  A theoretical formulation is proposed for forced mass transport by pressure
gradients in a liquid binary mixture around a spherical bubble undergoing
volume oscillations in a sound field. Assuming the impermeability of the bubble
wall to both species, diffusion driven by pressure gradients and classical
Fick-diffusion must cancel at the bubble wall, so that an oscillatory
concentration gradient arises in the vicinity of the bubble. The Peclet number
Pe is generally high in typical situations and Fick diffusion cannot restore
equilibrium immediately, so that an asymptotic average concentration profile
may progressively build up in the liquid over large times. Such a behavior is
reminiscent of the so-called rectified diffusion problem, leading to slow
growth of gas bubble oscillating in a sound field. A rigorous method formerly
proposed by Fyrillas & Szeri (1994) to solve the latter problem is used in this
paper to solve the present one. It is based on splitting the problem into a
smooth part and an oscillatory part. [...] For driving pressures sufficiently
high to yield inertial oscillations of the bubble, the oscillatory problem
predicts a periodic peak excess concentration of the heaviest species at the
bubble wall at each collapse, lingering on several tens of time the
characteristic duration of the bubble rebound. The two effects may compete for
large molecules and practical implications of this segregation phenomenon are
proposed for various processes involving acoustic cavitation.


  We present the G-MAPLE (Generalised Modal Analysis from the Poles of the
Laplace Expansion) software that allows decomposing data depending on a single
parameter (such as time series data) into a set of exponential functions having
complex amplitudes and arguments. The novelty is that G-MAPLE determines the
unknown number of exponentials in the data along with the corresponding complex
amplitudes and arguments.


  We propose a fiber-based environmental sensor that exploits the reflection
phase shift tunability provided by the use of layered coatings composed of
dielectric slabs spaced by conducting membranes. A transfer matrix study is
done in a simplified theoretical model, for which an enhanced sensitivity of
the reflection interference pattern to the output medium is demonstrated, in
the typical refractive index range of liquid media. An experimental
configuration using a cascaded Fabry-Perot microcavity coated by a graphene
oxide/polyethylenimine (GO/PEI) multilayered structure is demonstrated. Its
cost effective chemical production method makes graphene oxide-based hybrid
coatings excellent candidates for future real-life sensing devices.


  We demonstrate targeting and control over spatiotemporal chaos in an optical
feedback loop experiment. Different stationary target patterns are stabilized
in real-time by means of a two dimensional space extended perturbation field
driven by an interfaced computer and applied in real-space to a liquid crystal
display device inserted within a control optical loop. The flexibility of the
system in switching between different target patterns is also demonstrated.


  This commentary considers non-standard analysis and a recently introduced
computational methodology based on the notion of \G1 (this symbol is called
\emph{grossone}). The latter approach was developed with the intention to allow
one to work with infinities and infinitesimals numerically in a unique
computational framework and in all the situations requiring these notions.
Non-standard analysis is a classical purely symbolic technique that works with
ultrafilters, external and internal sets, standard and non-standard numbers,
etc. In its turn, the \G1-based methodology does not use any of these notions
and proposes a more physical treatment of mathematical objects separating the
objects from tools used to study them. It both offers a possibility to create
new numerical methods using infinities and infinitesimals in floating-point
computations and allows one to study certain mathematical objects dealing with
infinity more accurately than it is done traditionally. In these notes, we
explain that even though both methodologies deal with infinities and
infinitesimals, they are independent and represent two different philosophies
of Mathematics that are not in a conflict. It is proved that texts
\cite{Flunks, Gutman_Kutateladze_2008, Kutateladze_2011} asserting that the
\G1-based methodology is a part of non-standard analysis unfortunately contain
several logical fallacies. Their attempt to prove that the \G1-based
methodology is a part of non-standard analysis is similar to trying to show
that constructivism can be reduced to the traditional mathematics.


  Testing, contact tracing, and isolation (TTI) is an epidemic management and
control approach that is difficult to implement at scale because it relies on
manual tracing of contacts. Exposure notification apps have been developed to
digitally scale up TTI by harnessing contact data obtained from mobile devices;
however, exposure notification apps provide users only with limited binary
information when they have been directly exposed to a known infection source.
Here we demonstrate a scalable improvement to TTI and exposure notification
apps that uses data assimilation (DA) on a contact network. Network DA exploits
diverse sources of health data together with the proximity data from mobile
devices that exposure notification apps rely upon. It provides users with
continuously assessed individual risks of exposure and infection, which can
form the basis for targeting individual contact interventions. Simulations of
the early COVID-19 epidemic in New York City prove the concepts. In the
simulations, network DA identifies up to a factor 2 more infections than
contact tracing when both harness the same contact data and diagnostic test
data. This remains true even when only a relatively small fraction of the
population uses network DA. When a sufficiently large fraction of the
population ($\gtrsim 75\%$) uses network DA and complies with individual
contact interventions, targeting contact interventions with network DA reduces
deaths by up to a factor 4 relative to TTI. Network DA can be implemented by
expanding the computational backend of existing exposure notification apps,
thus greatly enhancing their capabilities. Implemented at scale, it has the
potential to precisely and effectively control future epidemics while
minimizing economic disruption.


  In common law jurisdictions, legal practitioners rely on precedents to
construct arguments, in line with the doctrine of \emph{stare decisis}. As the
number of cases grow over the years, prior case retrieval (PCR) has garnered
significant attention. Besides lacking real-world scale, existing PCR datasets
do not simulate a realistic setting, because their queries use complete case
documents while only masking references to prior cases. The query is thereby
exposed to legal reasoning not yet available when constructing an argument for
an undecided case as well as spurious patterns left behind by citation masks,
potentially short-circuiting a comprehensive understanding of case facts and
legal principles. To address these limitations, we introduce a PCR dataset
based on judgements from the European Court of Human Rights (ECtHR), which
explicitly separate facts from arguments and exhibit precedential practices,
aiding us to develop this PCR dataset to foster systems' comprehensive
understanding. We benchmark different lexical and dense retrieval approaches
with various negative sampling strategies, adapting them to deal with long text
sequences using hierarchical variants. We found that difficulty-based negative
sampling strategies were not effective for the PCR task, highlighting the need
for investigation into domain-specific difficulty criteria. Furthermore, we
observe performance of the dense models degrade with time and calls for further
research into temporal adaptation of retrieval models. Additionally, we assess
the influence of different views , Halsbury's and Goodhart's, in practice in
ECtHR jurisdiction using PCR task.


  We study the Thirring model in three spacetime dimensions, by means of Monte
Carlo simulation on lattice sizes 8^3 and 12^3, for numbers of fermion flavors
N_f=2,4,6. For sufficiently strong interaction strength, we find that
spontaneous chiral symmetry breaking occurs for N_f=2,4, in accordance with the
predictions of the Schwinger-Dyson approach. The phase transitions which occur
are continuous and with critical scaling behaviour depending on N_f. For N_f=6
our results are preliminary, and no firm conclusions about the existence or
otherwise of chiral symmetry breaking are possible


  We have developed a simple precipitation route to synthesize magnetite
(Fe3O4) nano-particles with controlled size without any requirement of
calcination step at high temperatures. The study of these nano-particles
indicates an enhancement in saturation magnetization with reduction in size
down to ~10 nm beyond which the magnetization reduces. The latter is attributed
to surface effects becoming predominant as surface to core volume ratio
increases. From the view -point of applications, 10 nm size of magnetite
particles seems to be the optimum.


  This paper considers correlation clustering on unweighted complete graphs. We
give a combinatorial algorithm that returns a single clustering solution that
is simultaneously $O(1)$-approximate for all $\ell_p$-norms of the disagreement
vector; in other words, a combinatorial $O(1)$-approximation of the all-norms
objective for correlation clustering. This is the first proof that minimal
sacrifice is needed in order to optimize different norms of the disagreement
vector. In addition, our algorithm is the first combinatorial approximation
algorithm for the $\ell_2$-norm objective, and more generally the first
combinatorial algorithm for the $\ell_p$-norm objective when $1 < p < \infty$.
It is also faster than all previous algorithms that minimize the $\ell_p$-norm
of the disagreement vector, with run-time $O(n^\omega)$, where $O(n^\omega)$ is
the time for matrix multiplication on $n \times n$ matrices. When the maximum
positive degree in the graph is at most $\Delta$, this can be improved to a
run-time of $O(n\Delta^2 \log n)$.


  Stereo video retargeting aims to resize an image to a desired aspect ratio.
The quality of retargeted videos can be significantly impacted by the stereo
videos spatial, temporal, and disparity coherence, all of which can be impacted
by the retargeting process. Due to the lack of a publicly accessible annotated
dataset, there is little research on deep learning-based methods for stereo
video retargeting. This paper proposes an unsupervised deep learning-based
stereo video retargeting network. Our model first detects the salient objects
and shifts and warps all objects such that it minimizes the distortion of the
salient parts of the stereo frames. We use 1D convolution for shifting the
salient objects and design a stereo video Transformer to assist the retargeting
process. To train the network, we use the parallax attention mechanism to fuse
the left and right views and feed the retargeted frames to a reconstruction
module that reverses the retargeted frames to the input frames. Therefore, the
network is trained in an unsupervised manner. Extensive qualitative and
quantitative experiments and ablation studies on KITTI stereo 2012 and 2015
datasets demonstrate the efficiency of the proposed method over the existing
state-of-the-art methods. The code is available at
https://github.com/z65451/SVR/.


  According to GHO (Global Health Observatory (GHO), the high prevalence of a
large variety of diseases such as Ischaemic heart disease, stroke, lung cancer
disease and lower respiratory infections have remained the top killers during
the past decade.
  The growth in the number of mortalities caused by these disease is due to the
very delayed symptoms'detection. Since in the early stages, the symptoms are
insignificant and similar to those of benign diseases (e.g. the flu ), and we
can only detect the disease at an advanced stage.
  In addition, The high frequency of improper practices that are harmful to
health, the hereditary factors, and the stressful living conditions can
increase the death rates.
  Many researches dealt with these fatal disease, and most of them applied
advantage machine learning models to deal with image diagnosis. However the
drawback is that imagery permit only to detect disease at a very delayed stage
and then patient can hardly be saved.
  In this Paper we present our new approach "DeepLCP" to predict fatal diseases
that threaten people's lives. It's mainly based on raw and heterogeneous data
of the concerned (or under-tested) person. "DeepLCP" results of a combination
combination of the Natural Language Processing (NLP) and the deep learning
paradigm.The experimental results of the proposed model in the case of Lung
cancer prediction have approved high accuracy and a low loss data rate during
the validation of the disease prediction.


  One efficient approach to control chip-wide thermal distribution in
multi-core systems is the optimization of online assignments of tasks to
processing cores. Online task assignment, however, faces several uncertainties
in real-world Systems and does not show a deterministic nature. In this paper,
we consider the operation of a thermal-aware task scheduler, dispatching tasks
from an arrival queue as well as setting the voltage and frequency of the
processing cores to optimize the mean temperature margin of the entire chip
(i.e., cores as well as the NoC routers). We model the decision process of the
task scheduler as a semi-Markov decision problem (SMDP). Then, to solve the
formulated SMDP, we propose two reinforcement learning algorithms that are
capable of computing the optimal task assignment policy without requiring the
statistical knowledge of the stochastic dynamics underlying the system states.
The proposed algorithms also rely on function approximation techniques to
handle the infinite length of the task queue as well as the continuous nature
of temperature readings. Compared to related research, the simulation results
show a nearly 6 Kelvin reduction in system average peak temperature and 66
milliseconds decrease in mean task service time.


  The concept of fluctuation-induced effective interactions is extended to
systems driven out of equilibrium. We compute the forces experienced by
macroscopic objects immersed in a soft material driven by external shaking
sources. We show that, in contrast with equilibrium Casimir forces induced by
thermal fluctuations, their sign, range and amplitude depends on specifics of
the shaking and can thus be tuned. We also comment upon the dispersion of these
shaking-induced forces, and discuss their potential application to phase
ordering in soft-materials.


  Secure compilation aims to build compilation chains that preserve security
properties when translating programs from a source to a target language. Recent
research led to the definition of secure compilation principles that, if met,
guarantee that the compilation chain in hand never violates specific families
of security properties. Still, to the best of our knowledge, no effective
procedure is available to check if a compilation chain meets such requirements.
Here, we outline our ongoing research inspired by translation validation, to
effectively check one of those principles.


  We aim to review the internal structure and dynamics of the Abell 1351
cluster, shown to host a radio halo with a quite irregular shape. Our analysis
is based on radial velocity data for 135 galaxies obtained at the Telescopio
Nazionale Galileo. We combine galaxy velocities and positions to select 95
cluster galaxy members and analyse the internal dynamics of the whole cluster.
We also examine X-ray data retrieved from Chandra and XMM archives. We measure
the cluster redshift, <z>=0.325, the line-of-sight (LOS) velocity dispersion,
\sigma_v~1500 km/s, and the X-ray temperature, kT~9 keV. From both X-ray and
optical data independently, we estimate a large cluster mass, in the 1--4
$10^{15}$ M$_\odot$ range. We attribute the extremely high value of \sigma_v to
the bimodality in the velocity distribution. We find evidence of a significant
velocity gradient and optical 3D substructure. The X-ray analysis also shows
many features in favour of a complex cluster structure, probably supporting an
ongoing merger of substructures in Abell 1351. The observational scenario
agrees with the presence of two main subclusters in the northern region, each
with its brightest galaxy (BCG1 and BCG2), detected as the two most important
X-ray substructures with a rest-frame LOS velocity difference of \Delta v~2500
km/s (in the rest frame) and probably being in large part aligned with the LOS.
We conclude that Abell 1351 is a massive merging cluster. The details of the
cluster structure allow us to interpret the quite asymmetric radio halo as a
`normal' halo plus a southern relic, strongly supporting a previous suggestion
based only on inspection of radio and preliminary X-ray data.


  For a smooth proper scheme over a local field of mixed characteristics which
has semistable reduction we define the category of its semistable etale sheaves
and under certain hypothesis we prove the appropriate semistable comparison
isomorphisms.


  We present a major upgrade of MGCAMB, a patch for the Einstein-Boltzmann
solver CAMB used for phenomenological tests of general relativity against
cosmological datasets. This new version is compatible with the latest CosmoMC
code and includes a consistent implementation of massive neutrinos and
dynamical dark energy. The code has been restructured to make it easier to
modify to fit the custom needs of specific users. To illustrate the
capabilities of the code, we present joint constraints on the modified growth,
massive neutrinos and the dark energy equation of state from the latest
cosmological observations, including the recent galaxy counts and weak lensing
measurements from the Dark Energy Survey, and find a good consistency with the
{\Lambda}CDM model.


  Moduli potential loses its minima due to external energy sources of inflaton
energy density or radiation produced at the end of inflation. But, the
non-existence of minima does not necessarily mean destabilization of moduli. In
fact, the destabilization of moduli is always dependent on the initial field
values of the fields. In this work, we study carefully how the effects of
reheating ease the problem of moduli destabilization. The associated time scale
to produce the thermal bath allows a larger initial field range to stabilize
the field. Contrary to the usual notion, the allowed initial field range is
larger for higher temperatures when the effective potential is of a run-away
nature. This eases the moduli destabilization problem for heavy mass moduli.
For low mass moduli ($\lesssim$ 30 TeV), the allowed field range still causes
the cosmological moduli problem by violating the BBN constraints unless its
initial abundance is suppressed.


  The physics of the strongly interacting Hubbard chain (with $t/U \ll 1$) at
finite temperatures undergoes a crossover to a spin incoherent regime when the
temperature is very small relative to the Fermi energy, but larger than the
characteristic spin energy scale. This crossover can be understood by means of
Ogata and Shiba's factorized wave function, where charge and spin are totally
decoupled, and assuming that the charge remains in the ground state, while the
spin is thermally excited and at an effective "spin temperature". We use the
time-dependent density matrix renormalization group method (tDMRG) to calculate
the dynamical contributions of the spin, to reconstruct the single-particle
spectral function of the electrons. The crossover is characterized by a
redistribution of spectral weight both in frequency and momentum, with an
apparent shift by $k_F$ of the minimum of the dispersion.


  We extend the setting of the right endpoint estimator introduced in Fraga
Alves and Neves (Statist. Sinica 24:1811--1835, 2014) to the broader class of
light-tailed distributions with finite endpoint, belonging to some domain of
attraction induced by the extreme value theorem. This stretch enables a general
estimator for the finite endpoint, which does not require estimation of the
(supposedly non-positive) extreme value index. A new testing procedure for
selecting max-domains of attraction also arises in connection with asymptotic
properties of the general endpoint estimator. The simulation study conveys that
the general endpoint estimator is a valuable complement to the most usual
endpoint estimators, particularly when the true extreme value index stays above
$-1/2$, embracing the most common cases in practical applications. An
illustration is provided via an extreme value analysis of supercentenarian
women data.


  Code generation, defined as automatically writing a piece of code to solve a
given problem for which an evaluation function exists, is a classic hard AI
problem. Its general form, writing code using a general language used by human
programmers from scratch is thought to be impractical. Adding constraints to
the code grammar, implementing domain specific concepts as primitives and
providing examples for the algorithm to learn, makes it practical. Formal
fields is a framework to do code generation across domains using the same
algorithms and language structure. Its ultimate goal is not just solving
different narrow problems, but providing necessary abstractions to integrate
many working solutions as a single lifelong reasoning system. It provides a
common grammar to define: a domain language, a problem and its evaluation. The
framework learns from examples of code snippets about the structure of the
domain language and searches completely new code snippets to solve unseen
problems in the same field. Formal fields abstract the search algorithm away
from the problem. The search algorithm is taken from existing reinforcement
learning algorithms. In our implementation it is an apropos Monte-Carlo Tree
Search (MCTS). We have implemented formal fields as a fully documented open
source project applied to the Abstract Reasoning Challenge (ARC). The
implementation found code snippets solving twenty two previously unsolved ARC
problems.


  In a recent paper (cond-mat/0506445) we derived an expression for the
replicated free energy of a liquid of hard spheres based on the HNC free energy
functional. An approximate equation of state for the glass and an estimate of
the random close packing density were obtained in d=3. Here we show that the
HNC approximation is not needed: the same expression can be obtained from the
full diagrammatic expansion of the replicated free energy. Then, we consider
the asymptotics of this expression when the space dimension d is very large. In
this limit, the entropy of the hard sphere liquid has been computed exactly.
Using this solution, we derive asymptotic expressions for the glass transition
density and for the random close packing density for hard spheres in large
space dimension.


  Stimulated emission of radiation has not been directly observed in
astrophysical situations up to this time. Here we demonstrate that photons from
pulsar B1641-45 stimulate pulses of excess 1720 MHz line emission in an
interstellar OH cloud. As this stimulated emission is driven by the pulsar, it
varies on a few millisecond timescale, orders of magnitude shorter than the
quickest OH maser variations previously detected.
  Our 1612 MHz spectra are inverted copies of the 1720 MHz spectra. This
"conjugate line" phenomenon enables us to constrain the properties of the
interstellar OH line-producing gas.
  We also show that pulsar signals suffer significantly deeper OH absorption
than do other background sources; confirming earlier tentative findings that OH
clouds are clumpier on small scales than neutral hydrogen clouds.


  The afterglows to gamma-ray bursts (GRBs) are due to synchrotron emission
from shocks generated as an ultra-relativistic outflow decelerates. A forward
and a reverse shock will form, however, where emission from the forward shock
is well studied as a potential counterpart to gravitational wave-detected
neutron star mergers the reverse shock has been neglected. Here, we show how
the reverse shock contributes to the afterglow from an off-axis and structured
outflow. The off-axis reverse shock will appear as a brightening feature in the
rising afterglow at radio frequencies. For bursts at $\sim100$ Mpc, the system
should be inclined $\lesssim20^\circ$ for the reverse shock to be observable at
$\sim0.1-10$ days post-merger. For structured outflows, enhancement of the
reverse shock emission by a strong magnetic field within the outflow is
required for the emission to dominate the afterglow at early times. Early radio
photometry of the afterglow could reveal the presence of a strong magnetic
field associated with the central engine.


  We derive a controlled expansion into mean field plus fluctuations for the
extended Bose-Hubbard model, involving interactions with many neighbors on an
arbitrary periodic lattice, and study the superfluid-supersolid phase
transition. Near the critical point, the impact of (thermal and quantum)
fluctuations on top of the mean field grows, which entails striking effects,
such as negative superfluid densities and thermodynamical instability of the
superfluid phase -- earlier as expected from mean-field dynamics. We also
predict the existence of long-lived "supercooled" states with anomalously large
quantum fluctuations.


  The positive-to-negative transition of spectral lag is an uncommon feature
reported in a small number of GRBs. An application of such a feature has been
made to constrain the critical quantum gravity energy ($E_{\rm QG}$) of the
light photons under the hypothesis that the Lorentz invariance might be
violated. Motivated by previous case studies, this paper systematically
examined the up-to-date Fermi/GBM GRB sample for the lag transition feature to
establish a comprehensive physical limit on the Lorentz Invariance Violation
(LIV). This search resulted in 32 GRBs with redshift available, which exhibit
the lag-transition phenomenon. We first fit each of the lag-E relations of the
32 GRBs with an empirical smoothly broken power law function, and found that
the lag transition occurs typically at about 400 keV. We then implemented the
LIV effect into the fit, which enabled us to constrain the lower limit of the
linear and quadratic values of $E_{\rm QG}$, which are typically distributed at
$1.5\times 10^{14}$ GeV and $8\times 10^{5}$ GeV, respectively.


  The member stars in globular cluster M15 show a substantial spread in the
abundances of r-process elements. We argue that a rare and prolific r-process
event enriched the natal cloud of M15 in an inhomogeneous manner. To critically
examine the possibility, we perform cosmological galaxy formation simulations
and study the physical conditions for the inhomogeneous enrichment. We explore
a large parameter space of the merger event time and the site. Our simulations
reproduce the large r-process abundance spread if a neutron-star merger occurs
at \sim 100 pc away from the formation site of the cluster and in a limited
time range of a few tens million years before the formation. Interestingly, a
bimodal feature is found in the Eu abundance distribution in some cases,
similarly to that inferred from recent observations. M15 member stars do not
show clear correlation between the abundances of Eu and light elements such as
Na that is expected in models with two stellar populations. We thus argue that
a majority of the stars in M15 are formed in a single burst. The ratio of heavy
to light r-process element abundance [Eu/Y] \sim 1.0 is consistent with that of
the so-called r-II stars, suggesting that a lanthanide-rich r-process event
dominantly enriched M15.


  The planetary nebulae He 2-436 and Wray 16-423 in the Sagittarius dwarf
galaxy appear to result from nearly twin stars, except that third-dredge-up
carbon is more abundant in He 2-436. A thorough photoionization-model analysis
implies that ratios Ne/O, S/O and Ar/O are significantly smaller in He 2-436,
indicative of third-dredge-up oxygen enrichment. The enrichment of oxygen with
respect to carbon is (7 +/- 4)%. Excess nitrogen in Wray 16-423 suggests third
dredge-up of late CN-cycle products even in these low-mass,
intermediate-metallicity stars.


  This paper considers the sum rate maximization problem of a two-user
multiple-input single-output interference channel with receivers that can
scavenge energy from the radio signals transmitted by the transmitters. We
first study the optimal transmission strategy for an ideal scenario where the
two receivers can simultaneously decode the information signal and harvest
energy. Then, considering the limitations of the current circuit technology, we
propose two practical schemes based on TDMA, where, at each time slot, the
receiver either operates in the energy harvesting mode or in the information
detection mode. Optimal transmission strategies for the two practical schemes
are respectively investigated. Simulation results show that the three schemes
exhibit interesting tradeoff between achievable sum rate and energy harvesting
requirement, and do not dominate each other in terms of maximum achievable sum
rate.


  B and L violating interactions of ordinary particles with their twin
particles from hypothetical mirror world can co-generate baryon asymmetries in
both worlds in comparable amounts, $\Omega'_B/\Omega_B \sim 5$ or so. On the
other hand, the same interactions induce the oscillation phenomena between the
neutral particles of two sectors which convert e.g. mirror neutrons into our
antineutrons. These oscillations are environment dependent and can have
fascinating physical consequences.


  In this article we report that there are two different types of destruction
of the phase synchronization regime of chaotic oscillators depending on the
parameter mismatch as well as in the case of the classical synchronization of
periodic oscillators. When the parameter mismatch of the interacting chaotic
oscillators is small enough the PS breaking takes place without the destruction
of the phase coherence of chaotic attractors of oscillators. Alternatively, due
to the large frequency detuning, the PS breaking is accomplished by loss of the
phase coherence of the chaotic attractors.


  Every nonconstant meromorphic function in the plane univalently covers
spherical discs of radii arbitrarily close to arctan(sqrt 8) ~ 70^\circ 32'. If
in addition all critical points of the function are multiple, then a similar
statement holds with pi/2. These constants are the best possible. The proof is
based on the consideration of negatively curved singular surfaces associated
with meromorphic functions.


  We present a dual-comb spectrometer based on two passively mode-locked
waveguide lasers integrated in a single Er-doped ZBLAN chip. This original
design yields two free-running frequency combs having a high level of mutual
stability. We developed in parallel a self-correction algorithm that
compensates residual relative fluctuations and yields mode-resolved spectra
without the help of any reference laser or control system. Fluctuations are
extracted directly from the interferograms using the concept of ambiguity
function, which leads to a significant simplification of the instrument that
will greatly ease its widespread adoption. Comparison with a correction
algorithm relying on a single-frequency laser indicates discrepancies of only
50 attoseconds on optical timings. The capacities of this instrument are
finally demonstrated with the acquisition of a high-resolution molecular
spectrum covering 20 nm. This new chip-based multi-laser platform is ideal for
the development of high-repetition-rate, compact and fieldable comb
spectrometers in the near- and mid-infrared.


  The growing complexity of cloud based software systems has resulted in
incident management becoming an integral part of the software development
lifecycle. Root cause analysis (RCA), a critical part of the incident
management process, is a demanding task for on-call engineers, requiring deep
domain knowledge and extensive experience with a team's specific services.
Automation of RCA can result in significant savings of time, and ease the
burden of incident management on on-call engineers. Recently, researchers have
utilized Large Language Models (LLMs) to perform RCA, and have demonstrated
promising results. However, these approaches are not able to dynamically
collect additional diagnostic information such as incident related logs,
metrics or databases, severely restricting their ability to diagnose root
causes. In this work, we explore the use of LLM based agents for RCA to address
this limitation. We present a thorough empirical evaluation of a ReAct agent
equipped with retrieval tools, on an out-of-distribution dataset of production
incidents collected at Microsoft. Results show that ReAct performs
competitively with strong retrieval and reasoning baselines, but with highly
increased factual accuracy. We then extend this evaluation by incorporating
discussions associated with incident reports as additional inputs for the
models, which surprisingly does not yield significant performance improvements.
Lastly, we conduct a case study with a team at Microsoft to equip the ReAct
agent with tools that give it access to external diagnostic services that are
used by the team for manual RCA. Our results show how agents can overcome the
limitations of prior work, and practical considerations for implementing such a
system in practice.


  We propose a trade-off between the Lipschitz constants of the position and
momentum probability distributions for arbitrary quantum states. We refer to
the trade-off as a quantum reciprocity relation. The Lipschitz constant of a
function may be considered to quantify the extent of fluctuations of that
function, and is, in general, independent of its spread. The spreads of the
position and momentum distributions are used to obtain the celebrated
Heisenberg quantum uncertainty relations. We find that the product of the
Lipschitz constants of position and momentum probability distributions is
bounded below by a number that is of the order of the inverse square of the
Planck's constant.


  We compute three-loop corrections to the Higgs-gluon form factor,
incorporating the top quark mass dependence. Our method is based on the
combination of expansions around the top threshold and for large top quark
mass, using conformal mapping and Pad\'e approximation to describe the form
factor over the full kinematic range.


  There is a group of binary post-AGB stars that show a conspicuous NIR excess,
usually assumed to arise from hot dust in very compact possibly rotating disks.
These stars are surrounded by significantly fainter nebulae than the
"standard", well studied protoplanetary and planetary nebulae (PPNe, PNe).
  We present high-sensitivity mm-wave observations of CO lines in 24 objects of
this type. CO emission is detected in most observed sources and the line
profiles show that the emissions very probably come from disks in rotation. We
derive typical values of the disk mass between 1e-3 and 1e-2 Mo, about two
orders of magnitude smaller than the (total) masses of "standard" PPNe. The
high-detection rate (upper limits being in fact not very significant) clearly
confirm that the NIR excess of these stars arises from compact disks in
rotation, very probably the inner parts of those found here. Low-velocity
outflows are also found in about eight objects, with moderate expansion
velocities of ~ 10 km/s, to be compared with the velocities of about 100 km/s
often found in "standard" PPNe. Except for two sources with complex profiles,
the outflowing gas in our objects represents a minor nebular component. Our
simple estimates of the disk typical sizes yields values ~ 0.5 - 1 arcsec, i.e.
between 5e15 and 3e16 cm. Estimates of the linear momenta carried by the
outflows, which can only be performed in a few well studied objects, also yield
moderate values, compared with the linear momenta that can be released by the
stellar radiation pressure (contrary, again, to the case of the very massive
and fast bipolar outflows in "standard" PPNe, that are strongly overluminous).
The mass and dynamics of nebulae around various classes of post-AGB stars
differ very significantly, and we can expect the formation of PNe with very
different properties.


  We prove that an almost zero-dimensional space $X$ is an Erd\H{o}s space
factor if and only if $X$ has a Sierpi\'{n}ski stratification of C-sets. We
apply this characterization to spaces which are countable unions of C-set
Erd\H{o}s space factors. We show that the Erd\H{o}s space $\mathfrak E$ is
unstable by giving strongly $\sigma$-complete and nowhere $\sigma$-complete
examples of almost zero-dimensional $F_{\sigma\delta}$-spaces which are not
Erd\H{o}s space factors. This answers a question by Dijkstra and van Mill.


  In this paper, we consider the nonparametric random regression model
$Y=f_1(X_1)+f_2(X_2)+\epsilon$ and address the problem of estimating the
function $f_1$. The term $f_2(X_2)$ is regarded as a nuisance term which can be
considerably more complex than $f_1(X_1)$. Under minimal assumptions, we prove
several nonasymptotic $L^2(\mathbb{P}^X)$-risk bounds for our estimators of
$f_1$. Our approach is geometric and based on considerations in Hilbert spaces.
It shows that the performance of our estimators is closely related to geometric
quantities, such as minimal angles and Hilbert-Schmidt norms. Our results
establish new conditions under which the estimators of $f_1$ have up to first
order the same sharp upper bound as the corresponding estimators of $f_1$ in
the model $Y=f_1(X_1)+\epsilon$. As an example we apply the results to an
additive model in which the number of components is very large or in which the
nuisance components are considerably less smooth than $f_1$. In particular, the
results apply to an asymptotic scenario in which the number of components is
allowed to increase with the sample size.


  Formulation of the Penrose inequality becomes ambiguous when the past and
future apparent horizons do cross. We test numerically several natural
possibilities of stating the inequality in punctured and boosted single- and
double- black holes, in a Dain-Friedrich class of initial data and in
conformally flat spheroidal data.The Penrose inequality holds true in vacuum
configurations for the outermost element amongst the set of disjoint future and
past apparent horizons (as expected)and (unexpectedly) for each of the
outermost past and future apparent horizons, whenever these two bifurcate from
an outermost minimal surface, regardless of whether they intersect or remain
disjoint. In systems with matter the conjecture breaks down only if matter does
not obey the dominant energy condition.


  For $B \subseteq \mathbb F_q^m$, the $n$-th affine extremal number of $B$ is
the maximum cardinality of a set $A \subseteq \mathbb F_q^n$ with no subset
which is affinely isomorphic to $B$. Furstenberg and Katznelson proved that for
any $B \subseteq \mathbb F_q^m$, the $n$-th affine extremal number of $B$ is
$o(q^n)$ as $n \to \infty$. By counting affine homomorphisms between subsets of
$\mathbb F_q^n$, we derive new bounds and give new proofs of some previously
known bounds for certain affine extremal numbers. At the same time, we
establish corresponding supersaturation results. We connect these bounds to
certain Ramsey-type numbers in vector spaces over finite fields. For $s,t \geq
1$, let $R_q(s,t)$ denote the minimum $n$ such that in every red-blue coloring
of the one-dimensional subspaces of $\mathbb F_q^n$, there is either a red
$s$-dimensional subspace or a blue $t$-dimensional subspace of $\mathbb F_q^n$.
The existence of these numbers is a special case of a well-known theorem of
Graham, Leeb, Rothschild. We improve the best known upper bounds on $R_2(2,t)$,
$R_3(2,t)$, $R_2(t,t)$, and $R_3(t,t)$.


  In industry, feature selection is a standard but necessary step to search for
an optimal set of informative feature fields for efficient and effective
training of deep Click-Through Rate (CTR) models. Most previous works measure
the importance of feature fields by using their corresponding continuous
weights from the model, then remove the feature fields with small weight
values. However, removing many features that correspond to small but not exact
zero weights will inevitably hurt model performance and not be friendly to
hot-start model training. There is also no theoretical guarantee that the
magnitude of weights can represent the importance, thus possibly leading to
sub-optimal results if using these methods.
  To tackle this problem, we propose a novel Learnable Polarizing Feature
Selection (LPFS) method using a smoothed-$\ell^0$ function in literature.
Furthermore, we extend LPFS to LPFS++ by our newly designed
smoothed-$\ell^0$-liked function to select a more informative subset of
features. LPFS and LPFS++ can be used as gates inserted at the input of the
deep network to control the active and inactive state of each feature. When
training is finished, some gates are exact zero, while others are around one,
which is particularly favored by the practical hot-start training in the
industry, due to no damage to the model performance before and after removing
the features corresponding to exact-zero gates. Experiments show that our
methods outperform others by a clear margin, and have achieved great A/B test
results in KuaiShou Technology.


  We study the large distance expansion of correlation functions in the free
massive Majorana theory at finite temperature, alias the Ising field theory at
zero magnetic field on a cylinder. We develop a method that mimics the spectral
decomposition, or form factor expansion, of zero-temperature correlation
functions, introducing the concept of "finite-temperature form factors". Our
techniques are different from those of previous attempts in this subject. We
show that an appropriate analytical continuation of finite-temperature form
factors gives form factors in the quantization scheme on the circle. We show
that finite-temperature form factor expansions are able to reproduce expansions
in form factors on the circle. We calculate finite-temperature form factors of
non-interacting fields (fields that are local with respect to the fundamental
fermion field). We observe that they are given by a mixing of their
zero-temperature form factors and of those of other fields of lower scaling
dimension. We then calculate finite-temperature form factors of order and
disorder fields. For this purpose, we derive the Riemann-Hilbert problem that
completely specifies the set of finite-temperature form factors of general
twist fields (order and disorder fields and their descendants). This
Riemann-Hilbert problem is different from the zero-temperature one, and so are
its solutions. Our results agree with the known form factors on the circle of
order and disorder fields.


  Much attention has been recently devoted to the possibility that quantum
gravity effects could lead to departures from Special Relativity in the form of
a deformed Poincar\`e algebra. These proposals go generically under the name of
Doubly or Deformed Special Relativity (DSR). In this article we further explore
a recently proposed class of quantum field theories, involving noncanonically
commuting complex scalar fields, which have been shown to entail a DSR-like
symmetry. An open issue for such theories is whether the DSR-like symmetry has
to be taken as a physically relevant symmetry, or if in fact the "true"
symmetries of the theory are just rotations and translations while boost
invariance has to be considered broken. We analyze here this issue by extending
the known results to curved spacetime under both of the previous assumptions.
We show that if the symmetry of the free theory is taken to be a DSR-like
realization of the Poincar\'e symmetry, then it is not possible to render such
a symmetry a gauge symmetry of the curved physical spacetime. However, it is
possible to introduce an auxiliary spacetime which allows to describe the
theory as a standard quantum field theory in curved spacetime. Alternatively,
taking the point of view that the noncanonical commutation of the fields
actually implies a breakdown of boost invariance, the physical spacetime
manifold has to be foliated in surfaces of simultaneity and the field theory
can be coupled to gravity by making use of the ADM prescription.


  We use the term protocol coding to denote the communication strategies in
which information is encoded through the actions taken by a certain
communication protocol. In this work we investigate strategies for protocol
coding via combinatorial ordering of the labelled user resources (packets,
channels) in an existing, primary system. This introduces a new, secondary
communication channel in the existing system, which has been considered in the
prior work exclusively in a steganographic context. Instead, we focus on the
use of secondary channel for reliable communication with newly introduced
secondary devices, that are low-complexity versions of the primary devices,
capable only to decode the robustly encoded header information in the primary
signals. In Part I of the work we have characterized the capacity of the
secondary channel through information-theoretic analysis. In this paper we
consider practical strategies for protocol coding inspired by the
information-theoretic analysis. It turns out that the insights from Part I are
instrumental for devising superior design of error-control codes. This is
demonstrated by comparing the error performance to the "na"{\i}ve" strategy
which is presumably available without carrying out the analysis in Part I.
These results are clearly outlining both the conceptual novelty behind the
discussed concept of secondary channel as well as its practical applicability.


  We present a theory characterizing the phases emerging as a consequence of
continuous symmetry-breaking in quantum and classical systems. In
symmetry-breaking phases, dynamics is restricted due to the existence of a set
of conserved charges derived from the order parameter of the phase transition.
Their expectation values are determined by the privileged direction appearing
in the ordered phase as a consequence of symmetry breaking, and thus they can
be used to determine whether this direction is well defined or it has quantum
fluctuations. Our theory is numerically exemplified via the two-dimensional
limit of the vibron model, a fully connected system invariant under a rotation
operator which generates the continuous symmetry-breaking.


  In this paper we investigate regular patterns of matrix elements of the
nuclear shell model Hamiltonian $H$, by sorting the diagonal matrix elements
from the smaller to larger values. By using simple plots of non-zero matrix
elements and lowest eigenvalues of artificially constructed "sub-matrices" $h$
of $H$, we propose a new and simple formula which predicts the lowest
eigenvalue with remarkable precisions.


  In this course we will give examples for experimental techniques used in
particle physics experiments. After a short introduction, we will discuss
applications in silicon microstrip detectors, wire chambers, and single photon
detection in Ring Imaging Cherenkov (RICH) counters. A short discussion of the
relevant physics processes, mainly different forms of energy loss in matter, is
enclosed.


  We demonstrate a new fabrication process for hybrid
semiconductor-superconductor heterostructures based on anodic oxidation (AO),
allowing controlled thinning of epitaxial Al films. Structural and transport
studies of oxidized epitaxial Al films grown on insulating GaAs substrates
reveal spatial non-uniformity and enhanced critical temperature and magnetic
fields. Oxidation of epitaxial Al on hybrid InAs heterostructures with a
conducting quantum well show similarly enhanced superconducting properties
transferred to the two-dimensional electron gas (2DEG) by proximity effect,
with critical perpendicular magnetic fields up to 3.5 T. An insulating AlOx
film, that passivates the heterostructure from exposure to air, is obtained by
complete oxidation of the Al. It simultaneously removes the need to strip Al
which damages the underlying semiconductor. AO passivation yielded 2DEG
mobilities two times higher than similar devices with Al removed by wet
etching. An AO-passivated Hall bar showed quantum Hall features emerging at a
transverse field of 2.5 T, below the critical transverse field of thinned
films, eventually allowing transparent coupling of quantum Hall effect and
superconductivity. AO thinning and passivation are compatible with standard
lithographic techniques, giving lateral resolution below <50 nm. We demonstrate
local patterning of AO by realizing a semiconductor-based Josephson junction
operating up to 0.3 T perpendicular.


  We defined the Gau\ss-Manin stratification of a stratified bundle with
respect to a smooth morphism and use it to study the homotopy sequence of
stratified fundamental group schemes.


  We study the linear perturbations about a nonrotating black hole solution of
Horndeski's theory, using a systematic approach that extracts the asymptotic
behaviour of perturbations (at spatial infinity and near the horizon) directly
from the first-order radial differential system governing these perturbations
instead of finding Schr\"odinger-like equations for their dynamics. We
illustrate this method in the case of a specific black hole solution. The
knowledge of the asymptotic behaviours of the perturbations paves the way for a
numerical computation of the quasinormal modes. Finally, the asymptotic form of
the modes also signals some pathologies in the scalar sector of the solution
considered here.


  Maritime traffic emissions are a major concern to governments as they heavily
impact the Air Quality in coastal cities. Ships use the Automatic
Identification System (AIS) to continuously report position and speed among
other features, and therefore this data is suitable to be used to estimate
emissions, if it is combined with engine data. However, important ship features
are often inaccurate or missing. State-of-the-art complex systems, like CALIOPE
at the Barcelona Supercomputing Center, are used to model Air Quality. These
systems can benefit from AIS based emission models as they are very precise in
positioning the pollution. Unfortunately, these models are sensitive to missing
or corrupted data, and therefore they need data curation techniques to
significantly improve the estimation accuracy. In this work, we propose a
methodology for treating ship data using Conditional Restricted Boltzmann
Machines (CRBMs) plus machine learning methods to improve the quality of data
passed to emission models. Results show that we can improve the default methods
proposed to cover missing data. In our results, we observed that using our
method the models boosted their accuracy to detect otherwise undetectable
emissions. In particular, we used a real data-set of AIS data, provided by the
Spanish Port Authority, to estimate that thanks to our method, the model was
able to detect 45% of additional emissions, of additional emissions,
representing 152 tonnes of pollutants per week in Barcelona and propose new
features that may enhance emission modeling.


  Compton telescopes provide a good sensitivity over a wide field of view in
the difficult energy range running from a few hundred keV to several MeV. Their
angular resolution is, however, poor and strongly energy dependent. We present
a novel experimental design associating a coded mask and a Compton detection
unit to overcome these pitfalls. It maintains the Compton performance while
improving the angular resolution by at least an order of magnitude in the field
of view subtended by the mask. This improvement is obtained only at the expense
of the efficiency that is reduced by a factor of two. In addition, the
background corrections benefit from the coded mask technique, i.e. a
simultaneous measurement of the source and background. This design is
implemented and tested using the IBIS telescope on board the INTEGRAL satellite
to construct images with a 12' resolution over a 29 degrees x 29 degrees field
of view in the energy range from 200 keV to a few MeV. The details of the
analysis method and the resulting telescope performance, particularly in terms
of sensitivity, are presented.


  We are dealing with the question whether every group or semigroup action
(with some additional property) on a continuum (with some additional property)
has a fixed point. One of such results was given in 2009 by Shi and Sun. They
proved that every nilpotent group action on a uniquely arcwise connected
continuum has a fixed point. We are seeking for this type of results with e.g.
commutative, compact or torsion groups and semigroups acting on dendrites,
dendroids, $\lambda$-dendroids and uniquely arcwise connected continua. We
prove that every continuous action of a compact or torsion group on a uniquely
arcwise connected continuum has a fixed point. We also prove that every
continuous action of a compact and commutative semigroup on a uniquely arcwise
connected continuum or on a tree-like continuum has a fixed point.


  We calculate the cross sections of inclusive J/psi production in
photoproduction and two-photon scattering, involving both direct and resolved
photons, and in e^+e^- annihilation at next-to-leading order (NLO) within the
factorization formalism of nonrelativistic quantum chromodynamics (NRQCD),
including the full relativistic corrections due to the intermediate
^1S_0^{[8]}, ^3S_1^{[8]}, and ^3P_J^{[8]} color-octet (CO) states. Exploiting
also our previous results on hadroproduction, we perform a combined fit of the
respective CO long-distance matrix elements (LDMEs) to all available
high-quality data of inclusive J/psi production, from KEKB, LEP~II, RHIC, HERA,
the Tevatron, and the LHC, comprising a total of 194 data points from 26 data
sets.


  The study of the production of nuclei and antinuclei in pp collisions has
proven to be a powerful tool to investigate the formation mechanism of loosely
bound states in high-energy hadronic collisions. In this paper, the production
of protons, deuterons and $^3$He and their charge conjugates at midrapidity is
studied as a function of the charged-particle multiplicity in inelastic pp
collisions at $\sqrt{s}=5.02$ TeV using the ALICE detector. Within the
uncertainties, the yields of nuclei in pp collisions at $\sqrt{s}=5.02$ TeV are
compatible with those in pp collisions at different energies and to those in
p-Pb collisions when compared at similar multiplicities. The measurements are
compared with the expectations of coalescence and Statistical Hadronisation
Models. The results suggest a common formation mechanism behind the production
of light nuclei in hadronic interactions and confirm that they do not depend on
the collision energy but on the number of produced particles.


  The paper develops high-order accurate physical-constraints-preserving finite
difference WENO schemes for special relativistic hydrodynamical (RHD)
equations, built on the local Lax-Friedrich splitting, the WENO reconstruction,
the physical-constraints-preserving flux limiter, and the high-order strong
stability preserving time discretization. They are extensions of the
positivity-preserving finite difference WENO schemes for the non-relativistic
Euler equations. However, developing physical-constraints-preserving methods
for the RHD system becomes much more difficult than the non-relativistic case
because of the strongly coupling between the RHD equations, no explicit
expressions of the primitive variables and the flux vectors, in terms of the
conservative vector, and one more physical constraint for the fluid velocity in
addition to the positivity of the rest-mass density and the pressure. The key
is to prove the convexity and other properties of the admissible state set and
discover a concave function with respect to the conservative vector replacing
the pressure which is an important ingredient to enforce the
positivity-preserving property for the non-relativistic case. Several one- and
two-dimensional numerical examples are used to demonstrate accuracy,
robustness, and effectiveness of the proposed physical-constraints-preserving
schemes in solving RHD problems with large Lorentz factor, or strong
discontinuities, or low rest-mass density or pressure etc.


  Knowledge distillation (KD) is a successful approach for deep neural network
acceleration, with which a compact network (student) is trained by mimicking
the softmax output of a pre-trained high-capacity network (teacher). In
tradition, KD usually relies on access to the training samples and the
parameters of the white-box teacher to acquire the transferred knowledge.
However, these prerequisites are not always realistic due to storage costs or
privacy issues in real-world applications. Here we propose the concept of
decision-based black-box (DB3) knowledge distillation, with which the student
is trained by distilling the knowledge from a black-box teacher (parameters are
not accessible) that only returns classes rather than softmax outputs. We start
with the scenario when the training set is accessible. We represent a sample's
robustness against other classes by computing its distances to the teacher's
decision boundaries and use it to construct the soft label for each training
sample. After that, the student can be trained via standard KD. We then extend
this approach to a more challenging scenario in which even accessing the
training data is not feasible. We propose to generate pseudo samples
distinguished by the teacher's decision boundaries to the largest extent and
construct soft labels for them, which are used as the transfer set. We evaluate
our approaches on various benchmark networks and datasets and experiment
results demonstrate their effectiveness. Codes are available at:
https://github.com/zwang84/zsdb3kd.


  In this paper we examine if hyperlink-based (webometric) indicators can be
used to rank academic websites. Therefore we analyzed the interlinking
structure of German university websites and compared our simple hyperlink-based
ranking with official and web-independent rankings of universities. We found
that link impact could not easily be seen as a prestige factor for
universities.


  General formulas describing the multiple scattering of electron by polyatomic
molecules have been derived within the framework of the model of
non-overlapping atomic potentials. These formulas are applied to different
carbon molecules, both for fixed-in-space and randomly oriented molecules.


  We introduce the notions of $\varepsilon$-approximate fixed point and weak
$\varepsilon$-approximate fixed point. We show that for a group of unitary
matrices even the existence of a nontrivial weak $\varepsilon$-approximate
fixed point for sufficiently small $\varepsilon$ gives an actual nontrivial
common eigenvector. We give estimates for $\varepsilon$ in terms of the size
$n$ of matrices and prove that the dependence is polynomial. Moreover, we show
that the common eigenvector is polynomially close to the starting weak
approximate fixed point.


  Fine-tuning is the primary methodology for tailoring pre-trained large
language models to specific tasks. As the model's scale and the diversity of
tasks expand, parameter-efficient fine-tuning methods are of paramount
importance. One of the most widely used family of methods is low-rank
adaptation (LoRA) and its variants. LoRA encodes weight update as the product
of two low-rank matrices. Despite its advantages, LoRA falls short of
full-parameter fine-tuning in terms of generalization error for certain tasks.
  We introduce Chain of LoRA (COLA), an iterative optimization framework
inspired by the Frank-Wolfe algorithm, to bridge the gap between LoRA and full
parameter fine-tuning, without incurring additional computational costs or
memory overheads. COLA employs a residual learning procedure where it merges
learned LoRA modules into the pre-trained language model parameters and
re-initilize optimization for new born LoRA modules. We provide theoretical
convergence guarantees as well as empirical results to validate the
effectiveness of our algorithm. Across various models (OPT and llama-2) and
seven benchmarking tasks, we demonstrate that COLA can consistently outperform
LoRA without additional computational or memory costs.


  A new model for monopole catalysis of nucleon decay is proposed. Unlike in
the earlier one, the only light fields in this model are the photon and Skyrme
(pion) field. The model admits the 't Hooft- Polyakov monopole and Skyrmion as
classical solutions, while baryon number non-conservation occurs through an
anomaly involving an intermediate mass axial vector field resembling W- and
Z-bosons. By considering spherically symmetric monopole-Skyrmion
configurations, we find that the Skyrmion looses essentially all its mass when
interacting with the monopole, which is phenomenologically identical to the
monopole catalysis of nucleon decay. Short-distance monopole-Skyrmion physics
in this model is interesting too, as there exist almost degenerate metastable
monopole-Skyrmion bound states separated by substantial energy barriers. Yet
the heights of the latter are much smaller than the physical nucleon mass, so
the complete disappearance of the normal undeformed Skyrmion remains perfectly
possible in Skyrmion-monopole scattering.


  There are two discrepant experimental results on the magnetic ground state of
Y$_{2}$Mn$_{2}$O$_{7}$, one study proposes a spin glass state, while another
introduces the material as a ferromagnet. In this study, we attempt to resolve
this issue by employing density functional theory and Monte Carlo simulations.
We derive different spin models by varying the Hubbard $U$ parameter in ab
initio GGA+$U$ calculations. For the most range of Hubbard $U$, We obtain that
the leading terms in the spin Hamiltonian are bi-quadratic and the nearest
neighbor Heisenberg exchange interactions. By comparing Monte Carlo simulations
of these models with the experiments, we find a ferromagnetic ground state for
Y$_{2}$Mn$_{2}$O$_{7}$ as the most compatible with experiments. We also
consider Y$_{2}$Mo$_{2}$O$_{7}$ as a prototype of the defect-free pyrochlore
system with spin-glass behavior and compare it with Y$_{2}$Mn$_{2}$O$_{7}$. The
orbital degrees of freedom are considered as a leading factor in converting a
defect-free pyrochlore such as Y$_{2}$Mn$_{2}$O$_{7}$ to a spin glass system.
By changing the $d$ orbital occupations of Mo atoms, our GGA+$U$ calculations
for Y$_{2}$Mo$_{2}$O$_{7}$ indicate many nearly degenerate states with
different $d$ orbital orientations which reveals $d$ orbital degrees of freedom
in this material. While for Y$_{2}$Mn$_{2}$O$_{7}$, we find a single ground
state with a fixed orbital orientation. Consequently, all of our ab initio
approaches confirm Y$_{2}$Mn$_{2}$O$_{7}$ as a ferromagnetic system.


  We choose a modified Chaplygin Gas Dark energy model for considering some its
cosmological behaviors. In this regards, we study different Rip singularity
scenarios and bouncing model of the universe in context of this model. We show
that by using suitable parameters can explain some cosmological aspects of the
model.


  There exist only a few known examples of subordinators for which the
transition probability density can be computed explicitly along side an
expression for its L\'evy measure and Laplace exponent. Such examples are
useful in several areas of applied probability, for example, they are used in
mathematical finance for modeling stochastic time change, they appear in
combinatorial probability to construct sampling formulae, which in turn is
related to a variety of issues in the theory of coalescence models, moreover,
they have also been extensively used in the potential analysis of subordinated
Brownian motion in dimension greater than or equal to 2. In this paper, we show
that Kendall's classic identity for spectrally negative L\'evy processes can be
used to construct new families of subordinators with explicit transition
probability semigroups. We describe the properties of these new subordinators
and emphasise some interesting connections with explicit and previously unknown
Laplace transform identities and with complete monotonicity properties of
certain special functions.


  Machine learning (ML) models have been shown to be vulnerable to Membership
Inference Attacks (MIA), which infer the membership of a given data point in
the target dataset by observing the prediction output of the ML model. While
the key factors for the success of MIA have not yet been fully understood,
existing defense mechanisms such as using L2 regularization
\cite{10shokri2017membership} and dropout layers \cite{salem2018ml} take only
the model's overfitting property into consideration. In this paper, we provide
an empirical analysis of the impact of both the data and ML model properties on
the vulnerability of ML techniques to MIA. Our results reveal the relationship
between MIA accuracy and properties of the dataset and training model in use.
In particular, we show that the size of shadow dataset, the class and feature
balance and the entropy of the target dataset, the configurations and fairness
of the training model are the most influential factors. Based on those
experimental findings, we conclude that along with model overfitting, multiple
properties jointly contribute to MIA success instead of any single property.
Building on our experimental findings, we propose using those data and model
properties as regularizers to protect ML models against MIA. Our results show
that the proposed defense mechanisms can reduce the MIA accuracy by up to 25\%
without sacrificing the ML model prediction utility.


  We investigate the application of the density-matrix renormalization group
(DMRG) algorithm to a one-dimensional harmonic oscillator chain and compare the
results with exact solutions, aiming to improve the algorithm efficiency. It
has been demonstrated that the algorithm can give quite accurate results if the
procedure is proper organized, for example, by using the optimized bases. The
errors of calculated ground state energy and the energy gap between the ground
state and the first excited state are analyzed, which are found to be
critically dependent upon the size of the system or the energy level structure
of the studied system and the number of states targeted during the DMRG
procedure.


  A model for the thermodynamics of a quantum heat bath is introduced. Under
the assumption that the bath molecules have finitely many degrees of freedom
and are weakly interacting, we present a general derivation of the equation of
state of the bath in the thermodynamic limit. The relation between the
temperature and the specific energy of the bath depends on (i) the spectral
properties of the molecules, and (ii) the choice of probability measure on the
state space of a representative molecule. The results obtained illustrate how
the microscopic features of the molecular constituents determine the
macroscopic thermodynamic properties of the bath. Our findings can thus be used
to compare the merits of different hypotheses for the equilibrium states of
quantum systems. Two examples of plausible choices for the probability measure
are considered in detail.


  We calculate the electromagnetic response functions of a Fractional Quantum
Hall system within the framework of the fermion Chern-Simons theory for the
Fractional Hall Effect (FQHE) which we developed before. Our results are valid
in a semiclassical expansion around the average field approximation (AFA). We
reexamine the AFA and the role of fluctuations. We argue that,
order-\-by-\-order in the semiclassical expansion, the response functions obey
the correct symmetry properties required by Galilean and Gauge Invariance and
by the incompressibility of the fluid. In particular, we find that the
low-momentum limit of the semiclassical approximation to the response functions
is exact and that it saturates the $f$-sum rule.We obtain the spectrum of
collective excitations of FQHE systems in the low-momentum limit. We find a
rich spectrum of modes which includes a host of quasiparticle- quasihole bound
states and, in general,two collective modes coalescing at the cyclotron
frequency. The Hall conductance is obtained from the current-density
correlation function, and it has the correct value already at the semiclassical
level. We applied these results to the problem of the screening of external
charges and fluxes by the electron fluid, and obtained asymptotic expressions
of the charge and current density profiles, for different types of
interactions. Finally, we reconsider the anyon superfluid within our scheme and
derive the spectrum of collective modes for interacting hard-core bosons and
semions. In addition to the gapless phase mode, we find a set of gapped
collective modes.


  Society is in constant transition to keep up with technological advancement,
we are seeing traditional paradigms being increasingly challenged. The
fundamentals of governance are one such paradigm. As society's values have
shifted, so have expectations of government shifted from the traditional model
to something commonly referred to as 'open governance'. Though a disputed term,
we take open governance to mean a concept, which encourages and facilitates
openness, accountability, and responsiveness to citizens. For the success of
open governance initiatives, there are some technologies, such as the internet,
that are crucial. These technologies enable access to both the data and to
engagement activities between citizens and government. There are also other
technologies, like blockchain and smart contacts, which could be utilised to
assist open governance. A sound starting point would be moving from a system
where information is tediously released by a government, on an 'as they please'
basis, to an infrastructure where critical actions are captured with strong
integrity, non-repudiation and evidential guarantees. With an added dimension
that facilitates these actions record be accessible to public scrutiny in near
real-time. One candidate technology for capturing such actions is blockchain.
The blockchains utility is being recognised through smart contracts -
potentially a vital building block to realising open and transparent government
activities. In this paper, we employ the concept of smart contracts to
government tendering activities. The proposed scheme is based on smart
contracts, enabling a fair, transparent and independently verifiable
(auditable) government tendering scheme. The scheme is then implemented on the
Ethereum platform to evaluate the performance and financial cost implications,
along with an evaluation of the potential security and auditability challenges.


  We study coherent excitation hopping in a spin chain realized using highly
excited individually addressable Rydberg atoms. The dynamics are fully
described in terms of an XY spin Hamiltonian with a long range resonant
dipole-dipole coupling that scales as the inverse third power of the lattice
spacing, $C_3/R^3$. The experimental data demonstrate the importance of next
neighbor interactions which are manifest as revivals in the excitation
dynamics. The results suggest that arrays of Rydberg atoms are ideally suited
to large scale, high-fidelity quantum simulation of spin dynamics.


  We study the domain of existence of a solution to a Riemann problem for the
pressure gradient equation in two space dimensions. The Riemann problem is the
expansion of a quadrant of gas of constant state into the other three vacuum
quadrants. The global existence of a smooth solution was established in Dai and
Zhang [Arch. Rational Mech. Anal., {\bf 155}(2000), 277-298] up to the free
boundary of vacuum. We prove that the vacuum boundary where the system is
degenerate is the trivial coordinate axes.


  Data from KEK on subthreshold antiproton as well as on pi(+-) and K(+-)
production in proton-nucleus reactions are described at projectile energies
between 3.5 and 12.0 GeV. We use a model which considers a hadron-nucleus
reaction as an incoherent sum over collisions of the projectile with a varying
number of target nucleons. It samples complete events and allows thus for the
simultaneous consideration of all particle species measured. The overall
reproduction of the data is quite satisfactory. It is shown that the
contributions from the interaction of the projectile with groups of several
target nucleons are decisive for the description of subthreshold production.
Since the collective features of subthreshold production become especially
significant far below the threshold, the results are extrapolated down to COSY
energies. It is concluded that an antiproton measurement at ANKE-COSY should be
feasible, if the high background of other particles can be efficiently
suppressed.


  The algebraic structure of S-Theory and its representations are described.
This structure includes up to 13 hidden dimensions. It implies the existence of
an SO(10,2) covariant supergravity theory as a limit of the secret theory
behind string theory. The black hole entropy is invariant under transformations
including the 11th and 12th dimensions. The discussion includes generalization
into curved spacetime via various contractions of the superalgebras OSp(1/32),
OSp(1/64) etc..


  The spectrum of the normalized complex Laplacian for electrical networks is
analyzed. We show that eigenvalues lie in a larger region compared to the case
of the real Laplacian. We show the existence of eigenvalues with negative real
part and absolute value greater than 2. An estimate from below for the first
non-vanishing eigenvalue in modulus is provided. We supplement the estimates
with examples, showing sharpness.


  A novel thermodynamic integration (TI) scheme is presented to compute the
crystal-liquid interfacial free energy ($\gamma_{\rm cl}$) from molecular
dynamics simulation. The scheme is applied to a Lennard-Jones system. By using
extremely short-ranged and impenetrable Gaussian flat walls to confine the
liquid and crystal phases, we overcome hysteresis problems of previous TI
schemes that stem from the translational movement of the crystal-liquid
interface. Our technique is applied to compute $\gamma_{\rm cl}$ for the (100),
(110) and (111) orientation of the crystalline phase at three temperatures
under coexistence conditions. For one case, namely the (100) interface at the
temperature $T=1.0$ (in reduced units), we demonstrate that finite-size scaling
in the framework of capillary wave theory can be used to estimate $\gamma_{\rm
cl}$ in the thermodynamic limit. Thereby, we show that our TI scheme is not
associated with the suppression of capillary wave fluctuations.


  Discriminating between Parkinson's Disease (PD) and Progressive Supranuclear
Palsy (PSP) is difficult due to overlapping symptoms, especially early on.
Saccades (rapid conjugate eye movements between fixation points) are affected
by both diseases but conventional saccade analyses exhibit group level
differences only. We hypothesized analyzing entire saccade raw time series
waveforms would permit superior individual level discrimination between PD,
PSP, and healthy controls (HC). 13,309 saccadic eye movements from 127
participants were analyzed using a novel, calibration-free waveform analysis
and hierarchical machine learning framework. Individual saccades were
classified based on which trained model could reconstruct each waveform with
minimum error, indicating the most likely condition. A hierarchical classifier
then predicted overall status (recently diagnosed and medication-naive 'de
novo' PD, 'established' PD on antiparkinsonian medication, PSP, and healthy
controls) by combining each participant's saccade results. This approach
substantially outperformed conventional metrics, achieving high AUROCs
distinguishing de novo PD from PSP (0.92-0.97), de novo PD from HC (0.72-0.89),
and PSP from HC (0.90-0.95), while the conventional model showed limited
performance (AUROC range: 0.45-0.75). This calibration-free waveform analysis
sets a new standard for precise saccadic classification of PD, PSP, and HC,
increasing potential for clinical adoption, remote monitoring, and screening.


  In this paper a new geometric characterization of the $n$th symmetric product
of a curve is given. Specifically, we assume that there exists a chain of
smooth subvarieties $V_i$ of dimension $i$, such that $V_i$ is an ample divisor
in $V_{i+1}$ and its intersection product with $V_1$ is one. That the Albanese
dimension of $V_2$ is $2$ and the genus of $V_1$ is equal to the irregularity
of the variety. We prove that in this case the variety is isomorphic to the
symmetric product of a curve.


  Neural network based classifiers are still prone to manipulation through
adversarial perturbations. State of the art attacks can overcome most of the
defense or detection mechanisms suggested so far, and adversaries have the
upper hand in this arms race. Adversarial examples are designed to resemble the
normal input from which they were constructed, while triggering an incorrect
classification. This basic design goal leads to a characteristic spatial
behavior within the context of Activation Spaces, a term coined by the authors
to refer to the hyperspaces formed by the activation values of the network's
layers. Within the output of the first layers of the network, an adversarial
example is likely to resemble normal instances of the source class, while in
the final layers such examples will diverge towards the adversary's target
class. The steps below enable us to leverage this inherent shift from one class
to another in order to form a novel adversarial example detector. We construct
Euclidian spaces out of the activation values of each of the deep neural
network layers. Then, we induce a set of k-nearest neighbor classifiers (k-NN),
one per activation space of each neural network layer, using the
non-adversarial examples. We leverage those classifiers to produce a sequence
of class labels for each nonperturbed input sample and estimate the a priori
probability for a class label change between one activation space and another.
During the detection phase we compute a sequence of classification labels for
each input using the trained classifiers. We then estimate the likelihood of
those classification sequences and show that adversarial sequences are far less
likely than normal ones. We evaluated our detection method against the state of
the art C&W attack method, using two image classification datasets (MNIST,
CIFAR-10) reaching an AUC 0f 0.95 for the CIFAR-10 dataset.


  ALICE is a general-purpose heavy-ion detector at the CERN LHC. We describe
the commissioning activities in the years 2007-2009 and the status of the
different subsystems at the time of this conference.


  In this work a system of non-linear elliptic equations is considered, where
the non-linear term is the sum of a quadratic form and a Sobolev sub-critical
term. An extra assumption is introduced on the sub-critical term, which is
minimal among the ones which guarantee the existence of standing-waves obtained
by estimating frequencies of minimizing sequences with the energy functional.


  We consider molecules made of two one-dimensional short-range-interacting
bosonic atoms. We show that in the process of scattering of these molecules off
a narrow barrier, odd incident waves produce \emph{no unbound atoms, even when
the incident energy exceeds the dissociation threshold}. This effect is a
consequence of a prohibition on chemical reactions acting in a generally
unphysical Bethe Ansatz integrable system of a $C_{2}$-type, with which our
system shares the spatially odd eigenstates. We suggest several experimental
implementations of the effect. We also propose to use the monomer production as
an alternative read-out channel in an atom interferometer: unlike in the
standard interferometric schemes, no spatial separation of the output channels
will be required.


  The coordinate Bethe Ansatz solution of the log-gamma polymer is extended to
boundary conditions with one fixed end and the other attached to one half of a
one-dimensional lattice. The large-time limit is studied using a saddle-point
approximation,and the cumulative distribution function of the rescaled free
energy of a long polymer is expressed as a Fredholm determinant. Scaling limits
of the kernel are identified, leading to a crossover from the GUE to the GOE
Tracy--Widom distributions. The continuum limit reproduces the crossover from
droplet to flat initial conditions of the Kardar--Parisi--Zhang equation.


  Deep reinforcement learning algorithms have shown an impressive ability to
learn complex control policies in high-dimensional tasks. However, despite the
ever-increasing performance on popular benchmarks, policies learned by deep
reinforcement learning algorithms can struggle to generalize when evaluated in
remarkably similar environments. In this paper we propose a protocol to
evaluate generalization in reinforcement learning through different modes of
Atari 2600 games. With that protocol we assess the generalization capabilities
of DQN, one of the most traditional deep reinforcement learning algorithms, and
we provide evidence suggesting that DQN overspecializes to the training
environment. We then comprehensively evaluate the impact of dropout and
$\ell_2$ regularization, as well as the impact of reusing learned
representations to improve the generalization capabilities of DQN. Despite
regularization being largely underutilized in deep reinforcement learning, we
show that it can, in fact, help DQN learn more general features. These features
can be reused and fine-tuned on similar tasks, considerably improving DQN's
sample efficiency.


  Narrowband x- and gamma-ray sources based on the inverse Compton scattering
of laser pulses suffer from a limitation of the allowed laser intensity due to
the onset of nonlinear effects that increase their bandwidth. It has been
suggested that laser pulses with a suitable frequency modulation could
compensate this ponderomotive broadening and reduce the bandwidth of the
spectral lines, which would allow to operate narrowband Compton sources in the
high-intensity regime. In this paper we, therefore, present the theory of
nonlinear Compton scattering in a frequency modulated intense laser pulse. We
systematically derive the optimal frequency modulation of the laser pulse from
the scattering matrix element of nonlinear Compton scattering, taking into
account the electron spin and recoil. We show that, for some particular
scattering angle, an optimized frequency modulation completely cancels the
ponderomotive broadening for all harmonics of the backscattered light. We also
explore how sensitive this compensation depends on the electron beam energy
spread and emittance, as well as the laser focusing.


  There are more than 5000 confirmed and validated planets beyond the solar
system to date, more than half of which were discovered by NASA's Kepler
mission. The catalog of Kepler's exoplanet candidates has only been extensively
analyzed under the assumption of white noise (i.i.d. Gaussian), which breaks
down on timescales longer than a day due to correlated noise (point-to-point
correlation) from stellar variability and instrumental effects. Statistical
validation of candidate transit events becomes increasingly difficult when they
are contaminated by this form of correlated noise, especially in the
low-signal-to-noise (S/N) regimes occupied by Earth--Sun and Venus--Sun
analogs. To diagnose small long-period, low-S/N putative transit signatures
with few (roughly 3--9) observed transit-like events (e.g., Earth--Sun
analogs), we model Kepler's photometric data as noise, treated as a Gaussian
process, with and without the inclusion of a transit model. Nested sampling
algorithms from the Python UltraNest package recover model evidences and
maximum a posteriori parameter sets, allowing us to disposition transit
signatures as either planet candidates or false alarms within a Bayesian
framework.


  We present a one-shot method to infer and render a photorealistic 3D
representation from a single unposed image (e.g., face portrait) in real-time.
Given a single RGB input, our image encoder directly predicts a canonical
triplane representation of a neural radiance field for 3D-aware novel view
synthesis via volume rendering. Our method is fast (24 fps) on consumer
hardware, and produces higher quality results than strong GAN-inversion
baselines that require test-time optimization. To train our triplane encoder
pipeline, we use only synthetic data, showing how to distill the knowledge from
a pretrained 3D GAN into a feedforward encoder. Technical contributions include
a Vision Transformer-based triplane encoder, a camera data augmentation
strategy, and a well-designed loss function for synthetic data training. We
benchmark against the state-of-the-art methods, demonstrating significant
improvements in robustness and image quality in challenging real-world
settings. We showcase our results on portraits of faces (FFHQ) and cats (AFHQ),
but our algorithm can also be applied in the future to other categories with a
3D-aware image generator.


  We investigate the particle production of a scalar field $\chi$ coupled to an
inflaton field $\phi$ ($g^2\phi^2\chi^2/2$) in the {\it oscillating inflation}
model, which was recently proposed by Damour and Mukhanov. Although the
fluctuation of the $\phi$ field can be effectively enhanced during a stage of
the oscillating inflation, the maximum fluctuation is suppressed as the
critical value $\phi_c$ which indicates the scale of the core part of the
inflaton potential decreases, in taking into account the back reaction effect
of created particles. As for the $\chi$ particle production, we find that
larger values of the coupling constant $g$ are required to lead to an efficient
parametric resonance with the decrease of $\phi_c$, because an effective mass
of inflaton around the minimum of its potential becomes larger. However, it is
possible to generate the superheavy $\chi$ particle whose mass is greater than
$10^{14}$ GeV, which would result in an important consequence for the GUT
baryogenesis.


  A geometric treatment of T-duality as an operation which acts on differential
forms in superspace allows us to derive the complete set of T-duality
transformation rules which relate the superfield potentials of D=10 type IIA
supergravity with those of type IIB supergravity including Ramond-Ramond
superfield potentials and fermionic supervielbeins. We show that these rules
are consistent with the superspace supergravity constraints.


  We present a theoretical study of single-stranded DNA under stretching.
Within the proposed framework, the effects of basepairing on the mechanical
response of the molecule can be studied in combination with an arbitrary
underlying model of chain elasticity. In a generic case, we show that the
stretching curve of ssDNA exhibits two distinct features: the second-order
"unfolding" phase transition, and a sharp crossover, reminiscent of the
first-order "unzipping" transition in dsDNA. We apply the theory to the
particular cases of Worm-like Chain (WLC) and Freely-Joint Chain (FJC) models,
and discuss the universal and model--dependent features of the mechanical
response of ssDNA. In particular, we show that variation of the width of the
unzipping crossover with interaction strength is very sensitive to the
energetics of hairpin loops. This opens a new way of testing the elastic
properties of ssDNA.


  We study the problem of designing an artificial Mott insulator in a
correlated oxide heterostructure. We consider the extreme limit of quantum
confinement based on ionic discontinuity doping, and argue that a unique dimer
Mott insulator can be achieved for the case of a single SrO layer in a
GdTiO$_3$ matrix. In the dimer Mott insulator, electrons are localized not to
individual atoms but to bonding orbitals on molecular dimers formed across a
bilayer of two TiO$_2$ planes, and is analogous to the Mott insulating state of
Hubbard ladders, studied in the 1990s. We verify the existence of the dimer
Mott insulator through both ab initio and model Hamiltonian studies, and find
for reasonable values of Hubbard $U$ that it is stable and ferromagnetic with a
clear bonding/anti-bonding splitting of order 0.65eV, and a significant smaller
Mott gap whose size depends upon $U$. The combined effects of polar
discontinuity, strong structural relaxation and electron correlations all
contribute to the realization of this unique ground state.


  Increasing evidence suggests that many deployed AI systems do not
sufficiently support end-user interaction and information needs. Engaging
end-users in the design of these systems can reveal user needs and
expectations, yet effective ways of engaging end-users in the AI explanation
design remain under-explored. To address this gap, we developed a design
method, called AI-DEC, that defines four dimensions of AI explanations that are
critical for the integration of AI systems -- communication content, modality,
frequency, and direction -- and offers design examples for end-users to design
AI explanations that meet their needs. We evaluated this method through
co-design sessions with workers in healthcare, finance, and management
industries who regularly use AI systems in their daily work. Findings indicate
that the AI-DEC effectively supported workers in designing explanations that
accommodated diverse levels of performance and autonomy needs, which varied
depending on the AI system's workplace role and worker values. We discuss the
implications of using the AI-DEC for the user-centered design of AI
explanations in real-world systems.


  We study differential forms invariant under a finite reflection group over a
field of arbitrary characteristic. In particular, we prove an analogue of
Saito's freeness criterion for invariant differential 1-forms. We also discuss
how twisted wedging endows the invariant forms with the structure of a free
exterior algebra in certain cases. Some of the results are extended to the case
of relative invariants with respect to a linear character.


  Let R be a polynomial ring in r variables and D a dual ring upon which R acts
as partial differential operators (classical apolarity). For a type two graded
level Artinian algebras A=R/I, of socle degree j we consider the family of
Artinian Gorenstein [AG] quotients of A having the same socle degree. By
Macaulay duality, A corresponds to a unique 2-dimensional vector space
W(A)=<F,G> of degree-j homogeneous elements (forms) in D, and each such AG
quotient of A corresponds to an element $F_\lambda=F+\lambda G$ in W(A), up to
non-zero constant multiple. Our main result is a lower bound for the Hilbert
function of the generic AG quotient of A in terms of the Hilbert functions of A
and the AG quotients of R determined by F and by G. This result restricts the
possible sequences H that may occur as the Hilbert function for a type two
level algebra A.
  This result may also be viewed as obtaining an optimal lower bound on the
dimensions of the spaces of degree-u partial derivatives of $F+\lambda G$ for a
generic lambda in terms of the corresponding dimensions for the forms F and G.


  The influence of the charging effects on the transport characteristics of a
molecular wire bridging two metallic electrodes in the limit of weak contacts
is studied by generalized Breit-Wigner formula. Molecule is modeled as a
quantum dot with discrete energy levels, while the coupling to the electrodes
is treated within a broad band theory. Owing to this model we find
self-consistent occupation of particular energy levels and orbital energies of
the wire in the presence of transport. The nonlinear conductance and
current-voltage characteristics are investigated as a function of bias voltage
in the case of symmetric and asymmetric coupling to the electrodes. It is shown
that the shape of that curves are determined by the combined effect of the
electronic structure of the molecule and by electron-electron repulsion.


  High accuracy frequency metrology on the 4s 2S1/2 - 4p 2P1/2 transition in
calcium ions is performed using laser cooled and crystallized ions in a linear
Paul trap. Calibration is performed with a frequency comb laser, resulting in a
transition frequency of f=755222766.2(1.7) MHz. The accuracy presents an
improvement of more than one order of magnitude, and will facilitate a
comparison with quasar data in a search for a possible change of the fine
structure constant on a cosmological time scale.


  We analyze the propagating degrees of freedom in gravity models where the
scalar curvature in the action is replaced by a generic function $f(R)$ of the
curvature. That these gravity models are equivalent to Einstein's gravity with
an extra scalar field had previously been shown by applying a conformal
transformation. We confirm this result by calculating the particle propagators.
This provides further evidence of the unability of these models to explain the
accelerated expansion of the Universe without contradicting solar system
experiments.


  We propose a simple yet highly effective method that addresses the
mode-collapse problem in the Conditional Generative Adversarial Network (cGAN).
Although conditional distributions are multi-modal (i.e., having many modes) in
practice, most cGAN approaches tend to learn an overly simplified distribution
where an input is always mapped to a single output regardless of variations in
latent code. To address such issue, we propose to explicitly regularize the
generator to produce diverse outputs depending on latent codes. The proposed
regularization is simple, general, and can be easily integrated into most
conditional GAN objectives. Additionally, explicit regularization on generator
allows our method to control a balance between visual quality and diversity. We
demonstrate the effectiveness of our method on three conditional generation
tasks: image-to-image translation, image inpainting, and future video
prediction. We show that simple addition of our regularization to existing
models leads to surprisingly diverse generations, substantially outperforming
the previous approaches for multi-modal conditional generation specifically
designed in each individual task.


  We study a decision-maker's problem of finding optimal monetary incentive
schemes for retention when faced with agents whose participation decisions
(stochastically) depend on the incentive they receive. Our focus is on policies
constrained to fulfill two fairness properties that preclude outcomes wherein
different groups of agents experience different treatment on average. We
formulate the problem as a high-dimensional stochastic optimization problem,
and study it through the use of a closely related deterministic variant. We
show that the optimal static solution to this deterministic variant is
asymptotically optimal for the dynamic problem under fairness constraints.
Though solving for the optimal static solution gives rise to a non-convex
optimization problem, we uncover a structural property that allows us to design
a tractable, fast-converging heuristic policy. Traditional schemes for
retention ignore fairness constraints; indeed, the goal in these is to use
differentiation to incentivize repeated engagement with the system. Our work
(i) shows that even in the absence of explicit discrimination, dynamic policies
may unintentionally discriminate between agents of different types by varying
the type composition of the system, and (ii) presents an asymptotically optimal
policy to avoid such discriminatory outcomes.


  We consider a model of algorithmic self-assembly of geometric shapes out of
square Wang tiles studied in SODA 2010, in which there are two types of tiles
(e.g., constructed out of DNA and RNA material) and one operation that destroys
all tiles of a particular type (e.g., an RNAse enzyme destroys all RNA tiles).
We show that a single use of this destruction operation enables much more
efficient construction of arbitrary shapes. In particular, an arbitrary shape
can be constructed using an asymptotically optimal number of distinct tile
types (related to the shape's Kolmogorov complexity), after scaling the shape
by only a logarithmic factor. By contrast, without the destruction operation,
the best such result has a scale factor at least linear in the size of the
shape, and is connected only by a spanning tree of the scaled tiles. We also
characterize a large collection of shapes that can be constructed efficiently
without any scaling.


  We consider the asymptotic quasinormal frequencies of various spin fields in
Schwarzschild and Reissner-Nordstr\"om black holes. In the Schwarzschild case,
the real part of the asymptotic frequency is ln3 for the spin 0 and the spin 2
fields, while for the spin 1/2, the spin 1, and the spin 3/2 fields it is zero.
For the non-extreme charged black holes, the spin 3/2 Rarita-Schwinger field
has the same asymptotic frequency as that of the integral spin fields. However,
the asymptotic frequency of the Dirac field is different, and its real part is
zero. For the extremal case, which is relevant to the supersymmetric
consideration, all the spin fields have the same asymptotic frequency, the real
part of which is zero. For the imaginary parts of the asymptotic frequencies,
it is interesting to see that it has a universal spacing of $1/4M$ for all the
spin fields in the single-horizon cases of the Schwarzschild and the extreme
Reissner-Nordstr\"om black holes. The implications of these results to the
universality of the asymptotic quasinormal frequencies are discussed.


  We obtained amorphous-FeAs-free SmFeAsO1-xFx using a low temperature
sintering with slow cooling. SmFeAsO1-xFx is sintered at 980 {\deg}C for 40
hours and cooled slowly down to 600 {\deg}C. The low temperature sintering
suppresses the formation of amorphous FeAs, and the slow cooling introduces
much fluorine into SmFeAsO1-xFx. The superconductivity of this sample appears
at 57.8 K and the superconducting volume fraction reaches 96 %. To study the
change of fluorine concentration during the cooling process, samples are
quenched by water at 950 {\deg}C, 900 {\deg}C, 850 {\deg}C, 800 {\deg}C, 750
{\deg}C and 700 {\deg}C. It is found that fluorine is substituted not only at
the maximum heating temperature but also during the cooling process. The low
temperature sintering with slow cooling is very effective to obtain a
homogeneous SmFeAsO1-xFx with high fluorine concentration.