title:
SAR-ShipNet: SAR-Ship Detection Neural Network via Bidirectional
  Coordinate Attention and Multi-resolution Feature Fusion

abstract:
  This paper studies a practically meaningful ship detection problem from
synthetic aperture radar (SAR) images by the neural network. We broadly extract
different types of SAR image features and raise the intriguing question that
whether these extracted features are beneficial to (1) suppress data variations
(e.g., complex land-sea backgrounds, scattered noise) of real-world SAR images,
and (2) enhance the features of ships that are small objects and have different
aspect (length-width) ratios, therefore resulting in the improvement of ship
detection. To answer this question, we propose a SAR-ship detection neural
network (call SAR-ShipNet for short), by newly developing Bidirectional
Coordinate Attention (BCA) and Multi-resolution Feature Fusion (MRF) based on
CenterNet. Moreover, considering the varying length-width ratio of arbitrary
ships, we adopt elliptical Gaussian probability distribution in CenterNet to
improve the performance of base detector models. Experimental results on the
public SAR-Ship dataset show that our SAR-ShipNet achieves competitive
advantages in both speed and accuracy.


title:
Probability of forming gaps in the GD-1 stream by close encounters of
  globular clusters

abstract:
  One of the most intriguing properties of the GD-1 stellar stream is the
existence of three gaps. If these gaps were formed by close encounters with
dark matter subhalos, the GD-1 stream opens an exciting window through which we
can see the size, mass, and velocity distributions of the dark matter subhalos
in the Milky Way. However, in order to use the GD-1 stream as a probe of the
dark matter substructure, we need to disprove that these gaps are not due to
the perturbations from baryonic components of the Milky Way. Here we ran a
large number of test particle simulations to investigate the probability that
each of the known globular clusters (GCs) can form a GD-1-like gap, by using
the kinematical data of the GD-1 stream and GCs from Gaia EDR3 and by fully
taking into account the observational uncertainty. We found that the
probability that all of the three gaps were formed by GCs is as low as
$1.2\times10^{-5}$ and the expected number of gaps formed by GCs is only
$0.057$ in our fiducial model. Our result highly disfavors a scenario in which
GCs form the gaps. Given that other baryonic perturbers (e.g., giant molecular
clouds) are even less likely to form a gap in the retrograde-moving GD-1
stream, we conclude that at least one of the gaps in the GD-1 stream was formed
by dark matter subhalos if the gaps were formed by flyby perturbations.


title: Constructing the relative Fukaya category

abstract:
  We give a definition of Seidel's `relative Fukaya category', for a smooth
complex projective variety, under a semipositivity assumption. We use the
Cieliebak--Mohnke approach to transversality via stabilizing divisors. Two
features of our construction are noteworthy: that we work relative to a normal
crossings divisor that supports an effective ample divisor but need not have
ample components; and that our relative Fukaya category is linear over a
certain ring of multivariate power series with integer coefficients.


title:
Representing `how you say' with `what you say': English corpus of
  focused speech and text reflecting corresponding implications

abstract:
  In speech communication, how something is said (paralinguistic information)
is as crucial as what is said (linguistic information). As a type of
paralinguistic information, English speech uses sentence stress, the heaviest
prominence within a sentence, to convey emphasis. While different placements of
sentence stress communicate different emphatic implications, current speech
translation systems return the same translations if the utterances are
linguistically identical, losing paralinguistic information. Concentrating on
focus, a type of emphasis, we propose mapping paralinguistic information into
the linguistic domain within the source language using lexical and grammatical
devices. This method enables us to translate the paraphrased text
representations instead of the transcription of the original speech and obtain
translations that preserve paralinguistic information. As a first step, we
present the collection of an English corpus containing speech that differed in
the placement of focus along with the corresponding text, which was designed to
reflect the implied meaning of the speech. Also, analyses of our corpus
demonstrated that mapping of focus from the paralinguistic domain into the
linguistic domain involved various lexical and grammatical methods. The data
and insights from our analysis will further advance research into
paralinguistic translation. The corpus will be published via LDC.


title:
Local variational quantum compilation of a large-scale Hamiltonian
  dynamics

abstract:
  Implementing time evolution operators on quantum circuits is important for
quantum simulation. However, the standard way, Trotterization, requires a huge
numbers of gates to achieve desirable accuracy. Here, we propose a local
variational quantum compilation (LVQC) algorithm, which allows to accurately
and efficiently compile a time evolution operators on a large-scale quantum
system by the optimization with smaller-size quantum systems. LVQC utilizes a
subsystem cost function, which approximates the fidelity of the whole circuit,
defined for each subsystem as large as approximate causal cones brought by the
Lieb-Robinson (LR) bound. We rigorously derive its scaling property with
respect to the subsystem size, and show that the optimization conducted on the
subsystem size leads to the compilation of whole-system time evolution
operators. As a result, LVQC runs with limited-size quantum computers or
classical simulators that can handle such smaller quantum systems. For
instance, finite-ranged and short-ranged interacting $L$-size systems can be
compiled with $O(L^0)$- or $O(\log L)$-size quantum systems depending on
observables of interest. Furthermore, since this formalism relies only on the
LR bound, it can efficiently construct time evolution operators of various
systems in generic dimension involving finite-, short-, and long-ranged
interactions. We also numerically demonstrate the LVQC algorithm for
one-dimensional systems. Employing classical simulation by time-evolving block
decimation, we succeed in compressing the depth of a time evolution operators
up to $40$ qubits by the compilation for $20$ qubits. LVQC not only provides
classical protocols for designing large-scale quantum circuits, but also will
shed light on applications of intermediate-scale quantum devices in
implementing algorithms in larger-scale quantum devices.


title: Learning Structured Gaussians to Approximate Deep Ensembles

abstract:
  This paper proposes using a sparse-structured multivariate Gaussian to
provide a closed-form approximator for the output of probabilistic ensemble
models used for dense image prediction tasks. This is achieved through a
convolutional neural network that predicts the mean and covariance of the
distribution, where the inverse covariance is parameterised by a sparsely
structured Cholesky matrix. Similarly to distillation approaches, our single
network is trained to maximise the probability of samples from pre-trained
probabilistic models, in this work we use a fixed ensemble of networks. Once
trained, our compact representation can be used to efficiently draw spatially
correlated samples from the approximated output distribution. Importantly, this
approach captures the uncertainty and structured correlations in the
predictions explicitly in a formal distribution, rather than implicitly through
sampling alone. This allows direct introspection of the model, enabling
visualisation of the learned structure. Moreover, this formulation provides two
further benefits: estimation of a sample probability, and the introduction of
arbitrary spatial conditioning at test time. We demonstrate the merits of our
approach on monocular depth estimation and show that the advantages of our
approach are obtained with comparable quantitative performance.


title:
CO in the ALMA Radio-Source Catalogue (ARC): the molecular gas content
  of radio galaxies as a function of redshift

abstract:
  To evaluate the role of radio activity in galaxy evolution, we designed a
large archival CO survey of radio galaxies (RGs) to determine their molecular
gas masses at different epochs. We used a sample of 120 RGs representative of
the NVSS 1.4GHz survey, when flux limited at 0.4Jy. Of those, 66 galaxies
belonged to the ALMA Radio-source Catalogue (ARC) of calibrators and had
spectral window tunings around CO (1-0), (2-1), (3-2), or (4-3). We reduced
their ALMA data, determined their H2 mass contents, and combined the results
with similar results for the remaining 54 galaxies from the literature. We
found that, while at all epochs the majority of RGs have undetectable
reservoirs, there is a rapid increase in the H2 mass content of the CO-detected
RGs with z. At 1<z<2.5, 1/4 of the RGs have at least as much molecular gas as
simulations would indicate for a typical halo mass of that epoch. These
galaxies plausibly have 'normal' or even starbursty hosts. Taking into account
the completeness correction of the sample, we created the corresponding H2 mass
functions at 0.005<z<0.3 and 1<z<2.5. The local mass function reveals that the
number density of low-z RGs with detectable molecular gas reservoirs is only a
little lower (a factor of ~4) than that of type 1 and 2 AGN in simulations. At
1<z<2.5, there is a significant decrease in the number density of high-z RGs
due to the rarity of bright radio galaxies. An estimate for the missing faint
RGs would, nonetheless, bring populations close again. Finally, we find that
the volume density of molecular gas locked up in the brightest 1/5000-1/7000
RGs is similar in the examined z bins. This result likely indicates that the
inflow rate on one hand and the star-formation depletion rate plus the
jet-driven expulsion rate on the other hand counteract each other in the most
luminous RGs of each epoch.


title: T-RAX: Transversely Resonant Axion eXperiment

abstract:
  We propose to use an elongated rectangular waveguide near its cutoff
frequency for axionic dark matter searches. The detector's large surface area
allows for significant signal power, while its narrow transverse dimension and
tapered-waveguide coupling suppress parasitic modes. The proposed system can
fit inside a solenoid magnet and is sensitive to the QCD-axion in the axion
mass $40-400\,\mu$eV. We describe the theoretical principles of the new design,
present simulation results, and discuss the implementation.


title: Over-the-Air Federated Learning via Second-Order Optimization

abstract:
  Federated learning (FL) is a promising learning paradigm that can tackle the
increasingly prominent isolated data islands problem while keeping users' data
locally with privacy and security guarantees. However, FL could result in
task-oriented data traffic flows over wireless networks with limited radio
resources. To design communication-efficient FL, most of the existing studies
employ the first-order federated optimization approach that has a slow
convergence rate. This however results in excessive communication rounds for
local model updates between the edge devices and edge server. To address this
issue, in this paper, we instead propose a novel over-the-air second-order
federated optimization algorithm to simultaneously reduce the communication
rounds and enable low-latency global model aggregation. This is achieved by
exploiting the waveform superposition property of a multi-access channel to
implement the distributed second-order optimization algorithm over wireless
networks. The convergence behavior of the proposed algorithm is further
characterized, which reveals a linear-quadratic convergence rate with an
accumulative error term in each iteration. We thus propose a system
optimization approach to minimize the accumulated error gap by joint device
selection and beamforming design. Numerical results demonstrate the system and
communication efficiency compared with the state-of-the-art approaches.


title: Fruit Mapping with Shape Completion for Autonomous Crop Monitoring

abstract:
  Autonomous crop monitoring is a difficult task due to the complex structure
of plants. Occlusions from leaves can make it impossible to obtain complete
views about all fruits of, e.g., pepper plants. Therefore, accurately
estimating the shape and volume of fruits from partial information is crucial
to enable further advanced automation tasks such as yield estimation and
automated fruit picking. In this paper, we present an approach for mapping
fruits on plants and estimating their shape by matching superellipsoids. Our
system segments fruits in images and uses their masks to generate point clouds
of the fruits. To combine sequences of acquired point clouds, we utilize a
real-time 3D mapping framework and build up a fruit map based on truncated
signed distance fields. We cluster fruits from this map and use optimized
superellipsoids for matching to obtain accurate shape estimates. In our
experiments, we show in various simulated scenarios with a robotic arm equipped
with an RGB-D camera that our approach can accurately estimate fruit volumes.
Additionally, we provide qualitative results of estimated fruit shapes from
data recorded in a commercial glasshouse environment.


title:
Neural representation of a time optimal, constant acceleration
  rendezvous

abstract:
  We train neural models to represent both the optimal policy (i.e. the optimal
thrust direction) and the value function (i.e. the time of flight) for a time
optimal, constant acceleration low-thrust rendezvous. In both cases we develop
and make use of the data augmentation technique we call backward generation of
optimal examples. We are thus able to produce and work with large dataset and
to fully exploit the benefit of employing a deep learning framework. We
achieve, in all cases, accuracies resulting in successful rendezvous (simulated
following the learned policy) and time of flight predictions (using the learned
value function). We find that residuals as small as a few m/s, thus well within
the possibility of a spacecraft navigation $\Delta V$ budget, are achievable
for the velocity at rendezvous. We also find that, on average, the absolute
error to predict the optimal time of flight to rendezvous from any orbit in the
asteroid belt to an Earth-like orbit is small (less than 4\%) and thus also of
interest for practical uses, for example, during preliminary mission design
phases.


title:
Improving the Learnability of Machine Learning APIs by Semi-Automated
  API Wrapping

abstract:
  A major hurdle for students and professional software developers who want to
enter the world of machine learning (ML), is mastering not just the scientific
background but also the available ML APIs. Therefore, we address the challenge
of creating APIs that are easy to learn and use, especially by novices.
However, it is not clear how this can be achieved without compromising
expressiveness. We investigate this problem for scikit-learn, a widely used ML
API. In this paper, we analyze its use by the Kaggle community, identifying
unused and apparently useless parts of the API that can be eliminated without
affecting client programs. In addition, we discuss usability issues in the
remaining parts, propose related design improvements and show how they can be
implemented by semi-automated wrapping of the existing third-party API.


title:
An overdetermined problem for sign-changing eigenfunctions in unbounded
  domains

abstract:
  We study the existence of non-trivial unbounded domains of $\Omega \subset
\mathbb{R}^2$ where the equation \begin{align} - \lambda u_{xx} -u_{tt} &= u
\qquad \text{in $\Omega$,}\nonumber u &=0 \qquad \text{on $\partial
\Omega$,}\nonumber \end{align} is solvable subject to the conditions
\begin{align} \frac{\partial u}{\partial \eta} =-1\quad \text{on $\partial
\Omega^+$} \quad \textrm{and}\quad \frac{\partial u}{\partial \eta} =+1\quad
\text{on $\partial \Omega^-$.} \end{align} For every integer $m\geq 0$, we
prove the existence of a family of unbounded domains $\Omega\subset
\mathbb{R}^2$ indexed by $0 \leqslant\ell\leqslant 2m$, where the above problem
admits periodic sign-changing solutions. The domains we construct are periodic
in the first coordinate in $\mathbb{R}^2$, and they bifurcate from suitable
strips.


title:
On the stable Harbourne conjecture for ideals defining space monomial
  curves

abstract:
  For the ideal $\mathfrak{p}$ in $k[x, y, z]$ defining a space monomial curve,
we show that $\mathfrak{p}^{(2 n - 1)} \subseteq \mathfrak{m} \mathfrak{p}^{n}$
for some positive integer $n$, where $\mathfrak{m}$ is the maximal ideal $(x,
y, z)$. Moreover, the smallest such $n$ is determined. It turns out that there
is a counterexample to a claim due to Grifo, Huneke, and Mukundan, which states
that $\mathfrak{p}^{(3)} \subseteq \mathfrak{m} \mathfrak{p}^2$ if $k$ is a
field of characteristic not $3$; however, the stable Harbourne conjecture holds
for space monomial curves as they claimed.


title: Comparing the Manipulability of Approval Voting and Borda

abstract:
  The Gibbard-Satterthwaite theorem established that no non-trivial voting rule
is strategy-proof, but that does not mean that all voting rules are equally
susceptible to strategic manipulation. Over the past fifty years numerous
approaches have been proposed to compare the manipulability of voting rules in
terms of the probability of manipulation, the domains on which manipulation is
possible, the complexity of finding such a manipulation, and others. In the
closely related field of matching, Pathak and Sonmez pioneered a notion of
manipulability based on case-by-case comparison of manipulable profiles. The
advantage of this approach is that it is independent of the underlying
statistical culture or the computational power of the agents, and it has proven
fruitful in the matching literature. In this paper, we extend the notion of
Pathak and Sonmez to voting, studying the families of $k$-approval and
truncated Borda scoring rules. We find that, with one exception, the notion
does not allow for a meaningful ordering of the manipulability of these rules.


title: Mesoscopic tunneling in strontium titanate

abstract:
  Spatial correlation between atoms can generate a depletion in the energy
dispersion of acoustic phonons. Two well known examples are rotons in
superfluid helium and the Kohn anomaly in metals. Here we report on the
observation of a large softening of the transverse acoustic mode in quantum
paraelectric SrTiO$_3$ by means of inelastic neutron scattering. In contrast to
other known cases, this softening occurs at a tiny wave vector implying spatial
correlation extending over a distance as long as 40 lattice parameters. We
attribute this to the formation of mesoscopic fluctuating domains due to the
coupling between local strain and quantum ferroelectric fluctuations. Thus, a
hallmark of the ground state of insulating SrTiO$_3$ is the emergence of
hybridized optical-acoustic phonons. Mesoscopic fluctuating domains play a role
in quantum tunneling, which impedes the emergence of a finite macroscopic
polarisation.


title: Analysis of Count-Min sketch under conservative update

abstract:
  Count-Min sketch is a hash-based data structure to represent a dynamically
changing associative array of counters. Here we analyse the counting version of
Count-Min under a stronger update rule known as \textit{conservative update},
assuming the uniform distribution of input keys. We show that the accuracy of
conservative update strategy undergoes a phase transition, depending on the
number of distinct keys in the input as a fraction of the size of the Count-Min
array. We prove that below the threshold, the relative error is asymptotically
$o(1)$ (as opposed to the regular Count-Min strategy), whereas above the
threshold, the relative error is $\Theta(1)$. The threshold corresponds to the
peelability threshold of random $k$-uniform hypergraphs. We demonstrate that
even for small number of keys, peelability of the underlying hypergraph is a
crucial property to ensure the $o(1)$ error.


title: A complement to the scalar wave theory of light

abstract:
  In this paper, we discuss how the concepts of Hamiltonian optics are
internally connected to the scalar wave theory of light rays. It is shown that
the solutions of the reduced wave equation are similar to Huygen's wavelets,
and they can be used to understand Snell's law of refraction. This model can
also be used to derive the coefficient of reflection consistent with Fresnel's
equation for $s$-polarized light.


title:
Powerful Physical Adversarial Examples Against Practical Face
  Recognition Systems

abstract:
  It is well-known that the most existing machine learning (ML)-based
safety-critical applications are vulnerable to carefully crafted input
instances called adversarial examples (AXs). An adversary can conveniently
attack these target systems from digital as well as physical worlds. This paper
aims to the generation of robust physical AXs against face recognition systems.
We present a novel smoothness loss function and a patch-noise combo attack for
realizing powerful physical AXs. The smoothness loss interjects the concept of
delayed constraints during the attack generation process, thereby causing
better handling of optimization complexity and smoother AXs for the physical
domain. The patch-noise combo attack combines patch noise and imperceptibly
small noises from different distributions to generate powerful
registration-based physical AXs. An extensive experimental analysis found that
our smoothness loss results in robust and more transferable digital and
physical AXs than the conventional techniques. Notably, our smoothness loss
results in a 1.17 and 1.97 times better mean attack success rate (ASR) in
physical white-box and black-box attacks, respectively. Our patch-noise combo
attack furthers the performance gains and results in 2.39 and 4.74 times higher
mean ASR than conventional technique in physical world white-box and black-box
attacks, respectively.


title: Marriage between neutrino mass and flavor anomalies

abstract:
  Experimental hints for lepton flavor universality violation in beauty-quark
decay both in neutral- and charged-current transitions require an extension of
the Standard Model for which scalar leptoquarks (LQs) are the prime candidates.
Besides, these same LQs can resolve the long-standing tension in the muon and
the recently reported deviation in the electron $g-2$ anomalies. These
tantalizing flavor anomalies have discrepancies in the range of
$2.5\sigma-4.2\sigma$, indicating that the Standard Model of particle physics
may finally be cracking. In this Letter, we propose a resolution to all these
anomalies within a unified framework that sheds light on the origin of neutrino
mass. In this model, the LQs that address flavor anomalies run through the
loops and generate neutrino mass at the two-loop order while satisfying all
constraints from collider searches, including those from flavor physics.


title:
An Unbiased Symmetric Matrix Estimator for Topology Inference under
  Partial Observability

abstract:
  Network topology inference is a fundamental problem in many applications of
network science, such as locating the source of fake news, brain connectivity
networks detection, etc. Many real-world situations suffer from a critical
problem that only a limited part of observations are available. This letter
considers the problem of network topology inference under the framework of
partial observability. Based on the vector autoregressive model, we propose a
novel unbiased estimator for the symmetric network topology with the Gaussian
noise and the Laplacian combination rule. Theoretically, we prove that it
converges to the network combination matrix in probability. Furthermore, by
utilizing the Gaussian mixture model algorithm, an effective algorithm called
network inference Gauss algorithm is developed to infer the network structure.
Finally, compared with the state-of-the-art methods, numerical experiments
demonstrate the proposed algorithm enjoys better performance in the case of
small sample sizes.


title:
Deep Learning for Encrypted Traffic Classification and Unknown Data
  Detection

abstract:
  Despite the widespread use of encryption techniques to provide
confidentiality over Internet communications, mobile device users are still
susceptible to privacy and security risks. In this paper, a new Deep Neural
Network (DNN) based user activity detection framework is proposed to identify
fine grained user activities performed on mobile applications (known as in-app
activities) from a sniffed encrypted Internet traffic stream. One of the
challenges is that there are countless applications, and it is practically
impossible to collect and train a DNN model using all possible data from them.
Therefore, in this work we exploit the probability distribution of DNN output
layer to filter the data from applications that are not considered during the
model training (i.e., unknown data). The proposed framework uses a time window
based approach to divide the traffic flow of an activity into segments, so that
in-app activities can be identified just by observing only a fraction of the
activity related traffic. Our tests have shown that the DNN based framework has
demonstrated an accuracy of 90% or above in identifying previously trained
in-app activities and an average accuracy of 79% in identifying previously
untrained in-app activity traffic as unknown data when this framework is
employed.


title: Analysis of OODA Loop based on Adversarial for Complex Game Environments

abstract:
  To address the problem of imperfect confrontation strategy caused by the lack
of information of game environment in the simulation of non-complete
information dynamic countermeasure modeling for intelligent game, the
hierarchical analysis game strategy of confrontation model based on OODA ring
(Observation, Orientation, Decision, Action) theory is proposed. At the same
time, taking into account the trend of unmanned future warfare, NetLogo
software simulation is used to construct a dynamic derivation of the
confrontation between two tanks. In the validation process, the OODA loop
theory is used to describe the operation process of the complex system between
red and blue sides, and the four-step cycle of observation, judgment, decision
and execution is carried out according to the number of armor of both sides,
and then the OODA loop system adjusts the judgment and decision time
coefficients for the next confrontation cycle according to the results of the
first cycle. Compared with traditional simulation methods that consider
objective factors such as loss rate and support rate, the OODA-loop-based
hierarchical game analysis can analyze the confrontation situation more
comprehensively.


title:
Cluster quantum Monte Carlo study of two-dimensional weakly-coupled
  frustrated trimer antiferromagnets

abstract:
  We report results from spin trimer-based cluster quantum Monte Carlo
simulations for the thermodynamic properties of two-dimensional frustrated
quantum antiferromagnets that are composed of weakly-coupled three-spin
(trimer) clusters. In particular, we consider the spin-1/2 kagome lattice with
a strong breathing distortion, and the triangle-square lattice model proposed
previously for the cuprate La${}_4$Cu${}_3$MoO${}_{12}$. For both cases, we
demonstrate that an appropriately chosen trimer-based computational basis
allows us to significantly reduce the quantum Monte Carlo sign problem down to
the low-temperature regime. Besides exploring the thermodynamic behavior for
the triangle-square lattice model we also assess a mean field theory-based
prediction for the onset of chiral order. For the breathing distorted kagome
lattice model, we observe a robust two-peak structure in the specific heat,
both in the quantum spin liquid and the lattice-nematic regimes.


title:
Improved DC Bus Voltage Control of Single-Phase Grid-Connected Voltage
  Source Converters for Minimising Bus Capacitance and Line Current Harmonics

abstract:
  There is a trade-off between transient performance and line current
distortion of the DC bus voltage control of single-phase grid-connected voltage
source converters. This paper presents an improved DC bus voltage control
scheme of such converters using a proportional-integral controller in series
with a first-order low pass filter. The extended symmetrical tuning method was
adopted in the design of a regulator parameter, which greatly reduced the
oscillating component at the double line frequency. The proposed control
methodology allowed the loop bandwidth to increase without distorting the line
current. Consequently, the DC bus voltage fluctuation or DC bus capacitance was
reduced with a shorter settling time during a step load, compared with the
conventional scheme. The proposed voltage scheme was found to be robust to the
grid voltage variation and was less susceptible to the line voltage harmonics.
Simulation and experimental results of a 1.5 kVA PWM rectifier verified the
proposed methodology.


title:
Laser-Induced Graphitisation of Diamond Under 30 fs Laser Pulse
  Irradiation

abstract:
  The degree of laser-induced graphitisation from a sp3-bonded to a sp2-bonded
carbon fraction in a single crystal chemical vapour deposited (CVD) diamond
under a varying fluence of an ultrashort pulsed laser (30 fs, 800 nm, 1 kHz)
irradiation has been studied. The tetrahedral CVD sp3-phase was found to
transition to primarily an sp2-aromatic crystalline graphitic fraction below
the critical fluence of 3.9 J/cm2, above which predominantly an amorphous
carbon was formed. A fractional increase of fluence from 3.3 J/cm2 to 3.9 J/cm2
(~ 20 %) resulted in a substantial (~ three-fold) increased depth of the
sp2-graphitised areas owing to the non-linear interactions associated with an
fs-laser irradiation. Additionally, formation of C=O carbonyl group was
observed below the critical threshold fluence; the C=O cleavage occurred
gradually with the increase of irradiation fluence of 30 fs laser light. The
implications for these findings on enhancement of fs-driven processing of
diamond are discussed.


title:
Trojan Horse Training for Breaking Defenses against Backdoor Attacks in
  Deep Learning

abstract:
  Machine learning (ML) models that use deep neural networks are vulnerable to
backdoor attacks. Such attacks involve the insertion of a (hidden) trigger by
an adversary. As a consequence, any input that contains the trigger will cause
the neural network to misclassify the input to a (single) target class, while
classifying other inputs without a trigger correctly. ML models that contain a
backdoor are called Trojan models. Backdoors can have severe consequences in
safety-critical cyber and cyber physical systems when only the outputs of the
model are available. Defense mechanisms have been developed and illustrated to
be able to distinguish between outputs from a Trojan model and a non-Trojan
model in the case of a single-target backdoor attack with accuracy > 96
percent. Understanding the limitations of a defense mechanism requires the
construction of examples where the mechanism fails. Current single-target
backdoor attacks require one trigger per target class. We introduce a new, more
general attack that will enable a single trigger to result in misclassification
to more than one target class. Such a misclassification will depend on the true
(actual) class that the input belongs to. We term this category of attacks
multi-target backdoor attacks. We demonstrate that a Trojan model with either a
single-target or multi-target trigger can be trained so that the accuracy of a
defense mechanism that seeks to distinguish between outputs coming from a
Trojan and a non-Trojan model will be reduced. Our approach uses the non-Trojan
model as a teacher for the Trojan model and solves a min-max optimization
problem between the Trojan model and defense mechanism. Empirical evaluations
demonstrate that our training procedure reduces the accuracy of a
state-of-the-art defense mechanism from >96 to 0 percent.


title:
Computation of Centroidal Voronoi Tessellations in High Dimensional
  spaces

abstract:
  Owing to the natural interpretation and various desirable mathematical
properties, centroidal Voronoi tessellations (CVT) have found a wide range of
applications and correspondingly a vast development in their literature.
However the computation of CVT in higher dimensional spaces still remains
difficult. In this paper, we exploit the non-uniqueness of CVTs in higher
dimensional spaces for their computation. We construct such high dimensional
tessellations from CVTs in one-dimensional spaces. We then prove that such a
tessellation is centroidal under the condition of independence among densities
over the one-dimensional spaces considered. Various numerical evaluations
backup the theoretical result through the low energy of the tessellations. The
resulting grid-like tessellations are obtained efficiently with minimal
computation time.


title: Improving Contrastive Learning with Model Augmentation

abstract:
  The sequential recommendation aims at predicting the next items in user
behaviors, which can be solved by characterizing item relationships in
sequences. Due to the data sparsity and noise issues in sequences, a new
self-supervised learning (SSL) paradigm is proposed to improve the performance,
which employs contrastive learning between positive and negative views of
sequences.
  However, existing methods all construct views by adopting augmentation from
data perspectives, while we argue that 1) optimal data augmentation methods are
hard to devise, 2) data augmentation methods destroy sequential correlations,
and 3) data augmentation fails to incorporate comprehensive self-supervised
signals.
  Therefore, we investigate the possibility of model augmentation to construct
view pairs. We propose three levels of model augmentation methods: neuron
masking, layer dropping, and encoder complementing.
  This work opens up a novel direction in constructing views for contrastive
SSL. Experiments verify the efficacy of model augmentation for the SSL in the
sequential recommendation. Code is
available\footnote{\url{https://github.com/salesforce/SRMA}}.


title:
Firefighter Problem with Minimum Budget: Hardness and Approximation
  Algorithm for Unit Disk Graphs

abstract:
  Unit disk graphs are the set of graphs which represent the intersection of
disk graphs and interval graphs. These graphs are of great importance due to
their structural similarity with wireless communication networks. Firefighter
problem on unit disk graph is interesting as it models the virus spreading in
an wireless network and asks for a solution to stop it. In this paper, we
consider the MIN-BUDGET firefighter problem where the goal is to determine the
minimum number of firefighters required and the nodes to place them at each
time instant to save a given set of vertices of a given graph and a fire
breakout node. We show that, the MIN-BUDGET firefighter problem in a unit disk
graph is NP-Hard. We also present a constant factor approximation algorithm.


title:
Achieving Guidance in Applied Machine Learning through Software
  Engineering Techniques

abstract:
  Development of machine learning (ML) applications is hard. Producing
successful applications requires, among others, being deeply familiar with a
variety of complex and quickly evolving application programming interfaces
(APIs). It is therefore critical to understand what prevents developers from
learning these APIs, using them properly at development time, and understanding
what went wrong when it comes to debugging. We look at the (lack of) guidance
that currently used development environments and ML APIs provide to developers
of ML applications, contrast these with software engineering best practices,
and identify gaps in the current state of the art. We show that current ML
tools fall short of fulfilling some basic software engineering gold standards
and point out ways in which software engineering concepts, tools and techniques
need to be extended and adapted to match the special needs of ML application
development. Our findings point out ample opportunities for research on
ML-specific software engineering.


title: Explaining random forest prediction through diverse rulesets

abstract:
  Tree-ensemble algorithms, such as random forest, are effective machine
learning methods popular for their flexibility, high performance, and
robustness to overfitting. However, since multiple learners are combined,they
are not as interpretable as a single decision tree. In this work we propose a
methodology, called Local Tree eXtractor (LTreeX) which is able to explain the
forest prediction for a given test instance with a few diverse rules. Starting
from the decision trees generated by a random forest, our method 1) pre-selects
a subset of them, 2) creates a vector representation, and 3) eventually
clusters such a representation. Each cluster prototype results in a rule that
explains the test instance prediction. We test the effectiveness of LTreeX on
71 real-world datasets and we demonstrate the validity of our approach for
binary classification, regression, multi-label classification and time-to-event
tasks. In all set-ups, we show that our extracted surrogate model manages to
approximate the performance of the corresponding ensemble model, while
selecting only few trees from the whole forest.We also show that our proposed
approach substantially outperforms other explainable methods in terms of
predictive performance.


title: Gravitational matter-antimatter impact interactions

abstract:
  The production of antihydrogen by several research groups will provide the
opportunity to measure the gravitational behaviour of antimatter in the
gravitational field of the Earth. The predictions in the literature reach from
normal attraction to repulsion. Applying our gravitational impact model, we
conclude, after a significant modification, that there will be neither
attraction nor repulsion. The modification consists of the assumption of a
symmetric graviton-antigraviton environment. The model, in addition, predicts
normal gravitation between antimatter and antimatter particles at large
distances, but strong repulsion at close range for matter pairs as well as for
antimatter pairs, whereas strong attraction will result for matter-antimatter
encounters.


title: Twin extreme ultraviolet waves in the solar corona

abstract:
  Solar extreme ultraviolet (EUV) waves are spectacular propagating
disturbances with EUV enhancements in annular shapes in the solar corona. These
EUV waves carry critical information about the coronal magnetised plasma that
can shed light on the elusive physical parameters (e.g. the magnetic field
strength) by global solar coronal magneto-seismology. EUV waves are closely
associated with a wide range of solar atmospheric eruptions, from violent
flares and coronal mass ejections (CMEs) to less energetic plasma jets or
mini-filament eruptions. However, the physical nature and driving mechanism of
EUV waves is still controversial. Here, we report the unique discovery of twin
EUV waves (TEWs) that were formed in a single eruption with observations from
two different perspectives. In all earlier studies, a single eruption was
associated at most with a single EUV wave. The newly found TEWs urge to
re-visit our theoretical understanding about the underlying formation
mechanism(s) of coronal EUV waves. Two distinct scenarios of TEWs were found.
In the first scenario, the two waves were separately associated with a filament
eruption and a precursor jet, while in another scenario the two waves were
successively associated with a filament eruption. Hence, we label these
distinguished scenarios as "fraternal TEWs" and "identical TEWs", respectively.
Further, we also suggest that impulsive lateral expansions of two distinct
groups of coronal loops are critical to the formation of TEWs in a single
eruption.


title:
Human Response to an AI-Based Decision Support System: A User Study on
  the Effects of Accuracy and Bias

abstract:
  Artificial Intelligence (AI) is increasingly used to build Decision Support
Systems (DSS) across many domains. This paper describes a series of experiments
designed to observe human response to different characteristics of a DSS such
as accuracy and bias, particularly the extent to which participants rely on the
DSS, and the performance they achieve. In our experiments, participants play a
simple online game inspired by so-called "wildcat" (i.e., exploratory) drilling
for oil. The landscape has two layers: a visible layer describing the costs
(terrain), and a hidden layer describing the reward (oil yield). Participants
in the control group play the game without receiving any assistance, while in
treatment groups they are assisted by a DSS suggesting places to drill. For
certain treatments, the DSS does not consider costs, but only rewards, which
introduces a bias that is observable by users. Between subjects, we vary the
accuracy and bias of the DSS, and observe the participants' total score, time
to completion, the extent to which they follow or ignore suggestions. We also
measure the acceptability of the DSS in an exit survey. Our results show that
participants tend to score better with the DSS, that the score increase is due
to users following the DSS advice, and related to the difficulty of the game
and the accuracy of the DSS. We observe that this setting elicits mostly
rational behavior from participants, who place a moderate amount of trust in
the DSS and show neither algorithmic aversion (under-reliance) nor automation
bias (over-reliance).However, their stated willingness to accept the DSS in the
exit survey seems less sensitive to the accuracy of the DSS than their
behavior, suggesting that users are only partially aware of the (lack of)
accuracy of the DSS.


title:
Gradient estimates for elliptic systems from composite materials with
  closely spaced stiff $C^{1,\gamma}$ inclusions

abstract:
  This paper is devoted to establishing the pointwise upper and lower bounds
estimates of the gradient of the solutions to a class of general elliptic
systems with H\"{o}lder continuous coefficients in a narrow region where the
upper and lower boundaries is $C^{1,\gamma},$ $0<\gamma<1$, weaker than the
previous $C^{2,\gamma}$ assumption. These estimates play a key role in the
damage analysis of composite materials. From our results, the damage may
initiate from the narrowest place.


title: Rich Feature Construction for the Optimization-Generalization Dilemma

abstract:
  There often is a dilemma between ease of optimization and robust
out-of-distribution (OoD) generalization. For instance, many OoD methods rely
on penalty terms whose optimization is challenging. They are either too strong
to optimize reliably or too weak to achieve their goals.
  In order to escape this dilemma, we propose to first construct a rich
representation (RFC) containing a palette of potentially useful features, ready
to be used by even simple models. On the one hand, a rich representation
provides a good initialization for the optimizer. On the other hand, it also
provides an inductive bias that helps OoD generalization. RFC is constructed in
a succession of training episodes. During each step of the discovery phase, we
craft a multi-objective optimization criterion and its associated datasets in a
manner that prevents the network from using the features constructed in the
previous iterations. During the synthesis phase, we use knowledge distillation
to force the network to simultaneously develop all the features identified
during the discovery phase.
  RFC consistently helps six OoD methods achieve top performance on challenging
invariant training benchmarks, ColoredMNIST (Arjovsky et al., 2020).
Furthermore, on the realistic Camelyon17 task, our method helps both OoD and
ERM methods outperform earlier compatable results by at least $5\%$, reduce
standard deviation by at least $4.1\%$, and makes hyperparameter tuning and
model selection more reliable.


title: A multimodal approach for Parkinson disease analysis

abstract:
  Parkinson's disease (PD) is the second most frequent neurodegenerative
disease with prevalence among general population reaching 0.1-1 %, and an
annual incidence between 1.3-2.0/10000 inhabitants. The mean age at diagnosis
of PD is 55 and most patients are between 50 and 80 years old. The most obvious
symptoms are movement-related; these include tremor, rigidity, slowness of
movement and walking difficulties. Frequently these are the symptoms that lead
to the PD diagnoses. Later, thinking and behavioral problems may arise, and
other symptoms include cognitive impairment and sensory, sleep and emotional
problems. In this paper we will present an ongoing project that will evaluate
if voice and handwriting analysis can be reliable predictors/indicators of
swallowing and balance impairments in PD. An important advantage of voice and
handwritten analysis is its low intrusiveness and easy implementation in
clinical practice. Thus, if a significant correlation between these simple
analyses and the gold standard video-fluoroscopic analysis will imply simpler
and less stressing diagnostic test for the patients as well as the use of
cheaper analysis systems.


title: On Atiyah-Segal completion for T-equivariant Hermitian K-theory

abstract:
  We show how derived completion can be used to prove an analogue of
Atiyah-Segal completion for the $T$-equivariant Hermitian K-theory of a scheme
$X$ with a trivial $T$-action, containing $\tfrac{1}{2}$ and satisfying the
resolution property, where $T$ is a split torus of rank $t$. This result is an
important first step towards a more general Atiyah-Segal completion theorem for
Hermitian K-theory.


title: Learning neural audio features without supervision

abstract:
  Deep audio classification, traditionally cast as training a deep neural
network on top of mel-filterbanks in a supervised fashion, has recently
benefited from two independent lines of work. The first one explores "learnable
frontends", i.e., neural modules that produce a learnable time-frequency
representation, to overcome limitations of fixed features. The second one uses
self-supervised learning to leverage unprecedented scales of pre-training data.
In this work, we study the feasibility of combining both approaches, i.e.,
pre-training learnable frontend jointly with the main architecture for
downstream classification. First, we show that pretraining two previously
proposed frontends (SincNet and LEAF) on Audioset drastically improves
linear-probe performance over fixed mel-filterbanks, suggesting that learnable
time-frequency representations can benefit self-supervised pre-training even
more than supervised training. Surprisingly, randomly initialized learnable
filterbanks outperform mel-scaled initialization in the self-supervised
setting, a counter-intuitive result that questions the appropriateness of
strong priors when designing learnable filters. Through exploratory analysis of
the learned frontend components, we uncover crucial differences in properties
of these frontends when used in a supervised and self-supervised setting,
especially the affinity of self-supervised filters to diverge significantly
from the mel-scale to model a broader range of frequencies.


title: Theory of the Dark State of Polyenes and Carotenoids

abstract:
  A theory is developed to describe the singlet dark state (usually labeled S1
or 2Ag) of polyenes and carotenoids. The theory assumes that in principle this
state is a linear combination of a singlet triplet-pair and an odd-parity
charge-transfer exciton. Crucially, these components only couple when the
triplet-pair occupies neighboring dimers, such that an electron transfer
between the triplets creates a nearest-neighbor charge-transfer excitation.
This local coupling stabilises the 2Ag state and induces a nearest neighbor
attraction between the triplets. In addition, because of the electron-hole
attraction in the exciton, the increased probability that the electron-hole
pair occupies neighboring dimers enhances the triplet-triplet attraction: the
triplet pair is `slaved' to the charge-transfer exciton. The theory also
predicts that as the Coulomb interaction is increased, the 2Ag state evolves
from a predominately odd-parity charge-transfer exciton state with a small
component of triplet-pair character to a state predominately composed of a
triplet-pair with some exciton character. Above a critical Coulomb interaction
there is a decoupling of the triplet-pair and charge-transfer exciton
subspaces, such that the 2Ag state becomes entirely composed of an unbound
spin-correlated triplet pair. The predictions of this theory are qualitatively
consistent with high-level density matrix renormalization group calculations of
the Pariser-Parr-Pople (or extended Hubbard) model.


title:
Universal control of superexchange in linear triple quantum dots with an
  empty mediator

abstract:
  Superexchange is one of the vital resources to realize long-range interaction
between distant spins for large-scale quantum computing. Recent experiments
have demonstrated coherent oscillations between logical states defined by
remote spins whose coupling is given by the superexchange interaction mediated
by central spins. Excavating the potential of superexchange requires a full
understanding of the interaction in terms of control parameters, which is still
lacking in literature. Here, using full configuration interaction calculation,
we study a linear triple-quantum-dot device in which the left and right dots
are occupied by a single electron each, whose spin states define the
computational basis states of a qubit. We have found that, when the detunings
at the left and right dots are leveled, the superexchange can exhibit a
non-monotonic behavior which ranges from positive to negative values as a
function of the middle-dot detuning. We further show that the larger relative
detuning between the left and right dots causes the magnitude of the
superexchange to increase (decrease) for an originally positive (negative)
superexchange. We then proceed to show that a much larger left-right dot
detuning can switch the superexchange from an originally negative value at zero
detuning to a positive one. Using a Hubbard-like model, we present analytical
expressions of the superexchange and have found that they conform well
qualitatively with the numerical results obtained from full configuration
interaction calculations. Our results suggest that even a simple configuration
of delocalized two-electron states in a linear triple-quantum-dot device
exhibits superexchange energy with non-trivial behavior, making it a promising
candidate for scaling up with various implementations.


title: Collision-Free Navigation using Evolutionary Symmetrical Neural Networks

abstract:
  Collision avoidance systems play a vital role in reducing the number of
vehicle accidents and saving human lives. This paper extends the previous work
using evolutionary neural networks for reactive collision avoidance. We are
proposing a new method we have called symmetric neural networks. The method
improves the model's performance by enforcing constraints between the network
weights which reduces the model optimization search space and hence, learns
more accurate control of the vehicle steering for improved maneuvering. The
training and validation processes are carried out using a simulation
environment - the codebase is publicly available. Extensive experiments are
conducted to analyze the proposed method and evaluate its performance. The
method is tested in several simulated driving scenarios. In addition, we have
analyzed the effect of the rangefinder sensor resolution and noise on the
overall goal of reactive collision avoidance. Finally, we have tested the
generalization of the proposed method. The results are encouraging; the
proposed method has improved the model's learning curve for training scenarios
and generalization to the new test scenarios. Using constrained weights has
significantly improved the number of generations required for the Genetic
Algorithm optimization.


title:
Heat-type equations on manifolds with fibered boundary I: Schauder
  estimates

abstract:
  In this paper we prove parabolic Schauder estimates for the Laplace-Beltrami
operator on a manifold $M$ with fibered boundary and a $\Phi$-metric $g_\Phi$.
This setting generalizes the asymptotically conical (scattering) spaces and
includes special cases of magnetic and gravitational monopoles. This paper,
combined with part II, lay the crucial groundwork for forthcoming discussions
on geometric flows in this setting; especially the Yamabe- and mean curvature
flow.


title:
Stable and unstable capillary fingering in porous media with a gradient
  in grains size

abstract:
  We present a theoretical and experimental investigation of slow drainage in
porous media with a gradient in the grains size (and hence in the typical
pores' throats), in an external gravitational field. We mathematically show
that such structural gradient and external force have a similar effect on the
obtained drainage patterns, when they stabilise the invasion front. With the
help of a newly introduced experimental set-up, based on the 3D-print of
transparent porous matrices, we illustrate this equivalence, and extend it to
the case where the front is unstable. We also present some invasion-percolation
simulations of the same phenomena, which are inline with our theoretical and
experimental results. In particular, we show that the width of stable drainage
fronts mainly scales with the spatial gradient of the average pore invasion
threshold and with the local distribution of this (disordered) threshold. The
scaling exponent results from percolation theory and is -0.57 for 2D systems.
Overall, we propose a unifying theory for the up-scaling of dual fluid flows in
most classical scenarii.


title:
Quantum Machine Learning Corrects Classical Force Fields: Stretching DNA
  Base Pairs in Explicit Solvent

abstract:
  In order to improve the accuracy of molecular dynamics simulations, classical
force fields are supplemented with a kernel-based machine learning method
trained on quantum-mechanical fragment energies. As an example application, a
potential-energy surface is generalised for a small DNA duplex, taking into
account explicit solvation and long-range electron exchange--correlation
effects. Study of the corrected potential energy versus extension shows that
leading classical DNA models have excessive stiffness with respect to
stretching. This discrepancy is found to be common across multiple forcefields.
The quantum correction is in qualitative agreement to the experimental
thermodynamics for larger DNA double helices, providing a candidate explanation
for the general and long-standing discrepancy between single molecule
stretching experiments and classical calculations of DNA stretching. The new
dataset of quantum calculations and the associated Kernel Modified Molecular
Dynamics (KMMD) method should be of general utility in biomolecular
simulations. KMMD is made available as part of the AMBER22 simulation software.


title:
Interactive Audio-text Representation for Automated Audio Captioning
  with Contrastive Learning

abstract:
  Automated Audio captioning (AAC) is a cross-modal task that generates natural
language to describe the content of input audio. Most prior works usually
extract single-modality acoustic features and are therefore sub-optimal for the
cross-modal decoding task. In this work, we propose a novel AAC system called
CLIP-AAC to learn interactive cross-modality representation with both acoustic
and textual information. Specifically, the proposed CLIP-AAC introduces an
audio-head and a text-head in the pre-trained encoder to extract audio-text
information. Furthermore, we also apply contrastive learning to narrow the
domain difference by learning the correspondence between the audio signal and
its paired captions. Experimental results show that the proposed CLIP-AAC
approach surpasses the best baseline by a significant margin on the Clotho
dataset in terms of NLP evaluation metrics. The ablation study indicates that
both the pre-trained model and contrastive learning contribute to the
performance gain of the AAC model.


title: Millikelvin scanning nitrogen-vacancy magnetometry

abstract:
  We report on the implementation of a scanning nitrogen-vacancy (NV)
magnetometer in a dry dilution refrigerator. Using low-power, pulsed optically
detected magnetic resonance combined with efficient microwave delivery through
a co-planar waveguide, we reach a base temperature of 350 mK, limited by
experimental heat load and thermalization of the probe. We demonstrate scanning
NV magnetometry by imaging the Abrikosov vortex lattice in a 50-nm-thin
aluminum microstructure. The sensitivity of our measurements is approximately 3
{\mu}T per square root Hz. Our work demonstrates the feasibility for performing
non-invasive magnetic field imaging with scanning NV centers at sub-Kelvin
temperatures.

categories: quant-ph cond-mat.mes-hall cond-mat.mtrl-sci cond-mat.supr-con physics.ins-det


title: Absorption spectrum of doped highly mismatched alloys

abstract:
  Highly mismatched alloys (HMA's) are a class of semiconductor alloys with
large electronegativity differences between the alloying elements. We predict
the absorption spectrum due to transitions between the split bands of a doped
highly mismatched alloy with a conduction band anticrossing. We analyze the
joint densities of states for both direct and indirect transitions between the
split bands. The resulting spectrum has features that reveal the unusual state
distribution that is characteristic of HMAs, hence providing valuable insight
into their electronic structure. In particular, we predict a peak near the
absorption edge, which arises due to the suppression of direct transitions at
large momenta. We present analytic forms for the near-absorption-edge and
large-energy spectra, showing that they are qualitatively different from those
in standard parabolic semiconductors. In particular, as a result of suppressed
direct transitions, indirect transitions dominate the spectrum away from the
edge of absorption.


title: Treatment Learning Transformer for Noisy Image Classification

abstract:
  Current top-notch deep learning (DL) based vision models are primarily based
on exploring and exploiting the inherent correlations between training data
samples and their associated labels. However, a known practical challenge is
their degraded performance against "noisy" data, induced by different
circumstances such as spurious correlations, irrelevant contexts, domain shift,
and adversarial attacks. In this work, we incorporate this binary information
of "existence of noise" as treatment into image classification tasks to improve
prediction accuracy by jointly estimating their treatment effects. Motivated
from causal variational inference, we propose a transformer-based architecture,
Treatment Learning Transformer (TLT), that uses a latent generative model to
estimate robust feature representations from current observational input for
noise image classification. Depending on the estimated noise level (modeled as
a binary treatment factor), TLT assigns the corresponding inference network
trained by the designed causal loss for prediction. We also create new noisy
image datasets incorporating a wide range of noise factors (e.g., object
masking, style transfer, and adversarial perturbation) for performance
benchmarking. The superior performance of TLT in noisy image classification is
further validated by several refutation evaluation metrics. As a by-product,
TLT also improves visual salience methods for perceiving noisy images.


title:
On $L^p$ estimates for positivity-preserving Riesz transforms related to
  Schr\"odinger operators

abstract:
  We study the $L^{p},$ $1\leqslant p\leqslant \infty,$ boundedness for Riesz
transforms of the form $V^{a}(-\frac{1}{2}\Delta+V)^{-a},$ where $a>0$ and $V$
is a non-negative potential. We prove that $V^{a}(-\frac{1}{2}\Delta+V)^{-a}$
is bounded on $L^p(\mathbb{R}^d)$ with $1< p\leqslant 2$ whenever $a\leqslant
1/p.$ We demonstrate that the $L^{\infty}(\mathbb{R}^d)$ boundedness holds if
$V$ satisfies an $a$-dependent integral condition that is resistant to small
perturbations. Similar results with stronger assumptions on $V$ are also
obtained on $L^{1}(\mathbb{R}^d).$ In particular our $L^{\infty}$ and $L^1$
results apply to non-negative potentials $V$ which globally have a power growth
or an exponential growth. We also discuss a counterexample showing that the
$L^{\infty}(\mathbb{R}^d)$ boundedness may fail.


title: Scalar Curvature Splittings II: Removal of Singularities

abstract:
  In Part I of this paper we have seen that any singular compact area minimizer
in a positive scalar curvature manifold admits a conformal deformation to some
minimal factor geometry that shares many properties with the minimizer, like
the Ahlfors regularity, the validity of Poincare inequalities and the presence
of tangent cones with positive scalar curvature. In this Part II we show that
these geometries admit surgery style arguments eliminating the singular sets.


title: Efficient flow equations for dissipative systems

abstract:
  Open quantum systems provide an essential theoretical basis for the
development of novel quantum technologies, since any real quantum system
inevitably interacts with its environment. Lindblad master equations capture
the effect of Markovian environments. Closed quantum systems can be treated
using flow equations with the particle conserving generator. We generalize this
generator to non-Hermitian matrices and open quantum systems governed by
Lindbladians, comparing our results with recently proposed generators by Rosso
et al. (arXiv:2007.12044). In comparison, we find that our advocated generator
provides an efficient flow with good accuracy in spite of truncations.


title: OSOP: A Multi-Stage One Shot Object Pose Estimation Framework

abstract:
  We present a novel one-shot method for object detection and 6 DoF pose
estimation, that does not require training on target objects. At test time, it
takes as input a target image and a textured 3D query model. The core idea is
to represent a 3D model with a number of 2D templates rendered from different
viewpoints. This enables CNN-based direct dense feature extraction and
matching. The object is first localized in 2D, then its approximate viewpoint
is estimated, followed by dense 2D-3D correspondence prediction. The final pose
is computed with PnP. We evaluate the method on LineMOD, Occlusion, Homebrewed,
YCB-V and TLESS datasets and report very competitive performance in comparison
to the state-of-the-art methods trained on synthetic data, even though our
method is not trained on the object models used for testing.


title:
Design and Implementation of Knowledge Base for Runtime Management of
  Software Defined Hardware

abstract:
  Runtime-reconfigurable software coupled with reconfigurable hardware is
highly desirable as a means towards maximizing runtime efficiency without
compromising programmability. Compilers for such software systems are extremely
difficult to design as they must leverage different types of hardware at
runtime. To address the need for static and dynamic compiler optimization of
workflows matched to dynamically reconfigurable hardware, we propose a novel
design of the central component of a dynamic software compiler for software
defined hardware. Our comprehensive design focuses not just on static knowledge
but also on semi-supervised extraction of knowledge from program executions and
developing their performance models. Specifically, our novel {\it dynamic and
extensible knowledge base} 1) continuously gathers knowledge during execution
of workflows 2) identifies {\it optimal} implementations of workflows on {\it
optimal} (available) hardware configurations. It plays a hub role in storing
information from, and providing information to other components of the
compiler, as well as the human analyst. Through a rich tripartite graph
representation, the knowledge base captures and learns extensive information on
decomposition and mapping of code steps to kernels and mapping of kernels to
available hardware configurations. The knowledge base is implemented using the
C++ Boost Library and is capable of quickly processing offline and online
queries and updates. We show that our knowledge base can answer queries in
$1ms$ regardless of the number of workflows it stores. To the best of our
knowledge, this is the first design of a dynamic and extensible knowledge base
to support compilation of high-level languages to leverage arbitrary
reconfigurable platforms.


title:
Game-theoretical trajectory planning enhances social acceptability for
  humans

abstract:
  Since humans and robots are increasingly sharing portions of their
operational spaces, experimental evidence is needed to ascertain the safety and
social acceptability of robots in human-populated environments. Although
several studies have aimed at devising strategies for robot trajectory planning
to perform \emph{safe} motion in populated environments, a few efforts have
\emph{measured} to what extent a robot trajectory is \emph{accepted} by humans.
Here, we present a navigation system for autonomous robotics that ensures
safety and social acceptability of robotic trajectories. We overcome the
typical reactive nature of state-of-the-art trajectory planners by leveraging
non-cooperative game theory to design a planner that encapsulates human-like
features of preservation of a vital space, recognition of groups, sequential
and strategized decision making, and smooth obstacle avoidance. Social
acceptability is measured through a variation of the Turing test administered
in the form of a survey questionnaire to a pool of 691 participants. Comparison
terms for our tests are a state-of-the-art navigation algorithm (Enhanced
Vector Field Histogram, VFH) and purely human trajectories. While all
participants easily recognized the non-human nature of VFH-generated
trajectories, the distinction between game-theoretical trajectories and human
ones were hardly revealed. These results mark a strong milestone toward the
full integration of robots in social environments.


title:
BARC: Learning to Regress 3D Dog Shape from Images by Exploiting Breed
  Information

abstract:
  Our goal is to recover the 3D shape and pose of dogs from a single image.
This is a challenging task because dogs exhibit a wide range of shapes and
appearances, and are highly articulated. Recent work has proposed to directly
regress the SMAL animal model, with additional limb scale parameters, from
images. Our method, called BARC (Breed-Augmented Regression using
Classification), goes beyond prior work in several important ways. First, we
modify the SMAL shape space to be more appropriate for representing dog shape.
But, even with a better shape model, the problem of regressing dog shape from
an image is still challenging because we lack paired images with 3D ground
truth. To compensate for the lack of paired data, we formulate novel losses
that exploit information about dog breeds. In particular, we exploit the fact
that dogs of the same breed have similar body shapes. We formulate a novel
breed similarity loss consisting of two parts: One term encourages the shape of
dogs from the same breed to be more similar than dogs of different breeds. The
second one, a breed classification loss, helps to produce recognizable
breed-specific shapes. Through ablation studies, we find that our breed losses
significantly improve shape accuracy over a baseline without them. We also
compare BARC qualitatively to WLDO with a perceptual study and find that our
approach produces dogs that are significantly more realistic. This work shows
that a-priori information about genetic similarity can help to compensate for
the lack of 3D training data. This concept may be applicable to other animal
species or groups of species. Our code is publicly available for research
purposes at https://barc.is.tue.mpg.de/.


title: On Metric Learning for Audio-Text Cross-Modal Retrieval

abstract:
  Audio-text retrieval aims at retrieving a target audio clip or caption from a
pool of candidates given a query in another modality. Solving such cross-modal
retrieval task is challenging because it not only requires learning robust
feature representations for both modalities, but also requires capturing the
fine-grained alignment between these two modalities. Existing cross-modal
retrieval models are mostly optimized by metric learning objectives as both of
them attempt to map data to an embedding space, where similar data are close
together and dissimilar data are far apart. Unlike other cross-modal retrieval
tasks such as image-text and video-text retrievals, audio-text retrieval is
still an unexplored task. In this work, we aim to study the impact of different
metric learning objectives on the audio-text retrieval task. We present an
extensive evaluation of popular metric learning objectives on the AudioCaps and
Clotho datasets. We demonstrate that NT-Xent loss adapted from self-supervised
learning shows stable performance across different datasets and training
settings, and outperforms the popular triplet-based losses.


title:
Optical and X-ray GRB Fundamental Planes as Cosmological Distance
  Indicators

abstract:
  Gamma-Ray Bursts (GRBs), observed up to $z=9.4$, can be employed as
standardized candles, extending the distance ladder beyond Supernovae Type Ia
(SNe Ia, $z=2.26$). We standardize GRBs using the 3D fundamental plane relation
among the rest-frame end time of the X-ray plateau emission, its corresponding
luminosity, and the peak prompt luminosity. Combining SNe Ia and GRBs, we
constrain the matter content $\Omega_{\text{M}}= 0.299 \pm 0.009$ assuming a
flat $\Lambda$CDM cosmology with and without correcting for selection biases
and redshift evolution. Using a similar 3D correlation in optical, we find that
our optical GRB sample is as efficacious in the determination of
$\Omega_{\text{M}}$ as the X-ray sample. We trimmed our GRB samples to achieve
tighter planes to be used to simulate additional GRBs. We determined how many
GRBs are needed to be used as standalone probes to achieve a comparable
precision on $\Omega_{\text{M}}$ to the one obtained by SNe Ia only. {\bf We
achieve the error measurements derived using SNe Ia in (Conley et al. 2011)
with 142 GRBs in optical considering the errorbars on the variables halved and
in Betoule et al. (2014) with 284 optical GRBs, by considering that the error
bars on the variables halved. Using a doubled sample (obtained by future
machine learning approaches that allow both a lightcurve reconstruction and the
estimates of GRBs for which the GRB is unknown) compared to our current one,
with errorbars halved we will reach the same precision of (Scolnic et al. 2018)
with 390 GRBs which will be uses as efficient standalone probes as SNe Ia by
2054.


title:
A second-order low-regularity correction of Lie splitting for the
  semilinear Klein--Gordon equation

abstract:
  The numerical approximation of the semilinear Klein--Gordon equation in the
$d$-dimensional space, with $d=1,2,3$, is studied by analyzing the consistency
errors in approximating the solution. By discovering and utilizing a new
cancellation structure in the semilinear Klein--Gordon equation, a
low-regularity correction of the Lie splitting method is constructed, which can
have second-order convergence in the energy space under the regularity
condition $(u,\partial_tu)\in L^\infty(0,T;H^{1+\frac{d}{4}}\times
H^{\frac{d}{4}})$, where $d=1,2,3$ denotes the dimension of space. In one
dimension, the proposed method is shown to have a convergence order arbitrarily
close to $\frac53$ in the energy space for solutions in the same space, i.e. no
additional regularity in the solution is required. Rigorous error estimates are
presented for a fully discrete spectral method with the proposed low-regularity
time-stepping scheme. Numerical examples are provided to support the
theoretical analysis and to illustrate the performance of the proposed method
in approximating both nonsmooth and smooth solutions of the semilinear
Klein--Gordon equation.


title: Pushchino multibeam pulsar search: I. Targeted search of weak pulsars

abstract:
  The search for pulsars in a sample of pulsar candidates found based on a
multi-year survey conducted with low (6 channels; sampling 0.1s) time-frequency
resolution on declinations -9^o < \delta < +42^o was carried out. A Large
Phased Array (LPA) transit telescope operating at 111 MHz in the 2.5 MHz band
was used. Search, analysis and evidence of pulsar detection were carried out
using a visualization program of summed up power spectra obtained from the
survey data with high 32 channels; sampling 12.5ms) time-frequency resolution.
11 new pulsars with periods P0 = 0.41-3.75 s and dispersion measure DM=15-154
pc/cm^3 have been discovered. In total, in the survey with a low time-frequency
resolution for the period 2016-2021 in a blind search, 208 pulsars were found,
of them 42 new and 166 known pulsars. It is shown that in the search on the
data with high time-frequency resolution accumulated over a time interval of 7
years, pulsars with a flux density of 0.1 - 0.2 mJy at the frequency of 111 MHz
can be detected. When searching for pulsars with regular (periodic) emission at
declinations +21^o < \delta < +42^o, all pulsars located outside the galactic
plane, having P0 \ge 0.5 s, DM \le 100 pc/cm^3 and the flux density S \ge 0.5
mJy, can be detected


title:
Hardware efficient quantum simulation of non-abelian gauge theories with
  qudits on Rydberg platforms

abstract:
  Non-abelian gauge theories underlie our understanding of fundamental forces
in nature, and developing tailored quantum hardware and algorithms to simulate
them is an outstanding challenge in the rapidly evolving field of quantum
simulation. Here we take an approach where gauge fields, discretized in
spacetime, are represented by qudits and are time-evolved in Trotter steps with
multi-qudit quantum gates. This maps naturally and hardware-efficiently to an
architecture based on Rydberg tweezer arrays, where long-lived internal atomic
states represent qudits, and the required quantum gates are performed as
error-tolerant holonomic operations supported by a Rydberg blockade mechanism.
We illustrate our proposal for a minimal digitization of SU(2) gauge fields.


title:
Modeling Users' Contextualized Page-wise Feedback for Click-Through Rate
  Prediction in E-commerce Search

abstract:
  Modeling user's historical feedback is essential for Click-Through Rate
Prediction in personalized search and recommendation. Existing methods usually
only model users' positive feedback information such as click sequences which
neglects the context information of the feedback. In this paper, we propose a
new perspective for context-aware users' behavior modeling by including the
whole page-wisely exposed products and the corresponding feedback as
contextualized page-wise feedback sequence. The intra-page context information
and inter-page interest evolution can be captured to learn more specific user
preference. We design a novel neural ranking model RACP(i.e., Recurrent
Attention over Contextualized Page sequence), which utilizes page-context aware
attention to model the intra-page context. A recurrent attention process is
used to model the cross-page interest convergence evolution as denoising the
interest in the previous pages. Experiments on public and real-world industrial
datasets verify our model's effectiveness.


title: Expansive Multisets: Asymptotic Enumeration

abstract:
  Consider a non-negative sequence $c_n = h(n) \cdot n^{\alpha-1} \cdot
\rho^{-n}$, where $h$ is slowly varying, $\alpha>0$, $0<\rho<1$ and
$n\in\mathbb{N}$. We investigate the coefficients of $G(x,y) =
\prod_{k\ge1}(1-x^ky)^{-c_k}$, which is the bivariate generating series of the
multiset construction of combinatorial objects. By a powerful blend of
probabilistic methods based on the Boltzmann model and analytic techniques
exploiting the well-known saddle-point method we determine the number of
multisets of total size $n$ with $N$ components, that is, the coefficient of
$x^ny^N$ in $G(x,y)$, asymptotically as $n\to\infty$ and for all ranges of $N$.
Our results reveal a phase transition in the structure of the counting formula
that depends on the ratio $n/N$ and that demonstrates a prototypical passage
from a bivariate local limit to an univariate one.


title: Graph Neural Networks are Dynamic Programmers

abstract:
  Recent advances in neural algorithmic reasoning with graph neural networks
(GNNs) are propped up by the notion of algorithmic alignment. Broadly, a neural
network will be better at learning to execute a reasoning task (in terms of
sample complexity) if its individual components align well with the target
algorithm. Specifically, GNNs are claimed to align with dynamic programming
(DP), a general problem-solving strategy which expresses many polynomial-time
algorithms. However, has this alignment truly been demonstrated and
theoretically quantified? Here we show, using methods from category theory and
abstract algebra, that there exists an intricate connection between GNNs and
DP, going well beyond the initial observations over individual algorithms such
as Bellman-Ford. Exposing this connection, we easily verify several prior
findings in the literature, and hope it will serve as a foundation for building
stronger algorithmically aligned GNNs.


title:
A simple criterion and experiments for onset of flocculation in kaolin
  clay suspensions

abstract:
  Cohesive effects between fine-grained sediment particles greatly influence
their effective settling rate and erodibility. Many studies have observed a
qualitative difference in settling dynamics between clays in freshwater, where
particles remain dispersed, and in saltwater, where aggregates form and settle
rapidly. The critical coagulation concentration (CCC) of salt that separates
the two regimes however remains under-investigated, even though knowledge of
the CCC is crucial to understanding aggregation in settings such as estuaries,
where large salt concentration gradients occur. Furthermore, no simple
criterion exists to predict the CCC for clay suspensions. In this study,
systematic experiments are performed to determine the CCC, by measuring
transmitted light intensity through clay suspensions. To investigate the effect
of ion valence, sodium chloride (NaCl) and calcium chloride (CaCl$_2$) are
used. For kaolin clay, the results show a CCC of 0.6mM NaCl ($\approx 0.04$ppt
NaCl $=$ 0.04 PSU), and of 0.04mM CaCl$_2$ ($\approx 0.004$ppt CaCl$_2$).
Because these salinities are lower than those commonly observed in nature,
these findings indicate that kaolin clay should flocculate in nearly all
natural aquatic environments. Furthermore, due to the fact that tap water often
has salinities higher than this threshold, these results imply that great care
is needed in experiments, especially in large facilities where using distilled
water is not feasible. In addition, a simple criterion to estimate the CCC for
a kaolin clay suspension is derived. This criterion predicts that flocculation
occurs at extremely low salt concentrations and is approximately independent of
clay concentration, in agreement with the experimental observations and
consistent with experimental evidence from the literature.


title:
Modelling Carbon Capture on Metal-Organic Frameworks with Quantum
  Computing

abstract:
  Despite the recent progress in quantum computational algorithms for
chemistry, there is a dearth of quantum computational simulations focused on
material science applications, especially for the energy sector, where next
generation sorbing materials are urgently needed to battle climate change. To
drive their development, quantum computing is applied to the problem of CO$_2$
adsorption in Al-fumarate Metal-Organic Frameworks. Fragmentation strategies
based on Density Matrix Embedding Theory are applied, using a variational
quantum algorithm as a fragment solver, along with active space selection to
minimise qubit number. By investigating different fragmentation strategies and
solvers, we propose a methodology to apply quantum computing to Al-fumarate
interacting with a CO$_2$ molecule, demonstrating the feasibility of treating a
complex porous system as a concrete application of quantum computing. Our work
paves the way for the use of quantum computing techniques in the quest of
sorbents optimisation for more efficient carbon capture and conversion
applications.

categories: quant-ph cond-mat.mtrl-sci physics.atom-ph physics.chem-ph physics.comp-ph


title: ME-CapsNet: A Multi-Enhanced Capsule Networks with Routing Mechanism

abstract:
  Convolutional Neural Networks need the construction of informative features,
which are determined by channel-wise and spatial-wise information at the
network's layers. In this research, we focus on bringing in a novel solution
that uses sophisticated optimization for enhancing both the spatial and channel
components inside each layer's receptive field. Capsule Networks were used to
understand the spatial association between features in the feature map.
Standalone capsule networks have shown good results on comparatively simple
datasets than on complex datasets as a result of the inordinate amount of
feature information. Thus, to tackle this issue, we have proposed ME-CapsNet by
introducing deeper convolutional layers to extract important features before
passing through modules of capsule layers strategically to improve the
performance of the network significantly. The deeper convolutional layer
includes blocks of Squeeze-Excitation networks which use a stochastic sampling
approach for progressively reducing the spatial size thereby dynamically
recalibrating the channels by reconstructing their interdependencies without
much loss of important feature information. Extensive experimentation was done
using commonly used datasets demonstrating the efficiency of the proposed
ME-CapsNet, which clearly outperforms various research works by achieving
higher accuracy with minimal model complexity in complex datasets.


title: Image Segmentation with Adaptive Spatial Priors from Joint Registration

abstract:
  Image segmentation is a crucial but challenging task that has many
applications. In medical imaging for instance, intensity inhomogeneity and
noise are common. In thigh muscle images, different muscles are closed packed
together and there are often no clear boundaries between them. Intensity based
segmentation models cannot separate one muscle from another. To solve such
problems, in this work we present a segmentation model with adaptive spatial
priors from joint registration. This model combines segmentation and
registration in a unified framework to leverage their positive mutual
influence. The segmentation is based on a modified Gaussian mixture model
(GMM), which integrates intensity inhomogeneity and spacial smoothness. The
registration plays the role of providing a shape prior. We adopt a modified sum
of squared difference (SSD) fidelity term and Tikhonov regularity term for
registration, and also utilize Gaussian pyramid and parametric method for
robustness. The connection between segmentation and registration is guaranteed
by the cross entropy metric that aims to make the segmentation map (from
segmentation) and deformed atlas (from registration) as similar as possible.
This joint framework is implemented within a constraint optimization framework,
which leads to an efficient algorithm. We evaluate our proposed model on
synthetic and thigh muscle MR images. Numerical results show the improvement as
compared to segmentation and registration performed separately and other joint
models.


title: Invariance Learning based on Label Hierarchy

abstract:
  Deep Neural Networks inherit spurious correlations embedded in training data
and hence may fail to predict desired labels on unseen domains (or
environments), which have different distributions from the domain used in
training. Invariance Learning (IL) has been developed recently to overcome this
shortcoming; using training data in many domains, IL estimates such a predictor
that is invariant to a change of domain. However, the requirement of training
data in multiple domains is a strong restriction of IL, since it often needs
high annotation cost. We propose a novel IL framework to overcome this problem.
Assuming the availability of data from multiple domains for a higher level of
classification task, for which the labeling cost is low, we estimate an
invariant predictor for the target classification task with training data in a
single domain. Additionally, we propose two cross-validation methods for
selecting hyperparameters of invariance regularization to solve the issue of
hyperparameter selection, which has not been handled properly in existing IL
methods. The effectiveness of the proposed framework, including the
cross-validation, is demonstrated empirically, and the correctness of the
hyperparameter selection is proved under some conditions.


title:
Harmonic flow correlations in Au+Au reactions at 1.23 AGeV: A new
  testing ground for the Equation-of-State and expansion geometry

abstract:
  Correlations between the harmonic flow coefficients $v_1$, $v_2$, $v_3$ and
$v_4$ of nucleons in semi-peripheral Au+Au collisions at a beam energy of
1.23~AGeV are investigated within the hadronic transport approach
Ultra-relativistic Quantum Molecular Dynamics (UrQMD). In contrast to
ultra-relativistic collision energies (where the flow coefficients are
evaluated with respect to the respective event plane), we predict strong
correlations between the flow harmonics with respect to the reaction plane.
Based on an event-by-event selection of the midrapidity final state elliptic
flow of protons we show that as a function of rapidity, I) the sign of the
triangular flow changes, II) that the shape of $v_4$ changes from convex to
concave, and III) that $v_3\propto v_1v_2$ and $v_4\propto v_2^2$ for all
different event classes, indicating strong correlations between all
investigated harmonic flow coefficients.


title: Bourgain's slicing problem and KLS isoperimetry up to polylog

abstract:
  We prove that Bourgain's hyperplane conjecture and the
Kannan-Lov\'asz-Simonovits (KLS) isoperimetric conjecture hold true up to a
factor that is polylogarithmic in the dimension.


title:
Locally maximizing orbits for the non-standard generating function of
  convex billiards and applications

abstract:
  Given an exact symplectic map $T$ of a cylinder with a generating function
$H$ satisfying the so-called negative twist condition, $H_{12}>0$, we study the
locally maximizing orbits of $T$, that is, configurations which are local
maxima of the action functional $\sum_n H(q_n,q_{n+1})$. We provide a necessary
and sufficient condition for a configuration to be locally maximizing. Using
it, we consider a situation where $T$ has two generating functions with respect
to two different sets of symplectic coordinates. We suggest a simple geometric
condition which guarantees that the set of locally maximizing orbits with
respect to both of these generating functions coincide. As the main application
we show that the two generating functions for planar Birkhoff billiards satisfy
this geometric condition. We apply it to get the following result: consider a
centrally symmetric curve $\gamma$, for which the Birkhoff billiard map has a
rotational invariant curve $\alpha$ of $4$-periodic orbits. We prove that a
certain $L^2$-distance between $\gamma$ and its "best approximating" ellipse
can be bounded from above in terms of the measure of the complement of the set
filled by locally maximizing orbits lying between $\alpha$ and the boundary of
the phase cylinder. Moreover, this estimate is sharp, giving an effective
version of a recent result on Birkhoff conjecture for centrally symmetric
curves. We also get a similar bound for arbitrary curves $\gamma$, that relates
the measure of the complement of the set of locally maximizing orbits with the
$L^2$-distance between $\gamma$ and its "best approximating" circle.


title:
Optimal control and selectivity of qubits in contact with a structured
  environment

abstract:
  We present a theoretical study of the optimal control of a qubit interacting
with a structured environment. We consider a model system in which the bath is
a bosonic reservoir at zero temperature and the qubit frequency is the only
control parameter. Using optimal control techniques, we show the extent to
which qubit population and relaxation effects can be manipulated. The reachable
qubit states by a shaped control with a fixed maximum intensity are found
numerically. We analyze the role of standard control mechanisms and the
structure of the reachable set with respect to the coupling strength between
the system and the environment. This investigation is used as a starting point
to explore the selectivity problem of two uncoupled qubits interacting with
their own baths and characterized by a specific coupling strength. We
numerically derive the optimal control solution for a wide range of parameters
and we show that the control law is close to a sinusoidal function with a
specific frequency in some peculiar cases.


title:
Propagation of singularities by Osgood vector fields and for 2D inviscid
  incompressible fluids

abstract:
  We show that certain singular structures (H\"{o}lderian cusps and mild
divergences) are transported by the flow of homeomorphisms generated by an
Osgood velocity field. The structure of these singularities is related to the
modulus of continuity of the velocity and the results are shown to be sharp in
the sense that slightly more singular structures cannot generally be
propagated. For the 2D Euler equation, we prove that certain singular
structures are preserved by the motion, e.g. a system of $\log\log_+(1/|x|)$
vortices (and those that are slightly less singular) travel with the fluid in a
nonlinear fashion, up to bounded perturbations. We also give stability results
for weak Euler solutions away from their singular set.


title:
Progression models for repeated measures: Estimating novel treatment
  effects in progressive diseases

abstract:
  Mixed Models for Repeated Measures (MMRMs) are ubiquitous when analyzing
outcomes of clinical trials. However, the linearity of the fixed-effect
structure in these models largely restrict their use to estimating treatment
effects that are defined as linear combinations of effects on the outcome
scale. In some situations, alternative quantifications of treatment effects may
be more appropriate, in which case the MMRM may be insufficient. In progressive
diseases, for example, one may want to estimate if a drug has cumulative
effects resulting in increasing efficacy over time or whether it slows the time
progression of disease. This paper introduces a class of nonlinear
mixed-effects models called Progression Models for Repeated Measures (PMRMs)
that, based on a continuous-time extension of the categorical-time
parametrization of MMRMs, enables estimation of novel types of treatment
effects, including measures of slowing or delay of the time progression of
disease. Compared to conventional estimates of treatment effects where the unit
matches that of the outcome scale (e.g. 2 points benefit on a cognitive scale),
the time-based treatment effects can offer better interpretability and clinical
meaningfulness (e.g. 6 months delay in progression of cognitive decline). The
PMRM class includes conventionally used MMRMs and variants of previously
proposed disease progression models as special cases. The potential of the PMRM
framework is illustrated using both simulated and historical data from clinical
trials in Alzheimer's disease with different types of artificially simulated
treatment effects. Compared to conventional MMRMs it is shown that PMRMs can
offer substantially increased power to detect disease-modifying treatment
effects where the benefit is increasing with treatment duration.


title: Training Compute-Optimal Large Language Models

abstract:
  We investigate the optimal model size and number of tokens for training a
transformer language model under a given compute budget. We find that current
large language models are significantly undertrained, a consequence of the
recent focus on scaling language models whilst keeping the amount of training
data constant. By training over \nummodels language models ranging from 70
million to over 16 billion parameters on 5 to 500 billion tokens, we find that
for compute-optimal training, the model size and the number of training tokens
should be scaled equally: for every doubling of model size the number of
training tokens should also be doubled. We test this hypothesis by training a
predicted compute-optimal model, \chinchilla, that uses the same compute budget
as \gopher but with 70B parameters and 4$\times$ more more data. \chinchilla
uniformly and significantly outperforms \Gopher (280B), GPT-3 (175B),
Jurassic-1 (178B), and Megatron-Turing NLG (530B) on a large range of
downstream evaluation tasks. This also means that \chinchilla uses
substantially less compute for fine-tuning and inference, greatly facilitating
downstream usage. As a highlight, \chinchilla reaches a state-of-the-art
average accuracy of 67.5\% on the MMLU benchmark, greater than a 7\%
improvement over \gopher.


title:
Near-Field Hierarchical Beam Management for RIS-Enabled Millimeter Wave
  Multi-Antenna Systems

abstract:
  In this paper, we present a low overhead beam management approach for
near-field millimeter-wave multi-antenna communication systems enabled by
Reconfigurable Intelligent Surfaces (RISs). We devise a novel variable-width
hierarchical phaseshift codebook suitable for both the near- and far-field of
the RIS, and present a fast alignment algorithm for the RIS phase shifts and
the transceiver beamformers. Indicative performance evaluation results are
shown, verifying the effectiveness of the proposed approach in comparison with
various benchmark schemes.


title: Wildfire risk forecast: An optimizable fire danger index

abstract:
  Wildfire events have caused severe losses in many places around the world and
are expected to increase with climate change. Throughout the years many
technologies have been developed to identify fire events early on and to
simulate fire behavior once they have started. Another particularly helpful
technology is fire risk indices, which use weather forcing to make advanced
predictions of the risk of fire. Predictions of fire risk indices can be used,
for instance, to allocate resources in places with high risk. These indices
have been developed over the years as empirical models with parameters that
were estimated in lab experiments and field tests. These parameters, however,
may not fit well all places where these models are used. In this paper we
propose a novel implementation of one index (NFDRS IC) as a differentiable
function in which one can optimize its internal parameters via gradient
descent. We leverage existing machine learning frameworks (PyTorch) to
construct our model. This approach has two benefits: (1) the NFDRS IC
parameters can be improved for each region using actual observed fire events,
and (2) the internal variables remain intact for interpretations by specialists
instead of meaningless hidden layers as in traditional neural networks. In this
paper we evaluate our strategy with actual fire events for locations in the USA
and Europe.


title: Multifidelity Orbit Uncertainty Propagation using Taylor Polynomials

abstract:
  A new multifidelity method is developed for nonlinear orbit uncertainty
propagation. This approach guarantees improved computational efficiency and
limited accuracy losses compared to fully high-fidelity counterparts. The
initial uncertainty is modeled as a weighted sum of Gaussian distributions
whose number is adapted online to satisfy the required accuracy. As needed,
univariate splitting libraries are used to split the mixture components along
the direction of maximum nonlinearity. Differential Algebraic techniques are
used to propagate these Gaussian kernels and compute a measure of nonlinearity
required for the split decision and direction identification. Taylor expansions
of the flow of the dynamics are computed using a low-fidelity dynamical model
to maximize computational efficiency and corrected with selected high-fidelity
samples to minimize accuracy losses. The effectiveness of the proposed method
is demonstrated for different dynamical regimes combining SGP4 theory and
numerical propagation as low- and high-fidelity models respectively.


title:
Orbital-selective Band Hybridisation at the Charge Density Wave
  Transition in Monolayer TiTe$_2$

abstract:
  An anomalous $(2\times2)$ charge density wave (CDW) phase emerges in
monolayer 1T-TiTe$_2$ which is absent for the bulk compound, and whose origin
is still poorly understood. Here, we investigate the electronic band structure
evolution across the CDW transition using temperature-dependent angle-resolved
photoemission spectroscopy. Our study reveals an orbital-selective band
hybridisation between the backfolded conduction and valence bands occurring at
the CDW phase transition, which in turn leads to a significant electronic
energy gain, underpinning the CDW transition. For the bulk compound, we show
how this energy gain is almost completely suppressed due to the
three-dimensionality of the electronic band structure, including via a
$k_z$-dependent band inversion which switches the orbital character of the
valence states. Our study thus sheds new light on how control of the electronic
dimensionalilty can be used to trigger the emergence of new collective states
in 2D materials.


title: Algorithmic Improvement and GPU Acceleration of the GenASM Algorithm

abstract:
  We improve on GenASM, a recent algorithm for genomic sequence alignment, by
significantly reducing its memory footprint and bandwidth requirement. Our
algorithmic improvements reduce the memory footprint by 24$\times$ and the
number of memory accesses by 12$\times$. We efficiently parallelize the
algorithm for GPUs, achieving a 4.1$\times$ speedup over a CPU implementation
of the same algorithm, a 62$\times$ speedup over minimap2's CPU-based KSW2 and
a 7.2$\times$ speedup over the CPU-based Edlib for long reads.