  Recently, the idea of using FP8 as a number format for neural network
training has been floating around the deep learning world. Given that most
training is currently conducted with entire networks in FP32, or sometimes FP16
with mixed-precision, the step to having some parts of a network run in FP8
with 8-bit weights is an appealing potential speed-up for the generally costly
and time-intensive training procedures in deep learning. A natural question
arises regarding what this development means for efficient inference on edge
devices. In the efficient inference device world, workloads are frequently
executed in INT8. Sometimes going even as low as INT4 when efficiency calls for
it. In this whitepaper, we compare the performance for both the FP8 and INT
formats for efficient on-device inference. We theoretically show the difference
between the INT and FP formats for neural networks and present a plethora of
post-training quantization and quantization-aware-training results to show how
this theory translates to practice. We also provide a hardware analysis showing
that the FP formats are somewhere between 50-180% less efficient in terms of
compute in dedicated hardware than the INT format. Based on our research and a
read of the research field, we conclude that although the proposed FP8 format
could be good for training, the results for inference do not warrant a
dedicated implementation of FP8 in favor of INT8 for efficient inference. We
show that our results are mostly consistent with previous findings but that
important comparisons between the formats have thus far been lacking. Finally,
we discuss what happens when FP8-trained networks are converted to INT8 and
conclude with a brief discussion on the most efficient way for on-device
deployment and an extensive suite of INT8 results for many models.


  This paper describes various design considerations for deep neural networks
that enable them to operate efficiently and accurately on processing-in-memory
accelerators. We highlight important properties of these accelerators and the
resulting design considerations using experiments conducted on various
state-of-the-art deep neural networks with the large-scale ImageNet dataset.


  We prove in a uniform way that all Denjoy--Carleman differentiable function
classes of Beurling type $C^{(M)}$ and of Roumieu type $C^{\{M\}}$, admit a
convenient setting if the weight sequence $M=(M_k)$ is log-convex and of
moderate growth: For $\mathcal C$ denoting either $C^{(M)}$ or $C^{\{M\}}$, the
category of $\mathcal C$-mappings is cartesian closed in the sense that
$\mathcal C(E,\mathcal C(F,G))\cong \mathcal C(E\times F, G)$ for convenient
vector spaces. Applications to manifolds of mappings are given: The group of
$\mathcal C$-diffeomorphisms is a regular $\mathcal C$-Lie group if $\mathcal C
\supseteq C^\omega$, but not better.


  We present controllable generation of various kinds of highly nonclassical
states of light, including the single photon state and superposition states of
mesoscopically distinct components. The high nonclassicality of the generated
states is measured by the negativity of the Wigner function, which is largest
ever observed to our knowledge. Our scheme is based on photon subtraction from
a nearly pure squeezed vacuum, generated from an optical parametric oscillator
with a periodically-poled KTiOPO$_4$ crystal as a nonlinear medium. This is an
important step to realize basic elements of universal quantum gates, and to
serve as a highly nonclassical input probe for spectroscopy and the study of
quantum memory.


  We present the band structure of CeIr$_3$ superconductor calculated within
the dynamical mean field theory (DMFT). Standard GGA and GGA+U methods fail to
reproduce the experimental electronic specific heat coefficient $\gamma_{\rm
expt.}$ due to the underestimated density of states at the Fermi level $N(E_F)$
followed by an overestimated strength of electron-phonon coupling (EPC)
calculated as a renormalization of $\gamma_{\rm expt.}$. The DMFT study shows a
strong hybridization of $4f$ states of Ce with $5d$-states of Ir, which leads
to the larger $N(E_F)$ giving the correct $\gamma_{\rm expt.}$ with a moderate
EPC in agreement with the experimental data.


  We impose unitarity constraints on the S-wave isoscalar pion-pion amplitudes
extracted from the analysis of the pi- p ---> pi+ pi- n data which have been
measured by the CERN-Cracow-Munich collaboration on a transversely polarized
target at 17.2 GeV/c pi- momentum. Two "steep" solutions contain a narrow
S-wave f0(750) resonance under the rho(770) and exhibit a considerable
inelasticity eta which is in disagreement with the four pion production data
below the KKbar threshold. We impose eta= 1 for all data points and examine
four sets of solutions for the S-wave isoscalar phase-shifts. The "down-flat"
and "up-flat" solutions easily pass the eta=1 constraint but the remaining
"down-steep" and "up-steep" are eliminated. We conclude that the 17.2 GeV data
cannot be described by a relatively narrow f0(750).


  Recent numerical simulations of gravitational collapse show that there exists
a special foliation of the spacetime where matter and entropy accumulate
directly on the inside of the horizon surface. In this foliation, the time
coincides with the proper time of the asymptotic static observer (ASO) and for
spherical symmetry, this corresponds to isotropic coordinates. In this gauge,
the three-volume in the interior shrinks to zero and only the horizon area
remains at the end of collapse. In a different foliation, matter and entropy
accumulate in the volume. The entropy is however independent of the foliation.
Black hole holography is therefore a mapping from an arbitrary foliation, where
information resides in the volume, to the special ASO frame, where it resides
directly on the horizon surface.


  Let $f\colon\mathbb{C} \to\mathbb{C}$ be a transcendental entire function. In
1989, Eremenko asked the following question concerning the set $I(f)$ of points
that tend to infinity under iteration: can every point of $I(f)$ be joined to
$\infty$ by a curve in $I(f)$? This is known as the strong Eremenko conjecture
and was disproved in 2011 by Rottenfu{\ss}er, R\"uckert, Rempe and Schleicher.
The function has relatively small infinite order: it can be chosen such that
$\log \log \,\lvert f(z)\rvert = (\log \lvert z\rvert)^{1+o(1)}$ as $f(z)\to
\infty$. Moreover, $f$ belongs to the \emph{Eremenko--Lyubich class
$\mathcal{B}$}. Rottenfu{\ss}er et al also show that the strong Eremenko
conjecture does hold for any $f\in\mathcal{B}$ of finite order. We consider how
slow a counterexample $f\in\mathcal{B}$ can grow. Suppose that $\Theta\colon
[t_0,\infty)\to [0,\infty)$ satisfies $\Theta(t) \to 0$ and \[ (\log t)^{-\beta
\Theta(\log t)}/\Theta(t) \to 0 \quad\text{ as $t\to \infty$} \] for some
$0<\beta<1$, along with a certain regularity assumption. Then there exists a
counterexample $f\in\mathcal{B}$ as above such that \[ \log \log \vert
f(z)\vert = O ( (\log \vert z \vert)^{1 + \Theta( \log \vert z \vert )}) \] as
$\vert f(z)\vert \to\infty$. The hypotheses are satisfied, in particular, for
$\Theta(t) = 1/(\log \log t)^{\alpha}$, for any $\alpha>0$.


  The observations combined with theory of neutron star (NS) cooling play a
crucial role in achieving the intriguing information of the stellar interior,
such as the equation of state (EOS), composition and superfluidity of dense
matter. The traditional NS cooling theory is based on the assumption that the
stellar structure does not change with time. The validity of such a static
description has not yet been confirmed. We generalize the theory to a dynamic
treatment; that is, continuous change of the NS structure (rearrangement of the
stellar density distribution with the total baryon number fixed) as the
decrease of temperature during the thermal evolution, is taken into account. It
is found that the practical thermal energy used for the cooling is slightly
lower than that is estimated in static situation, and hence the cooling of NSs
is accelerated correspondingly but the effect is rather weak. Therefore, the
static treatment is a good approximation in the calculations of NS cooling.


  A well-known empirical rule for the demand of wireless communication systems
is that of Edholm's law of bandwidth. It states that the demand for bandwidth
in wireless short-range communications doubles every 18 months. With the
growing demand for bandwidth and the decreasing cell size of wireless systems,
terahertz (THz) communication systems are expected to become increasingly
important in modern day applications. With this expectation comes the need for
protecting users' privacy and security in the best way possible. With that in
mind, we show that quantum key distribution can operate in the THz regime and
we derive the relevant secret key rates against realistic collective attacks.
In the extended THz range (from 0.1 to 50 THz), we find that below 1 THz, the
main detrimental factor is thermal noise, while at higher frequencies it is
atmospheric absorption. Our results show that high-rate THz quantum
cryptography is possible over distances varying from a few meters using direct
reconciliation, to about 220m via reverse reconciliation. We also give a
specific example of the physical hardware and architecture that could be used
to realize our THz quantum key distribution scheme.


  Observations of galaxies at sub-millimeter wavelengths, where the emission is
mainly due to cold dust, are required to constrain the dust physical properties
and provide important insight on the gas content of galaxies. We mapped NGC1365
at 870um with LABOCA, the Large APEX Bolometer Camera, allowing us to probe the
central mass concentration as well as the rate at which the gas flows to the
center. We obtained the dust physical properties both globally and locally for
different locations in the galaxy. A 20 K modified black body represents about
98% of the total dust content of the galaxy, the rest can be represented by a
warmer dust component of 40 K. The bar exhibits an east-west asymmetry in the
dust distribution: The eastern bar is heavier than the western bar by more than
a factor of 4. Integrating the dust SED, we derive a total infrared (IR)
luminosity of 9.8 x 10^{10} L_{\odot} leading to a dust-enshrouded star
formation rate of ~16.7 M_{\odot} per year in NGC1365. We derive the gas mass
from the measurements of the dust emission resulting in a CO-to-H2 conversion
factor of X_{CO}~1.2x10^{20} mol cm^{-2} (K km s^{-1})^{-1} in the central disk
including the bar. Taking into account the metallicity variation, the central
gas mass concentration is only ~20% at R<40" (3.6 kpc). On the other hand, the
time-scale with which the gas flows into the center, ~300 Myr, is rather short.
This indicates that the current central mass in NGC1365 is evolving fast due to
the strong bar.


  A phase transition in high-dimensional random geometry is analyzed as it
arises in a variety of problems. A prominent example is the feasibility of a
minimax problem that represents the extremal case of a class of financial risk
measures, among them the current regulatory market risk measure Expected
Shortfall. Others include portfolio optimization with a ban on short selling,
the storage capacity of the perceptron, the solvability of a set of linear
equations with random coefficients, and competition for resources in an
ecological system. These examples shed light on various aspects of the
underlying geometric phase transition, create links between problems belonging
to seemingly distant fields and offer the possibility for further
ramifications.


  In complex processes, various events can happen in different sequences. The
prediction of the next event given an a-priori process state is of importance
in such processes. Recent methods have proposed deep learning techniques such
as recurrent neural networks, developed on raw event logs, to predict the next
event from a process state. However, such deep learning models by themselves
lack a clear representation of the process states. At the same time, recent
methods have neglected the time feature of event instances. In this paper, we
take advantage of Petri nets as a powerful tool in modeling complex process
behaviors considering time as an elemental variable. We propose an approach
which starts from a Petri net process model constructed by a process mining
algorithm. We enhance the Petri net model with time decay functions to create
continuous process state samples. Finally, we use these samples in combination
with discrete token movement counters and Petri net markings to train a deep
learning model that predicts the next event. We demonstrate significant
performance improvements and outperform the state-of-the-art methods on nine
real-world benchmark event logs.


  We study scaling limits of a family of planar random growth processes in
which clusters grow by the successive aggregation of small particles. In these
models, clusters are encoded as a composition of conformal maps and the
location of each successive particle is distributed according to the density of
harmonic measure on the cluster boundary, raised to some power. We show that,
when this power lies within a particular range, the macroscopic shape of the
cluster converges to a disk, but that as the power approaches the edge of this
range the fluctuations approach a critical point, which is a limit of
stability. The methodology developed in this paper provides a blueprint for
analysing more general random growth models, such as the Hastings-Levitov
family.


  Multiple-input, multiple-output (MIMO) technology provides high data rate and
enhanced QoS for wireless com- munications. Since the benefits from MIMO result
in a heavy computational load in detectors, the design of low-complexity
sub-optimum receivers is currently an active area of research.
Lattice-reduction-aided detection (LRAD) has been shown to be an effective
low-complexity method with near-ML performance. In this paper we advocate the
use of systolic array architectures for MIMO receivers, and in particular we
exhibit one of them based on LRAD. The "LLL lattice reduction algorithm" and
the ensuing linear detections or successive spatial-interference cancellations
can be located in the same array, which is con- siderably hardware-efficient.
Since the conventional form of the LLL algorithm is not immediately suitable
for parallel processing, two modified LLL algorithms are considered here for
the systolic array. LLL algorithm with full-size reduction (FSR-LLL) is one of
the versions more suitable for parallel processing. Another variant is the
all-swap lattice-reduction (ASLR) algorithm for complex-valued lattices, which
processes all lattice basis vectors simultaneously within one iteration. Our
novel systolic array can operate both algorithms with different external logic
controls. In order to simplify the systolic array design, we replace the
Lov\'asz condition in the definition of LLL-reduced lattice with the looser
Siegel condition. Simulation results show that for LR- aided linear detections,
the bit-error-rate performance is still maintained with this relaxation.
Comparisons between the two algorithms in terms of bit-error-rate performance,
and average FPGA processing time in the systolic array are made, which shows
that ASLR is a better choice for a systolic architecture, especially for
systems with a large number of antennas.


  I show, in canonical formulation of flat space Maxwell theory, that its
duality rotation invariance must have constant parameter, cannot be promoted to
a local one by adding a compensating field, and that these conclusions hold in
the curved space Maxwell-Einstein extension.


  The non-centrosymmetric superconductor CePt$_3$Si is believed to have a line
node in the energy gap arising from coexistence of s-wave and p-wave pairing.
We show that a weak c-axis magnetic field will remove this line node, since it
has no topological stability against time-reversal symmetry breaking
perturbations. Conversely a field in the $a-b$ plane is shown to remove the
line node on some regions of the Fermi surface, while bifurcating the line node
in other directions, resulting in two 'boomerang'-like shapes. These line node
topological changes are predicted to be observable experimentally in the low
temperature heat capacity.


  Heavy quarks serve as ideal probes of the QGP properties produced in
energetic nuclear collisions, and provide a unique opportunity to study the
mass effects on parton energy loss. We develop a multi-stage approach for heavy
quark evolution inside the QGP, in which heavy quarks first undergo a
rare-scattering multiple-emission evolution at momenta large compared to their
mass (sensitive only to the transverse diffusion coefficient $\hat{q}$ ), and
then evolve through a single-scattering induced emission (Gunion-Bertsch) stage
at momenta comparable to their mass [sensitive to not only $\hat{q}$, but also
the longitudinal drag $\hat{e}$ and diffusion $\hat{e}_2$ coefficients]. This
multi-stage approach is coupled to a (2+1)-D viscous hydrodynamic model for a
quantitative investigation of charm vs. beauty quark energy loss inside the
QGP. Based on this approach, we find that drag induced radiation has a
considerable impact on the energy loss of intermediate $p_T$ massive beauty
quarks. This effect increases the suppression of B mesons and narrows the
difference between the $R_{AA}$ of B and D mesons. Our results are consistent
with the experimental data at the LHC and contribute to a more quantitative
understanding of the transverse momentum dependence of the mass hierarchy of
parton energy loss inside the QGP.


  High-energy photons and other particles carrying non-zero orbital angular
momentum (OAM) emerge as a new tool in high-energy physics. Recently, it was
suggested to generate high-energy photons with non-zero OAM (twisted photons)
by the Compton backscattering of laser twisted photons on relativistic electron
beams. Twisted electrons in the intermediate energy range have also been
demostrated experimentally; twisted protons and other particles can in
principle be created in a similar way. Collisions of energetic twisted states
can offer a new look at particle properties and interactions. A theoretical
description of twisted particle scattering developed previously treated them as
pure Bessel states and ran into difficulty when describing the OAM of the final
twisted particle at non-zero scattering angles. Here we develop further this
formalism by incorporating two additional important features. First, we treat
the initial OAM state as a wave packet of a finite transverse size rather than
a pure Bessel state. This realistic assumption allows us to resolve the
existing controversy between two theoretical analyses for non-forward
scattering. Second, we describe the final twisted particle in terms of the
orbital helicity --- the OAM projection on its average direction of propagation
rather than on the fixed reaction axis. Using this formalism, we determine to
what extent the twisted state is transferred from the initial to final OAM
particle in a generic scattering kinematics. As a particular application, we
prove that in the Compton backscattering the orbital helicity of the final
photon stays close to the OAM projection of the initial photon.


  We present a concise point of view on the first and the second Korn's
inequality for general exponent $p$ and for a class of domains that includes
Lipschitz domains. Our argument is conceptually very simple and, for $p = 2$,
uses only the classical Riesz representation theorem in Hilbert spaces.
Moreover, the argument for the general exponent $1<p<\infty$ remains the same,
the only change being invoking now the $q$-Riesz representation theorem (with
$q$ the harmonic conjugate of $p$). We also complement the analysis with
elementary derivations of Poincar\'e-Korn inequalities in bounded and unbounded
domains, which are essential tools in showing the coercivity of variational
problems of elasticity but also propedeutic to the proof of the first Korn
inequality.


  The Herbig Ae/Be stars (HAeBes) allow an exploration of the properties of
Pre-Main Sequence(PMS) stars above the low-mass range ($<2{\rm\thinspace
M_{\odot}}$) and those bordering the high-mass range ($>8{\rm\thinspace
M_{\odot}}$). This paper is the second in a series exploring accretion in 91
HAeBes with Very Large Telescope/X-shooter spectra. Equivalent width
measurements are carried out on 32 different lines, spanning the UV to NIR, in
order to obtain their line luminosities. The line luminosities were compared to
accretion luminosities, which were determined directly from measurements of an
UV-excess. When detected, emission lines always demonstrate a correlation with
the accretion luminosity, regardless of detection frequency. The average
relationship between accretion luminosity and line luminosity is found to be
${\thinspace L_{\rm acc}}\propto{\thinspace L_{\rm line}}^{1.16 \pm 0.15}$.
This is in agreement with the findings in Classical T Tauri stars, although the
HAeBe relationship is generally steeper, particularly towards the Herbig Be
mass range. Since all observed lines display a correlation with the accretion
luminosity, all of them can be used as accretion tracers. This has increased
the number of accretion diagnostic lines in HAeBes tenfold. However, questions
still remain on the physical origin of each line, which may not be due to
accretion.


  The brief discussion of the studies of the radiative QCD corrections to the
decay widths and branching ratios of the Standard Model Higgs boson with the
mass 2 m_b < M_H < 2 M_W is given. The numerical results are obtained with the
help of our FORTRAN code SEEHIGGS.


  Motivated by the possibility of experimental determination of top quark form
factors in upcoming e+ e- experiments, as recently discussed by Atwood and
Soni, we show at the one loop level that the conventionally defined magnetic
dipole moment (MDM) form factor of the top quark is gauge dependent, in the
class of renormalizable ($R_{\xi}$) gauges. We explicitly calculate its gauge
dependence, which, due to the massiveness of the top quark, turns out to be
numerically very sizable. We show how to use the S-matrix pinch technique in
order to define gauge independent form factors. The real and imaginary parts of
the gauge independent MDM form factor are calculated and their dependence on
the top quark and Higgs boson masses is discussed. Most noticeably, the
dependence on the Higgs mass turns out to be stronger than that of the cross
section as a whole.


  Self-interacting scalar field configurations which are non-minimally coupled
($\zeta\neq0$) to the gravity of a strictly stationary black hole with
non-rotating horizon are studied. It is concluded that for analytical
configurations the corresponding domain of outer communications is static.


  In this paper, we study the temperature and density properties of multiple
structural components of coronal mass ejections (CMEs) using differential
emission measure (DEM) analysis. The DEM analysis is based on the six-passband
EUV observations of solar corona from the Atmospheric Imaging Assembly onboard
the \emph{Solar Dynamic Observatory}. The structural components studied include
the hot channel in the core region (presumably the magnetic flux rope of the
CME), the bright loop-like leading front (LF), and coronal dimming in the wake
of the CME. We find that the presumed flux rope has the highest average
temperature ($>$8 MK) and density ($\sim$1.0 $\times10^{9}$ cm$^{-3}$),
resulting in an enhanced emission measure (EM) over a broad temperature range
(3 $\leq$ T(MK) $\leq$ 20). On the other hand, the CME LF has a relatively cool
temperature ($\sim$2 MK) and a narrow temperature distribution similar to the
pre-eruption coronal temperature (1 $\leq$ T(MK) $\leq$ 3). The density in the
LF, however, is increased by 2% to 32% compared with that of the pre-eruption
corona, depending on the event and location. In coronal dimmings, the
temperature is more broadly distributed (1 $\leq$ T(MK) $\leq$ 4), but the
density decreases by $\sim$35% to $\sim$40%. These observational results show
that: (1) CME core regions are significantly heated, presumably through
magnetic reconnection, (2) CME LFs are a consequence of compression of ambient
plasma caused by the expansion of the CME core region, and (3) the dimmings are
largely caused by the plasma rarefaction associated with the eruption.


  We calculate the first homology group of the mapping class group with
coefficients in the first rational homology group of the universal abelian $\Z
/ L \Z$-cover of the surface. If the surface has one marked point, then the
answer is $\Q^{\tau(L)}$, where $\tau(L)$ is the number of positive divisors of
$L$. If the surface instead has one boundary component, then the answer is
$\Q$. We also perform the same calculation for the level $L$ subgroup of the
mapping class group. Set $H_L = H_1(\Sigma_g;\Z/L\Z)$. If the surface has one
marked point, then the answer is $\Q[H_L]$, the rational group ring of $H_L$.
If the surface instead has one boundary component, then the answer is $\Q$.


  The density-matrix renormalization group method is used to study the ground
state of the two-chain zigzag-bond Hubbard model at quarter filling. We show
that, with a proper choice of the signs of hopping integrals, the ring exchange
mechanism yields ferromagnetic spin correlations between interchain neighboring
sites, and produces the attractive interaction between electrons as well as the
long-range pair correlations in the spin-triplet channel, thereby leading the
system to triplet superconductivity. We argue that this novel mechanism may
have possible relevance to observed superconductivity in Bechgaard salts.


  Mass differences of the charmonium mesons $\psi$(3770), $X$(3872),
$\chi$(3929), $X$(3940), $\psi$(4040), $\psi$(4160), $X$(4259) and $\psi$(4415)
have been studied. The analysis of data has shown, 5 of 7 mass differences with
$\psi$(4415) are precisely interpreted by using of harmonic quarks and their
complete oscillators. Upper part of the charmonium spectrum consists of two
groups. Except for two particles ($\psi$(3770) and $\psi$(4040)) connected
among themselves, the others 5 particles are connected with a meson
$\psi$(4415). The levels are separated by elementary groups of harmonic quarks
and completed oscillators. Experimental and calculated data are in the good
consent. The masses of mesons $\psi$(4415), $X$(4260), $\psi$(4160), $X$(3943)
and $\chi$(3929) were recalculated. The second group demonstrates that harmonic
quarks can also well work below threshold $D\bar{D}$.


  Using a suite of $N$-body simulations we study the angular clustering of
galaxies, halos, and dark matter in $\mathrm{\Lambda \text{CDM}}$ and Modified
Gravity (MG) scenarios. We consider two general categories of such MG models,
one is the $f(R)$ gravity, and the other is the normal branch of the
Dvali-Gabadadze-Porrati brane world (nDGP). To measure angular clustering we
construct a set of observer-frame lightcones and resulting mock sky catalogs.
We focus on the area-averaged angular correlation functions, $W_J$, and the
associated reduced cumulants, $S_J\equiv W_J/W_2^{(J-1)}$, and robustly measure
them up to the 9th order using counts-in-cells (CIC). We find that $0.15 < z <
0.3$ is the optimal redshift range to maximize the MG signal in our lightcones.
Analyzing various scales for the two types of statistics, we identify up to
20\% relative departures in MG measurements from general relativity (GR), with
varying signal significance. For the case of halos and galaxies, we find that
$3$rd order statistics offer the most sensitive probe of the different
structure formation scenarios, with both $W_3$ and the reduced skewness, $S_3$,
reaching from $2\sigma$ to $4\sigma$ significance at angular scales $\theta
\sim 0.13 ^\circ$. The MG clustering of the smooth dark matter field is
characterized by even stronger deviations ($\stackrel{>}{{}_\sim} 5\sigma$)
from GR, albeit at a bit smaller scales of $\theta\sim0.08^\circ$, where
baryonic physics is already important. Finally, we stress out that our mock
halo and galaxy catalogs are characterized by rather low surface number
densities when compared to existing and forthcoming state-of-the-art
photometric surveys. This opens up exciting potential for testing GR and MG
using angular clustering in future applications, with even higher precision and
significance than reported here.


  This chapter is based on lectures on Randomized Numerical Linear Algebra from
the 2016 Park City Mathematics Institute summer school on The Mathematics of
Data.


  The $S$-matrix of a quantum field theory is unchanged by field redefinitions,
and so only depends on geometric quantities such as the curvature of field
space. Whether the Higgs multiplet transforms linearly or non-linearly under
electroweak symmetry is a subtle question since one can make a coordinate
change to convert a field that transforms linearly into one that transforms
non-linearly. Renormalizability of the Standard Model (SM) does not depend on
the choice of scalar fields or whether the scalar fields transform linearly or
non-linearly under the gauge group, but only on the geometric requirement that
the scalar field manifold ${\mathcal M}$ is flat. We explicitly compute the
one-loop correction to scalar scattering in the SM written in non-linear
Callan-Coleman-Wess-Zumino (CCWZ) form, where it has an infinite series of
higher dimensional operators, and show that the $S$-matrix is finite. Standard
Model Effective Field Theory (SMEFT) and Higgs Effective Field Theory (HEFT)
have curved ${\mathcal M}$, since they parametrize deviations from the flat SM
case. We show that the HEFT Lagrangian can be written in SMEFT form if and only
if ${\cal M}$ has a $SU(2)_L \times U(1)_Y$ invariant fixed point. Experimental
observables in HEFT depend on local geometric invariants of ${\mathcal M}$ such
as sectional curvatures, which are of order $1/\Lambda^2$, where $\Lambda$ is
the EFT scale. We give explicit expressions for these quantities in terms of
the structure constants for a general $\mathcal G \to \mathcal H$ symmetry
breaking pattern. (Full abstract in pdf)


  The one-loop QCD corrections to the light-front wave-function for the
quark-antiquark Fock state inside a transverse or longitudinal off-shell photon
are explicitly calculated, both in full momentum space and in mixed space
(a.k.a. dipole space). These results provide one of the main contributions to
virtual NLO corrections to many DIS observables (inclusive or not) in the
dipole factorization formalism at low Bjorken x.
  In a follow-up article, these one-loop corrections are combined with earlier
results on the wave-function for the quark-antiquark-gluon Fock state, in order
to get the full set of NLO corrections to the DIS structure functions $F_2$ and
$F_L$ in the dipole factorization formalism, valid at low Bjorken x.


  We prove that all known examples of weakly o-minimal non-valuational
structures have no definable Skolem functions. We show, however, that such
structures eliminate imaginaries up to (definable families of) definable cuts.
Along the way we give some new examples of weakly o-minimal non-valuational
structures.


  The Anderson delocalization-localization transition is studied in
multilayered systems with randomly placed interlayer bonds of density $p$ and
strength $t$. In the absence of diagonal disorder (W=0), following an
appropriate perturbation expansion, we estimate the mean free paths in the main
directions and verify by scaling of the conductance that the states remain
extended for any finite $p$, despite the interlayer disorder. In the presence
of additional diagonal disorder ($W > 0$) we obtain an Anderson transition with
critical disorder $W_c$ and localization length exponent $\nu$ independently of
the direction. The critical conductance distribution $P_{c}(g)$ varies,
however, for the parallel and the perpendicular directions. The results are
discussed in connection to disordered anisotropic materials.


  Numerous sleep disorders are characterised by movement during sleep, these
include rapid-eye movement sleep behaviour disorder (RBD) and periodic limb
movement disorder. The process of diagnosing movement related sleep disorders
requires laborious and time-consuming visual analysis of sleep recordings. This
process involves sleep clinicians visually inspecting electromyogram (EMG)
signals to identify abnormal movements. The distribution of characteristics
that represent movement can be diverse and varied, ranging from brief moments
of tensing to violent outbursts. This study proposes a framework for automated
limb-movement detection by fusing data from two EMG sensors (from the left and
right limb) through a Dirichlet process mixture model. Several features are
extracted from 10 second mini-epochs, where each mini-epoch has been classified
as 'leg-movement' or 'no leg-movement' based on annotations of movement from
sleep clinicians. The distributions of the features from each category can be
estimated accurately using Gaussian mixture models with the Dirichlet process
as a prior. The available dataset includes 36 participants that have all been
diagnosed with RBD. The performance of this framework was evaluated by a
10-fold cross validation scheme (participant independent). The study was
compared to a random forest model and outperformed it with a mean accuracy,
sensitivity, and specificity of 94\%, 48\%, and 95\%, respectively. These
results demonstrate the ability of this framework to automate the detection of
limb movement for the potential application of assisting clinical diagnosis and
decision-making.


  We consider the problem of finding a control policy for a Markov Decision
Process (MDP) to maximize the probability of reaching some states while
avoiding some other states. This problem is motivated by applications in
robotics, where such problems naturally arise when probabilistic models of
robot motion are required to satisfy temporal logic task specifications. We
transform this problem into a Stochastic Shortest Path (SSP) problem and
develop a new approximate dynamic programming algorithm to solve it. This
algorithm is of the actor-critic type and uses a least-square temporal
difference learning method. It operates on sample paths of the system and
optimizes the policy within a pre-specified class parameterized by a
parsimonious set of parameters. We show its convergence to a policy
corresponding to a stationary point in the parameters' space. Simulation
results confirm the effectiveness of the proposed solution.


  We present a catalogue of refined positions of 68 gamma ray burst (GRB)
afterglows observed by the Swift X-ray Telescope (XRT) from the launch up to
2005 Oct 16. This is a result of the refinement of the XRT boresight
calibration. We tested this correction by means of a systematic study of a
large sample of X-ray sources observed by XRT with well established optical
counterparts. We found that we can reduce the systematic error radius of the
measurements by a factor of two, from 6.5" to 3.2" (90% of confidence). We
corrected all the positions of the afterglows observed by XRT in the first 11
months of the Swift mission. This is particularly important for the 37 X-ray
afterglows without optical counterpart. Optical follow-up of dark GRBs, in
fact, will be more efficient with the use of the more accurate XRT positions.


  Contagion processes are strongly linked to the network structures on which
they propagate, and learning these structures is essential for understanding
and intervention on complex network processes such as epidemics and
(mis)information propagation. However, using contagion data to infer network
structure is a challenging inverse problem. In particular, it is imperative to
have appropriate measures of uncertainty in network structure estimates,
however these are largely ignored in most machine-learning approaches. We
present a probabilistic framework that uses samples from the distribution of
networks that are compatible with the dynamics observed to produce network and
uncertainty estimates. We demonstrate the method using the well known
independent cascade model to sample from the distribution of networks P(G)
conditioned on the observation of a set of infections C. We evaluate the
accuracy of the method by using the marginal probabilities of each edge in the
distribution, and show the bene ts of quantifying uncertainty to improve
estimates and understanding, particularly with small amounts of data.


  For an odd prime $p$ and a supersingular elliptic curve over a number field,
this article introduces a fine signed residual Selmer group, under certain
hypotheses on the base field. This group depends purely on the residual
representation at $p$, yet captures information about the Iwasawa theoretic
invariants of the signed $p^\infty$-Selmer group that arise in supersingular
Iwasawa theory. Working in this residual setting provides a natural framework
for studying congruences modulo $p$ in Iwasawa theory.


  We compute the 2n-point renormalized coupling constants in the symmetric
phase of the 3d Ising model on the sc lattice in terms of the high temperature
expansions O(beta^{17}) of the Fourier transformed 2n-point connected
correlation functions at zero momentum. Our high temperature estimates of these
quantities, which enter into the small field expansion of the effective
potential for a 3d scalar field at the IR fixed point or, equivalently, in the
critical equation of state of the 3d Ising model universality class, are
compared with recent results obtained by renormalization group methods, strong
coupling, stochastic simulations as well as previous high temperature
expansions.


  The elimination of a fast time scale from the Vlasov equation by
Lie-transform methods is an important step in deriving a reduced Vlasov
equation such as the drift-kinetic Vlasov equation or the gyrokinetic Vlasov
equation. It is shown here that this dynamical reduction also leads to the
introduction of polarization and magnetization effects in the reduced Maxwell
equations, which ensure that the reduced Vlasov-Maxwell equations possess an
exact energy-momentum conservation law.


  A linear electron-positron collider operating at TeV scale energies will
provide high precision measurements and allow, for example, precision studies
of the Higgs boson as well as searches for physics beyond the standard model. A
future linear collider should produce collisions at high energy, with high
luminosity and with a good wall plug to beam power transfer efficiency. The
luminosity per power consumed is a key metric that can be used to compare
linear collider concepts. The plasma wakefield accelerator has demonstrated
high-gradient, high-efficiency acceleration of an electron beam, and is
therefore a promising technology for a future linear collider. We will go
through the opportunities of using plasma wakefield acceleration technology for
a collider, as well as a few of the collider-specific challenges that must be
addressed in order for a high-energy, high luminosity-per-power plasma
wakefield collider to become a reality.


  Evaluating observational estimators of causal effects demands information
that is rarely available: unconfounded interventions and outcomes from the
population of interest, created either by randomization or adjustment. As a
result, it is customary to fall back on simulators when creating benchmark
tasks. Simulators offer great control but are often too simplistic to make
challenging tasks, either because they are hand-designed and lack the nuances
of real-world data, or because they are fit to observational data without
structural constraints. In this work, we propose a general, repeatable strategy
for turning observational data into sequential structural causal models and
challenging estimation tasks by following two simple principles: 1) fitting
real-world data where possible, and 2) creating complexity by composing simple,
hand-designed mechanisms. We implement these ideas in a highly configurable
software package and apply it to the well-known Adult income data set to
construct the \tt IncomeSCM simulator. From this, we devise multiple estimation
tasks and sample data sets to compare established estimators of causal effects.
The tasks present a suitable challenge, with effect estimates varying greatly
in quality between methods, despite similar performance in the modeling of
factual outcomes, highlighting the need for dedicated causal estimators and
model selection criteria.


  Recently vehicle-to-vehicle (V2V) communication emerged as a key enabling
technology to ensure traffic safety and other mission-critical applications. In
this paper, a novel proximity and quality-of-service (QoS)-aware resource
allocation for V2V communication is proposed. The proposed approach exploits
the spatial-temporal aspects of vehicles in terms of their physical proximity
and traffic demands, to minimize the total transmission power while considering
queuing latency and reliability. Due to the overhead caused by frequent
information exchange between vehicles and the roadside unit (RSU), the
centralized problem is decoupled into two interrelated subproblems. First, a
novel RSU-assisted virtual clustering mechanism is proposed to group vehicles
in zones based on their physical proximity. Given the vehicles' traffic demands
and their QoS requirements, resource blocks are assigned to each zone. Second,
leveraging techniques from Lyapunov stochastic optimization, a power
minimization solution is proposed for each V2V pair within each zone.
Simulation results for a Manhattan model have shown that the proposed scheme
outperforms the baseline in terms of average queuing latency reduction up to
97% and significant improvement in reliability.


  This paper presents the first measurements of the charge independent (CI) and
charge dependent (CD) two-particle transverse momentum correlators $G_{2}^{\rm
CI}$ and $G_{2}^{\rm CD}$ in Pb--Pb collisions at $\sqrt{s_{\text{NN}}} =
2.76\;\text{\TeVe}$ by the ALICE collaboration. The two-particle transverse
momentum correlator $G_{2}$ was introduced as a measure of the momentum current
transfer between neighbouring system cells. The correlators are measured as a
function of pair separation in pseudorapidity ($\Delta \eta$) and azimuth
($\Delta \varphi$) and as a function of collision centrality. From peripheral
to central collisions, the correlator $G_{2}^{\rm CI}$ exhibits a longitudinal
broadening while undergoing a monotonic azimuthal narrowing. By contrast,
$G_{2}^{\rm CD}$ exhibits a narrowing along both dimensions. These features are
not reproduced by models such as HIJING and AMPT. However, the observed
narrowing of the correlators from peripheral to central collisions is expected
to result from the stronger transverse flow profiles produced in more central
collisions and the longitudinal broadening is predicted to be sensitive to
momentum currents and the shear viscosity per unit of entropy density $\eta/s$
of the matter produced in the collisions. The observed broadening is found to
be consistent with the hypothesized lower bound of $\eta/s$ and is in
qualitative agreement with values obtained from anisotropic flow measurements.


  Deep image relighting allows photo enhancement by illumination-specific
retouching without human effort and so it is getting much interest lately. Most
of the existing popular methods available for relighting are run-time intensive
and memory inefficient. Keeping these issues in mind, we propose the use of
Stacked Deep Multi-Scale Hierarchical Network, which aggregates features from
each image at different scales. Our solution is differentiable and robust for
translating image illumination setting from input image to target image.
Additionally, we have also shown that using a multi-step training approach to
this problem with two different loss functions can significantly boost
performance and can achieve a high quality reconstruction of a relighted image.


  The Hypergraph Partitioning (HGP) problem is a well-studied problem that
finds applications in a variety of domains. The literature on the HGP problem
has heavily focused on developing fast heuristic approaches. In several
application domains, such as the VLSI design and database migration planning,
the quality of the solution is more of a concern than the running time of the
algorithm. KaHyPar-E is the first multilevel memetic algorithm designed for the
HGP problem and it returns better quality solutions, compared to the heuristic
algorithms, if sufficient computation time is given. In this work, we introduce
novel problem-specific recombination and mutation operators, and develop a new
multilevel memetic algorithm by combining KaHyPar-E with these operators. The
performance of our algorithm is compared with the state-of-the-art HGP
algorithms on $150$ real-life instances taken from the benchmark datasets used
in the literature. In the experiments, which would take $39,000$ hours in a
single-core computer, each algorithm is given $2, 4$, and $8$ hours to compute
a solution for each instance. Our algorithm outperforms all others and finds
the best solutions in $112$, $115$, and $125$ instances in $2, 4$, and $8$
hours, respectively.


  For nonnegative real numbers $\alpha$, $\beta$, $\gamma$, $A$, $B$ and $C$
such that $B+C>0$ and $\alpha+\beta+\gamma >0$, the difference equation
  \begin{equation*} x_{n+1}=\displaystyle\frac{\alpha +\beta x_{n}+\gamma
x_{n-1}}{A+B x_{n}+C x_{n-1}}, \quad n=0,1,2,... %, \quad x_{-1},x_{0}\in
[0,\infty) \end{equation*} has a unique positive equilibrium. A proof is given
here for the following statements: \medskip
  \noindent Theorem 1. {\it For every choice of positive parameters $\alpha$,
$\beta$, $\gamma$, $A$, $B$ and $C$, all solutions to the difference equation
  \begin{equation*} x_{n+1}=\displaystyle\frac{\alpha +\beta x_{n}+\gamma
x_{n-1}}{A+B x_{n}+C x_{n-1}}, \quad n=0,1,2,..., \quad x_{-1},x_{0}\in
[0,\infty) \end{equation*} converge to the positive equilibrium or to a prime
period-two solution.} \medskip
  \noindent Theorem 2. {\it For every choice of positive parameters $\alpha$,
$\beta$, $\gamma$, $A$, $B$ and $C$, all solutions to the difference equation
\begin{equation*} x_{n+1}= \displaystyle\frac{\alpha +\beta x_{n}+\gamma
x_{n-1}}{B x_{n}+C x_{n-1}}, \quad n=0,1,2,..., \quad x_{-1},x_{0}\in
(0,\infty) \end{equation*} converge to the positive equilibrium or to a prime
period-two solution.}


  In this letter, we propose a framework for adapting the controller's
parameters based on learning optimal solutions from contextual black-box
optimization problems. We consider a class of control design problems for
dynamical systems operating in different environments or conditions represented
by contextual parameters. The overarching goal is to identify the controller
parameters that maximize the controlled system's performance, given different
realizations of the contextual parameters. We formulate a contextual Bayesian
optimization problem in which the solution is actively learned using Gaussian
processes to approximate the controller adaptation strategy. We demonstrate the
efficacy of the proposed framework with a simulation-to-real example. We learn
the optimal weighting strategy of a model predictive control for connected and
automated vehicles interacting with human-driven vehicles from simulations and
then deploy it in a real-time experiment.


  Numerical radiation-hydrodynamics (RHD) for non-relativistic flows is a
challenging problem because it encompasses processes acting over a very broad
range of timescales, and where the relative importance of these processes often
varies by orders of magnitude across the computational domain. Here we present
a new implicit-explicit (IMEX) method for numerical RHD that has a number of
desirable properties that have not previously been combined in a single method.
Our scheme is based on moments and allows machine-precision conservation of
energy and momentum, making it highly suitable for adaptive mesh refinement
applications; it requires no more communication than hydrodynamics and includes
no non-local iterative steps, making it highly suitable for massively parallel
and GPU-based systems where communication is a bottleneck; and we show that it
is asymptotically-accurate in the streaming, static diffusion, and dynamic
diffusion limits, including in the so-called asymptotic diffusion regime where
the computational grid does not resolve the photon mean free path. We implement
our method in the GPU-accelerated RHD code QUOKKA and show that it passes a
wide range of numerical tests.


  We consider ultraweak variational formulations for (parametrized) linear
first order transport equations in time and/or space. Computationally feasible
pairs of optimally stable trial and test spaces are presented, starting with a
suitable test space and defining an optimal trial space by the application of
the adjoint operator. As a result, the inf-sup constant is one in the
continuous as well as in the discrete case and the computational realization is
therefore easy. In particular, regarding the latter, we avoid a stabilization
loop within the greedy algorithm when constructing reduced models within the
framework of reduced basis methods. Several numerical experiments demonstrate
the good performance of the new method.


  Density Functional Theory's Kohn-Sham (KS) potential emerges as the
minimizing effective potential in an unconstrained variational scheme that does
not involve fixing the unknown single-electron density. The physical content
behind the virtual KS system, that of a non-interacting electronic system
representing optimally the interacting one, is brought to light.


  We introduce a class of metrics on $\mathbb{R}^n$ generalizing the classical
Grushin plane. These are length metrics defined by the line element $ds =
d_E(\cdot,Y)^{-\beta}ds_E$ for a closed nonempty subset $Y \subset
\mathbb{R}^n$ and $\beta \in [0,1)$. We prove that, assuming a H\"older
condition on the metric, these spaces are quasisymmetrically equivalent to
$\mathbb{R}^n$ and can be embedded in some larger Euclidean space under a
bi-Lipschitz map. Our main tool is an embedding characterization due to Seo,
which we strengthen by removing the hypothesis of uniform perfectness. In the
two-dimensional case, we give another proof of bi-Lipschitz embeddability based
on growth bounds on sectional curvature.


  Azimuthal particle correlations have been extensively studied in the past at
various collider energies in p-p, p-A, and A-A collisions. Hadron-correlation
measurements in heavy-ion collisions have mainly focused on studies of
collective (flow) effects at low-$p_T$ and parton energy loss via jet quenching
in the high-$p_T$ regime. This was usually done without event-by-event particle
identification. In this paper, we present two-particle correlations with
identified trigger hadrons and identified associated hadrons at mid-rapidity in
Monte Carlo generated events. The primary purpose of this study was to
investigate the effect of quantum number conservation and the flavour balance
during parton fragmentation and hadronization. The simulated p-p events were
generated with PYTHIA 6.4 with the Perugia-0 tune at $\sqrt{s}=7$ TeV. HIJING
was used to generate $0-10\%$ central Pb-Pb events at $\sqrt{s_{\rm NN}}=2.76$
TeV. We found that the extracted identified associated hadron spectra for
charged pion, kaon, and proton show identified trigger-hadron dependent
splitting. Moreover, the identified trigger-hadron dependent correlation
functions vary in different $p_T$ bins, which may show the presence of
collective/nuclear effects.


  Multimessenger astronomy is arguably the branch of the astroparticle physics
field that has seen the most significant developments in recent years. In this
manuscript, we will review the state-of-the-art, the recent observations, and
the prospects and challenges for the near future. We will give special emphasis
to the observation carried out with neutrino telescopes.


  The recent discovery of correlated insulator states and superconductivity in
magic-angle twisted bilayer graphene has paved the way to the experimental
investigation of electronic correlations in tunable flat band systems realized
in twisted van der Waals heterostructures. This novel twist angle degree of
freedom and control should be generalizable to other 2D systems, which may
exhibit similar correlated physics behavior while at the same time enabling new
techniques to tune and control the strength of electron-electron interactions.
Here, we report on a new highly tunable correlated system based on small-angle
twisted bilayer-bilayer graphene (TBBG), consisting of two rotated sheets of
Bernal-stacked bilayer graphene. We find that TBBG exhibits a rich phase
diagram, with tunable correlated insulators states that are highly sensitive to
both twist angle and to the application of an electric displacement field, the
latter reflecting the inherent polarizability of Bernal-stacked bilayer
graphene. We find correlated insulator states that can be switched on and off
by the displacement field at all integer electron fillings of the moir\'{e}
unit cell. The response of these correlated states to magnetic fields points
towards evidence of electrically switchable magnetism. Moreover, the strong
dependence of the resistance at low temperature and near the correlated
insulator states indicates possible proximity to a superconducting phase.
Furthermore, in the regime of lower twist angles, TBBG shows multiple sets of
flat bands near charge neutrality, resulting in numerous correlated states
corresponding to half-filling of each of these flat bands. Our results pave the
way to the exploration of novel twist-angle and electric-field controlled
correlated phases of matter in novel multi-flat band twisted superlattices.


  The experimentally observed nonlinearities of the current-voltage
characteristics (CVCs) of tantalum-based point homo- and hetero- contacts in
both normal and superconducting states related to electron-phonon interaction
(EPI) were analyzed. It was taken into account that additional nonlinearity of
CVCs arising upon contact transition to the superconducting state
(superconducting spectral component) is formed not only near the constriction
in the region roughly equal to the contact diameter (as is the case for the
normal state, and as predicted theoretically for the superconducting state),
but also in a markedly larger region that is about the size of the coherence
length. In this case, a considerable role in the formation of this
superconducting component is played by nonequilibrium phonons with low group
velocity, which account for the experimentally observed sharpening of the
phonon peaks in the EPI spectra (the second derivatives of the CVCs) during the
superconducting transition of the contacts, instead of the theoretically
expected peak broadening (spreading), and for the increase in the
superconducting contribution to the point contact spectrum in the low and
medium energy regions. The high-energy part of the EPI spectrum changes much
less significantly during the superconducting transition, which is attributable
to suppression of the excess contact current by nonequilibrium quasi-particles.
A detailed procedure was proposed for the recovery of the EPI spectral function
from the point contact spectrum contribution (the second derivative of the CVC)
that arises during the superconducting transition of one or both contacting
metals.


  We investigate the spectral fluctuation properties of constrained ensembles
of random matrices (defined by the condition that a number N(Q) of matrix
elements vanish identically; that condition is imposed in unitarily invariant
form) in the limit of large matrix dimension. We show that as long as N(Q) is
smaller than a critical value (at which the quadratic level repulsion of the
Gaussian unitary ensemble of random matrices may be destroyed) all spectral
fluctuation measures have the same form as for the Gaussian unitary ensemble.


  An extensive analysis of the presence of different altmetric indicators
provided by Altmetric.com across scientific fields is presented, particularly
focusing on their relationship with citations. Our results confirm that the
presence and density of social media altmetric counts are still very low and
not very frequent among scientific publications, with 15%-24% of the
publications presenting some altmetric activity and concentrating in the most
recent publications, although their presence is increasing over time.
Publications from the social sciences, humanities and the medical and life
sciences show the highest presence of altmetrics, indicating their potential
value and interest for these fields. The analysis of the relationships between
altmetrics and citations confirms previous claims of positive correlations but
relatively weak, thus supporting the idea that altmetrics do not reflect the
same concept of impact as citations. Also, altmetric counts do not always
present a better filtering of highly cited publications than journal citation
scores. Altmetrics scores (particularly mentions in blogs) are able to identify
highly cited publications with higher levels of precision than journal citation
scores (JCS), but they have a lower level of recall. The value of altmetrics as
a complementary tool of citation analysis is highlighted, although more
research is suggested to disentangle the potential meaning and value of
altmetric indicators for research evaluation.


  Let M be a foliated manifold and G a discrete group acting on M by
diffeomorphisms mapping leaves to leaves. Then G naturally acts by
automorphisms on the algebra of Heisenberg pseudodifferential operators on the
foliation. Our main result is an index theorem for hypoelliptic-type operators
which belong to the crossed product of the Heisenberg pseudodifferential
operators with the group G. As a corollary, we get a solution to
Connes-Moscovici's transverse problem in arbitrary codimensions, by exhibiting
an explicit formula in terms of characteristic classes of equivariant vector
bundles over M, for the Chern-Connes character associated to their hypoelliptic
signature operator.


  Tree perception is an essential building block toward autonomous forestry
operations. Current developments generally consider input data from lidar
sensors to solve forest navigation, tree detection and diameter estimation
problems. Whereas cameras paired with deep learning algorithms usually address
species classification or forest anomaly detection. In either of these cases,
data unavailability and forest diversity restrain deep learning developments
for autonomous systems. So, we propose two densely annotated image datasets -
43k synthetic, 100 real - for bounding box, segmentation mask and keypoint
detections to assess the potential of vision-based methods. Deep neural network
models trained on our datasets achieve a precision of 90.4% for tree detection,
87.2% for tree segmentation, and centimeter accurate keypoint estimations. We
measure our models' generalizability when testing it on other forest datasets,
and their scalability with different dataset sizes and architectural
improvements. Overall, the experimental results offer promising avenues toward
autonomous tree felling operations and other applied forestry problems. The
datasets and pre-trained models in this article are publicly available on
\href{https://github.com/norlab-ulaval/PercepTreeV1}{GitHub}
(https://github.com/norlab-ulaval/PercepTreeV1).


  Understanding human motion behavior is critical for autonomous moving
platforms (like self-driving cars and social robots) if they are to navigate
human-centric environments. This is challenging because human motion is
inherently multimodal: given a history of human motion paths, there are many
socially plausible ways that people could move in the future. We tackle this
problem by combining tools from sequence prediction and generative adversarial
networks: a recurrent sequence-to-sequence model observes motion histories and
predicts future behavior, using a novel pooling mechanism to aggregate
information across people. We predict socially plausible futures by training
adversarially against a recurrent discriminator, and encourage diverse
predictions with a novel variety loss. Through experiments on several datasets
we demonstrate that our approach outperforms prior work in terms of accuracy,
variety, collision avoidance, and computational complexity.


  The quantum Hall effect, with a Berry's phase of $\pi$ is demonstrated here
on a single graphene layer grown on the C-face of 4H silicon carbide. The
mobility is $\sim$ 20,000 cm$^2$/V$\cdot$s at 4 K and ~15,000 cm$^2$/V$\cdot$s
at 300 K despite contamination and substrate steps. This is comparable to the
best exfoliated graphene flakes on SiO$_2$ and an order of magnitude larger
than Si-face epitaxial graphene monolayers. These and other properties indicate
that C-face epitaxial graphene is a viable platform for graphene-based
electronics.


  This paper investigates how to mitigate the impact of adjacent channel
interference (ACI) in vehicular broadcast communication, using scheduling and
power control. Our objective is to maximize the number of connected vehicles.
First, we formulate the joint scheduling and power control problem as a mixed
Boolean linear programming (MBLP) problem. From this problem formulation, we
derive scheduling alone problem as Boolean linear programming (BLP) problem,
and power control alone problem as an MBLP problem. Due to the hardness in
solving joint scheduling and power control for multiple timeslots, we propose a
column generation method to reduce the computational complexity. We also
observe that the problem is highly numerically sensitive due to the high
dynamic range of channel parameters and adjacent channel interference ratio
(ACIR) values. Therefore, we propose a novel sensitivity reduction technique,
which can compute the optimal solution. Finally, we compare the results for
optimal scheduling, near-optimal joint scheduling and power control schemes,
and conclude that the effective scheduling and power control schemes indeed
significantly improve the performance.


  (Ga$_{1-x}$,Fe$_x$)Sb is one of the promising ferromagnetic semiconductors
for spintronic device applications because its Curie temperature ($T_{\rm C}$)
is above 300 K when the Fe concentration $x$ is equal to or higher than ~0.20.
However, the origin of the high $T_{\rm C}$ in (Ga,Fe)Sb remains to be
elucidated. To address this issue, we use resonant photoemission spectroscopy
(RPES) and first-principles calculations to investigate the $x$ dependence of
the Fe 3$d$ states in (Ga$_{1-x}$,Fe$_x$)Sb ($x$ = 0.05, 0.15, and 0.25) thin
films. The observed Fe 2$p$-3$d$ RPES spectra reveal that the Fe-3$d$ impurity
band (IB) crossing the Fermi level becomes broader with increasing $x$, which
is qualitatively consistent with the picture of double-exchange interaction.
Comparison between the obtained Fe-3$d$ partial density of states and the
first-principles calculations suggests that the Fe-3$d$ IB originates from the
minority-spin ($\downarrow$) $e$ states. The results indicate that enhancement
of the interaction between $e_\downarrow$ electrons with increasing $x$ is the
origin of the high $T_{\rm C}$ in (Ga,Fe)Sb.


  Noisy labels composed of correct and corrupted ones are pervasive in
practice. They might significantly deteriorate the performance of convolutional
neural networks (CNNs), because CNNs are easily overfitted on corrupted labels.
To address this issue, inspired by an observation, deep neural networks might
first memorize the probably correct-label data and then corrupt-label samples,
we propose a novel yet simple self-paced resistance framework to resist
corrupted labels, without using any clean validation data. The proposed
framework first utilizes the memorization effect of CNNs to learn a curriculum,
which contains confident samples and provides meaningful supervision for other
training samples. Then it adopts selected confident samples and a proposed
resistance loss to update model parameters; the resistance loss tends to smooth
model parameters' update or attain equivalent prediction over each class,
thereby resisting model overfitting on corrupted labels. Finally, we unify
these two modules into a single loss function and optimize it in an alternative
learning. Extensive experiments demonstrate the significantly superior
performance of the proposed framework over recent state-of-the-art methods on
noisy-label data. Source codes of the proposed method are available on
https://github.com/xsshi2015/Self-paced-Resistance-Learning.


  Our results demonstrated that a previously reported protein name
co-occurrence method (5-mention PubGene) which was not based on a hypothesis
testing framework, it is generally statistically more significant than the 99th
percentile of Poisson distribution-based method of calculating co-occurrence.
It agrees with previous methods using natural language processing to extract
protein-protein interaction from text as more than 96% of the interactions
found by natural language processing methods to overlap with the results from
5-mention PubGene method. However, less than 2% of the gene co-expressions
analyzed by microarray were found from direct co-occurrence or interaction
information extraction from the literature. At the same time, combining
microarray and literature analyses, we derive a novel set of 7 potential
functional protein-protein interactions that had not been previously described
in the literature.


  To ensure preservation of local or global bounds for numerical solutions of
conservation laws, we constrain a baseline finite element discretization using
optimization-based (OB) flux correction. The main novelty of the proposed
methodology lies in the use of flux potentials as control variables and targets
of inequality-constrained optimization problems for numerical fluxes. In
contrast to optimal control via general source terms, the discrete conservation
property of flux-corrected finite element approximations is guaranteed without
the need to impose additional equality constraints. Since the number of flux
potentials is less than the number of fluxes in the multidimensional case, the
potential-based version of optimal flux control involves fewer unknowns than
direct calculation of optimal fluxes. We show that the feasible set of a
potential-state potential-target (PP) optimization problem is nonempty and
choose a primal-dual Newton method for calculating the optimal flux potentials.
The results of numerical studies for linear advection and anisotropic diffusion
problems in 2D demonstrate the superiority of the new OB-PP algorithms to
closed-form flux limiting under worst-case assumptions.


  A main feature of high-energy scattering in QCD is saturation in the number
density of gluons. This phenomenon is described by non-linear evolution
equations, JIMWLK and BK, which have been derived at leading logarithmic
accuracy. In this paper we generalize this framework to include running
coupling corrections to the evolution kernel. We develop a dispersive
representation of the dressed gluon propagator in the background of Weiszacker
Williams fields and use it to compute O(beta_0^{n-1} alpha_s^n) corrections to
the kernel to all orders in perturbation theory. The resummed kernels present
infrared-renormalon ambiguities, which are indicative of the form and
importance of non-perturbative power corrections. We investigate numerically
the effect of the newly computed perturbative corrections as well as the power
corrections on the evolution and find that at present energies they are both
significant.


  We study the problem of privately estimating the parameters of
$d$-dimensional Gaussian Mixture Models (GMMs) with $k$ components. For this,
we develop a technique to reduce the problem to its non-private counterpart.
This allows us to privatize existing non-private algorithms in a blackbox
manner, while incurring only a small overhead in the sample complexity and
running time. As the main application of our framework, we develop an
$(\varepsilon, \delta)$-differentially private algorithm to learn GMMs using
the non-private algorithm of Moitra and Valiant [MV10] as a blackbox.
Consequently, this gives the first sample complexity upper bound and first
polynomial time algorithm for privately learning GMMs without any boundedness
assumptions on the parameters. As part of our analysis, we prove a tight (up to
a constant factor) lower bound on the total variation distance of
high-dimensional Gaussians which can be of independent interest.


  Silicon photonics enables scaling of quantum photonic systems by allowing the
creation of extensive, low-loss, reconfigurable networks linking various
functional on-chip elements. Inclusion of single quantum emitters onto photonic
circuits, acting as on-demand sources of indistinguishable photons or
single-photon nonlinearities, may enable large-scale chip-based quantum
photonic circuits and networks. Towards this, we use low-temperature
$\textit{in situ}$ electron-beam lithography to deterministically produce
hybrid GaAs/Si$_3$N$_4$ photonic devices containing single InAs quantum dots
precisely located inside nanophotonic structures, which act as efficient,
Si$_3$N$_4$ waveguide-coupled on-chip, on-demand single-photon sources. The
precise positioning afforded by our scalable fabrication method furthermore
allows observation of post-selected indistinguishable photons. This indicates a
promising path towards significant scaling of chip-based quantum photonics,
enabled by large fluxes of indistinguishable single-photons produced on-demand,
directly on-chip.


  We examine MHD simulations of the propagation of a strong shock wave through
the interstellar two-phase medium composed of small-scale cloudlets and diffuse
warm neutral medium in two-dimensional geometry. The pre-shock two-phase medium
is provided as a natural consequence of the thermal instability that is
expected to be ubiquitous in the interstellar medium. We show that the
shock-compressed shell becomes turbulent owing to the preshock density
inhomogeneity and magnetic field amplification takes place in the shell. The
maximum field strength is determined by the condition that plasma beta ~ 1,
which gives the field strength on the order of 1 mG in the case of shock
velocity ~ 1,000 km/s. The strongly magnetized region shows filamentary and
knot-like structures in two-dimensional simulations. The spatial scale of the
regions with magnetic field of 1 mG in our simulation is roughly 0.05 pc which
is comparable to the spatial scale of the X-ray hot spots recently discovered
in supernova remnants where the magnetic field strength is indicated to be
amplified up to the order of 1 mG. This result may also suggest that the
turbulent region with locally strong magnetic field is expected to be spread
out in the region with frequent supernova explosions, such as in the Galactic
center and starburst galaxies.


  The problem of transformation selection is thoroughly treated from a Bayesian
perspective. Several families of transformations are considered with a view to
achieving normality: the Box-Cox, the Modulus, the Yeo & Johnson and the Dual
transformation. Markov chain Monte Carlo algorithms have been constructed in
order to sample from the posterior distribution of the transformation parameter
$\lambda_T$ associated with each competing family $T$. We investigate different
approaches to constructing compatible prior distributions for $\lambda_T$ over
alternative transformation families, using a unit-information power-prior
approach and an alternative normal prior with approximate unit-information
interpretation. Selection and discrimination between different transformation
families is attained via posterior model probabilities. We demonstrate the
efficiency of our approach using a variety of simulated datasets. Although
there is no choice of transformation family that can be universally applied to
all problems, empirical evidence suggests that some particular data structures
are best treated by specific transformation families. For example, skewness is
associated with the Box-Cox family while fat-tailed distributions are
efficiently treated using the Modulus transformation.


  We study the entanglement of a two-qubit system in a superconducting quantum
dot (SQD) lattice in the presence of magnetic flux and gate voltage
inhomogeneity. We observe a universal feature for the half-integer magnetic
flux quantum which completely washes out the entanglement of the system both at
zero and finite temperature. We observe that the ground state is always in a
maximally entangled Bell state when there is no inhomogeneity in gate voltage
in the superconducting quantum dot lattice. We find an important constraint in
magnetic flux for ground state entanglement. We also observe few behavior of
entanglement at finite temperature is in contrast with the zero temperature
behavior.


  Quantum computing devices in the NISQ era share common features and
challenges like limited connectivity between qubits. Since two-qubit gates are
allowed on limited qubit pairs, quantum compilers must transform original
quantum programs to fit the hardware constraints. Previous works on qubit
mapping assume different gates have the same execution duration, which limits
them to explore the parallelism from the program. To address this drawback, we
propose a Multi-architecture Adaptive Quantum Abstract Machine (maQAM) and a
COntext-sensitive and Duration-Aware Remapping algorithm (CODAR). The CODAR
remapper is aware of gate duration difference and program context, enabling it
to extract more parallelism from programs and speed up the quantum programs by
1.23 in simulation on average in different architectures and maintain the
fidelity of circuits when running on Origin Quantum noisy simulator.


  The Spectrometer/Telescope for Imaging X-rays (STIX) is one of the 10
instruments on-board the scientific payload of ESA's Solar Orbiter mission.
STIX provides hard X-ray imaging spectroscopy in the 4-150~keV energy range,
observing hard X-ray bremsstrahlung emission from the Sun. These observations
provide diagnostics of the hottest thermal plasmas ($>$10~MK) and information
on the non-thermal energetic electrons accelerated above 10~keV during solar
flares. STIX has a spectral resolution of 1~keV, and employs the use of
in-direct bi-grid Fourier imaging to spatially locate hard X-ray emission.
Given that STIX provides critical information about accelerated electrons at
the Sun through hard X-ray diagnostics, it is a powerful contribution to the
Solar Orbiter suite and has a significant role to explore the dynamics of solar
inputs to the heliosphere. This chapter describes the STIX instrument, its
design, objectives, first observations and outlines the new perspectives STIX
provides over the mission lifetime of Solar Orbiter.


  In this paper, I mention a few processes which may play a role in the
evolution of the central regions of galaxies. In this context, I briefly
discuss some issues regarding the formation of bulges in spirals, the role of
supermassive black holes, and the importance of nuclear density waves.


  Context. The long-term evolution of an atmosphere and the remote
detectability of its chemical constituents are susceptible to how the
atmospheric gas responds to stellar irradiation. The response remains poorly
characterized for water and its dissociation products, however, this knowledge
is relevant to our understanding of hypothetical water-rich exoplanets. Aims:
Our work investigates the effect of photoelectrons, namely, the non-thermal
electrons produced by photoionizing stellar radiation on the heating and
ionization of extended atmospheres dominated by the dissociation products of
water. Methods: We used a Monte Carlo model and up-to-date collision cross
sections to simulate the slowing down of photoelectrons in O-H mixtures for a
range of fractional ionizations and photoelectron energies. Results: We find
that that the fraction of energy of a photoelectron that goes into heating is
similar in a pure H gas and in O-H mixtures, except for very low fractional
ionizations, whereby the O atom remains an efficient sink of energy. The O-H
mixtures will go on to produce more electrons because the O atom is
particularly susceptible to ionization. We quantified all that information and
present it in a way that can be easily incorporated into
photochemical-hydrodynamical models. Conclusions: Neglecting the role of
photoelectrons in models of water-rich atmospheres will result in
overestimations of the atmospheric heating and, foreseeably, the mass-loss
rates as well. It will also underestimate the rate at which the atmospheric gas
becomes ionized, which may have implications for the detection of extended
atmospheres with Lyman-{\alpha} transmission spectroscopy. Our simulations for
the small exoplanets {\pi} Men c and TRAPPIST-1 b reveal that they respond very
differently to irradiation from their host stars, with water remaining in
molecular form at lower pressures in the latter case.


  In this paper, globular star clusters which contain a sub-system of
stellar-mass black holes (BH) are investigated. This is done by considering
two-component models, as these are the simplest approximation of more realistic
multi-mass systems, where one component represents the BH population and the
other represents all the other stars. These systems are found to undergo a long
phase of evolution where the centre of the system is dominated by a dense BH
sub-system. After mass segregation has driven most of the BH into a compact
sub-system, the evolution of the BH sub-system is found to be influenced by the
cluster in which it is contained. The BH sub-system evolves in such a way as to
satisfy the energy demands of the whole cluster, just as the core of a one
component system must satisfy the energy demands of the whole cluster. The BH
sub-system is found to exist for a significant amount of time. It takes
approximately 10t_{rh,i}, where t_{rh,i} is the initial half-mass relaxation
time, from the formation of the compact BH sub-system up until the time when
90% of the sub-system total mass is lost (which is of order 10^{3} times the
half-mass relaxation time of the BH sub-system at its time of formation). Based
on theoretical arguments the rate of mass loss from the BH sub-system
(\dot{M}_2) is predicted to be -(beta*zeta*M)/(alpha*t_{rh}), where M is the
total mass, t_{rh} is the half-mass relaxation time, and alpha, beta, zeta are
three dimensionless parameters (see Section 2 for details). An interesting
consequence of this is that the rate of mass loss from the BH sub-system is
approximately independent of the stellar mass ratio (m_2/m_1) and the total
mass ratio (M_2/M_1) (in the range m_2/m_1 >~ 10 and M_2/M_1 ~ 10^{-2}, where
m_1, m_2 are the masses of individual low-mass and high-mass particles
respectively, and M_1, M_2 are the corresponding total masses).


  A strong coupling expansion of the SU(2) Yang-Mills quantum Hamiltonian is
carried out in the form of an expansion in the number of spatial derivatives,
using the symmetric gauge \epsilon_{ijk} A_{jk}=0. Introducing an infinite
lattice with box length a, I obtain a systematic strong coupling expansion of
the Hamiltonian in \lambda\equiv g^{-2/3}, with the free part being the sum of
Hamiltonians of Yang-Mills quantum mechanics of constant fields for each box,
and interaction terms of higher and higher number of spatial derivatives
connecting different boxes. The corresponding deviation from the free glueball
spectrum, obtained earlier for the case of the Yang-Mills quantum mechanics of
spatially constant fields, is calculated using perturbation theory in \lambda.
As a first step, the interacting glueball vacuum and the energy spectrum of the
interacting spin-0 glueball are obtained to order \lambda^2. Its relation to
the renormalisation of the coupling constant in the IR is discussed, indicating
the absence of infrared fixed points.


  Roman Jackiw has made highly original and influential contributions to
several areas of physics that have grown and blossomed, notably including the
quantum physics of domain walls, magnetic monopoles, and fractional quantum
numbers. Here I offer three small pieces that take off from those themes. I
discuss the emergence of topological surface structure in materials, the
emergence of a shape-space magnetic monopole in a simple mechanical system, and
the emergence of fractional angular momentum in an even simpler quantum
mechanical (molecular) system.


  Spin Hall effect (SHE) and voltage-controlled magnetic anisotropy (VCMA) are
two promising methods for low-power electrical manipulation of magnetization.
Recently, magnetic field-free switching of perpendicular magnetization through
SHE has been reported with the aid of an exchange bias from an
antiferromagnetic IrMn layer. In this letter, we experimentally demonstrate
that the IrMn/CoFeB/MgO structure exhibits a VCMA effect of 39 fJ/Vm, which is
comparable to that of the Ta/CoFeB/MgO structure. Magnetization dynamics under
a combination of the SHE and VCMA are modeled and simulated. It is found that,
by applying a voltage of 1.5 V, the critical SHE switching current can be
decreased by 10 times owing to the VCMA effect, leading to low-power
operations. Furthermore, a high-density spintronic memory structure can be
built with multiple magnetic tunnel junctions (MTJs) located on a single IrMn
strip. Through hybrid CMOS/MTJ simulations, we demonstrate that fast-speed
write operations can be achieved with power consumption of only 8.5 fJ/bit.
These findings reveal the possibility to realize high-density and low-power
spintronic memory manipulated by voltage-gated SHE.


  We have optically identified a recently discovered INTEGRAL source, IGR
J08390--4833, with a cataclysmic variable, i.e. an accreting white dwarf in a
binary system. The spectrum exhibits a rising blue continuum together with
Balmer and HeII emission lines. Analysis of the light curve of the source shows
clear presence of intrinsic variability on a time scale of the order of an
hour, although we do not claim that this variability is periodic. Therefore we
are not yet able to classify the object into a specific CV subclass.


  In this paper we present a detailed study of the peculiar early-type galaxy
NGC1947. The main goal of this work is to constrain the dynamical status and
the formation history of NGC1947 by comparing the observed properties with the
predictions derived from different galaxy formation scenarios. To this aim, we
derived the photometric and kinematical properties of NGC1947. Due to the
presence of an extended dust-lane, which crosses the galaxy center along the
photometric minor axis, we used near-infrared images (J and K bands) to derive
an accurate analysis of the stellar light distribution. Optical images (in the
V and R bands) are used to derive the color profiles and color maps to study
the structure of the dust-lane. The observed kinematics confirm the presence of
two components with decoupled angular momentum: gas and dust rotate along the
minor axis, while the rotation velocities of the stars are observed along the
major axis. The complex structure observed in NGC1947 support the hypothesis
that some kind of interactions happened in the evolution of this object. We
analyzed two alternatives: a merging process and an accretion event. We
discussed how the observed properties strongly suggest that the decoupled ring
of gas and dust have been accreted from outside.


  A two-dimensional dense fluid of frictional grains is shown to exhibit
time-chaotic, spatially heterogeneous flow in a range of stress values,
$\sigma$, chosen in the unstable region of s-shaped flow curves. Stress
controlled simulations reveal a phase diagram with reentrant stationary flow
for small and large stress $\sigma$. In between no steady flow state can be
reached, instead the system either jams or displays time dependent
heterogeneous strain rates $\dot\gamma({\bf r},t)$. The results of simulations
are in agreement with the stability analysis of a simple hydrodynamic model,
coupling stress and microstructure which we tentatively associate with the
frictional contact network.


  Gait analysis, an expanding research area, employs non invasive sensors and
machine learning techniques for a range of applicatio ns. In this study, we
concentrate on gait analysis for detecting cognitive decline in Parkinson's
disease (PD) and under dual task conditions. Using convolutional neural
networks (CNNs) and explainable machine learning, we objectively analyze gait
data and associate findings with clinically relevant biomarkers. This is
accomplished by connecting machine learning outputs to decisions based on human
visual observations or derived quantitative gait parameters, which are tested
and routinely implemented in curr ent healthcare practice. Our analysis of gait
deterioration due to cognitive decline in PD enables robust results using the
proposed methods for assessing PD severity from ground reaction force (GRF)
data. We achieved classification accuracies of 98% F1 sc ores for each
PhysioNet.org dataset and 95.5% F1 scores for the combined PhysioNet dataset.
By linking clinically observable features to the model outputs, we demonstrate
the impact of PD severity on gait. Furthermore, we explore the significance of
cognit ive load in healthy gait analysis, resulting in robust classification
accuracies of 100% F1 scores for subject identity verification. We also
identify weaker features crucial for model predictions using Layer Wise
Relevance Propagation. A notable finding o f this study reveals that cognitive
deterioration's effect on gait influences body balance and foot landing/lifting
dynamics in both classification cases: cognitive load in healthy gait and
cognitive decline in PD gait.


  Some thermodynamic quantities of nonrelativistic ideal boson and fermion
gases in the static Taub universe are derived to first order in a small
anisotropy parameter d which measuring the deformation from the spherical
Einstein universe. They are used to investigate the problem of how the
curvature anisotropy affects the thermodynamic behaviors of an ideal gas. It is
found that, when the universe is in the oblate configuration (i.e., d > 0), the
effect of curvature anisotropy is to increase the number of the fraction in the
Bose-Einstein condensation and to decrease the fermion distribution function at
low temperature. When the universe is in the prolate configuration (i.e., d <
0), the effects of curvature anisotropy on the thermodynamic quantities is
contrary to that in the oblate configuration. The density matrix of a two
particle system is evaluated and it is used to define the "statistical
interparticle potential" as an attempt to give a "statistical interpretation"
about the found thermodynamic behaviors. It is found that when the universe is
in the oblate (prolate) configuration the curvature anisotropy will enhance
(reduce) both the "statistic attraction" among the bosons and "statistical
repulsion" among the fermions. It is expected that such a behavior will also be
shown in the relativistic system.


  We investigate the impact of a fourth sterile neutrino at reactor and
Spallation Neutron Source neutrino detectors. Specifically, we explore the
discovery potential of the TEXONO and COHERENT experiments to subleading
sterile neutrino effects through the measurement of the coherent elastic
neutrino-nucleus scattering event rate. Our dedicated $\chi^2$-sensitivity
analysis employs realistic nuclear structure calculations adequate for high
purity sub-keV threshold Germanium detectors.


  Four rounds of surveys of slum dwellers in Dhaka city during the 2020-21
COVID-19 pandemic raise questions about whether the slum dwellers possess some
form of immunity to the effects of COVID-19? If the working poor of Bangladesh
are practically immune to COVID-19, why has this question not been more
actively investigated? We shed light on some explanations for these pandemic
questions and draw attention to the role of intellectual elites and public
policy, suggesting modifications needed for pandemic research.


  We present a simple representation for analytically continued nested harmonic
sums for the arbitrary complex argument. This representation can be obtained
for a wide range of nested harmonic sums from a precomputed database for the
pole expressions of these sums near negative integers. We describe the
procedure for the precise numerical evaluations of the corresponding results
from the dispersion representation.


  We obtain the Lifschitz tail asymptotics for the integrated density of states
of the subordinate $\alpha-$stable processes on the Sierpi\'nski gasket
$\mathcal G,$ evolving among killing Poissonian obstacles. Simultaneously, we
derive the large-time asymptotics for the volume of the $\alpha-$stable sausage
on the gasket.


  Are introduced six examples of non-braidable tensor categories which are
extensions of the category Comod(H), for H a super-group algebra; and two
examples of braided categories where the only possible braiding is the trivial
braiding.


  We consider a dynamic game with asymmetric information where each player
observes privately a noisy version of a (hidden) state of the world V,
resulting in dependent private observations. We study structured perfect
Bayesian equilibria that use private beliefs in their strategies as sufficient
statistics for summarizing their observation history. The main difficulty in
finding the appropriate sufficient statistic (state) for the structured
strategies arises from the fact that players need to construct (private)
beliefs on other players' private beliefs on V, which in turn would imply that
an infinite hierarchy of beliefs on beliefs needs to be constructed, rendering
the problem unsolvable. We show that this is not the case: each player's belief
on other players' beliefs on V can be characterized by her own belief on V and
some appropriately defined public belief. We then specialize this setting to
the case of a Linear Quadratic Gaussian (LQG) non-zero-sum game and we
characterize linear structured PBE that can be found through a backward/forward
algorithm akin to dynamic programming for the standard LQG control problem.
Unlike the standard LQG problem, however, some of the required quantities for
the Kalman filter are observation-dependent and thus cannot be evaluated
off-line through a forward recursion.


  We present numerical results for the nonplanar lightlike cusp and collinear
anomalous dimension at four loops in ${\mathcal N} = 4$ SYM theory, which we
infer from a calculation of the Sudakov form factor. The latter is expressed as
a rational linear combination of uniformly transcendental integrals for
arbitrary colour factor. Numerical integration in the nonplanar sector reveals
explicitly the breakdown of quadratic Casimir scaling at the four-loop order. A
thorough analysis of the reported numerical uncertainties is carried out.


  Random walkers absorbing on a boundary sample the Harmonic Measure linearly
and independently: we discuss how the recurrence times between impacts enable
non-linear moments of the measure to be estimated. From this we derive a new
technique to simulate Dielectric Breakdown Model growth which is governed
nonlinearly by the Harmonic Measure. Recurrence times are shown to be accurate
and effective in probing the multifractal growth measure of diffusion limited
aggregation. For the Dielectric Breakdown Model our new technique grows large
clusters efficiently and we are led to significantly revise earlier exponent
estimates. Previous results by two conformal mapping techniques were less
converged than expected, and in particular a recent theoretical suggestion of
superuniversality is firmly refuted.


  Ordinary quantum mechanics is formulated on the basis of the existence of an
ideal classical clock external to the system under study. This is clearly an
idealization. As emphasized originally by Salecker and Wigner and more recently
by other authors, there exist limits in nature to how ``classical'' even the
best possible clock can be. When one introduces realistic clocks, quantum
mechanics ceases to be unitary and a fundamental mechanism of decoherence of
quantum states arises. We estimate the rate of universal loss of unitarity
using optimal realistic clocks. In particular we observe that the rate is rapid
enough to eliminate the black hole information puzzle: all information is lost
through the fundamental decoherence before the black hole can evaporate. This
improves on a previous calculation we presented with a sub-optimal clock in
which only part of the information was lost by the time of evaporation.


  We study the statistics of the work done by globally changing in time with a
generic protocol the mass in a free bosonic field theory with relativistic
dispersion and the transverse field in the one-dimensional Ising chain both
globally and locally. In the latter case we make the system start from the
critical point and we describe it in the scaling limit. We provide exact
formulas in all these cases for the full statistics of the work and we show
that the low energy part of the distribution of the work displays an edge
singularity whose exponent does not depend on the specifics of the protocol
that is chosen, and may only depend on the position of the initial and final
value with respect to the critical point of the system. We also show that the
condensation transition found in the bosonic system for sudden quenches [A.
Gambassi and A. Silva, Phys. Rev. Lett. {\bf 109}, 250602 (2012)] is robust
with respect to the choice of the protocol.


  This book discusses the necessity and perhaps urgency for the regulation of
algorithms on which new technologies rely; technologies that have the potential
to re-shape human societies. From commerce and farming to medical care and
education, it is difficult to find any aspect of our lives that will not be
affected by these emerging technologies. At the same time, artificial
intelligence, deep learning, machine learning, cognitive computing, blockchain,
virtual reality and augmented reality, belong to the fields most likely to
affect law and, in particular, administrative law. The book examines
universally applicable patterns in administrative decisions and judicial
rulings. First, similarities and divergence in behavior among the different
cases are identified by analyzing parameters ranging from geographical location
and administrative decisions to judicial reasoning and legal basis. As it turns
out, in several of the cases presented, sources of general law, such as
competition or labor law, are invoked as a legal basis, due to the lack of
current specialized legislation. This book also investigates the role and
significance of national and indeed supranational regulatory bodies for
advanced algorithms and considers ENISA, an EU agency that focuses on network
and information security, as an interesting candidate for a European regulator
of advanced algorithms. Lastly, it discusses the involvement of representative
institutions in algorithmic regulation.


  Synchronization control of coupled continuous-time linear systems is studied.
For identical systems that are stabilizable, a linear feedback law obtained via
algebraic Riccati equation is shown to synchronize any fixed directed network
of any number of coupled systems provided that the coupling is strong enough.
The strength of coupling is determined by the smallest distance of a nonzero
eigenvalue of the coupling matrix to the imaginary axis. A dual problem where
detectable systems that are coupled via their outputs is also considered and
solved.


  This paper introduces a method to capture network traffic from medical IoT
devices and automatically detect cleartext information that may reveal
sensitive medical conditions and behaviors. The research follows a three-step
approach involving traffic collection, cleartext detection, and metadata
analysis. We analyze four popular consumer medical IoT devices, including one
smart medical device that leaks sensitive health information in cleartext. We
also present a traffic capture and analysis system that seamlessly integrates
with a home network and offers a user-friendly interface for consumers to
monitor and visualize data transmissions of IoT devices in their homes.


  Mumbai, a densely populated city, experiences frequent extreme rainfall
events leading to floods and waterlogging. However, the lack of real-time flood
monitoring and detailed past flooding data limits the scientific analysis to
extreme rainfall assessment. To address this, we explore the usability of
crowdsourced data for identifying flood hotspots and extracting reliable flood
information from the past. Through an automated program, we filter and retrieve
flood-related data from Twitter, using location information to generate flood
maps for past heavy rainfall events. The validity of the retrieved data is
confirmed by comparing it with volunteered geographic information (VGI) which
is more accurate but less abundant. In the absence of direct flood information,
Twitter data is cross-verified with the Height above the Nearest Drainage
(HAND) map, which serves as a proxy for elevation. Interestingly, while extreme
rainfall events are increasing in frequency, recent Twitter-based information
shows a decrease in flood reporting, attributed to effective mitigation
measures implemented at various flood hotspots. Local surveys support this
finding and highlight measures such as underground storage tanks and pumping
stations that have reduced flood severity. Our study demonstrates the value of
crowdsourced data in identifying urban flood hotspots and its potential for
real-time flood monitoring and forecasting. This approach can be adapted for
data-sparse urban regions to generate location-specific warnings, contributing
to improved early warnings and mitigating the impact on lives and property.