title: Migration-Based Synchronization

abstract:
  A fundamental challenge in multi- and many-core systems is the correct
execution of concurrent access to shared data. A common drawback from existing
synchronization mechanisms is the loss of data locality as the shared data is
transferred between the accessing cores. In real-time systems, this is
especially important as knowledge about data access times is crucial to
establish bounds on execution times and guarantee the meeting of deadlines.We
propose in this paper a refinement of our previously sketched approach of
Migration-Based Synchronization (MBS) as well as its first practical
implementation. The core concept of MBS is the replacement of data migration
with control-flow migration to achieve synchronized memory accesses with
guaranteed data locality. This leads to both shorter and more predictable
execution times for critical sections. As MBS can be used as a substitute for
classical locks, it can be employed in legacy applications without code
alterations.We further examine how the gained data locality improves the
results of worst-case timing analyses and results in tighter bounds on
execution and response time. We reason about the similarity of MBS to existing
synchronization approaches and how it enables us to reuse existing analysis
techniques.Finally, we evaluate our prototype implementation, showing that MBS
can exploit data locality with similar overheads as traditional locking
mechanisms.


title: k^{th} order Slant Hankel Operators on the Polydisk

abstract:
  In this paper, we initiate the notion of k^{th} order slant Hankel operators
on L^2(T^n) for k greater than or equal to 2 and n greater than or equal to 1
where T^n denotes the n-torus. We give the necessary and sufficient condition
for a bounded operator on L^2(T^n) to be a k^{th} order slant Hankel and
discuss their commutative, compactness, hyponormal and isometric property.


title:
Snowflake Point Deconvolution for Point Cloud Completion and Generation
  with Skip-Transformer

abstract:
  Most existing point cloud completion methods suffered from discrete nature of
point clouds and unstructured prediction of points in local regions, which
makes it hard to reveal fine local geometric details. To resolve this issue, we
propose SnowflakeNet with Snowflake Point Deconvolution (SPD) to generate the
complete point clouds. SPD models the generation of complete point clouds as
the snowflake-like growth of points, where the child points are progressively
generated by splitting their parent points after each SPD. Our insight of
revealing detailed geometry is to introduce skip-transformer in SPD to learn
point splitting patterns which can fit local regions the best. Skip-transformer
leverages attention mechanism to summarize the splitting patterns used in
previous SPD layer to produce the splitting in current SPD layer. The locally
compact and structured point clouds generated by SPD precisely reveal the
structure characteristic of 3D shape in local patches, which enables us to
predict highly detailed geometries. Moreover, since SPD is a general operation,
which is not limited to completion, we further explore the applications of SPD
on other generative tasks, including point cloud auto-encoding, generation,
single image reconstruction and upsampling. Our experimental results outperform
the state-of-the-art methods under widely used benchmarks.


title: Mixture-of-Experts with Expert Choice Routing

abstract:
  Sparsely-activated Mixture-of-experts (MoE) models allow the number of
parameters to greatly increase while keeping the amount of computation for a
given token or a given sample unchanged. However, a poor expert routing
strategy (e.g. one resulting in load imbalance) can cause certain experts to be
under-trained, leading to an expert being under or over-specialized. Prior work
allocates a fixed number of experts to each token using a top-k function
regardless of the relative importance of different tokens. To address this, we
propose a heterogeneous mixture-of-experts employing an expert choice method.
Instead of letting tokens select the top-k experts, we have experts selecting
the top-k tokens. As a result, each token can be routed to a variable number of
experts and each expert can have a fixed bucket size. We systematically study
pre-training speedups using the same computational resources of the Switch
Transformer top-1 and GShard top-2 gating of prior work and find that our
method improves training convergence time by more than 2x. For the same
computational cost, our method demonstrates higher performance in fine-tuning
11 selected tasks in the GLUE and SuperGLUE benchmarks. For a smaller
activation cost, our method outperforms the T5 dense model in 7 out of the 11
tasks.


title:
Exact bistability and time pseudo-crystallization of driven-dissipative
  fermionic lattices

abstract:
  The existence of bistability in quantum optical systems remains a intensely
debated open question beyond the mean-field approximation. Quantum fluctuations
are finite-size corrections to the mean-field approximation used because the
full exact solution is unobtainable. Usually, quantum fluctuations destroy the
bistability present on the mean-field level. Here, by identifying and using
exact modulated semi-local dynamical symmetries in a certain quantum optical
models of driven-dissipative fermionic chains we exactly prove bistability in
precisely the quantum fluctuations. Surprisingly, rather than destroying
bistability, the quantum fluctuations themselves exhibit bistability, even
though it is absent on the mean-field level for our systems. Moreover, the
models studied acquire additional thermodynamic dynamical symmetries that imply
persistent periodic oscillations in the quantum fluctuations, constituting
pseudo-variants of boundary time crystals. Physically, these emergent operators
correspond to finite-frequency and finite-momentum semi-local Goldstone modes.
Our work therefore provides to the best of our knowledge the first example of a
provably bistable quantum optical system.

categories: quant-ph cond-mat.quant-gas cond-mat.stat-mech physics.atom-ph


title:
Comparison of Updated Electron Capture Rates in the N=50 Region using 1D
  Simulations of Core-collapse Supernovae

abstract:
  Recent studies have highlighted the sensitivity of core-collapse supernovae
(CCSNe) models to electron-capture (EC) rates on neutron-rich nuclei near the
N=50 closed-shell region. In this work, we perform a large suite of
one-dimensional (1D) CCSN simulations for 200 stellar progenitors using
recently-updated EC rates in this region. For comparison, we repeat the
simulations using two previous implementations of EC rates: a microphysical
library with parametrized N=50 rates (LMP), and an older single-nucleus
approximation (SNA). We follow the simulations through shock revival up to
several seconds post-bounce, and show that the EC rates produce a consistent
imprint on CCSN properties, often surpassing the role of the progenitor itself.
Notable impacts include the timescale of core collapse, the electron fraction
and mass of the inner core at bounce, the accretion rate through the shock, the
success or failure of revival, and the properties of the central compact
remnant. We also compare the observable neutrino signal of the neutronization
burst in a DUNE-like detector, and find consistent impacts to the counts and
mean energies. Overall, the updated rates result in properties that are
intermediate between LMP and SNA, and yet slightly more favorable to explosion
than both.


title:
The ALPs from the Top: Searching for long lived axion-like particles
  from exotic top decays

abstract:
  We propose a search for long lived axion-like particles (ALPs) in exotic top
decays. Flavour-violating ALPs appear as low energy effective theories for
various new physics scenarios such as t-channel dark sectors or
Froggatt-Nielsen models. In this case the top quark may decay to an ALP and an
up- or charm-quark. For masses in the few GeV range, the ALP is long lived
across most of the viable parameter space, suggesting a dedicated search. We
propose to search for these long lived ALPs in $t\bar{t}$ events, using one top
quark as a trigger. We focus on ALPs decaying in the hadronic calorimeter, and
show that the ratio of energy deposits in the electromagnetic and hadronic
calorimeters as well as track vetoes can efficiently suppress Standard Model
backgrounds. Our proposed search can probe exotic top branching ratios smaller
than $10^{-4}$ with a conservative strategy at the upcoming LHC run, and
potentially below the $10^{-7}$ level with more advanced methods. Finally we
also show that measurements of single top production probe these branching
ratios in the very short and very long lifetime limit at the $10^{-3}$ level.


title:
Quantum Optimization of Maximum Independent Set using Rydberg Atom
  Arrays

abstract:
  Realizing quantum speedup for practically relevant, computationally hard
problems is a central challenge in quantum information science. Using Rydberg
atom arrays with up to 289 qubits in two spatial dimensions, we experimentally
investigate quantum algorithms for solving the Maximum Independent Set problem.
We use a hardware-efficient encoding associated with Rydberg blockade, realize
closed-loop optimization to test several variational algorithms, and
subsequently apply them to systematically explore a class of graphs with
programmable connectivity. We find the problem hardness is controlled by the
solution degeneracy and number of local minima, and experimentally benchmark
the quantum algorithm's performance against classical simulated annealing. On
the hardest graphs, we observe a superlinear quantum speedup in finding exact
solutions in the deep circuit regime and analyze its origins.

categories: quant-ph cond-mat.dis-nn cond-mat.quant-gas physics.atom-ph


title:
Requirements on common solutions to the LSND and MiniBooNE excesses: a
  post-MicroBooNE study

abstract:
  The strong statistical significance of an observed electron-like event excess
in the MiniBooNE (MB) experiment, along with an earlier similar excess seen in
the Liquid Scintillator Neutrino Detector (LSND), when interpreted in
conjunction with recent MicroBooNE results may have brought us to the cusp of
new physics discoveries. This has led to many attempts to understand these
observations, both for each experiment individually and in conjunction, via
physics beyond the Standard Model (SM). We provide an overview of the current
situation, and discuss three major categories under which the many proposals
for new physics fall. The possibility that the same new, non-oscillation
physics explains both anomalies leads to new restrictions and requirements. An
important class of such common solutions, which we focus on in this work,
consists of a heavy ${\cal O}$(MeV$-$sub-GeV) sterile neutral fermion produced
in the detectors, (via up-scattering of the incoming muon neutrinos), and
subsequently decaying to photons or $e^+e^-$ pairs which mimic the observed
signals. Such solutions are subject to strong demands from a) cross section
requirements which would yield a sufficient number of total events in both LSND
and MB, b) requirements imposed by the measured energy and angular
distributions in both experiments and finally, c) consistency and compatibility
of the new physics model and its particle content with other bounds from a
diverse swathe of particle physics experiments. We find that these criteria
often pull proposed solutions in different directions, and stringently limit
the viable set of proposals which could resolve both anomalies. Our conclusions
are relevant for both the general search for new physics and for the ongoing
observations and analyses of the MicroBooNE experiment.


title:
Explaining, Evaluating and Enhancing Neural Networks' Learned
  Representations

abstract:
  Most efforts in interpretability in deep learning have focused on (1)
extracting explanations of a specific downstream task in relation to the input
features and (2) imposing constraints on the model, often at the expense of
predictive performance. New advances in (unsupervised) representation learning
and transfer learning, however, raise the need for an explanatory framework for
networks that are trained without a specific downstream task. We address these
challenges by showing how explainability can be an aid, rather than an
obstacle, towards better and more efficient representations. Specifically, we
propose a natural aggregation method generalizing attribution maps between any
two (convolutional) layers of a neural network. Additionally, we employ such
attributions to define two novel scores for evaluating the informativeness and
the disentanglement of latent embeddings. Extensive experiments show that the
proposed scores do correlate with the desired properties. We also confirm and
extend previously known results concerning the independence of some common
saliency strategies from the model parameters. Finally, we show that adopting
our proposed scores as constraints during the training of a representation
learning task improves the downstream performance of the model.


title:
Ephemeral Learning -- Augmenting Triggers with Online-Trained
  Normalizing Flows

abstract:
  The large data rates at the LHC require an online trigger system to select
relevant collisions. Rather than compressing individual events, we propose to
compress an entire data set at once. We use a normalizing flow as a deep
generative model to learn the probability density of the data online. The
events are then represented by the generative neural network and can be
inspected offline for anomalies or used for other analysis purposes. We
demonstrate our new approach for a toy model and a correlation-enhanced bump
hunt.


title:
Using Artificial Intelligence and real galaxy images to constrain
  parameters in galaxy formation simulations

abstract:
  Cosmological galaxy formation simulations are still limited by their
spatial/mass resolution and cannot model from first principles some of the
processes, like star formation, that are key in driving galaxy evolution. As a
consequence they still rely on a set of 'effective parameters' that try to
capture the scales and the physical processes that cannot be directly resolved
in the simulation. In this study we show that it is possible to use Machine
Learning techniques applied to real and simulated images of galaxies to
discriminate between different values of these parameters by making use of the
full information content of an astronomical image instead of collapsing it into
a limited set of values like size, or stellar/ gas masses. In this work we
apply our method to the NIHAO simulations and the THINGS and VLA-ANGST
observations of HI maps in nearby galaxies to test the ability of different
values of the star formation density threshold $n$ to reproduce observed HI
maps. We show that observations indicate the need for a high value of $n
\gtrsim 80$ ,cm$^{-3}$ (although the exact numerical value is model-dependent),
which has important consequences for the dark matter distribution in galaxies.
Our study shows that with innovative methods it is possible to take full
advantage of the information content of galaxy images and compare simulations
and observations in an interpretable, non-parametric and quantitative manner.


title:
Exploring the physical properties of lensed star-forming clumps at
  $2\lesssim z \lesssim6$

abstract:
  We study the physical properties (size, stellar mass, luminosity, star
formation rate) and scaling relations for a sample of 166 star-forming clumps
with redshift $z \sim 2-6.2$. They are magnified by the Hubble Frontier Field
galaxy cluster MACS~J0416 and have robust lensing amplification ($2\lesssim \mu
\lesssim 82$) computed by using our high-precision lens model, based on 182
multiple images. Our sample extends by $\sim 3$ times the number of
spectroscopically-confirmed lensed clumps at $z \gtrsim 2$. We identify clumps
in ultraviolet continuum images and find that, whenever the effective spatial
resolution (enhanced by gravitational lensing) increases, they fragment into
smaller entities, likely reflecting the hierarchically-organized nature of star
formation. Kpc-scale clumps, most commonly observed in non-lensed fields, are
not found in our sample. The physical properties of our sample extend the
parameter space typically probed by $z \gtrsim 1$ non-lensed observations and
simulations, by populating the low mass (M$_\star \lesssim 10^7$ M$_\odot$),
low star formation rate (SFR $\lesssim 0.5$ M$_\odot$ yr$^{-1}$), and small
size (R$_\mathrm{eff} \lesssim 100$ pc) regime. The new domain probed by our
study approaches the regime of compact stellar complexes and star clusters. In
the mass-size plane our sample span the region between galaxies and globular
clusters, with a few clumps laying in the region populated by young star
clusters and globular-clusters. For the bulk of our sample, we measure
star-formation rates which are higher than those observed locally in compact
stellar systems, indicating different conditions for star formation at high
redshift and in the local Universe.


title:
The Very Knotty Lenser: exploring the role of regularization in source
  and potential reconstructions using Gaussian Process Regression

abstract:
  Reconstructing lens potentials and lensed sources can easily become an
underconstrained problem, even when the degrees of freedom are low, due to
degeneracies, particularly when potential perturbations superimposed on a
smooth lens are included. Regularization has traditionally been used to
constrain the solutions where the data failed to do so, e.g. in unlensed parts
of the source. In this exploratory work, we go beyond the usual choices of
regularization and adopt observationally motivated priors for the source
brightness. We also perform a similar comparison when reconstructing lens
potential perturbations, which are assumed to be stationary, i.e. permeate the
entire field of view. We find that physically motivated priors lead to lower
residuals, avoid overfitting, and are decisively preferred within a Bayesian
quantitative framework in all the examples considered. For the perturbations,
choosing the wrong regularization can have a detrimental effect that even
high-quality data cannot correct for, while using a purely smooth lens model
can absorb them to a very high degree and lead to biased solutions. Finally,
our new implementation of the semi-linear inversion technique provides the
first quantitative framework for measuring degeneracies between the source and
the potential perturbations.


title:
Hints of Early Dark Energy in Planck, SPT, and ACT data: new physics or
  systematics?

abstract:
  We investigate constraints on early dark energy (EDE) using ACT DR4, SPT-3G
2018, Planck polarization, and restricted Planck temperature data (at
$\ell<650$), finding a $3.3\sigma$ preference ($\Delta\chi^2=-16.2$ for 3
additional degrees of freedom) for EDE over $\Lambda$CDM. The EDE contributes a
maximum fractional energy density of $f_{\rm EDE}(z_c)=0.163^{+0.047}_{-0.04}$
at a redshift $z_c=3357\pm200$ and leads to a CMB inferred value of the Hubble
constant $H_0=74.2^{+1.9}_{-2.1}$ km/s/Mpc. We find that Planck and ACT DR4
data provide the majority of the improvement in $\chi^2$, and that the
inclusion of SPT-3G pulls the posterior of $f_{\rm EDE}(z_c)$ away from
$\Lambda$CDM. This is the first time that a moderate preference for EDE has
been reported for these three combined CMB data sets. We find that including
measurements of supernovae luminosity distances and the baryon acoustic
oscillation standard ruler only minimally affects the preference ($3.0\sigma$),
while measurements that probe the clustering of matter at late times - the
lensing potential power spectrum from Planck and $f \sigma_8$ from BOSS -
decrease the significance of the preference to 2.6$\sigma$. Conversely, adding
a prior on the $H_0$ value as reported by the SH0ES collaboration increases the
preference to the $4-5\sigma$ level. In the absence of this prior, the
inclusion of Planck TT data at $\ell>1300$ reduces the preference from
$3.0\sigma$ to $2.3\sigma$ and the constraint on $f_{\rm EDE}(z_c)$ becomes
compatible with $\Lambda$CDM at $1\sigma$. We explore whether systematic errors
in the Planck polarization data may affect our conclusions and find that
changing the TE polarization efficiencies significantly reduces the Planck
preference for EDE. More work will be necessary to establish whether these
hints for EDE within CMB data alone are the sole results of systematic errors
or an opening to new physics.


title: Efficient Template Bank Generation with Differentiable Waveforms

abstract:
  The most sensitive search pipelines for gravitational waves from compact
binary mergers use matched filters to extract signals from the noisy data
stream coming from gravitational wave detectors. Matched-filter searches
require banks of template waveforms covering the physical parameter space of
the binary system. Unfortunately, template bank construction can be a
time-consuming task. Here we present a new method for efficiently generating
template banks that utilizes automatic differentiation to calculate the
parameter space metric. Principally, we demonstrate that automatic
differentiation enables accurate computation of the metric for waveforms
currently used in search pipelines, whilst being computationally cheap.
Additionally, by combining random template placement and a Monte Carlo method
for evaluating the fraction of the parameter space that is currently covered,
we show that search-ready template banks for frequency-domain waveforms can be
rapidly generated. Finally, we argue that differentiable waveforms offer a
pathway to accelerating stochastic placement algorithms. We implement all our
methods into an easy-to-use Python package based on the jax framework,
diffbank, to allow the community to easily take advantage of differentiable
waveforms for future searches.


title: Synthetic Disinformation Attacks on Automated Fact Verification Systems

abstract:
  Automated fact-checking is a needed technology to curtail the spread of
online misinformation. One current framework for such solutions proposes to
verify claims by retrieving supporting or refuting evidence from related
textual sources. However, the realistic use cases for fact-checkers will
require verifying claims against evidence sources that could be affected by the
same misinformation. Furthermore, the development of modern NLP tools that can
produce coherent, fabricated content would allow malicious actors to
systematically generate adversarial disinformation for fact-checkers.
  In this work, we explore the sensitivity of automated fact-checkers to
synthetic adversarial evidence in two simulated settings: AdversarialAddition,
where we fabricate documents and add them to the evidence repository available
to the fact-checking system, and AdversarialModification, where existing
evidence source documents in the repository are automatically altered. Our
study across multiple models on three benchmarks demonstrates that these
systems suffer significant performance drops against these attacks. Finally, we
discuss the growing threat of modern NLG systems as generators of
disinformation in the context of the challenges they pose to automated
fact-checkers.


title:
Probing neutron stars with the full pre-merger and post-merger
  gravitational wave signal from binary coalescences

abstract:
  The gravitational wave signal emitted during the coalescence of two neutron
stars carries information about the stars' internal structure. During the long
inspiral phase the main matter observable is the tidal interaction between the
binary components, an effect that can be parametrically modeled with
compact-binary solutions to General Relativity. After the binary merger the
main observable is frequency modes of the remnant, most commonly giving rise to
a short-duration signal accessible only through numerical simulations. The
complicated morphology and the decreasing detector sensitivity in the relevant
frequencies currently hinder detection of the post-merger signal and motivate
separate analyses for the pre-merger and post-merger data. However, planned and
ongoing detector improvements could soon put the post-merger signal within
reach. In this study we target the whole pre-merger and post-merger signal
without an artificial separation at the binary merger. We construct a hybrid
analysis that models the inspiral with templates based on analytical
calculations and calibrated to numerical relativity and the post-merger signal
with a flexible morphology-independent analysis. Applying this analysis to
GW170817 we find, as expected, that the post-merger signal remains undetected.
We further study simulated signals and find that we can reconstruct the full
signal and simultaneously estimate both the pre-merger tidal deformation and
the post-merger signal frequency content. Our analysis allows us to study
neutron star physics using all the data available and directly test the
pre-merger and post-merger signal for consistency thus probing effects such as
the onset of the hadron-quark phase transition.


title:
A Bayesian Hierarchical Time Series Model for Reconstructing
  Hydroclimate from Multiple Proxies

abstract:
  We propose a Bayesian model which produces probabilistic reconstructions of
hydroclimatic variability in Queensland Australia. The approach uses
instrumental records of hydroclimate indices such as rain and evaporation, as
well as palaeoclimate proxy records derived from natural archives such as
sediment cores, speleothems, ice cores and tree rings. The method provides a
standardised approach to using multiple palaeoclimate proxy records for
hydroclimate reconstruction. Our approach combines time series modelling with
an inverse prediction approach to quantify the relationships between the
hydroclimate and proxies over the instrumental period and subsequently
reconstruct the hydroclimate back through time. Further analysis of the model
output allows us to estimate the probability that a hydroclimate index in any
reconstruction year was lower (higher) than the minimum (maximum) hydroclimate
value observed over the instrumental period. We present model-based
reconstructions of the Rainfall Index (RFI) and Standardised
Precipitation-Evapotranspiration Index (SPEI) for two case study catchment
areas, namely Brisbane and Fitzroy. In Brisbane, we found that the RFI is
unlikely (probability between 0 and 20%) to have exhibited extremes beyond the
minimum/maximum of what has been observed between 1889 and 2017. However, in
Fitzroy there are several years during the reconstruction period where the RFI
is likely (> 50% probability) to have exhibited behaviour beyond the
minimum/maximum of what has been observed. For SPEI, the probability of
observing such extremes since the end of the instrumental period in 1889
doesn't exceed 50% in any reconstruction year in the Brisbane or Fitzroy
catchments.


title: Orbits of actions of group superschemes

abstract:
  Working over an algebraically closed field $\Bbbk$, we prove that all orbits
of a left action of an algebraic group superscheme $G$ on a superscheme $X$ of
finite type are locally closed. Moreover, such an orbit $Gx$, where $x$ is a
$\Bbbk$-point of $X$, is closed if and only if $G_{ev}x$ is closed in $X_{ev}$,
or equivalently, if and only if $G_{res}x$ is closed in $X_{res}$. Here
$G_{ev}$ is the largest purely even group super-subscheme of $G$ and $G_{res}$
is $G_{ev}$ regarded as a group scheme. Similarly, $X_{ev}$ is the largest
purely even super-subscheme of $X$ and $X_{res}$ is $X_{ev}$ regarded as a
scheme. We also prove that
$\mathrm{sdim}(Gx)=\mathrm{sdim}(G)-\mathrm{sdim}(G_x)$, where $G_x$ is the
stabilizer of $x$.


title: SMEFT interpretation of $\Delta$F = 2 transitions

abstract:
  A model-independent anatomy of $\Delta F= 2$ transitions in the context of
the Weak Effective Theory (WET) below the electroweak scale (EW) and the
Standard Model Effective Field Theory (SMEFT) above the EW scale is discussed.
Two master formulae for the BSM contribution of the mixing amplitude $M_{12}$,
in terms of Wilson coefficients are presented. The coefficients entering these
formulae contain all the information below the EW scale and the NP scale
$\Lambda$, respectively. The renormalization group evolution from the top-quark
Yukawa coupling has the largest impact on the result. The obtained expressions
depend on whether the down-basis or the up-basis for SMEFT operators is
considered.


title: Mid and Far-Infrared Color-Color Relations within Local Galaxies

abstract:
  We present an extensive archival analysis of a sample of local galaxies,
combining multi-wavelength data from GALEX, Spitzer and Herschel to investigate
"blue-side" mid-infrared (MIR) and "red-side" far-infrared (FIR) color-color
correlations within the observed infrared spectral energy distributions (IR
SEDs). Our sample largely consists of the KINGFISH galaxies, with the important
addition of a select few including NGC5236 (M83) and NGC4449. With data from
the far-ultraviolet FUV (${\sim}0.15$ $\mu$m) through 500 $\mu$m convolved to
common angular resolution, we measure photometry of $kpc$-scale star-forming
regions 36$"\times$36$"$ in size. Star formation rates (SFRs), stellar masses
and metallicity distributions are derived throughout our sample. Focusing on
the $f_{70}/f_{500}$ "FIR" and $f_{8}/f_{24}$ "MIR" flux density ratios
(colors), we find that a sub-sample of galaxies demonstrate a strong IR
color-color correlation within their star-forming regions, while others
demonstrate uncorrelated colors. This division is driven by two main effects:
1) the local strength of star formation (SF) and 2) the metal content of the
interstellar medium (ISM). Galaxies uniformly dominated by high surface
densities of SF (e.g. NGC5236) demonstrate strong IR color-color correlations,
while galaxies that exhibit lower levels of SF and mixed environments (e.g.
NGC5457) demonstrate weaker or no correlation--explained by the increasing
effect of varying ISM heating and metal content on the IR colors, specifically
in the MIR. We find large dispersion in the SFR-$L_{8}$ (8 $\mu$m luminosity)
relation that is traced by the metallicity distributions, consistent with
extant studies, highlighting its problematic use as a SFR indicator across
diverse systems/samples.


title:
A mixed-integer programming model for identifying intuitive ambulance
  dispatching policies

abstract:
  Markov decision process models and algorithms can be used to identify optimal
policies for dispatching ambulances to spatially distributed customers, where
the optimal policies indicate the ambulance to dispatch to each customer type
in each state. Since the optimal solutions are dependent on Markov state
variables, they may not always correspond to a simple set of rules when
implementing the policies in practice. Restricted policies that conform to a
priority list for each type of customer may be desirable for use in practice,
since such policies are transparent and easy to implement. A priority list
policy is an ordered list of ambulances that indicates the preferred order to
dispatch the ambulances to a customer type subject to ambulance availability.
This paper proposes a constrained Markov decision process model for identifying
optimal priority list policies that is formulated as a mixed integer
programming model, does not extend the Markov state space, and can be solved
using standard algorithms. A series of computational examples illustrate the
approach. The optimal mixed integer programming solutions to the computational
examples have objective function values that are close to those of the
unrestricted model and are superior to those of heuristics.


title:
A Molecular Prior Distribution for Bayesian Inference Based on Wilson
  Statistics

abstract:
  Background and Objective: Wilson statistics describe well the power spectrum
of proteins at high frequencies. Therefore, it has found several applications
in structural biology, e.g., it is the basis for sharpening steps used in
cryogenic electron microscopy (cryo-EM). A recent paper gave the first rigorous
proof of Wilson statistics based on a formalism of Wilson's original argument.
This new analysis also leads to statistical estimates of the scattering
potential of proteins that reveal a correlation between neighboring Fourier
coefficients. Here we exploit these estimates to craft a novel prior that can
be used for Bayesian inference of molecular structures. Methods: We describe
the properties of the prior and the computation of its hyperparameters. We then
evaluate the prior on two synthetic linear inverse problems, and compare
against a popular prior in cryo-EM reconstruction at a range of SNRs. Results:
We show that the new prior effectively suppresses noise and fills-in low SNR
regions in the spectral domain. Furthermore, it improves the resolution of
estimates on the problems considered for a wide range of SNR and produces
Fourier Shell Correlation curves that are insensitive to masking effects.
Conclusions: We analyze the assumptions in the model, discuss relations to
other regularization strategies, and postulate on potential implications for
structure determination in cryo-EM.


title: Black-box Node Injection Attack for Graph Neural Networks

abstract:
  Graph Neural Networks (GNNs) have drawn significant attentions over the years
and been broadly applied to vital fields that require high security standard
such as product recommendation and traffic forecasting. Under such scenarios,
exploiting GNN's vulnerabilities and further downgrade its classification
performance become highly incentive for adversaries. Previous attackers mainly
focus on structural perturbations of existing graphs. Although they deliver
promising results, the actual implementation needs capability of manipulating
the graph connectivity, which is impractical in some circumstances. In this
work, we study the possibility of injecting nodes to evade the victim GNN
model, and unlike previous related works with white-box setting, we
significantly restrict the amount of accessible knowledge and explore the
black-box setting. Specifically, we model the node injection attack as a Markov
decision process and propose GA2C, a graph reinforcement learning framework in
the fashion of advantage actor critic, to generate realistic features for
injected nodes and seamlessly merge them into the original graph following the
same topology characteristics. Through our extensive experiments on multiple
acknowledged benchmark datasets, we demonstrate the superior performance of our
proposed GA2C over existing state-of-the-art methods. The data and source code
are publicly accessible at: https://github.com/jumxglhf/GA2C.


title:
Higher dimensional CFTs as 2D conformally-equivariant topological field
  theories

abstract:
  Two and three-point functions of primary fields in four dimensional CFT have
a simple space-time dependences factored out from the combinatoric structure
which enumerates the fields and gives their couplings. This has led to the
formulation of two dimensional topological field theories with $so(4,2)$
equivariance which are conjectured to be equivalent to higher dimensional
conformal field theories. We review this CFT4/TFT2 construction in the simplest
possible setting of a free scalar field, which gives an algebraic construction
of the correlators in terms of an infinite dimensional $so(4,2)$ equivariant
algebra with finite dimensional subspaces at fixed scaling dimension. Crossing
symmetry of the CFT4 is related to associativity of the algebra. This
construction is then extended to describe perturbative CFT4, by making use of
deformed co-products. Motivated by the Wilson-Fisher CFT we outline the
construction of U(so($d$,2)) equivariant TFT2 for non-integer $d$, in terms of
diagram algebras and their representations.


title:
Counterfactual Analysis of the Impact of the IMF Program on Child
  Poverty in the Global-South Region using Causal-Graphical Normalizing Flows

abstract:
  This work demonstrates the application of a particular branch of causal
inference and deep learning models: \emph{causal-Graphical Normalizing Flows
(c-GNFs)}. In a recent contribution, scholars showed that normalizing flows
carry certain properties, making them particularly suitable for causal and
counterfactual analysis. However, c-GNFs have only been tested in a simulated
data setting and no contribution to date have evaluated the application of
c-GNFs on large-scale real-world data. Focusing on the \emph{AI for social
good}, our study provides a counterfactual analysis of the impact of the
International Monetary Fund (IMF) program on child poverty using c-GNFs. The
analysis relies on a large-scale real-world observational data: 1,941,734
children under the age of 18, cared for by 567,344 families residing in the 67
countries from the Global-South. While the primary objective of the IMF is to
support governments in achieving economic stability, our results find that an
IMF program reduces child poverty as a positive side-effect by about
1.2$\pm$0.24 degree (`0' equals no poverty and `7' is maximum poverty). Thus,
our article shows how c-GNFs further the use of deep learning and causal
inference in AI for social good. It shows how learning algorithms can be used
for addressing the untapped potential for a significant social impact through
counterfactual inference at population level (ACE), sub-population level
(CACE), and individual level (ICE). In contrast to most works that model ACE or
CACE but not ICE, c-GNFs enable personalization using \emph{`The First Law of
Causal Inference'}.


title: Smooth time optimal trajectory generation for drones

abstract:
  In this paper, we address a minimum-time steering problem for a drone modeled
as point mass with bounded acceleration, across a set of desired waypoints in
the presence of gravity. We first provide a method to solve for the
minimum-time control input that will steer the point mass between two waypoints
based on a continuous-time problem formulation which we address by using
Pontryagin's Minimum Principle. Subsequently, we solve for the time-optimal
trajectory across the given set of waypoints by discretizing in the time domain
and formulating the minimum-time problem as a nonlinear program (NLP). The
velocities at each waypoint obtained from solving the NLP in the discretized
domain are then used as boundary conditions to extend our two-point solution
across those multiple waypoints. We apply this planning methodology to execute
a surveying task that minimizes the time taken to completely explore a target
area or volume. Numerical simulations and theoretical analyses of this new
planning methodology are presented. The results from our approach are also
compared to traditional polynomial trajectories like minimum snap planning.


title:
Information Decomposition Diagrams Applied beyond Shannon Entropy: A
  Generalization of Hu's Theorem

abstract:
  In information theory, one major goal is to find useful functions that
summarize the amount of information contained in the interaction of several
random variables. Specifically, one can ask how the classical Shannon entropy,
mutual information, and higher interaction information functions relate to each
other. This is formally answered by Hu's theorem, which is widely known in the
form of information diagrams: it relates disjoint unions of shapes in a Venn
diagram to summation rules of information functions; this establishes a bridge
from set theory to information theory. While a proof of this theorem is known,
to date it was not analyzed in detail in what generality it could be
established. In this work, we view random variables together with the joint
operation as a monoid that acts by conditioning on information functions, and
entropy as the unique function satisfying the chain rule of information. This
allows us to abstract away from Shannon's theory and to prove a generalization
of Hu's theorem, which applies to Shannon entropy of countably infinite
discrete random variables, Kolmogorov complexity, Tsallis entropy, (Tsallis)
Kullback-Leibler Divergence, cross-entropy, submodular information functions,
and the generalization error in machine learning. Our result implies for
Chaitin's prefix-free Kolmogorov complexity that the higher-order interaction
complexities of all degrees are in expectation close to Shannon interaction
information. For well-behaved probability distributions on increasing sequence
lengths, this shows that asymptotically, the per-bit expected interaction
complexity and information coincide, thus showing a strong bridge between
algorithmic and classical information theory.


title: Algebraic cycles and functorial lifts from $G_2$ to $\mathrm{PGSp}_6$

abstract:
  We establish instances of Beilinson-Tate conjectures for automorphic
representations of $\mathrm{PGSp}_6$ whose Spin $L$-function has a pole at
$s=1$. Using the exceptional theta correspondence between the split group of
type $G_2$ and $\mathrm{PGSp}_6$ and assuming the non-vanishing of a certain
archimedean integral, this allows us to confirm a conjecture of Gross and Savin
on rank $7$ motives of type $G_2$.


title:
A new visit to the variable stars in M56 and its colour magnitude
  diagram structure

abstract:
  We present a $VI$ CCD photometric study and a membership analysis of the
globular cluster M56 (NGC 6779). This produced a decontaminated CMD from field
stars, which enabled a better confrontation with theoretical isochrones;
Zero-Age Horizontal Branches (ZAHB) and post-ZAHB evolutionary tracks. Post
He-flash evolutionary models with a He-core mass of 0.5 M$_\odot$ and envelopes
of 0.04 - 0.18 M$_\odot$, cover the complete Horizontal Branch. Models with
total mass $\sim$ 0.68 M$_\odot$ explain the RR Lyrae, while those with a mass
$\sim$ 0.56 M$_\odot$ and a very subtle envelope, explain the Pop II cepheids
with a progenitor in the blue tail of the HB. %Using the mean $(V-I)$ at
minimum color curve for the RRab stars, we derived a value for the reddening of
$E(B-V)$ = 0.26 $\pm$ 0.02. Based on the Fourier decomposition of the $V$ light
curve of a single cluster member RRc star, we determined a metallicity of $\rm
[Fe/H]_{ ZW}$ = -1.96 $\pm$ 0.09. Several independent distance determination
approaches lead to a mean distance to M56 of $\big< d \big>$ = 9.4 $\pm$ 0.4
kpc. Finally, we report 5 new variables: 1 SX Phe, 3 EB, and 1 RRc.


title:
Real space representation of the quasiparticle self-consistent $GW$
  self-energy and its application to defect calculations

abstract:
  The quasiparticle self-consistent QS$GW$ approach incorporates the
corrections of the quasiparticle energies from their Kohn-Sham density
functional theory (DFT) eigenvalues by means of an energy independent and
Hermitian self-energy matrix usually given in the basis set of the DFT
eigenstates. By expanding these into an atom-centered basis set (specifically
here the linearized muffin-tin orbitals) a real space representation of the
self-energy corrections becomes possible. We show that this representation is
relatively short-ranged. This offers new opportunities to construct the
self-energy of a complex system from parts of the system by a cut-and-paste
method. Specifically for a point defect, represented in a large supercell, the
self-eneregy can be constructed from those of the host and a smaller defect
containing cell. The self-energy of the periodic host can be constructed simply
from a $GW$ calculation for the primitive cell. We show for the case of the
As$_\mathrm{Ga}$ in GaAs that the defect part can already be well represented
by a minimal 8 atom cell and allows us to construct the self-energy for a 64
cell in good agreement with direct QS$GW$ calculations for the large cell.
Using this approach to an even larger 216 atom cell shows the defect band
approaches an isolated defect level. The calculations also allow to identify a
second defect band which appears as a resonance near the conduction band
minimum. The results on the extracted defect levels agree well with Green's
function calculations for an isolated defect and with experimental data.


title: The theta invariants and the volume function on arithmetic varieties

abstract:
  We introduce a new arithmetic invariant for hermitian line bundles on an
arithmetic variety. We use this invariant to measure the variation of the
volume function with respect to the metric. The main result of this paper is a
generalized Hodge index theorem on arithmetic toric varieties.


title:
Provably Private Distributed Averaging Consensus: An
  Information-Theoretic Approach

abstract:
  In this work, we focus on solving a decentralized consensus problem in a
private manner. Specifically, we consider a setting in which a group of nodes,
connected through a network, aim at computing the mean of their local values
without revealing those values to each other. The distributed consensus problem
is a classic problem that has been extensively studied and its convergence
characteristics are well-known. Alas, state-of-the-art consensus methods build
on the idea of exchanging local information with neighboring nodes which leaks
information about the users' local values. We propose an algorithmic framework
that is capable of achieving the convergence limit and rate of classic
consensus algorithms while keeping the users' local values private. The key
idea of our proposed method is to carefully design noisy messages that are
passed from each node to its neighbors such that the consensus algorithm still
converges precisely to the average of local values, while a minimum amount of
information about local values is leaked. We formalize this by precisely
characterizing the mutual information between the private message of a node and
all the messages that another adversary collects over time. We prove that our
method is capable of preserving users' privacy for any network without a
so-called "generalized leaf", and formalize the trade-off between privacy and
convergence time. Unlike many private algorithms, any desired accuracy is
achievable by our method, and the required level of privacy only affects the
convergence time.


title: States of finite effect algebras

abstract:
  In this paper we characterize finite effect algebras which have a state. We
construct two matrices $A$ and $B$ assigned to a finite effect algebra $E$ and
show that if $E$ has a state then rank$A=$ rank$B$.


title: Equivariant Transporter Network

abstract:
  Transporter Net is a recently proposed framework for pick and place that is
able to learn good manipulation policies from a very few expert demonstrations.
A key reason why Transporter Net is so sample efficient is that the model
incorporates rotational equivariance into the pick module, i.e. the model
immediately generalizes learned pick knowledge to objects presented in
different orientations. This paper proposes a novel version of Transporter Net
that is equivariant to both pick and place orientation. As a result, our model
immediately generalizes place knowledge to different place orientations in
addition to generalizing pick knowledge as before. Ultimately, our new model is
more sample efficient and achieves better pick and place success rates than the
baseline Transporter Net model.


title: Density Matrices of Seniority-Zero Geminal Wavefunctions

abstract:
  Scalar products and density matrix elements of closed-shell pair geminal
wavefunctions are evaluated directly in terms of the pair amplitudes, resulting
in an analogue of Wick's theorem for fermions or bosons. This expression is in
general intractable, but it is shown how it becomes feasible in three distinct
ways for Richardson-Gaudin (RG) states, the antisymmetrized geminal power, and
the antisymmetrized product of strongly-orthogonal geminals. Dissociation
curves for hydrogen chains are computed with off-shell RG states and the
antisymmetrized product of interacting geminals. Both are near exact suggesting
that the incorrect results observed with ground state RG states are fixable
using a different RG state.


title: Controlling two-dimensional chaotic transients with the safety function

abstract:
  In this work we deal with the H\'enon and the Lozi map for a choice of
parameters where they show transient chaos. Orbits close to the chaotic saddle
behave chaotically for a while to eventually escape to an external attractor.
Traditionally, to prevent such an escape, the partial control technique has
been applied. This method stands out for considering disturbances (noise)
affecting the map and for finding a special region of the phase space, called
the safe set, where the control required to sustain the orbits is small.
However, in this work we will apply a new approach of the partial control
method that has been recently developed. This new approach is based on finding
a special function called the safety function, which allows to automatically
find the minimum control necessary to avoid the escape of the orbits.
Furthermore, we will show the strong connection between the safety function and
the classical safe set. To illustrate that, we will compute for the first time,
safety functions for the two-dimensional H\'enon and Lozi maps, where we also
show the strong dependence of this function with the magnitude of disturbances
affecting the map, and how this change drastically impacts the controlled
orbits.


title:
Multiple Ancillary Services Provision by Distributed Energy Resources in
  Active Distribution Networks

abstract:
  The electric power system is currently experiencing radical changes stemming
from the increasing share of renewable energy resources and the consequent
decommissioning of conventional power plants based on synchronous generators.
Since the principal providers of ancillary services are being phased out, new
flexibility and reserve providers are needed. The proliferation of Distributed
Energy Resources (DERs) in modern distribution networks has opened new
possibilities for distribution system operators, enabling them to fill the
market gap by harnessing the DER flexibility. This paper introduces a novel
centralized MPC-based controller that enables the concurrent provision of
voltage support, primary and secondary frequency control by adjusting the
setpoints of a heterogeneous group of DERs in active distribution grids. The
input-multirate control framework is used to accommodate the distinct
timescales and provision requirements of each ancillary service and to ensure
that the available resources are properly allocated. Furthermore, an efficient
way for incorporating network constraints in the formulation is proposed, where
network decomposition is applied to a linear power flow formulation together
with network reduction. In addition, different timescale dynamics of the
employed DERs and their capability curves are included. The performance of the
proposed controller is evaluated on several case studies via dynamic
simulations of the IEEE 33-bus system.


title: A minimizing problem of a polyharmonic operator with Critical Exponent

abstract:
  In this work, we study the two following minimization problems for $r \in
\mathbb{N}^{*}$, \begin{equation*} \begin{array}{ccc}
S_{0,r}(\varphi)=\displaystyle\inf_{u\in
H_{0}^{r}(\Omega)\,|u+\varphi\|_{L^{2^{*r}}}=1}\|u\|_{r}^{2}& \textrm{and}&
S_{\theta,r}(\varphi)=\displaystyle\inf_{u\in H_{\theta}^{r}(\Omega)\,
\|u+\varphi\|_{L^{2^{*r}}}=1}\|u\|_{r}^{2}, \end{array} \end{equation*} where
$\Omega \subset \mathbb{R}^{N}, $ $N > 2r$, is a smooth bounded domain,
$2^{*r}=\frac{2N}{N-2 r}$, $\varphi\in L^{2^{*r}} (\Omega) \cap C(\Omega)$ and
the norm $\|. \|_{r}=\displaystyle{ \int_{\Omega} |(-\Delta)^{\alpha}
.|^{2}dx}$ where $ \alpha=\frac{r}{2} $ if $r$ is even and $\|.
\|_{r}=\displaystyle{ \int_{\Omega} |\nabla(-\Delta)^{\alpha} . |^{2}dx }$
where $\alpha = \frac{r-1}{2}$ if $r$ is odd. Firstly, we prove that, when
$\varphi \not\equiv 0, $ the infimum in $S_{0,r}(\varphi)$ and
$S_{\theta,r}(\varphi)$ are attained. Secondly, we show that $
S_{\theta,r}(\varphi)< S_{0,r}(\varphi) $ for a large class of $ \varphi$.


title: A two-phase rank-based algorithm for low-rank matrix completion

abstract:
  Matrix completion aims to recover an unknown low-rank matrix from a small
subset of its entries. In many applications, the rank of the unknown target
matrix is known in advance. In this paper, first we revisit a recently proposed
rank-based heuristic for "known-rank" matrix completion and establish a
condition under which the generated sequence is quasi-Fej\'er convergent to the
solution set. Then, by including an acceleration mechanism similar to
Nesterov's acceleration, we obtain a new heuristic. Even though the convergence
of such heuristic cannot be granted in general, it turns out that it can be
very useful as a warm-start phase, providing a suitable estimate for the
regularization parameter and a good starting-point, to an accelerated
Soft-Impute algorithm. Numerical experiments with both synthetic and real data
show that the resulting two-phase rank-based algorithm can recover low-rank
matrices, with relatively high precision, faster than other well-established
matrix completion algorithms.


title: Optical quantum super-resolution imaging and hypothesis testing

abstract:
  Estimating the angular separation between two incoherent thermal sources is a
challenging task for direct imaging, especially when it is smaller than or
comparable to the Rayleigh length. In addition, the task of discriminating
whether there are one or two sources followed by detecting the faint emission
of a secondary source in the proximity of a much brighter one is in itself a
severe challenge for direct imaging. Here, we experimentally demonstrate two
tasks for superresolution imaging based on quantum state discrimination and
quantum imaging techniques. We show that one can significantly reduce the
probability of error for detecting the presence of a weak secondary source,
especially when the two sources have small angular separations. In this work,
we reduce the experimental complexity down to a single two-mode interferometer:
we show that (1) this simple set-up is sufficient for the state discrimination
task, and (2) if the two sources are of equal brightness, then this measurement
can super-resolve their angular separation, saturating the quantum Cram\'er-Rao
bound. By using a collection baseline of 5.3~mm, we resolve the angular
separation of two sources that are placed 15~$\mu$m apart at a distance of
1.0~m with an accuracy of $1.7\%$--this is between 2 to 3 orders of magnitudes
more accurate than shot-noise limited direct imaging.


title:
Blockchain Driven Privacy Preserving Contact Tracing Framework in
  COVID-19

abstract:
  Contact tracing has been proven an effective approach to mitigate the virus
spread in pandemics like COVID-19. As an emerging powerful decentralized data
sharing and storage technique, blockchain has been explored to ensure data
privacy and security in contact tracing processes. Existing work mostly treats
blockchain as a separate storage system requiring third-party servers to
perform data collection and computation, and avoids revealing detailed interior
design of blockchain storage. However, blockchain system is capable to work as
a fully automatic distributed contact tracing system without any third-party
server by tackling the following challenges: 1) how to ensure the contact
tracing correctness without compromising privacy; 2) how to design efficient
and effective consensus mechanism to ensure the system security and robustness;
3) how to design an incentive mechanism to motivate people to behave honestly.
In this article, we present the Blockchain Based Contact Tracing Framework for
the contact tracing problem. In the framework, RSA encryption based transaction
verification algorithm (RSA-TVA) is proposed to ensure contact tracing
correctness, which can achieve more than 97% contact case recording accuracy
even when each person has 60% chance of failing to verify the contact
information. Reputation Corrected Delegated Proof of Stack (RC-DPoS) consensus
mechanism is applied to jointly work with the proposed incentive mechanism,
which can ensure timeliness of reporting contact cases and meanwhile balance
the reward gained by different people. A novel contact tracing simulation
environment is created in the simulation part, which considers three different
contact scenarios. The simulation results demonstrate the effectiveness,
robustness and attack resistance of RSA-TVA and RC-DPoS in the proposed
Blockchain Based Contact Tracing Framework.


title:
Unsupervised strategies for identifying optimal parameters in Quantum
  Approximate Optimization Algorithm

abstract:
  As combinatorial optimization is one of the main quantum computing
applications, many methods based on parameterized quantum circuits are being
developed. In general, a set of parameters are being tweaked to optimize a cost
function out of the quantum circuit output. One of these algorithms, the
Quantum Approximate Optimization Algorithm stands out as a promising approach
to tackle combinatorial problems, due to many interesting properties. However
finding the appropriate parameters is a difficult task. Although QAOA exhibits
concentration properties, they can depend on instances characteristics which
may not be easy to identify, but may nonetheless offer useful information to
find good parameters. In this work, we study unsupervised Machine Learning
approaches for setting these parameters without optimization. We perform
clustering with the angle values but also instances encodings (using instance
features or the output of a variational graph autoencoder), and compare
different approaches. These angle-fiding strategies can be used to reduce calls
to quantum circuits when leveraging QAOA as a subroutine. We showcase them
within Recursive-QAOA up to depth $3$ where the number of QAOA parameters used
per iteration is limited to 3, achieving a median approximation ratio of 0.94
for MaxCut over 200 Erd\H{o}s-R\'{e}nyi graphs. We obtain similar performances
to the case where we extensively optimize the angles, hence saving numerous
circuit calls.


title:
Differentially Private Federated Learning via Inexact ADMM with Multiple
  Local Updates

abstract:
  Differential privacy (DP) techniques can be applied to the federated learning
model to statistically guarantee data privacy against inference attacks to
communication among the learning agents. While ensuring strong data privacy,
however, the DP techniques hinder achieving a greater learning performance. In
this paper we develop a DP inexact alternating direction method of multipliers
algorithm with multiple local updates for federated learning, where a sequence
of convex subproblems is solved with the objective perturbation by random
noises generated from a Laplace distribution. We show that our algorithm
provides $\bar{\epsilon}$-DP for every iteration, where $\bar{\epsilon}$ is a
privacy budget controlled by the user. We also present convergence analyses of
the proposed algorithm. Using MNIST and FEMNIST datasets for the image
classification, we demonstrate that our algorithm reduces the testing error by
at most $31\%$ compared with the existing DP algorithm, while achieving the
same level of data privacy. The numerical experiment also shows that our
algorithm converges faster than the existing algorithm.


title: Power-efficient soliton microcombs

abstract:
  Laser frequency combs are enabling some of the most exciting scientific
endeavours in the 21st century, ranging from the development of optical clocks
to the calibration of the astronomical spectrographs used for searching
Earth-like exoplanets. Today, dissipative Kerr solitons generated in
microresonators offer the prospect of attaining frequency combs in miniaturized
systems by capitalizing on advances in photonic integration. Most of the
applications based on soliton microcombs rely on tuning a continuous-wave laser
into a longitudinal mode of a microresonator whose dimensions are engineered to
display anomalous dispersion at the pump laser frequency. In this
configuration, however, nonlinear physics precludes from attaining dissipative
Kerr solitons with high power conversion efficiency, with typical comb powers
amounting to ~1% of the available laser power. Here, we demonstrate that this
fundamental limitation can be overcome by inducing a controllable frequency
shift to a selected cavity resonance. Experimentally, we realize this shift
using two linearly coupled anomalous-dispersion microresonators (a photonic
molecule), resulting in a coherent dissipative Kerr soliton with a conversion
efficiency exceeding 50% and excellent line spacing stability. We describe the
physical soliton dynamics in this configuration, and discover the system
displays unusual characteristics, such as the possibility to backwards initiate
solitons and stable operation with a blue detuned pump laser. By optimizing the
microcomb power available on chip, these results facilitate the practical
implementation of a scalable integrated photonic architecture for
energy-efficient applications.


title:
Minimizing travelling waves for the Gross-Pitaevskii equation on
  $\mathbb{R} \times \mathbb{T}$

abstract:
  We study the Gross-Pitaevskii equation in dimension two with periodic
conditions in one direction, or equivalently on the product space $\mathbb{R}
\times \mathbb{T}_L$ where $L > 0$ and $\mathbb{T}_L = \mathbb{R} / L
\mathbb{Z}.$ We focus on the variational problem consisting in minimizing the
Ginzburg-Landau energy under a fixed momentum constraint. We prove that there
exists a threshold value for $L$ below which minimizers are the one-dimensional
dark solitons, and above which no minimizer can be one-dimensional.


title: SN 2020jfo: A short plateau Type II supernova from a low mass progenitor

abstract:
  We present spectroscopic and photometric observations of the Type IIP
supernova, SN 2020jfo, in ultraviolet and optical wavelengths. SN 2020jfo
occurred in the spiral galaxy M61 (NGC 4303), with eight observed supernovae in
the past 100 years. SN 2020jfo exhibited a short plateau lasting < 65 d, and
achieved a maximum brightness in V-band of $M_V$ = -17.4 $\pm$ 0.4 mag at about
8.0 $\pm$ 0.5 d since explosion. From the bolometric light curve, we have
estimated the mass of $^{56}$Ni synthesised in the explosion to be 0.033 $\pm$
0.006 $M_\odot$. The observed spectral features are typical for a type IIP
supernova except for shallow H$\alpha$ absorption throughout the evolution and
the presence of stable $^{58}$Ni feature at 7378 \r{A}, in the nebular phase.
Using hydrodynamical modelling in the MESA + STELLA framework, an ejecta mass
of ~ 5 $M_\odot$ is estimated. Models also indicate SN 2020jfo could be the
result of a Red Super Giant progenitor with $M_{ZAMS}$ ~ 12 $M_\odot$.
Bolometric light curve modelling revealed the presence of a secondary radiation
source for initial ~ 20 d, which has been attributed to interaction with a
circumstellar material of mass ~ 0.2 $M_\odot$, which most likely was ejected
due to enhanced mass loss about 20 years prior to the supernova explosion.


title: Milligram mass metrology for quantitative deposition of liquid samples

abstract:
  Quantitative dispensing of liquids by mass becomes increasingly difficult as
sample volume decreases. Whereas ml quantities of liquid can be directly
weighed as dispensed on an analytical balance or using pycnometer methods,
{\mu}l samples are more challenging due to their milligram-scale mass and rapid
evaporation. Here, a method for quantitative determination of the mass of 1
{\mu}l droplets of liquid is proposed. The weighing of a microcapillary
pycnometer combined with traceable calibration masses and corrections for
sample evaporation allows determination of the droplet mass on the order of 1
mg with relative combined expanded uncertainty of 0.3 %.


title:
Functional Optical Coherence Tomography for Intrinsic Signal
  Optoretinography: Recent Developments and Deployment Challenges

abstract:
  Intrinsic optical signal (IOS) imaging of the retina, also termed as
optoretinography (ORG), promises a noninvasive method for objective assessment
of retinal function. By providing unparalleled capability to differentiate
individual layers of the retina, functional optical coherence tomography (OCT)
has been actively investigated for intrinsic signal ORG measurements. However,
clinical deployment of functional OCT for quantitative ORG is still challenging
due to the lack of a standardized imaging protocol and the complication of IOS
sources and mechanisms. This article aims to summarize recent developments of
functional OCT for ORG measurement, OCT intensity- and phase-based IOS
processing. Technical challenges and perspectives of quantitative IOS analysis
and ORG interpretations are discussed.

categories: physics.med-ph eess.IV physics.bio-ph physics.optics


title: Navier-Stokes equations in the half space with non compatible data

abstract:
  In this paper we shall consider the Navier-Stokes equations in the half plane
with Euler-type initial conditions, i.e. initial conditions which have a
non-zero tangential component at the boundary. Under analyticity assumptions
for the data, we shall prove that the solution exists for a small time
independent of the viscosity. The solution is constructed through a composite
asymptotic expansion involving the solutions of the Euler and Prandtl
equations, plus an error term. The norm of the error goes to zero with the
square root of the viscosity. The Prandtl solution contains a singular term,
which influences the regularity of the error. The error term is written as the
sum of a first order Euler correction, a first order Prandtl correction, and a
further error term. The use of an analytic setting is mainly due to the Prandtl
equation.


title: Harmonic Conjugation in Harmonic Matroids

abstract:
  We study a generalization of the concept of harmonic conjugation from
projective geometry and full algebraic matroids to a larger class of matroids
called \emph{harmonic matroids}. We use harmonic conjugation to construct a
projective plane of prime order in harmonic matroids without using the axioms
of projective geometry. As a particular case we have a combinatorial
construction of a projective plane of prime order in full algebraic matroids.


title:
Deep Vision-Inspired Bubble Dynamics on Hybrid Nanowires with Dual
  Wettability

abstract:
  The boiling efficacy is intrinsically tethered to trade-offs between the
desire for bubble nucleation and necessity of vapor removal. The solution to
these competing demands requires the separation of bubble activity and liquid
delivery, often achieved through surface engineering. In this study, we
independently engineer bubble nucleation and departure mechanisms through the
design of heterogeneous and segmented nanowires with dual wettability with the
aim of pushing the limit of structure-enhanced boiling heat transfer
performances. The demonstration of separating liquid and vapor pathways
outperforms state-of-the-art hierarchical nanowires, in particular, at low heat
flux regimes while maintaining equal performances at high heat fluxes. A
machine vision-based framework realizes the autonomous curation and extraction
of hidden big data along with bubble dynamics. The combined efforts of
materials design, deep learning techniques, and data-driven approach shed light
on the mechanistic relationship between vapor/liquid pathways, bubble
statistics, and phase change performance.


title:
Uniting Control and Data Parallelism: Towards Scalable Memory-Driven
  Dynamic Graph Processing

abstract:
  Control parallelism and data parallelism is mostly reasoned and optimized as
separate functions. Because of this, workloads that are irregular, fine-grain
and dynamic such as dynamic graph processing become very hard to scale. An
experimental research approach to computer architecture that synthesizes prior
techniques of parallel computing along with new innovations is proposed in this
paper. We establish the background and motivation of the research undertaking
and provide a detailed description of the proposed omputing system that is
highly parallel non-von Neumann, memory-centric and memory-driven. We also
present a message-driven (or even-driven) programming model called "diffusive
computation" and provide insights into its properties using SSSP and Triangle
Counting problems as examples.


title: Optical and Near-Infrared Excesses are Correlated in T Tauri Stars

abstract:
  Accretion is one of the defining characteristics of classical T Tauri stars,
fueled by the presence of a circumstellar disk comprised of dust and gas.
Accretion produces a UV and optical excess, while re-radiated emission at the
inner edge of the dust component of the disk produces a near-infrared (NIR)
excess. The interplay between stars and their disks helps regulate
protoplanetary disk evolution and dispersal, which is key to a full
understanding of planet formation. To investigate the relations between NIR
excess and optical excess in both single and binary stars, we used an archival
sample of spectroscopically characterized members of the Taurus star-forming
region ($\tau \sim$ 1-2 Myr) with measured luminosities, spectral types, and
optical veiling. We combined the archival sample with 2MASS and WISE NIR
photometry and high-resolution imaging surveys. We found that NIR and optical
excesses are correlated in multiple NIR photometric bands, suggesting that they
are closely related, likely because more massive disks have higher inner dust
disk walls and are also associated with higher accretion rates. We also found
that multiplicity has no impact on accretion or inner disk properties in a
sample with a wide range of separations, but the sample was too small to
specifically investigate close binaries, where the effects of multiplicity on
disk properties should be most significant.


title: Partitioning Dense Graphs with Hardware Accelerators

abstract:
  Graph partitioning is a fundamental combinatorial optimization problem that
attracts a lot of attention from theoreticians and practitioners due to its
broad applications. From multilevel graph partitioning to more general-purpose
optimization solvers such as Gurobi and CPLEX, a wide range of approaches have
been developed. Limitations of these approaches are important to study in order
to break the computational optimization barriers of this problem. As we
approach the limits of Moore's law, there is now a need to explore ways of
solving such problems with special-purpose hardware such as quantum computers
or quantum-inspired accelerators. In this work, we experiment with solving the
graph partitioning on the Fujitsu Digital Annealer (a special-purpose hardware
designed for solving combinatorial optimization problems) and compare it with
the existing top solvers. We demonstrate limitations of existing solvers on
many dense graphs as well as those of the Digital Annealer on sparse graphs
which opens an avenue to hybridize these approaches.


title:
Parallel inverse-problem solver for time-domain optical tomography with
  perfect parallel scaling

abstract:
  This paper presents an efficient parallel radiative transfer-based
inverse-problem solver for time-domain optical tomography. The radiative
transfer equation provides a physically accurate model for the transport of
photons in biological tissue, but the high computational cost associated with
its solution has hindered its use in time-domain optical-tomography and other
areas. In this paper this problem is tackled by means of a number of
computational and modeling innovations, including 1) A spatial
parallel-decomposition strategy with perfect parallel scaling for the forward
and inverse problems of optical tomography on parallel computer systems; and,
2) A Multiple Staggered Source method (MSS) that solves the inverse transport
problem at a computational cost that is independent of the number of sources
employed, and which significantly accelerates the reconstruction of the optical
parameters: a six-fold MSS acceleration factor is demonstrated in this paper.
Finally, this contribution presents 3) An intuitive derivation of the
adjoint-based formulation for evaluation of functional gradients, including the
highly-relevant general Fresnel boundary conditions -- thus, in particular,
generalizing results previously available for vacuum boundary conditions.
Solutions of large and realistic 2D inverse problems are presented in this
paper, which were produced on a 256-core computer system. The combined
parallel/MSS acceleration approach reduced the required computing times by
several orders of magnitude, from months to a few hours.


title:
Communication-Efficient Actor-Critic Methods for Homogeneous Markov
  Games

abstract:
  Recent success in cooperative multi-agent reinforcement learning (MARL)
relies on centralized training and policy sharing. Centralized training
eliminates the issue of non-stationarity MARL yet induces large communication
costs, and policy sharing is empirically crucial to efficient learning in
certain tasks yet lacks theoretical justification. In this paper, we formally
characterize a subclass of cooperative Markov games where agents exhibit a
certain form of homogeneity such that policy sharing provably incurs no
suboptimality. This enables us to develop the first consensus-based
decentralized actor-critic method where the consensus update is applied to both
the actors and the critics while ensuring convergence. We also develop
practical algorithms based on our decentralized actor-critic method to reduce
the communication cost during training, while still yielding policies
comparable with centralized training.


title: Route Discovery and Capacity of Ad hoc Networks

abstract:
  Throughput capacity of large ad hoc networks has been shown to scale
adversely with the size of network $n$. However the need for the nodes to find
or repair routes has not been analyzed in this context. In this paper, we
explicitly take route discovery into account and obtain the scaling law for the
throughput capacity under general assumptions on the network environment, node
behavior, and the quality of route discovery algorithms. We also discuss a
number of possible scenarios and show that the need for route discovery may
change the scaling for the throughput capacity dramatically.


title: Probing Magnetars Using Spectral Lines with Future Telescopes

abstract:
  We present our findings on magnetar spectral line analysis in the context of
upcoming high resolution, high effective area, high throughput X-ray telescopes
for two cases: persistent magnetar emission and magnetar bursts. For magnetars
in quiescence, we present our preliminary work on modelling for phase-resolved
emission. Our results reveal the necessity of constraining line depth and width
concurrently with line energy to conclusively determine hotspot emission and
corresponding magnetic field geometry. We then present the results of our
simulations using effective area and response of various current and upcoming
X-ray telescopes for magnetar spectral line detection and expand on the
exciting opportunities upcoming telescopes provide to probe quiescent and burst
emission region geometry and propagation in the extreme magnetic field of a
magnetar


title: The Fundamentality of Fields

abstract:
  There is debate as to whether quantum field theory is, at bottom, a quantum
theory of fields or particles. One can take a field approach to the theory,
using wave functionals over field configurations, or a particle approach, using
wave functions over particle configurations. This article argues for a field
approach, presenting three advantages over a particle approach: (1) particle
wave functions are not available for photons, (2) a classical field model of
the electron gives a superior account of both spin and self-interaction as
compared to a classical particle model, and (3) the space of field wave
functionals appears to be larger than the space of particle wave functions. The
article also describes two important tasks facing proponents of a field
approach: (1) legitimize or excise the use of Grassmann numbers for fermionic
field values and wave functional amplitudes, and (2) describe how quantum
fields give rise to particle-like behavior.


title: Curved wave rays in a ripple tank

abstract:
  In its broadest sense the term bent wave rays hints at light or
electromagnetic waves bending in a strong gravitational field or in their
progress through a transparent medium of nonuniform index of refraction.
However, there are instances where curved wave rays are observed as a result of
the superposition of two or more waves in an elastic medium. Amongst others,
this paper aims at presenting a detailed examination of the phenomenon of
interference of waves emanating from two synchronous and coherent sources and
disturbing the surface of a liquid that is supposed ideally devoid of
viscosity. Hence, it provides people with explicit parametric equations of the
bent wave rays as well as those of their corresponding curved wavefronts
originating from the superposition of two waves in a material medium. To ensure
the formality of the equations of the wave rays and those of the corresponding
wavefronts, a preliminary introduction to the mathematics of confocal conics is
given in a quite intensive fashion. Therefrom, and for the sake of simplicity,
and with no loss of generality, the study will be confined to transverse waves
of the simple harmonic type having equal frequencies and equal amplitudes,
thus, avoiding unnecessary complex situations that transcends the desired
scope.


title: Predicting deformation mechanisms in architected metamaterials using GNN

abstract:
  The present paradigm in design and modelling of lattice architected
mechanical metamaterials is mostly limited to traditional numerical methods
like finite element analysis. Recently, the use of machine learning and
artificial intelligence techniques have become popular and here we extend these
ideas to architected metamaterials. We show that truss based lattices have a
natural resemblance to computational graphs which serve as an input for the
rapidly emerging field of graph neural networks (GNNs). A dataset comprising
thousands of such extremely complex lattices is trained using a GNN to predict
the underlying dominant deformation mechanism viz. stretching and bending. The
trained GNN achieves > 90% accuracy on a previously unseen complex lattice
dataset. Such graph-based learning of metamaterials has the capability to
predict a range of properties, from elastic moduli to fracture toughness and
promises AI driven discovery of emergent metamaterials possessing superlative
properties.


title:
Microstructure and phase transformation of nickel-titanium shape memory
  alloy fabricated by directed energy deposition with in-situ heat treatment

abstract:
  Additive manufacturing has been vastly applied to fabricate various
structures of nickel-titanium (NiTi) shape memory alloys due to its flexibility
to create complex structures with minimal defects. However, the microstructure
heterogeneity and secondary phase formation are two main problems that impede
the further application of NiTi alloys. Although post-heat treatment is usually
adopted to improve or manipulate NiTi alloy properties, it cannot realize the
spatial control of thermal and/or mechanical properties of NiTi alloys. To
overcome the limitations of uniform post-heat treatment, this study proposes an
in-situ heat treatment strategy that is integrated into the directed energy
deposition of NiTi alloys. The proposed method will potentially lead to new
manufacturing capabilities to achieve location-dependent performance or
property manipulation. The influences of in-situ heat treatment on the thermal
and mechanical properties of printed NiTi structures were investigated. The
investigations were carried out in terms of thermal cycling, microstructure
evolution, and mechanical properties by 3D finite element simulations and
experimental characterizations. A low-power laser beam was adopted to localize
the in-situ heat treatment only to the current printed layer, facilitating a
reverse peritectic reaction and a transient high solution treatment
successively. The proposed in-situ heat treatment on the specimen results in a
more obvious phase transformation peak in the differential scanning calorimetry
curves, about 50%~70% volume reduction for the Ti2Ni phase, and approximately
35 HV reduction on microhardness.


title: The local logarithmic Brunn-Minkowski inequality for zonoids

abstract:
  The aim of this note is to show that the local form of the logarithmic
Brunn-Minkowski conjecture holds for zonoids. The proof uses a variant of the
Bochner method due to Shenfeld and the author.


title: The eROSITA extragalactic CalPV serendipitous catalog

abstract:
  Context: The eROSITA X-ray telescope on board the SRG observatory performed
Calibration and Performance Verification (CalPV) observations between September
2019 and December 2019, ahead of the planned four-year all-sky surveys. Most of
them were deep, pointing-mode observations. Aims: We present here the X-ray
catalog detected from the set of extra-galactic CalPV observations released to
the public by the German eROSITA consortium, and the multi-band counterparts of
these X-ray sources. Methods: We develop a source detection method optimized
for point-like X-ray sources by including extended X-ray emission in the
background measurement. The multi-band counterparts are identified using a
Bayesian method from the CatWISE catalog. Results: Combining 11 CalPV fields,
we present a catalog containing 9522 X-ray sources, whose X-ray fluxes are
measured through spectral fitting. CatWISE counterparts are presented for 77%
of the sources. Significant variabilities are found in 179 of the sources,
which are also presented with this paper. Most of these fields show similar
number counts of point sources as typical extragalactic fields, and a few
harbor particular stellar populations.


title:
Rashba-induced spin texture and spin-layer-locking effects in
  antiferromagnetic CrI3 bilayer

abstract:
  Antiferromagnetic (AFM) CrI3 bilayer is a particularly interesting
representative of the van-der Waals two-dimensional semiconductors currently
studied for their magnetism and for their potential in spintronics. Using
ab-initio density-functional theory calculations, we investigate the spin
texture in momentum space of the states of the (doubly degenerate) highest
valence band of AFM CrI3 bilayer with Cr-spin moments perpendicular to the
layers. We find the existence of a Rashba in-plane spin texture of opposite
signs on the two layers, resulting from the intrinsic local electric fields
acting on each layer. In order to study spatial segregation of the
wavefunctions, we apply a small electric field which splits the degenerate
states according to their layer occupancy. We find that, nearly over the whole
Brillouin zone, the wavefunctions of the highest valence band are spatially
segregated on one of the two layers with opposite in-plane spin textures. The
corresponding layer locking of the in-plane-canted spin is related to the
separation in energy of the highest AFM band. We also interpret the mechanism
by which interactions among the states destroy layer locking and we compare the
layer locking of the in-plane-canted spin in our AFM bilayer system to the
hidden polarization in centrosymmetric nonmagnetic materials. We note that a
similar Rashba effect with layer locking of in-plane-canted spin could occur in
other van der Waals AFM bilayers with strong spin-orbit coupling and an
isolated energy band.


title:
Ionization-Gasdynamic Simulations of Wind-Blown Nebulae around Massive
  Stars

abstract:
  Using a code that employs a self-consistent method for computing the effects
of photo-ionization on circumstellar gas dynamics, we model the formation of
wind-driven nebulae around massive stars. We take into account changes in
stellar properties and mass-loss over the star's evolution. Our simulations
show how various properties, such as the density and ionization fraction,
change throughout the evolution of the star. The multi-dimensional simulations
reveal the presence of strong ionization front instabilities in the
main-sequence phase, similar to those seen in galactic ionization fronts.
Hydrodynamic instabilities at the interfaces lead to the formation of filaments
and clumps that are continually being stripped off and mixed with the low
density interior. Even though the winds start out as completely radial, the
spherical symmetry is quickly destroyed, and the shocked wind region is
manifestly asymmetrical. The simulations demonstrate that it is important to
include the effects of the photoionizing photons from the star, and simulations
that do not include this may fail to reproduce the observed density profile and
ionization structure of wind-blown bubbles around massive stars.


title: iMARS: An In-Memory-Computing Architecture for Recommendation Systems

abstract:
  Recommendation systems (RecSys) suggest items to users by predicting their
preferences based on historical data. Typical RecSys handle large embedding
tables and many embedding table related operations. The memory size and
bandwidth of the conventional computer architecture restrict the performance of
RecSys. This work proposes an in-memory-computing (IMC) architecture (iMARS)
for accelerating the filtering and ranking stages of deep neural network-based
RecSys. iMARS leverages IMC-friendly embedding tables implemented inside a
ferroelectric FET based IMC fabric. Circuit-level and system-level evaluation
show that \fw achieves 16.8x (713x) end-to-end latency (energy) improvement
compared to the GPU counterpart for the MovieLens dataset.


title: ESCAPE to Precaution against Leader Failures

abstract:
  Leader-based consensus protocols must undergo a view-change phase to elect a
new leader when the current leader fails. The new leader is often decided upon
a candidate server that collects votes from a quorum of servers. However,
voting-based election mechanisms intrinsically cause competition in leadership
candidacy when each candidate collects only partial votes. This split-vote
scenario can result in no leadership winner and prolong the undesired
view-change period. In this paper, we investigate a case study of Raft's leader
election mechanism and propose a new leader election protocol, called ESCAPE,
that fundamentally solves split votes by prioritizing servers based on their
log responsiveness. ESCAPE dynamically assigns servers with a configuration
that offers different priorities through Raft's periodic heartbeat. In each
assignment, ESCAPE keeps track of server log responsiveness and assigns
configurations that are inclined to win an election to more up-to-date servers,
thereby preparing a pool of prioritized candidates. Consequently, when the next
election takes place, the candidate with the highest priority will defeat its
counterparts and becomes the next leader without competition. The evaluation
results show that ESCAPE progressively reduces the leader election time when
the cluster scales up, and the improvement becomes more significant under
message loss.


title:
Critical density triplets for the arrestment of a sphere falling in a
  sharply stratified fluid

abstract:
  We study the motion of a rigid sphere falling in a two layer stratified fluid
under the action of gravity in the potential flow regime. Experiments at
moderate Reynolds number of approximately 20 to 450 indicate that a sphere with
the precise critical density, higher than the bottom layer density, can display
behaviors such as bounce or arrestment after crossing the interface. We
experimentally demonstrate that such a critical sphere density decreases as the
fluid density transition layer thickness increases, and increases linearly as
the bottom fluid density increases with a fixed top fluid density. We propose
an estimation of the critical density based on the potential energy, which
constitute an upper bound of the critical density with less than 0.05 relative
difference within the experimental density regime 0.997 g/cm^{3} ~1.09 g/cm^{3}


title: Renormalization Group Approach to Percolation in Hierarchical Lattices

abstract:
  Percolation refers to an interesting class of problems related to the
properties of disordered systems, usually formulated in terms of objects
randomly placed on an underlying lattice or continuum. Despite the simplicity
of the setup, most percolative systems undergo a phase transition from a
disconnected state with many disjoint clusters to a state where a finite
fraction of the lattice sites are connected to a single cluster. As in the case
of thermodynamic phase transitions, power law dependencies generically near the
critical percolation threshold. The origin of these dependencies can be
understood through the lens of scaling and renormalization, and indeed many
quantitative results can be acquired using these tools. In this paper we study
the percolation problem on a hierarchical lattice, where exact results for the
critical exponents can be obtained from a decimation procedure. We calculate
analytic results for the full set of geometric critical exponents and confirm
their consistency with simulation. Finally, we set up an interesting
renormalization group for the conductivity of the system and use it to
computationally extract the conductivity exponent t.


title:
The need for a local nuclear physics feature in the neutron-rich
  rare-earths to explain solar $r$-process abundances

abstract:
  We apply Markov Chain Monte Carlo to predict the masses required to form the
observed solar $r$-process rare-earth abundance peak. Given highly distinct
astrophysical outflows and nuclear inputs, we find that results are most
sensitive to the $r$-process dynamics (i.e. overall competition between
reactions and decays), with similar mass trends predicted given similar
dynamics. We show that regardless of whether fission deposits into the
rare-earths or not, our algorithm consistently predicts the need for a local
nuclear physics feature of enhanced stability in the neutron-rich lanthanides.


title:
Energy-Efficient Throughput Maximization in mmWave MU-Massive-MIMO-OFDM:
  Genetic Algorithm based Resource Allocation

abstract:
  This paper develops a new genetic algorithm based resource allocation (GA-RA)
technique for energy-efficient throughout maximization in multi-user massive
multiple-input multiple-output (MU-mMIMO) systems using orthogonal frequency
division multiplexing (OFDM) based transmission. We employ a hybrid precoding
(HP) architecture with three stages: (i) radio frequency (RF) beamformer, (ii)
baseband (BB) precoder, (iii) resource allocation (RA) block. First, a single
RF beamformer block is built for all subcarriers via the slow time-varying
angle-of-departure (AoD) information. For enhancing the energy efficiency, the
RF beamformer aims to reduce the hardware cost/complexity and total power
consumption via a low number of RF chains. Afterwards, the reduced-size
effective channel state information (CSI) is utilized in the design of a
distinct BB precoder and RA block for each subcarrier. The BB precoder is
developed via regularized zero-forcing technique. Finally, the RA block is
built via the proposed GA-RA technique for throughput maximization by
allocating the power and subcarrier resources. The illustrative results show
that the throughput performance in the MU-mMIMO-OFDM systems is greatly
enhanced via the proposed GA-RA technique compared to both equal RA (EQ-RA) and
particle swarm optimization based RA (PSO-RA). Moreover, the performance gain
ratio increases with the increasing number of subcarriers, particularly for low
transmission powers.


title: Enabling Volatile Caches for Energy Harvesting Systems

abstract:
  Energy harvesting systems have shown their unique benefit of ultra-long
operation time without maintenance and are expected to be more prevalent in the
era of Internet of Things. However, due to the batteryless nature, they suffer
unpredictable frequent power outages. They thus require a lightweight mechanism
for crash consistency since saving/restoring checkpoints across the outages can
limit forward progress by consuming hard-won energy. For the reason, energy
harvesting systems have been designed with a non-volatile memory (NVM) only.
The use of a volatile data cache has been assumed to be not viable or at least
challenging due to the difficulty to ensure cacheline persistence. In this
paper, we propose ReplayCache, a software-only crash consistency scheme that
enables commodity energy harvesting systems to exploit a volatile data cache.
ReplayCache does not have to ensure the persistence of dirty cachelines or
record their logs at run time. Instead, ReplayCache recovery runtime
re-executes the potentially unpersisted stores in the wake of power failure to
restore the consistent NVM state, from which interrupted program can safely
resume. To support store replay during recovery, ReplayCache partitions program
into a series of regions in a way that store operand registers remain intact
within each region, and checkpoints all registers just before power failure
using the crash consistency mechanism of the commodity systems. For
performance, ReplayCache enables region-level persistence that allows the
stores in a region to be asynchronously persisted until the region ends,
exploiting ILP. The evaluation with 23 benchmark applications show that
compared to the baseline with no caches, ReplayCache can achieve about 10.72x
and 8.5x-8.9x speedup (on geometric mean) for the scenarios without and with
power outages, respectively.


title:
Emergent Synchronization and Flocking in Purely Repulsive
  Self-Navigating Particles

abstract:
  We study the collective dynamics of large numbers of repulsive homing active
Brownian particles (HABPs) - self-propelled particles whose orientation relaxes
at a finite rate towards a particle-specific absorbing target in $2d$
continuous space. At intermediary relaxation rates, such particles form
synchronised system-wide chiral flocks, in spite of the absence of explicit
alignment interactions. We describe how this synchronization evolves when
changing of the system size and density, and check that it is robust against
the addition of noise, polydispersity in the orbit sizes, and bounding walls.
We then show that the synchronization can be interpreted as the magnetisation
of an off-lattice, ferromagnetic XY model. In this picture, tuning the
relaxation rate is akin to changing the range of ferromagnetic interactions:
the high-synchronization regime is then mean-field-like, while low
synchronization is associated to topological defects.


title:
Lindstr\"om's conjecture on a class of algebraically non-representable
  matroids

abstract:
  Gordon introduced a class of matroids $M(n)$, for prime $n\ge 2$, such that
$M(n)$ is algebraically representable, but only in characteristic $n$.
Lindstr\"om proved that $M(n)$ for general $n\ge 2$ is not algebraically
representable if $n>2$ is an even number, and he conjectured that if $n$ is a
composite number it is not algebraically representable. We introduce a new kind
of matroid called {\it harmonic matroids}, of which full algebraic matroids are
an example. We prove the conjecture in this more general case.


title:
The logistic elliptic equation with the nonlinear boundary condition
  arising from coast fishery harvesting

abstract:
  Let $0<q<1<p$. In this study, we investigate positive solutions of the
logistic elliptic equation $-\Delta u = u(1-u^{p-1})$ in a smooth bounded
domain $\Omega$ of $\mathbb{R}^N$, $N\geq1$, with the nonlinear boundary
condition $\frac{\partial u}{\partial \nu}=-\lambda u^q$ on $\partial\Omega$.
This nonlinear boundary condition arises from coast fishery harvesting. When
$p>1$ is subcritical, we prove that in the case of $\lambda_{\Omega}>1$, there
exist at least two positive solutions for $\lambda>0$ sufficiently small but no
positive solutions for $\lambda>0$ large enough. In the case of
$\lambda_{\Omega}<1$, there exists at least one positive solution for every
$\lambda>0$. Here, $\lambda_{\Omega}>0$ is the smallest eigenvalue of $-\Delta$
under the Dirichlet boundary condition. An interpretation of our main results
from an ecological viewpoint is presented.


title:
Detecting few-body quantum chaos: out-of-time ordered correlators at
  saturation

abstract:
  We study numerically and analytically the time dependence and saturation of
out-of-time ordered correlators (OTOC) in chaotic few-body quantum-mechanical
systems: quantum Henon-Heiles system (weakly chaotic), BMN matrix quantum
mechanics (strongly chaotic) and Gaussian random matrix ensembles. The growth
pattern of quantum-mechanical OTOC is complex and nonuniversal, with no clear
exponential regime at relevant timescales in any of the examples studied (which
is not in contradiction to the exponential growth found in the literature for
many-body systems, i.e. fields). On the other hand, the plateau (saturated)
value of OTOC reached at long times decreases with temperature in a simple and
universal way: $\exp(\mathrm{const.}/T^2)$ for strong chaos (including random
matrices) and $\exp(\mathrm{const.}/T)$ for weak chaos. For small matrices and
sufficiently complex operators, there is also another, high-temperature regime
where the saturated OTOC grows with temperature. Therefore, the plateau OTOC
value is a meaningful indicator of few-body quantum chaos. We also discuss some
general consequences of our findings for the AdS/CFT duality.

categories: hep-th cond-mat.stat-mech nlin.CD quant-ph


title: Lightweight Soft Error Resilience for In-Order Cores

abstract:
  Acoustic-sensor-based soft error resilience is particularly promising, since
it can verify the absence of soft errors and eliminate silent data corruptions
at a low hardware cost. However, the state-of-the-art work incurs a significant
performance overhead for in-order cores due to frequent structural/data hazards
during the verification. To address the problem, this paper presents Turnpike,
a compiler/architecture co-design scheme that can achieve lightweight yet
guaranteed soft error resilience for in-order cores. The key idea is that many
of the data computed in the core can bypass the soft error verification without
compromising the resilience. Along with simple microarchitectural support for
realizing the idea, Turnpike leverages compiler optimizations to further reduce
the performance overhead. Experimental results with 36 benchmarks demonstrate
that Turnpike only incurs a 0-14% run-time overhead on average while the
state-of-the-art incurs a 29-84% overhead when the worst-case latency of the
sensor based error detection is 10-50 cycles.