to $\Delta(G)+2$ (which is best possible). To complement
these results, we show that 4-cycles are unique in having this property.
Specifically, let $S$ be a finite list of positive integers, with $4\notin S$.
For each constant $C$, we construct a planar graph $G_{S,C}$ with no cycle with
length in $S$, but for which $\chi(G_{S,C}^2) > \Delta(G_{S,C})+C$.


  We present a formalism to infer the presence of merging by comparing the time
derivative of the observed galaxy stellar mass function (MF) to the change of
the MF expected from the star formation rate (SFR) in galaxies as a function of
mass and time. We present the SFR in as a function of stellar mass and time
spanning 9<logM<12 and 0<z<5. We show that at z>=3 the average SFR, is a power
law of stellar mass (SFR~M^0.6). The average SFR in the most massive objects at
this redshift is 100-500 Msun/yr. At z~3, the SFR starts to drop at the high
mass end. As z decreases further, the SFR drops at progressively lower masses
(downsizing), dropping most rapidly for high mass (logM>11) galaxies. The mass
at which the SFR starts to deviate from the power-law form (break mass)
progresses smoothly from logM~13 at z~5 to logM~10.9 at z~0.5. The break mass
evolves with redshift as M(z)=2.7x10^10 (1+z)^2.1. We directly observe a
relationship between SFH and mass. More massive galaxies have steeper and
earlier onsets of SF, their SFR peaks earlier and higher, and the following
decay has a shorter e-folding time. The SFR observed in high mass galaxies at
z~4 is sufficient to explain their rapid increase in number density. Within
large uncertainties, at most 0.8 major mergers per Gyr are consistent with the
high-z data, yet enough to transform most high mass objects into ellipticals
contemporaneously with their major star formation episode. In contrast, at
z<1.5 and at logM>11, mergers contribute 0.1-0.2 Gyr^-1 to the relative
increase in number density (~1 major merger per massive object at 1.5>z>0). At
10<logM<11, galaxies are being preferably destroyed in mergers at high z, while
at later times the change in their numbers turns positive. This shows the
top-down buildup of the red sequence suggested by other observations.


  Adaptive video streaming plays a crucial role in ensuring high-quality video
streaming services. Despite extensive research efforts devoted to Adaptive
BitRate (ABR) techniques, the current reinforcement learning (RL)-based ABR
algorithms may benefit the average Quality of Experience (QoE) but suffers from
fluctuating performance in individual video sessions. In this paper, we present
a novel approach that combines imitation learning with the information
bottleneck technique, to learn from the complex offline optimal scenario rather
than inefficient exploration. In particular, we leverage the deterministic
offline bitrate optimization problem with the future throughput realization as
the expert and formulate it as a mixed-integer non-linear programming (MINLP)
problem. To enable large-scale training for improved performance, we propose an
alternative optimization algorithm that efficiently solves the MINLP problem.
To address the issues of overfitting due to the future information leakage in
MINLP, we incorporate an adversarial information bottleneck framework. By
compressing the video streaming state into a latent space, we retain only
action-relevant information. Additionally, we introduce a future adversarial
term to mitigate the influence of future information leakage, where Model
Prediction Control (MPC) policy without any future information is employed as
the adverse expert. Experimental results demonstrate the effectiveness of our
proposed approach in significantly enhancing the quality of adaptive video
streaming, providing a 7.30\% average QoE improvement and a 30.01\% average
ranking reduction.


  Advances in the development of graphene-based technology have enabled
improvements in DC resistance metrology. Devices made from epitaxially grown
graphene have replaced the GaAs-based counterparts, leading to an easier and
more accessible realization of the ohm. By optimizing the scale of the growth,
it has become possible to fabricate quantized Hall array resistance standards
(QHARS) with nominal values between 1 k{\Omega} and 1.29 M{\Omega}. One of
these QHARS device designs accommodates a value of about 1.01 M{\Omega}, which
made it an ideal candidate to pursue a proof-of-concept that graphene-based
QHARS devices are suitable for forming wye-delta resistance networks. In this
work, the 1.01 M{\Omega} array output nearly 20.6 M{\Omega} due to the
wye-delta transformation, which itself is a special case of star-mesh
transformations. These mathematical equivalence principles allow one to extend
the QHR to the 100 M{\Omega} and 10 G{\Omega} resistance levels with fewer
array elements than would be necessary for a single array with many more
elements in series. The 1.01 M{\Omega} device shows promise that the wye-delta
transformation can shorten the calibration chain, and, more importantly,
provide a chain with a more direct line to the quantum SI.


  An important result of Koml\'os [Tiling Tur\'an theorems, Combinatorica,
2000] yields the asymptotically exact minimum degree threshold that ensures a
graph $G$ contains an $H$-tiling covering an $x$th proportion of the vertices
of $G$ (for any fixed $x \in (0,1)$ and graph $H$). We give a degree sequence
strengthening of this result which allows for a large proportion of the
vertices in the host graph $G$ to have degree substantially smaller than that
required by Koml\'os' theorem. We also demonstrate that for certain graphs $H$,
the degree sequence condition is essentially best possible in more than one
sense.


  With the promising progress of deep neural networks, layer aggregation has
been used to fuse information across layers in various fields, such as computer
vision and machine translation. However, most of the previous methods combine
layers in a static fashion in that their aggregation strategy is independent of
specific hidden states. Inspired by recent progress on capsule networks, in
this paper we propose to use routing-by-agreement strategies to aggregate
layers dynamically. Specifically, the algorithm learns the probability of a
part (individual layer representations) assigned to a whole (aggregated
representations) in an iterative way and combines parts accordingly. We
implement our algorithm on top of the state-of-the-art neural machine
translation model TRANSFORMER and conduct experiments on the widely-used WMT14
English-German and WMT17 Chinese-English translation datasets. Experimental
results across language pairs show that the proposed approach consistently
outperforms the strong baseline model and a representative static aggregation
model.


  A teenager's experience of chronic pain reverberates through multiple
interacting aspects of their lives. To self-manage their symptoms, they need to
understand how factors such as their sleep, social interactions, emotions and
pain intersect; supporting this capability must underlie an effective
personalized healthcare solution. While adult use of personal informatics for
self-management of various health factors has been studied, solutions intended
for adults are rarely workable for teens, who face this complex and confusing
situation with unique perspectives, skills and contexts. In this design study,
we explore a means of facilitating self-reflection by youth living with chronic
pain, through visualization of their personal health data. In collaboration
with pediatric chronic pain clinicians and a health-tech industry partner, we
designed and deployed MyWeekInSight, a visualization-based self-reflection tool
for youth with chronic pain. We discuss our staged design approach with this
intersectionally vulnerable population, in which we balanced reliance on proxy
users and data with feedback from youth viewing their own data. We report on
extensive formative and in-situ evaluation, including a three-week clinical
deployment, and present a framework of challenges and barriers faced in
clinical deployment with mitigations that can aid fellow researchers. Our
reflections on the design process yield principles, surprises, and open
questions.


  $Al_xGa_{1-x}N$ is a critical ultra-wide bandgap material for
optoelectronics, but the deposition of thick, high quality epitaxial layers has
been hindered by a lack of lattice-matched substrates. Here we identify the
(111) face of transition metal carbides as a suitable class of materials for
substrates lattice matched to (0001) $Al_xGa_{1-x}N$ and demonstrate the growth
of thin film TaC which has an effective hexagonal lattice constant matched to
$Al_{0.45}Ga_{0.55}N$. We explore growth conditions for sputtered TaC on
sapphire substrates and investigate the effects of sputter power, layer
thickness and incident plasma angle on film structure and in- and out-of-plane
strain. We then show critical improvements to film quality by annealing films
in a face-to-face configuration at 1600 $^\circ$C, which significantly reduces
full width at half max (FWHM) of in- and out-of-plane diffraction peaks and
results in a step-and-terrace surface morphology. This work presents a path
toward electrically conductive, lattice matched, thermally compatible
substrates for $Al_xGa_{1-x}N$ heteroepitaxy, a critical step for vertical
devices and other power electronics applications.


  Generalized BCS equations which consistently include the projection on the
particle-number parity are derived from a systematic variational method.
Numerical solutions are given that are illustrative of ultra-small metallic
grains. Compared to the BCS approximation, the pairing is slightly enhanced in
even and noticeably inhibited in odd systems. In the latter case, the gap
increases with T near T=0; for a suitably small pairing this can result in a
reentrance phenomenon. Paramagnetic effects are evaluated as functions of the
temperature and the magnetic field.


  This paper provides a mathematical framework for estimation of the service
time distribution and the expected service time of an infinite-server queueing
system with a nonhomogeneous Poisson arrival process, in the case of partial
information, where only the number of busy servers are observed over time. The
problem is reduced to a statistical deconvolution problem, which is solved by
using Laplace transform techniques and kernels for regularization. Upper bounds
on the mean squared error of the proposed estimators are derived. Some concrete
simulation experiments are performed to illustrate how the method can be
applied and to provide some insight in the practical performance.


  The MAKET zero-power heavy water reactor has been used at ITEP to measure the
fission characteristics of the Np, Pu, Am, and Cm isotopes in the
0.52NaF+0.48ZrF_4 melt-filled salt blanket micromodel. The 237Np(n,f),
238Pu(n,f), 239Pu(n,f), 240Pu(n,f), 241Pu(n,f), 242m^Am(n,f), 243Cm(n,f),
245Cm(n,f), 247Cm(n,f), 238U(n,f), 238U(n,g), 235U(n,f) fission reaction rates
have been measured. The neutron spectrum in the isotope irradiation locations
was monitored by measuring the rates of the (235U(n,f), 238U(n,g), 55Mn(n,g),
63Cu(n,g), 176Lu(n,g), 197Au(n,g), 115In(n,n'), 27Al(n,4He), and 64Zn(n,p))
reactions whose cross sections have been commonly accepted. The measured
functionals are compared with the respective results of MCNP code simulation
obtained using the ENDF/B6 and JENDL3.2 neutron databases.


  Exact equations are derived that relate velocity structure functions of
arbitrary order with other statistics. "Exact" means that no approximations are
used except that the Navier-Stokes equation and incompressibility condition are
assumed to be accurate. The exact equations are used to determine the
structure-function equations of all orders for locally homogeneous but
anisotropic turbulence as well as for the locally isotropic case. These
equations can be used for investigating the approach to local homogeneity and
to local isotropy as well as the balance of the equations and identification of
scaling ranges.


  We consider the tensor power $V=(C^N)^{\otimes n}$ of the vector
representation of $gl_N$ and its weight decomposition
$V=\oplus_{\lambda=(\lambda_1,...,\lambda_N)}V[\lambda]$. For $\lambda =
(\lambda_1 \geq ... \geq \lambda_N)$, the trivial bundle $V[\lambda]\times
\C^n\to\C^n$ has a subbundle of q-conformal blocks at level l, where $l =
\lambda_1-\lambda_N$ if $\lambda_1-\lambda_N> 0$ and l=1 if
$\lambda_1-\lambda_N=0$. We construct a polynomial section
$I_\lambda(z_1,...,z_n,h)$ of the subbundle. The section is the main object of
the paper. We identify the section with the generating function
$J_\lambda(z_1,...,z_n,h)$ of the extended Joseph polynomials of orbital
varieties, defined in [DFZJ05,KZJ09].
  For l=1, we show that the subbundle of q-conformal blocks has rank 1 and
$I_\lambda(z_1,...,z_n,h)$ is flat with respect to the quantum
Knizhnik-Zamolodchikov discrete connection.
  For N=2 and l=1, we represent our polynomial as a multidimensional
q-hypergeometric integral and obtain a q-Selberg type identity, which says that
the integral is an explicit polynomial.


  The entanglement evolution after a quantum quench became one of the tools to
distinguish integrable versus chaotic (non-integrable) quantum many-body
dynamics. Following this line of thoughts, here we propose that the revivals in
the entanglement entropy provide a finite-size diagnostic benchmark for the
purpose. Indeed, integrable models display periodic revivals manifested in a
dip in the block entanglement entropy in a finite system. On the other hand, in
chaotic systems, initial correlations get dispersed in the global degrees of
freedom (information scrambling) and such a dip is suppressed. We show that
while for integrable systems the height of the dip of the entanglement of an
interval of fixed length decays as a power law with the total system size, upon
breaking integrability a much faster decay is observed, signalling strong
scrambling. Our results are checked by exact numerical techniques in
free-fermion and free-boson theories, and by time-dependent density matrix
renormalisation group in interacting integrable and chaotic models.


  We discuss new proofs, and new forms, of a reverse logarithmic Sobolev
inequality, with respect to the standard Gaussian measure, for low complexity
functions, measured in terms of Gaussian-width. In particular, we provide a
dimension-free improvement for a related result given in [Eldan '18].


  A single quantum of excitation of a mechanical oscillator is a textbook
example of the principles of quantum physics. Mechanical oscillators, despite
their pervasive presence in nature and modern technology, do not generically
exist in an excited Fock state. In the past few years, careful isolation of
GHz-frequency nano-scale oscillators has allowed experimenters to prepare such
states at milli-Kelvin temperatures. These developments illustrate the tension
between the basic predictions of quantum mechanics that should apply to all
mechanical oscillators existing even at ambient conditions, and the complex
experiments in extreme conditions required to observe those predictions. We
resolve the tension by creating a single Fock state of a vibration mode of a
crystal at room temperature using a technique that can be applied to any
Raman-active system. After exciting a bulk diamond with a femtosecond laser
pulse and detecting a Stokes-shifted photon, the 40~THz Raman-active internal
vibrational mode is prepared in the Fock state $|1>$ with $98.5\%$ probability.
The vibrational state is read out by a subsequent pulse, which when subjected
to a Hanbury-Brown-Twiss intensity correlation measurement reveals the
sub-Poisson number statistics of the vibrational mode. By controlling the delay
between the two pulses we are able to witness the decay of the vibrational Fock
state over its $3.9$ ps lifetime at room temperature. Our technique is agnostic
to specific selection rules, and should thus be applicable to any Raman-active
medium, opening a new generic approach to the experimental study of quantum
effects related to vibrational degrees of freedom in molecules and solid-state
systems.


  Regularization of control policies using entropy can be instrumental in
adjusting predictability of real-world systems. Applications benefiting from
such approaches range from, e.g., cybersecurity, which aims at maximal
unpredictability, to human-robot interaction, where predictable behavior is
highly desirable. In this paper, we consider entropy regularization for
interval Markov decision processes (IMDPs). IMDPs are uncertain MDPs, where
transition probabilities are only known to belong to intervals. Lately, IMDPs
have gained significant popularity in the context of abstracting stochastic
systems for control design. In this work, we address robust minimization of the
linear combination of entropy and a standard cumulative cost in IMDPs, thereby
establishing a trade-off between optimality and predictability. We show that
optimal deterministic policies exist, and devise a value-iteration algorithm to
compute them. The algorithm solves a number of convex programs at each step.
Finally, through an illustrative example we show the benefits of penalizing
entropy in IMDPs.


  In preparation for the planned linear collider TESLA, DESY is designing the
required buildings and facilities. The accelerator and infrastructure
components have to be allocated to buildings, and their required areas for
installation, operation and maintenance have to be determined.
Interdisciplinary working groups specify the project from different viewpoints
and need to develop a common vision as a precondition for an optimal solution.
A commercial requirements database is used as a collaborative tool, enabling
concurrent requirements specification by independent working groups. The
requirements database ensures long term storage and availability of the
emerging knowledge, and it offers a central platform for communication which is
available for all project members. It is successfully operating since summer
2002 and has since then become an important tool for the design team.


  We use atomistic computer simulations to provide a microscopic description of
the brittle failure of amorphous materials, and we assess the role of rare
events and quenched disorder. We argue that brittle yielding originates at rare
soft regions, similarly to Griffiths effects in disordered systems. We
numerically demonstrate how localized plastic events in such soft regions
trigger macroscopic failure via the propagation of a shear band. This physical
picture, which no longer holds in poorly annealed ductile materials, allows us
to discuss the role of finite size effects in brittle yielding and reinforces
the similarities between yielding and other disorder-controlled nonequilibrium
phase transitions.


  The SELEX experiment (E781) at Fermilab has observed two statistically
compelling high mass states near 3.6 GeV/c^2, decaying to Lambda_c+ K- pi+ and
Lambda_c+ K- pi+ pi+. These final states are Cabibbo-allowed decay modes of
doubly charmed baryons $\Xi_{cc}^+$ and $\Xi_{cc}^{++}$, respectively. The
masses are in the range expected from theoretical considerations, but the
spectroscopy is surprising. SELEX also has weaker preliminary evidence for a
state near 3.8 GeV/$\rm{c}^2$, a high mass state decaying to Lambda_c+ K- pi+
pi+, possibly an excited $\Xi_{cc}^{++}$ (ccu*). Data are presented and
discussed.


  Gyration radius of individual's trajectory plays a key role in quantifying
human mobility patterns. Of particular interests, empirical analyses suggest
that the growth of gyration radius is slow versus time except the very early
stage and may eventually arrive to a steady value. However, up to now, the
underlying mechanism leading to such a possibly steady value has not been well
understood. In this Letter, we propose a simplified human mobility model to
simulate individual's daily travel with three sequential activities: commuting
to workplace, going to do leisure activities and returning home. With the
assumption that individual has constant travel speed and inferior limit of time
at home and work, we prove that the daily moving area of an individual is an
ellipse, and finally get an exact solution of the gyration radius. The
analytical solution well captures the empirical observation reported in [M. C.
Gonz`alez et al., Nature, 453 (2008) 779]. We also find that, in spite of the
heterogeneous displacement distribution in the population level, individuals in
our model have characteristic displacements, indicating a completely different
mechanism to the one proposed by Song et al. [Nat. Phys. 6 (2010) 818].


  We study limit models in the abstract elementary class of modules with
embeddings as algebraic objects. We characterize parametrized noetherian rings
using the degree of injectivity of certain limit models.
  We show that the number of limit models and how close a ring is from being
noetherian are inversely proportional.
  $\textbf{Theorem.}$ Let $n \geq 0$ The following are equivalent.
  1. $R$ is left $(<\aleph_{n } )$-noetherian but not left $(< \aleph_{n -1
})$-noetherian.
  2.The abstract elementary class of modules with embeddings has exactly $n +1$
non-isomorphic $\lambda$-limit models for every $\lambda \geq
(\operatorname{card}(R) + \aleph_0)^+$ such that the class is stable in
$\lambda$.
  We further show that there are rings such that the abstract elementary class
of modules with embeddings has exactly $\kappa$ non-isomorphic $\lambda$-limit
models for every infinite cardinal $\kappa$.


  We derive a new effective macroscopic Cahn-Hilliard equation whose
homogeneous free energy is represented by 4-th order polynomials, which form
the frequently applied double-well potential. This upscaling is done for
perforated/strongly het- erogeneous domains. To the best knowledge of the
authors, this seems to be the first attempt of upscaling the Cahn-Hilliard
equation in such domains. The new homog- enized equation should have a broad
range of applicability due to the well-known versatility of phase-field models.
The additionally introduced feature of systemati- cally and reliably accounting
for confined geometries by homogenization allows for new modeling and numerical
perspectives in both, science and engineering. Our results are applied to
wetting dynamics in porous media and to a single channel with strongly
heterogeneous walls.


  The quantum measurement problem considered for measuring system (MS) consist
of measured state S (particle), detector D and information processing device
(observer) O. It's shown that O states selfreference structure results in
principal nonobservability of MS interference terms which discriminate pure and
mixed S states. Such observables restriction permit to construct for MS states
subjective representation (SR) which describes probabilistic evolution for
measurement events observed by $O$ and his subjective information about S
values. SR is dual and nonequivalent to MS Hilbert space $H$ for external
observer $O'$. Due to it SR evolution is compatible with Schrodinger linear MS
evolution observed by $O'$. It's argued that SR evolution corresponds to S
state collapse for individual events observed by $O$.


  We study the family of ideals generated by minors of mixed size contained in
a ladder of a symmetric matrix from the point of view of liaison theory. We
prove that they can be obtained from ideals of linear forms by ascending
G-biliaison. In particular, they are glicci.


  Radiation damage is considered to be the major problem that still prevents
imaging an individual biological molecule for structural analysis. So far, all
known mapping techniques using sufficient short wave-length radiation, be it
X-rays or high energy electrons, circumvent this problem by averaging over many
molecules. Averaging, however, leaves conformational details uncovered. Even
the anticipated use of ultra-short but extremely bright X-ray bursts of a Free
Electron Laser shall afford averaging over 10^6 molecules to arrive at atomic
resolution. Here we present direct experimental evidence for non-destructive
imaging of individual DNA molecules. In fact, we show that DNA withstands
coherent low energy electron radiation with deBroglie wavelength in the
Angstrom regime despite a vast dose of 10^8 electrons/nm^2 accumulated over
more than one hour.


  The central star of the planetary nebula (CSPN) M 2-29 shows an extraordinary
R Coronae Borealis-like fading event in its optical lightcurve. The only other
CSPN to show these events are CPD-568032 (Hen 3-1333) and V651 Mon (NGC 2346).
Dust cloud formation in the line of sight appears responsible but the exact
triggering mechanism is not well understood. Understanding how planetary
nebulae (PNe) trigger dust obscuration events may help understand the same
process in a wide range of objects including Population-I WC9 stars, symbiotic
stars and perhaps Asymptotic Giant Branch (AGB) stars with long secondary
periods (LSPs). A binary scenario involving an eccentric, wide companion that
triggers dust formation via interaction at periastron is a potential
explanation that has been suggested for LSP variables. Model fits to the
lightcurves of CPD-568032 and M 2-29 show the dust forms in excess of 70 AU at
the inner edge of a dust disk. In the case of CPD-568032 this radius is far too
large to coincide with a binary companion trigger, although a binary may have
been responsible for the formation of the dust disk. We find no direct evidence
to support previous claims of binarity in M 2-29 either from the OGLE
lightcurve or deep medium-resolution VLT FLAMES spectroscopy of the CSPN. We
classify the CSPN as Of(H) with T_eff=50+-10 kK and log g=4.0+-0.3. We find a
mean distance of 7.4+-1.8 kpc to M 2-29 at which the M_V=-0.9 mag CSPN could
potentially hide a subgiant luminosity or fainter companion. A companion would
help explain the multiple similarities with D'-type symbiotic stars whose outer
nebulae are thought to be bona-fide PNe. The 7.4 kpc distance, oxygen abundance
of 8.3 dex and Galactic coordinates (l=4.0, b=-3.0) prove that M 2-29 is a
Galactic Bulge PN and not a Halo PN as commonly misconceived.


  WMAP has provided CMB maps of the full sky. The raw data is subject to
foreground contamination, in particular near to the Galactic plane. Foreground
cleaned maps have been derived, e.g., the internal linear combination (ILC) map
of Bennett et al. and the reduced foreground TOH map of Tegmark et al. Using S
statistics we examine whether residual foreground contamination is left over in
the foreground cleaned maps. In particular, we specify which parts of the
foreground cleaned maps are sufficiently accurate for the circle-in-the-sky
signature. We generalise the S statistic, called D statistic, such that the
circle test can deal with CMB maps in which the contaminated regions of the sky
are excluded with masks.


  A summary is presented of a meeting on Particles and Fields in Radio Galaxies
held in Oxford in August 2000. Recent detailed studies of radio maps and the
first X-ray images from Chandra X-ray Observatory, together with new
capabilities in simulating hydromagnetic flows, are transforming our
understanding of how jets are formed by accretion disks orbiting massive black
holes, how these jets dissipate and how they inflate the giant radio-emitting
lobes by which they were first identified. As they can be imaged in such
detail, extragalactic radio sources remain central to the study of jet outflows
in general although there is a strong convergence with corresponding studies of
Galactic superluminal sources, gamma ray bursts and young stellar objects.


  The latent code of the recent popular model StyleGAN has learned disentangled
representations thanks to the multi-layer style-based generator. Embedding a
given image back to the latent space of StyleGAN enables wide interesting
semantic image editing applications. Although previous works are able to yield
impressive inversion results based on an optimization framework, which however
suffers from the efficiency issue. In this work, we propose a novel
collaborative learning framework that consists of an efficient embedding
network and an optimization-based iterator. On one hand, with the progress of
training, the embedding network gives a reasonable latent code initialization
for the iterator. On the other hand, the updated latent code from the iterator
in turn supervises the embedding network. In the end, high-quality latent code
can be obtained efficiently with a single forward pass through our embedding
network. Extensive experiments demonstrate the effectiveness and efficiency of
our work.


  The principle and experimental realization of a novel Raman spectroscopic
technique entitled stimulated Raman phase shift (SRPS) spectroscopy was
demonstrated. This technique depends on the measurement of the stimulated Raman
scattering (SRS) induced phase shift of Stokes light field ($\Delta$ $\phi$)
that is related to the real part of the third order nonlinear susceptibility of
SRS. In principle, the spectral lineshape of 1/|$\Delta$ $\phi$| is a delta
function waveform, which is insensitive to the fluctuation of Stokes light
intensity, the decoherence of phonon in materials, as well as the inhomogeneous
fluorescence background. In order to measure 1/|$\Delta$ $\phi$|, a SRPS
including a Mach-Zender interferometer and a signal processing device was
developed. Using the developed spectrometer, the SRPS and stimulated Raman gain
(SRG) spectra of neat dimethyl sulfoxide were detected simultaneously. Seven
Raman peaks corresponding to specific molecule vibrational and rotational modes
were observed in the SRPS spectra, while only two peaks could be identified in
the SRG spectra without a priori knowledge. The linewidth of the Raman peak
centered at 2913.283 cm$^{-1}$ indicating the v$_s$(CH$_3$)stretching mode of
the methyl groups was less than 0.00036 cm$^{-1}$ in the measured SRPS spectra,
which was almost four orders of magnitude narrower than that in the measured
SRG spectra. Meanwhile, the detection signal-to-noise ratio of the Raman peak
centered at 2913.283 cm$^{-1}$ was 25.3 dB, representing an increase of 14.3 dB
compared to the SRG spectra. The reliability of SRPS technique was verified by
10 independent measurements, and the standard deviation of the Raman peak
frequency was less than $\pm$0.338 cm$^{-1}$ . The SRPS technique paves the way
for characterizing the hyperfine fingerprint of materials.


  Spin-hosting graphene nanostructures are promising metal-free systems for
elementary quantum spintronic devices. Conventionally, spins are protected from
quenching by electronic bandgaps, which also hinder electronic access to their
quantum state. Here, we present a narrow graphene nanoribbon substitutionally
doped with boron heteroatoms that combines a metallic character with the
presence of localized spin 1/2 states in its interior. The ribbon was
fabricated by on-surface synthesis on a Au(111) substrate. Transport
measurements through ribbons suspended between the tip and the sample of a
scanning tunnelling microscope revealed their ballistic behavior,
characteristic of metallic nanowires. Conductance spectra show fingerprints of
localized spin states in form of Kondo resonances and inelastic tunnelling
excitations. Density functional theory rationalizes the metallic character of
the graphene nanoribbon due to the partial depopulation of the valence band
induced by the boron atoms. The transferred charge builds localized magnetic
moments around the boron atoms. The orthogonal symmetry of the spin-hosting
state's and the valence band's wavefunctions protects them from mixing,
maintaining the spin states localized. The combination of ballistic transport
and spin localization into a single graphene nanoribbon offers the perspective
of electronically addressing and controlling carbon spins in real device
architectures.


  By assuming factorization of the GPD under the deconvolution integral for the
hand-bag diagram, we develop a method of solving this integral beyond the
cross-over line. As examples we use explicit models of deeply virtual Compton
scattering (DVCS) amplitudes to get solution for relevant GPDs.


  We present K-band spectroscopy of short period, "sub-gap" cataclysmic
variable (CV) systems obtained using ISAAC on the VLT. We show the infrared
spectra (IR) for nine systems below the 2-3 hour period gap: V2051 Oph, V436
Cen, EX Hya, VW Hyi, Z Cha, WX Hyi, V893 Sco, RZ Leo, and TY PsA. We are able
to clearly detect the secondary star in all but WX Hyi, V893 Sco, and TY PsA.
We present the first direct detection of the secondary stars of V2051 Oph, V436
Cen, and determine new spectral classifications for EX Hya, VW Hyi, Z Cha, and
RZ Leo. We find that the CO band strengths of all but Z Cha appear normal for
their spectral types, in contrast to their longer period cousins above the
period gap. This brings the total number of CVs and pre-CVs with moderate
resolution (R >~ 1500) IR spectroscopy to sixty-one systems: nineteen pre-CVs,
thirty-one non-magnetic systems, and eleven magnetic or partially magnetic
systems. We discuss the trends seen in the IR abundance patterns thus far, and
highlight a potential link between anomalous abundances seen in the IR with the
C IV/N V anomaly seen in the ultraviolet. We present a compilation of all
systems with sufficient resolution IR observations to assess the CO band
strengths, and, by proxy, obtain an estimate on the C abundance on the
secondary star.


  We study sheaves in the context of a duality theory for lattice structure
endowed with extra operations, and in the context of forcing in a topos. Using
Sheaf duality theory of Comer for cylindric algebras, we give a representation
theorem of of distributive bounded lattices expanded by modalities (functions
distributing over joins) as the continuous sections of sheaves. Our
representation is defined via a contravariant functor from an algebraic
category to a category of sheaves. We show that if our category is a small site
(cartesian closed with a stability condition on pullbacks), then we can define
a notion of forcing using this category. In particular, we define fuzzy forcing
by interpreting the additional Lukasiewicz conjunction $\otimes$ as induced by
a tensor product in the target monodial category of pre-sheaves. We also study
topoi as semantics for higher order logic of many sorted theories in connection
to set theory, and the quasi-topoi based on $MV$ algebras, for fuzzy logic. We
show that the interpretation of a theory $T$, in this case into $Set_{\Omega}$
where $\Omega$ is an almost sub-object classifier in a quasi-topos $CAT$
defined from $T$, is completed by defining semantics for $\otimes$, and this is
done similarly to its defining clause in forcing. We give applications to
many-valued logics and various modifications of first order logic and
multi-modal logic, set in an algebraic framework.


  The proliferation of large language models (LLMs) in generating content
raises concerns about text copyright. Watermarking methods, particularly
logit-based approaches, embed imperceptible identifiers into text to address
these challenges. However, the widespread use of watermarking across diverse
LLMs has led to an inevitable issue known as watermark collision during common
tasks like question answering and paraphrasing. This study focuses on dual
watermark collisions, where two watermarks are present simultaneously in the
same text. The research demonstrates that watermark collision poses a threat to
detection performance for detectors of both upstream and downstream watermark
algorithms.


  We introduce a class of critical states which are embedded in the continuum
(CSC) of one-dimensional optical waveguide array with one non-Hermitian defect.
These states are at the verge of being fractal and have real propagation
constant. They emerge at a phase transition which is driven by the imaginary
refractive index of the defect waveguide and it is accompanied by a mode
segregation which reveals analogies with the Dicke super -radiance. Below this
point the states are extended while above they evolve to exponentially
localized modes. An addition of a background gain or loss can turn these
localized states to bound states in the continuum.


  We establish a defect relation of holomorphic curves from a general open
Riemann surface into a normal complex projective variety, with Zariski-dense
image intersecting effective Cartier divisors.


  We present a detailed investigation of static dipole polarizability of
lithium clusters containing up to 22 atoms. We first build a database of
lithium clusters by optimizing several candidate structures for the ground
state geometry for each size. The full polarizability tensor is determined for
about 5-6 isomers of each cluster size using the finite-field method. All
calculations are performed using large Gaussian basis sets, and within the
generalized gradient approximation to the density functional theory, as
implemented in the NRLMOL suite of codes. The average polarizability per atom
varies from 11 to 9 Angstrom^3, within the 8-22 size range and show smoother
decrease with increase in cluster size than the experimental values. While the
average polarizability exhibits a relatively weak dependence on cluster
conformation, significant changes in the degree of anisotropy of the
polarizability tensor are observed. Interestingly, in addition to the expected
even odd (0 and 1 $\mu_B$) magnetic states, our results show several cases
where clusters with an odd number of Li atoms exhibit elevated spin states
(e.g. 3 $\mu_B$).


  The paper deals with the problem of the existence of a normal form for a
nearly-integrable real-analytic Hamiltonian with aperiodically time-dependent
perturbation decaying (slowly) in time. In particular, in the case of an
isochronous integrable part, the system can be cast in an exact normal form,
regardless of the properties of the frequency vector. The general case is
treated by a suitable adaptation of the finite order normalization techniques
usually used for Nekhoroshev arguments. The key point is that the so called
"geometric part" is not necessary in this case. As a consequence, no hypotheses
on the integrable part are required, apart from analyticity. The work, based on
two different perturbative approaches developed by A.Giorgilli et al., is a
generalisation of the techniques used by the same authors to treat more
specific aperiodically time-dependent problems.


  We show that Euclidean axion wormholes in theories of gravity coupled to a
single axion have several independent inhomogeneous perturbations that lower
the Euclidean action. Our analysis relies on a judiciously chosen
gauge-invariant variable which makes the negative mode structure about axion
wormholes transparent. Perturbations lowering the action are concentrated in
the neck region and exist for wormholes in flat space and in AdS. Their
presence means axion wormholes are not relevant saddle points of the functional
integral in quantum gravity. This resolves the paradoxes associated with these
solutions from the viewpoint of AdS/CFT. We conjecture that the lower action
configurations one flows to are ensembles of microscopic `quantum wormholes'
with unit charge.


  The Hamiltonian describing fermion pair production from an arbitrarily
time-varying electric field in two dimensions is studied using a
group-theoretic approach. We show that this Hamiltonian can be encompassed by
two, commuting SU(2) algebras, and that the two-dimensional problem can
therefore be reduced to two one-dimensional problems. We compare the group
structure for the two-dimensional problem with that previously derived for the
one-dimensional problem, and verify that the Schwinger result is obtained under
the appropriate conditions.


  We discuss different proposals for the degree of polarization of quantum
fields. The simplest approach, namely making a direct analogy with the
classical description via the Stokes operators, is known to produce
unsatisfactory results. Still, we argue that these operators and their
properties should be basic for any measure of polarization. We compare
alternative quantum degrees and put forth that they order various states
differently. This is to be expected, since, despite being rooted in the Stokes
operators, each of these measures only captures certain characteristics.
Therefore, it is likely that several quantum degrees of polarization will
coexist, each one having its specific domain of usefulness.


  We develop a timeout based extension of propositional linear temporal logic
(which we call TLTL) to specify timing properties of timeout based models of
real time systems. TLTL formulas explicitly refer to a running global clock
together with static timing variables as well as a dynamic variable abstracting
the timeout behavior. We extend LTL with the capability to express timeout
constraints. From the expressiveness view point, TLTL is not comparable with
important known clock based real-time logics including TPTL, XCTL, and MTL,
i.e., TLTL can specify certain properties, which cannot be specified in these
logics (also vice-versa). We define a corresponding timeout tableau for
satisfiability checking of the TLTL formulas. Also a model checking algorithm
over timeout Kripke structure is presented. Further we prove that the validity
checking for such an extended logic remains PSPACE-complete even in the
presence of timeout constraints and infinite state models. Under discrete time
semantics, with bounded timeout increments, the model-checking problem that if
a TLTL-formula holds in a timeout Kripke structure is also PSPACE complete. We
further prove that when TLTL is interpreted over discrete time, it can be
embedded in the monadic second order logic with time, and when TLTL is
interpreted over dense time without the condition of non-zenoness, the
resulting logic becomes $\Sigma_1^1$-complete.


  Interactions between swimming cells and surfaces are essential to many
microbiological processes, from bacterial biofilm formation to human
fertilization. However, in spite of their fundamental importance, relatively
little is known about the physical mechanisms that govern the scattering of
flagellated or ciliated cells from solid surfaces. A more detailed
understanding of these interactions promises not only new biological insights
into structure and dynamics of flagella and cilia, but may also lead to new
microfluidic techniques for controlling cell motility and microbial locomotion,
with potential applications ranging from diagnostic tools to therapeutic
protein synthesis and photosynthetic biofuel production. Due to fundamental
differences in physiology and swimming strategies, it is an open question
whether microfluidic transport and rectification schemes that have recently
been demonstrated for pusher-type microswimmers such as bacteria and sperm
cells, can be transferred to puller-type algae and other motile eukaryotes, as
it is not known whether long-range hydrodynamic or short-range mechanical
forces dominate the surface interactions of these microorganisms. Here, using
high-speed microscopic imaging, we present direct experimental evidence that
the surface scattering of both mammalian sperm cells and unicellular green
algae is primarily governed by direct ciliary contact interactions. Building on
this insight, we predict and verify experimentally the existence of optimal
microfluidic ratchets that maximize rectification of initially uniform
Chlamydomonas reinhardtii suspensions. Since mechano-elastic properties of
cilia are conserved across eukaryotic species, we expect that our results apply
to a wide range of swimming microorganisms.


  Inspired by work of Montgomery on Fourier series and Donoho-Strak in signal
processing, we investigate two families of rearrangement inequalities for the
Fourier transform. More precisely, we show that the $L^2$ behavior of a Fourier
transform of a function over a small set is controlled by the $L^2$ behavior of
the Fourier transform of its symmetric decreasing rearrangement. In the $L^1$
case, the same is true if we further assume that the function has a support of
finite measure. As a byproduct, we also give a simple proof and an extension of
a result of Lieb about the smoothness of a rearrangement. Finally, a
straightforward application to solutions of the free Shr\"odinger equation is
given.


  This paper considers the problem of robust stability for a class of uncertain
quantum systems subject to unknown perturbations in the system coupling
operator. A general stability result is given for a class of perturbations to
the system coupling operator. Then, the special case of a nominal linear
quantum system is considered with non-linear perturbations to the system
coupling operator. In this case, a robust stability condition is given in terms
of a scaled strict bounded real condition.


  We generalise the usual notion of fibred category; first to fibred
2-categories and then to fibred bicategories. Fibred 2-categories correspond to
2-functors from a 2-category into 2-Cat. Fibred bicategories correspond to
trihomomorphisms from a bicategory into Bicat. We describe the Grothendieck
construction for each kind of fibration and present a few examples of each.
Fibrations in our sense, between bicategories, are closed under composition and
are stable under equiv-comma. The free such fibration on a homomorphism is
obtained by taking an oplax comma along an identity.


  Experiments are done by colliding a swinging bat with a stationary baseball
or softball. Each collision was recorded with high-speed cameras, from which
the post-impact speed, launch angle, and spin of the ball could be determined.
Initial bat speeds were in the range 63-88 mph, producing launch angles in the
range 0$^\circ$-30$^\circ$ and spins in the range 0-3500 rpm. The results are
analyzed in the context of a ball-bat collision model and the parameters of
that model are determined. For both baseballs and softballs, the data are
consistent with a mechanism whereby the ball grips the surface of the bat,
stretching the ball in the transverse direction and resulting in a spin that
was up to 40$\%$ greater than would be obtained by rolling contact of rigid
bodies. Using a lumped parameter contact model, baseballs are shown to be less
compliant tangentially than softballs. Implications of our results for batted
balls in game situations are presented.


  We explore the relationship between the nonlinear matter power spectrum and
the various Lagrangian and Standard Perturbation Theories (LPT and SPT). We
first look at it in the context of one dimensional (1-d) dynamics, where 1LPT
is exact at the perturbative level and one can exactly resum the SPT series
into the 1LPT power spectrum. Shell crossings lead to non-perturbative effects,
and the PT ignorance can be quantified in terms of their ratio, which is also
the transfer function squared in the absence of stochasticity. At the order of
PT we work, this parametrization is equivalent to the results of effective
field theory (EFT), and can thus be expanded in terms of the same parameters.
We find that its radius of convergence is larger than the SPT loop expansion.
The same EFT parametrization applies to all SPT loop terms and, if
stochasticity can be ignored, to all N-point correlators. In 3-d, the LPT
structure is considerably more complicated, and we find that LPT models with
parametrization motivated by the EFT exhibit running with $k$ and that SPT is
generally a better choice. Since these transfer function expansions contain
free parameters that change with cosmological model their usefulness for
broadband power is unclear. For this reason we test the predictions of these
models on baryonic acoustic oscillations (BAO) and other primordial
oscillations, including string monodromy models, for which we ran a series of
simulations with and without oscillations. Most models are successful in
predicting oscillations beyond their corresponding PT versions, confirming the
basic validity of the model.


  Due to various sources of uncertainty, emergent behavior, and ongoing
changes, the reliability of many socio-technical systems depends on an
iterative and collaborative process in which organizations (1) analyze and
learn from system failures, and then (2) co-evolve both the technical and human
parts of their systems based on what they learn. Many organizations have
defined processes for learning from failure, often involving postmortem
analyses conducted after any system failures that are judged to be sufficiently
severe. Despite established processes and tool support, our preliminary
research, and professional experience, suggest that it is not straightforward
to take what was learned from a failure and successfully improve the
reliability of the socio-technical system. To better understand this
collaborative process and the associated challenges, we are conducting a study
of how teams learn from failure. We are gathering incident reports from
multiple organizations and conducting interviews with engineers and managers
with relevant experience. Our analytic interest is in what is learned by teams
as they reflect on failures, the learning processes involved, and how they use
what is learned. Our data collection and analysis are not yet complete, but we
have so far analyzed 13 incident reports and seven interviews. In this short
paper we (1) present our preliminary findings, and (2) outline our broader
research plans.


  By using models based on Regge phenomenology we analyse the coherent
photoproduction of charmonium in peripheral heavy-ion collisions at the Large
Hadron Collider (LHC). The centrality dependence is investigated and compared
to the experimental results for coherent $J/\psi$ production in lead-lead LHC
runs at the energies of 2.76 and 5.02 TeV. Theoretical uncertainties and
possible limitations of the formalism are also discussed.


  We describe the wave functional model for the minimal (symmetric)
Sturm-Liouville operator on the finite interval. We construct the wave spectrum
of this operator, then, following the abstract scheme, we construct the model
space of functions on the wave spectrum and introduce in that space the model
operator. The latter is a matrix Sturm-Liouville operator which is unitarily
equivalent to the original.


  Across- and within-recording variabilities in electroencephalographic (EEG)
activity is a major limitation in EEG-based brain-computer interfaces (BCIs).
Specifically, gradual changes in fatigue and vigilance levels during long EEG
recording durations and BCI system usage bring along significant fluctuations
in BCI performances even when these systems are calibrated daily. We address
this in an experimental offline study from EEG-based BCI speller usage data
acquired for one hour duration. As the main part of our methodological
approach, we propose the concept of adversarial invariant feature learning for
BCIs as a regularization approach on recently expanding EEG deep learning
architectures, to learn nuisance-invariant discriminative features. We
empirically demonstrate the feasibility of adversarial feature learning on
eliminating drowsiness effects from event related EEG activity features, by
using temporal recording block ordering as the source of drowsiness
variability.