  With the accelerated adoption of end-to-end encryption, there is an
opportunity to re-architect security and anti-abuse primitives in a manner that
preserves new privacy expectations. In this paper, we consider two novel
protocols for on-device blocklisting that allow a client to determine whether
an object (e.g., URL, document, image, etc.) is harmful based on threat
information possessed by a so-called remote enforcer in a way that is both
privacy-preserving and trustworthy. Our protocols leverage a unique combination
of private set intersection to promote privacy, cryptographic hashes to ensure
resilience to false positives, cryptographic signatures to improve
transparency, and Merkle inclusion proofs to ensure consistency and
auditability. We benchmark our protocols -- one that is time-efficient, and the
other space-efficient -- to demonstrate their practical use for applications
such as email, messaging, storage, and other applications. We also highlight
remaining challenges, such as privacy and censorship tensions that exist with
logging or reporting. We consider our work to be a critical first step towards
enabling complex, multi-stakeholder discussions on how best to provide
on-device protections.


  Extriangulated categories were introduced by Nakaoka and Palu as a
simultaneous generalization of exact categories and triangulated categories. In
this paper, we show that the idempotent completion of an extriangulated
category admits a natural extriangulated structure. As applications, we prove
that cotorsion pairs in an extriangulated category induce cotorsion pairs in
its idempotent completion under certain condition, and the idempotent
completion of a recollement of extriangulated categories is still a
recollement.


  Biomolecular condensates help organize the cell cytoplasm and nucleoplasm
into spatial compartments with different chemical compositions. A key feature
of such compositional patterning is the local enrichment of enzymatically
active biomolecules which, after transient binding via molecular interactions,
catalyze reactions among their substrates. Thereby, biomolecular condensates
provide a spatial template for non-uniform concentration profiles of
substrates. In turn, the concentration profiles of substrates, and their
molecular interactions with enzymes, drive enzyme fluxes which can enable novel
non-equilibrium dynamics. To analyze this generic class of systems, with a
current focus on self-propelled droplet motion, we here develop a
self-consistent sharp interface theory. In our theory, we diverge from the
usual bottom-up approach, which involves calculating the dynamics of
concentration profiles based on a given chemical potential gradient. Instead,
reminiscent of control theory, we take the reverse approach by deriving the
chemical potential profile and enzyme fluxes required to maintain a desired
condensate form and dynamics. The chemical potential profile and currents of
enzymes come with a corresponding power dissipation rate, which allows us to
derive a thermodynamic consistency criterion for the passive part of the system
(here, reciprocal enzyme-enzyme interactions). As a first use case of our
theory, we study the role of reciprocal interactions, where the transport of
substrates due to reactions and diffusion is, in part, compensated by
redistribution due to molecular interactions. More generally, our theory
applies to mass-conserved active matter systems with moving phase boundaries.


  We present a new mechanism for baryogenesis: at preheating after inflation
fermions acquire a varying mass by their coupling to a time dependent field.
Their CP-violating mass matrix can generate a charge asymmetry to be
transformed into a lepton asymmetry through decay into standard model particles
and heavy Majorana neutrinos. In a concrete model of hybrid inflation we
compare ``coherent baryogenesis'' with nonthermal leptogenesis by perturbative
decay of the inflation condensates.


  We perform a complete study of prompt double $J/\psi$ hadroproduction at
leading order in the nonrelativistic-QCD factorization framework by including
all possible pairings of the $c\bar{c}$ Fock states ${}^1\!S_0^{[8]}$,
${}^3\!S_1^{[1,8]}$, and ${}^3\!P_J^{[1,8]}$ with $J=0,1,2$. We find that the
${}^1\!S_0^{[8]}$ and ${}^3\!P_J^{[8]}$ channels of $J/\psi$ and $\psi^\prime$
production and the ${}^3\!P_J^{[1]}$ and ${}^3\!S_1^{[8]}$ channels of
$\chi_{cJ}$ production, which have been overlooked so far, greatly dominate at
large invariant masses and rapidity separations of the $J/\psi$ pair, and that
their inclusion nearly fills the large gap between previous incomplete
predictions within the color-singlet model and the recent measurement by the
CMS Collaboration at the CERN LHC, leaving room for next-to-leading-order
corrections of typical size.


  A systematic numerical study of non-pairwise vortex interaction forces in the
Ginzburg-Landau model for single- and multicomponent superconductivity is
presented. The interactions are obtained by highly accurate numerical free
energy minimization. In particular a three-body interaction is defined as the
difference between the total interaction and sum of pairwise interactions in a
system of three vortices and such interactions are studied for single and
two-component type-1, type-2, and type-1.5 superconductors. In the investigated
regimes, the three-body interaction is found to be short-range repulsive but
long-range attractive in the type-1 case, zero in the critical {\kappa}
(Bogomoln'y) case, attractive in the type-2 case and repulsive in the type-1.5
case. Some systems of four vortices are also studied and results indicate that
four-body forces are of substantially less significance than the three-body
interactions.


  The aim of this paper is to define and study the constructions of alternating
and symmetric (super)powers of metric generalized Jordan (super)pairs. These
constructions are obtained by transference via the Faulkner construction. The
construction of tensor (super)products for metric generalized Jordan
(super)pairs is revisited. We assume that the base field $\mathbb{F}$ has
characteristic different from $2$.


  Proper scoring rules incentivize experts to accurately report beliefs,
assuming predictions cannot influence outcomes. We relax this assumption and
investigate incentives when predictions are performative, i.e., when they can
influence the outcome of the prediction, such as when making public predictions
about the stock market. We say a prediction is a fixed point if it accurately
reflects the expert's beliefs after that prediction has been made. We show that
in this setting, reports maximizing expected score generally do not reflect an
expert's beliefs, and we give bounds on the inaccuracy of such reports. We show
that, for binary predictions, if the influence of the expert's prediction on
outcomes is bounded, it is possible to define scoring rules under which optimal
reports are arbitrarily close to fixed points. However, this is impossible for
predictions over more than two outcomes. We also perform numerical simulations
in a toy setting, showing that our bounds are tight in some situations and that
prediction error is often substantial (greater than 5-10%). Lastly, we discuss
alternative notions of optimality, including performative stability, and show
that they incentivize reporting fixed points.


  Using experiments and simulations, we investigate the clusters that form when
colloidal spheres stick irreversibly to -- or "park" on -- smaller spheres. We
use either oppositely charged particles or particles labeled with complementary
DNA sequences, and we vary the ratio $\alpha$ of large to small sphere radii.
Once bound, the large spheres cannot rearrange, and thus the clusters do not
form dense or symmetric packings. Nevertheless, this stochastic aggregation
process yields a remarkably narrow distribution of clusters with nearly 90%
tetrahedra at $\alpha=2.45$. The high yield of tetrahedra, which reaches 100%
in simulations at $\alpha=2.41$, arises not simply because of packing
constraints, but also because of the existence of a long-time lower bound that
we call the "minimum parking" number. We derive this lower bound from solutions
to the classic mathematical problem of spherical covering, and we show that
there is a critical size ratio $\alpha_c=(1+\sqrt{2})\approx 2.41$, close to
the observed point of maximum yield, where the lower bound equals the upper
bound set by packing constraints. The emergence of a critical value in a random
aggregation process offers a robust method to assemble uniform clusters for a
variety of applications, including metamaterials.


  In this paper, zero-sum mean-field type games (ZSMFTG) with linear dynamics
and quadratic utility are studied under infinite-horizon discounted utility
function. ZSMFTG are a class of games in which two decision makers whose
utilities sum to zero, compete to influence a large population of agents. In
particular, the case in which the transition and utility functions depend on
the state, the action of the controllers, and the mean of the state and the
actions, is investigated. The game is analyzed and explicit expressions for the
Nash equilibrium strategies are derived. Moreover, two policy optimization
methods that rely on policy gradient are proposed for both model-based and
sample-based frameworks. In the first case, the gradients are computed exactly
using the model whereas they are estimated using Monte-Carlo simulations in the
second case. Numerical experiments show the convergence of the two players'
controls as well as the utility function when the two algorithms are used in
different scenarios.


  We introduce a de novo elastography method to learn the elasticity of solids
from measured strains. The deep neural network in our new method is supervised
by the theory of elasticity and does not require labeled data for training.
Results show that the proposed method can learn the hidden elasticity of solids
accurately and is robust when it comes to noisy and missing measurements. A
probable elasticity distribution for areas without measurements may also be
reconstructed by the neural network based on the elasticity distribution in
nearby regions. The neural network learns the hidden elasticity of solids as a
function of positions and thus it can generate elasticity images with an
arbitrary resolution. This feature is applied to create super-resolution
elasticity images in this study. We demonstrate that the neural network can
also learn the hidden physics when strain and elasticity distributions are both
given. The proposed method has various unique features and can be applied to a
broad range of elastography applications.


  We present atomistic molecular dynamics simulations of two Polyethylene
systems where all entanglements are trapped: a perfect network, and a melt with
grafted chain ends. We examine microscopically at what level topological
constraints can be considered as a collective entanglement effect, as in tube
model theories, or as certain pairwise uncrossability interactions, as in
slip-link models. A pairwise parameter, which varies between these limiting
cases, shows that, for the systems studied, the character of the entanglement
environment is more pairwise than collective.
  We employ a novel methodology, which analyzes entanglement constraints into a
complete set of pairwise interactions, similar to slip links. Entanglement
confinement is assembled by a plethora of links, with a spectrum of confinement
strengths, from strong to weak. The strength of interactions is quantified
through a link `persistence', which is the fraction of time for which the links
are active. By weighting links according to their strength, we show that
confinement is imposed mainly by the strong ones, and that the weak, trapped,
uncrossability interactions cannot contribute to the low frequency modulus of
an elastomer, or the plateau modulus of a melt.
  A self-consistent scheme for mapping topological constraints to specific,
strong binary links, according to a given entanglement density, is proposed and
validated. Our results demonstrate that slip links can be viewed as the
strongest pairwise interactions of a collective entanglement environment. The
methodology developed provides a basis for bridging the gap between atomistic
simulations and mesoscopic slip link models.


  We answer in the affirmative the following question raised by H. H. Corson in
1961: "Is it possible to cover every Banach space X by bounded convex sets with
nonempty interior in such a way that no point of X belongs to infinitely many
of them?" Actually we show the way to produce in every Banach space X a bounded
convex tiling of order 2, i.e. a covering of X by bounded convex closed sets
with nonempty interior (tiles) such that the interiors are pairwise disjoint
and no point of X belongs to more than two tiles.


  A brief overview of the phenomenology related to the seesaw mechanism and the
baryogenesis via leptogenesis is presented. In particular, it is explained how
large but not maximal lepton mixing can be achieved within the type II seesaw
mechanism. Moreover, the consequences for leptogenesis are explored, including
flavor effects.


  In this paper we investigate the perturbation theory of the asymptotically
safe inflation and we find that all modes of gravitational waves perturbation
become ghosts in order to achieve a large enough number of e-folds. Formally we
can calculate the power spectrum of gravitational waves perturbation, but we
find that it is negative. It indicates that there is serious trouble with the
asymptotically safe inflation.


  We derive a construction of the beta process that allows for the atoms with
significant measure to be drawn first. Our representation is based on an
extension of the Sethuraman (1994) construction of the Dirichlet process, and
therefore we refer to it as a stick-breaking construction. Our first proof uses
a limiting case argument of finite arrays. To this end, we present a finite
sieve approximation to the beta process that parallels that of Ishwaran &
Zarepour (2002) and prove its convergence to the beta process. We give a second
proof of the construction using Poisson process machinery. We use the Poisson
process to derive almost sure truncation bounds for the construction. We
conclude the paper by presenting an efficient sampling algorithm for
beta-Bernoulli and beta-negative binomial process models.


  We investigate the nature of transverse kink oscillations of loops expanding
through the solar corona and how can oscillations be used to diagnose the
plasma parameters and the magnetic field. In particular, we aim to analyse how
the temporal dependence of the loop length (here modelling the expansion) will
affect the P1 /P2 period ratio of transverse loop oscillations.
  Due to the uncertainty of the loop's shape through its expansion, we discuss
separately the case of the loop that maintains its initial semi-circular shape
and the case of the loop that from a semi-circular shape evolve into an
elliptical shape loop. The equations that describe the oscillations in
expanding flux tube are complicated due to the spatial and temporal dependence
of coefficients. Using the WKB approximation we find approximative values for
periods and their evolution, as well as the period ratio. For small values of
time (near the start of the expansion) we can employ a regular perturbation
method to find approximative relations for eigenfunctions and eigenfrequencies.
  Using simple analytical and numerical methods we show that the period of
oscillations are affected by the rising of the coronal loop. The change in the
period due to the increase in the loop's length is more pronounced for those
loops that expand into a more structured (or cooler corona). The deviation of
periods will have significant implications in determining the degree of
stratification in the solar corona. The effect of expansion on the periods of
oscillations is considerable only in the process of expansion of the loop but
not when it reached its final stage.


  We study the effect of noise-induced dephasing on collisional phase-diffusion
in the two-site Bose-Hubbard model. Dephasing of the quasi-momentum modes may
slow down phase-diffusion in the quantum Zeno limit. Remarkably, the degree of
suppression is enhanced by a bosonic factor of order $N/\log{N}$ as the
particle number $N$ increases.


  This submission contains all publications from the ACIS 2015 Doctoral
Consortium held in Adelaide, Australia from November 30 - December 1, 2015.


  This short note describes and proves a connectedness property which was
introduced in Blocher et al. [2023] in the context of data depth functions for
partial orders. The connectedness property gives a structural insight into
union-free generic sets. These sets, presented in Blocher et al. [2023], are
defined by using a closure operator on the set of all partial orders which
naturally appears within the theory of formal concept analysis. In the language
of formal concept analysis, the property of connectedness can be vividly
proven. However, since within Blocher et al. [2023] we did not discuss formal
concept analysis, we outsourced the proof to this note.


  Scientific models describe natural phenomena at different levels of
abstraction. Abstract descriptions can provide the basis for interventions on
the system and explanation of observed phenomena at a level of granularity that
is coarser than the most fundamental account of the system. Beckers and Halpern
(2019), building on work of Rubenstein et al. (2017), developed an account of
abstraction for causal models that is exact. Here we extend this account to the
more realistic case where an abstract causal model offers only an approximation
of the underlying system. We show how the resulting account handles the
discrepancy that can arise between low- and high-level causal models of the
same system, and in the process provide an account of how one causal model
approximates another, a topic of independent interest. Finally, we extend the
account of approximate abstractions to probabilistic causal models, indicating
how and where uncertainty can enter into an approximate abstraction.


  Most of the theoretical results on the kinematic amplification of small-scale
magnetic fluctuations by turbulence have been confined to the model of
white-noise-like advecting turbulent velocity field. In this work, the
statistics of the passive magnetic field in the diffusion-free regime are
considered for the case when the advecting flow is finite-time correlated. A
new method is developed that allows one to systematically construct the
correlation-time expansion for statistical characteristics of the field. The
expansion is valid provided the velocity correlation time is smaller than the
characteristic growth time of the magnetic fluctuations. This expansion is
carried out up to first order in the general case of a d-dimensional
arbitrarily compressible advecting flow. The growth rates for all moments of
the magnetic field are derived. The effect of the first-order corrections is to
reduce these growth rates. It is shown that introducing a finite correlation
time leads to the loss of the small-scale statistical universality, which was
present in the limit of the delta-correlated velocity field. Namely, the shape
of the velocity time-correlation profile and the large-scale spatial structure
of the flow become important. The latter is a new effect, that implies, in
particular, that the approximation of a locally-linear shear flow does not
fully capture the effect of nonvanishing correlation time. Physical
applications of this theory include the small-scale kinematic dynamo in the
interstellar medium and protogalactic plasmas.


  Let $G$ (resp. $H$) be the group of orientation preserving
self-homeomorphisms of the unit circle (resp. real line). In previous work, the
first two authors constructed pre-Tannakian categories
$\underline{\mathrm{Rep}}(G)$ and $\underline{\mathrm{Rep}}(H)$ associated to
these groups. In the predecessor to this paper, we analyzed the category
$\underline{\mathrm{Rep}}(H)$ (which we named the ``Delannoy category'') in
great detail, and found it to have many special properties. In this paper, we
study $\underline{\mathrm{Rep}}(G)$. The primary difference between these two
categories is that $\underline{\mathrm{Rep}}(H)$ is semi-simple, while
$\underline{\mathrm{Rep}}(G)$ is not; this introduces new complications in the
present case. We find that $\underline{\mathrm{Rep}}(G)$ is closely related to
the combinatorics of objects we call Delannoy loops, which seem to have not
previously been studied.


  It is well-known in thermodynamics that the creation of correlations costs
work. It seems then a truism that if a thermodynamic transformation A->B is
impossible, so will be any transformation that in sending A to B also
correlates among them some auxiliary systems C. Surprisingly, we show that this
is not the case for non-equilibrium thermodynamics of microscopic systems. On
the contrary, the creation of correlations greatly extends the set of
accessible states, to the point that we can perform on individual systems and
in a single shot any transformation that would otherwise be possible only if
the number of systems involved was very large. We also show that one only ever
needs to create a vanishingly small amount of correlations (as measured by
mutual information) among a small number of auxiliary systems (never more than
three). The many, severe constraints of microscopic thermodynamics are reduced
to the sole requirement that the non-equilibrium free energy decreases in the
transformation. This shows that, in principle, reliable extraction of work
equal to the free energy of a system can be performed by microscopic engines.


  Photonic integrated circuits (PICs) with rapid prototyping and reprogramming
capabilities promise revolutionary impacts on a plethora of photonic
technologies. Here, we report direct-write and rewritable photonic circuits on
a low-loss phase change material (PCM) thin film. Complete end-to-end PICs are
directly laser written in one step without additional fabrication processes,
and any part of the circuit can be erased and rewritten, facilitating rapid
design modification. We demonstrate the versatility of this technique for
diverse applications, including an optical interconnect fabric for
reconfigurable networking, a photonic crossbar array for optical computing, and
a tunable optical filter for optical signal processing. By combining the
programmability of the direct laser writing technique with PCM, our technique
unlocks opportunities for programmable photonic networking, computing, and
signal processing. Moreover, the rewritable photonic circuits enable rapid
prototyping and testing in a convenient and cost-efficient manner, eliminate
the need for nanofabrication facilities, and thus promote the proliferation of
photonics research and education to a broader community.


  We study a nonlocal traffic flow model with an Arrhenius type look-ahead
interaction. We show a sharp critical threshold condition on the initial data
which distinguishes the global smooth solutions and finite time wave
break-down.


  Since annotating pixel-level labels for semantic segmentation is laborious,
leveraging synthetic data is an attractive solution. However, due to the domain
gap between synthetic domain and real domain, it is challenging for a model
trained with synthetic data to generalize to real data. In this paper,
considering the fundamental difference between the two domains as the texture,
we propose a method to adapt to the texture of the target domain. First, we
diversity the texture of synthetic images using a style transfer algorithm. The
various textures of generated images prevent a segmentation model from
overfitting to one specific (synthetic) texture. Then, we fine-tune the model
with self-training to get direct supervision of the target texture. Our results
achieve state-of-the-art performance and we analyze the properties of the model
trained on the stylized dataset with extensive experiments.


  The interplay between the structural relaxation and the rheological response
of a binary LJ glass former is studied via MD simulations. In the quiescent
state, the model is well known for its sluggish dynamics and a two step
relaxation of correlation functions at low temperatures. An ideal glass
transition temperature of $T_c = 0.435$ has been identified in the previous
studies via the analysis of the system's dynamics in the frame work of the mode
coupling theory of the glass transition [W. Kob and H.C. Andersen, PRE 51, 4626
(1995)]. Here, we test wether a signature of this ideal glass transition can
also be found under shear. Indeed, the following distinction in the structural
relaxation is found: In the supercooled state, the structural relaxation is
dominated by the shear at relatively high shear rates, $\dot{\gamma}$, whereas
at sufficiently low $\dot{\gamma}$ the (shear-independent) equilibrium
relaxation is recovered. In contrast to this, the structural relaxation of a
\emph{glass} is always driven by shear. This distinct behavior of the
correlation functions is also reflected in the rheological response. In the
supercooled state, the shear viscosity, $\eta$, decreases with increasing shear
rate (shear thinning) at high shear rates, but then converges toward a constant
as the $\dot{\gamma}$ is decreased below a (temperature-dependent) threshold
value. Below $T_c$, on the other hand, the shear viscosity grows as $\eta
\propto 1/\dot{\gamma}$ suggesting a divergence at $\dot{\gamma} =0$. Thus,
within the accessible observation time window, a transition toward a
non-ergodic state seems to occur in the driven glass as the driving force
approaches zero.


  We present the NNLO QCD virtual corrections for qurak-antiquark -> gluon
photon, quark-antiquark -> photon photon and the NNLO QED virtual corrections
for electron positron -> photon photon and all processes related by crossing
symmetry. We perform an explicit evaluation of the two-loop diagrams in
conventional dimensional regularisation, and our results are renormalised in
the MSbar scheme. The infrared pole structure of the amplitudes is in agreement
with the prediction of Catani's general formalism for the singularities of
two-loop amplitudes, while expressions for the finite remainder are given for
all processes in terms of logarithms and polylogarithms that are real in the
physical region.


  The inconsistency of cond-mat/0007299 and Phys. Rev. B 63 094510 (2001) with
ARPES and resistivity data is pointed-out.


  This paper proposes a novel method that can replace compression-based
dissimilarity measure (CDM) in composer estimation task. The main features of
the proposed method are clarity and scalability. First, since the proposed
method is formalized by the information quantity, reproduction of the result is
easier compared with the CDM method, where the result depends on a particular
compression program. Second, the proposed method has a lower computational
complexity in terms of the number of learning data compared with the CDM
method. The number of correct results was compared with that of the CDM for the
composer estimation task of five composers of 75 piano musical scores. The
proposed method performed better than the CDM method that uses the file size
compressed by a particular program.


  Realizing Refsdal's original idea from 1964, we present estimates of the
Hubble constant that are complementary to and potentially competitive with
those of other cosmological probes. We use the observed positions of 89
multiple images, with extensive spectroscopic information, from 28 background
sources and the measured time delays between the images S1-S4 and SX of
supernova "Refsdal" ($z = 1.489$), which were obtained thanks to Hubble Space
Telescope (HST) deep imaging and Multi Unit Spectroscopic Explorer (MUSE) data.
We extend the strong lensing modeling of the Hubble Frontier Fields (HFF)
galaxy cluster MACS J1149.5$+$2223 ($z = 0.542$), published by Grillo et al.
(2016), and explore different $\Lambda$CDM models. Taking advantage of the
lensing information associated to the presence of very close pairs of multiple
images at various redshifts and to the extended surface brightness distribution
of the SN Refsdal host, we can reconstruct the total mass density profile of
the cluster very precisely. The combined dependence of the multiple image
positions and time delays on the cosmological parameters allows us to infer the
values of $H_{0}$ and $\Omega_{\rm m}$ with relative (1$\sigma$) statistical
errors of, respectively, 6% (7%) and 31% (26%) in flat (general) cosmological
models, assuming a conservative 3% uncertainty on the final time delay of image
SX and, remarkably, no priors from other cosmological experiments. Our best
estimate of $H_{0}$, based on the model described in this work, will be
presented when the final time-delay measurement becomes available. Our results
show that it is possible to utilize time delays in lens galaxy clusters as an
important alternative tool for measuring the expansion rate and the geometry of
the Universe.


  A graph is said to be well-covered if all its maximal independent sets are of
the same size. In 1999, Yamashita and Kameda introduced a subclass of
well-covered graphs, called localizable graphs and defined as graphs having a
partition of the vertex set into strong cliques, where a clique in a graph is
strong if it intersects all maximal independent sets. Yamashita and Kameda
observed that all well-covered trees are localizable, pointed out that the
converse inclusion fails in general, and asked for a characterization of
localizable graphs.
  In this paper we obtain several structural and algorithmic results about
localizable graphs. Our results include a proof of the fact that every very
well-covered graph is localizable and characterizations of localizable graphs
within the classes of line graphs, triangle-free graphs, $C_4$-free graphs, and
cubic graphs, each leading to a polynomial time recognition algorithm. On the
negative side, we prove NP-hardness of recognizing localizable graphs within
the classes of weakly chordal graphs, complements of line graphs, and graphs of
independence number three. Furthermore, using localizable graphs we disprove a
conjecture due to Zaare-Nahandi about $k$-partite well-covered graphs having
all maximal cliques of size $k$. Our results unify and generalize several
results from the literature.


  Examples are presented of applications of the Lema\^{i}tre - Tolman model to
problems of astrophysics and gravitation theory. They are: 1. Inferring the
spatial distribution of matter by interpretation of observations; 2.
Interaction of inhomogeneities in matter distribution with the CMB radiation;
3. Evolution of voids; 4. Singularities; 5. Influence of electromagnetic field
on gravitational collapse. This review is meant to demonstrate that the theory
is already well-prepared to meet the challenges posed by an inhomogeneous
Universe.


  In rhombohedral-stacked few-layer graphene, the very flat energy bands near
the charge neutrality point are unstable to electronic interactions, giving
rise to states with spontaneous broken symmetries. Using transport measurements
on suspended rhombohedral-stacked tetralayer graphene, we observe an insulating
ground state with a large interaction-induced gap up to 80 meV. This gapped
state can be enhanced by a perpendicular magnetic field, and suppressed by an
interlayer potential, carrier density, or a critical temperature of ~ 40 K.


  There may exist seven $\bar D^{(*)} \Sigma_c^{(*)}$ hadronic molecular
states. We construct their corresponding interpolating currents, and calculate
their masses and decay constants using QCD sum rules. Based on these results,
we calculate their relative production rates in $\Lambda_b^0$ decays through
the current algebra, i.e., $\mathcal{B}(\Lambda_b^0 \to P_c
K^-):\mathcal{B}(\Lambda_b^0 \to P_c^\prime K^-)$ with $P_c$ and $P_c^\prime$
two different states. We also study their decay properties through the Fierz
rearrangement, and further calculate these ratios in the $J/\psi p$ mass
spectrum, i.e., $\mathcal{B}(\Lambda_b^0 \to P_c K^- \to J/\psi p
K^-):\mathcal{B}(\Lambda_b^0 \to P_c^\prime K^- \to J/\psi p K^-)$. Our results
suggest that the $\bar D^{*} \Sigma_c^{*}$ molecular states of $J^P = 1/2^-$
and $3/2^-$ are possible to be observed in future experiments.


  Vector-like quarks, usually dubbed top partners, are a common presence in
composite Higgs models. Being composite objects, their mass is expected to be
of the order of their inverse size, that is the condensation scale of the new
strong interactions. Light top partners, while not being a generic prediction,
are however often considered in phenomenological models. We suggest that their
lightness may be due to the matching of global 't Hooft anomalies of the
underlying theory. We check this mechanism in explicit models showing that, in
one case, composite fermions with the quantum numbers of the top quark obtain a
mass which is controlled by a soft breaking term and can be made parametrically
small.


  In this paper we shall address this problem: Is quantum gravity constraints
algebra closed and what are the quantum Einstein equations. We shall
investigate this problem in the de-Broglie--Bohm quantum theory framework. It
is shown that the constraint algebra is weakly closed and the quantum
Einstein's equations are derived.


  In this thesis we elaborate on two different aspects of the Effective Field
Theory (EFT) approach to a Binary Coalescing system in General Relativity (GR).
First, we consider the issue of hereditary effects in the Post-Newtonian (PN)
perturbative scheme, and we compute hereditary diagrams in the far zone region
up to 5PN order, using both Feynman and Schwinger-Keldysh formalism, comparing
our results with those appearing in literature. Then, we focus on the
Post-Minkowskian (PM) perturbative scheme, and we compute the bending angle of
a massless scalar field under the influence of a massive scalar in the EFT of
GR, up to one loop order in dimensional regularisation. Our analysis points to
the existence of relevant terms that were not accounted for in previous study,
which may modify the predictions for physical observables.


  For any $\ell > 0$, we present an algorithm which takes as input a
semi-algebraic set, $S$, defined by $P_1 \leq 0,...,P_s \leq 0$, where each
$P_i \in \R[X_1,...,X_k]$ has degree $\leq 2,$ and computes the top $\ell$
Betti numbers of $S$, $b_{k-1}(S), ..., b_{k-\ell}(S),$ in polynomial time. The
complexity of the algorithm, stated more precisely, is $ \sum_{i=0}^{\ell+2} {s
\choose i} k^{2^{O(\min(\ell,s))}}. $ For fixed $\ell$, the complexity of the
algorithm can be expressed as $s^{\ell+2} k^{2^{O(\ell)}},$ which is polynomial
in the input parameters $s$ and $k$. To our knowledge this is the first
polynomial time algorithm for computing non-trivial topological invariants of
semi-algebraic sets in $\R^k$ defined by polynomial inequalities, where the
number of inequalities is not fixed and the polynomials are allowed to have
degree greater than one. For fixed $s$, we obtain by letting $\ell = k$, an
algorithm for computing all the Betti numbers of $S$ whose complexity is
$k^{2^{O(s)}}$.


  This paper presents a novel analysis of two feed-in tariffs (FIT) under
market and regulatory uncertainty, namely a sliding premium with cap and floor
and a minimum price guarantee. Regulatory uncertainty is modeled with a Poisson
process, whereby a jump event may reduce the tariff before the signature of the
contract. Using a semi-analytical real options framework, we derive the project
value, the optimal investment threshold, and the value of the investment
opportunity for these schemes. Taking into consideration the optimal investment
threshold, we also compare the two aforementioned FITs with the fixed-price FIT
and the fixed-premium FIT, which are policy schemes that have been extensively
studied in the literature. Our results show that increasing the likelihood of a
jump event lowers the investment threshold for all the schemes; moreover, the
investment threshold also decreases when the tariff reduction increases. We
also compare the four schemes in terms of the corresponding optimal investment
thresholds. For example, we find that the investment threshold of the sliding
premium is lower than the minimum price guarantee. This result suggests that
the first regime is a better policy than the latter because it accelerates the
investment while avoiding excessive earnings to producers.


  In this tutorial paper, we provide an introduction to the briskly expanding
research field of Majorana fermions in topological superconductors. We discuss
several aspects of topological superconductivity and the advantages it brings
to quantum computing. Mathematical derivation of the Kitaev model and BdG
Hamiltonian is carried out to explain the phenomena of superconductivity and
Majorana fermions. The Majorana fermions and the Non-Abelian statistics are
described in detail along with their significance for quantum engineers. The
theory provided led towards the engineering of the topological qubits using
Majoranas.


  The motion with intersections of relativistic gravitating shells in the
Schwarzschild gravitational field of a central body is considered. Formulas are
derived for calculating parameters of the shells after intersection via their
parameters before intersection. Such special cases as the Newtonian
approximation, intersections of light shells, and intersections of a test shell
with a gravitating shell are also considered. The ejection of one of the shells
to infinity in the relativistic region is described. The equations of motion
for the shells are analyzed numerically.


  The book deals with a stochastic formulation of path integration in real
time, by rotating the_space_ variables over exp(i pi/4). Preliminary chapters
deal with quantum and classical mechanics, probability theory and stochastic
calculus, and the conventional approach to path integration through the Trotter
product formula. Subsequently, the stochastic formulation in complex space is
established, and applied to semi-classical expansions, nonlinear oscillations,
quantum dynamics on analytic manifolds, dissipative systems (i.e., those which
are open in that they interact with an environment), tunneling, field theory,
and computer simulations.


  Automatic classification of running styles can enable runners to obtain
feedback with the aim of optimizing performance in terms of minimizing energy
expenditure, fatigue, and risk of injury. To develop a system capable of
classifying running styles using wearables, we collect a dataset from 10
healthy runners performing 8 different pre-defined running styles. Five
wearable devices are used to record accelerometer data from different parts of
the lower body, namely left and right foot, left and right medial tibia, and
lower back. Using the collected dataset, we develop a deep learning solution
which consists of a Convolutional Neural Network and Long Short-Term Memory
network to first automatically extract effective features, followed by learning
temporal relationships. Score-level fusion is used to aggregate the
classification results from the different sensors. Experiments show that the
proposed model is capable of automatically classifying different running styles
in a subject-dependant manner, outperforming several classical machine learning
methods (following manual feature extraction) and a convolutional neural
network baseline. Moreover, our study finds that subject-independent
classification of running styles is considerably more challenging than a
subject-dependant scheme, indicating a high level of personalization in such
running styles. Finally, we demonstrate that by fine-tuning the model with as
few as 5% subject-specific samples, considerable performance boost is obtained.


  In the theory of superfluidity and superconductivity, a jump of the free
energy was discovered theoretically and was naturally called a {\it
zeroth-order phase transition}.
  We present an example of an exactly solvable problem in which such a phase
transition occurs.


  The higher Nash blowup of an algebraic variety replaces singular points with
limits of certain spaces carrying higher-order data associated to the variety
at non-singular points. In this note we will define a higher-order Jacobian
matrix that will allow us to make explicit computations concerning the higher
Nash blowup of hypersurfaces. Firstly, we will generalize a known method to
compute the fiber of this modification. Secondly, we will give an explicit
description of the ideal whose blowup gives the higher Nash blowup. As a
consequence, we will deduce a higher-order version of Nobile's theorem for
normal hypersurfaces.


  A fresh approach to the data from experiments with hard sphere colloids
yields seminal insights into the glass transition. The precise determination of
the fraction of particles caged by their neighbours is unprecedented and
provides cornerstone of our study. We show that the so-called "dramatic
slowing" that accompanies super-packing is due to correlated cage fluctuations,
rather than caging per se. Their statistics establish that the amorphous
assemblies of caged particles are in states of self-organised criticality. The
implied connection between the onset of caging and the thermodynamic 1st order
freezing transition challenges the status quo, as does our contention that the
dynamically critical amorphous assemblies are precursory to that transition.


  We return to interpreting the historical SN~1987A neutrino data from a modern
perspective. To this end, we construct a suite of spherically symmetric
supernova models with the Prometheus-Vertex code, using four different
equations of state and five choices of final baryonic neutron-star (NS) mass in
the 1.36-1.93 M$_\odot$ range. Our models include muons and proto-neutron star
(PNS) convection by a mixing-length approximation. The time-integrated signals
of our 1.44 M$_\odot$ models agree reasonably well with the combined data of
the four relevant experiments, IMB, Kam-II, BUST, and LSD, but the
high-threshold IMB detector alone favors a NS mass of 1.7-1.8 M$_\odot$,
whereas Kam-II alone prefers a mass around 1.4 M$_\odot$. The cumulative energy
distributions in these two detectors are well matched by models for such NS
masses, and the previous tension between predicted mean neutrino energies and
the combined measurements is gone, with and without flavor swap. Generally, our
predicted signals do not strongly depend on assumptions about flavor mixing,
because the PNS flux spectra depend only weakly on antineutrino flavor. While
our models show compatibility with the events detected during the first
seconds, PNS convection and nucleon correlations in the neutrino opacities lead
to short PNS cooling times of 5-9 s, in conflict with the late event bunches in
Kam-II and BUST after 8-9 s, which are also difficult to explain by background.
Speculative interpretations include the onset of fallback of transiently
ejected material onto the NS, a late phase transition in the nuclear medium,
e.g., from hadronic to quark matter, or other effects that add to the standard
PNS cooling emission and either stretch the signal or provide a late source of
energy. More research, including systematic 3D simulations, is needed to assess
these open issues.


  This paper is focused on the design of phase sequences with good (aperiodic)
autocorrelation properties in terms of Peak Sidelobe Level (PSL) and Integrated
Sidelobe Level (ISL). The problem is formulated as a bi-objective Pareto
optimization forcing either a continuous or a discrete phase constraint at the
design stage. An iterative procedure based on the coordinate descent method is
introduced to deal with the resulting optimization problems which are
non-convex and NP-hard in general. Each iteration of the devised method
requires the solution of a non-convex min-max problem. It is handled either
through a novel bisection or an FFT-based method for the continuous and the
discrete phase constraint, respectively. Additionally, a heuristic approach to
initialize the procedures employing the lp-norm minimization technique is
proposed. Simulation results illustrate that the proposed methodologies can
outperform some counterparts providing sequences with good autocorrelation
features especially in the discrete phase/binary case.


  A simple technique for decoding an unknown modulated chaotic time-series is
presented. We point out that, by fitting a polynomial model to the modulated
chaotic signal, the error in the fit gives sufficient information to decode the
modulating signal.
  For analog implementation, a lowpass filter can be used for fitting. This
method is simple and easy to implement in hardware.


  We investigate statistics of dynamical exchange events in coarse--grained
models of supercooled liquids in spatial dimensions $d=1$, 2, and 3. The
models, based upon the concept of dynamical facilitation, capture generic
features of statistics of exchange times and persistence times. Here,
distributions for both times are related, and calculated for cases of strong
and fragile glass formers over a range of temperatures. Exchange time
distributions are shown to be particularly sensitive to the model parameters
and dimensions, and exhibit more structured and richer behavior than
persistence time distributions. Mean exchange times are shown to be Arrhenius,
regardless of models and spatial dimensions. Specifically, $<t_{\rm x}> \sim
c^{-2}$, with $c$ being the excitation concentration. Different dynamical
exchange processes are identified and characterized from the underlying
trajectories. We discuss experimental possibilities to test some of our
theoretical findings.


  When a flow is not allowed to be reoriented the Maximum Residual Flow Problem
with $k$-Arc Destruction is known to be $NP$-hard for $k=2$. We show that when
a flow is allowed to be adaptive the problem becomes polynomial for every fixed
$k$.


  Opinion mining in outdoor images posted by users during different activities
can provide valuable information to better understand urban areas. In this
regard, we propose a framework to classify the sentiment of outdoor images
shared by users on social networks. We compare the performance of
state-of-the-art ConvNet architectures, and one specifically designed for
sentiment analysis. We also evaluate how the merging of deep features and
semantic information derived from the scene attributes can improve
classification and cross-dataset generalization performance. The evaluation
explores a novel dataset, namely OutdoorSent, and other datasets publicly
available. We observe that the incorporation of knowledge about semantic
attributes improves the accuracy of all ConvNet architectures studied. Besides,
we found that exploring only images related to the context of the study,
outdoor in our case, is recommended, i.e., indoor images were not significantly
helpful. Furthermore, we demonstrated the applicability of our results in the
city of Chicago, USA, showing that they can help to improve the knowledge of
subjective characteristics of different areas of the city. For instance,
particular areas of the city tend to concentrate more images of a specific
class of sentiment, which are also correlated with median income, opening up
opportunities in different fields.


  In the standard cosmological model the dark energy (DE) and nonrelativistic
(NR) matter densities are observationally determined to be comparable at the
present time, in spite of their greatly different evolution histories. This
`cosmic coincidence' enigma -- also referred to as the `why now? problem' --
relies, by its very definition, on the implicit prior expectation for our
`typicality' in the cosmic (expanding) spacetime volume. Otherwise, this
conundrum does not exist in the first place. It is shown here that this
apparent coincidence could be explained as a non-anthropic observational
selection effect: for us to be typical observers in the comoving (static)
spacetime volume, the cosmic energy budget must contain a non-vanishing DE
component. In addition, it is shown that irrespective of the cosmological
initial conditions and assuming no `new physics', the Universe is most likely
to be observed at a time when the conformal Hubble radius, $\mathcal{H}^{-1}$,
attains a maximum. The latter takes place at the epoch when $\rho_{DE}$ and
$\rho_{m}$, the energy densities of DE and NR matter, respectively, are
comparable. Specifically, our presumed `typicality' along the conformal
timeline, coupled to a few other plausible assumptions, implies that
$R\equiv\rho_{DE}/\rho_{m}$ is `sampled' from a Beta Prime probability
distribution function. A priori 68\% (95\%) confidence range for the ratio is
$0.20<R<3.46$ ($0.033<R<17.20$), with an expectation value of $\bar{R}=3.5$.
These are in agreement with the observationally inferred value, $R_{obs}=2.23$.


  We investigate here the spectrum of gravitational collapse endstates when
arbitrarily small perfect fluid pressures are introduced in the classic black
hole formation scenario as described by Oppenheimer, Snyder and Datt (OSD) [1].
This extends a previous result on tangential pressures [2] to the more
physically realistic scenario of perfect fluid collapse. The existence of
classes of pressure perturbations is shown explicitly, which has the property
that injecting any smallest pressure changes the final fate of the dynamical
collapse from a black hole to a naked singularity. It is therefore seen that
any smallest neighborhood of the OSD model, in the space of initial data,
contains collapse evolutions that go to a naked singularity outcome. This gives
an intriguing insight on the nature of naked singularity formation in
gravitational collapse.


  Wind is a critical component of the Earth system and has unmistakable impacts
on everyday life. The CYGNSS satellite mission improves observational coverage
of ocean winds via a fleet of eight micro-satellites that use reflected GNSS
signals to infer surface wind speed. We present analyses characterizing
variability in wind speed measurements among the eight CYGNSS satellites and
between antennas. In particular, we use a carefully constructed Gaussian
process model that leverages comparisons between CYGNSS and Jason-3 during a
one-year period from September 2019 to September 2020. The CYGNSS sensors
exhibit a range of biases, most of them between -1.0 m/s and +0.2 m/s with
respect to Jason-3, indicating that some CYGNSS sensors are biased with respect
to one another and with respect to Jason-3. The biases between the starboard
and port antennas within a CYGNSS satellite are smaller. Our results are
consistent with, yet sharper than, a more traditional paired comparison
analysis. We also explore the possibility that the bias depends on wind speed,
finding some evidence that CYGNSS satellites have positive biases with respect
to Jason-3 at low wind speeds. However, we argue that there are subtle issues
associated with estimating wind speed-dependent biases, so additional careful
statistical modeling and analysis is warranted.


  We examine the growth of the Weyl curvature in two examples of naked
singularity formation in spherical gravitational collapse - dust and the Vaidya
spacetime. We find that the Weyl scalar diverges along outgoing radial null
geodesics as they meet the naked singularity in the past. The implications of
this result for the Weyl curvature hypothesis are discussed. We mention the
possibility that although classical general relativity admits naked singularity
solutions arising from gravitational collapse, the second law of thermodynamics
could forbid their occurrence in nature. The method can also be used to compare
the relative importance of initial data and that of the energy-momentum tensor
in deciding the metric solution in any general case.


  Music source separation represents the task of extracting all the instruments
from a given song. Recent breakthroughs on this challenge have gravitated
around a single dataset, MUSDB, only limited to four instrument classes. Larger
datasets and more instruments are costly and time-consuming in collecting data
and training deep neural networks (DNNs). In this work, we propose a fast
method to evaluate the separability of instruments in any dataset without
training and tuning a DNN. This separability measure helps to select
appropriate samples for the efficient training of neural networks. Based on the
oracle principle with an ideal ratio mask, our approach is an excellent proxy
to estimate the separation performances of state-of-the-art deep learning
approaches such as TasNet or Open-Unmix. Our results contribute to revealing
two essential points for audio source separation: 1) the ideal ratio mask,
although light and straightforward, provides an accurate measure of the audio
separability performance of recent neural nets, and 2) new end-to-end learning
methods such as Tasnet, that operate directly on waveforms, are, in fact,
internally building a Time-Frequency (TF) representation, so that they
encounter the same limitations as the TF based-methods when separating audio
pattern overlapping in the TF plane.


  An exponential increase in the data rate demand prompted several technical
innovations. Multi User Multiple Input Multiple Output (MU-MIMO) is one of the
most promising schemes. This has been evolved into Massive MIMO technology in
5G to further stretch the network throughput. Massive MIMO tackles the rising
data rate with the increase in the number of antenna. This comes at the price
of a higher energy consumption. Moreover the high velocity users in MU-MIMO
scheme experiences a frequent unpredictable change in the channel condition
that degrade its downlink performance. Therefore a proper number of antenna
selection is of paramount importance. This issue has been addressed using
machine learning techniques and Channel State Information (CSI) but only for
static users. In this study we propose to introduce antenna diversity in
spatial multiplexing MU-MIMO transmission scheme by operating more number of
reception antenna compare to the number of transmission antenna. The diversity
improves the downlink performance of high velocity users. In general our
results can be interpreted for large scale antenna systems like Massive MIMO.
The proposed method can be easily implemented in the existing network
architectures with minimal complexity. Also it has the potential for solving
real-life problems like call drops and low data rate to be experienced by
cellular users traveling through high-speed transportation systems like Dhaka
MRT project


  We report on the discovery of HAT-P-12b, a transiting extrasolar planet
orbiting the moderately bright V=12.8 K4 dwarf GSC 03033-00706, with a period P
= 3.2130598 +- 0.0000021 d, transit epoch Tc = 2454419.19556 +- 0.00020 (BJD)
and transit duration 0.0974 +- 0.0006 d. The host star has a mass of 0.73 +-
0.02 Msun, radius of 0.70 +- ^0.02_0.01 Rsun, effective temperature 4650 +- 60
K and metallicity [Fe/H] = -0.29 +- 0.05. We find a slight correlation between
the observed spectral line bisector spans and the radial velocity, so we
consider, and rule out, various blend configurations including a blend with a
background eclipsing binary, and hierarchical triple systems where the
eclipsing body is a star or a planet. We conclude that a model consisting of a
single star with a transiting planet best fits the observations, and show that
a likely explanation for the apparent correlation is contamination from
scattered moonlight. Based on this model, the planetary companion has a mass of
0.211 +- 0.012 MJup, and a radius of 0.959 +- ^0.029_0.021 RJup yielding a mean
density of 0.295 +- 0.025 g cm^-3. Comparing these observations with recent
theoretical models we find that HAT-P-12b is consistent with a ~ 1-4.5 Gyr,
mildly irradiated, H/He dominated planet with a core mass Mc <~ 10 Mearth.
HAT-P-12b is thus the least massive H/He dominated gas giant planet found to
date. This record was previously held by Saturn.


  We use high-low frequency methods developed in the context of decoupling to
prove sharp (up to $C_\epsilon R^\epsilon$) square function estimates for the
moment curve $(t,t^2,\ldots,t^n)$ in $\mathbb{R}^n$. Our inductive scheme
incorporates sharp square function estimates for auxiliary conical sets, which
allows us to fully exploit lower dimensional information.


  We study the effects of interparticle interactions and power-law tunneling
couplings on quantum walks executed by both a single one and a pair of
hard-core bosons moving in clean and disordered one-dimensional lattices. For
this purpose, we perform exact diagonalization to explicitly evaluate the short
and long time probabilities of finding the walkers within a surveillance area.
Our main conclusions, summarized in phase diagrams in the disorder-power-law
and interaction-disorder spaces, allowed us to discern two different scenarios
for the single and two quantum walkers dynamics. While in the single particle
case the transition to localized and extended regimes is identified for well
defined values of the disorder amplitude and power law hopping, those frontiers
are replaced by diffuse contours in the interacting two particle case. In fact,
counterintuitive transport regimes as diffusion enhanced by disorder, and space
constrained dynamics assisted by both interactions and short tunneling range
are found. Our results are of direct relevance for quantum systems with
long-range interactions that are currently realized in the laboratory.


  The recently measured experimental data of $^{79,81,83}$Se isotopes have been
interpreted in terms of shell model calculations. The calculations have been
performed in ${f_{5/2}pg_{9/2}}$ space with the recently derived interactions,
namely with JUN45 and jj44b. To study the importance of the proton excitations
across the $Z=28$ shell in this region. We have also performed calculation in
${fpg_{9/2}}$ valence space using an ${fpg}$ effective interaction with
$^{48}$Ca core and imposing a truncation. Excitation energies, $B(2)$ values,
quadrupole moments and magnetic moments are compared with experimental data
when available. Present study reveals the importance of proton excitations
across the $Z=28$ shell for predicting quadrupole and magnetic moments.


  We study numerically the onset of higher-level excitations and resonance
frequency shifts in the generalized multilevel Rabi model with dispersive
coupling under strong driving. The response to a weak probe is calculated using
the Floquet method, which allows us to calculate the probe spectrum and extract
the resonance frequency. We test our predictions using a superconducting
circuit consisting of transmon coupled capacitively to a coplanar waveguide
resonator. This system is monitored by a weak probe field, and at the same time
driven at various powers by a stronger microwave tone. We show that the
transition from the quantum to the classical regime is accompanied by a rapid
increase of the transmon occupation and, consequently that the qubit
approximation is valid only in the extreme quantum limit.


  We study quark thermal recombination as a function of energy density during
the evolution of a heavy-ion collision in a numerical model that reproduces
aspects of QCD phenomenology. We show that starting with a set of free quarks
(or quarks and antiquarks) the probability to form colorless clusters of three
quarks differs from that to form colorless clusters of quark-antiquark and that
the former has a sharp jump at a critical energy density whereas the latter
transits smoothly from the low to the high energy density domains. We interpret
this as a quantitative difference in the production of baryons and mesons with
energy density. We use this approach to compute the proton and pion spectra in
a Bjorken scenario that incorporates the evolution of these probabilities with
energy density, and therefore with proper time. From the spectra, we compute
the proton to pion ratio and compare to data at the highest RHIC energies. We
show that for a standard choice of parameters, this ratio reaches one, though
the maximum is very sensitive to the initial evolution proper time.


  How can we control for latent discrimination in predictive models? How can we
provably remove it? Such questions are at the heart of algorithmic fairness and
its impacts on society. In this paper, we define a new operational fairness
criteria, inspired by the well-understood notion of omitted variable-bias in
statistics and econometrics. Our notion of fairness effectively controls for
sensitive features and provides diagnostics for deviations from fair decision
making. We then establish analytical and algorithmic results about the
existence of a fair classifier in the context of supervised learning. Our
results readily imply a simple, but rather counter-intuitive, strategy for
eliminating latent discrimination. In order to prevent other features proxying
for sensitive features, we need to include sensitive features in the training
phase, but exclude them in the test/evaluation phase while controlling for
their effects. We evaluate the performance of our algorithm on several
real-world datasets and show how fairness for these datasets can be improved
with a very small loss in accuracy.


  Let $x=a+ib$ be a complex number, so we have the following inequality
$$(1/\sqrt{2})|a+b|\leq |x|\leq |a|+|b|$$ We give an operator version of above
inequality. Also we obtain some results for normal operators.


  We demonstrate that endonuclease deficient Clustered Regularly Interspaced
Short Palindromic Repeats CRISPR-associated Cas9 protein (dCas9) fused to the
photo-convertible fluorescence protein monomeric mEos3.1 (dCas9-mEos3) can be
used to resolve sub-diffraction limited features of repetitive gene elements,
thus providing a new route to investigate high-order chromatin organization at
these sites.


  We apply techniques introduced by Clerc, Kobayashi, Orsted and Pevzner to
study the degenerate principal series of Sp(n,C). An explicit description of
the K-types is provided and Knapp-Stein normalised operators are realised a
symplectic Fourier transforms, and their K-spectrum explicitely computed.
Reducibility phenomena are analysed in terms of K-types and eigenvalues of
intertwining operators. We also construct a new model for these
representations, in which Knapp-Stein intertwiners take an algebraic form.


  Multi-spectral frequency combs provide frontier architectures for laser
standoff spectroscopy, optical clockwork, and high-capacity optical
communications. Frequency microcombs, aided by their high-quality resonances
and inherent third-order nonlinear susceptibility, have demonstrated remarkable
impact in frequency metrology and synthesis. However, microcombs are often with
limited spectral bandwidth bounded by the intrinsic second-order chromatic
dispersion and the consequently low intensities at the spectral edges. To
enhance the applications of frequency combs, a spectrally-broad comb generation
scheme is often desired. Here we report coherent satellite clusters in
multi-spectral regenerative frequency microcombs with enhanced intensities at
the octave points and engineered frequency span. Beyond the conventional
bandwidth of parametric oscillation, the regenerative satellites are
facilitated by higher-order dispersion control allowing for
multi-phase-matching in the microcavities. The frequency span of the
multi-spectral regenerative satellites is deterministically controlled from 57
THz to 126 THz by pumping at C- and L- bands. We demonstrate that the
regenerative satellite preserves the coherence with the central comb through
the nonlinear parametric process. We further show the mirrored appearance of
the satellite transition dynamics including each comb state that are
simultaneously observed at the central comb. These multi-spectral regenerative
satellites extend the scope of parametric-based frequency combs and provide a
unique platform with wide applications.


  We provide an explicit analytical solution of the planar Poiseuille flow of a
viscoplastic fluid governed by the constitutive equation proposed by De Kee and
Turcotte (Chem. Eng. Commun. 6 (1980) 273-282). Formulae for the velocity and
the flow rate are derived, making use of the Lambert W function. It is shown
that a solution does not always exist because the flow curve is bounded from
above and hence the rheological model can accommodate stresses only up to a
certain limit. In fact, the flow curve reaches a peak at a critical shear rate,
beyond which it exhibits a negative slope, giving rise to unstable solutions.


  We investigate exponential phase moments of the s-parametrized
quasidistributions (smoothed Wigner functions). We show that the knowledge of
these moments as functions of s provides, together with photon-number
statistics, a complete description of the quantum state. We demonstrate that
the exponential phase moments can be directly sampled from the data recorded in
balanced homodyne detection and we present simple expressions for the sampling
kernels. The phase moments are Fourier coefficients of phase distributions
obtained from the quasidistributions via integration over the radial variable
in polar coordinates. We performed Monte Carlo simulations of the homodyne
detection and we demonstrate the feasibility of direct sampling of the moments
and subsequent reconstruction of the phase distribution.


  In this work, inspired by the observation of charmoniumlike
$Z_{c}^{+}(4200)$% , we study the photoproduction of charged charmoniumlike
$Z_{c}^{+}(4200)$ with an effective Lagrangian approach and the Regge
trajectories model. The numerical results indicate that the Reggeized treatment
can lead to a lower total cross section of the $Z_{c}^{+}(4200)$
photoproduction and the peak position of cross section was moved to the higher
energy point when the Reggeized treatment was added. {Moreover, using the data
from the COMPASS experiment and presented theoretical predictions, an upper
limit of the decay width of $Z_{c}(4200)\rightarrow J/\psi \pi $ is estimated.}
The relevant results not only shed light on the further experiment of searching
for the charmoniumlike $Z_{c}(4200)$ state via meson photoproduction, but also
provide valuable informations for having a better comprehension of the nature
of charmoniumlike $Z_{c}(4200)$ state.


  The FN method is an accurate and efficient numerical method for the
one-dimensional radiative transport equation. In this paper the FN method is
extended to three dimensions using rotated reference frames. To demonstrate the
method, the exiting flux from structured illumination reflected by a medium
occupying the half space is calculated.


  Recently, coded distributed computing (CDC), with advantages in intensive
computation and reduced latency, has attracted a lot of research interest for
edge computing, in particular, IoT applications, including IoT data
pre-processing and data analytics. Nevertheless, it can be challenging for edge
infrastructure providers (EIPs) with limited edge resources to support IoT
applications performed in a CDC approach in edge networks, given the additional
computational resources required by CDC. In this paper, we propose coded edge
federation, in which different EIPs collaboratively provide edge resources for
CDC tasks. To study the Nash equilibrium, when no EIP has an incentive to
unilaterally alter its decision on edge resource allocation, we model the coded
edge federation based on evolutionary game theory. Since the replicator
dynamics of the classical evolutionary game are unable to model economic-aware
EIPs which memorize past decisions and utilities, we propose fractional
replicator dynamics with a power-law fading memory via Caputo fractional
derivatives. The proposed dynamics allow us to study a broad spectrum of EIP
dynamic behaviors, such as EIP sensitivity and aggressiveness in strategy
adaptation, which classical replicator dynamics cannot capture. Theoretical
analysis and extensive numerical results justify the existence, uniqueness, and
stability of the equilibrium in the fractional evolutionary game. The influence
of the content and the length of the memory on the rate of convergence is also
investigated.


  We introduce MAGNeT, a masked generative sequence modeling method that
operates directly over several streams of audio tokens. Unlike prior work,
MAGNeT is comprised of a single-stage, non-autoregressive transformer. During
training, we predict spans of masked tokens obtained from a masking scheduler,
while during inference we gradually construct the output sequence using several
decoding steps. To further enhance the quality of the generated audio, we
introduce a novel rescoring method in which, we leverage an external
pre-trained model to rescore and rank predictions from MAGNeT, which will be
then used for later decoding steps. Lastly, we explore a hybrid version of
MAGNeT, in which we fuse between autoregressive and non-autoregressive models
to generate the first few seconds in an autoregressive manner while the rest of
the sequence is being decoded in parallel. We demonstrate the efficiency of
MAGNeT for the task of text-to-music and text-to-audio generation and conduct
an extensive empirical evaluation, considering both objective metrics and human
studies. The proposed approach is comparable to the evaluated baselines, while
being significantly faster (x7 faster than the autoregressive baseline).
Through ablation studies and analysis, we shed light on the importance of each
of the components comprising MAGNeT, together with pointing to the trade-offs
between autoregressive and non-autoregressive modeling, considering latency,
throughput, and generation quality. Samples are available on our demo page
https://pages.cs.huji.ac.il/adiyoss-lab/MAGNeT.


  A central theme in condensed matter physics is to create and understand the
exotic states of matter by incorporating magnetism into topological materials.
One prime example is the quantum anomalous Hall (QAH) state. Recently, MnBi2Te4
has been demonstrated to be an intrinsic magnetic topological insulator and the
QAH effect was observed in exfoliated MnBi2Te4 flakes. Here, we used molecular
beam epitaxy (MBE) to grow MnBi2Te4 films with thickness down to 1 septuple
layer (SL) and performed thickness-dependent transport measurements. We
observed a non-square hysteresis loop in the antiferromagnetic state for films
with thickness greater than 2 SL. The hysteresis loop can be separated into two
AH components. Through careful analysis, we demonstrated that one AH component
with the larger coercive field is from the dominant MnBi2Te4 phase, while the
other AH component with the smaller coercive field is from the minor Mn-doped
Bi2Te3 phase in the samples. The extracted AH component of the MnBi2Te4 phase
shows a clear even-odd layer-dependent behavior, a signature of
antiferromagnetic thin films. Our studies reveal insights on how to optimize
the MBE growth conditions to improve the quality of MnBi2Te4 films, in which
the QAH and other exotic states are predicted.


  We use scale invariant scattering theory to exactly determine the
renormalization group fixed points of a $q$-state Potts model coupled to an
$r$-state Potts model in two dimensions. For integer values of $q$ and $r$ the
fixed point equations are very constraining and show in particular that scale
invariance in coupled Potts ferromagnets is limited to the Ashkin-Teller case
($q=r=2$). Since our results extend to continuous values of the number of
states, we can access the limit $r\to 1$ corresponding to correlated
percolation, and show that the critical properties of Potts spin clusters
cannot in general be obtained from those of Fortuin-Kasteleyn clusters by
analytical continuation.


  In this paper we present the analytic solutions for two test problems
involving two-fluid mixtures of dust and gas in an astrophysical context. The
solutions provide a means of benchmarking numerical codes designed to simulate
the non-linear dynamics of dusty gas. The first problem, dustybox, consists of
two interpenetrating homogeneous fluids moving with relative velocity
difference. We provide exact solutions to the full non-linear problem for a
range of drag formulations appropriate to astrophysical fluids (i.e., various
prescriptions for Epstein and Stokes drag in different regimes). The second
problem, dustywave consists of the propagation of linear acoustic waves in a
two-fluid gas-dust mixture. We provide the analytic solution for the case when
the two fluids are interacting via a linear drag term. Both test problems are
simple to set up in any numerical code and can be run with periodic boundary
conditions. The solutions we derive are completely general with respect to both
the dust-to-gas ratio and the amplitude of the drag coefficient. A stability
analysis of waves in a gas-dust system is also presented, showing that sound
waves in an astrophysical dust-gas mixture are linearly stable.


  Conventional domain adaptation algorithms aim to achieve better
generalization by aligning only the task-discriminative causal factors between
a source and target domain. However, we find that retaining the spurious
correlation between causal and non-causal factors plays a vital role in
bridging the domain gap and improving target adaptation. Therefore, we propose
to build a framework that disentangles and supports causal factor alignment by
aligning the non-causal factors first. We also investigate and find that the
strong shape bias of vision transformers, coupled with its multi-head
attention, make it a suitable architecture for realizing our proposed
disentanglement. Hence, we propose to build a Causality-enforcing Source-Free
Transformer framework (C-SFTrans) to achieve disentanglement via a novel
two-stage alignment approach: a) non-causal factor alignment: non-causal
factors are aligned using a style classification task which leads to an overall
global alignment, b) task-discriminative causal factor alignment: causal
factors are aligned via target adaptation. We are the first to investigate the
role of vision transformers (ViTs) in a privacy-preserving source-free setting.
Our approach achieves state-of-the-art results in several DA benchmarks.


  We describe a new class of packet injection attacks called Man-on-the-Side
Attacks (MotS), previously only seen where state actors have "compromised" a
number of telecommunication companies. MotS injection attacks have not been
widely investigated in scientific literature, despite having been discussed by
news outlets and security blogs. MotS came to attention after the Edward
Snowden revelations, which described large scale pervasive monitoring of the
Internet's infrastructure. For an advanced adversary attempting to interfere
with IT connected systems, the next logical step is to adapt this class of
attack to a smaller scale, such as enterprise or critical infrastructure
networks. MotS is a weaker form of attack compared to a Man-in-the-Middle
(MitM). A MotS attack allows an adversary to read and inject packets, but not
modify packets sent by other hosts. This paper presents practical experiments
where we have implemented and performed MotS attacks against two testbeds: 1)
on HTTP connections, by redirecting a victim to a host controlled by an
adversary; and 2) on an Industrial Control network, where we inject falsified
command responses to the victim. In both cases, the victims accept the injected
packets without generating a suspiciously large number of unusual packets on
the network. We then perform an analysis of three leading Network IDS to
determine whether the attacks are detected, and discuss mitigation methods.


  The role of synchronous firing in sensory coding and cognition remains
controversial. While studies, focusing on its mechanistic consequences in
attentional tasks, suggest that synchronization dynamically boosts sensory
processing, others failed to find significant synchronization levels in such
tasks. We attempt to understand both lines of evidence within a coherent
theoretical framework. We conceptualize synchronization as an independent
control parameter to study how the postsynaptic neuron transmits the average
firing activity of a presynaptic population, in the presence of
synchronization. We apply the Berger-Levy theory of energy efficient
information transmission to interpret simulations of a Hodgkin-Huxley-type
postsynaptic neuron model, where we varied the firing rate and synchronization
level in the presynaptic population independently. We find that for a fixed
presynaptic firing rate the simulated postsynaptic interspike interval
distribution depends on the synchronization level and is well-described by a
generalized extreme value distribution. For synchronization levels of 15% to
50%, we find that the optimal distribution of presynaptic firing rate,
maximizing the mutual information per unit cost, is maximized at ~30%
synchronization level. These results suggest that the statistics and energy
efficiency of neuronal communication channels, through which the input rate is
communicated, can be dynamically adapted by the synchronization level.


  We present a completely automatic synthesis framework for oracle functions, a
central part in many quantum algorithms. The proposed framework for
resource-constrained oracle synthesis (ROS) is a LUT-based hierarchical method
in which every step is specifically tailored to address hardware resource
constraints. ROS embeds a LUT mapper designed to simplify the successive
synthesis steps, costing each LUT according to the resources used by its
corresponding quantum circuit. In addition, the framework exploits a SAT-based
quantum garbage management technique. Those two characteristics give ROS the
ability to beat the state-of-the-art hierarchical method both in number of
qubits and in number of operations. The efficiency of the framework is
demonstrated by synthesizing quantum oracles for Grover's algorithm.


  X-ray Fluorescence Ghost Imaging (XRF-GI) was recently demonstrated for x-ray
lab sources. It has the potential to reduce acquisition time and deposited dose
by choosing their trade-off with spatial resolution, while alleviating the
focusing constraints of the probing beam. Here, we demonstrate the realization
of synchrotron-based XRF-GI: We present both an adapted experimental setup and
its corresponding required computational technique to process the data. This
extends the above-mentioned potential advantages of GI to synchrotron XRF
imaging. In addition, it enables new strategies to improve resilience against
drifts at all scales, and the study of previously inaccessible samples, such as
liquids.


  Hierarchical inverse dynamics based on cascades of quadratic programs have
been proposed for the control of legged robots. They have important benefits
but to the best of our knowledge have never been implemented on a torque
controlled humanoid where model inaccuracies, sensor noise and real-time
computation requirements can be problematic. Using a reformulation of existing
algorithms, we propose a simplification of the problem that allows to achieve
real-time control. Momentum-based control is integrated in the task hierarchy
and a LQR design approach is used to compute the desired associated closed-loop
behavior and improve performance. Extensive experiments on various balancing
and tracking tasks show very robust performance in the face of unknown
disturbances, even when the humanoid is standing on one foot. Our results
demonstrate that hierarchical inverse dynamics together with momentum control
can be efficiently used for feedback control under real robot conditions.


  In a recent paper (hep-th/0103228) a new initial value formulation of
fermionic QFT was presented that is applicable to an arbitrary observer in any
electromagnetic background. This approach suggests a consistent particle
interpretation at all times, with the concept of `radar time' used to
generalise this interpretation to an arbitrarily moving observer. In the
present paper we extend this formalism to allow for gravitational backgrounds.
The observer-dependent particle interpretation generalises Gibbons' definition
to non-stationary spacetimes. This allows any observer to be considered,
providing a particle interpretation that depends {\it only} on the observer's
motion and the background, not on any choice of coordinates or gauge, or on
details of their particle detector. Consistency with known results is
demonstrated for the cases of Rindler space and deSitter space. Radar time is
also considered for an arbitrarily moving observer in an arbitrary 1+1
dimensional spacetime, and for a comoving observer in a 3+1 dimensional FRW
universe with arbitrary scale factor $a(t)$. Finite volume measurements and
their fluctuations are also discussed, allowing one to say with definable
precision where and when the particles are observed.


  We have measured electron energy distribution curves (EDCs) of the
laser-induced field emission from a tungsten tip. Field emission from
photo-excited nonequilibrium electron distributions were clearly observed,
while no enhanced field emission due to optical electric fields appeared up to
values of 1.3 V/nm. Thus, we experimentally confirm the emission mechanism.
Simulated transient EDCs show that electron dynamics plays a significant role
in the laser-induced field emission. The results should be useful to find
optimal parameters for defining the temporal and spectral characteristics of
electron pulses for many applications based on pulsed field emission.


  Racks and quandles are rich algebraic structures that are strong enough to
classify knots. Here we develop several fundamental categorical aspects of the
theories of racks and quandles and their relation to the theory of
permutations. In particular, we compute the centers of the categories and
describe power operations on them, thereby revealing free extra structure that
is not apparent from the definitions. This also leads to precise
characterizations of these theories in the form of universal properties.


  We show that there exists a recursive coloring function $c$ such that any
$\Pi^0_3$ set is not a solution to $c$ for Hindman's theorem. We also show that
there exists a recursive coloring function $c$ such that any $\Delta^0_3$ set
is not a solution to $c$ for Hindman's theorem restricted on sum of at most
three numbers.


  The internet has become a central medium through which `networked publics'
express their opinions and engage in debate. Offensive comments and personal
attacks can inhibit participation in these spaces. Automated content moderation
aims to overcome this problem using machine learning classifiers trained on
large corpora of texts manually annotated for offence. While such systems could
help encourage more civil debate, they must navigate inherently normatively
contestable boundaries, and are subject to the idiosyncratic norms of the human
raters who provide the training data. An important objective for platforms
implementing such measures might be to ensure that they are not unduly biased
towards or against particular norms of offence. This paper provides some
exploratory methods by which the normative biases of algorithmic content
moderation systems can be measured, by way of a case study using an existing
dataset of comments labelled for offence. We train classifiers on comments
labelled by different demographic subsets (men and women) to understand how
differences in conceptions of offence between these groups might affect the
performance of the resulting models on various test sets. We conclude by
discussing some of the ethical choices facing the implementers of algorithmic
moderation systems, given various desired levels of diversity of viewpoints
amongst discussion participants.


  Force-gradient decomposition methods are used to improve the energy
preservation of symplectic schemes applied to Hamiltonian systems. If the
potential is composed of different parts with strongly varying dynamics, this
multirate potential can be exploited by coupling force-gradient decomposition
methods with splitting techniques for multi-time scale problems to further
increase the accuracy of the scheme and reduce the computational costs. In this
paper, we derive novel force-gradient nested methods and test them numerically.
Such methods can be used to increase the acceptance rate for the molecular
dynamics step of the Hybrid Monte Carlo algorithm (HMC) and hence improve its
computational efficiency.


  This paper addresses the problem of decentralized tube-based nonlinear Model
Predictive Control (NMPC) for a class of uncertain nonlinear continuous-time
multi-agent systems with additive and bounded disturbance. In particular, the
problem of robust navigation of a multi-agent system to predefined states of
the workspace while using only local information is addressed, under certain
distance and control input constraints. We propose a decentralized feedback
control protocol that consists of two terms: a nominal control input, which is
computed online and is the outcome of a Decentralized Finite Horizon Optimal
Control Problem (DFHOCP) that each agent solves at every sampling time, for its
nominal system dynamics; and an additive state feedback law which is computed
offline and guarantees that the real trajectories of each agent will belong to
a hyper-tube centered along the nominal trajectory, for all times. The volume
of the hyper-tube depends on the upper bound of the disturbances as well as the
bounds of the derivatives of the dynamics. In addition, by introducing certain
distance constraints, the proposed scheme guarantees that the initially
connected agents remain connected for all times. Under standard assumptions
that arise in nominal NMPC schemes, controllability assumptions as well as
communication capabilities between the agents, we guarantee that the
multi-agent system is ISS (Input to State Stable) with respect to the
disturbances, for all initial conditions satisfying the state constraints.
Simulation results verify the correctness of the proposed framework.


  We prove that the central sequence algebra of a separable C*-algebra is
either subhomogeneous or non-exact, confirming a conjecture of Enders and
Shulman. We also prove analogous dichotomy for other massive C*-algebras.


  An elastic rod model for semi-flexible polymers is presented. Theory for a
continuum rod is reviewed, and it is shown that a popular discretised model
used in numerical simulations gives the correct continuum limit. Correlation
functions relating to both bending and twisting of the rod are derived for both
continuous and discrete cases, and results are compared with numerical
simulations. Finally, two possible implementations of the discretised model in
the multi-purpose molecular dynamics software package LAMMPS are described.


  Let $T_{f}$ be a circle homeomorphism with two break points $a_{b},c_{b}$ and
irrational rotation number $\varrho_{f}$. Suppose that the derivative $Df$ of
its lift $f$ is absolutely continuous on every connected interval of the set
$S^{1}\backslash\{a_{b},c_{b}\}$, that $DlogDf \in L^{1}$ and the product of
the jump ratios of $ Df $ at the break points is nontrivial, i.e.
$\frac{Df_{-}(a_{b})}{Df_{+}(a_{b})}\frac{Df_{-}(c_{b})}{Df_{+}(c_{b})}\neq1$.
We prove that the unique $T_{f}$- invariant probability measure $\mu_{f}$ is
then singular with respect to Lebesgue measure $l$ on $S^{1}$.


  Recent work on fairness in machine learning has primarily emphasized how to
define, quantify, and encourage "fair" outcomes. Less attention has been paid,
however, to the ethical foundations which underlie such efforts. Among the
ethical perspectives that should be taken into consideration is
consequentialism, the position that, roughly speaking, outcomes are all that
matter. Although consequentialism is not free from difficulties, and although
it does not necessarily provide a tractable way of choosing actions (because of
the combined problems of uncertainty, subjectivity, and aggregation), it
nevertheless provides a powerful foundation from which to critique the existing
literature on machine learning fairness. Moreover, it brings to the fore some
of the tradeoffs involved, including the problem of who counts, the pros and
cons of using a policy, and the relative value of the distant future. In this
paper we provide a consequentialist critique of common definitions of fairness
within machine learning, as well as a machine learning perspective on
consequentialism. We conclude with a broader discussion of the issues of
learning and randomization, which have important implications for the ethics of
automated decision making systems.


  We analyse the family of $C^1$-Virtual Elements introduced in
\cite{Brezzi:Marini:plates} for fourth-order problems and prove optimal
estimates in $L^2$ and in $H^1$ via classical duality arguments.


  This article defines and formulates the problem of observation learning in
robotic systems.


  Semiclassical systems being symmetric under Lie group are studied. A state of
a semiclassical system may be viewed as a set (X,f) of a classical state X and
a quantum state f in the external classical background X. Therefore, the set of
all semiclassical states may be considered as a bundle ("semiclassical
bundle"). Its base {X} is the set of all classical states, while a fibre is a
Hilbert space of quantum states in the external background X. Symmetry
transformation of a semiclassical system may be viewed as an automorphism of
the semiclassical bundle. Automorphism groups can be investigated with the help
of sections of the bundle: to any automorphism of the bundle one assigns a
transformation of section of the bundle. Infinitesimal properties of
transformations of sections are investigated; correspondence between Lie groups
and Lie algebras is discussed. For gauge theories, some points of the
semiclassical bundle are identified: a gauge group acts on the bundle. For this
case, only gauge-invariant sections of the semiclassical bundle are taken into
account.


  In domains where agents interact strategically, game theory is applied widely
to predict how agents would behave. However, game-theoretic predictions are
based on the assumption that agents are fully rational and believe in
equilibrium plays, which unfortunately are mostly not true when human decision
makers are involved. To address this limitation, a number of behavioral
game-theoretic models are defined to account for the limited rationality of
human decision makers. The "quantal cognitive hierarchy" (QCH) model, which is
one of the more recent models, is demonstrated to be the state-of-art model for
predicting human behaviors in normal-form games. The QCH model assumes that
agents in games can be both non-strategic (level-0) and strategic (level-$k$).
For level-0 agents, they choose their strategies irrespective of other agents.
For level-$k$ agents, they assume that other agents would be behaving at levels
less than $k$ and best respond against them. However, an important assumption
of the QCH model is that the distribution of agents' levels follows a Poisson
distribution. In this paper, we relax this assumption and design a
learning-based method at the population level to iteratively estimate the
empirical distribution of agents' reasoning levels. By using a real-world
dataset from the Swedish lowest unique positive integer game, we demonstrate
how our refined QCH model and the iterative solution-seeking process can be
used in providing a more accurate behavioral model for agents. This leads to
better performance in fitting the real data and allows us to track an agent's
progress in learning to play strategically over multiple rounds.


  We discuss classical gravitational aspects of the AdS/CFT correspondence,
with the aim of obtaining a rigorous (mathematical) understanding of the
semi-classical limit of the gravitational partition function. The paper surveys
recent progress in the area, together with a selection of new results and open
problems.


  Artificial Neural Networks (ANNs) have received increasing attention in
recent years with applications that span a wide range of disciplines including
vital domains such as medicine, network security and autonomous transportation.
However, neural network architectures are becoming increasingly complex and
with an increasing need to obtain real-time results from such models, it has
become pivotal to use parallelization as a mechanism for speeding up network
training and deployment. In this work we propose an implementation of Network
Parallel Training through Cannon's Algorithm for matrix multiplication. We show
that increasing the number of processes speeds up training until the point
where process communication costs become prohibitive; this point varies by
network complexity. We also show through empirical efficiency calculations that
the speedup obtained is superlinear.


  The CMS hadron Calorimeter is made of alternating layers of scintillating
tiles and metals, such as brass or iron. The original photo detectors were
hybrid units with a single accelerating gap called Hybrid Photo Diodes (HPD).
Scintillating light was transmitted to the HPDs by means of optical fibers.
During data taking at the Large Hadron Collider (LHC), the signal strength of
scintillator tiles of detector units in the forward region degraded
significantly due to the damage related to the amount of radiation to which the
scintillator was exposed to. Scintillators suffer damage when exposed to
radiation, however, the amount of damage observed was more than originally
estimated. Several HPDs were removed during a detector shut down period.
Microscopic scans of relative quantum efficiencies for few of these HPDs were
made. The damage of the photocathode was determined to vary with the amount of
optical signal transmitted by optical fibers to the HPD. Imprints of each fiber
(1 mm) on the photocathode with varying damage within the same pixel were
observed. Most of the observed reduction of the calorimeter signal can be
attributed to localised damage of the photocathode.


  Advanced Virgo is the project to upgrade the Virgo interferometric detector
of gravitational waves, with the aim of increasing the number of observable
galaxies (and thus the detection rate) by three orders of magnitude. The
project is now in an advanced construction phase and the assembly and
integration will be completed by the end of 2015. Advanced Virgo will be part
of a network with the two Advanced LIGO detectors in the US and GEO HF in
Germany, with the goal of contributing to the early detections of gravitational
waves and to opening a new observation window on the universe. In this paper we
describe the main features of the Advanced Virgo detector and outline the
status of the construction.


  To perform polarization sensitive THz-TDS, it is necessary to be able to
measure two (preferably orthogonal) electric field components of a terahertz
transient. We have developed a polarization sensitive detector by fabricating a
three-contact photoconductive receiver.


  Topological corner state (TCS) and topological edge state (TES) have provided
new approaches to control the propagation of light. The construction of
topological coupled cavity-waveguide system (TCCWS) based on TCS and TES is
worth looking forward to, due to its research prospects in realizing
high-performance micro-nano integrated photonic devices. In this Letter, TCCWS
is proposed in two-dimensional (2D) photonic crystal (PC), which possesses
strong optical localization, high quality factor and excellent robustness
compared with the conventional coupled cavity-waveguide system (CCCWS). This
work will provide the possibility to design high-performance logic gates,
lasers, filters and other micro-nano integrated photonics devices and expand
their applications.


  In this note, we provide the first 22-step collisions for SHA-256 and
SHA-512. Detailed technique of generating these collisions will be provided in
the next revision of this note.


  In this paper we consider integration and $L_2$-approximation for functions
over $\RR^s$ from weighted Hermite spaces. The first part of the paper is
devoted to a comparison of several weighted Hermite spaces that appear in
literature, which is interesting on its own. Then we study tractability of the
integration and $L_2$-approximation problem for the introduced Hermite spaces,
which describes the growth rate of the information complexity when the error
threshold $\varepsilon$ tends to 0 and the problem dimension $s$ grows to
infinity. Our main results are characterizations of tractability in terms of
the involved weights, which model the importance of the successive coordinate
directions for functions from the weighted Hermite spaces.


  We consider decentralized stochastic multi-armed bandit problem with multiple
players in the case of different communication probabilities between players.
Each player makes a decision of pulling an arm without cooperation while aiming
to maximize his or her reward but informs his or her neighbors in the end of
every turn about the arm he or she pulled and the reward he or she got.
Neighbors of players are determined according to an Erdos-Renyi graph with
which is reproduced in the beginning of every turn. We consider i.i.d. rewards
generated by a Bernoulli distribution and assume that players are unaware about
the arms' probability distributions and their mean values. In case of a
collision, we assume that only one of the players who is randomly chosen gets
the reward where the others get zero reward. We study the effects of
connectivity, the degree of communication between players, on the cumulative
regret using well-known algorithms UCB1, epsilon-Greedy and Thompson Sampling.


  We study the weak-value amplification (WVA) in a phase measurement with an
optical interferometer in which shot noise limits the sensitivity. We compute
the signal and the shot noise including the full-order interaction terms of the
WVA, and show that the shot-noise contribution to a phase shift in a pointer
variable is always larger than the final variance of the pointer variable. This
yields difference in estimating noise level up to a factor of 1.5. To clarify
an advantage for practical uses of the WVA, we discuss signal-to-noise ratio
and its optimization in the presence of the shot noise.


  Consider a rowwise independent triangular array of gamma random variables
with varying parameters. Under several different conditions on the shape
parameter, we show that the sequence of row-maximums converges weakly after
linear or power transformation. Depending on the parameter combinations, we
obtain both Gumbel and non-Gumbel limits.
  The weak limits for maximum of the coordinates of certain Dirichlet vectors
of increasing dimension are also obtained using the gamma representation.