  We study distributed algorithms for adjusting beamforming vectors and
receiver filters in multiple-input multiple-output (MIMO) interference
networks, with the assumption that each user uses a single beam and a linear
filter at the receiver. In such a setting there have been several distributed
algorithms studied for maximizing the sum-rate or sum-utility assuming perfect
channel state information (CSI) at the transmitters and receivers. The focus of
this paper is to study adaptive algorithms for time-varying channels, without
assuming any CSI at the transmitters or receivers. Specifically, we consider an
adaptive version of the recent Max-SINR algorithm for a time-division duplex
system. This algorithm uses a period of bi-directional training followed by a
block of data transmission. Training in the forward direction is sent using the
current beam-formers and used to adapt the receive filters. Training in the
reverse direction is sent using the current receive filters as beams and used
to adapt the transmit beamformers. The adaptation of both receive filters and
beamformers is done using a least-squares objective for the current block. In
order to improve the performance when the training data is limited, we also
consider using exponentially weighted data from previous blocks. Numerical
results are presented that compare the performance of the algorithms in
different settings.


  Graphs are very important mathematical structures used in many applications,
one of which is transportation science. When dealing with transportation
networks, one deals not only with the network structure, but also with
information related to the utilization of the elements of the network, which
can be shown using flow and origin-destination matrices. This paper extends an
algebraic model used to relate all these components by deriving additional
relationships and constructing a more structured understanding of the model.
Specifically, the paper introduces the concept of mutually exclusive matrices,
and shows their effect when decomposing the components of a Hadamard product on
matrices


  We propose masked particle modeling (MPM) as a self-supervised method for
learning generic, transferable, and reusable representations on unordered sets
of inputs for use in high energy physics (HEP) scientific data. This work
provides a novel scheme to perform masked modeling based pre-training to learn
permutation invariant functions on sets. More generally, this work provides a
step towards building large foundation models for HEP that can be generically
pre-trained with self-supervised learning and later fine-tuned for a variety of
down-stream tasks. In MPM, particles in a set are masked and the training
objective is to recover their identity, as defined by a discretized token
representation of a pre-trained vector quantized variational autoencoder. We
study the efficacy of the method in samples of high energy jets at collider
physics experiments, including studies on the impact of discretization,
permutation invariance, and ordering. We also study the fine-tuning capability
of the model, showing that it can be adapted to tasks such as supervised and
weakly supervised jet classification, and that the model can transfer
efficiently with small fine-tuning data sets to new classes and new data
domains.


  The famous Dirac's Theorem gives an exact bound on the minimum degree of an
$n$-vertex graph guaranteeing the existence of a hamiltonian cycle. We prove
exact bounds of similar type for hamiltonian Berge cycles in $r$-uniform,
$n$-vertex hypergraphs for all $3\leq r< n$. The bounds are different for
$r<n/2$ and $r\geq n/2$. We also give bounds on the minimum degree guaranteeing
existence of Berge cycles of length at least $k$ in such hypergraphs; the
bounds are exact for all $k\geq n/2$.


  The Ultraviolet Coronagraph Spectrometer on board the Solar and Heliospheric
Observatory (SOHO) often observes low ionization state coronal mass ejection
(CME) plasma at ultraviolet wavelengths. The CME plasmas are often detected in
O VI (3x10^5K), C III (8x10^4K), LyA, and LyB, with the low ionization plasma
confined to bright filaments or blobs that appear in small segments of the UVCS
slit. On the other hand, in situ observations by the Solar Wind Ion Composition
Spectrometer (SWICS) on board Advanced Composition Explorer (ACE) have shown
mostly high ionization state plasmas in the magnetic clouds in interplanetary
coronal mass ejections (ICME) events, while low ionization states are rarely
seen. In this analysis, we investigate whether the low ionization state CME
plasmas observed by UVCS occupy small enough fractions of the CME to be
consistent with the small fraction of ACE ICMEs that show low ionization
plasma, or whether the CME plasma must be further ionized after passing the
UVCS slit. To do this, we determine the covering factors of low ionization
state plasma for 10 CME events. We find that the low ionization state plasmas
in CMEs observed by UVCS show average covering factors below 10%. This
indicates that the lack of low ionization state ICME plasmas observed by the
ACE results from a small probability that the spacecraft passes through a
region of low ionization plasma. We also find that the low ionization state
plasma covering factors in faster CMEs are smaller than in slower CMEs.


  The employment of extremely large antenna arrays and high-frequency signaling
makes future 6G wireless communications likely to operate in the near-field
region. In this case, the spherical wave assumption which takes into account
both the user angle and distance is more accurate than the conventional planar
one that is only related to the user angle. Therefore, the conventional planar
wave based far-field channel model as well as its associated estimation
algorithms needs to be reconsidered. Here we first propose a
distance-parameterized angular-domain sparse model to represent the near-field
channel. In this model, the user distance is included in the dictionary as an
unknown parameter, so that the number of dictionary columns depends only on the
angular space division. This is different from the existing polar-domain
near-field channel model where the dictionary is constructed on an
angle-distance two-dimensional (2D) space. Next, based on this model, joint
dictionary learning and sparse recovery based channel estimation methods are
proposed for both line of sight (LoS) and multi-path settings. To further
demonstrate the effectiveness of the suggested algorithms, recovery conditions
and computational complexity are studied. Our analysis shows that with the
decrease of distance estimation error in the dictionary, the angular-domain
sparse vector can be exactly recovered after a few iterations. The high storage
burden and dictionary coherence issues that arise in the polar-domain 2D
representation are well addressed. Finally, simulations in multi-user
communication scenarios support the superiority of the proposed near-field
channel sparse representation and estimation over the existing polar-domain
method in channel estimation error.


  In this paper, we define the vanishing-off subgroup of a nonabelian group. We
study the structure of the quotient of this subgroup and a central series
obtained from this subgroup.


  A future linear e+e- collider with a clean environment, tunable collision
energy, high luminosity, polarized incoming beams, and additional e-e-, e-gamma
and gamma-gamma modes, will offer precision tools to explore new physics. Here
we summarize three papers submitted to the ICHEP04 conference in which
polarized e+e- and gamma-gamma beams are exploited to search for CP violation,
and universal extra dimensions (UED).


  Beam management (BM) protocols are critical for establishing and maintaining
connectivity between network radio nodes and User Equipments (UEs). In
Distributed Multiple Input Multiple Output systems (D-MIMO), a number of access
points (APs), coordinated by a central processing unit (CPU), serves a number
of UEs. At mmWave frequencies, the problem of finding the best AP and beam to
serve the UEs is challenging due to a large number of beams that need to be
sounded with Downlink (DL) reference signals. The objective of this paper is to
investigate whether the best AP/beam can be reliably inferred from sounding
only a small subset of beams and leveraging AI/ML for inference of best
beam/AP. We use Random Forest (RF), MissForest (MF) and conditional Generative
Adversarial Networks (c-GAN) for demonstrating the performance benefits of
inference.


  Richard Olshen was born in Portland, Oregon, on May 17, 1942. Richard spent
his early years in Chevy Chase, Maryland, but has lived most of his life in
California. He received an A.B. in Statistics at the University of California,
Berkeley, in 1963, and a Ph.D. in Statistics from Yale University in 1966,
writing his dissertation under the direction of Jimmie Savage and Frank
Anscombe. He served as Research Staff Statistician and Lecturer at Yale in
1966-1967. Richard accepted a faculty appointment at Stanford University in
1967, and has held tenured faculty positions at the University of Michigan
(1972-1975), the University of California, San Diego (1975-1989), and Stanford
University (since 1989). At Stanford, he is Professor of Health Research and
Policy (Biostatistics), Chief of the Division of Biostatistics (since 1998) and
Professor (by courtesy) of Electrical Engineering and of Statistics. At various
times, he has had visiting faculty positions at Columbia, Harvard, MIT,
Stanford and the Hebrew University. Richard's research interests are in
statistics and mathematics and their applications to medicine and biology. Much
of his work has concerned binary tree-structured algorithms for classification,
regression, survival analysis and clustering. Those for classification and
survival analysis have been used with success in computer-aided diagnosis and
prognosis, especially in cardiology, oncology and toxicology. He coauthored the
1984 book Classification and Regression Trees (with Leo Brieman, Jerome
Friedman and Charles Stone) which gives motivation, algorithms, various
examples and mathematical theory for what have come to be known as CART
algorithms. The approaches to tree-structured clustering have been applied to
problems in digital radiography (with Stanford EE Professor Robert Gray) and to
HIV genetics, the latter work including studies on single nucleotide
polymorphisms, which has helped to shed light on the presence of hypertension
in certain subpopulations of women.


  We present in this work energy levels, oscillator strengths, radiative decay
rates and fine structure collision strengths for the Mg III and Al IV ions. The
11 configurations:(1s$^{2}$) 2s$^{2}$2p$^{6}$, 2s$^{2}$2p$^{5}3l$,
2s2p$^{6}3l$, 2s$^{2}$2p$^{5}4l$ ($l\leq n-1$, where $n$ is the principal
quantum number), yielding the lowest 75 levels are used. The collisional data
for these two ions are missing in the literature, especially the database
CHIANTI, this is the principal motivation behind the present work. Calculations
have been performed using the AUTOSTRUCTURE code. AUTOSTRUCTURE treats the
scattering problem in the distorted wave approach. Fine structure collision
strengths are calculated for a range of electron energies from 10 Ry to 240 Ry.
The atomic structure data are compared to available experimental and
theoretical results.


  We consider the class of higher derivative field equations whose wave
operator is a square of another self-adjoint operator of lower order. At the
free level, the models of this class are shown to admit a two-parameter series
of integrals of motion. The series includes the canonical energy. Every
conserved quantity is unbounded in this series. The interactions are included
into the equations of motion such that a selected representative in conserved
quantity series is preserved at the non-linear level. The interactions are not
necessarily Lagrangian, but they admit Hamiltonian form of dynamics. The theory
is stable if the integral of motion is bounded from below due to the
interaction. The motions are finite in the vicinity of the conserved quantity
minimum. The equations of motion for fluctuations have the derived form with no
resonance. The general constructions are exemplified by the models of the
Pais-Uhlenbeck oscillator with multiple frequency and Podolsky electrodynamics.
The example is also considered of stable non-abelian Yang-Mills theory with
higher derivatives.


  Semantic segmentation is key in autonomous driving. Using deep visual
learning architectures is not trivial in this context, because of the
challenges in creating suitable large scale annotated datasets. This issue has
been traditionally circumvented through the use of synthetic datasets, that
have become a popular resource in this field. They have been released with the
need to develop semantic segmentation algorithms able to close the visual
domain shift between the training and test data. Although exacerbated by the
use of artificial data, the problem is extremely relevant in this field even
when training on real data. Indeed, weather conditions, viewpoint changes and
variations in the city appearances can vary considerably from car to car, and
even at test time for a single, specific vehicle. How to deal with domain
adaptation in semantic segmentation, and how to leverage effectively several
different data distributions (source domains) are important research questions
in this field. To support work in this direction, this paper contributes a new
large scale, synthetic dataset for semantic segmentation with more than 100
different source visual domains. The dataset has been created to explicitly
address the challenges of domain shift between training and test data in
various weather and view point conditions, in seven different city types.
Extensive benchmark experiments assess the dataset, showcasing open challenges
for the current state of the art. The dataset will be available at:
https://idda-dataset.github.io/home/ .


  One of the most promising solutions for uncertainty quantification in
high-dimensional statistics is the debiased LASSO that relies on unconstrained
$\ell_1$-minimization. The initial works focused on real Gaussian designs as a
toy model for this problem. However, in medical imaging applications, such as
compressive sensing for MRI, the measurement system is represented by a
(subsampled) complex Fourier matrix. The purpose of this work is to extend the
method to the MRI case in order to construct confidence intervals for each
pixel of an MR image. We show that a sufficient amount of data is $n \gtrsim
\max\{ s_0\log^2 s_0\log p, s_0 \log^2 p \}$.


  It was shown in Alur et al. [1] that the problem of verifying finite
concurrent systems through Linearizability is in EXPSPACE. However, there was
still a complexity gap between the easy to obtain PSPACE lower bound and the
EXPSPACE upper bound. We show in this paper that Linearizability is
EXPSPACE-complete.


  The two-dimensional Active Brownian Particles system is meant to be composed
of hard disks, that show excluded volume interactions, usually simulated via
molecular dynamics using pure repulsive potentials. We show that the softness
of the chosen potential plays a role in the result of the simulation, focusing
on the case of the emergence of Motility Induced Phase Separation. In a pure
hard-sphere system with no traslational diffusion,the phase diagram should be
completely determined by their density and P\'eclet number. However, we have
found two additional effects that affect the phase diagram in the ABP model we
simulate: the relative strength of the traslational diffusion compared to the
propulsion term and the overlapping of the particles. As we show, the second
effect can be strongly mitigated if we use, instead of the standard
Weeks-Chandler- Andersen potential, a harder one, the pseudo-hard spheres
potential. Moreover, in determining the boundary of our phase space, we have
tried different approaches to detect MIPS and concluded that observing
dynamical features, via the non-Gaussian parameter, is more efficient than
observing structural ones, via the local density distribution function. We also
demonstrate that the Vogel-Fulcher equation successfully reproduces the decay
of the diffusion as a function of density, except for very high density cases.
Thus, the ABP system behaves similarly to a fragile glass in this regard.


  Thanks to asteroseismology, constraints on the core rotation rate are
available for hundreds of low- and intermediate-mass stars in evolved phases.
Current physical processes tested in stellar evolution models cannot reproduce
the evolution of these core rotation rates. We investigate the efficiency of
the internal angular momentum redistribution in red giants during the hydrogen
shell and core-helium burning phases based on the asteroseismic determinations
of their core rotation rates. We compute stellar evolution models with rotation
and model the transport of angular momentum by the action of a sole dominant
diffusive process parametrized by an additional viscosity. We constrain the
values of this viscosity to match the mean core rotation rates of red giants
and their behaviour with mass and evolution along the red giant branch and in
the red clump. For red giants in the hydrogen shell-burning phase the transport
of angular momentum must be more efficient in more massive stars. The
additional viscosity is found to vary by approximately two orders of magnitude
in the mass range M $\sim$ 1 - 2.5 M$_{\odot}$. As stars evolve along the red
giant branch, the efficiency of the internal transport of angular momentum must
increase for low-mass stars (M $\lesssim$ 2 M$_{\odot}$) and remain
approximately constant for slightly higher masses (2.0 M$_{\odot}$ $\lesssim$ M
$\lesssim$ 2.5 M$_{\odot}$). In red-clump stars, the additional viscosities
must be an order of magnitude higher than in younger red giants of similar mass
during the hydrogen shell-burning phase. In combination with previous efforts,
we obtain a clear picture of how the physical processes acting in stellar
interiors should redistribute angular momentum from the end of the main
sequence until the core-helium burning phase for low- and intermediate-mass
stars to satisfy the asteroseismic constraints.


  In this paper, we study the number of limit cycles that can bifurcate from a
periodic annulus in discontinuous planar piecewise linear Hamiltonian
differential system with three zones separated by two parallel straight lines,
such that the linear differential systems that define the piecewise one have a
center and two saddles. That is, the linear differential system in the region
between the two parallel lines (i.e. the central subsystem) has a center and
the others subsystems have saddles. We prove that if the central subsystem has
a real or a boundary center, then we have at least six limit cycles bifurcating
from the periodic annulus by linear perturbations, four passing through the
three zones and two passing through the two zones. Now, if the central
subsystem has a virtual center, then we have at least four limit cycles
bifurcating from the periodic annulus by linear perturbations, three passing
through the three zones and one passing through the two zones. For this, we
obtain a normal form for these piecewise Hamiltonian systems and study the
number of zeros of its Melnikov functions defined in two and three zones


  Realizing precise control over multiquanta emission is crucial for quantum
information processing, especially when integrated with advanced techniques of
manipulating quantum states. Here, by spinning the resonator to induce the
Sagnac effect, we can obtain nonreciprocal photon-phonon and photon-magnon
super-Rabi oscillations under conditions of optically driving resonance
transitions. Opening dissipative channels for such super-Rabi oscillations
enables the realization of directional bundle emissions of entangled
photon-phonon pairs and photon-magnon pairs by transferring pure multiquanta
state to bundled multiquanta outside of the system. This nonreciprocal emission
is a flexible switch that can be controlled with precision, and simultaneous
emissions of different entangled pairs (such as photon-phonon or photon-magnon
pairs) can even emerge but in opposite directions by driving the resonator from
different directions. This ability to flexibly manipulate the system allows us
to achieve directional entangled multiquanta emitters, and has also potential
applications for building hybrid quantum networks and on-chip quantum
communications.


  A family of plane oriented continuous paths depending on a fixed real
positive number $R$ is considered. For any point $x$ on the path, the previous
points lie out of any circle of radius $R$ having at $x$ interior normal in a
suitable tangent cone to the path at $x$. These paths are locally descent
curves of a nested family sets of reach $R$. Avoiding any smoothness
requirements, we get angle estimate and not intersection property. Afterwards
we are able to estimate the lenght and detour of this curve.


  We find that the way we choose to represent data labels can have a profound
effect on the quality of trained models. For example, training an image
classifier to regress audio labels rather than traditional categorical
probabilities produces a more reliable classification. This result is
surprising, considering that audio labels are more complex than simpler
numerical probabilities or text. We hypothesize that high dimensional, high
entropy label representations are generally more useful because they provide a
stronger error signal. We support this hypothesis with evidence from various
label representations including constant matrices, spectrograms, shuffled
spectrograms, Gaussian mixtures, and uniform random matrices of various
dimensionalities. Our experiments reveal that high dimensional, high entropy
labels achieve comparable accuracy to text (categorical) labels on the standard
image classification task, but features learned through our label
representations exhibit more robustness under various adversarial attacks and
better effectiveness with a limited amount of training data. These results
suggest that label representation may play a more important role than
previously thought. The project website is at
\url{https://www.creativemachineslab.com/label-representation.html}.


  Semiconducting transition metal dichalcogenides (TMDs) are desired as active
materials in optoelectronic devices due to their strong excitonic effects. They
can be exfoliated from their parent layered materials with low-cost and for
mass production via a liquid exfoliation method. However, the device
application of TMDs prepared by liquid phase exfoliation is limited by their
poor photoluminescence quantum efficiencies (PLQE). It is crucial to understand
the reason to low PLQE for their practical device development. Here we evaluate
the quality of monolayer-enriched liquid phase exfoliated (LPE) WS$_2$
dispersions by systematically investigating their optical and photophysical
properties and contrasting with mechanically exfoliated (ME) WS2 monolayers. An
in-depth understanding of the exciton dynamics is gained with ultrafast
pump-probe measurements. We reveal that the energy transfer between monolayer
and few-layers in LPE WS$_2$ dispersions is a substantial reason for their
quenched PL. In addition, we show that LPE WS$_2$ is promising to build high
performance optoelectronic devices with excellent optical quality.


  Bipartite Bell inequalities can be simultaneously violated by two different
pairs of observers when weak measurements and signaling is employed. Here we
experimentally demonstrate the violation of two simultaneous CHSH inequalities
by exploiting a two-photon polarization maximally entangled state. Our results
demonstrate that large double violation is experimentally achievable. Our
demonstration may have impact for Quantum Key Distribution or certification of
Quantum Random Number generators based on weak measurements.


  Spin transfer torque oscillators (STOs) based on magnetic tunnel junction
(MTJ) devices are emerging as a possible replacement for complementary
metal-oxide semiconductors for radio-frequency (RF) signal generation.
Advantages include low power consumption, small device area, and large
frequency tunability. But such a single device cannot achieve the necessary
noise performance for RF applications. It has been reported lately that a
network of globally coupled STOs achieves significant improvement in phase
noise. The study here is to propose use of such coupled STOs as
self-oscillating RF mixers. Critical mixer performance parameters, including
conversion gain, output power, and linearity, are discussed.


  When atoms are exposed to intense laser or microwave pulses ~10% of the atoms
are found in Rydberg states subsequent to the pulse, even if it is far more
intense than required for static field ionization. The optical spectra of the
surviving Li atoms in the presence of a 38 GHz microwave field suggest how
atoms survive an intense pulse. The spectra exhibit a periodic train of peaks
38 GHz apart. One peak is just below the limit, and with a 90 V/cm field
amplitude the train extends from 300 GHz above the limit to 3000 GHz below it.
The spectra and quantum mechanical calculations imply that the atoms survive in
quasi stable states in which the Rydberg electron is in a weakly bound orbit
infrequently returning to the ionic core during the intense pulse.


  Instead of sampling a function at a single point, average sampling takes the
weighted sum of function values around the point. Such a sampling strategy is
more practical and more stable. In this note, we present an explicit method
with an exponentially-decaying approximation error to reconstruct a
multivariate bandlimited function from its finite average oversampling data.
The key problem in our analysis is how to extend a function so that its Fourier
transform decays at an optimal rate to zero at infinity.


  Soccer ball detection is identified as one of the critical challenges in the
RoboCup competition. It requires an efficient vision system capable of handling
the task of detection with high precision and recall and providing robust and
low inference time. In this work, we present a novel convolutional neural
network (CNN) approach to detect the soccer ball in an image sequence. In
contrast to the existing methods where only the current frame or an image is
used for the detection, we make use of the history of frames. Using history
allows to efficiently track the ball in situations where the ball disappears or
gets partially occluded in some of the frames. Our approach exploits
spatio-temporal correlation and detects the ball based on the trajectory of its
movements. We present our results with three convolutional methods, namely
temporal convolutional networks (TCN), ConvLSTM, and ConvGRU. We first solve
the detection task for an image using fully convolutional encoder-decoder
architecture, and later, we use it as an input to our temporal models and
jointly learn the detection task in sequences of images. We evaluate all our
experiments on a novel dataset prepared as a part of this work. Furthermore, we
present empirical results to support the effectiveness of using the history of
the ball in challenging scenarios.


  We study the possibility of complete family unification in higher-dimensional
space-time. Three families of matters in SU(5) grand unified theory are derived
from a single bulk multiplet of SU(N) gauge group (N >= 9) in the framework of
S^1/Z_2 orbifold models. In the case of the direct orbifold breaking down to
the standard model gauge group, there are models in which bulk fields from a
single multiplet and a few brane fields compose three families of quarks and
leptons.


  We consider the eternal inflation scenario of the slow-roll/chaotic type with
the additional element of an objective collapse of the wave function. The
incorporation of this new agent to the traditional inflationary setting might
represent a possible solution to the quantum measurement problem during
inflation, a subject that has not reached a consensus among the community.
Specifically, it could provide an explanation for the generation of the
primordial anisotropies and inhomogeneities, starting from a perfectly
symmetric background and invoking symmetric dynamics. We adopt the continuous
spontaneous localization model, in the context of inflation, as the dynamical
reduction mechanism that generates the primordial inhomogeneities. Furthermore,
when enforcing the objective reduction mechanism, the condition for eternal
inflation can be bypassed. In particular, the collapse mechanism incites the
wave function, corresponding to the inflaton, to localize itself around the
zero mode of the field. Then the zero mode will evolve essentially unperturbed,
driving inflation to an end in any region of the Universe where inflation
occurred. Also, our approach achieves a primordial spectrum with an amplitude
and shape consistent with the one that best fits the observational data.


  In modern biomedical research, it is ubiquitous to have multiple data sets
measured on the same set of samples from different views (i.e., multi-view
data). For example, in genetic studies, multiple genomic data sets at different
molecular levels or from different cell types are measured for a common set of
individuals to investigate genetic regulation. Integration and reduction of
multi-view data have the potential to leverage information in different data
sets, and to reduce the magnitude and complexity of data for further
statistical analysis and interpretation. In this paper, we develop a novel
statistical model, called supervised integrated factor analysis (SIFA), for
integrative dimension reduction of multi-view data while incorporating
auxiliary covariates. The model decomposes data into joint and individual
factors, capturing the joint variation across multiple data sets and the
individual variation specific to each set respectively. Moreover, both joint
and individual factors are partially informed by auxiliary covariates via
nonparametric models. We devise a computationally efficient
Expectation-Maximization (EM) algorithm to fit the model under some
identifiability conditions. We apply the method to the Genotype-Tissue
Expression (GTEx) data, and provide new insights into the variation
decomposition of gene expression in multiple tissues. Extensive simulation
studies and an additional application to a pediatric growth study demonstrate
the advantage of the proposed method over competing methods.


  Experimental investigations of the scaling behavior of Barkhausen avalanches
in out-of-plane ferromagnetic films yield widely different results for the
values of the critical exponents despite similar labyrinthine domain
structures, suggesting that universality may not hold for this class of
materials. Analyzing a phase field model for magnetic reversal, we show that
avalanche scaling is bounded by characteristic lengthscales arising from the
competition between dipolar forces and exchange interactions. We compare our
results with the experiments and find a good qualitative and quantitative
agreement, reconciling apparent contradictions. Finally, we make some
prediction, amenable to experimental verification, on the dependence of the
avalanches behavior from the film thickness and disorder.


  We present an analysis of the complex gas hydrodynamics in the X-ray luminous
galaxy cluster RXJ1347.5-1145 caught in the act of merging with a subcluster to
its southeast using a combined $186$ ks Chandra exposure, $2.5$ times greater
than previous analyses. The primary cluster hosts a sloshing cold front spiral
traced by four surface brightness edges $5.^{\prime \prime}85^{+0.04}_{-0.03}$
west, $7.^{\prime \prime}10^{+0.07}_{-0.03}$ southeast, $11.^{\prime
\prime}5^{+1.3}_{-1.2}$ east, and $16.^{\prime \prime}7^{+0.3}_{-0.5}$
northeast from the primary central dominant galaxy, suggesting the merger is in
the plane of the sky. We measure temperature and density ratios across these
edges, confirming they are sloshing cold fronts. We observe the eastern edge of
the subcluster infall shock, confirming the observed subcluster is traveling
from the southwest to the northeast in a clockwise orbit. We measure a shock
density contrast of $1.38^{+0.16}_{-0.15}$ and infer a Mach number
$1.25\pm0.08$ and a shock velocity of $2810^{+210}_{-240}$ km s$^{-1}$.
Temperature and entropy maps show cool, low entropy gas trailing the subcluster
in a southwestern tail, consistent with core shredding. Simulations suggest a
perturber in the plane of the sky on a clockwise orbit would produce a sloshing
spiral winding counterclockwise, opposite to that observed. The most compelling
solution to this discrepancy is that the observed southeastern subcluster is on
its first passage, shock heating gas during its clockwise infall, while the
main cluster's clockwise cold front spiral formed from earlier encounters with
a second perturber orbiting counterclockwise.


  This paper presents a deep-learned facial recognition method for underwater
robots to identify scuba divers. Specifically, the proposed method is able to
recognize divers underwater with faces heavily obscured by scuba masks and
breathing apparatus. Our contribution in this research is towards robust facial
identification of individuals under significant occlusion of facial features
and image degradation from underwater optical distortions. With the ability to
correctly recognize divers, autonomous underwater vehicles (AUV) will be able
to engage in collaborative tasks with the correct person in human-robot teams
and ensure that instructions are accepted from only those authorized to command
the robots. We demonstrate that our proposed framework is able to learn
discriminative features from real-world diver faces through different data
augmentation and generation techniques. Experimental evaluations show that this
framework achieves a 3-fold increase in prediction accuracy compared to the
state-of-the-art (SOTA) algorithms and is well-suited for embedded inference on
robotic platforms.


  Recent advances in unsupervised speech representation learning discover new
approaches and provide new state-of-the-art for diverse types of speech
processing tasks. This paper presents an investigation of using wav2vec 2.0
deep speech representations for the speaker recognition task. The proposed
fine-tuning procedure of wav2vec 2.0 with simple TDNN and statistic pooling
back-end using additive angular margin loss allows to obtain deep speaker
embedding extractor that is well-generalized across different domains. It is
concluded that Contrastive Predictive Coding pretraining scheme efficiently
utilizes the power of unlabeled data, and thus opens the door to powerful
transformer-based speaker recognition systems. The experimental results
obtained in this study demonstrate that fine-tuning can be done on relatively
small sets and a clean version of data. Using data augmentation during
fine-tuning provides additional performance gains in speaker verification. In
this study speaker recognition systems were analyzed on a wide range of
well-known verification protocols: VoxCeleb1 cleaned test set, NIST SRE 18
development set, NIST SRE 2016 and NIST SRE 2019 evaluation set, VOiCES
evaluation set, NIST 2021 SRE, and CTS challenges sets.


  Given any finite graph, we offer a simple realization of the
graph-associahedron polytope using integer coordinates.


  In this paper we describe weighting techniques used for the optimal
coaddition of CCD frames with differing characteristics. Optimal means maximum
signal-to-noise (s/n) for stellar objects. We derive formulae for four
applications: 1) object detection via matched filter, 2) object detection
identical to DAOFIND, 3) aperture photometry, and 4) ALLSTAR profile-fitting
photometry. We have included examples involving 21 frames for which either the
sky brightness or image resolution varied by a factor of three. The gains in
s/n were modest for most of the examples, except for DAOFIND detection with
varying image resolution which exhibited a substantial s/n increase. Even
though the only consideration was maximizing s/n, the image resolution was seen
to improve for most of the variable resolution examples. Also discussed are
empirical fits for the weighting and the availability of the program, WEIGHT,
used to generate the weighting for the individual frames. Finally, we include
appendices describing the effects of clipping algorithms and a scheme for
star/galaxy and cosmic ray/star discrimination.


  The great majority of engineered products are subject to thermo-mechanical
loads which vary with the product environment during the various phases of its
life-cycle (machining, assembly, intended service use...). Those load
variations may result in different values of the parts nominal dimensions,
which in turn generate corresponding variation of the effective clearance
(functional requirement) in the assembly. Usually, and according to the
contractual drawings, the parts are measured after the machining stage, whereas
the interesting measurement values are the ones taken in service for they allow
the prediction of clearance value under operating conditions. Unfortunately,
measurement in operating conditions may not be practical to obtain. Hence, the
main purpose of this research is to create, through computations and
simulations, links between the values of the loads, dimensions and functional
requirements during the successive phases of the life cycle of some given
product. [...]


  Fermat ideals define planar point configurations that are closely related to
the intersection locus of the members of a specific pencil of curves. These
ideals have gained recent popularity as counterexamples to some proposed
containments between symbolic and ordinary powers. We give a systematic
treatment of the family of Fermat ideals, describing explicitly the minimal
generators and the minimal free resolutions of all their ordinary powers as
well as many symbolic powers. We use these to study the ordinary and the
symbolic Rees algebra of Fermat ideals. Specifically, we show that the symbolic
Rees algebras of Fermat ideals are Noetherian. Along the way, we give formulas
for the Castelnuovo-Mumford regularity of the powers of Fermat ideals and we
determine their reduction ideals.


  We calculate the jet quenching parameter in medium with chemical potential
from AdS/CFT correspondence. Our result is summarized in a plot. Moreover, we
extract the explicit form of the jet quenching parameter of medium with small
chemical potential for phases of dual SYM corresponding to large and small
black holes. For the former phase, the jet quenching is increased as the charge
density increases, however, for the latter it is the opposite though the
background is thermodynamically unstable.


  We investigate a charge asymmetry in $t \bar t \gamma$ production at the LHC
that provides complementary information to the measured asymmetries in $t \bar
t$ production. We estimate the experimental uncertainty in its measurement at
the LHC with 8 and 14 TeV. For new physics models that simultaneously reproduce
the asymmetry excess in $t \bar t$ at the Tevatron and the SM-like asymmetry at
the LHC, the measurement in $t \bar t \gamma$ at the LHC could exhibit
significant deviations with respect to the SM prediction.


  We present the kinematics of a sample of 398 DA white dwarfs from the SPY
project (ESO SN Ia Progenitor surveY) and discuss kinematic criteria for
distinguishing thin-disk, thick-disk, and halo populations. This is the largest
homogeneous sample of white dwarfs for which 3D space motions have been
determined. Since the percentage of old stars among white dwarfs is higher than
among main-sequence stars, they are presumably valuable tools in studies of old
populations, such as the halo and the thick disk. Radial velocities and
spectroscopic distances were combined with our measurements of proper motions
to derive 3D space motions. Galactic orbits were computed. Our kinematic
criteria for assigning population membership were deduced from a sample of F
and G stars, for which chemical criteria can be used to distinguish between a
thin-disk, a thick-disk and a halo star. Our kinematic population
classification scheme is based on the position in the U-V-velocity diagram, the
position in the Jz-eccentricity diagram, and the Galactic orbit. We combined
this with age information and found seven halo and 27 thick-disk white dwarfs
in this brightness limited sample. Correspondingly 2% of the white dwarfs
belong to the halo and 7% to the thick disk. The mass contribution of the
thick-disk white dwarfs is found to be substantial, but is insufficient to
account for the missing dark matter. (abbreviated)


  We study collision of two atoms with formation of Feshbach resonance at
combined interaction with the external magnetic field and laser radiation. In
cases of one- and two-photon resonances of laser radiation with two discrete
vibrational molecular levels, we show that Feshbach resonances appear at
interaction of external magnetic field with dressed states formed via
Autler-Townes effect. In addition, in case of one-photon resonance the lower
vibrational molecular state is coupled by laser radiation with the continuum of
the elastic channel and forms laser-induced Feshbach resonance via both
Autler-Townes effect and LICS mechanism. We study the combined process of
formation of Feshbach resonances; this enables the control of Feshbach
resonance by varying the magnetic field and intensity and frequency of laser
radiation. We obtain the cross-sections of elastic and inelastic scattering and
show that quenching of resonance occurs at the energy equal to that of the
systems ground state. Dependence of the cross-sections on the magnetic field
and laser intensity is examined in detail. In all considered cases, the
scattering length is obtained depending on the magnetic and laser fields are
studied. In the absence of magnetic interaction if the hyperfine substates of
the quasibound state in the closed channel and those of individual colliding
atoms in the open channel are the same, Feshbach resonances may arise via weak
interaction between nuclear and electronic motions, which leads to transitions
between electronic states. The obtained results can be employed in new studies
of collisions of cold atoms, e.g., of alkali metal atoms and for interpretation
of new experiments in BECs.


  In this note we give a survey on the DDVV conjecture which is also called the
"normal scalar curvature conjecture".


  Let $R$ be a Dedekind ring, $K$ its quotient field, and $L=K(\alpha)$ a
finite field extension of $K$ defined by a monic irreducible polynomial
$f(x)\in R[x]$. We give an easy version of Dedekind's criterion which
computationally improves those versions know in the literature. We further use
this result to give a sufficient condition for the integral closedness of
$R[\alpha]$ when $f(x)=x^n-a$. In case $R$ is a ring of integers of a number
field, we give yet sufficient and necessary conditions for this to hold,
generalizing and improving in both cases some known results in this direction.
Some highlighting examples are also given.


  Spatial L{\'{e}}vy-like flights are introduced as a way in the absorbing
phase transitions to produce non-local interactions. We utilize the
autoencoder, an unsupervised learning method, to predict the critical points
for $(1+1)$-d directed percolation with such spatial long-range interactions.
After making a global coverage of the reaction-diffusion distance and taking a
series of different values for the parameter ${\beta}$ in the distribution
$P(r){\sim}1/r^{\beta}$, the critical points $P_c$ that can be continuously
varied are obtained. And the dynamic decay of the particle density under the
critical points was counted as a way to determine the critical exponent
${\delta}$ of the survival rate. We also investigate the active behavior of the
system's particles under the critical point with increasing time steps, which
allows us to determine the characteristic time $t_f$ of the finite-scale
systems. And the dynamic exponents $z$ are obtained using the scaling relation
$t_f{\sim}L^{z}$. We find that the autoencoder can identify this characteristic
evolutionary behavior of particles. Finally, we discuss the compliance of the
scaling form $1/{\delta}-({\beta}-2)/{\delta}z=2$ in different ${\beta}$
intervals as well as a method to introduce a global scaling mechanism by
generating a random walking step using the L{\'{e}}vy distribution.


  The regularized trace of the heat kernel of a one-dimensional Schr\"odinger
operator with a singular two-particle contact interaction being of Lieb-Liniger
type is considered. We derive a complete small-time asymptotic expansion in
(fractional) powers of the time, $t$. Most importantly, we do not invoke
standard parametrix constructions for the heat kernel. Instead, we first derive
the large-energy expansion of the regularized trace of the resolvent for the
considered operator. Then, we exploit that the resolvent may be obtained by a
Laplace transformation of the heat semi-group, and an application of a suitable
inverse Watson lemma eventually yields the small-$t$ asymptotic expansion of
the heat-kernel trace.


  The optical trapping and manipulation of small particles is an important tool
for probing fluid properties at the microscale. In particular, microrheology
exploits the manipulation and rotation of micron-scale particles to probe local
viscosity, especially where these properties may be perturbed as a function of
their local environment, for example in the vicinity of cells. To this end,
birefringent particles are useful as they can be readily controlled using
optically induced forces and torques, and thereby used to probe their local
environment. However the magnitude of optical torques that can be induced in
birefringent particles is small, and a function of the particle diameter,
meaning that rotational flow cannot readily be probed on length scales much
small than the micron level. Here we show modelling that demonstrates that
eccentric spherical core-shell nanoparticles can be used to generate
considerable optical torques. The eccentricity is a result of the displacement
of the centre of the core from the shell. Our results show that, for particles
ranging from 90~nm to 180~nm in diameter, we may achieve rotation rates
exceeding 800~Hz. This fills a missing size gap in the rotation of
microparticles with optical forces. The diameter of particle we may rotate is
almost an order of magnitude smaller than the smallest birefringent particles
that have been successfully rotated to date. The rotation of eccentric
core-shell nanoparticles therefore makes an important contribution to
biophotonics and creates new opportunities for rheology in nanoscale
environments.


  We present Finslerian perturbation for the $\Lambda$CDM model, which breaks
the isotropic symmetry of the universe. The analysis on the Killing vectors
shows that the Randers-Finsler spacetime breaks the isotropic symmetry even if
the scalar perturbations of the FRW metric vanish. In Randers-Finsler
spacetime, the modified geodesic equation deduces a modified Boltzmann
equation. We propose a perturbational version of the gravitational field
equation in Randers-Finsler spacetime, where we have omitted the curvature
tensor that does not belong to the base space of the tangent bundle. The
gravitational field equations for the gravitational wave are also presented.
The primordial power spectrum of the gravitational wave is investigated. We
show that the primordial power spectrum for super-horizon perturbations is
unchanged. For sub-horizon perturbations, however, the power spectrum is
modified.


  We describe the outcome of a data challenge conducted as part of the Dark
Machines Initiative and the Les Houches 2019 workshop on Physics at TeV
colliders. The challenged aims at detecting signals of new physics at the LHC
using unsupervised machine learning algorithms. First, we propose how an
anomaly score could be implemented to define model-independent signal regions
in LHC searches. We define and describe a large benchmark dataset, consisting
of >1 Billion simulated LHC events corresponding to $10~\rm{fb}^{-1}$ of
proton-proton collisions at a center-of-mass energy of 13 TeV. We then review a
wide range of anomaly detection and density estimation algorithms, developed in
the context of the data challenge, and we measure their performance in a set of
realistic analysis environments. We draw a number of useful conclusions that
will aid the development of unsupervised new physics searches during the third
run of the LHC, and provide our benchmark dataset for future studies at
https://www.phenoMLdata.org. Code to reproduce the analysis is provided at
https://github.com/bostdiek/DarkMachines-UnsupervisedChallenge.


  In this paper we describe the solution of a stochastic bistable system from a
dynamical perspective. We show how a single framework with variable noise can
explain hysteresis at zero temperature and two-state coexistence in the
presence of noise. This feature is similar to the phase transition of
thermodynamics. Our mathematical model for bistable systems also explains how
the width of a hysteresis loop shrinks in the presence of noise, and how
variation in initial conditions can take such systems to different final
states.


  In position dependent mass (PDM) problems, the quantum dynamics of the
associated systems have been understood well in the literature for particular
orderings. However, no efforts seem to have been made to solve such PDM
problems for general orderings to obtain a global picture. In this connection,
we here consider the general ordered quantum Hamiltonian of an interesting
position dependent mass problem, namely the Mathews-Lakshmanan oscillator, and
try to solve the quantum problem for all possible orderings including Hermitian
and non-Hermitian ones. The other interesting point in our study is that for
all possible orderings, although the Schr\"odinger equation of this
Mathews-Lakshmanan oscillator is uniquely reduced to the associated Legendre
differential equation, their eigenfunctions cannot be represented in terms of
the associated Legendre polynomials with integral degree and order. Rather the
eigenfunctions are represented in terms of associated Legendre polynomials with
non-integral degree and order. We here explore such polynomials and represent
the discrete and continuum states of the system. We also exploit the connection
between associated Legendre polynomials with non-integral degree with other
orthogonal polynomials such as Jacobi and Gegenbauer polynomials.


  We compute the effective Kahler potential for matter fields in warped
compactifications, starting from five dimensional gauged supergravity, as a
function of the matter fields localization. We show that truncation to zero
modes is inconsistent and the tree-level exchange of the massive gravitational
multiplet is needed for consistency of the four-dimensional theory. In addition
to the standard Kahler coming from dimensional reduction, we find the quartic
correction coming from integrating out the gravity multiplet. We apply our
result to the computation of scalar masses, by assuming that the SUSY breaking
field is a bulk hypermultiplet. In the limit of extreme opposite localization
of the matter and the spurion fields, we find zero scalar masses, consistent
with sequestering arguments. Surprisingly enough, for all the other cases the
scalar masses are tachyonic. This suggests the holographic interpretation that
a CFT sector always generates operators contributing in a tachyonic way to
scalar masses. Viability of warped su- persymmetric compactifications
necessarily asks then for additional contributions. We discuss the case of
additional bulk vector multiplets with mixed boundary conditions, which is a
partic- ularly simple and attractive way to generate large positive scalar
masses. We show that in this case successful fermion mass matrices implies
highly degenerate scalar masses for the first two generations of squarks and
sleptons.


  The collisional electron-impact line widths of the 3s-3p transitions in
Li-like ions from B III to Ne VIII are calculated with the convergent
close-coupling (CCC) method from the atomic collision theory. The elastic and
inelastic contributions to the line broadening and their Z-scaling are
discussed in detail, and comparisons with recent experimental and theoretical
results are also presented. It is found that similar to our previous study of
line broadening in Be-like ions, the difference between experimental and CCC
results monotonically increases with the spectroscopic charge of an ion.


  The short note is devoted to the setting free of hidden symmetries in Verma
modules over sl(2,C) by the noncommutative Veronese mappings.


  For several configurations of charges in the presence of conductors, the
method of images permits us to obtain some observables associated with such a
configuration by replacing the conductors with some image charges. However,
simple inspection shows that the potential energy associated with both systems
does not coincide. Nevertheless, it can be shown that for a system of a
grounded or neutral conductor and a distribution of charges outside, the
external potential energy associated with the real charge distribution embedded
in the field generated by the set of image charges is twice the value of the
internal potential energy associated with the original system. This assertion
is valid for any size and shape of the conductor, and regardless of the
configuration of images required. In addition, even in the case in which the
conductor is not grounded nor neutral, it is still possible to calculate the
internal potential energy of the original configuration through the method of
images. These results show that the method of images could also be useful for
calculations of the internal potential energy of the original system.


  The rapid growth in the scale of Large Language Models (LLMs) has led to
significant computational and memory costs, making model compression techniques
such as network pruning increasingly crucial for their efficient deployment.
Recent LLMs such as LLaMA2 and Mistral have adopted GLU-based MLP
architectures. However, current LLM pruning strategies are primarily based on
insights from older LLM architectures, necessitating a reevaluation of these
strategies to suit the new architectural characteristics. Contrary to
traditional beliefs, we find that outliers play a diminished role in the input
projections of GLU-based MLPs. Leveraging this new insight, we propose
Dependency-aware Semi-structured Sparsity (DaSS), a novel pruning method for
GLU-based LLMs. DaSS balances the flexibility of unstructured pruning and the
structural consistency of dependency-based structured pruning by considering
both of weight magnitude and corresponding intermediate activation norms in
weight pruning metric. Empirical evaluations on the Mistral, Gemma, and LLaMA2
model families demonstrate the consistent effectiveness of DaSS in the
prevailing GLU variants.


  A pipeline CAMAC controller with PC/104-Plus single board computer has been
developed. The architecture of the pipeline CAMAC controller is completely
different from that of traditional CAMAC controller. The pipeline CAMAC
controller adopted truly pipelined command and data paths from/to computer
memory to/from CAMAC. The pipeline method enabled the controller maximum speed
of CAMAC, approximately up to 3 MB/sec with 24-bit data. The controller also
has a DAQ function such as event numbering for co-existence with other
equipment such as VME. Not only the functionality and the performance but also
the pipeline architecture and the design concept are described.


  Genetic differences between individuals associated to quantitative phenotypic
traits, including disease states, are usually found in non-coding genomic
regions. These genetic variants are often also associated to differences in
expression levels of nearby genes (they are "expression quantitative trait
loci" or eQTLs for short) and presumably play a gene regulatory role, affecting
the status of molecular networks of interacting genes, proteins and
metabolites. Computational systems biology approaches to reconstruct causal
gene networks from large-scale omics data have therefore become essential to
understand the structure of networks controlled by eQTLs together with other
regulatory genes, and to generate detailed hypotheses about the molecular
mechanisms that lead from genotype to phenotype. Here we review the main
analytical methods and softwares to identify eQTLs and their associated genes,
to reconstruct co-expression networks and modules, to reconstruct causal
Bayesian gene and module networks, and to validate predicted networks in
silico.


  Planning problems where effects of actions are non-deterministic can be
modeled as Markov decision processes. Planning problems are usually
goal-directed. This paper proposes several techniques for exploiting the
goal-directedness to accelerate value iteration, a standard algorithm for
solving Markov decision processes. Empirical studies have shown that the
techniques can bring about significant speedups.


  Plasmas are highly nonlinear and multi-scale, motivating a hierarchy of
models to understand and describe their behavior. However, there is a scarcity
of plasma models of lower fidelity than magnetohydrodynamics (MHD). Galerkin
models, obtained by projection of the MHD equations onto a truncated modal
basis, can furnish this gap in the lower levels of the model hierarchy. In the
present work, we develop low-dimensional Galerkin plasma models which preserve
global conservation laws by construction. This additional model structure
enables physics-constrained machine learning algorithms that can discover these
types of low-dimensional plasma models directly from data. This formulation
relies on an energy-based inner product which takes into account all of the
dynamic variables. The theoretical results here build a bridge to the extensive
Galerkin literature in fluid mechanics, and facilitate the development of
physics-constrained reduced-order models from plasma data.


  The low-metallicity RV Tauri star MACHO47.2496.8, recently discovered in the
Large Magellanic Cloud, is highly enriched in carbon and heavy elements
produced by the slow neutron capture process (s-process), and is most probably
a genuine post-C(N-type) asymptotic giant branch (AGB) star. We use the
analysis of the abundances of MACHO47.2496.8 to constrain free parameters in
AGB models. We test which values of the free parameters describing uncertain
physical mechanisms in AGB stars, namely the third dredge-up and the features
of the 13C neutron source, produce models that better match the abundances
observed in MACHO47.2496.8. We carry out stellar population synthesis coupled
with s-process nucleosynthesis using a synthetic stellar evolution code. The
s-process ratios observed in MACHO47.2496.8 can be matched by the same models
that explain the s-process ratios of Galactic AGB and post-AGB stars of
metallicity > Z_sun/10, except for the choice of the effectiveness of 13C as a
neutron source, which has to be lower by roughly a factor of 3 to 6. The less
effective neutron source for lower metallicities is also required when
comparing population synthesis results to observations of Galactic halo
$s$-enhanced stars, such as Pb stars. The 12C/13C ratio in MACHO47.2496.8
cannot be matched simultaneously and requires the occurrence of extra-mixing
processes. The confirmed trend of the decreased efficiency of the 13C neutron
source with metallicity requires an explanation from AGB s-process models. The
present work is to date the first comparison between theoretical models and the
detailed abundances of an extragalactic post-AGB star.


  A growing effort in NLP aims to build datasets of human explanations.
However, the term explanation encompasses a broad range of notions, each with
different properties and ramifications. Our goal is to provide an overview of
diverse types of explanations and human limitations, and discuss implications
for collecting and using explanations in NLP. Inspired by prior work in
psychology and cognitive sciences, we group existing human explanations in NLP
into three categories: proximal mechanism, evidence, and procedure. These three
types differ in nature and have implications for the resultant explanations.
For instance, procedure is not considered explanations in psychology and
connects with a rich body of work on learning from instructions. The diversity
of explanations is further evidenced by proxy questions that are needed for
annotators to interpret and answer open-ended why questions. Finally,
explanations may require different, often deeper, understandings than
predictions, which casts doubt on whether humans can provide useful
explanations in some tasks.


  Anisotropic cloud string cosmological model with Bianchi type I space time is
investigated in the context of general relativity. The exact solutions of
Einstein field equations are obtained with when the source for energy momentum
tensor is generated by a cloud of strings with particles attached to them. The
dynamical and physical properties of the model universe are discussed by
comparing with the present observational findings. The interesting feature
obtained here is that our model universe starts with a big bang and as time
passes both particle density and string tension density decreases with
expansion of our Universe so that in the late time string vanishes and thus
leaving only the particles.


  We examine the effect of interactions between the electrons on the
conductances of some systems of quantum wires with different geometries. The
systems include a wire with a stub in the middle, a wire containing a ring
which can enclose a magnetic flux, and a system of four wires which are
connected in the middle through a fifth wire. Each of the wires is taken to be
a weakly interacting Tomonaga-Luttinger liquid, and scattering matrices are
introduced at all the junctions. Using a renormalization group method developed
recently for studying the flow of scattering matrices for interacting systems
in one dimension, we compute the conductances of these systems as functions of
the temperature and the wire lengths. We present results for all three regimes
of interest, namely, high, intermediate and low temperature. These correspond
respectively to the thermal coherence length being smaller than, comparable to
and larger than the smallest wire length in the different systems, i.e., the
length of the stub or each arm of the ring or the fifth wire. The
renormalization group procedure and the formulae used to compute the
conductances are different in the three regimes. We present a
phenomenologically motivated formalism for studying the conductances in the
intermediate regime where there is only partial coherence. At low temperatures,
we study the line shapes of the conductances versus the electron energy near
some of the resonances; the widths of the resonances go to zero with decreasing
temperature. Our results show that the conductances of various systems of
experimental interest depend on the temperature and lengths in a non-trivial
way when interactions are taken into account.


  We consider a cloud-based control architecture for a linear plant with
Gaussian process noise, where the state of the plant contains a client's
sensitive information. We assume that the cloud tries to estimate the state
while executing a designated control algorithm. The mutual information between
the client's actual state and the cloud's estimate is adopted as a measure of
privacy loss. We discuss the necessity of uplink and downlink privacy masks.
After observing that privacy is not necessarily a monotone function of the
noise levels of privacy masks, we discuss the joint design procedure for uplink
and downlink privacy masks. Finally, the trade-off between privacy and control
performance is explored.


  We present measurements of electromagnetically induced transparency with an
ensemble of donor- bound electrons in low-doped n-GaAs. We used optical
transitions from the Zeeman-split electron spin states to a bound trion state
in samples with optical densities of 0.3 and 1.0. The electron spin dephasing
time T* \approx 2 ns was limited by hyperfine coupling to fluctuating nuclear
spins. We also observe signatures of dynamical nuclear polarization, but find
these effects to be much weaker than in experiments that use electron spin
resonance and related experiments with quantum dots.


  Investigations of possible violations of the Pauli Exclusion Principle
represent critical tests of the microscopic space-time structure and
properties. Space-time non-commutativity provides a class of universality for
several Quantum Gravity models. In this context the VIP-2 Lead experiment sets
the strongest bounds, searching for Pauli Exclusion Principle violating
atomic-transitions in lead, excluding the $\theta$-Poincar\'e Non Commutative
Quantum Gravity models far above the Planck scale for non-vanishing
$\theta_{\mu \nu}$ ``electric-like'' components, and up to $6.9 \cdot 10^{-2}$
Planck scales if $\theta_{0i} = 0$.


  There is little doubt that hydrodynamic flow has been observed in heavy ion
collisions at the LHC and RHIC, mainly based on results on azimuthal
anisotropies, but also on particle spectra of identified particles, perfectly
compatible with hydrodynamic expansions. Surprisingly, in p-Pb collisions one
observes a very similar behavior. So do we see flow even in p-Pb? We will try
to answer this question.


  The annihilation of antiprotons on deuterons at rest are investigated for the
case when pion-nucleon and pion-delta-isobar pairs are produced. The two-step
mechanism is investigated by analysing these processes when either neutral
particles and charged ones are produced. Some predictions for the branching
ratios are presented.


  The Cotter-Holm Slice Model (CHSM) was introduced to study the behavior of
whether and specifically the formulation of atmospheric fronts, whose
prediction is fundamental in meteorology. Considered herein is the influence of
stochastic forcing on the Incompressible Slice Model (ISM) in a smooth 2D
bounded domain, which can be derived by adapting the Lagrangian function in
Hamilton's principle for CHSM to the Euler-Boussinesq Eady incompressible case.
First, we establish the existence and uniqueness of local pathwise solution
(probability strong solution) to the ISM perturbed by nonlinear multiplicative
stochastic forcing in Banach spaces $W^{k,p}(D)$ with $k>1+1/p$ and $p\geq 2$.
The solution is obtained by introducing suitable cut-off operators applied to
the $W^{1,\infty}$-norm of the velocity and temperature fields, using the
stochastic compactness method and the Yamada-Watanabe type argument based on
the Gy\"{o}ngy-Krylov characterization of convergence in probability. Then,
when the ISM is perturbed by linear multiplicative stochastic forcing and the
potential temperature does not vary linearly on the $y$-direction, we prove
that the associated Cauchy problem admits a unique global-in-time pathwise
solution with high probability, provided that the initial data is sufficiently
small or the diffusion parameter is large enough. The results partially answer
the problems left open in Alonso-Or{\'a}n et al. (Physica D 392:99--118, 2019,
pp. 117).


  We present an adaptive regularization algorithm that can be effectively
applied to the optimization problem in deep learning framework. Our
regularization algorithm aims to take into account the fitness of data to the
current state of model in the determination of regularity to achieve better
generalization. The degree of regularization at each element in the target
space of the neural network architecture is determined based on the residual at
each optimization iteration in an adaptive way. Our adaptive regularization
algorithm is designed to apply a diffusion process driven by the heat equation
with spatially varying diffusivity depending on the probability density
function following a certain distribution of residual. Our data-driven
regularity is imposed by adaptively smoothing a simplified objective function
in which the explicit regularization term is omitted in an alternating manner
between the evaluation of residual and the determination of the degree of its
regularity. The effectiveness of our algorithm is empirically demonstrated by
the numerical experiments in the application of image classification problems,
indicating that our algorithm outperforms other commonly used optimization
algorithms in terms of generalization using popular deep learning models and
benchmark datasets.


  We present observable upper bounds of squared concurrence, which are the dual
inequalities of the observable lower bounds introduced in [F. Mintert and A.
Buchleitner, Phys. Rev. Lett. 98, 140505 (2007)] and [L. Aolita, A. Buchleitner
and F. Mintert, Phys. Rev. A 78, 022308 (2008)]. These bounds can be used to
estimate entanglement for arbitrary experimental unknown finite-dimensional
states by few experimental measurements on a twofold copy $\rho\otimes\rho$ of
the mixed states. Furthermore, the degree of mixing for a mixed state and some
properties of the linear entropy also have certain relations with its upper and
lower bounds of squared concurrence.


  We have used fibre spectroscopy to establish cluster membership and examine
pre-main-sequence (PMS) lithium depletion for low-mass stars (spectral types F
to M) in the sparse young (~30 Myr) cluster IC 4665. We present a filtered
candidate list of 40 stars that should contain 75 per cent of single cluster
members with V of 11.5 to 18 in the central square degree of the cluster.
Whilst F- and G-type stars in IC 4665 have depleted little or no lithium, the
K- and early M-type stars have depleted more Li than expected when compared
with similar stars in other clusters of known age. An empirical age estimate
based on Li-depletion among the late-type stars of IC 4665 would suggest it is
older than 100 Myr. This disagrees entirely with ages determined either from
the nuclear turn-off, from isochronal matches to low-mass stars or from the
re-appearance of lithium previously found in much lower mass stars (the
``lithium depletion boundary''). We suggest that other parameters besides age,
perhaps composition or rotation, are very influential in determining the degree
of PMS Li-depletion in stars with M greater than 0.5 Msun. Further work is
required to identify and assess the effects of these additional parameters,
particularly to probe conditions at the interface between the sub-photospheric
convection zone and developing radiative core. Until then, PMS Li depletion in
F- to early M-type stars cannot be confidently used as a precise age indicator
in young clusters, kinematic groups or individual field stars.


  In this work, we study the personalized federated $\mathcal{X}$-armed bandit
problem, where the heterogeneous local objectives of the clients are optimized
simultaneously in the federated learning paradigm. We propose the
\texttt{PF-PNE} algorithm with a unique double elimination strategy, which
safely eliminates the non-optimal regions while encouraging federated
collaboration through biased but effective evaluations of the local objectives.
The proposed \texttt{PF-PNE} algorithm is able to optimize local objectives
with arbitrary levels of heterogeneity, and its limited communications protects
the confidentiality of the client-wise reward data. Our theoretical analysis
shows the benefit of the proposed algorithm over single-client algorithms.
Experimentally, \texttt{PF-PNE} outperforms multiple baselines on both
synthetic and real life datasets.


  We initiate the rigorous study of classification in semimetric spaces, which
are point sets with a distance function that is non-negative and symmetric, but
need not satisfy the triangle inequality. For metric spaces, the doubling
dimension essentially characterizes both the runtime and sample complexity of
classification algorithms --- yet we show that this is not the case for
semimetrics. Instead, we define the {\em density dimension} and discover that
it plays a central role in the statistical and algorithmic feasibility of
learning in semimetric spaces. We present nearly optimal sample compression
algorithms and use these to obtain generalization guarantees, including fast
rates. The latter hold for general sample compression schemes and may be of
independent interest.


  A phenomenological theory of mode collapse-revival for a system with a weak
nonlinearity is presented. The theory takes into account fluctuations of the
number n of quanta of oscillations. The collapse time $\tau _c ^{-1}$ for a
mode of frequency $\omega _0$ turns out to be $\omega _0(\sqrt{n/2}\hbar \omega
_0 \mid \nu \mid)$, where \nu is a coefficient in a nonlinear correction to the
mode frequency with respect to the oscillation energy E. For a Bose gas in a
harmonic trap $\tau _c$ is order of $\omega _0^{-1}\sqrt{\mu N/E\hbar \omega
_0}$. This value is order of 250 ms. for typical experimental conditions. The
coefficient \nu is calculated for the breathing mode in an isotropic trap.


  The quantum walk formalism is a widely used and highly successful framework
for modeling quantum systems, such as simulations of the Dirac equation,
different dynamics in both the low and high energy regime, and for developing a
wide range of quantum algorithms. Here we present the circuit-based
implementation of a discrete-time quantum walk in position space on a
five-qubit trapped-ion quantum processor. We encode the space of walker
positions in particular multi-qubit states and program the system to operate
with different quantum walk parameters, experimentally realizing a Dirac
cellular automaton with tunable mass parameter. The quantum walk circuits and
position state mapping scale favorably to a larger model and physical systems,
allowing the implementation of any algorithm based on discrete-time quantum
walks algorithm and the dynamics associated with the discretized version of the
Dirac equation.


  Twisted magnons (TMs) carrying orbital angular momentum (OAM) have attracted
much attention from the magnonic community. The fabrication of such novel
magnon state however is still challenging. Here we present a simple method to
generate TMs with arbitrary radial and azimuthal quantum numbers through the
spin-to-orbital angular momentum conversion. The conversion rate from
plane-wave magnons to twisted ones is shown to be insensitive to the quantum
index. The spectrum of TMs in thin nanodisks is solved analytically, showing a
good agreement with micromagnetic simulations. Moreover, we numerically study
the propagation of TMs in magnetic nanodisk arrays and obtain the quantitative
dependence of the decay length on quantum indexes. Our results are helpful for
realizing TMs with large OAMs that are indispensable for future high-capacity
magnonic communications and computings.


  As a generic model for liquid-vapour type transitions in random porous media,
the Asakura-Oosawa model for colloid-polymer mixtures is studied in a matrix of
quenched spheres using extensive Monte Carlo (MC) simulations. Since such
systems at criticality, as well as in the two-phase region, exhibit lack of
self-averaging, the analysis of MC data via finite size scaling requires
special care. After presenting the necessary theoretical background and the
resulting subtleties of finite size scaling in random-field Ising-type systems,
we present data on the order parameter distribution (and its moments) as a
function of colloid and polymer fugacities for a broad range of system sizes,
and for many (thousands) realizations of the porous medium. Special attention
is paid to the connected and disconnected susceptibilities, and their
respective critical behavior. We show that both susceptibilities diverge at the
critical point, and we demonstrate that this is compatible with the predicted
scenario of random-field Ising universality.


  Here we show how the Minimally Entangled States (MES) of a 2d system with
topological order can be identified using the geometric measure of
entanglement. We show this by minimizing this measure for the doubled semion,
doubled Fibonacci and toric code models on a torus with non-trivial topological
partitions. Our calculations are done either quasi-exactly for small system
sizes, or using the tensor network approach in [R. Orus, T.-C. Wei, O.
Buerschaper, A. Garcia-Saez, arXiv:1406.0585] for large sizes. As a byproduct
of our methods, we see that the minimisation of the geometric entanglement can
also determine the number of Abelian quasiparticle excitations in a given
model. The results in this paper provide a very efficient and accurate way of
extracting the full topological information of a 2d quantum lattice model from
the multipartite entanglement structure of its ground states.


  The continuous displacement cluster variation method (CDCVM) has introduced
local atomic displacements into the theoretical framework of the cluster
variation method (CVM) by viewing an atom displaced from a Bravais lattice
point as a particular atomic species located at the lattice point. This idea of
conversion from a freedom of local displacements into configurational freedom
is extended in this paper to magnetic freedoms. Various magnitudes of local
magnetic moments are considered, as well as two spin directions, on up-spins
and down-spins. The approach is applied to pure Ni and its Curie temperature is
explored with the entropy formula of the tetrahedron approximation in the CVM,
using the first-nearest-neighbor pair interaction energies extracted from the
total energies of various spin configurations, which are estimated from
electronic-structure calculations.


  We address the problem of imitation learning with multi-modal demonstrations.
Instead of attempting to learn all modes, we argue that in many tasks it is
sufficient to imitate any one of them. We show that the state-of-the-art
methods such as GAIL and behavior cloning, due to their choice of loss
function, often incorrectly interpolate between such modes. Our key insight is
to minimize the right divergence between the learner and the expert
state-action distributions, namely the reverse KL divergence or I-projection.
We propose a general imitation learning framework for estimating and minimizing
any f-Divergence. By plugging in different divergences, we are able to recover
existing algorithms such as Behavior Cloning (Kullback-Leibler), GAIL (Jensen
Shannon) and Dagger (Total Variation). Empirical results show that our
approximate I-projection technique is able to imitate multi-modal behaviors
more reliably than GAIL and behavior cloning.


  We study the three-body baryonic decay $ \bar{B}^{0}\rightarrow\Lambda^+_c
\bar{p} \pi^0$ based on the factorization approach. The most important Feynman
diagrams for this decay mode are factorizable and non-factorizable
contributions, so that the former plays the key role in the decay amplitude and
the latter affects the amplitude coefficient $a_2$. We calculate the decay
branching ratio for some values of $a_2$ in the range of $0.16-0.40$ and we
find that the value of $a_2=0.17$ leads us to the result of
$(1.50\pm0.21)\times10^{-4}$ which evidently agrees with the experimental
result $(1.55\pm0.17\pm0.08)\times 10^{-4}$ reported by BABAR


  A conceptual model can be used to manage complexity in both the design and
implementation phases of the system development life cycle. Such a model
requires a firm grasp of the abstract principles on which a system is based, as
well as an understanding of the high-level nature of the representation of
entities and processes. In this context, models can have distinct architectural
characteristics. This paper discusses model multiplicity (e.g., unified
modeling language [UML]), model singularity (e.g., object-process methodology
[OPM], thinging machine [TM]), and a heterogeneous model that involves
multiplicity and singularity. The basic idea of model multiplicity is that it
is not possible to present all views in a single representation, so a number of
models are used, with each model representing a different view. The model
singularity approach uses only a single unified model that assimilates its
subsystems into one system. This paper is concerned with current approaches,
especially in software engineering texts, where multimodal UML is introduced as
the general-purpose modeling language (i.e., UML is modeling). In such a
situation, we suggest raising the issue of multiplicity versus singularity in
modeling. This would foster a basic appreciation of the UML advantages and
difficulties that may be faced during modeling, especially in the educational
setting. Furthermore, we advocate the claim that a multiplicity of views does
not necessitate a multiplicity of models. The model singularity approach can
represent multiple views (static, behavior) without resorting to a collection
of multiple models with various notations. We present an example of such a
model where the static representation is developed first. Then, the dynamic
view and behavioral representations are built by incorporating a decomposition
strategy interleaved with the notion of time.


  Graph Machine Learning (GML) is receiving growing interest within the
pharmaceutical and biotechnology industries for its ability to model
biomolecular structures, the functional relationships between them, and
integrate multi-omic datasets - amongst other data types. Herein, we present a
multidisciplinary academic-industrial review of the topic within the context of
drug discovery and development. After introducing key terms and modelling
approaches, we move chronologically through the drug development pipeline to
identify and summarise work incorporating: target identification, design of
small molecules and biologics, and drug repurposing. Whilst the field is still
emerging, key milestones including repurposed drugs entering in vivo studies,
suggest graph machine learning will become a modelling framework of choice
within biomedical machine learning.


  We present the reweighted complex Langevin method, which enlarges the
applicability range of the complex Langevin method by reweighting the complex
trajectories. In this reweighting procedure both the auxiliary and target
ensembles have a complex action. We validate the method by applying it to
two-dimensional strong-coupling QCD at nonzero chemical potential, and observe
that it gives access to parameter regions that could otherwise not be reached
with the complex Langevin method.


  In this paper we revisit the problem of finding hidden symmetries in quantum
mechanical systems. Our interest in this problem was renewed by nontrivial
degeneracies of a simple spin Hamiltonian used to model spin relaxation in
alkali-metal vapors. We consider this spin Hamiltonian in detail and use this
example to outline a general approach to finding symmetries when eigenvalues
and eigenstates of the Hamiltonian are known. We extract all nontrivial
symmetries responsible for the degeneracy and show that the symmetry group of
the Hamiltonian is SU(2). The symmetry operators have a simple meaning which
becomes transparent in the limit of large spin. As an additional example we
apply the method to the Hydrogen atom.


  Topological superconductors are exotic gapped phases of matter hosting
Majorana mid-gap states on their boundary. In conventional topological
superconductors, Majorana in-gap states appear in the form of either localized
zero-dimensional modes or propagating spin-1/2 fermions with a
quasi-relativistic dispersion relation. Here we show that unconventional
propagating Majorana states can emerge on the surface of three-dimensional
topological superconductors protected by rotational symmetry. The
unconventional Majorana surface states fall into three different categories: a
spin-$S$ Majorana fermion with $(2S+1)$-fold degeneracy $(S\geq3/2)$, a
Majorana Fermi line carrying two distinct topological charges, and a quartet of
spin-1/2 Majorana fermions related by fourfold rotational symmetry. The
spectral properties of the first two kinds, which go beyond the conventional
spin-1/2 fermions, are unique to topological superconductors and have no
counterpart in topological insulators. We show that the unconventional Majorana
surface states can be obtained in the superconducting phase of doped $Z_2$
topological insulators or Dirac semimetals with rotational symmetry.


  Graphene foams have recently attracted a great deal of interest for their
possible use in technological applications, such as electrochemical storage
devices, wearable electronics, and chemical sensing. In this work, we present
computational investigations, performed by using molecular dynamics with
reactive potentials, of the mechanical and thermal properties of graphene
random nanofoams. In particular, we assess the mechanical and thermal
performances of four families of random foams characterized by increasing mass
density and decreasing average pore size. We find that the foams' mechanical
performances under tension cannot be rationalized in terms of mass density,
while they are principally related to their topology. Under compression,
higher-density foams show the typical slope change in the stress-strain curve
at 5-10% strain, moving from linear elasticity to bending stress plateau. At
variance, lower density foams display a quasi-linear behaviour up to 35%
strain. Furthermore, we assess the thermal conductivity of these random foams
using the Green-Kubo approach. While foam thermal conductivity is affected by
both connectivity and defects, nevertheless we obtain similar values for all
the investigated families.


  Phytochemical composition of ethanol leaf extract of Diodella sarmentosa was
profiled with GC-MS and the inhibitory property of the extract against total
microbial dehydrogenases were assessed. The major constituents of the extract
were squalene (29.50%), Phytol (24.68%), phenol, 3-pentadecyl- (18.58%),
1-Butanol, 3-methyl- (9.09%) and n-Hexadecanoic acid (7.78%). The minimum
inhibitory concentration (MIC) of the extract against broad spectrum of
microbial population was assessed. Bacillus subtilis, Candidas spp, and
Penicillium spp were more sensitive to the treatment and thus; were further
investigated using Dehydrogenase activity assay method. Total dehydrogenase
activities of Bacillus subtilis, Candidas spp, and Penicillium spp at the
extract concentration range of 0 to 2000mg/ml were progressively inhibited at
increasing extract concentrations. The threshold inhibitory concentrations
(IC50) of the extracts against Candidas spp, Penicillium spp and Bacillus
subtilis were 275micro. g/ml, 322 micro. g/ml and 411 micro. g/ml respectively.
Our findings suggested the extract as a useful source of antimicrobial
phytochemicals for pharmaceutical use.


  A Lagrangian definition of a large family of (0,2) supersymmetric conformal
field theories may be made by an appropriate gauge invariant combination of a
gauged Wess-Zumino-Witten model, right-moving supersymmetry fermions, and
left-moving current algebra fermions. Throughout this paper, use is made of the
interplay between field theoretic and algebraic techniques (together with
supersymmetry) which is facilitated by such a definition. These heterotic coset
models are thus studied in some detail, with particular attention paid to the
(0,2) analogue of the N=2 minimal models, which coincide with the `monopole'
theory of Giddings, Polchinski and Strominger. A family of modular invariant
partition functions for these (0,2) minimal models is presented. Some examples
of N=1 supersymmetric four dimensional string theories with gauge groups E_6 X
G and SO(10) X G are presented, using these minimal models as building blocks.
The factor G represents various enhanced symmetry groups made up of products of
SU(2) and U(1).


  Giant gravitons in AdS_5 x S^5, and its orbifolds, have a dual field theory
representation as states created by chiral primary operators. We argue that
these operators are not single-trace operators in the conformal field theory,
but rather are determinants and subdeterminants of scalar fields; the stringy
exclusion principle applies to these operators. Evidence for this
identification comes from three sources: (a) topological considerations in
orbifolds, (b) computation of protected correlators using free field theory and
(c) a Matrix model argument. The last argument applies to AdS_7 x S^4 and the
dual (2,0) theory, where we use algebraic aspects of the fuzzy 4-sphere to
compute the expectation value of a giant graviton operator along the Coulomb
branch of the theory.


  A series of high-pressure resistivity measurements on single crystals of
TbTe3 reveal a complex phase diagram involving the interplay of
superconducting, antiferromagnetic and charge density wave orders. The onset of
superconductivity reaches a maximum of ~ 3.5 K (onset) near 75 kbar.


  Curved algebras are algebras endowed with a predifferential, which is an
endomorphism of degree -1 whose square is not necessarily 0. This makes the
usual definition of quasi-isomorphism meaningless and therefore the homotopical
study of curved algebras cannot follow the same path as differential graded
algebras.
  In this article, we propose to study curved algebras by means of curved
operads. We develop the theory of bar and cobar constructions adapted to this
new notion as well as Koszul duality theory. To be able to provide meaningful
definitions, we work in the context of objects which are filtered and complete
and become differential graded after applying the associated graded functor.
  This setting brings its own difficulties but it nevertheless permits us to
define a combinatorial model category structure that we can transfer to the
category of curved operads and to the category of algebras over a curved operad
using free-forgetful adjunctions.
  We address the case of curved associative algebras. We recover the notion of
curved Aoo-algebras, and we show that the homotopy categories of curved
associative algebras and of curved Aoo-algebras are Quillen equivalent.


  There are various types of global and local spacetime invariant in general
relativity. Here I focus on the local invariants obtainable from the curvature
tensor and its derivatives. The number of such invariants at each order of
differentiation that are algebraically independent will be discussed. There is
no universally valid choice of a minimal set. The number in a complete set will
also be discussed. The invariants can then be used to characterize solutions of
the Einstein equations (locally), to test apparently distinct solutions for
equivalence, and to construct solutions. Other applications concern limits of
families of spacetimes, and the characterization of horizons and singularities.
Further uses are briefly mentioned.


  We prove a variant of a square function estimate for the extension operator
associated to the moment curve in non-archimedean local fields. The arguments
rely on a structural analysis of congruences (sublevel sets) of univariate
polynomials over field extensions of the base field. Our analysis can be
adapted to the archimedean setting as well.


  We consider possible mechanisms via which electroweak scale WIMPs \chi^0
could provide the source of the INTEGRAL/SPI 511 keV photon flux from the
galactic centre. We consider scenarios where the WIMP spectrum contains
near-degeneracies, with MeV-scale splitting, and focus on three possible
production mechanisms for galactic positrons: (i) collisional excitation of the
WIMP to a nearby charged state, \chi^0 + \chi^0 -> \chi^+ + \chi^-, with the
subsequent decay producing positrons; (ii) capture of the WIMP by nuclei in the
galactic interstellar medium, \chi^0 + N -> e^+ + (\chi^- N); and (iii) the
decay of a nearby long-lived state surviving from the Big Bang, \chi^0_2 ->
\chi_1^0 + e^+ + e^-. We find that process (i) requires a cross-section which
is significantly larger than the unitarity bound, process (ii) is allowed by
unitarity, but is impractical due to terrestrial bounds on the \chi-N
cross-section, while process (iii) is viable and we construct a simple model
realization with singlet dark matter fields interacting with the Standard Model
via the Higgs sector.


  Human Activity Recognition (HAR) is one of the key applications of health
monitoring that requires continuous use of wearable devices to track daily
activities. This paper proposes an Adaptive CNN for energy-efficient HAR (AHAR)
suitable for low-power edge devices. Unlike traditional early exit architecture
that makes the exit decision based on classification confidence, AHAR proposes
a novel adaptive architecture that uses an output block predictor to select a
portion of the baseline architecture to use during the inference phase.
Experimental results show that traditional early exit architectures suffer from
performance loss whereas our adaptive architecture provides similar or better
performance as the baseline one while being energy-efficient. We validate our
methodology in classifying locomotion activities from two datasets- Opportunity
and w-HAR. Compared to the fog/cloud computing approaches for the Opportunity
dataset, our baseline and adaptive architecture shows a comparable weighted F1
score of 91.79%, and 91.57%, respectively. For the w-HAR dataset, our baseline
and adaptive architecture outperforms the state-of-the-art works with a
weighted F1 score of 97.55%, and 97.64%, respectively. Evaluation on real
hardware shows that our baseline architecture is significantly energy-efficient
(422.38x less) and memory-efficient (14.29x less) compared to the works on the
Opportunity dataset. For the w-HAR dataset, our baseline architecture requires
2.04x less energy and 2.18x less memory compared to the state-of-the-art work.
Moreover, experimental results show that our adaptive architecture is 12.32%
(Opportunity) and 11.14% (w-HAR) energy-efficient than our baseline while
providing similar (Opportunity) or better (w-HAR) performance with no
significant memory overhead.


  In recent years, the prosperity of deep learning has revolutionized the
Artificial Neural Networks. However, the dependence of gradients and the
offline training mechanism in the learning algorithms prevents the ANN for
further improvement. In this study, a gradient-free training framework based on
data assimilation is proposed to avoid the calculation of gradients. In data
assimilation algorithms, the error covariance between the forecasts and
observations is used to optimize the parameters. Feedforward Neural Networks
(FNNs) are trained by gradient decent, data assimilation algorithms (Ensemble
Kalman Filter (EnKF) and Ensemble Smoother with Multiple Data Assimilation
(ESMDA)), respectively. ESMDA trains FNN with pre-defined iterations by
updating the parameters using all the available observations which can be
regard as offline learning. EnKF optimize FNN when new observation available by
updating parameters which can be regard as online learning. Two synthetic cases
with the regression of a Sine Function and a Mexican Hat function are assumed
to validate the effectiveness of the proposed framework. The Root Mean Square
Error (RMSE) and coefficient of determination (R2) are used as criteria to
assess the performance of different methods. The results show that the proposed
training framework performed better than the gradient decent method. The
proposed framework provides alternatives for online/offline training the
existing ANNs (e.g., Convolutional Neural Networks, Recurrent Neural Networks)
without the dependence of gradients.


  Topical discussion networks (TDNs) are networks centered around a discourse
concerning a particular concept, whether in real life or online. This paper
analogises the population of such networks to populations encountered in
mathematical ecology, and seeks to evaluate whether three metrics of diversity
used in ecology - Shannon's $H'$, Simpson's $\lambda$ and $E_{var}$ proposed by
Smith and Wilson - give valuable information about the composition and
diversity of TDNs. It concludes that each metric has its particular use, and
the choice of metric is best understood in the context of the particular
research question.


  We investigate electron transport through a mixed-valence molecular complex
in which an excess electron can tunnel between hetero-valent transition metal
ions, each having a fixed localized spin. We show that in this class of
molecules the interplay of the spins and the vibrational breathing modes of the
ionic ligand-shells allows the total molecular spin to be detected as well as
controlled by nonequilibrium transport. Due to a spin-dependent pseudo
Jahn-Teller effect electronic transitions with different spin values can be
distinguished by their vibronic conductance side peaks, without using an
external magnetic field. Conversely, we show that the spin state of the entire
molecule can also be controlled via the nonequilibrium quantized molecular
vibrations due to a vibration-induced spin-blockade.


  By correcting an example by Polyanskii, we show that there exist reduced
polytopes in three-dimensional Euclidean space. This partially answers the
question posed by Lassak on the existence of reduced polytopes in
$d$-dimensional Euclidean space for $d\geq 3$.


  In this paper, an Extreme Learning Machine (ELM) based technique for
Multi-label classification problems is proposed and discussed. In multi-label
classification, each of the input data samples belongs to one or more than one
class labels. The traditional binary and multi-class classification problems
are the subset of the multi-label problem with the number of labels
corresponding to each sample limited to one. The proposed ELM based multi-label
classification technique is evaluated with six different benchmark multi-label
datasets from different domains such as multimedia, text and biology. A
detailed comparison of the results is made by comparing the proposed method
with the results from nine state of the arts techniques for five different
evaluation metrics. The nine methods are chosen from different categories of
multi-label methods. The comparative results shows that the proposed Extreme
Learning Machine based multi-label classification technique is a better
alternative than the existing state of the art methods for multi-label
problems.