  Voice Activity Detection (VAD) is an important pre-processing step in a wide
variety of speech processing systems. VAD should in a practical application be
able to detect speech in both noisy and noise-free environments, while not
introducing significant latency. In this work we propose using an adversarial
multi-task learning method when training a supervised VAD. The method has been
applied to the state-of-the-art VAD Waveform-based Voice Activity Detection.
Additionally the performance of the VADis investigated under different
algorithmic delays, which is an important factor in latency. Introducing
adversarial multi-task learning to the model is observed to increase
performance in terms of Area Under Curve (AUC), particularly in noisy
environments, while the performance is not degraded at higher SNR levels. The
adversarial multi-task learning is only applied in the training phase and thus
introduces no additional cost in testing. Furthermore the correlation between
performance and algorithmic delays is investigated, and it is observed that the
VAD performance degradation is only moderate when lowering the algorithmic
delay from 398 ms to 23 ms.


  As users migrate their analytical workloads to cloud databases, it is
becoming just as important to reduce monetary costs as it is to optimize query
runtime. In the cloud, a query is billed based on either its compute time or
the amount of data it processes. We observe that analytical queries are either
compute- or IO-bound and each query type executes cheaper in a different
pricing model. We exploit this opportunity and propose methods to build cheaper
execution plans across pricing models that complete within user-defined runtime
constraints. We implement these methods and produce execution plans spanning
multiple pricing models that reduce the monetary cost for workloads by as much
as 56%. We reduce individual query costs by as much as 90%. The prices chosen
by cloud vendors for cloud services also impact savings opportunities. To study
this effect, we simulate our proposed methods with different cloud prices and
observe that multi-cloud savings are robust to changes in cloud vendor prices.
These results indicate the massive opportunity to save money by executing
workloads across multiple pricing models.


  In this report, experimental prospects for the inclusive analysis of the
radiative B decays and electroweak penguin decays at the super B factories are
presented.


  Hara and Yoshida introduced a notion of $\aaa$-tight closure in 2003, and
they proved that the test ideals given by this operation correspond to
multiplier ideals. However, their operation is not a true closure. The
alternative operation introduced here is a true closure. Moreover, we define a
joint Hilbert-Kunz multiplicity that can be used to test for membership in this
closure. We study the connections between the Hara-Yoshida operation and the
one introduced here, primarily from the point of view of test ideals. We also
consider variants with positive real exponents.


  We propose a new graph-theoretic benchmark in this paper. The benchmark is
developed to address shortcomings of an existing widely-used graph benchmark.
We thoroughly studied a large number of traditional and contemporary graph
algorithms reported in the literature to have clear understanding of their
algorithmic and run-time characteristics. Based on this study, we designed a
suite of kernels, each of which represents a specific class of graph
algorithms. The kernels are designed to capture the typical run-time behavior
of target algorithms accurately, while limiting computational and spatial
overhead to ensure its computation finishes in reasonable time. We expect that
the developed benchmark will serve as a much needed tool for evaluating
different architectures and programming models to run graph algorithms.


  Minimum Description Length (MDL) provides a framework and an objective for
principled model evaluation. It formalizes Occam's Razor and can be applied to
data from non-stationary sources. In the prequential formulation of MDL, the
objective is to minimize the cumulative next-step log-loss when sequentially
going through the data and using previous observations for parameter
estimation. It thus closely resembles a continual- or online-learning problem.
In this study, we evaluate approaches for computing prequential description
lengths for image classification datasets with neural networks. Considering the
computational cost, we find that online-learning with rehearsal has favorable
performance compared to the previously widely used block-wise estimation. We
propose forward-calibration to better align the models predictions with the
empirical observations and introduce replay-streams, a minibatch incremental
training technique to efficiently implement approximate random replay while
avoiding large in-memory replay buffers. As a result, we present description
lengths for a suite of image classification datasets that improve upon
previously reported results by large margins.


  We present in this paper a new architecture, named Convolutional vision
Transformer (CvT), that improves Vision Transformer (ViT) in performance and
efficiency by introducing convolutions into ViT to yield the best of both
designs. This is accomplished through two primary modifications: a hierarchy of
Transformers containing a new convolutional token embedding, and a
convolutional Transformer block leveraging a convolutional projection. These
changes introduce desirable properties of convolutional neural networks (CNNs)
to the ViT architecture (\ie shift, scale, and distortion invariance) while
maintaining the merits of Transformers (\ie dynamic attention, global context,
and better generalization). We validate CvT by conducting extensive
experiments, showing that this approach achieves state-of-the-art performance
over other Vision Transformers and ResNets on ImageNet-1k, with fewer
parameters and lower FLOPs. In addition, performance gains are maintained when
pretrained on larger datasets (\eg ImageNet-22k) and fine-tuned to downstream
tasks. Pre-trained on ImageNet-22k, our CvT-W24 obtains a top-1 accuracy of
87.7\% on the ImageNet-1k val set. Finally, our results show that the
positional encoding, a crucial component in existing Vision Transformers, can
be safely removed in our model, simplifying the design for higher resolution
vision tasks. Code will be released at \url{https://github.com/leoxiaobin/CvT}.


  Magnetic anisotropy of uranium monochalcogenides, US, USe and UTe, is studied
by means of fully-relativistic spin-polarized band structure calculations
within the local spin-density approximation. It is found that the size of the
magnetic anisotropy is fairly large (about 10 meV/unit formula), which is
comparable with experiment. This strong anisotropy is discussed in view of a
pseudo-gap formation, of which crucial ingredients are the exchange splitting
of U 5f states and their hybridization with chalcogen p states (f-p
hybridization). An anomalous trend in the anisotropy is found in the series
(US>>USe<UTe) and interpreted in terms of competition between localization of
the U 5f states and the f-p hybridization. It is the spin-orbit interaction on
the chalcogen p states that plays an essential role in enlarging the strength
of the f-p hybridization in UTe, leading to an anomalous systematic trend in
the magnetic anisotropy.


  This paper shows that, with high probability, randomly punctured Reed-Solomon
codes over fields of polynomial size achieve the list decoding capacity. More
specifically, we prove that for any $\epsilon>0$ and $R\in (0,1)$, with high
probability, randomly punctured Reed-Solomon codes of block length $n$ and rate
$R$ are $\left(1-R-\epsilon, O({1}/{\epsilon})\right)$ list decodable over
alphabets of size at least $2^{\mathrm{poly}(1/\epsilon)}n^2$. This extends the
recent breakthrough of Brakensiek, Gopi, and Makam (STOC 2023) that randomly
punctured Reed-Solomon codes over fields of exponential size attain the
generalized Singleton bound of Shangguan and Tamo (STOC 2020).


  We discuss an orbifold of the toroidally compactified heterotic string which
gives a global reduction of the dimension of the moduli space while preserving
the supersymmetry. This construction yields the moduli space of the first of a
series of reduced rank theories with maximal supersymmetry discovered recently
by Chaudhuri, Hockney, and Lykken. Such moduli spaces contain non-simply-laced
enhanced symmetry points in any spacetime dimension D<10. Precisely in D=4 the
set of allowed gauge groups is invariant under electric-magnetic duality,
providing further evidence for S-duality of the D=4 heterotic string.


  In this work, we analyze two models beyond the Standard Models descriptions
that make ad hoc hypotheses of three point-like lepton and quark generations
without explanations of their physical origins. Instead of using the same Dirac
equation involving four anti-commutative matrices for all such structure-less
elementary particles, we consider in the first model the use of sixteen
direct-product matrices of quaternions that are related to Diracs gamma
matrices. This associative direct-product matrix model could not generate three
fermion generations satisfying Einsteins mass-energy relation. We show that
sedenion algebra contains five distinct quaternion sub-algebras and three
octonion sub-algebras but with a common intersecting quaternion algebra. This
model naturally leads to precisely three generations as each of the
non-associative octonion sub-algebra leads to one fermion generation. Moreover,
we demonstrate the use of basic sedenion.


  We analyze multi-wavelength and multi-viewpoint observations of a large-scale
event viewed on 7 April 2011 originating from an active region complex. The
activity leads to a white-light jet being formed in the outer corona. The
topology and evolution of the coronal structures were imaged in high resolution
using the Atmospheric Imaging Assembly (AIA) onboard the Solar Dynamics
Observatory (SDO). In addition, large field-of-view images of the corona were
obtained using the Sun Watcher using Active Pixel System detector and Image
Processing (SWAP) telescope onboard the PRoject for Onboard Autonomy (PROBA2)
microsatellite, providing evidence for the connectivity of the coronal
structures with outer coronal features that were imaged with the Large Angle
Spectrometric Coronagraph (LASCO) C2 on Solar and Heliospheric Observatory
(SOHO). The data-sets reveal an Eiffel-tower type jet configuration extending
into a narrow jet in the outer corona. The event starts from the growth of a
dark area in the central part of the structure. The darkening was also observed
in projection on the disk by the Solar TErrestrial RElations Observatory-Ahead
(STEREO-A) spacecraft from a different point of view. We assume that the dark
volume in the corona descends from a coronal cavity of a flux rope that moved
up higher in the corona but still failed to erupt. The quadrupolar magnetic
configuration corresponds to a saddle-like shape of the dark volume and
provides a possibility for the plasma to escape along the open field lines into
the outer corona, forming the white-light jet.


  We define the {\it rest-frame instant form} of tetrad gravity restricted to
Christodoulou-Klainermann spacetimes. After a study of the Hamiltonian group of
gauge transformations generated by the 14 first class constraints of the
theory, we define and solve the multitemporal equations associated with the
rotation and space diffeomorphism constraints, finding how the cotriads and
their momenta depend on the corresponding gauge variables. This allows to find
quasi-Shanmugadhasan canonical transformation to the class of 3-orthogonal
gauges and to find the Dirac observables for superspace in these gauges.
  The construction of the explicit form of the transformation and of the
solution of the rotation and supermomentum constraints is reduced to solve a
system of elliptic linear and quasi-linear partial differential equations. We
then show that the superhamiltonian constraint becomes the Lichnerowicz
equation for the conformal factor of the 3-metric and that the last gauge
variable is the momentum conjugated to the conformal factor. The gauge
transformations generated by the superhamiltonian constraint perform the
transitions among the allowed foliations of spacetime, so that the theory is
independent from its 3+1 splittings. In the special 3-orthogonal gauge defined
by the vanishing of the conformal factor momentum we determine the final Dirac
observables for the gravitational field even if we are not able to solve the
Lichnerowicz equation. The final Hamiltonian is the weak ADM energy restricted
to this completely fixed gauge.


  In this paper we will study the representations of isomorphisms between bases
of topological spaces. It turns out that the perfect setting for this study is
that of regular open subsets of complete metric spaces, but we have achieved
some results about arbitrary bases in complete metric spaces and also about
regular open subsets of Hausdorff regular topological spaces.


  In this work, we study massive multiple-input multiple-output (MIMO)
precoders optimizing power consumption while achieving the users' rate
requirements. We first characterize analytically the solutions for narrowband
and wideband systems minimizing the power amplifiers (PAs) consumption in low
system load, where the per-antenna power constraints are not binding. After, we
focus on the asymptotic wideband regime. The power consumed by the whole base
station (BS) and the high-load scenario are then also investigated. We obtain
simple solutions, and the optimal strategy in the asymptotic case reduces to
finding the optimal number of active antennas, relying on known precoders among
the active antennas. Numerical results show that large savings in power
consumption are achievable in the narrowband system by employing antenna
selection, while all antennas need to be activated in the wideband system when
considering only the PAs consumption, and this implies lower savings. When
considering the overall BS power consumption and a large number of subcarriers,
we show that significant savings are achievable in the low-load regime by using
a subset of the BS antennas. While optimization based on transmit power pushes
to activate all antennas, optimization based on consumed power activates a
number of antennas proportional to the load.


  We consider the balayage of a measure $\mu$ defined on a domain $\Omega$ onto
its boundary $\partial \Omega$. Assuming that $\Omega$ has a corner of opening
$\pi \alpha$ at a point $z_0 \in \partial \Omega$ for some $0 < \alpha \leq 2$
and that $d\mu(z) \asymp |z-z_{0}|^{2b-2}d^{2}z$ as $z\to z_0$ for some $b >
0$, we obtain the precise rate of vanishing of the balayage of $\mu$ near
$z_{0}$. The rate of vanishing is universal in the sense that it only depends
on $\alpha$ and $b$. We also treat the case when the domain has multiple
corners at the same point. Moreover, when $2b\leq \frac{1}{\alpha}$, we provide
explicit constants for the upper and lower bounds.


  Many state-of-the-art delineation methods rely on supervised machine learning
algorithms. As a result, they require manually annotated training data, which
is tedious to obtain. Furthermore, even minor classification errors may
significantly affect the topology of the final result. In this paper we propose
a generic approach to addressing both of these problems by taking into account
the influence of a potential misclassification on the resulting delineation. In
an Active Learning context, we identify parts of linear structures that should
be annotated first in order to train a classifier effectively. In a
proofreading context, we similarly find regions of the resulting reconstruction
that should be verified in priority to obtain a nearly-perfect result. In both
cases, by focusing the attention of the human expert on potential
classification mistakes which are the most critical parts of the delineation,
we reduce the amount of required supervision. We demonstrate the effectiveness
of our approach on microscopy images depicting blood vessels and neurons.


  A large amount of research activity in power systems areas has focused on
developing computational methods to solve load flow equations where a key
question is the maximum number of isolated solutions.Though several concrete
upper bounds exist, recent studies have hinted that much sharper upper bounds
that depend the topology of underlying power networks may exist. This paper
establishes such a topology dependent solution bound which is actually the best
possible bound in the sense that it is always attainable. We also develop a
geometric construction called adjacency polytope which accurately captures the
topology of the underlying power network and is immensely useful in the
computation of the solution bound. Finally we highlight the significant
implications of the development of such solution bound in solving load flow
equations.


  Solid-state electronic spins are extensively studied in quantum information
science, as their large magnetic moments offer fast operations for computing
and communication, and high sensitivity for sensing. However, electronic spins
are more sensitive to magnetic noise, but engineering of their spectroscopic
properties, e.g. using clock transitions and isotopic engineering, can yield
remarkable spin coherence times, as for electronic spins in GaAs, donors in
silicon and vacancy centres in diamond. Here we demonstrate simultaneously
induced clock transitions for both microwave and optical domains in an
isotopically purified $^{171}$Yb$^{3+}$:Y$_2$SiO$_5$ crystal, reaching
coherence times of above 100 $\mu$s and 1 ms in the optical and microwave
domain, respectively. This effect is due to the highly anisotropic hyperfine
interaction, which makes each electronic-nuclear state an entangled Bell state.
Our results underline the potential of $^{171}$Yb$^{3+}$:Y$_2$SiO$_5$ for
quantum processing applications relying on both optical and spin manipulation,
such as optical quantum memories, microwave-tooptical quantum transducers, and
single spin detection, while they should also be observable in a range of
different materials with anisotropic hyperfine interaction.


  This paper aims to provide insights related to the impact assessment and
evaluation results from the use of CITS services in the Greek pilot of the
CRoads Greece project, i.e., Attica Tollway and Egnatia Odos Tollway. The
impact assessment and evaluation of the CITS services includes aspects related
to user acceptance, real world pilot logs collected from the two pilots, and
simulation experiments that were conducted for the impact assessment of the
CITS services. The paper concludes with a roadmap and guidelines for the
extended deployment of CITS services in the Greek highway and urban road
networks.


  The internal luminescence quantum efficiency ($Q_\mathrm{i}^\mathrm{lum}$)
provides an excellent assessment of the optoelectronic quality of
semiconductors. To determine $Q_\mathrm{i}^\mathrm{lum}$ of perovskite films
from the experimentally accessible external luminescence quantum efficiency
($Q_\mathrm{e}^\mathrm{lum}$) it is essential to account for photon recycling,
and this requires knowledge of the photon escape probability ($p_\mathrm{e}$).
Here, we establish an analysis procedure based on a curve fitting model that
accurately determines $p_\mathrm{e}$ of perovskite films from photoluminescence
(PL) spectra measured with a confocal microscope and an integrating sphere
setup. We show that scattering-induced outcoupling of initially-trapped PL
explains commonly observed red-shifted and broadened PL spectral shapes and
leads to $p_\mathrm{e}$ being more than 10% higher in absolute terms compared
to earlier assumptions. Applying our model to CH$_3$NH$_3$PbI$_3$ films with
exceptionally high $Q_\mathrm{e}^\mathrm{lum}$ up to 47.4% sets a real
benchmark for $Q_\mathrm{i}^\mathrm{lum}$ at $78.0 \pm 0.5\%$, revealing there
is beyond a factor of two more scope for reducing non-radiative recombination
than previously thought.


  We compute the cohomology with trivial coefficients of two graded
infinite-dimensional Lie algebras of maximal class, give explicit formulas for
their representative cocycles. Also we discuss the relations with combinatorics
and representation theory.


  Methods based on vector embeddings of knowledge graphs have been actively
pursued as a promising approach to knowledge graph completion.However,
embedding models generate storage-inefficient representations, particularly
when the number of entities and relations, and the dimensionality of the
real-valued embedding vectors are large. We present a binarized
CANDECOMP/PARAFAC(CP) decomposition algorithm, which we refer to as B-CP, where
real-valued parameters are replaced by binary values to reduce model size.
Moreover, we show that a fast score computation technique can be developed with
bitwise operations. We prove that B-CP is fully expressive by deriving a bound
on the size of its embeddings. Experimental results on several benchmark
datasets demonstrate that the proposed method successfully reduces model size
by more than an order of magnitude while maintaining task performance at the
same level as the real-valued CP model.


  The superconducting phase in iron based high Tc superconductors (FeSC) as in
other unconventional superconductors such as the cuprates neighbours a
magnetically ordered one in the phase diagram. This proximity hints at the
importance of electron correlation effects in these materials, and Hund
exchange interaction has been suggested to be the dominant correlation effect
in FeSCs because of their multiband nature. By this reasoning, correlation
should be strongest for materials closest to a half filled 3d shell (Mn
compounds, hole doped FeSCs) and decrease for systems with both higher
(electron doped FeSCs) and lower (Cr pnictides) 3d counts. Here we address the
strength of correlation effects in BaCr2As2 by means of angle resolved
photoelectron spectroscopy (ARPES) and first principles calculations. This
combination provides us with two handles on the strength of correlation, First,
a comparison of the experimental and calculated effective masses yields the
correlation induced mass renormalisation. In addition, the lifetime broadening
of the experimentally observed dispersions provides another measure of the
correlation strength. Both approaches reveal a reduction of electron
correlation in BaCr2As2 with respect to systems with a 3d count closer to five.
Our results thereby support the theoretical predictions that Hund's exchange
interaction is important in these materials.


  We study the charged t-->b W h^0 and t-->b W A^0 decays in the framework of
the general two Higgs doublet model, so called model III and beyond. Here, we
take the Yukawa couplings complex and introduce a new complex parameter due to
the physics beyond the model III, to switch on the CP violating effects. We
predict the branching ratios as BR (t--> b W h^0) ~ 10^{-6} and BR (t--> b W
A^0) ~ 10^{-8}. Furthermore, we observe a measurable CP asymmetry, at the order
of 10^{-2}, for both decays.


  We show that the criticism of our paper [Phys. Rev. B 65, 125109 (2002)] by
Wang, Millis, and Das Sarma [cond-mat/0206203] is based on a trivial
mathematical mistake they have committed.


  In the realm of ubiquitous computing, Human Activity Recognition (HAR) is
vital for the automation and intelligent identification of human actions
through data from diverse sensors. However, traditional machine learning
approaches by aggregating data on a central server and centralized processing
are memory-intensive and raise privacy concerns. Federated Learning (FL) has
emerged as a solution by training a global model collaboratively across
multiple devices by exchanging their local model parameters instead of local
data. However, in realistic settings, sensor data on devices is
non-independently and identically distributed (Non-IID). This means that data
activity recorded by most devices is sparse, and sensor data distribution for
each client may be inconsistent. As a result, typical FL frameworks in
heterogeneous environments suffer from slow convergence and poor performance
due to deviation of the global model's objective from the global objective.
Most FL methods applied to HAR are either designed for overly ideal scenarios
without considering the Non-IID problem or present privacy and scalability
concerns. This work addresses these challenges, proposing CDFL, an efficient
federated learning framework for image-based HAR. CDFL efficiently selects a
representative set of privacy-preserved images using contrastive learning and
deep clustering, reduces communication overhead by selecting effective clients
for global model updates, and improves global model quality by training on
privacy-preserved data. Our comprehensive experiments carried out on three
public datasets, namely Stanford40, PPMI, and VOC2012, demonstrate the
superiority of CDFL in terms of performance, convergence rate, and bandwidth
usage compared to state-of-the-art approaches.


  We study the influence of the surrounding gas in the dynamics of drop impact
on a smooth surface. We use an axisymmetric 3D model for which both the gas and
the liquid are incompressible; lubrication regime applies for the gas film
dynamics and the liquid viscosity is neglected. In the absence of surface
tension a finite time singularity whose properties are analysed is formed and
the liquid touches the solid on a circle. When surface tension is taken into
account, a thin jet emerges from the zone of impact, skating above a thin gas
layer. The thickness of the air film underneath this jet is always smaller than
the mean free path in the gas suggesting that the liquid film eventually wets
the surface. We finally suggest an aerodynamical instability mechanism for the
splash.


  In the present paper, we investigate the polarization properties of the
cholesteric liquid crystals (CLCs) with an isotropic/anisotropic defect inside
them. Possibilities of amplification of the polarization plane rotation and
stabilization of the light polarization azimuth by these systems are
investigated in details.


  In a global and technology oriented world the requirements that products and
services have to fulfill are increasing and are getting more complicated.
Research and development (R&D) is becoming increasingly important in creating
the knowledge that makes research and business more competitive. Companies are
obliged to produce more rapidly, more effectively and more efficiently. In
order to meet these requirements and to secure the viability of business
processes, services and products R&D teams need to access and retrieve
information from as many sources as possible. From the other perspective
virtual teams are important mechanisms for organizations seeking to leverage
scarce resources across geographic and other boundaries moreover; virtual
collaboration has become vital for most organizations. This is particularly
true in the context of designing new product and service innovation. Such
collaboration often involves a network of partners located around the world.
However at the R&D project level, dealing with such distributed teams
challenges both managers and specialists. In new product development, it is
necessary to put together the growing different capabilities and services with
the goal, through cooperation between suppliers and customers, service
providers and scientific institutions to achieve innovations of high quality.
In this paper based on comprehensive literature review of recent articles, at
the first step provides an primary definition and characterization of virtual
R&D team; next, the potential value created by virtual R&D teams for new
product development is explored and lastly along with a guide line for future
study, it is argued that the establishing of virtual R&D teams should be given
consideration in the management of R&D projects.


  A new hierarchy of "exact" unification types is introduced, motivated by the
study of admissibility for equational classes and non-classical logics. In this
setting, unifiers of identities in an equational class are preordered, not by
instantiation, but rather by inclusion over the corresponding sets of unified
identities. Minimal complete sets of unifiers under this new preordering always
have a smaller or equal cardinality than those provided by the standard
instantiation preordering, and in significant cases a dramatic reduction may be
observed. In particular, the classes of distributive lattices, idempotent
semigroups, and MV-algebras, which all have nullary unification type, have
unitary or finitary exact type. These results are obtained via an algebraic
interpretation of exact unification, inspired by Ghilardi's algebraic approach
to equational unification.


  We present the results of second epoch simultaneous multifrequency
observations of 45 sources out of the 55 in the sample of High Frequency
Peakers (HFP) by Dallacasa et al. (2000). Seven of the stellar sources (i.e.
16% of the total number of sources and 26% of the stellar fraction) no longer
show a convex spectrum and are thus likely to be flat spectrum quasars in an
outburst phase during the first epoch of observations. The rest frame peak
frequency distribution of HFP quasars extends up to ~45 GHz while that of HFP
galaxies is confined to $\le 17$ GHz. Imaging at 1.465 GHz and 1.665 GHz has
revealed extended emission for 31% of sources, a substantially larger fraction
than found for GPS sources (~10%). In the case of HFP quasars, but not of HFP
galaxies, extended emission is associated with strong variability, consistent
with a core-jet structure.


  We present a unified analytical framework within which power control, rate
allocation, routing, and congestion control for wireless networks can be
optimized in a coherent and integrated manner. We consider a multi-commodity
flow model with an interference-limited physical-layer scheme in which power
control and routing variables are chosen to minimize the sum of convex link
costs reflecting, for instance, queuing delay. Distributed network algorithms
where joint power control and routing are performed on a node-by-node basis are
presented. We show that with appropriately chosen parameters, these algorithms
iteratively converge to the global optimum from any initial point with finite
cost. Next, we study refinements of the algorithms for more accurate link
capacity models, and extend the results to wireless networks where the
physical-layer achievable rate region is given by an arbitrary convex set, and
the link costs are strictly quasiconvex. Finally, we demonstrate that
congestion control can be seamlessly incorporated into our framework, so that
algorithms developed for power control and routing can naturally be extended to
optimize user input rates.


  Results on two-particle angular correlations for charged particles produced
in pp collisions at a center-of-mass energy of 13 TeV are presented. The data
were taken with the CMS detector at the LHC and correspond to an integrated
luminosity of about 270 inverse nanobarns. The correlations are studied over a
broad range of pseudorapidity (abs(eta) < 2.4) and over the full azimuth (phi)
as a function of charged particle multiplicity and transverse momentum (pt). In
high-multiplicity events, a long-range (abs(Delta eta) > 2.0), near-side (Delta
phi approximately 0) structure emerges in the two-particle Delta eta-Delta phi
correlation functions. The magnitude of the correlation exhibits a pronounced
maximum in the range 1.0 < pt < 2.0 GeV/c and an approximately linear increase
with the charged particle multiplicity, with an overall correlation strength
similar to that found in earlier pp data at sqrt(s) = 7 TeV. The present
measurement extends the study of near-side long-range correlations up to
charged particle multiplicities of N[ch] approximately 180, a region so far
unexplored in pp collisions. The observed long-range correlations are compared
to those seen in pp, pPb, and PbPb collisions at lower collision energies.


  We consider the possibility of enhancing the inflationary tensor mode by
introducing a spectator scalar field with a small sound speed which induces
gravitational waves as a second order effect. We analytically obtain the power
spectra of gravitational waves and curvature perturbation induced by the
spectator scalar field. We found that the small sound speed amplifies the
curvature perturbation much more than the tensor mode and the current
observational constraint forces the induced gravitational waves to be
negligible compared with those from the vacuum fluctuation during inflation.


  As large language models (LLMs) become easily accessible nowadays, the
trade-off between safety and helpfulness can significantly impact user
experience. A model that prioritizes safety will cause users to feel less
engaged and assisted while prioritizing helpfulness will potentially cause
harm. Possible harms include teaching people how to build a bomb, exposing
youth to inappropriate content, and hurting users' mental health. In this work,
we propose to balance safety and helpfulness in diverse use cases by
controlling both attributes in LLM. We explore training-free and fine-tuning
methods that do not require extra human annotations and analyze the challenges
of controlling safety and helpfulness in LLMs. Our experiments demonstrate that
our method can rewind a learned model and unlock its controllability.


  Fe$_4$GeTe$_2$, an itinerant vdW ferromagnet (FM) having Curie temperature
(T$_C$) close to room temperature ($\sim 270$ K), exhibits another transition
(T$_{SR}$ $\sim$ 120 K) where the easy axis of magnetization changes from
in-plane to the out-of-plane direction in addition to T$_C$. Here, we have
studied the magnetotransport in a multilayer Hall bar device fabricated on 300
nm Si/SiO$_2$ substrate. Interestingly, the zero field resistivity shows a
negligible change in resistivity near T$_C$ unlike the typical metallic FM,
whereas, it exhibits a dramatic fall below T$_{SR}$. Also, the resistivity
shows a weak anomaly at T $ \sim $ 38 K (T$_Q$), below which the resistivity
shows a quadratic temperature dependence according to the Fermi liquid
behavior. Temperature-dependent Hall data exhibits important consequences. The
ordinary Hall coefficient changes sign near T$_{SR}$ indicating the change in
majority carriers. In a similar manner, the magnetoresistance (MR) data shows
significantly large negative MR near T$_{SR}$ and becomes positive below T$_Q$.
The observations of anomaly in the resistivity, sign-change of the ordinary
Hall coefficient and maximum negative MR near T$_{SR}$, together suggest a
possible Fermi surface reconstruction associated with the spin reorientation
transition. Furthermore, analysis of the Hall data reveals a significant
anomalous Hall conductivity (AHC) from $\sim 123 \Omega^{-1}$ cm$^{-1}$ (at T
$\approx$ 5 K) to the maximum value of $\sim 366 \Omega^{-1}$ cm$^{-1}$ near
T$_{SR}$. While the low-temperature part may originate due to the intrinsic KL
mechanism, our analysis indicates that the temperature-dependent AHC is
primarily appearing due to the side-jump mechanism as a result of the spin-flip
electron-magnon scattering. Our study demonstrates an interplay between
magnetism and band topology and its consequence on electron transport in
Fe$_4$GeTe$_2$.


  A central challenge in neuroscience is to understand neural computations and
circuit mechanisms that underlie the encoding of ethologically relevant,
natural stimuli. In multilayered neural circuits, nonlinear processes such as
synaptic transmission and spiking dynamics present a significant obstacle to
the creation of accurate computational models of responses to natural stimuli.
Here we demonstrate that deep convolutional neural networks (CNNs) capture
retinal responses to natural scenes nearly to within the variability of a
cell's response, and are markedly more accurate than linear-nonlinear (LN)
models and Generalized Linear Models (GLMs). Moreover, we find two additional
surprising properties of CNNs: they are less susceptible to overfitting than
their LN counterparts when trained on small amounts of data, and generalize
better when tested on stimuli drawn from a different distribution (e.g. between
natural scenes and white noise). Examination of trained CNNs reveals several
properties. First, a richer set of feature maps is necessary for predicting the
responses to natural scenes compared to white noise. Second, temporally precise
responses to slowly varying inputs originate from feedforward inhibition,
similar to known retinal mechanisms. Third, the injection of latent noise
sources in intermediate layers enables our model to capture the sub-Poisson
spiking variability observed in retinal ganglion cells. Fourth, augmenting our
CNNs with recurrent lateral connections enables them to capture contrast
adaptation as an emergent property of accurately describing retinal responses
to natural scenes. These methods can be readily generalized to other sensory
modalities and stimulus ensembles. Overall, this work demonstrates that CNNs
not only accurately capture sensory circuit responses to natural scenes, but
also yield information about the circuit's internal structure and function.


  This paper tackles the problem of data abstraction in the context of 3D point
sets. Our method classifies points into different geometric primitives, such as
planes and cones, leading to a compact representation of the data. Being based
on a semi-global Hough voting scheme, the method does not need initialization
and is robust, accurate, and efficient. We use a local, low-dimensional
parameterization of primitives to determine type, shape and pose of the object
that a point belongs to. This makes our algorithm suitable to run on devices
with low computational power, as often required in robotics applications. The
evaluation shows that our method outperforms state-of-the-art methods both in
terms of accuracy and robustness.


  In this work, we analyze a sample of $\sim$4000 massive ($M_*\geq 10^{11}
M_\odot$ at $z=0$) galaxies in TNG300, the $(300 \mathrm{Mpc})^3$ box of the
IllustrisTNG simulation suite. We characterize the shape and kinematics of
these galaxies with a focus on the kinematic misalignment ($\Psi_\mathrm{int}$)
between the angular momentum (AM) and morphological major axis. We find that
the traditional purely shape- or kinematics-based classifications are
insufficient to characterize the diversity of our sample and define a new set
of classes based on the rates of change of the galaxies' morphological and
kinematic axes. We show that these classes are mostly stable over time and
correspond to six distinct populations of galaxies: the rapid AM reorienters
(58% of our sample), unsettled galaxies (10%), spinning disks (10%), twirling
cigars (16%), misaligned slow reorienters (3%), and regular prolate rotators
(galaxies that display major axis rotation; 2%). We demonstrate that the
most-recent significant (mass-ratio $\mu>1/10$) mergers of these galaxies are
the primary cause for their present-day properties and find that these mergers
are best characterized at the point of the satellite's final infall -- that is,
much closer to the final coalescence than has been previously thought. We show
that regular prolate rotators evolve from spinning disk progenitors that
experience a radial merger along their internal AM direction. Finally, we argue
that these regular prolate rotators are distinct from the similarly-sized
population of rapid AM reorienters with large $\Psi_\mathrm{int}$, implying
that a large $\Psi_\mathrm{int}$ is not a sufficient condition for major axis
rotation.


  We reinforce the observations of almost stable scattering in nonintegrable
models and show that $\mathcal{PT}$-symmetry can be used as a guiding principle
to select relevant systems also when it comes to integrability properties. We
show that the presence of unbroken $\mathcal{PT}$-symmetry in classical field
theories produces quasi-integrable excitations with asymptotically conserved
charges.


  A grouping-circular-based (GCB) greedy algorithm is proposed to promote the
efficiency of mesh deformation. By incorporating the multigrid concept that the
computational errors on the fine mesh can be approximated with those on the
coarse mesh, this algorithm stochastically divides all boundary nodes into $m$
groups and uses the locally maximum radial basis functions (RBF) interpolation
error of each group as an approximation to the globally maximum one of all
boundary nodes in each iterative procedure for reducing the RBF support nodes.
For this reason, it avoids the interpolation conducted at all boundary nodes
and thus reduces the corresponding computational complexity from
$O\left({N_c^2{N_b}} \right)$ to $O\left( {N_c^3} \right)$. Besides, after $m$
iterations, the interpolation errors of all boundary nodes are computed once,
thus allowing all boundary nodes can contribute to error control. Two canonical
deformation problems of the ONERA M6 wing and the DLR-F6
Wing-Body-Nacelle-Pylon configuration are computed to validate the GCB greedy
algorithm. The computational results show that the GCB greedy algorithm is able
to remarkably promote the efficiency of computing the interpolation errors in
the data reducing procedure by dozens of times. Because an increase of $m$
results in an increase of $N_c$, an appropriate range of $\left[
{{N_b}/{N_c},{\rm{ }}2{N_b}/{N_c}}\right]$ for $m$ is suggested to prevent too
much additional computations for solving the linear algebraic system and
computing the displacements of volume nodes induced by the increase of $N_c $.
The results also show that the GCB greedy algorithm tends to generate a more
significant efficiency improvement for mesh deformation when a larger-scale
mesh is applied.


  Fujita and Kawarabayashi [J. Combin. Theory, Ser. B 98 (2008), 805--811]
conjectured that for all positive integers $k$, $m$, there is a (least)
non-negative integer $f_{k}(m)$ such that every $k$-connected graph $G$ with
$\delta(G)\geq \lfloor\frac{3k}{2}\rfloor+ f_{k}(m)-1$ contains a connected
subgraph $W$ of order $m$ such that $G-V(W)$ is still $k$-connected. Mader
confirmed Fujita-Kawarabayashi's conjecture by proving $f_{k}(m)=m$ and $W$ is
a path. In this paper, the authors will confirm Fujita-Kawarabayashi's
conjecture again by proving $f_{k}(m)=m$ and $W$ is a spider by a new method,
where a spider is a tree with at most one vertex of degree at least three.
Meanwhile, this result will verify a conjecture proposed by Mader [J. Graph
Theory 65 (2010), 61--69] for the case of the spider.


  In 1992 Bir\'{o}, Hujter and Tuza introduced, for every fixed connected graph
$H$, the class of $H$-graphs, defined as the intersection graphs of connected
subgraphs of some subdivision of $H$. Recently, quite a lot of research has
been devoted to understanding the tractability border for various computational
problems, such as recognition or isomorphism testing, in classes of $H$-graphs
for different graphs $H$. In this work we undertake this research topic,
focusing on the recognition problem. Chaplick, T\"{o}pfer, Voborn\'{\i}k, and
Zeman showed, for every fixed tree $T$, a polynomial-time algorithm recognizing
$T$-graphs. Tucker showed a polynomial time algorithm recognizing $K_3$-graphs
(circular-arc graphs). On the other hand, Chaplick at al. showed that
recognition of $H$-graphs is $NP$-hard if $H$ contains two different cycles
sharing an edge.
  The main two results of this work narrow the gap between the $NP$-hard and
$P$ cases of $H$-graphs recognition. First, we show that recognition of
$H$-graphs is $NP$-hard when $H$ contains two different cycles. On the other
hand, we show a polynomial-time algorithm recognizing $L$-graphs, where $L$ is
a graph containing a cycle and an edge attached to it ($L$-graphs are called
lollipop graphs). Our work leaves open the recognition problems of $M$-graphs
for every unicyclic graph $M$ different from a cycle and a lollipop. Other
results of this work, which shed some light on the cases that remain open, are
as follows. Firstly, the recognition of $M$-graphs, where $M$ is a fixed
unicyclic graph, admits a polynomial time algorithm if we restrict the input to
graphs containing particular holes (hence recognition of $M$-graphs is probably
most difficult for chordal graphs). Secondly, the recognition of medusa graphs,
which are defined as the union of $M$-graphs, where $M$ runs over all unicyclic
graphs, is $NP$-complete.


  Advanced transition elements are of utmost importance in many applications of
the finite element method (FEM) where a local mesh refinement is required.
Considering problems that exhibit singularities in the solution, an adaptive
hp-refinement procedure must be applied. Even today, this is a very demanding
task especially if only quadrilateral/hexahedral elements are deployed and
consequently the hanging nodes problem is encountered. These element types,
are, however, favored in computational mechanics due to the improved accuracy
compared to triangular/tetrahedral elements. Therefore, we propose a compatible
transition element - xNy-element - which provides the capability of coupling
different element types. The adjacent elements can exhibit different element
sizes, shape function types, and polynomial orders. Thus, it is possible to
combine independently refined h- and p-meshes. The approach is based on the
transfinite mapping concept and constitutes an extension/generalization of the
pNh-element concept. By means of several numerical examples, the convergence
behavior is investigated in detail, and the asymptotic rates of convergence are
determined numerically. Overall, it is found that the proposed approach
provides very promising results for local mesh refinement procedures.


  A model of chemical bonding between ions in manganites involving covalent
one-electron $\sigma$ bonding is suggested. The covalent one-electron $\sigma$
bonding gives rise to a strongly correlated state of electrons resulting from
the exchange interaction between electrons when they are simultaneously at
cation and anion orbitals. The manifestation of the correlatred state is the
spin and spatial ordering of the electrons resulting in the formation of a
spin- ordered electron lattice. The conductivity of manganites in this model is
the consequence of displacement of the electron lattice (or its part) from one
localization site to another and depends on the type of spin ordering of the
electrons in the electron lattice and the localization energy determined by the
energy of the one-electron $\sigma$ bond. The model also assumes a strong
polarization of an anion by cations, which facilitates the 3s2p hybridization
of the anion and transition of one of the pairs of 2p electrons from the
singlet state into the triplet state. This transition leads to formation of the
spin-polarized electron lattice (electron spins are parallel) and ferromagnetic
ordering of manganese ions. In the model, the effect of colossal
magnetoresistance is explained by a change of the conductivity mechanism on
application of an external magnetic field, i.e., transition from the
conductivity mechnism typical of an ionic crystal to the conductivity provided
by the spin-polarized electron lattice.


  In this paper, optical properties of Chiral Graphene Nanoribbons both in
longitude and transverse polarization have been studied using density
functional theory calculation. It has been shown that the selection rule which
have been reported before for Armchair and Zigzag Graphene Nanoribbons are no
longer valid due to breaking symmetry on these new categorize of graphene
nanoribbons. However, still the edge states play a critical role in optical
absorption. It have been illustrated that depending on the polarization of
incident beam the absorption peaks are different while it is spread in the same
energy range. It is also suggested that the absorption of light is sensitive to
the chiral vector on the edges and direction of the light polarization. Due to
breaking symmetry in chiral graphene nanoribbons, absorption peak is changed
and it would be around 1000nm, introducing a new potential of graphene
nanoribbons for optoelectronic devices.


  We investigate the transport characteristics of monolayer graphene p-n
junctions by means of the non-equilibrium Green's function technique. It is
shown that thanks to the high interband tunneling of chiral fermions and to a
finite bandgap opening when the inversion symmetry of graphene plane is broken,
a strong negative-differential-conductance behavior with peak-to-valley ratio
as large as a few tens can be achieved even at room temperature. The dependence
of this behavior on the device parameters such as the Fermi energy, the barrier
height, and the transition length is then discussed.


  This paper deals with the hot, evergreen topic of the relationship between
privacy and technology. We give extensive motivation for why the privacy debate
is still alive for private citizens and institutions, and we investigate the
privacy concept. This paper proposes a novel vision of the privacy ecosystem,
introducing privacy dimensions, the related users' expectations, the privacy
violations, and the changing factors. We provide a critical assessment of the
Privacy by Design paradigm, strategies, tactics, patterns, and
Privacy-Enhancing Technologies, highlighting the current open issues. We
believe that promising approaches to tackle the privacy challenges move in two
directions: (i) identification of effective privacy metrics; and (ii) adoption
of formal tools to design privacy-compliant applications.


  The distribution of baryons provides a significant way to understand the
formation of galaxy clusters by revealing the details of its internal structure
and changes over time. In this paper, we present theoretical studies on the
scaled profiles of physical properties associated with the baryonic components,
including gas density, temperature, metallicity, pressure and entropy as well
as stellar mass, metallicity and satellite galaxy number density in galaxy
clusters from $z=4$ to $z=0$ by tracking their progenitors. These mass-complete
simulated galaxy clusters are coming from THE THREE HUNDRED with two runs:
GIZMO-SIMBA and Gadget-X. Through comparisons between the two simulations, and
with observed profiles which are generally available at low redshift, we find
that (1) the agreements between the two runs and observations are mostly at
outer radii $r \gtrsim 0.3r_{500}$, in line with the self-similarity
assumption. While Gadget-X shows better agreements with the observed gas
profiles in the central regions compared to GIZMO-SIMBA; (2) the evolution
trends are generally consistent between the two simulations with slightly
better consistency at outer radii. In detail, the gas density profile shows
less discrepancy than the temperature and entropy profiles at high redshift.
The differences in the cluster centre and gas properties imply different
behaviours of the AGN models between Gadget-X and GIZMO-SIMBA, with the latter,
maybe too strong for this cluster simulation. The high-redshift difference may
be caused by the star formation and feedback models or hydrodynamics treatment,
which requires observation constraints and understanding.


  Linguistic relations in oral conversations present how opinions are
constructed and developed in a restricted time. The relations bond ideas,
arguments, thoughts, and feelings, re-shape them during a speech, and finally
build knowledge out of all information provided in the conversation. Speakers
share a common interest to discuss. It is expected that each speaker's reply
includes duplicated forms of words from previous speakers. However, linguistic
adaptation is observed and evolves in a more complex path than just
transferring slightly modified versions of common concepts. A conversation
aiming a benefit at the end shows an emergent cooperation inducing the
adaptation. Not only cooperation, but also competition drives the adaptation or
an opposite scenario and one can capture the dynamic process by tracking how
the concepts are linguistically linked. To uncover salient complex dynamic
events in verbal communications, we attempt to discover self-organized
linguistic relations hidden in a conversation with explicitly stated winners
and losers. We examine open access data of the United States Supreme Court. Our
understanding is crucial in big data research to guide how transition states in
opinion mining and decision-making should be modeled and how this required
knowledge to guide the model should be pinpointed, by filtering large amount of
data.


  Let (E, \varphi) be a flat Higgs bundle on a compact special affine manifold
M equipped with an affine Gauduchon metric. We prove that (E, \varphi) is
polystable if and only if it admits an affine Yang-Mills-Higgs metric.


  We construct a new Godunov type relativistic hydrodynamics code in Milne
coordinates, using a Riemann solver based on the two-shock approximation which
is stable under the existence of large shock waves. We check the correctness of
the numerical algorithm by comparing numerical calculations and analytical
solutions in various problems, such as shock tubes, expansion of matter into
the vacuum, the Landau-Khalatnikov solution, and propagation of fluctuations
around Bjorken flow and Gubser flow. We investigate the energy and momentum
conservation property of our code in a test problem of longitudinal
hydrodynamic expansion with an initial condition for high-energy heavy-ion
collisions. We also discuss numerical viscosity in the test problems of
expansion of matter into the vacuum and conservation properties. Furthermore,
we discuss how the numerical stability is affected by the source terms of
relativistic numerical hydrodynamics in Milne coordinates.


  The H.E.S.S. collaboration has reported a high-energy spherically symmetric
diffuse gamma-ray emission in the inner 50 pc of the Milky Way, up to ~ 50 TeV.
Here we propose a leptonic model which provides an alternative to the hadronic
scenario presented by the H.E.S.S. collaboration, and connects the newly
reported TeV emission to the Fermi-LAT Galactic center GeV excess. Our model
relies on a combination of inverse Compton emission from a population of
millisecond pulsars---which can account for the GeV excess---and a supermassive
black hole-induced spike of heavy (~ 60 TeV) dark matter particles annihilating
into electrons with a sub-thermal cross-section. With an up-to-date
interstellar radiation field, as well as a standard magnetic field and
diffusion set-up, our model accounts for the spectral morphology of the
detected emission. Moreover, we show that the dark matter induced emission
reproduces the spatial morphology of the H.E.S.S. signal above ~ 10 TeV, while
we obtain a slightly more extended component from pulsars at lower energies,
which could be used as a prediction for future H.E.S.S. observations.


  The microscopic control available over cold atoms in optical lattices has
opened new opportunities to study the properties of quantum spin models. While
a lot of attention is focussed on experimentally realizing ground or thermal
states via adiabatic loading, it would often be more straightforward to prepare
specific simple product states and to probe the properties of interacting spins
by observing their dynamics. We explore this possibility for spin-1/2 and
spin-1 models that can be realized with bosons in optical lattices, and which
exhibit \textit{XY}-ferromagnetic (or counterflow spin superfluid) phases. We
consider the dynamics of initial spin-rotated states corresponding to a
mean-field version of the phases of interest. Using matrix product state
methods in one dimension, we compute both non-equilibrium dynamics and
ground/thermal states for these systems. We compare and contrast their
behaviour in terms of correlation functions and induced spin currents, which
should be directly observable with current experimental techniques. We find
that although spin correlations decay substantially at large distances and on
long timescales, for induction of spin currents, the rotated states behave
similarly to the ground states on experimentally observable timescales.


  The classification of opinion texts in positive and negative is becoming a
subject of great interest in sentiment analysis. The existence of many labeled
opinions motivates the use of statistical and machine-learning methods.
First-order statistics have proven to be very limited in this field. The Opinum
approach is based on the order of the words without using any syntactic and
semantic information. It consists of building one probabilistic model for the
positive and another one for the negative opinions. Then the test opinions are
compared to both models and a decision and confidence measure are calculated.
In order to reduce the complexity of the training corpus we first lemmatize the
texts and we replace most named-entities with wildcards. Opinum presents an
accuracy above 81% for Spanish opinions in the financial products domain. In
this work we discuss which are the most important factors that have impact on
the classification performance.


  We study the distribution of the common zero sets of $m$-tuples of
holomorphic sections of powers of $m$ singular Hermitian pseudo-effective line
bundles on a compact K\"ahler manifold. As an application, we obtain sufficient
conditions which ensure that the wedge product of the curvature currents of
these line bundles can be approximated by analytic cycles.


  In recent years, denoising problems have become intertwined with the
development of deep generative models. In particular, diffusion models are
trained like denoisers, and the distribution they model coincide with denoising
priors in the Bayesian picture. However, denoising through diffusion-based
posterior sampling requires the noise level and covariance to be known,
preventing blind denoising. We overcome this limitation by introducing Gibbs
Diffusion (GDiff), a general methodology addressing posterior sampling of both
the signal and the noise parameters. Assuming arbitrary parametric Gaussian
noise, we develop a Gibbs algorithm that alternates sampling steps from a
conditional diffusion model trained to map the signal prior to the family of
noise distributions, and a Monte Carlo sampler to infer the noise parameters.
Our theoretical analysis highlights potential pitfalls, guides diagnostic
usage, and quantifies errors in the Gibbs stationary distribution caused by the
diffusion model. We showcase our method for 1) blind denoising of natural
images involving colored noises with unknown amplitude and spectral index, and
2) a cosmology problem, namely the analysis of cosmic microwave background
data, where Bayesian inference of "noise" parameters means constraining models
of the evolution of the Universe.


  Clock synchronization is ubiquitous in wireless systems for communication,
sensing and control. In this paper we design a scalable system in which an
indefinite number of passively receiving wireless units can synchronize to a
single master clock at the level of discrete clock ticks. Accurate
synchronization requires an estimate of the node positions. If such information
is available the framework developed here takes position uncertainties into
account. In the absence of such information we propose a mechanism which
enables simultaneous synchronization and positioning. Furthermore we derive the
Cramer-Rao bounds for the system which show that it enables synchronization
accuracy at sub-nanosecond levels. Finally, we develop and evaluate an online
estimation method which is statistically efficient.


  Thin-film lithium niobate (TFLN) in the form of x- or z-cut
lithium-niobate-on-insulator (LNOI) has recently popped up as a very promising
and novel platform for developing integrated optoelectronic (nano)devices and
exploring fundamental research. Here, we investigate the coherent interaction
length $l_{c}$ of optical second-harmonic (SH) microscopy in such samples, that
are purposely prepared into a wedge shape, in order to elegantly tune the
geometrical confinement from bulk thicknesses down to $\approx$ 50 nm. SH
microscopy is a very powerful and non-invasive tool for the investigation of
structural properties in the biological and solid-state sciences, especially
also for visualizing and analyzing ferroelectric domains and domain walls.
However, unlike bulk LN, SH microscopy in TFLN is largely affected by
interfacial reflections and resonant enhancement that both rely on film
thickness and substrate material. In this paper we show that the dominant SHG
contribution measured in back-reflection, is the co-propagating phase-matched
SH signal and \textit{not} the counter-propagating SH portion as is the case
for bulk LN samples. Moreover, $l_{c}$ dramatically depends also on the
incident pump laser wavelength (sample dispersion) but even more on the
numerical aperture of the focussing objective in use. These experimental
findings on x- and z-cut TFLN are excellently backed up by our advanced
numerical simulations.


  We demonstrate that generalized entanglement [Barnum {\em et al.}, Phys. Rev.
A {\bf 68}, 032308 (2003)] provides a natural and reliable indicator of quantum
chaotic behavior. Since generalized entanglement depends directly on a choice
of preferred observables, exploring how generalized entanglement increases
under dynamical evolution is possible without invoking an auxiliary coupled
system or decomposing the system into arbitrary subsystems. We find that, in
the chaotic regime, the long-time saturation value of generalized entanglement
agrees with random matrix theory predictions. For our system, we provide
physical intuition into generalized entanglement within a single system by
invoking the notion of extent of a state. The latter, in turn, is related to
other signatures of quantum chaos.


  Validation is one of the most important aspects of clustering, but most
approaches have been batch methods. Recently, interest has grown in providing
incremental alternatives. This paper extends the incremental cluster validity
index (iCVI) family to include incremental versions of Calinski-Harabasz (iCH),
I index and Pakhira-Bandyopadhyay-Maulik (iI and iPBM), Silhouette (iSIL),
Negentropy Increment (iNI), Representative Cross Information Potential (irCIP)
and Representative Cross Entropy (irH), and Conn_Index (iConn_Index).
Additionally, the effect of under- and over-partitioning on the behavior of
these six iCVIs, the Partition Separation (PS) index, as well as two other
recently developed iCVIs (incremental Xie-Beni (iXB) and incremental
Davies-Bouldin (iDB)) was examined through a comparative study. Experimental
results using fuzzy adaptive resonance theory (ART)-based clustering methods
showed that while evidence of most under-partitioning cases could be inferred
from the behaviors of all these iCVIs, over-partitioning was found to be a more
challenging scenario indicated only by the iConn_Index. The expansion of
incremental validity indices provides significant novel opportunities for
assessing and interpreting the results of unsupervised learning.


  We derive the path integral of the semiclassical, one dimensional anharmonic
Holstein model assuming that the electron motion takes place in a bath of non
linear oscillators with quartic on site hard (and soft) potentials. The
interplay between {\it e-ph} coupling and anharmonic force constant is analysed
both in the adiabatic and antiadiabatic regime. In the latter we find much
larger anharmonic features on the thermodynamic properties of low energy
oscillators. Soft on site potentials generate attractive centres at large
amplitude oscillator paths and contribute to the anomalous shape of the {\it
heat capacity over temperature} ratio in the intermediate to low $T$ range.
This anharmonic lattice effect is superimposed to the purely electronic
contribution associated to a temperature dependent hopping with variable range
inducing local disorder in the system.


  Deep neural networks (DNN) have made impressive progress in the
interpretation of image data, so that it is conceivable and to some degree
realistic to use them in safety critical applications like automated driving.
From an ethical standpoint, the AI algorithm should take into account the
vulnerability of objects or subjects on the street that ranges from "not at
all", e.g. the road itself, to "high vulnerability" of pedestrians. One way to
take this into account is to define the cost of confusion of one semantic
category with another and use cost-based decision rules for the interpretation
of probabilities, which are the output of DNNs. However, it is an open problem
how to define the cost structure, who should be in charge to do that, and
thereby define what AI-algorithms will actually "see". As one possible answer,
we follow a participatory approach and set up an online survey to ask the
public to define the cost structure. We present the survey design and the data
acquired along with an evaluation that also distinguishes between perspective
(car passenger vs. external traffic participant) and gender. Using simulation
based $F$-tests, we find highly significant differences between the groups.
These differences have consequences on the reliable detection of pedestrians in
a safety critical distance to the self-driving car. We discuss the ethical
problems that are related to this approach and also discuss the problems
emerging from human-machine interaction through the survey from a psychological
point of view. Finally, we include comments from industry leaders in the field
of AI safety on the applicability of survey based elements in the design of AI
functionalities in automated driving.


  Based on Touchard's identity, a simple derivation is given for the
enumeration of the N/S/E/W walks that remain on the north side of the origin.


  We prove a Kahane-Khinchin type result with a few random vectors, which are
distributed independently with respect to an arbitrary log-concave probability
measure on $\R^n$. This is an application of small ball estimate and Chernoff's
method, that has been recently used in the context of Asymptotic Geometric
Analysis in [1], [2].


  Local spatial models such as Geographically Weighted Regression (GWR) and
Multiscale Geographically Weighted Regression (MGWR) serve as instrumental
tools to capture intrinsic contextual effects through the estimates of the
local intercepts and behavioral contextual effects through estimates of the
local slope parameters. GWR and MGWR provide simple implementation yet powerful
frameworks that could be extended to various disciplines that handle spatial
data. This bibliography aims to serve as a comprehensive compilation of
peer-reviewed papers that have utilized GWR or MGWR as a primary analytical
method to conduct spatial analyses and acts as a useful guide to anyone
searching the literature for previous examples of local statistical modeling in
a wide variety of application fields.


  Theories over strings are among the most heavily researched logical theories
in the SMT community in the past decade, owing to the error-prone nature of
string manipulations, which often leads to security vulnerabilities (e.g.
cross-site scripting and code injection). The majority of the existing decision
procedures and solvers for these theories are themselves intricate; they are
complicated algorithmically, and also have to deal with a very rich vocabulary
of operations. This has led to a plethora of bugs in implementation, which have
for instance been discovered through fuzzing. In this paper, we present
CertiStr, a certified implementation of a string constraint solver for the
theory of strings with concatenation and regular constraints. CertiStr aims to
solve string constraints using a forward-propagation algorithm based on
symbolic representations of regular constraints as symbolic automata, which
returns three results: sat, unsat, and unknown, and is guaranteed to terminate
for the string constraints whose concatenation dependencies are acyclic. The
implementation has been developed and proven correct in Isabelle/HOL, through
which an effective solver in OCaml was generated. We demonstrate the
effectiveness and efficiency of CertiStr against the standard Kaluza benchmark,
in which 80.4% tests are in the string constraint fragment of CertiStr. Of
these 80.4% tests, CertiStr can solve 83.5% (i.e. CertiStr returns sat or
unsat) within 60s.


  This paper proposes a novel framework for lung sound event detection,
segmenting continuous lung sound recordings into discrete events and performing
recognition on each event. Exploiting the lightweight nature of Temporal
Convolution Networks (TCNs) and their superior results compared to their
recurrent counterparts, we propose a lightweight, yet robust, and completely
interpretable framework for lung sound event detection. We propose the use of a
multi-branch TCN architecture and exploit a novel fusion strategy to combine
the resultant features from these branches. This not only allows the network to
retain the most salient information across different temporal granularities and
disregards irrelevant information, but also allows our network to process
recordings of arbitrary length. Results: The proposed method is evaluated on
multiple public and in-house benchmarks of irregular and noisy recordings of
the respiratory auscultation process for the identification of numerous
auscultation events including inhalation, exhalation, crackles, wheeze,
stridor, and rhonchi. We exceed the state-of-the-art results in all
evaluations. Furthermore, we empirically analyse the effect of the proposed
multi-branch TCN architecture and the feature fusion strategy and provide
quantitative and qualitative evaluations to illustrate their efficiency.
Moreover, we provide an end-to-end model interpretation pipeline that
interprets the operations of all the components of the proposed framework. Our
analysis of different feature fusion strategies shows that the proposed feature
concatenation method leads to better suppression of non-informative features,
which drastically reduces the classifier overhead resulting in a robust
lightweight network.The lightweight nature of our model allows it to be
deployed in end-user devices such as smartphones, and it has the ability to
generate predictions in real-time.


  Current image generation models struggle to reliably produce well-formed
visual text. In this paper, we investigate a key contributing factor: popular
text-to-image models lack character-level input features, making it much harder
to predict a word's visual makeup as a series of glyphs. To quantify this
effect, we conduct a series of experiments comparing character-aware vs.
character-blind text encoders. In the text-only domain, we find that
character-aware models provide large gains on a novel spelling task
(WikiSpell). Applying our learnings to the visual domain, we train a suite of
image generation models, and show that character-aware variants outperform
their character-blind counterparts across a range of novel text rendering tasks
(our DrawText benchmark). Our models set a much higher state-of-the-art on
visual spelling, with 30+ point accuracy gains over competitors on rare words,
despite training on far fewer examples.


  In this paper we obtain an explicit formula for the number of curves in two
dimensional complex projective space, of degree d, passing through
d(d+3)/2-(k+1) generic points and having one node and one codimension k
singularity, where k is at most 6. Our main tool is a classical fact from
differential topology: the number of zeros of a generic smooth section of a
vector bundle V over M, counted with a sign, is the Euler class of V evaluated
on the fundamental class of M.


  We explore a system composed of scalar bosons and SU($3$) fermions in one
dimension. Considering only local intra- and interspecies interactions, the
system is described by the Bose-Fermi-Hubbard Hamiltonian, which is studied
using the density matrix renormalization group method. In addition to the
common gapless and mixed Mott insulator phases, we identify unknown gapped
states, in which bosons couple with some or all flavors of fermions. We unveil
different flavor-selective states characterized by itinerant fermions that
coexist with an insulator state where the bosons tie with only one- or
two-flavor fermions. The states reported here could be implemented in
cold-atoms experiments.


  Agile autonomous drones are becoming increasingly popular in research due to
the challenges they represent in fields like control, state estimation, or
perception at high speeds. When all algorithms are computed onboard the uav,
the computational limitations make the task of agile and robust flight even
more difficult. One of the most computationally expensive tasks in agile flight
is the generation of optimal trajectories that tackles the problem of planning
a minimum time trajectory for a quadrotor over a sequence of specified
waypoints. When these trajectories must be updated online due to changes in the
environment or uncertainties, this high computational cost can leverage to not
reach the desired waypoints or even crash in cluttered environments. In this
paper, a fast lightweight dynamic trajectory modification approach is presented
to allow modifying computational heavy trajectories using Local Gaussian
Modifiers (LGMs), when recalculating a trajectory is not possible due to the
time of computation.
  Our approach was validated in simulation, being able to pass through a race
circuit with dynamic gates with top speeds up to 16.0 m/s, and was also
validated in real flight reaching speeds up to 4.0 m/s in a fully autonomous
onboard computing condition.


  We use three dimensional magnetohydrodynamic simulations, in a
pseudo-Newtonian potential, to study geometrically thin accretion disc flows
crossing the marginally stable circular orbit around black holes. We
concentrate on vertically unstratified and isothermal disk models, but also
consider a model that includes stratification. In all cases, we find that the
sonic point lies just inside the last stable orbit, with a modest increase in
the importance of magnetic field energy, relative to the thermal energy,
observed interior to this radius. The time-averaged gradient of the specific
angular momentum of the flow, (dl/dr), is close to zero within the marginally
stable orbit, despite the presence of large fluctuations and continuing
magnetic stress in the plunging region. The result that the specific angular
momentum is constant within the last stable orbit is in general agreement with
traditional disk models computed using a zero-torque boundary condition at the
last stable orbit.


  Researchers have long proposed using economic approaches to resource
allocation in computer systems. However, few of these proposals became
operational, let alone commercial. Questions persist about the economic
approach regarding its assumptions, value, applicability, and relevance to
system design. The goal of this paper is to answer these questions. We find
that market-based resource allocation is useful, and more importantly, that
mechanism design and system design should be integrated to produce systems that
are both economically and computationally efficient.


  The human face conveys a significant amount of information. Through facial
expressions, the face is able to communicate numerous sentiments without the
need for verbalisation. Visual emotion recognition has been extensively
studied. Recently several end-to-end trained deep neural networks have been
proposed for this task. However, such models often lack generalisation ability
across datasets. In this paper, we propose the Deep Facial Expression Vector
ExtractoR (DeepFEVER), a new deep learning-based approach that learns a visual
feature extractor general enough to be applied to any other facial emotion
recognition task or dataset. DeepFEVER outperforms state-of-the-art results on
the AffectNet and Google Facial Expression Comparison datasets. DeepFEVER's
extracted features also generalise extremely well to other datasets -- even
those unseen during training -- namely, the Real-World Affective Faces (RAF)
dataset.


  Let $G$ be a discrete countable group and $C$ its central subgroup with $G/C$
treeable. We show that for any treeable action of $G/C$ on a standard
probability space $X$, the groupoid $G\ltimes X$ is isomorphic to the direct
product of $C$ and $(G/C)\ltimes X$, through cohomology of groupoids. We apply
this to show that any group in the minimal class of groups containing treeable
groups and closed under taking direct products, commensurable groups and
central extensions has the Haagerup property.


  We answer the question: "Does local progress (on batches) imply global
progress (on the entire dataset) for mini-batch $k$-means?". Specifically, we
consider mini-batch $k$-means which terminates only when the improvement in the
quality of the clustering on the sampled batch is below some threshold.
  Although at first glance it appears that this algorithm might execute
forever, we answer the above question in the affirmative and show that if the
batch is of size $\tilde{\Omega}((d/\epsilon)^2)$, it must terminate within
$O(d/\epsilon)$ iterations with high probability, where $d$ is the dimension of
the input, and $\epsilon$ is a threshold parameter for termination. This is
true regardless of how the centers are initialized. When the algorithm is
initialized with the $k$-means++ initialization scheme, it achieves an
approximation ratio of $O(\log k)$ (the same as the full-batch version).
  Finally, we show the applicability of our results to the mini-batch $k$-means
algorithm implemented in the scikit-learn (sklearn) python library.


  We analyze the decisive role played by the complexity of economic systems at
the onset of the industrialization process of countries over the past 50 years.
Our analysis of the input growth dynamics, based on a recently introduced
measure of economic complexity, reveals that more differentiated and more
complex economies face a lower barrier (in terms of GDP per capita) when
starting the transition towards industrialization. Moreover, adding the
complexity dimension to the industrialization process description helps to
reconcile current theories with empirical findings.


  Nowadays, clinical research routinely uses omics data, such as gene
expression, for predicting clinical outcomes or selecting markers.
Additionally, so-called co-data are often available, providing complementary
information on the covariates, like p-values from previously published studies
or groups of genes corresponding to pathways. Elastic net penalisation is
widely used for prediction and covariate selection. Group-adaptive elastic net
penalisation learns from co-data to improve the prediction and covariate
selection, by penalising important groups of covariates less than other groups.
Existing methods are, however, computationally expensive. Here we present a
fast method for marginal likelihood estimation of group-adaptive elastic net
penalties for generalised linear models. We first derive a low-dimensional
representation of the Taylor approximation of the marginal likelihood and its
first derivative for group-adaptive ridge penalties, to efficiently estimate
these penalties. Then we show by using asymptotic normality of the linear
predictors that the marginal likelihood for elastic net models may be
approximated well by the marginal likelihood for ridge models. The ridge group
penalties are then transformed to elastic net group penalties by using the
variance function. The method allows for overlapping groups and unpenalised
variables. We demonstrate the method in a model-based simulation study and an
application to cancer genomics. The method substantially decreases computation
time and outperforms or matches other methods by learning from co-data.


  We studied the adsorption of a charged protein onto an oppositely charged
membrane, composed of mobile phospholipids of differing valence, using a
statistical-thermodynamical approach. A two-block model was employed, one block
corresponding to the protein-affected region on the membrane, referred to as
the adsorption domain, and the other to the unaffected remainder of the
membrane. We calculated the protein-induced lipid rearrangement in the
adsorption domain as arising from the interplay between the electrostatic
interactions in the system and the mixing entropy of the lipids. Equating the
electrochemical potentials of the lipids in the two blocks yields an expression
for the relations among the various lipid fractions in the adsorption domain,
indicating a sensitive dependence of lipid fraction on valence. This expression
is a result of the two-block picture but does not depend on further details of
the protein-membrane interaction. We subsequently calculated the lipid
fractions themselves using the Poisson-Boltzmann theory. We examined the
dependence of lipid enrichment, i.e., the ratio between the lipid fractions
inside and outside the adsorption domain, on various parameters such as ionic
strength and lipid valence. Maximum enrichment was found for lipid valence of
about (-3) to (-4) in physiological conditions. Our results are in qualitative
agreement with recent experimental studies on the interactions between peptides
having a domain of basic residues and membranes containing a small fraction of
the polyvalent phosphatidylinositol 4,5-bisphosphate (PIP2). This study
provides theoretical support for the suggestion that proteins adsorbed onto
membranes through a cluster of basic residues may sequester PIP2 and other
polyvalent lipids.


  Fermat's Last Theorem is proved by using the philosophical and mathematical
knowledge of 1637 when the French mathematician Pierre de Fermat claimed to
have a truly marvelous proof of his conjecture. Our approach consists of
setting three variables of Fermat's equation as integers and then evaluating
whether the remaining variable can be an integer as well. Pythagorean triples
play a fundamental role in claiming that at least an irrational number is
needed to satisfy Fermat's equation. As a result, we confirm that Fermat's Last
Theorem is valid.


  A given order type in the plane can be represented by a point set. However,
it might be difficult to recognize the orientations of some point triples.
Recently, Aichholzer \etal \cite{abh19} introduced exit graphs for visualizing
order types in the plane. We present a new class of geometric graphs, called
{\em OT-graphs}, using abstract order types and their axioms described in the
well-known book by Knuth \cite{k92}. Each OT-graph corresponds to a unique
order type. We develop efficient algorithms for recognizing OT-graphs and
computing a minimal OT-graph for a given order type in the plane. We provide
experimental results on all order types of up to nine points in the plane
including a comparative analysis of exit graphs and OT-graphs.


  We construct several pairings in Hopf-cyclic cohomology of (co)module
(co)algebras with arbitrary coefficients. The key ideas instrumental in
constructing these pairings are the derived functor interpretation of
Hopf-cyclic and equivariant cyclic (co)homology, and the Yoneda interpretation
of Ext-groups. As a special case of one of these pairings, we recover the
Connes-Moscovici characteristic map in Hopf-cyclic cohomology. We also prove
that this particular pairing, along with few others, would stay the same if we
replace the derived category of (co)cyclic modules with the homotopy category
of (special) towers of $X$-complexes, or the derived category of mixed
complexes.


  Automated brain structure segmentation is important to many clinical
quantitative analysis and diagnoses. In this work, we introduce MixNet, a 2D
semantic-wise deep convolutional neural network to segment brain structure in
multi-modality MRI images. The network is composed of our modified deep
residual learning units. In the unit, we replace the traditional convolution
layer with the dilated convolutional layer, which avoids the use of pooling
layers and deconvolutional layers, reducing the number of network parameters.
Final predictions are made by aggregating information from multiple scales and
modalities. A pyramid pooling module is used to capture spatial information of
the anatomical structures at the output end. In addition, we test three
architectures (MixNetv1, MixNetv2 and MixNetv3) which fuse the modalities
differently to see the effect on the results. Our network achieves the
state-of-the-art performance. MixNetv2 was submitted to the MRBrainS challenge
at MICCAI 2018 and won the 3rd place in the 3-label task. On the MRBrainS2018
dataset, which includes subjects with a variety of pathologies, the overall DSC
(Dice Coefficient) of 84.7% (gray matter), 87.3% (white matter) and 83.4%
(cerebrospinal fluid) were obtained with only 7 subjects as training data.


  A set S of vertices of a graph G is a geodesic transversal of G if every
maximal geodesic of G contains at least one vertex of S. We determine a
smallest geodesic transversal in certain interconnection networks such as mesh
of trees, and some well-known chemical structures such as silicate networks and
carbon nanosheets. Some useful general bounds for the corresponding graph
invariant are obtained along the way.


  We have analyzed the most recent available Super-Kamiokande data in a three
flavor neutrino oscillation model. We have here neglected possible matter
effects and performed a fit to atmospheric and solar Super-Kamiokande data. We
have investigated a large parameter range, where the mixing angles were
restricted to $0 \leq \theta_i \leq \pi/2$, $ i=1,2,3$, and the mass squared
differences were taken to be in the intervals $10^{-11} {\rm eV}^2 \leq \Delta
m^2 \leq 10^{-2} {\rm eV}^2$ and $10^{-4} {\rm eV}^2 \leq \Delta M^2 \leq 10
{\rm eV}^2$. This yielded a best solution characterized by the parameter values
$\theta_1 \simeq 45^\circ$, $\theta_2 \simeq 10^\circ$, $\theta_3 \simeq
45^\circ$, $\Delta m^2 \simeq 4.4 \times 10^{-10} {\rm eV}^2$, and $\Delta M^2
\simeq 1.01 \times 10^{-3} {\rm eV}^2$, which shows that the analyzed
experimental data speak in favor of a bimaximal mixing scenario with one of the
mass squared differences in the ``just-so'' domain and the other one in the
range capable of providing a solution to the atmospheric neutrino problem.


  The abundance of elliptical, S0 and spiral galaxies in clusters is presented
as a function of redshift comparing the results of the HST distant cluster
studies with new data at z~0.2 and with the standard local comparison samples.
This analysis confirms and strengthens the conclusion that the proportion of S0
galaxies increases toward lower redshifts at the expense of the spiral
population. The dependence of the relative occurrence of S0s and ellipticals
and of the morphology-density relation on the cluster concentration are also
discussed.


  We present the results of a survey of N-body simulations aimed at exploring
the evolution of compact binaries in multiple-population globular clusters.We
show that as a consequence of the initial differences in the structural
properties of the first-generation (FG) and the second-generation (SG)
populations and the effects of dynamical processes on binary stars, the SG
binary fraction decreases more rapidly than that of the FG population. The
difference between the FG and SG binary fraction is qualitatively similar to
but quantitatively smaller than that found for wider binaries in our previous
investigations.The evolution of the radial variation of the binary fraction is
driven by the interplay between binary segregation, ionization and ejection.
Ionization and ejection counteract in part the effects of mass segregation but
for compact binaries the effects of segregation dominate and the inner binary
fraction increases during the cluster evolution. We explore the variation of
the difference between the FG and the SG binary fraction with the distance from
the cluster centre and its dependence on the binary binding energy and cluster
structural parameters. The difference between the binary fraction in the FG and
the SG populations found in our simulations is consistent with the results of
observational studies finding a smaller binary fraction in the SG population.


  We examine linear sums of primitive roots and their inverses in finite
fields. In particular, we refine a result by Li and Han, and show that every
$p> 13$ has a pair of primitive roots $a$ and $b$ such that $a+ b$ and $a^{-1}
+ b^{-1}$ are also primitive roots mod $p$.


  Is the Chandrasekhar mass limit for white dwarfs (WDs) set in stone? Not
anymore -- recent observations of over-luminous, peculiar type Ia supernovae
can be explained if significantly super-Chandrasekhar WDs exist as their
progenitors, thus barring them to be used as cosmic distance indicators.
However, there is no estimate of a mass limit for these super-Chandrasekhar WD
candidates yet. Can they be arbitrarily large? In fact, the answer is no! We
arrive at this revelation by exploiting the flux freezing theorem in observed,
accreting, magnetized WDs, which brings in Landau quantization of the
underlying electron degenerate gas. This essay presents the calculations which
pave the way for the ultimate (significantly super-Chandrasekhar) mass limit of
WDs, heralding a paradigm shift 80 years after Chandrasekhar's discovery.


  We measure the far-infrared emission of the general quasar (QSO) population
using Planck observations of the Baryon Oscillation Spectroscopic Survey QSO
sample. By applying multi-component matched multi-filters to the seven highest
Planck frequencies, we extract the amplitudes of dust, synchrotron and thermal
Sunyaev-Zeldovich (SZ) signals for nearly 300,000 QSOs over the redshift range
$0.1<z<5$. We bin these individually low signal-to-noise measurements to obtain
the mean emission properties of the QSO population as a function of redshift.
The emission is dominated by dust at all redshifts, with a peak at $z \sim 2$,
the same location as the peak in the general cosmic star formation rate.
Restricting analysis to radio-loud QSOs, we find synchrotron emission with a
monochromatic luminosity at $100\,\rm{GHz}$ (rest-frame) rising from
$\overline{L_{\rm synch}}=0$ to $0.2 \, {\rm L_\odot} {\rm Hz}^{-1}$ between
$z=0$ and 3. The radio-quiet subsample does not show any synchrotron emission,
but we detect thermal SZ between $z=2.5$ and 4; no significant SZ emission is
seen at lower redshifts. Depending on the supposed mass for the halos hosting
the QSOs, this may or may not leave room for heating of the halo gas by
feedback from the QSO.


  An interlaced method to learn and control nonlinear system dynamics from a
set of demonstrations is proposed, under a constrained optimization framework
for the unsupervised learning process. The nonlinear system is modelled as a
mixture of Gaussians and the Sontag's formula together with its associated
Control Lyapunov Function is proposed for learning and control. Lyapunov
stability and robustness in noisy data environments are guaranteed, as a result
of the inclusion of control in the learning-optimization problem. The
performances are validated through a well-known dataset of demonstrations with
handwriting complex trajectories, succeeding in all of them and outperforming
previous methods under bounded disturbances, possibly coming from inaccuracies,
imperfect demonstrations or noisy datasets. As a result, the proposed
interlaced solution yields a good performance trade-off between reproductions
and robustness. The proposed method can be used to program nonlinear
trajectories in robotic systems through human demonstrations.


  We consider the problem of inference in Difference-in-Differences (DID) when
there are few treated units and errors are spatially correlated. We first show
that, when there is a single treated unit, some existing inference methods
designed for settings with few treated and many control units remain
asymptotically valid when errors are weakly dependent. However, these methods
may be invalid with more than one treated unit. We propose alternatives that
are asymptotically valid in this setting, even when the relevant distance
metric across units is unavailable.


  We investigate full counting statistics of quantum heat transfer in a
collective-qubit system, constructed by multi-qubits interacting with two
thermal baths. The nonequilibrium polaron-transformed Redfield approach
embedded with an auxiliary counting field is applied to obtain the steady state
heat current and fluctuations, which enables us to study the impact of the
qubit-bath interaction in a wide regime. The heat current, current noise and
skewness are all found to clearly unify the limiting results in the weak and
strong couplings, respectively. Moreover, the superradiant heat transfer is
clarified as a system-size-dependent effect, and large number of qubits
dramatically suppresses the nonequilibrium superradiant signature.


  Tensor network contractions are widely used in statistical physics, quantum
computing, and computer science. We introduce a method to efficiently
approximate tensor network contractions using low-rank approximations, where
each intermediate tensor generated during the contractions is approximated as a
low-rank binary tree tensor network. The proposed algorithm has the flexibility
to incorporate a large portion of the environment when performing low-rank
approximations, which can lead to high accuracy for a given rank. Here, the
environment refers to the remaining set of tensors in the network, and low-rank
approximations with larger environments can generally provide higher accuracy.
For contracting tensor networks defined on lattices, the proposed algorithm can
be viewed as a generalization of the standard boundary-based algorithms. In
addition, the algorithm includes a cost-efficient density matrix algorithm for
approximating a tensor network with a general graph structure into a tree
structure, whose computational cost is asymptotically upper-bounded by that of
the standard algorithm that uses canonicalization. Experimental results
indicate that the proposed technique outperforms previously proposed
approximate tensor network contraction algorithms for multiple problems in
terms of both accuracy and efficiency.


  Improper health insurance payments resulting from fraud and upcoding result
in tens of billions of dollars in excess health care costs annually in the
United States, motivating machine learning researchers to build anomaly
detection models for health insurance claims. This article describes two such
strategies specifically for ER claims. The first is an upcoding model based on
severity code distributions, stratified by hierarchical diagnosis code
clusters. A statistically significant difference in mean upcoding anomaly
scores is observed between free-standing ERs and acute care hospitals, with
free-standing ERs being more anomalous. The second model is a random forest
that minimizes improper payments by optimally sorting ER claims within review
queues. Depending on the percentage of claims reviewed, the random forest saved
12% to 40% above a baseline approach that prioritized claims by billed amount.


  This paper initiates a limit theory of permutation valued processes, building
on the recent theory of permutons. We apply this to study the asymptotic
behaviour of random sorting networks. We prove that the Archimedean path, the
conjectured limit of random sorting networks, is the unique path from the
identity to the reverse permuton having minimal energy in an appropriate
metric. Together with a recent large deviations result (Kotowski, 2016), it
implies the Archimedean limit for the model of relaxed random sorting networks.


  We propose a simple way to explain Univerasal method of Yu. Nesterov. Based
on this method and using the restart technique we propose Universal method for
strictly convex optimization problems. We consider general proximal set up (not
necessarily euclidian one).


  Given a power-bounded linear operator T in a Banach space and a probability F
on the non-negative integers, one can form a `subordinated' operator S = \sum_k
F(k) T^k. We obtain asymptotic properties of the subordinated discrete
semigroup (S^n: n=1,2,...) under certain conditions on F. In particular, we
study probabilities F with the property that S satisfies the Ritt resolvent
condition whenever T is power-bounded. Examples and counterexamples of this
property are discussed. The hypothesis of power-boundedness of T can sometimes
be replaced by the weaker Kreiss resolvent condition.


  Resonances are essential for understanding the interactions between light and
matter in photonic systems. The real frequency response of the non-Hermitian
systems depends on the complex-valued resonance frequencies, which are the
poles of electromagnetic response functions. The zeros of the response
functions are often used for designing devices, since the zeros can be located
close to the real axis, where they have significant impact on scattering
properties. While methods are available to determine the locations of the
poles, there is a lack of appropriate approaches to find the zeros in photonic
systems. We present an approach to compute poles and zeros based on contour
integration of electromagnetic quantities. This also allows to extract
sensitivities with respect to geometrical or other parameters enabling
efficient device design. The approach is applied to a topical example in
nanophotonics, an illuminated metasurface, where the emergence of reflection
zeros due to the underlying resonance poles is explored using residue-based
modal expansions. The generality and simplicity of the theory allows
straightforward transfer to other areas of physics. We expect that easy access
to zeros will enable new computer-aided design methods in photonics and other
fields.


  Reentrant cavities are microwave resonant devices employed in a number of
different areas of physics. They are appealing due to their simple frequency
tuning mechanism, which offers large tuning ranges. Reentrant cavities are, in
essence, 3D lumped LC circuits consisting of a conducting central post embedded
in a resonant cavity. The lowest order reentrant mode (which transforms from
the $TM_{010}$ mode) has been extensively studied in past publications. In this
work we show the existence of higher order reentrant post modes (which
transform from the $TM_{01n}$ mode family). We characterize these new modes in
terms of their frequency tuning, filling factors and quality factors, as well
as discuss some possible applications of these modes in fundamental physics
tests. The appendix contains a comment on a paper related to this work.