  We characterize a natural class of modular categories of prime power
Frobenius-Perron dimension as representation categories of twisted doubles of
finite p-groups. We also show that a nilpotent braided fusion category C admits
an analogue of the Sylow decomposition. If the simple objects of C have
integral Frobenius-Perron dimensions then C is group-theoretical. As a
consequence, we obtain that semisimple quasi-Hopf algebras of prime power
dimension are group-theoretical. Our arguments are based on a reconstruction of
twisted group doubles from Lagrangian subcategories of modular categories (this
is reminiscent to the characterization of doubles of quasi-Lie bialgebras in
terms of Manin pairs).


  A new classification scheme for pairs of real numbers is given, generalizing
earlier work of the author that used continued fraction, which in turn was
motivated by ideas from statistical mechanics in general and work of Knauf and
Fiala and Kleban in particular. Critical for this classification are the number
theoretic and geometric properties of the triangle map, a type of
multi-dimensional continued fraction.


  Predicting the incidence of complex chronic conditions such as heart failure
is challenging. Deep learning models applied to rich electronic health records
may improve prediction but remain unexplainable hampering their wider use in
medical practice. We developed a novel Transformer deep-learning model for more
accurate and yet explainable prediction of incident heart failure involving
100,071 patients from longitudinal linked electronic health records across the
UK. On internal 5-fold cross validation and held-out external validation, our
model achieved 0.93 and 0.93 area under the receiver operator curve and 0.69
and 0.70 area under the precision-recall curve, respectively and outperformed
existing deep learning models. Predictor groups included all community and
hospital diagnoses and medications contextualised within the age and calendar
year for each patient's clinical encounter. The importance of contextualised
medical information was revealed in a number of sensitivity analyses, and our
perturbation method provided a way of identifying factors contributing to risk.
Many of the identified risk factors were consistent with existing knowledge
from clinical and epidemiological research but several new associations were
revealed which had not been considered in expert-driven risk prediction models.


  Lifelong or continual learning remains to be a challenge for artificial
neural network, as it is required to be both stable for preservation of old
knowledge and plastic for acquisition of new knowledge. It is common to see
previous experience get overwritten, which leads to the well-known issue of
catastrophic forgetting, especially in the scenario of class-incremental
learning (Class-IL). Recently, many lifelong learning methods have been
proposed to avoid catastrophic forgetting. However, models which learn without
replay of the input data, would encounter another problem which has been
ignored, and we refer to it as prior information loss (PIL). In training
procedure of Class-IL, as the model has no knowledge about following tasks, it
would only extract features necessary for tasks learned so far, whose
information is insufficient for joint classification. In this paper, our
empirical results on several image datasets show that PIL limits the
performance of current state-of-the-art method for Class-IL, the orthogonal
weights modification (OWM) algorithm. Furthermore, we propose to combine
self-supervised learning, which can provide effective representations without
requiring labels, with Class-IL to partly get around this problem. Experiments
show superiority of proposed method to OWM, as well as other strong baselines.


  This paper studies inverse problems in quantitative photoacoustic tomography
with additional optical current data supplemented from diffuse optical
tomography. We propose a three-stage image reconstruction method for the
simultaneous recovery of the absorption, diffusion, and Gr\"uneisen
coefficients. We demonstrate, through numerical simulations, that: (i) when the
Gr\"uneisen coefficient is known, the addition of the optical measurements
allows a more accurate reconstruction of the scattering and absorption
coefficients; and (ii) when the Gr\"uneisen coefficient is not known, the
addition of optical current measurements allows us to reconstruct uniquely the
Gr\"uneisen, the scattering and absorption coefficients. Numerical simulations
based on synthetic data are presented to demonstrate the effectiveness of the
proposed idea.


  In this paper we present ZKlaims: a system that allows users to present
attribute-based credentials in a privacy-preserving way. We achieve a
zero-knowledge property on the basis of Succinct Non-interactive Arguments of
Knowledge (SNARKs). ZKlaims allow users to prove statements on credentials
issued by trusted third parties. The credential contents are never revealed to
the verifier as part of the proving process. Further, ZKlaims can be presented
non-interactively, mitigating the need for interactive proofs between the user
and the verifier. This allows ZKlaims to be exchanged via fully decentralized
services and storages such as traditional peer-to-peer networks based on
distributed hash tables (DHTs) or even blockchains. To show this, we include a
performance evaluation of ZKlaims and show how it can be integrated in
decentralized identity provider services.


  Improving the generalization ability of modern deep neural networks (DNNs) is
a fundamental challenge in machine learning. Two branches of methods have been
proposed to seek flat minima and improve generalization: one led by
sharpness-aware minimization (SAM) minimizes the worst-case neighborhood loss
through adversarial weight perturbation (AWP), and the other minimizes the
expected Bayes objective with random weight perturbation (RWP). While RWP
offers advantages in computation and is closely linked to AWP on a mathematical
basis, its empirical performance has consistently lagged behind that of AWP. In
this paper, we revisit the use of RWP for improving generalization and propose
improvements from two perspectives: i) the trade-off between generalization and
convergence and ii) the random perturbation generation. Through extensive
experimental evaluations, we demonstrate that our enhanced RWP methods achieve
greater efficiency in enhancing generalization, particularly in large-scale
problems, while also offering comparable or even superior performance to SAM.
The code is released at https://github.com/nblt/mARWP.


  Nonlinear transport through a quantum dot is studied in the limit of weak and
strong intra-dot Coulomb interaction. For the latter regime the nonequilibrium
self-consistent mean field equations for energies and spectral weights of
one-electron transitions are formulated. The increase of the bias-voltage
window leads to a strong deviation from Gibbs statistics: the populations of
states involved into a tunnelling are equalizing in this limit even at low
temperature. For a symmetric coupling of a quantum dot to two leads we provide
simple analytical relations between heights of the current steps and degeneracy
of a spectrum in a two-dimensional parabolic dot in a perpendicular magnetic
field in the both regimes.


  The Quantum Alternating Operator Ansatz (QAOA) is a hybrid classical-quantum
algorithm that aims to sample the optimal solution(s) of discrete combinatorial
optimization problems. We present optimized QAOA circuit constructions for
sampling MAX $k$-SAT problems, specifically for $k=3$ and $k=4$. The novel
$4$-SAT QAOA circuit construction we present uses measurement based
uncomputation, followed by classical feed forward conditional operations. The
QAOA circuit parameters for $3$-SAT are optimized via exact classical
(noise-free) simulation, using HPC resources to simulate up to $20$ rounds on
$10$ qubits. In order to explore the limits of current NISQ devices we execute
these optimized QAOA circuits for random $3$-SAT test instances with
clause-to-variable ratio $4$ on four trapped ion quantum computers: Quantinuum
H1-1 (20 qubits), IonQ Harmony (11 qubits), IonQ Aria 1 (25 qubits), and IonQ
Forte (30 qubits). The QAOA circuits that are executed include $n=10$ up to
$p=20$, and $n=22$ for $p=1$ and $p=2$. The high round circuits use upwards of
9,000 individual gate instructions, making these some of the largest QAOA
circuits executed on NISQ devices. Our main finding is that current NISQ
devices perform best at low round counts (i.e., $p = 1,\ldots, 5$) and then --
as expected due to noise -- gradually start returning satisfiability truth
assignments that are no better than randomly picked solutions as the number of
QAOA rounds are further increased.


  We investigate the question of how many subgroups of a finite group are not
in its Chermak-Delgado lattice. The Chermak-Delgado lattice for a finite group
is a self-dual lattice of subgroups with many intriguing properties. Fasol\u{a}
and T\u{a}rn\u{a}uceanu asked how many subgroups are not in the Chermak-Delgado
lattice and classified all groups with two or less subgroups not in the
Chermak-Delgado lattice. We extend their work by classifying all groups with
less than five subgroups not in the Chermak-Delgado lattice. In addition, we
show that a group with less than five subgroups not in the Chermak--Delgado
lattice is nilpotent. In this vein we also show that the only non-nilpotent
group with five or fewer subgroups in the Chermak-Delgado lattice is S_3.


  Discovering the underlying dynamics of complex systems from data is an
important practical topic. Constrained optimization algorithms are widely
utilized and lead to many successes. Yet, such purely data-driven methods may
bring about incorrect physics in the presence of random noise and cannot easily
handle the situation with incomplete data. In this paper, a new iterative
learning algorithm for complex turbulent systems with partial observations is
developed that alternates between identifying model structures, recovering
unobserved variables, and estimating parameters. First, a causality-based
learning approach is utilized for the sparse identification of model
structures, which takes into account certain physics knowledge that is
pre-learned from data. It has unique advantages in coping with indirect
coupling between features and is robust to the stochastic noise. A practical
algorithm is designed to facilitate the causal inference for high-dimensional
systems. Next, a systematic nonlinear stochastic parameterization is built to
characterize the time evolution of the unobserved variables. Closed analytic
formula via an efficient nonlinear data assimilation is exploited to sample the
trajectories of the unobserved variables, which are then treated as synthetic
observations to advance a rapid parameter estimation. Furthermore, the
localization of the state variable dependence and the physics constraints are
incorporated into the learning procedure, which mitigate the curse of
dimensionality and prevent the finite time blow-up issue. Numerical experiments
show that the new algorithm succeeds in identifying the model structure and
providing suitable stochastic parameterizations for many complex nonlinear
systems with chaotic dynamics, spatiotemporal multiscale structures,
intermittency, and extreme events.


  A good hydrogen storage material should adsorb hydrogen in high
concentrations and with optimal binding energies. Numerous mixed carbon boron
fullerenes which are decorated with metal atoms were previously constructed by
hand and proposed as a promising material in this context. We present a fully
ab-initio, unbiased structure search in the configurational space of decorated
C48B12 and find that most of the hitherto postulated ground state structures
are not ground states. We determine the energetically lowest configurations for
Be, Ca, Li and Sc decorated C48B12 clusters.


  Self-training is a competitive approach in domain adaptive segmentation,
which trains the network with the pseudo labels on the target domain. However
inevitably, the pseudo labels are noisy and the target features are dispersed
due to the discrepancy between source and target domains. In this paper, we
rely on representative prototypes, the feature centroids of classes, to address
the two issues for unsupervised domain adaptation. In particular, we take one
step further and exploit the feature distances from prototypes that provide
richer information than mere prototypes. Specifically, we use it to estimate
the likelihood of pseudo labels to facilitate online correction in the course
of training. Meanwhile, we align the prototypical assignments based on relative
feature distances for two different views of the same target, producing a more
compact target feature space. Moreover, we find that distilling the already
learned knowledge to a self-supervised pretrained model further boosts the
performance. Our method shows tremendous performance advantage over
state-of-the-art methods. We will make the code publicly available.


  Building on the recent progress in solving Chern-Simons-matter theories in
the planar limit, we compute the scaling dimensions of a large class of
disorder ("monopole") operators in $U(N)_k$ Chern-Simons-fermion theories at
all 't Hooft couplings $\lambda = N/k$. We find that the lowest-dimension
operator of this sort has dimension $\frac23 k^{3/2}$. We comment on the
implications of these results to analyzing maps of fermionic disorder operators
under 3D bosonization.


  In black hole X-ray binaries, a non-thermal high-energy component is
sometimes detected at energies above 200 keV. The origin of this component is
debated and distinct spectral modelizations can lead to different
interpretations. High-energy polarimetry measurements with INTEGRAL allow new
diagnostics on the physics responsible for the MeV spectral component. In this
work, we aim to investigate the high-energy behavior of three bright sources
discovered by the MAXI: MAXI J1535-571, MAXI J1820+070 and MAXI J1348-630. We
take advantage of their brightness to investigate their soft gamma-ray (0.1-2
MeV) properties with INTEGRAL. We use both spectral and polarimetric approaches
to probe their high-energy emission with the aim to bring new constraints on
the ~ MeV emission. We first study the spectral characteristics of the sources
in the 3-2000 keV using JEM-X, IBIS and SPI with a semi-phenomenological
description of the data. We then use IBIS as a Compton telescope in order to
evaluate the polarization properties of the sources above 300 keV.
  A high-energy component is detected during the HIMS and SIMS of MAXI
J1535-571, the LHS of MAXI J1820+070 and the LHS of MAXI J1348-630. The
components detected in MAXI J1820+070 and MAXI J1348-630 are polarized with a
polarization fraction of 26 +/- 9{\deg} and > 56 % in the 300-1000 keV,
respectively. With no polarization information for MAXI J1535-571, the
component detected could either come from the jets or the corona. In the case
of MAXI J1820+070, the extrapolation of the synchrotron spectrum measured in
the infrared indicates that the component is likely due to a non-thermal
distribution of electrons from a hybrid corona. For MAXI J1348-630, the high
fraction of polarization points towards a jets origin, however, we cannot
formally conclude without any infrared data giving information on the optically
thin part of the synchrotron spectrum.


  We present a detailed theoretical and numerical study discussing the
application and optimization of phase estimation algorithms (PEAs) to diamond
spin magnetometry. We compare standard Ramsey magnetometry, the non-adaptive
PEA (NAPEA) and quantum PEA (QPEA) incorporating error-checking. Our results
show that the NAPEA requires lower measurement fidelity, has better dynamic
range, and greater consistency in sensitivity. We elucidate the importance of
dynamic range to Ramsey magnetic imaging with diamond spins, and introduce the
application of PEAs to time-dependent magnetometry.


  Modeling human behavioral data is challenging due to its scale, sparseness
(few observations per individual), heterogeneity (differently behaving
individuals), and class imbalance (few observations of the outcome of
interest). An additional challenge is learning an interpretable model that not
only accurately predicts outcomes, but also identifies important factors
associated with a given behavior. To address these challenges, we describe a
statistical approach to modeling behavioral data called the structured
sum-of-squares decomposition (S3D). The algorithm, which is inspired by
decision trees, selects important features that collectively explain the
variation of the outcome, quantifies correlations between the features, and
partitions the subspace of important features into smaller, more homogeneous
blocks that correspond to similarly-behaving subgroups within the population.
This partitioned subspace allows us to predict and analyze the behavior of the
outcome variable both statistically and visually, giving a medium to examine
the effect of various features and to create explainable predictions. We apply
S3D to learn models of online activity from large-scale data collected from
diverse sites, such as Stack Exchange, Khan Academy, Twitter, Duolingo, and
Digg. We show that S3D creates parsimonious models that can predict outcomes in
the held-out data at levels comparable to state-of-the-art approaches, but in
addition, produces interpretable models that provide insights into behaviors.
This is important for informing strategies aimed at changing behavior,
designing social systems, but also for explaining predictions, a critical step
towards minimizing algorithmic bias.


  Binary and multiple stars have long provided an effective empirical method of
testing stellar formation and evolution theories. In particular, the existence
of wide binary systems (separations $>$20,000 au) is particularly challenging
to binary formation models as their physical separations are beyond the typical
size of a collapsing cloud core ($\sim$5,000$-$10,000 au). We mined the
recently published Gaia-DR2 catalog to identify bright comoving systems in the
five-dimensional space (sky position, parallax, and proper motion). We
identified 3,741 comoving binary and multiple stellar candidate systems, out of
which 575 have compatible RVs for all the members of the system. The candidate
systems have separations between $\sim$400 and 500,000 au. We used the analysis
tools of the Virtual Observatory to characterize the comoving system members
and to assess their reliability. The comparison with previous comoving systems
catalogs obtained from TGAS showed that these catalogs contain a large number
of false systems. In addition, we were not able to confirm the ultra-wide
binary population presented in these catalogs. The robustness of our
methodology is demonstrated by the identification of well known comoving star
clusters and by the low contamination rate for comoving binary systems with
projected physical separations $<$50,000 au. These last constitute a reliable
sample for further studies. The catalog is available online at the Spanish
Virtual Observatory portal
(http://svo2.cab.inta-csic.es/vocats/v2/comovingGaiaDR2/).


  The field of Multi-Agent System (MAS) is an active area of research within
Artificial Intelligence, with an increasingly important impact in industrial
and other real-world applications. Within a MAS, autonomous agents interact to
pursue personal interests and/or to achieve common objectives. Distributed
Constraint Optimization Problems (DCOPs) have emerged as one of the prominent
agent architectures to govern the agents' autonomous behavior, where both
algorithms and communication models are driven by the structure of the specific
problem. During the last decade, several extensions to the DCOP model have
enabled them to support MAS in complex, real-time, and uncertain environments.
This survey aims at providing an overview of the DCOP model, giving a
classification of its multiple extensions and addressing both resolution
methods and applications that find a natural mapping within each class of
DCOPs. The proposed classification suggests several future perspectives for
DCOP extensions, and identifies challenges in the design of efficient
resolution algorithms, possibly through the adaptation of strategies from
different areas.


  Covid-19 is primarily spread through contact with the virus which may survive
on surfaces with lifespan of more than hours. To curb its spread, it is hence
of vital importance to detect and quarantine those who have been in contact
with the virus for sustained period of time, the so-called close contacts. In
this work, we study, for the first time, automatic contact detection when the
virus has a lifespan. Leveraging upon the ubiquity of WiFi signals, we propose
a novel, private, and fully distributed WiFi-based approach called vContact.
Users installing an app continuously scan WiFi and store its hashed IDs. Given
a confirmed case, the signals of the major places he/she visited are then
uploaded to a server and matched with the stored signals of users to detect
contact. vContact is not based on phone pairing, and no information of any
other users is stored locally. The confirmed case does not need to have
installed the app for it to work properly. As WiFi data are sampled
sporadically, we propose efficient signal processing approaches and similarity
metric to align and match signals of any time. We conduct extensive indoor and
outdoor experiments to evaluate the performance of vContact. Our results
demonstrate that vContact is efficient and robust for contact detection. The
precision and recall of contact detection are high (in the range of 50-90%) for
close contact proximity (2m). Its performance is robust with respect to signal
lengths (AP numbers) and phone heterogeneity. By implementing vContact as an
app, we present a case study to demonstrate the validity of our design in
notifying its users their exposure to virus with lifespan.


  We present a rigidity analysis on a large number of X-ray crystal structures
of the enzyme HIV-1 protease using the 'pebble game' algorithm of the software
FIRST. We find that although the rigidity profile remains similar across a
comprehensive set of high resolution structures, the profile changes
significantly in the presence of an inhibitor. Our study shows that the action
of the inhibitors is to restrict the flexibility of the beta-hairpin flaps
which allow access to the active site. The results are discussed in the context
of full molecular dynamics simulations as well as data from NMR experiments.


  We investigate high density state of SU(2) QCD in the case of the Wilson
fermions with Iwasaki improved action. Based on the Woodbery formula, the ratio
of fermion determinants is evaluated at each step of the Metropolis link
update. At $\beta=0.7$, for $\kappa = 0.150$, and $\kappa = 0.175$, we
calculate thermodynamical quantities, such as, baryon number density, Polyakov
line, and the energy density of gluon sector with chemical potential $\mu$=0 to
0.9 on the $4^{3} \times 8$ lattice. The susceptibility of the Polyakov line to
the chemical potential shows peak at around $\mu = 0.7$ which indicates the
deconfinement phase transition. Behavior of the meson and diquark propagators
with finite chemical potential are also investigated at both side of the peak.


  The dynamics of spinning particles in curved space-time is discussed,
emphasizing the hamiltonian formulation. Different choices of hamiltonians
allow for the description of different gravitating systems. We give full
results for the simplest case with minimal hamiltonian, constructing constants
of motion including spin. The analysis is illustrated by the example of motion
in Schwarzschild space-time. We also discuss a non-minimal extension of the
hamiltonian giving rise to a gravitational equivalent of the Stern-Gerlach
force. We show that this extension respects a large class of known constants of
motion for the minimal case.


  We study the process of equilibration between two non-extensive subsystems in
the framework of a particular non-extensive Boltzmann equation. We have found
that even subsystems with different non-extensive properties achieve a common
equilibrium distribution.


  This paper introduces DensePoint, a densely sampled and annotated point cloud
dataset containing over 10,000 single objects across 16 categories, by merging
different kind of information from two existing datasets. Each point cloud in
DensePoint contains 40,000 points, and each point is associated with two sorts
of information: RGB value and part annotation. In addition, we propose a method
for point cloud colorization by utilizing Generative Adversarial Networks
(GANs). The network makes it possible to generate colours for point clouds of
single objects by only giving the point cloud itself. Experiments on DensePoint
show that there exist clear boundaries in point clouds between different parts
of an object, suggesting that the proposed network is able to generate
reasonably good colours. Our dataset is publicly available on the project page.


  The algebraic Bethe Ansatz is a prosperous and well-established method for
solving one-dimensional quantum models exactly. The solution of the complex
eigenvalue problem is thereby reduced to the solution of a set of algebraic
equations. Whereas the spectrum is usually obtained directly, the eigenstates
are available only in terms of complex mathematical expressions. This makes it
very hard in general to extract properties from the states, like, for example,
correlation functions. In our work, we apply the tools of Tensor Network States
to describe the eigenstates approximately as Matrix Product States. From the
Matrix Product State expression, we then obtain observables like correlation
functions directly.


  Environmental sound classification (ESC) is a challenging problem due to the
unstructured spatial-temporal relations that exist in the sound signals.
Recently, many studies have focused on abstracting features from convolutional
neural networks while the learning of semantically relevant frames of sound
signals has been overlooked. To this end, we present an end-to-end framework,
namely feature pyramid attention network (FPAM), focusing on abstracting the
semantically relevant features for ESC. We first extract the feature maps of
the preprocessed spectrogram of the sound waveform by a backbone network. Then,
to build multi-scale hierarchical features of sound spectrograms, we construct
a feature pyramid representation of the sound spectrograms by aggregating the
feature maps from multi-scale layers, where the temporal frames and spatial
locations of semantically relevant frames are localized by FPAM. Specifically,
the multiple features are first processed by a dimension alignment module.
Afterward, the pyramid spatial attention module (PSA) is attached to localize
the important frequency regions spatially with a spatial attention module
(SAM). Last, the processed feature maps are refined by a pyramid channel
attention (PCA) to localize the important temporal frames. To justify the
effectiveness of the proposed FPAM, visualization of attention maps on the
spectrograms has been presented. The visualization results show that FPAM can
focus more on the semantic relevant regions while neglecting the noises. The
effectiveness of the proposed methods is validated on two widely used ESC
datasets: the ESC-50 and ESC-10 datasets. The experimental results show that
the FPAM yields comparable performance to state-of-the-art methods. A
substantial performance increase has been achieved by FPAM compared with the
baseline methods.


  Colloidal quantum dots (cQDs) recently emerged as building blocks for
semiconductor materials with tuneable properties. Electro-hydrodynamic printing
can be used to obtain sub-micrometre patterns of cQDs without elaborate and
aggressive photolithography steps. Post-deposition ligand exchange is necessary
for the introduction of new functionalities into cQD solids. However, achieving
a complete bulk exchange is challenging and conventional infrared spectroscopy
lacks the required spatial resolution. Infrared nanospectroscopy (AFM-IR)
enables quantitative analysis of the evolution of vibrational signals and
structural topography on the nano-metre scale upon ligand substitution on lead
sulphide (PbS) cQDs. A solution of ethane-dithiol in acetonitrile demonstrated
rapid (~60 s) and controllable exchange of approximately 90% of the ligands,
encompassing structures up to ~800 nm in thickness. Prolonged exposures (>1 h)
led to the degradation of the microstructures, with a systematic removal of
cQDs regulated by surface-to-bulk ratios and solvent interactions. This study
establishes a method for the development of devices through a combination of
tuneable photoactive materials, additive manufacturing of microstructures, and
their quantitative nanometre-scale analysis.


  The theoretical description of Drell-Yan (DY) transverse momentum spectra
over wide kinematic regions in energy, mass and transverse momentum requires
not only fixed-order perturbative QCD calculations but also soft-gluon QCD
resummations to all orders of perturbation theory. The latter are traditionally
accomplished either by Parton Showers (PS) with Monte Carlo event generators or
by (different versions of) analytical procedures. In this work we focus on
issues involved in the matching of the fixed-order calculation and resummation,
especially in the moderate to low mass and $p_{\bot}$ region. In particular we
address the DY region accessible at fixed target experiments. We present a
Parton Branching (PB) formulation in which transverse momentum dependent (TMD)
evolution is matched with MCatNLO calculations of NLO matrix elements. Using
this formulation, we show a good theoretical description of DY data from
experiments in very different kinematic ranges: NuSea, R209, Phenix, LHC
$8\;\textrm{TeV}$ and $13\;\textrm{TeV}$.


  Dynamic Random Access Memory (DRAM) is the de-facto choice for main memory
devices due to its cost-effectiveness. It offers a larger capacity and higher
bandwidth compared to SRAM but is slower than the latter. With each passing
generation, DRAMs are becoming denser. One of its side-effects is the deviation
of nominal parameters: process, voltage, and temperature. DRAMs are often
considered as the bottleneck of the system as it trades off performance with
capacity. With such inherent limitations, further deviation from nominal
specifications is undesired. In this paper, we investigate the impact of
variations in conventional DRAM devices on the aspects of performance,
reliability, and energy requirements. Based on this study, we model a
variation-aware framework, called VAR-DRAM, targeted for modern-day DRAM
devices. It provides enhanced power management by taking variations into
account. VAR-DRAM ensures faster execution of programs as it internally remaps
data from variation affected cells to normal cells and also ensures data
preservation. On extensive experimentation, we find that VAR-DRAM achieves peak
energy savings of up to 48.8% with an average of 29.54% on DDR4 memories while
improving the access latency of the DRAM compared to a variation affected
device by 7.4%.


  I review (1) Physics of Star Formation & Open Questions; (2) Structure &
Dynamics of Star-Forming Clouds & Young Clusters; (3) Star Formation Rates:
Observations & Theoretical Implications.


  We report a study of quantum oscillations (QO) in the magnetic torque of the
nodal-line Dirac semimetal ZrSiS in the magnetic fields up to 35 T and the
temperature range from 40 K down to 2 K, enabling high resolution mapping of
the Fermi surface (FS) topology in the $k_z=\pi$ (Z-R-A) plane of the first
Brillouin zone (FBZ). It is found that the oscillatory part of the measured
magnetic torque signal consists of low frequency (LF) contributions
(frequencies up to 1000 T) and high frequency (HF) contributions (several
clusters of frequencies from 7-22 kT). Increased resolution and angle-resolved
measurements allow us to show that the high oscillation frequencies originate
from magnetic breakdown (MB) orbits involving clusters of individual $\alpha$
hole and $\beta$ electron pockets from the diamond shaped FS in the Z-R-A
plane. Analyzing the HF oscillations we have unequivocally shown that the QO
frequency from the dog-bone shaped Fermi pocket ($\beta$ pocket) amounts
$\beta=591(15)$ T. Our findings suggest that most of the frequencies in the LF
part of QO can also be explained by MB orbits when intraband tunneling in the
dog-bone shaped $\beta$ electron pocket is taken into account. Our results give
a new understanding of the novel properties of the FS of the nodal-line Dirac
semimetal ZrSiS and sister compounds.


  Can one hear the shape of a drum? was proposed by Kac in 1966. The simple
answer is NO as shown through the construction of iso-spectral domains. There
already exists 17 families of planar domains which are non-isometric but
display the same spectra of frequencies. These frequencies, deduced from the
eigenvalues of the Laplacian, are determined by solving the wave equation in a
domain, which is subject to Dirichlet boundary conditions. This paper revisits
the serials of reflection rule inherent in the 17 families of iso-spectral
domains. In accordance with the reflection rule visualized by red-blue-black,
we construct real 3D isospectral models successfully. What is more,
accompanying with the proof of transplantation method, we also use the
numerical method to verify the isospectrality of the 3D models.


  We present techniques for maintaining subgraph frequencies in a dynamic
graph, using data structures that are parameterized in terms of h, the h-index
of the graph. Our methods extend previous results of Eppstein and Spiro for
maintaining statistics for undirected subgraphs of size three to directed
subgraphs and to subgraphs of size four. For the directed case, we provide a
data structure to maintain counts for all 3-vertex induced subgraphs in O(h)
amortized time per update. For the undirected case, we maintain the counts of
size-four subgraphs in O(h^2) amortized time per update. These extensions
enable a number of new applications in Bioinformatics and Social Networking
research.


  We propose a new kernel that quantifies success for the task of computing a
core-periphery partition for an undirected network. Finding the associated
optimal partitioning may be expressed in the form of a quadratic unconstrained
binary optimization (QUBO) problem, to which a state-of-the-art quantum
annealer may be applied. We therefore make use of the new objective function to
(a) judge the performance of a quantum annealer, and (b) compare this approach
with existing heuristic core-periphery partitioning methods. The quantum
annealing is performed on the commercially available D-Wave machine. The QUBO
problem involves a full matrix even when the underlying network is sparse.
Hence, we develop and test a sparsified version of the original QUBO which
increases the available problem dimension for the quantum annealer. Results are
provided on both synthetic and real data sets, and we conclude that the
QUBO/quantum annealing approach offers benefits in terms of optimizing this new
quantity of interest.


  We consider Schr\"odinger operators with periodic potentials on periodic
discrete graphs. Their spectrum consists of a finite number of bands. We obtain
two-sided estimates of the total bandwidth for the Schr\"odinger operators in
terms of geometric parameters of the graph and the potentials. In particular,
we show that these estimates are sharp. It means that these estimates become
identities for specific graphs and potentials. The proof is based on the
Floquet theory and trace formulas for fiber operators. The traces are expressed
as finite Fourier series of the quasimomentum with coefficients depending on
the potentials and cycles of the quotient graph from some specific cycle sets.
In order to obtain our results we estimate these Fourier coefficients in terms
of geometric parameters of the graph and the potentials.


  We use spatially resolved magneto-optical Kerr microscopy to track the
complete microstates of arrays of perpendicular anisotropy nanomagnets during
magnetization hysteresis cycles. These measurements allow us to disentangle the
intertwined effects of nearest neighbor interaction, disorder, and
stochasticity on magnetization switching. We find that the nearest neighbor
correlations depend on both interaction strength and disorder. We also find
that although the global characteristics of the hysteretic switching are
repeatable, the exact microstate sampled is stochastic with the behavior of
individual islands varying between nonminally identical runs.


  The number of users adopting Tor to protect their online privacy is
increasing rapidly. With a limited number of volunteered relays in the network,
the number of clients' connections sharing the same relays is increasing to the
extent that it is starting to affect the performance. Recently, Tor's resource
allocation among circuits has been studied as one cause of poor Tor network
performance. In this paper, we propose two scheduling approaches that guarantee
proportional fairness between circuits that are sharing the same connection. In
our evaluation, we show that the average-rate-base scheduler allocates Tor's
resources in an optimal fair scheme, increasing the total throughput achieved
by Tor's relays. However, our second proposed approach, an optimization-based
scheduler, maintains acceptable fairness while reducing the latency experienced
by Tor's clients.


  We show explicitly how the T-model, E-model, and Hilltop inflations are
obtained from the inflation models with a non-canonical kinetic term and an
arbitrary potential. By this method, any attractor of observables $n_s$ and $r$
is possible. The presence of attractors poses a challenge to differentiate
inflation models.


  We construct stable manifolds for a class of degenerate evolution equations
including the steady Boltzmann equation, establishing in the process
exponential decay of associated kinetic shock and boundary layers to their
limiting equilibrium states. Our analysis is from a classical dynamical systems
point of view, but with a number of interesting modifications to accomodate
ill-posedness with respect to the Cauchy problem of the underlying evolution
equation.


  Effectiveness of using laser field to produce entanglement between two
dipole-interacting identical two-level atoms is considered in detail. The
entanglement is achieved by driving the system with a carefully designed laser
pulse transferring the system's population to one of the maximally entangled
Dicke states in a way analogous to population inversion by a resonant
$\pi$-pulse in a two-level atom. It is shown that for the optimally chosen
pulse frequency, power and duration, the fidelity of generating a maximally
entangled state approaches unity as the distance between the atoms goes to
zero.


  In present work the generalization of Einstein's special theory of relativity
on 5-dimentional space is considered, in which as fifth coordinates we consider
the interval s of a particle. 5-dimentional vectors in this space are isotropic
both for mass, and for massless of particles.
  In extended space, offered by the authors, there is a possibility in addition
with usual Lorentz transformations, to enter two new transformations, therefore
massive and massless of a particle can reversible converted to each other.


  Automatic few-shot font generation is a practical and widely studied problem
because manual designs are expensive and sensitive to the expertise of
designers. Existing few-shot font generation methods aim to learn to
disentangle the style and content element from a few reference glyphs, and
mainly focus on a universal style representation for each font style. However,
such approach limits the model in representing diverse local styles, and thus
makes it unsuitable to the most complicated letter system, e.g., Chinese, whose
characters consist of a varying number of components (often called "radical")
with a highly complex structure. In this paper, we propose a novel font
generation method by learning localized styles, namely component-wise style
representations, instead of universal styles. The proposed style
representations enable us to synthesize complex local details in text designs.
However, learning component-wise styles solely from reference glyphs is
infeasible in the few-shot font generation scenario, when a target script has a
large number of components, e.g., over 200 for Chinese. To reduce the number of
reference glyphs, we simplify component-wise styles by a product of component
factor and style factor, inspired by low-rank matrix factorization. Thanks to
the combination of strong representation and a compact factorization strategy,
our method shows remarkably better few-shot font generation results (with only
8 reference glyph images) than other state-of-the-arts, without utilizing
strong locality supervision, e.g., location of each component, skeleton, or
strokes. The source code is available at https://github.com/clovaai/lffont.


  We present the stellar and ionized gas kinematics of 13 bright peculiar Virgo
cluster galaxies observed with the DensePak Integral Field Unit at the WIYN
3.5-meter telescope, to seek kinematic evidence that these galaxies have
experienced gravitational interactions or gas stripping. 2-Dimensional maps of
the stellar velocity $V$, and stellar velocity dispersion $\sigma$ and the
ionized gas velocity (H$\beta$ and/or [\ion{O}{3}]) are presented for galaxies
in the sample. The stellar rotation curves and velocity dispersion profiles are
determined for 13 galaxies, and the ionized gas rotation curves are determined
for 6 galaxies. Misalignments between the optical and kinematical major axis
are found in several galaxies. While in some cases this is due to a bar, in
other cases it seems associated with a gravitational interaction or ongoing ram
pressure stripping. Non-circular gas motions are found in nine galaxies, with
various causes including bars, nuclear outflows, or gravitational disturbances.
Several galaxies have signatures of kinematically distinct stellar components,
which are likely signatures of accretion or mergers. We compute for all
galaxies the angular momentum parameter $\lambda_{\rm R}$. An evaluation of the
galaxies in the $\lambda_{\rm R}$-ellipticity plane shows that all but 2 of the
galaxies have significant support from random stellar motions, and have likely
experienced gravitational interactions. This includes some galaxies with very
small bulges and truncated/compact H$\alpha$ morphologies, indicating that such
galaxies cannot be fully explained by simple ram pressure stripping, but must
have had significant gravitational encounters. Most of the sample galaxies show
evidence for ICM-ISM stripping as well as gravitational interactions,
indicating that the evolution of a significant fraction of cluster galaxies is
likely strongly impacted by both effects.


  In primate brains, tactile and proprioceptive inputs are relayed to the
somatosensory cortex which is known for somatotopic representations, or,
"homunculi". Our research centers on understanding the mechanisms of the
formation of these and more higher-level body representations (body schema) by
using humanoid robots and neural networks to construct models. We specifically
focus on how spatial representation of the body may be learned from
somatosensory information in self-touch configurations. In this work, we target
the representation of proprioceptive inputs, which we take to be joint angles
in the robot. The inputs collected in different body postures serve as inputs
to a Self-Organizing Map (SOM) with a 2D lattice on the output. With
unrestricted, all-to-all connections, the map is not capable of representing
the input space while preserving the topological relationships, because the
intrinsic dimensionality of the body posture space is too large. Hence, we use
a method we developed previously for tactile inputs (Hoffmann, Straka et al.
2018) called MRF-SOM, where the Maximum Receptive Field of output neurons is
restricted so they only learn to represent specific parts of the input space.
This is in line with the receptive fields of neurons in somatosensory areas
representing proprioception that often respond to combination of few joints
(e.g. wrist and elbow).


  A fractional time derivative is introduced into the Burger's equation to
model losses of nonlinear waves. This term amounts to a time convolution
product, which greatly penalizes the numerical modeling. A diffusive
representation of the fractional derivative is adopted here, replacing this
nonlocal operator by a continuum of memory variables that satisfy local-in-time
ordinary differential equations. Then a quadrature formula yields a system of
local partial differential equations, well-suited to numerical integration. The
determination of the quadrature coefficients is crucial to ensure both the
well-posedness of the system and the computational efficiency of the diffusive
approximation. For this purpose, optimization with constraint is shown to be a
very efficient strategy. Strang splitting is used to solve successively the
hyperbolic part by a shock-capturing scheme, and the diffusive part exactly.
Numerical experiments are proposed to assess the efficiency of the numerical
modeling, and to illustrate the effect of the fractional attenuation on the
wave propagation.


  We construct an infinite particle/infinite volume Langevin dynamics on the
space of configurations in $\R^d$ having velocities as marks. The construction
is done via a limiting procedure using $N$-particle dynamics in cubes
$(-\lambda,\lambda]^d$ with periodic boundary conditions. A main step to this
result is to derive an (improved) Ruelle bound for the canonical correlation
functions of $N$-particle systems in $(-\lambda,\lambda]^d$ with periodic
boundary conditions. After proving tightness of the laws of finite particle
dynamics, the identification of accumulation points as martingale solutions of
the Langevin equation is based on a general study of properties of measures on
configuration space (and their weak limit) fulfilling a uniform Ruelle bound.
Additionally, we prove that the initial/invariant distribution of the
constructed dynamics is a tempered grand canonical Gibbs measure. All proofs
work for general repulsive interaction potentials $\phi$ of Ruelle type (e.g.
the Lennard-Jones potential) and all temperatures, densities and dimensions
$d\geq 1$.


  We study how isotropic and homogeneous far-from-equilibrium quantum systems
relax to nonthermal attractors, which are of interest for cold atoms and
nuclear collisions. We demonstrate that a first-order ordinary differential
equation governs the self-similar approach to nonthermal attractors, i.e., the
prescaling. We also show that certain natural scaling-breaking terms induce
logarithmically slow corrections that prevent the scaling exponents from
reaching the constant values during the system's lifetime. We propose that,
analogously to hydrodynamic attractors, the appropriate mathematical structure
to describe such dynamics is the transseries. We verify our analytic
predictions with state-of-the-art 2PI simulations of the large-N vector model
and QCD kinetic theory.


  Upcoming and planned experiments combining increasingly intense lasers and
energetic particle beams will access new regimes of nonlinear, relativistic,
quantum effects. This improved experimental capability has driven substantial
progress in QED in intense background fields. We review here the advances made
during the last decade, with a focus on theory and phenomenology. As ever
higher intensities are reached, it becomes necessary to consider processes at
higher orders in both the number of scattered particles and the number of
loops, and to account for non-perturbative physics (e.g. the Schwinger effect),
with extreme intensities requiring resummation of the loop expansion. In
addition to increased intensity, experiments will reach higher accuracy, and
these improvements are being matched by developments in theory such as in
approximation frameworks, the description of finite-size effects, and the range
of physical phenomena analysed. Topics on which there has been substantial
progress include: radiation reaction, spin and polarisation, nonlinear quantum
vacuum effects and connections to other fields including physics beyond the
Standard Model.


  Beamline components, such as superconducting radio frequency cavities and
focusing lenses, need to be assembled together in a string while in a cleanroom
environment. The present contribution identifies and characterizes materials
for additive manufacturing that can be used in a cleanroom. The well known
advantages of additive manufacturing processes would highly benefit the design
and development of tooling needed for the mechanical support and alignment of
string components. Cleanliness, mechanical properties, and leak tightness of
the chosen materials are the main focus of this contribution, which also paves
the way for the integration of such materials in cryomodule assemblies. Results
reported here were obtained in the framework of the PIP-II project at Fermilab.


  We continue our study of the full set of translation-invariant splitting
Gibbs measures (TISGMs, translation-invariant tree-indexed Markov chains) for
the $q$-state Potts model on a Cayley tree. In our previous work \cite{KRK} we
gave a full description of the TISGMs, and showed in particular that at
sufficiently low temperatures their number is $2^{q}-1$.
  In this paper we find some regions for the temperature parameter ensuring
that a given TISGM is (non-)extreme in the set of all Gibbs measures.
  In particular we show the existence of a temperature interval for which there
are at least $2^{q-1} + q$ extremal TISGMs.
  For the Cayley tree of order two we give explicit formulae and some numerical
values.


  In this work we continue our research on nonharmonic analysis of boundary
value problems as initiated in our recent paper (IMRN 2016). There, we assumed
that the eigenfunctions of the model operator on which the construction is
based do not have zeros. In this paper we have weakened this condition
extending the applicability of the developed pseudo-differential analysis.
Also, we do not assume that the underlying set is bounded.


  The production of $\phi$ mesons in the reaction $e^{+}p \rightarrow e^{+}
\phi p$ ($\phi \rightarrow K^{+}K^{-}$), for $7 < Q^2 < 25$ GeV$^2$ and for
virtual photon-proton centre of mass energies ($W$) in the range 42-134 GeV,
has been studied with the ZEUS detector at HERA. When compared to lower energy
data at similar $Q^2$, the results show that the $\gamma^*p \rightarrow \phid
p$ cross section rises strongly with $W$. This behaviour is similar to that
previously found for the $\gamma^*p \rightarrow \rho^0 p$ cross section. This
strong dependence cannot be explained by production through soft pomeron
exchange. It is, however, consistent with perturbative QCD expectations, where
it reflects the rise of the gluon momentum density in the proton at small $x$.
The ratio of $\sigma (\phi) / \sigma (\rho^0)$, which has previously been
determined by ZEUS to be 0.065 $\pm$ 0.013 (stat.) in photoproduction at a mean
$W$ of 70 GeV, is measured to be 0.18 $\pm $ 0.05 (stat.) $\pm$ 0.03 (syst.) at
a mean $Q^2$ of 12.3 GeV$^2$ and mean $W$ of $\approx$ 100 GeV and is thus
approaching at large $Q^2$ the value of 2/9 predicted from the quark charges of
the vector mesons and a flavour independent production mechanism.


  The EXperiment for Cryogenic Large-Aperture Intensity Mapping (EXCLAIM) is a
cryogenic balloon-borne instrument that will map carbon monoxide and
singly-ionized carbon emission lines across redshifts from 0 to 3.5, using an
intensity mapping approach. EXCLAIM will broaden our understanding of these
elemental and molecular gases and the role they play in star formation
processes across cosmic time scales. The focal plane of EXCLAIM's cryogenic
telescope features six {\mu}-Spec spectrometers. {\mu}-Spec is a compact,
integrated grating-analog spectrometer, which uses meandered superconducting
niobium microstrip transmission lines on a single-crystal silicon dielectric to
synthesize the grating. It features superconducting aluminum microwave kinetic
inductance detectors (MKIDs), also in a microstrip architecture. The
spectrometers for EXCLAIM couple to the telescope optics via a hybrid planar
antenna coupled to a silicon lenslet. The spectrometers operate from 420 to 540
GHz with a resolving power R={\lambda}/{\Delta}{\lambda}=512 and employ an
array of 355 MKIDs on each spectrometer. The spectrometer design targets a
noise equivalent power (NEP) of 2x10-18W/\sqrt{Hz} (defined at the input to the
main lobe of the spectrometer lenslet beam, within a 9-degree half width),
enabled by the cryogenic telescope environment, the sensitive MKID detectors,
and the low dielectric loss of single-crystal silicon. We report on these
spectrometers under development for EXCLAIM, providing an overview of the
spectrometer and component designs, the spectrometer fabrication process,
fabrication developments since previous prototype demonstrations, and the
current status of their development for the EXCLAIM mission.


  We analyze the 3-year WMAP data and look for a deviation from Gaussianity in
the form of a 3-point function that has either of the two theoretically
motivated shapes: local and equilateral. There is no evidence of departure from
Gaussianity and the analysis gives the presently tightest bounds on the
parameters f_ NL^local and f_NL^equil., which define the amplitude of
respectively the local and the equilateral non-Gaussianity: -36 < f_NL^local <
100, -256 < f_NL^equil. < 332 at 95% C.L.


  Self-consistent treatment of time-dependent ionization in hydrodynamical
calculations of the X-ray emission from young supernova remnants has been
performed. The novel feature of the calculations is that K_alpha lines from
species produced by inner-shell collisional ionization are included. Parameters
of the shocked ejecta are found from fitting the model spectrum to the observed
one. The application of the method to Tycho SNR using the classical
deflagration model W7 for the explosion enables us to well reproduce the
observed X-ray spectra and radial brightness profiles of the remnant.


  Star formation in half of massive galaxies was quenched by the time the
Universe was three billion years old. Very low amounts of molecular gas appear
responsible for this, at least in some cases, though morphological gas
stabilization, shock heating, or activity associated with accretion onto a
central supermassive black hole is invoked in other cases. Recent studies of
quenching by gas depletion have been based upon upper limits that are
insufficiently sensitive to determine this robustly, or stacked emission with
its problems of averaging. Here we report 1.3mm observations of dust emission
from six strongly lensed galaxies where star formation has been quenched, with
magnifications of up to a factor of 30. Four of the six galaxies are undetected
in dust emission, with an estimated upper limit on the dust mass of 0.0001
times the stellar mass, and by proxy (assuming a Milky Way molecular
gas-to-dust ratio) 0.01 times the stellar mass in molecular gas. This is two
orders of magnitude less molecular gas per unit stellar mass than seen in star
forming galaxies at similar redshifts. It remains difficult to extrapolate from
these small samples, but these observations establish that gas depletion is
responsible for a cessation of star formation in some fraction of high-redshift
galaxies.


  It is a matter of debate whether a shrinking proportion of scholarly
literature is getting most of the references over time. It is also less well
understood how a narrowing literature usage would affect the circulation of
ideas in the sciences. Here we show, that the utilization of scientific
literature follows dual tendencies over time: while a larger proportion of
literature is cited at least a few times, citations are also concentrating more
on the top of the citation distribution. Parallel to the latter trend, a
paper's future importance increasingly depends on its past citation
performance. A random network model shows that the citation concentration is
directly related to the greater stability of citation performance. The
presented evidence suggests that the growing heterogeneity of citation impact
restricts the mobility of research articles that do not gain attention early
on. While concentration grows from the beginning of the studied period in 1970,
citation dispersion manifest itself significantly only from the mid-1990s when
the popularity of freshly published papers has also risen. Most likely,
advanced information technologies to disseminate papers are behind both of
these latter trends.


  Randomized benchmarking (RB) is a powerful method for determining the error
rate of experimental quantum gates. Traditional RB, however, is restricted to
gatesets, such as the Clifford group, that form a unitary 2-design. The
recently introduced character RB can benchmark more general gates using
techniques from representation theory; up to now, however, this method has only
been applied to "multiplicity-free" groups, a mathematical restriction on these
groups. In this paper, we extend the original character RB derivation to
explicitly treat non-multiplicity-free groups, and derive several applications.
First, we derive a rigorous version of the recently introduced subspace RB,
which seeks to characterize a set of one- and two-qubit gates that are
symmetric under SWAP. Second, we develop a new leakage RB protocol that applies
to more general groups of gates. Finally, we derive a scalable RB protocol for
the matchgate group, a group that like the Clifford group is non-universal but
becomes universal with the addition of one additional gate. This example
provides one of the few examples of a scalable non-Clifford RB protocol. In all
three cases, compared to existing theories, our method requires similar
resources, but either provides a more accurate estimate of gate fidelity, or
applies to a more general group of gates. In conclusion, we discuss the
potential, and challenges, of using non-multiplicity-free character RB to
develop new classes of scalable RB protocols and methods of characterizing
specific gates.


  We study the transverse momentum spectrum of hadrons in jets. By measuring
the transverse momentum with respect to a judiciously chosen axis, we find that
this observable is insensitive to (the recoil of) soft radiation. Furthermore,
for small transverse momenta we show that the effects of the jet boundary
factorize, leading to a new transverse-momentum-dependent (TMD) fragmentation
function. In contrast to the usual TMD fragmentation functions, it does not
involve rapidity divergences and is universal in the sense that it is
independent of the type of process and number of jets. These results directly
apply to sub-jets instead of hadrons. We discuss potential applications, which
include studying nuclear modification effects in heavy-ion collisions and
identifying boosted heavy resonances.


  A new perspective toward thermodynamic phase space of Reisser-Nordstrom (RN)
black holes in an anti-de-Sitter (AdS) spaces was recently proposed [Phys.
Lett. B 768 (2017) 235], where the square of the electric charge $(Q^2)$ of
black hole was regarded as a thermodynamic variable and the cosmological
constant (pressure) as a fixed quantity. In this paper, we address the
universality class and critical properties of any AdS black hole in this
alternative phase space. We disclose the critical behavior of AdS black hole in
the alternative phase space in which a continuous phase transition happens and
in a very general framework, independent of the spacetime metric. Based on the
expansion of the equation of state and Landau thermodynamic potential in the
neighborhood of a critical point in the alternative phase space, we confirm
that the set of values for critical exponents for generic black hole is
analogous to the Van der Waals fluid system. Finally, we reveal that the scalar
curvature in geometry thermodynamic diverges at the critical point of black
hole. Our study shows that the approach here is powerful enough to investigate
the critical behavior of any black holes and further supports the viability of
the alternative viewpoint toward phase space of black holes suggested in [Phys.
Lett. B 768 (2017) 235].


  An optimal control problem with an infinite horizon quadratic cost functional
for a linear system with a known additive disturbance is considered. The
feature of this problem is that a weight matrix of the control cost in the cost
functional is singular. Due to this singularity, the problem can be solved
neither by application of the Pontriagin's Maximum Principle, nor using the
Hamilton-Jacobi-Bellman equation approach, i.e. this problem is singular. Since
the weight matrix of the control cost, being singular, is not in general zero,
only a part of the control coordinates is singular, while the others are
regular. This problem is solved by a regularization method. Namely, it is
associated with a new optimal control problem for the same equation of
dynamics. The cost functional in this new problem is the sum of the original
cost functional and an infinite horizon integral of the squares of the singular
control coordinates with a small positive weight. Due to a smallness of this
coefficient, the new problem is a partial cheap control problem. Using a
perturbation technique, an asymptotic analysis of this partial cheap control
problem is carried out. Based on this analysis, the infimum of the cost
functional in the original problem is derived, and a minimizing sequence of
state-feedback controls is designed. An illustrative example of a singular
trajectory tracking is presented.


  Effective Prognostics and Health Management (PHM) relies on accurate
prediction of the Remaining Useful Life (RUL). Data-driven RUL prediction
techniques rely heavily on the representativeness of the available
time-to-failure trajectories. Therefore, these methods may not perform well
when applied to data from new units of a fleet that follow different operating
conditions than those they were trained on. This is also known as domain
shifts. Domain adaptation (DA) methods aim to address the domain shift problem
by extracting domain invariant features. However, DA methods do not distinguish
between the different phases of operation, such as steady states or transient
phases. This can result in misalignment due to under- or over-representation of
different operation phases. This paper proposes two novel DA approaches for RUL
prediction based on an adversarial domain adaptation framework that considers
the different phases of the operation profiles separately. The proposed
methodologies align the marginal distributions of each phase of the operation
profile in the source domain with its counterpart in the target domain. The
effectiveness of the proposed methods is evaluated using the New Commercial
Modular Aero-Propulsion System (N-CMAPSS) dataset, where sub-fleets of turbofan
engines operating in one of the three different flight classes (short, medium,
and long) are treated as separate domains. The experimental results show that
the proposed methods improve the accuracy of RUL predictions compared to
current state-of-the-art DA methods.


  The variability and low signal-to-noise ratio in financial data, combined
with the necessity for interpretability, make the alpha factor mining workflow
a crucial component of quantitative investment. Transitioning from early manual
extraction to genetic programming, the most advanced approach in this domain
currently employs reinforcement learning to mine a set of combination factors
with fixed weights. However, the performance of resultant alpha factors
exhibits inconsistency, and the inflexibility of fixed factor weights proves
insufficient in adapting to the dynamic nature of financial markets. To address
this issue, this paper proposes a two-stage formulaic alpha generating
framework AlphaForge, for alpha factor mining and factor combination. This
framework employs a generative-predictive neural network to generate factors,
leveraging the robust spatial exploration capabilities inherent in deep
learning while concurrently preserving diversity. The combination model within
the framework incorporates the temporal performance of factors for selection
and dynamically adjusts the weights assigned to each component alpha factor.
Experiments conducted on real-world datasets demonstrate that our proposed
model outperforms contemporary benchmarks in formulaic alpha factor mining.
Furthermore, our model exhibits a notable enhancement in portfolio returns
within the realm of quantitative investment.


  In this paper, we study the topological rigidity and its relationship with
the positivity of scalar curvature. Precisely, we show that any complete
contractible $3$-manifold with non-negative scalar curvature is homeomorphic to
$\mathbf{R}^3$.


  Red-teaming, or identifying prompts that elicit harmful responses, is a
critical step in ensuring the safe and responsible deployment of large language
models (LLMs). Developing effective protection against many modes of attack
prompts requires discovering diverse attacks. Automated red-teaming typically
uses reinforcement learning to fine-tune an attacker language model to generate
prompts that elicit undesirable responses from a target LLM, as measured, for
example, by an auxiliary toxicity classifier. We show that even with explicit
regularization to favor novelty and diversity, existing approaches suffer from
mode collapse or fail to generate effective attacks. As a flexible and
probabilistically principled alternative, we propose to use GFlowNet
fine-tuning, followed by a secondary smoothing phase, to train the attacker
model to generate diverse and effective attack prompts. We find that the
attacks generated by our method are effective against a wide range of target
LLMs, both with and without safety tuning, and transfer well between target
LLMs. Finally, we demonstrate that models safety-tuned using a dataset of
red-teaming prompts generated by our method are robust to attacks from other
RL-based red-teaming approaches.


  This work establishes distribution-free upper and lower bounds on the minimax
label complexity of active learning with general hypothesis classes, under
various noise models. The results reveal a number of surprising facts. In
particular, under the noise model of Tsybakov (2004), the minimax label
complexity of active learning with a VC class is always asymptotically smaller
than that of passive learning, and is typically significantly smaller than the
best previously-published upper bounds in the active learning literature. In
high-noise regimes, it turns out that all active learning problems of a given
VC dimension have roughly the same minimax label complexity, which contrasts
with well-known results for bounded noise. In low-noise regimes, we find that
the label complexity is well-characterized by a simple combinatorial complexity
measure we call the star number. Interestingly, we find that almost all of the
complexity measures previously explored in the active learning literature have
worst-case values exactly equal to the star number. We also propose new active
learning strategies that nearly achieve these minimax label complexities.


  Evolved stars are among the largest and brightest stars and they are ideal
targets for the new generation of sensitive, high resolution instrumentation
that provides spectrophotometric, interferometric, astrometric, and imaging
observables. The interpretation of the complex stellar surface images requires
numerical simulations of stellar convection that take into account
multi-dimensional time-dependent radiation hydrodynamics with realistic input
physics. We show how the evolved star simulations are obtained using the
radiative hydrodynamics code CO5BOLD and how the accurate observables are
computed with the post-processing radiative transfer code Optim3D. The synergy
between observations and theoretical work is supported by a proper and
quantitative analysis using these simulations, and by strong constraints from
the observational side.


  We study a 2-scale version of the Landau-Lifshitz system of ferromagnetism,
introduced by Starynkevitch to modelize hysteresis: the response of the
magnetization is fast compared to a slowly varying applied magnetic fi eld.
Taking the exchange term into account, in space dimension 3, we prove that,
under some natural stability assumption on the equilibria of the system, the
strong solutions follow the dynamics of these equilibria. We also give explicit
examples of relevant equilibria and exterior magnetic fields, when the
ferromagnetic medium occupies some ellipsoidal domain.


  The well known Haldane map from spin chains into the $O(3)$ non linear sigma
model is generalized to the case of spin ladders. This map allows us to explain
the different qualitative behaviour between even and odd ladders, exactly in
the same way it explains the difference between integer and half-integer spin
chains. Namely, for even ladders the topological term in the sigma model action
is absent, while for odd ladders the $\theta$ parameter, which multiplies the
topological term, is equal to $2 \pi S$, where $S$ is the spin of the ladder.
Hence even ladders should have a dynamically generated spin gap, while odd
ladders with half-integer spin should stay gapless, and physically equivalent
to a perturbed $SU(2)_1$ Wess-Zumino -Witten model in the infrared regime. We
also derive some consequences from the dependence of the sigma model coupling
constant on the ladder Heisenberg couplings constants.


  The sunflower conjecture is one of the most well-known open problems in
combinatorics. It has several applications in theoretical computer science, one
of which is DNF compression, due to Gopalan, Meka and Reingold [Computational
Complexity 2013]. In this paper, we show that improved bounds for DNF
compression imply improved bounds for the sunflower conjecture, which is the
reverse direction of [Computational Complexity 2013]. The main approach is
based on regularity of set systems and a structure-vs-pseudorandomness approach
to the sunflower conjecture.


  In this study we used nonequilibrium simulation method to investigate the
temperature dependent divergence of thermal conductivity in one dimensional
momentum conserving system with asymmetric double well nearest-neighbor
interaction potential. We show that the value of divergence exponent ($\alpha$)
in the power law divergence of thermal conductivity depends on the temperature
of the system. At low and high temperatures $\alpha$ reaches close to $\sim0.5$
and $\sim0.33$ respectively. Whereas in the intermediate temperature the
divergence of thermal conductivity with the chain length saturates with
$\alpha\sim0.07$. Subsequent analysis showed that the predicted value of
$\alpha$ in the intermediate temperature may not have reached its thermodynamic
limit. Further calculations of local $\alpha$ revealed that its approach
towards the thermodynamic limit crucially dependent on the temperature of the
system. At low and high temperatures local $\alpha$ reaches its thermodynamic
limits in shorter chain lengths. On the contrary in case of intermediate
temperature it's progress towards the asymptotic limit is nonmonotonous.


  Differential privacy is a promising privacy-preserving paradigm for
statistical query processing over sensitive data. It works by injecting random
noise into each query result, such that it is provably hard for the adversary
to infer the presence or absence of any individual record from the published
noisy results. The main objective in differentially private query processing is
to maximize the accuracy of the query results, while satisfying the privacy
guarantees. Previous work, notably the matrix mechanism, has suggested that
processing a batch of correlated queries as a whole can potentially achieve
considerable accuracy gains, compared to answering them individually. However,
as we point out in this paper, the matrix mechanism is mainly of theoretical
interest; in particular, several inherent problems in its design limit its
accuracy in practice, which almost never exceeds that of naive methods. In
fact, we are not aware of any existing solution that can effectively optimize a
query batch under differential privacy. Motivated by this, we propose the
Low-Rank Mechanism (LRM), the first practical differentially private technique
for answering batch queries with high accuracy, based on a low rank
approximation of the workload matrix. We prove that the accuracy provided by
LRM is close to the theoretical lower bound for any mechanism to answer a batch
of queries under differential privacy. Extensive experiments using real data
demonstrate that LRM consistently outperforms state-of-the-art query processing
solutions under differential privacy, by large margins.


  Neural operator architectures employ neural networks to approximate operators
mapping between Banach spaces of functions; they may be used to accelerate
model evaluations via emulation, or to discover models from data. Consequently,
the methodology has received increasing attention over recent years, giving
rise to the rapidly growing field of operator learning. The first contribution
of this paper is to prove that for general classes of operators which are
characterized only by their $C^r$- or Lipschitz-regularity, operator learning
suffers from a ``curse of parametric complexity'', which is an
infinite-dimensional analogue of the well-known curse of dimensionality
encountered in high-dimensional approximation problems. The result is
applicable to a wide variety of existing neural operators, including PCA-Net,
DeepONet and the FNO. The second contribution of the paper is to prove that
this general curse can be overcome for solution operators defined by the
Hamilton-Jacobi equation; this is achieved by leveraging additional structure
in the underlying solution operator, going beyond regularity. To this end, a
novel neural operator architecture is introduced, termed HJ-Net, which
explicitly takes into account characteristic information of the underlying
Hamiltonian system. Error and complexity estimates are derived for HJ-Net which
show that this architecture can provably beat the curse of parametric
complexity related to the infinite-dimensional input and output function
spaces.


  We make a detailed study on the properties of the total decay width of Higgs
decay channel $H\to gg$ up to $\alpha_s^6$-order QCD corrections by using the
newly suggested infinite-order scale-setting approach, which is based on the
ideas of both the principle of maximum conformality and the intrinsic
conformality. This approach is called as the PMC$_\infty$ approach. By using
the PMC$_\infty$ approach, we observe that the conventional renormalization
scale ambiguity in perturbative QCD calculation is eliminated and the residual
scale dependence due to unknown higher-order terms can also be highly
suppressed. We then obtain an accurate perturbative QCD prediction on the total
decay width, e.g. $\Gamma (H \to gg)|_{\rm PMC_\infty} =336.42^{+7.01}_{-6.92}$
KeV, where the errors are squared averages of those from all the mentioned
error sources.


  We present some results of the deepest extragalactic survey performed by the
INTEGRAL satellite. The fraction of very absorbed AGN is quite large. The sharp
decrease in the absorption fraction with X-ray luminosity observed at
lower-energy X-rays is not observed. The current lack of truly Compton-thick
objects, with an upper limit of 14% to the size of this population, is just
compatible with recent modeling of the cosmic X-ray background.
  We also study the prospects for a future hard X-ray serendipitous survey with
Simbol-X. We show that Simbol-X will easily detect a large number of
serendipitous AGN, allowing us to study the evolution of AGN up to redshifts
about 2, opening the door to the cosmological study of hard X-ray selected AGN,
which is barely possible with existing satellites like Swift and INTEGRAL.


  First protostellar cores are young stellar objects in the earliest
evolutionary stage. They are hydrostatic objects formed soon after the central
portions of star-forming cores become optically thick to dust emission. We
consider their characteristics in the emitted radiation, and discuss their
evolution with increasing mass of the cores. Particular attention is paid to
detailed radiative and chemical processes in the postshock relaxation layer
located at the surface of the core, where the majority of radiation is emitted.
Most of the radiation is originally emitted in the dust continuum in
mid-infrared wavelength (~10-30 micron), which reprocessed to far-infrared with
~100-200 micron. Although some fraction (~0.1) of the radiation energy is
emitted in the H2O lines at the accretion shock, most is absorbed and reemitted
in the dust continuum in the envelope. The H2O lines account for at most ~1/100
of the observed luminosity. If a cavity is present in the envelope due to
outflow or rotation, the dust and H2O line emission in the mid-infrared
wavelength from the shock can be observed directly, or as a reflection nebula.
Among forthcoming observational facillities, SPICA is the most suitable for
detecting either direct or processed radiation from first-core objects.


  In this paper we find a connection between the macroscopic classical laws of
gases and the quantum mechanical description of molecules, composing an ideal
gas. In such a gas, the motion of each individual molecule can be considered
independently on all other molecules, and thus the macroscopic parameters of
ideal gas, like pressure P and temperature T, can be introduced as a result of
simple averaging over all individual motions of molecules. It is shown that for
an ideal gas enclosed in a macroscopic cubic box of volume V, the constant, in
the classical law of adiabatic expansion, i.e.PV^5/3=const, can be derived,
based on quantum mechanics. Physical implications of the result we disclose are
discussed. In any case, our finding proves, seemingly for the first time, a
macroscopic manifestation of a quantum mechanical behavior, and this in
relation to classical thermodynamics.


  For the first time in visible part of the $D_2$ emission spectrum the pseudo
doublets representing partly resolved fine structure of rovibronic lines have
been observed. They are characterized by splitting values about 0.2 cm$^{-1}$
and relative intensity of the doublet components close to 2.0. It is shown that
they are determined by triplet splitting in lower rovibronic levels of various
$^3\Lambda_g^\pm \to c^3\Pi_u^-$ electronic transitions. It is proposed to use
an existence of such partly resolved fine structure patterns for identification
of numerous unassigned spectral lines of the $D_2$ molecule coming from great
variety of triplet "gerade" electronic states to vibro-rotational levels of the
$c^3\Pi_u^-$ state.


  In a previous paper the CSCR domain was defined. Here this is taken to the
next stage where we consider the design of a particular Collaborative Research
Environment to support Students and Supervisors CRESS. Following the CSCR
structure a preliminary design for CRESS has been established and a portal
framework analysis is undertaken in order to determine the most appropriate set
of tools for its implementation.


  Inhomogeneous quantum cosmology is modeled as a dynamical system of discrete
patches, whose interacting many-body equations can be mapped to a non-linear
minisuperspace equation by methods analogous to Bose-Einstein condensation.
Complicated gravitational dynamics can therefore be described by
more-manageable equations for finitely many degrees of freedom, for which
powerful solution procedures are available, including effective equations. The
specific form of non-linear and non-local equations suggests new questions for
mathematical and computational investigations, and general properties of
non-linear wave equations lead to several new options for physical effects and
tests of the consistency of loop quantum gravity. In particular, our quantum
cosmological methods show how sizeable quantum corrections in a low-curvature
universe can arise from tiny local contributions adding up coherently in large
regions.


  We present a suite of FIRE-2 cosmological zoom-in simulations of isolated
field dwarf galaxies, all with masses of $M_\mathrm{halo} \approx
10^{10}\,$M$_\odot$ at $z=0$, across a range of dark matter models. For the
first time, we compare how both self-interacting dark matter (SIDM) and/or warm
dark matter (WDM) models affect the assembly histories as well as the central
density structure in fully hydrodynamical simulations of dwarfs. Dwarfs with
smaller stellar half-mass radii (r$_{1/2}<500$ pc) have lower
$\sigma_\star/V_\mathrm{max}$ ratios, reinforcing the idea that smaller dwarfs
may reside in halos that are more massive than is naively expected. The
majority of dwarfs simulated with self-interactions actually experience
contraction of their inner density profiles with the addition of baryons
relative to the cores produced in dark-matter-only runs, though the simulated
dwarfs are always less centrally dense than in $\Lambda$CDM. The
V$_{1/2}-$r$_{1/2}$ relation across all simulations is generally consistent
with observations of Local Field dwarfs, though compact objects such as Tucana
provide a unique challenge. Spatially-resolved rotation curves in the central
regions ($<400$ pc) of small dwarfs could provide a way to distinguish between
CDM, WDM, and SIDM, however: at the masses probed in this simulation suite,
cored density profiles in dwarfs with small r$_{1/2}$ values can only originate
from dark matter self-interactions.


  We consider the class of optimization problems arising from computationally
intensive L1-regularized M-estimators, where the function or gradient values
are very expensive to compute. A particular instance of interest is the
L1-regularized MLE for learning Conditional Random Fields (CRFs), which are a
popular class of statistical models for varied structured prediction problems
such as sequence labeling, alignment, and classification with label taxonomy.
L1-regularized MLEs for CRFs are particularly expensive to optimize since
computing the gradient values requires an expensive inference step. In this
work, we propose the use of a carefully constructed proximal quasi-Newton
algorithm for such computationally intensive M-estimation problems, where we
employ an aggressive active set selection technique. In a key contribution of
the paper, we show that the proximal quasi-Newton method is provably
super-linearly convergent, even in the absence of strong convexity, by
leveraging a restricted variant of strong convexity. In our experiments, the
proposed algorithm converges considerably faster than current state-of-the-art
on the problems of sequence labeling and hierarchical classification.


  Let $T$ be a theory with a definable topology. $T$ is t-minimal in the sense
of Mathews if every definable set in one variable has finite boundary. If $T$
is t-minimal, we show that there is a good dimension theory for definable sets,
satisfying properties similar to dp-rank in dp-minimal theories, with one key
exception: the dimension of $\operatorname{dom}(f)$ can be less than the
dimension of $\operatorname{im}(f)$ for a definable function $f$. Using the
dimension theory, we show that any definable field in a t-minimal theory is
perfect.
  We then specialize to the case where $T$ is visceral in the sense of Dolich
and Goodrick, meaning that $T$ is t-minimal and the definable topology comes
from a definable uniformity (i.e., a definable uniform structure). We show that
almost all of Dolich and Goodrick's tame topology theorems for visceral
theories hold without their additional assumptions of definable finite choice
(DFC) and no space-filling functions (NSFF). Lastly, we produce an example of a
visceral theory with a space-filling curve, answering a question of Dolich and
Goodrick.


  Targeted therapeutic interventions utilizing low-inten\-sity ultrasound
(LIUS) exhibit substantial potential for hindering the proliferation of cancer
stem cells. This investigation introduces a multiscale model and computational
framework to comprehensively explore the therapeutic LIUS on poroelastic tumor
dynamics, thereby unraveling the intricacies of mechanotransduction mechanisms
at play. Our model includes both macroscopic timescales encompassing days and
rapid timescales spanning from microseconds to seconds, facilitating an
in-depth comprehension of tumor behavior. We unveil the discerning suppression
or reorientation of cancer cell proliferation and migration, enhancing a
notable redistribution of cellular phases and stresses within the tumor
microenvironment. Our findings defy existing paradigms by elucidating the
impact of LIUS on cancer stem cell behavior. This endeavor advances our
fundamental understanding of mechanotransduction phenomena in the context of
LIUS therapy, thus underscoring its promising as a targeted therapeutic
modality for cancer treatment. Furthermore, our results make a substantial
contribution to the broader scientific community by shedding light on the
intricate interplay between mechanical forces, cellular responses, and the
spatiotemporal evolution of tumors. These insights hold the promising to
promote a new perspective for the future development of pioneering and highly
efficacious therapeutic strategies for combating cancer in a personalized
manner.


  In this paper we propose SCDP, a general-purpose data transport protocol for
data centres that, in contrast to all other protocols proposed to date,
supports efficient one-to-many and many-to-one communication, which is
extremely common in modern data centres. SCDP does so without compromising on
efficiency for short and long unicast flows. SCDP achieves this by integrating
RaptorQ codes with receiver-driven data transport, packet trimming and
Multi-Level Feedback Queuing (MLFQ); (1) RaptorQ codes enable efficient
one-to-many and many-to-one data transport; (2) on top of RaptorQ codes,
receiver-driven flow control, in combination with in-network packet trimming,
enable efficient usage of network resources as well as multi-path transport and
packet spraying for all transport modes. Incast and Outcast are eliminated; (3)
the systematic nature of RaptorQ codes, in combination with MLFQ, enable fast,
decoding-free completion of short flows. We extensively evaluate SCDP in a wide
range of simulated scenarios with realistic data centre workloads. For
one-to-many and many-to-one transport sessions, SCDP performs significantly
better compared to NDP and PIAS. For short and long unicast flows, SCDP
performs equally well or better compared to NDP and PIAS.


  We revisit the problem of fair clustering, first introduced by Chierichetti
et al., that requires each protected attribute to have approximately equal
representation in every cluster; i.e., a balance property. Existing solutions
to fair clustering are either not scalable or do not achieve an optimal
trade-off between clustering objective and fairness. In this paper, we propose
a new notion of fairness, which we call $tau$-fair fairness, that strictly
generalizes the balance property and enables a fine-grained efficiency vs.
fairness trade-off. Furthermore, we show that simple greedy round-robin based
algorithms achieve this trade-off efficiently. Under a more general setting of
multi-valued protected attributes, we rigorously analyze the theoretical
properties of the our algorithms. Our experimental results suggest that the
proposed solution outperforms all the state-of-the-art algorithms and works
exceptionally well even for a large number of clusters.


  In this paper we derive the condition providing the on-shell equivalence of
$L_{\infty}$-type and WZW-like formulations for superstring field theory. We
construct the NS string products $L= \{L_{n}\}_{n=1}^{\infty}$ of
$L_{\infty}$-type formulation and the shifted BRST operator $Q_{\mathcal{G}}$
in WZW-like formulation by the similarity transformations of the BRST operator
$Q$. Utilizing the similarity transformations, we can consider a morphism
connecting the $L_{\infty}$-algebras on both sides. It naturally induces the
field redefinitions and guarantees the equivalence of the on-shell conditions
in two formulations. In addition, we have confirmed up to quartic order that
the on-shell equivalence condition also provides the off-shell equivalence.
Then partial-gauge-fixing conditions giving $L_{\infty}$-relations in WZW-like
formulation naturally appear.


  We study the Vlasov-Poisson-Fokker-Planck system with uncertainty and
multiple scales. Here the uncertainty, modeled by random variables, enters the
solution through initial data, while the multiple scales lead the system to its
high-field or parabolic regimes. With the help of proper Lyapunov-type
inequalities, under some mild conditions on the initial data, the regularity of
the solution in the random space, as well as exponential decay of the solution
to the global Maxwellian, are established under Sobolev norms, which are ${\it
uniform}$ in terms of the scaling parameters. These are the first
hypocoercivity results for a nonlinear kinetic system with random input, which
are important for the understanding of the sensitivity of the system under
random perturbations, and for the establishment of spectral convergence of
popular numerical methods for uncertainty quantification based on (spectrally
accurate) polynomial chaos expansions.


  We studied the hadronic decays of excited states of heavy mesons (D, D_s, B
and B_s) to lighter states by emission of pi, eta or K. Wavefunctions and
energy levels of these excited states are determined using a Dirac equation for
the light quark in the potential generated by the heavy quark (including first
order corrections in the heavy quark expansion). Transition amplitudes are
computed in the context of the Heavy Chiral Quark Model.


  In this paper we study the low temperature kinetics of the electrons in the
system composed of a quantum dot connected to two leads by solving the equation
of motion. The decoherence and the relaxation of the system caused by the gate
voltage noise and electron-phonon scattering are investigated. In order to take
account of the strong correlation of the electrons in this system, the
quasi-exact wave functions are calculated using an improved matrix product
states algorithm. This algorithm enables us to calculate the wave functions of
the ground state and the low lying excited states with satisfied accuracy and
thus enables us to study the kinetics of the system more effectively. It is
found that although both of these two mechanisms are proportional to the
electron number operator in the dot, the kinetics are quite different. The
noise induced decoherence is much more effective than the energy relaxation,
while the energy relaxation and decoherence time are of the same order for the
electron-phonon scattering. Moreover, the noise induced decoherence increases
with the lowering of the dot level, but the relaxation and decoherence due to
the electron-phonon scattering decrease.


  The Son Of X-Shooter (SOXS) is a single object spectrograph offering
simultaneous spectral coverage in UV-VIS (350-850 nm) and NIR (800-2000 nm)
wavelength regimes with an average of R close to 4500 for a 1 slit. SOXS also
has imaging capabilities in the visible wavelength regime. It is designed and
optimized to observe all kinds of transients and variable sources. The final
destination of SOXS is the Nasmyth platform of the ESO NTT at La Silla, Chile.
The SOXS consortium has a relatively large geographic spread, and therefore the
Assembly Integration and Verification (AIV) of this medium-class instrument
follows a modular approach. Each of the five main sub-systems of SOXS, namely
the Common Path, the Calibration Unit, the Acquisition Camera, the UV-VIS
Spectrograph, and the NIR Spectrograph, are undergoing (or undergone) internal
alignment and testing in the respective consortium institutes.
INAF-Osservatorio Astronomico di Padova delivers the Common Path sub-system,
the backbone of the entire instrument. We report the Common Path internal
alignment starting from the assembly of the individual components to the final
testing of the optical quality, and the efficiency of the complete sub-system.


  The magnetic properties and spin dynamics of the spin frustrated
polyoxometalate {W72V30}, where 30 V^{4+} ions (s = 1/2) occupy the sites of an
icosidodecahedron, have been investigated by low temperature magnetization,
magnetic susceptibility, proton and vanadium nuclear magnetic resonance, and
theoretical studies. The field-dependent magnetization at 0.5 K increases
monotonically up to 50 T without any sign of staircase behavior. This
low-temperature behavior cannot be explained by a Heisenberg model based on a
single value of the nearest-neighbor exchange coupling. We analyze this
behavior upon assuming a rather broad distribution of nearest-neighbor exchange
interactions. Slow spin dynamics of {W72V30} at low temperatures is observed
from the magnetic field and temperature dependence of nuclear spin-lattice
relaxation rate 1/T_1 measurements.


  The mechanisms of localization of Jahn-Teller deformations and vibronic
wavefunctions in isotope substituted dynamical Jahn-Teller systems are
elucidated. It is found that the localization in the trough is of potential
type in the case of strong vibronic coupling, while it becomes of kinetic type
in the case of intermediate and weak coupling. It is shown that the vibronic
levels in the linear $E\otimes e$-problem remain double degenerate upon
arbitrary isotope substitution on the reasons similar to time reversal symmetry
in which the role of spin is played by orbital pseudospin.


  Visual Question Answering (VQA) has emerged as a highly engaging field in
recent years, attracting increasing research efforts aiming to enhance VQA
accuracy through the deployment of advanced models such as Transformers.
Despite this growing interest, there has been limited exploration into the
comparative analysis and impact of textual modalities within VQA, particularly
in terms of model complexity and its effect on performance. In this work, we
conduct a comprehensive comparison between complex textual models that leverage
long dependency mechanisms and simpler models focusing on local textual
features within a well-established VQA framework. Our findings reveal that
employing complex textual encoders is not invariably the optimal approach for
the VQA-v2 dataset. Motivated by this insight, we introduce an improved model,
ConvGRU, which incorporates convolutional layers to enhance the representation
of question text. Tested on the VQA-v2 dataset, ConvGRU achieves better
performance without substantially increasing parameter complexity.


  Basing on the simplest single-mode field source, we investigate the role of
the various covariance matrices for reconstructing the field state and
describing its quantum statistical properties. In spite of the fact that the
intracavity field is a single-mode field, we take into account the natural
multimode structure arising in the field, when it leaves the cavity for the
free-space propagation. We show how the purity of the field state can be
calculated using the different covariance matrices.


  We consider evolution equations of the form \begin{equation*}\label{Abstract
equation} \dot u(t)+ A(t)u(t)=0,\ \ t\in[0,T],\ \ u(0)=u_0, \end{equation*}
where $A(t),\ t\in [0,T],$ are associated with a non-autonomous sesquilinear
form $\mathfrak a(t,\cdot,\cdot)$ on a Hilbert space $H$ with constant domain
$V\subset H.$ In this note we continue the study of fundamental operator
theoretical properties of the solutions. We give a sufficient condition for
norm-continuity of evolution families on each spaces $V, H$ and on the dual
space $V'$ of $V.$ The abstract results are applied to a class of equations
governed by time dependent Robin boundary conditions on exterior domains and by
Schr\"odinger operator with time dependent potentials.


  Interferometric inversion involves recovery of a signal from
cross-correlations of its linear transformations. A close relative of
interferometric inversion is the generalized phase retrieval problem, which
consists of recovering a signal from the auto-correlations of its linear
transformations. Recently, significant advancements have been made in phase
retrieval methods despite the ill-posed, and non-convex nature of the problem.
One such method is Wirtinger Flow (WF), a non-convex optimization framework
that provides high probability guarantees of exact recovery under certain
measurement models, such as coded diffraction patterns, and Gaussian sampling
vectors. In this paper, we develop a generalization of WF for interferometric
inversion, which we refer to as Generalized Wirtinger Flow (GWF). GWF theory
extends the probabilistic exact recovery results of WF to arbitrary measurement
models characterized in the equivalent lifted problem, hence covers a larger
class of measurement models. Our framework unifies the theory of low rank
matrix recovery (LRMR) and the non-convex optimization approach of WF, thereby
establishes theoretical advantages of the non-convex approach over LRMR. We
identify a new sufficient condition on the lifted forward model that directly
implies exact recovery conditions of standard WF. This condition is less
stringent than those of LRMR, which is the state of the art approach for exact
interferometric inversion. We establish our sufficient condition for the
cross-correlations of linear measurements collected by complex Gaussian
sampling vectors, and show that the exact recovery conditions of standard WF
imply our sufficient condition. As a result, the regularity condition of WF
becomes redundant in solving the interferometric inversion problem. Finally, we
demonstrate the effectiveness of GWF numerically in a deterministic
multi-static radar imaging scenario.