  Gaussian Splatting has revolutionized the world of novel view synthesis by
achieving high rendering performance in real-time. Recently, studies have
focused on enriching these 3D representations with semantic information for
downstream tasks. In this paper, we introduce RT-GS2, the first generalizable
semantic segmentation method employing Gaussian Splatting. While existing
Gaussian Splatting-based approaches rely on scene-specific training, RT-GS2
demonstrates the ability to generalize to unseen scenes. Our method adopts a
new approach by first extracting view-independent 3D Gaussian features in a
self-supervised manner, followed by a novel View-Dependent / View-Independent
(VDVI) feature fusion to enhance semantic consistency over different views.
Extensive experimentation on three different datasets showcases RT-GS2's
superiority over the state-of-the-art methods in semantic segmentation quality,
exemplified by a 8.01% increase in mIoU on the Replica dataset. Moreover, our
method achieves real-time performance of 27.03 FPS, marking an astonishing 901
times speedup compared to existing approaches. This work represents a
significant advancement in the field by introducing, to the best of our
knowledge, the first real-time generalizable semantic segmentation method for
3D Gaussian representations of radiance fields.


  A fundamental question lies in almost every application of deep neural
networks: what is the optimal neural architecture given a specific dataset?
Recently, several Neural Architecture Search (NAS) frameworks have been
developed that use reinforcement learning and evolutionary algorithm to search
for the solution. However, most of them take a long time to find the optimal
architecture due to the huge search space and the lengthy training process
needed to evaluate each candidate. In addition, most of them aim at accuracy
only and do not take into consideration the hardware that will be used to
implement the architecture. This will potentially lead to excessive latencies
beyond specifications, rendering the resulting architectures useless. To
address both issues, in this paper we use Field Programmable Gate Arrays
(FPGAs) as a vehicle to present a novel hardware-aware NAS framework, namely
FNAS, which will provide an optimal neural architecture with latency guaranteed
to meet the specification. In addition, with a performance abstraction model to
analyze the latency of neural architectures without training, our framework can
quickly prune architectures that do not satisfy the specification, leading to
higher efficiency. Experimental results on common data set such as ImageNet
show that in the cases where the state-of-the-art generates architectures with
latencies 7.81x longer than the specification, those from FNAS can meet the
specs with less than 1% accuracy loss. Moreover, FNAS also achieves up to
11.13x speedup for the search process. To the best of the authors' knowledge,
this is the very first hardware aware NAS.


  We survey recent developments in the study of SYZ mirror symmetry for compact
toric and toric Calabi-Yau varieties, with a special emphasis on works of the
author and his collaborators.


  Dirac plasmons in graphene are very sensitive to the dielectric properties of
the environment. We show that this can be used to probe the structure and
composition of van der Waals heterostructures (vdWh) put underneath a single
graphene layer. In order to do so, we assess vdWh composed of hexagonal boron
nitride and different types of transition metal dichalcogenides (TMDs). By
performing realistic simulations that account for the contribution of each
layer of the vdWh separately and including the importance of the substrate
phonons, we show that one can achieve single-layer resolution by investigating
the nonlocal nature of the Dirac plasmon-polaritons. The composition of the
vdWh stack can be inferred from the plasmon-phonon coupling once it is composed
by more than two TMD layers. Furthermore, we show that the bulk character of
TMD stacks for plasmonic screening properties in the terahertz regime is
reached only beyond 100 layers.


  Dilute multi $\alpha$ cluster condensed states with spherical and axially
deformed shapes are studied with the Gross-Pitaevskii equation and Hill-Wheeler
equation, where the $\alpha$ cluster is treated as a structureless boson.
Applications to self-conjugate $4N$ nuclei show that the dilute $N\alpha$
states of $^{12}$C to $^{40}$Ca with $J^\pi=0^+$ appear in the energy region
from threshold up to about 20 MeV, and the critical number of $\alpha$ bosons
that the dilute $N\alpha$ system can sustain as a self-bound nucleus is
estimated roughly to be $N_{cr}\sim10$. We discuss the characteristics of the
dilute $N\alpha$ states with emphasis on the $N$ dependence of their energies
and rms radii.


  Some links between Lorentz and Finsler geometries have been developed in the
last years, with applications even to the Riemannian case. Our purpose is to
give a brief description of them, which may serve as an introduction to recent
references. As a motivating example, we start with Zermelo navigation problem,
where its known Finslerian description permits a Lorentzian picture which
allows for a full geometric understanding of the original problem. Then, we
develop some issues including: (a) the accurate description of the Lorentzian
causality using Finsler elements, (b) the non-singular description of some
Finsler elements (such as Kropina metrics or complete extensions of Randers
ones with constant flag curvature), (c) the natural relation between the
Lorentzian causal boundary and the Gromov and Busemann ones in the Finsler
setting, and (d) practical applications to the propagation of waves and
firefronts.


  We revisit an elementary recursive approach to local conservation laws and
the Hamiltonian formalism for the Toda hierarchy.


  The unexpectedly large branching ratios for $B\to \eta' K (\eta' X_s)$ decays
could be of gluonic origin. We study the implications for $B\to \eta K (\eta
X_s)$ and $P K (PX_s)$, where $P$ is the pseudoscalar glueball. In the
mechanism proposed by Fritzsch, large branching ratios are predicted for these
modes. The $B\to \eta K$ rate is barely within the experimental limit, and
$B\to P K$, $PX_s$ could be at the 0.1% and 1% level, respectively. Smaller but
less definite results are found for the mechanism of $g^* \to \eta' g$ via the
gluon anomaly.


  Let $\mathbf{H}$ be the Cartesian product of a family of finite abelian
groups. Via a polynomial approach, we give sufficient conditions for a
partition of $\mathbf{H}$ induced by weighted poset metric to be reflexive,
which also become necessary for some special cases. Moreover, by examining the
roots of the Krawtchouk polynomials, we establish non-reflexive partitions of
$\mathbf{H}$ induced by combinatorial metric. When $\mathbf{H}$ is a vector
space over a finite field $\mathbb{F}$, we consider the property of admitting
MacWilliams identity (PAMI) and the MacWilliams extension property (MEP) for
partitions of $\mathbf{H}$. With some invariance assumptions, we show that two
partitions of $\mathbf{H}$ admit MacWilliams identity if and only if they are
mutually dual and reflexive, and any partition of $\mathbf{H}$ satisfying the
MEP is in fact an orbit partition induced by some subgroup of
$\Aut_{\mathbb{F}}(\mathbf{H})$, which is necessarily reflexive. As an
application of the aforementioned results, we establish partitions of
$\mathbf{H}$ induced by combinatorial metric that do not satisfy the MEP, which
further enable us to provide counter-examples to a conjecture proposed by
Pinheiro, Machado and Firer in \cite{39}.


  We report the identification of LSR J0745+2627 in the United Kingdom InfraRed
Telescope Infrared Deep Sky Survey (UKIDSS) Large Area Survey (LAS) as a cool
white dwarf with kinematics and age compatible with the thick-disk/halo
population. LSR J0745+2627 has a high proper motion (890 mas/yr) and a high
reduced proper motion value in the J band (H_J=21.87). We show how the
infrared-reduced proper motion diagram is useful for selecting a sample of cool
white dwarfs with low contamination. LSR J0745+2627 is also detected in the
Sloan Digital Sky Survey (SDSS) and the Wide-field Infrared Survey Explorer
(WISE). We have spectroscopically confirmed this object as a cool white dwarf
using X-Shooter on the Very Large Telescope. A detailed analysis of its
spectral energy distribution reveals that its atmosphere is compatible with a
pure-H composition model with an effective temperature of 3880+-90 K. This
object is the brightest pure-H ultracool white dwarf (Teff<4000 K) ever
identified. We have constrained the distance (24-45 pc), space velocities and
age considering different surface gravities. The results obtained suggest that
LSR J0745+2627 belongs to the thick-disk/halo population and is also one of the
closest ultracool white dwarfs.


  We consider the convolution model where i.i.d. random variables $X_i$ having
unknown density $f$ are observed with additive i.i.d. noise, independent of the
$X$'s. We assume that the density $f$ belongs to either a Sobolev class or a
class of supersmooth functions. The noise distribution is known and its
characteristic function decays either polynomially or exponentially
asymptotically. We consider the problem of goodness-of-fit testing in the
convolution model. We prove upper bounds for the risk of a test statistic
derived from a kernel estimator of the quadratic functional $\int f^2$ based on
indirect observations. When the unknown density is smoother enough than the
noise density, we prove that this estimator is $n^{-1/2}$ consistent,
asymptotically normal and efficient (for the variance we compute). Otherwise,
we give nonparametric upper bounds for the risk of the same estimator. We give
an approach unifying the proof of nonparametric minimax lower bounds for both
problems. We establish them for Sobolev densities and for supersmooth densities
less smooth than exponential noise. In the two setups we obtain exact testing
constants associated with the asymptotic minimax rates.


  It is now straightforward to carry out S-matrix to potential inversion over a
very wide range of energies and for a wide range of projectile-target
combinations. Inversion is possible in many cases involving spin. IP inversion
also permits direct scattering data-to-potential inversion and furnishes
powerful tools for the phenomenological analysis of nuclear scattering. The
resulting single particle potentials exhibit various generic properties which
challenge fundamental reaction theories as well as yield information on
densities, provide input for reaction calculations. S-matrix to potential
inversion is also a powerful tool for directly investigating theoretical
processes which contribute to inter-nuclear potentials. Various studies have
given insight into contributions to the dynamic polarisation potential (DPP)
due to breakup processes and due to collective and reaction channel coupling
and have also illuminated the role played by exchange processes in leading to
non-locality and parity dependence of the potentials. A case study involving d
+ He-4 is a model for ways in which inversion applied jointly to theory and
experiment might illuminate the scattering of exotic nuclei.


  A recent composite-dark-matter scenario assumes that the dominant fraction of
dark matter consists of O-helium (OHe) dark atoms, in which a lepton-like
doubly charged particle O is bound with a primordial helium nucleus. It
liberates the physics of dark matter from unknown features of new physics, but
it demands a deep understanding of the details of known nuclear and atomic
physics, which are still unclear. Here, we consider in detail the physics of
the binding of OHe to various nuclei of interest for direct dark matter
searches. We show that standard quantum mechanics leads to bound states in the
keV region, but does not seem to provide a simple mechanism that stabilizes
them. The crucial role of a barrier in the OHe-nucleus potential is confirmed
for such a stabilization.


  Sourav Chatterjee, Persi Diaconis, Allan Sly and Lingfu Zhang, prompted by a
question of Ramis Movassagh, renewed the study of a process proposed in the
early 1980s by Jean Bourgain. A state vector $v \in \mathbb R^n$, labeled with
the vertices of a connected graph, $G$, changes in discrete time steps
following the simple rule that at each step a random edge $(i,j)$ is picked and
$v_i$ and $v_j$ are both replaced by their average $(v_i+v_j)/2$. It is easy to
see that the value associated with each vertex converges to $1/n$. The question
was how quickly will $v$ be $\epsilon$-close to uniform in the $L^{1}$ norm in
the case of the complete graph, $K_{n}$, when $v$ is initialized as a standard
basis vector that takes the value 1 on one coordinate, and zeros everywhere
else. They have established a sharp cutoff of $\frac{1}{2\log 2}n\log n +
O(n\sqrt{\log n})$. Our main result is to prove, that
$\frac{(1-\epsilon)}{2\log2}n\log n-O(n)$ is a general lower bound for all
connected graphs on $n$ nodes. We also get sharp magnitude of $t_{\epsilon,1}$
for several important families of graphs, including star, expander, dumbbell,
and cycle. In order to establish our results we make several observations about
the process, such as the worst case initialization is always a standard basis
vector. Our results add to the body of work of Aldous, Aldous and Lanoue,
Quattropani and Sau, Cao, Olshevsky and Tsitsiklis, and others. The renewed
interest is due to an analogy to a question related to the Google's supremacy
circuit. For the proof of our main theorem we employ a concept that we call
'augmented entropy function' which may find independent interest in the
computer science and probability theory communities.


  We show that a Skoda complex with a general plurisubharmonic weight function
is exact if its 'degree' is sufficiently large. This answers a question of
Lazarsfeld and implies that not every integrally closed ideal is equal to a
multiplier ideal even if we allow general plurisubharmonic weights for the
multiplier ideal, extending the result of Lazarsfeld and Lee \cite{LL}.


  The role of HeH+ has been newly assessed with the aid of newly calculated
rates which use entirely ab initio methods, thereby allowing us to compute more
accurately the relevant abundances within the global chemical network of the
early universe. A comparison with the similar role of the ionic molecule LiH+
is also presented. Quantum calculations have been carried out for the gas-phase
reaction of HeH+ with H atoms with our new in-house code, based on the negative
imaginary potential method. Integral cross sections and reactive rate
coefficients obtained under the general conditions of early universe chemistry
are presented and discussed. With the new reaction rate, the abundance of HeH+
in the early universe is more than one order of magnitude larger than in
previous studies. Our more accurate findings further buttress the possibility
to detect cosmological signatures of HeH+.


  "Which Generative Adversarial Networks (GANs) generates the most plausible
images?" has been a frequently asked question among researchers. To address
this problem, we first propose an \emph{incomplete} U-statistics estimate of
maximum mean discrepancy $\mathrm{MMD}_{inc}$ to measure the distribution
discrepancy between generated and real images. $\mathrm{MMD}_{inc}$ enjoys the
advantages of asymptotic normality, computation efficiency, and model
agnosticity. We then propose a GANs analysis framework to select and test the
"best" member in GANs family using the Post Selection Inference (PSI) with
$\mathrm{MMD}_{inc}$. In the experiments, we adopt the proposed framework on 7
GANs variants and compare their $\mathrm{MMD}_{inc}$ scores.


  Scaling relations, such as the IPAT equation and the Kaya identity, are
useful for quickly gauging the scale of economic, technological, and
demographic changes required to reduce environmental impacts and pressures; in
the case of the Kaya identity, the environmental pressure is greenhouse gas
emissions. However, when considering large-scale economic transformation, as
with a shift to a low-carbon economy, the IPAT and Kaya identities and their
cousins fail to capture the legacy of existing capital, on the one hand, and
the need for new investment, on the other. While detailed models can capture
these factors, they do not allow for rapid exploration of widely different
alternatives, which is the appeal of the IPAT and Kaya identities. In this
paper we present an extended Kaya identity that includes investment and capital
stocks. The identity we propose is a sum of terms, rather than a simple scaling
relation. Nevertheless, it allows for quick analysis and rapid exploration of a
variety of different possible paths toward a low-carbon economy.


  We study a multispecies one-dimensional Calogero model with two- and
three-body interactions. Using an algebraic approach (Fock space analysis), we
construct ladder operators and find infinitely many, but not all, exact
eigenstates of the model Hamiltonian. Besides the ground state energy, we
deduce energies of the excited states. It turns out that the spectrum is linear
in quantum numbers and that the higher-energy levels are degenerate. The
dynamical symmetry responsible for degeneracy is SU(2). We also find the
universal critical point at which the model exhibits singular behaviour.
Finally, we make contact with some special cases mentioned in the literature.


  We show that the complete static black p-brane supergravity solution with a
single charge contains two and only two branches with respect to behavior at
infinity in the transverse space. One branch is the standard family of
asymptotically flat black branes, and another is the family of black branes
which asymptotically approach the linear dilaton background with antisymmetric
form flux (LDB). Such configurations were previously obtained in the
near-horizon near-extreme limit of the dilatonic asymptotically flat p-branes,
and used to describe the thermal phase of field theories involved in the DW/QFT
dualities and the thermodynamics of little string theory in the case of the
NS5-brane. Here we show by direct integration of the Einstein equations that
the asymptotically LDB p-branes are indeed exact supergravity solutions, and we
prove a new uniqueness theorem for static p-brane solutions satisfying cosmic
censorship. In the non-dilatonic case, our general non-asymptotically flat
p-branes are uncharged black branes on the background $AdS_{p+2}\times
S^{D-p-2}$ supported by the form flux. We develop the general formalism of
quasilocal quantities for non-asymptotically flat supergravity solutions with
antisymmetric form fields, and show that our solutions satisfy the first law of
theormodynamics. We also suggest a constructive procedure to derive rotating
asymptotically LDB brane solutions.


  On a symplectic manifold $M$, the quantum product defines a complex, one
parameter family of flat connections called the A-model or Dubrovin
connections. Let $\hbar$ denote the parameter. Associated to them is the
quantum $\mathcal{D}$ - module ${\mathcal{D}}/I$ over the Heisenberg algebra of
first order differential operators on a complex torus. An element of $I$ gives
a relation in the quantum cohomology of $M$ by taking the limit as $\hbar\to
0$. Givental (HomGeom), discovered that there should be a structure of a
$\mathcal{D}$ - module on the (as yet not rigorously defined) ${S^1}$
equivariant Floer cohomology of the loop space of $M$ and conjectured that the
two modules should be equal. Based on that, we formulate a conjecture about how
to compute the quantum cohomology $\mathcal{D}$ - module in terms of Morse
theoretic data for the symplectic action functional. The conjecture is proven
in the case of toric manifolds with $\int_d{c_1}> 0$ for all nonzero classes
$d$ of rational curves in $M$.


  We prove analogues of some of Kostant's theorems for infinitesimal Cherednik
algebras of gl_n. As a consequence, it follows that in positive characteristic
the Azumaya and smooth loci of the center of these algebras coincide.


  A regular language has the zero-one law if its asymptotic density converges
to either zero or one. We prove that the class of all zero-one languages is
closed under Boolean operations and quotients. Moreover, we prove that a
regular language has the zero-one law if and only if its syntactic monoid has a
zero element. Our proof gives both algebraic and automata characterisation of
the zero-one law for regular languages, and it leads the following two
corollaries: (i) There is an O(n log n) algorithm for testing whether a given
regular language has the zero-one law. (ii) The Boolean closure of existential
first-order logic over finite words has the zero-one law.


  We exploit the first public data release of VIPERS to investigate
environmental effects in galaxy evolution between $z\sim0.5$ and $0.9$. The
large number of spectroscopic redshifts over an area of about
$10\,\mathrm{deg}^2$ provides a galaxy sample with high statistical power. The
accurate redshift measurements, with $\sigma_z = 0.00047(1+z_\mathrm{spec})$,
allow us to robustly isolate galaxies living in the lowest- and highest-density
environments, as defined in terms of spatial 3D density contrast. We estimate
the stellar mass function (SMF) of galaxies residing in these two environments,
and constrain its high-mass end with unprecedented precision. We find that the
galaxy SMF in the densest regions has a different shape than that measured at
low densities, with an enhancement of massive galaxies and a hint of a flatter
(less negative) slope at $z<0.8$. We normalise each SMF to the comoving volume
occupied by the corresponding environment, and relate estimates from different
redshift bins. We observe an evolution of the SMF of VIPERS galaxies in high
densities, while the low-density one is nearly constant. We compare these
results to semi-analytical models and find consistent environmental signatures.
We discuss how the halo mass function and fraction of central/satellite
galaxies depend on the environments considered, making intrinsic and
environmental properties of galaxies physically coupled, and therefore
difficult to disentangle. The evolution of our low-density regions is well
described by the formalism introduced by Peng et al.~(2010), and is consistent
with the idea that galaxies become progressively passive because of internal
physical processes. The same formalism could also describe the evolution of the
SMF in the high density regions, but only if a significant contribution from
dry mergers is considered. [Abridged]


  About twenty years ago, we proposed the mathematical formulation of
Heisenberg's uncertainty principle, and further, we concluded that Heisenberg's
uncertainty principle and EPR-paradox are not contradictory. This is true,
however we now think that we should have argued about it under a certain firm
interpretation of quantum mechanics. Recently we proposed the linguistic
quantum interpretation (called quantum and classical measurement theory), which
was characterized as a kind of metaphysical and linguistic turn of the
Copenhagen interpretation. This turn from physics to language does not only
extend quantum theory to classical systems but also yield the quantum
mechanical world view (i.e., the philosophy of quantum mechanics, in other
words, quantum philosophy). In fact, we can consider that traditional
philosophies have progressed toward quantum philosophy. In this paper, we first
review the linguistic quantum interpretation, and further, clarify the relation
between EPR-paradox and Heisenberg's uncertainty principle. That is, the
linguistic interpretation says that EPR-paradox is closely related to the fact
that syllogism does not generally hold in quantum physics. This fact should be
compared to the non-locality of Bell's inequality.


  We present an analytical stability theory for the onset of the Faraday
instability, applying over a wide frequency range between shallow water gravity
and deep water capillary waves. For sufficiently thin fluid layers the surface
is predicted to occur in harmonic rather than subharmonic resonance with the
forcing. An experimental confirmation of this result is given. PACS: 47.20.Ma,
47.20.Gv, 47.15.Cb


  Given a probabilistic transition system (PTS) $\cal A$ partially observed by
an attacker, and an $\omega$-regular predicate $\varphi$over the traces of
$\cal A$, measuring the disclosure of the secret $\varphi$ in $\cal A$ means
computing the probability that an attacker who observes a run of $\cal A$ can
ascertain that its trace belongs to $\varphi$. In the context of refinement, we
consider specifications given as Interval-valued Discrete Time Markov Chains
(IDTMCs), which are underspecified Markov chains where probabilities on edges
are only required to belong to intervals. Scheduling an IDTMC $\cal S$ produces
a concrete implementation as a PTS and we define the worst case disclosure of
secret $\varphi$ in ${\cal S}$ as the maximal disclosure of $\varphi$ over all
PTSs thus produced. We compute this value for a subclass of IDTMCs and we prove
that refinement can only improve the opacity of implementations.


  Supergiant X-ray Binaries (SgXB) host a compact object, often a neutron star
(NS), orbiting an evolved O/B star. Mass transfer proceeds through the intense
line-driven wind of the stellar donor, a fraction of which is captured by the
gravitational field of the NS. The subsequent accretion process onto the NS is
responsible for the abundant X-ray emission from SgXB. They also display
peak-to-peak variability of the X-ray flux by a factor of a few 10 to 100,
along with changes in the hardness ratios possibly due to varying absorption
along the line-of-sight. We use recent radiation-hydrodynamic simulations of
inhomogeneities (aka clumps) in the non-stationary wind of massive hot stars to
evaluate their impact on the time-variable accretion process. For this, we run
3D hydrodynamic simulations of the wind in the vicinity of the accretor to
investigate the formation of the bow shock and follow the inhomogeneous flow
over several spatial orders of magnitude, down to the NS magnetosphere. In
particular, we show that the impact of the wind clumps on the time-variability
of the intrinsic mass accretion rate is severely tempered by the crossing of
the shock, compared to the purely ballistic Bondi-Hoyle-Lyttleton estimation.
We also account for the variable absorption due to clumps passing by the
line-of-sight and estimate the final effective variability of the column
density and mass accretion rate for different orbital separations. Finally, we
compare our results to the most recent analysis of the X-ray flux and the
hardness ratio in Vela X-1.


  The reservoir memory effects can lead to information backflow and recurrence
of the previously lost quantum correlations. We establish connections between
the direction of information flow and variation of the geometric quantum
discords (GQDs) measured respectively by the trace distance, the Hellinger
distance, and the Bures distance for two qubits subjecting to the bosonic
structured reservoirs, and unveil their dependence on a factor whose derivative
signifies the (non-)Markovianity of the dynamics. By considering the reservoirs
with Lorentzian and Ohmic-like spectra, we further demonstrated that the
non-Markovianity induced by the backflow of information from the reservoirs to
the system enhances the GQDs in most of the parameter regions. This highlights
the potential of non-Markovianity as a resource for protecting the GQDs.


  Radiation-enhanced precipitation of Cr-rich {\alpha}' in irradiated Fe-Cr
alloys, which results in hardening and embrittlement, depends on the
irradiating particle and the displacement per atom (dpa) rate. Here, we utilize
a Cahn-Hilliard phase-field based approach, that includes simple models for
nucleation, irradiating particle and rate dependent radiation-enhanced
diffusion and cascade mixing to simulate {\alpha}' evolution under neutrons,
heavy ions, and electron irradiations. Different irradiating particles manifest
very different cascade mixing efficiencies. The model was calibrated using
neutron data. For cascade inducing neutron/heavy-ion dpa rates at 300 {\deg}C
between 10^-^8 and 10^-^6 dpa/s the model predicts approximately constant
number density, decreasing radius, decreasing {\alpha}' Cr composition, and
lower {\alpha}' volume fraction. The model then predicts a dramatic transition
to no {\alpha}' formation above approximately 10^-^5 dpa/s, while electron
irradiation, with weak mixing, had little effect at dpa rates up to 10^-^3
dpa/s. These model predictions are consistent with experiments. We explain the
results in terms of the flux dependence of the radiation enhanced diffusion,
cascade mixing, and their ratio, which all vary significantly in relevant flux
ranges for neutron and cascade inducing ion irradiations. These results show
that both cascade mixing and radiation enhanced diffusion must be accounted for
when attempting to emulate neutron-irradiation effects using accelerated ion
irradiations.


  Volcanic seismicity at Mt. Etna is studied. It is found that the associated
stochastic process exhibits a subdiffusive phenomenon. The jump probability
distribution well obeys an exponential law, whereas the waiting-time
distribution follows a power law in a wide range. Although these results would
seem to suggest that the phenomenon could be described by temporally-fractional
kinetic theory based on the viewpoint of continuous-time random walks, the
exponent of the power-law waiting-time distribution actually lies outside of
the range allowed in the theory. In addition, there exists the aging phenomenon
in the event-time averaged mean squared displacement, in contrast to the
picture of fractional Brownian motion. Comments are also made on possible
relevances of random walks on fractals as well as nonlinear kinetics. Thus,
problems of volcanic seismicity are highly challenging for science of complex
systems.


  The complexity of biological systems, and the increasingly large amount of
associated experimental data, necessitates that we develop mathematical models
to further our understanding of these systems. As biological systems are
generally not well understood, most mathematical models of these systems are
based on experimental data, resulting in a seemingly heterogeneous collection
of models that ostensibly represent the same system. To understand the system
we therefore need to know how the different models are related, with a view to
obtaining a unified mathematical description. This goal is complicated by the
fact that distinct mathematical formalisms may be used to represent the same
system, making direct comparison of the models very difficult. In previous work
we developed an appropriate framework for model comparison where we represent
models as labelled simplicial complexes and compare them with two general
methodologies: comparison by distance or equivalence. In this article we
continue the development of our model comparison methodology in two directions.
First, we present a rigorous and automatable methodology for the core process
of comparison by equivalence, namely determining the vertices in a simplicial
representation, corresponding to model components, that are conceptually
related and the identification of these vertices via simplicial operations. Our
methodology is based on considerations of vertex symmetry in the simplicial
representation, for which we develop the required mathematical theory of group
actions on simplicial complexes. This methodology greatly simplifies and
expedites the process of determining model equivalence. Second, we provide an
alternative mathematical framework for our model-comparison methodology by
representing models as groups, which allows for the direct application of
group-theoretic techniques within our model-comparison methodology.


  We investigate the formation of TeV spectrum of a distant blazar PKS 1424+240
residing at a redshift $z\geq0.6$ in two scenarios in the frame of a
lepto-hadronic jet model taking both the uncertainties of the extragalactic
background light (EBL) and its redshift into account. In the first scenario,
TeV emission is attributed to the synchrotron emission of pair cascades
resulting from $p\gamma$ interaction; in the second scenario, TeV emission is
attributed to the proton-synchrotron emission, and an internal absorption due
to interaction with the photons around the jet is included. The results show
that in the first scenario the 68\% upper limit of its redshift within which
this scenario can explain the VERITAS TeV spectrum in 2009 well is $\sim0.75$;
in the second scenario, this upper limit of the redshift becomes $\sim1.03$.
However, the second scenario can be excluded because it requires an
unreasonable photon field around the jet with a luminosity of $\sim10^{43}\rm \
erg\ s^{-1}$. In conclusion, the jet model can explain its TeV spectrum with a
low EBL density if $0.6<z<0.75$.


  Our aim is to derive a self-consistent age, distance and composition for the
globular cluster $47\,$Tucanae ($47\,$Tuc; NGC104). First, we reevaluate the
reddening towards the cluster resulting in a nominal $E(B-V)=0.03\pm0.01$ as
the best estimate. The $T_{\rm eff}$ of the components of the eclipsing binary
member V69 is found to be $5900\pm72$ K from both photometric and spectroscopic
evidence. This yields a true distance modulus $(m-M)_0=13.21\pm0.06$(random)$
\pm0.03 $(systematic) to $47\,$Tuc when combined with existing measurements of
V69 radii and luminosity ratio. We then present a new completely
self-consistent isochrone fitting method to ground based and $\textit{HST}$
cluster colour-magnitude diagrams and the eclipsing binary member V69. The
analysis suggests that the composition of V69, and by extension one of the
populations of $47\,$Tuc, is given by [Fe/H]$\sim-0.70$, [O/Fe]$\sim+0.60$, and
$Y\sim0.250$ on the solar abundance scale of Asplund, Grevesse & Sauval.
However, this depends on the accuracy of the model $T_{\rm eff}$ scale which is
50-75 K cooler than our best estimate but within measurement uncertainties. Our
best estimate of the age of $47\,$Tuc is 11.8 Gyr, with firm ($3 \sigma$) lower
and upper limits of 10.4 and 13.4 Gyr, respectively, in satisfactory agreement
with the age derived from the white dwarf cooling sequence if our determination
of the distance modulus is adopted.


  We use the superspace formulation of supergravity in eleven and ten
dimensions to compute fermion couplings on the M2-brane and on D$p$-branes. In
this formulation fermionic couplings arise naturally from the
$\theta$-expansion of the superfields from which the brane actions are
constructed. The techniques we use and develop can in principle be applied to
determine the fermionic couplings to general background fields up to arbitrary
order. Starting with the superspace formulation of 11-dimensional supergravity,
we use a geometric technique known as the `normal coordinate' method to obtain
the $\theta$-expansion of the M2-brane action. We then present a method which
allows us to translate the knowledge of fermionic couplings on the M2-brane to
knowledge of such couplings on the D2-brane, and then to any D$p$-brane. This
method is based on superspace generalizations of both the compactification
taking 11-dimensional supergravity to type IIA supergravity and the T-duality
rules connecting the type IIA and type IIB supergravities.


  Talagrand's positivity principle states that one can slightly perturb a
Hamiltonian in the Sherrington-Kirkpatrick model in such a way that the overlap
of two configurations under the perturbed Gibbs' measure will become typically
nonnegative. In this note we observe that abstracting from the setting of the
SK model only improves the result and does not require any modifications in
Talagrand's argument. In this version, for example, positivity principle
immediately applies to the setting of Aizenman-Sims-Starr interpolation. Also,
abstracting from the SK model improves the conditions in the Ghirlanda-Guerra
identities and as a consequence results in a perturbation of smaller order
necessary to ensure positivity of the overlap.


  The k-means algorithm is a well-known method for partitioning n points that
lie in the d-dimensional space into k clusters. Its main features are
simplicity and speed in practice. Theoretically, however, the best known upper
bound on its running time (i.e. O(n^{kd})) can be exponential in the number of
points. Recently, Arthur and Vassilvitskii [3] showed a super-polynomial
worst-case analysis, improving the best known lower bound from \Omega(n) to
2^{\Omega(\sqrt{n})} with a construction in d=\Omega(\sqrt{n}) dimensions. In
[3] they also conjectured the existence of superpolynomial lower bounds for any
d >= 2.
  Our contribution is twofold: we prove this conjecture and we improve the
lower bound, by presenting a simple construction in the plane that leads to the
exponential lower bound 2^{\Omega(n)}.


  In a range of recent works, object-centric architectures have been shown to
be suitable for unsupervised scene decomposition in the vision domain. Inspired
by these methods we present AudioSlots, a slot-centric generative model for
blind source separation in the audio domain. AudioSlots is built using
permutation-equivariant encoder and decoder networks. The encoder network based
on the Transformer architecture learns to map a mixed audio spectrogram to an
unordered set of independent source embeddings. The spatial broadcast decoder
network learns to generate the source spectrograms from the source embeddings.
We train the model in an end-to-end manner using a permutation invariant loss
function. Our results on Libri2Mix speech separation constitute a proof of
concept that this approach shows promise. We discuss the results and
limitations of our approach in detail, and further outline potential ways to
overcome the limitations and directions for future work.


  Using a physically motivated parameterization based on the reggeized diquark
model we perform a flavor separation of the chiral-odd generalized parton
distributions obtained from both $\pi^0$ and $\eta$ exclusive
electroproduction. In our approach we exploit a connection between the
chiral-even and chiral-odd reduced helicity amplitudes using Parity
transformations that are relevant for a class of models that includes
two-component models. We compare our results for $\eta$ production to the
previously obtained $\pi^0$ results, and we make predictions for the transverse
single spin asymmetry components which will be measured within the Jefferson
Lab 12 GeV program.


  We report the results of extended high--resolution numerical integrations of
the Vlasov--Poisson equation for the collapse of spherically symmetric WDM
halos. For thermal relics with mass $m=1\,$keV/$c^2$, we find collapsed halos
with cores of size from $0.1$ to $0.6\,$kpc. The typical core is hollow, with
the mass density decreasing towards the core center by almost three orders of
magnitude from its maximum near the core radius $r_{\rm c}$. The core is in
equilibrium with the diffused part of the halo but far from virialization.
These properties are rooted in the conservation of the squared angular momentum
and in the original excess, proper of WDM initial conditions, of kinetic energy
in the core region. In a sample of more than one hundred simulated collapses,
the values of $r_{\rm c}$ and of the core density $\rho_{\rm c}$ are in the
range typical of dwarf spheroids, while the maximal circular velocities $V_{\rm
max}$ are proper of small disk galaxies. The product $\mu_{\rm c}=\rho_{\rm
c}r_{\rm c}$ takes values between $116\,M_\odot/$pc$^2$ and
$283\,M_\odot/$pc$^2$, while the surface density $\mu_0$, as determined from a
Burkert fit, is roughly three times larger. From these data and data obtained
at smaller values of $m$, we extrapolate for one particular halo $\mu_{\rm
c}=263(308)\,M_\odot/$pc$^2$ and $\mu_0=754(855)\,M_\odot/$pc$^2$ at
$m=2(3.3)\,$keV/$c^2$, to be compared with the observed value $140^{+83}_{-52}
M_\odot$/pc$^2$. In view of the many improvements and enhancements available,
we conclude that WDM is a viable solution for explaining the presence and the
size of cores in low mass galaxies.


  We use the technique developed in Moughal's doctoral thesis to analyse the
joint spike transition, revealing new groups of worldlines which undergo
distinct transitions, and correcting misconceptions about spikes.


  We report time- and angle-resolved photoemission spectroscopy measurements on
the Sb(111) surface. We observe band- and momentum-dependent binding-energy
oscillations in the bulk and surface bands driven by $A_{1g}$ and $E_{g}$
coherent phonons. While the bulk band shows simultaneous $A_{1g}$ and $E_{g}$
oscillations, the surface bands show either $A_{1g}$ or $E_{g}$ oscillations.
The observed behavior is reproduced by frozen-phonon calculations based on
density-functional theory. This evidences the connection between
electron-phonon coupling and coherent binding energy dynamics.


  We study the $Za_1$ decay mode of a heavy CP-even Higgs boson of the NMSSM,
$h_2$, where $a_1$ is the lightest CP-odd Higgs state of this scenario, the
former produced in association with a bottom-antibottom pair, and find that,
despite small event rates, a significant (in fact essentially background free)
signal should be extractable at the LHC with very high luminosity, so long that
a $Z\to jj$ (where $j$ represents a jet) and $a_1\to \tau^+\tau^-$ final state
is exploited, in presence of $b$-tagging


  Compact gamma cameras with a square-shaped monolithic scintillator crystal
and an array of silicon photomultipliers (SiPMs) are actively being developed
for applications in areas such as small animal imaging, cancer diagnostics and
radiotracer guided surgery. Statistical methods of position reconstruction,
which are potentially superior to the traditional centroid method, require
accurate knowledge of the spatial response of each photomultiplier. Using both
Monte Carlo simulations and experimental data obtained with a camera prototype,
we show that the spatial response of all photomultipliers (light response
functions) can be parameterized with axially symmetric functions obtained
iteratively from flood field irradiation data. The study was performed with a
camera prototype equipped with a 30 x 30 x 2 mm3 LYSO crystal and an 8 x 8
array of SiPMs for 140 keV gamma rays. The simulations demonstrate that the
images, reconstructed with the maximum likelihood method using the response
obtained with the iterative approach, exhibit only minor distortions: the
average difference between the reconstructed and the true positions in X and Y
directions does not exceed 0.2 mm in the central area of 22 x 22 mm2 and 0.4 mm
at the periphery of the camera. A similar level of image distortions is shown
experimentally with the camera prototype.


  When eliciting probability models from experts, knowledge engineers may
compare the results of the model with expert judgment on test scenarios, then
adjust model parameters to bring the behavior of the model more in line with
the expert's intuition. This paper presents a methodology for analytic
computation of sensitivity values to measure the impact of small changes in a
network parameter on a target probability value or distribution. These values
can be used to guide knowledge elicitation. They can also be used in a gradient
descent algorithm to estimate parameter values that maximize a measure of
goodness-of-fit to both local and holistic probability assessments.


  Optimum distance (Ropt) is a distance from the shower core in which the
density calculated by lateral distribution function, has its minimum
uncertainty. In this paper, Using CORSIKA code, proton, carbon and iron primary
in the energy range between 1013 - 3*1015eV are simulated to find Ropt for
Alborz-I array located at an altitude of 1200 m above sea level. It is shown
that Ropt is approximately independent of characteristics of primary particle
and it is only dependent to array configuration. Dependency of Ropt on layout
and detector spacing for 20 Alborz-I array detectors, are studied. It is shown
that the Alborz-I array layout and its detector spacing result into the best
(minimum uncertainty) Ropt for its number of detectors. In this work Ropt for
Alborz-I array is obtained about 9 m. In addition, it is shown that, by finding
dependency of primary energy to density in optimum distance, energy of primary
particle can be estimated well. An energy estimation function is suggested and
the function is examined by another set of simulated showers.


  The author has recently introduced abstract algebraic frameworks of
analogical proportions and similarity within the general setting of universal
algebra. The purpose of this paper is to build a bridge from similarity to
analogical proportions by formulating the latter in terms of the former. The
benefit of this similarity-based approach is that the connection between
proportions and similarity is built into the framework and therefore evident
which is appealing since proportions and similarity are both at the center of
analogy; moreover, future results on similarity can directly be applied to
analogical proportions.


  We consider a generalization of the mKdV equation, which contains dissipation
terms similar to those contained in both the Benjamin-Bona-Mahoney equation and
the famous Camassa-Holm and Degasperis-Procesi equations. Our objective is the
construction of classical (solitons) and non-classical (peakons and cuspons)
solitary wave solutions of this equation.


  In recent years, Spiking Neural Networks (SNNs) have demonstrated great
successes in completing various Machine Learning tasks. We introduce a method
for learning image features by \textit{locally connected layers} in SNNs using
spike-timing-dependent plasticity (STDP) rule. In our approach, sub-networks
compete via competitive inhibitory interactions to learn features from
different locations of the input space. These \textit{Locally-Connected SNNs}
(LC-SNNs) manifest key topological features of the spatial interaction of
biological neurons. We explore biologically inspired n-gram classification
approach allowing parallel processing over various patches of the the image
space. We report the classification accuracy of simple two-layer LC-SNNs on two
image datasets, which match the state-of-art performance and are the first
results to date. LC-SNNs have the advantage of fast convergence to a dataset
representation, and they require fewer learnable parameters than other SNN
approaches with unsupervised learning. Robustness tests demonstrate that
LC-SNNs exhibit graceful degradation of performance despite the random deletion
of large amounts of synapses and neurons.


  Deep Neural Networks (DNN) will emerge as a cornerstone in automotive
software engineering. However, developing systems with DNNs introduces novel
challenges for safety assessments. This paper reviews the state-of-the-art in
verification and validation of safety-critical systems that rely on machine
learning. Furthermore, we report from a workshop series on DNNs for perception
with automotive experts in Sweden, confirming that ISO 26262 largely
contravenes the nature of DNNs. We recommend aerospace-to-automotive knowledge
transfer and systems-based safety approaches, e.g., safety cage architectures
and simulated system test cases.


  Biological systems exploit self-assembly to create complex structures whose
arrangements are finely controlled from molecular to mesoscopic level. Herein
we report an example of using fully synthetic systems that mimic two levels of
self-assembly. We show the formation of vesicles using amphiphilic copolymers
whose chemical nature is chosen to control both membrane formation and
membrane-confined interactions. We report polymersomes with patterns that
emerge by engineering interfacial tension within the polymersome surface. This
allows the formation of domains whose topology is tailored by the chemical
synthesis paving the avenue to complex supramolecular designs functionally
similar to those found in viruses and trafficking vesicles.


  Academic success is distributed unequally; a few top scientists receive the
bulk of attention, citations, and resources. However, do these ``superstars"
foster leadership in scientific innovation? We introduce three
information-theoretic measures that quantify novelty, innovation, and impact
from scholarly citation networks, and compare the scholarly output of
scientists who are either not connected or strongly connected to superstar
scientists. We find that while connected scientists do indeed publish more,
garner more citations, and produce more diverse content, this comes at a cost
of lower innovation and higher redundancy of ideas. Further, once one removes
papers co-authored with superstars, the academic output of these connected
scientists diminishes. In contrast, authors that produce innovative content
without the benefit of collaborations with scientific superstars produce papers
that connect a greater diversity of concepts, publish more, and have comparable
citation rates, once one controls for transferred prestige of superstars. On
balance, our results indicate that academia pays a price by focusing attention
and resources on superstars.


  This paper introduces an unsupervised technique to detect the changed region
of multitemporal images on a same reference plane with the help of rough
clustering. The proposed technique is a soft-computing approach, based on the
concept of rough set with rough clustering and Pawlak's accuracy. It is less
noisy and avoids pre-deterministic knowledge about the distribution of the
changed and unchanged regions. To show the effectiveness, the proposed
technique is compared with some other approaches.


  Model predictive control (MPC) achieves stability and constraint satisfaction
for general nonlinear systems, but requires computationally expensive online
optimization. This paper studies approximations of such MPC controllers via
neural networks (NNs) to achieve fast online evaluation. We propose safety
augmentation that yields deterministic guarantees for convergence and
constraint satisfaction despite approximation inaccuracies. We approximate the
entire input sequence of the MPC with NNs, which allows us to verify online if
it is a feasible solution to the MPC problem. We replace the NN solution by a
safe candidate based on standard MPC techniques whenever it is infeasible or
has worse cost. Our method requires a single evaluation of the NN and forward
integration of the input sequence online, which is fast to compute on
resource-constrained systems. The proposed control framework is illustrated on
three non-linear MPC benchmarks of different complexity, demonstrating
computational speedups orders of magnitudes higher than online optimization. In
the examples, we achieve deterministic safety through the safety-augmented NNs,
where naive NN implementation fails.


  We consider the brane universe in the bulk background of the charged
topological AdS black holes. The evolution of the brane universe is described
by the Friedmann equations for a flat or an open FRW-universe containing
radiation and stiff matter. We find that the temperature and entropy of the
dual CFT are simply expressed in terms of the Hubble parameter and its time
derivative, and the Friedmann equations coincide with thermodynamic formulas of
the dual CFT at the moment when the brane crosses the black hole horizon. We
obtain the generalized Cardy-Verlinde formula for the CFT with an R-charge, for
any values of the curvature parameter k in the Friedmann equations.


  By dressing Polyakov lines with appropriate functionals of the gauge fields,
we construct observables describing a fundamental representation static
quark-antiquark pair in the singlet, adjoint and average channels of SU(N) pure
gauge theory. Each of the potentials represents a gauge invariant eigenvalue of
the Hamiltonian. Numerical simulations are performed for SU(2) in 2+1
dimensions. The adjoint channel is found to be repulsive at small and confining
at large separations, suggesting the existence of a metastable (N^2-1)-plet
bound state. For small distances and temperatures above the deconfinement
transition, the leading order perturbative prediction for the ratio of singlet
and adjoint potentials is reproduced by the lattice data.


  We study the use of U-Nets in reconstructing the linear dark matter density
field and its consequences for constraining cosmological parameters, in
particular primordial non-Gaussianity. Our network is able to reconstruct the
initial conditions of redshift $z=0$ density fields from N-body simulations
with $90\%$ accuracy out to $k \leq 0.4$ h/Mpc, competitive with
state-of-the-art reconstruction algorithms at a fraction of the computational
cost. We study the information content of the reconstructed $z=0$ density field
with a Fisher analysis using the QUIJOTE simulation suite, including
non-Gaussian initial conditions. Combining the pre- and post-reconstructed
power spectrum and bispectrum data up to $k_{\rm max} = 0.52$ h/Mpc, we find
significant improvements on all parameters. Most notably, we find a factor
$3.65$ (local), $3.54$ (equilateral) and $2.90$ (orthogonal) improvement on the
marginalized errors of $f_{\rm NL}$ as compared to only using the
pre-reconstructed data. We show that these improvements can be attributed to a
combination of reduced data covariance and parameter degeneracy. The results
constitute an important step towards more optimal inference of primordial
non-Gaussianity from non-linear scales.


  Unique-sink orientations (USOs) are an abstract class of orientations of the
n-cube graph. We consider some classes of USOs that are of interest in
connection with the linear complementarity problem. We summarise old and show
new lower and upper bounds on the sizes of some such classes. Furthermore, we
provide a characterisation of K-matrices in terms of their corresponding USOs.


  Most mobile network operators generate revenues by directly charging users
for data plan subscriptions. Some operators now also offer users data rewards
to incentivize them to watch mobile ads, which enables the operators to collect
payments from advertisers and create new revenue streams. In this work, we
analyze and compare two data rewarding schemes: a Subscription-Aware Rewarding
(SAR) scheme and a Subscription-Unaware Rewarding (SUR) scheme. Under the SAR
scheme, only the subscribers of the operators' data plans are eligible for the
rewards; under the SUR scheme, all users are eligible for the rewards (e.g.,
the users who do not subscribe to the data plans can still get SIM cards and
receive data rewards by watching ads). We model the interactions among an
operator, users, and advertisers by a two-stage Stackelberg game, and
characterize their equilibrium strategies under both the SAR and SUR schemes.
We show that the SAR scheme can lead to more subscriptions and a higher
operator revenue from the data market, while the SUR scheme can lead to better
ad viewership and a higher operator revenue from the ad market. We further show
that the operator's optimal choice between the two schemes is sensitive to the
users' data consumption utility function and the operator's network capacity.
We provide some counter-intuitive insights. For example, when each user has a
logarithmic utility function, the operator should apply the SUR scheme (i.e.,
reward both subscribers and non-subscribers) if and only if it has a small
network capacity.


  Skirlo et al., in 'Binary matrices of optimal autocorrelations as alignment
marks' [Journal of Vacuum Science and Technology Series B 33(2) (2015) 1-7],
defined a new class of binary matrices by maximizing the peak-sidelobe
distances in the aperiodic autocorrelations and, by exhaustive computer
searches, found the optimal square matrices of dimension up to 7 x 7, and
optimal diagonally symmetric matrices of dimensions 8 x 8 and 9 x 9. We make an
initial investigation into and propose a strategy for (deterministically)
constructing binary matrices with good peak-sidelobe distances. We construct
several classes of these and compare their distances to those of the optimal
matrices found by Skirlo et al. Our constructions produce matrices that are
near optimal for small dimension. Furthermore, we formulate a tight upper bound
on the peak-sidelobe distance of a cer- tain class of circulant matrices.
Interestingly, binary matrices corresponding to certain difference sets and
almost difference sets have peak-sidelobe distances meeting this upper bound.


  We consider the category of depth $0$ representations of a $p$-adic
quasi-split reductive group with coefficients in
$\overline{\mathbb{Z}}[\frac{1}{p}]$. We prove that the blocks of this category
are in natural bijection with the connected components of the space of tamely
ramified Langlands parameters for $G$ over
$\overline{\mathbb{Z}}[\frac{1}{p}]$. As a particular case, this depth $0$
category is thus indecomposable when the group is tamely ramified. Along the
way we prove a similar result for finite reductive groups. As an application,
we deduce that the semi-simple local Langlands correspondence $\pi\mapsto
\varphi_{\pi}$ constructed by Fargues and Scholze takes depth $0$
representations to tamely ramified parameters, using a motivic version of their
construction recently announced by Scholze. We also bound the restriction of
$\varphi_{\pi}$ to tame inertia in terms of the Deligne-Lusztig parameter of
$\pi$ and show, in particular, that $\varphi_{\pi}$ is unramified if $\pi$ is
unipotent.


  In the full effective potential (EFP) approach, we find that spontaneous
symmetry breaking (SSB) can, with the convexity of the full EFP, lead to the
prediction of the IR confinement of Higgs particles, adding difficulty to the
experimental identification of Higgs particles. The short distance behaviors
remain intact. The whole presentation is given in the understanding that any
QFT becomes UV well defined in a more complete underyling theory according to a
recently proposed strategy.


  In real-world multi-agent reinforcement learning (MARL) applications, agents
may not have perfect state information (e.g., due to inaccurate measurement or
malicious attacks), which challenges the robustness of agents' policies. Though
robustness is getting important in MARL deployment, little prior work has
studied state uncertainties in MARL, neither in problem formulation nor
algorithm design. Motivated by this robustness issue and the lack of
corresponding studies, we study the problem of MARL with state uncertainty in
this work. We provide the first attempt to the theoretical and empirical
analysis of this challenging problem. We first model the problem as a Markov
Game with state perturbation adversaries (MG-SPA) by introducing a set of state
perturbation adversaries into a Markov Game. We then introduce robust
equilibrium (RE) as the solution concept of an MG-SPA. We conduct a fundamental
analysis regarding MG-SPA such as giving conditions under which such a robust
equilibrium exists. Then we propose a robust multi-agent Q-learning (RMAQ)
algorithm to find such an equilibrium, with convergence guarantees. To handle
high-dimensional state-action space, we design a robust multi-agent
actor-critic (RMAAC) algorithm based on an analytical expression of the policy
gradient derived in the paper. Our experiments show that the proposed RMAQ
algorithm converges to the optimal value function; our RMAAC algorithm
outperforms several MARL and robust MARL methods in multiple multi-agent
environments when state uncertainty is present. The source code is public on
\url{https://github.com/sihongho/robust_marl_with_state_uncertainty}.


  Much work has been devoted to studying percolation of networks and
interdependent networks under varying levels of failures. Researchers have
considered many different realistic network structures, but thus far no study
has incorporated the hierarchical structure of many networks. For example,
infrastructure across cities will likely be distributed such that nodes are
tightly connected within small neighborhoods, somewhat less connected across
the whole city, and have even fewer connections between cities. Furthermore,
while previous work identified interconnected nodes, those nodes with links
outside their neighborhood, to be more likely to be attacked, here we have
various levels of interconnections (between neighborhoods, between cities,
etc.). We consider the nodes with interconnections at the highest level most
likely to be attacked, followed by those with interconnections at the next
level, etc. We develop an analytic solution for both single and interdependent
networks of this structure and verify our theory through simulations. We find
that depending on the number of levels in the hierarchy there may be multiple
transitions in the giant component (fraction of interconnected nodes), as the
network separates at the various levels. Our results show that these multiple
jumps are a feature of hierarchical networks and can affect the vulnerability
of infrastructure networks.


  In Digital Geometry, gaps are some basic portion of a digital object that a
discrete ray can cross without intersecting any voxel of the object itself.
Such a notion is quite important in combinatorial image analysis and it is
strictly connected with some applications in fields as CAD and Computer
graphics. In this paper we prove that the number of $0$-gaps of a $3$D digital
curve can be expressed as a linear combination of the number of its $i$-cells
(with $i = 0, \ldots, 3$).


  People with special needs like blind and visually impaired (BVI) people can
particularly benefit from using voice assistants providing spoken information
input and output in everyday life. However, it is crucial to understand their
needs and to include these in the development of accessible and useful
assistance systems. By conducting an online survey with 145 BVI people, this
paper revealed that common voice assistants like Apple's Siri or Amazon's Alexa
are used by a majority of BVI people and are also considered helpful. In
particular, features in the context of audio entertainment, internet access and
everyday life practical things like weather queries, time-related information
(e.g. setting an alarm clock), checking calendar entries and taking notes are
particularly often used and appreciated. The participants also indicated that
the integration of smart home devices, the optimization of existing
functionalities and voice input are important, but also potentially negative
aspects such as data privacy and data security are relevant. Therefore, it
seems particularly interesting to implement an online data processing as far as
possible. Our results contribute to this development by providing an overview
of empirically collected requirements for functions and implementation-related
aspects.


  Learning to navigate in a visual environment following natural language
instructions is a challenging task because natural language instructions are
highly variable, ambiguous, and under-specified. In this paper, we present a
novel training paradigm, Learn from EveryOne (LEO), which leverages multiple
instructions (as different views) for the same trajectory to resolve language
ambiguity and improve generalization. By sharing parameters across
instructions, our approach learns more effectively from limited training data
and generalizes better in unseen environments. On the recent Room-to-Room (R2R)
benchmark dataset, LEO achieves 16% improvement (absolute) over a greedy agent
as the base agent (25.3% $\rightarrow$ 41.4%) in Success Rate weighted by Path
Length (SPL). Further, LEO is complementary to most existing models for
vision-and-language navigation, allowing for easy integration with the existing
techniques, leading to LEO+, which creates the new state of the art, pushing
the R2R benchmark to 62% (9% absolute improvement).


  Some general formulas are derived for the solutions of a BRST quantization on
inner product spaces of finite dimensional bosonic gauge theories invariant
under arbitrary Lie groups. A detailed analysis is then performed of SL(2,R)
invariant models and some possible geometries of the Lagrange multipliers are
derived together with explicit results for a class of SL(2,R) models. Gauge
models invariant under a nonunimodular gauge group are also studied in some
detail.


  In this paper, we consider the problem of safety assessment for Markov
decision processes without explicit knowledge of the model. We aim to learn
probabilistic safety specifications associated with a given policy without
compromising the safety of the process. To accomplish our goal, we characterize
a subset of the state-space called proxy set, which contains the states that
are near in a probabilistic sense to the forbidden set consisting of all unsafe
states. We compute the safety function using the single-step temporal
difference method. To this end, we relate the safety function computation to
that of the value function estimation using temporal difference learning. Since
the given control policy could be unsafe, we use a safe baseline subpolicy to
generate data for learning. We then use an off-policy temporal difference
learning method with importance sampling to learn the safety function
corresponding to the given policy. Finally, we demonstrate our results using a
numerical example


  Classical models for multivariate or spatial extremes are mainly based upon
the asymptotically justified max-stable or generalized Pareto processes. These
models are suitable when asymptotic dependence is present, i.e., the joint tail
decays at the same rate as the marginal tail. However, recent environmental
data applications suggest that asymptotic independence is equally important
and, unfortunately, existing spatial models in this setting that are both
flexible and can be fitted efficiently are scarce. Here, we propose a new
spatial copula model based on the generalized hyperbolic distribution, which is
a specific normal mean-variance mixture and is very popular in financial
modeling. The tail properties of this distribution have been studied in the
literature, but with contradictory results. It turns out that the proofs from
the literature contain mistakes. We here give a corrected theoretical
description of its tail dependence structure and then exploit the model to
analyze a simulated dataset from the inverted Brown-Resnick process, hindcast
significant wave height data in the North Sea, and wind gust data in the state
of Oklahoma, USA. We demonstrate that our proposed model is flexible enough to
capture the dependence structure not only in the tail but also in the bulk.


  We provide a proximal average with repect to a $1$-coercive Legendre
function. In the sense of Bregman distance, the Bregman envelope of the
proximal average is a convex combination of Bregman envelopes of individual
functions. The Bregman proximal mapping of the average is a convex combination
of convexified proximal mappings of individual functions. Techniques from
variational analysis provide the keys for the Bregman proximal average.


  This paper studies sufficient conditions for deriving the kappa distribution
in polytropic plasmas by an improved method compared with the previous work [R.
Guo, Phys. Plasmas \textbf{27}, 122104 (2020)]. We find that the polytropic
equation of state can lead to the kappa distribution without any other
assumptions in one dimension. In higher dimensions, an extra assumption that
the global distribution must only depend on the energy is still needed. In
addition, the self-consistence of the electrostatic potential is investigated
in such plasmas. The study implies that in single-component plasmas, the
potential is constrained by the Lane-Emden equation. However, in two-component
plasmas, any arbitrary potential could exist with a suitable background ion
density. Finally, we discuss the connections between the kappa distribution and
the polytrope.


  A bijection $(l,r)$ between $M^2$ and $M$ is said to be a pairing function
with no cycles, if any composition of its coordinate functions has no fixed
point. We compute here the Grothendieck ring of the pairing function without
cycles to be isomorphic to $\mathbb{Z}^2\simeq \mathbb{Z}[X]/(X-X^2)$. More
generally, for any $n\in \mathbb{N}^*$ and any bijetion without cycles betwen
$M$ and $M^n$, the exact same method proves that
$K_0(M)=\mathbb{Z}[X]/(X-X^n)$.


  Localization phenomena in light scattering from random fluctuations of matter
fields and spacetime metric near a black hole horizon were predicted to produce
a pronounced peak in the angular distribution of second harmonic light in the
direction normal to the horizon. Therefore, detection of second harmonic
generation may become a viable observational tool to study spacetime physics
near event horizons of astronomical black holes. The light localization
phenomena near the horizon may be facilitated by the existence of surface
electromagnetic wave solutions. In this communication we study such surface
electromagnetic wave solutions near the horizon of a Schwarzschild metric
describing a black hole in vacuum. We demonstrate that such surface wave
solutions must appear when quantum gravity effects are taken into account.
Potential observational evidence of this effect are also discussed.


  Here we present graphene-based nanoelectromechanical switch with the vertical
carbon nanotube electrode via classical molecular dynamics simulations. The
carbon nanotube is grown in the center of the square hole and the graphene
covers on the hole. The potential difference between the bottom of the hole and
the graphene is applied to deflect the graphene. By performing classical
molecular dynamic simulations, we investigate the electromechanical properties
of graphene-based nanoelectro- mechanical switch with carbon nanotube
electrode, which can be switched by the externally applied force. This
simulation work explicitly demonstrated that such devices are applicable to
nanoscale sensors and quantum computing, as well as ultra-fast-response
switching devices.


  Employing one plus two-body random matrix ensembles for bosons, temperature
and entropy are calculated, using different definitions, as a function of the
two-body interaction strength \lambda for a system with 10 bosons (m=10) in
five single particle levels (N=5). It is found that in a region \lambda \sim
\lambda_t, different definitions give essentially same values for temperature
and entropy, thus defining a thermalization region. Also, (m,N) dependence of
\lambda_t has been derived. It is seen that \lambda_t is much larger than the
\lambda values where level fluctuations change from Poisson to GOE and strength
functions change from Breit-Wigner to Gaussian.


  We obtain nontrivial solutions of a $(N,q)$-Laplacian problem with a critical
Trudinger-Moser nonlinearity in a bounded domain. In addition to the usual
difficulty of the loss of compactness associated with problems involving
critical nonlinearities, this problem lacks a direct sum decomposition suitable
for applying the classical linking theorem. We show that every Palais-Smale
sequence at a level below a certain energy threshold admits a subsequence that
converges weakly to a nontrivial critical point of the variational functional.
Then we prove an abstract critical point theorem based on a cohomological index
and use it to construct a minimax level below this threshold.


  A review is given on the recently proposed idea that air showers with energy
$> 10^{20}$ eV beyond the Greisen-Zatsepin-Kuz'min cut-off may be due to
neutrinos having acquired a strong interaction at these energies, as suggested
by the Dualized Standard Model. Such a hypothesis is shown to be consistent
with the so far known facts. Further, by linking the astrophysical puzzle of
post-GZK air showers through electric-magnetic duality to the problem of
fermion generations in particle physics, one obtains on the one hand estimates
for the rates of some flavour-changing neutral current decays which are
accessible to experiments being planned, and on the other direct tests on the
hypothesis performable by new air shower detectors such as Auger. The
suggestion does not exclude other explanations for post-GZK showers given in
the literature. However, we diagree with a recent paper by Burdman, Halzen and
Gandhi which sweepingly claimed to have excluded nearly all explanations by new
particle physics including ours.


  This is a review of the Quantum Chromodynamics Cosmological Phase
Transitions, the Quark-Gluon Plasma, the production of heavy quark states via
p-p collisions and RHIC (Relativistic Heavy Ion Collisions) using the mixed
hybrid theory for the $\Psi(2S)$ and $\Upsilon(3S)$ states; and the possible
detection of the Quark-Gluon Plasma via heavy quark production using RHIC.
Recent research on fragmentation for the production of D mesons is reviewed, as
is future theoretical and experimental research on the Collins and Sivers
fragmentation functions for pions produced in polarized p-p collisions.


  Valuation networks have been proposed as graphical representations of
valuation-based systems (VBSs). The VBS framework is able to capture many
uncertainty calculi including probability theory, Dempster-Shafer's
belief-function theory, Spohn's epistemic belief theory, and Zadeh's
possibility theory. In this paper, we show how valuation networks encode
conditional independence relations. For the probabilistic case, the class of
probability models encoded by valuation networks includes undirected graph
models, directed acyclic graph models, directed balloon graph models, and
recursive causal graph models.


  Virtual Reality is used successfully to treat people for regular phobias. A
new challenge is to develop Virtual Reality Exposure Training for social
skills. Virtual actors in such systems have to show appropriate social behavior
including emotions, gaze, and keeping distance. The behavior must be realistic
and real-time. Current approaches consist of four steps: 1) trainee social
signal detection, 2) cognitive-affective interpretation, 3) determination of
the appropriate bodily responses, and 4) actuation. The "cognitive" detour of
such approaches does not match the directness of human bodily reflexes and
causes unrealistic responses and delay. Instead, we propose virtual reflexes as
concurrent sensory-motor processes to control virtual actors. Here we present a
virtual reflexes architecture, explain how emotion and cognitive modulation are
embedded, detail its workings, and give an example description of an aggression
training application.


  In this article, we study local Sobolev-Poincar\'e imbedding domains. The
main result reads as below. \begin{enumerate} \item for $1\leq p\leq n$, a
bounded uniform domain is also a local Sobolev-Poincar\'e imbedding domain of
order $p$; conversely a local Sobolev-Poincar\'e imbedding domain of order $p$
is locally linearly connected $(LLC)$. A uniform domain is $(LLC)$. Conversely,
with some very weak connecting assumption, a $(LLC)$ domain is uniform. \item
for $n<p<\fz$, a bounded domain is a local Sobolev-Poincar\'e imbedding domain
of order $p$ if and only if it is an $\alpha$-cigar domain for
$\alpha=(p-n)/(p-1)$. Hence, a domain is a local Sobolev-Poincar\'e imbedding
domain of oder $p$ if and only if it is a (global) Sobolev-Poincar\'e imbedding
domain. \end{enumerate}


  Convexity and quasiconvexity are two properties that capture the concept of
diversification for risk measures. Between the two, there is natural
quasiconvexity, an old but not so well-known property weaker than convexity but
stronger than quasiconvexity. A detailed discussion on natural quasiconvexity
is still missing and this paper aims to fill this gap in the setting of
conditional risk measures. We relate natural quasiconvexity to additively
decomposable sums. The notion of convexity index, defined in 1980s for
finite-dimensional vector spaces, plays a crucial role in the discussion of
decomposable sums. We propose a general treatment of convexity index in
topological vector spaces and use it to study naturally quasiconvex risk
measures. We prove that natural quasiconvexity and convexity are equivalent for
conditional risk measures on $L^p$ spaces, $p \geq 1$, under mild continuity
and locality conditions. Finally, we discuss an alternative notion of locality
with respect to an orthonormal basis in $L^2$.


  We demonstrate a die-level balanced homodyne detector with a high
quantum-to-classical noise clearance of 19.1dB over 3GHz bandwidth. We evaluate
this receiver as a high-quality entropy source for random number generation at
>10Gb/s and prove the functional integration of a classical 10Gb/s duobinary
communication channel.


  We model the formation of the Galactic stellar halo via the accretion of
satellite galaxies onto a time-dependent semi-cosmological galactic potential.
Our goal is to characterize the substructure left by these accretion events in
a close manner to what may be possible with the {\it Gaia} mission. We have
created a synthetic {\it Gaia} Solar Neighbourhood catalogue by convolving the
6D phase-space coordinates of stellar particles from our disrupted satellites
with the latest estimates of the {\it Gaia} measurement errors, and included
realistic background contamination due to the Galactic disc(s) and bulge. We
find that, even after accounting for the expected observational errors, the
resulting phase-space is full of substructure. We are able to successfully
isolate roughly 50% of the different satellites contributing to the `Solar
Neighbourhood' by applying the Mean-Shift clustering algorithm in
energy-angular momentum space. Furthermore, a Fourier analysis of the space of
orbital frequencies allows us to obtain accurate estimates of time since
accretion for approximately 30% of the recovered satellites.


  Seidel and Smith introduced the graded fixed-point symplectic Khovanov
cohomology group Kh_{symp,inv}(K) for a knot K inside S^{3}, as well as a
spectral sequence converging to the Heegaard Floer homology-hat group for the
connected sum of the double branched cover with a copy of S^{2}xS^{1}. The
E^{1}-page of this spectral sequence is isomorphic to a factor of
Kh_{symp,inv}(K). Seidel and Smith proved that Kh_{symp,inv} is a knot
invariant. We show here that the higher pages of their spectral sequence are
knot invariants also.


  We consider the distributed version of the Multiple Knapsack Problem (MKP),
where $m$ items are to be distributed amongst $n$ processors, each with a
knapsack. We propose different distributed approximation algorithms with a
tradeoff between time and message complexities. The algorithms are based on the
greedy approach of assigning the best item to the knapsack with the largest
capacity. These algorithms obtain a solution with a bound of $\frac{1}{n+1}$
times the optimum solution, with either $\mathcal{O}\left(m\log n\right)$ time
and $\mathcal{O}\left(m n\right)$ messages, or $\mathcal{O}\left(m\right)$ time
and $\mathcal{O}\left(mn^{2}\right)$ messages.


  This paper deals with global asymptotic stability of prolongations of flows
induced by specific vector fields and their prolongations. The method used is
based on various estimates of the flows.


  The projective unitary group PU(n) is the quotient of the unitary group U(n)
by its center. We compute the integral cohomology ring of PU(n) using explicit
constructed generators.


  A reanalysis of the fluorine abundance in three Galactic AGB carbon stars (TX
Psc, AQ Sgr and R Scl) has been performed from the molecular HF (1-0) R9 line
at 2.3358 $\mu$m. High-resolution (R$\sim 50000$) and high signal to noise
spectra obtained with the CRIRES spectrograph and the VLT telescope or from the
NOAO archive (for TX Psc) have been used. Our abundance analysis uses the
latest generation of MARCS model atmospheres for cool carbon rich stars. Using
spectral synthesis in LTE we derive for these stars fluorine abundances that
are systematically lower by $\sim 0.8$ dex in average with respect to the sole
previous estimates by Jorissen, Smith & Lambert (1992). The possible reasons of
this discrepancy are explored. We conclude that the difference may rely on the
blending with C-bearing molecules (CN and C$_2$) that were not properly taken
into account in the former study. The new F abundances are in better agreement
with the prediction of full network stellar models of low mass AGB stars. These
models also reproduce the $s$-process elements distribution in the sampled
stars. This result, if confirmed in a larger sample of AGB stars, might
alleviate the current difficulty to explain the largest [F/O] ratios found by
Jorissen et al. In particular, it may not be necessary to search for
alternative nuclear chains affecting the production of F in AGB stars.


  Recently there have been discussions about which complex metrics should be
allowable in quantum gravity. These discussions assumed that the matter fields
were real valued. We make the observation that for compactified solutions it
makes sense to demand convergence of the theory's path integral in the
higher-dimensional parent theory. Upon compactification this allows for more
general matter configurations in the lower-dimensional theory, in particular it
allows for complex scalar fields, with a bound on their imaginary parts.
Similar considerations apply to metric rescalings in the presence of higher
curvature corrections. We illustrate this effect with the example of the
no--boundary proposal, in which scalar fields are typically required to take
complex values. We find that complex no-boundary solutions exist, and satisfy
the derived bound, if the potential is sufficiently flat. For instance, for a
compactification from $D$ dimensions, the bound on the imaginary part
$\textrm{Im}\,(\phi)$ of the internal volume modulus reads $V_{,\phi}/V <
\sqrt{\frac{D-4}{D-2}}/3\sqrt{2}.$ This leads to a mild tension with swampland
conjectures.


  In formal language theory, one of the most fundamental tools, known as
pumping lemmas, is extremely useful for regular and context-free languages.
However, there are natural properties for which the pumping lemmas are of
little use. One of such examples concerns a notion of advice, which depends
only on the size of an underlying input. A standard pumping lemma encounters
difficulty in proving that a given language is not regular in the presence of
advice. We develop its substitution, called a swapping lemma for regular
languages, to demonstrate the non-regularity of a target language with advice.
For context-free languages, we also present a similar form of swapping lemma,
which serves as a technical tool to show that certain languages are not
context-free with advice.


  Homomorphic encryption (HE) is a promising technique used for
privacy-preserving computation. Since HE schemes only support primitive
polynomial operations, homomorphic evaluation of polynomial approximations for
non-polynomial functions plays an important role in privacy-preserving machine
learning. In this paper, we introduce a simple solution to approximating any
functions, which might be overmissed by researchers: just using the neural
networks for regressions. By searching decent superparameters, neural networks
can achieve near-optimal computation depth for a given function with fixed
precision, thereby reducing the modulus consumed.
  There are three main reasons why we choose neural networks for homomorphic
evaluation of polynomial approximations. Firstly, neural networks with
polynomial activation functions can be used to approximate whatever functions
are needed in an encrypted state. This means that we can compute by one unified
process for any polynomial approximation, such as that of Sigmoid or of ReLU.
Secondly, by carefully finding an appropriate architecture, neural networks can
efficiently evaluate a polynomial using near-optimal multiplicative depth,
which would consume less modulus and therefore employ less ciphertext
refreshing. Finally, as popular tools, model neural networks have many
well-studied techniques that can conveniently serve our solution.
  Experiments showed that our method can be used for approximation of various
functions. We exploit our method to the evaluation of the Sigmoid function on
large intervals $[-30, +30]$, $[-50, +50]$, and $[-70, +70]$, respectively.


  We make use of recent estimates for the parameters of the Milky Way's halo
globular clusters and study the influence of the galactic bar on the dynamics
of these clusters by computing their orbits. We use both an axisymmetric and
non-axisymmetric galactic potentials, which include the rotating elongated
bar/bulge structure. We account for observational errors both in the positions
and in the velocities of the globular clusters and explore the influence of the
bar on cluster's evolution. This is contained in the angular momentum-total
energy plane, (Lz,E), which is widely exploited as an indicator of the groups
of globular clusters that originated from the same accretion event. Particular
attention is devoted to the Gaia-Sausage/Enceladus and Pontus structures
identified recently as two independent accretion events. Our study shows that
it is not possible to identify GSE and Pontus as different merger events.


  The scalars in vector multiplets of N=2 supersymmetric theories in 4
dimensions exhibit `special Kaehler geometry', related to duality symmetries,
due to their coupling to the vectors. In the literature there is some confusion
on the definition of special geometry. We show equivalences of some definitions
and give examples which show that earlier definitions are not equivalent, and
are not sufficient to restrict the Kaehler metric to one that occurs in N=2
supersymmetry. We treat the rigid as well as the local supersymmetry case. The
connection is made to moduli spaces of Riemann surfaces and Calabi-Yau 3-folds.
The conditions for the existence of a prepotential translate to a condition on
the choice of canonical basis of cycles.


  Recent studies have shown that the medium-Earth orbit (MEO) region of the
Global Navigation Satellite Systems is permeated by a devious network of
lunisolar secular resonances, which can interact to produce chaotic and
diffusive motions. The precarious state of the four navigation constellations,
perched on the threshold of instability, makes it understandable why all past
efforts to define stable graveyard orbits, especially in the case of Galileo,
were bound to fail; the region is far too complex to allow of an adoption of
the simple geosynchronous disposal strategy. We retrace one such recent
attempt, funded by ESA's General Studies Programme in the frame of the GreenOPS
initiative, that uses a systematic parametric approach and the straightforward
maximum-eccentricity method to identify long-term stable regions, suitable for
graveyards, as well as large-scale excursions in eccentricity, which can be
used for post-mission deorbiting of constellation satellites. We then apply our
new results on the stunningly rich dynamical structure of the MEO region toward
the analysis of these disposal strategies for Galileo, and discuss the
practical implications of resonances and chaos in this regime. We outline how
the identification of the hyperbolic and elliptic fixed points of the
resonances near Galileo can lead to explicit criteria for defining optimal
disposal strategies.


  Personalized medicine seeks to identify the causal effect of treatment for a
particular patient as opposed to a clinical population at large. Most
investigators estimate such personalized treatment effects by regressing the
outcome of a randomized clinical trial (RCT) on patient covariates. The
realized value of the outcome may however lie far from the conditional
expectation. We therefore introduce a method called Dirac Delta Regression
(DDR) that estimates the entire conditional density from RCT data in order to
visualize the probabilities across all possible outcome values. DDR transforms
the outcome into a set of asymptotically Dirac delta distributions and then
estimates the density using non-linear regression. The algorithm can identify
significant differences in patient-specific outcomes even when no population
level effect exists. Moreover, DDR outperforms state-of-the-art algorithms in
conditional density estimation by a large margin even in the small sample
regime. An R package is available at https://github.com/ericstrobl/DDR.


  TOTEM is an LHC experiment dedicated to forward hadronic physics. In this
contribution, two main parts of its physics programme - proton-proton elastic
scattering and total cross-section - are discussed. The analysis procedures are
outlined and their status is reviewed.


  From the analysis of the quantum and relativistic properties of the particles
it results the unified quantum-relativistic dynamics of the physical reality
(Universe).


  We introduce LaGTran, a novel framework that utilizes text supervision to
guide robust transfer of discriminative knowledge from labeled source to
unlabeled target data with domain gaps. While unsupervised adaptation methods
have been established to address this problem, they show limitations in
handling challenging domain shifts due to their exclusive operation within the
pixel-space. Motivated by our observation that semantically richer text
modality has more favorable transfer properties, we devise a transfer mechanism
to use a source-trained text-classifier to generate predictions on the target
text descriptions, and utilize these predictions as supervision for the
corresponding images. Our approach driven by language guidance is surprisingly
easy and simple, yet significantly outperforms all prior approaches on
challenging datasets like GeoNet and DomainNet, validating its extreme
effectiveness. To further extend the scope of our study beyond images, we
introduce a new benchmark called Ego2Exo to study ego-exo transfer in videos
and find that our language-aided approach LaGTran yields significant gains in
this highly challenging and non-trivial transfer setting. Code, models, and
proposed datasets are publicly available at
https://tarun005.github.io/lagtran/.


  We studied in detail the low temperature/high magnetic field phases of
URu$_{2}$Si$_{2}$ single crystals with specific heat, magnetocaloric effect,
and magnetoresistance in magnetic fields up to 45 T. Data obtained down to 0.5
K, and extrapolated to T = 0, show a suppression of the hidden order phase at
H$_{o}$(0) = 35.9 $\pm$ 0.35 T and the appearance of a new phase for magnetic
fields in excess of H$_{1}$(0) = 36.1 $\pm$ 0.35 T observed \textit{only} at
temperatures lower than 6 K. In turn, complete suppression of this high field
state is attained at a critical magnetic field H$_{2}$(0) = 39.7 $\pm$ 0.35 T.
No phase transitions are observed above 40 T. We discuss our results in the
context of itinerant vs. localized \textit{f}-electron behavior and consider
the implications for the hidden order phase.


  With the explosive 3D data growth, the urgency of utilizing zero-shot
learning to facilitate data labeling becomes evident. Recently, methods
transferring language or language-image pre-training models like Contrastive
Language-Image Pre-training (CLIP) to 3D vision have made significant progress
in the 3D zero-shot classification task. These methods primarily focus on 3D
object classification with an aligned pose; such a setting is, however, rather
restrictive, which overlooks the recognition of 3D objects with open poses
typically encountered in real-world scenarios, such as an overturned chair or a
lying teddy bear. To this end, we propose a more realistic and challenging
scenario named open-pose 3D zero-shot classification, focusing on the
recognition of 3D objects regardless of their orientation. First, we revisit
the current research on 3D zero-shot classification, and propose two benchmark
datasets specifically designed for the open-pose setting. We empirically
validate many of the most popular methods in the proposed open-pose benchmark.
Our investigations reveal that most current 3D zero-shot classification models
suffer from poor performance, indicating a substantial exploration room towards
the new direction. Furthermore, we study a concise pipeline with an iterative
angle refinement mechanism that automatically optimizes one ideal angle to
classify these open-pose 3D objects. In particular, to make validation more
compelling and not just limited to existing CLIP-based methods, we also pioneer
the exploration of knowledge transfer based on Diffusion models. While the
proposed solutions can serve as a new benchmark for open-pose 3D zero-shot
classification, we discuss the complexities and challenges of this scenario
that remain for further research development. The code is available publicly at
https://github.com/weiguangzhao/Diff-OP3D.


  Reviews statistical methods for 1- and 2-dimensional analysis of cosmic ray
data including recent Bayesian analyses.


  We identify two families of ten-point Feynman diagrams that generalize the
elliptic double box, and show that they can be expressed in terms of the same
class of elliptic multiple polylogarithms to all loop orders. Interestingly,
one of these families can also be written as a dlog form. For both families of
diagrams, we provide new 2l-fold integral representations that are linearly
reducible in all but one variable and that make the above properties manifest.
We illustrate the simplicity of this integral representation by directly
integrating the three-loop representative of both families of diagrams. These
families also satisfy a pair of second-order differential equations, making
them ideal examples on which to develop bootstrap techniques involving elliptic
symbol letters at high loop orders.


  We describe the measurement of the beam profiles and window functions for the
Atacama Cosmology Telescope (ACT), which operated from 2007 to 2010 with
kilo-pixel bolometer arrays centered at 148, 218, and 277 GHz. Maps of Saturn
are used to measure the beam shape in each array and for each season of
observations. Radial profiles are transformed to Fourier space in a way that
preserves the spatial correlations in the beam uncertainty, to derive window
functions relevant for angular power spectrum analysis. Several corrections are
applied to the resulting beam transforms, including an empirical correction
measured from the final CMB survey maps to account for the effects of mild
pointing variation and alignment errors. Observations of Uranus made regularly
throughout each observing season are used to measure the effects of atmospheric
opacity and to monitor deviations in telescope focus over the season. Using the
WMAP-based calibration of the ACT maps to the CMB blackbody, we obtain precise
measurements of the brightness temperatures of the Uranus and Saturn disks at
effective frequencies of 149 and 219 GHz. For Uranus we obtain thermodynamic
brightness temperatures T_U^{149} = 106.7 \pm 2.2 K and T_U^{219} = 100.1 \pm
3.1 K. For Saturn, we model the effects of the ring opacity and emission using
a simple model and obtain resulting (unobscured) disk temperatures of T_S^{149}
= 137.3 \pm 3.2 K and T_S^{219} = 137.3 \pm 4.7 K.


  The decay of a passive scalar in a three-dimensional chaotic flow is studied
using high-resolution numerical simulations. The (volume-preserving) flow
considered is a three-dimensional extension of the randomised alternating sine
flow employed extensively in studies of mixing in two dimensions. It is used to
show that theoretical predictions for two-dimensional flows with small
diffusivity carry over to three dimensions even though the stretching
properties differ significantly. The variance decay rate, scalar field
structure, and time evolution of statistical moments confirm that there are two
distinct regimes of scalar decay: a locally controlled regime, which applies
when the domain size is comparable to the characteristic lengthscale of the
velocity field, and a globally controlled regime, which when applies when the
domain is larger. Asymptotic predictions for the variance decay rate in both
regimes show excellent agreement with the numerical results. Consideration of
both the forward flow and its time reverse makes it possible to compare the
scalar evolution in flows with one or two expanding directions; simulations
confirm the theoretical prediction that the decay rate of the scalar is the
same in both flows, despite the very different scalar field structures.


  When we talk about databases there have always been problems concerning data
synchronization. The latter is a technique for maintaining consistency among
different copies of data (often called replicas). In general, there is no
universal solution to this problem and often a particular situation requires a
particular approach driven by specific conditions. This paper presents an
approach tackling the issue of data synchronization in a distributed
multi-branch enterprise database. The proposed solution is based on MSMQ
(Microsoft Message Queue), a mechanism for asynchronous messaging.


  The discrete Fourier transform test is a randomness test included in NIST
SP800-22. However, the variance of the test statistic is smaller than expected
and the theoretical value of the variance is not known. Hitherto, the mechanism
explaining why the former variance is smaller than expected has been
qualitatively explained based on Parseval's theorem. In this paper, we explore
this quantitatively and derive the variance using Parseval's theorem under
particular assumptions. Numerical experiments are then used to show that this
derived variance is robust.


  Reply suggestion models help users process emails and chats faster. Previous
work only studies English reply suggestion. Instead, we present MRS, a
multilingual reply suggestion dataset with ten languages. MRS can be used to
compare two families of models: 1) retrieval models that select the reply from
a fixed set and 2) generation models that produce the reply from scratch.
Therefore, MRS complements existing cross-lingual generalization benchmarks
that focus on classification and sequence labeling tasks. We build a generation
model and a retrieval model as baselines for MRS. The two models have different
strengths in the monolingual setting, and they require different strategies to
generalize across languages. MRS is publicly available at
https://github.com/zhangmozhi/mrs.