Abstract:
  Cancer diseases constitute one of the most significant societal challenges.
In this paper we introduce a novel histopathological dataset for prostate
cancer detection. The proposed dataset, consisting of over 2.6 million tissue
patches extracted from 430 fully annotated scans, 4675 scans with assigned
binary diagnosis, and 46 scans with diagnosis given independently by a group of
histopathologists, can be found at https://ai-econsilio.diag.pl. Furthermore,
we propose a machine learning framework for detection of cancerous tissue
regions and prediction of scan-level diagnosis, utilizing thresholding and
statistical analysis to abstain from the decision in uncertain cases. During
the experimental evaluation we identify several factors negatively affecting
the performance of considered models, such as presence of label noise, data
imbalance, and quantity of data, that can serve as a basis for further
research. The proposed approach, composed of ensembles of deep neural networks
operating on the histopathological scans at different scales, achieves 94.6%
accuracy in patch-level recognition, and is compared in a scan-level diagnosis
with 9 human histopathologists.


Abstract:
  This paper is concerned with the output feedback boundary stabilization of
general 1-D reaction diffusion PDEs in the presence of an arbitrarily large
input delay. We consider the cases of Dirichlet/Neumann/Robin boundary
conditions for the both boundary control and boundary condition. The boundary
measurement takes the form of a either Dirichlet or Neumann trace. The adopted
control strategy is composed of a finite-dimensional observer estimating the
first modes of the PDE coupled with a predictor to compensate the input delay.
In this context, we show for any arbitrary value of the input delay that the
control strategy achieves the exponential stabilization of the closed-loop
system, for system trajectories evaluated in $H^1$ norm (also in $L^2$ norm in
the case of a Dirichlet boundary measurement), provided the dimension of the
observer is selected large enough. The reported proof of this result requires
to perform both control design and stability analysis using simultaneously the
(non-homogeneous) original version of the PDE and one of its equivalent
homogeneous representations.


Abstract:
  For graphene nanoribbons with Rashba spin-orbit coupling, the peculiar
magnetic response due to the presence of a magnetization and geometric
confinement are analyzed within a tight-binding model. We observe a sizable
transverse susceptibility that can be considered as a gate voltage-induced
magnetoelectric torque without the need of a bias voltage, with different
directions for zigzag and armchair ribbons. The local torque generates
non-collinear spin polarization between the two edges and/or along the ribbon,
and the net torque averages to zero if the magnetization is homogeneous.
Nevertheless, a nonzero net torque can appear in partially magnetized
nanoribbons or in nanoflakes of irregular shapes. The equilibrium spin current
produced by the spin-orbit coupling also appears in nanoribbons, but the
component flowing in the direction of confinement is strongly suppressed. Even
without the magnetization, an out-of-plane polarized chiral edge spin current
is produced, resembling that in the quantum spin Hall effect. Moreover, a
magnetization pointing perpendicular to the edge produces a laminar flow of
edge charge currents, whose flow direction is symmetric (non chiral) or
antisymmetric (chiral) between the two edges depends on whether the
magnetization points in-plane or out-of-plane.


Abstract:
  In many applications, the governing PDE to be solved numerically contains a
stiff component. When this component is linear, an implicit time stepping
method that is unencumbered by stability restrictions is often preferred. On
the other hand, if the stiff component is nonlinear, the complexity and cost
per step of using an implicit method is heightened, and explicit methods may be
preferred for their simplicity and ease of implementation. In this article, we
analyze new and existing linearly stabilized schemes for the purpose of
integrating stiff nonlinear PDEs in time. These schemes compute the nonlinear
term explicitly and, at the cost of solving a linear system with a matrix that
is fixed throughout, are unconditionally stable, thus combining the advantages
of explicit and implicit methods. Applications are presented to illustrate the
use of these methods.


Abstract:
  With the rapid evolution of social media, fake news has become a significant
social problem, which cannot be addressed in a timely manner using manual
investigation. This has motivated numerous studies on automating fake news
detection. Most studies explore supervised training models with different
modalities (e.g., text, images, and propagation networks) of news records to
identify fake news. However, the performance of such techniques generally drops
if news records are coming from different domains (e.g., politics,
entertainment), especially for domains that are unseen or rarely-seen during
training. As motivation, we empirically show that news records from different
domains have significantly different word usage and propagation patterns.
Furthermore, due to the sheer volume of unlabelled news records, it is
challenging to select news records for manual labelling so that the
domain-coverage of the labelled dataset is maximized. Hence, this work: (1)
proposes a novel framework that jointly preserves domain-specific and
cross-domain knowledge in news records to detect fake news from different
domains; and (2) introduces an unsupervised technique to select a set of
unlabelled informative news records for manual labelling, which can be
ultimately used to train a fake news detection model that performs well for
many domains while minimizing the labelling cost. Our experiments show that the
integration of the proposed fake news model and the selective annotation
approach achieves state-of-the-art performance for cross-domain news datasets,
while yielding notable improvements for rarely-appearing domains in news
datasets.


Abstract:
  Variational quantum algorithms (VQAs) promise efficient use of near-term
quantum computers. However, training VQAs often requires an extensive amount of
time and suffers from the barren plateau problem where the magnitude of the
gradients vanishes with increasing number of qubits. Here, we show how to
optimally train VQAs for learning quantum states. Parameterized quantum
circuits can form Gaussian kernels, which we use to derive adaptive learning
rates for gradient ascent. We introduce the generalized quantum natural
gradient that features stability and optimized movement in parameter space.
Both methods together outperform other optimization routines in training VQAs.
Our methods also excel at numerically optimizing driving protocols for quantum
control problems. The gradients of the VQA do not vanish when the fidelity
between the initial state and the state to be learned is bounded from below. We
identify a VQA for quantum simulation with such a constraint that thus can be
trained free of barren plateaus. Finally, we propose the application of
Gaussian kernels for quantum machine learning.


Abstract:
  NASA's Kepler, K2 and TESS missions employ Simple Aperture Photometry (SAP)
to derive time-series photometry, where an aperture is estimated for each star,
and pixels containing each star are summed to create a single light curve. This
method is simple, but in crowded fields the derived time-series can be highly
contaminated. The alternate method of fitting a Point Spread Function (PSF) to
the data is able to account for crowding, but is computationally expensive. In
this paper, we present a new approach to extracting photometry from these
time-series missions, which fits the PSF directly, but makes simplifying
assumptions in order to greatly reduce the computation expense. Our method
fixes the scene of the field in each image, estimates the PSF shape of the
instrument with a linear model, and allows only source flux and position to
vary. We demonstrate that our method is able to separate the photometry from
blended targets in the Kepler dataset that are separated by less than a pixel.
Our method is fast to compute, and fully accounts for uncertainties from
degeneracies due to crowded fields. We name the method described in this work
Linearized Field Deblending (LFD). We demonstrate our method on the false
positive Kepler target \koi. We are able to separate the photometry of the two
sources in the data, and demonstrate the contaminating transiting signal is
consistent with a small, sub-stellar companion with a radius of $2.67R_{jup}$
($0.27R_{sol}$). Our method is equally applicable to extracting photometry from
NASA's TESS mission.


Abstract:
  The main purpose of this paper is to describe the category of isotropic
cellular spectra over flexible fields. Guided by [9], we show that it is
equivalent as a stable $\infty$-category equipped with a $t$-structure to the
derived category of left comodules over the dual of the classical topological
Steenrod algebra. In order to obtain this result, the category of isotropic
cellular modules over the motivic Brown-Peterson spectrum is also studied and
isotropic Adams and Adams-Novikov spectral sequences are developed. As a
consequence, we also compute hom-sets in the category of isotropic Tate motives
between motives of isotropic cellular spectra.


Abstract:
  As neural networks gain widespread adoption in embedded devices, there is a
need for model compression techniques to facilitate deployment in
resource-constrained environments. Quantization is one of the go-to methods
yielding state-of-the-art model compression. Most approaches take a fully
trained model, apply different heuristics to determine the optimal
bit-precision for different layers of the network, and retrain the network to
regain any drop in accuracy. Based on Activation Density (AD)-the proportion of
non-zero activations in a layer-we propose an in-training quantization method.
Our method calculates bit-width for each layer during training yielding a mixed
precision model with competitive accuracy. Since we train lower precision
models during training, our approach yields the final quantized model at lower
training complexity and also eliminates the need for re-training. We run
experiments on benchmark datasets like CIFAR-10, CIFAR-100, TinyImagenet on
VGG19/ResNet18 architectures and report the accuracy and energy estimates for
the same. We achieve ~4.5x benefit in terms of estimated
multiply-and-accumulate (MAC) reduction while reducing the training complexity
by 50% in our experiments. To further evaluate the energy benefits of our
proposed method, we develop a mixed-precision scalable Process In Memory (PIM)
hardware accelerator platform. The hardware platform incorporates shift-add
functionality for handling multi-bit precision neural network models.
Evaluating the quantized models obtained with our proposed method on the PIM
platform yields ~5x energy reduction compared to 16-bit models. Additionally,
we find that integrating AD based quantization with AD based pruning (both
conducted during training) yields up to ~198x and ~44x energy reductions for
VGG19 and ResNet18 architectures respectively on PIM platform compared to
baseline 16-bit precision, unpruned models.


Abstract:
  The lanthanide elements have well-documented similarities in their chemical
behavior, which makes the valuable trivalent lanthanide cations (Ln(III))
particularly difficult to separate from each other in water. In this work, we
apply ab initio molecular dynamics simulations to compare the free energies
(Delta G(ads)) associated with the adsorption of lanthanide cations to silica
surfaces at a pH condition where SiO- groups are present. The predicted Delta
G(ads) for lutetium (Lu(III)) and europium (Eu(III)) are similar within
statistical uncertainties; this is in qualitative agreement with our batch
adsorption measurements on silica. This finding is remarkable because the two
cations exhibit hydration free energies (Delta G(hyd}) that differ by >2 eV,
different hydration numbers, and different hydrolysis behavior far from silica
surfaces. We observe that the similarity in Lu(III) and Eu(III) Delta G(ads) is
the result of a delicate cancellation between the difference in Eu(III) and
Lu(III) hydration (Delta G(hyd})), and their difference in binding energies to
silica. We propose that disrupting this cancellation at the two end points,
either for adsorbed or completely desorbed lanthanides (e.g., via
nanoconfinment or mixed solvents), will lead to effective Ln separation.


Abstract:
  The main objective of the M5 competition, which focused on forecasting the
hierarchical unit sales of Walmart, was to evaluate the accuracy and
uncertainty of forecasting methods in the field in order to identify best
practices and highlight their practical implications. However, whether the
findings of the M5 competition can be generalized and exploited by retail firms
to better support their decisions and operation depends on the extent to which
the M5 data is sufficiently similar to unit sales data of retailers that
operate in different regions, sell different types of products, and consider
different marketing strategies. To answer this question, we analyze the
characteristics of the M5 time series and compare them with those of two
grocery retailers, namely Corporaci\'on Favorita and a major Greek supermarket
chain, using feature spaces. Our results suggest that there are only small
discrepancies between the examined data sets, supporting the representativeness
of the M5 data.


Abstract:
  We derive a general expression for the threshold resummation of transverse
momentum distributions for processes with a colorless final state, by suitably
generalizing the renormalization-group based approach to threshold resummation
previously pursued by two of us. The ensuing expression holds to all
logarithmic orders, and it can be used to extend available results in the
literature, which only hold up to the next-to-leading log (NLL) level. We check
agreement of our result with the existing NLL result, as well as against the
known fixed next-to-leading order results for the Higgs transverse momentum
distribution in gluon fusion, and we provide explicit expressions at the
next-to-next-to-leading log level.


Abstract:
  We utilise an isobar model to investigate the $K^+ \Sigma^-$ photoproduction
off a neutron in the resonance region. Except for the Born terms, we include
high-spin (spin-3/2 and spin-5/2) nucleon resonances in the consistent
formalism together with a few $\Delta$ and kaon resonances to achieve an
acceptable agreement with data. Interestingly, we reveal that no hyperon
resonances are needed to achieve a reasonable description of data. On the other
hand the $N(1720)3/2^+$ resonance was found to be very important for correct
description of data. The free parameters of the model were fitted to
experimental data from the LEPS and CLAS Collaborations on either differential
cross sections or photon beam asymmetry. The novel feature of the fitting
procedure is the use of a regularization method, the Least Absolute Shrinkage
Selection Operator, and information criteria in order to choose the best fit.


Abstract:
  We show two sphere theorems for the Riemannian manifolds with scalar
curvature bounded below and the non-collapsed $\mathrm{RCD}(n-1,n)$ spaces with
mean distance close to $\frac{\pi}{2}$.


Abstract:
  The two-dimensional transition metal dichalcogenides (TMDs) have been
proposed as candidates for the channel material in future field effect
transistor designs. The heterophase design which utilizes the metallic T- or T'
phase of the TMD as contacts to the semiconducting H phase channel has shown
promising results in terms of bringing down the contact resistance of the
device. In this work, we use ab-initio calculations to demonstrate how
atomic-scale and quantum effects influence the ballistic transport properties
in such heterophase transistors with channel lengths up to 20 nm. We
investigate how the charge transfer depends on the carrier density both in T'-H
MoTe$_2$ Schottky contacts and planar T'-H-T' MoTe$_2$ transistors. We find
that the size of the Schottky barrier and the charge transfer is dominated by
the local atomic arrangements at the interface and the doping level.
Furthermore, two types of quantum states have a large influence on the charge
transport; interface states and standing waves in the semiconductor due to
quantum confinement. We find that the latter can be associated with rises in
the current by more than an order of magnitude due to resonant tunneling. Our
results demonstrate the quantum mechanical nature of these 2D transistors and
highlight several challenges and possible solutions for achieving a competitive
performance of such devices.


Abstract:
  Graph representation learning has become a ubiquitous component in many
scenarios, ranging from social network analysis to energy forecasting in smart
grids. In several applications, ensuring the fairness of the node (or graph)
representations with respect to some protected attributes is crucial for their
correct deployment. Yet, fairness in graph deep learning remains
under-explored, with few solutions available. In particular, the tendency of
similar nodes to cluster on several real-world graphs (i.e., homophily) can
dramatically worsen the fairness of these procedures. In this paper, we propose
a novel biased edge dropout algorithm (FairDrop) to counter-act homophily and
improve fairness in graph representation learning. FairDrop can be plugged in
easily on many existing algorithms, is efficient, adaptable, and can be
combined with other fairness-inducing solutions. After describing the general
algorithm, we demonstrate its application on two benchmark tasks, specifically,
as a random walk model for producing node embeddings, and to a graph
convolutional network for link prediction. We prove that the proposed algorithm
can successfully improve the fairness of all models up to a small or negligible
drop in accuracy, and compares favourably with existing state-of-the-art
solutions. In an ablation study, we demonstrate that our algorithm can flexibly
interpolate between biasing towards fairness and an unbiased edge dropout.
Furthermore, to better evaluate the gains, we propose a new dyadic group
definition to measure the bias of a link prediction task when paired with
group-based fairness metrics. In particular, we extend the metric used to
measure the bias in the node embeddings to take into account the graph
structure.


Abstract:
  Most heat transfer models for bulk crystal growth rely on the classical
Stefan formulation to evaluate interface motion during phase change. However,
when the interface is non-smooth the use of the classical Stefan formulation
may lead to singularities. To address this problem, we propose a simulation
model based on the weak formulation of the Stefan problem. Numerical solutions
of the weak Stefan formulation are obtained using the finite volume method.
This approach provides an energy conserving discretization scheme that
accurately evaluates heat transfer around non-smooth interfaces. We apply the
weak formulation to numerically simulate the solidification of silicon in the
horizontal ribbon growth process. Results exhibit a limitation on the ribbon's
pull speed, which previous classical Stefan models have failed to demonstrate.
A comparison of heat transfer between radiation and gas cooling shows that gas
cooling increases the pull speed limit for the same amount of heat removed.


Abstract:
  Despite the rapid progress in understanding the first intrinsic magnetic
topological insulator MnBi$_2$Te$_4$, its electronic structure remains a topic
under debates. Here we perform a thorough spectroscopic investigation into the
electronic structure of MnBi$_2$Te$_4$ via laser-based angle-resolved
photoemission spectroscopy. Through quantitative analysis, we estimate an upper
bound of 3 meV for the gap size of the topological surface state. Furthermore,
our circular dichroism measurements reveal band chiralities for both the
topological surface state and quasi-2D bands, which can be well reproduced in a
band hybridization model. A numerical simulation of energy-momentum dispersions
based on a four-band model with an additional step potential near the surface
provides a promising explanation for the origin of the quasi-2D bands. Our
study represents a solid step forward in reconciling the existing controversies
in the electronic structure of MnBi$_2$Te$_4$, and provides an important
framework to understand the electronic structures of other relevant topological
materials MnBi$_{2n}$Te$_{3n+1}$.


Abstract:
  Recently, referring image segmentation has aroused widespread interest.
Previous methods perform the multi-modal fusion between language and vision at
the decoding side of the network. And, linguistic feature interacts with visual
feature of each scale separately, which ignores the continuous guidance of
language to multi-scale visual features. In this work, we propose an encoder
fusion network (EFN), which transforms the visual encoder into a multi-modal
feature learning network, and uses language to refine the multi-modal features
progressively. Moreover, a co-attention mechanism is embedded in the EFN to
realize the parallel update of multi-modal features, which can promote the
consistent of the cross-modal information representation in the semantic space.
Finally, we propose a boundary enhancement module (BEM) to make the network pay
more attention to the fine structure. The experiment results on four benchmark
datasets demonstrate that the proposed approach achieves the state-of-the-art
performance under different evaluation metrics without any post-processing.


Abstract:
  This article presents a study on the quality and execution of research code
from publicly-available replication datasets at the Harvard Dataverse
repository. Research code is typically created by a group of scientists and
published together with academic papers to facilitate research transparency and
reproducibility. For this study, we define ten questions to address aspects
impacting research reproducibility and reuse. First, we retrieve and analyze
more than 2000 replication datasets with over 9000 unique R files published
from 2010 to 2020. Second, we execute the code in a clean runtime environment
to assess its ease of reuse. Common coding errors were identified, and some of
them were solved with automatic code cleaning to aid code execution. We find
that 74\% of R files crashed in the initial execution, while 56\% crashed when
code cleaning was applied, showing that many errors can be prevented with good
coding practices. We also analyze the replication datasets from journals'
collections and discuss the impact of the journal policy strictness on the code
re-execution rate. Finally, based on our results, we propose a set of
recommendations for code dissemination aimed at researchers, journals, and
repositories.


Abstract:
  We study covariant equations in quantum cosmology of an extended
minisuperspace obtained by the Eisenhart--Duval lift. We find that a Dirac-type
equation is naturally introduced in the extended minisuperspace. Explicit forms
of the fundamental solutions are yielded for specific models. The possible
further development in this direction is also discussed.


Abstract:
  We show that if a hyperbolic 3-manifold admits a partially hyperbolic
diffeomorphism then it also admits an Anosov flow. Moreover, we give a complete
classification of partially hyperbolic diffeomorphism in hyperbolic 3-manifolds
as well as partially hyperbolic diffeomorphisms in Seifert manifolds inducing
pseudo-Anosov dynamics in the base. This classification is given in terms of
the structure of their center (branching) foliations and the notion of
collapsed Anosov flows.


Abstract:
  Understanding the Bondi-Metzner-Sachs (BMS) frame of the gravitational waves
produced by numerical relativity is crucial for ensuring that analyses on such
waveforms are performed properly. It is also important that models are built
from waveforms in the same BMS frame. Up until now, however, the BMS frame of
numerical waveforms has not been thoroughly examined, largely because the
necessary tools have not existed. In this paper, we show how to analyze and map
to a suitable BMS frame for numerical waveforms calculated with the Spectral
Einstein Code (SpEC). However, the methods and tools that we present are
general and can be applied to any numerical waveforms. We present an extensive
study of 13 binary black hole systems that broadly span parameter space. From
these simulations, we extract the strain and also the Weyl scalars using both
SpECTRE's Cauchy-characteristic extraction module and also the standard
extrapolation procedure with a displacement memory correction applied during
postprocessing. First, we show that the current center-of-mass correction used
to map these waveforms to the center-of-mass frame is not as effective as
previously thought. Consequently, we also develop an improved correction that
utilizes asymptotic Poincar\'e charges instead of a Newtonian center-of-mass
trajectory. Next, we map our waveforms to the post-Newtonian (PN) BMS frame
using a PN strain waveform. This helps us find the unique BMS transformation
that minimizes the $L^{2}$ norm of the difference between the numerical and PN
strain waveforms during the early inspiral phase. We find that once the
waveforms are mapped to the PN BMS frame, they can be hybridized with a PN
strain waveform much more effectively than if one used any of the previous
alignment schemes, which only utilize the Poincar\'e transformations.


Abstract:
  We consider the approximability of constraint satisfaction problems in the
streaming setting. For every constraint satisfaction problem (CSP) on $n$
variables taking values in $\{0,\ldots,q-1\}$, we prove that improving over the
trivial approximability by a factor of $q$ requires $\Omega(n)$ space even on
instances with $O(n)$ constraints. We also identify a broad subclass of
problems for which any improvement over the trivial approximability requires
$\Omega(n)$ space. The key technical core is an optimal,
$q^{-(k-1)}$-inapproximability for the case where every constraint is given by
a system of $k-1$ linear equations $\bmod\; q$ over $k$ variables. Prior to our
work, no such hardness was known for an approximation factor less than $1/2$
for any CSP. Our work builds on and extends the work of Kapralov and Krachun
(Proc. STOC 2019) who showed a linear lower bound on any non-trivial
approximation of the max cut in graphs. This corresponds roughly to the case of
Max $k$-LIN-$\bmod\; q$ with $k=q=2$. Each one of the extensions provides
non-trivial technical challenges that we overcome in this work.


Abstract:
  Depth estimation is an important computer vision problem with many practical
applications to mobile devices. While many solutions have been proposed for
this task, they are usually very computationally expensive and thus are not
applicable for on-device inference. To address this problem, we introduce the
first Mobile AI challenge, where the target is to develop an end-to-end deep
learning-based depth estimation solutions that can demonstrate a nearly
real-time performance on smartphones and IoT platforms. For this, the
participants were provided with a new large-scale dataset containing RGB-depth
image pairs obtained with a dedicated stereo ZED camera producing
high-resolution depth maps for objects located at up to 50 meters. The runtime
of all models was evaluated on the popular Raspberry Pi 4 platform with a
mobile ARM-based Broadcom chipset. The proposed solutions can generate VGA
resolution depth maps at up to 10 FPS on the Raspberry Pi 4 while achieving
high fidelity results, and are compatible with any Android or Linux-based
mobile devices. A detailed description of all models developed in the challenge
is provided in this paper.


Abstract:
  Aerial navigation in GPS-denied, indoor environments, is still an open
challenge. Drones can perceive the environment from a richer set of viewpoints,
while having more stringent compute and energy constraints than other
autonomous platforms. To tackle that problem, this research displays a
biologically inspired deep-learning algorithm for simultaneous localization and
mapping (SLAM) and its application in a drone navigation system. We propose an
unsupervised representation learning method that yields low-dimensional latent
state descriptors, that mitigates the sensitivity to perceptual aliasing, and
works on power-efficient, embedded hardware. The designed algorithm is
evaluated on a dataset collected in an indoor warehouse environment, and
initial results show the feasibility for robust indoor aerial navigation.


Abstract:
  We extend upper bounds on the quantum independence number and the quantum
Shannon capacity of graphs to their counterparts in the commuting operator
model. We introduce a von Neumann algebraic generalization of the fractional
Haemers bound (over $\mathbb{C}$) and prove that the generalization upper
bounds the commuting quantum independence number. We call our bound the tracial
Haemers bound, and we prove that it is multiplicative with respect to the
strong product. In particular, this makes it an upper bound on the Shannon
capacity. The tracial Haemers bound is incomparable with the Lov\'asz theta
function, another well-known upper bound on the Shannon capacity. We show that
separating the tracial and fractional Haemers bounds would refute Connes'
embedding conjecture.
  Along the way, we prove that the tracial rank and tracial Haemers bound are
elements of the (commuting quantum) asymptotic spectrum of graphs (Zuiddam,
Combinatorica, 2019). We also show that the inertia bound (an upper bound on
the quantum independence number) upper bounds the commuting quantum
independence number.


Abstract:
  High-temperature conventional superconductivity in hydrogen-rich materials
under high pressure has been reportedly found in twelve different compounds in
recent years. However, the experimental evidence on which these claims are
based has recently been called into question. Here we discuss the measurement
of trapped magnetic flux, that should establish definitively that these
materials are indeed high-temperature superconductors. Its absence should
confirm claims to the contrary.


Abstract:
  We present a novel framework to learn to convert the perpixel photometric
information at each view into spatially distinctive and view-invariant
low-level features, which can be plugged into existing multi-view stereo
pipeline for enhanced 3D reconstruction. Both the illumination conditions
during acquisition and the subsequent per-pixel feature transform can be
jointly optimized in a differentiable fashion. Our framework automatically
adapts to and makes efficient use of the geometric information available in
different forms of input data. High-quality 3D reconstructions of a variety of
challenging objects are demonstrated on the data captured with an illumination
multiplexing device, as well as a point light. Our results compare favorably
with state-of-the-art techniques.


Abstract:
  Scalable data science requires access to metadata, which is increasingly
managed by databases called data catalogs. With today's data catalogs, users
choose between designs that make it easy to store or retrieve metadata, but not
both. We find this problem arises because catalogs lack an easy to understand
mental model.
  In this paper, we present 5W1H+R, a new catalog mental model that is
comprehensive in the metadata it represents, and comprehensible in that it
permits users to locate metadata easily. We demonstrate these properties via a
user study. We then study different schema designs for the new mental model
implementation and evaluate them on different backends to understand their
relative merits. We conclude mental models are important to make data catalogs
more useful and to boost metadata management efforts.


Abstract:
  Existing 3D human pose estimators suffer poor generalization performance to
new datasets, largely due to the limited diversity of 2D-3D pose pairs in the
training data. To address this problem, we present PoseAug, a new
auto-augmentation framework that learns to augment the available training poses
towards a greater diversity and thus improve generalization of the trained
2D-to-3D pose estimator. Specifically, PoseAug introduces a novel pose
augmentor that learns to adjust various geometry factors (e.g., posture, body
size, view point and position) of a pose through differentiable operations.
With such differentiable capacity, the augmentor can be jointly optimized with
the 3D pose estimator and take the estimation error as feedback to generate
more diverse and harder poses in an online manner. Moreover, PoseAug introduces
a novel part-aware Kinematic Chain Space for evaluating local joint-angle
plausibility and develops a discriminative module accordingly to ensure the
plausibility of the augmented poses. These elaborate designs enable PoseAug to
generate more diverse yet plausible poses than existing offline augmentation
methods, and thus yield better generalization of the pose estimator. PoseAug is
generic and easy to be applied to various 3D pose estimators. Extensive
experiments demonstrate that PoseAug brings clear improvements on both
intra-scenario and cross-scenario datasets. Notably, it achieves 88.6% 3D PCK
on MPI-INF-3DHP under cross-dataset evaluation setup, improving upon the
previous best data augmentation based method by 9.1%. Code can be found at:
https://github.com/jfzhang95/PoseAug.


Abstract:
  The design of recurrent neural networks (RNNs) to accurately process
sequential inputs with long-time dependencies is very challenging on account of
the exploding and vanishing gradient problem. To overcome this, we propose a
novel RNN architecture which is based on a structure preserving discretization
of a Hamiltonian system of second-order ordinary differential equations that
models networks of oscillators. The resulting RNN is fast, invertible (in
time), memory efficient and we derive rigorous bounds on the hidden state
gradients to prove the mitigation of the exploding and vanishing gradient
problem. A suite of experiments are presented to demonstrate that the proposed
RNN provides state of the art performance on a variety of learning tasks with
(very) long-time dependencies.


Abstract:
  Machine Learning (ML) is a fundamental part of modern perception systems. In
the last decade, the performance of computer vision using trained deep neural
networks has outperformed previous approaches based on careful feature
engineering. However, the opaqueness of large ML models is a substantial
impediment for critical applications such as in the automotive context. As a
remedy, Gradient-weighted Class Activation Mapping (Grad-CAM) has been proposed
to provide visual explanations of model internals. In this paper, we
demonstrate how Grad-CAM heatmaps can be used to increase the explainability of
an image recognition model trained for a pedestrian underpass. We argue how the
heatmaps support compliance to the EU's seven key requirements for Trustworthy
AI. Finally, we propose adding automated heatmap analysis as a pipe segment in
an MLOps pipeline. We believe that such a building block can be used to
automatically detect if a trained ML-model is activated based on invalid pixels
in test images, suggesting biased models.


Abstract:
  Recent work on mid-infrared (MIR) detection through the process of
non-degenerate two-photon absorption (NTA) in semiconducting materials has
shown that wide-field MIR imaging can be achieved with standard Si cameras.
While this approach enables MIR imaging at high pixel densities, the low
nonlinear absorption coefficient of Si prevents fast NTA-based imaging at lower
illumination doses. Here we overcome this limitation by using InGaAs as the
photosensor. Taking advantage of the much higher nonlinear absorption
coefficient of this direct bandgap semiconductor, we demonstrate high-speed MIR
imaging up to 500 fps with under 1 ms exposure per frame, enabling 2D or 3D
mapping without pre- or post-processing of the image.


Abstract:
  Winner-take-all competitions in forecasting and machine-learning suffer from
distorted incentives. Witkowski et al. 2018 identified this problem and
proposed ELF, a truthful mechanism to select a winner. We show that, from a
pool of $n$ forecasters, ELF requires $\Theta(n\log n)$ events or test data
points to select a near-optimal forecaster with high probability. We then show
that standard online learning algorithms select an $\epsilon$-optimal
forecaster using only $O(\log(n) / \epsilon^2)$ events, by way of a strong
approximate-truthfulness guarantee. This bound matches the best possible even
in the nonstrategic setting. We then apply these mechanisms to obtain the first
no-regret guarantee for non-myopic strategic experts.


Abstract:
  We propose a subvolume method to study the $\theta$ dependence of the free
energy density of the four-dimensional SU($N$) Yang-Mills theory on the
lattice. As an attempt, the method is first applied to SU(2) Yang-Mills theory
at $T=1.2\,T_c$ to understand the systematics of the method. We then proceed to
the calculation of the vacuum energy density and obtain the $\theta$ dependence
qualitatively different from the high temperature case. The numerical results
combined with the theoretical requirements provide the evidence for the
spontaneous CP violation at $\theta = \pi$, which is in accordance with the
large $N$ prediction and indicates that the similarity between 4d SU($N$) and
2d CP$^{N-1}$ theories does not hold for $N$=2.


Abstract:
  While work in fields of CSCW (Computer Supported Collaborative Work),
Psychology and Social Sciences have progressed our understanding of team
processes and their effect performance and effectiveness, current methods rely
on observations or self-report, with little work directed towards studying team
processes with quantifiable measures based on behavioral data. In this report
we discuss work tackling this open problem with a focus on understanding
individual differences and its effect on team adaptation, and further explore
the effect of these factors on team performance as both an outcome and a
process. We specifically discuss our contribution in terms of methods that
augment survey data and behavioral data that allow us to gain more insight on
team performance as well as develop a method to evaluate adaptation and
performance across and within a group. To make this problem more tractable we
chose to focus on specific types of environments, Alternate Reality Games
(ARGs), and for several reasons. First, these types of games involve setups
that are similar to a real-world setup, e.g., communication through slack or
email. Second, they are more controllable than real environments allowing us to
embed stimuli if needed. Lastly, they allow us to collect data needed to
understand decisions and communications made through the entire duration of the
experience, which makes team processes more transparent than otherwise
possible. In this report we discuss the work we did so far and demonstrate the
efficacy of the approach.


Abstract:
  For a set W of vertices and a vertex v in a graph G, the k-vector r2(v|W) =
(aG(v,w1),...,aG(v,wk)) is the adjacency representation of v with respect to W,
where W = {w1,...,wk} and aG(x,y) is the minimum of 2 and the distance between
the vertices x and y. The set W is an adjacency resolving set for G if distinct
vertices of G have distinct adjacency representations with respect to W. The
minimum cardinality of an adjacency resolving set for G is its adjacency
dimension. It is clear that the adjacency dimension of an n-vertex graph G is
between 1 and n-1. The graphs with adjacency dimension 1 and n-1 are known. All
graphs with adjacency dimension 2, and all n-vertex graphs with adjacency
dimension n-2 are studied in this paper. In terms of the diameter and order of
G, a sharp upper bound is found for adjacency dimension of G. Also, a sharp
lower bound for adjacency dimension of G is obtained in terms of order of G.
Using these two bounds, all graphs with adjacency dimension 2, and all n-vertex
graphs with adjacency dimension n-2 are characterized.


Abstract:
  The purpose of the present paper is to derive a partial differential equation
(PDE) for the single-time single-point probability density function (PDF) of
the velocity field of a turbulent flow. The PDF PDE is a highly non-linear
parabolic-transport equation, which depends on two conditional statistical
numerics of important physical significance. The PDF PDE is a general form of
the classical Reynolds mean flow equation, and is a precise formulation of the
PDF transport equation. The PDF PDE provides us with a new method for modelling
turbulence. An explicit example is constructed, though the example is seemingly
artificial, but it demonstrates the PDF method based on the new PDF PDE.


Abstract:
  Individual investors are now massively using online brokers to trade stocks
with convenient interfaces and low fees, albeit losing the advice and
personalization traditionally provided by full-service brokers. We frame the
problem faced by online brokers of replicating this level of service in a
low-cost and automated manner for a very large number of users. Because of the
care required in recommending financial products, we focus on a risk-management
approach tailored to each user's portfolio and risk profile. We show that our
hybrid approach, based on Modern Portfolio Theory and Collaborative Filtering,
provides a sound and effective solution. The method is applicable to stocks as
well as other financial assets, and can be easily combined with various
financial forecasting models. We validate our proposal by comparing it with
several baselines in a domain expert-based study.


Abstract:
  Artificial neural networks are a valuable tool for radio-frequency (RF)
signal classification in many applications, but digitization of analog signals
and the use of general purpose hardware non-optimized for training make the
process slow and energetically costly. Recent theoretical work has proposed to
use nano-devices called magnetic tunnel junctions, which exhibit intrinsic RF
dynamics, to implement in hardware the Multiply and Accumulate (MAC) operation,
a key building block of neural networks, directly using analogue RF signals. In
this article, we experimentally demonstrate that a magnetic tunnel junction can
perform multiplication of RF powers, with tunable positive and negative
synaptic weights. Using two magnetic tunnel junctions connected in series we
demonstrate the MAC operation and use it for classification of RF signals.
These results open the path to embedded systems capable of analyzing RF signals
with neural networks directly after the antenna, at low power cost and high
speed.


Abstract:
  Capsule networks (see e.g. Hinton et al., 2018) aim to encode knowledge and
reason about the relationship between an object and its parts. % In this paper
we focus on a clean version of this problem, where data is generated from
multiple geometric objects (e.g. triangles, squares) at arbitrary translations,
rotations and scales, and the observed datapoints (parts) come from the corners
of all objects, without any labelling of the objects.
  We specify a generative model for this data, and derive a variational
algorithm for inferring the transformation of each object and the assignments
of points to parts of the objects.
  Recent work by Kosiorek et al. [2019] has used amortized inference via
stacked capsule autoencoders (SCA) to tackle this problem -- our results show
that we significantly outperform them.
  We also investigate inference for this problem using a RANSAC-type algorithm.


Abstract:
  Two-dimensional materials on metallic surfaces or stacked one on top of the
other can form a variety of moir\'e superstructures depending on the possible
parameter and symmetry mismatch and misorientation angle. In most cases, such
as incommensurate lattices or identical lattices but with a small twist angle,
the common periodicity may be very large, thus making numerical simulations
prohibitive. We propose here a general procedure to determine the minimal
simulation cell which approximates, within a certain tolerance and a certain
size, the primitive cell of the common superlattice, given the two interfacing
lattices and the relative orientation angle. As case studies to validate our
procedure, we report two applications of particular interest: the case of
misaligned hexagonal/hexagonal identical lattices, describing a twisted
graphene bilayer or a graphene monolayer grown on Ni(111), and the case of
hexagonal/square lattices, describing for instance a graphene monolayer grown
on Ni(100) surface. The first one, which has also analytic solutions,
constitutes a solid benchmark for the algorithm; the second one shows that a
very nice description of the experimental observations can be obtained also
using the resulting relatively small coincidence cells.


Abstract:
  We consider the regression problem where the dependence of the response Y on
a set of predictors X is fully captured by the regression function E(Y |
X)=g(B'X), for an unknown function g and low rank parameter B matrix. We
combine neural networks with sufficient dimension reduction in order to remove
the limitation of small p and n of the latter. We show in simulations that the
proposed estimator is on par with competing sufficient dimension reduction
methods in small p and n settings, such as minimum average variance estimation
and conditional variance estimation. Among those, it is the only
computationally applicable in large p and n problems.


Abstract:
  We present a class of new explicit and stable numerical algorithms to solve
the spatially discretized linear heat or diffusion equation. After discretizing
the space and the time variables like conventional finite difference methods,
we do not approximate the time derivatives by finite differences, but use
constant neighbor and linear neighbour approximations to decouple the ordinary
differential equations and solve them analytically. During this process, the
timestep-size appears not in polynomial, but in exponential form with negative
exponents, which guarantees stability. We compare the performance of the new
methods with analytical and numerical solutions. According to our results, the
methods are first and second order in time and can be much faster than the
commonly used explicit or implicit methods, especially in the case of extremely
large stiff systems.


Abstract:
  Many organic chemical reactions are governed by potential energy surfaces
that have a region with the topographical features of a caldera. If the caldera
has a symmetry then trajectories transiting the caldera region are observed to
exhibit a phenomenon that is referred to as dynamical matching. Dynamical
matching is a constraint that restricts the way in which a trajectory can exit
the caldera based solely on how it enters the caldera. In this paper we show
that bifurcations of the critical points of the caldera potential energy
surface can destroy dynamical matching even when the symmetry of the caldera is
not affected by the bifurcation.


Abstract:
  The fundamental interaction between free electrons and light stands at the
base of both classical and quantum physics, with applications in free-electron
acceleration, radiation sources, and electron microscopy. Yet, to this day, all
experiments involving free-electron light interactions are fully explained by
describing the light as a classical wave, disregarding its quantum nature.
Here, we observe quantum statistics effects of photons on free-electron-light
interactions. We demonstrate interactions passing continuously from Poissonian
to super-Poissonian and up to thermal statistics, unveiling a surprising
manifestation of Bohr's Correspondence Principle: the transition from quantum
walk to classical random walk on the free-electron energy ladder. The electron
walker serves as the probe in non-destructive quantum detection, measuring the
photon-correlation ${g^{(2)} (0)}$ and higher-orders ${g^{(n)} (0)}$. Unlike
conventional quantum-optical detectors, the electron can perform both quantum
weak measurements and projective measurements by evolving into an entangled
joint-state with the photons. Our findings suggest free-electron-based
non-destructive quantum tomography of light, and constitute an important step
towards combined attosecond-temporal and sub-A-spatial resolution microscopy.


Abstract:
  The asymptotic decision theory by Le Cam and Hajek has been given a lucid
perspective by the Ibragimov-Hasminskii theory on convergence of the likelihood
random field. Their scheme has been applied to stochastic processes by
Kutoyants, and today this plot is called the IHK program. This scheme ensures
that asymptotic properties of an estimator follow directly from the convergence
of the random field if a large deviation estimate exists. The quasi-likelihood
analysis (QLA) proved a polynomial type large deviation (PLD) inequality to go
through a bottleneck of the program. A conclusion of the QLA is that if the
quasi-likelihood random field is asymptotically quadratic and if a key index
reflecting identifiability the random field has is non-degenerate, then the PLD
inequality is always valid, and as a result, the IHK program can run. Many
studies already took advantage of the QLA theory. However, not a few of them
are using it in an inefficient way yet. The aim of this paper is to provide a
reformed and simplified version of the QLA and to improve accessibility to the
theory. As an example of the effects of the theory based on the PLD, the user
can obtain asymptotic properties of the quasi-Bayesian estimator by only
verifying non-degeneracy of the key index.


Abstract:
  Reliable and efficient trajectory generation methods are a fundamental need
for autonomous dynamical systems of tomorrow. The goal of this article is to
provide a comprehensive tutorial of three major convex optimization-based
trajectory generation methods: lossless convexification (LCvx), and two
sequential convex programming algorithms known as SCvx and GuSTO. In this
article, trajectory generation is the computation of a dynamically feasible
state and control signal that satisfies a set of constraints while optimizing
key mission objectives. The trajectory generation problem is almost always
nonconvex, which typically means that it is not readily amenable to efficient
and reliable solution onboard an autonomous vehicle. The three algorithms that
we discuss use problem reformulation and a systematic algorithmic strategy to
nonetheless solve nonconvex trajectory generation tasks through the use of a
convex optimizer. The theoretical guarantees and computational speed offered by
convex optimization have made the algorithms popular in both research and
industry circles. To date, the list of applications includes rocket landing,
spacecraft hypersonic reentry, spacecraft rendezvous and docking, aerial motion
planning for fixed-wing and quadrotor vehicles, robot motion planning, and
more. Among these applications are high-profile rocket flights conducted by
organizations like NASA, Masten Space Systems, SpaceX, and Blue Origin. This
article aims to give the reader the tools and understanding necessary to work
with each algorithm, and to know what each method can and cannot do. A publicly
available source code repository supports the provided numerical examples. By
the end of the article, the reader should be ready to use the methods, to
extend them, and to contribute to their many exciting modern applications.


Abstract:
  We propose a general and tunable platform to realize high-density arrays of
quantum spin-valley Hall kink (QSVHK) states with spin-valley-momentum locking
based on a two-dimensional hexagonal topological insulator. Through the
analysis of Berry curvature and topological charge, the QSVHK states are found
to be topologically protected by the valley-inversion and time-reversal
symmetries. Remarkably, the conductance of QSVHK states remains quantized
against either nonmagnetic or long-range magnetic disorder, verified by the
Green function calculations. Based on first-principles results, we show that
QSVHK states, protected with a gap up to 287 meV, can be realized in bismuthene
by alloy engineering, surface functionalization, or electric field, supporting
non-volatile applications of spin-valley filters, valves, and waveguides even
at room temperature.


Abstract:
  We propose new variants of the sketch-and-project method for solving large
scale ridge regression problems. Firstly, we propose a new momentum alternative
and provide a theorem showing it can speed up the convergence of
sketch-and-project, through a fast $\textit{sublinear}$ convergence rate. We
carefully delimit under what settings this new sublinear rate is faster than
the previously known linear rate of convergence of sketch-and-project without
momentum. Secondly, we consider combining the sketch-and-project method with
new modern sketching methods such as the count sketch, subcount sketch (a new
method we propose), and subsampled Hadamard transforms. We show experimentally
that when combined with the sketch-and-project method, the (sub)count sketch is
very effective on sparse data and the standard subsample sketch is effective on
dense data. Indeed, we show that these sketching methods, combined with our new
momentum scheme, result in methods that are competitive even when compared to
the Conjugate Gradient method on real large scale data. On the contrary, we
show the subsampled Hadamard transform does not perform well in this setting,
despite the use of fast Hadamard transforms, and nor do recently proposed
acceleration schemes work well in practice. To support all of our experimental
findings, and invite the community to validate and extend our results, with
this paper we are also releasing an open source software package:
$\texttt{RidgeSketch}$. We designed this object-oriented package in Python for
testing sketch-and-project methods and benchmarking ridge regression solvers.
$\texttt{RidgeSketch}$ is highly modular, and new sketching methods can easily
be added as subclasses. We provide code snippets of our package in the
appendix.


Abstract:
  Recent inclusive and differential cross section measurements of the
associated production of top quark pairs with gauge bosons or heavy-flavor jets
are reported. A search for physics beyond the standard model in the top quark
sector is also presented. All measurements are based on data samples of
proton-proton collisions at $\sqrt{s}=13$ TeV collected by the ATLAS and CMS
experiments at the CERN LHC. No significant deviation from the standard model
predictions is observed.


Abstract:
  We prove a structure theorem for the isometry group Iso(M, g) of a compact
Lorentz manifold, under the assumption that a closed subgroup has exponential
growth. We don't assume anything about the identity component of Iso(M, g), so
that our results apply for discrete isometry groups. We infer a full
classification of lattices that can act isometrically on compact Lorentz
manifolds. Moreover, without any growth hypothesis, we prove a Tits alternative
for discrete subgroups of Iso(M, g).


Abstract:
  Given $r>n$ general hyperplanes in $\mathbb P^n,$ a star configuration of
points is the set of all the $n$-wise intersection of them. We introduce {\it
contact star configurations}, which are star configurations where all the
hyperplanes are osculating to the same rational normal curve. In this paper we
find a relation between this construction and Hadamard products of linear
varieties. Moreover, we study the union of contact star configurations on a
same conic in $\mathbb P^2$, we prove that the union of two contact star
configurations has a special $h$-vector and, in some cases, this is a complete
intersection.


Abstract:
  Networks have been widely used to represent the relations between objects
such as academic networks and social networks, and learning embedding for
networks has thus garnered plenty of research attention. Self-supervised
network representation learning aims at extracting node embedding without
external supervision. Recently, maximizing the mutual information between the
local node embedding and the global summary (e.g. Deep Graph Infomax, or DGI
for short) has shown promising results on many downstream tasks such as node
classification. However, there are two major limitations of DGI. Firstly, DGI
merely considers the extrinsic supervision signal (i.e., the mutual information
between node embedding and global summary) while ignores the intrinsic signal
(i.e., the mutual dependence between node embedding and node attributes).
Secondly, nodes in a real-world network are usually connected by multiple edges
with different relations, while DGI does not fully explore the various
relations among nodes. To address the above-mentioned problems, we propose a
novel framework, called High-order Deep Multiplex Infomax (HDMI), for learning
node embedding on multiplex networks in a self-supervised way. To be more
specific, we first design a joint supervision signal containing both extrinsic
and intrinsic mutual information by high-order mutual information, and we
propose a High-order Deep Infomax (HDI) to optimize the proposed supervision
signal. Then we propose an attention based fusion module to combine node
embedding from different layers of the multiplex network. Finally, we evaluate
the proposed HDMI on various downstream tasks such as unsupervised clustering
and supervised classification. The experimental results show that HDMI achieves
state-of-the-art performance on these tasks.


Abstract:
  Correlation clustering is a central topic in unsupervised learning, with many
applications in ML and data mining. In correlation clustering, one receives as
input a signed graph and the goal is to partition it to minimize the number of
disagreements. In this work we propose a massively parallel computation (MPC)
algorithm for this problem that is considerably faster than prior work. In
particular, our algorithm uses machines with memory sublinear in the number of
nodes in the graph and returns a constant approximation while running only for
a constant number of rounds. To the best of our knowledge, our algorithm is the
first that can provably approximate a clustering problem on graphs using only a
constant number of MPC rounds in the sublinear memory regime. We complement our
analysis with an experimental analysis of our techniques.


Abstract:
  We explore the relationships between scattering states and bound states of
different non-analytic segments (depending on $|x|$) of the exponential
potential, and elucidate the status of the special scattering states found in
an earlier publication by Ahmed et al. A similar analysis can be made of
non-analytic segments of power potentials such as $x^3$.


Abstract:
  This paper is concerned with an inverse source problem for the stochastic
biharmonic operator wave equation. The driven source is assumed to be a
microlocally isotropic Gaussian random field with its covariance operator being
a classical pseudo-differential operator. The well-posedness of the direct
problem is examined in the distribution sense and the regularity of the
solution is discussed for the given rough source. For the inverse problem, the
strength of the random source, involved in the principal symbol of its
covariance operator, is shown to be uniquely determined by a single realization
of the magnitude of the wave field averaged over the frequency band with
probability one. Numerical experiments are presented to illustrate the validity
and effectiveness of the proposed method for the case that the random source is
the white noise.


Abstract:
  In this paper, we compute the distribution of the first letter statistic on
nine avoidance classes of permutations corresponding to two pairs of patterns
of length four. In particular, we show that the distribution is the same for
each class and is given by the entries of a new Schr\"oder number triangle.
This answers in the affirmative a recent conjecture of Lin and Kim. We employ a
variety of techniques to prove our results, including generating trees, direct
bijections and the kernel method. For the latter, we make use of in a creative
way what we are trying to show in three cases to aid in solving a system of
functional equations satisfied by the associated generating functions.


Abstract:
  The big rip, the little rip and the little sibling of the big rip are
cosmological doomsdays predicted by some phantom dark energy models that could
describe the future evolution of our Universe. When the universe evolves
towards either of these future cosmic events all bounded structures and,
ultimately, space-time itself are ripped apart. Nevertheless, it is commonly
belief that quantum gravity effects may smooth or even avoid these classically
predicted singularities. In this review, we discuss the classical and quantum
occurrence of these riplike events in the scheme of metric $f(R)$ theories of
gravity. The quantum analysis is performed in the framework of $f(R)$ quantum
geometrodynamics. In this context, we analyse the fulfilment of the DeWitt
criterion for the avoidance of these singular fates.


Abstract:
  Using a large sample of sub-L$_*$ galaxies, with similar UV magnitudes,
M$_{\rm UV}\simeq -19$ at $z\simeq6$, extracted from the FirstLight
simulations, we show the diversity of galaxies at the end of the reionization
epoch. We find a factor $\sim$40 variation in the specific star-formation rate
(sSFR). This drives a $\sim$1 dex range in equivalent width of the
[OIII]$\lambda$5007 line. Variations in nebular metallicity and ionization
parameter within HII regions lead to a scatter in the equivalent widths and
[OIII]/H$\alpha$ line ratio at a fixed sSFR. [OIII]-bright emitters
([OIII]/H$\alpha$>1) have higher ionization parameters and/or higher
metallicities than H$\alpha$-bright ([OIII]/H$\alpha$<1) galaxies. According to
the surface brightness maps in both [OIII] and H$\alpha$, [OIII]-bright
emitters are more compact than H$\alpha$-bright galaxies. H$\alpha$ luminosity
is higher than [OIII] if star formation is distributed over extended regions.
OIII dominates if it is concentrated in compact clumps. In both cases, the
H$\alpha$-emitting gas is significantly more extended than [OIII].


Abstract:
  Within the intersection of Ehrhart theory, commutative algebra, and algebraic
geometry lie lattice polytopes. Ehrhart theory is concerned with lattice point
enumeration in dilates of polytopes; lattice polytopes provide a sandbox in
which to test many conjectures in commutative algebra; and many properties of
projectively normal toric varieties in algebraic geometry are encoded through
corresponding lattice polytopes. In this article we focus on reflexive
simplices and work to identify when these have the integer decomposition
property (IDP), or equivalently, when certain weighted projective spaces are
projectively normal. We characterize the reflexive, IDP simplices whose
associated weighted projective spaces have one projective coordinate with
weight fixed to unity and for which the remaining coordinates can assume one of
three distinct weights. We show that several subfamilies of such reflexive
simplices have unimodal $h^\ast$-polynomials, thereby making progress towards
conjectures and questions of Stanley, Hibi-Ohsugi, and others regarding the
unimodality of their $h^\ast$-polynomials. We also provide computational
results and introduce the notion of reflexive stabilizations to explore the
(non-)ubiquity of reflexive simplices that are simultaneously IDP and
$h^\ast$-unimodal.


Abstract:
  The research into insulating ferrimagnetic garnets has gained enormous
momentum in the past decade. This is partly due to the improvement in the
techniques to grow high-quality ultrathin films with desirable properties and
the advances in understanding the spin transport within the ferrimagnetic
garnets and through their interfaces with conducting materials. In recent
years, we have seen remarkable progress in controlling the magnetization state
of ferrimagnetic garnets by electrical means in suitable heterostructures and
device architectures. These advances have readily placed ferrimagnetic garnets
in a favorable position for the future development of insulating spintronic
concepts. The purpose of this article is to review recent experimental results
of the current-induced magnetization control and associated phenomena in
ferrimagnetic garnets, as well as to discuss future directions in this rapidly
evolving area of spintronics.


Abstract:
  Interference alignment (IA) is a promising scheme to increase the throughput
of the system with shared mediums. In this paper, we propose a novel IA scheme
in molecular interference channels (IFCs) based on the choice of
releasing/sampling times. To cancel the aligned interference signals and reduce
the signal dependent noise, we use molecular reaction in the proposed IA
scheme. We obtain the feasible region for the releasing/sampling times in the
IA scheme using reaction. Further, we investigate the error performance of the
proposed scheme. Our results show that the proposed IA scheme using reaction
improves the performance significantly.


Abstract:
  The $t$-$J$-$U$ model of high-$T_c$ copper-oxide superconductors incorporates
both the on-site Coulomb repulsion and kinetic exchange interaction and yields
a semi-quantitative description of the static properties of those materials. We
extend this analysis to dynamic quantities and address collective spin- and
charge excitations in the correlated metallic state of the $t$-$J$-$U$ model.
We employ VWF+$1/\mathcal{N}_f$ approach that combines the variational wave
function (VWF) approach with the expansion in the inverse number of fermionic
flavors ($1/\mathcal{N}_f$). It is shown that the resonant (paramagnon)
contribution to the dynamic magnetic susceptibility remains robust as one
interpolates between the Hubbard- and $t$-$J$-model limits, whereas the
incoherent continuum undergoes substantial renormalization. Energy of the
collective charge mode diminishes as the strong-coupling limit is approached.
We also introduce the concept of effective kinetic exchange interaction that
allows for a unified interpretation of magnetic dynamics in the Hubbard,
$t$-$J$, and $t$-$J$-$U$ models. The results are discussed in the context of
recent resonant inelastic $x$-ray scattering experiments for the high-$T_c$
cuprates.


Abstract:
  We present a catalog of 536 fast radio bursts (FRBs) detected by the Canadian
Hydrogen Intensity Mapping Experiment Fast Radio Burst (CHIME/FRB) Project
between 400 and 800 MHz from 2018 July 25 to 2019 July 1, including 62 bursts
from 18 previously reported repeating sources. The catalog represents the first
large sample, including bursts from repeaters and non-repeaters, observed in a
single survey with uniform selection effects. This facilitates comparative and
absolute studies of the FRB population. We show that repeaters and apparent
non-repeaters have sky locations and dispersion measures (DMs) that are
consistent with being drawn from the same distribution. However, bursts from
repeating sources differ from apparent non-repeaters in intrinsic temporal
width and spectral bandwidth. Through injection of simulated events into our
detection pipeline, we perform an absolute calibration of selection effects to
account for systematic biases. We find evidence for a population of FRBs -
comprising a large fraction of the overall population - with a scattering time
at 600 MHz in excess of 10 ms, of which only a small fraction are observed by
CHIME/FRB. We infer a power-law index for the cumulative fluence distribution
of $\alpha=-1.40\pm0.11(\textrm{stat.})^{+0.06}_{-0.09}(\textrm{sys.})$,
consistent with the $-3/2$ expectation for a non-evolving population in
Euclidean space. We find $\alpha$ is steeper for high-DM events and shallower
for low-DM events, which is what would be expected when DM is correlated with
distance. We infer a sky rate of
$[820\pm60(\textrm{stat.})^{+220}_{-200}({\textrm{sys.}})]/\textrm{sky}/\textrm{day}$
above a fluence of 5 Jy ms at 600 MHz, with scattering time at $600$ MHz under
10 ms, and DM above 100 pc cm$^{-3}$.


Abstract:
  We consider the problem of embedding a dynamic network, to obtain
time-evolving vector representations of each node, which can then be used to
describe the changes in behaviour of a single node, one or more communities, or
the entire graph. Given this open-ended remit, we wish to guarantee stability
in the spatio-temporal positioning of the nodes: assigning the same position,
up to noise, to nodes behaving similarly at a given time (cross-sectional
stability) and a constant position, up to noise, to a single node behaving
similarly across different times (longitudinal stability). These properties are
defined formally within a generic dynamic latent position model. By showing how
this model can be recast as a multilayer random dot product graph, we
demonstrate that unfolded adjacency spectral embedding satisfies both stability
conditions, allowing, for example, spatio-temporal clustering under the dynamic
stochastic block model. We also show how alternative methods, such as omnibus,
independent or time-averaged spectral embedding, lack one or the other form of
stability.


Abstract:
  We prove that the grades of simple modules indexed by boolean permutations,
over the incidence algebra of the symmetric group with respect to the Bruhat
order, are given by Lusztig's a-function. Our arguments are combinatorial, and
include a description of the intersection of two principal order ideals when at
least one permutation is boolean. An important object in our work is a reduced
word written as minimally many runs of consecutive integers, and one step of
our argument shows that this minimal quantity is equal to the length of the
second row in the permutation's shape under the Robinson-Schensted
correspondence. We also prove that a simple module over the above-mentioned
incidence algebra is perfect if and only if its index is the longest element of
a parabolic subgroup.


Abstract:
  Motivated by recent Parker Solar Probe (PSP) observations of "switchbacks"
(abrupt, large-amplitude reversals in the radial magnetic field, which exhibit
Alfv\'enic correlations) we examine the dynamics of large-amplitude Alfv\'en
waves in the expanding solar wind. We develop an analytic model which makes
several predictions: switchbacks should preferentially occur in regions where
the solar wind plasma has undergone a greater expansion, the switchback
fraction at radii comparable to PSP should be an increasing function of radius,
and switchbacks should have their gradients preferentially perpendicular to the
mean magnetic field direction. The expansion of the plasma generates small
compressive components as part of the wave's nonlinear evolution: these are
maximized when the normalized fluctuation amplitude is comparable to
$\sin\theta$, where $\theta$ is the angle between the propagation direction and
the mean magnetic field. These compressive components steepen the primary
Alfv\'enic waveform, keeping the solution in a state of nearly constant
magnetic field strength as its normalized amplitude $\delta B/B$ grows due to
expansion. The small fluctuations in the magnetic-field-strength are minimized
at a particular $\theta$-dependent value of $\beta$, usually of order unity,
and the density and magnetic-field-strength fluctuations can be correlated or
anticorrelated depending on $\beta$ and $\theta$. Example solutions of our
dynamical equation are presented; some do indeed form magnetic-field reversals.
Our predictions appear to match some previously unexplained phenomena in
observations and numerical simulations, providing evidence that the observed
switchbacks result from the nonlinear evolution of the initially
small-amplitude Alfv\'en waves already known to be present at the coronal base.


Abstract:
  Many ionic surfactants with wide applications in personal-care and house-hold
detergency show limited water solubility at lower temperatures (Krafft point).
This drawback can be overcome by using mixed solutions, where the ionic
surfactant is incorporated in mixed micelles with another surfactant, which is
soluble at lower temperatures. The solubility and electrolytic conductivity for
a binary surfactant mixture of anionic methyl ester sulfonates (MES) with
nonionic alkyl polyglucoside and alkyl polyoxyethylene ether at 5 deg C during
long-term storage were measured. Phase diagrams were established; a general
theoretical model for their explanation was developed and checked
experimentally. The binary and ternary phase diagrams for studied surfactant
mixtures include phase domains: mixed micelles; micelles and crystallites;
crystallites, and molecular solution. The proposed general methodology, which
utilizes the equations of molecular thermodynamics at minimum number of
experimental measurements, is convenient for construction of such phase
diagrams. The results could increase the range of applicability of
MES-surfactants with relatively high Krafft temperature, but with various
useful properties such as excellent biodegradability and skin compatibility;
stability in hard water; good wetting and cleaning performance.


Abstract:
  The long-term variations of the orbit of the Earth govern the insolation on
its surface and hence its climate. The use of the astronomical signal, whose
imprint has been recovered in the geological records, has revolutionized the
determination of the geological time scales (e.g. Gradstein & Ogg 2020).
However, the orbital variations beyond 60 Myr cannot be reliably predicted
because of the chaotic dynamics of the planetary orbits in the Solar System
(Laskar 1989). Taking into account this dynamical uncertainty is necessary for
a complete astronomical calibration of geological records. Our work addresses
this problem with a statistical analysis of 120 000 orbital solutions of the
secular model of the Solar System ranging from 500 Myr to 5 Gyr. We obtain the
marginal probability density functions of the fundamental secular frequencies
using kernel density estimation. The uncertainty of the density estimation is
also obtained here in the form of confidence intervals determined by the moving
block bootstrap method. The results of the secular model are shown to be in
good agreement with those of the direct integrations of a comprehensive model
of the Solar System. Application of our work is illustrated on two geological
data: the Newark-Hartford records and the Libsack core.


Abstract:
  It has become increasingly common to collect high-dimensional binary data;
for example, with the emergence of new sampling techniques in ecology. In
smaller dimensions, multivariate probit (MVP) models are routinely used for
inferences. However, algorithms for fitting such models face issues in scaling
up to high dimensions due to the intractability of the likelihood, involving an
integral over a multivariate normal distribution having no analytic form.
Although a variety of algorithms have been proposed to approximate this
intractable integral, these approaches are difficult to implement and/or
inaccurate in high dimensions. We propose a two-stage Bayesian approach for
inference on model parameters while taking care of the uncertainty propagation
between the stages. We use the special structure of latent Gaussian models to
reduce the highly expensive computation involved in joint parameter estimation
to focus inference on marginal distributions of model parameters. This
essentially makes the method embarrassingly parallel for both stages. We
illustrate performance in simulations and applications to joint species
distribution modeling in ecology.


Abstract:
  This paper presents an automatic finite element simulation scheme accounting
for high geometric nonlinearity and the difference between linear and nonlinear
buckling of composite thin-walled lenticular tubes (CTLTs). Parameterizing of
cross-section shapes and generation of design space for CTLTs with both
circular and parabolic arcs were accomplished, and several key factors were
identified, in particular the contrary effect of lumbus length and parabolic
coefficient on the bending stiffness anisotropy. The first quantitative
comparison of triangular rollable and collapsible (TRAC) booms and CTLTs is
given in terms of bending performance in two directions, showing that the
optimal CTLT carefully selected from the design space demonstrates a comparable
or even better performance than the TRAC boom. This is of great importance from
both academic and engineering perspectives. Our efforts enhance the
understanding of nonlinear buckling and post-buckling behavior of CTLTs, and
provide guidelines for future design of CTLTs with desirable performance.


Abstract:
  We demonstrate high fidelity enhancement of planetary digital elevation
models (DEMs) using optical images and deep learning with convolutional neural
networks. Enhancement can be applied recursively to the limit of available
optical data, representing a 90x resolution improvement in global Mars DEMs.
Deep learning-based photoclinometry robustly recovers features obscured by
non-ideal lighting conditions. Method can be automated at global scale.
Analysis shows enhanced DEM slope errors are comparable with high resolution
maps using conventional, labor intensive methods.


Abstract:
  We present mGENRE, a sequence-to-sequence system for the Multilingual Entity
Linking (MEL) problem -- the task of resolving language-specific mentions to a
multilingual Knowledge Base (KB). For a mention in a given language, mGENRE
predicts the name of the target entity left-to-right, token-by-token in an
autoregressive fashion. The autoregressive formulation allows us to effectively
cross-encode mention string and entity names to capture more interactions than
the standard dot product between mention and entity vectors. It also enables
fast search within a large KB even for mentions that do not appear in mention
tables and with no need for large-scale vector indices. While prior MEL works
use a single representation for each entity, we match against entity names of
as many languages as possible, which allows exploiting language connections
between source input and target name. Moreover, in a zero-shot setting on
languages with no training data at all, mGENRE treats the target language as a
latent variable that is marginalized at prediction time. This leads to over 50%
improvements in average accuracy. We show the efficacy of our approach through
extensive evaluation including experiments on three popular MEL benchmarks
where mGENRE establishes new state-of-the-art results. Code and pre-trained
models at https://github.com/facebookresearch/GENRE.


Abstract:
  We propose Anticipative Video Transformer (AVT), an end-to-end
attention-based video modeling architecture that attends to the previously
observed video in order to anticipate future actions. We train the model
jointly to predict the next action in a video sequence, while also learning
frame feature encoders that are predictive of successive future frames'
features. Compared to existing temporal aggregation strategies, AVT has the
advantage of both maintaining the sequential progression of observed actions
while still capturing long-range dependencies--both critical for the
anticipation task. Through extensive experiments, we show that AVT obtains the
best reported performance on four popular action anticipation benchmarks:
EpicKitchens-55, EpicKitchens-100, EGTEA Gaze+, and 50-Salads; and it wins
first place in the EpicKitchens-100 CVPR'21 challenge.


Abstract:
  Generalization is a central problem in machine learning, especially when data
is limited. Using prior information to enforce constraints is the principled
way of encouraging generalization. In this work, we propose to leverage the
prior information embedded in pretrained language models (LM) to improve
generalization for intent classification and slot labeling tasks with limited
training data. Specifically, we extract prior knowledge from pretrained LM in
the form of synthetic data, which encode the prior implicitly. We fine-tune the
LM to generate an augmented language, which contains not only text but also
encodes both intent labels and slot labels. The generated synthetic data can be
used to train a classifier later. Since the generated data may contain noise,
we rephrase the learning from generated data as learning with noisy labels. We
then utilize the mixout regularization for the classifier and prove its
effectiveness to resist label noise in generated data. Empirically, our method
demonstrates superior performance and outperforms the baseline by a large
margin.


Abstract:
  Second-harmonic generation (SHG) is a direct measure of the strength of
second-order nonlinear optical effects, which also include frequency mixing and
parametric oscillations. Natural and artificial materials with broken
center-of-inversion symmetry in their unit cell display high SHG efficiency,
however the silicon-foundry compatible group-IV semiconductors (Si, Ge) are
centrosymmetric, thereby preventing full integration of second-order
nonlinearity in silicon photonics platforms. Here we demonstrate strong SHG in
Ge-rich quantum wells grown on Si wafers. The symmetry breaking is artificially
realized with a pair of asymmetric coupled quantum wells (ACQW), in which three
of the quantum-confined states are equidistant in energy, resulting in a double
resonance for SHG. Laser spectroscopy experiments demonstrate a giant
second-order nonlinearity at mid-infrared pump wavelengths between 9 and 12
microns. Leveraging on the strong intersubband dipoles, the nonlinear
susceptibility almost reaches 10^5 pm/V


Abstract:
  Meta learning with multiple objectives can be formulated as a Multi-Objective
Bi-Level optimization Problem (MOBLP) where the upper-level subproblem is to
solve several possible conflicting targets for the meta learner. However,
existing studies either apply an inefficient evolutionary algorithm or linearly
combine multiple objectives as a single-objective problem with the need to tune
combination weights. In this paper, we propose a unified gradient-based
Multi-Objective Meta Learning (MOML) framework and devise the first
gradient-based optimization algorithm to solve the MOBLP by alternatively
solving the lower-level and upper-level subproblems via the gradient descent
method and the gradient-based multi-objective optimization method,
respectively. Theoretically, we prove the convergence properties of the
proposed gradient-based optimization algorithm. Empirically, we show the
effectiveness of the proposed MOML framework in several meta learning problems,
including few-shot learning, neural architecture search, domain adaptation, and
multi-task learning.


Abstract:
  Terahertz frequency bands will likely be used for the next-generation
wireless communication systems to provide data rates of hundreds of Gbps or
even Tbps because of the wide swaths of unused and unexplored spectrum. This
paper presents two outdoor wideband measurement campaigns in downtown Brooklyn
(urban microcell environment) in the sub-THz band of 140 GHz with TX-RX
separation distance up to 100 m: i) terrestrial urban microcell measurement
campaign, and ii) rooftop surrogate satellite and backhaul measurement
campaign. Outdoor omnidirectional and directional path loss models for both
line-of-sight and non-line-of-sight scenarios, as well as foliage loss (signal
attenuation through foliage), are provided at 140 GHz for urban microcell
environments. These measurements and models provide an understanding of both
the outdoor terrestrial (e.g., 6G cellular and backhaul) and non-terrestrial
(e.g., satellite and unmanned aerial vehicle communications) wireless channels,
and prove the feasibility of using THz frequency bands for outdoor fixed and
mobile cellular communications. This paper can be used for future outdoor
wireless system design at frequencies above 100 GHz.


Abstract:
  This paper examines a class of barrier options-multi-step barrier options,
which can have any finite number of barriers of any level. We obtain a general,
explicit expression of option prices of this type under the Black-Scholes
model. Multi-step barrier options are not only useful in that they can handle
barriers of different levels and time steps, but can also approximate options
with arbitrary barriers. Moreover, they can be embedded in financial products
such as deposit insurances based on jump models with simple barriers. Along the
way, we derive multi-step reflection principle, which generalizes the
reflection principle of Brownian motion.


Abstract:
  We analyze the effective field equations of the Randall-Sundrum braneworld
coupled with a Klein-Gordon scalar field through the minimal geometric
deformation decoupling method (MGD-decoupling). We introduce two different ways
to apply the MGD-decoupling method to obtain new solutions for this enlarged
system. We also compare the behavior of the new solutions with those obtained
directly from the Randall-Sumdrum braneworld without coupling to the scalar
field.


Abstract:
  The next technological breakthrough in millimeter-submillimeter astronomy is
3D imaging spectrometry with wide instantaneous spectral bandwidths and wide
fields of view. The total optimization of the focal-plane instrument, the
telescope, the observing strategy, and the signal-processing software must
enable efficient removal of foreground emission from the Earth's atmosphere,
which is time-dependent and highly nonlinear in frequency. Here we present
TiEMPO: Time-Dependent End-to-End Model for Post-process Optimization of the
DESHIMA Spectrometer. TiEMPO utilizes a dynamical model of the atmosphere and
parametrized models of the astronomical source, the telescope, the instrument,
and the detector. The output of TiEMPO is a time-stream of sky brightness
temperature and detected power, which can be analyzed by standard
signal-processing software. We first compare TiEMPO simulations with an on-sky
measurement by the wideband DESHIMA spectrometer and find good agreement in the
noise power spectral density and sensitivity. We then use TiEMPO to simulate
the detection of a line emission spectrum of a high-redshift galaxy using the
DESHIMA 2.0 spectrometer in development. The TiEMPO model is open source. Its
modular and parametrized design enables users to adapt it to design and
optimize the end-to-end performance of spectroscopic and photometric
instruments on existing and future telescopes.


Abstract:
  Magnetic droplets are dissipative magnetodynamical solitons that can form
under current driven nanocontacts in magnetic layers with large perpendicular
magnetic anisotropy. Here, we extend the original droplet theory by studying
the impact of the dipolar interactions on the dynamics of droplet solitons. By
varying the thickness of the free layer of a spin torque nano-oscillator, we
systematically tune the internal field of the free layer to investigate the
dynamics of droplet solitons. Our numerical results show that increasing the
free layer thickness increases the droplet threshold current, decreases the
droplet frequency and diameter, enlarges the current hysteresis and also
modifies the structure of the droplet. The Oersted field of the current breaks
the phase coherency and deteriorates the stability of the droplet in free
layers with larger thicknesses. Moreover, our findings show a simple relation
to determine the impact of the free layer thickness on the droplet nucleation
boundaries. Our study presents the missing brick on the physics behind magnetic
droplet solitons, and further illustrates that magnetic droplets in thinner
layers possess more promising characteristics for spintronic applications and
enable devices with higher speed of operation.


Abstract:
  In this paper, we propose MINE to perform novel view synthesis and depth
estimation via dense 3D reconstruction from a single image. Our approach is a
continuous depth generalization of the Multiplane Images (MPI) by introducing
the NEural radiance fields (NeRF). Given a single image as input, MINE predicts
a 4-channel image (RGB and volume density) at arbitrary depth values to jointly
reconstruct the camera frustum and fill in occluded contents. The reconstructed
and inpainted frustum can then be easily rendered into novel RGB or depth views
using differentiable rendering. Extensive experiments on RealEstate10K, KITTI
and Flowers Light Fields show that our MINE outperforms state-of-the-art by a
large margin in novel view synthesis. We also achieve competitive results in
depth estimation on iBims-1 and NYU-v2 without annotated depth supervision. Our
source code is available at https://github.com/vincentfung13/MINE


Abstract:
  We compute the tautological ring for a Hilbert modular variety at an
unramified prime. The method generalises that of van der Geer from the Siegel
case.


Abstract:
  In this document, some general results in approximation theory and matrix
analysis with applications to sparse identification of time series models and
nonlinear discrete-time dynamical systems are presented. The aforementioned
theoretical methods are translated into algorithms that can be used for sparse
model identification of discrete-time dynamical systems, based on structured
data measured from the systems. The approximation of the state-transition
operators that are determined primarily by matrices of parameters to be
identified based on data measured from a given system, is approached by
identifying conditions for the existence of low-rank approximations of
submatrices of the trajectory matrices corresponding to the measured data, that
can be used to compute approximate sparse representations of the matrices of
parameters. One of the main advantages of the low-rank approximation approach
presented in this document, concerns the parameter estimation for linear and
nonlinear models where numerical or measurement noise could affect the
estimates significantly. Prototypical algorithms based on the aforementioned
techniques together with some applications to approximate identification and
predictive simulation of time series models with symmetries and nonlinear
structured dynamical systems in theoretical physics, fluid dynamics and weather
forecasting are presented.


Abstract:
  We construct the first ever multimodal sarcasm dataset for Spanish. The
audiovisual dataset consists of sarcasm annotated text that is aligned with
video and audio. The dataset represents two varieties of Spanish, a Latin
American variety and a Peninsular Spanish variety, which ensures a wider
dialectal coverage for this global language. We present several models for
sarcasm detection that will serve as baselines in the future research. Our
results show that results with text only (89%) are worse than when combining
text with audio (91.9%). Finally, the best results are obtained when combining
all the modalities: text, audio and video (93.1%).


Abstract:
  A common assumption in the social learning literature is that agents exchange
information in an unselfish manner. In this work, we consider the scenario
where a subset of agents aims at deceiving the network, meaning they aim at
driving the network beliefs to the wrong hypothesis. The adversaries are
unaware of the true hypothesis. However, they will "blend in" by behaving
similarly to the other agents and will manipulate the likelihood functions used
in the belief update process to launch inferential attacks. We will
characterize the conditions under which the network is misled. Then, we will
explain that it is possible for such attacks to succeed by showing that
strategies exist that can be adopted by the malicious agents for this purpose.
We examine both situations in which the agents have access to information about
the network model as well as the case in which they do not. For the first case,
we show that there always exists a way to construct fake likelihood functions
such that the network is deceived regardless of the true hypothesis. For the
latter case, we formulate an optimization problem and investigate the
performance of the derived attack strategy by establishing conditions under
which the network is deceived. We illustrate the learning performance of the
network in the aforementioned adversarial setting via simulations. In a
nutshell, we clarify when and how a network is deceived in the context of
non-Bayesian social learning.


Abstract:
  We study the polyhedral convex hull structure of a mixed-integer set which
arises in a class of cardinality-constrained concave submodular minimization
problems. This class of problems has an objective function in the form of
$f(a^\top x)$, where $f$ is a univariate concave function, $a$ is a
non-negative vector, and $x$ is a binary vector of appropriate dimension. Such
minimization problems frequently appear in applications that involve
risk-aversion or economies of scale. We propose three classes of strong valid
linear inequalities for this convex hull and specify their facet conditions
when $a$ has two distinct values. We show how to use these inequalities to
obtain valid inequalities for general $a$ that contains multiple values. We
further provide a complete linear convex hull description for this
mixed-integer set when $a$ contains two distinct values and the cardinality
constraint upper bound is two. Our computational experiments on the mean-risk
optimization problem demonstrate the effectiveness of the proposed inequalities
in a branch-and-cut framework.


Abstract:
  In order to run Computational Fluid Dynamics (CFD) codes on large scale
infrastructures, parallel computing has to be used because of the computational
intensive nature of the problems. In this paper we investigate the ADAPT
platform where we couple flow Partial Differential Equations and a Poisson
equation. This leads to a linear system which we solve using direct methods.
The implementation deals with the MUMPS parallel multi-frontal direct solver
and mesh partitioning methods using METIS to improve the performance of the
framework. We also investigate, in this paper, how the mesh partitioning
methods are able to optimize the mesh cell distribution for the ADAPT solver.
The experience gained in this paper facilitates the move to the 3D version of
ADAPT and the move to a Service Oriented view of ADAPT as future work.


Abstract:
  We show that if we suppose n>3 and the (2n-1)-stem in the stable homotopy
groups of spheres has no 2-torsion, then the Whitehead squares of the identity
maps of (2n+1) and (4n+3)-spheres are divisible by 2. By applying the result of
G. Wang and Z. Xu on the 61-stem in the stable homotopy groups of spheres, we
find that the Kervaire invariant one elements in dimensions 62 and 126 exist.


Abstract:
  When documenting oral-languages, Unsupervised Word Segmentation (UWS) from
speech is a useful, yet challenging, task. It can be performed from phonetic
transcriptions, or in the absence of these, from the output of unsupervised
speech discretization models. These discretization models are trained using raw
speech only, producing discrete speech units which can be applied for
downstream (text-based) tasks. In this paper we compare five of these models:
three Bayesian and two neural approaches, with regards to the exploitability of
the produced units for UWS. Two UWS models are experimented with and we report
results for Finnish, Hungarian, Mboshi, Romanian and Russian in a low-resource
setting (using only 5k sentences). Our results suggest that neural models for
speech discretization are difficult to exploit in our setting, and that it
might be necessary to adapt them to limit sequence length. We obtain our best
UWS results by using the SHMM and H-SHMM Bayesian models, which produce high
quality, yet compressed, discrete representations of the input speech signal.


Abstract:
  In odd dimensions the integrated conformal anomaly is entirely due to the
boundary terms \cite{Solodukhin:2015eca}. In this paper we present a detailed
analysis of the anomaly in five dimensions. We give the complete list of the
boundary conformal invariants that exist in five dimensions. Additionally to 8
invariants known before we find a new conformal invariant that contains the
derivatives of the extrinsic curvature along the boundary. Then, for a
conformal scalar field satisfying either the Dirichlet or the conformal
invariant Robin boundary conditions we use the available general results for
the heat kernel coefficient $a_5$, compute the conformal anomaly and identify
the corresponding values of all boundary conformal charges.


Abstract:
  Extensions to the Standard Model that use strictly off-shell degrees of
freedom - the fakeons - allow for new measurable interactions at energy scales
usually precluded by the constraints that target the on-shell propagation of
new particles. Here we employ the interactions between a new fake scalar
doublet and the muon to explain the recent Fermilab measurement of its
anomalous magnetic moment. Remarkably, unlike in the case of usual particles,
the experimental result can be matched for fakeon masses below the electroweak
scale without contradicting the stringent precision data and collider bounds on
new light degrees of freedom. Our analysis, therefore, demonstrates that the
fakeon approach offers unexpected viable possibilities to model new physics
naturally at low scales.


Abstract:
  Microresonator-based platforms with $\chi^{(2)}$ nonlinearities have the
potential to perform frequency conversion at high efficiencies and ultralow
powers with small footprints. The standard doctrine for achieving high
conversion efficiency in cavity-based devices requires "perfect matching", that
is, zero phase mismatch while all relevant frequencies are precisely at a
cavity resonance, which is difficult to achieve in integrated platforms due to
fabrication errors and limited tunabilities. In this Letter, we show that the
violation of perfect matching does not necessitate a reduction in conversion
efficiency. On the contrary, in many cases, mismatches should be intentionally
introduced to improve the efficiency or tunability of conversion. We identify
the universal conditions for maximizing the efficiency of cavity-based
frequency conversion and show a straightforward approach to fully compensate
for parasitic processes such as thermorefractive and photorefractive effects
that, typically, can limit the conversion efficiency. We also show rigorously
that these high-efficiency states are stable.