title: Growing Isotropic Neural Cellular Automata

abstract:
  Modeling the ability of multicellular organisms to build and maintain their
bodies through local interactions between individual cells (morphogenesis) is a
long-standing challenge of developmental biology. Recently, the Neural Cellular
Automata (NCA) model was proposed as a way to find local system rules that
produce a desired global behaviour, such as growing and persisting a predefined
pattern, by repeatedly applying the same rule over a grid starting from a
single cell. In this work we argue that the original Growing NCA model has an
important limitation: anisotropy of the learned update rule. This implies the
presence of an external factor that orients the cells in a particular
direction. In other words, 'physical' rules of the underlying system are not
invariant to rotation, thus prohibiting the existence of differently oriented
instances of the target pattern on the same grid. We propose a modified
Isotropic NCA model that does not have this limitation. We demonstrate that
cell systems can be trained to grow accurate asymmetrical patterns through
either of two methods: by breaking symmetries using structured seeds; or by
introducing a rotation-reflection invariant training objective and relying on
symmetry breaking caused by asynchronous cell updates.


title:
Amplitude modulation in binary gravitational lensing of gravitational
  waves

abstract:
  We investigate the detectability of gravitational waves (GWs) lensed by a
system that consists of binary black holes as lenses using time-domain
numerical simulations. The gravitational lensing potential of this system is no
longer static but evolves with time. When GWs from the source pass through the
binary lens, their amplitudes can be modulated, which is similar to the
phenomenon of amplitude modulation (AM) in radio communication. We find that
even the frequency of the binary lens itself is too low to be detected by the
LISA detection band, the sidebands in the spectrum of the lensed GWs due to AM
can still be within the sensitive range of the detection band. Moreover, we
also calculate the relative differences of SNR (mismatch) between the lensed
and unlensed GWs. We find that the {\it mismatch} can be as significant as
9.18%. Since mismatch does not depend on the amplitude of wavefrom, the
differences between the binary lensed and unlensed waveforms are substantial.
This provides a robust way to identify the lensing event for the LISA project
in the future.


title:
SpineNetV2: Automated Detection, Labelling and Radiological Grading Of
  Clinical MR Scans

abstract:
  This technical report presents SpineNetV2, an automated tool which: (i)
detects and labels vertebral bodies in clinical spinal magnetic resonance (MR)
scans across a range of commonly used sequences; and (ii) performs radiological
grading of lumbar intervertebral discs in T2-weighted scans for a range of
common degenerative changes. SpineNetV2 improves over the original SpineNet
software in two ways: (1) The vertebral body detection stage is significantly
faster, more accurate and works across a range of fields-of-view (as opposed to
just lumbar scans). (2) Radiological grading adopts a more powerful
architecture, adding several new grading schemes without loss in performance. A
demo of the software is available at the project website:
http://zeus.robots.ox.ac.uk/spinenet2/.


title:
Effect of Random Histogram Equalization on Breast Calcification Analysis
  Using Deep Learning

abstract:
  Early detection and analysis of calcifications in mammogram images is crucial
in a breast cancer diagnosis workflow. Management of calcifications that
require immediate follow-up and further analyzing its benignancy or malignancy
can result in a better prognosis. Recent studies have shown that deep
learning-based algorithms can learn robust representations to analyze
suspicious calcifications in mammography. In this work, we demonstrate that
randomly equalizing the histograms of calcification patches as a data
augmentation technique can significantly improve the classification performance
for analyzing suspicious calcifications. We validate our approach by using the
CBIS-DDSM dataset for two classification tasks. The results on both the tasks
show that the proposed methodology gains more than 1% mean accuracy and
F1-score when equalizing the data with a probability of 0.4 when compared to
not using histogram equalization. This is further supported by the t-tests,
where we obtain a p-value of p<0.0001, thus showing the statistical
significance of our approach.


title: Deep Sequence Modeling for Anomalous ISP Traffic Prediction

abstract:
  Internet traffic in the real world is susceptible to various external and
internal factors which may abruptly change the normal traffic flow. Those
unexpected changes are considered outliers in traffic. However, deep sequence
models have been used to predict complex IP traffic, but their comparative
performance for anomalous traffic has not been studied extensively. In this
paper, we investigated and evaluated the performance of different deep sequence
models for anomalous traffic prediction. Several deep sequences models were
implemented to predict real traffic without and with outliers and show the
significance of outlier detection in real-world traffic prediction. First, two
different outlier detection techniques, such as the Three-Sigma rule and
Isolation Forest, were applied to identify the anomaly. Second, we adjusted
those abnormal data points using the Backward Filling technique before training
the model. Finally, the performance of different models was compared for
abnormal and adjusted traffic. LSTM_Encoder_Decoder (LSTM_En_De) is the best
prediction model in our experiment, reducing the deviation between actual and
predicted traffic by more than 11\% after adjusting the outliers. All other
models, including Recurrent Neural Network (RNN), Long Short-Term Memory
(LSTM), LSTM_En_De with Attention layer (LSTM_En_De_Atn), Gated Recurrent Unit
(GRU), show better prediction after replacing the outliers and decreasing
prediction error by more than 29%, 24%, 19%, and 10% respectively. Our
experimental results indicate that the outliers in the data can significantly
impact the quality of the prediction. Thus, outlier detection and mitigation
assist the deep sequence model in learning the general trend and making better
predictions.


title: Smart City Intersections: Intelligence Nodes for Future Metropolises

abstract:
  Traffic intersections are the most suitable locations for the deployment of
computing, communications, and intelligence services for smart cities of the
future. The abundance of data to be collected and processed, in combination
with privacy and security concerns, motivates the use of the edge-computing
paradigm which aligns well with physical intersections in metropolises. This
paper focuses on high-bandwidth, low-latency applications, and in that context
it describes: (i) system design considerations for smart city intersection
intelligence nodes; (ii) key technological components including sensors,
networking, edge computing, low latency design, and AI-based intelligence; and
(iii) applications such as privacy preservation, cloud-connected vehicles, a
real-time "radar-screen", traffic management, and monitoring of pedestrian
behavior during pandemics. The results of the experimental studies performed on
the COSMOS testbed located in New York City are illustrated. Future challenges
in designing human-centered smart city intersections are summarized.


title: NNLO QCD corrections to $Wb\bar{b}$ production at the LHC

abstract:
  We compute theoretical predictions for the production of a W-boson in
association with a bottom-quark pair at hadron colliders at
next-to-next-to-leading order (NNLO) in QCD, including the leptonic decay of
the W-boson, while treating the bottom quark as massless. This calculation
constitutes the very first $2 \to 3$ process with a massive external particle
to be studied at such a perturbative order. We derive an analytic expression
for the required two-loop five-particle amplitudes in the leading colour
approximation employing finite-field methods. Numerical results for the cross
section and differential distributions are presented for the Large Hadron
Collider at $\sqrt{s} = 8$ TeV. We observe an improvement of the perturbative
convergence for the inclusive case and for the prediction with a jet veto upon
the inclusion of the NNLO QCD corrections.


title:
The effect of the deforming dark matter haloes of the Milky Way and the
  Large Magellanic Cloud on the Orphan-Chenab stream

abstract:
  It has recently been shown that the Large Magellanic Cloud (LMC) has a
substantial effect on the Milky Way's stellar halo and stellar streams. Here,
we explore how deformations of the Milky Way and LMC's dark matter haloes
affect stellar streams, and whether these effects are observable. In
particular, we focus on the Orphan-Chenab (OC) stream which passes particularly
close to the LMC, and spans a large portion of the Milky Way's halo. We
represent the Milky Way--LMC system using basis function expansions that
capture their evolution in an $N$-body simulation. We present the properties of
this system, such as the evolution of the densities and force fields of each
galaxy. The OC stream is evolved in this time-dependent, deforming potential,
and we investigate the effects of the various moments of the Milky Way and the
LMC. We find that the simulated OC stream is strongly influenced by the
deformations of both the Milky Way and the LMC, and that this effect is much
larger than current observational errors. In particular, the Milky Way dipole
has the biggest impact on the stream, followed by the evolution of the LMC's
monopole, and the LMC's quadrupole. Detecting these effects would confirm a key
prediction of collisionless, cold dark matter, and would be a powerful test of
alternative dark matter and alternative gravity models.


title: Inflationary Gravitational Leptogenesis

abstract:
  We consider the generation of the baryon asymmetry in models with
right-handed neutrinos produced through gravitational scattering of the
inflaton during reheating. The right-handed neutrinos later decay and generate
a lepton asymmetry, which is partially converted to a baryon asymmetry by
Standard Model sphaleron processes. We find that a sufficient asymmetry can be
generated for a wide range of right-handed neutrino masses and reheating
temperatures. We also show that the same type of gravitational scattering
produces Standard Model Higgs bosons, which can achieve inflationary reheating
consistent with the production of a baryon asymmetry.


title: Characterising Atmospheres of Cloudy Temperate Mini-Neptunes with JWST

abstract:
  The upcoming James Webb Space Telescope (JWST) promises a generational shift
in the study of temperate mini-Neptune atmospheres using transit spectroscopy.
High-altitude clouds however threaten to impede their atmospheric
characterisation by muting spectral features. In this study, we systematically
investigate JWST instrument configurations for characterising cloudy
mini-Neptune atmospheres, assessing the importance of instrument choice and
wavelength coverage, focusing on NIRISS and NIRSpec. We consider two temperate
mini-Neptunes orbiting nearby M dwarfs, K2-18 b and TOI-732 c, with equilibrium
temperatures below 400 K, as case studies and assess observations using
different instrument configurations with one transit per instrument. We find
that their JWST transmission spectra with modest observing time and adequate
wavelength coverage can provide precise abundance constraints of key molecules
H2O, CH4, and NH3 even in the presence of clouds at significantly high
altitudes. The best constraints are obtained by combining all three
high-resolution NIRSpec gratings (G140H+G235H+G395H) that together span the
~1-5 $\mu$m range. Single-transit observations with this three-instrument
configuration allow precise abundance constraints for cloud-top pressures as
low as 3 mbar and 0.1 mbar for K2-18 b and TOI-732 c, respectively, assuming a
nominal 10x solar metallicity. The constraints vary with instrument
combinations. We find that NIRSpec G235H+G395H is the optimal two-instrument
configuration, while NIRISS or NIRSpec G235H is optimal for single-instrument
observations. Absent high-altitude clouds, even single-instrument observations
can provide good abundance constraints for these planets. Our findings
underscore the promise of JWST transmission spectroscopy for characterising
temperate mini-Neptunes orbiting nearby M dwarfs.


title:
Observational manifestations of jet-ejecta interaction from 3D GRMHD
  simulations of binary neutron star merger aftermath

abstract:
  Short $\gamma$-ray burst (sGRB) jets form in the aftermath of a neutron star
merger, drill through disk winds and dynamical ejecta, and extend over 5 orders
of magnitude in distance before reaching the photosphere. We present the first
3D general-relativistic magnetohydrodynamic sGRB simulations to span this
enormous scale separation. They feature three possible outcomes: jet+cocoon,
cocoon, and neither. Typical sGRB jets break out of the dynamical ejecta if:
(i) the bound ejecta isotropic equivalent mass along the pole at the time of
the BH formation is $ M_p\lesssim10^{-4}~M_\odot $, setting a limit on the
delay time between the merger and BH formation, otherwise the jets perish
inside the ejecta and leave the jet-inflated cocoon to power a low-luminosity
sGRB; (ii) post-merger remnant disk contains strong large-scale vertical
magnetic field, $\gtrsim10^{15}$ G; (iii) if the jet are weak
($\lesssim10^{50}$ erg), $ M_p $ must be small ($\lesssim10^{-2}~M_\odot$).
Generally, the jet structure is shaped by the early interaction with disk winds
rather than the dynamical ejecta. As long as our jets break out of the ejecta,
they reach the photosphere at $\sim10^{10.5}$ cm while retaining significant
magnetization ($\lesssim1$), suggesting that magnetic reconnection is a
fundamental property of sGRB emission. The angular structure of the outflow
isotropic equivalent energy after breakout consistently features a flat core
followed by a steep power-law distribution (slope $\gtrsim3$), similar to
hydrodynamic jets. In the cocoon-only outcome, the dynamical ejecta broadens
the outflow angular distribution and flattens it (slope $\sim1.5$).


title: Phase Information in Cosmological Collider Signals

abstract:
  Massive particles produced during the cosmic inflation can imprint in the
primordial non-Gaussianities as characteristic oscillating functions of various
momentum ratios, known as cosmological collider signals. We initiate a study of
the phase of the oscillating signals which can be unambiguously defined and
measured. The phase can provide useful new information about the spin and the
couplings of the intermediate heavy particles that cannot be obtained from the
signal frequency and angular dependences alone. We also present new analytical
results for full nonlocal signals from two typical 1-loop processes, enabling
precise determination of the signal phase away from the squeezed limit.


title:
Signatures of mass ratio reversal in gravitational waves from merging
  binary black holes

abstract:
  The spins of merging binary black holes offer insights into their formation
history. Recently it has been argued that in isolated binary evolution of two
massive stars the firstborn black hole is slowly rotating, whilst the
progenitor of the second-born black hole can be tidally spun up if the binary
is tight enough. Naively, one might therefore expect that only the less massive
black hole in merging binaries exhibits non-negligible spin. However, if the
mass ratio of the binary is "reversed" (typically during the first mass
transfer episode), it is possible for the tidally spun up second-born to become
the more massive black hole. We study the properties of such mass-ratio
reversed (MRR) binary black hole mergers using a large set of 560 population
synthesis models. We find that the more massive black hole is formed second in
$\gtrsim 70\%$ of binary black holes observable by LIGO, Virgo, and KAGRA for
most model variations we consider, with typical total masses $\gtrsim 20$
M$_{\odot}$ and mass ratios $q = m_2 / m_1 \sim 0.7$ (where $m_1 > m_2$). The
formation history of these systems typically involves only stable mass transfer
episodes. The second-born black hole has non-negligible spin ($\chi > 0.05$) in
up to $25\%$ of binary black holes, with among those the more (less) massive
black hole spinning in $0\%$--$80\%$ ($20\%$--$100\%$) of cases, varying
greatly in our models. We discuss our models in the context of several observed
gravitational-wave events and the observed mass ratio - effective spin
correlation.


title:
End2End Multi-View Feature Matching using Differentiable Pose
  Optimization

abstract:
  Learning-based approaches have become indispensable for camera pose
estimation. However, feature detection, description, matching, and pose
optimization are often approached in an isolated fashion. In particular,
erroneous feature matches have severe impact on subsequent camera pose
estimation and often require additional measures such as outlier rejection. Our
method tackles this challenge by addressing feature matching and pose
optimization jointly: first, we integrate information from multiple views into
the matching by spanning a graph attention network across multiple frames to
predict their matches all at once. Second, the resulting matches along with
their predicted confidences are used for robust pose optimization with a
differentiable Gauss-Newton solver. End-to-end training combined with
multi-view feature matching boosts the pose estimation metrics compared to
SuperGlue by 8.9% on ScanNet and 10.7% on MegaDepth on average. Our approach
improves both pose estimation and matching accuracy over state-of-the-art
matching networks. Training feature matching across multiple views with
gradients from pose optimization naturally learns to disregard outliers,
thereby rendering additional outlier handling unnecessary, which is highly
desirable for pose estimation systems.


title:
Young, blue, and isolated stellar systems in the Virgo Cluster. II. A
  new class of stellar system

abstract:
  We discuss five blue stellar systems in the direction of the Virgo cluster,
analogous to the enigmatic object SECCO 1 (AGC 226067). These objects were
identified based on their optical and UV morphology and followed up with HI
observations with the VLA (and GBT), MUSE/VLT optical spectroscopy, and HST
imaging. These new data indicate that one system is a distant group of
galaxies. The remaining four are extremely low mass ($M_\ast \sim 10^5 \;
\mathrm{M_\odot}$), are dominated by young, blue stars, have highly irregular
and clumpy morphologies, are only a few kpc across, yet host an abundance of
metal-rich, $12 + \log (\mathrm{O/H}) > 8.2$, HII regions. These high
metallicities indicate that these stellar systems formed from gas stripped from
much more massive galaxies. Despite the young age of their stellar populations,
only one system is detected in HI, while the remaining three have minimal (if
any) gas reservoirs. Furthermore, two systems are surprisingly isolated and
have no plausible parent galaxy within $\sim$30' ($\sim$140 kpc). Although
tidal stripping cannot be conclusively excluded as the formation mechanism of
these objects, ram pressure stripping more naturally explains their properties,
in particular their isolation, owing to the higher velocities, relative to the
parent system, that can be achieved. Therefore, we posit that most of these
systems formed from ram pressure stripped gas removed from new infalling
cluster members, and survived in the intracluster medium long enough to become
separated from their parent galaxies by hundreds of kiloparsecs, and that they
thus represent a new type of stellar system.


title:
Induced gravitational waves from slow-roll inflation after an enhancing
  phase

abstract:
  The primordial spectrum of fluctuations may present a large peak as a result
of enhancing features during inflation. This may include, but is not limited
to, bumps in the inflaton's potential, phases of ultra-slow-roll or turns in
multi-field space. However, in many models, inflation does not end immediately
after the enhancing feature and it is likely to continue with a second phase of
slow-roll. We show that the resulting induced gravitational waves may probe the
primordial spectrum from the second inflationary phase, even if its amplitude
is too small to directly induce detectable gravitational waves. This is
because, if there are sharp peaks in the primordial spectrum, the total
gravitational wave spectrum is not simply the sum of gravitational waves
induced by a peaked and scale-invariant primordial spectra separately, but
cross terms from interaction between these modes also become important. We also
find that such cross terms always have a characteristic slope. We discuss the
parameter space that may be probed by future gravitational waves detectors in
the presence of these signals.


title:
Event Generation and Density Estimation with Surjective Normalizing
  Flows

abstract:
  Normalizing flows are a class of generative models that enable exact
likelihood evaluation. While these models have already found various
applications in particle physics, normalizing flows are not flexible enough to
model many of the peripheral features of collision events. Using the framework
of Nielsen et al. (2020), we introduce several surjective and stochastic
transform layers to a baseline normalizing flow to improve modelling of
permutation symmetry, varying dimensionality and discrete features, which are
all commonly encountered in particle physics events. We assess their efficacy
in the context of the generation of a matrix element-level process, and in the
context of anomaly detection in detector-level LHC events.


title: On Circuit Depth Scaling For Quantum Approximate Optimization

abstract:
  Variational quantum algorithms are the centerpiece of modern quantum
programming. These algorithms involve training parameterized quantum circuits
using a classical co-processor, an approach adapted partly from classical
machine learning. An important subclass of these algorithms, designed for
combinatorial optimization on currrent quantum hardware, is the quantum
approximate optimization algorithm (QAOA). It is known that problem density - a
problem constraint to variable ratio - induces under-parametrization in fixed
depth QAOA. Density dependent performance has been reported in the literature,
yet the circuit depth required to achieve fixed performance (henceforth called
critical depth) remained unknown. Here, we propose a predictive model, based on
a logistic saturation conjecture for critical depth scaling with respect to
density. Focusing on random instances of MAX-2-SAT, we test our predictive
model against simulated data with up to 15 qubits. We report the average
critical depth, required to attain a success probability of 0.7, saturates at a
value of 10 for densities beyond 4. We observe the predictive model to describe
the simulated data within a $3\sigma$ confidence interval. Furthermore, based
on the model, a linear trend for the critical depth with respect problem size
is recovered for the range of 5 to 15 qubits.


title: New $W$-Boson mass in the light of doubly warped braneworld model

abstract:
  The recent observation by CDF collaboration has disclosed a modification in
the mass of the $W$ boson. In this regard we show that this modification in the
mass of the $W$ boson can be well explained in the background of a
6-dimensional warped geometry model, where the double warping is associated
with the two extra spatial dimensions. We consider that all the Standard Model
fields are residing in the bulk, where the bulk Higgs field gives rise to the
spontaneous symmetry breaking in the 6-dimensional spacetime. Allowing a little
hierarchy between the two moduli we exactly obtain the observed mass for the
$W$ boson, which is identified as the lowest lying Kaluza-Klein mass mode of
the bulk $W$ boson on the $(3+1)$ dimensional visible brane. The essential
feature of the 5-dimensional Randall-Sundrum scenario such as the resolution of
the gauge hierarchy problem without introducing any intermediate scale between
the Planck and the TeV scale, remains intact.


title: Classification of Galaxy Cluster Membership with Machine Learning

abstract:
  We present a classification algorithm based on the Random Forest machine
learning model that differentiates galaxies into orbiting, infalling and
background (interloper) populations, using phase space information as input. We
train and test our model with the galaxies from UniverseMachine mock catalog
based on Multi-Dark Planck 2 N-body simulations. We show that we can recover
the distribution of orbiting and infalling galaxies in 3D and 2D position and
velocity distribution with $<1\%$ error when using probabilistic predictions in
the presence of interlopers in the projected phase space. Our machine
learning-based galaxy classification method enables a percent level
measurements of the dynamics of cluster galaxies. We discuss potential
applications of this technique to improve cluster cosmology and galaxy
quenching.


title:
CDF $m_W$ and the muon $g-2$ through the Higgs-phobic light pseudoscalar
  in type-X two-Higgs-doublet model

abstract:
  The recent measurement of the $W$ boson mass by the CDF collaboration adds an
exciting anomaly to the long-standing anomaly of the muon $g-2$. Type-X in
two-Higgs-doublet model provides an attractive solution to two anomalies with
light pseudoscalar $A$. But type-X confronts with the exotic Higgs decays of
$h\to AA$ and the lepton flavor universality data in the $\tau$ and $Z$ decays,
because of the light $M_A$ itself. We propose to adopt the Higgs-phobic $A$
with $\lambda_{hAA}=0$ in type-X, which is shown to explain the two anomalies
as well as the theoretical and experimental constraints including the lepton
flavor universality data. Additionally requiring the cutoff scale above 10 TeV
through the RGE analysis, we find that the parameter space is severely limited
such that $\tan\beta\in[36,\,65]$, $M_A \in [11,\,38]\,{\rm GeV}$, $M_{H^\pm}
\in [283,\,338]\,{\rm GeV}$, and $M_H \in[ 249,\,306]\,{\rm GeV}$. Due to
sizable mass gaps between $M_A$ and $M_{H.H^\pm}$, originated from the CDF
$m_W$ measurement, dominant decay modes of $H$ and $H^\pm$ are bosonic such as
$H \to Z A$ and $H^\pm\to W^\pm A$. With $\mathcal{B}(A\to\tau\tau)\simeq
100\%$, this characteristic provides a unique signal of the $4\tau$ states
associated with two gauge bosons at the LHC.


title:
Strongly-overdoped La$_{2-x}$Sr$_x$CuO$_4$: Evidence for
  Josephson-coupled grains of strongly-correlated superconductor

abstract:
  The interpretation of how superconductivity disappears in cuprates at large
hole doping has been controversial. To address this issue, we present an
experimental study of single-crystal and thin film samples of
La$_{2-x}$Sr$_x$CuO$_4$ (LSCO) with $x\ge0.25$. In particular, measurements of
bulk susceptibility on LSCO crystals with $x=0.25$ indicate an onset of
diamagnetism at $T_{c1}=38.5$ K, with a sharp transition to a phase with full
bulk shielding at $T_{c2}=18$ K, independent of field direction. Strikingly,
the in-plane resistivity only goes to zero at $T_{c2}$. Inelastic neutron
scattering on $x=0.25$ crystals confirms the presence of low-energy
incommensurate magnetic excitations with reduced strength compared to lower
doping levels. The ratio of the spin gap to $T_{c2}$ is anomalously large. Our
results are consistent with a theoretical prediction for strongly overdoped
cuprates by Spivak, Oreto, and Kivelson, in which superconductivity initially
develops within disconnected self-organized grains characterized by a reduced
hole concentration, with bulk superconductivity occurring only after
superconductivity is induced by proximity effect in the surrounding medium of
higher hole concentration. Beyond the superconducting-to-metal transition,
local differential conductance measurements on an LSCO thin film suggest that
regions with pairing correlations survive, but are too dilute to support
superconducting order. Future experiments will be needed to test the degree to
which these results apply to overdoped cuprates in general.


title: Improving In-Context Few-Shot Learning via Self-Supervised Training

abstract:
  Self-supervised pretraining has made few-shot learning possible for many NLP
tasks. But the pretraining objectives are not typically adapted specifically
for in-context few-shot learning. In this paper, we propose to use
self-supervision in an intermediate training stage between pretraining and
downstream few-shot usage with the goal to teach the model to perform
in-context few shot learning. We propose and evaluate four self-supervised
objectives on two benchmarks. We find that the intermediate self-supervision
stage produces models that outperform strong baselines. Ablation study shows
that several factors affect the downstream performance, such as the amount of
training data and the diversity of the self-supervised objectives.
Human-annotated cross-task supervision and self-supervision are complementary.
Qualitative analysis suggests that the self-supervised-trained models are
better at following task requirements.


title:
A high-fidelity and large-scale reconfigurable photonic processor for
  NISQ applications

abstract:
  Reconfigurable linear optical networks are a key component for the
development of optical quantum information processing platforms in the NISQ era
and beyond. We report the implementation of such a device based on an
innovative design that uses the mode mixing of a multimode fiber in combination
with the programmable wavefront shaping of a SLM. The capabilities of the
platform are explored in the classical regime. For up to a record number of
8~inputs and 38~outputs we achieve fidelities in excess of $93\%$, week-long
stability and losses below $6.5\textrm{dB}$. The device was built inside a
standard server rack to allow for real world use.


title:
$f$-mode Imprints in Gravitational Waves from Coalescing Binaries
  involving Spinning Neutron Stars

abstract:
  The excitation of $f$-mode in a neutron star member of coalescing binaries
accelerates the merger course, and thereby introduces a phase shift in the
gravitational waveform. Emphasising on the tidal phase shift by rotating stars,
we provide an accurate, yet economical, method to generate $f$-mode-involved,
pre-merger waveforms using realistic spin-modulated $f$-mode frequencies for
some viable equations of state. We find for slow-rotating stars that the
dephasing effects of the dynamical tides can be uniquely, EOS-independently
determined by the direct observables (chirp mass ${\cal M}$, symmetric ratio
$\eta$ and the mutual tidal deformability ${\tilde \Lambda}$), while this
universality is gradually lost for increasing spin. Although a high cutoff
waveform frequency combined with large signal-to-noise ratio (SNR) is needed to
trace the tidal dephasing if binary members rotate slowly, for binaries with
fast rotating members ($\lesssim800\text{ Hz}$) the phase shift due to $f$-mode
will exceed the uncertainty in the waveform phase at reasonable SNR ($\rho=25$)
and cutoff frequency of $\gtrsim400\text{ Hz}$. In addition, a significant
phase shift of $\gtrsim100$ rads can be found for a high cutoff frequency of
$10^3\text{ Hz}$. For systems involving a rapidly-spinning star (potentially
the secondary of GW190814), neglecting $f$-mode effect in the waveform
templates can therefore lead to considerable systemic errors in the relevant
analysis.


title: Object Class Aware Video Anomaly Detection through Image Translation

abstract:
  Semi-supervised video anomaly detection (VAD) methods formulate the task of
anomaly detection as detection of deviations from the learned normal patterns.
Previous works in the field (reconstruction or prediction-based methods) suffer
from two drawbacks: 1) They focus on low-level features, and they (especially
holistic approaches) do not effectively consider the object classes. 2)
Object-centric approaches neglect some of the context information (such as
location). To tackle these challenges, this paper proposes a novel two-stream
object-aware VAD method that learns the normal appearance and motion patterns
through image translation tasks. The appearance branch translates the input
image to the target semantic segmentation map produced by Mask-RCNN, and the
motion branch associates each frame with its expected optical flow magnitude.
Any deviation from the expected appearance or motion in the inference stage
shows the degree of potential abnormality. We evaluated our proposed method on
the ShanghaiTech, UCSD-Ped1, and UCSD-Ped2 datasets and the results show
competitive performance compared with state-of-the-art works. Most importantly,
the results show that, as significant improvements to previous methods,
detections by our method are completely explainable and anomalies are localized
accurately in the frames.


title: MemSE: Fast MSE Prediction for Noisy Memristor-Based DNN Accelerators

abstract:
  Memristors enable the computation of matrix-vector multiplications (MVM) in
memory and, therefore, show great potential in highly increasing the energy
efficiency of deep neural network (DNN) inference accelerators. However,
computations in memristors suffer from hardware non-idealities and are subject
to different sources of noise that may negatively impact system performance. In
this work, we theoretically analyze the mean squared error of DNNs that use
memristor crossbars to compute MVM. We take into account both the quantization
noise, due to the necessity of reducing the DNN model size, and the programming
noise, stemming from the variability during the programming of the memristance
value. Simulations on pre-trained DNN models showcase the accuracy of the
analytical prediction. Furthermore the proposed method is almost two order of
magnitude faster than Monte-Carlo simulation, thus making it possible to
optimize the implementation parameters to achieve minimal error for a given
power constraint.


title:
Convergence Analysis of Waveform Relaxation Method to Compute Coupled
  Advection-Diffusion-Reaction Equations

abstract:
  We study the computation of coupled advection-diffusion-reaction equations by
the Schwarz waveform relaxation method. The study starts with linear equations,
and it analyzes the convergence of the computation with a Dirichlet condition,
a Robin condition, and a combination of them as the transmission conditions.
Then, an optimized algorithm for the Dirichlet condition is presented to
accelerate the convergence, and numerical examples show a substantial speedup
in the convergence. Furthermore, the optimized algorithm is extended to the
computation of nonlinear equations, including the viscous Burgers equation, and
numerical experiments indicate the algorithm may largely remain effective in
the speedup of convergence.


title:
Femtosecond laser-induced sub-wavelength plasma inside dielectrics: I.
  Field enhancement

abstract:
  The creation of high energy density ($\gtrsim10^6$ joules per cm$^3$)
over-critical plasmas in a large volume has essential applications in the study
of warm dense matter, being present in the hot cores of stars and planets. It
was recently shown that femtosecond Bessel beams enable creating over-critical
plasmas inside sapphire with sub-wavelength radius and several tens of
micrometers in length. Here, the dependence of field structure and absorption
mechanism on the plasma density transverse profile are investigated by
performing self-consistent Particle-In-Cell (PIC) simulations. Two { limiting}
cases are considered: one is a homogeneous step-like profile, that can sustain
plasmon formation, the second is an inhomogeneous Gaussian profile, where
resonance absorption occurs. Comparing experimental absorption measures to
analytical predictions allows determining the plasma parameters used in PIC
simulations. The PIC simulation results are in good agreement with experimental
diagnostics of total absorption, near-field fluence distribution, and far-field
radiation pattern. We show that in each case an ambipolar field forms at the
plasma surface due to the expansion of the hot electrons and that electron
sound waves propagate into the over-critical region.


title:
An exploration of the properties of cluster profiles for the thermal and
  kinetic Sunyaev-Zel'dovich effects

abstract:
  With the advent of high-resolution, low-noise CMB measurements, the ability
to extract cosmological information from thermal Sunyaev-Zel'dovich effect and
kinetic Sunyaev-Zel'dovich effect will be limited not by statistical
uncertainties but rather by systematic and theoretical uncertainties. The
theoretical uncertainty is driven by the lack of knowledge about the electron
pressure and density. Thus we explore the electron pressure and density
distributions in the IllustrisTNG hydrodynamical simulations, and we
demonstrate that the cluster properties exhibit a strong dependence on the halo
concentration -- providing some of the first evidence of cluster assembly bias
in the electron pressure and density. Further, our work shows evidence for a
broken power-law mass dependence, with lower pressure in lower mass halos than
previous work and a strong evolution with mass of the radial correlations in
the electron density and pressure. Both of these effects highlight the
differing impact of active galactic nuclei and supernova feedback on the gas in
galaxy groups compared to massive clusters. We verified that we see
qualitatively similar features in the SIMBA hydro-dynamical simulations,
suggesting these effects could be generic features. Finally, we provide a
parametric formula for the electron pressure and density profile as a function
of dark matter halo mass, halo concentration, and redshift. These fitting
formulae can reproduce the distribution of density and pressure of clusters and
will be useful in extracting cosmological information from upcoming CMB
surveys.


title: On the Level Crossing Rate of Fluid Antenna Systems

abstract:
  Multiple-input multiple-output (MIMO) technology has significantly impacted
wireless communication, by providing extraordinary performance gains. However,
a minimum inter-antenna space constraint in MIMO systems does not allow its
integration in devices with limited space. In this context, the concept of
fluid antenna systems (FASs) appears to be a potent solution, where there is no
such restriction. In this paper, we investigate the average level crossing rate
(LCR) of such FASs. Specifically, we derive closed-form analytical expressions
of the LCR of such systems and extensive Monte-Carlo simulations validate the
proposed analytical framework. Moreover, we also demonstrate that under certain
conditions, the LCR obtained coincides with that of a conventional selection
combining-based receiver. Finally, the numerical results also provide insights
regarding the selection of appropriate parameters that enhance the system
performance.


title: Evolving black hole with scalar field accretion

abstract:
  We obtain approximate analytical solutions of the Einstein equations close to
the trapping horizon for a dynamical spherically symmetric black hole in the
presence of a minimally coupled self-interacting scalar field. This is made
possible by a new parametrization of the metric, in which the displacement from
the horizon as well as its expansion rate feature explicitly. Our results are
valid in a neighbourhood of the horizon and hold for any scalar field potential
and spacetime asymptotics. An exact equation for the accretion rate is also
obtained, which generalizes the standard Bondi formula. We also develop a
dynamical system approach to study near-equilibrium black holes; using this
formalism, we focus on a simple model to show that the near-equilibrium
dynamics is characterised by scaling relations among dynamical variables.
Moreover, we show that solutions with purely ingoing energy-momentum flux never
reach equilibrium.


title: A Typechecker for a Set-Based Constraint Logic Programming Language

abstract:
  {log} (read 'setlog') is a Constraint Logic Programming (CLP) language and
satisfiability solver whose constraint domain is the theory of finite sets.
Rooted in CLP and Prolog, {log} essentially provides an untyped language. As
such it can accept formulas that make the solver to produce unwanted behaviors.
Besides, {log} users may make mistakes in their programs that would normally be
caught by a typechecker. In particular, {log} has been proposed as a
prototyping language for B and Z specifications, which are typed formalisms.
Then, without a type system for {log} there is a gap that users need to fill
manually. Therefore, in this paper we define a type system and implement a
typechecker for {log}. The type system is proved to be safe (sound) by adapting
the functional programming formulation of type safety to the CLP context. We
also show how types and CLP can be combined to provide stronger assurances on
program correctness. Finally, we apply the type system, the typechecker and
their combination with CLP to a real-world case study from the aeronautic
domain.


title:
Don't sweat the small stuff, classify the rest: Sample Shielding to
  protect text classifiers against adversarial attacks

abstract:
  Deep learning (DL) is being used extensively for text classification.
However, researchers have demonstrated the vulnerability of such classifiers to
adversarial attacks. Attackers modify the text in a way which misleads the
classifier while keeping the original meaning close to intact. State-of-the-art
(SOTA) attack algorithms follow the general principle of making minimal changes
to the text so as to not jeopardize semantics. Taking advantage of this we
propose a novel and intuitive defense strategy called Sample Shielding. It is
attacker and classifier agnostic, does not require any reconfiguration of the
classifier or external resources and is simple to implement. Essentially, we
sample subsets of the input text, classify them and summarize these into a
final decision. We shield three popular DL text classifiers with Sample
Shielding, test their resilience against four SOTA attackers across three
datasets in a realistic threat setting. Even when given the advantage of
knowing about our shielding strategy the adversary's attack success rate is
<=10% with only one exception and often < 5%. Additionally, Sample Shielding
maintains near original accuracy when applied to original texts. Crucially, we
show that the `make minimal changes' approach of SOTA attackers leads to
critical vulnerabilities that can be defended against with an intuitive
sampling strategy.


title: Electron-scale reconnection in three-dimensional shock turbulence

abstract:
  Magnetic reconnection has been observed in the transition region of
quasi-parallel shocks. In this work, the particle-in-cell method is used to
simulate three-dimensional reconnection in a quasi-parallel shock. The shock
transition region is turbulent, leading to the formation of reconnecting
current sheets with various orientations. Two reconnection sites with weak and
strong guide fields are studied, and it is shown that reconnection is fast and
transient. Reconnection sites are characterized using diagnostics including
electron flows and magnetic flux transport. In contrast to two-dimensional
simulations, weak guide field reconnection is realized. Furthermore, the
current sheets in these events form in a direction almost perpendicular to
those found in two-dimensional simulations, where the reconnection geometry is
constrained.


title:
Experiments with Unit Disk Cover Algorithms for Covering Massive
  Pointsets

abstract:
  Given a set of $n$ points in the plane, the Unit Disk Cover (UDC) problem
asks to compute the minimum number of unit disks required to cover the points,
along with a placement of the disks. The problem is NP-hard and several
approximation algorithms have been designed over the last three decades. In
this paper, we have engineered and experimentally compared practical
performances of some of these algorithms on massive pointsets. The goal is to
investigate which algorithms run fast and give good approximation in practice.
  We present a simple $7$-approximation algorithm for UDC that runs in $O(n)$
expected time and uses $O(s)$ extra space, where $s$ denotes the size of the
generated cover. In our experiments, it turned out to be the speediest of all.
We also present two heuristics to reduce the sizes of covers generated by it
without slowing it down by much.
  To our knowledge, this is the first work that experimentally compares
geometric covering algorithms. Experiments with them using massive pointsets
(in the order of millions) throw light on their practical uses. We share the
engineered algorithms via GitHub -
https://github.com/ghoshanirban/UnitDiskCoverAlgorithms for broader uses and
future research in the domain of geometric optimization.


title:
Individualized treatment effect was predicted best by modeling baseline
  risk in interaction with treatment assignment

abstract:
  Objective: To compare different risk-based methods for optimal prediction of
treatment effects. Methods: We simulated RCT data using diverse assumptions for
the average treatment effect, a baseline prognostic index of risk (PI), the
shape of its interaction with treatment (none, linear, quadratic or
non-monotonic), and the magnitude of treatment-related harms (none or constant
independent of the PI). We predicted absolute benefit using: models with a
constant relative treatment effect; stratification in quarters of the PI;
models including a linear interaction of treatment with the PI; models
including an interaction of treatment with a restricted cubic spline (RCS)
transformation of the PI; an adaptive approach using Akaike's Information
Criterion. We evaluated predictive performance using root mean squared error
and measures of discrimination and calibration for benefit. Results: The
linear-interaction model displayed optimal or close-to-optimal performance
across many simulation scenarios with moderate sample size (N=4,250 patients; ~
785 events). The RCS-model was optimal for strong non-linear deviations from a
constant treatment effect, particularly when sample size was larger (N=17,000).
The adaptive approach also required larger sample sizes. These findings were
illustrated in the GUSTO-I trial. Conclusion: An interaction between baseline
risk and treatment assignment should be considered to improve treatment effect
predictions.


title:
Development of an Organic Plastic Scintillator based Muon Veto Operating
  at Sub-Kelvin Temperatures for the NUCLEUS Experiment

abstract:
  The NUCLEUS experiment aims at measuring the coherent elastic scattering of
nuclear reactor antineutrinos off nuclei using cryogenic calorimeters.
Operating at an overburden of 3m.w.e., muon-induced backgrounds are expected to
be dominant. It is therefore essential to develop an efficient muon veto, with
a detection efficiency of more than 99%. This will be realized in NUCLEUS
through a compact cube assembly of plastic scintillator panels. In order to
prevent a large unshielded area where the cryostat intersects the shielding
arrangement without unnecessarily increasing the induced detector dead time, a
novel concept has been investigated, featuring a plastic scintillator based
active muon veto operating inside the NUCLEUS cryostat at sub-Kelvin
temperatures. The verification of the key physical aspects of this cryogenic
muon veto detector led to the first reported measurements of organic plastic
scintillators at sub-Kelvin temperatures. The functionality of the principal
scintillation process of organic plastic scintillators at these temperatures
has been confirmed. On the basis of these findings, a disc-shape plastic
scintillator equipped with wavelength shifting fibers and a silicon
photomultiplier to guide and detect the scintillation light has been developed.
The NUCLEUS cryogenic muon veto will be the first of its kind to be operated at
sub-Kelvin temperatures.


title:
Observation of the bands with $d_{xy}$ orbital character near the Fermi
  level in NdFeAs$_{1-x}$P$_{x}$O$_{0.9}$F$_{0.1}$ using angle-resolved
  photoemission spectroscopy

abstract:
  We studied the band structure of NdFeAs$_{1-x}$P$_{x}$O$_{0.9}$F$_{0.1}$ ($x$
= 0, 0.2, 0.4 and 0.6) using angle-resolved photoemission spectroscopy (ARPES)
measurements. Two of the hole bands, ${\alpha}_1$ $(d_{xz})$ and ${\alpha}_3$
$(d_{z^2})$, were observed at the Brillouin zone center in the $P$-polarized
light configuration, while the other two hole bands, ${\alpha}_2$ $(d_{yz})$
and ${\gamma}$ $(d_{xy})$, were observed in the $S$-polarized alternative. The
observed ${\gamma}$ band shifts downwards as $x$ increases, which is consistent
with the theoretical prediction for the change in bond angle of As/P-Fe-As/P.
Furthermore, a small amount of the $d_{xy}$ orbital component was observed at
the same binding energy as that of the top of the ${\alpha}_1$ band, thus
indicating the band reconstruction of the originally degenerate ${\alpha}_1$
and ${\alpha}_2$ $(d_{xz}/d_{yz})$ bands by the unoccupied $d_{xy}$ band. The
change in the energy level of the ${\alpha}_1$ band top with $d_{xy}$ orbital
character is accompanied by a $T_{c}$ upturn at $0.2 < x < 0.4$. The $T_{c}$
continues to increase as the ${\alpha}_1$ band shifts downward, crossing the
Fermi level. The incipient band with the $d_{xy}$ orbital character on its top
could be an important ingredient for high $T_{c}$ 1111-type iron-based
superconductors.


title:
Quadrupolar photovoltaic effect in the terahertz range in a
  two-dimensional spin-3/2 hole system

abstract:
  We identify a strong photovoltaic response due to the non-linear optical
transition between heavy and light hole sub-bands enabled by T_d-symmetry in a
quantum well, which we term the quadrupolar photovoltaic effect (QPE). The
photovoltaic current exhibits a strong resonance in the vicinity of the heavy
hole-light hole splitting, with a magnitude governed by the momentum relaxation
time, which can reach nanoseconds in GaAs holes. Since the heavy hole-light
hole splitting can be tuned from a few meV to nearly 100 meV the QPE could
serve as the basis for a terahertz photo-detector. We discuss strategies for
experimental observation and device applications.


title: In Defense of Image Pre-Training for Spatiotemporal Recognition

abstract:
  Image pre-training, the current de-facto paradigm for a wide range of visual
tasks, is generally less favored in the field of video recognition. By
contrast, a common strategy is to directly train with spatiotemporal
convolutional neural networks (CNNs) from scratch. Nonetheless, interestingly,
by taking a closer look at these from-scratch learned CNNs, we note there exist
certain 3D kernels that exhibit much stronger appearance modeling ability than
others, arguably suggesting appearance information is already well disentangled
in learning. Inspired by this observation, we hypothesize that the key to
effectively leveraging image pre-training lies in the decomposition of learning
spatial and temporal features, and revisiting image pre-training as the
appearance prior to initializing 3D kernels. In addition, we propose
Spatial-Temporal Separable (STS) convolution, which explicitly splits the
feature channels into spatial and temporal groups, to further enable a more
thorough decomposition of spatiotemporal features for fine-tuning 3D CNNs. Our
experiments show that simply replacing 3D convolution with STS notably improves
a wide range of 3D CNNs without increasing parameters and computation on both
Kinetics-400 and Something-Something V2. Moreover, this new training pipeline
consistently achieves better results on video recognition with significant
speedup. For instance, we achieve +0.6% top-1 of Slowfast on Kinetics-400 over
the strong 256-epoch 128-GPU baseline while fine-tuning for only 50 epochs with
4 GPUs. The code and models are available at
https://github.com/UCSC-VLAA/Image-Pretraining-for-Video.


title: Optimal Time-Backlog Tradeoffs for the Variable-Processor Cup Game

abstract:
  The \emph{$ p$-processor cup game} is a classic and widely studied scheduling
problem that captures the setting in which a $p$-processor machine must assign
tasks to processors over time in order to ensure that no individual task ever
falls too far behind. The problem is formalized as a multi-round game in which
two players, a filler (who assigns work to tasks) and an emptier (who schedules
tasks) compete. The emptier's goal is to minimize backlog, which is the maximum
amount of outstanding work for any task.
  Recently, Kuszmaul and Westover (ITCS, 2021) proposed the
\emph{variable-processor cup game}, which considers the same problem, except
that the amount of resources available to the players (i.e., the number $p$ of
processors) fluctuates between rounds of the game. They showed that this
seemingly small modification fundamentally changes the dynamics of the game:
whereas the optimal backlog in the fixed $p$-processor game is $\Theta(\log
n)$, independent of $p$, the optimal backlog in the variable-processor game is
$\Theta(n)$. The latter result was only known to apply to games with
\emph{exponentially many} rounds, however, and it has remained an open question
what the optimal tradeoff between time and backlog is for shorter games.
  This paper establishes a tight trade-off curve between time and backlog in
the variable-processor cup game. Importantly, we prove that for a game
consisting of $t$ rounds, the optimal backlog is $\Theta(n)$ if and only if $t
\ge \Omega(n^3)$. Our techniques also allow for us to resolve several other
open questions concerning how the variable-processor cup game behaves in
beyond-worst-case-analysis settings.


title:
The distribution of density matrices at fixed purity for arbitrary
  dimensions

abstract:
  We present marginal cumulative distribution functions (CDF) for density
matrices $\rho$ of fixed purity
$\tfrac{1}{N}\le\mu_N(\rho)=\textrm{Tr}[\rho^2]\le 1$ for arbitrary dimension
$N$. We give closed form analytic formulas for the cases $N=2$ (trivial), $N=3$
and $N=4$, and present a prescription for CDFs of higher arbitrary dimensions.
These formulas allows one to uniformly sample density matrices at a user
selected, fixed constant purity, and also detail how these density matrices are
distributed nonlinearly in the range $\mu_N(\rho)\in[\tfrac{1}{N}, 1]$. As an
illustration of these formulas, we compare the logarithmic negativity and
quantum discord to the (Wootter's) concurrence spanning a range of fixed purity
values in $\mu_4(\rho)\in[\tfrac{1}{4}, 1]$ for the case of $N=4$ (two qubits).
We also investigate the distribution of eigenvalues of a reduced
$N$-dimensional obtained by tracing out the reservoir of its higher-dimensional
purification. Lastly, we numerically investigate a recently proposed
complementary-quantum correlation conjecture which lower bounds the quantum
mutual information of a bipartite system by the sum of classical mutual
informations obtained from two pairs of mutually unbiased measurements.
Finally, numerical implementation issues for the computation of the CDFs and
inverse CDFs necessary for uniform sampling $\rho$ for fixed purity at very
high dimension are briefly discussed.


title: License Plate Privacy in Collaborative Visual Analysis of Traffic Scenes

abstract:
  Traffic scene analysis is important for emerging technologies such as smart
traffic management and autonomous vehicles. However, such analysis also poses
potential privacy threats. For example, a system that can recognize license
plates may construct patterns of behavior of the corresponding vehicles' owners
and use that for various illegal purposes. In this paper we present a system
that enables traffic scene analysis while at the same time preserving license
plate privacy. The system is based on a multi-task model whose latent space is
selectively compressed depending on the amount of information the specific
features carry about analysis tasks and private information. Effectiveness of
the proposed method is illustrated by experiments on the Cityscapes dataset,
for which we also provide license plate annotations.


title:
Many-Fermion Simulation from the Contracted Quantum Eigensolver without
  Fermionic Encoding of the Wave Function

abstract:
  Quantum computers potentially have an exponential advantage over classical
computers for the quantum simulation of many-fermion quantum systems.
Nonetheless, fermions are more expensive to simulate than bosons due to the
fermionic encoding -- a mapping by which the qubits are encoded with fermion
statistics. Here we generalize the contracted quantum eigensolver (CQE) to
avoid fermionic encoding of the wave function. In contrast to the variational
quantum eigensolver, the CQE solves for a many-fermion stationary state by
minimizing the contraction (projection) of the Schr\"odinger equation onto two
fermions. We avoid fermionic encoding of the wave function by contracting the
Schr\"odinger equation onto an unencoded pair of particles. Solution of the
resulting contracted equation by a series of unencoded two-body exponential
transformations generates an unencoded wave function from which the energy and
two-fermion reduced density matrix (2-RDM) can be computed. We apply the
unencoded and the encoded CQE algorithms to the hydrogen fluoride molecule, the
dissociation of oxygen O$_{2}$, and a series of hydrogen chains. Both
algorithms show comparable convergence towards the exact ground-state energies
and 2-RDMs, but the unencoded algorithm has computational advantages in terms
of state preparation and tomography.


title:
Accelerated Convergence of Contracted Quantum Eigensolvers through a
  Quasi-Second-Order, Locally Parameterized Optimization

abstract:
  A contracted quantum eigensolver (CQE) finds a solution to the many-electron
Schr\"odinger equation by solving its integration (or contraction) to the
2-electron space -- a contracted Schr\"odinger equation (CSE) -- on a quantum
computer. When applied to the anti-Hermitian part of the CSE (ACSE), the CQE
iterations optimize the wave function with respect to a general product ansatz
of two-body exponential unitary transformations that can exactly solve the
Schr\"odinger equation. In this work, we accelerate the convergence of the CQE
and its wavefunction ansatz via tools from classical optimization theory. By
treating the CQE algorithm as an optimization in a local parameter space, we
can apply quasi-second-order optimization techniques, such as quasi-Newton
approaches or non-linear conjugate gradient approaches. Practically these
algorithms result in superlinear convergence of the wavefunction to a solution
of the ACSE. Convergence acceleration is important because it can both minimize
the accumulation of noise on near-term intermediate-scale quantum (NISQ)
computers and achieve highly accurate solutions on future fault-tolerant
quantum devices. We demonstrate the algorithm, as well as some heuristic
implementations relevant for cost-reduction considerations, comparisons with
other common methods such as variational quantum eigensolvers, and a
fermionic-encoding-free form of the CQE.


title:
How to choose features to improve prediction performance in
  lane-changing intention: A meta-analysis

abstract:
  Lane-change is a fundamental driving behavior and highly associated with
various types of collisions, such as rear-end collisions, sideswipe collisions,
and angle collisions and the increased risk of a traffic crash. This study
investigates effectiveness of different features categories combination in
lane-changing intention prediction. Studies related to lane-changing intention
prediction have been selected followed by strict standards. Then the
meta-analysis was employed to not only evaluate the effectiveness of different
features categories combination in lane-changing intention but also capture
heterogeneity, effect size combination, and publication bias. According to the
meta-analysis and reviewed research papers, results indicate that using input
features from different types can lead to different performances. And vehicle
input type has a better performance in lane-changing intention, prediction,
compared with environment or even driver combination input type. Finally, some
potential future research directions are proposed based on the findings of the
paper.


title: A Secure File Sharing System Based on IPFS and Blockchain

abstract:
  There is a great interest in many approaches towards blockchain in providing
a solution to record transactions in a decentralized way. However, there are
some limitations when storing large files or documents on the blockchain. In
order to meet the requirements of storing relatively large data, a
decentralized storage medium is produced. IPFS is a distributed file system
which is content-addressable. It works very similar to the blockchain network.
There are some attempts which take advantage of the blockchain concept and IPFS
to design new approaches. Unfortunately, there are some inefficiencies in
sharing data using the combination of IPFS and blockchain. In this paper, we
proposed a secure file sharing system that brings a distributed access control
and group key management by the adoption of the IPFS proxy. The IPFS proxy
which plays an important role in the design is adopted to take responsibility
for the control policies. The combination of the IPFS server and the blockchain
network with the adoption of the IPFS proxy make a secure file sharing system
which the members on the system can create new groups or join different groups
by their own choice. Although there is no access control mechanism in IPFS
server and blockchain network, the secure file sharing system manages the
access control policies. The members access files only belong to the group they
authorized.


title: Pre-RTL DNN Hardware Evaluator With Fused Layer Support

abstract:
  With the popularity of the deep neural network (DNN), hardware accelerators
are demanded for real time execution. However, lengthy design process and fast
evolving DNN models make hardware evaluation hard to meet the time to market
need. This paper proposes a pre-RTL DNN hardware evaluator that supports
conventional layer-by-layer processing as well as the fused layer processing
for low external bandwidth requirement. The evaluator supports two
state-of-the-art accelerator architectures and finds the best hardware and
layer fusion group The experimental results show the layer fusion scheme can
achieve 55.6% memory bandwidth reduction, 36.7% latency improvement and 49.2%
energy reduction compared with layer-by-layer operation.


title:
Quiz Design Task: Helping Teachers Create Quizzes with Automated
  Question Generation

abstract:
  Question generation (QGen) models are often evaluated with standardized NLG
metrics that are based on n-gram overlap. In this paper, we measure whether
these metric improvements translate to gains in a practical setting, focusing
on the use case of helping teachers automate the generation of reading
comprehension quizzes. In our study, teachers building a quiz receive question
suggestions, which they can either accept or refuse with a reason. Even though
we find that recent progress in QGen leads to a significant increase in
question acceptance rates, there is still large room for improvement, with the
best model having only 68.4% of its questions accepted by the ten teachers who
participated in our study. We then leverage the annotations we collected to
analyze standard NLG metrics and find that model performance has reached
projected upper-bounds, suggesting new automatic metrics are needed to guide
QGen research forward.


title:
Themes of Revenge: Automatic Identification of Vengeful Content in
  Textual Data

abstract:
  Revenge is a powerful motivating force reported to underlie the behavior of
various solo perpetrators, from school shooters to right wing terrorists. In
this paper, we develop an automated methodology for identifying vengeful themes
in textual data. Testing the model on four datasets (vengeful texts from social
media, school shooters, Right Wing terrorist and Islamic terrorists), we
present promising results, even when the methodology is tested on extremely
imbalanced datasets. The paper not only presents a simple and powerful
methodology that may be used for the screening of solo perpetrators but also
validate the simple theoretical model of revenge.


title: Turbulent Heating in a Stratified Medium

abstract:
  There is considerable evidence for widespread subsonic turbulence in galaxy
clusters, most notably from {\it Hitomi}. Turbulence is often invoked to offset
radiative losses in cluster cores, both by direct dissipation and by enabling
turbulent heat diffusion. However, in a stratified medium, buoyancy forces
oppose radial motions, making turbulence anisotropic. This can be quantified
via the Froude number ${\rm Fr}$, which decreases inward in clusters as
stratification increases. We exploit analogies with MHD turbulence to show that
wave-turbulence interactions increase cascade times and reduces dissipation
rates $\epsilon \propto {\rm Fr}$. Equivalently, for a given energy
injection/dissipation rate $\epsilon$, turbulent velocities $u$ must be higher
compared to Kolmogorov scalings. High resolution hydrodynamic simulations show
excellent agreement with the $\epsilon \propto {\rm Fr}$ scaling, which sets in
for ${\rm Fr} < 0.1$. We also compare previously predicted scalings for the
turbulent diffusion coefficient $D \propto {\rm Fr}^2$ and find excellent
agreement, for ${\rm Fr} < 1$. However, we find a different normalization,
corresponding to stronger diffusive suppression by more than an order of
magnitude. Our results imply that turbulent diffusion is more heavily
suppressed by stratification, over a much wider radial range, than turbulent
dissipation. Thus, the latter potentially dominates. Furthermore, this shift
implies significantly higher turbulent velocities required to offset cooling,
compared to previous models. These results are potentially relevant to
turbulent metal diffusion (which is likewise suppressed), and to planetary
atmospheres.


title: Application of belief functions to medical image segmentation: A review

abstract:
  Belief function theory, a formal framework for uncertainty analysis and
multiple evidence fusion, has made significant contributions in the medical
domain, especially since the development of deep learning. Medical image
segmentation with belief function theory has shown significant benefits in
clinical diagnosis and medical image research. In this paper, we provide a
review of medical image segmentation methods using belief function theory. We
classify the methods according to the fusion step and explain how information
with uncertainty or imprecision is modeled and fused with belief function
theory. In addition, we discuss the challenges and limitations of present
belief function-based medical image segmentation and propose orientations for
future research. Future research could investigate both belief function theory
and deep learning to achieve more promising and reliable segmentation results.


title: Squared distance matrices of trees with matrix weights

abstract:
  Let $T$ be a tree on $n$ vertices whose edge weights are positive definite
matrices of order $s$. The squared distance matrix of $T$, denoted by $\Delta$,
is the $ns \times ns$ block matrix with $\Delta_{ij}=d(i,j)^2$, where $d(i,j)$
is the sum of the weights of the edges in the unique $(i,j)$-path. In this
article, we obtain a formula for the determinant of $\Delta$ and find
${\Delta}^{-1}$ under some conditions.


title:
Substructure, supernovae, and time resolved star formation history for
  Upper Scorpius

abstract:
  The improved astrometry precision of Gaia-eDR3 allows us to perform a
detailed study of the Upper Scorpius OB association and revisit its kinematic,
spatial, and age substructure. We achieve this by combining the Convergent
Point method with clustering techniques, complementing with age estimations
based on Gaia photometry. We obtain a set of 4028 candidate members for Upper
Scorpius with completeness $\sim$99\% and contamination $\sim$11\%, extending
the current census with Gaia by at least $\sim$9\%. We extract an
astrometrically clean sample of 3114 sources with contamination $\sim$6\%. We
show that Upper Scorpius can be divided into at least 3 main kinematic groups.
We systematically investigate and characterize the Upper Scorpius' internal
structure, revealing that at least $\sim 34\%$ of its stellar populations are
contained in 7 spatial substructures, with well defined boundaries, kinematics
and relative ages, with suggested names: $\pi$ Scorpii (20 $^{\pm2}_{\pm1}$
Myr), $\alpha$ Scorpii (14$^{\pm2}_{\pm1}$ Myr), $\delta$ Scorpii
(9$^{\pm2}_{\pm1}$ Myr), $\beta$ Scorpii (8$^{\pm1}_{\pm1}$ Myr), $\omega$
Scorpii (8$^{\pm1}_{\pm1}$ Myr), $\nu$ Scorpii (7$^{\pm1}_{\pm1}$ Myr), after
their brightest member, and the well known $\rho$ Ophiuchi (4$^{\pm1}_{\pm1}$
Myr). We find a clear correlation in (1) density-age suggesting that these
substructures are expanding at a measurable rate, and (2) tangential
velocity-age, providing constrains on the dynamics of these substructures, and
the position of potential past triggering events. We estimate the time at which
4 potential supernovae events occurred in Upper Scorpius. Based on these
findings, we suggest a star formation scenario for Upper Scorpius with
unprecedented temporal resolution.


title: Krylov-aware stochastic trace estimation

abstract:
  We introduce an algorithm for estimating the trace of a matrix function f(A)
using implicit products with a symmetric matrix A. Existing methods for
implicit trace estimation of a matrix function tend to treat matrix-vector
products with f(A) as a black-box to be computed by a Krylov subspace method.
Like other algorithms for implicit trace estimation, our approach is based on a
combination of deflation and stochastic trace estimation. However, we take a
closer look at how products with f(A) are integrated into these approaches
which enables several efficiencies not present in previously studied methods.


title: Surfaces and their Profile Curves

abstract:
  This paper examines the relationship between a profile curve of a surface in
3 dimensions and the isotopy class of the surface.


title:
Cartographing gravity-mediated scattering amplitudes: scalars and
  photons

abstract:
  The effective action includes all quantum corrections arising in a given
quantum field theory. Thus it serves as a powerful generating functional from
which quantum-corrected scattering amplitudes can be constructed via tree-level
computations. In this work we use this framework for studying gravity-mediated
two-to-two scattering processes involving scalars and photons as external
particles. We construct a minimal basis of interaction monomials capturing all
contributions to these processes. This classification goes beyond the
expansions used in effective field theory since it retains the most general
momentum dependence in the propagators and couplings. In this way, we derive
the most general scattering amplitudes compatible with a relativistic quantum
field theory. Comparing to tree-level scattering in general relativity, we
identify the differential cross sections which are generated by the non-trivial
momentum dependence of the interaction vertices.


title:
Intelligent Reflecting Surface Networks with Multi-Order-Reflection
  Effect: System Modelling and Critical Bounds

abstract:
  In this paper, we model, analyze and optimize the multi-user and
multi-order-reflection (MUMOR) intelligent reflecting surface (IRS) networks.
We first derive a complete MUMOR IRS network model applicable for the arbitrary
times of reflections, size and number of IRSs/reflectors. The optimal condition
for achieving sum-rate upper bound with one IRS in a closed-form function and
the analytical condition to achieve interference-free transmission are derived,
respectively. Leveraging this optimal condition, we obtain the MUMOR sum-rate
upper bound of the IRS network with different network topologies, where the
linear graph (LG), complete graph (CG) and null graph (NG) topologies are
considered. Simulation results verify our theories and derivations and
demonstrate that the sum-rate upper bounds of different network topologies are
under a K-fold improvement given K-piece IRS.


title:
Detailed chemical abundances of stars in the outskirts of the Tucana II
  ultra-faint dwarf galaxy

abstract:
  We present chemical abundances and velocities of five stars between 0.3 kpc
to 1.1 kpc from the center of the Tucana II ultra-faint dwarf galaxy (UFD) from
high-resolution Magellan/MIKE spectroscopy. We find that every star is
deficient in metals (-3.6 < [Fe/H] < -1.9) and in neutron-capture elements as
is characteristic of UFD stars, unambiguously confirming their association with
Tucana II. Other chemical abundances (e.g., C, iron-peak) largely follow UFD
trends and suggest that faint core-collapse supernovae (SNe) dominated the
early evolution of Tucana II. We see a downturn in [$\alpha$/Fe] at [Fe/H]
$\approx -2.8$, indicating the onset of Type Ia SN enrichment and somewhat
extended chemical evolution. The most metal-rich star has strikingly low
[Sc/Fe] = $-1.29 \pm 0.48$ and [Mn/Fe] = $-1.33 \pm 0.33$, implying significant
enrichment by a sub-Chandrasekhar mass Type Ia SN. We do not detect a radial
velocity gradient in Tucana II
($\text{d}v_{\text{helio}}/\text{d}\theta_1=-2.6^{+3.0}_{-2.9}$ km s$^{-1}$
kpc$^{-1}$) reflecting a lack of evidence for tidal disruption, and derive a
dynamical mass of $M_{1/2} (r_h) = 1.6^{+1.1}_{-0.7}\times 10^6$ M$_{\odot}$.
We revisit formation scenarios of the extended component of Tucana II in light
of its stellar chemical abundances. We find no evidence that Tucana II had
abnormally energetic SNe, suggesting that if SNe drove in-situ stellar halo
formation then other UFDs should show similar such features. Although not a
unique explanation, the decline in [$\alpha$/Fe] is consistent with an early
galactic merger triggering later star formation. Future observations may
disentangle such formation channels of UFD outskirts.


title:
Comparison of CoModGANs, LaMa and GLIDE for Art Inpainting- Completing
  M.C Escher's Print Gallery

abstract:
  Digital art restoration has benefited from inpainting models to correct the
degradation or missing sections of a painting. This work compares three current
state-of-the art models for inpainting of large missing regions. We provide
qualitative and quantitative comparison of the performance by CoModGANs, LaMa
and GLIDE in inpainting of blurry and missing sections of images. We use
Escher's incomplete painting Print Gallery as our test study since it presents
several of the challenges commonly present in restorative inpainting.


title: QCD equation of state with Tsallis statistics for heavy-ion collisions

abstract:
  Nonextensive statistics has attracted attention as a description of particle
spectra in nuclear collisions at QCD energies. First, we construct the equation
of state by incorporating Tsallis statistics based on the hadron resonance gas
and parton gas models. Thermodynamic conditions are found to impose constraints
on the $q$-parameter of Tsallis distribution. Next, we apply the equation of
state to the relativistic hydrodynamic modeling of nuclear collisions. The
Cooper-Frye prescription is consistently modified. Numerical demonstrations
indicate that the model may describe charged particle spectra at Large Hadron
Collider in the transverse momentum range up to 6-8 GeV. Elliptic flow, on the
other hand, suggests a narrower range of applicability.


title:
Three-phase generalized raking and multiple imputation estimators to
  address error-prone data

abstract:
  Validation studies are often used to obtain more reliable information in
settings with error-prone data. Validated data on a subsample of subjects can
be used together with error-prone data on all subjects to improve estimation.
In practice, more than one round of data validation may be required, and direct
application of standard approaches for combining validation data into analyses
may lead to inefficient estimators since the information available from
intermediate validation steps is only partially considered or even completely
ignored. In this paper, we present two novel extensions of multiple imputation
and generalized raking estimators that make full use of all available data. We
show through simulations that incorporating information from intermediate steps
can lead to substantial gains in efficiency. This work is motivated by and
illustrated in a study of contraceptive effectiveness among 82,957 women living
with HIV whose data were originally extracted from electronic medical records,
of whom 4855 had their charts reviewed, and a subsequent 1203 also had a
telephone interview to validate key study variables.


title:
Asymptotic behaviour of solutions to non-commensurate fractional-order
  planar systems

abstract:
  This paper is devoted to studying non-commensurate fractional order planar
systems. Our contributions are to derive sufficient conditions for the global
attractivity of non-trivial solutions to fractional-order inhomogeneous linear
planar systems and for the Mittag-Leffler stability of an equilibrium point to
fractional order nonlinear planar systems. To achieve these goals, our approach
is as follows. Firstly, based on Cauchy's argument principle in complex
analysis, we obtain various explicit sufficient conditions for the asymptotic
stability of linear systems whose coefficient matrices are constant. Secondly,
by using Hankel type contours, we derive some important estimates of special
functions arising from a variation of constants formula of solutions to
inhomogeneous linear systems. Then, by proposing new weighted norms combined
with the Banach fixed point theorem for appropriate Banach spaces, we get the
desired conclusions. Finally, numerical examples are provided to illustrate the
effect of the main theoretical results.


title: Nonparametric inference under a monotone hazard ratio order

abstract:
  The ratio of the hazard functions of two populations or two strata of a
single population plays an important role in time-to-event analysis. Cox
regression is commonly used to estimate the hazard ratio under the assumption
that it is constant in time, which is known as the proportional hazards
assumption. However, this assumption is often violated in practice, and when it
is violated, the parameter estimated by Cox regression is difficult to
interpret. The hazard ratio can be estimated in a nonparametric manner using
smoothing, but smoothing-based estimators are sensitive to the selection of
tuning parameters, and it is often difficult to perform valid inference with
such estimators. In some cases, it is known that the hazard ratio function is
monotone. In this article, we demonstrate that monotonicity of the hazard ratio
function defines an invariant stochastic order, and we study the properties of
this order. Furthermore, we introduce an estimator of the hazard ratio function
under a monotonicity constraint. We demonstrate that our estimator converges in
distribution to a mean-zero limit, and we use this result to construct
asymptotically valid confidence intervals. Finally, we conduct numerical
studies to assess the finite-sample behavior of our estimator, and we use our
methods to estimate the hazard ratio of progression-free survival in pulmonary
adenocarcinoma patients treated with Gefitinib or carboplatin-paclitaxel.


title: Heliospheric Compression due to Recent Nearby Supernova Explosions

abstract:
  The widespread detection of 60Fe in geological and lunar archives provides
compelling evidence for recent nearby supernova explosions within $\sim 100$ pc
around 3 Myr and 7 Myr ago. The blasts from these explosions had a profound
effect on the heliosphere. We perform new calculations to study the compression
of the heliosphere due to a supernova blast. Assuming a steady but
non-isotropic solar wind, we explore a range of properties appropriate for
supernova distances inspired by recent 60Fe data, and for a 20 pc supernova
proposed to account for mass extinctions at the end-Devonian period. We examine
the locations of the termination shock decelerating the solar wind and the
heliopause that marks the boundary between the solar wind and supernova
material. Pressure balance scaling holds, consistent with studies of other
astrospheres. Solar wind anisotropy does not have an appreciable effect on
shock geometry. We find that supernova explosions at 50 pc (95 pc) lead to
heliopause locations at 16 au (23 au) when the forward shock arrives. Thus, the
outer solar system was directly exposed to the blast, but the inner planets --
including the Earth -- were not. This finding reaffirms that the delivery of
supernova material to the Earth is not from the blast plasma itself, but likely
is from supernova dust grains. After the arrival of the forward shock, the
weakening supernova blast will lead to a gradual rebound of the heliosphere,
taking $\sim100$s of kyr to expand beyond 100 au. Prospects for future work are
discussed.


title: PBW filtration and monomial bases for Demazure modules in types A and C

abstract:
  We characterise the symplectic Weyl group elements such that the FFLV basis
is compatible with the PBW filtration on symplectic Demazure modules, extending
type A results by the second author. Surprisingly, the number of such elements
depends not on the type A or C of the Lie algebra but on the rank only.


title:
Bounds on the Capacity of the Multiple Access Diamond Channel with
  Cooperating Base-Stations

abstract:
  A diamond network is considered in which the central processor is connected,
via backhaul noiseless links, to multiple conferencing base stations, which
communicate with a single user over a multiple access channel. We propose
coding techniques along with lower and upper bounds on the capacity. Our
achievability scheme uses a common cloud coding strategy based on the technique
proposed by Wand, Wigger, and Zaidi (2018) and extends it beyond two relays.
Our upper bounds generalize the method proposed by Bidokhti and Kramer for the
two relay diamond network without cooperation (2016) and lead to new bounds for
the multiple relay setting. Specializing our upper bounds for the two relay
scenario (with cooperation), we provide new bounds and improve
state-of-the-art.


title: Mixed-effects transformers for hierarchical adaptation

abstract:
  Language use differs dramatically from context to context. To some degree,
modern language models like GPT-3 are able to account for such variance by
conditioning on a string of previous input text, or prompt. Yet prompting is
ineffective when contexts are sparse, out-of-sample, or extra-textual; for
instance, accounting for when and where the text was produced or who produced
it. In this paper, we introduce the mixed-effects transformer (MET), a novel
approach for learning hierarchically-structured prefixes -- lightweight modules
prepended to the input -- to account for structured variation. Specifically, we
show how the popular class of mixed-effects models may be extended to
transformer-based architectures using a regularized prefix-tuning procedure
with dropout. We evaluate this approach on several domain-adaptation
benchmarks, finding that it efficiently adapts to novel contexts with minimal
data while still effectively generalizing to unseen contexts.


title: Diffusion Approximation for Transport Equations with Dissipative Drifts

abstract:
  We study stochastic differential equations(SDEs) with a small perturbation
parameter. Under the dissipative condition on the drift coefficient and the
local Lipschitz condition on the drift and diffusion coefficients we prove the
existence and uniqueness result for the perturbed SDE, also the convergence
result for the solution of the perturbed system to the solution of the
unperturbed system when the perturbation parameter approaches zero.We consider
the application of the above-mentioned results to the Cauchy problem and the
transport equations.


title:
On monoaural speech enhancement for automatic recognition of real noisy
  speech using mixture invariant training

abstract:
  In this paper, we explore an improved framework to train a monoaural neural
enhancement model for robust speech recognition. The designed training
framework extends the existing mixture invariant training criterion to exploit
both unpaired clean speech and real noisy data. It is found that the unpaired
clean speech is crucial to improve quality of separated speech from real noisy
speech. The proposed method also performs remixing of processed and unprocessed
signals to alleviate the processing artifacts. Experiments on the
single-channel CHiME-3 real test sets show that the proposed method improves
significantly in terms of speech recognition performance over the enhancement
system trained either on the mismatched simulated data in a supervised fashion
or on the matched real data in an unsupervised fashion. Between 16% and 39%
relative WER reduction has been achieved by the proposed system compared to the
unprocessed signal using end-to-end and hybrid acoustic models without
retraining on distorted data.


title:
Evolutionary optimization of cosmological parameters using metropolis
  acceptance criterion

abstract:
  We introduce a novel evolutionary method that takes leverage from the MCMC
method that can be used for constraining the parameters and theoretical models
of Cosmology. Unlike the MCMC technique, which is essentially a non-parallel
algorithm by design, the newly proposed algorithm is able to obtain the full
potential of multi-core machines. With this algorithm, we could obtain the
best-fit parameters of the Lambda CDM cosmological model and identify the
discrepancy in the Hubble parameter $H_0$. In the present work we discuss the
design principle of this novel approach and also the results from the analysis
of Pantheon, OHD and Planck datasets are reported here. The estimation of
parameters shows significant consistency with the previously reported values as
well as a higher computational performance compared to the other similar
exercises.


title: Self-focusing virtual screening with active design space pruning

abstract:
  High-throughput virtual screening is an indispensable technique utilized in
the discovery of small molecules. In cases where the library of molecules is
exceedingly large, the cost of an exhaustive virtual screen may be prohibitive.
Model-guided optimization has been employed to lower these costs through
dramatic increases in sample efficiency compared to random selection. However,
these techniques introduce new costs to the workflow through the surrogate
model training and inference steps. In this study, we propose an extension to
the framework of model-guided optimization that mitigates inferences costs
using a technique we refer to as design space pruning (DSP), which irreversibly
removes poor-performing candidates from consideration. We study the application
of DSP to a variety of optimization tasks and observe significant reductions in
overhead costs while exhibiting similar performance to the baseline
optimization. DSP represents an attractive extension of model-guided
optimization that can limit overhead costs in optimization settings where these
costs are non-negligible relative to objective costs, such as docking.


title: B\'ezier Curve Gaussian Processes

abstract:
  Probabilistic models for sequential data are the basis for a variety of
applications concerned with processing timely ordered information. The
predominant approach in this domain is given by neural networks, which
incorporate either stochastic units or components. This paper proposes a new
probabilistic sequence model building on probabilistic B\'ezier curves. Using
Gaussian distributed control points, these parametric curves pose a special
case for Gaussian processes (GP). Combined with a Mixture Density network,
Bayesian conditional inference can be performed without the need for mean field
variational approximation or Monte Carlo simulation, which is a requirement of
common approaches. For assessing this hybrid model's viability, it is applied
to an exemplary sequence prediction task. In this case the model is used for
pedestrian trajectory prediction, where a generated prediction also serves as a
GP prior. Following this, the initial prediction can be refined using the GP
framework by calculating different posterior distributions, in order to adapt
more towards a given observed trajectory segment.


title: The Disc-structure space

abstract:
  We study the Disc-structure space $S^{\rm Disc}_\partial(M)$ of a compact
smooth manifold $M$. Informally speaking, this space measures the difference
between $M$, together with its diffeomorphisms, and the diagram of ordered
framed configuration spaces of $M$ with point-forgetting and point-splitting
maps between them, together with its derived automorphisms. As the main
results, we show that in high dimensions, the Disc-structure space a) only
depends on the tangential 2-type of $M$, b) is an infinite loop space, and c)
is nontrivial as long as $M$ is spin. The proofs involve intermediate results
that may be of independent interest, including an enhancement of embedding
calculus to the level of bordism categories, results on the behaviour of
derived mapping spaces between operads under rationalisation, and an answer to
a question of Dwyer and Hess in that we show that the map ${\rm BTop}(d)\to
{\rm BAut}(E_d)$ is an equivalence if and only if $d$ is at most $2$.


title:
Social Practice Cards: Research material to study social contexts as
  interwoven practice constellations

abstract:
  Studying how social contexts shape technology interactions and how we
experience them is hard. One challenge is that social contexts are very dynamic
and shaped by the situated practices of everyone involved. As a result, the
same human-technology interaction can be experienced quite differently
depending on what other people around us do. As a first step to study
interpersonal and interpractice dynamics, we collected a broad range of visual
representations of practices, such as "riding a bike" or "skipping the rope".
This material can be used to further explore how different, co-located
practices relate to each other.


title:
XLTime: A Cross-Lingual Knowledge Transfer Framework for Temporal
  Expression Extraction

abstract:
  Temporal Expression Extraction (TEE) is essential for understanding time in
natural language. It has applications in Natural Language Processing (NLP)
tasks such as question answering, information retrieval, and causal inference.
To date, work in this area has mostly focused on English as there is a scarcity
of labeled data for other languages. We propose XLTime, a novel framework for
multilingual TEE. XLTime works on top of pre-trained language models and
leverages multi-task learning to prompt cross-language knowledge transfer both
from English and within the non-English languages. XLTime alleviates problems
caused by a shortage of data in the target language. We apply XLTime with
different language models and show that it outperforms the previous automatic
SOTA methods on French, Spanish, Portuguese, and Basque, by large margins.
XLTime also closes the gap considerably on the handcrafted HeidelTime method.


title: Differentiable Simulation of Soft Multi-body Systems

abstract:
  We present a method for differentiable simulation of soft articulated bodies.
Our work enables the integration of differentiable physical dynamics into
gradient-based pipelines. We develop a top-down matrix assembly algorithm
within Projective Dynamics and derive a generalized dry friction model for soft
continuum using a new matrix splitting strategy. We derive a differentiable
control framework for soft articulated bodies driven by muscles, joint torques,
or pneumatic tubes. The experiments demonstrate that our designs make soft body
simulation more stable and realistic compared to other frameworks. Our method
accelerates the solution of system identification problems by more than an
order of magnitude, and enables efficient gradient-based learning of motion
control with soft robots.


title:
Explain and Conquer: Personalised Text-based Reviews to Achieve
  Transparency

abstract:
  There are many contexts where dyadic data is present. Social networking is a
well-known example, where transparency has grown on importance. In these
contexts, pairs of items are linked building a network where interactions play
a crucial role. Explaining why these relationships are established is core to
address transparency. These explanations are often presented using text, thanks
to the spread of the natural language understanding tasks.
  We have focused on the TripAdvisor platform, considering the applicability to
other dyadic data contexts. The items are a subset of users and restaurants and
the interactions the reviews posted by these users. Our aim is to represent and
explain pairs (user, restaurant) established by agents (e.g., a recommender
system or a paid promotion mechanism), so that personalisation is taken into
account. We propose the PTER (Personalised TExt-based Reviews) model. We
predict, from the available reviews for a given restaurant, those that fit to
the specific user interactions.
  PTER leverages the BERT (Bidirectional Encoders Representations from
Transformers) language model. We customised a deep neural network following the
feature-based approach. The performance metrics show the validity of our
labelling proposal. We defined an evaluation framework based on a clustering
process to assess our personalised representation. PTER clearly outperforms the
proposed adversary in 5 of the 6 datasets, with a minimum ratio improvement of
4%.


title: Non-identical moir\'e twins in bilayer graphene

abstract:
  The superlattice obtained by aligning a monolayer graphene and boron nitride
(BN) inherits from the hexagonal lattice a $60^{\circ}$ periodicity with the
layer alignment. This implies that the mechanical, optical and electronic
properties of the heterostructure must be identical for $0^{\circ}$ and
$60^{\circ}$ of layer alignment. Surprisingly, this periodicity apply only for
the monolayer case. Here, we demonstrate that the moir\'e superlattice formed
by a bilayer graphene aligned with BN, is present every $60^{\circ}$, but the
symmetry is broken between the $0^{\circ}$ and $60^{\circ}$ alignments,
creating non-identical 'moir\'e twins' with different electronic properties. In
particular, electron transport measurements display a fully developed valley
Hall effect at $0^{\circ}$ while on the contrary, it is completely absent at
$60^{\circ}$. We explain this effect by performing numerical simulations, which
highlight the central role of the atomic-scale structural relaxation of the
second graphene layer. This in-plane atomic relaxation, different for the two
alignments, has a profound impact on the electronic band structure of our
system. Our results demonstrate that controlling rotational order provides a
unique insight on the interplay between mechanical and electronic properties,
and increases the posibilities for band-structure engineering on van der Waals
heterostructures.


title: Fingering convection in the stably-stratified layers of planetary cores

abstract:
  Stably-stratified layers may be present at the top of the
electrically-conducting fluid layers of many planets either because the
temperature gradient is locally subadiabatic or because a stable composition
gradient is maintained by the segregation of chemical elements. Here we study
the double-diffusive processes taking place in such a stable layer, considering
the case of Mercury's core where the temperature gradient is stable but the
composition gradient is unstable over a 800km-thick layer. The large difference
in the molecular diffusivities leads to the development of buoyancy-driven
instabilities that drive radial flows known as fingering convection. We model
fingering convection using hydrodynamical simulations in a rotating spherical
shell and varying the rotation rate and the stratification strength. For small
Rayleigh numbers (i.e. weak background temperature and composition gradients),
fingering convection takes the form of columnar flows aligned with the rotation
axis and with an azimuthal size comparable with the layer thickness. For larger
Rayleigh numbers, the flows retain a columnar structure but the azimuthal size
is drastically reduced leading to thin sheet-like structures that are elongated
in the meridional direction. The azimuthal length decreases when the thermal
stratification increases, following closely the scaling law expected from the
linear non-rotating planar theory (Stern, 1960). We find that the radial flows
always remain laminar with local Reynolds number of order 1-10.
Equatorially-symmetric zonal flows form due to latitudinal variations of the
axisymmetric composition. The zonal velocity exceeds the non-axisymmetric
velocities at the largest Rayleigh numbers. We discuss plausible implications
for planetary magnetic fields.


title: EpIC: novel Monte Carlo generator for exclusive processes

abstract:
  We present the EpIC Monte Carlo event generator for exclusive processes
sensitive to generalised parton distributions. EpIC utilises the PARTONS
framework, which provides a flexible software architecture and a variety of
modelling options for the partonic description of the nucleon. The generator
offers a comprehensive set of features, including multi-channel capabilities
and radiative corrections. It may be used both in analyses of experimental
data, as well as in impact studies, especially for future electron-ion
colliders.