  Artificial neural networks have recently shown great results in many
disciplines and a variety of applications, including natural language
understanding, speech processing, games and image data generation. One
particular application in which the strong performance of artificial neural
networks was demonstrated is the recognition of objects in images, where deep
convolutional neural networks are commonly applied. In this survey, we give a
comprehensive introduction to this topic (object recognition with deep
convolutional neural networks), with a strong focus on the evolution of network
architectures. Therefore, we aim to compress the most important concepts in
this field in a simple and non-technical manner to allow for future researchers
to have a quick general understanding.
  This work is structured as follows:
  1. We will explain the basic ideas of (convolutional) neural networks and
deep learning and examine their usage for three object recognition tasks: image
classification, object localization and object detection.
  2. We give a review on the evolution of deep convolutional neural networks by
providing an extensive overview of the most important network architectures
presented in chronological order of their appearances.


  Grounding has been argued to be a crucial component towards the development
of more complete and truly semantically competent artificial intelligence
systems. Literature has divided into two camps: While some argue that grounding
allows for qualitatively different generalizations, others believe it can be
compensated by mono-modal data quantity. Limited empirical evidence has emerged
for or against either position, which we argue is due to the methodological
challenges that come with studying grounding and its effects on NLP systems.
  In this paper, we establish a methodological framework for studying what the
effects are - if any - of providing models with richer input sources than
text-only. The crux of it lies in the construction of comparable samples of
populations of models trained on different input modalities, so that we can
tease apart the qualitative effects of different input sources from
quantifiable model performances. Experiments using this framework reveal
qualitative differences in model behavior between cross-modally grounded,
cross-lingually grounded, and ungrounded models, which we measure both at a
global dataset level as well as for specific word representations, depending on
how concrete their semantics is.


  We consider the bilinear pseudo-differential operators with symbols in the
bilinear H\"ormander classes $BS_{\rho, \rho}^m$, $0 < \rho < 1$. In this
paper, we show that the condition $1/p = 1/p_1 + 1/p_2$ is necessary when we
consider the boudnedness from $H^{p_1} \times H^{p_2}$ to $L^p$ of those
operators for the critical case.


  We present standard model predictions for a four fermion reaction e+ e- ->
mu+ mu- bar_b b being one of the best detection channels of a low mass Higgs
boson produced through the Higgsstrahlung mechanism at a linear collider. We
include leading virtual and real QED corrections to all the lowest order
Feynman diagrams of e+ e- -> mu+ mu- bar_b b and the complete O(alpha)
electroweak corrections to the Z-Higgs production and to the Z and Higgs boson
decay widths in the double pole approximation.


  The $\beta\gamma$ system is generalized by complex(rational) powers of the
fields, which leads to a corresponding extension on the Fock space. Two
different approaches to compute the Green functions of the physical operators
are proposed. First the complex(rational) powers are defined via an integral
representation,that allows to compute the conformal blocks, Green functions and
structure constants of OPA. Next an approach based on a system of recursion
equations for the Green functions is developed. A number of solutions of the
system is found. A lot of possible applications is briefly discussed.


  Bound-state-like wave functions are used to determine the scattering matrix
corresponding to low energy $N-d$ and $p-^3$He collisions. To this end, the
coupled channel form of the integral relations derived from the Kohn
variational principle is used. The construction of degenerate bound-state-like
wave functions belonging to the continuum spectrum of the Hamiltonian is
discussed. Examples are shown using realistic nucleon-nucleon forces.


  Nonadiabatic geometric quantum computation (NGQC) and nonadiabatic holonomic
quantum computation (NHQC) have been proposed to reduce the run time of
geometric quantum gates. However, in terms of robustness against experimental
control errors, the existing NGQC and NHQC scenarios have no advantage over
standard dynamical gates in most cases. Here, we give the reasons why
nonadiabatic geometric gates are sensitive to the control errors and, further,
we propose a scheme of super-robust nonadiabatic geometric quantum control, in
which the super-robust condition can guarantee both high speed and robustness
of the geometric gate. To illustrate the working mechanism of super-robust
geometric quantum gates, we give two simple examples of SR-NGQC and SR-NHQC for
two- and three-level quantum systems, respectively. Theoretical and numerical
results with the experimental parameters indicate that our scheme can
significantly improve the gate performance compared to the previous NGQC, NHQC,
and standard dynamical schemes. Super-robust geometric quantum computation can
be applied to various physical platforms such as superconducting qubits,
quantum dots, and trapped ions. All of these sufficiently show that our scheme
provides a promising way towards robust geometric quantum computation.


  This article discusses entanglement between two subsystems, one with discrete
degrees of freedom and the other with continuous degrees of freedom. The
overlap integral between continuous variable wave functions emerges as an
important parameter to characterize this kind entanglement. ``Beam-like''
entanglement and ``shape-like'' entanglement are contrasted. One example of
this kind of entanglement is between between the spin degrees of freedom and
the momentum degrees of freedom for a non-relativistic particle. This
intraparticle entanglement is Galilean invariant.


  Tensor ring (TR) decomposition has been widely applied as an effective
approach in a variety of applications to discover the hidden low-rank patterns
in multidimensional data. A well-known method for TR decomposition is the
alternating least squares (ALS). However, it often suffers from the notorious
intermediate data explosion issue, especially for large-scale tensors. In this
paper, we provide two strategies to tackle this issue and design three
ALS-based algorithms. Specifically, the first strategy is used to simplify the
calculation of the coefficient matrices of the normal equations for the ALS
subproblems, which takes full advantage of the structure of the coefficient
matrices of the subproblems and hence makes the corresponding algorithm perform
much better than the regular ALS method in terms of computing time. The second
strategy is to stabilize the ALS subproblems by QR factorizations on TR-cores,
and hence the corresponding algorithms are more numerically stable compared
with our first algorithm. Extensive numerical experiments on synthetic and real
data are given to illustrate and confirm the above results. In addition, we
also present the complexity analyses of the proposed algorithms.


  We synthesized the YBamCu1+mOy superconductors; m = 2,3,4,5 that were Y123
(YBa2Cu3O7-x), Y134 (YBa3Cu4O9-x), Y145 (YBa4Cu5O11-x), Y156 (YBa5Cu6O13-x), by
solid state reaction with the Y2O3, BaCO3 and CuO as the beginning materials.
The calcination temperature was 950 C and varied the sintering temperature to
be 950 C and 980 C. The resistivity measurement by four-point-probe technique
showed that the Tconset of Y123, Y134, Y145, Y156 were at 97 K, 93 K, 91K, 85
K, respectively. The XRD and Rietveld full-profile analysis method were used
and found that the crystal structure was in the orthorhombic with Pmmm space
group with the ratio c/a were 3.0, 4.0, 5.0 and 6.0 for Y123, Y134, Y145 and
Y156, respectively. The oxygen content was characterized by Iodometric
titration. The (Cu3+/Cu2+and Oxygen content) were (0.28, 6.83), (0.19, 8.81),
(0.13, 10.79), (0.16, 12.92) of Y123, Y134, Y145, Y156 respectively. We also
found that the increasing of sintering temperature has reduced the oxygen
content and the critical temperature of all samples.


  It is showed that complex scalar fields with a self-interaction potential may
propagate along null geodesics on flat Friedmann--Lema\^itre--Robertson--Walker
universes with different time-dependent scale factors. This occurs provided
they self interact adequately, for different forms of potentials, and
  even for the massive case.


  Channel prediction is critical to address the channel aging issue in mobile
scenarios. Existing channel prediction techniques are mainly designed for
discrete channel prediction, which can only predict the future channel in a
fixed time slot per frame, while the other intra-frame channels are usually
recovered by interpolation. However, these approaches suffer from a serious
interpolation loss, especially for mobile millimeter wave communications. To
solve this challenging problem, we propose a tensor neural ordinary
differential equation (TN-ODE) based continuous-time channel prediction scheme
to realize the direct prediction of intra-frame channels. Specifically,
inspired by the recently developed continuous mapping model named neural ODE in
the field of machine learning, we first utilize the neural ODE model to predict
future continuous-time channels. To improve the channel prediction accuracy and
reduce computational complexity, we then propose the TN-ODE scheme to learn the
structural characteristics of the high-dimensional channel by low dimensional
learnable transform. Simulation results show that the proposed scheme is able
to achieve higher intra-frame channel prediction accuracy than existing
schemes.


  It is widely accepted that temporal asymmetry is largely a cosmological
problem; the task of explaining temporal asymmetry reduces in the main to that
of explaining an aspect of the condition of the early universe. However,
cosmologists who discuss these issues often make mistakes similar to those that
plagued nineteenth century discussions of the statistical foundations of
thermodynamics. In particular, they are often guilty of applying temporal
"double standards" of various kinds---e.g., in failing to recognise that
certain statistical arguments apply with equal force in either temporal
direction. This paper aims to clarify the issue as to what would count as
adequate explanation of cosmological time asymmetry. A particular concern is
the question whether it is possible to explain why entropy is low near the Big
Bang without showing that it must also be low near a Big Crunch, in the event
that the universe recollapses. I criticise some of the objections raised to
this possibility, showing that these too often depend on a temporal double
standard. I also discuss briefly some issues that arise if we take the view
seriously. (Could we observe a time- reversing future, for example?)


  Many biological studies involve inferring the genealogical history of a
sample of individuals from a large population and interpreting the
reconstructed tree. Such an ascertained tree typically represents only a small
part of a comprehensive population tree and is distorted by survivorship and
sampling biases. Inferring evolutionary parameters from ascertained trees
requires modeling both the underlying population dynamics and the ascertainment
process. A crucial component of this phylodynamic modeling involves tree
simulation, which is used to benchmark probabilistic inference methods. To
simulate an ascertained tree, one must first simulate the full population tree
and then prune unobserved lineages. Consequently, the computational cost is
determined not by the size of the final simulated tree, but by the size of the
population tree in which it is embedded. In most biological scenarios,
simulations of the entire population are prohibitively expensive due to
computational demands placed on lineages without sampled descendants. Here, we
address this challenge by proving that, for any partially ascertained process
from a general multi-type birth-death-mutation-sampling (BDMS) model, there
exists an equivalent pure birth process (i.e., no death) with mutation and
complete sampling. The final trees generated under these processes have exactly
the same distribution. Leveraging this property, we propose a highly efficient
algorithm for simulating trees under a general BDMS model. Our algorithm scales
linearly with the size of the final simulated tree and is independent of the
population size, enabling simulations from extremely large populations beyond
the reach of current methods but essential for various biological applications.
We anticipate that this unprecedented speedup will significantly advance the
development of novel inference methods that require extensive training data.


  Motivated by the developments in cyber risk treatment in the finance
industry, we propose a general framework of cyber bond, whose main purpose is
to insure (compensate) losses of a cyber attack. Based on a database of
publicly available cyber events, we determine cyber loss distribution
parameters and use them to numerically simulate cyber bond price, yield, and
other characteristics. We also consider two possible approaches to cyber bond
coupon calculation.


  Detailed electronic many-body configurations are extracted from
quantitatively measured timeresolved nonlinear absorption spectra of resonantly
excited GaAs quantum wells. The microscopic theory assigns the observed
spectral changes to a unique mixture of electron-hole plasma, exciton, and
polarization effects. Strong transient gain is observed only under co-circular
pump-probe conditions and is attributed to the transfer of pump-induced
coherences to the probe.


  We introduce and investigate a class of non-separable tree-like Banach
spaces. As a consequence, we prove that we can not achieve a satisfactory
extension of Rosenthal's $\ell_1$-theorem to spaces of the type
$\ell_1(\kappa)$, for $\kappa$ an uncountable cardinal.


  We present the first results of an near-ultraviolet (NUV) survey of RR Lyrae
stars from the Ultraviolet Optical Telescope (UVOT) aboard the Swift Gamma-Ray
Burst Mission. It is well-established that RR Lyrae have large amplitudes in
the far- and near-ultraviolet. We have used UVOT's unique wide-field NUV
imaging capability to perform the first systematic NUV survey of variable stars
in the Galactic globular clusters M 3 and M 15. We identify 280 variable stars,
comprising 275 RR Lyrae, two anomalous Cepheids, one classical Cepheid, one SX
Phoenicis star and one possible long-period or irregular variable. Only two of
these are new discoveries. We compare our results to previous investigations
and find excellent agreement in the periods with significantly larger
amplitudes in the NUV. We map out, for the first time, an NUV Bailey diagram
from globular clusters, showing the usual loci for fundamental mode RRab and
first overtone RRc pulsators. We show the unique sensitivity of NUV photometry
to both the temperatures and the surface gravities of RR Lyrae stars. Finally,
we show evidence of an NUV period-metallicity-luminosity relationship. Future
investigations will further examine the dependence of NUV pulsation parameters
on metallicity and Oosterhoff classification.


  We present the CDF results using 1.0 fb^{-1} of data on the mixing frequency
measurement in the B_s system and the lifetime measurements of several B
hadrons. We obtain \Delta m_s=17.77 +- 0.1 +- 0.07 ps^{-1} and
c\tau(\Lambda_b)=473.8 +- 23.1 +- 3.5 \mu m. The later one is more than 3 sigma
above the world average, but in reasonable agreement with HQE calculations.


  Near-infrared (2.2 um) long baseline interferometric observations of Vega are
presented. The stellar disk of the star has been resolved, and the data have
been fitted with a limb darkened stellar disk of diameter Theta_{LD} = 3.28 +/-
0.01 mas. The derived effective temperature is T_eff = 9553 +/- 111 K. However,
the residuals resulting from the stellar disk model appear to be significant
and display organized structure. Instrumental artifacts, stellar surface
structure, stellar atmosphere structure, and extended emission/scattering from
the debris disk are discussed as possible sources of the residuals. While the
current dataset cannot uniquely determine the origin of the residuals, the
debris disk is found to be the most likely source. A simple debris disk model,
with 3-6% of Vega's flux emanating from the disk at r < 4 AU, can explain the
residuals.


  Video anomalies detection is the intersection of anomaly detection and visual
intelligence. It has commercial applications in surveillance, security,
self-driving cars and crop monitoring. Videos can capture a variety of
anomalies. Due to efforts needed to label training data, unsupervised
approaches to train anomaly detection models for videos is more practical An
autoencoder is a neural network that is trained to recreate its input using
latent representation of input also called a bottleneck layer. Variational
autoencoder uses distribution (mean and variance) as compared to latent vector
as bottleneck layer and can have better regularization effect. In this paper we
have demonstrated comparison between performance of convolutional LSTM versus a
variation convolutional LSTM autoencoder


  An explicitely gauge invariant polynomial action for massive gauge fields is
proposed. For different values of parameters it describes massive Yang-Mills
field, the Higgs-Kibble model, the model with spontaneously broken symmetry and
two scalar mesons.


  We analyze the physical aspects and origins of currently proposed oracles for
(absolute) randomness.


  Smoothed Particle Hydrodynamics (SPH) is a Lagrangian method for solving the
fluid equations that is commonplace in astrophysics, prized for its natural
adaptivity and stability. The choice of variable to smooth in SPH has been the
topic of contention, with smoothed pressure (P-SPH) being introduced to reduce
errors at contact discontinuities relative to smoothed density schemes.
Smoothed pressure schemes produce excellent results in isolated hydrodynamics
tests; in more complex situations however, especially when coupling to the
`sub-grid' physics and multiple time-stepping used in many state-of-the-art
astrophysics simulations, these schemes produce large force errors that can
easily evade detection as they do not manifest as energy non-conservation. Here
two scenarios are evaluated: the injection of energy into the fluid (common for
stellar feedback) and radiative cooling. In the former scenario, force and
energy conservation errors manifest (of the same order as the injected energy),
and in the latter large force errors that change rapidly over a few timesteps
lead to instability in the fluid (of the same order as the energy lost to
cooling). Potential ways to remedy these issues are explored with solutions
generally leading to large increases in computational cost. Schemes using a
Density-based formulation do not create these instabilities and as such it is
recommended that they are preferred over Pressure-based solutions when combined
with an energy diffusion term to reduce errors at contact discontinuities.


  Giant micelles are elongated, polymer-like objects created by the
self-assembly of amphiphilic molecules (such as detergents) in solution. Giant
micelles are typically flexible, and can become highly entangled even at modest
concentrations. The resulting viscoelastic solutions show fascinating flow
behaviour (rheology) which we address theoretically in this article at two
levels. First, we summarise advances in understanding linear viscoelastic
spectra and steady-state nonlinear flows, based on microscopic constitutive
models that combine the physics of polymer entanglement with the reversible
kinetics of self-assembly. Such models were first introduced two decades ago,
and since then have been shown to explain robustly several distinctive features
of the rheology in the strongly entangled regime, including extreme
shear-thinning. We then turn to more complex rheological phenomena,
particularly involving spatial heterogeneity, spontaneous oscillation,
instability, and chaos. Recent understanding of these complex flows is based
largely on grossly simplified models which capture in outline just a few
pertinent microscopic features, such as coupling between stresses and other
order parameters such as concentration. The role of `structural memory' (the
dependence of structural parameters such as the micellar length distribution on
the flow history) in explaining these highly nonlinear phenomena is addressed.
Structural memory also plays an intriguing role in the little-understood
shear-thickening regime, which occurs in a concentration regime close to but
below the onset of strong entanglement, and which is marked by a shear-induced
transformation from an inviscid to a gelatinous state.


  In this paper, we prove the existence and uniqueness result of the reflected
BSDE with two continuous barriers under monotonicity and general increasing
condition on $y$, with Lipschitz condition on $z$.


  In this paper we consider the degree/diameter problem, namely, given natural
numbers {\Delta} \geq 2 and D \geq 1, find the maximum number N({\Delta},D) of
vertices in a graph of maximum degree {\Delta} and diameter D. In this context,
the Moore bound M({\Delta},D) represents an upper bound for N({\Delta},D).
Graphs of maximum degree {\Delta}, diameter D and order M({\Delta},D), called
Moore graphs, turned out to be very rare. Therefore, it is very interesting to
investigate graphs of maximum degree {\Delta} \geq 2, diameter D \geq 1 and
order M({\Delta},D) - {\epsilon} with small {\epsilon} > 0, that is,
({\Delta},D,-{\epsilon})-graphs. The parameter {\epsilon} is called the defect.
Graphs of defect 1 exist only for {\Delta} = 2. When {\epsilon} > 1,
({\Delta},D,-{\epsilon})-graphs represent a wide unexplored area. This paper
focuses on graphs of defect 2. Building on the approaches developed in [11] we
obtain several new important results on this family of graphs. First, we prove
that the girth of a ({\Delta},D,-2)-graph with {\Delta} \geq 4 and D \geq 4 is
2D. Second, and most important, we prove the non-existence of
({\Delta},D,-2)-graphs with even {\Delta} \geq 4 and D \geq 4; this outcome,
together with a proof on the non-existence of (4, 3,-2)-graphs (also provided
in the paper), allows us to complete the catalogue of (4,D,-{\epsilon})-graphs
with D \geq 2 and 0 \leq {\epsilon} \leq 2. Such a catalogue is only the second
census of ({\Delta},D,-2)-graphs known at present, the first being the one of
(3,D,-{\epsilon})-graphs with D \geq 2 and 0 \leq {\epsilon} \leq 2 [14]. Other
results of this paper include necessary conditions for the existence of
({\Delta},D,-2)-graphs with odd {\Delta} \geq 5 and D \geq 4, and the
non-existence of ({\Delta},D,-2)-graphs with odd {\Delta} \geq 5 and D \geq 5
such that {\Delta} \equiv 0, 2 (mod D).


  Exotic-deformation effects in 46Ti nucleus were investigated by analysing the
high-energy gamma-ray and the alpha-particle energy spectra. One of the
experiments was performed using the charged-particle multi-detector array ICARE
together with a large volume (4"x4") BGO detector. The study focused on
simultaneous measurement of light charged particles and gamma-rays in
coincidence with the evaporation residues. The experimental data show a
signature of very large deformations of the compound nucleus in the Jacobi
transition region at the highest spins. These results are compared to data from
previous experiments performed with the HECTOR array coupled to the EUROBALL
array, where it was found that the GDR strength function is highly fragmented,
strongly indicating a presence of nuclei with very large deformation.


  In many fields where the main goal is to produce sequential forecasts for
decision making problems, the good understanding of the contemporaneous
relations among different series is crucial for the estimation of the
covariance matrix. In recent years, the modified Cholesky decomposition
appeared as a popular approach to covariance matrix estimation. However, its
main drawback relies on the imposition of the series ordering structure. In
this work, we propose a highly flexible and fast method to deal with the
problem of ordering uncertainty in a dynamic fashion with the use of Dynamic
Order Probabilities. We apply the proposed method in two different forecasting
contexts. The first is a dynamic portfolio allocation problem, where the
investor is able to learn the contemporaneous relationships among different
currencies improving final decisions and economic performance. The second is a
macroeconomic application, where the econometrician can adapt sequentially to
new economic environments, switching the contemporaneous relations among
macroeconomic variables over time.


  Generalising an example by Girondo and Wolfart, we use finite group theory to
construct Riemann surfaces admitting two or more regular dessins (i.e.
orientably regular hypermaps) with automorphism groups of the same order, and
in many cases with isomorphic automorphism groups.


  Learning to Rank (LETOR) algorithms are usually trained on annotated corpora
where a single relevance label is assigned to each available document-topic
pair. Within the Cranfield framework, relevance labels result from merging
either multiple expertly curated or crowdsourced human assessments. In this
paper, we explore how to train LETOR models with relevance judgments
distributions (either real or synthetically generated) assigned to
document-topic pairs instead of single-valued relevance labels. We propose five
new probabilistic loss functions to deal with the higher expressive power
provided by relevance judgments distributions and show how they can be applied
both to neural and GBM architectures. Moreover, we show how training a LETOR
model on a sampled version of the relevance judgments from certain probability
distributions can improve its performance when relying either on traditional or
probabilistic loss functions. Finally, we validate our hypothesis on real-world
crowdsourced relevance judgments distributions. Overall, we observe that
relying on relevance judgments distributions to train different LETOR models
can boost their performance and even outperform strong baselines such as
LambdaMART on several test collections.


  We explore the possibility of dynamical quantum phase transitions (DQPTs)
occurring during the temporal evolution of a quenched transverse field Ising
chain coupled to a particle loss type of bath (local in Jordan-Wigner fermion
space) using two versions of the Loschmidt overlap (LO), namely, the fidelity
induced LO and the interferometric phase induced LO. The bath, on the one hand,
dictates the dissipative evolution following a sudden quench and on the other,
plays a role in dissipative mixed state preparation in the later part of the
study. During a dissipative evolution following a sudden quench, no trace of
DQPTs are revealed in both the fidelity and the interferometric phase
approaches; however, remarkably the interferometric phase approach reveals the
possibility of inter-steady state DQPTs in passage from one steady state to the
other when the system is subjected to a quench after having reached the first
steady state. We further probe the occurrences of DQPTs when the system evolves
unitarily after being prepared in a mixed state of engineered purity by ramping
the transverse field in a linear fashion in the presence of the bath. In this
case though the fidelity approach fails to indicate any DQPT, the
interferometric approach indeed unravels the possibility of occurrence of DQPTs
which persists even up to a considerable loss of purity of the engineered
initial state as long as a constraint relation involving the dissipative
coupling and ramping time (rate) is satisfied. This constraint relation also
marks the boundary between two dynamically inequivalent phases; in one the LO
vanishes for the critical momentum mode (and hence DQPTs exist) while in the
other no such critical mode can exist and hence the LO never vanishes.


  The Yoccoz puzzle is a fundamental tool in Holomorphic Dynamics. The original
combinatorial argument by Yoccoz, based on the Branner-Hubbard tableau, counts
the preimages of a non-degenerate annulus in the puzzle. However, in some
important new applications of the puzzle (notably, matings of quadratic
polynomials) there is no non-degenerate annulus. We develop a general
combinatorial argument to handle this situation. It allows to derive
corollaries, such as the local connectedness of the Julia set, for suitable
families of rational maps.


  We investigate, for two water models displaying a liquid-liquid critical
point, the relation between changes in dynamic and thermodynamic anomalies
arising from the presence of the liquid-liquid critical point. We find a
correlation between the dynamic fragility transition and the locus of specific
heat maxima $C_P^{\rm max}$ (``Widom line'') emanating from the critical point.
Our findings are consistent with a possible relation between the previously
hypothesized liquid-liquid phase transition and the transition in the dynamics
recently observed in neutron scattering experiments on confined water. More
generally, we argue that this connection between $C_P^{\rm max}$ and dynamic
crossover is not limited to the case of water, a hydrogen bond network forming
liquid, but is a more general feature of crossing the Widom line. Specifically,
we also study the Jagla potential, a spherically-symmetric two-scale potential
known to possess a liquid-liquid critical point, in which the competition
between two liquid structures is generated by repulsive and attractive ramp
interactions.


  In statistics, experimental designs are methods for making efficient
experiments. E-optimal designs are the multisets of experimental conditions
which minimize the maximum axis of the confidence ellipsoid of estimators. The
aim of this thesis is to propose a new algorithm for constructing E-optimal
designs approximately for weighted polynomial regression with a nonnegative
weight function.
  First, an algorithm to calculate E-optimal designs for weighted polynomial
regression of particular weight functions is discussed. Next a new algorithm
for constructing E-optimal designs approximately is proposed. Notions of the
Tchebycheff systems and orthogonal polynomials are used in the proposed
algorithm. Finally in this thesis, the results of numerical examples are shown
in order to verify the accuracy of the E-optimal designs computed by the
proposed algorithm.


  Trained LLMs are typically sparse in that most of the parameters are zero,
raising questions on efficiency. In response, we inquire into efficient LLMs,
i.e. those with the fewest parameters that achieve the desired accuracy on a
training corpus. Specifically, we compare theoretical and empirical estimates
for training loss at current scale to obtain upper and lower bounds on the
number of unique sequences in a natural training corpus as a function of its
size. Our result implies (1) to double the number of skills represented in a
training corpus, the corpus must scale roughly between three and five fold (2)
for efficient LLMs, the number of parameters $N$ and the size $D$ of a natural
training corpus scale as $N \sim D^{0.58}$ (3) if the number of parameters of
an LLM is smaller than the number of unique sequences in the training corpus,
scaling up can uncover emergent skills.


  We find new constructions of infinite families of skew Hadamard difference
sets in elementary abelian groups under the assumption of the existence of
cyclotomic strongly regular graphs. Our construction is based on choosing
cyclotomic classes in finite fields.


  Bosonic channels describe quantum-mechanically many practical communication
links such as optical, microwave, and radiofrequency. We investigate the
maximum rates for the bosonic multiple access channel (MAC) in the presence of
thermal noise added by the environment and when the transmitters utilize
Gaussian state inputs. We develop an outer bound for the capacity region for
the thermal-noise lossy bosonic MAC. We additionally find that the use of
coherent states at the transmitters is capacity-achieving in the limits of high
and low mean input photon numbers. Furthermore, we verify that coherent states
are capacity-achieving for the sum rate of the channel. In the non-asymptotic
regime, when a global mean photon-number constraint is imposed on the
transmitters, coherent states are the optimal Gaussian state. Surprisingly
however, the use of single-mode squeezed states can increase the capacity over
that afforded by coherent state encoding when each transmitter is photon number
constrained individually.


  In the present paper we study the interaction of a photonic field with a
particle in a magnetic field. We use formalism similar to the Born-Oppenheimer
approximation of molecular physics and we eliminate the photonic field
variables by using a method related to the Berry phase to obtain an effective
Hamiltonian and then extract the propagator of the particle. As an initial
photonic state we assume a coherent one.


  High resolution laser-based angle-resolved photoemission measurements have
been carried out on Sb(111) single crystal. Two kinds of Fermi surface sheets
are observed that are derived from the topological surface states: one small
hexagonal electron-like Fermi pocket around $\Gamma$ point and the other six
elongated lobes of hole-like Fermi pockets around the electron pocket. Clear
Rashba-type band splitting due to the strong spin-orbit coupling is observed
that is anisotropic in the momentum space. Our super-high-resolution ARPES
measurements reveal no obvious kink in the surface band dispersions indicating
a weak electron-phonon interaction in the surface states. In particular, the
electron scattering rate for these topological surface states is nearly a
constant over a large energy window near the Fermi level that is unusual in
terms of the conventional picture.


  We prove that every 1-planar graph G has a z-parallel visibility
representation, i.e., a 3D visibility representation in which the vertices are
isothetic disjoint rectangles parallel to the xy-plane, and the edges are
unobstructed z-parallel visibilities between pairs of rectangles. In addition,
the constructed representation is such that there is a plane that intersects
all the rectangles, and this intersection defines a bar 1-visibility
representation of G.


  In this work, we present some easily verifiable sufficient conditions that
guarantee the controllability of wave equations with non-constant coefficients.
These conditions work as complements for those obtained by Peng-fei Yao in SIAM
J. Control Optim.1999


  Game AI designers must manage complex interactions between the AI character,
the game world, and the player, while achieving their design visions.
Computational co-creativity tools can aid them, but first, AI and HCI
researchers must gather requirements and determine design heuristics to build
effective co-creative tools. In this work, we present a participatory design
study that categorizes and analyzes game AI designers' workflows, goals, and
expectations for such tools. We evince deep connections between game AI design
and the design of co-creative tools, and present implications for future
co-creativity tool research and development.


  Aiming at a complete classification of unitary N=2 minimal models (where the
assumption of space-time supersymmetry has been dropped), it is shown that each
modular invariant candidate of a partition function for such a theory is indeed
the partition function of a minimal model. A family of models constructed via
orbifoldings of either the diagonal model or of the space-time supersymmetric
exceptional models demonstrates that there exists a unitary N=2 minimal model
for every one of the allowed partition functions in the list obtained from
Gannon's work.
  Kreuzer and Schellekens' conjecture that all simple current invariants can be
obtained as orbifolds of the diagonal model, even when the extra assumption of
higher-genus modular invariance is dropped, is confirmed in the case of the
unitary N=2 minimal models by simple counting arguments.


  The class of inhomogeneous phase-type distributions (IPH) was recently
introduced in Albrecher and Bladt (2019) as an extension of the classical
phase-type (PH) distributions. Like PH distributions, the class of IPH is dense
in the class of distributions on the positive halfline, but leads to more
parsimonious models in the presence of heavy tails. In this paper we propose a
fitting procedure for this class to given data. We furthermore consider an
analogous extension of Kulkarni's multivariate phase-type class (Kulkarni,
1989) to the inhomogeneous framework and study parameter estimation for the
resulting new and flexible class of multivariate distributions. As a
by-product, we amend a previously suggested fitting procedure for the
homogeneous multivariate phase-type case and provide appropriate adaptations
for censored data. The performance of the algorithms is illustrated in several
numerical examples, both for simulated and real-life insurance data.


  Large-scale UAV switching formation tracking control has been widely applied
in many fields such as search and rescue, cooperative transportation, and UAV
light shows. In order to optimize the control performance and reduce the
computational burden of the system, this study proposes an event-triggered
optimal formation tracking controller for discrete-time large-scale UAV systems
(UASs). And an optimal decision - optimal control framework is completed by
introducing the Hungarian algorithm and actor-critic neural networks (NNs)
implementation. Finally, a large-scale mixed reality experimental platform is
built to verify the effectiveness of the proposed algorithm, which includes
large-scale virtual UAV nodes and limited physical UAV nodes. This compensates
for the limitations of the experimental field and equipment in realworld
scenario, ensures the experimental safety, significantly reduces the
experimental cost, and is suitable for realizing largescale UAV formation light
shows.


  Nano-particle agglomeration plays an important role in processes such as
spray drying and particle flame synthesis. These processes have in common that
nano-particles collide at low concentrations and get irreversibly linked at the
point of contact due to plastic deformation. In this paper, we investigate
several models of irreversible connections, which require only local
interactions between the colliding nano-particles and thus allow for scalable
simulations. The models investigated here connect the particles upon collision
by non-bonded strongly attractive interactions, bonded interactions or by
binding agents placed at the point of contact. Models using spherically
symmetric interactions form compact agglomerates and are therefore unsuitable
to study agglomeration. In contrast, models that are either based on both
central and angular potentials (type one) or on binding agents (type two)
efficiently prevent restructuring of the agglomerates, and are therefore useful
for modeling contacts formed by plastic deformation. Moreover, both types of
models allow to control the rigidity and by that the degree of restructuring.
The first type of model is computationally more efficient at low fractional
dimensions of the aggregates, while the second gives easy access to local shear
forces, which is important when breaking of agglomerates is to be considered.
As example applications, we reproduce the well-known diffusion-limited
agglomeration (DLA) and report results on soot aggregation.


  Artificial molecular machines are often driven by the periodic variation of
an external parameter. This external control exerts work on the system of which
a part can be extracted as output if the system runs against an applied load.
Usually, the thermodynamic cost of the process that generates the external
control is ignored. Here, we derive a refined second law for such small
machines that include this cost, which is, for example, generated by free
energy consumption of a chemical reaction that modifies the energy landscape
for such a machine. In the limit of irreversible control, this refined second
law becomes the standard one. Beyond this ideal limiting case, our analysis
shows that due to a new entropic term unexpected regimes can occur: The control
work can be smaller than the extracted work and the work required to generate
the control can be smaller than this control work. Our general inequalities are
illustrated by a paradigmatic three-state system.


  We show that Breuer-Laine-Piilo (BLP) non-Markovianity cannot capture the
dynamical information in the non-unital aspect of the quantum dynamics.
Moreover, we provide a measure on the effect of the non-unitality of quantum
processes on the infinitesimal non-divisibility. This measure can be used as a
supplement to BLP non-Markovianity for non-unital quantum processes. A measure
on the degree of the non-unital behavior of quantum processes is also given in
this paper.


  In this paper, we consider an energy-harvesting (EH) relay system consisting
of a source, a destination, and multiple EH decode-and-forward (DF) relays that
can harvest the energy from their received radio signals. A power-splitting
ratio is employed at an EH DF relay to characterize a trade-off between an
energy used for decoding the source signal received at the relay and the
remaining energy harvested for retransmitting the decode outcome. We propose an
optimal power splitting and relay selection (OPS-RS) framework and also
consider the conventional equal power splitting and relay selection (EPS-RS)
for comparison purposes. We derive closed-form expressions of outage
probability for both the OPS-RS and EPS-RS schemes and characterize their
diversity gains through an asymptotic outage analysis in the high
signal-to-noise ratio region. We further examine an extension of our OPS-RS
framework to an energy-harvesting battery (EHB) enabled cooperative relay
scenario, where the EH relays are equipped with batteries used to store their
harvested energies. We propose an amplify-and-forward (AF) based EHB-OPS-RS and
a DF based EHB-OPS-RS schemes for AF and DF relay networks, respectively.
Numerical results show that the proposed OPS-RS always outperforms the EPS-RS
in terms of outage probability. Moreover, the outage probabilities of AF and DF
based EHB-OPS-RS schemes are much smaller than that of OPS-RS and EPS-RS
methods, demonstrating the benefit of exploiting the batteries in EH relays.
Additionally, the DF based EHB-OPS-RS substantially outperforms the AF based
EHB-OPS-RS and such an outage advantage becomes more significant, as the number
of EH relays increases.


  Manipulation of light-beams with subwavelenth metallic devices has motivated
intensive studies, following the discovery of extraordinary transmission of
electromagnetic waves through sub-wavelength apertures in thin noble-metal
films. The propagation of light in these holes can be investigated at greately
improved spatial resolution by means of focused electron-beams. Here we
demonstrate direct e-beam excitation of radiative cavity modes well below the
surface plasmon (SP) frequency, of isolated rectangular holes in gold films,
illuminating the hotly debated phenomenon of the extraordinary optical
transmission through subwavelength holes. The exceptionally long range e-beam
interaction with the metal through the vacuum, involving electromagnetic
excitations within the light cone, is allowed by momentum conservation
breakdown along the e-beam axis. Two types of lowlying excited modes are
revealed: radiative cavity modes which are nearly unaffected by SPs, and SP
polariton modes with waveguide character in the near field region of the slit
walls, which in spite of the strong hybridization preserve the waveguide cutoff
frequencies and symmetry characteristics.


  In this work, we study some thermodynamical aspects associated with torsion
in a flat FLRW spacetime cosmic evolution. By implementing two Ansatze for the
torsion term, we find that the model admits a phantom regime or a quintessence
behavior. This scheme differs from the $\Lambda$CDM model at the
thermodynamical level. The resulting cosmic expansion is not adiabatic, the
fulfillment of the second law of thermodynamics requires a positive torsion
term, and the temperature of the cosmic fluid is always positive. The entropy
of the torsion phantom scenario is negative, but introducing chemical potential
solves this issue. For a Dirac-Milne type Universe, the torsion leads to a
growing behavior for the temperature of the fluid but has no incidence on the
rate of expansion.


  The aim of this paper is to study a new methodological framework for systemic
risk measures by applying deep learning method as a tool to compute the optimal
strategy of capital allocations. Under this new framework, systemic risk
measures can be interpreted as the minimal amount of cash that secures the
aggregated system by allocating capital to the single institutions before
aggregating the individual risks. This problem has no explicit solution except
in very limited situations. Deep learning is increasingly receiving attention
in financial modelings and risk management and we propose our deep learning
based algorithms to solve both the primal and dual problems of the risk
measures, and thus to learn the fair risk allocations. In particular, our
method for the dual problem involves the training philosophy inspired by the
well-known Generative Adversarial Networks (GAN) approach and a newly designed
direct estimation of Radon-Nikodym derivative. We close the paper with
substantial numerical studies of the subject and provide interpretations of the
risk allocations associated to the systemic risk measures. In the particular
case of exponential preferences, numerical experiments demonstrate excellent
performance of the proposed algorithm, when compared with the optimal explicit
solution as a benchmark.


  We study the shape of the F-signature function of a $d$-dimensional quotient
singularity $\mathbb{k}[[ x_1,\ldots,x_d]]^G$, and we show that it is a
quasi-polynomial. We prove that the second coefficient is always zero and we
describe the other coefficients in terms of invariants of the finite acting
group $G\subseteq \mathrm{Gl}(d,\Bbbk)$. When $G$ is cyclic, we obtain more
specific formulas for the coefficients of the quasi-polynomial, which allow us
to compute the general form of the function in several examples.


  Among theoretical issues in General Relativity the problem of constructing
its Hamiltonian formulation is still of interest. The most of attempts to
quantize Gravity are based upon Dirac generalization of Hamiltonian dynamics
for system with constraints. At the same time there exists another way to
formulate Hamiltonian dynamics for constrained systems guided by the idea of
extended phase space. We have already considered some features of this approach
in the previous MG12 Meeting by the example of a simple isotropic model. Now we
apply the approach to a generalized spherically symmetric model which imitates
the structure of General Relativity much better. In particular, making use of a
global BRST symmetry and the Noether theorem, we construct the BRST charge that
generates correct gauge transformations for all gravitational degrees of
freedom.


  Several interesting features of coset models "without fixed points" are
easily understood via Chern-Simons theory. In this paper we derive explicit
formulae for the handle-squashing operator in these cosets. These operators are
fixed, linear combinations of the irreducible representations of the coset. As
a simple application of these curious formulae, we compute the traces of all
genus-one operators for several common cosets. (1 Figure, at end in
encapsulated postscript- to locate use 'find figs')


  We study the realization of chiral and parity transformations within a
particle-like path-integral representation for Dirac fields, showing how those
transformations can be implemented in a natural way within the formalism. We
then obtain a representation for the chiral fermion propagator and determinant
within this framework, and also formulate a way to define the average of the
propagator over a random mass in $d=2n$ dimensions.


  While cities around the world are looking for smart ways to use new advances
in data collection, management, and analysis to address their problems, the
complex nature of urban issues and the overwhelming amount of available data
have posed significant challenges in translating these efforts into actionable
insights. In the past few years, urban visual analytics tools have
significantly helped tackle these challenges. When analyzing a feature of
interest, an urban expert must transform, integrate, and visualize different
thematic (e.g., sunlight access, demographic) and physical (e.g., buildings,
street networks) data layers, oftentimes across multiple spatial and temporal
scales. However, integrating and analyzing these layers require expertise in
different fields, increasing development time and effort. This makes the entire
visual data exploration and system implementation difficult for programmers and
also sets a high entry barrier for urban experts outside of computer science.
With this in mind, in this paper, we present the Urban Toolkit (UTK), a
flexible and extensible visualization framework that enables the easy authoring
of web-based visualizations through a new high-level grammar specifically built
with common urban use cases in mind. In order to facilitate the integration and
visualization of different urban data, we also propose the concept of knots to
merge thematic and physical urban layers. We evaluate our approach through use
cases and a series of interviews with experts and practitioners from different
domains, including urban accessibility, urban planning, architecture, and
climate science. UTK is available at urbantk.org.


  Aiming at recognizing the samples from novel categories with few reference
samples, few-shot learning (FSL) is a challenging problem. We found that the
existing works often build their few-shot model based on the image-level
feature by mixing all local-level features, which leads to the discriminative
location bias and information loss in local details. To tackle the problem,
this paper returns the perspective to the local-level feature and proposes a
series of local-level strategies. Specifically, we present (a) a local-agnostic
training strategy to avoid the discriminative location bias between the base
and novel categories, (b) a novel local-level similarity measure to capture the
accurate comparison between local-level features, and (c) a local-level
knowledge transfer that can synthesize different knowledge transfers from the
base category according to different location features. Extensive experiments
justify that our proposed local-level strategies can significantly boost the
performance and achieve 2.8%-7.2% improvements over the baseline across
different benchmark datasets, which also achieves state-of-the-art accuracy.


  We propose the possible detection of broken mirror symmetries in correlated
two-dimensional materials by elastotransport measurements. Using linear
response theory we calculate the shearconductivity $\Gamma_{xx,xy}$, defined as
the linear change of the longitudinal conductivity $\sigma_{xx}$ due to a shear
strain $\epsilon_{xy}$. This quantity can only be non-vanishing when in-plane
mirror symmetries are broken and we discuss how candidate states in the cuprate
pseudogap regime (e.g. various loop current or charge orders) may exhibit a
finite shearconductivity. We also provide a realistic experimental protocol for
detecting such a response.


  We consider the problem of existence of a (unique) weak solution to the SDE
describing symmetric $\alpha$-stable process with a locally unbounded drift
$b:\mathbb R^d \rightarrow \mathbb R^d$, $d \geq 3$, $1<\alpha<2$. In this
paper, $b$ belongs to the class of weakly form-bounded vector fields. The
latter arises as the class providing the $L^2$ theory of the non-local operator
behind the SDE, i.e.\,$(-\Delta)^{\frac{\alpha}{2}} + b \cdot \nabla$, and
contains as proper sub-classes the other classes of singular vector fields
studied in the literature in connection with this operator, such as the Kato
class, weak $L^{\frac{d}{\alpha-1}}$ class and the Campanato-Morrey class
(thus, $b$ can be so singular that it destroys the standard heat kernel
estimates in terms of the heat kernel of the fractional Laplacian). We show
that for such $b$ the operator $-(-\Delta)^{\frac{\alpha}{2}} - b \cdot \nabla$
admits a realization as a Feller generator, and that the probability measures
determined by the Feller semigroup (uniquely in appropriate sense) admit
description as weak solutions to the corresponding SDE. The proof is based on
detailed regularity theory of $(-\Delta)^{\frac{\alpha}{2}} + b \cdot \nabla$
in $L^p$, $p>d-\alpha+1$.


  Employing an extended three flavor version of the NJL model we discuss in
detail the phase diagram of quark matter. The presence of quark as well as of
diquark condensates gives raise to a rich structure of the phase diagram. We
study in detail the chiral phase transition and the color superconductivity as
well as color flavor locking as a function of the temperature and chemical
potentials of the system.


  We study a number of variants of an abstract scheduling problem inspired by
the scheduling of reclaimers in the stockyard of a coal export terminal. We
analyze the complexity of each of the variants, providing complexity proofs for
some and polynomial algorithms for others. For one, especially interesting
variant, we also develop a constant factor approximation algorithm.


  In this work, we present a dual learning approach for unsupervised text to
path and path to text transfers in Commonsense Knowledge Bases (KBs). We
investigate the impact of weak supervision by creating a weakly supervised
dataset and show that even a slight amount of supervision can significantly
improve the model performance and enable better-quality transfers. We examine
different model architectures, and evaluation metrics, proposing a novel
Commonsense KB completion metric tailored for generative models. Extensive
experimental results show that the proposed method compares very favorably to
the existing baselines. This approach is a viable step towards a more advanced
system for automatic KB construction/expansion and the reverse operation of KB
conversion to coherent textual descriptions.


  We discuss neutrino masses from higher than d=5 effective operators in a
supersymmetric framework, where we explicitly demonstrate which operators could
be the leading contribution to neutrino mass in the MSSM and NMSSM. As an
example, we focus on the d=7 operator L L H_u H_u H_d H_u, for which we
systematically derive all tree-level decompositions. We argue that many of
these lead to a linear or inverse see-saw scenario with two extra neutral
fermions, where the lepton number violating term is naturally suppressed by a
heavy mass scale when the extra mediators are integrated out. We choose one
example, for which we discuss possible implementations of the neutrino flavor
structure. In addition, we show that the heavy mediators, in this case SU(2)
doublet fermions, may indeed be observable at the LHC, since they can be
produced by Drell-Yan processes and lead to displaced vertices when they decay.
However, the direct observation of lepton number violating processes is on the
edge at LHC.


  The dynamical evolution of super star clusters (SSCs) moving in the
background of dark matter halo has been investigated as a possible event
causing the observed correlation between the mass of galactic bulge,
$M_{bulge}$, and the mass of its central black hole, $M_{BH}$. The involved
physical processes are the sinking of SSCs due to the dynamical friction, and
the stripping of SSCs on their way to the center. Model calculations show that
only sinking of circumnuclear SSCs contribute to both the growth of the central
object and the formation of the galactic bulge at the early stage. On the
assumption of a universal density profile for the dark matter halo, and an
isothermal model for the SSCs, our simulations have yielded the mass ratio of
the central objects to the bulges formed this way to be about a few times
$10^{-4}$, less than the observed median value for early type galaxies. It is,
however, consistent with the observed mass ratio for disk spirals, implying
that the proposed scenario might be a possible event for the formation of
bulges and central black holes of late type galaxies and for $M_{BH} -
M_{bulge}$ correlation of disk galaxies.


  We propose a fast integrator to a class of dynamical systems with several
temporal scales. The proposed method is developed as an extension of the
variable step size Heterogeneous Multiscale Method (VSHMM), which is a
two-scale integrator developed by the authors. While iterated applications of
multiscale integrators for two different scales increase the computational
complexity exponentially as the number of different scales increases, the
proposed method, on the other hand, has computational complexity linearly
proportional to the number of different scales. This efficiency is achieved by
solving different scale components of the vector fields with variable time
steps. It is shown that variable time stepping of different force components
has an effect of fast integration for the effective force of the slow dynamics.
The proposed fast integrator is numerically tested on problems with several
different scales which are dissipative and highly oscillatory including
multiscale partial differential equations with sparsity in the solution space.


  Assume that $p\in[1,\infty]$ and $u=P_{h}[\phi]$,
  where $\phi\in L^{p}(\mathbb{S}^{n-1},\mathbb{R}^n)$ and $u(0) = 0$. Then we
obtain the sharp inequality $|u(x)|\le G_p(|x|)\|\phi\|_{L^{p}}$ for some
smooth function $G_p$ vanishing at $0$. Moreover, we obtain an explicit form of
the sharp constant $C_p$ in the inequality $\|Du(0)\|\le C_p\|\phi\|_{L^{p}}$.
These two results generalize and extend some known result from harmonic mapping
theory (\cite[Theorem 2.1]{kalaj2018}) and hyperbolic harmonic theory
(\cite[Theorem 1]{bur}).


  We present a kinematical and photometrical study of a member, NGC4778, of the
nearest (z=0.0137) compact group: Hickson 62. Our analysis reveals that Hickson
62a, also known as NGC4778, is an S0 galaxy with kinematical and morphological
peculiarities, both in its central regions (r < 5'') and in the outer halo. In
the central regions, the rotation curve shows the existence of a kinematically
decoupled stellar component, offset with respect to the photometric center. In
the outer halo we find an asymmetric rotation curve and a velocity dispersion
profile showing a rise on the SW side, in direction of the galaxy NGC4776.


  The relation between the Hubble constant and the scale of supersymmetry
breaking is investigated in models of inflation dominated by a string modulus.
Usually in this kind of models the gravitino mass is of the same order of
magnitude as the Hubble constant which is not desirable from the
phenomenological point of view. It is shown that slow-roll saddle point
inflation may be compatible with a low scale of supersymmetry breaking only if
some corrections to the lowest order Kahler potential are taken into account.
However, choosing an appropriate Kahler potential is not enough. There are also
conditions for the superpotential, and e.g. the popular racetrack
superpotential turns out to be not suitable. A model is proposed in which
slow-roll inflation and a light gravitino are compatible. It is based on a
superpotential with a triple gaugino condensation and the Kahler potential with
the leading string corrections. The problem of fine tuning and experimental
constraints are discussed for that model.


  Low-rank adaptation (LoRA) has become the default approach to fine-tune large
language models (LLMs) due to its significant reduction in trainable
parameters. However, trainable parameter demand for LoRA increases with
increasing model embedding dimensions, leading to high compute costs.
Additionally, its backward updates require storing high-dimensional
intermediate activations and optimizer states, demanding high peak GPU memory.
In this paper, we introduce large model fine-tuning via spectrally decomposed
low-dimensional adaptation (LaMDA), a novel approach to fine-tuning large
language models, which leverages low-dimensional adaptation to achieve
significant reductions in trainable parameters and peak GPU memory footprint.
LaMDA freezes a first projection matrix (PMA) in the adaptation path while
introducing a low-dimensional trainable square matrix, resulting in substantial
reductions in trainable parameters and peak GPU memory usage. LaMDA gradually
freezes a second projection matrix (PMB) during the early fine-tuning stages,
reducing the compute cost associated with weight updates to enhance parameter
efficiency further. We also present an enhancement, LaMDA++, incorporating a
``lite-weight" adaptive rank allocation for the LoRA path via normalized
spectrum analysis of pre-trained model weights. We evaluate LaMDA/LaMDA++
across various tasks, including natural language understanding with the GLUE
benchmark, text summarization, natural language generation, and complex
reasoning on different LLMs. Results show that LaMDA matches or surpasses the
performance of existing alternatives while requiring up to 17.7x fewer
parameter updates and up to 1.32x lower peak GPU memory usage during
fine-tuning. Code will be publicly available.


  The formulation of QCD which contains no divergences and no renormalization
procedure is presented. It contains both perturbative and non-perturbative
phenomena. It is shown that, due to its asymptotically free nature, the theory
is not defined uniquely. The chiral symmetry breaking and the nature of the
octet of pseudo-scalar particles as quasi-Goldstone states are analysed in the
theory with massless and massive quarks. The U(1) problem is discussed.


  Nuclear response theory beyond the one-loop approximation is formulated for
the case of finite temperature. For this purpose, the time blocking
approximation to the time-dependent part of the in-medium nucleon-nucleon
interaction amplitude is adopted for the thermal (imaginary-time) Green's
function formalism. We found that introducing a soft blocking, instead of a
sharp blocking at zero temperature, brings the Bethe-Salpeter equation to a
single frequency variable equation also at finite temperatures. The method is
implemented self-consistently in the framework of Quantum Hadrodynamics and
designed to connect the high-energy scale of heavy mesons and the low-energy
domain of nuclear medium polarization effects in a parameter-free way. In this
framework, we investigate the temperature dependence of dipole spectra in the
even-even nuclei $^{48}$Ca, $^{120}$Sn and $^{132}$Sn with a special focus on
the giant dipole resonance's width problem and on the low-energy dipole
strength distribution.


  In the present paper we continue the project of systematic construction of
invariant differential operators on the example of the non-compact algebras
sp(n,R), in detail for n=6. Our choice of these algebras is motivated by the
fact that they belong to a narrow class of algebras, which we call 'conformal
Lie algebras', which have very similar properties to the conformal algebras of
Minkowski space-time. We give the main multiplets and the main reduced
multiplets of indecomposable elementary representations for n=6, including the
necessary data for all relevant invariant differential operators. In fact, this
gives by reduction also the cases for n<6, since the main multiplet for fixed n
coincides with one reduced case for n+1.


  Beta decay and electron capture on isotopes of chromium are advocated to play
a key role in the stellar evolution process. In this paper we endeavor to study
charge-changing transitions for 24 isotopes of chromium ($^{42-65}$Cr). These
include neutron-rich and neutron-deficient isotopes of chromium. Three
different models from the QRPA genre, namely the pn-QRPA, the Pyatov method
(PM) and the Schematic model (SM), were selected to calculate and study the
Gamow-Teller (GT) transitions in chromium isotopes. The SM was employed
separately in the particle-particle ($pp$) and $pp$ + particle-hole ($ph$)
channels. To study the effect of deformation, the SM was first used assuming
the nuclei to be spherical and later to be deformed. The PM was used both in
$pp$ and $pp$ + $ph$ channels but only for the case of spherical nuclei. The
pn-QRPA calculation was done by considering both $pp$ and $ph$ forces and
taking deformation of nucleus into consideration. A recent study proved this
version of pn-QRPA to be the best for calculation of GT strength distributions
amongst the QRPA models. The pn-QRPA model calculated GT distributions had
low-lying centroids and small widths as compared to other QRPA models. Our
calculation results were also compared with other theoretical models and
measurements wherever available. Our results are in decent agreement with
previous measurements and shell model calculations.


  Reverse Engineering(RE) has been a fundamental task in software engineering.
However, most of the traditional Java reverse engineering tools are strictly
rule defined, thus are not fault-tolerant, which pose serious problem when
noise and interference were introduced into the system. In this paper, we view
reverse engineering as a statistical machine translation task instead of
rule-based task, and propose a fault-tolerant Java decompiler based on machine
translation models. Our model is based on attention-based Neural Machine
Translation (NMT) and Transformer architectures. First, we measure the
translation quality on both the redundant and purified datasets. Next, we
evaluate the fault-tolerance(anti-noise ability) of our framework on test sets
with different unit error probability (UEP). In addition, we compare the
suitability of different word segmentation algorithms for decompilation task.
Experimental results demonstrate that our model is more robust and
fault-tolerant compared to traditional Abstract Syntax Tree (AST) based
decompilers. Specifically, in terms of BLEU-4 and Word Error Rate (WER), our
performance has reached 94.50% and 2.65% on the redundant test set; 92.30% and
3.48% on the purified test set.


  A study of proper conformal vector field in non conformally flat
cylindrically symmetric static space-times is given by using direct integration
technique. Using the above mentioned technique we have shown that a very
special class of the above space-time admits proper conformal vector field.


  In this paper we study long slit spectra in the region of H$\alpha$ emission
line of a sample of 111 spiral galaxies with recognizable and well defined
spiral morphology and with a well determined environmental status, ranging from
isolation to non-disruptive interaction with satellites or companions. The form
and properties of the rotation curves are considered as a function of the
isolation degree, morphological type and luminosity. The line ratios are used
to estimate the metallicity of all the detected HII regions, thus producing a
composite metallicity profile for different types of spirals. We have found
that isolated galaxies tend to be of later types and lower luminosity than the
interacting galaxies. The outer parts of the rotation curves of isolated
galaxies tend to be flatter than in interacting galaxies, but they show similar
relations between global parameters. The scatter of the Tully-Fisher relation
defined by isolated galaxies is significantly lower than that of interacting
galaxies. The [NII]/H$\alpha$ ratios, used as metallicity indicator, show a
clear trend between Z and morphological type, t, with earlier spirals showing
larger ratios; this trend is tighter when instead of t the gradient of the
inner rotation curve, G, is used; no trend is found with the interaction
status. The Z-gradient of the disks depends on the type, being almost flat for
early spirals, and increasing for later types. The [NII]/H$\alpha$ ratios
measured for disk HII regions of interacting galaxies are higher than for
normal/isolated objects, even if all the galaxy families present similar
distributions of H$\alpha$ Equivalent Width.


  Volatiles, notably water, are key to the habitability of rocky planets. The
presence of water in planetary material can be inferred from the atmospheric
oxygen abundances of polluted white dwarfs, but this interpretation is often
complex. We study the accretion process, and find that ices may sublimate and
accrete before more refractory minerals reach the star. As a result, a white
dwarf's relative photospheric abundances may vary with time during a single
accretion event, and do not necessarily reflect the bulk composition of a
pollutant. We offer two testable predictions for this hypothesis: 1. cooler
stars will more often be inferred to have accreted wet pollutants, and 2. there
will be rare occurrences of accretion events with inferred volatile levels far
exceeding those of pristine comets. To observationally test these predictions,
we statistically constrain the water content of white dwarf pollutants. We find
that in the current sample, only three stars show statistically significant
evidence of water at the 2$\sigma$ level, due to large typical uncertainties in
atmospheric abundances and accretion states. In the future, an expanded sample
of polluted white dwarfs with hydrogen-dominated atmospheres will allow for the
corroboration of our theoretical predictions. Our work also shows the
importance of interpreting pollutant compositions statistically, and emphasizes
the requirement to reduce uncertainties on measured abundances to allow for
statistically significant constraints on their water content.


  Due to globalization, geographic boundaries no longer serve as effective
shields for the spread of infectious diseases. In order to aid bio-surveillance
analysts in disease tracking, recent research has been devoted to developing
information retrieval and analysis methods utilizing the vast corpora of
publicly available documents on the internet. In this work, we present methods
for the automated retrieval and classification of documents related to active
public health events. We demonstrate classification performance on an
auto-generated corpus, using recurrent neural network, TF-IDF, and Naive Bayes
log count ratio document representations. By jointly modeling the title and
description of a document, we achieve 97% recall and 93.3% accuracy with our
best performing bio-surveillance event classification model: logistic
regression on the combined output from a pair of bidirectional recurrent neural
networks.


  We prove phase transition for the non-symmetric continuum Potts model with
background interaction, by generalizing the methods introduced in the symmetric
case by Georgii and H\"aggstr\"om. The proof relies on a Fortuin-Kasteleyn
representation, percolation and stochastic domination arguments.


  We report on X-ray spectral and timing results of the new black hole
candidate (BHC) MAXI J1659-152 with the orbital period of 2.41 hours (shortest
among BHCs) in the 2010 outburst from 65 Rossi X-ray Timing Explorer (RXTE)
observations and 8 simultaneous Swift and RXTE observations. According to the
definitions of the spectral states in Remillard & McClintock (2006), most of
the observations have been classified into the intermediate state. All the
X-ray broadband spectra can be modeled by a multi-color disk plus a power-law
with an exponential cutoff or a multi-color disk plus a Comptonization
component. During the initial phase of the outburst, a high energy cutoff was
visible at 30-40 keV. The innermost radius of the disk gradually decreased by a
factor of more than 3 from the onset of the outburst and reached a constant
value of 35 d_10 cos i^-1/2 km, where d_10 is the distance in units of 10 kpc
and $i$ is the inclination. The type-C quasi-periodic oscillation (QPO)
frequency varied from 1.6 Hz to 7.3 Hz in association with a change of the
innermost radius, while the innermost radius remained constant during the
type-B QPO detections at 1.6-4.1 Hz. Hence, we suggest that the origin of the
type-B QPOs is different from that of type-C QPOs, the latter of which would
originate from the disk truncation radius. Assuming the constant innermost
radius in the latter phase of the outburst as the innermost stable circular
orbit, the black hole mass in MAXI J1659-152 is estimated to be 3.6-8.0 M_solar
for a distance of 5.3-8.6 kpc and an inclination angle of 60-75 degrees.


  We analyse logic actions of Polish groups which arise in continuous logic. We
extend the generalised model theory of H.Becker to the case of Polish G-spaces
when G is an arbitrary Polish group.


  In this paper we present a new approach for distributed DBMSs called P4DB,
that uses a programmable switch to accelerate OLTP workloads. The main idea of
P4DB is that it implements a transaction processing engine on top of a
P4-programmable switch. The switch can thus act as an accelerator in the
network, especially when it is used to store and process hot (contended) tuples
on the switch. In our experiments, we show that P4DB hence provides significant
benefits compared to traditional DBMS architectures and can achieve a speedup
of up to 8x.


  The electronic ground states of the actinide dioxides AnO2 (with An=U, Np,
Pu, Am, and Cm) are investigated employing first-principles calculations within
the framework of the local density approximation +U (LDA+U) approach,
implemented in a full-potential linearized augmented plane-wave scheme. A
systematic analysis of the An-5f states is performed which provides intuitive
connections between the electronic structures and the local crystalline fields
of the f states in the AnO2 series. Particularly the mechanisms leading to the
experimentally observed insulating ground states are investigated. These are
found to be caused by the strong spin-orbit and Coulomb interactions of the 5f
orbitals; however, as a result of the different configurations, this mechanism
works in distinctly different ways for each of the AnO2 compounds. In agreement
with experimental observations, the nonmagnetic states of plutonium and curium
dioxide are computed to be insulating, whereas those of uranium, neptunium, and
americium dioxides require additional symmetry breaking to reproduce the
insulator ground states, a condition which is met with magnetic phase
transitions. We show that the occupancy of the An-f orbitals is closely
connected to each of the appearing insulating mechanisms. We furthermore
investigate the detailed constitution of the noncollinear multipolar moments
for transverse 3q magnetic ordered states in UO2 and longitudinal 3q high-rank
multipolar ordered states in NpO2 and AmO2.


  Sensors are an integral part of modern Internet of Things (IoT) applications.
There is a critical need for the analysis of heterogeneous multivariate
temporal data obtained from the individual sensors of these systems. In this
paper we particularly focus on the problem of the scarce amount of training
data available per sensor. We propose a novel federated multi-task hierarchical
attention model (FATHOM) that jointly trains classification/regression models
from multiple sensors. The attention mechanism of the proposed model seeks to
extract feature representations from the input and learn a shared
representation focused on time dimensions across multiple sensors. The
underlying temporal and non-linear relationships are modeled using a
combination of attention mechanism and long-short term memory (LSTM) networks.
We find that our proposed method outperforms a wide range of competitive
baselines in both classification and regression settings on activity
recognition and environment monitoring datasets. We further provide
visualization of feature representations learned by our model at the input
sensor level and central time level.


  Recent progress in the understanding of the complex magnetic properties of
the family of rare-earth strontium oxides, SrLn2O4, is reviewed. These
compounds consisting of hexagons and triangles are affected by geometrical
frustration and therefore exhibit its characteristic features, such as a
significant reduction of magnetic ordering temperatures and complex phase
diagrams in an applied field. Some of the observed features appear to be rather
remarkable even in the context of the unusual behavior associated with
geometrically frustrated magnetic systems. Of particular interest is the
coexistence at the lowest temperature of different magnetic structures
(exhibiting either long or short-range order) characterized by different
propagation vectors in materials without significant chemical or structural
disorder.


  Several classification methods assume that the underlying distributions
follow tree-structured graphical models. Indeed, trees capture statistical
dependencies between pairs of variables, which may be crucial to attain low
classification errors. The resulting classifier is linear in the
log-transformed univariate and bivariate densities that correspond to the tree
edges. In practice, however, observed data may not be well approximated by
trees. Yet, motivated by the importance of pairwise dependencies for accurate
classification, here we propose to approximate the optimal decision boundary by
a sparse linear combination of the univariate and bivariate log-transformed
densities. Our proposed approach is semi-parametric in nature: we
non-parametrically estimate the univariate and bivariate densities, remove
pairs of variables that are nearly independent using the Hilbert-Schmidt
independence criteria, and finally construct a linear SVM on the retained
log-transformed densities. We demonstrate using both synthetic and real data
that our resulting classifier, denoted SLB (Sparse Log-Bivariate density), is
competitive with popular classification methods.


  We introduce a new algorithm for modularity-based community detection in
large networks. The algorithm, which we refer to as a smart local moving
algorithm, takes advantage of a well-known local moving heuristic that is also
used by other algorithms. Compared with these other algorithms, our proposed
algorithm uses the local moving heuristic in a more sophisticated way. Based on
an analysis of a diverse set of networks, we show that our smart local moving
algorithm identifies community structures with higher modularity values than
other algorithms for large-scale modularity optimization, among which the
popular 'Louvain algorithm' introduced by Blondel et al. (2008). The
computational efficiency of our algorithm makes it possible to perform
community detection in networks with tens of millions of nodes and hundreds of
millions of edges. Our smart local moving algorithm also performs well in small
and medium-sized networks. In short computing times, it identifies community
structures with modularity values equally high as, or almost as high as, the
highest values reported in the literature, and sometimes even higher than the
highest values found in the literature.


  Artificial intelligence (AI)-empowered industrial fault diagnostics is
important in ensuring the safe operation of industrial applications. Since
complex industrial systems often involve multiple industrial plants (possibly
belonging to different companies or subsidiaries) with sensitive data collected
and stored in a distributed manner, collaborative fault diagnostic model
training often needs to leverage federated learning (FL). As the scale of the
industrial fault diagnostic models are often large and communication channels
in such systems are often not exclusively used for FL model training, existing
deployed FL model training frameworks cannot train such models efficiently
across multiple institutions. In this paper, we report our experience
developing and deploying the Federated Opportunistic Block Dropout (FEDOBD)
approach for industrial fault diagnostic model training. By decomposing
large-scale models into semantic blocks and enabling FL participants to
opportunistically upload selected important blocks in a quantized manner, it
significantly reduces the communication overhead while maintaining model
performance. Since its deployment in ENN Group in February 2022, FEDOBD has
served two coal chemical plants across two cities in China to build industrial
fault prediction models. It helped the company reduce the training
communication overhead by over 70% compared to its previous AI Engine, while
maintaining model performance at over 85% test F1 score. To our knowledge, it
is the first successfully deployed dropout-based FL approach.


  An important theme in modern inverse problems is the reconstruction of
time-dependent data from only finitely many measurements. To obtain
satisfactory reconstruction results in this setting it is essential to strongly
exploit temporal consistency between the different measurement times. The
strongest consistency can be achieved by reconstructing data directly in phase
space, the space of positions and velocities. However, this space is usually
too high-dimensional for feasible computations. We introduce a novel dimension
reduction technique, based on projections of phase space onto lower-dimensional
subspaces, which provably circumvents this curse of dimensionality: Indeed, in
the exemplary framework of superresolution we prove that known exact
reconstruction results stay true after dimension reduction, and we additionally
prove new error estimates of reconstructions from noisy data in optimal
transport metrics which are of the same quality as one would obtain in the
non-dimension-reduced case.


  Hilbert schemes of suitable smooth, projective 3-fold scrolls over the
Hirzebruch surface F_e, with e > 1, are studied. An irreducible component of
the Hilbert scheme parametrizing such varieties is shown to be generically
smooth of the expected dimension and the general point of such a component is
described. This article generalizes the study of Hilbert schemes done in
arXiv:1110.5464 for e=1.


  Abstract- The vision of the Linked Open Data (LOD) initiative is to provide a
distributed model for publishing and meaningfully interlinking open data. The
realization of this goal depends strongly on the quality of the data that is
published as a part of the LOD. This paper focuses on the systematic quality
assessment of datasets prior to publication on the LOD cloud. To this end, we
identify important quality deficiencies that need to be avoided and/or resolved
prior to the publication of a dataset. We then propose a set of metrics to
measure these quality deficiencies in a dataset. This way, we enable the
assessment and identification of undesirable quality characteristics of a
dataset through our proposed metrics. This will help publishers to filter out
low-quality data based on the quality assessment results, which in turn enables
data consumers to make better and more informed decisions when using the open
datasets.


  We consider a class of stochastic dynamical systems, called piecewise
deterministic Markov processes, with states $(x, \s)\in \O\times \G$, $\O$
being a region in $\bbR^d$ or the $d$--dimensional torus, $\G$ being a finite
set. The continuous variable $x$ follows a piecewise deterministic dynamics,
the discrete variable $\s$ evolves by a stochastic jump dynamics and the two
resulting evolutions are fully--coupled. We study stationarity, reversibility
and time--reversal symmetries of the process. Increasing the frequency of the
$\s$--jumps, we show that the system behaves asymptotically as deterministic
and we investigate the structure of fluctuations (i.e. deviations from the
asymptotic behavior), recovering in a non Markovian frame results obtained by
Bertini et al. \cite{BDGJL1, BDGJL2, BDGJL3, BDGJL4}, in the context of
Markovian stochastic interacting particle systems. Finally, we discuss a
Gallavotti--Cohen--type symmetry relation with involution map different from
time--reversal. For several examples the above results are recovered by
explicit computations.


  A quadrilateral inequality established by C. Sch\"otz in the context of
Hilbert spaces is extended to the framework of Banach spaces. Our approach is
based on the theory of majorization and a substitute for the parallelogram law
associated with Clarkson's notion of von Neumann-Jordan constant. Several
examples (concerning matrix theory and geometry of Banach spaces) that
illustrate our new results are also included.


  The pressure of hot QCD has recently been determined to the last
perturbatively computable order g^6 ln(g) by Kajantie et al. using
three-dimensional effective theories. A similar method is applied here to the
pressure in the presence of small but non-vanishing quark chemical potentials,
and the result is used to derive the quark number susceptibilities in the limit
mu = 0. The diagonal quark number susceptibility of QCD with n_f flavours of
massless quarks is evaluated to order g^6ln(g) and compared with recent lattice
simulations. It is observed that the results qualitatively resemble the lattice
ones, and that when combined with the fully perturbative but yet undetermined
g^6 term they may well explain the behaviour of the lattice data for a wide
range of temperatures.


  Combined gravitational-wave (GW) and electromagnetic (EM) observations of
compact binary mergers should enable detailed studies of astrophysical
processes in the strong-field gravity regime. Networks of GW interferometers
have poor angular resolution on the sky and their EM signatures are predicted
to be faint. Therefore, a challenging goal will be to unambiguously pinpoint
the EM counterparts to GW mergers. We perform the first comprehensive
end-to-end simulation that focuses on: i) GW sky localization, distance
measures and volume errors with two compact binary populations and four
different GW networks, ii) subsequent detectability by a slew of
multiwavelength telescopes and, iii) final identification of the merger
counterpart amidst a sea of possible astrophysical false-positives. First, we
find that double neutron star (NS) binary mergers can be detected out to a
maximum distance of 400 Mpc (or 750 Mpc) by three (or five) detector GW
networks respectively. NS -- black-hole (BH) mergers can be detected a factor
of 1.5 further out. The sky localization uncertainties for NS-BH mergers are
50--170 sq. deg. (or 6--65 sq. deg.) for a three (or five detector) GW network
respectively. Second, we quantify relative fractions of optical counterparts
that are detectable by different size telescopes. Third, we present five case
studies to illustrate the diversity of challenges in secure identification of
the EM counterpart at low and high Galactic latitudes. For the first time, we
demonstrate how construction of low-latency GW volumes in conjunction with
local universe galaxy catalogs can help solve the problem of false positives.


  The coherent photogalvanic effect leads to the generation of a current under
the absorption interference of coherent beams and allows for the inscription of
space-charge gratings leading to an effective second-order susceptibility
($\chi^{(2)}$). The inscribed grating automatically results in
quasi-phase-matching between the interfering beams. Theoretical and
experimental studies have been carried out, mostly focusing on the degenerate
case of second-harmonic generation, showing significant conversion efficiency
enhancements. However, the link between the theory and experiment was not fully
established such that general guidelines and achievable conversion efficiency
for a given material platform are still unclear. In this work, we theoretically
analyze the phenomenological model of coherent photogalvanic effect in optical
waveguides. Our model predicts the existence of non-degenerate sum-frequency
generation quasi-phase-matching gratings, which is confirmed experimentally for
the first time. In addition, we rigorously formulate the time dynamics of the
space-charge grating inscription in coherent photogalvanic process. Based on
developed theoretical equations for the time dynamics of the space-charge
grating formation, we extract the material parameters governing the process for
our experimental platform, stoichiometric silicon nitride. The results obtained
provides a basis to compare the performances and potentials of different
platforms. This work not only supplements the theory of coherent photogalvanic
effect, but also enables us to identify critical parameters and limiting
factors for the inscription of $\chi^{(2)}$ gratings.


  The diffusion model is capable of generating high-quality data through a
probabilistic approach. However, it suffers from the drawback of slow
generation speed due to the requirement of a large number of time steps. To
address this limitation, recent models such as denoising diffusion implicit
models (DDIM) focus on generating samples without directly modeling the
probability distribution, while models like denoising diffusion generative
adversarial networks (GAN) combine diffusion processes with GANs. In the field
of speech synthesis, a recent diffusion speech synthesis model called
DiffGAN-TTS, utilizing the structure of GANs, has been introduced and
demonstrates superior performance in both speech quality and generation speed.
In this paper, to further enhance the performance of DiffGAN-TTS, we propose a
speech synthesis model with two discriminators: a diffusion discriminator for
learning the distribution of the reverse process and a spectrogram
discriminator for learning the distribution of the generated data. Objective
metrics such as structural similarity index measure (SSIM), mel-cepstral
distortion (MCD), F0 root mean squared error (F0 RMSE), short-time objective
intelligibility (STOI), perceptual evaluation of speech quality (PESQ), as well
as subjective metrics like mean opinion score (MOS), are used to evaluate the
performance of the proposed model. The evaluation results show that the
proposed model outperforms recent state-of-the-art models such as FastSpeech2
and DiffGAN-TTS in various metrics. Our implementation and audio samples are
located on GitHub.


  Was Einstein wrong? This paper provides a detailed technical review of
Einstein's special and general relativity from an astrophysical perspective,
including the historical development of the theories, experimental tests,
modern applications to black holes, cosmology and parallel universes, and last
but not least, novel ways of expressing their seven most important equations.


  Let $X$ be a smooth complex projective variety such that the Albanese map of
$X$ is generically finite onto its image. Here we study the so-called eventual
$m$-paracanonical map of $X$ (when $m=1$ we also assume $\chi(K_X)>0$). We show
that for $m=1$ this map behaves in a similar way to the canonical map of a
surface of general type, while it is birational for $m>1$. We also describe it
explicitly in several examples.


  Quasinormal modes of spacetimes with event horizons are typically governed by
a non-normal operator. This gives rise to spectral instabilities, a topic of
recent interest in the black hole pseudospectrum programme. In this work we
show that non-normality leads to the existence of arbitrarily long-lived sums
of short-lived quasinormal modes, corresponding to localising packets of energy
near the future horizon. There exist sums of $M$ quasinormal modes whose
lifetimes scale as $\log{M}$. This transient behaviour results from large
cancellations between non-orthogonal quasinormal modes. We provide simple
closed-form examples for a massive scalar field in the static patch of
dS$_{d+1}$ and the BTZ black hole. We also provide numerical examples for
scalar perturbations of Schwarzschild-AdS$_{d+1}$, and gravitational
perturbations of Schwarzschild in asymptotically flat spacetime, using
hyperboloidal foliations. The existence of these perturbations is linked to
certain properties of black hole pseudospectra. We comment on implications for
thermalisation times in holographic plasmas.


  I propose a variation of the standard worm--like chain model to account for
internal order parameter (helix/coil) fields on the polymer chain. This
internal order parameter field influences polymer conformational statistics by
locally modifying the persistence length of the chain. Using this model, I make
predictions for the bending and stretching response of an alpha-helical domain
of a protein. In particular, I show that alpha-helical protein domains will
buckle under applied torque. This highly nonlinear elastic behavior may be
important in the understanding of allosteric control of biochemical pathways.


  As a mathematically rigorous framework that has amassed a rich theoretical
literature, differential privacy is considered by many experts to be the gold
standard for privacy-preserving data analysis. Others argue that while
differential privacy is a clean formulation in theory, it poses significant
challenges in practice. Both perspectives are, in our view, valid and
important. To bridge the gaps between differential privacy's promises and its
real-world usability, researchers and practitioners must work together to
advance policy and practice of this technology. In this paper, we outline
pressing open questions towards building usable differential privacy and offer
recommendations for the field, such as developing risk frameworks to align with
user needs, tailoring communications for different stakeholders, modeling the
impact of privacy-loss parameters, investing in effective user interfaces, and
facilitating algorithmic and procedural audits of differential privacy systems.


  The links between the mean families of Lehmer and H\"older and the weighted
maximum likelihood estimator have recently been established in the case of a
regular univariate exponential family. In this article, we will extend the
outcomes obtained to the multivariate case. This extension provides a
probabilistic interpretation of these families of means and could therefore
broaden their uses in various applications.


  Development of digital content has increased the necessity of copyright
protection by means of watermarking. Imperceptibility and robustness are two
important features of watermarking algorithms. The goal of watermarking methods
is to satisfy the tradeoff between these two contradicting characteristics.
Recently watermarking methods in transform domains have displayed favorable
results. In this paper, we present an adaptive blind watermarking method which
has high transparency in areas that are important to human visual system. We
propose a fuzzy system for adaptive control of the embedding strength factor.
Features such as saliency, intensity, and edge-concentration, are used as fuzzy
attributes. Redundant embedding in discrete cosine transform (DCT) of wavelet
domain has increased the robustness of our method. Experimental results show
the efficiency of the proposed method and better results are obtained as
compared to comparable methods with same size of watermark logo.


  Motivated by recent experimental progress in establishing the likely
existence of (variants of) exotic hadrons, predicted to be formed by the strong
interactions, various proposed concepts and ideas are compiled in an attempt to
draft a coherent picture of the achievable improvement in the theoretical
interpretation of exotic hadrons in terms of the underlying quantum field
theory of strong interactions.


  The mean-squared displacement (MSD) is an averaged quantity widely used to
assess anomalous diffusion. In many cases, such as molecular motors with finite
processivity, dynamics of the system of interest produce trajectories of
varying duration. Here we explore the effects of finite processivity on
different measures of the MSD. We do so by investigating a deceptively simple
dynamical system: a one-dimensional random walk (with equidistant jump lengths,
symmetric move probabilities, and constant step duration) with an
origin-directed detachment bias. By tuning the time dependence of the
detachment bias, we find through analytical calculations and trajectory
simulations that the system can exhibit a broad range of anomalous diffusion,
extending beyond conventional diffusion to superdiffusion and even
superballistic motion. We analytically determine that protocols with a
time-increasing detachment lead to an ensemble-averaged velocity increasing in
time, thereby providing the effective acceleration that is required to push the
system above the ballistic threshold. MSD analysis of burnt-bridges ratchets
similarly reveals superballistic behavior. Because superdiffusive MSDs are
often used to infer biased, motor-like dynamics, these findings provide a
cautionary tale for dynamical interpretation.


  The goal of this paper is to compare surface-based and volumetric 3D object
shape representations, as well as viewer-centered and object-centered reference
frames for single-view 3D shape prediction. We propose a new algorithm for
predicting depth maps from multiple viewpoints, with a single depth or RGB
image as input. By modifying the network and the way models are evaluated, we
can directly compare the merits of voxels vs. surfaces and viewer-centered vs.
object-centered for familiar vs. unfamiliar objects, as predicted from RGB or
depth images. Among our findings, we show that surface-based methods outperform
voxel representations for objects from novel classes and produce higher
resolution outputs. We also find that using viewer-centered coordinates is
advantageous for novel objects, while object-centered representations are
better for more familiar objects. Interestingly, the coordinate frame
significantly affects the shape representation learned, with object-centered
placing more importance on implicitly recognizing the object category and
viewer-centered producing shape representations with less dependence on
category recognition.