title:
PICT@DravidianLangTech-ACL2022: Neural Machine Translation On Dravidian
  Languages

abstract:
  This paper presents a summary of the findings that we obtained based on the
shared task on machine translation of Dravidian languages. We stood first in
three of the five sub-tasks which were assigned to us for the main shared task.
We carried out neural machine translation for the following five language
pairs: Kannada to Tamil, Kannada to Telugu, Kannada to Malayalam, Kannada to
Sanskrit, and Kannada to Tulu. The datasets for each of the five language pairs
were used to train various translation models, including Seq2Seq models such as
LSTM, bidirectional LSTM, Conv2Seq, and training state-of-the-art as
transformers from scratch, and fine-tuning already pre-trained models. For some
models involving monolingual corpora, we implemented backtranslation as well.
These models' accuracy was later tested with a part of the same dataset using
BLEU score as an evaluation metric.


title: A Maximum Entropy Approach to Defining Cell State

abstract:
  The past few decades have seen great leaps in technologies to analyze cells
and tissues. Omics methods in particular now allow us unprecedented access to
their the molecular composition where the base-level resolution of transcripts
and their numbers can be determined at a single cell level. Existing methods to
analyze the resulting data make use of the count data while discarding the
information present in the sequences themselves. In this paper we used a
maximum entropy approach to develop a method to analyze RNA-seq data using both
the sequence and count information. By mapping sequences to vectors of spins
and defining an energy function on them, we were able to identify specific
states in a biological process using mean energies and their associated
Boltzmann-probabilities. This approach opens up new avenues in the quantitative
analysis of -omics data and analysis of biological function.


title:
Ultra-high-energy cosmic ray acceleration by magnetic reconnection in
  relativistic jets and the origin of very high energy emission

abstract:
  Relativistic jets are believed to be born magnetically dominated. Very and
ultra-high energy cosmic rays can be efficiently accelerated by magnetic
reconnection in these sources. We here demonstrate this directly, with no
extrapolations to large scales, by means of three-dimensional relativistic
magnetohydrodynamical (3D-RMHD) simulations of a Poyinting flux dominated jet.
We inject thousands of low-energy protons in the region of a relativistic jet
that corresponds to the transition from magnetically to kinetically dominated,
where its magnetization parameter is $\sigma \sim 1$. In this region, there is
efficient fast magnetic reconnection which is naturally driven by
current-driven-kink instability (CDKI) turbulence in the helical magnetic
fields of the jet. We find that the particles are accelerated by Fermi process
in the reconnection regions (and by drift in the final stages) up to energies
$E \sim 10^{18}$ eV for background magnetic fields $B \sim 0.1$ G, and $E \sim
10^{20}$ eV for $B \sim 10$ G. We have also derived from the simulations the
acceleration rate due to magnetic reconnection which has a weak dependence on
the particles energy $ r_{acc} \propto E^{-0.1}$, characteristic of exponential
growth. The energy spectrum of the accelerated particles develops a power-law
tail with spectral index $p \sim -1.2$. This hardness of the spectrum must
decrease when particle losses and feedback into the background plasma are
included. Our results can explain observed flux variability in the emission of
blazars at the very high energy band as well as the associated neutrino
emission. Successful applications of our results to the blazars MRK 421 and TXS
0506+056 are also discussed.


title:
Shape anisotropy effect on magnetic domain wall dynamics in nanowires
  under thermal gradient

abstract:
  We investigate the magnetic-domain wall (DW) dynamics in
uniaxial/biaxial-nanowires under a thermal gradient (TG). The findings reveal
that the DW propagates toward the hotter region in both nanowires. In
uniaxial-nanowire, the DW propagates accompanying a rotation of the DW-plane.
In biaxial nanowire, DW propagates in the hotter region, and the so-called
Walker breakdown phenomenon is observed. The main physics of such DW dynamics
is the magnonic angular momentum transfer to the DW. The hard (shape)
anisotropy exists in biaxial-nanowire, which contributes an additional torque;
hence DW speed is larger than that in uniaxial-nanowire. But rotational speed
is lower initially as hard anisotropy suppresses the DW-rotation. After certain
TG, DW-plane overcomes the hard anisotropy; thus, the rotational speed
increases again. DW dynamics show a decreasing trend with the damping since the
magnon propagation length decreases. Therefore, the above findings might be
useful to realize the spintronics (i.e., fast racetrack memory) devices.


title:
New Aspects of Optical Coherence and their Potential for Quantum
  Technologies

abstract:
  Currently, optical technology impacts most of our lives, from light used in
scientific measurement to the fiber optic cables that makeup the backbone of
the internet. However, as our current optical infrastructure grows, we discover
that these technologies are not limitless. However, our current optical
technology functions on classical principles, and can be easily improved by
incorporating our knowledge of quantum optics. In order to implement quantum
technologies, our understanding of quantum coherence must improve. Through this
knowledge we can maintain quantum states, and therefore their information,
longer. In this dissertation, I will demonstrate that with sufficient knowledge
of coherent properties, a simple algebra can be derived which can provide rules
for graph reductions on a quantum network graph. Using this knowledge, I then
provide a rudimentary algorithm which can find the optimal subgraph for
communication on a quantum network. Next, I demonstrate that by measuring the
photon statistics and second-order quantum coherence of a field, one can create
a neural network capable of distinguishing the light sources on a pixel. Which
is then applied to develop an imaging scheme capable of surpassing the
Abbe-Rayleigh Criterion. Lastly, I present a multiphoton quantum version of the
van Cittert-Zernike theorem. This provides formalism capable of determining the
propagation of quantum coherence throughout a system. I then demonstrate the
usefulness of the theorem by demonstrating sub-Poissonian statistics created by
a linear system with an incident thermal beam, obtainable only by
post-selection. Altogether, this provides incite into new applications of
coherence to quantum technologies and the formalism to extending our knowledge
even further.


title:
Antiferromagnetic structure and magnetic properties of Dy2O2Te: An
  isostructural analog of the rare-earth superconductors R2O2Bi

abstract:
  The rare-earth compounds R2O2Bi (R=Tb, Dy, Er, Lu, Y) are newly discovered
superconductors in the vicinity of a rare-earth magnetic long-range order. In
this work, we determine the magnetic order of the parent compound Dy2O2Te by
neutron scattering as the A-type antiferromagnetic structure below the N\'eel
temperature TN=9.7K. The large staggered magnetic moment 9.4(1) {\mu}B per Dy
at T=3.5K lies in the basal ab plane. In a magnetic field, anomalous magnetic
properties including the bifurcation between zero-field- and field-cooling
magnetization, a butterfly-shaped magnetic hysteresis, and slow magnetic
relaxation emerge, which are related to the field-induced metamagnetic
transitions in Dy2O2Te. Our experimental findings could stimulate further
research on the relation between antiferromagnetism and superconductivity in
these rare-earth compounds.


title: City Motifs as Revealed by Similarity Between Hierarchical Features

abstract:
  Several natural and theoretical networks can be broken down into smaller
portions, or subgraphs corresponding to neighborhoods. The more frequent of
these neighborhoods can then be understood as motifs of the network, being
therefore important for better characterizing and understanding of the overall
structure. Several developments in network science have relied on this
interesting concept, with ample applications in areas including systems
biology, computational neuroscience, economy and ecology. The present work aims
at reporting an unsupervised methodology capable of identifying motifs
respective to streets networks, the latter corresponding to graphs obtained
from city plans by considering street junctions and terminations as nodes while
the links are defined by the streets. Remarkable results are described,
including the identification of nine stable and informative motifs, which have
been allowed by three critically important factors: (i) adoption of five
hierarchical measurements to locally characterize the neighborhoods of nodes in
the streets networks; (ii) adoption of an effective coincidence methodology for
translating datasets into networks; and (iii) definition of the motifs in
statistical terms by using community finding methodology. The nine identified
motifs are characterized and discussed from several perspective, including
their mutual similarity, visualization, histograms of measurements, and
geographical adjacency in the original cities. Also presented is the analysis
of the effect of the adopted features on the obtained networks as well as a
simple supervised learning method capable of assigning reference motifs to
cities.


title:
An improved central limit theorem and fast convergence rates for
  entropic transportation costs

abstract:
  We prove a central limit theorem for the entropic transportation cost between
subgaussian probability measures, centered at the population cost. This is the
first result which allows for asymptotically valid inference for entropic
optimal transport between measures which are not necessarily discrete. In the
compactly supported case, we complement these results with new, faster,
convergence rates for the expected entropic transportation cost between
empirical measures. Our proof is based on strengthening convergence results for
dual solutions to the entropic optimal transport problem.


title:
Identifying Near-Optimal Single-Shot Attacks on ICSs with Limited
  Process Knowledge

abstract:
  Industrial Control Systems (ICSs) rely on insecure protocols and devices to
monitor and operate critical infrastructure. Prior work has demonstrated that
powerful attackers with detailed system knowledge can manipulate exchanged
sensor data to deteriorate performance of the process, even leading to full
shutdowns of plants. Identifying those attacks requires iterating over all
possible sensor values, and running detailed system simulation or analysis to
identify optimal attacks. That setup allows adversaries to identify attacks
that are most impactful when applied on the system for the first time, before
the system operators become aware of the manipulations.
  In this work, we investigate if constrained attackers without detailed system
knowledge and simulators can identify comparable attacks. In particular, the
attacker only requires abstract knowledge on general information flow in the
plant, instead of precise algorithms, operating parameters, process models, or
simulators. We propose an approach that allows single-shot attacks, i.e.,
near-optimal attacks that are reliably shutting down a system on the first try.
The approach is applied and validated on two use cases, and demonstrated to
achieve comparable results to prior work, which relied on detailed system
information and simulations.


title: Cycle lengths modulo $k$ in expanders

abstract:
  Given a constant $\alpha>0$, an $n$-vertex graph is called an
$\alpha$-expander if every set $X$ of at most $n/2$ vertices in $G$ has an
external neighborhood of size at least $\alpha|X|$. Addressing a question posed
by Friedman and Krivelevich in [Combinatorica, 41(1), (2021), pp. 53--74], we
prove the following result: Let $k>1$ be an integer with smallest prime divisor
$p$. Then for $\alpha>\frac{1}{p-1}$ every sufficiently large
$\alpha$-expanding graph contains cycles of length congruent to any given
residue modulo $k$. This result is almost best possible, in the following
sense: There exists an absolute constant $c>0$ such that for every integer $k$
with smallest prime divisor $p$ and for every positive $\alpha<\frac{c}{p-1}$,
there exist arbitrarily large $\alpha$-expanding graphs with no cycles of
length $r$ modulo $k$, for some $r \in \{0,\ldots,k-1\}$.


title: Sintel: A Machine Learning Framework to Extract Insights from Signals

abstract:
  The detection of anomalies in time series data is a critical task with many
monitoring applications. Existing systems often fail to encompass an end-to-end
detection process, to facilitate comparative analysis of various anomaly
detection methods, or to incorporate human knowledge to refine output. This
precludes current methods from being used in real-world settings by
practitioners who are not ML experts. In this paper, we introduce Sintel, a
machine learning framework for end-to-end time series tasks such as anomaly
detection. The framework uses state-of-the-art approaches to support all steps
of the anomaly detection process. Sintel logs the entire anomaly detection
journey, providing detailed documentation of anomalies over time. It enables
users to analyze signals, compare methods, and investigate anomalies through an
interactive visualization tool, where they can annotate, modify, create, and
remove events. Using these annotations, the framework leverages human knowledge
to improve the anomaly detection pipeline. We demonstrate the usability,
efficiency, and effectiveness of Sintel through a series of experiments on
three public time series datasets, as well as one real-world use case involving
spacecraft experts tasked with anomaly analysis tasks. Sintel's framework,
code, and datasets are open-sourced at https://github.com/sintel-dev/.


title:
From Spoken Thoughts to Automated Driving Commentary: Predicting and
  Explaining Intelligent Vehicles' Actions

abstract:
  Commentary driving is a technique in which drivers verbalise their
observations, assessments and intentions. By speaking out their thoughts, both
learning and expert drivers are able to create a better understanding and
awareness of their surroundings. In the intelligent vehicle context, automated
driving commentary can provide intelligible explanations about driving actions,
and thereby assist a driver or an end-user during driving operations in
challenging and safety-critical scenarios. In this paper, we conducted a field
study in which we deployed a research vehicle in an urban environment to obtain
data. While collecting sensor data of the vehicle's surroundings, we obtained
driving commentary from a driving instructor using the think-aloud protocol. We
analysed the driving commentary and uncovered an explanation style; the driver
first announces his observations, announces his plans, and then makes general
remarks. He also made counterfactual comments. We successfully demonstrated how
factual and counterfactual natural language explanations that follow this style
could be automatically generated using a simple tree-based approach. Generated
explanations for longitudinal actions (e.g., stop and move) were deemed more
intelligible and plausible by human judges compared to lateral actions, such as
lane changes. We discussed how our approach can be built on in the future to
realise more robust and effective explainability for driver assistance as well
as partial and conditional automation of driving functions.


title:
Councils in Action: Automating the Curation of Municipal Governance Data
  for Research

abstract:
  Large scale comparative research into municipal governance is often
prohibitively difficult due to a lack of high-quality data. But, recent
advances in speech-to-text algorithms and natural language processing has made
it possible to more easily collect and analyze data about municipal
governments. In this paper, we introduce an open-source platform, the Council
Data Project (CDP), to curate novel datasets for research into municipal
governance. The contribution of this work is two-fold: 1. We demonstrate that
CDP, as an infrastructure, can be used to assemble reliable comparative data on
municipal governance; 2. We provide exploratory analysis of three
municipalities to show how CDP data can be used to gain insight into how
municipal governments perform over time. We conclude by describing future
directions for research on and with CDP such as the development of machine
learning models for speaker annotation, outline generation, and named entity
recognition for improved linked data.


title:
Thermodynamic modeling with uncertainty quantification using the
  modified quasichemical model in quadruplet approximation: Implementation into
  PyCalphad and ESPEI

abstract:
  The modified quasichemical model in the quadruplet approximation (MQMQA)
considers the first- and the second-nearest-neighbor coordination and
interactions, becoming one of the best models to describe short-range ordering
in complex liquids such as molten salts, slag in metal processing, and
electrolytic solutions. The present work implements the MQMQA into Python based
open-source software PyCalphad for thermodynamic calculations using the CALPHAD
approach. This endeavor facilitates the development of MQMQA-based
thermodynamic database with uncertainty quantification (UQ) using the
open-source software ESPEI. New database format XML (extensible markup
language) has been proposed to extensively parse other standard database
formats. Taking the KF-NiF2 system as an example, we demonstrate the successful
implementation of MQMQA through thermodynamic calculations of, e.g., Gibbs
energy, equilibrium quadruplet fractions, and phase diagram, using PyCalphad,
as well as database development with UQ using ESPEI. The present implementation
indicates that the open-source PyCalphad/ESPEI could provide an accurate
alternative to perform CALPHAD modeling for complex liquids with short-range
ordering, making the MQMQA more accessible to the broad community.


title: Real-time capable CCD-based individual trapped-ion qubit measurement

abstract:
  Individual-qubit readout is a key ingredient for quantum simulation and
quantum computation. Furthermore, this readout must take place in real-time to
enable the application of quantum error-correction protocols. In this paper the
capability of an EMCCD camera with a real-time processing capable output is
demonstrated to determine the quantum state of a single $^9$Be$^+$ ion and the
required timing sequences are explored. The results are comparable to a PMT
based detection. Experiments on the individual detection of $^9$Be$^+$ qubit
states undergoing coherent excitation are reported. Sources of error and the
amount of crosstalk in the detection system are discussed. Error rates due to
known problems in the state preparation and measurement processes were
determined to be approximately 0.5 %.


title: Representation of short distances in structurally sparse graphs

abstract:
  A partial orientation $\vec{H}$ of a graph $G$ is a weak $r$-guidance system
if for any two vertices at distance at most $r$ in $G$, there exists a shortest
path $P$ between them such that $\vec{H}$ directs all but one edge in $P$
towards this edge. In case $\vec{H}$ has bounded maximum outdegree, this gives
an efficient representation of shortest paths of length at most $r$ in $G$. We
show that graphs from many natural graph classes admit such weak guidance
systems, and study the algorithmic aspects of this notion.


title:
Template and Temperature Controlled Polymorph Formation in Squaraine
  Thin Films

abstract:
  Controlling the polymorph formation in organic semiconductor thin films by
the choice of substrate and deposition temperature is a key factor for targeted
device performance. Small molecular semiconductors such as the quadrupolar
donor-acceptor-donor (D-A-D) type squaraine compounds allow both solution and
vapor phase deposition methods. A prototypical anilino squaraine with branched
butyl chains as terminal functionalization (SQIB) has been considered for
photovoltaic applications due to its broad absorption within the visible to
deep-red spectral range. Its opto-electronic properties depend on the formation
of the two known polymorphs adopting a monoclinic and orthorhombic crystal
phase. Both phases emerge with a strongly preferred out-of-plane and rather
random in-plane orientation in spincasted thin films depending on subsequent
thermal annealing. Upon vapor deposition on dielectric and conductive
substrates, such as silicon dioxide, potassium chloride, graphene and gold, the
polymorph expression depends on the choice of growth substrate. In all cases
the same pronounced out-of-plane orientation is adopted, but with a surface
templated in-plane alignment in case of crystalline substrates. Combining X-ray
diffraction, atomic force microscopy, ellipsometry and polarized
spectro-microscopy we identify the processing dependent evolution of the
crystal phases, correlating morphology and molecular orientations within the
textured SQIB films.


title: Simulating Interaction Movements via Model Predictive Control

abstract:
  We present a method to simulate movement in interaction with computers, using
Model Predictive Control (MPC). The method starts from understanding
interaction from an Optimal Feedback Control (OFC) perspective. We assume that
users aim to minimize an internalized cost function, subject to the constraints
imposed by the human body and the interactive system. In contrast to previous
linear approaches used in HCI, MPC can compute optimal controls for nonlinear
systems. This allows us to use state-of-the-art biomechanical models and handle
nonlinearities that occur in almost any interactive system. Instead of torque
actuation, our model employs second-order muscles acting directly at the
joints. We compare three different cost functions and evaluate the simulated
trajectories against user movements in a Fitts' Law type pointing study with
four different interaction techniques. Our results show that the combination of
distance, control, and joint acceleration cost matches individual users'
movements best, and predicts movements with an accuracy that is within the
between-user variance. To aid HCI researchers and designers, we introduce CFAT,
a novel method to identify maximum voluntary torques in joint-actuated models
based on experimental data, and give practical advice on how to simulate human
movement for different users, interaction techniques, and tasks.


title:
A Novel Fast Exact Subproblem Solver for Stochastic Quasi-Newton Cubic
  Regularized Optimization

abstract:
  In this work we describe an Adaptive Regularization using Cubics (ARC) method
for large-scale nonconvex unconstrained optimization using Limited-memory
Quasi-Newton (LQN) matrices. ARC methods are a relatively new family of
optimization strategies that utilize a cubic-regularization (CR) term in place
of trust-regions and line-searches. LQN methods offer a large-scale alternative
to using explicit second-order information by taking identical inputs to those
used by popular first-order methods such as stochastic gradient descent (SGD).
Solving the CR subproblem exactly requires Newton's method, yet using
properties of the internal structure of LQN matrices, we are able to find exact
solutions to the CR subproblem in a matrix-free manner, providing large
speedups and scaling into modern size requirements. Additionally, we expand
upon previous ARC work and explicitly incorporate first-order updates into our
algorithm. We provide experimental results when the SR1 update is used, which
show substantial speed-ups and competitive performance compared to Adam and
other second order optimizers on deep neural networks (DNNs). We find that our
new approach, ARCLQN, compares to modern optimizers with minimal tuning, a
common pain-point for second order methods.


title: An introduction to decomposition

abstract:
  We review work on `decomposition,' a property of two-dimensional theories
with 1-form symmetries and, more generally, d-dimensional theories with
(d-1)-form symmetries. Decomposition is the observation that such quantum field
theories are equivalent to (`decompose into') disjoint unions of other QFTs,
known in this context as "universes." Examples include two-dimensional gauge
theories and orbifolds with matter invariant under a subgroup of the gauge
group. Decomposition explains and relates several physical properties of these
theories -- for example, restrictions on allowed instantons arise as a
"multiverse interference effect" between contributions from constituent
universes. First worked out in 2006 as part of efforts to resolve technical
questions in string propagation on stacks, decomposition has been the driver of
a number of developments since. We give a general overview of decomposition,
describe features of decomposition arising in gauge theories, then dive into
specifics for orbifolds. We conclude with a discussion of the recent
application to anomaly resolution of Wang-Wen-Witten in two-dimensional
orbifolds. This is a contribution to the proceedings of the conference
Two-dimensional supersymmetric theories and related topics (Matrix Institute,
Australia, January 2022), giving an overview of a talk given there and
elsewhere.


title:
Time-resolved microstructural changes in large amplitude oscillatory
  shear of model single and double component soft gels

abstract:
  Soft particulate gels can reversibly yield when sufficient deformation is
applied, and the characteristics of this transition can be enhanced or limited
by designing hybrid hydrogel composites. While the microscopic dynamics and
macroscopic rheology of these systems have been studied separately in detail,
the development of direct connections between the two has been difficult,
particularly with regard to the non-linear rheology. To bridge this gap, we
perform a series of large amplitude oscillatory shear (LAOS) numerical
measurements on model soft particulate gels at different volume fractions using
coarse-grained molecular dynamics simulations. We first study a particulate
network with local bending stiffness and then we combine it with a second
component that can provide additional crosslinking to obtain two-component
networks. Through the sequence of physical processes (SPP) framework we define
time-resolved dynamic moduli and, by tracking the changes in these moduli
through the period, we can distinguish transitions in the material behavior as
a function of time. This approach helps us establish the microsopic origin of
the non-linear rheology by connecting the changes in dynamics moduli to the
corresponding microstructural changes during the deformation including the
non-affine displacement of particles, and the breakage, formation, and
orientation of bonds.


title:
Noninvasive Submillimeter-Precision Brain Stimulation by
  Optically-Driven Focused Ultrasound

abstract:
  High precision neuromodulation is a powerful tool to decipher neurocircuits
and treat neurological diseases. Current non-invasive neuromodulation methods
offer limited millimeter-level precision. Here, we report an optically-driven
focused ultrasound (OFUS) for non-invasive brain stimulation with submillimeter
precision. OFUS is generated by a soft optoacoustic pad (SOAP) fabricated
through embedding candle soot nanoparticles in a curved polydimethylsiloxane
film. SOAP generates a transcranial ultrasound focus at 15 MHz with a lateral
resolution of 83 micrometers, which is two orders of magnitude smaller than
that of conventional transcranial focused ultrasound (tFUS). Effective OFUS
neurostimulation in vitro with a single ultrasound cycle is shown.
Submillimeter transcranial stimulation of mouse motor cortex in vivo is
demonstrated. An acoustic energy of 0.02 J/cm^2, two orders of magnitude less
than that of tFUS, is sufficient for successful OFUS neurostimulation. By
delivering a submillimeter focus non-invasively, OFUS opens a new way for
neuroscience studies and disease treatments.


title:
Optical Remote Sensing Image Understanding with Weak Supervision:
  Concepts, Methods, and Perspectives

abstract:
  In recent years, supervised learning has been widely used in various tasks of
optical remote sensing image understanding, including remote sensing image
classification, pixel-wise segmentation, change detection, and object
detection. The methods based on supervised learning need a large amount of
high-quality training data and their performance highly depends on the quality
of the labels. However, in practical remote sensing applications, it is often
expensive and time-consuming to obtain large-scale data sets with high-quality
labels, which leads to a lack of sufficient supervised information. In some
cases, only coarse-grained labels can be obtained, resulting in the lack of
exact supervision. In addition, the supervised information obtained manually
may be wrong, resulting in a lack of accurate supervision. Therefore, remote
sensing image understanding often faces the problems of incomplete, inexact,
and inaccurate supervised information, which will affect the breadth and depth
of remote sensing applications. In order to solve the above-mentioned problems,
researchers have explored various tasks in remote sensing image understanding
under weak supervision. This paper summarizes the research progress of weakly
supervised learning in the field of remote sensing, including three typical
weakly supervised paradigms: 1) Incomplete supervision, where only a subset of
training data is labeled; 2) Inexact supervision, where only coarse-grained
labels of training data are given; 3) Inaccurate supervision, where the labels
given are not always true on the ground.


title:
Importance is in your attention: agent importance prediction for
  autonomous driving

abstract:
  Trajectory prediction is an important task in autonomous driving.
State-of-the-art trajectory prediction models often use attention mechanisms to
model the interaction between agents. In this paper, we show that the attention
information from such models can also be used to measure the importance of each
agent with respect to the ego vehicle's future planned trajectory. Our
experiment results on the nuPlans dataset show that our method can effectively
find and rank surrounding agents by their impact on the ego's plan.


title: Continuous cutting plane algorithms in integer programming

abstract:
  Cutting planes for mixed-integer linear programs (MILPs) are typically
computed in rounds by iteratively solving optimization problems. Instead, we
reframe the problem of finding good cutting planes as a continuous optimization
problem over weights parametrizing families of valid inequalities. We propose a
practical two-step algorithm, and demonstrate empirical gains when optimizing
Gomory mixed-integer inequalities over various families of MILPs. Finally, a
reinterpretation of our approach as optimization of the subadditive dual using
a deep neural network is provided. Code for reproducing the experiments can be
found at https://github.com/dchetelat/subadditive.


title:
GAM(e) changer or not? An evaluation of interpretable machine learning
  models based on additive model constraints

abstract:
  The number of information systems (IS) studies dealing with explainable
artificial intelligence (XAI) is currently exploding as the field demands more
transparency about the internal decision logic of machine learning (ML) models.
However, most techniques subsumed under XAI provide post-hoc-analytical
explanations, which have to be considered with caution as they only use
approximations of the underlying ML model. Therefore, our paper investigates a
series of intrinsically interpretable ML models and discusses their suitability
for the IS community. More specifically, our focus is on advanced extensions of
generalized additive models (GAM) in which predictors are modeled independently
in a non-linear way to generate shape functions that can capture arbitrary
patterns but remain fully interpretable. In our study, we evaluate the
prediction qualities of five GAMs as compared to six traditional ML models and
assess their visual outputs for model interpretability. On this basis, we
investigate their merits and limitations and derive design implications for
further improvements.


title:
Investigation of deflection angle for muon energy classification in muon
  scattering tomography via GEANT4 simulations

abstract:
  In muon scattering tomography, the investigated materials are discriminated
according to the scattering angle that mainly depends on the atomic number, the
density, and the thickness of the medium at a given energy value. The
scattering angles at different initial energies also provide the opportunity to
classify the incoming muons into a number of energy groups. In this study, by
employing the GEANT4 code, we show that the deflection angle exponentially
decays as a function of energy, and the numerical values for the current
configuration are below the detector accuracy except the initial energy bins
owing to the low-Z, low density, and low thickness of the current plastic
scintillators. This implies the necessity of additional components that provoke
the muon scattering. Therefore, we introduce stainless steel surfaces into the
top and bottom sections in order to amplify the deflection angle as well as to
reduce the uncertainty, thereby improving the detector performance.


title:
Mobility Analysis Workflow (MAW): An accessible, interoperable, and
  reproducible container system for processing raw mobile data

abstract:
  Mobility analysis, or understanding and modeling of people's mobility
patterns in terms of when, where, and how people move from one place to
another, is fundamentally important as such information is the basis for
large-scale investment decisions on the nation's multi-modal transportation
infrastructure. Recent rise of using passively generated mobile data from
mobile devices have raised questions on using such data for capturing the
mobility patterns of a population because: 1) there is a great variety of
different kinds of mobile data and their respective properties are unknown; and
2) data pre-processing and analysis methods are often not explicitly reported.
The high stakes involved with mobility analysis and issues associated with the
passively generated mobile data call for mobility analysis (including data,
methods and results) to be accessible to all, interoperable across different
computing systems, reproducible and reusable by others. In this study, a
container system named Mobility Analysis Workflow (MAW) that integrates data,
methods and results, is developed. Built upon the containerization technology,
MAW allows its users to easily create, configure, modify, execute and share
their methods and results in the form of Docker containers. Tools for
operationalizing MAW are also developed and made publicly available on GitHub.
One use case of MAW is the comparative analysis for the impacts of different
pre-processing and mobility analysis methods on inferred mobility patterns.
This study finds that different pre-processing and analysis methods do have
impacts on the resulting mobility patterns. The creation of MAW and a better
understanding of the relationship between data, methods and resulting mobility
patterns as facilitated by MAW represent an important first step toward
promoting reproducibility and reusability in mobility analysis with
passively-generated data.


title: Measuring the Soft X-Ray Quantum Efficiency of a Hybrid CMOS Detector

abstract:
  Next-generation X-ray observatories, such as the Lynx X-ray Observatory
Mission Concept or other similar concepts in the coming decade, will require
detectors with high quantum efficiency (QE) across the soft X-ray band to
observe the faint objects that drive their mission science objectives. Hybrid
CMOS Detectors (HCDs), a form of active-pixel sensor, are promising candidates
for use on these missions because of their fast read-out, low power
consumption, and intrinsic radiation hardness. In this work, we present QE
measurements of a Teledyne H2RG HCD, performed using a gas-flow proportional
counter as a reference detector. We find that this detector achieves high QE
across the soft X-ray band, with an effective QE of $94.6 \pm 1.1 \%$ at the Mn
K$\alpha$/K$\beta$ energies (5.90/6.49 keV), $98.3 \pm 1.9 \%$ at the Al
K$\alpha$ energy (1.49 keV), $85.6 \pm 2.8 \%$ at the O K$\alpha$ energy (0.52
keV), and $61.3 \pm 1.1 \%$ at the C K$\alpha$ energy (0.28 keV). These values
are in good agreement with our model, based on the absorption of detector
layers. We find similar results in a more restrictive analysis considering only
high-quality events, with only somewhat reduced QE at lower energies.


title:
X-ray observation of the Roche-lobe filling white dwarf plus hot
  subdwarf system ZTF J213056.71+442046.5

abstract:
  ZTF J213056.71+442046.5 is the prototype of a small class of recently
discovered compact binaries composed of a white dwarf and a hot subdwarf that
fills its Roche-lobe. Its orbital period of only 39 min is the shortest known
for the objects in this class. Evidence for a high orbital inclination (i=86
deg) and for the presence of an accretion disk has been inferred from a
detailed modeling of its optical photometric and spectroscopic data. We report
the results of an XMM-Newton observation carried out on 2021 January 7. ZTF
J213056.71+442046.5 was clearly detected by the Optical Monitor, which showed a
periodic variability in the UV band (200-400 nm), with a light curve similar to
that seen at longer wavelengths. Despite accretion on the white dwarf at an
estimated rate of the order of 10^{-9} M_sun/yr, no X-rays were detected with
the EPIC instrument, with a limit of ~10^{30} erg/s on the 0.2-12 keV
luminosity. We discuss possible explanations for the lack of a strong X-ray
emission from this system.


title: One hundred second bit-flip time in a two-photon dissipative oscillator

abstract:
  Current implementations of quantum bits (qubits) continue to undergo too many
errors to be scaled into useful quantum machines. An emerging strategy is to
encode quantum information in the two meta-stable pointer states of an
oscillator exchanging pairs of photons with its environment, a mechanism shown
to provide stability without inducing decoherence. Adding photons in these
states increases their separation, and macroscopic bit-flip times are expected
even for a handful of photons, a range suitable to implement a qubit. However,
previous experimental realizations have saturated in the millisecond range. In
this work, we aim for the maximum bit-flip time we could achieve in a
two-photon dissipative oscillator. To this end, we design a Josephson circuit
in a regime that circumvents all suspected dynamical instabilities, and employ
a minimally invasive fluorescence detection tool, at the cost of a two-photon
exchange rate dominated by single-photon loss. We attain bit-flip times of the
order of 100 seconds for states pinned by two-photon dissipation and containing
about 40 photons. This experiment lays a solid foundation from which the
two-photon exchange rate can be gradually increased, thus gaining access to the
preparation and measurement of quantum superposition states, and pursuing the
route towards a logical qubit with built-in bit-flip protection.


title: Small Shadows of Lattice Polytopes

abstract:
  The diameter of the graph of a $d$-dimensional lattice polytope $P \subseteq
[0,k]^{n}$ is known to be at most $dk$ due to work by Kleinschmidt and Onn.
However, it is an open question whether the monotone diameter, the shortest
guaranteed length of a monotone path, of a $d$-dimensional lattice polytope $P
= \{\mathbf{x}: A\mathbf{x} \leq \mathbf{b}\} \subseteq [0,k]^{n}$ is bounded
by a polynomial in $d$ and $k$. This question is of particular interest in
linear optimization, since paths traced by the Simplex method must be monotone.
We introduce partial results in this direction including a monotone diameter
bound of $3d$ for $k = 2$, a monotone diameter bound of $(d-1)m+1$ for
$d$-dimensional $(m+1)$-level polytopes, a pivot rule such that the Simplex
method is guaranteed to take at most $dnk||A||_{\infty}$ non-degenerate steps
to solve a LP on $P$, and a bound of $dk$ for lengths of paths from certain
fixed starting points. Finally, we present a constructive approach to a
diameter bound of $(3/2)dk$ and describe how to translate this final bound into
an algorithm that solves a linear program by tracing such a path.


title: Very Light Sterile Neutrinos at NOvA and T2K

abstract:
  Over the last several years, our understanding of neutrino oscillations has
developed significantly due to the long-baseline measurements of muon-neutrino
disappearance and muon-to-electron-neutrino appearance at the T2K and NOvA
experiments. However, when interpreted under the
standard-three-massive-neutrinos paradigm, a tension has emerged between the
two experiments' data. Here, we examine whether this tension can be alleviated
when a fourth, very light neutrino is added to the picture. Specifically, we
focus on the scenario in which this new neutrino has a mass similar to, or even
lighter than, the three mostly-active neutrinos that have been identified to
date. We find that, for some regions of parameter space, the four-neutrino
framework is favored over the three-neutrino one with moderate (a little under
two sigma) significance. Interpreting these results, we provide future outlook
for near-term and long-term experiments if this four-neutrino framework is
indeed true.


title:
A Unified Approach for Multi-Scale Synchronous Correlation Search in Big
  Time Series -- Full Version

abstract:
  The wide deployment of IoT sensors has enabled the collection of very big
time series across different domains, from which advanced analytics can be
performed to find unknown relationships, most importantly the correlations
between them. However, current approaches for correlation search on time series
are limited to only a single temporal scale and simple types of relations, and
cannot handle noise effectively. This paper presents the integrated SYnchronous
COrrelation Search (iSYCOS) framework to find multi-scale correlations in big
time series. Specifically, iSYCOS integrates top-down and bottom-up approaches
into a single auto-configured framework capable of efficiently extracting
complex window-based correlations from big time series using mutual information
(MI). Moreover, iSYCOS includes a novel MI-based theory to identify noise in
the data, and is used to perform pruning to improve iSYCOS performance.
Besides, we design a distributed version of iSYCOS that can scale out in a
Spark cluster to handle big time series. Our extensive experimental evaluation
on synthetic and real-world datasets shows that iSYCOS can auto-configure on a
given dataset to find complex multi-scale correlations. The pruning and
optimisations can improve iSYCOS performance up to an order of magnitude, and
the distributed iSYCOS can scale out linearly on a computing cluster.


title:
Solar system relativity tests, formulas for light deflection by a
  central mass, and modification of the lens equation, for a Weyl scaling
  invariant dark energy

abstract:
  We estimate the changes in solar system tests of general relativity when
"dark energy" arises from a Weyl scaling invariant action, using the modified
Schwarzschild-like spherically symmetric solution obtained in this case by
Adler and Ramazano\vglu. We show that the standard results of general
relativity are modified by amounts that are far below current experimental
errors. However, because the metric is not conformally flat when the central
mass $M$ vanishes, there is a contribution to light deflection proportional to
the cosmological constant $\Lambda$, with opposite sign to the one proportional
to $M$. This could have implications for gravitational lensing in cosmological
contexts, so we have calculated the corresponding correction to the "lens
equation" for gravitational lensing. We also give formulas for the light
deflection angle through order $\Lambda M$, assuming a parameterized
spherically symmetric metric for the external region of the central mass.


title:
Thermal Friction as a Solution to the Hubble and Large-Scale Structure
  Tensions

abstract:
  Thermal friction offers a promising solution to the Hubble and the
large-scale structure (LSS) tensions. This additional friction acts on a scalar
field in the early universe and extracts its energy density into dark
radiation, the cumulative effect being similar to that of an early dark energy
(EDE) scenario. The dark radiation automatically redshifts at the minimal
necessary rate to improve the Hubble tension. On the other hand, the addition
of extra radiation to the Universe can improve the LSS tension. We explore this
model in light of cosmic microwave background (CMB), baryon acoustic
oscillation and supernova data, including the SH0ES $H_0$ measurement and the
Dark Energy Survey Y1 data release in our analysis. Our results indicate a
preference for the regime where the scalar field converts to dark radiation at
very high redshifts, asymptoting effectively to an extra self-interacting
radiation species rather than an EDE-like injection. In this limit, thermal
friction can ease both the Hubble and the LSS tensions, but not resolve them.
We find the source of this preference to be the incompatibility of the CMB data
with the linear density perturbations of the dark radiation when injected at
redshifts close to matter-radiation equality.


title: Diverse Imagenet Models Transfer Better

abstract:
  A commonly accepted hypothesis is that models with higher accuracy on
Imagenet perform better on other downstream tasks, leading to much research
dedicated to optimizing Imagenet accuracy. Recently this hypothesis has been
challenged by evidence showing that self-supervised models transfer better than
their supervised counterparts, despite their inferior Imagenet accuracy. This
calls for identifying the additional factors, on top of Imagenet accuracy, that
make models transferable. In this work we show that high diversity of the
features learnt by the model promotes transferability jointly with Imagenet
accuracy. Encouraged by the recent transferability results of self-supervised
models, we propose a method that combines self-supervised and supervised
pretraining to generate models with both high diversity and high accuracy, and
as a result high transferability. We demonstrate our results on several
architectures and multiple downstream tasks, including both single-label and
multi-label classification.


title: Deep Learning nearby galaxy peculiar velocities

abstract:
  We explore how information in images of nearby galaxies can be used to
estimate their distance. We train a convolutional Neural Network (NN) to do
this, using galaxy images from the Illustris simulation. We show that if the NN
is trained on data with random errors added to the true distance (representing
training using spectroscopic redshift instead of actual distance), then the NN
can predict distances in a test dataset with greater accuracy than it was given
in the training set. This is not unusual, as often NNs are trained on data with
added noise, in order to increase robustness. In this case, however, it offers
a route to estimating peculiar velocities of nearby galaxies. Given a galaxy
with a known spectroscopic redshift one can use the NN-predicted distance to
make an estimate of the peculiar velocity. Trying this using relatively low
resolution (1.4 arcsec per pixel) simulated galaxy images we find fractional
RMS distance errors of 7.7% for galaxies at a mean distance of 75 Mpc from the
observer, leading to RMS peculiar velocity errors of 440 km/s. In a companion
paper we apply the technique to 145,115 nearby galaxies from the NASA Sloan
Atlas.


title: The White-Box Adversarial Data Stream Model

abstract:
  We study streaming algorithms in the white-box adversarial model, where the
stream is chosen adaptively by an adversary who observes the entire internal
state of the algorithm at each time step. We show that nontrivial algorithms
are still possible. We first give a randomized algorithm for the $L_1$-heavy
hitters problem that outperforms the optimal deterministic Misra-Gries
algorithm on long streams. If the white-box adversary is computationally
bounded, we use cryptographic techniques to reduce the memory of our
$L_1$-heavy hitters algorithm even further and to design a number of additional
algorithms for graph, string, and linear algebra problems. The existence of
such algorithms is surprising, as the streaming algorithm does not even have a
secret key in this model, i.e., its state is entirely known to the adversary.
One algorithm we design is for estimating the number of distinct elements in a
stream with insertions and deletions achieving a multiplicative approximation
and sublinear space; such an algorithm is impossible for deterministic
algorithms.
  We also give a general technique that translates any two-player deterministic
communication lower bound to a lower bound for {\it randomized} algorithms
robust to a white-box adversary. In particular, our results show that for all
$p\ge 0$, there exists a constant $C_p>1$ such that any $C_p$-approximation
algorithm for $F_p$ moment estimation in insertion-only streams with a
white-box adversary requires $\Omega(n)$ space for a universe of size $n$.
Similarly, there is a constant $C>1$ such that any $C$-approximation algorithm
in an insertion-only stream for matrix rank requires $\Omega(n)$ space with a
white-box adversary. Our algorithmic results based on cryptography thus show a
separation between computationally bounded and unbounded adversaries.
  (Abstract shortened to meet arXiv limits.)


title:
Sarkisov links from toric weighted blowups of $\mathbb{P}^3$ and
  $\mathbb{P}^4$ at a point

abstract:
  We study Sarkisov links initiated by the toric weighted blowup of a point in
$\mathbb{P}^3$ or $\mathbb{P}^4$ using variation of GIT. We completely classify
which of these initiate Sarkisov links and describe the links explicitly.
Moreover, if $X$ is the toric weighted blowup of $\mathbb{P}^d$ at a point, we
give a simple criterion in terms of the weights of the blowup that
characterises when $X$ is weak Fano.


title: RangeUDF: Semantic Surface Reconstruction from 3D Point Clouds

abstract:
  We present RangeUDF, a new implicit representation based framework to recover
the geometry and semantics of continuous 3D scene surfaces from point clouds.
Unlike occupancy fields or signed distance fields which can only model closed
3D surfaces, our approach is not restricted to any type of topology. Being
different from the existing unsigned distance fields, our framework does not
suffer from any surface ambiguity. In addition, our RangeUDF can jointly
estimate precise semantics for continuous surfaces. The key to our approach is
a range-aware unsigned distance function together with a surface-oriented
semantic segmentation module. Extensive experiments show that RangeUDF clearly
surpasses state-of-the-art approaches for surface reconstruction on four point
cloud datasets. Moreover, RangeUDF demonstrates superior generalization
capability across multiple unseen datasets, which is nearly impossible for all
existing approaches.


title:
OSSOS XXV: Large Populations and Scattering-Sticking in the Distant
  Transneptunian Resonances

abstract:
  There have been 77 TNOs discovered to be librating in the distant
transneptunian resonances (beyond the 2:1 resonance, at semimajor axes greater
than 47.7~AU) in four well-characterized surveys: the Outer Solar System
Origins Survey (OSSOS) and three similar prior surveys. Here we use the OSSOS
Survey Simulator to measure their intrinsic orbital distributions using an
empirical parameterized model. Because many of the resonances had only one or
very few detections, $j$:$k$ resonant objects were grouped by $k$ in order to
have a better basis for comparison between models and reality. We also use the
Survey Simulator to constrain their absolute populations, finding that they are
much larger than predicted by any published Neptune migration model to date; we
also find population ratios that are inconsistent with published models,
presenting a challenge for future Kuiper Belt emplacement models. The estimated
population ratios between these resonances are largely consistent with
scattering-sticking predictions, though further discoveries of resonant TNOs
with high-precision orbits will be needed to determine whether
scattering-sticking can explain the entire distant resonant population or not.


title: A Survey on Multi-hop Question Answering and Generation

abstract:
  The problem of Question Answering (QA) has attracted significant research
interest for long. Its relevance to language understanding and knowledge
retrieval tasks, along with the simple setting makes the task of QA crucial for
strong AI systems. Recent success on simple QA tasks has shifted the focus to
more complex settings. Among these, Multi-Hop QA (MHQA) is one of the most
researched tasks over the recent years. The ability to answer multi-hop
questions and perform multi step reasoning can significantly improve the
utility of NLP systems. Consequently, the field has seen a sudden surge with
high quality datasets, models and evaluation strategies. The notion of
`multiple hops' is somewhat abstract which results in a large variety of tasks
that require multi-hop reasoning. This implies that different datasets and
models differ significantly which makes the field challenging to generalize and
survey. This work aims to provide a general and formal definition of MHQA task,
and organize and summarize existing MHQA frameworks. We also outline the best
methods to create MHQA datasets. The paper provides a systematic and thorough
introduction as well as the structuring of the existing attempts to this highly
interesting, yet quite challenging task.


title: Automorphic Gluing

abstract:
  We prove a gluing theorem on the automorphic side of the geometric Langlands
correspondence: roughly speaking, we show that the difference between
$\mathrm{DMod}(\mathrm{Bun}_G)$ and its full subcategory
$\mathrm{DMod}(\mathrm{Bun}_G)^\mathrm{temp}$ of tempered objects is
compensated by the categories $\mathrm{DMod}(\mathrm{Bun}_M)^\mathrm{temp}$ for
all standard Levi subgroups $M \subsetneq G$. This theorem is designed to match
exactly with the spectral gluing theorem, an analogous result occurring on the
other side of the geometric Langlands conjecture. Along the way, we state and
prove several facts that might be of independent interest. For instance, for
any parabolic $P \subseteq G$, we show that the functors
$\mathrm{CT}_{P,*}:\mathrm{DMod}(\mathrm{Bun}_G) \to
\mathrm{DMod}(\mathrm{Bun}_M)$ and $\mathrm{Eis}_{P,*}:
\mathrm{DMod}(\mathrm{Bun}_M) \to \mathrm{DMod}(\mathrm{Bun}_G)$ preserve
tempered objects, whereas the standard Eisenstein functor $\mathrm{Eis}_{P,!}$
preserves anti-tempered objects.


title: Bi-Colored Expansions of Geometric Theories

abstract:
  This paper concerns the study of Bi-colored expansions of geometric theories
in the light of the Fra\"{i}ss\'{e}-Hrushovski construction method.
Substructures of models of a geometric theory $T$ are expanded by a color
predicate $p$, and the dimension function associated with the pre-geometry of
the $T$-algebraic closure operator together with a real number
$0<\alpha\leqslant 1$ is used to define a pre-dimension function
$\delta_{\alpha}$. The pair $(\mathcal{K}_{\alpha}^{+},\leqslant_{\alpha})$
consisting of all such expansions with a hereditary positive pre-dimension
along with the notion of substructure $\leqslant_{\alpha}$ associated to
$\delta_{\alpha}$ is then used as a natural setting for the study of generic
bi-colored expansions in the style of Fra\"{i}ss\'{e}-Hrushovski construction.
Imposing certain natural conditions on $T$, enables us to introduce a complete
axiomatization $\mathbb{T}_{\alpha}$ for the class of rich structures in this
class. We will show that if $T$ is a dependent theory (NIP) then so is
$\mathbb{T}_{\alpha}$. We further prove that whenever $\alpha$ is rational the
strong dependence transfers to $\mathbb{T}_{\alpha}$. We conclude by showing
that if $T$ defines a linear order and $\alpha$ is irrational then
$\mathbb{T}_{\alpha}$ is not strongly dependent.


title:
New evidence for a cosmological distribution of stellar mass primordial
  black holes

abstract:
  In this paper we show that to explain the observed distribution of amplitudes
in a large sample of quasar lightcurves, a significant contribution from
microlensing is required. This implies the existence of a cosmologically
distributed population of stellar mass compact bodies making up a large
fraction of the dark matter. Our analysis is based on the lightcurves of a
sample of over 1000 quasars, photometrically monitored over a period of 26
years. The intrinsic variations in quasar luminosity are derived from luminous
quasars where the quasar accretion disc is too large to be microlensed by
stellar mass bodies, and then synthetic lightcurves for the whole sample are
constructed with the same statistical properties. We then run microlensing
simulations for each quasar with convergence in compact bodies appropriate to
the quasar redshift assuming a $\Lambda$CDM cosmology. The synthetic lightcurve
is then superimposed on the amplification pattern to incorporate the effects of
microlensing. The distribution of the resulting amplitudes can then be compared
with observation, giving a very close match. This procedure does not involve
optimising parameters or fitting to the data, as all inputs such as lens mass
and quasar disc size come from independent observations in the literature. The
overall conclusion of the paper is that to account for the distribution of
quasar lightcurve amplitudes it is necessary to include the microlensing
effects of a cosmologically distributed population of stellar mass compact
bodies, most plausibly identified as stellar mass primordial black holes.


title:
The science case and challenges of space-borne sub-millimeter
  interferometry

abstract:
  Ultra-high angular resolution in astronomy has always been an important
vehicle for making fundamental discoveries. Recent results in direct imaging of
the vicinity of the supermassive black hole in the nucleus of the radio galaxy
M87 by the millimeter VLBI system Event Horizon Telescope and various
pioneering results of the Space VLBI mission RadioAstron provided new momentum
in high angular resolution astrophysics. In both mentioned cases, the angular
resolution reached the values of about 10-20 microrcseconds. Further
developments toward at least an order of magnitude "sharper" values are
dictated by the needs of astrophysical studies and can only be achieved by
placing millimeter and submillimeter wavelength interferometric systems in
space. A concept of such the system, called Terahertz Exploration and
Zooming-in for Astrophysics (THEZA), has been proposed in the framework of the
ESA Call for White Papers for the Voayage 2050 long term plan in 2019. In the
current paper we discuss several approaches for addressing technological
challenges of the THEZA concept. In particular, we consider a novel
configuration of a space-borne millimeter/sub-millimeter antenna which might
resolve several bottlenecks in creating large precise mechanical structures.
The paper also presents an overview of prospective space-qualified technologies
of low-noise analogue front-end instrumentation for millimeter/sub-millimeter
telescopes, data handling and processing. The paper briefly discusses
approaches to the interferometric baseline state vector determination and
synchronisation and heterodyning system. In combination with the original ESA
Voyage 2050 White Paper, the current work sharpens the case for the next
generation microarcsceond-level imaging instruments and provides starting
points for further in-depth technology trade-off studies.


title: ALBETO and DistilBETO: Lightweight Spanish Language Models

abstract:
  In recent years there have been considerable advances in pre-trained language
models, where non-English language versions have also been made available. Due
to their increasing use, many lightweight versions of these models (with
reduced parameters) have also been released to speed up training and inference
times. However, versions of these lighter models (e.g., ALBERT, DistilBERT) for
languages other than English are still scarce. In this paper we present ALBETO
and DistilBETO, which are versions of ALBERT and DistilBERT pre-trained
exclusively on Spanish corpora. We train several versions of ALBETO ranging
from 5M to 223M parameters and one of DistilBETO with 67M parameters. We
evaluate our models in the GLUES benchmark that includes various natural
language understanding tasks in Spanish. The results show that our lightweight
models achieve competitive results to those of BETO (Spanish-BERT) despite
having fewer parameters. More specifically, our larger ALBETO model outperforms
all other models on the MLDoc, PAWS-X, XNLI, MLQA, SQAC and XQuAD datasets.
However, BETO remains unbeaten for POS and NER. As a further contribution, all
models are publicly available to the community for future research.


title:
Complex symmetry of linear fractional composition operators on a
  half-plane

abstract:
  We investigate the bounded composition operators induced by linear fractional
self-maps of the right half-plane $\mathbb{C}_+$ on the Hardy space
$H^2(\mathbb{C}_+).$ We completely characterize which of these operators are
cohyponormal and we find conjugations for the linear fractional composition
operators that are complex symmetric.


title:
Magnetic field effects in biology from the perspective of the radical
  pair mechanism

abstract:
  A large and growing body of research shows that weak magnetic fields can
significantly influence various biological systems, including plants, animals,
and humans. However, the underlying mechanisms behind these phenomena remain
elusive. It is remarkable that the magnetic energies implicated in these
effects are much smaller than thermal energies. Here we review these
observations, of which there are now hundreds, and we suggest that a viable
explanation is provided by the radical pair mechanism, which involves the
quantum dynamics of the electron and nuclear spins of naturally occurring
transient radical molecules. While the radical pair mechanism has been studied
in detail in the context of avian magnetoreception, the studies reviewed here
show that magnetosensitivity is widespread throughout biology. We review
magnetic field effects on various physiological functions, organizing them
based on the type of the applied magnetic fields, namely static, hypomagnetic,
and oscillating magnetic fields, as well as isotope effects. We then review the
radical pair mechanism as a potential unifying model for the described magnetic
field effects, and we discuss plausible candidate molecules that might
constitute the radical pairs. We review recent studies proposing that the
quantum nature of the radical pairs provides promising explanations for xenon
anesthesia, lithium effects on hyperactivity, magnetic field and lithium
effects on the circadian clock, and hypomagnetic field effects on neurogenesis
and microtubule assembly. We conclude by discussing future lines of
investigation in this exciting new area of quantum biology related to weak
magnetic field effects.

categories: physics.bio-ph physics.chem-ph q-bio.NC quant-ph


title:
What Makes Instruction Learning Hard? An Investigation and a New
  Challenge in a Synthetic Environment

abstract:
  The instruction learning paradigm -- where a model learns to perform new
tasks from task descriptions alone -- has become popular in general-purpose
model research. The capabilities of large transformer models as instruction
learners, however, remain poorly understood. We use a controlled synthetic
environment to characterize such capabilities. Specifically, we use the task of
deciding whether a given string matches a regular expression (viewed as an
instruction) to identify properties of tasks, instructions, and instances that
make instruction learning challenging. For instance, we find that our model, a
fine-tuned T5-based text2text transformer, struggles with large regular
languages, suggesting that less precise instructions are challenging for
models. Additionally, instruction executions that require tracking longer
contexts of prior steps are also more difficult. We use our findings to
systematically construct a challenging instruction learning dataset, which we
call Hard RegSet. Fine-tuning on Hard RegSet, our large transformer learns to
correctly interpret only 65.6% of test instructions (with at least 90%
accuracy), and 11%-24% of the instructions in out-of-distribution
generalization settings. We propose Hard RegSet as a challenging instruction
learning task, and a controlled environment for studying instruction learning.


title: DialoKG: Knowledge-Structure Aware Task-Oriented Dialogue Generation

abstract:
  Task-oriented dialogue generation is challenging since the underlying
knowledge is often dynamic and effectively incorporating knowledge into the
learning process is hard. It is particularly challenging to generate both
human-like and informative responses in this setting. Recent research primarily
focused on various knowledge distillation methods where the underlying
relationship between the facts in a knowledge base is not effectively captured.
In this paper, we go one step further and demonstrate how the structural
information of a knowledge graph can improve the system's inference
capabilities. Specifically, we propose DialoKG, a novel task-oriented dialogue
system that effectively incorporates knowledge into a language model. Our
proposed system views relational knowledge as a knowledge graph and introduces
(1) a structure-aware knowledge embedding technique, and (2) a knowledge
graph-weighted attention masking strategy to facilitate the system selecting
relevant information during the dialogue generation. An empirical evaluation
demonstrates the effectiveness of DialoKG over state-of-the-art methods on
several standard benchmark datasets.


title: Entanglement and conserved quantities

abstract:
  The Einstein-Podolsky-Rosen paradox is resolved by reconsidering what
entangled state is actually prepared, what physical quantities are conserved
and the character of the correlation measurements employed.


title:
Multi-Camera Multiple 3D Object Tracking on the Move for Autonomous
  Vehicles

abstract:
  The development of autonomous vehicles provides an opportunity to have a
complete set of camera sensors capturing the environment around the car. Thus,
it is important for object detection and tracking to address new challenges,
such as achieving consistent results across views of cameras. To address these
challenges, this work presents a new Global Association Graph Model with Link
Prediction approach to predict existing tracklets location and link detections
with tracklets via cross-attention motion modeling and appearance
re-identification. This approach aims at solving issues caused by inconsistent
3D object detection. Moreover, our model exploits to improve the detection
accuracy of a standard 3D object detector in the nuScenes detection challenge.
The experimental results on the nuScenes dataset demonstrate the benefits of
the proposed method to produce SOTA performance on the existing vision-based
tracking dataset.


title: Feigin-Odesskii brackets, syzygies, and Cremona transformations

abstract:
  We identify Feigin-Odesskii brackets $q_{n,1}(C)$, associated with a normal
elliptic curve of degree $n$, $C\subset {\mathbb P}^{n-1}$, with the
skew-symmetric $n\times n$ matrix of quadratic forms introduced by Fisher in
arXiv:1510.04327 in connection with some minimal free resolutions related to
the secant varieties of $C$. On the other hand, we show that for odd $n$, the
generators of the ideal of the secant variety of $C$ of codimension $3$ give a
Cremona transformation of ${\mathbb P}^{n-1}$, generalizing the quadro-cubic
Cremona transformation of ${\mathbb P}^4$. We identify this transformation with
the one considered in arXiv:alg-geom/9712022 and find explict formulas for the
inverse transformation. We also find polynomial formulas for Cremona
transformations from arXiv:alg-geom/9712022 associated with higher rank bundles
on $C$.


title:
Characterization and Optimization of Integrated Silicon-Photonic Neural
  Networks under Fabrication-Process Variations

abstract:
  Silicon-photonic neural networks (SPNNs) have emerged as promising successors
to electronic artificial intelligence (AI) accelerators by offering orders of
magnitude lower latency and higher energy efficiency. Nevertheless, the
underlying silicon photonic devices in SPNNs are sensitive to inevitable
fabrication-process variations (FPVs) stemming from optical lithography
imperfections. Consequently, the inferencing accuracy in an SPNN can be highly
impacted by FPVs -- e.g., can drop to below 10% -- the impact of which is yet
to be fully studied. In this paper, we, for the first time, model and explore
the impact of FPVs in the waveguide width and silicon-on-insulator (SOI)
thickness in coherent SPNNs that use Mach-Zehnder Interferometers (MZIs).
Leveraging such models, we propose a novel variation-aware, design-time
optimization solution to improve MZI tolerance to different FPVs in SPNNs.
Simulation results for two example SPNNs of different scales under realistic
and correlated FPVs indicate that the optimized MZIs can improve the
inferencing accuracy by up to 93.95% for the MNIST handwritten digit dataset --
considered as an example in this paper -- which corresponds to a <0.5% accuracy
loss compared to the variation-free case. The proposed one-time optimization
method imposes low area overhead, and hence is applicable even to
resource-constrained designs


title:
Bandwidth bounds for wide-field-of-view dispersion-engineered achromatic
  metalenses

abstract:
  Optical systems with wide field-of-views (FOV) are crucial for many
applications such as high performance imaging, optical projection,
augmented/virtual reality, and miniaturized medical imaging tools. Typically,
aberration-free imaging with a wide FOV is achieved by stacking multiple
refractive lenses (as in a "fisheye" lens), adding to the size and weight of
the optical system. Single metalenses designed to have a wide FOV have the
potential to replace these bulky imaging systems and, moreover, they may be
dispersion engineered for spectrally broadband operation. In this paper, we
derive a fundamental bound on the spectral bandwidth of dispersion-engineered
wide-FOV achromatic metalenses. We show that for metalenses with a relatively
large numerical aperture (NA), there is a tradeoff between the maximum
achievable bandwidth and the FOV; interestingly, however, the bandwidth
reduction saturates beyond a certain FOV that depends on the NA of the
metalens. These findings may provide important information and insights for the
design of future wide-FOV achromatic flat lenses.


title: Approximating Persistent Homology for Large Datasets

abstract:
  Persistent homology is an important methodology from topological data
analysis which adapts theory from algebraic topology to data settings and has
been successfully implemented in many applications. It produces a statistical
summary in the form of a persistence diagram, which captures the shape and size
of the data. Despite its widespread use, persistent homology is simply
impossible to implement when a dataset is very large. In this paper we address
the problem of finding a representative persistence diagram for prohibitively
large datasets. We adapt the classical statistical method of bootstrapping,
namely, drawing and studying smaller multiple subsamples from the large
dataset. We show that the mean of the persistence diagrams of subsamples --
taken as a mean persistence measure computed from the subsamples -- is a valid
approximation of the true persistent homology of the larger dataset. We give
the rate of convergence of the mean persistence diagram to the true persistence
diagram in terms of the number of subsamples and size of each subsample. Given
the complex algebraic and geometric nature of persistent homology, we adapt the
convexity and stability properties in the space of persistence diagrams
together with random set theory to achieve our theoretical results for the
general setting of point cloud data. We demonstrate our approach on simulated
and real data, including an application of shape clustering on complex
large-scale point cloud data.


title:
Blow-up and lifespan estimate for wave equations with critical damping
  term of space-dependent type related to Glassey conjecture

abstract:
  The main purpose of the present paper is to study the blow-up problem of the
wave equation with space-dependent damping in the \textit{scale-invariant case}
and time derivative nonlinearity with small initial data. Under appropriate
initial data which are compactly supported, by using a test function method and
taking into account the effect of the damping term
($\frac{\mu}{\sqrt{1+|x|^2}}u_t$), we provide that in higher dimensions the
blow-up region is given by $p \in (1, p_G(N+\mu)]$ where $p_G(N)$ is the
Glassey exponent. Furthermore, we shall establish a blow-up region, independent
of $\mu$ given by $p\in (1, 1+\frac{2}{N}),$ for appropriate initial data in
the energy space with noncompact support.


title: Multifidelity Deep Operator Networks

abstract:
  Operator learning for complex nonlinear operators is increasingly common in
modeling physical systems. However, training machine learning methods to learn
such operators requires a large amount of expensive, high-fidelity data. In
this work, we present a composite Deep Operator Network (DeepONet) for learning
using two datasets with different levels of fidelity, to accurately learn
complex operators when sufficient high-fidelity data is not available.
Additionally, we demonstrate that the presence of low-fidelity data can improve
the predictions of physics-informed learning with DeepONets.


title:
Ternary Wide Band Gap Oxides for High-Power Electronics Identified
  Computationally

abstract:
  As electricity grids become more renewable energy-compliant, there will be a
need for novel semiconductors that can withstand high power, high voltage, and
high temperatures. Wide band gap (WBG) semiconductors tend to exhibit large
breakdown field, allowing high operating voltages. Currently explored WBG
materials for power electronics are costly (GaN), difficult to synthesize as
high-quality single crystals (SiC) and at scale (diamond, BN), have low thermal
conductivity ($\beta$-Ga$_2$O$_3$), or cannot be suitably doped (AlN). We
conduct a computational search for novel semiconductors across 1,340 known
metal-oxides using first-principles calculations and existing transport models.
We calculate the Baliga figure of merit (BFOM) and lattice thermal conductivity
($\kappa_L$) to identify top candidates for n-type power electronics. We find
40 mostly ternary oxides that have higher $\kappa_L$ than $\beta$-Ga$_2$O$_3$
and higher n-type BFOM than SiC and GaN. Among these, several material classes
emerge, including 2-2-7 stoichiometry thortveitites and pyrochlores, II-IV
spinels, and calcite-type borates. Within these classes, we propose
In$_2$Ge$_2$O$_7$, Mg$_2$GeO$_4$, and InBO$_3$ as they are the most favorable
for n-type doping based on our preliminary evaluation and could be grown as
single crystals or thin film heterostructures. These materials could help
advance power electronic devices for the future grid.


title:
Resonant and non-resonant integrated third order parametric
  down-conversion

abstract:
  Third order parametric down-conversion describes a class of nonlinear
processes in which a pump photon can be down-converted into triplets of
photons. It has been identified as a source of non-classical light, with
capabilities beyond those offered by better-established processes such as
spontaneous four-wave mixing. Here we discuss the implementation of TOPDC in
integrated photonic systems. We derive equations for the rates of TOPDC in a
non-resonant (waveguide) and resonant (microring) platform, such that the
scaling with experimental parameters can be plainly seen. We find that
generally non-resonant platforms should be pursued for spontaneous TOPDC
(SpTOPDC), whereas resonant platforms are more suitable for stimulated TOPDC
(StTOPDC). We present a sample calculation for TOPDC rates in sample systems
with conservative and accessible parameters. We find that StTOPDC should be
observable with the current fabrication technology, and that with some progress
in the design of TOPDC platforms, integrated SpTOPDC too could be demonstrated
in the near term.


title:
Statistical moments and integrability properties of monatomic gas
  mixtures with long range interactions

abstract:
  This document presents a priori estimates related to statistical moments and
integrability properties for solutions of systems of monatomic gas mixtures
modelled with the homogeneous Boltzmann equation with long range interactions
for hard potentials. We detail the conditions for the generation and
propagation of polynomial and exponential moments, and the integrability in
Lebesgue spaces.


title:
XUV ionization of the H$_2$ molecule studied with attosecond angular
  streaking

abstract:
  We study orientation and two-center interference effects in attosecond
time-resolved photoionization of the H$_2$ molecule. Time resolution of XUV
ionization of H$_2$ is gained through the phase retrieval capability of
attosecond angular streaking demonstrated earlier by Kheifets {\em et al}
[arXiv:2202.06147 (2022)]. Once applied to H$_2$, this technique delivers an
anisotropic phase and time delay which both depend sensitively on the molecular
axis orientation. In addition, the photoelectron momentum distribution displays
a very clear two-center interference pattern. When the interference formula due
to Walter and Briggs [J. Phys. B {\bf 32} 2487 (1999)] is applied, an effective
photoelectron momentum appears to be greater than the asymptotic momentum at
the detector. This effect is explained by a molecular potential well
surrounding the photoemission center.


title: Computational Adaptation of XR Interfaces Through Interaction Simulation

abstract:
  Adaptive and intelligent user interfaces have been proposed as a critical
component of a successful extended reality (XR) system. In particular, a
predictive system can make inferences about a user and provide them with
task-relevant recommendations or adaptations. However, we believe such adaptive
interfaces should carefully consider the overall \emph{cost} of interactions to
better address uncertainty of predictions. In this position paper, we discuss a
computational approach to adapt XR interfaces, with the goal of improving user
experience and performance. Our novel model, applied to menu selection tasks,
simulates user interactions by considering both cognitive and motor costs. In
contrast to greedy algorithms that adapt based on predictions alone, our model
holistically accounts for costs and benefits of adaptations towards adapting
the interface and providing optimal recommendations to the user.


title:
Discovery of a 500 au Protobinary in the Massive Prestellar Core
  G11.92-0.61 MM2

abstract:
  We present high-resolution (< ~160 au) Atacama Large Millimeter/submillimeter
Array (ALMA) 1.3 mm observations of the high-mass prestellar core candidate
G11.92-0.61 MM2, which reveal that this source is in fact a protobinary system
with a projected separation of 505 au. The binary components, MM2E and MM2W,
are compact (radii < 140 au) sources within the partially optically thick dust
emission with alpha$_{0.9 cm-1.3 mm}$=2.47-2.94. The 1.3 mm brightness
temperatures, T$_b$=68.4/64.6 K for MM2E/MM2W, imply internal heating and
minimum luminosities L$_*$ > 24.7 Lsun for MM2E and L$_*$ > 12.6 Lsun for MM2W.
The compact sources are connected by a "bridge" of lower-surface-brightness
dust emission and lie within more extended emission that may correspond to a
circumbinary disk. The circumprotostellar gas mass, estimated from ~0.2"
resolution VLA 0.9 cm observations assuming optically thin emission, is
6.8+/-0.9 Msun. No line emission is detected towards MM2E and MM2W in our
high-resolution 1.3 mm ALMA observations. The only line detected is 13CO J=2-1,
in absorption against the 1.3 mm continuum, which likely traces a layer of
cooler molecular material surrounding the protostars. We also report the
discovery of a highly asymmetric bipolar molecular outflow that appears to be
driven by MM2E and/or MM2W in new deep, ~0.5" resolution (1680 au) ALMA 0.82 mm
observations. This outflow, traced by low-excitation CH3OH emission, indicates
ongoing accretion onto the protobinary system. Overall, the super-Alfvenic
models of Mignon-Risse et al. (2021) agree well with the observed properties of
the MM2E/MM2W protobinary, suggesting that this system may be forming in an
environment with a weak magnetic field.


title:
Dynamically controlled double-well optical potential for colloidal
  particles

abstract:
  This preliminary study presents a simple modulation scheme to dynamically
create time-averaged optical potentials to trap colloidal particles using
acousto-optical modulation. The method provides access to control
experimentally relevant parameters of a tunable double-well potential. We show
experimental data successfully adjusting the trapping distance in the range
20-2000 nm and discuss situations arising when reconstructing time-averaged
optical potentials from trapped particle data. This is the first step towards
studying colloidal particles in dynamically modulated optical potentials to
explore the stochastic thermodynamics of mesoscopic systems and small-scale
thermo-mechanical machines.


title:
Test suite effectiveness metric evaluation: what do we know and what
  should we do?

abstract:
  Comparing test suite effectiveness metrics has always been a research
hotspot. However, prior studies have different conclusions or even contradict
each other for comparing different test suite effectiveness metrics. The
problem we found most troubling to our community is that researchers tend to
oversimplify the description of the ground truth they use. For example, a
common expression is that "we studied the correlation between real faults and
the metric to evaluate (MTE)". However, the meaning of "real faults" is not
clear-cut. As a result, there is a need to scrutinize the meaning of "real
faults". Without this, it will be half-knowledgeable with the conclusions. To
tackle this challenge, we propose a framework ASSENT (evAluating teSt Suite
EffectiveNess meTrics) to guide the follow-up research. In nature, ASSENT
consists of three fundamental components: ground truth, benchmark test suites,
and agreement indicator. First, materialize the ground truth for determining
the real order in effectiveness among test suites. Second, generate a set of
benchmark test suites and derive their ground truth order in effectiveness.
Third, for the benchmark test suites, generate the MTE order in effectiveness
by the metric to evaluate (MTE). Finally, calculate the agreement indicator
between the two orders. Under ASSENT, we are able to compare the accuracy of
different test suite effectiveness metrics. We apply ASSENT to evaluate
representative test suite effectiveness metrics, including mutation score
metrics and code coverage metrics. Our results show that, based on the real
faults, mutation score and subsuming mutation score are the best metrics to
quantify test suite effectiveness. Meanwhile, by using mutants instead of real
faults, MTEs will be overestimated by more than 20% in values.


title:
On the Performance Evaluation of Action Recognition Models on Transcoded
  Low Quality Videos

abstract:
  In the design of action recognition models, the quality of videos in the
dataset is an important issue, however the trade-off between the quality and
performance is often ignored. In general, action recognition models are trained
and tested on high-quality videos, but in actual situations where action
recognition models are deployed, sometimes it might not be assumed that the
input videos are of high quality. In this study, we report qualitative
evaluations of action recognition models for the quality degradation associated
with transcoding by JPEG and H.264/AVC. Experimental results are shown for
evaluating the performance of pre-trained models on the transcoded validation
videos of Kinetics400. The models are also trained on the transcoded training
videos. From these results, we quantitatively show the degree of degradation of
the model performance with respect to the degradation of the video quality.


title: Private measures, random walks, and synthetic data

abstract:
  Differential privacy is a mathematical concept that provides an
information-theoretic security guarantee. While differential privacy has
emerged as a de facto standard for guaranteeing privacy in data sharing, the
known mechanisms to achieve it come with some serious limitations. Utility
guarantees are usually provided only for a fixed, a priori specified set of
queries. Moreover, there are no utility guarantees for more complex - but very
common - machine learning tasks such as clustering or classification. In this
paper we overcome some of these limitations. Working with metric privacy, a
powerful generalization of differential privacy, we develop a polynomial-time
algorithm that creates a private measure from a data set. This private measure
allows us to efficiently construct private synthetic data that are accurate for
a wide range of statistical analysis tools. Moreover, we prove an
asymptotically sharp min-max result for private measures and synthetic data for
general compact metric spaces. A key ingredient in our construction is a new
superregular random walk, whose joint distribution of steps is as regular as
that of independent random variables, yet which deviates from the origin
logarithmicaly slowly.


title: Analyzing Gender Representation in Multilingual Models

abstract:
  Multilingual language models were shown to allow for nontrivial transfer
across scripts and languages. In this work, we study the structure of the
internal representations that enable this transfer. We focus on the
representation of gender distinctions as a practical case study, and examine
the extent to which the gender concept is encoded in shared subspaces across
different languages. Our analysis shows that gender representations consist of
several prominent components that are shared across languages, alongside
language-specific components. The existence of language-independent and
language-specific components provides an explanation for an intriguing
empirical observation we make: while gender classification transfers well
across languages, interventions for gender removal, trained on a single
language, do not transfer easily to others.


title:
A Scalable Deep Learning Framework for Multi-rate CSI Feedback under
  Variable Antenna Ports

abstract:
  Channel state information (CSI) at transmitter is crucial for massive MIMO
downlink systems to achieve high spectrum and energy efficiency. Existing works
have provided deep learning architectures for CSI feedback and recovery at the
eNB/gNB by reducing user feedback overhead and improving recovery accuracy.
However, existing DL architectures tend to be inflexible and non-scalable as
models are often trained according to a preset number of antennas for a given
compression ratio. In this work, we develop a flexible and scalable learning
framework based on a divide-and-conquer approach (DCA). This new DCA
architecture can flexibly accommodate different numbers of 3GPP antenna ports
and dynamic levels of feedback compression. Importantly, it also significantly
reduces computational complexity and memory size by allowing UEs to feedback
segmented downlink CSI. We further propose a multi-rate successive convolution
encoder with fewer than 1000 parameters. Test results demonstrate superior
performance, good scalability, and low complexity for both indoor and outdoor
channels.


title:
The regulator dependence in the functional renormalization group: a
  quantitative explanation

abstract:
  The search of controlled approximations to study strongly coupled systems
remains a very general open problem. Wilson's renormalization group has shown
to be an ideal framework to implement approximations going beyond perturbation
theory. In particular, the most employed approximation scheme in this context,
the derivative expansion, was recently shown to converge and yield accurate and
very precise results. However, this convergence strongly depends on the shape
of the employed regulator. In this letter we clarify the reason for this
dependence and justify, simultaneously, the most largely employed procedure to
fix this dependence, the principle of minimal sensitivity.


title: Learned Monocular Depth Priors in Visual-Inertial Initialization

abstract:
  Visual-inertial odometry (VIO) is the pose estimation backbone for most AR/VR
and autonomous robotic systems today, in both academia and industry. However,
these systems are highly sensitive to the initialization of key parameters such
as sensor biases, gravity direction, and metric scale. In practical scenarios
where high-parallax or variable acceleration assumptions are rarely met (e.g.
hovering aerial robot, smartphone AR user not gesticulating with phone),
classical visual-inertial initialization formulations often become
ill-conditioned and/or fail to meaningfully converge. In this paper we target
visual-inertial initialization specifically for these low-excitation scenarios
critical to in-the-wild usage. We propose to circumvent the limitations of
classical visual-inertial structure-from-motion (SfM) initialization by
incorporating a new learning-based measurement as a higher-level input. We
leverage learned monocular depth images (mono-depth) to constrain the relative
depth of features, and upgrade the mono-depth to metric scale by jointly
optimizing for its scale and shift. Our experiments show a significant
improvement in problem conditioning compared to a classical formulation for
visual-inertial initialization, and demonstrate significant accuracy and
robustness improvements relative to the state-of-the-art on public benchmarks,
particularly under motion-restricted scenarios. We further extend this
improvement to implementation within an existing odometry system to illustrate
the impact of our improved initialization method on resulting tracking
trajectories.


title: Node Deployment in Heterogeneous Rayleigh Fading Sensor Networks

abstract:
  We study a heterogeneous Rayleigh fading wireless sensor network (WSN) in
which densely deployed sensor nodes monitor an environment and transmit their
sensed information to base stations (BSs) using access points (APs) as relays
to facilitate the data transfer. We consider both large-scale and small-scale
propagation effects in our system model and formulate the node deployment
problem as an optimization problem aimed at minimizing the wireless
communication network's power consumption. By imposing a desired outage
probability constraint on all communication channels, we derive the necessary
conditions for the optimal deployment that not only minimize the power
consumption, but also guarantee all wireless links to have an outage
probability below the given threshold. In addition, we study the necessary
conditions for an optimal deployment given ergodic capacity constraints. We
compare our node deployment algorithms with similar algorithms in the
literature and demonstrate their efficacy and superiority.


title:
3D hyperbolic Navier-Stokes equations in a thin strip: global
  well-posedness and hydrostatic limit in Gevrey space

abstract:
  We consider the hyperbolic version of three-dimensional anisotropic
Naver-Stokes equations in a thin strip and its hydrostatic limit that is a
hyperbolic Prandtl type equations. We prove the global-in-time existence and
uniqueness for the two systems and the hydrostatic limit when the initial data
belong to the Gevrey function space with index 2. The proof is based on a
direct energy method by observing the damping effect in the systems.


title: On automorphisms of undirected Bruhat graphs

abstract:
  The (directed) Bruhat graph $\hat{\Gamma}(u,v)$ has the elements of the
Bruhat interval $[u,v]$ as vertices, with directed edges given by
multiplication by a reflection. Famously, $\hat{\Gamma}(e,v)$ is regular if and
only if the Schubert variety $X_v$ is smooth, and this condition on $v$ is
characterized by pattern avoidance. In this work, we classify when the
undirected Bruhat graph $\Gamma(e,v)$ is vertex-transitive; surprisingly this
class of permutations is also characterized by pattern avoidance and sits
nicely between the classes of smooth permutations and self-dual permutations.
This leads us to a general investigation of automorphisms of $\Gamma(u,v)$ in
the course of which we show that special matchings, which originally appeared
in the theory Kazhdan--Lusztig polynomials, can be characterized as certain
$\Gamma(u,v)$-automorphisms which are conjecturally sufficient to generate the
orbit of $e$ under $Aut(\Gamma(e,v))$.


title:
The super resonance effect paves the way for a new type of refractive
  index sensor concept based on a mesoscale dielectric sphere

abstract:
  Recently, we showed that dielectric mesoscale spheres could support so-called
super-resonance effect, i.e. high-order Mie resonance modes with giant field
localization and enhancement. Due to the presence of the surrounding medium
leads to a significant influence in the intensity of the field in the particle,
based on Mie theory we show for the first time that this effect may be use to
design refractive index sensor of medium. Using the example of air as an
environment, we have shown that the sensitivity of the proposed sensor concept
can reach from 10-6 to 10-8, depending on the accuracy of the sphere size
parameter, which is no worse than the accuracy of modern interference methods.


title:
Controllable emergent spatial spin modulation in Sr2IrO4 by in situ
  shear strain

abstract:
  Symmetric anisotropic interaction can be ferromagnetic and antiferromagnetic
at the same time but for different crystallographic axes. We show that inducing
competition of anisotropic interactions of orthogonal irreducible
representations represents a general route to obtain new exotic magnetic
states. We demonstrate it here by observing the emergence of a continuously
tunable 12-layer spatial spin modulation when distorting the square lattice
planes in the quasi-2D antiferromagnetic Sr2IrO4 under in situ shear strain.
This translation-symmetry-breaking phase is a result of an unusual strain
activated anisotropic interaction which is at the 4th order and competing with
the inherent quadratic anisotropic interaction. Such a mechanism of competing
anisotropy is distinct from that among the ferromagnetic, antiferromagnetic,
and/or the Dzyaloshinskii-Moriya interactions, and it could be widely
applicable and highly controllable in low dimensional magnets.


title: Lie Algebraic Cost Function Design for Control on Lie Groups

abstract:
  This paper presents a control framework on Lie groups by designing the
control objective in its Lie algebra. Control on Lie groups is challenging due
to its nonlinear nature and difficulties in system parameterization. Existing
methods to design the control objective on a Lie group and then derive the
gradient for controller design are non-trivial and can result in slow
convergence in tracking control. We show that with a proper left-invariant
metric, setting the gradient of the cost function as the tracking error in the
Lie algebra leads to a quadratic Lyapunov function that enables globally
exponential convergence. In the PD control case, we show that our controller
can maintain an exponential convergence rate even when the initial error is
approaching $\pi$ in SO(3). We also show the merit of this proposed framework
in trajectory optimization. The proposed cost function enables the iterative
Linear Quadratic Regulator (iLQR) to converge much faster than the Differential
Dynamic Programming (DDP) with a well-adopted cost function when the initial
trajectory is poorly initialized on SO(3).


title:
Counting and enumerating optimum cut sets for hypergraph
  $k$-partitioning problems for fixed $k$

abstract:
  We consider the problem of enumerating optimal solutions for two hypergraph
$k$-partitioning problems -- namely, Hypergraph-$k$-Cut and
Minmax-Hypergraph-$k$-Partition. The input in hypergraph $k$-partitioning
problems is a hypergraph $G=(V, E)$ with positive hyperedge costs along with a
fixed positive integer $k$. The goal is to find a partition of $V$ into $k$
non-empty parts $(V_1, V_2, \ldots, V_k)$ -- known as a $k$-partition -- so as
to minimize an objective of interest.
  1. If the objective of interest is the maximum cut value of the parts, then
the problem is known as Minmax-Hypergraph-$k$-Partition. A subset of hyperedges
is a minmax-$k$-cut-set if it is the subset of hyperedges crossing an optimum
$k$-partition for Minmax-Hypergraph-$k$-Partition.
  2. If the objective of interest is the total cost of hyperedges crossing the
$k$-partition, then the problem is known as Hypergraph-$k$-Cut. A subset of
hyperedges is a min-$k$-cut-set if it is the subset of hyperedges crossing an
optimum $k$-partition for Hypergraph-$k$-Cut.
  We give the first polynomial bound on the number of minmax-$k$-cut-sets and a
polynomial-time algorithm to enumerate all of them in hypergraphs for every
fixed $k$. Our technique is strong enough to also enable an $n^{O(k)}p$-time
deterministic algorithm to enumerate all min-$k$-cut-sets in hypergraphs, thus
improving on the previously known $n^{O(k^2)}p$-time deterministic algorithm,
where $n$ is the number of vertices and $p$ is the size of the hypergraph. The
correctness analysis of our enumeration approach relies on a structural result
that is a strong and unifying generalization of known structural results for
Hypergraph-$k$-Cut and Minmax-Hypergraph-$k$-Partition. We believe that our
structural result is likely to be of independent interest in the theory of
hypergraphs (and graphs).