  We present a demonstration of delensing the observed cosmic microwave
background (CMB) B-mode polarization anisotropy. This process of reducing the
gravitational-lensing generated B-mode component will become increasingly
important for improving searches for the B modes produced by primordial
gravitational waves. In this work, we delens B-mode maps constructed from
multi-frequency SPTpol observations of a 90 deg$^2$ patch of sky by subtracting
a B-mode template constructed from two inputs: SPTpol E-mode maps and a lensing
potential map estimated from the $\textit{Herschel}$ $500\,\mu m$ map of the
CIB. We find that our delensing procedure reduces the measured B-mode power
spectrum by 28% in the multipole range $300 < \ell < 2300$; this is shown to be
consistent with expectations from theory and simulations and to be robust
against systematics. The null hypothesis of no delensing is rejected at $6.9
\sigma$. Furthermore, we build and use a suite of realistic simulations to
study the general properties of the delensing process and find that the
delensing efficiency achieved in this work is limited primarily by the noise in
the lensing potential map. We demonstrate the importance of including realistic
experimental non-idealities in the delensing forecasts used to inform
instrument and survey-strategy planning of upcoming lower-noise experiments,
such as CMB-S4.


  We investigate the effects of a $Z_2$ symmetry in the CP-conserving
Two-Higgs-Doublet-Model (2HDM); which is often imposed to prevent
Flavor-Changing-Neutral-Currents (FCNCs) at tree-level. Specifically, we
analyze how a breaking of the $Z_2$ symmetry spreads during renormalization
group evolution; employing general 2-loop renormalization group equations that
we have derived. Evolving the model from the electroweak to the Planck scale,
we find that while the case of an exact $Z_2$ symmetric 2HDM is very
constrained, a soft breaking of the $Z_2$ symmetry extends the valid parameter
space regions. The effects of a hard $Z_2$ breaking in the scalar sector as
well as the stability of the flavor alignment ansatz are also investigated. We
find that while a hard breaking of the $Z_2$ symmetry in the potential is
problematic, since it speeds up the growth of quartic couplings, the generated
FCNCs are heavily suppressed. Conversely, we also find that hard $Z_2$ breaking
in the Yukawa sector at most gives moderate $Z_2$ breaking in the potential;
whereas the FCNCs can become quite sizable far away from the $Z_2$ symmetric
regions.


  Moir\'e-twisted materials have garnered significant research interest due to
their distinctive properties and intriguing physics. However, conducting
first-principles studies on such materials faces challenges, notably the
formidable computational cost associated with simulating ultra-large twisted
structures. This obstacle impedes the construction of a twisted materials
database crucial for datadriven materials discovery. Here, by using
high-throughput calculations and state-of-the-art neural network methods, we
construct a Deep-learning Database of density functional theory (DFT)
Hamiltonians for Twisted materials named DDHT. The DDHT database comprises
trained neural-network models of over a hundred homo-bilayer and hetero-bilayer
moir\'e-twisted materials. These models enable accurate prediction of the DFT
Hamiltonian for these materials across arbitrary twist angles, with an averaged
mean absolute error of approximately 1.0 meV or lower. The database facilitates
the exploration of flat bands and correlated materials platforms within
ultra-large twisted structures.


  Entanglement related properties work as nice fingerprint of the quantum
many-body wave function. However, those of fermionic models are hard to
evaluate in standard numerical methods because they suffer from finite size
effects. We show that a so-called density embedding theory (DET) can evaluate
them without size scaling analysis in comparably high quality with those
obtained by the large-size density matrix renormalization group analysis. This
method projects the large scale original many-body Hamiltonian to the small
number of basis sets defined on a local cluster, and optimizes the choice of
these bases by tuning the local density matrix. The DET entanglement spectrum
of one-dimensional interacting fermions perfectly reproduces the exact ones and
works as a marker of the phase transition point. It is further shown that the
phase transitions in two-dimension could be determined by the entanglement
entropy and the fidelity that reflects the change of the structure of the wave
function.


  Functional Magnetic Resonance Imaging (fMRI) is a primary modality for
studying brain activity. Modeling spatial dependence of imaging data at
different scales is one of the main challenges of contemporary neuroimaging,
and it could allow for accurate testing for significance in neural activity.
The high dimensionality of this type of data (on the order of hundreds of
thousands of voxels) poses serious modeling challenges and considerable
computational constraints. For the sake of feasibility, standard models
typically reduce dimensionality by modeling covariance among regions of
interest (ROIs) -- coarser or larger spatial units -- rather than among voxels.
However, ignoring spatial dependence at different scales could drastically
reduce our ability to detect activation patterns in the brain and hence produce
misleading results. To overcome these problems, we introduce a multi-resolution
spatio-temporal model and a computationally efficient methodology to estimate
cognitive control related activation and whole-brain connectivity. The proposed
model allows for testing voxel-specific activation while accounting for
non-stationary local spatial dependence within anatomically defined ROIs, as
well as regional dependence (between-ROIs). Furthermore, the model allows for
detection of interpretable connectivity patterns among ROIs using the graphical
Least Absolute Shrinkage Selection Operator (LASSO). The model is used in a
motor-task fMRI study to investigate brain activation and connectivity patterns
aimed at identifying associations between these patterns and regaining motor
functionality following a stroke.


  In this paper, we connect the rigidity problem and the coarse Baum-Connes
conjecture for Roe algebras. In particular, we show that if $X$ and $Y$ are two
uniformly locally finite metric spaces such that their Roe algebras are
$*$-isomorphic, then $X$ and $Y$ are coarsely equivalent provided either $X$ or
$Y$ satisfies the coarse Baum-Connes conjecture with coefficients. It is
well-known that coarse embeddability into a Hilbert space implies the coarse
Baum-Connes conjecture with coefficients. On the other hand, we provide a new
example of a finitely generated group satisfying the coarse Baum-Connes
conjecture with coefficients but which does not coarsely embed into a Hilbert
space.


  The absence of any tendency toward rotation in the Trouton-Noble experiment
is given a simple explanation.


  Through numerical solution of the time-dependent Schrodinger equation, we
demonstrate that magnetic chains with uniaxial anisotropy support stable
structures, separating ferromagnetic domains of opposite magnetization. These
structures, domain walls in a quantum system, are shown to remain stable if
they interact with a spin wave. We find that a domain wall transmits the
longitudinal component of the spin excitations only. Our results suggests that
continuous, classical spin models described by LLG equation cannot be used to
describe spin wave-domain wall interaction in microscopic magnetic systems.


  The flight characteristics of bats remarkably have been overlooked in aerial
drone designs. Unlike other animals, bats leverage the manipulation of inertial
dynamics to exhibit aerial flip turns when they perch. Inspired by this unique
maneuver, this work develops and uses a tiny robot called \textit{Harpoon} to
demonstrate that the preparation for upside-down landing is possible through:
1) reorientation towards the landing surface through zero-angular-momentum
turns and 2) reaching to the surface through shooting a detachable landing
gear. The closed-loop manipulations of inertial dynamics takes place based on a
symplectic description of the dynamical system (body and appendage), which is
known to exhibit an excellent geometric conservation properties.


  We present a new computation in a field-theoretical model of Coulomb gauge
QCD of the first radial and angular excitations of a qqq system in a SU(3)
flavor singlet state, Lambda_S. The traditional motivation for the study is
that the absence of flavor singlets in the lowest-lying spectrum is a direct
consequence of the color degree of freedom. (The calculation is tested with
decuplet baryons Delta(1232) and Omega(1672).) We also analyze decay branching
fractions of the flavor singlet baryon for various masses with the simplest
effective Lagrangians.


  We use ab-initio calculations to predict the thermal conductivity of cubic
SiC with different types of defects. An excellent quantitative agreement with
previous experimental measurements is found. The results unveil that
B$_\mathrm{C}$ substitution has a much stronger effect than any of the other
defect types in 3C-SiC, including vacancies. This finding contradicts the
prediction of the classical mass-difference model of impurity scattering,
according to which the effects of B$_\mathrm{C}$ and N$_\mathrm{C}$ would be
similar and much smaller than that of the C vacancy. The strikingly different
behavior of the B$_\mathrm{C}$ defect arises from a unique pattern of resonant
phonon scattering caused by the broken structural symmetry around the B
impurity.


  This paper presents an innovative framework that integrates Large Language
Models (LLMs) with an external Thinker module to enhance the reasoning
capabilities of LLM-based agents. Unlike augmenting LLMs with prompt
engineering, Thinker directly harnesses knowledge from databases and employs
various optimization techniques. The framework forms a reasoning hierarchy
where LLMs handle intuitive System-1 tasks such as natural language processing,
while the Thinker focuses on cognitive System-2 tasks that require complex
logical analysis and domain-specific knowledge. Our framework is presented
using a 9-player Werewolf game that demands dual-system reasoning. We introduce
a communication protocol between LLMs and the Thinker, and train the Thinker
using data from 18800 human sessions and reinforcement learning. Experiments
demonstrate the framework's effectiveness in deductive reasoning, speech
generation, and online game evaluation. Additionally, we fine-tune a 6B LLM to
surpass GPT4 when integrated with the Thinker. This paper also contributes the
largest dataset for social deduction games to date.


  A theoretical description of the interacting Bose-system is proposed. It is
based on the extrapolation of the results obtained for the systems with a small
number of particles N=2, 3, 4, etc. to the bulk case of N=\infty. It is shown
that already the system with N=12, 13 behaves almost as a bulk in a wide
temperature range. Special attention is paid to the phase transition in these
systems. The hard sphere potential is used in calculations. The sequence of
heat capacity maxima is approximated as C^{max}_N/N\simeq13.6-aN^{-\epsilon}
with \epsilon=0.0608 giving the value of bulk heat capacity as 13.6 while
experimental value is close to 16. The temperature of \lambda-transition is
estimated as 2.1-2.3 K versus experimental 2.17 K. Quite good qualitative and
satisfactory quantitative agreement with the experimental data has been
achieved.


  Conventional processes for analyzing datasets and extracting meaningful
information are often time-consuming and laborious. Previous work has
identified manual, repetitive coding and data collection as major obstacles
that hinder data scientists from undertaking more nuanced labor and high-level
projects. To combat this, we evaluated OpenAI's GPT-3.5 as a "Language Data
Scientist" (LDS) that can extrapolate key findings, including correlations and
basic information, from a given dataset. The model was tested on a diverse set
of benchmark datasets to evaluate its performance across multiple standards,
including data science code-generation based tasks involving libraries such as
NumPy, Pandas, Scikit-Learn, and TensorFlow, and was broadly successful in
correctly answering a given data science query related to the benchmark
dataset. The LDS used various novel prompt engineering techniques to
effectively answer a given question, including Chain-of-Thought reinforcement
and SayCan prompt engineering. Our findings demonstrate great potential for
leveraging Large Language Models for low-level, zero-shot data analysis.


  We present near-infrared coronagraphic imaging polarimetry of RY Tau. The
scattered light in the circumstellar environment was imaged at H-band at a high
resolution (~0".05) for the first time, using Subaru-HiCIAO. The observed
polarized intensity (PI) distribution shows a butterfly-like distribution of
bright emission with an angular scale similar to the disk observed at
millimeter wavelengths. This distribution is offset toward the blueshifted jet,
indicating the presence of a geometrically thick disk or a remnant envelope,
and therefore the earliest stage of the Class II evolutionary phase. We perform
comparisons between the observed PI distribution and disk models with: (1) full
radiative transfer code, using the spectral energy distribution (SED) to
constrain the disk parameters; and (2) monochromatic simulations of scattered
light which explore a wide range of parameters space to constrain the disk and
dust parameters. We show that these models cannot consistently explain the
observed PI distribution, SED, and the viewing angle inferred by millimeter
interferometry. We suggest that the scattered light in the near-infrared is
associated with an optically thin and geometrically thick layer above the disk
surface, with the surface responsible for the infrared SED. Half of the
scattered light and thermal radiation in this layer illuminates the disk
surface, and this process may significantly affect the thermal structure of the
disk.


  We review and extend results for mutation, selection, genetic drift, and
migration in a one-dimensional continuous population. The population is
described by a continuous limit of the stepping stone model, which leads to the
stochastic Fisher-Kolmogorov-Petrovsky-Piscounov equation with additional terms
describing mutations. Although the stepping stone model was first proposed for
population genetics, it is closely related to "voter models" of interest in
nonequilibrium statistical mechanics. The stepping stone model can also be
regarded as an approximation to the dynamics of a thin layer of actively
growing pioneers at the frontier of a colony of microorganisms undergoing a
range expansion on a Petri dish. We find that the population tends to segregate
into monoallelic domains. This segregation slows down genetic drift and
selection because these two evolutionary forces can only act at the boundaries
between the domains; the effects of mutation, however, are not significantly
affected by the segregation. Although fixation in the neutral well-mixed (or
"zero dimensional") model occurs exponentially in time, it occurs only
algebraically fast in the one-dimensional model. If selection is weak,
selective sweeps occur exponentially fast in both well-mixed and
one-dimensional populations, but the time constants are different. We also find
an unusual sublinear increase in the variance of the spatially averaged allele
frequency with time. Although we focus on two alleles or variants, q-allele
Potts-like models of gene segregation are considered as well. We also
investigate the effects of geometry at the frontier by considering growth of
circular colonies. Our analytical results are checked with simulations, and
could be tested against recent spatial experiments on range expansions off
linear inoculations of Escherichia coli and Saccharomyces cerevisiae.


  We use testing to check if a combinational circuit N always evaluates to 0.
The usual point of view is that to prove that N always evaluates to 0 one has
to check the value of N for all 2^|X| input assignments where X is the set of
input variables of N. We use the notion of a Stable Set of Assignments (SSA) to
show that one can build a complete test set (i.e. a test set proving that N
always evaluates to 0) that consists of less than 2^|X| tests. Given an
unsatisfiable CNF formula H(W), an SSA of H is a set of assignments to W
proving unsatisfiability of H. A trivial SSA is the set of all 2^|W|
assignments to W. Importantly, real-life formulas can have SSAs that are much
smaller than 2^|W|. Generating a complete test set for N using only the
machinery of SSAs is inefficient. We describe a much faster algorithm that
combines computation of SSAs with resolution derivation and produces a complete
test set for a "projection" of N on a subset of variables of N. We give
experimental results and describe potential applications of this algorithm.


  The effective force between two parallel DNA molecules is calculated as a
function of their mutual separation for different valencies of counter- and
salt ions and different salt concentrations. Computer simulations of the
primitive model are used and the shape of the DNA molecules is accurately
modelled using different geometrical shapes. We find that multivalent ions
induce a significant attraction between the DNA molecules whose strength can be
tuned by the averaged valency of the ions. The physical origin of the
attraction is traced back either to electrostatics or to entropic
contributions. For multivalent counter- and monovalent salt ions, we find a
salt-induced stabilization effect: the force is first attractive but gets
repulsive for increasing salt concentration. Furthermore, we show that the
multivalent-ion-induced attraction does not necessarily correlate with DNA
overcharging.


  We study Fiske steps in small Bi2Sr2CaCu2O8+x mesa structures, containing
only few stacked intrinsic Josephson junctions. Careful alignment of magnetic
field prevents penetration of Abrikosov vortices and facilitates observation of
a large variety of high quality geometrical resonances, including superluminal
with velocities larger than the slowest velocity of electromagnetic waves. A
small number of junctions limits the number of resonant modes and allows
accurate identification of modes and velocities. It is shown that superluminal
geometrical resonances can be excited by subluminal fluxon motion and that
flux-flow itself becomes superluminal at high magnetic fields. We argue that
observation of high-quality superluminal geometrical resonances is crucial for
realization of the coherent flux-flow oscillator in the THz frequency range.


  We present an approach to non-uniform complexity in which single-pass
instruction sequences play a key part, and answer various questions that arise
from this approach. We introduce several kinds of non-uniform complexity
classes. One kind includes a counterpart of the well-known non-uniform
complexity class P/poly and another kind includes a counterpart of the
well-known non-uniform complexity class NP/poly. Moreover, we introduce a
general notion of completeness for the non-uniform complexity classes of the
latter kind. We also formulate a counterpart of the well-known complexity
theoretic conjecture that NP is not included in P/poly. We think that the
presented approach opens up an additional way of investigating issues
concerning non-uniform complexity.


  Cloud computing provides great benefits for applications hosted on the Web
that also have special computational and storage requirements. This paper
proposes an extensible and flexible architecture for integrating Wireless
Sensor Networks with the Cloud. We have used REST based Web services as an
interoperable application layer that can be directly integrated into other
application domains for remote monitoring such as e-health care services, smart
homes, or even vehicular area networks (VAN). For proof of concept, we have
implemented a REST based Web services on an IP based low power WSN test bed,
which enables data access from anywhere. The alert feature has also been
implemented to notify users via email or tweets for monitoring data when they
exceed values and events of interest.


  Text matching is a core natural language processing research problem. How to
retain sufficient information on both content and structure information is one
important challenge. In this paper, we present a neural approach for
general-purpose text matching with deep mutual information estimation
incorporated. Our approach, Text matching with Deep Info Max (TIM), is
integrated with a procedure of unsupervised learning of representations by
maximizing the mutual information between text matching neural network's input
and output. We use both global and local mutual information to learn text
representations. We evaluate our text matching approach on several tasks
including natural language inference, paraphrase identification, and answer
selection. Compared to the state-of-the-art approaches, the experiments show
that our method integrated with mutual information estimation learns better
text representation and achieves better experimental results of text matching
tasks without exploiting pretraining on external data.


  We present a complete characterization of the asymptotic behaviour of a
correlated Bernoulli sequence { which depends on the parameter $\theta \in
[0,1]$. A martingale theory based approach will allow} us to prove versions of
the law of large numbers, quadratic strong law, law of iterated logarithm,
almost sure central limit theorem and functional central limit theorem, in the
case $\theta \le 1/2$. For $\theta > 1/2$, we will obtain a strong convergence
to a non-degenerated random variable, including a central limit theorem and a
law of iterated logarithm for the fluctuations.


  Wide field images obtained with WIRCam and MegaCam on the
Canada-France-Hawaii Telescope are used to probe the spatial distribution of
young stars in the Sc galaxy NGC 2403. Bright main sequence stars and blue
supergiants are detected out to projected galactocentric distances of ~14 kpc
(~7 disk scale lengths) along the major axis. The star formation rate (SFR) in
the disk of NGC 2403 during the past 10 Myr has been 1 solar masses per year
based on the number of bright main sequence stars. The radially-averaged number
density of red supergiants (RSGs) per unit r-band surface brightness is
constant throughout the disk, indicating that (1) RSGs follow the integrated
r-band light, and (2) the SFR per unit mass has been constant with radius when
averaged over time scales of a few tens of millions of years. The mean color of
RSGs varies with galactocentric distance, suggesting that there is a
metallicity gradient among recently formed stars. A comparison of the
radially-averaged number density of bright main sequence stars also indicates
that the SFR per unit stellar mass in NGC 2403 has been ~3 times higher than in
NGC 247 during recent epochs, and this is in rough agreement with what would be
predicted from the far infrared fluxes of these galaxies. Finally, the data are
used to investigate the extraplanar regions of NGC 2403. A population of M
giants with peak brightness M_K = -8 is detected at projected distances between
12 and 14 kpc above the disk plane, and six new globular cluster candidates are
identified.


  This chapter reviews the following items: 1. Energy deposition and track
structure of fast positrons: ionization slowing down, number of ion-electron
pairs, typical sizes, thermalization, electrostatic interaction between e+ and
its blob, effect of local heating; 2. Positronium formation in condensed media:
the Ore model, quasifree Ps state, intratrack mechanism of Ps formation; 3.
Fast intratrack diffusion-controlled reactions: Ps oxidation and ortho-para
conversion by radiolytic products, reaction rate constants, interpretation of
the PAL spectra in water at different temperatures; 4. Ps bubble models.
"Non-point" positronium: wave function, energy contributions, relationship
between the pick-off annihilation rate and the bubble radius.


  We present results for the isovector axial vector form factors obtained using
thirteen 2+1+1-flavor highly improved staggered quark (HISQ) ensembles
generated by the MILC collaboration. The calculation of nucleon two- and
three-point correlation functions has been done using Wilson-clover fermions.
In the analysis of these data, we quantify the sensitivity of the results to
strategies used for removing excited state contamination and invoke the
partially conserved axial current relation between the form factors to choose
between them. Our data driven analysis includes removing contributions from
multihadron $N \pi$ states that make significant contributions. Our final
results are: $g_A = 1.292 (53)_\text{stat}\,(24)_\text{sys}$ for the axial
charge; $g_S = 1.085 (50)_\text{stat}\, (103)_\text{sys}$ and $g_T = 0.991
(21)_\text{stat}\, (10)_\text{sys}$ for the scalar and tensor charges; $\langle
r_A^2 \rangle = 0.439 (56)_\text{stat} (34)_\text{sys}$ fm${}^2$ for the mean
squared axial charge radius, $g_P^\ast = 9.03(47)_\text{stat}(42)_\text{sys} $
for the induced pseudoscalar charge; and $g_{\pi NN} =
14.14(81)_\text{stat}(85)_\text{sys}$ for the pion-nucleon coupling. We also
provide a parameterization of the axial form factor $G_A(Q^2)$ over the range
$0 \le Q^2 \le 1$ GeV${}^2$ for use in phenomenology and a comparison with
other lattice determinations. We find that the various lattice data agree
within 10\% but are significantly different from the extraction of $G_A(Q^2)$
from the $\nu$-deuterium scattering data.


  A cranked shell model approach for the description of rotational bands in
$N\approx Z$ nuclei is formulated. The isovector neutron-proton pairing is
taken into account explicitly. The concept of spontaneous breaking and
subsequent restoration of the isospin symmetry turns out to be crucial. The
general rules to construct the near yrast-spectra for rotating nuclei are
presented. For the model case of particles in a j-shell, it is shown that
excitation spectra and the alignment processes are well described as compared
to the exact shell model calculation. Realistic cranked shell model
calculations are able to describe the experimental spectra of $^{72,73}$Kr and
$^{74}$Rb isotopes. \


  We have investigated the onset of collective motion in systems of model
microswimmers, by performing a comprehensive analysis of the binary collision
dynamics using three dimensional direct numerical simulations (DNS) with fully
resolved hydrodynamics. From this data, we have constructed a simplified binary
collision model (BCM) which accurately reproduces the collective behavior
obtained from the DNS for most cases. Thus, we show that global alignment can
mostly arise solely from binary collisions. Although the agreement between both
models (DNS and BCM) is not perfect, the parameter range in which notable
differences appear is also that for which strong density fluctuations are
present in the system (where pseudo-sound mound can be observed[1]).


  The large and diffuse galaxies NGC1052-DF2 and NGC1052-DF4 have been found to
have very low dark matter content and a population of luminous globular
clusters. Accurate distance measurements are key to interpreting these
observations. Recently, the distance to NGC1052-DF4 was found to be $20.0\pm
1.6$ Mpc by identifying the tip of the red giant branch (TRGB) in 12 orbits of
Hubble Space Telescope (HST) Advanced Camera for Surveys (ACS) imaging. Here we
present 40 orbits of HST ACS data for NGC1052-DF2 and use these data to measure
its TRGB. The TRGB is readily apparent in the color-magnitude diagram. Using a
forward model that incorporates photometric uncertainties, we find a TRGB
magnitude of $m_{\rm F814W, TRGB} = 27.67 \pm 0.10$ mag. The inferred distance
is $D_{\rm TRGB} = 22.1 \pm 1.2$ Mpc, consistent with the previous surface
brightness fluctuation distances to the bright elliptical galaxy NGC1052. The
new HST distance rules out the idea that some of NGC1052-DF2's unusual
properties can be explained if it were at $\sim 13$ Mpc; instead, it implies
that the galaxy's globular clusters are even more luminous than had been
derived using the previous distance of 20 Mpc. The distance from NGC1052-DF2 to
NGC1052-DF4 is well-determined at $2.1\pm 0.5$ Mpc, significantly larger than
the virial diameter of NGC1052. We discuss the implications for formation
scenarios of the galaxies and for the external field effect, which has been
invoked to explain the intrinsic dynamics of these objects in the context of
modified Newtonian dynamics.


  We study the nature of rapidly star-forming galaxies at z=2 in cosmological
hydrodynamic simulations, and compare their properties to observations of
sub-millimetre galaxies (SMGs). We identify simulated SMGs as the most rapidly
star-forming systems that match the observed number density of SMGs. In our
models, SMGs are massive galaxies sitting at the centres of large potential
wells, being fed by smooth infall and gas-rich satellites at rates comparable
to their star formation rates (SFR). They are not typically undergoing major
mergers that significantly boost their quiescent SFR, but they still often show
complex gas morphologies and kinematics. Our simulated SMGs have stellar masses
of log M*/Mo~11-11.7, SFRs of ~180-500 Mo/yr, a clustering length of 10 Mpc/h,
and solar metallicities. The SFRs are lower than those inferred from far-IR
data by a factor of 3, which we suggest may owe to one or more systematic
effects in the SFR calibrations. SMGs at z=2 live in ~10^13 Mo halos, and by
z=0 they mostly end up as brightest group galaxies in ~10^14 Mo halos. We
predict that higher-M* SMGs should have on average lower specific SFRs, less
disturbed morphologies, and higher clustering. We also predict that deeper
far-IR surveys will smoothly join SMGs onto the massive end of the SFR-M*
relationship defined by lower-mass z=2 galaxies. Overall, our simulated rapid
star-formers provide as good a match to available SMG data as merger-based
scenarios, offering an alternative scenario that emerges naturally from
cosmological simulations.


  We study the query complexity of quantum learning problems in which the
oracles form a group $G$ of unitary matrices. In the simplest case, one wishes
to identify the oracle, and we find a description of the optimal success
probability of a $t$-query quantum algorithm in terms of group characters. As
an application, we show that $\Omega(n)$ queries are required to identify a
random permutation in $S_n$. More generally, suppose $H$ is a fixed subgroup of
the group $G$ of oracles, and given access to an oracle sampled uniformly from
$G$, we want to learn which coset of $H$ the oracle belongs to. We call this
problem coset identification and it generalizes a number of well-known quantum
algorithms including the Bernstein-Vazirani problem, the van Dam problem and
finite field polynomial interpolation. We provide character-theoretic formulas
for the optimal success probability achieved by a $t$-query algorithm for this
problem. One application involves the Heisenberg group and provides a family of
problems depending on $n$ which require $n+1$ queries classically and only $1$
query quantumly.


  A composite quantum system comprising a finite number k of subsystems which
are described with position and momentum variables in Z_{n_{i}}, i=1,...,k, is
considered. Its Hilbert space is given by a k-fold tensor product of Hilbert
spaces of dimensions n_{1},...,n_{k}. Symmetry group of the respective finite
Heisenberg group is given by the quotient group of certain normalizer. This
paper extends our previous investigation of bipartite quantum systems to
arbitrary multipartite systems of the above type. It provides detailed
description of the normalizers and the corresponding symmetry groups. The new
class of symmetry groups represents a very specific generalization of
symplectic groups over modular rings. As an application, a new proof of
existence of the maximal set of mutually unbiased bases in Hilbert spaces of
prime power dimensions is provided.


  Video action detectors are usually trained using datasets with
fully-supervised temporal annotations. Building such datasets is an expensive
task. To alleviate this problem, recent methods have tried to leverage weak
labeling, where videos are untrimmed and only a video-level label is available.
In this paper, we propose RefineLoc, a novel weakly-supervised temporal action
localization method. RefineLoc uses an iterative refinement approach by
estimating and training on snippet-level pseudo ground truth at every
iteration. We show the benefit of this iterative approach and present an
extensive analysis of five different pseudo ground truth generators. We show
the effectiveness of our model on two standard action datasets, ActivityNet
v1.2 and THUMOS14. RefineLoc shows competitive results with the
state-of-the-art in weakly-supervised temporal localization. Additionally, our
iterative refinement process is able to significantly improve the performance
of two state-of-the-art methods, setting a new state-of-the-art on THUMOS14.


  We perform quantum process tomography (QPT) for both discrete- and
continuous-variable quantum systems by learning a process representation using
Kraus operators. The Kraus form ensures that the reconstructed process is
completely positive. To make the process trace-preserving, we use a constrained
gradient-descent (GD) approach on the so-called Stiefel manifold during
optimization to obtain the Kraus operators. Our ansatz uses a few Kraus
operators to avoid direct estimation of large process matrices, e.g., the Choi
matrix, for low-rank quantum processes. The GD-QPT matches the performance of
both compressed-sensing (CS) and projected least-squares (PLS) QPT in
benchmarks with two-qubit random processes, but shines by combining the best
features of these two methods. Similar to CS (but unlike PLS), GD-QPT can
reconstruct a process from just a small number of random measurements, and
similar to PLS (but unlike CS) it also works for larger system sizes, up to at
least five qubits. We envisage that the data-driven approach of GD-QPT can
become a practical tool that greatly reduces the cost and computational effort
for QPT in intermediate-scale quantum systems.


  We apply a renormalization group approach to the determination of the phase
diagram of the t-t' Hubbard model at the Van Hove filling, as function of t'/t,
for small values of U/t. The model presents ferromagnetic, antiferromagnetic
and d-wave superconducting phases. Antiferromagnetism and d-wave
superconductivity arise from the same interactions, and compete in the same
region of parameter space.


  We consider a simplified model for resonant neutron-nucleus interaction with
coupled channels. An analytical solution is given for two coupled channels and
arbitrary neutron orbital momentum. A case of a week channel coupling,
corresponding to narrow Breit-Wigner resonance, is analyzed in details. As far
as the total width of a resonance coincides with the neutron width, the model
is directly appropriate only for light nuclei. We study a mixing of two narrow
s- and p-wave resonances by P- and P-,T-noninvariant potentials in the first
order of perturbation theory. As an example a close-lying pair of s- and p-wave
resonances of the $^{35}$Cl nucleus is considered. The favorable possibilities
are shown to exist on thick targets for measurements beyond the resonance
widths. In particular, an interference minimum near s-wave resonance is of
interest for P-odd neutron spin rotation on light spinless nuclei.


  Many supervised learning problems involve high-dimensional data such as
images, text, or graphs. In order to make efficient use of data, it is often
useful to leverage certain geometric priors in the problem at hand, such as
invariance to translations, permutation subgroups, or stability to small
deformations. We study the sample complexity of learning problems where the
target function presents such invariance and stability properties, by
considering spherical harmonic decompositions of such functions on the sphere.
We provide non-parametric rates of convergence for kernel methods, and show
improvements in sample complexity by a factor equal to the size of the group
when using an invariant kernel over the group, compared to the corresponding
non-invariant kernel. These improvements are valid when the sample size is
large enough, with an asymptotic behavior that depends on spectral properties
of the group. Finally, these gains are extended beyond invariance groups to
also cover geometric stability to small deformations, modeled here as subsets
(not necessarily subgroups) of permutations.


  Massive MIMO is envisioned as a promising technology for 5G wireless networks
due to its high potential to improve both spectral and energy efficiency.
Although the massive MIMO system is based on innovations in the physical layer,
the upper layer techniques also play important roles in harvesting the
performance gains of massive MIMO. In this article, we begin with an analysis
of the benefits and challenges of massive MIMO systems. We then investigate the
multi-layer techniques for incorporating massive MIMO in several important
network deployment scenarios. We conclude this article with a discussion of
open and potential problems for future research.


  The best bound known on 2-locally distillable entanglement is that of Vedral
and Plenio, involving a certain measure of entanglement based on relative
entropy. It turns out that a related argument can be used to give an even
stronger bound; we give this bound, and examine some of its properties. In
particular, and in contrast to the earlier bounds, the new bound is not
additive in general. We give an example of a state for which the bound fails to
be additive, as well as a number of states for which the bound is additive.


  With an artificial (p', n')-system it has been proved that even numbers >
(p(x))^2 are the sum of two p > p(x).


  Quasiparticle energies and fundamental band gaps in particular are critical
properties of molecules and materials. It was rigorously established that the
generalized Kohn-Sham HOMO and LUMO orbital energies are the chemical
potentials of electron removal and addition and thus good approximations to
band edges and fundamental gaps from a density functional approximation (DFA)
with minimal delocalization error. For other quasiparticle energies, their
connection to the generalized Kohn-Sham orbital energies has not been
established but remains highly interesting. We provide the comparison of
experimental quasiparticle energies for many finite systems with calculations
from the GW Green's function and localized orbitals scaling correction (LOSC),
a recently developed correction to semilocal DFAs, which has minimal
delocalization error. Extensive results with over forty systems clearly show
that LOSC orbital energies achieve slightly better accuracy than the GW
calculations with little dependence on the semilocal DFA, supporting the use of
LOSC DFA orbital energies to predict quasiparticle energies. This also leads to
the calculations of excitation energies of the $N$-electron systems from the
ground state DFA calculations of the $\left(N-1\right)$-electron systems.
Results show good performance with accuracy similar to TDDFT and the delta SCF
approach for valence excitations with commonly used DFAs with or without LOSC.
For Rydberg states, good accuracy was obtained only with the use of LOSC DFA.
This work highlights the pathway to quasiparticle and excitation energies from
ground density functional calculations.


  We report a detailed investigation of the first stages of the growth of
self-organized Fe clusters on the reconstructed Au(111) surface by grazing
incidence X-ray diffraction. Below one monolayer coverage, the Fe clusters are
in "local epitaxy" whereas the subsequent layers adopt first a strained fcc
lattice and then a partly relaxed bcc(110) phase in a Kurdjumov-Sachs epitaxial
relationship. The structural evolution is discussed in relation with the
magnetic properties of the Fe clusters.


  We consider the unconstrained $L_2$-$L_p$ minimization: find a minimizer of
$\|Ax-b\|^2_2+\lambda \|x\|^p_p$ for given $A \in R^{m\times n}$, $b\in R^m$
and parameters $\lambda>0$, $p\in [0,1)$. This problem has been studied
extensively in variable selection and sparse least squares fitting for high
dimensional data. Theoretical results show that the minimizers of the
$L_2$-$L_p$ problem have various attractive features due to the concavity and
non-Lipschitzian property of the regularization function $\|\cdot\|^p_p$. In
this paper, we show that the $L_q$-$L_p$ minimization problem is strongly
NP-hard for any $p\in [0,1)$ and $q\ge 1$, including its smoothed version. On
the other hand, we show that, by choosing parameters $(p,\lambda)$ carefully, a
minimizer, global or local, will have certain desired sparsity. We believe that
these results provide new theoretical insights to the studies and applications
of the concave regularized optimization problems.


  We obtain a number of lower bounds on the running time of algorithms solving
problems on graphs of bounded treewidth. We prove the results under the Strong
Exponential Time Hypothesis of Impagliazzo and Paturi. In particular, assuming
that SAT cannot be solved in (2-\epsilon)^{n}m^{O(1)} time, we show that for
any e > 0; {\sc Independent Set} cannot be solved in (2-e)^{tw(G)}|V(G)|^{O(1)}
time, {\sc Dominating Set} cannot be solved in (3-e)^{tw(G)}|V(G)|^{O(1)} time,
{\sc Max Cut} cannot be solved in (2-e)^{tw(G)}|V(G)|^{O(1)} time, {\sc Odd
Cycle Transversal} cannot be solved in (3-e)^{tw(G)}|V(G)|^{O(1)} time, For any
$q \geq 3$, $q$-{\sc Coloring} cannot be solved in (q-e)^{tw(G)}|V(G)|^{O(1)}
time, {\sc Partition Into Triangles} cannot be solved in
(2-e)^{tw(G)}|V(G)|^{O(1)} time. Our lower bounds match the running times for
the best known algorithms for the problems, up to the e in the base.


  Let $\pi$ be a set of primes, and let $G$ be a finite $\pi$-separable group.
We consider the Isaacs ${\rm B}_\pi$-characters. We show that if $N$ is a
normal subgroup of $G$, then ${\rm B}_\pi (G/N) = {\rm Irr} (G/N) \cap {\rm
B}_\pi (G)$.


  In this paper we give stochastic solutions of conformable fractional Cauchy
problems. The stochastic solutions are obtained by running the processes
corresponding to Cauchy problems with a nonlinear deterministic clock.


  The anomalous magnetic moment of the muon has been measured to 0.5 ppm in a
series of precision experiments at the Brookhaven Alternating Gradient
Synchrotron. The individual results for each sign: a(mu+)= 11 659 204(7)(5)
E-10 and a(mu-) = 11 659 214(8)(3) E-10 are consistent with each other, so that
we can write the average anomaly as a(mu)(exp) = 11 659 208(6) E-10 (0.5 ppm).
A discrepancy between the measured value and the Standard Model (Delta a(mu))
is a signal for new physics. Assuming that such a discrepancy is due to
contributions from supersymmetric particles provides a framework which can be
used to constrain the mass of the dark matter particles, assumed to be the
lightest neutral supersymmetric particles. The deviation from the standard
model has varied between 1.5 sigma and 3 sigma significance, dominated by
uncertainties in the hadronic contribution to the standard model calculation.
Currently the standard model prediction is calculated to 0.6 ppm precision and
Delta a(mu) = 23.5 (9.0) E-10, representing a 2.6 sigma deviation. We expect
that the error on a(mu)(SM) will be reduced by a factor of two within the next
decade. To fully utilize this improvement, a new g-2 run is proposed for the
near future. If the mean Delta a(mu) remains the same, this would result in
close to a 6 sigma discrepancy. In this case, we would expect to see SUSY
particles at the LHC and use the g-2 results to measure tan beta. If, instead,
the Standard Model is confirmed to this precision, gauginos must have masses
higher than ~ 500 GeV/c2 and simple SUSY dark matter models will be severely
constrained.


  Pure luminosity evolution models for galaxies provide an unacceptable fit to
the redshifts and colors of faint galaxies. In this paper we demonstrate, using
HST morphological number counts derived both from the $I_{814}$-band of WFPC2
in the Medium Deep Survey (MDS) and the Hubble Deep Field (HDF) and from the
$H_{1.6}$-band of NICMOS, and ground-based spectroscopic data of the Hawaii
Deep Field and the Canada-France Redshift Survey, that number evolution is
necessary for galaxies, regardless of whether the cosmic geometry is flat,
open, or $\Lambda$-dominated. Furthermore, we show that the number evolution is
small at redshifts of z<1, but large at z>1, and that this conclusion is valid
for all the three cosmological models under consideration. If the universe is
open or $\Lambda$-dominated, the models, which are subject to the constraint of
the conservation of the comoving mass density of galaxies, naturally predict a
population of star-forming galaxies with the redshift distribution peaking at
$z=2\sim3$, which seems to be consistent with the recent findings from
Lyman-break photometric selection techniques. If the cosmological model is
flat, however, the conservation of the comoving mass density is invalid. Hence,
in order to account for the steep slope of B-band number counts at faint
magnitudes in the flat universe, such a star-forming galaxy population has to
be introduced ad hoc into the modelling alongside the merger assumption.


  The complex structures of warm and hot dense matter are essential to
understand the behaviors of materials in high energy density physics processes
and provide new features of matter constitutions. Here, along a new unified
first-principle determined Hugoniot curve of iron from normal condensed
condition up to 1 Gbar, the novel structures characterized by the ionic
clusters and separated "electron bubbles" are revolutionarily unraveled using
newly developed quantum Langevin molecular dynamics (QLMD). Subsistence of
complex clusters, with bonds formed by inner shell electrons of neighbor ions,
can persist in the time length of 50 femto-seconds dynamically with quantum
flowing bubbles, which are produced by the interplay of Fermi electron
degeneracy, the ionic coupling and the dynamical nature. With the inclusion of
those complicated features in QLMD, the present data could serve as a
first-principle benchmark in a wide range of temperatures and densities.


  We present new algorithms to detect and correct errors in the product of two
matrices, or the inverse of a matrix, over an arbitrary field. Our algorithms
do not require any additional information or encoding other than the original
inputs and the erroneous output. Their running time is softly linear in the
number of nonzero entries in these matrices when the number of errors is
sufficiently small, and they also incorporate fast matrix multiplication so
that the cost scales well when the number of errors is large. These algorithms
build on the recent result of Gasieniec et al (2017) on correcting matrix
products, as well as existing work on verification algorithms, sparse low-rank
linear algebra, and sparse polynomial interpolation.


  In this article we study, in their non-Euclidean versions, two important
mechanical systems that are very common in numerous devices. More precisely, we
study the laws governing the movement of pulley and gear systems in spherical
and hyperbolic geometries. And curiously, we were able to see an interesting
similarity between the determined laws.


  Azimuthal angular correlations of charged hadrons with respect to the axis of
a reconstructed (trigger) jet in Au+Au and p+p collisions at
$\sqrt{s_{\text{NN}}} = 200 \text{GeV}$ in STAR are presented. The trigger jet
population in Au+Au collisions is biased towards jets that have not interacted
with the medium, allowing easier matching of jet energies between Au+Au and p+p
collisions while enhancing medium effects on the recoil jet. The associated
hadron yield of the recoil jet is significantly suppressed at high transverse
momentum ($p_{\text{T}}^{\text{assoc}}$) and enhanced at low
$p_{\text{T}}^{\text{assoc}}$ in 0-20% central Au+Au collisions compared to p+p
collisions, which is indicative of medium-induced parton energy loss in
ultrarelativistic heavy-ion collisions.


  Dimension 4 isogenies have first been introduced in cryptography for the
cryptanalysis of Supersingular Isogeny Diffie-Hellman (SIDH) and have been used
constructively in several schemes, including SQIsignHD, a derivative of SQIsign
isogeny based signature scheme. Unlike in dimensions 2 and 3, we can no longer
rely on the Jacobian model and its derivatives to compute isogenies. In
dimension 4 (and higher), we can only use theta-models. Previous works by
Romain Cosset, David Lubicz and Damien Robert have focused on the computation
of $\ell$-isogenies in theta-models of level $n$ coprime to $\ell$ (which
requires to use $n^g$ coordinates in dimension $g$). For cryptographic
applications, we need to compute chains of $2$-isogenies, requiring to use
$\geq 3^g$ coordinates in dimension $g$ with state of the art algorithms.
  In this paper, we present algorithms to compute chains of $2$-isogenies
between abelian varieties of dimension $g\geq 1$ with theta-coordinates of
level $n=2$, generalizing a previous work by Pierrick Dartois, Luciano Maino,
Giacomo Pope and Damien Robert in dimension $g=2$. We propose an implementation
of these algorithms in dimension $g=4$ to compute endomorphisms of elliptic
curve products derived from Kani's lemma with applications to SQIsignHD and
SIDH cryptanalysis. We are now able to run a complete key recovery attack on
SIDH when the endomorphism ring of the starting curve is unknown within a few
seconds on a laptop for all NIST SIKE parameters.


  This note answers the following question: Is it consistent that for an
arbitrary tall summable ideal I_g there exists an I_g-ultrafilter which is not
rapid? We show that assuming Martin's Axiom for \sigma-centered posets such
ultrafilters exist for each tall summable ideal I_g.


  Full jet reconstruction in heavy-ion collisions enables a complete study of
the modification of jet structure due to energy loss in hot and dense QCD
matter, but is challenging due to the high multiplicity environment. The STAR
and PHENIX collaborations at RHIC have recently presented measurements of fully
reconstructed jets from p+p, Cu+Cu and Au+Au collisions at
$\sqrt{s_\mathrm{NN}} = 200 \mathrm{GeV}$. We review the first results on
inclusive jet spectra, di-jets and fragmentation functions and discuss their
implications on understanding of jet quenching.


  We propose a mechanism for a velocity-selective device, which exploits the
fundamental phenomenon of dynamical localization. It would allow packets of
cold atoms travelling through a pulsed optical lattice in one direction to pass
undisturbed, while dispersing atoms travelling in the opposite direction. This
mechanism is based on a chaotic diffusion process, with a momentum-dependent
diffusion coefficient. We analyse this classical effect, and we show that it is
enhanced by dynamical localization.


  Large Language Models (LLMs) stand at the forefront of a number of Natural
Language Processing (NLP) tasks. Despite the widespread adoption of LLMs in
NLP, much of their potential in broader fields remains largely unexplored, and
significant limitations persist in their design and implementation. Notably,
LLMs struggle with structured data, such as graphs, and often falter when
tasked with answering domain-specific questions requiring deep expertise, such
as those in biology and chemistry. In this paper, we explore a fundamental
question: Can LLMs effectively handle molecule prediction tasks? Rather than
pursuing top-tier performance, our goal is to assess how LLMs can contribute to
diverse molecule tasks. We identify several classification and regression
prediction tasks across six standard molecule datasets. Subsequently, we
carefully design a set of prompts to query LLMs on these tasks and compare
their performance with existing Machine Learning (ML) models, which include
text-based models and those specifically designed for analysing the geometric
structure of molecules. Our investigation reveals several key insights:
Firstly, LLMs generally lag behind ML models in achieving competitive
performance on molecule tasks, particularly when compared to models adept at
capturing the geometric structure of molecules, highlighting the constrained
ability of LLMs to comprehend graph data. Secondly, LLMs show promise in
enhancing the performance of ML models when used collaboratively. Lastly, we
engage in a discourse regarding the challenges and promising avenues to harness
LLMs for molecule prediction tasks. The code and models are available at
https://github.com/zhiqiangzhongddu/LLMaMol.


  We propose a new framework for deriving screening rules for convex
optimization problems. Our approach covers a large class of constrained and
penalized optimization formulations, and works in two steps. First, given any
approximate point, the structure of the objective function and the duality gap
is used to gather information on the optimal solution. In the second step, this
information is used to produce screening rules, i.e. safely identifying
unimportant weight variables of the optimal solution. Our general framework
leads to a large variety of useful existing as well as new screening rules for
many applications. For example, we provide new screening rules for general
simplex and $L_1$-constrained problems, Elastic Net, squared-loss Support
Vector Machines, minimum enclosing ball, as well as structured norm regularized
problems, such as group lasso.


  The Hermite functions are an orthonormalbasis of the space of square
integrable functions with favourable approximation properties. Allowing for a
flexible localization in position and momentum, the Hagedorn wavepackets
generalize the Hermite functions also to several dimensions. Using Hagedorn's
raising and lowering operators, we derive explicit formulas and recurrence
relations for the Wigner and FBI transform of the wavepackets and show their
relation to the Laguerre polyomials.


  We present an exact normalisable zero-energy chiral fermion solution for
abelian BPS dipoles. For a single dipole, this solution is contained within the
high temperature limit of the SU(2) caloron with non-trivial holonomy.


  A conjectural formula for the $k$-point generating function of Gromov--Witten
invariants of the Riemann sphere for all genera and all degrees was proposed in
\cite{DY2}. In this paper, we give a proof of this formula together with an
explicit analytic (as opposed to formal) expression for the corresponding
matrix resolvent. We also give a formula for the $k$-point function as a sum of
$(k-1)!$ products of hypergeometric functions of one variable. We show that the
$k$-point generating function coincides with the $\epsilon\rightarrow 0$
asymptotics of the analytic $k$-point function, and also compute three more
asymptotics of the analytic function for $\epsilon\rightarrow \infty$,
$q\rightarrow 0$, $q\rightarrow\infty$, thus defining new invariants for the
Riemann sphere.


  We review the scholarly contributions that utilise Natural Language
Processing (NLP) techniques to support the design process. Using a heuristic
approach, we gathered 223 articles that are published in 32 journals within the
period 1991-present. We present state-of-the-art NLP in-and-for design research
by reviewing these articles according to the type of natural language text
sources: internal reports, design concepts, discourse transcripts, technical
publications, consumer opinions, and others. Upon summarizing and identifying
the gaps in these contributions, we utilise an existing design innovation
framework to identify the applications that are currently being supported by
NLP. We then propose a few methodological and theoretical directions for future
NLP in-and-for design research.


  Antibodies have become an important class of therapeutic agents to treat
human diseases. To accelerate therapeutic antibody discovery, computational
methods, especially machine learning, have attracted considerable interest for
predicting specific interactions between antibody candidates and target
antigens such as viruses and bacteria. However, the publicly available datasets
in existing works have notable limitations, such as small sizes and the lack of
non-binding samples and exact amino acid sequences. To overcome these
limitations, we have developed AVIDa-hIL6, a large-scale dataset for predicting
antigen-antibody interactions in the variable domain of heavy chain of heavy
chain antibodies (VHHs), produced from an alpaca immunized with the human
interleukin-6 (IL-6) protein, as antigens. By leveraging the simple structure
of VHHs, which facilitates identification of full-length amino acid sequences
by DNA sequencing technology, AVIDa-hIL6 contains 573,891 antigen-VHH pairs
with amino acid sequences. All the antigen-VHH pairs have reliable labels for
binding or non-binding, as generated by a novel labeling method. Furthermore,
via introduction of artificial mutations, AVIDa-hIL6 contains 30 different
mutants in addition to wild-type IL-6 protein. This characteristic provides
opportunities to develop machine learning models for predicting changes in
antibody binding by antigen mutations. We report experimental benchmark results
on AVIDa-hIL6 by using machine learning models. The results indicate that the
existing models have potential, but further research is needed to generalize
them to predict effective antibodies against unknown mutants. The dataset is
available at https://avida-hil6.cognanous.com.


  We analyze exactly the simplest minimal superstring theory, using its dual
matrix model. Its target space is one dimensional (the Liouville direction),
and the background fields include a linear dilaton, a possible tachyon
condensate, and RR flux. The theory has both charged and neutral branes, and
these exhibit new and surprising phenomena. The smooth moduli space of charged
branes has different weakly coupled boundaries in which the branes have
different RR charges. This new duality between branes of different charges
shows that the semiclassical notion of localized charge is not precise in the
quantum theory, and that the charges of these branes can fluctuate.
Correspondingly, the RR flux in some parts of target space can also fluctuate
-- only the net flux at infinity is fixed. We substantiate our physical picture
with a detailed semiclassical analysis of the exact answers. Along the way, we
uncover new subtleties in super-Liouville theory.


  In this article we establish explicit isomorphisms between three realizations
of quantum twisted affine algebra $U_q(A_2^{(2)})$: the Drinfeld ("current")
realization, the Chevalley realization and the so-called $RLL$ realization,
investigated by Faddeev, Reshetikhin and Takhtajan.


  Emotion recognition (ER) technology is an integral part for developing
innovative applications such as drowsiness detection and health monitoring that
plays a pivotal role in contemporary society. This study delves into ER using
electroencephalography (EEG), within immersive virtual reality (VR)
environments. There are four main stages in our proposed methodology including
data acquisition, pre-processing, feature extraction, and emotion
classification. Acknowledging the limitations of existing 2D datasets, we
introduce a groundbreaking 3D VR dataset to elevate the precision of emotion
elicitation. Leveraging the Interaxon Muse headband for EEG recording and
Oculus Quest 2 for VR stimuli, we meticulously recorded data from 40
participants, prioritizing subjects without reported mental illnesses.
Pre-processing entails rigorous cleaning, uniform truncation, and the
application of a Savitzky-Golay filter to the EEG data. Feature extraction
encompasses a comprehensive analysis of metrics such as power spectral density,
correlation, rational and divisional asymmetry, and power spectrum. To ensure
the robustness of our model, we employed a 10-fold cross-validation, revealing
an average validation accuracy of 85.54\%, with a noteworthy maximum accuracy
of 90.20\% in the best fold. Subsequently, the trained model demonstrated a
commendable test accuracy of 82.03\%, promising favorable outcomes.


  The European Workshop on Photocathodes for (particle) Accelerator
Applications (EWPAA) brings together experts in the field of photocathode based
electron sources for use in particle accelerators, with the aim of sharing
their knowledge and latest research and development progress in this crucial
field of particle accelerator science. The workshop is convened every other
year, and is thus complementary to the P3 workshop (Photocathode Physics for
Particle accelerators) run in the USA. Consequently, there is a workshop
focusing on photocathodes for particle accelerator applications convened every
year, either in Europe or the USA. The EWPAA 2022 is the 4th meeting in this
workshop series. The event was hosted by the INFN LASA Institute in Milan in
collaboration with University of Milano between September 20th and 22nd. The
programme was organised with 7 working groups, with each oral contribution
assigned to the most appropriate group. This report presents, summarised by the
work group conveners, the innovative ideas, the challenges and the main points
raised by each of the speakers.


  Traditionally TCP bandwidth sharing has been investigated mainly by
stochastic approaches due to its seemingly chaotic nature. Even though of great
generality, the theories deal mainly with expectation values, which is prone to
misinterpretation with respect to the Quality-of-Experience (QoE). We
disassemble TCP operating conditions into dominating scenarios and show that
bandwidth sharing alone follows mostly deterministic rules. From the analysis
we derive significant root causes of well-known TCP aspects like unequal
sharing, burstiness of losses, global synchronization, and on buffer sizing. We
base our model on a detailed analysis of bandwidth sharing experiments with
subsequent mathematical reproduction.


  In this paper, we consider the relation between two nonabelian Fourier
transforms. The first one is defined in terms of the Langlands-Kazhdan-Lusztig
parameters for unipotent elliptic representations of a split p-adic group and
the second is defined in terms of the pseudocoefficients of these
representations and Lusztig's nonabelian Fourier transform for characters of
finite groups of Lie type. We exemplify this relation in the case of the p-adic
group of type G_2.


  Linearising the dynamics of nonlinear mechanical systems is an important and
open research area. In this paper, we adopt a data-driven and feedback control
approach to tackle this problem. A model predictive control architecture is
developed that builds upon data-driven dynamic models obtained using nonlinear
system identification. The overall methodology shows a high degree of
performance combined with significant robustness against imperfect modelling
and extrapolation. These findings are demonstrated using large set of synthetic
experiments conducted on a asymmetric Duffing oscillator and using an
experimental prototype of a high-precision motion system.


  Sequence parallelism (SP), which divides the sequence dimension of input
tensors across multiple computational devices, is becoming key to unlocking the
long-context capabilities of generative AI models. This paper investigates the
state-of-the-art SP approaches, i.e. DeepSpeed-Ulysses and Ring-Attention, and
proposes a unified SP approach, which is more robust to transformer model
architectures and network hardware topology. This paper compares the
communication and memory cost of SP and existing parallelism, including
data/tensor/zero/pipeline parallelism, and discusses the best practices for
designing hybrid 4D parallelism involving SP. We achieved 47% MFU on two 8xA800
nodes using SP for the LLAMA3-8B model training using sequence length 208K. Our
code is publicly available at
https://github.com/feifeibear/long-context-attention.


  We introduce new quantum key distribution protocols using quantum continuous
variables, that are secure against individual attacks for any transmission of
the optical line between Alice and Bob. In particular, it is not required that
this transmission is larger than 50 %. Though squeezing or entanglement may be
helpful, they are not required, and there is no need for quantum memories or
entanglement purification. These protocols can thus be implemented using
coherent states and homodyne detection, and they may be more efficient than
usual protocols using quantum discrete variables.


  Convolutional Neural Networks have been extensively explored in the task of
automatic music tagging. The problem can be approached by using either
engineered time-frequency features or raw audio as input. Modulation filter
bank representations that have been actively researched as a basis for timbre
perception have the potential to facilitate the extraction of perceptually
salient features. We explore end-to-end learned front-ends for audio
representation learning, ModNet and SincModNet, that incorporate a temporal
modulation processing block. The structure is effectively analogous to a
modulation filter bank, where the FIR filter center frequencies are learned in
a data-driven manner. The expectation is that a perceptually motivated filter
bank can provide a useful representation for identifying music features. Our
experimental results provide a fully visualisable and interpretable front-end
temporal modulation decomposition of raw audio. We evaluate the performance of
our model against the state-of-the-art of music tagging on the MagnaTagATune
dataset. We analyse the impact on performance for particular tags when
time-frequency bands are subsampled by the modulation filters at a
progressively reduced rate. We demonstrate that modulation filtering provides
promising results for music tagging and feature representation, without using
extensive musical domain knowledge in the design of this front-end.


  In this paper we study the superalgebra $A_n$, introduced by the authors in
previous work on categorification of Verma modules for quantum
$\mathfrak{sl}_2$. The superalgebra $A_n$ is akin to the nilHecke algebra, and
shares similar properties. In particular, we prove a uniqueness result about
2-Verma modules on $\Bbbk$-linear 2-categories.


  A class of elliptic-hyperbolic equations is placed in the context of a
geometric variational theory, in which the change of type is viewed as a change
in the character of an underlying metric. A fundamental example of a metric
which changes in this way is the extended projective disc, which is Riemannian
at ordinary points, Lorentzian at ideal points, and singular on the absolute.
Harmonic fields on such a metric can be interpreted as the hodograph image of
extremal surfaces in Minkowski 3-space. This suggests an approach to
generalized Plateau problems in 3-dimensional space-time via Hodge theory on
the extended projective disc. Analogous variational problems arise on
Riemannian-Lorentzian flow metrics in fiber bundles (twisted nonlinear Hodge
equations), and on certain singular Riemannian-Lorentzian manifolds which occur
in relativity and quantum cosmology. The examples surveyed come with natural
gauge theories and Hodge dualities. This paper is mainly a review, but some
technical extensions are proven.


  It has recently been shown that signatures of non-Hermitian topology can be
realized in a conventional quantum Hall device connected to multiple current
sources. These signatures manifest as robust current-voltage characteristics,
dictated by the presence of a nontrivial, non-Hermitian topological invariant
of the conductance matrix. Chiral edge states are believed to be responsible
for this non-Hermitian response, similar to how they lead to a quantized Hall
conductivity in the presence of a single current source. Here, we go beyond
this paradigm, showing that multi-terminal conductance matrices can exhibit
non-Hermitian topological phase transitions that cannot be traced back to the
presence and directionality of a boundary-localized chiral mode. By performing
quantum transport simulations in the quantum Hall regime of monolayer graphene,
we find that when the chemical potential is swept across the zeroth Landau
level, unavoidable device imperfections cause the appearance of an additional
non-Hermitian phase of the conductance matrix. This highlights graphene as an
ideal platform for the study of non-Hermitian topological phase transitions,
and is a first step towards exploring how the geometry of quantum devices can
be harnessed to produce robust, topologically-protected transport
characteristics.


  Several lines of evidence suggest that early-type galaxies might have been
very dusty during their initial evolutionary stages, characterized by intense
star formation activity. The radio selection has provided, by far, the richest
samples of high-$z$ galaxies, most likely of early type, which may therefore
yield crucial direct information on the period when the bulk of stars were
born. In order to investigate the role of dust in these phases, we have
analyzed recent observations of a number of high-$z$ radio galaxies in the
framework of a self-consistent scenario for the evolution of early type
galaxies. The available data can be fully accounted for by ``opaque'' models
similar to that already used by Mazzei and De Zotti (1994a) to fit the spectrum
of the hyperluminous galaxy IRAS~F$10214+4724$. However, there is still
considerable latitude for models. Good fits can be obtained with galactic ages
ranging from 0.05 to 2 Gyr. Crucial constraints may be provided by ground-based
sub-mm measurements and by observations with the forthcoming Infrared Space
Observatory (ISO).


  We use the thin-shell Darmois-Israel formalism to model and assess the
stability of the interfaces separating phases, e.g. the core and the crust,
within compact stars. We exemplify the relevance and non-triviality of this
treatment in the simplest case of an incompressible star, in constant pressure
phase transitions, and in the case of strange quark stars with crust.


  The definition and derivation of flavor neutrino states in the framework of
standard Quantum Field Theory is reviewed, clarifying some subtle points. It is
shown that a flavor neutrino state that describes a neutrino produced or
detected in a charged-current weak interaction process depends on the process
under consideration and is appropriate for the description of neutrino
oscillations as well as for the calculation of neutrino production or detection
rates. Hence, we have a consistent framework for the description of neutrino
oscillations and interactions in neutrino oscillation experiments. The standard
flavor neutrino states are obtained as approximations which describe neutrinos
in experiments that are not sensitive to the dependence of neutrino
interactions on the neutrino mass differences. It is also shown that the
oscillation probability can be derived either through the usual light-ray time
= distance approximation or through an average of the space-time dependent
oscillation probability over the unobserved propagation time.


  The large amount of deployed smart devices put tremendous traffic pressure on
networks. Caching at the edge has been widely studied as a promising technique
to solve this problem. To further improve the successful transmission
probability (STP) of cache-enabled cellular networks (CEN), we combine the
cooperative transmission technique with CEN and propose a novel transmission
scheme. Local channel state information (CSI) is introduced at each cooperative
base station (BS) to enhance the strength of the signal received by the user. A
tight approximation for the STP of this scheme is derived using tools from
stochastic geometry. The optimal content placement strategy of this scheme is
obtained using a numerical method to maximize the STP. Simulation results
demonstrate the optimal strategy achieves significant gains in STP over several
comparative baselines with the proposed scheme.


  We build analytic surfaces in $\mathbb{R}cubec$ represented by the most
general sixth Painlev\'e equation $P_{VI}$ in two steps. Firstly, the moving
frame of the surfaces built by Bonnet in 1867 is extrapolated to a new, second
order, isomonodromic matrix Lax pair of $P_{VI}$, whose elements depend
rationally on the dependent variable and quadratically on the monodromy
exponents $\theta_j$. Secondly, by converting back this Lax pair to a moving
frame, we obtain an extrapolation of Bonnet surfaces to surfaces with two more
degrees of freedom. Finally, we give a rigorous derivation of the quantum
correspondence for $P_{VI}$.


  The radiative decay of excited states of the negative ion of rhodium, Rh$^-$,
has been investigated experimentally and theoretically. The experiments were
conducted at the Double ElectroStatic Ion Ring Experiment (DESIREE) facility at
Stockholm University using selective photodetachment from a stored ion beam to
monitor the time evolution of the excited state populations. The lifetimes of
the Rh$^-$ $^3F_{3}$ and $^3F_{2}$ fine structure levels were measured to be
3.2(6)~s and 21(4)~s, respectively. An additional, previously unreported,
higher-lying bound state of mixed $^1D_2+^3P_2+(4d^95s)^1D_2+^3F_2$ composition
was observed and found to have a lifetime of 10.9(8)s. The binding energy of
this state was determined to be in the interval $0.1584(2) $ eV $ < E_b <
0.2669(2)$ eV, using laser photodetachment threshold (LPT) spectroscopy. An
autodetaching state with a lifetime of 480(10) microseconds was also observed.
Theoretical calculations of the excited-state compositions, energies, and
magnetic-dipole transition lifetimes were performed using the
multiconfiguration Dirac-Hartree-Fock and relativistic configuration
interaction methods. The calculated lifetimes of the $^3F_{3}$ and $^3F_{2}$
fine structure levels are in excellent agreement with the measured values. The
present study should provide valuable insights into electron correlation
effects in negative ions and forbidden radiative transitions.


  The problem of representing a given positive integer as a sum of four squares
of integers has been widely concerned for a long time, and for a given positive
odd $n$ one can find a representation by doing arithmetic in a maximal order of
quaternion algebra once a pair of (positive) integers $x,y$ with
$x^2+y^2\equiv-1\mod n$ is given. In this paper, we introduce a new method to
find a representation of odd integer $w$ given $x,y$ satisfying the above
requirement. This method can avoid the complicated non-commutative structure in
quaternion algebra, which is similar to the one we use to obtain a
representation of a prime $p\equiv1\mod4$ as sum of two squares by doing
continued fraction expansions, except that here we will expand complex number
using Hurwitz algorithm.


  We study the statistical mechanics of classical self-gravitating systems
confined within a box of radius $R$ in general relativity. It has been found
that the caloric curve $T_{\infty}(E)$ has the form of a double spiral whose
shape depends on the compactness parameter $\nu=GNm/Rc^2$. The double spiral
shrinks as $\nu$ increases and finally disappears when $\nu_{\rm max}=0.1764$.
Therefore, general relativistic effects render the system more unstable. On the
other hand, the cold spiral and the hot spiral move away from each other as
$\nu$ decreases. Using a normalization $\Lambda=-ER/GN^2m^2$ and $\eta=GNm^2/R
k_B T_{\infty}$ appropriate to the nonrelativistic limit, and considering
$\nu\rightarrow 0$, the hot spiral goes to infinity and the caloric curve tends
towards a limit curve (determined by the Emden equation) exhibiting a single
cold spiral, as found in former works. Using another normalization ${\cal
M}=GM/Rc^2$ and ${\cal B}={Rc^4}/{GNk_B T_{\infty}}$ appropriate to the
ultrarelativistic limit, and considering $\nu\rightarrow 0$, the cold spiral
goes to infinity and the caloric curve tends towards a limit curve (determined
by the general relativistic Emden equation) exhibiting a single hot spiral.
This result is new. We discuss the analogies and the differences between this
asymptotic caloric curve and the caloric curve of the self-gravitating
black-body radiation. Finally, we compare box-confined isothermal models with
heavily truncated isothermal distributions in Newtonian gravity and general
relativity.


  Measuring longitudinally polarised vector boson scattering in WW channel is a
promising way to investigate unitarity restoration with the Higgs mechanism and
to search for possible physics beyond the Standard Model. In order to perform
such a measurement, it is crucial to develop an efficient reconstruction of the
full W boson kinematics in leptonic decays with the focus on polarisation
measurements. We investigated several approaches, from traditional ones up to
advanced deep neural network structures, and we compared their ability to
reconstruct the W boson reference frame and to consequently measure the
longitudinal fraction W_L in both semi-leptonic and di-leptonic WW decay
channels.


  The introduction of robust optimisation has pushed the state-of-the-art in
defending against adversarial attacks. Notably, the state-of-the-art projected
gradient descent (PGD)-based training method has been shown to be universally
and reliably effective in defending against adversarial inputs. This robustness
approach uses PGD as a reliable and universal "first-order adversary". However,
the behaviour of such optimisation has not been studied in the light of a
fundamentally different class of attacks called backdoors. In this paper, we
study how to inject and defend against backdoor attacks for robust models
trained using PGD-based robust optimisation. We demonstrate that these models
are susceptible to backdoor attacks. Subsequently, we observe that backdoors
are reflected in the feature representation of such models. Then, this
observation is leveraged to detect such backdoor-infected models via a
detection technique called AEGIS. Specifically, given a robust Deep Neural
Network (DNN) that is trained using PGD-based first-order adversarial training
approach, AEGIS uses feature clustering to effectively detect whether such DNNs
are backdoor-infected or clean.
  In our evaluation of several visible and hidden backdoor triggers on major
classification tasks using CIFAR-10, MNIST and FMNIST datasets, AEGIS
effectively detects PGD-trained robust DNNs infected with backdoors. AEGIS
detects such backdoor-infected models with 91.6% accuracy (11 out of 12 tested
models), without any false positives. Furthermore, AEGIS detects the targeted
class in the backdoor-infected model with a reasonably low (11.1%) false
positive rate. Our investigation reveals that salient features of adversarially
robust DNNs could be promising to break the stealthy nature of backdoor
attacks.


  Ultra-wideband (UWB) electromagnetic pulses of nanosecond duration, or
nanopulses, are of considerable interest to the communications industry and are
being explored for various applications in biotechnology and medicine. The
propagation of a nanopulse through biological matter has been computed in the
time domain using the finite difference-time domain method (FDTD). The approach
required existing Cole-Cole model-based descriptions of dielectric properties
of biological matter to be re-parametrized using the Debye model, but without
loss of accuracy. The approach has been applied to several tissue types.
Results show that the electromagnetic field inside a biological tissue depends
on incident pulse rise time and width. Rise time dominates pulse behavior
inside a tissue as conductivity increases. It has also been found that the
amount of energy deposited by 20 $kV/m$ nanopulses is insufficient to change
the temperature of the exposed material for the pulse repetition rates of 1
$MHz$ or less.


  In this paper we study conditions which guarantee the existence of perfect
matchings and perfect fractional matchings in uniform hypergraphs. We reduce
this problem to an old conjecture by Erd\H{o}s on estimating the maximum number
of edges in a hypergraph when the (fractional) matching number is given, which
we are able to solve in some special cases using probabilistic techniques.
Based on these results, we obtain some general theorems on the minimum
$d$-degree ensuring the existence of perfect (fractional) matchings. In
particular, we asymptotically determine the minimum vertex degree which
guarantees a perfect matching in 4-uniform and 5-uniform hypergraphs. We also
discuss an application to a problem of finding an optimal data allocation in a
distributed storage system.


  In this paper, we establish the existence of both compressive stress and
charge transfer process in hydrothermally synthesized cobalt ferrite-graphene
oxide (CoFe2O4/GO) nanocomposites. Transmission electron microscopy (TEM)
results reveal the decoration of CoFe2O4 nanoparticles on GO sheets. Magnetic
response of nanocomposites was confirme from superconducting quantum
interference device (SQUID) magnetometer measurement. Optical properties of
these nanocomposites were investigated by Raman spectroscopy. Interfacial
compressive stress involved in this system is evaluated from observed blue
shift of characteristic G peak of graphene oxide. Increase in full width
half-maximum ( FWHM) as well as up shift in D and G peaks are clear indicator
of involvement of charge transfer process between GO sheets and dispersed
magnetic nanoparticles. The effect of charge transfer process is quantified in
terms of shifting of Fermi level of these nanocomposites. This is evaluated
from variation in contact surface potential difference (CPD) using Scanning
Kelvin probe microscopy (SKPM). XRD spectra of CoFe2O4/GO confirm the
polycrystalline nature of CoFe2O4 nanoparticles. Lattice strain estimated from
XRD peaks are correlated to the observed Raman shift.


  The Federated Learning setting has a central server coordinating the training
of a model on a network of devices. One of the challenges is variable training
performance when the dataset has a class imbalance. In this paper, we address
this by introducing a new loss function called Fed-Focal Loss. We propose to
address the class imbalance by reshaping cross-entropy loss such that it
down-weights the loss assigned to well-classified examples along the lines of
focal loss. Additionally, by leveraging a tunable sampling framework, we take
into account selective client model contributions on the central server to
further focus the detector during training and hence improve its robustness.
Using a detailed experimental analysis with the VIRTUAL (Variational Federated
Multi-Task Learning) approach, we demonstrate consistently superior performance
in both the balanced and unbalanced scenarios for MNIST, FEMNIST, VSN and HAR
benchmarks. We obtain a more than 9% (absolute percentage) improvement in the
unbalanced MNIST benchmark. We further show that our technique can be adopted
across multiple Federated Learning algorithms to get improvements.


  Perfect (optical) vortex (PV) beams are fields which are mooted to be
independent of the orbital angular momentum (OAM) they carry. To date, the best
experimental approximation of these modes is obtained from passing
Bessel-Gaussian beams through a Fourier lens. However, the OAM-dependent width
of these quasi-PVs is not precisely known and is often understated. We address
this here by deriving and experimentally confirming an explicit analytic
expression for the second moment width of quasi-PVs. We show that the width
scales in proportion to $\sqrt{\ell}$ in the best case, the same as most
"regular" vortex modes albeit with a much smaller proportionality constant. Our
work will be of interest to the large community who seek to use such structured
light fields in various applications, including optical trapping, tweezing and
communications.


  We implement the normal ordering technique to study the quantum dissipation
of a single mode harmonic oscillator system. The dynamic evolution of the
system is investigated for a reasonable initial state by solving the
Schr\"{o}dinger equation directly through the normal ordering technique. The
decoherence process of the system for the cases $T=0K$ and $T\neq0K$ is
investigated as an application.


  Recently it has been claimed that the warm dark matter (WDM) model cannot at
the same time reproduce the observed Lyman-{\alpha} forests in distant quasar
spectra and solve the small-scale issues in the cold dark matter (CDM) model.
As an alternative candidate, it was shown that the mixed dark matter (MDM)
model that consists of WDM and CDM can satisfy the constraint from
Lyman-{\alpha} forests and account for the "missing satellite problem" as well
as the reported 3.5 keV anomalous X-ray line. We investigate observational
constraints on the MDM model using strong gravitational lenses. We first
develop a fitting formula for the nonlinear power spectra in the MDM model by
performing N-body simulations and estimate the expected perturbations caused by
line-of-sight structures in four quadruply lensed quasars that show anomaly in
the flux ratios. Our analysis indicates that the MDM model is compatible with
the observed anomaly if the mass fraction of the warm component is smaller than
0.47 at the 95% confidence level. The MDM explanation to the anomalous X-ray
line and the small-scale issues is still viable even after this constraint is
taken into account.


  Effect of Ge substitution on first order ferrimagnetic (FRI) -
antiferromagnetic (AFM) transition in Mn$_2$Sb has been studied. It shows that
transition temperature (T$_t$) can be tuned between 119~K - 271~K by
substituting 2.5-10\% Ge at Sb site in Mn$_2$Sb. The variation of density of
state at Fermi level N(E$_f$) with Ge substitution shows that dN(E)/dE is
positive at E$_f$ in the AFM state. With the application of magnetic field
T$_t$ shifts to low temperature, which results in a giant negative
magnetoresistance (MR) reaching a value of 70\% for 2.5\% substitution. Our
results show that FRI to AFM transformation during cooling stops around 35 K,
even though it remains incomplete. It along with non-monotonic variation of
lower critical field, open loop in isothermal MR and increasing difference in
zero field cooled warming (ZFCW) and field cooled warming (FCW) resistivity
with increasing magnetic field shows that FRI to AFM transition is kinetically
arrested in the case of 2.5\% Ge substitution.


  Diamond anvil cell using boron-doped metallic diamond electrodes covered with
undoped diamond insulating layer have been developed for electrical transport
measurements under high pressure. These designed diamonds were grown on a
bottom diamond anvil via a nanofabrication process combining microwave
plasma-assisted chemical vapor deposition and electron beam lithography. The
resistance measurements of high quality FeSe superconducting single crystal
under high pressure were successfully demonstrated by just putting the sample
and gasket on the bottom diamond anvil directly. The superconducting transition
temperature of FeSe single crystal was enhanced up to 43 K by applying
uniaxial-like pressure.


  We explore the possibility of modifying the classical Gauss free energy
functional used in capillarity theory by considering surface tension energies
of nonlocal type. The corresponding variational principles lead to new
equilibrium conditions which are compared to the mean curvature equation and
Young's law found in classical capillarity theory. As a special case of this
family of problems we recover a nonlocal relative isoperimetric problem of
geometric interest.


  Transfer learning makes use of data or knowledge in one problem to help solve
a different, yet related, problem. It is particularly useful in brain-computer
interfaces (BCIs), for coping with variations among different subjects and/or
tasks. This paper considers offline unsupervised cross-subject
electroencephalogram (EEG) classification, i.e., we have labeled EEG trials
from one or more source subjects, but only unlabeled EEG trials from the target
subject. We propose a novel manifold embedded knowledge transfer (MEKT)
approach, which first aligns the covariance matrices of the EEG trials in the
Riemannian manifold, extracts features in the tangent space, and then performs
domain adaptation by minimizing the joint probability distribution shift
between the source and the target domains, while preserving their geometric
structures. MEKT can cope with one or multiple source domains, and can be
computed efficiently. We also propose a domain transferability estimation (DTE)
approach to identify the most beneficial source domains, in case there are a
large number of source domains. Experiments on four EEG datasets from two
different BCI paradigms demonstrated that MEKT outperformed several
state-of-the-art transfer learning approaches, and DTE can reduce more than
half of the computational cost when the number of source subjects is large,
with little sacrifice of classification accuracy.


  Motivated by the rank modulation scheme, a recent work by Sala and Dolecek
explored the study of constraint codes for permutations. The constraint studied
by them is inherited by the inter-cell interference phenomenon in flash
memories, where high-level cells can inadvertently increase the level of
low-level cells.
  In this paper, the model studied by Sala and Dolecek is extended into two
constraints. A permutation $\sigma \in S_n$ satisfies the \emph{two-neighbor
$k$-constraint} if for all $2 \leq i \leq n-1$ either
$|\sigma(i-1)-\sigma(i)|\leq k$ or $|\sigma(i)-\sigma(i+1)|\leq k$, and it
satisfies the \emph{asymmetric two-neighbor $k$-constraint} if for all $2 \leq
i \leq n-1$, either $\sigma(i-1)-\sigma(i) < k$ or $\sigma(i+1)-\sigma(i) < k$.
We show that the capacity of the first constraint is $(1+\epsilon)/2$ in case
that $k=\Theta(n^{\epsilon})$ and the capacity of the second constraint is 1
regardless to the value of $k$. We also extend our results and study the
capacity of these two constraints combined with error-correction codes in the
Kendall's $\tau$ metric.


  We investigate the one-loop corrections at zero, as well as finite
temperature, of a scalar field taking place in a braneworld motived warped
background. After to reach a well defined problem, we calculate the effective
action with the corresponding quantum corrections to each case.


  Let $\Omega$ be homogeneous of degree zero, have vanishing moment of order
one on the unit sphere $\mathbb {S}^{d-1}$($d\ge 2$). In this paper, our object
of investigation is the following rough non-standard singular integral operator
$$T_{\Omega,\,A}f(x)={\rm
p.\,v.}\int_{\mathbb{R}^d}\frac{\Omega(x-y)}{|x-y|^{d+1}}\big(A(x)-A(y)-\nabla
A(y)(x-y)\big)f(y){\rm d}y,$$ where $A$ is a function defined on $\mathbb{R}^d$
with derivatives of order one in ${\rm BMO}(\mathbb{R}^d)$. We show that
$T_{\Omega,\,A}$ enjoys the endpoint $L\log L$ type estimate and is $L^p$
bounded if $\Omega\in L(\log L)^{2}(\mathbb{S}^{d-1})$. These resuts
essentially improve the previous known results given by Hofmann for the $L^p$
boundedness of $T_{\Omega,\,A}$ under the condition $\Omega\in L^{q}(\mathbb
{S}^{d-1})$ $(q>1)$, Hu and Yang for the endpoint weak $L\log L$ type estimates
when $\Omega\in {\rm Lip}_{\alpha}(\mathbb{S}^{d-1})$ for some $\alpha\in
(0,\,1]$. Quantitative weighted strong and endpoint weak $L\log L$ type
inequalities are proved whenever $\Omega\in L^{\infty}(\mathbb {S}^{d-1})$. The
analysis of the weighted results relies heavily on two bilinear sparse
dominations of $T_{\Omega,\,A}$ established herein.


  BOOMERanG has recently resolved structures on the last scattering surface at
redshift $\sim$ 1100 with high signal to noise ratio. We review the technical
advances which made this possible, and we focus on the current results for maps
and power spectra, with special attention to the determination of the total
mass-energy density in the Universe and of other cosmological parameters.


  We use a cosmological galactic evolutionary approach to model the Milky Way.
A detailed treatment of the mass aggregation and dynamical history of the
growing dark halo is included, together with a self consistent physical
treatment for the star formation processes within the growing galactic disc.
This allows us to calculate the temporal evolution of star and gas surface
densities at all galactic radii, in particular, the star formation history
(SFH) at the solar radius. A large range of cosmological mass aggregation
histories (MAHs) is capable of producing a galaxy with the present day
properties of the Milky Way. The resulting SFHs for the solar neighbourhood
bracket the available observational data for this feature, the most probable
MAH yielding the optimal comparison with these observations. We also find that
the rotation curve for our Galaxy implies the presence of a constant density
core in its dark matter halo.


  In this paper we investigate the relationship between twisted and untwisted
character varieties via a specific instance of the Cohomological Hall algebra
for moduli of objects in 3-Calabi-Yau categories introduced by Kontsevich and
Soibelman. In terms of Donaldson--Thomas theory, this relationship is
completely understood via the calculations of Hausel and Villegas of the E
polynomials of twisted character varieties and untwisted character stacks. We
present a conjectural lift of this relationship to the cohomological Hall
algebra setting.


  We show that each orbit of a Borel subgroup $B_{n-1}$ of GL(n-1)
(respectively SO(n-1)) on the flag variety of GL(n) (respectively of SO(n)) is
a bundle over a $B_{n-1}$-orbit on a generalized flag variety of GL(n-1)
(respectively SO(n-1)), with fiber isomorphic to an orbit of an analogous
subgroup on a smaller flag variety. As a consequence, we develop an inductive
procedure to classify $B_{n-1}$-orbits on the flag variety. Our method is
essentially uniform in the two cases. As further consequences, in the sequel to
this paper we give an explicit combinatorial classification of orbits and
determine completely the closure relation between orbit closures. This further
develops work of Hashimoto in the general linear group case.


  We design models for helium in matrices like aerogel, Vycor or Geltech from a
manifestly microscopic point of view. For that purpose, we calculate the
dynamic structure function of 4He on Si substrates and between two Si walls as
a function of energy, momentum transfer, and the scattering angle. The
angle--averaged results are in good agreement with the neutron scattering data;
the remaining differences can be attributed to the simplified model used here
for the complex pore structure of the materials. A focus of the present work is
the detailed identification of coexisting layer modes and bulk--like
excitations, and, in the case of thick films, ripplon excitations. Involving
essentially two--dimensional motion of atoms, the layer modes are sensitive to
the scattering angle.


  We develop a method, based on a Bochner-type identity, to obtain estimates on
the exponential rate of decay of the relative entropy from equilibrium of
Markov processes in discrete settings. When this method applies the relative
entropy decays in a convex way. The method is shown to be rather powerful when
applied to a class of birth and death processes. We then consider other
examples, including inhomogeneous zero-range processes and Bernoulli-Laplace
models. For these two models, known results were limited to the homogeneous
case, and obtained via the martingale approach, whose applicability to
inhomogeneous models is still unclear.


  Let $f(x)$ be a separable polynomial over a local field. Montes algorithm
computes certain approximations to the different irreducible factors of $f(x)$,
with strong arithmetic properties. In this paper we develop an algorithm to
improve any one of these approximations, till a prescribed precision is
attained. The most natural application of this "single-factor lifting" routine
is to combine it with Montes algorithm to provide a fast polynomial
factorization algorithm. Moreover, the single-factor lifting algorithm may be
applied as well to accelerate the computational resolution of several global
arithmetic problems in which the improvement of an approximation to a single
local irreducible factor of a polynomial is required.


  We introduce a model of a quantum walk on a graph in which a particle jumps
between neighboring nodes and interacts with independent spins sitting on the
edges. Entanglement propagates with the walker. We apply this model to the case
of a one dimensional lattice, to investigate its magnetic and entanglement
properties. In the continuum limit, we recover a Landau-Lifshitz equation that
describes the precession of spins. A rich dynamics is observed, with regimes of
particle propagation and localization, together with spin oscillations and
relaxation. Entanglement of the asymptotic states follows a volume law for most
parameters (the coin rotation angle and the particle-spin coupling).


  Physics education research has probed for the relevance of physics in
students' everyday lives. Attitudinal and epistemological surveys have asked
students if they think of or use physics in their daily lives. We have
previously documented how it is uncommon that our life science students
describe using or even seeing physics in their daily life (Nair, 2018). This
result was unsurprising and aligns with previous scholarship of students
majoring in disciplines outside of physics; we have argued that it is
optimistic for scholars to expect students with disciplinary homes outside of
physics to see their experiences through a lens of physics. Methodologically,
we searched for a contrasting case (Sam). Sam is majoring in the life sciences
and articulates moments where she uses physics to reason through everyday
phenomena. We explore the ways in which courses can support students like Sam
to find physics relevant to their everyday experiences.