  We report on the discovery of consecutive bright radio pulses from the Vela
pulsar, a new phenomenon that may lead to a greater understanding of the pulsar
emission mechanism. This results from a total of 345 hr worth of observations
of the Vela pulsar using the University of Tasmania's 26 m radio telescope to
study the frequency and statistics of abnormally bright pulses and sub-pulses.
The bright pulses show a tendency to appear consecutively. The observations
found two groups of six consecutive bright pulses and many groups of two to
five bright pulses in a row. The strong radio emission process that produces
the six bright pulses lasts between 0.4 and 0.6 s. The numbers of bright pulses
in sequence far exceed what would be expected if individual bright pulses were
independent random events. Consecutive bright pulses must be generated by an
emission process that is long lived relative to the rotation period of the
neutron star.


  In this paper we study an asymmetric valley-ridge inflection point (VRI)
potential, whose energy surface (PES) features two sequential index-1 saddles
(the upper and the lower), with one saddle having higher energy than the other
and two potential wells separated by the lower index-1 saddle. We show how the
depth and the flatness of our potential changes as we modify the parameter that
controls the asymmetry as well as how the branching ratio (ratio of the
trajectories that enter each well) is changing as we modify the same parameter
and its correlation with the area of the lobes as they have formulated by the
stable and unstable manifolds that have been extracted from the gradient of the
LD scalar fields.


  A compact HTS cable that is able to carry large current density is crucial
for developing high field accelerator magnets. We are reporting a novel HTS
cable (named X-cable) that could achieve a high current density as the Roebel
cable, but is implemented by in-plane bending stacked HTS tapes directly to
realize the transposition. The cable is jointly developed with an industrial
company on a production line: ready for large scale production from the
beginning. Recently, a prototype cable with REBCO coated conductor has been
successfully fabricated. Test results show no significant degradation,
demonstrated the feasibility of such cable concept. In this paper, the cable's
design concept, in-plane bending performance of the REBCO tapes, fabrication
procedure and test results of this first prototype cable will be presented.


  We report on a search for pair production of first-generation scalar
leptoquarks ($LQ$) in $p \bar{p}$ collisions at $\sqrt{s}$=1.96 TeV using an
integrated luminosity of 203 $pb^{-1}$ collected at the Fermilab Tevatron
collider by the CDF experiment. We observe no evidence for $LQ$ production in
the topologies arising from $LQ \bar{LQ} \to eqeq$ and $LQ \bar{LQ} \to eq \nu
q$, and derive 95% C.L. upper limits on the $LQ$ production cross section. %as
a function of $\beta$, where $\beta$ is the branching fraction for $LQ \to eq$.
The results are combined with those obtained from a separately reported CDF
search in the topology arising from $LQ\bar{LQ} \to \nu q \nu q$ and 95% C.L.
lower limits on the LQ mass as a function of $\beta = BR(LQ \to eq) $ are
derived. The limits are 236, 205 and 145 GeV/c$^2$ for $\beta$ = 1, $\beta$ =
0.5 and $\beta$ = 0.1, respectively.


  This manuscript introduces an object deformability-agnostic framework for
co-carrying tasks that are shared between a person and multiple robots. Our
approach allows the full control of the co-carrying trajectories by the person
while sharing the load with multiple robots depending on the size and the
weight of the object. This is achieved by merging the haptic information
transferred through the object and the human motion information obtained from a
motion capture system. One important advantage of the framework is that no
strict internal communication is required between the robots, regardless of the
object size and deformation characteristics. We validate the framework with two
challenging real-world scenarios: co-transportation of a wooden rigid closet
and a bulky box on top of forklift moving straps, with the latter
characterizing deformable objects. In order to evaluate the generalizability of
the proposed framework, a heterogenous team of two mobile manipulators that
consist of an Omni-directional mobile base and a collaborative robotic arm with
different DoFs is chosen for the experiments. The qualitative comparison
between our controller and the baseline controller (i.e., an admittance
controller) during these experiments demonstrated the effectiveness of the
proposed framework especially when co-carrying deformable objects. Furthermore,
we believe that the performance of our framework during the experiment with the
lifting straps offers a promising solution for the co-transportation of bulky
and ungraspable objects.


  We consider the iso-spectral real manifolds of tridiagonal Hessenberg
matrices with real eigenvalues. The manifolds are described by the iso-spectral
flows of indefinite Toda lattice equations introduced by the authors [Physica,
91D (1996), 321-339]. These Toda lattices consist of $2^{N-1}$ different
systems with hamiltonians $H = (1/2) \sum_{k=1}^{N} y_k^2 + \sum_{k=1}^{N-1}
s_ks_{k+1} \exp(x_k-x_{k+1})$, where $s_i=\pm 1$. We compactify the manifolds
by adding infinities according to the Toda flows which blow up in finite time
except the case with all $s_is_{i+1}=1$. The resulting manifolds are shown to
be nonorientable for $N>2$, and the symmetric group is the semi-direct product
of $(\ZZ_2)^{N-1}$ and the permutation group $S_N$. These properties identify
themselves with ``small covers'' introduced by Davis and Januszkiewicz [Duke
Mathematical Journal, 62 (1991), 417-451]. As a corollary of our construction,
we give a formula on the total numbers of zeroes for a system of exponential
polynomials generated as Hankel determinant.


  Binarization plays a key role in the automatic information retrieval from
document images. This process is usually performed in the first stages of
documents analysis systems, and serves as a basis for subsequent steps. Hence
it has to be robust in order to allow the full analysis workflow to be
successful. Several methods for document image binarization have been proposed
so far, most of which are based on hand-crafted image processing strategies.
Recently, Convolutional Neural Networks have shown an amazing performance in
many disparate duties related to computer vision. In this paper we discuss the
use of convolutional auto-encoders devoted to learning an end-to-end map from
an input image to its selectional output, in which activations indicate the
likelihood of pixels to be either foreground or background. Once trained,
documents can therefore be binarized by parsing them through the model and
applying a threshold. This approach has proven to outperform existing
binarization strategies in a number of document domains.


  We study the space requirement of $m$-ary search trees under the random
permutation model when $m \geq 27$ is fixed. Chauvin and Pouyanne have shown
recently that $X_n$, the space requirement of an $m$-ary search tree on $n$
keys, equals $\mu (n+1) + 2\Re{[\Lambda n^{\lambda_2}]} + \epsilon_n
n^{\Re{\lambda_2}}$, where $\mu$ and $\lambda_2$ are certain constants,
$\Lambda$ is a complex-valued random variable, and $\epsilon_n \to 0$ a.s. and
in $L^2$ as $n \to \infty$. Using the contraction method, we identify the
distribution of $\Lambda$.


  Current magnetic-accretion models for classical T-Tauri stars rely on a
strong, dipolar magnetic field of stellar origin to funnel the disk material
onto the star, and assume a steady-state. In this paper, I critically examine
the physical basis of these models in light of the observational evidence and
our knowledge of magnetic fields in low-mass stars, and find it lacking.
  I also argue that magnetic accretion onto these stars is inherently a
time-dependent problem, and that a steady-state is not warranted.
  Finally, directions for future work towards fully-consistent models are
pointed out.


  Cosmic rays (CR), constrained by scattering on magnetic irregularities, are
believed to propagate diffusively. But a well-known defect of diffusive
approximation whereby some of the particles propagate unrealistically fast has
directed interest towards an alternative CR transport model based on the
"telegraph" equation. However, its derivations often lack rigor and
transparency leading to inconsistent results.
  We apply the classic Chapman-Enskog method to the CR transport problem. We
show that no "telegraph" (second order time derivative) term emerges in any
order of a proper asymptotic expansion with systematically eliminated short
time scales. Nevertheless, this term may formally be\emph{ converted} from the
\emph{fourth} order hyper-diffusive term of the expansion. But, both the
telegraph and hyperdiffusive terms may only be important for a short relaxation
period associated with either strong pitch-angle anisotropy or spatial
inhomogeneity of the initial CR distribution. Beyond this period the system
evolves diffusively in both cases. The term conversion, that makes the
telegraph and Chapman-Enskog approaches reasonably equivalent, is possible only
after this relaxation period. During this period, the telegraph solution is
argued to be unphysical. Unlike the hyperdiffusion correction, it is not
uniformly valid and introduces implausible singular components to the solution.
These dominate the solution during the relaxation period. As they are shown not
to be inherent in the underlying scattering problem, we argue that the
telegraph term is involuntarily acquired in an asymptotic reduction of the
problem.


  A Dirac-Fermi liquid (DFL)--a doped system with Dirac spectrum--is an
important example of a non-Galilean-invariant Fermi liquid (FL). Real-life
realizations of a DFL include, e.g., doped graphene, surface states of
three-dimensional (3D) topological insulators, and 3D Dirac/Weyl metals. We
study the optical conductivity of a DFL arising from intraband
electron-electron scattering. It is shown that the effective current relaxation
rate behaves as $1/\tau_{J}\propto \left(\omega^2+4\pi^2
T^2\right)\left(3\omega^2+8\pi^2 T^2\right)$ for $\max\{\omega, T\}\ll \mu$,
where $\mu$ is the chemical potential, with an additional logarithmic factor in
two dimensions. In graphene, the quartic form of $1/\tau_{J}$ competes with a
small FL-like term, $\propto\omega^2+4\pi^2 T^2$, due to trigonal warping of
the Fermi surface. We also calculated the dynamical charge susceptibility,
$\chi_\mathrm{c}({\bf q},\omega)$, outside the particle-hole continua and to
one-loop order in the dynamically screened Coulomb interaction. For a 2D DFL,
the imaginary part of $\chi_\mathrm{c}({\bf q},\omega)$ scales as
$q^2\omega\ln|\omega|$ and $q^4/\omega^3$ for frequencies larger and smaller
than the plasmon frequency at given $q$, respectively. The small-$q$ limit of
$\mathrm{Im} \chi_\mathrm{c}({\bf q},\omega)$ reproduces our result for the
conductivity via the Einstein relation.


  After reviewing the interpretation of laser operation as a non-equilibrium
Bose-Einstein condensation phase transition, we illustrate the novel features
arising from the non-equilibrium nature of photon and polariton Bose-Einstein
condensates recently observed in experiments. We then proposea quantitative
criterion to experimentally assess the equilibrium vs. non-equilibrium nature
of a specific condensation process, based on fluctuation-dissipation relations.
The power of this criterion is illustrated on two models which shows very
different behaviours.


  When a group of people strives to understand new information, struggle ensues
as various ideas compete for attention. Steep learning curves are surmounted as
teams learn together. To understand how these team dynamics play out in
software development, we explore Git logs, which provide a complete change
history of software repositories. In these repositories, we observe code
additions, which represent successfully implemented ideas, and code deletions,
which represent ideas that have failed or been superseded. By examining the
patterns between these commit types, we can begin to understand how teams adopt
new information. We specifically study what happens after a software library is
adopted by a project, i.e. when a library is used for the first time in the
project. We find that a variety of factors, including team size, library
popularity, and prevalence on Stack Overflow are associated with how quickly
teams learn and successfully adopt new software libraries.


  We compute the critical exponents for nonextensive $\lambda\phi^{3}$ scalar
field theory for all loop orders and $|q - 1| < 1$. We apply the results for
both nonextensive percolation and Lee-Yang edge singularity problems. The
corresponding systems are nonextensive generalizations of their extensive
counterparts. For that we employ tools from the recently introduced
nonextensive statistical field theory. The results for the nonextensive
critical exponents computed depend on the nonextensive parameter $q$, which
encodes global correlations among the degrees of freedom of the system. The
extensive results are recovered in the limit $q\rightarrow 1$. There is an
interplay between global correlations and fluctuations, once the nonextensive
critical exponents depend on $q$. This dependence is in agreement with the
universality hypothesis.


  This paper presents MADER, a 3D decentralized and asynchronous trajectory
planner for UAVs that generates collision-free trajectories in environments
with static obstacles, dynamic obstacles, and other planning agents. Real-time
collision avoidance with other dynamic obstacles or agents is done by
performing outer polyhedral representations of every interval of the
trajectories and then including the plane that separates each pair of polyhedra
as a decision variable in the optimization problem. MADER uses our recently
developed MINVO basis to obtain outer polyhedral representations with volumes
2.36 and 254.9 times, respectively, smaller than the Bernstein or B-Spline
bases used extensively in the planning literature. Our decentralized and
asynchronous algorithm guarantees safety with respect to other agents by
including their committed trajectories as constraints in the optimization and
then executing a collision check-recheck scheme. Finally, extensive simulations
in challenging cluttered environments show up to a 33.9% reduction in the
flight time, and a 88.8% reduction in the number of stops compared to the
Bernstein and B-Spline bases, shorter flight distances than centralized
approaches, and shorter total times on average than synchronous decentralized
approaches.


  In a clinical trial of a treatment for alcoholism, a common response variable
of interest is the number of alcoholic drinks consumed by each subject each
day, or an ordinal version of this response, with levels corresponding to
abstinence, light drinking and heavy drinking. In these trials, within-subject
drinking patterns are often characterized by alternating periods of heavy
drinking and abstinence. For this reason, many statistical models for time
series that assume steady behavior over time and white noise errors do not fit
alcohol data well. In this paper we propose to describe subjects' drinking
behavior using Markov models and hidden Markov models (HMMs), which are better
suited to describe processes that make sudden, rather than gradual, changes
over time. We incorporate random effects into these models using a hierarchical
Bayes structure to account for correlated responses within subjects over time,
and we estimate the effects of covariates, including a randomized treatment, on
the outcome in a novel way. We illustrate the models by fitting them to a large
data set from a clinical trial of the drug Naltrexone. The HMM, in particular,
fits this data well and also contains unique features that allow for useful
clinical interpretations of alcohol consumption behavior.


  Legacy software documents are hard to understand and visualize. The tag cloud
technique helps software developers to visualize the contents of software
documents. A tag cloud is a well-known and simple visualization technique. This
paper proposes a new method to visualize software documents, using a tag cloud.
In this paper, tags visualize in the cloud based on their frequency in an
alphabetical order. The most important tags are displayed with a larger font
size. The originality of this method is that it visualizes the contents of
JavaDoc as a tag cloud. To validate the JavaDocCloud method, it was applied to
NanoXML case study, the results of these experiments display the most common
and uncommon tags used in the software documents.


  Cycle consistent generative adversarial network (CycleGAN) and variational
autoencoder (VAE) based models have gained popularity in non-parallel voice
conversion recently. However, they often suffer from difficult training process
and unsatisfactory results. In this paper, we propose CVC, a contrastive
learning-based adversarial approach for voice conversion. Compared to previous
CycleGAN-based methods, CVC only requires an efficient one-way GAN training by
taking the advantage of contrastive learning. When it comes to non-parallel
one-to-one voice conversion, CVC is on par or better than CycleGAN and VAE
while effectively reducing training time. CVC further demonstrates superior
performance in many-to-one voice conversion, enabling the conversion from
unseen speakers.


  I present here the HFAG averages for the parameters that regulate flavor
mixing and \CP violation in the neutral $D$ meson system. I also discuss recent
results from the $B$ factories for the measurements of the mixing parameter
\yCP and the \CP violation parameter \deltaY (\AGamma) in the lifetime ratio
analysis of the transitions to the \CP-even eigenstates $\Dz\to
K^+K^-,\pi^+\pi^-$, relative to the transitions to the \CP-mixed state $\Dz \to
K^-\pi^+$.


  Prism-based LiDARs are more compact and cheaper than the conventional
mechanical multi-line spinning LiDARs, which have become increasingly popular
in robotics, recently. However, there are several challenges for these new
LiDAR sensors, including small field of view, severe motion distortions, and
irregular patterns, which hinder them from being widely used in LiDAR odometry,
practically. To tackle these problems, we present an effective continuous-time
LiDAR odometry (ECTLO) method for the Risley-prism-based LiDARs with
non-repetitive scanning patterns. A single range image covering historical
points in LiDAR's small FoV is adopted for efficient map representation. To
account for the noisy data from occlusions after map updating, a filter-based
point-to-plane Gaussian Mixture Model is used for robust registration.
Moreover, a LiDAR-only continuous-time motion model is employed to relieve the
inevitable distortions. Extensive experiments have been conducted on various
testbeds using the prism-based LiDARs with different scanning patterns, whose
promising results demonstrate the efficacy of our proposed approach.


  This paper provides a view of Maxwell's equations from the perspective of
complex variables. The study is made through complex differential forms and the
Hodge star operator in $\mathbb{C}^2$ with respect to the Euclidean and the
Minkowski metrics. It shows that holomorphic functions give rise to nontrivial
solutions, and the inner product between the electric and the magnetic fields
is considered in this case. Further, it obtains a simple necessary and
sufficient condition regarding harmonic solutions to the equations. In the end,
the paper gives an interpretation of the Lorenz gauge condition in terms of the
codifferential operator.


  Epidemic spread in a population is traditionally modeled via
compartmentalized models which represent the free evolution of disease in
absence of any intervention policies. In addition, these models assume full
observability of disease cases and do not account for under-reporting. We
present a mathematical model, namely PolSIRD, which accounts for the
under-reporting by introducing an observation mechanism. It also captures the
effects of intervention policies on the disease spread parameters by leveraging
intervention policy data along with the reported disease cases. Furthermore, we
allow our recurrent model to learn the initial hidden state of all compartments
end-to-end along with other parameters via gradient-based training. We apply
our model to the spread of the recent global outbreak of COVID-19 in the United
States, where our model outperforms the methods employed by the CDC in
predicting the spread. We also provide counterfactual simulations from our
model to analyze the effect of lifting the intervention policies prematurely
and our model correctly predicts the second wave of the epidemic.


  Machine learning algorithms are increasingly used to assist human
decision-making. When the goal of machine assistance is to improve the accuracy
of human decisions, it might seem appealing to design ML algorithms that
complement human knowledge. While neither the algorithm nor the human are
perfectly accurate, one could expect that their complementary expertise might
lead to improved outcomes. In this study, we demonstrate that in practice
decision aids that are not complementary, but make errors similar to human ones
may have their own benefits.
  In a series of human-subject experiments with a total of 901 participants, we
study how the similarity of human and machine errors influences human
perceptions of and interactions with algorithmic decision aids. We find that
(i) people perceive more similar decision aids as more useful, accurate, and
predictable, and that (ii) people are more likely to take opposing advice from
more similar decision aids, while (iii) decision aids that are less similar to
humans have more opportunities to provide opposing advice, resulting in a
higher influence on people's decisions overall.


  This short technical report demonstrates a simple technique that yields state
of the art results in medical image-text matching tasks. We analyze the use of
OpenAI's CLIP, a general image-text matching model, and observe that CLIP's
limited textual input size has negative impact on downstream performance in the
medical domain where encoding longer textual contexts is often required. We
thus train and release ClipMD, which is trained with a simple sliding window
technique to encode textual captions. ClipMD was tested on two medical
image-text datasets and compared with other image-text matching models. The
results show that ClipMD outperforms other models on both datasets by a large
margin. We make our code and pretrained model publicly available.


  The calculation of both spinor and tensor Green's functions in
four-dimensional conformally invariant field theories can be greatly simplified
by six-dimensional methods. For this purpose, four-dimensional fields are
constructed as projections of fields on the hypercone in six-dimensional
projective space, satisfying certain transversality conditions. In this way
some Green's functions in conformal field theories are shown to have structures
more general than those commonly found by use of the inversion operator. These
methods fit in well with the assumption of AdS/CFT duality. In particular, it
is transparent that if fields on AdS$_5$ approach finite limits on the boundary
of AdS$_5$, then in the conformal field theory on this boundary these limits
transform with conformal dimensionality zero if they are tensors (of any rank),
but with conformal dimension 1/2 if they are spinors or spinor-tensors.


  We show that the formulas for the sum rules for the eigenvalues of
inhomogeneous systems that we have obtained in two recent papers are incomplete
when the system contains a zero mode. We prove that there are finite
contributions of the zero mode to the sum rules and we explicitly calculate the
expressions for the sum rules of order one and two. The previous results for
systems that do not contain a zero mode are unaffected.


  This paper investigates the causality in the decision making of movie
recommendations through the users' affective profiles. We advocate a method of
assigning emotional tags to a movie by the auto-detection of the affective
features in the movie's overview. We apply a text-based Emotion Detection and
Recognition model, which trained by tweets short messages and transfers the
learned model to detect movie overviews' implicit affective features. We
vectorized the affective movie tags to represent the mood embeddings of the
movie. We obtain the user's emotional features by taking the average of all the
movies' affective vectors the user has watched. We apply five-distance metrics
to rank the Top-N movie recommendations against the user's emotion profile. We
found Cosine Similarity distance metrics performed better than other distance
metrics measures. We conclude that by replacing the top-N recommendations
generated by the Recommender with the reranked recommendations list made by the
Cosine Similarity distance metrics, the user will effectively get affective
aware top-N recommendations while making the Recommender feels like an Emotion
Aware Recommender.


  One approach to improving the running time of kernel-based machine learning
methods is to build a small sketch of the input and use it in lieu of the full
kernel matrix in the machine learning task of interest. Here, we describe a
version of this approach that comes with running time guarantees as well as
improved guarantees on its statistical performance. By extending the notion of
\emph{statistical leverage scores} to the setting of kernel ridge regression,
our main statistical result is to identify an importance sampling distribution
that reduces the size of the sketch (i.e., the required number of columns to be
sampled) to the \emph{effective dimensionality} of the problem. This quantity
is often much smaller than previous bounds that depend on the \emph{maximal
degrees of freedom}. Our main algorithmic result is to present a fast algorithm
to compute approximations to these scores. This algorithm runs in time that is
linear in the number of samples---more precisely, the running time is
$O(np^2)$, where the parameter $p$ depends only on the trace of the kernel
matrix and the regularization parameter---and it can be applied to the matrix
of feature vectors, without having to form the full kernel matrix. This is
obtained via a variant of length-squared sampling that we adapt to the kernel
setting in a way that is of independent interest. Lastly, we provide empirical
results illustrating our theory, and we discuss how this new notion of the
statistical leverage of a data point captures in a fine way the difficulty of
the original statistical learning problem.


  In text-to-SQL tasks -- as in much of NLP -- compositional generalization is
a major challenge: neural networks struggle with compositional generalization
where training and test distributions differ. However, most recent attempts to
improve this are based on word-level synthetic data or specific dataset splits
to generate compositional biases. In this work, we propose a clause-level
compositional example generation method. We first split the sentences in the
Spider text-to-SQL dataset into sub-sentences, annotating each sub-sentence
with its corresponding SQL clause, resulting in a new dataset Spider-SS. We
then construct a further dataset, Spider-CG, by composing Spider-SS
sub-sentences in different combinations, to test the ability of models to
generalize compositionally. Experiments show that existing models suffer
significant performance degradation when evaluated on Spider-CG, even though
every sub-sentence is seen during training. To deal with this problem, we
modify a number of state-of-the-art models to train on the segmented data of
Spider-SS, and we show that this method improves the generalization
performance.


  In this short note, we review some recent progress in understanding the
1-loop corrections to the Gibbons-Hawking entropy, which amounts to studying
free fields on the de Sitter static patch and the round sphere. After briefly
surveying the unitary irreducible representations of the de Sitter group
$SO(1,d+1)$ and their Harish-Chandra characters, we discuss the Lorentzian
interpretation for the 1-loop sphere path integral for a scalar. After that we
comment on how the results are modified by edge contributions for spinning
fields.


  The flavor-changing electromagnetic dipole operator O_7 gives the dominant
contribution to the B -> Xs gamma decay rate. We calculate two-loop QCD
corrections to its matrix element together with the corresponding
bremsstrahlung contributions. The optical theorem is applied, and the relevant
imaginary parts of three-loop diagrams are computed following the lines of our
recent t -> Xb W calculation. The complete result allows us to test the
validity of the naive non-abelianization (NNA) approximation that has been
previously applied to estimate the NNLO QCD correction to Gamma(B -> Xs
gamma)/Gamma(B -> Xu e nu). When both decay widths are normalized to m^5_{b,R}
in the same renormalization scheme R, the calculated O(alpha_s^2) correction is
sizeable (~ 6%), and the NNA estimate is about 1/3 too large. On the other
hand, when the ratio of the decay widths is written as
S*(m_b(m_b)/m_{b,pole})^2, the calculated O(alpha_s^2) correction to S is at
the level of 1% for both the complete and the NNA results.


  The nature of random errors in any data set that is Gaussian is a well
established fact according to the Central Limit Theorem. Supernovae type Ia
data have played a crucial role in major discoveries in cosmology. Unlike in
laboratory experiments, astronomical measurements can not be performed in
controlled situations. Thus, errors in astronomical data can be more severe in
terms of systematics and non-Gaussianity compared to those of laboratory
experiments. In this paper, we use the Kolmogorov-Smirnov statistic to test
non-Gaussianity in high-z supernovae data. We apply this statistic to four data
sets, i.e., Gold data(2004), Gold data(2007), Union2 catalogue and the Union2.1
data set for our analysis. Our results shows that in all four data sets the
errors are consistent with the Gaussian distribution.


  Hashing has recently sparked a great revolution in cross-modal retrieval
because of its low storage cost and high query speed. Recent cross-modal
hashing methods often learn unified or equal-length hash codes to represent the
multi-modal data and make them intuitively comparable. However, such unified or
equal-length hash representations could inherently sacrifice their
representation scalability because the data from different modalities may not
have one-to-one correspondence and could be encoded more efficiently by
different hash codes of unequal lengths. To mitigate these problems, this paper
exploits a related and relatively unexplored problem: encode the heterogeneous
data with varying hash lengths and generalize the cross-modal retrieval in
various challenging scenarios. To this end, a generalized and flexible
cross-modal hashing framework, termed Matrix Tri-Factorization Hashing (MTFH),
is proposed to work seamlessly in various settings including paired or unpaired
multi-modal data, and equal or varying hash length encoding scenarios. More
specifically, MTFH exploits an efficient objective function to flexibly learn
the modality-specific hash codes with different length settings, while
synchronously learning two semantic correlation matrices to semantically
correlate the different hash representations for heterogeneous data comparable.
As a result, the derived hash codes are more semantically meaningful for
various challenging cross-modal retrieval tasks. Extensive experiments
evaluated on public benchmark datasets highlight the superiority of MTFH under
various retrieval scenarios and show its competitive performance with the
state-of-the-arts.


  Parameter-efficient fine-tuning (PEFT) that was initially developed for
exploiting pre-trained large language models has recently emerged as an
effective approach to perform transfer learning on computer vision tasks.
However, the effectiveness of PEFT on medical vision foundation models is still
unclear and remains to be explored. As a proof of concept, we conducted a
detailed empirical study on applying PEFT to chest radiography foundation
models. Specifically, we delved into LoRA, a representative PEFT method, and
compared it against full-parameter fine-tuning (FFT) on two self-supervised
radiography foundation models across three well-established chest radiograph
datasets. Our results showed that LoRA outperformed FFT in 13 out of 18
transfer learning tasks by at most 2.9% using fewer than 1% tunable parameters.
Combining LoRA with foundation models, we set up new state-of-the-art on a
range of data-efficient learning tasks, such as an AUROC score of 80.6% using
1% labeled data on NIH ChestX-ray14. We hope this study can evoke more
attention from the community in the use of PEFT for transfer learning on
medical imaging tasks. Code and models are available at
https://github.com/RL4M/MED-PEFT.


  We propose a novel method for instance label segmentation of dense 3D voxel
grids. We target volumetric scene representations, which have been acquired
with depth sensors or multi-view stereo methods and which have been processed
with semantic 3D reconstruction or scene completion methods. The main task is
to learn shape information about individual object instances in order to
accurately separate them, including connected and incompletely scanned objects.
We solve the 3D instance-labeling problem with a multi-task learning strategy.
The first goal is to learn an abstract feature embedding, which groups voxels
with the same instance label close to each other while separating clusters with
different instance labels from each other. The second goal is to learn instance
information by densely estimating directional information of the instance's
center of mass for each voxel. This is particularly useful to find instance
boundaries in the clustering post-processing step, as well as, for scoring the
segmentation quality for the first goal. Both synthetic and real-world
experiments demonstrate the viability and merits of our approach. In fact, it
achieves state-of-the-art performance on the ScanNet 3D instance segmentation
benchmark.


  Wave scattering in chaotic systems can be characterized by its spectrum of
resonances, $z_n=E_n-i\frac{\Gamma_n}{2}$, where $E_n$ is related to the energy
and $\Gamma_n$ is the decay rate or width of the resonance. If the
corresponding ray dynamics is chaotic, a gap is believed to develop in the
large-energy limit: almost all $\Gamma_n$ become larger than some $\gamma$.
However, rare cases with $\Gamma<\gamma$ may be present and actually dominate
scattering events. We consider the statistical properties of these super-sharp
resonances. We find that their number does not follow the fractal Weyl law
conjectured for the bulk of the spectrum. We also test, for a simple model, the
universal predictions of random matrix theory for density of states inside the
gap and the hereby derived probability distribution of gap size.


  Entropy coding is the backbone data compression. Novel machine-learning based
compression methods often use a new entropy coder called Asymmetric Numeral
Systems (ANS) [Duda et al., 2015], which provides very close to optimal
bitrates and simplifies [Townsend et al., 2019] advanced compression techniques
such as bits-back coding. However, researchers with a background in machine
learning often struggle to understand how ANS works, which prevents them from
exploiting its full versatility. This paper is meant as an educational resource
to make ANS more approachable by presenting it from a new perspective of latent
variable models and the so-called bits-back trick. We guide the reader step by
step to a complete implementation of ANS in the Python programming language,
which we then generalize for more advanced use cases. We also present and
empirically evaluate an open-source library of various entropy coders designed
for both research and production use. Related teaching videos and problem sets
are available online.


  We discuss novel features of twisted-light absorption both by hydrogen-like
atoms and by micro-particles. First, we extend the treatment of atomic
photoexcitation by twisted photons to include atomic recoil, derive generalized
quantum selection rules and consider phenomena of forbidden atomic transitions.
Second, using the same electromagnetic potential for the twisted light beams,
we analyze the radiation pressure from these beams on micro-sized particles,
and verify that while the Poynting vector can in some circumstances point back
toward the source, a complete analysis nonetheless gives a repulsive radiation
pressure.


  A Mott insulator sometimes induces unconventional superconductivity in its
neighbors when doped and/or pressurized. Because the phase diagram should be
strongly related to the microscopic mechanism of the superconductivity, it is
important to obtain the global phase diagram surrounding the Mott insulating
state. However, the parameter available for controlling the ground state of
most Mott insulating materials is one-dimensional owing to technical
limitations. Here we present a two-dimensional ground-state mapping for a Mott
insulator using an organic field-effect device by simultaneously tuning the
bandwidth and bandfilling. The observed phase diagram showed many unexpected
features such as an abrupt first-order superconducting transition under
electron doping, a recurrent insulating phase in the heavily electron-doped
region, and a nearly constant superconducting transition temperature in a wide
parameter range. These results are expected to contribute toward elucidating
one of the standard solutions for the Mott-Hubbard model. Theoretical
calculations reproducing the main features of our experimental results have
also been conducted.


  We present the results of our optical monitoring campaign of the X-ray source
H 0507+164, a low luminosity Seyfert 1.5 galaxy at a redshift z = 0.018.
Spectroscopic observations were carried out during 22 nights in 2007, from the
21 of November to the 26 of December. Photometric observations in the R-band
for 13 nights were also obtained during the same period. The continuum and
broad line fluxes of the galaxy were found to vary during our monitoring
period. The R-band differential light curve with respect to a companion star
also shows a similar variability. Using cross correlation analysis, we
estimated a time delay of 3.01 days (in the rest frame), of the response of the
broad H-beta line fluxes to the variations in the optical continuum at 5100
angstroms. Using this time delay and the width of the H-beta line, we estimated
the radius for the Broad Line Region (BLR) of 2.53 x 10^{-3} parsec, and a
black hole mass of 9.62 x 10^{6} solar mass.


  The manuscript is devoted to the description of the results obtained in the
frame of the modified potential cluster model with the classification of states
according to Young tableaux for neutron and proton radiative capture processes
on 2H at thermal and astrophysical energies. It demonstrates methods of
application that were obtained on the basis of phase shift analysis and
characteristics of the bound states of 2H potentials for consideration of the
radiative capture processes. First reaction of the proton capture directly
takes part in the pp solar cycle, where it is the second reaction. The neutron
capture is not a part of usual thermonuclear cycles in the Sun and stars, but
can take part in the processes of primordial nucleosynthesis, following at
formation and evolution of our entire Universe.


  This work researches the impact of including a wider range of participants in
the strategy-making process on the performance of organizations which operate
in either moderately or highly complex environments. Agent-based simulation
demonstrates that the increased number of ideas generated from larger and
diverse crowds and subsequent preference aggregation lead to rapid discovery of
higher peaks in the organization's performance landscape. However, this is not
the case when the expansion in the number of participants is small. The results
confirm the most frequently mentioned benefit in the Open Strategy literature:
the discovery of better performing strategies.


  We provide a new and much simpler structure for quasi-ideal adequate
transversals of abundant semigroups in terms of spined products, which is
similar in nature to that given by Saito for weakly multiplicative inverse
transversals of regular semigroups. As a consequence we deduce a similar result
for multiplicative transversals of abundant semigroups and also consider the
case when the semigroups are in fact regular and provide some new structure
theorems for inverse transversals.


  Self-organized quasi periodicity is one of the most puzzling dynamical phases
observed in systems of non linear coupled oscillators. The single dynamical
units are not locked to the periodic mean field they produce, but they still
feature a coherent behavior, through an unexplained complex form of
correlation. We consider a class of leaky integrate-and-fire oscillators on
random sparse and massive networks with dynamical synapses, featuring
self-organized quasi periodicity, and we show how complex collective
oscillations arise from constructive interference of microscopic dynamics. In
particular, we find a simple quantitative relationship between two relevant
microscopic dynamical time scales and the macroscopic time scale of the global
signal. We show that the proposed relation is a general property of collective
oscillations, common to all the partially synchronous dynamical phases
analyzed. We argue that an analogous mechanism could be at the origin of
similar network dynamics.


  In this paper, we continue our work on video relocalization task. Based on
using graph convolution to extract intra-video and inter-video frame features,
we improve the method by using similarity-metric based graph convolution, whose
weighted adjacency matrix is achieved by calculating similarity metric between
features of any two different time steps in the graph. Experiments on
ActivityNet v1.2 and Thumos14 dataset show the effectiveness of this
improvement, and it outperforms the state-of-the-art methods.


  Dynamics under which a system of Ising spins relaxes to a stationary state
with Bolzmann-Gibbs measure and which do not fulfil the condition of detailed
balance are irreversible and asymmetric. We revisit the problem of the
determination of rates yielding such a stationary state for models with
single-spin flip dynamics. We add some supplementary material to this study and
confirm that Gibbsian irreversible Ising models exist for one and
two-dimensional lattices but not for the three-dimensional cubic lattice. We
also analyze asymmetric Gibbsian dynamics in the limit of infinite temperature.
We finally revisit the case of a linear chain of spins under asymmetric
conserved dynamics.


  Adapting machine translation systems in the real world is a difficult
problem. In contrast to offline training, users cannot provide the type of
fine-grained feedback (such as correct translations) typically used for
improving the system. Moreover, different users have different translation
needs, and even a single user's needs may change over time.
  In this work we take a different approach, treating the problem of adaptation
as one of selection. Instead of adapting a single system, we train many
translation systems using different architectures, datasets, and optimization
methods. Using bandit learning techniques on simulated user feedback, we learn
a policy to choose which system to use for a particular translation task. We
show that our approach can (1) quickly adapt to address domain changes in
translation tasks, (2) outperform the single best system in mixed-domain
translation tasks, and (3) make effective instance-specific decisions when
using contextual bandit strategies.


  We propose a new conjecture on the relation between the species doubling of
lattice fermions and the topology of manifold on which the fermion action is
defined. Our conjecture claims that the maximal number of fermion species on a
finite-volume and finite-spacing lattice defined by discretizing a
$D$-dimensional manifold is equal to the summation of the Betti numbers of the
manifold. We start with reconsidering species doubling of naive fermions on the
lattices whose topologies are torus ($T^{D}$), hyperball ($B^D$) and their
direct-product space ($T^{D} \times B^{d}$). We find that the maximal number of
species is in exact agreement with the sum of Betti numbers $\sum^{D}_{r=0}
\beta_{r}$ for these manifolds. Indeed, the $4D$ lattice fermion on torus has
up to $16$ species while the sum of Betti numbers of $T^4$ is $16$. This
coincidence holds also for the $D$-dimensional hyperball and their
direct-product space $T^{D} \times B^{d}$. We study several examples of lattice
fermions defined on discretized hypersphere ($S^{D}$), and find that it has up
to $2$ species, which is the same number as the sum of Betti numbers of
$S^{D}$. From these facts, we conjecture the equivalence of the maximal number
of fermion species and the summation of Betti numbers. We discuss a program for
proof of the conjecture in terms of Hodge theory and spectral graph theory.


  The process of polymerizing a protein by a ribosome, using a messenger RNA
(mRNA) as the corresponding template, is called {\it translation}. Ribosome may
be regarded as a molecular motor for which the mRNA template serves also as the
track. Often several ribosomes may translate the same (mRNA) simultaneously.
The ribosomes bound simultaneously to a single mRNA transcript are the members
of a polyribosome (or, simply, {\it polysome}). Experimentally measured {\it
polysome profile} gives the distribution of polysome {\it sizes}. Recently a
breakthrough in determining the instantaneous {\it positions} of the ribosomes
on a given mRNA track has been achieved and the technique is called {\it
ribosome profiling} \cite{ingolia10,guo10}. Motivated by the success of these
techniques, we have studied the spatio-temporal organization of ribosomes by
extending a theoretical model that we have reported elsewhere \cite{sharma11}.
This extended version of our model incorporates not only (i) mechano-chemical
cycle of individual ribomes, and (ii) their steric interactions, but also (iii)
the effects of (a) kinetic proofreading, (b) translational infidelity, (c)
ribosome recycling, and (d) sequence inhomogeneities. The theoretical framework
developed here will serve in guiding further experiments and in analyzing the
data to gain deep insight into various kinetic processes involved in
translation.


  Deep neural networks need large amounts of labeled data to achieve good
performance. In real-world applications, labels are usually collected from
non-experts such as crowdsourcing to save cost and thus are noisy. In the past
few years, deep learning methods for dealing with noisy labels have been
developed, many of which are based on the small-loss criterion. However, there
are few theoretical analyses to explain why these methods could learn well from
noisy labels. In this paper, we theoretically explain why the widely-used
small-loss criterion works. Based on the explanation, we reformalize the
vanilla small-loss criterion to better tackle noisy labels. The experimental
results verify our theoretical explanation and also demonstrate the
effectiveness of the reformalization.


  We study the most suitable procedure to measure the effective temperature in
off-equilibrium systems. We analyze the stationary current established between
an off-equilibrium system and a thermometer and the necessary conditions for
that current to vanish. We find that the thermometer must have a short
characteristic time-scale compared to the typical decorrelation time of the
glassy system to correctly measure the effective temperature. This general
conclusion is confirmed analyzing an ensemble of harmonic oscillators with
Monte Carlo dynamics as an illustrative example of a solvable model of a glass.
We also find that the current defined allows to extend Fourier's law to the
off-equilibrium regime by consistently defining effective transport
coefficients. Our results for the oscillator model explain why thermal
conductivities between thermalized and frozen degrees of freedom in structural
glasses are extremely small.


  We present a strategy for producing multi-qubit gates that promise high
fidelity with minimal tuning requirements. Our strategy combines gap protection
from the adiabatic theorem with dynamical decoupling in a complementary manner.
To avoid degenerate states and maximize the benefit of the gap protection, the
scheme is best suited when there are two different kinds of qubits (not
mutually resonant). Furthermore, we require a robust operating point in control
space where the qubits interact with little sensitivity to noise. This allows
us to circumvent a No-Go theorem that prevents block-box dynamically corrected
gates [Phys. Rev. A 80, 032314 (2009)]. We show how to apply our strategy to an
architecture in Si with P donors where we assume we can shuttle electrons
between different donors. Electron spins act as mobile ancillary qubits and P
nuclear spins act as long-lived data qubits. This system can have a very robust
operating point where the electron spin is bound to a donor in the quadratic
Stark shift regime. High fidelity single qubit gates may be performed using
well-established global magnetic resonance pulse sequences. Single electron
spin preparation and measurement has also been demonstrated. Putting this all
together, we present a robust universal gate set for quantum computation.


  Blockchain is a new technological approach that has gained popularity on the
market due to its application in several areas such as education, health,
security, and smart cities, among others. However, understanding how blockchain
works is not easy at first, especially for non-technical people, because it
relies on a non-trivial computational process. We have developed a game board -
called Blocktrain - whose game mechanics are based on the blockchain processing
model. This game gives people the opportunity to learn key blockchain concepts
while playing. In this paper, we describe the game design process and
assessment of the game as pedagogical instrument.


  In this paper we develop an isogeometric B\'ezier dual mortar method for the
biharmonic problem on multi-patch domains. The well-posedness of the discrete
biharmonic problem requires a discretization with $C^1$ continuous basis
functions. Hence, two Lagrange multipliers are required to apply both $C^0$ and
$C^1$ continuity constraints on each intersection. The dual mortar method
utilizes dual basis functions to discretize the Lagrange multiplier spaces. In
order to preserve the sparsity of the coupled problem, we develop a dual mortar
suitable $C^1$ constraint and utilize the B\'ezier dual basis to discretize the
Lagrange multiplier spaces. The B\'ezier dual basis functions are constructed
through B\'ezier projection and possess the same support size as the
corresponding B-spline basis functions. We prove that this approach leads to a
well-posed discrete problem and specify requirements to achieve optimal
convergence. Although the B\'ezier dual basis is sub-optimal due to the lack of
polynomial reproduction, our formulation successfully postpones the domination
of the consistency error for practical problems. We verify the theoretical
results and demonstrate the performance of the proposed formulation through
several benchmark problems.


  In the Unsplittable Flow on a Path problem UFP, we are given a path graph
with edge capacities and a collection of tasks. Each task is characterized by a
demand, a profit, and a subpath. Our goal is to select a maximum profit subset
of tasks such that the total demand of the selected tasks that use each edge
$e$ is at most the capacity of $e$. Bag-UFP is the generalization of UFP where
tasks are partitioned into bags, and we are allowed to select at most one task
per bag. UFP admits a PTAS [Grandoni,M{\"o}mke,Wiese'22] but not an EPTAS
[Wiese'17]. Bag-UFP is APX-hard [Spieksma'99] and the current best
approximation is $O(\log n/\log\log n)$ [Grandoni,Ingala,Uniyal'15], where $n$
is the number of tasks.
  In this paper, we study the mentioned two problems when parameterized by the
number $m$ of edges in the graph, with the goal of designing faster
parameterized approximation algorithms. We present a parameterized EPTAS for
Bag-UFP, and a substantially faster parameterized EPTAS for UFP (which is an
FPTAS for $m=O(1)$). We also show that a parameterized FPTAS for UFP (hence for
BagUFP) does not exist, therefore our results are qualitatively tight.


  Image deblurring is a fundamental problem in imaging, usually solved with
com-putationally intensive optimization procedures. We show that the
minimization can be significantly accelerated by leveraging the fact that
images and blur operators are compressible in the same orthogonal wavelet
basis. The proposed methodology consists of three ingredients: i) a sparse
approximation of the blur operator in wavelet bases, ii) a diagonal
preconditioner and iii) an implementation on massively parallel architectures.
Combing the three ingredients leads to acceleration factors ranging from 30 to
250 on a typical workstation. For instance, a 1024 x 1024 image can be
deblurred in 0.15 seconds, which corresponds to real-time.


  In this work we study qualitative properties of real analytic bounded maps.
The main tool is approximation of real valued functions analytic in rectangular
domains of the complex plane by continued g-fractions of Wall. As an
application, the Sundman-Poincar\'e method in the Newtonian three-body problem
is revisited and applications to collision detection problem are considered.


  We investigate various pursuit-evasion parameters on latin square graphs,
including the cop number, metric dimension, and localization number. The cop
number of latin square graphs is studied, and for $k$-MOLS$(n),$ bounds for the
cop number are given. If $n>(k+1)^2,$ then the cop number is shown to be $k+2.$
Lower and upper bounds are provided for the metric dimension and localization
number of latin square graphs. The metric dimension of back-circulant latin
squares shows that the lower bound is close to tight. Recent results on covers
and partial transversals of latin squares provide the upper bound of
$n+O\left(\frac{\log{n}}{\log{\log{n}}}\right)$ on the localization number of a
latin square graph of order $n.$


  In visible light communication (VLC) diverse types of traffic are supported
while the number of optical channels is limited. In this paper we propose a
dynamic channel reservation scheme for higher priority calls that does not
reduce the channel utilization. The number of reserved channels for each
traffic class is calculated using real time observation of the call arrival
rates of each traffic classes. The numerical results show that the proposed
scheme is able to reduce the call blocking probability of the higher priority
user within a reasonable range without sacrificing channel utilization.


  Among all cosmological quantum-gravity or quantum-gravity-inspired scenarios,
only very few predict a blue-tilted primordial tensor spectrum. We explore five
of them and check whether they can generate a stochastic gravitational-wave
background detectable by present and future interferometers: non-local quantum
gravity, string-gas cosmology, new ekpyrotic scenario, Brandenberger-Ho
non-commutative inflation and multi-fractional spacetimes. We show that
non-local quantum gravity is unobservable, while all the other models can reach
the strain sensitivity of DECIGO but not that of LIGO-Virgo-KAGRA, LISA or
Einstein Telescope. Other quantum-gravity models with red-tilted spectra (most
loop quantum cosmologies) or with exceptionally tiny quantum corrections
(Wheeler-DeWitt quantum cosmology) are found to be non-detectable.


  [Context]Advancement in technologies, popularity of small-batch manufacturing
and the recent trend of investing in hardware startups are among the factors
leading to the rise of hardware startups nowadays. It is essential for hardware
startups to be not only agile to develop their business but also efficient to
develop the right products. [Objective] We investigate how hardware startups
achieve agility when developing their products in early stages. [Methods] A
qualitative research is conducted with data from 20 hardware startups. [Result]
Preliminary results show that agile development is known to hardware
entrepreneurs, however it is adopted limitedly. We also found tactics in four
domains (1) strategy, (2) personnel, (3) artifact and (4) resource that enable
hardware startups agile in their early stage business and product development.
[Conclusions] Agile methodologies should be adopted with the consideration of
specific features of hardware development, such as up-front design and vendor
dependencies.


  A numerical framework to explore the Brill Wave Initial Value Problem is
presented along with a review of some of the theoretical predictions concerning
Brill waves. It is demonstrated that there is an agreement between the
numerically observed phenomena and theory, namely that the IVP parameterisation
of the metric function $q$ has a minimum and maximum amplitude for which a
solution to the Hamiltonian for the metric function $\phi$ exists, and a
critical regime in the middle for which there are and are not apparent horizons
present.


  We report on the development of highly sensitive SQUIDs featuring
sub-micrometer loop dimensions. The integration of high quality and low
capacitance SIS Nb/AlOx/Nb cross-type Josephson tunnel junctions results in
white flux noise levels as low as 66 n$\Phi_0$/Hz$^{ 1/2} $, well below
state-of-the-art values of their Nb-based counterparts based on constriction
type junctions. Estimation of the spin sensitivity of the best SQUIDs yield
$S_\mu^{ 1/2} < 7 \mu_B$/Hz$^{ 1/2} $ in the white noise region, suitable for
the investigation of small spin systems. We discuss fabrication challenges,
show results on the electrical characterization of devices with various pickup
loops, and describe options for further improvement, which may push the
sensitivity of such devices even to single spin resolution.


  Due to the pervasiveness of image capturing devices in every-day life, images
of individuals are routinely captured. Although this has enabled many benefits,
it also infringes on personal privacy. A promising direction in research on
obfuscation of facial images has been the work in the k-same family of methods
which employ the concept of k-anonymity from database privacy. However, there
are a number of deficiencies of k-anonymity that carry over to the k-same
methods, detracting from their usefulness in practice. In this paper, we first
outline several of these deficiencies and discuss their implications in the
context of facial obfuscation. We then develop a framework through which we
obtain a formal differentially private guarantee for the obfuscation of facial
images in generative machine learning models. Our approach provides a provable
privacy guarantee that is not susceptible to the outlined deficiencies of
k-same obfuscation and produces photo-realistic obfuscated output. In addition,
we demonstrate through experimental comparisons that our approach can achieve
comparable utility to k-same obfuscation in terms of preservation of useful
features in the images. Furthermore, we propose a method to achieve
differential privacy for any image (i.e., without restriction to facial images)
through the direct modification of pixel intensities. Although the addition of
noise to pixel intensities does not provide the high visual quality obtained
via generative machine learning models, it offers greater versatility by
eliminating the need for a trained model. We demonstrate that our proposed use
of the exponential mechanism in this context is able to provide superior visual
quality to pixel-space obfuscation using the Laplace mechanism.


  We advance the viewpoint that only relevant modes of the vacuum fluctuations,
namely, with wavelengths conditioned by the size, homogeneity, geometry and
topology of the Universe, do contribute into the cosmological constant. A
formula is derived which relates the cosmological constant with the size of the
Universe and the three fundamental constants: the velocity of light, Planck and
Newton gravitational constants. Then the current value of the cosmological
constant remarkably agrees with the value indicated by distant supernovae
observations, i.e. is of the order of the critical density. Thus the
cosmological constant had to be smaller than the matter density in the past and
will be bigger in the future.


  We consider the problem of few-shot scene adaptive crowd counting. Given a
target camera scene, our goal is to adapt a model to this specific scene with
only a few labeled images of that scene. The solution to this problem has
potential applications in numerous real-world scenarios, where we ideally like
to deploy a crowd counting model specially adapted to a target camera. We
accomplish this challenge by taking inspiration from the recently introduced
learning-to-learn paradigm in the context of few-shot regime. In training, our
method learns the model parameters in a way that facilitates the fast
adaptation to the target scene. At test time, given a target scene with a small
number of labeled data, our method quickly adapts to that scene with a few
gradient updates to the learned parameters. Our extensive experimental results
show that the proposed approach outperforms other alternatives in few-shot
scene adaptive crowd counting. Code is available at
https://github.com/maheshkkumar/fscc.


  We consider the limit of two-dimensional N=(2,2) superconformal minimal
models when the central charge approaches c=3. Starting from a geometric
description as non-linear sigma models, we show that one can obtain two
different limit theories. One is the free theory of two bosons and two
fermions, the other one is a continuous orbifold thereof. We substantiate this
claim by detailed conformal field theory computations.


  This is a review article on the development of the probe and enclosure
methods from past to present, focused on their central ideas together with
various applications.


  Programmable linear optical interferometers are promising for classical and
quantum applications. Their integrated design makes it possible to create more
scalable and stable devices. To use them in practice, one has to reconstruct
the whole device model taking the manufacturing errors into account. The
inability to address individual interferometer elements complicates the
reconstruction problem. A naive approach is to train the model via some complex
optimization procedure. A faster optimization-free algorithm has been recently
proposed [Opt. Express 31, 16729 (2023)]. However, it requires the full
transfer matrix tomography while a more practical setup measures only the
fields intensities at the interferometer output. In this paper, we propose the
modification of the fast algorithm, which uses additional set of interferometer
configurations in order to reconstruct the model in the case of intensity-only
measurements. We show that it performs slightly worse than the original fast
algorithm but it is more practical and still does not require intensive
numerical optimization.


  This paper is based on talks delivered in summer 2008 at the Conference on
Motives, QFT and Pseudodifferential Operators in Boston, and at the Trimester
programme Geometry and Physics, Hausdorff Institute for Mathematics in Bonn
  The paper surveys the pseudodifferential calculus with parameter, resolvent
expansions, Dixmier trace, noncommutative residue, Kontsevich-Vishik canonical
trace as well some aspects of regularized integrals on spaces of symbols.


  The infrared behaviour of the medium modified gluon propagator in
non-equilibrium situations is studied in the covariant gauge using the
Schwinger-Keldysh closed-time path formalism. It is shown that the magnetic
screening mass is non-zero at the one loop level whenever the initial gluon
distribution function is non isotropic with the assumption that the
distribution function of the gluon is not divergent at zero transverse
momentum. For isotropic gluon distribution functions, such as those describing
local equilibrium, the magnetic mass at one loop level is zero which is
consistent with finite temperature field theory results. Assuming that a
reasonable initial gluon distribution function can be obtained from a
perturbative QCD calculation of minijets, we determine these out of equilibrium
values for the initial magnetic and Debye screening masses at energy densities
appropriate to RHIC and LHC. We also compare the magnetic masses obtained here
with those obtained using finite temperature lattice QCD methods at similar
temperatures at RHIC and LHC.


  We show how to perform all logical Clifford gates on the [[144,12,12]]
bivariate bicycle code, also known as the gross code. The scheme adds about 100
ancilla qubits into the connectivity graph, and one of the twelve logical
qubits is sacrificed for gate synthesis. Logical measurements are combined with
the automorphism gates studied by Bravyi et al. (Nature 627, 778-782) to
implement 288 Pauli product measurements. We demonstrate the practicality of
our scheme through circuit-level noise simulations, leveraging a novel modular
decoder that combines BPOSD with matching. The main technical contribution is a
lower overhead logical measurement scheme based on gauge-fixing the
construction by Cohen et al. (Sci. Adv. 8, eabn1717). Our techniques apply to
general CSS codes and leverage expansion properties of the Tanner graph to give
rigorous guarantees on qubit count, code distance, fault distance, and decoding
distance of the modular decoder. In particular, we require $O(d/\beta)$
additional qubits where $\beta$ is the boundary Cheeger constant of the
subgraph supporting the logical operator being measured. By introducing $O(d)$
additional bridge qubits, we are also able to measure products of logical Pauli
operators and logical $Y$ operators.


  We consider the inverse problem of identification of degenerate diffusion
coefficient of the form $x^\alpha a(x)$ in a one dimensional parabolic equation
by some extra data. We first prove by energy methods the uniqueness and
Lipschitz stability results for the identification of a constant coefficient
$a$ and the power $\alpha$ by knowing an interior data at some time.
  On the other hand, we obtain the uniqueness result for the identification of
a general diffusion coefficients $a(x)$ and also the power $\alpha$ form a
boundary data on one side of the space interval. The proof is based on global
Carleman estimates for a hyperbolic problem and an inversion of the integral
transform similar to the Laplace transform. Finally, the theoretical results
are satisfactory verified by numerically experiments.


  We propose that at the beginning of the universe gravity existed in a limbo
either because it was switched off or because it was only conformally coupled
to all particles. This picture can be reverse-engineered from the requirement
that the cosmological perturbations be (nearly) scale-invariant without the
need for inflation. It also finds support in recent results in quantum gravity
suggesting that spacetime becomes two-dimensional at super-Planckian energies.
We advocate a novel top-down approach to cosmology based on the idea that
gravity and the Big Bang Universe are relics from the mechanism responsible for
breaking the fundamental conformal invariance. Such a mechanism should leave
clear signatures in departures from scale-invariance in the primordial power
spectrum and the level of gravity waves generated.


  A type IIA string (or F-theory) compactified on a Calabi-Yau threefold is
believed to be dual to a heterotic string on a K3 surface times a 2-torus (or
on a K3 surface). We consider how the resulting moduli space of hypermultiplets
is identified between these two pictures in the case of the E8xE8 heterotic
string. As examples we discuss SU(2)-bundles and G2-bundles on the K3 surface
and the case of point-like instantons. We are lead to a rather beautiful
identification between the integral cohomology of the Calabi-Yau threefold and
some integral structures on the heterotic side somewhat reminiscent of mirror
symmetry. We discuss the consequences for probing nonperturbative effects in
the both the type IIA string and the heterotic string.


  Galois rings are regarded as "building blocks" of a finite commutative ring
with identity. There have been many papers on classical error correction codes
over Galois rings published. As an important warm-up before exploring quantum
algorithms and quantum error correction codes over Galois rings, we study the
quantum Fourier transform (QFT) over Galois rings and prove it can be
efficiently preformed on a quantum computer. The properties of the QFT over
Galois rings lead to the quantum algorithm for hidden linear structures over
Galois rings.


  We present a new model of molecular cloud cores that originate from
filamentary clouds that are threaded by helical magnetic fields. Only modest
toroidal fields are required to produce elongated cores, with intrinsic axis
ratios in the range 0.1 <= q <= 0.9 and mean projected axis ratios in the range
0.3 <= <p> <= 1. Thus many of our models are in good agreement with the
observed shapes of cores (Myers et al 1991, Ryden 1996), which find axis ratios
distributed about the mean values <p> \approx 0.5-0.6 and q \approx 0.4-0.5. We
find that the Bonnor-Ebert critical mass is reduced by about 20% by the helical
field threading our models. We also find that models are generally most
elongated when the mass is significantly less than the Bonnor-Ebert critical
mass for unmagnetized cores.


  In this paper we deal with four generalized notions of amenability which are
called approximate, approximate weak, approximate cyclic and approximate
$n$-weak amenability. The first two were introduced and studied by Ghahramani
and Loy in [9]. We introduce the third and fourth ones and we show by means of
some examples, their distinction with their classic analogs.
  Our main result is that under some mild conditions on a given Banach algebra
$\A$, if its second dual $\A^{**}$ is $(2n-1)$-weakly [respectively
approximately/ approximately weakly/ approximately $n$-weakly] amenable, then
so is $\A$. Also if $\A$ is approximately $(n+2)$-weakly amenable, then it is
approximately $n$-weakly amenable. Moreover we show the relationship between
approximate trace extension property and approximate weak [respectively cyclic]
amenability. This answers question 9.1 of [9] for approximate weak and cyclic
amenability.


  We derive the explicit form of eigenvectors of selfadjoint extension $H_\xi$,
parametrized by $\xi \in \langle 0,\pi),$ of differential expression $
H=-\frac{d^2 }{d x^2} + \frac{x^2 }{4}$ together with the spectrum
$\sigma(H_\xi)$ on the space $L^2(\mathbb{R}^+).$ For each $\xi$ the set of
eigenvectors form an orthonormal basis of $L^2(\mathbb{R}^+).$


  Self-inducing structure of pentagonal piecewise isometry is applied to show
detailed description of periodic and aperiodic orbits, and further dynamical
properties. A Pisot number appears as a scaling constant and plays a crucial
role in the proof. Further generalization is discussed in the last section.


  Markov state models (MSMs) have become a popular approach for investigating
the conformational dynamics of proteins and other biomolecules. MSMs are
typically built from numerous molecular dynamics simulations by dividing the
sampled configurations into a large number of microstates based on geometric
criteria. The resulting microstate model can then be coarse-grained into a more
understandable macro state model by lumping together rapidly mixing microstates
into larger, metastable aggregates. However, finite sampling often results in
the creation of many poorly sampled microstates. During coarse-graining, these
states are mistakenly identified as being kinetically important because
transitions to/from them appear to be slow. In this paper we propose a
formalism based on an algebraic principle for matrix approximation, i.e. the
Nystrom method, to deal with such poorly sampled microstates. Our scheme builds
a hierarchy of microstates from high to low populations and progressively
applies spectral clustering on sets of microstates within each level of the
hierarchy. It helps spectral clustering identify metastable aggregates with
highly populated microstates rather than being distracted by lowly populated
states. We demonstrate the ability of this algorithm to discover the major
metastable states on two model systems, the alanine dipeptide and TrpZip2.


  The chiral optical Tamm state is a new localized state of light at the
interface between a polarization-preserving anisotropic mirror and an optically
chiral medium such as a cholesteric liquid crystal. In this study the
metal-free polarization-preserving mirror is used for efficient resonance
control. We stress the advantage of the all-dielectric structure in obtaining
high Q factor. The light is localized near the interface and the field
decreases exponentially with the distance from the interface. The penetration
of the field into the chiral medium is virtually blocked at wavelengths
corresponding to the photonic band gap and close to the pitch of the helix. The
polarization-preserving mirror has another photonic band gap as well. Our
analytics agrees well with precise calculations, enabling intelligent design
for laser and sensing applications.


  Over $\mathbb{C}$, Montgomery superized Herstein's construction of simple Lie
algebras from finite-dimensional associative algebras, found obstructions to
the procedure and applied it to $\mathbb{Z}/2$-graded associative algebra of
differential operators with polynomial coefficients.
  Since the 1990s, Vasiliev and Konstein with their co-authors constructed (via
the Herstein--Montgomery method, having rediscovered it) simple Lie
(super)algebras from the associative (super)algebra such as Vasiliev's higher
spin algebras (a.k.a. algebras of observables of the rational Calogero model)
and algebras of symplectic reflections.
  The "queerification" is another method for cooking a~simple Lie superalgebra
from the simple associative (super)algebra. The above examples of associative
(super)algebras, and Lie (super)algebras of "matrices of complex size" can be
"queerified" by adding new elements resembling Faddeev--Popov ghosts.
  Conjectures: 1) a "queerified" Hamiltonian describes a version of the
Calogero model with $1\vert 1$-dimensional time; 2) metabelean algebras and
inhomogeneous subalgebras of Lie superalgebras naturally widen supersymmetries
in future theories; 3) only graded-commutative algebras can imitate algebras of
functions in a reasonably rich non-commutative Geometry.


  The presence of gaps or regions of small numbers of stars in the main
sequence of the Hertzsprung Russell Diagram (HRD) of star clusters has been
reported in literature. This is interesting and significant as it could be
related to star formation and/or rapid evolution or instabilities. In this
paper, using Gaia DR3 photometry and confirmed membership data, we explore the
HRD of nine open clusters with reported gaps, identify them and assess their
importance and spectral types.


  In the Large Hardron Collider (LHC), multiple proton-proton collisions cause
pileup in reconstructing energy information for a single primary collision
(jet). This project aims to select the most important features and create a
model to accurately estimate jet energy. Different machine learning methods
were explored, including linear regression, support vector regression and
decision tree. The best result is obtained by linear regression with predictive
features and the performance is improved significantly from the baseline
method.


  Previous results on quasi-classical limit of the KP hierarchy and its
W-infinity symmetries are extended to the Toda hierarchy. The Planck constant
$\hbar$ now emerges as the spacing unit of difference operators in the Lax
formalism. Basic notions, such as dressing operators, Baker-Akhiezer functions
and tau function, are redefined. $W_{1+\infty}$ symmetries of the Toda
hierarchy are realized by suitable rescaling of the Date-Jimbo-Kashiara-Miwa
vertex operators. These symmetries are contracted to $w_{1+\infty}$ symmetries
of the dispersionless hierarchy through their action on the tau function. (A
few errors in the earlier version is corrected.)


  We consider quantum field theory in four-dimensional Minkowski spacetime,
with the position coordinates represented by twistors instead of the usual
world-vectors. Upon imposing canonical commutation relations between twistors
and dual twistors, quantum theory of fields described by non-holomorphic
functions of twistor variables becomes manifestly non-commutative, with Lorentz
symmetry broken by a time-like vector. We discuss the free field propagation
and its impact on the short- and long-distance behavior of physical amplitudes
in perturbation theory. In the ultraviolet limit, quantum field theories in
twistor space are generically less divergent than their commutative
counterparts. Furthermore, there is no infrared--ultraviolet mixing problem.


  Stickers, while widely recognized for enhancing empathetic communication in
online interactions, remain underexplored in current empathetic dialogue
research, notably due to the challenge of a lack of comprehensive datasets. In
this paper, we introduce the Agent for STICKERCONV (Agent4SC), which uses
collaborative agent interactions to realistically simulate human behavior with
sticker usage, thereby enhancing multimodal empathetic communication. Building
on this foundation, we develop a multimodal empathetic dialogue dataset,
STICKERCONV, comprising 12.9K dialogue sessions, 5.8K unique stickers, and 2K
diverse conversational scenarios. This dataset serves as a benchmark for
multimodal empathetic generation. To advance further, we propose PErceive and
Generate Stickers (PEGS), a multimodal empathetic response generation
framework, complemented by a comprehensive set of empathy evaluation metrics
based on LLM. Our experiments demonstrate PEGS's effectiveness in generating
contextually relevant and emotionally resonant multimodal empathetic responses,
contributing to the advancement of more nuanced and engaging empathetic
dialogue systems.


  Including human analysis has the potential to positively affect the
robustness of Deep Neural Networks and is relatively unexplored in the
Adversarial Machine Learning literature. Neural network visual explanation maps
have been shown to be prone to adversarial attacks. Further research is needed
in order to select robust visualizations of explanations for the image analyst
to evaluate a given model. These factors greatly impact Human-In-The-Loop
(HITL) evaluation tools due to their reliance on adversarial images, including
explanation maps and measurements of robustness. We believe models of human
visual attention may improve interpretability and robustness of human-machine
imagery analysis systems. Our challenge remains, how can HITL evaluation be
robust in this adversarial landscape?


  The main purpose of this Comment is to point out that besides the short time
scale of quantum chaos, confirmed once more in recent paper by Alicki et al,
there is generally another time scale t_R, which is much longer and on which a
partial quantum-classical correspondence persists. Namely, the quantum
diffusion closely follows the classical one even though the former is
dynamically stable. The absence of the long scale t_R in model studied by
Alicki et al. is a result of a special choice for one model's parameters value.


  Hecke operators relate characters of rational conformal field theories
(RCFTs) with different central charges, and extend the previously studied
Galois symmetry of modular representations and fusion algebras. We show that
the conductor $N$ of a RCFT and the quadratic residues mod $N$ play an
important role in the computation and classification of Galois permutations. We
establish a field correspondence in different theories through the picture of
effective central charge, which combines Galois inner automorphisms and the
structure of simple currents. We then make a first attempt to extend Hecke
operators to the full data of modular tensor categories. The Galois symmetry
encountered in the modular data appears in the fusion and the braiding matrices
as well, and yields isomorphic structures in theories related by Hecke
operators.


  Kerning is the task of setting appropriate horizontal spaces for all possible
letter pairs of a certain font. One of the difficulties of kerning is that the
appropriate space differs for each letter pair. Therefore, for a total of 52
capital and small letters, we need to adjust $52 \times 52 = 2704$ different
spaces. Another difficulty is that there is neither a general procedure nor
criterion for automatic kerning; therefore, kerning is still done manually or
with heuristics. In this paper, we tackle kerning by proposing two
machine-learning models, called pairwise and set-wise models. The former is a
simple deep neural network that estimates the letter space for two given letter
images. In contrast, the latter is a transformer-based model that estimates the
letter spaces for three or more given letter images. For example, the set-wise
model simultaneously estimates 2704 spaces for 52 letter images for a certain
font. Among the two models, the set-wise model is not only more efficient but
also more accurate because its internal self-attention mechanism allows for
more consistent kerning for all letters. Experimental results on about 2500
Google fonts and their quantitative and qualitative analyses show that the
set-wise model has an average estimation error of only about 5.3 pixels when
the average letter space of all fonts and letter pairs is about 115 pixels.


  We study the limit of a kinetic evolution equation involving a small
parameter and perturbed by a smooth random term which also involves the small
parameter. Generalizing the classical method of perturbed test functions, we
show the convergence to the solution of a stochastic diffusion equation.


  It is known from theory that, by means of a plasma physics approach, it is
possible to obtain a simple formula to calculate the approximate height of a
meteor (Foschini, 1999). This formula can be used in case of forward scatter of
radio waves and has the advantage that it does not depend on the diffusion
coefficient. On the other hand, it is possible to apply the formula to a
particular type of meteor only (overdense meteor type I), which is a small
fraction of the total number observed. We have carried out a statistical
analysis of several radio echoes from meteor showers recorded during last years
by a radio observer located in Belgium. Results are compared and discussed with
those obtained with other methods and available in literature.


  The Thermally-Pulsating Asymptotic Giant Branch (TP-AGB) phase of stellar
evolution has received attention only recently in galaxy evolution, but is now
an important player in our understanding of how galaxies form and evolve.
Because it is a short but very luminous phase, bright in the near-IR where dust
effects are small, the TP-AGB phase is a powerful tracer of intermediate-age
stars in galaxies up to high redshift. The spectral signature of TP-AGB stars
as defined by population synthesis models has been detected by the Spitzer
Space Telescope in high-redshift galaxies, whose spectra show an amazing
similarity to spectra of local stellar populations. Even accounting for the
high uncertainty affecting the theoretical modelling of this phase, stellar
population models including the TP-AGB have leveraged a better determination of
galaxy ages and hence stellar masses, fundamental quantities for studying
galaxy formation and evolution. They have also improved the results of
semi-analytic models, which can better reproduce colours and the K-band
luminosity function of high-z galaxies.


  We present optical, radio, and X-ray observations of SN2020bvc (=ASASSN20bs;
ZTF20aalxlis), a nearby ($z=0.0252$; $d$=114 Mpc) broad-lined (BL) Type Ic
supernova (SN). Our observations show that SN2020bvc shares several properties
in common with the Ic-BL SN2006aj, which was associated with the low-luminosity
gamma-ray burst (LLGRB) 060218. First, the 10 GHz radio light curve is on the
faint end of LLGRB-SNe ($L_\mathrm{radio} \approx 10^{37}$erg/s): we model our
VLA observations (spanning 13-43 d) as synchrotron emission from a mildly
relativistic ($v \gtrsim 0.3c$) forward shock. Second, with Swift and Chandra
we detect X-ray emission ($L_X \approx 10^{41}$erg/s) that is not naturally
explained as inverse Compton emission or as part of the same synchrotron
spectrum as the radio emission. Third, high-cadence ($6\times$/night) data from
the Zwicky Transient Facility (ZTF) shows a double-peaked optical light curve,
the first peak from shock-cooling emission from extended low-mass material
(mass $M<10^{-2} M_\odot$ at radius $R>10^{12}$cm) and the second peak from the
radioactive decay of Ni-56. SN2020bvc is the first confirmed double-peaked
Ic-BL SN discovered without a GRB trigger, and shows X-ray and radio emission
similar to LLGRB-SNe: this is consistent with models in which the same
mechanism produces both the LLGRB and the shock-cooling emission. For four of
the five other nearby ($z\lesssim0.05$) Ic-BL SNe with ZTF high-cadence data,
we rule out a first peak like that seen in SN2006aj and SN2020bvc, i.e. that
lasts $\approx 1$d and reaches a peak luminosity $M \approx -18$. X-ray and
radio follow-up observations of future such events will establish whether
double-peaked optical light curves are indeed predictive of LLGRB-like X-ray
and radio emission.


  We analyze the 3D kinematics of a sample of $\sim 4400$ red clump stars
ranging between 5 and 10 kpc from the Galactic center and up to 3 kpc from the
Galactic plane. This sample is representative for the metal-rich ([Fe/H] = -0.6
to 0.5) thick disk. Absolute proper motions are from the fourth release of the
Southern Proper Motion Program, and radial velocities from the second release
of the Radial Velocity Experiment. The derived kinematical properties of the
thick disk include: the rotational velocity gradient $\partial V_{\theta} /
\partial z = -25.2 \pm 2.1$ km s$^{-1}$ kpc$^{-1}$, velocity dispersions
$(\sigma_{V_R}, \sigma_{V_{\theta}}, \sigma_{V_z})|_{z=1} = (70.4, 48.0, 36.2)
\pm(4.1,8.3,4.0)$ km s$^{-1}$, and velocity-ellipsoid tilt angle $\alpha_{Rz} =
8.6\arcdeg \pm 1.8 \arcdeg$. Our dynamical estimate of the thin-disk scale
length is $R_{thin} = 2.0 \pm 0.4$ kpc and the thick-disk scale height is
$z_{thick} = 0.7 \pm 0.1$ kpc.
  The observed orbital eccentricity distribution compared with those from four
different models of the formation of the thick disk from Sales et al. favor the
gas-rich merger model and the minor merger heating model.
  Interestingly, when referred to the currently accepted value of the LSR,
stars more distant than 0.7 kpc from the Sun show a net average radial velocity
of $13 \pm3 $ km s$^{-1}$. This result is seen in previous kinematical
studiesusing other tracers at distances larger than $\sim 1$ kpc. We suggest
this motion reflects an inward perturbation of the locally-defined LSR induced
by the spiral density wave.


  Let $G$ be a finite group and $\text{cd}(G)$ denote the character degree set
for $G$. The prime graph $\Delta(G)$ is a simple graph whose vertex set
consists of prime divisors of elements in $\text{cd}(G)$, denoted $\rho(G)$.
Two primes $p,q\in \rho(G)$ are adjacent in $\Delta(G)$ if and only if $pq|a$
for some $a\in \text{cd}(G)$. We determine which simple 4-regular graphs occur
as prime graphs for some finite nonsolvable group.


  The artificial stacking of atomically thin crystals suffers from intrinsic
limitations in terms of control and reproducibility of the relative orientation
of exfoliated flakes. This drawback is particularly severe when the properties
of the system critically depend on the twist angle, as in the case of the
dodecagonal quasicrystal formed by two graphene layers rotated by 30$^\circ$.
Here we show that large-area 30$^\circ$-rotated bilayer graphene can be grown
deterministically by chemical vapor deposition on Cu, eliminating the need of
artificial assembly. The quasicrystals are easily transferred to arbitrary
substrates and integrated in high-quality hBN-encapsulated heterostructures,
which we process into dual-gated devices exhibiting carrier mobility up to
$10^5$ cm$^2$/Vs. From low-temperature magnetotransport, we find that the
graphene quasicrystals effectively behave as uncoupled graphene layers, showing
8-fold degenerate quantum Hall states: this result indicates that the Dirac
cones replica detected by previous photo-emission experiments do not contribute
to the electrical transport.


  The ability to perform computation on devices, such as smartphones, cars, or
other nodes present at the Internet of Things leads to constraints regarding
bandwidth, storage, and energy, as most of these devices are mobile and operate
on batteries. Using their computational power to perform locally machine
learning and analytics tasks can enable accurate and real-time predictions at
the network edge. A trained machine learning model requires high accuracy
towards the prediction outcome, as wrong decisions can lead to negative
consequences on the efficient conclusion of applications. Most of the data
sensed in these devices are contextual and personal requiring
privacy-preserving without their distribution over the network. When working
with these privacy-preserving data, not only the protection is important but,
also, the model needs the ability to adapt to regular occurring concept drifts
and data distribution changes to guarantee a high accuracy of the prediction
outcome. We address the importance of personalization and generalization in
edge devices to adapt to data distribution updates over continuously evolving
environments. The methodology we propose relies on the principles of Federated
Learning and Optimal Stopping Theory extended with a personalization component.
The privacy-efficient and quality-awareness of personalization and
generalization is the overarching aim of this work.


  The Weibel/filamentation instability is known to play a key role in the
physics of weakly magnetized collisionless shock waves. From the point of view
of high energy astrophysics, this instability also plays a crucial role because
its development in the shock precursor populates the downstream with a
small-scale magneto-static turbulence which shapes the acceleration and
radiative processes of suprathermal particles. The present work discusses the
physics of the dissipation of this Weibel-generated turbulence downstream of
relativistic collisionless shock waves. It calculates explicitly the
first-order non-linear terms associated to the diffusive nature of the particle
trajectories. These corrections are found to systematically increase the
damping rate, assuming that the scattering length remains larger than the
coherence length of the magnetic fluctuations. The relevance of such
corrections is discussed in a broader astrophysical perspective, in particular
regarding the physics of the external relativistic shock wave of a gamma-ray
burst.


  This paper develops the singular perturbation theory for a particular
discrete-time nonlinear system which models the saturating inductor buck
converter using cycle-by-cycle digital control.


  The CKM angle $\gamma$ is important for testing the unitarity of the CKM
matrix and searching for new physics. $\gamma$ can be extracted by the
interference between $b\to u$ and $b\to c$ in the B factory such as LHCb and
Belle-II. Determining $\gamma$ also needs strong parameter information from the
charm factory, such as the BESIII experiment. With quantum-correlated data
samples collected at BESIII, the $\gamma$ uncertainties from the charm sector
can be highly suppressed.


  The evolution of the so-called `41meV resonance' in the magnetic response of
YBCO cuprates is studied with slave-boson theory for the t-t'-J-model. The
resonance appears as a collective spin fluctuation in the d-wave
superconducting (SC) state. It is undamped at optimal doping due to a threshold
in the excitation energies of particle--hole pairs with relative wave vector
(pi,pi). When hole filling is reduced, the resonance moves to lower energies
and broadens. Below the resonance energy we find a crossover to an
incommensurate response in agreement with a recent experiment on
YBa_2CU_3O_6.6. We show that dynamic nesting in the d-wave SC state causes this
effect.


  We present a comprehensive and user-friendly framework built upon the pyiron
integrated development environment (IDE), enabling researchers to perform the
entire Machine Learning Potential (MLP) development cycle consisting of (i)
creating systematic DFT databases, (ii) fitting the Density Functional Theory
(DFT) data to empirical potentials or MLPs, and (iii) validating the potentials
in a largely automatic approach. The power and performance of this framework
are demonstrated for three conceptually very different classes of interatomic
potentials: an empirical potential (embedded atom method - EAM), neural
networks (high-dimensional neural network potentials - HDNNP) and expansions in
basis sets (atomic cluster expansion - ACE). As an advanced example for
validation and application, we show the computation of a binary
composition-temperature phase diagram for Al-Li, a technologically important
lightweight alloy system with applications in the aerospace industry.


  Given a nonparametric Hidden Markov Model (HMM) with two states, the question
of constructing efficient multiple testing procedures is considered, treating
one of the states as an unknown null hypothesis. A procedure is introduced,
based on nonparametric empirical Bayes ideas, that controls the False Discovery
Rate (FDR) at a user--specified level. Guarantees on power are also provided,
in the form of a control of the true positive rate. One of the key steps in the
construction requires supremum--norm convergence of preliminary estimators of
the emission densities of the HMM. We provide the existence of such estimators,
with convergence at the optimal minimax rate, for the case of a HMM with $J\ge
2$ states, which is of independent interest.


  We extend the results by R.P. Langlands on representations of (connected)
abelian algebraic groups. This is done by considering characters into any
divisible abelian topological group. With this we can then prove what is known
as the abelian case of the $p$-adic Langlands program.


  We present a new idea to adapt relational abstract domains to the analysis of
IEEE 754-compliant floating-point numbers in order to statically detect,
through abstract Interpretation-based static analyses, potential floating-point
run-time exceptions such as overflows or invalid operations. In order to take
the non-linearity of rounding into account, expressions are modeled as linear
forms with interval coefficients. We show how to extend already existing
numerical abstract domains, such as the octagon abstract domain, to efficiently
abstract transfer functions based on interval linear forms. We discuss specific
fixpoint stabilization techniques and give some experimental results.


  Quantum mechanics is often developed in the position representation, but this
is not necessary, and one can perform calculations in a
representation-independent fashion, even for wavefunctions. In this work, we
illustrate how one can determine wavefunctions, aside from normalization, using
only operators and how those operators act on state vectors. To do this in
plane polar and spherical coordinates requires one to convert the translation
operator into those coordinates. As examples of this approach, we illustrate
the solution of the Coulomb problem in two and three dimensions without needing
to express any operators in position space.


  In this dissertation, we present work towards characterizing various
conformal and nearly conformal quantum field theories nonperturbatively using a
combination of numerical and analytical techniques. A key area of interest is
the conformal window of four dimensional gauge theories with Dirac fermions and
its potential applicability to beyond the standard model physics.
  In the first chapter, we review some of the history of models of composite
Higgs scenarios in order to motivate the study of gauge theories near the
conformal window. In the second chapter we review lattice studies of a specific
theory, SU(3) gauge theory with eight flavors of Dirac fermions in the
fundamental representation of the gauge group. We place a particular emphasis
on the light flavor-singlet scalar state appearing in the spectrum of this
model and its possible role as a composite Higgs boson. We advocate an approach
to characterizing nearly conformal gauge theories in which lattice calculations
are used to identify the best low energy effective field theory (EFT)
description of such nearly conformal gauge theories, and the lattice and EFT
are then used as complementary tools to classify the generic features of the
low energy physics in these theories. We present new results for maximal
isospin $\pi\pi \rightarrow \pi\pi$ scattering on the lattice computed using
L\"uscher's finite volume method. This scattering study is intended to provide
further data for constraining the possible EFT descriptions of nearly conformal
gauge theory. In Chapter 3, we review the historical development of chiral
effective theory from current algebra methods up through the chiral Lagrangian
and modern effective field theory techniques. We present a new EFT framework
based on the linear sigma model for describing the low lying states of nearly
conformal gauge theories. We place a ... (full abstract within)