  Distributed learning has emerged as a leading paradigm for training large
machine learning models. However, in real-world scenarios, participants may be
unreliable or malicious, posing a significant challenge to the integrity and
accuracy of the trained models. Byzantine fault tolerance mechanisms have been
proposed to address these issues, but they often assume full participation from
all clients, which is not always practical due to the unavailability of some
clients or communication constraints. In our work, we propose the first
distributed method with client sampling and provable tolerance to Byzantine
workers. The key idea behind the developed method is the use of gradient
clipping to control stochastic gradient differences in recursive variance
reduction. This allows us to bound the potential harm caused by Byzantine
workers, even during iterations when all sampled clients are Byzantine.
Furthermore, we incorporate communication compression into the method to
enhance communication efficiency. Under general assumptions, we prove
convergence rates for the proposed method that match the existing
state-of-the-art (SOTA) theoretical results. We also propose a heuristic on
adjusting any Byzantine-robust method to a partial participation scenario via
clipping.


  We consider multi-label prediction problems with large output spaces under
the assumption of output sparsity -- that the target (label) vectors have small
support. We develop a general theory for a variant of the popular error
correcting output code scheme, using ideas from compressed sensing for
exploiting this sparsity. The method can be regarded as a simple reduction from
multi-label regression problems to binary regression problems. We show that the
number of subproblems need only be logarithmic in the total number of possible
labels, making this approach radically more efficient than others. We also
state and prove robustness guarantees for this method in the form of regret
transform bounds (in general), and also provide a more detailed analysis for
the linear prediction setting.


  We investigated the magnetic properties of Sendust (Fe-Al-Si) alloys not only
at 0 K but also at finite-temperatures by means of the first-principles
calculations assuming A2, B2, and DO3 structures. We confirmed that the
itinerant characteristics of 3d electrons of Fe are not negligible for A2 and
B2 structures and a significantly small exchange stiffness constant exists at
zero-temperature in a B2 structure. However, the calculated Curie temperatures
are in the same order for all structures; this indicates that the Curie
temperature cannot be determined only by the exchange interactions at
zero-temperature in itinerant electron systems. Temperature dependence of the
exchange interaction, namely spin configuration dependence, also might be
important for determining it. In addition, this property might also be related
to the unique behavior of the temperature dependence of the exchange stiffness
constant for the B2 structure, which does not decrease monotonically as
temperatures increase, contrary to the behavior expected from the Heisenberg
model. In addition, we investigated composition dependence on the exchange
stiffness constant at zero-temperature and confirmed that the substitution of
Si with Al could improve the amplitude of the exchange stiffness constant at
zero-temperature for all structures.


  It is well-know that deciding consistency for normal answer set programs
(ASP) is NP-complete, thus, as hard as the satisfaction problem for classical
propositional logic (SAT). The best algorithms to solve these problems take
exponential time in the worst case. The exponential time hypothesis (ETH)
implies that this result is tight for SAT, that is, SAT cannot be solved in
subexponential time. This immediately establishes that the result is also tight
for the consistency problem for ASP. However, accounting for the treewidth of
the problem, the consistency problem for ASP is slightly harder than SAT: while
SAT can be solved by an algorithm that runs in exponential time in the
treewidth k, it was recently shown that ASP requires exponential time in k
\cdot log(k). This extra cost is due checking that there are no self-supported
true atoms due to positive cycles in the program. In this paper, we refine the
above result and show that the consistency problem for ASP can be solved in
exponential time in k \cdot log({\lambda}) where {\lambda} is the minimum
between the treewidth and the size of the largest strongly-connected component
in the positive dependency graph of the program. We provide a dynamic
programming algorithm that solves the problem and a treewidth-aware reduction
from ASP to SAT that adhere to the above limit.


  While deep learning in 3D domain has achieved revolutionary performance in
many tasks, the robustness of these models has not been sufficiently studied or
explored. Regarding the 3D adversarial samples, most existing works focus on
manipulation of local points, which may fail to invoke the global geometry
properties, like robustness under linear projection that preserves the
Euclidean distance, i.e., isometry. In this work, we show that existing
state-of-the-art deep 3D models are extremely vulnerable to isometry
transformations. Armed with the Thompson Sampling, we develop a black-box
attack with success rate over 95% on ModelNet40 data set. Incorporating with
the Restricted Isometry Property, we propose a novel framework of white-box
attack on top of spectral norm based perturbation. In contrast to previous
works, our adversarial samples are experimentally shown to be strongly
transferable. Evaluated on a sequence of prevailing 3D models, our white-box
attack achieves success rates from 98.88% to 100%. It maintains a successful
attack rate over 95% even within an imperceptible rotation range $[\pm
2.81^{\circ}]$.


  In this paper we define the formal and tempered Deligne cohomology groups,
that are obtained by applying the Deligne complex functor to the complexes of
formal differential forms and tempered currents respectively. We then prove the
existence of a duality between them, a vanishing theorem for the former and a
semipurity property for the latter. The motivation of these results comes from
the study of covariant arithmetic Chow groups. The semi-purity property of
tempered Deligne cohomology implies, in particular, that several definitions of
covariant arithmetic Chow groups agree for projective arithmetic varieties.


  The design of an electrical machine can be quantified and evaluated by Key
Performance Indicators (KPIs) such as maximum torque, critical field strength,
costs of active parts, sound power, etc. Generally, cross-domain tool-chains
are used to optimize all the KPIs from different domains (multi-objective
optimization) by varying the given input parameters in the largest possible
design space. This optimization process involves magneto-static finite element
simulation to obtain these decisive KPIs. It makes the whole process a
vehemently time-consuming computational task that counts on the availability of
resources with the involvement of high computational cost. In this paper, a
data-aided, deep learning-based meta-model is employed to predict the KPIs of
an electrical machine quickly and with high accuracy to accelerate the full
optimization process and reduce its computational costs. The focus is on
analyzing various forms of input data that serve as a geometry representation
of the machine. Namely, these are the cross-section image of the electrical
machine that allows a very general description of the geometry relating to
different topologies and the classical way with scalar parametrization of
geometry. The impact of the resolution of the image is studied in detail. The
results show a high prediction accuracy and proof that the validity of a deep
learning-based meta-model to minimize the optimization time. The results also
indicate that the prediction quality of an image-based approach can be made
comparable to the classical way based on scalar parameters.


  Common regularization algorithms for linear regression, such as LASSO and
Ridge regression, rely on a regularization hyperparameter that balances the
tradeoff between minimizing the fitting error and the norm of the learned model
coefficients. As this hyperparameter is scalar, it can be easily selected via
random or grid search optimizing a cross-validation criterion. However, using a
scalar hyperparameter limits the algorithm's flexibility and potential for
better generalization. In this paper, we address the problem of linear
regression with l2-regularization, where a different regularization
hyperparameter is associated with each input variable. We optimize these
hyperparameters using a gradient-based approach, wherein the gradient of a
cross-validation criterion with respect to the regularization hyperparameters
is computed analytically through matrix differential calculus. Additionally, we
introduce two strategies tailored for sparse model learning problems aiming at
reducing the risk of overfitting to the validation data. Numerical examples
demonstrate that our multi-hyperparameter regularization approach outperforms
LASSO, Ridge, and Elastic Net regression. Moreover, the analytical computation
of the gradient proves to be more efficient in terms of computational time
compared to automatic differentiation, especially when handling a large number
of input variables. Application to the identification of over-parameterized
Linear Parameter-Varying models is also presented.


  We prove that every topologically transitive shift of finite type in one
dimension is topologically conjugate to a subshift arising from a primitive
random substitution on a finite alphabet. As a result, we show that the set of
values of topological entropy which can be attained by random substitution
subshifts contains all Perron numbers and so is dense in the positive real
numbers. We also provide an independent proof of this density statement using
elementary methods.


  Fast measurements of quantum devices is important in areas such as quantum
sensing, quantum computing and nanodevice quality analysis. Here, we develop a
superconductor-semiconductor multi-module microwave assembly to demonstrate
charge state readout at the state-of-the-art. The assembly consist of a
superconducting readout resonator interfaced to a silicon-on-insulator (SOI)
chiplet containing quantum dots (QDs) in a high-$\kappa$ nanowire transistor.
The superconducting chiplet contains resonant and coupling elements as well as
$LC$ filters that, when interfaced with the silicon chip, result in a resonant
frequency $f=2.12$ GHz, a loaded quality factor $Q=850$, and a resonator
impedance $Z=470$ $\Omega$. Combined with the large gate lever arms of SOI
technology, we achieve a minimum integration time for single and double QD
transitions of 2.77 ns and 13.5 ns, respectively. We utilize the assembly to
measure charge noise over 9 decades of frequency up to 500 kHz and find a 1/$f$
dependence across the whole frequency spectrum as well as a charge noise level
of 4 $\mu$eV/$\sqrt{\text{Hz}}$ at 1 Hz. The modular microwave circuitry
presented here can be directly utilized in conjunction with other quantum
device to improve the readout performance as well as enable large bandwidth
noise spectroscopy, all without the complexity of superconductor-semiconductor
monolithic fabrication.


  In the election of a hierarchical clustering method, theoretic properties may
give some insight to determine which method is the most suitable to treat a
clustering problem. Herein, we study some basic properties of two hierarchical
clustering methods: $\alpha$-unchaining single linkage or $SL(\alpha)$ and a
modified version of this one, $SL^*(\alpha)$. We compare the results with the
properties satisfied by the classical linkage-based hierarchical clustering
methods.


  Ferrofluids containing magnetic nanoparticles represent a special class of
magnetic materials due to the added freedom of particle tumbling in the fluids.
We studied this process, known as Brownian relaxation, and its effect on the
magnetic properties of ferrofluids with controlled magnetite nanoparticle
sizes. For small nanoparticles (below 10 nm diameter) the N\'eel process is
expected to dominate the magnetic response, whereas for larger particles,
Brownian relaxation becomes important. Temperature- and magnetic
field-dependent magnetization studies, differential scanning calorimetry, and
AC susceptibility measurements were carried out for 6, 8, 10.6, and 13.5 nm
diameter magnetite nanoparticles suspended in water. We identify clear
fingerprints of the Brownian relaxation for the sample of the large diameter
nanoparticles as both magnetic and thermal hysteresis develop at the water
freezing temperature, whereas the samples of small diameter nanoparticles
remain hysteresis-free down to the magnetic blocking temperature. This is
supported by the temperature-dependent AC susceptibility measurements: above
273 K, the data show a low-frequency Debye peak, which is characteristic of the
Brownian relaxation. This peak vanishes below 273 K.


  Let $\mathfrak A$ be a maximal subdiagonal algebra in a $\sigma$-finite von
Neumann algebra $\mathcal M$. If every right invariant subspace of $\mathfrak
A$ in the non-commutative Hardy space $H^2$ is of Beurling type, then we say
$\mathfrak A$ to be type 1. We determine generators of these algebras and
consider a Riesz type factorization theorem for the non-commutative $H^1$
space. We show that the right analytic Toeplitz algebra on the non-commutative
Hardy space $H^p$ associated with a type 1 subdiagonal algebra with
multiplicity 1 is hereditary reflexive.


  This research presents a novel multimodal data fusion methodology for pain
behavior recognition, integrating statistical correlation analysis with
human-centered insights. Our approach introduces two key innovations: 1)
integrating data-driven statistical relevance weights into the fusion strategy
to effectively utilize complementary information from heterogeneous modalities,
and 2) incorporating human-centric movement characteristics into multimodal
representation learning for detailed modeling of pain behaviors. Validated
across various deep learning architectures, our method demonstrates superior
performance and broad applicability. We propose a customizable framework that
aligns each modality with a suitable classifier based on statistical
significance, advancing personalized and effective multimodal fusion.
Furthermore, our methodology provides explainable analysis of multimodal data,
contributing to interpretable and explainable AI in healthcare. By highlighting
the importance of data diversity and modality-specific representations, we
enhance traditional fusion techniques and set new standards for recognizing
complex pain behaviors. Our findings have significant implications for
promoting patient-centered healthcare interventions and supporting explainable
clinical decision-making.


  Expanding a double tetrahedron formation of equal spheres arranged in fcc
structure correlation between the positions of the nucleons and quantum numbers
has been detected. The number of protons in the structure is not simply
consistent with all the corresponding quantum numbers but also bears the same
physical meaning as in quantum mechanics. The detected correlations between
lattice positions of the protons and quantum numbers raise the possibility the
solid nuclear structure might be able to provide an explanation for the single
particle properties of the nuclei.


  Nonstationarity is a major challenge in analyzing spatial data. For example,
daily precipitation measurements may have increased variability and decreased
spatial smoothness in areas with high mean rainfall. Common nonstationary
covariance models introduce parameters specific to each location, giving a
highly-parameterized model which is difficult to fit. We develop a
nonstationary spatial model that uses the mean to determine the covariance in a
region, resulting in a far simpler, albeit more specialized, model. We explore
inferential and predictive properties of the model under various simulated data
situations. We show that this model in certain circumstances improves
predictions compared to a standard stationary spatial model. We further propose
a computationally efficient approximation that has comparable predictive
accuracy. We also develop a test for nonstationary data and show it reliably
identifies nonstationarity. We apply these methods to daily precipitation in
Puerto Rico.


  Cooperative Multi-Agent Reinforcement Learning (CMARL) strategies are well
known to be vulnerable to adversarial perturbations. Previous works on
adversarial attacks have primarily focused on white-box attacks that directly
perturb the states or actions of victim agents, often in scenarios with a
limited number of attacks. However, gaining complete access to victim agents in
real-world environments is exceedingly difficult. To create more realistic
adversarial attacks, we introduce a novel method that involves injecting
traitor agents into the CMARL system. We model this problem as a Traitor Markov
Decision Process (TMDP), where traitors cannot directly attack the victim
agents but can influence their formation or positioning through collisions. In
TMDP, traitors are trained using the same MARL algorithm as the victim agents,
with their reward function set as the negative of the victim agents' reward.
Despite this, the training efficiency for traitors remains low because it is
challenging for them to directly associate their actions with the victim
agents' rewards. To address this issue, we propose the Curiosity-Driven
Adversarial Attack (CuDA2) framework. CuDA2 enhances the efficiency and
aggressiveness of attacks on the specified victim agents' policies while
maintaining the optimal policy invariance of the traitors. Specifically, we
employ a pre-trained Random Network Distillation (RND) module, where the extra
reward generated by the RND module encourages traitors to explore states
unencountered by the victim agents. Extensive experiments on various scenarios
from SMAC demonstrate that our CuDA2 framework offers comparable or superior
adversarial attack capabilities compared to other baselines.


  We propose an inertial forward-backward splitting algorithm to compute the
zero of a sum of two monotone operators allowing for stochastic errors in the
computation of the operators. More precisely, we establish almost sure
convergence in real Hilbert spaces of the sequence of iterates to an optimal
solution. Then, based on this analysis, we introduce two new classes of
stochastic inertial primal-dual splitting methods for solving structured
systems of composite monotone inclusions and prove their convergence. Our
results extend to the stochastic and inertial setting various types of
structured monotone inclusion problems and corresponding algorithmic solutions.
Application to minimization problems is discussed.


  In this paper we study the self-adjoint and co-isometry composition and
weighted composition operator on $H_E(\zeta).$ We also discuss the conditions
under which adjoint operator of a composition and weighted composition operator
on $H_E(\zeta)$ to be some composition and weighted composition operator,
respectively.


  We describe a moderate-resolution (20-25 km/s) FUSE study of the low-redshift
intergalactic medium. We report on studies of 7 extragalactic sightlines and 12
Ly-beta absorbers that correspond to Ly-alpha lines detected by HST/GHRS and
STIS. These absorbers appear to contain a significant fraction of the low-z
baryons and were a major discovery of the HST spectrographs. Using FUSE data,
with 40 mA (4-sigma) Lyb detection limits, we have employed the equivalent
width ratio of Lyb/Lya and occasionally higher Lyman lines, to determine the
doppler parameter, b, and accurate column densities, N(HI), for moderately
saturated lines. We detect Lyb absorption corresponding to all Lya lines with
EW > 200 mA. The Lyb/Lya ratios yield a preliminary distribution function of
doppler parameters, with mean <b> = 31.4 +/- 7.4 km/s and median b = 28 km/s,
comparable to values at redshifts z = 2.0-2.5. If thermal, these b-values
correspond to T(HI) ~ 50,000 K, although the inferred doppler parameters are
considerably less than the widths derived from Lya profile fitting,
<b(dopp)/b(width)> = 0.52. The typical increase in column density over that
derived from profile fitting is Delta[log N(HI)] = 0.3, but ranges up to 1.0
dex. Our data suggest that the low-z Lya absorbers contain sizable non-thermal
motions or velocity components in the line profile, perhaps arising from
cosmological expansion and infall.


  Immersive experiences, such as remote collaboration and augmented and virtual
reality, require delivery of large volumes of data with consistent ultra-low
latency across wireless networks in fluctuating network conditions. We describe
the high-level design behind a data delivery solution that meets these
requirements and provide synthetic simulations and test results running in
network conditions based on real-world measurements demonstrating the efficacy
of the solution.


  This paper studies the physical-constraints-preserving (PCP) Lagrangian
finite volume schemes for one- and two-dimensional special relativistic
hydrodynamic (RHD) equations. First, the PCP property (i.e. preserving the
positivity of the rest-mass density and the pressure and the bound of the
velocity) is proved for the first-order accurate Lagrangian scheme with the
HLLC Riemann solver and forward Euler time discretization. The key is that the
intermediate states in the HLLC Riemann solver are shown to be admissible or
PCP when the HLLC wave speeds are estimated suitably. Then, the higher-order
accurate schemes are proposed by using the high-order accurate strong stability
preserving (SSP) time discretizations and the scaling PCP limiter as well as
the WENO reconstruction. Finally, several one- and two-dimensional numerical
experiments are conducted to demonstrate the accuracy and the effectiveness of
the PCP Lagrangian schemes in solving the special RHD problems involving strong
discontinuities, or large Lorentz factor, or low rest-mass density or low
pressure etc.


  Models endowed with Higgs portals can probe into the hidden sectors of
particle physics while providing stable dark matter candidates. Previous
tree-level computations in such scenarios have shown that experimental bounds
constrain dark matter to a very narrow region in parameter space. Aiming at
improving the study of the implications of those constraints, we inspect
one-loop corrections to the annihilation cross section for scalar dark matter
into observable fermions. We find that these loop contributions might be enough
to drastically change those results by deforming in about 10% the allowed
parameter space for dark matter particles with masses even below 1 TeV. These
findings encourage further investigation.


  High radiation dose in CT scans increases a lifetime risk of cancer and has
become a major clinical concern. Recently, iterative reconstruction algorithms
with Total Variation (TV) regularization have been developed to reconstruct CT
images from highly undersampled data acquired at low mAs levels in order to
reduce the imaging dose. Nonetheless, TV regularization may lead to
over-smoothed images and lost edge information. To solve this problem, in this
work we develop an iterative CT reconstruction algorithm with edge-preserving
TV regularization to reconstruct CT images from highly undersampled data
obtained at low mAs levels. The CT image is reconstructed by minimizing an
energy consisting of an edge-preserving TV norm and a data fidelity term posed
by the x-ray projections. The edge-preserving TV term is proposed to
preferentially perform smoothing only on non-edge part of the image in order to
avoid over-smoothing, which is realized by introducing a penalty weight to the
original total variation norm. Our iterative algorithm is implemented on GPU to
improve its speed. We test our reconstruction algorithm on a digital NCAT
phantom, a physical chest phantom, and a Catphan phantom. Reconstruction
results from a conventional FBP algorithm and a TV regularization method
without edge preserving penalty are also presented for comparison purpose. The
experimental results illustrate that both TV-based algorithm and our
edge-preserving TV algorithm outperform the conventional FBP algorithm in
suppressing the streaking artifacts and image noise under the low dose context.
Our edge-preserving algorithm is superior to the TV-based algorithm in that it
can preserve more information of fine structures and therefore maintain
acceptable spatial resolution.


  Artificial Intelligence (AI) technologies have been applied in various
domains, including early childhood education (ECE). Integration of AI
educational technology is a recent significant trend in ECE. Currently, there
are more and more studies of AI in ECE. To date, there is a lack of survey
articles that discuss the studies of AI in ECE. In this paper, we provide an
up-to-date and in-depth overview of the key AI technologies in ECE that
provides a historical perspective, summarizes the representative works,
outlines open questions, discusses the trends and challenges through a detailed
bibliometric analysis, and provides insightful recommendations for future
research. We mainly discuss the studies that apply AI-based robots and AI
technologies to ECE, including improving the social interaction of children
with an autism spectrum disorder. This paper significantly contributes to
provide an up-to-date and in-depth survey that is suitable as introductory
material for beginners to AI in ECE, as well as supplementary material for
advanced users.


  We call a family of sets intersecting, if any two sets in the family
intersect. In this paper we investigate intersecting families $\mathcal{F}$ of
$k$-element subsets of $[n]:=\{1,\ldots, n\},$ such that every element of $[n]$
lies in the same (or approximately the same) number of members of
$\mathcal{F}$. In particular, we show that we can guarantee $|\mathcal{F}| =
o({n-1\choose k-1})$ if and only if $k=o(n)$.


  Let an n-algebra mean an algebra over the chain complex of the little n-cubes
operad. We give a proof of Kontsevich's conjecture, which states that for a
suitable notion of Hochschild cohomology in the category of n-algebras, the
Hochschild cohomology complex of an n-algebra is an (n+1)-algebra. This
generalizes a conjecture by Deligne for n=1, now proven by several authors.


  The recent discoveries of short-range charge-density wave fluctuations in the
normal state of several hole-doped cuprate superconductors constitute a
significant addition to the known intrinsic properties of these materials.
Besides likely being associated with the normal-state pseudogap, the
charge-density wave order presumably influences the build-up of known
superconducting correlations as the temperature is lowered toward the
superconducting state. As a pure magnetic probe, muon spin rotation ($\mu$SR)
is not directly sensitive to charge order, but may sense its presence via the
effect it has on the magnetic dipolar coupling of the muon with the host nuclei
at zero or low magnetic field. At higher field where $\mu$SR is completely
blind to the effects of charge order, experiments have revealed a universal
inhomogeneous normal-state response extending to temperatures well above $T_c$.
The measured inhomogeneous line broadening has been attributed to regions of
superconducting correlations that exhibit varying degrees of fluctuation
diamagnetism. Here, the compatibility of these results with other measurements
showing charge order correlations or superconducting fluctuations above $T_c$
is discussed.


  We study the dynamics of localized and fully backreacting anti-D3 branes at
the tip of the Klebanov-Strassler geometry. We use a non-supersymmetric version
of the Polchinski-Strassler analysis to compute the potential for anti-D3
branes to polarize into all kinds of five-brane shells in all possible
directions. We find that generically there is a direction along which the
brane-brane interaction is repulsive, which implies that anti-D3 branes are
tachyonic. Hence, even though anti-D3 branes can polarize into five-branes, the
solution will most likely be unstable. This indicates that anti-D3 brane uplift
may not result in stable de Sitter vacua.


  We summarize initial results for high-energy neutrino counterpart searches
coinciding with gravitational-wave events in LIGO/Virgo's GWTC-2 catalog using
IceCube's neutrino triggers. We did not find any statistically significant
high-energy neutrino counterpart and derived upper limits on the
time-integrated neutrino emission on Earth as well as the isotropic equivalent
energy emitted in high-energy neutrinos for each event.


  The advantages of mixed approach with using different kinds of programming
techniques for symbolic manipulation are discussed. The main purpose of
approach offered is merge the methods of object oriented programming that
convenient for presentation data and algorithms for user with advantages of
functional languages for data manipulation, internal presentation, and
portability of software.


  We present the results of a scaling analysis of isothermal magnetization M(H)
curves measured in the mixed state of high-Tc superconductors in the vicinity
of the established first order phase transition. The most surprising result of
our analysis is that the difference between the magnetization above and below
the transition may have either sign, depending on the particular chosen sample.
We argue that this observation, based on M(H) data available in the literature,
is inconsistent with the interpretation that the well known first order phase
transition in the mixed state of high-Tc superconductors always represents the
melting transition in the vortex system.


  Diffraction of light at lateral inhomogenities is a central process in the
near-field studies of nanoscale phenomena, especially the propagation of
surface waves. Theoretical description of this process is extremely challenging
due to breakdown of plane-wave methods. Here, we present and analyze an exact
solution for electromagnetic wave diffraction at the linear junction between
two-dimensional electron systems (2DES) with dissimilar surface conductivities.
The field at the junction is a combination of three components with different
spatial structure: free-field component, non-resonant edge component, and
surface plasmon-polariton (SPP). We find closed-form expressions for efficiency
of photon-to-plasmon conversion by the edge being the ratio of electric fields
in SPP and incident wave. Particularly, the conversion efficiency can
considerably exceed unity for the contact between metal and 2DES with large
impedance. Our findings can be considered as a first step toward quantitative
near-field microscopy of inhomogeneous systems and polaritonic interferometry.


  We present a novel view on principal component analysis (PCA) as a
competitive game in which each approximate eigenvector is controlled by a
player whose goal is to maximize their own utility function. We analyze the
properties of this PCA game and the behavior of its gradient based updates. The
resulting algorithm -- which combines elements from Oja's rule with a
generalized Gram-Schmidt orthogonalization -- is naturally decentralized and
hence parallelizable through message passing. We demonstrate the scalability of
the algorithm with experiments on large image datasets and neural network
activations. We discuss how this new view of PCA as a differentiable game can
lead to further algorithmic developments and insights.


  Using a new technique, we prove a rich family of special cases of the matroid
intersection conjecture. Roughly, we prove the conjecture for pairs of tame
matroids which have a common decomposition by 2-separations into finite parts.


  In this work, we study sequential choice bandits with feedback. We propose
bandit algorithms for a platform that personalizes users' experience to
maximize its rewards. For each action directed to a given user, the platform is
given a positive reward, which is a non-decreasing function of the action, if
this action is below the user's threshold. Users are equipped with a patience
budget, and actions that are above the threshold decrease the user's patience.
When all patience is lost, the user abandons the platform. The platform
attempts to learn the thresholds of the users in order to maximize its rewards,
based on two different feedback models describing the information pattern
available to the platform at each action. We define a notion of regret by
determining the best action to be taken when the platform knows that the user's
threshold is in a given interval. We then propose bandit algorithms for the two
feedback models and show that upper and lower bounds on the regret are of the
order of $\tilde{O}(N^{2/3})$ and $\tilde\Omega(N^{2/3})$, respectively, where
$N$ is the total number of users. Finally, we show that the waiting time of any
user before receiving a personalized experience is uniform in $N$.


  This paper reports an opto-electro-thermal analysis of VCSEL structures with
a surface-relief filter and different aperture diameters. 808-nm GaAs/AlGaAs
VCSELs with different aperture diameters and different surface-relief filter
diameters are studied and the LI curves, the optical spectra, and the
temperature peaks of the devices are obtained. Also, the impact of utilizing
different oxide apertures on the temperature peak is shown. The effect of the
surface-relief filter diameter on VCSEL's performance is also investigated and
the optimal filter diameter is obtained for maintaining single-mode operation
while producing as much output power as possible.


  We propose a new objective for option discovery that emphasizes the
computational advantage of using options in planning. In a sequential machine,
the speed of planning is proportional to the number of elementary operations
used to achieve a good policy. For episodic tasks, the number of elementary
operations depends on the number of options composed by the policy in an
episode and the number of options being considered at each decision point. To
reduce the amount of computation in planning, for a given set of episodic tasks
and a given number of options, our objective prefers options with which it is
possible to achieve a high return by composing few options, and also prefers a
smaller set of options to choose from at each decision point. We develop an
algorithm that optimizes the proposed objective. In a variant of the classic
four-room domain, we show that 1) a higher objective value is typically
associated with fewer number of elementary planning operations used by the
option-value iteration algorithm to obtain a near-optimal value function, 2)
our algorithm achieves an objective value that matches it achieved by two
human-designed options 3) the amount of computation used by option-value
iteration with options discovered by our algorithm matches it with the
human-designed options, 4) the options produced by our algorithm also make
intuitive sense--they seem to move to and terminate at the entrances of rooms.


  We present a study of transverse momentum ($p_{T}$) spectra of unidentified
charged particles in pp collisions at RHIC and LHC energies from $\sqrt{s}$ =
62.4 GeV to 13 TeV using Tsallis/Hagedorn function. The power law of
Tsallis/Hagedorn form gives excellent description of the hadron spectra in
$p_{T}$ range from 0.2 to 300 GeV/$c$. The power index $n$ of the $p_T$
distributions is found to follow a function of the type $a+b/\sqrt {s}$ with
asymptotic value $a = 5.72$. The parameter $T$ governing the soft bulk
contribution to the spectra remains almost same over wide range of collision
energies. We also provide a Tsallis/Hagedorn fit to the $p_{T}$ spectra of
hadrons in pPb and different centralities of PbPb collisions at $\sqrt{s_{NN}}$
= 5.02 TeV. The data/fit shows deviations from the Tsallis distribution which
become more pronounced as the system size increases. We suggest simple
modifications in the Tsallis/Hagedorn power law function and show that the
above deviations can be attributed to the transverse flow in low $p_T$ region
and to the in-medium energy loss in high $p_T$ region.


  The phonon thermal contribution to the melting temperature of nano-particles
is inspected. The discrete summation of phonon states and its corresponding
integration form as an approximation for a nano-particle or for a bulk system
have been analyzed. The discrete phonon energy levels of pure size effect and
the wave-vector shifts of boundary conditions were investigated in detail.
Unlike in macroscopic thermodynamics, the integration volume of zero-mode of
phonon for a nano-particle is not zero, and it plays an important role in pure
size effect and boundary condition effect. We found that a nano-particle will
have a rising melting temperature due to purely finite size effect; a lower
melting temperature bound exists for a nano-particle in various environments,
and the melting temperature of a nano-particle with free boundary condition
reaches this lower bound. We suggest an easy procedure to estimation the
melting temperature, in which the zero-mode contribution will be excluded, and
only several bulk quantities will be used as input. We would like to emphasize
that the quantum effect of discrete energy levels in nano-particles, which was
not present in early thermodynamic studies on finite size corrections to
melting temperature in small systems, should be included in future researches.


  Using a periodic electron beam bunch train to resonantly excite plasma
wakefields in the quasi-nonlinear (QNL) regime has distinct advantages over
employing a single, higher charge bunch. Resonant excitation in the QNL regime
can produce plasma electron blowout using very low emittance beams with a small
charge per pulse: the local density perturbation is extremely nonlinear,
achieving total rarefaction, yet the resonant response of the plasma electrons
at the plasma frequency is preserved. Such a pulse train, with inter-bunch
spacing equal to the plasma period, can be produced via inverse free-electron
laser bunching. To achieve resonance with a laser wavelength of a few microns,
a high plasma density is used, with the attendant possibility of obtaining
extremely large wakefield amplitude, near 1 TV/m for FACET-II parameters. In
this article, we use particle-in-cell simulations to study the plasma response,
the beam modulation evolution, and the instabilities encountered, that arise
when using a bunching scheme to resonantly excite waves in a dense plasma.


  Learning to fuse vision and language information and representing them is an
important research problem with many applications. Recent progresses have
leveraged the ideas of pre-training (from language modeling) and attention
layers in Transformers to learn representation from datasets containing images
aligned with linguistic expressions that describe the images. In this paper, we
propose learning representations from a set of implied, visually grounded
expressions between image and text, automatically mined from those datasets. In
particular, we use denotation graphs to represent how specific concepts (such
as sentences describing images) can be linked to abstract and generic concepts
(such as short phrases) that are also visually grounded. This type of
generic-to-specific relations can be discovered using linguistic analysis
tools. We propose methods to incorporate such relations into learning
representation. We show that state-of-the-art multimodal learning models can be
further improved by leveraging automatically harvested structural relations.
The representations lead to stronger empirical results on downstream tasks of
cross-modal image retrieval, referring expression, and compositional
attribute-object recognition. Both our codes and the extracted denotation
graphs on the Flickr30K and the COCO datasets are publically available on
https://sha-lab.github.io/DG.


  Large Transformer-based Pretrained Language Models (PLMs) dominate almost all
Natural Language Processing (NLP) tasks. Nevertheless, they still make mistakes
from time to time. For a model deployed in an industrial environment, fixing
these mistakes quickly and robustly is vital to improve user experiences.
Previous works formalize such problems as Model Editing (ME) and mostly focus
on fixing one mistake. However, the one-mistake-fixing scenario is not an
accurate abstraction of the real-world challenge. In the deployment of AI
services, there are ever-emerging mistakes, and the same mistake may recur if
not corrected in time. Thus a preferable solution is to rectify the mistakes as
soon as they appear nonstop. Therefore, we extend the existing ME into
Sequential Model Editing (SME) to help develop more practical editing methods.
Our study shows that most current ME methods could yield unsatisfying results
in this scenario. We then introduce Transformer-Patcher, a novel model editor
that can shift the behavior of transformer-based models by simply adding and
training a few neurons in the last Feed-Forward Network layer. Experimental
results on both classification and generation tasks show that
Transformer-Patcher can successively correct up to thousands of errors
(Reliability) and generalize to their equivalent inputs (Generality) while
retaining the model's accuracy on irrelevant inputs (Locality). Our method
outperforms previous fine-tuning and HyperNetwork-based methods and achieves
state-of-the-art performance for Sequential Model Editing (SME). The code is
available at https://github.com/ZeroYuHuang/Transformer-Patcher.


  In the Multi-Level Aggregation Problem (MLAP), requests arrive at the nodes
of an edge-weighted tree T, and have to be served eventually. A service is
defined as a subtree X of T that contains its root. This subtree X serves all
requests that are pending in the nodes of X, and the cost of this service is
equal to the total weight of X. Each request also incurs waiting cost between
its arrival and service times. The objective is to minimize the total waiting
cost of all requests plus the total cost of all service subtrees. MLAP is a
generalization of some well-studied optimization problems; for example, for
trees of depth 1, MLAP is equivalent to the TCP Acknowledgment Problem, while
for trees of depth 2, it is equivalent to the Joint Replenishment Problem.
Aggregation problem for trees of arbitrary depth arise in multicasting, sensor
networks, communication in organization hierarchies, and in supply-chain
management. The instances of MLAP associated with these applications are
naturally online, in the sense that aggregation decisions need to be made
without information about future requests.
  Constant-competitive online algorithms are known for MLAP with one or two
levels. However, it has been open whether there exist constant competitive
online algorithms for trees of depth more than 2. Addressing this open problem,
we give the first constant competitive online algorithm for networks of
arbitrary (fixed) number of levels. The competitive ratio is O(D^4 2^D), where
D is the depth of T. The algorithm works for arbitrary waiting cost functions,
including the variant with deadlines.
  We also show several additional lower and upper bound results for some
special cases of MLAP, including the Single-Phase variant and the case when the
tree is a path.


  We exploit offshell regions in the process $e^+e^-\rightarrow W^+W^-b\bar{b}$
to gain access to the top-quark width. Working at next-to-leading order in QCD
we show that carefully selected ratios of offshell regions to onshell regions
in the reconstructed top and antitop invariant mass spectra are,
\emph{independently} of the coupling $g_{tbW}$, sensitive to the top-quark
width. We explore this approach for different centre of mass energies and
initial-state beam polarisations at $e^+e^-$ colliders and briefly comment on
the applicability of this method for a measurement of the top-quark width at
the LHC.


  Deep Neural Nets have hit quite a crest, But physical networks are where they
must rest, And here we put them all to the test, To see which network
optimization is best.


  Many astrophysical and galaxy-scale cosmological problems require a well
determined gravitational potential which is often modeled by observers under
strong assumptions. Globular clusters (GCs) surrounding galaxies can be used as
dynamical tracers of the luminous and dark matter distribution at large (kpc)
scales. A natural assumption for modeling the gravitational potential is that
GCs accreted in the same dwarf galaxy merger event move at the present time on
similar orbits in the host galaxy and should therefore have similar actions. We
investigate this idea in one realistic Milky Way like galaxy of the
cosmological N-body simulation suite Auriga. We show how the actions of
accreted stellar particles in the simulation evolve and that minimizing the
standard deviation of GCs in action space, however, cannot constrain the true
potential. This approach known as adaptive dynamics does therefore not work for
accreted GCs.


  The ultimate goal of astrobiology is to determine the distribution and
diversity of life in the universe. But as the word "biosignature" suggests,
what will be detected is not life itself, but an observation implicating a
particular process associated with living systems. Technical constraints and
our limited access to other worlds suggest we are more likely to detect an
out-of-equilibrium suite of gasses than a writhing octopus. Yet, anything short
of a writhing octopus will raise skepticism among astrobiologists about what
has been detected. Resolving that skepticism requires a theory to delineate
processes due to life and those due solely to abiotic mechanisms. This poses an
existential question for the endeavor of life detection: How do astrobiologists
plan to detect life via features shared between non-living and living systems?
We argue that you cannot without an underlying theory of life. We illustrate
this by analyzing the hypothetical detection of an "Earth 2.0" exoplanet. In
the absence of a theory of life, we argue the community should focus on
identifying unambiguous features of life via four areas of active research:
understanding the principles of life on Earth, building life in the lab,
detecting life in the solar system and searching for technosignatures.
Ultimately, we ask, what exactly do astrobiologists hope to learn by searching
for life?


  We construct a set of supersymmetric geometries that represent regular
microstates of the D1-D5-P 3-charge system, using the solution generating
technique of hep-th/0311092. These solutions are constructed as perturbations
around the maximally rotating D1-D5 solution at the linear order, and depend on
the coordinate of S^1 on which the D1- and D5-branes are wrapped. In the
framework of six-dimensional supergravity developed by Gutowski, Martelli and
Reall [hep-th/0306235], these solutions have a 4-dimensional base that depend
on the S^1 coordinate v. The v-dependent base is expected of the superstratum
solutions which are parametrized by arbitrary surfaces, and these solutions
give a modest step toward their explicit construction.


  Studying the flow of baryons into and out of galaxies is an important part of
understanding the evolution of galaxies over time. We present a detailed case
study of the environment around an intervening Ly $\alpha$ absorption line
system at $z_{\rm abs} = 0.633$, seen towards the quasar J0423$-$0130 ($z_{\rm
QSO} = 0.915$). We detect with ALMA the $^{12}$CO(2--1), $^{12}$CO(3--2) and
$1.2$~mm continuum emission from a galaxy at the redshift of the Ly $\alpha$
absorber at a projected distance of $135$ kpc. From the ALMA detections, we
infer ISM conditions similar to those in low redshift Luminous Infrared
Galaxies. DDT MUSE integral field unit observations reveal the optical
counterpart of the $^{12}$CO emission line source and three additional emission
line galaxies at the absorber redshift, which together form a galaxy group. The
$^{12}$CO emission line detections originate from the most massive galaxy in
this group. While we cannot exclude that we miss a fainter host, we reach a
dust-uncorrected star-formation rate (SFR) limit of > $0.3 \text{M}_{\odot}
\text{ yr}^{-1}$ within $100$ kpc from the sightline to the background quasar.
We measure the dust-corrected SFR (ranging from $3$ to $50$ M$_{\odot}$
yr$^{-1}$), the morpho-kinematics and the metallicities of the four group
galaxies to understand the relation between the group and the neutral gas
probed in absorption. We find that the Ly $\alpha$ absorber traces either an
outflow from the most massive galaxy or intra-group gas. This case study
illustrates the power of combining ALMA and MUSE to obtain a census of the cool
baryons in a bounded structure at intermediate redshift.


  In spintronics applications, ultrafast spin dynamics have to be controlled at
femtosecond (fs) timescales via fs-laser radiation. At such ultrafast
timescales, the effect of the Gilbert damping factor {\alpha} on ultrafast
demagnetization time should be considered. In previous explorations for the
relationship between these two parameters, it was found that the theoretical
calculations based on the local spin-flip scattering model do not agree with
the experimental results. Here, we find that in Fe81Ga19(FeGa)/Ir20Mn80(IrMn)
bilayers, the unconventional IrMn thickness dependence of {\alpha} results from
the competition between spin currents pumped from the ferromagnetic (FM) FeGa
layer to the antiferromagnetic (AFM) IrMn layer and those pumped from the AFM
layer to the FM layer. More importantly, we establish a proportional
relationship between the change of the ultrafast demagnetization rate and the
enhancement of Gilbert damping induced by the spin currents via interfacial
spin chemical potential . Our work builds a bridge to connect the ultrafast
demagnetization time and Gilbert damping in ultrafast photo-induced spin
currents dominated systems, which not only explains the disagreement between
experimental and theoretical results in the relation of {\tau}_M with {\alpha},
but provides further insight into ultrafast spin dynamics as well.


  We compute the E-polynomials of the moduli spaces of representations of the
fundamental group of a complex curve of genus g=3 into $ SL(2,C), and also of
the moduli space of twisted representations. The case of genus g=1,2 has
already been done in [http://arxiv.org/abs/1106.6011]. We follow the geometric
technique introduced in [http://arxiv.org/abs/1106.6011], based on stratifying
the space of representations, and on the analysis of the behaviour of the
E-polynomial under fibrations.


  Generative modeling, which learns joint probability distribution from data
and generates samples according to it, is an important task in machine learning
and artificial intelligence. Inspired by probabilistic interpretation of
quantum physics, we propose a generative model using matrix product states,
which is a tensor network originally proposed for describing (particularly
one-dimensional) entangled quantum states. Our model enjoys efficient learning
analogous to the density matrix renormalization group method, which allows
dynamically adjusting dimensions of the tensors and offers an efficient direct
sampling approach for generative tasks. We apply our method to generative
modeling of several standard datasets including the Bars and Stripes, random
binary patterns and the MNIST handwritten digits to illustrate the abilities,
features and drawbacks of our model over popular generative models such as
Hopfield model, Boltzmann machines and generative adversarial networks. Our
work sheds light on many interesting directions of future exploration on the
development of quantum-inspired algorithms for unsupervised machine learning,
which are promisingly possible to be realized on quantum devices.


  We compute the cohomological Brauer groups of twists of weighted projective
spaces and weighted projective stacks.


  About 30-50% of the baryons in the local Universe are unaccounted for and are
likely in a hot phase, 10^5.5-10^8 K. A hot halo (10^6.3 K) is detected around
the Milky Way through the O VII and O VIII resonance absorption and emission
lines in the soft X-ray band. Current instruments are not sensitive enough to
detect this gas in absorption around other galaxies and galaxy groups, the two
most likely sites. We show that resonant line absorption by this hot gas can be
detected with current technology, with a collecting area exceeding about 300
cm^2 and a resolution R > 2000. For a few notional X-ray telescope
configurations that could be constructed as Explorer or Probe missions, we
calculate the differential number of O VII and O VIII absorbers as a function
of equivalent width through redshift space, dN/dz. The hot halos of individual
external galaxies produce absorption that should be detectable out to about
their virial radii. For the Milky Way, one can determine the radial
distribution of density, temperature, and metallicity, after making optical
depth corrections. Spectroscopic observations can determine the rotation of a
hot gaseous halo.


  We analyzed the evolution of the metallicity of the gas with the redshift for
a sample of AGNs in a very wide redshift range (0<z<4) using ultraviolet
emission-lines from the narrow-line regions (NLRs) and photoionization models.
The new index C43=log(CIV+CIII])/HeII is suggested as a metallicity indicator
for AGNs. Based on this indicator, we confirmed the no metallicity evolution of
NLRs with the redshift pointed out by previous works. We found that metallicity
of AGNs shows similar evolution than the one predicted by cosmic semi-analytic
models of galaxy formation set within the Cold Dark Matter merging hierarchy
(for z < 3). Our results predict a mean metallicity for local objects in
agreement with the solar value (12+log(O/H)=8.69). This value is about the same
that the maximum oxygen abundance value derived for the central parts of local
spiral galaxies. Very low metallicity log(Z/Z_{\odot})~ -0.8 for some objects
in the range 1.5 < z <3 is derived.


  We study monodromies of plane curve singularities and pseudo-periodic
homeomorphisms of oriented surfaces with boundary, following an original idea
of the first author: t\^ete-\`a-t\^ete graphs and twists. We completely
characterize mapping classes that can be represented by t\^ete-\`a-t\^ete
twists, and generalize the notion to be able to represent any class of the
mapping class group relative to the boundary which is boundary-free periodic.
This improves previous work on the subject by C. Graf. Furthermore, we
introduce the class of mixed t\^ete-\`a-t\^ete graphs and twists, and prove
that mixed t\^ete-\`a-t\^ete twists contain monodromies of irreducible plane
curve singularities. In a sequel paper, the fourth author and B. Sigurdsson
have extended this to the reducible case.


  This article is motivated by soccer positional passing networks collected
across multiple games. We refer to these data as replicated spatial passing
networks---to accurately model such data it is necessary to take into account
the spatial positions of the passer and receiver for each passing event. This
spatial registration and replicates that occur across games represent key
differences with usual social network data. As a key step before investigating
how the passing dynamics influence team performance, we focus on developing
methods for summarizing different team's passing strategies. Our proposed
approach relies on a novel multiresolution data representation framework and
Poisson nonnegative block term decomposition model, which automatically
produces coarse-to-fine low-rank network motifs. The proposed methods are
applied to detailed passing record data collected from the 2014 FIFA World Cup.


  The Dark Energy Survey is a stage III dark energy experiment, performing an
optical imaging survey to measure cosmological equation of state parameters
using four independent methods. The scope and complexity of the survey
introduced complex strategic and tactical scheduling problems that needed to be
addressed. We begin with an overview of the process used to develop DES
strategy and tactics, from the inception of the project, to task forces that
studied and developed strategy changes over the course of the survey, to the
nightly pre-observing meeting in which immediate tactical issues were
addressed. We then summarize the strategic choices made for each sub-survey,
including metrics, scheduling considerations, choice of time domain fields and
their sequences of exposures, and wide survey footprint and pointing layout
choices. We go on to describe the detailed process that determined which
specific exposures were taken at which specific times. We give a chronology of
the strategic and tactical peculiarities of each year of observing, including
the proposal and execution of a sixth year. We give an overview of obstac, the
implementation of the DES scheduler used to simulate and evaluate strategic and
tactical options, and automate exposure scheduling; and describe developments
in obstac for use after DES. Appendices describe further details of data
quality evaluation, tau, and t_eff; airmass calculation; and modeling of the
seeing and sky brightness. The significant corpus of DES data indicates that
the simple scaling relations for seeing as a function of wavelength and airmass
derived from the Kolmogorov turbulence model work adequately for exposure
planning purposes: deviations from these relations are modest in comparison
with short time-scale seeing variations.


  Computer architectures become more and more complex. It requires more effort
to develop techniques that improve the programs of performance and allow to
exploit material resources efficiently. As a result, many transformations are
applied on various levels of code abstraction. The first level is the high
level, where the representation is close to the high level language. The second
one is the low level, where the presentation is close to the machine code.
Those transformations are called code optimizations. Optimizing programs
requires deep expertise. On one hand, it is a tedious task, because it requires
a lot of tests to find out the best combination of optimizations to apply with
their best factors. On the other hand, this task is critical, because it may
degrade the performance of the program instead of improving it. The
automatization of this task can deal with this problem and permit to obtain
good results. Our end of study project consists on proposing a novel approach
based on neural networks to automatically optimize loops in Tiramisu. Tiramisu
is a new language to create a code of high performance. It allows to separate
between the algorithm and its optimizations. We have chosen loop unrolling as a
study case. Our contribution aims to automate the choice of the best loop
unrolling factor for a program written in Tiramisu.


  Recent theoretical studies suggest the existence of low-mass zero-metal stars
in the current universe. In order to study the basic properties of the
atmosphere of low-mass first stars, we performed one dimensional
magnetohydrodynamical simulations for the heating of coronal loops on low-mass
stars with various metallicities. While the simulated loops are heated up to
>$10^6$ K by the dissipation of Alfvenic waves originating from the convective
motion irrespectively of the metallicity, the coronal properties sensitively
depend on the metallicity. Lower-metal stars create hotter and denser coronae
because the radiative cooling is suppressed. The zero-metal star gives more
than ten times higher coronal density than the solar-metallicity counterpart,
and as a result, the UV and X-ray fluxes from the loop are (1-5) times higher
than those of the solar metallicity star. We also discuss the dependence of the
coronal properties on the length of the simulated coronal loops


  11 transient bright flashes were detected in Jupiter's atmosphere using the
UVS instrument on the Juno spacecraft. These bright flashes are only observed
in a single spin of the spacecraft and their brightness decays exponentially
with time, with a duration of ~1.4 ms. The spectra are dominated by H2 Lyman
band emission and based on the level of atmospheric absorption, we estimate a
source altitude of 260 km above the 1-bar level. Based on these
characteristics, we suggest that these are observations of Transient Luminous
Events (TLEs) in Jupiter's upper atmosphere. In particular, we suggest that
these are elves, sprites or sprite halos, three types of TLEs that occur in the
Earth's upper atmosphere in response to tropospheric lightning strikes. This is
supported by visible light imaging, which shows cloud features typical of
lightning source regions at the locations of several of the bright flashes.
TLEs have previously only been observed on Earth, although theoretical and
experimental work has predicted that they should also be present on Jupiter.


  We outline two methods of constructing improved composite operators using
Wilson fermions.


  We present results of a theoretical study of 4He films adsorbed on graphite,
based on the continuous space worm algorithm. In the first layer, we find a
domain-wall phase and a (7/16) registered structure between the commensurate
(1/3) and the incommensurate solid phases. For the second layer, we find only
superfluid and incommensurate solid phases. The commensurate phase found in
previous simulation work is only observed if first layer particles are kept
fixed; it disappears upon explicitly including their zero-point fluctuations.
No evidence of any "supersolid" phase is found.


  We demonstrate, in the context of Loop Quantum Gravity, the Quantum
Holographic Principle, according to which the area of the boundary surface
enclosing a region of space encodes a qubit per Planck unit. To this aim, we
introduce fermion fields in the bulk, whose boundary surface is the
two-dimensional sphere. The doubling of the fermionic degrees of freedom and
the use of the Bogoljubov transformations lead to pairs of spin network's edges
piercing the boundary surface with double punctures, giving rise to pixels of
area encoding a qubit. The proof is also valid in the case of a fuzzy sphere.


  We present a novel technique to minimize UV-induced damage in experiments
that employ second-harmonic generation cavities. The principle of our approach
is to reduce the duty cycle of the UV light as much as possible to prolong the
lifetime of the used optics. The low duty cycle is achieved by ramping the
cavity into resonance for a short time during the experimental cycle when the
light is used and tuning it to an off-resonant state otherwise. The necessary
fast ramp and length-stabilization control of the cavity is implemented with
the FPGA-based STEMlab platform. We demonstrate the utility of this method by
measuring the isotope shift of the electronic transition ($X^1\Sigma \leftarrow
A^1\Pi$) in AlCl at 261.5 nm in a pulsed molecular beam experiment.


  The multi-TeV proton and ion beams of the LHC would allow for the most
energetic fixed-target experiment ever. In particular, $pp$, $p$d and $p$A
collisions could be performed at $\sqrt{s_{NN}}$ = 115~GeV, as well as Pb$p$
and PbA collisions at $\sqrt{s_{NN}}$ = 72~GeV, in a parasitic way by making
use of the already existing LHCb and ALICE detectors in fixed-target mode. This
would offer the possibility to carry out a ground-breaking physics program, to
study the nucleon and nuclear structure at high $x$, the spin content of the
nucleon and the phases of the nuclear matter from a new rapidity viewpoint. In
this talk I focus on the spin physics axis of the full program developed so far
by the AFTER@LHC study group.


  Taking the Planck cosmic microwave background data and the more direct Hubble
constant measurement data as unaffected by systematic offsets, the values of
the Hubble constant $H_0$ interpreted within the $\Lambda$CDM cosmological
constant and cold dark matter cosmological model are in $\sim 3.3 \sigma$
tension. We show that the Parker vacuum metamorphosis model, physically
motivated by quantum gravitational effects and with the same number of
parameters as $\Lambda$CDM, can remove the $H_0$ tension, and can give an
improved fit to data (up to $\Delta\chi^2=-7.5$). It also ameliorates tensions
with weak lensing data and the high redshift Lyman alpha forest data. We
separately consider a scale dependent scaling of the gravitational lensing
amplitude, such as provided by modified gravity, neutrino mass, or cold dark
energy, motivated by the somewhat different cosmological parameter estimates
for low and high CMB multipoles. We find that no such scale dependence is
preferred.


  A central property of a classical geometry is that the geodesic distance
between two events is \emph{additive}. When considering quantum fluctuations in
the metric or a quantum or statistical superposition of different spacetimes,
additivity is generically lost at the level of expectation values. In the
presence of a superposition of metrics, distances can be made diffeomorphism
invariant by considering the frame of a family of free-falling observers or a
pressureless fluid, provided we work at sufficiently low energies. We propose
to use the average squared distance between two events $\langle
d^2(x,y)\rangle$ as a proxy for understanding the effective quantum (or
statistical) geometry and the emergent causal relations among such observers.
At each point, the average squared distance $\langle d^2(x,y)\rangle$ defines
an average metric tensor. However, due to non-additivity, $\langle
d^2(x,y)\rangle$ is not the (squared) geodesic distance associated with it. We
show that departures from additivity can be conveniently captured by a bi-local
quantity $C(x,y)$. Violations of additivity build up with the mutual separation
between $x$ and $y$ and can correspond to $C<0$ (subadditive) or $C>0$
(superadditive). We show that average Euclidean distances are always
subadditive: they satisfy the triangle inequality but generally fail to
saturate it. In Lorentzian signature there is no definite result about the sign
of $C$, most physical examples give $C<0$ but there exist counterexamples. The
causality induced by subadditive Lorentzian distances is unorthodox but not
pathological. Superadditivity violates the transitivity of causal relations. On
these bases, we argue that subadditive distances are the expected outcome of
dynamical evolution, if relatively generic physical initial conditions are
considered.


  We demonstrate the successful fabrication on CaF$_2$ substrates of
FeSe$_{1-x}$Te$_{x}$ films with $0 \le x \le 1$, including the region of $0.1
\le x \le 0.4$, which is well known to be the "phase-separation region", via
pulsed laser deposition which is a thermodynamically non-equilibrium method. In
the resulting films, we observe a giant enhancement of the superconducting
transition temperature, $T_\mathrm{c}$, in the region of $0.1 \le x \le 0.4$:
the maximum value reaches 23 K, which is approximately 1.5 times as large as
the values reported for bulk samples of FeSe$_{1-x}$Te$_{x}$. We present a
complete phase diagram of FeSe$_{1-x}$Te$_{x}$ films. Surprisingly, a sudden
suppression of $T_\mathrm{c}$ is observed at $0.1<x<0.2$, while $T_\mathrm{c}$
increases with decreasing $x$ for $0.2 \le x < 1$. Namely, there is a clear
difference between superconductivity realized in $x=0-0.1$ and in $x \ge 0.2$.
To obtain a film of FeSe$_{1-x}$Te$_{x}$ with high $T_\mathrm{c}$, the controls
of the Te content $x$ and the in-plane lattice strain are found to be key
factors.


  Young core-collapse supernovae with dense-wind progenitors may be able to
accelerate cosmic-ray hadrons beyond the knee of the cosmic-ray spectrum, and
this may result in measurable gamma-ray emission. We searched for gamma-ray
emission from ten supernovae observed with the High Energy Stereoscopic System
(H.E.S.S.) within a year of the supernova event. Nine supernovae were observed
serendipitously in the H.E.S.S. data collected between December 2003 and
December 2014, with exposure times ranging from 1.4 hours to 53 hours. In
addition we observed SN 2016adj as a target of opportunity in February 2016 for
13 hours. No significant gamma-ray emission has been detected for any of the
objects, and upper limits on the $>1$ TeV gamma-ray flux of the order of
$\sim$10$^{-13}$ cm$^{-2}$s$^{-1}$ are established, corresponding to upper
limits on the luminosities in the range $\sim$2 $\times$ 10$^{39}$ erg s$^{-1}$
to $\sim$1 $\times$ 10$^{42}$ erg s$^{-1}$. These values are used to place
model-dependent constraints on the mass-loss rates of the progenitor stars,
implying upper limits between $\sim$2 $\times 10^{-5}$ and $\sim$2 $\times
10^{-3}$M$_{\odot}$yr$^{-1}$ under reasonable assumptions on the particle
acceleration parameters.


  Viral kinetics have been extensively studied in the past through the use of
spatially well-mixed ordinary differential equations describing the time
evolution of the diseased state. However, emerging spatial structures such as
localized populations of dead cells might adversely affect the spread of
infection, similar to the manner in which a counter-fire can stop a forest fire
from spreading. In a previous publication (Beauchemin et al., 2005), a simple
2-D cellular automaton model was introduced and shown to be accurate enough to
model an uncomplicated infection with influenza A. Here, this model is used to
investigate the effects of relaxing the well-mixed assumption. Particularly,
the effects of the initial distribution of infected cells, the regeneration
rule for dead epithelial cells, and the proliferation rule for immune cells are
explored and shown to have an important impact on the development and outcome
of the viral infection in our model.


  We present a versatile formulation of the convolution operation that we term
a "mapped convolution." The standard convolution operation implicitly samples
the pixel grid and computes a weighted sum. Our mapped convolution decouples
these two components, freeing the operation from the confines of the image grid
and allowing the kernel to process any type of structured data. As a test case,
we demonstrate its use by applying it to dense inference on spherical data. We
perform an in-depth study of existing spherical image convolution methods and
propose an improved sampling method for equirectangular images. Then, we
discuss the impact of data discretization when deriving a sampling function,
highlighting drawbacks of the cube map representation for spherical data.
Finally, we illustrate how mapped convolutions enable us to convolve directly
on a mesh by projecting the spherical image onto a geodesic grid and training
on the textured mesh. This method exceeds the state of the art for spherical
depth estimation by nearly 17%. Our findings suggest that mapped convolutions
can be instrumental in expanding the application scope of convolutional neural
networks.


  The non-linear evolution of dense partonic systems has been suggested as one
of the novel physics mechanisms relevant to the dynamics of hadron-nucleus and
nucleus-nucleus collisions at collider energies. Here we study to what extent
the description of Cronin enhancement in the framework of this non-linear
evolution is consistent with the recent observation in 200 AGeV d--Au
collisions at the Relativistic Heavy Ion Collider. We solve the
Balitsky-Kovchegov evolution equation numerically for several initial
conditions encoding Cronin enhancement. We find that the properly normalized
nuclear gluon distribution is suppressed at all momenta relative to that of a
single nucleon. Calculating the resulting spectrum of produced gluons in p-A
and A-A collisions, we establish that the nonlinear QCD evolution is unable to
generate a Cronin type enhancement, and that it quickly erases any such
enhancement which may be present at lower energies.


  This paper introduces a new type of soft continuum robot, called SCoReS,
which is capable of self-controlling continuously its curvature at the segment
level; in contrast to previous designs which either require external forces or
machine elements, or whose variable curvature capabilities are discrete --
depending on the number of locking mechanisms and segments. The ability to have
a variable curvature, whose control is continuous and independent from external
factors, makes a soft continuum robot more adaptive in constrained
environments, similar to what is observed in nature in the elephant's trunk or
ostrich's neck for instance which exhibit multiple curvatures. To this end, our
soft continuum robot enables reconfigurable variable curvatures utilizing a
variable stiffness growing spine based on micro-particle granular jamming for
the first time. We detail the design of the proposed robot, presenting its
modeling through beam theory and FEA simulation -- which is validated through
experiments. The robot's versatile bending profiles are then explored in
experiments and an application to grasp fruits at different configurations is
demonstrated.


  Model-agnostic meta-learning (MAML) is a popular method for few-shot learning
but assumes that we have access to the meta-training set. In practice, training
on the meta-training set may not always be an option due to data privacy
concerns, intellectual property issues, or merely lack of computing resources.
In this paper, we consider the novel problem of repurposing pretrained MAML
checkpoints to solve new few-shot classification tasks. Because of the
potential distribution mismatch, the original MAML steps may no longer be
optimal. Therefore we propose an alternative meta-testing procedure and combine
MAML gradient steps with adversarial training and uncertainty-based stepsize
adaptation. Our method outperforms "vanilla" MAML on same-domain and
cross-domains benchmarks using both SGD and Adam optimizers and shows improved
robustness to the choice of base stepsize.


  The Bogomol'nyi-Prasad-Sommerfield (BPS) multiwall solutions are constructed
in a massive Kahler nonlinear sigma model on the complex quadric surface,
Q^N=SO(N+2)/[SO(N)\times SO(2)] in 3-dimensional space-time. The theory has a
non-trivial scalar potential generated by the Scherk-Schwarz dimensional
reduction from the massless nonlinear sigma model on Q^N in 4-dimensional
space-time and it gives rise to 2[N/2+1] discrete vacua. The BPS wall solutions
connecting these vacua are obtained based on the moduli matrix approach. It is
also shown that the moduli space of the BPS wall solutions is the complex
quadric surface Q^N.


  We comment on the changes in the constrained model studied earlier when
constituent massless vector fields are introduced. The new model acts like a
gauge-Higgs-Yukawa system, although its origin is different.


  The charm quark offers interesting opportunities to cross-check the mechanism
of CP violation precisely tested in the strange and beauty sectors. In this
paper, we exploit the angular and quantum correlations in the D\bar{D} pairs
produced through the decay of the psi(3770) resonance in a charm factory to
investigate CP-violation in two different ways. We build CP-violating
observables in psi(3770) -> D\bar{D} -> (V_1V_2)(V_3 V_4) to isolate specific
New Physics effects in the charm sector. We also consider the case of psi(3770)
-> D\bar{D} -> (V_1V_2)(K\pi) decays, which provide a new way to measure the
strong phase difference delta between Cabibbo-favored and doubly-Cabibbo
suppressed D decays required in the determination of the CKM angle gamma.
Neglecting the systematics, we give a first rough estimate of the sensitivities
of these measurements at BES-III with an integrated luminosity of 20 fb^-1 at
psi(3770) peak and at a future Super tau-charm factory with a luminosity of
10^35 cm^-2.s^-1.


  In this paper we develop three different subjects. We study and prove
alternative versions of Hrushovski's "Stabilizer Theorem", we generalize part
of the basic theory of definably amenable NIP groups to NTP2 theories, and
finally, we use all this machinery to study groups with f-generic types
definable in bounded PRC fields.


  Researchers have applied deep neural networks to image restoration tasks, in
which they proposed various network architectures, loss functions, and training
methods. In particular, adversarial training, which is employed in recent
studies, seems to be a key ingredient to success. In this paper, we show that
simple convolutional autoencoders (CAEs) built upon only standard network
components, i.e., convolutional layers and skip connections, can outperform the
state-of-the-art methods which employ adversarial training and sophisticated
loss functions. The secret is to employ an evolutionary algorithm to
automatically search for good architectures. Training optimized CAEs by
minimizing the $\ell_2$ loss between reconstructed images and their ground
truths using the ADAM optimizer is all we need. Our experimental results show
that this approach achieves 27.8 dB peak signal to noise ratio (PSNR) on the
CelebA dataset and 40.4 dB on the SVHN dataset, compared to 22.8 dB and 33.0 dB
provided by the former state-of-the-art methods, respectively.


  Memory hard functions (MHFs) are an important cryptographic primitive that
are used to design egalitarian proofs of work and in the construction of
moderately expensive key-derivation functions resistant to brute-force attacks.
Broadly speaking, MHFs can be divided into two categories: data-dependent
memory hard functions (dMHFs) and data-independent memory hard functions
(iMHFs). iMHFs are resistant to certain side-channel attacks as the memory
access pattern induced by the honest evaluation algorithm is independent of the
potentially sensitive input e.g., password. While dMHFs are potentially
vulnerable to side-channel attacks (the induced memory access pattern might
leak useful information to a brute-force attacker), they can achieve higher
cumulative memory complexity (CMC) in comparison than an iMHF. In this paper,
we introduce the notion of computationally data-independent memory hard
functions (ciMHFs). Intuitively, we require that memory access pattern induced
by the (randomized) ciMHF evaluation algorithm appears to be independent from
the standpoint of a computationally bounded eavesdropping attacker --- even if
the attacker selects the initial input. We then ask whether it is possible to
circumvent known upper bound for iMHFs and build a ciMHF with CMC
$\Omega(N^2)$. Surprisingly, we answer the question in the affirmative when the
ciMHF evaluation algorithm is executed on a two-tiered memory architecture
(RAM/Cache).
  See paper for the full abstract.


  An anecdotal account of the author's role in the origins of lattice gauge
theory, prepared for delivery on the thirtieth anniversary of the publication
of "Confinement of Quarks" [Phys. Rev. D10 (1974) 2445].


  In this paper we report on PD-SPH the new tree-sph code developed in Padua.
The main features of the code are described and the results of a new and
independent series of 1-D and 3-D tests are shown. The paper is mainly
dedicated to the presentation of the code and to the critical discussion of its
performances. In particular great attention is devoted to the convergency
analysis. The code is highly adaptive in space and time by means of individual
smoothing lengths and individual time steps. At present it contains both dark
and baryonic matter, this latter in form of gas and stars, cooling, thermal
conduction, star formation, and feed-back from Type I and II supernovae,
stellar winds, and ultraviolet flux from massive stars, and finally chemical
enrichment. New cooling rates that depend on the metal abundance of the
interstellar medium are employed, and the differences with respect to the
standard ones are outlined. Finally, we show the simulation of the dynamical
and chemical evolution of a disk-like galaxy with and without feed-back. The
code is suitably designed to study in a global fashion the problem of formation
and evolution of elliptical galaxies, and in particular to feed a
spectro-photometric code from which the integrated spectra, magnitudes, and
colors (together with their spatial gradients) can be derived.


  High-quality sentence embeddings are fundamental in many natural language
processing (NLP) tasks, such as semantic textual similarity (STS) and
retrieval-augmented generation (RAG). Nevertheless, most existing methods
leverage fixed-length embeddings from full-layer language models, which lack
the scalability to accommodate the diverse available resources across various
applications. Viewing this gap, we propose a novel sentence embedding model
$\mathrm{Espresso}$ $\mathrm{Sentence}$ $\mathrm{Embeddings}$ (ESE) with two
learning processes. First, the learn-to-express process encodes more salient
representations to lower layers. Second, the learn-to-compress process compacts
essential features into the initial dimensions using Principal Component
Analysis (PCA). This way, ESE can scale model depth via the former process and
embedding size via the latter. Extensive experiments on STS and RAG suggest
that ESE can effectively produce high-quality embeddings with less model depth
and embedding size, enhancing embedding inference efficiency.


  Consider the problem of minimizing the sum of a smooth convex function and a
separable nonsmooth convex function subject to linear coupling constraints.
Problems of this form arise in many contemporary applications including signal
processing, wireless networking and smart grid provisioning. Motivated by the
huge size of these applications, we propose a new class of first order
primal-dual algorithms called the block successive upper-bound minimization
method of multipliers (BSUM-M) to solve this family of problems. The BSUM-M
updates the primal variable blocks successively by minimizing locally tight
upper-bounds of the augmented Lagrangian of the original problem, followed by a
gradient type update for the dual variable in closed form. We show that under
certain regularity conditions, and when the primal block variables are updated
in either a deterministic or a random fashion, the BSUM-M converges to the set
of optimal solutions. Moreover, in the absence of linear constraints, we show
that the BSUM-M, which reduces to the block successive upper-bound minimization
(BSUM) method, is capable of linear convergence without strong convexity.


  We resolve a seeming paradox arising from a common misinterpretation of
Ben-Naim's theorem, which rests on a pairwise decomposition of the Hamiltonian
of a molecular solute/solvent system into pairwise solute-solvent and
solvent-solvent interactions. According to this theorem, also the solvation
entropy can be decomposed into a solute-solute term and a remaining term that
is -- perhaps misleadingly -- referred to as "solvent reorganization entropy".
Crucially, the latter equals the average solvent-solvent interaction energy,
such that these two solvent-solvent terms do not change the total solvation
free energy. This analytical result has often been used to argue that the
reorganization of the solvent cannot play a role in the solvation process, and
thus to rule out "iceberg"-type solvent shell ordering as a solvation driving
force. However, recent calculations based on atomistic simulations of a
solvated globular protein and spatially resolved mutual information expansions
revealed substantial contributions of many-body solvent correlations to the
solvation free energy. Here we resolved this seeming contradiction and
illustrate by two examples -- a simple Ising model and a solvated Lennard-Jones
particle -- that the solvent reorganization entropy and the actual entropy
contribution arising from many-body solvent correlations differ both
conceptually and numerically. Whereas the solvent reorganization entropy in
fact arises from both solvent-solvent as well as solute-solvent interactions
and thus fails to describe what the name suggests, the mutual information
expansion permits a straightforward interpretation in terms of the entropy
contribution of solvent-solvent correlations to the solvation free energy.


  We consider self-injective finite-dimensional graded algebras admitting a
triangular decomposition. In a preceding paper, we have shown that the graded
module category of such an algebra is a highest weight category and has tilting
objects in the sense of Ringel. In this paper we focus on the degree zero part
of the algebra, the core of the algebra. We show that the core captures
essentially all relevant information about the graded representation theory.
Using tilting theory, we show that the core is cellular. We then describe a
canonical construction of a highest weight cover, in the sense of Rouquier, of
this cellular algebra using a finite subquotient of the highest weight
category. Thus, beginning with a self-injective graded algebra admitting a
triangular decomposition, we canonically construct a quasi-hereditary algebra
which encodes key information, such as graded multiplicities, of the original
algebra. Our results are general and apply to a wide variety of examples,
including restricted enveloping algebras, Lusztig's small quantum groups,
hyperalgebras, finite quantum groups, and restricted rational Cherednik
algebras. We expect that the cell modules and quasi-hereditary algebras
introduced here will provide a new way of understanding these important
examples.


  Nuclear density functional theory is the prevalent theoretical framework for
accurately describing nuclear properties at the scale of the entire chart of
nuclides. Given an energy functional and a many-body scheme (e.g., single- or
multireference level), the predictive power of the theory depends strongly on
how the parameters of the energy functionals have been calibrated with
experimental data. Expanded algorithms and computing power have enabled recent
optimization protocols to include data in deformed nuclei in order to optimize
the coupling constants of the energy functional. The primary motivation of this
work is to test the robustness of such protocols with respect to some of the
technical and numerical details of the underlying calculations, especially when
the calibration explores a large parameter space. To this end, we quantify the
effect of these uncertainties on both the optimization and statistical
emulation of composite objective functions. We also emphasize that Bayesian
calibration can provide better estimates of the theoretical errors used to
define objective functions.


  Photodetectors at telecommunications-band with high efficiency and high speed
are becoming increasingly important as the booming of big data, 5G, internet of
things, cloud computing, artificial intelligence and relevent applications.
Silicon-based Germanium photodetectors exhibit great potential in reducing the
cost and power dissipation, due to its compatibility of monolithic integration
with signal-processing electronics. We report the first demonstration of normal
incident resonant-metasurface germanium photodetector, to address the trade-off
between quantum efficiency, bandwidth and wavelength coverage for free-space
detectors. With an ultra-thin intrinsic layer thickness of 350 nm, a high
external quantum efficiency of more than 60% and clearly open eyes at the speed
of 20 Gbps are achieved, for a 30 {\mu}m-diameter device. The photodetector
employs multiple trapped-mode resonances to enhance the localized
electromagnetic field, which not only enhances the external quantum efficiency
by more than 300% at 1550 nm, but also extends to the whole C band. Further
simulation and measurement with small incident spot size show the feasibility
to achieve >50 GHz 3 dB bandwidth by simply reducing the mesa size, with minor
sacrifice of the enhanced absorption. This work paves the way for the future
development of low-cost, high efficiency normal incident germanium
photodetectors operating at data rates of 50 Gbps or higher.


  Compared to basic fork-join queues, a job in (n, k) fork-join queues only
needs its k out of all n sub-tasks to be finished. Since (n, k) fork-join
queues are prevalent in popular distributed systems, erasure coding based cloud
storages, and modern network protocols like multipath routing, estimating the
sojourn time of such queues is thus critical for the performance measurement
and resource plan of computer clusters. However, the estimating keeps to be a
well-known open challenge for years, and only rough bounds for a limited range
of load factors have been given. In this paper, we developed a closed-form
linear transformation technique for jointly-identical random variables: An
order statistic can be represented by a linear combination of maxima. This
brand-new technique is then used to transform the sojourn time of non-purging
(n, k) fork-join queues into a linear combination of the sojourn times of basic
(k, k), (k+1, k+1), ..., (n, n) fork-join queues. Consequently, existing
approximations for basic fork-join queues can be bridged to the approximations
for non-purging (n, k) fork-join queues. The uncovered approximations are then
used to improve the upper bounds for purging (n, k) fork-join queues.
Simulation experiments show that this linear transformation approach is
practiced well for moderate n and relatively large k.


  We study a risk-averse optimal control problem for a finite-horizon Borel
model, where a cumulative cost is assessed via exponential utility. The setting
permits non-linear dynamics, non-quadratic costs, and continuous state and
control spaces but is less general than the problem of optimizing an expected
utility. Our contribution is to show the existence of an optimal risk-averse
controller without using state space augmentation and therefore offer a simpler
solution method from first principles compared to what is currently available
in the literature.


  Deep surveys with Atacama Large Millimeter Array (ALMA) have uncovered a
population of dusty star-forming galaxies which are faint or even undetected at
optical to near-infrared wavelengths. Their faintness at short wavelengths
makes detailed characterization of the population challenging. Here we present
a spectroscopic redshift identification and characterization of one of such
near-infrared-dark galaxy discovered by an ALMA deep survey. Detection of
[CI](1-0) and CO(4-3) emission lines determines the precise redshift of the
galaxy, ADF22.A2, to be z=3.9913+/-0.0008. On the basis of multi-wavelength
analysis, ADF22.A2 is found to be a massive, star-forming galaxy with stellar
mass Mstar = $1.1_{-0.6}^{+1.3}$ x 10^{11} Msun and SFR = $430_{-150}^{+230}$
Msun/yr. The molecular gas mass is derived to be M ($H_2$) = 5.9 +/-
1.5x10^{10} Msun, indicating a gas fraction of ~35%, and the ratios of $L_{\rm
[CI](1-0)}/L_{\rm IR}$ and $L_{\rm [CI](1-0)}/L_{\rm CO(4-3)}$ suggests that
the nature of the interstellar medium in ADF22.A2 is in accordance with those
of other bright submillimeter galaxies. The properties of ADF22.A2, including
redshift, star-formation rate, stellar mass, and depletion time scale
(tau~0.1-0.2 Gyr), also suggest that ADF22.A2 has the characteristics expected
for the progenitors of quiescent galaxies at z>3. Our results demonstrate the
power of ALMA contiguous mapping and line scan to obtain an unbiased view of
galaxy formation in the early Universe.


  This paper investigates antenna selection at a base station with large
antenna arrays and low-resolution analog-to-digital converters. For downlink
transmit antenna selection for narrowband channels, we show (1) a selection
criterion that maximizes sum rate with zero-forcing precoding equivalent to
that of a perfect quantization system; (2) maximum sum rate increases with
number of selected antennas; (3) derivation of the sum rate loss function from
using a subset of antennas; and (4) unlike high-resolution converter systems,
sum rate loss reaches a maximum at a point of total transmit power and
decreases beyond that point to converge to zero. For wideband
orthogonal-frequency-division-multiplexing (OFDM) systems, our results hold
when entire subcarriers share a common subset of antennas. For uplink receive
antenna selection for narrowband channels, we (1) generalize a greedy antenna
selection criterion to capture tradeoffs between channel gain and quantization
error; (2) propose a quantization-aware fast antenna selection algorithm using
the criterion; and (3) derive a lower bound on sum rate achieved by the
proposed algorithm based on submodular functions. For wideband OFDM systems, we
extend our algorithm and derive a lower bound on its sum rate. Simulation
results validate theoretical analyses and show increases in sum rate over
conventional algorithms.


  It is proven that dual superconductivity of QCD vacuum in the confining phase
is an intinsic property, independent on the choice of the abelian projection
used to define the monopoles.


  We present a convolutional autoencoder to denoise pulses from a p-type point
contact high-purity germanium detector similar to those used in several rare
event searches. While we focus on training procedures that rely on detailed
detector physics simulations, we also present implementations requiring only
noisy detector pulses to train the model. We validate our autoencoder on both
simulated data and calibration data from an $^{241}$Am source, the latter of
which is used to show that the denoised pulses are statistically compatible
with data pulses. We demonstrate that our denoising method is able to preserve
the underlying shapes of the pulses well, offering improvement over traditional
denoising methods. We also show that the shaping time used to calculate energy
with a trapezoidal filter can be significantly reduced while maintaining a
comparable energy resolution. Under certain circumstances, our denoising method
can improve the overall energy resolution. The methods we developed to remove
electronic noise are straightforward to extend to other detector technologies.
Furthermore, the latent representation from the encoder is also of use in
quantifying shape-based characteristics of the signals. Our work has great
potential to be used in particle physics experiments and beyond.


  Architectures based on Recurrent Neural Networks (RNNs) have been
successfully applied to many different tasks such as speech or handwriting
recognition with state-of-the-art results. The main contribution of this work
is to analyse the feasibility of RNNs for on-line signature verification in
real practical scenarios. We have considered a system based on Long Short-Term
Memory (LSTM) with a Siamese architecture whose goal is to learn a similarity
metric from pairs of signatures. For the experimental work, the BiosecurID
database comprised of 400 users and 4 separated acquisition sessions are
considered. Our proposed LSTM RNN system has outperformed the results of recent
published works on the BiosecurID benchmark in figures ranging from 17.76% to
28.00% relative verification performance improvement for skilled forgeries.


  We present a solution for the Einstein-Maxwell (EM) equations which unifies
both the magnetic Bertotti-Robinson (BR) and Melvin (ML) solutions as a single
metric in the axially symmetric coordinates ${t,\rho,z,\varphi} $. Depending on
the strength of magnetic field the spacetime manifold, unlike the cases of
separate BR and ML spacetime, develops singularity on the symmetry axis ($\rho
=0$). Our analysis shows, beside other things that there are regions
inaccessible to all null geodesics.


  A generic feature of systems with long-range interactions is the presence of
{\it quasi-stationary} states with non-Gaussian single particle velocity
distributions. For the case of the Hamiltonian Mean Field (HMF) model, we
demonstrate that a maximum entropy principle applied to the associated Vlasov
equation explains known features of such states for a wide range of initial
conditions. We are able to reproduce velocity distribution functions with an
analytical expression which is derived from the theory with no adjustable
parameters. A normal diffusion of angles is detected and a new dynamical
effect, two oscillating clusters surrounded by a halo, is also found and
theoretically justified.


  We present a gauge independent method to construct the effective action of
QCD, and calculate the one loop effective action of $SU(2)$ QCD in an arbitrary
constant background field. Our result establishes the existence of a dynamical
symmetry breaking by demonstrating that the effective potential develops a
unique and stable vacuum made of the monopole condensation in one loop
approximation. This provides a strong evidence for the magnetic confinement of
color through the dual Meissner effect in the non-Abelian gauge theory. The
result is obtained by separating the topological degrees which describe the
non-Abelian monopoles from the dynamical degrees of the gauge potential, and
integrating out all the dynamical degrees of QCD. We present three independent
arguments to support our result.


  We characterise the class of uniform limits of functions from Pawlak's class
$\mathcal B_1^{**}$. The resulting class $u\mathscr S_1$, which contains
functions with the oscillation rank one, is discussed in connection with its
linear span. We apply a general, topological space, setting in our discussion.


  Homonuclear correlation spectroscopy has played a crucial role in elucidating
structure of molecules in solution. In all kinds of homonuclear correlation,
the most intense peaks appear in the diagonal, and quite often due to its broad
dispersive tails obscure the useful cross peaks close to the diagonal. Herein,
we demonstrate that by editing the 1H magnetization by a C-13 nucleus in
natural abundance before starting the t1 evolution, it is possible to
discriminate the inphase 1H magnetizations that leads to the diagonal peaks
from the antiphase 1H magnetizations that generates the cross peaks. The 1H
magnetization is edited by a C-13 nucleus in natural abundance before starting
the t1 period in COSY. After the coherence transfer, the inphase transverse
magnetization directly attached to C-13 evolves under one bond heteronuclear
coupling for a time period 2{\tau} tuned to one bond heteronuclear coupling
while the antiphase transeverse magnetization directly attached to remote C-12
evolves under negligible long range heteronuclear couplings. As a result the
inphase magnetization can be eliminated as an unobservable heteronuclear
multiple quantum coherence leading to a diagonal free correlated spectrum. The
method is demonstrated for COSY.