  Lithium fluorinated carbon is one of the most promising chemistries for
high-energy-density primary energy storage systems in applications where
rechargeability is not required. Though Li/CFx demonstrates high energy density
under ambient conditions, achieving such a high energy density when exposed to
subzero temperatures remains a challenge, particularly under high current
density. Here, we report a liquefied gas electrolyte with an anion-pair
solvation structure based on dimethyl ether with a low melting point and low
viscosity, leading to high ionic conductivity between a wide temperature range.
Besides that, through systematic X-ray photoelectron spectroscopy integrated
with transmission electron microscopy characterizations, we evaluate the
interface of CFx for low-temperature performance. We conclude that the fast
transport and anion-pairing solvation structure of the electrolyte bring about
reduced charge transfer resistance at low temperatures, which resulted in
significantly enhanced performance of Li/CFx cells. Utilizing 50 mg/cm2 loading
electrodes, the Li/CFx still displayed 1530 Wh/kg at reduced temperature. This
work provides insights into the electrolyte design that may overcome the
operational limits of batteries in extreme environments.


  The exponential upper bounds for the convergence rate of the distribution of
restorable element with partially energized standby redundancy are founded, in
the case when all working and repair times are bounded by exponential random
variable (upper and lower), and working and repair times can by dependent. The
bounds for the convergence rate of the availability factor are estimated.
  --------------------------------------
  Polucheny ocenki sverhu dlya skorosti skhodimosti raspredeleniya sostoyaniya
vosstanavlivaemogo ehlementa s tyoplym rezervom v sluchae, kogda vremena raboty
i vosstanovleniya oboih ehlementov mogut byt' zavisimy, i oni ogranicheny
sverhu i snizu ehksponencial'nym raspredeleniem. Poluchennyj rezul'tat
pozvolyaet ocenivat' skorost' skhodimosti koehfficienta gotovnosti k
stacionarnomu znacheniyu


  The equitable coloring problem, introduced by Meyer in 1973, has received
considerable attention and research. Recently, Wu, Zhang and Li introduced the
concept of equitable $(t,k)$-tree-coloring, which can be regarded as a
generalization of proper equitable $t$-coloring. The \emph{strong equitable
vertex $k$-arboricity} of $G$, denoted by ${va_k}^\equiv(G)$, is the smallest
integer $t$ such that $G$ has an equitable $(t', k)$-tree-coloring for every
$t'\geq t$. The exact value of strong equitable vertex $k$-arboricity of
complete equipartition bipartite graph $K_{n,n}$ was studied by Wu, Zhang and
Li. In this paper, we first get a sharp upper bound of strong equitable vertex
arboricity of complete bipartite graph$K_{n,n+\ell} \ (1\leq \ell\leq n)$, that
is,
${va_2}^\equiv(K_{n,n+\ell})\leq2\left\lfloor{\frac{n+\ell+1}{3}}\right\rfloor$.
Next, we obtain a sufficient and necessary condition on an equitable
$(q,\infty)$-tree coloring of a complete equipartition tripartite graph, and
study the strong equitable vertex arboricity of forests. For a simple graph $G$
of order $n$, we show that $1\leq {va_k}^\equiv(G)\leq \lceil n/2 \rceil$.
Furthermore, graphs with
${va_k}^\equiv(G)=1,\lceil\frac{n}{2}\rceil,\lceil\frac{n}{2}\rceil-1$ are
characterized, respectively. In the end, we obtain the Nordhaus-Gaddum type
results of strong equitable vertex $k$-arboricity for general $k$.


  We present a novel approach to test for heteroscedasticity of a
non-stationary time series that is based on Gini's mean difference of
logarithmic local sample variances. In order to analyse the large sample
behaviour of our test statistic, we establish new limit theorems for
U-statistics of dependent triangular arrays. We derive the asymptotic
distribution of the test statistic under the null hypothesis of a constant
variance and show that the test is consistent against a large class of
alternatives, including multiple structural breaks in the variance. Our test is
applicable even in the case of non-stationary processes, assuming a locally
stationary mean function. The performance of the test and its comparatively low
computation time are illustrated in an extensive simulation study. As an
application, we analyse Google Trends data, monitoring the relative search
interest for the topic "global warming."


  Crumpled paper or drapery patterns are everyday examples of how elastic
sheets can respond to external forcing. In this Letter, we study experimentally
a novel sort of forcing. We consider a circular flexible plate clamped at its
center and subject to a uniform flow normal to its initial surface. As the flow
velocity is gradually increased, the plate exhibits a rich variety of bending
deformations: from a cylindrical taco-like shape, to isometric developable
cones with azimuthal periodicity two or three, to eventually a rolled-up
period-three cone. We show that this sequence of flow-induced deformations can
be qualitatively predicted by a linear analysis based on the balance between
elastic energy and pressure force work.


  Amorphous polymers are known as thermal insulators, increasing their thermal
conductivities have not been guided by fully understood physics. In this work,
we use molecular dynamics simulations to study the thermal transport mechanism
of solid polyelectrolytes, poly(acrylic acid) (PAA) and its ionized forms. The
thermal conductivity of PAA increases monotonically with the ionization
strength. Although stronger ionization induces larger Coulombic interactions,
the Coulombic interaction does not directly contribute to the thermal
conductivity enhancement. Instead, it enhances thermal transport through the
Lennard-Jones (LJ) interaction. The strong Coulombic force between the
counterion and the ionized carboxylic group shifts the LJ force to the stronger
LJ repulsive regime, which is mainly responsible for the improved thermal
conductivity. Applying a high pressure can further reduce the inter-atomic
distance and trigger the thermal transport through the LJ interaction. A
thermal conductivity of 1.09 W/m.K can be achieved at 11.2 GPa in an ionized
PAA.


  Quasiclassical generalized Weierstrass representation for highly corrugated
surfaces with slow modulation in the three-dimensional space is proposed.
Integrable deformations of such surfaces are described by the dispersionless
Veselov-Novikov hierarchy.


  The electrical conductivity and dielectric properties of Ni1.5Fe1.5O4 ferrite
has been controlled by varying the annealing temperature of the chemical routed
samples. The frequency activated conductivity obeyed Jonschers power law and
universal scaling suggested semiconductor nature. An unusual metal like state
has been revealed in the measurement temperature scale in between two
semiconductor states with different activation energy. The metal like state has
been affected by thermal annealing of the material. The analysis of electrical
impedance and modulus spectra has confirmed non-Debye dielectric relaxation
with contributions from grains and grain boundaries. The dielectric relaxation
process is thermally activated in terms of measurement temperature and
annealing temperature of the samples. The hole hopping process, due to presence
of Ni3+ ions in the present Ni rich ferrite, played a significant role in
determining the thermal activated conduction mechanism. This work has
successfully applied the technique of a combined variation of annealing
temperature and pH value during chemical reaction for tuning electrical
parameters in a wide range; for example dc limit of conductivity 10power(-4)
-10power(-12) S/cm, and unusually high activation energy 0.17-1.36 eV.


  The latest assessments of the emerging technologies for reconfigurable
intelligent surfaces (RISs) have indicated the concept's significant potential
for localization and sensing, either as individual or simultaneously realized
tasks. However, in the vast majority of those studies, the RIS state (i.e., its
position and rotation angles) is required to be known a priori. In this paper,
we address the problem of the joint three-dimensional (3D) localization of a
hybrid RIS (HRIS) and a user. The most cost- and power-efficient hybrid version
of an RIS is equipped with a single reception radio-frequency chain and
meta-atoms capable of simultaneous reconfigurable reflection and sensing. This
dual functionality is controlled by adjustable power splitters embedded at each
hybrid meta-atom. Focusing on a downlink scenario where a multi-antenna base
station transmits multicarrier signals to a user via an HRIS, we propose a
multistage approach to jointly estimate the metasurface's 3D position and 3D
rotation matrix (i.e., 6D parameter estimation) as well as the user's 3D
position. Our simulation results verify the validity of the proposed estimator
via extensive comparisons of the root-mean-square error of the state
estimations with the Cram\'{e}r-Rao lower bound (CRB), which is analytically
derived. Furthermore, it is showcased that there exists an optimal hybrid
reconfigurable intelligent surface (HRIS) power splitting ratio for the desired
multi-parameter estimation problem. We also study the robustness of the
proposed method in the presence of scattering points in the wireless
propagation environment.


  Sixteen pulsars have been discovered so far in blind searches of photons
collected with the Large Area Telescope on the Fermi Gamma-ray Space Telescope.
We here report the discovery of radio pulsations from two of them. PSR
J1741-2054, with period P=413ms, was detected in archival Parkes telescope data
and subsequently has been detected at the Green Bank Telescope (GBT). Its
received flux varies greatly due to interstellar scintillation and it has a
very small dispersion measure of DM=4.7pc/cc, implying a distance of ~0.4kpc
and possibly the smallest luminosity of any known radio pulsar. At this
distance, for isotropic emission, its gamma-ray luminosity above 0.1GeV
corresponds to 25% of the spin-down luminosity of dE/dt=9.4e33erg/s. The
gamma-ray profile occupies 1/3 of pulse phase and has three closely-spaced
peaks with the first peak lagging the radio pulse by delta=0.29P. We have also
identified a soft Swift source that is the likely X-ray counterpart. In many
respects PSR J1741-2054 resembles the Geminga pulsar. The second source, PSR
J2032+4127, was detected at the GBT. It has P=143ms, and its DM=115pc/cc
suggests a distance of ~3.6kpc, but we consider it likely that it is located
within the Cyg OB2 stellar association at half that distance. The radio
emission is nearly 100% linearly polarized, and the main radio peak precedes by
delta=0.15P the first of two narrow gamma-ray peaks that are separated by
Delta=0.50P. Faint, diffuse X-ray emission in a Chandra image is possibly its
pulsar wind nebula. PSR J2032+4127 likely accounts for the EGRET source 3EG
J2033+4118, while its pulsar wind is responsible for the formerly unidentified
HEGRA source TeV J2032+4130.


  Numerous problems of analysis (real and complex) and geometry (analytic,
algebraic, Diophantine e.a.) can be reduced to calculation of the ``number of
solutions'' of systems of equations, defined by algebraic equalities and
differential equations with algebraic right hand sides (both ordinary and
Pfaffian). In the purely algebraic context the paradigm is given by the
B\'ezout theorem: the number of isolated solutions of a system of polynomial
equations of degree $\le d$ in the $n$-dimensional space does not exceed $d^n$,
the bound polynomial in $d$ and exponential in $n$. This bound is optimal: if
we count solutions properly (i.e., with multiplicities, including complex
solutions and solutions on the infinite hyperplane), then the equality holds.
  This paradigm can be generalized for the transcendental case as described
above. It turns out some counting problems admit similar bounds depending only
on the degrees and dimensions, whereas other can be treated only locally, i.e.,
admit bounds for the number of solutions in some domains of limited size. There
is only one class of counting problems, which admits global bounds, but besides
the degree and dimension, the answer depends on the height of the corresponding
Pfaffian system.
  The unifying feature for these results is the core fact that lies at the
heart of their proofs. This fact can be regarded as a variety of distant
generalizations of the Rolle theorem known from the undergraduate calculus,
claiming that between any two roots of a univariate differentiable function on
a segment must lie a root of its derivative. We discuss generalizations of the
Rolle theorem for vector-valued and complex analytic functions (none of them
straightforward) and for germs of holomorphic maps.


  In this paper, we derive a microscopic master equation for a pair of
XY-coupled two-level systems interacting with the same memoryless reservoir. In
particular, we apply this master equation to the case of a pair of two-level
atoms in free space where we can clearly contrast the predictions made with the
microscopic master equation obtained here and the phenomenological approaches
where the atom-atom coupling is included just \textit{a posteriori}, i.e, not
taking into account in the derivation of the open system equation of motion. We
show, for instance, that the phenomenological approach fails completely in the
assessment of the role played by the symmetric and antisymmetric decay
channels. As a consequence, the predictions related to collective effects such
as superradiance, for instance, are misleading in the phenomenological
approach. We also obtain the fluorescence spectrum using the microscopic model
developed here.


  Let $C$ be a cone in a locally convex Hausdorff topological vector space $X$
containing $0$. We show that there exists a (essentially unique) nonempty
family $\mathscr{K}$ of nonempty subsets of the topological dual $X^\prime$
such that $$ C=\{x \in X: \forall K \in \mathscr{K}, \exists f \in K, \,\, f(x)
\ge 0\}. $$ Then, we identify the additional properties on the family
$\mathscr{K}$ which characterize, among others, closed convex cones, open
convex cones, closed cones, and convex cones. For instance, if $X$ is a Banach
space, then $C$ is a closed cone if and only if the family $\mathscr{K}$ can be
chosen with nonempty convex compact sets.
  These representations provide abstract versions of several recent results in
decision theory and give us the proper framework to obtain new ones. This
allows us to characterize preorders which satisfy the independence axiom over
certain probability measures, answering an open question in
[Econometrica~\textbf{87} (2019), no. 3, 933--980].


  We analyse the triviality-quantum decoherence of Euclidean quantum
chromodynamics in the gauge invariant sector in the presence of an external
U(infinite)-flavor constant charged white noise reservoir and producing thus a
proof for confinement in the Color-charge QCD through the modeling of the QCD
vacuum by constant gauge fields


  We investigate the ability of current and third-generation gravitational wave
(GW) detectors to determine the delay time distribution (DTD) of binary neutron
stars (BNS) through a direct measurement of the BNS merger rate as a function
of redshift. We assume that the DTD follows a power law distribution with a
slope $\Gamma$ and a minimum merger time $t_{\rm min}$, and also allow the
overall BNS formation efficiency per unit stellar mass to vary. By convolving
the DTD and mass efficiency with the cosmic star formation history, and then
with the GW detector capabilities, we explore two relevant regimes. First, for
the current generation of GW detectors, which are only sensitive to the local
universe, but can lead to precise redshift determinations via the
identification of electromagnetic counterparts and host galaxies, we show that
the DTD parameters are strongly degenerate with the unknown mass efficiency and
therefore cannot be determined uniquely. Second, for third-generation detectors
such as Einstein Telescope (ET) and Cosmic Explorer (CE), which will detect BNS
mergers at cosmological distances, but with a redshift uncertainty inherent to
GW-only detections ($\delta(z)/z\approx 0.1z$), we show that the DTD and mass
efficiency can be well-constrained to better than 10\% with a year of
observations. This long-term approach to determining the DTD through a direct
mapping of the BNS merger redshift distribution will be supplemented by more
near term studies of the DTD through the properties of BNS merger host galaxies
at $z\approx 0$ (Safarzadeh & Berger 2019).


  We define the continuous modeling property for first-order structures and
show that a first-order structure has the continuous modelling property if and
only if its age has the embedding Ramsey property. We use generalized
indiscernible sequences in continuous logic to study and characterize
$n$-dependence for continuous theories and first-order hyperdefinable sets in
terms of the collapse of indiscernible sequences.


  In present paper, we investigate the multifractality signatures in hourly
time series extracted from CoRoT spacecraft database. Our analysis is intended
to highlight the possibility that astrophysical time series can be members of a
particular class of complex and dynamic processes which require several
photometric variability diagnostics to characterize their structural and
topological properties. To achieve this goal, we search for contributions due
to nonlinear temporal correlation and effects caused by heavier tails than the
Gaussian distribution, using a detrending moving average algorithm for
one-dimensional multifractal signals (MFDMA). We observe that the correlation
structure is the main source of multifractality, while heavy-tailed
distribution plays a minor role in generating the multifractal effects. Our
work also reveals that rotation period of stars is inherently scaled by degree
of multifractality. As a result, analyzing the multifractal degree of referred
series, we uncover an evolution of multifractality from shorter to larger
periods.


  For the sake of complete theoretical research of atmospheric refraction, the
atmospheric refraction under the condition of lower angles of elevation is
still worthy to be analyzed and explored. In some engineering applications, the
objects with larger zenith distance must be observed sometimes. Carrying out
observational research of the atmospheric refraction at lower angles of
elevation has an important significance. It has been considered difficult to
measure the atmospheric refraction at lower angles of elevation. A new idea for
determining atmospheric refraction by utilizing differential measurement with
double fields of view is proposed. Taking the observational principle of
HIPPARCOS satellite as a reference, a schematic prototype with double fields of
view was developed. In August of 2013, experimental observations were carried
out and the atmospheric refractions at lower angles of elevation can be
obtained by the schematic prototype. The measured value of the atmospheric
refraction at the zenith distance of 78.8 degree is $240.23"\pm0.27"$, and the
feasibility of differential measurement of atmospheric refraction with double
fields of view was justified. The limitations of the schematic prototype such
as inadequate ability of gathering light, lack of accurate meteorological data
recording and lower automatic level of observation and data processing were
also pointed out, which need to be improved in subsequent work.


  We review the fundamental results of a new cosmological model, based on
conformal gravity, and apply them to the analysis of the early data of the
Pioneer anomaly. We show that our conformal cosmology can naturally explain the
anomalous acceleration of the Pioneer 10 and 11 spacecraft, in terms of a local
blueshift region extending around the solar system and therefore affecting the
frequencies of the navigational radio signals exchanged between Earth and the
spacecraft. On the contrary, conformal gravity corrections alone would not be
able to account for dynamical effects of such magnitude to be capable of
producing the observed Pioneer acceleration. By using our model, we explain the
numerical coincidence between the value of the anomalous acceleration and the
Hubble constant at the present epoch and also confirm our previous
determination of the cosmological parameters gamma ~ 10^(-28) cm^(-1) and delta
~ 10^(-4) - 10^(-5). New Pioneer data are expected to be publicly available in
the near future, which might enable more precise evaluations of these
parameters.


  Most existing vision-language pre-training methods focus on understanding
tasks and use BERT-like objectives (masked language modeling and image-text
matching) during pretraining. Although they perform well in many understanding
downstream tasks, e.g., visual question answering, image-text retrieval and
visual entailment, they do not possess the ability to generate. To tackle this
problem, we propose Unified multimodal pre-training for both Vision-Language
understanding and generation (UniVL). The proposed UniVL is capable of handling
both understanding tasks and generative tasks. We augment existing pretraining
paradigms that only use random masks with causal masks, i.e., triangular masks
that mask out future tokens, such that the pre-trained models can have
autoregressive generation abilities by design. We formulate several previous
understanding tasks as a text generation task and propose to use prompt-based
method for fine-tuning on different downstream tasks. Our experiments show that
there is a trade-off between understanding tasks and generation tasks while
using the same model, and a feasible way to improve both tasks is to use more
data. Our UniVL framework attains comparable performance to recent
vision-language pre-training methods on both understanding tasks and generation
tasks. Moreover, we demostrate that prompt-based finetuning is more
data-efficient - it outperforms discriminative methods in few-shot scenarios.


  With a widespread growth in the potential applications of Wireless Sensor
Networks, the need for reliable security mechanisms for them has increased
manifold. This paper proposes a scheme, Privacy for Police Patrols (PPP), to
provide secure data aggregation that relies on multilevel routing. Privacy
factors have been identified and implemented. Aggregates are prepared and the
summary of information is gathered and stored in a repository. The above
defined approaches are integrated in police patrol applications and preliminary
results are obtained.


  Understanding how the magnetic activity of low-mass stars depends on their
fundamental parameters is an important goal of stellar astrophysics. Previous
studies show that activity levels are largely determined by the stellar Rossby
number which is defined as the rotation period divided by the convective
turnover time. However, we currently have little information on the role that
chemical composition plays. In this work, we investigate how metallicity
affects magnetic activity using photometric variability as an activity proxy.
Similarly to other proxies, we demonstrate that the amplitude of photometric
variability is well parameterised by the Rossby number, although in a more
complex way. We also show that variability amplitude and metallicity are
generally positively correlated. This trend can be understood in terms of the
effect that metallicity has on stellar structure and, hence, the convective
turnover time (or, equivalently, the Rossby number). Lastly, we demonstrate
that the metallicity dependence of photometric variability results in a
rotation period detection bias whereby the periods of metal-rich stars are more
easily recovered for stars of a given mass.


  In this paper, we study the Liouville-type property for smooth solutions to
the steady 3D tropical climate model. We prove that if a smooth solution
$(u,v,\theta)$ satisfies $u \in L^3 (\mathbb{R}^3)$, $v \in L^2
(\mathbb{R}^3)$, and $\nabla \theta \in L^2 (\mathbb{R}^3)$, then $u=v=0$ and
$\theta$ is constant, which improves the previous result, Theorem 1.3 (Math.
Methods Appl. Sci. 44, 2021) by Ding and Wu.


  An adaptive analogue of the Yu. E. Nesterov method for variational
inequalities with a strongly monotone operator is proposed. Some estimates are
obtained for the parameters determining the quality of the solution of the
variational inequality depending on the number of iterations.


  We model the cooling of hybrid neutron stars combining a microscopic nuclear
equation of state in the Brueckner-Hartree-Fock approach with different quark
models. We then analyze the neutron star cooling curves predicted by the
different models and single out the preferred ones. We find that the
possibility of neutron p-wave pairing can be excluded in our scenario.


  The accuracy of LDA + U total energies is tested by predicting the high
pressure behavior of rocksalt-structured FeO, including metal-insulator and
magnetic transitions, equation of state, and lattice strain, as functions of
the Coulomb repulsion U. For U = 4.6 eV, 6.0 eV and 8.0 eV, two self-consistent
solutions, one with rhombohedral and one with monoclinic electronic symmetry,
are found, and for U = 4.6 eV the monoclinic solution becomes more stable than
the rhombohedral solution at 100 GPa, leading to an insulator-metal transition.
With increasing U, metallization occurs at higher pressures. Despite the
insulator-metal transition, the high-spin magnetic state persists to pressures
greater than 300 GPa for U > 0. The LDA + U method also gives improved
agreement with experiments for ground state properties as compared to LDA and
GGA methods.


  A sample of some relevant developments that have taken place during the last
twenty years in classical and quantum tomography are displayed. We will present
a general conceptual framework that provides a simple unifying mathematical
picture for all of them and, as an effective use of it, three subjects have
been chosen that offer a wide panorama of the scope of classical and quantum
tomography: tomography along lines and submanifolds, coherent state tomography
and tomography in the abstract algebraic setting of quantum systems.


  Many of today's probabilistic programming languages (PPLs) have brittle
inference performance: the performance of the underlying inference algorithm is
very sensitive to the precise way in which the probabilistic program is
written. A standard way of addressing this challenge in traditional programming
languages is via program optimizations, which seek to unburden the programmer
from writing low-level performant code, freeing them to work at a higher-level
of abstraction. The arsenal of applicable program optimizations for PPLs to
choose from is scarce in comparison to traditional programs; few of today's
PPLs offer significant forms of automated program optimization. In this work we
develop a new family of program optimizations specific to discrete-valued
knowledge compilation based PPLs. We identify a particular form of program
structure unique to these PPLs that tangibly affects exact inference
performance in these programs: redundant random variables -- variables with
repeated parameters and inconsistent path conditions. We develop a new program
analysis and associated optimization called flip-hoisting that identifies these
redundancies and optimizes them into a single random variable. We show that
flip-hoisting yields inference speedups of up to 60% on applications of
probabilistic programs such as Bayesian networks and probabilistic
verification.


  This note concerns the general formulation by Preiss and Uher of Kestelman's
influential result pertaining the change of variable, or substitution, formula
for the Riemann integral.


  As invisibility cloaking has recently become experimental reality, it is
interesting to explore ways to reveal remaining imperfections. In essence, the
idea of most invisibility cloaks is to recover the optical path lengths without
an object (to be made invisible) by a suitable arrangement around that object.
Optical path length is proportional to the time of flight of a light ray or to
the optical phase accumulated by a light wave. Thus, time-of-flight images
provide a direct and intuitive tool for probing imperfections. Indeed, recent
phase-sensitive experiments on the carpet cloak have already made early steps
in this direction. In the macroscopic world, time-of-flight images could be
measured directly by light detection and ranging (LIDAR). Here, we show
calculated time-of-flight images of the conformal Gaussian carpet cloak, the
conformal grating cloak, the cylindrical free-space cloak, and of the invisible
sphere. All results are obtained by using a ray-velocity equation of motion
derived from Fermat's principle.


  To investigate the barrier effect of grain boundaries on the propagation of
avalanche-like plasticity at the atomic-scale, we perform three-dimensional
molecular dynamics simulations by using simplified polycrystal models including
symmetric-tilt grain boundaries. The cut-offs of stress-drop distributions
following power-law distributions decrease as the size of the crystal grains
decreases. We show that some deformation avalanches are confined by grain
boundaries; on the other hand, unignorable avalanches penetrate all the grain
boundaries included in the models. The blocking probability that one grain
boundary hinders this system-spanning avalanche is evaluated by using an
elemental probabilistic model.


  In order to improve the precision of domain wall dynamics measurements, we
develop a coplanar waveguide-based setup where the domain wall motion should be
triggered by pulses of magnetic field. The latter are produced by the Oersted
field of the waveguide as a current pulse travels toward its termination, where
it is dissipated. Our objective is to eliminate a source of bias in domain wall
speed estimation while optimizing the field amplitude. Here, we present
implementations of this concept for magnetic force microscopy (MFM) and
synchrotron-based investigation.


  We numerically study the jamming transition in particulate systems with
attraction by investigating their mechanical response at zero temperature. We
find three regimes of mechanical behavior separated by two critical
transitions--connectivity and rigidity percolation. The transitions belong to
different universality classes than their lattice counterparts, due to force
balance constraints. We also find that these transitions are unchanged at low
temperatures and resemble gelation transitions in experiments on colloidal and
silica gels.


  In the present paper we study two sequences of real numbers associated to a
symplectic diffeomorphism: the uniform norm of the differential of its n-th
iteration and the word length of its n-th iteration. In the latter case we
assume that our diffeomorphism lies in a finitely generated group of symplectic
diffeomorphisms. We find lower bounds on the growth rates of these sequences in
a number of situations. These bounds depend on the symplectic geometry of the
manifold rather than on the specific choice of a diffeomorphism. They are
obtained by using recent results of Schwarz on Floer homology. Applications to
the Zimmer program are presented. We prove non-existence of certain non-linear
symplectic representations for finitely generated groups including some
lattices and Baumslag-Solitar groups.


  In this paper, we propose algorithms for signal detection in large-scale
multiuser {\em spatial modulation multiple-input multiple-output (SM-MIMO)}
systems. In large-scale SM-MIMO, each user is equipped with multiple transmit
antennas (e.g., 2 or 4 antennas) but only one transmit RF chain, and the base
station (BS) is equipped with tens to hundreds of (e.g., 128) receive antennas.
In SM-MIMO, in a given channel use, each user activates any one of its multiple
transmit antennas and the index of the activated antenna conveys information
bits in addition to the information bits conveyed through conventional
modulation symbols (e.g., QAM). We propose two different algorithms for
detection of large-scale SM-MIMO signals at the BS; one is based on {\em
message passing} and the other is based on {\em local search}. The proposed
algorithms are shown to achieve very good performance and scale well. Also, for
the same spectral efficiency, multiuser SM-MIMO outperforms conventional
multiuser MIMO (recently being referred to as massive MIMO) by several dBs; for
e.g., with 16 users, 128 antennas at the BS and 4 bpcu per user, SM-MIMO with 4
transmit antennas per user and 4-QAM outperforms massive MIMO with 1 transmit
antenna per user and 16-QAM by about 4 to 5 dB at $10^{-3}$ uncoded BER. The
SNR advantage of SM-MIMO over massive MIMO can be attributed to the following
reasons: (i) because of the spatial index bits, SM-MIMO can use a lower-order
QAM alphabet compared to that in massive MIMO to achieve the same spectral
efficiency, and (ii) for the same spectral efficiency and QAM size, massive
MIMO will need more spatial streams per user which leads to increased spatial
interference.


  Clean predictions are presented for all the spin-averaged heavy-light meson
spectroscopies. A new symmetry is identified wherein the energy eigenstates
have a universal dependence on both the light and heavy quark masses. This
universality is used in an efficient analysis of these mesons within the QCD
string/flux tube picture. Unique predictions for all the D, D_s, B, and B_s
type mesons in terms of just four measured quantities.


  Consider the random matrix obtained from the adjacency matrix of a random
d-regular graph by multiplying every entry by a random sign. The largest
eigenvalue converges, after proper scaling, to the Tracy--Widom distribution.


  We study entanglement entropy on the fuzzy sphere. We calculate it in a
scalar field theory on the fuzzy sphere, which is given by a matrix model. We
use a method that is based on the replica method and applicable to interacting
fields as well as free fields. For free fields, we obtain the results
consistent with the previous study, which serves as a test of the validity of
the method. For interacting fields, we perform Monte Carlo simulations at
strong coupling and see a novel behavior of entanglement entropy.


  The classical sharp threshold theorem of Friedgut and Kalai (1996) asserts
that any symmetric monotone function $f:\{0,1\}^{n}\to\{0,1\}$ exhibits a sharp
threshold phenomenon. This means that the expectation of $f$ with respect to
the biased measure $\mu_{p}$ increases rapidly from 0 to 1 as $p$ increases.
  In this paper we present `robust' versions of the theorem, which assert that
it holds also if the function is `almost' monotone, and admits a much weaker
notion of symmetry. Unlike the original proof of the theorem which relies on
hypercontractivity, our proof relies on a `regularity' lemma (of the class of
Szemer\'edi's regularity lemma and its generalizations) and on the `invariance
principle' of Mossel, O'Donnell, and Oleszkiewicz which allows (under certain
conditions) replacing functions on the cube $\{0,1\}^{n}$ with functions on
Gaussian random variables.
  The hypergraph removal lemma of Gowers (2007) and independently of Nagle,
R\"odl, Schacht, and Skokan (2006) says that if a $k$-uniform hypergraph on $n$
vertices contains few copies of a fixed hypergraph $H$, then it can be made
$H$-free by removing few of its edges. While this settles the `hypergraph
removal problem' in the case where $k$ and $H$ are fixed, the result is
meaningless when $k$ is large (e.g. $k>\log\log\log n$).
  Using our robust version of the Friedgut-Kalai Theorem, we obtain a
hypergraph removal lemma that holds for $k$ up to linear in $n$ for a large
class of hypergraphs. These contain all the hypergraphs such that both their
number of edges and the sizes of the intersections of pairs of their edges are
upper bounded by some constant.


  The thermodynamical quantities and response functions are useful to describe
the particle production in heavy-ion collisions as they reveal crucial
information about the produced system. While the study of isothermal
compressibility provides an inference about the viscosity of the medium, speed
of sound helps in understanding the equation of state. With an aim towards
understanding the system produced in the heavy-ion collision, we have made an
attempt to study isothermal compressibility and speed of sound as function of
charged particle multiplicity in heavy-ion collisions at $\sqrt{s_{NN}}$ =
$2.76$ TeV, $5.02$ TeV, and $5.44$ TeV using unified formalism.


  We study unsupervised multi-document summarization evaluation metrics, which
require neither human-written reference summaries nor human annotations (e.g.
preferences, ratings, etc.). We propose SUPERT, which rates the quality of a
summary by measuring its semantic similarity with a pseudo reference summary,
i.e. selected salient sentences from the source documents, using contextualized
embeddings and soft token alignment techniques. Compared to the
state-of-the-art unsupervised evaluation metrics, SUPERT correlates better with
human ratings by 18-39%. Furthermore, we use SUPERT as rewards to guide a
neural-based reinforcement learning summarizer, yielding favorable performance
compared to the state-of-the-art unsupervised summarizers. All source code is
available at https://github.com/yg211/acl20-ref-free-eval.


  Transformers have advanced the field of natural language processing (NLP) on
a variety of important tasks. At the cornerstone of the Transformer
architecture is the multi-head attention (MHA) mechanism which models pairwise
interactions between the elements of the sequence. Despite its massive success,
the current framework ignores interactions among different heads, leading to
the problem that many of the heads are redundant in practice, which greatly
wastes the capacity of the model. To improve parameter efficiency, we
re-formulate the MHA as a latent variable model from a probabilistic
perspective. We present cascaded head-colliding attention (CODA) which
explicitly models the interactions between attention heads through a
hierarchical variational distribution. We conduct extensive experiments and
demonstrate that CODA outperforms the transformer baseline, by $0.6$ perplexity
on \texttt{Wikitext-103} in language modeling, and by $0.6$ BLEU on
\texttt{WMT14 EN-DE} in machine translation, due to its improvements on the
parameter efficiency.\footnote{Our implementation is publicly available at
\url{https://github.com/LZhengisme/CODA}.}


  The rapid solidification of a binary mixture in the region of the interface
velocities $V$ close to the diffusion speed in the bulk of the liquid phase
$V_D$ is considered within the framework of the local nonequilibrium approach.
In this high-speed region the derivation of the analytical expression for the
response function "temperature-velocity" representing kinetic phase diagram is
given without using the concept of the equilibrium phase diagram.
  The modes of movement of the interface both without and with the drag effect
are analyzed. It is shown that the drag effect can be accompanied by a local
interface temperature maximum at $V = V_D$.


  Accurate and efficient pedestrian detection is crucial for the intelligent
transportation system regarding pedestrian safety and mobility, e.g., Advanced
Driver Assistance Systems, and smart pedestrian crosswalk systems. Among all
pedestrian detection methods, vision-based detection method is demonstrated to
be the most effective in previous studies. However, the existing vision-based
pedestrian detection algorithms still have two limitations that restrict their
implementations, those being real-time performance as well as the resistance to
the impacts of environmental factors, e.g., low illumination conditions. To
address these issues, this study proposes a lightweight Illumination and
Temperature-aware Multispectral Network (IT-MN) for accurate and efficient
pedestrian detection. The proposed IT-MN is an efficient one-stage detector.
For accommodating the impacts of environmental factors and enhancing the
sensing accuracy, thermal image data is fused by the proposed IT-MN with visual
images to enrich useful information when visual image quality is limited. In
addition, an innovative and effective late fusion strategy is also developed to
optimize the image fusion performance. To make the proposed model implementable
for edge computing, the model quantization is applied to reduce the model size
by 75% while shortening the inference time significantly. The proposed
algorithm is evaluated by comparing with the selected state-of-the-art
algorithms using a public dataset collected by in-vehicle cameras. The results
show that the proposed algorithm achieves a low miss rate and inference time at
14.19% and 0.03 seconds per image pair on GPU. Besides, the quantized IT-MN
achieves an inference time of 0.21 seconds per image pair on the edge device,
which also demonstrates the potentiality of deploying the proposed model on
edge devices as a highly efficient pedestrian detection algorithm.


  We study the large scale behaviour of a population consisting of two types
which evolve in dimension d = 1, 2 according to a spatial Lambda- Fleming-Viot
process subject to random time-independent selection. If one of the two types
is rare compared to the other, we prove that its evolution can be approximated
by a super-Brownian motion in a random (and singular) environment. Without the
sparsity assumption, a diffusion approximation leads to a Fisher-KPP equation
in a random potential. The proofs build on two-scale Schauder estimates and
semidiscrete approximations of the Anderson Hamiltonian.


  We study the massive scalar field equation $\Box_g \phi = m^2 \phi$ on a
stationary and spherically symmetric black hole $g$ (including in particular
the Schwarzschild and Reissner--Nordstr\"om black holes in the full
sub-extremal range) for solutions $\phi$ projected on a fixed spherical
harmonic. Our problem involves the scattering of an attractive long-range
potential (Coulomb-like) and thus cannot be treated perturbatively.
  We prove precise (point-wise) asymptotic tails of the form $t^{-5/6} f(t)+
O(t^{-1+\delta})$, where $f(t)$ is an explicit oscillating profile. Our
asymptotics appear to be the first rigorous decay result for a massive scalar
field on a black hole. Establishing these asymptotics is also an important step
in retrieving the assumptions used in work of the third author regarding the
interior of dynamical black holes and Strong Cosmic Censorship.


  The interplay between the image and comment on a social media post is one of
high importance for understanding its overall message. Recent strides in
multimodal embedding models, namely CLIP, have provided an avenue forward in
relating image and text. However the current training regime for CLIP models is
insufficient for matching content found on social media, regardless of site or
language. Current CLIP training data is based on what we call ``descriptive''
text: text in which an image is merely described. This is something rarely seen
on social media, where the vast majority of text content is ``commentative'' in
nature. The captions provide commentary and broader context related to the
image, rather than describing what is in it. Current CLIP models perform poorly
on retrieval tasks where image-caption pairs display a commentative
relationship. Closing this gap would be beneficial for several important
application areas related to social media. For instance, it would allow groups
focused on Open-Source Intelligence Operations (OSINT) to further aid efforts
during disaster events, such as the ongoing Russian invasion of Ukraine, by
easily exposing data to non-technical users for discovery and analysis. In
order to close this gap we demonstrate that training contrastive image-text
encoders on explicitly commentative pairs results in large improvements in
retrieval results, with the results extending across a variety of non-English
languages.


  Grazing Incidence X-ray Diffraction (GIXD) studies of monolayers of
biomolecules at the air-water interface give quantitative information of
in-plane packing, coherence lengths of the ordered diffracting crystalline
domains and the orientation of hydrocarbon chains. Rheo-GIXD measurements revel
quantitative changes in the monolayer under shear. Here we report GIXD studies
of monolayers of Alamethicin peptide, DPPC lipid and their mixtures at the
air-water interface under the application of steady shear stresses. The
Alamethicin monolayer and the mixed monolayer show flow jamming transition. On
the other hand, pure DPPC monolayer under the constant stress flows steadily
with a notable enhancement of area/molecule, coherence length, and the tilt
angle with increasing stress, suggesting fusion of nanocrystallites during
flow. The DPPC-Alamethicin mixed monolayer shows no significant change in the
area/DPPC molecule or in the DPPC chain tilt but the coherence length of both
phases (DPPC and Alamethicin) increases suggesting that the crystallites of
individual phases are merging to bigger size promoting more separation of
phases in the system during flow. Our results show that Rheo-GIXD has the
potential to explore in-situ molecular structural changes under rheological
conditions for a diverse range of confined biomolecules at the interfaces.


  This paper presents E5, a family of state-of-the-art text embeddings that
transfer well to a wide range of tasks. The model is trained in a contrastive
manner with weak supervision signals from our curated large-scale text pair
dataset (called CCPairs). E5 can be readily used as a general-purpose embedding
model for any tasks requiring a single-vector representation of texts such as
retrieval, clustering, and classification, achieving strong performance in both
zero-shot and fine-tuned settings. We conduct extensive evaluations on 56
datasets from the BEIR and MTEB benchmarks. For zero-shot settings, E5 is the
first model that outperforms the strong BM25 baseline on the BEIR retrieval
benchmark without using any labeled data. When fine-tuned, E5 obtains the best
results on the MTEB benchmark, beating existing embedding models with 40x more
parameters.


  Changing the form of the star cluster mass function (CMF) can effectively
change the upper end of the stellar initial mass function. The yields of from
supernovae are very sensitive to the mass of the progenitor star. We show that
by changing the parameters of the CMF, it is possible to change the yields of
oxygen and magnesium by a factor of $\sim 1.5$ and of metals in general by a
factor of $\sim 1.8$.


  The relationship between the star formation rate and surface density of
neutral gas within the disk of M33 is examined with new imaging observations of
CO J=1-0 emission gathered with the FCRAO 14m telescope and IRAS HiRes images
of the 60 micron and 100 micron emission. The Schmidt law, Sigma_SFR ~
Sigma_gas^n, is constructed using radial profiles of the HI 21cm, CO, and far
infrared emission. A strong correlation is identified between the star
formation rate and molecular gas surface density. This suggests that the
condensation of giant molecular clouds is the limiting step to star formation
within the M33 disk. The corresponding molecular Schmidt index, n_{mol}, is
1.36 +/- 0.08. The star formation rate has a steep dependence on total mass gas
surface density, (Sigma_{HI}+Sigma_{H_2}), owing to the shallow radial profile
of the atomic gas which dominates the total gas surface density for most radii.
The disk pressure of the gas is shown to play a prominent role in regulating
the molecular gas fraction in M33.


  Weighted model counting (WMC) consists of computing the weighted sum of all
satisfying assignments of a propositional formula. WMC is well-known to be
#P-hard for exact solving, but admits a fully polynomial randomized
approximation scheme (FPRAS) when restricted to DNF structures. In this work,
we study weighted model integration, a generalization of weighted model
counting which involves real variables in addition to propositional variables,
and pose the following question: Does weighted model integration on DNF
structures admit an FPRAS? Building on classical results from approximate
volume computation and approximate weighted model counting, we show that
weighted model integration on DNF structures can indeed be approximated for a
class of weight functions. Our approximation algorithm is based on three
subroutines, each of which can be a weak (i.e., approximate), or a strong
(i.e., exact) oracle, and in all cases, comes along with accuracy guarantees.
We experimentally verify our approach over randomly generated DNF instances of
varying sizes, and show that our algorithm scales to large problem instances,
involving up to 1K variables, which are currently out of reach for existing,
general-purpose weighted model integration solvers.


  Charge exchange (CE) reactions offer a major opportunity to excite nuclear
isovector modes, providing clues about the nuclear interaction in the medium.
Moreover, double charge exchange (DCE) reactions are proving to be a tempting
tool to access nuclear transition matrix elements (NME) related to double
beta-decay processes. Through a multi-channel experimental analysis and a
consistent theoretical approach of the $^{116}$Cd($^{20}$Ne,$^{20}$F)$^{116}$In
single charge exchange (SCE) reaction at 306 MeV, we aim at disentangling from
the experimental cross section the contribution of the competing mechanisms,
associated with second or higher order sequential transfer and inelastic
processes. We measured excitation energy spectra and absolute cross sections
for elastic + inelastic, one-proton transfer and SCE channels, using the MAGNEX
large acceptance magnetic spectrometer to detect the ejectiles. For the first
two channels, we also extracted the experimental cross section angular
distributions. The experimental data are compared with theoretical predictions
obtained by performing two-step distorted wave Born approximation and coupled
reaction channel calculations. We employ spectroscopic amplitudes for
single-particle transitions derived within a large-scale shell model approach
and different optical potentials for modeling the initial and the final state
interactions. The present study significantly mitigates the possible model
dependence existing in the description of these complex reaction mechanisms,
thanks to the reproduction of several channels at once. In particular, our work
demonstrates that the two-step transfer mechanisms produce a non negligible
contribution to the total cross section of the
$^{116}$Cd($^{20}$Ne,$^{20}$F)$^{116}$In reaction channel, although a relevant
fraction is still missing, being ascribable to the direct SCE mechanism, which
is not addressed here.


  With the rapid development of big data and computing devices, low-latency
automatic trading platforms based on real-time information acquisition have
become the main components of the stock trading market, so the topic of
quantitative trading has received widespread attention. And for non-strongly
efficient trading markets, human emotions and expectations always dominate
market trends and trading decisions. Therefore, this paper starts from the
theory of emotion, taking East Money as an example, crawling user comment
titles data from its corresponding stock bar and performing data cleaning.
Subsequently, a natural language processing model BERT was constructed, and the
BERT model was fine-tuned using existing annotated data sets. The experimental
results show that the fine-tuned model has different degrees of performance
improvement compared to the original model and the baseline model.
Subsequently, based on the above model, the user comment data crawled is
labeled with emotional polarity, and the obtained label information is combined
with the Alpha191 model to participate in regression, and significant
regression results are obtained. Subsequently, the regression model is used to
predict the average price change for the next five days, and use it as a signal
to guide automatic trading. The experimental results show that the
incorporation of emotional factors increased the return rate by 73.8\% compared
to the baseline during the trading period, and by 32.41\% compared to the
original alpha191 model. Finally, we discuss the advantages and disadvantages
of incorporating emotional factors into quantitative trading, and give possible
directions for further research in the future.


  In this paper we complete the proof of the X=K conjecture, that for every
family of nonexceptional affine algebras, the graded multiplicities of tensor
products of symmetric power Kirillov-Reshetikhin modules known as
one-dimensional sums, have a large rank stable limit X that has a simple
expression (called the K-polynomial) as nonnegative integer combination of
Kostka-Foulkes polynomials. We consider a subfamily of Lusztig's q-analogues of
weight multiplicity which we call stable KL polynomials and denote by KL. We
give a type-independent proof that K=KL. This proves that X=KL: the family of
stable one-dimensional sums coincides with family of stable KL polynomials. Our
result generalizes the theorem of Nakayashiki and Yamada which establishes the
above equality in the case of one-dimensional sums of affine type A and the
Lusztig q-analogue of type A, where both are Kostka-Foulkes polynomials.


  Video scene parsing in the wild with diverse scenarios is a challenging and
great significance task, especially with the rapid development of automatic
driving technique. The dataset Video Scene Parsing in the Wild(VSPW) contains
well-trimmed long-temporal, dense annotation and high resolution clips. Based
on VSPW, we design a Temporal Bilateral Network with Vision Transformer. We
first design a spatial path with convolutions to generate low level features
which can preserve the spatial information. Meanwhile, a context path with
vision transformer is employed to obtain sufficient context information.
Furthermore, a temporal context module is designed to harness the inter-frames
contextual information. Finally, the proposed method can achieve the mean
intersection over union(mIoU) of 49.85\% for the VSPW2021 Challenge test
dataset.


  The cross-correlation between the ROSAT all-sky survey (~150000 sources) and
the Tycho mission (~1000000 stars) catalogs has selected about 14000 stellar
X-ray sources (RasTyc sample, Guillout et al. 1999). About 200-300 stars have
been spectroscopically observed at high resolution both in the Halpha and LiI
lambda-6708 regions with Elodie and Aurelie spectrographs of the OHP
(Observatoire de l'Haute Provence, France). The aim was to classify the RasTyc
star sample in terms of age and chromospheric activity level and to detect
eventual binary systems. Photometric and spectroscopic follow-up observations
of a RasTyc sub-sample composed of particularly interesting objects (binaries
and very young stars) has been performed with the 91-cm telescope of the
Catania Astrophysical Observatory. In this work we present some results of this
monitoring. In particular, we have obtained good radial velocity curves and
solved for the orbits of three SB2 and three SB1 spectroscopic binaries. In
addition, for near all sources we have detected a photometric modulation
ascribable to photospheric surface inhomogeneities and chromospheric Halpha
line variation.


  In 2024, we will hold a research paper competition (the third Human
Understanding AI Paper Challenge) for the research and development of
artificial intelligence technologies to understand human daily life. This
document introduces the datasets that will be provided to participants in the
competition, and summarizes the issues to consider in data processing and
learning model development.


  In this article, we mainly study the products of commutator ideals of
Lie-admissible algebras such as Novikov algebras, bicommutative algebras, and
assosymmetric algebras. More precisely, we first study the properties of the
lower central chains for Novikov algebras and bicommutative algebras. Then we
show that for every Lie nilpotent Novikov algebra or Lie nilpotent
bicommutative algebra $\mathcal{A}$, the ideal of $\mathcal{A}$ generated by
the set $\{ab - ba\mid a, b\in \mathcal{A}\}$ is nilpotent. Finally, we study
properties of the lower central chains for assosymmetric algebras, study the
products of commutator ideals of assosymmetric algebras and show that the
products of commutator ideals have a similar property as that for associative
algebras.


  The adaptation of numerical wind wave models to the local time-spatial
conditions is a problem that can be solved by using various calibration
techniques. However, the obtained sets of physical parameters become over-tuned
to specific events if there is a lack of observations. In this paper, we
propose a robust evolutionary calibration approach that allows to build the
stochastic ensemble of perturbed models and use it to achieve the trade-off
between quality and robustness of the target model. The implemented robust
ensemble-based evolutionary calibration (REBEC) approach was compared to the
baseline SPEA2 algorithm in a set of experiments with the SWAN wind wave model
configuration for the Kara Sea domain. Provided metrics for the set of
scenarios confirm the effectiveness of the REBEC approach for the majority of
calibration scenarios.


  We present the first numerical relativity waveforms for binary black hole
mergers produced using spectral methods that show both the displacement and the
spin memory effects. Explicitly, we use the SXS Collaboration's $\texttt{SpEC}$
code to run a Cauchy evolution of a binary black hole merger and then extract
the gravitational wave strain using $\texttt{SpECTRE}$'s version of a
Cauchy-characteristic extraction. We find that we can accurately resolve the
strain's traditional $m=0$ memory modes and some of the $m\not=0$ oscillatory
memory modes that have previously only been theorized. We also perform a
separate calculation of the memory using equations for the Bondi-Metzner-Sachs
charges as well as the energy and angular momentum fluxes at asymptotic
infinity. Our new calculation uses only the gravitational wave strain and two
of the Weyl scalars at infinity. Also, this computation shows that the memory
modes can be understood as a combination of a memory signal throughout the
binary's inspiral and merger phases, and a quasinormal mode signal near the
ringdown phase. Additionally, we find that the magnetic memory, up to numerical
error, is indeed zero as previously conjectured. Lastly, we find that
signal-to-noise ratios of memory for LIGO, the Einstein Telescope (ET), and the
Laser Interferometer Space Antenna (LISA) with these new waveforms and new
memory calculation are larger than previous expectations based on
post-Newtonian or Minimal Waveform models.


  The life of Ernst Ising and the steps to solving the model named after him
are reported in parallel. Wilhelm Lenz suggested his student Ernst Ising to
explain the existence of ferromagnetism on the basis of his publication in
1920. The result, published in 1925 was disappointing, because only the one
dimensional case could be solved with a negative result about the absence of
ferromagnetism. Wolfgang Pauli who was an assistant of Lenz in Hamburg
published in the same year his 'nonclassical ambiguity', later identified as
the spin of the electron, and the exclusion principle. He was the first - at
the Solvay Conference in 1930 - to present the Hamiltonian of the Ising model
as we know it today.
  Meanwhile Ising had left university research and due to the political
situation in 1938 had to leave Germany and fled to Luxemburg. This went in hand
with damaging the network of researchers dealing with the problem of
ferromagnetism and more generally with phase transitions and statistical
physics. In 1944, the year when Luxemburg was liberated by the American troops
and Ising and his family was rescued, Lars Onsager presented a solution of the
two-dimensional case. In 1952 Chen-Ning Yang solved the problem of Ising's
thesis in two dimensions; one year later Ising became the US citizenship. The
following development showed, that the model turned out to be a highway to
modern physics concepts applicable also in other fields, although the final
exact solution in three dimensions has not yet been reached.


  Calculations using the (exact) fermionic functional renormalization group are
usually truncated at the second order of the corresponding hierarchy of coupled
ordinary differential equations. We present a method for the systematic
determination of higher order vertex functions. This method is applied to a
study of transport properties of various correlated quantum dot systems. It is
shown that for large Coulomb correlations higher order vertex functions cannot
be neglected, and a static approximation is insufficient.


  Cryptographic scheme proposed by Bennett, Brassard, and Mermin [Phys. Rev.
Lett. {\bf 68}, 557 (1992)] is reformulated in a version involving two
polarizing Mach-Zehnder interferometers. Such a form, although physically
equivalent to the original one, makes its security explicit, suggestive and
easy to explain to non-experts.


  Multilayer perceptrons (MLPs) have been successfully used to represent 3D
shapes implicitly and compactly, by mapping 3D coordinates to the corresponding
signed distance values or occupancy values. In this paper, we propose a novel
positional encoding scheme, called Spline Positional Encoding, to map the input
coordinates to a high dimensional space before passing them to MLPs, for
helping to recover 3D signed distance fields with fine-scale geometric details
from unorganized 3D point clouds. We verified the superiority of our approach
over other positional encoding schemes on tasks of 3D shape reconstruction from
input point clouds and shape space learning. The efficacy of our approach
extended to image reconstruction is also demonstrated and evaluated.


  In the present paper we study subsolutions of the Dirac equation. The known
examples of such subsolutions are the Weyl neutrino and the Majorana neutrino.
It is shown that the Dirac equation for a massive non-interacting particle
admits another decomposition into two separate equations. It is suggested that
such particles can be identified with weakly interacting massive particles,
other than neutrinos and of non-exotic nature, which can be candidates for dark
matter.


  Towards the end of the BeppoSAX mission, the only operated Narrow Field
Instrument was the PDS, which covers the energy range 13-300 keV. Two blazars,
1ES 0507-040 (z=0.304) and PKS 1229-021 (z= 1.045), suitably located in the sky
for BeppoSAX pointing, were observed for about 2 days each in Spring 2002 with
the PDS and detected up to 50 keV. 1ES0507-040 had been already observed by the
BeppoSAX NFIs at an earlier epoch for a much shorter time. We have re-analysed
those data as well, and identified a possible contamination problem in the PDS
spectrum. We present our recent PDS data on both sources, combined with the
previous BeppoSAX data and with non-simultaneous observations at other
frequencies. The derived Spectral Energy Distributions allow us to discuss the
origin of the high energy component.


  Large language models (LLMs)-based image captioning has the capability of
describing objects not explicitly observed in training data; yet novel objects
occur frequently, necessitating the requirement of sustaining up-to-date object
knowledge for open-world comprehension. Instead of relying on large amounts of
data and/or scaling up network parameters, we introduce a highly effective
retrieval-augmented image captioning method that prompts LLMs with object names
retrieved from External Visual--name memory (EVCap). We build ever-changing
object knowledge memory using objects' visuals and names, enabling us to (i)
update the memory at a minimal cost and (ii) effortlessly augment LLMs with
retrieved object names by utilizing a lightweight and fast-to-train model. Our
model, which was trained only on the COCO dataset, can adapt to out-of-domain
without requiring additional fine-tuning or re-training. Our experiments
conducted on benchmarks and synthetic commonsense-violating data show that
EVCap, with only 3.97M trainable parameters, exhibits superior performance
compared to other methods based on frozen pre-trained LLMs. Its performance is
also competitive to specialist SOTAs that require extensive training.


  We demonstrate a compact, robust, and highly efficient source of
polarization-entangled photons, based on linear bi-directional down-conversion
in a novel 'folded sandwich' configuration. Bi-directionally pumping a single
periodically poled KTiOPO$_4$ (ppKTP) crystal with a 405-nm laser diode, we
generate entangled photon pairs at the non-degenerate wavelengths 784 nm
(signal) and 839 nm (idler), and achieve an unprecedented detection rate of
11.8 kcps for 10.4 $\mu$W of pump power (1.1 million pairs / mW), in a 2.9-nm
bandwidth, while maintaining a very high two-photon entanglement quality, with
a Bell-state fidelity of $99.3\pm0.3$%.


  We present a mathematical proof of theoretical predictions made by Arguin and
Saint-Aubin, as well as by Bauer, Bernard, and Kytola, about certain non-local
observables for the two-dimensional Ising model at criticality by combining
Smirnov's recent proof of the fact that the scaling limit of critical Ising
interfaces can be described by chordal SLE(3) with Kozdron and Lawler's
configurational measure on mutually avoiding chordal SLE paths. As an extension
of this result, we also compute the probability that an SLE(k) path, k in
(0,4], and a Brownian motion excursion do not intersect.


  In this paper, we show a connection between a certain online low-congestion
routing problem and an online prediction of graph labeling. More specifically,
we prove that if there exists a routing scheme that guarantees a congestion of
$\alpha$ on any edge, there exists an online prediction algorithm with mistake
bound $\alpha$ times the cut size, which is the size of the cut induced by the
label partitioning of graph vertices. With previous known bound of $O(\log n)$
for $\alpha$ for the routing problem on trees with $n$ vertices, we obtain an
improved prediction algorithm for graphs with high effective resistance.
  In contrast to previous approaches that move the graph problem into problems
in vector space using graph Laplacian and rely on the analysis of the
perceptron algorithm, our proof are purely combinatorial. Further more, our
approach directly generalizes to the case where labels are not binary.


  We discuss a hydrodynamical description of the eigenvalues of the Polyakov
line at large but finite $N_c$ for Yang-Mills theory in even and odd space-time
dimensions. The hydro-static solutions for the eigenvalue densities are shown
to interpolate between a uniform distribution in the confined phase and a
localized distribution in the de-confined phase. The resulting critical
temperatures are in overall agreement with those measured on the lattice over a
broad range of $N_c$, and are consistent with the string model results at
$N_c=\infty$. The stochastic relaxation of the eigenvalues of the Polyakov line
out of equilibrium is captured by a hydrodynamical instanton. An estimate of
the probability of formation of a Z(N$_c)$ bubble using a piece-wise sound wave
is suggested.


  MRI is an inherently slow process, which leads to long scan time for
high-resolution imaging. The speed of acquisition can be increased by ignoring
parts of the data (undersampling). Consequently, this leads to the degradation
of image quality, such as loss of resolution or introduction of image
artefacts. This work aims to reconstruct highly undersampled Cartesian or
radial MR acquisitions, with better resolution and with less to no artefact
compared to conventional techniques like compressed sensing. In recent times,
deep learning has emerged as a very important area of research and has shown
immense potential in solving inverse problems, e.g. MR image reconstruction. In
this paper, a deep learning based MR image reconstruction framework is
proposed, which includes a modified regularised version of ResNet as the
network backbone to remove artefacts from the undersampled image, followed by
data consistency steps that fusions the network output with the data already
available from undersampled k-space in order to further improve reconstruction
quality. The performance of this framework for various undersampling patterns
has also been tested, and it has been observed that the framework is robust to
deal with various sampling patterns, even when mixed together while training,
and results in very high quality reconstruction, in terms of high SSIM (highest
being 0.990$\pm$0.006 for acceleration factor of 3.5), while being compared
with the fully sampled reconstruction. It has been shown that the proposed
framework can successfully reconstruct even for an acceleration factor of 20
for Cartesian (0.968$\pm$0.005) and 17 for radially (0.962$\pm$0.012) sampled
data. Furthermore, it has been shown that the framework preserves brain
pathology during reconstruction while being trained on healthy subjects.


  Discussed in the paper is the possibility of introducing the concept of
Brownian motion of various mesoparticles in the ballistic regime. The case in
point is the effect of collisions between thermal excitations in the liquid and
the test mesoparticle (allowing to trace its position) on the thermal motion of
the latter. The standard criterion for the brownian nature of the particle
motion assumes that the inequality $l_s<R_i$ is satisfied and, consequently, a
large number of collisions with the averaged response of the mesoparticle to
such a bombardment (here $l_s$ is the mean free path of helium excitations,
$R_i$ is the effective mesoparticle radius). However, the opposite limit
$l_s>R_i$, which we refer to as the ballistic regime, is also possible. It is
characterized with the specific features in the behavior of mesoparticles. The
emphasis is made on the already available evidences indicating this behavior in
the thermal motion of helium ions.


  Formulations of magnetic monopoles in a Hilbert-space formulation of quantum
mechanics require Dirac's quantization condition of magnetic charge, which
implies a large value that can easily be ruled out for elementary particles by
standard atomic spectroscopy. However, an algebraic formulation of
non-associative quantum mechanics is mathematically consistent with fractional
magnetic charges of small values. Here, spectral properties in non-associative
quantum mechanics are derived, applied to the ground state of hydrogen with a
magnetically charged nucleus. The resulting energy leads to new strong upper
bounds for the magnetic charge of various elementary particles that can appear
as the nucleus of hydrogen-like atoms, such as the muon or the antiproton.


  Let $\Sigma_{2n}$ be the set of all partitions of the even integers from the
interval $(4,2n], n>2,$ into two odd prime parts. We select a partition from
the set $\Sigma_{2n}$ uniformly at random. Let $2G_n$ be the number partitioned
by this selection. $2G_n$ is sometimes called a Goldbach number. In [6] we
showed that $G_n/n$ converges weakly to the maximum $T$ of two random variables
which are independent copies of a uniformly distributed random variable in the
interval $(0,1)$. In this note we show that the mean and the variance of
$G_n/n$ tend to the mean $\mu_T=2/3$ and variance $\sigma_T^2=1/18$ of $T$,
respectively. Our method of proof is based on generating functions and on a
Tauberian theorem due to Hardy-Littlewood-Karamata.


  Two component model for a description of the electromagnetic nucleon
structure functions in low-x region, based on generalized vector dominance and
color dipole approaches is briefly described. The model operates with the
mesons of rho-family having the mass spectrum which is equidistant on square
mass and takes into account the nondiagonal transitions in meson-nucleon
scattering. The special cut-off factors are introduced in the model, to exclude
the gamma-qq-V transitions in the case of the narrow qq-pairs. For the color
dipole part of the model the well known FKS-parameterization is used.


  Let $G$ be a graph on $n$ vertices, $p$ the order of a longest path and
$\kappa$ the connectivity of $G$. In 1989, Bauer, Broersma Li and Veldman
proved that if $G$ is a 2-connected graph with $d(x)+d(y)+d(z)\ge n+\kappa$ for
all triples $x,y,z$ of independent vertices, then $G$ is hamiltonian. In this
paper we improve this result by reducing the lower bound $n+\kappa$ to
$p+\kappa$.


  Tapered optical fibers (TOFs) are used in many areas of physics and optical
technologies ranging from coupling light into nanophotonic components to
optical sensing and amplification to interfacing quantum emitters. Here, we
study the fundamental torsional mechanical mode of the nanofiber-waist of a TOF
using laser light. We find that this oscillator features a quality factor of up
to $10^7$ and a $Qf$ product of 1 THz. We damp the thermal motion from room
temperature to 28(7) mK by means of active feedback. Our results might enable
new types of fiber-based sensors and lay the foundation for a novel hybrid
quantum optomechanical platform.


  A generalized quadrangle is a point-line incidence geometry $\mathcal{Q}$
such that: (i) any two points lie on at most one line, and (ii) given a line
$\ell$ and a point $P$ not incident with $\ell$, there is a unique point of
$\ell$ collinear with $P$. The finite Moufang generalized quadrangles were
classified by Fong and Seitz (1973), and we study a larger class of generalized
quadrangles: the \emph{antiflag-transitive} quadrangles. An antiflag of a
generalized quadrangle is a non-incident point-line pair $(P, \ell)$, and we
say that the generalized quadrangle $\mathcal{Q}$ is antiflag-transitive if the
group of collineations is transitive on the set of all antiflags. We prove that
if a finite thick generalized quadrangle $\mathcal{Q}$ is antiflag-transitive,
then $\mathcal{Q}$ is either a classical generalized quadrangle or is the
unique generalized quadrangle of order $(3,5)$ or its dual.


  In the Letter, Xu et al. reported that edge modes disappear in the expanded
structure of Wu-Hu model characterized by $\mathbb{Z}_2$ topological index,
while appear in the trivial shrunken structure, when the edge cuts through the
hexagonal unit cell. They then concluded that these edge modes are defect modes
lacking topological protection. Unfortunately, their approach is not justified,
rendering the conclusion unsolid.


  The first phase of stellar evolution in the history of the universe may be
Dark Stars, powered by dark matter heating rather than by fusion. Weakly
interacting massive particles, which are their own antiparticles, can
annihilate and provide an important heat source for the first stars in the
universe. This and the following contribution present the story of Dark Stars.
In this first part, we describe the conditions under which dark stars form in
the early universe: 1) high dark matter densities, 2) the annihilation products
get stuck inside the star, and 3) dark matter heating wins over all other
cooling or heating mechanisms.


  In light of the growing number of user privacy violations in centralized
social networks, the need to define effective platforms for decentralized
online social networks (DOSNs) is deeply felt. Interesting solutions have been
proposed in the past, which own the necessary mechanisms to allow users keeping
control over their personal information and setting the rules to regulate the
access of other users. Unfortunately, the effectiveness of this type of
solutions is severely reduced by the fact that different user communities with
a shared interest could be disconnected/separated from each other. This
translates into a reduced ability in effectively spreading data of common
interest towards all interested users, as it currently happens in centralized
social networks. In order to overcome the cited limitation, this paper proposes
a disruptive approach, which exploits the availability of a new class of
Internet of Things (IoT) devices with autonomous social behaviors and cognitive
abilities. Such devices can be leveraged as friendship intermediaries between
devices' owners who are connected to a DOSN platform and share the same
interest. We will demonstrate that clear advantages can be achieved in terms of
increased percentage of Interested Reachable Nodes (a specific measure of
Delivery Ratio) in distributed social networks among humans, when enhanced with
so called Mediator Objects adhering to the well-known social IoT (SIoT)
paradigm.


  Most secondary sources of cosmic microwave background anisotropy (radio
sources, dusty galaxies -DSFGs-, thermal Sunyaev Zel'dovich -tSZ- distortions
from hot gas, and gravitational lensing) are highly non-Gaussian. Statistics
beyond the power spectrum are therefore potentially important sources of
information about the physics of these processes. We combine data from the
Atacama Cosmology Telescope and with data from the Planck satellite (only using
Planck data in the overlapping region) to constrain the amplitudes of a set of
theoretical bispectrum templates from the tSZ effect, DSFGs, gravitational
lensing, and radio galaxies. We make a strong detection of radio galaxies
(>5$\sigma$) and have hints of non-Gaussianity arising from the tSZ effect,
DSFGs, from cross-correlations between the tSZ effect and DSFGs and from
cross-correlations among the tSZ effect, DSFGs and radio galaxies. These
results suggest that the same halos host radio sources, DSFGs, and have tSZ
signal. We present a new method to calculate the non-Gaussian contributions to
the template covariances. Using this method we find significant non-Gaussian
contributions to the variance and covariance of our templates, with templates
involving the tSZ effect most effected. Strong degeneracies exist between the
various sources at the current noise levels. In light of these degeneracies,
combined with theoretical uncertainty in the templates, these results are a
demonstration of this technique. With these caveats, we demonstrate the utility
of future bispectrum measurements by using the tSZ bispectrum measurement to
constrain a combination of the amplitude of matter fluctuations and the matter
density to be $\sigma_8 \Omega_m^{0.17}=0.65^{+0.05}_{-0.06}$. Improvements in
signal to noise from upcoming observations will enable the separation of
bispectrum components and robust constraints on cosmological parameters.


  Self-avoiding polymers in strictly two-dimensional ($d=2$) melts are
investigated by means of molecular dynamics simulation of a standard
bead-spring model with chain lengths ranging up to N=2048. % The chains adopt
compact configurations of typical size $R(N) \sim N^{\nu}$ with $\nu=1/d$. %
The precise measurement of various distributions of internal chain distances
allows a direct test of the contact exponents $\Theta_0=3/8$, $\Theta_1=1/2$
and $\Theta_2=3/4$ predicted by Duplantier. % Due to the segregation of the
chains the ratio of end-to-end distance $\Rend(N)$ and gyration radius
$\Rgyr(N)$ becomes $\Rend^2(N)/\Rgyr^2(N) \approx 5.3 < 6$ for $N \gg 100$ and
the chains are more spherical than Gaussian phantom chains. % The second
Legendre polynomial $P_2(s)$ of the bond vectors decays as $P_2(s) \sim
1/s^{1+\nu\Theta_2}$ measuring thus the return probability of the chain after
$s$ steps. % The irregular chain contours are shown to be characterized by a
perimeter length $L(N) \sim R(N)^{\dc}$ of fractal line dimension $\dc =
d-\Theta_2 =5/4$. % % In agreement with the generalized Porod scattering of
compact objects with fractal contour the Kratky representation of the
intramolecular structure factor $F(q)$ reveals a strong non-monotonous behavior
with $q^dF(q) \sim 1/(q R(N))^{\Theta_2}$ in the intermediate regime of the
wave vector $q$. This may allow to confirm the predicted contour fractality in
a real experiment.


  A rigid body, with an interior cavity entirely filled with a Navier-Stokes
liquid, moves in absence of external torques relative to the center of mass of
the coupled system body-liquid (inertial motions). The only steady-state
motions allowed are then those where the system, as a whole rigid body, rotates
uniformly around one of the central axes of inertia (permanent rotations).
Objective of this article is two-fold. On the one hand, we provide sufficient
conditions for the asymptotic, exponential stability of permanent rotations, as
well as for their instability. On the other hand, we study the asymptotic
behavior of the generic motion in the class of weak solutions and show that
there exists a time $t_0$ after that all such solutions must decay
exponentially fast to a permanent rotation. This result provides a {\em full}
and rigorous explanation of Zhukovsky's conjecture, and explains, likewise,
other interesting phenomena that are observed in both lab and numerical
experiments.


  We discuss the existence of glueball states for N=1 SYM within the
Maldacena-Nunez model. We find that for this model the existence of an area law
in the Wilson loop operator does not imply the existence of a discrete glueball
spectrum. We suggest that implementing the model with an upper hard cut-off can
amend the lack of spectrum. As a result the model can be only interpreted in
the infra-red region. A direct comparison with the lattice data allows us to
fix the scale up to where the model is sensible to describe low-energy
observables. Nevertheless, taking for granted the lattice results, the
resulting spectrum does not follow the general trends found in other
supergravity backgrounds. We further discuss the decoupling of the non-singlet
Kaluza-Klein states by analysing the associated supergravity equation of
motion. The inclusion of non-commutative effects is also analysed and we find
that leads to an enhancement on the value of the masses.


  The analysis of Current-voltage characteristic and Ampere-Gauss
characteristic for graphene with Hubbard interaction was carried out depending
on frequency of external variable field and magnetic field. The regions of the
absolute negative conductivity were found.


  Recently, an all-order conjecture for the anomalous-dimension matrix of n-jet
operators in SCET was proposed, which allows one to predict the structure of
the infrared divergences of dimensionally regularized, massless gauge-theory
scattering amplitudes with an arbitrary number of legs and loops. The
conjecture is severely constrained by soft-collinear factorization, non-abelian
exponentiation, and the behavior of amplitudes in collinear limits. Using these
constraints, a diagrammatic analysis has shown that the anomalous dimension
involves only two-parton correlators up to three loop order. The only exception
is given by a single color structure multiplying a function of conformal cross
ratios depending on the momenta of four external partons, which would have to
vanish in all two-particle collinear limits. We extend this analysis by
completing the diagrammatic analysis at four loop, and we find that additional
functions which vanish in all two-particle collinear limits may arise.


  A quintessential Inflation (QI) scenario from Mimetic Dark Matter (MDM) is
presented in this paper. This scenario, which is based on the MDM model
presented by Chamseddine and Mukhanov \cite{chams}, uses a potential that is
defined on three time intervals:the first during inflation, the second
$10^{-65}s$ after the end of inflation and the third after that. The resulting
energy density of the universe is constant during inflation, followed by that
of a matter/radiation dominated universe, and finally ends with a constant
energy density corresponding to dark energy. The scale factor has an
accelerating expansion nature during and after inflation. It will be shown how
this is still a viable scenario, even if the scale factor after inflation is
not that of a decelerating De Sitter universe.


  We show the results of two-dimensional, azimuthally symmetric simulations of
advection dominated fluid flows around black holes. We use the SPH method, and
the alpha-prescription for the viscosity, including all terms in the stress
tensor. We have performed calculations with 0.001 < alpha < 0.2 and find strong
circulation patterns in the flow, on spatial scales that depend on the
magnitude of the viscosity.


  We theoretically investigate the collisional heating of a cold atom system
subjected to time-periodic forces. We show within the Floquet framework that
this heating rate due to two-body collisions has a general semiclassical
expression $\mathcal{P}\propto \rho \sigma v_{\rm col} E_0$, depending on the
kinetic energy $E_0$ associated with the shaking, particle number density
$\rho$, elastic collision cross section $\sigma$, and an effective collisional
velocity $v_{\rm col}$ determined by the dominant energy scale in the system.
We further show that the collisional heating is suppressed by Pauli blocking in
cold fermionic systems, and by the modified density of states in systems in
lower dimensions. Our results provide an exactly solvable example and reveal
some general features of Floquet heating in interacting systems.


  By enabling control of valley degrees of freedom in transition-metal
dichalcogenides, valley-selective circular dichroism has become a key concept
in valleytronics. In this manuscript, we show that valley excitons -- bound
electron-hole pairs formed at either the K or K' valleys upon absorption of
circularly-polarized light -- are chiral quasiparticles characterized by a
finite orbital angular momentum (OAM). We further formulate an ab-initio
many-body theory of valley-selective circular dichroism and valley excitons
based on the Bethe-Salpeter equation. Beside governing the interaction with
circularly polarized light, the OAM confers excitons a finite magnetization
which manifests itself through an excitonic Zeeman splitting upon interaction
with external magnetic fields. The good agreement between our ab-initio
calculations and recent experimental measurements of the exciton Zeeman shifts
corroborate this picture.


  The (Mahler-Popken) complexity $\| n \|$ of a natural number $n$ is the
smallest number of ones that can be used via combinations of multiplication and
addition to express $n$, with parentheses arranged in such a way so as to form
legal nestings. We generalize $\| \cdot \|$ by defining $\| n \|_{m}$ as the
smallest number of possibly repeated selections from $\{ 1, 2, \ldots, m \}$
(counting repetitions), for fixed $m \in \mathbb{N}$, that can be used to
express $n$ with the same operational and bracket symbols as before. There is a
close relationship, as we explore, between $\|\cdot\|_{2}$ and lengths of
shortest addition chains for a given natural number. This illustrates how
remarkable it is that $(\| n \|_{2} : n \in \mathbb{N} )$ is not currently
included in the On-Line Encyclopedia of Integer Sequences and has, apparently,
not been studied previously. This, in turn, motivates our exploration of the
complexity function $\| \cdot\|_{2}$, in which we prove explicit upper and
lower bounds for $\|\cdot\|_{2}$ and describe some problems and further areas
of research concerning $\|\cdot\|_{2}$.


  Passive linear networks are used in a wide variety of engineering
applications, but the best studied are electrical circuits made of resistors,
inductors and capacitors. We describe a category where a morphism is a circuit
of this sort with marked input and output terminals. In this category,
composition describes the process of attaching the outputs of one circuit to
the inputs of another. We construct a functor, dubbed the "black box functor",
that takes a circuit, forgets its internal structure, and remembers only its
external behavior. Two circuits have the same external behavior if and only if
they impose same relation between currents and potentials at their terminals.
The space of these currents and potentials naturally has the structure of a
symplectic vector space, and the relation imposed by a circuit is a Lagrangian
linear relation. Thus, the black box functor goes from our category of circuits
to the category of symplectic vector spaces and Lagrangian linear relations. We
prove that this functor is We prove that this functor is symmetric monoidal and
indeed a hypergraph functor. We assume the reader is familiar with category
theory, but not with circuit theory or symplectic linear algebra.


  A Brownian motion tree (BMT) model is a Gaussian model whose associated set
of covariance matrices is linearly constrained according to common ancestry in
a phylogenetic tree. We study the complexity of inferring the maximum
likelihood (ML) estimator for a BMT model by computing its ML-degree. Our main
result is that the ML-degree of the BMT model on a star tree with $n + 1$
leaves is $2^{n+1}-2n-3$, which was previously conjectured by Am\'endola and
Zwiernik. We also prove that the ML-degree of a BMT model is independent of the
choice of the root. The proofs rely on the toric geometry of concentration
matrices in a BMT model. Toward this end, we produce a combinatorial formula
for the determinant of the concentration matrix of a BMT model, which
generalizes the Cayley-Pr\"ufer theorem to complete graphs with weights given
by a tree.


  The preliminary analyses on a multiscale model of intestinal crypt dynamics
are here presented. The model combines a morphological model, based on the
Cellular Potts Model (CPM), and a gene regulatory network model, based on Noisy
Random Boolean Networks (NRBNs). Simulations suggest that the stochastic
differentiation process is itself sufficient to ensure the general homeostasis
in the asymptotic states, as proven by several measures.


  Studies examining gender differences in introductory physics show a consensus
when it comes to a gender gap on conceptual assessments; however, the story is
not as clear when it comes to differences in gendered performance on exams.
This study examined whether gendered differences exist on midterm and final
exams in introductory physics courses and if such differences were correlated
with a gender difference in final course grades. The population for this study
included more than 10,000 students enrolled in algebra- and calculus-based
introductory physics courses between spring 2007 and spring 2019. We found a
small but statistically significant difference, with a weak effect size, in
final letter grades for only one out of four courses: algebra-based mechanics.
By looking at midterm exam grades, statistically significant differences were
noted for some exams in three out of four courses, with algebra-based
electricity and magnetism being the exception. In all statistically significant
cases, the effect size was small or weak, indicating that performance on exams
and final letter grades was not strongly dependent on gender. As an added
dimension examining gendered differences, we investigated if differences exist
when accounting for instructor gender. Additionally, a questionnaire was
administered in fall 2019 to more than 1,600 students in both introductory
sequences to explore students' perceptions of performance, class contributions,
and inclusion. We observed some differences between students' perception of
their performance and contribution when grouped by gender, but no difference on
perception of inclusion.


  The first short Chandra and XMM-Newton observations of the young and
energetic pulsar J1357-6429 provided strong indications of a tail-like
pulsar-wind nebula associated with this object, as well as pulsations of its
X-ray flux with a pulsed fraction above 50% and a thermal component dominating
at lower photon energies (below 2 keV). The elongated nebula is very compact in
size and may be interpreted as evidence for a pulsar jet. The thermal radiation
is most plausibly emitted from the entire neutron star surface of a 10 km
radius and a 1.0+/-0.1 K temperature, covered with a magnetized hydrogen
atmosphere. At higher energies the pulsar's emission is of a nonthermal
(magnetospheric) origin, with a power-law spectrum of a photon index of
1.1-1.3. This makes the X-ray properties of PSR J1357-6429 very similar to
those of the youngest pulsars J1119-6127 and Vela with a detected thermal
radiation.


  We analyze the cluster formation in a non-ergodic stochastic system as a
result of counter-flow, with the aid of an exactly solvable model. To
illustrate the clustering, a two species asymmetric simple exclusion process
with impurities on a periodic lattice is considered, where the impurity can
activate flips between the two non-conserved species. Exact analytical results,
supported by Monte Carlo simulations, show two distinct phases, free flowing
phase and clustering phase. The clustering phase is characterized by constant
density and vanishing current of the non-conserved species, whereas the free
flowing phase is identified with non-monotonic density and non-monotonic finite
current of the same. The $n$-point spatial correlation between $n$ consecutive
vacancies grows with increasing $n$ in the clustering phase, indicating the
formation of two macroscopic clusters in this phase, one of the vacancies and
the other consisting of all the particles. We define a rearrangement parameter
that permutes the ordering of particles in the initial configuration, keeping
all the input parameters fixed. This rearrangement parameter reveals the
significant effect of non-ergodicity on the onset of clustering. For a special
choice of the microscopic dynamics, we connect the present model to a system of
run and tumble particles used to model active matter, where the two species
having opposite net bias manifest the two possible run directions of the run
and tumble particles, and the impurities act as tumbling reagents that enable
the tumbling process.


  Adversarial robustness corresponds to the susceptibility of deep neural
networks to imperceptible perturbations made at test time. In the context of
image tasks, many algorithms have been proposed to make neural networks robust
to adversarial perturbations made to the input pixels. These perturbations are
typically measured in an $\ell_p$ norm. However, robustness often holds only
for the specific attack used for training. In this work we extend the above
setting to consider the problem of training of deep neural networks that can be
made simultaneously robust to perturbations applied in multiple natural
representation spaces. For the case of image data, examples include the
standard pixel representation as well as the representation in the discrete
cosine transform~(DCT) basis. We design a theoretically sound algorithm with
formal guarantees for the above problem. Furthermore, our guarantees also hold
when the goal is to require robustness with respect to multiple $\ell_p$ norm
based attacks. We then derive an efficient practical implementation and
demonstrate the effectiveness of our approach on standard datasets for image
classification.


  The centroid-based model for extractive document summarization is a simple
and fast baseline that ranks sentences based on their similarity to a centroid
vector. In this paper, we apply this ranking to possible summaries instead of
sentences and use a simple greedy algorithm to find the best summary.
Furthermore, we show possi- bilities to scale up to larger input docu- ment
collections by selecting a small num- ber of sentences from each document prior
to constructing the summary. Experiments were done on the DUC2004 dataset for
multi-document summarization. We ob- serve a higher performance over the orig-
inal model, on par with more complex state-of-the-art methods.


  In 1959, R.V. Kadison and I.M. Singer asked whether each pure state of the
algebra of bounded diagonal operators on $\ell^2$, admits a unique state
extension to $B(\ell^2)$. The positive answer was given in June 2013 by A.
Marcus, D. Spielman and N. Srivastava, who took advantage of a series of
translations of the original question, due to C. Akemann, J. Anderson, N.
Weaver,... Ultimately, the problem boils down to an estimate of the largest
zero of the expected characteristic polynomial of the sum of independent random
variables taking values in rank 1 positive matrices in the algebra of n-by-n
matrices.


  In this paper, we study the existence of solution to BSDE with quadratic
growth and unbounded terminal value. We apply a localization procedure together
with a priori bounds. As a byproduct, we apply the same method to extend a
result on BSDEs with integrable terminal condition.


  We present a novel meta-learning approach for 6D pose estimation on unknown
objects. In contrast to ``instance-level" and ``category-level" pose estimation
methods, our algorithm learns object representation in a category-agnostic way,
which endows it with strong generalization capabilities across object
categories. Specifically, we employ a neural process-based meta-learning
approach to train an encoder to capture texture and geometry of an object in a
latent representation, based on very few RGB-D images and ground-truth
keypoints. The latent representation is then used by a simultaneously
meta-trained decoder to predict the 6D pose of the object in new images.
Furthermore, we propose a novel geometry-aware decoder for the keypoint
prediction using a Graph Neural Network (GNN), which explicitly takes geometric
constraints specific to each object into consideration. To evaluate our
algorithm, extensive experiments are conducted on the \linemod dataset, and on
our new fully-annotated synthetic datasets generated from Multiple Categories
in Multiple Scenes (MCMS). Experimental results demonstrate that our model
performs well on unseen objects with very different shapes and appearances.
Remarkably, our model also shows robust performance on occluded scenes although
trained fully on data without occlusion. To our knowledge, this is the first
work exploring \textbf{cross-category level} 6D pose estimation.


  We present a formulation of the relative depth estimation from a single image
problem, as a ranking problem. By reformulating the problem this way, we were
able to utilize literature on the ranking problem, and apply the existing
knowledge to achieve better results. To this end, we have introduced a listwise
ranking loss borrowed from ranking literature, weighted ListMLE, to the
relative depth estimation problem. We have also brought a new metric which
considers pixel depth ranking accuracy, on which our method is stronger.