  The behavior of hot carriers in metal-halide perovskites (MHPs) present a
valuable foundation for understanding the details of carrier-phonon coupling in
the materials as well as the prospective development of highly efficient hot
carrier and carrier multiplication solar cells. Whilst the carrier population
dynamics during cooling have been intensely studied, the evolution of the hot
carrier properties, namely the hot carrier mobility, remain largely unexplored.
To address this, we introduce a novel ultrafast visible pump - infrared push -
terahertz probe spectroscopy (PPP-THz) to monitor the real-time conductivity
dynamics of cooling carriers in methylammonium lead iodide. We find a decrease
in mobility upon optically depositing energy into the carriers, which is
typical of band-transport. Surprisingly, the conductivity recovery dynamics are
incommensurate with the intraband relaxation measured by an analogous
experiment with an infrared probe (PPP- IR), and exhibit a negligible
dependence on the density of hot carriers. These results and the kinetic
modelling reveal the importance of highly-localized lattice heating on the
mobility of the hot electronic states. This collective polaron-lattice
phenomenon may contribute to the unusual photophysics observed in MHPs and
should be accounted for in devices that utilize hot carriers.


  Data on the nucleon spin structure suggests that the $u$ (and $d$) quark
distributions in the $\Lambda$ hyperon may be polarized. If this correlation
carries over into fragmentation, then the study of polarized $\Lambda$'s in the
current fragmentation region of deep inelastic lepton scattering will be a
sensitive probe of nucleon spin structure. $\Lambda$ production by polarized
electrons from unpolarized targets can search for this correlation. If it is
signficant, $\Lambda$ production by unpolarized electrons from longitudinally
and transversely polarized targets can probe the $u$-quark helicity and
transversity distributions in the nucleon. We review what is known about quark
polarization in the $\Lambda$, summarize electroproduction of polarized
$\Lambda$s, and estimate the sensitivity to quark polarizations in the nucleon.
We also describe polarization phenomena associated with vector meson
electroproduction that can be observed in the same experimental regime.


  Quantum feedback networks have been introduced in quantum optics as a set of
rules for constructing arbitrary networks of quantum mechanical systems
connected by uni-directional quantum optical fields, and has allowed for a
system theoretic approach to open quantum optics systems. Our aim here is to
establish a network theory for quantum transport systems where typically the
mediating fields between systems are bi-directional. Mathematically this leads
us to study quantum feedback networks where fields arrive at ports in
input-output pairs, which is then just a specially case of the uni-directional
theory. However, it is conceptually important to develop this theory in the
context of quantum transport theory, and the resulting theory extends
traditional approaches which tends to view the components in quantum transport
as scatterers for the various fields, in the process allows us to consider
emission and absorption of field quanta by these components. The quantum
feedback network theory is applicable to both Bose and Fermi fields, moreover
it applies to nonlinear dynamics for the component systems. In this first paper
on the subject, we advance the general theory, but study the case of linear
passive quantum components in some detail.


  Multimodal Large Language Models (MLLMs) have demonstrated remarkable
proficiency in diverse tasks across different domains, with an increasing focus
on improving their zero-shot generalization capabilities for unseen multimodal
tasks. Multimodal instruction tuning has emerged as a successful strategy for
achieving zero-shot generalization by fine-tuning pre-trained models on diverse
multimodal tasks through instructions. As MLLMs grow in complexity and size,
the need for parameter-efficient fine-tuning methods like Low-Rank Adaption
(LoRA), which fine-tunes with a minimal set of parameters, becomes essential.
However, applying LoRA in multimodal instruction tuning presents the challenge
of task interference, which leads to performance degradation, especially when
dealing with a broad array of multimodal tasks. To address this, this paper
introduces a novel approach that integrates multimodal instruction tuning with
Conditional Mixture-of-LoRA (MixLoRA). It innovates upon LoRA by dynamically
constructing low-rank adaptation matrices tailored to the unique demands of
each input instance, aiming to mitigate task interference. Experimental results
on various multimodal evaluation datasets indicate that MixLoRA not only
outperforms the conventional LoRA with the same or even higher ranks,
demonstrating its efficacy and adaptability in diverse multimodal tasks.


  We consider a kinetic relaxation model and an associated macroscopic scalar
nonlinear hyperbolic equation on a network. Coupling conditions for the
macroscopic equations are derived from the kinetic coupling conditions via an
asymptotic analysis near the nodes of the network. This analysis leads to the
combination of kinetic half-space problems with Riemann problems at the
junction. Detailed numerical comparisons between the different models show the
agreement of the coupling conditions for the case of tripod networks.


  We study signals for beyond standard model physics and consider the virtues
of single photon signals or associated photons in the final states in
identifying different scenarios of new physics models in a very efficient and
novel way.


  Measurements of angular correlations of hadrons with a (semi-)hard trigger
hadron in heavy-ion collisions at RHIC show large angular structures opposite
to the trigger which were a priori unexpected. These away side large angle
correlations were first observed in two-particle correlations and have recently
also been investigated in three-particle correlation measurements by the PHENIX
and STAR collaborations. We show that the correlation signal can be understood
in terms of sonic shockwaves ('Mach cones') excited by hard partons
supersonically traversing the medium. The propagation of such shocks through
the medium evolution is treated in a Monte Carlo (MC) framework. We demonstrate
that two- and especially three-particle correlations offer non-trivial insight
into the medium-averaged speed of sound and the evolution of flow. Our findings
imply that the assumption of "deflected jets" is not necessary to account for
the observed correlations.


  Experiments show that when a monolayer of cells cultured on an elastic
substrate is subject to a cyclic stretch, cells tend to re-orient either
perpendicularly or at an oblique angle with respect to the main direction of
the stretch. Due to stochastic effects, however, the distribution of angles
achieved by the cells is broader and, experimentally, histrograms over the
interval [0, 90] are reported. Here we will determine the evolution and the
stationary state of probability density functions describing the statistical
distribution of the orientations of the cells using Fokker-Planck equations
derived from microscopic rules for the evolution of the orientation of the
cell. As a first attempt, we shall use a stochastic differential equation
related to a very general elastic energy and we will show that the results of
the time integration and of the stationary state of the related forward
Fokker-Planck equation compare very well with experimental results obtained by
different researchers. Then, in order to model more accurately the microscopic
process of cell re-orientation, we consider discrete in time random processes
that allow to recover Fokker- Planck equations through the well known technique
of quasi-invariant limit. In particular, we shall introduce a non-local rule
related to the evaluation of the state of stress experienced by the cell
extending its protrusions, and a model of re-orientation as a result of an
optimal control internally activated by the cell. Also in the latter case the
results match very well with experiments.


  Generalised hyperbolic (GH) processes are a class of stochastic processes
that are used to model the dynamics of a wide range of complex systems that
exhibit heavy-tailed behavior, including systems in finance, economics,
biology, and physics. In this paper, we present novel simulation methods based
on subordination with a generalised inverse Gaussian (GIG) process and using a
generalised shot-noise representation that involves random thinning of infinite
series of decreasing jump sizes. Compared with our previous work on GIG
processes, we provide tighter bounds for the construction of rejection sampling
ratios, leading to improved acceptance probabilities in simulation.
Furthermore, we derive methods for the adaptive determination of the number of
points required in the associated random series using concentration
inequalities. Residual small jumps are then approximated using an appropriately
scaled Brownian motion term with drift. Finally the rejection sampling steps
are made significantly more computationally efficient through the use of
squeezing functions based on lower and upper bounds on the L\'evy density.
Experimental results are presented illustrating the strong performance under
various parameter settings and comparing the marginal distribution of the GH
paths with exact simulations of GH random variates. The new simulation
methodology is made available to researchers through the publication of a
Python code repository.


  We present a theory of the local field corrections to the spontaneous
emission rate for the array of silicon nanocrystals in silicon dioxide. An
analytical result for the Purcell factor is obtained. We demonstrate that the
local-field corrections are sensitive to the volume fill factor of the
nanocrystals in the sample and are suppressed for large values of the fill
factor. The local-field corrections and the photonic density of states are
shown to be described by two different effective permittivities: the harmonic
mean between the nanocrystal and the matrix permittivities and the
Maxwell-Garnett permittivity.


  Black hole binary transients undergo dramatic evolution in their X-ray timing
and spectral behaviour during outbursts. In recent years a paradigm has arisen
in which "soft" X-ray states are associated with an inner disc radius at, or
very close to, the innermost stable circular orbit (ISCO) around the black
hole, while in "hard" X-ray states the inner edge of the disc is further from
the black hole. Models of advective flows suggest that as the X-ray luminosity
drops in hard states, the inner disc progressively recedes, from a few to
hundreds gravitational radii. Recent observations which show broad iron line
detections and estimates of the disc component strength suggest that a
non-recessed disc could still be present in bright hard states. In this study
we present a comprehensive analysis of the spectral components associated with
the inner disc, utilising data from instruments with sensitive low-energy
responses and including reanalyses of previously published results. A key
component of the study is to fully estimate systematic uncertainties by e.g.
investigating in detail the effect of having a hydrogen column density that is
fixed or free to vary. We conclude that for L_x > 0.01 of the Eddington limit,
spectral fits allow us to constrain the disc to be < 10R_g. There is, however,
clear evidence that when L_x is between 10^-2-- 10^-3 Eddington, the disc does
begin to recede. We include measurements of disc radii in two quiescent black
hole binaries, and present the inferred evolution of accretion parameters in
the entire range of bolometric luminosities 10^-8 -- 1 Eddington. We compare
our results with theoretical models and note that the implied rate of disc
recession with luminosity is consistent with recent empirical results on the
X-ray timing behaviour of black holes of all masses.


  We explore the MRI driven dynamo in a radiatively inefficient accretion flow
(RIAF) using the mean field dynamo paradigm. Using singular value decomposition
(SVD) we obtain the least squares fitting dynamo coefficients $\alpha$ and
$\gamma$ by comparing the time series of the turbulent electromotive force and
the mean magnetic field. Our study is the first one to show the poloidal
distribution of these dynamo coefficients in global accretion flow simulations.
Surprisingly, we obtain a high value of the turbulent pumping coefficient
$\gamma$ which transports the mean magnetic flux radially outward. This would
have implications for the launching of magnetised jets which are produced
efficiently in presence a large-scale poloidal magnetic field close to the
compact object. We present a scenario of a truncated disc beyond the RIAF where
a large scale dynamo-generated poloidal magnetic field can aid jet-launching
close to the black hole. Magnitude of all the calculated coefficients decreases
with radius. Meridional variations of $\alpha_{\phi \phi}$, responsible for
toroidal to poloidal field conversion, is very similar to that found in
shearing box simulations using the `test field' (TF) method. By estimating the
relative importance of $\alpha$-effect and shear, we conclude that the MRI
driven large-scale dynamo, which operates at high latitudes beyond a disc scale
height, is essentially of the $\alpha-\Omega$ type.


  Training large models requires a large amount of data, as well as abundant
computation resources. While collaborative learning (e.g., federated learning)
provides a promising paradigm to harness collective data from many
participants, training large models remains a major challenge for participants
with limited resources like mobile devices. We introduce MSfusion, an effective
and efficient collaborative learning framework, tailored for training larger
models on resourceconstraint machines through model splitting. Specifically, a
double shifting model splitting scheme is designed such that in each training
round, each participant is assigned a subset of model parameters to train over
local data, and aggregates with sub-models of other peers on common parameters.
While model splitting significantly reduces the computation and communication
costs of individual participants, additional novel designs on adaptive model
overlapping and contrastive loss functions help MSfusion to maintain training
effectiveness, against model shift across participants. Extensive experiments
on image and NLP tasks illustrate significant advantages of MSfusion in
performance and efficiency for training large models, and its strong
scalability: computation cost of each participant reduces significantly as the
number of participants increases.


  We propose a new method to conserve the total energy to round-off error in
grid-based codes for hydrodynamic simulations with self-gravity. A formula for
the energy flux due to the work done by the the self-gravitational force is
given, so the change in total energy can be written in conservative form.
Numerical experiments with the code Athena show that the total energy is indeed
conserved with our new algorithm and the new algorithm is second order
accurate. We have performed a set of tests that show the numerical errors in
the traditional, non-conservative algorithm can affect the dynamics of the
system. The new algorithm only requires one extra solution of the Poisson
equation, as compared to the traditional algorithm which includes self-gravity
as a source term. If the Poisson solver takes a negligible fraction of the
total simulation time, such as when FFTs are used, the new algorithm is almost
as efficient as the original method. This new algorithm is useful in Eulerian
hydrodynamic simulations with self-gravity, especially when results are
sensitive to small energy errors, as for radiation pressure dominated flow.


  Suppose $E \subseteq \mathbb{R}$ is nowhere dense. If $(\mathbb{R},<,+,(x
\mapsto \lambda x)_{\lambda \in \mathbb{R} }, E)$ does not define every bounded
Borel subset of every $\mathbb{R}^n$ then for every $s > 0$ we have $$ | \{ k
\in \mathbb{Z}, -m \leq k \leq m - 1 : [k,k+1] \cap E \neq \emptyset \} | < m^s
$$ for sufficiently large $m \in \mathbb{N}$. Then there is an $n \in
\mathbb{N}$ and a linear $T : \mathbb{R}^n \to \mathbb{R}$ such that $T(E^n)$
is dense. It follows that if $E$ is in addition nowhere dense then
$(\mathbb{R},<,+,0,(x \mapsto \lambda x)_{\lambda \in \mathbb{R}}, E)$ defines
every bounded Borel subset of every $\mathbb{R}^n$.


  We consider a class of warped higher dimensional brane models with topology
$M \times \Sigma \times S^1/Z_2$, where $\Sigma$ is a $D_2$ dimensional
manifold. Two branes of codimension one are embedded in such a bulk space-time
and sit at the orbifold fixed points. We concentrate on the case where an
exponential warp factor (depending on the distance along the orbifold)
accompanies the Minkowski $M$ and the internal space $\Sigma$ line elements. We
evaluate the moduli effective potential induced by bulk scalar fields in these
models, and we show that generically this can stabilize the size of the extra
dimensions. As an application, we consider a scenario where supersymmetry is
broken not far below the cutoff scale, and the hierarchy between the
electroweak and the effective Planck scales is generated by a combination of
redshift and large volume effects. The latter is efficient due to the shrinking
of $\Sigma$ at the negative tension brane, where matter is placed. In this
case, we find that the effective potential can stabilize the size of the extra
dimensions (and the hierarchy) without fine tuning provided that the internal
space $\Sigma$ is flat.


  The dominating NLP paradigm of training a strong neural predictor to perform
one task on a specific dataset has led to state-of-the-art performance in a
variety of applications (eg. sentiment classification, span-prediction based
question answering or machine translation). However, it builds upon the
assumption that the data distribution is stationary, ie. that the data is
sampled from a fixed distribution both at training and test time. This way of
training is inconsistent with how we as humans are able to learn from and
operate within a constantly changing stream of information. Moreover, it is
ill-adapted to real-world use cases where the data distribution is expected to
shift over the course of a model's lifetime.
  The first goal of this thesis is to characterize the different forms this
shift can take in the context of natural language processing, and propose
benchmarks and evaluation metrics to measure its effect on current deep
learning architectures. We then proceed to take steps to mitigate the effect of
distributional shift on NLP models. To this end, we develop methods based on
parametric reformulations of the distributionally robust optimization
framework. Empirically, we demonstrate that these approaches yield more robust
models as demonstrated on a selection of realistic problems. In the third and
final part of this thesis, we explore ways of efficiently adapting existing
models to new domains or tasks. Our contribution to this topic takes
inspiration from information geometry to derive a new gradient update rule
which alleviate catastrophic forgetting issues during adaptation.


  The development and application of various remote sensing platforms result in
the production of huge amounts of satellite image data. Therefore, there is an
increasing need for effective querying and browsing in these image databases.
In order to take advantage and make good use of satellite images data, we must
be able to extract meaningful information from the imagery. Hence we proposed a
new algorithm for SAR image segmentation. In this paper we propose segmentation
using vector quantization technique on entropy image. Initially, we obtain
entropy image and in second step we use Kekre's Fast Codebook Generation (KFCG)
algorithm for segmentation of the entropy image. Thereafter, a codebook of size
128 was generated for the Entropy image. These code vectors were further
clustered in 8 clusters using same KFCG algorithm and converted into 8 images.
These 8 images were displayed as a result. This approach does not lead to over
segmentation or under segmentation. We compared these results with well known
Gray Level Co-occurrence Matrix. The proposed algorithm gives better
segmentation with less complexity.


  We compared statistics of major digital libraries and we tried to see if
there is a relationship between the volume of digital libraries and online
visibility of each digitized document. Finally, we analyzed the consequences of
the diffusion strategies of French digital libraries. The statistics were
obtained by survey, gray literature, alexa.com, and Google Trends.


  Single-view 3D shape retrieval is a challenging task that is increasingly
important with the growth of available 3D data. Prior work that has studied
this task has not focused on evaluating how realistic occlusions impact
performance, and how shape retrieval methods generalize to scenarios where
either the target 3D shape database contains unseen shapes, or the input image
contains unseen objects. In this paper, we systematically evaluate single-view
3D shape retrieval along three different axes: the presence of object
occlusions and truncations, generalization to unseen 3D shape data, and
generalization to unseen objects in the input images. We standardize two
existing datasets of real images and propose a dataset generation pipeline to
produce a synthetic dataset of scenes with multiple objects exhibiting
realistic occlusions. Our experiments show that training on occlusion-free data
as was commonly done in prior work leads to significant performance degradation
for inputs with occlusion. We find that that by first pretraining on our
synthetic dataset with occlusions and then finetuning on real data, we can
significantly outperform models from prior work and demonstrate robustness to
both unseen 3D shapes and unseen objects.


  We study superstrings on AdS_3 x S^3 x T^4 supported by a combination of
Ramond-Ramond and Neveu-Schwarz-Neveu-Schwarz three form fluxes, and construct
a set of finite-gap equations that describe the classical string spectrum.
Using the recently proposed all-loop S-matrix we write down the all-loop Bethe
ansatz equations for the massive sector. In the thermodynamic limit the Bethe
ansatz reproduces the finite-gap equations. As part of this derivation we
propose expressions for the leading order dressing phases. These phases differ
from the well-known Arutyunov-Frolov-Staudacher phase that appears in the pure
Ramond-Ramond case. We also consider the one-loop quantization of the algebraic
curve and determine the one-loop corrections to the dressing phases. Finally we
consider some classical string solutions including finite size giant magnons
and circular strings.


  The use of partition function zeros in the study of phase transition is
growing in the last decade mainly due to improved numerical methods as well as
novel formulations and analysis. In this paper the impact of different
parameters choice for the energy probability distribution (EPD) zeros recently
introduced by Costa et al is explored in search for optimal values. Our results
indicate that the EPD method is very robust against parameter variations and
only small deviations on estimated critical temperatures are found even for
large variation of parameters, allowing to obtain accurate results with low
computational cost. A proposal to circumvent potential convergence issues of
the original algorithm is presented and validated for the case where it occurs.


  Graph neural networks (GNNs) have emerged as a series of competent graph
learning methods for diverse real-world scenarios, ranging from daily
applications like recommendation systems and question answering to cutting-edge
technologies such as drug discovery in life sciences and n-body simulation in
astrophysics. However, task performance is not the only requirement for GNNs.
Performance-oriented GNNs have exhibited potential adverse effects like
vulnerability to adversarial attacks, unexplainable discrimination against
disadvantaged groups, or excessive resource consumption in edge computing
environments. To avoid these unintentional harms, it is necessary to build
competent GNNs characterised by trustworthiness. To this end, we propose a
comprehensive roadmap to build trustworthy GNNs from the view of the various
computing technologies involved. In this survey, we introduce basic concepts
and comprehensively summarise existing efforts for trustworthy GNNs from six
aspects, including robustness, explainability, privacy, fairness,
accountability, and environmental well-being. Additionally, we highlight the
intricate cross-aspect relations between the above six aspects of trustworthy
GNNs. Finally, we present a thorough overview of trending directions for
facilitating the research and industrialisation of trustworthy GNNs.


  This PhD dissertation covers a range of topics in Finsler geometry and
Finsler gravity, most notably: (i) the characterization of Berwald spaces, (ii)
pseudo-Riemann (non-)metrizability of Berwald spaces, (iii)
$(\alpha,\beta)$-metrics, (iv) exact solutions to Pfeifer and Wohlfarth's
vacuum field equation in Finsler gravity, and (v) Finsler gravitational waves
and their observational signature. An extended abstract can be found in the
dissertation itself.


  In this paper we show that every three-dimensional closed hyperbolic manifold
admits no locally geometric $1$-parameter family of closed minimal surfaces.


  This paper presents a hybrid variational quantum algorithm that finds a
random eigenvector of a unitary matrix with a known quantum circuit. The
algorithm is based on the SWAP test on trial states generated by a parametrized
quantum circuit. The eigenvector is described by a compact set of classical
parameters that can be used to reproduce the found approximation to the
eigenstate on demand. This variational eigenvector finder can be adapted to
solve the generalized eigenvalue problem, to find the eigenvectors of normal
matrices and to perform quantum principal component analysis (QPCA) on unknown
input mixed states. These algorithms can all be run with low depth quantum
circuits, suitable for an efficient implementation on noisy intermediate state
quantum computers (NISQC) and, with some restrictions, on linear optical
systems. Limitations and potential applications are discussed.


  Legged locomotion is a highly promising but under-researched subfield within
the field of soft robotics. The compliant limbs of soft-limbed robots offer
numerous benefits, including the ability to regulate impacts, tolerate falls,
and navigate through tight spaces. These robots have the potential to be used
for various applications, such as search and rescue, inspection, surveillance,
and more. The state-of-the-art still faces many challenges, including limited
degrees of freedom, a lack of diversity in gait trajectories, insufficient limb
dexterity, and limited payload capabilities. To address these challenges, we
develop a modular soft-limbed robot that can mimic the locomotion of pinnipeds.
By using a modular design approach, we aim to create a robot that has improved
degrees of freedom, gait trajectory diversity, limb dexterity, and payload
capabilities. We derive a complete floating-base kinematic model of the
proposed robot and use it to generate and experimentally validate a variety of
locomotion gaits. Results show that the proposed robot is capable of
replicating these gaits effectively. We compare the locomotion trajectories
under different gait parameters against our modeling results to demonstrate the
validity of our proposed gait models.


  We study the problem of finding unitary submatrices of the $N \times N$
discrete Fourier transform matrix, in the context of interpolating a discrete
bandlimited signal using an orthogonal basis. This problem is related to a
diverse set of questions on idempotents on $\mathbb{Z}_N$ and tiling
$\mathbb{Z}_N$. In this work, we establish a graph-theoretic approach and
connections to the problem of finding maximum cliques. We identify the key
properties of these graphs that make the interpolation problem tractable when
$N$ is a prime power, and we identify the challenges in generalizing to
arbitrary $N$. Finally, we investigate some connections between graph
properties and the spectral-tile direction of the Fuglede conjecture.


  The Mountain pine beetle (MPB) is a destructive forest pest that undergoes
boom-bust cycles every 20-40 years. It also has unusual life-history traits,
including a chemosensory affinity for large pine trees, coordinated attacks
facilitated by aggregation and anti-aggregation pheromones, and variable
dispersal that increases in the absence of trees. Using an empirically
calibrated model, we show that MPB's distinctive life-history characteristics
are responsible for its distinctive population dynamics. Specifically, three
life-history traits are necessary for episodic boom-bust dynamics:
density-dependent dispersal, where beetles disperse once most of the large
trees have been killed; an Allee effect, which requires a threshold number of
beetles to overcome tree defenses; and a short generation time, notably in
comparison with their pine tree hosts. In addition to explaining the
qualitative behavior of MPB dynamics, these three ingredients explain residual
tree density, the duration of outbreaks, and the average waiting time between
outbreaks. The peak number of beetles, believed to be the primary factor
driving range expansion, is mostly a consequence of MPB's constitutive high
fecundity. However, two life history traits -- MPB's size-dependent fecundity
and preference for large trees -- are responsible for 25% of the peak number of
beetles. By identifying patterns across the extensive MPB literature, and by
integrating data across multiple sources, we develop a highly accurate
description of MPB's density-dependent interactions with lodgepole pine. We
conclude with a simplified mechanistic model, distilled to two difference
equations, effectively capturing the essence of MPB outbreak dynamics.


  In a preceding paper we introduced a formalism to study the scattering of low
intensity fields from a system of multi-level emitters embedded in a $3$D
dielectric medium. Here we show how this photon-scattering relation can be used
to analyze the scattering of single photons and weak coherent states from any
generic multi-level quantum emitter coupled to a $1$D waveguide. The reduction
of the photon-scattering relation to $1$D waveguides provides for the first
time a direct solution of the scattering problem involving low intensity fields
in the waveguide QED regime. To show how our formalism works, we consider
examples of multi-level emitters and evaluate the transmitted and reflected
field amplitude. Furthermore, we extend our study to include the dynamical
response of the emitters for scattering of a weak coherent photon pulse. As our
photon-scattering relation is based on the Heisenberg picture, it is quite
useful for problems involving photo-detection in the waveguide architecture. We
show this by considering a specific problem of state generation by
photo-detection in a multi-level emitter, where our formalism exhibits its full
potential. Since the considered emitters are generic, the $1$D results apply to
a plethora of physical systems like atoms, ions, quantum dots, superconducting
qubits, and nitrogen-vacancy centers coupled to a $1$D waveguide or
transmission line.


  The existence and uniqueness of the local strong solution to the
three-dimensional compressible viscoelastic fluids near the equilibrium is
established. In addition to the uniform estimates on the velocity, some
essential uniform estimates on the density and the deformation gradient are
also obtained.


  We present an oriented numerical summarizer algorithm, applied to producing
automatic summaries of scientific documents in Organic Chemistry. We present
its implementation named Yachs (Yet Another Chemistry Summarizer) that combines
a specific document pre-processing with a sentence scoring method relying on
the statistical properties of documents. We show that Yachs achieves the best
results among several other summarizers on a corpus of Organic Chemistry
articles.


  Small-angle correlations of pairs of nonidentical light charged particles
produced in central collisions of heavy ions in the A=100 mass region at a beam
energy of 400 AMeV are investigated with the FOPI detector system at GSI
Darmstadt. The difference of longitudinal correlation functions with the
relative velocity parallel and anti-parallel to the center-of-mass velocity of
the pair in the central source frame is studied. This method allows extracting
the apparent space-time differences of the emission of the charged particles.
Comparing the correlations with results of a final-state interaction model
delivers quantitative estimates of these asymmetries. Time delays as short as 1
fm/c or - alternatively - source radius differences of a few tenth fm are
resolved. The strong collective expansion of the participant zone introduces
not only an apparent reduction of the source radius but also a modification of
the emission times. After correcting for both effects a complete sequence of
space-time emission points of p, d, t, 3He, alpha particles is presented for
the first time.


  As an ubiquitous method in natural language processing, word embeddings are
extensively employed to map semantic properties of words into a dense vector
representation. They capture semantic and syntactic relations among words but
the vectors corresponding to the words are only meaningful relative to each
other. Neither the vector nor its dimensions have any absolute, interpretable
meaning. We introduce an additive modification to the objective function of the
embedding learning algorithm that encourages the embedding vectors of words
that are semantically related to a predefined concept to take larger values
along a specified dimension, while leaving the original semantic learning
mechanism mostly unaffected. In other words, we align words that are already
determined to be related, along predefined concepts. Therefore, we impart
interpretability to the word embedding by assigning meaning to its vector
dimensions. The predefined concepts are derived from an external lexical
resource, which in this paper is chosen as Roget's Thesaurus. We observe that
alignment along the chosen concepts is not limited to words in the Thesaurus
and extends to other related words as well. We quantify the extent of
interpretability and assignment of meaning from our experimental results.
Manual human evaluation results have also been presented to further verify that
the proposed method increases interpretability. We also demonstrate the
preservation of semantic coherence of the resulting vector space by using
word-analogy and word-similarity tests. These tests show that the
interpretability-imparted word embeddings that are obtained by the proposed
framework do not sacrifice performances in common benchmark tests.


  Response functions are a fundamental aspect of physics; they represent the
link between experimental observations and the underlying quantum many-body
state. However, this link is often under-appreciated, as the Lehmann formalism
for obtaining response functions in linear response has no direct link to
experiment. Within the context of quantum computing, and by using a linear
response framework, we restore this link by making the experiment an
inextricable part of the quantum simulation. This method can be frequency- and
momentum-selective, avoids limitations on operators that can be directly
measured, and is ancilla-free. As prototypical examples of response functions,
we demonstrate that both bosonic and fermionic Green's functions can be
obtained, and apply these ideas to the study of a charge-density-wave material
on ibm_auckland. The linear response method provides a robust framework for
using quantum computers to study systems in physics and chemistry. It also
provides new paradigms for computing response functions on classical computers.


  We show that, under suitable assumptions, the systems of Hecke eigenvalues
arising from (mod p) modular forms of PEL-type associated to an algebraic group
G of type A or C coincide with the Hecke eigensystems arising from (mod p)
algebraic modular forms for an inner form of G. We treat explicitly the case of
unitary groups, and we give in this case an asymptotic formula for the number
of eigensystems. The paper generalizes results of J-P. Serre for GL(2) and A.
Ghitza for GSp(2g).


  We investigate the transport properties of LixCoO2 thin films whose
resistivities are nearly an order of magnitude lower than those of the bulk
polycrystals. A metal-nonmetal transition occurs at ~0.8 in a biphasic domain,
and the Seebeck coefficient (S) is drastically increased at ~140 K (= T*) with
increasing the Li concentration to show a peak of magnitude ~120 \muV/K in the
S-T curve of x = 0.87. We show that T* corresponds to a crossover temperature
in the conduction, most likely reflecting the correlation-induced temperature
dependence in the low-energy excitations.


  We analyze the management of the Italian pandemic during the five identified
waves. We considered the following problems: (i) The composition of the CTS
("Scientific Technical Committee"), which was composed entirely of doctors,
mainly virologists, without mathematical epidemiologists, statisticians,
physicists, etc. In fact, a pandemic has a behavior described by mathematical,
stochastic and probabilistic criteria; (ii) Political interference in security
measures and media propaganda; (iii) The initial stages of the vaccination
campaign, ignoring the age factor, and (iv) The persistence of the pandemic due
to the population unvaccinated (anti-vax or "no-vax"), which amounts to about
six to seven million people, including 10% of anti-vax doctors.


  The ANTARES Collaboration is completing the deployment of a 12 lines
underwater detector, 2500m deep in the Mediterranean Sea, dedicated to high
energy neutrino astronomy. Starting with the first line in 2006, 10 lines were
continuously recording data by the end of 2007, which allow us to reconstruct
downward-going cosmic muons, and search for the first upward-going
neutrino-induced muons. Calibration topics will be described and preliminary
results presented.


  We address the problem of causal interpretation of the graphical structure of
Bayesian belief networks (BBNs). We review the concept of causality explicated
in the domain of structural equations models and show that it is applicable to
BBNs. In this view, which we call mechanism-based, causality is defined within
models and causal asymmetries arise when mechanisms are placed in the context
of a system. We lay the link between structural equations models and BBNs
models and formulate the conditions under which the latter can be given causal
interpretation.


  Systems of coupled sine circle maps show regimes of spatiotemporally
intermittent behaviour with associated scaling exponents which belong to the DP
class, as well as regimes of spatially intermittent behaviour (with associated
regular dynamical behaviour) which do not belong to the DP class. Both types of
behaviour are seen along the bifurcation boundaries of the synchronized
solutions, and contribute distinct signatures to the dynamical characterizers
of the system, viz. the distribution of eigenvalues of the one step stability
matrix. Within the spatially intermittent (SI) class, the temporal behaviour of
the burst solutions can be quasi-periodic or travelling wave. The usual
characterizers of bifurcations, i.e. the eigenvalues of the stability matrix
crossing the unit circle, pick up the bifurcation from the synchronized
solution to SI with quasi-periodic bursts but are unable to pick up the
bifurcation of the synchronized solution to SI with TW bursts. Other
characterizers, such as the Shannon entropy of the eigenvalue distribution, and
the rate of change of the largest eigenvalue with parameter are required to
pick up this bifurcation. This feature has also been seen for other
bifurcations in this system, e.g. that from the synchronized solution to kink
solutions. We therefore conjecture that in the case of high dimensional
systems, entropic characterizers provide better signatures of bifurcations from
one ordered solution to another.


  The CDF Central Preshower and Crack Detector Upgrade consist of scintillator
tiles with embedded wavelength-shifting fibers, clear-fiber optical cables, and
multi-anode photomultiplier readout. A description of the detector design, test
results from R&D studies, and construction phase are reported. The upgrade was
installed late in 2004, and a large amount of proton-antiproton collider data
has been collected since then. Detector studies using those data are also
discussed.


  People are increasingly bringing Internet of Things (IoT) devices into their
homes without understanding how their data is gathered, processed, and used. We
describe PrivacyCube, a novel data physicalization designed to increase privacy
awareness within smart home environments. PrivacyCube visualizes IoT data
consumption by displaying privacy-related notices. PrivacyCube aims to assist
smart home occupants to (i) understand their data privacy better and (ii) have
conversations around data management practices of IoT devices used within their
homes. Using PrivacyCube, households can learn and make informed privacy
decisions collectively. To evaluate PrivacyCube, we used multiple research
methods throughout the different stages of design. We first conducted a focus
group study in two stages with six participants to compare PrivacyCube to text
and state-of-the-art privacy policies. We then deployed PrivacyCube in a
14-day-long field study with eight households. Our results show that
PrivacyCube helps home occupants comprehend IoT privacy better with
significantly increased privacy awareness at p < .05 (p=0.00041, t= -5.57).
Participants preferred PrivacyCube over text privacy policies because it was
comprehensive and easier to use. PrivacyCube and Privacy Label, a
state-of-the-art approach, both received positive reviews from participants,
with PrivacyCube being preferred for its interactivity and ability to encourage
conversations. PrivacyCube was also considered by home occupants as a piece of
home furniture, encouraging them to socialize and discuss IoT privacy
implications using this device.


  Recent advances in ultracold atoms in optical lattices and developments in
surface science have allowed for the creation of artificial lattices as well as
the control of many-body interactions. Such systems provide new settings to
investigate interaction-driven instabilities and non-trivial topology. In this
paper, we explore the interplay between molecular electric dipoles on a
two-dimensional triangular lattice with fermions hopping on the dual decorated
honeycomb lattice which hosts Dirac and flat band states. We show that
short-range dipole-dipole interaction can lead to ordering into various stripe
and vortex crystal ground states. We study these ordered states and their
thermal transitions as a function of the interaction range using simulated
annealing and Monte Carlo methods. For the special case of zero wave vector
ferrodipolar order, incorporating dipole-electron interactions and integrating
out the electrons leads to a six-state clock model for the dipole ordering.
Finally, we discuss the impact of the various dipole orders on the electronic
band structure and the local tunneling density of states. Our work may be
relevant to studies of "molecular graphene" -- CO molecules arranged on the
Cu(111) surface -- which have been explored using scanning tunneling
spectroscopy, as well as ultracold molecule-fermion mixtures in optical
lattices.


  The two-dimensional quantum lattice Toda model for the affine and simple Lie
algebras of the type A is considered. For its known L-operator a correction of
the second order in the lattice parameter is found. It is proved that the
equation determining a correction of the third order has no solutions.


  Quantum robots and their interactions with environments of quantum systems
are described and their study justified. A quantum robot is a mobile quantum
system that includes a quantum computer and needed ancillary systems on board.
Quantum robots carry out tasks whose goals include specified changes in the
state of the environment or carrying out measurements on the environment. Each
task is a sequence of alternating computation and action phases. Computation
phase activities include determination of the action to be carried out in the
next phase and possible recording of information on neighborhood environmental
system states. Action phase activities include motion of the quantum robot and
changes of neighborhood environment system states. Models of quantum robots and
their interactions with environments are described using discrete space and
time. To each task is associated a unitary step operator T that gives the
single time step dynamics. T = T_{a}+T_{c} is a sum of action phase and
computation phase step operators. Conditions that T_{a} and T_{c} should
satisfy are given along with a description of the evolution as a sum over paths
of completed phase input and output states. A simple example of a task carrying
out a measurement on a very simple environment is analyzed. A decision tree for
the task is presented and discussed in terms of sums over phase paths. One sees
that no definite times or durations are associated with the phase steps in the
tree and that the tree describes the successive phase steps in each path in the
sum.


  The vertical structure of stationary thin accretion discs is calculated from
the energy balance equation with heat generation due to microscopic ion
viscosity {\eta} and electron heat conductivity {\kappa}, both depending on
temperature. In the optically thin discs it is found that for the heat
conductivity increasing with temperature, the vertical temperature gradient
exceeds the adiabatic value at some height, suggesting convective instability
in the upper disc layer. There is a critical Prandtl number, Pr = 4/9, above
which a Keplerian disc become fully convective. The vertical density
distribution of optically thin laminar accretion discs as found from the
hydrostatic equilibrium equation cannot be generally described by a polytrope
but in the case of constant viscosity and heat conductivity. In the optically
thick discs with radiation heat transfer, the vertical disc structure is found
to be convectively stable for both absorption dominated and scattering
dominated opacities, unless a very steep dependence of the viscosity
coefficient on temperature is assumed. A polytropic-like structure in this case
is found for Thomson scattering dominated opacity.


  We present the nonequilibrium implementation of the two-particle
self-consistent (TPSC) approach, which has been shown to provide a reliable
equilibrium description of interacting lattice systems in the weak- and
intermediate-correlation regime. This method captures the effects of local and
nonlocal correlations in two- and higher-dimensional systems and satisfies the
Mermin-Wagner theorem. We demonstrate the versatility of nonequilibrium TPSC
with calculations of the time-dependent spin and charge response functions and
the evolution of effective temperatures extracted from different correlation
functions, after interaction ramps in the two-dimensional Hubbard model.


  In this work, the polymerization of Limonene Oxide (LO) has been achieved
using an Earth abundant metal-based catalyst developed in our group, that is
very active in ring opening polymerization (ROP) processes. The bio-based
polylimonene ether (PLO) obtained had low molecular weight and good thermal
properties, thus being a potential green polymeric additive for other bio-based
polymers such as PLA. Hence, we have explored its ability to influence PLA
properties. The addition of only 10 wt %, led to the modification and
improvement of PLA properties in terms of flexibility, thermal stability, and
hydrophobicity. The results obtained are promising and open up the potential
industrial application of polylimonene oxide (PLO) for the melt-processing of
blends based on PLA/PLO. These new materials are totally based on renewable
sources and may be interesting for many applications where biodegradability and
reduced water adsorption is required, such as food packaging or agricultural
mulch films.


  Entity alignment (EA) aims to find equivalent entities in different knowledge
graphs (KGs). Current EA approaches suffer from scalability issues, limiting
their usage in real-world EA scenarios. To tackle this challenge, we propose
LargeEA to align entities between large-scale KGs. LargeEA consists of two
channels, i.e., structure channel and name channel. For the structure channel,
we present METIS-CPS, a memory-saving mini-batch generation strategy, to
partition large KGs into smaller mini-batches. LargeEA, designed as a general
tool, can adopt any existing EA approach to learn entities' structural features
within each mini-batch independently. For the name channel, we first introduce
NFF, a name feature fusion method, to capture rich name features of entities
without involving any complex training process. Then, we exploit a name-based
data augmentation to generate seed alignment without any human intervention.
Such design fits common real-world scenarios much better, as seed alignment is
not always available. Finally, LargeEA derives the EA results by fusing the
structural features and name features of entities. Since no widely-acknowledged
benchmark is available for large-scale EA evaluation, we also develop a
large-scale EA benchmark called DBP1M extracted from real-world KGs. Extensive
experiments confirm the superiority of LargeEA against state-of-the-art
competitors.


  In this paper, we investigate the synthesis problem of terminating reactive
systems from quantitative specifications. Such systems are modeled as finite
transducers whose executions are represented as finite words in $(I\times
O)^*$, where $I,O$ are finite sets of input and output symbols, respectively. A
weighted specification $S$ assigns a rational value (or $-\infty$) to words in
$(I\times O)^*$, and we consider three kinds of objectives for synthesis,
namely threshold objectives where the system's executions are required to be
above some given threshold, best-value and approximate objectives where the
system is required to perform as best as it can by providing output symbols
that yield the best value and $\varepsilon$-best value respectively w.r.t. $S$.
We establish a landscape of decidability results for these three objectives and
weighted specifications with partial domain over finite words given by
deterministic weighted automata equipped with sum, discounted-sum and average
measures. The resulting objectives are not regular in general and we develop an
infinite game framework to solve the corresponding synthesis problems, namely
the class of (weighted) critical prefix games.


  Squeezed states of light reduce the signal-normalized photon counting noise
of measurements without increasing the light power and enable fundamental
research on quantum entanglement in hybrid systems of light and matter.
Furthermore, the completion of squeezed states with cryo-cooling has high
potential. First, measurement sensitivities are usually limited by quantum
noise and thermal noise. Second, squeezed states allow for reducing the heat
load on cooled devices without losing measurement precision. Here, we
demonstrate squeezed-light position sensing of a cryo-cooled micro-mechanical
membrane. The sensing precision is improved by up to 4.8 dB below photon
counting noise, limited by optical loss in two Faraday rotators, at a membrane
temperature of about 20K, limited by our cryo-cooler. We prove that realising a
high interference contrast in a cryogenic Michelson interferometer is feasible.
Our setup is the first conceptual demonstration towards the envisioned European
gravitational-wave detector, the 'Einstein Telescope', which is planned to use
squeezed states of light together with cryo-cooling of its mirror test masses.


  We consider a supersymmetric mirror Twin Higgs model in a gravity-mediated
supersymmetry-breaking scenario. We point out that the Polonyi field can be odd
under the $Z_{2}$ symmetry exchanging the Standard Model with the mirror sector
while gaugino masses are generated at tree-level. We discuss the dynamics of
the Polonyi field during and after inflation and show that the Polonyi problem
is absent. The Polonyi field couples to the two sectors with opposite signs,
which may serve as origin of the $Z_{2}$-breaking of the Higgs potential in
Twin Higgs models. We also estimate the $Z_2$-breaking in soft masses of
supersymmetric particles.


  Let $G$ be a finite group and $N_{\Omega}(G)$ be the intersection of the
normalizers of all subgroups belonging to the set $\Omega(G),$ where
$\Omega(G)$ is a set of all subgroups of $G$ which have some theoretical group
property. In this paper, we show that $N_{\Omega}(G)= Z_{\infty}(G)$ if
$\Omega(G)$ is one of the following: (i) the set of all self-normalizing
subgroups of $G$; (ii) the set of all subgroups of $G$ satisfying the
subnormalizer condition in $G$; (iii) the set of all pronormal subgroups of
$G$; (iv) the set of all $\mathscr{H}$-subgroups of $G$; (v) the set of all
weakly normal subgroups of $G$; (vi) the set of all $NE$-subgroups of $G$.


  We generalize Cauchy's celebrated theorem on the global rigidity of convex
polyhedra in Euclidean $3$-space $\mathbb{E}^{3}$ to the context of circle
polyhedra in the $2$-sphere $\mathbb{S}^{2}$. We prove that any two convex and
proper non-unitary c-polyhedra with M\"obius-congruent faces that are
consistently oriented are M\"obius-congruent. Our result implies the global
rigidity of convex inversive distance circle packings in the Riemann sphere as
well as that of certain hyperideal hyperbolic polyhedra in $\mathbb{H}^{3}$.


  The energy transition towards an increased supply of renewable energy raises
concerns that existing electricity grids, built to connect few centralized
large power plants with consumers, may become more difficult to control and
stabilized with a rising number of decentralized small scale generators. Here,
we aim to study therefore, how local phase perturbations which may be caused by
local power fluctuations, affect the AC grid stability. To this end, we start
from nonlinear power balance equations and map them to complex linear wave
equations, yielding stationary solutions with phases $\varphi_i$ at generator
and consumer sites $i$. Next, we study deviations from these stationary
solutions. Starting with an initially localized perturbation, it is found to
spread in a periodic grid diffusively throughout the grid. We derive the
parametric dependence of diffusion constant $D$. We apply the same solution
strategy to general grid topologies and analyse their stability against local
perturbations. The perturbation remains either localized or becomes
delocalized, depending on grid topology, power capacity and distribution of
consumers and generators $P_i$. Delocalization is found to increase the
lifetime of perturbations and thereby their influence on grid stability, while
localization results in an exponentiallyfast decay of perturbations at all grid
sites. These results may therefore lead to new strategies to control the
stability of electricity grids.


  The data created from virtual communication platforms presents the
opportunity to explore automated measures for monitoring team performance. In
this work, we explore one important characteristic of successful teams -
Psychological Safety - or the belief that a team is safe for interpersonal
risk-taking. To move towards an automated measure of this phenomenon, we derive
virtual communication characteristics and message keywords related to elements
of Psychological Safety from the literature. Using a mixed methods approach, we
investigate whether these characteristics are present in the Slack messages
from two design teams - one high in Psychological Safety, and one low. We find
that some usage characteristics, such as replies, reactions, and user mentions,
might be promising metrics to indicate higher levels of Psychological Safety,
while simple keyword searches may not be nuanced enough. We present the first
step towards the automated detection of this important, yet complex, team
characteristic.


  The study of the production of two forward jets with a large interval of
rapidity at hadron colliders was proposed by Mueller and Navelet as a possible
test of the high energy dynamics of QCD. We analyze this process within a
complete next-to-leading logarithm framework, supplemented by the use of the
Brodsky-Lepage-Mackenzie procedure extended to the perturbative Regge dynamics,
to find the optimal renormalization scale. This leads to a very good
description of the recent CMS data at LHC for the azimuthal correlations of the
jets.


  Phylogenetic networks model reticulate evolutionary histories. The last two
decades have seen an increased interest in establishing mathematical results
and developing computational methods for inferring and analyzing these
networks. A salient concept underlying a great majority of these developments
has been the notion that a network displays a set of trees and those trees can
be used to infer, analyze, and study the network.
  In this paper, we show that in the presence of coalescence effects, the set
of displayed trees is not sufficient to capture the network. We formally define
the set of parental trees of a network and make three contributions based on
this definition. First, we extend the notion of anomaly zone to phylogenetic
networks and report on anomaly results for different networks. Second, we
demonstrate how coalescence events could negatively affect the ability to infer
a species tree that could be augmented into the correct network. Third, we
demonstrate how a phylogenetic network can be viewed as a mixture model that
lends itself to a novel inference approach via gene tree clustering.
  Our results demonstrate the limitations of focusing on the set of trees
displayed by a network when analyzing and inferring the network. Our findings
can form the basis for achieving higher accuracy when inferring phylogenetic
networks and open up new venues for research in this area, including new
problem formulations based on the notion of a network's parental trees.


  A normalized analytic function f is shown to be univalent in the open unit
disk D if its second coefficient is sufficiently small and relates to its
Schwarzian derivative through a certain inequality. New criteria for analytic
functions to be in certain subclasses of functions are established in terms of
the Schwarzian derivatives and the second coefficients. These include obtaining
a sufficient condition for functions to be strongly $\alpha$-Bazilevi\^c of
order $\beta$.


  In this paper, particle-hopping cellular automaton (CA) models of elastic
demand are used to investigate the value added to plug-in electric vehicles
(PEV) aggregators by adopting vehicle-to-grid (V2G) responsive services. CA
models used earlier to study load-sifting responses are modified to capture
discharge/ recharge capabilities of V2G. Results on ramping responses from CA
are then analysed to discuss the small contribution to system controllability
added by V2G responsive services.


  We study the crackling noise emerging during single crack propagation in a
specimen under three-point bending conditions. Computer simulations are carried
out in the framework of a discrete element model where the specimen is
discretized in terms of convex polygons and cohesive elements are represented
by beams. Computer simulations revealed that fracture proceeds in bursts whose
size and waiting time distributions have a power law functional form with an
exponential cutoff. Controlling the degree of brittleness of the sample by the
amount of disorder, we obtain a scaling form for the characteristic quantities
of crackling noise of quasi-brittle materials. Analyzing the spatial structure
of damage we show that ahead of the crack tip a process zone is formed as a
random sequence of broken and intact mesoscopic elements. We characterize the
statistics of the shrinking and expanding steps of the process zone and
determine the damage profile in the vicinity of the crack tip.


  Currently it is thought that in order to explain the phenomenon of
superconductivity is necessary to understand the mechanism of formation of
electron pairs. However, the paired electrons cannot form a superconducting
condensate. They perform disorderly zero-point oscillations and there are no
attractive forces in their ensemble. To create a unified ensemble of particles,
the pairs must order their zero-point fluctuations so that an attraction
between the particles appears. For this reason, the ordering of zero-point
oscillations in the electron gas is the cause of superconductivity and the
parameters characterizing this order determine the properties of
superconductors. The model of condensation of zero-point oscillations creates
the possibility to obtain estimates for the critical parameters of elementary
superconductors, which are also in the satisfactory agreement with measured
data. On the another hand, the phenomenon of superfluidity in He-4 and He-3 can
be similarly explained due to the ordering of zero-point fluctuations. Thus it
is established that the both related phenomena are based on the same physical
mechanism.


  Gaussian white noise is frequently used to model fluctuations in physical
systems. In Fokker-Planck theory, this leads to a vanishing probability density
near the absorbing boundary of threshold models. Here we derive the boundary
condition for the stationary density of a first-order stochastic differential
equation for additive finite-grained Poisson noise and show that the response
properties of threshold units are qualitatively altered. Applied to the
integrate-and-fire neuron model, the response turns out to be instantaneous
rather than exhibiting low-pass characteristics, highly non-linear, and
asymmetric for excitation and inhibition. The novel mechanism is exhibited on
the network level and is a generic property of pulse-coupled systems of
threshold units.


  Almost contact manifolds with B-metric are considered. There are studied
three natural connections (i.e. linear connections preserving the structure
tensors) determined by conditions for their torsions. These connections are
investigated on a family of Lie groups considered as 5-dimensional almost
contact B-metric manifolds.


  It is known that the Frenet-Serret apparatus of a space curve in
three-dimensional Euclidean space determines the local geometry of curves. In
particular, the Frenet-Serret apparatus specifies important geometric
invariants, including the curvature and the torsion of a curve. It is also
acknowledged in quantum information science that low complexity and high
efficiency are essential features to achieve when cleverly manipulating quantum
states that encode quantum information about a physical system. In this paper,
we propose a geometric perspective on how to quantify the bending and the
twisting of quantum curves traced by dynamically evolving state vectors.
Specifically, we propose a quantum version of the Frenet-Serret apparatus for a
quantum trajectory in projective Hilbert space traced by a parallel-transported
pure quantum state evolving unitarily under a stationary Hamiltonian specifying
the Schrodinger equation. Our proposed constant curvature coefficient is given
by the magnitude squared of the covariant derivative of the tangent vector to
the state vector and represents a useful measure of the bending of the quantum
curve. Our proposed constant torsion coefficient, instead, is defined in terms
of the magnitude squared of the projection of the covariant derivative of the
tangent vector, orthogonal to both the tangent vector and the state vector. The
torsion coefficient provides a convenient measure of the twisting of the
quantum curve. Remarkably, we show that our proposed curvature and torsion
coefficients coincide with those existing in the literature, although
introduced in a completely different manner...


  Recently, subsampling or refining images generated from unconditional GANs
has been actively studied to improve the overall image quality. Unfortunately,
these methods are often observed less effective or inefficient in handling
conditional GANs (cGANs) -- conditioning on a class (aka class-conditional
GANs) or a continuous variable (aka continuous cGANs or CcGANs). In this work,
we introduce an effective and efficient subsampling scheme, named conditional
density ratio-guided rejection sampling (cDR-RS), to sample high-quality images
from cGANs. Specifically, we first develop a novel conditional density ratio
estimation method, termed cDRE-F-cSP, by proposing the conditional Softplus
(cSP) loss and an improved feature extraction mechanism. We then derive the
error bound of a density ratio model trained with the cSP loss. Finally, we
accept or reject a fake image in terms of its estimated conditional density
ratio. A filtering scheme is also developed to increase fake images' label
consistency without losing diversity when sampling from CcGANs. We extensively
test the effectiveness and efficiency of cDR-RS in sampling from both
class-conditional GANs and CcGANs on five benchmark datasets. When sampling
from class-conditional GANs, cDR-RS outperforms modern state-of-the-art methods
by a large margin (except DRE-F-SP+RS) in terms of effectiveness. Although the
effectiveness of cDR-RS is often comparable to that of DRE-F-SP+RS, cDR-RS is
substantially more efficient. When sampling from CcGANs, the superiority of
cDR-RS is even more noticeable in terms of both effectiveness and efficiency.
Notably, with the consumption of reasonable computational resources, cDR-RS can
substantially reduce Label Score without decreasing the diversity of
CcGAN-generated images, while other methods often need to trade much diversity
for slightly improved Label Score.


  We consider a random matrix model which interpolates between the chiral
Gaussian unitary ensemble and the Gaussian unitary ensemble while preserving
chiral symmetry. This ensemble describes flavor symmetry breaking for staggered
fermions in 3d QCD as well as in 4d QCD at high temperature or in 3d QCD at
finite isospin chemical potential. Our model is an Osborn-type two-matrix model
which is equivalent to the elliptic ensemble but we consider the singular value
statistics rather than the complex eigenvalue statistics. We report on exact
results for the partition function and the microscopic level density of the
Dirac operator in the $\varepsilon$-regime of QCD. We compare these analytical
results with Monte Carlo simulations of the matrix model.


  We consider the problem of inferring a latent function in a probabilistic
model of data. When dependencies of the latent function are specified by a
Gaussian process and the data likelihood is complex, efficient computation
often involve Markov chain Monte Carlo sampling with limited applicability to
large data sets. We extend some of these techniques to scale efficiently when
the problem exhibits a sequential structure. We propose an approximation that
enables sequential sampling of both latent variables and associated parameters.
We demonstrate strong performance in growing-data settings that would otherwise
be unfeasible with naive, non-sequential sampling.


  The growing carbon footprint of artificial intelligence (AI) models,
especially large ones such as GPT-3, has been undergoing public scrutiny.
Unfortunately, however, the equally important and enormous water (withdrawal
and consumption) footprint of AI models has remained under the radar. For
example, training GPT-3 in Microsoft's state-of-the-art U.S. data centers can
directly evaporate 700,000 liters of clean freshwater, but such information has
been kept a secret. More critically, the global AI demand may be accountable
for 4.2 -- 6.6 billion cubic meters of water withdrawal in 2027, which is more
than the total annual water withdrawal of 4 -- 6 Denmark or half of the United
Kingdom. This is very concerning, as freshwater scarcity has become one of the
most pressing challenges shared by all of us in the wake of the rapidly growing
population, depleting water resources, and aging water infrastructures. To
respond to the global water challenges, AI models can, and also must, take
social responsibility and lead by example by addressing their own water
footprint. In this paper, we provide a principled methodology to estimate the
water footprint of AI models, and also discuss the unique spatial-temporal
diversities of AI models' runtime water efficiency. Finally, we highlight the
necessity of holistically addressing water footprint along with carbon
footprint to enable truly sustainable AI.


  Learning physics requires understanding the applicability of fundamental
principles in a variety of contexts that share deep features. One way to help
students learn physics is via analogical reasoning. Students can be taught to
make an analogy between situations that are more familiar or easier to
understand and another situation where the same physics principle is involved
but that is more difficult to handle. Here, we examine introductory physics
students' ability to use analogies in solving problems involving Newton's
second law. Students enrolled in an algebra-based introductory physics course
were given a solved problem involving tension in a rope and were then asked to
solve another problem for which the physics is very similar but involved a
frictional force. They were asked to point out the similarities between the two
problems and then use the analogy to solve the friction problem.


  The properties of 939 star clusters in the Large and Small Magellanic Clouds
were determined from ground-based CCD images in UBVR passbands. The areal
coverage was extensive, corresponding to 11.0 sq-kpc in the LMC and 8.3 sq-kpc
in the SMC. After corrections for reddening, the colors and magnitudes of the
clusters were converted to ages and masses, and the resulting mass
distributions were searched for the effects of fading, evaporation, and
size-of-sample bias. The data show a clear signature of cluster fading below
the detection threshold. The initial cluster mass function (ICMF) was
determined by fitting the mass and age distributions with cluster population
models. These models suggest a new method to determine the ICMF that is nearly
independent of fading or disruption and is based on the slope of a correlation
between age and the maximum cluster mass in equally spaced intervals of
log-age. For a nearly uniform star formation rate, this correlation has a slope
equal to 1/(alpha-1) for an ICMF of dn(M)/dM proportional to M^-alpha. We
determine that alpha is between 2 and 2.4 for the LMC and SMC using this method
plus another method in which models are fit to the mass distribution integrated
over age and to the age distribution integrated over mass. The maximum mass
method also suggests that the cluster formation rate in the LMC age gap between
3 and 13 Gy is about a factor of ten below that in the period from 0.1 Gy to 1
Gy. The oldest clusters correspond in age and mass to halo globular clusters in
the Milky Way. They do not fit the trends for lower-mass clusters but appear to
be a separate population that either had a very high star formation rate and
became depleted by evaporation or formed with only high masses.


  Post-hoc interpretability methods play a critical role in explainable
artificial intelligence (XAI), as they pinpoint portions of data that a trained
deep learning model deemed important to make a decision. However, different
post-hoc interpretability methods often provide different results, casting
doubts on their accuracy. For this reason, several evaluation strategies have
been proposed to understand the accuracy of post-hoc interpretability. Many of
these evaluation strategies provide a coarse-grained assessment -- i.e., they
evaluate how the performance of the model degrades on average by corrupting
different data points across multiple samples. While these strategies are
effective in selecting the post-hoc interpretability method that is most
reliable on average, they fail to provide a sample-level, also referred to as
fine-grained, assessment. In other words, they do not measure the robustness of
post-hoc interpretability methods. We propose an approach and two new metrics
to provide a fine-grained assessment of post-hoc interpretability methods. We
show that the robustness is generally linked to its coarse-grained performance.


  We classify possible finite groups of symplectic automorphisms of K3 surfaces
of order divisible by 11. The characteristic of the ground field must be equal
to 11. The complete list of such groups consists of five groups: the cyclic
group of order 11, $11\rtimes 5$, $L_2(11)$ and the Mathieu groups $M_{11}$,
$M_{22}$. We also show that a surface $X$ admitting an automorphism $g$ of
order 11 admits a $g$-invariant elliptic fibration with the Jacobian fibration
isomorphic to one of explicitly given elliptic K3 surfaces.


  The photonic platform holds great promise for quantum computing.
Nevertheless, the intrinsic probabilistic characteristics of its native fusion
operations introduces substantial randomness into the computing process, posing
significant challenges to achieving scalability and efficiency in program
execution. In this paper, we introduce a randomness-aware compilation framework
designed to concurrently achieve scalability and efficiency. Our approach
leverages an innovative combination of offline and online optimization passes,
with a novel intermediate representation serving as a crucial bridge between
them. Through a comprehensive evaluation, we demonstrate that this framework
significantly outperforms the most efficient baseline compiler in a scalable
manner, opening up new possibilities for realizing scalable photonic quantum
computing.


  We characterize the positive radial continuous and rotation invariant
valuations $V$ defined on the star bodies of $\mathbb R^n$ as the applications
on star bodies which admit an integral representation with respect to the
Lebesgue measure. That is, $$V(K)=\int_{S^{n-1}}\theta(\rho_K)dm,$$ where
$\theta$ is a positive continuous function, $\rho_K$ is the radial function
associated to $K$ and $m$ is the Lebesgue measure on $S^{n-1}$. As a corollary,
we obtain that every such valuation can be uniformly approximated on bounded
sets by a linear combination of dual quermassintegrals.


  This paper surveys, in the first place, some basic facts from the
classification theory of normal complex singularities, including details for
the low dimensions 2 and 3. Next, it describes how the toric singularities are
located within the class of rational singularities, and recalls their main
properties. Finally, it focuses, in particular, on a toric version of Reid's
desingularization strategy in dimension three


  The problem of structured matrix estimation has been studied mostly under
strong noise dependence assumptions. This paper considers a general framework
of noisy low-rank-plus-sparse matrix recovery, where the noise matrix may come
from any joint distribution with arbitrary dependence across entries. We
propose an incoherent-constrained least-square estimator and prove its
tightness both in the sense of deterministic lower bound and matching minimax
risks under various noise distributions. To attain this, we establish a novel
result asserting that the difference between two arbitrary low-rank incoherent
matrices must spread energy out across its entries, in other words cannot be
too sparse, which sheds light on the structure of incoherent low-rank matrices
and may be of independent interest. We then showcase the applications of our
framework to several important statistical machine learning problems. In the
problem of estimating a structured Markov transition kernel, the proposed
method achieves the minimax optimality and the result can be extended to
estimating the conditional mean operator, a crucial component in reinforcement
learning. The applications to multitask regression and structured covariance
estimation are also presented. We propose an alternating minimization algorithm
to approximately solve the potentially hard optimization problem. Numerical
results corroborate the effectiveness of our method which typically converges
in a few steps.


  In this article, we propose CANDIES (Combined Approach for Novelty Detection
in Intelligent Embedded Systems), a new approach to novelty detection in
technical systems. We assume that in a technical system several processes
interact. If we observe these processes with sensors, we are able to model the
observations (samples) with a probabilistic model, where, in an ideal case, the
components of the parametric mixture density model we use, correspond to the
processes in the real world. Eventually, at run-time, novel processes emerge in
the technical systems such as in the case of an unpredictable failure. As a
consequence, new kinds of samples are observed that require an adaptation of
the model. CANDIES relies on mixtures of Gaussians which can be used for
classification purposes, too. New processes may emerge in regions of the
models' input spaces where few samples were observed before (low-density
regions) or in regions where already many samples were available (high-density
regions). The latter case is more difficult, but most existing solutions focus
on the former. Novelty detection in low- and high-density regions requires
different detection strategies. With CANDIES, we introduce a new technique to
detect novel processes in high-density regions by means of a fast online
goodness-of-fit test. For detection in low-density regions we combine this
approach with a 2SND (Two-Stage-Novelty-Detector) which we presented in
preliminary work. The properties of CANDIES are evaluated using artificial data
and benchmark data from the field of intrusion detection in computer networks,
where the task is to detect new kinds of attacks.


  We report observations of PSR's 0740-28, 1737-30, 1822-09, 1915+13 and
1916+14 with ROSAT. In the 0.1-2.1 keV range upper limits to luminosity are
derived for power law and blackbody spectra, using a range of $N_{H}$
estimates. The upper limit to the blackbody luminosity from PSR1822-09 turns
out to be consistent with standard cooling curves. For the other pulsars the
upper limits are not restrictive as they are much larger than the luminosities
predicted by the models.


  In this talk I propose the first fast methods which can analyze CMB data
taking into account correlated noise, arbitrary beam shapes, non-uniform
distribution of integration time on the sky, and partial sky coverage, without
the need for approximations. These ring torus methods work by performing the
analysis in the time ordered domain (TOD) rather than on the sky map of
fluctuations. They take advantage of the simplicity of noise correlations in
the TOD as well as certain properties of the group of rotations SO(3). These
properties single out a family of scanning strategies as favorable, namely
those which scan on rings and have the geometry of an n-torus. This family
includes the strategies due to TOPHAT, MAP and Planck. I first develop the
tools to model the time ordered signal, using Fast Fourier Transform methods
for convolution of two arbitrary functions on the sphere (Wandelt and G\'orski
2000). Then I apply these ideas to show that in the case of a 2-torus one can
reduce the time taken for CMB power spectrum analysis from an unfeasible order
N^3 to order N^2, where N ~ 10^5-10^8 is the number of resolution elements
(Wandelt and Hansen, in preparation).


  In this paper, we present a new experimental apparatus for the measurement of
the detection efficiency of free-space single-photon detectors based on the
substitution method. For the first time, we extend the analysis to account for
the wavelength dependence introduced by the transmissivity of the optical
window in front of the detector's active area. Our method involves measuring
the detector's response at different wavelengths and comparing it to a
calibrated reference detector. This allows us to accurately quantify the
efficiency variations due to the optical window's transmissivity. The results
provide a comprehensive understanding of the wavelength-dependent efficiency,
which is crucial for optimizing the performance of single-photon detectors in
various applications, including quantum communication and photonics research.
This characterization technique offers a significant advancement in the
precision and reliability of single-photon detection efficiency measurements.


  We examine the subgroup $D(G)$ of a transitive permutation group $G$ which is
generated by the derangements in $G$. Our main results bound the index of this
subgroup: we conjecture that, if $G$ has degree $n$ and is not a Frobenius
group, then $|G:D(G)|\leqslant\sqrt{n}-1$; we prove this except when $G$ is a
primitive affine group. For affine groups, we translate our conjecture into an
equivalent form regarding $|H:R(H)|$, where $H$ is a linear group on a finite
vector space and $R(H)$ is the subgroup of $H$ generated by elements having
eigenvalue~$1$.
  If $G$ is a Frobenius group, then $D(G)$ is the Frobenius kernel, and so
$G/D(G)$ is isomorphic to a Frobenius complement. We give some examples where
$D(G)\ne G$, and examine the group-theoretic structure of $G/D(G)$; in
particular, we construct groups $G$ in which $G/D(G)$ is not a Frobenius
complement.


  The Argon Dark Matter (ArDM-1t) experiment is a ton-scale liquid argon (LAr)
double-phase time projection chamber designed for direct Dark Matter searches.
Such a device allows to explore the low energy frontier in LAr with a charge
imaging detector. The ionization charge is extracted from the liquid into the
gas phase and there amplified by the use of a Large Electron Multiplier in
order to reduce the detection threshold. Direct detection of the ionization
charge with fine spatial granularity, combined with a measurement of the
amplitude and time evolution of the associated primary scintillation light,
provide powerful tools for the identification of WIMP interactions against the
background due to electrons, photons and possibly neutrons if scattering more
than once. A one ton LAr detector is presently installed on surface at CERN to
fully test all functionalities and it will be soon moved to an underground
location. We will emphasize here the lessons learned from such a device for the
design of a large LAr TPC for neutrino oscillation, proton decay and
astrophysical neutrinos searches.


  We perform an analysis of Lattice QCD data in the open-charm sector based on
the chiral SU(3) Lagrangian. The low-energy constants are adjusted to recover
the open-charm meson masses on Lattice QCD ensembles from HPQCD, ETMC and HSC
with pion and kaon masses smaller than 550 MeV. A significant set of low-energy
parameters is obtainable only if the most recent information from HSC on
scattering observables is included in our global fit. For the first time our
analysis considers the effect of left-hand cuts as developed in terms of a
generalized potential approach (GPA) previously by one of the authors. Here we
use coupled-channel interaction terms at the one-loop level. The elastic s-wave
and p-wave $D\,\pi$, $D K $ and $D \bar K $ scattering phase shifts on
ensembles with nominal pion masses of about 239 MeV and 391 MeV are reproduced
faithfully. Based on such low-energy parameters we predict s- and p-wave phase
shifts and inelasticities at physical quark masses, where the statistical
uncertainties in the phase shifts are smaller than 1 degree always. Most
striking would be the exotic s-wave $D_s \pi$ channel, for which we predict a
resonance state at about 2.287 GeV where the phase shift passes through 90
degrees.


  We study $\ell^\infty$ norms of $\ell^2$-normalized eigenfunctions of quantum
cat maps. For maps with short quantum periods (constructed by Bonechi and de
Bi\`evre), we show that there exists a sequence of eigenfunctions $u$ with
$\|u\|_{\infty}\gtrsim (\log N)^{-1/2}$. For general eigenfunctions we show the
upper bound $\|u\|_\infty\lesssim (\log N)^{-1/2}$. Here the semiclassical
parameter is $h=(2\pi N)^{-1}$. Our upper bound is analogous to the one proved
by B\'{e}rard for compact Riemannian manifolds without conjugate points.


  We study the electronic structure of gated graphene sheets. We consider both
infinite graphene and finite width ribbons. The effect of Coulomb interactions
between the electrically injected carriers and the coupling to the external
gate are computed self-consistently in the Hartree approximation. We compute
the average density of extra carriers, $n_{2D}$, the number of occupied
subbands and the density profiles as a function of the gate potential $V_g$. We
discuss quantum corrections to the classical capacitance and we calculate the
threshold $V_g$ above which semiconducting armchair ribbons conduct. We find
that the ideal conductance of perfectly transmitting wide ribbons is
proportional to the square root of the gate voltage.


  We compare three independent, cosmological linear perturbation theory codes
to asses the level of agreement between them and to improve upon it by
investigating the sources of discrepancy. By eliminating the major sources of
numerical instability the final level of agreement between the codes was
improved by an order of magnitude. The relative error is now below 0.1% for the
dark matter power spectrum. For the cosmic microwave background anisotropies
the agreement is below the sampling variance up to l=3000, with close to 0.1%
accuracy reached over most of this range of scales. The same level of agreement
is also achieved for the polarization spectrum and the temperature-polarization
cross-spectrum. Linear perturbation theory codes are thus well prepared for the
present and upcoming high precision cosmological observations.


  This paper presents a research through design exploration using tangible
interactions in order to seamlessly integrate technology in a historical house
museum. The study addresses a longstanding concern in museum exhibition design
that interactive technologies may distract from the artifacts on display.
Through an iterative design process including user studies, a co-creation
workshop with museum staff and several prototypes, we developed an interactive
installation called The Diary of Niels that combines physical objects, RFID
sensors and an elaborate fiction in order to facilitate increased visitor
engagement. Insights from the research process and user tests indicate that the
integration of technology and artifacts is meaningful and engaging for users,
and helps introduce museum visitors to the historic theme of the exhibition and
the meaning of the artifacts. The study also points to continued challenges in
integrating such hybrid experiences fully with the rest of the exhibition.


  As users often express their preferences with binary behavior data~(implicit
feedback), such as clicking items or buying products, implicit feedback based
Collaborative Filtering~(CF) models predict the top ranked items a user might
like by leveraging implicit user-item interaction data. For each user, the
implicit feedback is divided into two sets: an observed item set with limited
observed behaviors, and a large unobserved item set that is mixed with negative
item behaviors and unknown behaviors. Given any user preference prediction
model, researchers either designed ranking based optimization goals or relied
on negative item mining techniques for better optimization. Despite the
performance gain of these implicit feedback based models, the recommendation
results are still far from satisfactory due to the sparsity of the observed
item set for each user. To this end, in this paper, we explore the unique
characteristics of the implicit feedback and propose Set2setRank framework for
recommendation. The optimization criteria of Set2setRank are two folds: First,
we design an item to an item set comparison that encourages each observed item
from the sampled observed set is ranked higher than any unobserved item from
the sampled unobserved set. Second, we model set level comparison that
encourages a margin between the distance summarized from the observed item set
and the most "hard" unobserved item from the sampled negative set. Further, an
adaptive sampling technique is designed to implement these two goals. We have
to note that our proposed framework is model-agnostic and can be easily applied
to most recommendation prediction approaches, and is time efficient in
practice. Finally, extensive experiments on three real-world datasets
demonstrate the superiority of our proposed approach.


  The Kerr solution is generated from the Schwarzschild solution by a simple
combination of real global coordinate transformations and of invariance
transformations acting on the space of stationary solutions of the
Einstein-Maxwell equations. The same transformation can be used to generate a
spinning field configuration from any static axisymmetric configuration. We
illustrate this by generating from the continuous family of Voorhees--Zipoy
vacuum solutions a family of solutions endowed with mass, angular momentum,
dipole magnetic moment and quadrupole electric moment.


  We present an approximate method for solving nonlinear control problems over
long time horizons, in which the full nonlinear model is preserved over an
initial part of the horizon, while the remainder of the horizon is modeled
using a linear relaxation. As this approximate problem may still be too large
to solve directly, we present a Benders decomposition-based solution algorithm
that iterates between solving the nonlinear and linear parts of the horizon.
This extends the Dual Dynamic Programming approach commonly employed for
optimization of linearized hydro power systems. We prove that the proposed
algorithm converges after a finite number of iterations, even when the
nonlinear initial stage problems are solved inexactly. We also bound the
suboptimality of the split-horizon method with respect to the original
nonlinear problem, in terms of the properties of a map between the linear and
nonlinear state-input trajectories. We then apply this method to a case study
concerning a multiple reservoir hydro system, approximating the nonlinear head
effects in the second stage using McCormick envelopes. We demonstrate that
near-optimal solutions can be obtained in a shrinking horizon setting when the
full nonlinear model is used for only a short initial section of the horizon.
For this example, the approach is shown to be more practical than both
conventional dynamic programming and a multi-cell McCormick envelope
approximation from literature.


  It is widely accepted that the large obliquity of Uranus is the result of a
great tangential collision (GC) with an Earth size proto-planet at the end of
the accretion. We attempt to constraint the GC scenario as the cause of Uranus'
obliquity as well as on the mechanisms able to give origin to the Uranian
irregulars. Different capture mechanisms for irregulars operate at different
stages on the giant planets formation process. The mechanisms able to capture
the uranian irregulars before and after the GC are analysed. Assuming that they
were captured before the GC, we calculate the orbital transfer of the nine
irregulars by the impulse imparted by the GC. If their orbital transfer results
dynamically implausible, they should have originated after the GC. We
investigate and discuss the dissipative mechanisms able to operate later. In
particular Prospero could not exist at the time of the GC. Different capture
mechanisms for Prospero after the GC are investigated. Gas drag by
Uranus'envelope and pull-down capture are not plausible mechanisms. Capture of
Prospero through a collisionless interaction seems to be difficult. The GC
itself provides a mechanism of permanent capture. However, the capture of
Prospero by the GC is a low probable event. Catastrophic collisions could be a
possible mechanism for the birth of Prospero and the other irregulars after the
GC. Orbital and physical clusterings should then be expected. Either Prospero
had to originate after the GC or the GC did not occur. In the former case, the
mechanism for the origin of Prospero after the GC remains an open question. In
the latter case, another theory to account for Uranus' obliquity and the
formation of the Uranian regular satellites on the equatorial plane of the
planet would be needed.


  Bit-serial architectures can handle Neural Networks (NNs) with different
weight precisions, achieving higher resource efficiency compared with
bit-parallel architectures. Besides, the weights contain abundant zero bits
owing to the fault tolerance of NNs, indicating that bit sparsity of NNs can be
further exploited for performance improvement. However, the irregular
proportion of zero bits in each weight causes imbalanced workloads in the
Processing Element (PE) array, which degrades performance or induces overhead
for sparse processing. Thus, this paper proposed a bit-sparsity quantization
method to maintain the bit sparsity ratio of each weight to no more than a
certain value for balancing workloads, with little accuracy loss. Then, we
co-designed a sparse bit-serial architecture, called Bit-balance, to improve
overall performance, supporting weight-bit sparsity and adaptive bitwidth
computation. The whole design was implemented with 65nm technology at 1 GHz and
performs at 326-, 30-, 56-, and 218-frame/s for AlexNet, VGG-16, ResNet-50, and
GoogleNet respectively. Compared with sparse bit-serial accelerator, Bitlet,
Bit-balance achieves 1.8x~2.7x energy efficiency (frame/J) and 2.1x~3.7x
resource efficiency (frame/mm2).


  We have calculated the electromagnetic elastic form factor of a two body
scalar bound state. The Bethe-Salpeter equation is solved directly in the
Minkowski space using the Perturbation Theory Integral Representation. At soft
coupling regime the obtained results are compared with those following from a
quasipotential spectator (Gross) approximation.


  Decoherence in superconducting qubits has long been attributed to two level
systems arising from the surfaces and interfaces present in real devices. A
recent significant step in reducing decoherence was the replacement of
superconducting niobium by superconducting tantalum, resulting in a tripling of
transmon qubit lifetimes (T1). One of these surface variables, the identity,
thickness, and quality of the native surface oxide, is thought to play a major
role as tantalum only has one oxide whereas niobium has several. Here we report
the development of a thermodynamic metric to rank materials based on their
potential to form a well-defined, thin, surface oxide. We first compute this
metric for known binary and ternary metal alloys using data available from
Materials Project, and experimentally validate the strengths and limits of this
metric through preparation and controlled oxidation of 8 known metal alloys.
Then we train a convolutional neural network to predict the value of this
metric from atomic composition and atomic properties. This allows us to compute
the metric for materials that are not present in materials project, including a
large selection of known superconductors, and, when combined with Tc, allow us
to identify new candidate superconductors for quantum information science
(QISE) applications. We test the oxidation resistance of a pair of these
predictions experimentally. Our results are expected to lay the foundation for
tailored and rapid selection of improved superconductors for QISE.


  The SQL group-by operator plays an important role in summarizing and
aggregating large datasets in a data analytic stack.While the standard group-by
operator, which is based on equality, is useful in several applications,
allowing similarity aware grouping provides a more realistic view on real-world
data that could lead to better insights. The Similarity SQL-based Group-By
operator (SGB, for short) extends the semantics of the standard SQL Group-by by
grouping data with similar but not necessarily equal values. While existing
similarity-based grouping operators efficiently materialize this approximate
semantics, they primarily focus on one-dimensional attributes and treat
multidimensional attributes independently. However, correlated attributes, such
as in spatial data, are processed independently, and hence, groups in the
multidimensional space are not detected properly. To address this problem, we
introduce two new SGB operators for multidimensional data. The first operator
is the clique (or distance-to-all) SGB, where all the tuples in a group are
within some distance from each other. The second operator is the
distance-to-any SGB, where a tuple belongs to a group if the tuple is within
some distance from any other tuple in the group. We implement and test the new
SGB operators and their algorithms inside PostgreSQL. The overhead introduced
by these operators proves to be minimal and the execution times are comparable
to those of the standard Group-by. The experimental study, based on TPC-H and a
social check-in data, demonstrates that the proposed algorithms can achieve up
to three orders of magnitude enhancement in performance over baseline methods
developed to solve the same problem.


  The ultra-wide optical coherence tomography angiography (OCTA) has become an
important imaging modality in diabetic retinopathy (DR) diagnosis. However,
there are few researches focusing on automatic DR analysis using ultra-wide
OCTA. In this paper, we present novel and practical deep-learning solutions
based on ultra-wide OCTA for the Diabetic Retinopathy Analysis Challenge
(DRAC). In the segmentation of DR lesions task, we utilize UNet and UNet++ to
segment three lesions with strong data augmentation and model ensemble. In the
image quality assessment task, we create an ensemble of InceptionV3,
SE-ResNeXt, and Vision Transformer models. Pre-training on the large dataset as
well as the hybrid MixUp and CutMix strategy are both adopted to boost the
generalization ability of our model. In the DR grading task, we build a Vision
Transformer (ViT) and fnd that the ViT model pre-trained on color fundus images
serves as a useful substrate for OCTA images. Our proposed methods ranked 4th,
3rd, and 5th on the three leaderboards of DRAC, respectively. The source code
will be made available at https://github.com/FDU-VTS/DRAC.