Abstract:
  Carbon contamination is a significant concern for proton-conducting oxides in
the cerate and zirconate family, particularly for BaCeO$_3$. Here, we use
first-principles calculations to evaluate carbon stability in SrCeO$_3$,
BaCeO$_3$, SrZrO$_3$, and BaZrO$_3$. The cerates require more carbon-poor
environments to prevent carbonate formation, though this requirement can be
loosened through the use of more oxygen-poor growth conditions. Carbonate
formation is not the only concern, however. We find that interstitial carbon
has lower formation energies in the cerates relative to the zirconates, leading
to higher carbon concentrations that compete with the desired oxygen vacancy
formation. We also examine the mobility of carbon interstitials, finding that
both migration barriers and binding energies to acceptors are lower in the
cerates. As a result, the cerates are likely to degrade when exposed to carbon
at operating temperatures. Our results show definitively why the cerates are
less stable than the zirconates with respect to carbon and elucidate the
mechanisms contributing to their instability, thereby helping to explain why
alloying with zirconium will enhance their operational efficiency.


Abstract:
  Mass number dependence of the nuclear radii is closely related to the nuclear
matter properties. It is known that the most of nuclei exhibit some
deformation. We discuss how the nuclear density profile is modified by the
nuclear deformation to elucidate the enhancement mechanism of the nuclear radii
through a systematic investigation of neutron-rich Ne, Mg, Si, S, Ar, Ti, Cr,
and Fe isotopes. Skyrme-Hartree-Fock calculations are performed in a
three-dimensional Cartesian grid to describe the nuclear deformation in a
non-empirical way. The role of the nuclear deformation on the nuclear density
profiles is explored in comparison to calculations with spherical limit. We
find correlations between the nuclear deformation and the internal nuclear
density. The evolution of the nuclear radii appears to follow the core swelling
mechanism recently proposed in spherical nuclei [Phys. Rev. C 101, 061301(R)
(2020)], and the radius is further enhanced by the nuclear deformation. This
study demands further theoretical and experimental investigations for the
internal density.


Abstract:
  Our purpose of this paper is to study isolated singular solutions of
semilinear Helmholtz equation $$ -\Delta u-u=Q|u|^{p-1}u \quad{\rm in}\ \
\mathbb{R}^N\setminus\{0\},\ \qquad\lim_{|x|\to0}u(x)=+\infty, $$ where $N\geq
2$, $p>1$ and the potential $Q: \mathbb{R}^N\to (0,+\infty)$ is a H\"older
continuous function satisfying extra decaying conditions at infinity. We give
the classification of the isolated singularity in the Serrin's subcritical case
and then isolated singular solutions is derived with the form $u_k=k\Phi+v_k$
via the Schauder fixed point theorem for the integral equation
$$v_k=\Phi\ast\big(Q|kw_\sigma+v_k|^{p-1}(kw_\sigma+v_k)\big)\quad{\rm in}\ \,
\mathbb{R}^N,$$ where $\Phi$ is the real valued fundamental solution
$-\Delta-1$ and $w_\sigma$ is a also a real valued solution
$(-\Delta-1)w_\sigma=\delta_0$ with the asymptotic behavior at infinity
controlled by $|x|^{-\sigma}$ for some $\sigma\leq \frac{N-1}{2}$.


Abstract:
  We study the dissipative dynamics of a single quantum harmonic oscillator
subjected to a parametric driving with in an effective Hamiltonian approach.
Using Liouville von Neumann approach, we show that the time evolution of a
parametrically driven dissipative quantum oscillator has a strong connection
with the classical damped Mathieu equation. Based on the numerical analysis of
the Monodromy matrix, we demonstrate that the dynamical instability generated
by the parametric driving are reduced by the effect of dissipation. Further, we
obtain a closed relationship between the localization of the Wigner function
and the stability of the damped Mathieu equation.


Abstract:
  In this paper, we propose a general scheme to analyze the gradient vanishing
phenomenon, also known as the barren plateau phenomenon, in training quantum
neural networks with the ZX-calculus. More precisely, we extend the barren
plateaus theorem from unitary 2-design circuits to any parameterized quantum
circuits under certain reasonable assumptions. The main technical contribution
of this paper is representing certain integrations as ZX-diagrams and computing
them with the ZX-calculus. The method is used to analyze four concrete quantum
neural networks with different structures. It is shown that, for the hardware
efficient ansatz and the MPS-inspired ansatz, there exist barren plateaus,
while for the QCNN ansatz and the tree tensor network ansatz, there exists no
barren plateau.


Abstract:
  Resolvent analysis has demonstrated encouraging results for modeling coherent
structures in jets when compared against their data-educed counterparts from
high-fidelity large-eddy simulations (LES). We formulate resolvent analysis as
an acoustic analogy that relates the near-field resolvent forcing to the near-
and far-field pressure. We use an LES database of round, isothermal, Mach 0.9
and 1.5 jets to produce an ensemble of realizations for the acoustic field that
we project onto a limited set of resolvent modes. In the near-field, we perform
projections on a restricted acoustic output domain, $r/D = [5,6]$, while the
far-field projections are performed on a Kirchhoff surface comprising a
100-diameter arc centered at the nozzle. This allows the LES realizations to be
expressed in the resolvent basis via a data-deduced, low-rank, cross-spectral
density matrix. We find that a single resolvent mode reconstructs the most
energetic regions of the acoustic field across Strouhal numbers, $St = [0-1]$,
and azimuthal wavenumbers, $m=[0,2]$. Finally, we present a simple function
that results in a rank-1 resolvent model agreeing within 2dB of the peak noise
for both jets.


Abstract:
  In this paper, we first generalize a result of Bishop and Steger
[Representation theoretic rigidity in PSL(2, R). Acta Math., 170, (1993),
121-149] by proving that for a Fuchsian group $G$ of divergence type and
non-lattice, if $h$ is a quasi-symmetric homeomorphism of the real axis
$\mathbb{R}$ corresponding to a quasi-conformal compact deformation of $G$.
Then for any $E\subset \mathbb{R}$, we have max(dim$E$,
dim$h(\mathbb{R}\setminus E))=1$. Furthermore, we showed that Bishop and
steger's result does not hold for the covering groups of all '$d$-dimensional
jungle gym' (d is any positive integer) which generalizes G\"onye's results [
Differentiability of quasi-conformal maps on the jungle gym. Trans. Amer. Math.
Soc. Vol 359 (2007), 9-32] where the author discussed the case of
'$1$-dimensional jungle gym'.


Abstract:
  Given N data points drawn from a chi-square distribution, we use Bayesian
inference to determine most likely values and N-dependent confidence intervals
for the width sigma and the number k of degrees of freedom of that
distribution. Using reduced partial neutron widths measured in a number of
nuclei, a guessed value of sigma, and a maximum-likelihood approach (different
from Bayesian inference), Koehler et al. and Koehler have determined the most
likely k-values of chi-square distributions that fit the data. In all cases
they find values for k that differ substantially from k = 1 (the value
characterizing the Porter-Thomas distribution (PTD) predicted by random-matrix
theory). The authors conclude that the validity of the PTD must be rejected
with considerable statistical significance. We show that the value of sigma
guessed in these papers lies far outside the Bayesian confidence interval for
sigma, casting serious doubt on the results of and the conclusions drawn there.
We also show that sigma and k must both be determined from the data. Comparison
of the results with the Bayesian confidence intervals would then decide on
acceptance or rejection of the PTD.


Abstract:
  In this paper, we propose a new technique based on program synthesis for
extracting information from webpages. Given a natural language query and a few
labeled webpages, our method synthesizes a program that can be used to extract
similar types of information from other unlabeled webpages. To handle websites
with diverse structure, our approach employs a neurosymbolic DSL that
incorporates both neural NLP models as well as standard language constructs for
tree navigation and string manipulation. We also propose an optimal synthesis
algorithm that generates all DSL programs that achieve optimal F1 score on the
training examples. Our synthesis technique is compositional, prunes the search
space by exploiting a monotonicity property of the DSL, and uses transductive
learning to select programs with good generalization power. We have implemented
these ideas in a new tool called WebQA and evaluate it on 25 different tasks
across multiple domains. Our experiments show that WebQA significantly
outperforms existing tools such as state-of-the-art question answering models
and wrapper induction systems.


Abstract:
  Dealing with quadratic payments, marginal probability is usually considered
ideally constant, maybe for the sake of initial simplicity. Considering the
voting scenario depicted in "Quadratic Payments: A Primer" by Vitalik Buterin,
firstly its math foundations are made explicit. Developing a simple referendum
model, more realistic outcome probability and marginal probability qualitative
shapes are introduced. Enforcing seemingly reasonable assumptions, quadratic
payments are then generalized to take into account these new functions shapes,
and the way they are still quadratic is discussed. Closing remarks underline
the emerging of trade-off constraints not existing in ideal case.


Abstract:
  Neural Architecture Search (NAS) has shown excellent results in designing
architectures for computer vision problems. NAS alleviates the need for
human-defined settings by automating architecture design and engineering.
However, NAS methods tend to be slow, as they require large amounts of GPU
computation. This bottleneck is mainly due to the performance estimation
strategy, which requires the evaluation of the generated architectures, mainly
by training them, to update the sampler method. In this paper, we propose
EPE-NAS, an efficient performance estimation strategy, that mitigates the
problem of evaluating networks, by scoring untrained networks and creating a
correlation with their trained performance. We perform this process by looking
at intra and inter-class correlations of an untrained network. We show that
EPE-NAS can produce a robust correlation and that by incorporating it into a
simple random sampling strategy, we are able to search for competitive
networks, without requiring any training, in a matter of seconds using a single
GPU. Moreover, EPE-NAS is agnostic to the search method, since it focuses on
the evaluation of untrained networks, making it easy to integrate into almost
any NAS method.


Abstract:
  Based on decision trees, many fields have arguably made tremendous progress
in recent years. In simple words, decision trees use the strategy of
"divide-and-conquer" to divide the complex problem on the dependency between
input features and labels into smaller ones. While decision trees have a long
history, recent advances have greatly improved their performance in
computational advertising, recommender system, information retrieval, etc. We
introduce common tree-based models (e.g., Bayesian CART, Bayesian regression
splines) and training techniques (e.g., mixed integer programming, alternating
optimization, gradient descent). Along the way, we highlight probabilistic
characteristics of tree-based models and explain their practical and
theoretical benefits. Except machine learning and data mining, we try to show
theoretical advances on tree-based models from other fields such as statistics
and operation research. We list the reproducible resource at the end of each
method.


Abstract:
  For many psychiatric disorders, neuroimaging offers a potential for
revolutionizing diagnosis, and potentially treatment, by providing access to
preverbal mental processes. In their study "Machine learning of neural
representations of suicide and emotion concepts identifies suicidal youth."1,
Just and colleagues report that a Naive Bayes classifier, trained on voxelwise
fMRI responses in human participants during the presentation of words and
concepts related to mortality, can predict whether an individual had reported
having suicidal ideations with a classification accuracy of 91%. Here we report
a reappraisal of the methods employed by the authors, including re-analysis of
the same data set, that calls into question the accuracy of the authors
findings. The analysis is a case study in the dangers of overfitting in machine
learning.


Abstract:
  Galaxy clusters exhibit a rich morphology during the early and intermediate
stages of mass assembly, especially beyond their boundary. A classification
scheme based on shapefinders deduced from the Minkowski functionals is examined
to fully account for the morphological diversity of galaxy clusters, including
relaxed and merging clusters, clusters fed by filamentary structures, and
cluster-pair bridges. These configurations are conveniently treated with
idealised geometric models and analytical formulae, some of which are novel.
Examples from CLASH and LC$^2$ clusters and observed cluster-pair bridges are
discussed.


Abstract:
  Human-AI collaborative policy synthesis is a procedure in which (1) a human
initializes an autonomous agent's behavior, (2) Reinforcement Learning improves
the human specified behavior, and (3) the agent can explain the final optimized
policy to the user. This paradigm leverages human expertise and facilitates a
greater insight into the learned behaviors of an agent. Existing approaches to
enabling collaborative policy specification involve black box methods which are
unintelligible and are not catered towards non-expert end-users. In this paper,
we develop a novel collaborative framework to enable humans to initialize and
interpret an autonomous agent's behavior, rooted in principles of
human-centered design. Through our framework, we enable humans to specify an
initial behavior model in the form of unstructured, natural language, which we
then convert to lexical decision trees. Next, we are able to leverage these
human-specified policies, to warm-start reinforcement learning and further
allow the agent to optimize the policies through reinforcement learning.
Finally, to close the loop on human-specification, we produce explanations of
the final learned policy, in multiple modalities, to provide the user a final
depiction about the learned policy of the agent. We validate our approach by
showing that our model can produce >80% accuracy, and that human-initialized
policies are able to successfully warm-start RL. We then conduct a novel
human-subjects study quantifying the relative subjective and objective benefits
of varying XAI modalities(e.g., Tree, Language, and Program) for explaining
learned policies to end-users, in terms of usability and interpretability and
identify the circumstances that influence these measures. Our findings
emphasize the need for personalized explainable systems that can facilitate
user-centric policy explanations for a variety of end-users.


Abstract:
  High harmonic generation (HHG) in crystals has revealed a wealth of
perspectives such as all-optical mapping of the electronic band structure,
ultrafast quantum information and the creation of novel all-solid-state
attosecond sources. Significant efforts have been made to understand the
microscopic aspects of HHG in crystals, whereas the macroscopic effects, such
as non-linear propagation effects of the driving pulse inside the dense solid
media and its impact on the HHG process is often overlooked. In this work, we
study macroscopic effects by comparing two materials with distinct optical
properties, silicon (Si) and zinc oxide (ZnO). By scanning the focal position
of 85 fs, 2.123 $\mu$m wavelength pulses inside the crystals (Z-scan) we reveal
spectral shifts in the generated harmonics. We interpret the overall blueshift
of the emitted harmonic spectrum as an imprint of the driving field spectral
modulation occurring during the propagation inside the crystal. This is
supported with numerical simulations. This study demonstrates that through
manipulation of the fundamental driving field through non-linear propagation
effects, precise control of the emitted HHG spectrum in solids can be realised.
This method could offer a robust way to tailor HHG spectra for a range of
spectroscopic applications.


Abstract:
  In this paper, we present a novel passive single Degree-of-Freedom (DoF)
manipulator design and its integration on an autonomous drone to capture a
moving target. The end-effector is designed to be passive, to disengage the
moving target from a flying UAV and capture it efficiently in the presence of
disturbances, with minimal energy usage. It is also designed to handle target
sway and the effect of downwash. The passive manipulator is integrated with the
drone through a single Degree of Freedom (DoF) arm, and experiments are carried
out in an outdoor environment. The rack-and-pinion mechanism incorporated for
this manipulator ensures safety by extending the manipulator beyond the body of
the drone to capture the target. The autonomous capturing experiments are
conducted using a red ball hanging from a stationary drone and subsequently
from a moving drone. The experiments show that the manipulator captures the
target with a success rate of 70\% even under environmental/measurement
uncertainties and errors.


Abstract:
  Orientation is a crucial skill for football players that becomes a
differential factor in a large set of events, especially the ones involving
passes. However, existing orientation estimation methods, which are based on
computer-vision techniques, still have a lot of room for improvement. To the
best of our knowledge, this article presents the first deep learning model for
estimating orientation directly from video footage. By approaching this
challenge as a classification problem where classes correspond to orientation
bins, and by introducing a cyclic loss function, a well-known convolutional
network is refined to provide player orientation data. The model is trained by
using ground-truth orientation data obtained from wearable EPTS devices, which
are individually compensated with respect to the perceived orientation in the
current frame. The obtained results outperform previous methods; in particular,
the absolute median error is less than 12 degrees per player. An ablation study
is included in order to show the potential generalization to any kind of
football video footage.


Abstract:
  Weakly supervised object detection (WSOD) has recently attracted much
attention. However, the method, performance and speed gaps between WSOD and
fully supervised detection prevent WSOD from being applied in real-world tasks.
To bridge the gaps, this paper proposes a new framework, Salvage of Supervision
(SoS), with the key idea being to harness every potentially useful supervisory
signal in WSOD: the weak image-level labels, the pseudo-labels, and the power
of semi-supervised object detection. This paper shows that each type of
supervisory signal brings in notable improvements, outperforms existing WSOD
methods (which mainly use only the weak labels) by large margins. The proposed
SoS-WSOD method achieves 64.4 $m\text{AP}_{50}$ on VOC2007, 61.9
$m\text{AP}_{50}$ on VOC2012 and 16.4 $m\text{AP}_{50:95}$ on MS-COCO, and also
has fast inference speed. Ablations and visualization further verify the
effectiveness of SoS.


Abstract:
  We present geometric optics expansion in the subleading order for the
circularly polarized gravitational waves on curved spacetimes. We call spin
optics to the subleading order geometric optics expansion. Spin optics involves
the modification of the standard eikonal function by including a specially
chosen helicity dependent correction. We show that the techniques developed for
the propagation of electromagnetic waves could be applied to gravitational
waves, in the limit of spin optics also. However, we should account for the
difference in their helicity.


Abstract:
  We present a detailed analysis for a subset of the high resolution (~35 mas,
or 5 au) ALMA observations from the Disk Substructures at High Angular
Resolution Project (DSHARP) to search for faint 1.3 mm continuum emission
associated with dusty circumplanetary material located within the narrow annuli
of depleted emission (gaps) in circumstellar disks. This search used the
Jennings et al. (2020) $\tt{frank}$ modeling methodology to mitigate
contamination from the local disk emission, and then deployed a suite of
injection-recovery experiments to statistically characterize point-like
circumplanetary disks in residual images. While there are a few putative
candidates in this sample, they have only marginal local signal-to-noise ratios
and would require deeper measurements to confirm. Associating a 50% recovery
fraction with an upper limit, we find these data are sensitive to
circumplanetary disks with flux densities $\gtrsim 50-70$ $\mu$Jy in most
cases. There are a few examples where those limits are inflated ($\gtrsim 110$
$\mu$Jy) due to lingering non-axisymmetric structures in their host
circumstellar disks, most notably for a newly identified faint spiral in the HD
143006 disk. For standard assumptions, this analysis suggests that these data
should be sensitive to circumplanetary disks with dust masses $\gtrsim
0.001-0.2$ M$_\oplus$. While those bounds are comparable to some theoretical
expectations for young giant planets, we discuss how plausible system
properties (e.g., relatively low host planet masses or the efficient radial
drift of solids) could require much deeper observations to achieve robust
detections.


Abstract:
  A posteriori probability (APP) and max-log APP detection is widely used in
soft-input soft-output detection. In contrast to bijective modulation schemes,
there are important differences when applying these algorithms to non-bijective
symbol constellations. In this letter the main differences are highlighted.


Abstract:
  Face clustering is a useful tool for applications like automatic face
annotation and retrieval. The main challenge is that it is difficult to cluster
images from the same identity with different face poses, occlusions, and image
quality. Traditional clustering methods usually ignore the relationship between
individual images and their neighbors which may contain useful context
information. In this paper, we repurpose the well-known Transformer and
introduce a Face Transformer for supervised face clustering. In Face
Transformer, we decompose the face clustering into two steps: relation encoding
and linkage predicting. Specifically, given a face image, a \textbf{relation
encoder} module aggregates local context information from its neighbors and a
\textbf{linkage predictor} module judges whether a pair of images belong to the
same cluster or not. In the local linkage graph view, Face Transformer can
generate more robust node and edge representations compared to existing
methods. Experiments on both MS-Celeb-1M and DeepFashion show that our method
achieves state-of-the-art performance, e.g., 91.12\% in pairwise F-score on
MS-Celeb-1M.


Abstract:
  We consider smooth families of Lie groups (group bundles) and connections
that are compatible with the group operation. We characterize the space of
group connections on a group bundle as an affine space modeled over the vector
space of $1$-forms with values cocycles in the Lie algebra bundle of the
aforementioned group bundle. We show that group connections satisfy the
Ambrose-Singer theorem and that group bundles can be seen as a particular case
of associated bundles realizing group connections as associated connections. We
give a construction of the Moduli space of group connections with fixed base
and fiber, as an space of representations of the fundamental group of the base.


Abstract:
  The potential of the process $\gamma \gamma \rightarrow \tau
\bar{\tau}\gamma$ is examined in a model-independent way using the effective
Lagrangian approach for the ILC which is designed with standard configurations
of 0.25 TeV/2000 fb$^{-1}$, 0.35 TeV/200 fb$^{-1}$ and 0.5 TeV/4000 fb$^{-1}$.
The limits obtained for $\tilde{a}_{\tau}$ and $\tilde{{d}_{\tau}}$ parameters
defining the anomalous $\tau \bar{\tau}\gamma$ couplings at $95\%$ confidence
level and systematic uncertainties of $\delta_{sys}=0,5,10 \%$ are compared
with the experimental results. The best limits obtained without systematic
error on the anomalous couplings are $-0.00082<\tilde{a}_{\tau}<0.00050$ and
$|\tilde{{d}_{\tau}}|<3.5969 \times 10^{-18}$ $e\,cm$, respectively. Thus, our
results show that $\gamma \gamma$ collisions at the ILC lead to a remarkable
improvement in the existing experimental limits on the anomalous magnetic and
electric dipole moments of the tau lepton.


Abstract:
  Chromatic properties of the multi-prism and prism-array X-ray lenses (MPL and
PAL) can potentially be utilized for efficient energy filtering and dose
reduction in mammography. The line-shaped foci of the lenses are optimal for
coupling to photon-counting silicon strip detectors in a scanning system. A
theoretical model was developed and used to investigate the benefit of two
lenses compared with an absorption-filtered reference system. The dose
reduction of the MPL filter was ~15% compared with the reference system at
matching scan time, and the spatial resolution was higher. The dose of the
PAL-filtered system was found to be ~20% lower than for the reference system at
equal scan time and resolution, and only ~20% higher than for a monochromatic
beam. An investigation of some practical issues remains, including the
feasibility of brilliant-enough X-ray sources and manufacturing of a polymer
PAL.


Abstract:
  Uniqueness theorems are considered for various types of almost periodic
objects: functions, measures, distributions, multisets, holomorphic and
meromorphic functions.


Abstract:
  Alweiss, Lovett, Wu, and Zhang introduced $q$-spread hypergraphs in their
breakthrough work regarding the sunflower conjecture, and since then $q$-spread
hypergraphs have been used to give short proofs of several outstanding problems
in probabilistic combinatorics. A variant of $q$-spread hypergraphs was
implicitly used by Kahn, Narayanan, and Park to determine the threshold for
when a square of a Hamiltonian cycle appears in the random graph $G_{n,p}$. In
this paper we give a common generalization of the original notion of $q$-spread
hypergraphs and the variant used by Kahn et al.


Abstract:
  Let $F$ be a number field and $p$ an odd prime. We estimate the kernels and
cokernels of the codescent maps of the \'etale wild kernels over various
$p$-adic Lie extensions. For this, we propose a novel approach of viewing the
\'etale wild kernel as an appropriate fine Selmer group in the sense of
Coates-Sujatha. This viewpoint reduces the problem to a control theorem of the
said fine Selmer groups, which in turn allows us to employ the strategies
developed by Mazur and Greenberg. As applications of our estimates on the
kernels and cokernels of the codescent maps, we establish asymptotic growth
formulas for the \'etale wild kernels in the various said $p$-adic Lie
extensions. We then relate these growth formulas to the Greenberg's conjecture
(and its noncommutative analogue). Finally, we shall give some examples to
illustrate our results.


Abstract:
  Objective: In this work, we set out to investigate the accuracy of direct
attenuation correction (AC) in the image domain for the myocardial perfusion
SPECT imaging (MPI-SPECT) using two residual (ResNet) and UNet deep
convolutional neural networks. Methods: The MPI-SPECT 99mTc-sestamibi images of
99 participants were retrospectively examined. UNet and ResNet networks were
trained using SPECT non-attenuation corrected images as input and CT-based
attenuation corrected SPECT images (CT-AC) as reference. The Chang AC approach,
considering a uniform attenuation coefficient within the body contour, was also
implemented. Quantitative and clinical evaluation of the proposed methods were
performed considering SPECT CT-AC images of 19 subjects as reference using the
mean absolute error (MAE), structural similarity index (SSIM) metrics, as well
as relevant clinical indices such as perfusion deficit (TPD). Results: Overall,
the deep learning solution exhibited good agreement with the CT-based AC,
noticeably outperforming the Chang method. The ResNet and UNet models resulted
in the ME (count) of ${-6.99\pm16.72}$ and ${-4.41\pm11.8}$ and SSIM of
${0.99\pm0.04}$ and ${0.98\pm0.05}$, respectively. While the Change approach
led to ME and SSIM of ${25.52\pm33.98}$ and ${0.93\pm0.09}$, respectively.
Similarly, the clinical evaluation revealed a mean TPD of ${12.78\pm9.22}$ and
${12.57\pm8.93}$ for the ResNet and UNet models, respectively, compared to
${12.84\pm8.63}$ obtained from the reference SPECT CT-AC images. On the other
hand, the Chang approach led to a mean TPD of ${16.68\pm11.24}$. Conclusion: We
evaluated two deep convolutional neural networks to estimate SPECT-AC images
directly from the non-attenuation corrected images. The deep learning solutions
exhibited the promising potential to generate reliable attenuation corrected
SPECT images without the use of transmission scanning.


Abstract:
  Backdoor attack intends to inject hidden backdoor into the deep neural
networks (DNNs), such that the prediction of infected models will be
maliciously changed if the hidden backdoor is activated by the attacker-defined
trigger. Currently, most existing backdoor attacks adopted the setting of
static trigger, $i.e.,$ triggers across the training and testing images follow
the same appearance and are located in the same area. In this paper, we revisit
this attack paradigm by analyzing trigger characteristics. We demonstrate that
this attack paradigm is vulnerable when the trigger in testing images is not
consistent with the one used for training. As such, those attacks are far less
effective in the physical world, where the location and appearance of the
trigger in the digitized image may be different from that of the one used for
training. Moreover, we also discuss how to alleviate such vulnerability. We
hope that this work could inspire more explorations on backdoor properties, to
help the design of more advanced backdoor attack and defense methods.


Abstract:
  In the first part of this dissertation research, we develop a modular
framework that can serve as a recipe for constructing and analyzing iterative
algorithms for convex optimization. Specifically, our work casts optimization
as iteratively playing a two-player zero-sum game. Many existing optimization
algorithms including Frank-Wolfe and Nesterov's acceleration methods can be
recovered from the game by pitting two online learners with appropriate
strategies against each other. Furthermore, the sum of the weighted average
regrets of the players in the game implies the convergence rate. As a result,
our approach provides simple alternative proofs to these algorithms. Moreover,
we demonstrate that our approach of optimization as iteratively playing a game
leads to three new fast Frank-Wolfe-like algorithms for some constraint sets,
which further shows that our framework is indeed generic, modular, and
easy-to-use.
  In the second part, we develop a modular analysis of provable acceleration
via Polyak's momentum for certain problems, which include solving the classical
strongly quadratic convex problems, training a wide ReLU network under the
neural tangent kernel regime, and training a deep linear network with an
orthogonal initialization. We develop a meta theorem and show that when
applying Polyak's momentum for these problems, the induced dynamics exhibit a
form where we can directly apply our meta theorem.
  In the last part of the dissertation, we show another advantage of the use of
Polyak's momentum -- it facilitates fast saddle point escape in smooth
non-convex optimization. This result, together with those of the second part,
sheds new light on Polyak's momentum in modern non-convex optimization and deep
learning.


Abstract:
  Spin-1/2 Heisenberg model on the Shastry-Sutherland lattice is considered
within the many-body perturbation theory developed from the exactly solved
spin-1/2 Ising-Heisenberg model with the Heisenberg intradimer and Ising
interdimer interactions. The former model is widely used for a description of
magnetic properties of the layered compound SrCu$_2$(BO$_3$)$_2$, which
exhibits a series of fractional magnetization plateaux at sufficiently low
temperatures. Using the novel type of many-body perturbation theory we have
found the effective model of interacting triplet excitations with the extended
hard-core repulsion, which accurately recovers 1/8, 1/6 and 1/4 magnetization
plateaux for moderate values of the interdimer coupling. A possible existence
of a striking quantum phase of bound triplons is also revealed at low enough
magnetic fields.


Abstract:
  We prove orbital stability result for physical ground states of a nonlinear
Schr\"{o}dinger (NLS) equation in the sense that the set of these ground states
is contained in the set of prescribed mass solutions which is orbital stable by
the Cazenave-Lions theorem. We apply the nonlinear generalized Rayleigh
quotients method which allows establishing a one-to-one correspondence between
the values of the mass $m$, the frequency $\lambda$, and the action level $S$
of the physical ground states.


Abstract:
  We study the formation of over 6000 compact groups (CGs) of galaxies
identified in mock redshift-space galaxy catalogues built from semi-analytical
models of galaxy formation (SAMs) run on the Millennium Simulations. We select
CGs of 4 members in our mock SDSS galaxy catalogues and, for each CG, we trace
back in time the real-space positions of the most massive progenitors of their
4 galaxies. By analysing the evolution of the distance of the galaxy members to
the centre of mass of the group, we identify 4 channels of CG formation. The
classification of these assembly channels is performed with an automatic recipe
inferred from a preliminary visual inspection and based on the orbit of the
galaxy with the fewest number of orbits. Most CGs show late assembly, with the
last galaxy arriving on its first or second passage, while only 10-20 per cent
form by the gradual contraction of their orbits by dynamical friction, and only
a few per cent forming early with little subsequent contraction. However, a SAM
from a higher resolution simulation leads to earlier assembly. Assembly
histories of CGs also depend on cosmological parameters. At similar resolution,
CGs assemble later in SAMs built on parent cosmological simulations of high
density parameter. Several observed properties of mock CGs correlate with their
assembly history: early-assembling CGs are smaller, with shorter crossing
times, and greater magnitude gaps between their brightest two members, and
their brightest galaxies have smaller spatial offsets and are more passive.


Abstract:
  We establish a series of results showing that the Jacobian ideal is contained
in the test ideal. We first prove a new result in characteristic $p$ for
complete rings over a field $K$. Then we prove some results showing that
Jacobian ideals are contained in test ideals in equal characteristic 0 for
affine, analytic, affine-analytic and semianalytic $K$-algebras.


Abstract:
  In this paper we obtain the genus field of a general Kummer extension of a
global rational function field. We study first the case of a general Kummer
extension of degree a power of a prime. Then we prove that the genus field of a
composite of two abelian extensions of a global rational function field with
relatively prime degrees is equal to the composite of their respective genus
fields. Our main result, the genus of a general Kummer extension of a global
rational function field, is a direct consequence of this fact.


Abstract:
  In this paper, the notions of integral closure of hyperrings and hyperideals
in a Krasner hyperring $(R, +, \cdot)$ are defined and some basics properties
of them are studied. We define also the notion of hypervaluation hyperideals
and then a relations between hypervaluations, integral closure of hyperideals
and primary hyperideals are studied. In fact it is shown that the integral
closure of a hyperideal is determined by the hypervaluation Krasner hyperrings.


Abstract:
  We discuss the applicability of elementary band representations (EBRs) to
diagnose spatial- and time-reversal-symmetry protected topological phases in
interacting insulators in terms of their single-particle Green's functions. We
do so by considering an auxiliary non-interacting system
$H_{\textrm{T}}(\mathbf{k}) = -G^{-1}(0, \mathbf{k})$, known as the topological
Hamiltonian, whose bands can be labeled by EBRs. This labeling is stable if
neither (i) the gap in the spectral function at zero frequency closes, (ii) the
Green's function has a zero at zero frequency or (iii) the Green's function
breaks a protecting symmetry. We demonstrate the use of EBRs applied to the
Green's function on the one-dimensional Su-Schrieffer-Heeger model with Hubbard
interactions, which we solve by exact diagonalization for a finite number of
unit cells. Finally, the use of EBRs for the Green's function to diagnose
so-called symmetry protected topological phases is discussed, but remains an
open question.


Abstract:
  We give examples of birational selfmaps of $\mathbb{P}^d, d \geq 3,$ whose
dynamical degree is a transcendental number. This contradicts a conjecture by
Bellon and Viallet. The proof uses a combination of techniques from algebraic
dynamics and diophantine approximation.


Abstract:
  We propose a method for generating nodes for kernel quadrature by a
point-wise gradient descent method. For kernel quadrature, most methods for
generating nodes are based on the worst case error of a quadrature formula in a
reproducing kernel Hilbert space corresponding to the kernel. In typical ones
among those methods, a new node is chosen among a candidate set of points in
each step by an optimization problem with respect to a new node. Although such
sequential methods are appropriate for adaptive quadrature, it is difficult to
apply standard routines for mathematical optimization to the problem. In this
paper, we propose a method that updates a set of points one by one with a
simple gradient descent method. To this end, we provide an upper bound of the
worst case error by using the fundamental solution of the Laplacian on
$\mathbf{R}^{d}$. We observe the good performance of the proposed method by
numerical experiments.


Abstract:
  A graph whose nodes have degree 1 or 3 is called a $\{1,3\}$-graph. Liu and
Osserman associated a polytope to each $\{1,3\}$-graph and studied the Ehrhart
quasi-polynomials of these polytopes. They showed that the vertices of these
polytopes have coordinates in the set $\{0,\frac14,\frac12,1\}$, which implies
that the period of their Ehrhart quasi-polynomials is either 1, 2, or 4. We
show that the period of the Ehrhart quasi-polynomial of these polytopes is at
most 2 if the graph is a tree or a cubic graph, and it is equal to 4 otherwise.
  In the process of proving this theorem, several interesting combinatorial and
geometric properties of these polytopes were uncovered, arising from the
structure of their associated graphs. The tools developed here may find other
applications in the study of Ehrhart quasi-polynomials and enumeration problems
for other polytopes that arise from graphs. Additionally, we have identified
some interesting connections with triangulations of 3-manifolds.


Abstract:
  This paper presents Poisoning MorphNet, the first backdoor attack method on
point clouds. Conventional adversarial attack takes place in the inference
stage, often fooling a model by perturbing samples. In contrast, backdoor
attack aims to implant triggers into a model during the training stage, such
that the victim model acts normally on the clean data unless a trigger is
present in a sample. This work follows a typical setting of clean-label
backdoor attack, where a few poisoned samples (with their content tampered yet
labels unchanged) are injected into the training set. The unique contributions
of MorphNet are two-fold. First, it is key to ensure the implanted triggers
both visually imperceptible to humans and lead to high attack success rate on
the point clouds. To this end, MorphNet jointly optimizes two objectives for
sample-adaptive poisoning: a reconstruction loss that preserves the visual
similarity between benign / poisoned point clouds, and a classification loss
that enforces a modern recognition model of point clouds tends to mis-classify
the poisoned sample to a pre-specified target category. This implicitly
conducts spectral separation over point clouds, hiding sample-adaptive triggers
in fine-grained high-frequency details. Secondly, existing backdoor attack
methods are mainly designed for image data, easily defended by some point cloud
specific operations (such as denoising). We propose a third loss in MorphNet
for suppressing isolated points, leading to improved resistance to
denoising-based defense. Comprehensive evaluations are conducted on ModelNet40
and ShapeNetcorev2. Our proposed Poisoning MorphNet outstrips all previous
methods with clear margins.


Abstract:
  The paper deals with the fast-slow motions setups in the continuous time
$\frac {dX^\ve(t)}{dt}=\frac 1\ve
B(X^\ve(t),\xi(t/\ve^2))+b(X^\ve(t),\,\xi(t/\ve^2)),\, t\in [0,T]$ and the
discrete time $X^\ve((n+1)\ve^2)=X^\ve(n\ve^2)+\ve B(X^\ve(n\ve^2),\xi(n))
+\ve^2b(X^\ve(n\ve^2),\xi(n))$, $n=0,1,...,[T/\ve^2]$ where $B$ and $b$ are
smooth vector functions and $\xi$ is a stationary vector stochastic process
such that $EB(x,\xi(0))=0$ for all $x\in\bbR^d$. Unlike \cite{Ki20} the
assumptions imposed on the process $\xi$ allow applications to a wide class of
observables $g$ in the dynamical systems setup so that $\xi$ can be taken in
the form $\xi(t)=g(F^t\xi(0))$ or $\xi(n)=g(F^n\xi(0))$ where $F$ is either a
flow or a diffeomorphism with some hyperbolicity and $g$ is a vector function.
In this paper we show that both $X^\ve$ and a family of diffusions $\Xi^\ve$
can be redefined on a common sufficiently rich probability space so that
$E\sup_{0\leq t\leq T}|X^\ve(t)-\Xi^\ve(t)|^{p}\leq C\ve^\del,\, p\geq 1$ for
some $C,\del>0$ and all $\ve>0$, where all $\Xi^\ve,\, \ve>0$ have the same
diffusion coefficients but underlying Brownian motions may change with $\ve$.


Abstract:
  We present the structural and initial magnetic characterization of the
previously unreported materials NdPd5Ge3 and NdPd5Ge3. These materials, with
1-dimensional Nd chains, crystallize in the orthorhombic YNi5Si3-type structure
in space group Pnma. Magnetic ordering is observed for both compounds at ~ 2.2
K for NdPd5Ge3 and ~ 3.0 K for NdPt5Ge3. A magnetic-field-induced transition is
clearly observed for NdPd5Ge3 below 2K, at an applied magnetic field of around
1.6 Tesla. The heat capacity data reveals that essentially all the available
magnetic entropy is released at the magnetic ordering transition for NdPd5Ge3
but that only 29% is seen for NdPt5Ge3 at temperatures down to 1.5 K, the data
implying that a second magnetic transition can be expected at lower
temperatures.


Abstract:
  A source node updates its status as a point process and also forwards its
updates to a network of observer nodes. Within the network of observers, these
updates are forwarded as point processes from node to node. Each node wishes
its knowledge of the source to be as timely as possible. In this network,
timeliness is measured by a discrete form of age of information: each status
change at the source is referred to as a version and the age at a node is how
many versions out of date is its most recent update from the source. This work
introduces a method for evaluating the average version age at each node in the
network when nodes forward updates using a memoryless gossip protocol. This
method is then demonstrated by version age analysis for a collection of simple
networks. For gossip on a complete graph with symmetric updating rates, it is
shown that each node has average age that grows as the logarithm of the network
size.


Abstract:
  We report on temperature-dependent soft X-ray absorption spectroscopy (XAS)
measurements utilizing linearly polarized synchrotron radiation to probe
magnetic phase transitions in iron-rich Fe1+yTe. X-ray magnetic linear
dichroism (XMLD) signals, which sense magnetic ordering processes at surfaces,
start to increase monotonically below the N\'eel temperature TN = 57 K. This
increase is due to a progressive bicollinear antiferromagnetic (AFM) alignment
of Fe spins of the monoclinic Fe1+yTe parent phase. This AFM alignment was
achieved by a [100]-oriented biasing field favoring a single-domain state
during cooling across TN. Our specific heat and magnetization measurements
confirm the bulk character of this AFM phase transition. On longer time scales,
however, we observe that the field-biased AFM state is highly unstable even at
the lowest temperature of T = 3 K. After switching off the biasing field, the
XMLD signal decays exponentially with a time constant {\tau} = 1506 s. The
initial XMLD signal is restored only upon repeating a cycle consisting of
heating and field-cooling through TN. We explain the time effect by a gradual
formation of a multi-domain state with 90 deg rotated AFM domains, promoted by
structural disorder, facilitating the motion of twin-domains. Significant
disorder in our Fe1+yTe sample is evident from our X-ray diffraction and
specific heat data. The stability of magnetic phases in Fe-chalcogenides is an
important material property, since the Fe(Te1-xSex) phase diagram shows
magnetism intimately connected with superconductivity.


Abstract:
  Many ecological studies and conservation policies are based on field
observations of species, which can be affected by systematic variability
introduced by the observation process. A recently introduced causal modeling
technique called 'half-sibling regression' can detect and correct for
systematic errors in measurements of multiple independent random variables.
However, it will remove intrinsic variability if the variables are dependent,
and therefore does not apply to many situations, including modeling of species
counts that are controlled by common causes. We present a technique called
'three-quarter sibling regression' to partially overcome this limitation. It
can filter the effect of systematic noise when the latent variables have
observed common causes. We provide theoretical justification of this approach,
demonstrate its effectiveness on synthetic data, and show that it reduces
systematic detection variability due to moon brightness in moth surveys.


Abstract:
  Summary statistics of the likelihood, such as the Bayesian evidence, offer a
principled way of comparing models and assessing tension between, or within,
the results of physical experiments. Noisy realisations of the data induce
scatter in these model comparison statistics. For a realistic case of
cosmological inference from large-scale structure we show that the logarithm of
the Bayes factor attains scatter of order unity, increasing significantly with
stronger tension between the models under comparison. We develop an approximate
procedure that quantifies the sampling distribution of the evidence at small
additional computational cost and apply it to real data to demonstrate the
impact of the scatter, which acts to reduce the significance of any model
discrepancies. Data compression is highlighted as a potential avenue to
suppressing noise in the evidence to negligible levels, with a proof of concept
on Planck cosmic microwave background data.


Abstract:
  We will describe several pioneering efforts in the study of electromagnetic
radiative corrections to semileptonic decay processes, with particular emphasis
on the role of lattice QCD. These studies are essential for the precise
extraction of the matrix element $V_{ud}$ from beta decays of pion, free
neutron and $J^P=0^+$ nuclei, and are crucial to address several
recently-emerged anomalies involving $V_{ud}$ and $V_{us}$, which may provide
hints for physics beyond the Standard Model.


Abstract:
  We present a study of fixed and partial-node approximations in Slater
determinant basis sets, using full configuration interaction quantum Monte
Carlo (FCIQMC) to perform sampling. Walker annihilation in the FCIQMC method
allows partial-node simulations to be performed, relaxing the nodal constraint
to converge to the FCI solution. This is applied to ab initio molecular
systems, using symmetry-projected Jastrow mean-field wave functions for
complete active space (CAS) problems. Convergence and the sign problem within
the partial-node approximation are studied, which is shown to eventually be
limited in its use due to the large walker populations required. However the
fixed-node approximation results in an accurate and practical method. We apply
these approaches to various molecular systems and active spaces, including
ferrocene and acenes. This also provides a test of symmetry-projected Jastrow
mean-field wave functions in variational Monte Carlo (VMC) for a new set of
problems. For trans-polyacetylene molecules and acenes we find that the time to
perform a constant number of fixed-node FCIQMC iterations scales as O(N^{1.44})
and O(N^{1.75}) respectively, resulting in an efficient method for CAS-based
problems that can be applied accurately to large active spaces.


Abstract:
  A Type IV-II Z4-code is a self-dual code over Z4 with the property that all
Euclidean weights are divisible by eight and all codewords have even Hamming
weight. In this paper we use generalized bent functions for a construction of
self-orthogonal codes over Z4 of length $2^m$, for $m$ odd, $m \geq 3$, and
prove that for $m \geq 5$ those codes can be extended to Type IV-II Z4-codes.
From that family of Type IV-II Z4-codes, we obtain a family of self-dual Type
II binary codes by using Gray map. We also consider the weight distributions of
the obtained codes and the structure of the supports of the minimum weight
codewords.


Abstract:
  Sentiment analysis is a research topic focused on analysing data to extract
information related to the sentiment that it causes. Applications of sentiment
analysis are wide, ranging from recommendation systems, and marketing to
customer satisfaction. Recent approaches evaluate textual content using Machine
Learning techniques that are trained over large corpora. However, as social
media grown, other data types emerged in large quantities, such as images.
Sentiment analysis in images has shown to be a valuable complement to textual
data since it enables the inference of the underlying message polarity by
creating context and connections. Multimodal sentiment analysis approaches
intend to leverage information of both textual and image content to perform an
evaluation. Despite recent advances, current solutions still flounder in
combining both image and textual information to classify social media data,
mainly due to subjectivity, inter-class homogeneity and fusion data
differences. In this paper, we propose a method that combines both textual and
image individual sentiment analysis into a final fused classification based on
AutoML, that performs a random search to find the best model. Our method
achieved state-of-the-art performance in the B-T4SA dataset, with 95.19%
accuracy.


Abstract:
  For every fixed class of regular languages, there is a natural hierarchy of
increasingly more general problems: Firstly, the membership problem asks
whether a given language belongs to the fixed class of languages. Secondly, the
separation problem asks for two given languages whether they can be separated
by a language from the fixed class. And thirdly, the covering problem is a
generalization of separation problem to more than two given languages. Most
instances of such problems were solved by the connection of regular languages
and finite monoids. Both the membership problem and the separation problem were
also extended to ordered monoids. The computation of pointlikes can be
interpreted as the algebraic counterpart of the covering problem. In this
paper, we consider the extension the computation of pointlikes to ordered
monoids. This leads to the notion of conelikes for the corresponding algebraic
framework.
  We apply this framework to the Trotter-Weil hierarchy and both the full and
the half levels of the $\text{FO}^2$ quantifier alternation hierarchy. As a
consequence, we solve the covering problem for the resulting subvarieties of
$\mathbf{DA}$. An important combinatorial tool are uniform ranker
characterizations for all subvarieties under consideration; these
characterizations stem from order comparisons of ranker positions.


Abstract:
  The partition function for a system of non-interacting $N-$particles can be
found by summing over all the states of the system. The classical partition
function for an ideal gas differs from Bosonic or Fermionic partition function
in the classical regime. Students find it difficult to follow the differences
arising out of incorrect counting by the classical partition function by
missing out on the indistinguishability of particles and Fermi-Bose statistics.
We present a pedagogical computer-based experiment to probe and demonstrate the
key differences in the partition functions (i) $Q$, for the system of
distinguishable and indistinguishable particles (ii) $B$, for Bosons and (ii)
$F$, for Fermions without formally using the single-particle partition
function.


Abstract:
  The current increasing need for privacy-preserving voice communications is
leading to new ideas for securing voice transmission. This paper refers to a
relatively new concept of sending encrypted data or speech as pseudo-speech in
the audio domain over existing voice communication infrastructures, like 3G
cellular network and Voice over IP (VoIP). The distinctive characteristic of
such a communication system is that it relies on the robust transmission of
binary information in the form of audio signal.
  This work presents a novel Data over Voice (DoV) technique based on codebooks
of short harmonic waveforms. The technique provides a sufficiently fast and
reliable data rate over cellular networks and many VoIP applications. The new
method relies on general principles of Linear Predictive Coding for voice
compression (LPC voice coding) and is more versatile compared to solutions
trained on exact channel models. The technique gives by design a high control
over the desired rate of transmission and provides robustness to channel
distortion. In addition, an efficient codebook design approach inspired by
quaternary error correcting codes is proposed.
  The usability of the proposed DoV technique for secure voice communication
over cellular networks and VoIP has been successfully validated by empirical
experiments. The paper details the system parameters, putting a special
emphasis on system's security and technical challenges.


Abstract:
  We propose a curriculum-driven learning strategy for solving difficult
multi-agent coordination tasks. Our method is inspired by a study of animal
communication, which shows that two straightforward design features (mutual
reward and decentralization) support a vast spectrum of communication protocols
in nature. We highlight the importance of similarly interpreting emergent
communication as a spectrum. We introduce a toroidal, continuous-space
pursuit-evasion environment and show that naive decentralized learning does not
perform well. We then propose a novel curriculum-driven strategy for
multi-agent learning. Experiments with pursuit-evasion show that our approach
enables decentralized pursuers to learn to coordinate and capture a superior
evader, significantly outperforming sophisticated analytical policies. We argue
through additional quantitative analysis -- including influence-based measures
such as Instantaneous Coordination -- that emergent implicit communication
plays a large role in enabling superior levels of coordination.


Abstract:
  In this work, we present polarization profiles for 23 millisecond pulsars
observed at 820 MHz and 1500 MHz with the Green Bank Telescope as part of the
NANOGrav pulsar timing array. We calibrate the data using Mueller matrix
solutions calculated from observations of PSRs B1929+10 and J1022+1001. We
discuss the polarization profiles, which can be used to constrain pulsar
emission geometry, and present both the first published radio polarization
profiles for nine pulsars and the discovery of very low intensity average
profile components ("microcomponents") in four pulsars. Using the Faraday
rotation measures, we measure for each pulsar and use it to calculate the
Galactic magnetic field parallel to the line of sight for different lines of
sight through the interstellar medium. We fit for linear and sinusoidal trends
in time in the dispersion measure and Galactic magnetic field and detect
magnetic field variations with a period of one year in some pulsars, but
overall find that the variations in these parameters are more consistent with a
stochastic origin.


Abstract:
  Quantum features of the baryon number fluctuations in subsystems of a hot and
dense relativistic gas of fermions are analyzed. We find that the fluctuations
in small systems are significantly increased compared to their values known
from the statistical physics, and diverge in the limit where the system size
goes to zero. The numerical results obtained for a broad range of the
thermodynamic parameters expected in heavy-ion collisions are presented. They
can be helpful to interpret and shed new light on the experimental data.


Abstract:
  Background: One-neutron removal reactions are used to study the
single-particle structure of unstable nuclei, and in particular the exotic halo
nuclei. The Eikonal Reaction Theory (ERT) has been developed by Yahiro, Ogata
and Minomo in Prog. Theor. Phys. 126, 167 (2011) to include dynamical effects,
which are missing in the usual eikonal description of these reactions.
Encouraging results have been obtained for total breakup cross sections in
comparison to more elaborate reaction models.
  Purpose: We extend these comparisons to more differential breakup cross
sections expressed as functions of the relative energy or parallel momentum
between the core and halo neutron.
  Method: ERT predictions of these cross sections are compared to
state-of-the-art calculations.
  Results: The hypotheses upon which the ERT is based are confirmed and their
range of validity is made clearer. The actual ordering of the evolution
operators {affects ERT differential} cross sections and a specific choice leads
to excellent agreement with the reference calculation. Dynamical effects in the
treatment of the neutron-target interaction can be significant in the
parallel-momentum observable.
  Conclusions: The role of the different interactions in the dynamics of
breakup reactions of one-neutron halo nuclei are better understood and
improvements to the ERT are suggested.


Abstract:
  Much has been learned about universal properties of the eigenstate
entanglement entropy for one-dimensional lattice models, which is described by
a Hermitian Hamiltonian. While very less of it has been understood for
non-Hermitian systems. In the present work we study a non-Hermitian,
non-interacting model of fermions which is invariant under combined $PT$
transformation. Our models show a phase transition from $PT$ unbroken phase to
broken phase as we tune the hermiticity breaking parameter. Entanglement
entropy of such systems can be defined in two different ways, depending on
whether we consider only right (or equivalently only left) eigenstates or a
combination of both left and right eigenstates which form a complete set of
bi-orthonormal eigenstates. We demonstrate that the entanglement entropy of the
ground state and also of the typical excited states show some unique features
in both of these phases of the system. Most strikingly, entanglement entropy
obtained taking a combination of both left and right eigenstates shows an
exponential divergence with system size at the transition point. While in the
$PT$-unbroken phase, the entanglement entropy obtained from only the right (or
equivalently left) eigenstates shows identical behavior as of an equivalent
Hermitian system which is connected to the non-Hermitian system by a similarity
transformation.


Abstract:
  We report the detection of a single transit-like signal in the Kepler data of
the slightly evolved F star KIC4918810. The transit duration is ~45 hours, and
while the orbital period ($P\sim10$ years) is not well constrained, it is one
of the longest among companions known to transit. We calculate the size of the
transiting object to be $R_P = 0.910$ $R_J$. Objects of this size vary by
orders of magnitude in their densities, encompassing masses between that of
Saturn ($0.3$ $M_J$) and stars above the hydrogen-burning limit (~80 $M_J$).
Radial-velocity observations reveal that the companion is unlikely to be a
star. The mass posterior is bimodal, indicating a mass of either ~0.24 $M_J$ or
~26 $M_J$. Continued spectroscopic monitoring should either constrain the mass
to be planetary or detect the orbital motion, the latter of which would yield a
benchmark long-period brown dwarf with a measured mass, radius, and age.


Abstract:
  This paper is devoted to the development of algorithms for finding unit
distance graphs with chromatic number greater than 4, embedded in a
two-dimensional sphere or plane. Such graphs provide a lower bound for the
Nelson-Hadwiger problem on the chromatic number of the plane and its
generalizations to the case of the sphere. A series of 5-chromatic unit
distance graphs on 64513 vertices embedded into the plane is constructed.
Unlike previously known examples, these graphs do not contain the Moser spindle
as a subgraph. The construction of 5-chromatic graphs embedded in a sphere at
two values of the radius is given. Namely, the 5-chromatic unit distance graph
on 372 vertices embedded into the circumsphere of an icosahedron with a unit
edge length, and the 5-chromatic graph on 972 vertices embedded into the
circumsphere of a great icosahedron are constructed.


Abstract:
  In a graph $G$, a vertex dominates itself and its neighbors. A subset
$S\subseteq V(G)$ is said to be a double dominating set of $G$ if $S$ dominates
every vertex of $G$ at least twice. The double domination number
$\gamma_{\times 2}(G)$ is the minimum cardinality of a double dominating set of
$G$. We show that if $G$ is a maximal outerplanar graph on $n\geq 3$ vertices,
then $\gamma_{\times 2}(G)\leq \lfloor \frac{2n}{3}\rfloor$. Further, if $n\geq
4$, then $\gamma_{\times 2}(G)\leq \min \{\lfloor \frac{n+t}{2}\rfloor, n-t\}$,
where $t$ is the number of vertices of degree $2$ in $G$. These bounds are
shown to be tight. In addition, we also study the case that $G$ is a striped
maximal outerplanar graph.


Abstract:
  The advent of the big data era drives the media data owner to seek help from
the cloud platform for data hosting and sharing. Sharing media data through the
cloud suffers three key security/privacy problems including the leakage of data
privacy, the infringement on the data owner's copyright, and the infringement
on the user's right. Existing techniques such as attribute-based encryption,
proxy re-encryption, and asymmetric fingerprinting are insufficient to solve
all three problems. In this work, we consider the scheme design of being
capable of addressing these three problems simultaneously. Associating the
additive homomorphic proxy re-encryption technique with the asymmetric
fingerprinting based on user-side embedding, we bring forward two novel cloud
media sharing schemes: CREAMS-I and CREAMS-II. Among them, CREAMS-II has better
security performance, while CREAMS-I has more outstanding cloud-side
efficiency. It is demonstrated that both proposed schemes can solve the
existing three problems well and have advantages over existing peers. In
addition, these two schemes can also be seen as an instantiation of
privacy-preserving outsourcing of asymmetric fingerprinting, from which the
owner can reap substantial savings in local storage, communication, and
computing resources. The feasibility of CREAMS-I and CREAMS-II is also verified
by simulation.


Abstract:
  Legged robots can traverse challenging terrain, use perception to plan their
safe foothold positions, and navigate the environment. Such unique mobility
capabilities make these platforms a perfect candidate for scenarios such as
search and rescue, inspection, and exploration tasks. While traversing through
such terrains, the robot's instability is a significant concern. Many times the
robot needs to switch gaits depending on its environment. Due to the complex
dynamics of quadruped robots, classical PID control fails to provide high
stability. Thus, there is a need for advanced control methods like the Model
Predictive Control (MPC) which uses the system model and the nature of the
terrain in order to predict the stable body pose of the robot. The controller
also provides correction to any external disturbances that result in a change
in the desired behavior of the robot. The MPC controller is designed in MATLAB,
for full body torque control. The controller performance was verified on Boston
Dynamics Spot in Webots simulator. The robot is able to provide correction for
external perturbations up to 150 N and also resist falls till 80 cm.


Abstract:
  Exotic phases of matter emerge from the interplay between strong electron
interactions and non-trivial topology. Owing to their lack of dispersion at the
single-particle level, systems harboring flat bands are excellent testbeds for
strongly interacting physics, with twisted bilayer graphene serving as a prime
example. On the other hand, existing theoretical models for obtaining flat
bands in crystalline materials, such as the line-graph formalism, are often too
restrictive for real-life material realizations. Here we present a generic
technique for constructing perfectly flat bands from bipartite crystalline
lattices. Our prescription encapsulates and generalizes the various flat band
models in the literature, being applicable to systems with any orbital content,
with or without spin-orbit coupling. Using Topological Quantum Chemistry, we
build a complete topological classification in terms of symmetry eigenvalues of
all the gapped and gapless flat bands, for all 1651 Magnetic Space Groups. In
addition, we derive criteria for the existence of symmetry-protected band
touching points between the flat and dispersive bands, and we identify the
gapped flat bands as prime candidates for fragile topological phases. Finally,
we show that the set of all (gapped and gapless) perfectly flat bands is
finitely generated and construct the corresponding bases for all 1651 Shubnikov
Space Groups.


Abstract:
  The Seebeck coefficient and electrical conductivity are two critical
quantities to optimize simultaneously in designing thermoelectric materials,
and they are determined by the dynamics of carrier scattering. We uncover a new
regime where the co-existence at the Fermi level of multiple bands with
different effective masses leads to strongly energy-dependent carrier lifetimes
due to intrinsic electron-phonon scattering. In this anomalous regime,
electrical conductivity decreases with carrier concentration, Seebeck
coefficient reverses sign even at high doping, and power factor exhibits an
unusual second peak. We discuss the origin and magnitude of this effect using
first-principles Boltzmann transport calculations and simplified models. We
also identify general design rules for using this paradigm to engineer enhanced
performance in thermoelectric materials.


Abstract:
  An element $g$ of a free group $F$ depends on a subgroup $H < F$, or is
algebraic over $H$, if it satisfies a univariate equation over $H$.
Equivalently, when $H$ is finitely-generated, the rank of $\gen{H,g}$ is at
most the rank of $H$. We study the elements that depend on subgroups of $F$,
the subgroups they generate and the equations that they satisfy.


Abstract:
  Macroscopic, phenomenological models have proven useful as concise framings
of our understandings in fields from statistical physics to economics to
biology. Constructing a phenomenological model for development would provide a
framework for understanding the complicated, regulatory nature of oogenesis and
embryogenesis. Here, we use a data-driven approach to infer quantitative,
precise models of human oocyte maturation and pre-implantation embryo
development, by analyzing existing clinical In-Vitro Fertilization (IVF) data
on 7,399 IVF cycles resulting in 57,827 embryos. Surprisingly, we find that
both oocyte maturation and early embryo development are quantitatively
described by simple models with minimal interactions. This simplicity suggests
that oogenesis and embryogenesis are composed of modular processes that are
relatively siloed from one another. In particular, our analysis provides strong
evidence that (i) pre-antral follicles produce anti-M{\"u}llerian hormone
independently of effects from other follicles, (ii) oocytes mature to
metaphase-II independently of the woman's age, her BMI, and other factors,
(iii) early embryo development is memoryless for the variables assessed here,
in that the probability of an embryo transitioning from its current
developmental stage to the next is independent of its previous stage. Our
results both provide insight into the fundamentals of oogenesis and
embryogenesis and have implications for the clinical practice of IVF.


Abstract:
  We investigate features of the deconfinement phase transition in an $SU(N_c)$
gauge theory as revealed by fluctuations of the order parameter. The tool of
choice is an effective model built from one-loop expressions of the field
determinants of gluon and ghost, in the presence of a Polyakov loop background
field. We show that the curvature masses associated with the Cartan angles,
which serve as a proxy to study the $A_0$-gluon screening mass, show a
characteristic dip in the vicinity of the transition temperature. The strength
of the observables, which reflects a competition between the confining and the
deconfining forces, is sensitive to assumptions of dynamics, thus provides an
interesting link between the $Z(N_c)$ vacuum structure and the properties of
gluon and ghost propagators.


Abstract:
  This paper evaluates the heterogenous impacts of the America Invents Act of
2011 (AIA) on patent applications for small and large businesses. Using data
collected from the United States Patent and Trademark Office and Google
Patents, I compare how the probability of successfully overcoming an initial
rejection is affected by the AIA for small- and large-business applicants,
respectively. This comparison is achieved by analyzing the data using a
difference-in-differences approach. Results suggest that after the enactment of
the AIA, small-business applicants were relatively favored when compared
against large-business applicants. This effect is statistically significant and
also practically large.


Abstract:
  Phenomena in chemistry, biology and neuroscience are often modelled using
ordinary differential equations (ODEs) in which the right-hand-side is
comprised of terms which correspond to individual 'processes' or 'fluxes'.
Frequently, these ODEs are characterised by multiple time-scale phenomena due
to order of magnitude differences between contributing processes and the
presence of switching, i.e., dominance or sub-dominance of particular terms as
a function of state variables. We outline a heuristic procedure for the
identification of small parameters in ODE models of this kind, with a
particular emphasis on the identification of small parameters relating to
switching behaviours. This procedure is outlined informally in generality, and
applied in detail to a model for intracellular calcium dynamics characterised
by switching and multiple (more than two) time-scale dynamics. A total of five
small parameters are identified, and related to a single perturbation parameter
by a polynomial scaling law based on order of magnitude comparisons. The
resulting singular perturbation problem has a time-scale separation which
depends on the region of state space. We prove the existence and uniqueness of
stable relaxation oscillations with three distinct time-scales using a
coordinate-independent formulation of GSPT in combination with the blow-up
method. We also provide an estimate for the period of the oscillations, and
consider a number of possibilities for their onset under parameter variation.


Abstract:
  The reconstruction of accurate three-dimensional environment models is one of
the most fundamental goals in the field of photogrammetry. Since satellite
images provide suitable properties for obtaining large-scale environment
reconstructions, there exist a variety of Stereo Matching based methods to
reconstruct point clouds for satellite image pairs. Recently, the first
Structure from Motion (SfM) based approach has been proposed, which allows to
reconstruct point clouds from multiple satellite images. In this work, we
propose an extension of this SfM based pipeline that allows us to reconstruct
not only point clouds but watertight meshes including texture information. We
provide a detailed description of several steps that are mandatory to exploit
state-of-the-art mesh reconstruction algorithms in the context of satellite
imagery. This includes a decomposition of finite projective camera calibration
matrices, a skew correction of corresponding depth maps and input images as
well as the recovery of real-world depth maps from reparameterized depth
values. The paper presents an extensive quantitative evaluation on multi-date
satellite images demonstrating that the proposed pipeline combined with current
meshing algorithms outperforms state-of-the-art point cloud reconstruction
algorithms in terms of completeness and median error. We make the source code
of our pipeline publicly available.


Abstract:
  The World Wide Web is not only one of the most important platforms of
communication and information at present, but also an area of growing interest
for scientific research. This motivates a lot of work and projects that require
large amounts of data. However, there is no dataset that integrates the
parameters and visual appearance of Web pages, because its collection is a
costly task in terms of time and effort. With the support of various computer
tools and programming scripts, we have created a large dataset of 49,438 Web
pages. It consists of visual, textual and numerical data types, includes all
countries worldwide, and considers a broad range of topics such as art,
entertainment, economy, business, education, government, news, media, science,
and environment, covering different cultural characteristics and varied design
preferences. In this paper, we describe the process of collecting, debugging
and publishing the final product, which is freely available. To demonstrate the
usefulness of our dataset, we expose a binary classification model for
detecting error Web pages, and a multi-class Web subject-based categorization,
both problems using convolutional neural networks.


Abstract:
  In this paper, we give some properties of fuzzy Riesz homomorphism on fuzzy
Riesz space. We give definitions of fuzzy quotient spaces, and some
characterizations of fuzzy Archimedean quotient spaces. We prove the properties
which fuzzy Riesz homomorphism is fuzzy order continuous, and study the
extension of fuzzy lattice homomorphism and the extension of fuzzy order
continuous lattice homomorphism. Finally, we investigate the factorization by
fuzzy Riesz homomorphisms.


Abstract:
  Participatory budgeting (PB) is a democratic process where citizens jointly
decide on how to allocate public funds to indivisible projects. This paper
focuses on PB processes where citizens may give additional money to projects
they want to see funded. We introduce a formal framework for this kind of PB
with donations. Our framework also allows for diversity constraints, meaning
that each project belongs to one or more types, and there are lower and upper
bounds on the number of projects of the same type that can be funded. We
propose three general classes of methods for aggregating the citizens'
preferences in the presence of donations and analyze their axiomatic
properties. Furthermore, we investigate the computational complexity of
determining the outcome of a PB process with donations and of finding a
citizen's optimal donation strategy.


Abstract:
  Materials with exceptional magnetism and superconductivity usually conceive
emergent physical phenomena. Here, we investigate the physical properties of
the (Eu,La)FeAs2 system with double magnetic sublattices. The parent EuFeAs2
shows anisotropy-associated magnetic behaviors, such as Eu-related moment
canting and exchange bias. Through La doping, the magnetic anisotropy is
enhanced with ferromagnetism of Eu2+ realized in the overdoped region, and a
special exchange bias of the superposed ferromagnetic/superconducting loop
revealed in Eu0.8La0.2FeAs2. Meanwhile, the Fe-related antiferromagnetism shows
unusual robustness against La doping. Theoretical calculation and 57Fe
M\"ossbauer spectroscopy investigation reveal a doping-tunable dual
itinerant/localized nature of the Fe-related antiferromagnetism. Coexistence of
the Eu-related ferromagnetism, Fe-related robust antiferromagnetism, and
superconductivity is further revealed in Eu0.8La0.2FeAs2, providing a platform
for further exploration of potential applications and emergent physics.
Finally, an electronic phase diagram is established for (Eu,La)FeAs2 with the
whole superconducting dome adjacent to the Fe-related antiferromagnetic phase,
which is of benefit for seeking underlying clues to high-temperature
superconductivity.


Abstract:
  Louveau showed that if a Borel set in a Polish space happens to be in a Borel
Wadge class $\Gamma$, then its $\Gamma$-code can be obtained from its Borel
code in a hyperarithmetical manner. We extend Louveau's theorem to Borel
functions: If a Borel function on a Polish space happens to be a
$\Sigma_t$-function, then one can effectively find its $\Sigma_t$-code
hyperarithmetically relative to its Borel code. More generally, we prove
extension-type, domination-type, and decomposition-type variants of Louveau's
theorem for Borel functions.


Abstract:
  Augmented Reality is the fusion of virtual components and our real
surroundings. The simultaneous visibility of generated and natural objects
often requires users to direct their selective attention to a specific target
that is either real or virtual. In this study, we investigated whether this
target is real or virtual by using machine learning techniques to classify
electroencephalographic (EEG) data collected in Augmented Reality scenarios. A
shallow convolutional neural net classified 3 second data windows from 20
participants in a person-dependent manner with an average accuracy above 70\%
if the testing data and training data came from different trials.
Person-independent classification was possible above chance level for 6 out of
20 participants. Thus, the reliability of such a Brain-Computer Interface is
high enough for it to be treated as a useful input mechanism for Augmented
Reality applications.


Abstract:
  We study linear peer effects models where peers interact in groups,
individual's outcomes are linear in the group mean outcome and characteristics,
and group effects are random. Our specification is motivated by the moment
conditions imposed in Graham 2008. We show that these moment conditions can be
cast in terms of a linear random group effects model and lead to a class of GMM
estimators that are generally identified as long as there is sufficient
variation in group size. We also show that our class of GMM estimators contains
a Quasi Maximum Likelihood estimator (QMLE) for the random group effects model,
as well as the Wald estimator of Graham 2008 and the within estimator of Lee
2007 as special cases. Our identification results extend insights in Graham
2008 that show how assumptions about random group effects as well as variation
in group size can be used to overcome the reflection problem in identifying
peer effects. Our QMLE and GMM estimators can easily be augmented with
additional covariates and are valid in situations with a large but finite
number of different group sizes. Because our estimators are general moment
based procedures, using instruments other than binary group indicators in
estimation is straight forward. Monte-Carlo simulations show that the bias of
the QMLE estimator decreases with the number of groups and the variation in
group size, and increases with group size. We also prove the consistency and
asymptotic normality of the estimator under reasonable assumptions.


Abstract:
  In this paper, we consider an extension of cycle-complete graph Ramsey
numbers to Berge cycles in hypergraphs: for $k \geq 2$, a {\em non-trivial
Berge $k$-cycle} is a family of sets $e_1,e_2,\dots,e_k$ such that $e_1 \cap
e_2, e_2 \cap e_3,\dots,e_k \cap e_1$ has a system of distinct representatives
and $e_1 \cap e_2 \cap \dots \cap e_k = \emptyset$. In the case that all the
sets $e_i$ have size three, let $\mathcal{B}_k$ denotes the family of all
non-trivial Berge $k$-cycles. The {\em Ramsey numbers} $R(t,\mathcal{B}_k)$
denote the minimum $n$ such that every $n$-vertex $3$-uniform hypergraph
contains either a non-trivial Berge $k$-cycle or an independent set of size
$t$. We prove \[ R(t, \mathcal{B}_{2k}) \leq t^{1 + \frac{1}{2k-1} +
\frac{4}{\sqrt{\log t}}}\] and moreover, we show that if a conjecture of
Erd\H{o}s and Simonovits \cite{ES} on girth in graphs is true, then this is
tight up to a factor $t^{o(1)}$ as $t \rightarrow \infty$.


Abstract:
  We study variance estimation and associated confidence intervals for
parameters characterizing genetic effects from genome-wide association studies
(GWAS) misspecified mixed model analysis. Previous studies have shown that, in
spite of the model misspecification, certain quantities of genetic interests
are estimable, and consistent estimators of these quantities can be obtained
using the restricted maximum likelihood (REML) method under a misspecified
linear mixed model. However, the asymptotic variance of such a REML estimator
is complicated and not ready to be implemented for practical use. In this
paper, we develop practical and computationally convenient methods for
estimating such asymptotic variances and constructing the associated confidence
intervals. Performance of the proposed methods is evaluated empirically based
on Monte-Carlo simulations and real-data application.


Abstract:
  Evolving attacks on the vulnerabilities of the computing systems demand novel
defense strategies to keep pace with newer attacks. This report discusses
previous works on side-channel attacks (SCAs) and defenses for cache-targeted
and physical proximity attacks. We then discuss the proposed Entropy-Shield as
a defense against timing SCAs, and explain how we can extend the same to
hardware-based implementations of crypto applications as "Entropy-Shield for
FPGA". We then discuss why we want to build newer attacks with the hope of
coming up with better defense strategies.


Abstract:
  Functional near-infrared spectroscopy (fNIRS) is a non-invasive, low-cost
method used to study the brain's blood flow pattern. Such patterns can enable
us to classify performed by a subject. In recent research, most classification
systems use traditional machine learning algorithms for the classification of
tasks. These methods, which are easier to implement, usually suffer from low
accuracy. Further, a complex pre-processing phase is required for data
preparation before implementing traditional machine learning methods. The
proposed system uses a Bi-Directional LSTM based deep learning architecture for
task classification, including mental arithmetic, motor imagery, and idle state
using fNIRS data. Further, this system will require less pre-processing than
the traditional approach, saving time and computational resources while
obtaining an accuracy of 81.48\%, which is considerably higher than the
accuracy obtained using conventional machine learning algorithms for the same
data set.


Abstract:
  We present a method to estimate dense depth by optimizing a sparse set of
points such that their diffusion into a depth map minimizes a multi-view
reprojection error from RGB supervision. We optimize point positions, depths,
and weights with respect to the loss by differential splatting that models
points as Gaussians with analytic transmittance. Further, we develop an
efficient optimization routine that can simultaneously optimize the 50k+ points
required for complex scene reconstruction. We validate our routine using ground
truth data and show high reconstruction quality. Then, we apply this to light
field and wider baseline images via self supervision, and show improvements in
both average and outlier error for depth maps diffused from inaccurate sparse
points. Finally, we compare qualitative and quantitative results to image
processing and deep learning methods. http://visual.cs.brown.edu/diffdiffdepth


Abstract:
  We introduce a new class of non-linear models for functional data based on
neural networks. Deep learning has been very successful in non-linear modeling,
but there has been little work done in the functional data setting. We propose
two variations of our framework: a functional neural network with continuous
hidden layers, called the Functional Direct Neural Network (FDNN), and a second
version that utilizes basis expansions and continuous hidden layers, called the
Functional Basis Neural Network (FBNN). Both are designed explicitly to exploit
the structure inherent in functional data. To fit these models we derive a
functional gradient based optimization algorithm. The effectiveness of the
proposed methods in handling complex functional models is demonstrated by
comprehensive simulation studies and real data examples.


Abstract:
  As HPC systems grow in complexity, efficient and manageable operation is
increasingly critical. Many centers are thus starting to explore the use of
Operational Data Analytics (ODA) techniques, which extract knowledge from
massive amounts of monitoring data and use it for control and visualization
purposes. As ODA is a multi-faceted problem, much effort has gone into
researching its separate aspects: however, accounts of production ODA
experiences are still hard to come across.
  In this work we aim to bridge the gap between ODA research and production use
by presenting our experiences with ODA in production, involving in particular
the control of cooling infrastructures and visualization of job data on two HPC
systems. We cover the entire development process, from design to deployment,
highlighting our insights in an effort to drive the community forward. We rely
on open-source tools, which make for a generic ODA framework suitable for most
scenarios.


Abstract:
  Everyday thousands of meteoroids enter the Earth's atmosphere. The vast
majority burn up harmlessly during the descent, but the larger objects survive,
occasionally experiencing intense fragmentation events, and reach the ground.
These events can pose a threat for a village or a small city; therefore, models
of asteroid fragmentation, along with accurate post-breakup trajectory and
strewn field estimation, are needed to enable a reliable risk assessment. In
this work, a methodology to describe meteoroids entry, fragmentation, descent,
and strewn field is presented by means of a continuum approach. At breakup, a
modified version of the NASA Standard Breakup Model is used to generate the
distribution of the fragments in terms of their area-to-mass ratio and ejection
velocity. This distribution, combined with the meteoroid state, is directly
propagated using the continuity equation coupled with the non-linear entry
dynamics. At each time step, the probability density evolution of the fragments
is reconstructed using GMM interpolation. Using this information is then
possible to estimate the meteoroid's ground impact probability. This approach
departs from the current state-of-the-art models: it has the flexibility to
include large fragmentation events while maintaining a continuum formulation
for a better physical representation of the phenomenon. The methodology is also
characterised by a modular structure, so that updated asteroids fragmentation
models can be readily integrated into the framework, allowing a continuously
improving prediction of re-entry and fragmentation events. The propagation of
the fragments' density and its reconstruction, currently considering only one
fragmentation point, is first compared against Monte Carlo simulations, and
then against real observations. Both deceleration due to atmospheric drag and
ablation due to aerothermodynamics effects have been considered.


Abstract:
  Machine learning (ML) and AI toolboxes such as scikit-learn or Weka are
workhorses of contemporary data scientific practice -- their central role being
enabled by usable yet powerful designs that allow to easily specify, train and
validate complex modeling pipelines. However, despite their universal success,
the key design principles in their construction have never been fully analyzed.
In this paper, we attempt to provide an overview of key patterns in the design
of AI modeling toolboxes, taking inspiration, in equal parts, from the field of
software engineering, implementation patterns found in contemporary toolboxes,
and our own experience from developing ML toolboxes. In particular, we develop
a conceptual model for the AI/ML domain, with a new type system, called
scientific types, at its core. Scientific types capture the scientific meaning
of common elements in ML workflows based on the set of operations that we
usually perform with them (i.e. their interface) and their statistical
properties. From our conceptual analysis, we derive a set of design principles
and patterns. We illustrate that our analysis can not only explain the design
of existing toolboxes, but also guide the development of new ones. We intend
our contribution to be a state-of-art reference for future toolbox engineers, a
summary of best practices, a collection of ML design patterns which may become
useful for future research, and, potentially, the first steps towards a
higher-level programming paradigm for constructing AI.


Abstract:
  Determining the trajectories of cells and their lineages or ancestries in
live-cell experiments are fundamental to the understanding of how cells behave
and divide. This paper proposes novel online algorithms for jointly tracking
and resolving lineages of an unknown and time-varying number of cells from
time-lapse video data. Our approach involves modeling the cell ensemble as a
labeled random finite set with labels representing cell identities and
lineages. A spawning model is developed to take into account cell lineages and
changes in cell appearance prior to division. We then derive analytic filters
to propagate multi-object distributions that contain information on the current
cell ensemble including their lineages. We also develop numerical
implementations of the resulting multi-object filters. Experiments using
simulation, synthetic cell migration video, and real time-lapse sequence, are
presented to demonstrate the capability of the solutions.


Abstract:
  A regularized minimization model with $l_1$-norm penalty (RP) is introduced
for training the autoencoders that belong to a class of two-layer neural
networks. We show that the RP can act as an exact penalty model which shares
the same global minimizers, local minimizers, and d(irectional)-stationary
points with the original regularized model under mild conditions. We construct
a bounded box region that contains at least one global minimizer of the RP, and
propose a linearly constrained regularized minimization model with $l_1$-norm
penalty (LRP) for training autoencoders. A smoothing proximal gradient
algorithm is designed to solve the LRP. Convergence of the algorithm to a
generalized d-stationary point of the RP and LRP is delivered. Comprehensive
numerical experiments convincingly illustrate the efficiency as well as the
robustness of the proposed algorithm.


Abstract:
  Riemannian manifold Hamiltonian Monte Carlo is traditionally carried out
using the generalized leapfrog integrator. However, this integrator is not the
only choice and other integrators yielding valid Markov chain transition
operators may be considered. In this work, we examine the implicit midpoint
integrator as an alternative to the generalized leapfrog integrator. We discuss
advantages and disadvantages of the implicit midpoint integrator for
Hamiltonian Monte Carlo, its theoretical properties, and an empirical
assessment of the critical attributes of such an integrator for Hamiltonian
Monte Carlo: energy conservation, volume preservation, and reversibility.
Empirically, we find that while leapfrog iterations are faster, the implicit
midpoint integrator has better energy conservation, leading to higher
acceptance rates, as well as better conservation of volume and better
reversibility, arguably yielding a more accurate sampling procedure.


Abstract:
  Understanding the dynamics of charge exchange between a solid surface and a
liquid is fundamental to various situations, ranging from nanofiltration to
catalysis and electrochemistry. Charge transfer is ultimately determined by
physicochemical processes (surface group dissociation, ion adsorption, etc...)
occurring in the few layers of molecules at the interface between the solid and
the liquid. Unfortunately, these processes remain largely uncharted due to the
experimental challenges in probing interfacial charge dynamics with
sufficiently high spatial and temporal resolution. Here, we resolve at the
single-charge scale, the dynamics of proton charges at the interface between an
hBN crystal and binary mixtures of water and organic amphiphilic solvents (e.g.
alcohol), evidencing a dramatic influence of solvation on interfacial dynamics.
Our observations rely on the application of spectral Single Molecule
Localization Microscopy (sSMLM) to two types of optically active defects at the
hBN surface, which act as intrinsic optical markers for both surface
protonation and interaction with apolar alkyl groups of the organic solvent. We
use sSMLM to reveal interfacial proton charge transport as a succession of
jumps between the titratable surface defects, mediated by the transport of the
solvated proton charge along the solid/liquid interface. By changing the
relative concentration of water in binary mixtures, we evidence a non-trivial
effect on interfacial proton charge dynamics, leading at intermediate water
concentration to an increased affinity of the proton charge to the solid
surface, accompanied by an increased surface diffusivity. These measurements
confirm the strong role of solvation on interfacial proton charge transport and
establish the potential of single-molecule localization techniques to probe a
wide range of dynamic processes at solid/liquid interfaces.


Abstract:
  Avoiding collisions is the core problem in multi-agent navigation. In
decentralized settings, when agents have limited communication and sensory
capabilities, collisions are typically avoided in a reactive fashion, relying
on local observations/communications. Prominent collision avoidance techniques,
e.g. ORCA, are computationally efficient and scale well to a large number of
agents. However, in numerous scenarios, involving navigation through the tight
passages or confined spaces, deadlocks are likely to occur due to the egoistic
behaviour of the agents and as a result, the latter can not achieve their
goals. To this end, we suggest an application of the locally confined
multi-agent path finding (MAPF) solvers that coordinate sub-groups of the
agents that appear to be in a deadlock (to detect the latter we suggest a
simple, yet efficient ad-hoc routine). We present a way to build a grid-based
MAPF instance, typically required by modern MAPF solvers. We evaluate two of
them in our experiments, i.e. Push and Rotate and a bounded-suboptimal version
of Conflict Based Search (ECBS), and show that their inclusion into the
navigation pipeline significantly increases the success rate, from 15% to 99%
in certain cases.


Abstract:
  Monocular depth prediction plays a crucial role in understanding 3D scene
geometry. Although recent methods have achieved impressive progress in terms of
evaluation metrics such as the pixel-wise relative error, most methods neglect
the geometric constraints in the 3D space. In this work, we show the importance
of the high-order 3D geometric constraints for depth prediction. By designing a
loss term that enforces a simple geometric constraint, namely, virtual normal
directions determined by randomly sampled three points in the reconstructed 3D
space, we significantly improve the accuracy and robustness of monocular depth
estimation. Significantly, the virtual normal loss can not only improve the
performance of learning metric depth, but also disentangle the scale
information and enrich the model with better shape information. Therefore, when
not having access to absolute metric depth training data, we can use virtual
normal to learn a robust affine-invariant depth generated on diverse scenes. In
experiments, We show state-of-the-art results of learning metric depth on NYU
Depth-V2 and KITTI. From the high-quality predicted depth, we are now able to
recover good 3D structures of the scene such as the point cloud and surface
normal directly, eliminating the necessity of relying on additional models as
was previously done. To demonstrate the excellent generalizability of learning
affine-invariant depth on diverse data with the virtual normal loss, we
construct a large-scale and diverse dataset for training affine-invariant
depth, termed Diverse Scene Depth dataset (DiverseDepth), and test on five
datasets with the zero-shot test setting. Code is available at:
https://git.io/Depth