  We describe a new method for simulating ionizing radiation and supernova
feedback in the analogues of low-redshift galactic disks. In this method, which
we call star-forming molecular cloud (SFMC) particles, we use a ray-tracing
technique to solve the radiative transfer equation for ultraviolet photons
emitted by thousands of distinct particles on the fly. Joined with high
numerical resolution of 3.8 pc, the realistic description of stellar feedback
helps to self-regulate star formation. This new feedback scheme also enables us
to study the escape of ionizing photons from star-forming clumps and from a
galaxy, and to examine the evolving environment of star-forming gas clumps. By
simulating a galactic disk in a halo of 2.3e11 Msun, we find that the average
escape fraction from all radiating sources on the spiral arms (excluding the
central 2.5 kpc) fluctuates between 0.08% and 5.9% during a ~20 Myr period with
a mean value of 1.1%. The flux of escaped photons from these sources is not
strongly beamed, but manifests a large opening angle of more than 60 degree
from the galactic pole. Further, we investigate the escape fraction per SFMC
particle, f_esc(i), and how it evolves as the particle ages. We discover that
the average escape fraction f_esc is dominated by a small number of SFMC
particles with high f_esc(i). On average, the escape fraction from a SFMC
particle rises from 0.27% at its birth to 2.1% at the end of a particle
lifetime, 6 Myrs. This is because SFMC particles drift away from the dense gas
clumps in which they were born, and because the gas around the star-forming
clumps is dispersed by ionizing radiation and supernova feedback. The framework
established in this study brings deeper insight into the physics of photon
escape fraction from an individual star-forming clump, and from a galactic
disk.


  In this paper, we establish the existence of one solution for a
Schr\"{o}dinger equation with jumping nonlinearities: $-\Delta u+V(x)u=f(x,u)$,
$x\in \mathbb {R}^N$, and $u(x)\to 0$, $|x|\to +\infty$, where $V$ is a
potential function on which we make hypotheses, and in particular allow $V$
which is unbounded below, and $f(x,u)=au^-+bu^++g(x,u)$. No restriction on $b$
is required, which implies that $f(x,s)s^{-1}$ may interfere with the essential
spectrum of $ -\Delta+V$ for $s\to +\infty$. Using the truncation method and
the Morse theory, we can compute critical groups of the corresponding
functional at zero and infinity, then prove the existence of one negative
solution.


  Recent advancements in large language models (LLMs) have opened new pathways
for many domains. However, the full potential of LLMs in financial investments
remains largely untapped. There are two main challenges for typical deep
learning-based methods for quantitative finance. First, they struggle to fuse
textual and numerical information flexibly for stock movement prediction.
Second, traditional methods lack clarity and interpretability, which impedes
their application in scenarios where the justification for predictions is
essential. To solve the above challenges, we propose Ploutos, a novel financial
LLM framework that consists of PloutosGen and PloutosGPT. The PloutosGen
contains multiple primary experts that can analyze different modal data, such
as text and numbers, and provide quantitative strategies from different
perspectives. Then PloutosGPT combines their insights and predictions and
generates interpretable rationales. To generate accurate and faithful
rationales, the training strategy of PloutosGPT leverage rearview-mirror
prompting mechanism to guide GPT-4 to generate rationales, and a dynamic token
weighting mechanism to finetune LLM by increasing key tokens weight. Extensive
experiments show our framework outperforms the state-of-the-art methods on both
prediction accuracy and interpretability.


  As quantum computing hardware steadily increases in qubit count and quality,
one important question is how to allocate these resources to mitigate the
effects of hardware noise. In a transitional era between noisy small-scale and
fully fault-tolerant systems, we envisage a scenario in which we are only able
to error correct a small fraction of the qubits required to perform an
interesting computation. In this work, we develop concrete constructions of
logical operations on a joint system of a collection of noisy and a collection
of error-corrected logical qubits. Within this setting and under Pauli noise
assumptions, we provide analytical evidence that brick-layered circuits display
on average slower concentration to the "useless" uniform distribution with
increasing circuit depth compared to fully noisy circuits. We corroborate these
findings by numerical demonstration of slower decoherence with an increasing
fraction of error-corrected qubits under a noise model inspired by a real
device. We find that this advantage only comes when the number of
error-corrected qubits passes a specified threshold which depends on the number
of couplings between error-corrected and noisy registers.


  We give natural descriptions of the homology and cohomology algebras of
regular quotient ring spectra of even E-infinity ring spectra. We show that the
homology is a Clifford algebra with respect to a certain bilinear form
naturally associated to the quotient ring spectrum F. To identify the
cohomology algebra, we first determine the derivations of F and then prove that
the cohomology is isomorphic to the exterior algebra on the module of
derivations. We treat the example of the Morava K-theories in detail.


  This paper focuses on the study of an original combination of the Multilevel
Monte Carlo method introduced by Giles [10] and the popular importance sampling
technique. To compute the optimal choice of the parameter involved in the
importance sampling method, we rely on Robbins-Monro type stochastic
algorithms. On the one hand, we extend our previous work [2] to the Multilevel
Monte Carlo setting. On the other hand, we improve [2] by providing a new
adaptive algorithm avoiding the discretization of any additional process.
Furthermore, from a technical point of view, the use of the same stochastic
algorithms as in [2] appears to be problematic. To overcome this issue, we
employ an alternative version of stochastic algorithms with projection (see
e.g. Laruelle, Lehalle and Pag\`es [20]). In this setting, we show innovative
limit theorems for a doubly indexed stochastic algorithm which appear to be
crucial to study the asymptotic behavior of the new adaptive Multilevel Monte
Carlo estimator. Finally, we illustrate the efficiency of our method through
applications from quantitative finance.


  Solar eruptions generally refer to coronal mass ejections (CMEs) and flares.
Both are important sources of space weather. Solar flares cause sudden change
in the ionization level in the ionosphere. CMEs cause solar energetic particle
(SEP) events and geomagnetic storms. A flare with unusually high intensity
and/or a CME with extremely high energy can be thought of examples of extreme
events on the Sun. These events can also lead to extreme SEP events and/or
geomagnetic storms. Ultimately, the energy that powers CMEs and flares are
stored in magnetic regions on the Sun, known as active regions. Active regions
with extraordinary size and magnetic field have the potential to produce
extreme events. Based on current data sets, we estimate the sizes of
one-in-hundred and one-in-thousand year events as an indicator of the
extremeness of the events. We consider both the extremeness in the source of
eruptions and in the consequences. We then compare the estimated 100-year and
1000-year sizes with the sizes of historical extreme events measured or
inferred.


  Blockchain and smart contract technology are novel approaches to data and
code management that facilitate trusted computing by allowing for development
in a distributed and decentralized manner. Testing smart contracts comes with
its own set of challenges which have not yet been fully identified and
explored. Although existing tools can identify and discover known
vulnerabilities and their interactions on the Ethereum blockchain through
random search or symbolic execution, these tools generally do not produce test
suites suitable for human oracles. In this paper, we present AGSOLT (Automated
Generator of Solidity Test Suites). We demonstrate its efficiency by
implementing two search algorithms to automatically generate test suites for
stand-alone Solidity smart contracts, taking into account some of the
blockchain-specific challenges. To test AGSOLT, we compared a random search
algorithm and a genetic algorithm on a set of 36 real-world smart contracts. We
found that AGSOLT is capable of achieving high branch coverage with both
approaches and even discovered some errors in some of the most popular Solidity
smart contracts on Github.


  Building on Papert (1980)'s idea of children talking to computers, we propose
ChatLogo, a hybrid natural-programming language interface for agent-based
modeling and programming. We build upon previous efforts to scaffold ABM & P
learning and recent development in leveraging large language models (LLMs) to
support the learning of computational programming. ChatLogo aims to support
conversations with computers in a mix of natural and programming languages,
provide a more user-friendly interface for novice learners, and keep the
technical system from over-reliance on any single LLM. We introduced the main
elements of our design: an intelligent command center, and a conversational
interface to support creative expression. We discussed the presentation format
and future work. Responding to the challenges of supporting open-ended
constructionist learning of ABM & P and leveraging LLMs for educational
purposes, we contribute to the field by proposing the first constructionist
LLM-driven interface to support computational and complex systems thinking.


  The accurate prediction of intense precipitation events is one of the main
objectives of operational weather services. This task is even more relevant
nowadays, with the rapid progression of global warming which intensifies these
events. Numerical weather prediction models have improved continuously over
time, providing uncertainty estimation with dynamical ensembles. However,
direct precipitation forecasting is still challenging. Greater availability of
machine learning tools paves the way to a hybrid forecasting approach, with the
optimal combination of physical models, event statistics, and user-oriented
post-processing. Here we describe a specific chain, based on a random forest
pipeline, specialised in recognizing favourable synoptic conditions leading to
precipitation extremes and subsequently classifying extremes into predefined
types. The application focuses on Northern and Central Italy, taken as a
testbed region, but is seamlessly extensible to other regions and timescales.
The system is called MaLCoX (Machine Learning model predicting Conditions for
eXtreme precipitation) and is running daily at the Italian regional weather
service of ARPAE Emilia-Romagna. MalCoX has been trained with the ARCIS gridded
high-resolution precipitation dataset as the target truth, using the last 20
years of the ECMWF re-forecast dataset as input predictors. We show that, with
a long enough training period, the optimal blend of larger-scale information
with direct model output improves the probabilistic forecast accuracy of
extremes in the medium range. In addition, with specific methods, we provide a
useful diagnostic to convey to forecasters the underlying physical storyline
which makes a meteorological event extreme.


  Let $ACC \circ THR$ be the class of constant-depth circuits comprised of AND,
OR, and MOD$m$ gates (for some constant $m > 1$), with a bottom layer of gates
computing arbitrary linear threshold functions. This class of circuits can be
seen as a "midpoint" between $ACC$ (where we know nontrivial lower bounds) and
depth-two linear threshold circuits (where nontrivial lower bounds remain
open).
  We give an algorithm for evaluating an arbitrary symmetric function of
$2^{n^{o(1)}}$ $ACC \circ THR$ circuits of size $2^{n^{o(1)}}$, on all possible
inputs, in $2^n \cdot poly(n)$ time. Several consequences are derived:
  $\bullet$ The number of satisfying assignments to an $ACC \circ THR$ circuit
of subexponential size can be computed in $2^{n-n^{\varepsilon}}$ time (where
$\varepsilon > 0$ depends on the depth and modulus of the circuit).
  $\bullet$ $NEXP$ does not have quasi-polynomial size $ACC \circ THR$
circuits, nor does $NEXP$ have quasi-polynomial size $ACC \circ SYM$ circuits.
Nontrivial size lower bounds were not known even for $AND \circ OR \circ THR$
circuits.
  $\bullet$ Every 0-1 integer linear program with $n$ Boolean variables and $s$
linear constraints is solvable in $2^{n-\Omega(n/((\log M)(\log s)^{5}))}\cdot
poly(s,n,M)$ time with high probability, where $M$ upper bounds the bit
complexity of the coefficients. (For example, 0-1 integer programs with weights
in $[-2^{poly(n)},2^{poly(n)}]$ and $poly(n)$ constraints can be solved in
$2^{n-\Omega(n/\log^6 n)}$ time.)
  We also present an algorithm for evaluating depth-two linear threshold
circuits (a.k.a., $THR \circ THR$) with exponential weights and $2^{n/24}$ size
on all $2^n$ input assignments, running in $2^n \cdot poly(n)$ time. This is
evidence that non-uniform lower bounds for $THR \circ THR$ are within reach.


  Electroweak bosons produced in conjunction with jets in high-energy collider
experiments is one of the principle final-state channels that can be used to
test the accuracy of perturbative Quantum Chromodynamics calculations and to
assess the potential to uncover new physics through comparison between data and
theory. In this paper we present results for the $Z^0/\gamma^*$+jet production
cross sections at the LHC at leading and next-to-leading orders. In
proton-proton reactions we elucidate up to ${\cal O}(G_F\alpha_s^2)$ the
constraints that jet tagging via the $Z^0/\gamma^*$ decay dileptons provides on
the momentum distribution of jets. In nucleus-nucleus reactions we demonstrate
that tagged jets can probe important aspects of the dynamics of quark and gluon
propagation in hot and dense nuclear matter and characterize the properties of
the medium-induced parton showers in ways not possible with more inclusive
measurements. Finally, we present specific predictions for the anticipated
suppression of the $Z^0/\gamma^*$+jet production cross section in the
quark-gluon plasma that is expected to be created in central lead-lead
collisions at the LHC relative to the naive superposition of independent
nucleon-nucleon scatterings.


  We determine the secrecy capacity of the compound channel with quantum
wiretapper and channel state information at the transmitter. Moreover, we
derive a lower bound on the secrecy capacity of this channel without channel
state information and determine the secrecy capacity of the compound
classical-quantum wiretap channel with channel state information at the
transmitter. We use this result to derive a new proof for a lower bound on the
entanglement generating capacity of compound quantum channel. We also derive a
new proof for the entanglement generating capacity of compound quantum channel
with channel state information at the encoder.


  We investigated regular black holes with fuzzy sources in three and four
dimensions. The density distributions of such fuzzy sources are inspired by
noncommutative geometry and given by Gaussian or generalized Gaussian
functions. We utilized mass functions to give a physical interpretation of the
horizon formation condition for the black holes. In particular, we investigated
three-dimensional BTZ-like black holes and four-dimensional Schwarzschild-like
black holes in detail, and found that the number of horizons is related to the
spacetime dimensions, and the existence of a void in the vicinity of the center
of the spacetime is significant, rather than noncommutativity. As an
application, we considered a three-dimensional black hole with the fuzzy disc
which is a disc-shaped region known in the context of noncommutative geometry
as a source. We also analyzed a four-dimensional black hole with a source whose
density distribution is an extension of the fuzzy disc, and investigated the
horizon formation condition for it.


  The propagation of excitation along a one-dimensional chain of atoms is
simulated by means of NMR. The physical system used as an analog quantum
computer is a nucleus of 133-Cs (spin 7/2) in a liquid crystalline matrix. The
Hamiltonian of migration is simulated by using a special 7-frequency pulse, and
the dynamics is monitored by following the transfer of population from one of
the 8 spin energy levels to the other.


  New nonlinear connection formulae of the q-orthogonal polynomials, such
continuous q-Laguerre, continuous big q-Hermite, q-Meixner-Pollaczek and
q-Gegenbauer polynomials, in terms of their respective classical analogues are
obtained using a special realization of the q-exponential function as infinite
multiplicative series of ordinary exponential function.


  The success of employment is highly related to a job seeker's capability of
communicating and collaborating with others. While leveraging one's network
during the job-seeking process is intuitive to the neurotypical, this can be
challenging for people with autism. Recent empirical findings have started to
show how facilitating collaboration between people with autism and their social
surroundings through new design can improve their chances of employment. This
work aims to provide actionable guidelines and conceptual frameworks that
future researchers and practitioners can apply to improve collaborative design
for job-seekers with autism. Built upon the literature on past technological
interventions built for supporting job-seekers with autism, we define three
major research challenges of (1) communication support, (2) employment
stage-wise support, and (3) group work support. For each challenge, we review
the current state-of-the-art practices and possible future solutions. We then
suggest future designs that can provide breakthroughs from the
interdisciplinary lens of human-AI collaboration, health services, group work,
accessibility computing, and natural language processing.


  Research on offline signature verification has explored a large variety of
methods on multiple signature datasets, which are collected under controlled
conditions. However, these datasets may not fully reflect the characteristics
of the signatures in some practical use cases. Real-world signatures extracted
from the formal documents may contain different types of occlusions, for
example, stamps, company seals, ruling lines, and signature boxes. Moreover,
they may have very high intra-class variations, where even genuine signatures
resemble forgeries. In this paper, we address a real-world writer independent
offline signature verification problem, in which, a bank's customers'
transaction request documents that contain their occluded signatures are
compared with their clean reference signatures. Our proposed method consists of
two main components, a stamp cleaning method based on CycleGAN and signature
representation based on CNNs. We extensively evaluate different verification
setups, fine-tuning strategies, and signature representation approaches to have
a thorough analysis of the problem. Moreover, we conduct a human evaluation to
show the challenging nature of the problem. We run experiments both on our
custom dataset, as well as on the publicly available Tobacco-800 dataset. The
experimental results validate the difficulty of offline signature verification
on real-world documents. However, by employing the stamp cleaning process, we
improve the signature verification performance significantly.


  In this paper we have analyzed the production of pair charmonium mesons in
the reactions $e^+ e^- \to J/ \Psi \chi_{c0}, e^+ e^-\to \Psi(2S) \chi_{c0}$ at
energy $\sqrt s = 10.6$GeV in the framework of the light cone formalism. In
comparison with NRQCD the numerical results for the cross sections are in
better agreement with experiment.


  I discuss some problems featuring scattering due to discrete edges on certain
structures. These problems stem from linear difference equations and the
underlying basic issue can be mapped to Wiener-Hopf factorization on an annulus
in the complex plane. In most of these problems, the relevant factorization
involves a scalar function, while in some cases a nxn matrix kernel, with n>=2,
appears. For the latter, I give examples of two non-trivial cases where it can
be further reduced to a scalar problem but in general this is not the case.
Some of the problems that I have presented in this paper can be also
interpreted as discrete analogues of well-known scattering problems, notably a
few of which are still open, in Wiener-Hopf factorization on an infinite strip
in complex plane.


  This paper proposes a realistic image generation method for visualization in
endoscopic simulation systems. Endoscopic diagnosis and treatment are performed
in many hospitals. To reduce complications related to endoscope insertions,
endoscopic simulation systems are used for training or rehearsal of endoscope
insertions. However, current simulation systems generate non-realistic virtual
endoscopic images. To improve the value of the simulation systems, improvement
of reality of their generated images is necessary. We propose a realistic image
generation method for endoscopic simulation systems. Virtual endoscopic images
are generated by using a volume rendering method from a CT volume of a patient.
We improve the reality of the virtual endoscopic images using a virtual-to-real
image-domain translation technique. The image-domain translator is implemented
as a fully convolutional network (FCN). We train the FCN by minimizing a cycle
consistency loss function. The FCN is trained using unpaired virtual and real
endoscopic images. To obtain high quality image-domain translation results, we
perform an image cleansing to the real endoscopic image set. We tested to use
the shallow U-Net, U-Net, deep U-Net, and U-Net having residual units as the
image-domain translator. The deep U-Net and U-Net having residual units
generated quite realistic images.


  We study transport of interacting electrons in a low-dimensional disordered
system at low temperature $T$. In view of localization by disorder, the
conductivity $\sigma(T)$ may only be non-zero due to electron-electron
scattering. For weak interactions, the weak-localization regime crosses over
with lowering $T$ into a dephasing-induced "power-law hopping". As $T$ is
further decreased, the Anderson localization in Fock space crucially affects
$\sigma(T)$, inducing a transition at $T=T_c$, so that $\sigma(T<T_c)=0$. The
critical behavior of $\sigma(T)$ above $T_c$ is $\ln\sigma(T)\propto -
(T-T_c)^{-1/2}$. The mechanism of transport in the critical regime is
many-particle transitions between distant states in Fock space.


  I present near-IR photometry of a selected sample of E+A galaxies observed in
the southern hemisphere. The sample includes 50 galaxies from nearby clusters
(z ~ 0.05) and distant clusters (z ~ 0.31) as well as E+A galaxies from the
field (z ~ 0.15). I have also observed 13 normal early-type galaxies from the
field and from clusters, to be compared with the E+A sample. The photometry
includes J, H and Ks apparent magnitudes and colors. I investigate systematic
properties of the E+A sample as a function of their environment, contrasting
the observed colors with spectrophotometric models of galaxy evolution.


  We show that in cohomogeneity 3 there are G-manifolds with any given number
of isolated singular orbits and an invariant metric of positive Ricci
curvature. We show that the corresponding result is also true in cohomogeneity
5 provided the number of singular orbits is even.


  An extension of the Kinetic Ising model with nonuniform coupling constants on
a one-dimensional lattice with boundaries is investigated, and the relaxation
of such a system towards its equilibrium is studied. Using a transfer matrix
method, it is shown that there are cases where the system exhibits a dynamical
phase transition. There may be two phases, the fast phase and the slow phase.
For some region of the parameter space, the relaxation time is independent of
the reaction rates at the boundaries. Changing continuously the reaction rates
at the boundaries, however, there is a point where the relaxation times begins
changing, as a continuous (nonconstant) function of the reaction rates at the
boundaries, so that at this point there is a jump in the derivative of the
relaxation time with respect to the reaction rates at the boundaries.


  Smart home IoT systems utilize trigger-action platforms, e.g., IFTTT, to
manage devices from various vendors. However, they may be abused by triggering
malicious rule execution with forged IoT devices or events violating the
execution integrity and the intentions of the users. To address this issue, we
propose a ledger based IoT platform called Ruledger, which ensures the correct
execution of rules by verifying the authenticity of the corresponding
information. Ruledger utilizes smart contracts to enforce verifying the
information associated with rule executions, e.g., the user and configuration
information from users, device events, and triggers in the trigger-action
platforms. In particular, we develop three algorithms to enable ledger-wallet
based applications for Ruledger and guarantee that the records used for
verification are stateful and correct. Thus, the execution integrity of rules
is ensured even if devices and platforms in the smart home systems are
compromised. We prototype Ruledger in a real IoT platform, i.e., IFTTT, and
evaluate the performance with various settings. The experimental results
demonstrate Ruledger incurs an average of 12.53% delay, which is acceptable for
smart home systems.


  An effective two-spin density matrix (TSDM) for a pair of spin-$1/2$ degree
of freedom, residing at a distance of $R$ in a spinful Fermi sea, can be
obtained from the two-electron density matrix following the framework
prescribed in Phys. Rev. A 69, 054305 (2004). We note that the single spin
density matrix (SSDM) obtained from this TSDM for generic spin-degenerate
systems of free fermions is always pinned to the maximally mixed state $i.e.$
$(1/2) \ \mathbb{I}$, independent of the distance $R$ while the TSDM confirms
to the form for the set of maximally entangled mixed state (the so called
"X-state") at finite $R$. The X-state reduces to a pure state (a singlet) in
the $R\rightarrow 0$ limit while it saturates to an X-state with largest
allowed value of von-Neumann entropy of $2 \ln2$ as $R \rightarrow \infty$
independent of the value of chemical potential. However, once an external
magnetic field is applied to lift the spin-degeneracy, we find that the
von-Neumann entropy of SSDM becomes a function of the distance $R$ between the
two spins. We also show that the von-Neumann entropy of TSDM in the
$R\rightarrow \infty$ limit becomes a function of the chemical potential and it
saturate to $2 \ln2$ only when the band in completely filled unlike the
spin-degenerate case. Finally we extend our study to include spin-orbit
coupling and show that it does effect these asymptotic results. Our findings
are in sharp contrast with previous works which were based on continuum models
owing to physics which stem from the lattice model.


  We survey results related to the important Hossz\'u-Gluskin Theorem on
$n$-ary groups adding also several new results and comments. The aim of this
paper is to write all such results in uniform and compressive forms. Therefore
some proofs of new results are only sketched or omitted if their completing
seems to be not too difficult for readers. In particular, we show as the
Hossz\'u-Gluskin Theorem can be used for evaluation how many different $n$-ary
groups (up to isomorphism) exist on some small sets. Moreover, we sketch as the
mentioned theorem can be also used for investigation of
$\mathcal{Q}$-independent subsets of semiabelian $n$-ary groups for some
special families $\mathcal{Q}$ of mappings.


  We consider groups that act on spherically symmetric rooted trees and study
the associated representation of the group on the space of locally constant
functions on the boundary of the tree. We introduce and discuss the new notion
of locally 2-transitive actions. Assuming local 2-transitivity our main theorem
yields a precise decomposition of the boundary representation into irreducible
constituents.
  The method can be used to study Gelfand pairs and enables us to answer a
question of Grigorchuk. To provide examples, we analyse in detail the local
2-transitivity of GGS-groups. Moreover, our results can be used to determine
explicit formulae for zeta functions of induced representations defined by
Klopsch and the author.


  We consider an Hamiltonian with ultraviolet and infrared cutoffs, describing
the interaction of relativistic electrons and positrons in the Coulomb
potential with photons in Coulomb gauge. The interaction includes both
interaction of the current density with transversal photons and the Coulomb
interaction of charge density with itself. We prove that the Hamiltonian is
self-adjoint and has a ground state for sufficiently small coupling constants.


  The aim of this paper is to compare the performances of the optimal strategy
under parameters mis-specification and of a technical analysis trading
strategy. The setting we consider is that of a stochastic asset price model
where the trend follows an unobservable Ornstein-Uhlenbeck process. For both
strategies, we provide the asymptotic expectation of the logarithmic return as
a function of the model parameters. Finally, numerical examples find that an
investment strategy using the cross moving averages rule is more robust than
the optimal strategy under parameters mis-specification.


  We argue for Brackets Consistency to be a `Pillar of Geometry', i.e. a
foundational approach, other Pillars being 1) Euclid's constructive approach,
2) the algebraic approach, 3) the projective approach, and 4) the geometrical
automorphism groups `Erlangen' approach. We proceed via a distinct Pillar 5) -
Killing's computational route to Erlangen's groups - firstly by picking out the
ensuing geometrically significant brackets subalgebras as consistent
possibilities. Secondly, by considering which generators, arising piecemeal
from different generalized Killing equations, combine brackets-consistently,
recovering the affine-conformal and projective-conformal alternatives. Thirdly,
we cease to rest on Killing's Pillar altogether, replacing it by mere
polynomial ans\"{a}taze as source of generators. Even in this case - demanding
(Lie) Brackets Consistency - suffices to recover notions of geometry, as
indicated by the below alternative just dropping out of the ensuing brackets
algebra, and is thus declared to be Pillar 6. The current article is further
motivated, firstly, by the gradually accumulating successes in reformulating
Theoretical Physics to run on the Dirac Algorithm for classical constraint
algebraic structure consistency. Secondly, this is sufficiently driven by
Brackets Consistency alone to transcend to whichever context possesses such
brackets. Thirdly, we derive the conformal-or-projective alternative in
top-automorphism-group $d \geq 3$ Flat Geometry. In a manner by now familiar in
Dirac Algorithm applications, a) these two top geometries moreover arise side
by side as roots of a merely algebraic quadratic equation. b) This equation
arises from requiring that the self-bracket of a general ansatz for generators
be strongly zero. c) For, if not, an infinite cascade of generators would
follow, by which the ansatz would not support a finite Shape Theory.


  Finding optimal configurations in a geometric space is a key challenge in
many technological disciplines. Current approaches either rely heavily on human
domain expertise and are difficult to scale. In this paper we show it is
possible to solve configuration optimization problems for whole-page
recommendation using reinforcement learning. The proposed \textit{Tile
Networks} is a neural architecture that optimizes 2D geometric configurations
by arranging items on proper positions. Empirical results on real dataset
demonstrate its superior performance compared to traditional learning to rank
approaches and recent deep models.


  We construct a privacy-preserving, distributed and decentralized marketplace
where parties can exchange data for tokens. In this market, buyers and sellers
make transactions in a blockchain and interact with a third party, called
notary, who has the ability to vouch for the authenticity and integrity of the
data.
  We introduce a protocol for the data-token exchange where neither party gains
more information than what it is paying for, and the exchange is fair: either
both parties gets the other's item or neither does. No third party involvement
is required after setup, and no dispute resolution is needed.


  We introduce a nonlinear, nonhierarchical generalization of Derrida's GREM
and establish through a Sanov-type large deviation analysis both a
Boltzmann-Gibbs principle as well as a Parisi formula for the limiting free
energy. In line with the predictions of the Parisi theory, the free energy is
given by the minimal value over all Parisi functionals/hierarchical structures
in which the original model can be coarse-grained.


  We construct exact analytical Taub-NUT solutions in the context of $f(T)$
gravity. We study the physical properties of the solutions, and compare them
with those of the Taub-NUT solution in Einstein gravity.


  Acute kidney injury (AKI) is a common and serious complication after a
surgery which is associated with morbidity and mortality. The majority of
existing perioperative AKI risk score prediction models are limited in their
generalizability and do not fully utilize the physiological intraoperative
time-series data. Thus, there is a need for intelligent, accurate, and robust
systems, able to leverage information from large-scale data to predict
patient's risk of developing postoperative AKI. A retrospective single-center
cohort of 2,911 adult patients who underwent surgery at the University of
Florida Health has been used for this study. We used machine learning and
statistical analysis techniques to develop perioperative models to predict the
risk of AKI (risk during the first 3 days, 7 days, and until the discharge day)
before and after the surgery. In particular, we examined the improvement in
risk prediction by incorporating three intraoperative physiologic time series
data, i.e., mean arterial blood pressure, minimum alveolar concentration, and
heart rate. For an individual patient, the preoperative model produces a
probabilistic AKI risk score, which will be enriched by integrating
intraoperative statistical features through a machine learning stacking
approach inside a random forest classifier. We compared the performance of our
model based on the area under the receiver operating characteristics curve
(AUROC), accuracy and net reclassification improvement (NRI). The predictive
performance of the proposed model is better than the preoperative data only
model. For AKI-7day outcome: The AUC was 0.86 (accuracy was 0.78) in the
proposed model, while the preoperative AUC was 0.84 (accuracy 0.76).
Furthermore, with the integration of intraoperative features, we were able to
classify patients who were misclassified in the preoperative model.


  We demonstrate that the two major complementary pictures of large N_c baryon
resonances--as single-quark orbital excitations about a closed-shell core
[giving SU(2N_F) x O(3) multiplets], and as resonances in meson-baryon
scattering amplitudes--are completely compatible in a specific sense: Both
pictures give rise to a set of multiplets of degenerate states, for which any
complete spin-flavor multiplet within one picture fills the quantum numbers of
complete multiplets in the other picture. This result is demonstrated by: (i)
straightforward computation of the lowest multiplets in both pictures; (ii) a
study of the nature of quark excitations in a hedgehog picture; (iii) direct
group-theoretical comparison of the constraints in the two pictures.


  The paper analyses cointegration in vector autoregressive processes (VARs)
for the cases when both the number of coordinates, $N$, and the number of time
periods, $T$, are large and of the same order. We propose a way to examine a
VAR of order $1$ for the presence of cointegration based on a modification of
the Johansen likelihood ratio test. The advantage of our procedure over the
original Johansen test and its finite sample corrections is that our test does
not suffer from over-rejection. This is achieved through novel asymptotic
theorems for eigenvalues of matrices in the test statistic in the regime of
proportionally growing $N$ and $T$. Our theoretical findings are supported by
Monte Carlo simulations and an empirical illustration. Moreover, we find a
surprising connection with multivariate analysis of variance (MANOVA) and
explain why it emerges.


  Data driven control of a continuum manipulator requires a lot of data for
training but generating sufficient amount of real time data is not cost
efficient. Random actuation of the manipulator can also be unsafe sometimes.
Meta learning has been used successfully to adapt to a new environment. Hence,
this paper tries to solve the above mentioned problem using meta learning. We
consider two cases for that. First, this paper proposes a method to use
simulation data for training the model using MAML(Model-Agnostic
Meta-Learning). Then, it adapts to the real world using gradient steps.
Secondly,if the simulation model is not available or difficult to formulate,
then we propose a CGAN(Conditional Generative adversial network)-MAML based
method for it. The model is trained using a small amount of real time data and
augmented data for different loading conditions. Then, adaptation is done in
the real environment. It has been found out from the experiments that the
relative positioning error for both the cases are below 3%. The proposed models
are experimentally verified on a real continuum manipulator.


  When the Standard Model is interpreted as the renormalizable sector of a
low-energy effective theory, the effects of new physics are encoded into a set
of higher dimensional operators. These operators potentially deform the shapes
of Standard Model differential distributions of final states observable at
colliders. We describe a simple and systematic method to obtain optimal
estimations of these deformations when using numerical tools, like Monte Carlo
simulations. A crucial aspect of this method is minimization of the estimation
uncertainty: we demonstrate how the operator coefficients have to be set in the
simulations in order to get optimal results. The uncertainty on the
interference term turns out to be the most difficult to control and grows very
quickly when the interference is suppressed. We exemplify our method by
computing the deformations induced by the ${\cal O}_{3W}$ operator in $W^+W^-$
production at the LHC, and by deriving a bound on ${\cal O}_{3W}$ using $8$ TeV
CMS data.


  Following a review of our present knowledge about blue subdwarf stars in
globular clusters, we present an overview of the results of searches for close
binaries among these stars, including results previously published in the
literature and reporting recent and preliminary results of new data. Previous
investigations revealed a lack of close systems in NGC 6752, which we confirm
with new, more extensive observations. Our estimate of the close binary
fraction in this cluster is only 4%. From a review of the relevant literature,
there are indications that a low close binary fraction among extreme horizontal
branch (EHB) stars is a common feature in globular star clusters. On the other
hand, the field EHB population shows evidence of a remarkably high binary
fraction. Such a difference among globular cluster and field populations,
although not yet explained in detail on the basis of theoretical models, must
necessarily be related to different formation histories for EHB stars in the
field and in clusters. In this framework, preliminary results indicate that
close systems could be relatively common in the peculiar globular cluster NGC
2808, although the sample of studied stars is still small. This would imply
that not all clusters share the same behavior, as far as EHB star formation is
concerned. We briefly explore possible explanations for these results.


  The objectives of this chapter are: (i) to introduce a concise overview of
regularization; (ii) to define and to explain the role of a particular type of
regularization called total variation norm (TV-norm) in computer vision tasks;
(iii) to set up a brief discussion on the mathematical background of TV
methods; and (iv) to establish a relationship between models and a few existing
methods to solve problems cast as TV-norm. For the most part, image-processing
algorithms blur the edges of the estimated images, however TV regularization
preserves the edges with no prior information on the observed and the original
images. The regularization scalar parameter {\lambda} controls the amount of
regularization allowed and it is an essential to obtain a high-quality
regularized output. A wide-ranging review of several ways to put into practice
TV regularization as well as its advantages and limitations are discussed.


  The scalability of massively parallel algorithms is a fundamental question in
computer science. We study the scalability and the efficiency of a conservative
massively parallel algorithm for discrete-event simulations where the discrete
events are Poisson arrivals. The parallel algorithm is applicable to a wide
range of problems, including dynamic Monte Carlo simulations for large
asynchronous systems with short-range interactions. The evolution of the
simulated time horizon is analogous to a growing and fluctuating surface, and
the efficiency of the algorithm corresponds to the density of local minima of
this surface. In one dimension we find that the steady state of the macroscopic
landscape is governed by the Edwards-Wilkinson Hamiltonian, which implies that
the algorithm is scalable. Preliminary results for higher-dimensional logical
topologies are discussed.


  The majority of nearby early-type galaxies contains detectable amounts of
emission-line gas at their centers. The emission-line ratios and gas kinematics
potentially form a valuable diagnostic of the nuclear activity and
gravitational potential well. The observed central gas velocity dispersion
often exceeds the stellar velocity dispersion. This could be due to either the
gravitational potential of a black hole or turbulent shocks in the gas. Here we
try to discriminate between these two scenarios.


  How can we segment varying numbers of objects where each specific object
represents its own separate class? To make the problem even more realistic, how
can we add and delete classes on the fly without retraining or fine-tuning?
This is the case of robotic applications where no datasets of the objects exist
or application that includes thousands of objects (E.g., in logistics) where it
is impossible to train a single model to learn all of the objects. Most current
research on object segmentation for robotic grasping focuses on class-level
object segmentation (E.g., box, cup, bottle), closed sets (specific objects of
a dataset; for example, YCB dataset), or deep learning-based template matching.
In this work, we are interested in open sets where the number of classes is
unknown, varying, and without pre-knowledge about the objects' types. We
consider each specific object as its own separate class. Our goal is to develop
an object detector that requires no fine-tuning and can add any object as a
class just by capturing a few images of the object. Our main idea is to break
the segmentation pipelines into two steps by combining unseen object
segmentation networks cascaded by class-adaptive classifiers. We evaluate our
class-adaptive object detector on unseen datasets and compare it to a trained
Mask R-CNN on those datasets. The results show that the performance varies from
practical to unsuitable depending on the environment setup and the objects
being handled. The code is available in our DoUnseen library repository.


  We study systems of nonlinear ordinary differential equations where the
dominant term, with respect to large spatial variables, causes blow-ups and is
positively homogeneous of a degree $1+\alpha$ for some $\alpha>0$. We prove
that the asymptotic behavior of a solution $y(t)$ near a finite blow-up time
$T_*$ is $(T_*-t)^{-1/\alpha}\xi_*$ for some nonzero vector $\xi_*$. Specific
error estimates for $|(T_*-t)^{1/\alpha}y(t)-\xi_*|$ are provided. In some
typical cases, they can be a positive power of $(T_*-t)$ or $1/|\ln(T_*-t)|$.
This depends on whether the decaying rate of the lower order term, relative to
the size of the dominant term, is of a power or logarithmic form. Similar
results are obtained for a class of nonlinear differential inequalities with
finite time blow-up solutions. Our results cover larger classes of nonlinear
equations, differential inequalities and error estimates than those in the
previous work.


  The ladder-rainbow truncation of the set of Dyson-Schwinger equations is used
to study a variety of electroweak and strong processes involving light mesons.
The parameters in the effective interaction are constrained by the chiral
condensate and f_\pi; the current quark masses are fitted to m_\pi and m_K. The
obtained electromagnetic form factors are in good agreement with the data. Also
the weak K_{l3} decay and the radiative and strong decays of the vector mesons
agree reasonably well with the data. Finally, we indicate how processes such as
\pi-\pi scattering can be described within this framework as well.


  In many quantum quench experiments involving cold atom systems the
post-quench system can be described by a quantum field theory of free scalars
or fermions, typically in a box or in an external potential. We work with free
scalars in arbitrary dimensions generalizing the techniques employed in our
earlier work \cite{Mandal:2015kxi} in 1+1 dimensions. In this paper, we
generalize to $d$ spatial dimensions for arbitrary $d$. The system is
considered in a box much larger than any other scale of interest. We start with
the ground state, or a squeezed state, with a high mass and suddenly quench the
system to zero mass ("critical quench"). We explicitly compute time-dependence
of local correlators and show that at long times they are described by a
generalized Gibbs ensemble (GGE), which, in special cases, reduce to a thermal
(Gibbs) ensemble. The equilibration of {\it local} correlators can be regarded
as `subsystem thermalization' which we simply call 'thermalization' here (the
notion of thermalization here also includes equlibration to GGE). The rate of
approach to equilibrium is exponential or power law depending on whether $d$ is
odd or even respectively. As in 1+1 dimensions, details of the quench protocol
affect the long time behaviour; this underlines the importance of irrelevant
operators at IR in non-equilibrium situations. We also discuss quenches from a
high mass to a lower non-zero mass, and find that in this case the approach to
equilibrium is given by a power law in time, for all spatial dimensions $d$,
even or odd.


  The magnetic properties of anisotropic films have been studied using
3D-neutron polarization analysis. The experimental facts refer to essential
distinction of the sample states, magnetized in opposite directions. For an
explanation of asymmetrical effects the model is offered, in which the
fundamental theoretical principles of structural phase transitions are used.


  Statistical latent class models are widely used in social and psychological
researches, yet it is often difficult to establish the identifiability of the
model parameters. In this paper we consider the identifiability issue of a
family of restricted latent class models, where the restriction structures are
needed to reflect pre-specified assumptions on the related assessment. We
establish the identifiability results in the strict sense and specify which
types of restriction structure would give the identifiability of the model
parameters. The results not only guarantee the validity of many of the
popularly used models, but also provide a guideline for the related
experimental design, where in the current applications the design is usually
experience based and identifiability is not guaranteed. Theoretically, we
develop a new technique to establish the identifiability result, which may be
extended to other restricted latent class models.


  We explore the recently discovered solution of the driven Tavis-Cummings
model (DTCM). It describes interaction of arbitrary number of two-level systems
with a bosonic mode that has linearly time-dependent frequency. We derive
compact and tractable expressions for transition probabilities in terms of the
well known special functions. In the new form, our formulas are suitable for
fast numerical calculations and analytical approximations. As an application,
we obtain the semiclassical limit of the exact solution and compare it to prior
approximations. We also reveal connection between DTCM and $q$-deformed
binomial statistics.


  Accurately estimating the snowpack in key mountainous basins is critical for
water resource managers to make decisions that impact local and global
economies, wildlife, and public policy. Currently, this estimation requires
multiple LiDAR-equipped plane flights or in situ measurements, both of which
are expensive, sparse, and biased towards accessible regions. In this paper, we
demonstrate that fusing spatial and temporal information from multiple,
openly-available satellite and weather data sources enables estimation of
snowpack in key mountainous regions. Our multisource model outperforms
single-source estimation by 5.0 inches RMSE, as well as outperforms sparse in
situ measurements by 1.2 inches RMSE.


  We realize a Laughlin state of two rapidly rotating fermionic atoms in an
optical tweezer. By utilizing a single atom and spin resolved imaging
technique, we sample the Laughlin wavefunction, thereby revealing its
distinctive features, including a vortex distribution in the relative motion,
correlations in the particles' relative angle, and suppression of the
inter-particle interactions. Our work lays the foundation for atom-by-atom
assembly of fractional quantum Hall states in rotating atomic gases.


  We consider general reaction diffusion systems posed on rectangular lattices
in two or more spatial dimensions. We show that travelling wave solutions to
such systems that propagate in rational directions are nonlinearly stable under
small perturbations. We employ recently developed techniques involving
point-wise Green's functions estimates for functional differential equations of
mixed type (MFDEs), allowing our results to be applied even in situations where
comparison principles are not available.


  In this paper we consider a variation of the Vafa-Witten equations on
compact, oriented and smooth 4-manifolds, and construct a set of perturbation
terms to establish the transversality of that equations. The new perturbed
equations provide us a priori estimates of the solutions, while the original
reduced Vafa-Witten equations does not. By applying the a priori estimates we
show that the singular of the solutions can be removed, and then construct the
Ulhenbeck closure of the moduli spaces.


  We disprove a conjecture stating that the integral cohomology of any
crystallographic group Z^n \rtimes Z_m is given by the cohomology of Z_m with
coefficients in the cohomology of the group Z^n, by providing a complete list
of counterexamples up to dimension 5. We also find a counterexample with odd
order holonomy, m=9, in dimension 8 and finish the computations of the
cohomology of 6-dimensional crystallographic groups arising as orbifold
fundamental groups of certain Calabi-Yau toroidal orbifolds.


  Questions concerning the proof-theoretic strength of classical versus
non-classical theories of truth have received some attention recently. A
particularly convenient case study concerns classical and nonclassical
axiomatizations of fixed-point semantics. It is known that nonclassical
axiomatizations in four- or three-valued logics are substantially weaker than
their classical counterparts. In this paper we consider the addition of a
suitable conditional to First-Degree Entailment -- a logic recently studied by
Hannes Leitgeb under the label `HYPE'. We show in particular that, by
formulating the theory PKF over HYPE one obtains a theory that is sound with
respect to fixed-point models, while being proof-theoretically on a par with
its classical counterpart KF. Moreover, we establish that also its schematic
extension -- in the sense of Feferman -- is as strong as the schematic
extension of KF, thus matching the strength of predicative analysis.


  We derive analytical expressions for the power spectra at the end of
inflation in theories with two inflaton fields and non-canonical kinetic terms.
We find that going beyond the slow-roll approximation is necessary and that the
nature of the non-canonical terms have an important impact on the final power
spectra at the end of inflation. We study five models numerically and find
excellent agreement with our analytical results. Our results emphasise the fact
that going beyond the slow-roll approximation is important in times of
high-precision data coming from cosmological observations.


  In this paper the deformation quantization is constructed in the case of
scalar fields on Minkowski space-time. We construct the star products at three
level concerning fields, Hamiltonian functionals and their underlying structure
called Hamiltonian functions in a special sense. Which mean the star products
of fields, functionals, Hamiltonian functions, and ones between the fields and
functionals. As bases of star products the Poisson brackets at different level
are generalized, constructed and discussed in a systematical way, where the
Poisson brackets like canonical and time-equal ones. For both of the star
products and Poisson brackets the construction at level of Hamiltonian
functions plays the essential role. Actually, the discussion for the case of
Hamiltonian functions includes the key information about Poisson brackets and
the star products in our approach. All of other Poisson brackets and star
products in this paper are based on ones of Hamiltonian functions, and the
Poisson brackets and the star products at three level are compatible. To
discuss the Poisson brackets and the star products in the case of scalar
fields, we introduce the notion of algebra of Euler-Lagrange operators which
related to variation calculus closely.


  Facts extraction is pivotal for constructing knowledge graphs. Recently, the
increasing demand for temporal facts in downstream tasks has led to the
emergence of the task of temporal fact extraction. In this paper, we
specifically address the extraction of temporal facts from natural language
text. Previous studies fail to handle the challenge of establishing
time-to-fact correspondences in complex sentences. To overcome this hurdle, we
propose a timeline-based sentence decomposition strategy using large language
models (LLMs) with in-context learning, ensuring a fine-grained understanding
of the timeline associated with various facts. In addition, we evaluate the
performance of LLMs for direct temporal fact extraction and get unsatisfactory
results. To this end, we introduce TSDRE, a method that incorporates the
decomposition capabilities of LLMs into the traditional fine-tuning of smaller
pre-trained language models (PLMs). To support the evaluation, we construct
ComplexTRED, a complex temporal fact extraction dataset. Our experiments show
that TSDRE achieves state-of-the-art results on both HyperRED-Temporal and
ComplexTRED datasets.


  Channel estimation is a difficult problem in MIMO systems. Using a physical
model allows to ease the problem, injecting a priori information based on the
physics of propagation. However, such models rest on simplifying assumptions
and require to know precisely the system configuration, which is unrealistic.In
this paper, we propose to perform online learning for channel estimation in a
massive MIMO context, adding flexibility to physical models by unfolding a
channel estimation algorithm (matching pursuit) as a neural network. This leads
to a computationally efficient neural network that can be trained online when
initialized with an imperfect model. The method allows a base station to
automatically correct its channel estimation algorithm based on incoming data,
without the need for a separate offline training phase.It is applied to
realistic channels and shows great performance, achieving channel estimation
error almost as low as one would get with a perfectly calibrated system.


  The online increasing subsequence problem is a stochastic optimisation task
with the objective to maximise the expected length of subsequence chosen from a
random series by means of a nonanticipating decision strategy. We study the
structure of optimal and near-optimal subsequences in a standardised planar
Poisson framework. Following a long-standing suggestion by Bruss and Delbaen
(Stoch. Proc. Appl. 114, 2004), we prove a joint functional limit theorem for
the transversal fluctuations about the diagonal of the running maximum and the
length processes. The limit is identified explicitly with a Gaussian
time-inhomogeneous diffusion. In particular, the running maximum converges to a
Brownian bridge, and the length process has another explicit non-Markovian
limit.


  To train a robust deep learning model, one usually needs a balanced set of
categories in the training data. The data acquired in a medical domain,
however, frequently contains an abundance of healthy patients, versus a small
variety of positive, abnormal cases. Moreover, the annotation of a positive
sample requires time consuming input from medical domain experts. This scenario
would suggest a promise for one-class classification type approaches. In this
work we propose a general one-class classification model for histology, that is
meta-trained on multiple histology datasets simultaneously, and can be applied
to new tasks without expensive re-training. This model could be easily used by
pathology domain experts, and potentially be used for screening purposes.


  We present the BRAIN Experiment, a project of B-mode experiment using a novel
technology, bolometric interferometry. This technique is a promising
alternative to direct imaging experiments since it combines the advantages of
interferometry in terms of systematic effects handling and those of bolometry
in terms of sensitivity. We briefly introduce some of the bolometric
interferometry key concepts and difficulties. We then give the specifications
of the BRAIN future detector. A first module of the final instrument is planned
to be installed at Dome C in 2010. We hope to constrain a tensor to scalar
modes ratio of 0.01 with nine modules and one effective year of data. BRAIN is
a collaboration between France, Italy and United Kingdom.


  We continue our study on the logarithmic balanced model metric initiated in
our previous work. By a non-trivial refinement of the set of tools developed in
our previous work, we are able to confirm partially a conjecture we made in our
previous work on the asymptotic behavior of the balanced metric near infinity.


  A theoretical possibility is shown for the bound state of a tetraneutron to
exist in the case of the proposed neutron-neutron potentials in the singlet
state with two attractive wells separated by a repulsive barrier. The anomalous
behaviours are revealed for the calculated size, density distribution, and pair
correlation functions of a hypothetical tetraneutron.


  We have analyzed high velocity resolution HI synthesis observations of the
nearly face-on Sc galaxy NGC 1232. The neutral gas distribution extends well
beyond the optical extent of the galaxy. As expected, local peaks in the HI
column density are associated with the spiral arms. Further, the HI column
density drops precipitously near the center of the galaxy. Closed contours in
the velocity field suggest either that the system is warped, or that the
rotation curve declines. The velocity dispersion is approximately constant
throughout the system, with a median value of 9.9 +/- 1.8 km/s. When corrected
for rotational broadening, there is no indication of a radial trend in the
neutral gas velocity dispersion in this galaxy.


  We studied the microstructural and helium bubbling evolutions of Gd2Zr2O7
waste form with immobilized TRPO (50 wt%) under multi-energy He ion
irradiation. Three structurally heterogeneous regions for the Gd2Zr2O7 waste
form were found as a function of the depth from the He-irradiated surface.
Specifically, at a depth less than 40 nm below the He-irradiated surface
(Region I) the Gd2Zr2O7 waste form is completely amorphous with large spherical
He bubbles (5-25 nm). In the intermediate region, Region II, (40-800 nm)
partially amorphized Gd2Zr2O7 waste form accompanied with ribbon-like He
bubbles that may lead to the formation of microcracks is observed. The
crystallinity is not impacted in Region III for a depth of more than 800 nm.
For the first time, we elucidated that the Gd2Zr2O7 waste form, which was
considered to be structurally intact at 100 dpa, is completely amorphized at
6.5 dpa with the synergistic displacement damage, electronic energy loss, and
He concentration enabled. This study leads to new physical insights into
amorphization and He bubbles formation mechanisms of Gd2Zr2O7 waste form under
multi-energy He irradiation, which is essential for the design and optimization
of irradiation-resistant ceramic waste matrices.


  In this paper, we prove the local converse theorem for split even special
orthogonal groups over a non-Archimedean local field of characteristic zero.
This is the only case left on local converse theorems of split classical groups
and the difficulty is the existence of the outer automorphism. We apply new
ideas of considering the summation of partial Bessel functions and overcome
this difficulty. As a direct application, we obtain a weak rigidity theorem for
irreducible generic cuspidal representations of split even special orthogonal
groups.


  We report on a systematic temperature dependent X-band electron paramagnetic
resonance (EPR) study of nano-sized graphite particles prepared by
ball-milling. In as-prepared samples a very intense and sharp EPR resonance at
$g=2.0035$ has been measured. The EPR linewidth shows a Korringa-like linear
temperature dependence arising due to the coexistence and strong exchange
coupling of itinerant and localized edge states. With a prolonged aging in
inert atmosphere changes in the EPR signal suggest gradual structural
reconstruction where the localized edge-states dominate the EPR signal. In this
case the EPR spin susceptibility shows a maximum at $\approx 23\, {\rm K}$
indicating the development of antiferromagnetic correlations as expected for
the graphene lattice with a bipartite symmetry.


  A stabilization theorem for processes of opinion dynamics is presented. The
theorem is applicable to a wide class of models of continuous opinion dynamics
based on averaging (like the models of Hegselmann-Krause and
Weisbuch-Deffuant). The analysis detects self-confidence as a driving force of
stabilization.


  The Apriori algorithm that mines frequent itemsets is one of the most popular
and widely used data mining algorithms. Now days many algorithms have been
proposed on parallel and distributed platforms to enhance the performance of
Apriori algorithm. They differ from each other on the basis of load balancing
technique, memory system, data decomposition technique and data layout used to
implement them. The problems with most of the distributed framework are
overheads of managing distributed system and lack of high level parallel
programming language. Also with grid computing there is always potential
chances of node failures which cause multiple re-executions of tasks. These
problems can be overcome by the MapReduce framework introduced by Google.
MapReduce is an efficient, scalable and simplified programming model for large
scale distributed data processing on a large cluster of commodity computers and
also used in cloud computing. In this paper, we present the overview of
parallel Apriori algorithm implemented on MapReduce framework. They are
categorized on the basis of Map and Reduce functions used to implement them
e.g. 1-phase vs. k-phase, I/O of Mapper, Combiner and Reducer, using
functionality of Combiner inside Mapper etc. This survey discusses and analyzes
the various implementations of Apriori on MapReduce framework on the basis of
their distinguishing characteristics. Moreover, it also includes the advantages
and limitations of MapReduce framework.


  This paper presents a novel set-based computing method, called interval
superposition arithmetic, for enclosing the image set of multivariate
factorable functions on a given domain. In order to construct such enclosures,
the proposed arithmetic operates over interval superposition models which are
parameterized by a matrix with interval components. Every point in the domain
of a factorable function is then associated with a sequence of components of
this matrix and the superposition, i.e. Minkowski sum, of these elements
encloses the image of the function at this point. Interval superposition
arithmetic has a linear runtime complexity with respect to the number of
variables. Besides presenting a detailed theoretical analysis of the accuracy
and convergence properties of interval superposition arithmetic, the paper
illustrates its advantages compared to existing set arithmetics via numerical
examples.


  The Kardar-Parisi-Zhang (KPZ) equation is a paradigmatic model of
nonequilibrium low-dimensional systems with spatiotemporal scale invariance,
recently highlighting universal behavior in fluctuation statistics. Its space
derivative, namely the noisy Burgers equation, has played a very important role
in its study, predating the formulation of the KPZ equation proper, and being
frequently held as an equivalent system. We show that, while differences in the
scaling exponents for the two equations are indeed due to a mere space
derivative, the fluctuations behave in a remarkably different way: while KPZ
displays Tracy-Widom statistics, its derivative displays Gaussian behavior,
hence being in a different universality class. We reach this conclusion via
direct numerical simulations of the equations, supported by a dynamic
renormalization group analysis of field statistics.


  A specific model of parity-conserving lepton substructure is considered. We
show that a positive-definite contribution to the muon $(g-2)/2$ at the
possible level of about $4\times 10^{-9}$, can be related to a significant
increase in the interaction cross section for cosmic-ray neutrinos with
energies above about $10^{19}$ eV. The additional cross section at $\sim
10^{20}$ eV is calculated to be $\sim 10^{-29}$ cm$^2$, which is about 100
times the standard weak-interaction cross section. The model involves an
extremely massive, neutral lepton, with $m_L\cong 2\times 10^6$ GeV fixed by
the new contribution to $(g-2)/2$.


  Large-amplitude longitudinal oscillations (LALOs) in solar prominences have
been widely studied in the last decades. However, their damping and
amplification mechanisms are not well understood. In this study, we investigate
the attenuation and amplification of LALOs using high-resolution numerical
simulations with progressively increasing spatial resolutions. We performed
time-dependent numerical simulations of LALOs using the 2D magnetic
configuration that contains a dipped region. After the prominence mass loading
in the magnetic dips, we triggered LALOs by perturbing the prominence mass
along the magnetic field. We performed the experiments with four values of
spatial resolution. In the simulations with the highest resolution, the period
shows a good agreement with the pendulum model. The convergence experiment
revealed that the damping time saturates at the bottom prominence region with
improving the resolution, indicating the existence of a physical reason for the
damping of oscillations. At the prominence top, the oscillations are amplified
during the first minutes and then are slowly attenuated. The characteristic
time suggests more significant amplification in the experiments with the
highest spatial resolution. The analysis revealed that the energy exchange
between the bottom and top prominence regions is responsible for the
attenuation and amplification of LALOs. The high-resolution experiments are
crucial for the study of the periods and the damping mechanism of LALOs. The
period agrees with the pendulum model only when using high enough spatial
resolution. The results suggest that numerical diffusion in simulations with
insufficient spatial resolution can hide important physical mechanisms, such as
amplification of oscillations.


  Knowledge-graph-based reasoning has drawn a lot of attention due to its
interpretability. However, previous methods suffer from the incompleteness of
the knowledge graph, namely the interested link or entity that can be missing
in the knowledge graph(explicit missing). Also, most previous models assume the
distance between the target and source entity is short, which is not true on a
real-world KG like Freebase(implicit missing). The sensitivity to the
incompleteness of KG and the incapability to capture the long-distance link
between entities have limited the performance of these models on large KG. In
this paper, we propose a model that leverages the text corpus to cure such
limitations, either the explicit or implicit missing links. We model the
question answering on KG as a cooperative task between two agents, a knowledge
graph reasoning agent and an information extraction agent. Each agent learns
its skill to complete its own task, hopping on KG or select knowledge from the
corpus, via maximizing the reward for correctly answering the question. The
reasoning agent decides how to find an equivalent path for the given entity and
relation. The extraction agent provide shortcut for long-distance target entity
or provide missing relations for explicit missing links with messages from the
reasoning agent. Through such cooperative reward design, our model can augment
the incomplete KG strategically while not introduce much unnecessary noise that
could enlarge the search space and lower the performance.


  A graph $G$ is $H$-free if any subset of $V(G)$ does not induce a subgraph of
$G$ that is isomorphic to $H$. Given a graph $H$, we present sufficient and
necessary conditions for a graph $G$ such that $G/e$ is $H$-free for any edge
$e$ in $E(G)$. Thereafter, we use these conditions to characterize forests,
claw-free, $2K_{2}$-free, $C_{4}$-free, $C_{5}$-free, split, and pseudo-split
graphs.


  We derive the nilpotent (anti-)BRST symmetry transformations for the Dirac
(matter) fields of an interacting four (3+1)-dimensional 1-form non-Abelian
gauge theory by applying the theoretical arsenal of augmented superfield
formalism where (i) the horizontality condition, and (ii) the equality of a
gauge invariant quantity, on the six (4, 2)-dimensional supermanifold, are
exploited together. The above supermanifold is parameterized by four bosonic
spacetime coordinates x^\mu (with \mu = 0,1,2,3) and a couple of Grassmannian
variables \theta and \bar{\theta}. The on-shell nilpotent BRST symmetry
transformations for all the fields of the theory are derived by considering the
chiral superfields on the five (4, 1)-dimensional super sub-manifold and the
off-shell nilpotent symmetry transformations emerge from the consideration of
the general superfields on the full six (4, 2)-dimensional supermanifold.
Geometrical interpretations for all the above nilpotent symmetry
transformations are also discussed in the framework of augmented superfield
formalism.


  We present a brief review of CPV and mixing measurements in the charm sector,
with emphasys in results published or presented since the previous edition of
the Physics in Collision Symposia.


  We extend our analysis for scalar fields in a Robertson-Walker metric to the
electromagnetic field and Dirac fields by the method of invariants. The issue
of the relation between conformal properties and particle production is
re-examined and it is verified that the electromagnetic and massless spinor
actions are conformal invariant, while the massless conformally coupled scalar
field is not. For the scalar field case it is pointed out that the violation of
conformal simmetry due to surface terms, although ininfluential for the
equation of motion, does lead to effects in the quantized theory.


  After the discovery of a Standard Model-like boson with mass of about 125 GeV
the possibility of an enlarged scalar sector arises naturally. Here we present
the current status of the phenomenology of the two-Higgs-doublet models with a
special focus on the charged Higgs sector. If one considers a fermiophobic
charged Higgs (it does not couple to fermions at tree level), all present
experimental bounds are evaded trivially, therefore one needs to consider other
decay and production channels. In this work we also present some of the
interesting features of this specific scenario.


  Heavy ion collisions at RHIC and at the LHC give access to the medium-induced
suppression patterns of heavy-flavored single inclusive hadron spectra at high
transverse momentum. This opens novel opportunities for a detailed
characterization of the medium produced in the collision. In this note, we
point out that the capacity of a QCD medium to absorb the recoil of a partonic
projectile is an independent signature, which may differ for different media at
the same density. In particular, while the mass hierarchy (i.e., the projectile
mass dependence) of radiative energy loss depends solely on a property of the
projectile, the mass hierarchy of collisional energy loss depends significantly
on properties of the medium. By varying these properties in a class of models,
we find that the mass hierarchy of collisional parton energy loss can be
modified considerably and can even be inverted, compared to that of radiative
parton energy loss. This may help to disentangle the relative strengths of
radiative and collisional contributions to jet quenching, and it may be
employed to constrain properties of the produced QCD medium beyond its density.


  The superposition principle is one of the bizarre predictions of quantum
mechanics. Nevertheless, it has been experimentally verified using electrons,
photons, atoms, and molecules. In this article, using a $20~$nm levitated
ferromagnetic FePt nanoparticle, an exotic all optical spin polarization
technique and the matter-wave interferometry, we show that a mesoscopic spatial
Schrodinger cat can be created. Additionally, we argue that the maximum spatial
separation between the delocalized wavepackets can be $25~\mu m$ and is
significantly larger than the object itself.


  An operator C on a Hilbert space H dilates to an operator T on a Hilbert
space K if there is an isometry V from H to K such that C=V^*TV. A main result
of this paper is, for a positive integer d, the simultaneous dilation, up to a
sharp factor $\vartheta(d)$, of all d-by-d symmetric matrices of operator norm
at most one to a collection of commuting self-adjoint contraction operators on
a Hilbert space. An analytic formula for $\vartheta(d)$ is derived, which as a
by-product gives new probabilistic results for the binomial and beta
distributions.
  Dilating to commuting operators has consequences for the theory of linear
matrix inequalities (LMIs). Given a tuple A=(A_1,...,A_g) of symmetric matrices
of the same size, L(x):=I-\sum A_j x_j is a monic linear pencil. The solution
set S_L of the corresponding linear matrix inequality, consisting of those x in
R^g for which L(x) is positive semidefinite (PsD), is a spectrahedron. The set
D_L of tuples X=(X_1,...,X_g) of symmetric matrices (of the same size) for
which L(X):=I-\sum A_j \otimes X_j is PsD, is a free spectrahedron. A result
here is: any tuple X of d-by-d symmetric matrices in a bounded free
spectrahedron D_L dilates, up to a scale factor, to a tuple T of commuting
self-adjoint operators with joint spectrum in the corresponding spectrahedron
S_L. From another viewpoint, the scale factor measures the extent that a
positive map can fail to be completely positive.
  Given another monic linear pencil M, the inclusion D_L \subset D_M obviously
implies the inclusion S_L \subset S_M and thus can be thought of as its free
relaxation. Determining if one free spectrahedron contains another can be done
by solving an explicit LMI and is thus computationally tractable. The scale
factor for commutative dilation of D_L gives a precise measure of the worst
case error inherent in the free relaxation, over all monic linear pencils M of
size d.


  Using examples of compact complex 3-manifolds which arise as twistor spaces,
we show that the class of compact complex manifolds bimeromorphic to K\"ahler
manifolds is not stable under small deformations of complex structure.


  Motivated by experiments in nanoscopic systems, we study a generalized
Anderson, which consists of two spin degenerate doublets hybridized to a
singlet by promotion of an electron to two conduction bands, as a function of
the energy separation $\delta$ between both doublets. For $\delta$=0 or very
large, the model is equivalent to a one-level SU(N) Anderson model, with N=4
and 2 respectively. We study the evolution of the spectral density for both
doublets ($\rho_{1 \sigma}(\omega)$ and $\rho_{2 \sigma}(\omega)$) and their
width in the Kondo limit as $\delta$ is varied, using the non-crossing
approximation (NCA). As $\delta$ increases, the peak at the Fermi energy in the
spectral density (Kondo peak) splits and the density of the doublet of higher
energy $\rho_{2 \sigma}(\omega)$ shifts above the Ferrmi energy. The Kondo
temperature $T_K$ (determined by the half width at half maximum of the Kondo
peak in density of the doublet of lower energy $\rho_{1 \sigma}(\omega)$)
decreases dramatically. The variation of $T_K$ with $\delta$ is reproduced by a
simple variational calculation.


  We consider inference of the parameters of the diffusion term for
Cox-Ingersoll-Ross and similar processes with a power type dependence of the
diffusion coefficient from the underlying process. We suggest some original
pathwise estimates for this coefficient and for the power index based on an
analysis of an auxiliary continuous time complex valued process generated by
the underlying real valued process. These estimates do not rely on the
distribution of the underlying process and on a particular choice of the drift.
Some numerical experiments are used to illustrate the feasibility of the
suggested method.


  We introduce the notion of the "covering type" of a space, which is more
subtle that the notion of Lusternik Schnirelman category. It measures the
complexity of a space which arises from coverings by contractible subspaces
whose non-empty intersections are also contractible.


  In the literature, lines of the projective space $\mathrm{PG}(3,q)$ are
partitioned into classes, each of which is a union of line orbits under the
stabilizer group of the twisted cubic. The least studied class is named
$\mathcal{O}_6$. This class contains lines external to the twisted cubic which
are not its chords or axes and do not lie in any of its osculating planes. For
even and odd $q$, we propose a new family of orbits of $\mathcal{O}_6$ and
investigate in detail their stabilizer groups and the corresponding submatrices
of the point-line and plane-line incidence matrices. To obtain these
submatrices, we explored the number of solutions of cubic and quartic equations
connected with intersections of lines (including the tangents to the twisted
cubic), points, and planes in $\mathrm{PG}(3,q)$.


  This paper deals with the existence and multiplicity of nontrivial solutions
for $(p, Q)$-Laplace equations with the Stein-Weiss reaction under critical
exponential nonlinearity in the Heisenberg group $\mathbb{H}^N$. In addition, a
weight function and two positive parameters have also been included in the
nonlinearity. The developed analysis is significantly influenced by these two
parameters. Further, the mountain pass theorem, the Ekeland variational
principle, the Trudinger-Moser inequality, the doubly weighted
Hardy-Littlewood-Sobolev inequality and a completely new Br\'ezis-Lieb type
lemma for Choquard nonlinearity play key roles in our proofs.


  Nowadays massive amount of data are available for analysis in natural and
social systems. Inferring system structures from the data, i.e., the inverse
problem, has become one of the central issues in many disciplines and
interdisciplinary studies. In this Letter, we study the inverse problem of
stochastic dynamic complex networks. We derive analytically a simple and
universal inference formula called double correlation matrix (DCM) method.
Numerical simulations confirm that the DCM method can accurately depict both
network structures and noise correlations by using available kinetic data only.
This inference performance was never regarded possible by theoretical
derivation, numerical computation and experimental design.


  In this paper, we study affine Deligne--Lusztig varieties $X_w(b)$ when the
finite part of the element $w$ in the Iwahori--Weyl group is a partial
$\sigma$-Coxeter element. We show that such $w$ is a cordial element and
$X_w(b) \neq \emptyset$ if and only if $b$ satisfies a certain Hodge--Newton
indecomposability condition. The main result of this paper is that for such $w$
and $b$, $X_w(b)$ has a simple geometric structure: the $\sigma$-centralizer of
$b$ acts transitively on the set of irreducible components of $X_w(b)$; and
each irreducible component is an iterated fibration over a classical
Deligne--Lusztig variety of Coxeter type, and the iterated fibers are either
$\mathbb A^1$ or $\mathbb G_m$.


  In their study of physics beyond the first year of University -- termed
upper-division in the US, many of students' primary learning opportunities come
from working long, complex back-of-the-book style problems, and from trying to
develop an understanding of the underlying physics through solving such
problems. Some of the research at the upper-division focuses on how students
use mathematics in these problems, and what challenges students encounter along
the way. There are a number of different and diverse research studies on
students' use of mathematics in the upper-division. These typically utilize one
of two broad approaches, with some researchers primarily seeking out and
addressing challenges students face, and others working chiefly to unpack
students' in-the-moment reasoning. In this paper, we present and discuss both
approaches, and then review research efforts that strive to connect these two
approaches in order to make sense of students' use of mathematics as well as to
uncover particular challenges that students encounter. These recent efforts
represent a small step towards synthesizing the two approaches, which we argue
is necessary to more meaningfully impact student learning at the
upper-division. We close our review and discussion with suggested refinements
for future research questions for the physics education research community to
consider while it works to understand how students use math in upper-division
courses.


  We calculate the $\Lambda_b \to \Lambda_c \ell \nu$ form factors and decay
rates for all possible $b\to c \ell\bar\nu$ four-Fermi interactions beyond the
Standard Model, including nonzero charged lepton masses and terms up to order
$\alpha_s\, \Lambda_\text{QCD}/m_{c,b}$ and $\Lambda_\text{QCD}^2/m_c^2$ in the
heavy quark effective theory. At this order, we obtain model independent
predictions for semileptonic $\Lambda_b \to \Lambda_c$ decays in terms of only
two unknown sub-subleading Isgur-Wise functions, which can be determined from
fitting LHCb and lattice QCD data. We thus obtain model independent results for
$\Lambda_b\to \Lambda_c\ell\bar\nu$ decays, including predictions for the ratio
$R(\Lambda_c) = {\cal B}(\Lambda_b\to \Lambda_c \tau\bar\nu) / {\cal
B}(\Lambda_b\to \Lambda_c \mu\bar\nu)$ in the presence of new physics, that are
more precise than prior results in the literature, and systematically
improvable with better data on the decays with $\mu$ (or $e$) in the final
state. We also explore tests of factorization in $\Lambda_b \to \Lambda_c\pi$
decays, and emphasize the importance of measuring at LHCb the double
differential rate $d^2\Gamma(\Lambda_b\to\Lambda_c\ell\bar\nu) / (d q^2\,
d\cos\theta)$, in addition to the $q^2$ spectrum.


  A simple quantum mechanical model consisting of a discrete level resonantly
coupled to a continuum of finite width, where the coupling can be varied from
perturbative to strong (Fano-Anderson model), is considered. The particle is
initially localized at the discrete level, and the time dependence of the
amplitude to find the particle at the discrete level is calculated without
resorting to perturbation theory. The deviations from the exponential decay
law, predicted by the Fermi's Golden Rule, are discussed. We also study
analytic structure of the Green's function (GF) for the model. We analyze the
GF poles, branch points and Riemann surface, and show how the Fermi's Golden
Rule, valid in perturbative regime for not to large time, appears in this
context. The knowledge of analytic structure of the GF in frequency
representation opens opportunities for obtaining easy for numerical
calculations formulas for the GF in time representation, alternative to those
using the spectral density.


  We introduce a domain-specific algorithm for numerical optimization
operations used by quantum circuit instantiation, synthesis, and compilation
methods. QFactor uses a tensor network formulation together with analytic
methods and an iterative local optimization algorithm to reduce the number of
problem parameters. Besides tailoring the optimization process, the formulation
is amenable to portable parallelization across CPU and GPU architectures, which
is usually challenging in general purpose optimizers (GPO). Compared with
several GPOs, our algorithm achieves exponential memory and performance savings
with similar optimization success rates. While GPOs can handle directly
circuits of up to six qubits, QFactor can process circuits with more than 12
qubits. Within the BQSKit optimization framework, we enable optimizations of
100+ qubit circuits using gate deletion algorithms to scale out linearly with
the hardware resources allocated for compilation in GPU environments.


  We review M-theory description of 4d N=2 SQCD. Configurations of M-theory
fivebranes relevant to describe the moduli spaces of the Coulomb and Higgs
branches are studied using the Taub-NUT geometry. Minimal area membranes
related with the BPS states of N=2 SQCD are given explicitly. They almost
saturate the BPS bounds. The deviation from the bounds is due to their boundary
condition constrained by the fivebrane. The electric-magnetic duality at the
baryonic branch root is also examined from the M-theory viewpoint. In this
course, novel concepts such as creation of brane and exchange of branes in Type
II theory are explained in the framework of M-theory.


  We consider a linear autoencoder in which the latent variables are quantized,
or corrupted by noise, and the constraint is Schur-concave in the set of latent
variances. Although finding the optimal encoder/decoder pair for this setup is
a nonconvex optimization problem, we show that decomposing the source into its
principal components is optimal. If the constraint is strictly Schur-concave
and the empirical covariance matrix has only simple eigenvalues, then any
optimal encoder/decoder must decompose the source in this way. As one
application, we consider a strictly Schur-concave constraint that estimates the
number of bits needed to represent the latent variables under fixed-rate
encoding, a setup that we call \emph{Principal Bit Analysis (PBA)}. This yields
a practical, general-purpose, fixed-rate compressor that outperforms existing
algorithms. As a second application, we show that a prototypical
autoencoder-based variable-rate compressor is guaranteed to decompose the
source into its principal components.


  We have studied the giant dipole resonance (GDR) in the hot and rotating
nucleus $^{152}$Gd within the framework of thermal shape fluctuation model
(TSFM) built on the microscopic-macroscopic calculations of the free energies
with a macroscopic approach for the GDR. Our results for GDR cross sections are
in good agreement with the experimental values except for a component peaking
around 17 MeV where the data has large uncertainties. Such a component is
beyond our description which properly takes care of the splitting of GDR
components due to the deformation and Coriolis effects. Around this 17 MeV lies
the half maximum in experimental cross sections, and hence the extracted GDR
widths and deformations (estimated from these widths) turn out to be
overestimated and less reliable. Reproducing these widths with empirical
formulae could conceal the information contained in the cross sections. Fully
microscopic GDR calculations and a more careful look at the data could be
useful to understand the GDR component around 17 MeV. We also discuss the
occurrence of $\gamma$-softness in the free energy surfaces of $^{152}$Gd and
its role on GDR.


  It has been suggested that the cycles of activity of X-ray Binaries (XrB) are
triggered by a switch in the dominant disk torque responsible for accretion
(paper I). As the disk accretion rate increases, the disk innermost regions
would change from a jet-emitting disk (JED) to a standard accretion disk (SAD).
While JEDs have been proven to successfully reproduce hard states (paper II),
the existence of an outer cold SAD introduces an extra non local cooling term.
We investigate the thermal structure and associated spectra of such a hybrid
disk configuration. We use the 2T plasma code elaborated in paper II, allowing
to compute outside-in the disk local thermal equilibrium with self-consistent
advection and optically thin-to-thick transitions, in both radiation and gas
supported regimes. The non-local inverse Compton cooling introduced by the
external soft photons is computed by the BELM code. This additional term has a
profound influence on JED solutions, allowing a smooth temperature transition
from the outer SAD to the inner JED. We explore the full parameter space in
disk accretion rate and transition radius, and show that the whole domain in
X-ray fluxes and hardness ratios covered by standard XrB cycles is well
reproduced by such hybrid configurations. Precisely, a reasonable combination
of these parameters allows to reproduce the 3-200 keV spectra of five canonical
XrB states. Along with X-ray signatures, JED-SAD configurations also naturally
account for the radio emission whenever it is observed. By varying only the
transition radius and the accretion rate, hybrid disk configurations combining
an inner JED and an outer SAD are able to reproduce simultaneously the X-ray
spectral states and radio emission of X-ray binaries during their outburst.
Adjusting these two parameters, it is then possible to reproduce a full cycle.
This will be shown in a forthcoming paper (paper IV).


  Quantum phases provide us with important information for understanding the
fundamental properties of a system. However, the observation of quantum phases,
such as Berry's phase and the sign of the matrix element of the Hamiltonian
between two non-equivalent localized orbitals in a tight-binding formalism, has
been challenged by the presence of other factors, e.g., dynamic phases and
spin/valley degeneracy, and the absence of methodology. Here, we report a new
way to directly access these quantum phases, through polarization-dependent
angle-resolved photoemission spectroscopy (ARPES), using graphene as a
prototypical two-dimensional material. We show that the momentum- and
polarization-dependent spectral intensity provides direct measurements of (i)
the phase of the band wavefunction and (ii) the sign of matrix elements for
non-equivalent orbitals. Upon rotating light polarization by \pi/2, we found
that graphene with a Berry's phase of n\pi (n=1 for single- and n=2 for
double-layer graphene for Bloch wavefunction in the commonly used form)
exhibits the rotation of ARPES intensity by \pi/n, and that ARPES signals
reveal the signs of the matrix elements in both single- and double-layer
graphene. The method provides a new technique to directly extract fundamental
quantum electronic information on a variety of materials.


  We present a comprehensive multiphonon Raman and complementary infrared
analysis for bulk and monolayer MoS2.For the bulk the analysis consists of
symmetry assignment from which we obtain a broad set of allowed second order
transitions at the high symmetry M,K and gamma Brillouin zone points. The
attribution of about 80 transitions of up to fifth order Raman processes are
proposed in the low temperature(95K)resonant Raman spectrum measured with the
excitation energy of 1.96 eV,which is slightly shifted from the A exciton. We
propose that the main contributions come from four
phonons:A1g(M),E12g(M2),E22g(M1)(TA'(M))and E22g (M2)(LA'(M)). The last three
are single degenerate phonons at M with an origin of the E12g(gamma)and
E22g(gamma)phonons. Among the four phonons, we identify in the resonant Raman
spectra all(but one) of the second order overtones,combination and difference
bands and many of the third order bands. Consistent with the expectation that
at the M point only combinations with the same inversion symmetry (g or u)are
Raman allowed, the contribution of combinations with the LA(M)phonon can not be
considered with the above four phonons. Although minor,contribution from K
point and possibly gamma point phonons are also evident. The "2LA
band",measured at ~460 cm-1 is reassigned.Supported by the striking similarity
between this band, measured under off resonant conditions, and recently
published two phonon density of states, we propose that the lower part of the
band,previously attributed to 2LA(M),is due to a van Hove singularity between K
and M. The higher part,previously attributed exclusively to the
A2u(gamma)phonon,is mostly due to the LA and LA' phonons at M. For the
monolayer MoS2, the second order phonon processes from M and gamma Brillouin
zone points are also analyzed and are discussed within similar framework to
that of the bulk.


  A recursive approach to determine the Hilbert-Schmidt measure of pairwise
quantum discord in a special class of symmetric states of $k$ qubits is
presented. We especially focus on the reduced states of $k$ qubits obtained
from a balanced superposition of symmetric $n$-qubit states (multiqubit
Schr\"odinger cat states) by tracing out $n-k$ particles $(k=2,3, \cdots
,n-1)$. Two pairing schemes are considered. In the first one, the geometric
discord measuring the correlation between one qubit and the party grouping
$(k-1)$ qubits is explicitly derived. This uses recursive relations between the
Fano-Bloch correlation matrices associated with subsystems comprising $k$,
$k-1$, $\cdots$ and $2$ particles. A detailed analysis is given for two, three
and four qubit systems. In the second scheme, the subsystem comprising the
$(k-1)$ qubits is mapped into a system of two logical qubits. We show that
these two bipartition schemes are equivalents in evaluating the pairwise
correlation in multi-qubits systems. The explicit expressions of classical
states presenting zero discord are derived.


  The gram-negative bacterium Yersinia pestis, the aetiological agent of
bubonic plague, is one the deadliest pathogens known to man. Despite its
historical reputation, plague is a modern disease which annually afflicts
thousands of people. Public safety considerations greatly limit clinical
experimentation on this organism and thus development of theoretical tools to
analyze the capabilities of this pathogen is of utmost importance. Here, we
report the first genome-scale metabolic model of Yersinia pestis biovar
Mediaevalis based both on its recently annotated genome, and physiological and
biochemical data from literature. Our model demonstrates excellent agreement
with Y. pestis known metabolic needs and capabilities. Since Y. pestis is a
meiotrophic organism, we have developed CryptFind, a systematic approach to
identify all candidate cryptic genes responsible for known and theoretical
meiotrophic phenomena. In addition to uncovering every known cryptic gene for
Y. pestis, our analysis of the rhamnose fermentation pathway suggests that betB
is the responsible cryptic gene. Despite all of our medical advances, we still
do not have a vaccine for bubonic plague. Recent discoveries of antibiotic
resistant strains of Yersinia pestis coupled with the threat of plague being
used as a bioterrorism weapon compel us to develop new tools for studying the
physiology of this deadly pathogen. Using our theoretical model, we can study
the cells phenotypic behavior under different circumstances and identify
metabolic weaknesses which may be harnessed for the development of
therapeutics. Additionally, the automatic identification of cryptic genes
expands the usage of genomic data for pharmaceutical purposes.


  We obtain an asymptotic formula for the fourth moment of quadratic Dirichlet
$L$--functions over $\mathbb{F}_q[x]$, as the base field $\mathbb{F}_q$ is
fixed and the genus of the family goes to infinity. According to conjectures of
Andrade and Keating, we expect the fourth moment to be asymptotic to $q^{2g+1}
P(2g+1)$ up to an error of size $o(q^{2g+1})$, where $P$ is a polynomial of
degree $10$ with explicit coefficients. We prove an asymptotic formula with the
leading three terms, which agrees with the conjectured result.