Abstract:
  The accurate determination of electronic temperatures in metallic
nanostructures is essential for many technological applications, like
plasmon-enhanced catalysis or lithographic nanofabrication procedures. In this
Letter we demonstrate that the electronic temperature can be accurately
measured by the shape of the tunnel electroluminescence emission edge in tunnel
plasmonic nanocavities, which follows a universal thermal distribution with the
bias voltage as the chemical potential of the photon population. A significant
deviation between electronic and lattice temperatures is found below 30 K for
tunnel currents larger than 15 nA. This deviation is rationalized as the result
of a two-electron process in which the second electron excites plasmon modes
with an energy distribution that reflects the higher temperature following the
first tunneling event. These results dispel a long-standing controversy on the
nature of overbias emission in tunnel junctions and adds a new method for the
determination of electronic temperatures and quasiparticle dynamics.


Abstract:
  Continued improvements on existing reconstruction methods are vital to the
success of high-energy physics experiments, such as the IceCube Neutrino
Observatory. In IceCube, further challenges arise as the detector is situated
at the geographic South Pole where computational resources are limited.
However, to perform real-time analyses and to issue alerts to telescopes around
the world, powerful and fast reconstruction methods are desired. Deep neural
networks can be extremely powerful, and their usage is computationally
inexpensive once the networks are trained. These characteristics make a deep
learning-based approach an excellent candidate for the application in IceCube.
A reconstruction method based on convolutional architectures and hexagonally
shaped kernels is presented. The presented method is robust towards systematic
uncertainties in the simulation and has been tested on experimental data. In
comparison to standard reconstruction methods in IceCube, it can improve upon
the reconstruction accuracy, while reducing the time necessary to run the
reconstruction by two to three orders of magnitude.


Abstract:
  In this study, we will study investor attention measurement using the Search
Volume Index in the recent market. Since 2009, the popularity of mobile devices
and the spread of the Internet have made the speed of information delivery
faster and the investment information retrieval data for obtaining investment
information has increased dramatically. In these circumstances, investor
attention measurement using search volume data can be measured more accurately
and faster than before mobile device popularization. To confirm this, we will
compare the effect of measuring investor attention using search volume data
before and after mobile device popularization. In addition, it is confirmed
that the measured investor attention is that of retail traders, not
institutional traders or professional traders, and the relationship between
investor attention and short-term price pressure theory. Using SVI data
provided by Google Trends, we will experiment with Russell 3000 stocks and IPO
stocks and compare the results. In addition, the results of investigating the
investor's interest using the search volume data from various angles through
experiments such as the comparison of the results based on the inclusion of the
noise ticker group, the comparison of the limitations of the existing investor
attention measurement method, and the comparison of explanatory variables with
existing IPO related studies. We would like to verify its practicality and
significance.


Abstract:
  This paper proposes a new framework to evaluate unconditional quantile
effects (UQE) in a data combination model. The UQE measures the effect of a
marginal counterfactual change in the unconditional distribution of a covariate
on quantiles of the unconditional distribution of a target outcome. Under rank
similarity and conditional independence assumptions, we provide a set of
identification results for UQEs when the target covariate is continuously
distributed and when it is discrete, respectively. Based on these
identification results, we propose semiparametric estimators and establish
their large sample properties under primitive conditions. Applying our method
to a variant of Mincer's earnings function, we study the counterfactual
quantile effect of actual work experience on income.


Abstract:
  We prove that the genus-0 sector of the quartic analogue of the Kontsevich
model is completely governed by an involution identity which expresses the
meromorphic differential $\omega_{0,n}$ at a reflected point $\iota z$ in terms
of all $\omega_{0,m}$ with $m\leq n$ at the original point $z$. We prove that
the solution of the involution identity obeys blobbed topological recursion,
which confirms a previous conjecture about the quartic Kontsevich model.


Abstract:
  This compact review about gluonium focuses on a slate of theoretical efforts;
among the many standing works, I have selected several that are meant to assist
in the identification, among ordinary mesons, of the few Yang-Mills glueball
configurations that populate the energy region below 3 GeV. This includes
$J/\psi$ radiative and vector-meson decays, studies of scalar meson mixing, of
high-energy cross sections via the Pomeron and the odderon, glueball decays,
etc. The weight of accumulated evidence seems to support the $f_0(1710)$ as
having a large (and the largest) glueball component among the scalars, although
no single observable by itself is conclusive. Further tests would be welcome,
such as exclusive $f_J$ production at asymptotically high $s$ and $t$. No clear
experimental candidates for the pseudoscalar or tensor glueball stand out yet,
and continuing investigations trying to sort them out will certainly teach us
much more about mesons.


Abstract:
  Recently, some approaches are proposed to harness deep convolutional networks
to facilitate superpixel segmentation. The common practice is to first evenly
divide the image into a pre-defined number of grids and then learn to associate
each pixel with its surrounding grids. However, simply applying a series of
convolution operations with limited receptive fields can only implicitly
perceive the relations between the pixel and its surrounding grids.
Consequently, existing methods often fail to provide an effective context when
inferring the association map. To remedy this issue, we propose a novel
\textbf{A}ssociation \textbf{I}mplantation (AI) module to enable the network to
explicitly capture the relations between the pixel and its surrounding grids.
The proposed AI module directly implants the features of grid cells to the
surrounding of its corresponding central pixel, and conducts convolution on the
padded window to adaptively transfer knowledge between them. With such an
implantation operation, the network could explicitly harvest the pixel-grid
level context, which is more in line with the target of superpixel segmentation
comparing to the pixel-wise relation. Furthermore, to pursue better boundary
precision, we design a boundary-perceiving loss to help the network
discriminate the pixels around boundaries in hidden feature level, which could
benefit the subsequent inferring modules to accurately identify more boundary
pixels. Extensive experiments on BSDS500 and NYUv2 datasets show that our
method could not only achieve state-of-the-art performance but maintain
satisfactory inference efficiency.


Abstract:
  We present a novel method to reconstruct a spectral central view and its
aligned disparity map from spatio-spectrally coded light fields. Since we do
not reconstruct an intermediate full light field from the coded measurement, we
refer to this as principal reconstruction. The coded light fields correspond to
those captured by a light field camera in the unfocused design with a
spectrally coded microlens array. In this application, the spectrally coded
light field camera can be interpreted as a single-shot spectral depth camera.
  We investigate several multi-task deep learning methods and propose a new
auxiliary loss-based training strategy to enhance the reconstruction
performance. The results are evaluated using a synthetic as well as a new
real-world spectral light field dataset that we captured using a custom-built
camera. The results are compared to state-of-the art compressed sensing
reconstruction and disparity estimation.
  We achieve a high reconstruction quality for both synthetic and real-world
coded light fields. The disparity estimation quality is on par with or even
outperforms state-of-the-art disparity estimation from uncoded RGB light
fields.


Abstract:
  Understanding unsupervised domain adaptation has been an important task that
has been well explored. However, the wide variety of methods have not analyzed
the role of a classifier's performance in detail. In this paper, we thoroughly
examine the role of a classifier in terms of matching source and target
distributions. We specifically investigate the classifier ability by matching
a) the distribution of features, b) probabilistic uncertainty for samples and
c) certainty activation mappings. Our analysis suggests that using these three
distributions does result in a consistently improved performance on all the
datasets. Our work thus extends present knowledge on the role of the various
distributions obtained from the classifier towards solving unsupervised domain
adaptation.


Abstract:
  Cosmological phase transitions are thought to have taken place at the early
Universe imprinting their properties on the observable Universe. There is
strong evidence that, through the dynamics of a scalar field that lead a second
order phase transition, inflation shaped the Universe accounting for the most
conspicuous features of the observed Universe. It is argued that inflation has
also striking implications for the vacuum energy. Considerations for subsequent
second order phase transitions are also discussed.


Abstract:
  We investigate polariton quantum correlations in a coherently driven box
cavity in the low driving regime, with a particular focus on accounting for the
polarization degree of freedom. The possibility of having different interaction
strengths between co- and cross-circularly polarized polaritons as well as a
realistic linear-polarization splitting allows one to model the system as two
coupled nonlinear resonators with both self- and cross-Kerr-like
nonlinearities, thus making our results potentially relevant for other
experimental platforms. Within an effective wave-function approach, we obtain
analytical expressions for the steady-state polarization-resolved polariton
populations and second-order correlation functions, which agree very well with
our numerical results obtained from a Lindblad master equation. Notably, we
highlight that depending on the excitation polarization (circular or linear),
both the unconventional (interference-mediated) and conventional (mediated by
nonlinearities) antibunchings can be investigated in a single cavity. Moreover,
using our results, we argue that recent experiments on confined fiber-cavity
polaritons are likely to have probed a regime where the dominant interaction is
between cross-polarized polaritons, which is characteristic of the polariton
Feshbach resonance. We furthermore investigate the regime close to resonance
using a two-channel model, and we show that systems with large biexciton
binding energies, such as atomically thin semiconductors, are promising
platforms for realizing strong polariton antibunching.


Abstract:
  In this paper, we study the single-source shortest-path (SSSP) problem with
positive edge weights, which is a notoriously hard problem in the parallel
context. In practice, the $\Delta$-stepping algorithm proposed by Meyer and
Sanders has been widely adopted. However, $\Delta$-stepping has no known
worst-case bounds for general graphs. The performance of $\Delta$-stepping also
highly relies on the parameter $\Delta$. There have also been lots of
algorithms with theoretical bounds, such as Radius-stepping, but they either
have no implementations available or are much slower than $\Delta$-stepping in
practice.
  We propose a stepping algorithm framework that generalizes existing
algorithms such as $\Delta$-stepping and Radius-stepping. The framework allows
for similar analysis and implementations of all stepping algorithms. We also
propose a new ADT, lazy-batched priority queue (LaB-PQ), that abstracts the
semantics of the priority queue needed by the stepping algorithms. We provide
two data structures for LaB-PQ, focusing on theoretical and practical
efficiency, respectively. Based on the new framework and LaB-PQ, we show two
new stepping algorithms, $\rho$-stepping and $\Delta^*$-stepping, that are
simple, with non-trivial worst-case bounds, and fast in practice.
  The stepping algorithm framework also provides almost identical
implementations for three algorithms: Bellman-Ford, $\Delta^*$-stepping, and
$\rho$-stepping. We compare our code with four state-of-the-art
implementations. On five social and web graphs, $\rho$-stepping is 1.3--2.5x
faster than all the existing implementations. On two road graphs, our
$\Delta^*$-stepping is at least 14\% faster than existing implementations,
while $\rho$-stepping is also competitive. The almost identical implementations
for stepping algorithms also allow for in-depth analyses and comparisons among
the stepping algorithms in practice.


Abstract:
  Atomic-like defects in solids are not considered to be identical owing to the
imperfections of host lattice. Here, we found that even under ambient
conditions, negatively charged nitrogen-vacancy (NV$^-$) centers in diamond
could still manifest identical at Hz-precision level, corresponding to a
10$^{-7}$-level relative precision, while the lattice strain can destroy the
identity by tens of Hz. All parameters involved in the NV$^-$-$^{14}$N
Hamiltonian are determined by formulating six nuclear frequencies at
10-mHz-level precision and measuring them at Hz-level precision. The most
precisely measured parameter, the $^{14}$N quadrupole coupling $P$, is given by
-4945754.9(8) Hz, whose precision is improved by nearly four orders of
magnitude compared with previous measurements. We offer an approach for
performing precision measurements in solids and deepening our understandings of
NV centers as well as other solid-state defects. Besides, these high-precision
results imply a potential application of a robust and integrated atomic-like
clock based on ensemble NV centers.


Abstract:
  In this note, we completely classify left-invariant Riemann solitons on
three-dimensional Lorentzian Lie groups.


Abstract:
  In our recent work, we build a propagation scenario to simultaneously explain
the spectra and anisotropy of cosmic rays (CRs) by considering spatially
dependent propagation (SDP) model and nearby Geminga supernova remnant (SNR)
source. But the phase of anisotropy is still inconsistent with the experimental
data. Recent observations of CR anisotropy show that the phase is consistent
with local regular magnetic field (LRMF) observed by Interstellar Boundary
Explorer (IBEX) below 100 TeV, which indicates that diffusion along LRMF is
important. In this work, we further introduce the LRMF and take into account
the effect of corresponding anisotropic diffusion to explain the anisotropy of
CRs. We find that when the diffusion coefficient perpendicular to the LRMF is
much smaller than the parallel one, the phase of anisotropy points to $\sim
R.A.= 3^h$, which accords with experimental observation below 100 TeV. We also
analyze the influence of the ratio of perpendicular and parallel diffusion
coefficient on the anisotropy and the energy dependence of the ratio. The
results illustrate that with the decrease of perpendicular diffusion, the
anisotropic phase changes from the direction of nearby source to the LRMF below
100 TeV, meanwhile it changes from the galactic center (GC) to opposite
direction of LRMF above 100 TeV. When the perpendicular diffusion coefficient
grows faster than the parallel one with energy, the diffusion approaches to the
isotropic at high energy, the phase of anisotropy shifts from the LRMF to the
GC above 100 TeV. This could be helpful to ascertain the energy dependence of
diffusion coefficients.


Abstract:
  The recent boost in data transfer rates puts a daring strain on information
technology. Sustaining such a growth rate requires the development of sources,
detectors and systems working in the so-called TeraHertz (THz) gap covering the
frequency window from 0.1 to 10 THz (1 THz = 10$^{12}$~Hz). This gap represents
a challenge for conventional electronic devices due to carrier transit delays
($\sim$1-10ps), as well as for photonic devices due to thermal fluctuations
(300K$\sim$6THz). Nonetheless, designing efficient, room-temperature THz
sources would constitute a key enabler to applications spanning from
high-resolution imaging to extreme wide band wireless communication. Whereas
high-harmonic generation in solid is usually limited to less than ten
harmonics, broadband THz emission has been demonstrated using laser-induced
superdiffusive spin currents in magnetic bilayers composed of a ferromagnet
deposited on top of a noble metal. While promising, this technique presents the
major disadvantage of necessitating optical pumping and hence lacks
scalability. Here, we demonstrate that extremely high harmonic emission can be
achieved by exploiting conventional spin pumping, without the need of optical
excitation. We show that when the spin-orbit coupling strength is close to the
s-d exchange energy, a strongly non linear regime resulting from resonant spin
flip scattering occurs leading to the generation of a thousand of harmonics at
realistic antiferromagnetic precession frequencies, thereby enhancing both spin
and charge dynamics by two orders of magnitude, and allowing for an emission at
frequencies above 300 THz.


Abstract:
  Nowadays, neural vocoders can generate very high-fidelity speech when a bunch
of training data is available. Although a speaker-dependent (SD) vocoder
usually outperforms a speaker-independent (SI) vocoder, it is impractical to
collect a large amount of data of a specific target speaker for most real-world
applications. To tackle the problem of limited target data, a data augmentation
method based on speaker representation and similarity measurement of speaker
verification is proposed in this paper. The proposed method selects utterances
that have similar speaker identity to the target speaker from an external
corpus, and then combines the selected utterances with the limited target data
for SD vocoder adaptation. The evaluation results show that, compared with the
vocoder adapted using only limited target data, the vocoder adapted using
augmented data improves both the quality and similarity of synthesized speech.


Abstract:
  Disjoint paths are defined as paths between the source and destination nodes
where the intermediate nodes in any two paths are disjoint. They are helpful in
fault-tolerance routing and securing message distribution in the network.
Several research papers were proposed to solve the problem of finding disjoint
paths for variety of interconnection networks such as Hypercube, Generalized
Hypercube, Mesh, Torus, Gaussian, Eisenstein-Jacobi, and many other topologies.
In this research, we have developed a general real-time fault-tolerance
algorithm that constructs all node-to-node disjoint paths for symmetric
networks where all paths are shortest or close to shortest. In addition, we
have simulated the proposed algorithm on different networks. The solution of
unsolved problem in Cube-Connected-Cycles is given in the simulation results.


Abstract:
  For classic Lotka-Volterra systems governing many interacting species, we
establish an exclusion principle that rules out the existence of linearly
asymptotically stable steady states in subcommunitites of communities that
admit a stable state which is internally D-stable. This type of stability is
known to be ensured, e.g., by diagonal dominance or Volterra-Lyapunov stability
conditions. By consequence, the number of stable steady states of this type is
bounded by Sperner's lemma on anti-chains in a poset. The number of stable
steady states can nevertheless be very large if there are many groups of
species that strongly inhibit outsiders but have weak interactions among
themselves. By examples we also show that in general it is possible for a
stable community to contain a stable subcommunity consisting of a single
species. Thus a recent empirical finding to the contrary, in a study of random
competitive systems by Lischke and L\"offler (Theo. Pop. Biol. 115 (2017)
24--34), does not hold without qualification.


Abstract:
  Video relation detection problem refers to the detection of the relationship
between different objects in videos, such as spatial relationship and action
relationship. In this paper, we present video relation detection with
trajectory-aware multi-modal features to solve this task.
  Considering the complexity of doing visual relation detection in videos, we
decompose this task into three sub-tasks: object detection, trajectory proposal
and relation prediction. We use the state-of-the-art object detection method to
ensure the accuracy of object trajectory detection and multi-modal feature
representation to help the prediction of relation between objects. Our method
won the first place on the video relation detection task of Video Relation
Understanding Grand Challenge in ACM Multimedia 2020 with 11.74\% mAP, which
surpasses other methods by a large margin.


Abstract:
  The classical 1961 solution to the problem of determining the number of
perfect matchings (or dimer coverings) of a rectangular grid graph -- due
independently to Kasteleyn and to Temperley and Fisher -- consists of changing
the sign of some of the entries in the adjacency matrix so that the Pfaffian of
the new matrix gives the number of perfect matchings, and then evaluating this
Pfaffian. Another classical method is to use the Lindstr\"om-Gessel-Viennot
theorem on non-intersecting lattice paths to express the number of perfect
matchings as a determinant, and then evaluate this determinant. In this paper
we present a new method for solving the two dimensional dimer problem, which
relies on the Cauchy-Binet theorem. It only involves facts that were known in
the mid 1930's when the dimer problem was phrased, so it could have been
discovered while the dimer problem was still open.
  We provide explicit product formulas for both the square and the hexagonal
lattice. One advantage of our formula for the square lattice compared to the
original formula of Kasteleyn, Temperley and Fisher is that ours has a linear
number of factors, while the number of factors in the former is quadratic. Our
result for the hexagonal lattice yields a formula for the number of periodic
stepped surfaces that fit in an infinite tube of given cross-section, which can
be regarded as a counterpart of MacMahon's boxed plane partition theorem.


Abstract:
  This article proves the products, behaviors and simple zeros for the classes
of the entire functions associated with the Weierstrass-Hadamard product and
the Taylor series.


Abstract:
  Given a matrix $X\in \mathbb{R}_+^{m\times n}$ with nonnegative entries, a
Positive Semidefinite (PSD) factorization of $X$ is a collection of $r \times
r$-dimensional PSD matrices $\{A_i\}$ and $\{B_j\}$ satisfying $X_{ij}=
\mathrm{tr}(A_i B_j)$ for all $\ i\in [m],\ j\in [n]$. PSD factorizations are
fundamentally linked to understanding the expressiveness of semidefinite
programs as well as the power and limitations of quantum resources in
information theory. The PSD factorization task generalizes the Non-negative
Matrix Factorization (NMF) problem where we seek a collection of
$r$-dimensional nonnegative vectors $\{a_i\}$ and $\{b_j\}$ satisfying $X_{ij}=
a_i^\top b_j$, for all $i\in [m],\ j\in [n]$ -- one can recover the latter
problem by choosing matrices in the PSD factorization to be diagonal. The most
widely used algorithm for computing NMFs of a matrix is the Multiplicative
Update algorithm developed by Lee and Seung, in which nonnegativity of the
updates is preserved by scaling with positive diagonal matrices. In this paper,
we describe a non-commutative extension of Lee-Seung's algorithm, which we call
the Matrix Multiplicative Update (MMU) algorithm, for computing PSD
factorizations. The MMU algorithm ensures that updates remain PSD by congruence
scaling with the matrix geometric mean of appropriate PSD matrices, and it
retains the simplicity of implementation that Lee-Seung's algorithm enjoys.
Building on the Majorization-Minimization framework, we show that under our
update scheme the squared loss objective is non-increasing and fixed points
correspond to critical points. The analysis relies on Lieb's Concavity Theorem.
Beyond PSD factorizations, we use the MMU algorithm as a primitive to calculate
block-diagonal PSD factorizations and tensor PSD factorizations. We demonstrate
the utility of our method with experiments on real and synthetic data.


Abstract:
  The krypton electroluminescence yield was studied, at room temperature, as a
function of electric field in the gas scintillation gap. A large area avalanche
photodiode has been used to allow the simultaneous detection of the
electroluminescence pulses as well as the direct interaction of x-rays, the
latter being used as a reference for the calculation of the number of charge
carriers produced by the electroluminescence pulses and, thus, the
determination of the number of photons impinging the photodiode. An
amplification parameter of 113 photons per kV per drifting electron and a
scintillation threshold of 2.7 Td ( 0.7 kV/cm/bar at 293 K ) was obtained, in
good agreement with the simulation data reported in the literature. On the
other hand, the ionisation threshold in krypton was found to be around 13.5 Td
(3.4 kV/cm/bar), less than what had been obtained by the most recent simulation
work-package. The krypton amplification parameter is about 80% and 140% of
those measured for xenon and argon, respectively. The electroluminescence yield
in krypton is of great importance for modeling krypton-based double-phase or
high-pressure gas detectors, which may be used in future rare event detection
experiments.


Abstract:
  Many important challenges in science and technology can be cast as
optimization problems. When viewed in a statistical physics framework, these
can be tackled by simulated annealing, where a gradual cooling procedure helps
search for groundstate solutions of a target Hamiltonian. While powerful,
simulated annealing is known to have prohibitively slow sampling dynamics when
the optimization landscape is rough or glassy. Here we show that by
generalizing the target distribution with a parameterized model, an analogous
annealing framework based on the variational principle can be used to search
for groundstate solutions. Modern autoregressive models such as recurrent
neural networks provide ideal parameterizations since they can be exactly
sampled without slow dynamics even when the model encodes a rough landscape. We
implement this procedure in the classical and quantum settings on several
prototypical spin glass Hamiltonians, and find that it significantly
outperforms traditional simulated annealing in the asymptotic limit,
illustrating the potential power of this yet unexplored route to optimization.


Abstract:
  We study the excitation of asymmetric-top (including chiral) molecules by
two-color femtosecond laser pulses. In the cases of non-chiral asymmetric-top
molecules excited by an orthogonally polarized two-color pulse, we demonstrate,
classically and quantum mechanically, three-dimensional orientation. For chiral
molecules, we show that the orientation induced by a cross-polarized two-color
pulse is enantioselective along the laser propagation direction, namely, the
two enantiomers are oriented in opposite directions. On the short time scale,
the classical and quantum simulations give results that are in excellent
agreement, whereas on the longer time scale, the enantioselective orientation
exhibits quantum beats. These observations are qualitatively explained by
analyzing the interaction potential between the two-color pulse and molecular
(hyper-)polarizability. The prospects for utilizing the long-lasting
orientation for measuring and using the enantioselective orientation for
separating the individual enantiomers are discussed.


Abstract:
  We present new obstructions for a knot K in S^3 to admit purely cosmetic
surgeries, which arise from the study of Witten-Reshetikhin-Turaev invariants
at fixed level. In particular, we strengthen a recent result of Hanselman,
showing that if K has purely cosmetic surgeries then the slopes of the
surgeries are of the form 1/5k except if the Jones polynomial of K evaluated at
a 5-th root of unity is 1. For any odd prime r, we also give an obstruction for
K to have a 1/k surgery slope with k coprime to r that involves the values of
the first (r-3)/2 colored Jones polynomials of K at an r-th root of unity. We
verify the purely cosmetic surgery conjecture for all knots with at most 17
crossings.


Abstract:
  Despite the widespread availability of in-treatment room cone beam computed
tomography (CBCT) imaging, due to the lack of reliable segmentation methods,
CBCT is only used for gross set up corrections in lung radiotherapies. Accurate
and reliable auto-segmentation tools could potentiate volumetric response
assessment and geometry-guided adaptive radiation therapies. Therefore, we
developed a new deep learning CBCT lung tumor segmentation method. Methods: The
key idea of our approach called cross modality educed distillation (CMEDL) is
to use magnetic resonance imaging (MRI) to guide a CBCT segmentation network
training to extract more informative features during training. We accomplish
this by training an end-to-end network comprised of unpaired domain adaptation
(UDA) and cross-domain segmentation distillation networks (SDN) using unpaired
CBCT and MRI datasets. Feature distillation regularizes the student network to
extract CBCT features that match the statistical distribution of MRI features
extracted by the teacher network and obtain better differentiation of tumor
from background.} We also compared against an alternative framework that used
UDA with MR segmentation network, whereby segmentation was done on the
synthesized pseudo MRI representation. All networks were trained with 216
weekly CBCTs and 82 T2-weighted turbo spin echo MRI acquired from different
patient cohorts. Validation was done on 20 weekly CBCTs from patients not used
in training. Independent testing was done on 38 weekly CBCTs from patients not
used in training or validation. Segmentation accuracy was measured using
surface Dice similarity coefficient (SDSC) and Hausdroff distance at 95th
percentile (HD95) metrics.


Abstract:
  Input devices, such as buttons and sliders, are the foundation of any
interface. The typical user-centered design workflow requires the developers
and users to go through many iterations of design, implementation, and
analysis. The procedure is inefficient, and human decisions highly bias the
results. While computational methods are used to assist various design tasks,
there has not been any holistic approach to automate the design of input
components. My thesis proposed a series of Computational Input Design
workflows: I envision a sample-efficient multi-objective optimization algorithm
that cleverly selects design instances, which are instantly deployed on
physical simulators. A meta-reinforcement learning user model then simulates
the user behaviors when using the design instance upon the simulators. The new
workflows derive Pareto-optimal designs with high efficiency and automation. I
demonstrate designing a push-button via the proposed methods. The resulting
designs outperform the known baselines. The Computational Input Design process
can be generalized to other devices, such as joystick, touchscreen, mouse,
controller, etc.


Abstract:
  The Higgs sector of the standard model can be extended by introducing an
$SU(2)_L$ Higgs triplet $\Delta$ to generate the tiny neutrino masses in the
framework of type-II seesaw mechanism. In this paper, we study the pair
production of the introduced Higgs triplet at future $e^{-}p$ colliders. The
corresponding production cross sections via vector boson fusion process at
FCC-ep and ILC$\otimes$FCC are predicted, where the production of a pair of
doubly charged Higgs is found to be dominant and then used to investigate the
collider phenomenology of the Higgs triplet. Depending on the size of the Higgs
triplet vacuum expectation value, the doubly charged Higgs may decay into a
pair of same-sign charged leptons or a pair of same-sign $W$ bosons. In order
to explore the discovery potential of the doubly charged Higgs at future
$e^{-}p$ colliders, we discuss these two decay scenarios in detail and show
respectively the detection sensitivity on the mass of the doubly charged Higgs.


Abstract:
  We introduce a very simple queue implementation with the singly linked list.
With the help of the rear blank node instead of the usual header node, we avoid
additional check steps for the dequeue operation in the traditional
implementations existing for many decades. The essence of our representation is
the half-opened pointer interval with the same direction of the queue
operations, which can guarantee the uniform treatment even in the empty queue
case. The simplification of queue implementations cuts off unnecessary steps,
and it minimizes the number of steps in the dequeue operation with the time
limitation of enqueue operation, which could contribute to the performance of
the real-time systems. We extend the linked queue to the circularly linked
queue, which can also be used to implement stack and take advantage of the
maximal information of the single direction in the circularly linked list, and
it actually constructs the output-restricted deque. We also present a variant:
lazy circularly linked queue, which is more efficient in some special cases,
especially for the dequeue operations.


Abstract:
  Graphical calculi such as the ZH-calculus are powerful tools in the study and
analysis of quantum processes, with links to other models of quantum
computation such as quantum circuits, measurement-based computing, etc.
  A somewhat compact but systematic way to describe a quantum process is
through the use of quantum multiple-valued decision diagrams (QMDDs), which
have already been used for the synthesis of quantum circuits as well as for
verification.
  We show in this paper how to turn a QMDD into an equivalent ZH-diagram, and
vice-versa, and show how reducing a QMDD translates in the ZH-Calculus, hence
allowing tools from one formalism to be used into the other.


Abstract:
  We propose an effective consistency training framework that enforces a
training model's predictions given original and perturbed inputs to be similar
by adding a discrete noise that would incur the highest divergence between
predictions. This virtual adversarial discrete noise obtained by replacing a
small portion of tokens while keeping original semantics as much as possible
efficiently pushes a training model's decision boundary. Moreover, we perform
an iterative refinement process to alleviate the degraded fluency of the
perturbed sentence due to the conditional independence assumption. Experimental
results show that our proposed method outperforms other consistency training
baselines with text editing, paraphrasing, or a continuous noise on
semi-supervised text classification tasks and a robustness benchmark.


Abstract:
  We prove that a large class of Paschke dual algebras of simple unital
C*-algebras are $K_1$-injective. As a consequence, we obtain interesting
$KK$-uniqueness theorems which generalize the Brown--Douglas--Fillmore
essential codimension property.


Abstract:
  Digital services have been offered through remote systems for decades. The
questions of how these systems can be built in a trustworthy manner and how
their security properties can be understood are given fresh impetus by recent
hardware developments, allowing a fuller, more general, exploration of the
possibilities than has previously been seen in the literature. Drawing on and
consolidating the disparate strains of research, technologies and methods
employed throughout the adaptation of confidential computing, we present a
novel, dedicated Confidential Remote Computing (CRC) model. CRC proposes a
compact solution for next-generation applications to be built on strong
hardware-based security primitives, control of secure software products'
trusted computing base, and a way to make correct use of proofs and evidence
reports generated by the attestation mechanisms. The CRC model illustrates the
trade-offs between decentralisation, task size and transparency overhead. We
conclude the paper with six lessons learned from our approach, and suggest two
future research directions.


Abstract:
  We consider the problem of variance reduction in randomized controlled
trials, through the use of covariates correlated with the outcome but
independent of the treatment. We propose a machine learning regression-adjusted
treatment effect estimator, which we call MLRATE. MLRATE uses machine learning
predictors of the outcome to reduce estimator variance. It employs
cross-fitting to avoid overfitting biases, and we prove consistency and
asymptotic normality under general conditions. MLRATE is robust to poor
predictions from the machine learning step: if the predictions are uncorrelated
with the outcomes, the estimator performs asymptotically no worse than the
standard difference-in-means estimator, while if predictions are highly
correlated with outcomes, the efficiency gains are large. In A/A tests, for a
set of 48 outcome metrics commonly monitored in Facebook experiments the
estimator has over 70% lower variance than the simple difference-in-means
estimator, and about 19% lower variance than the common univariate procedure
which adjusts only for pre-experiment values of the outcome.


Abstract:
  Let $\mathcal{A}_1,\ldots,\mathcal{A}_m$ be families of $k$-subsets of an
$n$-set. Suppose that one cannot choose pairwise disjoint edges from $s+1$
distinct families. Subject to this condition we investigate the maximum of
$|\mathcal{A}_1|+\ldots+|\mathcal{A}_m|$. Note that the subcase $m=s+1$,
$\mathcal{A}_1=\ldots=\mathcal{A}_m$ is the Erd\H{o}s Matching Conjecture, one
of the most important open problems in extremal set theory. We provide some
upper bounds, a general conjecture and its solution for the range $n\geq
4k^2s$.


Abstract:
  In the cubic chiral magnet Fe$_{1-x}$Co$_{x}$Si a metastable state comprising
of topologically nontrivial spin whirls, so-called skyrmions, may be preserved
down to low temperatures by means of field cooling the sample. This metastable
skyrmion state is energetically separated from the topologically trivial ground
state by a considerable potential barrier, a phenomenon also referred to as
topological protection. Using magnetic force microscopy on the surface of a
bulk crystal, we show that certain positions are preferentially and
reproducibly decorated with metastable skyrmions, indicating that surface
pinning plays a crucial role. Increasing the magnetic field allows an
increasing number of skyrmions to overcome the potential barrier, and hence to
transform into the ground state. Most notably, we find that the unwinding of
individual skyrmions may be triggered by the magnetic tip itself, however, only
when its magnetization is aligned parallel to the external field. This implies
that the stray field of the tip is key for locally overcoming the topological
protection. Both the control of the position of topologically nontrivial states
as well as their creation and annihilation on demand pose important challenges
in the context of potential skyrmionic applications.


Abstract:
  One of the main issues in hadron spectroscopy is to identify the origin of
threshold or near-threshold enhancement. Prior to our study, there is no
straightforward way of distinguishing even the lowest channel
threshold-enhancement of the nucleon-nucleon system using only the
cross-sections. The difficulty lies in the proximity of either a bound or
virtual state pole to the threshold which creates an almost identical structure
in the scattering region. Identifying the nature of the pole causing the
enhancement falls under the general classification problem and supervised
machine learning using a feed-forward neural network is known to excel in this
task. In this study, we discuss the basic idea behind deep neural network and
how it can be used to identify the nature of the pole causing the enhancement.
The applicability of the trained network can be explored by using an exact
separable potential model to generate a validation dataset. We find that within
some acceptable range of the cut-off parameter, the neural network gives high
accuracy of inference. The result also reveals the important role played by the
background singularities in the training dataset. Finally, we apply the method
to nucleon-nucleon scattering data and show that the network was able to give
the correct nature of pole, i.e. virtual pole for ${}^1S_0$ partial
cross-section and bound state pole for ${}^3S_0$.


Abstract:
  Predicting the future interaction of objects when they come into contact with
their environment is key for autonomous agents to take intelligent and
anticipatory actions. This paper presents a perception framework that fuses
visual and tactile feedback to make predictions about the expected motion of
objects in dynamic scenes. Visual information captures object properties such
as 3D shape and location, while tactile information provides critical cues
about interaction forces and resulting object motion when it makes contact with
the environment. Utilizing a novel See-Through-your-Skin (STS) sensor that
provides high resolution multimodal sensing of contact surfaces, our system
captures both the visual appearance and the tactile properties of objects. We
interpret the dual stream signals from the sensor using a Multimodal
Variational Autoencoder (MVAE), allowing us to capture both modalities of
contacting objects and to develop a mapping from visual to tactile interaction
and vice-versa. Additionally, the perceptual system can be used to infer the
outcome of future physical interactions, which we validate through simulated
and real-world experiments in which the resting state of an object is predicted
from given initial conditions.


Abstract:
  In this paper, a novel convolutional neural network (CNN)-based framework is
developed for light field reconstruction from a sparse set of views. We
indicate that the reconstruction can be efficiently modeled as angular
restoration on an epipolar plane image (EPI). The main problem in direct
reconstruction on the EPI involves an information asymmetry between the spatial
and angular dimensions, where the detailed portion in the angular dimensions is
damaged by undersampling. Directly upsampling or super-resolving the light
field in the angular dimensions causes ghosting effects. To suppress these
ghosting effects, we contribute a novel "blur-restoration-deblur" framework.
First, the "blur" step is applied to extract the low-frequency components of
the light field in the spatial dimensions by convolving each EPI slice with a
selected blur kernel. Then, the "restoration" step is implemented by a CNN,
which is trained to restore the angular details of the EPI. Finally, we use a
non-blind "deblur" operation to recover the spatial high frequencies suppressed
by the EPI blur. We evaluate our approach on several datasets, including
synthetic scenes, real-world scenes and challenging microscope light field
data. We demonstrate the high performance and robustness of the proposed
framework compared with state-of-the-art algorithms. We further show extended
applications, including depth enhancement and interpolation for unstructured
input. More importantly, a novel rendering approach is presented by combining
the proposed framework and depth information to handle large disparities.


Abstract:
  Kernel methods are a highly effective and widely used collection of modern
machine learning algorithms. A fundamental limitation of virtually all such
methods are computations involving the kernel matrix that naively scale
quadratically (e.g., constructing the kernel matrix and matrix-vector
multiplication) or cubically (solving linear systems) with the size of the data
set $N.$ We propose the Fast Kernel Transform (FKT), a general algorithm to
compute matrix-vector multiplications (MVMs) for datasets in moderate
dimensions with quasilinear complexity. Typically, analytically grounded fast
multiplication methods require specialized development for specific kernels. In
contrast, our scheme is based on auto-differentiation and automated symbolic
computations that leverage the analytical structure of the underlying kernel.
This allows the FKT to be easily applied to a broad class of kernels, including
Gaussian, Matern, and Rational Quadratic covariance functions and physically
motivated Green's functions, including those of the Laplace and Helmholtz
equations. Furthermore, the FKT maintains a high, quantifiable, and
controllable level of accuracy -- properties that many acceleration methods
lack. We illustrate the efficacy and versatility of the FKT by providing timing
and accuracy benchmarks and by applying it to scale the stochastic neighborhood
embedding (t-SNE) and Gaussian processes to large real-world data sets.


Abstract:
  With the growing commercial interest in blockchain, permissioned
implementations have received increasing attention. Unfortunately, existing BFT
consensus protocols that are the backbone of permissioned blockchains, either
scale poorly or offer limited throughput. Most of these algorithms require at
least one process to receive and validate the votes from all other processes
and then broadcast the result, which is inherently non-scalable. Some
algorithms avoid this bottleneck by using aggregation trees to collect and
validate votes. However, to the best of our knowledge, such algorithms offer
limited throughput and degrade quickly in the presence of faults. In this paper
we propose \thesystem, the first BFT communication abstraction that organizes
participants in a tree to perform scalable vote aggregation and that, in faulty
runs, is able to terminate the protocol within an optimal number of
reconfigurations ($f+1$). We define precisely which aggregation trees allow for
optimal reconfiguration and show that, unlike previous protocols, when using
these configurations, \thesystem scales to large number of processes and
outperforms HotStuff's throughput by up to 38x.


Abstract:
  In this article, we propose a method to design loss functions based on
component trees which can be optimized by gradient descent algorithms and which
are therefore usable in conjunction with recent machine learning approaches
such as neural networks. We show how the altitudes associated to the nodes of
such hierarchical image representations can be differentiated with respect to
the image pixel values. This feature is used to design a generic loss function
that can select or discard image maxima based on various attributes such as
extinction values. The possibilities of the proposed method are demonstrated on
simulated and real image filtering.


Abstract:
  The analog of the Kepler system defined on the Heisenberg group introduced by
Montgomery and Shanbrom in [Fields Inst. Commun., Vol. 73, Springer, New York,
2015, 319-342, arXiv:1212.2713] is integrable on the zero level of the
Hamiltonian. We show that in all other cases the system is not Liouville
integrable due to the lack of additional meromorphic first integrals. We prove
that the analog of the two-body problem on the Heisenberg group is not
integrable in the Liouville sense.


Abstract:
  It is shown that the general relativity has a one-parameter compact symmetry
and this symmetry is analogous to the phase symmetry of quantum mechanics


Abstract:
  We theoretically investigate how the spin susceptibility of a planar
Josephson junction is affected when the system transits into the topological
superconducting state. We show that the magnetic flux and magnetic field
dependence of the spin susceptibility closely maps the phase diagram of the
system. In the absence of an external magnetic flux the system can self-tune
into the topological superconducting state by minimizing its free energy.
Self-tuned topological transitions are accompanied by sharp peaks in the spin
susceptibility, which can therefore be use as measurable fingerprints for
detecting the topological superconducting state. Away from the peaks, the
amplitude of the spin susceptibility can provide qualitative information about
the relative size of the topological gap. The signatures in the spin
susceptibility are robust, even in junctions with narrow superconducting leads,
where critical current minima may no longer serve as an indication of
topological phase transitions. Numerical simulations are complemented with
simplified analytical models capable of capturing the main features predicted
for the spin susceptibility behavior. The predicted results could be
particularly relevant for future experiments on realization and detection of
the topological superconducting state in planar Josephson junctions.


Abstract:
  We present the first X-ray detections of ultracool dwarfs (UCDs) from the
first all-sky survey of the extended ROentgen Survey with an Imaging Telescope
Array (eROSITA) onboard the Russian Spektrum-Roentgen-Gamma (SRG) mission. We
use three publicly available input catalogs of spectroscopically confirmed UCDs
and Gaia-selected UCD candidates that together comprise nearly 20000 objects.
We first extracted all X-ray sources from the catalog of the first eROSITA
survey, eRASS1, that have a UCD or candidate within three times their
positional uncertainty. Then we examined all Gaia objects in the vicinity of
these 96 X-ray sources and we associated them to the most plausible counterpart
on the basis of their spatial separation to the X-ray position and their
multiwavelength properties. This way we find 40 UCDs that have a secure
identification with an X-ray source and 18 plausible UCD X-ray emitters.
Twenty-one of these X-ray emitting UCDs have a spectroscopic confirmation,
while the others have been selected based on Gaia photometry and we computed
spectral types from the G-J color. The spectral types of the X-ray emitting
UCDs and candidates range between M5 and M9, and the distances range from 3.5
to 190 pc. The majority of the UCDs from the eRASS1 sample show a ratio of
X-ray to bolometric luminosity well above the canonical saturation limit of log
(Lx/Lbol) ~ -3. For the two most extreme outliers, we showed through an
analysis of the eRASS1 light curve that these high values are due to flaring
activity. The X-ray spectra of the two brightest objects both reveal an
emission-measure weighted plasma temperature of kT ~ 0.75 keV. These
observations demonstrate the potential of eROSITA for advancing our knowledge
on the faint coronal X-ray emission from UCDs by building statistical samples
for which the average X-ray brightness, flares, and coronal temperatures can be
derived.


Abstract:
  Let $G$ be a finite group, and let $\Delta(G)$ be the prime graph built on
its set of conjugacy class sizes: this is the (simple undirected) graph whose
vertices are the prime numbers dividing some conjugacy class size of $G$, and
two distinct vertices $p, q$ are adjacent if and only if $pq$ divides some
class size of $G$. In this paper, we characterise the structure of those groups
$G$ whose prime graph $\Delta(G)$ is a block square.


Abstract:
  We investigate the magnetic nanoparticles (fluid) hyperthermia in
non-adiabatic conditions through the calorimetric method. Specifically, we
propose a theoretical approach to magnetic hyperthermia from a thermodynamic
point of view. To test the robustness of the approach, we perform hyperthermia
experiments and analyze the thermal behavior of magnetite and magnesium ferrite
magnetic nanoparticles dispersed in water submitted to an alternating magnetic
field. From our findings, besides estimating the specific loss power value from
a non-adiabatic process, thus enhancing the accuracy in the determination of
this quantity, we provide physical meaning to parameters found in literature
that still remained not fully understood, and bring to light how they can be
obtained experimentally.


Abstract:
  We study possibilities of creation and detection of oscillating gravitational
fields from lab-scale high energy, relativistic sources. The sources considered
are high energy laser beams in an optical cavity and the ultra-relativistic
proton bunches circulating in the beam of the Large Hadron Collider (LHC) at
CERN. These sources allow for signal frequencies much higher and far narrower
in bandwidth than what most celestial sources produce. In addition, by
modulating the beams, one can adjust the source frequency over a very broad
range, from Hz to GHz. The gravitational field of these sources and responses
of a variety of detectors are analyzed.
  We optimize a mechanical oscillator such as a pendulum or torsion balance as
detector and find parameter regimes such that -- combined with the planned
high-luminosity upgrade of the LHC as a source -- a signal-to-noise ratio
substantially larger than 1 should be achievable at least in principle,
neglecting all sources of technical noise. This opens new perspectives of
studying general relativistic effects and possibly quantum-gravitational
effects with ultra-relativistic, well-controlled terrestrial sources.


Abstract:
  We theoretically and numerically study the terahertz auto oscillations in
thin-film metallic non-collinear coplanar antiferromagnets (AFMs), such as
$\mathrm{Mn_{3}Sn}$ and $\mathrm{Mn_{3}Ir}$, under the effect of anti-damping
spin-torque with spin polarization perpendicular to the plane of the film. To
obtain the order parameter dynamics in these AFMs, we solve three
Landau-Lifshitz-Gilbert equations coupled by exchange interactions assuming
both single- and multi-domain (micromagnetics) dynamical processes. In the
limit of strong exchange interaction, the oscillatory dynamics of the order
parameter in these AFMs, which have opposite chiralities, could be mapped to
that of a linear damped-driven pendulum in the case of $\mathrm{Mn_{3}Sn}$, and
a non-linear damped-driven pendulum in case of $\mathrm{Mn_{3}Ir}$. The
theoretical framework allows us to identify the input current requirements as a
function of the material and geometry parameters for exciting an oscillatory
response. We also obtain a closed-form approximate solution of the oscillation
frequency for large input currents in case of both $\mathrm{Mn_{3}Ir}$ and
$\mathrm{Mn_{3}Sn}$. Our analytical predictions of threshold current and
oscillation frequency agree well with the numerical results and thus can be
used as compact models to design and optimize the auto oscillator. Employing a
circuit model, based on the principle of tunnel anisotropy magnetoresistance,
we present detailed models of the output power and efficiency versus
oscillation frequency of the auto oscillator. Finally, we explore the spiking
dynamics of two unidirectional as well as bidirectional coupled AFM oscillators
using non-linear damped-driven pendulum equations.


Abstract:
  We investigate the training of sparse layers that use different parameters
for different inputs based on hashing in large Transformer models.
Specifically, we modify the feedforward layer to hash to different sets of
weights depending on the current token, over all tokens in the sequence. We
show that this procedure either outperforms or is competitive with
learning-to-route mixture-of-expert methods such as Switch Transformers and
BASE Layers, while requiring no routing parameters or extra terms in the
objective function such as a load balancing loss, and no sophisticated
assignment algorithm. We study the performance of different hashing techniques,
hash sizes and input features, and show that balanced and random hashes focused
on the most local features work best, compared to either learning clusters or
using longer-range context. We show our approach works well both on large
language modeling and dialogue tasks, and on downstream fine-tuning tasks.


Abstract:
  We prove a factorization formula for the Taylor series coefficients of a zero
of a polynomial as a function of the polynomial's coefficients. This result
extends to more general functions which we call "complex-exponent polynomials".
To prove this formula, we prove theorems about derivations on commutative
rings. We also show that, when applied to polynomials, our formula recovers the
results of Sturmfels obtained with GKZ systems ("Solving algebraic equations in
terms of $\mathcal{A}$-hypergeometric series". Discrete Math. 210 (2000) pp.
171-181)


Abstract:
  Motivated by work of Erd\H{o}s, Ota determined the maximum size $g(n,k)$ of a
$k$-connected nonhamiltonian graph of order $n$ in 1995. But for some pairs
$n,k,$ the maximum size is not attained by a graph of connectivity $k.$ For
example, $g(15,3)=77$ is attained by a unique graph of connectivity $7,$ not
$3.$ In this paper we obtain more precise information by determining the
maximum size of a nonhamiltonian graph of order $n$ and connectivity $k,$ and
determining the extremal graphs. Consequently we solve the corresponding
problem for nontraceable graphs.


Abstract:
  The ongoing COVID-19 pandemic has profoundly impacted people's life around
the world, including how they interact with mobile technologies. In this paper,
we seek to develop an understanding of how the dynamic trajectory of a pandemic
shapes mobile phone users' experiences. Through the lens of app popularity, we
approach this goal from a cross-country perspective. We compile a dataset
consisting of six-month daily snapshots of the most popular apps in the iOS App
Store in China and the US, where the pandemic has exhibited distinct
trajectories. Using this longitudinal dataset, our analysis provides detailed
patterns of app ranking during the pandemic at both category and individual app
levels. We reveal that app categories' rankings are correlated with the
pandemic, contingent upon country-specific development trajectories. Our work
offers rich insights into how the COVID-19, a typical global public health
crisis, has influence people's day-to-day interaction with the Internet and
mobile technologies.


Abstract:
  We use spherical harmonic expansions to prove that any star body equal to its
intersection body is a ball about the origin.


Abstract:
  The performance of modern electronics is associated with the multi-layered
interconnects, encouraging the development of a low-k dielectrics. Herein, we
studied the effects of phase transition from crystalline to amorphous on
dielectric, optical and electrical properties of MIL-100 (Fe) and Basolite F300
metal-organic framework (MOF) obtained using different synthesis techniques in
both the radio (4-1.5 MHz) and infrared (1.2-150 THz) frequency regimes, which
are important for the microelectronics, infrared optical sensors, and
high-frequency telecommunications. The impact of amorphization on broadband
dielectric response was established based on: (1) The comparison in the
dielectric characteristics of a commercially available amorphous Basolite F300
versus a mechanochemically synthesized crystalline MIL-100 in the MHz region.
(2) By tracking the frequency shifts in the vibrational modes of MIL-100
structure in the far-IR (phonons) and mid-IR regions. We showed that various
parameters such as pelleting pressure, temperature, frequency, density, and
degree of amorphization greatly affect the dielectric properties of the
framework. We also investigated the influence of temperature (20-100 {\deg}C)
on the electric and dielectric response in the MHz region, crucial for all
electronic devices.


Abstract:
  Results from the last FTU campaigns on the deuterium large (wrt FTU volume)
pellet REs suppression capability, mainly due to the induced burst MHD activity
expelling REs seed are presented for discharges with 0.5 MA and 5.3T. Clear
indications of avalanche multiplication of REs following single pellet
injection on 0.36 MA flat-top discharges is shown together with quantitative
indications of dissipative effects in terms of critical electrical field
increase due to fan-like instabilities. Analysis of large fan-like
instabilities on post-disruption RE beams, that seem to be correlated with low
electrical field and background density drops, reveal their strong RE energy
suppression capability suggesting a new strategy for RE energy suppression
controlling large fan instabilities. We demonstrate how such density drops can
be induced using modulated ECRH power on post-disruption beams.


Abstract:
  In this paper, we propose dualFace, a portrait drawing interface to assist
users with different levels of drawing skills to complete recognizable and
authentic face sketches. dualFace consists of two-stage drawing assistance to
provide global and local visual guidance: global guidance, which helps users
draw contour lines of portraits (i.e., geometric structure), and local
guidance, which helps users draws details of facial parts (which conform to
user-drawn contour lines), inspired by traditional artist workflows in portrait
drawing. In the stage of global guidance, the user draws several contour lines,
and dualFace then searches several relevant images from an internal database
and displays the suggested face contour lines over the background of the
canvas. In the stage of local guidance, we synthesize detailed portrait images
with a deep generative model from user-drawn contour lines, but use the
synthesized results as detailed drawing guidance. We conducted a user study to
verify the effectiveness of dualFace, and we confirmed that dualFace
significantly helps achieve a detailed portrait sketch. see
http://www.jaist.ac.jp/~xie/dualface.html


Abstract:
  Many of the models used nowadays in mathematical epidemiology, in particular
in COVID-19 research, belong to a certain sub-class of compartmental models
whose classes may be divided into three "(x, y, z)" groups, which we will call
respectively "susceptible/entrance, diseased, and output" (in the classic SIR
case, there is only one class of each type). Roughly, the ODE dynamics of these
models contain only linear terms, with the exception of products between x and
y terms. It has long been noticed that the basic reproduction number R has a
very simple formula (3.3) in terms of the matrices which define the model, and
an explicit first integral formula (3.8) is also available. These results can
be traced back at least to [ABvdD+07] and [Fen07], respectively, and may be
viewed as the "basic laws of SIR-type epidemics"; however many papers continue
to reprove them in particular instances (by the next-generation matrix method
or by direct computations, which are unnecessary). This motivated us to redraw
the attention to these basic laws and provide a self-contained reference of
related formulas for (x, y, z) models. We propose to rebaptize the class to
which they apply as matrix SIR epidemic models, abbreviated as SYR, to
emphasize the similarity to the classic SIR case. For the case of one
susceptible class, we propose to use the name SIR-PH, due to a simple
probabilistic interpretation as SIR models where the exponential infection time
has been replaced by a PH-type distribution. We note that to each SIR-PH model,
one may associate a scalar quantity Y(t) which satisfies "classic SIR
relations", see (3.8). In the case of several susceptible classes, this
generalizes to (5.10); in a future paper, we will show that (3.8), (5.10) may
be used to obtain approximate control policies which compare well with the
optimal control of the original model.


Abstract:
  A simple and natural explanation for the minimum period of millisecond
pulsars follows from a correlation between the accretion rate and the frozen
surface dipole magnetic field resulting from Ohmic diffusion through the
neutron star crust in initial stages of accretion in low mass X-ray binaries.


Abstract:
  About two dozens of exactly solvable Markov chains on one-dimensional finite
and semi-infinite integer lattices are constructed in terms of convolutions of
orthogonality measures of the Krawtchouk, Hahn, Meixner, Charlier, $q$-Hahn,
$q$-Meixner and little $q$-Jacobi polynomials. By construction, the stationary
probability distributions, the complete sets of eigenvalues and eigenvectors
are provided by the polynomials and the orthogonality measures. An interesting
property possessed by these stationary probability distributions, called
`convolutional self-similarity,' is demonstrated.


Abstract:
  In threshold-based anomaly detection, we want to tune the threshold of a
detector to achieve an acceptable false alarm rate. However, tuning the
threshold is often a non-trivial task due to unknown detector output
distributions. A detector threshold that provides an acceptable false alarm
rate is equivalent to a specific quantile of the detector output distribution.
Therefore, we use quantile estimators based on order statistics to estimate the
detector threshold. The estimation of quantiles from sample data has a more
than a century long tradition and we provide three different distribution-free
finite sample guarantees for a class of quantile estimators. The first is based
on the Dworetzky-Kiefer-Wolfowitz inequality, the second utilizes Chebyshev's
inequality, and the third is based on exact confidence intervals for a binomial
distribution. These guarantees are then compared and used in the detector
threshold tuning problem. We use both simulated data as well as data obtained
from an experimental setup with the Temperature Control Lab to validate the
guarantees provided.


Abstract:
  We derive the optimal signed variable in general case kernels for the
classical statistic density estimation, which are some generalization of the
famous Epanechnikov's ones.


Abstract:
  We give a brief review of the current understanding of renormalons of the
static QCD potential in coordinate and momentum spaces. We also reconsider
estimate of the normalization constant of the $u=3/2$ renormalon and propose a
new way to improve the estimate.


Abstract:
  With the instantaneous growth of text information, retrieving domain-oriented
information from the text data has a broad range of applications in Information
Retrieval and Natural language Processing. Thematic keywords give a compressed
representation of the text. Usually, Domain Identification plays a significant
role in Machine Translation, Text Summarization, Question Answering,
Information Extraction, and Sentiment Analysis. In this paper, we proposed the
Multichannel LSTM-CNN methodology for Technical Domain Identification for
Telugu. This architecture was used and evaluated in the context of the ICON
shared task TechDOfication 2020 (task h), and our system got 69.9% of the F1
score on the test dataset and 90.01% on the validation set.


Abstract:
  Recent studies constructing direct interactions between the claim and each
single user response (a comment or a relevant article) to capture evidence have
shown remarkable success in interpretable claim verification. Owing to
different single responses convey different cognition of individual users
(i.e., audiences), the captured evidence belongs to the perspective of
individual cognition. However, individuals' cognition of social things is not
always able to truly reflect the objective. There may be one-sided or biased
semantics in their opinions on a claim. The captured evidence correspondingly
contains some unobjective and biased evidence fragments, deteriorating task
performance. In this paper, we propose a Dual-view model based on the views of
Collective and Individual Cognition (CICD) for interpretable claim
verification. From the view of the collective cognition, we not only capture
the word-level semantics based on individual users, but also focus on
sentence-level semantics (i.e., the overall responses) among all users and
adjust the proportion between them to generate global evidence. From the view
of individual cognition, we select the top-$k$ articles with high degree of
difference and interact with the claim to explore the local key evidence
fragments. To weaken the bias of individual cognition-view evidence, we devise
inconsistent loss to suppress the divergence between global and local evidence
for strengthening the consistent shared evidence between the both. Experiments
on three benchmark datasets confirm that CICD achieves state-of-the-art
performance.


Abstract:
  Introduces HIVE-4-MAT - Helping Interdisciplinary Vocabulary Engineering for
Materials Science, an automatic linked data ontology application. Covers
contextual background for materials science, shared ontology infrastructures,
and reviews the knowledge extraction and indexing process. HIVE-4-MAT's
vocabulary browsing, term search and selection, and knowledge extraction and
indexing are reviewed, and plans to integrate named entity recognition.
Conclusion highlights next steps with relation extraction to support better
ontologies.


Abstract:
  The main motivation of this work is to assess the validity of a LWR traffic
flow model to model measurements obtained from trajectory data, and propose
extensions of this model to improve it. A formulation for a discrete dynamical
system is proposed aiming at reproducing the evolution in time of the density
of vehicles along a road, as observed in the measurements. This system is
formulated as a chemical reaction network where road cells are interpreted as
compartments, the transfer of vehicles from one cell to the other is seen as a
chemical reaction between adjacent compartment and the density of vehicles is
seen as a concentration of reactant. Several degrees of flexibility on the
parameters of this system, which basically consist of the reaction rates
between the compartments, can be considered: a constant value or a function
depending on time and/or space. Density measurements coming from trajectory
data are then interpreted as observations of the states of this system at
consecutive times. Optimal reaction rates for the system are then obtained by
minimizing the discrepancy between the output of the system and the state
measurements. This approach was tested both on simulated and real data, proved
successful in recreating the complexity of traffic flows despite the
assumptions on the flux-density relation.


Abstract:
  In this paper, we derive the information theoretic performance bounds on
communication data rates and errors in parameter estimation, for a joint radar
and communication (JRC) system. We assume that targets are semi-passive, i.e.
they use active components for signal reception, and passive components to
communicate their own information. Specifically, we let the targets to have
control over their passive reflectors in order to transmit their own
information back to the radar via reflection-based beamforming or
backscattering. We derive the Cramer-Rao lower bounds (CRBs) for the mean
squared error in the estimation of target parameters. The concept of a target
ambiguity function (TAF) arises naturally in the derivation CRBs. Using these
TAFs as cost function, we propose a waveform optimization technique based on
calculus of variations. Further, we derive lower bounds on the data rates for
communication on forward and reverse channels, in radar-only and joint radar
and communications scenarios. Through numerical examples, we demonstrate the
utility of this framework for transmit waveform design, codebook construction,
and establishing the corresponding data rate bounds.


Abstract:
  Recently single-beam intracavity optical tweezers have been reported and
achieved orders-of-magnitude higher confinement than standard optical tweezers.
However, there is only one feedback loop between the trapped particle's
three-dimensional position and the scattering loss of the intracavity laser.
That leads to the coupling effect between the particle's radial and axial
motion, and aggravates the axial confinement efficiency. Here, we present and
demonstrate the dual-beam intracavity optical trap enabling independent radial
and axial self-feedback control of the trapped particle, through offsetting the
foci of the clockwise and counter-clockwise beams. We have achieved the axial
confinement efficiency of 1.6*10^(-4) mW^(-1) experimentally at very low
numerical aperture (NA=0.25), which is the highest axial confinement efficiency
of the optical trap to date, to the best of our knowledge. The dual-beam
intracavity optical trap will significantly expand the range of applications in
the further studies of biology and physics, especially for a sample that is
extremely sensitive to heat.


Abstract:
  Here we present a liquid nitrogen-cooled Ca^+ optical clock with an overall
systematic uncertainty of 3*10^-18. In contrast with the room-temperature Ca^+
optical clock that we have reported previously, the temperature of the
blackbody radiation (BBR) shield in vacuum has been reduced to 82(5) K using
liquid nitrogen. An ion trap with a lower heating rate and improved cooling
lasers were also introduced. This allows cooling the ion temperature to the
Doppler cooling limit during the clock operation, and the systematic
uncertainty due to the ion's secular (thermal) motion is reduced to < 1*10^-18.
The uncertainty due to the probe laser light shift and the servo error are also
reduced to < 1*10^-19 and 4*10^-19 with the hyper-Ramsey method and the
higher-order servo algorithm, respectively. By comparing the output frequency
of the cryogenic clock to that of a room-temperature clock, the differential
BBR shift between the two was measured with a fractional statistical
uncertainty of 7*10^-18. The differential BBR shift was used to calculate the
static differential polarizability, and it was found in excellent agreement
with our previous measurement with a different method. This work suggests that
the BBR shift of optical clocks can be well suppressed in a liquid nitrogen
environment. This is advantageous because conventional liquid-helium cryogenic
systems for optical clocks are more expensive and complicated. Moreover, the
proposed system can be used to suppress the BBR shift significantly in other
types of optical clocks such as Yb^+, Sr^+, Yb, Sr, etc.


Abstract:
  By linking to external websites, Wikipedia can act as a gateway to the Web.
To date, however, little is known about the amount of traffic generated by
Wikipedia's external links. We fill this gap in a detailed analysis of usage
logs gathered from Wikipedia users' client devices. Our analysis proceeds in
three steps: First, we quantify the level of engagement with external links,
finding that, in one month, English Wikipedia generated 43M clicks to external
websites, in roughly even parts via links in infoboxes, cited references, and
article bodies. Official links listed in infoboxes have by far the highest
click-through rate (CTR), 2.47% on average. In particular, official links
associated with articles about businesses, educational institutions, and
websites have the highest CTR, whereas official links associated with articles
about geographical content, television, and music have the lowest CTR. Second,
we investigate patterns of engagement with external links, finding that
Wikipedia frequently serves as a stepping stone between search engines and
third-party websites, effectively fulfilling information needs that search
engines do not meet. Third, we quantify the hypothetical economic value of the
clicks received by external websites from English Wikipedia, by estimating that
the respective website owners would need to pay a total of $7--13 million per
month to obtain the same volume of traffic via sponsored search. Overall, these
findings shed light on Wikipedia's role not only as an important source of
information, but also as a high-traffic gateway to the broader Web ecosystem.


Abstract:
  Soft robots, in contrast to their rigid counter parts, have infinite degrees
of freedom that are coupled with their interaction with the environment. We
consider the locomotion of an untethered robot, in the granular medium,
comprised of multiple flexible flagella that rotate about an axis by a motor.
Drag from the grains causes the flagella to deform and the deformed shape
generates a net forward propulsion. This external drag force depends on the
shape of the flagella, while the change in flagellar shape is the result of the
competition between the external loading and elastic forces. We introduce a
numerical tool that couples discrete differential geometry based simulation of
elastic rods - our model for flagella - and a resistive force theory based
model for the drag. In parallel with simulations, we conduct experiments to
quantify the propulsive speed of this class of robots. We find reasonable
quantitative agreement between experiments and simulations. Owing to a
rod-based kinematic representation of the robot, the simulation runs faster
than real-time, and, therefore, we can use it as a design tool for this class
of soft robots. We find that there is an optimal rotational speed at which
maximum efficiency is achieved. Moreover, both experiments and simulations show
that increasing the number of flagella decreases the speed of the robot. We
also gain insight into the mechanics of granular medium - while resistive force
theory can successfully describe the propulsion at low number of flagella, it
fails when more flagella are added to the robot.


Abstract:
  Automated proof search with connection tableaux, such as implemented by
Otten's leanCoP prover, depends on backtracking for completeness. Otten's
restricted backtracking strategy loses completeness, yet for many problems, it
significantly reduces the time required to find a proof. I introduce a new,
less restricted backtracking strategy based on the notion of exclusive cuts. I
implement the strategy in a new prover called meanCoP and show that it greatly
improves upon the previous best strategy in leanCoP.


Abstract:
  This paper proposes a flexible and analytically tractable class of
frequency-severity models based on neural networks to parsimoniously capture
important empirical observations. In the proposed two-part model, mean
functions of frequency and severity distributions are characterized by neural
networks to incorporate the non-linearity of input variables. Furthermore, it
is assumed that the mean function of the severity distribution is an affine
function of the frequency variable to account for a potential linkage between
frequency and severity. We provide explicit closed-form formulas for the mean
and variance of the aggregate loss within our modelling framework. Components
of the proposed model including parameters of neural networks and distribution
parameters can be estimated by minimizing the associated negative
log-likelihood functionals with neural network architectures. Furthermore, we
leverage the Shapely value and recent developments in machine learning to
interpret the outputs of the model. Applications to a synthetic dataset and
insurance claims data illustrate that our method outperforms the existing
methods in terms of interpretability and predictive accuracy.


Abstract:
  The current work is motivated by the need for robust statistical methods for
precision medicine; as such, we address the need for statistical methods that
provide actionable inference for a single unit at any point in time. We aim to
learn an optimal, unknown choice of the controlled components of the design in
order to optimize the expected outcome; with that, we adapt the randomization
mechanism for future time-point experiments based on the data collected on the
individual over time. Our results demonstrate that one can learn the optimal
rule based on a single sample, and thereby adjust the design at any point t
with valid inference for the mean target parameter. This work provides several
contributions to the field of statistical precision medicine. First, we define
a general class of averages of conditional causal parameters defined by the
current context for the single unit time-series data. We define a nonparametric
model for the probability distribution of the time-series under few
assumptions, and aim to fully utilize the sequential randomization in the
estimation procedure via the double robust structure of the efficient influence
curve of the proposed target parameter. We present multiple
exploration-exploitation strategies for assigning treatment, and methods for
estimating the optimal rule. Lastly, we present the study of the data-adaptive
inference on the mean under the optimal treatment rule, where the target
parameter adapts over time in response to the observed context of the
individual. Our target parameter is pathwise differentiable with an efficient
influence function that is doubly robust - which makes it easier to estimate
than previously proposed variations. We characterize the limit distribution of
our estimator under a Donsker condition expressed in terms of a notion of
bracketing entropy adapted to martingale settings.


Abstract:
  The interpretation of the nuclear cross sections measured using accelerator
neutrino beams involve severe difficulties, arising primarily from the average
over the incoming neutrino flux. The broad energy distribution of the beam
particles hampers the determination of the energy transfer to the nuclear
target, the knowledge of which is needed to pin down the dominant reaction
mechanism. Overcoming this problem requires the development of a theoretical
approach suitable to describe neutrino interactions at energies ranging from
hundreds of MeV to few GeV. In this paper, it is argued that the approach based
on the factorisation of the nuclear cross section provides a consistent
framework for the calculation of neutrino-nucleus interactions in both the
quasi elastic and inelastic channels. The near-degeneracy between theoretical
models based on different assumptions, and the use of electron scattering data
to advance the understanding of neutrino-nucleus cross sections are also
discussed.


Abstract:
  In Savage's classic decision-theoretic framework, actions are formally
defined as functions from states to outcomes. But where do the state space and
outcome space come from? Expanding on recent work by Blume, Easley, and Halpern
(BEH), we consider a language-based framework in which actions are identified
with (conditional) descriptions in a simple underlying language, while states
and outcomes (along with probabilities and utilities) are constructed as part
of a representation theorem. Our work expands the role of language from that of
BEH by using it not only for the conditions that determine which actions are
taken, but also the effects. More precisely, we take the set of actions to be
built from those of the form "do(phi)", for formulas phi in the underlying
language. This presents a problem: how do we interpret the result of do(phi)
when phi is underspecified (i.e., compatible with multiple states)? We answer
this using tools familiar from the semantics of counterfactuals: roughly
speaking, do(phi) maps each state to the "closest" phi-state. This notion of
"closest" is also something we construct as part of the representation theorem;
in effect, then, we prove that (under appropriate assumptions) the agent is
acting as if each underspecified action is first made definite and then
evaluated (i.e., by maximizing expected utility). Of course, actions in the
real world are often not presented in a fully precise manner, yet agents reason
about and form preferences among them all the same. Our work brings the
abstract tools of decision theory into closer contact with such real-world
scenarios.


Abstract:
  We investigate a class of weighted planar stochastic lattice (WPSL1) created
by random sequential nucleation of seed from which a crack is grown parallel to
one of the sides of the chosen block and ceases to grow upon hitting another
crack. It results in the partitioning of the square into contiguous and
non-overlapping blocks. Interestingly, we find that the dynamics of WPSL1 is
governed by infinitely many conservation laws and each of the conserved
quantities, except the trivial conservation of total mass or area, is a
multifractal measure. On the other hand, the dual of the lattice is a
scale-free network as its degree distribution exhibits a power-law $P(k)\sim
k^{-\gamma}$ with $\gamma=4.13$. The network is also a small-world network as
we find that (i) the total clustering coefficient $C$ is high and independent
of the network size and (ii) the mean geodesic path length grows
logarithmically with $N$. Besides, the clustering coefficient $C_k$ of the
nodes which have degree $k$ decreases exactly as $2/(k-1)$ revealing that it is
also a nested hierarchical network.


Abstract:
  Hypergraphs are a common model for multiway relationships in data, and
hypergraph semi-supervised learning is the problem of assigning labels to all
nodes in a hypergraph, given labels on just a few nodes. Diffusions and label
spreading are classical techniques for semi-supervised learning in the graph
setting, and there are some standard ways to extend them to hypergraphs.
However, these methods are linear models, and do not offer an obvious way of
incorporating node features for making predictions. Here, we develop a
nonlinear diffusion process on hypergraphs that spreads both features and
labels following the hypergraph structure, which can be interpreted as a
hypergraph equilibrium network. Even though the process is nonlinear, we show
global convergence to a unique limiting point for a broad class of
nonlinearities, which is the global optimum of a interpretable, regularized
semi-supervised learning loss function. The limiting point serves as a node
embedding from which we make predictions with a linear model. Our approach is
much more accurate than several hypergraph neural networks, and also takes less
time to train.


Abstract:
  The ability to persuade others is critical to professional and personal
success. However, crafting persuasive messages is demanding and poses various
challenges. We conducted nine exploratory case studies to identify adaptations
that professional and non-professional writers make in written scenarios to
increase their subjective persuasiveness. Furthermore, we identified challenges
that those writers faced and identified strategies to resolve them with
persuasive natural language generation, i.e., artificial intelligence. Our
findings show that humans can achieve high degrees of persuasiveness (more so
for professional-level writers), and artificial intelligence can complement
them to achieve increased celerity and alignment in the process.


Abstract:
  The widespread deployment of deep nets in practical applications has lead to
a growing desire to understand how and why such black-box methods perform
prediction. Much work has focused on understanding what part of the input
pattern (an image, say) is responsible for a particular class being predicted,
and how the input may be manipulated to predict a different class. We focus
instead on understanding which of the internal features computed by the neural
net are responsible for a particular class. We achieve this by mimicking part
of the neural net with an oblique decision tree having sparse weight vectors at
the decision nodes. Using the recently proposed Tree Alternating Optimization
(TAO) algorithm, we are able to learn trees that are both highly accurate and
interpretable. Such trees can faithfully mimic the part of the neural net they
replaced, and hence they can provide insights into the deep net black box.
Further, we show we can easily manipulate the neural net features in order to
make the net predict, or not predict, a given class, thus showing that it is
possible to carry out adversarial attacks at the level of the features. These
insights and manipulations apply globally to the entire training and test set,
not just at a local (single-instance) level. We demonstrate this robustly in
the MNIST and ImageNet datasets with LeNet5 and VGG networks.


Abstract:
  We review complicated problems in the Lifshitz theory describing the Casimir
force between real material plates made of metals and dielectrics including
different approaches to their resolution. It has been shown that both for
metallic plates with perfect crystal lattices and for any dielectric plates the
Casimir entropy calculated in the framework of the Lifshitz theory violates the
Nernst heat theorem when the well approved dielectric functions are used in
computations. The respective theoretical Casimir forces are excluded by the
measurement data of numerous precision experiments. In the literature this
situation received the names of the Casimir puzzle and the Casimir conundrum
for the cases of metallic and dielectric plates, respectively. The review
presents a summary of the main facts on this subject on both theoretical and
experimental sides. Next, we discuss the main approaches proposed in the
literature in order to bring the Lifshitz theory in agreement with the
measurement data and with the laws of thermodynamics. Special attention is paid
to the recently suggested spatially nonlocal Drude-like response functions
which take into account the relaxation properties of conduction electrons, as
does the standard Drude model, but lead to the theoretical results in agreement
with both thermodynamics and the measurement data through the alternative
response to quantum fluctuations off the mass shell. Further advances and
trends in this field of research are discussed.


Abstract:
  The dynamics of the 4-body problem allows for two binary collisions to occur
simultaneously. It is known that in the collinear 4-body problem this
simultaneous binary collision (SBC) can be block-regularised, but that the
resulting block map is only $C^{8/3}$ differentiable. In this paper, it is
proved that the $C^{8/3}$ differentiability persists for the SBC in the planar
4-body problem. The proof uses several geometric tools, namely, blow-up, normal
forms, dynamics near normally hyperbolic manifolds of equilibrium points, and
Dulac maps.


Abstract:
  Eluder dimension and information gain are two widely used methods of
complexity measures in bandit and reinforcement learning. Eluder dimension was
originally proposed as a general complexity measure of function classes, but
the common examples of where it is known to be small are function spaces
(vector spaces). In these cases, the primary tool to upper bound the eluder
dimension is the elliptic potential lemma. Interestingly, the elliptic
potential lemma also features prominently in the analysis of linear
bandits/reinforcement learning and their nonparametric generalization, the
information gain. We show that this is not a coincidence -- eluder dimension
and information gain are equivalent in a precise sense for reproducing kernel
Hilbert spaces.


Abstract:
  Instance segmentation is an active topic in computer vision that is usually
solved by using supervised learning approaches over very large datasets
composed of object level masks. Obtaining such a dataset for any new domain can
be very expensive and time-consuming. In addition, models trained on certain
annotated categories do not generalize well to unseen objects. The goal of this
paper is to propose a method that can perform unsupervised discovery of
long-tail categories in instance segmentation, through learning instance
embeddings of masked regions. Leveraging rich relationship and hierarchical
structure between objects in the images, we propose self-supervised losses for
learning mask embeddings. Trained on COCO dataset without additional
annotations of the long-tail objects, our model is able to discover novel and
more fine-grained objects than the common categories in COCO. We show that the
model achieves competitive quantitative results on LVIS as compared to the
supervised and partially supervised methods.


Abstract:
  Temporally shaping the density of electron beams using light forms the basis
for a wide range of established and emerging technologies, including
free-electron lasers and attosecond electron microscopy. The modulation depth
of compressed electron pulses is a key figure of merit limiting applications.
In this work, we present an approach for generating background-free attosecond
electron pulse trains by sequential inelastic electron-light scattering.
Harnessing quantum interference in the fractional Talbot effect, we suppress
unwanted background density in electron compression by several orders of
magnitude. Our results will greatly enhance applications of coherent
electron-light scattering, such as stimulated cathodoluminescence and
streaking.


Abstract:
  Suspensions of motile active particles with space dependent activity form
characteristic polarization and density patterns. Recent single-particle
studies for planar activity landscapes identified several quantities associated
with emergent density-polarization patterns, including swim pressure, that are
solely determined by bulk variables and thus constitute state functions. We
show that for radially symmetric activity steps these variables depend on the
curvature of the active-passive interface. In the specific case of total
polarization and swim pressure, we generalize this result for arbitrary
radially symmetric activity landscapes. Our exact as well as approximate
analytical results agree with exact numerical calculations. We expect
qualitatively the same results to hold for arbitrarily curved activity
landscapes.


Abstract:
  Recent work on hyperparameters optimization (HPO) has shown the possibility
of training certain hyperparameters together with regular parameters. However,
these online HPO algorithms still require running evaluation on a set of
validation examples at each training step, steeply increasing the training
cost. To decide when to query the validation loss, we model online HPO as a
time-varying Bayesian optimization problem, on top of which we propose a novel
\textit{costly feedback} setting to capture the concept of the query cost.
Under this setting, standard algorithms are cost-inefficient as they evaluate
on the validation set at every round. In contrast, the cost-efficient GP-UCB
algorithm proposed in this paper queries the unknown function only when the
model is less confident about current decisions. We evaluate our proposed
algorithm by tuning hyperparameters online for VGG and ResNet on CIFAR-10 and
ImageNet100. Our proposed online HPO algorithm reaches human expert-level
performance within a single run of the experiment, while incurring only modest
computational overhead compared to regular training.


Abstract:
  We present simultaneous multiwavelength observations of the 4.66 ms redback
pulsar PSR J1048+2339. We performed phase-resolved spectroscopy with the Very
Large Telescope (VLT) searching for signatures of a residual accretion disk or
intra-binary shock emission, constraining the companion radial velocity
semi-amplitude ($K_2$), and estimating the neutron star mass ($M_{\rm NS}$).
Using the FORS2-VLT intermediate-resolution spectra, we measured a companion
velocity of $291 < K_2 < 348$ km s$^{-1}$ and a binary mass ratio of $0.209 < q
< 0.250$. Combining our results for $K_2$ and $q$, we constrained the mass of
the neutron star and the companion to $(1.0 < M_{\rm NS} < 1.6){\rm
sin}^{-3}i\,M_{\odot}$ and $(0.24 < M_2 < 0.33){\rm sin}^{-3}i\,M_{\odot}$,
respectively, where $i$ is the system inclination. The Doppler map of the
H$\alpha$ emission line exhibits a spot feature at the expected position of the
companion star and an extended bright spot close to the inner Lagrangian point.
We interpret this extended emission as the effect of an intra-binary shock
originating from the interaction between the pulsar relativistic wind and the
matter leaving the companion star. The mass loss from the secondary star could
be either due to Roche-lobe overflow or to the ablation of its outer layer by
the energetic pulsar wind. Contrastingly, we find no evidence for an accretion
disk. We report on the results of the SRT and the LOFAR simultaneous radio
observations at three different frequencies (150 MHz, 336 MHz, and 1400 MHz).
No pulsed radio signal is found in our search. This is probably due to both
scintillation and the presence of material expelled from the system which can
cause the absorption of the radio signal at low frequencies. Finally, we report
on an attempt to search for optical pulsations using IFI+Iqueye mounted at the
1.2 m Galileo telescope at the Asiago Observatory.


Abstract:
  As we enter the era of large-scale imaging surveys with the up-coming
telescopes such as LSST and SKA, it is envisaged that the number of known
strong gravitational lensing systems will increase dramatically. However, these
events are still very rare and require the efficient processing of millions of
images. In order to tackle this image processing problem, we present Machine
Learning techniques and apply them to the Gravitational Lens Finding Challenge.
The Convolutional Neural Networks (CNNs) presented have been re-implemented
within a new modular, and extendable framework, LEXACTUM. We report an Area
Under the Curve (AUC) of 0.9343 and 0.9870, and an execution time of 0.0061s
and 0.0594s per image, for the Space and Ground datasets respectively, showing
that the results obtained by CNNs are very competitive with conventional
methods (such as visual inspection and arc finders) for detecting gravitational
lenses.


Abstract:
  A survey is given of approaches to the problem of distributed consensus,
focusing particularly on methods based on cellular automata and related
systems. A variety of new results are given, as well as a history of the field
and an extensive bibliography. Distributed consensus is of current relevance in
a new generation of blockchain-related systems.