title: On limitations of uniplex networks for modeling multiplex contagion

abstract:
  Many substantive processes are inherently multiplex in nature, yet are often
reduced to processes on uniplex networks in analytic practice. We look at how
data modeling choices can affect the predictions of contagion processes. We
demonstrate that multiplex contagion processes are not simply the union of
contagion processes over their constituent uniplex networks. We use multiplex
network data from two different contexts -- a behavioral network to represent
their potential for infectious disease transmission using a "simple"
epidemiological model, the other from online social network site users to
represent their potential for information spread using a threshold-based
"complex" contagion process. Our results show that contagion on multiplex data
is not represented accurately in models developed from the uniplex networks
even when they are combined, and that the nature of the differences between the
(combined) uniplex and multiplex results depends on the specific spreading
process over these networks.


title: Error Correction for Discrete Tomography

abstract:
  Discrete tomography focuses on the reconstruction of functions $f: A \to
\mathbb{R}$ from their line sums in a finite number $d$ of directions, where
$A$ is a finite subset of $\mathbb{Z}^2$. Consequently, the techniques of
discrete tomography often find application in areas where only a small number
of projections are available. In 1978 M.B. Katz gave a necessary and sufficient
condition for the uniqueness of the solution. Since then, several
reconstruction methods have been introduced. Recently Pagani and Tijdeman
developed a fast method to reconstruct $f$ if it is uniquely determined.
Subsequently Ceko, Pagani and Tijdeman extended the method to the
reconstruction of a function with the same line sums of $f$ in the general
case. Up to here we assumed that the line sums are exact. In this paper we
investigate the case where a small number of line sums are incorrect as may
happen when discrete tomography is applied for data storage or transmission. We
show how less than $d/2$ errors can be corrected and that this bound is the
best possible.


title: Entropic Plots for Tail Type Classification

abstract:
  This article proposes a non-parametric tail classifier to aid tail
classification among discrete thick-tail distributions. Theoretical results in
this article show that certain distributional tail types can be identified
among a sequence of plots based on the tail profile. Such distributional tails
include power, sub-exponential, near-exponential, and exponential or thinner
decaying tails. The proposed method does not hypothesize the distribution
parameters values for classification. The method can be used practically as a
preliminary tool to narrow down possible parametric models for refined
statistical analysis with the unbiased estimators of the tail profile. Besides,
simulation studies suggest that the proposed classification method performs
well under various situations.


title: Reinforcement Learning Generation of 4-Qubits Entangled States

abstract:
  We have devised an artificial intelligence algorithm with machine
reinforcement learning (Q-learning) to construct remarkable entangled states
with 4 qubits. This way, the algorithm is able to generate representative
states for some of the 49 true SLOCC classes of the four-qubit entanglement
states. In particular, it is possible to reach at least one true SLOCC class
for each of the nine entanglement families. The quantum circuits synthesized by
the algorithm may be useful for the experimental realization of these important
classes of entangled states and to draw conclusions about the intrinsic
properties of our universe. We introduce a graphical tool called the state-link
graph (SLG) to represent the construction of the Quality matrix (Q-matrix) used
by the algorithm to build a given objective state belonging to the
corresponding entanglement class. This allows us to discover the necessary
connections between specific entanglement features and the role of certain
quantum gates that the algorithm needs to include in the quantum gate set of
actions. The quantum circuits found are optimal by construction with respect to
the quantum gate-set chosen. These SLGs make the algorithm simple, intuitive
and a useful resource for the automated construction of entangled states with a
low number of qubits.


title:
Equation of states in the curved spacetime of slowly rotating degenerate
  stars

abstract:
  We compute the equation of state for an ensemble of degenerate fermions by
using the curved spacetime of a slowly rotating axially symmetric star. We show
that the equation of state computed in such curved spacetime depends on the
gravitational time dilation as well as on the dragging of inertial frames,
unlike an equation of state computed in a globally flat spacetime. The effect
of gravitational time dilation leads to a significant enhancement of the
maximum mass limit of a degenerate neutron star. However, such an enhancement
due to the frame-dragging effect is extremely small.


title: Casimir contribution to the interfacial Hamiltonian for 3D wetting

abstract:
  Previous treatments of three-dimensional (3D) short-ranged wetting
transitions have missed an entropic or low temperature Casimir contribution to
the binding potential describing the interaction between the unbinding
interface and wall. This we determine by exactly deriving the interfacial model
for 3D wetting from a more microscopic Landau-Ginzburg-Wilson Hamiltonian. The
Casimir term changes the interpretation of fluctuation effects occurring at
wetting transitions so that, for example, mean-field predictions are no longer
obtained when interfacial fluctuations are ignored. While the Casimir
contribution does not alter the surface phase diagram, it significantly
increases the adsorption near a first-order wetting transition and changes
completely the predicted critical singularities of tricritical wetting,
including the non-universality occurring in 3D arising from interfacial
fluctuations. Using the numerical renormalization group we show that, for
critical wetting, the asymptotic regime is extremely narrow with the growth of
the parallel correlation length characterised by an effective exponent in
quantitative agreement with Ising model simulations, resolving a longstanding
controversy.


title: Exploring dust growth in the episodic WCd system WR140

abstract:
  The wind collision region (WCR) in a colliding wind binary (CWB) is a
particularly violent place, as such, it is surprising that it is also a region
where significant quantities of interstellar dust can form. In extreme cases,
approximately 30% of the total mass loss rate of a system can be converted into
dust. These regions are poorly understood, as observation and simulation of
these systems are difficult. In our previous paper we simulated dust growth in
CWB systems using an advected scalar model and found our model to be suitable
for qualitative study. For this paper we simulated the periodic dust forming
CWB (WCd) system WR140 with our dust model, to determine how dust growth
changes over the systems periastron passage. We found that dust production
increases significantly at periastron passage, which is consistent with IR
emission of the surrounding dusty shell. We also find that the dust production
rate of the system decreases rapidly as the stars recede from each other,
though the rate of decrease is significantly lower than the rate of increase
during periastron passage. This was found to be due to strong cooling and its
associated thermal instabilities, resulting in cool, high-density pockets of
gas in the WCR where dust forms. The WCR also shows a degree of hysteresis,
resulting in a radiative post-shock flow even when the stars are separated
enough for the region to behave adiabatically.


title:
Measurement of cross-sections for production of a $Z$ boson in
  association with a flavor-inclusive or doubly $b$-tagged large-radius jet in
  proton-proton collisions at $\sqrt{s} = 13~$TeV with the ATLAS experiment

abstract:
  We present measurements of cross-sections for production of a leptonically
decaying $Z$ boson in association with a large-radius jet in $13~$TeV
proton-proton collisions at the LHC, using $36~\mathrm{fb}^{-1}$ of data from
the ATLAS detector. Integrated and differential cross-sections are measured at
particle-level in both a flavor-inclusive and a doubly $b$-tagged fiducial
phase-space. The large-radius jet mass and transverse momentum, its kinematic
relationship to the $Z$ boson, and the angular separation of $b$-tagged
small-radius track-jets within the large-radius jet are measured. This
measurement constitutes an important test of perturbative quantum
chromodynamics in kinematic and flavor configurations relevant to several Higgs
boson and beyond-Standard-Model physics analyses. The results highlight issues
with modeling of additional hadronic activity in the flavor-inclusive
selection, and a distinction between flavor-number schemes in the $b$-tagged
phase-space.


title: Search for hidden-charm pentaquark states in three-body final states

abstract:
  The three pentaquark states, $P_c(4312)$, $P_c(4440)$ and $P_c(4457)$,
discovered by the LHCb Collaboration in 2019, are widely recognized as
$\bar{D}^{(\ast)}\Sigma_{c}$ hadronic molecules. Together with their four
$\bar{D}^{(*)}\Sigma_{c}^{\ast}$ partners dictated by heavy quark spin symmetry
they present a complete multiplet of hadronic molecules of
$\bar{D}^{(\ast)}\Sigma_{c}^{(\ast)}$. It is widely recognized that to
understand their nature, other discovery channels play an important role. In
this work, we investigate two three-body decay modes of the
$\bar{D}^{(\ast)}\Sigma_{c}^{(\ast)}$ molecules. The tree-level modes proceed
via off-shell $\Sigma_{c}^{(\ast)}$ baryons,
$\bar{D}^{(\ast)}\Sigma_{c}^{(\ast)} \to
\bar{D}^{(\ast)}\left(\Sigma_{c}^{(\ast)}\to
\Lambda_{c}\pi\right)\to\bar{D}^{(\ast)}\Lambda_{c}\pi$, while the
triangle-loop modes proceed through $\bar{D}^{\ast}\Sigma_{c}^{(\ast)}\to
J/\psi N\pi$, $\eta_{c}N\pi$ via $\bar{D}\Sigma_{c}^{(\ast)}$ rescattering to
$J/\psi N$ and $\eta_{c}N$. Our results indicate that the decay widths of the
$P_{c}(4457)$ and $\bar{D}^{(\ast)}\Sigma_{c}^{\ast}$ states into
$\bar{D}^{(\ast)}\Lambda_{c}\pi$ are several MeV, as a result can be observed
in the upcoming Run 3 and Run 4 of LHC. The partial decay widths into
$\bar{D}^{(\ast)}\Lambda_{c}\pi$ of the $P_{c}(4312)$ and $P_{c}(4440)$ states
range from tens to hundreds of keV. In addition, the partial decay widths of
$\bar{D}^{\ast}\Sigma_{c}$ molecules into $J/\psi N \pi$ and $\eta_c N \pi$ are
several keV and tens of keV, respectively, and the partial decay widths of
$\bar{D}^{\ast}\Sigma_{c}^{\ast}$ molecules into $J/\psi N \pi$ vary from
several keV to tens of keV. These three-body decay modes of the pentaquark
states are of great value to further observations of the pentaquark states and
to a better understanding of their nature.


title: Direct 3D Printing of Soft Fluidic Actuators with Graded Porosity

abstract:
  New additive manufacturing methods are needed to realize more complex soft
robots. One example is soft fluidic robotics, which exploits fluidic power and
stiffness gradients. Porous structures are an interesting type for this
approach, as they are flexible and allow for fluid transport. Within this work,
the Infill-Foam (InFoam) is proposed to print structures with graded porosity
by liquid rope coiling (LRC). By exploiting LRC, the InFoam method could
exploit the repeatable coiling patterns to print structures. To this end, only
the characterization of the relation between nozzle height and coil radius and
the extruded length were necessary (at a fixed temperature). Then by adjusting
the nozzle height and/or extrusion speed the porosity of the printed structure
could be set. The InFoam method was demonstrated by printing porous structures
using styrene-ethylene-butylene-styrene (SEBS) with porosities ranging from
46\% to 89\%. In compression tests, the cubes showed large changes in modulus
(more than 200 times), density (-89\% compared to bulk), and energy
dissipation. The InFoam method combined coiling and normal plotting to realize
a large range of porosity gradients. This grading was exploited to realize
rectangular structures with varying deformation patterns, which included
twisting, contraction, and bending. Furthermore, the InFoam method was shown to
be capable of programming the behavior of bending actuators by varying the
porosity. Both the output force and stroke showed correlations similar to those
of the cubes. Thus, the InFoam method can fabricate and program the mechanical
behavior of a soft fluidic (porous) actuator by grading porosity.


title: Polylogarithmic Sketches for Clustering

abstract:
  Given $n$ points in $\ell_p^d$, we consider the problem of partitioning
points into $k$ clusters with associated centers. The cost of a clustering is
the sum of $p^{\text{th}}$ powers of distances of points to their cluster
centers. For $p \in [1,2]$, we design sketches of size
poly$(\log(nd),k,1/\epsilon)$ such that the cost of the optimal clustering can
be estimated to within factor $1+\epsilon$, despite the fact that the
compressed representation does not contain enough information to recover the
cluster centers or the partition into clusters. This leads to a streaming
algorithm for estimating the clustering cost with space
poly$(\log(nd),k,1/\epsilon)$. We also obtain a distributed memory algorithm,
where the $n$ points are arbitrarily partitioned amongst $m$ machines, each of
which sends information to a central party who then computes an approximation
of the clustering cost. Prior to this work, no such streaming or
distributed-memory algorithm was known with sublinear dependence on $d$ for $p
\in [1,2)$.


title: Changes in Retirement Savings During the COVID Pandemic

abstract:
  This paper documents changes in retirement saving patterns at the onset of
the COVID-19 pandemic. We construct a large panel of U.S. tax data, including
tens of millions of person-year observations, and measure retirement savings
contributions and withdrawals. We use these data to document several important
changes in retirement savings patterns during the pandemic relative to the
years preceding the pandemic or the Great Recession. First, unlike during the
Great Recession, contributions to retirement savings vehicles did not
meaningfully decline. Second, driven by the suspension of required minimum
distribution rules, IRA withdrawals substantially declined in 2020 for those
older than age 72. Third, potentially driven by partial suspension of the early
withdrawal penalty, employer-plan withdrawals increased for those under age 60.


title:
Onset of common envelope evolution during a core helium flash by rapid
  envelope expansion

abstract:
  Applying the suggestion that vigorous core convection during core helium
flash on the tip of the red giant branch (RGB) of low mass stars excites waves
that carry energy to the envelope and inflate it for few years, we find that in
binary systems this process leads to a sharp peak of extreme horizontal branch
(EHB; sdB and sdO) stars with masses of ~0.47Mo and ease the formation of
elliptical planetary nebulae (PNe) by such low mass stars. Using the
open-source mesa-binary we follow the evolution of a number of eccentric binary
systems with an initial primary stellar mass of 1.6Mo. The energy that the
waves carry to the envelope leads to envelope expansion at the tip of the RGB.
The inflated RGB star engulfs many secondary stars to start a common envelope
evolution (CEE) that otherwise would not occur. If the secondary star manages
to remove most of the RGB envelope the primary evolves to become an EHB star
with a mass of ~0.47Mo. In cases where the secondary star does not have time to
spiral-in to close orbits, it ends at a large orbit and leaves a massive enough
envelope for the primary star to later evolve along the asymptotic giant branch
and to engulf the secondary star, therefore forming a non-spherical planetary
nebula.


title: Tile Computer-on-Module for the ATLAS Tile Calorimeter Phase-II upgrades

abstract:
  The Tile PreProcessor (TilePPr) is the core element of the Tile Calorimeter
(TileCal) off-detector electronics for High-luminosity Large Hadron Collider
(HL-LHC). The TilePPr comprises FPGA-based boards to operate and read out the
TileCal on-detector electronics. The Tile Computer on Module (TileCoM)
mezzanine is embedded within TilePPr to carry out three main functionalities.
These include remote configuration of on-detector electronics and TilePPr
FPGAs, interface the TilePPr with the ATLAS Trigger and Data Acquisition (TDAQ)
system, and interfacing the TilePPr with the ATLAS Detector Control System
(DCS) by providing monitoring data. The TileCoM is a 10-layer board with a Zynq
UltraScale+ ZU2CG for processing data, interface components to integrate with
TilePPr and the power supply to be connected to the Advanced Telecommunication
Computing Architecture carrier. A CentOS embedded Linux is deployed on the
TileCoM to implement the required functionalities for the HL-LHC. In this paper
we present the hardware and firmware developments of the TileCoM system in
terms of remote programming, interface with ATLAS TDAQ system and DCS system.


title: The carbon footprint of IRAP

abstract:
  We present an assessment of the greenhouse gases emissions of the Institute
for Research in Astrophysics and Planetology (IRAP), located in Toulouse
(France). It was performed following the established "Bilan Carbone"
methodology, over a large scope compared to similar previous studies, including
in particular the contribution from the purchase of goods and services as well
as IRAP's use of external research infrastructures, such as ground-based
observatories and space-borne facilities. The carbon footprint of the institute
for the reference year 2019 is 7400 +/- 900 tCO2e. If we exclude the
contribution from external research infrastructures to focus on a restricted
perimeter over which the institute has some operational control, IRAP's
emissions in 2019 amounted to 3300 +/- 400 tCO2e. Over the restricted
perimeter, the contribution from purchasing goods and services is dominant,
about 40% of the total, slightly exceeding the contribution from professional
travel including hotel stays, which accounts for 38%. Local infrastructures
make a smaller contribution to IRAP's carbon footprint, about 25% over the
restricted perimeter. We note that this repartition may be specific to IRAP,
since the energy used to produce the electricity and heating has a relatively
low carbon footprint. Over the full perimeter, the large share from the use of
ground-based observatories and space-borne facilities and the fact that the
majority of IRAP purchases are related to instrument development indicate that
research infrastructures represent the most significant challenge for reducing
the carbon footprint of research at our institute. With ~260 staff members
employed, our results imply that performing research in astronomy and
astrophysics at IRAP according to the standards of 2019 produces average GHG
emissions of 28 tCO2e/yr per person involved in that activity (Abridged).


title: Causal Transportability for Visual Recognition

abstract:
  Visual representations underlie object recognition tasks, but they often
contain both robust and non-robust features. Our main observation is that image
classifiers may perform poorly on out-of-distribution samples because spurious
correlations between non-robust features and labels can be changed in a new
environment. By analyzing procedures for out-of-distribution generalization
with a causal graph, we show that standard classifiers fail because the
association between images and labels is not transportable across settings.
However, we then show that the causal effect, which severs all sources of
confounding, remains invariant across domains. This motivates us to develop an
algorithm to estimate the causal effect for image classification, which is
transportable (i.e., invariant) across source and target environments. Without
observing additional variables, we show that we can derive an estimand for the
causal effect under empirical assumptions using representations in deep models
as proxies. Theoretical analysis, empirical results, and visualizations show
that our approach captures causal invariances and improves overall
generalization.


title:
Is the orbital distribution of multiplanet systems influenced by pure
  three-planet resonances?

abstract:
  We analyze the distribution of known multi-planet systems ($N \geq 3$) in the
plane of mean-motion ratios, and compare it with the resonance web generated by
two-planet mean-motion resonances (2P-MMR) and pure 3-planet commensurabilities
(pure 3P-MMR). We find intriguing evidence of a statistically significant
correlation between the observed distribution of compact low-mass systems and
the resonance structure, indicating a possible causal relation. While resonance
chains such as Kepler-60, Kepler-80 and TRAPPIST-1 are strong contributors,
most of the correlation appears to be caused by systems not identified as
resonance chains. Finally, we discuss their possible origin through planetary
migration during the last stages of the primordial disc and/or an eccentricity
damping process.


title:
Explaining Adverse Actions in Credit Decisions Using Shapley
  Decomposition

abstract:
  When a financial institution declines an application for credit, an adverse
action (AA) is said to occur. The applicant is then entitled to an explanation
for the negative decision. This paper focuses on credit decisions based on a
predictive model for probability of default and proposes a methodology for AA
explanation. The problem involves identifying the important predictors
responsible for the negative decision and is straightforward when the
underlying model is additive. However, it becomes non-trivial even for linear
models with interactions. We consider models with low-order interactions and
develop a simple and intuitive approach based on first principles. We then show
how the methodology generalizes to the well-known Shapely decomposition and the
recently proposed concept of Baseline Shapley (B-Shap). Unlike other Shapley
techniques in the literature for local interpretability of machine learning
results, B-Shap is computationally tractable since it involves just function
evaluations. An illustrative case study is used to demonstrate the usefulness
of the method. The paper also discusses situations with highly correlated
predictors and desirable properties of fitted models in the credit-lending
context, such as monotonicity and continuity.


title:
Robust Audio-Visual Instance Discrimination via Active Contrastive Set
  Mining

abstract:
  The recent success of audio-visual representation learning can be largely
attributed to their pervasive property of audio-visual synchronization, which
can be used as self-annotated supervision. As a state-of-the-art solution,
Audio-Visual Instance Discrimination (AVID) extends instance discrimination to
the audio-visual realm. Existing AVID methods construct the contrastive set by
random sampling based on the assumption that the audio and visual clips from
all other videos are not semantically related. We argue that this assumption is
rough, since the resulting contrastive sets have a large number of faulty
negatives. In this paper, we overcome this limitation by proposing a novel
Active Contrastive Set Mining (ACSM) that aims to mine the contrastive sets
with informative and diverse negatives for robust AVID. Moreover, we also
integrate a semantically-aware hard-sample mining strategy into our ACSM. The
proposed ACSM is implemented into two most recent state-of-the-art AVID methods
and significantly improves their performance. Extensive experiments conducted
on both action and sound recognition on multiple datasets show the remarkably
improved performance of our method.


title:
ROMA: Cross-Domain Region Similarity Matching for Unpaired Nighttime
  Infrared to Daytime Visible Video Translation

abstract:
  Infrared cameras are often utilized to enhance the night vision since the
visible light cameras exhibit inferior efficacy without sufficient
illumination. However, infrared data possesses inadequate color contrast and
representation ability attributed to its intrinsic heat-related imaging
principle. This makes it arduous to capture and analyze information for human
beings, meanwhile hindering its application. Although, the domain gaps between
unpaired nighttime infrared and daytime visible videos are even huger than
paired ones that captured at the same time, establishing an effective
translation mapping will greatly contribute to various fields. In this case,
the structural knowledge within nighttime infrared videos and semantic
information contained in the translated daytime visible pairs could be utilized
simultaneously. To this end, we propose a tailored framework ROMA that couples
with our introduced cRoss-domain regiOn siMilarity mAtching technique for
bridging the huge gaps. To be specific, ROMA could efficiently translate the
unpaired nighttime infrared videos into fine-grained daytime visible ones,
meanwhile maintain the spatiotemporal consistency via matching the cross-domain
region similarity. Furthermore, we design a multiscale region-wise
discriminator to distinguish the details from synthesized visible results and
real references. Extensive experiments and evaluations for specific
applications indicate ROMA outperforms the state-of-the-art methods. Moreover,
we provide a new and challenging dataset encouraging further research for
unpaired nighttime infrared and daytime visible video translation, named
InfraredCity. In particular, it consists of 9 long video clips including City,
Highway and Monitor scenarios. All clips could be split into 603,142 frames in
total, which are 20 times larger than the recently released daytime
infrared-to-visible dataset IRVI.


title: Coalgebraic Partition Refinement For All Functors

abstract:
  Coalgebraic partition refinement generalizes classical transition system
minimization to general system types equipped with a coalgebraic equivalence
notion, subsuming strong, weighted, and probabilistic bisimilarity. The
asymptotically fastest algorithm requires an ad-hoc condition on the system
type and uses large amounts of memory, limiting the size of the transition
system that can be handled. A subsequent distributed algorithm is able to
handle larger systems by distributing the memory requirement over several
compute nodes, but this algorithm is asymptotically slower. We present an
algorithm that is applicable to all computable set functors, and runs in time
$O(k^2 n \log n)$, where n is the number of states and k is the number of
transitions per state. This algorithm is asymptotically slower than the fastest
algorithm by a factor of k, but asymptotically faster than the distributed
algorithm by a factor of n. In practice, our algorithm uses much less time and
memory on existing benchmarks. Transition systems that previously required half
an hour on HPC clusters can be minimized in seconds on a single core of a
laptop by our algorithm.


title: Origin of primeval seed magnetism in spinning astrophysical bodies

abstract:
  We show that a primeval seed magnetic field arises as a natural consequence
of spin-degeneracy breaking of fermions caused by the dragging of inertial
frames in the curved spacetime of spinning astrophysical bodies. This seed
magnetism would arise even due to electrically neutral fermions such as
neutrons. As an example, we show that an ideal neutron star spinning at $500$
revolutions per second, having mass $0.83$ M$_{\odot}$ and described by an
ensemble of degenerate neutrons would have $0.12$ Gauss seed magnetic field at
its center arising through the breaking of spin-degeneracy.


title:
Finite field transport response of a dilute magnetic topological
  insulator based Josephson junction

abstract:
  Hybrid samples combining superconductors with magnetic topological insulators
are a promising platform for exploring exotic new transport physics. We examine
a Josephson junction of such a system, based on the dilute magnetic topological
insulator (Hg,Mn)Te and the type II superconductor MoRe. In the zero and very
low field limit, to the best of our knowledge, the device shows, for the first
time, induced supercurrent through a magnetically doped semiconductor, in this
case a topological insulator. At higher fields, a rich and hysteretic
magnetoresistance is revealed. Careful analysis shows that the explanation of
this behaviour can be found in magnetic flux focusing stemming from the
Meissner effect in the superconductor, without invoking any role of proximity
induced superconductivity. The phenomena is important, as it will ubiquitously
co-exist with any exotic new physics that may be present in this class of
devices.


title:
Social learning spontaneously emerges by searching optimal heuristics
  with deep reinforcement learning

abstract:
  How have individuals of social animals in nature evolved to learn from each
other, and what would be the optimal strategy for such learning in a specific
environment? Here, we address both problems by employing a deep reinforcement
learning model to optimize the social learning strategies (SLSs) of agents in a
cooperative game in a multi-dimensional landscape. Throughout the training for
maximizing the overall payoff, we find that the agent spontaneously learns
various concepts of social learning, such as copying, focusing on frequent and
well-performing neighbors, self-comparison, and the importance of balancing
between individual and social learning, without any explicit guidance or prior
knowledge about the system. The SLS from a fully trained agent outperforms all
of the traditional, baseline SLSs in terms of mean payoff. We demonstrate the
superior performance of the reinforcement learning agent in various
environments, including temporally changing environments and real social
networks, which also verifies the adaptability of our framework to different
social settings.


title: Ribbon cobordisms as a partial order

abstract:
  We show that the notion of ribbon rational homology cobordism yields a
partial order on the set of aspherical $3$-manifolds, thus supporting a
conjecture formulated by Daemi, Lidman, Vela-Vick and Wong. Our proof is built
on Agol's recent proof of the fact that ribbon concordance yields a partial
order on the set of knots in the 3-sphere.


title: Quantum control of molecules for fundamental physics

abstract:
  The extraordinary success in laser cooling, trapping, and coherent
manipulation of atoms has energized the efforts in extending this exquisite
control to molecules. Not only are molecules ubiquitous in nature, but the
control of their quantum states offers unparalleled access to fundamental
constants and possible physics beyond the Standard Model. Quantum state
manipulation of molecules can enable high-precision measurements including
tests of fundamental symmetries and searches for new particles and fields. At
the same time, their complex internal structure presents experimental
challenges to overcome in order to gain sensitivity to new physics. In this
Perspective, we review recent developments in this thriving new field.
Moreover, throughout the text we discuss many current and future research
directions that have the potential to place molecules at the forefront of
fundamental science.


title:
An empirical equilibrium model of formal and informal credit markets in
  developing countries

abstract:
  I develop and estimate a dynamic equilibrium model of risky entrepreneurs'
borrowing and savings decisions incorporating both formal and local-informal
credit markets. Households have access to an exogenous formal credit market and
to an informal credit market in which the interest rate is endogenously
determined by the local demand and supply of credit. I estimate the model via
Simulated Maximum Likelihood using Thai village data during an episode of
formal credit market expansion. My estimates suggest that a 49 percent
reduction in fixed costs increased the proportion of households borrowing
formally by 36 percent, and that a doubling of the collateralized borrowing
limits lowered informal interest rates by 24 percent. I find that more
productive households benefited from the policies that expanded borrowing
access, but less productive households lost in terms of welfare due to
diminished savings opportunities. Gains are overall smaller than would be
predicted by models that do not consider the informal credit market.


title: Hetero-interface of electrolyte/2D materials

abstract:
  Electrochemical gating has been demonstrated as a powerful tool to tune the
physical properties of two-dimensional (2D) materials, leading to lots of
fascinating quantum phenomena. However, the reported liquid-nature electrolytes
(e.g, ionic liquid and ion-gel) cover the top surface of 2D materials,
introduce the strain at the hetero-interface, and present sensitivity to
humidity, which strongly limits the further exploration of the hetero-interface
between electrolyte and 2D materials, and their wide applications for
electronics and optoelectronics. Herein, by introducing a lithium-ion
solid-state electrolyte, the character of the electric double layer (EDL) at
hetero-interface and its effect on the optical property of transition metal
chalcogenides (TMDs) have been revealed by Kelvin probe force microscopy (KPFM)
and (time-resolved) photoluminescence measurements. The work function of TMDs
can be strongly tailored by electrochemical gating, up to 0.7eV for WSe2 and
0.3 eV for MoS2, respectively. Besides, from the gate-dependent surface
potential of TMDs with different thicknesses, the potential drop across the EDL
has been quantitatively revealed. Furthermore, from the gate-dependent PL
emission at room temperature, monolayer WS2 exhibits only neutral exciton
emission in the whole range of gate voltage applied, which also exhibits
exciton-exciton annihilation. Our results demonstrate that lithium-ion
substrate is a promising alternative to explore the physics of 2D materials and
the hetero-interface of electrolyte/2D materials by easily integrating both
scanning probe and optical techniques.


title: Structural Rules and Algebraic Properties of Intersection Types

abstract:
  In this paper we define several notions of term expansion, used to define
terms with less sharing, but with the same computational properties of terms
typable in an intersection type system. Expansion relates terms typed by
associative, commutative and idempotent intersections with terms typed in the
Curry type system and the relevant type system, terms typed by non-idempotent
intersections with terms typed in the affine and linear type systems and terms
typed by non-idempotent and non-commutative intersections with terms typed in
an ordered type system. Finally, we show how idempotent intersection is related
with the contraction rule, commutative intersection with the exchange rule and
associative intersection with the lack of structural rules in a type system.


title:
A puzzling insensitivity of magnon spin diffusion to the presence of
  180$^\circ$ domain walls in a ferrimagnetic insulator

abstract:
  We present room-temperature measurements of magnon spin diffusion in
epitaxial ferrimagnetic insulator MgAl$_{0.5}$Fe$_{1.5}$O$_{4}$ (MAFO) thin
films near zero applied magnetic field where the sample forms a multi-domain
state. Due to a weak uniaxial magnetic anisotropy, the domains are separated
primarily by 180$^\circ$ domain walls. We find, surprisingly, that the presence
of the domain walls has very little effect on the spin diffusion -- nonlocal
spin transport signals in the multi-domain state retain at least 95% of the
maximum signal strength measured for the spatially-uniform magnetic state, over
distances at least five times the typical domain size. This result is in
conflict with simple models of interactions between magnons and static domain
walls, which predict that the spin polarization carried by the magnons reverses
upon passage through a 180$^\circ$ domain wall.


title:
Performance Analysis of Out-of-Distribution Detection on Trained Neural
  Networks

abstract:
  Several areas have been improved with Deep Learning during the past years.
Implementing Deep Neural Networks (DNN) for non-safety related applications
have shown remarkable achievements over the past years; however, for using DNNs
in safety critical applications, we are missing approaches for verifying the
robustness of such models. A common challenge for DNNs occurs when exposed to
out-of-distribution samples that are outside of the scope of a DNN, but which
result in high confidence outputs despite no prior knowledge of such input.
  In this paper, we analyze three methods that separate between in- and
out-of-distribution data, called supervisors, on four well-known DNN
architectures. We find that the outlier detection performance improves with the
quality of the model. We also analyse the performance of the particular
supervisors during the training procedure by applying the supervisor at a
predefined interval to investigate its performance as the training proceeds. We
observe that understanding the relationship between training results and
supervisor performance is crucial to improve the model's robustness and to
indicate, what input samples require further measures to improve the robustness
of a DNN. In addition, our work paves the road towards an instrument for safety
argumentation for safety critical applications. This paper is an extended
version of our previous work presented at 2019 SEAA (cf. [1]); here, we
elaborate on the used metrics, add an additional supervisor and test them on
two additional datasets.


title:
An analytically divergence-free collocation method for the
  incompressible Navier-Stokes equations on the rotating sphere

abstract:
  In this work, we develop a high-order collocation method using radial basis
function (RBF) for the incompressible Navier-Stokes equation (NSE) on the
rotating sphere. The method is based on solving the projection of the NSE on
the space of divergence-free functions. For that, we use matrix valued kernel
functions which allow an analytically divergence-free approximation of the
velocity field. Using kernel functions which lead to rotation-free
approximations, the pressure can be recovered by a simple kernel exchange in
one of the occurring approximations, without solving an additional Poisson
problem. We establish precise error estimates for the velocity and the pressure
functions for the semi-discretised solution. In the end, we give a short
estimate of the numerical cost and apply the new method to an experimental test
case.


title:
Multi-task Learning for Concurrent Prediction of Thermal Comfort,
  Sensation, and Preference

abstract:
  Indoor thermal comfort immensely impacts the health and performance of
occupants. Therefore, researchers and engineers have proposed numerous
computational models to estimate thermal comfort (TC). Given the impetus toward
energy efficiency, the current focus is on data-driven TC prediction solutions
that leverage state-of-the-art machine learning (ML) algorithms. However, an
indoor occupant's perception of indoor thermal comfort (TC) is subjective and
multi-dimensional. Different aspects of TC are represented by various standard
metrics/scales viz., thermal sensation (TSV), thermal comfort (TCV), and
thermal preference (TPV). The current ML-based TC prediction solutions adopt
the Single-task Learning approach, i.e., one prediction model per metric.
Consequently, solutions often focus on only one TC metric. Moreover, when
several metrics are considered, multiple TC models for a single indoor space
lead to conflicting predictions, making real-world deployment infeasible. This
work addresses these problems. With the vision toward energy conservation and
real-world application, naturally ventilated primary school classrooms are
considered. First, month-long field experiments are conducted in 5 schools and
14 classrooms, including 512 unique student participants. Further,
"DeepComfort," a Multi-task Learning inspired deep-learning model is proposed.
DeepComfort predicts multiple TC output metrics viz., TSV, TPV, and TCV,
simultaneously, through a single model. It demonstrates high F1-scores,
Accuracy (>90%), and generalization capability when validated on the ASHRAE-II
database and the dataset created in this study. DeepComfort is also shown to
outperform 6 popular metric-specific single-task machine learning algorithms.
To the best of our knowledge, this work is the first application of Multi-task
Learning to thermal comfort prediction in classrooms.


title: Analysis on simple Lie groups and lattices

abstract:
  We present a simple tool to perform analysis with groups such as SL(n,R) and
SL(n,Z), that has been introduced by Vincent Lafforgue in his study of
non-unitary representations, in connection with the Baum-Connes conjecture and
strong property (T). It has been later applied in various contexts: operator
algebras, Fourier analysis, geometry of Banach spaces or dynamics. The idea is
to first restrict to compact subgroups and then exploit how they sit inside the
whole group.


title:
Variation in defects and properties in composite of ZnO and
  {\alpha}-Fe2O3 for methyl blue dye removal

abstract:
  The plasma deposition wall coated composite of ZnO and {\alpha}-Fe2O3 (ZF-W)
after exposure to ~ 2000 {\deg}C, mostly considered as waste-materials and
cleaned out from the deposition unit, was subjected to anneal at 300, 500 and
1000 {\deg}C to manipulate the structural properties. An evolution of defect
states along with the structural changes has been identified as annealing
temperature was varied. As a consequence, an unstable state of ZnFe2O4 was
found to be stabilized at 500 {\deg}C and migration of Zn from ZnO causes the
phase transformation from the {\alpha}-Fe2O3 to ZnFe2O4. While implemented for
methyl blue adsorption/degradation without the effect of any external sources,
the degradation for ZF-W annealed at 300 {\deg}C, 500 {\deg}C and 1000 {\deg}C
were 84%, 68% and 82%, respectively. Compared to annealed structures, pristine
ZF-W delivered the highest methyl blue adsorption efficiency of 86%. The
changes in adsorption/degradation properties have been correlated with the
simultaneous evolution of defects and structural properties of ZF-W as annealed
at different temperatures. The plausible mechanism on the interaction of methyl
blue with the composites on the adsorption/degradation is proposed. These
findings give a clear indication on the importance of defects presence in the
mixed metal oxide composite to obtain high-performance degradation/adsorption
properties for sustainable wastewater treatment.


title: Density matrix reconstruction using non-negative matrix product states

abstract:
  Quantum state tomography is a central technique for quantum information
processing, but is challenging due to the exponential growth of its complexity
with the system size. In this work, we propose an algorithm which iteratively
finds the best non-negative matrix product state approximation based on a set
of measurement outcomes whose size does not necessarily grow exponentially.
Compared to the tomography approach based on neural network states, our scheme
utilizes a so-called tensor train representation that allows straightforward
recovery of the unknown density matrix in matrix product state form. As
applications, our algorithm is numerically demonstrated for the ground state of
the XXZ spin chain under depolarization noise.


title: Qunity: A Unified Language for Quantum and Classical Computing

abstract:
  We introduce Qunity, a new quantum programming language designed around the
central goal of treating quantum computing as a natural generalization of
classical computing. Qunity presents a unified syntax where familiar
programming constructs can have both quantum and classical effects. For
example, one can use sum types to implement the direct sum of linear operators,
exception handling syntax to implement projective measurements, and aliasing to
induce entanglement. Further, Qunity takes advantage of the overlooked BQP
subroutine theorem, allowing one to construct reversible subroutines from
irreversible quantum algorithms through the uncomputation of "garbage" outputs.
Unlike existing languages that enable quantum aspects with a separate add-on
(e.g., gates added to a classical language), we unify quantum and classical
computing through novel compositional semantics based on Kraus operators. We
present Qunity's syntax, type system, and denotational semantics, showing how
it can cleanly express several quantum algorithms. We also outline how Qunity
could be compiled to OpenQASM, demonstrating the realizability of our design.


title:
Outcome coding choice in randomized trials of programs to reduce
  violence

abstract:
  Over the last decade, the number of randomized trials of programs to reduce
intimate partner violence (IPV) has grown precipitously. However, most trials
continue to measure and code violence using standards originally designed for
global prevalence surveys. This choice may have consequences in terms of bias,
power, and efficiency of trial estimates and may limit what we can learn about
how programs are working. In this paper, we return to first principles to
develop a generative model for violence reduction. We then use this model to
better understand trade-offs in outcome coding choices via simulation. We
re-analyze results from seven recent trials in Southern and Eastern Africa to
highlight some of our findings. We conclude with a discussion of key take-aways
for trialists.


title:
Learning Meta Word Embeddings by Unsupervised Weighted Concatenation of
  Source Embeddings

abstract:
  Given multiple source word embeddings learnt using diverse algorithms and
lexical resources, meta word embedding learning methods attempt to learn more
accurate and wide-coverage word embeddings.
  Prior work on meta-embedding has repeatedly discovered that simple vector
concatenation of the source embeddings to be a competitive baseline.
  However, it remains unclear as to why and when simple vector concatenation
can produce accurate meta-embeddings.
  We show that weighted concatenation can be seen as a spectrum matching
operation between each source embedding and the meta-embedding, minimising the
pairwise inner-product loss.
  Following this theoretical analysis, we propose two \emph{unsupervised}
methods to learn the optimal concatenation weights for creating meta-embeddings
from a given set of source embeddings.
  Experimental results on multiple benchmark datasets show that the proposed
weighted concatenated meta-embedding methods outperform previously proposed
meta-embedding learning methods.


title: Chern-Simons theory, link invariants and the Askey-Wilson algebra

abstract:
  The occurrence of the Askey-Wilson (AW) algebra in the $SU(2)$ Chern-Simons
(CS) theory and in the Reshetikhin-Turaev (RT) link invariant construction with
quantum algebra $U_q(\mathfrak{su}_2)$ is explored. Tangle diagrams with three
strands with some of them enclosed in a spin-$1/2$ closed loop are associated
to the generators of the AW algebra. It is shown in both the CS theory and RT
construction that the link invariant of these tangles obey the relations of the
AW generators. It follows that the expectation values of certain Wilson loops
in the CS theory satisfy relations dictated by the AW algebra and that the link
invariants do not distinguish the corresponding linear combinations of links.


title: Photon conservation in trans-luminal metamaterials

abstract:
  Structures which appear to move at or near the velocity of light contain
singular points. Energy generated by the motion accumulates at these points
into ever-narrowing peaks. In this paper we show that energy is generated by a
curious process that conserves the number of photons, adding energy by forcing
photons already present to climb a ladder of increasing frequency. We present
both a classical proof based on conservation of lines of force, and a more
formal QED proof demonstrating the absence of unpaired creation and
annihilation operators. Exceptions to this rule are found when negative
frequencies make an appearance. Finally we make a connection to
laboratory-based models of black holes and Hawking radiation.


title: Single-Photon Storage in a Ground-State Vapor Cell Quantum Memory

abstract:
  Interfaced single-photon sources and quantum memories for photons together
form a foundational component of quantum technology. Achieving compatibility
between heterogeneous, state-of-the-art devices is a long-standing challenge.
We built and successfully interfaced a heralded single-photon source based on
cavity-enhanced spontaneous parametric down-conversion in ppKTP and a matched
memory based on electromagnetically induced transparency in warm $^{87}$Rb
vapor. The bandwidth of the photons emitted by the source is 370 MHz, placing
its speed in the technologically relevant regime while remaining well within
the acceptance bandwidth of the memory. Simultaneously, the experimental
complexity is kept low, with all components operating at or above room
temperature. Read-out noise of the memory is considerably reduced by exploiting
polarization selection rules in the hyperfine structure of spin-polarized
atoms. For the first time, we demonstrate single-photon storage and retrieval
in a ground-state vapor cell memory, with $g_{c,\text{ret}}^{(2)}=0.177(23)$
demonstrating the single-photon character of the retrieved light. Our platform
of single-photon source and atomic memory is attractive for future experiments
on room-temperature quantum networks operating at high bandwidth.


title:
Quantum-classical convolutional neural networks in radiological image
  classification

abstract:
  Quantum machine learning is receiving significant attention currently, but
its usefulness in comparison to classical machine learning techniques for
practical applications remains unclear. However, there are indications that
certain quantum machine learning algorithms might result in improved training
capabilities with respect to their classical counterparts -- which might be
particularly beneficial in situations with little training data available. Such
situations naturally arise in medical classification tasks. Within this paper,
different hybrid quantum-classical convolutional neural networks (QCCNN) with
varying quantum circuit designs and encoding techniques are proposed. They are
applied to two- and three-dimensional medical imaging data, e.g. featuring
different, potentially malign, lesions in computed tomography scans. The
performance of these QCCNNs is already similar to the one of their classical
counterparts -- therefore encouraging further studies towards the direction of
applying these algorithms within medical imaging tasks.


title:
Correlation of Magnetism and Disordered Shiba Bands in Fe Monolayer
  Islands on Nb(110)

abstract:
  Two-dimensional (2D) magnet-superconductor hybrid systems are intensively
studied due to their potential for the realization of 2D topological
superconductors with Majorana edge modes. It is theoretically predicted that
this quantum state is ubiquitous in spin-orbit coupled ferromagnetic or
skyrmionic 2D spin-lattices in proximity to an s-wave superconductor. However,
recent examples suggest that the requirements for topological superconductivity
are complicated by the multi-orbital nature of the magnetic components and
disorder effects. Here, we investigate Fe monolayer islands grown on a surface
of the s-wave superconductor with the largest gap of all elemental
superconductors, Nb, with respect to magnetism and superconductivity using
spin-resolved scanning tunneling spectrosopy. We find three types of Fe
monolayer islands which differ by their reconstruction inducing disorder, the
magnetism and the sub-gap electronic states. All three types are ferromagnetic
with different coercive fields indicating diverse exchange and anisotropy
energies. On all three islands, there is finite spectral weight throughout the
substrate's energy gap at the expense of the coherence peak intensity,
indicating the formation of Shiba bands overlapping with the Fermi energy. The
gap filling and coherence peak reduction is strongest for the island with
largest coercive field. A strong lateral variation of the spectral weight of
the Shiba bands signifies substantial disorder on the order of the substrate's
pairing energy with a length scale of the period of the three different
reconstructions. There are neither signs of topological gaps within these bands
nor of any kind of edge modes. Our work illustrates that a reconstructed growth
mode of magnetic layers on superconducting surfaces is detrimental for the
formation of 2D topological superconductivity.


title:
PAC-Bayes training for neural networks: sparsity and uncertainty
  quantification

abstract:
  We study the Gibbs posterior distribution from PAC-Bayes theory for sparse
deep neural nets in a nonparametric regression setting. To access the posterior
distribution, an efficient MCMC algorithm based on backpropagation is
constructed. The training yields a Bayesian neural network with a joint
distribution on the network parameters. Using a mixture over uniform priors on
sparse sets of networks weights, we prove an oracle inequality which shows that
the method adapts to the unknown regularity and hierarchical structure of the
regression function. Studying the Gibbs posterior distribution from a
frequentist Bayesian perspective, we analyze the diameter and show high
coverage probability of the resulting credible sets. The method is illustrated
in a simulation example.


title: On Fragile Features and Batch Normalization in Adversarial Training

abstract:
  Modern deep learning architecture utilize batch normalization (BN) to
stabilize training and improve accuracy. It has been shown that the BN layers
alone are surprisingly expressive. In the context of robustness against
adversarial examples, however, BN is argued to increase vulnerability. That is,
BN helps to learn fragile features. Nevertheless, BN is still used in
adversarial training, which is the de-facto standard to learn robust features.
In order to shed light on the role of BN in adversarial training, we
investigate to what extent the expressiveness of BN can be used to robustify
fragile features in comparison to random features. On CIFAR10, we find that
adversarially fine-tuning just the BN layers can result in non-trivial
adversarial robustness. Adversarially training only the BN layers from scratch,
in contrast, is not able to convey meaningful adversarial robustness. Our
results indicate that fragile features can be used to learn models with
moderate adversarial robustness, while random features cannot


title: Angular coefficients in W+j production at the LHC with high precision

abstract:
  The extraction of the W-boson mass, a fundamental parameter of the Standard
Model, from hadron-hadron collision requires precise theory predictions. In
this regard, angular coefficients are crucial to model the dynamics of W-boson
production. In this work, we provide, for the first time, angular coefficients
at NNLO QCD + NLO EW accuracy for finite transverse momentum W-boson at the
LHC. The corrections can reach up to 10% in certain regions of phase space.
They are accompanied by a significant reduction of the scale uncertainty. This
work should, besides providing reference values for theory-data comparison,
provide state-of-the-art theory input for W-boson mass measurements.


title:
Using HITRAN to Model Opacities for Planetary Atmospheres: Test case of
  Microwave Spectra of NH$_3$, SO$_2$ and PH$_3$

abstract:
  The high-resolution transmission molecular spectroscopic database (HITRAN)
has recently introduced line-by-line broadening parameters (and their
temperature dependence) appropriate for the dominant constituents of planetary
atmospheres. The latest version of the database, HITRAN2020, has recently been
released and constitutes a large and expansive update. In this work, line shape
codes suitable for calculating microwave spectra have been implemented within
the HITRAN Application Programming Interface (HAPI). These new additions allow
for spectroscopic calculations of microwave absorbing species pertinent to
current and future studies of the atmospheres of Jupiter and Venus, and more
generally for the atmospheres of gas giants and rocky planets. The inversion
spectrum of the NH$_3$ molecule broadened by H$_2$, He and H$_2$O dominates the
microwave region of Jupiter. Whereas for Venus, accurate spectroscopic data of
SO$_2$ broadened by CO$_2$ is necessary in order to determine its significance,
if any, on the reported detection of PH$_3$ in the Venusian upper cloud deck.
Comparisons have been made to available microwave laboratory opacities and the
following results illustrate that HITRAN data can be used in conjunction with
HAPI to reproduce the existing experimental measurements and provide reliable
calculation of planetary opacities.


title: Frequency chirped Fourier-Transform spectroscopy

abstract:
  Fast (sub-second) spectroscopy with high spectral resolution is of vital
importance for revealing quantum chemistry kinetics of complex chemical and
biological reactions. Fourier transform (FT) spectrometers can achieve high
spectral resolution and operate at hundreds of ms time scales in rapid-scan
mode. However, the linear translation of a scanning mirror imposes stringent
time-resolution limitations to these systems, which makes simultaneous high
spectral and temporal resolution impossible. Here, we demonstrate an FT
spectrometer whose operational principle is based on continuous rotational,
rather than linear, motion of the scanning mirror, decoupling the spectral
resolution from the temporal one. This enables 0.5 cm${}^{-1}$ resolution on
sub-ms time scales. Furthermore, we show that such rotational FT spectrometers
can perform dual-comb spectroscopy with a single comb source, since the
Doppler-shifted version of the comb serves as the second comb. In this way, we
combine the advantages of dual-comb and FT spectroscopy using a single quantum
cascade laser frequency comb as a light source. Our technique does not require
any diffractive or dispersive optical elements and hence preserve the
Jacquinot's-, Fellgett's-, and Connes'-advantages of FT spectrometers. The
system supports a large optical bandwidth from visible to THz frequencies. The
combination of a rotational delay line with collimated coherent or non-coherent
light sources pave the way for FT spectrometers in applications where high
speed, large optical bandwidth, and high spectral resolution are desired.


title: Tolerant Bipartiteness Testing in Dense Graphs

abstract:
  Bipartite testing has been a central problem in the area of property testing
since its inception in the seminal work of Goldreich, Goldwasser and Ron
[FOCS'96 and JACM'98]. Though the non-tolerant version of bipartite testing has
been extensively studied in the literature, the tolerant variant is not well
understood. In this paper, we consider the following version of tolerant
bipartite testing: Given a parameter $\varepsilon \in (0,1)$ and access to the
adjacency matrix of a graph $G$, we can decide whether $G$ is
$\varepsilon$-close to being bipartite or $G$ is at least
$(2+\Omega(1))\varepsilon$-far from being bipartite, by performing
$\widetilde{\mathcal{O}}\left(\frac{1}{\varepsilon ^3}\right)$ queries and in
$2^{\widetilde{\mathcal{O}}(1/\varepsilon)}$ time. This improves upon the
state-of-the-art query and time complexities of this problem of
$\widetilde{\mathcal{O}}\left(\frac{1}{\varepsilon ^6}\right)$ and
$2^{\widetilde{\mathcal{O}}(1/\varepsilon^2)}$, respectively, from the work of
Alon, Fernandez de la Vega, Kannan and Karpinski (STOC'02 and JCSS'03), where
$\widetilde{\mathcal{O}}(\cdot)$ hides a factor polynomial in $\log
\frac{1}{\varepsilon}$.


title:
Slow spin dynamics and quantum tunneling of magnetization in the dipolar
  antiferromagnet DyScO$_3$

abstract:
  We present a comprehensive study of static and dynamic magnetic properties in
the Ising-like dipolar antiferromagnet (AFM) DyScO$_3$\ by means of DC and AC
magnetization measurements supported by classical Monte-Carlo calculations. Our
AC-susceptibility data show that the magnetic dynamics exhibit a clear
crossover from an Arrhenius-like regime to quantum tunneling of magnetization
(QTM) at $T^* = 10$ K. Below $T_{\mathrm{N}} = 3.2$ K DyScO$_3$ orders in an
antiferromagnetic $GxAy$-type magnetic structure and the magnetization dynamics
slow down to the minute timescale. The low-temperature magnetization curves
exhibit complex hysteretic behavior, which depends strongly on the magnetic
field sweep rate. We demonstrate that the low-field anomalies on the
magnetization curve are related to the metamagnetic transition, while the
hysteresis at higher fields is induced by a strong magnetocaloric effect. Our
theoretical calculations, which take into account dipolar interaction between
Dy$^{3+}$ moments, reproduce essential features of the magnetic behavior of
DyScO$_3$. We demonstrate that DyScO$_3$ represents a rare example of inorganic
compound, which exhibits QTM at a single-ion level and magnetic order due to
classical dipolar interaction.


title: Streaming Algorithms for High-Dimensional Robust Statistics

abstract:
  We study high-dimensional robust statistics tasks in the streaming model. A
recent line of work obtained computationally efficient algorithms for a range
of high-dimensional robust estimation tasks. Unfortunately, all previous
algorithms require storing the entire dataset, incurring memory at least
quadratic in the dimension. In this work, we develop the first efficient
streaming algorithms for high-dimensional robust statistics with near-optimal
memory requirements (up to logarithmic factors). Our main result is for the
task of high-dimensional robust mean estimation in (a strengthening of) Huber's
contamination model. We give an efficient single-pass streaming algorithm for
this task with near-optimal error guarantees and space complexity nearly-linear
in the dimension. As a corollary, we obtain streaming algorithms with
near-optimal space complexity for several more complex tasks, including robust
covariance estimation, robust regression, and more generally robust stochastic
optimization.


title:
Robust measurements of n-point correlation functions of
  driven-dissipative quantum systems on a digital quantum computer

abstract:
  We propose and demonstrate a unified hierarchical method to measure n-point
correlation functions that can be applied to driven, dissipative, or otherwise
non-equilibrium systems. In this method, the time evolution of the system is
repeatedly interrupted by interacting an ancillary qubit with the system
through a controlled operation, and measuring the ancilla immediately
afterwards. We discuss robustness of this method versus ancilla-enabled
interferometric techniques (such as the Hadamard test), and implement the
method on a quantum computer in order to measure single-particle Green's
functions of a driven-dissipative fermionic system. This work shows that
dynamical correlation functions for driven-dissipative systems can be measured
with near-term quantum computers.


title: Jet Functors in Noncommutative Geometry

abstract:
  In this paper we construct three infinite families of endofunctors
$J_d^{(n)}$, $J_d^{[n]}$, and $J_d^n$ on the category of left $A$-modules,
where $A$ is a unital associative algebra over a commutative ring $\mathbb{k}$,
equipped with an exterior algebra $\Omega^\bullet_d$. We prove that these
functors generalize the corresponding classical notions of nonholonomic,
semiholonomic, and holonomic jet functors, respectively. Our functors come
equipped with natural transformations from the identity functor to the
corresponding jet functors, which play the r\^{o}les of the classical
prolongation maps. This allows us to define the notion of linear differential
operators with respect to $\Omega^{\bullet}_d$. We show that if $\Omega^1_d$ is
a flat right $A$-module, the semiholonomic jet functor satisfies the
semiholonomic jet exact sequence $0 \rightarrow \bigotimes^n_A \Omega^1_d
\rightarrow J^{[n]}_d\rightarrow J^{[n-1]}_d \rightarrow 0$. Moreover, we
construct a functor of quantum symmetric forms $S^n_d$ associated to
$\Omega^\bullet_d$, and proceed to introduce the corresponding noncommutative
analogue of the Spencer $\delta$-complex. We give necessary and sufficient
conditions under which the holonomic jet functor $J_d^n$ satisfies the
(holonomic) jet exact sequence, $0\rightarrow S^n_d \rightarrow J_d^n
\rightarrow J_d^{n-1} \rightarrow 0$. In particular, for $n=1$ the sequence is
always exact, for $n=2$ it is exact for $\Omega^1_d$ flat as a right
$A$-module, and for $n\ge 3$, it is sufficient to have $\Omega^1_d$,
$\Omega^2_d$, and $\Omega^3_d$ flat as right $A$-modules and the vanishing of
the Spencer cohomology $H^{\bullet,2}$.


title:
Understanding the Impact of Edge Cases from Occluded Pedestrians for ML
  Systems

abstract:
  Machine learning (ML)-enabled approaches are considered a substantial support
technique of detection and classification of obstacles of traffic participants
in self-driving vehicles. Major breakthroughs have been demonstrated the past
few years, even covering complete end-to-end data processing chain from sensory
inputs through perception and planning to vehicle control of acceleration,
breaking and steering. YOLO (you-only-look-once) is a state-of-the-art
perception neural network (NN) architecture providing object detection and
classification through bounding box estimations on camera images. As the NN is
trained on well annotated images, in this paper we study the variations of
confidence levels from the NN when tested on hand-crafted occlusion added to a
test set. We compare regular pedestrian detection to upper and lower body
detection. Our findings show that the two NN using only partial information
perform similarly well like the NN for the full body when the full body NN's
performance is 0.75 or better. Furthermore and as expected, the network, which
is only trained on the lower half body is least prone to disturbances from
occlusions of the upper half and vice versa.


title:
Magnetic Excitations and Interactions in the Kitaev Hyperhoneycomb
  Iridate $\beta$-Li$_2$IrO$_3$

abstract:
  We present a thorough experimental study of the three-dimensional
hyperhoneycomb Kitaev magnet $\beta$-Li$_2$IrO$_3$, using a combination of
inelastic neutron scattering (INS), time-domain THz spectroscopy, and heat
capacity measurements. The main results include a massive low-temperature
reorganization of the INS spectral weight that evolves into a broad peak
centered around 12 meV, and a distinctive peak in the THz data at 2.8(1) meV. A
detailed comparison to powder-averaged spin-wave theory calculations reveals
that the positions of these two features are controlled by the anisotropic
$\Gamma$ coupling and the Heisenberg exchange $J$, respectively. The refined
microscopic spin model places $\beta$-Li$_2$IrO$_3$ in close proximity to the
Kitaev spin liquid phase.


title:
Knowledge Transfer in Engineering Fleets: Hierarchical Bayesian
  Modelling for Multi-Task Learning

abstract:
  We propose a population-level analysis to address issues of data sparsity
when building predictive models of engineering infrastructure. By sharing
information between similar assets, hierarchical Bayesian modelling is used to
improve the survival analysis of a truck fleet (hazard curves) and power
prediction in a wind farm (power curves). In each example, a set of correlated
functions are learnt over the asset fleet, in a combined inference, to learn a
population model. Parameter estimation is improved when sub-fleets of assets
are allowed to share correlated information at different levels in the
hierarchy. In turn, groups with incomplete data automatically borrow
statistical strength from those that are data-rich. The correlations can be
inspected to inform which assets share information for which effect (i.e.
parameter).


title: Confined Penrose process and black-hole bomb

abstract:
  We consider decay of a particle with some energy $E_{0}>0$ inside the
ergosphere of a black hole. After the first decay one of particles with the
energy $E_{1}<0$ falls towards a black hole while the second one with $%
E_{2}>E_{0}\,\ $moves in the outward direction. It bounces back from a
reflecting shell and, afterwards, the process repeats. For radial motion of
charged particles in the Reissner-Nordst\"{o}m metric, the result depends
strongly on a concrete scenario. In particular, an indefinitely large growth of
energy inside a shell is possible that gives rise to a black-hole bomb. We also
consider a similar multiple process with neutral particles in the background of
a rotating axially symmetric stationary black hole. We demonstrate that, if
decay occurs in the turning point, a black-hole bomb in this case is impossible
at all. For a generic point inside the ergoregion, there is condition for a
black-hole bomb to exist. It relates the ratio of masses before and after decay
and the velocity of a fragment in the center of mass frame.


title:
A survey on attention mechanisms for medical applications: are we moving
  towards better algorithms?

abstract:
  The increasing popularity of attention mechanisms in deep learning algorithms
for computer vision and natural language processing made these models
attractive to other research domains. In healthcare, there is a strong need for
tools that may improve the routines of the clinicians and the patients.
Naturally, the use of attention-based algorithms for medical applications
occurred smoothly. However, being healthcare a domain that depends on
high-stake decisions, the scientific community must ponder if these
high-performing algorithms fit the needs of medical applications. With this
motto, this paper extensively reviews the use of attention mechanisms in
machine learning (including Transformers) for several medical applications.
This work distinguishes itself from its predecessors by proposing a critical
analysis of the claims and potentialities of attention mechanisms presented in
the literature through an experimental case study on medical image
classification with three different use cases. These experiments focus on the
integrating process of attention mechanisms into established deep learning
architectures, the analysis of their predictive power, and a visual assessment
of their saliency maps generated by post-hoc explanation methods. This paper
concludes with a critical analysis of the claims and potentialities presented
in the literature about attention mechanisms and proposes future research lines
in medical applications that may benefit from these frameworks.


title:
Quantum error mitigation by hidden inverses protocol in superconducting
  quantum devices

abstract:
  We present a method to improve the convergence of variational algorithms
based on hidden inverses to mitigate coherent errors. In the context of error
mitigation, this means replacing the on hardware implementation of certain
Hermitian gates with their inverses. Doing so results in noise cancellation and
a more resilient quantum circuit. This approach improves performance in a
variety of two-qubit error models where the noise operator also inverts with
the gate inversion. We apply the mitigation scheme on superconducting quantum
processors running the variational quantum eigensolver (VQE) algorithm to find
the H$_{\rm 2}$ ground-state energy. When implemented on superconducting
hardware we find that the mitigation scheme effectively reduces the energy
fluctuations in the parameter learning path in VQE, reducing the number of
iterations for a converged value. We also provide a detailed numerical
simulation of VQE performance under different noise models and explore how
hidden inverses \& randomized compiling affect the underlying loss landscape of
the learning problem. These simulations help explain our experimental hardware
outcomes, helping to connect lower-level gate performance to
application-specific behavior in contrast to metrics like fidelity which often
do not provide an intuitive insight into observed high-level performance.


title:
MILES: Visual BERT Pre-training with Injected Language Semantics for
  Video-text Retrieval

abstract:
  Dominant pre-training work for video-text retrieval mainly adopt the
"dual-encoder" architectures to enable efficient retrieval, where two separate
encoders are used to contrast global video and text representations, but ignore
detailed local semantics. The recent success of image BERT pre-training with
masked visual modeling that promotes the learning of local visual context,
motivates a possible solution to address the above limitation. In this work, we
for the first time investigate masked visual modeling in video-text
pre-training with the "dual-encoder" architecture. We perform Masked visual
modeling with Injected LanguagE Semantics (MILES) by employing an extra
snapshot video encoder as an evolving "tokenizer" to produce reconstruction
targets for masked video patch prediction. Given the corrupted video, the video
encoder is trained to recover text-aligned features of the masked patches via
reasoning with the visible regions along the spatial and temporal dimensions,
which enhances the discriminativeness of local visual features and the
fine-grained cross-modality alignment. Our method outperforms state-of-the-art
methods for text-to-video retrieval on four datasets with both zero-shot and
fine-tune evaluation protocols. Our approach also surpasses the baseline models
significantly on zero-shot action recognition, which can be cast as
video-to-text retrieval.


title: Distributed controller synthesis for deadlock avoidance

abstract:
  We consider the distributed control synthesis problem for systems with locks.
The goal is to find local controllers so that the global system does not
deadlock. With no restriction this problem is undecidable even for three
processes each using a fixed number of locks. We propose two restrictions that
make distributed control decidable. The first one is to allow each process to
use at most two locks. The problem then becomes Sigma2P-complete, and even in
PTIME under some additional assumptions. The dining philosophers problem
satisfies these assumptions. The second restriction is a nested usage of locks.
In this case the synthesis problem is NEXPTIME-complete. The drinking
philosophers problem falls in this case.


title:
Isoperimetric lower bounds for critical exponents for long-range
  percolation

abstract:
  We study independent long-range percolation on $\mathbb{Z}^d$ where the
vertices $x$ and $y$ are connected with probability
$1-e^{-\beta\|x-y\|^{-d-\alpha}}$ for $\alpha > 0$. Provided the critical
exponents $\delta$ and $2-\eta$ defined by $\delta = \lim_{n\to \infty}
\frac{-\log(n)}{\mathbb{P}_{\beta_c}\left(|K_0|\geq n\right)}$ and $2-\eta =
\lim_{x \to \infty} \frac{\log\left(\mathbb{P}_{\beta_c}\left(0\leftrightarrow
x\right)\right)}{\log(\|x\|)} + d$ exist, we show that
  \begin{equation*}
  \delta \geq \frac{d+(\alpha\wedge 1)}{d-(\alpha\wedge 1)} \ \text{ and } \
2-\eta \geq \alpha \wedge 1 \text.
  \end{equation*} The lower bound on $\delta$ is believed to be sharp for $d =
1, \alpha \in \left[\frac{1}{3},1\right)$ and for $d = 2, \alpha \in
\left[\frac{2}{3},1\right]$, whereas the lower bound on $2-\eta$ is sharp for
$d=1, \alpha \in (0,1)$, and for $\alpha \in \left(0,1\right]$ for $d>1$, and
is not believed to be sharp otherwise. Our main tool is a connection between
the critical exponents and the isoperimetry of cubes inside $\mathbb{Z}^d$.


title:
Energy dynamics, information and heat flow in quenched cooling and the
  crossover from quantum to classical thermodynamics

abstract:
  The dynamics when a hot many-body quantum system is brought into
instantaneous contact with a cold many-body quantum system can be understood as
a combination of early time quantum correlation (von Neumann entropy) gain and
late time energy relaxation. We show that at the shortest timescales there is
an energy increase in each system linked to the entropy gain, even though
equilibrium thermodynamics does not apply. This energy increase is of quantum
origin and results from the collective binding energy between the two systems.
Counter-intuitively, this implies that also the hotter of the two systems
generically experiences an initial energy increase when brought into contact
with the other colder system. In the limit where the energy relaxation
overwhelms the (quantum) correlation build-up, classical energy dynamics
emerges where the energy in the hot system decreases immediately upon contact
with a cooler system. We use both strongly correlated SYK systems and weakly
correlated mixed field Ising chains to exhibit these characteristics, and
comment on its implications for both black hole evaporation and quantum
thermodynamics.


title:
Polynomiality of the faithful dimension of nilpotent groups over finite
  truncated valuation rings

abstract:
  The faithful dimension of a finite group $\mathrm G$ over $\mathbb C$,
denoted by $m_\mathrm{faithful}(\mathrm G)$, is the smallest integer $n$ such
that $\mathrm G$ can be embedded in $\mathrm{GL}_n(\mathbb C)$. Continuing our
previous work (arXiv:1712.02019), we address the problem of determining the
faithful dimension of a finite $p$-group of the form $\mathcal
G_R:=\exp(\mathfrak g_R)$ associated to $\mathfrak g_R:=\mathfrak g
\otimes_\mathbb Z R $ in the Lazard correspondence, where $\mathfrak g$ is a
nilpotent $\mathbb Z$-Lie algebra and $R$ ranges over finite truncated
valuation rings.
  Our first main result is that if $R$ is a finite field with $p^f$ elements
and $p$ is sufficiently large, then $m_\mathrm{faithful}(\mathcal G_R)=fg(p^f)$
where $g(T)$ belongs to a finite list of polynomials $g_1,\ldots,g_k$, with
non-negative integer coefficients. The list of polynomials is uniquely
determined by the Lie algebra $\mathfrak g$. Furthermore, for $1\leq i\leq k$
the set of pairs $(p,f)$ for which $g=g_i$ is a finite union of Cartesian
products $\mathcal P\times \mathcal F$, where $\mathcal P$ is a Frobenius set
of prime numbers and $\mathcal F$ is a subset of $\mathbb N$ that belongs to
the Boolean algebra generated by arithmetic progressions.
  Next we formulate a conjectural polynomiality property for
$m_\mathrm{faithful}(\mathcal G_R)$ in the more general setting where $R$ is a
finite truncated valuation ring, and prove special cases of this conjecture. In
particular, we show that for a vast class of Lie algebras $\mathfrak g $ that
are defined by partial orders, $m_\mathrm{faithful}(\mathcal G_R)$ is given by
a single polynomial-type formula.
  Finally, we compute $m_\mathrm{faithful}(\mathcal G_R)$ precisely in the case
where $\mathfrak g$ is the free metabelian nilpotent Lie algebra of class $c$
on $n$ generators and $R$ is a finite truncated valuation ring.


title:
Search for a massive scalar resonance decaying to a light scalar and a
  Higgs boson in the four b quarks final state with boosted topology

abstract:
  We search for new massive scalar particles X and Y through the resonant
process X$\to$YH$\to$$\mathrm{b\bar{b}b\bar{b}}$, where H is the standard model
Higgs boson. Data from CERN LHC proton-proton collisions are used, collected at
a centre-of-mass energy of 13 TeV in 2016-2018 and corresponding to an
integrated luminosity of 138 fb$^{-1}$. The search is performed in mass ranges
of 0.9-4 TeV for X and 60-600 GeV for Y, where both Y and H are reconstructed
as Lorentz-boosted single large-area jets. The results are interpreted in the
context of the next-to-minimal supersymmetric standard model and also in an
extension of the standard model with two additional singlet scalar fields. The
95% confidence level upper limits for the production cross section vary between
0.1 and 150 fb depending on the X and Y masses, and represent a significant
improvement over results from previous searches.


title: On a class of interpolation inequalities on the 2D sphere

abstract:
  We prove estimates for the $L^p$-norms of systems of functions and divergence
free vector functions that are orthonormal in the Sobolev space $H^1$ on the 2D
sphere. As a corollary, order sharp constants in the embedding
$H^1\hookrightarrow L^q$, $q<\infty$, are obtained in the Gagliardo--Nirenberg
interpolation inequalities.


title:
Automatic Monitoring of Fruit Ripening Rooms by UHF RFID Sensor Network
  and Machine Learning

abstract:
  Accelerated ripening through the exposure of fruits to controlled
environmental conditions and gases is nowadays one of the most assessed food
technologies, especially for climacteric and exotic products. However, a fine
granularity control of the process and consequently of the quality of the goods
is still missing, so the management of the ripening rooms is mainly based on
qualitative estimations only. Following the modern paradigms of Industry 4.0,
this contribution proposes a non-destructive RFID-based system for the
automatic evaluation of the live ripening of avocados. The system, coupled with
a properly trained automatic classification algorithm based on Support Vector
Machines (SVMs), can discriminate the stage of ripening with an accuracy
greater than 85%.


title:
XSS for the Masses: Integrating Security in a Web Programming Course
  using a Security Scanner

abstract:
  Cybersecurity education is considered an important part of undergraduate
computing curricula, but many institutions teach it only in dedicated courses
or tracks. This optionality risks students graduating with limited exposure to
secure coding practices that are expected in industry. An alternative approach
is to integrate cybersecurity concepts across non-security courses, so as to
expose students to the interplay between security and other sub-areas of
computing. In this paper, we report on our experience of applying the security
integration approach to an undergraduate web programming course. In particular,
we added a practical introduction to secure coding, which highlighted the OWASP
Top 10 vulnerabilities by example, and demonstrated how to identify them using
out-of-the-box security scanner tools (e.g. ZAP). Furthermore, we incentivised
students to utilise these tools in their own course projects by offering bonus
marks. To assess the impact of this intervention, we scanned students' project
code over the last three years, finding a reduction in the number of
vulnerabilities. Finally, in focus groups and a survey, students shared that
our intervention helped to raise awareness, but they also highlighted the
importance of grading incentives and the need to teach security content
earlier.


title: UAS Imagery and Computer Vision for Site-Specific Weed Control in Corn

abstract:
  Currently, weed control in a corn field is performed by a blanket application
of herbicides which do not consider spatial distribution information of weeds
and also uses an extensive amount of chemical herbicides. In order to reduce
the amount of chemicals, we used drone based high-resolution imagery and
computer-vision techniwue to perform site-specific weed control in corn.


title:
Bifrost: End-to-End Evaluation and Optimization of Reconfigurable DNN
  Accelerators

abstract:
  Reconfigurable accelerators for deep neural networks (DNNs) promise to
improve performance such as inference latency. STONNE is the first
cycle-accurate simulator for reconfigurable DNN inference accelerators which
allows for the exploration of accelerator designs and configuration space.
However, preparing models for evaluation and exploring configuration space in
STONNE is a manual developer-timeconsuming process, which is a barrier for
research. This paper introduces Bifrost, an end-to-end framework for the
evaluation and optimization of reconfigurable DNN inference accelerators.
Bifrost operates as a frontend for STONNE and leverages the TVM deep learning
compiler stack to parse models and automate offloading of accelerated
computations. We discuss Bifrost's advantages over STONNE and other tools, and
evaluate the MAERI and SIGMA architectures using Bifrost. Additionally, Bifrost
introduces a module leveraging AutoTVM to efficiently explore accelerator
designs and dataflow mapping space to optimize performance. This is
demonstrated by tuning the MAERI architecture and generating efficient dataflow
mappings for AlexNet, obtaining an average speedup of $50\times$ for the
convolutional layers and $11\times$ for the fully connected layers. Our code is
available at www.github.com/gicLAB/bifrost.


title:
A geometric proof for the root-independence of the greedoid polynomial
  of Eulerian branching greedoids

abstract:
  We define the root polytope of a regular oriented matroid, and show that the
greedoid polynomial of an Eulerian branching greedoid rooted at vertex $v_0$ is
equivalent to the $h^*$-polynomial of the root polytope of the dual of the
graphic matroid.
  As the definition of the root polytope is independent of the vertex $v_0$,
this gives a geometric proof for the root-independence of the greedoid
polynomial for Eulerian branching greedoids, a fact which was first proved by
Swee Hong Chan, and K\'evin Perrot and Trung Van Pham using sandpile models. We
also obtain that the greedoid polynomial does not change if we reverse each
edge of an Eulerian digraph.


title:
Interpretable Battery Cycle Life Range Prediction Using Early
  Degradation Data at Cell Level

abstract:
  Battery cycle life prediction using early degradation data has many potential
applications throughout the battery product life cycle. Various data-driven
methods have been proposed for point prediction of battery cycle life with
minimum knowledge of the battery degradation mechanisms. However, management of
batteries at end-of-life with lower economic and technical risk requires
prediction of cycle life with quantified uncertainty, which is still lacking.
The interpretability (i.e., the reason for high prediction accuracy) of these
advanced data-driven methods is also worthy of investigation. Here, a
physics-informed Quantile Regression Forest (QRF) model is introduced to make
cycle life range prediction with uncertainty quantified as the length of the
prediction interval, in addition to point predictions with high accuracy. The
hyperparameters of the QRF model are tuned with a proposed area-based
performance evaluation metric so that the coverage probabilities associated
with the prediction intervals are calibrated. The interpretability of the final
QRF model is explored with two global model-agnostic methods, namely
permutation importance, and partial dependence plot. The final QRF model
facilitates dual-criteria decision-making to select the high-cycle-life
charging protocol with consideration of both point predictions and uncertainty
associated with the prediction.


title:
Disambiguation of morpho-syntactic features of African American English
  -- the case of habitual be

abstract:
  Recent research has highlighted that natural language processing (NLP)
systems exhibit a bias against African American speakers. The bias errors are
often caused by poor representation of linguistic features unique to African
American English (AAE), due to the relatively low probability of occurrence of
many such features in training data. We present a workflow to overcome such
bias in the case of habitual "be". Habitual "be" is isomorphic, and therefore
ambiguous, with other forms of "be" found in both AAE and other varieties of
English. This creates a clear challenge for bias in NLP technologies. To
overcome the scarcity, we employ a combination of rule-based filters and data
augmentation that generate a corpus balanced between habitual and non-habitual
instances. With this balanced corpus, we train unbiased machine learning
classifiers, as demonstrated on a corpus of AAE transcribed texts, achieving
.65 F$_1$ score disambiguating habitual "be".


title:
Energy dependent ergodicity and scarring phenomena in two component
  Bose-Josephson junction

abstract:
  We consider a Bose-Josephson junction (BJJ) formed by binary mixture of
ultracold atoms to investigate the manifestation of coherent collective
dynamics on ergodicity and quantum scars, unfolding the connection between
them. By tuning the inter and intra-species interaction, we reveal a rich
structure of steady states, which has a significant influence on the overall
ergodic behavior, although its degree varies across the energy band. The
signature of underlying classicality can be revealed from the entanglement
spectrum, which is used to identify the quantum scars of unstable steady states
and periodic orbits, leading to athermal behavior. The energy dependent
ergodicity and scarring phenomena can be probed from the auto-correlation
function as well from the phase fluctuation of the condensates, which has
relevance in cold atom experiments.

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


title:
RadioPathomics: Multimodal Learning in Non-Small Cell Lung Cancer for
  Adaptive Radiotherapy

abstract:
  The current cancer treatment practice collects multimodal data, such as
radiology images, histopathology slides, genomics and clinical data. The
importance of these data sources taken individually has fostered the recent
raise of radiomics and pathomics, i.e. the extraction of quantitative features
from radiology and histopathology images routinely collected to predict
clinical outcomes or to guide clinical decisions using artificial intelligence
algorithms. Nevertheless, how to combine them into a single multimodal
framework is still an open issue. In this work we therefore develop a
multimodal late fusion approach that combines hand-crafted features computed
from radiomics, pathomics and clinical data to predict radiation therapy
treatment outcomes for non-small-cell lung cancer patients. Within this
context, we investigate eight different late fusion rules (i.e. product,
maximum, minimum, mean, decision template, Dempster-Shafer, majority voting,
and confidence rule) and two patient-wise aggregation rules leveraging the
richness of information given by computer tomography images and whole-slide
scans. The experiments in leave-one-patient-out cross-validation on an in-house
cohort of 33 patients show that the proposed multimodal paradigm with an AUC
equal to $90.9\%$ outperforms each unimodal approach, suggesting that data
integration can advance precision medicine. As a further contribution, we also
compare the hand-crafted representations with features automatically computed
by deep networks, and the late fusion paradigm with early fusion, another
popular multimodal approach. In both cases, the experiments show that the
proposed multimodal approach provides the best results.


title: On Exceptional 't Hooft Lines in 4D-Chern-Simons Theory

abstract:
  We study 't Hooft lines and the associated $\mathcal{L}$- operators in
topological 4D Chern-Simons theory with gauge symmetry given by the exceptional
groups E$_{6}$ and E$_{7}$. We give their oscillator realisations and propose
topological gauge quivers encoding the properties of these topological lines
where Darboux coordinates are interpreted in terms of topological fundamental
matter. Other related aspects are also described.


title: Bioblox 2.5D -- Developing an Educational Game Based on Protein Docking

abstract:
  We present the development process of Bioblox2-5D, an educational biology
game aimed at teenagers. The game content refers to protein docking and aims to
improve learning about molecular shape complexity, the roles of charges in
molecular docking and the scoring function to calculate binding affinity. We
developed the game as part of a collaboration between the Computing Department
at Goldsmiths, University of London, and the Structural Bioinformatics group at
Imperial College London. The team at Imperial provided the content requirements
and validated the technical solution adopted in the game. The team at
Goldsmiths designed and implemented the content requirements into a fun and
stimulating educational puzzle game that supports teaching and motivates
students to engage with biology. We illustrate the game design choices, the
compromises and solutions that we applied to accomplish the desired learning
outcomes. This paper aims to illustrate useful insights and inspirations in the
context of educational game development for biology students.


title: Time-triggered Federated Learning over Wireless Networks

abstract:
  The newly emerging federated learning (FL) framework offers a new way to
train machine learning models in a privacy-preserving manner. However,
traditional FL algorithms are based on an event-triggered aggregation, which
suffers from stragglers and communication overhead issues. To address these
issues, in this paper, we present a time-triggered FL algorithm (TT-Fed) over
wireless networks, which is a generalized form of classic synchronous and
asynchronous FL. Taking the constrained resource and unreliable nature of
wireless communication into account, we jointly study the user selection and
bandwidth optimization problem to minimize the FL training loss. To solve this
joint optimization problem, we provide a thorough convergence analysis for
TT-Fed. Based on the obtained analytical convergence upper bound, the
optimization problem is decomposed into tractable sub-problems with respect to
each global aggregation round, and finally solved by our proposed online search
algorithm. Simulation results show that compared to asynchronous FL (FedAsync)
and FL with asynchronous user tiers (FedAT) benchmarks, our proposed TT-Fed
algorithm improves the converged test accuracy by up to 12.5% and 5%,
respectively, under highly imbalanced and non-IID data, while substantially
reducing the communication overhead.


title:
Nonreciprocal electrical transport in multiferroic semiconductor
  (Ge,Mn)Te

abstract:
  We have investigated the nonreciprocal electrical transport, that is a
nonlinear resistance effect depending on the current direction, in multiferroic
Rashba semiconductor (Ge,Mn)Te. Due to coexistence of ferromagnetic and
ferroelectric orders, (Ge,Mn)Te provides a unique platform for exploring the
nonreciprocal electrical transport in a bulk form. (Ge,Mn)Te thin films shows a
large nonreciprocal resistance compared to GeTe, the nonmagnetic counterpart
with the same crystal structure. The magnetic-field-angle dependence of the
nonreciprocal resistance is maximized when magnetic field is orthogonal to both
current and electric polarization, in accord with the symmetry argument. From
the analysis of temperature and magnetic field dependence, we deduce that
inelastic scatterings of electrons mediated by magnons dominantly contribute to
the observed nonreciprocal response. Furthermore, the nonreciprocal resistance
is significantly enhanced by lowering hole density. The Fermi level dependence
is attributed to the deformation of the Rashba band in which the spin-momentum
locked single Fermi surface appears by exchange field from the in-plane
magnetization. The present study provides a key insight to the mechanisms of
novel transport phenomena caused by the interplay of ferroelectric and
ferromagnetic orders in a semiconductor.


title:
Chirality-Induced Noncollinear Magnetization and Asymmetric Domain-Wall
  Propagation in Hydrogenated CoPd Thin Films

abstract:
  Array-patterned CoPd-based heterostructures are created through e-beam
lithography and plasma pretreatment that induces oxidation with depth gradient
in the CoPd alloy films, breaking the central symmetry of the structure.
Effects on the magnetic properties of the follow-up hydrogenation of the thin
film are observed via magneto-optic Kerr effect microscopy. The system exhibits
strong vertical and lateral antiferromagnetic coupling in the perpendicular
component between the areas with and without plasma pretreatment, and
asymmetric domain-wall propagation in the plasma-pretreated areas during
magnetization reversal. These phenomenon exhibit evident magnetic chirality and
can be interpreted with the Ruderman-Kittel-Kasuya-Yosida coupling and the
Dzyaloshinskii-Moriya interaction (DMI). The sample processing demonstrated in
this study allows easy incorporation of lithography techniques that can define
areas with or without DMI to create intricate magnetic patterns on the sample,
which provides an avenue towards more sophisticate control of canted spin
textures in future spintronic devices.


title: A Quantum Optical Microphone in the Audio Band

abstract:
  The ability to perform high-precision optical measurements is paramount to
science and engineering. Laser interferometry enables interaction-free sensing
with a precision ultimately limited by shot noise. Quantum optical sensors can
surpass this limit, but single- or multi-photon schemes are challenged by low
experimental sampling rates, while squeezed-light approaches require complex
optical setups and sophisticated time gating. Here, we introduce a simple
method that infers optical phase shifts through standard intensity measurements
while still maintaining the quantum advantage in the measurement precision.
Capitalising on the robustness and high sampling rates of our device, we
implement a quantum optical microphone in the audio band. Its performance is
benchmarked against a classical laser microphone in a standardised
medically-approved speech recognition test on 45 subjects. We find that
quantum-recorded words improve the speech recognition threshold by $-0.57\,
\text{dB}_{\text{SPL}}$, thus making the quantum advantage audible. Not only do
these results open the door towards applications in quantum nonlinear
interferometry, but they also show that quantum phenomena can be experienced by
humans.


title: Federated Progressive Sparsification (Purge, Merge, Tune)+

abstract:
  To improve federated training of neural networks, we develop FedSparsify, a
sparsification strategy based on progressive weight magnitude pruning. Our
method has several benefits. First, since the size of the network becomes
increasingly smaller, computation and communication costs during training are
reduced. Second, the models are incrementally constrained to a smaller set of
parameters, which facilitates alignment/merging of the local models and
improved learning performance at high sparsification rates. Third, the final
sparsified model is significantly smaller, which improves inference efficiency
and optimizes operations latency during encrypted communication. We show
experimentally that FedSparsify learns a subnetwork of both high sparsity and
learning performance. Our sparse models can reach a tenth of the size of the
original model with the same or better accuracy compared to existing pruning
and nonpruning baselines.