Abstract:
  A new search method for large polarization kernels is proposed. The algorithm
produces a kernel with given partial distances by employing depth-first search
combined with some methods which reduce the search space. Using the proposed
method, we improved almost all existing lower bounds on the maximum rate of
polarization for kernels of size from 17 to 27. We also obtained kernels which
admit low complexity processing by the recently proposed recursive trellis
algorithm. Numerical results demonstrate the advantage of polar codes with the
proposed kernels compared with shortened polar codes and polar codes with small
kernels.


Abstract:
  Conventional breast cancer imaging techniques are nowadays based on the use
of ionising radiations or ultrasound waves for the inspection of breast areas.
Nevertheless, these conventional techniques present some drawbacks related to
patient safety, processing time and resolution issues. In this framework,
microwave imaging can represent a valid alternative or a complementary
technique compared to other conventional medical imaging modalities since it is
safe (using non-ionising radiations), relatively cheap and more comfortable
from patient point of view. Unfortunately, it is slow and computationally
expensive, which strongly limit its use in clinical scenarios. In this paper,
an artificial neural network for effective and almost real-time breast imaging
is proposed. First, a realistic breast-like phantom generator was developed for
training the network. Subsequently, numerical analyses have been conducted for
the optimisation and the performance evaluation of the approach. The results
seem very promising in terms of recovery performance as well as for the
computation burden.


Abstract:
  Realtime model learning proves challenging for complex dynamical systems,
such as drones flying in variable wind conditions. Machine learning technique
such as deep neural networks have high representation power but is often too
slow to update onboard. On the other hand, adaptive control relies on simple
linear parameter models can update as fast as the feedback control loop. We
propose an online composite adaptation method that treats outputs from a deep
neural network as a set of basis functions capable of representing different
wind conditions. To help with training, meta-learning techniques are used to
optimize the network output useful for adaptation. We validate our approach by
flying a drone in an open air wind tunnel under varying wind conditions and
along challenging trajectories. We compare the result with other adaptive
controller with different basis function sets and show improvement over
tracking and prediction errors.


Abstract:
  Large scale contextual representation models have significantly advanced NLP
in recent years, understanding the semantics of text to a degree never seen
before. However, they need to process large amounts of data to achieve
high-quality results. Joining and accessing all these data from multiple
sources can be extremely challenging due to privacy and regulatory reasons.
Federated Learning can solve these limitations by training models in a
distributed fashion, taking advantage of the hardware of the devices that
generate the data. We show the viability of training NLP models, specifically
Word2Vec, with the Federated Learning protocol. In particular, we focus on a
scenario in which a small number of organizations each hold a relatively large
corpus. The results show that neither the quality of the results nor the
convergence time in Federated Word2Vec deteriorates as compared to centralised
Word2Vec.


Abstract:
  The exponential rise of online social media has enabled the creation,
distribution, and consumption of information at an unprecedented rate. However,
it has also led to the burgeoning of various forms of online abuse. Increasing
cases of online antisemitism have become one of the major concerns because of
its socio-political consequences. Unlike other major forms of online abuse like
racism, sexism, etc., online antisemitism has not been studied much from a
machine learning perspective. To the best of our knowledge, we present the
first work in the direction of automated multimodal detection of online
antisemitism. The task poses multiple challenges that include extracting
signals across multiple modalities, contextual references, and handling
multiple aspects of antisemitism. Unfortunately, there does not exist any
publicly available benchmark corpus for this critical task. Hence, we collect
and label two datasets with 3,102 and 3,509 social media posts from Twitter and
Gab respectively. Further, we present a multimodal deep learning system that
detects the presence of antisemitic content and its specific antisemitism
category using text and images from posts. We perform an extensive set of
experiments on the two datasets to evaluate the efficacy of the proposed
system. Finally, we also present a qualitative analysis of our study.


Abstract:
  The extremely large magnetoresistance (XMR) observed in many topologically
nontrivial and trivial semimetals has attracted much attention in relation to
its underlying physical mechanism. In this paper, by combining the band
structure and Fermi surface (FS) calculations with the Hall resistivity and de
Haas-Van Alphen (dHvA) oscillation measurements, we studied the anisotropy of
magnetoresistance (MR) of ReO$_3$ with a simple cubic structure, an "ordinary"
nonmagnetic metal considered previously. We found that ReO$_3$ exhibits almost
all the characteristics of XMR semimetals: the nearly quadratic field
dependence of MR, a field-induced upturn in resistivity followed by a plateau
at low temperatures, high mobilities of charge carriers. It was found that for
magnetic field \emph{H} applied along the \emph{c} axis, the MR exhibits an
unsaturated \emph{H}$^{1.75}$ dependence, which was argued to arise from the
complete carrier compensation supported by the Hall resistivity measurements.
For \emph{H} applied along the direction of 15$^\circ$ relative to the \emph{c}
axis, an unsaturated \emph{H}$^{1.90}$ dependence of MR up to
9.43~$\times$~$10^3$$\%$ at 10~K and 9~T was observed, which was explained by
the existence of electron open orbits extending along the $k_{x}$ direction.
Two mechanisms responsible for XMR observed usually in the semimetals occur
also in the simple metal ReO$_3$ due to its peculiar FS (two closed electron
pockets and one open electron pocket), once again indicating that the details
of FS topology are a key factor for the observed XMR in materials.


Abstract:
  Previous studies have shown significant differences in the enhancement of the
star-formation rate (SFR) and the star-formation efficiency (SFE=SFR/M_mol)
between spiral-spiral and spiral-elliptical mergers. In order to shed light on
the physical mechanism of these differences, we present NOEMA observations of
the molecular gas distribution and kinematics (linear resolutions of ~ 2kpc) in
two representative close major-merger star-forming pairs: the spiral-elliptical
pair Arp142 and the spiral-spiral pair Arp238. The CO in Arp142 is widely
distributed over a highly distorted disk without any nuclear concentration, and
an off-centric ring-like structure is discovered in channel maps. The SFE
varies significantly within Arp142, with a starburst region (Region 1) near the
eastern tip of the distorted disk showing an SFE ~0.3 dex above the mean of the
control sample of isolated galaxies, and the SFE of the main disk (Region 4)
0.43 dex lower than the mean of the control sample. In contrast, the CO
emission in Arp238 is detected only in two compact sources at the galactic
centers. Compared to the control sample, Arp238-E shows an SFE enhancement of
more than 1 dex whereas Arp238-W has an enhancement of ~0.7 dex. We suggest
that the extended CO distribution and the large SFE variation in Arp142 are due
to an expanding large-scale ring triggered by a recent high-speed head-on
collision between the spiral galaxy and the elliptical galaxy, and the compact
CO sources with high SFEs in Arp238 are associated with nuclear starbursts
induced by gravitational tidal torques in a low-speed coplanar interaction.


Abstract:
  We propose a complexity measure of a neural network mapping function based on
the diversity of the set of tangent spaces from different inputs. Treating each
tangent space as a linear PAC concept we use an entropy-based measure of the
bundle of concepts in order to estimate the conceptual capacity of the network.
The theoretical maximal capacity of a ReLU network is equivalent to the number
of its neurons. In practice however, due to correlations between neuron
activities within the network, the actual capacity can be remarkably small,
even for very big networks. Empirical evaluations show that this new measure is
correlated with the complexity of the mapping function and thus the
generalisation capabilities of the corresponding network. It captures the
effective, as oppose to the theoretical, complexity of the network function. We
also showcase some uses of the proposed measure for analysis and comparison of
trained neural network models.


Abstract:
  We have further extended our compartmental model describing the spread of the
infection in Italy. The model is based on the assumption that the time
evolution of all of the observable quantities (number of people still positive
to the infection, hospitalized and fatalities cases, healed people, and total
number of people that has contracted the infection) depend on average
parameters, namely people diffusion coefficient, infection cross-section, and
population density. The model provides precious information on the tight
relationship between the variation of the reported infection cases and a well
defined observable physical quantity: the average number of people that lie
within the daily displacement area of any single person. The extension of the
model now includes self-consistent evaluation of the reproduction index, effect
of immunization due to vaccination, and potential impact of virus variants on
the dynamical evolution of the outbreak. The model fits very well the epidemic
data, and allows us to strictly relate the time evolution of the number of
hospitalized case and fatalities to the change of people mobility, vaccination
rate, and appearance of an initial concentration of people positives for new
variants of the virus.


Abstract:
  Recently discovered superconductivity in hole-doped nickelate
Nd$_{0.8}$Sr$_{0.2}$NiO$_2$ caught intensive attention in the field. An
immediate question is how to improve its superconducting properties. Guided by
the key characteristics of electronic structures of the cuprates and the
nickelates, we propose that nickel chalcogenides with a similar lattice
structure should be a promising family of materials. Using NdNiS$_2$ as an
example, we find this particular crystal structure a stable one, through
first-principle structural optimization and phonon calculation. We justify our
proposal by comparing with CaCuO$_2$ and NdNiO$_2$ the strength of the
charge-transfer characteristics and the trend in their low-energy many-body
effective Hamiltonians of doped hole carriers. These analysis indicates that
nickel chalcogenides host low-energy physics closer to that of the cuprates,
with stronger magnetic interaction than the nickelates, and thus deserve
further experimental exploration. Our proposal also opens up the possibility of
a wide range of parameter tuning through ligand substitution among
chalcogenides, to further improve superconducting properties.


Abstract:
  In this paper, we present a dataset of inter-language knowledge propagation
in Wikipedia. Covering the entire 309 language editions and 33M articles, the
dataset aims to track the full propagation history of Wikipedia concepts, and
allow follow up research on building predictive models of them. For this
purpose, we align all the Wikipedia articles in a language-agnostic manner
according to the concept they cover, which results in 13M propagation
instances. To the best of our knowledge, this dataset is the first to explore
the full inter-language propagation at a large scale. Together with the
dataset, a holistic overview of the propagation and key insights about the
underlying structural factors are provided to aid future research. For example,
we find that although long cascades are unusual, the propagation tends to
continue further once it reaches more than four language editions. We also find
that the size of language editions is associated with the speed of propagation.
We believe the dataset not only contributes to the prior literature on
Wikipedia growth but also enables new use cases such as edit recommendation for
addressing knowledge gaps, detection of disinformation, and cultural
relationship analysis.


Abstract:
  We investigate, within the framework of Quantum Field Theory in curved space,
the correlations across the horizon of a black hole in order to highlight the
pair creation mechanism at the origin of Hawking radiation. The analysis
concerns both acoustic black holes, formed by Bose-Einstein condensates, and
gravitational black holes. We find a striking disagreement between the two
cases. This is a consequence of the existence of a 'quantum atmosphere'
displaced from the horizon as locus of the origin of Hawking radiation together
with the presence of a central singularity in the gravitational case swallowing
everything is trapped inside the horizon.


Abstract:
  The existence of quasinormal modes (QNMs) for waves propagating on pure de
Sitter space has been called into question in several works. We definitively
prove the existence of quasinormal modes for massless and massive scalar fields
in all dimensions and for all scalar field masses, and present a simple method
for the explicit calculation of QNMs and the corresponding mode solutions. By
passing to coordinates which are regular at the cosmological horizon, we
demonstrate that certain QNMs only appear in the QNM expansion of the field
when the initial data do not vanish near the cosmological horizon. The key
objects in the argument are dual resonant states. These are distributional mode
solutions of the adjoint field equation satisfying a generalized incoming
condition at the horizon, and they characterize the amplitudes with which QNMs
contribute to the QNM expansion of the field.


Abstract:
  We analyze the properties of adversarial training for learning adversarially
robust halfspaces in the presence of agnostic label noise. Denoting
$\mathsf{OPT}_{p,r}$ as the best robust classification error achieved by a
halfspace that is robust to perturbations of $\ell_{p}$ balls of radius $r$, we
show that adversarial training on the standard binary cross-entropy loss yields
adversarially robust halfspaces up to (robust) classification error $\tilde
O(\sqrt{\mathsf{OPT}_{2,r}})$ for $p=2$, and $\tilde O(d^{1/4}
\sqrt{\mathsf{OPT}_{\infty, r}} + d^{1/2} \mathsf{OPT}_{\infty,r})$ when
$p=\infty$. Our results hold for distributions satisfying anti-concentration
properties enjoyed by log-concave isotropic distributions among others. We
additionally show that if one instead uses a nonconvex sigmoidal loss,
adversarial training yields halfspaces with an improved robust classification
error of $O(\mathsf{OPT}_{2,r})$ for $p=2$, and $O(d^{1/4}\mathsf{OPT}_{\infty,
r})$ when $p=\infty$. To the best of our knowledge, this is the first work to
show that adversarial training provably yields robust classifiers in the
presence of noise.


Abstract:
  Modern systems on a chip (SoCs) utilize heterogeneous architectures where
multiple IP cores have concurrent access to on-chip shared resources. In
security-critical applications, IP cores have different privilege levels for
accessing shared resources, which must be regulated by an access control
system. AKER is a design and verification framework for SoC access control.
AKER builds upon the Access Control Wrapper (ACW) -- a high performance and
easy-to-integrate hardware module that dynamically manages access to shared
resources. To build an SoC access control system, AKER distributes the ACWs
throughout the SoC, wrapping controller IP cores, and configuring the ACWs to
perform local access control. To ensure the access control system is
functioning correctly and securely, AKER provides a property-driven security
verification using MITRE common weakness enumerations. AKER verifies the SoC
access control at the IP level to ensure the absence of bugs in the
functionalities of the ACW module, at the firmware level to confirm the secure
operation of the ACW when integrated with a hardware root-of-trust (HRoT), and
at the system level to evaluate security threats due to the interactions among
shared resources. The performance, resource usage, and security of access
control systems implemented through AKER is experimentally evaluated on a
Xilinx UltraScale+ programmable SoC, it is integrated with the OpenTitan
hardware root-of-trust, and it is used to design an access control system for
the OpenPULP multicore architecture.


Abstract:
  The binary of the pulsar PSRB1259$-$63 and the Be star LS 2883 has been
observed at the 2010 and 2014 periastron passages in the near-infrared (NIR)
bands using the IRSF/SIRIUS and SIRPOL. The light curves in the J-,H-, and
Ks-bands are almost identical in these periastron passages. A flare starts no
later than 10 days before periastron and the maximum brightening of about 0.1
magnitude is observed 12--17 days after periastron. The rising part of the
light curve is steeper and reaches a peak slightly earlier in the Ks-band than
in the other bands, thus a characteristic track appears on the NIR
color-magnitude diagram. The time lag between the NIR light curves indicates
that the variation in the Be circumstellar disk first occurs in an outer
region. We propose that the initial rapid contraction followed by the gradual
expansion of the disk is evoked by the rapidly changing tidal torque around
periastron and the resultant change of the optically thick area causes the
observed NIR light curves.


Abstract:
  We develop a word mechanism applied in knot and link diagrams for the
illustration of a diagrammatic property. We also give a necessary condition for
determining incompressible and pairwise incompressible surfaces, that are
embedded in knot or link complements. Finally, we give a finiteness theorem and
an upper bound on the Euler characteristic of such surfaces.


Abstract:
  For solutions of ${\rm div}\,(DF(Du))=f$ we show that the quasiconformality
of $z\mapsto DF(z)$ is the key property leading to the Sobolev regularity of
the stress field $DF(Du)$, in relation with the summability of $f$. This class
of nonlinearities encodes in a general way the notion of uniform ellipticity
and encompasses all known instances where the stress field is known to be
Sobolev regular. We provide examples showing the optimality of this assumption
and present three applications: the study of the strong locality of the
operator ${\rm div}\,(DF(Du))$, a nonlinear Cordes condition for equations in
divergence form, and some partial results on the $C^{p'}$-conjecture.


Abstract:
  Numerical experiments are the primary method of studying the evolution of
circumbinary disks due to the strong nonlinearities involved. Many circumbinary
simulations also require the use of numerical mass sinks: source terms which
prevent gas from unphysically accumulating around the simulated point masses by
removing gas at a given rate. However, special care must be taken when drawing
physical conclusions from such simulations to ensure that results are not
biased by numerical artifacts. We demonstrate how the use of improved sink
methods reduces some of these potential biases in vertically-integrated
simulations of aspect ratio 0.1 accretion disks around binaries with mass
ratios between 0.1 and 1. Specifically, we show that sink terms that do not
reduce the angular momentum of gas relative to the accreting object: 1) reduce
the dependence on the sink rate of physical quantities such as the torque on
the binary, distribution of accretion between binary components, and evolution
of the binary semi-major axis; 2) reduce the degree to which the sink rate
affects the structure of the accretion disks around each binary component; 3)
alter the inferred variability of accretion onto the binary, making it more
regular in time. We also investigate other potential sources of systematic
error, such as the precise from of gravitational softening and previously
employed simplifications to the viscous stress tensor. Because of the strong
dependence of the orbital evolution of the binary on both the torque and
distribution of mass between binary components, the sink methods employed can
have a significant effect on the inferred orbital evolution of the binary.


Abstract:
  The problem of long-tailed recognition, where the number of examples per
class is highly unbalanced, is considered. While training with class-balanced
sampling has been shown effective for this problem, it is known to over-fit to
few-shot classes. It is hypothesized that this is due to the repeated sampling
of examples and can be addressed by feature space augmentation. A new feature
augmentation strategy, EMANATE, based on back-tracking of features across
epochs during training, is proposed. It is shown that, unlike class-balanced
sampling, this is an adversarial augmentation strategy. A new sampling
procedure, Breadcrumb, is then introduced to implement adversarial
class-balanced sampling without extra computation. Experiments on three popular
long-tailed recognition datasets show that Breadcrumb training produces
classifiers that outperform existing solutions to the problem.


Abstract:
  This paper concerns the homogenization of Schrodinger equations for
non-crystalline matter, that is to say the coefficients are given by the
composition of stationary functions with stochastic deformations. Two rigorous
results of so-called effective mass theorems in solid state physics are
obtained: a general abstract result (beyond the classical stationary ergodic
setting), and one for quasi-perfect materials (i.e. the disorder in the
non-crystalline matter is limited). The former relies on the double-scale
limits and the wave function is spanned on the Bloch basis. Therefore, we have
extended the Bloch Theory which was restrict until now to crystals (periodic
setting). The second result relies on the Perturbation Theory and a special
case of stochastic deformations, namely stochastic perturbation of the
identity.


Abstract:
  Green's functions with continuum spectra are a way of avoiding the strong
bounds on new physics from the absence of new narrow resonances in experimental
data. We model such a situation with a five-dimensional model with two branes
along the extra dimension $z$, the ultraviolet (UV) and the infrared (IR) one,
such that the metric between the UV and the IR brane is AdS$_5$, thus solving
the hierarchy problem, and beyond the IR brane the metric is that of a linear
dilaton model, which extends to $z\to\infty$. This simplified metric, which can
be considered as an approximation of a more complicated (and smooth) one, leads
to analytical Green's functions (with a mass gap $m_g \sim \textrm{TeV}$ and a
continuum for $s > m_g^2$) which could then be easily incorporated in the
experimental codes. The theory contains Standard Model gauge bosons in the bulk
with Neumann boundary conditions in the UV brane. To cope with electroweak
observables the theory is also endowed with an extra custodial gauge symmetry
in the bulk, with gauge bosons with Dirichlet boundary conditions in the UV
brane, and without zero (massless) modes. All Green's functions have analytical
expressions and exhibit poles in the second Riemann sheet of the complex plane
at $s=M_n^2-i M_n\Gamma_n$, denoting a discrete (infinite) set of broad
resonances with masses ($M_n$) and widths ($\Gamma_n$). For gauge bosons with
Neumann or Dirichlet boundary conditions, the masses and widths of resonances
satisfy the (approximate) equation $s= - 4 m_g^2 \mathcal W_n^2[\pm (1+i)/4]$,
where $\mathcal W_n$ is the $n$-th branch of the Lambert function.


Abstract:
  We advance a novel explanation of similarity-based interference effects in
subject-verb and reflexive pronoun agreement processing, grounded in surprisal
values computed from a pretrained large-scale Transformer model, GPT-2.
Specifically, we show that surprisal of the verb or reflexive pronoun predicts
facilitatory interference effects in ungrammatical sentences, where a
distractor noun that matches in number with the verb or pronoun leads to faster
reading times, despite the distractor not participating in the agreement
relation. We review the human empirical evidence for such effects, including
recent meta-analyses and large-scale studies. We also show that attention
patterns (indexed by entropy and other measures) in the Transformer show
patterns of diffuse attention in the presence of similar distractors,
consistent with cue-based retrieval models of parsing. But in contrast to these
models, the attentional cues and memory representations are learned entirely
from the simple self-supervised task of predicting the next word.


Abstract:
  We adapt the theory of anisotropic rubber elasticity to model cross-linked
double-twist liquid crystal cylinders such as exhibited in biological systems.
In mechanical extension we recover strain-straightening, but with an exact
expression in the small twist-angle limit. In compression, we observe
coexistence between high and low twist phases. Coexistence begins at small
compressive strains and is robustly observed for any anisotropic cross-links
and for general double-twist functions -- but disappears at large twist angles.
Within the coexistence region, significant compression of double-twist
cylinders is allowed at constant stress. Our results are qualitatively
consistent with previous observations of swollen or compressed collagen
fibrils, indicating that this phenomenon may be readily accessible
experimentally.


Abstract:
  We utilize 3-D fully convolutional neural networks (CNN) to segment gliomas
and its constituents from multimodal Magnetic Resonance Images (MRI). The
architecture uses dense connectivity patterns to reduce the number of weights
and residual connections and is initialized with weights obtained from training
this model with BraTS 2018 dataset. Hard mining is done during training to
train for the difficult cases of segmentation tasks by increasing the dice
similarity coefficient (DSC) threshold to choose the hard cases as epoch
increases. On the BraTS2020 validation data (n = 125), this architecture
achieved a tumor core, whole tumor, and active tumor dice of 0.744, 0.876,
0.714,respectively. On the test dataset, we get an increment in DSC of tumor
core and active tumor by approximately 7%. In terms of DSC, our network
performances on the BraTS 2020 test data are 0.775, 0.815, and 0.85 for
enhancing tumor, tumor core, and whole tumor, respectively. Overall survival of
a subject is determined using conventional machine learning from rediomics
features obtained using a generated segmentation mask. Our approach has
achieved 0.448 and 0.452 as the accuracy on the validation and test dataset.


Abstract:
  With the advances in the Internet of Things technology, electric vehicles
(EVs) have become easier to schedule in daily life, which is reshaping the
electric load curve. It is important to design efficient charging algorithms to
mitigate the negative impact of EV charging on the power grid. This paper
investigates an EV charging scheduling problem to reduce the charging cost
while shaving the peak charging load, under unknown future information about
EVs, such as arrival time, departure time, and charging demand. First, we
formulate an EV charging problem to minimize the electricity bill of the EV
fleet and study the EV charging problem in an online setting without knowing
future information. We develop an actor-critic learning-based smart charging
algorithm (SCA) to schedule the EV charging against the uncertainties in EV
charging behaviors. The SCA learns an optimal EV charging strategy with
continuous charging actions instead of discrete approximation of charging. We
further develop a more computationally efficient customized actor-critic
learning charging algorithm (CALC) by reducing the state dimension and thus
improving the computational efficiency. Finally, simulation results show that
our proposed SCA can reduce EVs' expected cost by 24.03%, 21.49%, 13.80%,
compared with the Eagerly Charging Algorithm, Online Charging Algorithm,
RL-based Adaptive Energy Management Algorithm, respectively. CALC is more
computationally efficient, and its performance is close to that of SCA with
only a gap of 5.56% in the cost.


Abstract:
  We give a brief overview how to couple general relativity to the Standard
Model of elementary particles, within the higher gauge theory framework,
suitable for the spinfoam quantization procedure. We begin by providing a short
review of all relevant mathematical concepts, most notably the idea of a
categorical ladder, 3-groups and generalized parallel transport. Then, we give
an explicit construction of the algebraic structure which describes the full
Standard Model coupled to Einstein-Cartan gravity, along with the classical
action, written in the form suitable for the spinfoam quantization procedure.
We emphasize the usefulness of the 3-group concept as a superior tool to
describe gauge symmetry, compared to an ordinary Lie group, as well as the
possibility to employ this new structure to classify matter fields and study
their spectrum, including the origin of fermion families.


Abstract:
  We consider a discrete Schr\"odinger operator $ H_\varepsilon=
-\varepsilon^2\Delta_\varepsilon + V_\varepsilon$ on $\ell^2(\varepsilon
\mathbb Z^d)$, where $\varepsilon>0$ is a small parameter and the potential
$V_\varepsilon$ is defined in terms of a multiwell energy landscape $f$ on
$\mathbb R^d$. This operator can be seen as a discrete analog of the
semiclassical Witten Laplacian of $\mathbb R^d$. It is unitarily equivalent to
the generator of a diffusion on $\varepsilon \mathbb Z^d$, satisfying the
detailed balance condition with respect to the Boltzmann weight
$\exp{(-f/\varepsilon)}$. These type of diffusions exhibit metastable behaviour
and arise in the context of disordered mean field models in Statistical
Mechanics. We analyze the bottom of the spectrum of $H_\varepsilon$ in the
semiclassical regime $\varepsilon\ll1$ and show that there is a one-to-one
correspondence between exponentially small eigenvalues and local minima of $f$.
Then we analyze in more detail the bistable case and compute the precise
asymptotic splitting between the two exponentially small eigenvalues. Through
this purely spectral-theoretical analysis of the discrete Witten Laplacian we
recover in a self-contained way the Eyring-Kramers formula for the metastable
tunneling time of the underlying stochastic process.


Abstract:
  Understanding the structure of loss landscape of deep neural networks
(DNNs)is obviously important. In this work, we prove an embedding principle
that the loss landscape of a DNN "contains" all the critical points of all the
narrower DNNs. More precisely, we propose a critical embedding such that any
critical point, e.g., local or global minima, of a narrower DNN can be embedded
to a critical point/hyperplane of the target DNN with higher degeneracy and
preserving the DNN output function. The embedding structure of critical points
is independent of loss function and training data, showing a stark difference
from other nonconvex problems such as protein-folding. Empirically, we find
that a wide DNN is often attracted by highly-degenerate critical points that
are embedded from narrow DNNs. The embedding principle provides an explanation
for the general easy optimization of wide DNNs and unravels a potential
implicit low-complexity regularization during the training. Overall, our work
provides a skeleton for the study of loss landscape of DNNs and its
implication, by which a more exact and comprehensive understanding can be
anticipated in the near


Abstract:
  We simulate a feasible experimental scenario that reconstruct the density
matrix of an electronic qubit via spin-polarized tunneling currents. Our system
is composed of a single level quantum dot, attached to ferromagnetic leads. By
properly rotating the leads magnetizations we can select the spin component to
be detected. Counting the number of tunneling events taken in four different
magnetic alignments we find the Stokes parameters, that are used as a train
data set into a supervised machine learning algorithm. The predicted model
allows the complete knowledge of the open system density matrix, including both
probability amplitudes and relative phases.


Abstract:
  We present a formal geometric framework for the study of adiabatic quantum
mechanics for arbitrary finite-dimensional non-degenerate Hamiltonians. This
framework generalizes earlier holonomy interpretations of the geometric phase
to non-cyclic states appearing for non-Hermitian Hamiltonians. We start with an
investigation of the space of non-degenerate operators on a finite-dimensional
state space. We then show how the energy bands of a Hamiltonian family form a
covering space. Likewise, we show that the eigenrays form a bundle, a
generalization of a principal bundle, which admits a natural connection
yielding the (generalized) geometric phase. This bundle provides in addition a
natural generalization of the quantum geometric tensor and derived tensors, and
we show how it can incorporate the non-geometric dynamical phase as well. We
finish by demonstrating how the bundle can be recast as a principal bundle, so
that both the geometric phases and the permutations of eigenstates can be
expressed simultaneously by means of standard holonomy theory.


Abstract:
  The present article is a part of the study of solvable Leibniz algebras with
a given nilradical. In this paper solvable Leibniz algebras, whose nilradicals
is naturally graded quasi-filiform algebra and the complemented space to the
nilradical has maximal dimension, are described up to isomorphism.


Abstract:
  A unified system integrating a compact object detector and a surrounding
environmental condition classifier for enhancing the robustness of object
detection scheme in advanced driver assistance systems (ADAS) is proposed in
this paper. ADAS are invented to improve traffic safety and effectiveness in
autonomous driving systems where object detection plays an extremely important
role. However, modern object detectors integrated in ADAS are still unstable
due to high latency and the variation of the environmental contexts in the
deployment phase. Our system is proposed to address the aforementioned
problems. The proposed system includes two main components: (1) a compact
one-stage object detector which is expected to be able to perform at a
comparable accuracy compared to state-of-the-art object detectors, and (2) an
environmental condition detector that helps to send a warning signal to the
cloud in case the self-driving car needs human actions due to the significance
of the situation. The empirical results prove the reliability and the
scalability of the proposed system to realistic scenarios.


Abstract:
  Autonomous vehicles face tremendous challenges while interacting with human
drivers in different kinds of scenarios. Developing control methods with safety
guarantees while performing interactions with uncertainty is an ongoing
research goal. In this paper, we present a real-time safe control framework
using bi-level optimization with Control Barrier Function (CBF) that enables an
autonomous ego vehicle to interact with human-driven cars in ramp merging
scenarios with a consistent safety guarantee. In order to explicitly address
motion uncertainty, we propose a novel extension of control barrier functions
to a probabilistic setting with provable chance-constrained safety and analyze
the feasibility of our control design. The formulated bi-level optimization
framework entails first choosing the ego vehicle's optimal driving style in
terms of safety and primary objective, and then minimally modifying a nominal
controller in the context of quadratic programming subject to the probabilistic
safety constraints. This allows for adaptation to different driving strategies
with a formally provable feasibility guarantee for the ego vehicle's safe
controller. Experimental results are provided to demonstrate the effectiveness
of our proposed approach.


Abstract:
  In this paper, we generalize Witten's non-abelian bosonization in $(1+1)$-D
to two and three spatial dimensions. Our theory applies to fermions with
relativistic dispersion. The bosonized theories are non-linear sigma models
with level-1 Wess-Zumino-Witten terms. We apply the bosonization results to the
$SU(2)$ gauge theory of the $\pi$-flux phase, critical spin liquids in 1,2,3
spatial dimensions, and twisted bilayer graphene.


Abstract:
  Vegetation, trees in particular, sequester carbon by absorbing carbon dioxide
from the atmosphere. However, the lack of efficient quantification methods of
carbon stored in trees renders it difficult to track the process. We present an
approach to estimate the carbon storage in trees based on fusing multi-spectral
aerial imagery and LiDAR data to identify tree coverage, geometric shape, and
tree species -- key attributes to carbon storage quantification. We demonstrate
that tree species information and their three-dimensional geometric shapes can
be estimated from aerial imagery in order to determine the tree's biomass.
Specifically, we estimate a total of $52,000$ tons of carbon sequestered in
trees for New York City's borough Manhattan.


Abstract:
  As collaborative learning allows joint training of a model using multiple
sources of data, the security problem has been a central concern. Malicious
users can upload poisoned data to prevent the model's convergence or inject
hidden backdoors. The so-called backdoor attacks are especially difficult to
detect since the model behaves normally on standard test data but gives wrong
outputs when triggered by certain backdoor keys. Although Byzantine-tolerant
training algorithms provide convergence guarantee, provable defense against
backdoor attacks remains largely unsolved. Methods based on randomized
smoothing can only correct a small number of corrupted pixels or labels;
methods based on subset aggregation cause a severe drop in classification
accuracy due to low data utilization. We propose a novel framework that
generalizes existing subset aggregation methods. The framework shows that the
subset selection process, a deciding factor for subset aggregation methods, can
be viewed as a code design problem. We derive the theoretical bound of data
utilization ratio and provide optimal code construction. Experiments on non-IID
versions of MNIST and CIFAR-10 show that our method with optimal codes
significantly outperforms baselines using non-overlapping partition and random
selection. Additionally, integration with existing coding theory results shows
that special codes can track the location of the attackers. Such capability
provides new countermeasures to backdoor attacks.


Abstract:
  In this work, we extend the analyses devoted to Newtonian viscous fluids
previously reported by Ribe [Physical Review E 68, 036305 (2003)], by
investigating shear thickening (dilatant) and shear thinning (pseudoplastic)
effects on the development of folding instabilities in non-Newtonian viscous
sheets of which viscosity is given by a power-law constitutive equation. Such
instabilities are trigged by compression stresses acting on viscous sheets that
leave a channel at a very small initial velocity, fall, and then hit a solid
surface or a fluid substrate. Our study is conducted through a mixed approach
combining direct numerical simulations, energy budget analyses, scaling laws,
and experiments. The numerical results are based on an adaptive variational
multi-scale method for multiphase flows, while Carpobol gel sheets are
considered for the conducted experiments. Two folding regimes are observed: (1)
the viscous regime; and (2) the gravitational one. Interestingly, only the
latter is affected by shear thinning/thickening manifestations within the
material. In short, when gravity is balanced by viscous forces along the
non-Newtonian viscous sheet, both the folding amplitude and the folding
frequency are given by a power-law function of the sheet slenderness, the
Galileo number (the ratio of the gravitational stress to the viscous one), and
the flow behaviour index. Highly shear thickening materials develop large
amplitude (and low frequency) instabilities, which, in contrast, tend to be
suppressed by shear thinning effects, and eventually cease. Lastly,
nonNewtonian effects on folding onset/cessation are also carefully explored. As
a result, non-Newtonian folding onset and cessation criteria are presented.


Abstract:
  Expressive reading, considered the defining attribute of oral reading
fluency, comprises the prosodic realization of phrasing and prominence. In the
context of evaluating oral reading, it helps to establish the speaker's
comprehension of the text. We consider a labeled dataset of children's reading
recordings for the speaker-independent detection of prominent words using
acoustic-prosodic and lexico-syntactic features. A previous well-tuned random
forest ensemble predictor is replaced by an RNN sequence classifier to exploit
potential context dependency across the longer utterance. Further, deep
learning is applied to obtain word-level features from low-level acoustic
contours of fundamental frequency, intensity and spectral shape in an
end-to-end fashion. Performance comparisons are presented across the different
feature types and across different feature learning architectures for prominent
word prediction to draw insights wherever possible.


Abstract:
  Although deep learning models have driven state-of-the-art performance on a
wide array of tasks, they are prone to spurious correlations that should not be
learned as predictive clues. To mitigate this problem, we propose a
causality-based training framework to reduce the spurious correlations caused
by observed confounders. We give theoretical analysis on the underlying general
Structural Causal Model (SCM) and propose to perform Maximum Likelihood
Estimation (MLE) on the interventional distribution instead of the
observational distribution, namely Counterfactual Maximum Likelihood Estimation
(CMLE). As the interventional distribution, in general, is hidden from the
observational data, we then derive two different upper bounds of the expected
negative log-likelihood and propose two general algorithms, Implicit CMLE and
Explicit CMLE, for causal predictions of deep learning models using
observational data. We conduct experiments on both simulated data and two
real-world tasks: Natural Language Inference (NLI) and Image Captioning. The
results show that CMLE methods outperform the regular MLE method in terms of
out-of-domain generalization performance and reducing spurious correlations,
while maintaining comparable performance on the regular evaluations.


Abstract:
  Low excitation radio galaxies (LERGs) are weakly accreting active galactic
nuclei (AGN) believed to be fuelled by radiatively inefficient accretion
processes. Despite this, recent works have shown evidence for ionized and
neutral hydrogen gas outflows in these galaxies. To investigate the potential
drivers of such outflows we select a sample of 802 LERGs using the Best &
Heckman (2012) catalogue of radio galaxies. By modelling the [O III] $\lambda
5007$ profile in Sloan Digital Sky Survey spectra of a sample of 802 LERGs, we
determine that the ionized outflows are present in $\sim 1.5\%$ of the
population. Using $1.4~\text{GHz}$ imaging from the Faint Images of the Radio
Sky at Twenty Centimeters survey we analyze the radio morphology of LERGs with
outflows and find these to be consistent with the parent LERG population.
However, we note that unlike the majority of the LERG population, those LERGs
showing outflows have Eddington scaled accretion rates close to $1\%$. This is
indicative that ionized outflows in LERGs are driven by the radiation pressure
from the accretion disk of the AGN rather than the radio jets. We report
specific star formation rates in the range of $10^{-12} < \text{sSFR} <
10^{-9}~\text{yr}^{-1}$. Moreover, we observe higher mass outflow rates of
$7-150~M_{\odot}~\text{yr}^{-1}$ for these LERGs than luminous quasars for a
given bolometric luminosity, which could possibly be due to the radio source in
LERGs boosting the mass-loading. This scenario could indicate that these
outflows could potentially drive feedback in LERGs.


Abstract:
  We systematically investigate the ground-state properties of self-bound
droplets of quasi-two-dimensional binary Bose gases by using the Gaussian state
theory. We find that quantum droplets consists two macroscopic squeezed phases
and a macroscopic coherent phase. We map out the phase diagram and determine
all phase boundaries via both numerical and nearly analytical methods. In
particular, we find three easily accessible signatures for the quantum phases
and the stablization mechanism of the self-bound droplets by precisely
measuring their radial size. Our studies indicate that binary droplets
represent an ideal platform for in-depth investigations of the quantum nature
of the droplet state.


Abstract:
  We utilize a recently proposed cubic nilpotent superfield to realize
inflation in supergravity with the minimal degrees of freedom: the inflaton,
graviton, and massive gravitino. As an advantage, the resultant model is free
from the catastrophic production of gravitinos due to its vanishing propagation
speed. However, the model suffers from the standard gravitino problem, and its
viability depends on the mass spectrum and the thermal history of the universe.


Abstract:
  Feedback-driven winds from star formation or active galactic nuclei might be
a relevant channel for the abrupt quenching star formation in massive galaxies.
However, both observations and simulations support the idea that these
processes are non-conflictingly co-evolving and self-regulating. Furthermore,
evidence of disruptive events that are capable of fast quenching is rare, and
constraints on their statistical prevalence are lacking. Here we present a
massive starburst galaxy at z=1.4 which is ejecting $46 \pm 13$\% of its
molecular gas mass at a startling rate of $\gtrsim 10,000$ M$_{\odot}{\rm
yr}^{-1}$. A broad component that is red-shifted from the galaxy emission is
detected in four (low- and high-J) CO and [CI] transitions and in the ionized
phase, which ensures a robust estimate of the expelled gas mass. The implied
statistics suggest that similar events are potentially a major star-formation
quenching channel. However, our observations provide compelling evidence that
this is not a feedback-driven wind, but rather material from a merger that has
been probably tidally ejected. This finding challenges some literature studies
in which the role of feedback-driven winds might be overstated.


Abstract:
  Objective: This study aims to develop an end-to-end natural language
processing pipeline for triage and diagnosis of COVID-19 from patient-authored
social media posts, in order to provide researchers and other interested
parties with additional information on the symptoms, severity and prevalence of
the disease. Materials and Methods: The text processing pipeline first extracts
COVID-19 symptoms and related concepts such as severity, duration, negations,
and body parts from patients posts using conditional random fields. An
unsupervised rule-based algorithm is then applied to establish relations
between concepts in the next step of the pipeline. The extracted concepts and
relations are subsequently used to construct two different vector
representations of each post. These vectors are applied separately to build
support vector machine learning models to triage patients into three categories
and diagnose them for COVID-19. Results: We report that macro- and
micro-averaged F1 scores in the range of 71-96% and 61-87%, respectively, for
the triage and diagnosis of COVID-19, when the models are trained on human
labelled data. Our experimental results indicate that similar performance can
be achieved when the models are trained using predicted labels from concept
extraction and rule-based classifiers, thus yielding end-to-end machine
learning. Also, we highlight important features uncovered by our diagnostic
machine learning models and compare them with the most frequent symptoms
revealed in another COVID-19 dataset. In particular, we found that the most
important features are not always the most frequent ones.


Abstract:
  For quasiperiodic Schr\"odinger operators with one-frequency analytic
potentials, from dynamical systems side, it has been proved that the
corresponding quasiperiodic Schr\"odinger cocycle is either rotations reducible
or has positive Lyapunov exponent for all irrational frequency and almost every
energy. From spectral theory side, the "Schr\"odinger conjecture" and the
"Last's intersection spectrum conjecture" have been verified. The proofs of
above results crucially depend on the analyticity of the potentials. People are
curious about if the analyticity is essential for those problems, see open
problems by Fayad-Krikorian and Jitomirskaya-Mar. In this paper, we prove the
above mentioned results for ultra-differentiable potentials.


Abstract:
  Recent advances on Vision Transformer (ViT) and its improved variants have
shown that self-attention-based networks surpass traditional Convolutional
Neural Networks (CNNs) in most vision tasks. However, existing ViTs focus on
the standard accuracy and computation cost, lacking the investigation of the
intrinsic influence on model robustness and generalization. In this work, we
conduct systematic evaluation on components of ViTs in terms of their impact on
robustness to adversarial examples, common corruptions and distribution shifts.
We find some components can be harmful to robustness. By using and combining
robust components as building blocks of ViTs, we propose Robust Vision
Transformer (RVT), which is a new vision transformer and has superior
performance with strong robustness. We further propose two new plug-and-play
techniques called position-aware attention scaling and patch-wise augmentation
to augment our RVT, which we abbreviate as RVT*. The experimental results on
ImageNet and six robustness benchmarks show the advanced robustness and
generalization ability of RVT compared with previous ViTs and state-of-the-art
CNNs. Furthermore, RVT-S* also achieves Top-1 rank on multiple robustness
leaderboards including ImageNet-C and ImageNet-Sketch. The code will be
available at \url{https://git.io/Jswdk}.


Abstract:
  We prove in this paper the Ax-Schanuel conjecture for all admissible
variations of mixed Hodge structures.


Abstract:
  In this paper, we propose a quadruple-UPA architecture for realizing the
beamforming with wide coverage in terahertz (THz) communication. Considering
the severe path loss suffered in the THz wave propagation, the pilot signals of
conventional channel estimation strategies may not be effectively detected. In
sight of this, we propose a fast 3D beam training strategy to jointly realize
the angle estimation and the beamforming. Specifically, we first develop a
novel hierarchical codebook that predefines some codewords (including narrow
beams and wide beams) stage by stage. The narrow beams are designed as the
beamforming solution candidates, whereas the wide beams are designed for
reducing the training complexity. Then, we propose a 3D beam training procedure
to find the optimal beamforming solution, i.e., the optimal narrow-beam pair,
in a fast manner. Numerical results are presented to validate the effectiveness
of the proposed scheme.


Abstract:
  Conditional Random Field (CRF) based neural models are among the most
performant methods for solving sequence labeling problems. Despite its great
success, CRF has the shortcoming of occasionally generating illegal sequences
of tags, e.g. sequences containing an "I-" tag immediately after an "O" tag,
which is forbidden by the underlying BIO tagging scheme. In this work, we
propose Masked Conditional Random Field (MCRF), an easy to implement variant of
CRF that impose restrictions on candidate paths during both training and
decoding phases. We show that the proposed method thoroughly resolves this
issue and brings consistent improvement over existing CRF-based models with
near zero additional cost.


Abstract:
  The presence of snow and ice on runway surfaces reduces the available
tire-pavement friction needed for retardation and directional control and
causes potential economic and safety threats for the aviation industry during
the winter seasons. To activate appropriate safety procedures, pilots need
accurate and timely information on the actual runway surface conditions. In
this study, XGBoost is used to create a combined runway assessment system,
which includes a classifcation model to predict slippery conditions and a
regression model to predict the level of slipperiness. The models are trained
on weather data and data from runway reports. The runway surface conditions are
represented by the tire-pavement friction coefficient, which is estimated from
flight sensor data from landing aircrafts. To evaluate the performance of the
models, they are compared to several state-of-the-art runway assessment
methods. The XGBoost models identify slippery runway conditions with a ROC AUC
of 0.95, predict the friction coefficient with a MAE of 0.0254, and outperforms
all the previous methods. The results show the strong abilities of machine
learning methods to model complex, physical phenomena with a good accuracy when
domain knowledge is used in the variable extraction. The XGBoost models are
combined with SHAP (SHapley Additive exPlanations) approximations to provide a
comprehensible decision support system for airport operators and pilots, which
can contribute to safer and more economic operations of airport runways.


Abstract:
  In this work, we investigate the problem of privacy-preserving supervisory
control against an external passive intruder via co-synthesis of dynamic mask,
edit function, and supervisor for opacity enforcement and requirement
satisfaction. We attempt to achieve the following goals: 1) the system secret
cannot be inferred by the intruder, i.e., opacity of secrets against the
intruder, and the existence of the dynamic mask and the edit function should
not be discovered by the intruder, i.e., covertness of dynamic mask and edit
function against the intruder; 2) the closed-loop system behaviors should
satisfy some safety and nonblockingness requirement. We assume the intruder can
eavesdrop both the sensing information generated by the sensors and the control
commands issued to the actuators, and we refer to such an intruder as a
sensor-actuator eavesdropping intruder. Our approach is to model the
co-synthesis problem as a distributed supervisor synthesis problem in the
Ramadge-Wonham supervisory control framework, and we propose an incremental
synthesis heuristic to incrementally synthesize a dynamic mask, an edit
function, and a supervisor, which consists of three steps: 1) we first
synthesize an ensemble ME of dynamic mask and edit function to ensure the
opacity and the covertness against a sensor eavesdropping but command
non-eavesdropping intruder, and marker-reachability; 2) we then decompose ME
into a dynamic mask and an edit function by using a constraint-based approach,
with the help of a Boolean satisfiability (SAT) solver; 3) finally, we
synthesize a supervisor such that opacity and covertness can be ensured against
the sensor-actuator eavesdropping intruder, and at the same time safety and
nonblockingness requirement can be ensured. The effectiveness of our approach
is illustrated on an example about the enforcement of location privacy for an
autonomous vehicle.


Abstract:
  We study single-field inflationary models with steep step-like features in
the potential that lead to the temporary violation of the slow-roll conditions
during the evolution of the inflaton. These features enhance the power spectrum
of the curvature perturbations by several orders of magnitude at certain scales
and also produce prominent oscillatory patterns. We study analytically and
numerically the inflationary dynamics. We describe quantitatively the size of
the enhancement, as well as the profile of the oscillations, which are shaped
by the number and position of the features in the potential. The induced tensor
power spectrum inherits the distinctive oscillatory profile of the curvature
spectrum and is potentially detectable by near-future space interferometers.
The enhancement of the power specrtum by step-like features, though
significant, may be insufficient to trigger the production of a sizeable number
of primordial black holes if radiation dominates the energy density of the
early universe. However, it can result in sufficient black hole production if
the universe is dominated by non-relativistic matter. For the latter scenario,
we find that deviations from the standard monochromatic profile of the mass
spectrum of primordial black holes are possible because of the multiple-peak
structure of the curvature power spectrum.


Abstract:
  In this paper we prove two matrix weighted norm inequalities for the
commutator of a fractional integral operator and multiplication by a matrix
symbol. More precisely, we extend the recent results of the second author,
Pott, and Treil on two matrix weighted norm inequalities for commutators of
Calderon-Zygmund operators and multiplication by a matrix symbol to the
fractional integral operator setting. In particular, we completely extend the
fractional Bloom theory of Holmes, Rahm, and Spencer to the two matrix weighted
setting with a matrix


Abstract:
  We study d-minimal expansions of ordered fields, and dense pairs thereof. We
also consider other generalizations of o-minimality.


Abstract:
  In this paper we introduce Stein's square function associated with bilinear
Bochner-Riesz means and develop a systematic study of its $L^p$ boundedness
properties. We also discuss applications of bilinear Bochner-Riesz square
function in the context of bilinear fractional Schr\"{o}dinger multipliers,
generalized bilinear spherical maximal function and more general bilinear
multipliers defined on $\mathbb{R}^{2n}$ of the form $(\xi,\eta)\rightarrow
m\left(|(\xi,\eta)|^2\right)$.


Abstract:
  For a given graph $G$, the metric and edge metric dimensions of $G$,
$\dim(G)$ and ${\rm edim}(G)$, are the cardinalities of the smallest possible
subsets of vertices in $V(G)$ such that they uniquely identify the vertices and
the edges of $G$, respectively, by means of distances. It is already known that
metric and edge metric dimensions are not in general comparable. Infinite
families of graphs with pendant vertices in which the edge metric dimension is
smaller than the metric dimension are already known. In this article, we
construct a 2-connected graph $G$ such that $\dim(G)=a$ and ${\rm edim}(G)=b$
for every pair of integers $a,b$, where $4\le b<a$. For this we use
subdivisions of complete graphs, whose metric dimension is in some cases
smaller than the edge metric dimension. Along the way, we present an upper
bound for the metric and edge metric dimensions of subdivision graphs under
some special conditions.


Abstract:
  We establish long-time existence for a projected Sobolev gradient flow of
generalized integral Menger curvature in the Hilbert case, and provide
$C^{1,1}$-bounds in time for the solution that only depend on the initial
curve. The self-avoidance property of integral Menger curvature guarantees that
the knot class of the initial curve is preserved under the flow, and the
projection ensures that each curve along the flow is parametrized with the same
speed as the initial configuration. Finally, we describe how to simulate this
flow numerically with substantially higher efficiency than in the corresponding
numerical $L^2$ gradient descent or other optimization methods.


Abstract:
  A novel structure of sheet-pile groin consists of row piles and tied sheet
has been introduced to resist the impact of tidal bore in recent years.
However, the dynamic responses of the structure under tidal bore have not been
carefully studied before. This paper proposed an analytical solution for the
dynamic responses of the sheet-pile groin subjected to a transient lateral
excitation. In order to take the flexural and longitudinal vibrations into
consideration, the piles and the sheet are modeled by the Timoshenko beam (TB)
theory and a one-dimensional rod, respectively. The soil around the piles is
simulated by the dynamic Winkler foundation model. The excitation is considered
by a lateral concentrated point load acting on the front pile. On this basis,
the governing equations are constructed in time domain and solved in frequency
domain, while the time-domain responses are finally obtained using the discrete
Inverse Fourier Transform. The accuracy of the presented analytical solution is
validated by comparing with the results obtained from the FEM simulation and a
model test. The effects of properties of the sheet, the piles and the soil on
the dynamic responses are investigated through a comprehensive parametric
study.


Abstract:
  It has often been conjectured that the effectiveness of line drawings can be
explained by the similarity of edge images to line drawings. This paper
presents several problems with explaining line drawing perception in terms of
edges, and how the recently-proposed Realism Hypothesis of Hertzmann (2020)
resolves these problems. There is nonetheless existing evidence that edges are
often the best features for predicting where people draw lines; this paper
describes how the Realism Hypothesis can explain this evidence.


Abstract:
  The generation of high-intensity optical fields from harmonic-wave photons,
interacting via a cross-phase modulation with dark solitons both propagating in
a Kerr nonlinear medium, is examined. The focus is on a pump consisting of
time-entangled dark-soliton patterns, forming a periodic waveguide along the
path of the harmonic-wave probe. It is shown that an increase of the strength
of cross-phase modulation respective to the self-phase modulation, favors
soliton-mode proliferation in the bound-state spectrum of the trapped
harmonic-wave probe. The induced soliton modes, which display the structures of
periodic soliton lattices, are not just rich in numbers, they also form a great
diversity of population of soliton crystals with a high degree of degeneracy.


Abstract:
  Here, we report both ac and dc magnetization, thermodynamic and electric
properties of hexagonal Ba$_3$NiIr$_2$O$_9$. The Ni$^{2+}$ (spin-1) forms
layered triangular-lattice and interacts antiferromagnetically while Ir$^{5+}$
is believed to act as magnetic link between the layers. This complex magnetic
interaction results in magnetic frustration leading to a spin-glass transition
at $T_f$ $\sim$ 8.5 K. The observed magnetic relaxation and aging effect also
confirms the nonequilibrium ground state. The system further shows large
exchange bias which is tunable with cooling field. Below the Curie-Weiss
temperature $\theta_{CW}$ ($\sim$ -29 K), the magnetic specific heat $C_m$
displays a broad hump and at low temperature follows $C_m = \gamma T^\alpha$
dependence where both $\gamma$ and $\alpha$ show dependence on temperature and
magnetic field. A sign change in magnetoresistace is observed which is due to
an interplay among magnetic moment, field and spin-orbit coupling.


Abstract:
  We study Alexandrov curvature in the tropical projective torus with respect
to the tropical metric. Alexandrov curvature is a generalization of classical
Riemannian sectional curvature to more general metric spaces; it is determined
by a comparison of triangles in an arbitrary metric space to corresponding
triangles in Euclidean space. In the polyhedral setting of tropical geometry,
triangles are a combinatorial object, which adds a combinatorial dimension to
our analysis. We study the effect that the triangle types have on curvature,
and what can be revealed about these types from the curvature. We find that
positive, negative, zero, and undefined Alexandrov curvature can exist
concurrently in tropical settings and that there is a tight connection between
triangle combinatorial type and curvature. Our results are established both by
proof and numerical experiments, and shed light on the intricate geometry of
the tropical projective torus.
  This paper is dedicated to Bernd Sturmfels on the occasion of his 60th
birthday.


Abstract:
  I propose a new type of common envelope jets supernova (CEJSN) events where
instead of a single neutron star (NS; or a black hole; BH) a tight binary
system of a NS and a main sequence star enters a common envelope evolution
(CEE) with a red supergiant. The NS and the main sequence star of the tight
binary system merge inside the red supergiant envelope and enter a CEE of their
own. The NS accretes some mass through an accretion disk and launches jets that
explodes the main sequence star. I estimate that the two jets that the NS
launches at this phase carry an energy of ~10^{52} erg, about the same order of
magnitude as the energy that the jets will carry when the NS or its BH remnant
will enter the core in a later phase. For that, I term the entire event a
double CEJSN. The outcome of the double CEJSN is a very long, months to years,
and very energetic event, a total energy of ~10^{52} - 10^{53} erg, that will
be observationally classified as a peculiar super-energetic event. I crudely
estimate that new transient surveys should detect about one CEJSN event from a
triple star system per year.


Abstract:
  We consider a generalised oriented site percolation model (GOSP) on $\mathbb
Z^d$ with arbitrary neighbourhood. The key additional difficulties as compared
to standard oriented percolation (OP) are the lack of symmetry and, in two
dimensions, of planarity. We establish that, despite these deficiencies, in the
supercritical regime GOSP behaves qualitatively like OP.


Abstract:
  Gaussian processes are widely employed as versatile modelling and predictive
tools in spatial statistics, functional data analysis, computer modelling and
diverse applications of machine learning. They have been widely studied over
Euclidean spaces, where they are specified using covariance functions or
covariograms for modelling complex dependencies. There is a growing literature
on Gaussian processes over Riemannian manifolds in order to develop richer and
more flexible inferential frameworks. While Gaussian processes have been
extensively studied for asymptotic inference on Euclidean spaces using
well-defined (positive definite) covariograms, such results are relatively
sparse on Riemannian manifolds. We undertake analogous developments for
Gaussian processes constructed over compact Riemannian manifolds. Building upon
the recently introduced Mat\'ern covariograms on a compact Riemannian manifold,
we employ formal notions and conditions for the equivalence of two Mat\'ern
Gaussian random measures on compact manifolds to derive the microergodic
parameters and formally establish the consistency of their maximum likelihood
estimates as well as asymptotic optimality of the best linear unbiased
predictor. The circle and sphere are studied as two specific examples of
compact Riemannian manifolds with numerical experiments to illustrate and
corroborate the theory.


Abstract:
  Two cavities in different size with the photonic crystal structure have been
developed for axion searches. In the cavities, the dispersion relation in the
photonic crystal is utilised, and so was named to "DRiPC cavities". The size of
the smaller one is 100 mm x 100 mm x 10 mm, where 16 cylindrical metal poles
with a diameter of 4 mm are introduced in a 4x4 grid at 20 mm intervals. In
this study, the grid interval in x direction in the small size cavity, Lx, was
changed to investigate resonance frequency, Q-value, and electric field profile
at each Lx. The lowest three frequencies have been compared with the ones
simulated by the finite element method to be found in excellent agreement. The
lowest frequency mode could be tuned from 5.10 GHz (Lx = 25.0 mm) to 6.72 GHz
(13.9 mm), centering on 5.87 GHz at Lx = 20 mm. This wide range tunability,
27.7%, was suitable for a search with a modest Q-value. By examining the
electric field distributions with the bead pull method, the lowest frequency
mode at Lx = 16.0 - 25.0 mm were TM010-like. This mode was also obtained in a
larger size cavity (180 mm x 180 mm x 20 mm x 2) with the same photonic crystal
structure. These results led us to conclude a DRiPC cavity has the noble
features for future axion search experiments.


Abstract:
  If dark matter has mass lower than around 1 GeV, it will not impart enough
energy to cause detectable nuclear recoils in many direct-detection
experiments. However, if dark matter is upscattered to high energy by
collisions with cosmic rays, it may be detectable in both direct-detection
experiments and neutrino experiments. We report the results of a dedicated
search for boosted dark matter upscattered by cosmic rays using the PROSPECT
reactor antineutrino experiment. We show that such a flux of upscattered dark
matter would display characteristic diurnal sidereal modulation, and use this
to set new experimental constraints on sub-GeV dark matter exhibiting large
interaction cross-sections.


Abstract:
  Polyelectrolyte-surfactant complexes (PESCs) have long been employed as
oil-in-water (o/w) emulsions stabilizers, but never in the structure of
colloidal complex coacervates providing a Pickering effect. The complexed state
of PESCs could make them unsuitable o/w Pickering emulsifiers, which instead
require a balance between colloidal structure and stability, amphiphilicity and
wettability. Here we hypothesize that PESCs coacervates are efficient Pickering
stabilizers. Instead of classical surfactants, we employ sophorolipid (SL)
biosurfactants, atypical anionic/neutral stimuli-responsive biosurfactants.
Despite their tunable charge and mild amphiphilic character, they can be used
in combination with cationic/neutral polyelectrolytes (chitosan, CHL, or
poly-L-lysine, PLL) to form PESC coacervates for the development of biobased,
but also pH-switchable, Pickering emulsions.Aqueous solutions of SL-CHL (or
SL-PLL) complex coacervates are emulsified with dodecane. Confocal laser
scanning microscopy (CLSM) and scanning electron microscopy under cryogenic
conditions (cryo-SEM) demonstrate the Pickering effect, while optical
microscopy and oscillatory rheology respectively assess the emulsion formation
and relative viscoelastic properties.Both SL-CHL and SL-PLL PESCs stabilize o/w
emulsions up to $\Phi$oil of 0.7 only in the pH region of complex coacervation
(6 < pH < 9): outside this range, phase separation occurs. Rheology shows a
typical solid-like response and mechanical recovery upon applying large
deformations. CLSM and cryo-SEM highlight a colloidal structure, associated to
the complex coacervates, of the oil/water interface and suggest a Pickering
effect. These findings demonstrate the Pickering effect from PESC coacervates
and the possibility to use biobased and biocompatible components, with
application potential in cosmetics, food science, or oil recovery.


Abstract:
  Autonomous trading robots have been studied in artificial intelligence area
for quite some time. Many AI techniques have been tested for building
autonomous agents able to trade financial assets. These initiatives include
traditional neural networks, fuzzy logic, reinforcement learning but also more
recent approaches like deep neural networks and deep reinforcement learning.
Many developers claim to be successful in creating robots with great
performance when simulating execution with historical price series, so called
backtesting. However, when these robots are used in real markets frequently
they present poor performance in terms of risks and return. In this paper, we
propose an open source framework, called mt5se, that helps the development,
backtesting, live testing and real operation of autonomous traders. We built
and tested several traders using mt5se. The results indicate that it may help
the development of better traders. Furthermore, we discuss the simple
architecture that is used in many studies and propose an alternative multiagent
architecture. Such architecture separates two main concerns for portfolio
manager (PM) : price prediction and capital allocation. More than achieve a
high accuracy, a PM should increase profits when it is right and reduce loss
when it is wrong. Furthermore, price prediction is highly dependent of asset's
nature and history, while capital allocation is dependent only on analyst's
prediction performance and assets' correlation. Finally, we discuss some
promising technologies in the area.


Abstract:
  Reinforcement learning (RL) is always the preferred embodiment to construct
the control strategy of complex tasks, like asymmetric assembly tasks. However,
the convergence speed of reinforcement learning severely restricts its
practical application. In this paper, the convergence is first accelerated by
combining RL and compliance control. Then a completely innovative progressive
extension of action dimension (PEAD) mechanism is proposed to optimize the
convergence of RL algorithms. The PEAD method is verified in DDPG and PPO. The
results demonstrate the PEAD method will enhance the data-efficiency and
time-efficiency of RL algorithms as well as increase the stable reward, which
provides more potential for the application of RL.


Abstract:
  We numerically investigate the propagation of plane gravitational waves in
the form of an initial boundary value problem with de Sitter initial data. The
full non-linear Einstein equations with positive cosmological constant
$\lambda$ are written in the Friedrich-Nagy gauge which yields a wellposed
system. The propagation of a single wave and the collision of two with colinear
polarization are studied and contrasted with their Minkowskian analogues.
Unlike with $\lambda=0$, critical behaviours are found with $\lambda>0$ and are
based on the relationship between the wave profile and $\lambda$. We find that
choosing boundary data close to one of these bifurcations results in a "false"
steady state which violates the constraints. Simulations containing
(approximate) impulsive wave profiles are run and general features are
discussed. Analytic results of Woodard and Tsamis, which describe how
gravitational waves could affect an expansion rate at an initial instance of
time, are explored and generalized to the entire space-time. Finally we put
forward boundary conditions that, at least locally, slow down the expansion
considerably for a time.


Abstract:
  Atmospheric neutrinos are one of the most relevant natural neutrino sources
that can be exploited to infer properties about cosmic rays and neutrino
oscillations. The Jiangmen Underground Neutrino Observatory (JUNO) experiment,
a 20 kton liquid scintillator detector with excellent energy resolution is
currently under construction in China. JUNO will be able to detect several
atmospheric neutrinos per day given the large volume. A study on the JUNO
detection and reconstruction capabilities of atmospheric $\nu_e$ and $\nu_\mu$
fluxes is presented in this paper. In this study, a sample of atmospheric
neutrino Monte Carlo events has been generated, starting from theoretical
models, and then processed by the detector simulation. The excellent timing
resolution of the 3'' PMT light detection system of JUNO detector and the much
higher light yield for scintillation over Cherenkov allow to measure the time
structure of the scintillation light with very high precision. Since $\nu_e$
and $\nu_\mu$ interactions produce a slightly different light pattern, the
different time evolution of light allows to discriminate the flavor of primary
neutrinos. A probabilistic unfolding method has been used, in order to infer
the primary neutrino energy spectrum from the detector experimental
observables. The simulated spectrum has been reconstructed between 100 MeV and
10 GeV, showing a great potential of the detector in the atmospheric low energy
region.


Abstract:
  Recommender systems (RSs) have emerged as very useful tools to help customers
with their decision-making process, find items of their interest, and alleviate
the information overload problem. There are two different lines of approaches
in RSs: (1) general recommenders with the main goal of discovering long-term
users' preferences, and (2) sequential recommenders with the main focus of
capturing short-term users' preferences in a session of user-item interaction
(here, a session refers to a record of purchasing multiple items in one
shopping event). While considering short-term users' preferences may satisfy
their current needs and interests, long-term users' preferences provide users
with the items that they may interact with, eventually. In this thesis, we
first focus on improving the performance of general RSs. Most of the existing
general RSs tend to exploit the users' rating patterns on common items to
detect similar users. The data sparsity problem (i.e. the lack of available
information) is one of the major challenges for the current general RSs, and
they may fail to have any recommendations when there are no common items of
interest among users. We call this problem data sparsity with no feedback on
common items (DSW-n-FCI). To overcome this problem, we propose a
personality-based RS in which similar users are identified based on the
similarity of their personality traits.


Abstract:
  This paper provides a general framework for Stein's density method for
multivariate continuous distributions. The approach associates to any
probability density function a canonical operator and Stein class, as well as
an infinite collection of operators and classes which we call standardizations.
These in turn spawn an entire family of Stein identities and characterizations
for any continuous distribution on $\mathbb{R}^d$, among which we highlight
those based on the score function and the Stein kernel. A feature of these
operators is that they do not depend on normalizing constants. A new definition
of Stein kernel is introduced and examined; integral formulas are obtained
through a connection with mass transport, as well as ready-to-use explicit
formulas for elliptical distributions. The flexibility of the kernels is used
to compare in Stein discrepancy (and therefore 2-Wasserstein distance) between
two normal distributions, Student and normal distributions, as well as two
normal-gamma distributions. Upper and lower bounds on the 1-Wasserstein
distance between continuous distributions are provided, and computed for a
variety of examples: comparison between different normal distributions
(improving on existing bounds in some regimes), posterior distributions with
different priors in a Bayesian setting (including logistic regression), centred
Azzalini-Dalla Valle distributions. Finally the notion of weak Stein equation
and weak Stein factors is introduced, and new bounds are obtained for Lipschitz
test functions if the distribution admits a Poincar\'e constant, which we use
to compare in 1-Wasserstein distance between different copulas on the unit
square.


Abstract:
  Graph kernels are often used in bioinformatics and network applications to
measure the similarity between graphs; therefore, they may be used to construct
efficient graph classifiers. Many graph kernels have been developed thus far,
but to the best of our knowledge there is no existing graph kernel that
considers all subgraphs to measure similarity. We propose a novel graph kernel
that applies a quantum computer to measure the graph similarity taking all
subgraphs into account by fully exploiting the power of quantum superposition
to encode every subgraph into a feature. For the construction of the quantum
kernel, we develop an efficient protocol that removes the index information of
subgraphs encoded in the quantum state. We also prove that the quantum computer
requires less query complexity to construct the feature vector than the
classical sampler used to approximate the same vector. A detailed numerical
simulation of a bioinformatics problem is presented to demonstrate that, in
many cases, the proposed quantum kernel achieves better classification accuracy
than existing graph kernels.


Abstract:
  Matrix valued asymmetric truncated Toeplitz operators are compressions of
multiplication operators acting between two possibly different model spaces. In
this paper, we characterize matrix valued asymmetric truncated Toeplitz
operators by using compressed shifts.


Abstract:
  Let $R$ be a commutative ring with identity. In this paper, we introduce the
concept of quasi $J$-ideal which is a generalization of $J$-ideal. A proper
ideal of $R$ is called a quasi $J$-ideal if its radical is a $J$-ideal. Many
characterizations of quasi $J$-ideals in some special rings are obtained. We
characterize rings in which every proper ideal is quasi $J$-ideal. Further, as
a generalization of presimplifiable rings, we define the notion of quasi
presimplifiable rings. We call a ring $R$ a quasi presimplifiable ring if
whenever $a,b\in R$ and $a=ab$, then either $a$ is a nilpotent or $b$ is a
unit. It is shown that a proper ideal $I$ that is contained in the Jacobson
radical is a quasi $J$-ideal (resp. $J$-ideal) if and only if $R/I$ is a quasi
presimplifiable (resp. presimplifiable) ring.


Abstract:
  Rydberg atoms in optical tweezer arrays provide a playground for
nonequilibrium quantum many-body physics. The PXP model describes the dynamics
of such systems in the strongly interacting Rydberg blockade regime and notably
exhibits weakly nonergodic dynamics due to quantum many-body scars. Here, we
study the PXP model in a strong staggered external field, which has been
proposed to manifest quasiparticle confinement in light of a mapping to a
lattice gauge theory. We characterize this confining regime using both
numerical exact diagonalization and perturbation theory around the strong-field
limit. In addition to the expected emergent symmetry generated by the staggered
field, we find a second emergent symmetry that is special to the PXP model. The
interplay between these emergent symmetries and the Rydberg blockade constraint
dramatically slows down the system's dynamics beyond naive expectations. We
devise a nested Schrieffer-Wolff perturbation theory to properly account for
the new emergent symmetry and show that this treatment is essential to
understand the numerically observed relaxation time scales. We also discuss
connections to Hilbert space fragmentation and trace the origin of the new
emergent symmetry to a "nearly-$SU(2)$" algebra discovered in the context of
many-body scarring.


Abstract:
  New transitions in neutron rich $^{100}$Y have been identified in a
$^9$Be+$^{238}$U experiment with mass- and Z- gates to provide full fragment
identification. These transitions and high spin levels of $^{100}$Y have been
investigated by analyzing the high statistics $\gamma$-$\gamma$-$\gamma$ and
$\gamma$-$\gamma$-$\gamma$-$\gamma$ coincidence data from the spontaneous
fission of $^{252}$Cf at the Gammasphere detector array. Two new bands, 14 new
levels and 23 new transitions have been identified. The $K^{\pi}=4^+$ new band
decaying to an 1s isomeric state is assigned to be the high-$K$
Gallagher-Moszkowski (GM) partner of the known $K^{\pi}=1^+$ band, with the
$\pi 5/2[522] \otimes \nu 3/2[411]$ configuration. This 4$^+$ band is also
proposed to be the pseudo spin partner of the new $K^{\pi}=5^+$ band with a
5$^{+}$ $\pi 5/2[422] \otimes \nu 5/2[413]$ configuration, to form a $\pi
5/2[422] \otimes \nu [312$ $5/2,3/2]$ neutron pseudospin doublet. Constrained
triaxial covariant density functional theory and quantal particle rotor model
calculations have been applied to interpret the band structure and available
electromagnetic transition probabilities and are found in good agreement with
experimental values.


Abstract:
  Zero-shot learning (ZSL) aims to transfer knowledge from seen classes to
semantically related unseen classes, which are absent during training. The
promising strategies for ZSL are to synthesize visual features of unseen
classes conditioned on semantic side information and to incorporate
meta-learning to eliminate the model's inherent bias towards seen classes.
While existing meta generative approaches pursue a common model shared across
task distributions, we aim to construct a generative network adaptive to task
characteristics. To this end, we propose an Attribute-Modulated generAtive
meta-model for Zero-shot learning (AMAZ). Our model consists of an
attribute-aware modulation network, an attribute-augmented generative network,
and an attribute-weighted classifier. Given unseen classes, the modulation
network adaptively modulates the generator by applying task-specific
transformations so that the generative network can adapt to highly diverse
tasks. The weighted classifier utilizes the data quality to enhance the
training procedure, further improving the model performance. Our empirical
evaluations on four widely-used benchmarks show that AMAZ outperforms
state-of-the-art methods by 3.8% and 3.1% in ZSL and generalized ZSL settings,
respectively, demonstrating the superiority of our method. Our experiments on a
zero-shot image retrieval task show AMAZ's ability to synthesize instances that
portray real visual characteristics.


Abstract:
  Echo-enabled harmonic generation free-electron lasers (EEHG FELs) are
promising candidates to produce fully coherent soft x-ray pulses by virtue of
efficient high harmonic frequency up-conversion from UV lasers. The ultimate
spectral limit of EEHG, however, remains unclear, because of the broadening and
distortions induced in the output spectrum by residual broadband energy
modulations in the electron beam. We present a mathematical description of the
impact of incoherent (broadband) energy modulations on the bunching spectrum
produced by the microbunching instability through both the accelerator and the
EEHG line. The model is in agreement with a systematic experimental
characterization of the FERMI EEHG FEL in the photon energy range $130-210$ eV.
We find that amplification of electron beam energy distortions primarily in the
EEHG dispersive sections explains an observed reduction of the FEL spectral
brightness that is proportional to the EEHG harmonic number. Local maxima of
the FEL spectral brightness and of the spectral stability are found for a
suitable balance of the dispersive sections' strength and the first seed laser
pulse energy. Such characterization provides a benchmark for user experiments
and future EEHG implementations designed to reach shorter wavelengths.


Abstract:
  Clinical diagnosis, which aims to assign diagnosis codes for a patient based
on the clinical note, plays an essential role in clinical decision-making.
Considering that manual diagnosis could be error-prone and time-consuming, many
intelligent approaches based on clinical text mining have been proposed to
perform automatic diagnosis. However, these methods may not achieve
satisfactory results due to the following challenges. First, most of the
diagnosis codes are rare, and the distribution is extremely unbalanced. Second,
existing methods are challenging to capture the correlation between diagnosis
codes. Third, the lengthy clinical note leads to the excessive dispersion of
key information related to codes. To tackle these challenges, we propose a
novel framework to combine the inheritance-guided hierarchical assignment and
co-occurrence graph propagation for clinical automatic diagnosis. Specifically,
we propose a hierarchical joint prediction strategy to address the challenge of
unbalanced codes distribution. Then, we utilize graph convolutional neural
networks to obtain the correlation and semantic representations of medical
ontology. Furthermore, we introduce multi attention mechanisms to extract
crucial information. Finally, extensive experiments on MIMIC-III dataset
clearly validate the effectiveness of our method.


Abstract:
  Several speculative visions are conjecturing on what 6G services will be able
to offer at the horizon of 2030. Nevertheless, the 6G design process is at its
preliminary stages. The reality today is that hardware, technologies and new
materials required to effectively meet the unprecedented performance targets
required for future 6G services and network operation, have not been designed,
tested or even do not exist yet. Today, a solid vision on the cost-benefit
trade-offs of machine learning and artificial intelligence support for 6G
network and services operation optimization is missing. This includes the
possible support from hardware efficiency, operation effectiveness and, the
immeasurable cost due to data acquisition-transfer-processing. The contribution
of this paper is three-fold. This is the first paper deriving crucial 6G key
performance indicators on hardware and technology design. Second, we present a
new hardware technologies design methodology conceived to enable the effective
software-hardware components integration required to meet the challenging
performance envisioned for future 6G networks. Third, we suggest a paradigm
shift towards goal-oriented and semantic communications, in which a totally new
opportunity of joint design of hardware, artificial intelligence and effective
communication is offered. The proposed vision is consolidated by our recent
results on hardware, technology and machine learning performance.


Abstract:
  In order to consider j-wise relative r-primality conditions that do not
necessarily require all j-tuples of elements in a Dedekind domain to be
relatively r-prime, we define the notion of j-wise relative r-primality with
respect to a fixed j-uniform hypergraph H. This allows us to provide further
generalisations to several results on natural densities not only for a ring of
algebraic integers, but also for the ring F_q[x].


Abstract:
  Humans leverage the dynamics of the environment and their own bodies to
accomplish challenging tasks such as grasping an object while walking past it
or pushing off a wall to turn a corner. Such tasks often involve switching
dynamics as the robot makes and breaks contact. Learning these dynamics is a
challenging problem and prone to model inaccuracies, especially near contact
regions. In this work, we present a framework for learning composite dynamical
behaviors from expert demonstrations. We learn a switching linear dynamical
model with contacts encoded in switching conditions as a close approximation of
our system dynamics. We then use discrete-time LQR as the differentiable policy
class for data-efficient learning of control to develop a control strategy that
operates over multiple dynamical modes and takes into account discontinuities
due to contact. In addition to predicting interactions with the environment,
our policy effectively reacts to inaccurate predictions such as unanticipated
contacts. Through simulation and real world experiments, we demonstrate
generalization of learned behaviors to different scenarios and robustness to
model inaccuracies during execution.


Abstract:
  Molecular dynamics facilitates the simulation of a complex system to be
analyzed at molecular and atomic levels. Simulations can last a long period of
time, even months. Due to this cause the graphics processing units (GPUs) and
multi-core systems are used as solutions to overcome this impediment. The
current paper describes a comparison done between these two kinds of systems.
The first system used implies the graphics processing unit, respectively CUDA
with the OpenMM molecular dynamics package and OpenCL that allows the kernels
to run on the GPU. This simulation is done on a new thermostat which mixes the
Berendsen thermostat with the Langevin dynamics. The second comprises the
molecular dynamics simulation and energy minimization package GROMACS which is
based on a parallelization through MPI (Message Passing Interface) on
multi-core systems. The second simulation uses another new thermostat algorithm
related respectively, dissipative particle dynamics - isotropic type (DPD-ISO).
Both thermostats are innovative, based on a new theory developed by us. Results
show that parallelization on multi-core systems has a performance up to 33
times greater than the one performed on the graphics processing unit. In both
cases temperature of the system was maintained close to the one taken as
reference. For the simulation using the CUDA GPU, the faster runtime was
obtained when the number of processors was equal to four, the simulation speed
being 3.67 times faster compared to the case of only one processor.


Abstract:
  We are proposing a novel fabrication method for single crystal diamond
scanning probes, exploiting the method of Faraday cage angled etching (FCAE).
Electron-beam lithography (EBL) and inductively coupled plasma (ICP) etching as
state-of-the-art method is often limiting the achievable diamond structure
geometries in aspect ratio and complexity. This poses challenges, e.g., for
realizing nitrogen vacancy (NV) centre magnetometry scanning probes. Combining
a planar design with FCAE offers several advantages over the established
fabrication technology of nano-opto-mechanical diamond devices. Here, we report
on the direct comparison of both approaches and present first proof-of-concept
FCAE-prototypes for scanning probe applications.


Abstract:
  Polycyclic Aromatic Hydrocarbons (PAHs) have long been invoked in the study
of interstellar and protostellar sources, but the unambiguous identification of
any individual PAH has proven elusive until very recently. As a result, the
formation mechanisms for this important class of molecules remain poorly
constrained. Here we report the first interstellar detection of a pure
hydrocarbon PAH, indene (C$_9$H$_8$), as part of the GBT Observations of TMC-1:
Hunting for Aromatic Molecules (GOTHAM) survey. This detection provides a new
avenue for chemical inquiry, complementing the existing detections of
CN-functionalized aromatic molecules. From fitting the GOTHAM observations,
indene is found to be the most abundant organic ring detected in TMC-1 to date.
And from astrochemical modeling with NAUTILUS, the observed abundance is
greater than the model's prediction by several orders of magnitude suggesting
that current formation pathways in astrochemical models are incomplete. The
detection of indene in relatively high abundance implies related species such
as cyanoindene, cyclopentadiene, toluene, and styrene may be detectable in dark
clouds.


Abstract:
  We examine the use of time series data, derived from Electric Cell-substrate
Impedance Sensing (ECIS), to differentiate between standard mammalian cell
cultures and those infected with a mycoplasma organism. We perform
feature-based classification, extracting interpretable features from the ECIS
time courses. We can achieve high classification accuracy using only two
features, which depend on the cell line under examination. Initial results also
show the existence of experimental variation between plates and suggest types
of features that may prove more robust to such variation. Our paper is the
first to perform a broad examination of ECIS time course features in the
context of detecting contamination, and to describe and suggest possibilities
for ameliorating plate-to-plate variation.


Abstract:
  We investigated microscopic lattice states in the donor-acceptor ionic Mott
insulator, TTF-BA, by $^{79}$Br-NQR spectroscopy to explore cross-correlated
fluctuations between spin, charge and lattice. A ferroelectric transition with
lattice dimerization is captured by a NQR line splitting with the critical
exponent $\beta$ of 0.40, as expected in the 3D Ising universality class, and a
peak formation in the spin-lattice relaxation rate $T_1^{-1}$ at the transition
temperature, $T_\mathrm{c}$, of 53 K. Notably, $T_1^{-1}$ does not obey the
conventional $T^2$ law expected for the Raman process of phonons even far above
$T_\mathrm{c}$, indicating the emergence of extraordinary lattice fluctuations.
They are very probably associated with polar fluctuations in the paraelectric
and paramagnetic phase of TTF-BA and explain the previous observation of the
anomalously suppressed paramagnetic spin susceptibility, which was conjectured
to be due to the local spin-singlet pairing prior to the nonmagnetic
ferroelectric order [K. Sunami $et$ $al$., Phys. Rev. Res. 2, 043333 (2020)].


Abstract:
  We launched a community platform for collecting the ATC speech world-wide in
the ATCO2 project. Filtering out unseen non-English speech is one of the main
components in the data processing pipeline. The proposed English Language
Detection (ELD) system is based on the embeddings from Bayesian subspace
multinomial model. It is trained on the word confusion network from an ASR
system. It is robust, easy to train, and light weighted. We achieved 0.0439
equal-error-rate (EER), a 50% relative reduction as compared to the
state-of-the-art acoustic ELD system based on x-vectors, in the in-domain
scenario. Further, we achieved an EER of 0.1352, a 33% relative reduction as
compared to the acoustic ELD, in the unseen language (out-of-domain) condition.
We plan to publish the evaluation dataset from the ATCO2 project.


Abstract:
  Active aging technologies are increasingly designed to support an active
lifestyle. However, the way in which they are designed can raise different
barriers to acceptance of and use by older adults. Their designers can adopt a
negative stereotype of aging. Thorough understanding of user requirements is
central to this problem. This paper investigates user requirements for
technologies that encourage an active lifestyle and provide older people with
the means to self-manage their physical, mental, and emotional health. This
requires consideration of the person and the sociotechnical context of use. We
describe our work in collecting and analyzing older adults' requirements for a
technology which enables an active lifestyle. The main contribution of the
paper is a model of user requirements for inclusive technology for older
people.