  Obtaining sufficient labeled data for training deep models is often
challenging in real-life applications. To address this issue, we propose a
novel solution for single-source domain generalized semantic segmentation.
Recent approaches have explored data diversity enhancement using hallucination
techniques. However, excessive hallucination can degrade performance,
particularly for imbalanced datasets. As shown in our experiments, minority
classes are more susceptible to performance reduction due to hallucination
compared to majority classes. To tackle this challenge, we introduce a
dual-stage Feature Transform (dFT) layer within the Adversarial Semantic
Hallucination+ (ASH+) framework. The ASH+ framework performs a dual-stage
manipulation of hallucination strength. By leveraging semantic information for
each pixel, our approach adaptively adjusts the pixel-wise hallucination
strength, thus providing fine-grained control over hallucination. We validate
the effectiveness of our proposed method through comprehensive experiments on
publicly available semantic segmentation benchmark datasets (Cityscapes and
SYNTHIA). Quantitative and qualitative comparisons demonstrate that our
approach is competitive with state-of-the-art methods for the Cityscapes
dataset and surpasses existing solutions for the SYNTHIA dataset. Code for our
framework will be made readily available to the research community.


  The nonlinear responses of different materials provide useful mechanisms for
optical switching, low noise amplification, and harmonic frequency generation.
However, the nonlinear processes usually have an extremely weak nature and
require high input power to be excited. To alleviate this severe limitation, we
propose new designs of ultrathin nonlinear metasurfaces composed of patterned
graphene micro-ribbons to significantly enhance third harmonic generation (THG)
at far-infrared and terahertz (THz) frequencies. The incident wave is tightly
confined and significantly boosted along the surface of graphene in these
configurations due to the excitation of highly localized plasmons. The
bandwidth of the resonant response becomes narrower due to the introduction of
a metallic substrate below the graphene micro-ribbons, which leads to zero
transmission and standing waves inside the intermediate dielectric spacer
layer. The enhancement of the incident field, combined with the large nonlinear
conductivity of graphene, can dramatically increase the THG conversion
efficiency by several orders of magnitude. In addition, the resonant frequency
of the metasurface can be adjusted by dynamically tuning the Fermi energy of
graphene via electrical or chemical doping. As a result, the third harmonic
generated wave can be optimized and tuned to be emitted at different
frequencies without the need to change the nonlinear metasurface geometry. The
proposed nonlinear metasurfaces provide a new way to realize compact and
efficient nonlinear sources at the far-infrared and THz frequency ranges, as
well as new frequency generation and wave mixing devices which are expected to
be useful for nonlinear THz spectroscopy and noninvasive THz imaging
applications.


  The teacher-free online Knowledge Distillation (KD) aims to train an ensemble
of multiple student models collaboratively and distill knowledge from each
other. Although existing online KD methods achieve desirable performance, they
often focus on class probabilities as the core knowledge type, ignoring the
valuable feature representational information. We present a Mutual Contrastive
Learning (MCL) framework for online KD. The core idea of MCL is to perform
mutual interaction and transfer of contrastive distributions among a cohort of
networks in an online manner. Our MCL can aggregate cross-network embedding
information and maximize the lower bound to the mutual information between two
networks. This enables each network to learn extra contrastive knowledge from
others, leading to better feature representations, thus improving the
performance of visual recognition tasks. Beyond the final layer, we extend MCL
to intermediate layers and perform an adaptive layer-matching mechanism trained
by meta-optimization. Experiments on image classification and transfer learning
to visual recognition tasks show that layer-wise MCL can lead to consistent
performance gains against state-of-the-art online KD approaches. The
superiority demonstrates that layer-wise MCL can guide the network to generate
better feature representations. Our code is publicly avaliable at
https://github.com/winycg/L-MCL.


  One recent promising approach to the Visual Place Recognition (VPR) problem
has been to fuse the place recognition estimates of multiple complementary VPR
techniques using methods such as SRAL and multi-process fusion. These
approaches come with a substantial practical limitation: they require all
potential VPR methods to be brute-force run before they are selectively fused.
The obvious solution to this limitation is to predict the viable subset of
methods ahead of time, but this is challenging because it requires a predictive
signal within the imagery itself that is indicative of high performance
methods. Here we propose an alternative approach that instead starts with a
known single base VPR technique, and learns to predict the most complementary
additional VPR technique to fuse with it, that results in the largest
improvement in performance. The key innovation here is to use a dimensionally
reduced difference vector between the query image and the top-retrieved
reference image using this baseline technique as the predictive signal of the
most complementary additional technique, both during training and inference. We
demonstrate that our approach can train a single network to select performant,
complementary technique pairs across datasets which span multiple modes of
transportation (train, car, walking) as well as to generalise to unseen
datasets, outperforming multiple baseline strategies for manually selecting the
best technique pairs based on the same training data.


  Let $G$ be a countable monoid and let $A$ be an Artinian group (resp. an
Artinian module). Let $\Sigma \subset A^G$ be a closed subshift which is also a
subgroup (resp. a submodule) of $A^G$. Suppose that $\Gamma$ is a finitely
generated monoid consisting of pairwise commuting cellular automata $\Sigma \to
\Sigma$ that are also homomorphisms of groups (resp. homomorphisms of modules)
with monoid binary operation given by composition of maps. We show that the
valuation action of $\Gamma$ on $\Sigma$ satisfies a natural intrinsic
shadowing property. Generalizations are also established for families of
endomorphisms of admissible group subshifts.


  We show that non-Hermitian Ginibre random matrix behaviors emerge in
spatially-extended many-body quantum chaotic systems in the space direction,
just as Hermitian random matrix behaviors emerge in chaotic systems in the time
direction. Starting with translational invariant models, which can be
associated with dual transfer matrices with complex-valued spectra, we show
that the linear ramp of the spectral form factor necessitates that the dual
spectra have non-trivial correlations, which in fact fall under the
universality class of the Ginibre ensemble, demonstrated by computing the level
spacing distribution and the dissipative spectral form factor. As a result of
this connection, the exact spectral form factor for the Ginibre ensemble can be
used to universally describe the spectral form factor for translational
invariant many-body quantum chaotic systems in the scaling limit where $t$ and
$L$ are large, while the ratio between $L$ and $L_{\mathrm{Th}}$, the many-body
Thouless length is fixed. With appropriate variations of Ginibre models, we
analytically demonstrate that our claim generalizes to models without
translational invariance as well. The emergence of the Ginibre ensemble is a
genuine consequence of the strongly interacting and spatially extended nature
of the quantum chaotic systems we consider, unlike the traditional emergence of
Hermitian random matrix ensembles.


  We develop a quantitative method to assess the style of American poems and to
visualize a collection of poems in relation to one another. Qualitative poetry
criticism helped guide our development of metrics that analyze various
orthographic, syntactic, and phonemic features. These features are used to
discover comprehensive stylistic information from a poem's multi-layered latent
structure, and to compute distances between poems in this space. Visualizations
provide ready access to the analytical components. We demonstrate our method on
several collections of poetry, showing that it better delineates poetry style
than the traditional word-occurrence features that are used in typical text
analysis algorithms. Our method has potential applications to academic research
of texts, to research of the intuitive personal response to poetry, and to
making recommendations to readers based on their favorite poems.


  Young Stellar Objects (YSOs) in the inner Galactic region $10^0 < l < 15^0$
and $-1^0 < b < 1^0$ are studied using GLIMPSE images and GLIMPSE data
catalogue. A total number of 1107 Class I and 1566 Class II sources are
identified in this Galactic region. With the help of GLIMPSE 5.8 $\mu$m & 8
$\mu$m images, we have identified the presence of 10 major star forming sites
in the Galactic midplane, of which 8 of them are filamentary while 2 are
possible clusters of Class I $&$ II sources. The length of the identified
filaments are estimated as 8'-33' ($\sim$ 9 - 56 pc). Occurrence of
Hub-Filamentary System (HFS) is observed in many filamentary star forming
sites. Most of the Class I sources are found to be aligned along the length of
these filamentary structures, while Class II sources have a random
distribution. Mass and age distribution of 425 Class I and 241 Class II sources
associated with filaments & clusters are studied through their SED analysis.
Most of the Class I sources detected have mass $>$ 8M$_\odot$, while Class II
sources have relatively low mass regime. Class I sources have ages $\le$ 0.5
Myr, while Class II sources have ages in the range $\sim$0.1 - 3 Myr. Along
with the help of high mass star forming tracers, we demonstrate that the 10
regions studied here are forming a large number of high-mass stars.


  Why does a microwave oven work? How does biological tissue absorb
electromagnetic radiation? Astonishingly, we do not have a definite answer to
these simple questions because the microscopic processes governing the
absorption of electromagnetic waves by water are largely unclarified. This
absorption can be quantified by dielectric loss spectra, which reveal a huge
peak at a frequency of the exciting electric field of about 20 GHz and a
gradual tailing off towards higher frequencies. The microscopic interpretation
of such spectra is highly controversial and various superpositions of
relaxation and resonance processes ascribed to single-molecule or
molecule-cluster motions have been proposed for their analysis. By combining
dielectric, microwave, THz, and far-infrared spectroscopy, here we provide
nearly continuous temperature-dependent broadband spectra of water. Moreover,
we find that corresponding spectra for aqueous solutions reveal the same
features as pure water. However, in contrast to the latter, crystallization in
these solutions can be avoided by supercooling. As different spectral
contributions tend to disentangle at low temperatures, this enables to
deconvolute them when approaching the glass transition under cooling. We find
that the overall spectral development, including the 20 GHz feature (employed
for microwave heating), closely resembles the behavior known for common
supercooled liquids. Thus, water's absorption of electromagnetic waves at room
temperature is not unusual but very similar to that of glass-forming liquids at
elevated temperatures, deep in the low-viscosity liquid regime, and should be
interpreted along similar lines.


  Engaging with diverse political views is important for reaching better
collective decisions, however, users online tend to remain confined within
ideologically homogeneous spaces. In this work, we study users who are members
of these spaces but who also show a willingness to engage with diverse views,
as they have the potential to introduce more informational diversity into their
communities. Across four Reddit communities (r/Conservative, r/The_Donald,
r/ChapoTrapHouse, r/SandersForPresident), we find that these users tend to use
less hostile and more advanced and personable language, but receive fewer
social rewards from their peers compared to others. We also find that social
sanctions on the discussion community r/changemyview are insufficient to drive
them out in the short term, though they may play a role over the longer term.


  In this study, a path-based Statistical Static Timing Analysis (SSTA) is
formulated as a problem within the statistics of correlated extremes. For
extreme value statistics with correlations, a novel approach to studying such
systems, when the correlations are small, is developed. The approach considers
a system from the first principals, starting from the multivariable
characteristic function. Analytical solutions to the problem of weakly
correlated extremes are obtained in the form of corrections to the Gumbel
distribution. These solutions are compared with Monte Carlo simulations. The
applicability limits of the proposed solutions are studied. An algorithm to
estimate the covariance matrix of a timing graph is proposed.


  Stochastic dynamics with random resetting leads to a non-equilibrium steady
state. Here, we consider the thermodynamics of resetting by deriving the first
and second law for reset processes far from equilibrium. We identify the
contributions to the entropy production of the system which arise due to
resetting and show that they correspond to the rate with which information is
either erased or created. Using Landauer's principle, we derive a bound on the
amount of work that is required to maintain a resetting process. We discuss
different regimes of resetting, including a Maxwell's demon scenario where heat
is extracted from a bath at constant temperature.


  The Hofstadter problem is the lattice analog of the quantum Hall effect and
is the paradigmatic example of topology induced by an applied magnetic field.
Conventionally, the Hofstadter problem involves adding $\sim 10^4$ T magnetic
fields to a trivial band structure. In this work, we show that when a magnetic
field is added to an initially topological band structure, a wealth of
remarkable possible phases emerges. Remarkably, we find topological phases
which cannot be realized in any crystalline insulators. We prove that threading
magnetic flux through a Hamiltonian with nonzero Chern number enforces a phase
transition at fixed filling and that a 2D Hamiltonian with nontrivial Kane-Mele
invariant produces a 3D TI or 3D weak TI phase in periodic flux. We then study
fragile topology protected by the product of two-fold rotation and
time-reversal and show that there exists a 3D higher order TI phase where
corner modes are pumped by flux. We show that a model of twisted bilayer
graphene realizes this phase. Our results rely primarily on the magnetic
translation group which exists at rational values of the flux. The advent of
Moir\'e lattices also renders our work relevant experimentally. In Moir\'e
lattices, it is possible for fields of order $1-30$ T to reach one flux per
plaquette and allow access to our proposed Hofstadter topological phase.


  We solve the ghost-gluon system of Yang-Mills theory using Graphics
Processing Units (GPUs). Working in Landau gauge, we use the Dyson-Schwinger
formalism for the mathematical description as this approach is well-suited to
directly benefit from the computing power of the GPUs. With the help of a
Chebyshev expansion for the dressing functions and a subsequent appliance of a
Newton-Raphson method, the non-linear system of coupled integral equations is
linearized. The resulting Newton matrix is generated in parallel using OpenMPI
and CUDA(TM). Our results show, that it is possible to cut down the run time by
two orders of magnitude as compared to a sequential version of the code. This
makes the proposed techniques well-suited for Dyson-Schwinger calculations on
more complicated systems where the Yang-Mills sector of QCD serves as a
starting point. In addition, the computation of Schwinger functions using GPU
devices is studied.


  We introduce a smooth variance sum associated to a pair of positive definite
symmetric integral matrices $A_{m\times m}$ and $B_{n\times n}$, where $m\geq
n$. By using the oscillator representation, we give a formula for this variance
sum in terms of a smooth sum over the square of a functional evaluated on the
$B$-th Fourier coefficients of the vector valued holomorphic Siegel modular
forms which are Hecke eigenforms and obtained by the theta transfer from
$O_{A_{m\times m}}$. By using the Ramanujan bound on the Fourier coefficients
of the holomorphic cusp forms, we give a sharp upper bound on this variance
when $n=1$. As applications, we prove a cutoff phenomenon for the probability
that a unimodular lattice of dimension $m$ represents a given even number. This
gives an optimal upper bound on the sphere packing density of almost all even
unimodular lattices. Furthermore, we generalize the result of Bourgain, Rudnick
and Sarnak~\cite{Bourgain}, and also give an optimal bound on the diophantine
exponent of the $p$-integral points on any positive definite $d$-dimensional
quadric, where $d\geq 3$. This improves the best known bounds due to Ghosh,
Gorodnik and Nevo~\cite{GGN} into an optimal bound.


  A classification algorithm, called the Linear Centralization Classifier
(LCC), is introduced. The algorithm seeks to find a transformation that best
maps instances from the feature space to a space where they concentrate towards
the center of their own classes, while maximimizing the distance between class
centers. We formulate the classifier as a quadratic program with quadratic
constraints. We then simplify this formulation to a linear program that can be
solved effectively using a linear programming solver (e.g., simplex-dual). We
extend the formulation for LCC to enable the use of kernel functions for
non-linear classification applications. We compare our method with two standard
classification methods (support vector machine and linear discriminant
analysis) and four state-of-the-art classification methods when they are
applied to eight standard classification datasets. Our experimental results
show that LCC is able to classify instances more accurately (based on the area
under the receiver operating characteristic) in comparison to other tested
methods on the chosen datasets. We also report the results for LCC with a
particular kernel to solve for synthetic non-linear classification problems.


  The robust estimator presented in this paper processes each structure
independently. The scales of the structures are estimated adaptively and no
threshold is involved in spite of different objective functions. The user has
to specify only the number of elemental subsets for random sampling. After
classifying all the input data, the segmented structures are sorted by their
strengths and the strongest inlier structures come out at the top. Like any
robust estimators, this algorithm also has limitations which are described in
detail. Several synthetic and real examples are presented to illustrate every
aspect of the algorithm.


  Finite-temperature spin transport in integrable isotropic spin chains (i.e.,
spin chains with continuous nonabelian symmetries) is known to be
superdiffusive, with anomalous transport properties displaying remarkable
robustness to isotropic integrability-breaking perturbations. Using a
discrete-time classical model, we numerically study the crossover to
conventional diffusion resulting from both noisy and Floquet isotropic
perturbations of strength $\lambda$. We identify an anomalously-long crossover
time scale $t_\star \sim \lambda^{-\alpha}$ with $\alpha \approx 6$ in both
cases. We discuss our results in terms of a kinetic theory of transport that
characterizes the lifetimes of large solitons responsible for superdiffusion.


  The study of crowd dynamics is interesting because of the various
self-organization phenomena resulting from the interactions of many
pedestrians, which may improve or obstruct their flow. Besides formation of
lanes of uniform walking direction and oscillations at bottlenecks at moderate
densities, it was recently discovered that stop-and-go waves [D. Helbing et
al., Phys. Rev. Lett. 97, 168001 (2006)] and a phenomenon called "crowd
turbulence" can occur at high pedestrian densities [D. Helbing et al., Phys.
Rev. E 75, 046109 (2007)]. Although the behavior of pedestrian crowds under
extreme conditions is decisive for the safety of crowds during the access to or
egress from mass events as well as for situations of emergency evacuation,
there is still a lack of empirical studies of extreme crowding. Therefore, this
paper discusses how one may study high-density conditions based on suitable
video data. This is illustrated at the example of pilgrim flows entering the
previous Jamarat Bridge in Mina, 5 kilometers from the Holy Mosque in Makkah,
Saudi-Arabia. Our results reveal previously unexpected pattern formation
phenomena and show that the average individual speed does not go to zero even
at local densities of 10 persons per square meter. Since the maximum density
and flow are different from measurements in other countries, this has
implications for the capacity assessment and dimensioning of facilities for
mass events. When conditions become congested, the flow drops significantly,
which can cause stop-and-go waves and a further increase of the density until
critical crowd conditions are reached. Then, "crowd turbulence" sets in, which
may trigger crowd disasters.


  We prove that the simple condition on the potential V, \int exp(-t V) <
\infty for all t>0, is sufficient for the lattice approximation of the trace
Tr[A exp(-b H)] with (Re b)>0 to work for all bounded functions A and a large
class of potentials. As a by-product we obtain an explicit bound for the
real-temperature lattice kernels.


  We theoretically characterize the semiclassical dynamics of an ensemble of
atoms after a sudden quench across a driven-dissipative second-order phase
transition. The atoms are driven by a laser and interact via conservative and
dissipative long-range forces mediated by the photons of a single-mode cavity.
These forces can cool the motion and, above a threshold value of the laser
intensity, induce spatial ordering. We show that the relaxation dynamics
following the quench exhibits a long prethermalizing behaviour which is first
dominated by coherent long-range forces, and then by their interplay with
dissipation. Remarkably, dissipation-assisted prethermalization is orders of
magnitude longer than prethermalization due to the coherent dynamics. We show
that it is associated with the creation of momentum-position correlations,
which remain nonzero for even longer times than mean-field predicts. This
implies that cavity cooling of an atomic ensemble into the selforganized phase
can require longer time scales than the typical experimental duration. In
general, these results demonstrate that noise and dissipation can substantially
slow down the onset of thermalization in long-range interacting many-body
systems.


  We examine the Bose-Einstein-condensate (BEC) equivalent of transverse
aspects of laser resonators. We model numerically repeated focussing of a
2-dimensional BEC, which could be achieved in practice by a series of far
off-resonant light pulses. We show for a range of non-linear coefficients that
such a series of light pulses traps the BEC. We also model a combination of
repeated focussing and loss, which could be achieved with a series of light
pulses, some far off-resonant, some resonant. In analogy to light repeatedly
being focussed and locally absorbed by passing through a laser resonator, a
small proportion (in our model up to 10%) of the BEC is shaped into
Hermite-Gaussian-like modes; the remainder of the BEC is lost. This happens
irrespective of the presence of a harmonic trap. We show that astigmatic
focussing of the resulting Hermite-Gaussian-like modes, which can be achieved
by a pair of off-resonant light pulses, results in a specific number of
vortices.


  A new straightforward universal blind detection algorithm for linear Gaussian
channel with ISI is given. A new error exponent is derived, which is better
than Gallager's random coding error exponent.


  We show that the dynamics of a laser driven Rydberg gas in the limit of
strong dephasing is described by a master equation with manifest kinetic
constraints. The equilibrium state of the system is uncorrelated but the
constraints in the dynamics lead to spatially correlated collective relaxation
reminiscent of glasses. We study and quantify the evolution towards equilibrium
in one and two dimensions, and analyze how the degree of glassiness and the
relaxation time are controlled by the interaction strength between Rydberg
atoms. We also find that spontaneous decay of Rydberg excitations leads to an
interruption of glassy relaxation that takes the system to a highly correlated
non-equilibrium stationary state. The results presented here, which are in
principle also applicable other systems such as polar molecules and atoms with
large magnetic dipole moments, show that the collective behavior of cold atomic
and molecular ensembles can be similar to that found in soft condensed-matter
systems.


  We demonstrate laser slowing of a hot thulium atomic beam using the nearly
closed cycling transition
$4\textrm{f}^{13}6\textrm{s}^2(^2\textrm{F}^\circ)(J=7/2)\leftrightarrow4\textrm{f}^{12}(^3\textrm{H}_5)5\textrm{d}_{3/2}6\textrm{s}^2(J=9/2)$
at 410.6 nm. Atoms are decelerated to velocities around 25 m/s by a 40 cm
Zeeman slower. The flux of slowed atoms is evaluated as $10^7
\textrm{s}^{-1}\textrm{cm}^{-2}$. The experiment explicitly indicates the
possibility of trapping Tm atoms in a magneto-optical trap.


  We study the effect of electron interaction on the spin-splitting and the
$g$-factor in graphene in perpendicular magnetic field using the Hartree and
Hubbard approximations within the Thomas-Fermi model. We found that the
$g$-factor is enhanced in comparison to its free electron value $g=2$ and
oscillates as a function of the filling factor $\nu $ in the range $2\leq
g^{\ast}\lesssim 4$ reaching maxima at even $\nu $ and minima at odd $\nu $. We
outline the role of charged impurities in the substrate, which are shown to
suppress the oscillations of the $g^{\ast}$-factor. This effect becomes
especially pronounced with the increase of the impurity concentration, when the
effective $g$-factor becomes independent of the filling factor reaching a value
of $g^{\ast}\approx 2.3$. A relation to the recent experiment is discussed.


  We propose to measure tagged deep inelastic scattering from light nuclei
(deuterium and $^4$He) by detecting the low energy nuclear spectator recoil (p,
$^3$H and $^3$He) in addition to the scattered electron. The proposed
experiment will provide stringent tests leading to clear differentiation
between the many models describing the EMC effect, by accessing the bound
nucleon virtuality through its initial momentum at the point of interaction.
Indeed, conventional nuclear physics explanations of the EMC effect mainly
based on Fermi motion and binding effects yield very different predictions than
more exotic scenarios, where bound nucleons basically loose their identity when
embedded in the nuclear medium. By distinguishing events where the interacting
nucleon was slow, as described by a mean field scenario, or fast, very likely
belonging to a correlated pair, will clearly indicate which phenomenon is
relevant to explain the EMC effect. An important challenge for such
measurements using nuclear spectators is the control of the theoretical
framework and, in particular, final state interactions. This experiment will
directly provide the necessary data needed to test our understanding of
spectator tagging and final state interactions in $^2$H and $^4$He and their
impact on the semi-inclusive measurements of the EMC effect described above.


  We derive a large deviation principle for the empirical measure of zeros of
random polynomials with i.i.d. exponential coefficients.


  Understanding how the predictions of deep learning models are formed during
the training process is crucial to improve model performance and fix model
defects, especially when we need to investigate nontrivial training strategies
such as active learning, and track the root cause of unexpected training
results such as performance degeneration.
  In this work, we propose a time-travelling visual solution DeepVisualInsight
(DVI), aiming to manifest the spatio-temporal causality while training a deep
learning image classifier. The spatio-temporal causality demonstrates how the
gradient-descent algorithm and various training data sampling techniques can
influence and reshape the layout of learnt input representation and the
classification boundaries in consecutive epochs. Such causality allows us to
observe and analyze the whole learning process in the visible low dimensional
space. Technically, we propose four spatial and temporal properties and design
our visualization solution to satisfy them. These properties preserve the most
important information when inverse-)projecting input samples between the
visible low-dimensional and the invisible high-dimensional space, for causal
analyses. Our extensive experiments show that, comparing to baseline
approaches, we achieve the best visualization performance regarding the
spatial/temporal properties and visualization efficiency. Moreover, our case
study shows that our visual solution can well reflect the characteristics of
various training scenarios, showing good potential of DVI as a debugging tool
for analyzing deep learning training processes.


  There has been much discussion of the right to explanation in the EU General
Data Protection Regulation, and its existence, merits, and disadvantages.
Implementing a right to explanation that opens the black box of algorithmic
decision-making faces major legal and technical barriers. Explaining the
functionality of complex algorithmic decision-making systems and their
rationale in specific cases is a technically challenging problem. Some
explanations may offer little meaningful information to data subjects, raising
questions around their value. Explanations of automated decisions need not
hinge on the general public understanding how algorithmic systems function.
Even though such interpretability is of great importance and should be pursued,
explanations can, in principle, be offered without opening the black box.
Looking at explanations as a means to help a data subject act rather than
merely understand, one could gauge the scope and content of explanations
according to the specific goal or action they are intended to support. From the
perspective of individuals affected by automated decision-making, we propose
three aims for explanations: (1) to inform and help the individual understand
why a particular decision was reached, (2) to provide grounds to contest the
decision if the outcome is undesired, and (3) to understand what would need to
change in order to receive a desired result in the future, based on the current
decision-making model. We assess how each of these goals finds support in the
GDPR. We suggest data controllers should offer a particular type of
explanation, unconditional counterfactual explanations, to support these three
aims. These counterfactual explanations describe the smallest change to the
world that can be made to obtain a desirable outcome, or to arrive at the
closest possible world, without needing to explain the internal logic of the
system.


  We examine large-eddy-simulation modeling approaches and computational
performance of two open-source computational fluid dynamics codes for the
simulation of atmospheric boundary layer (ABL) flows that are of direct
relevance to wind energy production. The first is NekRS, a high-order,
unstructured-grid, spectral element code. The second, AMR-Wind, is a
block-structured, second-order finite-volume code with adaptive-mesh-refinement
capabilities. The objective of this study is to co-develop these codes in order
to improve model fidelity and performance for each. These features will be
critical for running ABL-based applications such as wind farm analysis on
advanced computing architectures. To this end, we investigate the performance
of NekRS and AMR-Wind on the Oak Ridge Leadership Facility supercomputers
Summit, using 4 to 800 nodes (24 to 4,800 NVIDIA V100 GPUs), and Crusher, the
testbed for the Frontier exascale system using 18 to 384 Graphics Compute Dies
on AMD MI250X GPUs. We compare strong- and weak-scaling capabilities, linear
solver performance, and time to solution. We also identify leading inhibitors
to parallel scaling.


  We study the Kondo effect and related transport properties in orbitally
degenerate vertical quantum dot systems with plural electrons. Applying the
non-crossing approximation to the three-orbital Anderson impurity model with
the finite Coulomb interaction and Hund-coupling, we investigate the
magnetic-field dependence of the conductance and thermopower. We also introduce
an additional orbital splitting to take account of the realistic many-body
effect in the vertical quantum dot system. It is clarified how the
three-orbital Kondo effect influences the transport properties via the
modulation of the Kondo temperature and unitary limit of transport quantities
due to the change of the symmetry in the system.


  With every family of finitely many subsets of a finite-dimensional vector
space over the Galois-field with two elements we associate a cyclic transversal
polytope. It turns out that those polytopes generalize several well-known
polytopes that are relevant in combinatorial optimization, among them cut
polytopes as well as stable set and matching polytopes. We introduce the class
of lifted odd-set inequalities and prove results demonstrating their strength.
In particular, we show that they suffice to describe cyclic transversal
polytopes if the union of the sets in the family has rank at most two. We also
describe extended formulations for cyclic transversal polytopes and introduce a
special relaxation hierarchy for them.


  We briefly review some results in the theory of quantum $W_3$ gravity in the
chiral gauge. We compare them with similar results in the analogous but simpler
cases of $d=2$ induced gauge theories and $d=2$ induced gravity.


  This review describes the main experimental facts and a number of theoretical
models concerning the pseudogap state in high - temperature superconductors. On
the phase diagram of HTSC - cuprates the pseudogap state is observed in the
region of current carrier concentrations less than optimal and leads to a
number of anomalies of electronic properties. These pseudogap anomalies
apparently are due to fluctuations of antiferromagnetic short - range order
developing as system moves towards antiferromagnetic region on the phase
diagram. Electron interaction with these fluctuations leads to anisotropic
renormalization of electronic spectrum and non - Fermi liquid behavior on
certain parts of the Fermi surface. We also discuss some simple theoretical
models describing basic properties of the pseudogap state, including the
anomalies of superconducting state due to pseudogap renormalization of
electronic spectrum.


  Constructing high-fidelity control fields that are robust to control, system,
and/or surrounding environment uncertainties is a crucial objective for quantum
information processing. Using the two-state Landau-Zener model for illustrative
simulations of a controlled qubit, we generate optimal controls for \pi/2- and
\pi-pulses, and investigate their inherent robustness to uncertainty in the
magnitude of the drift Hamiltonian. Next, we construct a quantum-control
protocol to improve system-drift robustness by combining environment-decoupling
pulse criteria and optimal control theory for unitary operations. By
perturbatively expanding the unitary time-evolution operator for an open
quantum system, previous analysis of environment-decoupling control pulses has
calculated explicit control-field criteria to suppress environment-induced
errors up to (but not including) third order from \pi/2- and \pi-pulses. We
systematically integrate this criteria with optimal control theory,
incorporating an estimate of the uncertain parameter, to produce improvements
in gate fidelity and robustness, demonstrated via a numerical example based on
double quantum dot qubits. For the qubit model used in this work, post facto
analysis of the resulting controls suggests that realistic control-field
fluctuations and noise may contribute just as significantly to gate errors as
system and environment fluctuations.


  Inrecent time a rapid increase in the number of smart devices and user
applications have generated an intensity volume of data traffic from/to a
cellular network. So the Long Term Evaluation(LTE)network is facing some
issuesdifficulties ofthebase station and infrastructure in terms of upgrade and
configuration becausethere is no concept of BSC (Base Station Controller) of 2G
and RNC (Radio Network Controller) of 3G to control several BTS/NB. Only 4G
(LTE) all the eNBs areinterconnected for traffic flow from UE (user equipment)
to core switch. Determination of capacity of a linkof such a network is a
challenging job since each node offers its own traffic andat the same time
conveys traffic of other nodes.In this paper, we apply maximum flow algorithm
including the binary search techniqueto solve the traffic flow of radio
networkandinterconnected eNBs of the LTE network. The throughput of the LTE
network shown graphically under the QPSK and 16-QAM


  This article gives a new insight of kernel-based (approximation) methods to
solve the high-dimensional stochastic partial differential equations. We will
combine the techniques of meshfree approximation and kriging interpolation to
extend the kernel-based methods for the deterministic data to the stochastic
data. The main idea is to endow the Sobolev spaces with the probability
measures induced by the positive definite kernels such that the Gaussian random
variables can be well-defined on the Sobolev spaces. The constructions of these
Gaussian random variables provide the kernel-based approximate solutions of the
stochastic models. In the numerical examples of the stochastic Poisson and heat
equations, we show that the approximate probability distributions are
well-posed for various kinds of kernels such as the compactly supported kernels
(Wendland functions) and the Sobolev-spline kernels (Mat\'ern functions).


  We present a tool, keep me around (kma), a suite of python scripts and an R
package that finds retained introns in RNA-Seq experiments and incorporates
biological replicates to reduce the number of false positives when detecting
retention events. kma uses the results of existing quantification tools that
probabilistically assign multi-mapping reads, thus interfacing easily with
transcript quantification pipelines. The data is represented in a convenient,
database style format that allows for easy aggregation across introns, genes,
samples, and conditions to allow for further exploratory analysis.


  We introduce a new class of $(\alpha,\beta)$-type exact solutions in Finsler
gravity closely related to the well-known pp-waves in general relativity. Our
class contains most of the exact solutions currently known in the literature as
special cases. The linearized versions of these solutions may be interpretted
as Finslerian gravitational waves, and we investigate the physical effect of
such waves. More precisely, we compute the Finslerian correction to the radar
distance along an nterferometer arm at the moment a Finslerian gravitational
wave passes a detector. We come to the remarkable conclusion that the effect of
a Finslerian gravitational wave on an interferometer is indistinguishable from
that of standard gravitational wave in general relativity. Along the way we
also physically motivate a modification of the Randers metric and prove that it
has some very interesting properties.


  One key issue of the foundation of statistical mechanics is the emergence of
equilibrium ensembles in isolated and closed quantum systems. Recently, it was
predicted that in the thermodynamic ($N\rightarrow\infty$) limit of large
quantum many-body systems canonical density matrices emerge for small
subsystems from almost all pure states. This notion of canonical typicality is
assumed to originate from the entanglement between subsystem and environment
and the resulting intrinsic quantum complexity of the many-body state. For
individual eigenstates it has been shown that local observables show thermal
properties provided the eigenstate thermalization hypothesis holds, which
requires the system to be quantum chaotic. In the present paper, we study the
emergence of thermal states in the regime of a quantum analog of a mixed phase
space. Specifically, we study the emergence of the canonical density matrix of
an impurity upon reduction from isolated energy eigenstates of a large but
finite quantum system the impurity is embedded in. Our system can be tuned by
means of a single parameter from quantum integrability to quantum chaos and
corresponds in between to a system with mixed quantum phase space. We show that
the probability for finding a canonical density matrix when reducing the
ensemble of energy eigenstates of the finite many-body system can be
quantitatively controlled and tuned by the degree of quantum chaos present. For
the transition from quantum integrability to quantum chaos we find a continuous
and universal (i.e. size independent) relation between the fraction of
canonical eigenstates and the degree of chaoticity as measured by the Brody
parameter or the Shannon entropy.


  A new method is discussed which vastly simplifies one of the two integrals
over AdS(d+1) required to compute exchange graphs for 4-point functions of
scalars in the AdS/CFT correspondence. The explicit form of the bulk-to-bulk
propagator is not required. Previous results for scalar, gauge boson and
graviton exchange are reproduced, and new results are given for massive
vectors. It is found that precisely for the cases that occur in the AdS(5) X
S(5) compactification of Type IIB supergravity, the exchange diagrams reduce to
a finite sum of graphs with quartic scalar vertices. The analogous integrals in
n-point scalar diagrams for n>4 are also evaluated.


  Preliminary results on inclusive neutral pion production in d-Au collisions
at sqrt(s_NN) = 200 GeV in the pseudo-rapidity range 0<eta<1 are presented. The
measurement is performed using the STAR Barrel Electromagnetic calorimeter
(BEMC). In this paper, the analysis of the first BEMC hadron measurement is
described and the results are compared with earlier RHIC findings. The pi0
invariant differential cross sections show good agreement with next-to-leading
order (NLO) perturbative QCD calculations.


  The quasistatic rate-independent damage combined with linearized plasticity
with hardening at small strains is investigated. The fractional-step time
discretisation is devised with the purpose to obtain a numerically efficient
scheme converging possibly to a physically relevant stress-driven solutions,
which however is to be verified a-posteriori by using a suitable integrated
variant of the maximum-dissipation principle. Gradient theories both for damage
and for plasticity are considered to make the scheme numerically stable with
guaranteed convergence within the class of weak solutions. After finite-element
approximation, this scheme is computationally implemented and illustrative
2-dimensional simulations are performed.


  In the (M+1)SSM an additional gauge singlet Weyl spinor appears in the
neutralino sector. For a large part of the parameter space this approximative
eigenstate is the true LSP. Then most sparticle decays proceed via an
additional cascade involving the NLSP -> LSP transition, where the NLSP is the
non-singlet next-to-lightest neutralino. We present a comprehensive list of all
processes, which contribute to the NLSP -> LSP transition, the partial widths
and the total NLSP decay rate. We perform a scan of the parameters of the model
compatible with universal soft terms, and find that the NLSP life time can be
quite large, leading to macroscopically displaced vertices. Our results imply
that the signatures for sparticle production in the (M+1)SSM can be very
different from the MSSM, and are important for calculations of the abundance of
dark matter in this model.


  Azimuthal decorrelations between the two central jets with the largest
transverse momenta are sensitive to the dynamics of events with multiple jets.
We present a measurement of the normalized differential cross section based on
the full dataset (L=36/pb) acquired by the ATLAS detector during the 2010
sqrt(s)=7 TeV proton-proton run of the LHC. The measured distributions include
jets with transverse momenta up to 1.3 TeV, probing perturbative QCD in a high
energy regime.


  We propose the study of mathematical ludology, which aims to formally
interrogate questions of interest to game studies and game design in
particular. The goal is to extend our mathematical understanding of complex
games beyond decision-making---the typical focus of game theory and artificial
intelligence efforts---to explore other aspects such as game mechanics,
structure, relationships between games, and connections between game rules and
user-interfaces, as well as exploring related gameplay phenomena and typical
player behavior. In this paper, we build a basic foundation for this line of
study by developing a hierarchy of game descriptions, mathematical formalism to
compactly describe complex discrete games, and equivalence relations on the
space of game systems.


  Based on the intermediate energy radioactive Ion Beam Line in Lanzhou (RIBLL)
of Heavy Ion Research Facility in Lanzhou (HIRFL) and Low Energy Radioactive
Ion Beam Line (GIRAFFE) of Beijing National Tandem Accelerator Lab (HI13), the
radioactive ion beam physics and nuclear astrophysics will be researched in
detail. The key scientific problems are: the nuclear structure and reaction for
nuclear far from $\beta$-stability line; the synthesize of new nuclides near
drip lines and new super heavy nuclides; the properties of asymmetric nuclear
matter with extra large isospin and some nuclear astro- reactions.


  We propose that the overlapping shock fronts from young supernova remnants
produce a locally unsteady, but globally steady large scale spiral shock front
in spiral galaxies, where star formation and therefore massive star explosions
correlate geometrically with spiral structure. This global shock front with its
steep gradients in temperature, pressure and associated electric fields will
produce drifts, which in turn give rise to a strong sheet-like electric
current, we propose. This sheet current then produces a large scale magnetic
field, which is regular, and connected to the overall spiral structure. This
rejuvenates the overall magnetic field continuously, and also allows to
understand that there is a regular field at all in disk galaxies. This proposal
connects the existence of magnetic fields to accretion in disks. We not yet
address all the symmetries of the magnetic field here; the picture proposed
here is not complete. X-ray observations may be able to test it already.


  We report on a NuSTAR observation of the young, energetic pulsar PSR
J1617-5055. Parkes Observatory 3 GHz radio observations of the pulsar (taken
about 7 years before the NuSTAR observations) are also reported here. NuSTAR
detected pulsations at a frequency of $f\approx14.4$ Hz ($P\approx69.44$ ms)
and, in addition, the observation was long enough to measure the source's
frequency derivative, $\dot{f}\approx-2.8\times10^{-11}$ Hz s$^{-1}$. We find
that the pulsar shows one peak per period at both hard X-ray and radio
wavelengths, but that the hard X-ray pulse is broader (having a duty cycle of
$\sim 0.7$), than the radio pulse (having a duty cycle of $\sim 0.08$).
Additionally, the radio pulse is strongly linearly polarized. J1617's
phase-integrated hard X-ray spectrum is well fit by an absorbed power-law
model, with a photon index $\Gamma=1.59\pm 0.02$. The hard X-ray pulsations are
well described by three Fourier harmonics, and have a pulsed fraction that
increases with energy. We also fit the phase-resolved NuSTAR spectra with an
absorbed power-law model in five phase bins and find that the photon index
varies with phase from $\Gamma = 1.52\pm 0.03$ at phases around the flux
maximum to $\Gamma=1.79\pm 0.06$ around the flux minimum. Lastly, we compare
our results with other pulsars whose magnetospheric emission is detected at
hard X-ray energies and find that, similar to previous studies, J1617's hard
X-ray properties are more similar to the MeV pulsars than the GeV pulsars.


  A completely non-perturbative estimate is given for the u/d and strange quark
masses in quenched QCD using O(a) improved fermions and, for comparison, Wilson
fermions. For improved fermions we find m_{u/d}^MSbar(\mu=2 GeV) = 4.4(2) MeV,
m_s^MSbar(\mu=2 GeV) = 105(4) MeV when using r_0 to set the physical scale.


  Quasar redshifts require the best possible precision and accuracy for a
number of applications, such as setting the velocity scale for outflows as well
as measuring small-scale quasar-quasar clustering. The most reliable redshift
standard in luminous quasars is arguably the narrow [OIII]
$\lambda\lambda$4959,5007 emission line doublet in the rest-frame optical. We
use previously published [OIII] redshifts obtained using near-infrared spectra
in a sample of 45 high-redshift (z > 2.2) quasars to evaluate redshift
measurement techniques based on rest-frame ultraviolet spectra. At redshifts
above z = 2.2 the MgII $\lambda$2798 emission line is not available in
observed-frame optical spectra, and the most prominent unblended and unabsorbed
spectral feature available is usually CIV $\lambda$1549. Peak and centroid
measurements of the CIV profile are often blueshifted relative to the
rest-frame of the quasar, which can significantly bias redshift determinations.
We show that redshift determinations for these high-redshift quasars are
significantly correlated with the emission-line properties of CIV (i.e., the
equivalent width, or EW, and the full width at half maximum, or FWHM) as well
as the luminosity, which we take from the Sloan Digital Sky Survey Data Release
7. We demonstrate that empirical corrections based on multiple regression
analyses yield significant improvements in both the precision and accuracy of
the redshifts of the most distant quasars and are required to establish
consistency with redshifts determined in more local quasars.


  In this correspondence, we study the secure multiantenna transmission with
artificial noise (AN) under imperfect channel state information in the presence
of spatially randomly distributed eavesdroppers. We derive the optimal
solutions of the power allocation between the information signal and the AN for
minimizing the secrecy outage probability (SOP) under a target secrecy rate and
for maximizing the secrecy rate under a SOP constraint, respectively. Moreover,
we provide an interesting insight that channel estimation error affects the
optimal power allocation strategy in opposite ways for the above two
objectives. When the estimation error increases, more power should be allocated
to the information signal if we aim to decrease the rate-constrained SOP,
whereas more power should be allocated to the AN if we aim to increase the
SOP-constrained secrecy rate.


  News media is expected to uphold unbiased reporting. Yet they may still
affect public opinion by selectively including or omitting events that support
or contradict their ideological positions. Prior work in NLP has only studied
media bias via linguistic style and word usage. In this paper, we study to
which degree media balances news reporting and affects consumers through event
inclusion or omission. We first introduce the task of detecting both partisan
and counter-partisan events: events that support or oppose the author's
political ideology. To conduct our study, we annotate a high-quality dataset,
PAC, containing 8,511 (counter-)partisan event annotations in 304 news articles
from ideologically diverse media outlets. We benchmark PAC to highlight the
challenges of this task. Our findings highlight both the ways in which the news
subtly shapes opinion and the need for large language models that better
understand events within a broader context. Our dataset can be found at
https://github.com/launchnlp/Partisan-Event-Dataset.


  The diagonal subvariety on a product of two CM elliptic curves, is presented
as an example of a dimension one subvariety, that is pre-periodic only if the
respective projection on the product of two projective lines is also
pre-periodic.


  We propose an efficient method for calculating level anti-crossing spectra
(LAC spectra) of interacting paramagnetic defect centers in crystals. By LAC
spectra we mean the magnetic field dependence of the photoluminescence
intensity of paramagnetic color centers: such field dependences often exhibit
sharp features, such as peaks or dips, originating from LACs in the spin
system. Our approach takes into account the electronic Zeeman interaction with
the external magnetic field, dipole-dipole interaction of paramagnetic centers,
hyperfine coupling of paramagnetic defects to magnetic nuclei and zero-field
splitting. By using this method, we can not only obtain the positions of lines
in LAC spectra, but also reproduce their shapes as well as the relative
amplitudes of different lines. As a striking example, we present a calculation
of LAC spectra in diamond crystals containing negatively charged NV centers.


  We obtain the kinematic distributions of stars (synthetic model line
absorption) and ionized gas (H$\alpha$ line emission) for star-forming regions
residing in CALIFA survey tidally perturbed (perturbed) and non-tidally
perturbed (control) galaxies. We set the uncertainties of the velocity
dispersion by measuring the statistical variability of the datasets themselves.
Using these adopted uncertainties and considering the sensitivity of the
grating device, we establish thresholds of reliability that allow us to select
reliable velocity dispersions. From this selection, we pair the star-forming
spaxels between control and perturbed galaxies at the closest shifts in
velocity (de-redshifting). We compare their respective distributions of
velocity dispersion. In perturbed galaxies, median velocity dispersions for the
stellar and gaseous components are minimally higher and equal, respectively,
than those in control galaxies. The spread in velocity dispersion and the
velocity shift - velocity dispersion space agree with this result. Unlike the
well-known trend in strongly interacting systems, the stellar and ionized-gas
motions are not disturbed by the influence of close companions. For the gaseous
component, this result is due to the poor statistical variability of its data,
a consequence of the tightness in velocity dispersion derived from high
spectral line intensities. This analysis concludes the series, which previously
showed star-forming regions in galaxies with close companions undergoing more
prominent gas inflows, resulting in differences in their star formation and
consequent metal content.


  In this short paper, we review the Euler-Rodrigues formula for
three-dimensional rotation via fractional powers of matrices. We derive the
rotations by any angle through the spectral behavior of the fractional powers
of the rotation matrix by $\frac{\pi}{2}$ in $\mathbb{R}^3$ about some axis.


  The fullerene C$_{60}$ has four infrared-active vibrational transitions at
7.0, 8.5, 17.4 and 18.9 $\mu$m. We have previously observed emission features
at 17.4 and 18.9 $\mu$m in the reflection nebula NGC 7023 and demonstrated
spatial correlations suggestive of a common origin. We now confirm our earlier
identification of these features with C$_{60}$ by detecting a third emission
feature at 7.04 $\pm$ 0.05 $\mu$m in NGC 7023. We also report the detection of
these three C$_{60}$ features in the reflection nebula NGC 2023. Our
spectroscopic mapping of NGC 7023 shows that the 18.9 $\mu$m C$_{60}$ feature
peaks on the central star and that the 16.4 $\mu$m emission feature due to
polycyclic aromatic hydrocarbons peaks between the star and a nearby
photodissociation front. The observed features in NGC 7023 are consistent with
emission from UV-excited gas-phase C$_{60}$. We find that 0.1-0.6% of
interstellar carbon is in C$_{60}$; this abundance is consistent with those
from previous upper limits and possible fullerene detections in the
interstellar medium. This is the first firm detection of neutral C$_{60}$ in
the interstellar medium.


  Pre-trained models have proved to be powerful in enhancing task-oriented
dialog systems. However, current pre-training methods mainly focus on enhancing
dialog understanding and generation tasks while neglecting the exploitation of
dialog policy. In this paper, we propose GALAXY, a novel pre-trained dialog
model that explicitly learns dialog policy from limited labeled dialogs and
large-scale unlabeled dialog corpora via semi-supervised learning.
Specifically, we introduce a dialog act prediction task for policy optimization
during pre-training and employ a consistency regularization term to refine the
learned representation with the help of unlabeled dialogs. We also implement a
gating mechanism to weigh suitable unlabeled dialog samples. Empirical results
show that GALAXY substantially improves the performance of task-oriented dialog
systems, and achieves new state-of-the-art results on benchmark datasets:
In-Car, MultiWOZ2.0 and MultiWOZ2.1, improving their end-to-end combined scores
by 2.5, 5.3 and 5.5 points, respectively. We also show that GALAXY has a
stronger few-shot ability than existing models under various low-resource
settings.


  Rapid miniaturization and cost reduction of computing, along with the
availability of wearable and implantable physiological sensors have led to the
growth of human Body Area Network (BAN) formed by a network of such sensors and
computing devices. One promising application of such a network is wearable
health monitoring where the collected data from the sensors would be
transmitted and analyzed to assess the health of a person. Typically, the
devices in a BAN are connected through wireless (WBAN), which suffers from
energy inefficiency due to the high-energy consumption of wireless
transmission. Human Body Communication (HBC) uses the relatively low loss human
body as the communication medium to connect these devices, promising order(s)
of magnitude better energy-efficiency and built-in security compared to WBAN.
In this paper, we demonstrate a health monitoring device and system built using
Commercial-Off-The- Shelf (COTS) sensors and components, that can collect data
from physiological sensors and transmit it through a) intra-body HBC to another
device (hub) worn on the body or b) upload health data through HBC-based
human-machine interaction to an HBC capable machine. The system design
constraints and signal transfer characteristics for the implemented HBC-based
wearable health monitoring system are measured and analyzed, showing reliable
connectivity with >8x power savings compared to Bluetooth lowenergy (BTLE).


  We consider the theory of a higher-derivative (HD) real scalar field $\phi$
coupled to a complex scalar $\sigma$, the coupling of the $\phi$ and $\sigma$
being given by two types, $\lambda_{\sigma\phi}\sigma^\dagger \sigma\phi^{2}$
and $\xi_{\sigma\phi}\sigma^\dagger \sigma\left(\partial_{\mu}\phi\right)^{2}$.
We evaluate $\phi$ one-loop corrections $\delta V(\sigma)$ to the effective
potential of $\sigma$, both the contribution from the positive norm part of
$\phi$ and that from the {\it negative norm part} (ghost). We show that $\delta
V(\sigma_{\rm cl})$ at $\sigma_{\rm cl}\to \infty$, where $\sigma_{\rm cl}$ is
a classical value of $\sigma$, is positive, implying the stability of $\delta
V(\sigma_{\rm cl})$ by the HD 1-loop radiative corrections at high energy.


  We consider a dense, ad hoc wireless network, confined to a small region. The
wireless network is operated as a single cell, i.e., only one successful
transmission is supported at a time. Data packets are sent between
sourcedestination pairs by multihop relaying. We assume that nodes
self-organise into a multihop network such that all hops are of length d
meters, where d is a design parameter. There is a contention based multiaccess
scheme, and it is assumed that every node always has data to send, either
originated from it or a transit packet (saturation assumption). In this
scenario, we seek to maximize a measure of the transport capacity of the
network (measured in bit-meters per second) over power controls (in a fading
environment) and over the hop distance d, subject to an average power
constraint. We first argue that for a dense collection of nodes confined to a
small region, single cell operation is efficient for single user decoding
transceivers. Then, operating the dense ad hoc wireless network (described
above) as a single cell, we study the hop length and power control that
maximizes the transport capacity for a given network power constraint.


  We look at a real scalar field in thermal equilibrium in the context of the
new normal ordering and field split defined by Evans and Steer. We show that
the field split defines a natural canonical transformation, but that this
transformation differs from others known in thermal field theory.


  There have been several approaches to provisioning traffic between core
network nodes in Internet Service Provider (ISP) networks. Such approaches aim
to minimize network delay, increase network capacity, and enhance network
security services. MATE (Multipath Adaptive Traffic Engineering) protocol has
been proposed for multipath adaptive traffic engineering between an ingress
node (source) and an egress node (destination). Its novel idea is to avoid
network congestion and attacks that might exist in edge and node disjoint paths
between two core network nodes.
  This paper builds an adaptive, robust, and reliable traffic engineering
scheme for better performance of communication network operations. This will
also provision quality of service (QoS) and protection of traffic engineering
to maximize network efficiency. Specifically, we present a new approach, S-MATE
(secure MATE) is developed to protect the network traffic between two core
nodes (routers or switches) in a cloud network. S-MATE secures against a single
link attack/failure by adding redundancy in one of the operational paths
between the sender and receiver. The proposed scheme can be built to secure
core networks such as optical and IP networks.


  New THz data on the optical conductivity of Pb are presented as well as a
detailed Eliashberg analysis with particular emphasis on phonon-assisted
processes not included in a BCS approach. Consideration of the optical
self-energy instead of the conductivity itself helps highlight the differences
with BCS predictions. Predicted coherence peaks are observed in the optical
scattering rates. Impurities enhance the optical effective mass at zero
frequency by an order of magnitude and induce a large peak at twice the gap in
agreement with theory. This work illustrates the usefulness of the optical
self-energy for the analysis of data.


  Exact ground states, charge densities and excitation energies are found using
valence bond methods for N-site modified Hubbard models with uniform spacing.
At the neutral-ionic transition (NIT), the ground state has a symmetry
crossover in 4n, 4n+2 rings with periodic and antiperiodic boundary conditions,
respectively. The modified Hubbard model has a continuous NIT between a
diamagnetic band insulator on the paired side and a paramagnetic Mott insulator
on the covalent side. The singlet-triplet (ST), singlet-singlet (SS) and charge
gaps for finite N indicate that the ST and SS gaps close at the NIT with
increasing U and that the charge gap vanishes only there. Finite-N excitations
constrain all singularities to about 0.1t of the symmetry crossover. The NIT is
interpreted as a localized ground state (gs) with finite gaps on the paired
side and an extended gs with vanishing ST and SS gaps on the covalent side. The
charge gap and charge stiffness indicate a metallic gs at the transition that,
however, is unconditionally unstable to dimerization.


  We present the orbital solution for the newly discovered transient Be X-ray
binary Swift J0243.6+6124 based on the data from gamma-ray burst monitor
onboard Fermi obtained during the Oct 2017 outburst. We model the Doppler
induced and intrinsic spin variations of the neutron star assuming that the
later is driven by accretion torque and discuss the implications of the
observed spin variations for the parameters of the neutron star and the binary.
In particular we conclude that the neutron star must be strongly magnetized,
and estimate the distance to the source at $\sim$5 kpc.


  Monte Carlo simulations are increasingly used in scintigraphic imaging to
model imaging systems and to develop and assess tomographic reconstruction
algorithms and correction methods for improved image quantitation. GATE (GEANT
4 Application for Tomographic Emission) is a new Monte Carlo simulation
platform based on GEANT4 dedicated to nuclear imaging applications. This paper
describes the GATE simulation of a prototype of scintillation camera dedicated
to small animal imaging and consisting of a CsI(Tl) crystal array coupled to a
position sensitive photomultiplier tube. The relevance of GATE to model the
camera prototype was assessed by comparing simulated 99mTc point spread
functions, energy spectra, sensitivities, scatter fractions and image of a
capillary phantom with the corresponding experimental measurements. Results
showed an excellent agreement between simulated and experimental data:
experimental spatial resolutions were predicted with an error less than 100 mu
m. The difference between experimental and simulated system sensitivities for
different source-to-collimator distances was within 2%. Simulated and
experimental scatter fractions in a [98-182 keV] energy window differed by less
than 2% for sources located in water. Simulated and experimental energy spectra
agreed very well between 40 and 180 keV. These results demonstrate the ability
and flexibility of GATE for simulating original detector designs. The main
weakness of GATE concerns the long computation time it requires: this issue is
currently under investigation by the GEANT4 and the GATE collaborations


  In this paper we study bond percolation on a one-dimensional chain with
power-law bond probability $C/ r^{1+\sigma}$, where $r$ is the distance length
between distinct sites. We introduce and test an order $N$ Monte Carlo
algorithm and we determine as a function of $\sigma$ the critical value $C_{c}$
at which percolation occurs. The critical exponents in the range $0<\sigma<1$
are reported and compared with mean-field and $\varepsilon$-expansion results.
Our analysis is in agreement, up to a numerical precision $\approx 10^{-3}$,
with the mean field result for the anomalous dimension $\eta=2-\sigma$, showing
that there is no correction to $\eta$ due to correlation effects.


  Experiments with low-Z powder injection in DIII-D high confinement discharges
demonstrated increased divertor dissipation and detachment while maintaining
good core energy confinement. Lithium (Li), boron (B), and boron nitride (BN)
powders were injected in high-confinement mode plasmas ($I_p=$1 MA, $B_t=$2 T,
$P_{NB}=$6 MW, $\langle n_e\rangle=3.6-5.0\cdot10^{19}$ m$^{-3}$) into the
upper small-angle slot (SAS) divertor for 2-s intervals at constant rates of
3-204 mg/s. The multi-species BN powders at a rate of 54 mg/s showed the most
substantial increase in divertor neutral compression by more than an order of
magnitude and lasting detachment with minor degradation of the stored magnetic
energy $W_{mhd}$ by 5%. Rates of 204 mg/s of boron nitride powder further
reduce ELM-fluxes on the divertor but also cause a drop in confinement
performance by 24% due to the onset of an $n=2$ tearing mode. The application
of powders also showed a substantial improvement of wall conditions manifesting
in reduced wall fueling source and intrinsic carbon and oxygen content in
response to the cumulative injection of non-recycling materials. The results
suggest that low-Z powder injection, including mixed element compounds, is a
promising new core-edge compatible technique that simultaneously enables
divertor detachment and improves wall conditions during high confinement
operation.


  We consider a bosonic $s$ and $p$ orbital system in a face-centered cubic
(FCC) optical lattice, and predict a fluctuation-induced instability towards
the orbital analogue of Palmer-Chalker state, which is originally proposed in
an electronic spin system. For bosons loaded in the FCC optical lattice, the
single-particle spectrum has four degenerate band minima with their crystal
momenta forming a tetrahedron in Brillouin zone. In the weakly interacting
regime, the ensuing many-particle ground state, at the classical level,
underlies a four-sublattice tetrahedral supercell of spontaneously generated
$p$-orbital angular momenta through the Bravias-Bloch duality between real and
momentum space, and is macroscopically degenerate originating from the
geometric frustration. The fluctuations on top of the classical ground state
lift its degeneracy and select the Palmer-Chalker ordering of $p$-orbital
angular momenta as the quantum ground state through order-by-disorder
mechanism. These findings raise the exciting possibility of simulating the
Palmer-Chalker state with its orbital counterpart in ultracold atomic gases.


  The renormalization group equations for the general 2 by 2, complex, neutrino
mass matrix are shown to have exact, analytic solutions. Simple formulas are
given for the physical mixing angle, complex phase and mass ratio in terms of
their initial values and the energy scales. We also establish a (complex)
renormalization invariant relating these parameters. The qualitative features
of the physical parameters' renormalization are clearly illustrated in vector
field plots. In both the SM and MSSM, maximal mixing is a saddle point.


  We study number theoretic properties of the map $x \mapsto x^{x} \mod{p}$,
where $x \in \{1,2,\ldots,p-1\}$, and improve on some recent upper bounds, due
to Kurlberg, Luca, and Shparlinski, on the number of primes $p < N$ for which
the map only has the trivial fixed point $x=1$.
  A key technical result, possibly of independent interest, is the existence of
subsets $\mathscr{N}_{q} \subset \{2,3,\ldots,q-1\}$ such that almost all
$k$-tuples of distinct integers $n_{1}, n_{2},\ldots,n_{k} \in \mathscr{N}_q$
are multiplicatively independent (if $k$ is not too large), and
$|\mathscr{N}_q| = q \cdot (1+o(1))$ as $q \to \infty$. For $q$ a large prime,
this is used to show that the number of solutions to a certain large and sparse
system of $\mathbb{F}_q$-linear forms $\{ \mathscr{L}_{n} \}_{n=2}^{q-1}$
"behaves randomly" in the sense that $|\{ \mathbf{v} \in \mathbb{F}_{q}^{d} :
\mathscr{L}_{n}(\mathbf{v}) =1, n = 2,3, \ldots, q-1 \}| \sim q^{d}(1-1/q)^{q}
\sim q^{d}/e$. (Here $d=\pi(q-1)$ and the coefficents of $\mathscr{L}_{n}$ are
given by the exponents in the prime power factorization of $n$.)


  Deutsch and Aguirre have recently shown that the solutions of certain
dynamical systems typically contain a point of minimum size that they identify
as an entropy minimum and from which the size and entropy increase to infinity
in both directions of time. They argue that in such systems entropic arrows of
time exist without the need for a special condition imposed in the past. In
this paper I sharpen and extend the conditions under which such solutions exist
but argue that the resulting arrows of time should not be interpreted as
entropic since they point towards greater order and not disorder.


  We present the first measurement of the correlation between the $Z^0$ spin
and the three-jet plane orientation in polarized $Z^0$ decays into three jets
in the SLD experiment at SLAC utilizing a longitudinally polarized electron
beam. The CP-even and T-odd triple product $\vec{S_Z}\cdot(\vec{k_1}\times
\vec{k_2})$ formed from the two fastest jet momenta, $\vec{k_1}$ and
$\vec{k_2}$, and the $Z^0$ polarization vector $\vec{S_Z}$, is sensitive to
physics beyond the Standard Model. We measure the expectation value of this
quantity to be consistent with zero and set 95\% C.L. limits of $-0.022 < \beta
< 0.039$ on the correlation between the $Z^0$-spin and the three-jet plane
orientation.


  Although the thermal conductivity of molecular gases can be measured
straightforwardly and accurately, it is difficult to experimentally determine
its separate contributions from the translational and internal motions of gas
molecules. Yet this information is critical in rarefied gas dynamics as the
rarefaction effects corresponding to these motions are different. In this
paper, we propose a novel methodology to extract the translational thermal
conductivity (or equivalently, the translational Eucken factor) of molecular
gases from the Rayleigh-Brillouin scattering (RBS) experimental data. From the
numerical simulation of the \cite{LeiJFM2015} model we find that, in the
kinetic regime, in addition to bulk viscosity, the RBS spectrum is sensitive to
the translational Eucken factor, even when the total thermal conductivity is
fixed. Thus it is not only possible to extract the bulk viscosity, but also the
translational Eucken factor of molecular gases from RBS light scattering
spectra measurements. Such experiments bear the additional advantage that
gas-surface interactions do not affect the measurements. For the first time,
bulk viscosities (due to the rotational relaxation of gas molecules only) and
translational Eucken factors of N2, CO2 and SF6 are simultaneously extracted
from RBS experiments.


  The Outer Space Treaty makes it clear that the Moon is the province of all
mankind, with the latter ordinarily understood to exclude state or private
appropriation of any portion of its surface. However, there are indeterminacies
in the Treaty and in space law generally over the issue of appropriation. These
indeterminacies might permit a close approximation to a property claim or some
manner of quasi-property. The recently revealed highly inhomogeneous
distribution of lunar resources changes the context of these issues. We
illustrate this altered situation by considering the Peaks of Eternal Light.
They occupy about one square kilometer of the lunar surface. We consider a
thought experiment in which a Solar telescope is placed on one of the Peaks of
Eternal Light at the lunar South pole for scientific research. Its operation
would require nondisturbance, and hence that the Peak remain unvisited by
others, effectively establishing a claim of protective exclusion and de facto
appropriation. Such a telescope would be relatively easy to emplace with todays
technology and so poses a near-term property issue on the Moon. While effective
appropriation of a Peak might proceed without raising some of the familiar
problems associated with commercial development (especially lunar mining), the
possibility of such appropriation nonetheless raises some significant issues
concerning justice and the safeguarding of scientific practice on the lunar
surface. We consider this issue from scientific, technical, ethical and policy
viewpoints.


  Multimodal single-cell technologies enable the simultaneous collection of
diverse data types from individual cells, enhancing our understanding of
cellular states. However, the integration of these datatypes and modeling the
interrelationships between modalities presents substantial computational and
analytical challenges in disease biomarker detection and drug discovery.
Established practices rely on isolated methodologies to investigate individual
molecular aspects separately, often resulting in inaccurate analyses. To
address these obstacles, distinct Machine Learning Techniques are leveraged,
each of its own kind to model the co-variation of DNA to RNA, and finally to
surface proteins in single cells during hematopoietic stem cell development,
which simplifies understanding of underlying cellular mechanisms and immune
responses. Experiments conducted on a curated subset of a 300,000-cell time
course dataset, highlights the exceptional performance of Echo State Networks,
boasting a remarkable state-of-the-art correlation score of 0.94 and 0.895 on
Multi-omic and CiteSeq datasets. Beyond the confines of this study, these
findings hold promise for advancing comprehension of cellular differentiation
and function, leveraging the potential of Machine Learning.


  An important task in trajectory analysis is clustering. The results of a
clustering are often summarized by a single representative trajectory and an
associated size of each cluster. We study the problem of computing a suitable
representative of a set of similar trajectories. To this end we define a
central trajectory $\mathcal{C}$, which consists of pieces of the input
trajectories, switches from one entity to another only if they are within a
small distance of each other, and such that at any time $t$, the point
$\mathcal{C}(t)$ is as central as possible. We measure centrality in terms of
the radius of the smallest disk centered at $\mathcal{C}(t)$ enclosing all
entities at time $t$, and discuss how the techniques can be adapted to other
measures of centrality. We first study the problem in $\mathbb{R}^1$, where we
show that an optimal central trajectory $\mathcal{C}$ representing $n$
trajectories, each consisting of $\tau$ edges, has complexity $\Theta(\tau
n^2)$ and can be computed in $O(\tau n^2 \log n)$ time. We then consider
trajectories in $\mathbb{R}^d$ with $d\geq 2$, and show that the complexity of
$\mathcal{C}$ is at most $O(\tau n^{5/2})$ and can be computed in $O(\tau n^3)$
time.


  The selfconsistent approach to the 2D Ising Model with quenched random bonds
is extended to the full lattice theory of four real fermions. The additional
degrees of freedom, neglected in the renormalization-group theory, lead to a
new phase between the ferromagnetic and the paramagnetic phase. The disorder
averaged spin-spin correlation function decays exponentially with distance. The
corresponding correlation length is $1/\eta^2$, where $\eta$ denotes the order
parameter of the new phase introduced by Ziegler.


  This paper describes Mull, an open-source tool for mutation testing based on
the LLVM framework. Mull works with LLVM IR, a low-level intermediate
representation, to perform mutations, and uses LLVM JIT for just-in-time
compilation. This design choice enables the following two capabilities of Mull:
language independence and fine-grained control over compilation and execution
of a tested program and its mutations. Mull can work with code written in any
programming language that supports compilation to LLVM IR, such as C, C++,
Rust, or Swift. Direct manipulation of LLVM IR allows Mull to do less work to
generate mutations: only modified fragments of IR code are recompiled, and this
results in faster processing of mutated programs. To our knowledge, no existing
mutation testing tool provides these capabilities for compiled programming
languages. We describe the algorithm and implementation details of Mull,
highlight current limitations of Mull, and present the results of our
evaluation of Mull on real-world projects such as RODOS, OpenSSL, LLVM.


  Observations of energetic neutral atoms (ENAs) are a fruitful tool for remote
diagnosis of the plasma in the heliosphere and its vicinity. So far,
instruments detecting ENAs from the heliosphere were configured for
observations of hydrogen atoms. Here, we estimate emissions of ENAs of the
heavy chemical elements helium, oxygen, nitrogen, and neon. A large portion of
the heliospheric ENAs is created in the inner heliosheath from neutralized
interstellar pick-up ions (PUIs). We modeled this process and calculated
full-sky intensities of ENAs for energies 0.2-130 keV/nuc. We found that the
largest fluxes among considered species are expected for helium, smaller for
oxygen and nitrogen, and smallest for neon. The obtained intensities are
50-10^6 times smaller than the hydrogen ENA intensities observed by IBEX. The
detection of heavy ENAs will be possible if a future ENA detector is equipped
with the capability to measure the masses of observed atoms. Because of
different reaction cross-sections among the different species, observations of
heavy ENAs can allow for a better understanding of global structure of the
heliosphere as well as the transport and energization of PUIs in the
heliosphere.


  In recent years, the use of machine learning classifiers is of great value in
solving a variety of problems in text classification. Sentiment mining is a
kind of text classification in which, messages are classified according to
sentiment orientation such as positive or negative. This paper extends the idea
of evaluating the performance of various classifiers to show their
effectiveness in sentiment mining of online product reviews. The product
reviews are collected from Amazon reviews. To evaluate the performance of
classifiers various evaluation methods like random sampling, linear sampling
and bootstrap sampling are used. Our results shows that support vector machine
with bootstrap sampling method outperforms others classifiers and sampling
methods in terms of misclassification rate.


  Let X be a path-connected topological space admitting a universal cover. Let
Homeo(X,a) denote the group of homeomorphisms of X preserving degree one
cohomology class a.
  We investigate the distortion in Homeo(X,a). Let g be an element of
Homeo(X,a). We define a Nielsen-type equivalence relation on the space of
g-invariant Borel probability measures on X and prove that if a homeomorphism g
admits two nonequivalent invariant measures then it is undistorted. We also
define a local rotation number of a homeomorphism generalising the notion of
the rotation of a homeomorphism of the circle. Then we prove that a
homeomorphism is undistorted if its rotation number is nonconstant.


  Applications of face recognition systems for authentication purposes are
growing rapidly. Although state-of-the-art (SOTA) face recognition systems have
high recognition accuracy, the features which are extracted for each user and
are stored in the system's database contain privacy-sensitive information.
Accordingly, compromising this data would jeopardize users' privacy. In this
paper, we propose a new cancelable template protection method, dubbed MLP-hash,
which generates protected templates by passing the extracted features through a
user-specific randomly-weighted multi-layer perceptron (MLP) and binarizing the
MLP output. We evaluated the unlinkability, irreversibility, and recognition
accuracy of our proposed biometric template protection method to fulfill the
ISO/IEC 30136 standard requirements. Our experiments with SOTA face recognition
systems on the MOBIO and LFW datasets show that our method has competitive
performance with the BioHashing and IoM Hashing (IoM-GRP and IoM-URP) template
protection algorithms. We provide an open-source implementation of all the
experiments presented in this paper so that other researchers can verify our
findings and build upon our work.


  We show that two independent elephant random walks on the integer lattice
$\mathbb{Z}$ meet each other finitely often or infinitely often depends on
whether the memory parameter $p$ is strictly larger than $3/4$ or not.
Asymptotic results for the distance between them are also obtained.


  Given a rank-1 bimatrix game (A,B), i.e., where rank(A+B)=1, we construct a
suitable linear subspace of the rank-1 game space and show that this subspace
is homeomorphic to its Nash equilibrium correspondence. Using this
homeomorphism, we give the first polynomial time algorithm for computing an
exact Nash equilibrium of a rank-1 bimatrix game. This settles an open question
posed in Kannan and Theobald (SODA 2007) and Theobald (2007). In addition, we
give a novel algorithm to enumerate all the Nash equilibria of a rank-1 game
and show that a similar technique may also be applied for finding a Nash
equilibrium of any bimatrix game. This technique also proves the existence,
oddness and the index theorem of Nash equilibria in a bimatrix game. Further,
we extend the rank-1 homeomorphism result to a fixed rank game space, and give
a fixed point formulation on $[0,1]^k$ for solving a rank-k game. The
homeomorphism and the fixed point formulation are piece-wise linear and
considerably simpler than the classical constructions.


  We propose a measurement of leading neutrons spectra at LHC in order to
extract inclusive $\pi^+ p$ and $\pi^+\pi^+$ cross-sections with high $p_T$
jets production. The cross-sections for these processes are simulated with the
use of parton distributions in hadrons. In this work we estimate the
possibility to extract parton distributions in the pion from the data on these
cross-sections and also search for signatures of fundamental differences in the
pion and proton structure.


  Floating gate SONOS (Silicon-Oxygen-Nitrogen-Oxygen-Silicon) transistors can
be used to train neural networks to ideal accuracies that match those of
floating point digital weights on the MNIST dataset when using multiple devices
to represent a weight or within 1% of ideal accuracy when using a single
device. This is enabled by operating devices in the subthreshold regime, where
they exhibit symmetric write nonlinearities. A neural training accelerator core
based on SONOS with a single device per weight would increase energy efficiency
by 120X, operate 2.1X faster and require 5X lower area than an optimized SRAM
based ASIC.


  Due to their susceptibility to adversarial perturbations, neural networks
(NNs) are hardly used in safety-critical applications. One measure of
robustness to such perturbations in the input is the Lipschitz constant of the
input-output map defined by an NN. In this work, we propose a framework to
train multi-layer NNs while at the same time encouraging robustness by keeping
their Lipschitz constant small, thus addressing the robustness issue. More
specifically, we design an optimization scheme based on the Alternating
Direction Method of Multipliers that minimizes not only the training loss of an
NN but also its Lipschitz constant resulting in a semidefinite programming
based training procedure that promotes robustness. We design two versions of
this training procedure. The first one includes a regularizer that penalizes an
accurate upper bound on the Lipschitz constant. The second one allows to
enforce a desired Lipschitz bound on the NN at all times during training.
Finally, we provide two examples to show that the proposed framework
successfully increases the robustness of NNs.


  We construct the explicit worldvolume effective actions of the type IIB
NS-5-brane and KK-monopole. These objects are obtained through a T-duality
transformation from the IIA KK-monopole and the IIA NS-5-brane respectively. We
show that the worldvolume field content of these actions is precisely that
necessary to describe their worldvolume solitons. The IIB NS-5-brane effective
action is shown to be related to the D-5-brane's by an S-duality
transformation, suggesting the way to construct (p,q) 5-brane multiplets. The
IIB KK-monopole is described by a gauged sigma model, in agreement with the
general picture for KK-monopoles, and behaves as a singlet under S-duality. We
derive the explicit T-duality rules NS-5-brane -- KK, which we use for the
construction of the previous actions, as well as NS-5 -- NS-5, and KK -- KK.


  The dispersion relations and S-matrix of the one-dimensional Hubbard model at
half filling are considered in a certain scaling limit. (In the process we
derive a useful small-coupling expansion of the exact lattice dispersion
relations.) The resulting scattering theory is consistently identified as that
of the SU(2) chiral-invariant Thirring (or Gross-Neveu) field theory,
containing both massive and massless sectors.


  The hadronic decays of $\chi_{c1}\to VV$ and $\chi_{c2}\to VP$ are supposed
to be suppressed by the helicity selection rule in the pQCD framework. With an
effective Lagrangian method, we show that the intermediate charmed meson loops
can provide a mechanism for the evasion of the helicity selection rule, and
result in sizeable decay branching ratios in some of those channels. The
theoretical predictions can be examined by the forthcoming BES-III data in the
near future.


  This study proposes a radar-based heartbeat measurement method that uses the
absolute value of the second derivative of the complex radar signal, rather
than its phase, and the variational mode extraction method, which is a type of
mode decomposition algorithm. We show that the proposed second-derivative-based
approach can amplify the heartbeat component in radar signals effectively and
also confirm that use of the variational mode extraction method represents an
efficient way to emphasize the heartbeat component amplified via the
second-derivative-based approach. We demonstrate estimation of the heart
interbeat intervals using the proposed approach in combination with the
topology method, which is an accurate interbeat interval estimation method. The
performance of the proposed method is evaluated quantitatively using data
obtained from eleven participants that were measured using a millimeter-wave
radar system. When compared with conventional methods based on the phase of the
complex radar signal, our proposed method can achieve higher accuracy when
estimating the heart interbeat intervals; the correlation coefficient for the
proposed method was increased by 0.20 and the root-mean-square error decreased
by 23%.


  A ferromagnet can resonantly absorbs rf radiation to sustain a steady
precession of the magnetization around an internal or applied magnetic field.
We show that under these ferromagnetic resonance (FMR) conditions, a dc voltage
is generated at a normal-metal electric contact to a ferromagnet with spin-flip
scattering. This mechanism allows an easy electric detection of magnetization
dyamics.


  We report initial results from AO327, a drift survey for pulsars with the
Arecibo telescope at 327 MHz. The first phase of AO327 will cover the sky at
declinations of -1 to 28 degrees, excluding the region within 5 degrees of the
Galactic plane, where high scattering and dispersion make low-frequency surveys
sub-optimal. We record data from a 57 MHz bandwidth with 1024 channels and 125
us sampling time. The 60 s transit time through the AO327 beam means that the
survey is sensitive to very tight relativistic binaries even with no
acceleration searches. To date we have detected 44 known pulsars with periods
ranging from 3 ms to 2.21 s and discovered 24 new pulsars. The new discoveries
include three millisecond pulsars, three objects with periods of a few tens of
milliseconds typical of young as well as mildly recycled pulsars, a nuller, and
a rotating radio transient. Five of the new discoveries are in binary systems.
The second phase of AO327 will cover the sky at declinations of 28 to 38
degrees. We compare the sensitivity and search volume of AO327 to the Green
Bank North Celestial Cap survey and the GBT350 drift survey, both of which
operate at 350 MHz.


  We perform high resolution X-ray diffraction on GaMnAs mixed crystals as well
as on GaMnAs/GaAs and GaAs/MnAs superlattices for samples grown by low
temperature molecular beam epitaxy under different growth conditions. Although
all samples are of high crystalline quality and show narrow rocking curve
widths and pronounced finite thickness fringes, the lattice constant variation
with increasing manganese concentration depends strongly on the growth
conditions: For samples grown at substrate temperatures of 220 and 270 degrees
C the extrapolated relaxed lattice constant of Zincblende MnAs is 0.590 nm and
0.598 nm respectively. This is in contrast to low temperature GaAs, for which
the lattice constant decreases with increasing substrate temperature.


  We observe a trend regarding restart strategies used in SAT solvers. A few
years ago, most state-of-the-art solvers restarted on average after a few
thousands of backtracks. Currently, restarting after a dozen backtracks results
in much better performance. The main reason for this trend is that heuristics
and data structures have become more restart-friendly. We expect further
continuation of this trend, so future SAT solvers will restart even more
rapidly. Additionally, we present experimental results to support our
observations.


  Low-energy supersymmetry is a theoretical extension of the Standard Model of
particle physics in which supersymmetry is invoked to explain the origin of the
electroweak scale. In this approach, the energy scale of supersymmetry breaking
can be no larger than about 1 TeV. In these lectures, a pedagogical account of
softly broken supersymmetric gauge theories is presented. The minimal
supersymmetric extension of the Standard Model (MSSM) is defined and
constraints on its parameters are explored. The implications of supersymmetric
tree-level interactions and its one-loop corrections are discussed. Low-energy
supersymmetric model alternatives to the MSSM are briefly mentioned.


  Recently, a connection between quantum coherence and quantum steering was
established and criteria for quantum steering or in other words, nonlocal
advantage of quantum coherence (NAQC) were derived for two-qubit states. Here,
we derive a set of complementarity relations between the steering or NAQC
inequalities achieved by various criteria. We also extend the idea in the
multi-partite scenario, specifically, in the three-qubit scenario, which can
easily be generalized to the multi-partite scenario.


  We address the problem of unsupervised domain adaptation when the source
domain differs from the target domain because of a shift in the distribution of
a latent subgroup. When this subgroup confounds all observed data, neither
covariate shift nor label shift assumptions apply. We show that the optimal
target predictor can be non-parametrically identified with the help of concept
and proxy variables available only in the source domain, and unlabeled data
from the target. The identification results are constructive, immediately
suggesting an algorithm for estimating the optimal predictor in the target. For
continuous observations, when this algorithm becomes impractical, we propose a
latent variable model specific to the data generation process at hand. We show
how the approach degrades as the size of the shift changes, and verify that it
outperforms both covariate and label shift adjustment.


  We investigate the three-dimensional asymmetrical kinematics and present time
stamps of the Milky Way disk between Galactocentric distances of $R=12$ and 15
\,kpc, using red clump stars selected from the LAMOST Galactic survey, also
with proper motion measurements provided by the Gaia DR2. We discover velocity
substructure above the Galactic plane corresponding to a density dip found
recently ("south-middle opposite" density structure[R $\sim$ 12-15 \,kpc, Z
$\sim$ 1.5 \,kpc] discovered in \citet{Wang2018b, Wang2018c}) in the radial and
azimuthal velocity. For the vertical velocity, we detect clear vertical bulk
motions or bending mode motions, which has no clear north-south asymmetry
corresponding to the in-plane asymmetrical features. In the subsample of stars
with different ages, we find that there is little temporal evolution of the
in-plane asymmetry from 0$-$14 \,Gyr, which means the structure is sensitive to
the perturbations in almost cosmic time possibly. We propose that the possible
scenario of this asymmetric velocity structure is caused by the mechanisms
generated in-plane, rather than vertical perturbations.


  Word vector representations are well developed tools for various NLP and
Machine Learning tasks and are known to retain significant semantic and
syntactic structure of languages. But they are prone to carrying and amplifying
bias which can perpetrate discrimination in various applications. In this work,
we explore new simple ways to detect the most stereotypically gendered words in
an embedding and remove the bias from them. We verify how names are masked
carriers of gender bias and then use that as a tool to attenuate bias in
embeddings. Further, we extend this property of names to show how names can be
used to detect other types of bias in the embeddings such as bias based on
race, ethnicity, and age.


  We succeeded to isolate a special class of concave Young-functions enjoying
the so-called \emph{density-level property}. In this class there is a proper
subset whose members have each the so-called degree of contraction denoted by
$c^{\ast}$, and map bijectively the interval $[ c^{\ast}, \infty) $ onto
itself. We constructed the fixed point of each of these functions. Later we
proved that every positive number $b$ is the fixed point of a concave
Young-function having $b$ as degree of contraction. We showed that every
concave Young-function is square integrable with respect to a specific Lebesgue
measure. We also proved that the concave Young-functions possessing the
density-level property constitute a dense set in the space of concave
Young-functions with respect to the distance induced by the $L^{2}$-norm.


  We point out that heavy new physics contributions in leptonic dipole moments
and high-energy cross-sections of lepton pairs into Higgs bosons and photons
are connected model-independently. In particular, we demonstrate that a muon
collider, running at center-of-mass energies of several TeV, can provide a
unique test of new physics in the muon g-2 through the study of high-energy
processes such as mu+ mu- -> h gamma. This high-energy test would be of the
utmost importance to shed light on the long-standing muon g-2 anomaly as it is
not affected by the hadronic and experimental uncertainties entering the
current low-energy determination of the muon g-2. Furthermore, we show that the
current bound on the muon electric dipole moment can be improved by three
orders of magnitude, down to few x 10^-22 e cm.