title: Non-Pauli Errors in the Three-Dimensional Surface Code

abstract:
  A powerful feature of stabiliser error correcting codes is the fact that
stabiliser measurement projects arbitrary errors to Pauli errors, greatly
simplifying the physical error correction process as well as classical
simulations of code performance. However, logical non-Clifford operations can
map Pauli errors to non-Pauli (Clifford) errors, and while subsequent
stabiliser measurements will project the Clifford errors back to Pauli errors
the resulting distributions will possess additional correlations that depend on
both the nature of the logical operation and the structure of the code.
Previous work has studied these effects when applying a transversal $T$ gate to
the three-dimensional colour code and shown the existence of a non-local
"linking charge" phenomenon between membranes of intersecting errors. In this
work we generalise these results to the case of a $CCZ$ gate in the
three-dimensional surface code and find that many aspects of the problem are
much more easily understood in this setting. In particular, the emergence of
linking charge is a local effect rather than a non-local one. We use the
relative simplicity of Clifford errors in this setting to simulate their effect
on the performance of a single-shot magic state preparation process (the first
such simulation to account for the full effect of these errors) and find that
while they lead to an increase in the logical error rate their effect on the
threshold is minimal.


title: Incentive Compatible Queues Without Money

abstract:
  For job scheduling systems, where jobs require some amount of processing and
then leave the system, it is natural for each user to provide an estimate of
their job's time requirement in order to aid the scheduler. However, if there
is no incentive mechanism for truthfulness, each user will be motivated to
provide estimates that give their job precedence in the schedule, so that the
job completes as early as possible.
  We examine how to make such scheduling systems incentive compatible, without
using monetary charges, under a natural queueing theory framework. In our
setup, each user has an estimate of their job's running time, but it is
possible for this estimate to be incorrect. We examine scheduling policies
where if a job exceeds its estimate, it is with some probability "punished" and
re-scheduled after other jobs, to disincentivize underestimates of job times.
However, because user estimates may be incorrect (without any malicious
intent), excessive punishment may incentivize users to overestimate their job
times, which leads to less efficient scheduling. We describe two natural
scheduling policies, BlindTrust and MeasuredTrust. We show that, for both of
these policies, given the parameters of the system, we can efficiently
determine the set of punishment probabilities that are incentive compatible, in
that users are incentivized to provide their actual estimate of the job time.
Moreover, we prove for MeasuredTrust that in the limit as estimates converge to
perfect accuracy, the range of punishment probabilities that are incentive
compatible converges to $[0,1]$. Our formalism establishes a framework for
studying further queue-based scheduling problems where job time estimates from
users are utilized, and the system needs to incentivize truthful reporting of
estimates.


title:
Borrowing from yourself: Faster future video segmentation with partial
  channel update

abstract:
  Semantic segmentation is a well-addressed topic in the computer vision
literature, but the design of fast and accurate video processing networks
remains challenging. In addition, to run on embedded hardware, computer vision
models often have to make compromises on accuracy to run at the required speed,
so that a latency/accuracy trade-off is usually at the heart of these real-time
systems' design. For the specific case of videos, models have the additional
possibility to make use of computations made for previous frames to mitigate
the accuracy loss while being real-time.
  In this work, we propose to tackle the task of fast future video segmentation
prediction through the use of convolutional layers with time-dependent channel
masking. This technique only updates a chosen subset of the feature maps at
each time-step, bringing simultaneously less computation and latency, and
allowing the network to leverage previously computed features. We apply this
technique to several fast architectures and experimentally confirm its benefits
for the future prediction subtask.


title:
Constrained Optimization with Dynamic Bound-scaling for Effective
  NLPBackdoor Defense

abstract:
  We develop a novel optimization method for NLPbackdoor inversion. We leverage
a dynamically reducing temperature coefficient in the softmax function to
provide changing loss landscapes to the optimizer such that the process
gradually focuses on the ground truth trigger, which is denoted as a one-hot
value in a convex hull. Our method also features a temperature rollback
mechanism to step away from local optimals, exploiting the observation that
local optimals can be easily deter-mined in NLP trigger inversion (while not in
general optimization). We evaluate the technique on over 1600 models (with
roughly half of them having injected backdoors) on 3 prevailing NLP tasks, with
4 different backdoor attacks and 7 architectures. Our results show that the
technique is able to effectively and efficiently detect and remove backdoors,
outperforming 4 baseline methods.


title:
Bounded nonlinear forecasts of partially observed geophysical systems
  with physics-constrained deep learning

abstract:
  The complexity of real-world geophysical systems is often compounded by the
fact that the observed measurements depend on hidden variables. These latent
variables include unresolved small scales and/or rapidly evolving processes,
partially observed couplings, or forcings in coupled systems. This is the case
in ocean-atmosphere dynamics, for which unknown interior dynamics can affect
surface observations. The identification of computationally-relevant
representations of such partially-observed and highly nonlinear systems is thus
challenging and often limited to short-term forecast applications. Here, we
investigate the physics-constrained learning of implicit dynamical embeddings,
leveraging neural ordinary differential equation (NODE) representations. A key
objective is to constrain their boundedness, which promotes the generalization
of the learned dynamics to arbitrary initial condition. The proposed
architecture is implemented within a deep learning framework, and its relevance
is demonstrated with respect to state-of-the-art schemes for different
case-studies representative of geophysical dynamics.


title: GitHub Sponsors: Exploring a New Way to Contribute to Open Source

abstract:
  GitHub Sponsors, launched in 2019, enables donations to individual open
source software (OSS) developers. Financial support for OSS maintainers and
developers is a major issue in terms of sustaining OSS projects, and the
ability to donate to individuals is expected to support the sustainability of
developers, projects, and community. In this work, we conducted a mixed-methods
study of GitHub Sponsors, including quantitative and qualitative analyses, to
understand the characteristics of developers who are likely to receive
donations and what developers think about donations to individuals. We found
that: (1) sponsored developers are more active than non-sponsored developers,
(2) the possibility to receive donations is related to whether there is someone
in their community who is donating, and (3) developers are sponsoring as a new
way to contribute to OSS. Our findings are the first step towards data-informed
guidance for using GitHub Sponsors, opening up avenues for future work on this
new way of financially sustaining the OSS community.


title:
Parameter uncertainty estimation for exponential semi-variogram models:
  Two generalized bootstrap methods with check- and quantile-based filtering

abstract:
  The estimation of parameter standard errors for semi-variogram models is
challenging, given the two-step process required to fit a parametric model to
spatially correlated data. Motivated by an application in the
social-epidemiology, we focus on exponential semi-variogram models fitted to
data between 500 to 2000 observations and little control over the sampling
design. Previously proposed methods for the estimation of standard errors
cannot be applied in this context. Approximate closed form solutions are too
costly using generalized least squares in terms of memory capacities. The
generalized bootstrap proposed by Olea and Pardo-Ig\'uzquiza is nonetheless
applicable with weighted instead of generalized least squares. However, the
standard error estimates are hugely biased and imprecise. Therefore, we propose
a filtering method added to the generalized bootstrap. The new development is
presented and evaluated with a simulation study which shows that the
generalized bootstrap with check-based filtering leads to massively improved
results compared to the quantile-based filter method and previously developed
approaches. We provide a case study using birthweight data.


title: A multiwavelength-motivated X-ray model for the Circinus Galaxy

abstract:
  Reprocessed X-ray emission in Active Galactic Nuclei (AGN) can provide
fundamental information about the circumnuclear environments of supermassive
black holes. Recent mid-infrared studies have shown evidence of an extended
dusty structure perpendicular to the torus plane. In this work, we build a
self-consistent X-ray model for the Circinus Galaxy including the different
physical components observed at different wavelengths and needed to reproduce
both the morphological and spectral properties of this object in the
mid-infrared. The model consists of four components: the accretion disk, the
broad line region (BLR), a flared disk in the equatorial plane and a hollow
cone in the polar direction. Our final model reproduces well the 3--70 keV
Chandra and NuSTAR spectra of Circinus, including the complex Fe K$\alpha$ zone
and the spectral curvature, although several additional Gaussian lines,
associated to either ionized iron or to broadened Fe K$\alpha$/K$\beta$ lines,
are needed. We find that the flared disk is Compton thick ($ N_{\rm H,d}= \rm
1.01^{+0.03}_{-0.24}\times 10^{25}\: cm^{-2}$) and geometrically thick
($CF=0.55^{+0.01}_{-0.05}$), and that the hollow cone has a Compton-thin column
density ($ N_{\rm H,c}= \rm 2.18^{+0.47}_{-0.43}\times 10^{23}\: cm^{-2}$),
which is consistent with the values inferred by mid-infrared studies. Including
also the BLR, the effective line of sight column density is $ N_{\rm H}= \rm
1.47^{+0.03}_{-0.24}\times 10^{25}\: cm^{-2}$. This approach to X-ray
modelling, i.e. including all the different reprocessing structures, will be
very important to fully exploit data from future X-ray missions.


title:
"Forbidden" polarisation and extraordinary piezoelectric effect in
  organometallic lead halide perovskites

abstract:
  Organometallic lead halide perovskites are highly efficient materials for
solar cells and other optoelectronic applications due to their high quantum
efficiency and exceptional semiconducting properties. A peculiarity of these
perovskites is the substantial ionic motion under external forces. Here, we
reveal that electric field-and light-induced ionic motion in MAPbX3 crystals
(X=Cl, Br, I and MA=CH3NH3) leads to unexpected piezoelectric-like response, an
order of magnitude larger than in ferroelectric perovskite oxides. The nominal
macroscopic symmetry of the crystals is broken by redistribution of ionic
species, which can be controlled deterministically by light and electric field.
The revealed piezoelectric response is possibly present in other materials with
significant ionic activity but the unique feature of organometallic perovskites
is the strong effect on the piezoelectric response of interplay of ionic motion
(MA+ and X-1) and photoelectrons generated with illumination.


title: Sub-Fibonacci behavior in numerical semigroup enumeration

abstract:
  In 2013, Zhai proved that most numerical semigroups of a given genus have
depth at most $3$ and that the number $n_g$ of numerical semigroups of a genus
$g$ is asymptotic to $S\varphi^g$, where $S$ is some positive constant and
$\varphi \approx 1.61803$ is the golden ratio. In this paper, we prove
exponential upper and lower bounds on the factors that cause $n_g$ to deviate
from a perfect exponential, including the number of semigroups with depth at
least $4$. Among other applications, these results imply the sharpest known
asymptotic bounds on $n_g$ and shed light on a conjecture by Bras-Amor\'os
(2008) that $n_g \geq n_{g-1} + n_{g-2}$. Our main tools are the use of Kunz
coordinates, introduced by Kunz (1987), and a result by Zhao (2011) bounding
weighted graph homomorphisms.


title:
A Novel Speech Intelligibility Enhancement Model based on
  CanonicalCorrelation and Deep Learning

abstract:
  Current deep learning (DL) based approaches to speech intelligibility
enhancement in noisy environments are often trained to minimise the feature
distance between noise-free speech and enhanced speech signals. Despite
improving the speech quality, such approaches do not deliver required levels of
speech intelligibility in everyday noisy environments .
Intelligibility-oriented (I-O) loss functions have recently been developed to
train DL approaches for robust speech enhancement. Here, we formulate, for the
first time, a novel canonical correlation based I-O loss function to more
effectively train DL algorithms. Specifically, we present a
canonical-correlation based short-time objective intelligibility (CC-STOI) cost
function to train a fully convolutional neural network (FCN) model. We carry
out comparative simulation experiments to show that our CC-STOI based speech
enhancement framework outperforms state-of-the-art DL models trained with
conventional distance-based and STOI-based loss functions, using objective and
subjective evaluation measures for case of both unseen speakers and noises.
Ongoing future work is evaluating the proposed approach for design of robust
hearing-assistive technology.


title: Product-Coproduct Prographs and Triangulations of the Sphere

abstract:
  In this paper, we explain how the classical Catalan families of objects
involving paths, tableaux, triangulations, parentheses configurations and more
generalize canonically to a three-dimensional version. In particular, we
present product-coproduct prographs as central objects explaining the
combinatorics of the triangulations of the sphere. Then we expose a natural way
to extend the Tamari lattice to the product-coproduct prographs.


title:
Using Random Perturbations to Mitigate Adversarial Attacks on Sentiment
  Analysis Models

abstract:
  Attacks on deep learning models are often difficult to identify and therefore
are difficult to protect against. This problem is exacerbated by the use of
public datasets that typically are not manually inspected before use. In this
paper, we offer a solution to this vulnerability by using, during testing,
random perturbations such as spelling correction if necessary, substitution by
random synonym, or simply dropping the word. These perturbations are applied to
random words in random sentences to defend NLP models against adversarial
attacks. Our Random Perturbations Defense and Increased Randomness Defense
methods are successful in returning attacked models to similar accuracy of
models before attacks. The original accuracy of the model used in this work is
80% for sentiment classification. After undergoing attacks, the accuracy drops
to accuracy between 0% and 44%. After applying our defense methods, the
accuracy of the model is returned to the original accuracy within statistical
significance.


title:
The Open Cluster Chemical Abundances and Mapping Survey: V. Chemical
  Abundances of CTIO/Hydra Clusters using The Cannon

abstract:
  Open clusters are key chemical and age tracers of Milky Way evolution. While
open clusters provide significant constraints on galaxy evolution, their use
has been limited due to discrepancies in measuring abundances from different
studies. We analyze medium resolution (R~19,000) CTIO/Hydra spectra of giant
stars in 58 open clusters using The Cannon to determine [Fe/H], [Mg/Fe],
[Si/Fe], [Al/Fe], and [O/Fe]. This work adds an additional 55 primarily
southern hemisphere open clusters calibrated to the SDSS/APOGEE DR16
metallicity system. This uniform analysis is compared to previous studies
[Fe/H] measurements for 23 clusters and we present spectroscopic metallicities
for the first time for 35 open clusters.


title: Assessing Privacy Risks from Feature Vector Reconstruction Attacks

abstract:
  In deep neural networks for facial recognition, feature vectors are numerical
representations that capture the unique features of a given face. While it is
known that a version of the original face can be recovered via "feature
reconstruction," we lack an understanding of the end-to-end privacy risks
produced by these attacks. In this work, we address this shortcoming by
developing metrics that meaningfully capture the threat of reconstructed face
images. Using end-to-end experiments and user studies, we show that
reconstructed face images enable re-identification by both commercial facial
recognition systems and humans, at a rate that is at worst, a factor of four
times higher than randomized baselines. Our results confirm that feature
vectors should be recognized as Personal Identifiable Information (PII) in
order to protect user privacy.


title: Stochastic dynamics of social patch foraging decisions

abstract:
  Animals typically forage in groups. Social foraging can help animals avoid
predation and decrease their uncertainty about the richness of food resources.
Despite this, theoretical mechanistic models of patch foraging have
overwhelmingly focused on the behavior of single foragers. In this study, we
develop a mechanistic model describing the behavior of individuals foraging
together and departing food patches following an evidence accumulation process.
Each individual's belief about patch quality is represented by a stochastically
accumulating variable coupled to others' belief, representing the transfer of
information. We consider a cohesive group, and model information sharing as
either intermittent pulsatile coupling (communicate decision to leave) or
continuous diffusive coupling (communicate online belief). Foraging efficiency
under pulsatile coupling has a stronger dependence on the coupling strength
parameter compared to diffusive. Despite employing minimal information
transfer, pulsatile coupling can still provide similar or higher foraging
efficiency compared to diffusive coupling. Conversely, diffusive coupling is
more robust to parameter detuning and performs better when individuals have
heterogeneous departure criteria and social information weighting. Efficiency
is measured by a reward rate function that balances the amount of energy
accumulated against the time spent in a patch, computed by solving an ordered
first passage time problem for the patch departures of each individual. Using
synthetic data we show that we can distinguish between the two modes of
communication and identify the model parameters. Our model establishes a social
patch foraging framework to parse and identify deliberative decision
strategies, to distinguish different forms of social communication, and to
allow model fitting to real world animal behavior data.


title:
Towards controllable Si-doping in oxide molecular beam epitaxy using a
  solid SiO source: Application to $\beta$-Ga2O3

abstract:
  The oxidation-related issues in controlling Si doping from the Si source
material in oxide molecular beam epitaxy (MBE) is addressed by using solid SiO
as an alternative source material in a conventional effusion cell.
Line-of-sight quadrupole mass spectrometry of the direct SiO-flux
($\Phi_{SiO}$) from the source at different temperatures ($T_{SiO}$) confirmed
SiO molecules to sublime with an activation energy of 3.3eV. The
$T_{SiO}$-dependent $\Phi_{SiO}$ was measured in vacuum before and after
subjecting the source material to an O$_{2}$-background of $10^{-5}$ mbar
(typical oxide MBE regime). The absence of a significant $\Phi_{SiO}$
difference indicates negligible source oxidation in molecular O$_{2}$. Mounted
in an oxygen plasma-assisted MBE, Si-doped $\beta$-Ga2O3 layers were grown
using this source. The $\Phi_{SiO}$ at the substrate was evaluated [from
2.9x10$^{9}$ cm$^{-2}$s$^{-1}$ ($T_{SiO}$=700{\deg}C) to 5.5x10$^{13}$
cm$^{-2}$s$^{-1}$ (T$_{SiO}$=1000{\deg}C)] and Si-concentration in the
$\beta$-Ga2O3 layers measured by secondary ion mass spectrometry highlighting
unprecedented control of continuous Si-doping for oxide MBE, i.e., $N_{Si}$
from 4x10$^{17}$ cm$^{-3}$ ($T_{SiO}$=700{\deg}C) up to 1.7x10$^{20}$ cm$^{-3}$
($T_{SiO}$=900{\deg}C). For a homoepitaxial $\beta$-Ga2O3 layer an Hall charge
carrier concentration of 3x10$^{19}$ cm$^{-3}$ in line with the provided
$\Phi_{SiO}$ ($T_{SiO}$=800{\deg}C) is demonstrated. No SiO-incorporation
difference was found between $\beta$-Ga2O3(010) layers homoepitaxially grown at
750{\deg}C and $\beta$-Ga2O3(-201) layers heteroepitaxially grown at
550{\deg}C. The presence of activated oxygen (plasma) resulted in partial
source oxidation and related decrease of doping concentration (particularly at
$T_{SiO}$<800{\deg}C) which has been tentatively explained with a simple model.
Degassing the source at 1100{\deg}C reverted the oxidation.


title: Light-pulse atom interferometry with entangled atom-optical elements

abstract:
  The analogs of optical elements in light-pulse atom interferometers are
generated from the interaction of matter waves with light fields. As such,
these fields possess quantum properties, which fundamentally lead to a reduced
visibility in the observed interference. This loss is a consequence of the
encoded information about the atom's path. However, the quantum nature of the
atom-optical elements also gives an additional degree of freedom to reduce such
effects: We demonstrate that entanglement between all light fields can be used
to erase information about the atom's path and by that to partially recover the
visibility. Thus, our work highlights the role of complementarity on
atom-interferometric experiments.


title: Transit effects for non-linear index measurement in hot atomic vapors

abstract:
  Hot atomic vapors are widely used in non-linear and quantum optics due to
their large Kerr non-linearity. While the linear refractive index and the
transmission are precisely measured and well modeled theoretically, similar
characterization remains partial for the $\chi^{(3)}$ non-linear part of the
susceptibility. In this work, we present a set of tools to measure and estimate
numerically the non-linear index of hot atomic vapors both in the steady state
and during the transient response of the medium. We apply these techniques for
the characterization of a hot vapor of rubidium and we evidence the critical
role played by transit effects, due to finite beam sizes, in the measurement of
the non-linear index.


title:
Plane curves with a large linear automorphism group in characteristic
  $p$

abstract:
  Let $G$ be a subgroup of the three dimensional projective group
$\mathrm{PGL}(3,q)$ defined over a finite field $\mathbb{F}_q$ of order $q$,
viewed as a subgroup of $\mathrm{PGL}(3,K)$ where $K$ is an algebraic closure
of $\mathbb{F}_q$. For the seven nonsporadic, maximal subgroups $G$ of
$\mathrm{PGL}(3,q)$, we investigate the (projective, irreducible) plane curves
defined over $K$ that are left invariant by $G$. For each, we compute the
minimum degree $d(G)$ of $G$-invariant curves, provide a classification of all
$G$-invariant curves of degree $d(G)$, and determine the first gap
$\varepsilon(G)$ in the spectrum of the degrees of all $G$-invariant curves. We
show that the curves of degree $d(G)$ belong to a pencil depending on $G$,
unless they are uniquely determined by $G$. We also point out that
$G$-invariant curves of degree $d(G)$ have particular geometric features such
as Frobenius nonclassicality and an unusual variation of the number of
$\mathbb{F}_{q^i}$-rational points. For most examples of plane curves left
invariant by a large subgroup of $\mathrm{PGL}(3,q)$, the whole automorphism
group of the curve is linear, i.e., a subgroup of $\mathrm{PGL}(3,K)$. Although
this appears to be a general behavior, we show that the opposite case can also
occur for some irreducible plane curves, that is, the curve has a large group
of linear automorphisms, but its full automorphism group is nonlinear.


title: Learning via nonlinear conjugate gradients and depth-varying neural ODEs

abstract:
  The inverse problem of supervised reconstruction of depth-variable
(time-dependent) parameters in a neural ordinary differential equation (NODE)
is considered, that means finding the weights of a residual network with time
continuous layers. The NODE is treated as an isolated entity describing the
full network as opposed to earlier research, which embedded it between pre- and
post-appended layers trained by conventional methods. The proposed parameter
reconstruction is done for a general first order differential equation by
minimizing a cost functional covering a variety of loss functions and penalty
terms. A nonlinear conjugate gradient method (NCG) is derived for the
minimization. Mathematical properties are stated for the differential equation
and the cost functional. The adjoint problem needed is derived together with a
sensitivity problem. The sensitivity problem can estimate changes in the
network output under perturbation of the trained parameters. To preserve
smoothness during the iterations the Sobolev gradient is calculated and
incorporated. As a proof-of-concept, numerical results are included for a NODE
and two synthetic datasets, and compared with standard gradient approaches (not
based on NODEs). The results show that the proposed method works well for deep
learning with infinite numbers of layers, and has built-in stability and
smoothness.


title: A PDE-Based Analysis of the Symmetric Two-Armed Bernoulli Bandit

abstract:
  This work addresses a version of the two-armed Bernoulli bandit problem where
the sum of the means of the arms is one (the symmetric two-armed Bernoulli
bandit). In a regime where the gap between these means goes to zero and the
number of prediction periods approaches infinity, we obtain the leading order
terms of the expected regret and pseudoregret for this problem by associating
each of them with a solution of a linear parabolic partial differential
equation. Our results improve upon the previously known results; specifically
we explicitly compute the leading order term of the optimal regret and
pseudoregret in three different scaling regimes for the gap. Additionally, we
obtain new non-asymptotic bounds for any given time horizon.


title:
Finding the Shape of Lacunae of the Wave Equation Using Artificial
  Neural Networks

abstract:
  We apply a fully connected neural network to determine the shape of the
lacunae in the solutions of the wave equation. Lacunae are the regions of
quietness behind the trailing fronts of the propagating waves. The network is
trained using a computer simulated data set containing a sufficiently large
number of samples. The network is then shown to correctly reconstruct the shape
of lacunae including the configurations when it is fully enclosed.


title: The Benefit of Hindsight: Tracing Edge-Cases in Distributed Systems

abstract:
  Today's distributed tracing frameworks are ill-equipped to troubleshoot rare
edge-case requests. The crux of the problem is a trade-off between specificity
and overhead. On the one hand, frameworks can indiscriminately select requests
to trace when they enter the system (head sampling), but this is unlikely to
capture a relevant edge-case trace because the framework cannot know which
requests will be problematic until after-the-fact. On the other hand,
frameworks can trace everything and later keep only the interesting edge-case
traces (tail sampling), but this has high overheads on the traced application
and enormous data ingestion costs.
  In this paper we circumvent this trade-off for any edge-case with symptoms
that can be programmatically detected, such as high tail latency, errors, and
bottlenecked queues. We propose a lightweight and always-on distributed tracing
system, Hindsight, which implements a retroactive sampling abstraction: instead
of eagerly ingesting and processing traces, Hindsight lazily retrieves trace
data only after symptoms of a problem are detected. Hindsight is analogous to a
car dash-cam that, upon detecting a sudden jolt in momentum, persists the last
hour of footage. Developers using Hindsight receive the exact edge-case traces
they desire without undue overhead or dependence on luck. Our evaluation shows
that Hindsight scales to millions of requests per second, adds nanosecond-level
overhead to generate trace data, handles GB/s of data per node, transparently
integrates with existing distributed tracing systems, and successfully persists
full, detailed traces in real-world use cases when edge-case problems are
detected.


title: Reliability function for streaming over a DMC with feedback

abstract:
  Conventionally, posterior matching is investigated in channel coding and
block encoding contexts -- the source symbols are equiprobably distributed and
are entirely known by the encoder before the transmission. In this paper, we
consider a streaming source, whose symbols progressively arrive at the encoder
at a sequence of deterministic times. We derive the joint source-channel coding
(JSCC) reliability function for streaming over a discrete memoryless channel
(DMC) with feedback under regularity conditions. We propose a novel
instantaneous encoding phase that operates during the symbol arriving period
and that achieves the JSCC reliability function for streaming when followed by
a block code that achieves the JSCC reliability function for a classical source
whose symbols are fully accessible before the transmission. The instantaneous
encoding phase partitions the evolving message alphabet into groups whose
priors are close to the capacity-achieving distribution, and randomizes the
group indices to ensure that the transmitted group index has the
capacity-achieving distribution. Surprisingly, the JSCC reliability function
for streaming is equal to that for a fully accessible source, implying that the
knowledge of the entire symbol sequence before the transmission offers no
advantage in terms of the reliability function. For equiprobably distributed
source symbols, we design a low complexity algorithm for the instantaneous
encoding phase. It groups the source sequences into sets we call types, which
enable the encoder and the decoder to track the priors and the posteriors of
source sequences jointly, leading to a log-linear complexity in time.


title:
Torque Computation with the Isogeometric Mortar Method for the
  Simulation of Electric Machines

abstract:
  In this work isogeometric mortaring is used for the simulation of a six pole
permanent magnet synchronous machine. Isogeometric mortaring is especially well
suited for the efficient computation of rotating electric machines as it allows
for an exact geometry representation for arbitrary rotation angles without the
need of remeshing. The appropriate B-spline spaces needed for the solution of
Maxwell's equations and the corresponding mortar spaces are introduced. Unlike
in classical finite element methods their construction is straightforward in
the isogeometric case. The torque in the machine is computed using two
different methods, i.e., Arkkio's method and by using the Lagrange multipliers
from the mortaring.


title:
New insights on carbon black suspension rheology -- anisotropic
  thixotropy and anti-thixotropy

abstract:
  We report a detailed experimental study of peculiar thixotropic dynamics of
carbon black (CB, Vulcan XC-72) suspensions in mineral oil, specifically the
observation of sequential stress increase then decrease at a fixed shear rate
in a step-down test. We verify that such dynamics, though peculiar, come from a
true material response rather than experimental artifacts. We also reveal how
this long-time stress decay is associated with anti-thixotropy, rather than
viscoelasticity, by using orthogonal superposition (OSP) rheometry to probe
viscoelastic moduli during the step-down tests. The orthogonal storage and loss
modulus are present, showing this two-timescale recovery then decay response,
which demonstrates that this response is anti-thixotropic, and it involves
shear-induced structuring. We further show a mechanical anisotropy in the CB
suspension under shear using OSP. Based on the rheological results, a
microstructural schematic is proposed, considering qualitatively thixotropic
structure build-up, anti-thixotropic densification, and anisotropic structure
evolution. Our observation for these CB suspensions is outside the standard
paradigm of thixotropic structure-parameter models, and the elastic response
provides us with new insight into the transient dynamics of CB suspensions.


title: Visualising Multiplayer Game Spaces

abstract:
  We compare four different `game-spaces' in terms of their usefulness in
characterising multi-player tabletop games, with a particular interest in any
underlying change to a game's characteristics as the number of players changes.
In each case we take a 16-dimensional feature space, and reduce it to a
2-dimensional visualizable landscape.
  We find that a space obtained from optimization of parameters in Monte Carlo
Tree Search (MCTS) is the most directly interpretable to characterise our set
of games in terms of the relative importance of imperfect information,
adversarial opponents and reward sparsity. These results do not correlate with
a space defined using attributes of the game-tree.
  This dimensionality reduction does not show any general effect as the number
of players. We therefore consider the question using the original features to
classify the games into two sets; those for which the characteristics of the
game changes significantly as the number of players changes, and those for
which there is no such effect.


title:
Ultra-Compact accurate wave functions for He-like and Li-like
  iso-electronic sequences and variational calculus. III. Spin-quartet state of
  the Lithium sequence

abstract:
  As a continuation of Part I, dedicated to the ground state of He-like and
Li-like isoelectronic sequences for nuclear charges $Z \leq 20$, and Part II,
dedicated to two excited states of He-like sequence, two ultra-compact wave
functions in the form of generalized Guevara-Harris-Turbiner functions are
constructed for Li-like sequence. They describe accurately the domain of
applicability of the Quantum Mechanics of Coulomb Charges (QMCC) for energies
(2-3 significant digits (s.d.)) of the spin-quartet state $1^40^+$ of Li-like
ions (in static approximation with point-like, infinitely heavy nuclei).
Variational parameters are fitted in $Z$ by 2nd degree polynomials. The most
accurate ultra-compact function leads to the absolute accuracy $\sim
10^{-3}$\,a.u. for energy, and $\sim 10^{-4}$ for the normalized
electron-nuclear cusp parameter for $Z \leq 20$. Critical charge $Z=Z_B$, where
the ultra-compact trial function for the $1^40^+$ state looses its
square-integrability, is estimated, $Z_B(1^4\,0^+) \sim 1.26 - 1.30$. As a
complement to Part I, square integrability for the compact functions
constructed for the {\it ground, spin-doublet state} $1^2\,0^+$ of the Li-like
sequence is discussed. The critical charge, for which these functions stop to
be normalizable, is estimated as $Z_B( 1^2\,0^+) = 1.62 - 1.65$. It implies
that at $Z=2$ - the negative helium ion He${}^-$ - both states $1^2\,0^+$ and
$1^4\,0^+$ exist as states embedded to continuum.


title: Inference of Multiscale Gaussian Graphical Model

abstract:
  Gaussian Graphical Models (GGMs) are widely used for exploratory data
analysis in various fields such as genomics, ecology, psychometry. In a
high-dimensional setting, when the number of variables exceeds the number of
observations by several orders of magnitude, the estimation of GGM is a
difficult and unstable optimization problem. Clustering of variables or
variable selection is often performed prior to GGM estimation. We propose a new
method allowing to simultaneously infer a hierarchical clustering structure and
the graphs describing the structure of independence at each level of the
hierarchy. This method is based on solving a convex optimization problem
combining a graphical lasso penalty with a fused type lasso penalty. Results on
real and synthetic data are presented.


title: Privately Estimating Graph Parameters in Sublinear time

abstract:
  We initiate a systematic study of algorithms that are both differentially
private and run in sublinear time for several problems in which the goal is to
estimate natural graph parameters. Our main result is a differentially-private
$(1+\rho)$-approximation algorithm for the problem of computing the average
degree of a graph, for every $\rho>0$. The running time of the algorithm is
roughly the same as its non-private version proposed by Goldreich and Ron
(Sublinear Algorithms, 2005). We also obtain the first differentially-private
sublinear-time approximation algorithms for the maximum matching size and the
minimum vertex cover size of a graph.
  An overarching technique we employ is the notion of coupled global
sensitivity of randomized algorithms. Related variants of this notion of
sensitivity have been used in the literature in ad-hoc ways. Here we formalize
the notion and develop it as a unifying framework for privacy analysis of
randomized approximation algorithms.


title: Metastability of the Potts ferromagnet on random regular graphs

abstract:
  We study the performance of Markov chains for the $q$-state ferromagnetic
Potts model on random regular graphs. It is conjectured that their performance
is dictated by metastability phenomena, i.e., the presence of "phases"
(clusters) in the sample space where Markov chains with local update rules,
such as the Glauber dynamics, are bound to take exponential time to escape, and
therefore cause slow mixing. The phases that are believed to drive these
metastability phenomena in the case of the Potts model emerge as local, rather
than global, maxima of the so-called Bethe functional, and previous approaches
of analysing these phases based on optimisation arguments fall short of the
task.
  Our first contribution is to detail the emergence of the metastable phases
for the $q$-state Potts model on the $d$-regular random graph for all integers
$q,d\geq 3$, and establish that for an interval of temperatures, delineated by
the uniqueness and the Kesten-Stigum thresholds on the $d$-regular tree, the
two phases coexist. The proofs are based on a conceptual connection between
spatial properties and the structure of the Potts distribution on the random
regular graph, rather than complicated moment calculations.
  Based on this new structural understanding of the model, we obtain various
algorithmic consequences. We first complement recent fast mixing results for
Glauber dynamics by Blanca and Gheissari below the uniqueness threshold,
showing an exponential lower bound on the mixing time above the uniqueness
threshold. Then, we obtain tight results even for the non-local Swendsen-Wang
chain, where we establish slow mixing/metastability for the whole interval of
temperatures where the chain is conjectured to mix slowly on the random regular
graph. The key is to bound the conductance of the chains using a random graph
"planting" argument combined with delicate bounds on random-graph percolation.


title:
White-Box Attacks on Hate-speech BERT Classifiers in German with
  Explicit and Implicit Character Level Defense

abstract:
  In this work, we evaluate the adversarial robustness of BERT models trained
on German Hate Speech datasets. We also complement our evaluation with two
novel white-box character and word level attacks thereby contributing to the
range of attacks available. Furthermore, we also perform a comparison of two
novel character-level defense strategies and evaluate their robustness with one
another.


title: The Evolution of Blockchain: from Lit to Dark

abstract:
  Transactions submitted through the blockchain peer-to-peer (P2P) network may
leak out exploitable information. We study the economic incentives behind the
adoption of blockchain dark venues, where users' transactions are observable
only by miners on these venues. We show that miners may not fully adopt dark
venues to preserve rents extracted from arbitrageurs, hence creating execution
risk for users. The dark venue neither eliminates frontrunning risk nor reduces
transaction costs. It strictly increases the payoff of miners, weakly increases
the payoff of users, and weakly reduces arbitrageurs' profits. We provide
empirical support for our main implications, and show that they are
economically significant. A 1% increase in the probability of being frontrun
raises users' adoption rate of the dark venue by 0.6%. Arbitrageurs'
cost-to-revenue ratio increases by a third with a dark venue.


title: A Modern Self-Referential Weight Matrix That Learns to Modify Itself

abstract:
  The weight matrix (WM) of a neural network (NN) is its program. The programs
of many traditional NNs are learned through gradient descent in some error
function, then remain fixed. The WM of a self-referential NN, however, can keep
rapidly modifying all of itself during runtime. In principle, such NNs can
meta-learn to learn, and meta-meta-learn to meta-learn to learn, and so on, in
the sense of recursive self-improvement. While NN architectures potentially
capable of implementing such behavior have been proposed since the '90s, there
have been few if any practical studies. Here we revisit such NNs, building upon
recent successes of fast weight programmers and closely related linear
Transformers. We propose a scalable self-referential WM (SRWM) that uses outer
products and the delta update rule to modify itself. We evaluate our SRWM in
supervised few-shot learning and in multi-task reinforcement learning with
procedurally generated game environments. Our experiments demonstrate both
practical applicability and competitive performance of the proposed SRWM. Our
code is public.


title: Holographic superconductivity in Einsteinian Cubic Gravity

abstract:
  We study the condensation of a charged scalar field in a $(3+1)$-dimensional
asymptotically AdS background in the context of Einsteinian cubic gravity,
featuring a holographic superconductor with higher curvature corrections
corresponding to a CFT with a non-vanishing value of the stress tensor
three-point function $t_4$. As it was previously noticed for higher dimensional
Gauss-Bonnet theory, we observe that the critical temperature of the
superconducting phase transition is lowered as the higher curvature coupling
grows.


title:
Parameterizing the AGN radius -- luminosity relation from the
  Eigenvector 1 viewpoint

abstract:
  The study of the broad-line region (BLR) using reverberation mapping has
allowed us to establish an empirical relation between the size of this line
emitting region and the continuum luminosity that drives the line emission
(i.e. the R$_{\rm BLR}$-L$_{\rm 5100}$ relation). To realize its full
potential, the intrinsic scatter in the R$_{\rm BLR}$-L$_{\rm 5100}$ relation
needs to be understood better. The Eddington ratio plays a key role in
addressing this problem. On the other hand, the Eigenvector 1 schema has helped
to reveal an almost clear connection between the Eddington ratio and the
strength of the optical FeII emission which has its origin from the BLR. This
paper aims to reveal the connection between theoretical entities, like, the
ionization parameter (U) and cloud mean density (n$_{\rm H}$) of the BLR, with
physical observables obtained directly from the spectra, such as optical FeII
strength (R$_{\rm FeII}$) that has immense potential to trace the accretion
rate. We utilize the photoionization code CLOUDY and perform a suite of models
to reveal the BLR in the U-n parameter space and estimate RFeII. We compare the
SEDs for a prototypical Population A and Population B source, I Zw 1 and NGC
5548, respectively, in this study. The results from the photoionization
modelling are combined with existing reverberation mapped sources with observed
R$_{\rm FeII}$ estimates, allowing us to provide an analytical formulation to
tie together the aforementioned quantities. We utilize the comparison of the
modelled equivalent widths for the low-ionization emission lines to their
observed values to identify the optimal (U,n$_{\rm H}$). The recovery of the
correct physical conditions in the BLR suggests that the BLR `sees' a
different, filtered ionizing continuum with only a very small fraction (~1-10%)
that leads to the line emission in the dustless, low-ionization BLR.


title:
From Symplectic to Poisson. A Study of Reduction and a Proposal Towards
  Implosion

abstract:
  The imploded cross-section of a symplectic manifold is a stratified space
allowing for an abelianization of its symplectic reduction. After recalling
symplectic and Poisson reduction and reviewing the basics of symplectic
implosion, we prove a cross-section theorem for Poisson manifolds, generalizing
the Guillemin-Sternberg theorem for symplectic manifolds, which constitutes a
first step towards Poisson implosion. On our way, we find and fix a mistake in
the proof of Guillemin-Sternberg's theorem, and we identify Poisson
transversals as the right analogue to symplectic submanifolds in this context.


title:
Exact results for duality-covariant integrated correlators in
  $\mathcal{N}=4$ SYM with general classical gauge groups

abstract:
  We present exact expressions for certain integrated correlators of four
superconformal primary operators in the stress tensor multiplet of
$\mathcal{N}=4$ supersymmetric Yang--Mills (SYM) theory with classical gauge
group, $G_N$ $= SO(2N)$, $SO(2N+1)$, $USp(2N)$. These integrated correlators
are expressed as two-dimensional lattice sums by considering derivatives of the
localised partition functions, generalising the expression obtained for $SU(N)$
in our previous works. These expressions are manifestly covariant under
Goddard-Nuyts-Olive duality. The integrated correlators can also be formally
written as infinite sums of non-holomorphic Eisenstein series with integer
indices and rational coefficients. Furthermore, the action of the hyperbolic
Laplace operator with respect to the complex coupling $\tau=\theta/(2\pi) +
4\pi i /g^2_{_{YM}}$ on any integrated correlator for gauge group $G_N$ relates
it to a linear combination of correlators with gauge groups $G_{N+1}$, $G_N$
and $G_{N-1}$. These "Laplace-difference equation" determine the expressions of
integrated correlators for all classical gauge groups for any value of $N$ in
terms of the correlator for the gauge group $SU(2)$. The perturbation
expansions of these integrated correlators for any finite value of $N$ agree
with properties obtained from perturbative Yang--Mills quantum field theory,
together with various multi-instanton calculations which are also shown to
agree with those determined by supersymmetric localisation. The coefficients of
terms in the large-$N$ expansion are sums of non-holomorphic Eisenstein series
with half-integer indices, which extend recent results and make contact with
low order terms in the low energy expansion of type IIB superstring theory in
an $AdS_5\times S^5/\mathbb{Z}_2$ background.


title: Affine nil-Hecke algebras and Quantum cohomology

abstract:
  Let $G$ be a compact, connected Lie group and $T \subset G$ a maximal torus.
Let $(M,\omega)$ be a monotone closed symplectic manifold equipped with a
Hamiltonian action of $G$. We construct a module action of the affine nil-Hecke
algebra $\hat{H}_*^{S^1 \times T}(LG/T)$ on the $S^1 \times T$-equivariant
quantum cohomology of $M$, $QH^*_{S^1 \times T}(M).$ Our construction
generalizes the theory of shift operators for Hamiltonian torus actions
[OP,LJ]. We show that, as in the abelian case, this action behaves well with
respect to the quantum connection. As an application of our construction, we
show that when $G$ is semi-simple, the $G$-equivariant quantum cohomology
$QH_G^*(M)$ defines a canonical holomorphic Lagrangian subvariety
$\mathbb{L}_G(M) \hookrightarrow BFM(G_{\mathbb{C}}^{\vee})$ in the BFM-space
of the Langlands dual group, confirming an expectation of Teleman from [T1].


title: Multi-Modal Knowledge Graph Construction and Application: A Survey

abstract:
  Recent years have witnessed the resurgence of knowledge engineering which is
featured by the fast growth of knowledge graphs. However, most of existing
knowledge graphs are represented with pure symbols, which hurts the machine's
capability to understand the real world. The multi-modalization of knowledge
graphs is an inevitable key step towards the realization of human-level machine
intelligence. The results of this endeavor are Multi-modal Knowledge Graphs
(MMKGs). In this survey on MMKGs constructed by texts and images, we first give
definitions of MMKGs, followed with the preliminaries on multi-modal tasks and
techniques. We then systematically review the challenges, progresses and
opportunities on the construction and application of MMKGs respectively, with
detailed analyses of the strength and weakness of different solutions. We
finalize this survey with open research problems relevant to MMKGs.


title: The word problem for free groups cannot be solved in linear time*

abstract:
  *by a standard (one-tape) Turing machine. It is well-known that the word
problem for hyperbolic groups, whence in particular for free groups, can be
solved in linear time. However, these algorithms run on machines more
complicated than a standard Turing machine. By contrast, in this note we show
that a standard Turing machine cannot solve the word problem for the free group
on two generators in less than quadratic time.


title: A Fixed Point Decomposition of Twisted Equivariant K-Theory

abstract:
  We present a decomposition of rational twisted $G$-equivariant K-theory, $G$
a finite group, into cyclic group equivariant K-theory groups of fixed point
spaces. This generalises the untwisted decomposition by Atiyah and Segal as
well as the decomposition by Adem and Ruan for twists coming from group
cocycles.


title: A constraint on the dynamics of wealth concentration

abstract:
  In Ref. [1] the authors show that under minimal hypothesis, in a free,
growing economy the wealth concentration as measured by the Gini coefficient
$G_t$ is bounded to reach its maximum, $G_t \to 1$. Under their hypothesis the
wealth growth is on average proportional to the wealth itself, thus leaving no
room for a salary component independent of the individual's wealth. In addition
the state of zero wealth is absorbing, meaning that once an individual loses
all its wealth, it is forced to remain in that state. Here we further
generalize the result of Ref. [1], introducing a salary component of wealth
growth and thus allowing for the possibility to escape from the state of zero
wealth. We arrive at the same conclusions of the previous study, unless a
minimum salary component is introduced and kept proportional to the average
wealth.


title:
97 Eclipsing Quadruple Star Candidates Discovered in TESS Full Frame
  Images

abstract:
  We present a catalog of 97 uniformly-vetted candidates for quadruple star
systems. The candidates were identified in TESS Full Frame Image data from
Sectors 1 through 42 through a combination of machine learning techniques and
visual examination, with major contributions from a dedicated group of citizen
scientists. All targets exhibit two sets of eclipses with two different
periods, both of which pass photocenter tests confirming that the eclipses are
on-target. This catalog outlines the statistical properties of the sample,
nearly doubles the number of known multiply-eclipsing quadruple systems, and
provides the basis for detailed future studies of individual systems. Several
important discoveries have already resulted from this effort, including the
first sextuply-eclipsing sextuple stellar system and the first transiting
circumbinary planet detected from one sector of TESS data.


title:
The Power of Adaptivity in SGD: Self-Tuning Step Sizes with Unbounded
  Gradients and Affine Variance

abstract:
  We study convergence rates of AdaGrad-Norm as an exemplar of adaptive
stochastic gradient methods (SGD), where the step sizes change based on
observed stochastic gradients, for minimizing non-convex, smooth objectives.
Despite their popularity, the analysis of adaptive SGD lags behind that of non
adaptive methods in this setting. Specifically, all prior works rely on some
subset of the following assumptions: (i) uniformly-bounded gradient norms, (ii)
uniformly-bounded stochastic gradient variance (or even noise support), (iii)
conditional independence between the step size and stochastic gradient. In this
work, we show that AdaGrad-Norm exhibits an order optimal convergence rate of
$\mathcal{O}\left(\frac{\mathrm{poly}\log(T)}{\sqrt{T}}\right)$ after $T$
iterations under the same assumptions as optimally-tuned non adaptive SGD
(unbounded gradient norms and affine noise variance scaling), and crucially,
without needing any tuning parameters. We thus establish that adaptive gradient
methods exhibit order-optimal convergence in much broader regimes than
previously understood.


title: Co-Design quantum simulation of nanoscale NMR

abstract:
  Quantum computers have the potential to efficiently simulate the dynamics of
nanoscale NMR systems. In this work we demonstrate that a noisy
intermediate-scale quantum computer can be used to simulate and predict
nanoscale NMR resonances. In order to minimize the required gate fidelities, we
propose a superconducting application-specific Co-Design quantum processor that
reduces the number of SWAP gates by over 90 % for chips with more than 20
qubits. The processor consists of transmon qubits capacitively coupled via
tunable couplers to a central co-planar waveguide resonator with a quantum
circuit refrigerator (QCR) for fast resonator reset. The QCR implements the
non-unitary quantum operations required to simulate nuclear hyperpolarization
scenarios.


title: Answer Set Planning: A Survey

abstract:
  Answer Set Planning refers to the use of Answer Set Programming (ASP) to
compute plans, i.e., solutions to planning problems, that transform a given
state of the world to another state. The development of efficient and scalable
answer set solvers has provided a significant boost to the development of
ASP-based planning systems. This paper surveys the progress made during the
last two and a half decades in the area of answer set planning, from its
foundations to its use in challenging planning domains. The survey explores the
advantages and disadvantages of answer set planning. It also discusses typical
applications of answer set planning and presents a set of challenges for future
research.


title: Transport coefficients of magnetized neutron star cores

abstract:
  We review the calculations of the kinetic coefficients (thermal conductivity,
shear viscosity, momentum transfer rates) of the neutron star core matter
within the framework of the Landau Fermi-liquid theory. We restrict ourselves
to the case of normal (i.e. non-superfluid) matter. As an example we consider
simplest $npe\mu$ composition of neutron star core matter. Utilizing the
CompOSE database of dense matter equations of state and several microscopic
interactions we analyze the uncertainties in calculations of the kinetic
coefficients that result from the insufficient knowledge of the properties of
the dense nuclear matter and suggest possible approximate treatment. In our
study we also take into account non-quantizing magnetic field. The presence of
magnetic field makes transport anisotropic leading to the tensor structure of
kinetic coefficients. We find that the moderate ($B\lesssim 10^{12}$ G)
magnetic field do not affect considerably thermal conductivity of neutron star
core matter, since the latter is mainly governed by the electrically neutral
neutrons. In contrast, shear viscosity is affected even by the moderate $B\sim
10^8 - 10^{10}$ G. Based on the in-vacuum nucleon interactions we provide
practical expressions for calculation of transport coefficients for any
equation of state of dense matter.


title:
Meta-learning with GANs for anomaly detection, with deployment in
  high-speed rail inspection system

abstract:
  Anomaly detection has been an active research area with a wide range of
potential applications. Key challenges for anomaly detection in the AI era with
big data include lack of prior knowledge of potential anomaly types, highly
complex and noisy background in input data, scarce abnormal samples, and
imbalanced training dataset. In this work, we propose a meta-learning framework
for anomaly detection to deal with these issues. Within this framework, we
incorporate the idea of generative adversarial networks (GANs) with appropriate
choices of loss functions including structural similarity index measure (SSIM).
Experiments with limited labeled data for high-speed rail inspection
demonstrate that our meta-learning framework is sharp and robust in identifying
anomalies. Our framework has been deployed in five high-speed railways of China
since 2021: it has reduced more than 99.7% workload and saved 96.7% inspection
time.


title: Topological complexity of spherical bundles

abstract:
  Parametrized motion planning algorithms have high degree of flexibility and
universality, they can work under a variety of external conditions, which are
viewed as parameters and form part of the input of the algorithm. In this paper
we analyse the parameterized motion planning problem in the case of sphere
bundles. Our main results provide upper and lower bounds for the parametrized
topological complexity; the upper bounds typically involve sectional categories
of the associated fibrations and the lower bounds are given in terms of
characteristic classes and their properties. We explicitly compute the
parametrized topological complexity in many examples and show that it may
assume arbitrarily large values.


title: Distributionally Robust Data Join

abstract:
  Suppose we are given two datasets: a labeled dataset and unlabeled dataset
which also has additional auxiliary features not present in the first dataset.
What is the most principled way to use these datasets together to construct a
predictor?
  The answer should depend upon whether these datasets are generated by the
same or different distributions over their mutual feature sets, and how similar
the test distribution will be to either of those distributions. In many
applications, the two datasets will likely follow different distributions, but
both may be close to the test distribution. We introduce the problem of
building a predictor which minimizes the maximum loss over all probability
distributions over the original features, auxiliary features, and binary
labels, whose Wasserstein distance is $r_1$ away from the empirical
distribution over the labeled dataset and $r_2$ away from that of the unlabeled
dataset. This can be thought of as a generalization of distributionally robust
optimization (DRO), which allows for two data sources, one of which is
unlabeled and may contain auxiliary features.


title:
The Dual Form of Neural Networks Revisited: Connecting Test Time
  Predictions to Training Patterns via Spotlights of Attention

abstract:
  Linear layers in neural networks (NNs) trained by gradient descent can be
expressed as a key-value memory system which stores all training datapoints and
the initial weights, and produces outputs using unnormalised dot attention over
the entire training experience. While this has been technically known since the
'60s, no prior work has effectively studied the operations of NNs in such a
form, presumably due to prohibitive time and space complexities and impractical
model sizes, all of them growing linearly with the number of training patterns
which may get very large. However, this dual formulation offers a possibility
of directly visualizing how an NN makes use of training patterns at test time,
by examining the corresponding attention weights. We conduct experiments on
small scale supervised image classification tasks in single-task, multi-task,
and continual learning settings, as well as language modelling, and discuss
potentials and limits of this view for better understanding and interpreting
how NNs exploit training patterns. Our code is public.


title:
Rate-matching the regret lower-bound in the linear quadratic regulator
  with unknown dynamics

abstract:
  The theory of reinforcement learning currently suffers from a mismatch
between its empirical performance and the theoretical characterization of its
performance, with consequences for, e.g., the understanding of sample
efficiency, safety, and robustness. The linear quadratic regulator with unknown
dynamics is a fundamental reinforcement learning setting with significant
structure in its dynamics and cost function, yet even in this setting there is
a gap between the best known regret lower-bound of $\Omega_p(\sqrt{T})$ and the
best known upper-bound of $O_p(\sqrt{T}\,\text{polylog}(T))$. The contribution
of this paper is to close that gap by establishing a novel regret upper-bound
of $O_p(\sqrt{T})$. Our proof is constructive in that it analyzes the regret of
a concrete algorithm, and simultaneously establishes an estimation error bound
on the dynamics of $O_p(T^{-1/4})$ which is also the first to match the rate of
a known lower-bound. The two keys to our improved proof technique are (1) a
more precise upper- and lower-bound on the system Gram matrix and (2) a
self-bounding argument for the expected estimation error of the optimal
controller.


title:
A Newton-type algorithm for federated learning based on incremental
  Hessian eigenvector sharing

abstract:
  There is a growing interest in the decentralized optimization framework that
goes under the name of Federated Learning (FL). In particular, much attention
is being turned to FL scenarios where the network is strongly heterogeneous in
terms of communication resources (e.g., bandwidth) and data distribution. In
these cases, communication between local machines (agents) and the central
server (Master) is a main consideration. In this work, we present an original
communication-constrained Newton-type (NT) algorithm designed to accelerate FL
in such heterogeneous scenarios. The algorithm is by design robust to non
i.i.d. data distributions, handles heterogeneity of agents' communication
resources (CRs), only requires sporadic Hessian computations, and achieves
super-linear convergence. This is possible thanks to an incremental strategy,
based on a singular value decomposition (SVD) of the local Hessian matrices,
which exploits (possibly) outdated second-order information. The proposed
solution is thoroughly validated on real datasets by assessing (i) the number
of communication rounds required for convergence, (ii) the overall amount of
data transmitted and (iii) the number of local Hessian computations required.
For all these metrics, the proposed approach shows superior performance against
state-of-the art techniques like GIANT and FedNL.


title: Parametrized motion planning and topological complexity

abstract:
  In this paper we study paramertized motion planning algorithms which provide
universal and flexible solutions to diverse motion planning problems. Such
algorithms are intended to function under a variety of external conditions
which are viewed as parameters and serve as part of the input of the algorithm.
Continuing a recent paper, we study further the concept of parametrized
topological complexity. We analyse in full detail the problem of controlling a
swarm of robots in the presence of multiple obstacles in Euclidean space which
served for us a natural motivating example. We present an explicit parametrized
motion planning algorithm solving the motion planning problem for any number of
robots and obstacles.. This algorithm is optimal, it has minimal possible
topological complexity for any d odd. Besides, we describe a modification of
this algorithm which is optimal for d even. We also analyse the parametrized
topological complexity of sphere bundles using the Stiefel - Whitney
characteristic classes.


title:
Improved early-warning estimates of luminosity distance and orbital
  inclination of compact binary mergers using higher modes of gravitational
  radiation

abstract:
  The pre-merger (early-warning) gravitational-wave (GW) detection and
localization of a compact binary merger would enable astronomers to capture
potential electromagnetic (EM) emissions around the time of the merger, thus
shedding light on the complex physics of the merger. While early detection and
sky localization are of primary importance to the multimessenger follow-up of
the event, improved estimates of luminosity distance and orbital inclination
could also provide insights on the observability of the EM emission. In this
work, we demonstrate that the inclusion of higher modes of gravitational
radiation, which vibrate at higher multiples of the orbital frequency than the
dominant mode, would significantly improve the earlywarning estimates of the
luminosity distance and orbital inclination of the binary. This will help
astronomers to better determine their follow-up strategy. Focusing on future
observing runs of the ground-based GW detector network [O5 run of
LIGOVirgo-KAGRA, Voyager, and third-generation (3G) detectors], we show that
for a range of masses spanning the neutron-star black-hole binaries that are
potentially EM-bright, the inclusion of higher modes improve the luminosity
distance estimates by a factor of ~ 1 - 1.5 (1.1 - 2) [1.1 - 5] for the O5
(Voyager) [3G] observing scenario, 45 (45) [300] seconds before the merger for
the sources located at 100 Mpc. There are significant improvements in orbital
inclination estimates as well. We also investigate these improvements with
varying sky-location and polarization angle. Combining the luminosity distance
uncertainties with localization skyarea estimates, we find that the number of
galaxies within localization volume is reduced by a factor of ~ 1 - 2.5 (1.2 -
4) [1.2 - 10] with the inclusion of higher modes at early-warning time of 45
(45) [300] seconds in O5 (Voyager) [3G].


title:
High Harmonic Generation from Isolated Bound States: Tunable Emission
  Spectra and Mollow Triplets

abstract:
  High harmonic generation (HHG) is a highly nonlinear emission process in
which systems driven by intense laser pulses emit integer multiples (harmonics)
of the driving field. This feature is considered universal to all occurrences
of HHG. Here we show that a strong nonlinear response of certain systems can
split the HHG spectrum into non-harmonic Mollow-type triplets, imprinting the
internal electronic characteristics and confining potential on the HHG
spectrum. The spectral lines become tunable by the driver field. These
phenomena are universal to any material system with isolated bound states. We
identify the conditions under which our predictions become accessible and
propose experimental systems to observe them.


title: Subconvexity in the inhomogeneous cubic Vinogradov system

abstract:
  When $\mathbf h\in \mathbb Z^3$, denote by $B(X;\mathbf h)$ the number of
integral solutions to the system \[ \sum_{i=1}^6(x_i^j-y_i^j)=h_j\quad (1\le
j\le 3), \] with $1\le x_i,y_i\le X$ $(1\le i\le 6)$. When $h_1\ne 0$ and
appropriate local solubility conditions on $\mathbf h$ are met, we obtain an
asymptotic formula for $B(X;\mathbf h)$, thereby establishing a subconvex
local-global principle in the inhomogeneous cubic Vinogradov system. We obtain
similar conclusions also when $h_1=0$, $h_2\ne 0$ and $X$ is sufficiently large
in terms of $h_2$. Our arguments involve minor arc estimates going beyond
square-root cancellation.


title:
"Stairway to Heaven" -- Spectroscopy of Particle Couplings with
  Gravitational Waves

abstract:
  We discuss the possibility to measure particle couplings with stochastic
gravitational wave backgrounds (SGWBs). Under certain circumstances a sequence
of peaks of different amplitude and frequency -- a $stairway$ -- emerges in a
SGWB spectrum, with each peak probing a different coupling. The detection of
such signature opens the possibility to reconstruct couplings (spectroscopy) of
particle species involved in high energy phenomena generating SGWBs.
Stairway-like signatures may arise in causally produced backgrounds in the
early Universe, e.g. from preheating or first order phase transitions. As a
proof of principle we study a preheating scenario with an inflaton $\phi$
coupled to multiple $daughter$ fields $\lbrace \chi_j \rbrace$ with different
coupling strengths. As a clear stairway signature is imprinted in the SGWB
spectrum, we reconstruct the relevant couplings with various detectors.


title: Evaluating MT Systems: A Theoretical Framework

abstract:
  This paper outlines a theoretical framework using which different automatic
metrics can be designed for evaluation of Machine Translation systems. It
introduces the concept of {\em cognitive ease} which depends on {\em adequacy}
and {\em lack of fluency}. Thus, cognitive ease becomes the main parameter to
be measured rather than comprehensibility. The framework allows the components
of cognitive ease to be broken up and computed based on different linguistic
levels etc. Independence of dimensions and linearly combining them provides for
a highly modular approach.
  The paper places the existing automatic methods in an overall framework, to
understand them better and to improve upon them in future. It can also be used
to evaluate the newer types of MT systems, such as speech to speech translation
and discourse translation.


title:
QED and strong isospin corrections in the hadronic vacuum polarization
  contribution to the anomalous magnetic moment of the muon

abstract:
  Recently, the Budapest-Marseille-Wuppertal collaboration achieved sub-percent
precision in the evaluation of the lowest-order hadronic vacuum polarization
contribution to the muon $g_\mu-2$ (arXiv:hep-lat/2002.12347v3). At this level
of precision, isospin-symmetric QCD is not sufficient. In this contribution we
review how QED and strong-isospin-breaking effects have been included in our
work. Isospin breaking is implemented by expanding the relevant correlation
functions to second order in the electric charge $e$ and to first order in
$m_u-m_d$. The correction terms are then computed using isospin-symmetric
configurations. The choice of this approach allows us to better distribute the
available computing resources among the various contributions.


title: Investigating Power laws in Deep Representation Learning

abstract:
  Representation learning that leverages large-scale labelled datasets, is
central to recent progress in machine learning. Access to task relevant labels
at scale is often scarce or expensive, motivating the need to learn from
unlabelled datasets with self-supervised learning (SSL). Such large unlabelled
datasets (with data augmentations) often provide a good coverage of the
underlying input distribution. However evaluating the representations learned
by SSL algorithms still requires task-specific labelled samples in the training
pipeline. Additionally, the generalization of task-specific encoding is often
sensitive to potential distribution shift. Inspired by recent advances in
theoretical machine learning and vision neuroscience, we observe that the
eigenspectrum of the empirical feature covariance matrix often follows a power
law. For visual representations, we estimate the coefficient of the power law,
$\alpha$, across three key attributes which influence representation learning:
learning objective (supervised, SimCLR, Barlow Twins and BYOL), network
architecture (VGG, ResNet and Vision Transformer), and tasks (object and scene
recognition). We observe that under mild conditions, proximity of $\alpha$ to
1, is strongly correlated to the downstream generalization performance.
Furthermore, $\alpha \approx 1$ is a strong indicator of robustness to label
noise during fine-tuning. Notably, $\alpha$ is computable from the
representations without knowledge of any labels, thereby offering a framework
to evaluate the quality of representations in unlabelled datasets.


title:
Hard X-ray luminosity functions of cataclysmic variables: Joint
  Swift/BAT and Gaia data

abstract:
  Cataclysmic variables (CVs) are the most numerous population among the
Galactic objects emitting in hard X-rays. Most probably, they are responsible
for the extended hard X-ray emission of the Galactic ridge and the central
Galactic regions. Here we consider the sample of CVs detected in the all-sky
hard X-ray Swift/BAT survey which were also detected by Gaia and thus have
reliable distance estimates. Using these data, we derive accurate estimates for
local number density per solar mass (\rho_M = 1.37^{+0.3}_{-0.16} x 10^{-5}
M_sun^{-1}) and luminosity density per solar mass (\rho_L = 8.95^{+0.15}_{-0.1}
x 10^{26} erg s^{-1} M_sun^{-1}) for objects in the sample. These values appear
to be in good agreement with the integrated Galactic ridge X-ray emission and
Nuclear Stellar Cluster luminosities. Analysis of the differential luminosity
functions d\rho_M/d(\log_{10} L_x) and d\rho_L/d(\log_{10} L_x) confirms that
there are two populations of hard X-ray emitting CVs. Intermediate polars
dominate at luminosities L > 10^{33} erg s^{-1}, whereas non-magnetic CVs and
polars are much more numerous but have lower luminosities on average. As a
consequence, the contribution of these populations to the observed hard X-ray
luminosity is almost equivalent.


title:
An a posteriori error estimator for the spectral fractional power of the
  Laplacian

abstract:
  We develop a novel a posteriori error estimator for the L2 error committed by
the finite element discretization of the solution of the fractional Laplacian.
Our a posteriori error estimator takes advantage of the semi-discretization
scheme using a rational approximation which allows to reformulate the
fractional problem into a family of non-fractional parametric problems. The
estimator involves applying the implicit Bank-Weiser error estimation strategy
to each parametric non-fractional problem and reconstructing the fractional
error through the same rational approximation used to compute the solution to
the original fractional problem. We provide several numerical examples in both
two and three-dimensions demonstrating the effectivity of our estimator for
varying fractional powers and its ability to drive an adaptive mesh refinement
strategy.


title: Overhead Image Factors for Underwater Sonar-based SLAM

abstract:
  Simultaneous localization and mapping (SLAM) is a critical capability for any
autonomous underwater vehicle (AUV). However, robust, accurate state estimation
is still a work in progress when using low-cost sensors. We propose enhancing a
typical low-cost sensor package using widely available and often free prior
information; overhead imagery. Given an AUV's sonar image and a partially
overlapping, globally-referenced overhead image, we propose using a
convolutional neural network (CNN) to generate a synthetic overhead image
predicting the above-surface appearance of the sonar image contents. We then
use this synthetic overhead image to register our observations to the provided
global overhead image. Once registered, the transformation is introduced as a
factor into a pose SLAM factor graph. We use a state-of-the-art simulation
environment to perform validation over a series of benchmark trajectories and
quantitatively show the improved accuracy of robot state estimation using the
proposed approach. We also show qualitative outcomes from a real AUV field
deployment. Video attachment: https://youtu.be/_uWljtp58ks


title: Distributed saddle point problems for strongly concave-convex functions

abstract:
  In this paper, we propose GT-GDA, a distributed optimization method to solve
saddle point problems of the form: $\min_{\mathbf{x}} \max_{\mathbf{y}}
\{F(\mathbf{x},\mathbf{y}) :=G(\mathbf{x}) + \langle \mathbf{y}, \overline{P}
\mathbf{x} \rangle - H(\mathbf{y})\}$, where the functions $G(\cdot)$,
$H(\cdot)$, and the the coupling matrix $\overline{P}$ are distributed over a
strongly connected network of nodes. GT-GDA is a first-order method that uses
gradient tracking to eliminate the dissimilarity caused by heterogeneous data
distribution among the nodes. In the most general form, GT-GDA includes a
consensus over the local coupling matrices to achieve the optimal (unique)
saddle point, however, at the expense of increased communication. To avoid
this, we propose a more efficient variant GT-GDA-Lite that does not incur the
additional communication and analyze its convergence in various scenarios. We
show that GT-GDA converges linearly to the unique saddle point solution when
$G(\cdot)$ is smooth and convex, $H(\cdot)$ is smooth and strongly convex, and
the global coupling matrix $\overline{P}$ has full column rank. We further
characterize the regime under which GT-GDA exhibits a network
topology-independent convergence behavior. We next show the linear convergence
of GT-GDA to an error around the unique saddle point, which goes to zero when
the coupling cost ${\langle \mathbf y, \overline{P} \mathbf x \rangle}$ is
common to all nodes, or when $G(\cdot)$ and $H(\cdot)$ are quadratic. Numerical
experiments illustrate the convergence properties and importance of GT-GDA and
GT-GDA-Lite for several applications.


title: Transit Timing Variations for AU Microscopii b & c

abstract:
  We explore the transit timing variations (TTVs) of the young (22 Myr) nearby
AU Mic planetary system. For AU Mic b, we introduce three Spitzer (4.5 $\mu$m)
transits, five TESS transits, 11 LCO transits, one PEST transit, one Brierfield
transit, and two transit timing measurements from Rossiter-McLaughlin
observations; for AU Mic c, we introduce three TESS transits. We present two
independent TTV analyses. First, we use EXOFASTv2 to jointly model the Spitzer
and ground-based transits and to obtain the midpoint transit times. We then
construct an O--C diagram and model the TTVs with Exo-Striker. Second, we
reproduce our results with an independent photodynamical analysis. We recover a
TTV mass for AU Mic c of 10.8$^{+2.3}_{-2.2}$ M$_{E}$. We compare the
TTV-derived constraints to a recent radial-velocity (RV) mass determination. We
also observe excess TTVs that do not appear to be consistent with the dynamical
interactions of b and c alone, and do not appear to be due to spots or flares.
Thus, we present a hypothetical non-transiting "middle-d" candidate exoplanet
that is consistent with the observed TTVs, the candidate RV signal, and would
establish the AU Mic system as a compact resonant multi-planet chain in a 4:6:9
period commensurability. These results demonstrate that the AU Mic planetary
system is dynamically interacting producing detectable TTVs, and the implied
orbital dynamics may inform the formation mechanisms for this young system. We
recommend future RV and TTV observations of AU Mic b and c to further constrain
the masses and to confirm the existence of possible additional planet(s).


title: Cosmic-ray generated bubbles around their sources

abstract:
  Cosmic rays are thought to escape their sources streaming along the local
magnetic field lines. We show that this phenomenon generally leads to the
excitation of both resonant and non-resonant streaming instabilities. The
self-generated magnetic fluctuations induce particle diffusion in extended
regions around the source, so that cosmic rays build up a large pressure
gradient. By means of two-dimensional (2D) and three-dimensional (3D) hybrid
particle-in-cell simulations, we show that such a pressure gradient excavates a
cavity around the source and leads to the formation of a cosmic-ray dominated
bubble, inside which diffusivity is strongly suppressed. Based on the trends
extracted from self-consistent simulations, we estimate that, in the absence of
severe damping of the self-generated magnetic fields, the bubble should keep
expanding until pressure balance with the surrounding medium is reached,
corresponding to a radius of $\sim 10-50$ pc. The implications of the formation
of these regions of low diffusivity for sources of Galactic cosmic rays are
discussed. Special care is devoted to estimating the self-generated diffusion
coefficient and the grammage that cosmic rays might accumulate in the bubbles
before moving into the interstellar medium. Based on the results of 3D
simulations, general considerations on the morphology of the $\gamma$-ray and
synchrotron emission from these extended regions also are outlined.


title:
Semialgebraic and Continuous Solution of Linear Equations with
  Semialgebraic Coefficients

abstract:
  Starting from the approach proposed by Charles Fefferman and Janos Kollar in
"Continuous Solutions of Linear Equations", we generalize and prove, by using
Fefferman's techniques of Glaeser refinement, a part of the result that was
proved by Kollar by using techniques from algebraic geometry. In particular we
consider a system of linear equation with semialgebraic coefficients and we get
a necessary and sufficient condition for the existence of a continuous and
semialgebraic solution. Namely, we prove that a continuous and semialgebraic
solution exists if and only if there is a continuous solution i.e., if the
Glaeser-stable bundle associated to the system has no empty fiber.


title: Why just FRET when you can Refactor? Retuning FRETISH Requirements

abstract:
  Formal verification of a software system relies on formalising the
requirements to which it should adhere, which can be challenging. While
formalising requirements from natural-language, we have dependencies that lead
to duplication of information across many requirements, meaning that a change
to one requirement causes updates in several places. We propose to adapt code
refactorings for NASA's Formal Requirements Elicitation Tool (FRET), our
tool-of-choice. Refactoring is the process of reorganising software to improve
its internal structure without altering its external behaviour; it can also be
applied to requirements, to make them more manageable by reducing repetition.
FRET automatically translates requirements (written in its input language
Fretish) into Temporal Logic, which enables us to formally verify that
refactoring has preserved the requirements' underlying meaning. In this paper,
we present four refactorings for Fretish requirements and explain their
utility. We describe the application of one of these refactorings to the
requirements of a civilian aircraft engine software controller, to decouple the
dependencies from the duplication, and analyse how this changes the number of
requirements and the number of repetitions. We evaluate our approach using
Spot, a tool for checking equivalence of Temporal Logic specifications.


title:
The HaMSE Ontology: Using Semantic Technologies to support Music
  Representation Interoperability and Musicological Analysis

abstract:
  The use of Semantic Technologies - in particular the Semantic Web - has
revealed to be a great tool for describing the cultural heritage domain and
artistic practices. However, the panorama of ontologies for musicological
applications seems to be limited and restricted to specific applications. In
this research, we propose HaMSE, an ontology capable of describing musical
features that can assist musicological research. More specifically, HaMSE
proposes to address sues that have been affecting musicological research for
decades: the representation of music and the relationship between quantitative
and qualitative data. To do this, HaMSE allows the alignment between different
music representation systems and describes a set of musicological features that
can allow the music analysis at different granularity levels.


title: Modularity lifting theorems

abstract:
  Updated version of 2013 Arizona WInter School notes on modularity lifting
theorems for for two-dimensional p-adic representations, using wherever
possible arguments that go over to the n-dimensional (self-dual) case.


title: Nonprehensile Manipulation of a Stick Using Impulsive Forces

abstract:
  The problem of nonprehensile manipulation of a stick in three-dimensional
space using intermittent impulsive forces is considered. The objective is to
juggle the stick between a sequence of configurations that are rotationally
symmetric about the vertical axis. The dynamics of the stick is described by
five generalized coordinates and three control inputs. Between two consecutive
rotationally symmetric configurations, the dynamics is conveniently represented
by a Poincar\'e map in the reference frame of the juggler. Stabilization of the
orbit associated with a desired juggling motion is accomplished by stabilizing
a fixed point on the Poincar\'e map. The Impulse Controlled Poincar\'e Map
approach is used to stabilize the orbit, and numerical simulations are used
demonstrate convergence to the desired juggling motion from an arbitrary
initial configuration. In the limiting case, where consecutive rotationally
symmetric configurations are chosen arbitrarily close, it is shown that the
dynamics reduces to that of steady precession of the stick on a hoop.


title: Expectation Consistent Plug-and-Play for MRI

abstract:
  For image recovery problems, plug-and-play (PnP) methods have been developed
that replace the proximal step in an optimization algorithm with a call to an
application-specific denoiser, often implemented using a deep neural network.
Although such methods have been successful, they can be improved. For example,
the denoiser is often trained using white Gaussian noise, while PnP's denoiser
input error is often far from white and Gaussian, with statistics that are
difficult to predict from iteration to iteration. PnP methods based on
approximate message passing (AMP) are an exception, but only when the forward
operator behaves like a large random matrix. In this work, we design a PnP
method using the expectation consistent (EC) approximation algorithm, a
generalization of AMP, that offers predictable error statistics at each
iteration, from which a deep-net denoiser can be effectively trained.


title:
PEg TRAnsfer Workflow recognition challenge report: Does multi-modal
  data improve recognition?

abstract:
  This paper presents the design and results of the "PEg TRAnsfert Workflow
recognition" (PETRAW) challenge whose objective was to develop surgical
workflow recognition methods based on one or several modalities, among video,
kinematic, and segmentation data, in order to study their added value. The
PETRAW challenge provided a data set of 150 peg transfer sequences performed on
a virtual simulator. This data set was composed of videos, kinematics, semantic
segmentation, and workflow annotations which described the sequences at three
different granularity levels: phase, step, and activity. Five tasks were
proposed to the participants: three of them were related to the recognition of
all granularities with one of the available modalities, while the others
addressed the recognition with a combination of modalities. Average
application-dependent balanced accuracy (AD-Accuracy) was used as evaluation
metric to take unbalanced classes into account and because it is more
clinically relevant than a frame-by-frame score. Seven teams participated in at
least one task and four of them in all tasks. Best results are obtained with
the use of the video and the kinematics data with an AD-Accuracy between 93%
and 90% for the four teams who participated in all tasks. The improvement
between video/kinematic-based methods and the uni-modality ones was significant
for all of the teams. However, the difference in testing execution time between
the video/kinematic-based and the kinematic-based methods has to be taken into
consideration. Is it relevant to spend 20 to 200 times more computing time for
less than 3% of improvement? The PETRAW data set is publicly available at
www.synapse.org/PETRAW to encourage further research in surgical workflow
recognition.


title: CLIPasso: Semantically-Aware Object Sketching

abstract:
  Abstraction is at the heart of sketching due to the simple and minimal nature
of line drawings. Abstraction entails identifying the essential visual
properties of an object or scene, which requires semantic understanding and
prior knowledge of high-level concepts. Abstract depictions are therefore
challenging for artists, and even more so for machines. We present an object
sketching method that can achieve different levels of abstraction, guided by
geometric and semantic simplifications. While sketch generation methods often
rely on explicit sketch datasets for training, we utilize the remarkable
ability of CLIP (Contrastive-Language-Image-Pretraining) to distill semantic
concepts from sketches and images alike. We define a sketch as a set of
B\'ezier curves and use a differentiable rasterizer to optimize the parameters
of the curves directly with respect to a CLIP-based perceptual loss. The
abstraction degree is controlled by varying the number of strokes. The
generated sketches demonstrate multiple levels of abstraction while maintaining
recognizability, underlying structure, and essential visual components of the
subject drawn.


title: Consistent Bayesian community recovery in multilayer networks

abstract:
  Revealing underlying relations between nodes in a network is one of the most
important tasks in network analysis. Using tools and techniques from a variety
of disciplines, many community recovery methods have been developed for
different scenarios. Despite the recent interest on community recovery in
multilayer networks, theoretical results on the accuracy of the estimates are
few and far between. Given a multilayer, e.g. temporal, network and a
multilayer stochastic block model, we derive bounds for sufficient separation
between intra- and inter-block connectivity parameters to achieve posterior
exact and almost exact community recovery. These conditions are comparable to a
well known threshold for community detection by a single-layer stochastic block
model. A simulation study shows that the derived bounds translate to
classification accuracy that improves as the number of observed layers
increases.


title:
Bell's inequality violation by dynamical Casimir photons in
  superconducting microwave circuit

abstract:
  We study the Bell's inequality violation by dynamical Casimir radiation with
pseudospin measurement. We consider a circuit quantum electrodynamical set-up
where a relativistically moving mirror is simulated by variable external
magnetic flux in a SQUID terminating a superconducting-microwave waveguide. We
analytically obtain expectation values of the Bell operator optimized with
respect to channel orientations, in terms of the system parameters. We consider
the effects of local noise in the microwave field modes, asymmetry between the
field modes resulting from nonzero detuning, and signal loss. Our analysis
provides ranges of the above experimental parameters for which Bell violation
can be observed. We show that Bell violation can be observed in this set-up up
to 40 mK temperature as well as up to 65 % signal loss.


title: Birth Quota of Non-Generic Degeneracy Points

abstract:
  Weyl points are generic and stable features in the energy spectrum of
Hamiltonians that depend on a three-dimensional parameter space. Non-generic
isolated two-fold degeneracy points, such as multi-Weyl points, split into Weyl
points upon a generic perturbation that removes the fine-tuning or protecting
symmetry. The number of the resulting Weyl points is at least $|Q|$, where $Q$
is the topological charge associated to the non-generic degeneracy point. Here,
we show that such a non-generic degeneracy point also has a birth quota, i.e.,
a maximum number of Weyl points that can be born from it upon any perturbation.
The birth quota is a local multiplicity associated to the non-generic
degeneracy point, an invariant of map germs known from singularity theory. This
holds not only for the case of a three-dimensional parameter space with a
Hermitian Hamiltonian, but also for the case of a two-dimensional parameter
space with a chiral-symmetric Hamiltonian. We illustrate the power of this
result for electronic band structures of two- and three-dimensional crystals.
Our work establishes a strong connection between singularity theory and
topological band structures, and more broadly, parameter-dependent quantum
systems.


title:
End-to-end Algorithm Synthesis with Recurrent Networks: Logical
  Extrapolation Without Overthinking

abstract:
  Machine learning systems perform well on pattern matching tasks, but their
ability to perform algorithmic or logical reasoning is not well understood. One
important reasoning capability is logical extrapolation, in which models
trained only on small/simple reasoning problems can synthesize complex
algorithms that scale up to large/complex problems at test time. Logical
extrapolation can be achieved through recurrent systems, which can be iterated
many times to solve difficult reasoning problems. We observe that this approach
fails to scale to highly complex problems because behavior degenerates when
many iterations are applied -- an issue we refer to as "overthinking." We
propose a recall architecture that keeps an explicit copy of the problem
instance in memory so that it cannot be forgotten. We also employ a progressive
training routine that prevents the model from learning behaviors that are
specific to iteration number and instead pushes it to learn behaviors that can
be repeated indefinitely. These innovations prevent the overthinking problem,
and enable recurrent systems to solve extremely hard logical extrapolation
tasks, some requiring over 100K convolutional layers, without overthinking.


title:
Automated Architecture Search for Brain-inspired Hyperdimensional
  Computing

abstract:
  This paper represents the first effort to explore an automated architecture
search for hyperdimensional computing (HDC), a type of brain-inspired neural
network. Currently, HDC design is largely carried out in an
application-specific ad-hoc manner, which significantly limits its application.
Furthermore, the approach leads to inferior accuracy and efficiency, which
suggests that HDC cannot perform competitively against deep neural networks.
Herein, we present a thorough study to formulate an HDC architecture search
space. On top of the search space, we apply reinforcement-learning to
automatically explore the HDC architectures. The searched HDC architectures
show competitive performance on case studies involving a drug discovery dataset
and a language recognition task. On the Clintox dataset, which tries to learn
features from developed drugs that passed/failed clinical trials for toxicity
reasons, the searched HDC architecture obtains the state-of-the-art ROC-AUC
scores, which are 0.80% higher than the manually designed HDC and 9.75% higher
than conventional neural networks. Similar results are achieved on the language
recognition task, with 1.27% higher performance than conventional methods.


title:
Cross-caps, triple points and a linking invariant for finitely
  determined germs

abstract:
  It was recently proved that for finitely determined germs $ \Phi: (
\mathbb{C}^2, 0) \to ( \mathbb{C}^3, 0) $ the number $C(\Phi)$ of Whitney
umbrella points and the number $T(\Phi)$ of triple values of a stable
deformation are topological invariants. The proof uses the fact that the
combination $C(\Phi)-3T(\Phi)$ is topological since it equals the linking
invariant of the associated immersion $S^3 \looparrowright S^5$ introduced by
Ekholm and Sz\H{u}cs. We provide a new, direct proof for this equality. We also
clarify the relation between various definitions of the latter invariant.


title:
Clean quantum point contacts in an InAs quantum well grown on a
  lattice-mismatched InP substrate

abstract:
  Strong spin-orbit coupling, the resulting large $g$-factor, and small
effective mass make InAs an attractive material platform for inducing
topological superconductivity. The surface Fermi level pinning in the
conduction band enables highly transparent ohmic contact without excessive
doping. We investigate electrostatically-defined quantum point contacts (QPCs)
in deep-well InAs 2DEGs. Despite the 3.3\% lattice mismatch between the InAs
quantum well and the InP substrate, we report clean QPCs with up to eight
pronounced quantized conductance plateaus at zero magnetic field. Source-drain
DC bias spectroscopy reveals a harmonic confinement potential with a nearly $5$
meV subband spacing. We find a many-body exchange interaction enhancement for
the out-of-plane $g$-factor $|g_{\perp}^*| = 27 \pm 1$, whereas the in-plane
$g$-factor is isotropic $|g^*_{x}| = |g^*_{y}| = 12 \pm 2$, close to the bulk
value for InAs.


title:
Learning Fast Samplers for Diffusion Models by Differentiating Through
  Sample Quality

abstract:
  Diffusion models have emerged as an expressive family of generative models
rivaling GANs in sample quality and autoregressive models in likelihood scores.
Standard diffusion models typically require hundreds of forward passes through
the model to generate a single high-fidelity sample. We introduce
Differentiable Diffusion Sampler Search (DDSS): a method that optimizes fast
samplers for any pre-trained diffusion model by differentiating through sample
quality scores. We also present Generalized Gaussian Diffusion Models (GGDM), a
family of flexible non-Markovian samplers for diffusion models. We show that
optimizing the degrees of freedom of GGDM samplers by maximizing sample quality
scores via gradient descent leads to improved sample quality. Our optimization
procedure backpropagates through the sampling process using the
reparametrization trick and gradient rematerialization. DDSS achieves strong
results on unconditional image generation across various datasets (e.g., FID
scores on LSUN church 128x128 of 11.6 with only 10 inference steps, and 4.82
with 20 steps, compared to 51.1 and 14.9 with strongest DDPM/DDIM baselines).
Our method is compatible with any pre-trained diffusion model without
fine-tuning or re-training required.


title: Character sheaves for classical graded Lie algebras

abstract:
  In this note we study character sheaves for graded Lie algebras arising from
inner automorphisms of special linear groups and Vinberg's type II classical
graded Lie algebras.