  Monolayer transition metal dichalcogenides (TMDs) have been in focus of
current research, among others due to their remarkable exciton landscape
consisting of bright and dark excitonic states. Although dark excitons are not
directly visible in optical spectra, they have a large impact on exciton
dynamics and hence their understanding is crucial for potential TMD-based
applications. Here, we study brightening mechanisms of dark excitons via
interaction with phonons and in-plane magnetic fields. We show clear signatures
of momentum- and spin-dark excitons in WS$_2$, WSe$_2$ and MoS$_2$, while the
photoluminescence of MoSe$_2$ is only determined by the bright exciton. In
particular, we reveal the mechanism behind the brightening of states that are
both spin- \textit{and} momentum-dark in MoS$_2$. Our results are in good
agreement with recent experiments and contribute to a better microscopic
understanding of the exciton landscape in TMDs.


  We prove an integrability criterion of order 3 for a homogeneous potential of
degree -1 in the plane. Still, this criterion depends on some integer and it is
impossible to apply it directly except for families of potentials whose
eigenvalues are bounded. To address this issue, we use holonomic and asymptotic
computations with error control of this criterion and apply it to the potential
of the form V(r,\theta)=r^{-1} h(\exp(i\theta)) with h a polynomial of degree
less than 3. We find then all meromorphically integrable potentials of this
form.


  What should researchers do when their baseline model is refuted? We provide
four constructive answers. First, researchers can measure the extent of
falsification. To do this, we consider continuous relaxations of the baseline
assumptions of concern. We then define the falsification frontier: The smallest
relaxations of the baseline model which are not refuted. This frontier provides
a quantitative measure of the extent of falsification. Second, researchers can
present the identified set for the parameter of interest under the assumption
that the true model lies somewhere on this frontier. We call this the
falsification adaptive set. This set generalizes the standard baseline estimand
to account for possible falsification. Third, researchers can present the
identified set for a specific point on this frontier. Finally, as a sensitivity
analysis, researchers can present identified sets for points beyond the
frontier. To illustrate these four ways of salvaging falsified models, we study
overidentifying restrictions in two instrumental variable models: a homogeneous
effects linear model, and heterogeneous effect models with either binary or
continuous outcomes. In the linear model, we consider the classical
overidentifying restrictions implied when multiple instruments are observed. We
generalize these conditions by considering continuous relaxations of the
classical exclusion restrictions. By sufficiently weakening the assumptions, a
falsified baseline model becomes non-falsified. We obtain analogous results in
the heterogeneous effect models, where we derive identified sets for marginal
distributions of potential outcomes, falsification frontiers, and falsification
adaptive sets under continuous relaxations of the instrument exogeneity
assumptions. We illustrate our results in four different empirical
applications.


  We propose an birefringence route to perfect electromagnetic (EM) wave
tunneling through a metallic film which relies on homogeneous birefringent
coatings with moderate and positive parameters only. EM transparency is
achieved in such an birefringent-metal-birefringent (BMB) structure for both
polarizations and over nearly all incident angles. The stringent restrictions
in conventional dielectric-metal-dielectric media, i.e., dielectrics with
extremely negative permittivity, high magnetic field and polarization
dependence (only for TE waves), are not required in our method. The criterion
for perfect transmission is obtained by analyzing the effective medium theory
and EM fields of such a birefringent structure. The solutions hold for lossless
and lossy cases in a quite large frequency range.


  We propose a simple model for studying small-x physics in which we take only
the collinearly enhanced part of leading and subleading kernels, for all
possible transverse momentum orderings. The small-x equation reduces to a
second order differential equation in t=log k^2/Lambda^2 space, whose
perturbative and strong-coupling features are investigated both analytically
and numerically. For two-scale processes, we clarify the transition mechanism
between the perturbative, non Regge regime and the strong-coupling Pomeron
behavior.


  We study the fully-packed dimer model on the bilayer square lattice with
fugacity equal to $z$ ($1$) for inter-layer (intra-layer) dimers, and
intra-layer interaction $V$ between neighbouring parallel dimers on any
elementary plaquette in either layer. For a range of not-too-large $z> 0$ and
repulsive interactions $0< V < V_s$ (with $V_s \approx 2.1$), we demonstrate
the existence of a {\em bilayer Coulomb phase} with purely dipolar two-point
functions, {\em i.e.}, without the power-law columnar order that characterizes
the usual Coulomb phase of square and honeycomb lattice dimer models. The
transition line $z_{c}(V)$ separating this bilayer Coulomb phase from a
large-$z$ disordered phase is argued to be in the inverted Kosterlitz-Thouless
universality class. Additionally, we argue for the possibility of a tricritical
point at which the bilayer Coulomb phase, the large-$z$ disordered phase and
the large-$V$ staggered phase meet in the large-$z$, large-$V$ part of the
phase diagram. In contrast, for the attractive case with $ V_{cb} < V \leq 0$
($V_{cb} \approx -1.2$), we argue that any $z > 0$ destroys the power-law
correlations of the $z=0$ decoupled layers, and leads immediately to a
short-range correlated state, albeit with a slow crossover for small $|V|$. For
$V_{c} < V < V_{cb}$ ($V_{c} \approx -1.55$), we predict that any small nonzero
$z$ immediately gives rise to long-range {\em bilayer columnar order} although
the $z=0$ decoupled layers remain power-law correlated in this regime; this
implies a non-monotonic $z$ dependence of the columnar order parameter for
fixed $V$ in this regime. This bilayer columnar ordered state is separated from
the large-$z$ disordered state by a line of Ashkin-Teller transitions $z_{\rm
AT}(V)$.


  We propose an interferometric scheme based on an untrapped nano-object
subjected to gravity. The motion of the center of mass (c.m.) of the free
object is coupled to its internal spin system magnetically, and a free flight
scheme is developed based on coherent spin control. The wavepacket of the test
object, under a spin-dependent force, may then be delocalized to a macroscopic
scale. A gravity induced dynamical phase (accrued solely on the spin state, and
measured through a Ramsey scheme) is used to reveal the above spatially
delocalised superposition of the spin-nano-object composite system that arises
during our scheme. We find a remarkable immunity to the motional noise in the
c.m. (initially in a thermal state with moderate cooling), and also a dynamical
decoupling nature of the scheme itself. Together they secure a high visibility
of the resulting Ramsey fringes. The mass independence of our scheme makes it
viable for a nano-object selected from an ensemble with a high mass
variability. Given these advantages, a quantum superposition with $100$ nm
spatial separation for a massive object of $10^9$ amu is achievable
experimentally, providing a route to test postulated modifications of quantum
theory such as continuous spontaneous localisation.


  Numerous reports claim that quantum advantage, which should emerge as a
direct consequence of the advent of quantum computers, will herald a new era of
chemical research because it will enable scientists to perform the kinds of
quantum chemical simulations that have not been possible before. Such
simulations on quantum computers, promising a significantly greater accuracy
and speed, are projected to exert a great impact on the way we can probe
reality, predict the outcomes of chemical experiments, and even drive design of
drugs, catalysts, and materials. In this work we review the current status of
quantum hardware and algorithm theory and examine whether such popular claims
about quantum advantage are really going to be transformative. We go over
subtle complications of quantum chemical research that tend to be overlooked in
discussions involving quantum computers. We estimate quantum computer resources
that will be required for performing calculations on quantum computers with
chemical accuracy for several types of molecules. In particular, we directly
compare the resources and timings associated with classical and quantum
computers for the molecules H$_2$ for increasing basis set sizes, and Cr$_2$
for a variety of complete active spaces (CAS) within the scope of the CASCI and
CASSCF methods. The results obtained for the chromium dimer enable us to
estimate the size of the active space at which computations of non-dynamic
correlation on a quantum computer should take less time than analogous
computations on a classical computer. Using this result, we speculate on the
types of chemical applications for which the use of quantum computers would be
both beneficial and relevant to industrial applications in the short term.


  The $\Delta$-edge stability number ${\rm es}_{\Delta}(G)$ of a graph $G$ is
the minimum number of edges of $G$ whose removal results in a subgraph $H$ with
$\Delta(H) = \Delta(G)-1$. Sets whose removal results in a subgraph with
smaller maximum degree are called mitigating sets. It is proved that there
always exists a mitigating set which induces a disjoint union of paths of order
$2$ or $3$. Minimum mitigating sets which induce matchings are characterized.
It is proved that to obtain an upper bound of the form ${\rm es}_{\Delta}(G)
\leq c |V(G)|$ for an arbitrary graph $G$ of given maximum degree $\Delta$,
where $c$ is a given constant, it suffices to prove the bound for
$\Delta$-regular graphs. Sharp upper bounds of this form are derived for
regular graphs. It is proved that if $\Delta(G) \geq\frac{|V(G)|-2}{3}$ or the
induced subgraph on maximum degree vertices has a $\Delta(G)$-edge coloring,
then ${\rm es}_{\Delta}(G) \le |V(G)|/2$.


  The realization of an efficient quantum optical interface for multi-qubit
systems is an outstanding challenge in science and engineering. Using two atoms
in individually-controlled optical tweezers coupled to a nanofabricated
photonic crystal cavity, we demonstrate entanglement generation, fast
non-destructive readout, and full quantum control of atomic qubits. The
entangled state is verified in free space after being transported away from the
cavity by encoding the qubits into long-lived states and using dynamical
decoupling. Our approach bridges quantum operations at an optical link and in
free space with a coherent one-way transport, potentially enabling an
integrated optical interface for atomic quantum processors.


  It has long been known that cylindrical algebraic decompositions (CADs) can
in theory be used for robot motion planning. However, in practice even the
simplest examples can be too complicated to tackle. We consider in detail a
"Piano Mover's Problem" which considers moving an infinitesimally thin piano
(or ladder) through a right-angled corridor.
  Producing a CAD for the original formulation of this problem is still
infeasible after 25 years of improvements in both CAD theory and computer
hardware. We review some alternative formulations in the literature which use
differing levels of geometric analysis before input to a CAD algorithm. Simpler
formulations allow CAD to easily address the question of the existence of a
path. We provide a new formulation for which both a CAD can be constructed and
from which an actual path could be determined if one exists, and analyse the
CADs produced using this approach for variations of the problem.
  This emphasises the importance of the precise formulation of such problems
for CAD. We analyse the formulations and their CADs considering a variety of
heuristics and general criteria, leading to conclusions about tackling other
problems of this form.


  We present BVRI light curves of the afterglow of GRB000301C, one of the
brightest ever detected at a day time scale interval after GRB trigger. The
monitoring started 1.5 days after the GRB and ended one month later. Inspection
of the extremely well sampled R band light curve and comparison with BVI data
has revealed a complex behavior, with a long term flux decrease and various
short time scale features superimposed. These are uncommon among other observed
afterglows, and might trace either intrinsic variability within the
relativistic shock (re-acceleration and re-energization) or inhomogeneities in
the medium in which the shock propagates.


  We study the problem of deep joint source-channel coding (D-JSCC) for
correlated image sources, where each source is transmitted through a noisy
independent channel to the common receiver. In particular, we consider a pair
of images captured by two cameras with probably overlapping fields of view
transmitted over wireless channels and reconstructed in the center node. The
challenging problem involves designing a practical code to utilize both source
and channel correlations to improve transmission efficiency without additional
transmission overhead. To tackle this, we need to consider the common
information across two stereo images as well as the differences between two
transmission channels. In this case, we propose a deep neural networks solution
that includes lightweight edge encoders and a powerful center decoder. Besides,
in the decoder, we propose a novel channel state information aware cross
attention module to highlight the overlapping fields and leverage the relevance
between two noisy feature maps.Our results show the impressive improvement of
reconstruction quality in both links by exploiting the noisy representations of
the other link. Moreover, the proposed scheme shows competitive results
compared to the separated schemes with capacity-achieving channel codes.


  We propose a theoretical model of the light-induced director slippage effect.
In this effect the bulk director reorientation contributes to the surface
director reorientation It is found that the director and ellipticity profiles,
obtained in the geometric optics approximation, are dependent on the
ellipticity of the incident light wave. The director distribution is spatially
modulated in linearly polarized light but grows monotonically in circularly
polarized light. The surface director deviation has been examined, and
comparison made with existing experimental data, which then permits the
magnitude of the orientational nonlinearity coefficient to be calculated.


  We continue to explore the consequences of the recently discovered Minkowski
space structure of the Higgs potential in the two-Higgs-doublet model. Here, we
focus on the vacuum properties. The search for extrema of the Higgs potential
is reformulated in terms of 3-quadrics in the 3+1-dimensional Minkowski space.
We prove that 2HDM cannot have more than two local minima in the orbit space
and that a twice-degenerate minimum can arise only via spontaneous violation of
a discrete symmetry of the Higgs potential. Investigating topology of the
3-quadrics, we give concise criteria for existence of non-contractible paths in
the Higgs orbit space. We also study explicit symmetries of the Higgs
potential/lagrangian and their spontaneous violation from a wider perspective
than usual.


  Self-organization properties of sustained magnetized plasma are applied to
selected solar data to understand solar magnetic fields. Torsional oscillations
are speed-up and slow-down bands of the azimuthal flow that correlate with the
solar cycle, and they imply the existence of a symmetric solar dynamo with a
measured polar flux of 3x10^14 Wb. It is shown that the solar dynamo is thin
(~0.1 Mm gradient scale size) and powerful (~10^23 W). These properties are
found from the amplitude of the torsional oscillations and the relationship of
their velocity contours to solar magnetograms supports the result. The dynamo
has enough power to heat the chromosphere and to power the corona and the solar
wind. The dynamo also causes a rigid rotation of the heliosphere out to at
least the corona and the relationship of the rotation of the corona to solar
magnetograms supports this result as well. The thin solar dynamo sustains a
thin stable minimum energy state that seems to be covering most of the solar
surface just below the photosphere. The magnetic field lines of the minimum
energy state should be parallel to the solar surface and rotate with distance
from the surface with 2{\pi} radians of rotation in ~1 Mm Resistive diffusion
helps to push the magnetic fields to the surface and the global magnetic
structure (GMS) seems to lose {\pi} radians every 11 years, causing the
observed 180 degree flipping of the solar magnetic field. The thin sheets of
magnetized plasma in solar prominences may be the lost thin sheets of the GMS.
For completeness, the formation of sunspots, CMEs and flares is discussed.


  CP-violation in the Higgs sector remains a possible source of the baryon
asymmetry of the universe. Recent differential measurements of signed angular
distributions in Higgs boson production provide a general experimental probe of
the CP structure of Higgs boson interactions. We interpret these measurements
using the Standard Model Effective Field Theory and show that they do not
distinguish the various CP-violating operators that couple the Higgs and gauge
fields. However, the constraints can be sharpened by measuring additional
CP-sensitive observables and exploiting phase-space-dependent effects. Using
these observables, we demonstrate that perturbatively meaningful constraints on
CP-violating operators can be obtained at the LHC with luminosities of
${\cal{O}}$(100/fb). Our results provide a roadmap to a global Higgs boson
coupling analysis that includes CP-violating effects.


  As Deep Neural Networks (DNNs) are considered the state-of-the-art in many
classification tasks, the question of their semantic generalizations has been
raised. To address semantic interpretability of learned features, we introduce
a novel idea of classification by re-generation based on variational
autoencoder (VAE) in which a separate encoder-decoder pair of VAE is trained
for each class. Moreover, the proposed architecture overcomes the scalability
issue in current DNN networks as there is no need to re-train the whole network
with the addition of new classes and it can be done for each class separately.
We also introduce a criterion based on Kullback-Leibler divergence to reject
doubtful examples. This rejection criterion should improve the trust in the
obtained results and can be further exploited to reject adversarial examples.


  We discuss the free cyclic submodules over an associative ring $R$ with
unity. Special attention is paid to those, which are generated by outliers.
This paper describes all orbits of such submodules in the ring of lower
triangular $3$x$3$ matrices over a field $F$ under the action of the general
linear group. Besides rings with outliers generating free cyclic submodules,
there are also rings with outliers generating only torsion cyclic submodules
and without any outliers. We give examples of all cases.


  In this paper, we will present several new congruences involving binomial
coefficients under integer moduli, which are the continuation of the previous
two work by Cai \textit{et al.} (2002, 2007).


  Language model capabilities predictably improve from scaling a model's size
and training data. Motivated by this, increasingly large language models have
been trained, yielding an array of impressive capabilities. Yet these models
are vulnerable to adversarial prompts, such as "jailbreaks" that hijack models
to perform undesired behaviors, posing a significant risk of misuse. Prior work
indicates that computer vision models become more robust with model and data
scaling, raising the question: does language model robustness also improve with
scale? We study this question empirically, finding that larger models respond
substantially better to adversarial training, but there is little to no benefit
from model scale in the absence of explicit defenses.


  The hydration phase diagram for sodium cobalt oxyhydrate, Na0.3CoO2*yH2O
(y=0, 0.6, 1.3), was determined as a function of relative humidity at 298K. It
is found that greater than 75% relative humidity is needed for complete
hydration of anhydrous Na0.3CoO2 to the superconducting phase Na0.3CoO2*1.3H2O.
Dehydration studies show that a minimum of 43% relative humidity is needed to
maintain the stability of the fully hydrated superconducting phase. The
intermediate hydrate, Na0.3CoO2*0.6H2O, is stable between 10% and 50% relative
humidity on hydration, and 35% to 0% relative humidity on dehydration.


  We use first principles density functional theory to investigate the
softening of polar phonon modes in rutile TiO$_2$ under tensile (110)-oriented
strain. We show that the system becomes unstable against a ferroelectric
distortion with polarization along (110) for experimentally accessible strain
values. The resulting polarization, estimated from the Born effective charges,
even exceeds the bulk polarization of BaTiO$_3$. Our calculations demonstrate
the different strain dependence of polar modes polarized along (110) and (001)
directions, and we discuss the possibility of strain engineering the
polarization direction, and the resulting dielectric and piezoelectric
response, in thin films of TiO$_2$ grown on suitable substrates.


  In the reversible Schrodinger-Newton equation a complex Newton coupling
G*exp(-i*alpha) is proposed in place of G. The equation becomes irreversible
and all initial one-body states are expected to converge to solitonic
stationary states. This feature is verified numerically. For two-body solutions
we point out that an effective Newtonian interaction is induced by the
imaginary mean-fields as if they were real. The effective strength of such
induced gravity depends on the local wave functions of the participating
distant bodies.


  The measurement of the charge asymmetry in top quark pair events with highly
Lorentz-boosted top quarks decaying to a single lepton and jets is presented.
The analysis is performed using proton-proton collisions at $\sqrt{s}$ = 13 TeV
with the CMS detector at the LHC and corresponding to an integrated luminosity
of 138 fb$^{-1}$. The selection is optimized for top quarks produced with large
Lorentz boosts, resulting in nonisolated leptons and overlapping jets. The top
quark charge asymmetry is measured for events with a $\mathrm{t\bar{t}}$
invariant mass larger than 750 GeV and corrected for detector and acceptance
effects using a binned maximum likelihood fit. The measured top quark charge
asymmetry of (0.42 $_{-0.69}^{+0.64}$)% is in good agreement with the standard
model prediction at next-to-next-to-leading order in quantum chromodynamic
perturbation theory with next-to-leading-order electroweak corrections. The
result is also presented for two invariant mass ranges, 750-900 and $\gt$ 900
GeV.


  Starting from a discussion of the black hole information loss paradox, we
argue that Primordial Black Hole Remnants (PBHRs) of Planck mass, should they
exist, are a plausible candidate for dark energy. We call this proposal the
PBHR model. We also find an instability of de Sitter space towards a space with
power-law accelerating expansion, the time dependence of the cosmological
constant and the resulting form of the metric, and propose that this is the
dominant instability of de Sitter space. We conjecture that a similar
instability does not afflict a space sourced by PBHRs. We find that the PBHR
model satisfies the upper limits on $\beta$, the fraction of the Universe's
mass in PBHs at their formation time, for a fair range of initial PBH masses.
We derive information on the present day gravitational wave background due to
the PBHs in our scenario.


  We study the spectral statistics of primary operators in the recently
formulated ensemble average of Narain's family of free boson conformal field
theories, which provides an explicit (though exotic) example of an averaged
holographic duality. In particular we study moments of the partition function
by explicit computation of higher-degree Eisenstein series. This describes the
analog of wormhole contributions coming from a sum of over geometries in the
dual theory of "U(1) gravity" in AdS$_3$. We give an exact formula for the
two-point correlation function of the density of primary states. We compute the
spectral form factor and show that the wormhole sum reproduces precisely the
late time plateau behaviour related to the discreteness of the spectrum. The
spectral form factor does not exhibit a linear ramp.


  The single degenerate (SD) model, one of the leading models for the
progenitors of Type Ia supernovae (SNe Ia), predicts that there should be
binary companions that survive the supernova explosion which, in principle,
should be detectable in the Galaxy. The discovery of such surviving companions
could therefore provide conclusive support for the SD model. Several years ago,
a new type of mysterious variables was discovered, the so-called blue
large-amplitude pulsators (BLAPs). Here we show that all the properties of
BLAPs can be reasonably well reproduced if they are indeed such surviving
companions, in contrast to other proposed channels. This suggests that BLAPs
could potentially be the long-sought surviving companions of SNe Ia. Our model
also predicts a new channel for forming single hot subdwarf stars, consistent
with a small group in the present hot-subdwarf-star sample.


  A second quantised theory of electrons and positrons in a deep time-dependent
potential well is discussed. It is shown that positron production from the well
is a natural consequence of Dirac's hole theory when the strength of the well
becomes supercritical. A formalism is developed whereby the amplitude for
emission of a positron of a given momentum can be calculated. The difference
between positron production and electron-positron pair production is
demonstrated. Considerations of the vacuum charge and of Levinson's theorem are
required for a full description of the problem.


  The asymmetry in the energy spectra between mirror nuclei (the Thomas-Ehrman
shifts) around $^{16}$O is investigated from a phenomenological viewpoint. The
recent data on proton-rich nuclei indicates that the residual nuclear
interaction is reduced for the loosely bound s-orbit by as much as 30%, which
originates in the broad radial distribution of the proton single-particle wave
function.


  We prove a quantum-ergodicity theorem on large graphs, for eigenfunctions of
Schr\"odinger operators in a very general setting. We consider a sequence of
finite graphs endowed with discrete Schr\"odinger operators, assumed to have a
local weak limit. We assume that our graphs have few short loops, in other
words that the limit model is a random rooted tree endowed with a random
discrete Schr\"odinger operator. We show that absolutely continuous spectrum
for the infinite model, reinforced by a good control of the moments of the
Green function, imply "quantum ergodicity", a form of spatial delocalization
for eigenfunctions of the finite graphs approximating the tree. This roughly
says that the eigenfunctions become equidistributed in phase space. Our result
applies in particular to graphs converging to the Anderson model on a regular
tree, in the r\'egime of extended states studied by Klein and Aizenman-Warzel.


  Assessment of job performance, personalized health and psychometric measures
are domains where data-driven and ubiquitous computing exhibits the potential
of a profound impact in the future. Existing techniques use data extracted from
questionnaires, sensors (wearable, computer, etc.), or other traits, to assess
well-being and cognitive attributes of individuals. However, these techniques
can neither predict individual's well-being and psychological traits in a
global manner nor consider the challenges associated to processing the data
available, that is incomplete and noisy. In this paper, we create a benchmark
for predictive analysis of individuals from a perspective that integrates:
physical and physiological behavior, psychological states and traits, and job
performance. We design data mining techniques as benchmark and uses real noisy
and incomplete data derived from wearable sensors to predict 19 constructs
based on 12 standardized well-validated tests. The study included 757
participants who were knowledge workers in organizations across the USA with
varied work roles. We developed a data mining framework to extract the
meaningful predictors for each of the 19 variables under consideration. Our
model is the first benchmark that combines these various instrument-derived
variables in a single framework to understand people's behavior by leveraging
real uncurated data from wearable, mobile, and social media sources. We verify
our approach experimentally using the data obtained from our longitudinal
study. The results show that our framework is consistently reliable and capable
of predicting the variables under study better than the baselines when
prediction is restricted to the noisy, incomplete data.


  The research interest in specialized hardware accelerators for deep neural
networks (DNN) spikes recently owing to their superior performance and
efficiency. However, today's DNN accelerators primarily focus on accelerating
specific "kernels" such as convolution and matrix multiplication, which are
vital but only part of an end-to-end DNN-enabled application. Meaningful
speedups over the entire application often require supporting computations that
are, while massively parallel, ill-suited to DNN accelerators. Integrating a
general-purpose processor such as a CPU or a GPU incurs significant data
movement overhead and leads to resource under-utilization on the DNN
accelerators.
  We propose Simultaneous Multi-mode Architecture (SMA), a novel architecture
design and execution model that offers general-purpose programmability on DNN
accelerators in order to accelerate end-to-end applications. The key to SMA is
the temporal integration of the systolic execution model with the GPU-like SIMD
execution model. The SMA exploits the common components shared between the
systolic-array accelerator and the GPU, and provides lightweight
reconfiguration capability to switch between the two modes in-situ. The SMA
achieves up to 63% performance improvement while consuming 23% less energy than
the baseline Volta architecture with TensorCore.


  Vulnerabilities of complex networks have became a trend topic in complex
systems recently due to its real world applications. Most real networks tend to
be very fragile to high betweenness adaptive attacks. However, recent
contributions have shown the importance of interconnected nodes in the
integrity of networks and module-based attacks have appeared promising when
compared to traditional malicious non-adaptive attacks. In the present work we
deeply explore the trade-off associated with attack procedures, introducing a
generalized robustness measure and presenting an attack performance index that
takes into account both robustness of the network against the attack and the
run-time needed to obtained the list of targeted nodes for the attack. Besides,
we introduce the concept of deactivation point aimed to mark the point at which
the network stops to function properly. We then show empirically that
non-adaptive module-based attacks perform better than high degree and
betweenness adaptive attacks in networks with well defined community structures
and consequent high modularity.


  We review some important results from the PHENIX experiment at RHIC. They
were obtained in a unique environment for studying QCD bulk matter at
temperatures and densities that sur- pass the limits where hadrons exist as
individual entities, so raising to prominence the quark- gluon degrees of
freedom. We present measurements of nuclear modification factors for neutral
pions, light favors (strange hadrons), direct-photons and non-photonic
electrons from decays of particles carrying charm or beauty quarks. We
interpret the large suppression of hadron produc- tion at high transverse
momenta as resulting from a large energy loss by the precursor parton on its
path through the dense matter, primarily driven by gluon radiation. This dense
QCD matter responds to energy loss in a pattern consistent with that expected
from a hydrodynamic fluid. Further, its elliptic flow measurements suggest that
the hadronization of bulk partonic matter exhibits collectivity with effective
partonic degrees of freedom. The results are shown as a function of transverse
momentum, centrality in different collision systems and energies.


  Opaque detectors are a recently proposed novel detector concept where an
opaque scintillator aligned with wavelength-shifting fibers is used to enable
the discrimination of electron neutrinos and antineutrinos with a rather low
energy threshold. In this work, we investigate the potential effects of the
enhanced detection capabilities of the opaque detectors in accelerator neutrino
experiments. Focusing on the energy threshold, energy resolution, detection
efficiency and background suppression in the analysis of electron-like events,
we determine whether using opaque detectors could lead to improvements in the
CP violation and light sterile neutrino searches in the future accelerator
neutrino experiments. We also identify the minimum requirements for the opaque
detectors to reach the designated physics goals in the simulated experiments.
We find that a 75.6% fraction of $\delta_{CP}$ values could be reached for CP
violation discovery by 3$\sigma$ confidence level or better when opaque
detectors of 120 kton and 130 kton fiducial masses are used together with
neutrino beams from J-PARC and MOMENT, respectively, whereas near detectors
placed about 250 m from sources are sufficient to exclude the gallium anomaly
at 2$\sigma$ confidence level.


  Brighton [Bri13] proved the Liouville theorem for bounded harmonic functions
on weighted manifolds satisfying non-negative curvature dimension condition,
i.e. $CD(0,\infty).$ In this paper, we provide a new proof of this result by
using the reverse Poincar\'e inequality. Moreover, we adopt this approach to
prove the Liouville theorem for bounded harmonic functions on graphs satisfying
the $CD(0,\infty)$ condition.


  Machine learning-based automatic scoring can be challenging if students'
responses are unbalanced across scoring categories, as it introduces
uncertainty in the machine training process. To meet this challenge, we
introduce a novel text data augmentation framework using GPT-4, a generative
large language model, specifically tailored for unbalanced datasets in
automatic scoring. Our experimental dataset comprised student-written responses
to two science items. We crafted prompts for GPT-4 to generate responses
resembling student-written answers, particularly for the minority scoring
classes, to augment the data. We then finetuned DistillBERT for automatic
scoring based on the augmented and original datasets. Model performance was
assessed using accuracy, precision, recall, and F1 score. We incorporate varied
amounts of augmented data to examine scoring performance, and our findings
revealed remarkedly improved model performance. The average maximum increase
observed across two items is: 3.5% for accuracy, 30.6% for precision, 21.1% for
recall, and 24.2% for F1 score. Notably, using just 5% of the augmented data
led to substantial improvements: 2.6%, 29.2%, 15.1%, and 19.6%. Interestingly,
the extent of improvement varied depending on specific datasets. Moreover, we
found that a varying amount of augmented data (5%-40%) was needed to obtain a
stable improvement. We also compare models trained with GPT-4 augmented data
and those trained with additional student-written responses. The findings
indicate that former ones match or even exceed the performance of the latter.
Specifically, there is an average difference of 1.7%, 1.9%, 11.0%, and 7.8% for
four metrics separately. This research underscores the potential and
effectiveness of data augmentation techniques utilizing GPT-4 in addressing
unbalanced datasets within automated assessment.


  In this paper, we propose a path following replicator dynamic, and
investigate its potentials in uncovering the underlying cluster structure of a
graph. The proposed dynamic is a generalization of the discrete replicator
dynamic. The replicator dynamic has been successfully used to extract dense
clusters of graphs; however, it is often sensitive to the degree distribution
of a graph, and usually biased by vertices with large degrees, thus may fail to
detect the densest cluster. To overcome this problem, we introduce a dynamic
parameter, called path parameter, into the evolution process. The path
parameter can be interpreted as the maximal possible probability of a current
cluster containing a vertex, and it monotonically increases as evolution
process proceeds. By limiting the maximal probability, the phenomenon of some
vertices dominating the early stage of evolution process is suppressed, thus
making evolution process more robust. To solve the optimization problem with a
fixed path parameter, we propose an efficient fixed point algorithm. The time
complexity of the path following replicator dynamic is only linear in the
number of edges of a graph, thus it can analyze graphs with millions of
vertices and tens of millions of edges on a common PC in a few minutes.
Besides, it can be naturally generalized to hypergraph and graph with edges of
different orders. We apply it to four important problems: maximum clique
problem, densest k-subgraph problem, structure fitting, and discovery of
high-density regions. The extensive experimental results clearly demonstrate
its advantages, in terms of robustness, scalability and flexility.


  Recent progress in the applications of covariant density functional theory
(CDFT) to the description of the spectroscopy of the heaviest nuclei is
reviewed. The analysis of quasiparticle spectra in actinides and the heaviest A
~ 250 nuclei provides a measure of the accuracy of the description of
single-particle energies in CDFT and an additional constraint for the choice of
effective interactions for the description of superheavy nuclei. The response
of these nuclei to the rotation is rather well described by cranked
relativistic Hartree+Bogoliubov theory and it serves as a supplementary tool in
configuration assignment in odd-mass nuclei. A systematic analysis of the
fission barriers with allowance for triaxial deformation shows that covariant
density functional theory is able to describe fission barriers on a level of
accuracy comparable with the best phenomenological macroscopic+microscopic
approaches.


  Semi-supervised text classification-based paradigms (SSTC) typically employ
the spirit of self-training. The key idea is to train a deep classifier on
limited labeled texts and then iteratively predict the unlabeled texts as their
pseudo-labels for further training. However, the performance is largely
affected by the accuracy of pseudo-labels, which may not be significant in
real-world scenarios. This paper presents a Rank-aware Negative Training (RNT)
framework to address SSTC in learning with noisy label manner. To alleviate the
noisy information, we adapt a reasoning with uncertainty-based approach to rank
the unlabeled texts based on the evidential support received from the labeled
texts. Moreover, we propose the use of negative training to train RNT based on
the concept that ``the input instance does not belong to the complementary
label''. A complementary label is randomly selected from all labels except the
label on-target. Intuitively, the probability of a true label serving as a
complementary label is low and thus provides less noisy information during the
training, resulting in better performance on the test data. Finally, we
evaluate the proposed solution on various text classification benchmark
datasets. Our extensive experiments show that it consistently overcomes the
state-of-the-art alternatives in most scenarios and achieves competitive
performance in the others. The code of RNT is publicly available
at:https://github.com/amurtadha/RNT.


  The software package developed in the MS thesis research implements functions
for the intelligent guessing of polynomial sequence formulas based on
user-defined expected sequence factors of the input coefficients. We present a
specialized hybrid approach to finding exact representations for polynomial
sequences that is motivated by the need for an automated procedures to discover
the precise forms of these sums based on user guidance, or intuition, as to
special sequence factors present in the formulas. In particular, the package
combines the user input on the expected special sequence factors in the
polynomial coefficient formulas with calls to the existing functions as
subroutines that then process formulas for the remaining sequence terms already
recognized by these packages.
  The factorization-based approach to polynomial sequence recognition is unique
to this package and allows the search functions to find expressions for
polynomial sums involving Stirling numbers and other special triangular
sequences that are not readily handled by other software packages. In contrast
to many other sequence recognition and summation software, the package not
provide an explicit proof, or certificate, for the correctness of these
sequence formulas -- only computationally guided educated guesses at a complete
identity generating the sequence over all $n$. The thesis contains a number of
concrete, working examples of the package that are intended to both demonstrate
its usage and to document its current sequence recognition capabilities.


  The convergence of Boltzmann Fokker Planck solution can become arbitrarily
slow with iterative procedures like source iteration. This paper derives and
investigates a nonlinear diffusion acceleration scheme for the solution of the
Boltzmann Fokker Planck equation in slab geometry. This method is a
conventional high order low order scheme with a traditional
diffusion-plus-drift low-order system. The method, however, differs from the
earlier variants as the definition of the low order equation, which is adjusted
according to the zeroth and first moments of the Boltzmann Fokker Planck
equation. For the problems considered, we observe that the NDA-accelerated
solution follows the unaccelerated well and provides roughly an order of
magnitude savings in iteration count and runtime compared to source iteration.


  Using a complex model potential, we have calculated the total, integrated
elastic, momentum transfer, absorption, and differential cross sections for
positrons scattered from molecular hydrogen. The widely available software
package GAUSSIAN is used to generate the radial electronic charge density of
the molecule which is used to produce the interaction potentials. The quasifree
absorption potential, previously developed and used for positron-atom
scattering, is extended to positron scattering from molecular targets. It is
shown that this model potential approach produces accurate results even into
the low-energy regime.


  We use the nudged elastic band method from computational chemistry to analyze
high-dimensional data. Our approach is inspired by Morse theory, and as output
we produce an increasing sequence of small cell complexes modeling the dense
regions of the data. We test the method on data sets arising in social networks
and in image processing. Furthermore, we apply the method to identify new
topological structure in a data set of optical flow patches.


  This report presents azimuthal dependent and polarisation dependent x-ray
resonant magnetic scattering at the Ir L3 edge for the bilayered iridate
compound, Sr3Ir2O7. Two magnetic wave vectors, k1=(1/2,1/2,0) and
k2=(1/2,-1/2,0), result in domains of two symmetry-related G-type
antiferromagnetic structures, noted A and B, respectively. These domains are
approximately 0.02 mm^2 and are independent of the thermal history. An
understanding of this key aspect of the magnetism is necessary for an overall
picture of the magnetic behaviour in this compound. Azimuthal and polarisation
dependence of magnetic reflections, relating to both magnetic wave vectors,
show that the Ir magnetic moments in the bilayer compound are oriented along
the c axis. This contrasts with single layer Sr2IrO4 where the moments are
confined to the ab plane.


  We classify the topology of quench dynamics by homotopy groups. A relation
between the topological invariant of a post-quench order parameter and the
topological invariant of a static Hamiltonian is shown in one, two and three
dimensions. The mid-gap states in the entanglement spectrum of the post-quench
state reveal its topological nature. When a trivial quantum state under a
sudden quench to a Chern insulator, the mid-gap states in entanglement spectrum
form rings. These rings are analogous to the boundary Fermi rings in the Hopf
insulators. Finally, we show a post-quench state in 3+1 dimensions can be
characterized by the second Chern number. The number of Dirac cones in the
entanglement spectrum is equal to the second Chern number.


  The dynamics of a nucleate cell in shear flow is of great relevance in cancer
cells and circulatory tumor cells where they dominate the dynamics of blood.
Buoyed by the success of Giant Unilamellar vesicles in explaining the dynamics
of anucleate cells such as Red Blood cells, compound vesicles have been
suggested as a simple model for nucleate cells. In this work, a theoretical
model is presented to study the deformation and dynamics of a compound vesicle
in linear shear flow using small deformation theory and spherical harmonics
with higher order approximation to the membrane forces. The results indicate
that size of the inner vesicle does not affect the tank-treading dynamics of
the outer vesicle. The inner vesicle admits a greater inclination angle than
the outer vesicle. However, the transition to trembling-swinging and tumbling
is significantly affected. The inner and outer vesicle exhibit identical
dynamics in most of the modified viscosity-shear rate parameter space. At
moderate size of the inner vesicle, a swinging mode is observed for the inner
vesicle while the outer vesicle exhibits tumbling. The inner vesicle also
exhibits modification of the TU mode to IUS-Intermediate Tumbling Swinging
mode. Moreover, synchronization of the two vesicles at higher inner vesicle
size and a capillary number sensitive motion at small inner vesicle size is
observed in the tumbling regime. These results are in accordance with the few
experimental observations reported by Levant and Steinberg2014. A reduction in
the inclination angle is observed with an increase in size of the inner vesicle
when the inner vesicle is a solid inclusion. Additionally a very elaborate
phase diagram is presented in the modified viscosity-shear rate parameter
space, which could be tested in future experiments or numerical simulations


  The tensor polarization of particles and nuclei becomes constant in the
coordinate system rotating with the same angular velocity as the spin and
rotates in the lab frame with the above angular velocity. The general equation
defining the time dependence of the tensor polarization is derived. An explicit
form of dynamics of this polarization is found in the case when the initial
polarization is axially symmetric.


  We extend the Lifshitz theory of the Casimir force to the case of two
parallel magnetic metal plates possessing a spatially nonlocal dielectric
response. By solving Maxwell equations in the configuration of an
electromagnetic wave incident on the boundary plane of a magnetic metal
semispace, the exact surface impedances are expressed in terms of its magnetic
permeability and longitudinal and transverse dielectric functions. This allows
application of the Lifshitz theory with reflection coefficients written via the
surface impedances for calculation of the Casimir pressure between magnetic
metal (Ni) plates whose dielectric responses are described by the alternative
nonlocal response functions introduced for the case of nonmagnetic media. It is
shown that at separations from 100 to 800~nm the Casimir pressures computed
using the alternative nonlocal and local plasma response functions differ by
less than 1\%. At separations of a few micrometers, the predictions of these
two approaches differ between themselves and between that one obtained using
the Drude function by several tens of percent. We also compute the gradient of
the Casimir force between Ni-coated surfaces of a sphere and a plate using the
alternative nonlocal response functions and find a very good agreement with the
measurement data. Implications of the obtained results determined by the
off-shell quantum fluctuations to a resolution of long-standing problems in the
Casimir physics are discussed.


  Silicon monoxide maser emission has been detected in the circumstellar
envelopes of many evolved stars in various vibrationally-excited rotational
transitions. It is considered a good tracer of the wind dynamics close to the
photosphere of the star. We have investigated the polarization morphology in
the circumstellar envelope of an AGB star, R Cas. We mapped the linear and
circular polarization of SiO masers in the v=1, J=1-0 transition. The linear
polarization is typically a few tens of percent while the circular polarization
is a few percent. The fractional polarization tends to be higher for emission
of lower total intensity. We found that, in some isolated features the
fractional linear polarization appears to exceed 100%. We found the Faraday
rotation is not negligible but is ~15 deg., which could produce small scale
structure in polarized emission whilst total intensity is smoother and partly
resolved out. The polarization angles vary considerably from feature to feature
but there is a tendency to favour the directions parallel or perpendicular to
the radial direction with respect to the star. In some features, the
polarization angle abruptly flips 90 deg. We found that our data are in the
regime where the model of Goldreich et al (1973) can be applied and the
polarization angle flip is caused when the magnetic field is at close to 55
deg. to the line of sight. The polarization angle configuration is consistent
with a radial magnetic field although other configurations are not excluded.


  We propose a quantum charging scheme fueled by measurements on ancillary
qubits serving as disposable chargers. A stream of identical qubits are
sequentially coupled to a quantum battery of $N+1$ levels and measured by
projective operations after joint unitary evolutions of optimized intervals. If
charger qubits are prepared in excited state and measured on ground state, then
their excitations (energy) can be near-perfectly transferred to battery by
iteratively updating the optimized measurement intervals. Starting from its
ground state, the battery could be constantly charged to an even higher energy
level. Starting from a thermal state, the battery could also achieve a
near-unit ratio of ergotropy and energy through less than $N$ measurements,
when a population inversion is realized by measurements. If charger qubits are
prepared in ground state and measured on excited state, useful work extracted
by measurements alone could transform the battery from a thermal state to a
high-ergotropy state before the success probability vanishes. Our operations in
charging are more efficient than those without measurements and do not invoke
the initial coherence in both battery and chargers. Particularly, our finding
features quantum measurement in shaping nonequilibrium systems.


  For applications to quasi-exactly solvable Schr\"odinger equations in quantum
mechanics, we establish the general conditions that have to be satisfied by the
coefficients of a second-order differential equation with at most $k+1$
singular points in order that this equation has particular solutions which are
$n$th-degree polynomials. In a first approach, we extend the Nikiforov-Uvarov
method, which was devised to deal with hypergeometric-type equations (i.e., for
$k=2$), and show that the conditions involve $k-2$ integration constants. In a
second approach, we consider the functional Bethe ansatz method in its most
general form. Comparing the two approaches, we prove that under the assumption
that the roots of the polynomial solutions are real and distinct, the $k-2$
integration constants of the extended Nikiforov-Uvarov method can be expressed
as linear combinations of monomial symmetric polynomials in those roots,
corresponding to partitions into no more than two parts.


  This paper presents the PlanMiner-N algorithm, a domain learning technique
based on the PlanMiner domain learning algorithm. The algorithm presented here
improves the learning capabilities of PlanMiner when using noisy data as input.
The PlanMiner algorithm is able to infer arithmetic and logical expressions to
learn numerical planning domains from the input data, but it was designed to
work under situations of incompleteness making it unreliable when facing noisy
input data. In this paper, we propose a series of enhancements to the learning
process of PlanMiner to expand its capabilities to learn from noisy data. These
methods preprocess the input data by detecting noise and filtering it and study
the learned action models learned to find erroneous preconditions/effects in
them. The methods proposed in this paper were tested using a set of domains
from the International Planning Competition (IPC). The results obtained
indicate that PlanMiner-N improves the performance of PlanMiner greatly when
facing noisy input data.


  Time-dependent Ginzburg-Landau model with noise for neutral s-wave
superconductor is derived from real-time finite-temperature quantum field
theory by integrating over fermions in the density matrix propagator. Quantum
decoherence due to environment of pair breaking fluctuations is identified.
Dynamics is described by Langevin equation in the near critical regime and by a
nonlinear Schrodinger equation at zero temperature.


  We present a measurement of the correlated $b\bar{b}$ production cross
section. The data used in this analysis were taken with the upgraded CDF
detector (CDF II) at the Fermilab Tevatron collider, and correspond to an
integrated luminosity of 742 pb$^{-1}$. We utilize muon pairs with invariant
mass $5 \leq m_{\mu\mu} \leq 80
  \gevcc$ produced by $b\bar{b}$ double semileptonic decays. For muons with
$p_T \geq 3 \gevc$ and $|\eta| \leq 0.7$, that are produced by $b$ and
$\bar{b}$ quarks with $p_T \geq 2 \gevc$ and $|y| \leq 1.3$, we measure
$\sigma_{b\to\mu,\bar{b}\to \mu}= 1549 \pm 133$ pb. We compare this result with
theoretical predictions and previous measurements. We also report the
measurement of $\sigma_{c\to\mu,\bar{c}\to \mu}$, a by-product of the study of
the background to $b\bar{b}$ production.


  Circular Rydberg atoms (CRAs), i.e., Rydberg atoms with maximal orbital
momentum, ideally combine long coherence times and strong interactions, a key
property of quantum systems, in particular for the development of quantum
technologies. However, the dipole-dipole interaction between CRAs has not been
observed so far. We report the measurement and characterization of the resonant
dipole-dipole interaction between two CRAs, individually trapped in optical
tweezers, and find excellent agreement with theoretical predictions. We
demonstrate a dynamic control over the strength of the interaction by tuning
the orientation of an electric field. We use the interaction between the CRAs
as a meter for the interatomic distance, and record the relative motion between
two atoms in their traps. This motion, that we induce through the interaction
between Rydberg levels with permanent electric dipoles, transiently populated
during the preparation of the circular states, is a signature of spin-motion
coupling.


  Stackelberg games are a classic example of bilevel optimization problems,
which are often encountered in game theory and economics. These are complex
problems with a hierarchical structure, where one optimization task is nested
within the other. Despite a number of studies on handling bilevel optimization
problems, these problems still remain a challenging territory, and existing
methodologies are able to handle only simple problems with few variables under
assumptions of continuity and differentiability. In this paper, we consider a
special case of a multi-period multi-leader-follower Stackelberg competition
model with non-linear cost and demand functions and discrete production
variables. The model has potential applications, for instance in aircraft
manufacturing industry, which is an oligopoly where a few giant firms enjoy a
tremendous commitment power over the other smaller players. We solve cases with
different number of leaders and followers, and show how the entrance or exit of
a player affects the profits of the other players. In the presence of various
model complexities, we use a computationally intensive nested evolutionary
strategy to find an optimal solution for the model. The strategy is evaluated
on a test-suite of bilevel problems, and it has been shown that the method is
successful in handling difficult bilevel problems.


  Solid-state devices made from correlated oxides such as perovskite nickelates
are promising for neuromorphic computing by mimicking biological synaptic
function. However, comprehending dopant action at the nanoscale poses a
formidable challenge to understanding the elementary mechanisms involved. Here,
we perform operando infrared nanoimaging of hydrogen-doped correlated
perovskite, neodymium nickel oxide (H-NdNiO3) devices and reveal how an applied
field perturbs dopant distribution at the nanoscale. This perturbation leads to
stripe phases of varying conductivity perpendicular to the applied field, which
define the macroscale electrical characteristics of the devices. Hyperspectral
nano-FTIR imaging in conjunction with density functional theory calculations
unveil a real-space map of multiple vibrational states of H-NNO associated with
OH stretching modes and their dependence on the dopant concentration. Moreover,
the localization of excess charges induces an out-of-plane lattice expansion in
NNO which was confirmed by in-situ - x-ray diffraction and creates a strain
that acts as a barrier against further diffusion. Our results and the
techniques presented here hold great potential to the rapidly growing field of
memristors and neuromorphic devices wherein nanoscale ion motion is
fundamentally responsible for function.


  The temperature and field dependence of reversible magnetization have been
measured on a YBa$_2$Cu$_3$O$_{7-\delta}$ single crystal at six different
doping concentrations. It is found that the data above 2 T can be described by
the scaling law based on the GL-LLL (lowest Landau level approach based on
Ginzburg-Landau theory) critical fluctuation theory yielding the values of the
slope of upper critical field $-\mathrm{d}H_{\mathrm{c2}}(T)/\mathrm{d}T$ near
$T_\mathrm{c}$. This set of values is self-consistent with that obtained in
doing the universal scaling for the six samples. Based on a simple
Ginzburg-Landau approach, we determined the doping dependence of the coherence
length $\xi$ which behaves in a similar way as that determined from $\xi= \hbar
v_\mathrm{F}/E_\mathrm{sc}$ with $E_\mathrm{sc}$ the superconducting energy
scale. Our results may suggest a growing coherence length towards more
underdoping.


  This paper presents distributed adaptive algorithms based on the conjugate
gradient (CG) method for distributed networks. Both incremental and diffusion
adaptive solutions are all considered. The distributed conventional (CG) and
modified CG (MCG) algorithms have an improved performance in terms of mean
square error as compared with least-mean square (LMS)-based algorithms and a
performance that is close to recursive least-squares (RLS) algorithms . The
resulting algorithms are distributed, cooperative and able to respond in real
time to changes in the environment.


  We present a theory that is able to account quantitatively for the surface
and interfacial tensions of different electrolyte solutions. It is found that
near the interface, ions can be separated into two classes: the kosmotropes and
the chaotropes. While the kosmotropes remain hydrated near the interface and
are repelled from it, the chaotropes loose their hydration sheath and become
adsorbed to the surface. The anionic adsorption is strongly correlated with the
Jones-Dole viscosity B-coefficient. Both hydration and polarizability must be
taken into account to obtain a quantitative agreement with the experiments. To
calculate the excess interfacial tension of the oil-electrolyte interface, the
dispersion interactions must also be included. The theory can also be used to
calculate the surface and the interfacial tensions of acid solutions,
predicting a strong surface adsorption of hydronium ion.


  This paper presents a study of the interaction between Alfv\'en modes and
zonal structures, considering a realistic ASDEX Upgrade equilibrium. The
results of gyrokinetic simulations with the global, electromagnetic,
particle-in-cell code ORB5 are presented, where the modes are driven unstable
by energetic particles with a bump-on-tail equilibrium distribution function,
with radial density gradient. Two regimes have been observed: at low energetic
particles concentration, the Alfv\'en mode saturates at much higher level in
presence of zonal structures; on the other hand at high energetic particles
concentration the difference is less pronounced. The former regime is
characterized by the zonal structure (identified as an energetic particle
driven geodesic acoustic mode), being more unstable than the Alfv\'en mode. In
the latter regime the Alfv\'en mode is more unstable than the zonal structure.
The theoretical explanation is given in terms of a 3-wave coupling of the
energetic particle driven geodesic acoustic mode and Alfv\'en mode, mediated by
the curvature-pressure coupling term of the energetic particles.


  With the increasing use of black-box Machine Learning (ML) techniques in
critical applications, there is a growing demand for methods that can provide
transparency and accountability for model predictions. As a result, a large
number of local explainability methods for black-box models have been developed
and popularized. However, machine learning explanations are still hard to
evaluate and compare due to the high dimensionality, heterogeneous
representations, varying scales, and stochastic nature of some of these
methods. Topological Data Analysis (TDA) can be an effective method in this
domain since it can be used to transform attributions into uniform graph
representations, providing a common ground for comparison across different
explanation methods.
  We present a novel topology-driven visual analytics tool, Mountaineer, that
allows ML practitioners to interactively analyze and compare these
representations by linking the topological graphs back to the original data
distribution, model predictions, and feature attributions. Mountaineer
facilitates rapid and iterative exploration of ML explanations, enabling
experts to gain deeper insights into the explanation techniques, understand the
underlying data distributions, and thus reach well-founded conclusions about
model behavior. Furthermore, we demonstrate the utility of Mountaineer through
two case studies using real-world data. In the first, we show how Mountaineer
enabled us to compare black-box ML explanations and discern regions of and
causes of disagreements between different explanations. In the second, we
demonstrate how the tool can be used to compare and understand ML models
themselves. Finally, we conducted interviews with three industry experts to
help us evaluate our work.


  We use high-resolution angle-resolved photoemission spectroscopy (ARPES) and
density functional theory (DFT) calculations to study the electronic and
magnetic properties of La2Ni7, an itinerant magnetic system with a series of
three magnetic transition temperatures upon cooling, which end in a weak
itinerant antiferromagnetic (wAFM) ground state. Our APRES data reveal several
electron and hole pockets that have hexagonal symmetry near the $\Gamma$ point.
We observe significant reconstruction of the band structure upon successive
magnetic transitions at T1 = 61 K, T2 = 57 K and T3 = 42 K. The experimental
data are in a reasonable agreement with DFT calculations, demonstrating their
applicability to itinerant antiferromagnet systems. Our results detail the
effects of magnetic ordering on the electronic structure in a Ni-based weak
antiferromagnet.


  We show that the class of CAT(0) spaces is closed under suitable conformal
changes. In particular, any CAT(0) space admits a large variety of non-trivial
deformations.


  We present the most general actions of a single scalar field and two scalar
fields coupled to gravity, consistent with second order field equations in four
dimensions, possessing local scale invariance. We apply two different methods
to arrive at our results. One method, Ricci gauging, was known to the
literature and we find this to produce the same result for the case of one
scalar field as a more efficient method presented here. However, we also find
our more efficient method to be much more general when we consider two scalar
fields. Locally scale invariant actions are also presented for theories with
more than two scalar fields coupled to gravity and we explain how one could
construct the most general actions for any number of scalar fields. Our
generalised scale invariant actions have obvious applications to early universe
cosmology, and include, for example, the Bezrukov-Shaposhnikov action as a
subset.


  A method is reported for creating, generating, and measuring parametrically
shaped pulses for time-bandwidth product >>5, which consists of a parametric
pulse-shaping algorithm, a spatial light modulation system and a single shot
interferometric characterization scheme (SEA TADPOLE) . The utilization of
these tools marks the inception of a new method called SPECIFIC, shaped-pulse
electric-field construction and interferometric characterization, capable of
producing complex shaped laser pulses for coherent control experiments.


  Based on recent COSY-11 results of measurements of total cross sections for
the quasi-free pn-->pn eta reaction we determine the isospin I=0 component of
the total cross section for the NN-->NN eta reaction down to the threshold. We
show that the energy dependence of the total cross section ratios
{\sigma_{I=0}(pn-->pn eta)}/{\sigma(pp-->pp eta)} and \sigma_{I=0}(pn-->pn
eta)}/{\sigma(pn-->d eta)} can be described using the Faldt and Wilkin
analitical parametrization of the nucleon-nucleon final state interaction.


  Few-shot Class-Incremental Learning (FSCIL) presents a unique challenge in
Machine Learning (ML), as it necessitates the Incremental Learning (IL) of new
classes from sparsely labeled training samples without forgetting previous
knowledge. While this field has seen recent progress, it remains an active
exploration area. This paper aims to provide a comprehensive and systematic
review of FSCIL. In our in-depth examination, we delve into various facets of
FSCIL, encompassing the problem definition, the discussion of the primary
challenges of unreliable empirical risk minimization and the
stability-plasticity dilemma, general schemes, and relevant problems of IL and
Few-shot Learning (FSL). Besides, we offer an overview of benchmark datasets
and evaluation metrics. Furthermore, we introduce the Few-shot
Class-incremental Classification (FSCIC) methods from data-based,
structure-based, and optimization-based approaches and the Few-shot
Class-incremental Object Detection (FSCIOD) methods from anchor-free and
anchor-based approaches. Beyond these, we present several promising research
directions within FSCIL that merit further investigation.


  Millimeter-wave (mmWave) communications provide access to spectra with
bandwidths and in abundance. However, the high susceptibility of mmWave to
blockage imposes crucial challenges, especially to low-latency services. In
this paper, a novel intelligent reflecting surface (IRS) assisted mmWave scheme
is proposed to overcome the impact of blockage. New methods are developed to
minimize the user power for a multi-user mmWave system by jointly optimizing
individual device power, multi-user detection matrix and passive beamforming,
subject to delay requirements. An alternating optimization framework is
delivered so that the joint optimization problem can be decomposed into three
subproblems iteratively optimized till convergence. In particular, closed-form
expressions are devised for the update of the powers and multi-user detection
vectors. The configuration of the IRS is formulated as a sum-of-inverse
minimization (SIMin) fractional programming problem and solved by developing a
new solution based on the alternating direction method of multipliers (ADMM).
The configuration is also interpreted as a latency residual maximization
problem, and solved efficiently by designing a new complex circle manifold
optimization (CCMO) method. Numerical results corroborate the feasibility and
effectiveness of our algorithms in terms of power saving, as compared with an
existing semidefinite relaxation technique.


  To ensure agreement between theoretical calculations and experimental data,
parameters to selected nuclear physics models, are perturbed, and fine-tuned in
nuclear data evaluations. This approach assumes that the chosen set of models
accurately represents the `true' distribution. Furthermore, the models are
chosen globally, indicating their applicability across the entire energy range
of interest. However, this approach overlooks uncertainties inherent in the
models themselves. As a result, achieving satisfactory fits to experimental
data within certain energy regions for specific channels becomes challenging,
as the evaluation is constrained by the deficiencies of the selected models. In
this work, we propose that instead of selecting globally a winning model set
and proceeding with it as if it was the `true' model set, we instead, take a
weighted average over multiple models within a BMA framework, each weighted by
its posterior probability. The method involves executing a set of TALYS
calculations by randomly varying multiple nuclear physics models and their
parameters to yield a vector of calculated observables. Next, the likelihood
function was computed at each considered incident energy point for selected
cross sections by comparing the vector of calculated observables with that of
the selected differential experimental data. As the cross sections and elastic
angular distributions were updated locally on a per-energy-point basis, the
approach typically results in discontinuities or "kinks" in the curves, and
these were addressed using spline interpolation. The proposed BMA method was
applied to the evaluation of proton induced reactions on $^{58}$Ni within 1 -
100 MeV. The results demonstrate favorable comparisons with experimental data,
as well as with the TENDL-2021 evaluation.


  Previous results indicate that while chaos can lead to substantial entropy
production, thereby maximizing dynamical entanglement, this still falls short
of maximality. Random Matrix Theory (RMT) modeling of composite quantum
systems, investigated recently, entails an universal distribution of the
eigenvalues of the reduced density matrices. We demonstrate that these
distributions are realized in quantized chaotic systems by using a model of two
coupled and kicked tops. We derive an explicit statistical universal bound on
entanglement, that is also valid for the case of unequal dimensionality of the
Hilbert spaces involved, and show that this describes well the bounds observed
using composite quantized chaotic systems such as coupled tops.


  Open quantum systems have been shown to host a plethora of exotic dynamical
phases. Measurement-induced entanglement phase transitions in monitored quantum
systems are a striking example of this phenomena. However, naive realizations
of such phase transitions requires an exponential number of repetitions of the
experiment which is practically unfeasible on large systems. Recently, it has
been proposed that these phase transitions can be probed locally via entangling
reference qubits and studying their purification dynamics. In this work, we
leverage modern machine learning tools to devise a neural network decoder to
determine the state of the reference qubits conditioned on the measurement
outcomes. We show that the entanglement phase transition manifests itself as a
stark change in the learnability of the decoder function. We study the
complexity and scalability of this approach and discuss how it can be utilized
to detect entanglement phase transitions in generic experiments.


  We predict the existence of chiral spin waves collective modes in a
two-dimensional Fermi liquid with the Rashba or Dresselhaus spin-orbit
coupling. Starting from the phenomenological Landau theory, we show that the
long-wavelength dynamics of magnetization is governed by the Klein- Gordon
equations. The standing-wave solutions of these equations describe "particles"
with effective masses, whose magnitudes and signs depend on the strength of the
electron-electron interaction. The spectrum of the spin-chiral modes for
arbitrary wavelengths is determined from the Dyson equation for the interaction
vertex. We propose to observe spin-chiral modes via microwave absorption of
standing waves confined by an in-plane profile of the spin-orbit splitting.


  Quantum phase interference and spin-parity effects are studied in biaxial
molecular magnets in a magnetic field at an arbitrarily directed angle. The
calculations of the ground-state tunnel splitting are performed on the basis of
the instanton technique in the spin-coherent-state path-integral
representation, and complemented by exactly numerical diagonalization. Both the
Wentzel-Kramers-Brillouin exponent and the preexponential factor are obtained
for the entire region of the direction of the field. Our results show that the
tunnel splitting oscillates with the field for the small field angle, while for
the large field angle the oscillation is completely suppressed. This distinct
angular dependence, together with the dependence of the tunnel splitting on the
field strengh, provide an independent test for spin-parity effects in biaxial
molecular magnets. The analytical results for the molecular Fe$_{8}$ magnet,
are found to be in good areement with the numerical simulations, which suggests
that even the molecular magnet with total spin S=10 is large enough to be
treated as a giant spin system.


  We propose a comprehensive framework exploiting the polarization sensitive
array (PLA) to improve the physical layer security of wireless communications.
More specifically, the polarization difference among signals is utilized to
improve the secrecy rate of wireless communications, especially when these
signals are spatially indistinguishable. We firstly investigate the PLA based
secure communications for point-to-point wireless systems from the perspectives
of both total power minimization and secrecy rate maximization. We then take a
step further to apply the PLA based secure beamforming designs to relaying
networks. The secrecy rate maximization for relaying networks is discussed in
detail under both the perfect channel state information and the polarization
sensitive array pointing error. In the later case, a robust scheme to achieve
secure communications for relaying networks is proposed. Simulation results
show that the proposed PLA based algorithms achieve lower total power
consumption and better security performance compared with the conventional
scalar array designs, especially under challenging environments where all
received signals at destination are difficult to be distinguished from the
spatial domain.


  In these lectures, given in '96 summer schools in Beg-Rohu (France) and
Budapest, I discuss the fundamental principles of thermodynamic and dynamic
Monte Carlo methods in a simple light-weight fashion. The keywords are MARKOV
CHAINS, SAMPLING, DETAILED BALANCE, A PRIORI PROBABILITIES, REJECTIONS,
ERGODICITY, "FASTER THAN THE CLOCK ALGORITHMS".
  The emphasis is on ORIENTATION, which is difficult to obtain (all the
mathematics being simple). A firm sense of orientation helps to avoid getting
lost, especially if you want to leave safe trodden-out paths established by
common usage.
  Even though I remain quite basic (and, I hope, readable), I make every effort
to drive home the essential messages, which are easily explained: the
crystal-clearness of detail balance, the main problem with Markov chains, the
great algorithmic freedom, both in thermodynamic and dynamic Monte Carlo, and
the fundamental differences between the two problems.


  We give an explicit formula for the projectively invariant quantization map
between the space of symbols of degree three and the space of third-order
linear differential operators, both viewed as modules over the group of
diffeomorphisms and the Lie algebra of vector fields on a manifold.


  We show that integrable vertex and RSOS models with trigonometric Boltzmann
weights and appropriate inhomogeneities provide a convenient lattice
regularization for massive field theories and for the recently studied massless
field theories that interpolate between two non trivial conformal field
theories. Massive and massless S matrices are computed from the lattice Bethe
ansatz.


  This is a sequel to our previous work on LHC phenomenology of the type II
seesaw model in the nondegenerate case. In this work, we further study the pair
and associated production of the neutral scalars H^0/A^0. We restrict ourselves
to the so-called negative scenario characterized by the mass order
M_{H^{\pm\pm}}>M_{H^\pm}>M_{H^0/A^0}, in which the H^0/A^0 production receives
significant enhancement from cascade decays of the charged scalars
H^{\pm\pm},~H^\pm. We consider three important signal
channels---b\bar{b}\gamma\gamma, b\bar{b}\tau^+\tau^-,
$b\bar{b}\ell^+\ell^-\cancel{E}_T$---and perform detailed simulations. We find
that at the 14 TeV LHC with an integrated luminosity of 3000/fb, a 5\sigma mass
reach of 151, 150, and 180 GeV, respectively, is possible in the three channels
from the pure Drell-Yan H^0A^0 production, while the cascade-decay-enhanced
H^0/A^0 production can push the mass limit further to 164, 177, and 200 GeV.
The neutral scalars in the negative scenario are thus accessible at LHC run II.


  Engineering the electromagnetic environment of a quantum emitter gives rise
to a plethora of exotic light-matter interactions. In particular, photonic
lattices can seed long-lived atom-photon bound states inside photonic band
gaps. Here we report on the concept and implementation of a novel microwave
architecture consisting of an array of compact, high-impedance superconducting
resonators forming a 1 GHz-wide pass band, in which we have embedded two
frequency-tuneable artificial atoms. We study the atom-field interaction and
access previously unexplored coupling regimes, in both the single- and
double-excitation subspace. In addition, we demonstrate coherent interactions
between two atom-photon bound states, in both resonant and dispersive regimes,
that are suitable for the implementation of SWAP and CZ two-qubit gates. The
presented architecture holds promise for quantum simulation with tuneable-range
interactions and photon transport experiments in nonlinear regime.


  The pressure dependence of the inverse square of the magnetic penetration
depth $\lambda ^{- 2}$ in $\kappa$-$($ET$)_2$Cu(NCS)$_2$ was measured by
Larkin, et al According to the paper, $\lambda ^{- 2}$ behaves differently
under low pressure and under high pressure. Under low pressure, the development
of $\lambda ^{- 2}$ just below $T=T_c$ is rapid compared to the case under high
pressure. Moreover, $T_c$ in $\kappa$-$($ET$)_2$Cu(NCS)$_2$ increases under
c-axis pressure up to 1kbar and decreases under higher pressure, while $T_c$
decreases monotonically under the hydrostatic pressure, or under the uniaxial
pressure parallel to other axes. In order to explain these behaviors, we
calculate $T_c$ and $\lambda ^{- 2}$ for $\kappa$-$($ET$)_2$Cu(NCS)$_2$ under
pressure. In the calculation we mainly use an effective dimer Hubbard model. In
conclusion, the behavior of $\lambda ^{- 2}$ results from three effects: the
variation of the bandwidth of quasiparticles, the change of the Fermi surfaces,
and the effect of vertex correction. This is a different mechanism from that of
$\lambda ^{- 2}$ in cuprates which we observe when the doping varies. Moreover,
we explain the increase in $T_c$ under the c-axis pressure up to 1kbar and the
decrease in $T_c$ over 1kbar from our calculation. With the increase in the
c-axis pressure, two competitive effects with respect to $T_c$ appear. One is
the approach of the Fermi surface to the antiferromagnetic Brillouin zone
boundary, and the other is the suppression of the electron correlation. Under
the low c-axis pressure, $T_c$ increases since the former effect is dominant.
On the other hand, $T_c$ decreases since the latter effect is dominant under
the high c-axis pressure.


  We study here the $\Lambda_b^0 \to J/\psi p \pi^-$ reaction in analogy to the
$\Lambda^0_b \to J/\psi p K^-$ one, and we note that in both decays there is a
sharp structure (dip or peak) in the $J/\psi p$ mass distribution around $4450$
MeV, which is associated in the $\Lambda^0_b \to J/\psi p K^-$ experiment to an
exotic pentaquark baryonic state, although in $\Lambda_b^0 \to J/\psi p \pi^-$
it shows up with relatively low statistics. We analyze the $\Lambda^0_b \to
J/\psi p \pi^-$ interaction along the same lines as the $\Lambda^0_b \to J/\psi
p K^-$ one, with the main difference stemming from the reduced Cabibbo strength
in the former and the consideration of the $\pi^-p$ final state interaction
instead of the $K^-p$ one. We find that with a minimal input, introducing the
$\pi^-p$ and $J/\psi p$ interaction in $S$-wave with realistic interactions,
and the empirical $P$-wave and $D$-wave contributions, one can accomplish a
qualitative description of the $\pi^-p$ and $J/\psi p$ mass distributions. More
importantly, the peak structure followed by a dip of the experimental $J/\psi
p$ mass distribution is reproduced with the same input as used to describe the
data of $\Lambda^0_b\rightarrow J/\psi p K^-$ reaction. The repercussion for
the triangular singularity mechanism, invoked in some works to explain the
pentaquark peak, is discussed.


  We give a hybrid two stage design which can be useful to estimate the
reliability of a parallel-series and/or by duality a series-parallel system,
when the component reliabilities are unknown as well as the total numbers of
units allowed to be tested in each subsystem. When a total sample size is fixed
large, asymptotic optimality is proved systematically and validated via Monte
Carlo simulation.


  The low-energy physics of systems with spontaneous symmetry breaking is
governed by the associated Nambu-Goldstone (NG) bosons. While NG bosons in
Lorentz-invariant systems are well understood, the precise characterization of
their number and dispersion relations in a general quantum many-body system is
still an open problem. An inequality relating the number of NG bosons and their
dispersion relations to the number of broken symmetry generators was found by
Nielsen and Chadha. In this paper, we give a presumably first example of a
system in which the Nielsen-Chadha inequality is actually not saturated. We
suggest that the number of NG bosons is exactly equal to the number of broken
generators minus the number of pairs of broken generators whose commutator has
a nonzero vacuum expectation value. This naturally leads us to a proposal for a
different classification of NG bosons.


  Safety monitoring of power operations in power stations is crucial for
preventing accidents and ensuring stable power supply. However, conventional
methods such as wearable devices and video surveillance have limitations such
as high cost, dependence on light, and visual blind spots. WiFi-based human
pose estimation is a suitable method for monitoring power operations due to its
low cost, device-free, and robustness to various illumination conditions.In
this paper, a novel Channel State Information (CSI)-based pose estimation
framework, namely PowerSkel, is developed to address these challenges.
PowerSkel utilizes self-developed CSI sensors to form a mutual sensing network
and constructs a CSI acquisition scheme specialized for power scenarios. It
significantly reduces the deployment cost and complexity compared to the
existing solutions. To reduce interference with CSI in the electricity
scenario, a sparse adaptive filtering algorithm is designed to preprocess the
CSI. CKDformer, a knowledge distillation network based on collaborative
learning and self-attention, is proposed to extract the features from CSI and
establish the mapping relationship between CSI and keypoints. The experiments
are conducted in a real-world power station, and the results show that the
PowerSkel achieves high performance with a PCK@50 of 96.27%, and realizes a
significant visualization on pose estimation, even in dark environments. Our
work provides a novel low-cost and high-precision pose estimation solution for
power operation.


  We show that the free Banach lattice $\mathrm{FBL}(A)$ may be constructed as
the completion of $\mathrm{FVL}(A)$ with respect to the maximal lattice
seminorm $\nu$ on $\mathrm{FVL}(A)$ with $\nu(a)\le 1$ for all $a\in A$. We
present a similar construction for the free Banach lattice $\mathrm{FBL}[E]$
generated by a Banach space $E$.


  In this paper, we study equivariant cohomolgy theory of Hom Lie Triple
Systems. Using this cohomology, we study 1-parameter formal deformation and
central extensions of Hom Lie Triple Systems in the equivariant context.


  In 2017 an estimated 3 billion people used polluting fuels and technologies
as their primary cooking solution, with 3.8 million deaths annually attributed
to household exposure to the resulting fine particulate matter air pollution.
Currently, health burdens are calculated using aggregations of fuel types, e.g.
solid fuels, as country-level estimates of the use of specific fuel types, e.g.
wood and charcoal, are unavailable. To expand the knowledge base about impacts
of household air pollution on health, we develop and implement a Bayesian
hierarchical model, based on Generalized Dirichlet Multinomial distributions,
that jointly estimates non-linear trends in the use of eight key fuel types,
overcoming several data-specific challenges including missing or combined fuel
use values. We assess model fit using within-sample predictive analysis and an
out-of-sample prediction experiment to evaluate the model's forecasting
performance.


  In a previous paper (arXiv:1608.02262), we used computer-assisted methods to
find explicit expressions for the moments of the size of a uniform random
(n,n+1)-core partition with distinct parts. In particular, we conjectured that
the distribution is asymptotically normal. However, our analysis hinged on a
characterization of (n,n+1)-core partitions given by Straub, which is not
readily generalized to other families of simultaneous core partitions.
  In another paper (arXiv:1611.05775) with Doron Zeilberger, we made use of the
characterization in terms of posets to analyze (2n+1,2n+3)-core partitions with
distinct parts; here, the distribution was found not to be asymptotically
normal. Our method involved finding recursive structure in the relevant
sequence of posets. We remarked that this method is applicable to other
families of core partitions, provided that one can understand the corresponding
posets.
  Here, we use the poset method (and, as before, a computer) to analyze
(n,dn-1)-core partitions with distinct parts, where d is a natural number.
(This problem was introduced by Straub in arXiv:1601.07161.) We exhibit
formulas for the moments of the size, as functions of d with n fixed, and vice
versa. We conjecture that the distribution is asymptotically normal as n
approaches infinity. Finally, we find formulas for the first few moments, as
functions of both n and d.


  The state-of-the-art linked Gaussian process offers a way to build analytical
emulators for systems of computer models. We generalize the closed form
expressions for the linked Gaussian process under the squared exponential
kernel to a class of Mat\'ern kernels, that are essential in advanced
applications. An iterative procedure to construct linked Gaussian processes as
surrogate models for any feed-forward systems of computer models is presented
and illustrated on a feed-back coupled satellite system. We also introduce an
adaptive design algorithm that could increase the approximation accuracy of
linked Gaussian process surrogates with reduced computational costs on running
expensive computer systems, by allocating runs and refining emulators of
individual sub-models based on their heterogeneous functional complexity.


  We present the transition amplitude for a particle moving in a space with two
times and D space dimensions having a Sp(2,R) local symmetry and an SO(D,2)
rigid symmetry. It was obtained from the BRST-BFV quantization with a unique
gauge choice. We show that by constraining the initial and final points of this
amplitude to lie on some hypersurface of the D+2 space the resulting amplitude
reproduces well known systems in lower dimensions. This provides an alternative
physical interpretation for two times physics which is derived in a single
framework.


  Manipulation of wave-matter interactions in systems with loss and gain have
opened new mechanisms to control wave propagation at will. Metamaterials and
metasurfaces having spatially inhomogeneous loss and gain have been studied in
the past few years by exploiting parity-time (PT) symmetry concepts inspired
from quantum-physics. In this work we theoretically study the control of
light-matter interactions in spatially unbounded metamaterials having a
time-modulated permittivity whose imaginary part is temporally modulated to
induce loss and gain, while the real part stays unchanged. We show both
numerically and theoretically how such temporally modulated multistepped
metamaterials with loss and gain can be equivalent to a temporal effective
metamaterial having an effective permittivity modelled by a step function in
time. Interestingly, it is shown how the amplitude of a monochromatic
electromagnetic wave traveling inside such temporal metamaterials can
experience spatiotemporal decay or amplification depending on the values of
loss and gain added into the system, while its wavenumber is preserved. We
envision that our findings may open new avenues in exploration of potential
applications of temporal metamaterials in signal amplification and loss
mitigation.


  We investigate the fermionic condensate and the vacuum expectation value of
the energy-momentum tensor for a massive spinor field in the geometry of a
straight cosmic string on background of de Sitter spacetime. By using the
Abel-Plana summation formula, we explicitly extract form the expectation values
the contribution associated with purely de Sitter space, remaining the
expectation values induced by the cosmic string. The latter presents
information about de Sitter gravity as well. Because the investigation of the
fermionic quantum fluctuations in de Sitter space have been investigated in
literature, here we are mainly interested in the cosmic string-induced
contributions. For a massless field, the fermionic condensate vanishes and the
presence of the string does not break chiral symmetry of the massless theory.
Unlike to the case of a scalar field, for a massive fermionic field the vacuum
expectation value of the energy-momentum tensor is diagonal and the axial and
radial stresses are equal to the energy density. At large distances from the
string the behavior of the string-induced parts in the vacuum densities is
damping oscillatory with the amplitude decaying as the inverse fourth power of
the distance. This is in contrast to the case of flat spacetime, in which the
string-induced vacuum densities for a massive field decay exponentially with
distance from the string. In the limit of the large curvature radius of de
Sitter space we recover the results for a cosmic string in flat spacetime.


  Deep CCD photometry (NTT, HST data) for the dwarf galaxy in Tucana is
presented, down to V=27.5. Distance modulus and metallicity are derived,
together with an estimate of the age of the galaxy.


  We consider classes of noncompact n-folds with trivial canonical bundle, that
are linear foliations on nonsingular projective varieties, in general without a
projection to the base. We obtain them as first-order deformations of total
spaces of vector bundles on those varieties.


  Large Language Models (LLMs) have performed well on various reasoning tasks,
but their inaccessibility and numerous parameters hinder wide application in
practice. One promising way is distilling the reasoning ability from LLMs to
small models by the generated chain-of-thought reasoning paths. In some cases,
however, LLMs may produce incorrect reasoning chains, especially when facing
complex mathematical problems. Previous studies only transfer knowledge from
positive samples and drop the synthesized data with wrong answers. In this
work, we illustrate the merit of negative data and propose a model
specialization framework to distill LLMs with negative samples besides positive
ones. The framework consists of three progressive steps, covering from training
to inference stages, to absorb knowledge from negative data. We conduct
extensive experiments across arithmetic reasoning tasks to demonstrate the role
of negative data in distillation from LLM.


  Micro-swimmers such as bacteria perform random walks known as run-and-tumbles
to move up chemo-attractant gradients and as a result aggregate with others. It
is also known that such micro-swimmers can self-organize into macroscopic
patterns due to interactions with neighboring cells through the fluidic
environment they live in. While the pattern formation resulting from
chemotactic and hydrodynamic interactions separately and together have been
previously investigated, the effect of the anisotropy in the tumbles of
micro-swimmers has been unexplored. Here we show through linear analysis and
full nonlinear simulations that the slight anisotropy in the individual swimmer
tumbles can alter the collective pattern formation in non-trivial ways. We show
that tumbling anisotropy diminishes the magnitude of the chemotactic aggregates
but may result in more such aggregation peaks.


  We use the Athena hydrodynamics code to model propagating two-dimensional
relativistic jets as approximations to the growth of radio-loud active galactic
nuclei for various input jet velocities and jet-to-ambient matter density
ratios. Using results from these simulations we estimate the changing
synchrotron emission by summing the fluxes from a vertical strip of zones
behind the reconfinement shock, which is nearly stationary, and from which a
substantial portion of the flux variability should arise. We explore a wide
range of time scales by considering two light curves from each simulation; one
uses a relativistic turbulence code with bulk velocities taken from our
simulations as input, while the other uses the bulk velocity data to compute
fluctuations caused by variations in the Doppler boosting due to changes in the
direction and the speed of the flow through all zones in the strip. We then
calculate power spectral densities (PSDs) from the light curves for both
turbulent and bulk velocity origins for variability. The range of the power-law
slopes of the PSDs for the turbulence induced variations is -1.8 to -2.3, while
for the bulk velocity produced variations this range is -2.1 to -2.9; these are
in agreement with most observations. When superimposed, these power spectra
span a very large range in frequency (about five decades), with the turbulent
fluctuations yielding most of the shorter timescale variations and the bulk
flow changes dominating the longer periods.


  Measurement error in observational datasets can lead to systematic bias in
inferences based on these datasets. As studies based on observational data are
increasingly used to inform decisions with real-world impact, it is critical
that we develop a robust set of techniques for analyzing and adjusting for
these biases. In this paper we present a method for estimating the distribution
of an outcome given a binary exposure that is subject to underreporting. Our
method is based on a missing data view of the measurement error problem, where
the true exposure is treated as a latent variable that is marginalized out of a
joint model. We prove three different conditions under which the outcome
distribution can still be identified from data containing only error-prone
observations of the exposure. We demonstrate this method on synthetic data and
analyze its sensitivity to near violations of the identifiability conditions.
Finally, we use this method to estimate the effects of maternal smoking and
opioid use during pregnancy on childhood obesity, two import problems from
public health. Using the proposed method, we estimate these effects using only
subject-reported drug use data and substantially refine the range of estimates
generated by a sensitivity analysis-based approach. Further, the estimates
produced by our method are consistent with existing literature on both the
effects of maternal smoking and the rate at which subjects underreport smoking.