Abstract:
  With the rapid development of railways, especially high-speed railways, there
is an increasingly urgent demand for new wireless communication system for
railways. Taking the mature 5G technology as an opportunity, 5G-railways (5G-R)
have been widely regarded as a solution to meet the diversified demands of
railway wireless communications. For the design, deployment and improvement of
5G-R networks, radio communication scenario classification plays an important
role, affecting channel modeling and system performance evaluation. In this
paper, a standardized radio communication scenario classification, including 18
scenarios, is proposed for 5G-R. This paper analyzes the differences of 5G-R
scenarios compared with the traditional cellular networks and GSM-railways,
according to 5G-R requirements and the unique physical environment and
propagation characteristics. The proposed standardized scenario classification
helps deepen the research of 5G-R and promote the development and application
of the existing advanced technologies in railways.


Abstract:
  The evolution of massive stars is influenced by the mass lost to stellar
winds over their lifetimes. These winds limit the masses of the stellar
remnants (such as black holes) that the stars ultimately produce. We use radio
astrometry to refine the distance to the black hole X-ray binary Cygnus X-1,
which we find to be $2.22^{+0.18}_{-0.17}$ kiloparsecs. When combined with
previous optical data, this implies a black hole mass of $21.2\pm2.2$ solar
masses, higher than previous measurements. The formation of such a high-mass
black hole in a high-metallicity system constrains wind mass loss from massive
stars.


Abstract:
  The energy available in Micro Grid (MG) that is powered by solar energy is
tightly related to the weather conditions in the moment of generation. Very
short-term forecast of solar irradiance provides the MG with the capability of
automatically controlling the dispatch of energy. We propose to achieve this
using a data acquisition systems (DAQ) that simultaneously records sky imaging
and Global Solar Irradiance (GSI) measurements, with the objective of
extracting features from clouds and use them to forecast the power produced by
a Photovoltaic (PV) system. The DAQ system is nicknamed as the \emph{Girasol
Machine} (Girasol means Sunflower in Spanish). The sky imaging system consists
of a longwave infrared (IR) camera and a visible (VI) light camera with a
fisheye lens attached to it. The cameras are installed inside a weatherproof
enclosure that it is mounted on an outdoor tracker. The tracker updates its pan
an tilt every second using a solar position algorithm to maintain the Sun in
the center of the IR and VI images. A pyranometer is situated on a horizontal
support next to the DAQ system to measure GSI. The dataset, composed of IR
images, VI images, GSI measurements, and the Sun's positions, has been tagged
with timestamps.


Abstract:
  The $\mu\nu$SSM is a highly predictive alternative model to the MSSM. In
particular, the electroweak sector of the model can explain the longstanding
discrepancy between the experimental result for the anomalous magnetic moment
of the muon, $(g-2)_\mu$, and its Standard Model prediction, while being in
agreement with all other theoretical and experimental constraints. The recently
published MUON G-2 result is within $0.8\,\sigma$ in agreement with the older
BNL result on $(g-2)_\mu$. The combined result was announced as $a_\mu^{\rm
exp} = (11659206.1 \pm 4.1) \times 10^{-10}$, yielding a new deviation from the
Standard Model prediction of $\Delta a_\mu = (25.1 \pm 5.9) \times 10^{-10}$,
corresponding to $4.2\,\sigma$. Using this improved bound we update the
analysis in the $\mu\nu$SSM as presented in Ref. [1] and set new limits on the
allowed parameters space of the electroweak sector of the model. We conclude
that significant regions of the model can explain the new $(g-2)_\mu$ data.


Abstract:
  It is well known that the existence of traveling wave solutions (TWS) for
many partial differential equations (PDE) is a consequence of the fact that an
associated planar ordinary differential equation (ODE) has certain types of
solutions defined for all time. In this paper we address the problem of
persistence of TWS of a given PDE under small perturbations. Our main results
deal with the situation where the associated ODE has a center and, as a
consequence, the original PDE has a continuum of periodic traveling wave
solutions. We prove that the TWS that persist are controlled by the zeroes of
some Abelian integrals. We apply our results to several famous PDE, like the
Ostrovsky, Klein-Gordon, sine-Gordon, Korteweg-de Vries, Rosenau-Hyman,
Camassa-Holm, and Boussinesq equations.


Abstract:
  Dirac points are found to emerge due to the crossing of bands in the
electronic structure of bilayer graphene for configurations in which the
alignment between two hexagonal lattices preserves the parallelism of the
armchair/zigzag lines between two layers. On the base of electronic
calculations using a tight-binding model for the $\pi$ bands it is shown that
the crossing of the energy-band dispersion curves occurs in the vicinity of the
corner points of the hexagonal Brillouin zone. Group representation theory
analysis confirms the emergence of such Dirac points. It is demonstrated that
the band crossings at generic $\mathbf{k}$ points are guaranteed by the
compatibility relations between the symmetries of eigenstates at the
high-symmetry $\mathbf{k}$ points in the Brillouin zone. The presence of Dirac
points governs the geometrical properties of the energy surfaces, and thus the
topological structure of the Fermi energy surface and the energy spectrum.


Abstract:
  We give a brief overview of phenomenological developments in the analysis of
angular observables for exclusive decay modes of $B$ mesons and $\Lambda_b$
baryons, with focus on recent results and some important aspects related to the
theoretical background (which mostly concern the treatment of hadronic
uncertainties).


Abstract:
  Three-dimensional topological insulator (TI) nanowires with quantized surface
subband spectra are studied as a main component of Majorana bound states (MBS)
devices. However, such wires are known to have large concentration $N \sim
10^{19}$ cm$^{-3}$ of Coulomb impurities. It is believed that a MBS device can
function only if the amplitude of long-range fluctuations of the random Coulomb
potential $\Gamma$ is smaller than the subband gap $\Delta$. Here we calculate
$\Gamma$ for recently experimentally studied large-dielectric-constant
(Bi$_{1-x}$Sb$_x$)$_2$Te$_{3}$ wires in a small-dielectric-constant environment
(no superconductor). We show that provided by such a dielectric-constant
contrast, the confinement of electric field of impurities within the wire
allows more distant impurities to contribute into $\Gamma$, leading to $\Gamma
\sim 3\Delta$. We also calculate a TI wire resistance as a function of the
Fermi level and carrier concentration due to scattering on Coulomb and neutral
impurities, and do not find observable discrete subband-spectrum related
oscillations at $N \gtrsim 10^{18}$ cm$^{-3}$.


Abstract:
  We discuss the role of quantum coherence in the energy fluctuations of open
quantum systems. To this aim, we introduce a protocol, to which we refer to as
the end-point-measurement scheme, allowing to define the statistics of energy
changes as a function of energy measurements performed only after the evolution
of the initial state. At the price of an additional uncertainty on the initial
energies, this approach prevents the loss of initial quantum coherences and
enables the estimation of their effects on energy fluctuations. We demonstrate
our findings by running an experiment on the IBM Quantum Experience
superconducting qubit platform.


Abstract:
  In this work, we introduce the notion of decisional width of a finite
relational structure and the notion of decisional width of a regular class of
finite structures. Our main result states that given a first-order formula
{\psi} over a vocabulary {\tau}, and a finite automaton F over a suitable
alphabet B({\Sigma},w,{\tau}) representing a width-w regular-decisional class
of {\tau}-structures C, one can decide in time f({\tau},{\Sigma},{\psi},w)|F|
whether some {\tau}-structure in C satisfies {\psi}. Here, f is a function that
depends on the parameters {\tau},{\Sigma},{\psi},w, but not on the size of the
automaton F representing the class. Therefore, besides implying that the
first-order theory of any given regular-decisional class of finite structures
is decidable, it also implies that when the parameters {\tau}, {\psi}, {\Sigma}
and w are fixed, decidability can be achieved in linear time on the size of the
input automaton F. Building on the proof of our main result, we show that the
problem of counting satisfying assignments for a first-order logic formula in a
given structure A of width w is fixed-parameter tractable with respect to w,
and can be solved in quadratic time on the length of the input representation
of A.


Abstract:
  Off-policy evaluation (OPE) leverages data generated by other policies to
evaluate a target policy. Previous OPE methods mainly focus on precisely
estimating the true performance of a policy. We observe that in many
applications, (1) the end goal of OPE is to compare two or multiple candidate
policies and choose a good one, which is actually a much simpler task than
evaluating their true performance; and (2) there are usually multiple policies
that have been deployed in real-world systems and thus whose true performance
is known through serving real users. Inspired by the two observations, in this
work, we define a new problem, supervised off-policy ranking (SOPR), which aims
to rank a set of new/target policies based on supervised learning by leveraging
off-policy data and policies with known performance. We further propose a
method for supervised off-policy ranking that learns a policy scoring model by
correctly ranking training policies with known performance rather than
estimating their precise performance. Our method leverages logged states and
policies to learn a Transformer based model that maps offline interaction data
including logged states and the actions taken by a target policy on these
states to a score. Experiments on different games, datasets, training policy
sets, and test policy sets show that our method outperforms strong baseline OPE
methods in terms of both rank correlation and performance gap between the truly
best and the best of the ranked top three policies. Furthermore, our method is
more stable than baseline methods.


Abstract:
  We address the problem of class incremental learning, which is a core step
towards achieving adaptive vision intelligence. In particular, we consider the
task setting of incremental learning with limited memory and aim to achieve
better stability-plasticity trade-off. To this end, we propose a novel
two-stage learning approach that utilizes a dynamically expandable
representation for more effective incremental concept modeling. Specifically,
at each incremental step, we freeze the previously learned representation and
augment it with additional feature dimensions from a new learnable feature
extractor. This enables us to integrate new visual concepts with retaining
learned knowledge. We dynamically expand the representation according to the
complexity of novel concepts by introducing a channel-level mask-based pruning
strategy. Moreover, we introduce an auxiliary loss to encourage the model to
learn diverse and discriminate features for novel concepts. We conduct
extensive experiments on the three class incremental learning benchmarks and
our method consistently outperforms other methods with a large margin.


Abstract:
  This paper presents models and optimization algorithms to jointly optimize
the design and control of the transmission of electric vehicles equipped with
one central electric motor (EM). First, considering the required traction power
to be given, we identify a convex speed-dependent loss model for the EM.
Second, we leverage such a model to devise computationally-efficient algorithms
to determine the energy-optimal design and control strategies for the
transmission. In particular, with the objective of minimizing the EM energy
consumption on a given drive cycle, we analytically compute the optimal
gear-ratio trajectory for a continuously variable transmission (CVT) and the
optimal gear-ratio design for a fixed-gear transmission (FGT) in closed form,
whilst efficiently solving the combinatorial joint gear-ratio design and
control problem for a multiple-gear transmission (MGT), combining convex
analysis and dynamic programming in an iterative fashion. Third, we validate
our models with nonlinear simulations, and benchmark the optimality of our
methods with mixed-integer quadratic programming. Finally, we showcase our
framework in a case-study for a family vehicle, whereby we leverage the
computational efficiency of our methods to jointly optimize the EM size via
exhaustive search. Our numerical results show that by using a 2-speed MGT, the
energy consumption of an electric vehicle can be reduced by 3% and 2.5%
compared to an FGT and a CVT, respectively, whilst further increasing the
number of gears may even be detrimental due to the additional weight


Abstract:
  In adaptive sliding mode control methods, an updating gain strategy
associated with finite-time convergence to the sliding set is essential to deal
with matched bounded perturbations with unknown upper-bound. However, the
estimation of the finite time of any adaptive design is a complicated task
since it depends not only on the upper-bound of unknown perturbation but also
on the size of initial conditions. This brief proposes a uniform adaptive
reaching phase strategy (ARPS) within a predefined reaching-time. Moreover, as
a case of study, the barrier function approach is extended for perturbed MIMO
systems with uncertain control matrix. The usage of proposed ARPS in the MIMO
case solves simultaneously two issues: giving a uniform reaching phase with a
predefined reaching-time and adapting to the perturbation norm while in a
predefined vicinity of the sliding manifold.


Abstract:
  An independent broadcast on a graph $G$ is a function $f: V \longrightarrow
\{0,\ldots,{\rm diam}(G)\}$ such that $(i)$ $f(v)\leq e(v)$ for every vertex
$v\in V(G)$, where $\operatorname{diam}(G)$ denotes the diameter of $G$ and
$e(v)$ the eccentricity of vertex $v$, and $(ii)$ $d(u,v) > \max \{f(u),
f(v)\}$ for every two distinct vertices $u$ and $v$ with $f(u)f(v)>0$. The
broadcast independence number $\beta_b(G)$ of $G$ is then the maximum value of
$\sum_{v \in V} f(v)$, taken over all independent broadcasts on $G$. We prove
that every circulant graph of the form $C(n;1,a)$, $3\le a\le
\lfloor\frac{n}{2} \rfloor$, admits an optimal $2$-bounded independent
broadcast, that is, an independent broadcast~$f$ satisfying $f(v)\le 2$ for
every vertex $v$, except when $n=2a+1$, or $n=2a$ and $a$ is even. We then
determine the broadcast independence number of various classes of such
circulant graphs, and prove that, for most of these classes, the equality
$\beta_b(C(n;1,a)) = \alpha(C(n;1,a))$ holds, where $\alpha(C(n;1,a))$ denotes
the independence number of $C(n;1,a)$.


Abstract:
  We consider two particles hopping on a chain with a contact interaction
between them. At strong interaction, there is a molecular bound state separated
by a direct gap from a continuous band of atomic states. Introducing weak
disorder in the interaction, the molecular state becomes Anderson localized. At
stronger disorder, part of the molecular band delocalizes and dissociates due
to its hybridization to the atomic band. We characterize these different
regimes by computing the density of states, the inverse participation ratio,
the level-spacing statistics and the survival probability of an initially
localized state. The atomic band is best described as that of a rough billiard
for a single particle on a square lattice that shows signatures of quantum
chaos. In addition to typical "chaotic states", we find states that are
localized along only one direction. These "separatrix states" are more
localized than chaotic states, and similar in this respect to scarred states,
but their existence is due to the separatrix iso-energy line in the
interaction-free dispersion relation, rather than to unstable periodic orbits.


Abstract:
  We consider normal velocity of smooth sets evolving by the $s-$fractional
diffusion. We prove that for small time, the normal velocity of such sets is
nearly proportional to the mean curvature of the boundary of the initial set
for $s\in [\frac{1}{2}, 1)$ while, for $s\in (0, \frac{1}{2})$, it is nearly
proportional to the fractional mean curvature of the initial set. Our results
show that the motion by (fractional) mean curvature flow can be approximated by
fractional heat diffusion and by a diffusion by means of harmonic extension of
smooth sets.


Abstract:
  This work is devoted to the study of integral $p$-adic Hodge theory in the
context of Artin stacks. For a Hodge-proper stack, using the formalism of
prismatic cohomology, we establish a version of $p$-adic Hodge theory with the
\'etale cohomology of the Raynaud generic fiber as an input. In particular, we
show that the corresponding Galois representation is crystalline and that the
associated Breuil-Kisin module is given by the prismatic cohomology. An
interesting new feature of the stacky setting is that the natural map between
\'etale cohomology of the algebraic and the Raynaud generic fibers is often an
equivalence even outside of the proper case. In particular, we show that this
holds for global quotients $[X/G]$ where $X$ is a smooth proper scheme and $G$
is a reductive group. As applications we deduce Totaro's conjectural inequality
and also set up a theory of $A_{\mathrm{inf}}$-characteristic classes.


Abstract:
  Hermitian theories play a major role in understanding the physics of most
phenomena. It has been found only in the past decade that non-Hermiticity
enables unprecedented effects such as exceptional points, spectral
singularities and bulk Fermi arcs. Recent studies further show that
non-Hermiticity can fundamentally change the topological band theory, leading
to the non-Hermitian band topology and non-Hermitian skin effect, as confirmed
in one-dimensional (1D) systems. However, in higher dimensions, these
non-Hermitian effects remain unexplored in experiments. Here, we demonstrate
the spin-polarized, higher-order non-Hermitian skin effect in two-dimensional
(2D) acoustic metamaterials. Using a lattice of coupled whisper-gallery
acoustic resonators, we realize a spinful 2D higher-order topological insulator
(HOTI) where the spin-up and spin-down states are emulated by the
anti-clockwise and clockwise modes, respectively. We find that the
non-Hermiticity drives wave localizations toward opposite edge boundaries
depending on the spin polarizations. More interestingly, for finite systems
with both edge and corner boundaries, the higher-order non-Hermitian skin
effect leads to wave localizations toward two corner boundaries for the bulk,
edge and corner states in a spin-dependent manner. We further show that such a
non-Hermitian skin effect enables rich wave manipulation through the loss
configuration in each unit-cell. The reported spin-dependent, higher-order
non-Hermitian skin effect reveals the interplay between higher-order topology
and non-Hermiticity, which is further enriched by the spin degrees of freedom.
This unveils a new horizon in the study of non-Hermitian physics and the design
of non-Hermitian metamaterials.


Abstract:
  Recent studies have revealed a security threat to natural language processing
(NLP) models, called the Backdoor Attack. Victim models can maintain
competitive performance on clean samples while behaving abnormally on samples
with a specific trigger word inserted. Previous backdoor attacking methods
usually assume that attackers have a certain degree of data knowledge, either
the dataset which users would use or proxy datasets for a similar task, for
implementing the data poisoning procedure. However, in this paper, we find that
it is possible to hack the model in a data-free way by modifying one single
word embedding vector, with almost no accuracy sacrificed on clean samples.
Experimental results on sentiment analysis and sentence-pair classification
tasks show that our method is more efficient and stealthier. We hope this work
can raise the awareness of such a critical security risk hidden in the
embedding layers of NLP models. Our code is available at
https://github.com/lancopku/Embedding-Poisoning.


Abstract:
  Fast and light-weight methods for animating 3D characters are desirable in
various applications such as computer games. We present a learning-based
approach to enhance skinning-based animations of 3D characters with vivid
secondary motion effects. We design a neural network that encodes each local
patch of a character simulation mesh where the edges implicitly encode the
internal forces between the neighboring vertices. The network emulates the
ordinary differential equations of the character dynamics, predicting new
vertex positions from the current accelerations, velocities and positions.
Being a local method, our network is independent of the mesh topology and
generalizes to arbitrarily shaped 3D character meshes at test time. We further
represent per-vertex constraints and material properties such as stiffness,
enabling us to easily adjust the dynamics in different parts of the mesh. We
evaluate our method on various character meshes and complex motion sequences.
Our method can be over 30 times more efficient than ground-truth physically
based simulation, and outperforms alternative solutions that provide fast
approximations.


Abstract:
  With the ever-increasing complexity of neural language models, practitioners
have turned to methods for understanding the predictions of these models. One
of the most well-adopted approaches for model interpretability is feature-based
interpretability, i.e., ranking the features in terms of their impact on model
predictions. Several prior studies have focused on assessing the fidelity of
feature-based interpretability methods, i.e., measuring the impact of dropping
the top-ranked features on the model output. However, relatively little work
has been conducted on quantifying the robustness of interpretations. In this
work, we assess the robustness of interpretations of neural text classifiers,
specifically, those based on pretrained Transformer encoders, using two
randomization tests. The first compares the interpretations of two models that
are identical except for their initializations. The second measures whether the
interpretations differ between a model with trained parameters and a model with
random parameters. Both tests show surprising deviations from expected
behavior, raising questions about the extent of insights that practitioners may
draw from interpretations.


Abstract:
  The cybersecurity of smart grids has become one of key problems in developing
reliable modern power and energy systems. This paper introduces a
non-stationary adversarial cost with a variation constraint for smart grids and
enables us to investigate the problem of optimal smart grid protection against
cyber attacks in a relatively practical scenario. In particular, a Bayesian
multi-node bandit (MNB) model with adversarial costs is constructed and a new
regret function is defined for this model. An algorithm called Thompson-Hedge
algorithm is presented to solve the problem and the superior performance of the
proposed algorithm is proven in terms of the convergence rate of the regret
function. The applicability of the algorithm to real smart grid scenarios is
verified and the performance of the algorithm is also demonstrated by numerical
examples.


Abstract:
  Recent high-resolution interferometric images of submillimetre galaxies
(SMGs) reveal fascinatingly complex morphologies. This raises a number of
questions: how does the relative orientation of a galaxy affect its observed
submillimetre emission, and does this result in an `orientation bias' in the
selection and analysis of such galaxies in flux-limited cosmological surveys?
We investigate these questions using the Simba cosmological simulation paired
with the dust radiative transfer code Powderday. We select eight simulated SMGs
($S_{850}\gtrsim2$ mJy) at $z = 2$, and measure the variance of their
`observed' emission over 50 random orientations. Each galaxy exhibits
significant scatter in its emission close to the peak of the thermal dust
emission, with variation in flux density of up to $\sim$50 mJy at the peak.
This results in an appreciable dispersion in the inferred dust temperatures and
infrared luminosities ($16^{\mathrm{th}}-84^{\mathrm{th}}$ percentile ranges of
5 K and 0.1 dex, respectively) and therefore a fundamental uncertainty in
derived parameters such as dust mass and star formation rate ($\sim$30% for the
latter using simple calibrations). Using a Monte Carlo simulation we also
assess the impact of orientation on flux-limited surveys, finding a bias in the
selection of SMGs towards those with face-on orientations, as well as those at
lower redshifts. We predict that the orientation bias will affect flux-limited
single-dish surveys, most significantly at THz frequencies, and this bias
should be taken into account when placing the results of targeted follow-up
studies in a statistical context.


Abstract:
  In this letter, we report a polarization-entangled photon-pair source based
on type-II spontaneous parametric down conversion at telecom O-band in
periodically poled silica fiber (PPSF). The photon-pair source exhibits more
than 130 nm (~24 THz) emission bandwidth centered at 1306.6 nm. The broad
emission spectrum results in a short biphoton correlation time and we
experimentally demonstrate a Hong-Ou-Mandel interference dip with a full width
of 26.6 fs at half maximum. Owing to the low birefringence of the PPSF, the
biphotons generated from type-II SPDC are polarization-entangled over the
entire emission bandwidth, with a measured fidelity to a maximally entangled
state greater than 95.4%. The biphoton source provides the broadest bandwidth
entangled biphotons at O-band to our knowledge.


Abstract:
  Using response theory, we calculate the charge-current vortex generated by
spin pumping at a point-like contact in a system with Rashba spin-orbit
coupling. We discuss the spatial profile of the current density for finite
temperature and for the zero-temperature limit. The main observation is that
the Rashba spin precession leads to a charge current that oscillates as a
function of the distance from the spin-pumping source, which is confirmed by
numerical simulations. In our calculations, we consider a Rashba model on a
square lattice, for which we first review the basic properties related to
charge and spin transport. In particular, we define the charge- and
spin-current operators for the tight-binding Hamiltonian as the currents
coupled linearly with the U(1) and SU(2) gauge potentials, respectively. By
analogy to the continuum model, the spin-orbit-coupling Hamiltonian on the
lattice is then introduced as the generator of the spin current.


Abstract:
  The first work of Dyson relating to random matrix theory, "The dynamics of a
disordered linear chain", is reviewed. Contained in this work is an exact
solution of a so-called Type I chain in the case of the disorder variables
being given by a gamma distribution. The exact solution exhibits a singularity
in the density of states about the origin, which has since been shown to be
universal for one-dimensional tight binding models with off diagonal disorder.
We discuss this context and also point out some universal features of the weak
disorder expansion of the exact solution near the band edge. Further, a link
between the exact solution, and a tridiagonal formalism of anti symmetric
Gaussian $\beta$-ensembles with $\beta$ proportional to $1/N$, is made.


Abstract:
  Deep learning is finding its way into high energy physics by replacing
traditional Monte Carlo simulations. However, deep learning still requires an
excessive amount of computational resources. A promising approach to make deep
learning more efficient is to quantize the parameters of the neural networks to
reduced precision. Reduced precision computing is extensively used in modern
deep learning and results to lower execution inference time, smaller memory
footprint and less memory bandwidth. In this paper we analyse the effects of
low precision inference on a complex deep generative adversarial network model.
The use case which we are addressing is calorimeter detector simulations of
subatomic particle interactions in accelerator based high energy physics. We
employ the novel Intel low precision optimization tool (iLoT) for quantization
and compare the results to the quantized model from TensorFlow Lite. In the
performance benchmark we gain a speed-up of 1.73x on Intel hardware for the
quantized iLoT model compared to the initial, not quantized, model. With
different physics-inspired self-developed metrics, we validate that the
quantized iLoT model shows a lower loss of physical accuracy in comparison to
the TensorFlow Lite model.


Abstract:
  In this paper, we investigate the newly developed $f(R,\mathbf{T}^2)$ theory
($R$ is the Ricci scalar and $\mathbf{T}^2=T_{\alpha\beta}T^{\alpha\beta},~T
_{\alpha\beta}$ demonstrates the energy-momentum tensor) to explore some viable
cosmological models. For this purpose, we use the Noether symmetry approach in
the context of flat Friedmann-Robertson-Walker (FRW) universe. We solve the
Noether equations of this modified theory for two types of models and obtain
the symmetry generators as well as corresponding conserved quantities. We also
evaluate exact solutions and investigate their physical behavior via different
cosmological parameters. For the prospective models, the graphical behavior of
these parameters indicate consistency with recent observations representing
accelerated expansion of the universe. In the first case, we take a special
model of this theory and obtain new class of exact solutions with the help of
conserved quantities. Secondly, we consider minimal and non-minimal coupling
models of $f(R,\mathbf{T} ^{2})$ gravity. We conclude that conserved quantities
are very useful to derive the exact solutions that are used to study the cosmic
accelerated expansion.


Abstract:
  The homological theory of Auslander-Platzeck-Todorov on idempotent ideals
laid much of the groundwork for higher Auslander-Reiten theory, providing the
key technical lemmas for both higher Auslander correspondence as well as the
construction of higher Nakayama algebras, among other results. Given a
finite-dimensional algebra $A$ and idempotent $e$, we expand on a criterion of
Jasso-K\"ulshammer in order to determine a correspondence between the
$2$-precluster-tilting subcategories of $\mathrm{mod}(A)$ and
$\mathrm{mod}(A/\langle e\rangle)$. This is then applied in the context of
generalising dimer algebras on surfaces with boundary idempotent.


Abstract:
  The success of deep learning in computer vision has inspired the scientific
community to explore new analysis methods. Within the field of neuroscience,
specifically in electrophysiological neuroimaging, researchers are starting to
explore leveraging deep learning to make predictions on their data without
extensive feature engineering. This paper compares deep learning using
minimally processed EEG raw data versus deep learning using EEG spectral
features using two different deep convolutional neural architectures. One of
them from Putten et al. (2018) is tailored to process raw data; the other was
derived from the VGG16 vision network (Simonyan and Zisserman, 2015) which is
designed to process EEG spectral features. We apply them to classify sex on
24-channel EEG from a large corpus of 1,574 participants. Not only do we
improve on state-of-the-art classification performance for this type of
classification problem, but we also show that in all cases, raw data
classification leads to superior performance as compared to spectral EEG
features. Interestingly we show that the neural network tailored to process EEG
spectral features has increased performance when applied to raw data
classification. Our approach suggests that the same convolutional networks used
to process EEG spectral features yield superior performance when applied to EEG
raw data.


Abstract:
  For infinitesimal learning rates, stochastic gradient descent (SGD) follows
the path of gradient flow on the full batch loss function. However moderately
large learning rates can achieve higher test accuracies, and this
generalization benefit is not explained by convergence bounds, since the
learning rate which maximizes test accuracy is often larger than the learning
rate which minimizes training loss. To interpret this phenomenon we prove that
for SGD with random shuffling, the mean SGD iterate also stays close to the
path of gradient flow if the learning rate is small and finite, but on a
modified loss. This modified loss is composed of the original loss function and
an implicit regularizer, which penalizes the norms of the minibatch gradients.
Under mild assumptions, when the batch size is small the scale of the implicit
regularization term is proportional to the ratio of the learning rate to the
batch size. We verify empirically that explicitly including the implicit
regularizer in the loss can enhance the test accuracy when the learning rate is
small.


Abstract:
  A Stick graph G=(A\cup B, E) is the intersection graph of a set A of
horizontal segments and a set B of vertical segments in the plane, whose left
and respectively bottom endpoints lie on the same ground line with slope -1.
These endpoints are respectively called A-origins and B-origins. When a total
order is provided for the A-origins, the resulting graphs are called A-Stick
graphs.
  In this paper, we propose a characterization of the class of A-Stick graphs
using forced pairs, which are pairs of segments in B with the property that
only one left-to-right order of their origins is possible on the ground line.
We deduce a recognition algorithm for A-Stick graphs running in O(|A|+|B|+|E|)
time, thus improving the running time of O(|A|\cdot |B|) of the best current
algorithm. We also introduce the problem of finding, for a Stick graph, a
representation using segments of minimum total length. The canonical order on
the A- and B-origins, output by our recognition algorithm, allows us to obtain
partial results on this problem.


Abstract:
  It is generally recognized that finite learning rate (LR), in contrast to
infinitesimal LR, is important for good generalization in real-life deep nets.
Most attempted explanations propose approximating finite-LR SGD with Ito
Stochastic Differential Equations (SDEs), but formal justification for this
approximation (e.g., (Li et al., 2019)) only applies to SGD with tiny LR.
Experimental verification of the approximation appears computationally
infeasible. The current paper clarifies the picture with the following
contributions: (a) An efficient simulation algorithm SVAG that provably
converges to the conventionally used Ito SDE approximation. (b) A theoretically
motivated testable necessary condition for the SDE approximation and its most
famous implication, the linear scaling rule (Goyal et al., 2017), to hold. (c)
Experiments using this simulation to demonstrate that the previously proposed
SDE approximation can meaningfully capture the training and generalization
properties of common deep nets.


Abstract:
  Text classifiers are at the core of many NLP applications and use a variety
of algorithmic approaches and software. This paper introduces infrastructure
and methodologies for text classifiers based on large-scale regular
expressions. In particular, we describe how Facebook determines if a given
piece of text - anything from a hashtag to a post - belongs to a narrow topic
such as COVID-19. To fully define a topic and evaluate classifier performance
we employ human-guided iterations of keyword discovery, but do not require
labeled data. For COVID-19, we build two sets of regular expressions: (1) for
66 languages, with 99% precision and recall >50%, (2) for the 11 most common
languages, with precision >90% and recall >90%. Regular expressions enable
low-latency queries from multiple platforms. Response to challenges like
COVID-19 is fast and so are revisions. Comparisons to a DNN classifier show
explainable results, higher precision and recall, and less overfitting. Our
learnings can be applied to other narrow-topic classifiers.


Abstract:
  This paper is dealing with another multiple person game model under the
antagonistic duel type setup. The most flexible multiple person duel game is
analytically solved and the explicit formulas are solved to determine the time
dependent duel game model by using the first exceed theory. Unlike conventional
two-person duel game, multiple battle fields are introduced in the paper and
each battle field becomes shooting ground of pairwise players. This model is
targeted for real-world situations especially for selected target shooting
scenarios. An analogue of the theory in the paper is designed for solving the
best shooting time within multiple battle fields. This new proposed model is
fully mathematically explained to be adapted in various domains including the
strategies and operations.


Abstract:
  Topological superconductors host surface Andreev bound states which can be
classified as chiral or helical. Chiral superconductors belong to $Z$ symmetry
class which breaks time-reversal symmetry (TRS) while helical superconductors
belong to $Z_2$ symmetry class which preserves TRS. Chiral and helical
superconductors can be further categorized by the parity-even or odd, of their
order parameter. In this paper using Hanbury Brown and Twiss (HBT) shot noise
correlations and the non-local conductance, we probe metal/unconventional
superconductor/metal junctions in order to better understand the pairing
helical vs chiral in topological superconductors. We also compare these to
non-topological superconductors. Topological superconductors are carriers of
Majorana fermions which are important for topological quantum computation. By
distinguishing chiral superconductors, wherein Majorana bound states (MBS)
persist even in presence of a magnetic field, from helical superconductors
where MBS are fragile, our study will help in the search for stable Majorana
fermions which will be useful for topological quantum computation.


Abstract:
  Neural Architecture Search (NAS) often trains and evaluates a large number of
architectures. Recent predictor-based NAS approaches attempt to alleviate such
heavy computation costs with two key steps: sampling some
architecture-performance pairs and fitting a proxy accuracy predictor. Given
limited samples, these predictors, however, are far from accurate to locate top
architectures due to the difficulty of fitting the huge search space. This
paper reflects on a simple yet crucial question: if our final goal is to find
the best architecture, do we really need to model the whole space well?. We
propose a paradigm shift from fitting the whole architecture space using one
strong predictor, to progressively fitting a search path towards the
high-performance sub-space through a set of weaker predictors. As a key
property of the weak predictors, their probabilities of sampling better
architectures keep increasing. Hence we only sample a few well-performed
architectures guided by the previously learned predictor and estimate a new
better weak predictor. This embarrassingly easy framework, dubbed WeakNAS,
produces coarse-to-fine iteration to gradually refine the ranking of sampling
space. Extensive experiments demonstrate that WeakNAS costs fewer samples to
find top-performance architectures on NAS-Bench-101 and NAS-Bench-201. Compared
to state-of-the-art (SOTA) predictor-based NAS methods, WeakNAS outperforms all
with notable margins, e.g., requiring at least 7.5x less samples to find global
optimal on NAS-Bench-101. WeakNAS can also absorb their ideas to boost
performance more. Further, WeakNAS strikes the new SOTA result of 81.3% in the
ImageNet MobileNet Search Space. The code is available at
https://github.com/VITA-Group/WeakNAS.


Abstract:
  We extend a study by Lempp and Hirst of infinite versions of some problems
from finite complexity theory, using an intuitionistic version of reverse
mathematics and techniques of Weihrauch analysis.


Abstract:
  Twitter has become a major social media platform since its launching in 2006,
while complaints about bot accounts have increased recently. Although extensive
research efforts have been made, the state-of-the-art bot detection methods
fall short of generalizability and adaptability. Specifically, previous bot
detectors leverage only a small fraction of user information and are often
trained on datasets that only cover few types of bots. As a result, they fail
to generalize to real-world scenarios on the Twittersphere where different
types of bots co-exist. Additionally, bots in Twitter are constantly evolving
to evade detection. Previous efforts, although effective once in their context,
fail to adapt to new generations of Twitter bots. To address the two challenges
of Twitter bot detection, we propose SATAR, a self-supervised representation
learning framework of Twitter users, and apply it to the task of bot detection.
In particular, SATAR generalizes by jointly leveraging the semantics, property
and neighborhood information of a specific user. Meanwhile, SATAR adapts by
pre-training on a massive number of self-supervised users and fine-tuning on
detailed bot detection scenarios. Extensive experiments demonstrate that SATAR
outperforms competitive baselines on different bot detection datasets of
varying information completeness and collection time. SATAR is also proved to
generalize in real-world scenarios and adapt to evolving generations of social
media bots.


Abstract:
  A new approach for image reconstruction in THz computed tomography (THz-CT)
is presented. Based on a geometrical optics model containing the THz signal
amplitude and phase, a novel algorithm for extracting an average phase from the
measured THz signals is derived. Applying the algorithm results in a
phase-contrast sinogram, which is further used for image reconstruction. For
experimental validation, a fast THz time-domain spectrometer (THz-TDS) in
transmission geometry is employed, enabling CT measurements within several
minutes. Quantitative evaluation of reconstructed 3D printed plastic profiles
reveals the potential of our approach in non-destructive testing of plastic
profiles.


Abstract:
  Automatic analysis of bioacoustic signals is a fundamental tool to evaluate
the vitality of our planet. Frogs and bees, for instance, may act like
biological sensors providing information about environmental changes. This task
is fundamental for ecological monitoring still includes many challenges such as
nonuniform signal length processing, degraded target signal due to
environmental noise, and the scarcity of the labeled samples for training
machine learning. To tackle these challenges, we present a bioacoustic signal
classifier equipped with a discriminative mechanism to extract useful features
for analysis and classification efficiently. The proposed classifier does not
require a large amount of training data and handles nonuniform signal length
natively. Unlike current bioacoustic recognition methods, which are
task-oriented, the proposed model relies on transforming the input signals into
vector subspaces generated by applying Singular Spectrum Analysis (SSA). Then,
a subspace is designed to expose discriminative features. The proposed model
shares end-to-end capabilities, which is desirable in modern machine learning
systems. This formulation provides a segmentation-free and noise-tolerant
approach to represent and classify bioacoustic signals and a highly compact
signal descriptor inherited from SSA. The validity of the proposed method is
verified using three challenging bioacoustic datasets containing anuran, bee,
and mosquito species. Experimental results on three bioacoustic datasets have
shown the competitive performance of the proposed method compared to commonly
employed methods for bioacoustics signal classification in terms of accuracy.


Abstract:
  In order to deal with the high development time of exact and approximation
algorithms for NP-hard combinatorial optimisation problems and the high running
time of exact solvers, deep learning techniques have been used in recent years
as an end-to-end approach to find solutions. However, there are issues of
representation, generalisation, complex architectures, interpretability of
models for mathematical analysis etc. using deep learning techniques. As a
compromise, machine learning can be used to improve the run time performance of
exact algorithms in a matheuristics framework. In this paper, we use a pruning
heuristic leveraging machine learning as a pre-processing step followed by an
exact Integer Programming approach. We apply this approach to sparsify
instances of the classical travelling salesman problem. Our approach learns
which edges in the underlying graph are unlikely to belong to an optimal
solution and removes them, thus sparsifying the graph and significantly
reducing the number of decision variables. We use carefully selected features
derived from linear programming relaxation, cutting planes exploration,
minimum-weight spanning tree heuristics and various other local and statistical
analysis of the graph. Our learning approach requires very little training data
and is amenable to mathematical analysis. We demonstrate that our approach can
reliably prune a large fraction of the variables in TSP instances from
TSPLIB/MATILDA (>85%$) while preserving most of the optimal tour edges. Our
approach can successfully prune problem instances even if they lie outside the
training distribution, resulting in small optimality gaps between the pruned
and original problems in most cases. Using our learning technique, we discover
novel heuristics for sparsifying TSP instances, that may be of independent
interest for variants of the vehicle routing problem.


Abstract:
  Despite significant interest in developing general purpose fact checking
models, it is challenging to construct a large-scale fact verification dataset
with realistic claims that would occur in the real world. Existing claims are
either authored by crowdworkers, thereby introducing subtle biases that are
difficult to control for, or manually verified by professional fact checkers,
causing them to be expensive and limited in scale. In this paper, we construct
a challenging, realistic, and large-scale fact verification dataset called
FaVIQ, using information-seeking questions posed by real users who do not know
how to answer. The ambiguity in information-seeking questions enables
automatically constructing true and false claims that reflect confusions arisen
from users (e.g., the year of the movie being filmed vs. being released). Our
claims are verified to be natural, contain little lexical bias, and require a
complete understanding of the evidence for verification. Our experiments show
that the state-of-the-art models are far from solving our new task. Moreover,
training on our data helps in professional fact-checking, outperforming models
trained on the most widely used dataset FEVER or in-domain data by up to 17%
absolute. Altogether, our data will serve as a challenging benchmark for
natural language understanding and support future progress in professional fact
checking.


Abstract:
  How can we model affect in a general fashion, across dissimilar tasks, and to
which degree are such general representations of affect even possible? To
address such questions and enable research towards general affective computing
this paper introduces the Affect Game AnnotatIoN (AGAIN) dataset. AGAIN is a
large-scale affective corpus that features over 1,100 in-game videos (with
corresponding gameplay data) from nine different games, which are annotated for
arousal from 124 participants in a first-person continuous fashion. Even though
AGAIN is created for the purpose of investigating the generality of affective
computing across dissimilar tasks, affect modelling can be studied within each
of its 9 specific interactive games. To the best of our knowledge AGAIN is the
largest - over 37 hours of annotated video and game logs - and the most diverse
publicly available affective dataset based on games as interactive affect
elicitors.


Abstract:
  We study multielectron bubble phases in the $N=2$ and $N=3$ Landau levels in
a high mobility GaAs/AlGaAs sample. We found that the longitudinal
magnetoresistance versus temperature curves in the multielectron bubble region
exhibit sharp peaks, irrespective of the Landau level index. We associate these
peaks with an enhanced scattering caused by thermally fluctuating domains of a
bubble phase and a uniform uncorrelated electron liquid at the onset of the
bubble phases. Within the $N=3$ Landau level, onset temperatures of
three-electron and two-electron bubbles exhibit linear trends with respect to
the filling factor; the onset temperatures of three-electron bubbles are
systematically higher than those of two-electron bubbles. Furthermore, onset
temperatures of the two-electron bubble phases across $N=2$ and $N=3$ Landau
levels are similar, but exhibit an offset. This offset and the dominant nature
of the three-electron bubbles in the $N=3$ Landau level reveals the role of the
short-range part of the electron-electron interaction in the formation of the
bubbles.


Abstract:
  The sizable linear polarization signals produced by the scattering of
anisotropic radiation in the core of the Ca I 4227 $\mathrm{\r{A}}$ line
constitute an important observable for probing the inhomogeneous and dynamic
plasma of the lower solar chromosphere. Here we show the results of a
three-dimensional (3D) radiative transfer complete frequency redistribution
(CRD) investigation of the line's scattering polarization in a
magneto-hydrodynamical 3D model of the solar atmosphere. We take into account
not only the Hanle effect produced by the model's magnetic field, but also the
symmetry breaking caused by the horizontal inhomogeneities and macroscopic
velocity gradients. The spatial gradients of the horizontal components of the
macroscopic velocities produce very significant forward scattering polarization
signals without the need of magnetic fields, while the Hanle effect tends to
depolarize them at the locations where the model's magnetic field is stronger
than about 5 G. The standard 1.5D approximation is found to be unsuitable for
understanding the line's scattering polarization, but we introduce a novel
improvement to this approximation that produces results in qualitative
agreement with the full 3D results. The instrumental degradation of the
calculated polarization signals is also investigated, showing what can we
expect to observe with the Visible Spectro-Polarimeter at the upcoming Daniel
K. Inouye Solar Telescope.


Abstract:
  GW170817 has provided valuable constraints on the equations of state of
merging binary neutron stars, which can be considered as the most probable
candidate for the source of gravitational waves. On the other hand, these
natural laboratories of extreme temperature and density may lead to the
estimation of some exotic matter like {deconfined quark matter in their cores}.
In this paper, we investigate the neutron star matter equation of state (EoS)
with the lowest order constrained variational (LOCV) method considering the
excluded volume effect (VLOCV) for nucleons {to} compute the tidal
deformability of binary neutron star mergers (BNSMs). Within this approach, the
size of nucleons makes the EoS {so} stiff that requires a phase transition in
order to avoid causality violation. Therefore, this phase transition {may lead}
to the appearance of the third family of compact stars {including} ``twin
star'' configurations. {Our EoS models are confronted with observations from
GW170817, GW190814, GW190425, and also NICER. We find out that regarding all
these constraints, the EoS models having the {transition}
pressure$\approx$30-100 MeV/fm$^{3}$ and the energy density discontinuity
$\Delta\varepsilon$$\lesssim$300 MeV/fm$^{3}$ are preferable.


Abstract:
  Compactifications of the heterotic string on special T^d/Z_2 orbifolds
realize a landscape of string models with 16 supercharges and a gauge group on
the left-moving sector of reduced rank d+8. The momenta of untwisted and
twisted states span a lattice known as the Mikhailov lattice II_{(d)}, which is
not self-dual for d > 1. By using computer algorithms which exploit the
properties of lattice embeddings, we perform a systematic exploration of the
moduli space for d=1 and 2, and give a list of maximally enhanced points where
the U(1)^{d+8} enhances to a rank d+8 non-Abelian gauge group. For d = 1, these
groups are simply-laced and simply-connected, and in fact can be obtained from
the Dynkin diagram of E_{10}. For d = 2 there are also symplectic and
doubly-connected groups. For the latter we find the precise form of their
fundamental groups from embeddings ofof lattices into the dual of II_{(2)}.
  Our results easily generalize to d > 2.


Abstract:
  Pulmonary respiratory motion artifacts are common in four-dimensional
computed tomography (4DCT) of lungs and are caused by missing, duplicated, and
misaligned image data. This paper presents a geodesic density regression (GDR)
algorithm to correct motion artifacts in 4DCT by correcting artifacts in one
breathing phase with artifact-free data from corresponding regions of other
breathing phases. The GDR algorithm estimates an artifact-free lung template
image and a smooth, dense, 4D (space plus time) vector field that deforms the
template image to each breathing phase to produce an artifact-free 4DCT scan.
Correspondences are estimated by accounting for the local tissue density change
associated with air entering and leaving the lungs, and using binary artifact
masks to exclude regions with artifacts from image regression. The
artifact-free lung template image is generated by mapping the artifact-free
regions of each phase volume to a common reference coordinate system using the
estimated correspondences and then averaging. This procedure generates a fixed
view of the lung with an improved signal-to-noise ratio. The GDR algorithm was
evaluated and compared to a state-of-the-art geodesic intensity regression
(GIR) algorithm using simulated CT time-series and 4DCT scans with clinically
observed motion artifacts. The simulation shows that the GDR algorithm has
achieved significantly more accurate Jacobian images and sharper template
images, and is less sensitive to data dropout than the GIR algorithm. We also
demonstrate that the GDR algorithm is more effective than the GIR algorithm for
removing clinically observed motion artifacts in treatment planning 4DCT scans.
Our code is freely available at https://github.com/Wei-Shao-Reg/GDR.


Abstract:
  Hydrogenic excited states of a 2D exciton are degenerate. In the presence of
a weak magnetic field, the $S$-states with a zero momentum of the center of
mass get coupled to the $P$-states with finite momentum of the center of mass.
This field-induced coupling leads to a strong modification of the dispersion
branches of the exciton spectrum. Namely, the lower branch acquires a shape of
a "mexican hat" with a minimum at a finite momentum. At certain magnetic field,
exciton branches exhibit a linear crossing, similarly to the spectrum of a 2D
electron in the presence of spin-orbit coupling. While spin is not involved,
degenerate $S$ and $P$ states play the role of the spin projections. Lifting of
degeneracy due to diamagnetic shifts and deviation of electron-hole attraction
from purely Coulomb suppresses the linear crossing.


Abstract:
  We give a sufficient condition for a Banach space with which the homogeneous
extension of a surjective isometry from the unit sphere of it onto another one
is real-linear. The condition is satisfied by a uniform algebra and a certain
extremely $C$-regular space of real-valued continuous functions.


Abstract:
  While deep learning is a powerful tool for natural language processing (NLP)
problems, successful solutions to these problems rely heavily on large amounts
of annotated samples. However, manually annotating data is expensive and
time-consuming. Active Learning (AL) strategies reduce the need for huge
volumes of labeled data by iteratively selecting a small number of examples for
manual annotation based on their estimated utility in training the given model.
In this paper, we argue that since AL strategies choose examples independently,
they may potentially select similar examples, all of which may not contribute
significantly to the learning process. Our proposed approach,
Active$\mathbf{^2}$ Learning (A$\mathbf{^2}$L), actively adapts to the deep
learning model being trained to eliminate further such redundant examples
chosen by an AL strategy. We show that A$\mathbf{^2}$L is widely applicable by
using it in conjunction with several different AL strategies and NLP tasks. We
empirically demonstrate that the proposed approach is further able to reduce
the data requirements of state-of-the-art AL strategies by an absolute
percentage reduction of $\approx\mathbf{3-25\%}$ on multiple NLP tasks while
achieving the same performance with no additional computation overhead.


Abstract:
  In this paper, we consider the problem of determining the \emph{exact} number
of periodic orbits for polynomial planar flows. This problem is a variant of
Hilbert's 16th problem. Using a natural definition of computability, we show
that the problem is noncomputable on the one hand and, on the other hand,
computable uniformly on the set of all structurally stable systems defined on
the unit disk. We also prove that there is a family of polynomial planar
systems which does not have a computable sharp upper bound on the number of its
periodic orbits.


Abstract:
  Neutron-star mergers were recently confirmed as sites of
rapid-neutron-capture (r-process) nucleosynthesis. However, in Galactic
chemical evolution models, neutron-star mergers alone cannot reproduce the
observed element abundance patterns of extremely metal-poor stars, which
indicates the existence of other sites of r-process nucleosynthesis. These
sites may be investigated by studying the element abundance patterns of
chemically primitive stars in the halo of the Milky Way, because these objects
retain the nucleosynthetic signatures of the earliest generation of stars. Here
we report the element abundance pattern of the extremely metal-poor star SMSS
J200322.54-114203.3. We observe a large enhancement in r-process elements, with
very low overall metallicity. The element abundance pattern is well matched by
the yields of a single 25-solar-mass magnetorotational hypernova. Such a
hypernova could produce not only the r-process elements, but also light
elements during stellar evolution, and iron-peak elements during explosive
nuclear burning. Hypernovae are often associated with long-duration gamma-ray
bursts in the nearby Universe. This connection indicates that similar
explosions of fast-spinning strongly magnetized stars occurred during the
earliest epochs of star formation in our Galaxy.


Abstract:
  Cartesian fibrations were originally defined by Lurie in the context of
quasi-categories and are commonly used in $(\infty,1)$-category theory to study
presheaves valued in $(\infty,1)$-categories. In this work we define and study
fibrations modeling presheaves valued in simplicial spaces and their
localizations. This includes defining a model structure for these fibrations
and giving effective tools to recognize its fibrations and weak equivalences.
This in particular gives us a new method to construct Cartesian fibrations via
complete Segal spaces. In addition to that, it allows us to define and study
fibrations modeling presheaves of Segal spaces.


Abstract:
  In this paper, we explore the construction of natural language explanations
for news claims, with the goal of assisting fact-checking and news evaluation
applications. We experiment with two methods: (1) an extractive method based on
Biased TextRank -- a resource-effective unsupervised graph-based algorithm for
content extraction; and (2) an abstractive method based on the GPT-2 language
model. We perform comparative evaluations on two misinformation datasets in the
political and health news domains, and find that the extractive method shows
the most promise.


Abstract:
  Group-IV color centers in diamond have attracted significant attention as
solid-state spin qubits because of their excellent optical and spin properties.
Among these color centers, the tin-vacancy (SnV$^{\,\textrm{-}}$) center is of
particular interest because its large ground-state splitting enables long spin
coherence times at temperatures above 1$\,$K. However, color centers typically
suffer from inhomogeneous broadening, which can be exacerbated by
nanofabrication-induced strain, hindering the implementation of quantum nodes
emitting indistinguishable photons. Although strain and Raman tuning have been
investigated as promising techniques to overcome the spectral mismatch between
distinct group-IV color centers, other approaches need to be explored to find
methods that can offer more localized control without sacrificing emission
intensity. Here, we study electrical tuning of SnV$^{\,\textrm{-}}$ centers in
diamond via the direct-current Stark effect. We demonstrate a tuning range
beyond 1.7$\,$GHz. We observe both quadratic and linear dependence on the
applied electric field. We also confirm that the tuning effect we observe is a
result of the applied electric field and is distinct from thermal tuning due to
Joule heating. Stark tuning is a promising avenue toward overcoming detunings
between emitters and enabling the realization of multiple identical quantum
nodes.


Abstract:
  The Lorentzian Engle-Pereira-Rovelli-Livine/Freidel-Krasnov (EPRL/FK)
spinfoam model and the Conrady-Hnybida (CH) timelike-surface extension can be
expressed in the integral form $\int e^S$. This work studies the analytic
continuation of the spinfoam action $S$ to the complexification of the
integration domain. Our work extends our knowledge from the real critical
points well-studied in the spinfoam large-$j$ asymptotics to general complex
critical points of $S$ analytic continued to the complexified domain. The
complex critical points satisfying critical equations of the analytic continued
$S$. In the large-$j$ regime, the complex critical points give subdominant
contributions to the spinfoam amplitude when the real critical points are
present. But the contributions from the complex critical points can become
dominant when the real critical point are absent. Moreover the contributions
from the complex critical points cannot be neglected when the spins $j$ are not
large. In this paper, we classify the complex critical points of the spinfoam
amplitude, and find a subclass of complex critical points that can be
interpreted as 4-dimensional simplicial geometries. In particular, we identify
the complex critical points corresponding to the Riemannian simplicial
geometries although we start with the Lorentzian spinfoam model. The
contribution from these complex critical points of Riemannian geometry to the
spinfoam amplitude give $e^{-S_{Regge}}$ in analogy with the Euclidean path
integral, where $S_{Regge}$ is the Riemannian Regge action on simplicial
complex.


Abstract:
  Robot programming methods for industrial robots are time consuming and often
require operators to have knowledge in robotics and programming. To reduce
costs associated with reprogramming, various interfaces using augmented reality
have recently been proposed to provide users with more intuitive means of
controlling robots in real-time and programming them without having to code.
However, most solutions require the operator to be close to the real robot's
workspace which implies either removing it from the production line or shutting
down the whole production line due to safety hazards. We propose a novel
augmented reality interface providing the users with the ability to model a
virtual representation of a workspace which can be saved and reused to program
new tasks or adapt old ones without having to be co-located with the real
robot. Similar to previous interfaces, the operators then have the ability to
program robot tasks or control the robot in real-time by manipulating a virtual
robot. We evaluate the intuitiveness and usability of the proposed interface
with a user study where 18 participants programmed a robot manipulator for a
disassembly task.


Abstract:
  We study the extragradient method for solving vector quasi-equilibrium
problems in Banach spaces, which generalizes the extragradient method for
vector equilibrium problems and scalar quasi-equilibrium problems. We propose a
regularization procedure which ensures strong convergence of the generated
sequence to a solution of the vector quasi-equilibrium problem, under standard
assumptions on the problem without assuming neither any monotonicity assumption
on the vector valued bifunction nor any weak continuity assumption of $f$ in
its arguments that in the many well-known methods have been used. Also, we show
that the boundedness of the generated sequences implies that the solution set
of the vector quasi-equilibrium problem is nonempty, and prove the strong
convergence of the generated sequences to a solution of the problem. Finally,
we give some examples of vector quasi-equilibrium problems in several Banach
spaces to which our main theorem can be applied. We also present some numerical
experiments.


Abstract:
  We prove that the genuine $C_{2^n}$-spectrum
$N_{C_{2}}^{C_{2^n}}MU_{\mathbb{R}}$ is cofree, for all $n$. Our proof is a
formal argument using chromatic hypercubes and the Slice Theorem of Hill,
Hopkins, and Ravenel. We show that this gives a new proof of the Segal
conjecture for $C_2$, independent of Lin's theorem.


Abstract:
  The inferior electrical contact to two-dimensional (2D) materials is a
critical challenge for their application in post-silicon very large-scale
integrated circuits. Electrical contacts were generally related to their
resistive effect, quantified as contact resistance. With a systematic
investigation, this work demonstrates a capacitive
metal-insulator-semiconductor (MIS) field-effect at the electrical contacts to
2D materials: the field-effect depletes or accumulates charge carriers,
redistributes the voltage potential, and give rise to abnormal current
saturation and nonlinearity. On the one hand, the current saturation hinders
the devices' driving ability, which can be eliminated with carefully engineered
contact configurations. On the other hand, by introducing the nonlinearity to
monolithic analog artificial neural network circuits, the circuits' perception
ability can be significantly enhanced, as evidenced using a COVID-19 critical
illness prediction model. This work provides a comprehension of the
field-effect at the electrical contacts to 2D materials, which is fundamental
to the design, simulation, and fabrication of electronics based on 2D material.


Abstract:
  The snowlines of various volatile species in protoplanetary disks are
associated with abrupt changes in gas composition and dust physical properties.
Volatiles may affect dust growth, as they cover grains with icy mantles that
can change the fragmentation velocity of the grains. In turn, dust coagulation,
fragmentation, and drift through the gas disk can contribute to the
redistribution of volatiles between the ice and gas phases. Here we present the
hydrodynamic model FEOSAD for protoplanetary disks with two dust populations
and volatile dynamics. We compute the spatial distributions of major volatile
molecules (H$_2$O, CO$_2$, CH$_4$, and CO) in the gas, on small and grown dust,
and analyze the composition of icy mantles over the initial 0.5 Myr of disk
evolution. We show that most of ice arrives to the grown dust through
coagulation with small grains. Spiral structures and dust rings forming in the
disk, as well as photodissociation in the outer regions, lead to the formation
of complex snowline shapes and multiple snowlines for each volatile species.
During the considered disk evolution, the snowlines shift closer to the star,
with their final position being a factor $4-5$ smaller than that at the disk
formation epoch. We demonstrate that volatiles tend to collect in the vicinity
of their snowlines, both in the ice and gas phases, leading to the formation of
thick icy mantles potentially important for dust dynamics. The dust size is
affected by a lower fragmentation velocity of bare grains in the model with a
higher turbulent viscosity.


Abstract:
  False data injection attacks (FDIA) are a main category of cyber-attacks
threatening the security of power systems. Contrary to the detection of these
attacks, less attention has been paid to identifying the attacked units of the
grid. To this end, this work jointly studies detecting and localizing the
stealth FDIA in power grids. Exploiting the inherent graph topology of power
systems as well as the spatial correlations of measurement data, this paper
proposes an approach based on the graph neural network (GNN) to identify the
presence and location of the FDIA. The proposed approach leverages the
auto-regressive moving average (ARMA) type graph filters (GFs) which can better
adapt to sharp changes in the spectral domain due to their rational type filter
composition compared to the polynomial type GFs such as Chebyshev. To the best
of our knowledge, this is the first work based on GNN that automatically
detects and localizes FDIA in power systems. Extensive simulations and
visualizations show that the proposed approach outperforms the available
methods in both detection and localization of FDIA for different IEEE test
systems. Thus, the targeted areas can be identified and preventive actions can
be taken before the attack impacts the grid.


Abstract:
  We develop a nonperturbative method through the Hartree factorization to
examine the quantum fluctuation effects on the single-field inflationary models
in a spatially flat FRW cosmological space-time. Apart from the background
field equation as well as the Friedmann equation with the corrections of
quantum field fluctuations, the modified Mukhanov-Sasaki equations for the mode
functions of the quantum scalar field are also derived by introducing the
nonzero $\Delta_B$ term. We consider the Universe undergoing the slow roll
(SR)-ultra slow roll (USR) -slow roll (SR) inflation where in particular the
presence of the USR inflation triggers the huge growth of $\Delta_B$ that in
turn gives the boost effects to the curvature perturbations for the modes that
leave horizon in the early times of the inflation. However, the cosmic friction
term in the mode equation given by the Hubble parameter presumably prohibits
the boost effects. Here we propose two representative models to illustrate
these two competing terms.


Abstract:
  For quadratic forms in $4$ variables defined over the rational function field
in one variable over $\mathbb C(\!(t)\!)$, the validity of the local-global
principle for isotropy with respect to different sets of discrete valuations is
examined.


Abstract:
  Two different ab initio potential energy surfaces are employed to investigate
the efficiency of the rotational excitation channels for the polar molecular
ion HeH$^+$ interacting with He atoms. We further use them to investigate the
quantum dynamics of both the proton-exchange reaction and the purely rotational
inelastic collisions over a broad range of temperatures. In current modeling
studies, this cation is considered to be one of the possible cooling sources
under early universe conditions after the recombination era and has recently
been found to exist in the Interstellar Medium. Results from the present
calculations are able to show the large efficiency of the state-changing
channels involving rotational states of this cation. In fact, we find them to
be similar in size and behaviour to the inelastic and to the reaction rate
coefficients obtained in previous studies where H atoms were employed as
projectiles. The same rotational excitation processes, occurring when free
electrons are the collision partners of this cation, are also compared with the
present findings. The relative importance of the reactive, proton-exchange
channel and the purely inelastic channels is also analysed and discussed. The
rotational de-excitation processes are also investigated for the cooling
kinetics of the present cation under cold trap conditions with He as the buffer
gas. The implications of the present results for setting up more comprehensive
numerical models to describe the chemical evolution networks in different
environments are briefly discussed.


Abstract:
  We study properties of an accretion ring in a steady mass flow from a
companion star to a compact object in an X-ray binary. The accretion ring is a
place where matter inflowing from a companion star sojourns for a while to
bifurcate to accretion and excretion flows due to angular momentum transfer in
it. The matter in the accretion ring rotates along the Keplerian circular orbit
determined by the intrinsic specific angular momentum of the inflowing matter
and forms a thick ring-envelope. Two internal flows are expected to appear in
the thick envelope. One is a mass spreading flow bifurcating to a thick
accretion flow and a thick excretion flow, as a result of the angular momentum
transfer within the ring-envelope. The other is a cooling flow toward the
envelope center governed by radiative cooling under an effect of X-ray
irradiation. This cooling flow eventually forms a core in the torus, from which
a thin accretion disk and a thin excretion disk spread out as a result of the
angular momentum transfer there again. Evaluating and comparing the time scales
for the two internal flows, the accretion ring is shown to generally originate
a two-layer accretion flow in which a thin accretion disk is sandwiched by a
thick accretion flow, unless the accretion rate is very low. Properties of the
thin excretion disk and the thick excretion flow are also investigated. The
thin excretion disk is expected to terminate at a distance 4 times as large as
the accretion ring radius and to form another ring there, unless tidal effects
from the companion star exist. The thick excretion flow is, on the other hand,
likely to turn to a super-sonic wind-flow reaching the infinity.


Abstract:
  In applications such as wireless communication, it is important to study the
statistical properties of $L_{2}$, the minimal arc distance between a random
point (e.g., a cellphone user) uniformly distributed on a sphere to a set of
pre-defined seeds (e.g., wireless towers) on that sphere. In this study, we
first derive the distribution (CDF) and density (PDF) functions of the arc
distance between a selected vertex of a spherical triangle to a random point
uniformly distributed within this triangle. Next, using computational
techniques based on spherical Voronoi diagram and triangular partition of
Voronoi cells, we derive moments of $L_{2}$ and $\cos L_{2}$. These results are
verified by extensive Monte Carlo simulations.


Abstract:
  Parker (1972) first proposed that coronal heating was the necessary outcome
of an energy flux caused by the tangling of coronal magnetic field lines by
photospheric flows. In this paper we discuss how this model has been modified
by subsequent numerical simulations outlining in particular the substantial
differences between the "nanoflares" introduced by Parker and "elementary
events", defined here as small-scale spatially and temporally isolated heating
events resulting from the continuous formation and dissipation of field-aligned
current sheets within a coronal loop. We present numerical simulations of the
compressible 3-D MHD equations using the HYPERION code. We use two clustering
algorithms to investigate the properties of the simulated elementary events: an
IDL implementation of a Density-Based Spatial Clustering of Applications with
Noise (DBSCAN) technique; and our own Physical Distance Clustering (PDC)
algorithm. We identify and track elementary heating events in time, both in
temperature and in Joule heating space. For every event we characterize
properties such as: density, temperature, volume, aspect ratio, length,
thickness, duration and energy. The energies of the events are in the range
$10^{18}-10^{21}$ ergs, with durations shorter than 100 seconds. A few events
last up to 200 seconds and release energies up to $10^{23}$ ergs. While high
temperature are typically located at the flux tube apex, the currents extend
all the way to the footpoints. Hence a single elementary event cannot at
present be detected. The observed emission is due to the superposition of many
elementary events distributed randomly in space and time within the loop.


Abstract:
  The chemical master equation (CME), which describes the discrete and
stochastic molecule number dynamics associated with biological processes like
transcription, is difficult to solve analytically. It is particularly hard to
solve for models involving bursting/gene switching, a biological feature that
tends to produce heavy-tailed single cell RNA counts distributions. In this
paper, we present a novel method for computing exact and analytic solutions to
the CME in such cases, and use these results to explore approximate solutions
valid in different parameter regimes, and to compute observables of interest.
Our method leverages tools inspired by quantum mechanics, including ladder
operators and Feynman-like diagrams, and establishes close formal parallels
between the dynamics of bursty transcription, and the dynamics of bosons
interacting with a single fermion. We focus on two problems: (i) the chemical
birth-death process coupled to a switching gene/the telegraph model, and (ii) a
model of transcription and multistep splicing involving a switching gene and an
arbitrary number of downstream splicing steps. We work out many special cases,
and exhaustively explore the special functionology associated with these
problems. This is Part I in a two-part series of papers; in Part II, we explore
an alternative solution approach that is more useful for numerically solving
these problems, and apply it to parameter inference on simulated RNA counts
data.


Abstract:
  Stabbing Planes (also known as Branch and Cut) is a proof system introduced
very recently which, informally speaking, extends the DPLL method by branching
on integer linear inequalities instead of single variables. The techniques
known so far to prove size and depth lower bounds for Stabbing Planes are
generalizations of those used for the Cutting Planes proof system established
via communication complexity arguments. As such they work for the lifted
version of combinatorial statements. Rank lower bounds for Cutting Planes are
also obtained by geometric arguments called protection lemmas.
  In this work we introduce two new geometric approaches to prove size/depth
lower bounds in Stabbing Planes working for any formula: (1) the antichain
method, relying on Sperner's Theorem and (2) the covering method which uses
results on essential coverings of the boolean cube by linear polynomials, which
in turn relies on Alon's combinatorial Nullenstellensatz.
  We demonstrate their use on classes of combinatorial principles such as the
Pigeonhole principle, the Tseitin contradictions and the Linear Ordering
Principle. By the first method we prove almost linear size lower bounds and
optimal logarithmic depth lower bounds for the Pigeonhole principle and
analogous lower bounds for the Tseitin contradictions over the complete graph
and for the Linear Ordering Principle. By the covering method we obtain a
superlinear size lower bound and a logarithmic depth lower bound for Stabbing
Planes proof of Tseitin contradictions over a grid graph.


Abstract:
  Deep neural networks (DNNs) trained on one set of medical images often
experience severe performance drop on unseen test images, due to various domain
discrepancy between the training images (source domain) and the test images
(target domain), which raises a domain adaptation issue. In clinical settings,
it is difficult to collect enough annotated target domain data in a short
period. Few-shot domain adaptation, i.e., adapting a trained model with a
handful of annotations, is highly practical and useful in this case. In this
paper, we propose a Polymorphic Transformer (Polyformer), which can be
incorporated into any DNN backbones for few-shot domain adaptation.
Specifically, after the polyformer layer is inserted into a model trained on
the source domain, it extracts a set of prototype embeddings, which can be
viewed as a "basis" of the source-domain features. On the target domain, the
polyformer layer adapts by only updating a projection layer which controls the
interactions between image features and the prototype embeddings. All other
model weights (except BatchNorm parameters) are frozen during adaptation. Thus,
the chance of overfitting the annotations is greatly reduced, and the model can
perform robustly on the target domain after being trained on a few annotated
images. We demonstrate the effectiveness of Polyformer on two medical
segmentation tasks (i.e., optic disc/cup segmentation, and polyp segmentation).
The source code of Polyformer is released at
https://github.com/askerlee/segtran.


Abstract:
  The need for reliable systems to determine fingerprint presentation attacks
grows with the rising use of the fingerprint for authentication. This work
presents a new approach to single-class classification for software-based
fingerprint presentation attach detection. The described method utilizes a
Wasserstein GAN to apply transfer learning to a deep convolutional autoencoder.
By doing so, the autoencoder could be pretrained and finetuned on the
LivDet2021 Dermalog sensor dataset with only 1122 bona fide training samples.
Without making use of any presentation attack samples, the model could archive
an average classification error rate of 16.79%. The Wasserstein GAN implemented
to pretrain the autoencoders weights can further be used to generate
realistic-looking artificial fingerprint patches. Extensive testing of
different autoencoder architectures and hyperparameters led to coarse
architectural guidelines as well as multiple implementations which can be
utilized for future work.


Abstract:
  In the 2016 experiment by Crossno et al. [Science 351, 1058 (2016)],
electronic contribution to the thermal conductivity of graphene was found to
violate the well-known Wiedemann-Franz (WF) law for metals. At liquid nitrogen
temperatures, the thermal to electrical conductivity ratio of charge-neutral
samples was more than 10 times higher than predicted by the WF law, what was
attributed to interactions between particles leading to collective behavior
described by hydrodynamics. Here we show, by adapting the handbook derivation
of the WF law to the case of massless Dirac fermions, that significantly
enhanced thermal conductivity should appear also in few- or even sub-kelvin
temperatures, where the role of interactions can be neglected. The comparison
with numerical results obtained within the Landauer-B\"uttiker formalism for
rectangular and disk-shaped (Corbino) devices in ballistic graphene is also
provided.


Abstract:
  In this article we construct a compact Riemannian manifold of high dimension
on which the time dependent Euler equations are Turing complete. More
precisely, the halting of any Turing machine with a given input is equivalent
to a certain global solution of the Euler equations entering a certain open set
in the space of divergence-free vector fields. In particular, this implies the
undecidability of whether a solution to the Euler equations with an initial
datum will reach a certain open set or not in the space of divergence-free
fields. This result goes one step further in Tao's programme to study the
blow-up problem for the Euler and Navier-Stokes equations using fluid
computers. As a remarkable spin-off, our method of proof allows us to give a
counterexample to a conjecture of Moore dating back to 1998 on the
non-existence of analytic maps on compact manifolds that are Turing complete.


Abstract:
  ThumbTrak is a novel wearable input device that recognizes 12 micro-finger
poses in real-time. Poses are characterized by the thumb touching each of the
12 phalanges on the hand. It uses a thumb-ring, built with a flexible printed
circuit board, which hosts nine proximity sensors. Each sensor measures the
distance from the thumb to various parts of the palm or other fingers.
ThumbTrak uses a support-vector-machine (SVM) model to classify finger poses
based on distance measurements in real-time. A user study with ten participants
showed that ThumbTrak could recognize 12 micro finger poses with an average
accuracy of 93.6%. We also discuss potential opportunities and challenges in
applying ThumbTrak in real-world applications.


Abstract:
  We study Jackiw-Teitelboim gravity with dynamical end of the world branes in
asymptotically nearly AdS$_2$ spacetimes. We quantize this theory in Lorentz
signature, and compute the Euclidean path integral summing over topologies
including dynamical branes. The latter will be seen to exactly match with a
modification of the SSS matrix model. The resolution of UV divergences in the
gravitational instantons involving the branes will lead us to understand the
matrix model interpretation of the Wilsonian effective theory perspective on
the gravitational theory. We complete this modified SSS matrix model
nonperturbatively by extending the integration contour of eigenvalues into the
complex plane. Furthermore, we give a new interpretation of other phases in
such matrix models. We derive an effective $W(\Phi)$ dilaton gravity, which
exhibits similar physics semiclassically. In the limit of a large number of
flavors of branes, the effective extremal entropy $S_{0,\text{eff}}$ has the
form of counting the states of these branes.


Abstract:
  Despite ongoing advancements in quantum computing, businesses are still faced
with the problem to decide if they would benefit from investing into this novel
technology for building a business critical application. This uncertainty is
not only owing to the limitations in the current state of the technology but
also due to the gap between the level at which business applications are
analyzed (e.g., using high level semi-formal languages) and the level at which
quantum computing related information is currently available (e.g., formally
specified computational problems, their algorithmic solutions with
computational complexity theoretic analysis) to make informed decisions. To
fill the discourse gap, in this paper, we present design of an interactive
advisor, which augments users while deciding to invest into quantum software
development as a plausible future option in their application context. Towards
that we apply business process modeling and natural language similarity
analysis using text-embeddings to associated business context with
computational problems and formulate constraints in terms of quantum speedup
and resource requirements to select software development platforms.


Abstract:
  Modern machine learning techniques, such as deep neural networks, are
transforming many disciplines ranging from image recognition to language
understanding, by uncovering patterns in big data and making accurate
predictions. They have also shown promising results for synthesizing new
designs, which is crucial for creating products and enabling innovation.
Generative models, including generative adversarial networks (GANs), have
proven to be effective for design synthesis with applications ranging from
product design to metamaterial design. These automated computational design
methods can support human designers, who typically create designs by a
time-consuming process of iteratively exploring ideas using experience and
heuristics. However, there are still challenges remaining in automatically
synthesizing `creative' designs. GAN models, however, are not capable of
generating unique designs, a key to innovation and a major gap in AI-based
design automation applications. This paper proposes an automated method, named
CreativeGAN, for generating novel designs. It does so by identifying components
that make a design unique and modifying a GAN model such that it becomes more
likely to generate designs with identified unique components. The method
combines state-of-art novelty detection, segmentation, novelty localization,
rewriting, and generative models for creative design synthesis. Using a dataset
of bicycle designs, we demonstrate that the method can create new bicycle
designs with unique frames and handles, and generalize rare novelties to a
broad set of designs. Our automated method requires no human intervention and
demonstrates a way to rethink creative design synthesis and exploration.


Abstract:
  Performance degradation of an Automatic Speech Recognition (ASR) system is
commonly observed when the test acoustic condition is different from training.
Hence, it is essential to make ASR systems robust against various environmental
distortions, such as background noises and reverberations. In a multi-stream
paradigm, improving robustness takes account of handling a variety of unseen
single-stream conditions and inter-stream dynamics. Previously, a practical
two-stage training strategy was proposed within multi-stream end-to-end ASR,
where Stage-2 formulates the multi-stream model with features from Stage-1
Universal Feature Extractor (UFE). In this paper, as an extension, we introduce
a two-stage augmentation scheme focusing on mismatch scenarios: Stage-1
Augmentation aims to address single-stream input varieties with data
augmentation techniques; Stage-2 Time Masking applies temporal masks on UFE
features of randomly selected streams to simulate diverse stream combinations.
During inference, we also present adaptive Connectionist Temporal
Classification (CTC) fusion with the help of hierarchical attention mechanisms.
Experiments have been conducted on two datasets, DIRHA and AMI, as a
multi-stream scenario. Compared with the previous training strategy,
substantial improvements are reported with relative word error rate reductions
of 29.7-59.3% across several unseen stream combinations.


Abstract:
  Modern and future observation Space missions face increasingly more demanding
pointing performance requirements. This is accompanied with the development of
larger lightweight flexible structures. This paper outlines a methodology for
modeling a generic multi-body flexible spacecraft in an end-to-end fashion
within the Two-Inputs Two-Outputs Port framework: from disturbance all the way
to pointing performance by tacking into account all the uncertainties of each
sub-system. A particular focus is dedicated to a novel generic model for Solar
Drive Array Mechanisms and its harmonic disturbances produced by the
micro-stepping driver and the gearbox imperfections. The proposed model is
validated with the on-board telemetries of a European spacecraft. Moreover this
paper shows how the proposed model-based approach can be easily used for
control design and closed-loop robust performance analysis. Worst-case
scenarios of mechanical parameters combination are investigated to provide
robust performance certificate of the reference study case. A Linear
Parameter-Varying observer, scheduled by the solar array rotor angle, is
finally proposed in order to estimate the disturbance torques induced by the
gearbox imperfections just using the measurements coming from a classical
attitude control system: a star tracker and a gyrometer.


Abstract:
  With the expansion of scientific research, the number of scientific research
results is increasing. How to summarize these data has become an urgent
problem. Therefore, knowledge mapping methods come into being, providing a lot
of management and application functions. However, it is still a problem to
fully understand the knowledge map, especially in the field of sociology. In
this paper, a three-dimensional knowledge map is proposed with time, space and
number based on category and numericity, which concludes all the scientific
problems related to numericity interdisciplinary. Compared with the traditional
way, this map is normative, and puts forward the general production criteria of
labeling and digitization. It is also intuitive and readable, on which nature,
society and formal science are expressed in the same picture. The scientific
methodologies are summarized, so that the methods with similar logic between
different disciplines can be used for reference in development. Some social
subjects are expressed more vividly than traditional text-based expressions,
and are compatible with the natural science system. Mathematics also show its
importance on the map as formal Science, indicating that it is the key to the
development of science. This is not only a preliminary model of a comprehensive
scientific worldview, but also a preliminary framework for the connection and
cooperation of various disciplines in the future.


Abstract:
  Tropical ideals are a class of ideals in the tropical polynomial semiring
that combinatorially abstracts the possible collections of supports of all
polynomials in an ideal over a field. We study zero-dimensional tropical ideals
I with Boolean coefficients in which all underlying matroids are paving
matroids, or equivalently, in which all polynomials of minimal support have
support of size deg(I) or deg(I)+1 -- we call them paving tropical ideals. We
show that paving tropical ideals of degree d+1 are in bijection with $\mathbb
Z^n$-invariant d-partitions of $\mathbb Z^n$. This implies that
zero-dimensional tropical ideals of degree 3 with Boolean coefficients are in
bijection with $\mathbb Z^n$-invariant 2-partitions of quotient groups of the
form $\mathbb Z^n/L$. We provide several applications of these techniques,
including a construction of uncountably many zero-dimensional degree-3 tropical
ideals in one variable with Boolean coefficients, and new examples of
non-realizable zero-dimensional tropical ideals.


Abstract:
  An assumption of smooth response to small parameter changes, of statistics or
long-time averages of a chaotic system, is generally made in the field of
sensitivity analysis, and the parametric derivatives of statistical quantities
are critically used in science and engineering. In this paper, we propose a
numerical procedure to assess the differentiability of statistics with respect
to parameters in chaotic systems. We numerically show that the existence of the
derivative depends on the Lebesgue-integrability of a certain density gradient
function, which we define as the derivative of logarithmic SRB density along
the unstable manifold. We develop a recursive formula for the density gradient
that can be efficiently computed along trajectories, and demonstrate its use in
determining the differentiability of statistics. Our numerical procedure is
illustrated on low-dimensional chaotic systems whose statistics exhibit both
smooth and rough regions in parameter space.


Abstract:
  Finite element methods for symmetric linear hyperbolic systems using
unstructured advancing fronts (satisfying a causality condition) are considered
in this work. Convergence results and error bounds are obtained for mapped tent
pitching schemes made with standard discontinuous Galerkin discretizations for
spatial approximation on mapped tents. Techniques to study semidiscretization
on mapped tents, design fully discrete schemes, prove local error bounds, prove
stability on spacetime fronts, and bound error propagated through unstructured
layers are developed.


Abstract:
  Any $C^d$ conservative map $f$ of the $d$-dimensional unit ball $\mathbb B^d$
can be realized by renormalized iteration of a $C^d$ perturbation of identity:
there exists a conservative diffeomorphism of $\mathbb B^d$, arbitrarily close
to identity in the $C^d$ topology, that has a periodic disc on which the return
dynamics after a $C^d$ change of coordinates is exactly $f$.


Abstract:
  Humans possess a unique social cognition capability; nonverbal communication
can convey rich social information among agents. In contrast, such crucial
social characteristics are mostly missing in the existing scene understanding
literature. In this paper, we incorporate different nonverbal communication
cues (e.g., gaze, human poses, and gestures) to represent, model, learn, and
infer agents' mental states from pure visual inputs. Crucially, such a mental
representation takes the agent's belief into account so that it represents what
the true world state is and infers the beliefs in each agent's mental state,
which may differ from the true world states. By aggregating different beliefs
and true world states, our model essentially forms "five minds" during the
interactions between two agents. This "five minds" model differs from prior
works that infer beliefs in an infinite recursion; instead, agents' beliefs are
converged into a "common mind". Based on this representation, we further devise
a hierarchical energy-based model that jointly tracks and predicts all five
minds. From this new perspective, a social event is interpreted by a series of
nonverbal communication and belief dynamics, which transcends the classic
keyframe video summary. In the experiments, we demonstrate that using such a
social account provides a better video summary on videos with rich social
interactions compared with state-of-the-art keyframe video summary methods.


Abstract:
  We have studied the optical and electrical spectra from an n i p LED as a
function of magnetic field. This sample incorporated three GaAs quantum wells
in the intrinsic region. This device had excess n type doping and as a result.
The quantum wells were populated by a two dimension Landau electron gas. The
broad B0 field emission band evolved into a series of discrete features in the
presence of a magnetic field. These were identified as interband transitions
between the different values of l. Landau levels associated with the sub-bands,
with the selection rule. An energy splitting between the two polarised
components was observed for each Landau level transition. This was found to be
equal to the sum of the conduction and valence band spin splittings. We used
the know value of electron g factor to determine the valence band spin
splittings. Our experimental values were compared to the numerically calculated
values and were found to be in reasonable agreement.