Abstract:
  The optomechanical character of molecules was discovered by Raman about one
century ago. Today, molecules are promising contenders for high-performance
quantum optomechanical platforms because their small size and large
energy-level separations make them intrinsically robust against thermal
agitations. Moreover, the precision and throughput of chemical synthesis can
ensure a viable route to quantum technological applications. The challenge,
however, is that the coupling of molecular vibrations to environmental phonons
limits their coherence to picosecond time scales. Here, we improve the
optomechanical quality of a molecule by several orders of magnitude through
phononic engineering of its surrounding. By dressing a molecule with long-lived
high-frequency phonon modes of its nanoscopic environment, we achieve storage
and retrieval of photons at millisecond time scales and allow for the emergence
of single-photon strong coupling in optomechanics. Our strategy can be extended
to the realization of molecular optomechanical networks.


Abstract:
  In this paper we propose $\epsilon$-Consistent Mixup ($\epsilon$mu).
$\epsilon$mu is a data-based structural regularization technique that combines
Mixup's linear interpolation with consistency regularization in the Mixup
direction, by compelling a simple adaptive tradeoff between the two. This
learnable combination of consistency and interpolation induces a more flexible
structure on the evolution of the response across the feature space and is
shown to improve semi-supervised classification accuracy on the SVHN and
CIFAR10 benchmark datasets, yielding the largest gains in the most challenging
low label-availability scenarios. Empirical studies comparing $\epsilon$mu and
Mixup are presented and provide insight into the mechanisms behind
$\epsilon$mu's effectiveness. In particular, $\epsilon$mu is found to produce
more accurate synthetic labels and more confident predictions than Mixup.


Abstract:
  We present a novel phase locking scheme for the coherent combination of beam
arrays in the filled aperture configuration. Employing a phase dithering
mechanism for the different beams similar to LOCSET, dithering frequencies for
sequential combination steps are reused. By applying an additional phase
alternating scheme, this allows to use standard synchronized multichannel
lock-in electronics for phase locking a large number of channels even when the
frequency bandwidth of the employed phase actuators is limited.


Abstract:
  Short text is a popular avenue of sharing feedback, opinions and reviews on
social media, e-commerce platforms, etc. Many companies need to extract
meaningful information (which may include thematic content as well as semantic
polarity) out of such short texts to understand users' behaviour. However,
obtaining high quality sentiment-associated and human interpretable themes
still remains a challenge for short texts. In this paper we develop ELJST, an
embedding enhanced generative joint sentiment-topic model that can discover
more coherent and diverse topics from short texts. It uses Markov Random Field
Regularizer that can be seen as a generalisation of skip-gram based models.
Further, it can leverage higher-order semantic information appearing in word
embedding, such as self-attention weights in graphical models. Our results show
an average improvement of 10% in topic coherence and 5% in topic
diversification over baselines. Finally, ELJST helps understand users'
behaviour at more granular levels which can be explained. All these can bring
significant values to the service and healthcare industries often dealing with
customers.


Abstract:
  Using $980~\rm fb^{-1}$ of data on and around the $\Upsilon(nS)(n=1,2,3,4,5)$
resonances collected with the Belle detector at the KEKB asymmetric-energy
$e^+e^-$ collider, the two-photon process $\gamma\gamma\to \gamma\psi(2S)$ is
studied from $\sqrt{s} = 3.7$ to $4.2~{\rm GeV}$ for the first time. Evidence
is found for a structure in the invariant mass distribution of $\gamma\psi(2S)$
at $M_1 = 3921.3\pm 2.4 \pm 1.6~{\rm MeV}/c^2$ with a width of $\Gamma_1 =
0.0\pm 5.3 \pm 2.0~{\rm MeV}$ and a significance of $4.0\sigma$ including
systematic uncertainties, and another structure is seen at $M_2 = 4014.4\pm 4.1
\pm 0.5~{\rm MeV}/c^2$ with a width of $\Gamma_2 = 6\pm 16 \pm 6~{\rm MeV}$ and
a global significance of $2.8\sigma$ including the look-elsewhere effect, if
the mass spectrum is parametrized with the incoherent sum of two Breit-Wigner
functions. The upper limits of the widths are determined to be $\Gamma_1^{\rm
UL} = 11.5~{\rm MeV}$ and $\Gamma_2^{\rm UL} = 39.3~{\rm MeV}$ at 90\%
confidence level. The production rates are determined to be
$\Gamma_{\gamma\gamma}{\cal B}(R_1\to\gamma\psi(2S)) = 8.2\pm 2.3\pm 0.9~{\rm
eV}$ assuming $(J^{PC}, |\lambda|) =(0^{++}, 0)$ and $1.6\pm 0.5\pm 0.2~{\rm
eV}$ with $(2^{++}, 2)$ for the first structure and $\Gamma_{\gamma\gamma}{\cal
B}(R_2\to\gamma\psi(2S)) = 5.3\pm 2.7\pm 2.5~{\rm eV}$ with $(0^{++}, 0)$ and
$1.1\pm 0.5\pm 0.5~{\rm eV}$ with $(2^{++}, 2)$ for the second one. Here, the
first errors are statistical and the second systematic.


Abstract:
  The influence of ligands on the low frequency vibration of different
thicknesses cadmium selenide colloidal nanoplatelets is investigated using
resonant low frequency Raman scattering. The strong vibration frequency shifts
induced by ligand modifications as well as the sharp spectral linewidths make
low frequency Raman scattering a tool of choice to follow ligand exchange as
well as the nano-mechanical properties of the NPLs, as evidenced by a
carboxylate to thiolate exchange study. Apart from their molecular weight, the
nature of the ligands, such as the sulfur to metal bond of thiols, induces a
modification of the NPLs as a whole, increasing the thickness by one monolayer.
Moreover, as the weight of the ligands increases, the discrepancy between the
massload model and the experimental measurements increase. These effects are
all the more important when the number of layers is small and can only be
explained by a modification of the longitudinal sound velocity. This
modification takes its origin in a change of lattice structure of the NPLs,
that reflects on its elastic properties. These nanobalances are finally used to
characterize ligands affinity with the surface using binary thiols mixtures,
illustrating the potential of low frequency Raman scattering to finely
characterize nanocrystals surfaces.


Abstract:
  Motivated by entropic optimal transport, we investigate an extended notion of
solution to the parabolic equation $( \partial_t + b\cdot \nabla + \Delta _{
a}/2 +V)g
  =0$ with a final boundary condition. It is well-known that the viscosity
solution $g$ of this PDE is represented by the Feynman-Kac formula when the
drift $b$, the diffusion matrix $a$ and the scalar potential $V$ are regular
enough and not growing too fast. In this article, $b$ and $V$ are not assumed
to be regular and their growth is controlled by a finite entropy condition,
allowing for instance $V$ to belong to some Kato class. We show that the
Feynman-Kac formula represents a solution, in an extended sense, to the
parabolic equation. This notion of solution is trajectorial and expressed with
the semimartingale extension of the Markov generator $ b\cdot \nabla + \Delta
_{ a}/2.$ Our probabilistic approach relies on stochastic derivatives,
semimartingales, Girsanov's theorem and the Hamilton-Jacobi-Bellman equation
satisfied by $\log g$.


Abstract:
  In continuous-variable quantum information processing, quantum error
correction of Gaussian errors requires simultaneous estimation of both
quadrature components of displacements on phase space. However, quadrature
operators $x$ and $p$ are non-commutative conjugate observables, whose
simultaneous measurement is prohibited by the uncertainty principle.
Gottesman-Kitaev-Preskill (GKP) error correction deals with this problem using
complex non-Gaussian states called GKP states. On the other hand, simultaneous
estimation of displacement using experimentally feasible non-Gaussian states
has not been well studied. In this paper, we consider a multi-parameter
estimation problem of displacements assuming an isotropic Gaussian prior
distribution and allowing post-selection of measurement outcomes. We derive a
lower bound for the estimation error when only Gaussian operations are used,
and show that even simple non-Gaussian states such as single-photon states can
beat this bound. Based on Ghosh's bound, we also obtain a lower bound for the
estimation error when the maximum photon number of the input state is given.
Our results reveal the role of non-Gaussianity in the estimation of
displacements, and pave the way toward the error correction of Gaussian errors
using experimentally feasible non-Gaussian states.


Abstract:
  In this paper we discuss two canonical transformations that turn St\"{a}ckel
separable Hamiltonians of Benenti type into polynomial form: transformation to
Vi\`ete coordinates and transformation to Newton coordinates. Transformation to
Newton coordinates has been applied to these systems only very recently and in
this paper we present a new proof that this transformation indeed leads to
polynomial form of St\"{a}ckel Hamiltonians of Benenti type. Moreover we
present all geometric ingredients of these Hamiltonians in both Vi\`ete and
Newton coordinates.


Abstract:
  In this article we obtained the harmonic oscillator solution for quaternionic
quantum mechanics ($\mathbbm{H}$QM) in the real Hilbert space, both in the
analytic method and in the algebraic method. The quaternionic solutions have
many additional possibilities if compared to complex quantum mechanics
($\mathbbm{C}$QM), and thus there are many possible applications to these
results in future research.


Abstract:
  Recently unpolarized and polarized $J/\psi \,(\Upsilon)$ production at the
Electron-Ion Collider (EIC) has been proposed as a new way to extract two
poorly known color-octet NRQCD long-distance matrix elements:
$\langle0\vert{\cal O}_{8}^{J/\psi}(^{1}S_{0})\vert0\rangle$ and
$\langle0\vert{\cal O}_{8}^{J/\psi}(^{3}P_{0})\vert0\rangle$. The proposed
method is based on a comparison to open heavy-quark pair production ideally
performed at the same kinematics. In this paper we analyze this proposal in
more detail and provide predictions for the EIC based on the available
determinations of the color-octet matrix elements. We also propose two
additional methods that do not require comparison to open heavy-quark pair
production.


Abstract:
  Computational models based on the depth-averaged shallow water equations
(SWE) offer an efficient choice to analyse velocity fields around hydraulic
structures. Second-order finite volume (FV2) solvers have often been used for
this purpose subject to adding an eddy viscosity term at sub-meter resolution,
but have been shown to fall short of capturing small-scale field transients
emerging from wave-structure interactions. The second-order discontinuous
Galerkin (DG2) alternative is significantly more resistant to the growth of
numerical diffusion and leads to faster convergence rates. These properties
make the DG2 solver a promising modelling tool for detailed velocity field
predictions. This paper focuses on exploring this DG2 capability with reference
to an FV2 counterpart for a selection of test cases that require well-resolved
velocity field predictions. The findings of this work lead to identifying a
particular setting for the DG2 solver that allows for obtaining more accurate
and efficient depth-averaged velocity fields incorporating small-scale
transients.


Abstract:
  We develop a variational framework to understand the properties of functions
learned by fitting deep neural networks with rectified linear unit activations
to data. We propose a new function space, which is reminiscent of classical
bounded variation-type spaces, that captures the compositional structure
associated with deep neural networks. We derive a representer theorem showing
that deep ReLU networks are solutions to regularized data fitting problems over
functions from this space. The function space consists of compositions of
functions from the Banach spaces of second-order bounded variation in the Radon
domain. These are Banach spaces with sparsity-promoting norms, giving insight
into the role of sparsity in deep neural networks. The neural network solutions
have skip connections and rank bounded weight matrices, providing new
theoretical support for these common architectural choices. The variational
problem we study can be recast as a finite-dimensional neural network training
problem with regularization schemes related to the notions of weight decay and
path-norm regularization. Finally, our analysis builds on techniques from
variational spline theory, providing new connections between deep neural
networks and splines.


Abstract:
  The next wave of wireless technologies is proliferating in connecting things
among themselves as well as to humans. In the era of the Internet of things
(IoT), billions of sensors, machines, vehicles, drones, and robots will be
connected, making the world around us smarter. The IoT will encompass devices
that must wirelessly communicate a diverse set of data gathered from the
environment for myriad new applications. The ultimate goal is to extract
insights from this data and develop solutions that improve quality of life and
generate new revenue. Providing large-scale, long-lasting, reliable, and near
real-time connectivity is the major challenge in enabling a smart connected
world. This paper provides a comprehensive survey on existing and emerging
communication solutions for serving IoT applications in the context of
cellular, wide-area, as well as non-terrestrial networks. Specifically,
wireless technology enhancements for providing IoT access in fifth-generation
(5G) and beyond cellular networks, and communication networks over the
unlicensed spectrum are presented. Aligned with the main key performance
indicators of 5G and beyond 5G networks, we investigate solutions and standards
that enable energy efficiency, reliability, low latency, and scalability
(connection density) of current and future IoT networks. The solutions include
grant-free access and channel coding for short-packet communications,
non-orthogonal multiple access, and on-device intelligence. Further, a vision
of new paradigm shifts in communication networks in the 2030s is provided, and
the integration of the associated new technologies like artificial
intelligence, non-terrestrial networks, and new spectra is elaborated. Finally,
future research directions toward beyond 5G IoT networks are pointed out.


Abstract:
  Human gesture recognition has drawn much attention in the area of computer
vision. However, the performance of gesture recognition is always influenced by
some gesture-irrelevant factors like the background and the clothes of
performers. Therefore, focusing on the regions of hand/arm is important to the
gesture recognition. Meanwhile, a more adaptive architecture-searched network
structure can also perform better than the block-fixed ones like Resnet since
it increases the diversity of features in different stages of the network
better. In this paper, we propose a regional attention with
architecture-rebuilt 3D network (RAAR3DNet) for gesture recognition. We replace
the fixed Inception modules with the automatically rebuilt structure through
the network via Neural Architecture Search (NAS), owing to the different shape
and representation ability of features in the early, middle, and late stage of
the network. It enables the network to capture different levels of feature
representations at different layers more adaptively. Meanwhile, we also design
a stackable regional attention module called dynamic-static Attention (DSA),
which derives a Gaussian guidance heatmap and dynamic motion map to highlight
the hand/arm regions and the motion information in the spatial and temporal
domains, respectively. Extensive experiments on two recent large-scale RGB-D
gesture datasets validate the effectiveness of the proposed method and show it
outperforms state-of-the-art methods. The codes of our method are available at:
https://github.com/zhoubenjia/RAAR3DNet.


Abstract:
  Neural networks (NNs) and linear stochastic estimation (LSE) have widely been
utilized as powerful tools for fluid-flow regressions. We investigate
fundamental differences between them considering two canonical fluid-flow
problems: 1. the estimation of high-order proper orthogonal decomposition
coefficients from low-order their counterparts for a flow around a
two-dimensional cylinder, and 2. the state estimation from wall characteristics
in a turbulent channel flow. In the first problem, we compare the performance
of LSE to that of a multi-layer perceptron (MLP). With the channel flow
example, we capitalize on a convolutional neural network (CNN) as a nonlinear
model which can handle high-dimensional fluid flows. For both cases, the
nonlinear NNs outperform the linear methods thanks to nonlinear activation
functions. We also perform error-curve analyses regarding the estimation error
and the response of weights inside models. Our analysis visualizes the
robustness against noisy perturbation on the error-curve domain while revealing
the fundamental difference of the covered tools for fluid-flow regressions.


Abstract:
  In-volume ultrafast laser direct writing of silicon is generally limited by
strong nonlinear propagation effects preventing the initiation of
modifications. By employing a triple-optimization procedure in the spectral,
temporal and spatial domains, we demonstrate that modifications can be
repeatably produced inside silicon. Our approach relies on irradiation at
$\approx 2$-$\mu$m wavelength with temporally-distorted femtosecond pulses.
These pulses are focused in a way that spherical aberrations of different
origins counterbalance, as predicted by point spread function analyses and in
good agreement with nonlinear propagation simulations. We also establish the
laws governing modification growth on a pulse-to-pulse basis, which allows us
to demonstrate transverse inscription inside silicon with various line
morphologies depending on the irradiation conditions. We finally show that the
production of single-pulse repeatable modifications is a necessary condition
for reliable transverse inscription inside silicon.


Abstract:
  In the past decade, remarkable progress has been achieved in deep learning
related systems and applications. In the post Moore's Law era, however, the
limit of semiconductor fabrication technology along with the increasing data
size have slowed down the development of learning algorithms. In parallel, the
fast development of quantum computing has pushed it to the new ear. Google
illustrates quantum supremacy by completing a specific task (random sampling
problem), in 200 seconds, which is impracticable for the largest classical
computers. Due to the limitless potential, quantum based learning is an area of
interest, in hopes that certain systems might offer a quantum speedup. In this
work, we propose a novel architecture QuClassi, a quantum neural network for
both binary and multi-class classification. Powered by a quantum
differentiation function along with a hybrid quantum-classic design, QuClassi
encodes the data with a reduced number of qubits and generates the quantum
circuit, pushing it to the quantum platform for the best states, iteratively.
We conduct intensive experiments on both the simulator and IBM-Q quantum
platform. The evaluation results demonstrate that QuClassi is able to
outperform the state-of-the-art quantum-based solutions, Tensorflow-Quantum and
QuantumFlow by up to 53.75% and 203.00% for binary and multi-class
classifications. When comparing to traditional deep neural networks, QuClassi
achieves a comparable performance with 97.37% fewer parameters.


Abstract:
  We consider a linear minimum mean squared error (LMMSE) estimation framework
with model mismatch where the assumed model order is smaller than that of the
underlying linear system which generates the data used in the estimation
process. By modelling the regressors of the underlying system as random
variables, we analyze the average behaviour of the mean squared error (MSE).
Our results quantify how the MSE depends on the interplay between the number of
samples and the number of parameters in the underlying system and in the
assumed model. In particular, if the number of samples is not sufficiently
large, neither increasing the number of samples nor the assumed model
complexity is sufficient to guarantee a performance improvement.


Abstract:
  We discuss the anatomy of the $L_{VV}$ observable designed as a ratio of the
longitudinal components of $B_s \to VV$ versus $B_d \to VV$ decays. We focus on
the particular case of $B_{d,s} \to K^{*0} {\bar K^{*0}}$ where we find for the
SM prediction $L_{K^* \bar{K}^*}=19.5^{+9.3}_{-6.8}$ implying a 2.6$\sigma$
tension with respect to data. The interpretation of this tension in a model
independent way identifies two Wilson coefficients ${\cal C}_{4}$ and ${\cal
C}_{8g}$ as possible sources. The example of one simplified model including a
Kaluza-Klein (KK) gluon is discussed. This KK gluon embedded inside a
composite/extra dimensional model combined with a $Z^\prime$ can explain also
the $b\to s\ell\ell$ anomalies albeit with a significant amount of fine-tuning.


Abstract:
  We review the calculation of the solar axion flux from axion-photon and
axion-electron interactions and discuss the size of various effects neglected
in current calculations. For the Primakoff flux we then explicitly include the
partial degeneracy of electrons. We survey the available solar models and
opacity codes and develop a publicly available C++/Python code to quantify the
associated systematic differences and statistical uncertainties. The number of
axions emitted in helioseismological solar models is systematically larger by
about 5% compared to photospheric models, while the overall statistical
uncertainties in solar models are typically at the percent level in both
helioseismological and photospheric models. However, for specific energies, the
statistical fluctuations can reach up to about 5% as well. Taking these
uncertainties into account, we investigate the ability of the upcoming
helioscope IAXO to discriminate KSVZ axion models. Such a discrimination is
possible for a number of models, and a discovery of KSVZ axions with high $E/N$
ratios could potentially help to solve the solar abundance problem. We discuss
limitations of the axion emission calculations and identify potential
improvements, which would help to determine axion model parameters more
accurately.


Abstract:
  In this paper, we prove existence and regularity of positive solutions for
singular quasilinear elliptic systems involving gradient terms. Our approach is
based on comparison properties, a priori estimates and Schauder's fixed point
theorem.


Abstract:
  A novel and compact tri-band planar antenna for 2.4/5.2/5.8-GHz wireless
local area network (WLAN), 2.3/3.5/5.5GHz Worldwide Interoperability for
Microwave Access (WiMAX) and Bluetooth applications is proposed and studied in
this paper. The antenna comprises of a L-shaped element which is coupled with a
ground shorted parasitic resonator to generate three resonant modes for
tri-band operation. The L-shaped element which is placed on top of the
substrate is fed by a 50$\Omega$ microstrip feed line and is responsible for
the generation of a wide band at 5.5 GHz. The parasitic resonator is placed on
the other side of the substrate and is directly connected to the ground plane.
The presence of the parasitic resonator gives rise to two additional resonant
bands at 2.3 GHz and 3.5 GHz. Thus, together the two elements generate three
resonant bands to cover WLAN, WiMAX and Bluetooth bands of operation. A
thorough parametric study has been performed on the antenna and it has been
found that the three bands can be tuned by varying certain dimensions of the
antenna. Hence, the same design can be used for frequencies in adjacent bands
as well with minor changes in its dimensions. Important antenna parameters such
as return loss, radiation pattern and peak gains in the operating bands have
been studied in detail to prove that the proposed design is a promising
candidate for the aforementioned wireless technologies.


Abstract:
  We present a novel mapping approach for WENO schemes through the use of an
approximate constant mapping function which is constructed by employing an
approximation of the classic signum function. The new approximate constant
mapping function is designed to meet the overall criteria for a proper mapping
function required in the design of the WENO-PM6 scheme. The WENO-PM6 scheme was
proposed to overcome the potential loss of accuracy of the WENO-M scheme which
was developed to recover the optimal convergence order of the WENO-JS scheme at
critical points. Our new mapped WENO scheme, denoted as WENO-ACM, maintains
almost all advantages of the WENO-PM6 scheme, including low dissipation and
high resolution, while decreases the number of mathematical operations
remarkably in every mapping process leading to a significant improvement of
efficiency. The convergence rates of the WENO-ACM scheme have been shown
through one-dimensional linear advection equation with various initial
conditions. Numerical results of one-dimensional Euler equations for the
Riemann problems, the Mach 3 shock-density wave interaction and the
Woodward-Colella interacting blastwaves are improved in comparison with the
results obtained by the WENO-JS, WENO-M and WENO-PM6 schemes. Numerical
experiments with two-dimensional problems as the 2D Riemann problem, the
shock-vortex interaction, the 2D explosion problem, the double Mach reflection
and the forward-facing step problem modeled via the two dimensional Euler
equations have been conducted to demonstrate the high resolution and the
effectiveness of the WENO-ACM scheme. The WENO-ACM scheme provides
significantly better resolution than the WENO-M scheme and slightly better
resolution than the WENO-PM6 scheme, and compared to the WENO-M and WENO-PM6
schemes, the extra computational cost is reduced by more than 83% and 93%,
respectively.


Abstract:
  Hundreds of millions of speakers of bidirectional (BiDi) languages rely on
writing systems that mix the native right-to-left script with left-to-right
strings. The global reach of interactive digital technologies requires special
attention to these people, whose perception of interfaces is affected by this
script mixture. However, empirical research on this topic is scarce. Although
leading software vendors provide guidelines for BiDi design, bidirectional
interfaces demonstrate inconsistent and incorrect directionality of UI
elements, which may cause user confusion and errors. Through a websites'
review, we identified problematic UI items and considered reasons for their
existence. In an online survey with 234 BiDi speakers, we observed that in many
cases, users' direction preferences were inconsistent with the guidelines. The
findings provide potential insights for design rules and empirical evidence for
the problem's complexity, suggesting the need for further empirical research
and greater attention by the HCI community to the BiDi design problem.


Abstract:
  Grasping objects in cluttered scenarios is a challenging task in robotics.
Performing pre-grasp actions such as pushing and shifting to scatter objects is
a way to reduce clutter. Based on deep reinforcement learning, we propose a
Fast-Learning Grasping (FLG) framework, that can integrate pre-grasping actions
along with grasping to pick up objects from cluttered scenarios with reduced
real-world training time. We associate rewards for performing moving actions
with the change of environmental clutter and utilize a hybrid triggering
method, leading to data-efficient learning and synergy. Then we use the output
of an extended fully convolutional network as the value function of each pixel
point of the workspace and establish an accurate estimation of the grasp
probability for each action. We also introduce a mask function as prior
knowledge to enable the agents to focus on the accurate pose adjustment to
improve the effectiveness of collecting training data and, hence, to learn
efficiently. We carry out pre-training of the FLG over simulated environment,
and then the learnt model is transferred to the real world with minimal
fine-tuning for further learning during actions. Experimental results
demonstrate a 94% grasp success rate and the ability to generalize to novel
objects. Compared to state-of-the-art approaches in the literature, the
proposed FLG framework can achieve similar or higher grasp success rate with
lesser amount of training in the real world. Supplementary video is available
at https://youtu.be/e04uDLsxfDg.


Abstract:
  Structured matrices, such as those derived from Kronecker products (KP), are
effective at compressing neural networks, but can lead to unacceptable accuracy
loss when applied to large models. In this paper, we propose the notion of
doping -- addition of an extremely sparse matrix to a structured matrix. Doping
facilitates additional degrees of freedom for a small number of parameters,
allowing them to independently diverge from the fixed structure. To train LSTMs
with doped structured matrices, we introduce the additional parameter matrix
while slowly annealing its sparsity level. However, we find that performance
degrades as we slowly sparsify the doping matrix, due to co-matrix adaptation
(CMA) between the structured and the sparse matrices. We address this over
dependence on the sparse matrix using a co-matrix dropout regularization (CMR)
scheme. We provide empirical evidence to show that doping, CMA and CMR are
concepts generally applicable to multiple structured matrices (Kronecker
Product, LMF, Hybrid Matrix Decomposition). Additionally, results with doped
kronecker product matrices demonstrate state-of-the-art accuracy at large
compression factors (10 - 25x) across 4 natural language processing
applications with minor loss in accuracy. Doped KP compression technique
outperforms previous state-of-the art compression results by achieving 1.3 -
2.4x higher compression factor at a similar accuracy, while also beating strong
alternatives like pruning and low-rank methods by a large margin (8% or more).
Additionally, we show that doped KP can be deployed on commodity hardware using
the current software stack and achieve 2.5 - 5.5x inference run-time speed-up
over baseline.


Abstract:
  Recently, deep neural network (DNN)-based speech enhancement (SE) systems
have been used with great success. During training, such systems require clean
speech data - ideally, in large quantity with a variety of acoustic conditions,
many different speaker characteristics and for a given sampling rate (e.g.,
48kHz for fullband SE). However, obtaining such clean speech data is not
straightforward - especially, if only considering publicly available datasets.
At the same time, a lot of material for automatic speech recognition (ASR) with
the desired acoustic/speaker/sampling rate characteristics is publicly
available except being clean, i.e., it also contains background noise as this
is even often desired in order to have ASR systems that are noise-robust.
Hence, using such data to train SE systems is not straightforward. In this
paper, we propose two improvements to train SE systems on noisy speech data.
First, we propose several modifications of the loss functions, which make them
robust against noisy speech targets. In particular, computing the median over
the sample axis before averaging over time-frequency bins allows to use such
data. Furthermore, we propose a noise augmentation scheme for mixture-invariant
training (MixIT), which allows using it also in such scenarios. For our
experiments, we use the Mozilla Common Voice dataset and we show that using our
robust loss function improves PESQ by up to 0.19 compared to a system trained
in the traditional way. Similarly, for MixIT we can see an improvement of up to
0.27 in PESQ when using our proposed noise augmentation.


Abstract:
  Temporal, spectral, and sample-to-sample fluctuations in coherence properties
of qubits form an outstanding challenge for the development of upscaled
fault-tolerant quantum computers. A ubiquitous source for these fluctuations in
superconducting qubits is a set of atomic-scale defects with a two-level
structure. Here we propose a way to mitigate these fluctuations and stabilize
the qubit performance. We show that frequency modulation of a qubit or,
alternatively, of the two-level defects, leads to averaging of the qubit
relaxation rate over a wide interval of frequencies.


Abstract:
  In this work, we introduce NU-Wave, the first neural audio upsampling model
to produce waveforms of sampling rate 48kHz from coarse 16kHz or 24kHz inputs,
while prior works could generate only up to 16kHz. NU-Wave is the first
diffusion probabilistic model for audio super-resolution which is engineered
based on neural vocoders. NU-Wave generates high-quality audio that achieves
high performance in terms of signal-to-noise ratio (SNR), log-spectral distance
(LSD), and accuracy of the ABX test. In all cases, NU-Wave outperforms the
baseline models despite the substantially smaller model capacity (3.0M
parameters) than baselines (5.4-21%). The audio samples of our model are
available at https://mindslab-ai.github.io/nuwave, and the code will be made
available soon.


Abstract:
  We obtain the asymptotic formula with an error term $O(X^{\frac{1}{2} +
\varepsilon})$ for the smoothed first moment of quadratic twists of modular
$L$-functions. We also give a similar result for the smoothed first moment of
the first derivative of quadratic twists of modular $L$-functions. The argument
is largely based on Young's recursive method [19,20].


Abstract:
  Convolutional neural network training can suffer from diverse issues like
exploding or vanishing gradients, scaling-based weight space symmetry and
covariant-shift. In order to address these issues, researchers develop weight
regularization methods and activation normalization methods. In this work we
propose a weight soft-regularization method based on the Oblique manifold. The
proposed method uses a loss function which pushes each weight vector to have a
norm close to one, i.e. the weight matrix is smoothly steered toward the
so-called Oblique manifold. We evaluate our method on the very popular
CIFAR-10, CIFAR-100 and ImageNet 2012 datasets using two state-of-the-art
architectures, namely the ResNet and wide-ResNet. Our method introduces
negligible computational overhead and the results show that it is competitive
to the state-of-the-art and in some cases superior to it. Additionally, the
results are less sensitive to hyperparameter settings such as batch size and
regularization factor.


Abstract:
  In the online balanced graph repartitioning problem, one has to maintain a
clustering of $n$ nodes into $\ell$ clusters, each having $k = n / \ell$ nodes.
During runtime, an online algorithm is given a stream of communication requests
between pairs of nodes: an inter-cluster communication costs one unit, while
the intra-cluster communication is free. An algorithm can change the
clustering, paying unit cost for each moved node.
  This natural problem admits a simple $O(\ell^2 \cdot k^2)$-competitive
algorithm COMP, whose performance is far apart from the best known lower bound
of $\Omega(\ell \cdot k)$. One of open questions is whether the dependency on
$\ell$ can be made linear; this question is of practical importance as in the
typical datacenter application where virtual machines are clustered on physical
servers, $\ell$ is of several orders of magnitude larger than $k$. We answer
this question affirmatively, proving that a simple modification of COMP is
$(\ell \cdot 2^{O(k)})$-competitive.
  On the technical level, we achieve our bound by translating the problem to a
system of linear integer equations and using Graver bases to show the existence
of a ``small'' solution.


Abstract:
  We prove a pair of sharp reverse isoperimetric inequalities for domains in
nonpositively curved surfaces: (1) metric disks centered at the vertex of a
Euclidean cone of angle at least $2\pi$ have minimal area among all
nonpositively curved disks of the same perimeter and the same total curvature;
(2) geodesic triangles in a Euclidean (resp. hyperbolic) cone of angle at least
$2\pi$ have minimal area among all nonpositively curved geodesic triangles
(resp. all geodesic triangles of curvature at most $-1$) with the same side
lengths and angles.


Abstract:
  We derive bounds on the error, in high-order Sobolev norms, incurred in the
approximation of Sobolev-regular as well as analytic functions by neural
networks with the hyperbolic tangent activation function. These bounds provide
explicit estimates on the approximation error with respect to the size of the
neural networks. We show that tanh neural networks with only two hidden layers
suffice to approximate functions at comparable or better rates than much deeper
ReLU neural networks.


Abstract:
  This paper addresses the problem of time-varying bearing formation control in
$d$ $(d\ge 2)$-dimensional Euclidean space by exploring Persistence of
Excitation (PE) of the desired bearing reference. A general concept of Bearing
Persistently Exciting (BPE) formation defined in $d$-dimensional space is here
fully developed. By providing a desired formation that is BPE, distributed
control laws for multi-agent systems under both single- and double-integrator
dynamics are proposed using bearing measurements (along with velocity
measurements when the agents are described by double-integrator dynamics),
which guarantee uniform exponential stabilization of the desired formation in
terms of shape and scale. A key contribution of this work is to show that the
classical bearing rigidity condition on the graph topology, required for
achieving the stabilization of a formation up to a scaling factor, is relaxed
and extended in a natural manner by exploring PE conditions imposed either on a
specific set of desired bearing vectors or on the whole desired formation.
Simulation results are provided to illustrate the performance of the proposed
control method.


Abstract:
  By investigating the exam scores of introductory economics in a business
school in Taiwan between 2008 and 2019, we find three sets of results: First,
we find no significant difference between genders in the exam scores. Second,
students' majors are significantly associated with their exam scores, which
likely reflects their academic ability measured at college admission. Third,
the exam scores are strong predictors of students' future academic performance.


Abstract:
  We explicitly determine the defining relations of all quantum symmetric pair
coideal subalgebras of quantized enveloping algebras of Kac-Moody type. Our
methods are based on star products on noncommutative $\mathbb{N}$-graded
algebras. The resulting defining relations are expressed in terms of continuous
q-Hermite polynomials and a new family of deformed Chebyshev polynomials.


Abstract:
  The usage of smartphone-collected respiratory sound, trained with deep
learning models, for detecting and classifying COVID-19 becomes popular
recently. It removes the need for in-person testing procedures especially for
rural regions where related medical supplies, experienced workers, and
equipment are limited. However, existing sound-based diagnostic approaches are
trained in a fully supervised manner, which requires large scale well-labelled
data. It is critical to discover new methods to leverage unlabelled respiratory
data, which can be obtained more easily. In this paper, we propose a novel
self-supervised learning enabled framework for COVID-19 cough classification. A
contrastive pre-training phase is introduced to train a Transformer-based
feature encoder with unlabelled data. Specifically, we design a random masking
mechanism to learn robust representations of respiratory sounds. The
pre-trained feature encoder is then fine-tuned in the downstream phase to
perform cough classification. In addition, different ensembles with varied
random masking rates are also explored in the downstream phase. Through
extensive evaluations, we demonstrate that the proposed contrastive
pre-training, the random masking mechanism, and the ensemble architecture
contribute to improving cough classification performance.


Abstract:
  We report the discovery of a circumstellar debris disk viewed nearly edge-on
and associated with the young, K1 star BD+45$^{\circ}$598 using high-contrast
imaging at 2.2$\mu$m obtained at the W.M.~Keck Observatory. We detect the disk
in scattered light with a peak significance of $\sim$5$\sigma$ over three
epochs, and our best-fit model of the disk is an almost edge-on $\sim$70 AU
ring, with inclination angle $\sim$87$^\circ$. Using the NOEMA interferometer
at the Plateau de Bure Observatory operating at 1.3mm, we find resolved
continuum emission aligned with the ring structure seen in the 2.2$\mu$m
images. We estimate a fractional infrared luminosity of $L_{IR}/L_{tot}$
$\simeq6^{+2}_{-1}$$\times$$10^{-4}$, higher than that of the debris disk
around AU Mic. Several characteristics of BD+45$^{\circ}$598, such as its
galactic space motion, placement in a color-magnitude diagram, and strong
presence of Lithium, are all consistent with its membership in the $\beta$
Pictoris Moving Group with an age of 23$\pm$3 Myr. However, the galactic
position for BD+45$^{\circ}$598 is slightly discrepant from previously-known
members of the $\beta$ Pictoris Moving Group, possibly indicating an extension
of members of this moving group to distances of at least 70pc.
BD+45$^{\circ}$598 appears to be an example from a population of young
circumstellar debris systems associated with newly identified members of young
moving groups that can be imaged in scattered light, key objects for mapping
out the early evolution of planetary systems from $\sim$10-100 Myr. This target
will also be ideal for northern-hemisphere, high-contrast imaging platforms to
search for self-luminous, planetary mass companions residing in this system.


Abstract:
  We study the fair division problem on divisible heterogeneous resources (the
cake cutting problem) with strategic agents, where each agent can manipulate
his/her private valuation in order to receive a better allocation. A
(direct-revelation) mechanism takes agents' reported valuations as input, and
outputs an allocation that satisfies a given fairness requirement. A natural
and fundamental open problem, first raised by [Chen et al., 2010] and
subsequently raised by [Procaccia, 2013] [Aziz and Ye, 2014] [Branzei and
Miltersen, 2015] [Menon and Larson, 2017] [Bei et al., 2017] [Bei et al.,
2020], etc., is whether there exists a deterministic, truthful and envy-free
(or even proportional) cake cutting mechanism. In this paper, we resolve this
open problem by proving that there does not exist a deterministic, truthful and
proportional cake cutting mechanism, even in the special case where all of the
followings hold: 1. there are only two agents; 2. each agent's valuation is a
piecewise-constant function; 3. each agent is hungry: each agent has a strictly
positive value on any part of the cake. The impossibility result extends to the
case where the mechanism is allowed to leave some part of the cake unallocated.
  To circumvent this impossibility result, we aim to design mechanisms that
possess certain degree of truthfulness. Motivated by the kind of truthfulness
possessed by the classical I-cut-you-choose protocol, we define a weaker notion
of truthfulness: the risk-averse truthfulness. We show that the well-known
moving-knife procedure and Even-Paz algorithm do not have this truthful
property. We propose a mechanism that is risk-averse truthful and envy-free,
and a mechanism that is risk-averse truthful and proportional that always
outputs allocations with connected pieces.


Abstract:
  The Deep Extragalactic VIsible Legacy Survey (DEVILS) is an ongoing
high-completeness, deep spectroscopic survey of $\sim$60,000 galaxies to
Y$<$21.2 mag, over $\sim$6 deg2 in three well-studied deep extragalactic
fields: D10 (COSMOS), D02 (XMM-LSS) and D03 (ECDFS). Numerous DEVILS projects
all require consistent, uniformly-derived and state-of-the-art photometric data
with which to measure galaxy properties. Existing photometric catalogues in
these regions either use varied photometric measurement techniques for
different facilities/wavelengths leading to inconsistencies, older imaging data
and/or rely on source detection and photometry techniques with known problems.
Here we use the ProFound image analysis package and state-of-the-art imaging
datasets (including Subaru-HSC, VST-VOICE, VISTA-VIDEO and UltraVISTA-DR4) to
derive matched-source photometry in 22 bands from the FUV to 500{\mu}m. This
photometry is found to be consistent, or better, in colour-analysis to previous
approaches using fixed-size apertures (which are specifically tuned to derive
colours), but produces superior total source photometry, essential for the
derivation of stellar masses, star-formation rates, star-formation histories,
etc. Our photometric catalogue is described in detail and, after internal
DEVILS team projects, will be publicly released for use by the broader
scientific community.


Abstract:
  The Kepler and TESS missions delivered high-precision, long-duration
photometric time series for hundreds of main-sequence stars with
gravito-inertial (g) pulsation modes. This high precision allows us to evaluate
increasingly detailed theoretical stellar models. Recent theoretical work
extended the traditional approximation of rotation (TAR), a framework to
evaluate the effect of the Coriolis acceleration on g-modes, to include the
effects of the centrifugal acceleration in the approximation of slightly
deformed stars, which so far had mostly been neglected in asteroseismology.
This extension of the TAR was conceived by rederiving the TAR in a
centrifugally deformed, spheroidal coordinate system. We explore the effect of
the centrifugal acceleration on g modes and assess its detectability in
space-based photometry. We implement the new framework to calculate the
centrifugal deformation of precomputed 1D spherical stellar structure models
and compute the corresponding g-mode frequencies, assuming uniform rotation.
The framework is evaluated for a grid of stellar structure models covering a
relevant parameter space for observed g-mode pulsators. The centrifugal
acceleration modifies the effect of the Coriolis acceleration on g modes,
narrowing the equatorial band in which they are trapped. Furthermore, the
centrifugal acceleration causes the pulsation periods and period spacings of
the most common g modes (prograde dipole modes and r modes) to increase with
values similar to the observational uncertainties in Kepler and TESS data. The
effect of the centrifugal acceleration on g~modes is formally detectable in
modern space photometry. Implementation of the new theoretical framework in
stellar structure and pulsation codes will allow for more precise asteroseismic
modelling of centrifugally deformed stars, to assess its effect on mode
excitation, -trapping and -damping.


Abstract:
  The analysis of Magnetic Resonance Imaging (MRI) sequences enables clinical
professionals to monitor the progression of a brain tumor. As the interest for
automatizing brain volume MRI analysis increases, it becomes convenient to have
each sequence well identified. However, the unstandardized naming of MRI
sequences makes their identification difficult for automated systems, as well
as makes it difficult for researches to generate or use datasets for machine
learning research. In the face of that, we propose a system for identifying
types of brain MRI sequences based on deep learning. By training a
Convolutional Neural Network (CNN) based on 18-layer ResNet architecture, our
system can classify a volumetric brain MRI as a FLAIR, T1, T1c or T2 sequence,
or whether it does not belong to any of these classes. The network was
evaluated on publicly available datasets comprising both, pre-processed (BraTS
dataset) and non-pre-processed (TCGA-GBM dataset), image types with diverse
acquisition protocols, requiring only a few slices of the volume for training.
Our system can classify among sequence types with an accuracy of 96.81%.


Abstract:
  Neural machine translation (NMT) has recently gained widespread attention
because of its high translation accuracy. However, it shows poor performance in
the translation of long sentences, which is a major issue in low-resource
languages. It is assumed that this issue is caused by insufficient number of
long sentences in the training data. Therefore, this study proposes a simple
data augmentation method to handle long sentences. In this method, we use only
the given parallel corpora as the training data and generate long sentences by
concatenating two sentences. Based on the experimental results, we confirm
improvements in long sentence translation by the proposed data augmentation
method, despite its simplicity. Moreover, the translation quality is further
improved by the proposed method, when combined with back-translation.


Abstract:
  It is commonly known that Killing vectors and tensors are in one-to-one
correspondence with polynomial first integrals of the geodesic equation. In
this work, metrics admitting nonpolynomial first integrals of the geodesic
equation are constructed, each of which revealing a chain of generalised
Killing vectors.


Abstract:
  Motivated by the philosophy that $C^*$-algebras reflect noncommutative
topology, we investigate the stable homotopy theory of the (opposite) category
of $C^*$-algebras. We focus on $C^*$-algebras which are non-commutative
CW-complexes in the sense of [ELP]. We construct the stable $\infty$-category
of noncommutative CW-spectra, which we denote by $\mathtt{NSp}$. Let
$\mathcal{M}$ be the full spectral subcategory of $\mathtt{NSp}$ spanned by
"noncommutative suspension spectra" of matrix algebras. Our main result is that
$\mathtt{NSp}$ is equivalent to the $\infty$-category of spectral presheaves on
$\mathcal{M}$.
  To prove this we first prove a general result which states that any compactly
generated stable $\infty$-category is naturally equivalent to the
$\infty$-category of spectral presheaves on a full spectral subcategory spanned
by a set of compact generators. This is an $\infty$-categorical version of a
result by Schwede and Shipley [ScSh1]. In proving this we use the language of
enriched $\infty$-categories as developed by Hinich [Hin2,Hin3].
  We end by presenting a "strict" model for $\mathcal{M}$. That is, we define a
category $\mathcal{M}_s$ strictly enriched in a certain monoidal model category
of spectra $\mathtt{Sp^M}$. We give a direct proof that the category of
$\mathtt{Sp^M}$-enriched presheaves $\mathcal{M}_s^{op}\to\mathtt{Sp^M}$ with
the projective model structure models $\mathtt{NSp}$ and conclude that
$\mathcal{M}_s$ is a strict model for $\mathcal{M}$.


Abstract:
  We consider a hierarchy of four typed call-by-value languages with either
higher-order or ground-type references and with either callcc or no control
operator.Our first result is a fully abstract trace model for the most
expressive setting, featuring both higher-order references and callcc,
constructed in the spirit of operational game semantics. Next we examine the
impact of suppressing higher-order references and callcc in contexts and
provide an operational explanation for the game-semantic conditions known as
visibility and bracketing respectively.This allows us to refine the original
model to provide fully abstract trace models of interaction with contexts that
need not use higher-order references or callcc. Along the way, we discuss the
relationship between error- and termination-based contextual testing in each
case, and relate the two to trace and complete trace equivalence
respectively.Overall, the paper provides a systematic development of
operational game semantics for all four cases, which represent the state-based
face of the so-called semantic cube.


Abstract:
  We describe a functional framework suitable to the analysis of the
Cahn-Hilliard equation on an evolving surface whose evolution is assumed to be
given \textit{a priori}. The model is derived from balance laws for an order
parameter with an associated Cahn-Hilliard energy functional and we establish
well-posedness for general regular potentials, satisfying some prescribed
growth conditions, and for two singular nonlinearities -- the thermodynamically
relevant logarithmic potential and a double obstacle potential. We identify,
for the singular potentials, necessary conditions on the initial data and the
evolution of the surfaces for global-in-time existence of solutions, which
arise from the fact that integrals of solutions are preserved over time, and
prove well-posedness for initial data on a suitable set of admissible initial
conditions. We then briefly describe an alternative derivation leading to a
model that instead preserves a weighted integral of the solution, and explain
how our arguments can be adapted in order to obtain global-in-time existence
without restrictions on the initial conditions. Some illustrative examples and
further research directions are given in the final sections.


Abstract:
  While unbiased machine learning models are essential for many applications,
bias is a human-defined concept that can vary across tasks. Given only
input-label pairs, algorithms may lack sufficient information to distinguish
stable (causal) features from unstable (spurious) features. However, related
tasks often share similar biases -- an observation we may leverage to develop
stable classifiers in the transfer setting. In this work, we explicitly inform
the target classifier about unstable features in the source tasks.
Specifically, we derive a representation that encodes the unstable features by
contrasting different data environments in the source task. We achieve
robustness by clustering data of the target task according to this
representation and minimizing the worst-case risk across these clusters. We
evaluate our method on both text and image classifications. Empirical results
demonstrate that our algorithm is able to maintain robustness on the target
task, outperforming the best baseline by 22.9% in absolute accuracy across 12
transfer settings. Our code is available at https://github.com/YujiaBao/Tofu.


Abstract:
  A comb domain is defined to be the entire complex plain with a collection of
vertical slits, symmetric over the real axis, removed. In this paper, we
consider the question of determining whether the exit time of planar Brownian
motion from such a domain has finite $p$-th moment. This question has been
addressed before in relation to starlike domains, but these previous results do
not apply to comb domains. Our main result is a sufficient condition on the
location of the slits which ensures that the $p$-th moment of the exit time is
finite. Several auxiliary results are also presented, including a construction
of a comb domain whose exit time has infinite $p$-th moment for all $p \geq
1/2$.


Abstract:
  Word embeddings are often used in natural language processing as a means to
quantify relationships between words. More generally, these same word embedding
techniques can be used to quantify relationships between features. In this
paper, we first consider multiple different word embedding techniques within
the context of malware classification. We use hidden Markov models to obtain
embedding vectors in an approach that we refer to as HMM2Vec, and we generate
vector embeddings based on principal component analysis. We also consider the
popular neural network based word embedding technique known as Word2Vec. In
each case, we derive feature embeddings based on opcode sequences for malware
samples from a variety of different families. We show that we can obtain better
classification accuracy based on these feature embeddings, as compared to HMM
experiments that directly use the opcode sequences, and serve to establish a
baseline. These results show that word embeddings can be a useful feature
engineering step in the field of malware analysis.


Abstract:
  Machine learning inference is increasingly being executed locally on mobile
and embedded platforms, due to the clear advantages in latency, privacy and
connectivity. In this paper, we present approaches for online resource
management in heterogeneous multi-core systems and show how they can be applied
to optimise the performance of machine learning workloads. Performance can be
defined using platform-dependent (e.g. speed, energy) and platform-independent
(accuracy, confidence) metrics. In particular, we show how a Deep Neural
Network (DNN) can be dynamically scalable to trade-off these various
performance metrics. Achieving consistent performance when executing on
different platforms is necessary yet challenging, due to the different
resources provided and their capability, and their time-varying availability
when executing alongside other workloads. Managing the interface between
available hardware resources (often numerous and heterogeneous in nature),
software requirements, and user experience is increasingly complex.


Abstract:
  Basic physics of drift-wave turbulence and zonal flows has long been studied
within the framework of wave-kinetic theory. Recently, this framework has been
re-examined from first principles, which has led to more accurate yet still
tractable "improved" wave-kinetic equations. In particular, these equations
reveal an important effect of the zonal-flow "curvature" (the second radial
derivative of the flow velocity) on dynamics and stability of drift waves and
zonal flows. We overview these recent findings and present a consolidated
high-level picture of (mostly quasilinear) zonal-flow physics within reduced
models of drift-wave turbulence.


Abstract:
  A search for the single material system that simultaneously exhibits
topological phase and intrinsic superconductivity has been largely limited,
although such a system is far more favorable especially for the quantum device
applications. Except artificially engineered topological superconductivity in
heterostructure systems, another alternative is to have superconductivity
arising from the topological materials by pressure or other clean technology.
Here, based on first-principles calculations, we first show that
quasi-one-dimensional compound (NbSe4)2I represents a rare example of a chiral
Weyl semimetal in which the set of symmetry-related Weyl points (WPs) exhibit
the same chiral charge at a certain energy. The net chiral charge (NCC) of the
below Fermi level EF (or a certain energy) can be tuned by pressure. In
addition, a partial disorder induced by pressure accompanied with
superconductivity emerges. Although amorphization of the iodine sub-lattice
under high pressure, the one-dimensional NbSe4 chains in (NbSe4)2I remain
intact and provide a superconducting channel in one dimension. Our combined
theoretical and experimental research provide critical insight into a new phase
of the one-dimensional system, in which distinctive phase transitions and
correlated topological states emerge upon compression.


Abstract:
  Most chatbot literature that focuses on improving the fluency and coherence
of a chatbot, is dedicated to making chatbots more human-like. However, very
little work delves into what really separates humans from chatbots -- humans
intrinsically understand the effect their responses have on the interlocutor
and often respond with an intention such as proposing an optimistic view to
make the interlocutor feel better. This paper proposes an innovative framework
to train chatbots to possess human-like intentions. Our framework includes a
guiding chatbot and an interlocutor model that plays the role of humans. The
guiding chatbot is assigned an intention and learns to induce the interlocutor
to reply with responses matching the intention, for example, long responses,
joyful responses, responses with specific words, etc. We examined our framework
using three experimental setups and evaluated the guiding chatbot with four
different metrics to demonstrate flexibility and performance advantages.
Additionally, we performed trials with human interlocutors to substantiate the
guiding chatbot's effectiveness in influencing the responses of humans to a
certain extent. Code will be made available to the public.


Abstract:
  We study the key features of the Josephson transport through a curved
semiconducting nanowire. Based on numerical simulations and analytical
estimates within the framework of the Bogoliubov-de Gennes equations we find
the ground-state phase difference $\varphi_0$ between the superconducting leads
tuned by the spin splitting field $h$ driving the system from the topologically
trivial to the nontrivial superconducting state. The phase $\varphi_0$ vanishes
for rather small $h$, grows in a certain field range around the topological
transition, and then saturates at large $h$ in the Kitaev regime. Both the
subgap and the continuum quasiparticle levels are responsible for the above
behavior of the anomalous Josephson phase. It is demonstrated that the
crossover region on $\varphi_0(h)$ dependencies reveals itself in the
superconducting diode effect. The resulting tunable phase battery can be used
as a probe of topological transitions in Majorana networks and can become a
useful element of various quantum computation devices.


Abstract:
  In this paper we give an alternative construction of a certain class of
Deformed Double Current Algebras. These algebras are deformations of $U({\rm
End}(\Bbbk^r)[x,y])$ and they were initially defined and studied by N.Guay in
his papers. Here we construct them as algebras of endomorphisms in Deligne
category. We do this by taking an ultraproduct of spherical subalgebras of the
extended Cherednik algebras of finite rank.


Abstract:
  Call centers, in which human operators attend clients using textual chat, are
very common in modern e-commerce. Training enough skilled operators who are
able to provide good service is a challenge. We suggest an algorithm and a
method to train and implement an assisting agent that provides on-line advice
to operators while they attend clients. The agent is domain-independent and can
be introduced to new domains without major efforts in design, training and
organizing structured knowledge of the professional discipline. We demonstrate
the applicability of the system in an experiment that realizes its full
life-cycle on a specific domain and analyze its capabilities.


Abstract:
  Currently several Bayesian approaches are available to estimate large sparse
precision matrices, including Bayesian graphical Lasso (Wang, 2012), Bayesian
structure learning (Banerjee and Ghosal, 2015), and graphical horseshoe (Li et
al., 2019). Although these methods have exhibited nice empirical performances,
in general they are computationally expensive. Moreover, we have limited
knowledge about the theoretical properties, e.g., posterior contraction rate,
of graphical Bayesian Lasso and graphical horseshoe. In this paper, we propose
a new method that integrates some commonly used continuous shrinkage priors
into a quasi-Bayesian framework featured by a pseudo-likelihood. Under mild
conditions, we establish an optimal posterior contraction rate for the proposed
method. Compared to existing approaches, our method has two main advantages.
First, our method is computationally more efficient while achieving similar
error rate; second, our framework is more amenable to theoretical analysis.
Extensive simulation experiments and the analysis on a real data set are
supportive of our theoretical results.


Abstract:
  3D face recognition has shown its potential in many application scenarios.
Among numerous 3D face recognition methods, deep-learning-based methods have
developed vigorously in recent years. In this paper, an end-to-end deep
learning network entitled Sur3dNet-Face for point-cloud-based 3D face
recognition is proposed. The network uses PointNet as the backbone, which is a
successful point cloud classification solution but does not work properly in
face recognition. Supplemented with modifications in network architecture and a
few-data guided learning framework based on Gaussian process morphable model,
the backbone is successfully modified for 3D face recognition. Different from
existing methods training with a large amount of data in multiple datasets, our
method uses Spring2003 subset of FRGC v2.0 for training which contains only 943
facial scans, and the network is well trained with the guidance of such a small
amount of real data. Without fine-tuning on the test set, the Rank-1
Recognition Rate (RR1) is achieved as follows: 98.85% on FRGC v2.0 dataset and
99.33% on Bosphorus dataset, which proves the effectiveness and the
potentiality of our method.


Abstract:
  In this perspective we discuss recent theoretical and experimental concepts
giving a route to a better understanding of conventional and unconventional
pairing mechanisms between opposite-spin fermions arising in one-dimensional
mesoscopic systems. With special attention, we focus on the problem of
experimental detectability of correlations between particles. We argue that
state-of-the-art experiments with few ultracold fermions may finally break an
impasse and give pioneering and unquestionable verification of the existence of
correlated pairs with non-zero center-of-mass momentum.


Abstract:
  Current intent classification approaches assign binary intent class
memberships to natural language utterances while disregarding the inherent
vagueness in language and the corresponding vagueness in intent class
boundaries. In this work, we propose a scheme to address the ambiguity in
single-intent as well as multi-intent natural language utterances by creating
degree memberships over fuzzified intent classes. To our knowledge, this is the
first work to address and quantify the impact of the fuzzy nature of natural
language utterances over intent category memberships. Additionally, our
approach overcomes the sparsity of multi-intent utterance data to train
classification models by using a small database of single intent utterances to
generate class memberships over multi-intent utterances. We evaluate our
approach over two task-oriented dialog datasets, across different fuzzy
membership generation techniques and approximate string similarity measures.
Our results reveal the impact of lexical overlap between utterances of
different intents, and the underlying data distributions, on the fuzzification
of intent memberships. Moreover, we evaluate the accuracy of our approach by
comparing the defuzzified memberships to their binary counterparts, across
different combinations of membership functions and string similarity measures.


Abstract:
  We study neologism use in two samples of early English correspondence, from
1640--1660 and 1760--1780. Of especial interest are the early adopters of new
vocabulary, the social groups they represent, and the types and functions of
their neologisms. We describe our computer-assisted approach and note the
difficulties associated with massive variation in the corpus. Our findings
include that while male letter-writers tend to use neologisms more frequently
than women, the eighteenth century seems to have provided more opportunities
for women and the lower ranks to participate in neologism use as well. In both
samples, neologisms most frequently occur in letters written between close
friends, which could be due to this less stable relationship triggering more
creative language use. In the seventeenth-century sample, we observe the
influence of the English Civil War, while the eighteenth-century sample appears
to reflect the changing functions of letter-writing, as correspondence is
increasingly being used as a tool for building and maintaining social
relationships in addition to exchanging information.


Abstract:
  With the aim of matching a pair of instances from two different modalities,
cross modality mapping has attracted growing attention in the computer vision
community. Existing methods usually formulate the mapping function as the
similarity measure between the pair of instance features, which are embedded to
a common space. However, we observe that the relationships among the instances
within a single modality (intra relations) and those between the pair of
heterogeneous instances (inter relations) are insufficiently explored in
previous approaches. Motivated by this, we redefine the mapping function with
relational reasoning via graph modeling, and further propose a GCN-based
Relational Reasoning Network (RR-Net) in which inter and intra relations are
efficiently computed to universally resolve the cross modality mapping problem.
Concretely, we first construct two kinds of graph, i.e., Intra Graph and Inter
Graph, to respectively model intra relations and inter relations. Then RR-Net
updates all the node features and edge features in an iterative manner for
learning intra and inter relations simultaneously. Last, RR-Net outputs the
probabilities over the edges which link a pair of heterogeneous instances to
estimate the mapping results. Extensive experiments on three example tasks,
i.e., image classification, social recommendation and sound recognition,
clearly demonstrate the superiority and universality of our proposed model.


Abstract:
  High-altitude balloon experiments are becoming very popular among
universities and research institutes as they can be used for testing
instruments eventually intended for space, and for simple astronomical
observations of Solar System objects like the Moon, comets, and asteroids,
difficult to observe from the ground due to atmosphere. Further, they are one
of the best platforms for atmospheric studies. In this experiment, we build a
simple 1U CubeSat and, by flying it on a high-altitude balloon to an altitude
of about 30 km, where the total payload weighted 4.9 kg and examine how some
parameters, such as magnetic field, humidity, temperature or pressure, vary as
a function of altitude. We also calibrate the magnetometer to remove the hard
iron and soft iron errors. Such experiments and studies through a stratospheric
balloon flights can also be used to study the performance of easily available
commercial sensors in extreme conditions as well. We present the results of the
first flight, which helped us study the functionality of the various sensors
and electronics at low temperatures reaching about -40 degrees Celsius. Further
the motion of the payload has been tracked throughout this flight. This
experiment took place on 8 March 2020 from the CREST campus of the Indian
Institute of Astrophysics, Bangalore. Using the results from this flight, we
identify and rectify the errors to obtain better results from the subsequent
flights.


Abstract:
  Matrix elements between nonorthogonal Slater determinants represent an
essential component of many emerging electronic structure methods. However,
evaluating nonorthogonal matrix elements is conceptually and computationally
harder then their orthogonal counterparts. While several different approaches
have been developed, these are predominantly derived from the first-quantised
generalised Slater-Condon rules and usually require biorthogonal occupied
orbitals to be computed for each matrix element. For coupling terms between
nonorthogonal excited configurations, a second-quantised approach such as the
nonorthogonal Wick's theorem is more desirable, but this fails when the two
reference determinants have a zero many-body overlap. In this contribution, we
derive an entirely generalised extension to the nonorthogonal Wick's theorem
that is applicable to all pairs of determinants with nonorthogonal orbitals.
Our approach creates a universal methodology for evaluating any nonorthogonal
matrix element and allows Wick's theorem and the generalised Slater-Condon
rules to be unified for the first time. Furthermore, we present a simple
well-defined protocol for deriving arbitrary coupling terms between
nonorthogonal excited configurations. In the case of overlap and one-body
operators, this protocol recovers efficient formulae with reduced scaling,
promising significant computational acceleration for methods that rely on such
terms.


Abstract:
  We present a new class of Langevin based algorithms, which overcomes many of
the known shortcomings of popular adaptive optimizers that are currently used
for the fine tuning of deep learning models. Its underpinning theory relies on
recent advances of Euler's polygonal approximations for stochastic differential
equations (SDEs) with monotone coefficients. As a result, it inherits the
stability properties of tamed algorithms, while it addresses other known
issues, e.g. vanishing gradients in neural networks. In particular, we provide
a nonasymptotic analysis and full theoretical guarantees for the convergence
properties of an algorithm of this novel class, which we named TH$\varepsilon$O
POULA (or, simply, TheoPouLa). Finally, several experiments are presented with
different types of deep learning models, which show the superior performance of
TheoPouLa over many popular adaptive optimization algorithms.


Abstract:
  Distributing points on a (possibly high-dimensional) sphere with minimal
energy is a long-standing problem in and outside the field of mathematics. This
paper considers a novel energy function that arises naturally from statistics
and combinatorial optimization, and studies its theoretical properties. Our
result solves both the exact optimal spherical point configurations in certain
cases and the minimal energy asymptotics under general assumptions. Connections
between our results and the L1-Principle Component Analysis and Quasi-Monte
Carlo methods are also discussed.


Abstract:
  Detecting cancers at early stages can dramatically reduce mortality rates.
Therefore, practical cancer screening at the population level is needed. Here,
we develop a comprehensive detection system to classify all common cancer
types. By integrating artificial intelligence deep learning neural network and
noncoding RNA biomarkers selected from massive data, our system can accurately
detect cancer vs healthy object with 96.3% of AUC of ROC (Area Under Curve of a
Receiver Operating Characteristic curve). Intriguinely, with no more than 6
biomarkers, our approach can easily discriminate any individual cancer type vs
normal with 99% to 100% AUC. Furthermore, a comprehensive marker panel can
simultaneously multi-classify all common cancers with a stable 78% of accuracy
at heterological cancerous tissues and conditions. This provides a valuable
framework for large scale cancer screening. The AI models and plots of results
were available in https://combai.org/ai/cancerdetection/


Abstract:
  We present a multi-instrument spectroscopic analysis of the unique Li/Na-rich
giant star 25664 in Omega Centauri using spectra acquired with FLAMES-GIRAFFE,
X-SHOOTER, UVES and HARPS. Li and Na abundances have been derived from the UVES
spectrum using transitions weakly sensitive to non-local thermodynamic
equilibrium and assumed isotopic ratio. This new analysis confirms the
surprising Li and Na abundances of this star (A(Li) =+2.71+-0.07 dex,
[Na/Fe]=+1.00+-0.05 dex). Additionally, we provide new pieces of evidence for
its chemical characterisation. The 12C/13C isotopic ratio (15+-2) shows that
this star has not yet undergone the extra-mixing episode usually associated
with the red giant branch bump. Therefore, we can rule out the scenario of
efficient deep extra-mixing during the red giant branch phase envisaged to
explain the high Li and Na abundances. Also, the star exhibits high abundances
of both C and N ([C/Fe]=+0.45+-0.16 dex and [N/Fe]=+0.99+-0.20 dex), not
compatible with the typical C-N anticorrelation observed in globular cluster
stars. We found evidence of a radial velocity variability in 25664, suggesting
that the star could be part of a binary system, likely having accreted material
from a more massive companion when the latter was evolving in the AGB phase.
Viable candidates for the donor star are AGB stars with 3-4 Msun and super-AGB
stars (~7-8 Msun), both able to produce Li- and Na-rich material.
Alternatively, the star could be formed from the pure ejecta of a super-AGB
stars, before the dilution with primordial gas occurs.


Abstract:
  A sample of 46 stars, host of exoplanets, is used to search for a connection
between their formation process and the formation of the planets rotating
around them. Separating our sample in two, stars hosting high-mass exoplanets
(HMEs) and low-mass exoplanets (LMEs), we found the former to be more massive
and to rotate faster than the latter. We also found the HMEs to have higher
orbital angular momentum than the LMEs and to have lost more angular momentum
through migration. These results are consistent with the view that the more
massive the star and higher its rotation, the more massive was its
protoplanetarys disk and rotation, and the more efficient the extraction of
angular momentum from the planets.


Abstract:
  We present 63 new multi-site radial velocity measurements of the K1III giant
HD 76920, which was recently reported to host the most eccentric planet known
to orbit an evolved star. We focussed our observational efforts on the time
around the predicted periastron passage and achieved near-continuous phase
coverage of the corresponding radial velocity peak. By combining our radial
velocity measurements from four different instruments with previously published
ones, we confirm the highly eccentric nature of the system, and find an even
higher eccentricity of $e=0.8782 \pm 0.0025$, an orbital period of
$415.891^{+0.043}_{-0.039}\,\mathrm{d}$, and a minimum mass of
$3.13^{+0.41}_{-0.43}\,\mathrm{M_J}$ for the planet. The uncertainties in the
orbital elements are greatly reduced, especially for the period and
eccentricity. We also performed a detailed spectroscopic analysis to derive
atmospheric stellar parameters, and thus the fundamental stellar parameters
($M_*, R_*, L_*$), taking into account the parallax from Gaia DR2, and
independently determined the stellar mass and radius using asteroseismology.
Intriguingly, at periastron the planet comes to within 2.4 stellar radii of its
host star's surface. However, we find that the planet is not currently
experiencing any significant orbital decay and will not be engulfed by the
stellar envelope for at least another $50-80$ Myr. Finally, while we calculate
a relatively high transit probability of $16\%$, we did not detect a transit in
the TESS photometry.


Abstract:
  In this paper, we first address the general fractional integrals and
derivatives with the Sonine kernels that possess the integrable singularities
of power function type at the point zero. Both particular cases and
compositions of these operators are discussed. Then we proceed with a
construction of an operational calculus of the Mikusi\'nski type for the
general fractional derivatives with the Sonine kernels. This operational
calculus is applied for analytical treatment of some initial value problems for
the fractional differential equations with the general fractional derivatives.
The solutions are expressed in form of the convolution series that generalize
the power series for the exponential and the Mittag-Leffler functions.


Abstract:
  Beam matching is a common technique that is routinely employed in accelerator
design with the aim of minimizing beam losses and preservation of beam
brightness. Despite being widely used, a full theoretical understanding of beam
matching in 6D remains elusive. Here, we present an analytical treatment of 6D
beam matching of a high-intensity beam onto an RF structure. We begin our
analysis within the framework of a linear model, and apply the averaging method
to a set of 3D beam envelope equations. Accordingly, we obtain a matched
solution that is comprised of smoothed envelopes and periodic terms, describing
envelope oscillations with the period of the focusing structure. We then
consider the nonlinear regime, where the beam size is comparable with the
separatrix size. Stating with a Hamiltonian analysis in 6D phase space, we
attain a self-consistent beam profile and show that it is significantly
different from the commonly used ellipsoidal shape. Subsequently, we analyze
the special case of an equilibrium with equal space charge depression between
all degrees of freedom. Comparison of beam dynamics for equipartitioned, equal
space charge depression, and equal emittances beams is given. Finally, we
present experimental results on beam matching in the LANSCE linac.


Abstract:
  Due to the global pandemic, in March 2020 we in academia and industry were
abruptly forced into working from home. Yet teaching never stopped, and neither
did developing software, fixing software, and expanding into new markets.
Demands for flexible ways of working, responding to new requirements, have
never been so high. How did we manage to continue working, when we had to
suddenly switch all communication to online and virtual forms of contact? In
this short paper we describe how Ocuco Ltd., a medium-sized organization
headquartered in Ireland, managed our software development teams--distributed
throughout Ireland, Europe, Asia and America during the COVID-19 pandemic. We
describe how we expanded, kept our customers happy, and our teams motivated. We
made changes, some large, such as providing emergency financial support; others
small, like implementing regular online social pizza evenings. Technology and
process changes were minor, an advantage of working in globally distributed
teams since 2016, when development activities were coordinated according to the
Scaled Agile Framework (SAFe). The results of implementing the changes were
satisfying; productivity went up, we gained new customers, and preliminary
results from our wellness survey indicate that everyone feels extremely
well-supported by management to achieve their goals. However, the anonymised
survey responses did show some developers' anxiety levels were slightly raised,
and many are working longer hours. Administering this survey is very
beneficial, as now we know, so we can act.


Abstract:
  We show that the cross section for diffractive dissociation of a small onium
off a large nucleus at total rapidity $Y$ and requiring a minimum rapidity gap
$Y_{\text{gap}}$ can be identified, in a well-defined parametric limit, with a
simple classical observable on the stochastic process representing the
evolution of the state of the onium, as its rapidity increases, in the form of
color dipole branchings: It formally coincides with twice the probability that
an even number of these dipoles effectively participate in the scattering, when
viewed in a frame in which the onium is evolved to the rapidity
$Y-Y_{\text{gap}}$. Consequently, finding asymptotic solutions to the
Kovchegov-Levin equation, which rules the $Y$-dependence of the diffractive
cross section, boils down to solving a probabilistic problem. Such a
formulation authorizes the derivation of a parameter-free analytical expression
for the gap distribution. Interestingly enough, events in which many dipoles
interact simultaneously play an important role, since the distribution of the
number $k$ of dipoles participating in the interaction turns out to be
proportional to $1/[k(k-1)]$.


Abstract:
  In this paper, the effect on the ultrasonic attenuation of the grain size
heterogeneity in polycrystals is analyzed. First, new analytical developments
allowing the extension of the unified theory of Stanke and Kino to general
grain size distributions are presented. It is then shown that one can
additively decompose the attenuation coefficient provided that groups of grains
are defined. Second, the study is specialized to a bimodal distribution of the
grain size for which microstructures are numerically modeled by means of the
software Neper. The additive partition of the attenuation coefficient into
contributions coming from large and small grains motivates the derivation of an
optimization procedure for characterizing the grain size distribution. The
aforementioned approach, which is based on a least squares minimization, is at
last presented and illustrated on both analytical and numerical attenuation
data. It is thus shown that the method provides satisfying approximations of
volume fractions of large grains and modal equivalent diameters from the
frequency-dependent attenuation coefficient.


Abstract:
  This paper proposes an enhanced coarray transformation model (EDCTM) and a
mixed greedy maximum likelihood algorithm called List-Based Maximum Likelihood
Orthogonal Matching Pursuit (LBML-OMP) for direction-of-arrival estimation with
non-uniform linear arrays (NLAs). The proposed EDCTM approach obtains improved
estimates when Khatri-Rao product-based models are used to generate difference
coarrays under the assumption of uncorrelated sources. In the proposed LBML-OMP
technique, for each iteration a set of candidates is generated based on the
correlation-maximization between the dictionary and the residue vector.
LBML-OMP then chooses the best candidate based on a reduced-complexity
asymptotic maximum likelihood decision rule. Simulations show the improved
results of EDCTM over existing approaches and that LBML-OMP outperforms
existing sparse recovery algorithms as well as Spatial Smoothing Multiple
Signal Classification with NLAs.


Abstract:
  Let $0 \in \Gamma$ and $\Gamma \setminus \{0\}$ be an abelian group under
multiplication, where $\Gamma \setminus \{0\} \subseteq \{ z\in \mathbb{C}:
|z|=1 \}$. Define $\mathcal{H}_{n}(\Gamma)$ to be the set of all $n\times n$
Hermitian matrices with entries in $\Gamma$, whose diagonal entries are zero.
We introduce the notion of switching equivalence on $\mathcal{H}_{n}(\Gamma)$.
We find a characterization, in terms of fundamental cycles of graphs, of
switching equivalence of matrices in $\mathcal{H}_{n}(\Gamma)$. We give
sufficient conditions to characterize the cospectral matrices in
$\mathcal{H}_{n}(\Gamma)$. We find bounds on the number of switching
equivalence classes of all mixed graphs with the same underlying graph. We also
provide the size of all switching equivalence classes of mixed cycles, and give
a formula that calculates the size of a switching equivalence class of a mixed
planar graph. We also discuss the action of automorphism group of a graph on
switching equivalence classes.


Abstract:
  In photosynthetic organisms, the energy of light during illumination is
absorbed by the antenna complexes, which is transmitted by excitons and is
either absorbed by the reaction centers (RCs), which have been closed in this
way, or emitted by fluorescence. The basic components of the dynamics of light
absorption have been integrated into a simple model of exciton migration, which
contains two parameters: the exciton hopping probability and the exciton
lifetime. During continuous radiation with light the fraction of closed RCs,
$x$, continuously increases and at a critical threshold, $x_c$, a percolation
transition takes place. Performing extensive Monte Carlo simulations we study
the properties of the transition in this correlated percolation model. We
measure the spanning probability in the vicinity of $x_c$, as well as the
fractal properties of the critical percolating cluster, both in the bulk and at
the surface.


Abstract:
  Consider a truck filled with boxes of varying size and unknown mass and an
industrial robot with end-effectors that can unload multiple boxes from any
reachable location. In this work, we investigate how would the robot with the
help of a simulator, learn to maximize the number of boxes unloaded by each
action. Most high-fidelity robotic simulators like ours are time-consuming.
Therefore, we investigate the above learning problem with a focus on minimizing
the number of simulation runs required. The optimal decision-making problem
under this setting can be formulated as a multi-class classification problem.
However, to obtain the outcome of any action requires us to run the
time-consuming simulator, thereby restricting the amount of training data that
can be collected. Thus, we need a data-efficient approach to learn the
classifier and generalize it with a minimal amount of data. A high-fidelity
physics-based simulator is common in general for complex manipulation tasks
involving multi-body interactions. To this end, we train an optimal decision
tree as the classifier, and for each branch of the decision tree, we reason
about the confidence in the decision using a Probably Approximately Correct
(PAC) framework to determine whether more simulator data will help reach a
certain confidence level. This provides us with a mechanism to evaluate when
simulation can be avoided for certain decisions, and when simulation will
improve the decision making. For the truck unloading problem, our experiments
show that a significant reduction in simulator runs can be achieved using the
proposed method as compared to naively running the simulator to collect data to
train equally performing decision trees.


Abstract:
  We report on a Rosenbluth separation using previously published data by the
CLAS collaboration in Hall B, Jefferson Lab for exclusive $\pi^{0}$ deeply
virtual electroproduction (DVEP) from the proton at a mean $Q^{2}$ of $\approx$
2 (GeV/c)$^{2}$. The central question we address is the applicability of
factorization in $\pi^0$ DVEP at these kinematics. The results of our
Rosenbluth separation clearly demonstrate the dominance of the longitudinal
contribution to the cross section. The extracted longitudinal and transverse
contributions are in agreement with previous data from Hall A at Jefferson Lab,
but over a much wider $-t$ range (0.12 - 1.8 (GeV/c)$^{2}$). The measured
dominance of the longitudinal contribution at $Q^{2} \approx$ 2 (GeV/c)$^{2}$
is consistent with the expectation of the handbag factorization theorem. We
find that $\sigma_L(t) \sim 1/(-t)$ for $-t >$ 0.5 (GeV/c)$^2$. Determination
of both longitudinal and transverse contributions to the deeply virtual
$\pi^{0}$ electroproduction cross section allows extraction of additional GPDs.


Abstract:
  Accurate segmentation for medical images is important for clinical diagnosis.
Existing automatic segmentation methods are mainly based on fully supervised
learning and have an extremely high demand for precise annotations, which are
very costly and time-consuming to obtain. To address this problem, we proposed
an automatic CT segmentation method based on weakly supervised learning, by
which one could train an accurate segmentation model only with weak annotations
in the form of bounding boxes. The proposed method is composed of two steps: 1)
generating pseudo masks with bounding box annotations by k-means clustering,
and 2) iteratively training a 3D U-Net convolutional neural network as a
segmentation model. Some data pre-processing methods are used to improve
performance. The method was validated on four datasets containing three types
of organs with a total of 627 CT volumes. For liver, spleen and kidney
segmentation, it achieved an accuracy of 95.19%, 92.11%, and 91.45%,
respectively. Experimental results demonstrate that our method is accurate,
efficient, and suitable for clinical use.


Abstract:
  A key open issue in condensed matter physics is how quantum and classical
correlations emerge in an unconventional superconductor from the underlying
normal state. We study this problem in a doped Mott insulator with information
theory tools on the two-dimensional Hubbard model at finite temperature with
cluster dynamical mean-field theory. We find that the local entropy detects the
superconducting state and that the difference in the local entropy between the
superconducting and normal states follows the same difference in the potential
energy. We find that the thermodynamic entropy is suppressed in the
superconducting state and monotonically decreases with decreasing doping. The
maximum in entropy found in the normal state above the overdoped region of the
superconducting dome is obliterated by superconductivity. The total mutual
information, which quantifies quantum and classical correlations, is amplified
in the superconducting state of the doped Mott insulator for all doping levels,
and shows a broad peak versus doping, as a result of competing quantum and
classical effects.


Abstract:
  Knowledge Graph (KG) alignment aims at finding equivalent entities and
relations (i.e., mappings) between two KGs. The existing approaches utilize
either reasoning-based or semantic embedding-based techniques, but few studies
explore their combination. In this demonstration, we present PRASEMap, an
unsupervised KG alignment system that iteratively computes the Mappings with
both Probabilistic Reasoning (PR) And Semantic Embedding (SE) techniques.
PRASEMap can support various embedding-based KG alignment approaches as the SE
module, and enables easy human computer interaction that additionally provides
an option for users to feed the mapping annotations back to the system for
better results. The demonstration showcases these features via a stand-alone
Web application with user friendly interfaces. The demo is available at
https://prasemap.qizhy.com.


Abstract:
  Face forgery detection is raising ever-increasing interest in computer vision
since facial manipulation technologies cause serious worries. Though recent
works have reached sound achievements, there are still unignorable problems: a)
learned features supervised by softmax loss are separable but not
discriminative enough, since softmax loss does not explicitly encourage
intra-class compactness and interclass separability; and b) fixed filter banks
and hand-crafted features are insufficient to capture forgery patterns of
frequency from diverse inputs. To compensate for such limitations, a novel
frequency-aware discriminative feature learning framework is proposed in this
paper. Specifically, we design a novel single-center loss (SCL) that only
compresses intra-class variations of natural faces while boosting inter-class
differences in the embedding space. In such a case, the network can learn more
discriminative features with less optimization difficulty. Besides, an adaptive
frequency feature generation module is developed to mine frequency clues in a
completely data-driven fashion. With the above two modules, the whole framework
can learn more discriminative features in an end-to-end manner. Extensive
experiments demonstrate the effectiveness and superiority of our framework on
three versions of the FF++ dataset.


Abstract:
  When collaborating with an AI system, we need to assess when to trust its
recommendations. If we mistakenly trust it in regions where it is likely to
err, catastrophic failures may occur, hence the need for Bayesian approaches
for probabilistic reasoning in order to determine the confidence (or epistemic
uncertainty) in the probabilities in light of the training data. We propose an
approach to overcome the independence assumption behind most of the approaches
dealing with a large class of probabilistic reasoning that includes Bayesian
networks as well as several instances of probabilistic logic. We provide an
algorithm for Bayesian learning from sparse, albeit complete, observations, and
for deriving inferences and their confidences keeping track of the dependencies
between variables when they are manipulated within the unifying computational
formalism provided by probabilistic circuits. Each leaf of such circuits is
labelled with a beta-distributed random variable that provides us with an
elegant framework for representing uncertain probabilities. We achieve better
estimation of epistemic uncertainty than state-of-the-art approaches, including
highly engineered ones, while being able to handle general circuits and with
just a modest increase in the computational effort compared to using point
probabilities.


Abstract:
  We propose and demonstrate a single-photon sensitive technique for optical
vibrometry. It uses high speed photon counting to sample the modulated
backscattering from a vibrating target. Designed for remote vibration sensing
with ultralow photon flux, we show that this technique can detect small
displacements down to 110 nm and resolve vibration frequencies from DC up to
several kilohertz, with less than 0.01 detected photons per pulse. This
single-photon sensitive optical vibrometry may find important applications in
acousto-optic sensing and imaging, especially in photon-starved environments.


Abstract:
  We present a novel algorithm to solve a non-linear system of equations, whose
solution can be interpreted as a tight lower bound on the vector of expected
hitting times of a Markov chain whose transition probabilities are only
partially specified. We also briefly sketch how this method can be modified to
solve a conjugate system of equations that gives rise to the corresponding
upper bound. We prove the correctness of our method, and show that it converges
to the correct solution in a finite number of steps under mild conditions on
the system. We compare the runtime complexity of our method to a previously
published method from the literature, and identify conditions under which our
novel method is more efficient.