Abstract:
  This paper studies the traffic monitoring problem in a road network using a
team of aerial robots. The problem is challenging due to two main reasons.
First, the traffic events are stochastic, both temporally and spatially.
Second, the problem has a non-homogeneous structure as the traffic events
arrive at different locations of the road network at different rates.
Accordingly, some locations require more visits by the robots compared to other
locations. To address these issues, we define an uncertainty metric for each
location of the road network and formulate a path planning problem for the
aerial robots to minimize the network's average uncertainty. We express this
problem as a partially observable Markov decision process (POMDP) and propose a
distributed and scalable algorithm based on deep reinforcement learning to
solve it. We consider two different scenarios depending on the communication
mode between the agents (aerial robots) and the traffic management center
(TMC). The first scenario assumes that the agents continuously communicate with
the TMC to send/receive real-time information about the traffic events. Hence,
the agents have global and real-time knowledge of the environment. However, in
the second scenario, we consider a challenging setting where the observation of
the aerial robots is partial and limited to their sensing ranges. Moreover, in
contrast to the first scenario, the information exchange between the aerial
robots and the TMC is restricted to specific time instances. We evaluate the
performance of our proposed algorithm in both scenarios for a real road network
topology and demonstrate its functionality in a traffic monitoring system.


Abstract:
  The joint use of multiple imaging modalities for medical image segmentation
has been widely studied in recent years. The fusion of information from
different modalities has demonstrated to improve the segmentation accuracy,
with respect to mono-modal segmentations, in several applications. However,
acquiring multiple modalities is usually not possible in a clinical setting due
to a limited number of physicians and scanners, and to limit costs and scan
time. Most of the time, only one modality is acquired. In this paper, we
propose KD-Net, a framework to transfer knowledge from a trained multi-modal
network (teacher) to a mono-modal one (student). The proposed method is an
adaptation of the generalized distillation framework where the student network
is trained on a subset (1 modality) of the teacher's inputs (n modalities). We
illustrate the effectiveness of the proposed framework in brain tumor
segmentation with the BraTS 2018 dataset. Using different architectures, we
show that the student network effectively learns from the teacher and always
outperforms the baseline mono-modal network in terms of segmentation accuracy.


Abstract:
  Teleoperation provides a way for human operators to guide robots in
situations where full autonomy is challenging or where direct human
intervention is required. It can also be an important tool to teach robots in
order to achieve autonomous behaviour later on. The increased availability of
collaborative robot arms and Virtual Reality (VR) devices provides ample
opportunity for development of novel teleoperation methods. Since robot arms
are often kinematically different from human arms, mapping human motions to a
robot in real-time is not trivial. Additionally, a human operator might steer
the robot arm toward singularities or its workspace limits, which can lead to
undesirable behaviour. This is further accentuated for the orchestration of
multiple robots. In this paper, we present a VR interface targeted to multi-arm
payload manipulation, which can closely match real-time input motion. Allowing
the user to manipulate the payload rather than mapping their motions to
individual arms we are able to simultaneously guide multiple collaborative
arms. By releasing a single rotational degree of freedom, and by using a local
optimization method, we can improve each arm's manipulability index, which in
turn lets us avoid kinematic singularities and workspace limitations. We apply
our approach to predefined trajectories as well as real-time teleoperation on
different robot arms and compare performance in terms of end effector position
error and relevant joint motion metrics.


Abstract:
  This Full Paper in the Research-To-Practice Category presents a long-term
study about the effects of a student-centered course on communication and
professional skills on students' thoughts, attitudes, and behavior. The course
is offered at a European university as part of a computer science master's
program. This paper shares the design and challenges of a longitudinal study
that reaches ten years behind and employs a mixed-methods approach. Besides
presenting and interpreting the findings, we shed light on which features tend
to stay on students' minds and impact their way of being and acting in society.
Moreover, we suggest implications for the design and practice in comparable
courses to maximize constructive, sustainable effects, such as improved active
listening, presentation skills, and openness to other perspectives. These are
essential (not only) for computer science professionals. Our findings suggest
that the course provided significant learning for the vast majority of
respondents, including aspects such as presenting while keeping the other side
in mind, managing one's stress, and becoming less shy to speak up. All in all,
we aim to contribute an evidence-based source of motivation for instructors in
technically focused curricula who hold a student-centered stance.


Abstract:
  Training convolutional neural networks with a Lipschitz constraint under the
$l_{2}$ norm is useful for provable adversarial robustness, interpretable
gradients, stable training, etc. While 1-Lipschitz networks can be designed by
imposing a 1-Lipschitz constraint on each layer, training such networks
requires each layer to be gradient norm preserving (GNP) to prevent gradients
from vanishing. However, existing GNP convolutions suffer from slow training,
lead to significant reduction in accuracy and provide no guarantees on their
approximations. In this work, we propose a GNP convolution layer called Skew
Orthogonal Convolution (SOC) that uses the following mathematical property:
when a matrix is {\it Skew-Symmetric}, its exponential function is an {\it
orthogonal} matrix. To use this property, we first construct a convolution
filter whose Jacobian is Skew-Symmetric. Then, we use the Taylor series
expansion of the Jacobian exponential to construct the SOC layer that is
orthogonal. To efficiently implement SOC, we keep a finite number of terms from
the Taylor series and provide a provable guarantee on the approximation error.
Our experiments on CIFAR-10 and CIFAR-100 show that SOC allows us to train
provably Lipschitz, large convolutional neural networks significantly faster
than prior works while achieving significant improvements for both standard and
certified robust accuracies.


Abstract:
  The plants, Althaea officinalis, Tilia cordata and Psidium guaja have been
used traditionally to treat respiratory infection symptoms. Flowers of A.
officinalis and leaves of T. cordata and P. guaja have been used to treat
cough, sore throat, catarrh, oral and pharyngeal mucosa irritation. Therefore,
this study was designed to examine the antibacterial and antibiofilm effects of
these plants individually as well as in combination, as a formula against
respiratory infections causing pathogens. The tested pathogens were Extended
Spectrum Beta-Lactamase producing Escherichia coli (ESBL), Beta-Lactamase
producing Escherichia coli (BL), Beta-Lactamase producing Klebsiella pneumoniae
(BL), Beta-Lactamase producing Pseudomonas aeruginosa (BL), Enterobacter
cloacae, and Beta-Lactamase producing Staphylococcus aureus (BL). The tested
plants were extracted using ethanol and then fractionated using different
polarity solvents (hexane, ethyl acetate and water). Disc diffusion and
microdilution (Minimum Inhibitory Concentration) methods were used to evaluate
the antibacterial activity while the antibiofilm activity was tested using
crystal violet assay. The results showed that A. officinalis and T. cordata
extracts and fractions exhibited weak antibacterial activity having MIC values
ranged from 6.25 to 12.5 mg/mL. P. guaja exhibited moderate antibacterial
activity with MIC values ranged from 6.25 to 1.56 mg/mL. Combination between
these plants extracts and fractions in equal proportion provides stronger
antibacterial (with MIC values ranged from 6.25 to 0.8 mg/mL) and antibiofilm
activities (MBIC50 was 0.2 mg/mL). Therefore, this study provides a valuable
scientific knowledge to support the use of plants in combination rather than
individually.


Abstract:
  The coronavirus disease (COVID-19) outbreak was declared a pandemic in March
2020 and since then it has had a significant effect on all aspects of life.
Although we live in an information era, we do not have accurate information
about this disease. Online social networks (OSNs) play a vital role in society,
especially people who do not have trust in the government would tend to have
more confidence in the evidence that is formed by social networks. The
advantages of OSNs in the COVID-19 era are clear. For instance, social media
enables people to connect with each other without the need for real-world
face-to-face social interaction. Social media networks also act as a collective
intelligence in the absence of world leadership. Therefore, in this study,
considering the phenomenon of information diffusion in OSNs, we focus on the
effects of COVID-19 on user sentiment and show the user behavior trend during
the early months of the pandemic through mining and analyzing OSN data.
Moreover, we propose a data-driven model to demonstrate how user sentiment
changes over a period of time and how OSNs help us to obtain information on
user behavior that is very important for the accurate prediction of future
behavior. For this purpose, this study uses tweet texts about COVID-19 and the
related network structure to extract significant features, and then presents a
model attempting to provide a more comprehensive real picture of current and
future conditions.


Abstract:
  In this paper, we propose a unified SWIPT signal and its architecture design
in order to take advantage of both single tone and multi-tone signaling by
adjusting only the power allocation ratio of a unified signal. For this, we
design a novel unified and integrated receiver architecture for the proposed
unified SWIPT signaling, which consumes low power with an envelope detection.
To relieve the computational complexity of the receiver, we propose an adaptive
control algorithm by which the transmitter adjusts the communication mode
through temporal convolutional network (TCN) based asymmetric processing. To
this end, the transmitter optimizes the modulation index and power allocation
ratio in short-term scale while updating the mode switching threshold in
long-term scale. We demonstrate that the proposed unified SWIPT system improves
the achievable rate under the self-powering condition of low-power IoT devices.
Consequently it is foreseen to effectively deploy low-power IoT networks that
concurrently supply both information and energy wirelessly to the devices by
using the proposed unified SWIPT and adaptive control algorithm in place at the
transmitter side.


Abstract:
  Observation of high energy cosmic neutrinos by ICECUBE has ushered in a new
era in exploring both cosmos and new physics beyond the Standard Model (SM). In
the standard picture, although mostly $\nu_\mu$ and $\nu_e$ are produced in the
source, oscillation will produce $\nu_\tau$ {\it en route}. Certain beyond SM
scenarios, like interaction with ultralight DM can alter this picture. Thus,
the flavor composition of the cosmic neutrino flux can open up the possibility
of exploring certain beyond the SM scenarios that are inaccessible otherwise.
We show that the $\tau$ flavor holds a special place among the neutrino flavors
in elucidating new physics. Interpreting the two anomalous events observed by
ANITA as $\nu_\tau$ events makes the tau flavor even more intriguing. We study
how the detection of the two tau events by ICECUBE constrains the interaction
of the neutrinos with ultralight dark matter and discuss the implications of
this interaction for even higher energy cosmic neutrinos detectable by future
radio telescopes such as ARA, ARIANNA and GRAND. We also revisit the $3+1$
neutrino scheme as a solution to the two anomalous ANITA events and clarify a
misconception that exists in the literature about the evolution of high energy
neutrinos in matter within the $3+1$ scheme with a possibility of scattering
off nuclei. We show that the existing bounds on the flux of $\nu_\tau$ with
energy of EeV rules out this solution for the ANITA events. We show that the
$3+1$ solution can be saved from both this bound and from the bound on the
extra relativistic degrees of freedom in the early universe by turning on the
interaction of neutrinos with ultralight dark matter.


Abstract:
  The spectatorship experience for virtual reality (VR) games differs strongly
from its non-VR precursor. When watching non-VR games on platforms such as
Twitch, spectators just see what the player sees, as the physical interaction
is mostly unimportant for the overall impression. In VR, the immersive
full-body interaction is a crucial part of the player experience. Hence,
content creators, such as streamers, often rely on green screens or similar
solutions to offer a mixed-reality third-person view to disclose their
full-body actions. Our work compares the most popular realizations of the
first-person and the third-person perspective in an online survey (N=217) with
three different VR games. Contrary to the current trend to stream in
third-person, our key result is that most viewers prefer the first-person
version, which they attribute mostly to the better focus on in-game actions and
higher involvement. Based on the study insights, we provide design
recommendations for both perspectives.


Abstract:
  Various planning-based know-how logics have been studied in the recent
literature. In this paper, we use such a logic to do know-how-based planning
via model checking. In particular, we can handle the higher-order epistemic
planning involving know-how formulas as the goal, e.g., find a plan to make
sure p such that the adversary does not know how to make p false in the future.
We give a PTIME algorithm for the model checking problem over finite epistemic
transition systems and axiomatize the logic under the assumption of perfect
recall.


Abstract:
  The speculation that gravity is the key to solving the quantum measurement
problem has been alive for decades, without any convincing demonstration of a
solution. One necessary factor in the relevant proposals is that the
gravitational energy of mutual interaction, which scales quadratically with the
mass, facilitates the spontaneous collapse of the wavefunctions in spatially
separated superpositions. Relying on a simple physical input from
electrodynamics, supported by robust first principle calculations, we show that
the speculations connecting gravity and the hypothetical spontaneous collapse
of the wavefunction are inconsistent and not tenable. The result suggests that
the gravitational solution to the problem of the collapse of the wavefunction
be put to rest.


Abstract:
  We performed a systematic search for X-ray bursts of the SGR J1935+2154 using
the Fermi Gamma-ray Burst Monitor continuous data dated from Jan 2013 to
October 2021. Eight bursting phases, which consist of a total of 353 individual
bursts, are identified. We further analyze the periodic properties of our
sample using the Lomb-Scargle periodogram. The result suggests that those
bursts exhibit a period of ~ 238 days with a ~ 63.2% duty cycle. Based on our
analysis, we further predict two upcoming active windows of the X-ray bursts.
Since July 2021, the beginning date of our first prediction has been confirmed
by the ongoing X-ray activities of the SGR J1935+2154.


Abstract:
  The Norton product is defined on each eigenspace of a distance regular graph
by the orthogonal projection of the entry-wise product. The resulting algebra,
known as the Norton algebra, is a commutative nonassociative algebra that is
useful in group theory due to its interesting automorphism group. We provide a
formula for the Norton product on each eigenspace of a Hamming graph using
linear characters. We construct a large subgroup of automorphisms of the Norton
algebra of a Hamming graph and completely describe the automorphism group in
some cases. We also show that the Norton product on each eigenspace of a
Hamming graph is as nonassociative as possible, except for some special cases
in which it is either associative or equally as nonassociative as the so-called
double minus operation previously studied by the author, Mickey, and Xu. Our
results restrict to the hypercubes and extend to the halved and/or folded
cubes, the bilinear forms graphs, and more generally, all Cayley graphs of
finite abelian groups.


Abstract:
  Current approaches to design flood-sensitive infrastructure typically assume
a stationary rainfall distribution and neglect many uncertainties. These
assumptions are inconsistent with observations that suggest intensifying
extreme precipitation events and the uncertainties surrounding projections of
the coupled natural-human systems. Here we demonstrate a safety factor approach
to designing urban infrastructure in a changing climate. Our results show that
assuming climate stationarity and neglecting deep uncertainties can drastically
underestimate flood risks and lead to poor infrastructure design choices. We
find that climate uncertainty dominates the socioeconomic and engineering
uncertainties that impact the hydraulic reliability in stormwater drainage
systems. We quantify the upfront costs needed to achieve higher hydraulic
reliability and robustness against the deep uncertainties surrounding
projections of rainfall, surface runoff characteristics, and infrastructure
lifetime. Depending on the location, we find that adding safety factors of 1.4
to 1.7 to the standard stormwater pipe design guidance produces robust
performance to the considered deep uncertainties. The insights gained from this
study highlight the need for updating traditional engineering design strategies
to improve infrastructure reliability under socioeconomic and environmental
changes.


Abstract:
  The partially overlapped ferroelastic/martensitic and para-ferromagnetic
phase transitions of a Ni$_{50.53}$Mn${33.65}$In$_{15.82}$ metamagnetic shape
memory alloy have been studied from calorimetric, magnetic and acoustic
emission measurement. We have taken advantage of the existence of thermal
hysteresis of the first order ferroelastic/martensitic phase transition
($\sim2.5$K) to discriminate the latent heat contribution $\Delta$Ht = 7.21(15)
kJ/kg and the specific heat contribution $\Delta$Hc = 216(1) J/kg to the total
excess enthalpy of the phase transition. The specific heat was found to follow
a step-like behavior at this phase transition. The intermittent dynamics of the
ferroelastic/martensitic transition has been characterized as a series of
avalanches detected both from acoustic emission and calorimetric measurements.
The energy distribution of these avalanche events was found to follow a power
law with a characteristic energy exponent $\epsilon\sim2$ which is in agreement
with the expected value for a system undergoing a symmetry change from cubic to
monoclinic. Finally, the critical behavior of the para-ferromagnetic austenite
phase transition that takes place at $\sim 311$K has been studied from the
behavior of the specific heat. A critical exponent $\alpha\sim0.09$ has been
obtained, which has been shown to be in agreement with previous values reported
for Ni-Mn-Ga alloys but different from the critical divergence reported for
pure Ni.


Abstract:
  We study the causal interpretation of regressions on multiple dependent
treatments and flexible controls. Such regressions are often used to analyze
randomized control trials with multiple intervention arms, and to estimate
institutional quality (e.g. teacher value-added) with observational data. We
show that, unlike with a single binary treatment, these regressions do not
generally estimate convex averages of causal effects-even when the treatments
are conditionally randomly assigned and the controls fully address omitted
variables bias. We discuss different solutions to this issue, and propose as a
solution anew class of efficient estimators of weighted average treatment
effects.


Abstract:
  In partially observable reinforcement learning, offline training gives access
to latent information which is not available during online training and/or
execution, such as the system state. Asymmetric actor-critic methods exploit
such information by training a history-based policy via a state-based critic.
However, many asymmetric methods lack theoretical foundation, and are only
evaluated on limited domains. We examine the theory of asymmetric actor-critic
methods which use state-based critics, and expose fundamental issues which
undermine the validity of a common variant, and its ability to address high
partial observability. We propose an unbiased asymmetric actor-critic variant
which is able to exploit state information while remaining theoretically sound,
maintaining the validity of the policy gradient theorem, and introducing no
bias and relatively low variance into the training process. An empirical
evaluation performed on domains which exhibit significant partial observability
confirms our analysis, and shows the unbiased asymmetric actor-critic converges
to better policies and/or faster than symmetric actor-critic and standard
asymmetric actor-critic baselines.


Abstract:
  Magnonics attracts increasing attention in the view of novel low-energy
computation technologies based on spin waves. Recently, spin-wave propagation
in longitudinally magnetized nano-scaled spin-wave conduits was demonstrated,
proving the fundamental feasibility of magnonics at the sub-100 nm scale.
Transversely magnetized nano-conduits, which are of great interest in this
regard as they offer a large group velocity and a potentially chirality-based
protected transport of energy, have not yet been investigated due to their
complex internal magnetic field distribution. Here, we present a study of
propagating spin waves in a transversely magnetized nanoscopic yttrium iron
garnet conduit of 50 nm width. Space and time-resolved micro-focused
Brillouin-light-scattering spectroscopy is employed to measure the spin-wave
group velocity and decay length. A long-range spin-wave propagation is observed
with a decay length of up to (8.0+-1.5) {\mu}m and a large spin-wave lifetime
of up to (44.7+-9.1) ns. The results are supported with micromagnetic
simulations, revealing a single-mode dispersion relation in contrast to the
common formation of localized edge modes for microscopic systems. Furthermore,
a frequency non-reciprocity for counter-propagating spin waves is observed in
the simulations and the experiment, caused by the trapezoidal cross-section of
the structure. The revealed long-distance spin-wave propagation on the
nanoscale is particularly interesting for an application in spin-wave devices,
allowing for long-distance transport of information in magnonic circuits, as
well as novel low-energy device architectures.


Abstract:
  We analyze equilibrium behavior and optimal policy within a
Susceptible-Infected-Recovered epidemic model augmented with potentially
undiagnosed agents who infer their health status and a social planner with
imperfect enforcement of social distancing. We define and prove the existence
of a perfect Bayesian Markov competitive equilibrium and contrast it with the
efficient allocation subject to the same informational constraints. We identify
two externalities, static (individual actions affect current risk of infection)
and dynamic (individual actions affect future disease prevalence), and study
how they are affected by limitations on testing and enforcement. We prove that
a planner with imperfect enforcement will always wish to curtail activity, but
that its incentives vanish as testing becomes perfect. When a vaccine arrives
far into the future, the planner with perfect enforcement may encourage
activity before herd immunity. We find that lockdown policies have modest
welfare gains, whereas quarantine policies are effective even with imperfect
testing.


Abstract:
  The correct evaluation of gradients is at the cornerstone of the smoothed
particle hydrodynamics (SPH) technique. Using an integral approach to estimate
gradients has proven to enhance accuracy substantially. Such approach retains
the Lagrangian structure of SPH equations and is fully conservative. But, in
practice, it is difficult to make the Lagrangian formulation totally consistent
to an exact partition of unit.
  In this paper we study, among other things, the connection between the choice
of the volume elements (VEs), which enters in the SPH summations, and the
accuracy in the gradient estimation within the integral approach scheme (ISPH).
A new variant of VEs are proposed which improve the partition of unit and is
fully compatible with the Lagrangian formulation of SPH, including the grad-h
corrections. Using analytic considerations, simple static toy models in 1D, and
a few full 3D test cases, we show that any improvement in the partition of unit
also leads to a better calculation of gradients when the integral approach is
used jointly. Additionally, we propose an easy-to-implement modification of the
ISPH scheme, which makes it more flexible and better suited to handle sharp
density contrasts.
  The ISPH code built with the proposed scheme has been validated with a good
number of standard tests, some of them involving contact discontinuities. The
performance of the code was excellent in all of them, always showing that an
improvement in the partition of unit is not detrimental of the good
conservation of energy, momentum, and entropy typical of Lagrangian schemes.


Abstract:
  Recently, sparse training methods have started to be established as a de
facto approach for training and inference efficiency in artificial neural
networks. Yet, this efficiency is just in theory. In practice, everyone uses a
binary mask to simulate sparsity since the typical deep learning software and
hardware are optimized for dense matrix operations. In this paper, we take an
orthogonal approach, and we show that we can train truly sparse neural networks
to harvest their full potential. To achieve this goal, we introduce three novel
contributions, specially designed for sparse neural networks: (1) a parallel
training algorithm and its corresponding sparse implementation from scratch,
(2) an activation function with non-trainable parameters to favour the gradient
flow, and (3) a hidden neurons importance metric to eliminate redundancies. All
in one, we are able to break the record and to train the largest neural network
ever trained in terms of representational power -- reaching the bat brain size.
The results show that our approach has state-of-the-art performance while
opening the path for an environmentally friendly artificial intelligence era.


Abstract:
  Debiased machine learning is a meta algorithm based on bias correction and
sample splitting to calculate confidence intervals for functionals (i.e. scalar
summaries) of machine learning algorithms. For example, an analyst may desire
the confidence interval for a treatment effect estimated with a neural network.
We provide a nonasymptotic debiased machine learning theorem that encompasses
any global or local functional of any machine learning algorithm that satisfies
a few simple, interpretable conditions. Formally, we prove consistency,
Gaussian approximation, and semiparametric efficiency by finite sample
arguments. The rate of convergence is root-n for global functionals, and it
degrades gracefully for local functionals. Our results culminate in a simple
set of conditions that an analyst can use to translate modern learning theory
rates into traditional statistical inference. The conditions reveal a new
double robustness property for ill posed inverse problems.


Abstract:
  We present observations of ionized gas outflows in eleven z$ =1.39-2.59$
radio-loud quasar host galaxies. Data was taken with the integral field
spectrograph (IFS) OSIRIS and the adaptive optics system at the W.M. Keck
Observatory targeting nebular emission lines (H$\beta$, [OIII], H$\alpha$,
[NII] and [SII]) redshifted into the near-infrared (1-2.4 \micron). Outflows
with velocities of 500 - 1700 km\,s$^{-1}$ are detected in 10 systems on scales
ranging from $<1$ kpc to 10 kpc with outflow rates from 8-2400
M$_\odot$yr$^{-1}$. For five sources, the outflow momentum rates are 4-80 times
$L_{AGN}$/c, consistent with outflows being driven by an energy conserving
shock. The five other outflows are either driven by radiation pressure or an
isothermal shock. The outflows are the dominant source of gas depletion, and we
find no evidence for star formation along the outflow paths. For eight objects,
the outflow paths are consistent with the orientation of the jets. Yet, given
the calculated pressures, we find no evidence of the jets currently doing work
on these galactic-scale ionized outflows. We find that galactic-scale feedback
occurs well before galaxies establish a substantial fraction of their stellar
mass, as expected from local scaling relationships.


Abstract:
  Task-driven semantic video/image coding has drawn considerable attention with
the development of intelligent media applications, such as license plate
detection, face detection, and medical diagnosis, which focuses on maintaining
the semantic information of videos/images. Deep neural network (DNN)-based
codecs have been studied for this purpose due to their inherent end-to-end
optimization mechanism. However, the traditional hybrid coding framework cannot
be optimized in an end-to-end manner, which makes task-driven semantic fidelity
metric unable to be automatically integrated into the rate-distortion
optimization process. Therefore, it is still attractive and challenging to
implement task-driven semantic coding with the traditional hybrid coding
framework, which should still be widely used in practical industry for a long
time. To solve this challenge, we design semantic maps for different tasks to
extract the pixelwise semantic fidelity for videos/images. Instead of directly
integrating the semantic fidelity metric into traditional hybrid coding
framework, we implement task-driven semantic coding by implementing semantic
bit allocation based on reinforcement learning (RL). We formulate the semantic
bit allocation problem as a Markov decision process (MDP) and utilize one RL
agent to automatically determine the quantization parameters (QPs) for
different coding units (CUs) according to the task-driven semantic fidelity
metric. Extensive experiments on different tasks, such as classification,
detection and segmentation, have demonstrated the superior performance of our
approach by achieving an average bitrate saving of 34.39% to 52.62% over the
High Efficiency Video Coding (H.265/HEVC) anchor under equivalent task-related
semantic fidelity.


Abstract:
  Future prospects for solar spectroscopy missions operating in the extreme
ultraviolet (EUV) and soft X-ray (SXR) wavelength ranges, 1.2-1600 Angstroms,
are discussed. NASA is the major funder of Solar Physics missions, and brief
summaries of the opportunities for mission development under NASA are given.
Upcoming major solar missions from other nations are also described. The
methods of observing the Sun in the two wavelength ranges are summarized with a
discussion of spectrometer types, imaging techniques and detector options. The
major spectral features in the EUV and SXR regions are identified, and then the
upcoming instruments and concepts are summarized. The instruments range from
large spectrometers on dedicated missions, to tiny, low-cost CubeSats launched
through rideshare opportunities.


Abstract:
  Increasing the light scattering efficiency of nanostructured materials is
becoming an active field of research both in fundamental science and commercial
applications. In this context, the challenge is to use inexpensive organic
materials that come with a lower refractive index than currently used mineral
nanoparticles, which are under increased scrutiny for their toxicity. Although
several recent investigations have reported different disordered systems to
optimise light scattering by morphological design, no systematic studies
comparing and explaining how different topological features contribute to
optical properties have been reported yet. Using in silico synthesis and
numerical simulations, we demonstrate that the reflectance is primarily
determined by second order statistics. While remaining differences are
explained by surface area and integrated mean curvature, an equal reflectance
can be obtained by further tuning the structural anisotropy. Our results
suggest a topological invariance for light scattering, demonstrating that any
disordered system can be optimised for whiteness.


Abstract:
  Massive multiple-input multiple-output (MIMO) systems are considered as one
of the leading technologies employed in the next generations of wireless
communication networks (5G), which promise to provide higher spectral
efficiency, lower latency, and more reliability. Due to the massive number of
devices served by the base stations (BS) equipped with large antenna arrays,
massive-MIMO systems need to perform high-dimensional signal processing in a
considerably short amount of time. The computational complexity of such data
processing, while satisfying the energy and latency requirements, is beyond the
capabilities of the conventional widely-used digital electronics-based
computing, i.e., Field-Programmable Gate Arrays (FPGAs) and
Application-Specific Integrated Circuits (ASICs). In this paper, the speed and
lossless propagation of light is exploited to introduce a photonic computing
approach that addresses the high computational complexity required by
massive-MIMO systems. The proposed computing approach is based on photonic
implementation of multiply and accumulate (MAC) operation achieved by
broadcast-and-weight (B&W) architecture. The B&W protocol is limited to real
and positive values to perform MAC operations. In this work, preprocessing
steps are developed to enable the proposed photonic computing architecture to
accept any arbitrary values as the input. This is a requirement for wireless
communication systems that typically deal with complex values. Numerical
analysis shows that the performance of the wireless communication system is not
degraded by the proposed photonic computing architecture, while it provides
significant improvements in time and energy efficiency for massive-MIMO systems
as compared to the most powerful Graphics Processing Units (GPUs).


Abstract:
  Dark patterns have emerged as a set of methods to exploit cognitive biases to
trick users to make decisions that are more aligned with a third party than to
their own. These patterns can have consequences that might range from
inconvenience to global disasters. We present a case of a drug company and an
electronic medical record vendor who colluded to modify the medical record's
interface to induce clinicians to increase the prescription of extended-release
opioids, a class of drugs that has a high potential for addiction and has
caused almost half a million additional deaths in the past two decades. Through
this case, we present the use and effects of dark patterns in healthcare,
discuss the current challenges, and offer some recommendations on how to
address this pressing issue.


Abstract:
  A representation of general translation-invariant star products in the
algebra of M(C) = lim_N\to \infty M_N (C) is introduced which results in the
Moyal-Weyl-Wigner quantization. It provides a matrix model for general
translation-invariant noncommutative quantum field theories in terms of the
noncommutative calculus on differential graded algebras. Upon this machinery a
cohomology theory, the so called star-cohomology, with groups H^k_*(C), is
worked out which provides a cohomological framework to formulate general
translation-invariant noncommutative quantum field theories based on the
achievements for the commutative fields, and is comparable to the
Seiberg-Witten map for the Moyal case. Employing the Chern-Weil theory via the
integral classes of H^k_*(Z) a noncommutative version of the Chern character is
defined as an equivariant form which contains topological information about the
corresponding translation-invariant noncommutative Yang-Mills theory. Thereby
we study the mentioned Yang-Mills theories with three types of actions of the
gauge fields on the spinors, the ordinary, the inverse, and the adjoint action,
and then some exact solutions for their anomalous behaviors are worked out via
employing the homotopic correlation on the integral classes of *-cohomology.
Finally, the corresponding consistent anomalies are also derived from this
topological Chern character in the *-cohomology.


Abstract:
  Simple upper and lower bounds are established for the integral
$\int_0^x\mathrm{e}^{-\beta u}u^\nu t_{\mu,\nu}(u)\,\mathrm{d}u$, where $x>0$,
$0<\beta<1$, $\mu+\nu>-2$, $\mu-\nu\geq-3$, and $t_{\mu,\nu}(x)$ is the
modified Lommel function of the first kind. Our bounds complement and improve
on existing bounds for this integral, by either being sharper or increasing the
range of validity. Our bounds also generalise recent bounds for an integral
involving the modified Struve function of the first kind, and in some cases
more a direct approach lead to sharper bounds when our general bounds are
specialised to the modified Struve case.


Abstract:
  Decentralized finance (DeFi) has become one of the most successful
applications of blockchain and smart contracts. The DeFi ecosystem enables a
wide range of crypto-financial activities, while the underlying smart contracts
often contain bugs, with many vulnerabilities arising from the unforeseen
consequences of composing DeFi protocols together. In this paper, we propose a
formal process-algebraic technique that models DeFi protocols in a
compositional manner to allow for efficient property verification. We also
conduct a case study to demonstrate the proposed approach in analyzing the
composition of two interacting DeFi protocols, namely, Curve and Compound.
Finally, we discuss how the proposed modeling and verification approach can be
used to analyze financial and security properties of interest.


Abstract:
  Motivated by applications in cybersecurity and epidemiology, we consider the
problem of detecting an abrupt change in the intensity of a Poisson process,
characterised by a jump (non transitory change) or a bump (transitory change)
from constant. We propose a complete study from the nonasymptotic minimax
testing point of view, when the constant baseline intensity is known or
unknown. The question of minimax adaptation with respect to each parameter
(height, location, length) of the change is tackled, leading to a comprehensive
overview of the various minimax separation rate regimes. We exhibit three such
regimes and identify the factors of the two phase transitions, by giving the
cost of adaptation to each parameter. For each alternative hypothesis,
depending on the knowledge or not of each change parameter, we propose minimax
or minimax adaptive tests based on linear statistics, close to CUSUM
statistics, or quadratic statistics more adapted to the L 2-distance considered
in our minimax criteria and typically more powerful in practice, as our
simulation study shows. When the change location or length is unknown, our
adaptive tests are constructed from a scan aggregation principle combined with
Bonferroni or min-p level correction, and a conditioning trick when the
baseline intensity is unknown.


Abstract:
  Abstract. We determine the Grothendieck rings of the category of
finite-dimensional modules over Queer Lie superalgebras via their rings of
characters. In particular, we show that the ring of characters of the Queer Lie
supergroup $Q(n)$ is isomorphic to the ring of Laurent polynomials in
$x_{1},\ldots,x_{n}$ for which the evaluation $x_{n-1}=-x_{n}=t$ is independent
of $t$. We thus complete the description of Grothendieck rings for all
classical Lie superalgebras.


Abstract:
  Feature alignment is an approach to improving robustness to distribution
shift that matches the distribution of feature activations between the training
distribution and test distribution. A particularly simple but effective
approach to feature alignment involves aligning the batch normalization
statistics between the two distributions in a trained neural network. This
technique has received renewed interest lately because of its impressive
performance on robustness benchmarks. However, when and why this method works
is not well understood. We investigate the approach in more detail and identify
several limitations. We show that it only significantly helps with a narrow set
of distribution shifts and we identify several settings in which it even
degrades performance. We also explain why these limitations arise by
pinpointing why this approach can be so effective in the first place. Our
findings call into question the utility of this approach and Unsupervised
Domain Adaptation more broadly for improving robustness in practice.


Abstract:
  Elemental gallium can exist in several phases under ambient conditions. The
stable $\alpha$ phase has a superconducting transition temperature, $T_c$, of
0.9~K. By contrast, the $T_c$ of the metastable $\beta$ phase is around 6~K. To
understand the significant improvement in $T_c$ in the $\beta$ phase, we first
calculate the electronic structure, phonon dispersion, and the electron-phonon
coupling of gallium in the $\alpha$ and $\beta$ phase. Next, we solve the
Eliashberg equations to obtain the superconducting gaps and the transition
temperatures. Using these results, we relate the increased $T_c$ in the $\beta$
phase to structural differences between the phases that affect the electronic
and phonon properties. The structure motif of the $\alpha$ phase is Ga$_2$
dimers, which form strong covalent bonds leading to bonding and antibonding
states that suppress the density of states at the Fermi level. The $\beta$-Ga
structure consists of arrays of Ga chains that favor strong coupling between
the lattice vibrations and the electronic states near the Fermi level. The
increased density of states and strong coupling to the phonons for the
$\beta$-Ga chains compared to the $\alpha$ Ga$_2$ dimers enhance
superconductivity in the $\beta$-Ga phase.


Abstract:
  We analyze the instability of a vortex column in a dilute polymer solution at
large $\textit{Re}$ and $\textit{De}$ with $\textit{El} =
\textit{De}/\textit{Re}$, the elasticity number, being finite. Here,
$\textit{Re} = \Omega_0 a^2/\nu_s$ and $\textit{De} = \Omega_0 \tau$ are,
respectively, the Reynolds and Deborah numbers based on the core angular
velocity ($\Omega_0$), the radius of the column ($a$), the solvent-based
kinematic viscosity ($\nu_s = \mu_s/\rho$), and the polymeric relaxation time
($\tau$). The stability of small-amplitude perturbations in this distinguished
limit is governed by the elastic Rayleigh equation whose spectrum is
parameterized by $ \textrm{E} = \textit{El}(1-\beta)$, $\beta$ being the ratio
of the solvent to the solution viscosity. The neglect of the relaxation terms,
in the said limit, implies that the polymer solution supports undamped elastic
shear waves propagating relative to the base-state flow. The existence of these
shear waves leads to multiple (three) continuous spectra associated with the
elastic Rayleigh equation in contrast to just one for the original Rayleigh
equation. Further, unlike the neutrally stable inviscid case, an instability of
the vortex column arises for finite E due to a pair of elastic shear waves
being driven into a resonant interaction under the differential convection by
the irrotational shearing flow outside the core. An asymptotic analysis for the
Rankine profile shows the absence of an elastic threshold; although, for small
E, the growth rate of the unstable discrete mode is transcendentally small,
being O$(\textrm{E}^2e^{-1/\textrm{E}^{\frac{1}{2}}})$. An accompanying
numerical investigation shows that the instability persists for smooth
vorticity profiles, provided the radial extent of the transition region (from
the rotational core to the irrotational exterior) is less than a certain
$\textrm{E}$-dependent threshold.


Abstract:
  It is known that $G$-functions solutions of a linear differential equation of
order 1 with coefficients in $\overline{\mathbb{Q}}(z)$, are algebraic (of a
very precise form). No general result is known when the order is 2. In this
paper, we determine the form of a $G$-function solution of an inhomogeneous
equation of order 1 with coefficients in $\overline{\mathbb{Q}}(z)$, as well as
that of a $G$-function $f$ of differential order 2 over
$\overline{\mathbb{Q}}(z)$, and such that $f$ and $f'$ are algebraically
dependent over $\mathbb{C}(z)$. Our results apply more generally to
Nilsson-Gevrey arithmetic series of order 0 that encompass $G$-functions.


Abstract:
  Traditional aerial vehicles are usually custom-designed for specific tasks.
Although they offer an efficient solution, they are not always able to adapt to
changes in the task specification, e.g., increasing the payload. This applies
to quadrotors, having a maximum payload and only four controllable degrees of
freedom, limiting their adaptability to the task's variations. We propose a
versatile modular robotic system that can increase its payload and degrees of
freedom by assembling heterogeneous modules; we call it H-ModQuad. It consists
of cuboid modules propelled by quadrotors with tilted propellers that can
generate forces in different directions. By connecting different types of
modules, an H-ModQuad can increase its controllable degrees of freedom from 4
to 5 and 6. We model the general structure and propose three controllers, one
for each number of controllable degrees of freedom. We extend the concept of
the actuation ellipsoid to find the best reference orientation that can
maximize the performance of the structure. Our approach is validated with
experiments using actual robots, showing the independence of the translation
and orientation of a structure.


Abstract:
  Wide dynamic range (WDR) image tone mapping is in high demand in many
applications like film production, security monitoring, and photography. It is
especially crucial for mobile devices because most of the images taken today
are from mobile phones, hence such technology is highly demanded in the
consumer market of mobile devices and is essential for a good customer
experience. However, high-quality and high-performance WDR image tone mapping
implementations are rarely found in the mobile-end. In this paper, we introduce
a high performance, mobile-end WDR image tone mapping implementation. It
leverages the tone mapping results of multiple receptive fields and calculates
a suitable value for each pixel. The utilization of integral image and integral
histogram significantly reduce the required computation. Moreover, GPU parallel
computation is used to increase the processing speed. The experimental results
indicate that our implementation can process a high-resolution WDR image within
a second on mobile devices and produce appealing image quality.


Abstract:
  Teeth gestures become an alternative input modality for different situations
and accessibility purposes. In this paper, we present TeethTap, a novel
eyes-free and hands-free input technique, which can recognize up to 13 discrete
teeth tapping gestures. TeethTap adopts a wearable 3D printed earpiece with an
IMU sensor and a contact microphone behind both ears, which works in tandem to
detect jaw movement and sound data, respectively. TeethTap uses a support
vector machine to classify gestures from noise by fusing acoustic and motion
data, and implements K-Nearest-Neighbor (KNN) with a Dynamic Time Warping (DTW)
distance measurement using motion data for gesture classification. A user study
with 11 participants demonstrated that TeethTap could recognize 13 gestures
with a real-time classification accuracy of 90.9% in a laboratory environment.
We further uncovered the accuracy differences on different teeth gestures when
having sensors on single vs. both sides. Moreover, we explored the activation
gesture under real-world environments, including eating, speaking, walking and
jumping. Based on our findings, we further discussed potential applications and
practical challenges of integrating TeethTap into future devices.


Abstract:
  Shape completion is the problem of completing partial input shapes such as
partial scans. This problem finds important applications in computer vision and
robotics due to issues such as occlusion or sparsity in real-world data.
However, most of the existing research related to shape completion has been
focused on completing shapes by learning a one-to-one mapping which limits the
diversity and creativity of the produced results. We propose a novel multimodal
shape completion technique that is effectively able to learn a one-to-many
mapping and generates diverse complete shapes. Our approach is based on the
conditional Implicit MaximumLikelihood Estimation (IMLE) technique wherein we
condition our inputs on partial 3D point clouds. We extensively evaluate our
approach by comparing it to various baselines both quantitatively and
qualitatively. We show that our method is superior to alternatives in terms of
completeness and diversity of shapes.


Abstract:
  We consider a minimal residual discretization of a simultaneous space-time
variational formulation of parabolic evolution equations. Under the usual `LBB'
stability condition on pairs of trial- and test spaces we show quasi-optimality
of the numerical approximations without assuming symmetry of the spatial part
of the differential operator. Under a stronger LBB condition we show error
estimates in an energy-norm which are independent of this spatial differential
operator.


Abstract:
  Until recently, artificial neural networks were typically designed with a
fixed network structure. Here, I argue that network structure is highly
relevant to function, and therefore neural networks should be livewired
(Eagleman 2020): dynamically rewired to reflect relationships between higher
order representations of the external environment identified by coincident
activations in individual neurons. I discuss how this approach may enable such
networks to build compositional world models that operate on symbols and that
achieve few-shot learning, capabilities thought by many to be critical to
human-level cognition. Here, I also 1) discuss how such livewired neural
networks maximize the information the environment provides to a model, 2)
explore evidence indicating that livewiring is implemented in the brain, guided
by glial cells, 3) discuss how livewiring may give rise to the associative
emergent behaviors of brains, and 4) suggest paths for future research using
livewired networks to understand and create human-like reasoning.


Abstract:
  Let $A$ and $B$ be rings, $U$ a $(B,A)$-bimodule and $T=\begin{pmatrix}
A&0\\U&B \end{pmatrix}$ the triangular matrix ring. In this paper, several
notions in relative Gorenstein algebra over a triangular matrix ring are
investigated. We first study how to construct w-tilting (tilting,
semidualizing) over $T$ using the corresponding ones over $A$ and $B$. We show
that when $U$ is relative (weakly) compatible we are able to describe the
structure of $G_C$-projective modules over $T$. As an application, we study
when a morphism in $T$-Mod has a special $G_CP(T)$-precover and when the class
$G_CP(T)$ is a special precovering class. In addition, we study the relative
global dimension of $T$. In some cases, we show that it can be computed from
the relative global dimensions of $A$ and $B$. We end the paper with a
counterexample to a result that characterizes when a $T$-module has a finite
projective dimension.


Abstract:
  A dataset is a shred of crucial evidence to describe a task. However, each
data point in the dataset does not have the same potential, as some of the data
points can be more representative or informative than others. This unequal
importance among the data points may have a large impact in rehearsal-based
continual learning, where we store a subset of the training examples (coreset)
to be replayed later to alleviate catastrophic forgetting. In continual
learning, the quality of the samples stored in the coreset directly affects the
model's effectiveness and efficiency. The coreset selection problem becomes
even more important under realistic settings, such as imbalanced continual
learning or noisy data scenarios. To tackle this problem, we propose Online
Coreset Selection (OCS), a simple yet effective method that selects the most
representative and informative coreset at each iteration and trains them in an
online manner. Our proposed method maximizes the model's adaptation to a target
dataset while selecting high-affinity samples to past tasks, which directly
inhibits catastrophic forgetting. We validate the effectiveness of our coreset
selection mechanism over various standard, imbalanced, and noisy datasets
against strong continual learning baselines, demonstrating that it improves
task adaptation and prevents catastrophic forgetting in a sample-efficient
manner.


Abstract:
  There is a striking similarity between Macdonald's reduced word formula and
the image of the Schubert class in the cohomology ring of the permutahedral
variety $\mathrm{Perm}_n$ as computed by Klyachko. Toward understanding this
better, we undertake an in-depth study of a $q$-deformation of the
$\mathbb{S}_n$-invariant part of the rational cohomology ring of
$\mathrm{Perm}_n$, which we call the $q$-Klyachko algebra. We uncover intimate
links between expansions in the basis of squarefree monomials in this algebra
and various notions in algebraic combinatorics, thereby connecting seemingly
unrelated results by finding a common ground to study them.
  Our main results are as follows. 1) A $q$-analog of divided symmetrization
($q$-DS) using Yang-Baxter elements in the Hecke algebra. It is a linear form
that picks up coefficients in the squarefree basis. 2) A relation between
$q$-DS and the ideal of quasisymmetric polynomials involving work of
Aval--Bergeron--Bergeron. 3) A family of polynomials in $q$ with nonnegative
integral coefficients that specialize to Postnikov's mixed Eulerian numbers
when $q=1$. We refer to these new polynomials as remixed Eulerian numbers. For
$q>0$, their normalized versions occur as probabilities in the internal
diffusion limited aggregation (IDLA) stochastic process. 4) A lift of
Macdonald's reduced word identity in the $q$-Klyachko algebra. 5) The Schubert
expansion of the Chow class of the standard split Deligne--Lusztig variety in
type $A$, when $q$ is a prime power.


Abstract:
  We consider the homogenization of a Poisson problem or a Stokes system in a
randomly punctured domain with Dirichlet boundary conditions. We assume that
the holes are spherical and have random centres and radii. We impose that the
average distance between the balls is of size $\eps$ and their average radius
is $\varepsilon^{\alpha}$, $\alpha \in (1; 3)$. We prove that, as in the
periodic case [G. Allaire, ``Homogenization of the Navier-Stokes equations in
domains perforated with tiny holes. II''], the solutions converge to the
solution of Darcy's law (or its scalar analogue in the case of Poisson). In the
same spirit of [A. Giunti, R. H\"ofer and J. Vel\'azquez, ``Homogenization of
the Poisson equation in randomly perforated domains under minimal assumptions
on the size of the holes''], we work under minimal conditions on the
integrability of the random radii. These ensure that the problem is
well-defined but do not rule out the onset of clusters of holes.


Abstract:
  Policymakers worldwide draft privacy laws that require trading-off between
safeguarding consumer privacy and preventing economic loss to companies that
use consumer data. However, little empirical knowledge exists as to how privacy
laws affect companies' performance. Accordingly, this paper empirically
quantifies the effects of the enforcement of the EU's General Data Protection
Regulation (GDPR) on online user behavior over time, analyzing data from 6,286
websites spanning 24 industries during the 10 months before and 18 months after
the GDPR's enforcement in 2018. A panel differences estimator, with a synthetic
control group approach, isolates the short- and long-term effects of the GDPR
on user behavior. The results show that, on average, the GDPR's effects on user
quantity and usage intensity are negative; e.g., the numbers of total visits to
a website decrease by 4.9% and 10% due to GDPR in respectively the short- and
long-term. These effects could translate into average revenue losses of $7
million for e-commerce websites and almost $2.5 million for ad-based websites
18 months after GDPR. The GDPR's effects vary across websites, with some
industries even benefiting from it; moreover, more-popular websites suffer
less, suggesting that the GDPR increased market concentration.


Abstract:
  Developing the isolation and control of ultracold atomic systems to the level
of single quanta has led to significant advances in quantum sensing, yet
demonstrating a quantum advantage in real world applications by harnessing
entanglement remains a core task. Here, we realize a many-body quantum-enhanced
sensor to detect weak displacements and electric fields using a large crystal
of $\sim 150$ trapped ions. The center of mass vibrational mode of the crystal
serves as high-Q mechanical oscillator and the collective electronic spin as
the measurement device. By entangling the oscillator and the collective spin
before the displacement is applied and by controlling the coherent dynamics via
a many-body echo we are able to utilize the delicate spin-motion entanglement
to map the displacement into a spin rotation such that we avoid quantum
back-action and cancel detrimental thermal noise. We report quantum enhanced
sensitivity to displacements of $8.8 \pm 0.4~$dB below the standard quantum
limit and a sensitivity for measuring electric fields of
$240\pm10~\mathrm{nV}\mathrm{m}^{-1}$ in $1$ second
($240~\mathrm{nV}\mathrm{m}^{-1}/\sqrt{\mathrm{Hz}}$).


Abstract:
  Prescribed burns have been increasingly administered to forest management
with the assumption that they help reduce the risk of wildfires; however, this
hypothesis has yet to be rigorously tested. We leverage the historical data of
forest fires from multiple states to show that the sandpile model of
self-organized criticality accurately represents the real-world incidence of
fire by describing a negative linear relationship between the logarithm of fire
size and the logarithm of the fire incidence number of that size. We then
investigate the association between the size of prescribed burn and the slope
of the negative linear relationship which represents the relative risk of
destructive wildfires. The results demonstrate that increases in the area
subject to prescribed burning generally reduce the risk of destructive
wildfires. This is consistent with the Florida data, which shows a trend in
reduction of destructive wildfires as prescribed burns have been progressively
introduced to forest management. Our study justifies the application of the
sandpile model to wildfire research and establishes a novel method of the
analysis of slope estimated from the sandpile model for facilitating the
investigation of potential risk factors of destructive wildfires and the
development of an optimal strategy for prescribed burning.


Abstract:
  To get channel state information (CSI) at a base station (BS), most of
researches on massive multiple-input multiple-output (MIMO) systems consider
time division duplexing (TDD) to get benefit from the uplink and downlink
channel reciprocity. Even in TDD, however, the BS still needs to transmit
downlink training signals, which are referred to as channel state information
reference signals (CSI-RSs) in the 3GPP standard, to support spatial
multiplexing in practice. This is because there are many cases that the number
of transmit antennas is less than the number of receive antennas at a user
equipment (UE) due to power consumption and circuit complexity issues. Because
of this mismatch, uplink sounding reference signals (SRSs) from the UE are not
enough for the BS to obtain full downlink MIMO CSI. Therefore, after receiving
the downlink CSI-RSs, the UE needs to feed back quantized CSI to the BS using a
pre-defined codebook to support spatial multiplexing. In this paper, possible
approaches to reconstruct full downlink MIMO CSI at the BS are proposed by
exploiting both the SRS and quantized downlink CSI considering practical
antenna structures with reduced downlink CSI-RS overhead. Numerical results
show that the spectral efficiencies by spatial multiplexing based on the
proposed downlink MIMO CSI reconstruction techniques outperform the
conventional methods solely based on the quantized CSI.


Abstract:
  Two gravitational wave events, i.e. GW200105 and GW200115, were observed by
the Advanced LIGO and Virgo detectors recently. In this work, we show that they
can be explained by a scenario of primordial black hole binaries that are
formed in the early Universe. The merger rate predicted by such a scenario
could be consistent with the one estimated from LIGO and Virgo, even if
primordial black holes constitute a fraction of cold dark matter. The required
abundance of primordial black holes is compatible with the existing upper
limits from microlensing, caustic crossing and cosmic microwave background
observations.


Abstract:
  Recent studies have shown that it is possible to characterize subject bias
and variance in subjective assessment tests. Apparent differences among
subjects can, for the most part, be explained by random factors. Building on
that theory, we propose a subjective test design where three to four team
members each rate the stimuli multiple times. The results are comparable to a
high performing objective metric. This provides a quick and simple way to
analyze new technologies and perform pre-tests for subjective assessment.


Abstract:
  At the time of publication of H. Everett's Relative-State Formulation (1957)
and DeWitt's Many-Worlds Interpretation (1970), quantum mechanics was available
in a more modern and adequate version than the one used by these authors. We
show that with the more modern quantum theory, quantum measurement could have
been analyzed along more conventional lines in a one-world cosmology. Bell
criticized the Everett-DeWitt theory quite sharply in 1987 but this seems not
to have affected the acceptance of the old quantum mechanics as the framework
for analysis of measurement.


Abstract:
  We develop the general many-body perturbation theory for a superconducting
quantum dot represented by a single-impurity Anderson model attached to
superconducting leads. We build our approach on a thermodynamically consistent
mean-field approximation with a two-particle self-consistency of the parquet
type. The two-particle self-consistency leading to a screening of the bare
interaction proves substantial for suppressing the spurious transitions of the
Hartree-Fock solution. We demonstrate that the magnetic field plays a
fundamental role in the extension of the perturbation theory beyond the weakly
correlated $0$-phase. It controls the critical behavior of the $0-\pi$ quantum
transition, lifts the degeneracy in the $\pi$-phase, where the limits to zero
temperature and zero magnetic field do not commute. The response to the
magnetic field is quite different in $0$- and $\pi$-phases. While the magnetic
susceptibility vanishes in the $0$-phase it becomes of the Curie type and
diverges in the $\pi$-phase at zero temperature.


Abstract:
  Training a large multilayer neural network can present many difficulties due
to the large number of useless stationary points. These points usually attract
the minimization algorithm used during the training phase, which therefore
results inefficient. Extending some results proposed in literature for shallow
networks, we propose the mathematical characterization of a class of such
stationary points that arise in deep neural networks training. Availing such a
description, we are able to define an incremental training algorithm that
avoids getting stuck in the region of attraction of these undesirable
stationary points.


Abstract:
  Localization on 3D data is a challenging task for unmanned vehicles,
especially in long-term dynamic urban scenarios. Due to the generality and
long-term stability, the pole-like objects are very suitable as landmarks for
unmanned vehicle localization in time-varing scenarios. In this paper, a
long-term LiDAR-only localization algorithm based on semantic cluster map is
proposed. At first, the Convolutional Neural Network(CNN) is used to infer the
semantics of LiDAR point clouds. Combined with the point cloud segmentation,
the long-term static objects pole/trunk in the scene are extracted and
registered into a semantic cluster map. When the unmanned vehicle re-enters the
environment again, the relocalization is completed by matching the clusters of
the local map with the clusters of the global map. Furthermore, the continuous
matching between the local and global maps stably outputs the global pose at
2Hz to correct the drift of the 3D LiDAR odometry. The proposed approach
realizes localization in the long-term scenarios without maintaining the
high-precision point cloud map. The experimental results on our campus dataset
demonstrate that the proposed approach performs better in localization accuracy
compared with the current state-of-the-art methods. The source of this paper is
available at: http://www.github.com/HITSZ-NRSL/long-term-localization.


Abstract:
  While the Berezinskii-Kosterlitz-Thouless transition (BKT) has been under
intense scrutiny for decades, unambiguous experimental signatures in magnetic
systems remain elusive. Here, we investigate the interplay between electronic
and magnetic degrees of freedom near the BKT transition. Focusing on a metal
with easy-plane ferromagnetic order, we establish a framework that accounts
both for the coupling between the charge current and the flow of topological
magnetic defects and for electron scattering on their inhomogeneous spin
texture. We show that electron scattering is responsible for a
temperature-dependent magnetoresistance effect scaling as the density of the
topological defects, which is expected to increase dramatically above the BKT
transition temperature. Our findings call for further experimental
investigations.


Abstract:
  Anterior uveitis is the most common form of intraocular inflammation, and one
of its main signs is the presence of white blood cells (WBCs) in the anterior
chamber (AC). Clinically, the true composition of cells can currently only be
obtained using AC paracentesis, an invasive procedure to obtain AC fluid
requiring needle insertion into the AC. We previously developed a spectroscopic
optical coherence tomography (SOCT) analysis method to differentiate between
populations of RBCs and subtypes of WBCs, including granulocytes, lymphocytes
and monocytes, both in vitro and in ACs of excised porcine eyes. We have shown
that different types of WBCs have distinct characteristic size distributions,
extracted from the backscattered reflectance spectrum of individual cells using
Mie theory. Here, we further develop our method to estimate the composition of
blood cell mixtures, both in vitro and in vivo. To do so, we estimate the size
distribution of unknown cell mixtures by fitting the distribution observed
using SOCT with a weighted combination of reference size distributions of each
WBC type calculated using kernel density estimation. We validate the accuracy
of our estimation in an in vitro study, by comparing our results for a given
WBC sample mixture with the cellular concentrations measured by a hemocytometer
and SOCT images before mixing. We also conducted a small in vivo quantitative
cell mixture validation pilot study which demonstrates congruence between our
method and AC paracentesis in two patients with uveitis. The SOCT based method
appears promising to provide quantitative diagnostic information of cellular
responses in the ACs of patients with uveitis.


Abstract:
  In order to understand stellar evolution, it is crucial to efficiently
determine stellar surface rotation periods. An efficient tool to automatically
determine reliable rotation periods is needed when dealing with large samples
of stellar photometric datasets. The objective of this work is to develop such
a tool. Random forest learning abilities are exploited to automate the
extraction of rotation periods in Kepler light curves. Rotation periods and
complementary parameters are obtained from three different methods: a wavelet
analysis, the autocorrelation function of the light curve, and the composite
spectrum. We train three different classifiers: one to detect if rotational
modulations are present in the light curve, one to flag close binary or
classical pulsators candidates that can bias our rotation period determination,
and finally one classifier to provide the final rotation period. We test our
machine learning pipeline on 23,431 stars of the Kepler K and M dwarf reference
rotation catalog of Santos et al. (2019) for which 60% of the stars have been
visually inspected. For the sample of 21,707 stars where all the input
parameters are provided to the algorithm, 94.2% of them are correctly
classified (as rotating or not). Among the stars that have a rotation period in
the reference catalog, the machine learning provides a period that agrees
within 10% of the reference value for 95.3% of the stars. Moreover, the yield
of correct rotation periods is raised to 99.5% after visually inspecting 25.2%
of the stars. Over the two main analysis steps, rotation classification and
period selection, the pipeline yields a global agreement with the reference
values of 92.1% and 96.9% before and after visual inspection. Random forest
classifiers are efficient tools to determine reliable rotation periods in large
samples of stars. [abridged]


Abstract:
  This paper presents a compositional framework for the construction of
symbolic models for a network composed of a countably infinite number of
finite-dimensional discrete-time control subsystems. We refer to such a network
as infinite network. The proposed approach is based on the notion of
alternating simulation functions. This notion relates a concrete network to its
symbolic model with guaranteed mismatch bounds between their output behaviors.
We propose a compositional approach to construct a symbolic model for an
infinite network, together with an alternating simulation function, by
composing symbolic models and alternating simulation functions constructed for
subsystems. Assuming that each subsystem is incrementally input-to-state stable
and under some small-gain type conditions, we present an algorithm for orderly
constructing local symbolic models with properly designed quantization
parameters. In this way, the proposed compositional approach can provide us a
guideline for constructing an overall symbolic model with any desired
approximation accuracy. A compositional controller synthesis scheme is also
provided to enforce safety properties on the infinite network in a
decentralized fashion. The effectiveness of our result is illustrated through a
road traffic network consisting of infinitely many road cells.


Abstract:
  A well-known strategy for building effective preconditioners for higher-order
discretizations of some PDEs, such as Poisson's equation, is to leverage
effective preconditioners for their low-order analogs. In this work, we show
that high-quality preconditioners can also be derived for the Taylor-Hood
discretization of the Stokes equations in much the same manner. In particular,
we investigate the use of geometric multigrid based on the $\boldsymbol{
\mathbb{Q}}_1iso\boldsymbol{ \mathbb{Q}}_2/ \mathbb{Q}_1$ discretization of the
Stokes operator as a preconditioner for the $\boldsymbol{
\mathbb{Q}}_2/\mathbb{Q}_1$ discretization of the Stokes system. We utilize
local Fourier analysis to optimize the damping parameters for Vanka and
Braess-Sarazin relaxation schemes and to achieve robust convergence. These
results are then verified and compared against the measured multigrid
performance. While geometric multigrid can be applied directly to the
$\boldsymbol{ \mathbb{Q}}_2/\mathbb{Q}_1$ system, our ultimate motivation is to
apply algebraic multigrid within solvers for $\boldsymbol{
\mathbb{Q}}_2/\mathbb{Q}_1$ systems via the $\boldsymbol{
\mathbb{Q}}_1iso\boldsymbol{ \mathbb{Q}}_2/ \mathbb{Q}_1$ discretization, which
will be considered in a companion paper.


Abstract:
  Given a projective smooth curve $X$ over any field $k$ of characteristic not
$2$, we define the global $\mathbb{A}^1$ degree of a finite morphism of smooth
curves $f: X \to \mathbb{P}^1_k$ satisfying certain conditions, a
generalization of Morel's construction of $\mathbb{A}^1$-Brouwer degree of a
morphism $f: \mathbb{P}^1_k \to \mathbb{P}^1_k$ as well as Kass and
Wickelgren's construction of Euler numbers of global sections. We then prove
that under certain conditions on $N$, the global $\mathbb{A}^1$ degrees of
covering maps between modular curves $X_0(N) \to X(1)$, $X_1(N) \to X(1)$, and
$X(N) \to X(1)$ are sums of hyperbolic elements $\langle 1 \rangle + \langle -1
\rangle$ in the Grothendieck-Witt ring $\operatorname{GW}(k)$ for any field $k$
whose characteristic is coprime to $N$ and the pullback of
$\mathscr{O}_{\operatorname{P}^1}(1)$ is relatively orientable.


Abstract:
  Spectral properties of many finite convolution integral operators have been
understood by finding differential operators that commute with them. In this
paper we compile a complete list of such commuting pairs, extending previous
work to complex-valued and non self-adjoint operators. In addition, we
introduce a new kind of commutation relation, which we call sesquicommutation,
that also has implications for the spectral properties of the integral
operator. In this case we also compute a complete list of sequicommuting pairs
of integral and differential operators.


Abstract:
  Here, we conducted a photometric and astrometric study of two open stellar
clusters Dolidze-18 and Ruprecht-70, which have not been photometrically
studied before. The most important thing for using Gaia (DR2) database lies in
the positions, parallax, and proper motions, which make us split cluster
members from the field ones and get precise astrophysical parameters. On
studying the radial density profiles of these clusters, the actual sizes are
estimated and found larger using Gaia. From the color-magnitude diagrams and
theoretical isochrones, we simultaneously determined the ages, distance moduli,
and reddening of the two clusters. However, considering the parallaxes of Gaia
(DR2) for the cluster members, we calculated the cluster distance and confirmed
what we obtained from the color-magnitude diagram. Then, the Cartesian
galactocentric coordinates (Xo, Yo, Zo), and the distances from the galactic
center (Rg) were also estimated. According to the luminosity and mass
functions, the total luminosity and total mass of the clusters are estimated.
Our study shows that Ruprecht-70 is recently dynamically relaxed, while Dolidze
18 is not relaxed yet.


Abstract:
  We develop a theory of multiplicities and mixed multiplicities of
filtrations, extending the theory for filtrations of $m$-primary ideals to
arbitrary (not necessarily Noetherian) filtrations. The mixed multiplicities of
$r$ filtrations on an analytically unramified local ring $R$ come from the
coefficients of a suitable homogeneous polynomial in $r$ variables of degree
equal to the dimension of the ring, analogously to the classical case of the
mixed multiplicities of $m$-primary ideals in a local ring. We prove that the
Minkowski inequalities hold for arbitrary filtrations. The characterization of
equality in the Minkowski inequality for m-primary ideals in a local ring by
Teissier, Rees and Sharp and Katz does not extend to arbitrary filtrations, but
we show that they are true in a large and important subcategory of filtrations.
We define divisorial and bounded filtrations. The filtration of powers of a
fixed ideal is a bounded filtration, as is a divisorial filtration. We show
that in an excellent local domain, the characterization of equality in the
Minkowski equality is characterized by the condition that the integral closures
of suitable Rees like algebras are the same, strictly generalizing the theorem
of Teissier, Rees and Sharp and Katz. We also prove that a theorem of Rees
characterizing the inclusion of ideals with the same multiplicity generalizes
to bounded filtrations in excellent local domains. We give a number of other
applications, extending classical theorems for ideals.


Abstract:
  Aims. We present observations of the first coronal mass ejection (CME)
observed at the Solar Orbiter spacecraft on April 19, 2020, and the associated
Forbush decrease (FD) measured by its High Energy Telescope (HET). This CME is
a multispacecraft event also seen near Earth the next day. Methods. We
highlight the capabilities of HET for observing small short-term variations of
the galactic cosmic ray count rate using its single detector counters. The
analytical ForbMod model is applied to the FD measurements to reproduce the
Forbush decrease at both locations. Input parameters for the model are derived
from both in situ and remote-sensing observations of the CME. Results. The very
slow (~350 km/s) stealth CME caused a FD with an amplitude of 3 % in the
low-energy cosmic ray measurements at HET and 2 % in a comparable channel of
the Cosmic Ray Telescope for the Effects of Radiation (CRaTER) on the Lunar
Reconnaissance Orbiter, as well as a 1 % decrease in neutron monitor
measurements. Significant differences are observed in the expansion behavior of
the CME at different locations, which may be related to influence of the
following high speed solar wind stream. Under certain assumptions, ForbMod is
able to reproduce the observed FDs in low-energy cosmic ray measurements from
HET as well as CRaTER, but with the same input parameters, the results do not
agree with the FD amplitudes at higher energies measured by neutron monitors on
Earth. We study these discrepancies and provide possible explanations.
Conclusions. This study highlights that the novel measurements of the Solar
Orbiter can be coordinated with other spacecraft to improve our understanding
of space weather in the inner heliosphere. Multi-spacecraft observations
combined with data-based modeling are also essential to understand the
propagation and evolution of CMEs as well as their space weather impacts.


Abstract:
  This paper presents flexible storage codes, a class of error-correcting codes
that can recover information from a flexible number of storage nodes. As a
result, one can make a better use of the available storage nodes in the
presence of unpredictable node failures and reduce the data access latency. Let
us assume a storage system encodes $k\ell$ information symbols over a finite
field $\mathbb{F}$ into $n$ nodes, each of size $\ell$ symbols. The code is
parameterized by a set of tuples $\{(R_j,k_j,\ell_j): 1 \le j \le a\}$,
satisfying $k_1\ell_1=k_2\ell_2=...=k_a\ell_a$ and $k_1>k_2>...>k_a = k,
\ell_a=\ell$, such that the information symbols can be reconstructed from any
$R_j$ nodes, each node accessing $\ell_j$ symbols. In other words, the code
allows a flexible number of nodes for decoding to accommodate the variance in
the data access time of the nodes. Code constructions are presented for
different storage scenarios, including LRC (locally recoverable) codes, PMDS
(partial MDS) codes, and MSR (minimum storage regenerating) codes. We analyze
the latency of accessing information and perform simulations on Amazon clusters
to show the efficiency of presented codes.


Abstract:
  Correlations between a system and its environment lead to errors in an open
quantum system. Detecting those correlations would be valuable for avoiding
and/or correcting those errors. Here we show that we can detect correlations by
only measuring the system itself if we know the cause of the interaction
between the two, for example in the case of a dipole-dipole interaction. We
investigate the unitary $U$ which is associated with the exchange Hamiltonian
and examine the ability to detect initial correlations between a system and its
environment for various types of initial states. The states we select are
motivated by realistic experimental conditions and we provide bounds for when
we can state with certainty that there are initial system-environment
correlations given experimental data.


Abstract:
  This open-source book represents our attempt to make deep learning
approachable, teaching readers the concepts, the context, and the code. The
entire book is drafted in Jupyter notebooks, seamlessly integrating exposition
figures, math, and interactive examples with self-contained code. Our goal is
to offer a resource that could (i) be freely available for everyone; (ii) offer
sufficient technical depth to provide a starting point on the path to actually
becoming an applied machine learning scientist; (iii) include runnable code,
showing readers how to solve problems in practice; (iv) allow for rapid
updates, both by us and also by the community at large; (v) be complemented by
a forum for interactive discussion of technical details and to answer
questions.


Abstract:
  We present a ground-based optical transmission spectrum for the warm
Saturn-mass exoplanet WASP-110b from two transit observations made with the
FOcal Reducer and Spectrograph (FORS2) on the Very Large Telescope (VLT). The
spectrum covers the wavelength range from 4000 to 8333\AA, which is binned in
46 transit depths measured to an averaged precision of 220 parts per million
(ppm) over an averaged 80\AA~bin for a Vmag=12.8 star. The measured transit
depths are unaffected by a dilution from a close A-type field dwarf, which was
fully resolved. The overall main characteristic of the transmission spectrum is
an increasing radius with wavelength and a lack of the theoretically predicted
pressure-broadened sodium and potassium absorption features for a cloud-free
atmosphere. We analyze archival high-resolution optical spectroscopy and find
evidence for low to moderate activity of the host star, which we take into
account in the atmospheric retrieval analysis. Using the AURA retrieval code,
we find that the observed transmission spectrum can be best explained by a
combination of unocculted stellar faculae and a cloud deck. Transmission
spectra of cloud-free and hazy atmospheres are rejected at a high confidence.
With a possible cloud deck at its terminator, WASP-110b joins the increasing
population of irradiated hot-Jupiter exoplanets with cloudy atmospheres
observed in transmission.


Abstract:
  Equivariant neural networks have been successful in incorporating various
types of symmetries, but are mostly limited to vector representations of
geometric objects. Despite the prevalence of higher-order tensors in various
application domains, e.g. in quantum chemistry, equivariant neural networks for
general tensors remain underexplored. Previous strategies for learning
equivariant functions on tensors mostly rely on expensive tensor factorization
which is not scalable when the dimensionality of the problem becomes large. In
this work, we propose unitary $N$-body tensor equivariant neural network
(UNiTE), an architecture for a general class of symmetric tensors called
$N$-body tensors. The proposed neural network is equivariant with respect to
the actions of a unitary group, such as the group of 3D rotations. Furthermore,
it has a linear time complexity with respect to the number of non-zero elements
in the tensor. When applied to quantum chemistry, UNiTE in combination with a
low-cost physics-based molecular representation outperforms state-of-the-art
machine learning methods on multiple benchmarks. Finally, we show that UNiTE
achieves a robust zero-shot generalization performance on diverse down stream
chemistry tasks, while being three orders of magnitude faster than conventional
numerical methods with competitive accuracy.


Abstract:
  Traffic light management is a broad subject with various papers published
that put forth algorithms to efficiently manage traffic using traffic lights.
Two such algorithms are the OAF (oldest arrival first) and ITLC (intelligent
traffic light controller) algorithms. However, many traffic light algorithms do
not consider future traffic flow and therefore cannot mitigate traffic in such
a way as to reduce future traffic in the present. This paper presents the Edge
Load Management and Optimization through Pseudoflow Prediction (ELMOPP)
algorithm, which aims to solve problems detailed in previous algorithms;
through machine learning with nested long short-term memory (NLSTM) modules and
graph theory, the algorithm attempts to predict the near future using past data
and traffic patterns to inform its real-time decisions and better mitigate
traffic by predicting future traffic flow based on past flow and using those
predictions to both maximize present traffic flow and decrease future traffic
congestion. Furthermore, while ITLC and OAF require the use of GPS
transponders; and GPS, speed sensors, and radio, respectively, ELMOPP only uses
traffic light camera footage, something that is almost always readily available
in contrast to GPS and speed sensors. ELMOPP was tested against the ITLC and
OAF traffic management algorithms using a simulation modeled after the one
presented in the ITLC paper, a single-intersection simulation, and the
collected data supports the conclusion that ELMOPP statistically significantly
outperforms both algorithms in throughput rate, a measure of how many vehicles
are able to exit inroads every second.


Abstract:
  We consider abstraction-based design of output-feedback controllers for
non-linear dynamical systems against specifications over state-based predicates
in linear-time temporal logic (LTL). In this context, our contribution is
two-fold: (I) we generalize feedback-refinement relations for abstraction-based
output-feedback control to systems with arbitrary predicate and observation
maps, and (II) we introduce a new algorithm for the synthesis of abstract
output-feedback controllers w.r.t. LTL specifications over unobservable
state-based predicates.
  Our abstraction-based output-feedback controller synthesis algorithm consists
of two steps. First, we compute a finite state abstraction of the original
system using existing techniques. This process typically leads to an abstract
system with non-deterministic predicate and observation maps which are not
necessarily related to each other. Second, we introduce an algorithm to compute
an output-feedback controller for such abstract systems. Our algorithm is
inspired by reactive synthesis under partial observation and utilizes bounded
synthesis.


Abstract:
  We study solution sensitivity for nonlinear programs (NLPs) whose structures
are induced by graphs. These NLPs arise in many applications such as dynamic
optimization, stochastic optimization, optimization with partial differential
equations, and network optimization. We show that for a given pair of nodes,
the sensitivity of the primal-dual solution at one node against a data
perturbation at the other node decays exponentially with respect to the
distance between these two nodes on the graph. In other words, the solution
sensitivity decays as one moves away from the perturbation point. This result,
which we call exponential decay of sensitivity, holds under the strong
second-order sufficiency condition and the linear independence constraint
qualification. We also present conditions under which the decay rate remains
uniformly bounded; this allows us to characterize the sensitivity behavior of
NLPs defined over subgraphs of infinite graphs. The theoretical developments
are illustrated with numerical examples.


Abstract:
  We investigate the geometry of $C(K,X)$ and $\ell_{\infty}(X)$ spaces through
complemented subspaces of the form $\left(\bigoplus_{i\in
\varGamma}X_i\right)_{c_0}$. Concerning the geometry of $C(K,X)$ spaces we
extend some results of D. Alspach and E. M. Galego from \cite{AlspachGalego}.
On $\ell_{\infty}$-sums of Banach spaces we prove that if $\ell_{\infty}(X)$
has a complemented subspace isomorphic to $c_0(Y)$, then, for some $n \in
\mathbb{N}$, $X^n$ has a subspace isomorphic to $c_0(Y)$. We further prove the
following:
  (1) If $C(K)\sim c_0(C(K))$ and $C(L)\sim c_0(C(L))$ and
$\ell_{\infty}(C(K))\sim \ell_{\infty}(C(L))$, then $K$ and $L$ have the same
cardinality.
  (2) If $K_1$ and $K_2$ are infinite metric compacta, then
$\ell_{\infty}(C(K_1))\sim \ell_{\infty}(C(K_2))$ if and only if $C(K_1)$ is
isomorphic to $C(K_2)$.


Abstract:
  We analyse the forward error in the floating point summation of real numbers,
from algorithms that do not require recourse to higher precision or better
hardware. We derive informative explicit expressions, and new deterministic and
probabilistic bounds for errors in three classes of algorithms: general
summation,shifted general summation, and compensated (sequential) summation.
Our probabilistic bounds for general and shifted general summation hold to all
orders. For compensated summation, we also present deterministic and
probabilistic first and second order bounds, with a first order bound that
differs from existing ones. Numerical experiments illustrate that the bounds
are informative and that among the three algorithm classes, compensated
summation is generally the most accurate method.


Abstract:
  We discuss equilibration process in expanding universes as compared to the
thermalization process in Minkowski space--time. The final goal is to answer
the following question: Is the equilibrium reached before the rapid expansion
stops and quantum effects have a negligible effect on the background geometry
or stress--energy fluxes in a highly curved early Universe have strong effects
on the expansion rate and the equilibrium is reached only after the drastic
decrease of the space--time curvature? We argue that consideration of more
generic non--invariant states in theories with invariant actions is a necessary
ingredient to understand quantum field dynamics in strongly curved backgrounds.
We are talking about such states in which correlation functions are not
functions of such isometry invariants as geodesic distances, while having
correct UV behaviour. The reason to consider such states is the presence of IR
secular memory effects for generic time dependent backgrounds, which are
totally absent in equilibrium. These effects strongly affect the destiny of
observables in highly curved space--times.


Abstract:
  Quantum resource theory formulations of thermodynamics offer a versatile tool
for the study of fundamental limitations to the efficiency of physical
processes, independently of the microscopic details governing their dynamics.
Despite the ubiquitous presence of non-Markovian dynamics in open quantum
systems at the nanoscale, rigorous proofs of their beneficial effects on the
efficiency of quantum dynamical processes at the bio-molecular level have not
been reported yet. Here we combine the resource theory of athermality with
concepts from the theory of divisibility classes for quantum channels, to prove
that memory effects can increase the efficiency of photoisomerization to levels
that are not achievable under a purely thermal Markovian (i.e. memoryless)
evolution. This provides rigorous evidence that memory effects can provide a
resource in biological quantum dynamics, and, more generally, quantum
thermodynamics at the nanoscale.


Abstract:
  Let $\mathcal{T}$ be a rooted tree endowed with the natural partial order
$\preceq$. Let $(Z(v))_{v\in \mathcal{T}}$ be a sequence of independent
standard Gaussian random variables and let $\alpha = (\alpha_k)_{k=1}^\infty$
be a sequence of real numbers with $\sum_{k=1}^\infty \alpha_k^2<\infty$. Set
$\alpha_0 =0$ and define a Gaussian process on $\mathcal{T}$ in the following
way: \[ G(\mathcal{T}, \alpha; v): = \sum_{u\preceq v} \alpha_{|u|} Z(u), \quad
v \in \mathcal{T}, \] where $|u|$ denotes the graph distance between the vertex
$u$ and the root vertex. Under mild assumptions on $\mathcal{T}$, we obtain a
necessary and sufficient condition for the almost sure boundedness of the above
Gaussian process. Our condition is also necessary and sufficient for the almost
sure uniform convergence of the Gaussian process $G(\mathcal{T}, \alpha; v)$
along all rooted geodesic rays in $\mathcal{T}$.


Abstract:
  We study the effect of combining spin fluctuations and forward scattering
electron-phonon ({\eph}) coupling on the superconductivity in the
FeSe/SrTiO$_3$ system modeled by a phenomenological two-band Hubbard model with
long-range {\eph} interactions. We treat the electron and phonon degrees of
freedom on an equal footing using a \emph{fully} self-consistent FLEX plus
Migdal-Eliashberg calculation, which includes a self-consistent determination
of the spin fluctuation spectrum. Based on FeSe monolayers, we focus on the
case where one of the bands lies below the Fermi level (i.e. incipient), and
demonstrate that the combined interactions can enhance or suppress $T_c$,
depending on their relative strength. For a suitable choice of parameters, the
spin-fluctuation mechanism yields a $T_c \approx 46.8$ K incipient $s_\pm$
superconductor, consistent with surface-doped FeSe thin films. A
forward-focused {\eph} interaction further enhances the $T_c$, as observed in
monolayer FeSe on SrTiO$_3$.


Abstract:
  We study mechanisms for selling a single item when buyers have private values
for their outside options, which they forego by participating in the mechanism.
This substantially changes the revenue maximization problem. For example, the
seller can strictly benefit from selling lotteries already in the single-buyer
setting. We bound the menu size and the sample complexity for the optimal
single-buyer mechanism. We then show that posting a single price is in fact
optimal under the assumption that either (1) the outside option value is
independent of the item value, and the item value distribution has decreasing
marginal revenue or monotone hazard rate; or (2) the outside option value is a
concave function of the item value. Moreover, when there are multiple buyers,
we show that sequential posted pricing guarantees a large fraction of the
optimal revenue under similar conditions.


Abstract:
  In this paper we prove the case $dim(V_3)=3$ of a conjecture about the
exterior operad ${\Lambda}^{S^2}_{V_d}$. For this we introduce a collection of
natural involutions on the set of homogeneous cycle-free $d$-partitions of the
complete graph $K_{2d}$, and show that these involutions correspond to the
relations in ${\Lambda}^{S^2}_{V_d}(2d+1)$. When $d=3$ this correspondence
allows us to give an explicit description of a determinant-like map and to
settle the above mentioned conjecture.


Abstract:
  Stochastic hybrid systems involve a coupling between a discrete Markov chain
and a continuous stochastic process. If the latter evolves deterministically
between jumps in the discrete state, then the system reduces to a piecewise
deterministic Markov process (PDMP). Well known examples include stochastic
gene expression, voltage fluctuations in neurons, and motor-driven
intracellular transport. In this paper we use coherent spin states to construct
a new path integral representation of the probability density functional for
stochastic hybrid systems, which holds outside the weak noise regime. We use
the path integral to derive a system of Langevin equations in the
semi-classical limit, which extends previous diffusion approximations based on
a quasi-steady-state reduction. We then show how in the weak noise limit the
path integral is equivalent to an alternative representation that was
previously derived using Doi-Peliti operators. The action functional of the
latter is related to a large deviation principle for stochastic hybrid systems.


Abstract:
  Due to the low error tolerance of a qubit, detecting and correcting errors on
it is essential for fault-tolerant quantum computing. Surface code (SC)
associated with its decoding algorithm is one of the most promising quantum
error correction (QEC) methods. % One of the challenges of QEC is its high
complexity and computational demand. QEC needs to be very power-efficient since
the power budget is limited inside of a dilution refrigerator for
superconducting qubits by which one of the most successful quantum computers
(QCs) is built. In this paper, we propose an online-QEC algorithm and its
hardware implementation with SFQ-based superconducting digital circuits. We
design a key building block of the proposed hardware with an SFQ cell library
and evaluate it by the SPICE-level simulation. Each logic element is composed
of about 3000 Josephson junctions and power consumption is about 2.78 uW when
operating with 2 GHz clock frequency which meets the required decoding speed.
Our decoder is simulated on a quantum error simulator for code distances 5 to
13 and achieves a 1.0% accuracy threshold.


Abstract:
  We study scalarization of slowly rotating black holes in the
Einstein-scalar-Gauss-Bonnet (GB)-Chern-Simons (CS) theory. In the slow
rotation approximation of $a\ll1$ with rotation parameter $a$, the GB term is
given by a term for Schwarzschild black hole, whereas the CS term takes a
linear term of $a$. The tachyonic instability for slowly rotating black holes
represents the onset of spontaneous scalarization. We use the (2+1)-dimensional
hyperboloidal foliation method to show the tachyonic instability for slowly
rotating black holes by considering the time evolution of a spherically
symmetric scalar mode. A threshold (existence) curve is obtained from the
constant scalar modes under time evolution, which means the boundary between
stable and unstable black holes. It is found that the slowly rotating black
holes turn out to be unstable against a spherically symmetric scalar-mode
propagation for positive coupling $\alpha$. However, we could not find
tachyonic instability and any $a$-bound for scalarization for negative coupling
$\alpha$.