  The paper presents an ant colony optimization metaheuristic for collaborative
planning. Collaborative planning is used to coordinate individual plans of
self-interested decision makers with private information in order to increase
the overall benefit of the coalition. The method consists of a new search graph
based on encoded solutions. Distributed and private information is integrated
via voting mechanisms and via a simple but effective collaborative local search
procedure. The approach is applied to a distributed variant of the multi-level
lot-sizing problem and evaluated by means of 352 benchmark instances from the
literature. The proposed approach clearly outperforms existing approaches on
the sets of medium and large sized instances. While the best method in the
literature so far achieves an average deviation from the best known
non-distributed solutions of 46 percent for the set of the largest instances,
for example, the presented approach reduces the average deviation to only 5
percent.


  In the context of the effective field theory of dark energy (EFT) we perform
agnostic explorations of Horndeski gravity. We choose two parametrizations for
the free EFT functions, namely a power law and a dark energy density-like
behaviour on a non trivial Chevallier-Polarski-Linder background. We restrict
our analysis to those EFT functions which do not modify the speed of
propagation of gravitational waves. Among those, we prove that one specific
function cannot be constrained by data, since its contribution to the
observables is below the cosmic variance, although we show it has a relevant
role in defining the viable parameter space. We place constraints on the
parameters of these models combining measurements from present day cosmological
datasets and we prove that the next generation galaxy surveys can improve such
constraints by one order of magnitude. We then verify the validity of the
quasi-static limit within the sound horizon of the dark field, by looking at
the phenomenological functions $\mu$ and $\Sigma$, associated respectively to
clustering and lensing potentials. Furthermore, we notice up to $5\%$
deviations in $\mu, \Sigma$ with respect to General Relativity at scales
smaller than the Compton one. For the chosen parametrizations and in the
quasi-static limit, future constraints on $\mu$ and $\Sigma$ can reach the
$1\%$ level and will allow us to discriminate between certain models at more
than $3\sigma$, provided the present best-fit values remain.


  In the past decade, the number of known binary near-Earth asteroids has more
than quadrupled and the number of known large main belt asteroids with
satellites has doubled. Half a dozen triple asteroids have been discovered, and
the previously unrecognized populations of asteroid pairs and small main belt
binaries have been identified. The current observational evidence confirms that
small (<20 km) binaries form by rotational fission and establishes that the
YORP effect powers the spin-up process. A unifying paradigm based on rotational
fission and post-fission dynamics can explain the formation of small binaries,
triples, and pairs. Large (>20 km) binaries with small satellites are most
likely created during large collisions.


  We report on a new sensitive method to minimize excess micromotion of an ion
in a Paul trap. The ion is placed in an ultracold cloud of neutral Rb atoms in
which ionic micromotion induces atomic losses and heating. Micromotion is
minimized by applying static electric offset fields such that both loss and
heating are minimized. We achieve a compensation precision as high as the best
compensation methods to date. In contrast to these methods, our scheme is
applicable even for ions that cannot be optically probed. Furthermore, our
method avoids the formation of temporary patch charges which are a main issue
for the long-term stability of micromotion minimization.


  The problem of optimal stopping with finite horizon in discrete time is
considered in view of maximizing the expected gain. The algorithm proposed in
this paper is completely nonparametric in the sense that it uses observed data
from the past of the process up to time $-n+1$, $n\in\mathbb{N}$, not relying
on any specific model assumption. Kernel regression estimation of conditional
expectations and prediction theory of individual sequences are used as tools.
It is shown that the algorithm is universally consistent: the achieved expected
gain converges to the optimal value for $n\to\infty$ whenever the underlying
process is stationary and ergodic. An application to exercising American
options is given, and the algorithm is illustrated by simulated data.


  This work examines whether decoder-only Transformers such as LLaMA, which
were originally designed for large language models (LLMs), can be adapted to
the computer vision field. We first "LLaMAfy" a standard ViT step-by-step to
align with LLaMA's architecture, and find that directly applying a causal mask
to the self-attention brings an attention collapse issue, resulting in the
failure to the network training. We suggest to reposition the class token
behind the image tokens with a post-sequence class token technique to overcome
this challenge, enabling causal self-attention to efficiently capture the
entire image's information. Additionally, we develop a soft mask strategy that
gradually introduces a causal mask to the self-attention at the onset of
training to facilitate the optimization behavior. The tailored model, dubbed as
image LLaMA (iLLaMA), is akin to LLaMA in architecture and enables direct
supervised learning. Its causal self-attention boosts computational efficiency
and learns complex representation by elevating attention map ranks. iLLaMA
rivals the performance with its encoder-only counterparts, achieving 75.1%
ImageNet top-1 accuracy with only 5.7M parameters. Scaling the model to
$\sim$310M and pre-training on ImageNet-21K further enhances the accuracy to
86.0%. Extensive experiments demonstrate iLLaMA's reliable properties:
shape-texture bias, calibration, quantization compatibility, ADE20K
segmentation and CIFAR transfer learning. We hope our study can kindle fresh
views to visual architectures in the wave of LLMs and inspire the development
of unified multimodal models. Pre-trained models and codes are available
https://github.com/techmonsterwang/iLLaMA.


  A recent study has suggested a link between the surface level diurnal
temperature range (DTR) and variations in the cosmic ray (CR) flux. As the DTR
is an effective proxy for cloud cover, this result supports the notion that
widespread cloud changes may be induced by the CR flux. If confirmed, this
would have significant implications for our understanding of natural climate
forcings. Here, we perform a detailed investigation of the relationships
between DTR and solar activity (total solar irradiance and the CR flux) from
more than 60 years of NCEP/NCAR reanalysis data and observations from
meteorological station data. We find no statistically significant evidence to
suggest that the DTR is connected to either long-term solar periodicities (11
or 1.68 year) or short-term (daily-timescale) fluctuations in solar activity,
and we attribute previous reports on the contrary to an incorrect estimation of
the statistical significance of the data. If a CR-DTR relationship exists,
based on the estimated noise in DTR composites during Forbush decrease (FD)
events, the DTR response would need to be larger than 0.03{\deg}C per 1%
increase in the CR flux to be reliably detected. Compared with a much smaller
rough estimate of -0.005{\deg}C per 1% increase in the CR flux expected if
previous claims that FD events cause reductions in the cloud cover are valid,
we conclude it is not possible to detect a solar related responses in
station-based or reanalysis-based DTR datasets related to a hypothesized
CR-cloud link, as potential signals would be drowned in noise.


  For a binary-input memoryless symmetric channel $W$, we consider the
asymptotic behavior of the polarization process in the large block-length
regime when transmission takes place over $W$. In particular, we study the
asymptotics of the cumulative distribution $\mathbb{P}(Z_n \leq z)$, where
$\{Z_n\}$ is the Bhattacharyya process defined from $W$, and its dependence on
the rate of transmission. On the basis of this result, we characterize the
asymptotic behavior, as well as its dependence on the rate, of the block error
probability of polar codes using the successive cancellation decoder. This
refines the original bounds by Ar{\i}kan and Telatar. Our results apply to
general polar codes based on $\ell \times \ell$ kernel matrices.
  We also provide lower bounds on the block error probability of polar codes
using the MAP decoder. The MAP lower bound and the successive cancellation
upper bound coincide when $\ell=2$, but there is a gap for $\ell>2$.


  We imaged the continuum and molecular line emission from Orion Source I
(SrcI) with up to 30 mas (12 AU) resolution at 43, 99, 223, and 340 GHz in an
attempt to probe the structure and chemistry of the circumstellar disk and
bipolar outflow associated with this high mass protostar. The continuum
spectral index ranges from $\sim$2 along the midplane of the disk to $\sim$3
along the edges, consistent with dust that is optically thick in the midplane
but becomes optically thin at the periphery. Salt (NaCl) emission is visible
where the dust is optically thin; it provides a unique tracer of the velocity
field within the disk. All other molecules that we have mapped - H$_2$O, AlO,
SiO, SiS, SO, and SO$_2$ - appear to originate primarily in the bipolar
outflow. The base of the outflow is corotating with the disk. SiS shows a
filamentary structure that is most prominent along the edges of the outflow.
The molecular distributions suggest that Si and Al released from dust grains in
the disk react with oxygen derived from H$_2$O to form SiO and AlO, and with SO
and SO$_2$ to form SiS.


  We investigate the impact of various fluctuation mechanisms on the dc
resistance at the superconducting resistive transition (RT) in epitaxial
titanium nitride (TiN) films. The studied samples demonstrate a relatively
steep RT with $\delta T/T_\mathrm{c}$ $\sim 0.002 - 0.025$ depending on the
film thickness (20, 9, and 5\, nm) and device dimensions. The transition is
significantly broader than predicted by a conventional theory of
superconducting fluctuations ($\delta T/T_\mathrm{c} \ll 10^{-3}$), which can
be can be tackled by the effective medium theory of RT in an inhomogeneous
superconductor. We propose that the underlying inhomogeneity has intrinsic
origin and manifests via two microscopic mechanisms - static and dynamic. The
dynamic inhomogeneity is associated with spontaneous fluctuations of the
electronic temperature ($T$-fluctuations), whereas the static one is related to
random spatial distribution of surface magnetic disorder (MD) present in TiN
films. Our analysis reveals distinct dependencies of the RT width on the
material parameters and device dimensions for the two mechanisms, which allows
to disentangle their contributions in experiment. We find that while the
$T$-fluctuations may contribute a sizeable part of the measured RT width, the
main effect is related to the MD mechanism, which is quantitatively consistent
with the data in a wide range of sample dimensions. Given the fact that both
microscopic mechanisms are almost inevitably present in real devices, our
results provide a novel perspective of microscopic origin of the RT broadening
and inhomogeneity in thin superconducting films.


  The lack of reliable cluster samples at intermediate and high redshift has
motivated many optical/infrared and X-ray cluster searches. The motivation of
such searches is well justified: the abundance of massive collapsed regions
gives a direct constraint on Omega(matter). Alternatively, the
Sunyaev-Zeldovich effect (SZE) offers a robust method for the detection of
clusters which is, almost, independent of redshift. In this paper we comment on
the possibility of mapping the SZE at high resolution in the millimeter regime
using the Large Millimeter Telescope/Gran Telescopio Milimetrico (LMT/GTM) and
the bolometer array BOLOCAM. The construction of the LMT/GTM facility is
underway atop Cerro La Negra (latitude=18deg 59min; height=4600 m) in Mexico.


  We suggest an axiom system for a collection of matchings that describes the
triangulation of product of simplices.


  Cepheid stars are crucial objects for a variety of topics that range from
stellar pulsation and the evolution of intermediate-mass stars to the
understanding the structure of the Galaxy and the Universe through the distance
measurements they provide. The developments in hydrodynamical calculations, the
release of large ground-based surveys, and the advent of continuous,
space-based photometry revealed many puzzling phenomena about these stars in
the last few years. In this paper I collected some important and new results in
the topics of distance measurements and binarity investigations. I also
summarize the most recent discoveries in their light variations, such as period
doubling, modulation, low-amplitude additional modes, period jitter and the
signs of granulation, and discuss the new opportunities that current and future
space missions will offer for us.


  Euclidean wormholes -- geometries which connect disconnected boundaries --
present a challenge to a standard quantum mechanical interpretation of the
theory. One potential resolution is that the gravitational path integral
computes the ensemble average of many theories. The connected topologies
contribute to the simplest possible observable: the free energy, which is
computed using a replica trick. This is distinct from the replica trick used to
compute entanglement entropies, and appears in the computation of any extensive
quantity. We argue that both JT gravity and a simplified version of CGHS admit
a regime where the contribution of connected replica wormholes to the free
energy is larger than that of disconnected topologies. In both theories we find
evidence of replica symmetry breaking, which is reminiscent of the behavior of
certain spin glasses. We discuss possible insights about ensemble averaging in
gravity from this perspective.


  We present a financial market model, characterized by self-organized
criticality, that is able to generate endogenously a realistic price dynamics
and to reproduce well-known stylized facts. We consider a community of
heterogeneous traders, composed by chartists and fundamentalists, and focus on
the role of informative pressure on market participants, showing how the
spreading of information, based on a realistic imitative behavior, drives
contagion and causes market fragility. In this model imitation is not intended
as a change in the agent's group of origin, but is referred only to the price
formation process. We introduce in the community also a variable number of
random traders in order to study their possible beneficial role in stabilizing
the market, as found in other studies. Finally we also suggest some
counterintuitive policy strategies able to dampen fluctuations by means of a
partial reduction of information.


  We present Keck Interferometer observations of TW Hya that spatially resolve
its emission at 2 micron wavelength. Analyzing these data together with
existing K-band veiling and near-infrared photometric measurements, we conclude
that the inner disk consists of optically thin, sub-micron-sized dust extending
from ~4 AU to within 0.06 AU of the central star. The inner disk edge may be
magnetospherically truncated. Even if we account for the presence of gas in the
inner disk, these small dust grains have survival times against radiation
blow-out that are orders of magnitude shorter than the age of the system,
suggesting continual replenishment through collisions of larger bodies.


  Standard models for deep galaxy counts are based on luminosity functions (LF)
with relatively flat faint end ($\alpha\sim-1.0$). Galaxy counts in the B--band
exceed the prediction of such models by a factor of 2 to more than 5, forcing
the introduction of strong luminosity and/or density evolution. Recently Marzke
et al. (1994a) using the CfA redshift survey sample find that the number of
galaxies in the range $-16<M_{Zw}<-13$ exceeds the extrapolation of a flat
faint end LF by a factor of 2. Here we show that this steep LF substantially
contributes to justify the observed blue galaxy counts without invoking strong
luminosity and/or density evolution. Furthermore we show that taking into
account the variation of the $B-K$ color as a function of the morphological
types and assuming a mean value $<B-K><2.5$ for dwarf galaxies, we reproduce
well also the observed $K$--band deep galaxy counts. This assumption is
supported by the strong correlation we found between $B-K$ color of galaxies
and their infrared absolute magnitude: galaxies become bluer with decreasing
luminosity.


  To unravel the effect of likely interactions between the energetic ejections
of the galactic superluminal source GRS 1915+105 and its surrounding
interstellar medium, we observed its environment. Two IRAS sources are
symmetrically placed with respect to GRS 1915+105, and are aligned with the
sub-arcsec ejections of this source.
  We analyzed these two sources IRAS 19124+1106 and IRAS 19132+1035 through
near-infrared, millimeter and centimeter wavelengths. The evidence for these
regions being interaction zones seems inconclusive.


  There is literature that questions the veracity of the studies commissioned
by the transnational tobacco companies (TTC) to measure the illicit tobacco
trade. Furthermore, there are studies that indicate that the Empty Pack Surveys
(EPS) ordered by the TTCs are oversized. The novelty of this study is that, in
addition to detecting the anomalies analyzed in the EPSs, there are provinces
in which cigarette sales are higher than reasonable values, something that the
TTCs ignore. This study analyzed simultaneously, firstly, if the EPSs
established in each of the 47 Spanish provinces were fulfilled. Second,
anomalies observed in provinces where sales exceed expected values are
measured. To achieve the objective of the paper, provincial data on cigarette
sales, price and GDP per capita are used. These data are modeled with machine
learning techniques widely used to detect anomalies in other areas. The results
reveal that the provinces in which sales below reasonable values are observed
(as detected by the EPSs) present a clear geographical pattern. Furthermore,
the values provided by the EPSs in Spain, as indicated in the previous
literature, are slightly oversized. Finally, there are regions bordering other
countries or with a high tourist influence in which the observed sales are
higher than the expected values.


  Pollen grain classification has a remarkable role in many fields from
medicine to biology and agronomy. Indeed, automatic pollen grain classification
is an important task for all related applications and areas. This work presents
the first large-scale pollen grain image dataset, including more than 13
thousands objects. After an introduction to the problem of pollen grain
classification and its motivations, the paper focuses on the employed data
acquisition steps, which include aerobiological sampling, microscope image
acquisition, object detection, segmentation and labelling. Furthermore, a
baseline experimental assessment for the task of pollen classification on the
built dataset, together with discussion on the achieved results, is presented.


  Parametric down-conversion is a widely exploited technique in optics to
produce entangled states of photons for quantum information processing and
quantum sensing. In the microwave domain, devices based on Josephson junctions,
such as Josephson parametric amplifiers (JPAs) and voltage-biased Josephson
junctions, have been successfully utilized to generate such states. However,
their high susceptibility to magnetic fields has posed challenges in many
applications. Here we demonstrate the generation of two-mode squeezed states
via four-wave-mixing in a superconducting nanowire resonator patterned from
NbN. The NbN nanowire exhibits a strong Kerr nonlinearity, resulting in the
emission of a signal-idler pair with a cross-correlation of $g^{(2)}(0) =
11.9$. Owing to the magnetic resilience and high critical temperature ($T_c$)
of NbN, our microwave parametric sources based on kinetic inductance promise an
expanded range of potential applications.


  Knowledge graphs (KGs) complement Large Language Models (LLMs) by providing
reliable, structured, domain-specific, and up-to-date external knowledge.
However, KGs and LLMs are often developed separately and must be integrated
after training. We introduce Tree-of-Traversals, a novel zero-shot reasoning
algorithm that enables augmentation of black-box LLMs with one or more KGs. The
algorithm equips a LLM with actions for interfacing a KG and enables the LLM to
perform tree search over possible thoughts and actions to find high confidence
reasoning paths. We evaluate on two popular benchmark datasets. Our results
show that Tree-of-Traversals significantly improves performance on question
answering and KG question answering tasks. Code is available at
\url{https://github.com/amazon-science/tree-of-traversals}


  On the space $\mathbb Q_p^n$, where $p\ne 2$ and $p$ does not divide $n$, we
construct a p-adic counterpart of spherical coordinates. As applications, a
description of homogeneous distributions on $\mathbb Q_p^n$ and a skew product
decomposition of p-adic L\'evy processes are given.


This paper has been withdrawn.


  With the aim to construct a dynamical model with quantum group symmetry, the
$q$-deformed Schr\"odinger equation of the harmonic oscillator on the
$N$-dimensional quantum Euclidian space is investigated. After reviewing the
differential calculus on the $q$-Euclidian space, the $q$-analog of the
creation-annihilation operator is constructed. It is shown that it produces
systematically all eigenfunctions of the Schr\"odinger equation and
eigenvalues. We also present an alternative way to solve the Schr\"odinger
equation which is based on the $q$-analysis. We represent the Schr\"odinger
equation by the $q$-difference equation and solve it by using $q$-polynomials
and $q$-exponential functions. The problem of the involution corresponding to
the reality condition is discussed.


  Improving the energy efficiency of mobile applications is a topic that has
gained a lot of attention recently. It has been addressed in a number of ways
such as identifying energy bugs and developing a catalog of energy patterns.
Previous work shows that users discuss the battery-related issues (energy
inefficiency or energy consumption) of the apps in their reviews. However,
there is no work that addresses the automatic extraction of battery-related
issues from users' feedback. In this paper, we report on a visualization tool
that is developed to empirically study machine learning algorithms and text
features to automatically identify the energy consumption specific reviews with
the highest accuracy. Other than the common machine learning algorithms, we
utilize deep learning models with different word embeddings to compare the
results. Furthermore, to help the developers extract the main topics that are
discussed in the reviews, two states of the art topic modeling algorithms are
applied. The visualizations of the topics represent the keywords that are
extracted for each topic along with a comparison with the results of string
matching. The developed web-browser based interactive visualization tool is a
novel framework developed with the intention of giving the app developers
insights about running time and accuracy of machine learning and deep learning
models as well as extracted topics. The tool makes it easier for the developers
to traverse through the extensive result set generated by the text
classification and topic modeling algorithms. The dynamic-data structure used
for the tool stores the baseline-results of the discussed approaches and is
updated when applied on new datasets. The tool is open-sourced to replicate the
research results.


  We present detailed first-principles calculations of the electron-conduction
properties of a three-sodium-atom nanowire suspended between semi-infinite
crystalline Na(001) electrodes during its elongation. Our investigations reveal
that the conductance is ~1 G0 before the nanowire breaks and only one channel
with the characteristic of the $3s$ orbital of the center atom in the nanowire
contributes to the electron conduction. Moreover, the channel fully opens
around the Fermi level, and the behavior of the channel-current density is
insensitive to the structural deformation of the nanowire. These results verify
that the conductance trace as a function of the electrode spacing exhibits a
flat plateau at ~1 G0 during elongation.


  We perform a systematic study of the temporal dynamics emerging in the
asymmetrically driven dissipative Bose-Hubbard dimer model. This model
successfully describes the nonlinear dynamics of photonic diatomic molecules in
linearly coupled Kerr resonators coherently excited by a single laser beam.
Such temporal dynamics include self-pulsing oscillations, period doubled
oscillatory states, chaotic dynamics, and spikes. The different states and
dynamical regimes have been thoroughly characterized using bifurcation
analysis. This analysis has allowed us to identify the main instabilities, i.e.
bifurcations, responsible for the appearance of the previously stated dynamics.


  Active galaxies are the most powerful engines in the Universe for converting
gravitational energy into radiation, and their study at all epochs of evolution
is therefore important. Powerful radio-loud quasars and radio galaxies have the
added advantage that, since their radio jets need X-ray-emitting gas as a
medium in which to propagate, the sources can be used as cosmological probes to
trace significant atmospheres at high redshift. The radio emission can be used
as a measure of source orientation, and sensitive X-ray measurements,
especially when used in combination with multi-wavelength data, can be used to
derive important results on the physical structures on a range of sizes from
the cores to the large-scale components. In this paper we present new results
on a significant sample of powerful radio galaxies and quasars at z > 0.5,
drawn from the 3CRR catalogue and selected to sample a full range of source
orientation. Using high-quality observations from XMM-Newton and Chandra, we
discuss the X-ray properties of the cores, jets, lobes and cluster gas, and,
through the incorporation of multi-wavelength data, draw conclusions about the
nature of the emission from the different components.


  We present the first James Webb Space Telescope/NIRCam-led determination of
$7<z<9$ galaxy properties based on broadband imaging from 0.8 to 5~$\mathrm{\mu
m}$ as part of the GLASS-JWST Early Release Science program. This is the
deepest dataset acquired at these wavelengths to date, with an angular
resolution $\lesssim0.14$ arcsec. We robustly identify 13 galaxies with
$S/N\gtrsim8$ in F444W from 8 arcmin$^2$ of data at $m_{AB}\leq 28$ from a
combination of dropout and photometric redshift selection. From simulated data
modeling, we estimate the dropout sample purity to be $\gtrsim90\%$. We find
that the number density of these F444W-selected sources is broadly consistent
with expectations from the UV luminosity function determined from Hubble Space
Telescope data. We characterize galaxy physical properties using a Bayesian
Spectral Energy Distribution fitting method, finding median stellar mass
$10^{8.5}M_\odot$ and age 140 Myr, indicating they started ionizing their
surroundings at redshift $z>9.5$. Their star formation main sequence is
consistent with predictions from simulations. Lastly, we introduce an
analytical framework to constrain main-sequence evolution at $z>7$ based on
galaxy ages and basic assumptions, through which we find results consistent
with expectations from cosmological simulations. While this work only gives a
glimpse of the properties of typical galaxies that are thought to drive the
reionization of the universe, it clearly shows the potential of JWST to unveil
unprecedented details on galaxy formation in the first billion years.


  This work can be considered as a continuation of our previous one (J.Phys.,
26 (1993) 313), in which an explicit form of coherent states (CS) for all SU(N)
groups was constructed by means of representations on polynomials. Here we
extend that approach to any SU(l,1) group and construct explicitly
corresponding CS. The CS are parametrized by dots of a coset space, which is,
in that particular case, the open complex ball $CD^{l}$. This space together
with the projective space $CP^{l}$, which parametrizes CS of the SU(l+1) group,
exhausts all complex spaces of constant curvature. Thus, both sets of CS
provide a possibility for an explicit analysis of the quantization problem on
all the spaces of constant curvature.


  An approximation of the Standard Regge Calculus (SRC) was proposed by the
$Z_2$-Regge Model ($Z_2$RM). There the edge lengths of the simplicial complexes
are restricted to only two possible values, both always compatible with the
triangle inequalities. To examine the effect of discrete edge lengths, we
define two models to describe the transition from the $Z_2$RM to the SRC. These
models allow to choose the number of possible link lengths to be $n =
{4,8,16,32,64,...}$ and differ mainly in the scaling of the quadratic link
lengths. The first extension, the $X^1_n$-Model, keeps the edge lengths limited
and still behaves rather similar to the "spin-like" $Z_2$RM. The vanishing
critical cosmological constant is reproduced by the second extension, the
$X^C_n$-Model, which allows for increasing edge lengths. In addition the area
expectation values are consistent with the scaling relation of the SRC.


  We point out that the effective channel for the interfacial thermal
conductance, the inverse of Kapitza resistance, of
metal-insulator/semiconductor interfaces is governed by the electron-phonon
interaction mediated by the surface states allowed in a thin region near the
interface. Our detailed calculations demonstrate that the interfacial thermal
conductance across Pb/Pt/Al/Au-diamond interfaces are only slightly different
among these metals, and reproduce well the experimental results of the
interfacial thermal conductance across metal-diamond interfaces observed by
Stoner et al. [Phys. Rev. Lett. 68, 1563 (1992)] and most recently by Hohensee
et al. [Nature Commun. 6, 6578 (2015)].


  We present a new selection of 358 blue compact dwarf galaxies (BCDs) from
5,000 square degrees in the Dark Energy Survey (DES), and the spectroscopic
follow-up of a subsample of 68 objects. For the subsample of 34 objects with
deep spectra, we measure the metallicity via the direct T$_e$ method using the
auroral [\oiii]$\lambda$ 4363 emission line. These BCDs have an average oxygen
abundance of 12+log(O/H)= 7.8, with stellar masses between 10$^7$ to 10$^8$
M$_\odot$ and specific star formation rates between $\sim$ 10$^{-9}$ to
10$^{-7}$ yr$^{-1}$. We compare the position of our BCDs with the
Mass-metallicity (M-Z) and Luminosity-metallicity (L-Z) relation derived from
the Local Volume Legacy sample. We find the scatter about the M-Z relation is
smaller than the scatter about the L-Z relation. We identify a correlation
between the offsets from the M-Z and L-Z relation that we suggest is due to the
contribution of metal-poor inflows. Finally, we explore the validity of the
mass-metallicity-SFR fundamental plane in the mass range probed by our
galaxies. We find that BCDs with stellar masses smaller than
$10^{8}$M$_{\odot}$ do not follow the extrapolation of the fundamental plane.
This result suggests that mechanisms other than the balance between inflows and
outflows may be at play in regulating the position of low mass galaxies in the
M-Z-SFR space.


  We prove that a GIT chamber quotient of an affine variety $X=Spec(A)$ by a
reductive group $G$, where $A$ is an almost factorial domain, is a Mori dream
space if it is projective, regardless of the codimension of the unstable locus.
This includes an explicit description of the Picard number, the pseudoeffective
cone, and the Mori chambers in terms of GIT. We apply the results to quiver
moduli, to show that they are Mori dream spaces if the quiver contains no
oriented cycles, and if stability and semistability coincide. We give a formula
for the Picard number in quiver terms. As a second application we prove that
geometric quotients of Mori dream spaces are Mori dream spaces as well, which
again includes a description of the Picard number and the Mori chambers. Some
examples are given to illustrate the results.


  Deep Neural Networks (DNNs) come into the limelight in High Energy Physics
(HEP) in order to manipulate the increasing amount of data encountered in the
next generation of accelerators. Recently, the HEP community has suggested
Generative Adversarial Networks (GANs) to replace traditional time-consuming
Geant4 simulations based on the Monte Carlo method. In parallel with advances
in deep learning, intriguing studies have been conducted in the last decade on
quantum computing, including the Quantum GAN model suggested by IBM. However,
this model is limited in learning a probability distribution over discrete
variables, while we initially aim to reproduce a distribution over continuous
variables in HEP. We introduce and analyze a new prototype of quantum GAN
(qGAN) employed in continuous-variable (CV) quantum computing, which encodes
quantum information in a continuous physical observable. Two CV qGAN models
with a quantum and a classical discriminator have been tested to reproduce
calorimeter outputs in a reduced size, and their advantages and limitations are
discussed.


  Performance analysis, from the external point of view of a client who would
only have access to returns and holdings of a fund, evolved towards exact
attribution made in the context of portfolio optimisation, which is the
internal point of view of a manager controlling all the parameters of this
optimisation. Attribution is exact, that-is-to-say no residual "interaction"
term remains, and various contributions to the optimal portfolio can be
identified: predictive signals, constraints, benchmark. However constraints are
identified as a separate portfolio and attribution for each signal that are
used to predict future returns thus corresponds to unconstrained signal
portfolios. We propose a novel attribution method that put predictive signals
at the core of attribution and allows to include the effect of constraints in
portfolios attributed to every signal. We show how this can be applied to
various trading models and portfolio optimisation frameworks and explain what
kind of insights such an attribution provides.


  We report a 13.16 Tbit/s 54-channel DWDM free-space optical data transmission
field trial emulating the worst-case scenario for optical GEO satellite uplinks
through the atmosphere, and show the system performance under turbulent
conditions.


  We solve a problem mentioned in an article of Berger and Bourn: we prove that
in the context of an algebraically coherent semi-abelian category, two natural
definitions of the lower central series coincide. In a first, "standard"
approach, nilpotency is defined as in group theory via nested binary
commutators of the form $[[X,X],X]$. In a second approach, higher Higgins
commutators of the form $[X,X,X]$ are used to define nilpotent objects. The two
are known to be different in general; for instance, in the context of loops,
the definition of Bruck is of the former kind, while the commutator-associator
filtration of Mostovoy and his co-authors is of the latter type. Another
example, in the context of Moufang loops, is given in Berger and Bourn's paper.
In this article, we show that the two streams of development agree in any
algebraically coherent semi-abelian category. Such are, for instance, all
Orzech categories of interest. Our proof of this result is based on a
higher-order version of the Three Subobjects Lemma of Cigoli-Gray-Van der
Linden, which extends the classical Three Subgroups Lemma from group theory to
categorical algebra. It says that any $n$-fold Higgins commutator $[K_1,
\dots,K_n]$ of normal subobjects $K_i$ of an object $X$ may be decomposed into
a join of nested binary commutators.


  The principal submatrix localization problem deals with recovering a $K\times
K$ principal submatrix of elevated mean $\mu$ in a large $n\times n$ symmetric
matrix subject to additive standard Gaussian noise. This problem serves as a
prototypical example for community detection, in which the community
corresponds to the support of the submatrix. The main result of this paper is
that in the regime $\Omega(\sqrt{n}) \leq K \leq o(n)$, the support of the
submatrix can be weakly recovered (with $o(K)$ misclassification errors on
average) by an optimized message passing algorithm if $\lambda = \mu^2K^2/n$,
the signal-to-noise ratio, exceeds $1/e$. This extends a result by Deshpande
and Montanari previously obtained for $K=\Theta(\sqrt{n}).$ In addition, the
algorithm can be extended to provide exact recovery whenever
information-theoretically possible and achieve the information limit of exact
recovery as long as $K \geq \frac{n}{\log n} (\frac{1}{8e} + o(1))$. The total
running time of the algorithm is $O(n^2\log n)$.
  Another version of the submatrix localization problem, known as noisy
biclustering, aims to recover a $K_1\times K_2$ submatrix of elevated mean
$\mu$ in a large $n_1\times n_2$ Gaussian matrix. The optimized message passing
algorithm and its analysis are adapted to the bicluster problem assuming
$\Omega(\sqrt{n_i}) \leq K_i \leq o(n_i)$ and $K_1\asymp K_2.$ A sharp
information-theoretic condition for the weak recovery of both clusters is also
identified.


  A vast literature on the theory and phenomenology of Two-Higgs-Doublet models
(2HDM) exists since long. However, the present situation demands a revisit of
some 2HDM properties. Now that a 125 GeV scalar resonance has been discovered
at the LHC, with its couplings to other particles showing increasing affinity
to the Standard Model Higgs-like behavior, the 2HDM parameter space is more
squeezed than ever. We briefly review the different parametrizations of the
2HDM potential and discuss the constraints on the parameter space arising from
the unitarity and stability of the potential together with constraints from the
oblique electroweak $T$-parameter. We also differentiate the consequences of
imposing a global continuous U(1) symmetry on the potential from a discrete
$Z_2$ symmetry.


  It is well-known that the functions $f \in L^1(\mathbb{R}^d)$ whose
translates along a lattice $\Lambda$ form a tiling, can be completely
characterized in terms of the zero set of their Fourier transform. We construct
an example of a discrete set $\Lambda \subset \mathbb{R}$ (a small perturbation
of the integers) for which no characterization of this kind is possible: there
are two functions $f, g \in L^1(\mathbb{R})$ whose Fourier transforms have the
same set of zeros, but such that $f + \Lambda$ is a tiling while $g + \Lambda$
is not.


  We analyse four-dimensional, supersymmetric intersecting D-brane models in a
toroidal orientifold background from a statistical perspective. The
distribution and correlation of observables, like gauge groups and couplings,
are discussed. We focus on models with a Standard Model-like gauge sector,
derive frequency distributions for their occurence and analyse the properties
of the hidden sector.


  Machine learning (ML) is increasingly often used to inform high-stakes
decisions. As complex ML models (e.g., deep neural networks) are often
considered black boxes, a wealth of procedures has been developed to shed light
on their inner workings and the ways in which their predictions come about,
defining the field of 'explainable AI' (XAI). Saliency methods rank input
features according to some measure of 'importance'. Such methods are difficult
to validate since a formal definition of feature importance is, thus far,
lacking. It has been demonstrated that some saliency methods can highlight
features that have no statistical association with the prediction target
(suppressor variables). To avoid misinterpretations due to such behavior, we
propose the actual presence of such an association as a necessary condition and
objective preliminary definition for feature importance. We carefully crafted a
ground-truth dataset in which all statistical dependencies are well-defined and
linear, serving as a benchmark to study the problem of suppressor variables. We
evaluate common explanation methods including LRP, DTD, PatternNet,
PatternAttribution, LIME, Anchors, SHAP, and permutation-based methods with
respect to our objective definition. We show that most of these methods are
unable to distinguish important features from suppressors in this setting.


  The identification of out-of-distribution data is vital to the deployment of
classification networks. For example, a generic neural network that has been
trained to differentiate between images of dogs and cats can only classify an
input as either a dog or a cat. If a picture of a car or a kumquat were to be
supplied to this classifier, the result would still be either a dog or a cat.
In order to mitigate this, techniques such as the neural network watchdog have
been developed. The compression of the image input into the latent layer of the
autoencoder defines the region of in-distribution in the image space. This
in-distribution set of input data has a corresponding boundary in the image
space. The watchdog assesses whether inputs are in inside or outside this
boundary. This paper demonstrates how to sharpen this boundary using generative
network training data augmentation thereby bettering the discrimination and
overall performance of the watchdog.


  Transistors based on various types of non-silicon nanowires have shown great
potential for a variety of applications, especially for those require
transparency and low-temperature substrates. However, critical requirements for
circuit functionality such as saturated source-drain current, and matched
threshold voltages of individual nanowire transistors in a way that is
compatible with low temperature substrates, have not been achieved. Here we
show that femtosecond laser pulses can anneal individual transistors based on
In2O3 nanowires, improve the saturation of the source-drain current, and
permanently shift the threshold voltage to the positive direction. We applied
this technique and successfully shifted the switching threshold voltages of
NMOS based inverters and improved their noise margin, in both depletion and
enhancement modes. Our demonstration provides a method to trim the parameters
of individual nanowire transistors, and suggests potential for large-scale
integration of nanowire-based circuit blocks and systems.


  The Generalized P\'{o}lya Urn (GPU) is a popular urn model which is widely
used in many disciplines. In particular, it is extensively used in treatment
allocation schemes in clinical trials. In this paper, we propose a sequential
estimation-adjusted urn model (a nonhomogeneous GPU) which has a wide spectrum
of applications. Because the proposed urn model depends on sequential
estimations of unknown parameters, the derivation of asymptotic properties is
mathematically intricate and the corresponding results are unavailable in the
literature. We overcome these hurdles and establish the strong consistency and
asymptotic normality for both the patient allocation and the estimators of
unknown parameters, under some widely satisfied conditions. These properties
are important for statistical inferences and they are also useful for the
understanding of the urn limiting process. A superior feature of our proposed
model is its capability to yield limiting treatment proportions according to
any desired allocation target. The applicability of our model is illustrated
with a number of examples.


  In this study, we investigate a context-aware status updating system
consisting of multiple sensor-estimator pairs. A centralized monitor pulls
status updates from multiple sensors that are monitoring several
safety-critical situations (e.g., carbon monoxide density in forest fire
detection, machine safety in industrial automation, and road safety). Based on
the received sensor updates, multiple estimators determine the current
safety-critical situations. Due to transmission errors and limited
communication resources, the sensor updates may not be timely, resulting in the
possibility of misunderstanding the current situation. In particular, if a
dangerous situation is misinterpreted as safe, the safety risk is high. In this
paper, we introduce a novel framework that quantifies the penalty due to the
unawareness of a potentially dangerous situation. This situation-unaware
penalty function depends on two key factors: the Age of Information (AoI) and
the observed signal value. For optimal estimators, we provide an
information-theoretic bound of the penalty function that evaluates the
fundamental performance limit of the system. To minimize the penalty, we study
a pull-based multi-sensor, multi-channel transmission scheduling problem. Our
analysis reveals that for optimal estimators, it is always beneficial to keep
the channels busy. Due to communication resource constraints, the scheduling
problem can be modelled as a Restless Multi-armed Bandit (RMAB) problem. By
utilizing relaxation and Lagrangian decomposition of the RMAB, we provide a
low-complexity scheduling algorithm which is asymptotically optimal. Our
results hold for both reliable and unreliable channels. Numerical evidence
shows that our scheduling policy can achieve up to 100 times performance gain
over periodic updating and up to 10 times over randomized policy.


  Adversarial attacks on a convolutional neural network (CNN) -- injecting
human-imperceptible perturbations into an input image -- could fool a
high-performance CNN into making incorrect predictions. The success of
adversarial attacks raises serious concerns about the robustness of CNNs, and
prevents them from being used in safety-critical applications, such as medical
diagnosis and autonomous driving. Our work introduces a visual analytics
approach to understanding adversarial attacks by answering two questions: (1)
which neurons are more vulnerable to attacks and (2) which image features do
these vulnerable neurons capture during the prediction? For the first question,
we introduce multiple perturbation-based measures to break down the attacking
magnitude into individual CNN neurons and rank the neurons by their
vulnerability levels. For the second, we identify image features (e.g., cat
ears) that highly stimulate a user-selected neuron to augment and validate the
neuron's responsibility. Furthermore, we support an interactive exploration of
a large number of neurons by aiding with hierarchical clustering based on the
neurons' roles in the prediction. To this end, a visual analytics system is
designed to incorporate visual reasoning for interpreting adversarial attacks.
We validate the effectiveness of our system through multiple case studies as
well as feedback from domain experts.


  Massive and extended dark halos can inhibit the formation of long tidal tails
in galaxy collisions. We examine this effect using an extensive survey of
simulations with different dark halo potentials to constrain halo properties of
interacting galaxies. These constraints are compared to other observational
limits and theoretical predictions of halo structure. The dark halos predicted
by $\Omega=1$ cosmological models like CDM are too massive and extended to
produce the long tidal tails seen in nearby galaxy collisions. There is also a
conflict with the halo potentials inferred from satellite kinematics; such
halos would likewise inhibit tail formation in galaxy collisions.


  Domain generalization in semantic segmentation aims to alleviate the
performance degradation on unseen domains through learning domain-invariant
features. Existing methods diversify images in the source domain by adding
complex or even abnormal textures to reduce the sensitivity to domain specific
features. However, these approaches depend heavily on the richness of the
texture bank, and training them can be time-consuming. In contrast to importing
textures arbitrarily or augmenting styles randomly, we focus on the single
source domain itself to achieve generalization. In this paper, we present a
novel adaptive texture filtering mechanism to suppress the influence of texture
without using augmentation, thus eliminating the interference of
domain-specific features. Further, we design a hierarchical guidance
generalization network equipped with structure-guided enhancement modules,
which purpose is to learn the domain-invariant generalized knowledge. Extensive
experiments together with ablation studies on widely-used datasets are
conducted to verify the effectiveness of the proposed model, and reveal its
superiority over other state-of-the-art alternatives.


  The $k$-support norm is a regularizer which has been successfully applied to
sparse vector prediction problems. We show that it belongs to a general class
of norms which can be formulated as a parameterized infimum over quadratics. We
further extend the $k$-support norm to matrices, and we observe that it is a
special case of the matrix cluster norm. Using this formulation we derive an
efficient algorithm to compute the proximity operator of both norms. This
improves upon the standard algorithm for the $k$-support norm and allows us to
apply proximal gradient methods to the cluster norm. We also describe how to
solve regularization problems which employ centered versions of these norms.
Finally, we apply the matrix regularizers to different matrix completion and
multitask learning datasets. Our results indicate that the spectral $k$-support
norm and the cluster norm give state of the art performance on these problems,
significantly outperforming trace norm and elastic net penalties.


  A Brain Computer Interface based on imagined words can decode the word a
subject is thinking on through brain signals to control an external device. In
order to build a fully asynchronous Brain Computer Interface based on imagined
words in electroencephalogram signals as source, we need to solve the problem
of detecting the onset of the imagined words. Although there has been some
research in this field, the problem has not been fully solved. In this paper we
present an approach to solve this problem by using values from statistics,
information theory and chaos theory as features to correctly identify the onset
of imagined words in a continuous signal. On detecting the onsets of imagined
words, the highest True Positive Rate achieved by our approach was obtained
using features based on the generalized Hurst exponent, this True Positive Rate
was 0.69 and 0.77 with a timing error tolerance region of 3 and 4 seconds
respectively.


  Short Message Service (SMS) remains one of the most popular communication
channels since its introduction in 2G cellular networks. In this paper, we
demonstrate that merely receiving silent SMS messages regularly opens a
stealthy side-channel that allows other regular network users to infer the
whereabouts of the SMS recipient. The core idea is that receiving an SMS
inevitably generates Delivery Reports whose reception bestows a timing attack
vector at the sender. We conducted experiments across various countries,
operators, and devices to show that an attacker can deduce the location of an
SMS recipient by analyzing timing measurements from typical receiver locations.
Our results show that, after training an ML model, the SMS sender can
accurately determine multiple locations of the recipient. For example, our
model achieves up to 96% accuracy for locations across different countries, and
86% for two locations within Belgium. Due to the way cellular networks are
designed, it is difficult to prevent Delivery Reports from being returned to
the originator making it challenging to thwart this covert attack without
making fundamental changes to the network architecture.


  We analyze the density profiles and virial radii for a sample of 90 nearby
clusters, using galaxies with available redshifts and positions. Each cluster
has at least 20 redshifts measured within an Abell radius, and all the results
come from galaxy sets of at least 20 members. Most of the density profiles of
our clusters are well fitted by hydrostatic-isothermal-like profiles. The
slopes we find for many cluster density profiles are consistent with the
hypothesis that the galaxies are in equilibrium with the binding cluster
potential. The virial radii correlate with the core radii at a very high
significance level. The observed relationship between the two size estimates is
in agreement with the theoretical one computed by using the median values of
the density profile parameters fitted on our clusters. After correcting for
incompleteness in our cluster sample, we provide the universal distributions
functions of core and virial radii (obtained within half an Abell radius).


  A brief review is presented on the studies of the hole motion in a
two--dimensional quantum antiferromagnet. An extended introduction is given to
cover the background of the problem. The quantum Bogoliubov--de Gennes
formalism which treats the local distortion of the spin configuration and the
quantum renormalization process on an equal footing, is outlined. The latest
development on the central issue, whether a hole can freely propagate on an
antiferromagnetic background, is overviewed.


  This work proposes a conformable fractional predictor-corrector algorithm for
solving conformable fractional differential equations. Fractional calculus is
finding applications in various scientific fields, but existing numerical
methods might have limitations. This work addresses that gap by introducing a
new algorithm specifically designed for the conformable fractional derivative
using Adams-Bashforth and Adams-Moulton methods.


  This paper deals with an inverse problem for a non-self-adjoint Schr\"odinger
equation on a compact Riemannian manifold. Our goal is to stably determine a
real vector field from the dynamical Dirichlet-to Neumann map. We establish in
dimension n greater than 2, an H\"older type stability estimate for the inverse
problem under study. The proof is mainly based on the reduction to an
equivalent problem for an electro-magnetic Schr\"odinger equation and the use
of a Carleman estimate designed for elliptic operators.


  On several proof-of-stake blockchains, agents engaged in validating
transactions can open a pool to which others can delegate their stake in order
to earn higher returns. We develop a model of staking pool formation in the
presence of malicious agents and establish existence and uniqueness of
equilibria. We then identify potential and risk of staking pools. First,
allowing for staking pools lowers blockchain security. Yet, honest stake
holders obtain higher returns. Second, by choosing welfare optimal distribution
rewards, staking pools prevent that malicious agents receive large rewards.
Third, when pool owners can freely distribute the returns from validation to
delegators, staking pools disrupt blockchain operations, since malicious agents
attract most delegators by offering generous returns.


  We present a uniform assessment of existing near-infrared Spitzer Space
Telescope observations of planet-bearing stars. Using a simple four-parameter
blackbody thermal model, we analyze stars for which photometry in at least one
of Spitzer's IRAC bands has been obtained over either the entirety or a
significant fraction of the planetary orbit. Systems in this category comprise
ten well-studied systems with Hot Jupiters on circular or near-circular orbits
(HAT-P-7, HD 149026, HD 189733, HD 209458, WASP-12, WASP-14, WASP-18, WASP-19,
WASP-33, and WASP-43), as well as three stars harboring planets on
significantly eccentric orbits (GJ 436, HAT-P-2, and HD 80606). We find that
our simple model, in almost all cases, accurately reproduces the minimum and
maximum planetary emission, as well as the phase offsets of these extrema with
respect to transits/secondary eclipses. For one notable exception, WASP-12 b,
adding an additional parameter to account for its tidal distortion is not
sufficient to reproduce its photometric features. Full-orbit photometry is
available in multiple wavelengths for 10 planets. We find that the returned
parameter values for independent fits to each band are largely in agreement.
However, disagreements in night-side temperature suggest distinct atmospheric
layers, each with their own characteristic minimum temperature. In addition, a
diversity in albedos suggests variation in opacity of the photospheres. While
previous works have pointed out trends in photometric features based on system
properties, we cannot conclusively identify analogous trends for physical model
parameters. To make the connection between full-phase data and physical models
more robust, a higher signal-to-noise must come from both increased resolution
and a careful treatment of instrumental systematics.


  Automatically generating accurate summaries from clinical reports could save
a clinician's time, improve summary coverage, and reduce errors. We propose a
sequence-to-sequence abstractive summarization model augmented with
domain-specific ontological information to enhance content selection and
summary generation. We apply our method to a dataset of radiology reports and
show that it significantly outperforms the current state-of-the-art on this
task in terms of rouge scores. Extensive human evaluation conducted by a
radiologist further indicates that this approach yields summaries that are less
likely to omit important details, without sacrificing readability or accuracy.


  Floppy membranes are tensionless surfaces without extrinsic stiffness, whose
fluctuations are governed by fourth-order bending elasticity. This suppresses
spiky superstructures and ensures that floppy membranes remain smooth over any
distance, with Hausdorff dimension D=2, in contrast to surfaces with stiffness,
which are rough on the scale of some finite persistence length.


  It is shown that third-order 1D nonlinear dispersion equations admit single
point gradient catastrophe, described by blow-up-type similarity solutions.
After blow-up, the solutions admit shock wave-type self-similar extensions.
Snce such extensions are not unique, this implies the principle nonuniqueness
of shock-type solutions and also nonexistence of any entropy-type description
of proper unique solutions. A difficult free-boundary setting, with extra
conditions specified on shocks, are necessary to restore uniqueness in such
problems.


  We explore full/partial tidal disruption events (TDEs) of stars/planets by
stellar compact objects (Black holes; BHs; or neutron stars; NSs), which we
term micro-TDEs. Disruption of a star/planet with mass $M_{\star}$ may lead to
the formation of a debris disk around the BH/NS. Efficient accretion of a
fraction $(f_{acc}=0.1$ of the debris may then give rise to bright energetic
long ($10^{3}-10^{4}\,s$), X-ray/Gamma-ray flares, with total energies of up to
$(f_{acc}/0.1)\times10^{52}\,(M_{\star}/{0.6\,M_{\odot}})\,$ergs, possibly
resembling ultra-long GRBs/XRFs. The energy of such flares depends on the
poorly constrained accretion processes. Significantly fainter flares might be
produced if most of the disk mass is blown away through strong outflows. We
suggest three dynamical origins for such disruptions. In the first, a
star/planet is tidally disrupted following a close random encounter with a
BH/NS in a dense cluster. We estimate the BH (NS) micro-TDE rates from this
scenario to be few$\times10^{-6}$ (few$\times10^{-7}$) ${\rm yr}^{-1}$ per
Milky-Way galaxy. Another scenario involves the interaction of wide companions
due to perturbations by stars in the field, likely producing comparable but
lower rates. Finally, a third scenario involves a BH/NS which gain a natal
velocity kick at birth, leading to a close encounter with a binary companion
and the tidal disruption of that companion. Such events could be associated
with a supernova, or even with a preceding GRB/XRF event, and would likely
occur hours to days after the prompt explosion; the rates of such events could
be larger than those obtained from the other scenarios, depending on the
preceding complex binary stellar evolution.


  Heavy vector-boson hunting is one of the major analyses undertaken by the
experiments carried out at the LHC. I will explore two scenarios which have not
been investigated yet by the experimental collaborations. The first scenario
consists in searching for Z' bosons, predicted by U(1)' GUT-inspired models, by
studying its supersymmetric decays, leading to resonant final states with
charged leptons and missing energy. Afterwards, I shall discuss the 331 model
and the production of doubly-charged vector bileptons, yielding events with two
same-sign lepton pairs at the LHC.


  With the introduction of SNIP [arXiv:1810.02340v2], it has been demonstrated
that modern neural networks can effectively be pruned before training. Yet, its
sensitivity criterion has since been criticized for not propagating training
signal properly or even disconnecting layers. As a remedy, GraSP
[arXiv:2002.07376v1] was introduced, compromising on simplicity. However, in
this work we show that by applying the sensitivity criterion iteratively in
smaller steps - still before training - we can improve its performance without
difficult implementation. As such, we introduce 'SNIP-it'. We then demonstrate
how it can be applied for both structured and unstructured pruning, before
and/or during training, therewith achieving state-of-the-art
sparsity-performance trade-offs. That is, while already providing the
computational benefits of pruning in the training process from the start.
Furthermore, we evaluate our methods on robustness to overfitting,
disconnection and adversarial attacks as well.


  For a finite subgroup $\Gamma\subset {\mathrm{SL}}(2,\mathbb{C})$, we
identify fine moduli spaces of certain cornered quiver algebras, defined in
earlier work, with orbifold Quot schemes for the Kleinian orbifold
$[\mathbb{C}^2/\Gamma]$. We also describe the reduced schemes underlying these
Quot schemes as Nakajima quiver varieties for the framed McKay quiver of
$\Gamma$, taken at specific non-generic stability parameters. These schemes are
therefore irreducible, normal and admit symplectic resolutions. Our results
generalise our previous work on the Hilbert scheme of points on
$\mathbb{C}^2/\Gamma$; we present arguments that completely bypass the ADE
classification.


  We investigate coupled circle maps in presence of feedback and explore
various dynamical phases observed in this system of coupled high dimensional
maps. We observe an interesting transition from localized chaos to
spatiotemporal chaos. We study this transition as a dynamic phase transition.
We observe that persistence acts as an excellent quantifier to describe this
transition. Taking the location of the fixed point of circle map (which does
not change with feedback) as a reference point, we compute number of sites
which have been greater than (less than) the fixed point till time t. Though
local dynamics is high-dimensional in this case this definition of persistence
which tracks a single variable is an excellent quantifier for this transition.
In most cases, we also obtain a well defined persistence exponent at the
critical point and observe conventional scaling as seen in second order phase
transitions. This indicates that persistence could work as good order parameter
for transitions from fully or partially arrested phase. We also give an
explanation of gaps in eigenvalue spectrum of the Jacobian of localized state.


  We give the first correction to the suspension viscosity due to fluid
elasticity for a dilute suspension of spheres in a viscoelastic medium. Our
perturbation theory is valid to $O(\phi\mathrm{Wi}^2)$ in the Weissenberg
number $\mathrm{Wi}=\dot\gamma \lambda$, where $\dot\gamma$ is the typical
magnitude of the suspension velocity gradient, and $\lambda$ is the relaxation
time of the viscoelastic fluid. For shear flow we find that the suspension
shear-thickens due to elastic stretching in strain hot spots near the particle,
despite the fact that the stress inside the particles decreases relative to the
Newtonian case. We thus argue that it is crucial to correctly model the
extensional rheology of the suspending medium to predict the shear rheology of
the suspension. For uniaxial extensional flow we correct existing results at
$O(\phi\mathrm{Wi})$, and find dramatic strain-rate thickening at
$O(\phi\mathrm{Wi}^2)$. We validate our theory with fully resolved numerical
simulations.


  In this paper we derive a propagation of smallness result for a scalar second
elliptic equation in divergence form whose leading order coefficients are
Lipschitz continuous on two sides of a $C^2$ hypersurface that crosses the
domain, but may have jumps across this hypersurface. Our propagation of
smallness result is in the most general form regarding the locations of
domains, which may intersect the interface of discontinuity. At the end, we
also list some consequences of the propagation of smallness result, including
stability results for the associated Cauchy problem, a propagation of smallness
result from sets of positive measure, and a quantitative Runge approximation
property.


  We provide a symmetry result for n-mode positive solutions of a general class
of semi-linear elliptic systems under cooperative conditions on the
nonlinearities. Moreover, we apply the result to a class of H\'enon systems and
provide the existence of multiple n-mode positive solutions.


  This article discusses the question - how to estimate the number of solutions
of algebraic Diophantine equations with natural coefficients using Circular
method developed by Hardy and Littlewood. This paper considers the estimate of
the number of solutions of algebraic Diophantine equation:
$c_1x_1^{k_1}+c_2x_2^{k_2}+...+c_sx_s^{k_s}=n$. The author found the asymptotic
estimate for the number of solutions of this equation as a function of the
value $n$, if all coefficients and $n$ are natural. This article analyzes the
results and shows that these estimates of the number of natural solutions of
the equations have high accuracy.


  We present new observations of the faint "Sirius-like" companion discovered
to orbit HD 114174. Previous attempts to image HD 114174 B at mid-infrared
wavelengths using NIRC2 at Keck have resulted in a non-detection. Our new
L'-band observations taken with the Large Binocular Telescope and LMIRCam
recover the companion ($\Delta L$ = 10.15 $\pm$ 0.15 mag, $\rho$ = 0.675''
$\pm$ 0.016'') with a high signal-to-noise ratio (10 $\sigma$). This
measurement represents the deepest L' high-contrast imaging detection at
sub-arcsecond separations to date, including extrasolar planets. We confirm
that HD 114174 B has near-infrared colors consistent with the interpretation of
a cool white dwarf ($J-L'$ = 0.76 $\pm$ 0.19 mag, $K-L'$ = 0.64 $\pm$ 0.20).
New model fits to the object's spectral energy distribution indicate a
temperature $T_{\rm eff}$ = 4260 $\pm$ 360 K, surface gravity log g = 7.94
$\pm$ 0.03, a cooling age t$_{c} \approx$ 7.8 Gyr, and mass $M$ = 0.54 $\pm$
0.01 $M_{\odot}$. We find that the cooling age given by theoretical atmospheric
models do not agree with the age of HD 114174 A derived from both
isochronological and gyrochronological analyses. We speculate on possible
scenarios to explain the apparent age discrepancy between the primary and
secondary. HD 114174 B is a nearby benchmark white dwarf that will ultimately
enable a dynamical mass estimate through continued Doppler and astrometric
monitoring. Efforts to characterize its physical properties in detail will test
theoretical atmospheric models and improve our understanding of white dwarf
evolution, cooling, and progenitor masses.


  Mix-cumulants of conserved charge distributions are sensitive observables for
probing properties of the QCD medium and phase transition in heavy-ion
collisions. To perform precise measurements, efficiency correction is one of
the most important step. In this study, using the binomial efficiency model, we
derive efficiency correction formulas for mutually exclusive and inclusive
variables. The UrQMD model is applied to verify the validity of these formulas
for different types of correlations. Furthermore, we investigate the effect of
the multiplicity loss and contamination emerging from the particle
identifications. This study provides an important step toward future
measurements of mixed-cumulants in relativistic heavy-ion collisions.


  Neural radiance fields (NeRF) methods have demonstrated impressive novel view
synthesis performance. The core approach is to render individual rays by
querying a neural network at points sampled along the ray to obtain the density
and colour of the sampled points, and integrating this information using the
rendering equation. Since dense sampling is computationally prohibitive, a
common solution is to perform coarse-to-fine sampling.
  In this work we address a clear limitation of the vanilla coarse-to-fine
approach -- that it is based on a heuristic and not trained end-to-end for the
task at hand. We introduce a differentiable module that learns to propose
samples and their importance for the fine network, and consider and compare
multiple alternatives for its neural architecture. Training the proposal module
from scratch can be unstable due to lack of supervision, so an effective
pre-training strategy is also put forward. The approach, named `NeRF in detail'
(NeRF-ID), achieves superior view synthesis quality over NeRF and the
state-of-the-art on the synthetic Blender benchmark and on par or better
performance on the real LLFF-NeRF scenes. Furthermore, by leveraging the
predicted sample importance, a 25% saving in computation can be achieved
without significantly sacrificing the rendering quality.


  In this note we collect some known information and prove new results about
the small uncountable cardinal $\mathfrak q_0$. The cardinal $\mathfrak q_0$ is
defined as the smallest cardinality $|A|$ of a subset $A\subset \mathbb R$
which is not a $Q$-set (a subspace $A\subset\mathbb R$ is called a $Q$-set if
each subset $B\subset A$ is of type $F_\sigma$ in $A$). We present a simple
proof of a folklore fact that $\mathfrak p\le\mathfrak q_0\le\min\{\mathfrak
b,\mathrm{non}(\mathcal N),\log(\mathfrak c^+)\}$, and also establish the
consistency of a number of strict inequalities between the cardinal $\mathfrak
q_0$ and other standard small uncountable cardinals. This is done by combining
some known forcing results. A new result of the paper is the consistency of
$\mathfrak{p} < \mathfrak{lr} < \mathfrak{q}_0$, where $\mathfrak{lr}$ denotes
the linear refinement number. Another new result is the upper bound $\mathfrak
q_0\le\mathrm{non}(\mathcal I)$ holding for any $\mathfrak q_0$-flexible cccc
$\sigma$-ideal $\mathcal I$ on $\mathbb R$.


  Charm production as related to the determination of the strange sea density
in deep inelastic charged current processes is studied predominantly in the
framework of the $\overline{{\rm{MS}}}$ fixed flavor factorization scheme.
Perturbative stability within this formalism is demonstrated. The compatibility
of recent next-to-leading order strange quark distributions with the available
dimuon and $F^{\nu N}_2$ data is investigated. It is shown that final
conclusions concerning these distributions afford further analyses of presently
available and/or forthcoming neutrino data.


  Optimally-shaped electromagnetic fields have the capacity to coherently
control the dynamics of quantum systems and thus offer a promising means for
controlling molecular transformations relevant to chemical, biological, and
materials applications. Currently, advances in this area are hindered by the
prohibitive cost of the quantum dynamics simulations needed to explore the
principles and possibilities of molecular control. However, the emergence of
nascent quantum-computing devices suggests that efficient simulations of
quantum dynamics may be on the horizon. In this article, we study how quantum
computers could be employed to design optimally-shaped fields to control
molecular systems. We introduce a hybrid algorithm that utilizes a quantum
computer for simulating the field-induced quantum dynamics of a molecular
system in polynomial time, in combination with a classical optimization
approach for updating the field. Qubit encoding methods relevant for molecular
control problems are described, and procedures for simulating the quantum
dynamics and obtaining the simulation results are discussed. Numerical
illustrations are then presented that explicitly treat paradigmatic vibrational
and rotational control problems, and also consider how optimally-shaped fields
could be used to elucidate the mechanisms of energy transfer in
light-harvesting complexes. Resource estimates, as well as a numerical
assessment of the impact of hardware noise and the prospects of near-term
hardware implementations, are provided for the latter task.


  The diffuse X-ray background (DXB) emission near the Galactic plane ($l,b
\sim 25.6^{\circ},0.78^{\circ}$) has been observed with $ASCA$. The observed
region is toward a Galactic molecular cloud which was recently reported to cast
a deep X-ray shadow in the 0.5 $-$ 2.0 keV band DXB. The selection of this
particular region is intended to provide a constraint on the spatial
distribution of the DXB emission along the line of sight: i.e., the molecular
cloud is optically thick at $<$2 keV and so the bulk of the observed soft
X-rays {\it must} originate in the foreground of the cloud, which is at $\sim$3
kpc from the Sun. In the 0.8 $-$ 9.0 keV band, the observed spectrum is
primarily from multiple components of thermal plasmas. We here report a
detection of soft X-ray (0.5 $-$ 2 keV) emission from an $\sim10^{7}$ K thermal
plasma. Comparisons with the {\it ROSAT} data suggest that this soft X-ray
emission is absorbed by $N_H$ = 1 $-$ 3 $\times$ 10$^{21}$ cm$^{-2}$, which
implies a path-length through the soft X-ray emitting regions of $\la$1 kpc
from the Sun.


  Soybeans are an important crop for global food security. Every year, soybean
yields are reduced by numerous soybean diseases, particularly the soybean cyst
nematode (SCN). It is difficult to visually identify the presence of SCN in the
field, let alone its population densities or numbers, as there are no obvious
aboveground disease symptoms. The only definitive way to assess SCN population
densities is to directly extract the SCN cysts from soil and then extract the
eggs from cysts and count them. Extraction is typically conducted in commercial
soil analysis laboratories and university plant diagnostic clinics and involves
repeated steps of sieving, washing, collecting, grinding, and cleaning. Here we
present a robotic instrument to reproduce and automate the functions of the
conventional methods to extract nematode cysts from soil and subsequently
extract eggs from the recovered nematode cysts. We incorporated mechanisms to
actuate the stage system, manipulate positions of individual sieves using the
gripper, recover cysts and cyst-sized objects from soil suspended in water, and
grind the cysts to release their eggs. All system functions are controlled and
operated by a touchscreen interface software. The performance of the robotic
instrument is evaluated using soil samples infested with SCN from two farms at
different locations and results were comparable to the conventional technique.
Our new technology brings the benefits of automation to SCN soil diagnostics, a
step towards long-term integrated pest management of this serious soybean pest.


  Motivation: Researchers need a rich trove of genomic datasets that they can
leverage to gain a better understanding of the genetic basis of the human
genome and identify associations between phenotypes and specific parts of DNA.
However, sharing genomic datasets that include sensitive genetic or medical
information of individuals can lead to serious privacy-related consequences if
data lands in the wrong hands. Restricting access to genomic datasets is one
solution, but this greatly reduces their usefulness for research purposes. To
allow sharing of genomic datasets while addressing these privacy concerns,
several studies propose privacy-preserving mechanisms for data sharing.
Differential privacy (DP) is one of such mechanisms that formalize rigorous
mathematical foundations to provide privacy guarantees while sharing aggregated
statistical information about a dataset. However, it has been shown that the
original privacy guarantees of DP-based solutions degrade when there are
dependent tuples in the dataset, which is a common scenario for genomic
datasets (due to the existence of family members). Results: In this work, we
introduce a near-optimal mechanism to mitigate the vulnerabilities of the
inference attacks on differentially private query results from genomic datasets
including dependent tuples. We propose a utility-maximizing and
privacy-preserving approach for sharing statistics by hiding selective SNPs of
the family members as they participate in a genomic dataset. By evaluating our
mechanism on a real-world genomic dataset, we empirically demonstrate that our
proposed mechanism can achieve up to 40% better privacy than state-of-the-art
DP-based solutions, while near-optimally minimizing the utility loss.


  We consider several detection situations where, under the alternative
hypothesis, the signal admits a low complexity model and, under both the null
and the alternative hypotheses, the distribution of the background noise is
{unknown}. We present several detection strategies for such cases, whose design
relies on exogenous or on endogenous data. These testing procedures have been
inspired by and are applied to two specific problems in Astrophysics, namely
the detection of exoplanets from radial velocity curves and of distant galaxies
in hyperspectral datacubes.


  We extend and improve the work of Model Agnostic Anchors for explanations on
image classification through the use of generative adversarial networks (GANs).
Using GANs, we generate samples from a more realistic perturbation
distribution, by optimizing under a lower dimensional latent space. This
increases the trust in an explanation, as results now come from images that are
more likely to be found in the original training set of a classifier, rather
than an overlay of random images. A large drawback to our method is the
computational complexity of sampling through optimization; to address this, we
implement more efficient algorithms, including a diverse encoder. Lastly, we
share results from the MNIST and CelebA datasets, and note that our
explanations can lead to smaller and higher precision anchors.


  Enumerating nonisomorphic orthogonal arrays is an important, yet very
difficult, problem. Although orthogonal arrays with a specified set of
parameters have been enumerated in a number of cases, general results are
extremely rare. In this paper, we provide a complete solution to enumerating
nonisomorphic two-level orthogonal arrays of strength $d$ with $d+2$
constraints for any $d$ and any run size $n=\lambda2^d$. Our results not only
give the number of nonisomorphic orthogonal arrays for given $d$ and $n$, but
also provide a systematic way of explicitly constructing these arrays. Our
approach to the problem is to make use of the recently developed theory of
$J$-characteristics for fractional factorial designs. Besides the general
theoretical results, the paper presents some results from applications of the
theory to orthogonal arrays of strength two, three and four.


  Galaxies are surrounded by extended atmospheres, which are often called the
circumgalactic medium (CGM) and are the least understood part of galactic
ecosystems. The CGM serves as a reservoir of both diffuse, metal-poor gas
accreted from the intergalactic medium, and metal-rich gas that is either
ejected from galaxies by energetic feedback or stripped from infalling
satellites. As such, the CGM is empirically multi-phased and complex in
dynamics. Significant progress has been made in the past decade or so in
observing the cosmic-ray/B-field, as well as various phases of the CGM. But
basic questions remain to be answered. First, what are the energy, mass, and
metal contents of the CGM? More specifically, how are they spatially
distributed and partitioned in the different components? Moreover, how are they
linked to properties of host galaxies and their global clustering and
intergalactic medium environments? Lastly, what are the origin, state, and
life-cycle of the CGM? This question explores the dynamics of the CGM. Here we
illustrate how these questions may be addressed with multi-wavelength
observations of the CGM.


  Let $\mathbb{K}$ denote an algebraically closed field. Let $V$ denote a
vector space over $\mathbb{K}$ with finite positive dimension. By a Leonard
triple on $V$ we mean an ordered triple of linear transformations in ${\rm
End}(V)$ such that for each of these transformations there exists a basis of
$V$ with respect to which the matrix representing that transformation is
diagonal and the matrices representing the other two transformations are
irreducible tridiagonal. There is a family of Leonard triples said to have
QRacah type. This is the most general type of Leonard triple. We classify the
Leonard triples of QRacah type up to isomorphism. We show that any Leonard
triple of QRacah type satisfies the $\mathbb{Z}_3$-symmetric Askey-Wilson
relations.


  We report on observations of very high-energy gamma rays from the shell-type
supernova remnant Cassiopeia A with the VERITAS stereoscopic array of four
imaging atmospheric Cherenkov telescopes in Arizona. The total exposure time
for these observations is 22 hours, accumulated between September and November
of 2007. The gamma-ray source associated with the SNR Cassiopeia A was detected
above 200 GeV with a statistical significance of 8.3 s.d. The estimated
integral flux for this gamma-ray source is about 3% of the Crab-Nebula flux.
The photon spectrum is compatible with a power law dN/dE ~ E^(-Gamma) with an
index Gamma = 2.61 +/- 0.24(stat) +/- 0.2(sys). The data are consistent with a
point-like source. We provide a detailed description of the analysis results,
and discuss physical mechanisms that may be responsible for the observed
gamma-ray emission.


  This is the second of three papers about the Compression Theorem. We give
proofs of Gromov's theorem on directed embeddings [M Gromov, Partial
differential relations, Springer--Verlag (1986); 2.4.5 C'] and of the Normal
Deformation Theorem [The compression theorem I; 4.7], arxiv:math.GT/9712235.


  Context: Bug bisection is a common technique used to identify a revision that
introduces a bug or indirectly fixes a bug, and often involves executing
multiple revisions of a project to determine whether the bug is present within
the revision. However, many legacy revisions often cannot be successfully
compiled due to changes in the programming language or tools used in the
compilation process, adding complexity and preventing automation in the
bisection process.
  Objective: In this paper, we introduce an approach to repair test cases of
Java projects by performing dependency minimization. Our approach aims to
remove classes and methods that are not required for the execution of one or
more test cases. Unlike existing state-of-the-art techniques, our approach
performs minimization at source-level, which allows compile-time errors to be
fixed.
  Method: A standalone Java tool implementing our technique was developed, and
we evaluated our technique using subjects from Defects4J retargeted against
Java 8 and 17.
  Results: Our evaluation showed that a majority of subjects can be repaired
solely by performing minimization, including replicating the test results of
the original version. Furthermore, our technique is also shown to achieve
accurate minimized results, while only adding a small overhead to the bisection
process.
  Conclusion: Our proposed technique is shown to be effective for repairing
build failures with minimal overhead, making it suitable for use in automated
bug bisection. Our tool can also be adapted for use cases such as bug corpus
creation and refactoring.


  Recently, self-supervised neural networks have shown excellent image
denoising performance. However, current dataset free methods are either
computationally expensive, require a noise model, or have inadequate image
quality. In this work we show that a simple 2-layer network, without any
training data or knowledge of the noise distribution, can enable high-quality
image denoising at low computational cost. Our approach is motivated by
Noise2Noise and Neighbor2Neighbor and works well for denoising pixel-wise
independent noise. Our experiments on artificial, real-world camera, and
microscope noise show that our method termed ZS-N2N (Zero Shot Noise2Noise)
often outperforms existing dataset-free methods at a reduced cost, making it
suitable for use cases with scarce data availability and limited computational
resources. A demo of our implementation including our code and hyperparameters
can be found in the following colab notebook:
https://colab.research.google.com/drive/1i82nyizTdszyHkaHBuKPbWnTzao8HF9b


  We study the problem of scheduling $m$ tasks to $n$ selfish, unrelated
machines in order to minimize the makespan, where the execution times are
independent random variables, identical across machines. We show that the VCG
mechanism, which myopically allocates each task to its best machine, achieves
an approximation ratio of $O\left(\frac{\ln n}{\ln \ln n}\right)$. This
improves significantly on the previously best known bound of
$O\left(\frac{m}{n}\right)$ for prior-independent mechanisms, given by Chawla
et al. [STOC'13] under the additional assumption of Monotone Hazard Rate (MHR)
distributions. Although we demonstrate that this is in general tight, if we do
maintain the MHR assumption, then we get improved, (small) constant bounds for
$m\geq n\ln n$ i.i.d. tasks, while we also identify a sufficient condition on
the distribution that yields a constant approximation ratio regardless of the
number of tasks.


  In this Paper we analyze a model proposed recently with the purpose of
studying the effects of rotation on the interaction of a point charge with a
uniform magnetic field in an elastic medium with a spiral dislocation. In
particular we focus on the approximation proposed by the authors that consists
of changing the left boundary condition in order to obtain analytical results.
We show that this approximation leads to quantitative and qualitative errors,
the most relevant one being a wrong prediction of the level spacing.


  The quantum theory of multiphoton stimulated bremsstrahlung of charged
carriers on an arbitrary electrostatic potential of impurity ion in doped
bilayer graphene at the presence of coherent electromagnetic radiation is
developed. A terahertz wave field is considered exactly, while the
electrostatic potential of doped ions as a perturbation. The essentially
nonlinear response of bilayer graphene to a pump wave and significant
differences from the case of a single layer graphene are shown, which can be
associated to nonlinear parabolic dispersion. The latter opens new way to
manipulate with the electronic transport properties of conductive electrons of
bilayer graphene by coherent radiation field of terahertz or near-infrared
frequencies.


  The SNO+ detector, a new kiloton scale liquid scintillator detector capable
of recording geoneutrino events, will define the strength of the Earth
radiogenic heat. A detailed 3-D model of the regional crust, centered at SNO+
and based on compiled geological, geophysical and geochemical information, was
used to characterize the physical and chemical attributes of crust and assign
uncertainties to its structure. Monte Carlo simulations were used to predict
the U and Th abundances and uncertainties in crustal lithologies and to model
the regional crustal geoneutrino signal originating from the at SNO+.


  We calculate in chiral perturbation theory the double-pion photoproduction
processes $\pi^-\gamma \to \pi^- \pi^0\pi^0$ and $\pi^-\gamma\to
\pi^+\pi^-\pi^-$ at low energies. At leading order these reactions are governed
by the chiral pion-pion interaction. The next-to-leading order corrections
arise from pion-loop diagrams and chiral-invariant counterterms involving the
low-energy constants $\bar\ell_1,\, \bar\ell_2,\, \bar\ell_3$ and $\bar\ell_4$.
The pertinent production amplitudes $A_1$ and $A_2$ depending on five
kinematical variables are given in closed analytical form. We find that the
total cross section for neutral pion-pair production $\pi^-\gamma
\to\pi^-\pi^0\pi^0$ gets enhanced in the region $\sqrt{s}< 7m_\pi$ by a factor
$1.5 - 1.8$ by the next-to-leading order corrections. In contrast to this
behavior the total cross section for charged pion-pair production
$\pi^-\gamma\to \pi^+ \pi^-\pi^-$ remains almost unchanged in the region
$\sqrt{s}< 6m_\pi$ in comparison to its tree-level result. Although the
dynamics of the pion-pair production reactions is much richer, this observed
pattern can be understood from the different influence of the chiral
corrections on the pion-pion final state interaction ($\pi^+\pi^- \to
\pi^0\pi^0$ versus $\pi^-\pi^- \to \pi^-\pi^-$). We present also results for
the complete set of two-pion invariant mass spectra. The predictions of chiral
perturbation theory for the $\pi^-\gamma\to 3\pi$ processes can be tested by
the COMPASS experiment which uses Primakoff scattering of high-energy pions in
the Coulomb field of a heavy nucleus to extract cross sections for
$\pi^-\gamma$ reactions with various final states.


  For an $n$-by-$n$ matrix $A$, let $f_A$ be its "field of values generating
function" defined as $f_A\colon x\mapsto x^*Ax$. We consider two natural
versions of the continuity, which we call strong and weak, of $f_A^{-1}$ (which
is of course multi-valued) on the field of values $F(A)$. The strong continuity
holds, in particular, on the interior of $F(A)$, and at such points $z \in
\partial F(A)$ which are either corner points, belong to the relative interior
of flat portions of $\partial F(A)$, or whose preimage under $f_A$ is contained
in a one-dimensional set. Consequently, $f_A^{-1}$ is continuous in this sense
on the whole $F(A)$ for all normal, 2-by-2, and unitarily irreducible 3-by-3
matrices. Nevertheless, we show by example that the strong continuity of
$f_A^{-1}$ fails at certain points of $\partial F(A)$ for some (unitarily
reducible) 3-by-3 and (unitarily irreducible) 4-by-4 matrices. The weak
continuity, in its turn, fails for some unitarily reducible 4-by-4 and
untiarily irreducible 6-by-6 matrices.


  To identify emerging interdependencies between traded stocks we investigate
the behavior of the stocks of FTSE 100 companies in the period 2000-2015, by
looking at daily stock values. Exploiting the power of information theoretical
measures to extract direct influences between multiple time series, we compute
the information flow across stock values to identify several different regimes.
While small information flows is detected in most of the period, a dramatically
different situation occurs in the proximity of global financial crises, where
stock values exhibit strong and substantial interdependence for a prolonged
period. This behavior is consistent with what one would generally expect from a
complex system near criticality in physical systems, showing the long lasting
effects of crashes on stock markets.


  In this paper, the capacity region of the two-user linear deterministic (LD)
interference channel with noisy output feedback (IC-NOF) is fully
characterized. This result allows the identification of several asymmetric
scenarios in which imple- menting channel-output feedback in only one of the
transmitter- receiver pairs is as beneficial as implementing it in both links,
in terms of achievable individual rate and sum-rate improvements w.r.t. the
case without feedback. In other scenarios, the use of channel-output feedback
in any of the transmitter-receiver pairs benefits only one of the two pairs in
terms of achievable individual rate improvements or simply, it turns out to be
useless, i.e., the capacity regions with and without feedback turn out to be
identical even in the full absence of noise in the feedback links.


  We investigate the properties of minimizers of one-dimensional variational
problems when the Lagrangian has no higher smoothness than continuity. An
elementary approximation result is proved, but it is shown that this cannot be
in general of the form of a standard Lipschitz "variation". Part of this
investigation, but of interest in its own right, is an example of a nowhere
locally Lipschitz minimizer which serves as a counter-example to any putative
Tonelli partial regularity statement. Under these low assumptions we find it
nonetheless remains possible to derive necessary conditions for minimizers, in
terms of approximate continuity and equality of the one-sided derivatives.


  We validate the application of our recent orbital-free density functional
theory (DFT) approach, [Phys. Rev. Lett. 113, 155006 (2014)], for the
calculation of ionic and electronic transport properties of dense plasmas. To
this end, we calculate the self-diffusion coefficient, the viscosity
coefficient, the electrical and thermal conductivities, and the reflectivity
coefficient of hydrogen and aluminum plasmas. Very good agreement is found with
orbital-based Kohn-Sham DFT calculations at lower temperatures. Because the
method does not scale with temperature, we can produce results at much higher
temperatures than is accessible by the Kohn-Sham method. Our results for warm
dense aluminum at solid density are inconsistent with the recent experimental
results reported by Sperling et al. [Phys. Rev. Lett. 115, 115001 (2015)].


  The dynamics of a ring of vortices in two-dimensional Bose-Einstein
condensates (with and without an additional vortex at the center) is studied
for (1) a uniform condensate in a rigid cylinder and (2) a nonuniform trapped
condensate in the Thomas-Fermi limit. The sequence of ground states (within
these single-ring configurations) is determined as a function of the external
rotation frequency by comparing the free energy of the various states. For each
ground state, the Tkachenko-like excitations and the associated dynamical
stability are analyzed.


  A first-ever 2-dimensional celestial map of primary cosmic-ray flux was
obtained from 2.10x10^8 cosmic-ray muons accumulated in 1662.0 days of
Super-Kamiokande. The celestial map indicates an (0.104 \pm 0.020)% excess
region in the constellation of Taurus and a -(0.094 \pm 0.014)% deficit region
toward Virgo. Interpretations of this anisotropy are discussed.


  In this study, we present a novel approach for predicting genomic information
from medical imaging modalities using a transformer-based model. We aim to
bridge the gap between imaging and genomics data by leveraging transformer
networks, allowing for accurate genomic profile predictions from CT/MRI images.
Presently most studies rely on the use of whole slide images (WSI) for the
association, which are obtained via invasive methodologies. We propose using
only available CT/MRI images to predict genomic sequences. Our transformer
based approach is able to efficiently generate associations between multiple
sequences based on CT/MRI images alone. This work paves the way for the use of
non-invasive imaging modalities for precise and personalized healthcare,
allowing for a better understanding of diseases and treatment.


  Large language models (LLMs) have demonstrated impressive capability in
reasoning and planning when integrated with tree-search-based prompting
methods. However, since these methods ignore the previous search experiences,
they often make the same mistakes in the search process. To address this issue,
we introduce Reflection on search Trees (RoT), an LLM reflection framework
designed to improve the performance of tree-search-based prompting methods. It
uses a strong LLM to summarize guidelines from previous tree search experiences
to enhance the ability of a weak LLM. The guidelines are instructions about
solving this task through tree search which can prevent the weak LLMs from
making similar mistakes in the past search process. In addition, we proposed a
novel state selection method, which identifies the critical information from
historical search processes to help RoT generate more specific and meaningful
guidelines. In our extensive experiments, we find that RoT significantly
improves the performance of LLMs in reasoning or planning tasks with various
tree-search-based prompting methods (e.g., BFS and MCTS). Non-tree-search-based
prompting methods such as Chain-of-Thought (CoT) can also benefit from RoT
guidelines since RoT can provide task-specific knowledge collected from the
search experience.


  String theory can (in principle) describe gravity at all curvature scales,
and can be applied to cosmology to look back in time beyond the Planck epoch.
The duality symmetries of string theory suggest a cosmological picture in which
the imprint of a primordial, pre-big bang phase could still be accessible to
present observations. The predictive power of such a scenario relies, however,
on our ability to connect in a smooth way the pre-big bang to the present
cosmological regime. Classical radiation back reaction seems to play a key role
to this purpose, by isotropizing and turning into a final expansion any state
of anisotropic contraction possibly emerging from the pre-big bang at the
string scale.