  Creative writing is hard: Novelists struggle with writer's block daily. While
automatic story generation has advanced recently, it is treated as a "toy task"
for advancing artificial intelligence rather than helping people. In this
paper, we create a system that produces a short description that narrates a
predicted plot using existing story generation approaches. Our goal is to
assist writers in crafting a consistent and compelling story arc. We conducted
experiments on Amazon Mechanical Turk (AMT) to examine the quality of the
generated story plots in terms of consistency and storiability. The results
show that short descriptions produced by our frame-enhanced GPT-2 (FGPT-2) were
rated as the most consistent and storiable among all models; FGPT-2's outputs
even beat some random story snippets written by humans. Next, we conducted a
preliminary user study using a story continuation task where AMT workers were
given access to machine-generated story plots and asked to write a follow-up
story. FGPT-2 could positively affect the writing process, though people favor
other baselines more. Our study shed some light on the possibilities of future
creative writing support systems beyond the scope of completing sentences. Our
code is available at: https://github.com/appleternity/Story-Plot-Generation.


  The temperature fluctuations generated by viscous dissipation in an isotropic
turbulent flow are studied using direct numerical simulation. It is shown that
their scaling with Reynolds number is at odds with predictions from recent
investigations. The origin of the discrepancy is traced back to the anomalous
scaling of the dissipation rate fluctuations. Phenomenological arguments are
presented which explain the observed results. The study shows that previously
proposed models underpredict the variance of frictional temperature
fluctuations by a factor proportional to the square of the Taylor-scale
Reynolds number.


  A novel Gromov-Wasserstein learning framework is proposed to jointly match
(align) graphs and learn embedding vectors for the associated graph nodes.
Using Gromov-Wasserstein discrepancy, we measure the dissimilarity between two
graphs and find their correspondence, according to the learned optimal
transport. The node embeddings associated with the two graphs are learned under
the guidance of the optimal transport, the distance of which not only reflects
the topological structure of each graph but also yields the correspondence
across the graphs. These two learning steps are mutually-beneficial, and are
unified here by minimizing the Gromov-Wasserstein discrepancy with structural
regularizers. This framework leads to an optimization problem that is solved by
a proximal point method. We apply the proposed method to matching problems in
real-world networks, and demonstrate its superior performance compared to
alternative approaches.


  Low-momentum excitations of a dilute Bose-Einstein condensate behave as
phonons and move at a finite velocity v_s. Yet the atoms making up the phonon
excitation each move very slowly; v_a = p/m --> 0. A simple "cartoon picture"
is suggested to understand this phenomenon intuitively. It implies a relation
v_s/v_a = N_ex, where N_ex is the number of excited atoms making up the phonon.
This relation does indeed follow from the standard Bogoliubov theory.


  We present the identification of 634 variable stars in the Milky Way dSph
satellite Sculptor based on archival ground-based optical observations spanning
$\sim$24 years and covering $\sim$ 2.5 deg$^2$. We employed the same
methodologies as the "Homogeneous Photometry" series published by Stetson. In
particular, we have identified and characterized one of the largest (536) RR
Lyrae samples so far in a Milky Way dSph satellite. We have also detected four
Anomalous Cepheids, 23 SX Phoenicis stars, five eclipsing binaries, three field
variable stars, three peculiar variable stars located above the horizontal
branch - near to the locus of BL Herculis - that we are unable to classify
properly. Additionally we identify 37 Long Period Variables plus 23 probable
variable stars, for which the current data do not allow us to determine the
period. We report positions and finding charts for all the variable stars, and
basic properties (period, amplitude, mean magnitude) and light curves for 574
of them. We discuss the properties of the RR Lyrae stars in the Bailey diagram,
which supports the coexistence of subpopulations with different chemical
compositions. We estimate the mean mass of Anomalous Cepheids
($\sim$1.5M$_{\odot}$) and SX Phoenicis stars ($\sim$1M$_{\odot}$). We discuss
in detail the nature of the former. The connections between the properties of
the different families of variable stars are discussed in the context of the
star formation history of the Sculptor dSph galaxy.


  We consider two-band double exchange model and calculate the critical
temperature in ferromagnetic regime (Curie temperature). The localized spins
are represented in terms of the Schwinger-bosons, and two spin-singlet Fermion
operators are introduced. In terms of the new Fermi fields the on-site Hund's
interactions are in a diagonal form and one accounts for them exactly.
Integrating out the spin-singlet fermions we derive an effective Heisenberg
type model for a vector which describes the local orientations of the total
magnetization. The transversal fluctuations of the vector are the true magnons
in the theory, which is a base for Curie temperature calculation. The critical
temperature is calculated employing the Schwinger-bosons mean-field theory.
While approximate, this technic of calculation captures the essentials of the
magnon fluctuations in the theory, and for 2D systems one obtains zero Curie
temperature, in accordance with Mermin-Wagner theorem.


  In this paper we prove a sharp lower bound for the first nontrivial Neumann
eigenvalue $\mu_1(\Omega)$ for the $p$-Laplace operator in a Lipschitz, bounded
domain $\Omega$ in $\R^n$. Our estimate does not require any convexity
assumption on $\Omega$ and it involves the best isoperimetric constant relative
to $\Omega$.


  In this article the $(b, c)$-inverse will be studied. Several equivalent
conditions for the existence of the $(b,c)$-inverse in rings will be given. In
particular, the conditions ensuring the existence of the $(b,c)$-inverse, of
the annihilator $(b,c)$-inverse and of the hybrid $(b,c)$-inverse will be
proved to be equivalent, provided $b$ and $c$ are regular elements in a unitary
ring $R$. In addition, the set of all $(b,c)$-invertible elements will be
characterized and the reverse order law will be also studied. Moreover, the
relationship between the $(b,c)$-inverse and the Bott-Duffin inverse will be
considered. In the context of Banach algebras, integral, series and limit
representations will be given. Finally the continuity of the $(b,c)$-inverse
will be characterized


  We discuss the perturbatively calculable virtual photon structure functions.
First we present the framework for analyzing the structure functions of the
virtual photon and derive a first moment of $g_1^\gamma$ of the virtual photon.
We then investigate the three positivity constraints satisfied by the eight
structure functions of the virtual photon.


  In this work we introduce a new family of twelve-step linear multistep
methods for the integration of the Schr\"odinger equation. The new methods are
constructed by adopting a new methodology which improves the phase lag
characteristics by vanishing both the phase lag function and its first
derivatives at a specific frequency. This results in decreasing the sensitivity
of the integration method on the estimated frequency of the problem. The
efficiency of the new family of methods is proved via error analysis and
numerical applications.


  CT scans are the standard-of-care for many clinical ailments, and are needed
for treatments like external beam radiotherapy. Unfortunately, CT scanners are
rare in low and mid-resource settings due to their costs. Planar X-ray
radiography units, in comparison, are far more prevalent, but can only provide
limited 2D observations of the 3D anatomy. In this work, we propose a method to
generate CT volumes from few (<5) planar X-ray observations using a prior data
distribution, and perform the first evaluation of such a reconstruction
algorithm for a clinical application: radiotherapy planning. We propose a deep
generative model, building on advances in neural implicit representations to
synthesize volumetric CT scans from few input planar X-ray images at different
angles. To focus the generation task on clinically-relevant features, our model
can also leverage anatomical guidance during training (via segmentation masks).
We generated 2-field opposed, palliative radiotherapy plans on thoracic CTs
reconstructed by our method, and found that isocenter radiation dose on
reconstructed scans have <1% error with respect to the dose calculated on
clinically acquired CTs using <=4 X-ray views. In addition, our method is
better than recent sparse CT reconstruction baselines in terms of standard
pixel and structure-level metrics (PSNR, SSIM, Dice score) on the public LIDC
lung CT dataset. Code is available at: https://github.com/wanderinrain/Xray2CT.


  This paper proposes a polynomial-time algorithm to construct the monotone
stepwise curve that minimizes the sum of squared errors with respect to a given
cloud of data points. The fitted curve is also constrained on the maximum
number of steps it can be composed of and on the minimum step length. Our
algorithm relies on dynamic programming and is built on the basis that said
curve-fitting task can be tackled as a shortest-path type of problem. Numerical
results on synthetic and realistic data sets reveal that our algorithm is able
to provide the globally optimal monotone stepwise curve fit for samples with
thousands of data points in less than a few hours. Furthermore, the algorithm
gives a certificate on the optimality gap of any incumbent solution it
generates. From a practical standpoint, this piece of research is motivated by
the roll-out of smart grids and the increasing role played by the small
flexible consumption of electricity in the large-scale integration of renewable
energy sources into current power systems. Within this context, our algorithm
constitutes an useful tool to generate bidding curves for a pool of small
flexible consumers to partake in wholesale electricity markets.


  In this paper I outline a new strategy for the routine detection and imaging
of faint (sub-mJy and microJy) radio sources with VLBI and SVLBI. The strategy
relies on a combination of in-beam phase-referencing, wide-field VLBI imaging
and simultaneous correlation of multiple field centres. A combination of these
techniques, together with the steeply rising radio source counts observed at cm
wavelengths, permit routine high resolution observations of radio sources
previously considered too faint for conventional VLBI.


  When a Simple Harmonic Oscillator (SHO) wave function is used as an effective
wave function, a very important parameter in the SHO wave function is the
effective $\beta$ value. We obtain the analytical expression of $\beta_{eff}$
($\beta_{effective}$) of the SHO wave function in coordinate space and momentum
space. The expression is applied to the light meson system
$(u\bar{u},~u\bar{s})$ to compare the behavior of $\beta_{eff}$. The results
show that $\beta_ {eff,\mathbf{r}}$ in coordinate space and $\beta_
{eff,\mathbf{p}}$ in momentum space are significantly different in the ground
state, however, similar in the highly excited states with Cornell potential.


  We propose a new object-centric video prediction algorithm based on the deep
latent particle (DLP) representation. In comparison to existing slot- or
patch-based representations, DLPs model the scene using a set of keypoints with
learned parameters for properties such as position and size, and are both
efficient and interpretable. Our method, deep dynamic latent particles (DDLP),
yields state-of-the-art object-centric video prediction results on several
challenging datasets. The interpretable nature of DDLP allows us to perform
``what-if'' generation -- predict the consequence of changing properties of
objects in the initial frames, and DLP's compact structure enables efficient
diffusion-based unconditional video generation. Videos, code and pre-trained
models are available: https://taldatech.github.io/ddlp-web


  We report on an analysis of the high-energy muon component in near-vertical
extensive air showers detected by the surface array IceTop in coincidence with
the in-ice array of the IceCube Neutrino Observatory. In the coincidence
measurement, the predominantly electromagnetic signal measured by IceTop is
used to estimate the cosmic-ray primary energy, and the energy loss of the muon
bundle in the deep in-ice array is used to estimate the number of muons in the
shower with energies above 500 GeV ("TeV muons"). The average multiplicity of
these TeV muons is determined for cosmic-ray energies between 2.5 PeV and 100
PeV assuming three different hadronic interaction models: Sibyll 2.1,
QGSJet-II.04, and EPOS-LHC. For all models considered, the results are found to
be in good agreement with the expectations from simulations. A tension exists,
however, between the high-energy muon multiplicity and other observables; most
importantly the density of GeV muons measured by IceTop using QGSJet-II.04 and
EPOS-LHC.


  We study the distributions of money in a simple closed economic system for
different types of monetary transactions. We know that for arbitrary and random
sharing but locally conserving money transactions, the money distribution goes
to the Gibb's distribution of statistical mechanics. We then consider the
effects of savings, etc. and see how the distribution changes. We also propose
a new model where the agents invest equal amounts of money in each transaction.
We find that for short time-periods, the money distribution obeys a power-law
with an exponent very close to unity, and has an exponential tail; after a very
long time, this distribution collapses and the entire amount of money goes to a
tiny fraction of the population.


  Energy concepts are fundamental across the sciences, yet these concepts can
be fragmented along disciplinary boundaries, rather than integrated into a
coherent whole. To teach physics effectively to biology students, we need to
understand students' disciplinary perspectives. We present interview data from
an undergraduate student who displays multiple stances towards the concept of
energy. At times he views energy in macroscopic contexts as a separate entity
from energy in microscopic (particularly biological) contexts, while at other
times he uses macroscopic physics phenomena as productive analogies for
understanding energy in the microscopic biological context, and he reasons
about energy transformations between the microscopic and macroscopic scales.
This case study displays preliminary evidence for the context dependence of
students' ability to translate energy concepts across scientific disciplines.
This points to challenges that must be taken into account in developing
curricula for biology students that integrate physics and biology concepts.


  Knowing the pressure at all times in each node of a water distribution system
(WDS) facilitates safe and efficient operation. Yet, complete measurement data
cannot be collected due to the limited number of instruments in a real-life
WDS. The data-driven methodology of reconstructing all the nodal pressures by
observing only a limited number of nodes is presented in the paper. The
reconstruction method is based on K-localized spectral graph filters, wherewith
graph convolution on water networks is possible. The effect of the number of
layers, layer depth and the degree of the Chebyshev-polynomial applied in the
kernel is discussed taking into account the peculiarities of the application.
In addition, a weighting method is shown, wherewith information on friction
loss can be embed into the spectral graph filters through the adjacency matrix.
The performance of the proposed model is presented on 3 WDSs at different
number of nodes observed compared to the total number of nodes. The weighted
connections prove no benefit over the binary connections, but the proposed
model reconstructs the nodal pressure with at most 5% relative error on average
at an observation ratio of 5% at least. The results are achieved with shallow
graph neural networks by following the considerations discussed in the paper.


  The Local Group irregular galaxy IC 1613 has remained an enigma for many
years because of its apparent lack of star clusters. We report the successful
search for clusters among several of the candidate objects identified many
years ago on photographic plates. We have used a single HST WFPC2 pointing and
a series of images obtained with the WIYN telescope under exceptional seeing
conditions, examining a total of 23 of the previously published candidates. All
but six of these objects were found to be either asterisms or background
galaxies. Five of the six remaining candidates possibly are small, sparse
clusters and the sixth, C32, is an obvious cluster. It is a compact, young
object, with an age of less than 10 million years and a total absolute
magnitude of M_V = -5.78+/-0.16 within a radius of 13 pc.


  While large language models excel in a variety of natural language processing
(NLP) tasks, to perform well on spoken language understanding (SLU) tasks, they
must either rely on off-the-shelf automatic speech recognition (ASR) systems
for transcription, or be equipped with an in-built speech modality. This work
focuses on the former scenario, where LLM's accuracy on SLU tasks is
constrained by the accuracy of a fixed ASR system on the spoken input.
Specifically, we tackle speech-intent classification task, where a high
word-error-rate can limit the LLM's ability to understand the spoken intent.
Instead of chasing a high accuracy by designing complex or specialized
architectures regardless of deployment costs, we seek to answer how far we can
go without substantially changing the underlying ASR and LLM, which can
potentially be shared by multiple unrelated tasks. To this end, we propose
prompting the LLM with an n-best list of ASR hypotheses instead of only the
error-prone 1-best hypothesis. We explore prompt-engineering to explain the
concept of n-best lists to the LLM; followed by the finetuning of Low-Rank
Adapters on the downstream tasks. Our approach using n-best lists proves to be
effective on a device-directed speech detection task as well as on a keyword
spotting task, where systems using n-best list prompts outperform those using
1-best ASR hypothesis; thus paving the way for an efficient method to exploit
ASR uncertainty via LLMs for speech-based applications.


  The properties of the gauge invariant two-point quark Green's function are
studied in the large-Nc limit of two-dimensional QCD. The analysis is done by
means of an exact integrodifferential equation. The Green's function is found
infrared finite, with singularities in the momentum squared variable
represented by an infinite number of threshold type branch points with a power
-3/2, starting at positive mass squared values, with cuts lying on the positive
real axis. The expression of the Green's function is analytically determined.


  We study the computation contests where players compete for searching a
solution to a given problem with a winner-take-all reward. The search processes
are independent across the players and the search speeds of players are
proportional to their computational powers. One concrete application of this
abstract model is the mining process of proof-of-work type blockchain systems,
such as Bitcoin. Although one's winning probability is believed to be
proportional to his computational power in previous studies on Bitcoin, we show
that it is not the case in the strict sense.
  Because of the gaps between the winning probabilities and the proportions of
computational powers, the Matthew effect will emerge in the system, where the
rich get richer and the poor get poorer. In addition, we show that allowing the
players to pool with each other or reducing the number of solutions to the
problem may aggravate the Matthew effect and vice versa.


  We apply novel jet techniques to investigate the spin and CP quantum numbers
of a heavy resonance X, singly produced in pp -> X -> ZZ -> l(+)l(-)jj at the
LHC. We take into account all dominant background processes to show that this
channel, which has been considered unobservable until now, can qualify under
realistic conditions to supplement measurements of the purely leptonic decay
channels X -> ZZ -> 4l. We perform a detailed investigation of spin- and
CP-sensitive angular observables on the fully-simulated final state for various
spin and CP quantum numbers of the state X, tracing how potential sensitivity
communicates through all the steps of a subjet analysis. This allows us to
elaborate on the prospects and limitations of performing such measurements with
the semihadronic final state. We find our analysis particularly sensitive to a
CP-even or CP-odd scalar resonance, while, for tensorial and vectorial
resonances, discriminative features are diminished in the boosted kinematical
regime.


  When a detuned and strong laser pulse acts on an optical transition, a Stark
shift of the corresponding energies occurs. We analyze how this optical Stark
effect can be used to prepare and control the dark exciton occupation in a
semiconductor quantum dot. The coupling between the bright and dark exciton
states is facilitated by an external magnetic field. Using sequences of laser
pulses, we show how the dark exciton and different superposition states can be
prepared. We give simple analytic formulas, which yield a good estimate for
optimal preparation parameters. The preparation scheme is quite robust against
the influence of acoustic phonons. We further discuss the experimental
feasibility of the used Stark pulses. Giving a clear physical picture our
results will stimulate the usage of dark excitons in schemes to generate
photons from quantum dots.


  Local perturbations of a Brownian motion are considered. As a limit we obtain
a non-Markov process that behaves as a reflected Brownian motion on the
positive half line until its local time at zero reaches some exponential level,
then changes a sign and behaves as a reflected Brownian motion on the negative
half line until some stopping time, etc.


  Recently, the NA61/SHINE experiment at the CERN SPS has extended its program
on physics of strong interactions by measurements of charm hadron production in
nucleus-nucleus collisions. This charm program is here briefly summarized.


  Learning to predict masked tokens in a sequence has been shown to be a
helpful pretraining objective for powerful language models such as PaLM2. After
training, such masked language models (MLMs) can provide distributions of
tokens in the masked positions in a sequence. However, this paper shows that
distributions corresponding to different masking patterns can demonstrate
considerable inconsistencies, i.e., they cannot be derived from a coherent
joint distribution when considered together.
  This fundamental flaw in MLMs can lead to self-contradictory behaviors during
inference. On various benchmark datasets including MMLU, MLMs can give
different predictions to the same input question. From BERT-base to UL2-20B, we
show that such inconsistencies exist ubiquitously in MLMs of diverse sizes and
configurations. In light of our observations, we further propose an
inference-time strategy for MLMs called Ensemble of Conditionals. It jointly
considers a selected range of inconsistent conditionals directly produced by
the MLM for the final prediction, which often leads to considerable accuracy
improvement.


  Vision Transformers (ViTs) have proven to be effective, in solving 2D image
understanding tasks by training over large-scale image datasets; and meanwhile
as a somehow separate track, in modeling the 3D visual world too such as voxels
or point clouds. However, with the growing hope that transformers can become
the "universal" modeling tool for heterogeneous data, ViTs for 2D and 3D tasks
have so far adopted vastly different architecture designs that are hardly
transferable. That invites an (over-)ambitious question: can we close the gap
between the 2D and 3D ViT architectures? As a piloting study, this paper
demonstrates the appealing promise to understand the 3D visual world, using a
standard 2D ViT architecture, with only minimal customization at the input and
output levels without redesigning the pipeline. To build a 3D ViT from its 2D
sibling, we "inflate" the patch embedding and token sequence, accompanied with
new positional encoding mechanisms designed to match the 3D data geometry. The
resultant "minimalist" 3D ViT, named Simple3D-Former, performs surprisingly
robustly on popular 3D tasks such as object classification, point cloud
segmentation and indoor scene detection, compared to highly customized
3D-specific designs. It can hence act as a strong baseline for new 3D ViTs.
Moreover, we note that pursing a unified 2D-3D ViT design has practical
relevance besides just scientific curiosity. Specifically, we demonstrate that
Simple3D-Former naturally enables to exploit the wealth of pre-trained weights
from large-scale realistic 2D images (e.g., ImageNet), which can be plugged in
to enhancing the 3D task performance "for free".


  One approach to monitoring a dynamic system relies on decomposition of the
system into weakly interacting subsystems. An earlier paper introduced a notion
of weak interaction called separability, and showed that it leads to exact
propagation of marginals for prediction. This paper addresses two questions
left open by the earlier paper: can we define a notion of approximate
separability that occurs naturally in practice, and do separability and
approximate separability lead to accurate monitoring? The answer to both
questions is afirmative. The paper also analyzes the structure of approximately
separable decompositions, and provides some explanation as to why these models
perform well.


  This paper presents the statistical model for Ticker [1], a novel
probabilistic stereophonic single-switch text entry method for
visually-impaired users with motor disabilities who rely on single-switch
scanning systems to communicate. All terminology and notation are defined in
[1].


  The ALEPH data on the (non-strange) vector and axial-vector spectral
functions, extracted from tau-lepton decays, is used in order to search for
evidence for a dimension-two contribution, $C_{2 V,A}$, to the Operator Product
Expansion (other than $d=2$ quark mass terms). This is done by means of a
dimension-two Finite Energy Sum Rule, which relates QCD to the experimental
hadronic information. The average $C_{2} \equiv (C_{2V} + C_{2A})/2$ is
remarkably stable against variations in the continuum threshold, but depends
rather strongly on $\Lambda_{QCD}$. Given the current wide spread in the values
of $\Lambda_{QCD}$, as extracted from different experiments, we would
conservatively conclude from our analysis that $C_{2}$ is consistent with zero.


  Preventable or undiagnosed visual impairment and blindness affect billion of
people worldwide. Automated multi-disease detection models offer great
potential to address this problem via clinical decision support in diagnosis.
In this work, we proposed an innovative multi-disease detection pipeline for
retinal imaging which utilizes ensemble learning to combine the predictive
capabilities of several heterogeneous deep convolutional neural network models.
Our pipeline includes state-of-the-art strategies like transfer learning, class
weighting, real-time image augmentation and Focal loss utilization.
Furthermore, we integrated ensemble learning techniques like heterogeneous deep
learning models, bagging via 5-fold cross-validation and stacked logistic
regression models. Through internal and external evaluation, we were able to
validate and demonstrate high accuracy and reliability of our pipeline, as well
as the comparability with other state-of-the-art pipelines for retinal disease
prediction.


  Can machines design? Can they come up with creative solutions to problems and
build tools and artifacts across a wide range of domains? Recent advances in
the field of computational creativity and formal Artificial General
Intelligence (AGI) provide frameworks for machines with the general ability to
design. In this paper we propose to integrate a formal computational creativity
framework into the G\"odel machine framework. We call the resulting framework
design G\"odel machine. Such a machine could solve a variety of design problems
by generating novel concepts. In addition, it could change the way these
concepts are generated by modifying itself. The design G\"odel machine is able
to improve its initial design program, once it has proven that a modification
would increase its return on the utility function. Finally, we sketch out a
specific version of the design G\"odel machine which specifically addresses the
design of complex software and hardware systems. Future work aims at the
development of a more formal version of the design G\"odel machine and a proof
of concept implementation.


  A shortcoming of current binary black-hole initial data is the generation of
spurious gravitational radiation, so-called junk radiation, when they are
evolved. This problem is a consequence of an oversimplified modeling of the
binary's physics in the initial data. Since junk radiation is not
astrophysically realistic, it contaminates the actual waveforms of interest and
poses a numerical nuisance. The work here presents a further step towards
mitigating and understanding the origin of this issue, by incorporating
post-Newtonian results in the construction of constraint-satisfying binary
black-hole initial data. Here we focus on including realistic tidal
deformations of the black holes in the initial data, by building on the method
of superposing suitably chosen black hole metrics to compute the conformal
data. We describe the details of our initial data for an equal-mass and
nonspinning binary, compute the subsequent relaxation of horizon quantities in
evolutions, and quantify the amount of junk radiation that is generated. These
results are contrasted with those obtained with the most common choice of
conformally flat (CF) initial data, as well as superposed Kerr-Schild (SKS)
initial data. We find that when realistic tidal deformations are included, the
early transients in the horizon geometries are significantly reduced, along
with smaller deviations in the relaxed black hole masses and spins from their
starting values. Likewise, the junk radiation content in the $l=2$ modes is
reduced by a factor of $\sim$1.7 relative to CF initial data, but only by a
factor of $\sim$1.2 relative to SKS initial data. More prominently, the junk
radiation content in the $3\leq l\leq8$ modes is reduced by a factor of $\sim$5
relative to CF initial data, and by a factor of $\sim$2.4 relative to SKS
initial data.


  We show how recent angle resolved photoemission measurements addressing the
Fermi arcs in the cuprates reveal a very natural phenomenological description
of the complex superfluid phase. Importantly, this phenomenology is consistent
with a previously presented microscopic theory. By distinguishing the order
parameter and the excitation gap, we are able to demonstrate how the collapse
of the arcs below $T_c$ into well defined nodes is associated with the
\emph{smooth} emergence of superconducting coherence. Comparison of this theory
with experiment shows good semi-quantitative agreement.


  The cross-section of associated $J/\psi$-$\psi(2S)$ production in
proton-proton collisions at a centre-of-mass energy of $\sqrt{s}=13$ TeV is
measured using a data sample corresponding to an integrated luminosity of 4.2
fb$^{-1}$, collected by the LHCb experiment. The measurement is performed for
both $J/\psi$ and $\psi(2S)$ mesons having transverse momentum
$p_{\text{T}}<14$ GeV/$c$ and rapidity $2.0<y<4.5$, assuming negligible
polarisation of the $J/\psi$ and $\psi(2S)$ mesons. The production
cross-section is measured to be $4.5\pm0.7\pm0.3$ nb, where the first
uncertainty is statistical and the second systematic. The differential
cross-sections are measured as functions of several kinematic variables of the
$J/\psi$-$\psi(2S)$ candidates. The results are combined with a measurement of
$J/\psi$-$J/\psi$ production, giving a cross-section ratio between
$J/\psi$-$\psi(2S)$ and $J/\psi$-$J/\psi$ production of
$0.274\pm0.044\pm0.008$, where the first uncertainty is statistical and the
second systematic.


  Highly precise pulsar timing is very important for understanding the nature
of neutron stars and the implied physics, even being used to detect directly
gravitational waves. Unfortunately, the accuracy of the pulsar timing is
seriously affected by the spin-down irregularities, such as spin fluctuations
with a manifestation of low frequency structures (the so-called red noise
processes), and various activities of magnetospheres. Except from the random
timing noise, significant periodic or quasi-periodic components could also be
found in some timing residuals of pulsars, indicating the presence of
unmodelled deterministic effects. The physical origins of these effects still
remain unexplained. In this paper, we suggest a new mechanism involving the de
Haas-van Alphen magnetic oscillation, which could trigger the observed low
frequency structures. We find that the proposed magnetic oscillation period is
about 1-$10^{2}$ yr, which is about $10^{-4}$ times as long as the classical
characteristic time scale of interior magnetic field evolution for a normal
neutron star. Due to the magnetic oscillation, we estimate both the braking
index range between $10^{-5}$ and $10^{5}$ and the residuals range between 4 to
906 ms for some specific samples of quasi-periodic pulsars. Those are
consistent with the pulsar timing observations. Nonlinear phenomena of de
Haas-van Alphen effect and the Condon domain structure are considered.
Avalanche-like magnetized process of domain wall motion (rapidly magnetic
energy release) could associated with some special emitting manifestations of
neuron star radiation.


  The ability to simulate a fermionic system on a quantum computer is expected
to revolutionize chemical engineering, materials design, nuclear physics, to
name a few. Thus, optimizing the simulation circuits is of significance in
harnessing the power of quantum computers. Here, we address this problem in two
aspects. In the fault-tolerant regime, we optimize the $\rzgate$ and $\tgate$
gate counts along with the ancilla qubit counts required, assuming the use of a
product-formula algorithm for implementation. We obtain a savings ratio of two
in the gate counts and a savings ratio of eleven in the number of ancilla
qubits required over the state of the art. In the pre-fault tolerant regime, we
optimize the two-qubit gate counts, assuming the use of the variational quantum
eigensolver (VQE) approach. Specific to the latter, we present a framework that
enables bootstrapping the VQE progression towards the convergence of the
ground-state energy of the fermionic system. This framework, based on
perturbation theory, is capable of improving the energy estimate at each cycle
of the VQE progression, by about a factor of three closer to the known
ground-state energy compared to the standard VQE approach in the test-bed,
classically-accessible system of the water molecule. The improved energy
estimate in turn results in a commensurate level of savings of quantum
resources, such as the number of qubits and quantum gates, required to be
within a pre-specified tolerance from the known ground-state energy. We also
explore a suite of generalized transformations of fermion to qubit operators
and show that resource-requirement savings of up to more than $20\%$, in small
instances, is possible.


  Quantitative description of the statistics of intensity fluctuations within
spectral line data cubes introduced in our earlier work is extended to the
absorbing media. A possibility of extracting 3D velocity and density statistics
from both integrated line intensity as well as from the individual channel maps
is analyzed. We find that absorption enables the velocity effects to be seen
even if the spectral line is integrated over frequencies. This regime that is
frequently employed in observations is characterized by a non-trivial relation
between the spectral index of velocities and the spectral index of intensity
fluctuations. For instance when density is dominated by fluctuations at large
scales, i.e. when correlations scale as r^{-\gamma}, \gamma<0, the intensity
fluctuations exhibit a universal spectrum of fluctuations ~K^{-3} over a range
of scales. When small scale fluctuations of density contain most of the energy,
i.e. when correlations scale as r^{-\gamma}, \gamma>0, the resulting spectrum
of the integrated lines depends on the scaling of the underlying density and
scales as K^{-3+\gamma}. We show that if we take the spectral line slices that
are sufficiently thin we recover our earlier results for thin slice data
without absorption. As the result we extend the Velocity Channel Analysis (VCA)
technique to optically thick lines enabling studies of turbulence in molecular
clouds. In addition, the developed mathematical machinery enables a
quantitative approach to solving other problems that involved statistical
description of turbulence within emitting and absorbing gas.


  Process mining starts from event data. The ordering of events is vital for
the discovery of process models. However, the timestamps of events may be
unreliable or imprecise. To further complicate matters, also causally unrelated
events may be ordered in time. The fact that one event is followed by another
does not imply that the former causes the latter. This paper explores the
relationship between time and order. Moreover, it describes an approach to
preprocess event data having timestamp-related problems. This approach avoids
using accidental or unreliable orders and timestamps, creates partial orders to
capture uncertainty, and allows for exploiting domain knowledge to (re)order
events. Optionally, the approach also generates interleavings to be able to use
existing process mining techniques that cannot handle partially ordered event
data. The approach has been implemented using ProM and can be applied to any
event log.


  The morphology of interfaces is known to play fundamental role on the
efficiency of energy-related applications, such light harvesting or ion
intercalation. Altering the morphology on demand, however, is a very difficult
task. Here, we show ways the morphology of interfaces can be tuned by driven
electron transfer reactions. By using non-equilibrium thermodynamic stability
theory, we uncover the operating conditions that alter the interfacial
morphology. We apply the theory to ion intercalation and surface growth where
electrochemical reactions are described using Butler-Volmer or coupled
ion-electron transfer kinetics. The latter connects microscopic/quantum
mechanical concepts with the morphology of electrochemical interfaces. Finally,
we construct non-equilibrium phase diagrams in terms of the applied driving
force (current/voltage) and discuss the importance of engineering the density
of states of the electron donor in applications related to energy harvesting
and storage, electrocatalysis and photocatalysis.


  For a commutative ring $R$, the $F$-signature was defined by Huneke and
Leuschke \cite{H-L}. It is an invariant that measures the order of the rank of
the free direct summand of $R^{(e)}$. Here, $R^{(e)}$ is $R$ itself, regarded
as an $R$-module through $e$-times Frobenius action $F^e$.In this paper, we
show a connection of the F-signature of a graded ring with other invariants.
More precisely, for a graded $F$-finite Gorenstein ring $R$ of dimension $d$,
we give an inequality among the $F$-signature $s(R)$, $a$-invariant $a(R)$ and
Poincar\'{e} polynomial $P(R,t)$. \[
s(R)\le\frac{(-a(R))^d}{2^{d-1}d!}\lim_{t\rightarrow 1}(1-t)^dP(R,t)
\]Moreover, we show that $R^{(e)}$ has only one free direct summand for any
$e$, if and only if $R$ is $F$-pure and $a(R)=0$. This gives a characterization
of such rings.


  Motivated by applications in geomorphology, the aim of this paper is to
extend Morse-Smale theory from smooth functions to the radial distance function
(measured from an internal point), defining a convex polyhedron in
3-dimensional Euclidean space. The resulting polyhedral Morse-Smale complex may
be regarded, on one hand, as a generalization of the Morse-Smale complex of the
smooth radial distance function defining a smooth, convex body, on the other
hand, it could be also regarded as a generalization of the Morse-Smale complex
of the piecewise linear parallel distance function (measured from a plane),
defining a polyhedral surface. Beyond similarities, our paper also highlights
the marked differences between these three problems and it also relates our
theory to other methods. Our work includes the design, implementation and
testing of an explicit algorithm computing the Morse-Smale complex on a convex
polyhedron.


  In a (parameterized) graph edge modification problem, we are given a graph
$G$, an integer $k$ and a (usually well-structured) class of graphs
$\mathcal{G}$, and ask whether it is possible to transform $G$ into a graph $G'
\in \mathcal{G}$ by adding and/or removing at most $k$ edges. Parameterized
graph edge modification problems received considerable attention in the last
decades.
  In this paper, we focus on finding small kernels for edge modification
problems. One of the most studied problems is the Cluster Editing problem, in
which the goal is to partition the vertex set into a disjoint union of cliques.
Even if this problem admits a $2k$ kernel [Cao, 2012], this kernel does not
reduce the size of most instances. Therefore, we explore the question of
whether linear kernels are a theoretical limit in edge modification problems,
in particular when the target graphs are very structured (such as a partition
into cliques for instance). We prove, as far as we know, the first sublinear
kernel for an edge modification problem. Namely, we show that Clique +
Independent Set Deletion, which is a restriction of Cluster Deletion, admits a
kernel of size $O(k/\log k)$.
  We also obtain small kernels for several other edge modification problems. We
prove that Split Addition (and the equivalent Split Deletion) admits a linear
kernel, improving the existing quadratic kernel of Ghosh et al. [Ghosh et al.,
2015]. We complement this result by proving that Trivially Perfect Addition
admits a quadratic kernel (improving the cubic kernel of Guo [Guo, 2007]), and
finally prove that its triangle-free version (Starforest Deletion) admits a
linear kernel, which is optimal under ETH.


  The quantum entanglement is considered as one of the most important notions
of quantum computing. The entanglement is a feature of quantum systems and it
is used as a basis for many quantum algorithms and protocols. In this paper we
analyze the level of entanglement for the quantum switch, during its work. The
level of entanglement during the correct operating may be compared with the
situation when a noise is present in the analyzed system. The noise changes the
level of quantum entanglement and we utilize this observation to evaluate if
the switch works properly. We present the formulae describing the value of
entanglement with use of the Concurrence measure and also the value of entropy.
Both mentioned values may be utilized to detect the noise during the operating
of quantum switch.


  The main purpose of this note is to present a matrix version of Neeman's
forcing with sequences of models of two types. In some sense, the forcing
defined here can be seen as a generalization of Asper\'o-Mota's pure side
condition forcing from arXiv:1203.1235 and arXiv:1206.6724. In our case, the
forcing will consist of certain symmetric systems of countable and uncountable
elementary submodels of some $H(\kappa)$, which will be closed by the relevant
intersections, in Neeman's sense. The goal of this note is to show that this
forcing preserves all cardinals by showing that it is strongly proper for the
class of countable submodels of $H(\kappa)$ and for the class of countably
closed submodels of $H(\kappa)$ of size $\aleph_1$, and that it has has the
$\aleph_3$-cc. Moreover, we will show that it preserves
$2^{\aleph_1}=\aleph_2$.


  In this work we study the hadron-quark phase transition matching relativistic
hadrodynamical mean-field models (in the hadronic phase) with the more updated
versions of the Polyakov-Nambu-Jona-Lasinio models (on the quark side).
Systematic comparisons are performed showing that the predicted hadronic phases
of the matching named as RMF-PNJL, are larger than the confined phase obtained
exclusively by the Polyakov quark models. This important result is due to the
effect of the nuclear force that causes more resistance of hadronic matter to
isothermal compressions. For sake of comparison, we also obtain the matchings
of the hadronic models with the MIT bag model, named as RMF-MIT, showing that
it presents always larger hadron regions, while shows smaller mixed phases than
that obtained from the RMF-PNJL ones. Thus, studies of the confinement
transition in nuclear matter, done only with quark models, still need nuclear
degrees of freedom to be more reliable in the whole $T\times\mu$ phase diagram.


  We report a theoretical analysis of the half-polarized quantum Hall states
observed in a recent experiment. Our numerical results indicate that the ground
state energy of the quantum Hall $\nu= 2/3$ and $\nu= 2/5$ states versus spin
polarization has a downward cusp at half the maximal spin polarization. We map
the two-component fermion system onto a system of excitons and describe the
ground state as a liquid state of excitons with non-zero values of exciton
angular momentum.


  Posit has been a promising alternative to the IEEE-754 floating point format
for deep learning applications due to its better trade-off between dynamic
range and accuracy. However, hardware implementation of posit arithmetic
requires further exploration, especially for the dot-product operations
dominated in deep neural networks (DNNs). It has been implemented by either the
combination of multipliers and an adder tree or cascaded fused multiply-add
units, leading to poor computational efficiency and excessive hardware
overhead. To address this issue, we propose an open-source posit dot-product
unit, namely PDPU, that facilitates resource-efficient and high-throughput
dot-product hardware implementation. PDPU not only features the fused and
mixed-precision architecture that eliminates redundant latency and hardware
resources, but also has a fine-grained 6-stage pipeline, improving
computational efficiency. A configurable PDPU generator is further developed to
meet the diverse needs of various DNNs for computational accuracy. Experimental
results evaluated under the 28nm CMOS process show that PDPU reduces area,
latency, and power by up to 43%, 64%, and 70%, respectively, compared to the
existing implementations. Hence, PDPU has great potential as the computing core
of posit-based accelerators for deep learning applications.


  An experimentally feasible scheme for generating a 12-qubit hyperentangled
state via four photons, entangled in polarization, frequency and spatial mode,
is proposed. We study the nature of quantum non-locality of this hyperentangled
state by evaluating its violation degree to a Bell-type inequality, and find
that the result agrees well with quantum mechanics prediction while extremely
contradicts to the local realism constraint.


  The recent realization of mixed-dimensional systems of cold atoms has
attracted much attention from both experimentalists and theorists. Different
effective interactions and novel correlated quantum many-body phases may be
engineered in these systems, with the different phases being tunable via
external parameters. In this article we investigate a two-species Fermi atom
mixture: one species of atom exists in two hyperfine states and is confined to
move in a two-leg ladder, interacting with an on-site interaction, and the
other moves freely in a two dimensional square lattice that contains the
two-leg ladder. The two species of atoms interact via an on-site interaction on
the ladder. In the limit of weak inter-species interactions, the
two-dimensional gas can be integrated out, leading to an effective long-range
mediated interaction in the ladder, generated by to the on-site inter-species
interaction. We show that the form of the mediated interaction can be
controlled by the density of the two-dimensional gas and that it enhances the
charge density wave instability in the two-leg ladder after the renormalization
group transformation. Parameterizing the phase diagram with various
experimentally controllable quantities, we discuss the possible tuning of the
macroscopic quantum many-body phases of the two-leg ladder in this
mixed-dimensional fermionic cold atom system.


  We show that the blow-up of a generalized Kahler 4-manifold in a
nondegenerate complex point admits a generalized Kahler metric. As with the
blow-up of complex surfaces, this metric may be chosen to coincide with the
original outside a tubular neighbourhood of the exceptional divisor. To
accomplish this, we develop a blow-up operation for bi-Hermitian manifolds.


  Rotational misalignment or twisting of two mono-layers of graphene strongly
influences its electronic properties. Structurally, twisting leads to large
periodic supercell structures, which in turn can support intriguing strongly
correlated behaviour. Here, we propose a highly tunable scheme to synthetically
emulate twisted bilayer systems with ultracold atoms trapped in an optical
lattice. In our scheme, neither a physical bilayer nor twist is directly
realized. Instead, two synthetic layers are produced exploiting
coherently-coupled internal atomic states, and a supercell structure is
generated \emph{via} a spatially-dependent Raman coupling. To illustrate this
concept, we focus on a synthetic square bilayer lattice and show that it leads
to tunable quasi-flatbands and Dirac cone spectra under certain magic supercell
periodicities. The appearance of these features are explained using a
perturbative analysis. Our proposal can be implemented using available
state-of-the-art experimental techniques, and opens the route towards the
controlled study of strongly-correlated flat band accompanied by hybridization
physics akin to magic angle bilayer graphene in cold atom quantum simulators.


  We report the results of our classification-based machine translation model,
built upon the framework of a recurrent neural network using gated recurrent
units. Unlike other RNN models that attempt to maximize the overall conditional
log probability of sentences against sentences, our model focuses a
classification approach of estimating the conditional probability of the next
word given the input sequence. This simpler approach using GRUs was hoped to be
comparable with more complicated RNN models, but achievements in this
implementation were modest and there remains a lot of room for improving this
classification approach.


  We consider distributed systems of identical autonomous computational
entities, called robots, moving and operating in the plane in synchronous
Look-Compute-Move (LCM) cycles. The algorithmic capabilities of these systems
have been extensively investigated in the literature under four distinct models
(OBLOT, FSTA, FCOM, LUMI), each identifying different levels of memory
persistence and communication capabilities of the robots. Despite their
differences, they all always assume that robots have unlimited amounts of
energy.
  In this paper, we remove this assumption and start the study of the
computational capabilities of robots whose energy is limited, albeit renewable.
We first study the impact that memory persistence and communication
capabilities have on the computational power of such energy-constrained systems
of robots; we do so by analyzing the computational relationship between the
four models under this energy constraint. We provide a complete
characterization of this relationship.
  We then study the difference in computational power caused by the energy
restriction and provide a complete characterization of the relationship between
energy-constrained and unrestricted robots in each model. We prove that within
LUMI there is no difference; an integral part of the proof is the design and
analysis of an algorithm that in LUMI allows energy-constrained robots to
execute correctly any protocol for robots with unlimited energy. We then show
the (apparently counterintuitive) result that in all other models, the energy
constraint actually provides the robots with a computational advantage.


  Time series modeling is uniquely challenged by the presence of
autocorrelation in both historical and label sequences. Current research
predominantly focuses on handling autocorrelation within the historical
sequence but often neglects its presence in the label sequence. Specifically,
emerging forecast models mainly conform to the direct forecast (DF) paradigm,
generating multi-step forecasts under the assumption of conditional
independence within the label sequence. This assumption disregards the inherent
autocorrelation in the label sequence, thereby limiting the performance of
DF-based models. In response to this gap, we introduce the Frequency-enhanced
Direct Forecast (FreDF), which bypasses the complexity of label autocorrelation
by learning to forecast in the frequency domain. Our experiments demonstrate
that FreDF substantially outperforms existing state-of-the-art methods
including iTransformer and is compatible with a variety of forecast models.


  We calculate all irreducible representations over a subfamily of pointed Hopf
algebras with group-likes the dihedral group analyzing the possible
decompositions of the restriction to the dihedral group and calculating the
Jacobson radical of the Hopf algebra


  Many condensed matter systems are such that their collective excitations at
low energies can be described by fields satisfying equations of motion formally
indistinguishable from those of relativistic field theory. The finite speed of
propagation of the disturbances in the effective fields (in the simplest
models, the speed of sound) plays here the role of the speed of light in
fundamental physics. However, these apparently relativistic fields are immersed
in an external Newtonian world (the condensed matter system itself and the
laboratory can be considered Newtonian, since all the velocities involved are
much smaller than the velocity of light) which provides a privileged coordinate
system and therefore seems to destroy the possibility of having a perfectly
defined relativistic emergent world. In this essay we ask ourselves the
following question: In a homogeneous condensed matter medium, is there a way
for internal observers, dealing exclusively with the low-energy collective
phenomena, to detect their state of uniform motion with respect to the medium?
By proposing a thought experiment based on the construction of a
Michelson-Morley interferometer made of quasi-particles, we show that a real
Lorentz-FitzGerald contraction takes place, so that internal observers are
unable to find out anything about their `absolute ' state of motion. Therefore,
we also show that an effective but perfectly defined relativistic world can
emerge in a fishbowl world situated inside a Newtonian (laboratory) system.
This leads us to reflect on the various levels of description in physics, in
particular regarding the quest towards a theory of quantum gravity.


  An improved linear stability theory of small-amplitude oscillations of a
self-gravitating, infinitesimally thin gaseous disk of spiral galaxies has been
developed. It was shown that in the differentially rotating disks for
nonaxisymmetric perturbations Toomre's modified hydrodynamical simulations to
test the validity of the modified local criterion.


  We consider the heat equation for monolayer two-dimensional materials in the
presence of heat flow into a substrate and Joule heating due to electrical
current. We compare devices including a nanowire of constant width and a bow
tie (or wedge) constriction of varying width, and we derive approximate
one-dimensional heat equations for them; a bow tie constriction is described by
the modified Bessel equation of zero order. We compare steady state analytic
solutions of the approximate equations with numerical results obtained by a
finite element method solution of the two-dimensional equation. Using these
solutions, we describe the role of thermal conductivity, thermal boundary
resistance with the substrate and device geometry. The temperature in a device
at fixed potential difference will remain finite as the width shrinks, but will
diverge for fixed current, logarithmically with width for the bow tie as
compared to an inverse square dependence in a nanowire.


  In this paper, we attempt the theoretical modeling of the magnetic tunnel
junctions with embedded magnetic and nonmagnetic nanoparticles (NPs). A few
abnormal tunnel magnetoresistance (TMR) effects, observed in related
experiments, can be easily simulated within our model: we found, that the
suppressed TMR magnitudes and the TMR sign-reversing effect at small voltages
are related to the electron momentum states of the NP located inside the
insulating layer. All these TMR behaviors can be explained within the tunneling
model, where NP is simulated as a quantum well (QW). The coherent (direct)
double barrier tunneling is dominating over the single barrier one. The origin
of the TMR suppression is the quantized angle transparency for spin polarized
electrons being in one of the lowest QW states. The phenomenon was classified
as the quantized conductance regime due to restricted geometry.


  Double-beta decay matrix elements (ME) for $^{76}Ge$ are calculated with
different quasi random phase approximation (QRPA)-based methods. First, the ME
for the two-neutrino mode are computed using two choices for the single
particle (s.p.) basis: i) $2-4\hbar\omega$ full shells and ii) $3-4\hbar\omega$
full shells. When calculated with the renormalized QRPA (RQRPA) and full-RQRPA
their values are rather dependent on the size of the single particle basis
used, while calculated with proton-neutron QRPA (pnQRPA) and second-QRPA
approaches such a dependence was found to be small. The Ikeda sum rule was well
fulfilled within pnQRPA for both choices of the s.p. basis and with a good
approximation within second-QRPA, while the RQRPA and full-RQRPA methods give
deviations up to 21%. Further, the ME for the neutrinoless mode are calculated
with the pnQRPA, RQRPA and full-RQRPA methods. They all give close results for
the calculation with the smaller basis (i), while for the larger basis (ii),
the results differ significantly either from one method to another or within
the same method. Finally, using the most recent experimental limit for the
$0\nu\beta\beta$ decay half-life of $^{76}Ge$ a critical discussion on the
upper limits for the neutrino mass parameter obtained with different
theoretical approaches is given.


  We propose and demonstrate two methods for modal decomposition in multi-mode
fibres. Linearly polarized modes propagating in a slightly multi-mode fibre are
easily retrieved from intensity measurements at the fibre output surface. The
first method is an improvement of the so-called spectrally and spatially
imaging technique, which is limited to largemode-area optical fibers. The
second method is a new, simpler and faster solution for the characterization of
any kind of optical fibre, thus attractive in comparison to previously reported
methods, which are cumbersome, time-consuming and/or limited to large-more-area
fibres. Different kinds of multi-mode optical fibres are characterized. A
large-modearea photonic-bandgap fibre, a photonic-crystal small-core non-linear
fibre, and a standard index-stepped multi-mode fibre are characterized
successfully.


  Spectral precision matrix, the inverse of a spectral density matrix, is an
object of central interest in frequency-domain analysis of multivariate time
series. Estimation of spectral precision matrix is a key step in calculating
partial coherency and graphical model selection of stationary time series. When
the dimension of a multivariate time series is moderate to large, traditional
estimators of spectral density matrices such as averaged periodograms tend to
be severely ill-conditioned, and one needs to resort to suitable regularization
strategies involving optimization over complex variables.
  In this work, we propose complex graphical Lasso (CGLASSO), an
$\ell_1$-penalized estimator of spectral precision matrix based on local
Whittle likelihood maximization. We develop fast $\textit{pathwise coordinate
descent}$ algorithms for implementing CGLASSO on large dimensional time series
data sets. At its core, our algorithmic development relies on a ring
isomorphism between complex and real matrices that helps map a number of
optimization problems over complex variables to similar optimization problems
over real variables. This finding may be of independent interest and more
broadly applicable for high-dimensional statistical analysis with
complex-valued data. We also present a complete non-asymptotic theory of our
proposed estimator which shows that consistent estimation is possible in
high-dimensional regime as long as the underlying spectral precision matrix is
suitably sparse. We compare the performance of CGLASSO with competing
alternatives on simulated data sets, and use it to construct partial coherence
network among brain regions from a real fMRI data set.


  Propagation of fast-mode magnetohydrodynamic (MHD) compression waves is
traced in the Galactic Center with a poloidal magnetic cylinder. MHD waves
ejected from the nucleus are reflected and guided along the magnetic field,
exhibiting vertically stretched fronts. The radio threads and non-thermal
filaments are explained as due to tangential views of the waves driven by
sporadic activity in Sgr A$^*$, or by multiple supernovae. In the latter case,
the threads could be extremely deformed relics of old SNRs exploded in the
nucleus.


  The discovery of signatures of topological superconductivity in
superconducting bulk materials with topological surface states has attracted
intensive research interests recently. Utilizing angle-resolved photoemission
spectroscopy and first-principles calculations, here, we demonstrate the
existence of topological nodal-line states and drumheadlike surface states in
centrosymmetric superconductor SnTaS2, which is a type-II superconductor with a
critical transition temperature of about 3 K. The valence bands from Ta 5d
orbitals and the conduction bands from Sn 5p orbitals cross each other, forming
two nodal lines in the vicinity of the Fermi energy without the inclusion of
spin-orbit coupling (SOC), protected by the spatial-inversion symmetry and
time-reversal symmetry. The nodal lines are gapped out by SOC. The drumheadlike
surface states, the typical characteristics in nodal-line semimetals, are quite
visible near the Fermi level. Our findings indicate that SnTaS2 offers a
promising platform for exploring the exotic properties of the topological
nodal-line fermions and gives a help to study topological superconductivity.


  We report on a recent ~150-ks long Chandra observation of the ultraluminous
infrared galaxy merger NGC 6240, which allows a detailed investigation of the
diffuse galactic halo. Extended soft X-ray emission is detected at the 3-sigma
confidence level over a diamond-shaped region with projected physical size of
~110x80 kpc, and a single-component thermal model provides a reasonably good
fit to the observed X-ray spectrum. The hot gas has a temperature of ~7.5
million K, an estimated density of 2.5x10^{-3} cm^{-3}, and a total mass of
~10^10 M_sun, resulting in an intrinsic 0.4-2.5 keV luminosity of 4x10^41 erg
s^{-1}. The average temperature of 0.65 keV is quite high to be obviously
related to either the binding energy in the dark-matter gravitational potential
of the system or the energy dissipation and shocks following the galactic
collision, yet the spatially-resolved spectral analysis reveals limited
variations across the halo. The relative abundance of the main alpha-elements
with respect to iron is several times the solar value, and nearly constant as
well, implying a uniform enrichment by type II supernovae out to the largest
scales. Taken as a whole, the observational evidence is not compatible with a
superwind originated by a recent, nuclear starburst, but rather hints at
widespread, enhanced star formation proceeding at steady rate over the entire
dynamical timescale (~200 Myr). The preferred scenario is that of a
starburst-processed gas component gently expanding into, and mixing with, a
pre-existing halo medium of lower metallicity (Z ~ 0.1 solar) and temperature
(kT ~ 0.25 keV). This picture cannot be probed more extensively with the
present data, and the ultimate fate of the diffuse, hot gas remains uncertain.
Under some favorable conditions, at least a fraction of it might be retained
after the merger completion, and evolve into the hot halo of a young elliptical
galaxy.


  The vast majority of modern consumer-grade cameras employ a rolling shutter
mechanism, leading to image distortions if the camera moves during image
acquisition. In this paper, we present a novel deep network to solve the
generic rolling shutter correction problem with two consecutive frames. Our
pipeline is symmetrically designed to predict the global shutter image
corresponding to the intermediate time of these two frames, which is difficult
for existing methods because it corresponds to a camera pose that differs most
from the two frames. First, two time-symmetric dense undistortion flows are
estimated by using well-established principles: pyramidal construction,
warping, and cost volume processing. Then, both rolling shutter images are
warped into a common global shutter one in the feature space, respectively.
Finally, a symmetric consistency constraint is constructed in the image decoder
to effectively aggregate the contextual cues of two rolling shutter images,
thereby recovering the high-quality global shutter image. Extensive experiments
with both synthetic and real data from public benchmarks demonstrate the
superiority of our proposed approach over the state-of-the-art methods.


  Given a representation of a C*-algebra, thought of as an abstract collection
of physical observables, together with a unit vector, one obtains a state on
the algebra via restriction. We show that the Gelfand-Naimark-Segal (GNS)
construction furnishes a left adjoint of this restriction. To properly
formulate this adjoint, it must be viewed as a weak natural transformation, a
1-morphism in a suitable 2-category, rather than as a functor between
categories. Weak naturality encodes the functoriality and the universal
property of adjunctions encodes the characterizing features of the GNS
construction. Mathematical definitions and results are accompanied by physical
interpretations.


  In this paper, we study the stellar structure in terms of alternative theory
of gravity specially by f (R;T) gravity theory. Here, we consider the function
f (R;T) = R+2VT where R is the Ricci scalar, T is the stress-energy momentum
and V is the coupling constant. Using it we developed a stellar model that
briefly explains the isotropic matter distribution within the compact object
filled with perfect fluid. The stability of the model is shown by several
physical and stability conditions. With the accecptibility of our theory, we
were able to collect data for compact stars like PSR-B0943+10, CEN X-3, SMC
X-4, Her X-1 and 4U1538-52 with great accuracy.


  Dynamic Mode III interfacial fracture in a dissimilar square-cell lattice,
composed of two contrasting mass-spring lattice half-planes joined at an
interface, is considered. The fracture, driven by a remotely applied load, is
assumed to propagate at a constant speed along the interface. The choice of the
load allows the solution of the problem to be matched with the crack tip field
for a Mode III interfacial crack propagating between two dissimilar continuous
elastic materials. The lattice problem is reduced to a system of functional
equations of the Wiener-Hopf type through the Fourier transform. The derived
solution of the system fully characterises the process. We demonstrate the
existence of trapped vibration modes that propagate ahead of the crack along
the interface during the failure process. In addition, we show as the crack
propagates several preferential directions for wave radiation can emerge in the
structured medium that are determined by the lattice dissimilarity. The energy
attributed to the wave radiation as a result of the fracture process is studied
and admissible fracture regimes supported by the structure are identified. The
results are illustrated by numerical examples that demonstrate the influence of
the dissimilarity of the lattice on the existence of the steady failure modes
and the lattice dynamics.


  We discuss the prospects for setting limits on or discovering spin-1 $Z'$
bosons using early LHC data at 7 TeV. Our results are based on the narrow width
approximation in which the leptonic Drell-Yan $Z'$ boson production
cross-section only depends on the $Z'$ boson mass together with two parameters
$c_u$ and $c_d$. We carefully discuss the experimental cuts that should be
applied and tabulate the theoretical next-to-next-to-leading order corrections
which must be included. Using these results the approach then provides a safe,
convenient and unbiased way of comparing experiment to theoretical models which
avoids any built-in model dependent assumptions. We apply the method to three
classes of perturbative $Z'$ boson benchmark models: $E_6$ models, left-right
symmetric models and sequential standard models. We generalise each class of
model in terms of {mixing angles which continuously parametrize} linear
combinations of pairs of generators and lead to distinctive orbits in the
$c_u-c_d$ plane. We also apply this method to the strongly coupled four-site
benchmark model in which two $Z'$ bosons are predicted. By comparing the
experimental limits or discovery bands to the theoretical predictions on the
$c_u$-$c_d$ plane, we show that the LHC at 7 TeV with integrated luminosity of
500 pb$^{-1}$ will greatly improve on current Tevatron mass limits for the
benchmark models. If a $Z'$ is discovered our results show that measurement of
the mass and cross-section will provide a powerful discriminator between the
benchmark models using this approach.


  Can expressiveness of a drawing be traced with a computer? In this study a
neural network (perceptron) and a support vector machine are used to classify
line drawings. To do this the line drawings are attributed values according to
a kinematic model and a diffusion model for the lines they consist of. The
values for both models are related to looking times. Extreme values according
to these models, that is both extremely short and extremely long looking times,
are interpreted as indicating expressiveness. The results strongly indicate
that expressiveness in this sense can be detected, at least with a neural
network.


  We study the three-body problem from different perspectives, going from
classical Newtonian physics to general relativity. In the classical case, we
modeled the interactions in a typical chaotic configuration, identifying strong
interaction times (SITs), namely transients in which the system manifests
complex, highly energetic dynamics. By selecting one of such SITs as an initial
condition for the general relativistic case, we perform simulations of
numerical relativity, showing net differences with the Newtonian case. In the
relativistic case, comparing also with the more "quiet" two-body inspiral, we
observed strong nonlinear emission of gravitational waves. The spectrum of the
three-body signals during a SIT might be a guidance for future gravitational
waves observations.


  We study the relationship between bipartite entanglement, subsystem particle
number and topology in a half-filled free fermion system. It is proposed that
the spin-projected particle numbers can distinguish the quantum spin Hall state
from other states, and can be used to establish a new topological index for the
system. Furthermore, we apply the new topological invariant to a disordered
system and show that a topological phase transition occurs when the disorder
strength is increased beyond a critical value. It is also shown that the
subsystem particle number fluctuation displays behavior very similar to that of
the entanglement entropy. This provides a lower-bound estimation for the
entanglement entropy, which can be utilized to obtain an estimate of the
entanglement entropy experimentally.


  We study nuclear shadowing in $J/\psi$ and $\Upsilon$ production in
hadron-nucleus interactions and in nucleus-nucleus collisions at the
Relativistic Heavy Ion Collider and the Large Hadron Collider. %We define the
regions in $x_f$ where nuclear shadowing begins %to set in for \jp\ and \up. As
a consequence of the perturbative $Q^2$-dependence of gluon shadowing, we
predict that $\Upsilon$ production is less suppressed than the $J/\psi$. We
show that antishadowing leads to enhanced \jp\ production at $x_f \lesssim 0$,
an effect reduced for $\Upsilon$ production.


  Robotic platforms that can efficiently collaborate with humans in physical
tasks constitute a major goal in robotics. However, many existing robotic
platforms are either designed for social interaction or industrial object
manipulation tasks. The design of collaborative robots seldom emphasizes both
their social interaction and physical collaboration abilities. To bridge this
gap, we present the novel semi-humanoid NICOL, the Neuro-Inspired COLlaborator.
NICOL is a large, newly designed, scaled-up version of its well-evaluated
predecessor, the Neuro-Inspired COmpanion (NICO). NICOL adopts NICO's head and
facial expression display and extends its manipulation abilities in terms of
precision, object size, and workspace size. Our contribution in this paper is
twofold -- firstly, we introduce the design concept for NICOL, and secondly, we
provide an evaluation of NICOL's manipulation abilities by presenting a novel
extension for an end-to-end hybrid neuro-genetic visuomotor learning approach
adapted to NICOL's more complex kinematics. We show that the approach
outperforms the state-of-the-art Inverse Kinematics (IK) solvers KDL, TRACK-IK
and BIO-IK. Overall, this article presents for the first time the humanoid
robot NICOL, and contributes to the integration of social robotics and neural
visuomotor learning for humanoid robots.


  We explore the holographic properties of non-perturbative vacuum decay in
Anti-de Sitter ($\mathrm{AdS}$) geometries. To this end, we consider a
gravitational theory in a metastable $\mathrm{AdS}_3$ state, which decays into
an $\mathrm{AdS}_3$ of lower vacuum energy via bubble nucleation, and we employ
the Ryu-Takayanagi conjecture to compute the entanglement entropy
$S_\text{ent}$ in its alleged holographic dual. Our analysis leads us to infer
that the nucleation and growth of a vacuum bubble correspond, in the boundary
theory, to the introduction of a relevant deformation and a subsequent
Renormalization Group (RG) flow, where $S_\text{ent}$ provides a $c$-function.
We provide further evidence for the claim and comment on the holographic
interpretation of off-centred or multiple bubbles. We also frame the issue in
the formalism of Holographic Integral Geometry, discussing the consequences on
the structure of the holographic RG flow and recovering the standard
holographic RG as a limiting case.


  We study the localization properties of electrons in incommensurate twisted
bilayer graphene for small angles, encompassing the narrow-band regime, by
numerically exact means. Sub-ballistic states are found within the narrow-band
region around the magic angle. Such states are delocalized in momentum-space
and follow non-Poissonian level statistics, in contrast with their ballistic
counterparts found for close commensurate angles. Transport results corroborate
this picture: for large enough systems, the conductance decreases with system
size for incommensurate angles within the sub-ballistic regime. Our results
show that incommensurability effects are of crucial importance in the
narrow-band regime. The incommensurate nature of a general twist angle must
therefore be taken into account for an accurate description of magic-angle
twisted bilayer graphene.


  This work proposes an alternative approach to the so-called lattice of
embedded subsets, which is included in the product of the subset and partition
lattices of a finite set, and whose elements are pairs consisting of a subset
and a partition where the former is a block of the latter. The lattice
structure proposed in a recent contribution relies on ad-hoc definitions of
both the join operator and the bottom element, while also including
join-irreducible elements distinct from atoms. Conversely, here embedded
subsets obtain through a closure operator defined over the product of the
subset and partition lattices, where elements are generic pairs of a subset and
a partition. Those such pairs that coincide with their closure are precisely
embedded subsets, and since the Steinitz exchange axiom is also satisfied, what
results is a geometric (hence atomic) lattice given by a simple matroid (or
combinatorial geometry) included in the product of the subset and partition
lattices (as the partition lattice itself is the polygon matroid defined on the
edges of a complete graph). By focusing on its M\"obius function, this
geometric lattice of embedded subsets of a n-set is shown to be isomorphic to
the lattice of partitions of a n+1-set.


  Measurements play an important role in quantum computing (QC), by either
providing the nonlinearity required for two-qubit gates (linear optics QC), or
by implementing a quantum algorithm using single-qubit measurements on a highly
entangled initial state (cluster state QC). Parity measurements can be used as
building blocks for preparing arbitrary stabilizer states, and, together with
1-qubit gates are universal for quantum computing. Here we generalize parity
gates by using a higher dimensional (qudit) ancilla. This enables us to go
beyond the stabilizer/graph state formalism and prepare other types of
multi-particle entangled states. The generalized parity module introduced here
can prepare in one-shot, heralded by the outcome of the ancilla, a large class
of entangled states, including GHZ_n, W_n, Dicke states D_{n,k}, and, more
generally, certain sums of Dicke states, like G_n states used in secret
sharing. For W_n states it provides an exponential gain compared to linear
optics based methods.


  The use of naturalistic driving studies (NDSs) for driver behavior research
has skyrocketed over the past two decades. Intersections are a key target for
traffic safety, with up to 25-percent of fatalities and 50-percent injuries
from traffic crashes in the United States occurring at intersections. NDSs are
increasingly being used to assess driver behavior at intersections and devise
strategies to improve intersection safety. A common challenge in NDS
intersection research is the need for to combine spatial locations of
driver-visited intersections with concurrent video clips of driver trajectories
at intersections to extract analysis variables. The intersection identification
and driver trajectory video clip extraction process are generally complex and
repetitive. We developed a novel R package called ndsintxn to streamline this
process and automate best practices to minimize computational time, cost, and
manual labor. This paper provides details on the methods and illustrative
examples used in the ndsintxn R package.


  When a quantum system interacts with an external environment, it undergoes
the loss of quantum correlation (decoherence) and the loss of energy
(relaxation) and eventually all of the quantum information becomes classical.
Here we show a general principle to use unitary operations to establish and
preserve particular non-equilibrium states in arbitrary relaxing quantum
systems. We elucidate these concepts with examples of state preservation in
one-spin and two-spin entangled systems.


  The giant galaxies located at the centers of cluster cooling flows are
frequently sites of vigorous star formation. In some instances, star formation
appears to have been triggered by the galaxy's radio source. The colors and
spectral indices of the young populations are generally consistent with short
duration bursts or continuous star formation for durations much less than 1
Gyr, which is less than the presumed ages of cooling flows. The star formation
properties are inconsistent with fueling by a continuously accreting cooling
flow, although the prevalence of star formation is consistent with repeated
bursts and periodic refueling. Star formation may be fueled, in some cases, by
cold material stripped from neighboring cluster galaxies.


  We propose a novel algorithm to ensure $\epsilon$-differential privacy for
answering range queries on trajectory data. In order to guarantee privacy,
differential privacy mechanisms add noise to either data or query, thus
introducing errors to queries made and potentially decreasing the utility of
information. In contrast to the state-of-the-art, our method achieves
significantly lower error as it is the first data- and query-aware approach for
such queries. The key challenge for answering range queries on trajectory data
privately is to ensure an accurate count. Simply representing a trajectory as a
set instead of \emph{sequence} of points will generally lead to highly
inaccurate query answers as it ignores the sequential dependency of location
points in trajectories, i.e., will violate the consistency of trajectory data.
Furthermore, trajectories are generally unevenly distributed across a city and
adding noise uniformly will generally lead to a poor utility. To achieve
differential privacy, our algorithm adaptively adds noise to the input data
according to the given query set. It first privately partitions the data space
into uniform regions and computes the traffic density of each region. The
regions and their densities, in addition to the given query set, are then used
to estimate the distribution of trajectories over the queried space, which
ensures high accuracy for the given query set. We show the accuracy and
efficiency of our algorithm using extensive empirical evaluations on real and
synthetic data sets.


  Report in extended abstract form. Based on talk given at Strings '95, Los
Angeles, CA. This is a summary of the findings initially presented in
hep-th/9505080. Results of a step (the complete classification of free
fermionic solutions to $N=1$ spacetime) in the search for stringy
three-generation SO(10) SUSY-GUTs are reviewed.


  Topological Josephson junctions designed on the surface of a 3D-topological
insulator (TI) harbor Majorana bound states (MBS's) among a continuum of
conventional Andreev bound states. The distinct feature of these MBS's lies in
the $4\pi$-periodicity of their energy-phase relation that yields a fractional
ac Josephson effect and a suppression of odd Shapiro steps under $r\!f$
irradiation. Yet, recent experiments showed that a few, or only the first, odd
Shapiro steps are missing, casting doubts on the interpretation. Here, we show
that Josephson junctions tailored on the large bandgap 3D TI Bi$_2$Se$_3$
exhibit a fractional ac Josephson effect acting on the first Shapiro step only.
With a modified resistively shunted junction model, we demonstrate that the
resilience of higher order odd Shapiro steps can be accounted for by thermal
poisoning driven by Joule overheating. Furthermore, we uncover a residual
supercurrent at the nodes between Shapiro lobes, which provides a direct and
novel signature of the current carried by the MBS. Our findings showcase the
crucial role of thermal effects in topological Josephson junctions and lend
support to the Majorana origin of the partial suppression of odd Shapiro steps.


  Liquid-solid phase transition and the change of the frictional force of a
system with two hard spheres in a two-dimensional rectangular box are
discussed. Under controlling the pressure or the supply of energy from the
wall, the solid like state, the solid-liquid temporal coexistence state, and
the liquid like state are alternatively observed. The frictional force which
works on particles and the mobility of the system are measured under the supply
of the energy with an asymmetric external force. Characters of frictional
forces for the 0 mobility state and that for the large mobility state are
obtained like, respectively, that of the static and the dynamic frictions of
solid-on-solid system. The strong temperature dependency is also observed in
the profile of the relation between the above frictional force and the
mobility. From above results, the relation between the friction and the
velocity of a plate on granular layers which includes the hysteresis loop
[S.Nasuno et al.,Phys. Rev. Lett {\bf 79} (1997) 949 et. al.] is discussed.


  In this paper one studies the distribution of log-returns (tick-by-tick) in
the Lisbon stock market and shows that it is well adjusted by the solution of
the equation, {$\frac{dp_{x}}{d| x|}=-\beta_{q^{\prime
}}p_{x}^{q^{\prime}}-(\beta_{q}-\beta_{q^{\prime}}) p_{x}^{q}$}, which
corresponds to a generalization of the differential equation which has as
solution the power-laws that optimise the entropic form $S_{q}=-k \frac{1-\int
p_{x}^{q} dx}{1-q}$, base of present non-extensive statistical mechanics.


  We created this CORD-NER dataset with comprehensive named entity recognition
(NER) on the COVID-19 Open Research Dataset Challenge (CORD-19) corpus
(2020-03-13). This CORD-NER dataset covers 75 fine-grained entity types: In
addition to the common biomedical entity types (e.g., genes, chemicals and
diseases), it covers many new entity types related explicitly to the COVID-19
studies (e.g., coronaviruses, viral proteins, evolution, materials, substrates
and immune responses), which may benefit research on COVID-19 related virus,
spreading mechanisms, and potential vaccines. CORD-NER annotation is a
combination of four sources with different NER methods. The quality of CORD-NER
annotation surpasses SciSpacy (over 10% higher on the F1 score based on a
sample set of documents), a fully supervised BioNER tool. Moreover, CORD-NER
supports incrementally adding new documents as well as adding new entity types
when needed by adding dozens of seeds as the input examples. We will constantly
update CORD-NER based on the incremental updates of the CORD-19 corpus and the
improvement of our system.


  This paper studies deep learning methodologies for portfolio optimization in
the US equities market. We present a novel residual switching network that can
automatically sense changes in market regimes and switch between momentum and
reversal predictors accordingly. The residual switching network architecture
combines two separate residual networks (ResNets), namely a switching module
that learns stock market conditions, and the main module that learns momentum
and reversal predictors. We demonstrate that over-fitting noisy financial data
can be controlled with stacked residual blocks and further incorporating the
attention mechanism can enhance powerful predictive properties. Over the period
2008 to H12017, the residual switching network (Switching-ResNet) strategy
verified superior out-of-sample performance with an average annual Sharpe ratio
of 2.22, compared with an average annual Sharpe ratio of 0.81 for the ANN-based
strategy and 0.69 for the linear model.


  The aim of this article is the proof of the following result: Let M be a
connected manifold endowed with a regular Cartan geometry modelled on the
boundary X of the d-dimensional real (resp. complex, resp. quaternionic, resp.
octonionic) hyperbolic space. If the group of automorphisms of M does not act
properly on M, then M is geometrically isomorphic to:
  - X if M is compact.
  - X minus a point in the other cases.


  In this work, we characterize two data piling phenomenon for a
high-dimensional binary classification problem with heterogeneous covariance
models. The data piling refers to the phenomenon where projections of the
training data onto a direction vector have exactly two distinct values, one for
each class. This first data piling phenomenon occurs for any data when the
dimension $p$ is larger than the sample size $n$. We show that the second data
piling phenomenon, which refers to a data piling of independent test data, can
occur in an asymptotic context where $p$ grows while $n$ is fixed. We further
show that a second maximal data piling direction, which gives an asymptotic
maximal distance between the two piles of independent test data, can be
obtained by projecting the first maximal data piling direction onto the
nullspace of the common leading eigenspace. This observation provides a
theoretical explanation for the phenomenon where the optimal ridge parameter
can be negative in the context of high-dimensional linear classification. Based
on the second data piling phenomenon, we propose various linear classification
rules which ensure perfect classification of high-dimension low-sample-size
data under generalized heterogeneous spiked covariance models.


  Network-based biocomputation (NBC) is an alternative parallel computing
paradigm that encodes combinatorial problems into a nanofabricated device's
graphical network of channels, enabling cytoskeletal filaments propelled by
molecular motors to explore the problems' solution space. NBC promises to
require significantly less energy than traditional computers due to the high
energy efficiency of molecular motors. However, error rates associated with the
pass junction crossing, the primary path-regulating geometry, pose a bottleneck
for scaling up this technology. Here, we optimize the geometry of the pass
junction for low error rates for the actin-myosin system. To do so, we evaluate
various pass junction designs that differ in features, such as the nanochannel
width, junction crossing area, and angles of a funnel-shaped output part of the
junction. Error rates were measured experimentally by using gliding motility
assay and as well as by simulation methods. The final optimized design
displayed a decreased error rate of under 1 percent compared to the previous
2-4 percent. We anticipate this improvement will enable scaling up NBC networks
from tens to hundreds of pass junctions. However, the results of 2D junction
optimizations also suggest that further drastic reduction of error rates in
two-dimensional pass junctions is unlikely, necessitating three-dimensional
junctions, such as bridges or tunnels, for complete error rate mitigation.
Furthermore, the simulation results demonstrate that including a layer of
myosin motor on the channel provides a better fit between simulation and
experimental results.


  Evolution of beliefs of a society are a product of interactions between
people (horizontal transmission) in the society over generations (vertical
transmission). Researchers have studied both horizontal and vertical
transmission separately. Extending prior work, we propose a new theoretical
framework which allows application of tools from Markov chain theory to the
analysis of belief evolution via horizontal and vertical transmission. We
analyze three cases: static network, randomly changing network, and
homophily-based dynamic network. Whereas the former two assume network
structure is independent of beliefs, the latter assumes that people tend to
communicate with those who have similar beliefs. We prove under general
conditions that both static and randomly changing networks converge to a single
set of beliefs among all individuals along with the rate of convergence. We
prove that homophily-based network structures do not in general converge to a
single set of beliefs shared by all and prove lower bounds on the number of
different limiting beliefs as a function of initial beliefs. We conclude by
discussing implications for prior theories and directions for future work.


  We have investigated the possibility of calibrating the PMTs of scintillation
detectors, using the primary scintillation produced by X-rays to induce single
photoelectron response of the PMT. The high-energy tail of this response, can
be approximated to an exponential function, under some conditions. In these
cases, it is possible to determine the average gain for each PMT biasing
voltage from the inverse of the exponent of the exponential fit to the tail,
which can be done even if the background and/or noise cover-up most of the
distribution. We have compared our results with those obtained by the commonly
used single electron response (SER) method, which uses a LED to induce a single
photoelectron response of the PMT and determines the peak position of such
response, relative to the pedestal peak (the electronic noise peak, which
corresponds to 0 photoelectrons). The results of the exponential fit method
agree with those obtained by the SER method when the average number of
photoelectrons reaching the first dynode per light/scintillation pulse is
around 1.0. The SER method has higher precision, while the exponential fit
method has the advantage of being useful in situations where the PMT is already
in situ, being difficult or even impossible to apply the SER method, e.g. in
sealed scintillator/PMT devices.


  The discovery of a light Higgs boson at LHC may be suggesting that we need to
revise our model building paradigms to understand the origin of the weak scale.
We explore the possibility that the Fermi scale is not fundamental but rather a
derived one, i.e. a low energy mirage. We show that this scenario emerges in a
very natural way in models previously used to break the electroweak symmetry
dynamically and suggest a simple dynamical framework for this idea. In our
model the electroweak scale results from the interplay between two very high
energy scales, one typically of the order of $\Luv\sim 10^{10}$ GeV and the
other around $M_{\rm U} \sim 10^{16}$ GeV, although other values are also
possible.


  We study the boundary value problem with Radon measures for nonnegative
solutions of $-\Delta u+Vu=0$ in a bounded smooth domain $\Gw$, when $V$ is a
locally bounded nonnegative function. Introducing some specific capacity, we
give sufficient conditions on a Radon measure $\gm$ on $\prt\Gw$ so that the
problem can be solved. We study the reduced measure associated to this equation
as well as the boundary trace of positive solutions.


  A growing issue in the modern cyberspace world is the direct identification
of malicious activity over network connections. The boom of the machine
learning industry in the past few years has led to the increasing usage of
machine learning technologies, which are especially prevalent in the network
intrusion detection research community. When utilizing these fairly
contemporary techniques, the community has realized that datasets are pivotal
for identifying malicious packets and connections, particularly ones associated
with information concerning labeling in order to construct learning models.
However, there exists a shortage of publicly available, relevant datasets to
researchers in the network intrusion detection community. Thus, in this paper,
we introduce a method to construct labeled flow data by combining the packet
meta-information with IDS logs to infer labels for intrusion detection
research. Specifically, we designed a NetFlow-compatible format due to the
capability of a a large body of network devices, such as routers and switches,
to export NetFlow records from raw traffic. In doing so, the introduced method
at hand would aid researchers to access relevant network flow datasets along
with label information.


  We set out the novel bottom up procedure to aggregate or cluster cells with
small frequency counts together, in a two way classification while maintaining
dependence in the table. The procedure is model free. It combines cells in a
table into clusters based on independent log odds ratios. We use this procedure
to build a set of statistically efficient and robust imputation cells, for the
imputation of missing values of a continuous variable using a pair
classification variables. A nice feature of the procedure is it forms
aggregation groups homogeneous with respect to the cell response mean. Using a
series of simulation studies, we show IlocA only groups together independent
cells and does so in a consistent and credible way. While imputing missing
data, we show IlocAs generates close to an optimal number of imputation cells.
For ignorable non-response the resulting imputed means are accurate in general.
With non-ignorable missingness results are consistent with those obtained
elsewhere. We close with a case study applying our method to imputing missing
building energy performance data


  For a connected reductive algebraic group $G$ defined over a finite field
$\mathbb F_q$, Kawanaka introduced the generalized Gelfand-Graev
representations (GGGRs for short) of the finite group $G(\mathbb F_q)$ in the
case where $q$ is a power of a good prime for $G$. This representation has been
widely studied and used in various contexts. Recently, Geck proposed a
conjecture, characterizing Lusztig's special unipotent classes in terms of
weighted Dynkin diagrams. Based on this conjecture, he gave a guideline for
extending the definition of GGGRs to the case where $q$ is a power of a bad
prime for $G$. Here, we will give a proof of Geck's conjecture. Combined with
Geck's pioneer work, our proof verifies Geck's conjectural characterization of
special unipotent classes, and completes his definition of GGGRs in bad
characteristics.


  We compute here the Yang-Mills effective action on Moyal space by integrating
over the scalar fields in a noncommutative scalar field theory with harmonic
term, minimally coupled to an external gauge potential. We also explain the
special regularisation scheme chosen here and give some links to the Schwinger
parametric representation. Finally, we discuss the results obtained: a
noncommutative possibly renormalisable Yang-Mills theory.