Abstract:
  This paper presents a sparse solver based on the alternating direction method
of multipliers algorithm for a linear model predictive control (MPC)
formulation in which the terminal state is constrained to a given ellipsoid.
The motivation behind this solver is to substitute the typical polyhedral
invariant set used as a terminal constraint in many nominal and robust linear
MPC formulations with an invariant set in the form of an ellipsoid, which is
(typically) much easier to compute and results in an optimization problem with
significantly fewer constraints, even for average-sized systems. However, this
optimization problem is no longer the quadratic programming problem found in
most linear MPC approaches, thus meriting the development of a tailored solver.
The proposed solver is suitable for its use in embedded systems, since it is
sparse, has a small memory footprint and requires no external libraries. We
show the results of its implementation in an embedded system to control a
simulated multivariable plant, comparing it against other alternatives.


Abstract:
  We show, using the same Lagrangian for the $K_1(1270) \to \pi K^*_0(1430)$
and $K^*_0(1430) \to K_1(1270) \pi$ decays, that the present PDG data on the
partial decay width of $K_1(1270) \to \pi K^*_0(1430)$ implies a width for
$K^*_0(1430) \to K_1(1270) \pi$ decay which is about ten times larger than the
total $K^*_0(1430)$ width. A discussion on this inconsistency is done,
stressing its relationship to the existence of two $K_1(1270)$ states obtained
with the chiral unitary theory, which are not considered in the experimental
analyses of $K\pi\pi$ data.


Abstract:
  This article considers average marginal effects (AME) in a panel data fixed
effects logit model. Relating the identified set of the AME to an extremal
moment problem, we first show how to obtain sharp bounds on the AME
straightforwardly, without any optimization. Then, we consider two strategies
to build confidence intervals on the AME. In the first, we estimate the sharp
bounds with a semiparametric two-step estimator. The second, very simple
strategy estimates instead a quantity known to be at a bounded distance from
the AME. It does not require any nonparametric estimation but may result in
larger confidence intervals. Monte Carlo simulations suggest that both
approaches work well in practice, the second being often very competitive.
Finally, we show that our results also apply to average treatment effects, the
average structural functions and ordered, fixed effects logit models.


Abstract:
  Ram Pressure Stripping can remove gas from satellite galaxies in clusters via
a direct interaction between the intracluster medium (ICM) and the interstellar
medium. This interaction is generally thought of as a contact force per area,
however we point out that these gases must interact in a hydrodynamic fashion,
and argue that this will lead to mixing of the galactic gas with the ICM wind.
We develop an analytic framework for how mixing is related to the acceleration
of stripped gas from a satellite galaxy. We then test this model using three
"wind-tunnel" simulations of Milky Way-like galaxies interacting with a moving
ICM, and find excellent agreement with predictions using the analytic
framework. Focusing on the dense clumps in the stripped tails, we find that
they are nearly uniformly mixed with the ICM, indicating that all gas in the
tail mixes with the surroundings, and dense clumps are not separate entities to
be modeled differently than diffuse gas. We find that while mixing drives
acceleration of stripped gas, the density and velocity of the surrounding wind
will determine whether the mixing results in the heating of stripped gas into
the ICM, or the cooling of the ICM into dense clouds.


Abstract:
  Domain shift is a major challenge for object detectors to generalize well to
real world applications. Emerging techniques of domain adaptation for two-stage
detectors help to tackle this problem. However, two-stage detectors are not the
first choice for industrial applications due to its long time consumption. In
this paper, a novel Domain Adaptive YOLO (DA-YOLO) is proposed to improve
cross-domain performance for one-stage detectors. Image level features
alignment is used to strictly match for local features like texture, and
loosely match for global features like illumination. Multi-scale instance level
features alignment is presented to reduce instance domain shift effectively ,
such as variations in object appearance and viewpoint. A consensus
regularization to these domain classifiers is employed to help the network
generate domain-invariant detections. We evaluate our proposed method on
popular datasets like Cityscapes, KITTI, SIM10K and etc.. The results
demonstrate significant improvement when tested under different cross-domain
scenarios.


Abstract:
  Most of the recent work on terminology integration in machine translation has
assumed that terminology translations are given already inflected in forms that
are suitable for the target language sentence. In day-to-day work of
professional translators, however, it is seldom the case as translators work
with bilingual glossaries where terms are given in their dictionary forms;
finding the right target language form is part of the translation process. We
argue that the requirement for apriori specified target language forms is
unrealistic and impedes the practical applicability of previous work. In this
work, we propose to train machine translation systems using a source-side data
augmentation method that annotates randomly selected source language words with
their target language lemmas. We show that systems trained on such augmented
data are readily usable for terminology integration in real-life translation
scenarios. Our experiments on terminology translation into the morphologically
complex Baltic and Uralic languages show an improvement of up to 7 BLEU points
over baseline systems with no means for terminology integration and an average
improvement of 4 BLEU points over the previous work. Results of the human
evaluation indicate a 47.7% absolute improvement over the previous work in term
translation accuracy when translating into Latvian.


Abstract:
  The adaptive stochastic gradient descent (SGD) with momentum has been widely
adopted in deep learning as well as convex optimization. In practice, the last
iterate is commonly used as the final solution to make decisions. However, the
available regret analysis and the setting of constant momentum parameters only
guarantee the optimal convergence of the averaged solution. In this paper, we
fill this theory-practice gap by investigating the convergence of the last
iterate (referred to as individual convergence), which is a more difficult task
than convergence analysis of the averaged solution. Specifically, in the
constrained convex cases, we prove that the adaptive Polyak's Heavy-ball (HB)
method, in which only the step size is updated using the exponential moving
average strategy, attains an optimal individual convergence rate of
$O(\frac{1}{\sqrt{t}})$, as opposed to the optimality of $O(\frac{\log t}{\sqrt
{t}})$ of SGD, where $t$ is the number of iterations. Our new analysis not only
shows how the HB momentum and its time-varying weight help us to achieve the
acceleration in convex optimization but also gives valuable hints how the
momentum parameters should be scheduled in deep learning. Empirical results on
optimizing convex functions and training deep networks validate the correctness
of our convergence analysis and demonstrate the improved performance of the
adaptive HB methods.


Abstract:
  NASA's Transiting Exoplanet Survey Satellite (TESS) mission is expected to
discover hundreds of planets via single transits first identified in their
light curves. Determining the orbital period of these single transit candidates
typically requires a significant amount of follow-up work to observe a second
transit or measure a radial velocity orbit. In Yao et al. (2019), we developed
simulations that demonstrated the ability to use archival photometric data in
combination with TESS to "precover" the orbital period for these candidates
with a precision of several minutes, assuming circular orbits. In this work, we
incorporate updated models for TESS single transits, allowing for eccentric
orbits, along with an updated methodology to improve the reliability of the
results. Additionally, we explore how radial velocity (RV) observations can be
used to follow up single transit events, using strategies distinct from those
employed when the orbital period is known. We find that the use of an estimated
period based on a circular orbit to schedule reconnaissance RV observations can
efficiently distinguish eclipsing binaries from planets. For candidates that
pass reconnaissance RV observations, we simulate RV monitoring campaigns that
enable one to obtain an approximate orbital solution. We find this method can
regularly determine the orbital periods for planets more massive than 0.5 M_J
with orbital periods as long as 100 days.


Abstract:
  Current speech agent interactions are typically user-initiated, limiting the
interactions they can deliver. Future functionality will require agents to be
proactive, sometimes interrupting users. Little is known about how these spoken
interruptions should be designed, especially in urgent interruption contexts.
We look to inform design of proactive agent interruptions through investigating
how people interrupt others engaged in complex tasks. We therefore developed a
new technique to elicit human spoken interruptions of people engaged in other
tasks. We found that people interrupted sooner when interruptions were urgent.
Some participants used access rituals to forewarn interruptions, but most
rarely used them. People balanced speed and accuracy in timing interruptions,
often using cues from the task they interrupted. People also varied phrasing
and delivery of interruptions to reflect urgency. We discuss how our findings
can inform speech agent design and how our paradigm can help gain insight into
human interruptions in new contexts.


Abstract:
  A rank-adaptive integrator for the dynamical low-rank approximation of matrix
and tensor differential equations is presented. The fixed-rank integrator
recently proposed by two of the authors is extended to allow for an adaptive
choice of the rank, using subspaces that are generated by the integrator
itself. The integrator first updates the evolving bases and then does a
Galerkin step in the subspace generated by both the new and old bases, which is
followed by rank truncation to a given tolerance. It is shown that the adaptive
low-rank integrator retains the exactness, robustness and symmetry-preserving
properties of the previously proposed fixed-rank integrator. Beyond that, up to
the truncation tolerance, the rank-adaptive integrator preserves the norm when
the differential equation does, it preserves the energy for Schr\"odinger
equations and Hamiltonian systems, and it preserves the monotonic decrease of
the functional in gradient flows. Numerical experiments illustrate the
behaviour of the rank-adaptive integrator.


Abstract:
  SARS-CoV-2 is the third betacoronavirus to enter the human population in the
past 20 years, revealing a concerning pattern. Clearly, preventing a future
pandemic from such viruses is a critical priority. Previous studies have shown
that shRNAs can be powerful suppressors of RNA viruses in transgenic animals
and substantially reduce transmission. Thus, we propose the introduction of
anti-betacoronavirus shRNAs using CRISPR/CAS9 gene drive into the horseshoe bat
population, the natural reservoir of those viruses, to combat this pandemic
threat at its source. Importantly, our approach is not expected to create any
harm to bats and can benefit other animals in the ecosystem that contract
betacoronaviruses from bats. We map the ethical and the technical aspects and
suggest guidelines for moving forward with this proposal.


Abstract:
  Lattice-skin structures composed of a thin-shell skin and a lattice infill
are widespread in nature and large-scale engineering due to their efficiency
and exceptional mechanical properties. Recent advances in additive
manufacturing, or 3D printing, make it possible to create lattice-skin
structures of almost any size with arbitrary shape and geometric complexity. We
propose a novel gradient-based approach to optimising both the shape and infill
of lattice-skin structures to improve their efficiency further. The respective
gradients are computed by fully considering the lattice-skin coupling while the
lattice topology and shape optimisation problems are solved in a sequential
manner. The shell is modelled as a Kirchhoff-Love shell and analysed using
isogeometric subdivision surfaces, whereas the lattice is modelled as a
pin-jointed truss. The lattice consists of many cells, possibly of different
sizes, with each containing a small number of struts. We propose a penalisation
approach akin to the SIMP (solid isotropic material with penalisation) method
for topology optimisation of the lattice. Furthermore, a corresponding
sensitivity filter and a lattice extraction technique are introduced to ensure
the stability of the optimisation process and to eliminate scattered struts of
small cross-sectional areas. The developed topology optimisation technique is
suitable for non-periodic, non-uniform lattices. For shape optimisation of both
the shell and the lattice, the geometry of the lattice-skin structure is
parameterised using the free-form deformation technique. The topology and shape
optimisation problems are solved in an iterative, sequential manner. The
effectiveness of the proposed approach and the influence of different
algorithmic parameters are demonstrated with several numerical examples.


Abstract:
  The elastic energy of mixing for multi-component solid solutions is derived
by generalizing Eshelby's sphere-in-hole model for binary alloys. By surveying
the dependence of the elastic energy on chemical composition and lattice
misfit, we propose a lattice strain coefficient {\lambda}*. Applying to several
high-entropy alloys and superalloys, we found that most solid solution alloys
are stable when {\lambda}*<0.16, analogous to the Hume-Rothery atomic-size rule
for binary alloys. We also reveal that the polydispersity index {\delta},
frequently used for describing strain in multi-component alloys, is directly
related to the elastic energy (e) with e=q{\delta}^2, q being an elastic
constant. Furthermore, the effects of (i) the number and (ii) the atomic-size
distribution of constituting elements on the phase stability of high-entropy
alloys were quantified. The present derivations open for richer considerations
of elastic effects in high-entropy alloys, offering immediate support for
quantitative assessments of their thermodynamic properties and studying related
strengthening mechanisms.


Abstract:
  The formal semantics of an interpreted first-order logic (FOL) statement can
be given in Tarskian Semantics or a basically equivalent Game Semantics. The
latter maps the statement and the interpretation into a two-player semantic
game. Many combinatorial problems can be described using interpreted FOL
statements and can be mapped into a semantic game. Therefore, learning to play
a semantic game perfectly leads to the solution of a specific instance of a
combinatorial problem. We adapt the AlphaZero algorithm so that it becomes
better at learning to play semantic games that have different characteristics
than Go and Chess. We propose a general framework, Persephone, to map the FOL
description of a combinatorial problem to a semantic game so that it can be
solved through a neural MCTS based reinforcement learning algorithm. Our goal
for Persephone is to make it tabula-rasa, mapping a problem stated in
interpreted FOL to a solution without human intervention.


Abstract:
  The diamond is the graph obtained by removing an edge from the complete graph
on 4 vertices. A graph is ($P_6$, diamond)-free if it contains no induced
subgraph isomorphic to a six-vertex path or a diamond. In this paper we show
that the chromatic number of a ($P_6$, diamond)-free graph $G$ is no larger
than the maximum of 6 and the clique number of $G$. We do this by reducing the
problem to imperfect ($P_6$, diamond)-free graphs via the Strong Perfect Graph
Theorem, dividing the imperfect graphs into several cases, and giving a proper
colouring for each case. We also show that there is exactly one
6-vertex-critical ($P_6$, diamond, $K_6$)-free graph. Together with the
Lov\'asz theta function, this gives a polynomial time algorithm to compute the
chromatic number of ($P_6$, diamond)-free graphs.


Abstract:
  Distinguishability and predictability are part of complementarity relations
which apply to two different kinds of interference experiments, with and
without a path-detector, respectively. In [Opt. Comm. 179, 337 (2000)], Englert
and Bergou pointed out the possible connection between distinguishability,
predictability, and entanglement. They even conjectured that an entanglement
measure was hidden between the measures of distinguishability and
predictability. Here, we push forward this conjecture. We start defining a new
entropic distinguishability measure and suggesting an entanglement measure as
the difference between this entropic distinguishability and an entropic
predictability measure already defined in the literature. Besides, we prove
that it is possible to define an entanglement monotone from the largest value
of the distinguishability and the corresponding predictability, provided that
the predictability satisfy the criteria already established in the literature.
Thus, this result formally connects an entanglement monotone with
distinguishability and the corresponding predictability, without appealing to
specific measures.


Abstract:
  A matching is compatible to two or more labeled point sets of size $n$ with
labels $\{1,\dots,n\}$ if its straight-line drawing on each of these point sets
is crossing-free. We study the maximum number of edges in a matching compatible
to two or more labeled point sets in general position in the plane. We show
that for any two labeled convex sets of $n$ points there exists a compatible
matching with $\lfloor \sqrt {2n}\rfloor$ edges. More generally, for any $\ell$
labeled point sets we construct compatible matchings of size
$\Omega(n^{1/\ell})$. As a corresponding upper bound, we use probabilistic
arguments to show that for any $\ell$ given sets of $n$ points there exists a
labeling of each set such that the largest compatible matching has
${\mathcal{O}}(n^{2/({\ell}+1)})$ edges. Finally, we show that $\Theta(\log n)$
copies of any set of $n$ points are necessary and sufficient for the existence
of a labeling such that any compatible matching consists only of a single edge.


Abstract:
  Consider a Hamiltonian diffeomorphism $g$ on a surface. We describe several
scenarios where a curve $L$ and its image $g(L)$ provide a simple evidence that
$g$ is not autonomous.


Abstract:
  Balancing the needs of data privacy and predictive utility is a central
challenge for machine learning in healthcare. In particular, privacy concerns
have led to a dearth of public datasets, complicated the construction of
multi-hospital cohorts and limited the utilization of external machine learning
resources. To remedy this, new methods are required to enable data owners, such
as hospitals, to share their datasets publicly, while preserving both patient
privacy and modeling utility. We propose NeuraCrypt, a private encoding scheme
based on random deep neural networks. NeuraCrypt encodes raw patient data using
a randomly constructed neural network known only to the data-owner, and
publishes both the encoded data and associated labels publicly. From a
theoretical perspective, we demonstrate that sampling from a sufficiently rich
family of encoding functions offers a well-defined and meaningful notion of
privacy against a computationally unbounded adversary with full knowledge of
the underlying data-distribution. We propose to approximate this family of
encoding functions through random deep neural networks. Empirically, we
demonstrate the robustness of our encoding to a suite of adversarial attacks
and show that NeuraCrypt achieves competitive accuracy to non-private baselines
on a variety of x-ray tasks. Moreover, we demonstrate that multiple hospitals,
using independent private encoders, can collaborate to train improved x-ray
models. Finally, we release a challenge dataset to encourage the development of
new attacks on NeuraCrypt.


Abstract:
  The paper addresses an improved inner current reference calculation to be
employed in the control of modular multilevel converters operating during
either balanced or unbalanced conditions. The suggested reference calculation
is derived based on the AC and DC additive and differential voltage components
applied to the upper and lower arms of the converter. In addition, the impacts
caused not only by the AC network's impedances but also by the MMC's arm
impedances are also considered during the derivation of the AC additive current
reference expressions. Another issue discussed in this article regards that
singular voltage conditions, where the positive-sequence component is equal to
the negative one, may occur not only in the AC network but also internally
(within the converter's applied voltages). Several different inner current
reference calculation methods are compared and their applicability during the
former fault conditions is analyzed. The paper presents a detailed formulation
of the inner current reference calculation and applies it to different
unbalanced AC grid faults where it is shown that the presented approach can be
potentially used to maintain the internal energy of the converter balanced
during normal and fault conditions.


Abstract:
  The purpose of this technical report is to review the main properties of an
accelerated composite gradient (ACG) method commonly referred to as the Fast
Iterative Shrinkage-Thresholding Algorithm (FISTA). In addition, we state a
version of FISTA for solving both convex and strongly convex composite
minimization problems and derive its iteration complexities to generate
iterates satisfying various stopping criteria, including one which arises in
the course of solving other composite optimization problems via inexact
proximal point schemes. This report also discusses different reformulations of
the convex version of FISTA and how they relate to other formulations in the
literature.


Abstract:
  We present a Convolutional Neural Network (CNN) architecture for inverse
Raman amplifier design. This model aims at finding the pump powers and
wavelengths required for a target signal power evolution, both in distance
along the fiber and in frequency. Using the proposed framework, the prediction
of the pump configuration required to achieve a target power profile is
demonstrated numerically with high accuracy in C-band considering both
counter-propagating and bidirectional pumping schemes. For a distributed Raman
amplifier based on a 100 km single-mode fiber, a low mean set (0.51, 0.54 and
0.64 dB) and standard deviation set (0.62, 0.43 and 0.38 dB) of the maximum
test error are obtained numerically employing 2 and 3 counter, and 4
bidirectional propagating pumps, respectively.


Abstract:
  The intrinsic orbital magnetization of a TMD monolayer is usually calculated
for a plane unbounded system without mentioning the geometrical shape of
samples and boundary conditions (BCs) for electron wave functions. The method
of calculations includes allowing for the Berry curvature contribution also in
the case when the system is described by the two-band minimal model [9]. In the
present paper, we show that the geometrical and topological properties of the
specimen, as well as the BCs, play an important role in the problem of
magnetization even for a macroscopic specimen.


Abstract:
  A conventional approach to train neural ordinary differential equations
(ODEs) is to fix an ODE solver and then learn the neural network's weights to
optimize a target loss function. However, such an approach is tailored for a
specific discretization method and its properties, which may not be optimal for
the selected application and yield the overfitting to the given solver. In our
paper, we investigate how the variability in solvers' space can improve neural
ODEs performance. We consider a family of Runge-Kutta methods that are
parameterized by no more than two scalar variables. Based on the solvers'
properties, we propose an approach to decrease neural ODEs overfitting to the
pre-defined solver, along with a criterion to evaluate such behaviour.
Moreover, we show that the right choice of solver parameterization can
significantly affect neural ODEs models in terms of robustness to adversarial
attacks. Recently it was shown that neural ODEs demonstrate superiority over
conventional CNNs in terms of robustness. Our work demonstrates that the model
robustness can be further improved by optimizing solver choice for a given
task. The source code to reproduce our experiments is available at
https://github.com/juliagusak/neural-ode-metasolver.


Abstract:
  In this article, we are presenting the relationship between environmental
pollution and the income level of the selected twenty-four countries. We
implemented a data-based research analysis where, for each country, we analyzed
the related data for fifty-six years, from 1960 to 2016, to assess the
relationship between the carbon emission and income level. After performing the
related data analysis for each country, we concluded whether the results for
that country were in line with the Environmental Kuznets Curve (EKC)
hypothesis. The EKC hypothesis suggests that the carbon emission per capita
starts a declining trend when the country-specific high level of income is
reached. The results of our data analyses show that the EKC hypothesis is valid
for high-income countries and the declining trends of carbon emission are
clearly observed when the income level reaches a specific high enough level. On
the other hand, for the non-high income countries, our analysis results show
that it is too early to make an assessment at this growth stage of their
economies because they have not reached their related high-enough income per
capita levels yet. Furthermore, we performed two more additional analyses on
high-income countries. First, we analyzed the related starting years of their
carbon emission declining trends. The big variance in the starting years of the
carbon emission declining trends shows that the international policies are
clearly ineffective in initiating the declining trend in carbon emission. In
addition, for the high-income countries, we explained the differences in their
carbon emission per capita levels in 2014 with their SGI indices and their
dependence on high-carbon emission energy production.


Abstract:
  Momentum strategies are an important part of alternative investments and are
at the heart of commodity trading advisors (CTAs). These strategies have,
however, been found to have difficulties adjusting to rapid changes in market
conditions, such as during the 2020 market crash. In particular, immediately
after momentum turning points, where a trend reverses from an uptrend
(downtrend) to a downtrend (uptrend), time-series momentum (TSMOM) strategies
are prone to making bad bets. To improve the response to regime change, we
introduce a novel approach, where we insert an online changepoint detection
(CPD) module into a Deep Momentum Network (DMN) [1904.04912] pipeline, which
uses an LSTM deep-learning architecture to simultaneously learn both trend
estimation and position sizing. Furthermore, our model is able to optimise the
way in which it balances 1) a slow momentum strategy which exploits persisting
trends, but does not overreact to localised price moves, and 2) a fast
mean-reversion strategy regime by quickly flipping its position, then swapping
it back again to exploit localised price moves. Our CPD module outputs a
changepoint location and severity score, allowing our model to learn to respond
to varying degrees of disequilibrium, or smaller and more localised
changepoints, in a data driven manner. Back-testing our model over the period
1995-2020, the addition of the CPD module leads to an improvement in Sharpe
ratio of one-third. The module is especially beneficial in periods of
significant nonstationarity, and in particular, over the most recent years
tested (2015-2020) the performance boost is approximately two-thirds. This is
interesting as traditional momentum strategies have been underperforming in
this period.


Abstract:
  The forthcoming generation of multi-petawatt lasers opens the way to abundant
pair production by the nonlinear Breit-Wheeler process, i.e., the decay of a
photon into an electron-positron pair inside an intense laser field. In this
paper we explore the optimal conditions for Breit-Wheeler pair production in
the head-on collision of a laser pulse with gamma photons. The role of the
laser peak intensity versus the focal spot size and shape is examined keeping a
constant laser energy to match experimental constraints. A simple model for the
soft-shower case, where most pairs originate from the decay of the initial
gamma photons, is derived. This approach provides us with a semi-analytical
model for more complex situations involving either Gaussian or Laguerre-Gauss
(LG) laser beams. We then explore the influence of the order of the LG beams on
pair creation. Finally we obtain the result that, above a given threshold, a
larger spot size (or a higher order in the case of LG laser beams) is more
favorable than a higher peak intensity. Our results match very well with
three-dimensional particle-in-cell simulations and can be used to guide
upcoming experimental campaigns.


Abstract:
  Novel view synthesis is a long-standing problem in machine learning and
computer vision. Significant progress has recently been made in developing
neural scene representations and rendering techniques that synthesize
photorealistic images from arbitrary views. These representations, however, are
extremely slow to train and often also slow to render. Inspired by neural
variants of image-based rendering, we develop a new neural rendering approach
with the goal of quickly learning a high-quality representation which can also
be rendered in real-time. Our approach, MetaNLR++, accomplishes this by using a
unique combination of a neural shape representation and 2D CNN-based image
feature extraction, aggregation, and re-projection. To push representation
convergence times down to minutes, we leverage meta learning to learn neural
shape and image feature priors which accelerate training. The optimized shape
and image features can then be extracted using traditional graphics techniques
and rendered in real time. We show that MetaNLR++ achieves similar or better
novel view synthesis results in a fraction of the time that competing methods
require.


Abstract:
  The integral model of a GU(n-1,1) Shimura variety carries a universal abelian
scheme over it, and the dual top exterior power of its Lie algebra carries a
natural hermitian metric. We express the arithmetic volume of this metrized
line bundle, defined as an iterated self-intersection in the Gillet-Soule
arithmetic Chow ring, in terms of logarithmic derivatives of Dirichlet
L-functions.


Abstract:
  A recent line of work has studied the relationship between the Wishart matrix
$X^\top X$, where $X\in \mathbb{R}^{d\times n}$ has i.i.d. standard Gaussian
entries, and the corresponding Gaussian matrix with independent entries above
the diagonal. Jiang and Li (2015) and Bubeck et al. (2016) showed that these
two matrix ensembles converge in total variation whenever $d/n^3\to \infty$,
and Bubeck et al. (2016) showed this to be sharp. In this paper we aim to
identify the precise threshold for $d$ in terms of $n$ for subsets of Wishart
matrices to converge in total variation to independent Gaussians. It turns out
that the combinatorial structure of the revealed entries, viewed as the
adjacency matrix of a graph $G$, characterizes the distance from fully
independent. Specifically, we show that the threshold for $d$ depends on the
number of various small subgraphs in $G$. So, even when the number of revealed
entries is fixed, the threshold can vary wildly depending on their
configuration. Convergence of masked Wishart to independent Gaussians thus
inherently involves an interplay between both probabilistic and combinatorial
phenomena. Our results determine the sharp threshold for a large family of $G$,
including Erd\H{o}s-R\'enyi $G\sim \mathcal{G}(n,p)$ at all values $p\gtrsim
n^{-2}\mathrm{polylog}(n)$. Our proof techniques are both combinatorial and
information theoretic, which together allow us to carefully unravel the
dependencies in the masked Wishart ensemble.


Abstract:
  The realization of multifunctional two-dimensional (2D) materials is
fundamentally intriguing, such as combination of piezoelectricity with
topological insulating phase or ferromagnetism. In this work, a Janus monolayer
$\mathrm{SrAlGaSe_4}$ is built from 2D $\mathrm{MA_2Z_4}$ family with dynamic,
mechanical and thermal stabilities, which is piezoelectric due to lacking
inversion symmetry. The unstrained $\mathrm{SrAlGaSe_4}$ monolayer is a narrow
gap normal insulator (NI) with spin orbital coupling (SOC). However, the NI to
topological insulator (TI) phase transition can be induced by the biaxial
strain, and a piezoelectric quantum spin Hall insulator (PQSHI) can be
achieved. More excitingly, the phase transformation point is only about 1.01
tensile strain, and nontrivial band topology can hold until considered 1.16
tensile strain. Moreover, a Rashba spin splitting in the conduction bands can
exit in PQSHI due to the absence of a horizontal mirror symmetry and the
presence of SOC. For monolayer $\mathrm{SrAlGaSe_4}$, both in-plane and much
weak out-of-plane piezoelectric polarizations can be induced with a uniaxial
strain applied. The calculated piezoelectric strain coefficients $d_{11}$ and
$d_{31}$ of monolayer $\mathrm{SrAlGaSe_4}$ are -1.865 pm/V and -0.068 pm/V at
1.06 tensile strain as a representative TI. In fact, many PQSHIs can be
realized from 2D $\mathrm{MA_2Z_4}$ family. To confirm that, similar to
$\mathrm{SrAlGaSe_4}$, the coexistence of piezoelectricity and topological
orders can be realized by strain (about 1.04 tensile strain) in the
$\mathrm{CaAlGaSe_4}$ monolayer. Our works suggest that Janus monolayer
$\mathrm{SrAlGaSe_4}$ is a pure 2D system for PQSHI, enabling future studies
exploring the interplay between piezoelectricity and topological orders, which
can lead to novel applications in electronics and spintronics.


Abstract:
  Hyperfine-structure constants of odd Ra$^{+}$ due to the interactions of
nuclear magnetic dipole, electric quadrupole, and magnetic octupole moments
with the electrons are investigated in the framework of relativistic
coupled-cluster method within single- and double-excitation approximation. The
calculated energies and magnetic dipole hyperfine-structure constants $A$
exhibit a good agreement with available experimental values. Combining with the
experimental electric quadrupole hyperfine-structure constant, we also
extracted the electric quadrupole moments $Q$ of $^{209,211,221,223}$Ra. Our
$Q$($^{221}$Ra) and $Q$($^{223}$Ra) are consistent with the referenced values
from a semi-empirical analysis (Z. Phys. D: At., Mol. Clusters 11, 105 (1988)),
but $Q(^{211}$Ra)=$0.33(2)$ is smaller than the referenced value $0.48(4)$ by
about 30\%. Furthermore, we also performed a procedure for assessing the
contributions of magnetic octupole moment to the hyperfine splitting. The
sensitivity of hyperfine-structure interval measurements in $^{223}$Ra$^{+}$
that can reveal the effect caused by the nuclear octupole moment are found to
be on the order of kHz.


Abstract:
  Person search has recently emerged as a challenging task that jointly
addresses pedestrian detection and person re-identification. Existing
approaches follow a fully supervised setting where both bounding box and
identity annotations are available. However, annotating identities is
labor-intensive, limiting the practicability and scalability of current
frameworks. This paper inventively considers weakly supervised person search
with only bounding box annotations. We proposed to address this novel task by
investigating three levels of context clues (i.e., detection, memory and scene)
in unconstrained natural images. The first two are employed to promote local
and global discriminative capabilities, while the latter enhances clustering
accuracy. Despite its simple design, our CGPS achieves 80.0% in mAP on
CUHK-SYSU, boosting the baseline model by 8.8%. Surprisingly, it even achieves
comparable performance with several supervised person search models. Our code
is available at https://github.com/ljpadam/CGPS


Abstract:
  In the setting of Carnot groups, we prove the $q-$Logarithmic Sobolev
inequality for probability measures as a function of the Carnot-Carath\'eodory
distance. As an application, we use the Hamilton-Jacobi equation in the setting
of Carnot groups to prove the $p-$Talagrand inequality and hypercontractivity.


Abstract:
  The exploration of germanium (Ge) detectors with amorphous Ge (a-Ge) contacts
has drawn attention to the searches for rare-event physics such as dark matter
and neutrinoless double-beta decay. The charge barrier height (CBH) of the a-Ge
contacts deposited on the detector surface is crucial to suppress the leakage
current of the detector in order to achieve la ow-energy detection threshold
and high-energy resolution. The temperature-dependent CBH of a-Ge contacts for
three Ge detectors is analyzed to study the bulk leakage current (BLC)
characteristics. The detectors were fabricated at the University of South
Dakota using homegrown crystals. The CBH is determined from the BLC when the
detectors are operated in the reverse bias mode with a guard-ring structure,
which separates the BLC from the surface leakage current (SLC). The results
show that CBH is temperature dependent. The direct relation of the CBH
variation to temperature is related to the barrier inhomogeneities created on
the interface of a-Ge and crystalline Ge. The inhomogeneities that occur at the
interface were analyzed using the Gaussian distribution model for three
detectors. The CBH of a-Ge contact is projected to zero temperature. The
implication of the CBH at zero temperature is discussed for Ge detectors with
a-Ge contacts in searching for rare-event physics.


Abstract:
  Automated cooking machine is a goal for the future. The main aim is to make
the cooking process easier, safer, and create human welfare. To allow robots to
accurately perform the cooking activities, it is important for them to
understand the cooking environment and recognize the objects, especially
correctly identifying the state of the cooking objects. This will significantly
improve the correctness of the following cooking recipes. In this project,
several parts of the experiment were conducted to design a robust deep
convolutional neural network for classifying the state of the cooking objects
from scratch. The model is evaluated by using various techniques, such as
adjusting architecture layers, tuning key hyperparameters, and using different
optimization techniques to maximize the accuracy of state classification.


Abstract:
  Research in Cognitive Science suggests that humans understand and represent
knowledge of the world through causal relationships. In addition to
observations, they can rely on experimenting and counterfactual reasoning --
i.e. referring to an alternative course of events -- to identify causal
relations and explain atypical situations. Different instances of control
systems, such as smart homes, would benefit from having a similar causal model,
as it would help the user understand the logic of the system and better react
when needed. However, while data-driven methods achieve high levels of
correlation detection, they mainly fall short of finding causal relations,
notably being limited to observations only. Notably, they struggle to identify
the cause from the effect when detecting a correlation between two variables.
This paper introduces a new way to learn causal models from a mixture of
experiments on the environment and observational data. The core of our method
is the use of selected interventions, especially our learning takes into
account the variables where it is impossible to intervene, unlike other
approaches. The causal model we obtain is then used to generate Causal Bayesian
Networks, which can be later used to perform diagnostic and predictive
inference. We use our method on a smart home simulation, a use case where
knowing causal relations pave the way towards explainable systems. Our
algorithm succeeds in generating a Causal Bayesian Network close to the
simulation's ground truth causal interactions, showing encouraging prospects
for application in real-life systems.


Abstract:
  Studies of neutron stars are at their peak after the multi-messenger
observation of the binary merger event GW170817, which strongly constraints the
stellar parameters like tidal deformability, masses and radii. Although current
and future observations will provide stronger limits on the neutron stars
parameters, knowledge of explicit interior solutions to Einstein's equations,
which connect observed parameters with the internal structure, are crucial to
have a satisfactory description of the interior of these compact objects. A
well known exact solution, which has shown a relatively good approximation to a
neutron star, is the Tolman VII solution. In order to provide a better fitting
for the energy density profile, with the realistic equations of state for
neutron stars, recently Jiang and Yagi proposed a modified version of this
model which introduces an additional parameter $\alpha$ reflecting the
interplay of the quadratic and the newly added quartic term in the energy
density profile. Here we study the dynamical stability of this modified Tolman
VII solution using the theory of infinitesimal and adiabatic radial
oscillations developed by Chandrasekhar. For this purpose, we determine values
of the critical adiabatic index, for the onset of instability, considering
configurations with varying compactness and $\alpha$. We found that the new
models are stable against radial oscillations for a considerable range of
values of compactness and the new parameter $\alpha$, thus supporting their
applicability as a physically plausible approximation of realistic neutron
stars.


Abstract:
  We present single-mode nanowire (NW) lasers with ultralow threshold in the
near-infrared spectral range. To ensure the single-mode operation, the NW
diameter and length are reduced specifically to minimize the longitudinal and
transverse modes of the NW cavity. Increased optical losses and reduced gain
volume by the dimension reduction are compensated by excellent NW morphology
and InGaAs/GaAs multi-quantum disks. At 5 K, a threshold low as 1.6 {\mu}J/cm2
per pulse is achieved with a resulting quality factor exceeding 6400. By
further passivating the NW with an AlGaAs shell to suppress surface
non-radiative recombination, single-mode lasing operation is obtained with a
threshold of only 48 {\mu}J/cm2 per pulse at room temperature with a high
characteristic temperature of 223 K and power output of ~ 0.9 {\mu}W. These
single-mode, ultralow threshold, high power output NW lasers are promising for
the development of near-infrared nanoscale coherent light sources for
integrated photonic circuits, sensing, and spectroscopy.


Abstract:
  In reconfigurable intelligent surfaces (RISs) aided communications, the
existing passive beamforming (PB) design involves polynomial complexity in the
number of reflecting elements, and thus is difficult to implement due to a
massive number of reflecting elements. To overcome this difficulty, we propose
a reflection-angle-based cascaded channel model by adopting the generalized
Snell's law, in which the dimension of the variable space involved in
optimization is significantly reduced, resulting in a simplified hierarchical
passive beamforming (HPB) design. We develop an efficient two-stage HPB
algorithm, which exploits the angular domain property of the channel, to
maximize the achievable rate of the target user. Simulation results demonstrate
the appealing performance and low complexity of the proposed HPB design.


Abstract:
  In this paper a methodology is described to estimate multigroup neutron
source distributions which must be added into a subcritical system to drive it
to a steady state prescribed power distribution. This work has been motivated
by the principle of operation of the ADS (Accelerator Driven System) reactors,
which have subcritical cores stabilized by the action of external sources. We
use the energy multigroup two-dimensional neutron transport equation in the
discrete ordinates formulation (SN) and the equation which is adjoint to it,
whose solution is interpreted here as a distribution measuring the importance
of the angular flux of neutrons to a linear functional. These equations are
correlated through a reciprocity relation, leading to a relationship between
the interior sources of neutrons and the power produced by unit length of
height of the domain. A coarse-mesh numerical method of the spectral nodal
class, referred to as adjoint response matrix constant-nodal method, is applied
to numerically solve the adjoint SN equations. Numerical experiments are
performed to analyze the accuracy of the present methodology so as to
illustrate its potential practical applications.


Abstract:
  We show that in three-dimensional (3D) topological metals, a subset of the
van Hove singularities of the density of states sits exactly at the transitions
between topological and trivial gapless phases. We may refer to these as
topological van Hove singularities. By investigating two minimal models, we
show that they originate from energy saddle points located between Weyl points
with opposite chiralities, and we illustrate their topological nature through
their magnetotransport properties in the ballistic regime. We exemplify the
relation between van Hove singularities and topological phase transitions in
Weyl systems by analyzing the 3D Hofstadter model, which offers a simple and
interesting playground to consider different kinds of Weyl metals and to
understand the features of their density of states. In this model, as a
function of the magnetic flux, the occurrence of topological van Hove
singularities can be explicitly checked.


Abstract:
  In this paper we present a methodology for data accesses when solving batches
of Tridiagonal and Pentadiagonal matrices that all share the same
left-hand-side (LHS) matrix. The intended application is to the numerical
solution of Partial Differential Equations via the finite-difference method,
although the methodology is applicable more broadly. By only storing one copy
of this matrix, a significant reduction in storage overheads is obtained,
together with a corresponding decrease in compute time. Taken together, these
two performance enhancements lead to an overall more efficient implementation
over the current state of the art algorithms cuThomasBatch and cuPentBatch,
allowing for a greater number of systems to be solved on a single GPU. We
demonstrate the methodology in the case of the Diffusion Equation,
Hyperdiffusion Equation, and the Cahn--Hilliard Equation, all in one spatial
dimension. In this last example, we demonstrate how the method can be used to
perform $2^{20}$ independent simulations of phase separation in one dimension.
In this way, we build up a robust statistical description of the coarsening
phenomenon which is the defining behavior of phase separation. We anticipate
that the method will be of further use in other similar contexts requiring
statistical simulation of physical systems.


Abstract:
  Neural approaches have achieved state-of-the-art accuracy on machine
translation but suffer from the high cost of collecting large scale parallel
data. Thus, a lot of research has been conducted for neural machine translation
(NMT) with very limited parallel data, i.e., the low-resource setting. In this
paper, we provide a survey for low-resource NMT and classify related works into
three categories according to the auxiliary data they used: (1) exploiting
monolingual data of source and/or target languages, (2) exploiting data from
auxiliary languages, and (3) exploiting multi-modal data. We hope that our
survey can help researchers to better understand this field and inspire them to
design better algorithms, and help industry practitioners to choose appropriate
algorithms for their applications.


Abstract:
  Graphite is a ubiquitous electrode material with particular promise for use
in e.g., energy storage and desalination devices, but very little is known
about the properties of the graphite-electrolyte double layer at
technologically relevant concentrations. Here, the (electrified)
graphite-NaCl(aq) interface was examined using constant chemical potential
molecular dynamics simulations; this approach avoids ion depletion (due to
surface adsorption) and maintains a constant concentration of ions beyond the
surface. Specific Na+ adsorption at the graphite basal surface causes charging
of the interface in the absence of an applied potential. At moderate bulk
concentrations, this leads to accumulation of counter-ions in a diffuse layer
to balance the effective surface charge, consistent with established models of
the electrical double layer (DL). Beyond 0.6 M, however, a combination of
over-screening and ion crowding in the DL results in alternating compact layers
of ion density perpendicular to the interface. The transition to this regime is
marked by an increasing DL size and anomalous negative shifts to the potential
of zero charge with incremental changes to the bulk concentration. Our
observations are supported by changes to the position of the differential
capacitance minimum measured by electrochemical impedance spectroscopy.
Furthermore, a striking level of agreement between the differential capacitance
from simulations and experiments allows us to critically assess the accepted
norm that electrochemical capacitance measurements report simply on the density
of states of the graphite material. Finally, ion crowding at the highest
concentrations (beyond 5 M) leads to the formation of liquid-like NaCl clusters
confined to highly non-ideal regions of the double layer, where ion diffusion
is up to five times slower than in the bulk.


Abstract:
  The distance matrix $\mathcal{D}$ of a connected graph $G$ is the matrix
indexed by the vertices of $G$ which entry $\mathcal{D}_{i,j}$ equals the
distance between the vertices $v_i$ and $v_j$. The distance signless Laplacian
matrix $\mathcal{Q}(G)$ of graph $G$ is defined as
$\mathcal{Q}(G)=Diag(Tr)+\mathcal{D}(G)$, where $Diag(Tr)$ is the diagonal
matrix of the vertex transmissions in $G$. The largest eigenvalue of
$\mathcal{Q}(G)$ is called the distance signless Laplacian spectral radius of
$G$, written as $\eta_1(G)$. And a perfect matching in a graph is a set of
disadjacent edges covering every vertex of $G$. In this paper, we present two
suffcient conditions in terms of the distance signless Laplacian sepectral
radius for the exsitence of perfect matchings in graphs and bipatite graphs.


Abstract:
  A continuum, post-deposition mesoscopic model of a Moir\'e-regulated
self-assembly of metal nanoclusters on a twisted bilayer graphene is presented.
Quasi-two-dimensional nanocluster-like steady states at a low adsorbate
coverage are analytically determined for Pt, Ni, and Pb adsorbates, pointing
that nanoclusters self-assemble at the Moir\'e cells centers. This is followed
by the computations of nanoclusters self-assembly dynamics. Differences in the
self-assembly efficiency for three chosen metals are highlighted across three
typical values of an initial submonolayer coverage and for three temperature
regimes. Accounting for the adsorption potential of metal atoms onto graphene
leads to a significantly faster nanoclusters self-assembly and has a transient
impact on the nanoclusters morphologies. A model extensions to the cases of
nanoclusters self-assembly on a Moir\'e formed by a monolayer graphene over a
metal substrate, and the electromigration-guided self-assembly on such Moir\'e
are proposed.


Abstract:
  The inviscid limit for the two-dimensional compressible viscoelastic
equations on the half plane is considered under the no-slip boundary condition.
When the initial deformation tensor is a perturbation of the identity matrix
and the initial density is near a positive constant, we establish the uniform
estimates of solutions to the compressible viscoelastic flows in the conormal
Sobolev spaces. It is well-known that for the corresponding inviscid limit of
the compressible Navier-Stokes equations with the no-slip boundary condition,
one does not expect the uniform energy estimates of solutions due to the
appearance of strong boundary layers. However, when the deformation tensor
effect is taken into account, our results show that the deformation tensor
plays an important role in the vanishing viscosity process and can prevent the
formation of strong boundary layers. As a result we are able to justify the
inviscid limit of solutions for the compressible viscous flows under the
no-slip boundary condition governed by the viscoelastic equations, based on the
uniform conormal regularity estimates achieved in this paper.


Abstract:
  This paper develops an efficient procedure for designing low-complexity
codebooks for precoding in a full-dimension (FD) multiple-input multiple-output
(MIMO) system with a uniform planar array (UPA) antenna at the transmitter (Tx)
using tensor learning. In particular, instead of using statistical channel
models, we utilize a model-free data-driven approach with foundations in
machine learning to generate codebooks that adapt to the surrounding
propagation conditions. We use a tensor representation of the FD-MIMO channel
and exploit its properties to design quantized version of the channel
precoders. We find the best representation of the optimal precoder as a
function of Kronecker Product (KP) of two low-dimensional precoders,
respectively corresponding to the horizontal and vertical dimensions of the
UPA, obtained from the tensor decomposition of the channel. We then quantize
this precoder to design product codebooks such that an average loss in mutual
information due to quantization of channel state information (CSI) is
minimized. The key technical contribution lies in exploiting the constraints on
the precoders to reduce the product codebook design problem to an unsupervised
clustering problem on a Cartesian Product Grassmann manifold (CPM), where the
cluster centroids form a finite-sized precoder codebook. This codebook can be
found efficiently by running a $K$-means clustering on the CPM. With a suitable
induced distance metric on the CPM, we show that the construction of product
codebooks is equivalent to finding the optimal set of centroids on the factor
manifolds corresponding to the horizontal and vertical dimensions. Simulation
results are presented to demonstrate the capability of the proposed design
criterion in learning the codebooks and the attractive performance of the
designed codebooks.


Abstract:
  We establish a framework for doing second order conformal perturbation theory
for the symmetric orbifold Sym$^N(T^4)$ to all orders in $N$. This allows us to
compute how 1/4-BPS states of the D1-D5 system on $AdS_3\times S^3\times T^4$
are lifted as we move away from the orbifold point. As an application we
confirm a previous observation that in the large $N$ limit not all 1/4-BPS
states that can be lifted do get lifted. This provides evidence that the
supersymmetric index actually undercounts the number of 1/4-BPS states at a
generic point in the moduli space.


Abstract:
  Well ordered covers of square-free monomial ideals are subsets of the minimal
generating set ordered in a certain way that give rise to a Lyubeznik
resolution for the ideal, and have guaranteed nonvanishing Betti numbers in
certain degrees. This paper is about square-free monomial ideals which have a
well ordered cover. We consider the question of subadditivity of syzygies of
square-free monomial ideals via complements in the lcm lattice of the ideal,
and examine how lattice complementation breaks well ordered covers of the ideal
into (well ordered) covers of subideals. We also introduce a family of well
ordered covers called strongly disjoint sets of simplicial bouquets
(generalizing work of Kimura on graphs), which are relatively easy to identify
in simplicial complexes. We examine the subadditivity property via numerical
characteristics of these bouquets.


Abstract:
  Topological phases of matter is an exotic phenomena in modern condense matter
physics, which has attracted much attention due to the unique boundary states
and transport properties. Recently, this topological concept in electronic
materials has been exploited in many other fields of physics. Motivated by
designing and controlling the behavior of electromagnetic waves, in optical,
microwave, and sound frequencies, topological photonics emerges as a rapid
growing research field. Due to the flexibility and diversity of superconducting
quantum circuits system, it is an promising platform to realize exotic
topological phases of matter and to probe and explore topologically-protected
effects in new ways. Here, we review theoretical and experimental advances of
topological photonics on superconducting quantum circuits via the
experimentally demonstrated parametric tunable coupling techniques, including
using of the superconducting transmission line resonator, superconducting
qubits, and the coupled system of them. On superconducting circuits, the
flexible interactions and intrinsic nonlinearity making topological photonics
in this system not only a simple photonic analog of topological effects for
novel devices, but also a realm of exotic but less-explored fundamental
physics.


Abstract:
  Given a query patch from a novel class, one-shot object detection aims to
detect all instances of that class in a target image through the semantic
similarity comparison. However, due to the extremely limited guidance in the
novel class as well as the unseen appearance difference between query and
target instances, it is difficult to appropriately exploit their semantic
similarity and generalize well. To mitigate this problem, we present a
universal Cross-Attention Transformer (CAT) module for accurate and efficient
semantic similarity comparison in one-shot object detection. The proposed CAT
utilizes transformer mechanism to comprehensively capture bi-directional
correspondence between any paired pixels from the query and the target image,
which empowers us to sufficiently exploit their semantic characteristics for
accurate similarity comparison. In addition, the proposed CAT enables feature
dimensionality compression for inference speedup without performance loss.
Extensive experiments on COCO, VOC, and FSOD under one-shot settings
demonstrate the effectiveness and efficiency of our method, e.g., it surpasses
CoAE, a major baseline in this task by 1.0% in AP on COCO and runs nearly 2.5
times faster. Code will be available in the future.


Abstract:
  The scientific impact of current and upcoming photometric galaxy surveys is
contingent on our ability to obtain redshift estimates for large numbers of
faint galaxies. In the absence of spectroscopically confirmed redshifts,
broad-band photometric redshift point estimates (photo-$z$s) have been
superseded by photo-$z$ probability density functions (PDFs) that encapsulate
their nontrivial uncertainties. Initial applications of photo-$z$ PDFs in weak
gravitational lensing studies of cosmology have obtained the redshift
distribution function $\mathcal{N}(z)$ by employing computationally
straightforward stacking methodologies that violate the laws of probability. In
response, mathematically self-consistent models of varying complexity have been
proposed in an effort to answer the question, "What is the right way to obtain
the redshift distribution function $\mathcal{N}(z)$ from a catalog of photo-$z$
PDFs?" This letter aims to motivate adoption of such principled methods by
addressing the contrapositive of the more common presentation of such models,
answering the question, "Under what conditions do traditional stacking methods
successfully recover the true redshift distribution function $\mathcal{N}(z)$?"
By placing stacking in a rigorous mathematical environment, we identify two
such conditions: those of perfectly informative data and perfectly informative
prior information. Stacking has maintained its foothold in the astronomical
community for so long because the conditions in question were only weakly
violated in the past. These conditions, however, will be strongly violated by
future galaxy surveys. We therefore conclude that stacking must be abandoned in
favor of mathematically supported methods in order to advance observational
cosmology.


Abstract:
  The non-autoregressive models have boosted the efficiency of neural machine
translation through parallelized decoding at the cost of effectiveness when
comparing with the autoregressive counterparts. In this paper, we claim that
the syntactic and semantic structures among natural language are critical for
non-autoregressive machine translation and can further improve the performance.
However, these structures are rarely considered in the existing
non-autoregressive models. Inspired by this intuition, we propose to
incorporate the explicit syntactic and semantic structures of languages into a
non-autoregressive Transformer, for the task of neural machine translation.
Moreover, we also consider the intermediate latent alignment within target
sentences to better learn the long-term token dependencies. Experimental
results on two real-world datasets (i.e., WMT14 En-De and WMT16 En-Ro) show
that our model achieves a significantly faster speed, as well as keeps the
translation quality when compared with several state-of-the-art
non-autoregressive models.


Abstract:
  We present Wi-Lo, which allows to convert an ordinary 802.11 (WiFi) access
point into an internet of things (IoT) gateway supporting the low-power wide
area network (LPWAN) technology LoRa in the downlink. Our Wi-Lo system only
requires a software update and no additional hardware. It uses signal emulation
technique based on complementary code keying modulation from 802.11b in order
to emulate a downlink LoRa (long range) transmission. The Wi-Lo gateway can be
used by a normal WiFi-enabled smartphone to send packets to LoRa compliant IoT
devices like smart sensors. We implemented a prototype using commodity WiFi
hardware. Experimental results show that Wi-Lo enables a normal WiFi node to
communication to LoRa devices even over long distances, which is comparable to
the configurations using pure LoRa transmitter and receivers.


Abstract:
  Real-world imagery is often characterized by a significant imbalance of the
number of images per class, leading to long-tailed distributions. An effective
and simple approach to long-tailed visual recognition is to learn feature
representations and a classifier separately, with instance and class-balanced
sampling, respectively. In this work, we introduce a new framework, by making
the key observation that a feature representation learned with instance
sampling is far from optimal in a long-tailed setting. Our main contribution is
a new training method, referred to as Class-Balanced Distillation (CBD), that
leverages knowledge distillation to enhance feature representations. CBD allows
the feature representation to evolve in the second training stage, guided by
the teacher learned in the first stage. The second stage uses class-balanced
sampling, in order to focus on under-represented classes. This framework can
naturally accommodate the usage of multiple teachers, unlocking the information
from an ensemble of models to enhance recognition capabilities. Our experiments
show that the proposed technique consistently outperforms the state of the art
on long-tailed recognition benchmarks such as ImageNet-LT, iNaturalist17 and
iNaturalist18. The experiments also show that our method does not sacrifice the
accuracy of head classes to improve the performance of tail classes, unlike
most existing work.


Abstract:
  Learning representations of nodes in a low dimensional space is a crucial
task with numerous interesting applications in network analysis, including link
prediction, node classification, and visualization. Two popular approaches for
this problem are matrix factorization and random walk-based models. In this
paper, we aim to bring together the best of both worlds, towards learning node
representations. In particular, we propose a weighted matrix factorization
model that encodes random walk-based information about nodes of the network.
The benefit of this novel formulation is that it enables us to utilize kernel
functions without realizing the exact proximity matrix so that it enhances the
expressiveness of existing matrix decomposition methods with kernels and
alleviates their computational complexities. We extend the approach with a
multiple kernel learning formulation that provides the flexibility of learning
the kernel as the linear combination of a dictionary of kernels in data-driven
fashion. We perform an empirical evaluation on real-world networks, showing
that the proposed model outperforms baseline node embedding algorithms in
downstream machine learning tasks.


Abstract:
  Supply voltage scaling is one of the most effective techniques to reduce the
power consumption of microprocessors. However, technology limitations such as
aging and process variability enforce microprocessor designers to apply
pessimistic voltage guardbands to guarantee correct operation in the field for
any foreseeable workload. This worst-case design practice makes energy
efficiency hard to scale with technology evolution. Improving energy-efficiency
requires the identification of the chip design margins through time-consuming
and comprehensive characterization of its operational limits. Such a
characterization of state-of-the-art multi-core CPUs fabricated in aggressive
technologies is a multi-parameter process, which requires statistically
significant information. In this paper, we present an automated framework to
support system-level voltage and frequency scaling characterization of Applied
Micro's state-of-the-art ARMv8-based multicore CPUs used in the X-Gene 2
micro-server family. The fully automated framework can provide fine-grained
information of the system's state by monitoring any abnormal behavior that may
occur during reduced supply voltage conditions. We also propose a new metric to
quantify the behavior of a microprocessor when it operates beyond nominal
conditions. Our experimental results demonstrate potential uses of the
characterization framework to identify the limits of operation for improved
energy efficiency.


Abstract:
  In this technical report, we present our solution of KDD Cup 2021 OGB
Large-Scale Challenge - PCQM4M-LSC Track. We adopt Graphormer and ExpC as our
basic models. We train each model by 8-fold cross-validation, and additionally
train two Graphormer models on the union of training and validation sets with
different random seeds. For final submission, we use a naive ensemble for these
18 models by taking average of their outputs. Using our method, our team
MachineLearning achieved 0.1200 MAE on test set, which won the first place in
KDD Cup graph prediction track.


Abstract:
  Touch data, and in particular text-entry data, has been mostly collected in
the laboratory, under controlled conditions. While touch and text-entry data
have consistently shown its potential for monitoring and detecting a variety of
conditions and impairments, its deployment in-the-wild remains a challenge. In
this paper, we present WildKey, an Android keyboard toolkit that allows for the
usable deployment of in-the-wild user studies. WildKey is able to analyze
text-entry behaviors through implicit and explicit text-entry data collection
while ensuring user privacy. We detail each of the WildKey's components and
features, all of the metrics collected, and discuss the steps taken to ensure
user privacy and promote compliance.


Abstract:
  We propose nonabelian higher-rank gauge theories in 2+1D and 3+1D. The gauge
group is constructed from the volume-preserving diffeomorphisms of space. We
show that the intriguing physics of the lowest Landau level (LLL) limit can be
interpreted as the consequences of the symmetry. We derive the renowned
Girvin-MacDonald-Platzman (GMP) algebra as well as the topological Wen-Zee term
within our formalism. Using the gauge symmetry in 2+1D, we derive the LLL
effective action of vortex crystal in rotating Bose gas as well as Wigner
crystal of electron in an applied magnetic field. We show that the nonlinear
sigma models of ferromagnets in 2+1D and 3+1D exhibit the higher-rank gauge
symmetries that we introduce in this paper. We interpret the fractonic behavior
of the excitations on the lowest Landau level and of skyrmions in ferromagnets
as the consequence of the higher-rank gauge symmetry.


Abstract:
  For $G=G_{n, 1/2}$, the Erd\H{o}s--Renyi random graph, let $X_n$ be the
random variable representing the number of distinct partitions of $V(G)$ into
sets $A_1, \ldots, A_q$ so that the degree of each vertex in $G[A_i]$ is
divisible by $q$ for all $i\in[q]$. We prove that if $q\geq 3$ is odd then
$X_n\xrightarrow{d}{\mathrm{Po}(1/q!)}$, and if $q \geq 4$ is even then
$X_n\xrightarrow{d}{\mathrm{Po}(2^q/q!)}$. More generally, we show that the
distribution is still asymptotically Poisson when we require all degrees in
$G[A_i]$ to be congruent to $x_i$ modulo $q$ for each $i\in[q]$, where the
residues $x_i$ may be chosen freely. For $q=2$, the distribution is not
asymptotically Poisson, but it can be determined explicitly.


Abstract:
  We report a theoretical study of the coherence dynamics of spin qubits in
two-dimensional materials (2DMs) and van-der-Waals heterostructures, as a
function of the host thickness and the composition of the surrounding
environment. We focus on MoS$_2$ and WS$_2$, two promising systems for quantum
technology applications, and we consider the decoherence arising from the
interaction of the spin qubit with nuclear spins. We show that the Hahn-echo
coherence time is determined by a complex interplay between the source of
decoherence in the qubit host and in the environment, which in turn determines
whether the noise evolution is in a classical or quantum mechanical regime. We
suggest that the composition and thickness of van-der-Waals heterostructures
encapsulating a qubit host can be engineered to maximize coherence times.
Finally, we discuss how quantum sensors may be able to probe the dynamics of
the nuclear bath in 2DMs.


Abstract:
  The use of few-femtosecond, extreme ultraviolet (XUV) pulses, produced by
high-order harmonic generation, in combination with few-femtosecond infrared
(IR) pulses in pump-probe experiments has great potential to disclose ultrafast
dynamics in molecules, nanostructures and solids. A crucial prerequisite is a
reliable characterization of the temporal properties of the XUV and IR pulses.
Several techniques have been developed. The majority of them applies phase
reconstruction algorithms to a photoelectron spectrogram obtained by ionizing
an atomic target in a pump-probe fashion. If the ionizing radiation is a single
harmonic, all the information is encoded in a two-color two-photon signal
called sideband (SB). In this work, we present a simplified model to interpret
the time-frequency mapping of the SB signal and we show that the temporal
dispersion of the pulses directly maps onto the shape of its spectrogram.
Finally, we derive an analytical solution, which allows us to propose a novel
procedure to estimate the second-order dispersion of the XUV and IR pulses in
real time and with no need for iterative algorithms.


Abstract:
  We prove a slab theorem for convex ancient solutions to mean curvature flow
without any additional hypotheses (such as concavity of the arrival time,
bounded curvature on compact time intervals, or noncollapsing in the sense of
inscribed radii). By carefully exploiting this, we are able to obtain a
Harnack-type inequality and a corresponding compactness theorem for the entire
solutions.
  Using the compactness theorem, we are then able to give a short proof of the
equivalence of noncollapsing in the sense of inscribed radii and entire arrival
times for convex ancient mean curvature flows.
  Finally, we provide a useful characterization of the non-entire solutions. In
particular, we prove that they are necessarily asymptotic to at least one Grim
hyperplane.
  As a consequence, we rule out collapsing singularity models in (not
necessarily embedded) compact $(n-1)$-convex mean curvature flow. We also
remove the noncollapsing hypothesis from certain recent classification results
for ancient solutions.


Abstract:
  *The following abbreviates the abstract. Please refer to the thesis for the
full abstract.*
  After a disaster, locating and extracting victims quickly is critical because
mortality rises rapidly after the first two days. To assist search and rescue
teams and improve response times, teams of camera-equipped aerial robots can
engage in tasks such as mapping buildings and locating victims.
  These sensing tasks encapsulate difficult (NP-Hard) problems. One way to
simplify planning for these tasks is to focus on maximizing sensing performance
over a short time horizon. Specifically, consider the problem of how to select
motions for a team of robots to maximize a notion of sensing quality (the
sensing objective) over the near future, say by maximizing the amount of
unknown space in a map that robots will observe over the next several seconds.
By repeating this process regularly, the team can react quickly to new
observations as they work to complete the sensing task. In technical terms,
this planning and control process forms an example of receding-horizon control.
Fortunately, common sensing objectives benefit from well-known monotonicity
properties (e.g. submodularity), and greedy algorithms can exploit these
monotonicity properties to solve the receding-horizon optimization problems
that we study near-optimally.
  However, greedy algorithms typically force robots to make decisions
sequentially so that planning time grows with the number of robots. Further,
recent works that investigate sequential greedy planning, have demonstrated
that reducing the number of sequential steps while retaining suboptimality
guarantees can be hard or impossible.
  We demonstrate that halting growth in planning time is sometimes possible. To
do so, we introduce novel greedy algorithms involving fixed numbers of
sequential steps.


Abstract:
  Backdoor attacks are a kind of insidious security threat against machine
learning models. After being injected with a backdoor in training, the victim
model will produce adversary-specified outputs on the inputs embedded with
predesigned triggers but behave properly on normal inputs during inference. As
a sort of emergent attack, backdoor attacks in natural language processing
(NLP) are investigated insufficiently. As far as we know, almost all existing
textual backdoor attack methods insert additional contents into normal samples
as triggers, which causes the trigger-embedded samples to be detected and the
backdoor attacks to be blocked without much effort. In this paper, we propose
to use the syntactic structure as the trigger in textual backdoor attacks. We
conduct extensive experiments to demonstrate that the syntactic trigger-based
attack method can achieve comparable attack performance (almost 100% success
rate) to the insertion-based methods but possesses much higher invisibility and
stronger resistance to defenses. These results also reveal the significant
insidiousness and harmfulness of textual backdoor attacks. All the code and
data of this paper can be obtained at https://github.com/thunlp/HiddenKiller.


Abstract:
  Symmetries have proven to be important ingredients in the analysis of neural
networks. So far their use has mostly been implicit or seemingly coincidental.
  We undertake a systematic study of the role that symmetry plays. In
particular, we clarify how symmetry interacts with the learning algorithm. The
key ingredient in our study is played by Noether's celebrated theorem which,
informally speaking, states that symmetry leads to conserved quantities (e.g.,
conservation of energy or conservation of momentum). In the realm of neural
networks under gradient descent, model symmetries imply restrictions on the
gradient path. E.g., we show that symmetry of activation functions leads to
boundedness of weight matrices, for the specific case of linear activations it
leads to balance equations of consecutive layers, data augmentation leads to
gradient paths that have "momentum"-type restrictions, and time symmetry leads
to a version of the Neural Tangent Kernel.
  Symmetry alone does not specify the optimization path, but the more
symmetries are contained in the model the more restrictions are imposed on the
path. Since symmetry also implies over-parametrization, this in effect implies
that some part of this over-parametrization is cancelled out by the existence
of the conserved quantities.
  Symmetry can therefore be thought of as one further important tool in
understanding the performance of neural networks under gradient descent.


Abstract:
  Sensors are being extensively deployed and are expected to expand at
significant rates in the coming years. They typically generate a large volume
of data on the internet of things (IoT) application areas like smart cities,
intelligent traffic systems, smart grid, and e-health. Cloud, edge and fog
computing are potential and competitive strategies for collecting, processing,
and distributing IoT data. However, cloud, edge, and fog-based solutions need
to tackle the distribution of a high volume of IoT data efficiently through
constrained and limited resource network infrastructures. This paper addresses
the issue of conveying a massive volume of IoT data through a network with
limited communications resources (bandwidth) using a cognitive communications
resource allocation based on Reinforcement Learning (RL) with SARSA algorithm.
The proposed network infrastructure (PSIoTRL) uses a Publish/ Subscribe
architecture to access massive and highly distributed IoT data. It is
demonstrated that the PSIoTRL bandwidth allocation for buffer flushing based on
SARSA enhances the IoT aggregator buffer occupation and network link
utilization. The PSIoTRL dynamically adapts the IoT aggregator traffic flushing
according to the Pub/Sub topic's priority and network constraint requirements.


Abstract:
  We study odd parity perturbations of spherically symmetric black holes with
time-dependent scalar hair in shift-symmetric higher-order scalar-tensor
theories. The analysis is performed in a general way without assuming the
degeneracy conditions. Nevertheless, we end up with second-order equations for
a single master variable, similarly to cosmological tensor modes. We thus
identify the general form of the quadratic Lagrangian for the odd parity
perturbations, leading to a generalization of the Regge-Wheeler equation. We
also investigate the structure of the effective metric for the master variable
and refine the stability conditions. As an application of our generalized
Regge-Wheeler equation, we compute the quasi-normal modes of a certain
nontrivial black hole solution. Finally, our result is extended to include the
matter energy-momentum tensor as a source term.


Abstract:
  Superconducting qubits are a promising platform for building a larger-scale
quantum processor capable of solving otherwise intractable problems. In order
for the processor to reach practical viability, the gate errors need to be
further suppressed and remain stable for extended periods of time. With recent
advances in qubit control, both single- and two-qubit gate fidelities are now
in many cases limited by the coherence times of the qubits. Here we
experimentally employ closed-loop feedback to stabilize the frequency
fluctuations of a superconducting transmon qubit, thereby increasing its
coherence time by 26\% and reducing the single-qubit error rate from $(8.5 \pm
2.1)\times 10^{-4}$ to $(5.9 \pm 0.7)\times 10^{-4}$. Importantly, the
resulting high-fidelity operation remains effective even away from the qubit
flux-noise insensitive point, significantly increasing the frequency bandwidth
over which the qubit can be operated with high fidelity. This approach is
helpful in large qubit grids, where frequency crowding and parasitic
interactions between the qubits limit their performance.


Abstract:
  In this paper, we develop a method to assess the sensitivity of local average
treatment effect estimates to potential violations of the monotonicity
assumption of Imbens and Angrist (1994). We parameterize the degree to which
monotonicity is violated using two sensitivity parameters: the first one
determines the share of defiers in the population, and the second one measures
differences in the distributions of outcomes between compliers and defiers. For
each pair of values of these sensitivity parameters, we derive sharp bounds on
the outcome distributions of compliers in the first-order stochastic dominance
sense. We identify the robust region that is the set of all values of
sensitivity parameters for which a given empirical conclusion, e.g. that the
local average treatment effect is positive, is valid. Researchers can assess
the credibility of their conclusion by evaluating whether all the plausible
sensitivity parameters lie in the robust region. We obtain confidence sets for
the robust region through a bootstrap procedure and illustrate the sensitivity
analysis in an empirical application. We also extend this framework to analyze
treatment effects of the entire population.


Abstract:
  We report the discovery of an extended very-high-energy (VHE) gamma-ray
source around the location of the middle-aged (207.8 kyr) pulsar PSR J0622+3749
with the Large High Altitude Air Shower Observatory (LHAASO). The source is
detected with a significance of $8.2\sigma$ for $E>25$~TeV assuming a Gaussian
template. The best-fit location is (R.A.,
Dec.)$=(95^{\circ}\!.47\pm0^{\circ}\!.11,\,37^{\circ}\!.92 \pm0^{\circ}\!.09)$,
and the extension is $0^{\circ}\!.40\pm0^{\circ}\!.07$. The energy spectrum can
be described by a power-law spectrum with an index of ${-2.92 \pm 0.17_{\rm
stat} \pm 0.02_{\rm sys} }$. No clear extended multi-wavelength counterpart of
the LHAASO source has been found from the radio to sub-TeV bands. The LHAASO
observations are consistent with the scenario that VHE electrons escaped from
the pulsar, diffused in the interstellar medium, and scattered the interstellar
radiation field. If interpreted as the pulsar halo scenario, the diffusion
coefficient, inferred for electrons with median energies of $\sim160$~TeV, is
consistent with those obtained from the extended halos around Geminga and
Monogem and much smaller than that derived from cosmic ray secondaries. The
LHAASO discovery of this source thus likely enriches the class of so-called
pulsar halos and confirms that high-energy particles generally diffuse very
slowly in the disturbed medium around pulsars.


Abstract:
  This paper develops probabilistic PV forecasters by taking advantage of
recent breakthroughs in deep learning. It tailored forecasting tool, named
encoder-decoder, is implemented to compute intraday multi-output PV quantiles
forecasts to efficiently capture the time correlation. The models are trained
using quantile regression, a non-parametric approach that assumes no prior
knowledge of the probabilistic forecasting distribution. The case study is
composed of PV production monitored on-site at the University of Li\`ege
(ULi\`ege), Belgium. The weather forecasts from the regional climate model
provided by the Laboratory of Climatology are used as inputs of the deep
learning models. The forecast quality is quantitatively assessed by the
continuous ranked probability and interval scores. The results indicate this
architecture improves the forecast quality and is computationally efficient to
be incorporated in an intraday decision-making tool for robust optimization.


Abstract:
  Nuclear Magnetic Resonance (NMR) shielding constants of transition metals in
solvated complexes are computed at the relativistic density functional theory
(DFT) level. The solvent effects evaluated with subsystem-DFT approaches are
compared with the reference solvent shifts predicted from supermolecular
calculations. Two subsystem-DFT approaches are analyzed -- in the standard
frozen density embedding (FDE) scheme the transition metal complexes are
embedded in an environment of solvent molecules whose density is kept frozen,
in the second approach the densities of the complex and of its environment are
relaxed in the "freeze-and-thaw" procedure. The latter approach improves the
description of the solvent effects in most cases, nevertheless the FDE
deficiencies are rather large in some cases.


Abstract:
  Static Application Security Testing (SAST) is a popular quality assurance
technique in software engineering. However, integrating SAST tools into
industry-level product development and security assessment poses various
technical and managerial challenges. In this work, we reported a longitudinal
case study of adopting SAST as a part of a human-driven security assessment for
an open-source e-government project. We described how SASTs are selected,
evaluated, and combined into a novel approach for software security assessment.
The approach was preliminarily evaluated using semi-structured interviews. Our
result shows that (1) while some SAST tools out-perform others, it is possible
to achieve better performance by combining more than one SAST tools and (2)
SAST tools should be used towards a practical performance and in the
combination with triangulated approaches for human-driven vulnerability
assessment in real-world projects.


Abstract:
  This is the opening article of the abstract book of conference "Set-Theoretic
Topology and Topological Algebra" in honor of professor Alexander Arhangelskii
on the occasion of his 80th birthday held in 2018 at Moscow State University.


Abstract:
  For a non-negative separable random field $Z(t), t\in \mathbb{R}^d$
satisfying some mild assumptions we show that \begin{eqnarray*}
  H_Z^\delta =
  \lim_{T\to\infty} \frac{1}{T^d} E \{\sup_{ t\in [0,T]^d \cap \delta
\mathbb{Z}^d } Z(t) \} <\infty \end{eqnarray*} for $\delta \ge 0$ where $0
\mathbb{Z}^d := \mathbb{R}^d$ and prove that $H_Z^0$ can be approximated by
$H_Z^\delta$ if $\delta$ tends to 0. These results extend the classical
findings for the Pickands constants $H_{Z}^\delta$, defined for $Z(t)=
\exp\left( \sqrt{ 2} B_\alpha (t)- |t|^{2\alpha }\right), t\in \mathbb{R}$ with
$B_\alpha$ a standard fractional Brownian motion with Hurst parameter $\alpha
\in (0,1]$.
  The continuity of $H_{Z}^\delta$ at $\delta=0$ is additionally shown for two
particular extensions of Pickands constants.


Abstract:
  Characterizing plasticity mechanisms below the ductile-to-brittle transition
temperature is traditionally difficult to accomplish in asystematic fashion.
Here, we use a new experimental setup to perform in situ cryogenic mechanical
testing of pure Sn micropillars at room temperature and at 142{\deg}C.
Subsequent electron microscopy characterization of the micropillars shows a
clear difference in the deformation mechanisms at room temperature and at
cryogenic temperatures. At room temperature, the Sn micropillars deformed
through dislocation plasticity, while at142{\deg}C they exhibited both higher
strength and deformation twinning. Two different orientations were tested, a
symmetric (100) orientation and a non-symmetric (451) orientation. The
deformation mechanisms were found to be the same for both orientations


Abstract:
  Semiconductor emitter can possibly achieve sharp cutoff wavelength due to its
intrinsic bandgap absorption and almost zero sub-bandgap emission without
doping. A germanium wafer based selective emitter with front-side
antireflection and backside metal coating is studied here for
thermophotovoltaic (TPV) energy conversion. Optical simulation predicts the
spectral emittance above 0.9 in the wavelengths from 1 to 1.85 um and below 0.2
in the sub-bandgap range with sharp cutoff around the bandgap, indicating
superior spectral selectivity behavior. This is confirmed by excellent
agreement with indirectly measured spectral emittance of the fabricated
Ge-based selective emitter sample. Furthermore, the TPV efficiency by paring
the Ge-based selective emitter with a GaSb cell is theoretically analyzed at
different temperatures. This work will facilitate the development of the
semiconductor-based selective emitters for enhancing TPV performance.


Abstract:
  We prove the equivalence in the covariant phase space of the metric and
connection formulations for Palatini gravity, with nonmetricity and torsion, on
a spacetime manifold with boundary. To this end, we will rely on the
cohomological approach provided by the relative bicomplex framework. Finally,
we discuss some of the physical implications derived from this equivalence in
the context of singularity identification through curvature invariants.


Abstract:
  A common observation in data-driven applications is that high dimensional
data has a low intrinsic dimension, at least locally. In this work, we consider
the problem of estimating a $d$ dimensional sub-manifold of $\mathbb{R}^D$ from
a finite set of noisy samples. Assuming that the data was sampled uniformly
from a tubular neighborhood of $\mathcal{M}\in \mathcal{C}^k$, a compact
manifold without boundary, we present an algorithm that takes a point $r$ from
the tubular neighborhood and outputs $\hat p_n\in \mathbb{R}^D$, and
$\widehat{T_{\hat p_n}\mathcal{M}}$ an element in the Grassmanian $Gr(d, D)$.
We prove that as the number of samples $n\to\infty$ the point $\hat p_n$
converges to $p\in \mathcal{M}$ and $\widehat{T_{\hat p_n}\mathcal{M}}$
converges to $T_p\mathcal{M}$ (the tangent space at that point) with high
probability. Furthermore, we show that the estimation yields asymptotic rates
of convergence of $n^{-\frac{k}{2k + d}}$ for the point estimation and
$n^{-\frac{k-1}{2k + d}}$ for the estimation of the tangent space. These rates
are known to be optimal for the case of function estimation.


Abstract:
  Understanding the limits of list-decoding and list-recovery of Reed-Solomon
(RS) codes is of prime interest in coding theory and has attracted a lot of
attention in recent decades. However, the best possible parameters for these
problems are still unknown, and in this paper, we take a step in this
direction. We show the existence of RS codes that are list-decodable or
list-recoverable beyond the Johnson radius for \emph{any} rate, with a
polynomial field size in the block length. In particular, we show that for any
$\epsilon\in (0,1)$ there exist RS codes that are list-decodable from radius
$1-\epsilon$ and rate less than $\frac{\epsilon}{2-\epsilon}$, with constant
list size. We deduce our results by extending and strengthening a recent result
of Ferber, Kwan, and Sauermann on puncturing codes with large minimum distance
and by utilizing the underlying code's linearity.


Abstract:
  In this research, we propose a new low-precision framework, TENT, to leverage
the benefits of a tapered fixed-point numerical format in TinyML models. We
introduce a tapered fixed-point quantization algorithm that matches the
numerical format's dynamic range and distribution to that of the deep neural
network model's parameter distribution at each layer. An accelerator
architecture for the tapered fixed-point with TENT framework is proposed.
Results show that the accuracy on classification tasks improves up to ~31 %
with an energy overhead of ~17-30 % as compared to fixed-point, for ConvNet and
ResNet-18 models.


Abstract:
  Determining the clumpiness of matter around galaxies is pivotal to a full
understanding of the spatially inhomogeneous, multi-phase gas in the
circumgalactic medium (CGM). We combine high spatially resolved 3D observations
with hydrodynamical cosmological simulations to measure the cold circumgalactic
gas clumpiness. We present new adaptive-optics-assisted VLT/MUSE observations
of a quadruply lensed quasar, targeting the CGM of 2 foreground $z\sim$1
galaxies observed in absorption. We additionally use zoom-in FOGGIE simulations
with exquisite resolution ($\sim$0.1 kpc scales) in the CGM of galaxies to
compute the physical properties of cold gas traced by Mg\,II absorbers. By
contrasting these mock-observables with the VLT/MUSE observations, we find a
large spread of fractional variations of Mg\,II equivalent widths with physical
separation, both in observations and simulations. The simulations indicate a
dependence of the Mg\,II coherence length on the underlying gas morphology
(filaments vs clumps). The $z_{\rm abs}$=1.168 Mg\,II system shows coherence
over $\gtrsim$ 6 kpc and is associated with an [O\,II] emitting galaxy situated
89 kpc away, with SFR $\geq$ 4.6 $\pm$ {1.5} $\rm M_{\odot}$/yr and
$M_{*}=10^{9.6\pm0.2} M_{\odot}$. Based on this combined analysis, we determine
that the absorber is consistent with being an inflowing filament. The $z_{\rm
abs}$=1.393 Mg\,II system traces dense CGM gas clumps varying in strength over
$\lesssim$ 2 kpc physical scales. Our findings suggest that this absorber is
likely related to an outflowing clump. Our joint approach combining
3D-spectroscopy observations of lensed systems and simulations with extreme
resolution in the CGM put new constraints on the clumpiness of cold CGM gas, a
key diagnostic of the baryon cycle.


Abstract:
  We investigate how the addition of quantum resources changes the statistical
complexity of quantum circuits by utilizing the framework of quantum resource
theories. Measures of statistical complexity that we consider include the
Rademacher complexity and the Gaussian complexity, which are well-known
measures in computational learning theory that quantify the richness of classes
of real-valued functions. We derive bounds for the statistical complexities of
quantum circuits that have limited access to certain resources and apply our
results to two special cases: (1) stabilizer circuits that are supplemented
with a limited number of T gates and (2) instantaneous quantum polynomial-time
Clifford circuits that are supplemented with a limited number of CCZ gates. We
show that the increase in the statistical complexity of a quantum circuit when
an additional quantum channel is added to it is upper bounded by the free
robustness of the added channel. Finally, we derive bounds for the
generalization error associated with learning from training data arising from
quantum circuits.


Abstract:
  We analyze the interaction with uniform external fields of nematic liquid
crystals within a recent generalized free-energy posited by Virga and falling
in the class of quartic functionals in the spatial gradients of the nematic
director. We review some known interesting solutions, i. e., uniform
heliconical structures, which correspond to the so-called twist-bend nematic
phase and we also study the transition between this phase and the standard
uniform nematic one. Moreover, we find liquid crystal configurations, which
closely resemble some novel, experimentally detected, structures called
Skyrmion Tubes. Skyrmion Tubes are characterized by a localized
cylindrically-symmetric pattern surrounded by either twist-bend or uniform
nematic phase. We study the equilibrium differential equations and find
numerical solutions and analytical approximations.


Abstract:
  In the pursuit of natural language understanding, there has been a long
standing interest in tracking state changes throughout narratives. Impressive
progress has been made in modeling the state of transaction-centric dialogues
and procedural texts. However, this problem has been less intensively studied
in the realm of general discourse where ground truth descriptions of states may
be loosely defined and state changes are less densely distributed over
utterances. This paper proposes to turn to simplified, fully observable systems
that show some of these properties: Sports events. We curated 2,263 soccer
matches including time-stamped natural language commentary accompanied by
discrete events such as a team scoring goals, switching players or being
penalized with cards. We propose a new task formulation where, given paragraphs
of commentary of a game at different timestamps, the system is asked to
recognize the occurrence of in-game events. This domain allows for rich
descriptions of state while avoiding the complexities of many other real-world
settings. As an initial point of performance measurement, we include two
baseline methods from the perspectives of sentence classification with temporal
dependence and current state-of-the-art generative model, respectively, and
demonstrate that even sophisticated existing methods struggle on the state
tracking task when the definition of state broadens or non-event chatter
becomes prevalent.


Abstract:
  We consider Sobolev mappings $f\in W^{1,q}(\Omega,\IC)$, $1<q<\infty$,
between planar domains $\Omega\subset \IC$. We analyse the Radon-Riesz property
for convex functionals of the form \[f\mapsto \int_\Omega \Phi(|Df(z)|,J(z,f))
\; dz \]
  and show that under certain criteria, which hold in important cases, weak
convergence in $W_{loc}^{1,q}(\Omega)$ of (for instance) a minimising sequence
can be improved to strong convergence. This finds important applications in the
minimisation problems for mappings of finite distortion and the $L^p$ and
$Exp$\,-Teichm\"uller theories.


Abstract:
  Athreya, Bufetov, Eskin and Mirzakhani have shown the number of mapping class
group lattice points intersecting a closed ball of radius $R$ in
Teichm\"{u}ller space is asymptotic to $e^{hR}$, where $h$ is the dimension of
the Teichm\"{u}ller space. We show for any pseudo-Anosov mapping class $f$,
there exists a power $n$, such that the number of lattice points of the $f^n$
conjugacy class intersecting a closed ball of radius $R$ is coarsely asymptotic
to $e^{\frac{h}{2}R}$.