  Modelling of photonic devices traditionally involves solving the equations of
light-matter interaction and light propagation, and it is restrained by their
applicability. Here we demonstrate an alternative modelling methodology by
creating a "quantum copy" of the optical device in the quantum computer. As an
illustration, we simulate quantum interference of light on a thin absorbing
film. Such interference can lead to either perfect absorption or total
transmission of light through the film, the phenomena attracting attention for
data processing applications in classical and quantum information networks. We
map behaviour of the photon in the quantum interference experiment to the
evolution of a quantum state of transmon, a superconducting charge qubit of the
IBM quantum computer. Details of the real optical experiment are flawlessly
reproduced on the quantum computer. We argue that superiority of the "quantum
copy" methodology shall be apparent in modelling complex multi-photon optical
phenomena and devices.


  We extend the Schwinger-Dyson equation (SDE) on the complex plane, which was
treated in our previous research, to finite temperature. As a simple example,
we solve the SDE for a model with four-fermion interactions in the (1+1)
space-time dimensions at strong coupling region. We investigate the properties
of the effective mass and energy for the fermions, especially near the phase
transition temperature.


  In this paper we discussed the topological transition between trivial and
nontrivial phases of a quasi-periodic (Aubry-Andr\'e like) mechanical
Su-Schrieffer-Heeger (SSH) model. We find that there exists a nontrivial
boundary separating the two topological phases and an analytical expression for
this boundary is found. We discuss the localization of the vibrational modes
using the calculation of the inverse participation ratio (IPR) and access the
localization nature of the states of the system. We find three different
regimes: extended, localized, and critical, depending on the intensity of the
Aubry-Andr\'e spring. We further study the energy dependent mobility edge (ME)
separating localized from extended eigenstates and find its analytical
expression for both commensurate and incommensurate modulation wavelengths,
thus enlarging the library of models possessing analytical expressions for the
ME. Our results extend previous results for the theory of fermionic topological
insulators and localization theory in quantum matter to the classical realm.


  Quasi-phase matched direct laser acceleration (DLA) of electrons can be
realized with guided, radially polarized laser pulses in density-modulated
plasma waveguides. A 3-D particle-in-cell model has been developed to describe
the interactions among the laser field, injected electrons, and the background
plasma in the DLA process. Simulations have been conducted to study the scheme
in which seed electron bunches with moderate energies are injected into a
plasma waveguide and the DLA is performed by use of relatively low-power (0.5-2
TW) laser pulses. Selected bunch injection delays with respect to the laser
pulse, bunch lengths, and bunch transverse sizes have been studied in a series
of simulations of DLA in a plasma waveguide. The results show that the
injection delay is important for controlling the final transverse properties of
short electron bunches, but it also affects the final energy gain. With a long
injected bunch length, the enhanced ion-focusing force helps to collimate the
electrons and a relatively small final emittance can be obtained. DLA
efficiency is reduced when a bunch with a greater transverse size is injected;
in addition, micro-bunching is clearly observed due to the focusing and
defocusing of electrons by the radially directed Lorentz force. DLA should be
performed with a moderate laser power to maintain favorable bunch transverse
properties, while the waveguide length can be extended to obtain a higher
maximum energy gain, with the commensurate increase of laser pulse duration and
energy.


  Cooperation between mobile robots and wireless sensor networks is a line of
research that is currently attracting a lot of attention. In this context, we
study the following problem of barrier coverage by stationary wireless sensors
that are assisted by a mobile robot with the capacity to move sensors. Assume
that $n$ sensors are initially arbitrarily distributed on a line segment
barrier. Each sensor is said to cover the portion of the barrier that
intersects with its sensing area. Owing to incorrect initial position, or the
death of some of the sensors, the barrier is not completely covered by the
sensors. We employ a mobile robot to move the sensors to final positions on the
barrier such that barrier coverage is guaranteed. We seek algorithms that
minimize the length of the robot's trajectory, since this allows the
restoration of barrier coverage as soon as possible. We give an optimal
linear-time offline algorithm that gives a minimum-length trajectory for a
robot that starts at one end of the barrier and achieves the restoration of
barrier coverage. We also study two different online models: one in which the
online robot does not know the length of the barrier in advance, and the other
in which the online robot knows the length of the barrier. For the case when
the online robot does not know the length of the barrier, we prove a tight
bound of $3/2$ on the competitive ratio, and we give a tight lower bound of
$5/4$ on the competitive ratio in the other case. Thus for each case we give an
optimal online algorithm.


  We propose a setup that integrates a quantum point contact (QPC) and a
Josephson junction on a quantum spin Hall sample, experimentally realizable in
InAs/GaSb quantum wells. The confinement due to both the QPC and the
superconductor results in a Kramers pair of Majorana zero-energy bound states
when the superconducting phases in the two arms differ by an odd multiple of
$\pi$ across the Josephson junction. We investigate the detection of these
Majorana pairs with the integrated QPC, and find a robust switching from normal
to Andreev scattering across the edges due to the presence of Majorana Kramers
pairs. This transport signature is expected to be exhibited in measurements of
differential conductance and/or current cross-correlation at low bias.


  Auto-scheduling for tensor programs is a process where a search algorithm
automatically explores candidate schedules (program transformations) for a
given program on a target hardware platform to improve its performance. However
this can be a very time consuming process depending on the complexity of the
tensor program and the capacity of the target device, with often many thousands
of program variants being explored. To address this, in this paper we introduce
the idea of transfer-tuning, a novel approach to identify and reuse
auto-schedules between tensor programs. We demonstrate this concept using Deep
Neural Networks (DNNs), taking sets of auto-schedules from pre-tuned DNNs and
using them to reduce the inference time of a new DNN. We compare
transfer-tuning against the state-of-the-art Ansor auto-scheduler, defining the
maximum possible speedup for a given DNN model as what Ansor achieves using its
recommended full tuning time. On a server-class CPU and across 11 widely used
DNN models, we observe that transfer-tuning achieves up to $88.41\%$ ($49.13\%$
on average) of this maximum speedup, while Ansor requires $6.5\times$ more
search time on average to match it. We also evaluate transfer-tuning on a
constrained edge CPU and observe that the differences in search time are
exacerbated, with Ansor requiring $10.8\times$ more time on average to match
transfer-tuning's speedup, which further demonstrates its value. Our code is
available at https://www.github.com/gicLAB/transfer-tuning


  We present ALMA observations of the [CII] fine structure line and the
underlying far-infrared (FIR) dust continuum emission in J1120+0641, the most
distant quasar currently known (z=7.1). We also present observations targeting
the CO(2-1), CO(7-6) and [CI] 369 micron lines in the same source obtained at
the VLA and PdBI. We find a [CII] line flux of F_[CII]=1.11+/-0.10 Jy km/s and
a continuum flux density of S_227GHz=0.53+/-0.04 mJy/beam, consistent with
previous unresolved measurements. No other source is detected in continuum or
[CII] emission in the field covered by ALMA (~25"). At the resolution of our
ALMA observations (0.23", or 1.2 kpc, a factor ~70 smaller beam area compared
to previous measurements), we find that the majority of the emission is very
compact: a high fraction (~80%) of the total line and continuum flux is
associated with a region 1-1.5 kpc in diameter. The remaining ~20% of the
emission is distributed over a larger area with radius <4 kpc. The [CII]
emission does not exhibit ordered motion on kpc-scales: applying the virial
theorem yields an upper limit on the dynamical mass of the host galaxy of
(4.3+/-0.9)x10^10 M_sun, only ~20x higher than the central black hole. The
other targeted lines (CO(2-1), CO(7-6) and [CI]) are not detected, but the
limits of the line ratios with respect to the [CII] emission imply that the
heating in the quasar host is dominated by star formation, and not by the
accreting black hole. The star-formation rate implied by the FIR continuum is
105-340 M_sun/yr, with a resulting star-formation rate surface density of
~100-350 M_sun/yr/kpc^2, well below the value for Eddington-accretion-limited
star formation.


  Deep neural networks for semantic segmentation always require a large number
of samples with pixel-level labels, which becomes the major difficulty in their
real-world applications. To reduce the labeling cost, unsupervised domain
adaptation (UDA) approaches are proposed to transfer knowledge from labeled
synthesized datasets to unlabeled real-world datasets. Recently, some
semi-supervised learning methods have been applied to UDA and achieved
state-of-the-art performance. One of the most popular approaches in
semi-supervised learning is the entropy minimization method. However, when
applying the entropy minimization to UDA for semantic segmentation, the
gradient of the entropy is biased towards samples that are easy to transfer. To
balance the gradient of well-classified target samples, we propose the maximum
squares loss. Our maximum squares loss prevents the training process being
dominated by easy-to-transfer samples in the target domain. Besides, we
introduce the image-wise weighting ratio to alleviate the class imbalance in
the unlabeled target domain. Both synthetic-to-real and cross-city adaptation
experiments demonstrate the effectiveness of our proposed approach. The code is
released at https://github. com/ZJULearning/MaxSquareLoss.


  Lectures at the 53rd Winter School of Petersburg Nuclear Physics Institute
and European School on High Energy Physics ESHEP2019, Saint-Petersburg, Russia.


  Convolutional neural networks (CNNs) have been widely used over many areas in
compute vision. Especially in classification. Recently, FlowNet and several
works on opti- cal estimation using CNNs shows the potential ability of CNNs in
doing per-pixel regression. We proposed several CNNs network architectures that
can estimate optical flow, and fully unveiled the intrinsic different between
these structures.


  This is a brief note on a numerical computation of the dimension of the
center of the small quantum group u_q(sl_3) at the 5th root of unity. The
obtained dimension is 57. Using the description of the regular block of the
center for u_q(sl_3), given in arXiv:1604.07380, we conclude that the dimension
of the singular parabolic block of the center for u_q(sl_3) is 6, and obtain a
complete block decomposition of the center in this case.


  The CRESST experiment (Cryogenic Rare Event Search with Superconducting
Thermometers) searches for dark matter via the phonon and light signals of
elastic scattering processes in scintillating crystals. The discrimination
between a possible dark matter signal and background is based on the light
yield. We present a new method for evaluating the two characteristics of a
phonon/light detector module that determine how much of the deposited energy is
converted to scintillation light and how efficiently a module detects the
produced light. In contrast to former approaches with dedicated setups, we
developed a method which allows us to use data taken with the cryogenic setup,
during a dark matter search phase. In this way, we accounted for the entire
process that occurs in a detector module, and obtained information on the light
emission of the crystal as well as information on the performance of the module
(light transport and detection). We found that with the detectors operated in
CRESST-II phase 1, about 20% of the produced scintillation light is detected. A
part of the light is likely absorbed by creating meta-stable excitations in the
scintillating crystals. The light not detected is not absorbed entirely, as an
additional light detector can help to increase the fraction of detected light.


  Dynamic topic models (DTMs) model the evolution of prevalent themes in
literature, online media, and other forms of text over time. DTMs assume that
word co-occurrence statistics change continuously and therefore impose
continuous stochastic process priors on their model parameters. These dynamical
priors make inference much harder than in regular topic models, and also limit
scalability. In this paper, we present several new results around DTMs. First,
we extend the class of tractable priors from Wiener processes to the generic
class of Gaussian processes (GPs). This allows us to explore topics that
develop smoothly over time, that have a long-term memory or are temporally
concentrated (for event detection). Second, we show how to perform scalable
approximate inference in these models based on ideas around stochastic
variational inference and sparse Gaussian processes. This way we can train a
rich family of DTMs to massive data. Our experiments on several large-scale
datasets show that our generalized model allows us to find interesting patterns
that were not accessible by previous approaches.


  The compound BaMn2As2 with the tetragonal ThCr2Si2 structure is a
local-moment antiferromagnetic insulator with a Neel temperature TN = 625 K and
a large ordered moment mu = 3.9 mu_B/Mn. We demonstrate that this compound can
be driven metallic by partial substitution of Ba by K, while retaining the same
crystal and antiferromagnetic structures together with nearly the same high TN
and large mu. Ba_{1-x}K_xMn2As2 is thus the first metallic ThCr2Si2-type
MAs-based system containing local 3d transition metal M magnetic moments, with
consequences for the ongoing debate about the local moment versus itinerant
pictures of the FeAs-based superconductors and parent compounds. The
Ba_{1-x}K_xMn2As2 class of compounds also forms a bridge between the layered
iron pnictides and cuprates and may be useful to test theories of high Tc
superconductivity.


  Colossal magnetoresistance (CMR) is highly applicable in spintronic devices
such as magnetic sensors, magnetic memory, and hard drives. Typically, CMR is
found in Weyl semimetals characterized by perfect electron-hole symmetry or
exceptionally high electric conductivity and mobility. Our study explores this
phenomenon in a recently developed graphene moir$\acute{e}$ system, which
demonstrates CMR owing to its topological structure and high-quality crystal
formation. We specifically investigate the electronic properties of
three-dimensional (3D) intertwined twisted graphene spirals (TGS), manipulating
the screw dislocation axis to achieve a rotation angle of 7.3$^{\circ}$.
Notably, at 14 T and 2 K, the magnetoresistance of these structures reached
1.7$\times$10$^7$%, accompanied by an unexpected metal-to-insulator transition
as the temperature increased. This transition becomes noticeable when the
magnetic field exceeds a minimal threshold of approximately 0.1 T. These
observations suggest the existence of complex, correlated states within the
partially filled three-dimensional Landau levels of the 3D TGS system. Our
findings open up new possibilities for achieving CMR by engineering the
topological structure of 2D layered moir$\acute{e}$ systems.


  Suppose a Boolean function $f$ is symmetric under a group action $G$ acting
on the $n$ bits of the input. For which $G$ does this mean $f$ does not have an
exponential quantum speedup? Is there a characterization of how rich $G$ must
be before the function $f$ cannot have enough structure for quantum algorithms
to exploit?
  In this work, we make several steps towards understanding the group actions
$G$ which are "quantum intolerant" in this way. We show that sufficiently
transitive group actions do not allow a quantum speedup, and that a
"well-shuffling" property of group actions -- which happens to be preserved by
several natural transformations -- implies a lack of super-polynomial speedups
for functions symmetric under the group action. Our techniques are motivated by
a recent paper by Chailloux (2018), which deals with the case where $G=S_n$.
  Our main application is for graph symmetries: we show that any Boolean
function $f$ defined on the adjacency matrix of a graph (and symmetric under
relabeling the vertices of the graph) has a power $6$ relationship between its
randomized and quantum query complexities, even if $f$ is a partial function.
In particular, this means no graph property testing problems can have
super-polynomial quantum speedups, settling an open problem of Ambainis,
Childs, and Liu (2011).


  Turbulence and dynamo induced by the magnetorotational instability (MRI) are
analyzed using quasi-linear statistical simulation methods. It is found that
homogenous turbulence is unstable to a large scale dynamo instability, which
saturates to an inhomogenous equilibrium with a strong dependence on the
magnetic Prandtl number (Pm). Despite its enormously reduced nonlinearity, the
dependence of the angular momentum transport on Pm in the quasi-linear model is
qualitatively similar to that of nonlinear MRI turbulence. This indicates that
recent convergence problems may be related to large scale dynamo and suggests
how dramatically simplified models may be used to gain insight into the
astrophysically relevant regimes of very low or high Pm.


  Object detection in streaming images is a major step in different
detection-based applications, such as object tracking, action recognition,
robot navigation, and visual surveillance applications. In mostcases, image
quality is noisy and biased, and as a result, the data distributions are
disturbed and imbalanced. Most object detection approaches, such as the faster
region-based convolutional neural network (Faster RCNN), Single Shot Multibox
Detector with 300x300 inputs (SSD300), and You Only Look Once version 2
(YOLOv2), rely on simple sampling without considering distortions and noise
under real-world changing environments, despite poor object labeling. In this
paper, we propose an Incremental active semi-supervised learning (IASSL)
technology for unseen object detection. It combines batch-based active learning
(AL) and bin-based semi-supervised learning (SSL) to leverage the strong points
of AL's exploration and SSL's exploitation capabilities. A collaborative
sampling method is also adopted to measure the uncertainty and diversity of AL
and the confidence in SSL. Batch-based AL allows us to select more informative,
confident, and representative samples with low cost. Bin-based SSL divides
streaming image samples into several bins, and each bin repeatedly transfers
the discriminative knowledge of convolutional neural network (CNN) deep
learning to the next bin until the performance criterion is reached. IASSL can
overcome noisy and biased labels in unknown, cluttered data distributions. We
obtain superior performance, compared to state-of-the-art technologies such as
Faster RCNN, SSD300, and YOLOv2.


  Conventional Computed Tomography (CT) imaging recognition faces two
significant challenges: (1) There is often considerable variability in the
resolution and size of each CT scan, necessitating strict requirements for the
input size and adaptability of models. (2) CT-scan contains large number of
out-of-distribution (OOD) slices. The crucial features may only be present in
specific spatial regions and slices of the entire CT scan. How can we
effectively figure out where these are located? To deal with this, we introduce
an enhanced Spatial-Slice Feature Learning (SSFL++) framework specifically
designed for CT scan. It aim to filter out a OOD data within whole CT scan,
enabling our to select crucial spatial-slice for analysis by reducing 70%
redundancy totally. Meanwhile, we proposed Kernel-Density-based slice Sampling
(KDS) method to improve the stability when training and inference stage,
therefore speeding up the rate of convergence and boosting performance. As a
result, the experiments demonstrate the promising performance of our model
using a simple EfficientNet-2D (E2D) model, even with only 1% of the training
data. The efficacy of our approach has been validated on the COVID-19-CT-DB
datasets provided by the DEF-AI-MIA workshop, in conjunction with CVPR 2024.
Our source code is available at https://github.com/ming053l/E2D


  In this paper we characterize a mathematical model called Maximum Common
Subelement (MCS) Model and prove the existence of four different metrics on
such model. We generalize metrics on graphs previously proposed in the
literature and identify new ones by showing three different examples of MCS
Models on graphs based on (1) subgraphs, (2) induced subgraphs and (3) an
extended notion of subgraphs. This latter example can be used to model graphs
with complex labels (e.g., graphs whose labels are other graphs), and hence to
derive metrics on them. Furthermore, we also use (3) to show that graph edit
distance, when a metric, is related to a maximum common subelement in a
corresponding MCS Model.


  Structural results impose sufficient conditions on the model parameters of a
Markov decision process (MDP) so that the optimal policy is an increasing
function of the underlying state. The classical assumptions for MDP structural
results require supermodularity of the rewards and transition probabilities.
However, supermodularity does not hold in many applications. This paper uses a
sufficient condition for interval dominance (called I) proposed in the
microeconomics literature, to obtain structural results for MDPs under more
general conditions. We present several MDP examples where supermodularity does
not hold, yet I holds, and so the optimal policy is monotone; these include
sigmoidal rewards (arising in prospect theory for human decision making),
bi-diagonal and perturbed bi-diagonal transition matrices (in optimal
allocation problems). We also consider MDPs with TP3 transition matrices and
concave value functions. Finally, reinforcement learning algorithms that
exploit the differential sparse structure of the optimal monotone policy are
discussed.


  We present a description of the capabilities and performance of the NAval
Ultra-Trace Isotope Laboratory's Universal Spectrometer (NAUTILUS) at the U.S.
Naval Research Laboratory. The NAUTILUS combines secondary ion mass
spectrometry (SIMS) and single-stage accelerator mass spectrometry (SSAMS) into
a single unified instrument for spatially resolved trace element and isotope
analysis. The NAUTILUS instrument is essentially a fully functional SIMS
instrument with an additional molecule-filtering detector, the SSAMS. The
combination of these two techniques mitigates the drawbacks of each and enables
new measurement paradigms for SIMS-like microanalysis. Highlighted capabilities
include molecule-free raster ion imaging for direct spatially resolved analysis
of heterogeneous materials with or without perturbed isotopic compositions. The
NAUTILUS' sensitivity to trace elements is at least 10x better than commercial
SIMS instruments due to near-zero background conditions. We describe the design
and construction of the NAUTILUS, and its performance applied to topics in
nuclear materials analysis, cosmochemistry, and geochemistry.


  In this paper, we introduce a kind of homology which we call Hawaiian
homology to study and classify pointed topological spaces. The Hawaiian
homology group has advantages of Hawaiian groups. Moreover, the first Hawaiian
homology group is isomorphic to the abelianization of the first Hawaiian group
for path-connected and locally path-connected topological spaces. Since
Hawaiian homology has concrete elements and abelian structure, its calculations
are more routine. Thus we use Hawaiian homology groups to compare Hawaiian
groups, and then we obtain some information about Hawaiian groups of some wild
topological spaces.


  We study the strangeness $S=-2$ baryon-baryon interactions in relativistic
chiral effective field theory at leading order. Among the 15 relevant low
energy constants, eight of them are determined by fitting to the state of the
art lattice QCD data of the HAL QCD Collaboration (with $m_\pi=146$ MeV), and
the rest are either taken from the study of the $S=-1$ hyperon-nucleon systems,
assuming strict SU(3) flavor symmetry, or temporarily set equal to zero. Using
the so-obtained low energy constants, we extrapolate the results to the
physical point, and show that they are consistent with the available
experimental scattering data. Furthermore, we demonstrate that the
$\Lambda\Lambda$ and $\Xi N$ phase shifts near the $\Xi N$ threshold are very
sensitive to the lattice QCD data fitted, to the pion mass, and to isospin
symmetry breaking effects. As a result, any conclusion drawn from lattice QCD
data at unphysical pion masses (even close to the physical point) should be
taken with caution. Our results at the physical point, similar to the lattice
QCD data, show that a resonance/quasi-bound state may appear in the $I=0$
$\Lambda\Lambda$/$\Xi N$ channel.


  There have been increasing concerns about Artificial Intelligence (AI) due to
its unfathomable potential power. To make AI address ethical challenges and
shun undesirable outcomes, researchers proposed to develop socially responsible
AI (SRAI). One of these approaches is causal learning (CL). We survey
state-of-the-art methods of CL for SRAI. We begin by examining the seven CL
tools to enhance the social responsibility of AI, then review how existing
works have succeeded using these tools to tackle issues in developing SRAI such
as fairness. The goal of this survey is to bring forefront the potentials and
promises of CL for SRAI.


  We constructed physically stable sp2 negatively curved cubic carbon
structures which reticulate a Schwarz P-like surface. The method for
constructing such crystal structures is based on the notion of the standard
realization of abstract crystal lattices. In this paper, we expound on the
mathematical method to construct such crystal structures.


  We present a versatile framework for the computational co-design of legged
robots and dynamic maneuvers. Current state-of-the-art approaches are typically
based on random sampling or concurrent optimization. We propose a novel bilevel
optimization approach that exploits the derivatives of the motion planning
sub-problem (i.e., the lower level). These motion-planning derivatives allow us
to incorporate arbitrary design constraints and costs in an general-purpose
nonlinear program (i.e., the upper level). Our approach allows for the use of
any differentiable motion planner in the lower level and also allows for an
upper level that captures arbitrary design constraints and costs. It
efficiently optimizes the robot's morphology, payload distribution and actuator
parameters while considering its full dynamics, joint limits and physical
constraints such as friction cones. We demonstrate these capabilities by
designing quadruped robots that jump and trot. We show that our method is able
to design a more energy-efficient Solo robot for these tasks.


  These notes, connected to a "potpourri" topics class currently underway,
discuss some basic topics in analysis and connections with other areas of
mathematics.


  We present results for the matrix elements relevant for proton decay in Grand
Unified Theories (GUTs). The calculation is performed at a fixed lattice
spacing a^{-1}=1.73(3) GeV using 2+1 flavors of domain wall fermions on
lattices of size 16^3\times32 and 24^3\times64 with a fifth dimension of length
16. We use the indirect method which relies on an effective field theory
description of proton decay, where we need to estimate the low energy
constants, \alpha = -0.0112(25) GeV^3 and \beta = 0.0120(26) GeV^3. We relate
these low energy constants to the proton decay matrix elements using leading
order chiral perturbation theory. These can then be combined with experimental
bounds on the proton lifetime to bound parameters of individual GUTs.


  Bilayer graphene was theorized to host a moire miniband with flat dispersion
if the layers are stacked at specific twist angles known as the magic angles.
Recently, such twisted bilayer graphene (tBLG) with the first magic angle twist
was reported to exhibit correlated insulating state and superconductivity,
where the presence of the flat miniband in the system is thought to be
essential for the emergence of these ordered phases in the transport
measurements. Tunneling spectroscopy and electronic compressibility
measurements in tBLG have revealed a van Hove singularity that is consistent
with the presence of the flat miniband. However, a direct observation of the
flat dispersion in the momentum-space of such moire miniband in tBLG is still
elusive. Here, we report the visualization of the flat moire miniband by using
angle-resolved photoemission spectroscopy with nanoscale resolution
(nanoARPES). The high spatial resolution in nanoARPES enabled the measurement
of the local electronic structure of the tBLG. We clearly demonstrate the
existence of the flat moire band near the charge neutrality for tBLG close to
the magic angle at room temperature.


  We consider an infinite planar straight strip perforated by small holes along
a curve. In such domain, we consider a general second order elliptic operator
subject to classical boundary conditions on the holes. Assuming that the
perforation is non-periodic and satisfies rather weak assumptions, we describe
all possible homogenized problems. Our main result is the norm resolvent
convergence of the perturbed operator to a homogenized one in various operator
norms and the estimates for the rate of convergence. On the basis of the norm
resolvent convergence, we prove the convergence of the spectrum.


  Incorporating neural networks for the solution of Ordinary Differential
Equations (ODEs) represents a pivotal research direction within computational
mathematics. Within neural network architectures, the integration of the
intrinsic structure of ODEs offers advantages such as enhanced predictive
capabilities and reduced data utilization. Among these structural ODE forms,
the Lagrangian representation stands out due to its significant physical
underpinnings. Building upon this framework, Bhattoo introduced the concept of
Lagrangian Neural Networks (LNNs). Then in this article, we introduce a
groundbreaking extension (Genralized Lagrangian Neural Networks) to Lagrangian
Neural Networks (LNNs), innovatively tailoring them for non-conservative
systems. By leveraging the foundational importance of the Lagrangian within
Lagrange's equations, we formulate the model based on the generalized
Lagrange's equation. This modification not only enhances prediction accuracy
but also guarantees Lagrangian representation in non-conservative systems.
Furthermore, we perform various experiments, encompassing 1-dimensional and
2-dimensional examples, along with an examination of the impact of network
parameters, which proved the superiority of Generalized Lagrangian Neural
Networks(GLNNs).


  The predictive brain hypothesis suggests that perception can be interpreted
as the process of minimizing the error between predicted perception tokens
generated by an internal world model and actual sensory input tokens. When
implementing working examples of this hypothesis in the context of in-air
sonar, significant difficulties arise due to the sparse nature of the
reflection model that governs ultrasonic sensing. Despite these challenges,
creating consistent world models using sonar data is crucial for implementing
predictive processing of ultrasound data in robotics. In an effort to enable
robust robot behavior using ultrasound as the sole exteroceptive sensor
modality, this paper introduces EchoPT, a pretrained transformer architecture
designed to predict 2D sonar images from previous sensory data and robot
ego-motion information. We detail the transformer architecture that drives
EchoPT and compare the performance of our model to several state-of-the-art
techniques. In addition to presenting and evaluating our EchoPT model, we
demonstrate the effectiveness of this predictive perception approach in two
robotic tasks.


  Motivated by recent chemical attempts at assembling halogen-bridged
transition-metal complexes within a nanotube, we model and characterize a
platinum-halide triangular prism in terms of a Peierls-Hubbard Hamiltonian.
Based on a group-theoretical argument, we reveal a variety of valence
arrangements, including heterogeneous or partially metallic charge-density-wave
states. Quantum and thermal phase competitions are numerically demonstrated
with particular emphasis on novel insulator-to-metal and insulator-to-insulator
transitions under doping, the former of which is of the first order, while the
latter of which is of the second order.


  We present results on the low-frequency dynamical and transport properties of
random quantum systems whose low temperature ($T$), low-energy behavior is
controlled by strong disorder fixed points. We obtain the momentum and
frequency dependent dynamic structure factor in the Random Singlet (RS) phases
of both spin-1/2 and spin-1 random antiferromagnetic chains, as well as in the
Random Dimer (RD) and Ising Antiferromagnetic (IAF) phases of spin-1/2 random
antiferromagnetic chains. We show that the RS phases are unusual `spin metals'
with divergent low-frequency spin conductivity at T=0, and we also follow the
conductivity through novel `metal-insulator' transitions tuned by the strength
of dimerization or Ising anisotropy in the spin-1/2 case, and by the strength
of disorder in the spin-1 case. We work out the average spin and energy
autocorrelations in the one-dimensional random transverse field Ising model in
the vicinity of its quantum critical point. All of the above calculations are
valid in the frequency dominated regime $\omega \agt T$, and rely on previously
available renormalization group schemes that describe these systems in terms of
the properties of certain strong-disorder fixed point theories. In addition, we
obtain some information about the behavior of the dynamic structure factor and
dynamical conductivity in the opposite `hydrodynamic' regime $\omega < T$ for
the special case of spin-1/2 chains close to the planar limit (the quantum x-y
model) by analyzing the corresponding quantities in an equivalent model of
spinless fermions with weak repulsive interactions and particle-hole symmetric
disorder.


  In two-dimensional spectrographs, the optical distortions in the spatial and
dispersion directions produce variations in the sub-pixel sampling of the
background spectrum. Using knowledge of the camera distortions and the
curvature of the spectral features, one can recover information regarding the
background spectrum on wavelength scales much smaller than a pixel. As a
result, one can propagate this better-sampled background spectrum through
inverses of the distortion and rectification transformations, and accurately
model the background spectrum in two-dimensional spectra for which the
distortions have not been removed (i.e. the data have not been
rebinned/rectified). The procedure, as outlined in this paper, is extremely
insensitive to cosmic rays, hot pixels, etc. Because of this insensitivity to
discrepant pixels, sky modeling and subtraction need not be performed as one of
the later steps in a reduction pipeline. Sky-subtraction can now be performed
as one of the earliest tasks, perhaps just after dividing by a flat-field.
Because subtraction of the background can be performed without having to
``clean'' cosmic rays, such bad pixel values can be trivially identified after
removal of the two-dimensional sky background.


  The light absorption of a monolayer graphene-molybdenum disulfide
photovoltaic (GM-PV) cell in a wedge-shaped microcavity with a
spectrum-splitting structure is investigated theoretically. The GM-PV cell,
which is three times thinner than the traditional photovoltaic cell, exhibits
up to 98\% light absorptivity in a wide wavelength range. This rate exceeds the
fundamental limit of nanophotonic light trapping in solar cells. The effects of
defect layer thickness, GM-PV cell position in the microcavity, incident angle,
and lens aberration on the light absorption rate of the GM-PV cell is explored.
Regardless of errors, the GM-PV cell can still achieve at least 90\% light
absorptivity with the current technology. Our proposal provides different
methods to design light-trapping structures and apply spectrum-splitting
systems.


  Dynamics is central to living systems. In the last two decades, experiments
have revealed that the dynamics in diverse biological systems - from
intracellular cytoplasm to cellular and organismal aggregates - are remarkably
similar to that in dense systems of inanimate particles in equilibrium. They
show a glass transition from a solid-like jammed state to a fluid-like flowing
state, where a moderate change in control parameter leads to an enormous
variation in relaxation time. However, biological systems have crucial
differences from the equilibrium systems: the former have activity that drives
them out of equilibrium, novel control parameters, and enormous levels of
complexity. These active systems showing glassy dynamics are known as active
glasses. The field is at the interface of physics and biology, freely borrowing
tools from both disciplines and promising novel, fascinating discoveries. We
review the experiments that started this field, simulations that have been
instrumental for insights, and theories that have helped unify diverse
phenomena, reveal correlations, and make novel quantitative predictions. We
discuss the primary characteristics that define a glassy system. For most
concepts, we first discuss the known equilibrium scenario and then present the
key aspects when activity is introduced. We end the article with a discussion
of the challenges in the field and possible future directions.


  A $D_{\infty}$-topological Markov chain can be represented by a pair of
zero-one square matrices, which is called a flip pair. We introduce the
concepts of $D_{\infty}$-strong shift equivalence and $D_{\infty}$-shift
equivalence, which are equivalence relations between flip pairs. We investigate
the relationships between the existence of a $D_{\infty}$-conjugacy, the
existence of a $D_{\infty}$-strong shift equivalence, the existence of a
$D_{\infty}$-shift equivalence and the coincidence of the Lind zeta functions.


  Bit error rate (BER) minimization and SNR-gap maximization, two robustness
optimization problems, are solved, under average power and bit-rate
constraints, according to the waterfilling policy. Under peak-power constraint
the solutions differ and this paper gives bit-loading solutions of both
robustness optimization problems over independent parallel channels. The study
is based on analytical approach with generalized Lagrangian relaxation tool and
on greedy-type algorithm approach. Tight BER expressions are used for square
and rectangular quadrature amplitude modulations. Integer bit solution of
analytical continuous bit-rates is performed with a new generalized secant
method. The asymptotic convergence of both robustness optimizations is proved
for both analytical and algorithmic approaches. We also prove that, in
conventional margin maximization problem, the equivalence between SNR-gap
maximization and power minimization does not hold with peak-power limitation.
Based on a defined dissimilarity measure, bit-loading solutions are compared
over power line communication channel for multicarrier systems. Simulation
results confirm the asymptotic convergence of both allocation policies. In non
asymptotic regime the allocation policies can be interchanged depending on the
robustness measure and the operating point of the communication system. The low
computational effort of the suboptimal solution based on analytical approach
leads to a good trade-off between performance and complexity.


  We present X-ray spectral analyses of low mass X-ray binary Cir X-1 during
X-ray dips, using the Rossi X-ray Timing Explorer (RXTE) data. Each dip was
divided into several segments, and the spectrum of each segment was fitted with
a three-component blackbody model, in which two components are affected by
partial covering and the third one is unaffected. A Gaussian emission line is
also included in the spectral model to represent the Fe Ka line at ~ 6.4 keV.
The fitted temperatures of the two partially covered components are about 2 keV
and 1 keV, while the uncovered component has a temperature of ~ 0.5-0.6 keV.
The equivalent blackbody emission radius of the hottest component is the
smallest and that of the coolest component is the biggest. During dips, the
fluxes of the two hot components are linearly correlated, while that of the
third component doesn't show any significant variation. The Fe line flux
remains constant within errors during the short dips. However, during the long
dips the line flux changes significantly and is positively correlated with the
fluxes of the two hot components. These results suggest: (1) the temperature of
the X-ray emitting region decreases with radius, (2) the Fe Ka line emitting
region is close to the hot continuum emitting region, and (3) the size of the
Fe line emitting region is bigger than the size of the obscuring matters
causing short dips but smaller than the sizes of those causing long dips.


  Artificial neural networks can learn complex, salient data features to
achieve a given task. On the opposite end of the spectrum, mathematically
grounded methods such as topological data analysis allow users to design
analysis pipelines fully aware of data constraints and symmetries. We introduce
a class of persistence-based neural network layers. Persistence-based layers
allow the users to easily inject knowledge about symmetries (equivariance)
respected by the data, are equipped with learnable weights, and can be composed
with state-of-the-art neural architectures.


  The search for extremely massive high redshift blazars is essential to put
strong constraints on the supermassive black holes formation theories. Up to
now, the few blazars known to have a redshift larger than 4 have been
discovered serendipitously. We try a more systematic approach. Assuming
radio-loudness as a proxy for the jet orientation, we select a sample of
extremely radio-loud quasars. We measure their black hole masses with a method
based on fitting the thermal emission from the accretion disc. We achieve a
precision of a factor of two for our measures, thanks to the observations
performed with the Gamma-Ray Burst Optical Near-Infrared Detector (GROND). The
infrared to optical GROND data allow us to observe directly the peak of the
disc emission, thus constraining the overall disc luminosity. We obtain a small
range of masses, that peaks at 10^{9.3}Msun. If some of our candidates will be
confirmed as blazars, these results would introduce interesting constraints on
the mass function of extremely massive black holes at very high redshift.
Moreover, all our blazar candidates have high accretion rates. This result,
along with the high masses, opens an interesting view on the need of a fast
growth of the heaviest black holes at very high redshift.


  Most of the visible matter in the Universe is ionized, so that cosmic
magnetic fields are quite easy to generate and due to the lack of magnetic
monopoles hard to destroy. Magnetic fields have been measured in or around
practically all celestial objects, either by in-situ measurements of
spacecrafts or by the electromagnetic radiation of embedded cosmic rays, gas,
or dust. The Earth, the Sun, solar planets, stars, pulsars, the Milky Way,
nearby galaxies, distant radio galaxies, quasars, and even intergalactic space
in clusters of galaxies have significant magnetic fields. Even larger volumes
of the Universe may be permeated by so-far invisble magnetic fields.
Information on cosmic magnetic fields has increased enormously as the result of
the rapid development of observational methods, especially in radio astronomy.
In the Milky Way, a wealth of magnetic phenomena was discovered that are only
partly related to objects visible in other spectral ranges. The large-scale
structure of the Milky Way's magnetic field is still under debate. The
available data for external spiral galaxies can well be explained by field
amplification and ordering via the dynamo mechanism. The measured field
strengths and the similarity of field patterns and flow patterns of the diffuse
ionized gas give strong indication that galactic magnetic fields are
dynamically important. They may affect the formation of spiral arms, outflows,
and the general evolution of galaxies. In spite of our increasing knowledge on
magnetic fields, many important questions on the origin and evolution of
magnetic fields, like their first occurrence in young galaxies or the existence
of large-scale intergalactic fields, remain unanswered. Progress can be
expected from new-generation radio telescopes like LOFAR and the SKA, for which
"cosmic magnetism" is a key science project.


  We report a numerical analysis of Anderson localization in a model of a doped
semiconductor. The model incorporates the disorder arising from the random
spatial distribution of the donor impurities and takes account of the
electron-electron interactions between the carriers using density functional
theory in the local density approximation. Preliminary results suggest that the
model exhibits a metal-insulator transition.


  We have used the NIHAO simulations to explore how supernovae (SNe) affect
star formation in galaxies. We find that SN feedback operates on all scales
from the interstellar medium (ISM) to several virial radii. SNe regulate star
formation by preventing condensation of HI into H$_2$ and by moving cold
neutral gas to the hot HII phase. The first effect explains why the cold
neutral gas in dwarf galaxies forms stars inefficiently. The second maintains
the hot ISM of massive galaxies (HII vents out at lower masses). At $v_{\rm
vir}\simeq 67{\rm\,km\,s}^{-1}$, the outflow rate follows the relation:
$\dot{M}_{\rm out}=23\,(v_{\rm vir}/67{\rm\,km\,s}^{-1})^{-4.6}\,{\rm SFR}$.
$20\%$ to $70\%$ of the gas expelled from galaxies escapes from the halo
(ejective feedback) but outflows are dominated by cold swept-up gas, most of
which falls back onto the galaxy on a $\sim 1\,$Gyr timescale. This `fountain
feedback' reduces the masses of galaxies by a factor of two to four, since gas
spends half to three quarter of its time in the fountain. Less than $10\%$ of
the ejected gas mixes with the hot circumgalactic medium and this gas is
usually not reaccreted. On scales as large as $6r_{\rm vir}$, galactic winds
divert the incoming gas from cosmic filaments and prevent if from accreting
onto galaxies (pre-emptive feedback). This process is the main reason for the
low baryon content of ultradwarves.


  We construct an example of proof within the main formal system from
arXiv:1010.4760v3, which is intended to capture the bisimulation equivalence
for non-deterministic first-order grammars, and show that its conclusion is
semantically false. We then locate and analyze the flawed argument in the
soundness (meta)-proof of the above reference.


  We construct an $(\infty,2)$-version of the (lax) Gray tensor product. On the
1-categorical level, this is a binary (or more generally an $n$-ary) functor on
the category of $\Theta_2$-sets, and it is shown to be left Quillen with
respect to Ara's model structure. Moreover we prove that this tensor product
forms part of a "homotopical" (biclosed) monoidal structure, or more precisely
a normal lax monoidal structure that is associative up to homotopy.


  This study toward quantum gravity (QG) introduces an SU(N) gauge theory with
the \Theta vacuum term as a trial theory. Newton gravitation constant G_N is
realized as the effective coupling constant for a massive graviton, G_N
/\sqrt{2} = g_f g_g^2/8 M_G^2 \simeq 10^{-38} GeV^{-2} with the gauge boson
mass M_G = M_{Pl} \simeq 10^{19} GeV, the gravitational coupling constant g_g,
and the gravitational factor g_f. This scheme postulates the effective
cosmological constant as the effective vacuum energy represented by massive
gauge bosons, \Lambda_e = 8 \pi G_N M_G^4, and provides a plausible explanation
for the small cosmological constant at the present epoch \Lambda_0 \simeq
10^{-84} GeV^2 and the large value at the Planck epoch \Lambda_{Pl} \simeq
10^{38} GeV^2; the condensation of the singlet gauge field <\phi> triggers the
current anomaly and subtracts the gauge boson mass, M_G^2 = M_{Pl}^2 - g_f
g_g^2 <\phi>^2 = g_f g_g^2 (A_{0}^2 - <\phi>^2), as the vacuum energy.
Relations among QG, general relativity, and Newtonian mechanics are discussed.


  Microinstabilities play important roles in both entropy generation and
particle acceleration in collisionless shocks. Recent studies have suggested
that in the transition zone of quasi-perpendicular ($Q_{\perp}$) shocks in the
high-beta ($\beta=P_{\rm gas}/P_{\rm B}$) intracluster medium (ICM), the ion
temperature anisotropy due to the reflected-gyrating ions could trigger the
Alfv\'en ion cyclotron (AIC) instability and the ion-mirror instability, while
the electron temperature anisotropy induced by magnetic field compression could
excite the whistler instability and the electron-mirror instability. Adopting
the numerical estimates for ion and electron temperature anisotropies found in
particle-in-cell (PIC) simulations of $Q_{\perp}$-shocks with sonic Mach
numbers, $M_{\rm s}=2-3$, we carry out a linear stability analysis for these
microinstabilities. The kinetic properties of the microinstabilities and the
ensuing plasma waves on both ion and electron scales are described for wide
ranges of parameters, including the dependence on $\beta$ and the
ion-to-electron mass ratio. In addition, the nonlinear evolution of induced
plasma waves are examined by performing 2D PIC simulations with periodic
boundary conditions. We find that for $\beta\approx 20-100$, the AIC
instability could induce ion-scale waves and generate shock surface ripples in
supercritical shocks above the AIC critical Mach number, $M_{\rm AIC}^{*}
\approx 2.3$. Also electron-scale waves are generated primarily by the whistler
instability in these high-$\beta$ shocks. The resulting multi-scale waves from
electron to ion scales are thought to be essential in electron injection to the
diffusive shock acceleration mechanism in $Q_{\perp}$-shocks in the ICM.


  The thermodynamics of quantum systems coupled to periodically modulated heat
baths and work reservoirs is developed. By identifying affinities and fluxes,
the first and second law are formulated consistently. In the linear response
regime, entropy production becomes a quadratic form in the affinities.
Specializing to Lindblad-dynamics, we identify the corresponding kinetic
coefficients in terms of correlation functions of the unperturbed dynamics.
Reciprocity relations follow from symmetries with respect to time reversal. The
kinetic coefficients can be split into a classical and a quantum contribution
subject to a new constraint, which follows from a natural detailed balance
condition. This constraint implies universal bounds on efficiency and power of
quantum heat engines. In particular, we show that Carnot efficiency can not be
reached whenever quantum coherence effects are present, i.e., when the
Hamiltonian used for work extraction does not commute with the bare system
Hamiltonian. For illustration, we specialize our universal results to a driven
two-level system in contact with a heat bath of sinusoidally modulated
temperature.


  Recently, Smart Video Surveillance (SVS) systems have been receiving more
attention among scholars and developers as a substitute for the current passive
surveillance systems. These systems are used to make the policing and
monitoring systems more efficient and improve public safety. However, the
nature of these systems in monitoring the public's daily activities brings
different ethical challenges. There are different approaches for addressing
privacy issues in implementing the SVS. In this paper, we are focusing on the
role of design considering ethical and privacy challenges in SVS. Reviewing
four policy protection regulations that generate an overview of best practices
for privacy protection, we argue that ethical and privacy concerns could be
addressed through four lenses: algorithm, system, model, and data. As an case
study, we describe our proposed system and illustrate how our system can create
a baseline for designing a privacy perseverance system to deliver safety to
society. We used several Artificial Intelligence algorithms, such as object
detection, single and multi camera re-identification, action recognition, and
anomaly detection, to provide a basic functional system. We also use
cloud-native services to implement a smartphone application in order to deliver
the outputs to the end users.


  Nucleus segmentation and classification are the prerequisites in the workflow
of digital pathology processing. However, it is very challenging due to its
high-level heterogeneity and wide variations. This work proposes a deep neural
network to simultaneously achieve nuclear classification and segmentation,
which is designed using a unified framework with three different branches,
including segmentation, HoVer mapping, and classification. The segmentation
branch aims to generate the boundaries of each nucleus. The HoVer branch
calculates the horizontal and vertical distances of nuclear pixels to their
centres of mass. The nuclear classification branch is used to distinguish the
class of pixels inside the nucleus obtained from segmentation.


  Emoji have become ubiquitous in written communication, on the Web and beyond.
They can emphasize or clarify emotions, add details to conversations, or simply
serve decorative purposes. This casual use, however, barely scratches the
surface of the expressive power of emoji. To further unleash this power, we
present Emojinize, a method for translating arbitrary text phrases into
sequences of one or more emoji without requiring human input. By leveraging the
power of large language models, Emojinize can choose appropriate emoji by
disambiguating based on context (eg, cricket-bat vs bat) and can express
complex concepts compositionally by combining multiple emoji (eq, "Emojinize"
is translated to input-latin-letters right-arrow grinning-face). In a cloze
test--based user study, we show that Emojinize's emoji translations increase
the human guessability of masked words by 55%, whereas human-picked emoji
translations do so by only 29%. These results suggest that emoji provide a
sufficiently rich vocabulary to accurately translate a wide variety of words.
Moreover, annotating words and phrases with Emojinize's emoji translations
opens the door to numerous downstream applications, including children learning
how to read, adults learning foreign languages, and text understanding for
people with learning disabilities.


  Modern time series gene expression and other omics data sets have enabled
unprecedented resolution of the dynamics of cellular processes such as cell
cycle and response to pharmaceutical compounds. In anticipation of the
proliferation of time series data sets in the near future, we use the Hopfield
model, a recurrent neural network based on spin glasses, to model the dynamics
of cell cycle in HeLa (human cervical cancer) and S. cerevisiae cells. We study
some of the rich dynamical properties of these cyclic Hopfield systems,
including the ability of populations of simulated cells to recreate
experimental expression data and the effects of noise on the dynamics. Next, we
use a genetic algorithm to identify sets of genes which, when selectively
inhibited by local external fields representing gene silencing compounds such
as kinase inhibitors, disrupt the encoded cell cycle. We find, for example,
that inhibiting the set of four kinases BRD4, MAPK1, NEK7, and YES1 in HeLa
cells causes simulated cells to accumulate in the M phase. Finally, we suggest
possible improvements and extensions to our model.


  The complete quantitative description of the structure of dense and
supercooled liquids remains a notoriously difficult problem in statistical
physics. Most studies to date focus solely on two-body structural correlations,
and only a handful of papers have sought to consider additional three-body
correlations. Here, we go beyond the state of the art by extracting many-body
static structure factors from molecular dynamics simulations and by deriving
accurate approximations up to the six-body structure factor via density
functional theory. We find that supercooling manifestly increases four-body
correlations, akin to the two- and three-body case. However, at small wave
numbers, we observe that the four-point structure of a liquid drastically
changes upon supercooling, both qualitatively and quantitatively, which is not
the case in two-point structural correlations. This indicates that theories of
the structure or dynamics of dense liquids should incorporate many-body
correlations beyond the two-particle level to fully capture their intricate
behaviour.


  Several recent papers have shown a close relationship between entanglement
wedge reconstruction and the unitarity of black hole evaporation in AdS/CFT.
The analysis of these papers however has a rather puzzling feature: all
calculations are done using bulk dynamics which are essentially those Hawking
used to predict information loss, but applying ideas from entanglement wedge
reconstruction seems to suggest a Page curve which is consistent with
information conservation. Why should two different calculations in the same
model give different answers for the Page curve?
  In this note we present a new pair of models which clarify this situation.
Our first model gives a holographic illustration of unitary black hole
evaporation, in which the analogue of the Hawking radiation purifies itself as
expected, and this purification is reproduced by the entanglement wedge
analysis. Moreover a smooth black hole interior persists until the last stages
the evaporation process. Our second model gives an alternative holographic
interpretation of the situation where the bulk evolution leads to information
loss: unlike in the models proposed so far, this bulk information loss is
correctly reproduced by the entanglement wedge analysis. This serves as an
illustration that quantum extremal surfaces are in some sense kinematic: the
time-dependence of the entropy they compute depends on the choice of bulk
dynamics. In both models no bulk quantum corrections need to be considered:
classical extremal surfaces are enough to do the job. We argue that our first
model is the one which gives the right analogy for what actually happens to
evaporating black holes, but we also emphasize that any complete resolution of
the information problem will require an understanding of non-perturbative bulk
dynamics.


  Non-orthogonal multiple access (NOMA) is a promising multiple access
technique for beyond fifth generation (B5G) cellular wireless networks, where
several users can be served on a single time-frequency resource block, using
the concepts of superposition coding at the transmitter and selfinterference
cancellation (SIC) at the receiver. For terrestrial networks, the achievable
performance gains of NOMA over traditional orthogonal multiple access (OMA) are
well-known. However, the achievable performance of NOMA in aerial networks,
compared to terrestrial networks, is not well-understood. In this paper, we
provide a unified analytic framework to characterize the outage probabilities
of users considering various network settings, such as i) uplink and downlink
NOMA and OMA in aerial networks, and ii) uplink and downlink NOMA and OMA in
terrestrial networks. In particular, we derive closed-form rate outage
probability expressions for two users, considering line-of-sight (LOS) Rician
fading channels. Numerical results validate the derived analytical expressions
and demonstrate the difference of outage probabilities of users with OMA and
NOMA transmissions. Numerical results unveil that the optimal UAV height
increases with the increase in Rice-K factor, which implies strong
line-of-sight (LOS) conditions.


  The H.E.S.S. Imaging Atmospheric Cherenkov Telescope Array is currently the
most sensitive instrument for Very High Energy (VHE) gamma-ray observations in
the energy range of about 0.1-10 TeV. During more than two years of operation
with the complete 4-telescope array, many galactic and extragalactic VHE
gamma-ray sources have been discovered. With its superior sensitivity and its
large field-of-view camera, H.E.S.S. is particularly suited for surveys and
detailed studies of extended sources. A selection of recent H.E.S.S. results is
presented in this proceeding.


  I study an aspect of chiral perturbation theory (\chi PT) which enables one
to ``bridge'' different reactions. That is, an operator fixed in one of the
reactions can then be used to predict the other. For this purpose, I calculate
the partial wave amplitude for the p-wave pion production (pp\to pn\pi^+) using
the pion production operator from the lowest and the next nonvanishing orders.
The operator includes a contact operator whose coupling has been fixed using a
matrix element of a low-energy weak process (pp\to de^+\nu_e). I find that this
operator does not reproduce the partial wave amplitude extracted from
experimental data, showing that the bridging over the reactions with
significantly different kinematics is not necessarily successful. I study the
dependence of the amplitude on the various inputs such as the NN potential, the
\pi N\Delta coupling, and the cutoff. I argue the importance of a higher order
calculation. In order to gain an insight into a higher order calculation, I add
a higher order counter term to the operator used above, and fit the couplings
to both the low-energy weak process and the pion production. The energy
dependence of the partial wave amplitude for the pion production is described
by the operator consistently with the data. However, I find a result which
tells us to be careful about the convergence of the chiral expansion for the
pp\to pn\pi^+ reaction.


  We propose a flexible stochastic block model for multi-layer networks, where
layer-specific hidden Markov-chain processes drive the changes in the formation
of communities. The changes in block membership of a node in a given layer may
be influenced by its own past membership in other layers. This allows for
clustering overlap, clustering decoupling, or more complex relationships
between layers including settings of unidirectional, or bidirectional, block
causality. We cope with the overparameterization issue of a saturated
specification by assuming a Multi-Laplacian prior distribution within a
Bayesian framework. Data augmentation and Gibbs sampling are used to make the
inference problem more tractable. Through simulations, we show that the
standard linear models are not able to detect the block causality under the
great majority of scenarios. As an application to trade networks, we show that
our model provides a unified framework including community detection and
Gravity equation. The model is used to study the causality between trade
agreements and trade looking at the global topological properties of the
networks as opposed to the main existent approaches which focus on local
bilateral relationships. We are able to provide new evidence of unidirectional
causality from the free trade agreements network to the non-observable trade
barriers network structure for 159 countries in the period 1995-2017.


  Ground based extensive air showers arrays can observe GRBs in the 1-1000 GeV
energy range using the "single particle" techique. The sensitivity to detect a
GRB as a function of the burst parameters and the detector characteristics are
discussed. The rate of possible observations is evaluated, making reasonable
assumptions on the high energy emission, the absorbtion of gamma-rays in the
intergalactic space, the distribution of the sources in the universe and the
bursts luminosity function. We show that a large area detector located at high
mountain altitude has good prospects for positive detections, providing useful
informations on the high energy components of GRBs.


  In this paper we prove large deviations results for partial sums constructed
from the solution to a stochastic recurrence equation. We assume Kesten's
condition [Acta Math. 131 (1973) 207-248] under which the solution of the
stochastic recurrence equation has a marginal distribution with power law
tails, while the noise sequence of the equations can have light tails. The
results of the paper are analogs to those obtained by A. V. Nagaev [Theory
Probab. Appl. 14 (1969) 51-64; 193-208] and S. V. Nagaev [Ann. Probab. 7 (1979)
745-789] in the case of partial sums of i.i.d. random variables. In the latter
case, the large deviation probabilities of the partial sums are essentially
determined by the largest step size of the partial sum. For the solution to a
stochastic recurrence equation, the magnitude of the large deviation
probabilities is again given by the tail of the maximum summand, but the exact
asymptotic tail behavior is also influenced by clusters of extreme values, due
to dependencies in the sequence. We apply the large deviation results to study
the asymptotic behavior of the ruin probabilities in the model.


  Two types of semiclassical calculations have been used to study quantum
effects in black hole backgrounds, the WKB and the mean field approaches. In
this work we systematically reconstruct the logical implications of both
methods on quantum black hole physics and provide the link between these two
approaches. Our conclusions completely support our previous findings based
solely on the WKB method: quantum black holes are effectively p-brane
excitations and, consequently, no information loss paradox exists in this
problem.


  Training reinforcement learning agents that continually learn across multiple
environments is a challenging problem. This is made more difficult by a lack of
reproducible experiments and standard metrics for comparing different continual
learning approaches. To address this, we present TELLA, a tool for the Test and
Evaluation of Lifelong Learning Agents. TELLA provides specified, reproducible
curricula to lifelong learning agents while logging detailed data for
evaluation and standardized analysis. Researchers can define and share their
own curricula over various learning environments or run against a curriculum
created under the DARPA Lifelong Learning Machines (L2M) Program.


  The feedback of the geometrical Berry phase, accumulated in an electron
system, on the slow dynamics of classical degrees of freedom is governed by the
Berry curvature. Here, we study local magnetic moments, modelled as classical
spins, which are locally exchange coupled to the (spinful) Haldane model for a
Chern insulator. In the emergent equations of motion for the slow
classical-spin dynamics there is a an additional anomalous geometrical spin
torque, which originates from the corresponding spin-Berry curvature. Due to
the explicitly broken time-reversal symmetry, this is nonzero but usually small
in a condensed-matter system. We develop the general theory and compute the
spin-Berry curvature, mainly in the limit of weak exchange coupling, in various
parameter regimes of the Haldane model, particularly close to a topological
phase transition and for spins coupled to sites at the zigzag edge of the model
in a ribbon geometry. The spatial structure of the spin-Berry curvature tensor,
its symmetry properties, the distance dependence of its nonlocal elements and
further properties are discussed in detail. For the case of two classical
spins, the effect of the geometrical spin torque leads to an anomalous
non-Hamiltonian spin dynamics. It is demonstrated that the magnitude of the
spin-Berry curvature is decisively controlled by the size of the insulating
gap, the system size and the strength of local exchange coupling.


  In this paper, we present DuTongChuan, a novel context-aware translation
model for simultaneous interpreting. This model allows to constantly read
streaming text from the Automatic Speech Recognition (ASR) model and
simultaneously determine the boundaries of Information Units (IUs) one after
another. The detected IU is then translated into a fluent translation with two
simple yet effective decoding strategies: partial decoding and context-aware
decoding. In practice, by controlling the granularity of IUs and the size of
the context, we can get a good trade-off between latency and translation
quality easily. Elaborate evaluation from human translators reveals that our
system achieves promising translation quality (85.71% for Chinese-English, and
86.36% for English-Chinese), specially in the sense of surprisingly good
discourse coherence. According to an End-to-End (speech-to-speech simultaneous
interpreting) evaluation, this model presents impressive performance in
reducing latency (to less than 3 seconds at most times). Furthermore, we
successfully deploy this model in a variety of Baidu's products which have
hundreds of millions of users, and we release it as a service in our AI
platform.


  A weakly nonlinear model for two-dimensional Faraday waves over infinite
depth is derived and studied. Sideband instability of monochromatic standing
waves as well as non-monochromatic solutions are studied analytically.
Persistent irregular regimes are found numerically.


  The structure and routing architecture design is critical for achieving low
latency and high capacity in future LEO space networks (SNs). Existing studies
mainly focus on topologies of space networks, but there is a lack of analysis
on constellation structures, which can greatly affect network performance. In
addition, some routing architectures are designed for networks with a small
number of network nodes such as Iridium while they introduce significant
network overhead for high-density networks (i.e., mega-constellation networks
containing thousands of satellites). In this paper, we conduct the
quantitatively study on the design of network structure and routing
architecture in space. The high density, high dynamics, and large scale nature
of emerging Space Networks (SNs) pose significant challenges, such as unstable
routing paths, low network reachability, high latency, and large jitter. To
alleviate the above challenges, we design the structure of space network to
maximum the connectivity through wisely adjusting the inter-plane inter
satellite link. We further propose Multi-Protocol Location Forwarding (MPLF), a
distributed routing architecture, targeting at minimizing the propagation
latency with a distributed, convergence-free routing paradigm, while keeping
routing stable and maximum the path diversity. Comprehensive experiments are
conducted on a customized platform \textit{Space Networking Kits} (SNK) which
demonstrate that our solution can outperform existing related schemes by about
14\% reduction of propagation latency and 66\% reduction of hops-count on
average, while sustaining a high path diversity with only $O(1)$ time
complexity.


  Querying the knowledge base (KB) has long been a challenge in the end-to-end
task-oriented dialogue system. Previous sequence-to-sequence (Seq2Seq) dialogue
generation work treats the KB query as an attention over the entire KB, without
the guarantee that the generated entities are consistent with each other. In
this paper, we propose a novel framework which queries the KB in two steps to
improve the consistency of generated entities. In the first step, inspired by
the observation that a response can usually be supported by a single KB row, we
introduce a KB retrieval component which explicitly returns the most relevant
KB row given a dialogue history. The retrieval result is further used to filter
the irrelevant entities in a Seq2Seq response generation model to improve the
consistency among the output entities. In the second step, we further perform
the attention mechanism to address the most correlated KB column. Two methods
are proposed to make the training feasible without labeled retrieval data,
which include distant supervision and Gumbel-Softmax technique. Experiments on
two publicly available task oriented dialog datasets show the effectiveness of
our model by outperforming the baseline systems and producing entity-consistent
responses.


  We propose a new method to probe for variations in the fine structure
constant alpha using clusters of galaxies, opening up a window on a new
redshift range for such constraints. Hot clusters shine in the X-ray mainly due
to bremsstrahlung, while they leave an imprint on the CMB frequency spectrum
through the Sunyaev-Zel'dovich effect. These two physical processes can be
characterized by the integrated Comptonization parameter Y_SZ DA^2 and its
X-ray counterpart, the Y_X parameter. The ratio of these two quantities is
expected to be constant from numerical simulations and current observations. We
show that this fact can be exploited to constrain alpha, as the ratio of the
two parameters depends on the fine structure constant as alpha^{3.5}. We
determine current constraints from a combination of Planck SZ and XMM-Newton
data, testing different models of variation of alpha. When fitting for a
constant value of alpha, we find that current constraints are at the 1% level,
comparable with current CMB constraints. We discuss strategies for further
improving these constraints by almost an order of magnitude.


  We propose a new approach to video face recognition. Our component-wise
feature aggregation network (C-FAN) accepts a set of face images of a subject
as an input, and outputs a single feature vector as the face representation of
the set for the recognition task. The whole network is trained in two steps:
(i) train a base CNN for still image face recognition; (ii) add an aggregation
module to the base network to learn the quality value for each feature
component, which adaptively aggregates deep feature vectors into a single
vector to represent the face in a video. C-FAN automatically learns to retain
salient face features with high quality scores while suppressing features with
low quality scores. The experimental results on three benchmark datasets,
YouTube Faces, IJB-A, and IJB-S show that the proposed C-FAN network is capable
of generating a compact feature vector with 512 dimensions for a video sequence
by efficiently aggregating feature vectors of all the video frames to achieve
state of the art performance.


  In this work we investigate approaches to reconstruct generator models from
measurements available at the generator terminal bus using machine learning
(ML) techniques. The goal is to develop an emulator which is trained online and
is capable of fast predictive computations. The training is illustrated on
synthetic data generated based on available open-source dynamical generator
model. Two ML techniques were developed and tested: (a) standard vector
auto-regressive (VAR) model; and (b) novel customized long short-term memory
(LSTM) deep learning model. Trade-offs in reconstruction ability between
computationally light but linear AR model and powerful but computationally
demanding LSTM model are established and analyzed.


  The concepts of topology and geometry are of critical importance in exploring
exotic phases of quantum matter. Though they have been investigated on various
experimental platforms, to date a direct probe of topological and geometric
properties on a universal quantum computer even for a minimum model is still in
vain. In this work, we first show that a density matrix form of the quantum
geometric tensor (QGT) can be explicitly re-constructed from Pauli operator
measurements on a quantum circuit. We then propose two algorithms, suitable for
IBM quantum computers, to directly probe QGT. The first algorithm is a
variational quantum algorithm particularly suitable for Noisy
Intermediate-Scale Quantum (NISQ)-era devices, whereas the second one is a pure
quantum algorithm based on quantum imaginary time evolution. Explicit results
obtained from IBM Q simulating a Chern insulator model are presented and
analysed. Our results indicate that transmon qubit-based universal quantum
computers have the potential to directly simulate and investigate topological
and geometric properties of a quantum system.


  Many real-world optimisation problems can be stated in terms of submodular
functions. A lot of evolutionary multi-objective algorithms have recently been
analyzed and applied to submodular problems with different types of
constraints. We present a first runtime analysis of evolutionary
multi-objective algorithms for chance-constrained submodular functions. Here,
the constraint involves stochastic components and the constraint can only be
violated with a small probability of alpha. We show that the GSEMO algorithm
obtains the same worst case performance guarantees as recently analyzed greedy
algorithms. Furthermore, we investigate the behavior of evolutionary
multi-objective algorithms such as GSEMO and NSGA-II on different submodular
chance constrained network problems. Our experimental results show that this
leads to significant performance improvements compared to the greedy algorithm.


  We investigate the quantum dynamics of particles on graphs ("quantum random
walks"), with the aim of developing quantum algorithms for determining if two
graphs are isomorphic (related to each other by a relabeling of vertices). We
focus on quantum random walks of multiple non-interacting particles on strongly
regular graphs (SRGs), a class of graphs with high symmetry that is known to
have pairs of graphs that are hard to distinguish. Previous work has already
demonstrated analytically that two-particle non-interacting quantum walks
cannot distinguish non-isomorphic SRGs of the same family. Here, we demonstrate
numerically that three-particle non-interacting quantum walks have significant,
but not universal, distinguishing power for pairs of SRGs, proving a
fundamental difference between the distinguishing power of two-particle and
three-particle non-interacting walks. We analytically show why this
distinguishing power is possible, whereas it is forbidden for two-particle
non-interacting walks. Based on sampling of SRGs with up to 64 vertices, we
find no difference in the distinguishing power of bosonic and fermionic walks.
In addition, we find that the four-fermion non-interacting walk has greater
distinguishing power than the three-particle walks on SRGs, showing that
increasing particle number increases distinguishing power. However, we also
analytically show that no non-interacting walk with a fixed number of particles
can distinguish all SRGs, thus demonstrating a potential fundamental difference
between the distinguishing power of interacting and noninteracting walks.


  We introduce the concept of metasurface spatial processor, whose transmission
is remotely and coherently controlled by the superposition of an incident wave
and a control wave through the metasurface. The conceptual operation of this
device is analogous to both that of a transistor and a Mach-Zehnder
interferometer, while offering much more diversity in terms of electromagnetic
transformations. We demonstrate two metasurfaces, that perform the operation of
electromagnetic switching and amplification.


  We study the generalization of the Willmore functional for surfaces in the
three-Heisenberg group. Its construction is based on the spectral theory of the
Dirac operator coming to the Weierstrass representation of surfaces (see
math.DG/0503707). By using surfaces of revolution we demonstrate that it
resembles the Willmore functional for surfaces in the Euclidean space in many
geometrical respects. We also observe the relation of these functionals to the
isoperimetric problem.


  Automated surface inspection is an important task in many manufacturing
industries and often requires machine learning driven solutions. Supervised
approaches, however, can be challenging, since it is often difficult to obtain
large amounts of labeled training data. In this work, we instead perform
one-class unsupervised learning on fault-free samples by training a deep
convolutional neural network to complete images whose center regions are cut
out. Since the network is trained exclusively on fault-free data, it completes
the image patches with a fault-free version of the missing image region. The
pixel-wise reconstruction error within the cut out region is an anomaly image
which can be used for anomaly detection. Results on surface images of decorated
plastic parts demonstrate that this approach is suitable for detection of
visible anomalies and moreover surpasses all other tested methods.


This paper has been withdrawn due to a mistake in the previous version.


  Cloud computing (CC) is a centralized computing paradigm that accumulates
resources centrally and provides these resources to users through Internet.
Although CC holds a large number of resources, it may not be acceptable by
real-time mobile applications, as it is usually far away from users
geographically. On the other hand, edge computing (EC), which distributes
resources to the network edge, enjoys increasing popularity in the applications
with low-latency and high-reliability requirements. EC provides resources in a
decentralized manner, which can respond to users' requirements faster than the
normal CC, but with limited computing capacities. As both CC and EC are
resource-sensitive, several big issues arise, such as how to conduct job
scheduling, resource allocation, and task offloading, which significantly
influence the performance of the whole system. To tackle these issues, many
optimization problems have been formulated. These optimization problems usually
have complex properties, such as non-convexity and NP-hardness, which may not
be addressed by the traditional convex optimization-based solutions.
Computational intelligence (CI), consisting of a set of nature-inspired
computational approaches, recently exhibits great potential in addressing these
optimization problems in CC and EC. This paper provides an overview of research
problems in CC and EC and recent progresses in addressing them with the help of
CI techniques. Informative discussions and future research trends are also
presented, with the aim of offering insights to the readers and motivating new
research directions.


  While using a speaker verification (SV) based system in a commercial
application, it is important that customers have an inclusive experience
irrespective of their gender, age, or ethnicity. In this paper, we analyze the
impact of gender and age on SV and find that for a desired common False
Acceptance Rate (FAR) across different gender and age groups, the False
Rejection Rate (FRR) is different for different gender and age groups. To
optimize FRR for all users for a desired FAR, we propose a context (e.g.
gender, age) adaptive thresholding framework for SV. The context can be
available as prior information for many practical applications. We also propose
a concatenated gender/age detection model to algorithmically derive the context
in absence of such prior information. We experimentally show that our
context-adaptive thresholding method is effective in building a more efficient
inclusive SV system. Specifically, we show that we can reduce FRR for specific
gender for a desired FAR on the voxceleb1 test set by using gender-specific
thresholds. Similar analysis on OGI kids' speech corpus shows that by using an
age-specific threshold, we can significantly reduce FRR for certain age groups
for desired FAR.


  Acoustic howling suppression (AHS) is a critical challenge in audio
communication systems. In this paper, we propose a novel approach that
leverages the power of neural networks (NN) to enhance the performance of
traditional Kalman filter algorithms for AHS. Specifically, our method involves
the integration of NN modules into the Kalman filter, enabling refining
reference signal, a key factor in effective adaptive filtering, and estimating
covariance metrics for the filter which are crucial for adaptability in dynamic
conditions, thereby obtaining improved AHS performance. As a result, the
proposed method achieves improved AHS performance compared to both standalone
NN and Kalman filter methods. Experimental evaluations validate the
effectiveness of our approach.


  We present an astrometric analysis of the binary systems ABDorA /ABDorC and
ABDorBa / ABDorBb. These two systems of well-known late-type stars are
gravitationally associated and they constitute the quadruple ABDoradus system.
  From the astrometric data available at different wavelengths, we report: (i)
a determination of the orbit of ABDorC, the very low mass companion to ABDorA,
which confirms the mass estimate of 0.090Msun reported in previous works; (ii)
a measurement of the parallax of ABDorBa, which unambiguously confirms the
long-suspected physical association between this star and ABDorA; and (iii)
evidence of orbital motion of ABDorBa around ABDorA, which places an upper
bound of 0.4Msun on the mass of the pair ABDorBa / ABDorBb (50% probability).
Further astrometric monitoring of the system at all possible wavelengths would
determine with extraordinary precision the dynamical mass of its four
components.


  We give a detailed description of a polynomial optimization method allowing
to solve a problem in continuum mechanics: the determination of the elasticity
or the piezoelectricity tensor of a specific isotropy stratum the closest to a
given experimental tensor, and the calculation of the distance to the given
tensor from the considered isotropy stratum. We take advantage of the fact that
the isotropy strata are semialgebraic sets to show that the method, developed
by Lasserre and coworkers which consists in solving polynomial optimization
problems with semialgebraic constraints, successfully applies.


  This paper presents an agent-oriented approach to build a decision support
system aimed at helping emergency managers to detect and to manage risks. We
stress the flexibility and the adaptivity characteristics that are crucial to
build a robust and efficient system, able to resolve complex problems. The
system should be independent as much as possible from the subject of study.
Thereby, an original approach based on a mechanism of perception,
representation, characterisation and assessment is proposed. The work described
here is applied on the RoboCupRescue application. Experimentations and results
are provided.


  Several recent works have focused on the search for bright, high-z quasars
(QSOs) in the South. Among them, the QUasars as BRIght beacons for Cosmology in
the Southern hemisphere (QUBRICS) survey has now delivered hundreds of new
spectroscopically confirmed QSOs selected by means of machine learning
algorithms. Building upon the results obtained by introducing the probabilistic
random forest (PRF) for the QUBRICS selection, we explore in this work the
feasibility of training the algorithm on synthetic data to improve the
completeness in the higher redshift bins. We also compare the performances of
the algorithm if colours are used as primary features instead of magnitudes. We
generate synthetic data based on a composite QSO spectral energy distribution.
We first train the PRF to identify QSOs among stars and galaxies, then separate
high-z quasar from low-z contaminants. We apply the algorithm on an updated
dataset, based on SkyMapper DR3, combined with Gaia eDR3, 2MASS and WISE
magnitudes. We find that employing colours as features slightly improves the
results with respect to the algorithm trained on magnitude data. Adding
synthetic data to the training set provides significantly better results with
respect to the PRF trained only on spectroscopically confirmed QSOs. We
estimate, on a testing dataset, a completeness of ~86% and a contamination of
~36%. Finally, 207 PRF-selected candidates were observed: 149/207 turned out to
be genuine QSOs with z > 2.5, 41 with z < 2.5, 3 galaxies and 14 stars. The
result confirms the ability of the PRF to select high-z quasars in large
datasets.


  Let $R$ be a commutative noetherian local ring, and let $M$ be a finitely
generated $R$-module. Inspired by works of Vasconcelos and Briggs on
characterization of complete intersection local rings through the homological
properties of the conormal module, in this paper, we define the characteristic
module $\mathrm{T}_M$ and the cocharacteristic module $\mathrm{E}_M$ of $M$,
and investigate their properties. Our main results include characterizations of
Cohen--Macaulay and Gorenstein local rings.


  We calculate exactly matrix elements between states that are not eigenstates
of the quantum XY model for general anisotropy. Such quantities therefore
describe non equilibrium properties of the system; the Hamiltonian does not
contain any time dependence. These matrix elements are expressed as a sum of
Pfaffians. For single particle excitations on the ground state the Pfaffians in
the sum simplify to determinants.


  Machine learning research depends on objectively interpretable, comparable,
and reproducible algorithm benchmarks. We advocate the use of curated,
comprehensive suites of machine learning tasks to standardize the setup,
execution, and reporting of benchmarks. We enable this through software tools
that help to create and leverage these benchmarking suites. These are
seamlessly integrated into the OpenML platform, and accessible through
interfaces in Python, Java, and R. OpenML benchmarking suites (a) are easy to
use through standardized data formats, APIs, and client libraries; (b) come
with extensive meta-information on the included datasets; and (c) allow
benchmarks to be shared and reused in future studies. We then present a first,
carefully curated and practical benchmarking suite for classification: the
OpenML Curated Classification benchmarking suite 2018 (OpenML-CC18). Finally,
we discuss use cases and applications which demonstrate the usefulness of
OpenML benchmarking suites and the OpenML-CC18 in particular.


  In this paper we address two questions about the synchronization of coupled
oscillators in the Kuramoto model with all-to-all coupling. In the first part
we use some classical results in convex geometry to prove bounds on the size of
the frequency set supporting the existence of stable, phase locked solutions
and show that the set of such frequencies can be expressed by a seminorm which
we call the Kuramoto norm. In the second part we use some ideas from extreme
order statistics to compute upper and lower bounds on the probability of
synchronization for very general frequency distributions. We do so by computing
exactly the limiting extreme value distribution of a quantity that is
equivalent to the Kuramoto norm.


  BBR is a new congestion-based congestion control algorithm proposed by
Google. A BBR flow sequentially measures the bottleneck bandwidth and
round-trip delay of the network pipe, and uses the measured results to govern
its sending behavior, maximizing the delivery bandwidth while minimizing the
delay. However, our deployment in geo-distributed cloud servers reveals a
severe RTT fairness problem: a BBR flow with longer RTT dominates a competing
flow with shorter RTT.
  Somewhat surprisingly, our deployment of BBR on the Internet and an in-house
cluster unearthed a consistent bandwidth disparity among competing flows. Long
BBR flows are bound to seize bandwidth from short ones. Intrigued by this
unexpected behavior, we ask, is the phenomenon intrinsic to BBR? how's the
severity? and what's the root cause? To this end, we conduct thorough
measurements and develop a theoretical model on bandwidth dynamics. We find, as
long as the competing flows are of different RTTs, bandwidth disparities will
arise. With an RTT ratio of 10, even flow starvation can happen. We blame it on
BBR's connivance at sending an excessive amount of data when probing bandwidth.
Specifically, the amount of data is in proportion to RTT, making long RTT flows
overwhelming short ones. Based on this observation, we design a derivative of
BBR that achieves guaranteed flow fairness, at the meantime without losing any
merits. We have implemented our proposed solution in Linux kernel and evaluated
it through extensive experiments.


  Let $\hat{\boldsymbol x}$ be a normalised standard complex Gaussian vector,
and project an Hermitian matrix $A$ onto the hyperplane orthogonal to
$\hat{\boldsymbol x}$. In a recent paper Faraut [Tunisian J. Math. \textbf{1}
(2019), 585--606] has observed that the corresponding eigenvalue PDF has an
almost identical structure to the eigenvalue PDF for the rank 1 perturbation $A
+ b \hat{\boldsymbol x} \hat{\boldsymbol x}^\dagger$, and asks for an
explanation. We provide this by way of a common derivation involving the
secular equations and associated Jacobians. This applies too in related
setting, for example when $\hat{\boldsymbol x}$ is a real Gaussian and $A$
Hermitian, and also in a multiplicative setting $A U B U^\dagger$ where $A, B$
are fixed unitary matrices with $B$ a multiplicative rank 1 deviation from
unity, and $U$ is a Haar distributed unitary matrix. Specifically, in each case
there is a dual eigenvalue problem giving rise to a PDF of almost identical
structure.


  Let $M$ be an ordered matching of size $n$, that is, a partition of the set
$[2n]$ into 2-element subsets. The sock number of $M$ is the maximum size of a
sub-matching of $M$ in which all left-ends of the edges precede all the
right-ends (such matchings are also called bipartite). The name of this
parameter comes from an amusing "real-life" problem posed by Bosek, concerning
an on-line pairing of randomly picked socks from a drying machine. Answering
one of Bosek's questions we prove that the sock number of a random matching of
size $n$ is asymptotically equal to $n/2$. Moreover, we prove that the expected
average number of socks waiting for their match during the whole process is
equal to $\frac{2n+1}{6}$. Analogous results are obtained if socks come not in
pairs, but in sets of size $r\geq 2$, which corresponds to a similar problem
for random ordered $r$-matchings. We also attempt to enumerate matchings with a
given sock number.


  The state of protonation/deprotonation of surfaces has far-ranging
implications in all areas of chemistry: from acid-base catalysis$^1$ and the
electro- and photocatalytic splitting of water$^2$, to the behavior of
minerals$^3$ and biochemistry$^4$. The acidity of a molecule or a surface site
is described by its proton affinity (PA) and pK$_\mathrm{a}$ value (the
negative logarithm of the equilibrium constant of the proton transfer reaction
in solution). For solids, in contrast to molecules, the acidity of individual
sites is difficult to assess. For mineral surfaces such as oxides they are
estimated by semi-empirical concepts such as bond-order valence sums$^5$, and
also increasingly modeled with first-principles molecular dynamics
simulations$^{6,7}$. Currently such predictions cannot be tested - the
experimental measures used for comparison are typically average quantities
integrated over the whole surface or, in some cases, individual crystal
facets$^8$, such as the point of zero charge (pzc)$^9$. Here we assess
individual hydroxyls on In$_2$O$_3$(111), a model oxide with four different
types of surface oxygen atoms, and probe the strength of their hydrogen bond
with the tip of a non-contact atomic force microscope (AFM). The force curves
are in quantitative agreement with density-functional theory (DFT)
calculations. By relating the results to known proton affinities and
pK$_\mathrm{a}$ values of gas-phase molecules, we provide a direct measure of
proton affinity distributions at the atomic scale.


  Clusters of galaxies are large gravitationally bound systems which consist of
several observable components: hundreds of galaxies, hot gas between the
galaxies and sometimes relativistic particles. These components are emitting in
different wavelengths from radio to X-rays. We show that the combination of
observations at different frequencies and also theoretical models is giving now
a comprehensive picture of these massive objects. Topics presented here include
cluster masses, baryon fractions, the dynamical state of clusters, the physical
processes in clusters and cosmological parameters derived from cluster
observations.


  We study the partitioning of cosolute particles in a thin film of a
semi-flexible polymer network by a combination of coarse-grained
(implicit-solvent) stochastic dynamics simulations and mean-field theory. We
focus on a wide range of solvent qualities and cosolute-network interactions
for selected polymer flexibilities. Our investigated ensemble
(isothermal-isobaric) allows the network to undergo a volume transition from
extended to collapsed state while the cosolutes can distribute in bulk and
network, correspondingly. We find a rich topology of equilibrium states of the
network and transitions between them, qualitatively depending on solvent
quality, polymer flexibility, and cosolute-network interactions. In particular,
we find a novel `cosolute-induced' collapsed state, where strongly attractive
cosolutes bridge network monomers albeit the latter interact mutually
repulsive. Finally, the cosolutes' global partitioning `landscape', computed as
a function of solvent quality and cosolute-network interactions, exhibits very
different topologies depending on polymer flexibility. The simulation results
are supported by theoretical predictions obtained with a two-component
mean-field approximation for the Helmholtz free energy that considers the chain
elasticity and the particle interactions in terms of a virial expansion. Our
findings have implications on the interpretation of transport processes and
permeability in hydrogel films, as realized in filtration or macromolecular
carrier systems.