  We tackle the problem of classifying news articles pertaining to
disinformation vs mainstream news by solely inspecting their diffusion
mechanisms on Twitter. Our technique is inherently simple compared to existing
text-based approaches, as it allows to by-pass the multiple levels of
complexity which are found in news content (e.g. grammar, syntax, style). We
employ a multi-layer representation of Twitter diffusion networks, and we
compute for each layer a set of global network features which quantify
different aspects of the sharing process. Experimental results with two
large-scale datasets, corresponding to diffusion cascades of news shared
respectively in the United States and Italy, show that a simple Logistic
Regression model is able to classify disinformation vs mainstream networks with
high accuracy (AUROC up to 94%), also when considering the political bias of
different sources in the classification task. We also highlight differences in
the sharing patterns of the two news domains which appear to be
country-independent. We believe that our network-based approach provides useful
insights which pave the way to the future development of a system to detect
misleading and harmful information spreading on social media.


  With the growing demand for data connectivity, network service providers are
faced with the task of reducing their capital and operational expenses while
simultaneously improving network performance and addressing the increased
demand. Although Network Function Virtualization (NFV) has been identified as a
promising solution, several challenges must be addressed to ensure its
feasibility. In this paper, we address the Virtual Network Function (VNF)
migration problem by developing the VNF Neural Network for Instance Migration
(VNNIM), a migration strategy for VNF instances. The performance of VNNIM is
further improved through the optimization of the learning rate hyperparameter
through particle swarm optimization. Results show that the VNNIM is very
effective in predicting the post-migration server exhibiting a binary accuracy
of 99.07% and a delay difference distribution that is centered around a mean of
zero when compared to the optimization model. The greatest advantage of VNNIM,
however, is its run-time efficiency highlighted through a run-time analysis.


  Attempts to unravel the nature of magnetic ordering in LaSrCoO$_4$
(Co$^{3+}$), a compound intermediate between antiferromagnetic (AFM)
La$_2$CoO$_4$ (Co$^{2+}$) and ferromagnetic (FM) Sr$_2$CoO$_4$ (Co$^{4+}$),
have met with a limited success so far. In this report, the results of a
thorough investigation of dc magnetization and ac susceptibility (ACS) in
single-phase LaSrCoO$_4$ provide clinching evidence for a thermodynamic
paramagnetic (PM) - ferromagnetic (FM) phase transition at T$_{c}$ = 220.5 K,
followed at lower temperature (T$_{g}$ = 7.7 K) by a transition to the cluster
spin glass (CSG) state. Analysis of the low-field Arrott plot isotherms, in the
critical region near T$_{c}$, in terms of the Aharony-Pytte scaling equation of
state clearly establishes that the PM-FM transition is basically driven by
random magnetic anisotropy (RMA). For temperatures below $\approx$ 30 K, large
enough RMA destroys long-range FM order by breaking up the infinite FM network
into FM clusters of finite size and leads to the formation of a CSG state at
temperatures T $\lesssim$ 8 K by promoting freezing of finite FM clusters in
random orientations. Increasing strength of the single-ion magnetocrystalline
anisotropy (and hence RMA) with decreasing temperature is taken to reflect an
increase in the number of low-spin (LS) Co$^{3+}$ ions at the expense of that
of high-spin (HS) Co$^{3+}$ ions. At intermediate temperatures (30 K $\lesssim
T \lesssim$ 180 K), spin dynamics has contributions from the infinite FM
network (fast relaxation governed by a single anisotropy energy barrier) and
finite FM clusters (extremely slow stretched exponential relaxation due to
hierarchical energy barriers).


  We examine the relationship between the group x-ray luminosity in the 0.3-3.5
keV band and the measured velocity dispersion, and galaxy surface number
density. We find definite correlations. Richer groups follow the same relation
as rich clusters (cf. Quintana & Melnick 1982) with $L_x \propto \sigma^{4.0\pm
0.6}$, but the relation flattens for lower luminosity systems which have
velocity dispersions below 300 km/s). We suggest that the $L_x-\sigma$ relation
arises from a combination of extended cluster emission and emission associated
with individual galaxies. The x-ray emission for the richer groups is dominated
by emission from the intra-group medium, as for the richer clusters; emission
from the poorer clusters is dominated by less extended emission associated with
the individual group galaxies. This is a corrected version of the paper which
appeared in the Astronomical Journal (February 1994). We include a new version
of the affected figure.


  Pair production of W gauge bosons is an important process at the LHC entering
many experimental analyses, both as background in new-physics searches or Higgs
measurements and as signal in precision studies and tests of the Standard
Model. Therefore, accurate predictions for this class of processes are of great
interest in order to exploit the full potential of LHC measurements. We use the
LoopSim method to combine NLO QCD results for WW and WW+jet, as well as the
loop-squared gluon-fusion contribution, to obtain approximate NNLO predictions
for WW production. The cross sections are calculated with VBFNLO and include
leptonic decays of the W bosons as well as finite-width and off-shell effects.
We find that the size of the additional corrections beyond NLO can be
significant and well outside of the NLO error bands given by renormalization
and factorization scale variation. Applying a jet veto, we observe further
negative corrections at NNLO, which we relate to the presence of large Sudakov
logarithms.


  One of the methods to calculate tree-level multi-gluon scattering amplitudes
is to use the Berends-Giele recursion relation involving off-shell currents or
off-shell amplitudes, if working in the light cone gauge. As shown in recent
works using the light-front perturbation theory, solutions to these recursions
naturally collapse into gauge invariant and gauge-dependent components, at
least for some helicity configurations. In this work, we show that such
structure is helicity independent and emerges from analytic properties of
matrix elements of Wilson line operators, where the slope of the straight gauge
path is shifted in a certain complex direction. This is similar to the
procedure leading to the Britto-Cachazo-Feng-Witten (BCFW) recursion, however
we apply a complex shift to the Wilson line slope instead of the external
momenta. While in the original BCFW procedure the boundary integrals over the
complex shift vanish for certain deformations, here they are non-zero and are
equal to the off-shell amplitudes. The main result can thus be summarized as
follows: we derive a decomposition of a helicity-fixed off-shell current into
gauge invariant component given by a matrix element of a straight Wilson line
plus a reminder given by a sum of products of gauge invariant and gauge
dependent quantities. We give several examples realizing this relation,
including the five-point next-to-MHV helicity configuration.


  One of the defining features of both recent and historical cases of global
climate change is Arctic Amplification (AA). This is the more rapid change in
the surface air temperature (SAT) in the Arctic compared to some wider
reference region, such as the Northern Hemisphere (NH) mean. Many different
metrics have been developed to quantify the degree of AA based on SAT
anomalies, trends and variability. The use of different metrics, as well as the
choice of dataset to use can affect conclusions about the magnitude and
temporal variability of AA. Here we review the established metrics of AA to see
how well they agree upon the temporal signature of AA, such as the
multi-decadal variability, and assess the consistency in these metrics across
different commonly-used datasets which cover both the early and late 20th
century warming in the Arctic. We find the NOAA 20th Century Reanalysis most
closely matches the observations when using metrics based upon SAT trends (A2),
variability (A3) and regression (A4) of the SAT anomalies, and the ERA 20th
Century Reanalysis is closest to the observations in the SAT anomalies (A1) and
variability of SAT anomalies (A3). However, there are large seasonal
differences in the consistency between datasets. Moreover, the largest
differences between the century-long reanalysis products and observations are
during the early warming period, likely due to the sparseness of the
observations in the Arctic at that time. In the modern warming period, the high
density of observations strongly constrains all the reanalysis products,
whether they include satellite observations or only surface observations. Thus,
all the reanalysis and observation products produce very similar magnitudes and
temporal variability in the degree of AA during the recent warming period.


  Model-induced distribution shifts (MIDS) occur as previous model outputs
pollute new model training sets over generations of models. This is known as
model collapse in the case of generative models, and performative prediction or
unfairness feedback loops for supervised models. When a model induces a
distribution shift, it also encodes its mistakes, biases, and unfairnesses into
the ground truth of its data ecosystem. We introduce a framework that allows us
to track multiple MIDS over many generations, finding that they can lead to
loss in performance, fairness, and minoritized group representation, even in
initially unbiased datasets. Despite these negative consequences, we identify
how models might be used for positive, intentional, interventions in their data
ecosystems, providing redress for historical discrimination through a framework
called algorithmic reparation (AR). We simulate AR interventions by curating
representative training batches for stochastic gradient descent to demonstrate
how AR can improve upon the unfairnesses of models and data ecosystems subject
to other MIDS. Our work takes an important step towards identifying,
mitigating, and taking accountability for the unfair feedback loops enabled by
the idea that ML systems are inherently neutral and objective.


  Variational quantum eigensolvers (VQEs) are leading candidates to demonstrate
near-term quantum advantage. Here, we conduct density-matrix simulations of
leading gate-based VQEs for a range of molecules. We numerically quantify their
level of tolerable depolarizing gate-errors. We find that: (i) The
best-performing VQEs require gate-error probabilities between $10^{-6}$ and
$10^{-4}$ ( $10^{-4}$ and $10^{-2}$ with error mitigation) to predict, within
chemical accuracy, ground-state energies of small molecules with $4-14$
orbitals. (ii) ADAPT-VQEs that construct ansatz circuits iteratively outperform
fixed-circuit VQEs. (iii) ADAPT-VQEs perform better with circuits constructed
from gate-efficient rather than physically-motivated elements. (iv) The
maximally-allowed gate-error probability, $p_c$, for any VQE to achieve
chemical accuracy decreases with the number $\ncx$ of noisy two-qubit gates as
$p_c\approxprop\ncx^{-1}$. Additionally, $p_c$ decreases with system size, even
with error mitigation, implying that larger molecules require even lower
gate-errors. Thus, quantum advantage via gate-based VQEs is unlikely unless
gate-error probabilities are decreased by orders of magnitude.


  Cold atoms provide a flexible platform for synthesizing and characterizing
topolog-ical matter, where geometric phases play a central role. However, cold
atoms are intrinsically prone to thermal noise, which can overwhelm the
topological response and hamper promised applications. On the other hand,
geometric phases also de-termine the energy spectra of particles subjected to a
static force, based on the po-larization relation between Wannier-Stark ladders
and geometric Zak phases. By exploiting this relation, we develop a method to
extract geometric phases from en-ergy spectra of room-temperature superradiance
lattices, which are momentum-space lattices of timed Dicke states. In such
momentum-space lattices the thermal motion of atoms, instead of being a source
of noise, provides effective forces which lead to spectroscopic signatures of
the Zak phases. We measure Zak phases direct-ly from the anti-crossings between
Wannier-Stark ladders in the Doppler-broadened absorption spectra of
superradiance lattices. Our approach paves the way of measuring topological
invariants and developing their applications in room-temperature atoms.


  Eight decades ago, Arthur Holmes introducted the idea of mantle convection as
a mechanism for continental drift. Five decades ago, continental drift was
modified to become plate tectonics theory, which included mantle convection as
an absolutely critical component. Using the submarine design and operation
concept of "neutral buoyancy", which follows from Archimedes' discoveries, the
concept of mantle convection is proven to be incorrect, concomitantly refuting
plate tectonics, refuting all mantle convection models, and refuting all models
that depend upon mantle convection.


  In this work we describe the system built for the three English subtasks of
the SemEval 2016 Task 3 by the Department of Computer Science of the University
of Houston (UH) and the Pattern Recognition and Human Language Technology
(PRHLT) research center - Universitat Polit`ecnica de Val`encia: UH-PRHLT. Our
system represents instances by using both lexical and semantic-based similarity
measures between text pairs. Our semantic features include the use of
distributed representations of words, knowledge graphs generated with the
BabelNet multilingual semantic network, and the FrameNet lexical database.
Experimental results outperform the random and Google search engine baselines
in the three English subtasks. Our approach obtained the highest results of
subtask B compared to the other task participants.


  We start with a brief overview of the known facts about the spaces of
discrete Radon measures those may be considered as generalizations of
configuration spaces. Then we study three Markov dynamics on the spaces of
discrete Radon measures: analogues of the contact model, of the
Bolker--Dieckmann--Law--Pacala model, and of the Glauber-type dynamics. We show
how the results obtained previously for the configuration spaces can be
modified for the case of the spaces of discrete Radon measures.


  We consider the 2-dimensional spatially homogeneous Boltzmann equation for
hard potentials. We assume that the initial condition is a probability measure
that has some exponential moments and is not a Dirac mass. We prove some
regularization properties: for a class of very hard potentials, the solution
instantaneously belongs to $H^r$, for some $r\in (-1,2)$ depending on the
parameters of the equation. Our proof relies on the use of a well-suited
Malliavin calculus for jump processes.


  Biologically functional liquid-liquid phase separation of intrinsically
disordered proteins (IDPs) is driven by interactions encoded by their amino
acid sequences. Little is currently known about the molecular recognition
mechanisms for distributing different IDP sequences into various cellular
membraneless compartments. Pertinent physics was addressed recently by applying
random-phase-approximation (RPA) polymer theory to electrostatics, which is a
major energetic component governing IDP phase properties. RPA accounts for
charge patterns and thus has advantages over Flory-Huggins and Overbeek-Voorn
mean-field theories. To make progress toward deciphering the phase behaviors of
multiple IDP sequences, the RPA formulation for one IDP species plus solvent is
hereby extended to treat polyampholyte solutions containing two IDP species.
The new formulation generally allows for binary coexistence of two phases, each
containing a different set of volume fractions $(\phi_1,\phi_2)$ for the two
different IDP sequences. The asymmetry between the two predicted coexisting
phases with regard to their $\phi_1/\phi_2$ ratios for the two sequences
increases with increasing mismatch between their charge patterns. This finding
points to a multivalent, stochastic, "fuzzy" mode of molecular recognition that
helps populate various IDP sequences differentially into separate phase
compartments. An intuitive illustration of this trend is provided by
Flory-Huggins models, whereby a hypothetical case of ternary coexistence is
also explored. Augmentations of the present RPA theory with a relative
permittivity $\epsilon_{\rm r}(\phi)$ that depends on IDP volume fraction
$\phi=\phi_1+\phi_2$ lead to higher propensities to phase separate, in line
with the case with one IDP species we studied previously. ...


  Let $K=Z/pZ$ and let $A$ be a subset of $\GL_r(K)$ such that $<A>$ is
solvable. We reduce the study of the growth of $A$ under the group operation to
the nilpotent setting. Specifically we prove that either $A$ grows rapidly
(meaning $|A\cdot A\cdot A|\gg |A|^{1+\delta}$), or else there are groups $U_R$
and $S$, with $S/U_R$ nilpotent such that $A_k\cap S$ is large and
$U_R\subseteq A_k$, where $k$ is a bounded integer and $A_k = \{x_1 x_2...b x_k
: x_i \in A \cup A^{-1} \cup {1}}$. The implied constants depend only on the
rank $r$ of $\GL_r(K)$.
  When combined with recent work by Pyber and Szab\'o, the main result of this
paper implies that it is possible to draw the same conclusions without
supposing that $<A>$ is solvable.


  Spatial differentiability of solutions of stochastic differential equations
(SDEs) is a classical question in stochastic analysis. The case of coefficients
with globally Lipschitz continuous derivatives is well understood in the
literature. Counterexamples with smooth and bounded coefficients demonstrate
that the non-globally Lipschitz case is more subtle. In this article we
establish conditions, including a suitable local monotonicity property, which
provide existence of continuously differentiable solutions of SDEs, moment
estimates and strong local H\"older regularity.


  The elementary excitation in the antiferromagnetic spin-1 model known as the
Affleck-Kennedy-Lieb-Tasaki (AKLT) Hamiltonian has been described alternatively
as magnons or kink-like solitons (triplons). The latter, which we call the
triplon throughout this paper, has been proven equivalent descriptions of the
same magnon excitation and not an independent branch of excited states. On the
other hand, no careful examination of multi-magnon and multi-triplon
equivalence was made in the past. In this paper we prove that two-magnon and
two-triplon states are also identical descriptions of the same excited states,
and furthermore that their energies break down as the sum of one-triplon
energies exactly for the AKLT Hamiltonian. The statement holds despite the fact
that the model is non-integrable. Such magnon/triplon dichotomy is conjectured
to hold for arbitrary n-magnon and n-triplon states. The one- and two-triplon
states form orthogonal sets that can be used to span the low-energy Hilbert
space. We construct an effective version of the AKLT Hamiltonian within such
subspace, and work out the correction to the one-triplon energy gap that finds
excellent agreement with the known exact value.


  We consider episodic reinforcement learning in reward-mixing Markov decision
processes (RMMDPs): at the beginning of every episode nature randomly picks a
latent reward model among $M$ candidates and an agent interacts with the MDP
throughout the episode for $H$ time steps. Our goal is to learn a near-optimal
policy that nearly maximizes the $H$ time-step cumulative rewards in such a
model. Previous work established an upper bound for RMMDPs for $M=2$. In this
work, we resolve several open questions remained for the RMMDP model. For an
arbitrary $M\ge2$, we provide a sample-efficient
algorithm--$\texttt{EM}^2$--that outputs an $\epsilon$-optimal policy using
$\tilde{O} \left(\epsilon^{-2} \cdot S^d A^d \cdot \texttt{poly}(H, Z)^d
\right)$ episodes, where $S, A$ are the number of states and actions
respectively, $H$ is the time-horizon, $Z$ is the support size of reward
distributions and $d=\min(2M-1,H)$. Our technique is a higher-order extension
of the method-of-moments based approach, nevertheless, the design and analysis
of the \algname algorithm requires several new ideas beyond existing
techniques. We also provide a lower bound of $(SA)^{\Omega(\sqrt{M})} /
\epsilon^{2}$ for a general instance of RMMDP, supporting that super-polynomial
sample complexity in $M$ is necessary.


  Magnetic fields in cool stars can be investigated by measuring Zeeman line
broadening and polarization in atomic and molecular lines. Similar to the Sun,
these fields are complex and height-dependent. Many molecular lines dominating
M-dwarf spectra (e.g., FeH, CaH, MgH, and TiO) are temperature -- and Zeeman --
sensitive and form at different atmospheric heights, which makes them excellent
probes of magnetic fields on M dwarfs. Our goal is to analyze the complexity of
magnetic fields in M dwarfs. We investigate how magnetic fields vary with the
stellar temperature and how "surface" inhomogeneities are distributed in height
-- the dimension that is usually neglected in stellar magnetic studies. We have
determined effective temperatures of the photosphere and of magnetic features,
magnetic field strengths and filling factors for nine M dwarfs (M1-M7). Our
chi^2 analysis is based on a comparison of observed and synthetic intensity and
circular polarization profiles. Stokes profiles were calculated by solving
polarized radiative transfer equations. Properties of magnetic structures
depend on the analyzed atomic or molecular species and their formation heights.
Two types of magnetic features similar to those on the Sun have been found: a
cooler (starspots) and a hotter (network) one. The magnetic field strength in
both starspots and network is within 3 kG to 6 kG, on average it is 5 kG. These
fields occupy a large fraction of M dwarf atmospheres at all heights, up to
100%. The plasma beta is less than one, implying highly magnetized stars. A
combination of molecular and atomic species and a simultaneous analysis of
intensity and circular polarization spectra have allowed us to better decipher
the complexity of magnetic fields on M dwarfs, including their dependence on
the atmospheric height. This work provides an opportunity to investigate a
larger sample of M dwarfs and L-type brown dwarfs.


  The centrally extended su(2|2) superalgebra is an asymptotic symmetry of the
light-cone string sigma model on AdS5 x S5. We consider an evaluation
representation of the conventional Yangian built over a particular
16-dimensional long representation of the centrally extended su(2|2).
Interestingly, we find that S-matrices compatible with this evaluation
representation do not exist. On the other hand, by requiring centrally extended
su(2|2) invariance and explicitly solving the Yang-Baxter equation, we find a
scattering matrix for long-short representations of the Lie superalgebra. We
notice that this S-matrix is invariant under a different representation of
non-evaluation type, induced from the tensor product of short representations.
Our findings concern the conventional Yangian only, and are not applied to
possible algebraic extensions of the latter.


  For statistical inference of means of stationary processes, one needs to
estimate their time-average variance constants (TAVC) or long-run variances.
For a stationary process, its TAVC is the sum of all its covariances and it is
a multiple of the spectral density at zero. The classical TAVC estimate which
is based on batched means does not allow recursive updates and the required
memory complexity is O(n). We propose a faster algorithm which recursively
computes the TAVC, thus having memory complexity of order O(1) and the
computational complexity scales linearly in $n$. Under short-range dependence
conditions, we establish moment and almost sure convergence of the recursive
TAVC estimate. Convergence rates are also obtained.


  In this thesis, we study the critical dynamics near the QCD critical point.
  Near the critical point, the relevant modes for the critical dynamics are
identified as the hydrodynamic modes. Thus, we first study the linear dynamics
of them by the relativistic hydrodynamics.
  We show that the thermal diffusion mode is the most relevant mode, whereas
the sound mode is suppressed around the critical point. We also find that the
Landau equation, which is believed to be an acausal hydrodynamic equation, has
no problem to describe slowly varying fluctuations. Moreover, we find that the
Israel-Stewart equation, which is a causal one, gives the same result as the
Landau equation gives in the long-wavelength region.
  Next, we study the nonlinear dynamics of the hydrodynamic modes by the
nonlinear Langevin equation and the dynamic renormalization group (RG). In the
vicinity of the critical point, the usual hydrodynamics breaks down by large
fluctuations. Thus, we must consider the nonlinear Langevin equation. We
construct the nonlinear Langevin equation based on the generalized Langevin
theory. After the construction, we apply the dynamic RG to the Langevin
equation and derive the RG equation for the transport coefficients.
  We find that the resulting RG equation turns out to be the same as that for
the liquid-gas critical point except for an insignificant constant.
Consequently, the bulk viscosity and the thermal conductivity strongly diverge
at the critical point. Then, a system near the critical point can not be
described as a perfect fluid by their strong divergences.
  We also show that the thermal and viscous diffusion modes exhibit
critical-slowing down with the dynamic critical exponents z_{thermal} \sim 3
and z_{viscous} \sim 2, respectively.
  In contrast, the sound mode shows critical-speeding up with the negative
exponent z_{sound} \sim -0.8.


  Gauge-invariant systems in unconstrained configuration and phase spaces,
equivalent to second-class constraints systems upon a gauge-fixing, are
discussed. A mathematical pendulum on an $n-1$-dimensional sphere $S^{n-1}$ as
an example of a mechanical second-class constraints system and the O(n)
non-linear sigma model as an example of a field theory under second-class
constraints are discussed in details and quantized using the existence of
underlying dilatation gauge symmetry and by solving the constraint equations
explicitly. The underlying gauge symmetries involve, in general, velocity
dependent gauge transformations and new auxiliary variables in extended
configuration space. Systems under second-class holonomic constraints have
gauge-invariant counterparts within original configuration and phase spaces.
The Dirac's supplementary conditions for wave functions of first-class
constraints systems are formulated in terms of the Wigner functions which
admit, as we show, a broad set of physically equivalent supplementary
conditions. Their concrete form depends on the manner the Wigner functions are
extrapolated from the constraint submanifolds into the whole phase space.


  In order to understand the role of space in ecological communities where each
species produces a certain type of resource and has varying abilities to
exploit the resources produced by its own species and by the other species, we
carry out a comparative study of an interacting particle system and its
mean-field approximation. For a wide range of parameter values, we show both
analytically and numerically that the spatial model results in predictions that
significantly differ from its nonspatial counterpart, indicating that the use
of the mean-field approach to describe the evolution of communities in which
individuals only interact locally is invalid. In two-species communities, the
disagreements between the models appear either when both species compete by
producing resources that are more beneficial for their own species or when both
species cooperate by producing resources that are more beneficial for the other
species. In particular, while both species coexist if and only if they
cooperate in the mean-field approximation, the inclusion of space in the form
of local interactions may prevent coexistence even in cooperative communities.
Introducing additional species, cooperation is no longer the only mechanism
that promotes coexistence. We prove that, in three-species communities,
coexistence results either from a global cooperative behavior, or from
rock-paper-scissors type interactions, or from a mixture of these dynamics,
which excludes in particular all cases in which two species compete.


  Significant progress has been made to develop silicon pixel technologies for
use in the vertex and tracker regions of the proposed Compact Linear Collider
(CLIC) detector design. The electron-positron collisions generated by this
linear accelerator provide a clean, low-radiation environment for the inner
detectors. However, physics-driven performance targets, the CLIC beam
structure, and occupancies from beam-induced backgrounds place challenging
requirements on detector technologies for this region. A pixel pitch down to 25
x 25$\mu$m$^{-2}$, material budget $\leq$ 0.2-2$\%X_0$ per layer, average power
dissipation of down to 50mWcm$^{-2}$, position resolution of 3-7$\mu$m, and
timing resolution as low as 5ns are called for in the vertex and tracking
detectors. To this aim, a comprehensive R&D programme is ongoing to design and
test silicon pixel detectors to fulfil these specifications, including both
monolithic and hybrid devices. These studies involve Allpix$^2$ Monte Carlo and
TCAD simulations, advanced 65nm ASIC and sensor design, laboratory testing, and
beam tests of individual modules to determine the required performance
parameters. The characterisation and simulation modelling of these devices has
also lead to the development of a set of tools and software within the CLIC
detector and physics (CLICdp) collaboration. This publication will present
recent results from the technologies being developed and tested in view of the
CLIC vertex and tracking detector requirements, such as various monolithic CMOS
sensors, and fine pitch hybrid assemblies with planar sensors.


  This paper investigates the long-time behavior of double branching
annihilating random walkers with nearest-neighbor dependent rates. The system
consists of even number of particles which can execute nearest-neighbor random
walk and they can as well give birth in a parity conserving manner to two other
particles with rates $1$ and $b$, respectively, until they meet. Upon meeting,
each of the adjacent particles can branch with rate $p\cdot b$ while it can
annihilate, i.e. hop on, the other particle with rate $p$ for some $0< p\leq
1$. This process first appeared in the article by D. ben Avraham, F. Leyvraz
and S. Redner (Propagation and extinction in branching annihilating random
walks, Phys. Rev. E 50(3), 1994) and can be considered as the extension of
Sudbury's model (The branching annihilating process: an interacting particle
system, Ann. Probab., 18(2), 1990). We prove that in some region of the
parameters $(p,b)$, the process survives with positive probability. Combining
with Sudbury's extinction result it shows a phase transition phenomenon for
this model. In some sense our result also shows the sharpness of the
assumptions of the article by M. Bal\'azs and A. L. Nagy (Dependent double
branching annihilating random walk, Electron. J. Probab., 20(84), 2015). We use
similar arguments that was developed by M. Bramson and L. Gray in their article
titled "The survival of branching annihilating random walk" (Z. Wahrsch. Verw.
Gebiete, 68(4), 1985).


  In this paper, we study the set of absolute continuity of p-harmonic measure,
$\mu$, and $(n-1)-$dimensional Hausdorff measure, $\mathcal{H}^{n-1}$, on
locally flat domains in $\mathbb{R}^{n}$, $n\geq 2$. We prove that for fixed
$p$ with $2<p<\infty$ there exists a Reifenberg flat domain
$\Omega\subset\mathbb{R}^{n}$, $n\geq 2$, with
$\mathcal{H}^{n-1}(\partial\Omega)<\infty$ and a Borel set
$K\subset\partial\Omega$ such that $\mu(K)>0=\mathcal{H}^{n-1}(K)$ where $\mu$
is the p-harmonic measure associated to a positive weak solution to p-Laplace
equation in $\Omega$ with continuous boundary value zero on $\partial\Omega$.
We also show that there exists such a domain for which the same result holds
when $p$ is fixed with $2-\eta<p<2$ for some $\eta>0$ provided that $n\geq 3$.
  This work is a generalization of a recent result of Azzam, Mourgoglou, and
Tolsa when the measure $\mu$ is harmonic measure at $x$, $\omega=\omega^{x}$,
associated to the Laplace equation, i.e when $p=2$.


  Atom probe tomography (APT) helps elucidate the link between the nanoscale
chemical variations and physical properties, but it has limited structural
resolution. Field ion microscopy (FIM), a predecessor technique to APT, is
capable of attaining atomic resolution along certain sets of crystallographic
planes albeit at the expense of elemental identification. We demonstrate how
two commercially-available atom probe instruments, one with a straight flight
path and one fitted with a reflectron-lens, can be used to acquire
time-of-flight mass spectrometry data concomitant with a FIM experiment. We
outline various experimental protocols making use of temporal and spatial
correlations to best discriminate field evaporated signals from the large field
ionised background signal, demonstrating an unsophisticated yet efficient data
mining strategy to provide this discrimination. We discuss the remaining
experimental challenges that need be addressed, notably concerned with accurate
detection and identification of individual field evaporated ions contained
within the high field ionised flux that contributes to a FIM image. Our hybrid
experimental approach can, in principle, exhibit true atomic resolution with
elemental discrimination capabilities, neither of which atom probe nor field
ion microscopy can individually fully deliver - thereby making this new
approach, here broadly termed analytical field ion microscope (aFIM), unique.


  Synchronizations of processing elements (PEs) in massively parallel
simulations, which arise due to communication or load imbalances between PEs,
significantly affect the scalability of scientific applications. We have
recently proposed a method based on finite-difference schemes to solve partial
differential equations in an asynchronous fashion -- synchronization between
PEs is relaxed at a mathematical level. While standard schemes can maintain
their stability in the presence of asynchrony, their accuracy is drastically
affected. In this work, we present a general methodology to derive
asynchrony-tolerant (AT) finite difference schemes of arbitrary order of
accuracy, which can maintain their accuracy when synchronizations are relaxed.
We show that there are several choices available in selecting a stencil to
derive these schemes and discuss their effect on numerical and computational
performance. We provide a simple classification of schemes based on the stencil
and derive schemes that are representative of different classes. Their
numerical error is rigorously analyzed within a statistical framework to obtain
the overall accuracy of the solution. Results from numerical experiments are
used to validate the performance of the schemes.


  A deep understanding of two-phase displacement in porous media with
permeability contrast is essential for the design and optimisation of enhanced
oil recovery processes. In this paper, we investigate the forced imbibition
behaviour in two dual-permeability geometries that are of equal permeability
contrast. First, a mathematical model is developed for the imbibition in a pore
doublet, which shows that the imbibition dynamics can be fully described by the
viscosity ratio $\lambda$ and capillary number $Ca_m$ which creatively
incorporates the influence of channel width and length. Through the finite
difference solution of the mathematical model, a $\lambda-Ca_m$ phase diagram
is established to characterise the imbibition preference in the pore doublet.
We then investigate the imbibition process in a dual-permeability pore network
using a well-established lattice Boltzmann method, focusing on the competition
between the viscous and capillary forces. Like in the pore doublet, the
preferential imbibition occurs in high permeability zone at high $Ca_{m}$ but
in low permeability zone at low $Ca_{m}$. When $Ca_m$ is not sufficiently high,
an oblique advancing pattern is observed which is attributed to non-trivial
interfacial tension. Thanks to the newly defined capillary number, the critical
$Ca_{m}$ curve on which the breakthrough simultaneously occurs in both
permeability zones, is found to match perfectly with that from the pore doublet
and it is the optimal condition for maximising the imbibition efficiency in the
entire pore network.


  Personalized dialogue systems are an essential step toward better
human-machine interaction. Existing personalized dialogue agents rely on
properly designed conversational datasets, which are mostly monolingual (e.g.,
English), which greatly limits the usage of conversational agents in other
languages. In this paper, we propose a multi-lingual extension of Persona-Chat,
namely XPersona. Our dataset includes persona conversations in six different
languages other than English for building and evaluating multilingual
personalized agents. We experiment with both multilingual and cross-lingual
trained baselines, and evaluate them against monolingual and
translation-pipeline models using both automatic and human evaluation.
Experimental results show that the multilingual trained models outperform the
translation-pipeline and that they are on par with the monolingual models, with
the advantage of having a single model across multiple languages. On the other
hand, the state-of-the-art cross-lingual trained models achieve inferior
performance to the other models, showing that cross-lingual conversation
modeling is a challenging task. We hope that our dataset and baselines will
accelerate research in multilingual dialogue systems.


  The paper is devoted to several questions related to the notion of
Cohomological Hall algebra (COHA for short) introduced few years ago by Maxim
Kontsevich and the author. In particular we discuss a class of representations
of COHA in the cohomology groups of the moduli spaces of stable framed objects.
We speculate about possible sources of interesting representations as well as
about some applications to geometry and physics.


  To build a universal quantum computer from fragile physical qubits, effective
implementation of quantum error correction (QEC) is an essential requirement
and a central challenge. Existing demonstrations of QEC are based on a schedule
of discrete error syndrome measurements and adaptive recovery operations. These
active routines are hardware intensive, prone to introducing and propagating
errors, and expected to consume a vast majority of the processing power in a
large-scale quantum computer. In principle, QEC can be realized autonomously
and continuously by tailoring dissipation within the quantum system, but this
strategy has remained challenging so far. Here we encode a logical qubit in
Schr\"odinger cat-like multiphoton states of a superconducting cavity, and
demonstrate a corrective dissipation process that directly stabilizes an error
syndrome operator: the photon number parity. Implemented with continuous-wave
control fields only, this passive protocol realizes autonomous correction
against single-photon loss and boosts the coherence time of the multiphoton
qubit by over a factor of two. Notably, QEC is realized in a modest hardware
setup with neither high-fidelity readout nor fast digital feedback, in contrast
to the technological sophistication required for prior QEC demonstrations.
Compatible with other error suppression and phase stabilization techniques, our
experiment suggests reservoir engineering as a resource-efficient alternative
or supplement to active QEC in future quantum computing architectures.


  This work proposes improvements in the electroencephalogram (EEG) recording
protocols for motor imagery through the introduction of actual motor movement
and/or somatosensory cues. The results obtained demonstrate the advantage of
requiring the subjects to perform motor actions following the trials of
imagery. By introducing motor actions in the protocol, the subjects are able to
perform actual motor planning, rather than just visualizing the motor movement,
thus greatly improving the ease with which the motor movements can be imagined.
This study also probes the added advantage of administering somatosensory cues
in the subject, as opposed to the conventional auditory/visual cues. These
changes in the protocol show promise in terms of the aptness of the spatial
filters obtained on the data, on application of the well-known common spatial
pattern (CSP) algorithms. The regions highlighted by the spatial filters are
more localized and consistent across the subjects when the protocol is
augmented with somatosensory stimuli. Hence, we suggest that this may prove to
be a better EEG acquisition protocol for detecting brain activation in response
to intended motor commands in (clinically) paralyzed/locked-in patients.


  Adversarial training (AT) as a regularization method has proved its
effectiveness on various tasks. Though there are successful applications of AT
on some NLP tasks, the distinguishing characteristics of NLP tasks have not
been exploited. In this paper, we aim to apply AT on machine reading
comprehension (MRC) tasks. Furthermore, we adapt AT for MRC tasks by proposing
a novel adversarial training method called PQAT that perturbs the embedding
matrix instead of word vectors. To differentiate the roles of passages and
questions, PQAT uses additional virtual P/Q-embedding matrices to gather the
global perturbations of words from passages and questions separately. We test
the method on a wide range of MRC tasks, including span-based extractive RC and
multiple-choice RC. The results show that adversarial training is effective
universally, and PQAT further improves the performance.


  An extensive set of realistic equations of state for superdense matter with a
quark phase transition is derived on the basis of the three equations of state
for neutron matter and the eight variants of strange quark-gluon plasmas in the
MIT quark bag model. The characteristics of the phase transitions are described
and the calculated equations of state with a density jump are studied in
detail.


  The ratio of $\alpha$-elements to iron in galaxies holds valuable information
about the star formation history since their enrichment occurs on different
timescales. The fossil record of stars in galaxies has mostly been excavated
for passive galaxies, since the light of star-forming galaxies is dominated by
young stars which have much weaker atmospheric absorption features. Here we use
the largest reference cosmological simulation of the EAGLE project to
investigate the origin of variations in stellar $\alpha$-enhancement among
star-forming galaxies at $z=0$, and their impact on integrated spectra. The
definition of $\alpha$-enhancement in a composite stellar population is
ambiguous. We elucidate two definitions - termed `mean' and `galactic'
$\alpha$-enhancement - in more detail. While a star-forming galaxy has a high
`mean' $\alpha$-enhancement when its stars formed rapidly, a galaxy with a
large `galactic' $\alpha$-enhancement generally had a delayed star formation
history. We find that absorption-line strengths of Mg and Fe correlate with
variations in $\alpha$-enhancement. These correlations are strongest for the
`galactic' $\alpha$-enhancement. However, we show that these are mostly caused
by other effects which are cross-correlated with $\alpha$-enhancement, such as
variations in the light-weighted age. This severely complicates the retrieval
of $\alpha$-enhancements in star-forming galaxies. The ambiguity is not severe
for passive galaxies and we confirm that spectral variations in these galaxies
are caused by measurable variations in $\alpha$-enhancements. We suggest that
this more complex coupling between $\alpha$-enhancement and star formation
histories can guide the interpretation of new observations of star-forming
galaxies.


  We present an automatic multilayer power plane generation method to
accelerate the design of printed circuit boards (PCB). In PCB design, while
automatic solvers have been developed to predict important indicators such as
the IR-drop, power integrity, and signal integrity, the generation of the power
plane itself still largely relies on laborious manual methods. Our automatic
power plane generation approach is based on genetic optimization combined with
a multilayer perceptron and is able to automatically generate power planes
across a diverse set of problems with varying levels of difficulty. Our method
GOMLP consists of an outer loop genetic optimizer (GO) and an inner loop
multi-layer perceptron (MLP) that generate power planes automatically. The
critical elements of our approach include contour detection, feature expansion,
and a distance measure to enable island-minimizing complex power plane
generation. We compare our approach to a baseline solution based on A*. The A*
method consisting of a sequential island generation and merging process which
can produce less than ideal solutions. Our experimental results show that on
single layer power plane problems, our method outperforms A* in 71% of the
problems with varying levels of board layout difficulty. We further describe
H-GOMLP, which extends GOMLP to multilayer power plane problems using
hierarchical clustering and net similarities based on the Hausdorff distance.


  We show that in the angle resolved photoemission spectroscopy (ARPES)
near-surface induced fields can be useful for disentangling the surface and
bulk related emission. The jump of the dielectric function at the interface
results in a nonzero term $\operatorname{div}\textbf{A}$ in the photoemission
matrix element. The term happens to be significant approximately within the
first unit cell and leads to the circular dichroism for the states localized
therein. As an example we use ARPES spectra of an YBa$_2$Cu$_3$O$_{7-\delta}$
crystal to distinguish between the overdoped surface related component and its
bulk counterparts.


  Let $\xi(n, x)$ be the local time at $x$ for a recurrent one-dimensional
random walk in random environment after $n$ steps, and consider the maximum
$\xi^*(n) = \max_x \xi(n,x)$. It is known that $\limsup \xi^*(n)/n$ is a
positive constant a.s. We prove that $\liminf_n (\log\log\log n)\xi^*(n)/n$ is
a positive constant a.s.; this answers a question of P. R\'ev\'esz (1990). The
proof is based on an analysis of the {\em valleys /} in the environment,
defined as the potential wells of record depth. In particular, we show that
almost surely, at any time $n$ large enough, the random walker has spent almost
all of its lifetime in the two deepest valleys of the environment it has
encountered. We also prove a uniform exponential tail bound for the ratio of
the expected total occupation time of a valley and the expected local time at
its bottom.


  Basecalling, an essential step in many genome analysis studies, relies on
large Deep Neural Networks (DNNs) to achieve high accuracy. Unfortunately,
these DNNs are computationally slow and inefficient, leading to considerable
delays and resource constraints in the sequence analysis process. A
Computation-In-Memory (CIM) architecture using memristors can significantly
accelerate the performance of DNNs. However, inherent device non-idealities and
architectural limitations of such designs can greatly degrade the basecalling
accuracy, which is critical for accurate genome analysis. To facilitate the
adoption of memristor-based CIM designs for basecalling, it is important to (1)
conduct a comprehensive analysis of potential CIM architectures and (2) develop
effective strategies for mitigating the possible adverse effects of inherent
device non-idealities and architectural limitations.
  This paper proposes Swordfish, a novel hardware/software co-design framework
that can effectively address the two aforementioned issues. Swordfish
incorporates seven circuit and device restrictions or non-idealities from
characterized real memristor-based chips. Swordfish leverages various
hardware/software co-design solutions to mitigate the basecalling accuracy loss
due to such non-idealities. To demonstrate the effectiveness of Swordfish, we
take Bonito, the state-of-the-art (i.e., accurate and fast), open-source
basecaller as a case study. Our experimental results using Sword-fish show that
a CIM architecture can realistically accelerate Bonito for a wide range of real
datasets by an average of 25.7x, with an accuracy loss of 6.01%.


  Monotonic convergence is established for a general class of multiplicative
algorithms introduced by Silvey, Titterington and Torsney [Comm. Statist.
Theory Methods 14 (1978) 1379--1389] for computing optimal designs. A
conjecture of Titterington [Appl. Stat. 27 (1978) 227--234] is confirmed as a
consequence. Optimal designs for logistic regression are used as an
illustration.


  We report the observation of superconductivity with multiple magnetic
ordering and Rashba-type spin-orbit coupling in a layered polar semimetal
EuAuBi. Magnetic transition is observed at 4 K, followed by a superconducting
transition at 2.2 K, which is sensitive to the crystal surface conditions. The
upper critical field Hc2 of 9.8 T for the out-of-plane field is three times
higher than that for the in-plane field, which can be associated with the
two-dimensional structure or the surface state. On the basis of
first-principles calculations, it is found that the characteristic Hc2 possibly
reflects the anisotropic modification of the Fermi surface by the effective
combination of Rashba-type spin splitting and Zeeman spin splitting enhanced by
Eu moments.


  Nuclear matrix elements of the neutrinoless double beta decays of 96Zr,
100Mo, 116Cd, 128Te, 130Te and 136Xe are calculated for the light-neutrino
exchange mechanism by using the proton-neutron quasiparticle random-phase
approximation (pnQRPA) with a realistic nucleon-nucleon force. The g_pp
parameter of the pnQRPA is fixed by the data on the two-neutrino double beta
decays and single beta decays. The finite size of a nucleon, the higher-order
terms of nucleonic weak currents, and the nucleon-nucleon short-range
correlations (s.r.c) are taken into account. The s.r.c. are computed by the
traditional Jastrow method and by the more advanced unitary correlation
operator method (UCOM). Comparison of the results obtained by the two methods
is carried out. The UCOM computed matrix elements turn out to be considerably
larger than the Jastrow computed ones. This result is important for the
assessment of the neutrino-mass sensitivity of the present and future double
beta experiments.


  Scoring patent documents is very useful for technology management. However,
conventional methods are based on static models and, thus, do not reflect the
growth potential of the technology cluster of the patent. Because even if the
cluster of a patent has no hope of growing, we recognize the patent is
important if PageRank or other ranking score is high. Therefore, there arises a
necessity of developing citation network clustering and prediction of future
citations. In our research, clustering of patent citation networks by
Stochastic Block Model was done with the aim of enabling corporate managers and
investors to evaluate the scale and life cycle of technology. As a result, we
confirmed nested SBM is appropriate for graph clustering of patent citation
networks. Also, a high MAPE value was obtained and the direction accuracy
achieved a value greater than 50% when predicting growth potential for each
cluster by using LSTM.


  Various multi-agent models of wealth distributions defined by microscopic
laws regulating the trades, with or without a saving criterion, are reviewed.
We discuss and clarify the equilibrium properties of the model with constant
global saving propensity, resulting in Gamma distributions, and their
equivalence to the Maxwell-Boltzmann kinetic energy distribution for a system
of molecules in an effective number of dimensions $D_\lambda$, related to the
saving propensity $\lambda$ [M. Patriarca, A. Chakraborti, and K. Kaski, Phys.
Rev. E 70 (2004) 016104]. We use these results to analyze the model in which
the individual saving propensities of the agents are quenched random variables,
and the tail of the equilibrium wealth distribution exhibits a Pareto law $f(x)
\propto x^{-\alpha -1}$ with an exponent $\alpha=1$ [A. Chatterjee, B. K.
Chakrabarti, and S. S. Manna, Physica Scripta T106 (2003) 367]. Here, we show
that the observed Pareto power law can be explained as arising from the overlap
of the Maxwell-Boltzmann distributions associated to the various agents, which
reach an equilibrium state characterized by their individual Gamma
distributions. We also consider the influence of different types of saving
propensity distributions on the equilibrium state.


  In this article, we review our recently introduced methods for obtaining
strictly positive lower bounds on the top Lyapunov exponent of
high-dimensional, stochastic differential equations such as the weakly-damped
Lorenz-96 (L96) model or Galerkin truncations of the 2d Navier-Stokes
equations. This hallmark of chaos has long been observed in these models,
however, no mathematical proof had been made for either deterministic or
stochastic forcing.
  The method we proposed combines (A) a new identity connecting the Lyapunov
exponents to a Fisher information of the stationary measure of the Markov
process tracking tangent directions (the so-called "projective process"); and
(B) an $L^1$-based hypoelliptic regularity estimate to show that this
(degenerate) Fisher information is an upper bound on some fractional
regularity. For L96 and GNSE, we then further reduce the lower bound of the top
Lyapunov exponent to proving that the projective process satisfies
H\"ormander's condition. We review the recent contributions of the first and
third author on the verification of this condition for the 2d
Galerkin-Navier-Stokes equations in a rectangular, periodic box of any aspect
ratio. Finally, we briefly contrast this work with our earlier work on
Lagrangian chaos in the stochastic Navier-Stokes equations. We end the review
with a discussion of some open problems.


  After five years of data taking, the STAR experiment at the Relativistic
Heavy Ion Collider (RHIC) at Brookhaven National Laboratory provides precise
measurements of particle production at high transverse momentum in p-p, d-Au,
and Au-Au collisions at sqrt(s) = 200 GeV. We review recent results on the
flavor dependence of high pT particle suppression and hadron particle spectra
at sqrt(s) = 62.4 GeV. New results on two-particle angular correlations for
identified trigger particles and for low momentum associated charged hadrons in
p-p and Au-Au as well as near-side $\Delta\eta$ correlations will be presented
and discussed.


  The growth index $\gamma_L$ was proposed to investigate the possible
deviation from the standard $\Lambda$CDM model and Einstein's gravity theory in
a dynamical perspective. Recently, thanks to the measurement of the cosmic
growth rate via the redshift-space distortion, one can understand the evolution
of density contrast through $f\sigma_8(z)$, where $f(z)=d\ln \delta/d \ln a$ is
the growth rate of matter and $\sigma_8(z)$ is the rms amplitude of the density
contrast $\delta$ at the comoving $8h^{-1}$ Mpc scale. In this paper, we use
the redshift space distortion data points to study the growth index on the
bases of Einstein's gravity theory and a modified gravity theory under the
assumption of $f=\Omega_m(a)^{\gamma_L}$. The cosmic background evolution is
fixed by the cosmic observations from the type Ia supernovae SNLS3, cosmic
microwave background radiation data from {\it Planck} and baryon acoustic
oscillations. Via the Markov Chain Monte Carlo method, we found the $\gamma_L$
values for Einstein's gravity with a cosmological constant, $w=constant$ dark
energy and a modified gravity theory in the $1,2,3\sigma$ regions respectively:
$0.675_{-0.0662-0.120-0.155}^{+0.0611+0.129+0.178}$,
$0.745_{-0.0819-0.146-0.190}^{+0.0755+0.157+0.205}$ and
$0.555_{-0.0167-0.0373-0.0516}^{+0.0193+0.0335+0.0436}$. In the Einstein's
gravity theory, the values of growth index $\gamma_L$ show almost $2\sigma$
deviation from the theoretical prediction 6/11 for the $\Lambda$CDM model.
However in the modified gravity framework, a deviation from the Einstein's
relativity is not detected in $1\sigma$ region. That implies that the currently
available cosmic observations don't expect an alternative modified gravity
theory beyond the $\Lambda$CDM model under Einstein's gravity, but that the
simple assumption of $f=\Omega_m^{\gamma_L}$ should be improved.


  If a group $\Gamma$ acts geometrically on a CAT(0) space $X$ without 3-flats,
then either $X$ contains a $\Gamma$-periodic geodesic which does not bound a
flat half-plane, or else $X$ is a rank 2 Riemannian symmetric space, a
2-dimensional Euclidean building or non-trivially splits as a metric product.
Consequently all such groups satisfy a strong form of the Tits Alternative.


  We present an updated and improved global fit analysis of current flavor and
electroweak precision observables to derive bounds on unitarity deviations of
the leptonic mixing matrix and on the mixing of heavy neutrinos with the active
flavours. This new analysis is motivated by new and updated experimental
results on key observables such as $V_{ud}$, the invisible decay width of the
$Z$ boson and the $W$ boson mass. It also improves upon previous studies by
considering the full correlations among the different observables and
explicitly calibrating the test statistic, which may present significant
deviations from a $\chi^2$ distribution. The results are provided for three
different Type-I seesaw scenarios: the minimal scenario with only two
additional right-handed neutrinos, the next to minimal one with three extra
neutrinos, and the most general one with an arbitrary number of heavy neutrinos
that we parametrize via a generic deviation from a unitary leptonic mixing
matrix. Additionally, we also analyze the case of generic deviations from
unitarity of the leptonic mixing matrix, not necessarily induced by the
presence of additional neutrinos. This last case relaxes some correlations
among the parameters and is able to provide a better fit to the data.
Nevertheless, inducing only leptonic unitarity deviations avoiding both the
correlations implied by the right-handed neutrino extension as well as more
strongly constrained operators is challenging and would imply significantly
more complex UV completions.


  We investigate the potential of inclusive-jet production in deep-inelastic
scattering (DIS) at a future Electron-Ion Collider (EIC) to improve our current
knowledge of nuclear parton density functions (PDFs). We demonstrate that the
kinematic reach is extended similarly to inclusive DIS, but that the
uncertainty of the nuclear PDFs, in particular of the gluon density at low
Bjorken-x, is considerably reduced, by up to an order of magnitude compared to
the present situation. Using an approximate next-to-next-to-leading order
(aNNLO) calculation implemented in the program JetViP, we also make predictions
for three different EIC designs and for four different light and heavy nuclei.


  We prove that a quotient G/N of a subgroup G of Sym(n) by a nonabelian
minimal normal subgroup N of G embeds into Sym(m) for some $m<n$. Furthermore,
if G is transitive then we can take m \le 2n/5.


  We present theoretical predictions in the framework of the ANP model for
single pion production ($\pi^+, \pi^0$) in $\nu_\mu$ and $\bar\nu_\mu$
scattering off mineral oil and plastic. Our results for the total cross
sections and flux averaged differential distributions are compared to all
available data of the MiniBooNE and MINER$\nu$A experiments. While our
predictions slightly undershoot the MiniBooNE data they reproduce the
normalization of the MINER$\nu$A data for the kinetic energy distribution. For
the dependence on the polar angle we reproduce the shape of the arbitrarily
normalized data.


  The dominant production mechanism for heavy quark-antiquark bound states in
very high energy processes is fragmentation, the splitting of a high energy
parton into a quarkonium state and other partons. We show that the
fragmentation functions $D(z,\mu)$ describing these processes can be calculated
using perturbative QCD. We calculate the fragmentation functions for a gluon to
split into S-wave quarkonium states to leading order in the QCD coupling
constant. The leading logarithms of $\mu/m_Q$, where $\mu$ is the factorization
scale and $m_Q$ is the heavy quark mass, are summed up using Altarelli-Parisi
evolution equations.


  We derive a class of mesoscopic virial equations governing energy partition
between conjugate position and momentum variables of individual degrees of
freedom. They are shown to apply to a wide range of nonequilibrium steady
states with stochastic (Langevin) and deterministic (Nos\'e--Hoover) dynamics,
and to extend to collective modes for models of heat-conducting lattices. A
generalised macroscopic virial theorem ensues upon summation over all degrees
of freedom. This theorem allows for the derivation of nonequilibrium state
equations that involve dissipative heat flows on the same footing with state
variables, as exemplified for inertial Brownian motion with solid friction and
overdamped active Brownian particles subject to inhomogeneous pressure.


  Since the 1990s, the Environmental Kuznets Curve (EKC) hypothesis posits an
inverted U-shaped relationship between pollutants and economic development. The
hypothesis has attracted a lot of research. We provide here a review of more
than 2000 articles that have been published on the EKC. We aim at mapping the
development of this specialized research, both in term of actors and of
content, and to trace the transformation it has undergone from its beginning to
the present. To that end, we combine traditional bibliometric analysis and
semantic analysis with a novel method, that enables us to recover the type of
pollutants that are studied and the empirical claims made on EKC (whether the
hypothesis is invalidated or not). We principally exhibit the existence of a
few epistemic communities that are related to distinct time periods, topics
and, to some extent, proportion of positive results on EKC.


  In this work, we introduce a computational model for the study of the
host-bacteria interaction and the influence of the intestinal microbiota on the
behavior and feeding pattern of an individual. The model is based on digital
entities, which we've called Digital Beings (DBs), modeled using dynamic
systems and genetic algorithms. We have successfully tested the use of the DBs
by reproducing observation in previously made studies using rats and humans.
Among these studies, we highlight those on how the bacteria in an individual's
stomach could influence their eating behavior and how a controlled and
continuous diet can affect the longevity of a certain population. Our results
point that the Digital Beings can be used as a tool for supporting the devising
of experiments and corroborating with theoretical hypotheses, reducing the
number of in vivo tests.


  We consider the geometric optics problem of finding a system of two
reflectors that transform a spherical wavefront into a beam of parallel rays
with prescribed intensity distribution. Using techniques from optimal
transportation theory, it has been shown before that this problem is equivalent
to an infinite dimensional linear programming (LP) problem. We investigate
techniques for constructing the two reflectors numerically. A straightforward
discretization of this problem has the disadvantage that the number of
constraints increases rapidly with the mesh size. So with this technique only
very coarse meshes are practical. To address this well-known issue we propose
an iterative solution scheme. In each step an LP problem is solved. Information
from the previous iteration step is used to reduce the number of constraints
necessary. As a proof of concept we apply our proposed scheme to solve a
problem with synthetic data. We give evidence that the scheme converges. We
also show that it allows for much finer meshes than a simple discretization
scheme. There exists a growing literature for the application of optimal
transportation theory to other beam shaping problems. Our proposed scheme is
easy to adapt for these problems as well.


  Deep Material Network (DMN) has recently emerged as a data-driven surrogate
model for heterogeneous materials. Given a particular microstructural
morphology, the effective linear and nonlinear behaviors can be successfully
approximated by such physics-based neural-network like architecture. In this
work, a novel micromechanics-informed parametric DMN (MIpDMN) architecture is
proposed for multiscale materials with a varying microstructure characterized
by several parameters. A single-layer feedforward neural network is used to
account for the dependence of DMN fitting parameters on the microstructural
ones. Micromechanical constraints are prescribed both on the architecture and
the outputs of this new neural network. The proposed MIpDMN is also recast in a
multiple physics setting, where physical properties other than the mechanical
ones can also be predicted. In the numerical simulations conducted on three
parameterized microstructures, MIpDMN demonstrates satisfying generalization
capabilities when morphology varies. The effective behaviors of such parametric
multiscale materials can thus be predicted and encoded by MIpDMN with high
accuracy and efficiency.


  Quantum computers have the potential to solve some important industrial and
scientific problems with greater efficiency than classical computers. While
most current realizations focus on two-level qubits, the underlying physics
used in most hardware is capable of extending the concepts to a multi-level
logic - enabling the use of qudits, which promise higher computational power
and lower error rates. Based on a strong theoretical backing and motivated by
recent physical accomplishments, this also calls for methods and tools for
compiling quantum circuits to those devices. To enable efficient qudit
compilation, we introduce the concept of an energy coupling graph for
single-qudit systems and provide an adaptive algorithm that leverages this
representation for compiling arbitrary unitaries. This leads to significant
improvements over the state-of-the-art compilation scheme and, additionally,
provides an option to trade-off worst-case costs and run-time. The developed
compiler is available via github.com/cda-tum/qudit-compilation under an
open-source license.


  This work presents a deep-learning approach to estimate atmospheric density
profiles for use in planetary entry guidance problems. A long short-term memory
(LSTM) neural network is trained to learn the mapping between measurements
available onboard an entry vehicle and the density profile through which it is
flying. Measurements include the spherical state representation, Cartesian
sensed acceleration components, and a surface-pressure measurement. Training
data for the network is initially generated by performing a Monte Carlo
analysis of an entry mission at Mars using the fully numerical
predictor-corrector guidance (FNPEG) algorithm that utilizes an exponential
density model, while the truth density profiles are sampled from MarsGRAM. A
curriculum learning procedure is developed to refine the LSTM network's
predictions for integration within the FNPEG algorithm. The trained LSTM is
capable of both predicting the density profile through which the vehicle will
fly and reconstructing the density profile through which it has already flown.
The performance of the FNPEG algorithm is assessed for three different density
estimation techniques: an exponential model, an exponential model augmented
with a first-order fading-memory filter, and the LSTM network. Results
demonstrate that using the LSTM model results in superior terminal accuracy
compared to the other two techniques when considering both noisy and noiseless
measurements.


  Contrary to the usual picture that decoherence destroys quantum properties
and causes the quantum-to-classical transition, we argue that decoherence can
also play a constructive role in driving quantum dynamics and amplifying its
results to macroscopic scales. We support this perspective by presenting an
example system from spin chemistry, which is also of importance for biological
systems, e.g. in avian magnetoreception.


  In the minimal Standard Model (SM) with four generations (the so called SM4)
and in "standard" two Higgs doublets model (2HDM) setups, e.g., the type II
2HDM with four fermion generations, the contribution of the 4th family heavy
leptons to the muon magnetic moment is suppressed and cannot accommodate the
measured $ \sim 3 \sigma$ access with respect to the SM prediction. We show
that in a 2HDM for the 4th generation (the 4G2HDM), which we view as a low
energy effective theory for dynamical electroweak symmetry breaking, with one
of the Higgs doublets coupling only to the 4th family leptons and quarks (thus
effectively addressing their large masses), the loop exchanges of the heavy 4th
generation neutrino can account for the measured value of the muon anomalous
magnetic moment. We also discuss the sensitivity of the lepton flavor violating
decays $\mu \to e \gamma$ and $\tau \to \mu \gamma$ and of the decay $B_s \to
\mu \mu$ to the new couplings which control the muon g-2 in our model.


  We investigate the dynamics of continuous attractor neural networks (CANNs).
Due to the translational invariance of their neuronal interactions, CANNs can
hold a continuous family of stationary states. We systematically explore how
their neutral stability facilitates the tracking performance of a CANN, which
is believed to have wide applications in brain functions. We develop a
perturbative approach that utilizes the dominant movement of the network
stationary states in the state space. We quantify the distortions of the bump
shape during tracking, and study their effects on the tracking performance.
Results are obtained on the maximum speed for a moving stimulus to be
trackable, and the reaction time to catch up an abrupt change in stimulus.


  We consider the most general perturbatively renormalizable theory of vector
fields in four dimensions with a global SU(N) symmetry and massless couplings.
The Lagrangian contains 1 quadratic, 2 cubic and 4 quartic couplings. The RG
flow among this set of 7 couplings is computed to 1-loop and a rich phase
diagram is mapped out; in particular it is shown that a finite number of
asymptotically free RG-flows exist corresponding to non-trivial fixed points
for the ratios of the couplings. None of these are gauge theories, i.e. possess
only global SU(N) invariance but not a local one. We also include the most
general ghost couplings, still with global SU(N) invariance, and compute the RG
flow to 1-loop for all 9 resulting couplings. Again asymptotically free RG
flows exist with non-trivial fixed points for the ratios of couplings. It is
shown that Yang-Mills theory emerges at a particular fixed point. The theories
at the other fixed points are marginally relevant gauge symmetry violating
perturbations of Yang-Mills theory. The large-N limit is also investigated in
detail.


  Virial coefficients for the two-dimensional hard-disk fluid, when expressed
in powers of density relative to maximum close packing, lead to an accurate
closed equation-of-state for the equilibrium fluid, analogous to that recently
found for hard spheres. The 2D series also converges for all densities up to a
negative pole at close packing density. The virial pressure begins to deviate
from the thermodynamic fluid in the approach to the ordering transition.


  Visualizing the details of different cellular structures is of great
importance to elucidate cellular functions. However, it is challenging to
obtain high quality images of different structures directly due to complex
cellular environments. Fluorescence staining is a popular technique to label
different structures but has several drawbacks. In particular, label staining
is time consuming and may affect cell morphology, and simultaneous labels are
inherently limited. This raises the need of building computational models to
learn relationships between unlabeled microscopy images and labeled
fluorescence images, and to infer fluorescence labels of other microscopy
images excluding the physical staining process. We propose to develop a novel
deep model for virtual staining of unlabeled microscopy images. We first
propose a novel network layer, known as the global pixel transformer layer,
that fuses global information from inputs effectively. The proposed global
pixel transformer layer can generate outputs with arbitrary dimensions, and can
be employed for all the regular, down-sampling, and up-sampling operators. We
then incorporate our proposed global pixel transformer layers and dense blocks
to build an U-Net like network. We believe such a design can promote feature
reusing between layers. In addition, we propose a multi-scale input strategy to
encourage networks to capture features at different scales. We conduct
evaluations across various fluorescence image prediction tasks to demonstrate
the effectiveness of our approach. Both quantitative and qualitative results
show that our method outperforms the state-of-the-art approach significantly.
It is also shown that our proposed global pixel transformer layer is useful to
improve the fluorescence image prediction results.


  Recent experimental advances in neuroscience have opened new vistas into the
immense complexity of neuronal networks. This proliferation of data challenges
us on two parallel fronts. First, how can we form adequate theoretical
frameworks for understanding how dynamical network processes cooperate across
widely disparate spatiotemporal scales to solve important computational
problems? And second, how can we extract meaningful models of neuronal systems
from high dimensional datasets? To aid in these challenges, we give a
pedagogical review of a collection of ideas and theoretical methods arising at
the intersection of statistical physics, computer science and neurobiology. We
introduce the interrelated replica and cavity methods, which originated in
statistical physics as powerful ways to quantitatively analyze large highly
heterogeneous systems of many interacting degrees of freedom. We also introduce
the closely related notion of message passing in graphical models, which
originated in computer science as a distributed algorithm capable of solving
large inference and optimization problems involving many coupled variables. We
then show how both the statistical physics and computer science perspectives
can be applied in a wide diversity of contexts to problems arising in
theoretical neuroscience and data analysis. Along the way we discuss spin
glasses, learning theory, illusions of structure in noise, random matrices,
dimensionality reduction, and compressed sensing, all within the unified
formalism of the replica method. Moreover, we review recent conceptual
connections between message passing in graphical models, and neural computation
and learning. Overall, these ideas illustrate how statistical physics and
computer science might provide a lens through which we can uncover emergent
computational functions buried deep within the dynamical complexities of
neuronal networks.


  Efficient energy transfer from electromagnetic waves to ions has been
demanded to control laboratory plasmas for various applications and could be
useful to understand the nature of space and astrophysical plasmas. However,
there exists a severe unsolved problem that most of the wave energy is
converted quickly to electrons, but not to ions. Here, an energy conversion
process to ions in overdense plasmas associated with whistler waves is
investigated by numerical simulations and theoretical model. Whistler waves
propagating along a magnetic field in space and laboratories often form the
standing waves by the collision of counter-propagating waves or through the
reflection. We find that ions in the standing whistler waves acquire a large
amount of energy directly from the waves in a short timescale comparable to the
wave oscillation period. Thermalized ion temperature increases in proportion to
the square of the wave amplitude and becomes much higher than the electron
temperature in a wide range of wave-plasma conditions. This efficient
ion-heating mechanism applies to various plasma phenomena in space physics and
fusion energy sciences.


  Contemporary makeup approaches primarily hinge on unpaired learning
paradigms, yet they grapple with the challenges of inaccurate supervision
(e.g., face misalignment) and sophisticated facial prompts (including face
parsing, and landmark detection). These challenges prohibit low-cost deployment
of facial makeup models, especially on mobile devices. To solve above problems,
we propose a brand-new learning paradigm, termed "Data Amplify Learning (DAL),"
alongside a compact makeup model named "TinyBeauty." The core idea of DAL lies
in employing a Diffusion-based Data Amplifier (DDA) to "amplify" limited images
for the model training, thereby enabling accurate pixel-to-pixel supervision
with merely a handful of annotations. Two pivotal innovations in DDA facilitate
the above training approach: (1) A Residual Diffusion Model (RDM) is designed
to generate high-fidelity detail and circumvent the detail vanishing problem in
the vanilla diffusion models; (2) A Fine-Grained Makeup Module (FGMM) is
proposed to achieve precise makeup control and combination while retaining face
identity. Coupled with DAL, TinyBeauty necessitates merely 80K parameters to
achieve a state-of-the-art performance without intricate face prompts.
Meanwhile, TinyBeauty achieves a remarkable inference speed of up to 460 fps on
the iPhone 13. Extensive experiments show that DAL can produce highly
competitive makeup models using only 5 image pairs.


  It is shown that a channel flow of a dilute polymer solution between two
widely spaced cylinders hindering the flow is an important paradigm of an
unbounded flow in the case in which the channel wall is located sufficiently
far from the cylinders. The quantitative characterization of instabilities in a
creeping viscoelastic channel flow between two widely spaced cylinders reveals
two elastically driven transitions, which are associated with the breaking of
time-reversal and mirror symmetries: Hopf and forward bifurcations described by
two order parameters $\mbox{v}_{rms}$ and $\bar{\omega}$, respectively. We
suggest that a decrease of the normalized distance between the obstacles leads
to a collapse of the two bifurcations into a codimension-2 point, a situation
general for many non-equilibrium systems. However, the striking and unexpected
result is the discovery of a mechanism of the vorticity growth via an increase
of a vortex length at the preserved streamline curvature in a viscoelastic
flow, which is in sharp contrast to the well-known suppression of the vorticity
in a Newtonian flow by polymer additives.


  We derive a new flavor symmetry relation for the determination of the weak
phase $\beta=\phi_1$ from time-dependent CP asymmetries and $B \to J/\psi P$
decay rates. In this relation, the contributions to $\sin2\beta$ proportional
to $V_{ub}$ are parametrically suppressed compared to the contributions in the
$B \to J/\psi K^0$ time-dependent CP asymmetry alone. This relation uses only
$SU(3)$ flavor symmetry, and does not require further diagrammatic assumptions.
The current data either fluctuate at the $2\sigma$ level from expectations, or
may hint at effects of unexpected magnitude from contributions proportional to
$V_{ub}$ or from isospin breaking.


  The statistical mechanics of a ribbon polymer made up of two semiflexible
chains is studied using both analytical techniques and simulation. The system
is found to have a crossover transition at some finite temperature, from a type
of short range order to a fundamentally different sort of short range order. In
the high temperature regime, the 2-point correlation functions of the object
are identical to worm-like chains, while in the low temperature regime they are
different due to a twist structure. The crossover happens when the persistence
length of individual strands becomes comparable to the thickness of the ribbon.
In the low temperature regime, the ribbon is observed to have a novel
``kink-rod'' structure with a mutual exclusion of twist and bend in contrast to
smooth worm-like chain behaviour. This is due to its anisotropic rigidity and
corresponds to an {\it infinitely} strong twist-bend coupling. The
double-stranded polymer is also studied in a confined geometry. It is shown
that when the polymer is restricted in a particular direction to a size less
than the bare persistence length of the individual strands, it develops zigzag
conformations which are indicated by an oscillatory tangent-tangent correlation
function in the direction of confinement. Increasing the separation of the
confining plates leads to a crossover to the free behaviour, which takes place
at separations close to the bare persistence length. These results are expected
to be relevant for experiments which involve complexation of two or more stiff
or semiflexible polymers.


  Recently, heuristics based on the Douglas-Rachford splitting algorithm and
the alternating direction method of multipliers (ADMM) have found empirical
success in minimizing convex functions over nonconvex sets, but not much has
been done to improve the theoretical understanding of them. In this paper, we
investigate convergence of these heuristics. First, we characterize optimal
solutions of minimization problems involving convex cost functions over
nonconvex constraint sets. We show that these optimal solutions are related to
the fixed point set of the underlying nonconvex Douglas-Rachford operator.
Next, we establish sufficient conditions under which the Douglas-Rachford
splitting heuristic either converges to a point or its cluster points form a
nonempty compact connected set. In the case where the heuristic converges to a
point, we establish sufficient conditions for that point to be an optimal
solution. Then, we discuss how the ADMM heuristic can be constructed from the
Douglas-Rachford splitting algorithm. We show that, unlike in the convex case,
the algorithms in our nonconvex setup are not equivalent to each other and have
a rather involved relationship between them. Finally, we comment on convergence
of the ADMM heuristic and compare it with the Douglas-Rachford splitting
heuristic.


  Let P be a set of n points in $\mathbb{R}^d$. A point x is said to be a
centerpoint of P if x is contained in every convex object that contains more
than $dn\over d+1$ points of P. We call a point x a strong centerpoint for a
family of objects $\mathcal{C}$ if $x \in P$ is contained in every object $C
\in \mathcal{C}$ that contains more than a constant fraction of points of P. A
strong centerpoint does not exist even for halfspaces in $\mathbb{R}^2$. We
prove that a strong centerpoint exists for axis-parallel boxes in
$\mathbb{R}^d$ and give exact bounds. We then extend this to small strong
$\epsilon$-nets in the plane and prove upper and lower bounds for
$\epsilon_i^\mathcal{S}$ where $\mathcal{S}$ is the family of axis-parallel
rectangles, halfspaces and disks. Here $\epsilon_i^\mathcal{S}$ represents the
smallest real number in $[0,1]$ such that there exists an
$\epsilon_i^\mathcal{S}$-net of size i with respect to $\mathcal{S}$.


  While the question ``how many CNOT gates are needed to simulate an arbitrary
two-qubit operator'' has been conclusively answered -- three are necessary and
sufficient -- previous work on this topic assumes that one wants to simulate a
given unitary operator up to global phase. However, in many practical cases
additional degrees of freedom are allowed. For example, if the computation is
to be followed by a given projective measurement, many dissimilar operators
achieve the same output distributions on all input states. Alternatively, if it
is known that the input state is |0>, the action of the given operator on all
orthogonal states is immaterial. In such cases, we say that the unitary
operator is incompletely specified; in this work, we take up the practical
challenge of satisfying a given specification with the smallest possible
circuit. In particular, we identify cases in which such operators can be
implemented using fewer quantum gates than are required for generic completely
specified operators.


  The new interface of the Web of Science (of Thomson Reuters) enables users to
retrieve sets larger than 100,000 documents in a single search. This makes it
possible to compare publication trends for China, the USA, EU-27, and a number
of smaller countries. China no longer grew exponentially during the 2000s, but
linearly. Contrary to previous predictions on the basis of exponential growth
or Scopus data, the cross-over of the lines for China and the USA is postponed
to the next decade (after 2020) according to this data. These long
extrapolations, however, should be used only as indicators and not as
predictions. Along with the dynamics in the publication trends, one also has to
take into account the dynamics of the databases used for the measurement.


  We consider an Einstein-Gauss-Bonnet massive gravity model in $4D$ AdS
spacetime to obtain a possible black hole solution and discuss the horizon
structure of this black hole. The real roots of the vanishing metric function
lead to various types of horizons. Furthermore, we derive various thermodynamic
quantities, thus insuring the validity of the first-law of thermodynamics and
the Smarr relation. The thermodynamic quantities are modified in the presence
of the massive gravity parameter and also discuss the stability of the system
from the heat capacity. Black hole space times can not only possess standard
thermodynamics, but also possess phase structures when the cosmological
constant is treated as the thermodynamic pressure. The effects of massive
parameter and GB parameter on phase transition are also discussed. We also
examine the phase structure through Maxwell's equal area law. The first order
phase transition occurs only when pressure is lower than its critical value. At
critical pressure, the first order phase transition terminates, and the second
order phase transition occurs. No phase transition occurs when pressure is
greater than its critical value.


  The new proposals for high luminosity B/Flavor factories, near and on top of
the $\Upsilon$ resonances, allow for a detailed investigation of CP-violation
in the $\tau$-pair production. In particular, bounds on the tau electric dipole
moment can be obtained from genuine CP-odd observables related to the
$\tau$-pair production. We perform an independent analysis from low energy (10
GeV) data by means of linear spin observables. We show that, for a
longitudinally polarized electron beam, a CP-odd asymmetry, associated to the
normal polarization term, can be measured at these low energy facilities both
at resonant and non resonant energies. In this way, stringent and independent
bounds to the tau electric dipole moment, which are orders of magnitude below
other high or low energy bounds, can be obtained.


  The majority of massive stars, the progenitors of core-collapse supernovae
(SNe), are found in close binary systems. Zapartas et al. (2019) modeled the
fraction of hydrogen-rich, Type II SN progenitors which have their evolution
affected by mass exchange with their companion, finding this to be between 1/3
and 1/2 for most assumptions. Here we study in more depth the impact of this
binary history of Type II SN progenitors on their final pre-SN core mass
distribution, using population synthesis simulations. We find that binary star
progenitors of Type II SNe typically end their life with a larger core mass
than they would have had if they had lived in isolation, because they gained
mass or merged with a companion before explosion. The combination of the
diverse binary evolutionary paths typically lead to a marginally shallower
final core mass distribution. Discussing our results in the context of the red
supergiant problem, i.e., the reported lack of detected high luminosity
progenitors, we conclude that binary evolution does not seem to significantly
affect the issue. This conclusion is quite robust against our variations in the
assumptions of binary physics. We also predict that inferring the initial
masses of Type II SN progenitors from "age-dating" its surrounding environment
systematically yields lower masses compared to methods that probe the pre-SN
core mass or luminosity. A robust discrepancy between the inferred initial
masses of a SN progenitor from those different techniques could indicate an
evolutionary history of binary mass accretion or merging.


  We investigate numerically the transition between static equilibrium and
dynamic surface flow of a 2D cohesionless granular system driven by a
continuous gravity loading. This transition is characterized by intermittent
local dynamic rearrangements and can be described by an order parameter defined
as the density of critical contacts, e.g. contacts where the friction is fully
mobilized. Analysis of the spatial correlations of critical contacts shows the
occurence of ``fluidized'' clusters which exhibit a power-law divergence in
size at the approach of the stability limit. The results are compatible with
recent models that describe the granular system during the static/dynamic
transition as a multi-phase system.


  In this paper we develop a new approach to the investigation of the
bi-partite entanglement entropy in integrable quantum spin chains. Our method
employs the well-known replica trick, thus taking a replica version of the spin
chain model as starting point. At each site i of this new model we construct an
operator T_i which acts as a cyclic permutation among the n replicas of the
model. Infinite products of T_i give rise to local operators, precursors of
branch-point twist fields of quantum field theory. The entanglement entropy is
then expressed in terms of correlation functions of such operators. Employing
this approach we investigate the von Neumann and R\'enyi entropies of a
particularly interesting quantum state occurring as a limit (in a compact
convergence topology) of the antiferromagnetic XXZ quantum spin chain. We find
that, for large sizes, the entropy scales logarithmically, but not conformally.


  We experimentally and theoretically determine the magic wavelength of the
(5$s^2$)$^{1}S_{0}$$-$(5$s$5$p$)$^{3}P_{0}$ clock transition of $^{111}$Cd to
be 419.88(14) nm and 420.1(7) nm. To perform Lamb-Dicke spectroscopy of the
clock transition, we use narrow-line laser cooling on the
$^{1}S_{0}$$-$$^{3}P_{1}$ transition to cool the atoms to 6 $\mu$K and load
them into an optical lattice. Cadmium is an attractive candidate for optical
lattice clocks because it has a small sensitivity to blackbody radiation and
its efficient narrow-line cooling mitigates higher order light shifts. We
calculate the blackbody shift, including the dynamic correction, to be
fractionally 2.83(8)$\times$10$^{-16}$ at 300 K, an order of magnitude smaller
than that of Sr and Yb. We also report calculations of the Cd $^1P_1$ lifetime
and the ground state $C_6$ coefficient.


  We demonstrate that a planar and ultrathin binary lens can focus an
azimuthally polarized beam with vortical phase (APV) to a subwavelength spot of
transverse polarization. The results elaborates that, in the multi-layer
medium, this focused spot, which is beyond the Rayleigh diffraction limitation,
can be well maintained for several wavelengths after travelling through the
dielectric interfaces, which is not attainable by using other vector beams
(i.e., radially, linearly and circularly polarized beams) as the illuminating
light. This compact optical system can be valuable in data writing and defect
identification of wafer or silicon chips, owing to the enhanced polarized
focusing through interfaces. It also enables to be highly integrated with
traditional microscopy for the far-field super-resolution imaging, surface
scanning and detection, and subwavelength focusing, owing to the enhanced
focusing performance (reduced width and extended length) as well as the
planarized configuration of the ultrathin lens.


  Buddhism is an influential religion with a long-standing history and profound
philosophy. Nowadays, more and more people worldwide aspire to learn the
essence of Buddhism, attaching importance to Buddhism dissemination. However,
Buddhist scriptures written in classical Chinese are obscure to most people and
machine translation applications. For instance, general Chinese-English neural
machine translation (NMT) fails in this domain. In this paper, we proposed a
novel approach to building a practical NMT model for Buddhist scriptures. The
performance of our translation pipeline acquired highly promising results in
ablation experiments under three criteria.


  We predict that twisted bilayers of 1T-ZrS$_2$ realize a novel and tunable
platform to engineer two-dimensional topological quantum phases dominated by
strong spin-orbit interactions. At small twist angles, ZrS$_2$ heterostructures
give rise to an emergent and twist-controlled moir\'e Kagom\'e lattice,
combining geometric frustration and strong spin-orbit coupling to give rise to
a moir\'e quantum spin Hall insulator with highly controllable and
nearly-dispersionless bands. We devise a generic pseudo-spin theory for
group-IV transition metal dichalcogenides that relies on the two-component
character of the valence band maximum of the 1T structure at $\Gamma$, and
study the emergence of a robust quantum anomalous Hall phase as well as
possible fractional Chern insulating states from strong Coulomb repulsion at
fractional fillings of the topological moir\'e Kagom\'e bands. Our results
establish group-IV transition metal dichalcogenide bilayers as a novel moir\'e
platform to realize strongly-correlated topological phases in a twist-tunable
setting.


  Spatial entropy redistribution plays a key role in adiabatic cooling of
ultra-cold lattice gases. We show that high-spin fermions with a spatially
variable quadratic Zeeman coupling may allow for the creation of an inner
spin-1/2 core surrounded by high-spin wings. The latter are always more
entropic than the core at high temperatures and, remarkably, at all
temperatures in the presence of frustration. Combining thermodynamic Bethe
Ansatz with local density approximation, we study the spatial entropy
distribution for the particular case of one-dimensional spin-3/2 lattice
fermions in the Mott phase. Interestingly, this spatially dependent entropy
opens a possible path for an adiabatic cooling technique that, in contrast to
previous proposals, would specifically target the spin degree of freedom. We
discuss a possible realization of this adiabatic cooling, which may allow for a
highly efficient entropy decrease in the spin-1/2 core and help access
antiferromagnetic order in experiments on ultracold spinor fermions.


  We study a dynamical counterpart of bifurcation to invariant torus for a
system of interconnected fast phase variables and slowly varying parameters. We
show that in such a system, due to the slow evolution of parameters, there
arise transient processes from damping oscillations to multi-frequency ones,
asymptotically close to motions on the invariant torus.


  We show strong uniform convergence of monotone P1 finite element methods to
the viscosity solution of isotropic parabolic Hamilton-Jacobi-Bellman equations
with mixed boundary conditions on unstructured meshes and for possibly
degenerate diffusions. Boundary operators can generally be discontinuous across
face-boundaries and type changes. Robin-type boundary conditions are
discretised via a lower Dini derivative. In time the Bellman equation is
approximated through IMEX schemes. Existence and uniqueness of numerical
solutions follows through Howard's algorithm.
  Keywords: Finite element method, Hamilton-Jacobi-Bellman equation, Mixed
boundary conditions, Fully nonlinear equation, Viscosity solution


  We provide a new method to construct the S-matrix in quantum field theory.
This method implements crossing symmetry manifestly by erasing the a priori
distinction between in- and out-states. It allows the description of processes
where the interaction weakens with distance in space, but remains strong in the
center at all times. It should also be applicable to certain spacetimes where
the conventional method fails due to lack of temporal asymptotic states.


  Subsurface Charge Accumulation imaging is a cryogenic scanning probe
technique that has recently been used to spatially probe incompressible strips
formed in a two-dimensional electron system (2DES) at high magnetic fields. In
this paper, we present detailed numerical modeling of these data. At a basic
level, the method produces results that agree well with the predictions of
models based on simple circuit elements. Moreover, the modeling method is
sufficiently advanced to simulate the spatially resolved measurements. By
comparing directly the simulations to the experimentally measured data, we can
extract quantitatively local electronic features of the 2DES. In particular, we
deduce the electron density of states inside the incompressible strips and
electrical resistance across them.