  Trust region methods rigorously enabled reinforcement learning (RL) agents to
learn monotonically improving policies, leading to superior performance on a
variety of tasks. Unfortunately, when it comes to multi-agent reinforcement
learning (MARL), the property of monotonic improvement may not simply apply;
this is because agents, even in cooperative games, could have conflicting
directions of policy updates. As a result, achieving a guaranteed improvement
on the joint policy where each agent acts individually remains an open
challenge. In this paper, we extend the theory of trust region learning to
MARL. Central to our findings are the multi-agent advantage decomposition lemma
and the sequential policy update scheme. Based on these, we develop
Heterogeneous-Agent Trust Region Policy Optimisation (HATPRO) and
Heterogeneous-Agent Proximal Policy Optimisation (HAPPO) algorithms. Unlike
many existing MARL algorithms, HATRPO/HAPPO do not need agents to share
parameters, nor do they need any restrictive assumptions on decomposibility of
the joint value function. Most importantly, we justify in theory the monotonic
improvement property of HATRPO/HAPPO. We evaluate the proposed methods on a
series of Multi-Agent MuJoCo and StarCraftII tasks. Results show that HATRPO
and HAPPO significantly outperform strong baselines such as IPPO, MAPPO and
MADDPG on all tested tasks, therefore establishing a new state of the art.


  Undoped and Mn doped ZnO samples with different percentage of Mn content (1
mol%, 2 mol% and 3mol%) were synthesized by a simple solvo-thermal method. We
have studied the structural, chemical and optical properties of the samples by
using x-ray diffraction (XRD), scanning electron microscopy (SEM), energy
dispersive x-ray (EDX) analysis, Fourier transform infrared (FTIR) spectroscopy
and UV-VIS spectroscopy. The XRD spectra show that all the samples are
hexagonal wurtzite structures. The lattice parameters calculated for the Mn
doped ZnO from the XRD pattern were found to be slightly larger than those of
the undoped ZnO, which indicates substitution of Mn in ZnO lattice. SEM
photograph shows the grain size of undoped ZnO is bigger than the Mn doped ZnO
indicating hindrance of grain growth upon Mn doping. As the Mn doping increases
the optical band gap decreases for the range of Mn doping reported here.


  Despite achieving promising results in a breadth of medical image
segmentation tasks, deep neural networks require large training datasets with
pixel-wise annotations. Obtaining these curated datasets is a cumbersome
process which limits the applicability in scenarios. Mixed supervision is an
appealing alternative for mitigating this obstacle. In this work, we propose a
dual-branch architecture, where the upper branch (teacher) receives strong
annotations, while the bottom one (student) is driven by limited supervision
and guided by the upper branch. Combined with a standard cross-entropy loss
over the labeled pixels, our novel formulation integrates two important terms:
(i) a Shannon entropy loss defined over the less-supervised images, which
encourages confident student predictions in the bottom branch; and (ii) a KL
divergence term, which transfers the knowledge (i.e., predictions) of the
strongly supervised branch to the less-supervised branch and guides the entropy
(student-confidence) term to avoid trivial solutions. We show that the synergy
between the entropy and KL divergence yields substantial improvements in
performance. We also discuss an interesting link between Shannon-entropy
minimization and standard pseudo-mask generation, and argue that the former
should be preferred over the latter for leveraging information from unlabeled
pixels. We evaluate the effectiveness of the proposed formulation through a
series of quantitative and qualitative experiments using two publicly available
datasets. Results demonstrate that our method significantly outperforms other
strategies for semantic segmentation within a mixed-supervision framework, as
well as recent semi-supervised approaches. Our code is publicly available:
https://github.com/by-liu/ConfKD.


  Achieving desirable receiver sampling in ocean bottom acquisition is often
not possible because of cost considerations. Assuming adequate source sampling
is available, which is achievable by virtue of reciprocity and the use of
modern randomized (simultaneous-source) marine acquisition technology, we are
in a position to train convolutional neural networks (CNNs) to bring the
receiver sampling to the same spatial grid as the dense source sampling. To
accomplish this task, we form training pairs consisting of densely sampled data
and artificially subsampled data using a reciprocity argument and the
assumption that the source-site sampling is dense. While this approach has
successfully been used on the recovery monochromatic frequency slices, its
application in practice calls for wavefield reconstruction of time-domain data.
Despite having the option to parallelize, the overall costs of this approach
can become prohibitive if we decide to carry out the training and recovery
independently for each frequency. Because different frequency slices share
information, we propose the use the method of transfer training to make our
approach computationally more efficient by warm starting the training with CNN
weights obtained from a neighboring frequency slices. If the two neighboring
frequency slices share information, we would expect the training to improve and
converge faster. Our aim is to prove this principle by carrying a series of
carefully selected experiments on a relatively large-scale five-dimensional
data synthetic data volume associated with wide-azimuth 3D ocean bottom node
acquisition. From these experiments, we observe that by transfer training we
are able t significantly speedup in the training, specially at relatively
higher frequencies where consecutive frequency slices are more correlated.


  The model for the cuprates based on the modified electron-phonon pairing
mechanism has been tested. For this purpose, the superconductors with high
value of the critical temperature have been taken into consideration. In
particular: ${\rm YBa_{2}Cu_{3}O_{7-y}}$, ${\rm HgBa_{2}CuO_{4+y}}$, ${\rm
HgBa_{2}Cu_{1-x}Zn_{x}O_{4+y}}$, and ${\rm HgBa_{2}Ca_{2}Cu_{3}O_{8+y}}$. It
has been shown that the dependence of the ratio $R_{1}\equiv
2\Delta_{tot}^{(0)}/k_{B}T_{C}$ on the doping ($p$) can be properly predicted
in the framework of the presented theory; the symbol $\Delta_{tot}^{(0)}$
denotes the energy gap amplitude at the temperature of zero Kelvin, and $T_{C}$
is the critical temperature. The numerical results have been supplemented by
the formula which describes the function $R_{1}(p)$.


  This work provides a comprehensive scaling law and performance analysis for
multi-user massive MIMO relay networks, where the relay is equipped with
massive antennas and uses MRC/MRT for low-complexity processing. CSI error is
considered. First, a sum-rate lower bound is derived which manifests the effect
of system parameters including the numbers of relay antennas and users, the CSI
quality, and the transmit powers of the sources and the relay. Via a general
scaling model on the system parameters with respect to the relay antenna
number, the asymptotic scaling law of the SINR as a function of the parameter
scalings is obtained, which shows quantitatively the tradeoff between the
network parameters and their effect on the network performance. In addition, a
sufficient condition on the parameter scalings for the SINR to be
asymptotically deterministic is given, which covers existing studies on such
analysis as special cases. Then, the scenario where the SINR increases linearly
with the relay antenna number is studied. The sufficient and necessary
condition on the parameter scaling for this scenario is proved. It is shown
that in this case, the interference power is not asymptotically deterministic,
then its distribution is derived, based on which the outage probability and
average bit error rate of the relay network are analysed.


  The HI Parkes All-Sky Survey (HIPASS) gives an unprecedented view of the
local large-scale structures in the southern sky. I will review the results
from the HIPASS Bright Galaxy Catalog (BGC, Koribalski et al. 2004) and the
first version of the deep catalog (HICAT, Meyer et al. 2004) with particular
emphasis on galaxy structures across the Zone of Avoidance. Some previously
hardly noticed galaxy groups stand out quite distinctively in the HI sky
distribution and several large-scale structures are seen for the first time.
Because HI surveys preferentially detect late-type spiral and dwarf galaxies,
their distribution is less clustered than that of optically selected galaxies.
HI surveys also significantly enhance the connectivity of large-scale filaments
by filling in gaps populated by gas-rich galaxies.


  The flow in an evaporating glycerol-water binary sub-millimeter droplet with
Bond number Bo $\ll$ 1 is studied both experimentally and numerically. First,
we measure the flow fields near the substrate by micro-PIV for both sessile and
pendant droplets during the evaporation process, which surprisingly show
opposite radial flow directions -- inward and outward, respectively. This
observation clearly reveals that in spite of the small droplet size,
gravitational effects play a crucial role in controlling the flow fields in the
evaporating droplets. We theoretically analyze that this gravity-driven effect
is triggered by the lower volatility of glycerol which leads to a preferential
evaporation of water then the local concentration difference of the two
components leads to a density gradient that drives the convective flow. We show
that the Archimedes number Ar is the nondimensional control parameter for the
occurrence of the gravitational effects. We confirm our hypothesis by
experimentally comparing two evaporating microdroplet systems, namely a
glycerol-water droplet and a 1,2-propanediol-water droplet. We obtain different
Ar, larger or smaller than a unit by varying a series of droplet heights, which
corresponds to cases with or without gravitational effects, respectively.
Finally, we simulate the process numerically, finding good agreement with the
experimental results and again confirming our interpretation.


  Multi-domain learning (MDL) aims at obtaining a model with minimal average
risk across multiple domains. Our empirical motivation is automated microscopy
data, where cultured cells are imaged after being exposed to known and unknown
chemical perturbations, and each dataset displays significant experimental
bias. This paper presents a multi-domain adversarial learning approach, MuLANN,
to leverage multiple datasets with overlapping but distinct class sets, in a
semi-supervised setting. Our contributions include: i) a bound on the average-
and worst-domain risk in MDL, obtained using the H-divergence; ii) a new loss
to accommodate semi-supervised multi-domain learning and domain adaptation;
iii) the experimental validation of the approach, improving on the state of the
art on two standard image benchmarks, and a novel bioimage dataset, Cell.


  Thermodynamics and kinetics of lithium intercalation into C--SiO$_2$
nanocomposites are investigated. Dependencies of both differential capacity and
intercalation kinetics on the nanocomposite size are established. The processes
are analyzed in terms of the impedance model. The obtained results are
explained based on the quantum effect of interference blockade of electron
tunneling into a nonmetallic nanoparticle. Propositions for the new
electrochemical energy storage technology are presented.


  Nucleation and its mechanism of diamond on titanium substrates under very low
pressure was studied using hot-filament chemical vapor deposition. Very high
nucleation rates and densities (10^8-10^{10} cm^{-2}) were obtained under 1
torr, which were 1-3 orders of magnitude higher than the counterpart (10^7
cm^{-2}) under conventionally low pressure (tens of torr). The effects of
substrate temperature and methane concentration under very low pressure were
also investigated, revealing that, overly high substrate temperature leads to a
relatively low nucleation density, and that higher CH_4 concentration gives
rise to a higher density and a higher rate. The nucleation mechanism is
discussed in detail. While a large amount of atomic hydrogen creates nucleating
sites, sufficient supersaturation of carbon and/or hydrocarbon species on/near
the substrate surface is the key factor for nucleation, in competition against
the rapid formation of carbide. Very low pressure leads to long mean free path
and other benefiting effects, and hence, is critical for rapid, high-density
nucleation. Effects of substrate temperature and CH_4 concentration are also
important. This further implies that C_2H_x (x<6) and CH_4 also contribute to
nucleation, but CH_{1-3} dominates under very low pressure. The
very-low-pressure method seems to be the only candidate to make diamond
deposition on titanium films applicable. It also sheds light on how to increase
the diamond growth rate.


  Cross-lingual annotations of legislative texts enable us to explore major
themes covered in multilingual legal data and are a key facilitator of semantic
similarity when searching for similar documents. Multilingual probabilistic
topic models have recently emerged as a group of semi-supervised machine
learning models that can be used to perform thematic explorations on
collections of texts in multiple languages. However, these approaches require
theme-aligned training data to create a language-independent space, which
limits the amount of scenarios where this technique can be used. In this work,
we provide an unsupervised document similarity algorithm based on hierarchies
of multi-lingual concepts to describe topics across languages. The algorithm
does not require parallel or comparable corpora, or any other type of
translation resource. Experiments performed on the English, Spanish, French and
Portuguese editions of JCR-Acquis corpora reveal promising results on
classifying and sorting documents by similar content.


  Minimal $SU(5)$ Grand Unified models predict massless neutrinos and struggle
to achieve gauge coupling unification compatible with the observed lower limit
on the proton lifetime. Both of these issues can be resolved by embedding
minimal radiative neutrino mass models into $SU(5)$. We systematically analyze
the possible ways to realize radiative neutrino mass generation in $SU(5)$ and
provide a list of the minimal models. We find various models that have not been
considered in the literature and demonstrate the compatibility of radiative
neutrino masses with gauge coupling unification and proton decay for a new
class of models with vector-like fermions.


  Bianchi type III string cosmological models with bulk viscous fluid for
massive string are investigated. To get the determinate model of the universe,
we have assumed that the coefficient of bulk viscosity ($\xi$) is inversely
proportional to the expansion ($\theta$) in the model and expansion ($\theta$)
in the model is proportional to the shear ($\sigma$). This leads to $B = \ell
C^{n}$, $\ell$ and $n$ are constants. The behaviour of the model in presence
and absence of bulk viscosity, is discussed. The physical implications of the
models are also discussed in detail.


  I analyse our present understanding of the Froehlich polaron with emphasis on
the response properties, in particular optical absorption.


  Unsupervised 3D object detection aims to identify objects of interest from
unlabeled raw data, such as LiDAR points. Recent approaches usually adopt
pseudo 3D bounding boxes (3D bboxes) from clustering algorithm to initialize
the model training, and then iteratively updating both pseudo labels and the
trained model. However, pseudo bboxes inevitably contain noises, and such
inaccurate annotation accumulates to the final model, compromising the
performance. Therefore, in an attempt to mitigate the negative impact of pseudo
bboxes, we introduce a new uncertainty-aware framework. In particular, Our
method consists of two primary components: uncertainty estimation and
uncertainty regularization. (1) In the uncertainty estimation phase, we
incorporate an extra auxiliary detection branch alongside the primary detector.
The prediction disparity between the primary and auxiliary detectors is
leveraged to estimate uncertainty at the box coordinate level, including
position, shape, orientation. (2) Based on the assessed uncertainty, we
regularize the model training via adaptively adjusting every 3D bboxes
coordinates. For pseudo bbox coordinates with high uncertainty, we assign a
relatively low loss weight. Experiment verifies that the proposed method is
robust against the noisy pseudo bboxes, yielding substantial improvements on
nuScenes and Lyft compared to existing techniques, with increases of 6.9% in
AP$_{BEV}$ and 2.5% in AP$_{3D}$ on nuScenes, and 2.2% in AP$_{BEV}$ and 1.0%
in AP$_{3D}$ on Lyft.


  Intense geomagnetic storms are characterized by a minimum value of the Dst
index at or below -100 nT. It is well known that these storms are caused by the
southward magnetic fields in coronal mass ejections (CMEs) and corotating
interaction regions (CIRs). While CIR storms are confined to Dst values at or
above -150 nT, CME storms can reach Dst -500 nT or lower. In this report, we
illustrate the need to understand the storm evolution based on solar source and
solar wind parameters using a recent storm (2023 April 24) by way of providing
the motivation to catalog such events for a better understanding of the main
phase time structure of geomagnetic storms


  A summary of how black holes grow in full, non-linear general relativity is
presented. Specifically, a notion of "dynamical horizons" is introduced and
expressions of fluxes of energy and angular momentum carried by gravitational
waves across these horizons are obtained. Fluxes are local and the energy flux
is positive. Change in the horizon area is related to these fluxes. The flux
formulae also give rise to balance laws analogous to the ones obtained by Bondi
and Sachs at null infinity and provide generalizations of the first and second
laws of black hole mechanics.


  With the advent of the Internet, large amount of digital text is generated
everyday in the form of news articles, research publications, blogs, question
answering forums and social media. It is important to develop techniques for
extracting information automatically from these documents, as lot of important
information is hidden within them. This extracted information can be used to
improve access and management of knowledge hidden in large text corpora.
Several applications such as Question Answering, Information Retrieval would
benefit from this information. Entities like persons and organizations, form
the most basic unit of the information. Occurrences of entities in a sentence
are often linked through well-defined relations; e.g., occurrences of person
and organization in a sentence may be linked through relations such as employed
at. The task of Relation Extraction (RE) is to identify such relations
automatically. In this paper, we survey several important supervised,
semi-supervised and unsupervised RE techniques. We also cover the paradigms of
Open Information Extraction (OIE) and Distant Supervision. Finally, we describe
some of the recent trends in the RE techniques and possible future research
directions. This survey would be useful for three kinds of readers - i)
Newcomers in the field who want to quickly learn about RE; ii) Researchers who
want to know how the various RE techniques evolved over time and what are
possible future research directions and iii) Practitioners who just need to
know which RE technique works best in various settings.


  We find the mixing induced by a small swimming organism in a density
stratified fluid. We model the swimmer as a spherical squirmer and quantify
mixing through a mixing efficiency that is the ratio of rate of change of
potential energy of fluid to the total work done by the swimmer. Assessing
mixing in the near field and far field of the swimmer separately, we find that
the near-field mixing aligns with past work, but the overall mixing is much
larger than that caused by a point-sized swimmer (like force-dipole), although
still small in weak stratification. Equivalent results are also obtained for a
homogeneous dilute suspension of non-interacting swimmers. Our study highlights
the impact of swimmer size on mixing, revealing that small-sized swimmers
cannot induce substantial mixing. However, we propose few (unexplored) pathways
through which small swimmers could induce significant mixing, ultimately
contributing towards oceanic mixing.


  We derive an encoded universality representation for a generalized
anisotropic exchange Hamiltonian that contains cross-product terms in addition
to the usual two-particle exchange terms. The recently developed algebraic
approach is used to show that the minimal universality-generating encodings of
one logical qubit are based on three physical qubits. We show how to generate
both single- and two-qubit operations on the logical qubits, using suitably
timed conjugating operations derived from analysis of the commutator algebra.
The timing of the operations is seen to be crucial in allowing simplification
of the gate sequences for the generalized Hamiltonian to forms similar to that
derived previously for the symmetric (XY) anisotropic exchange Hamiltonian. The
total number of operations needed for a controlled-Z gate up to local
transformations is five. A scalable architecture is proposed.


  EE Cep is a unique system in which a Be star is eclipsed by a dark dusty
disk, making this star similar to the famous epsilon Aur in many respects. The
depth and the duration of the EE Cep eclipses change to a large extent. The
last two eclipses were observed in the framework of extensive international
campaigns. The joint analysis of these campaigns data and historical
photometry, enabled us to propose a model of this system, which implies a disk
precession with a period approximately 11-12 times larger than the orbital
period. This model predicts that the forthcoming eclipse should be among the
deepest observed, reaching about 2 mag. The next eclipse approaches - the
photometric minimum should occur around August 23, 2014. Here we would like to
announce a new, third international campaign with purpose to verify the disk
precession model and to put more constraints on the physical parameters of this
system.


  Feature-based transfer is one of the most effective methodologies for
transfer learning. Existing studies usually assume that the learned new feature
representation is \emph{domain-invariant}, and thus train a transfer model
$\mathcal{M}$ on the source domain. In this paper, we consider a more realistic
scenario where the new feature representation is suboptimal and small
divergence still exists across domains. We propose a new transfer model called
Randomized Transferable Machine (RTM) to handle such small divergence of
domains. Specifically, we work on the new source and target data learned from
existing feature-based transfer methods. The key idea is to enlarge source
training data populations by randomly corrupting the new source data using some
noises, and then train a transfer model $\widetilde{\mathcal{M}}$ that performs
well on all the corrupted source data populations. In principle, the more
corruptions are made, the higher the probability of the new target data can be
covered by the constructed source data populations, and thus better transfer
performance can be achieved by $\widetilde{\mathcal{M}}$. An ideal case is with
infinite corruptions, which however is infeasible in reality. We develop a
marginalized solution that enables to train an $\widetilde{\mathcal{M}}$
without conducting any corruption but equivalent to be trained using infinite
source noisy data populations. We further propose two instantiations of
$\widetilde{\mathcal{M}}$, which theoretically show the transfer superiority
over the conventional transfer model $\mathcal{M}$. More importantly, both
instantiations have closed-form solutions, leading to a fast and efficient
training process. Experiments on various real-world transfer tasks show that
RTM is a promising transfer model.


  In this paper we propose a novel distributed model predictive control (DMPC)
based algorithm with a trajectory predictor for a scenario of landing of
unmanned aerial vehicles (UAVs) on a moving unmanned surface vehicle (USV). The
algorithm is executing DMPC with exchange of trajectories between the agents at
a sufficient rate. In the case of loss of communication, and given the sensor
setup, agents are predicting the trajectories of other agents based on the
available measurements and prior information. The predictions are then used as
the reference inputs to DMPC. During the landing, the followers are tasked with
avoidance of USV-dependent obstacles and inter-agent collisions. In the
proposed distributed algorithm, all agents solve their local optimization
problem in parallel and we prove the convergence of the proposed algorithm.
Finally, the simulation results support the theoretical findings.


  We consider a modified Euler equation on $\mathbb R^2$. We prove existence of
weak global solutions for bounded (and fast decreasing at infinity) initial
conditions and construct Gibbs-type measures on function spaces which are
quasi-invariant for the Euler flow. Almost everywhere with respect to such
measures (and, in particular, for less regular initial conditions), the flow is
shown to be also globally defined.


  The goal in {\em reconfiguration problems} is to compute a {\em gradual
transformation} between two feasible solutions of a problem such that all
intermediate solutions are also feasible. In the {\em Matching Reconfiguration
Problem} (MRP), proposed in a pioneering work by Ito et al.\ from 2008, we are
given a graph $G$ and two matchings $M$ and $M'$, and we are asked whether
there is a sequence of matchings in $G$ starting with $M$ and ending at $M'$,
each resulting from the previous one by either adding or deleting a single edge
in $G$, without ever going through a matching of size $< \min\{|M|,|M'|\}-1$.
Ito et al.\ gave a polynomial time algorithm for the problem.
  In this paper we introduce a natural generalization of the MRP that depends
on an integer parameter $\Delta \ge 1$: here we are allowed to make $\Delta$
changes to the current solution rather than 1 at each step of the
{transformation procedure}. There is always a valid sequence of matchings
transforming $M$ to $M'$ if $\Delta$ is sufficiently large, and naturally we
would like to minimize $\Delta$. We first devise an optimal transformation
procedure for unweighted matching with $\Delta = 3$, and then extend it to
weighted matchings to achieve asymptotically optimal guarantees. The running
time of these procedures is linear.
  We further demonstrate the applicability of this generalized problem to
dynamic graph matchings. In this area, the number of changes to the maintained
matching per update step (the \emph{recourse bound}) is an important quality
measure. Nevertheless, the \emph{worst-case} recourse bounds of almost all
known dynamic matching algorithms are prohibitively large, much larger than the
corresponding update times. We fill in this gap via a surprisingly simple
black-box reduction: Any dynamic algorithm for maintaining [...]


  For $G$ and $H_1,\dots, H_n$ finite groups, does there exist a $3$-manifold
group with $G$ as a quotient but no $H_i$ as a quotient? We answer all such
questions in terms of the group cohomology of finite groups. We prove
non-existence with topological results generalizing the theory of
semicharacteristics. To prove existence of 3-manifolds with certain finite
quotients but not others, we use a probabilistic method, by first proving a
formula for the distribution of the (profinite completion of) the fundamental
group of a random 3-manifold in the Dunfield-Thurston model of random Heegaard
splittings as the genus goes to infinity. We believe this is the first
construction of a new distribution of random groups from its moments.


  Spin-orbit (SO) interactions in two dimensional systems split the Fermi
surface, and allow for the spatial separation of spin-states via transverse
magnetic focusing (TMF). In this work, we consider the case of combined Rashba
and Zeeman interactions, which leads to a Fermi surface without cylindrical
symmetry. While the classical trajectories are effectively unchanged, we
predict an additional contribution to the phase, linear in the applied in-plane
magnetic field. We show that this term is unique to TMF, and vanishes for
magnetic (Shubnikov de Haas) oscillations. Finally we propose some experimental
signatures of this phase.


  This is an expository paper on Garland's vanishing theorem specialized to the
case when the linear algebraic group is $\mathrm{SL}_n$. Garland's theorem can
be stated as a vanishing of the cohomology groups of certain finite simplicial
complexes. The method of the proof is quite interesting on its own. It relates
the vanishing of cohomology to the assertion that the minimal positive
eigenvalue of a certain combinatorial laplacian is sufficiently large. Since
the 1970's, this idea has found applications in a variety of problems in
representation theory, group theory, and combinatorics, so the paper might be
of interest to a wide audience. The paper is intended for non-specialists and
graduate students.


  Recent detection of gravitational waves from a neutron star (NS) merger event
GW170817 and identification of an electromagnetic counterpart provide a unique
opportunity to study the physical processes in NS mergers. To derive properties
of ejected material from the NS merger, we perform radiative transfer
simulations of kilonova, optical and near-infrared emissions powered by
radioactive decays of r-process nuclei synthesized in the merger. We find that
the observed near-infrared emission lasting for > 10 days is explained by 0.03
Msun of ejecta containing lanthanide elements. However, the blue optical
component observed at the initial phases requires an ejecta component with a
relatively high electron fraction (Ye). We show that both optical and
near-infrared emissions are simultaneously reproduced by the ejecta with a
medium Ye of ~ 0.25. We suggest that a dominant component powering the emission
is post-merger ejecta, which exhibits that mass ejection after the first
dynamical ejection is quite efficient. Our results indicate that NS mergers
synthesize a wide range of r-process elements and strengthen the hypothesis
that NS mergers are the origin of r-process elements in the Universe.


  We estimate the intrinsic neutral gas density in Damped Lyman Alpha systems
($\Omega_{HI}^{(DLA)}$) in the redshift range $ 2.2 \lesssim z \lesssim 5$ from
the DLA SDSS DR_3 sample of optically selected quasars. We take into account
self-consistently the obscuration on background quasars due to the dust present
in Damped Lyman Alpha systems. We model the column density and redshift
distribution of these systems by using both a non-parametric and a parametric
approach. Under conservative assumptions on the dust content of Damped Lyman
$\alpha$ systems, we show that selection effects lead to underestimating the
intrinsic neutral gas density by at least $15\%$ with respect to the observed
neutral gas density. Over the redshift range $[2.2;5.5]$ we find
$\Omega_{HI}^{(DLA)}=0.97^{+0.08+0.28}_{-0.06-0.15} \cdot 10^{-3}$, where the
first set of error bars gives the $1\sigma$ random errors and the second set
gives the modeling uncertainty dependent on the fraction of metals in dust -
from 0\% to 50\%. This value compares with
$\Omega_{HI}^{(DLA)}=0.82^{+0.05}_{-0.05}$ ($1\sigma$ error bars), which is
obtained when no correction for dust is introduced. In the model with half of
the metals mass in dust we cannot constraint $\Omega_{HI}^{(DLA)}$ at a
confidence level higher than $90\%$. In this case there is indeed a probability
of about $10\%$ that the intrinsic column density distribution of DLA systems
is a power law $f(N_{HI}) \propto 1/N_{HI}^{~1.95}$. In contrast, with $25 \%$
of the metals in dust - the most realistic estimate - a power law is ruled out
at $99.5\%$ of confidence level.


  In this paper, we investigate the effects exerted by the interplay among
Laplacian diffusion, chemotaxis cross diffusion and the fluid dynamic mechanism
on global existence and boundedness of the solutions. The mathematical model
considered herein appears as \begin{align}\left\{ \begin{array}{l}
n_t+u\cdot\nabla n=\Delta n-\nabla\cdot( nS(n)\nabla c)-nm,\quad x\in \Omega,
t>0, \disp{ c_{ t}+u\cdot\nabla c=\Delta c-c+w},\quad x\in \Omega, t>0,
\disp{w_{t}+u\cdot\nabla w=\Delta w-nw},\quad x\in \Omega, t>0,\\ u_t+\kappa(u
\cdot \nabla)u+\nabla P=\Delta u+(n+m)\nabla \phi,\quad x\in \Omega, t>0,\\
\nabla\cdot u=0,\quad x\in \Omega, t>0,\\ \end{array}\right.\eqno(KSNF)
\end{align} in a bounded domain $\Omega\subset \mathbb{R}^2$ with a smooth
boundary, which describes the process of coral fertilization occurring in ocean
flow. Here $\kappa\in \mathbb{R}$ is a given constant, $\phi\in
W^{2,\infty}(\Omega)$and $S(n) $ is a scalar function satisfies $|S(n)|\leq
C_S(1+n)^{-\alpha}$ {for all} $n\geq 0$ with some $C_S>0$ and
$\alpha\in\mathbb{R}$. It is proved that if either $\alpha>-1,\kappa=0$ or
$\alpha\geq-\frac{1}{2},\kappa\in\mathbb{R}$ is satisfied,then for any
reasonably smooth initial data, the corresponding
Neumann-Neumann-Neumann-Dirichlet initial-boundary problem $(KSNF)$ possesses a
globally classical solution. In case of the stronger assumption
$\alpha>-1,\kappa = 0$ or $\alpha>-\frac{1}{2},\kappa \in\mathbb{R},$ we
moreover show that the corresponding initial-boundary problem admits a unique
global classical solution which is uniformly bounded on
$\Omega\times(0,\infty)$.


  On the surface of 2D Topological insulators occur 1D quantum spin Hall(QSH)
edge modes with Dirac like dispersion. Unlike quantum Hall(QH) edge modes which
occur at high magnetic fields in 2DEGs, the occurrence of QSH edge modes is
because of spin-orbit scattering in the bulk of the material. These QSH edge
modes are spin dependent and chiral- opposite spins move in opposing
directions. Electronic spin has larger decoherence and relaxation time than
charge- in view of this its expected that QSH edge modes will be more robust to
disorder and inelastic scattering than QH edge modes which are charge dependent
and spin unpolarized. However, we notice no such advantage accrues to QSH edge
modes when subjected to same degree of contact disorder and/or inelastic
scattering in similar setups as QH edge modes. In fact we observe that QSH edge
modes are more susceptible to inelastic scattering and contact disorder than QH
edge modes. Further, while a single disordered contact has no effect on QH edge
modes it leads to a finite charge Hall current in case of quantum spin Hall
edge modes and thus vanishing of pure quantum spin Hall effect. For more than a
single disordered contact while quantum Hall states continue to remain immune
to disorder, quantum spin Hall edge modes become more susceptible- the Hall
resistance for quantum spin Hall effect changes sign with increasing disorder.
In case of many disordered contacts with inelastic scattering included while
quantization of Hall edge modes holds, for quantum spin Hall edge modes- a
finite charge Hall current still flows. For quantum spin Hall edge modes in the
inelastic scattering regime we distinguish between two cases: with spin-flip
and without spin-flip scattering. Finally, while asymmetry in sample geometry
can have a deleterious effect on quantum spin Hall case it has no impact in
quantum Hall case.


  We propose a new ensemble framework for supervised learning, called machine
collaboration (MaC), using a collection of base machines for prediction tasks.
Unlike bagging/stacking (a parallel & independent framework) and boosting (a
sequential & top-down framework), MaC is a type of circular & interactive
learning framework. The circular & interactive feature helps the base machines
to transfer information circularly and update their structures and parameters
accordingly. The theoretical result on the risk bound of the estimator from MaC
reveals that the circular & interactive feature can help MaC reduce risk via a
parsimonious ensemble. We conduct extensive experiments on MaC using both
simulated data and 119 benchmark real datasets. The results demonstrate that in
most cases, MaC performs significantly better than several other
state-of-the-art methods, including classification and regression trees, neural
networks, stacking, and boosting.


  A new theory for the dynamics of the magnetic particles and their magnetic
moments in ferrofluids is developed. Based on a generalized Lagrangian
formulation for the equations of motion of the colloidal particle, we introduce
its interaction with the solvent fluid via dissipative and random noise
torques, as well as the interactions between the particle and its magnetic
moment, treated as an independent physical entity and characterized by three
generalized coordinates, its two polar angles and its modulus. It has been
recognized recently that inertial effects, as well as the particle's rotational
Brownian motion, may play important roles on the dynamic susceptibility of a
class of magnetic fluids. No satisfactory theory existed, up to now, that takes
this effects into account. The theory presented here is a first-principles
3-dimensional approach, in contrast to some phenomenological 2-dimensional
approaches that can be found in the recent literature. It is appropriate for
superparamagnetic, non-superparamagnetic and mixed magnetic fluids. As a simple
application, the blocked limit (magnetic moment fixed in the particle) is
treated numerically. The rotational trajectory of the particles in presence of
a magnetic field, as well as the response functions and dynamic susceptibility
matrices are explicitly calculated for some values of the parameters


  Accurate measurements of the Cosmic Microwave Background (CMB) anisotropy
call for high precision and reliability of the in-flight calibration. For
extended surveys the CMB dipole provides an excellent calibration source at
frequencies lower than 200 GHz; however poorly known foreground emissions, such
as diffuse galactic components, complicate the signal and introduce a
systematic error in the calibration. We show that introducing a weight function
that takes into account the uncertainty in the a priori knowledge of the sky,
allows us to substantially improve the calibration accuracy with respect to
methods involving galactic latitude cuts. This new method is tested for
Planck-LFI radiometers at 30 and 100 GHz. On short time scales (less than 1
day) the absolute calibration of each channel can be recovered with an overall
1-2% accuracy. We also consider the effect of CMB anisotropy itself on the
calibration, and find that knowledge of the CMB pattern on large scales is
needed to keep the short-time scale calibration accuracy within 1%


  We characterise the existentially closed models of the theory of exponential
fields. They do not form an elementary class, but can be studied using positive
logic. We find the amalgamation bases and characterise the types over them. We
define a notion of independence and show that independent systems of higher
dimension can also be amalgamated. We extend some notions from classification
theory to positive logic and position the category of existentially closed
exponential fields in the stability hierarchy as NSOP$_1$ but TP$_2$.


  We present lattice QCD results along the freezeout curve of heavy-ion
collisions for the ongoing RHIC energy scan as well as the LHC heavy-ion runs
planned for this year. The skew and kurtosis of the event distribution of
baryon number are positive at all energies studied, We predict three ratios of
non-linear susceptibilities along the freezeout curve as functions of the
colliding energy, Sqrt[S_{NN}]. They have smooth behaviour except in the
vicinity of a critical point; this can have multiple benefits.


  We consider non-relativistic conformal quantum mechanical theories that arise
by doing discrete light cone quantization of field theories. If the field
theory has a gravity dual, then the conformal quantum mechanical theory can
have a gravity dual description in a suitable finite temperature and finite
density regime. Using this we compute the thermodynamic properties of the
system. We give an explicit example where we display both the conformal quantum
mechanical theory as well as the gravity dual. We also discuss the string
theory embedding of certain backgrounds with non-relativistic conformal
symmetry that were recently discussed. Using this, we construct finite
temperature and finite density solutions, with asymptotic non-relativistic
conformal symmetry. In addition, we derive consistent Kaluza-Klein truncations
of type IIB supergravity to a five dimensional theory with massive vector
fields.


  The author \mbox{(Appl. Math. Comput. 218(3):860-865, 2011)} introduced a new
fractional integral operator given by, \[ \big({}^\rho
\mathcal{I}^\alpha_{a+}f\big)(x) = \frac{\rho^{1- \alpha }}{\Gamma({\alpha})}
\int^x_a \frac{\tau^{\rho-1} f(\tau) }{(x^\rho - \tau^\rho)^{1-\alpha}}\,
d\tau, \] which generalizes the well-known Riemann-Liouville and the Hadamard
fractional integrals. In this paper we present a new fractional derivative
which generalizes the familiar Riemann-Liouville and the Hadamard fractional
derivatives to a single form. We also obtain two representations of the
generalized derivative in question. An example is given to illustrate the
results.


  We present multi-frequency multi-epoch Very Long Baseline Array (VLBA)
observations of J11584+2450. These observations clearly show this source,
previously classified as a core-jet, to be a compact symmetric object (CSO).
Comparisons between these new data and data taken over the last 9 years shows
the edge brightened hot spots retreating towards the core (and slightly to the
west) at approximately 0.3c. Whether this motion is strictly apparent or
actually physical in nature is discussed, as well as possible explanations, and
what implications a physical contraction of J11584+2450 would have for current
CSO models.


  We consider a system of coupled nonlinear Schr{\"o}dinger equations in one
space dimension. First, we prove the existence of multi-speed solitary waves,
i.e solutions to the system with each component behaving at large times as a
solitary wave. Then, we investigate numerically the interaction of two solitary
waves supported each on one component. Among the possible outcomes, we find
elastic and inelastic interactions, collision with mass extraction and
reflexion.


  The rational and hyperbolic Ruijsenaars-Schneider models and their
non-relativistic limits are maximally superintegrable since they admit action
variables with globally well-defined canonical conjugates. In the case of the
rational Ruijsenaars-Schneider model we present an alternative proof of the
superintegrability by explicitly exhibiting extra conserved quantities relying
on a generalization of the construction of Wojciechowski for the rational
Calogero model.


  This paper presents a comprehensive spectroscopic analysis of the nova RS
Ophiuchi during its quiescent stage, spanning a duration of approximately 13
years. The spectra exhibit prominent low-ionization emission features,
including hydrogen, helium, iron, and TiO absorption features originating from
the cool secondary component. The CLOUDY photoionization code is employed to
model these spectra, allowing us to estimate various physical parameters such
as temperature, luminosity, and hydrogen density, along with elemental
abundances and accretion rate. The central ionizing sources exhibit
temperatures in the range of $1.05 - 1.8~\times 10^4$ K and luminosities
between $0.1 - 7.9~\times 10^{30}$ \ergs. Notably, \ion{He}{} displays an
overabundance from 2008 to 2016, returning to solar values by 2020, while
\ion{Fe}{} appears subsolar from 2008 to 2014 but becomes overabundant from
2006 onward. The mean accretion rate, as calculated from the model, is
approximately $1.254 \times 10^{-8} M_{\odot}$ yr$^{-1}$. About 47\% of the
critical mass was accreted after April, 2020 ($\sim$15 months before the 2021
outburst), and approximately 88\% of the critical mass was accreted after July
20, 2018. This non-uniform accretion rate suggests a more rapid approach
towards reaching the critical mass in the final years, possibly attributed to
the heightened gravitational pull resulting from previously accreted matter,
influencing the accretion dynamics as the system approaches the critical mass
limit.


  We have studied the origin of the Tully-Fisher relation by analysing
hydrodynamical simulations in a Lambda-CDM universe. We found that smaller
galaxies exhibit lower stellar masses than those predicted by the linear fit to
high mass galaxies (fast rotators), consistently with observations. In this
model, these trends are generated by the more efficient action of Supernova
feedback in the regulation of the star formation in smaller galaxies. Without
introducing scale-dependent parameters, the model predicts that the
Tully-Fisher relation bends at a characteristic velocity of around 100 km/s, in
agreement with previous observational and theoretical findings.


  Current end-to-end code-switching Text-to-Speech (TTS) can already generate
high quality two languages speech in the same utterance with single speaker
bilingual corpora. When the speakers of the bilingual corpora are different,
the naturalness and consistency of the code-switching TTS will be poor. The
cross-lingual embedding layers structure we proposed makes similar syllables in
different languages relevant, thus improving the naturalness and consistency of
generated speech. In the end-to-end code-switching TTS, there exists problem of
prosody instability when synthesizing paragraph text. The text enhancement
method we proposed makes the input contain prosodic information and
sentence-level context information, thus improving the prosody stability of
paragraph text. Experimental results demonstrate the effectiveness of the
proposed methods in the naturalness, consistency, and prosody stability. In
addition to Mandarin and English, we also apply these methods to Shanghaiese
and Cantonese corpora, proving that the methods we proposed can be extended to
other languages to build end-to-end code-switching TTS system.


  Czochralski-grown boron-doped silicon crystals were studied by the techniques
of the low-angle mid-IR-light scattering and electron-beam-induced current. The
large-scale accumulations of electrically-active impurities detected in this
material were found to be different in their nature and formation mechanisms
from the well-known impurity clouds in a float zone-grown silicon. A
classifcation of the large-scale impurity accumulations in CZ Si:B is made and
point centers constituting them are analyzed in this paper. A model of the
large-scale impurity accumulations in CZ-grown Si:B is also proposed.


  Current and future experimental studies of the high-energy limit of QCD,
dominated by non-linear gluon dynamics in the low-x sector of the hadron
wavefunctions, are presented. Results at HERA (proton) and RHIC (nucleus)
pointing to the possible onset of parton saturation phenomena, and perspectives
at the LHC and new proposed DIS facilities are outlined.


  Motivated by recent developments in optical switching and reconfigurable
network design, we study dynamic binary search trees (BSTs) in the matching
model. In the classical dynamic BST model, the cost of both link traversal and
basic reconfiguration (rotation) is $O(1)$. However, in the matching model, the
BST is defined by two optical switches (that represent two matchings in an
abstract way), and each switch (or matching) reconfiguration cost is $\alpha$
while a link traversal cost is still $O(1)$. In this work, we propose
Arithmetic BST (A-BST), a simple dynamic BST algorithm that is based on dynamic
Shannon-Fano-Elias coding, and show that A-BST is statically optimal for
sequences of length $\Omega(n \alpha \log \alpha)$ where $n$ is the number of
nodes (keys) in the tree.


  We describe a completely automated large scale visual recommendation system
for fashion. Existing approaches have primarily relied on purely computational
models to solving this problem that ignore the role of users in the system. In
this paper, we propose to overcome this limitation by incorporating a
user-centric design of visual fashion recommendations. Specifically, we propose
a technique that augments 'user preferences' in models by exploiting elasticity
in fashion choices. We further design a user study on these choices and gather
results from the 'wisdom of crowd' for deeper analysis. Our key insights learnt
through these results suggest that fashion preferences when constrained to a
particular class, contain important behavioral signals that are often ignored
in recommendation design. Further, presence of such classes also reflect strong
correlations to visual perception which can be utilized to provide
aesthetically pleasing user experiences. Finally, we illustrate that user
approval of visual fashion recommendations can be substantially improved by
carefully incorporating these user-centric feedback into the system framework.


  This paper provides the details of implementing two important policy gradient
methods to solve the inverted pendulum problem. These are namely the Deep
Deterministic Policy Gradient (DDPG) and the Proximal Policy Optimization (PPO)
algorithm. The problem is solved by using an actor-critic model where an
actor-network is used to learn the policy function and a critic network is to
evaluate the actor-network by learning to estimate the Q function. Apart from
briefly explaining the mathematics behind these two algorithms, the details of
python implementation are provided which helps in demystifying the underlying
complexity of the algorithm. In the process, the readers will be introduced to
OpenAI/Gym, Tensorflow 2.x and Keras utilities used for implementing the above
concepts.


  Smooth model potentials with parameters selected to reproduce the spectrum of
one-electron atoms are used to approximate the singular Coulomb potential. Even
when the potentials do not mimic the Coulomb singularity, much of the spectrum
is reproduced within the chemical accuracy. For the Hydrogen atom, the smooth
approximations to the Coulomb potential are more accurate for higher angular
momentum states. The transferability of the model potentials from an attractive
interaction (Hydrogen atom) to a repulsive one (Harmonium and the uniform
electron gas) is discussed.


  Creating and collecting labeled data is one of the major bottlenecks in
machine learning pipelines and the emergence of automated feature generation
techniques such as deep learning, which typically requires a lot of training
data, has further exacerbated the problem. While weak-supervision techniques
have circumvented this bottleneck, existing frameworks either require users to
write a set of diverse, high-quality rules to label data (e.g., Snorkel), or
require a labeled subset of the data to automatically mine rules (e.g., Snuba).
The process of manually writing rules can be tedious and time consuming. At the
same time, creating a labeled subset of the data can be costly and even
infeasible in imbalanced settings. This is due to the fact that a random sample
in imbalanced settings often contains only a few positive instances.
  To address these shortcomings, we present Darwin, an interactive system
designed to alleviate the task of writing rules for labeling text data in
weakly-supervised settings. Given an initial labeling rule, Darwin
automatically generates a set of candidate rules for the labeling task at hand,
and utilizes the annotator's feedback to adapt the candidate rules. We describe
how Darwin is scalable and versatile. It can operate over large text corpora
(i.e., more than 1 million sentences) and supports a wide range of labeling
functions (i.e., any function that can be specified using a context free
grammar). Finally, we demonstrate with a suite of experiments over five
real-world datasets that Darwin enables annotators to generate
weakly-supervised labels efficiently and with a small cost. In fact, our
experiments show that rules discovered by Darwin on average identify 40% more
positive instances compared to Snuba even when it is provided with 1000 labeled
instances.


No abstract available.


  In the context of nucleon structure, the Wigner distribution has been
commonly used to visualize the phase-space distribution of quarks and gluons
inside the nucleon. However, the Wigner distribution does not allow for a
probabilistic interpretation because it takes negative values. In pursuit of a
positive phase-space distribution in QCD, we introduce the Husimi distribution
and demonstrate its advantages via a simple one-loop example. We also comment
on a possible connection to the semiclassical approach to saturation physics at
small-$x$.


  On the basis of DefakeHop, an enhanced lightweight Deepfake detector called
DefakeHop++ is proposed in this work. The improvements lie in two areas. First,
DefakeHop examines three facial regions (i.e., two eyes and mouth) while
DefakeHop++ includes eight more landmarks for broader coverage. Second, for
discriminant features selection, DefakeHop uses an unsupervised approach while
DefakeHop++ adopts a more effective approach with supervision, called the
Discriminant Feature Test (DFT). In DefakeHop++, rich spatial and spectral
features are first derived from facial regions and landmarks automatically.
Then, DFT is used to select a subset of discriminant features for classifier
training. As compared with MobileNet v3 (a lightweight CNN model of 1.5M
parameters targeting at mobile applications), DefakeHop++ has a model of 238K
parameters, which is 16% of MobileNet v3. Furthermore, DefakeHop++ outperforms
MobileNet v3 in Deepfake image detection performance in a weakly-supervised
setting.


  Motivated by the recent developments about the Hartle-Hawking wave function
associated to black holes, we formulate an entropy functional on the moduli
space of Calabi-Yau compactifications. We find that the maximization of the
entropy is correlated with the appearance of asymptotic freedom in the
effective field theory. The points where the entropy is maximized correspond to
points on the moduli which are maximal intersection points of walls of marginal
stability for BPS states. We also find an intriguing link between extremizing
the entropy functional and the points on the moduli space of Calabi-Yau
three-folds which admit a `quantum deformed' complex multiplication.


  We analyze the dissipative dynamics of a two-level quantum system subject to
low-frequency, e.g. 1/f noise, motivated by recent experiments with
superconducting quantum circuits. We show that the effect of transverse linear
coupling of the system to low-frequency noise is equivalent to that of
quadratic longitudinal coupling. We further find the decay law of quantum
coherent oscillations under the influence of both low- and high-frequency
fluctuations, in particular, for the case of comparable rates of relaxation and
pure dephasing.


  Supernovae (SNe) that show evidence of strong shock interaction between their
ejecta and pre-existing, slower circumstellar material (CSM) constitute an
interesting, diverse, and still poorly understood category of explosive
transients. The chief reason that they are extremely interesting is because
they tell us that in a subset of stellar deaths, the progenitor star may become
wildly unstable in the years, decades, or centuries before explosion. This is
something that has not been included in standard stellar evolution models, but
may significantly change the end product and yield of that evolution, and
complicates our attempts to map SNe to their progenitors. Another reason they
are interesting is because CSM interaction is an efficient engine for making
bright transients, allowing super-luminous transients to arise from normal SN
explosion energies, and allowing transients of normal SN luminosities to arise
from sub-energetic explosions or low radioactivity yield. CSM interaction
shrouds the fast ejecta in bright shock emission, obscuring our normal view of
the underlying explosion, and the radiation hydrodynamics of the interaction is
challenging to model. The CSM interaction may also be highly non-spherical,
perhaps linked to binary interaction in the progenitor system. In some cases,
these complications make it difficult to definitively tell the difference
between a core-collapse or thermonuclear explosion, or to discern between a
non-terminal eruption, failed SN, or weak SN. Efforts to uncover the physical
parameters of individual events and connections to possible progenitor stars
make this a rapidly evolving topic that continues to challenge paradigms of
stellar evolution.


  We show that topological waves at the interface between two magnetic domains
in a graphene device are possible. First, we consider the case of a linear
relation between the applied gate voltage and local density in the channel and,
secondly, we investigate the effect of non-local Coulomb interactions. We
obtain two distinct edge modes for each interaction type: a Yanai mode with
rotational flow and dispersion relation that extends to infinite wave-number,
and a Kelvin mode with purely longitudinal flow and bound dispersion relation.
The scattering matrix concept is applied to verify the infinite frequency
regime of the spectrum, and the bulk-edge correspondence principle is satisfied
if one takes into account the Kelvin modes that merge with an imaginary cut of
the bulk band.


  In this paper, we apply an equivalent color transform (ECT) for a minimal
$k$-coloring of any graph $G$. It contracts each color class of the graph to a
single vertex and produces a complete graph $K_k$ for $G$ by removing redundant
edges between any two vertices. Based on ECT, a simple proof for four color
theorem for planar graph is then proposed.


  In observational studies of treatment effects, matched samples are created so
treated and control groups are similar in terms of observable covariates.
Traditionally such matched samples consist of matched pairs. If a pair match
fails to make treated and control units sufficiently comparable, alternative
forms of matching may be necessary. One general strategy to improve balance is
to match a variable number of control units to each treated unit. A more
tailored strategy is to adopt a fine balance constraint. Under a fine balance
constraint, a nominal covariate is exactly balanced, but it does not require
individually matched treated and control subjects for this variable. In the
example, we seek to construct a matched sample for an ongoing evaluation of
Peer Health Exchange, an intervention in schools designed to decrease risky
health behaviors among youth. We find that an optimal pair match that minimizes
distances between pairs creates a matched sample where balance is poor. Here we
propose a method to allow for fine balance constraints when each treated unit
is matched to a variable number of control units, which is not currently
possible using existing matching algorithms. Our approach uses the entire
number to first determine the optimal number of controls for each treated unit.
For each strata of matched treated units, we can then apply a fine balance
constraint. We then demonstrate that a matched sample for the evaluation of the
Peer Health Exchange based on a variable number of controls and fine balance
constraint is superior to simply using a variable-ratio match.


  A low-dimensional model (LDM) for turbulent Rayleigh-Benard convection in a
Cartesian cell with square domain, based on the Galerkin projection of the
Boussinesq equations onto a finite set of empirical eigenfunctions, is
presented. The empirical eigenfunctions are obtained from a joint Proper
Orthogonal Decomposition (POD) of the velocity and temperature fields using the
Snapshot Method on the basis of a direct numerical simulation (DNS). The
resulting LDM is a quadratic inhomogeneous system of coupled ordinary
differential equations which we use to describe the long-time temporal
evolution of the large-scale mode amplitudes for a Rayleigh number of 1e5 and a
Prandtl number of 0.7. The truncation to a finite number of degrees of freedom,
that does not exceed a number of 310 for the present, requires the additional
implementation of an eddy viscosity-diffusivity to capture the missing
dissipation of the small-scale modes. The magnitude of this additional
dissipation mechanism is determined by requiring statistical stationarity and a
total dissipation that corresponds with the original DNS data. We compare the
performance of two models, a constant so-called Heisenberg
viscosity--diffusivity and a mode-dependent or modal one. The latter
viscosity--diffusivity model turns out to reproduce the large-scale properties
of the turbulent convection qualitatively well, even for a model with only a
few hundred POD modes.


  In this paper we relate some classical normal forms for complex elliptic
curves in terms of 4-point sets in the Riemann sphere. Our main result is an
alternative proof that every elliptic curve is isomorphic as a Riemann surface
to one in the Hesse normal form. In this setting, we give an alternative proof
of the equivalence between the Edwards and the Jacobi normal forms. Also, we
give a geometric construction of the cross ratios for 4-point sets in general
position.


  It is generally believed that the shadows of either a black hole or naked
singularity arise due to photon spheres developing in these spacetimes. Here we
propose a new spherically symmetric naked singularity spacetime solution of
Einstein equations which has no photon sphere, and we show that the singularity
casts a shadow in the absence of the photon sphere. We discuss some novel
features of this shadow and the lightlike geodesics in this spacetime. We
compare the shadow of the naked singularity here with shadows cast by
Schwarzschild black hole and the first type of Joshi-Malafarina-Narayan (JMN1)
naked singularity, where for the last two spacetimes the shadow is formed due
to the presence of a photon sphere. It is seen, in particular, that the size of
shadow of the singularity is considerably smaller than that of a black hole.
Our analysis shows that the shadow of this naked singularity is distinguishable
from the shadow of a Schwarzschild black hole and the JMN1 naked singularity.
These results are useful and important in the context of recent observations of
shadow of the M87 galactic center.


  A system of inelastic hard disks in a thin pipe capped by hot walls is
studied with the aim of investigating velocity correlations between particles.
Two effects lead to such correlations: inelastic collisions help to build
localized correlations, while momentum conservation and diffusion produce long
ranged correlations. In the quasi-elastic limit, the velocity correlation is
weak, but it is still important since it is of the same order as the deviation
from uniformity. For system with stronger inelasticity, the pipe contains a
clump of particles in highly correlated motion. A theory with empirical
parameters is developed. This theory is composed of equations similar to the
usual hydrodynamic laws of conservation of particles, energy, and momentum.
Numerical results show that the theory describes the dynamics satisfactorily in
the quasi-elastic limit, however only qualitatively for stronger inelasticity.


  The main goal of this paper is to derive the primordial power spectrum for
the scalar perturbations generated as a result of quantum fluctuations during
an inflationary pe- riod by an alternative approach of field quantization[1-3].
Formulas are derived for the gravitational waves, special cases of which
include power law inflation and inflation in the slow roll approximation, in
Krein space quantization.


  Spoof surface plasmon meta-couplers are compact antennas which link
propagating waves and surface waves. However, most of them are designed with a
fixed phase gradient and channel for the incident waves with specific
polarization, which limits their further applications in multichannel
scenarios. In this Letter, we propose, to the best of our knowledge, a new
method that combines the Brillouin folds theory with the Generalized Snell Law.
We demonstrate that when the phase gradient of the metasurface is large enough,
Brillouin folds effect will occur, which will create dual phase gradient space
in a single metasurface. With this method, we design two novel terahertz
meta-couplers with functionalities of symmetrical and asymmetrical
binary-channel/bidirectional SSP excitation. Furthermore, finite element method
(FEM) simulations are performed to demonstrate their functionalities.
Considering the orthogonality of the incident waves, there can be a total of
four independent space channels to excite SSP on one metasurface. This work may
open up new routes in multi-channel SSP meta-couplers and multi-beam surface
wave antennas.


  Top-k query processing finds a list of k results that have largest scores
w.r.t the user given query, with the assumption that all the k results are
independent to each other. In practice, some of the top-k results returned can
be very similar to each other. As a result some of the top-k results returned
are redundant. In the literature, diversified top-k search has been studied to
return k results that take both score and diversity into consideration. Most
existing solutions on diversified top-k search assume that scores of all the
search results are given, and some works solve the diversity problem on a
specific problem and can hardly be extended to general cases. In this paper, we
study the diversified top-k search problem. We define a general diversified
top-k search problem that only considers the similarity of the search results
themselves. We propose a framework, such that most existing solutions for top-k
query processing can be extended easily to handle diversified top-k search, by
simply applying three new functions, a sufficient stop condition sufficient(),
a necessary stop condition necessary(), and an algorithm for diversified top-k
search on the current set of generated results, div-search-current(). We
propose three new algorithms, namely, div-astar, div-dp, and div-cut to solve
the div-search-current() problem. div-astar is an A* based algorithm, div-dp is
an algorithm that decomposes the results into components which are searched
using div-astar independently and combined using dynamic programming. div-cut
further decomposes the current set of generated results using cut points and
combines the results using sophisticated operations. We conducted extensive
performance studies using two real datasets, enwiki and reuters. Our div-cut
algorithm finds the optimal solution for diversified top-k search problem in
seconds even for k as large as 2,000.


  It is shown how high energy neutrino beams from very distant sources can be
utilised to learn about many neutrino properties such as lifetimes, mass
hierarchy, mixing, minuscule pseudo-Dirac mass splittings and other exotic
properties. In addition, the production mechanism of neutrinos in the
astrophysical sources can also be elucidated.


  Artificial intelligence (AI) and neuroscience share a rich history, with
advancements in neuroscience shaping the development of AI systems capable of
human-like knowledge retention. Leveraging insights from neuroscience and
existing research in adversarial and continual learning, we introduce a novel
framework comprising two core concepts: feature distillation and
re-consolidation. Our framework, named Robust Rehearsal, addresses the
challenge of catastrophic forgetting inherent in continual learning (CL)
systems by distilling and rehearsing robust features. Inspired by the mammalian
brain's memory consolidation process, Robust Rehearsal aims to emulate the
rehearsal of distilled experiences during learning tasks. Additionally, it
mimics memory re-consolidation, where new experiences influence the integration
of past experiences to mitigate forgetting. Extensive experiments conducted on
CIFAR10, CIFAR100, and real-world helicopter attitude datasets showcase the
superior performance of CL models trained with Robust Rehearsal compared to
baseline methods. Furthermore, examining different optimization training
objectives-joint, continual, and adversarial learning-we highlight the crucial
role of feature learning in model performance. This underscores the
significance of rehearsing CL-robust samples in mitigating catastrophic
forgetting. In conclusion, aligning CL approaches with neuroscience insights
offers promising solutions to the challenge of catastrophic forgetting, paving
the way for more robust and human-like AI systems.


  Change detection process has recently progressed from a post-classification
method to an expert knowledge interpretation process of the time-series data.
The technique finds applications mainly in remote sensing images and can be
utilized to analyze urbanization and monitor forest regions. In this paper, a
framework to perform a knowledge based interpretation of the changes/no changes
observed in a spatiotemporal domain using tensor based approaches is presented.
An incremental approach to Boolean Tensor Factorization method is proposed in
this work, which is adopted to model the change patterns of objects/classes as
well as their associated features. The framework is evaluated under different
datasets to visualize the performance for the dependency factors. The algorithm
is also validated in comparison with the tradition Boolean Tensor Factorization
method and the results are substantial.


  A theorem of Esnault, Srinivas and Viehweg asserts that if the Chow group of
0-cycles of a smooth complete complex variety decomposes, then the top-degree
coherent cohomology group decomposes similarly. In this note, we prove that (a
weak version of) the converse holds for varieties of dimension at most 5 that
have finite-dimensional motive and satisfy the Lefschetz standard conjecture.
The proof is based on Vial's construction of a refined Chow-Kunneth
decomposition for these varieties.


  Multi-Domain Learning (MDL) refers to the problem of learning a set of models
derived from a common deep architecture, each one specialized to perform a task
in a certain domain (e.g., photos, sketches, paintings). This paper tackles MDL
with a particular interest in obtaining domain-specific models with an
adjustable budget in terms of the number of network parameters and
computational complexity. Our intuition is that, as in real applications the
number of domains and tasks can be very large, an effective MDL approach should
not only focus on accuracy but also on having as few parameters as possible. To
implement this idea we derive specialized deep models for each domain by
adapting a pre-trained architecture but, differently from other methods, we
propose a novel strategy to automatically adjust the computational complexity
of the network. To this aim, we introduce Budget-Aware Adapters that select the
most relevant feature channels to better handle data from a novel domain. Some
constraints on the number of active switches are imposed in order to obtain a
network respecting the desired complexity budget. Experimentally, we show that
our approach leads to recognition accuracy competitive with state-of-the-art
approaches but with much lighter networks both in terms of storage and
computation.


  This work proposes the application of independent component analysis to the
problem of ranking different alternatives by considering criteria that are not
necessarily statistically independent. In this case, the observed data (the
criteria values for all alternatives) can be modeled as mixtures of latent
variables. Therefore, in the proposed approach, we perform ranking by means of
the TOPSIS approach and based on the independent components extracted from the
collected decision data. Numerical experiments attest the usefulness of the
proposed approach, as they show that working with latent variables leads to
better results compared to already existing methods


  A knot K in the 3-sphere is superslice if there is a slice disk D in the
4-ball such that the double of D along K is the unknotted 2-sphere S in $S^4$.
Answering a question of Livingston-Meier, we find smoothly slice (in fact
doubly slice) knots in the 3-sphere with Alexander polynomial equal to 1 that
are not smoothly superslice.


  We give a H\"ormander-type localization principle for the Szeg\"o kernel
$S_\Omega(z)$. We also show that for each boundary point $z_0$,
$S_\Omega(z)\gtrsim|z-z_0|^{-\frac{1}{3}}$ holds non-tangentially for any
bounded pseudoconvex domain with smooth boundary in ${\mathbb C}^2$.


  This note completely describes the bounded or compact Riemann-Stieltjes
integral operators $T_g$ acting between the weighted Bergman space pairs
$(A^p_\alpha,A^q_\beta)$ in terms of particular regularities of the holomorphic
symbols $g$ on the open unit ball of $\Bbb C^n$.


  Insect-borne plant diseases recur commonly in wild plants and in agricultural
crops, and are responsible for severe losses in terms of produce yield and
monetary return. Mathematical models of insect-borne plant diseases are
therefore an essential tool to help predicting the progression of an epidemic
disease and aid in decision making when control strategies are to be
implemented in the field. While retaining a generalized applicability of the
proposed model to plant epidemics vectored by insects, we specifically
investigated the epidemics of Flavescence dor\'ee phytoplasma (FD) in grapevine
plant Vitis vinifera specifically transmitted by the leafhopper Scaphoideus
titanus. The epidemiological model accounted for life-cycle stage of S.
titanus, FD pathogen cycle within S. titanus and V. vinifera, vineyard setting,
and agronomic practices. The model was comprehensively tested against
biological S. titanus life cycle and FD epidemics data collected in various
research sites in Piemonte, Italy, over multiple years. The work presented here
represents a unique suite of governing equations tested on existing independent
data and sets the basis for further modelling advances and possible
applications to investigate effectiveness of real-case epidemics control
strategies and scenarios.


  The spectrum of hybrid electromagnetic-spin-acoustic waves for magnetic
having conical spiral ferromagnetic structure defined by heterogeneous exchange
and relativistic interactions have been received. The possibility of resonant
interaction of spin, electromagnetic and acoustic waves has been shown. The
electromagnetic waves reflectance from the half-infinity layer of magnetic
having conical spiral ferromagnetic order has been calculated for different
values of external magnetic field (angle of spiral). The acoustic Faradey
effect has been considered.


  We give a new formalism for pure gauge-theoretic scattering at tree-amplitude
level. We first describe a generalization of the Britto-Cachazo-Feng recursion
relation in which a significant restriction is removed. We then use twistor
diagrams to express all tree amplitudes in a form independent of helicity. A
formal procedure involving anticommuting elements is required. We illustrate
the results with specific calculations of interest, up to 8 interacting fields.


  A theory for the photon statistics of a random laser is presented. Noise is
described by Langevin operators, where both fluctuations of the electromagnetic
field and of the medium are included. The theory is valid for all lasers with
small outcoupling when the laser cavity is large compared to the wavelength of
the radiation. The theory is applied to a chaotic laser cavity with a small
opening. It is known that a large number of modes can be above threshold
simultaneously in such a cavity. It is shown the amount of fluctuations is
increased compared to the Poissonian value by an amount that depends on that
number.


  Let $\mathbb T_{q+1}$ denote the homogeneous tree of degree $q+1$ with the
standard graph distance $d$ and the canonical flow measure $\mu$. The metric
measure space $(\mathbb T_{q+1},d,\mu)$ is of exponential growth. Let
$\mathcal{L}$ denote the flow Laplacian, which is a probabilistic Laplacian
self-adjoint on $L^2(\mu)$. In this note, we prove some weighted
$L^1$-estimates for the heat kernel associated with $\mathcal{L}$ and its
gradient. As a consequence, we show that the first order Riesz transform
associated with the flow Laplacian on $\mathbb T_{q+1}$ is bounded on
$L^p(\mu)$, for $p \in (1,2]$ and of weak type $(1,1)$. The latter result was
proved in a previous paper by Hebisch and Steger: we give a different proof
that might pave the way to further generalizations.


  The MiniBooNE results have still not been able to comprehensively rule out
the oscillation interpretation of the LSND experiment. So far the so-called
short baseline experiments with energy in the MeV range and baseline of few
meters have been probing the existence of sterile neutrinos. We show how
signatures of these extra sterile states could be obtained in TeV energy range
atmospheric neutrinos travelling distances of thousands of kilometers.
Atmospheric neutrinos in the TeV range would be detected by the upcoming
neutrino telescopes. Of course vacuum oscillations of these neutrinos would be
very small. However, we show that resonant matter effects inside the Earth
could enhance these very tiny oscillations into near-maximal transitions, which
should be hard to miss. We show that imprint of sterile neutrinos could be
unambiguously obtained in this high energy atmospheric neutrino event sample.
Not only would neutrino telescopes tell the presence of sterile neutrinos, it
should also be possible for them to distinguish between the different possible
mass and mixing scenarios with additional sterile states.


  We study the effect of a rough wall on the controllability of micro-swimmers
made of several balls linked by thin jacks: the so-called 3-sphere and 4-sphere
swimmers. We show that a controllable swimmer (the 4-sphere swimmer) is not
impacted by the roughness. On the contrary, we show that the roughness changes
the dynamics of the 3-sphere swimmer, so that it can reach any direction almost
everywhere.


  The problem is addressed of defining the values of functions, whose variables
tend to infinity, from the knowledge of these functions at asymptotically small
variables close to zero. For this purpose, the extrapolation by means of
different types of self-similar approximants is employed. Two new variants of
such an extrapolation are suggested. The methods are illustrated by several
examples of systems typical of chemical physics, statistical physics, and
quantum physics. The developed methods make it possible to find good
approximations for the strong-coupling limits from the knowledge of the
weak-coupling expansions.


  In this work we propose a new approach for the numerical simulation of
kinetic equations through Monte Carlo schemes. We introduce a new technique
which permits to reduce the variance of particle methods through a matching
with a set of suitable macroscopic moment equations. In order to guarantee that
the moment equations provide the correct solutions, they are coupled to the
kinetic equation through a non equilibrium term. The basic idea, on which the
method relies, consists in guiding the particle positions and velocities
through moment equations so that the concurrent solution of the moment and
kinetic models furnishes the same macroscopic quantities.


  Online services are interested in solutions to opinion mining, which is the
problem of extracting aspects, opinions, and sentiments from text. One method
to mine opinions is to leverage the recent success of pre-trained language
models which can be fine-tuned to obtain high-quality extractions from reviews.
However, fine-tuning language models still requires a non-trivial amount of
training data. In this paper, we study the problem of how to significantly
reduce the amount of labeled training data required in fine-tuning language
models for opinion mining. We describe Snippext, an opinion mining system
developed over a language model that is fine-tuned through semi-supervised
learning with augmented data. A novelty of Snippext is its clever use of a
two-prong approach to achieve state-of-the-art (SOTA) performance with little
labeled training data through: (1) data augmentation to automatically generate
more labeled training data from existing ones, and (2) a semi-supervised
learning technique to leverage the massive amount of unlabeled data in addition
to the (limited amount of) labeled data. We show with extensive experiments
that Snippext performs comparably and can even exceed previous SOTA results on
several opinion mining tasks with only half the training data required.
Furthermore, it achieves new SOTA results when all training data are leveraged.
By comparison to a baseline pipeline, we found that Snippext extracts
significantly more fine-grained opinions which enable new opportunities of
downstream applications.


  We study the differences in the gamma ray spectra simulated by four Monte
Carlo event generator packages developed in particle physics. Two different
versions of PYTHIA and two of HERWIG are analyzed, namely PYTHIA 6.418 and
HERWIG 6.5.10 in Fortran and PYTHIA 8.165 and HERWIG 2.6.1 in C++. For all the
studied channels, the intrinsic differences between them are shown to be
significative and may play an important role in misunderstanding dark matter
signals.


  In the maximally Abelian gauge of SU(2), the clusters of monopole current are
found to divide into two distinct classes. The largest cluster permeates the
lattice, has a density that scales and produces the string tension. The
remaining clusters possess an approximate 1/l^3 number density distribution (l
is the cluster length), their radii vary as \sqrt l and their total current
density does not scale. Their contribution to the string tension is compatible
with being exactly zero. Their number density can be thought of as arising from
an underlying scale invariant distribution. This suggests that they are not
related to instantons. We also observe that when we locally smoothen the SU(2)
fields by cooling, the string tension due to monopoles becomes much smaller
than the SU(2) string tension. This dramatic loss of Abelian/monopole dominance
occurs even after just one cooling step.


  The effective allocation of cross-border trading capacities is one of the
central challenges for the implementation of a pan-European internal energy
market. In contrast to traditional power flow-ignorant methods like net
transfer capacities (NTC), flow-based market coupling (FBMC) has been shown to
increase price convergence between market areas, while improving congestion
management effectiveness. However, explicitly analysing FBMC for a future power
system with a very high share of intermittent renewable generation is often
overlooked in the current literature. This paper provides a comprehensive
summary on the technical specification of the FBMC process and FBMC modeling
methods. It discusses implications of policy considerations and explicitly
discusses the impact of high-shares of intermittent generation on FBMC
performance. Further, we propose probabilistic security margins compatible with
the current FBMC implementation to better account for renewable uncertainty in
FBMC modeling. We conduct numerical experiments on the IEEE 118 bus test system
to showcase the proposed model formulations and our data and implementation is
published open source.


  Large Language Models (LLMs) have shown exceptional performance in text
processing. Notably, LLMs can synthesize information from large datasets and
explain their decisions similarly to human reasoning through a chain of thought
(CoT). An emerging application of LLMs is the handling and interpreting of
numerical data, where fine-tuning enhances their performance over basic
inference methods. This paper proposes a novel approach to training LLMs using
knowledge transfer from a random forest (RF) ensemble, leveraging its
efficiency and accuracy. By converting RF decision paths into natural language
statements, we generate outputs for LLM fine-tuning, enhancing the model's
ability to classify and explain its decisions. Our method includes verifying
these rules through established classification metrics, ensuring their
correctness. We also examine the impact of preprocessing techniques on the
representation of numerical data and their influence on classification accuracy
and rule correctness


  The associated production of a $J/{\psi}$ and a $\pi$-meson in
antiproton-nucleon annihilation is studied in the framework of QCD collinear
factorization. In this approach, a hard subprocess responsible for the
production of the heavy quark-antiquark pair factorizes from soft hadronic
matrix elements, such as the antiproton (nucleon) distribution amplitude and
the nucleon-to-pion (antiproton-to-pion) transition distribution amplitude.
This reaction mechanism should dominate the forward and backward kinematical
regions, where the cross sections are expected to be measurable in the set-up
of the PANDA experiment at the GSI-Fair facility.


  The inertial subrange of turbulent scales is commonly reflected by a power
law signature in ensemble statistics such as the energy spectrum and structure
functions - both in theory and from observations. Despite promising findings on
the topic of fractal geometries in turbulence, there is no accepted image for
the physical flow features corresponding to this statistical signature in the
inertial subrange. The present study uses boundary layer turbulence
measurements to evaluate the self-similar geometric properties of velocity
isosurfaces and investigate their influence on statistics for the velocity
signal. The fractal dimension of streamwise velocity isosurfaces, indicating
statistical self-similarity in the size of "wrinkles" along each isosurface, is
shown to be constant only within the inertial subrange of scales. For the
transition between the inertial subrange and production range, it is inferred
that the largest wrinkles become increasingly confined by the overall size of
large-scale coherent velocity regions such as uniform momentum zones. The
self-similarity of isosurfaces yields power law trends in subsequent
one-dimensional statistics. For instance, the theoretical 2/3 power law
exponent for the structure function can be recovered by considering the
collective behavior of numerous isosurface level sets. The results suggest that
the physical presence of inertial subrange eddies is manifested in the
self-similar wrinkles of isosurfaces.


  Sensory receptor neurons match their dynamic range to ecologically relevant
stimulus intensities. How this tuning is achieved is poorly understood in most
receptors. We show that in the C. elegans URX O2 sensing neurons two putative
molecular O2 sensors, a neuroglobin and O2-binding soluble guanylate cyclases,
work antagonistically to sculpt a slowly sigmoidal O2 response curve tuned to
approach saturation when O2 reaches 21%. glb-5 imposes this sigmoidal function
by inhibiting O2-evoked Ca2+ responses in URX when O2 levels fall. Without
GLB-5, the URX response curve approaches saturation at 15% O2. Behaviorally,
GLB-5 signaling broadens the O2 preference of C. elegans while maintaining
strong avoidance of 21% O2. Our computational aerotaxis model suggests that the
relationship between GLB-5-modulated URX responses and reversal behavior is
sufficient to broaden O2-preference. Thus, a neuroglobin can shift neural
information coding leading to a change in perception and altered behavior.


  When models, e.g., for semantic segmentation, are applied to images that are
vastly different from training data, the performance will drop significantly.
Domain adaptation methods try to overcome this issue, but need samples from the
target domain. However, this might not always be feasible for various reasons
and therefore domain generalization methods are useful as they do not require
any target data. We present a new diffusion-based domain extension (DIDEX)
method and employ a diffusion model to generate a pseudo-target domain with
diverse text prompts. In contrast to existing methods, this allows to control
the style and content of the generated images and to introduce a high
diversity. In a second step, we train a generalizing model by adapting towards
this pseudo-target domain. We outperform previous approaches by a large margin
across various datasets and architectures without using any real data. For the
generalization from GTA5, we improve state-of-the-art mIoU performance by 3.8%
absolute on average and for SYNTHIA by 11.8% absolute, marking a big step for
the generalization performance on these benchmarks. Code is available at
https://github.com/JNiemeijer/DIDEX


  We analyse the divergences of the three-loop partition function at fixed area
in 2D quantum gravity. Considering the Liouville action in the Kahler
formalism, we extract the coefficient of the leading divergence in $\sim
A\Lambda^2 (\ln A\Lambda^2)^2$. This coefficient is non-vanishing. We discuss
the counterterms one can and must add and compute their precise contribution to
the partition function. This allows us to conclude that every local and
non-local divergence in the partition function can be balanced by local
counterterms, with the only exception of the maximally non-local divergence
$(\ln A\Lambda^2)^3$. Yet, this latter is computed and does cancel between the
different three-loop diagrams. Thus, requiring locality of the counterterms is
enough to renormalize the partition function. Finally, the structure of the new
counterterms strongly suggests that they can be understood as a renormalization
of the measure action.


  The automorphism group $G_{2}$ of the octonions changes when octonion
X,Y-product variants are used. I present here a general solution for how to go
from $G_{2}$ to its X,Y-product variant.


  Network Intrusion Detection Systems (NIDS) are computer systems which monitor
a network with the aim of discerning malicious from benign activity on that
network. While a wide range of approaches have met varying levels of success,
most IDSs rely on having access to a database of known attack signatures which
are written by security experts. Nowadays, in order to solve problems with
false positive alerts, correlation algorithms are used to add additional
structure to sequences of IDS alerts. However, such techniques are of no help
in discovering novel attacks or variations of known attacks, something the
human immune system (HIS) is capable of doing in its own specialised domain.
This paper presents a novel immune algorithm for application to the IDS
problem. The goal is to discover packets containing novel variations of attacks
covered by an existing signature base.


  We describe and report first results from PALM-3000, the second-generation
astronomical adaptive optics facility for the 5.1-m Hale telescope at Palomar
Observatory. PALM-3000 has been engineered for high-contrast imaging and
emission spectroscopy of brown dwarfs and large planetary mass bodies at
near-infrared wavelengths around bright stars, but also supports general
natural guide star use to V ~ 17. Using its unique 66 x 66 actuator deformable
mirror, PALM-3000 has thus far demonstrated residual wavefront errors of 141 nm
RMS under 1 arcsecond seeing conditions. PALM-3000 can provide phase
conjugation correction over a 6.4 x 6.4 arcsecond working region at an
observing wavelength of 2.2 microns, or full electric field (amplitude and
phase) correction over approximately one half of this field. With optimized
back-end instrumentation, PALM-3000 is designed to enable as high as 10e-7
contrast at ~1 arc second angular separation, after including post-observation
speckle suppression processing. While optimization of the adaptive optics
system is ongoing, we have already successfully commissioned five back-end
science instruments and begun a major exoplanet characterization survey,
Project 1640, with our partners at American Museum of Natural History and Jet
Propulsion Laboratory.


  I describe the MOND paradigm, which posits a departure from standard physics
below a certain acceleration scale. This acceleration as deduced from the
dynamics in galaxies is found mysteriously to agree with the cosmic
acceleration scales defined by the present day expansion rate and by the
density of `dark energy'. I put special emphasis on phenomenology and on
critical comparison with the competing paradigm based on classical dynamics
plus cold dark matter. I also describe briefly nonrelativistic and relativistic
MOND theories.


  A one phase Stefan problem in nonlinear conduction is considered. The problem
is shown to admit a unique solution for small times. An exact solution is
obtained which is a travelling front moving with constant speed.


  Coherent anti-Stokes Raman scattering (CARS) spectroscopy with time-delayed
ultrashort pulses and a single-pixel photodetector has shown great potential
for spectroscopic imaging and transient studies in chemistry and biological
research. However, those systems rely on mechanical delay lines or two
asynchronous optical combs with inflexible repetition frequencies, technically
limiting their acquisition speeds. Here, we demonstrate a hybrid dual-comb CARS
system involving a broadband fiber laser and a highly-flexible,
frequency-modulated electro-optic comb. We achieve multiplex CARS spectra
(2800-3200 cm-1), with a moderate resolution (22 cm-1), at a maximum refresh
rate of 1 MHz, limited by the radio-frequency synthesizer we use. Fast
spectroscopic CARS imaging is demonstrated for liquid mixtures. Our system
enables spectral measurements in the high-wavenumber C-H stretching region at a
record speed that is an order of magnitude higher than state-of-the-art
systems, which may open up new opportunities for fast chemical sensing and
imaging.


  Graphical models are useful tools for describing structured high-dimensional
probability distributions. Development of efficient algorithms for generating
unbiased and independent samples from graphical models remains an active
research topic. Sampling from graphical models that describe the statistics of
discrete variables is a particularly challenging problem, which is intractable
in the presence of high dimensions. In this work, we provide the first method
that allows one to provably generate unbiased and independent samples from
general discrete factor models with a quantum circuit. Our method is compatible
with multi-body interactions and its success probability does not depend on the
number of variables. To this end, we identify a novel embedding of the
graphical model into unitary operators and provide rigorous guarantees on the
resulting quantum state. Moreover, we prove a unitary Hammersley-Clifford
theorem -- showing that our quantum embedding factorizes over the cliques of
the underlying conditional independence structure. Importantly, the quantum
embedding allows for maximum likelihood learning as well as maximum a
posteriori state approximation via state-of-the-art hybrid quantum-classical
methods. Finally, the proposed quantum method can be implemented on current
quantum processors. Experiments with quantum simulation as well as actual
quantum hardware show that our method can carry out sampling and parameter
learning on quantum computers.


  To a crystallographic root system \Phi, and a positive integer k, there are
associated two Fuss-Catalan objects, the set of nonnesting partitions
NN^(k)(\Phi), and the cluster complex \Delta^(k)(\Phi). These posess a number
of enumerative coincidences, many of which are captured in a surprising
identity, first conjectured by Chapoton for k=1 and later generalized to k>1 by
Armstrong. We prove this conjecture, obtaining some structural and enumerative
results on NN^(k)(\Phi) along the way, including an earlier conjecture by Fomin
and Reading giving a refined enumeration by Fu{\ss}-Narayana numbers.


  Extensions of the standard model with universal extra dimensions are
interesting both as phenomenological templates as well as model-building
fertile ground. For instance, they are one the prototypes for theories
exhibiting compressed spectra, leading to difficult searches at the LHC since
the decay products of new states are soft and immersed in a large standard
model background. Here we study the phenomenology at the LHC of theories with
two universal extra dimensions. We obtain the current bound by using the
production of second level excitations of electroweak gauge bosons decaying to
a pair of leptons and study the reach of the LHC Run~II in this channel. We
also introduce a new channel originating in higher dimensional operators and
resulting in the single production of a second level quark excitation. Its
subsequent decay into a hard jet and lepton pair resonance would allow the
identification of a more model-specific process, unlike the more generic vector
resonance signal. We show that the sensitivity of this channel to the
compactification scale is very similar to the one obtained using the vector
resonance.


  We consider aggregative games with affine coupling constraints, where agents
have partial information on the aggregate value and can only communicate with
neighbouring agents. We propose a single-layer distributed algorithm that
reaches a variational generalized Nash equilibrium, under constant step sizes.
The algorithm works on a single timescale, i.e., does not require multiple
communication rounds between agents before updating their action. The
convergence proof leverages an invariance property of the aggregate estimates
and relies on a forward-backward splitting for two preconditioned operators and
their restricted (strong) monotonicity properties on the consensus subspace.