Abstract:
  As large Open Data are increasingly shared as RDF graphs today, there is a
growing demand to help users discover the most interesting facets of a graph,
which are often hard to grasp without automatic tools. We consider the problem
of automatically identifying the k most interesting aggregate queries that can
be evaluated on an RDF graph, given an integer k and a user-specified
interestingness function. Our problem departs from analytics in relational data
warehouses in that (i) in an RDF graph we are not given but we must identify
the facts, dimensions, and measures of candidate aggregates; (ii) the classical
approach to efficiently evaluating multiple aggregates breaks in the face of
multi-valued dimensions in RDF data. In this work, we propose an extensible
end-to-end framework that enables the identification and evaluation of
interesting aggregates based on a new RDF-compatible one-pass algorithm for
efficiently evaluating a lattice of aggregates and a novel early-stop technique
(with probabilistic guarantees) that can prune uninteresting aggregates.
Experiments using both real and synthetic graphs demonstrate the ability of our
framework to find interesting aggregates in a large search space, the
efficiency of our algorithms (with up to 2.9x speedup over a similar pipeline
based on existing algorithms), and scalability as the data size and complexity
grow.


Abstract:
  We discuss the emergence of black hole shadow and photon-sphere in the
context of $f(R)$ gravity. It is shown that the shadow is exponentially
sensitive to linear instabilities of metric coming from some $f(R)$ solutions.
Thus, the instabilities of photon circular trajectories, delimiting the black
hole photon-sphere, are double exponentialized. Specifically we individuate two
Lyapunov exponents, rather than only one, related to two different sources of
chaos in geodesic orbits as a sort of butterfly effect. Such a result violates
the black hole chaos bound proposed by Maldacena, Shenker and Stanford for
General Relativity. We also explore the impact of the black hole metric
instabilities in $f(R)$ gravity on the quasi-normal modes. In the framework of
Extended Theories of Gravity, our analysis suggests a new paradigm to deal with
black hole shadow and gravitational waves observations coming from black hole
merging in the ringdown phase.


Abstract:
  Every convex homogeneous polynomial (or form) is nonnegative. Blekherman has
shown that there exist convex forms that are not sums of squares via a
nonconstructive argument. We provide an explicit example of a convex form of
degree four in 272 variables that is not a sum of squares. The form is related
to the Cauchy-Schwarz inequality over the octonions. The proof uses symmetry
reduction together with the fact (due to Blekherman) that forms of even degree,
that are near-constant on the unit sphere, are convex. Using this same
connection, we obtain improved bounds on the approximation quality achieved by
the basic sum-of-squares relaxation for optimizing quaternary quartic forms on
the sphere.


Abstract:
  Principled decision making in emergency response management necessitates the
use of statistical models that predict the spatial-temporal likelihood of
incident occurrence. These statistical models are then used for proactive
stationing which allocates first responders across the spatial area in order to
reduce overall response time. Traditional methods that simply aggregate past
incidents over space and time fail to make useful short-term predictions when
the spatial region is large and focused on fine-grained spatial entities like
interstate highway networks. This is partially due to the sparsity of incidents
with respect to the area in consideration. Further, accidents are affected by
several covariates, and collecting, cleaning, and managing multiple streams of
data from various sources is challenging for large spatial areas. In this
paper, we highlight how this problem is being solved for the state of
Tennessee, a state in the USA with a total area of over 100,000 sq. km. Our
pipeline, based on a combination of synthetic resampling, non-spatial
clustering, and learning from data can efficiently forecast the spatial and
temporal dynamics of accident occurrence, even under sparse conditions. In the
paper, we describe our pipeline that uses data related to roadway geometry,
weather, historical accidents, and real-time traffic congestion to aid accident
forecasting. To understand how our forecasting model can affect allocation and
dispatch, we improve upon a classical resource allocation approach.
Experimental results show that our approach can significantly reduce response
times in the field in comparison with current approaches followed by first
responders.


Abstract:
  The posterior over Bayesian neural network (BNN) parameters is extremely
high-dimensional and non-convex. For computational reasons, researchers
approximate this posterior using inexpensive mini-batch methods such as
mean-field variational inference or stochastic-gradient Markov chain Monte
Carlo (SGMCMC). To investigate foundational questions in Bayesian deep
learning, we instead use full-batch Hamiltonian Monte Carlo (HMC) on modern
architectures. We show that (1) BNNs can achieve significant performance gains
over standard training and deep ensembles; (2) a single long HMC chain can
provide a comparable representation of the posterior to multiple shorter
chains; (3) in contrast to recent studies, we find posterior tempering is not
needed for near-optimal performance, with little evidence for a "cold
posterior" effect, which we show is largely an artifact of data augmentation;
(4) BMA performance is robust to the choice of prior scale, and relatively
similar for diagonal Gaussian, mixture of Gaussian, and logistic priors; (5)
Bayesian neural networks show surprisingly poor generalization under domain
shift; (6) while cheaper alternatives such as deep ensembles and SGMCMC methods
can provide good generalization, they provide distinct predictive distributions
from HMC. Notably, deep ensemble predictive distributions are similarly close
to HMC as standard SGLD, and closer than standard variational inference.


Abstract:
  Small to medium sized business enterprises (SMEs) generally thrive because
they have successfully done something unique within a niche market. For this
reason, SMEs may seek to protect their competitive advantage by avoiding any
standardization encouraged by the use of packaged software (PS). Packaged
software implementation at SMEs therefore presents challenges relating to how
best to respond to misfits between the functionality offered by the packaged
software and each SME's business needs. An important question relates to which
processes small software enterprises - or Small to Medium-Sized Software
Development Companies (SMSSDCs) - apply in order to identify and then deal with
these misfits. To explore the processes of packaged software (PS)
implementation, an ethnographic study was conducted to gain in-depth insights
into the roles played by analysts in two SMSSDCs. The purpose of the study was
to understand PS implementation in terms of requirements engineering (or
'PSIRE'). Data collected during the ethnographic study were analyzed using an
inductive approach. Based on our analysis of the cases we constructed a
theoretical model explaining the requirements engineering process for PS
implementation, and named it the PSIRE Parallel Star Model. The Parallel Star
Model shows that during PSIRE, more than one RE process can be carried out at
the same time. The Parallel Star Model has few constraints, because not only
can processes be carried out in parallel, but they do not always have to be
followed in a particular order. This paper therefore offers a novel
investigation and explanation of RE practices for packaged software
implementation, approaching the phenomenon from the viewpoint of the analysts,
and offers the first extensive study of packaged software implementation RE
(PSIRE) in SMSSDCs.


Abstract:
  We present a comprehensive study on the silver bismuthate Ag$_2$BiO$_3$,
synthesized under high-pressure high-temperature conditions, which has been the
subject of recent theoretical work on topologically complex electronic states.
We present X-ray photoelectron spectroscopy results showing two different
bismuth states, and X-ray absorption spectroscopy results on the oxygen
$K$-edge showing holes in the oxygen bands. These results support a bond
disproportionated state with holes on the oxygen atoms for Ag$_2$BiO$_3$. We
estimate a band gap of $\sim$1.25~eV for Ag$_2$BiO$_3$ from optical
conductivity measurements, which matches the band gap in density functional
calculations of the electronic band structure in the non-symmorphic space group
$Pnn2$, which supports two inequivalent Bi sites. In our band structure
calculations the disproportionated Ag$_2$BiO$_3$ is expected to host Weyl nodal
chains, one of which is located $\sim$0.5~eV below the Fermi level.
Furthermore, we highlight similarities between Ag$_2$BiO$_3$ and the well-known
disproportionated bismuthate BaBiO$_3$, including breathing phonon modes with
similar energy. In both compounds hybridization of Bi-$6s$ and O-$2p$ atomic
orbitals is important in shaping the band structure, but in contrast to the
Ba-$5p$ in BaBiO$_3$, the Ag-$4d$ bands in Ag$_2$BiO$_3$ extend up to the Fermi
level.


Abstract:
  Both Earth and Mars host populations of co-orbital minor bodies. A large
number of present-day Mars co-orbitals is probably associated with the fission
of the parent body of Mars Trojan 5261 Eureka (1990 MB) during a
rotation-induced YORP break-up event. Here, we use the statistical
distributions of the Tisserand parameter and the relative mean longitude of
Mars co-orbitals with eccentricity below 0.2 to estimate the importance of
rotation-induced Yarkovsky-O'Keefe-Radzievskii-Paddack (YORP) break-up events
in Martian co-orbital space. Machine-learning techniques (k-means++ and
agglomerative hierarchical clustering algorithms) are applied to assess our
findings. Our statistical analysis identified three new Mars Trojans: 2009 SE,
2018 EC4 and 2018 FC4. Two of them, 2018 EC4 and 2018 FC4, are probably linked
to Eureka but we argue that 2009 SE may have been captured, so it is not
related to Eureka. We also suggest that 2020 VT1, a recent discovery, is a
transient Martian co-orbital of the horseshoe type. When applied to Earth
co-orbital candidates with eccentricity below 0.2, our approach led us to
identify some clustering, perhaps linked to fission events. The cluster with
most members could be associated with Earth quasi-satellite 469219 Kamo`oalewa
(2016 HO3) that is a fast rotator. Our statistical analysis identified two new
Earth co-orbitals: 2020 PN1, that follows a horseshoe path, and 2020 PP1, a
quasi-satellite that is dynamically similar to Kamo`oalewa. For both Mars and
Earth co-orbitals, we found pairs of objects whose values of the Tisserand
parameter differ by very small amounts, perhaps hinting at recent disruption
events. Clustering algorithms and numerical simulations both suggest that 2020
KZ2 and Kamo`oalewa could be related.


Abstract:
  Subjective evaluations are critical for assessing the perceptual realism of
sounds in audio-synthesis driven technologies like augmented and virtual
reality. However, they are challenging to set up, fatiguing for users, and
expensive. In this work, we tackle the problem of capturing the perceptual
characteristics of localizing sounds. Specifically, we propose a framework for
building a general purpose quality metric to assess spatial localization
differences between two binaural recordings. We model localization similarity
by utilizing activation-level distances from deep networks trained for
direction of arrival (DOA) estimation. Our proposed metric (DPLM) outperforms
baseline metrics on correlation with subjective ratings on a diverse set of
datasets, even without the benefit of any human-labeled training data.


Abstract:
  Convolutional Neural Networks (CNNs) can play a key role in Medical Image
Analysis under large-scale annotated datasets. However, preparing such massive
dataset is demanding. In this context, Generative Adversarial Networks (GANs)
can generate realistic but novel samples, and thus effectively cover the real
image distribution. In terms of interpolation, the GAN-based medical image
augmentation is reliable because medical modalities can display the human
body's strong anatomical consistency at fixed position while clearly reflecting
inter-subject variability; thus, we propose to use noise-to-image GANs (e.g.,
random noise samples to diverse pathological images) for (i) medical Data
Augmentation (DA) and (ii) physician training. Regarding the DA, the
GAN-generated images can improve Computer-Aided Diagnosis based on supervised
learning. For the physician training, the GANs can display novel desired
pathological images and help train medical trainees despite
infrastructural/legal constraints. This thesis contains four GAN projects
aiming to present such novel applications' clinical relevance in collaboration
with physicians. Whereas the methods are more generally applicable, this thesis
only explores a few oncological applications.


Abstract:
  The problem of providing complete presentations of reduction algebras
associated to a pair of Lie algebras $(\mathfrak{G},\mathfrak{g})$ has
previously been considered by Khoroshkin and Ogievetsky in the case of the
diagonal reduction algebra for $\mathfrak{gl}(n)$. In this paper we consider
the diagonal reduction algebra of the pair of Lie superalgebras
$\left(\mathfrak{osp}(1|2) \times \mathfrak{osp}(1|2),
\mathfrak{osp}(1|2)\right)$ as a double coset space having an associative
diamond product and give a complete presentation in terms of generators and
relations. We also provide a PBW basis for this reduction algebra along with
Casimir-like elements and a subgroup of automorphisms.


Abstract:
  Can advanced deep learning technologies be applied to analyze some ancient
humanistic arts? Can deep learning technologies be directly applied to special
scenes such as facial expression analysis of Terracotta Warriors? The big
challenging is that the facial features of the Terracotta Warriors are very
different from today's people. We found that it is very poor to directly use
the models that have been trained on other classic facial expression datasets
to analyze the facial expressions of the Terracotta Warriors. At the same time,
the lack of public high-quality facial expression data of the Terracotta
Warriors also limits the use of deep learning technologies. Therefore, we
firstly use Generative Adversarial Networks (GANs) to generate enough
high-quality facial expression data for subsequent training and recognition. We
also verify the effectiveness of this approach. For the first time, this paper
uses deep learning technologies to find common facial expressions of general
and postured Terracotta Warriors. These results will provide an updated
technical means for the research of art of the Terracotta Warriors and shine
lights on the research of other ancient arts.


Abstract:
  A $k$-arc in PG($2,q$) is a set of $k$ points no three of which are
collinear. A hyperfocused $k$-arc is a $k$-arc in which the $k \choose 2$
secants meet some external line in exactly $k-1$ points. Hyperfocused $k$-arcs
can be viewed as 1-factorizations of the complete graph $K_k$ that embed in
PG($2,q$). We study the 526,915,620 1-factorizations of $K_{12}$, determine
which are embeddable in PG($2,q$), and classify hyperfocused $12$-arcs.
Specifically we show if a $12$-arc $\mathcal{K}$ is a hyperfocused arc in
PG($2,q$) then $q = 2^{5k}$ and $\mathcal{K}$ is a subset of a hyperconic
including the nucleus.


Abstract:
  The known exact solutions of Einstein's vacuum field equations modeling the
gravitational fields of pure gravitational radiation involve wave fronts which
are either planar or roughly spherical. We describe a scheme designed to check
explicitly whether or not the wave fronts collide. From the spacetime point of
view the scheme determines whether or not the null hypersurface histories of
the wave fronts intersect and, in particular, allows easy identification of the
cases in which the null hypersurfaces do not intersect.


Abstract:
  Let $R$ be a commutative ring with identity and $S$ a multiplicative subset
of $R$. First, we introduce and study the $S$-projective dimensions and
$S$-injective dimensions of $R$-modules, and then explore the $S$-global
dimension $S$-gl.dim$(R)$ of a commutative ring $R$ which is defined to be the
supremum of $S$-projective dimensions of all $R$-modules. Finally, we
investigated the $S$-global dimension of factor rings and polynomial rings.


Abstract:
  We show that Vizing's Theorem holds in the Borel context for graphs induced
by actions of 2-ended groups, and ask whether it holds more generally for
everywhere two ended Borel graphs.


Abstract:
  The novel coronavirus disease 2019 (COVID-19) characterized by atypical
pneumonia has caused millions of deaths worldwide. Automatically segmenting
lesions from chest Computed Tomography (CT) is a promising way to assist
doctors in COVID-19 screening, treatment planning, and follow-up monitoring.
However, voxel-wise annotations are extremely expert-demanding and scarce,
especially when it comes to novel diseases, while an abundance of unlabeled
data could be available. To tackle the challenge of limited annotations, in
this paper, we propose an uncertainty-guided dual-consistency learning network
(UDC-Net) for semi-supervised COVID-19 lesion segmentation from CT images.
Specifically, we present a dual-consistency learning scheme that simultaneously
imposes image transformation equivalence and feature perturbation invariance to
effectively harness the knowledge from unlabeled data. We then quantify the
segmentation uncertainty in two forms and employ them together to guide the
consistency regularization for more reliable unsupervised learning. Extensive
experiments showed that our proposed UDC-Net improves the fully supervised
method by 6.3% in Dice and outperforms other competitive semi-supervised
approaches by significant margins, demonstrating high potential in real-world
clinical practice.


Abstract:
  Neutron capture-induced nuclear recoils have emerged as an important tool for
detector calibrations in direct dark matter detection and coherent elastic
neutrino-nucleus scattering (CE${\nu}$NS). $\texttt{nrCascadeSim}$ is a
command-line tool for generating simulation data for energy deposits resulting
from neutron capture on pure materials. Presently, silicon, germanium, neon,
and argon are supported. While the software was developed for solid state
detector calibration, it can be used for any application which requires
simulated neutron capture-induced nuclear recoil data.


Abstract:
  With growing access to versatile robotics, it is beneficial for end users to
be able to teach robots tasks without needing to code a control policy. One
possibility is to teach the robot through successful task executions. However,
near-optimal demonstrations of a task can be difficult to provide and even
successful demonstrations can fail to capture task aspects key to robust skill
replication. Here, we propose a learning from demonstration (LfD) approach that
enables learning of robust task definitions without the need for near-optimal
demonstrations. We present a novel algorithmic framework for learning tasks
based on the ergodic metric -- a measure of information content in motion.
Moreover, we make use of negative demonstrations -- demonstrations of what not
to do -- and show that they can help compensate for imperfect demonstrations,
reduce the number of demonstrations needed, and highlight crucial task elements
improving robot performance. In a proof-of-concept example of cart-pole
inversion, we show that negative demonstrations alone can be sufficient to
successfully learn and recreate a skill. Through a human subject study with 24
participants, we show that consistently more information about a task can be
captured from combined positive and negative (posneg) demonstrations than from
the same amount of just positive demonstrations. Finally, we demonstrate our
learning approach on simulated tasks of target reaching and table cleaning with
a 7-DoF Franka arm. Our results point towards a future with robust,
data-efficient LfD for novice users.


Abstract:
  Man-Computer Symbiosis (MCS) was originally envisioned by the famous computer
pioneer J.C.R. Licklider in 1960, as a logical evolution of the then inchoate
relationship between computer and humans. In his paper, Licklider provided a
set of criteria by which to judge if a Man-Computer System is a symbiotic one,
and also provided some predictions about such systems in the near and far
future. Since then, innovations in computer networks and the invention of the
Internet were major developments towards that end. However, with most systems
based on conventional logical algorithms, many aspects of Licklider's MCS
remained unfulfilled. This paper explores the extent to which modern machine
learning systems in general, and deep learning ones in particular best
exemplify MCS systems, and why they are the prime contenders to achieve a true
Man-Computer Symbiosis as described by Licklider in his original paper in the
future. The case for deep learning is built by illustrating each point of the
original criteria as well as the criteria laid by subsequent research into MCS
systems, with specific examples and applications provided to strengthen the
arguments. The efficacy of deep neural networks in achieving Artificial General
Intelligence, which would be the perfect version of an MCS system is also
explored.


Abstract:
  We present a new random approximation method that yields the existence of a
discrete Beurling prime system $\mathcal{P}=\{p_{1}, p_{2}, \dotso\}$ which is
very close in a certain precise sense to a given non-decreasing,
right-continuous, nonnegative, and unbounded function $F$. This discretization
procedure improves an earlier discrete random approximation method due to H.
Diamond, H. Montgomery, and U. Vorhauer [Math. Ann. 334 (2006), 1-36], and
refined by W.-B. Zhang [Math. Ann. 337 (2007), 671-704].
  We obtain several applications. Our new method is applied to a question posed
by M. Balazard concerning Dirichlet series with a unique zero in their half
plane of convergence, to construct examples of very well-behaved generalized
number systems that solve a recent open question raised by T. Hilberdink and A.
Neamah in [Int. J. Number Theory 16 05 (2020), 1005-1011], and to improve the
main result from [Adv. Math. 370 (2020), Article 107240], where a Beurling
prime system with regular primes but extremely irregular integers was
constructed.


Abstract:
  Tidal interactions and planet evaporation processes impact the evolution of
close-in star-planet systems. We study the impact of stellar rotation on these
processes. We compute the time evolution of star-planet systems consisting of a
planet with initial mass between 0.02 and 2.5 M$_{ Jup}$ (6 and 800 M$_{
Earth}$), in a quasi-circular orbit with an initial orbital distance between
0.01 and 0.10 au, around a solar-type star evolving from the Pre-Main-Sequence
(PMS) until the end of the Main-Sequence (MS) phase. We account for the
evolution of: the stellar structure, the stellar angular momentum due to tides
and magnetic braking, the tidal interactions (equilibrium and dynamical tides
in stellar convective zones), the mass-evaporation of the planet, and the
secular evolution of the planetary orbit. We consider that at the beginning of
the evolution, the proto-planetary disk has fully dissipated and planet
formation is complete. Both a rapid initial stellar rotation, and a more
efficient angular momentum transport inside the star, in general, contribute
toward the enlargement of the domain which is devoid of planets after the PMS
phase, in the plane of planet mass vs. orbital distance. Comparisons with the
observed distribution of exoplanets orbiting solar mass stars, in the plane of
planet mass vs. orbital distance (addressing the "Neptunian desert" feature),
show an encouraging agreement with the present simulations, especially since no
attempts have been made to fine-tune initial parameters of the models to fit
the observations. We also obtain an upper limit for the orbital period of
bare-core planets, that agrees with observations of the "radius valley" feature
in the plane of planet radius vs. orbital period. The two effects, tides and
planet evaporation, should be accounted for simultaneously and in a consistent
way, with a detailed model for the evolution of the star.


Abstract:
  Symmetric Teleparallel Gravity is an exceptional theory of gravity that is
consistent with the vanishing affine connection. This theory is an alternative
and a simpler geometrical formulation of general relativity, where the
non-metricity $Q$ drives the gravitational interaction. Our interest lies in
exploring the cosmological bouncing scenarios in a flat
Friedmann-Lima\^itre-Robertson-Walker (FLRW) spacetime within this framework.
We explore bouncing scenarios with two different Lagrangian forms of $f(Q)$
such as a linearly and non-linearly dependence on $Q$. We have successfully
examined all the energy conditions and stability analysis for both models to
present a matter bounce.


Abstract:
  We introduce the new concept of computation coding. Similar to how
rate-distortion theory is concerned with the lossy compression of data,
computation coding deals with the lossy computation of functions.
  Particularizing to linear functions, we present an algorithm to reduce the
computational cost of multiplying an arbitrary given matrix with an unknown
column vector. The algorithm decomposes the given matrix into the product of
codebook wiring matrices whose entries are either zero or signed integer powers
of two.
  For a typical implementation of deep neural networks, the proposed algorithm
reduces the number of required addition units several times. To achieve the
accuracy of 16-bit signed integer arithmetic for 4k-vectors, no multipliers and
only 1.5 adders per matrix entry are needed.


Abstract:
  In this article, the outlier production mechanism of the conventional
Multiple Signal Classification (MUSIC) and the g-MUSIC Direction-of-Arrival
(DoA) estimation technique is investigated using tools from Random Matrix
Theory (RMT). A general Central Limit Theorem (CLT) is derived that allows to
analyze the asymptotic stochastic behavior of eigenvector-based cost functions
in the asymptotic regime where the number of snapshots and the number of
antennas increase without bound at the same rate. Furthermore, this CLT is used
to provide an accurate prediction of the resolution capabilities of the MUSIC
and the g-MUSIC DoA estimation method. The finite dimensional distribution of
the MUSIC and the g-MUSIC cost function is shown to be asymptotically jointly
Gaussian distributed in the asymptotic regime.


Abstract:
  Our aim is to find a complex continued fraction algorithm finding all the
best Diophantine approximations to a complex number. Using the sequence of
minimal vectors in a two dimensional lattice over Gaussian integers, we obtain
an algorithm defined on a submanifold of the space of unimodular two
dimensional Gauss lattices. This submanifold is transverse to the diagonal
flow. Thanks to the correspondence between minimal vectors and best Diophantine
approximations, the algorithm finds all the best approximations to a complex
number. A byproduct of the algorithm is the best constant for the complex
version of Dirichlet Theorem about approximations of complex numbers by
quotients of Gaussian integers.


Abstract:
  Observations of galactic white dwarfs with Gaia have allowed for
unprecedented modeling of white dwarf cooling, resolving core crystallization
and sedimentary heating from neutron rich nuclei. These cooling sequences are
sensitive to the diffusion coefficients of nuclei in Coulomb plasmas which have
order 10\% uncertainty and are often not valid across coupling regimes. Using
large scale molecular dynamics simulations we calculate diffusion coefficients
at high resolution in the regime relevant for white dwarf modeling. We present
a physically motivated law for diffusion with a semi-empirical correction which
is accurate at the percent level. Implemented along with linear mixing in
stellar evolution codes, this law should reduce the error from diffusion
coefficients by an order of magnitude.


Abstract:
  The author of this work proposes an overview of the recent semi-supervised
learning approaches and related works. Despite the remarkable success of neural
networks in various applications, there exist few formidable constraints
including the need for a large amount of labeled data. Therefore,
semi-supervised learning, which is a learning scheme in which the scarce labels
and a larger amount of unlabeled data are utilized to train models (e.g., deep
neural networks) is getting more important. Based on the key assumptions of
semi-supervised learning, which are the manifold assumption, cluster
assumption, and continuity assumption, the work reviews the recent
semi-supervised learning approaches. In particular, the methods in regard to
using deep neural networks in a semi-supervised learning setting are primarily
discussed. In addition, the existing works are first classified based on the
underlying idea and explained, and then the holistic approaches that unify the
aforementioned ideas are detailed.


Abstract:
  We study the existence of fully-heavy hidden-flavor $bc\bar{b}\bar{c}$
tetraquark states with various $J^{PC}=0^{\pm+}, 0^{--},1^{\pm\pm}, 2^{++}$, by
using the moment QCD sum rule method augmented by fundamental inequalities.
Using the moment sum rule analyses, our calculation shows that the masses for
the S-wave positive parity $bc\bar{b}\bar{c}$ tetraquark states are about
$12.2-12.4$ GeV in both
$[\mathbf{\bar{3}_c}]_{bc}\otimes[\mathbf{3_c}]_{\bar{b}\bar{c}}$ and
$[\mathbf{6_c}]_{bc}\otimes[\mathbf{\bar{6}_c}]_{\bar{b}\bar{c}}$ color
configuration channels. Except for two $0^{++}$ states, such results are below
the thresholds $T_{\eta_c\eta_b}/T_{\Upsilon\psi}$ and $T_{B_cB_c}$, implying
that these S-wave positive parity $bc\bar{b}\bar{c}$ tetraquark states are
probably stable against the strong interaction. For the P-wave negative parity
$bc\bar{b}\bar{c}$ tetraquarks, their masses in the
$[\mathbf{\bar{3}_c}]_{bc}\otimes[\mathbf{3_c}]_{\bar{b}\bar{c}}$ channel are
around $12.9-13.2$ GeV, while a bit higher in the
$[\mathbf{6_c}]_{bc}\otimes[\mathbf{\bar{6}_c}]_{\bar{b}\bar{c}}$ channel. They
can decay into the $c\bar c+b\bar b$ and $c\bar b+b\bar c$ final states via the
spontaneous dissociation mechanism, including the $J/\psi\Upsilon$,
$\eta_c\Upsilon$, $J/\psi\eta_b$, $B_c^+B_c^-$ channels.


Abstract:
  The adiabatic topological pumping is proposed by periodically modulating a
semiconductor nanowire double-quantum-dot chain. We demonstrate that the
quantized charge transport can be achieved by a nontrivial modulation of the
quantum-dot well and barrier potentials. When the quantum-dot well potential is
replaced by a time-dependent staggered magnetic field, the topological spin
pumping can be realized by periodically modulating the barrier potentials and
magnetic field. We also demonstrate that in the presence of Rashba spin-orbit
interaction, the double-quantum-dot chain can be used to implement the
topological spin pumping. However, the pumped spin in this case can have a
quantization axis other than the applied magnetic field direction. Moreover, we
show that all the adiabatic topological pumping are manifested by the presence
of gapless edge states traversing the band gap as a function of time.


Abstract:
  The large attention-based encoder-decoder network (Transformer) has become
prevailing recently due to its effectiveness. But the high computation
complexity of its decoder raises the inefficiency issue. By examining the
mathematic formulation of the decoder, we show that under some mild conditions,
the architecture could be simplified by compressing its sub-layers, the basic
building block of Transformer, and achieves a higher parallelism. We thereby
propose Compressed Attention Network, whose decoder layer consists of only one
sub-layer instead of three. Extensive experiments on 14 WMT machine translation
tasks show that our model is 1.42x faster with performance on par with a strong
baseline. This strong baseline is already 2x faster than the widely used
standard baseline without loss in performance.


Abstract:
  We demonstrate a smart laser-diffraction analysis technique for particle
mixture identification. We retrieve information about the size, geometry, and
ratio concentration of two-component heterogeneous particle mixtures with an
efficiency above 92%. In contrast to commonly-used laser diffraction schemes --
in which a large number of detectors is needed -- our machine-learning-assisted
protocol makes use of a single far-field diffraction pattern, contained within
a small angle ($\sim 0.26^{\circ}$) around the light propagation axis. Because
of its reliability and ease of implementation, our work may pave the way
towards the development of novel smart identification technologies for sample
classification and particle contamination monitoring in industrial
manufacturing processes.


Abstract:
  A critical subject in fully differential QED calculations originates from
numerical instabilities due to small fermion masses that act as regulators of
collinear singularities. At next-to-next-to-leading order (NNLO) a major
challenge is therefore to find a stable implementation of numerically delicate
real-virtual matrix elements. In the case of Bhabha scattering this has so far
prevented the development of a fixed-order Monte Carlo at NNLO accuracy. In
this paper we present a new method for stabilising the real-virtual matrix
element. It is based on the expansion for soft photon energies including the
non-universal subleading term calculated with the method of regions. We have
applied this method to Bhabha scattering to obtain a stable and efficient
implementation within the McMule framework. We therefore present for the first
time fully differential results for the photonic NNLO corrections to Bhabha
scattering.


Abstract:
  As an attempt to tackle the low-data-rate issue of the conventional LoRa
systems, we propose two novel frequency-bin-index (FBI) LoRa schemes. In scheme
I, the indices of starting frequency bins (SFBs) are utilized to carry the
information bits. To facilitate the actual implementation, the SFBs of each
LoRa signal are divided into several groups prior to the modulation process in
the proposed FBI-LoRa system. To further improve the system flexibility, we
formulate a generalized modulation scheme and propose scheme II by treating the
SFB groups as an additional type of transmission entity. In scheme II, the
combination of SFB indices and that of SFB group indices are both exploited to
carry the information bits. We derive the theoretical expressions for
bit-error-rate (BER) and throughput of the proposed FBI-LoRa system with two
modulation schemes over additive white Gaussian noise (AWGN) and Rayleigh
fading channels. Theoretical and simulation results show that the proposed
FBI-LoRa schemes can significantly increases the transmission throughput
compared with the existing LoRa systems at the expense of a slight loss in BER
performance. Thanks to the appealing superiorities, the proposed FBI-LoRa
system is a promising alternative for high-data-rate Internet of Things (IoT)
applications.


Abstract:
  Palomar 5 is one of the sparsest star clusters in the Galactic halo and is
best-known for its spectacular tidal tails, spanning over 20 degrees across the
sky. With N-body simulations we show that both distinguishing features can
result from a stellar-mass black hole population, comprising ~20% of the
present-day cluster mass. In this scenario, Palomar 5 formed with a `normal'
black hole mass fraction of a few per cent, but stars were lost at a higher
rate than black holes, such that the black hole fraction gradually increased.
This inflated the cluster, enhancing tidal stripping and tail formation. A
gigayear from now, the cluster will dissolve as a 100% black hole cluster.
Initially denser clusters end up with lower black hole fractions, smaller
sizes, and no observable tails. Black hole-dominated, extended star clusters
are therefore the likely progenitors of the recently discovered thin stellar
streams in the Galactic halo.


Abstract:
  This paper investigates fractal dimension of linear combination of fractal
continuous functions with the same or different fractal dimensions. It has been
proved that: (1) $BV_{I}$ all fractal continuous functions with bounded
variation is fractal linear space; (2) ${}^{1}D_{I}$ all fractal continuous
functions with Box dimension one is a fractal linear space; (3) ${}^{s}D_{I}$
all fractal continuous functions with identical Box dimension $s(1<s\leq 2)$ is
surprisingly a non-fractal linear space, even non-fractal linear manifold,
beyond our initial expectation, because the Box dimension of linear combination
of fractal continuous functions can take any real number in $[1,s)$ if it
exists, and some different upper and lower Box dimension if it does not exit.
This attracts our interests to probe into fractal characteristics of
${}^{s}D_{I}$, and get some suggesting results.


Abstract:
  We present an engaging levitation experiment that students can perform at
home or in a simple laboratory using everyday objects. A cork, modified to be
slightly denser than water, is placed in a jug containing tap water and coarse
kitchen salt delivered at the bottom without stirring. The salt gradually
diffuses and determines a stable density stratification of water, the bottom
layers being denser than the top ones. During the dissolution of salt, the cork
slowly rises at an increasing height, where at any instant its density is
balanced by that of the surrounding water. If the cork is gently pushed off its
temporary equilibrium position, it experiences a restoring force and starts to
oscillate. Students can perform many different measurements of the phenomena
involved and tackle non-trivial physical issues related to the behaviour of a
macroscopic body immersed in a stratified fluid. Despite its simplicity, this
experiment allows to introduce various theoretical concepts of relevance for
the physics of the atmosphere and stars and offers students the opportunity of
getting acquainted with a simple system that can serve as a model to understand
complex phenomena such as oscillations at the Brunt-V\"{a}is\"{a}l\"{a}
frequency and the propagation of internal gravity waves in a stratified medium.


Abstract:
  We show that every $r$-uniform hypergraph on $n$ vertices which does not
contain a tight cycle has at most $O(n^{r-1} (\log n)^5)$ edges. This is an
improvement on the previously best-known bound, of $n^{r-1} e^{O(\sqrt{\log
n})}$, due to Sudakov and Tomon, and our proof builds up on their work. A
recent construction of B. Janzer implies that our bound is tight up to an
$O((\log n)^4 \log \log n)$ factor.


Abstract:
  Gravitational wave observations of binary neutron star mergers provide
valuable information about neutron star structure and the equation of state of
dense nuclear matter. Numerous methods have been proposed to analyze the
population of observed neutron stars and previous work has demonstrated the
necessity of jointly fitting the astrophysical distribution and the equation of
state in order to accurately constrain the equation of state. In this work, we
introduce a new framework to simultaneously infer the distribution of binary
neutron star masses and the nuclear equation of state using Gaussian mixture
model density estimates which mitigates some of the limitations previously-used
methods suffer from. Using our method, we reproduce previous projections for
the expected precision of our joint mass distribution and equation of state
inference with tens of observations. We also show that mismodeling the equation
of state can bias our inference of the neutron star mass distribution. While we
focus on neutron star masses and matter effects, our method is widely
applicable to population inference problems.


Abstract:
  A strong correlation between magnetic interaction and topological symmetries
leads to unconventional magneto-transport behavior. Weyl fermions induce
topologically protected spin-momentum locking, which is closely related to
spin-wave gap formation in magnetic crystals. Ferromagnetic SrRuO3, regarded as
a strong candidate for Weyl semimetal, inherently possesses a nonzero spin-wave
gap owing to its strong magnetic anisotropy. In this paper, we propose a method
to control the spin-wave dynamics by nanolayer designing of the SrRuO3/SrTiO3
superlattices. In particular, the six-unit-cell-thick SrRuO3 layers within the
superlattices undergo a phase transition in crystalline symmetry from
orthorhombic to tetragonal, as the thickness of the SrTiO3 layers is modulated
with atomic-scale precision. Consequently, the magnetic anisotropy, anomalous
Hall conductivity, and spin-wave gap could be systematically manipulated. Such
customization of magnetic anisotropy via nanoscale heterostructuring offers a
novel control knob to tailor the magnon excitation energy for future spintronic
applications, including magnon waveguides and filters. Our nanolayer approach
unveils the important correlation between the tunable lattice degrees of
freedom and spin dynamics in topologically non-trivial magnetic materials.


Abstract:
  The concept of Nature-Based Solutions (NBS) has emerged to foster sustainable
development by transversally addressing social, economic, and environmental
urban challenges. However, there is still a considerable lack of agreement on
the conceptualization of NBS, especially concerning typologies, nomenclature,
and performance assessments in terms of ecosystem services (ES) and urban
challenges (UC). Therefore, this article consolidates the knowledge from 4
European projects to set a path for a common understanding of NBS and thus,
facilitate their mainstreaming.


Abstract:
  We study the merger rate of primordial black holes (PBHs) in the
self-interacting dark matter (SIDM) halo models. To explore a numerical
description for the density profile of the SIDM halo models, we use the result
of a previously performed simulation for the SIDM halo models with
$\sigma/m=10~cm^{2}g^{-1}$. We also propose a concentration-mass-time relation
that can explain the evolution of the halo density profile related to the SIDM
models. Furthermore, we investigate the encounter condition of PBHs that may
have been distributed in the medium of dark matter halos randomly. Under these
assumptions, we calculate the merger rate of PBHs within each halo considering
the SIDM halo models and compare the results with the one obtained for the cold
dark matter (CDM) halo models. We indicate that the merger rate of PBHs for the
SIDM halo models during the first epoch should be lower than the corresponding
result for the CDM halo models, while by the time entering the second epoch
sufficient PBH mergers in the SIDM halo models can be generated and even exceed
the one resulted from the CDM halo models. By considering the
spherical-collapse halo mass function, we obtain similar results for the
cumulative merger rate of PBHs. Moreover, we calculate the redshift evolution
of the PBH total merger rate. To determine a constraint on the PBH abundance,
we study the merger rate of PBHs in terms of their fraction and masses and
compare those with the black hole merger rate estimated by the Advanced LIGO
(aLIGO) detectors during the third observing run. The results demonstrate that
within the context of the SIDM halo models during the second epoch, the merger
rate of $10~M_{\odot}-10~M_{\odot}$ events falls within the aLIGO window. We
also estimate a relation between the fraction of PBHs and their masses, which
is well consistent with our findings.


Abstract:
  The critical point equation arises as a critical point of the total scalar
curvature functional defined on the space of constant scalar curvature metrics
of a unit volume on a compact manifold. In this equation, there exists a
function $f$ on the manifold that satisfies the following
  $$
  (1+f){\rm Ric} = Ddf + \frac{nf +n-1}{n(n-1)}sg.
  $$
  It has been conjectured that if $(g, f)$ is a solution of the critical point
equation, then $g$
  is Einstein and so $(M, g)$ is isometric to a standard sphere.
  In this paper, we show that this conjecture is true if the given Riemannian
metric has positive isotropic curvature.


Abstract:
  Numerous malware families rely on domain generation algorithms (DGAs) to
establish a connection to their command and control (C2) server. Counteracting
DGAs, several machine learning classifiers have been proposed enabling the
identification of the DGA that generated a specific domain name and thus
triggering targeted remediation measures. However, the proposed
state-of-the-art classifiers are based on deep learning models. The black box
nature of these makes it difficult to evaluate their reasoning. The resulting
lack of confidence makes the utilization of such models impracticable. In this
paper, we propose EXPLAIN, a feature-based and contextless DGA multiclass
classifier. We comparatively evaluate several combinations of feature sets and
hyperparameters for our approach against several state-of-the-art classifiers
in a unified setting on the same real-world data. Our classifier achieves
competitive results, is real-time capable, and its predictions are easier to
trace back to features than the predictions made by the DGA multiclass
classifiers proposed in related work.


Abstract:
  The robust topology optimization formulation that introduces the eroded and
dilated versions of the design has gained increasing popularity in recent
years, mainly because of its ability to produce designs satisfying a minimum
length scale. Despite its success in various topology optimization fields, the
robust formulation presents some drawbacks. This paper addresses one in
particular, which concerns the imposition of the minimum length scale. In the
density framework, the minimum size of the solid and void phases must be
imposed implicitly through the parameters that define the density filter and
the smoothed Heaviside projection. Finding these parameters can be time
consuming and cumbersome, hindering a general code implementation of the robust
formulation. Motivated by this issue, in this article we provide analytical
expressions that explicitly relate the minimum length scale and the parameters
that define it. The expressions are validated on a density-based framework. To
facilitate the reproduction of results, MATLAB codes are provided.
  As a side finding, this paper shows that to obtain simultaneous control over
the minimum size of the solid and void phases, it is necessary to involve the 3
fields (eroded, intermediate and dilated) in the topology optimization problem.
Therefore, for the compliance minimization problem subject to a volume
restriction, the intermediate and dilated designs can be excluded from the
objective function, but the volume restriction has to be applied to the dilated
design in order to involve all 3 designs in the formulation.


Abstract:
  What is the computational model behind a Transformer? Where recurrent neural
networks have direct parallels in finite state machines, allowing clear
discussion and thought around architecture variants or trained models,
Transformers have no such familiar parallel. In this paper we aim to change
that, proposing a computational model for the transformer-encoder in the form
of a programming language. We map the basic components of a transformer-encoder
-- attention and feed-forward computation -- into simple primitives, around
which we form a programming language: the Restricted Access Sequence Processing
Language (RASP). We show how RASP can be used to program solutions to tasks
that could conceivably be learned by a Transformer, and how a Transformer can
be trained to mimic a RASP solution. In particular, we provide RASP programs
for histograms, sorting, and Dyck-languages. We further use our model to relate
their difficulty in terms of the number of required layers and attention heads:
analyzing a RASP program implies a maximum number of heads and layers necessary
to encode a task in a transformer. Finally, we see how insights gained from our
abstraction might be used to explain phenomena seen in recent works.


Abstract:
  The problem of designing NLP solvers for math word problems (MWP) has seen
sustained research activity and steady gains in the test accuracy. Since
existing solvers achieve high performance on the benchmark datasets for
elementary level MWPs containing one-unknown arithmetic word problems, such
problems are often considered "solved" with the bulk of research attention
moving to more complex MWPs. In this paper, we restrict our attention to
English MWPs taught in grades four and lower. We provide strong evidence that
the existing MWP solvers rely on shallow heuristics to achieve high performance
on the benchmark datasets. To this end, we show that MWP solvers that do not
have access to the question asked in the MWP can still solve a large fraction
of MWPs. Similarly, models that treat MWPs as bag-of-words can also achieve
surprisingly high accuracy. Further, we introduce a challenge dataset, SVAMP,
created by applying carefully chosen variations over examples sampled from
existing datasets. The best accuracy achieved by state-of-the-art models is
substantially lower on SVAMP, thus showing that much remains to be done even
for the simplest of the MWPs.


Abstract:
  Terahertz time domain spectroscopy (THz TDS) is used to measure the melting
kinetics of fructose molecular crystals. Combining single crystal anisotropy
measurements with density functional calculations we assign the phonon
frequencies and interrogate how specific phonons behave with melting. While
nearly all the low frequency phonons continuously red shift with heating and
melting, the lowest energy phonon polarized along the c-axis blue shifts at the
melting temperature, suggesting an initial structural change immediately before
melting. We find that the kinetics follow a 3D growth model with large
activation energies consistent with previous differential scanning calorimetry
(DSC) measurements. The large activation energies indicate multiple H-bonds
must break collectively for the transition. The results suggest the generality
of the kinetics for molecular crystals and that THz TDS with picosecond
resolution could be used to measure ultra-fast kinetics.


Abstract:
  In this paper, we propose to enhance the pair-wise aspect and opinion terms
extraction (PAOTE) task by incorporating rich syntactic knowledge. We first
build a syntax fusion encoder for encoding syntactic features, including a
label-aware graph convolutional network (LAGCN) for modeling the dependency
edges and labels, as well as the POS tags unifiedly, and a local-attention
module encoding POS tags for better term boundary detection. During pairing, we
then adopt Biaffine and Triaffine scoring for high-order aspect-opinion term
pairing, in the meantime re-harnessing the syntax-enriched representations in
LAGCN for syntactic-aware scoring. Experimental results on four benchmark
datasets demonstrate that our model outperforms current state-of-the-art
baselines, meanwhile yielding explainable predictions with syntactic knowledge.


Abstract:
  Driven by the tremendous effort in researching novel deep learning (DL)
algorithms, the training cost of developing new models increases staggeringly
in recent years. We analyze GPU cluster usage statistics from a top research
institute for more insights into the hardware efficiency achieved by typical DL
training jobs. Our study reveals that single-accelerator training jobs can
dominate the cluster-wide resource consumption when launched repetitively
(e.g., for hyper-parameter tuning) while severely under-utilizing the hardware.
Fortunately, we observe that such workloads have the following unique
characteristics: (i) the models among jobs often have the same types of
operators with the same shapes, and (ii) the inter-model horizontal fusion of
such operators is mathematically equivalent to other already well-optimized
operators. Thus, to help DL researchers and practitioners effectively improve
the hardware utilization of their novel DL training workloads, we propose
Horizontally Fused Training Array (HFTA). HFTA is a new DL framework extension
library that horizontally fuses the models from different repetitive jobs
deeply down to operators and then trains them simultaneously on a shared
accelerator. To show the generality of our solution, we apply HFTA to six DL
models training on state-of-the-art accelerators (GPUs and TPUs). Our results
indicate that HFTA is highly effective in improving hardware utilization and
achieves up to $15.1 \times$ higher training throughput vs. the standard
practice of running each job on a separate accelerator.


Abstract:
  This paper considers grid formation of an unmanned aerial vehicle (UAV) swarm
for maximizing the secrecy rate in the presence of an unknown eavesdropper. In
particular, the UAV swarm performs coordinated beamforming onto the null space
of the legitimate channel to jam the eavesdropper located at an unknown
location. By nulling the channel between the legitimate receiver and the UAV
swarm, we obtain an optimal trajectory and jamming power allocation for each
UAV enabling wideband single ray beamforming to improve the secrecy rate.
Results obtained demonstrate the effectiveness of the proposed UAV-aided
jamming scheme as well as the optimal number of UAVs in the swarm necessary to
observe a saturation effect in the secrecy rate. We also show the optimal
radius of the unknown but constrained location of the eavesdropper.


Abstract:
  We show that if an exponential sum with multiplicative coefficients is large
then the associated multiplicative function is "pretentious". This leads to
applications in the circle method, and a natural interpretation of the
local-global principle.


Abstract:
  This paper presents a detailed computational protocol for atomistic
simulation of the formation and growth of metal-containing nanostructures
during the Focused Electron Beam Induced Deposition (FEBID) process. The
protocol is based upon the Irradiation-Driven Molecular Dynamics (IDMD) - a
novel and general methodology for computer simulations of irradiation-driven
transformations of complex molecular systems by means of the advanced software
packages MBN Explorer and MBN Studio. Atomistic simulations performed following
the formulated protocol provide valuable insights into the fundamental
mechanisms of electron-induced precursor fragmentation and the related
mechanism of nanostructure formation and growth using FEBID, which are
essential for the further advancement of FEBID-based nanofabrication. The
developed computational methodology is general and applicable to different
precursor molecules, substrate types, irradiation regimes, etc. The methodology
can also be adjusted to simulate the nanostructure formation by other
nanofabrication techniques using electron beams, such as direct electron beam
lithography. In the present study, the methodology is applied to the IDMD
simulation of the FEBID of Pt(PF$_3$)$_4$ - a widely studied precursor molecule
- on a SiO$_2$ surface. The simulations reveal the processes driving the
initial phase of nanostructure formation during FEBID, including nucleation of
Pt atoms, formation of small metal clusters on the surface, followed by their
aggregation and the formation of dendritic platinum nanostructures. The
analysis of the simulation results provides space resolved relative metal
content, height and the growth rate of the deposits which represent valuable
reference data for the experimental characterization of the nanostructures
grown by FEBID.


Abstract:
  In this conference paper we summarise the findings of a recent study, where
the impact of the ultra-relativistic regime on the production of a feebly
interacting dark matter particle is considered. As its population accumulates
over the thermal history, we inspected thoroughly the temperature window $T \gg
M$, which has been previously neglected in the context of dark matter models
with renormalizable operators. At high temperatures, and for the model
considered in our work, the production rate of the feebly interacting particle
is driven by multiple soft scatterings, as well as $2 \to 2$ processes, that
can give a large contribution to the dark matter energy density.


Abstract:
  Advanced resuscitative technologies, such as Extra Corporeal Membrane
Oxygenation (ECMO) cannulation or Resuscitative Endovascular Balloon Occlusion
of the Aorta (REBOA), are technically difficult even for skilled medical
personnel. This paper describes the core technologies that comprise a
teleoperated system capable of granting femoral vascular access, which is an
important step in both of these procedures and a major roadblock in their wider
use in the field. These technologies include a kinematic manipulator, various
sensing modalities, and a user interface. In addition, we evaluate our system
on a surgical phantom as well as in-vivo porcine experiments. These resulted
in, to the best of our knowledge, the first robot-assisted arterial
catheterizations; a major step towards our eventual goal of automatic catheter
insertion through the Seldinger technique.


Abstract:
  A diverse representation of different demographic groups in AI training data
sets is important in ensuring that the models will work for a large range of
users. To this end, recent efforts in AI fairness and inclusion have advocated
for creating AI data sets that are well-balanced across race, gender,
socioeconomic status, and disability status. In this paper, we contribute to
this line of work by focusing on the representation of age by asking whether
older adults are represented proportionally to the population at large in AI
data sets. We examine publicly-available information about 92 face data sets to
understand how they codify age as a case study to investigate how the subjects'
ages are recorded and whether older generations are represented. We find that
older adults are very under-represented; five data sets in the study that
explicitly documented the closed age intervals of their subjects included older
adults (defined as older than 65 years), while only one included oldest-old
adults (defined as older than 85 years). Additionally, we find that only 24 of
the data sets include any age-related information in their documentation or
metadata, and that there is no consistent method followed across these data
sets to collect and record the subjects' ages. We recognize the unique
difficulties in creating representative data sets in terms of age, but raise it
as an important dimension that researchers and engineers interested in
inclusive AI should consider.


Abstract:
  As a sequel to \cite{Licollapsing}, we study Calabi-Yau metrics collapsing
along a holomorphic fibration over a Riemann surface. Assuming at worst
canonical singular fibres, we prove a uniform diameter bound for all fibres in
the suitable rescaling. This has consequences on the geometry around the
singular fibres.


Abstract:
  Worldwide research shows that millions of lives lost per year because of
heart disease. The healthcare sector produces massive volumes of data on heart
disease that are sadly not used to locate secret knowledge for successful
decision making. One of the most important aspects at this moment is detecting
heart disease at an early stage. Researchers have applied distinct techniques
to the UCI Machine Learning heart disease dataset. Many researchers have tried
to apply some complex techniques to this dataset, where detailed studies are
still missing. In this paper, Principal Component Analysis (PCA) has been used
to reduce attributes. Apart from a Hybrid genetic algorithm (HGA) with k-means
used for final clustering. Typically, the k-means method is using for
clustering the data. This type of clustering can get stuck in the local optima
because this method is heuristic. We used the Hybrid Genetic Algorithm (HGA)
for data clustering to avoid this problem. Our proposed method can predict
early heart disease with an accuracy of 94.06%.


Abstract:
  We propose a novel method for finding the most innovative people in an
organization, using email to analyze structure and dynamics of the
organization's online communication. To illustrate our approach, we analyzed
the email archive of 2000 members of the R&D department of a US multinational
company. We use metrics of social network analysis extended with meta-data of
interaction dynamics to calculate features for individual employees: their
network positions, messages sent and received, pings to others and response
times. We find a distinction between innovation group leaders and subject
matter experts focused on publishing papers and patents. Innovation
administrators have a higher number of direct contacts, are more committed in
conversations and receive more messages than they send. We also found
significant differences between innovators oriented towards internal awards and
innovators more concerned with external recognition of their work.


Abstract:
  Containerization plays a crucial role in the de facto technology stack for
implementing microservices architecture (each microservice has its own database
in most cases). Nevertheless, there are still fierce debates on containerizing
production databases, mainly due to the data persistence issues and concerns.
Driven by a project of refactoring an Automated Machine Learning system, this
research proposes the container-native data persistence as a conditional
solution to running database containers in production. In essence, the proposed
solution distinguishes the stateless data access (i.e. reading) from the
stateful data processing (i.e. creating, updating, and deleting) in databases.
A master database handles the stateful data processing and dumps database
copies for building container images, while the database containers will keep
stateless at runtime, based on the preloaded dump in the image. Although there
are delays in the state/image update propagation, this solution is particularly
suitable for the read-only, the eventual consistency, and the asynchronous
processing scenarios. Moreover, with optimal tuning (e.g., disabling locking),
the portability and performance gains of a read-only database container would
outweigh the performance loss in accessing data across the underlying image
layers.


Abstract:
  In this paper, it is shown theoretically that spurious local minima are
common for deep fully-connected networks and convolutional neural networks
(CNNs) with piecewise linear activation functions and datasets that cannot be
fitted by linear models. A motivating example is given to explain the reason
for the existence of spurious local minima: each output neuron of deep
fully-connected networks and CNNs with piecewise linear activations produces a
continuous piecewise linear (CPWL) output, and different pieces of CPWL output
can fit disjoint groups of data samples when minimizing the empirical risk.
Fitting data samples with different CPWL functions usually results in different
levels of empirical risk, leading to prevalence of spurious local minima. This
result is proved in general settings with any continuous loss function. The
main proof technique is to represent a CPWL function as a maximization over
minimization of linear pieces. Deep ReLU networks are then constructed to
produce these linear pieces and implement maximization and minimization
operations.


Abstract:
  The muon anomalous magnetic moment is discussed within the model based on the
$SU(2)_L\times SU(2)_R\times U(1)_{B-L}$-gauge group. The contributions of the
neutrinos having the dipole magnetic moment (DMM) are calculated. The cases of
the Majorana and Dirac neutrino are considered. It is shown that to account for
the $(g-2)_{\mu}$ anomaly DMMs connected with heavy neutrinos should be on the
order of $\mbox{few}\times10^{-8}\mu_B$, that is at variance with the
theoretically predicted values.


Abstract:
  How does the accuracy of deep neural network models trained to classify
clinical images of skin conditions vary across skin color? While recent studies
demonstrate computer vision models can serve as a useful decision support tool
in healthcare and provide dermatologist-level classification on a number of
specific tasks, darker skin is underrepresented in the data. Most publicly
available data sets do not include Fitzpatrick skin type labels. We annotate
16,577 clinical images sourced from two dermatology atlases with Fitzpatrick
skin type labels and open-source these annotations. Based on these labels, we
find that there are significantly more images of light skin types than dark
skin types in this dataset. We train a deep neural network model to classify
114 skin conditions and find that the model is most accurate on skin types
similar to those it was trained on. In addition, we evaluate how an algorithmic
approach to identifying skin tones, individual typology angle, compares with
Fitzpatrick skin type labels annotated by a team of human labelers.


Abstract:
  To provide consistent emotional interaction with users, dialog systems should
be capable to automatically select appropriate emotions for responses like
humans. However, most existing works focus on rendering specified emotions in
responses or empathetically respond to the emotion of users, yet the individual
difference in emotion expression is overlooked. This may lead to inconsistent
emotional expressions and disinterest users. To tackle this issue, we propose
to equip the dialog system with personality and enable it to automatically
select emotions in responses by simulating the emotion transition of humans in
conversation. In detail, the emotion of the dialog system is transitioned from
its preceding emotion in context. The transition is triggered by the preceding
dialog context and affected by the specified personality trait. To achieve
this, we first model the emotion transition in the dialog system as the
variation between the preceding emotion and the response emotion in the
Valence-Arousal-Dominance (VAD) emotion space. Then, we design neural networks
to encode the preceding dialog context and the specified personality traits to
compose the variation. Finally, the emotion for response is selected from the
sum of the preceding emotion and the variation. We construct a dialog dataset
with emotion and personality labels and conduct emotion prediction tasks for
evaluation. Experimental results validate the effectiveness of the
personality-affected emotion transition.


Abstract:
  Alignment of non-spherical grains with magnetic fields is an important
problem as it lays the foundation of probing magnetic fields with polarized
dust thermal emissions. In this paper, we investigate the feasibility of
magnetic alignment in protoplanetary disks (PPDs). We use an alignment
condition that Larmor precession should be fast compared with the damping
timescale. We first show that the Larmor precession timescale is some three
orders of magnitude longer than the damping time for millimeter-sized grains
under conditions typical of PPDs, making the magnetic alignment unlikely. The
precession time can be shortened by superparamagnetic inclusions (SPIs), but
the reduction factor strongly depends on the size of the SPI clusters, which we
find is limited by the so-called "N\'{e}el's relaxation process." In
particular, the size limit of SPIs is set by the so-called "anisotropic energy
constant" of the SPI material, which describes the energy barrier needed to
change the direction of the magnetic moment of an SPI. For the most common
iron-bearing materials, we find maximum SPI sizes corresponding to a reduction
factor of the Larmor precession timescale of order $10^3$. We also find that
reaching this maximum reduction factor requires fine-tuning on the SPI sizes.
Lastly, we illustrate the effects of the SPI size limits on magnetic alignment
of dust grains with a simple disk model, and we conclude that it is unlikely
for relatively large grains of order 100 $\mu$m or more to be aligned with
magnetic fields even with SPIs.


Abstract:
  Finite-state Markov models are widely used for modeling wireless channels
affected by a variety of non-idealities, ranging from shadowing to
interference. In an industrial environment, the derivation of a Markov model
based on the wireless communication physics can be prohibitive as it requires a
complete knowledge of both the communication dynamics parameters and of the
disturbances/interferers. In this work, a novel methodology is proposed to
learn a Markov model of a fading channel via historical data of the
signal-to-interference-plus-noise-ratio (SINR). Such methodology can be used to
derive a Markov jump model of a wireless control network, and thus to design a
stochastic optimal controller that takes into account the interdependence
between the plant and the wireless channel dynamics. The proposed method is
validated by comparing its prediction accuracy and control performance with
those of a stationary finite-state Markov chain derived assuming perfect
knowledge of the physical channel model and parameters of a WirelessHART
point-to-point communication based on the IEEE-802.15.4 standard.


Abstract:
  Recursive Neural Networks (RvNNs), which compose sequences according to their
underlying hierarchical syntactic structure, have performed well in several
natural language processing tasks compared to similar models without structural
biases. However, traditional RvNNs are incapable of inducing the latent
structure in a plain text sequence on their own. Several extensions have been
proposed to overcome this limitation. Nevertheless, these extensions tend to
rely on surrogate gradients or reinforcement learning at the cost of higher
bias or variance. In this work, we propose Continuous Recursive Neural Network
(CRvNN) as a backpropagation-friendly alternative to address the aforementioned
limitations. This is done by incorporating a continuous relaxation to the
induced structure. We demonstrate that CRvNN achieves strong performance in
challenging synthetic tasks such as logical inference and ListOps. We also show
that CRvNN performs comparably or better than prior latent structure models on
real-world tasks such as sentiment analysis and natural language inference.


Abstract:
  Fast-pairwise neutrino oscillations potentially affect many aspects of
core-collapse supernova (CCSN): the explosion mechanism, neutrino signals, and
nucleosynthesis in the ejecta. This particular mode of collective neutrino
oscillations has a deep connection to the angular structure of neutrinos in
momentum space; for instance, the appearance of electron neutrinos lepton
number (ELN) angular crossings in momentum space is a good indicator of
occurrences of the flavor conversions. However, many multi-dimensional
(multi-D) CCSN simulations are carried out with approximate neutrino transport
(such as two-moment methods), which limits the access to the angular
distributions of neutrinos, i.e., inhibits ELN-crossing searches. In this
paper, we develop a new method of ELN-crossing search in these CCSN
simulations. The required data is the zero-th and first angular moments of
neutrinos and matter profile, all of which are available in CCSN models with
two-moment method. One of the novelties of our new method is to use a
ray-tracing neutrino transport to determine ELNs in the direction of the
stellar center. It is designed to compensate for shortcomings of the crossing
searches only with the two angular moments. We assess the capability of the
method by carrying out a detailed comparison to results of full Boltzmann
neutrino transport in 1D and 2D CCSN models. We find that the ray-tracing
neutrino transport improves the accuracy of crossing searches; indeed, the
appearance/disappearance of the crossings is accurately detected even in the
region of forward-peaked angular distributions. The new method is
computationally cheap and has a benefit of efficient parallelization; hence, it
will be useful for ELN-crossing searches in any CCSN models employed two-moment
neutrino transport.


Abstract:
  When stretched in one direction, most solids shrink in the transverse
directions. In soft silicone gels, however, we observe that small-scale
topographical features grow upon stretching. A quantitative analysis of the
topography shows that this counter-intuitive response is nearly linear,
allowing us to tackle it through a small-strain analysis. We find that the
surprising increase of small-scale topography with stretch is due to a delicate
interplay of the bulk and surface responses to strain. Specifically, we find
that surface tension changes as the material is deformed. This response is
expected on general grounds for solid materials, but challenges the standard
description of gel- and elastomer-surfaces.


Abstract:
  Propagation-invariant or non-diffracting optical beams have received
considerable attention during the last two decades. However, the pulsed nature
of light waves and the structured property of optical media like waveguides are
often overlooked. We here present a four-dimensional spatiotemporal approach
that extends and unifies both concepts of conical waves and helicon beams,
mainly studied in bulk media. By taking advantage of tight correlations between
the spatial modes, the topological charges, and the frequencies embedded in an
optical field, we reveal propagation-invariant (dispersion- and
diffraction-free) space-time wavepackets carrying orbital angular momentum
(OAM) that evolve on spiraling trajectories in both time and space in bulk
media or multimode fibers. Besides their intrinsic linear nature, we show that
such wave structures can spontaneously emerge when a rather intense ultrashort
pulse propagates nonlinearly in OAM modes. With emerging technologies of
pulse/beam shaping, multimode fibers and modal multiplexing, our proposed
scheme to create OAM-carrying helicon wavepackets could find a plethora of
applications.


Abstract:
  We present a thermodynamic model which explains the presence of a negative
slope in the melt curve, as observed in systems as diverse as the alkali metals
and molecular hydrogen at high pressure. We assume that components of the
system can be in one of two well defined states - one associated with low
energy, the other with low volume.
  The model exhibits a number of measurable features which are also observed in
these systems and are therefore expected to be associated with all negative
Clapeyron-slope systems: first order phase transitions, thermodynamic anomalies
along Widom lines.
  The melt curve maximum is a feature of the model, but appears well below the
pressures where the change in state occurs in the solid: the solid-solid
transition is related to the melt line minimum. An example of the model fitted
to the electride transition in potassium is discussed.


Abstract:
  For a graph $G$, $\chi(G)$ will denote its chromatic number, and $\omega(G)$
its clique number. A graph $G$ is said to be perfectly divisible if for all
induced subgraphs $H$ of $G$, $V(H)$ can be partitioned into two sets $A$, $B$
such that $H[A]$ is perfect and $\omega(H[B]) < \omega(H)$. An integer-valued
function $f$ is called a $\chi$-binding function for a hereditary class of
graphs $\cal C$ if $\chi(G) \leq f(\omega(G))$ for every graph $G\in \cal C$.
The fork is the graph obtained from the complete bipartite graph $K_{1,3}$ by
subdividing an edge once. The problem of finding a polynomial $\chi$-binding
function for the class of fork-free graphs is open. In this paper, we study the
structure of some classes of fork-free graphs; in particular, we study the
class of (fork,$F$)-free graphs $\cal G$ in the context of perfect
divisibility, where $F$ is a graph on five vertices with a stable set of size
three, and show that every $G\in \cal G$ satisfies $\chi(G)\leq \omega(G)^2$.
We also note that the class $\cal G$ does not admit a linear $\chi$-binding
function.


Abstract:
  We introduce a Metropolis-Hastings Markov chain for Boltzmann distributions
of classical spin systems. It relies on approximate tensor network contractions
to propose correlated collective updates at each step of the evolution. We
present benchmarks for a wide variety of instances of the two-dimensional Ising
model, including ferromagnetic, antiferromagnetic, (fully) frustrated and
Edwards-Anderson spin glass cases, and we show that, with modest computational
effort, our Markov chain achieves sizeable acceptance rates, even in the
vicinity of critical points. In each of the situations we have considered, the
Markov chain compares well with other Monte Carlo schemes such as the
Metropolis or Wolff algorithm: equilibration times appear to be reduced by a
factor that varies between 40 and 2000, depending on the model and the
observable being monitored. We also present an extension to three spatial
dimensions, and demonstrate that it exhibits fast equilibration for finite
ferro and antiferromagnetic instances. Additionally, and although it is
originally designed for a square lattice of finite degrees of freedom with open
boundary conditions, the proposed scheme can be used as such, or with slight
modifications, to study triangular lattices, systems with continuous degrees of
freedom, matrix models, a confined gas of hard spheres, or to deal with
arbitrary boundary conditions.


Abstract:
  Although instance segmentation has made considerable advancement over recent
years, it's still a challenge to design high accuracy algorithms with real-time
performance. In this paper, we propose a real-time instance segmentation
framework termed OrienMask. Upon the one-stage object detector YOLOv3, a mask
head is added to predict some discriminative orientation maps, which are
explicitly defined as spatial offset vectors for both foreground and background
pixels. Thanks to the discrimination ability of orientation maps, masks can be
recovered without the need for extra foreground segmentation. All instances
that match with the same anchor size share a common orientation map. This
special sharing strategy reduces the amortized memory utilization for mask
predictions but without loss of mask granularity. Given the surviving box
predictions after NMS, instance masks can be concurrently constructed from the
corresponding orientation maps with low complexity. Owing to the concise design
for mask representation and its effective integration with the anchor-based
object detector, our method is qualified under real-time conditions while
maintaining competitive accuracy. Experiments on COCO benchmark show that
OrienMask achieves 34.8 mask AP at the speed of 42.7 fps evaluated with a
single RTX 2080 Ti. The code is available at https://github.com/duwt/OrienMask.


Abstract:
  Keystone is a trusted execution environment, based on RISC-V architecture. It
divides the memory into a secure Keystone private memory and an unsecure
non-Keystone memory, and allows code that lies inside the Keystone private
memory to execute securely. Simple demand paging in Keystone ends up leaking
sensitive access patterns of Keystone application to the Operating System(OS),
that is assumed to be malicious. This is because, to access the unsecure
non-Keystone memory, Keystone needs support of the OS. To mitigate this,
Keystone needs to implement oblivious demand paging while obfuscating its page
access patterns by using Oblivious RAM(ORAM) techniques. This causes
substantial slowdown in the application execution.
  In this paper, we bridge the performance gap between application execution
time with unsecure and secure demand paging in Keystone by using Deterministic,
stash free, Write only ORAM (DetWoORAM) for oblivious demand paging. We also
show why DetWoORAM, that is a write-only ORAM, is sufficient for oblivious
demand paging. DetWoORAM logically partitions the memory into a main area and a
holding area. The actual pages are stored in main area. We propose two
enhancements over DetWoORAM that improves the application execution slowdown.
The first enhancement, which we call the Eager DetWoORAM, involves page
preloading that exploits the deterministic access pattern of DetWoORAM, and
tries to hide the ORAM latency. The second enhancement, which we call the
Parallel DetWoORAM, involves spawning multiple threads and each thread performs
a part of the DetWoORAM memory access algorithm. Compared to DetWoORAM that
shows slowdown of [1.4x, 2x, and 3.24x], Eager DetWoORAM and Parallel DetWoORAM
provide slowdown of [1.2x, 1.8x, and 3.2x] and [1.1x, 1.1x, and 1.4x], for k=
3, 7, and 15, respectively.


Abstract:
  The Chekanov-Eliashberg dg-algebra is a holomorphic curve invariant
associated to Legendrian submanifolds of a contact manifold. We extend the
definition to Legendrian embeddings of skeleta of Weinstein manifolds. Via
Legendrian surgery, the new definition gives direct proofs of wrapped Floer
cohomology push-out diagrams. It also leads to a proof of a conjectured
isomorphism between partially wrapped Floer cohomology and Chekanov-Eliashberg
dg-algebras with coefficients in chains on the based loop space.


Abstract:
  In the setting where we ask participants multiple similar possibly subjective
multi-choice questions (e.g. Do you like Bulbasaur? Y/N; do you like Squirtle?
Y/N), peer prediction aims to design mechanisms that encourage honest feedback
without verification. A series of works have successfully designed multi-task
peer prediction mechanisms where reporting truthfully is better than any other
strategy (dominantly truthful), while they require an infinite number of tasks.
A recent work proposes the first multi-task peer prediction mechanism,
Determinant Mutual Information (DMI)-Mechanism, where not only is dominantly
truthful but also works for a finite number of tasks (practical). However, the
existence of other practical dominantly-truthful multi-task peer prediction
mechanisms remains to be an open question. This work answers the above question
by providing 1. a new family of information-monotone information measures:
volume mutual information (VMI), where DMI is a special case; 2. a new family
of practical dominantly-truthful multi-task peer prediction mechanisms,
VMI-Mechanisms. To illustrate the importance of VMI-Mechanisms, we also provide
a tractable effort incentive optimization goal. We show that DMI-Mechanism may
not be not optimal but we can construct a sequence of VMI-Mechanisms that are
approximately optimal. The main technical highlight in this paper is a novel
geometric information measure, Volume Mutual Information, that is based on a
simple idea: we can measure an object A's information amount by the number of
objects that is less informative than A. Different densities over the object
lead to different information measures. This also gives Determinant Mutual
Information a simple geometric interpretation.


Abstract:
  We use interface-resolved simulations to study near-wall turbulence
modulation by small inertial particles, much denser than the fluid, in
dilute/semi-dilute conditions. We considered three bulk solid mass fractions,
$\Psi=0.34\%$, $3.37\%$ and $33.7\%$, with only the latter two showing
turbulence modulation. The increase of the drag is strong at $\Psi=3.37\%$, but
mild in the densest case. Actually, two distinct regimes of turbulence
modulation emerge: for smaller mass fractions, the turbulence statistics are
weakly affected and the near-wall particle accumulation increases the drag so
the flow appears as a single phase flow at slightly higher Reynolds number.
Conversely, at higher mass fractions, the particles modulate the turbulent
dynamics over the entire flow, and the interphase coupling becomes more
complex. In this case, fluid Reynolds stresses are attenuated, but the inertial
particle dynamics increases the drag via correlated velocity fluctuations
leading to an overall drag increase. Hence, we conclude that, although
particles at high mass fractions reduce the fluid turbulent drag, the solid
phase inertial dynamics may still increase the overall drag. However,
inspection of the streamwise momentum budget in the two-way coupling limit of
vanishing volume fraction, but finite mass fraction, indicates that this trend
could reverse at even higher particle load.


Abstract:
  A semi-supervised learning method for spiking neural networks is proposed.
The proposed method consists of supervised learning by backpropagation and
subsequent unsupervised learning by spike-timing-dependent plasticity (STDP),
which is a biologically plausible learning rule. Numerical experiments show
that the proposed method improves the accuracy without additional labeling when
a small amount of labeled data is used. This feature has not been achieved by
existing semi-supervised learning methods of discriminative models. It is
possible to implement the proposed learning method for event-driven systems.
Hence, it would be highly efficient in real-time problems if it were
implemented on neuromorphic hardware. The results suggest that STDP plays an
important role other than self-organization when applied after supervised
learning, which differs from the previous method of using STDP as pre-training
interpreted as self-organization.


Abstract:
  The necessary and sufficient conditions for a three-dimensional Riemannian
metric to admit a group of isometries of dimension $r$ acting on s-dimensional
orbits are obtained. These conditions are Intrinsic, Deductive, Explicit and
ALgorithmic and they offer an IDEAL labeling that improves previously known
invariant studies.


Abstract:
  It is well known from universal algebra that, for every signature $\Sigma$,
there exist algebras over $\Sigma$ which are absolutely free, meaning that they
do not satisfy any identities or, alternatively, satisfy the universal mapping
property for the class of $\Sigma$-algebras. Furthermore, once we fix a
cardinality of the generating set, they are, up to isomorphisms, unique, and
equal to algebras of terms (or propositional formulas, in the context of
logic). Equivalently, the forgetful functor, from the category of
$\Sigma$-algebras to $\textbf{Set}$, has a left adjoint. This result does not
extend to multialgebras. Not only multialgebras satisfying the universal
mapping property do not exist, but the forgetful functor $\mathcal{U}$, from
the category of $\Sigma$-multialgebras to $\textbf{Set}$, does not have a left
adjoint.
  In this paper we generalize, in a natural way, algebras of terms to
multialgebras of terms, whose family of submultialgebras enjoys many properties
of the former. One example is that, to every pair consisting of a function,
from a multialgebra of terms to another multialgebra, and a collection of
choices (which selects how a homomorphism approaches indeterminacies), it
corresponds a unique homomorphism, which ressembles the universal mapping
property. Another example is that the multialgebras of terms are generated by a
set that may be viewed as a strong basis, which we call the ground of the
multialgebra. Submultialgebras of multialgebras of terms are what we call
weakly free multialgebras. Finally, with these definitions at hand, we offer a
simple proof that multialgebras with the universal mapping property for the
class of all multialgebras do not exist and that $\mathcal{U}$ does not have a
left adjoint.


Abstract:
  Quantum error mitigation techniques can reduce noise on current quantum
hardware without the need for fault-tolerant quantum error correction. For
instance, the quasiprobability method simulates a noise-free quantum computer
using a noisy one, with the caveat of only producing the correct expected
values of observables. The cost of this error mitigation technique manifests as
a sampling overhead which scales exponentially in the number of corrected
gates. In this work, we present a new algorithm based on mathematical
optimization that aims to choose the quasiprobability decomposition in a
noise-aware manner. This directly leads to a significantly lower basis of the
sampling overhead compared to existing approaches. A key element of the novel
algorithm is a robust quasiprobability method that allows for a tradeoff
between an approximation error and the sampling overhead via semidefinite
programming.


Abstract:
  Low-energy magnon excitations in magnetoelectric LiFePO$_4$ have been
investigated by high-frequency high-field electron spin resonance spectroscopy
in magnetic fields up to B = 58 T and frequencies up to f = 745 GHz. For
magnetic fields applied along the easy magnetic axis, the excitation gap
softens and vanishes at the spin-flop field of BSF = 32 T before hardening
again at higher fields. In addition, for B smaller than BSF we observe a
resonance mode assigned to excitations due to Dzyaloshinskii-Moriya
(DM)-interactions, thereby evidencing sizable DM interaction of approx 150
micro eV in LiFePO4. Both the magnetisation and the excitations up to high
magnetic fields are described in terms of a mean-field theory model which
extends recent zero field inelastic neutron scattering results. Our results
imply that magnetic interactions as well as magnetic anisotropy have a sizable
quadratic field dependence which we attribute to significant magnetostriction.


Abstract:
  We dwell upon certain points concerning the meaning of quantum field theory,
among these the problems with the perturbative approach, and the question
raised by tHooft of the existence of the theory in a well defined mathematical
sense, as well as some of the few existent mathematically precise results on
fully quantized field theories. Emphasis is brought on how the mathematical
contributions help to elucidate or illuminate certain conceptual aspects of the
theory when applied to real physical phenomena, in particular, the singular
nature of quantum fields. In a first part, we present a comprehensive review of
divergent versus asymptotic series, with qed as background example, as well as
a method due to Terence Tao which conveys mathematical sense to divergent
series. In a second part we apply the method of Tao to the Casimir effect in
its simplest form, consisting of perfectly conducting parallel plates, arguing
that the usual theory, which makes use of the Euler-MacLaurin formula, still
contains a residual infinity, which is eliminated in our approach. In the third
part, we revisit the general theory of nonperturbative quantum fields, in the
form of newly proposed Wightman axioms for interacting field theories, together
with Christian Jaekel. Applications to dressed electrons in a theory with
massless particles, such as qed, as well as unstable particles, are included,
and various problems (mostly open) are discussed in connection with concrete
models.


Abstract:
  We propose that the symbol alphabet for classes of planar,
dual-conformal-invariant Feynman integrals can be obtained as truncated cluster
algebras purely from their kinematics, which correspond to boundaries of
(compactifications of) $G_+(4,n)/T$ for the $n$-particle massless kinematics.
For one-, two-, three-mass-easy hexagon kinematics with $n=7,8,9$, we find
finite cluster algebras $D_4$, $D_5$ and $D_6$ respectively, in accordance with
previous result on alphabets of these integrals. As the main example, we
consider hexagon kinematics with two massive corners on opposite sides and find
a truncated affine $D_4$ cluster algebra whose polytopal realization is a
co-dimension 4 boundary of that of $G_+(4,8)/T$ with 39 facets; the normal
vectors for 38 of them correspond to g-vectors and the remaining one gives a
limit ray, which yields an alphabet of $38$ rational letters and $5$ algebraic
ones with the unique four-mass-box square root. We construct the space of
integrable symbols with this alphabet and physical first-entry conditions,
whose dimension can be reduced using conditions from a truncated version of
cluster adjacency. Already at weight $4$, by imposing last-entry conditions
inspired by the $n=8$ double-pentagon integral, we are able to uniquely
determine an integrable symbol that gives the algebraic part of the most
generic double-pentagon integral. Finally, we locate in the space the $n=8$
double-pentagon ladder integrals up to four loops using differential equations
derived from Wilson-loop $d\log$ forms, and we find a remarkable pattern about
the appearance of algebraic letters.


Abstract:
  It has recently been suggested that black hole remnants of primordial origin
are not a viable dark matter candidate since they would have far too large a
velocity due to the recoil of Hawking radiation. We re-examined this
interesting claim in more details and found that it does not rule out such a
possibility. On the contrary, for models based on non-commutativity of
spacetime near the Planck scale, essentially the same argument can be used to
estimate the scale at which non-commutativity effect becomes important. If dark
matter "particles" are non-commutative black holes that have passed the maximum
temperature, this implies that the non-commutative scale is about 100 times the
Planck length. The same analysis applies to other black hole remnants whose
temperature reaches a maximum before cooling off, for example, black holes in
asymptotically safe gravity.


Abstract:
  During the past half century, a major approximation was natural in the field
of light-matter interaction: the point-dipole model. It was assumed that the
wavelength is much larger than the size of the emitting atom or molecule, so
that the emitter can be described as a single or a collection of elementary
dipoles. As it is legitimate for visible light, the approximation does no
longer hold near plasmonic nanostructures, where the effective wavelength can
drop below 10 nm. In that case deviations arise from the approximate model.
First, the emitter spatial extent influences the far-field spectrum. Second,
high-order transitions beyond the dipolar ones are not forbidden anymore. Going
beyond the approximation requires intensive numerical efforts to compute the
photonic response over the spatial extent of the emitter, since the complete
Green's function is required. Here, we develop a general model that computes
the multipolar transition rates of a quantum emitter in a photonic environment,
by computing the Green's function through an eigenpermittivity modal expansion.
We apply the method on graphene nanoislands, and we demonstrate a local
breakdown of the selection rules, with quadrupolar transition rates becoming
100 times larger than dipolar ones.


Abstract:
  Dedicated to the memory of Prof. Veltman, one of the founding fathers of our
discipline: his legacy lives on. Many times we have to turn back and follow his
footprints to find the right path. After reviewing general aspects of high
energy physics where he gave a seminal contribution we will introduce recent
developments in the standard model effective field theory, showing how the
whole movement from renormalization to predictions plays from Veltman to SMEFT.


Abstract:
  The smooth action of a compact Lie group on a compact manifold can be
resolved to an iterated space, as made explicit by Pierre Albin and the second
author. On the resolution the lifted action has fixed isotropy type
corresponding to the open stratum and also in an iterated sense, with
connecting equivariant fibrations over the boundary hypersurfaces covering the
resolutions of the other strata. This structure descends to a resolution of the
quotient as a stratified space. For an Abelian group action the equivariant
K-theory can then be described in terms of bundles over the bases `dressed' by
the representations of the isotropy types with morphisms covering the
connecting maps. A similar model is given here covering the non-Abelian case.
Now the reduced objects are torsion-twisted bundles over finite covers of the
bases, corresponding to the projective action of the normalizers on the
representations of the isotropy groups, again with morphisms over all the
boundaries. This leads to a closely related iterated deRham model for
equivariant cohomology and, now with values in forms twisted by flat bundles of
representation rings over the bases, for delocalized equivariant cohomology. We
show, as envisioned by Baum, Brylinksi and MacPherson, that the usual
equivariant Chern character, mapping to equivariant cohomology, factors through
a natural Chern character from equivariant K-theory to delocalized equivariant
cohomology with the latter giving an Atiyah-Hirzebruch isomorphism.


Abstract:
  How should representations from complementary sensors be integrated for
autonomous driving? Geometry-based sensor fusion has shown great promise for
perception tasks such as object detection and motion forecasting. However, for
the actual driving task, the global context of the 3D scene is key, e.g. a
change in traffic light state can affect the behavior of a vehicle
geometrically distant from that traffic light. Geometry alone may therefore be
insufficient for effectively fusing representations in end-to-end driving
models. In this work, we demonstrate that imitation learning policies based on
existing sensor fusion methods under-perform in the presence of a high density
of dynamic agents and complex scenarios, which require global contextual
reasoning, such as handling traffic oncoming from multiple directions at
uncontrolled intersections. Therefore, we propose TransFuser, a novel
Multi-Modal Fusion Transformer, to integrate image and LiDAR representations
using attention. We experimentally validate the efficacy of our approach in
urban settings involving complex scenarios using the CARLA urban driving
simulator. Our approach achieves state-of-the-art driving performance while
reducing collisions by 76% compared to geometry-based fusion.