  Hierarchical support vector regression (HSVR) models a function from data as
a linear combination of SVR models at a range of scales, starting at a coarse
scale and moving to finer scales as the hierarchy continues. In the original
formulation of HSVR, there were no rules for choosing the depth of the model.
In this paper, we observe in a number of models a phase transition in the
training error -- the error remains relatively constant as layers are added,
until a critical scale is passed, at which point the training error drops close
to zero and remains nearly constant for added layers. We introduce a method to
predict this critical scale a priori with the prediction based on the support
of either a Fourier transform of the data or the Dynamic Mode Decomposition
(DMD) spectrum. This allows us to determine the required number of layers prior
to training any models.


  A simple method for detection of melting event in laser-heated diamond anvil
cells (DACs) is introduced. The melting is registered optically by the
formation of spherical drops of the investigated material as heated in an inert
pressure transmitting medium. Feasibility of the method is demonstrated on the
examples of metal (iron and gold) and iron oxide (Fe2O3), materials molten at
pressures over 40 GPa employing a portable laser heating system.


  Magnetic skyrmions are topologically non-trivial spin textures of potential
interest for future information storage applications, and for such purposes,
the control and understanding of single skyrmion creation is required. A scheme
is analyzed to create single N\'{e}el-type and Bloch-type skyrmions in
helimagnetic thin films utilizing the dynamical excitations induced by the
Oersted field and the spin transfer torque given by a vertically injected
spin-polarized current. A topological charge analysis using a lattice version
of the topological charge provides insight into the locally triggered
transition from a trivial to a non-trivial topological spin texture of the
N\'{e}el or Bloch type skyrmion. The topological protection of the magnetic
skyrmion is determined by the symmetric Heisenberg exchange energy. The
critical switching current density is $\sim10^{7}\thinspace\textrm{A/cm}^{2}$,
which decreases with the easy-plane type uniaxial anisotropy and thermal
fluctuations. In-plane spin polarization of the injected current performs
better than out-of-plane polarization, and it provides ultrafast switching
times (within 100 ps) and reliable switching outcomes.


  Managing large numbers of incoming bug reports and finding the most critical
issues in hardware development is time consuming, but crucial in order to
reduce development costs. In this paper, we present an approach to predict the
time to fix, the risk and the complexity of debugging and resolution of a bug
report using different supervised machine learning algorithms, namely Random
Forest, Naive Bayes, SVM, MLP and XGBoost. Further, we investigate the effect
of the application of active learning and we evaluate the impact of different
text representation techniques, namely TF-IDF, Word2Vec, Universal Sentence
Encoder and XLNet on the model's performance. The evaluation shows that a
combination of text embeddings generated through the Universal Sentence Encoder
and MLP as classifier outperforms all other methods, and is well suited to
predict the risk and complexity of bug tickets.


  We report on the magnetic, resistive, and structural studies of perovskite
La$_{1/3}$Sr$_{2/3}$CoO$_{3-\delta}$. By using the relation of synthesis
temperature and oxygen partial pressure to oxygen stoichiometry obtained from
thermogravimetric analysis, we have synthesized a series of samples with
precisely controlled $\delta=0.00-0.49$. These samples show three structural
phases at $\delta=0.00-0.15$, $\approx0.25$, $\approx0.5$, and two-phase
behavior for other oxygen contents. The stoichiometric material with
$\delta=0.00$ is a cubic ferromagnetic metal with the Curie temperature $T_{\rm
C}=274$ K. The increase of $\delta$ to 0.15 is followed by a linear decrease of
$T_{\rm C}$ to $\approx$ 160 K and a metal-insulator transition near the
boundary of the cubic structure range. Further increase of $\delta$ results in
formation of a tetragonal $2a_p\times 2a_p \times 4a_p$ phase for
$\delta\approx 0.25$ and a brownmillerite phase for $\delta\approx0.5$. At low
temperatures, these are weak ferromagnetic insulators (canted antiferromagnets)
with magnetic transitions at $T_{\rm m}\approx230$ and 120 K, respectively. At
higher temperatures, the $2a_p\times 2a_p \times 4a_p$ phase is $G$-type
antiferromagnetic between 230 K and $\approx$360 K. Low temperature magnetic
properties of this system for $\delta<1/3$ can be described in terms of a
mixture of Co$^{3+}$ ions in the low-spin state and Co$^{4+}$ ions in the
intermediate-spin state and a possible spin transition of Co$^{3+}$ to the
intermediate-spin state above $T_{\rm C}$. For $\delta>1/3$, there appears to
be a combination of Co$^{2+}$ and Co$^{3+}$ ions, both in the high-spin state
with dominating antiferromagnetic interactions.


  Autoregressive large language models (LLMs) compress knowledge from their
training data through next-token conditional distributions. This limits
tractable querying of this knowledge to start-to-end autoregressive sampling.
However, many tasks of interest -- including sequence continuation, infilling,
and other forms of constrained generation -- involve sampling from intractable
posterior distributions. We address this limitation by using amortized Bayesian
inference to sample from these intractable posteriors. Such amortization is
algorithmically achieved by fine-tuning LLMs via diversity-seeking
reinforcement learning algorithms: generative flow networks (GFlowNets). We
empirically demonstrate that this distribution-matching paradigm of LLM
fine-tuning can serve as an effective alternative to maximum-likelihood
training and reward-maximizing policy optimization. As an important
application, we interpret chain-of-thought reasoning as a latent variable
modeling problem and demonstrate that our approach enables data-efficient
adaptation of LLMs to tasks that require multi-step rationalization and tool
use.


  Let $q > 2$ be a prime number and define $\lambda_q := \left( \frac{\tau}{q}
\right)$ where $\tau(n)$ is the number of divisors of $n$ and $\left(
\frac{\cdot}{q} \right)$ is the Legendre symbol. When $\tau(n)$ is a quadratic
residue modulo $q$, then $\left( \lambda_q \star \mathbf{1} \right) (n)$ could
be close to the number of divisors of $n$. This is the aim of this work to
compare the mean value of the function $\lambda_q \star \mathbf{1}$ to the well
known average order of $\tau$. The proof reveals that the results depend
heavily on the value of $\left( \frac{2}{q} \right)$. A bound for short sums in
the case $q=5$ is also given, using profound results from the theory of integer
points close to certain smooth curves.


  In this paper, we prove that a squarefree monomial ideal of height 2 whose
quotient ring is Cohen-Macaulay is set-theoretic complete intersection.


  Starting from a microscopic approach and using the formalism of quantum
Brownian motion, partition function of a system composed of two separated
pieces of anisotropic matter and a fluctuating medium in finite temperature is
obtained rigorously. A general expression for fluctuation-induced free energy
between the separated anisotropic pieces of matter is obtained and it is shown
that in the framework of induced-force, the free energy of mean-force and
effective free energy are equivalent.


  We study the unitary orbit of a normal operator $a\in \mathcal B(\mathcal
H)$, regarded as a homogeneous space for the action of unitary groups
associated with symmetrically normed ideals of compact operators. We show with
an unified treatment that the orbit is a submanifold of the differing ambient
spaces if and only if the spectrum of $a$ is finite, and in that case it is a
closed submanifold. For arithmetically mean closed ideals, we show that
nevertheless the orbit always has a natural manifold structure, modeled by the
kernel of a suitable conditional expectation. When the spectrum of $a$ is not
finite, we describe the closure of the orbits of $a$ for the different norm
topologies involved. We relate these results to the action of the groupoid of
the partial isometries via the moment map given by the range projection of
normal operators. We show that all these groupoid orbits also have
differentiable structures for which the target map is a smooth submersion. For
any normal operator $a$ we also describe the norm closure of its groupoid orbit
${\mathcal O}_a$, which leads to necessary and sufficient spectral conditions
on $a$ ensuring that ${\mathcal O}_a$ is norm closed and that ${\mathcal O}_a$
is a closed embedded submanifold of $\mathcal B(\mathcal H)$.


  We introduce a structure for the directed acyclic graph (DAG) and a mechanism
design based on that structure so that peers can reach consensus at large scale
based on proof of work (PoW). We also design a mempool transaction assignment
method based on the DAG structure to render negligible the probability that a
transaction being processed by more than one miners. The result is a
significant scale-up of the capacity without sacrificing security and
decentralization.


  Quantum walks are expected to provide useful algorithmic tools for quantum
computation. This paper introduces absorbing probability and time of quantum
walks and gives both numerical simulation results and theoretical analyses on
Hadamard walks on the line and symmetric walks on the hypercube from the
viewpoint of absorbing probability and time.


  We study the eikonal approximation to quantum mechanics on the Moyal plane.
Instead of using a star product, the analysis is carried out in terms of
operator-valued wavefunctions depending on noncommuting, operator-valued
coordinates.


  The possible inhomogeneity of superconductivity has been investigated by
means of bulk-sensitive probes, using La_2-x_Sr_x_CuO_4_ (LSCO) single crystals
from the underdoped to optimally doped regime. Measurements of the magnetic
susceptibility, chi, on field cooling and specific heat have revealed that both
the absolute value of chi at 2 K, regarded as corresponding to the
superconducting (SC) volume fraction in a sample, and the Sommerfeld constant,
reflecting the density of states of quasiparticles at the Fermi level, exhibit
significant dependence on x, namely, on the hole concentration. This is the
first experimental work strongly suggesting that a phase separation into SC and
normal-state regions takes place in the underodped regime of LSCO as well as in
the overdoped regime.


  Employing probabilistic techniques we compute best possible upper and lower
bounds on the price of an option on one or two assets with continuous piecewise
linear payoff function based on prices of simple call options of possibly
distinct maturities and the no-arbitrage condition, but without any assumption
on the price dynamics of underlying assets. We show that the problem reduces to
solving linear optimization problems that we explicitly characterize. We report
numerical results that illustrate the effectiveness of the algorithms we
develop.


  Connes' gauge theory on $M_4\times Z_2$ is reformulated in the Lagrangian
level. It is pointed out that the field strength in Connes' gauge theory is not
unique. We explicitly construct a field strength different from Connes' one and
prove that our definition leads to the generation-number independent Higgs
potential. It is also shown that the nonuniqueness is related to the assumption
that two different extensions of the differential geometry are possible when
the extra one-form basis $\chi$ is introduced to define the differential
geometry on $M_4\times Z_2$. Our reformulation is applied to the standard model
based on Connes' color-flavor algebra. A connection between the unimodularity
condition and the electric charge quantization is then discussed in the
presence or absence of $\nu_R$.


  Current multimodal sentiment analysis frames sentiment score prediction as a
general Machine Learning task. However, what the sentiment score actually
represents has often been overlooked. As a measurement of opinions and
affective states, a sentiment score generally consists of two aspects: polarity
and intensity. We decompose sentiment scores into these two aspects and study
how they are conveyed through individual modalities and combined multimodal
models in a naturalistic monologue setting. In particular, we build unimodal
and multimodal multi-task learning models with sentiment score prediction as
the main task and polarity and/or intensity classification as the auxiliary
tasks. Our experiments show that sentiment analysis benefits from multi-task
learning, and individual modalities differ when conveying the polarity and
intensity aspects of sentiment.


  The European Power Exchange has introduced day-ahead auctions and continuous
trading spot markets to facilitate the insertion of renewable electricity.
These markets are designed to balance excess or lack of power in short time
periods, which leads to a large stochastic variability of the electricity
prices. Furthermore, the different markets show different stochastic memory in
their electricity price time series, which seem to be the cause for the large
volatility. In particular, we show the antithetical temporal correlation in the
intraday 15 minutes spot markets in comparison to the day-ahead hourly market.
We contrast the results from Detrended Fluctuation Analysis (DFA) to a new
method based on the Kramers--Moyal equation in scale. For very short term
($<12$ hours), all price time series show positive temporal correlations (Hurst
exponent $H>0.5$) except for the intraday 15 minute market, which shows strong
negative correlations ($H<0.5$). For longer term periods covering up to two
days, all price time series are anti-correlated ($H<0.5$).


  We report the method of LC/ESI-MS analysis of underivatized amino acids with
corresponding mass spectrum and fragmentation patterns. Diagnostic product ions
determined by mass spectrometry can support the qualitative identification and
quantitative estimation of individual amino acids. Therefore, the optimization
of chromatographic separation using the ion-pairing reagent (i.e.,
Nonafluoropentanoic acid, NFPA) is useful for the evaluation of target amino
acid and for further compound-specific nitrogen isotope studies of amino acids.


  We investigate the evolutions of edge Majorana fermions (MFs) and unveil that
they can be used to char- acterize different topological phases and study the
topological phase transitions. For some limiting cases of the evolution process
for the one-dimensional Kitaev model and Su-Schrieffer-Heeger (SSH) model, we
give analytical expressions of the survival probabilities of the edge MFs,
which indicates that different topological phases correspond to different zero
point numbers of the defined survival probabilities at some times. For a
general case, we consider a dimerized Kitaev model and the Kitaev chain with
disorder chemical potential and numerically calculate the survival
probabilities of two edge correlation MFs. Our results show that both of them
equal to zero, one of them equals to zero at some times or neither of them
equal to zero correspond to the SSH-like topological, topological
superconductor and trivial phases respectively.


  We establish a unique continuation property for stochastic heat equations
evolving in a bounded domain $G$. Our result shows that the value of the
solution can be determined uniquely by means of its value on an arbitrary open
subdomain of $G$ at any given positive time constant. Further, when $G$ is
convex and bounded, we also give a quantitative version of the unique
continuation property. As applications, we get an observability estimate for
stochastic heat equations, an approximate result and a null controllability
result for a backward stochastic heat equation.


  Charge transport across the interfaces in complex oxides attracts a lot of
attention because it allows creating novel functionalities useful for device
applications. In particular, it has been observed that movable domain walls in
epitaxial BiFeO3 films possess enhanced conductivity that can be used for read
out in ferroelectric-based memories. In this work, the relation between the
polarization and conductivity in sol-gel BiFeO3 films with special emphasis on
grain boundaries as natural interfaces in polycrystalline ferroelectrics is
investigated. The grains exhibit self-organized domain structure in these
films, so that the "domain clusters" consisting of several grains with aligned
polarization directions are formed. Surprisingly, grain boundaries between
these clusters (with antiparallel polarization direction) have significantly
higher electrical conductivity in comparison to "inter-cluster" grain
boundaries, in which the conductivity was even smaller than in the bulk. As
such, polarization-dependent conductivity of the grain boundaries was observed
for the first time in ferroelectric thin films. The results are rationalized by
thermodynamic modelling combined with finite element simulations of the charge
and stress accumulation at the grain boundaries giving major contribution to
conductivity. The observed polarization-dependent conductivity of grain
boundaries in ferroelectrics opens up a new avenue for exploiting these
materials in electronic devices.


  The physics at the planned $e^+e^-$ colliders is discussed around three main
topics corresponding to different manifestations of symmetry breaking: $W$
physics in the no Higgs scenario, studies of the properties of the Higgs and
precision tests of SUSY. A comparison with the LHC is made for all these cases.
The $\gamma \gamma$ mode of the linear collider will also be reviewed.


  Quadrotors are extremely agile, so much in fact, that classic
first-principle-models come to their limits. Aerodynamic effects, while
insignificant at low speeds, become the dominant model defect during high
speeds or agile maneuvers. Accurate modeling is needed to design robust
high-performance control systems and enable flying close to the platform's
physical limits. We propose a hybrid approach fusing first principles and
learning to model quadrotors and their aerodynamic effects with unprecedented
accuracy. First principles fail to capture such aerodynamic effects, rendering
traditional approaches inaccurate when used for simulation or controller
tuning. Data-driven approaches try to capture aerodynamic effects with blackbox
modeling, such as neural networks; however, they struggle to robustly
generalize to arbitrary flight conditions. Our hybrid approach unifies and
outperforms both first-principles blade-element theory and learned residual
dynamics. It is evaluated in one of the world's largest motion-capture systems,
using autonomous-quadrotor-flight data at speeds up to 65km/h. The resulting
model captures the aerodynamic thrust, torques, and parasitic effects with
astonishing accuracy, outperforming existing models with 50% reduced prediction
errors, and shows strong generalization capabilities beyond the training set.


  We study the evolutionary and physical properties of evolved O stars in the
Small Magellanic Cloud (SMC), with a special focus on their surface abundances
to investigate the efficiency of rotational mixing as a function of age,
rotation and global metallicity. We analyse the UV + optical spectra of
thirteen SMC O-type giants and supergiants, using the stellar atmosphere code
CMFGEN to derive photospheric and wind properties. We compare the inferred
properties to theoretical predictions from evolution models. For a more
comprehensive analysis, we interpret the results together with those we
obtained for O-type dwarfs in a former study. Most dwarfs lie in the early
phases of the main-sequence. For a given initial mass, giants are farther along
the evolutionary tracks, confirming that they are more evolved than dwarfs.
Supergiants have higher initial masses and are located past the terminal age
main-sequence. We find no clear trend of a mass discrepancy, regardless of the
diagram that was used to estimate the evolutionary mass. CNO abundances are
consistent with nucleosynthesis from the CNO cycle. Comparisons to theoretical
predictions reveal that the initial mixture is important when the observed
trends in the N/C versus N/O diagram are to be reproduced. A trend for stronger
chemical evolution for more evolved objects is observed. More massive stars,
are on average, more chemically enriched at a given evolutionary phase,
qualitatively consistent with evolutionary models. Abundance ratios supports
the theoretical prediction that massive stars at low metallicity are more
chemically processed than their Galactic counterparts. Finally, models
including rotation generally reproduce the surface abundances and rotation
rates when different initial rotational velocities are considered.
Nevertheless, there are objects for which a stronger braking and/or more
efficient mixing is required.


  In this paper, we consider numerical approximations for the viscous
Cahn-Hilliard equation with hyperbolic relaxation. This type of equations
processes energy-dissipative structure. The main challenge in solving such a
diffusive system numerically is how to develop high order temporal
discretization for the hyperbolic and nonlinear terms, allowing large
time-marching step, while preserving the energy stability, i.e. the energy
dissipative structure at the time-discrete level. We resolve this issue by
developing two second-order time-marching schemes using the recently developed
"Invariant Energy Quadratization" approach where all nonlinear terms are
discretized semi-explicitly. In each time step, one only needs to solve a
symmetric positive definite (SPD) linear system. All the proposed schemes are
rigorously proven to be unconditionally energy stable, and the second-order
convergence in time has been verified by time step refinement tests
numerically. Various 2D and 3D numerical simulations are presented to
demonstrate the stability, accuracy and efficiency of the proposed schemes.


  Consider a very ample line bundle $ E \to X$ over a compact complex manifold,
endowed with a hermitian metric of curvature $-i \omega $, and the space
$\mathcal{O}(E)$ of its holomorphic sections. The Fubini--Study map associates
with positive definite inner products $\langle \, , \rangle$ on
$\mathcal{O}(E)$ functions FS$(\langle \, ,\rangle) \in
\mathcal{H}_{\omega}=\{u \in C^{\infty}(X):\omega +i\partial\overline{\partial}
u >0\}$. We prove that FS is an injective immersion, but its image in general
is not closed in $\mathcal{H}_{\omega}$. To obtain a closed range, FS has to be
extended to certain degenerate inner products. This we do by associating
Bergman kernels with general inner products on the dual $\mathcal{O}(E)^*$, and
the paper describes some simple properties of this association.


  The system of spinless fermions on a hexagonal lattice is studied . We have
considered tight-binding model with the hopping integrals between the
nearest-neighbor and next-nearest-neighbor lattice sites, that depend on the
direction of the link. The links are divided on three types depending on the
direction, the hopping integrals are defined by different phases along the
links. The energy of the system depends on the phase differences, the solutions
for the phases, that correspond to the minimums of the energy, lead to a
topological insulator state with the nontrivial Chern numbers. We have analyzed
distinct topological states and phase transitions, the behavior of the chiral
gapless edge modes, have defined the Chern numbers. The band structure of
topological insulator (TI) is calculated, the ground-state phase diagram in the
parameter space is obtained. We propose a novel mechanism of realization of TI,
when the TI state is result of spontaneous breaking of time-reversal symmetry
due to nontrivial stable solutions for the phases that determine the hopping
integrals along the links and show that the Haldane model can be implemented in
real compounds of the condensed matter physics.


  We introduce dark mediator Dark matter (dmDM) where the dark and visible
sectors are connected by at least one light mediator $\phi$ carrying the same
dark charge that stabilizes DM. $\phi$ is coupled to the Standard Model via an
operator $\bar q q \phi \phi^*/\Lambda$, and to dark matter via a Yukawa
coupling $y_\chi \overline{\chi^c}\chi \phi$. Direct detection is realized as
the $2\rightarrow3$ process $\chi N \rightarrow \bar \chi N \phi$ at tree-level
for $m_\phi \lesssim 10 \ \mathrm{keV}$ and small Yukawa coupling, or
alternatively as a loop-induced $2\rightarrow2$ process $\chi N \rightarrow
\chi N$. We explore the direct-detection consequences of this scenario and find
that a heavy $\mathcal{O}(100 \ \mathrm{GeV})$ dmDM candidate fakes different
$\mathcal{O}(10 \ \mathrm{GeV})$ standard WIMPs in different experiments. Large
portions of the dmDM parameter space are detectable above the irreducible
neutrino background and not yet excluded by any bounds. Interestingly, for the
$m_\phi$ range leading to novel direct detection phenomenology, dmDM is also a
form of Self-Interacting Dark Matter (SIDM), which resolves inconsistencies
between dwarf galaxy observations and numerical simulations.


  N2 association to the FeMo-cofactor of nitrogenase, including the recently
identified central N ligand, has been investigated using first-principles
electronic structure calculations. The oxidation state of the resting state of
the cofactor and its electronic structure has been identified. A single proton
is added to the sulfur bridges following each electron transfer to the
cofactor. During N2 association, the cofactor undergoes large rearrangements
resulting in opening the central Fe-cage of the cofactor. N2 binds axially
while the bond of the bridging SH group breaks. It is then able to insert
between the two Fe sites in a bridged configuration.


  There is a considerable body of work on data cleaning which employs various
principles to rectify erroneous data and transform a dirty dataset into a
cleaner one. One of prevalent approaches is probabilistic methods, including
Bayesian methods. However, existing probabilistic methods often assume a
simplistic distribution (e.g., Gaussian distribution), which is frequently
underfitted in practice, or they necessitate experts to provide a complex prior
distribution (e.g., via a programming language). This requirement is both
labor-intensive and costly, rendering these methods less suitable for
real-world applications. In this paper, we propose BClean, a Bayesian Cleaning
system that features automatic Bayesian network construction and user
interaction. We recast the data cleaning problem as a Bayesian inference that
fully exploits the relationships between attributes in the observed dataset and
any prior information provided by users. To this end, we present an automatic
Bayesian network construction method that extends a structure learning-based
functional dependency discovery method with similarity functions to capture the
relationships between attributes. Furthermore, our system allows users to
modify the generated Bayesian network in order to specify prior information or
correct inaccuracies identified by the automatic generation process. We also
design an effective scoring model (called the compensative scoring model)
necessary for the Bayesian inference. To enhance the efficiency of data
cleaning, we propose several approximation strategies for the Bayesian
inference, including graph partitioning, domain pruning, and pre-detection. By
evaluating on both real-world and synthetic datasets, we demonstrate that
BClean is capable of achieving an F-measure of up to 0.9 in data cleaning,
outperforming existing Bayesian methods by 2% and other data cleaning methods
by 15%.


  Examining cables using many conductor transmission line theory has shed light
on the modes supported by various cable types. However, so far the theory
disregards the fundamental surface wave mode whose lateral confinement
increases with frequency and hence is expected to play an important role in
high frequency applications. To address this issue, we propose an extension to
the theory which incorporates surface waves on uncoated, cylindrical wires.
Crucially, this requires new definitions of the per unit length transmission
line parameters which are derived using the single wire surface wave solution.
By closely examining a two wire and three wire system, we show that these new
parameters can predict surface waves as well as modes found using conventional
many conductor transmission line theory. Furthermore, all calculated modes are
validated experimentally by diagonalization of a measured channel transfer
matrix. Additionally, the theoretically predicted propagation constants for the
modes are validated against full numerical simulation for the two wire case and
good agreement is observed when proximity effects can be neglected.


  We prove that the space of mathematical instantons with second Chern class 5
over ${\mathbb P}_3$ is smooth and irreducible. Unified and simple proofs for
the same statements in case of second Chern class $\leq 4$ are contained.


  The repeating fast radio burst source FRB 121102 has been shown to have an
exceptionally high and variable Faraday rotation measure (RM), which must be
imparted within its host galaxy and likely by or within its local environment.
In the redshifted ($z=0.193$) source reference frame, the RM decreased from
$1.46\times10^5$~rad~m$^{-2}$ to $1.33\times10^5$~rad~m$^{-2}$ between January
and August 2017, showing day-timescale variations of $\sim200$~rad~m$^{-2}$.
Here we present sixteen FRB 121102 RMs from burst detections with the Arecibo
305-m radio telescope, the Effelsberg 100-m, and the Karl G. Jansky Very Large
Array, providing a record of FRB 121102's RM over a 2.5-year timespan. Our
observations show a decreasing trend in RM, although the trend is not linear,
dropping by an average of 15\% year$^{-1}$ and is
$\sim9.7\times10^4$~rad~m$^{-2}$ at the most recent epoch of August 2019.
Erratic, short-term RM variations of $\sim10^3$~rad~m$^{-2}$ week$^{-1}$ were
also observed between MJDs 58215--58247. A decades-old neutron star embedded
within a still-compact supernova remnant or a neutron star near a massive black
hole and its accretion torus have been proposed to explain the high RMs. We
compare the observed RMs to theoretical models describing the RM evolution for
FRBs originating within a supernova remnant. FRB 121102's age is unknown, and
we find that the models agree for source ages of $\sim6-17$~years at the time
of the first available RM measurements in 2017. We also draw comparisons to the
decreasing RM of the Galactic center magnetar, PSR J1745--2900.


  Although support vector machines (SVMs) are theoretically well understood,
their underlying optimization problem becomes very expensive, if, for example,
hundreds of thousands of samples and a non-linear kernel are considered.
Several approaches have been proposed in the past to address this serious
limitation. In this work we investigate a decomposition strategy that learns on
small, spatially defined data chunks. Our contributions are two fold: On the
theoretical side we establish an oracle inequality for the overall learning
method using the hinge loss, and show that the resulting rates match those
known for SVMs solving the complete optimization problem with Gaussian kernels.
On the practical side we compare our approach to learning SVMs on small,
randomly chosen chunks. Here it turns out that for comparable training times
our approach is significantly faster during testing and also reduces the test
error in most cases significantly. Furthermore, we show that our approach
easily scales up to 10 million training samples: including hyper-parameter
selection using cross validation, the entire training only takes a few hours on
a single machine. Finally, we report an experiment on 32 million training
samples. All experiments used liquidSVM (Steinwart and Thomann, 2017).


  In wireless data networks, communication is particularly susceptible to
eavesdropping due to its broadcast nature. Security and privacy systems have
become critical for wireless providers and enterprise networks. This paper
considers the problem of secret communication over the Gaussian broadcast
channel, where a multi-antenna transmitter sends independent confidential
messages to two users with perfect secrecy. That is, each user would like to
obtain its own message reliably and confidentially. First, a computable
Sato-type outer bound on the secrecy capacity region is provided for a
multi-antenna broadcast channel with confidential messages. Next, a dirty-paper
secure coding scheme and its simplified version are described. For each case,
the corresponding achievable rate region is derived under the perfect secrecy
requirement. Finally, two numerical examples demonstrate that the Sato-type
outer bound is consistent with the boundary of the simplified dirty-paper
coding secrecy rate region.


  The hitherto unexplored two-photon doubly-excited states
[Ne$^{*}$($2p^{-1}3s$)]$_{2}$ were experimentally identified using the seeded,
fully coherent, intense extreme ultraviolet free-electron laser FERMI. These
states undergo ultrafast interatomic Coulombic decay (ICD) which predominantly
produces singly-ionized dimers. In order to obtain the rate of ICD, the
resulting yield of Ne$_{2}^{+}$ ions was recorded as a function of delay
between the XUV pump and UV probe laser pulses. The extracted lifetimes of the
long-lived doubly-excited states, 390 (-130 / +450} fs, and of the short-lived
ones, less than 150~fs, are in good agreement with \emph{ab initio} quantum
mechanical calculations.


  In this paper, we prove the global existence of strong solutions to the
two-dimensional compressible MHD equations with density dependent viscosity
coefficients (known as Kazhikhov-Vaigant model) on 2D solid balls with
arbitrary large initial smooth data where shear viscosity $\mu$ being constant
and the bulk viscosity $\lambda$ be a polynomial of density up to power
$\beta$.
  The global existence of the radially symmetric strong solutions was
established under Dirichlet boundary conditions for $\beta>1$. Moreover, as
long as $\beta\in (\max\{1,\frac{\gamma+2}{4}\},\gamma]$, the density is shown
to be uniformly bounded with respect to time. This generalizes the previous
result of \cite{2022Li,2016Huang,2022Huang} of the compressible Navier-Stokes
equations on 2D bounded domains where they require $\beta>4/3$ and also
improves the result of \cite{chen2022,2015Mei-1,2015Mei-2} of compressible MHD
equations on 2D solid balls.


  Optical coherence tomography angiography (OCTA) performs non-invasive
visualization and characterization of microvasculature in research and clinical
applications mainly in ophthalmology and dermatology. A wide variety of
instruments, imaging protocols, processing methods and metrics have been used
to describe the microvasculature, such that comparing different study outcomes
is currently not feasible. With the goal of contributing to standardization of
OCTA data analysis, we report a user-friendly, open-source toolbox, OCTAVA
(OCTA Vascular Analyzer), to automate the pre-processing, segmentation, and
quantitative analysis of en face OCTA maximum intensity projection images in a
standardized workflow. We present each analysis step, including optimization of
filtering and choice of segmentation algorithm, and definition of metrics. We
perform quantitative analysis of OCTA images from different commercial and
non-commercial instruments and samples and show OCTAVA can accurately and
reproducibly determine metrics for characterization of microvasculature. Wide
adoption could enable studies and aggregation of data on a scale sufficient to
develop reliable microvascular biomarkers for early detection, and to guide
treatment, of microvascular disease.


  Chemical and morphological features of spores and pollens have been linked to
changes in solar ultraviolet radiation (specifically UVB, 280-315 nm) at
Earth's surface. Variation in UVB exposure as inferred from these features has
been suggested as a proxy for paleoaltitude. While UVB irradiance does increase
with altitude above sea level, a number of other factors affect the irradiance
at any given place and time. In this modeling study we use the TUV atmospheric
radiative transfer model to investigate dependence of surface-level UVB
irradiance and relative biological impact on a number of constituents in
Earth's atmosphere that are variable over long and short time periods. We
consider changes in O3 column density, and SO2 and sulfate aerosols due to
periods of volcanic activity, including that associated with the formation of
the Siberian Traps. We find that UVB irradiance may be highly variable under
volcanic conditions and variations in several of these atmospheric constituents
can easily mimic or overwhelm changes in UVB irradiance due to changes in
altitude. On the other hand, we find that relative change with altitude is not
very sensitive to different sets of atmospheric conditions. Any paleoaltitude
proxy based on UVB exposure requires confidence that the samples under
comparison were located at roughly the same latitude, under very similar O3 and
SO2 columns, with similar atmospheric aerosol conditions. In general, accurate
estimates of the surface-level UVB exposure at any time and location require
detailed radiative transfer modeling taking into account a number of
atmospheric factors; this result is important for paleoaltitude proxies as well
as attempts to reconstruct the UV environment through geologic time and to tie
extinctions, such as the end-Permian mass extinction, to UVB irradiance
changes.


  For a compact surface $ S $ with a finite set of marked points $ P $, we
define a 1-system to be a collection of arcs which are pairwise non-homotopic
and intersect pairwise at most once. We prove that, up to equivalence, there
are exactly 23 maximal 1-systems on $ (S, P) $ when $ S $ is a torus and $ |P|
= 2 $. Along the way, we generalize some of the results of a previous paper to
the context of surfaces with boundary. In particular, we prove that the maximal
cardinality of a 1-system on $ (S, P) $ is $ 2 |\chi| (|\chi| + 1) -
\frac{v}{2} $, where $ \chi $ is the Euler characteristic of $ (S, P) $ and $ v
$ is the number of marked points of $ P $ in the boundary of $ S $.


  We make progress on a question of Skandera by showing that a product of
Kazhdan-Lusztig basis elements indexed by maximal elements of parabolic
subgroups admits a Kazhdan-Lusztig basis element as a quotient arising from
operations in the Schur algebroid if and only if the sequence of parabolic
subgroups satisfy both a rigidity condition and a combinatorial criterion. For
Weyl groups, the rigidity condition specializes to a necessary condition for
smallness of Gelfand-MacPherson resolutions. For Schubert varieties indexed by
4231-avoiding permutations, we derive a stronger necessary condition that, up
to an appropriate equivalence, is satisfied by at most one Gelfand-MacPherson
resolution, and exactly one if and only if 45312 is also avoided. Moreover, we
apply the combinatorial criterion to prove the (essentially unique) resolution
is small when 34512 and 45123 are likewise avoided.
  We develop the combinatorial criterion as part of a generalization of
Deodhar's combinatorial setting for questions in Kazhdan-Lusztig theory, which
in the case of Weyl groups we show captures the Bya{\l}ynicki-Birula
decompositions of Gelfand-MacPherson resolutions. In particular, we obtain new
combinatorial interpretations of Kazhdan-Lusztig polynomials, new algorithms
for computing them, and in the case of Weyl groups, an equivalence of the
existence of small Gelfand-MacPherson resolutions with that of certain
factorizations and generating-function interpretations of the Poincar\'e
polynomial of intersection cohomology of the Schubert variety.


  We present the long-term X-ray spectral and temporal analysis of a 'bare-type
AGN' Ark 120. We consider the observations from XMM-Newton, Suzaku, Swift, and
NuSTAR from 2003 to 2018. The spectral properties of this source are studied
using various phenomenological and physical models present in the literature.
We report (a) the variations of several physical parameters, such as the
temperature and optical depth of the electron cloud, the size of the Compton
cloud, and accretion rate for the last fifteen years. The spectral variations
are explained from the change in the accretion dynamics; (b) the X-ray time
delay between 0.2-2 keV and 3-10 keV light-curves exhibited zero-delay in 2003,
positive delay of 4.71 \pm 2.1 ks in 2013, and negative delay of 4.15 \pm 1.5
ks in 2014. The delays are explained considering Comptonization, reflection,
and light-crossing time; (c) the long term intrinsic luminosities obtained
using nthcomp, of the soft-excess and the primary continuum show a correlation
with a Pearson Correlation Coefficient of 0.922. This indicates that the
soft-excess and the primary continuum are originated from the same physical
process. From a physical model fitting, we infer that the soft excess for Ark
120 could be due to a small number of scatterings in the Compton cloud. Using
Monte-Carlo simulations, we show that indeed the spectra corresponding to fewer
scatterings could provide a steeper soft-excess power-law in the 0.2-3 keV
range. Simulated luminosities are found to be in agreement with the observed
values.


  Detection and characterization of extended structures is a crucial goal in
high contrast imaging. However, these structures face challenges in data
reduction, leading to over-subtraction from speckles and self-subtraction with
most existing methods. Iterative post-processing methods offer promising
results, but their integration into existing pipelines is hindered by selective
algorithms, high computational cost, and algorithmic regularization. To address
this for reference differential imaging (RDI), here we propose the data
imputation concept to Karhunen-Lo\`eve transform (DIKL) by modifying two steps
in the standard Karhunen-Lo\`eve image projection (KLIP) method. Specifically,
we partition an image to two matrices: an anchor matrix which focuses only on
the speckles to obtain the DIKL coefficients, and a boat matrix which focuses
on the regions of astrophysical interest for speckle removal using DIKL
components. As an analytical approach, DIKL achieves high-quality results with
significantly reduced computational cost (~3 orders of magnitude less than
iterative methods). Being a derivative method of KLIP, DIKL is seamlessly
integrable into high contrast imaging pipelines for RDI observations.


  Discrete-time modeling of acoustic, mechanical and electrical systems is a
prominent topic in the musical signal processing literature. Such models are
mostly derived by discretizing a mathematical model, given in terms of ordinary
or partial differential equations, using established techniques. Recent work
has applied the techniques of machine-learning to construct such models
automatically from data for the case of systems which have lumped states
described by scalar values, such as electrical circuits. In this work, we
examine how similar techniques are able to construct models of systems which
have spatially distributed rather than lumped states. We describe several novel
recurrent neural network structures, and show how they can be thought of as an
extension of modal techniques. As a proof of concept, we generate synthetic
data for three physical systems and show that the proposed network structures
can be trained with this data to reproduce the behavior of these systems.


  We present the discovery of diffuse optical line emission in the Centaurus
cluster seen with the MUSE IFU. The unparalleled sensitivity of MUSE allows us
to detect the faint emission from these structures which extend well beyond the
bounds of the previously known filaments. Diffuse structures (emission
surrounding the filaments, a northern shell and an extended Halo) are detected
in many lines typical of the nebulae in cluster cores ([NII]$_{\lambda
6548\&6583}$ ,[SII]$_{\lambda 6716\&6731}$, [OI]$_{\lambda 6300}$,
[OIII]$_{\lambda 4959\&5007}$ etc.) but are more than an order of magnitude
fainter than the filaments, with the faint halo only detected through the
brightest line in the spectrum ([NII]$_{\lambda 6583}$). These structures are
shown to be kinematically distinct from the stars in the central galaxy and
have different physical and excitation states to the filaments. Possible
origins are discussed for each structure in turn and we conclude that shocks
and/or pressure imbalances are resulting in gas dispersed throughout the
cluster core, formed from either disrupted filaments or direct cooling, which
is not confined to the bright filaments.


  In this paper we construct two infinite sets of self-adjoint commuting
charges for a quite general CFT. They come out naturally by considering an
infinite embedding chain of Lie algebras, an underlying structure that share
all theories with gauge groups U(N), SO(N) and Sp(N). The generality of the
construction allows us to carry all gauge groups at the same time in a unified
framework, and so to understand the similarities among them. The eigenstates of
these charges are restricted Schur polynomials and their eigenvalues encode the
value of the correlators of two restricted Schurs. The existence of these
charges singles out restricted Schur polynomials among the number of bases of
orthogonal gauge invariant operators that are available in the literature.


  Disentanglement of constituent factors of a sensory signal is central to
perception and cognition and hence is a critical task for future artificial
intelligence systems. In this paper, we present a compute engine capable of
efficiently factorizing holographic perceptual representations by exploiting
the computation-in-superposition capability of brain-inspired hyperdimensional
computing and the intrinsic stochasticity associated with analog in-memory
computing based on nanoscale memristive devices. Such an iterative in-memory
factorizer is shown to solve at least five orders of magnitude larger problems
that cannot be solved otherwise, while also significantly lowering the
computational time and space complexity. We present a large-scale experimental
demonstration of the factorizer by employing two in-memory compute chips based
on phase-change memristive devices. The dominant matrix-vector multiply
operations are executed at O(1) thus reducing the computational time complexity
to merely the number of iterations. Moreover, we experimentally demonstrate the
ability to factorize visual perceptual representations reliably and
efficiently.


  While various avenues of research have been explored for iterative pruning,
little is known what effect pruning has on zero-shot test performance and its
potential implications on the choice of pruning criteria. This pruning setup is
particularly important for cross-lingual models that implicitly learn alignment
between language representations during pretraining, which if distorted via
pruning, not only leads to poorer performance on language data used for
retraining but also on zero-shot languages that are evaluated.
  In this work, we show that there is a clear performance discrepancy in
magnitude-based pruning when comparing standard supervised learning to the
zero-shot setting. From this finding, we propose two weight regularizers that
aim to maximize the alignment between units of pruned and unpruned networks to
mitigate alignment distortion in pruned cross-lingual models and perform well
for both non zero-shot and zero-shot settings.
  We provide experimental results on cross-lingual tasks for the zero-shot
setting using XLM-RoBERTa$_{\mathrm{Base}}$, where we also find that pruning
has varying degrees of representational degradation depending on the language
corresponding to the zero-shot test set. This is also the first study that
focuses on cross-lingual language model compression.


  Precise predictions for an $e^+e^-\rightarrow t\bar{t}$ cross-section are
presented in the energy region from 400 GeV to 800 GeV. Cross-sections are
estimated including the beam-polarization effects with full
$\mathcal{O}(\alpha)$, and also with the effects of the initial-state photon
emission. A radiator technique is used for the initial-state photon emission up
to two-loop orders. In this investigation, a weak correction is defined as the
full electroweak corrections without the initial-state photonic corrections. As
a result, it is determined that the total cross-section of a top quark
pair-production receives the weak corrections of $+4\%$ over the trivial
initial state corrections at a center of mass energy of 500 GeV. Among the
initial state contributions, a contribution from two-loop diagrams gives less
than $0.11\%$ correction over the one-loop ones at the center of mass energies
of from $400$ GeV to $800$ GeV. In addition, the effect of a running coupling
constant is also discussed.


  Mobility entropy is proposed to measure predictability of human movements,
based on which, the upper and lower bound of prediction accuracy is deduced,
but corresponding mathematical expressions of prediction accuracy keeps yet
open. In this work, we try to analyze and model prediction accuracy in terms of
entropy based on the 2-order Markov chain model empirical results on a large
scale CDR data set, which demonstrates the observation that users with the same
level of entropy achieve different levels of accuracy\cite{Empirical}. After
dividing entropy into intervals, we fit the probability density distributions
of accuracy in each entropy interval with Gaussian distribution and then we
estimate the corresponding mean and standard deviation of these distributions.
After observing that the parameters vary with increasing entropy, we then model
the relationship between parameters and entropy using least squares method. The
mean can be modelled as a linear function, while the standard deviation can be
modelled as a Gaussian distribution. Thus based on the above analysis, the
probability density function of accuracy given entropy can be expressed by
functional Gaussian distribution. The insights from our work is the first step
to model the correlation prediction accuracy and predictability entropy, thus
shed light on the further work in this direction.


  With the great success in simulating many intelligent behaviors using
computing devices, there has been an ongoing debate whether all conscious
activities are computational processes. In this paper, the answer to this
question is shown to be no. A certain phenomenon of consciousness is
demonstrated to be fully represented as a computational process using a quantum
computer. Based on the computability criterion discussed with Turing machines,
the model constructed is shown to necessarily involve a non-computable element.
The concept that this is solely a quantum effect and does not work for a
classical case is also discussed.


  In this paper we study the identification of the b quark inside a boosted
hadronically decaying top quark in the center-of-mass frame of the jet. We
demonstrate that the method can be used to greatly reduce the QCD jet
background even in a very high pileup condition. The method has a much smaller
fake rate for QCD jets compared to typical b quark identification algorithms in
jets at the same signal efficiency. When combining the b quark identification
in the center-of-mass frame of the jet with jet substructure information, we
can improve the rejection rate of QCD jet background by almost an order of
magnitude while maintaining the same identification efficiency for the boosted
top quark.


  We introduce an S-function formulation for the recently found r-th
dispersionless modified KP and r-th dispersionless Dym hierarchies, giving also
a connection of these $S$-functions with the Orlov functions of the
hierarchies. Then, we discuss a reduction scheme for the hierarchies that
together with the $S$-function formulation leads to hodograph systems for the
associated solutions. We consider also the connection of these reductions with
those of the dispersionless KP hierarchy and with hydrodynamic type systems. In
particular, for the 1-component and 2-component reduction we derive, for both
hierarchies, ample sets of examples of explicit solutions.


  Typically, real-world stochastic processes are not easy to analyze. In this
work we study the representation of any stochastic process as a memoryless
innovation process triggering a dynamic system. We show that such a
representation is always feasible for innovation processes taking values over a
continuous set. However, the problem becomes more challenging when the alphabet
size of the innovation is finite. In this case, we introduce both lossless and
lossy frameworks, and provide closed-form solutions and practical algorithmic
methods. In addition, we discuss the properties and uniqueness of our suggested
approach. Finally, we show that the innovation representation problem has many
applications. We focus our attention to Entropic Causal Inference, which has
recently demonstrated promising performance, compared to alternative methods.


  The electromagnetic calorimeter (ECAL) of the AMS-02 experiment is a
3-dimensional sampling calorimeter, made of lead and scintillating fibers. The
detector allows for a high granularity, with 18 samplings in the longitudinal
direction, and 72 sampling in the lateral direction. The ECAL primary goal is
to measure the energy of cosmic rays up to few TeV, however, thanks to the fine
grained structure, it can also provide the separation of positrons from
protons, in the GeV to TeV region. A direct measurement of high energy photons
with accurate energy and direction determination can also be provided.


  We consider the problem of learning in Linear Quadratic Control systems whose
transition parameters are initially unknown. Recent results in this setting
have demonstrated efficient learning algorithms with regret growing with the
square root of the number of decision steps. We present new efficient
algorithms that achieve, perhaps surprisingly, regret that scales only
(poly)logarithmically with the number of steps in two scenarios: when only the
state transition matrix $A$ is unknown, and when only the state-action
transition matrix $B$ is unknown and the optimal policy satisfies a certain
non-degeneracy condition. On the other hand, we give a lower bound that shows
that when the latter condition is violated, square root regret is unavoidable.


  We describe multiple correlations of Jordan and Cartan spectra for any finite
number of Anosov representations of a finitely generated group. This extends
our previous work on correlations of length and displacement spectra for rank
one convex cocompact representations. Examples include correlations of the
Hilbert length spectra for convex projective structures on a closed surface as
well as correlations of eigenvalue gaps and singular value gaps for Hitchin
representations. We relate the correlation problem to the counting problem for
Jordan and Cartan projections of an Anosov subgroup with respect to a family of
carefully chosen truncated {\it hypertubes}, rather than in tubes as in our
previous work. Hypertubes go to infinity in a linear subspace of directions,
while tubes go to infinity in a single direction and this feature presents a
novel difficulty in this higher rank correlation problem.


  Understanding the recovery of gas from reservoirs featuring pervasive
nanopores is essential for effective shale gas extraction. Classical theories
cannot accurately predict such gas recovery and many experimental observations
are not well understood. Here we report molecular simulations of the recovery
of gas from single nanopores, explicitly taking into account molecular gas-wall
interactions. We show that, in very narrow pores, the strong gas-wall
interactions are essential in determining the gas recovery behavior both
quantitatively and qualitatively. These interactions cause the total diffusion
coefficients of the gas molecules in nanopores to be smaller than those
predicted by kinetic theories, hence slowing down the rate of gas recovery.
These interactions also lead to significant adsorption of gas molecules on the
pore walls. Because of the desorption of these gas molecules during gas
recovery, the gas recovery from the nanopore does not exhibit the usual
diffusive scaling law (i.e., the accumulative recovery scales as $R \sim
t^{1/2}$ but follows a super-diffusive scaling law $R \sim t^n$ ($n>0.5$),
which is similar to that observed in some field experiments. For the system
studied here, the super-diffusive gas recovery scaling law can be captured well
by continuum models in which the gas adsorption and desorption from pore walls
are taken into account using the Langmuir model.


  In the present paper we study the asymptotic behavior of trigonometric
products of the form $\prod_{k=1}^N 2 \sin(\pi x_k)$ for $N \to \infty$, where
the numbers $\omega=(x_k)_{k=1}^N$ are evenly distributed in the unit interval
$[0,1]$. The main result are matching lower and upper bounds for such products
in terms of the star-discrepancy of the underlying points $\omega$, thereby
improving earlier results obtained by Hlawka in 1969. Furthermore, we consider
the special cases when the points $\omega$ are the initial segment of a
Kronecker or van der Corput sequence. The paper concludes with some
probabilistic analogues.


  A complex number $\alpha$ is said to satisfy the height reducing property if
there is a finite set $F\subset \mathbb{Z}$ such that
$\mathbb{Z}[\alpha]=F[\alpha]$, where $\mathbb{Z}$ is the ring of the rational
integers. It is easy to see that $\alpha$ is an algebraic number when it
satisfies the height reducing property. We prove the relation
$\operatorname{Card}(F)\geq \max\{2,\left\vert M_{\alpha}(0)\right\vert \},$
where $M_{\alpha}$ is the minimal polynomial of $\alpha$ over the field of the
rational numbers, and discuss the related optimal cases, for some classes of
algebraic numbers $\alpha$. In addition, we show that there is an algorithm to
determine the minimal height polynomial of a given algebraic number, provided
it has no conjugate of modulus one.


  In opinion polls, the public frequently claim to value their privacy.
However, individuals often seem to overlook the principle, contributing to a
disparity labelled the `Privacy Paradox'. The growth of the Internet-of-Things
(IoT) is frequently claimed to place privacy at risk. However, the Paradox
remains underexplored in the IoT. In addressing this, we first conduct an
online survey (N = 170) to compare public opinions of IoT and less-novel
devices. Although we find users perceive privacy risks, many still decide to
purchase smart devices. With the IoT rated less usable/familiar, we assert that
it constrains protective behaviour. To explore this hypothesis, we perform
contextualised interviews (N = 40) with the public. In these dialogues, owners
discuss their opinions and actions with a personal device. We find the Paradox
is significantly more prevalent in the IoT, frequently justified by a lack of
awareness. We finish by highlighting the qualitative comments of users, and
suggesting practical solutions to their issues. This is the first work, to our
knowledge, to evaluate the Privacy Paradox over a broad range of technologies.


  A $(\beta,\epsilon)$-$\textit{hopset}$ is, informally, a weighted edge set
that, when added to a graph, allows one to get from point $a$ to point $b$
using a path with at most $\beta$ edges ("hops") and length
$(1+\epsilon)\mathrm{dist}(a,b)$. In this paper we observe that Thorup and
Zwick's $\textit{sublinear additive}$ emulators are also actually
$(O(k/\epsilon)^k,\epsilon)$-hopsets for every $\epsilon>0$, and that with a
small change to the Thorup-Zwick construction, the size of the hopset can be
made $O(n^{1+\frac{1}{2^{k+1}-1}})$. As corollaries, we also shave "$k$"
factors off the size of Thorup and Zwick's sublinear additive emulators and the
sparsest known $(1+\epsilon,O(k/\epsilon)^{k-1})$-spanners, due to Abboud,
Bodwin, and Pettie.


  We revisit the theory of Cherenkov radiation in uniaxial crystals.
Historically, a number of flawed attempts have been made at explaining this
radiation phenomenon and a consistent error-free description is nowhere
available. We apply our calculation to a large modern day telescope - IceCube.
Being located at the Antarctica, this detector makes use of the naturally
occuring ice as a medium to generate Cherenkov radiation. However, due to the
high pressure at the depth of the detector site, large volumes of hexagonal ice
crystals are formed. We calculate how this affects the Cherenkov radiation
yield and angular dependence. We conclude that the effect is small, at most
about a percent, and would only be relevant in future high precision
instruments like e.g. Precision IceCube Next Generation Upgrade (PINGU). For
radio-Cherenkov experiments which use the presence of a clear Cherenkov cone to
determine the arrival direction, any variation in emission angle will directly
and linearly translate into a change in apparent neutrino direction. In
closing, we also describe a simple experiment to test this formalism, and
calculate the impact of anisotropy on light-yields from lead tungstate crystals
as used, for example, in the CMS calorimeter at the CERN LHC.


  We present the results of a deep optical survey in the Corona Australis and
Chamaeleon II star forming regions. Our optical photometry is combined with
available near- and mid-infrared photometry to identify very low-mass candidate
members in these dark clouds. In our Chamaeleon II field, only one object
exhibits clear H-alpha emission, but the discrepancy between its optical and
near-infrared colours suggests that it might be a foreground star. We also
identify two objects without H-alpha emission that could be planetary mass
members of Chamaeleon II. In Corona Australis, we find ten stars and three
brown dwarf candidates in the Coronet cluster. Five of our new members are
identified with ISOCAM sources. Only two of them have a mid-infrared excess,
indicating the presence of an accretion disk. On the other hand, one brown
dwarf candidate has a faint close companion, seen only in our deepest I-band
image. For many of the candidates in both clouds, membership could not be
inferred from their H-alpha emission or near-infrared colours; these objects
need spectroscopic confirmation of their status.


  Topological description of hierarchical sets of spectral gaps of Hofstadter
butterfly is found to be encoded in a quasicrystal where magnetic flux plays
the role of a phase factor that shifts the origin of the quasiperiodic order.
Revealing an intrinsic frustration at smallest energy scale, described by
$\zeta=2-\sqrt{3}$, this irrational number characterizes the universal
butterfly and is related to two quantum numbers that includes the Chern number
of quantum Hall states. With a periodic drive that induces phase transitions in
the system, the fine structure of the butterfly is shown to be amplified making
states with large topological invariants accessible experimentally .


  We conjecture the following entropy bound to be valid in all space-times
admitted by Einstein's equation: Let A be the area of any two-dimensional
surface. Let L be a hypersurface generated by surface-orthogonal null geodesics
with non-positive expansion. Let S be the entropy on L. Then S does not exceed
A/4.
  We present evidence that the bound can be saturated, but not exceeded, in
cosmological solutions and in the interior of black holes. For systems with
limited self-gravity it reduces to Bekenstein's bound. Because the conjecture
is manifestly time reversal invariant, its origin cannot be thermodynamic, but
must be statistical. Thus it places a fundamental limit on the number of
degrees of freedom in nature.


This paper has been withdrawn by the author


  We review the general interplay between Nuclear Physics and neutrino-nucleus
cross sections at intermediate and high energies. The effects of different
reaction mechanisms over the neutrino observables are illustrated with examples
in calculations using several nuclear models and ingredients.


  We report on a new class of fast-roll inflationary models. In a huge part of
its parameter space, inflationary perturbations exhibit quite unusual phenomena
such as scalar and tensor modes freezing out at widely different times, as well
as scalar modes reentering the horizon during inflation. In another, narrower
range of parameters, this class of models agrees with observations. One
specific point in parameter space is characterized by extraordinary behavior of
the scalar perturbations. Freeze-out of scalar perturbations as well as
particle production at horizon crossing are absent. Also the behavior of the
perturbations around this quasi-de Sitter background is dual to a quantum field
theory in flat space-time. Finally, the form of the primordial power spectrum
is determined by the interaction between different modes of scalar
perturbations.


  We show that the revised KKKP protocol proposed by Kye and Kim [Phys. Rev.
Lett. 95,040501(2005)] is still insecure with coherent states by a type of
beamsplitting attack. We then further revise the KKKP protocol so that it is
secure under such type of beamsplitting attack. The revised scheme can be used
for not-so-weak coherent state quantum key distribution.


  We have studied statistical characteristics of five share price time series.
For each stock price, we estimated a best fit quantitative model for the
monthly closing price as based on the decomposition into two defining consumer
price indices selected from a large set of CPIs. It was found that there are
two pairs of similar models (Bank of America/Morgan Stanley and Goldman
Sachs/JPMorgan Chase) with a standalone model for Franklin Resources. From each
pair, one can choose the company with the highest return depending on the
future evolution of defining CPIs


  Rare Objects SEarch with Bolometers UndergrounD) is an experiment which
attempts to detect low mass Weak Interacting Massive Particles (WIMPs) through
their elastic scattering off Al and O nuclei. It consists of three small
sapphire bolometers (of a total mass of 100 g) with NTD-Ge sensors in a
dilution refrigerator operating at 20 mK in the Canfranc Underground
Laboratory. We report in this paper the results of several runs (of about 10
days each) with successively improved energy thresholds, and the progressive
background reduction obtained by improvement of the radiopurity of the
components and subsequent modifications in the experimental assembly, including
the addition of old lead shields. Mid-term plans and perspectives of the
experiment are also presented.


  The Tate conjecture for squares of K3 surfaces over finite fields was
recently proved by Ito-Ito-Koshikawa. We give a more geometric proof when the
characteristic is at least 5. The main idea is to use twisted derived
equivalences between K3 surfaces to link the Tate conjecture to finiteness
results over finite fields, in the spirit of Tate.


  In recent years significant progress has been made in dealing with
challenging problems using reinforcement learning.Despite its great success,
reinforcement learning still faces challenge in continuous control tasks.
Conventional methods always compute the derivatives of the optimal goal with a
costly computation resources, and are inefficient, unstable and lack of
robust-ness when dealing with such tasks. Alternatively, derivative-based
methods treat the optimization process as a blackbox and show robustness and
stability in learning continuous control tasks, but not data efficient in
learning. The combination of both methods so as to get the best of the both has
raised attention. However, most of the existing combination works adopt complex
neural networks (NNs) as the policy for control. The double-edged sword of deep
NNs can yield better performance, but also makes it difficult for parameter
tuning and computation. To this end, in this paper we presents a novel method
called FiDi-RL, which incorporates deep RL with Finite-Difference (FiDi) policy
search.FiDi-RL combines Deep Deterministic Policy Gradients (DDPG)with Augment
Random Search (ARS) and aims at improving the data efficiency of ARS. The
empirical results show that FiDi-RL can improves the performance and stability
of ARS, and provide competitive results against some existing deep
reinforcement learning methods


  Deep reinforcement learning(DRL) has shown significant promise in a wide
range of applications including computer games and robotics. Yet, training DRL
policies consume extraordinary computing resources resulting in dense policies
which are prone to overfitting. Moreover, inference with dense DRL policies
limit their practical applications, especially in edge computing. Techniques
such as pruning and singular value decomposition have been used with deep
learning models to achieve sparsification and model compression to limit
overfitting and reduce memory consumption. However, these techniques resulted
in sub-optimal performance with notable decay in rewards. $L_1$ and $L_2$
regularization techniques have been proposed for neural network sparsification
and sparse auto-encoder development, but their implementation in DRL
environments has not been apparent. We propose a novel
$L_0$-norm-regularization technique using an optimal sparsity map to sparsify
DRL policies and promote their decomposition to a lower rank without decay in
rewards. We evaluated our $L_0$-norm-regularization technique across five
different environments (Cartpole-v1, Acrobat-v1, LunarLander-v2,
SuperMarioBros-7.1.v0 and Surgical Robot Learning) using several on-policy and
off-policy algorithms. We demonstrated that the $L_0$-norm-regularized DRL
policy in the SuperMarioBros environment achieved 93% sparsity and gained 70%
compression when subjected to low-rank decomposition, while significantly
outperforming the dense policy. Additionally, the $L_0$-norm-regularized DRL
policy in the Surgical Robot Learning environment achieved a 36% sparsification
and gained 46% compression when decomposed to a lower rank, while being
performant. The results suggest that our custom $L_0$-norm-regularization
technique for sparsification of DRL policies is a promising avenue to reduce
computational resources and limit overfitting.


  Doping of a two-dimensional (2D) material by impurity atoms occurs
\textit{via} two distinct mechanisms: absorption of the dopants by the 2D
crystal or adsorption on its surface. To distinguish the relevant mechanism, we
systematically dope 53 experimentally synthesized 2D monolayers by 65 different
chemical elements in both absorption and adsorption sites. The resulting 17,598
doped monolayer structures were generated using the newly developed ASE
\texttt{DefectBuilder} -- a Python tool to set up point defects in 2D and bulk
materials -- and subsequently relaxed by an automated high-throughput density
functional theory (DFT) workflow. We find that interstitial positions are
preferred for small dopants with partially filled valence electrons in host
materials with large lattice parameters. On the contrary, adatoms are favored
for dopants with a low number of valence electrons due to lower coordination of
adsorption sites compared to interstitials. The relaxed structures,
characterization parameters, defect formation energies, and magnetic moments
(spins) are available in an open database to help advance our understanding of
defects in 2D materials.


  Consensus clustering (or clustering aggregation) inputs $k$ partitions of a
given ground set $V$, and seeks to create a single partition that minimizes
disagreement with all input partitions. State-of-the-art algorithms for
consensus clustering are based on correlation clustering methods like the
popular Pivot algorithm. Unfortunately these methods have not proved to be
practical for consensus clustering instances where either $k$ or $V$ gets
large.
  In this paper we provide practical run time improvements for correlation
clustering solvers when $V$ is large. We reduce the time complexity of Pivot
from $O(|V|^2 k)$ to $O(|V| k)$, and its space complexity from $O(|V|^2)$ to
$O(|V| k)$ -- a significant savings since in practice $k$ is much less than
$|V|$. We also analyze a sampling method for these algorithms when $k$ is
large, bridging the gap between running Pivot on the full set of input
partitions (an expected 1.57-approximation) and choosing a single input
partition at random (an expected 2-approximation). We show experimentally that
algorithms like Pivot do obtain quality clustering results in practice even on
small samples of input partitions.


  Generative models such as GANs and diffusion models have demonstrated
impressive image generation capabilities. Despite these successes, these
systems are surprisingly poor at creating images with hands. We propose a novel
training framework for generative models that substantially improves the
ability of such systems to create hand images. Our approach is to augment the
training images with three additional channels that provide annotations to
hands in the image. These annotations provide additional structure that coax
the generative model to produce higher quality hand images. We demonstrate this
approach on two different generative models: a generative adversarial network
and a diffusion model. We demonstrate our method both on a new synthetic
dataset of hand images and also on real photographs that contain hands. We
measure the improved quality of the generated hands through higher confidence
in finger joint identification using an off-the-shelf hand detector.


  We investigate line formation processes in Type IIb supernovae (SNe) from 100
to 500 days post-explosion using spectral synthesis calculations. The modeling
identifies the nuclear burning layers and physical mechanisms that produce the
major emission lines, and the diagnostic potential of these. We compare the
model calculations with data on the three best observed Type IIb SNe to-date -
SN 1993J, SN 2008ax, and SN 2011dh. Oxygen nucleosynthesis depends sensitively
on the main-sequence mass of the star and modeling of the [O I] 6300, 6364
lines constrains the progenitors of these three SNe to the M_ZAMS=12-16 M_sun
range (ejected oxygen masses 0.3-0.9 M_sun), with SN 2011dh towards the lower
end and SN 1993J towards the upper end of the range. The high ejecta masses
from M_ZAMS >= 17 M_sun progenitors give rise to brighter nebular phase
emission lines than observed. Nucleosynthesis analysis thus supports a scenario
of low/moderate mass progenitors for Type IIb SNe, and by implication an origin
in binary systems. We demonstrate how oxygen and magnesium recombination lines
may be combined to diagnose the magnesium mass in the SN ejecta. For SN 2011dh,
a magnesium mass of of 0.02-0.14 M_sun is derived, which gives a Mg/O
production ratio consistent with the solar value. Nitrogen left in the He
envelope from CNO-burning gives strong [N II] 6548, 6583 emission lines that
dominate over H-alpha emission in our models. The hydrogen envelopes of Type
IIb SNe are too small and dilute to produce any noticeable H-alpha emission or
absorption after ~150 days, and nebular phase emission seen around 6550 A is in
many cases likely caused by [N II] 6548, 6583. Finally, the influence of
radiative transport on the emergent line profiles is investigated...(abridged)


  It has been shown that, in the context of the MSSM, the Supersymmetric
Flavour Problem cannot be solved by just letting the sfermions of the first two
generations be relatively heavy. The reason is twofold: naturalness of the
Fermi scale on one side, need for positive squared stop masses on the other.
The situation is much more promising in models without a light Higgs boson, in
which the goal can be met. The prices are: a relatively low messenger scale,
semiperturbativity before the GUT scale, and some amount of
degeneracy/alignement of order of the Cabibbo angle in the sfermion sector. A
crucial role is played by the increased quartic coupling. The resulting
phenomenology is quite different from the MSSM one in many respects.


  In processes involving Coulomb-type initial- and final-state interactions,
the Gamow factor has been traditionally used to take into account these
additional interactions. The Gamow factor needs to be modified when the
magnitude of the effective coupling constant increases or when the velocity
increases. For the production of a pair of particles under their mutual
Coulomb-type interaction, we obtain the modification of the Gamow factor in
terms of the overlap of the Feynman amplitude with the relativistic wave
function of the two particles. As a first example, we study the modification of
the Gamow factor for the production of two bosons. The modification is
substantial when the coupling constant is large.


  We study the turbulent flow of the density-stratified fluid around a small
translating (either passively or self-propelled) particle. It was found
recently [A. M. Ardekani and R. Stocker, Phys. Rev. Lett. vol. 105, 084502
(2010)] that without turbulence, the familiar Stokes flow is dramatically
altered by the stratification. Stratification-induced inhomogeneity "turns on"
the buoyancy introducing a new "cutoff" or "screening" length scale for the
flow, yielding closed streamlines and a faster (exponential-like) decay of
velocity. This result, however, did not account for the potential role of the
background turbulence, intrinsically present in many aquatic environments.
Turbulence mixes the density opposing the effect. Here we derive and solve the
advection-diffusion equation that describes the interplay of turbulent mixing,
diffusion of the stratifying agent and buoyancy. We derive an exact expression
for fluctuations due to weak background turbulence and show that stronger
turbulence can completely change the flow around the particle, canceling the
effect of stratification and restoring the unstratified Stokes flow.


  We consider stabilization of linear time-invariant (LTI) and single input
single output (SISO) plants in the frequency domain from a fresh perspective.
Compensators that are themselves stable are sometimes preferred because they
make starting the system easier. Such starting remains easy if there is a
stable compensator in parallel with the plant rather than in a feedback loop.
In such an arrangement, we explain why it is possible to stabilize all plants
whose transfer functions are proper rational functions of the Laplace variable
$s$. In our proposed architecture we have (i) an optional compensator $C_s(s)$
in series with the plant $P(s)$, (ii) a necessary compensator $C_p(s)$ in
parallel with $C_s(s)P(s)$, along with (iii) a feedback gain $K$ for the
combined new plant $C_s(s)P(s)+C_p(s)$. We show that stabilization with stable
$C_s(s)$ and $C_p(s)$ is always possible. In our proposed solution the
closed-loop plant is biproper and has all its zeros in the left half plane, so
there is a $K_0$ such that the plant is stable for $K>K_0$. We are not aware of
prior use of parallel compensators with such a goal. Our proposed architecture
works even for plants that are impossible to stabilize with stable compensators
in the usual single-loop feedback architecture. Several examples are provided.


  Traditionally, when recommender systems are formalized as multi-armed
bandits, the policy of the recommender system influences the rewards accrued,
but not the length of interaction. However, in real-world systems, dissatisfied
users may depart (and never come back). In this work, we propose a novel
multi-armed bandit setup that captures such policy-dependent horizons. Our
setup consists of a finite set of user types, and multiple arms with Bernoulli
payoffs. Each (user type, arm) tuple corresponds to an (unknown) reward
probability. Each user's type is initially unknown and can only be inferred
through their response to recommendations. Moreover, if a user is dissatisfied
with their recommendation, they might depart the system. We first address the
case where all users share the same type, demonstrating that a recent UCB-based
algorithm is optimal. We then move forward to the more challenging case, where
users are divided among two types. While naive approaches cannot handle this
setting, we provide an efficient learning algorithm that achieves
$\tilde{O}(\sqrt{T})$ regret, where $T$ is the number of users.


  In this report, we study the packet delay as a QoS metric in CR systems. The
packet delay includes the queue waiting time and the service time. In this
work, we study the effect of both the scheduling and the power allocation
algorithms on the delay performance of the SUs.
  To study the delay due to the service time we study a multichannel system
where the channels are sensed sequentially, we study the tradeoff between
throughput and delay. The problem is formulated as an optimal stopping rule
problem where there is a tradeoff between the service time and the throughput.
This tradeoff results from skipping low-quality channels to seek the
possibility of finding high-quality ones in the future at the expense of a
higher probability of being blocked from transmission since these future
channels might be busy.
  On the other hand, the queue waiting time is studied by considering a
multi-user single channel system. Specifically, we study the effect of
scheduling and power allocation on the delay performance of all SUs in the
system. We propose a delay optimal algorithm that protects the PUs from harmful
interference and provides the required delay guarantees to users. Conventional
scheduling algorithms do not provide such guarantees if the interference
channels are heterogeneous. This is because they are developed for conventional
non-CR wireless systems that neglect interference since channels are
orthogonal.
  Finally, we present two potential extensions to these studied problems.


  Leidenfrost drops were recently found to host strong dynamics. In the present
study, we investigate both experimentally and theoretically the {flows
structures and stability} inside a Leidenfrost water drop as it evaporates,
starting with a large puddle. As revealed by infrared mapping, the drop base is
warmer than its apex by typically 10$^{\circ}$C, which is likely to trigger
bulk thermobuoyant flows and Marangoni surface flows. Tracer particles unveil
complex and strong flows that undergo successive symmetry breakings as the drop
evaporates. We investigate the linear stability of the baseflows in a
non-deformable, quasi-static, levitating drop induced by thermobuoyancy and
effective thermocapillary surface stress, using only one adjustable parameter.
The stability analysis of nominally axisymmetric thermoconvective flows,
parametrized by the drop radius $R$, yields the most unstable, {thus, dominant,
azimuthal modes (of wavenumber $m$). Our theory predicts well the radii $R$ for
the mode transitions and cascade with decreasing wavenumbers from $m=3$, $m=2$,
down to $m=1$ (the eventual rolling mode that entails propulsion) as the drop
shrinks in size}. The effect of the escaping vapor is not taken into account
here, which may further destabilize the inner flow and couple to the
liquid/vapor interface to give rise to motion Bouillant et al. (2018) [8] and
Brandao et al. (2020) [9].


  We present a new operational framework for studying ``superpositions of
spacetimes'', which are of fundamental interest in the development of a theory
of quantum gravity. Our approach capitalizes on nonlocal correlations in curved
spacetime quantum field theory, allowing us to formulate a metric for spacetime
superpositions as well as characterizing the coupling of particle detectors to
a quantum field. We apply our approach to analyze the dynamics of a detector
(using the Unruh-deWitt model) in a spacetime generated by a BTZ black hole in
a superposition of masses. We find that the detector exhibits signatures of
quantum-gravitational effects corroborating and extending Bekenstein's seminal
conjecture concerning the quantized mass spectrum of black holes in quantum
gravity. Crucially, this result follows directly from the approach, without any
additional assumptions about the black hole mass properties.


  It was shown in our earlier work that the measurement error in the microphone
position affected the room impulse response (RIR) which in turn affected the
single-channel close microphone and multi-channel distant microphone speech
recognition. In this paper, as an extension, we systematically study to
identify the optimal location of the microphone, given an approximate and hence
erroneous location of the microphone in 3D space. The primary idea is to use
Monte-Carlo technique to generate a large number of random microphone positions
around the erroneous microphone position and select the microphone position
that results in the best performance of a general purpose automatic speech
recognition (gp-asr). We experiment with clean and noisy speech and show that
the optimal location of the microphone is unique and is affected by noise.


  Electrical breakdown of the dielectric nanolayer between film electrodes of
niobium and an alloy of 50% indium and 50% tin forms a bridge of this alloy
between the electrodes. The bridge resistance depends on the breakdown current.
The length of the bridge is equal to the thickness of the dielectric (30 nm),
and its diameter is 25 nm. The calculated coherence length of the alloy at 0 K
is close to the length of the bridge. The calculated critical current of a
bridge with a resistance of 1 {\Omega} at a temperature of 0 K is 2 mA. It is
concluded that such a bridge should have the properties of a Josephson contact
at a temperature lower than the critical temperature of the alloy (6.5 K).


  This report introduces researchers in AI to some of the concepts in quantum
heurisitics and quantum AI.


  It is shown that the algebraic structure of finite Heisenberg groups
associated with the tensor product of two Hilbert spaces leads to a simple
demonstration valid in all Hilbert space dimensions of the impossibility of
non-contextual hidden variables.


  Multi-behavior recommendation (MBR) aims to jointly consider multiple
behaviors to improve the target behavior's performance. We argue that MBR
models should: (1) model the coarse-grained commonalities between different
behaviors of a user, (2) consider both individual sequence view and global
graph view in multi-behavior modeling, and (3) capture the fine-grained
differences between multiple behaviors of a user. In this work, we propose a
novel Multi-behavior Multi-view Contrastive Learning Recommendation (MMCLR)
framework, including three new CL tasks to solve the above challenges,
respectively. The multi-behavior CL aims to make different user single-behavior
representations of the same user in each view to be similar. The multi-view CL
attempts to bridge the gap between a user's sequence-view and graph-view
representations. The behavior distinction CL focuses on modeling fine-grained
differences of different behaviors. In experiments, we conduct extensive
evaluations and ablation tests to verify the effectiveness of MMCLR and various
CL tasks on two real-world datasets, achieving SOTA performance over existing
baselines. Our code will be available on
\url{https://github.com/wyqing20/MMCLR}


  We explore the generation of topological defects in the course of a dynamical
phase transition in a ring with a weak link, i.e., a SSS Josephson junction,
from the AdS/CFT correspondence. By setting different parameters of the
junction (width, steepness, depth) and the final temperature of the quench, the
configurations of the charge density and condensate of the order parameters of
the dual field theory are presented. Meanwhile, we observe that in the final
equilibrium state, variations in parameters of the junctions only affect the
configurations of the charge density and condensate of the order parameters,
without altering their values outside the junction. However, variations in the
final temperature will directly affect the values of the charge density and
condensate of the order parameters outside of the junction. Moreover, in the
final equilibrium state, we propose an analytic relation between the
gauge-invariant velocity in the two superconducting states in the SSS Josephson
junction, which agrees well with the numerical results.


  We showed in another paper [arXiv:1103.1759] that every connected graph can
be realized as the cut locus of some point on some riemannian surface $S$.
Here, criteria for the orientability of $S$ are given, and are applied to
classify the distinct, orientable, cut locus structures on graphs with four
generating cycles.


  Radiology reports have been widely used for extraction of various clinically
significant information about patients' imaging studies. However, limited
research has focused on standardizing the entities to a common
radiology-specific vocabulary. Further, no study to date has attempted to
leverage RadLex for standardization. In this paper, we aim to normalize a
diverse set of radiological entities to RadLex terms. We manually construct a
normalization corpus by annotating entities from three types of reports. This
contains 1706 entity mentions. We propose two deep learning-based NLP methods
based on a pre-trained language model (BERT) for automatic normalization.
First, we employ BM25 to retrieve candidate concepts for the BERT-based models
(re-ranker and span detector) to predict the normalized concept. The results
are promising, with the best accuracy (78.44%) obtained by the span detector.
Additionally, we discuss the challenges involved in corpus construction and
propose new RadLex terms.


  Let $X$ and $Y$ be independent variance-gamma random variables with zero
location parameter; then the exact probability density function of the ratio
$X/Y$ is derived. Some basic distributional properties are also derived,
including identification of parameter regimes under which the density is
bounded, asymptotic approximations of tail probabilities, and fractional
moments; in particular, we see that the mean is undefined. In the case that $X$
and $Y$ are independent symmetric variance-gamma random variables, an exact
formula is also given for the cumulative distribution function of the ratio
$X/Y$.


  A hypergeometric type equation satisfying certain conditions defines either a
finite or an infinite system of orthogonal polynomials. The associated special
functions are eigenfunctions of some shape invariant operators. These operators
can be analysed together and the mathematical formalism we use can be extended
in order to define other shape invariant operators. All the considered shape
invariant operators are directly related to Schrodinger type equations.


  Ab initio has been used as a label in nuclear theory for over two decades.
Its meaning has evolved and broadened over the years. We present our
interpretation, briefly review its historical use, and discuss its present-day
relation to theoretical uncertainty quantification.


  In this paper we introduce and study absolute continuity and singularity of
positive operators acting on anti-dual pairs. We establish a general theorem
that can be considered as a common generalization of various earlier
Lebesgue-type decompositions. Different algebraic and topological
characterizations of absolute continuity and singularity are supplied and also
a complete description of uniqueness of the decomposition is provided. We apply
the developed decomposition theory to some concrete objects including Hilbert
space operators, Hermitian forms, representable functionals, and additive set
functions.


  This paper generalizes recent proposals of density forecasting models and it
develops theory for this class of models. In density forecasting, the density
of observations is estimated in regions where the density is not observed.
Identification of the density in such regions is guaranteed by structural
assumptions on the density that allows exact extrapolation. In this paper, the
structural assumption is made that the density is a product of one-dimensional
functions. The theory is quite general in assuming the shape of the region
where the density is observed. Such models naturally arise when the time point
of an observation can be written as the sum of two terms (e.g., onset and
incubation period of a disease). The developed theory also allows for a
multiplicative factor of seasonal effects. Seasonal effects are present in many
actuarial, biostatistical, econometric and statistical studies. Smoothing
estimators are proposed that are based on backfitting. Full asymptotic theory
is derived for them. A practical example from the insurance business is given
producing a within year budget of reported insurance claims. A small sample
study supports the theoretical results.


  With the advent of large scale galaxy surveys, constraints on primordial
non-Gaussianity (PNG) are expected to reach ${\cal O}(f_\text{NL}) \sim 1$. In
order to fully exploit the potential of these future surveys, a deep
theoretical understanding of the signatures imprinted by PNG on the large scale
structure of the Universe is necessary. In this paper, we explore the effect of
a stochastic moving barrier on the amplitude of the non-Gaussian bias induced
by local quadratic PNG. We show that, in the peak approach to halo clustering,
the amplitude of the non-Gaussian bias will generally differ from the
peak-background split prediction unless the barrier is flat and deterministic.
For excursion set peaks with a square-root barrier, which reproduce reasonably
well the linear bias $b_1$ and mass function $\bar{n}_\text{h}$ of SO haloes,
the non-Gaussian bias amplitude is $\sim 40$% larger than the peak-background
split expectation $d\ln\bar{n}_\text{h}/d\ln\sigma_8$ for haloes of mass $\sim
10^{13} {\it h}^{-1}M_\odot$ at $z=0$. Furthermore, we argue that the effect of
PNG on squeezed configurations of the halo bispectrum differs significantly
from that predicted by standard local bias approaches. Our predictions can be
easily confirmed, or invalidated, with N-body simulations.


  Enabling mobile robots for solving challenging and diverse shape, texture,
and motion related tasks with high fidelity vision requires the integration of
novel multimodal imaging sensors and advanced fusion techniques. However, it is
associated with high cost, power, hardware modification, and computing
requirements which limit its scalability. In this paper, we propose a novel
Simultaneously Learned Auto Encoder Domain Adaptation (SAEDA)-based transfer
learning technique to empower noisy sensing with advanced sensor suite
capabilities. In this regard, SAEDA trains both source and target auto-encoders
together on a single graph to obtain the domain invariant feature space between
the source and target domains on simultaneously collected data. Then, it uses
the domain invariant feature space to transfer knowledge between different
signal modalities. The evaluation has been done on two collected datasets
(LiDAR and Radar) and one existing dataset (LiDAR, Radar and Video) which
provides a significant improvement in quadruped robot-based classification
(home floor and human activity recognition) and regression (surface roughness
estimation) problems. We also integrate our sensor suite and SAEDA framework on
two real-time systems (vacuum cleaning and Mini-Cheetah quadruped robots) for
studying the feasibility and usability.


  In the present work, we investigate the production of $P_{c}$ and
$Z_c(3900)/Z_{cs}(3985)$ states in the processes of $\pi p \rightarrow
Z_{c}(3900) P_{c} $/$Kp\rightarrow Z_{cs}(3985) P_{c}$ with an effective
Lagrangian approach. Our estimations indicates that the cross sections depends
on the model parameters and beam energies, in particular, the cross sections
for $\pi p \to Z_c(3900) P_c(4312)$ and $\pi p \to Z_c(3900) P_c(4440)$ are
similar and can reach up to 10 nb, while those for $\pi p \to Z_c(3900)
P_c(4457)$ can be 30 nb. As for $Kp \to Z_{cs}(3985)P_c$, the cross sections
are about 2.5 times smaller than those for $\pi p \to Z_c(3900) P_c$. The
discussed processes in the present work, especially $\pi p \to Z_c(3900)
P_c(4457)$, may be accessible by the the high-energy pion and kaon beams in the
facilities of J-PARC and COMPASS.


  XMM-Newton archival data for the Small Magellanic Cloud have been examined
for the presence of previously undetected X-ray pulsars. One such pulsar, with
a period of 202 s, is detected. Its position is consistent with an early B star
in the SMC and we identify it as a high mass X-ray binary (HMXB). In the course
of this study we determined the pulse period of the possible AXP CXOU
J010043.1-721134 to be 8.0 s, correcting an earlier report (Lamb et al 2002b)
of a 5.4 s period for this object. Pulse profiles and spectra for each of these
objects are presented as well as for a recently discovered (Haberl & Pietsch
2004) 263 s X-ray pulsar. Properties of an ensemble of 24 optically identified
HMXB pulsars from the SMC are investigated. The locations of the pulsars and an
additional 22 X-ray pulsars not yet identified as having high mass companions
are located predominately in the young (ages $\le 3 \times 10^{7}$ years) star
forming regions of the SMC as expected on the basis of binary evolution models.
We find no significant difference between the distribution of spin periods for
the HMXB pulsars of the SMC compared with that of the Milky Way. For those HMXB
pulsars which have Be companions we note an inverse correlation between spin
period and maximum X-ray flux density. (This anti-correlation has been
previously noted for all X-ray binary pulsars by Stella, White & Rosner 1986).
The anti-correlation for the Be binaries may be a reflection of the fact that
the spin periods and orbital periods of Be HMXBs are correlated. We note a
similar correlation between X-ray luminosity and spin period for the Be HMXB
pulsars of the Milky Way and speculate that exploitation of the correlation
could serve as a distance indicator.