  We study the minimum cut problem in the presence of uncertainty and show how
to apply a novel robust optimization approach, which aims to exploit the
similarity in subsequent graph measurements or similar graph instances, without
posing any assumptions on the way they have been obtained. With experiments we
show that the approach works well when compared to other approaches that are
also oblivious towards the relationship between the input datasets.


  Safe deployment of AI models requires proactive detection of failures to
prevent costly errors. To this end, we study the important problem of detecting
failures in deep regression models. Existing approaches rely on epistemic
uncertainty estimates or inconsistency w.r.t the training data to identify
failure. Interestingly, we find that while uncertainties are necessary they are
insufficient to accurately characterize failure in practice. Hence, we
introduce PAGER (Principled Analysis of Generalization Errors in Regressors), a
framework to systematically detect and characterize failures in deep
regressors. Built upon the principle of anchored training in deep models, PAGER
unifies both epistemic uncertainty and complementary manifold non-conformity
scores to accurately organize samples into different risk regimes.


  Since 2011, significant and atypical arrival of two species of surface
dwelling algae, Sargassum natans and Sargassum Fluitans, have been detected in
the Mexican Caribbean. This massive accumulation of algae has had a great
environmental and economic impact. Therefore, for the government, ecologists,
and local businesses, it is important to keep track of the amount of sargassum
that arrives on the Caribbean coast. High-resolution satellite imagery is
expensive or may be time delayed. Therefore, we propose to estimate the amount
of sargassum based on ground-level smartphone photographs. From the computer
vision perspective, the problem is quite difficult since no information about
the 3D world is provided, in consequence, we have to model it as a
classification problem, where a set of five labels define the amount. For this
purpose, we have built a dataset with more than one thousand examples from
public forums such as Facebook or Instagram and we have tested several
state-of-the-art convolutional networks. As a result, the VGG network trained
under fine-tuning showed the best performance. Even though the reached accuracy
could be improved with more examples, the current prediction distribution is
narrow, so the predictions are adequate for keeping a record and taking quick
ecological actions.


  Transition metal dichalcogenide (TMD) materials have emerged as promising
candidates for thin film solar cells due to their wide bandgap range across the
visible wavelengths, high absorption coefficient and ease of integration with
both arbitrary substrates as well as conventional semiconductor technologies.
However, reported TMD-based solar cells suffer from relatively low external
quantum efficiencies (EQE) and low open circuit voltage due to unoptimized
design and device fabrication. This paper studies $Pt/WSe_2$ vertical Schottky
junction solar cells with various $WSe_2$ thicknesses in order to find the
optimum absorber thickness.Also, we show that the photovoltaic performance can
be improved via $Al_2O_3$ passivation which increases the EQE by up to 29.5% at
410 nm wavelength incident light. The overall resulting short circuit current
improves through antireflection coating, surface doping, and surface trap
passivation effects. Thanks to the ${Al_2O_3}$ coating, this work demonstrates
a device with open circuit voltage ($V_{OC}$) of 380 mV and short circuit
current density ($J_{SC}$) of 10.7 $mA/cm^2$. Finally, the impact of Schottky
barrier height inhomogeneity at the $Pt/WSe_2$ contact is investigated as a
source of open circuit voltage lowering in these devices


  The ridesharing economy is experiencing rapid growth and innovation.
Companies such as Uber and Lyft are continuing to grow at a considerable pace
while providing their platform as an organizing medium for ridesharing
services, increasing consumer utility as well as employing thousands in
part-time positions. However, many challenges remain in the modeling of
ridesharing services, many of which are not currently under wide consideration.
In this paper, an agent-based model is developed to simulate a ridesharing
service in the Washington D.C. metropolitan region. The model is used to
examine levels of utility gained for both riders (customers) and drivers
(service providers) of a generic ridesharing service. A description of the
Individual Agent Metro-Washington Area Ridesharing Model (IAMWARM) is provided,
as well as a description of a typical simulation run. We investigate the
financial gains of drivers for a 24-hour period under two scenarios and two
spatial movement behaviors. The two spatial behaviors were random movement and
Voronoi movement, which we describe. Both movement behaviors were tested under
a stationary run conditions scenario and a variable run conditions scenario. We
find that Voronoi movement increased drivers' utility gained but that emergence
of this system property was only viable under variable scenario conditions.
This result provides two important insights: The first is that driver movement
decisions prior to passenger pickup can impact financial gain for the service
and drivers, and consequently, rate of successful pickup for riders. The second
is that this phenomenon is only evident under experimentation conditions where
variability in passenger and driver arrival rates are administered.


  I gave a rambling talk about gravity and its many mysteries at Chen-Ning
Yang's 85th Birthday Celebration held in November 2007. I don't have any
answers.


  Ensuring that large language models (LMs) are fair, robust and useful
requires an understanding of how different modifications to their inputs impact
the model's behaviour. In the context of open-text generation tasks, however,
such an evaluation is not trivial. For example, when introducing a model with
an input text and a perturbed, "contrastive" version of it, meaningful
differences in the next-token predictions may not be revealed with standard
decoding strategies. With this motivation in mind, we propose Contrastive Input
Decoding (CID): a decoding algorithm to generate text given two inputs, where
the generated text is likely given one input but unlikely given the other. In
this way, the contrastive generations can highlight potentially subtle
differences in how the LM output differs for the two inputs in a simple and
interpretable manner. We use CID to highlight context-specific biases that are
hard to detect with standard decoding strategies and quantify the effect of
different input perturbations.


  Texture is one of the most important properties of visual surface that helps
in discriminating one object from another or an object from background. The
self-organizing map (SOM) is an excellent tool in exploratory phase of data
mining. It projects its input space on prototypes of a low-dimensional regular
grid that can be effectively utilized to visualize and explore properties of
the data. This paper proposes an enhancement extraction method for accurate
extracting features for efficient image representation it based on SOM neural
network. In this approach, we apply three different partitioning approaches as
a region of interested (ROI) selection methods for extracting different
accurate textural features from medical image as a primary step of our
extraction method. Fisherfaces feature selection is used, for selecting
discriminated features form extracted textural features. Experimental result
showed the high accuracy of medical image categorization with our proposed
extraction method. Experiments held on Mammographic Image Analysis Society
(MIAS) dataset.


  We explore modifications to the current scenario for the slow neutron capture
process in asymptotic giant branch (AGB) stars to account for the Pb deficiency
observed in post-AGB stars of low metallicity ([Fe/H] ~ -1.2) and low initial
mass (~ 1 - 1.5 Msun) in the Large and Small Magellanic Clouds. We calculated
the stellar evolution and nucleosynthesis for a 1.3 Msun star with [Fe/H]=-1.3
and tested different amounts and distributions of protons leading to the
production of the main neutron source within the 13C-pocket and proton
ingestion scenarios. No s-process models can fully reproduce the abundance
patterns observed in the post-AGB stars. When the Pb production is lowered the
abundances of the elements between Eu and Pb, such as Er, Yb, W, and Hf, are
also lowered to below those observed. Neutron-capture processes with neutron
densities intermediate between the s and the rapid neutron-capture processes
may provide a solution to this problem and be a common occurrence in low-mass,
low-metallicity AGB stars.


  The 135 classical novae that we have discovered in M87 with two
$\textit{Hubble Space Telescope}$ imaging surveys appear to be strongly
concentrated along that galaxy's jet. Detailed simulations show that the
likelihood that this distribution occurred by chance is of order $0.3\%$. The
novae near the jet display outburst characteristics (peak luminosities, colors
and decline rates) that are indistinguishable from novae far from the jet. We
explore whether the remarkable nova distribution could be caused by the jet's
irradiation of the hydrogen-rich donors in M87's cataclysmic binaries. This
explanation, and others extant in the literature which rely on increased binary
mass transfer rates, fail by orders of magnitude in explaining the enhanced
nova rate near the jet. An alternate explanation is the presence of a genuine
surplus of nova binary systems near the jet, perhaps due to jet-induced star
formation. This explanation fails to explain the lack of nova enhancement along
M87's counterjet. The enhanced rate of novae along M87's jet is now firmly
established, and unexplained.


  In this paper we define cycle-star graph CSk,n-k to be a graph on n vertices
consisting of the cycle of length k and n-k leafs appended to the same vertex
of the cycle. Also, we define cycle-path graph CPk,n-k to be a graph on n
vertices consisting of the cycle of length k and of path on n-k vertices whose
one end is linked to a vertex on a cycle. We establish that cycle-star graph
CS3,n-3 is the only maximal graph with respect to additively weighted Harary
index among all unicyclic graphs on n vertices, while cycle-path graph CP3,n-3
is the only minimal unicyclic graph (here n must be at least 5). The values of
additively weighted Harary index for extremal unicyclic graphs are established,
so these values are the upper and the lower bound for the value of additively
weighted Harary index on the class of unicyclic graphs on n vertices.


  We study finite-time future singularities in $\mathcal{F}(R,G)$-gravity,
where $R$ and $G$ are the Ricci scalar and the Gauss-Bonnet invariant,
respectively. In particular, we reconstruct the $F(G)$-gravity and
$\mathcal{F}(R,G)$-gravity models realizing the finite-time future
singularities. We discuss a possible way to cure the finite-time future
singularities in $\mathcal{F}(R,G)$-gravity by taking into account higher-order
curvature corrections or effects of viscous fluids.


  Lattice Quantum Chromodynamics (LQCD) studies of light nuclei have entered an
era when first results on structure and reaction properties of light nuclei
have emerged in recent years, complementing existing results on their
lowest-lying spectra. Although in these preliminary studies the quark masses
are still set to larger than the physical values, a few results at the physical
point can still be deduced from simple extrapolations in the quark masses. The
progress paves the road towards obtaining several important quantities in
nuclear physics, such as nuclear forces and nuclear matrix elements relevant
for pp fusion, single and double-beta decay processes, neutrino-nucleus
scattering, searches for CP violation, nuclear response in direct dark-matter
detection experiments, as well as gluonic structure of nuclei for an
Electron-Ion Collider (EIC) program. Some of the recent developments, the
results obtained, and the outlook of the field will be briefly reviewed in this
talk, with a focus on results obtained by the Nuclear Physics From LQCD
(NPLQCD) collaboration.


  We construct an explicit filtration of the ring of algebraic power series by
finite dimensional constructible sets, measuring the complexity of these
series. As an application, we give a bound on the dimension of the set of
algebraic power series of bounded complexity lying on an algebraic variety
defined over the field of power series.


  Motion of a test particle plays an important role in understanding the
properties of a spacetime. As a new type of the strong gravity system, boson
stars could mimic black holes located at the center of galaxies. Studying the
motion of a test particle in the spacetime of a rotating boson star will
provide the astrophysical observable effects if a boson star is located at the
center of a galaxy. In this paper, we investigate the timelike geodesic of a
test particle in the background of a rotating boson star with angular number
$m=(1, 2, 3)$. With the change of angular number and frequency, a rotating
boson star will transform from the low rotating state to the highly
relativistic rapidly rotating state, the corresponding Lense-Thirring effects
will be more and more significant and it should be studied in detail. By
solving the four-velocity of a test particle and integrating the geodesics, we
investigate the bound orbits with a zero and nonzero angular momentum. We find
that a test particle can stay more longer time in the central region of a boson
star when the boson star becomes from low rotating state to highly relativistic
rotating state. Comparing the periapse values of the orbits obtained in
rotating boson stars and the corresponding orbits of the observed stars
orbiting Sagittarius $A^*$, we discuss the possible observable effects of the
astrophysical boson star in the Galactic center.


  We study the problem of high-dimensional robust mean estimation in an online
setting. Specifically, we consider a scenario where $n$ sensors are measuring
some common, ongoing phenomenon. At each time step $t=1,2,\ldots,T$, the
$i^{th}$ sensor reports its readings $x^{(i)}_t$ for that time step. The
algorithm must then commit to its estimate $\mu_t$ for the true mean value of
the process at time $t$. We assume that most of the sensors observe independent
samples from some common distribution $X$, but an $\epsilon$-fraction of them
may instead behave maliciously. The algorithm wishes to compute a good
approximation $\mu$ to the true mean $\mu^\ast := \mathbf{E}[X]$. We note that
if the algorithm is allowed to wait until time $T$ to report its estimate, this
reduces to the well-studied problem of robust mean estimation. However, the
requirement that our algorithm produces partial estimates as the data is coming
in substantially complicates the situation.
  We prove two main results about online robust mean estimation in this model.
First, if the uncorrupted samples satisfy the standard condition of
$(\epsilon,\delta)$-stability, we give an efficient online algorithm that
outputs estimates $\mu_t$, $t \in [T],$ such that with high probability it
holds that $\|\mu-\mu^\ast\|_2 = O(\delta \log(T))$, where $\mu = (\mu_t)_{t
\in [T]}$. We note that this error bound is nearly competitive with the best
offline algorithms, which would achieve $\ell_2$-error of $O(\delta)$. Our
second main result shows that with additional assumptions on the input (most
notably that $X$ is a product distribution) there are inefficient algorithms
whose error does not depend on $T$ at all.


  The data reported in Planck's Early Release Compact Source Catalogue (ERCSC)
are exploited to measure the number counts (dN/dS) of extragalactic radio
sources at 30, 44, 70, 100, 143 and 217 GHz. Due to the full-sky nature of the
catalogue, this measurement extends to the rarest and brightest sources in the
sky. At lower frequencies (30, 44, and 70 GHz) our counts are in very good
agreement with estimates based on WMAP data, being somewhat deeper at 30 and 70
GHz, and somewhat shallower at 44 GHz. Planck's source counts at 143 and 217
GHz join smoothly with the fainter ones provided by the SPT and ACT surveys
over small fractions of the sky. An analysis of source spectra, exploiting
Planck's uniquely broad spectral coverage, finds clear evidence of a steepening
of the mean spectral index above about 70 GHz. This implies that, at these
frequencies, the contamination of the CMB power spectrum by radio sources below
the detection limit is significantly lower than previously estimated.


  Solitons and breathers are nonlinear modes that exist in a wide range of
physical systems. They are fundamental solutions of a number of nonlinear wave
evolution equations, including the uni-directional nonlinear Schr\"odinger
equation (NLSE). We report the observation of slanted solitons and breathers
propagating at an angle with respect to the direction of propagation of the
wave field. As the coherence is diagonal, the scale in the crest direction
becomes finite, consequently, a beam dynamics forms. Spatio-temporal
measurements of the water surface elevation are obtained by
stereo-reconstructing the positions of the floating markers placed on a regular
lattice and recorded with two synchronized high-speed cameras. Experimental
results, based on the predictions obtained from the (2D+1) hyperbolic NLSE
equation, are in excellent agreement with the theory. Our study proves the
existence of such unique and coherent wave packets and has serious implications
for practical applications in optical sciences and physical oceanography.
Moreover, unstable wave fields in this geometry may explain the formation of
directional large amplitude rogue waves with a finite crest length within a
wide range of nonlinear dispersive media, such as Bose-Einstein condensates,
plasma, hydrodynamics and optics.


  Multi-energy computed tomography (CT) with photon counting detectors (PCDs)
enables spectral imaging as PCDs can assign the incoming photons to specific
energy channels. However, PCDs with many spectral channels drastically increase
the computational complexity of the CT reconstruction, and bespoke
reconstruction algorithms need fine-tuning to varying noise statistics.
\rev{Especially if many projections are taken, a large amount of data has to be
collected and stored. Sparse view CT is one solution for data reduction.
However, these issues are especially exacerbated when sparse imaging scenarios
are encountered due to a significant reduction in photon counts.} In this work,
we investigate the suitability of learning-based improvements to the
challenging task of obtaining high-quality reconstructions from sparse
measurements for a 64-channel PCD-CT. In particular, to overcome missing
reference data for the training procedure, we propose an unsupervised denoising
and artefact removal approach by exploiting different filter functions in the
reconstruction and an explicit coupling of spectral channels with the nuclear
norm. Performance is assessed on both simulated synthetic data and the openly
available experimental Multi-Spectral Imaging via Computed Tomography (MUSIC)
dataset. We compared the quality of our unsupervised method to iterative total
nuclear variation regularized reconstructions and a supervised denoiser trained
with reference data. We show that improved reconstruction quality can be
achieved with flexibility on noise statistics and effective suppression of
streaking artefacts when using unsupervised denoising with spectral coupling.


  Humans learn powerful representations of objects and scenes by observing how
they evolve over time. Yet, outside of specific tasks that require explicit
temporal understanding, static image pretraining remains the dominant paradigm
for learning visual foundation models. We question this mismatch, and ask
whether video pretraining can yield visual representations that bear the
hallmarks of human perception: generalisation across tasks, robustness to
perturbations, and consistency with human judgements. To that end we propose a
novel procedure for curating videos, and develop a contrastive framework which
learns from the complex transformations therein. This simple paradigm for
distilling knowledge from videos, called VITO, yields general representations
that far outperform prior video pretraining methods on image understanding
tasks, and image pretraining methods on video understanding tasks. Moreover,
VITO representations are significantly more robust to natural and synthetic
deformations than image-, video-, and adversarially-trained ones. Finally,
VITO's predictions are strongly aligned with human judgements, surpassing
models that were specifically trained for that purpose. Together, these results
suggest that video pretraining could be a simple way of learning unified,
robust, and human-aligned representations of the visual world.


  This work addresses the design of optimal linear transmit filters for the
Multiple Input-Multiple Output (MIMO) Broadcast Channel (BC) when several
spatial streams are allocated to each user.We also consider that the Channel
State Information (CSI) is perfect at the receivers but is only partial at the
transmitter. A statistical model for the partial CSI is assumed and exploited
for the filter design. Similarly to the single-stream per user case, the
problem is solved via Mean Square Error (MSE) dualities and interference
functions. However, including more streams per user involves an additional
complexity level since we must determine how to distribute the per-user rates
among the streams. Such problem is solved using a projected gradient algorithm.


  We propose a logical qubit based on the Blume-Capel model: a higher spin
generalization of the Ising chain and which allows for an on-site anisotropy
preserving rotational invariance around the Ising axis. We show that such a
spin-3/2 Blume-Capel model can also support localized Majorana bound states at
the ends of the chain. Inspired by known braiding protocols of these Majorana
bound states, upon appropriate manipulation of the system parameters, we
demonstrate a set of universal gate operations which act on qubits encoded in
the doubly degenerate ground states of the chain.


  We develop an effective low energy theory for multi-channel scattering of
cold atomic alkali atoms with particular focus on Feshbach resonances. The
scattering matrix is expressed in terms of observables only and the theory
allows for the inclusion of many-body effects both in the open and in the
closed channels.
  We then consider the frequency and damping of collective modes for Fermi
gases and demonstrate how medium effects significantly increase the scattering
rate determining the nature of the modes. Our results obtained with no fitting
parameters are shown to compare well with experimental data.


  This paper surveys the quantum entanglement of two coupled harmonic
oscillators via angular momentum generating a magnetic coupling $\omega_{c}$.
The corresponding Hamiltonian is diagonalized by using three canonical
transformations and then the stationary wave function is obtained. Based on the
Schmidt decomposition, we explicitly determine the Schmidt modes $\lambda_{k}$
with $k\in\left\lbrace 0,1,\cdots,n+m\right\rbrace$, $n$ and $m$ being two
quantum numbers associated to the two oscillators. By studying the effect of
the anisotropy $ R=\omega_{1}^{2}/\omega_{2}^{2} $, $\omega_{c}$, asymmetry $
|n-m| $ and dynamics on the entanglement, we summarize our results as follows.
$ (i)- $ The entanglement becomes very large with the increase of $ (n,m) $. $
(ii)- $ The sensistivity to $\omega_c$ depends on $ (n,m) $ and $R$. $ (iii)- $
The periodic revival of entanglement strongly depends on the physical
parameters and quantum numbers.


  We show the model wavefunctions for the neutral collective modes in
fractional quantum Hall (FQH) states have simple analytic forms obtained from
judicially reducing the powers of selected pairs in the ground state Jastrow
factor. This scheme of "pair excitations" works for the magneto-roton modes of
single-component Abelian and non-Abeliean FQH states, as well as the neutral
fermion mode for the Moore-Read (MR) state. The analytic wavefunctions allow us
to compute the "quadrupole gap" of the magneto-roton mode in the thermodynamic
limit, which was previously inaccessible to the numerics. The quadrupole gap is
related to the fusion of the charges in the two-dimensional plasma picture,
extending the plasma analogy to neutral excitations. A lattice diagrammatic
method of representing these many-body wavefunctions and FQH elementary
excitations is also presented.


  The Possible-Winner problem asks, given an election where the voters'
preferences over the set of candidates is partially specified, whether a
distinguished candidate can become a winner. In this work, we consider the
computational complexity of Possible-Winner under the assumption that the voter
preferences are $partitioned$. That is, we assume that every voter provides a
complete order over sets of incomparable candidates (e.g., candidates are
ranked by their level of education). We consider elections with partitioned
profiles over positional scoring rules, with an unbounded number of candidates,
and unweighted voters. Our first result is a polynomial time algorithm for
voting rules with $2$ distinct values, which include the well-known
$k$-approval voting rule. We then go on to prove NP-hardness for a class of
rules that contain all voting rules that produce scoring vectors with at least
$4$ distinct values.


  The results of part I (hep-ph/9612284) are used to obtain full asymptotic
expansions of Feynman diagrams renormalized within the MS-scheme in the regimes
when some of the masses and external momenta are large with respect to the
others. The large momenta are Euclidean, and the expanded diagrams are regarded
as distributions with respect to them. The small masses may be equal to zero.
The asymptotic operation for integrals is defined and a simple combinatorial
techniques is developed to study its exponentiation. The asymptotic operation
is used to obtain the corresponding expansions of arbitrary Green functions.
Such expansions generalize and improve upon the well-known short-distance
operator-product expansions, the decoupling theorem etc.; e.g. the low-energy
effective Lagrangians are obtained to all orders of the inverse heavy mass. The
obtained expansions possess the property of perfect factorization of large and
small parameters, which is essential for meaningful applications to
phenomenology. As an auxiliary tool, the inversion of the R-operation is
constructed. The results are valid for arbitrary QFT models.


  Natural draft direct dry cooling systems (NDDDCSs) combine the benefits of
two traditional dry cooling systems, air-cooled condensers (ACCs) and indirect
natural draft dry cooling systems, to offer advantages such as reduced system
complexity, high thermal efficiency, and low parasitic power losses and
operational costs. These scalable systems can potentially operate in both large
coal-fired power plants (900 MWt) and medium-scale concentrated solar power
(CSP) plants (100 MWt). While prior research focused on NDDDCS steady-state
performance, this study delves into the transient behaviour and start-up
characteristics of this cooling system, crucial for power plants with rapid
start-up requirements or load ramps during electricity demand peaks. The paper
employs a transient 1-D mathematical model to characterize the NDDDCSs'
response to start-up and load ramp conditions under no-wind, presenting results
for both large- and medium scales while identifying limiting factors.


  The mass radius relationship of white dwarfs, near the Chandrasekhar Limit,
is derived for a toy model of uniform density, using the variational principle.
A power law scaling, reminiscent of those found in 2nd order phase transitions,
is obtained. The derived exponent is shown to explain the relationship obtained
by numerically integrating the TOV equations with the equation of state for a
relativistic Fermi gas of electrons.


  We consider a quantum particle interacting with $N$ obstacles, whose
positions are independently chosen according to a given probability density,
through a two-body potential of the form $N^2 V(Nx)$ (Gross-Pitaevskii
potential). We show convergence of the $N$ dependent one-particle Hamiltonian
to a limiting Hamiltonian where the quantum particle experiences an effective
potential depending only on the scattering length of the unscaled potential and
the density of the obstacles. In this sense our Lorentz gas model exhibits a
universal behavior for $N$ large. Moreover we explicitely characterize the
fluctuations around the limit operator. Our model can be considered as a
simplified model for scattering of slow neutrons from condensed matter.


  Electronic Line Calling is an auxiliary referee system used for tennis
matches based on binocular vision technology. While ELC has been widely used,
there are still many problems, such as complex installation and maintenance,
high cost and etc. We propose a monocular vision technology based ELC method.
The method has the following steps. First, locate the tennis ball's trajectory.
We propose a multistage tennis ball positioning approach combining background
subtraction and color area filtering. Then we propose a bouncing point
prediction method by minimizing the fitting loss of the uncertain point.
Finally, we find out whether the bouncing point of the ball is out of bounds or
not according to the relative position between the bouncing point and the court
side line in the two dimensional image. We collected and tagged 394 samples
with an accuracy rate of 99.4%, and 81.8% of the 11 samples with bouncing
points.The experimental results show that our method is feasible to judge if a
ball is out of the court with monocular vision and significantly reduce complex
installation and costs of ELC system with binocular vision.


  We outline a new approach to calculating the quantum mechanical propagator in
the presence of geometrically non-trivial Dirichlet boundary conditions based
upon a generalisation of an integral transform of the propagator studied in
previous work (the so-called ``hit function''), and a convergent sequence of
Pad\'e approximants. In this paper the generalised hit function is defined as a
many-point propagator and we describe its relation to the sum over trajectories
in the Feynman path integral. We then show how it can be used to calculate the
Feynman propagator. We calculate analytically all such hit functions in $D=1$
and $D=3$ dimensions, giving recursion relations between them in the same or
different dimensions and apply the results to the simple cases of propagation
in the presence of perfectly conducting planar and spherical plates. We use
these results to conjecture a general analytical formula for the propagator
when Dirichlet boundary conditions are present in a given geometry, also
explaining how it can be extended for application for more general,
non-localised potentials. Our work has resonance with previous results obtained
by Grosche in the study of path integrals in the presence of delta potentials.
We indicate the eventual application in a relativistic context to determining
Casimir energies using this technique.


  We develop a variational principle between mean dimension theory and rate
distortion theory. We consider a minimax problem about the rate distortion
dimension with respect to two variables (metrics and measures). We prove that
the minimax value is equal to the mean dimension for a dynamical system with
the marker property. The proof exhibits a new combination of ergodic theory,
rate distortion theory and geometric measure theory. Along the way of the
proof, we also show that if a dynamical system has the marker property then it
has a metric for which the upper metric mean dimension is equal to the mean
dimension.


  Flow 2.0 is an end-to-end easy-of-use software that allows us to quickly,
robustly and accurately perform a batch process real-time phase contrast data
and multivariate analysis of the effect of respiration on cerebral fluids
circulation. Synopsis (99/100) Real-time phase contrast sequences (RT-PC) have
potential value as a scientific and clinical tool in quantifying the effects of
respiration on cerebral circulation. To simplify its complicated
post-processing process, we developed Flow 2.0 software, which provides a
complete post-processing workflow including converting DICOM data, image
segmentation, image processing, data extraction, background field correction,
antialiasing filter, signal processing and analysis and a novel time-domain
method for quantifying the effect of respiration on the cerebral circulation.
This end-to-end software allows us to quickly, robustly and accurately perform
batch process RT-PC and multivariate analysis of the effects of respiration on
cerebral circulation.


  In the Proof of Stake (PoS) Ethereum ecosystem, users can stake ETH on Lido
to receive stETH, a Liquid Staking Derivative (LSD) that represents staked ETH
and accrues staking rewards. LSDs improve the liquidity of staked assets by
facilitating their use in secondary markets, such as for collateralized
borrowing on Aave or asset exchanges on Curve. The composability of Lido, Aave,
and Curve enables an emerging strategy known as leverage staking, where users
supply stETH as collateral on Aave to borrow ETH and then acquire more stETH.
This can be done directly by initially staking ETH on Lido or indirectly by
swapping ETH for stETH on Curve. While this iterative process enhances
financial returns, it also introduces potential risks.
  This paper explores the opportunities and risks of leverage staking. We
establish a formal framework for leverage staking with stETH and identify 442
such positions on Ethereum over 963 days. These positions represent a total
volume of 537,123 ETH (877m USD). Our data reveal that the majority (81.7%) of
leverage staking positions achieved an Annual Percentage Rate (APR) higher than
that of conventional staking on Lido. Despite the high returns, we also
recognize the risks of leverage staking. From the Terra crash incident, we
understand that token devaluation can greatly impact the market. Therefore, we
conduct stress tests under extreme conditions, particularly during stETH
devaluations, to thoroughly evaluate the associated risks. Our simulations
indicate that leverage staking can exacerbate the risk of cascading
liquidations by introducing additional selling pressures from liquidation and
deleveraging activities. Moreover, this strategy poses broader systemic risks
as it undermines the stability of ordinary positions by intensifying their
liquidations.


  We study the evolution of the band-gap structure in few-layer MoTe$_2$
crystals, by means of low-temperature micro-reflectance (MR) and
temperature-dependent photoluminescence (PL) measurements. The analysis of the
measurements indicate that, in complete analogy with other semiconducting
transition metal dichalchogenides (TMDs), the dominant PL emission peaks
originate from direct transitions associated to recombination of excitons and
trions. When we follow the evolution of the PL intensity as a function of layer
thickness, however, we observe that MoTe$_2$ behaves differently from other
semiconducting TMDs investigated earlier. Specifically, the exciton PL yield
(integrated PL intensity) is identical for mono and bilayer and it starts
decreasing for trilayers. A quantitative analysis of this behavior and of all
our experimental observations is fully consistent with mono and bilayer
MoTe$_2$ being direct band-gap semiconductors, with tetralayer MoTe$_2$ being
an indirect gap semiconductor, and with trilayers having nearly identical
direct and indirect gaps.This conclusion is different from the one reached for
other recently investigated semiconducting transition metal dichalcogenides,
for which only monolayers are found to be direct band-gap semiconductors, with
thicker layers having indirect band gaps that are significantly smaller, by
hundreds of meV, than the direct gap. We discuss the relevance of our findings
for experiments of fundamental interest and possible future device
applications.


  Mainly motivated by the problem of modelling directional dependence
relationships for multivariate count data in high-dimensional settings, we
present a new algorithm, called learnDAG, for learning the structure of
directed acyclic graphs (DAGs). In particular, the proposed algorithm tackled
the problem of learning DAGs from observational data in two main steps: (i)
estimation of candidate parent sets; and (ii) feature selection. We
experimentally compare learnDAG to several popular competitors in recovering
the true structure of the graphs in situations where relatively moderate sample
sizes are available. Furthermore, to make our algorithm is stronger, a
validation of the algorithm is presented through the analysis of real datasets.


  Tree-adjoining grammars are a generalization of context-free grammars that
are well suited to model human languages and are thus popular in computational
linguistics. In the tree-adjoining grammar recognition problem, given a grammar
$\Gamma$ and a string $s$ of length $n$, the task is to decide whether $s$ can
be obtained from $\Gamma$. Rajasekaran and Yooseph's parser (JCSS'98) solves
this problem in time $O(n^{2\omega})$, where $\omega < 2.373$ is the matrix
multiplication exponent. The best algorithms avoiding fast matrix
multiplication take time $O(n^6)$.
  The first evidence for hardness was given by Satta (J. Comp. Linguist.'94):
For a more general parsing problem, any algorithm that avoids fast matrix
multiplication and is significantly faster than $O(|\Gamma| n^6)$ in the case
of $|\Gamma| = \Theta(n^{12})$ would imply a breakthrough for Boolean matrix
multiplication.
  Following an approach by Abboud et al. (FOCS'15) for context-free grammar
recognition, in this paper we resolve many of the disadvantages of the previous
lower bound. We show that, even on constant-size grammars, any improvement on
Rajasekaran and Yooseph's parser would imply a breakthrough for the $k$-Clique
problem. This establishes tree-adjoining grammar parsing as a practically
relevant problem with the unusual running time of $n^{2\omega}$, up to lower
order factors.


  We propose a simple microscopic model for arching phenomena at bottlenecks.
The dynamics of particles in front of a bottleneck is described by a
one-dimensional stochastic cellular automaton on a semicircular geometry. The
model reproduces oscillation phenomena due to formation and collapsing of
arches. It predicts the existence of a critical bottleneck size for continuous
particle flows. The dependence of the jamming probability on the system size is
approximated by the Gompertz function. The analytical results are in good
agreement with simulations.


  We analyze a new spectral graph matching algorithm, GRAph Matching by
Pairwise eigen-Alignments (GRAMPA), for recovering the latent vertex
correspondence between two unlabeled, edge-correlated weighted graphs.
Extending the exact recovery guarantees established in the companion paper for
Gaussian weights, in this work, we prove the universality of these guarantees
for a general correlated Wigner model. In particular, for two Erd\H{o}s-R\'enyi
graphs with edge correlation coefficient $1-\sigma^2$ and average degree at
least $\operatorname{polylog}(n)$, we show that GRAMPA exactly recovers the
latent vertex correspondence with high probability when $\sigma \lesssim
1/\operatorname{polylog}(n)$. Moreover, we establish a similar guarantee for a
variant of GRAMPA, corresponding to a tighter quadratic programming relaxation
of the quadratic assignment problem. Our analysis exploits a resolvent
representation of the GRAMPA similarity matrix and local laws for the
resolvents of sparse Wigner matrices.


  Cache-enabled device-to-device (D2D) networks turn memory of the devices at
the network edge, such as smart phones and tablets, into bandwidth by enabling
asynchronous content sharing directly between proximate devices. Limited
storage capacity of the mobile devices necessitates the determination of
optimal set of contents to be cached on each device. In order to study the
problem of optimal cache placement, we model the locations of devices in a
finite region (e.g., coffee shop, sports bar, library) as a uniform binomial
point process (BPP). For this setup, we first develop a generic framework to
analyze the coverage probability of the target receiver (target-Rx) when the
requested content is available at the $k^{th}$ closest device to it. Using this
coverage probability result, we evaluate optimal caching probability of the
popular content to maximize the total hit probability. Our analysis concretely
demonstrates that optimal caching probability strongly depends on the number of
simultaneously active devices in the network.


  Bethe approximation is shown to violate Bravais lattices translational
invariance. A new scheme is then presented which goes over the one-site Weiss
model yet preserving initial lattice symmetry. A mapping to a one-dimensional
finite closed chain in an external field is obtained. Lattice topology
determines the chain size. Using recent results in percolation, lattice
connectivity between chains is argued to be $(q(d-1)-2)/(d)$ where $q$ is the
coordination number and $d$ is the space dimension. A new self-consistent
mean-field equation of state is derived. Critical temperatures are thus
calculated for a large variety of lattices and dimensions. Results are within a
few percent of exact estimates. Moreover onset of phase transitions is found to
occur in the range $(d-1)q> 2$. For the Ising hypercube it yields the Golden
number limit $d > (1+\sqrt 5)/(2)$.


  The dynamics of collapsing and expanding cylindrically symmetric
gravitational and matter fields with lightlike wave-fronts is studied in
General Relativity, using the Barrabes-Israel method. As an application of the
general formulae developed, the collapse of a matter field that satisfies the
condition R_{AB}g^{AB} = 0, (A, B = z, phi), in an otherwise flat spacetime
background is studied. In particular, it is found that the gravitational
collapse of a purely gravitational wave or a null dust fluid cannot be realized
in a flat spacetime background. The studies are further specified to the
collapse of purely gravitational waves and the general conditions for such
collapse are found. It is shown that after the waves arrive at the axis, in
general, part of them is reflected to spacelike infinity along the future light
cone, and part of it is focused to form spacetime singularities on the symmetry
axis. The cases where the collapse does not result in the formation of
spacetime singularities are also identified.


  It has long been known how to construct radiation boundary conditions for the
time dependent wave equation. Although arguments suggesting that they are
accurate have been given, it is only recently that rigorous error bounds have
been proved. Previous estimates show that the error caused by these methods
behaves like epsilon C exp(gamma t) for any gamma > 0. We improve these results
and show that the error behaves like epsilon t^2.


  We perform a calculation in quantum field theory of neutrino oscillation
probabilities, where we include simultaneously the source, detector, and
neutrino fields in the Hamiltonian. Within the appropriate limits associated
with current neutrino oscillation experiments, we recover the standard
oscillation formula. On the other hand, we find that the dominant contributions
to the amplitude are associated with different neutrino mass eigenstates being
emitted at different times, such that they arrive at the detector at the same
time. This is contrary to the neutrino wave packet picture, where they are
emitted simultaneously and separate as they travel to the detector. This has
direct consequences regarding the mechanisms that lead to a damping of neutrino
oscillations for very long baselines. Our analysis also provides a pedagogical
example of a measurement process in quantum mechanics.


  Research on multi-modal learning dominantly aligns the modalities in a
unified space at training, and only a single one is taken for prediction at
inference. However, for a real machine, e.g., a robot, sensors could be added
or removed at any time. Thus, it is crucial to enable the machine to tackle the
mismatch and unequal-scale problems of modality combinations between training
and inference. In this paper, we tackle these problems from a new perspective:
"Modalities Help Modalities". Intuitively, we present OmniBind, a novel
two-stage learning framework that can achieve any modality combinations and
interaction. It involves teaching data-constrained, a.k.a, student, modalities
to be aligned with the well-trained data-abundant, a.k.a, teacher, modalities.
This subtly enables the adaptive fusion of any modalities to build a unified
representation space for any combinations. Specifically, we propose Cross-modal
Alignment Distillation (CAD) to address the unequal-scale problem between
student and teacher modalities and effectively align student modalities into
the teacher modalities' representation space in stage one. We then propose an
Adaptive Fusion (AF) module to fuse any modality combinations and learn a
unified representation space in stage two. To address the mismatch problem, we
aggregate existing datasets and combine samples from different modalities by
the same semantics. This way, we build the first dataset for training and
evaluation that consists of teacher (image, text) and student (touch, thermal,
event, point cloud, audio) modalities and enables omni-bind for any of them.
Extensive experiments on the recognition task show performance gains over prior
arts by an average of 4.05 % on the arbitrary modality combination setting. It
also achieves state-of-the-art performance for a single modality, e.g., touch,
with a 4.34 % gain.


  Content-aware graphic layout generation aims to automatically arrange visual
elements along with a given content, such as an e-commerce product image. In
this paper, we argue that the current layout generation approaches suffer from
the limited training data for the high-dimensional layout structure. We show
that a simple retrieval augmentation can significantly improve the generation
quality. Our model, which is named Retrieval-Augmented Layout Transformer
(RALF), retrieves nearest neighbor layout examples based on an input image and
feeds these results into an autoregressive generator. Our model can apply
retrieval augmentation to various controllable generation tasks and yield
high-quality layouts within a unified architecture. Our extensive experiments
show that RALF successfully generates content-aware layouts in both constrained
and unconstrained settings and significantly outperforms the baselines.


  We describe a broadly applicable experimental proposal to search for the
violation of local Lorentz invariance (LLI) with atomic systems. The new scheme
uses dynamic decoupling and can be implemented in current atomic clocks
experiments, both with single ions and arrays of neutral atoms. Moreover, the
scheme can be performed on systems with no optical transitions, and therefore
it is also applicable to highly charged ions which exhibit particularly high
sensitivity to Lorentz invariance violation. We show the results of an
experiment measuring the expected signal of this proposal using a two-ion
crystal of $^{88}$Sr$^+$ ions. We also carry out a systematic study of the
sensitivity of highly charged ions to LLI to identify the best candidates for
the LLI tests.


  Feedback from an active galactic nucleus (AGN) is often implicated as a
mechanism that leads to the quenching of galactic star formation. However,
AGN-driven quenching is challenging to reconcile with observations that AGN
hosts tend to harbour equal (or even excess) amounts of gas compared with
inactive galaxies of similar stellar mass. In this paper, we investigate
whether AGN feedback happens on sub-galactic (kpc) scales, an effect that might
be difficult to detect with global gas measurements. Using kpc-scale
measurements of molecular gas (Sigma_H2) and stellar mass (Sigma_*) surface
densities taken from the EDGE-CALIFA survey, we show that the gas fractions of
central AGN regions are typically a factor of ~2 lower than in star-forming
regions. Based on four galaxies with the best spaxel statistics, the difference
between AGN and star-forming gas fractions is seen even within a given galaxy,
indicating that AGN feedback is able to deplete the molecular gas reservoir in
the central few kpc.


  A general procedure for constructing conserved electromagnetic current
operators, for both finite and infinite degree of freedom systems, is given. A
four-momentum operator consisting of matter, photon, and electromagnetic
interactions is assumed to be polynomial in photon creation and annihilation
operators. Commutators of the four-momentum operator with the four-vector
potential operator at the space-time point zero give the electromagnetic field
tensor and current operator at the space-time point zero. In this construction
there are no equations of motion to be satisfied and field operators at an
arbitrary space-time point-defined as the four-momentum translates of the
corresponding operators at the space-time point zero-are not local operators.
Several examples are given to show how the construction is carried out.


  The problem of detecting auxetic behavior, originating in materials science
and mathematical crystallography, refers to the property of a flexible periodic
bar-and-joint framework to widen, rather than shrink, when stretched in some
direction. The only known algorithmic solution for detecting infinitesimal
auxeticity is based on the rather heavy machinery of fixed-dimension
semi-definite programming. In this paper we present a new, simpler algorithmic
approach which is applicable to a natural family of 3D periodic bar-and-joint
frameworks with 3 degrees-of-freedom. This class includes most zeolite
structures, which are important for applications in computational materials
science. We show that the existence of auxetic deformations is related to
properties of an associated elliptic curve. A fast algorithm for recognizing
auxetic capabilities is obtained via the classical Aronhold invariants of the
cubic form defining the curve.


  This is a study on facial information analysis technology for estimating
gender and age, and poses are estimated using a transformation relationship
matrix between the camera coordinate system and the world coordinate system for
estimating the pose of a face image. Gender classification was relatively
simple compared to age estimation, and age estimation was made possible using
deep learning-based facial recognition technology. A comparative CNN was
proposed to calculate the experimental results using the purchased database and
the public database, and deep learning-based gender classification and age
estimation performed at a significant level and was more robust to
environmental changes compared to the existing machine learning techniques.


  We report the thermal conductance G of Au/Ti/graphene/SiO2 interfaces
(graphene layers 1 < n < 10) typical of graphene transistor contacts. We find G
~ 25 MW m-2 K-1 at room temperature, four times smaller than the thermal
conductance of a Au/Ti/SiO2 interface, even when n = 1. We attribute this
reduction to the thermal resistance of Au/Ti/graphene and graphene/SiO2
interfaces acting in series. The temperature dependence of G from 50 < T < 500
K also indicates that heat is predominantly carried by phonons through these
interfaces. Our findings indicate that metal contacts can limit not only
electrical transport, but also thermal dissipation from sub-micron graphene
devices.


  We give essentially tight bounds for, $\nu(d,k)$, the maximum number of
distinct neighbourhoods on a set $X$ of $k$ vertices in a graph with twin-width
at most~$d$. Using the celebrated Marcus-Tardos theorem, two independent works
[Bonnet et al., Algorithmica '22; Przybyszewski '22] have shown the upper bound
$\nu(d,k) \leqslant \exp(\exp(O(d)))k$, with a double-exponential dependence in
the twin-width. The work of [Gajarsky et al., ICALP '22], using the framework
of local types, implies the existence of a single-exponential bound (without
explicitly stating such a bound).
  We give such an explicit bound, and prove that it is essentially tight.
Indeed, we give a short self-contained proof that for every $d$ and $k$
$$\nu(d,k) \leqslant (d+2)2^{d+1}k = 2^{d+\log d+\Theta(1)}k,$$ and build a
bipartite graph implying $\nu(d,k) \geqslant 2^{d+\log d+\Theta(1)}k$, in the
regime when $k$ is large enough compared to~$d$.


  In this note, residual finiteness of quandles is defined and investigated. It
is proved that free quandles and knot quandles of tame knots are residually
finite and Hopfian. Residual finiteness of quandles arising from residually
finite groups (conjugation, core and Alexander quandles) is established.
Further, residual finiteness of automorphism groups of some residually finite
quandles is also discussed.


  This paper brings mathematical tools to bear on the study of package
dependencies in software systems. We introduce structures known as Dependency
Structures with Choice (DSC) that provide a mathematical account of such
dependencies, inspired by the definition of general event structures in the
study of concurrency. We equip DSCs with a particular notion of morphism and
show that the category of DSCs is isomorphic to the category of antimatroids.
We study the exactness properties of these equivalent categories, and show that
they are finitely complete, have finite coproducts but not all coequalizers.
Further, we construct a functor from a category of DSCs equipped with a certain
subclass of morphisms to the opposite of the category of finite distributive
lattices, making use of a simple finite characterization of the Bruns-Lakser
completion, and finally, we introduce a formal account of versions of packages
and introduce a mathematical account of package version-bound policies.


  Well known to the machine learning community, the random feature model is a
parametric approximation to kernel interpolation or regression methods. It is
typically used to approximate functions mapping a finite-dimensional input
space to the real line. In this paper, we instead propose a methodology for use
of the random feature model as a data-driven surrogate for operators that map
an input Banach space to an output Banach space. Although the methodology is
quite general, we consider operators defined by partial differential equations
(PDEs); here, the inputs and outputs are themselves functions, with the input
parameters being functions required to specify the problem, such as initial
data or coefficients, and the outputs being solutions of the problem. Upon
discretization, the model inherits several desirable attributes from this
infinite-dimensional viewpoint, including mesh-invariant approximation error
with respect to the true PDE solution map and the capability to be trained at
one mesh resolution and then deployed at different mesh resolutions. We view
the random feature model as a non-intrusive data-driven emulator, provide a
mathematical framework for its interpretation, and demonstrate its ability to
efficiently and accurately approximate the nonlinear parameter-to-solution maps
of two prototypical PDEs arising in physical science and engineering
applications: viscous Burgers' equation and a variable coefficient elliptic
equation.


  The ergodic control problem for a non-degenerate controlled diffusion
controlled through its drift is considered under a uniform stability condition
that ensures the well-posedness of the associated Hamilton-Jacobi-Bellman (HJB)
equation. A nonlinear parabolic evolution equation is then proposed as a
continuous time continuous state space analog of White's `relative value
iteration' algorithm for solving the ergodic dynamic programming equation for
the finite state finite action case. Its convergence to the solution of the HJB
equation is established using the theory of monotone dynamical systems and
also, alternatively, by using the theory of reverse martingales.


  Symmetry breaking can produce ``Alice'' strings, which alter scattered
charges and carry monopole number and charge when twisted into loops. Alice
behavior arises algebraically, when strings obstruct unbroken symmetries -- a
fragile criterion. We give a topological criterion, compelling Alice behavior
or deforming it away. Our criterion, that \pi_o(H) acts nontrivially on
\pi_1(H), links topologically Alice strings to topological monopoles. We twist
topologically Alice loops to form monopoles. We show that Alice strings of
condensed matter systems (nematic liquid crystals, helium 3A, and related
non-chiral Bose condensates and amorphous chiral superconductors) are
topologically Alice, and support fundamental monopole charge when twisted into
loops. Thus they might be observed indirectly, not as strings, but as loop-like
point defects. We describe other models, showing Alice strings failing our
topological criterion; and twisted Alice loops supporting deposited, but not
fundamental, monopole number.


  In the present paper, we consider elliptic operators
$L=-\textrm{div}(A\nabla)$ in a domain bounded by a chord-arc surface $\Gamma$
with small enough constant, and whose coefficients $A$ satisfy a weak form of
the Dahlberg-Kenig-Pipher condition of approximation by constant coefficient
matrices, with a small enough Carleson norm, and show that the elliptic measure
with pole at infinity associated to $L$ is $A_\infty$-absolutely continuous
with respect to the surface measure on $\Gamma$, with a small $A_\infty$
constant. In other words, we show that for relatively flat uniformly
rectifiable sets and for operators with slowly oscillating coefficients the
elliptic measure satisfies the $A_\infty$ condition with a small constant and
the logarithm of the Poisson kernel has small oscillations.


  We show under suitable finiteness conditions that a functor between abelian
categories induces a (not necessarily additive) map between their Grothendieck
groups. This is related to the derived functors of Dold and Puppe, and
generalizes a theorem of Dold.


  In this paper, the following two theorems are proved: $(1)$ every spherical
convex body $W$ of constant width $\Delta (W) \geq \frac{\pi}{2}$ may be
covered by a disk of radius $\Delta(W) + \arcsin \left( \frac{2\sqrt{3}}{3}
\cdot \cos \frac{\Delta(W)}{2}\right) - \frac{\pi}{2}$; $(2)$ every reduced
spherical convex body $R$ of thickness $\Delta(R)<\frac{\pi}{2}$ may be covered
by a disk of radius $\arctan \left( \sqrt{2} \cdot \tan
\frac{\Delta(R)}{2}\right)$.


  In this part of the series five-dimensional tangent vectors are introduced
first as equivalence classes of parametrized curves and then as
differential-algebraic operators that act on scalar functions. I then examine
their basic algebraic properties and their parallel transport in the particular
case where space-time possesses a special local symmetry. After that I give
definition to five-dimensional tangent vectors associated with dimensional
curve parameters and show that they can be identified with the five-vectors
introduced formally in part I (math-ph/9805004). In conclusion I speak about
differential forms associated with five-vectors.


  Distributed switched large-scale systems are composed by dynamically coupled
subsystems, in which interactions among subsystems vary over time according to
an exogenous input signal named switching signal. In this paper, we present a
distributed robust switched model-based predictive control (DSwMPC) to control
such systems that are subject to local state and input constraints. The
proposed method guarantees stabilizing the origin of the whole closed-loop
system and ensures the constraints satisfaction in the presence of an unknown
switching signal. In the distributed model-based predictive control (DMPC) used
in this work, by considering the interactions among subsystems as an additive
disturbance, the effect of the switch is reflected on the dynamic equation,
local, and consistency constraint sets of the nominal subsystems. In the
DSwMPC, to compensate the effect of switching signal which creates a
time-varying network topology, a robust tube-based switched model-based
predictive control (RSwMPC) with switched robust control invariant (switchRCI)
set as the target set robust to unknown mode switching is used as local
controller. The scheme performance is assessed using three typical examples. In
the first example, the switching times are unknown in prior, but the next
neighborhood sets are assumed to be known in prior. In the second and third
cases, both of them are supposed to be unknown in prior. The simulation results
show that the input and state constraints are satisfied by the proposed DSwMPC
at all times. They also validate that the closed-loop system converges to the
origin. Also, a comparison of the DSwMPC with a centralized SwMPC (CSwMPC) and
a decentralized SwMPC (DeSwMPC) shows that the DSwMPC outperforms the DecSwMPC
and also the shapes of response curves under the CSwMPC are very similar to
those obtained by the DSwMPC.


  In order to find out capable molecular source of astronomically well observed
infrared (IR) spectrum, asymmetric molecular configuration polycyclic aromatic
hydrocarbon (PAH) was analyzed by the density functional theory (DFT) analysis.
Starting molecules were benzene C6H6, naphthalene C10H8 and 1H-phenalene C13H9.
In interstellar space, those molecules will be attacked by high energy photon
and proton, which may bring cationic molecules as like C6H6n+ (n=0~3 in
calculation), C10H8n+, and C13H9n+, also CH lacked molecules C5H5n+, C9H7n+,
and C12H8n+. IR spectra of those molecules were analyzed based on DFT based
Gaussian program. Results suggested that symmetrical configuration molecules as
like benzene, naphthalene , 1H-phenalene and those cation ( +, 2+, and 3+) show
little resemblance with observed IR. Contrast to such symmetrical molecules,
several cases among cationic and asymmetric configuration molecules show fairly
good IR tendency. One typical example was C12H83+, of which calculated harmonic
IR wavelength were 3.2, 6.3, 7.5, 7.8, 8.7, 11.3, and 12.8 micro meter, which
correspond well to astronomically observed wavelength of 3.3, 6.2, 7.6, 7.8,
8.6, 11.2, and 12.7 micro meter. It was amazing agreement. Also, some cases
like C5H5+, C9H7+, C9H72+, C9H73+ and C12H82+ show fairly good coincidence.
Such results suggest that asymmetric and cationic PAH may be capable source of
interstellar dust.


  In this paper, we propose a model to perform style transfer of speech to
singing voice. Contrary to the previous signal processing-based methods, which
require high-quality singing templates or phoneme synchronization, we explore a
data-driven approach for the problem of converting natural speech to singing
voice. We develop a novel neural network architecture, called SymNet, which
models the alignment of the input speech with the target melody while
preserving the speaker identity and naturalness. The proposed SymNet model is
comprised of symmetrical stack of three types of layers - convolutional,
transformer, and self-attention layers. The paper also explores novel data
augmentation and generative loss annealing methods to facilitate the model
training. Experiments are performed on the
  NUS and NHSS datasets which consist of parallel data of speech and singing
voice. In these experiments, we show that the proposed SymNet model improves
the objective reconstruction quality significantly over the previously
published methods and baseline architectures. Further, a subjective listening
test confirms the improved quality of the audio obtained using the proposed
approach (absolute improvement of 0.37 in mean opinion score measure over the
baseline system).


  Rules are necessary to provide or shape the incentives of individuals and
organizations. This is particularly true when free markets lead to undesirable
outcomes. The Telecommunications Act of 1996 attempted to create incentives to
foster competition. Ambiguity as well as the timing of the Act has led to
delays in the clarification of rules and the rapid obsolescence of the
document. The paper presents the strategies that common carriers adopted to try
to tilt regulation in their favor, slow the entry of competitors, maintain
their market leadership, and expand into other segments. Some of the strategies
analyzed include lobbying efforts, court challenges, and lack of cooperation
with new entrants.


  We use the Wide-field Infrared Survey Explorer (WISE) and the Sloan Digital
Sky Survey (SDSS) to confirm a connection between dust-obscured active galactic
nuclei (AGNs) and galaxy merging. Using a new, volume-limited (z <= 0.08)
catalog of visually-selected major mergers and galaxy-galaxy interactions from
the SDSS, with stellar masses above 2x10^10 Msun, we find that major mergers
(interactions) are 5-17 (3-5) times more likely to have red [3.4]-[4.6] colors
associated with dust-obscured or `dusty' AGNs, compared to non-merging galaxies
with similar masses. Using published fiber spectral diagnostics, we map the
[3.4]-[4.6] versus [4.6]-[12] colors of different emission-line galaxies and
find one-quarter of Seyferts have colors indicative of a dusty AGN. We find
that AGNs are five times more likely to be obscured when hosted by a merging
galaxy, half of AGNs hosted by a merger are dusty, and we find no enhanced
frequency of optical AGNs in merging over non-merging galaxies. We conclude
that undetected AGNs missed at shorter wavelengths are at the heart of the
ongoing AGN-merger connection debate. The vast majority of mergers hosting
dusty AGNs are star-forming and located at the centers of Mhalo<10^13 Msun
groups. Assuming plausibly short duration dusty-AGN phases, we speculate that a
large fraction of gas-rich mergers experience a brief obscured AGN phase, in
agreement with the strong connection between central star formation and black
hole growth seen in merger simulations.


  When did the universe thermalize? In this talk I review the status of this
issue and its importance in establishing the expected properties of dark
matter, the growth of large-scale structure, and the viability of inflation
models when confronted with CMB observations. I also present a novel approach
to tackling the theoretical challenges surrounding inflationary (p)reheating,
which seeks to extend past work on the Effective Field Theory of Inflation to
the time of reheating.


  The recent discovery of TeV emission from gamma-ray bursts (GRBs) by the
MAGIC and H.E.S.S. Cherenkov telescopes confirmed that emission from these
transients can extend to very high energies. The TeV energy domain reaches the
most sensitive band of the Cherenkov Telescope Array (CTA). This newly
anticipated, improved sensitivity will enhance the prospects of
gravitational-wave follow-up observations by CTA to probe particle acceleration
and high-energy emission from binary black hole and neutron star mergers, and
stellar core-collapse events. Here we discuss the implications of TeV emission
on the most promising strategies of choice for the gravitational-wave follow-up
effort for CTA and Cherenkov telescopes more broadly. We find that TeV emission
(i) may allow more than an hour of delay between the gravitational-wave event
and the start of CTA observations; (ii) enables the use of CTA's small size
telescopes that have the largest fields of view. We characterize the number of
pointings needed to find a counterpart. (iii) We compute the annual follow-up
time requirements and find that prioritization will be needed. (iv) Even a few
telescopes could detect sufficiently nearby counterparts, raising the
possibility of adding a handful of small-size or medium-size telescopes to the
network at diverse geographic locations taking into account the positions of
CTA and the LIGO-Virgo-KAGRA network. (v) The continued operation of
VERITAS/H.E.S.S./MAGIC would be a useful compliment to CTA's follow-up
capabilities by increasing the sky area that can be rapidly covered, especially
for directions above and 'below' the United States in which the present network
of gravitational-wave detectors is more sensitive.


  We describe two new open source tools written in Python for performing
extreme deconvolution Gaussian mixture modeling (XDGMM) and using a conditioned
model to re-sample observed supernova and host galaxy populations. XDGMM is new
program for using Gaussian mixtures to do density estimation of noisy data
using extreme deconvolution (XD) algorithms that has functionality not
available in other XD tools. It allows the user to select between the AstroML
(Vanderplas et al. 2012; Ivezic et al. 2015) and Bovy et al. (2011) fitting
methods and is compatible with scikit-learn machine learning algorithms
(Pedregosa et al. 2011). Most crucially, it allows the user to condition a
model based on the known values of a subset of parameters. This gives the user
the ability to produce a tool that can predict unknown parameters based on a
model conditioned on known values of other parameters. EmpiriciSN is an example
application of this functionality that can be used for fitting an XDGMM model
to observed supernova/host datasets and predicting likely supernova parameters
using on a model conditioned on observed host properties. It is primarily
intended for simulating realistic supernovae for LSST data simulations based on
empirical galaxy properties.


  Accretion around black holes is very often characterized by distinctive X-ray
reflection features (mostly, iron inner-shell transitions), which arise due to
the primary radiation being reprocessed by a dense and relatively colder
medium, such as an accretion disk. Most reflection modeling assume that
emission stops at the inner-most stable circular orbit (ISCO), and that for
smaller radii - in the plunging region - the density drops and the accretion
flow is far too ionized for efficient line production. We investigate the
spectral features of the reflection in the plunging regions of optically-thick
and geometrically-thin accretion disks around black holes. We show that for
cases in which the density profile is considered constant (as expected in
highly magnetized flows), or in cases in which the disk density is high enough
such that the ionization still allows line formation within the ISCO, there is
a significant modification of the observed reflected spectrum. Consistent with
previous studies, we found that the impact of the radiation reprocessed in the
plunging region is stronger the lower the black hole spin, when the plunging
region subtends a larger area. Likewise, as for the case of standard reflection
modeling, the relativistic broadening of the iron line is more pronounced at
low inclination, whereas the blueshift and relativistic beaming effect is
dominant at high inclination. We also tested the effects of various
prescriptions of the stress at the ISCO radius on the reflection spectrum, and
found that several of these cases appear to show line profiles distinct enough
to be distinguishable with reasonably good quality observational data.


  Driven by the loss of energy, isolated rotating neutron stars (pulsars) are
gradually slowing down to lower frequencies, which increases the tremendous
compression of the matter inside of them. This increase in compression changes
both the global properties of rotating neutron stars as well as their hadronic
core compositions. Both effects may register themselves observationally in the
thermal evolution of such stars, as demonstrated in this Letter. The
rotation-driven particle process which we consider here is the direct Urca (DU)
process, which is known to become operative in neutron stars if the number of
protons in the stellar core exceeds a critical limit of around 11% to 15%. We
find that neutron stars spinning down from moderately high rotation rates of a
few hundred Hertz may be creating just the right conditions where the DU
process becomes operative, leading to an observable effect (enhanced cooling)
in the temperature evolution of such neutron stars. As it turns out, the
rotation-driven DU process could explain the unusual temperature evolution
observed for the neutron star in Cas A, provided the mass of this neutron star
lies in the range of 1.5 to 1.9 \msun and its rotational frequency at birth was
between 40 (400 Hz) and 70% (800 Hz) of the Kepler (mass shedding) frequency,
respectively.


  Despite the availability of commercial QRNG devices, distinguishing between
PRNG and QRNG outputs computationally remains challenging. This paper presents
two significant contributions from the perspectives of QRNG manufacturers and
users. For manufacturers, the conventional method of assessing the quantumness
of single-photon-based QRNGs through mean and variance comparisons of photon
counts is statistically unreliable due to finite sample sizes. Given the
sub-Poissonian statistics of single photons, confirming the underlying
distribution is crucial for validating a QRNG's quantumness. We propose a more
efficient two-fold statistical approach to ensure the quantumness of optical
sources with the desired confidence level. Additionally, we demonstrate that
the output of QRNGs from exponential and uniform distributions exhibit
similarity under device noise, deriving corresponding photon statistics and
conditions for $\epsilon$-randomness.
  From the user's perspective, the fundamental parameters of a QRNG are
quantumness (security), efficiency (randomness and random number generation
rate), and cost. Our analysis reveals that these parameters depend on three
factors, expected photon count per unit time, external reference cycle
duration, and detection efficiency. A lower expected photon count enhances
security but increases cost and decreases the generation rate. A shorter
external reference cycle boosts security but must exceed a minimum threshold to
minimize timing errors, with minor impacts on cost and rate. Lower detection
efficiency enhances security and lowers cost but reduces the generation rate.
Finally, to validate our results, we perform statistical tests like NIST,
Dieharder, AIS-31, ENT etc. over the data simulated with different values of
the above parameters. Our findings can empower manufacturers to customize QRNGs
to meet user needs effectively.


  We present an exact procedure that allows one to calculate the scattering
lenth for any potential expressed as an algebraic sum of inverse powers of the
interatomic distance. We apply it to (12, $s$) Lennard-Jones potentials, with
different values of $s$. The procedure is suitable for a very precise
determination of the intensities of the potential corresponding to zeros or
poles of the scattering length.


  We give a characterization of distance--preserving subgraphs of Johnson
graphs, i.e. of graphs which are isometrically embeddable into Johnson graphs
(the Johnson graph $J(m,\Lambda)$ has the subsets of cardinality $m$ of a set
$\Lambda$ as the vertex--set and two such sets $A,B$ are adjacent iff
$|A\triangle B|=2$). Our characterization is similar to the characterization of
D. \v{Z}. Djokovi\'c (J. Combin. Th. Ser. B 14 (1973), 263--267) of
distance--preserving subgraphs of hypercubes and provides an explicit
description of the wallspace (split system) defining the embedding.


  We theoretically study the tunnel magnetoresistance (TMR) effect in
(111)-oriented magnetic tunnel junctions (MTJs) with SrTiO$_{3}$ barriers,
Co/SrTiO$_{3}$/Co(111) and Ni/SrTiO$_{3}$/Ni(111). Our analysis combining the
first-principles calculation and the Landauer formula shows that the Co-based
MTJ has a high TMR ratio over 500%, while the Ni-based MTJ has a smaller value
(290%). Since the in-plane lattice periodicity of SrTiO$_{3}$ is about twice
that of the primitive cell of fcc Co (Ni), the original bands of Co (Ni) are
folded in the $k_x$-$k_y$ plane corresponding to the $ab$ plane of the MTJ
supercell. We find that this band folding gives a half-metallic band structure
in the $\Lambda_1$ state of Co (Ni) and the coherent tunneling of such a
half-metallic $\Lambda_1$ state yields a high TMR ratio. We also reveal that
the difference in the TMR ratio between the Co- and Ni-based MTJs can be
understood by different $s$-orbital weights in the $\Lambda_1$ band at the
Fermi level.


  In conventional scattering theory, to obtain an explicit result, one imposes
a precondition that the distance between target and observer is infinite. With
the help of this precondition, one can asymptotically replace the Hankel
function and the Bessel function with the sine functions so that one can
achieve an explicit result. Nevertheless, after such a treatment, the
information of the distance between target and observer is inevitably lost. In
this paper, we show that such a precondition is not necessary: without losing
any information of distance, one can still obtain an explicit result of a
scattering rigorously. In other words, we give an rigorous explicit scattering
result which contains the information of distance between target and observer.
We show that at a finite distance, a modification factor --- the Bessel
polynomial --- appears in the scattering amplitude, and, consequently, the
cross section depends on the distance, the outgoing wave-front surface is no
longer a sphere, and, besides the phase shift, there is an additional phase
(the argument of the Bessel polynomial) appears in the scattering wave
function.


  This paper deals with the Physics Institute of via Panisperna in Rome,
getting ready for investigation on neutron physics before Fermi's discovery of
neutron-induced radioactivity. The importance of nuclear research had been
acknowledged in the Physics Institute in Rome since 1929. The Institute had
been directed towards nuclear physics since then, but from the experimental
point of view, still in 1933, it was not yet engaged in nuclear experiments on
account of the lack of adequate supplies. An adjustment of the equipment and
supplies was undertaken, so that strong radioactive sources, Geiger-Mueller
counters and Wilson chambers were finally available at the end of 1933, thanks
largely to Rasetti's efforts.


  A self-consistent calculation of nuclear matrix elements of the neutrinoless
double beta decays (0nbb) of 76Ge, 82Se, 96Zr, 100Mo, 116Cd, 128Te, 130Te and
130Xe is presented in the framework of the renormalized quasiparticle random
phase approximation (RQRPA) and the standard QRPA. The pairing and residual
interactions as well as the two-nucleon short-range correlations are for the
first time derived from the same modern realistic nucleon-nucleon potentials,
namely from charge-dependent Bonn potential (CD-Bonn) and the Argonne V18
potential. In a comparison with the traditional approach of using the
Miller-Spencer Jastrow correlations matrix elements for the 0nbb-decay are
obtained, which are larger in magnitude. We analyze the differences among
various two-nucleon correlations including those of the unitary correlation
operator method (UCOM) and quantify the uncertainties in the calculated
0nbb-decay matrix elements.


  Convection in the liquid outer core of the Earth is driven by thermal and
chemical perturbations. The main purpose of this study is to examine the impact
of double-diffusive convection on magnetic field generation by means of 3D
global geodynamo models, in the so-called "top-heavy" regime of
double-diffusive convection, when both thermal and compositional background
gradients are destabilizing. Using a linear eigensolver, we begin by confirming
that, compared to the standard single-diffusive configuration, the onset of
convection is facilitated by the addition of a second buoyancy source. We next
carry out a systematic parameter survey by performing $79$ numerical dynamo
simulations. We show that a good agreement between simulated magnetic fields
and the geomagnetic field can be attained for any partitioning of the
convective input power between its thermal and chemical components. On the
contrary, the transition between dipole-dominated and multipolar dynamos is
found to strongly depend on the nature of the buoyancy forcing. Classical
parameters expected to govern this transition, such as the local Rossby number
or the degree of equatorial symmetry of the flow, fail to capture the dipole
breakdown. A scale-dependent analysis of the force balance instead reveals that
the transition occurs when the ratio of inertial to Lorentz forces at the
dominant length scale reaches $0.5$, regardless of the partitioning of the
buoyancy power. The ratio of integrated kinetic to magnetic energy $E_k/E_m$
provides a reasonable proxy of this force ratio. Given that $E_k/E_m \ll 1$ in
the Earth's core, the geodynamo is expected to operate far from the
dipole-multipole transition. It hence appears that the occurrence of
geomagnetic reversals is unlikely related to dramatic and punctual changes of
the amplitude of inertial forces in the Earth's core, and that another
mechanism must be sought.


  The level of current non-uniformity in NbTi CICCs sections near the joints in
combination with the magnet field profile needs attention in view of proper
joint design. The strand Joule power and current distribution at quench under
DC conditions of two samples of ITER Poloidal Field Coil conductors, as tested
in the SULTAN facility and of the so called PFCI Model Coil Insert, have been
analyzed with the numerical cable model JackPot. The precise trajectories of
all individual strands, joint design, cabling configuration, spatial
distribution of the magnetic field, sample geometry and using experimentally
determined interstrand resistance distributions have been taken into account.
Although unable to predict the quench point due to the lack of a
thermal-hydraulic routine, the model allows to assess the instantaneous strand
power at quench and its local distribution in the cable, showing the hot spots,
once the quench conditions in terms of current and temperature are
experimentally known., The analysis points out the relation of the above
mentioned factors with the DC quench stability of both short samples and coils.
The possible small scale and local electrical-thermal interactions were ignored
in order to examine the relevance of such effects in the overall prediction of
the CICC performance The electromagnetic code shows an excellent quantitative
predictive potential for CICC transport properties, excluding any freedom for
matching the results. The influence of the local thermal effects in the
modeling is identified as being marginal and far less than the generally
accepted temperature margin for safe operation.


  We give an extension of Casimir of Casimir $\cal{WA_N}$ algebras including a
vertex operator which depends on non-simple roots of $A_{N-1}$.


  The limit of a sequence by the definition with $\varepsilon$ is introduced by
the notion of checkmate in two moves. The idea is also extended to define the
limit of a function with $\varepsilon$ and $\delta$.


  We present a short elementary proof of the following Twelve Points Theorem:
  Let M be a convex polygon with vertices at the lattice points, containing a
single lattice point in its interior. Denote by m (resp. m*) the number of
lattice points in the boundary of M (resp. in the boundary of the dual
polygon). Then m+m*=12.


  The quantum computing paradigm in photonics currently relies on the
multi-port interference in linear optical devices, which is intrinsically based
on probabilistic measurements outcome and thus non-deterministic. Devising a
fully deterministic, universal, and practically achievable quantum computing
platform based on integrated photonic circuits is still an open challenge. Here
we propose to exploit weakly nonlinear photonic devices to implement
deterministic entangling quantum gates, following the definition of dual rail
photonic qubits. It is shown that a universal set of single- and two-qubit
gates can be designed by a suitable concatenation of few optical
interferometric elements, with optimal fidelities arbitrarily close to 100%
theoretically demonstrated through a bound constrained optimization algorithm.
The actual realization would require the concatenation of a few tens of
elementary operations, as well as on-chip optical nonlinearities that are
compatible with some of the existing quantum photonic platforms, as it is
finally discussed.


  Despite the stride made by machine learning (ML) based performance modeling,
two major concerns that may impede production-ready ML applications in EDA are
stringent accuracy requirements and generalization capability. To this end, we
propose hybrid graph neural network (GNN) based approaches towards highly
accurate quality-of-result (QoR) estimations with great generalization
capability, specifically targeting logic synthesis optimization. The key idea
is to simultaneously leverage spatio-temporal information from hardware designs
and logic synthesis flows to forecast performance (i.e., delay/area) of various
synthesis flows on different designs. The structural characteristics inside
hardware designs are distilled and represented by GNNs; the temporal knowledge
(i.e., relative ordering of logic transformations) in synthesis flows can be
imposed on hardware designs by combining a virtually added supernode or a
sequence processing model with conventional GNN models. Evaluation on 3.3
million data points shows that the testing mean absolute percentage error
(MAPE) on designs seen and unseen during training are no more than 1.2% and
3.1%, respectively, which are 7-15X lower than existing studies.


  Since 2015, with the restart of the LHC for its second run of data taking,
the LHCb experiment has been empowered with a dedicated computing model to
select and analyse calibration samples to measure the performance of the
particle identification (PID) detectors and algorithms. The novel technique was
developed within the framework of the innovative trigger model of the LHCb
experiment, which relies on online event reconstruction for most of the
datasets, reserving offline reconstruction to special physics cases. The
strategy to select and process the calibration samples, which includes a
dedicated data-processing scheme combining online and offline reconstruction,
is discussed. The use of the calibration samples to measure the detector PID
performance, and the efficiency of PID requirements across a large range of
decay channels, is described. Applications of the calibration samples in
data-quality monitoring and validation procedures are also detailed.


  We study the depinning transition of driven interfaces in thin ferromagnetic
films driven by external magnetic fields. Approaching the transition point the
correlation length increases with decreasing driving. If the correlation length
becomes of the order of the film thickness a crossover to two dimensional
behavior occurs. From the corresponding scaling analysis we determine the
exponents characterizing the transition of the three dimensional system.


  We present a technique to construct a spectropolarimetrically accurate
magneto-hydrostatic model of a large-scale solar magnetic field concentration,
mimicking a sunspot. Using the constructed model we perform a simulation of
acoustic wave propagation, conversion and absorption in the solar interior and
photosphere with the sunspot embedded into it. With the $6173\mathrm{\AA}$
magnetically sensitive photospheric absorption line of neutral iron, we
calculate observable quantities such as continuum intensities, Doppler
velocities, as well as full Stokes vector for the simulation at various
positions at the solar disk, and analyse the influence of non-locality of
radiative transport in the solar photosphere on helioseismic measurements.
Bisector shapes were used to perform multi-height observations. The differences
in acoustic power at different heights within the line formation region at
different positions at the solar disk were simulated and characterised. An
increase in acoustic power in the simulated observations of the sunspot umbra
away from the solar disk centre was confirmed as the slow magneto-acoustic
wave.


  We show that the local temperature dependence of thermalized electron and
phonon populations along metallic carbon nanotubes is the main reason behind
this non-linear transport characteristics in the high bias regime. Our model
that considers optical and zone boundary phonon emission as well as absorption
by charge carriers is based on the solution of the Boltzmann transport equation
that assumes a local temperature along the nanotube, determined
self-consistently with the heat transport equation. By using realistic
transport parameters, our results not only reproduce experimental data for
electronic transport, but also provide a coherent interpretation of thermal
breakdown under electric stress. In particular, electron and phonon
thermalization prohibits ballistic transport in short nanotubes.


  We report a simple one pot process for the preparation of lead sulphide (PbS)
nanocrystals in the conjugated polymer MEH-PPV, and we demonstrate electronic
coupling between the two components.


  In this note we consider a Markov chain formed by a finite system of
interacting birth-and-death processes on a finite state space. We study an
asymptotic behaviour of the Markov chain as its state space becomes large. In
particular, we show that the appropriately scaled Markov chain converges to a
diffusion process, and derive conditions for existence of the stationary
distribution of the limit diffusion process in special cases.


  This paper summarizes the basics of the notion of quantum discord and how it
relates to other types of correlations in quantum physics. We take the
fundamental information theoretic approach and illustrate our exposition with a
number of simple examples.


  Radon gas emanating from materials is of interest in environmental science
and also a major concern in rare event non-accelerator particle physics
experiments such as dark matter and double beta decay searches, where it is a
major source of background. Notable for dark matter experiments is the
production of radon progeny recoils (RPRs), the low energy (~100 keV) recoils
of radon daughter isotopes, which can mimic the signal expected from WIMP
interactions. Presented here are results of measurements of radon emanation
from detector materials in the 1 metre cubed DRIFT-II directional dark matter
gas time projection chamber experiment. Construction and operation of a radon
emanation facility for this work is described, along with an analysis to
continuously monitor DRIFT data for the presence of internal 222Rn and 218Po.
Applying this analysis to historical DRIFT data, we show how systematic
substitution of detector materials for alternatives, selected by this device
for low radon emanation, has resulted in a factor of ~10 reduction in internal
radon rates. Levels are found to be consistent with the sum from separate radon
emanation measurements of the internal materials and also with direct
measurement using an attached alpha spectrometer. The current DRIFT detector,
DRIFT-IId, is found to have sensitivity to 222Rn of 2.5 {\mu}Bq/l with current
analysis efficiency, potentially opening up DRIFT technology as a new tool for
sensitive radon assay of materials.


  Orbital-free density functional theory (OF-DFT) constitutes a computationally
highly effective tool for modeling electronic structures of systems ranging
from room-temperature materials to warm dense matter. Its accuracy critically
depends on the employed kinetic energy (KE) density functional, which has to be
supplied as an external input. In this work we consider several nonlocal and
Laplacian-level KE functionals and use an external harmonic perturbation to
compute the static density response at T=0 K in the linear and beyond linear
response regimes. We test for the satisfaction of exact conditions in the limit
of uniform densities and for how approximate KE functionals reproduce the
density response of realistic materials (e.g., Al and Si) against the Kohn-Sham
DFT reference which employs the exact KE. The results illustrate that several
functionals violate exact conditions in the UEG limit. We find a strong
correlation between the accuracy of the KE functionals in the UEG limit and in
the strongly inhomogeneous case. This empirically demonstrates the importance
of imposing the limit of UEG response for uniform densities and validates the
use of the Lindhard function in the formulation of kernels for nonlocal
functionals. This conclusion is substantiated by additional calculations for
bulk Aluminum (Al) with a face-centered cubic (fcc) lattice and Silicon (Si)
with an fcc lattice, body-centered cubic (bcc) lattice, and semiconducting
crystal diamond (cd) state. The analysis of fcc Al, and fcc as well as bcc Si
data follows closely the conclusions drawn for the UEG, allowing us to extend
our conclusions to realistic systems that are subject to density
inhomogeneities induced by ions.


  After summarizing the phenomenology of pressure amorphization (PA), we
present a theory of PA based on the notion that one or more branches of the
phonon spectrum soften and flatten with increasing pressure. The theory
expresses the anharmonic dynamics of the flat branches in terms of local modes,
represented by lattice Wannier functions, which are in turn used to construct
an effective Hamiltonian. When the low-pressure structure becomes metastable
with respect to the high-pressure equilibrium phase and the relevant branches
are sufficiently flat, transformation into an amorphous phase is shown to be
kinetically favored because of the exponentially large number of both amorphous
phases and reaction pathways. In effect, the critical-size nucleus for the
first-order phase transition is found to be reduced to a single unit cell, or
nearly so. Random nucleation into symmetrically equivalent local configurations
characteristic of the high-pressure structure is then shown to overwhelm any
possible domain growth, and an ``amorphous'' structure results.


  Supersymmetry and Grand Unification are the two most promising directions for
physics beyond the Standard Model. They receive indirect experimental support
from the apparent lightness of the Higgs boson, the values of the gauge
couplings measured at LEP and elsewhere, and the persistent solar neutrino
deficit. Phenomenological constraints and theoretical models constrain
predictions in interesting ways. All these ideas may be embedded in string
theory, which is shown by newly-discovered dualities to possess
previously-unsuspected richness and simplicity.


  How does one introduce randomness into a classical dynamical system in order
to produce something which is related to the `corresponding' quantum system? We
consider this question from a probabilistic point of view, in the context of
some integrable Hamiltonian systems.


  Due to complex interactions among various deep neural network (DNN)
optimization techniques, modern DNNs can have weights and activations that are
dense or sparse with diverse sparsity degrees. To offer a good trade-off
between accuracy and hardware performance, an ideal DNN accelerator should have
high flexibility to efficiently translate DNN sparsity into reductions in
energy and/or latency without incurring significant complexity overhead.
  This paper introduces hierarchical structured sparsity (HSS), with the key
insight that we can systematically represent diverse sparsity degrees by having
them hierarchically composed from multiple simple sparsity patterns. As a
result, HSS simplifies the underlying hardware since it only needs to support
simple sparsity patterns; this significantly reduces the sparsity acceleration
overhead, which improves efficiency. Motivated by such opportunities, we
propose a simultaneously efficient and flexible accelerator, named HighLight,
to accelerate DNNs that have diverse sparsity degrees (including dense). Due to
the flexibility of HSS, different HSS patterns can be introduced to DNNs to
meet different applications' accuracy requirements. Compared to existing works,
HighLight achieves a geomean of up to 6.4x better energy-delay product (EDP)
across workloads with diverse sparsity degrees, and always sits on the
EDP-accuracy Pareto frontier for representative DNNs


  The NPDGamma experiment measures the parity-violating asymmetry in
$\gamma$-ray emission in the capture of polarized neutrons on liquid
parahydrogen. The sensitivity to the asymmetry for each detector in the array
is used as a parameter in the extraction of the physics asymmetry from the
measured data. The detector array is approximately cylindrically symmetric
around the target and a step-wise sinusoidal function has been used for the
sensitivity in the previous iteration of the NPDGamma experiment, but
deviations from cylindrical symmetry necessitate the use of a Monte Carlo model
to determine corrections to the geometrical factors. For the calculations,
source code modifications to MCNPX were done in order to calculate the
sensitivity of each cesium iodide detector to the physics asymmetry. We
describe the MCNPX model and results from calculations and how the results are
validated through measurement of the parity violating asymmetry of
$\gamma$-rays from neutron capture on chlorine.


  Linear-infrastructure Mission Control (LiMiC) is an application for
autonomous Unmanned Aerial Vehicle (UAV) infrastructure inspection mission
planning developed in monolithic software architecture. The application
calculates routes along the infrastructure based on the users' inputs, the
number of UAVs participating in the mission, and UAVs' locations. LiMiC1.0 is
the latest application version migrated from monolith to microservices,
continuously integrated, and deployed using DevOps tools to facilitate future
features development, enable better traffic management, and improve the route
calculation processing time. Processing time was improved by refactoring the
route calculation algorithm into services, scaling them in the Kubernetes
cluster, and enabling asynchronous communication in between. In this paper, we
discuss the differences between the monolith and microservice architecture to
justify our decision for migration. We describe the methodology for the
application's migration and implementation processes, technologies we use for
continuous integration and deployment, and we present microservices improved
performance results compared with the monolithic application.