Abstract:
  We consider a multiphysics model for the flow of Newtonian fluid coupled with
Biot consolidation equations through an interface, and incorporating total
pressure as an unknown in the poroelastic region. A new mixed-primal finite
element scheme is proposed solving for the pairs fluid velocity - pressure and
displacement - total poroelastic pressure using Stokes-stable elements, and
where the formulation does not require Lagrange multipliers to set up the usual
transmission conditions on the interface. The stability and well-posedness of
the continuous and semi-discrete problems are analysed in detail. Our numerical
study is framed in the context of applicative problems pertaining to
heterogeneous geophysical flows and to eye poromechanics. For the latter, we
investigate different interfacial flow regimes in Cartesian and axisymmetric
coordinates that could eventually help describe early morphologic changes
associated with glaucoma development in canine species.


Abstract:
  In this work, we explore macroscopic transport phenomena associated with a
rotational system in the presence of an external orthogonal electromagnetic
field. Simply based on the lowest Landau level approximation, we derive
nontrivial expressions for chiral density and various currents consistently by
adopting small angular velocity expansion or Kubo formula. While the generation
of anomalous electric current is due to the pseudo gauge field effect of the
spin-rotation coupling, the chiral density and current can be simply explained
with the help of Lorentz boosts. Finally, Lorentz covariant forms can be
obtained by unifying our results and the magnetovorticity effect.


Abstract:
  We find an explicit formula that produces inductively the elliptic stable
envelopes of an arbitrary Nakajima variety associated to a quiver Q from the
ones of those Nakajima varieties whose framing vectors are the fundamental
vectors of the quiver Q, i.e. the dimension vectors with just one unitary
nonzero entry. The result relies on abelianization of stable envelopes. As an
application, we combine our result with Smirnov's formula for the elliptic
stable envelopes of the Hilbert scheme of points on the plane to produce the
elliptic stable envelopes of the instanton moduli space.


Abstract:
  Small-scale magnetic fields are not only the fundamental element of the solar
magnetism, but also closely related to the structure of the solar atmosphere.
The observations have shown that there is a ubiquitous tangled small-scale
magnetic field with a strength of 60 $\sim$ 130\,G in the canopy forming layer
of the quiet solar photosphere. On the other hand, the multi-dimensional MHD
simulations show that the convective overshooting expels the magnetic field to
form the magnetic canopies at a height of about 500\,km in the upper
photosphere. However, the distribution of such small-scale ``canopies" in the
solar photosphere cannot be rigorously constrained by either observations and
numerical simulations. Based on stellar standard models, we identify that these
magnetic canopies can act as a global magnetic-arch splicing layer, and find
that the reflections of the solar p-mode oscillations at this magnetic-arch
splicing layer results in significant improvement on the discrepancy between
the observed and calculated p-mode frequencies. The location of the
magnetic-arch splicing layer is determined at a height of about 630\,km, and
the inferred strength of the magnetic field is about 90\,G. These features of
the magnetic-arch splicing layer derived independently in the present study are
quantitatively in agreement with the presence of small-scale magnetic canopies
as those obtained by the observations and 3-D MHD simulations.


Abstract:
  In many real-world scenarios, the utility of a user is derived from the
single execution of a policy. In this case, to apply multi-objective
reinforcement learning, the expected utility of the returns must be optimised.
Various scenarios exist where a user's preferences over objectives (also known
as the utility function) are unknown or difficult to specify. In such
scenarios, a set of optimal policies must be learned. However, settings where
the expected utility must be maximised have been largely overlooked by the
multi-objective reinforcement learning community and, as a consequence, a set
of optimal solutions has yet to be defined. In this paper we address this
challenge by proposing first-order stochastic dominance as a criterion to build
solution sets to maximise expected utility. We also propose a new dominance
criterion, known as expected scalarised returns (ESR) dominance, that extends
first-order stochastic dominance to allow a set of optimal policies to be
learned in practice. We then define a new solution concept called the ESR set,
which is a set of policies that are ESR dominant. Finally, we define a new
multi-objective distributional tabular reinforcement learning (MOT-DRL)
algorithm to learn the ESR set in a multi-objective multi-armed bandit setting.


Abstract:
  This paper presents a theoretical framework for the design and analysis of
gradient descent-based algorithms for coverage control tasks involving robot
swarms. We adopt a multiscale approach to analysis and design to ensure
consistency of the algorithms in the large-scale limit. First, we represent the
macroscopic configuration of the swarm as a probability measure and formulate
the macroscopic coverage task as the minimization of a convex objective
function over probability measures. We then construct a macroscopic dynamics
for swarm coverage, which takes the form of a proximal descent scheme in the
$L^2$-Wasserstein space. Our analysis exploits the generalized geodesic
convexity of the coverage objective function, proving convergence in the
$L^2$-Wasserstein sense to the target probability measure. We then obtain a
consistent gradient descent algorithm in the Euclidean space that is
implementable by a finite collection of agents, via a "variational"
discretization of the macroscopic coverage objective function. We establish the
convergence properties of the gradient descent and its behavior in the
continuous-time and large-scale limits. Furthermore, we establish a connection
with well-known Lloyd-based algorithms, seen as a particular class of
algorithms within our framework, and demonstrate our results via numerical
experiments.


Abstract:
  The higher-order generalized singular value decomposition (HO-GSVD) is a
matrix factorization technique that extends the GSVD to $N \ge 2$ data
matrices, and can be used to identify shared subspaces in multiple large-scale
datasets with different row dimensions. The standard HO-GSVD factors $N$
matrices $A_i\in\mathbb{R}^{m_i\times n}$ as $A_i=U_i\Sigma_i V^\text{T}$, but
requires that each of the matrices $A_i$ has full column rank. We propose a
reformulation of the HO-GSVD that extends its applicability to rank-deficient
data matrices $A_i$. If the matrix of stacked $A_i$ has full rank, we show that
the properties of the original HO-GSVD extend to our reformulation. The HO-GSVD
captures shared right singular vectors of the matrices $A_i$, and we show that
our method also identifies directions that are unique to the image of a single
matrix. We also extend our results to the higher-order cosine-sine
decomposition (HO-CSD), which is closely related to the HO-GSVD. Our extension
of the standard HO-GSVD allows its application to datasets with $m_i < n$, such
as are encountered in bioinformatics, neuroscience, control theory or
classification problems.


Abstract:
  Multi-frame human pose estimation in complicated situations is challenging.
Although state-of-the-art human joints detectors have demonstrated remarkable
results for static images, their performances come short when we apply these
models to video sequences. Prevalent shortcomings include the failure to handle
motion blur, video defocus, or pose occlusions, arising from the inability in
capturing the temporal dependency among video frames. On the other hand,
directly employing conventional recurrent neural networks incurs empirical
difficulties in modeling spatial contexts, especially for dealing with pose
occlusions. In this paper, we propose a novel multi-frame human pose estimation
framework, leveraging abundant temporal cues between video frames to facilitate
keypoint detection. Three modular components are designed in our framework. A
Pose Temporal Merger encodes keypoint spatiotemporal context to generate
effective searching scopes while a Pose Residual Fusion module computes
weighted pose residuals in dual directions. These are then processed via our
Pose Correction Network for efficient refining of pose estimations. Our method
ranks No.1 in the Multi-frame Person Pose Estimation Challenge on the
large-scale benchmark datasets PoseTrack2017 and PoseTrack2018. We have
released our code, hoping to inspire future research.


Abstract:
  Versions of the following problem appear in several topics such as Gamma
Knife radiosurgery, studying objects with the X-ray transform, the 3SUM
problem, and the $k$-linear degeneracy testing. Suppose there are $n$ points on
a plane whose specific locations are unknown. We are given all the lines that
go through the points with a given slope. We show that the minimum number of
slopes needed, in general, to find all the point locations is $n+1$ and we
provide an algorithm to do so.


Abstract:
  Speech disorders often occur at the early stage of Parkinson's disease (PD).
The speech impairments could be indicators of the disorder for early diagnosis,
while motor symptoms are not obvious. In this study, we constructed a new
speech corpus of Mandarin Chinese and addressed classification of patients with
PD. We implemented classical machine learning methods with ranking algorithms
for feature selection, convolutional and recurrent deep networks, and an end to
end system. Our classification accuracy significantly surpassed
state-of-the-art studies. The result suggests that free talk has stronger
classification power than standard speech tasks, which could help the design of
future speech tasks for efficient early diagnosis of the disease. Based on
existing classification methods and our natural speech study, the automatic
detection of PD from daily conversation could be accessible to the majority of
the clinical population.


Abstract:
  In this paper we consider convex co-compact subgroups of the projective
linear group. We prove that such a group is relatively hyperbolic with respect
to a collection of virtually Abelian subgroups of rank two if and only if each
open face in the ideal boundary has dimension at most one. We also introduce
the "coarse Hilbert dimension" of a subset of a convex set and use it to
characterize when a naive convex co-compact subgroup is word hyperbolic or
relatively hyperbolic with respect to a collection of virtually Abelian
subgroups of rank two.


Abstract:
  Let $I(G)^{[k]}$ denote the $k$th squarefree power of the edge ideal of $G$.
When $G$ is a forest, we provide a sharp upper bound for the regularity of
$I(G)^{[k]}$ in terms of the $k$-admissable matching number of $G$. For any
positive integer $k$, we classify all forests $G$ such that $I(G)^{[k]}$ has
linear resolution. We also give a combinatorial formula for the regularity of
$I(G)^{[2]}$ for any forest $G$.


Abstract:
  Recently, deep learning approaches have become the main research frontier for
biological image reconstruction and enhancement problems thanks to their high
performance, along with their ultra-fast inference times. However, due to the
difficulty of obtaining matched reference data for supervised learning, there
has been increasing interest in unsupervised learning approaches that do not
need paired reference data. In particular, self-supervised learning and
generative models have been successfully used for various biological imaging
applications. In this paper, we overview these approaches from a coherent
perspective in the context of classical inverse problems, and discuss their
applications to biological imaging, including electron, fluorescence and
deconvolution microscopy, optical diffraction tomography and functional
neuroimaging.


Abstract:
  Given a graph with a source vertex $s$, the Single Source Replacement Paths
(SSRP) problem is to compute, for every vertex $t$ and edge $e$, the length
$d(s,t,e)$ of a shortest path from $s$ to $t$ that avoids $e$. A Single-Source
Distance Sensitivity Oracle (Single-Source DSO) is a data structure that
answers queries of the form $(t,e)$ by returning the distance $d(s,t,e)$. We
show how to deterministically compress the output of the SSRP problem on
$n$-vertex, $m$-edge graphs with integer edge weights in the range $[1,M]$ into
a Single-Source DSO of size $O(M^{1/2}n^{3/2})$ with query time
$\widetilde{O}(1)$. The space requirement is optimal (up to the word size) and
our techniques can also handle vertex failures.
  Chechik and Cohen [SODA 2019] presented a combinatorial, randomized
$\widetilde{O}(m\sqrt{n}+n^2)$ time SSRP algorithm for undirected and
unweighted graphs. Grandoni and Vassilevska Williams [FOCS 2012, TALG 2020]
gave an algebraic, randomized $\widetilde{O}(Mn^\omega)$ time SSRP algorithm
for graphs with integer edge weights in the range $[1,M]$, where $\omega<2.373$
is the matrix multiplication exponent. We derandomize both algorithms for
undirected graphs in the same asymptotic running time and apply our compression
to obtain deterministic Single-Source DSOs. The $\widetilde{O}(m\sqrt{n}+n^2)$
and $\widetilde{O}(Mn^\omega)$ preprocessing times are polynomial improvements
over previous $o(n^2)$-space oracles.
  On sparse graphs with $m=O(n^{5/4-\varepsilon}/M^{7/4})$ edges, for any
constant $\varepsilon > 0$, we reduce the preprocessing to randomized
$\widetilde{O}(M^{7/8}m^{1/2}n^{11/8})=O(n^{2-\varepsilon/2})$ time. This is
the first truly subquadratic time algorithm for building Single-Source DSOs on
sparse graphs.


Abstract:
  End-to-end DNN architectures have pushed the state-of-the-art in speech
technologies, as well as in other spheres of AI, leading researchers to train
more complex and deeper models. These improvements came at the cost of
transparency. DNNs are innately opaque and difficult to interpret. We no longer
understand what features are learned, where they are preserved, and how they
inter-operate. Such an analysis is important for better model understanding,
debugging and to ensure fairness in ethical decision making. In this work, we
analyze the representations trained within deep speech models, towards the task
of speaker recognition, dialect identification and reconstruction of masked
signals. We carry a layer- and neuron-level analysis on the utterance-level
representations captured within pretrained speech models for speaker, language
and channel properties. We study: is this information captured in the learned
representations? where is it preserved? how is it distributed? and can we
identify a minimal subset of network that posses this information. Using
diagnostic classifiers, we answered these questions. Our results reveal: (i)
channel and gender information is omnipresent and is redundantly distributed
(ii) complex properties such as dialectal information is encoded only in the
task-oriented pretrained network and is localised in the upper layers (iii) a
minimal subset of neurons can be extracted to encode the predefined property
(iv) salient neurons are sometimes shared between properties and can highlights
presence of biases in the network. Our cross-architectural comparison indicates
that (v) the pretrained models captures speaker-invariant information and (vi)
the pretrained CNNs models are competitive to the Transformers for encoding
information for the studied properties. To the best of our knowledge, this is
the first study to investigate neuron analysis on the speech models.


Abstract:
  AOSAT is a python package for the analysis of single-conjugate adaptive
optics (SCAO) simulation results. Python is widely used in the astronomical
community these days, and AOSAT may be used stand-alone, integrated into a
simulation environment, or can easily be extended according to a user's needs.
Standalone operation requires the user to provide the residual wavefront frames
provided by the SCAO simulation package used, the aperture mask (pupil) used
for the simulation, and a custom setup file describing the simulation/analysis
configuration. In its standard form, AOSAT's "tearsheet" functionality will
then run all standard analyzers, providing an informative plot collection on
properties such as the point-spread function (PSF) and its quality, residual
tip-tilt, the impact of pupil fragmentation, residual optical aberration modes
both static and dynamic, the expected high-contrast performance of suitable
instrumentation with and without coronagraphs, and the power spectral density
of residual wavefront errors.
  AOSAT fills the gap between the simple numerical outputs provided by most
simulation packages, and the full-scale deployment of instrument simulators and
data reduction suites operating on SCAO residual wavefronts. It enables
instrument designers and end-users to quickly judge the impact of design or
configuration decisions on the final performance of down-stream
instrumentation.


Abstract:
  It has recently been pointed out that Gaia is capable of detecting a
stochastic gravitational wave background in the sensitivity band between the
frequency of pulsar timing arrays and LISA. We argue that Gaia and THEIA has
great potential for early universe cosmology, since such a frequency range is
ideal for probing phase transitions in asymmetric dark matter, SIMP and the
cosmological QCD transition. Furthermore, there is the potential for detecting
primordial black holes in the solar mass range produced during such an early
universe transition and distinguish them from those expected from the QCD
epoch. Finally, we discuss the potential for Gaia and THEIA to probe
topological defects and the ability of Gaia to potentially shed light on the
recent NANOGrav results.


Abstract:
  This paper studies the recovery of a joint piece-wise linear trend from a
time series using L1 regularization approach, called L1 trend filtering (Kim,
Koh and Boyd, 2009). We provide some sufficient conditions under which a L1
trend filter can be well-behaved in terms of mean estimation and change point
detection. The result is two-fold: for the mean estimation, an almost optimal
consistent rate is obtained; for the change point detection, the slope change
in direction can be recovered in a high probability. In addition, we show that
the weak irrepresentable condition, a necessary condition for LASSO model to be
sign consistent (Zhao and Yu, 2006), is not necessary for the consistent change
point detection. The performance of the L1 trend filter is evaluated by some
finite sample simulations studies.


Abstract:
  It has recently been shown that superconductivity in magic-angle twisted
trilayer graphene survives to in-plane magnetic fields that are well in excess
of the Pauli limit, and much stronger than the in-plane critical magnetic
fields of magic-angle twisted bilayer graphene. The difference is surprising
because twisted bilayers and trilayers both support the magic-angle flat bands
thought to be the fountainhead of twisted graphene superconductivity. We show
here that the difference in critical magnetic fields can be traced to a
$\mathcal{C}_2 \mathcal{M}_{h}$ symmetry in trilayers that survives in-plane
magnetic fields, and also relative displacements between top and bottom layers
that are not under experimental control at present. An gate electric field
breaks the $\mathcal{C}_2 \mathcal{M}_{h}$ symmetry and therefore limits the
in-plane critical magnetic field.


Abstract:
  We analyze possibilities of second-order quantifier elimination for formulae
containing parameters -- constants or functions. For this, we use a constraint
resolution calculus obtained from specializing the hierarchical superposition
calculus. If saturation terminates, we analyze possibilities of obtaining
weakest constraints on parameters which guarantee satisfiability. If the
saturation does not terminate, we identify situations in which finite
representations of infinite saturated sets exist. We identify situations in
which entailment between formulae expressed using second-order quantification
can be effectively checked. We illustrate the ideas on a series of examples
from wireless network research.


Abstract:
  In the context of supervised learning of a function by a Neural Network (NN),
we claim and empirically justify that a NN yields better results when the
distribution of the data set focuses on regions where the function to learn is
steeper. We first traduce this assumption in a mathematically workable way
using Taylor expansion. Then, theoretical derivations allow to construct a
methodology that we call Variance Based Samples Weighting (VBSW). VBSW uses
local variance of the labels to weight the training points. This methodology is
general, scalable, cost effective, and significantly increases the performances
of a large class of NNs for various classification and regression tasks on
image, text and multivariate data. We highlight its benefits with experiments
involving NNs from shallow linear NN to Resnet or Bert.


Abstract:
  Multiple transition phenomena in divalent Eu compound EuAl$_4$ with the
tetragonal structure were investigated via the single-crystal time-of-flight
neutron Laue technique. At 30.0 K below a charge-density-wave (CDW) transition
temperature of $T_{\rm CDW}$ = 140 K, superlattice peaks emerge near nuclear
Bragg peaks described by an ordering vector $q_{\rm CDW}$=(0 0 ${\delta}_c$)
with ${\delta}_c{\sim}$0.19. In contrast, magnetic peaks appear at $q_2 =
({\delta}_2 {\delta}_2 0)$ with ${\delta}_2$ = 0.085 in a magnetic-ordered
phase at 13.5 K below $T_{\rm N1}$ = 15.4 K. By further cooling to below
$T_{\rm N3}$ = 12.2 K, the magnetic ordering vector changes into $q_1 =
({\delta}_1 0 0)$ with ${\delta}_1$ = 0.17 at 11.5 K and slightly shifts to
${\delta}_1$ = 0.194 at 4.3 K. No distinct change in the magnetic Bragg peak
was detected at $T_{\rm N2}$=13.2 K and $T_{\rm N4}$=10.0 K. The structural
modulation below $T_{\rm CDW}$ with $q_{\rm CDW}$ is characterized by the
absence of the superlattice peak in the (0 0 $l$) axis. As a similar CDW
transition was observed in SrAl$_4$, the structural modulation with $q_{\rm
CDW}$ could be mainly ascribed to the displacement of Al ions within the
tetragonal $ab$-plane. Complex magnetic transitions are in stark contrast to a
simple collinear magnetic structure in isovalent EuGa$_4$. This could stem from
different electronic structures with the CDW transition between two compounds.


Abstract:
  We propose a gauged $B-L$ extension of the standard model (SM) where light
neutrinos are of Dirac type by virtue of tiny Yukawa couplings with the SM
Higgs. To achieve leptogenesis, we include additional heavy Majorana fermions
without introducing any $B-L$ violation by two units. An additional scalar
doublet with appropriate $B-L$ charge can allow heavy fermion coupling with the
SM leptons so that out of equilibrium decay of the former can lead to
generation of lepton asymmetry. Due to the $B-L$ gauge interactions of the
decaying fermion, the criteria of successful Dirac leptogenesis can also
constrain the gauge sector couplings so as to keep the corresponding washout
processes under control. The same $B-L$ gauge sector parameter space can also
be constrained from dark matter requirements if the latter is assumed to be a
SM singlet particle with non-zero $B-L$ charge. The same $B-L$ gauge
interactions also lead to additional thermalised relativistic degrees of
freedom $\Delta N_{\rm eff}$ from light Dirac neutrinos which are tightly
constrained by Planck 2018 data. While there exists parameter space from the
criteria of successful low scale Dirac leptogenesis, dark matter and $\Delta
N_{\rm eff}$ even after incorporating the latest collider bounds, all the
currently allowed parameters can be probed by future measurements of $\Delta
N_{\rm eff}$.


Abstract:
  In two-dimensional loop models, the scaling properties of critical random
curves are encoded in the correlators of connectivity operators. In the dense
O($n$) loop model, any such operator is naturally associated to a standard
module of the periodic Temperley-Lieb algebra. We introduce a new family of
representations of this algebra, with connectivity states that have two marked
points, and argue that they define the fusion of two standard modules. We
obtain their decomposition on the standard modules for generic values of the
parameters, which in turn yields the structure of the operator product
expansion of connectivity operators.


Abstract:
  We seek to investigate the scalability of neuromorphic computing for computer
vision, with the objective of replicating non-neuromorphic performance on
computer vision tasks while reducing power consumption. We convert the deep
Artificial Neural Network (ANN) architecture U-Net to a Spiking Neural Network
(SNN) architecture using the Nengo framework. Both rate-based and spike-based
models are trained and optimized for benchmarking performance and power, using
a modified version of the ISBI 2D EM Segmentation dataset consisting of
microscope images of cells. We propose a partitioning method to optimize
inter-chip communication to improve speed and energy efficiency when deploying
multi-chip networks on the Loihi neuromorphic chip. We explore the advantages
of regularizing firing rates of Loihi neurons for converting ANN to SNN with
minimum accuracy loss and optimized energy consumption. We propose a percentile
based regularization loss function to limit the spiking rate of the neuron
between a desired range. The SNN is converted directly from the corresponding
ANN, and demonstrates similar semantic segmentation as the ANN using the same
number of neurons and weights. However, the neuromorphic implementation on the
Intel Loihi neuromorphic chip is over 2x more energy-efficient than
conventional hardware (CPU, GPU) when running online (one image at a time).
These power improvements are achieved without sacrificing the task performance
accuracy of the network, and when all weights (Loihi, CPU, and GPU networks)
are quantized to 8 bits.


Abstract:
  We put forward the concept of work extraction from thermal noise by
phase-sensitive (homodyne) measurements of the noisy input followed by
(outcome-dependent) unitary manipulations of the post-measured state. For
optimized measurements, noise input with more than one quantum on average is
shown to yield heat-to-work conversion with efficiency and power that grow with
the mean number of input quanta, detector efficiency and its inverse
temperature. This protocol is shown to be advantageous compared to common
models of information and heat engines.


Abstract:
  Different regimes of entanglement growth under measurement have been
demonstrated for quantum many-body systems, with an entangling phase for low
measurement rates and a disentangling phase for high rates (quantum Zeno
effect). Here we study entanglement growth on a disordered Bose-Fermi mixture
with the bosons playing the role of the effective self-induced measurement for
the fermions. Due to the interplay between the disorder and a non-Abelian
symmetry, the model features an entanglement growth resonance when the
boson-fermion interaction strength is varied. With the addition of a magnetic
field, the model acquires a dynamical symmetry leading to experimentally
measurable long-time local oscillations. At the entanglement growth resonance,
we demonstrate the emergence of the cleanest oscillations. Furthermore, we show
that this resonance is distinct from both noise enhanced transport and a
standard stochastic resonance. Our work paves the way for experimental
realizations of self-induced correlated phases in multi-species systems.


Abstract:
  Saturn's E ring consists of micron-sized particles launched from Enceladus by
that moon's geological activity. A variety of small-scale structures in the
E-ring's brightness have been attributed to tendrils of material recently
launched from Enceladus. However, one of these features occurs at a location
where Enceladus' gravitational perturbations should concentrate background
E-ring particles into structures known as satellite wakes. While satellite
wakes have been observed previously in ring material drifting past other moons,
these E-ring structures would be the first examples of wakes involving
particles following horseshoe orbits near Enceladus' orbit. The predicted
intensity of these wake signatures are particularly sensitive to the fraction
E-ring particles' on orbits with low eccentricities and semi-major axes just
outside of Enceladus' orbit, and so detailed analyses of these and other
small-scale E-ring features should place strong constraints on the orbital
properties and evolution of E-ring particles.


Abstract:
  In \cite{BH20} an elegant choice-free construction of a canonical extension
of a boolean algebra $B$ was given as the boolean algebra of regular open
subsets of the Alexandroff topology on the poset of proper filters of $B$. We
make this construction point-free by replacing the Alexandroff space of proper
filters of $B$ with the free frame $\mathcal{L}$ generated by the bounded
meet-semilattice of all filters of $B$ (ordered by reverse inclusion) and prove
that the booleanization of $\mathcal{L}$ is a canonical extension of $B$. Our
main result generalizes this approach to the category
$\boldsymbol{\mathit{ba}\ell}$ of bounded archimedean $\ell$-algebras, thus
yielding a point-free construction of canonical extensions in
$\boldsymbol{\mathit{ba}\ell}$. We conclude by showing that the algebra of
normal functions on the Alexandroff space of proper archimedean $\ell$-ideals
of $A$ is a canonical extension of $A\in\boldsymbol{\mathit{ba}\ell}$, thus
providing a generalization of the result of \cite{BH20} to
$\boldsymbol{\mathit{ba}\ell}$.


Abstract:
  We report on the measurement of inclusive charmless semileptonic B decays $B
\to X_{u} \ell \nu$. The analysis makes use of hadronic tagging and is
performed on the full data set of the Belle experiment comprising 772 million
$B\bar{B}$ pairs. In the proceedings, the preliminary results of measurements
of partial branching fractions and the CKM matrix element $|V_{ub}|$ are
presented.


Abstract:
  Superconductivity and magnetism are generally incompatible because of the
opposing requirement on electron spin alignment. When combined, they produce a
multitude of fascinating phenomena, including unconventional superconductivity
and topological superconductivity. The emergence of two-dimensional (2D)layered
superconducting and magnetic materials that can form nanoscale junctions with
atomically sharp interfaces presents an ideal laboratory to explore new
phenomena from coexisting superconductivity and magnetic ordering. Here we
report tunneling spectroscopy under an in-plane magnetic field of
superconductor-ferromagnet-superconductor (S/F/S) tunnel junctions that are
made of 2D Ising superconductor NbSe2 and ferromagnetic insulator CrBr3. We
observe nearly 100% tunneling anisotropic magnetoresistance (AMR), that is,
difference in tunnel resistance upon changing magnetization direction from
out-of-plane to inplane. The giant tunneling AMR is induced by
superconductivity, particularly, a result of interfacial magnetic exchange
coupling and spin-dependent quasiparticle scattering. We also observe an
intriguing magnetic hysteresis effect in superconducting gap energy and
quasiparticle scattering rate with a critical temperature that is 2 K below the
superconducting transition temperature. Our study paves the path for exploring
superconducting spintronic and unconventional superconductivity in van der
Waals heterostructures.


Abstract:
  This paper explores how different ideas of racial equity in machine learning,
in justice settings in particular, can present trade-offs that are difficult to
solve computationally. Machine learning is often used in justice settings to
create risk assessments, which are used to determine interventions, resources,
and punitive actions. Overall aspects and performance of these machine
learning-based tools, such as distributions of scores, outcome rates by levels,
and the frequency of false positives and true positives, can be problematic
when examined by racial group. Models that produce different distributions of
scores or produce a different relationship between level and outcome are
problematic when those scores and levels are directly linked to the restriction
of individual liberty and to the broader context of racial inequity. While
computation can help highlight these aspects, data and computation are unlikely
to solve them. This paper explores where values and mission might have to fill
the spaces computation leaves.


Abstract:
  Affect recognition based on subjects' facial expressions has been a topic of
major research in the attempt to generate machines that can understand the way
subjects feel, act and react. In the past, due to the unavailability of large
amounts of data captured in real-life situations, research has mainly focused
on controlled environments. However, recently, social media and platforms have
been widely used. Moreover, deep learning has emerged as a means to solve
visual analysis and recognition problems. This paper exploits these advances
and presents significant contributions for affect analysis and recognition
in-the-wild. Affect analysis and recognition can be seen as a dual knowledge
generation problem, involving: i) creation of new, large and rich in-the-wild
databases and ii) design and training of novel deep neural architectures that
are able to analyse affect over these databases and to successfully generalise
their performance on other datasets. The paper focuses on large in-the-wild
databases, i.e., Aff-Wild and Aff-Wild2 and presents the design of two classes
of deep neural networks trained with these databases. The first class refers to
uni-task affect recognition, focusing on prediction of the valence and arousal
dimensional variables. The second class refers to estimation of all main
behavior tasks, i.e. valence-arousal prediction; categorical emotion
classification in seven basic facial expressions; facial Action Unit detection.
A novel multi-task and holistic framework is presented which is able to jointly
learn and effectively generalize and perform affect recognition over all
existing in-the-wild databases. Large experimental studies illustrate the
achieved performance improvement over the existing state-of-the-art in affect
recognition.


Abstract:
  The Segerdahl process (Segerdahl (1955)), characterized by exponential claims
and affine drift, has drawn a considerable amount of interest -- see, for
example, (Tichy (1984); Avram and Usabel (2008), due to its economic interest
(it is the simplest risk process which takes into account the effect of
interest rates). It is also the simplest non-Levy, non-diffusion example of a
spectrally negative Markov risk model. Note that for both spectrally negative
Levy and diffusion processes, first passage theories which are based on
identifying two basic monotone harmonic functions/martingales have been
developped. This means that for these processes many control problems involving
dividends, capital injections, etc., may be solved explicitly once the two
basic functions have been obtained. Furthermore, extensions to general
spectrally negative Markov processes are possible (Landriault et al. (2017),
Avram et al. (2018); Avram and Goreac (2019); Avram et al. (2019b).
Unfortunately, methods for computing the basic functions are still lacking
outside the Levy and diffusion classes, with the notable exception of the
Segerdahl process, for which the ruin probability has been computed (Paulsen
and Gjessing (1997). However, there is a striking lack of numerical results in
both cases. This motivated us to review several approaches, with the purpose of
drawing attention to connections between them, and underlying open problems.


Abstract:
  Ultralight bosons are possible fundamental building blocks of nature, and
promising dark matter candidates. They can trigger superradiant instabilities
of spinning black holes (BHs) and form long-lived "bosonic clouds" that slowly
dissipate energy through the emission of gravitational waves (GWs). Previous
studies constrained ultralight bosons by searching for the stochastic
gravitational wave background (SGWB) emitted by these sources in LIGO data,
focusing on the most unstable dipolar and quadrupolar modes. Here we focus on
scalar bosons and extend previous works by: (i) studying in detail the impact
of higher modes in the SGWB; (ii) exploring the potential of future proposed
ground-based GW detectors, such as the Neutron Star Extreme Matter Observatory,
the Einstein Telescope and Cosmic Explorer, to detect this SGWB. We find that
higher modes largely dominate the SGWB for bosons with masses $\gtrsim
10^{-12}$ eV, which is particularly relevant for future GW detectors. By
estimating the signal-to-noise ratio of this SGWB, due to both stellar-origin
BHs and from a hypothetical population of primordial BHs, we find that future
ground-based GW detectors could observe or constrain bosons in the mass range
$\sim [7\times 10^{-14}, 2\times 10^{-11}]$ eV and significantly improve on
current and future constraints imposed by LIGO and Virgo observations.


Abstract:
  Army cadets obtain occupations through a centralized process. Three
objectives -- increasing retention, aligning talent, and enhancing trust --
have guided reforms to this process since 2006. West Point's mechanism for the
Class of 2020 exacerbated challenges implementing Army policy aims. We
formulate these desiderata as axioms and study their implications theoretically
and with administrative data. We show that the Army's objectives not only
determine an allocation mechanism, but also a specific priority policy, a
uniqueness result that integrates mechanism and priority design. These results
led to a re-design of the mechanism, now adopted at both West Point and ROTC.


Abstract:
  We associate a certain tensor product lattice to any primitive integer
lattice and ask about its typical shape. These lattices are related to the
tangent bundle of Grassmannians and their study is motivated by Peyre's
programme on "freeness" for rational points of bounded height on Fano
varieties.


Abstract:
  Static (DC) and dynamic (AC, at 14 MHz and 8 GHz) magnetic susceptibilities
of single crystals of a ferromagnetic superconductor,
$\textrm{EuFe}_{2}(\textrm{As}_{1-x}\textrm{P}_{x})_{2}$ (x = 0.23), were
measured in pristine state and after different doses of 2.5 MeV electron or 3.5
MeV proton irradiation. The superconducting transition temperature, $T_{c}(H)$,
shows an extraordinarily large decrease. It starts at
$T_{c}(H=0)\approx24\:\textrm{K}$ in the pristine sample for both AC and DC
measurements, but moves to almost half of that value after moderate irradiation
dose. Our results suggest that in
$\textrm{EuFe}_{2}(\textrm{As}_{1-x}\textrm{P}_{x})_{2}$ superconductivity is
affected by local-moment ferromagnetism mostly via the spontaneous internal
magnetic fields induced by the FM subsystem. Another mechanism is revealed upon
irradiation where magnetic defects created in ordered $\text{Eu}^{2+}$ lattice
act as efficient pairbreakers leading to a significant $T_{c}$ reduction upon
irradiation compared to other 122 compounds. On the other hand, the exchange
interactions seem to be weakly screened by the superconducting phase leading to
a modest increase of $T_{m}$ (less than 1 K) after the irradiation drives
$T_{c}$ to below $T_{m}$. The results suggest that FM and SC phases coexist
microscopically in the same volume.


Abstract:
  We propose a new network architecture, the Fractal Pyramid Networks (PFNs)
for pixel-wise prediction tasks as an alternative to the widely used
encoder-decoder structure. In the encoder-decoder structure, the input is
processed by an encoding-decoding pipeline that tries to get a semantic
large-channel feature. Different from that, our proposed PFNs hold multiple
information processing pathways and encode the information to multiple separate
small-channel features. On the task of self-supervised monocular depth
estimation, even without ImageNet pretrained, our models can compete or
outperform the state-of-the-art methods on the KITTI dataset with much fewer
parameters. Moreover, the visual quality of the prediction is significantly
improved. The experiment of semantic segmentation provides evidence that the
PFNs can be applied to other pixel-wise prediction tasks, and demonstrates that
our models can catch more global structure information.


Abstract:
  Black-box quantum state preparation is a fundamental primitive in quantum
algorithms. Starting from Grover, a series of techniques have been devised to
reduce the complexity. In this work, we propose to perform black-box state
preparation using the technique of linear combination of unitaries (LCU). We
provide two algorithms based on a different structure of LCU. Our algorithms
improve upon the existed best results by reducing the required additional
qubits and Toffoli gates to 2log(n) and n, respectively, in the bit precision
n. We demonstrate the algorithms using the IBM Quantum Experience cloud
services. The further reduced complexity of the present algorithms brings the
black-box quantum state preparation closer to reality.


Abstract:
  Three-dimensional topological insulators (TIs) host helical Dirac surface
states at the interface with a trivial insulator. In quasi-one-dimensional TI
nanoribbon structures the wave function of surface charges extends
phase-coherently along the perimeter of the nanoribbon, resulting in a
quantization of transverse surface modes. Furthermore, as the inherent
spin-momentum locking results in a Berry phase offset of $\pi$ of
self-interfering charge carriers an energy gap within the surface state
dispersion appears and all states become spin-degenerate. We investigate and
compare the magnetic field dependent surface state dispersion in selectively
deposited Bi$_2$Te$_3$ TI micro- and nanoribbon structures by analysing the
gate voltage dependent magnetoconductance at cryogenic temperatures. While in
wide microribbon devices the field effect mainly changes the amount of bulk
charges close to the top surface we identify coherent transverse surface states
along the perimeter of the nanoribbon devices responding to a change in top
gate potential. We quantify the energetic spacing in between these quantized
transverse subbands by using an electrostatic model that treats an initial
difference in charge carrier densities on the top and bottom surface as well as
remaining bulk charges. In the gate voltage dependent transconductance we find
oscillations that change their relative phase by $\pi$ at half-integer values
of the magnetic flux quantum applied coaxial to the nanoribbon, which is a
signature for a magnetic flux dependent topological phase transition in narrow,
selectively deposited TI nanoribbon devices.


Abstract:
  Interference between light waves is one of the widely known phenomena in
physics, which is widely used in modern optics, ranging from precise detection
at the nanoscale to gravitational-wave observation. Akin to light, both
classical and quantum interferences between surface plasmon polaritons (SPPs)
have been demonstrated. However, to actively control the SPP interference
within subcycle in time (usually less than several femtoseconds in the visible
range) is still missing, which hinders the ultimate manipulation of SPP
interference on ultrafast time scale. In this paper, the interference between
SPPs launched by a hole dimer, which was excited by a grazing incident free
electron beam without direct contact, was manipulated through both propagation
and initial phase difference control. Particularly, using cathodoluminescence
spectroscopy, the appearance of higher-order interference orders was obtained
through propagation phase control by increasing separation distances of the
dimer. Meanwhile, the peak-valley-peak evolution at a certain wavelength
through changing the accelerating voltages was observed, which originates from
the initial phase difference control of hole launched SPPs. In particular, the
time resolution of this kind of control is shown to be in the ultrafast
attosecond (as) region. Our work suggests that fast electron beams can be an
efficient tool to control polarition interference in subcycle temporal scale,
which can be potentially used in ultrafast optical processing or sensing.


Abstract:
  We investigate Bayesian predictive distributions for Wishart distributions
under the Kullback--Leibler divergence. We consider a recently introduced class
of prior distributions, called the family of enriched standard conjugate prior
distributions and compare the Bayesian predictive distributions based on these
prior distributions. We study the performance of the Bayesian predictive
distribution based on the reference prior distribution in the family. We show
that there exists a prior distribution in the family that dominates the
reference prior distribution.


Abstract:
  The construction of an ontology of scientific knowledge objects, presented
here, is part of the development of an approach oriented towards the
visualization of scientific knowledge. It is motivated by the fact that the
concepts that are used to organize scientific knowledge (theorem, law,
experience, proof, etc.) appear in existing ontologies but that none of these
ontologies is centered on this topic and presents them in a simple and easily
understandable organization. This ontology has been constructed by 1) selecting
concepts that appear in high level ontologies or in ontologies of knowledge
objects of specific fields and 2) by interviewing scientists in different
fields. We have aligned this ontology with some of the sources used, which has
allowed us to verify its consistency with respect to them. The validation of
the ontology consists in using it to formalize knowledge from various sources,
which we have begun to do in the field of physics.


Abstract:
  We consider an input-constrained differential-drive robot with actuator
dynamics. For this system, we establish asymptotic stability of the origin on
arbitrary compact, convex sets using Model Predictive Control (MPC) without
stabilizing terminal conditions despite the presence of state constraints and
actuator dynamics. We note that the problem without those two additional
ingredients was essentially solved beforehand, despite the fact that the
linearization is not stabilizable. We propose an approach successfully solving
the task at hand by combining the theory of barriers to characterize the
viability kernel and an MPC framework based on so-called cost controllability.
Moreover, we present a numerical case study to derive quantitative bounds on
the required length of the prediction horizon. To this end, we investigate the
boundary of the viability kernel and a neighbourhood of the origin, i.e. the
most interesting areas.


Abstract:
  Anomaly detection is a crucial and challenging subject that has been studied
within diverse research areas. In this work, we explore the task of log anomaly
detection (especially computer system logs and user behavior logs) by analyzing
logs' sequential information. We propose LAMA, a multi-head attention based
sequential model to process log streams as template activity (event) sequences.
  A next event prediction task is applied to train the model for anomaly
detection. Extensive empirical studies demonstrate that our new model
outperforms existing log anomaly detection methods including statistical and
deep learning methodologies, which validate the effectiveness of our proposed
method in learning sequence patterns of log data.


Abstract:
  In this paper, we consider fully connected feed-forward deep neural networks
where weights and biases are independent and identically distributed according
to Gaussian distributions. Extending previous results (Matthews et al.,
2018a;b; Yang, 2019) we adopt a function-space perspective, i.e. we look at
neural networks as infinite-dimensional random elements on the input space
$\mathbb{R}^I$. Under suitable assumptions on the activation function we show
that: i) a network defines a continuous Gaussian process on the input space
$\mathbb{R}^I$; ii) a network with re-scaled weights converges weakly to a
continuous Gaussian process in the large-width limit; iii) the limiting
Gaussian process has almost surely locally $\gamma$-H\"older continuous paths,
for $0 < \gamma <1$. Our results contribute to recent theoretical studies on
the interplay between infinitely wide deep neural networks and Gaussian
processes by establishing weak convergence in function-space with respect to a
stronger metric.


Abstract:
  In this article, we wish to establish some first order differential
subordination relations for certain Carath\'{e}odory functions with nice
geometrical properties. Moreover, several implications are determined so that
the normalized analytic function belongs to various subclasses of starlike
functions.


Abstract:
  We study the Du Bois complex of a hypersurface $Z$ in a smooth complex
algebraic variety in terms of the minimal exponent $\widetilde{\alpha}(Z)$ and
give various applications. We show that if $\widetilde{\alpha}(Z)\geq p+1$,
then the canonical morphism $\Omega_Z^p\to \underline{\Omega}_Z^p$ is an
isomorphism. On the other hand, if $Z$ is singular and
$\widetilde{\alpha}(Z)>p\geq 2$, then ${\mathcal
H}^{p-1}(\underline{\Omega}_Z^{n-p})\neq 0$.


Abstract:
  We determine the asymptotic spreading speed of the solutions of a Fisher-KPP
reaction-diffusion equation, starting from compactly supported initial data,
when the diffusion coefficient is a fixed bounded monotone profile that is
shifted at a given forcing speed and satisfies a general uniform ellipticity
condition. Depending on the monotony of the profile, we are able to
characterize this spreading speed as a function of the forcing speed and the
two linear spreading speeds associated to the asymptotic problems. Most
notably, when the profile of the coefficient diffusion is increasing we show
that there is an intermediate range for the forcing speed where spreading
actually occurs at a speed which is larger than the linear speed associated
with the homogeneous state around the position of the front. We complement our
study with the construction of strictly monotone traveling front solutions with
strong exponential decay near the unstable state when the profile of the
coefficient diffusion is decreasing and in the regime where the forcing speed
is precisely the selected spreading speed.


Abstract:
  In this paper, we study the rainbow Erd\H{o}s-Rothschild problem with respect
to 3-term arithmetic progressions. We obtain the asymptotic number of
$r$-colorings of $[n]$ without rainbow 3-term arithmetic progressions, and we
show that the typical colorings with this property are 2-colorings. We also
prove that $[n]$ attains the maximum number of rainbow 3-term arithmetic
progression-free $r$-colorings among all subsets of $[n]$. Moreover, the exact
number of rainbow 3-term arithmetic progression-free $r$-colorings of
$\mathbb{Z}_p$ is obtained, where $p$ is any prime and $\mathbb{Z}_p$ is the
cyclic group of order $p$.


Abstract:
  In recent times, the Internet has been plagued by a tremendous amount of
misinformation. Online markets such as Amazon are also not free from
misinformation. In this work, we study the misinformation propagated to
consumers through the form of Amazon reviews. There exists a vast underground
market where reviews by real Amazon users are purchased and sold. While such a
practice violates Amazon's terms of service, we observe that there exists a
complex network consisting of thousands of sellers and agents, who provide a
rebate to consumers for leaving positive reviews to over $5000$ products. Based
on interviews with members involved in the reviews market, we understand the
working of this market, and the tactics used to avoid detection by Amazon. We
also present a set of recommendations of features that Amazon and similar
online markets can take into consideration to detect such reviews.


Abstract:
  Spatially separated bodies in relative motion through vacuum experience a
tiny friction force known as quantum friction. This force has so far eluded
experimental detection due to its small magnitude and short range. Quantitative
details revealing traces of the quantum friction in the degradation of the
quantum coherence of a particle are presented. Environmentally induced
decoherence for a particle sliding over a dielectric sheet can be decomposed
into contributions of different signatures: one solely induced by the
electromagnetic vacuum in presence of the dielectric and another induced by
motion. As the geometric phase has been proved to be a fruitful venue of
investigation to infer features of the quantum systems, herein we propose to
use the accumulated geometric phase acquired by a particle as a quantum
friction sensor. Furthermore, an innovative experiment designed to track traces
of quantum friction by measuring the velocity dependence of corrections to the
geometric phase and coherence is proposed. The experimentally viable scheme
presented can spark renewed optimism for the detection of non-contact friction,
with the hope that this non-equilibrium phenomenon can be readily measured
soon.


Abstract:
  Multicore fibers can be used for Radio over Fiber transmission of mmwave
signals for phased array antennas in 5G networks. The inter-core skew of these
fibers distort the radiation pattern. We propose an efficient method to
compensate the differential delays, without full equalization of the
transmission path lengths, reducing the power loss and complexity.


Abstract:
  The interplay between security and reliability is poorly understood. This
paper shows how triple modular redundancy affects a side-channel attack (SCA).
Our counterintuitive findings show that modular redundancy can increase SCA
resiliency.


Abstract:
  An intelligent reflecting surface (IRS) can greatly improve the channel
quality over a frequency-flat channel, if it is configured to reflect the
incident signal as a beam towards the receiver. However, the fundamental
limitations of the IRS technology become apparent over practical
frequency-selective channels, where the same configuration must be used over
the entire bandwidth. In this paper, we consider a wideband orthogonal
frequency-division multiplexing (OFDM) system that is supported by a fairly
realistic IRS setup with two unbalanced states per element and also mutual
coupling. We describe the simulation setup considered in the IEEE Signal
Processing Cup 2021, propose a low-complexity solution for channel estimation
and IRS configuration, and evaluate it on that setup.


Abstract:
  In this paper, we propose a novel DNN watermarking method that utilizes a
learnable image transformation method with a secret key. The proposed method
embeds a watermark pattern in a model by using learnable transformed images and
allows us to remotely verify the ownership of the model. As a result, it is
piracy-resistant, so the original watermark cannot be overwritten by a pirated
watermark, and adding a new watermark decreases the model accuracy unlike most
of the existing DNN watermarking methods. In addition, it does not require a
special pre-defined training set or trigger set. We empirically evaluated the
proposed method on the CIFAR-10 dataset. The results show that it was resilient
against fine-tuning and pruning attacks while maintaining a high
watermark-detection accuracy.


Abstract:
  For a typical Scene Graph Generation (SGG) method, there is often a large gap
in the performance of the predicates' head classes and tail classes. This
phenomenon is mainly caused by the semantic overlap between different
predicates as well as the long-tailed data distribution. In this paper, a
Predicate Correlation Learning (PCL) method for SGG is proposed to address the
above two problems by taking the correlation between predicates into
consideration. To describe the semantic overlap between strong-correlated
predicate classes, a Predicate Correlation Matrix (PCM) is defined to quantify
the relationship between predicate pairs, which is dynamically updated to
remove the matrix's long-tailed bias. In addition, PCM is integrated into a
Predicate Correlation Loss function ($L_{PC}$) to reduce discouraging gradients
of unannotated classes. The proposed method is evaluated on Visual Genome
benchmark, where the performance of the tail classes is significantly improved
when built on the existing methods.


Abstract:
  Developers in data science and other domains frequently use computational
notebooks to create exploratory analyses and prototype models. However, they
often struggle to incorporate existing software engineering tooling into these
notebook-based workflows, leading to fragile development processes. We
introduce Assembl\'{e}, a new development environment for collaborative data
science projects, in which promising code fragments of data science pipelines
can be contributed as pull requests to an upstream repository entirely from
within JupyterLab, abstracting away low-level version control tool usage. We
describe the design and implementation of Assembl\'{e} and report on a user
study of 23 data scientists.


Abstract:
  Predicting the permeability of porous media in saturated and partially
saturated conditions is of crucial importance in many geo-engineering areas,
from water resources to vadose zone hydrology or contaminant transport
predictions. Many models have been proposed in the literature to estimate the
permeability from properties of the porous media such as porosity, grain size
or pore size. In this study, we develop a model of the permeability for porous
media saturated by one or two fluid phases with all physically-based parameters
using a fractal upscaling technique. The model is related to microstructural
properties of porous media such as fractal dimension for pore space, fractal
dimension for tortuosity, porosity, maximum radius, ratio of minimum pore
radius and maximum pore radius, water saturation and irreducible water
saturation. The model is favorably compared to existing and widely used models
from the literature. Then, comparison with published experimental data for both
unconsolidated and consolidated samples, we show that the proposed model
estimate the permeability from the medium properties very well.


Abstract:
  This paper is one in a series that investigates topological measures on
locally compact spaces. A topological measure is a set function which is
finitely additive on the collection of open and compact sets, inner regular on
open sets, and outer regular on closed sets. We examine semisolid sets and give
a way of constructing topological measures from solid-set functions on locally
compact, connected, locally connected spaces. For compact spaces our approach
produces a simpler method than the current one. We give examples of finite and
infinite topological measures on locally compact spaces and present an easy way
to generate topological measures on spaces whose one-point compactification has
genus 0. Results of this paper are necessary for various methods for
constructing topological measures, give additional properties of topological
measures, and provide a tool for determining whether two topological measures
or quasi-linear functionals are the same.


Abstract:
  We propose a novel neural model compression strategy combining data
augmentation, knowledge transfer, pruning, and quantization for device-robust
acoustic scene classification (ASC). Specifically, we tackle the ASC task in a
low-resource environment leveraging a recently proposed advanced neural network
pruning mechanism, namely Lottery Ticket Hypothesis (LTH), to find a
sub-network neural model associated with a small amount non-zero model
parameters. The effectiveness of LTH for low-complexity acoustic modeling is
assessed by investigating various data augmentation and compression schemes,
and we report an efficient joint framework for low-complexity multi-device ASC,
called \emph{Acoustic Lottery}. Acoustic Lottery could compress an ASC model up
to $1/10^{4}$ and attain a superior performance (validation accuracy of 79.4%
and Log loss of 0.64) compared to its not compressed seed model. All results
reported in this work are based on a joint effort of four groups, namely
GT-USTC-UKE-Tencent, aiming to address the "Low-Complexity Acoustic Scene
Classification (ASC) with Multiple Devices" in the DCASE 2021 Challenge Task
1a.


Abstract:
  Models of the geomagnetic field rely on magnetic data of high spatial and
temporal resolution. The magnetic data from low-Earth orbit satellites of
dedicated magnetic survey missions such as CHAMP and Swarm play a key role in
the construction of such models. Unfortunately, there are no magnetic data from
such satellites after the end of CHAMP in 2010 and before the launch of Swarm
in late 2013. This limits our ability to recover signals on timescales of 3
years and less during this gap period. The magnetic data from platform
magnetometers carried by satellites for navigational purposes may help address
this data gap provided that they are carefully calibrated. Earlier studies have
demonstrated that platform magnetometer data can be calibrated using a fixed
reference field model. However, this approach can lead to biased calibration
parameters. An alternative has been developed in the form of a co-estimation
scheme which consists of simultaneously estimating both the calibration
parameters and a model of the internal geomagnetic field. Here, we develop a
scheme, based on the CHAOS framework, that involves the co-estimation of a
geomagnetic field model along with calibration parameters of platform
magnetometers. Using our implementation, we are able to derive a geomagnetic
field model from 2008 to 2018 with satellite magnetic data from CHAMP, Swarm,
secular variation data from ground observatories, and platform magnetometer
data from CryoSat-2 and GRACE. Through experiments, we explore correlations
between the estimates of the geomagnetic field and the calibration parameters,
and suggest how these may be avoided. We find that platform magnetometer data
provide additional information on the secular acceleration, especially in the
Pacific during the gap period. This study adds to the evidence that it is
beneficial to use platform magnetometer data in geomagnetic field modeling.


Abstract:
  We generalize the continuous observation privacy setting from Dwork et al.
'10 and Chan et al. '11 by allowing each event in a stream to be a subset of
some (possibly unknown) universe of items. We design differentially private
(DP) algorithms for histograms in several settings, including top-$k$
selection, with privacy loss that scales with polylog$(T)$, where $T$ is the
maximum length of the input stream. We present a meta-algorithm that can use
existing one-shot top-$k$ DP algorithms as a subroutine to continuously release
private histograms from a stream. Further, we present more practical DP
algorithms for two settings: 1) continuously releasing the top-$k$ counts from
a histogram over a known domain when an event can consist of an arbitrary
number of items, and 2) continuously releasing histograms over an unknown
domain when an event has a limited number of items.


Abstract:
  Electronic instabilities in transition metal compounds often spontaneously
form orbital molecules, which consist of orbital-coupled metal ions at low
temperature. Recent local structural studies utilizing the pair distribution
function revealed that preformed orbital molecules appear disordered even in
the high-temperature paramagnetic phase. However, it is unclear whether
preformed orbital molecules are dynamic or static. Here, we provide clear
experimental evidence of the slow dynamics of disordered orbital molecules
realized in the high-temperature paramagnetic phase of LiVS2, which exhibits
vanadium trimerization upon cooling below 314 K. Unexpectedly, the preformed
orbital molecules appear as a disordered zigzag chain that fluctuate in both
time and space under electron irradiation. Our findings should advance studies
on soft matter physics realized in an inorganic material due to disordered
orbital molecules.


Abstract:
  In the classical survey (Chapter 16.2, {\it Mathematics in industrial
problem}, Vol. 24, Springer-Verlag, New York, 1989), A. Friedman proposed an
open problem on the collision of two incompressible jets emerging from two
axially symmetric nozzles. In this paper, we concerned with the mathematical
theory on this collision problem, and establish the well-posedness theory on
hydrodynamic impinging outgoing jets issuing from two coaxial axially symmetric
nozzles. More precisely, we showed that for any given mass fluxes $M_1>0$ and
$M_2<0$ in two nozzles respectively, that there exists an incompressible,
inviscid impinging outgoing jet with contact discontinuity, which issues from
two given semi-infinitely long axially symmetric nozzles and extends to
infinity. Moreover, the constant pressure free stream surfaces of the impinging
jet initiate smoothly from the mouths of the two nozzles and shrink to some
asymptotic conical surface. There exists a smooth surface separating the two
incompressible fluids and the contact discontinuity occurs on the surface.
Furthermore, we showed that there is no stagnation point in the flow field and
its closure, except one point on the symmetric axis. Some asymptotic behavior
of the impinging jet in upstream and downstream, geometric properties of the
free stream surfaces are also obtained. The main results in this paper solved
the open problem on the collision of two incompressible axially symmetric jets
in [24].


Abstract:
  The existence of multiple datasets for sarcasm detection prompts us to apply
transfer learning to exploit their commonality. The adversarial neural transfer
(ANT) framework utilizes multiple loss terms that encourage the source-domain
and the target-domain feature distributions to be similar while optimizing for
domain-specific performance. However, these objectives may be in conflict,
which can lead to optimization difficulties and sometimes diminished transfer.
We propose a generalized latent optimization strategy that allows different
losses to accommodate each other and improves training dynamics. The proposed
method outperforms transfer learning and meta-learning baselines. In
particular, we achieve 10.02% absolute performance gain over the previous state
of the art on the iSarcasm dataset.


Abstract:
  This is the third series of the lab manuals for virtual teaching of
introductory physics classes. This covers fluids, waves, thermodynamics,
optics, interference, photoelectric effect, atomic spectra, and radiation
concepts. A few of these labs can be used within Physics I and a few other labs
within Physics II depending on the syllabi of Physics I and II classes. Virtual
experiments in this lab manual and our previous Physics I (arXiv.2012.09151)
and Physics II (arXiv.2012.13278) lab manuals were designed for 2.45 hrs long
lab classes (algebra-based and calculus-based). However, all the virtual labs
in these three series can be easily simplified to align with conceptual type or
short time physics lab classes as desired. All the virtual experiments were
based on open education resource (OER) type simulations. Virtual experiments
were designed to simulate in-person physical laboratory experiments. Student
learning outcomes (understand, apply, analyze and evaluate) were studied with
detailed lab reports per each experiment and end of the semester written exam
which was based on experiments. Special emphasis was given to study the student
skill development of computational data analysis.


Abstract:
  A pearl's distinguished beauty and toughness are attributable to the periodic
stacking of aragonite tablets known as nacre. Nacre has naturally occurring
mesoscale periodicity that remarkably arises in the absence of discrete
translational symmetry. Gleaning the inspiring biomineral design of a pearl
requires quantifying its structural coherence and understanding the stochastic
processes that influence formation. By characterizing the entire structure of
pearls (~3 mm) in cross-section at high resolution, we show nacre has
medium-range mesoscale periodicity. Self-correcting growth mechanisms actively
remedy disorder and topological defects of the tablets and act as a
countervailing process to long-range disorder. Nacre has a correlation length
of roughly 16 tablets (~5.5 um) despite persistent fluctuations and topological
defects. For longer distances (> 25 tablets, ~8.5 um), the frequency spectrum
of nacre tablets follows f^(-1.5) behavior suggesting growth is coupled to
external stochastic processes-a universality found across disparate natural
phenomena which now includes pearls.


Abstract:
  Let $F$ be a field of characteristic $p$ and let $\Omega^n(F)$ be the
$F$-vector space of $n$-differential forms. In this work, we will study the
annihilator of differential forms, give specific descriptions for special cases
and show a connection between these annihilators and the kernels of the
restriction map $\Omega^n(F) \to \Omega^n(E)$ for purely inseparable field
extensions $E/F$.


Abstract:
  Despite abundant negotiation strategies in literature, the complexity of
automated negotiation forbids a single strategy from being dominant against all
others in different negotiation scenarios. To overcome this, one approach is to
use mixture of experts, but at the same time, one problem of this method is the
selection of experts, as this approach is limited by the competency of the
experts selected. Another problem with most negotiation strategies is their
incapability of adapting to dynamic variation of the opponent's behaviour
within a single negotiation session resulting in poor performance. This work
focuses on both, solving the problem of expert selection and adapting to the
opponent's behaviour with our Autonomous Negotiating Agent Framework. This
framework allows real-time classification of opponent's behaviour and provides
a mechanism to select, switch or combine strategies within a single negotiation
session. Additionally, our framework has a reviewer component which enables
self-enhancement capability by deciding to include new strategies or replace
old ones with better strategies periodically. We demonstrate an instance of our
framework by implementing maximum entropy reinforcement learning based
strategies with a deep learning based opponent classifier. Finally, we evaluate
the performance of our agent against state-of-the-art negotiators under varied
negotiation scenarios.


Abstract:
  Software Transactional Memory (STM) algorithms provide programmers with a
synchronisation mechanism for concurrent access to shared variables. Basically,
programmers can specify transactions (reading from and writing to shared state)
which execute "seemingly" atomic. This property is captured in a correctness
criterion called opacity. For model checking opacity of an STM algorithm, we --
in principle -- need to check opacity for all possible combinations of
transactions writing to and reading from potentially unboundedly many
variables.
  To still apply automatic model checking techniques to opacity checking, a so
called small model theorem has been proven which states that model checking on
two variables and two transactions is sufficient for correctness verification
of STMs. In this paper, we take a fresh look at this small model theorem and
investigate its applicability to opacity checking of STM algorithms.


Abstract:
  The electronic nematic phase emerging with spontaneous rotation symmetry
breaking is a central issue of modern condensed matter physics. In particular,
various nematic phases in iron-based superconductors and high-$T_{\rm c}$
cuprate superconductors are extensively studied recently. Electric quadrupole
moments (EQMs) are one of the order parameters characterizing these nematic
phases in a unified way, and elucidating EQMs is a key to understanding these
nematic phases. However, the quantum-mechanical formulation of the EQMs in
crystals is a nontrivial issue because the position operators are non-periodic
and unbound. Recently, the EQMs have been formulated by local thermodynamics,
and such {\it thermodynamic EQMs} may be used to characterize the fourfold
rotation symmetry breaking in materials. In this paper, we calculate the
thermodynamic EQMs in iron-based superconductors LaFeAsO and FeSe as well as a
cuprate superconductor La$_2$CuO$_4$ by a first-principles calculation. We show
that owing to the orbital degeneracy the EQMs in iron-based superconductors are
mainly determined by the geometric properties of wave functions. This result is
in sharp contrast to the cuprate superconductor, in which the EQMs are
dominated by distortion of the Fermi surface.


Abstract:
  Escape from a potential well through an index-1 saddle can be widely found in
some important physical systems. Knowing the criteria and phase space geometry
that govern escape events plays an important role in making use of such
phenomenon, particularly when realistic frictional or dissipative forces are
present. We aim to extend the study the escape dynamics around the saddle from
two degrees of freedom to three degrees of freedom, presenting both a
methodology and phase space structures. Both the ideal conservative system and
a perturbed, dissipative system are considered. We define the five-dimensional
transition region, $\mathcal{T}_h$, as the set of initial conditions of a given
initial energy $h$ for which the trajectories will escape from one side of the
saddle to another. Invariant manifold arguments demonstrate that in the
six-dimensional phase space, the boundary of the transition region, $\partial
\mathcal{T}_h$, is topologically a four-dimensional hyper-cylinder in the
conservative system, and a four-dimensional hyper-sphere in the dissipative
system. The transition region $\mathcal{T}_h$ can be constructed by a solid
three-dimensional ellipsoid (solid three-dimensional cylinder) in the
three-dimensional configuration space, where at each point, there is a cone of
velocity -- the velocity directions leading to transition are given by cones,
with velocity magnitude given by the initial energy and the direction by two
spherical angles with given limits. To illustrate our analysis, we consider an
example system which has two potential minima connected by an index 1 saddle.


Abstract:
  Scale is often seen as a given, disturbing factor in many vision tasks. When
doing so it is one of the factors why we need more data during learning. In
recent work scale equivariance was added to convolutional neural networks. It
was shown to be effective for a range of tasks. We aim for accurate
scale-equivariant convolutional neural networks (SE-CNNs) applicable for
problems where high granularity of scale and small kernel sizes are required.
Current SE-CNNs rely on weight sharing and kernel rescaling, the latter of
which is accurate for integer scales only. To reach accurate scale
equivariance, we derive general constraints under which scale-convolution
remains equivariant to discrete rescaling. We find the exact solution for all
cases where it exists, and compute the approximation for the rest. The discrete
scale-convolution pays off, as demonstrated in a new state-of-the-art
classification on MNIST-scale and on STL-10 in the supervised learning setting.
With the same SE scheme, we also improve the computational effort of a
scale-equivariant Siamese tracker on OTB-13.


Abstract:
  Crowdsourcing is widely used to create data for common natural language
understanding tasks. Despite the importance of these datasets for measuring and
refining model understanding of language, there has been little focus on the
crowdsourcing methods used for collecting the datasets. In this paper, we
compare the efficacy of interventions that have been proposed in prior work as
ways of improving data quality. We use multiple-choice question answering as a
testbed and run a randomized trial by assigning crowdworkers to write questions
under one of four different data collection protocols. We find that asking
workers to write explanations for their examples is an ineffective stand-alone
strategy for boosting NLU example difficulty. However, we find that training
crowdworkers, and then using an iterative process of collecting data, sending
feedback, and qualifying workers based on expert judgments is an effective
means of collecting challenging data. But using crowdsourced, instead of expert
judgments, to qualify workers and send feedback does not prove to be effective.
We observe that the data from the iterative protocol with expert assessments is
more challenging by several measures. Notably, the human--model gap on the
unanimous agreement portion of this data is, on average, twice as large as the
gap for the baseline protocol data.


Abstract:
  We theoretically investigate pattern formation and nonlinear dynamics in an
array of equally-coupled, optically driven, Kerr nonlinear microresonators. We
show that the nonlinear dynamics of the system can be associated with an
effective two dimensional space due to the multimiode structure of each
resonator. As a result, two fundamentally different dynamical regimes (elliptic
and hyperbolic) arise at different regions of the hybridized dispersion
surface. We demonstrate the formation of global nonlinear optical patterns in
both regimes which correspond to coherent optical frequency combs on the
individual resonator level. In addition we show that the presence of an
additional dimension leads to the observation of wave collapse.


Abstract:
  We apply the method of differential inequalities for the computation of upper
bounds for the rate of convergence to the limiting regime for one specific
class of (in)homogeneous continuous-time Markov chains. To obtain these
estimates, we investigate the corresponding forward system of Kolmogorov
differential equations.


Abstract:
  Recently, advances in film synthesis methods have enabled a study of
extremely overdoped $La_{2-x}Sr_{x}CuO_{4}$. This has revealed a surprising
behavior of the superfluid density as a function of doping and temperature, the
explanation of which is vividly debated. One popular class of models posits
electronic phase separation, where the superconducting phase fraction decreases
with doping, while some competing phase (e.g. ferromagnetic) progressively
takes over. A problem with this scenario is that all the way up to the dome
edge the superconducting transition remains sharp, according to mutual
inductance measurements. However, the physically relevant scale is the Pearl
penetration depth, $\Lambda_{P}$, and this technique probes the sample on a
length scale $L$ that is much larger than $\Lambda_{P}$. In the present paper,
we use local scanning SQUID measurements that probe the susceptibility of the
sample on the scale $L << \Lambda_{P}$. Our SQUID maps show uniform landscapes
of susceptibility and excellent overall agreement of the local penetration
depth data with the bulk measurements. These results contribute an important
piece to the puzzle of how high-temperature superconductivity vanishes on the
overdoped side of the cuprates phase diagram.


Abstract:
  Social media often acts as breeding grounds for different forms of offensive
content. For low resource languages like Tamil, the situation is more complex
due to the poor performance of multilingual or language-specific models and
lack of proper benchmark datasets. Based on this shared task, Offensive
Language Identification in Dravidian Languages at EACL 2021, we present an
exhaustive exploration of different transformer models, We also provide a
genetic algorithm technique for ensembling different models. Our ensembled
models trained separately for each language secured the first position in
Tamil, the second position in Kannada, and the first position in Malayalam
sub-tasks. The models and codes are provided.


Abstract:
  Overnight, Apple has turned its hundreds-of-million-device ecosystem into the
world's largest crowd-sourced location tracking network called offline finding
(OF). OF leverages online finder devices to detect the presence of missing
offline devices using Bluetooth and report an approximate location back to the
owner via the Internet. While OF is not the first system of its kind, it is the
first to commit to strong privacy goals. In particular, OF aims to ensure
finder anonymity, untrackability of owner devices, and confidentiality of
location reports. This paper presents the first comprehensive security and
privacy analysis of OF. To this end, we recover the specifications of the
closed-source OF protocols by means of reverse engineering. We experimentally
show that unauthorized access to the location reports allows for accurate
device tracking and retrieving a user's top locations with an error in the
order of 10 meters in urban areas. While we find that OF's design achieves its
privacy goals, we discover two distinct design and implementation flaws that
can lead to a location correlation attack and unauthorized access to the
location history of the past seven days, which could deanonymize users. Apple
has partially addressed the issues following our responsible disclosure.
Finally, we make our research artifacts publicly available.


Abstract:
  We develop a constitutive model allowing for the description of the rheology
of two-dimensional soft dense suspensions above jamming. Starting from a
statistical description of the particle dynamics, we derive, using a set of
approximations, a non-linear tensorial evolution equation linking the
deviatoric part of the stress tensor to the strain-rate and vorticity tensors.
The coefficients appearing in this equation can be expressed in terms of the
packing fraction and of particle-level parameters. This constitutive equation
rooted in the microscopic dynamic qualitatively reproduces a number of salient
features of the rheology of jammed soft suspensions, including the presence of
yield stresses for the shear component of the stress and for the normal stress
difference. More complex protocols like the relaxation after a preshear are
also considered, showing a smaller stress after relaxation for a stronger
preshear.


Abstract:
  We study the MaxCut problem for graphs $G=(V,E)$. The problem is NP-hard,
there are two main approximation algorithms with theoretical guarantees: (1)
the Goemans \& Williamson algorithm uses semi-definite programming to provide a
0.878MaxCut approximation (which, if the Unique Games Conjecture is true, is
the best that can be done in polynomial time) and (2) Trevisan proposed an
algorithm using spectral graph theory from which a 0.614MaxCut approximation
can be obtained. We discuss a new approach using a specific quadratic program
and prove that its solution can be used to obtain at least a 0.502MaxCut
approximation. The algorithm seems to perform well in practice.


Abstract:
  In many applications, a large number of features are collected with the goal
to identify a few important ones. Sometimes, these features lie in a metric
space with a known distance matrix, which partially reflects their
co-importance pattern. Proper use of the distance matrix will boost the power
of identifying important features. Hence, we develop a new multiple testing
framework named the Distance Assisted Recursive Testing (DART). DART has two
stages. In stage 1, we transform the distance matrix into an aggregation tree,
where each node represents a set of features. In stage 2, based on the
aggregation tree, we set up dynamic node hypotheses and perform multiple
testing on the tree. All rejections are mapped back to the features. Under mild
assumptions, the false discovery proportion of DART converges to the desired
level in high probability converging to one. We illustrate by theory and
simulations that DART has superior performance under various models compared to
the existing methods. We applied DART to a clinical trial in the allogeneic
stem cell transplantation study to identify the gut microbiota whose abundance
will be impacted by the after-transplant care.


Abstract:
  Trusted AI literature to date has focused on the trust needs of users who
knowingly interact with discrete AIs. Conspicuously absent from the literature
is a rigorous treatment of public trust in AI. We argue that public distrust of
AI originates from the under-development of a regulatory ecosystem that would
guarantee the trustworthiness of the AIs that pervade society. Drawing from
structuration theory and literature on institutional trust, we offer a model of
public trust in AI that differs starkly from models driving Trusted AI efforts.
This model provides a theoretical scaffolding for Trusted AI research which
underscores the need to develop nothing less than a comprehensive and visibly
functioning regulatory ecosystem. We elaborate the pivotal role of externally
auditable AI documentation within this model and the work to be done to ensure
it is effective, and outline a number of actions that would promote public
trust in AI. We discuss how existing efforts to develop AI documentation within
organizations -- both to inform potential adopters of AI components and support
the deliberations of risk and ethics review boards -- is necessary but
insufficient assurance of the trustworthiness of AI. We argue that being
accountable to the public in ways that earn their trust, through elaborating
rules for AI and developing resources for enforcing these rules, is what will
ultimately make AI trustworthy enough to be woven into the fabric of our
society.


Abstract:
  Consider two batches of independent or interdependent exponentiated
location-scale distributed heterogeneous random variables. This article
investigates ordering results for the second-order statistics from these
batches when a vector of parameters is switched to another vector of parameters
in the specified model. Sufficient conditions for the usual stochastic order
and the hazard rate order are derived. Some applications of the established
results are presented.


Abstract:
  We determine the starspot detection rate in exoplanetary transit light curves
for M and K dwarf stars observed by the Transiting Exoplanet Survey Satellite
(TESS) using various starspot filling factors and starspot distributions. We
used $3.6\times10^9$ simulations of planetary transits around spotted stars
using the transit-starspot model \prism. The simulations cover a range of
starspot filling factors using one of three distributions: uniform,
polar-biased, and mid-latitude. After construction of the stellar disc and
starspots, we checked the transit cord for starspots and examined the change in
flux of each starspot to determine whether or not a starspot anomaly would be
detected. The results were then compared to predicted planetary detections for
TESS. The results show that for the case of a uniform starspot distribution,
$64\pm9$ M dwarf and $23\pm4$ K dwarf transit light curves observed by TESS
will contain a starspot anomaly. This reduces to $37\pm6$ M dwarf and $12\pm2$
K dwarf light curves for a polar-biased distribution and $47\pm7$ M dwarf and
$21\pm4$ K dwarf light curves for a mid-latitude distribution. Currently there
are only 17 M dwarf and 10 K dwarf confirmed planetary systems from TESS, none
of which are confirmed as showing starspot anomalies. All three starspot
distributions can explain the current trend. However, with such a small sample,
a firm conclusion can not be made at present. In the coming years when more
TESS M and K dwarf exoplanetary systems have been detected and characterised,
it will be possible to determine the dominant starspot distribution.


Abstract:
  In this paper, we propose a novel data augmentation strategy named
Cut-Thumbnail, that aims to improve the shape bias of the network. We reduce an
image to a certain size and replace the random region of the original image
with the reduced image. The generated image not only retains most of the
original image information but also has global information in the reduced
image. We call the reduced image as thumbnail. Furthermore, we find that the
idea of thumbnail can be perfectly integrated with Mixed Sample Data
Augmentation, so we put one image's thumbnail on another image while the ground
truth labels are also mixed, making great achievements on various computer
vision tasks. Extensive experiments show that Cut-Thumbnail works better than
state-of-the-art augmentation strategies across classification, fine-grained
image classification, and object detection. On ImageNet classification,
ResNet-50 architecture with our method achieves 79.21\% accuracy, which is more
than 2.8\% improvement on the baseline.


Abstract:
  This essay summarizes the efforts required to build a program of a unified,
low-dimension topology that allows characterizing all these flat space-times.
Since spatiotemporal manifolds are topological spaces equipped with metrics,
their properties are characterized by Clifford algebras in hypercomplex rings
associative with unity, so that Galileo's transformations are induced by a dual
number; the Lorentz transformations, by a perplexed number and the Euclid
transformations, by a complex number. This fact led us to establish an internal
automorphism in the ring of hybrid numbers that acts as a map of the manifolds
and induces the space-time metric based on the quality (characteristic) of the
associated hypercomplex unit. From this automorphism, we built hybrid
trigonometric functions, which we call Poincar\'e functions, which allowed us
to deduce general properties of space-time, hyperbolic, parabolic and
elliptical geometries and the groups SO (3), SO (4) and SO (1, 3). This
approach allows us to highlight the global properties of space-time, suggests
methods for geodynamic models and allows us to interpret anti-matter as matter
in a Euclidean space-time where the nature of time is imaginary.


Abstract:
  Global value chains (GVCs) are formed through value-added trade, and some
regions promote economic integration by concluding regional trade agreements to
promote these chains. However, there is no way to quantitatively assess the
scope and extent of economic integration involving various sectors in multiple
countries. In this study, we used the World Input--Output Database to create a
cross-border sector-wise trade in value-added network (international
value-added network (IVAN)) covering the period of 2000--2014 and evaluated
them using network science methods. By applying Infomap to the IVAN, we
confirmed for the first time the existence of two regional communities: Europe
and the Pacific Rim. Helmholtz--Hodge decomposition was used to decompose the
value flows within the region into potential and circular flows, and the annual
evolution of the potential and circular relationships between countries and
sectors was clarified. The circular flow component of the decomposition was
used to define an economic integration index, and findings confirmed that the
degree of economic integration in Europe declined sharply after the economic
crisis in 2009 to a level lower than that in the Pacific Rim. The European
economic integration index recovered in 2011 but again fell below that of the
Pacific Rim in 2013. Moreover, sectoral analysis showed that the economic
integration index captured the effect of Russian mineral resources, free
movement of labor in Europe, and international division of labor in the Pacific
Rim, especially in GVCs for the manufacture of motor vehicles and high-tech
products.


Abstract:
  Unitarity demands that the black-hole final state (what remains inside the
event horizon at complete evaporation) must be unique. Assuming a UV theory
with infinitely many fields, we propose that the uniqueness of the final state
can be achieved via a mechanism analogous to the quantum-mechanical description
of dissipation.


Abstract:
  \textit{Resolve} onboard the X-ray satellite XRISM is a cryogenic instrument
with an X-ray microcalorimeter in a Dewar. A lid partially transparent to
X-rays (called gate valve, or GV) is installed at the top of the Dewar along
the optical axis. Because observations will be made through the GV for the
first few months, the X-ray transmission calibration of the GV is crucial for
initial scientific outcomes. We present the results of our ground calibration
campaign of the GV, which is composed of a Be window and a stainless steel
mesh. For the stainless steel mesh, we measured its transmission using the
X-ray beamline at ISAS. For the Be window, we used synchrotron facilities to
measure the transmission and modeled the data with (i) photoelectric absorption
and incoherent scattering of Be, (ii) photoelectric absorption of contaminants,
and (iii) coherent scattering of Be changing at specific energies. We discuss
the physical interpretation of the transmission discontinuity caused by the
Bragg diffraction in poly-crystal Be, which we incorporated into our
transmission phenomenological model. We present the X-ray diffraction
measurement on the sample to support our interpretation. The measurements and
the constructed model meet the calibration requirements of the GV. We also
performed a spectral fitting of the Crab nebula observed with Hitomi SXS and
confirmed improvements of the model parameters.


Abstract:
  The uptake of machine learning (ML) approaches in the social and health
sciences has been rather slow, and research using ML for social and health
research questions remains fragmented. This may be due to the separate
development of research in the computational/data versus social and health
sciences as well as a lack of accessible overviews and adequate training in ML
techniques for non data science researchers. This paper provides a meta-mapping
of research questions in the social and health sciences to appropriate ML
approaches, by incorporating the necessary requirements to statistical analysis
in these disciplines. We map the established classification into description,
prediction, and causal inference to common research goals, such as estimating
prevalence of adverse health or social outcomes, predicting the risk of an
event, and identifying risk factors or causes of adverse outcomes. This
meta-mapping aims at overcoming disciplinary barriers and starting a fluid
dialogue between researchers from the social and health sciences and
methodologically trained researchers. Such mapping may also help to fully
exploit the benefits of ML while considering domain-specific aspects relevant
to the social and health sciences, and hopefully contribute to the acceleration
of the uptake of ML applications to advance both basic and applied social and
health sciences research.


Abstract:
  We develop a new three-dimensional time-dependent radiative transfer code,
TRINITY (Time-dependent Radiative transfer In Near-Infrared TomographY), for
in-vivo diffuse optical tomography (DOT). The simulation code is based on the
design of long radiation rays connecting boundaries of a computational domain,
which allows us to calculate light propagation with little numerical diffusion.
We parallelize the code with MPI using the domain decomposition technique and
confirm the high parallelization efficiency so that simulations with a spatial
resolution of $\sim 1~\rm mm$ can be performed in practical time. As a first
application, we study the light propagation for a pulse collimated within
$\theta \sim 15^\circ$ in a phantom, which is a uniform medium made of
polyurethane mimicking biological tissue. We show that the pulse spreads in all
forward directions over $\sim$ a few mm due to the multiple scattering process
of photons. Our simulations successfully reproduce the time-resolved signals
measured with eight detectors for the phantom. We also introduce the effects of
reflection and refraction at the boundary of medium with different refractive
index and demonstrate the faster propagation of photons in an air hole that is
an analogue for the respiratory tract.


Abstract:
  Topological phases of materials are characterized by topological invariants
that are conventionally calculated by different means according to the
dimension and symmetry class of the system. For topological materials described
by Dirac models, we introduce a wrapping number as a unified approach to obtain
the topological invariants in arbitrary dimensions and symmetry classes. Given
a unit vector that parametrizes the momentum-dependence of the Dirac model, the
wrapping number describes the degree of the map from the Brillouin zone torus
to the sphere formed by the unit vector that we call Dirac sphere. This method
is gauge-invariant and originates from the intrinsic features of the Dirac
model, and moreover places all known topological invariants, such as Chern
number, winding number, Pfaffian, etc, on equal footing.


Abstract:
  Recently monolayer jacutingaite (Pt2HgSe3), a naturally occurring exfoliable
mineral, discovered in Brazil in 2008, has been theoretically predicted as a
candidate quantum spin Hall system with a 0.5 eV band gap, while the bulk form
is one of only a few known dual-topological insulators which may host different
surface states protected by symmetries. In this work, we systematically
investigate both structure and electronic evolution of bulk Pt2HgSe3 under high
pressure up to 96 GPa. The nontrivial topology persists up to the structural
phase transition observed in the high-pressure regime. Interestingly, we found
that this phase transition is accompanied by the appearance of
superconductivity at around 55 GPa and the critical transition temperature Tc
increases with applied pressure. Our results demonstrate that Pt2HgSe3 with
nontrivial topology of electronic states displays new ground states upon
compression and raises potentials in application to the next-generation
spintronic devices.


Abstract:
  We make two observations on the motion of coupled particles in a periodic
potential. Coupled pendula, or the space-discretized sine-Gordon equation is an
example of this problem. Linearized spectrum of the synchronous motion turns
out to have a hidden asymptotic periodicity in its dependence on the energy;
this is the gist of the first observation. Our second observation is the
discovery of a special property of the purely sinusoidal potentials: the
linearization around the synchronous solution is equivalent to the classical
Lam\`e equation. As a consequence, {\it all but one instability zones of the
linearized equation collapse to a point for the one-harmonic potentials}. This
provides a new example where Lam\'e's finite zone potential arises in the
simplest possible setting.