title: A two term Kuznecov sum formula

abstract:
  The Kuznecov sum formula, proved by Zelditch in the Riemannian setting, is an
asymptotic sum formula $$N(\lambda) := \sum_{\lambda_j \leq \lambda} \left|
\int_H e_j \, dV_H \right|^2 = C_{H,M} \lambda^{\operatorname{codim} H} +
O(\lambda^{\operatorname{codim} H - 1})$$ where $e_j$ constitute a Hilbert
basis of Laplace-Beltrami eigenfunctions on a Riemannian manifold $M$ with
$\Delta_g e_j = -\lambda_j^2 e_j$, and $H$ is an embedded submanifold. We show
for some suitable definition of `$\sim$', $$ N(\lambda) \sim C_{H,M}
\lambda^{\operatorname{codim} H} + Q(\lambda) \lambda^{\operatorname{codim} H -
1} + o(\lambda^{\operatorname{codim} H - 1}) $$ where $Q$ is a bounded
oscillating term and is expressed in terms of the geodesics which depart and
arrive $H$ in the normal directions.
  In work by Canzani, Galkowski, and Toth, they establish (as a corollary to a
stronger result involving defect measures) that if the set of recurrent
directions of geodesics normal to $H$ has measure zero, then we obtain improved
bounds on the individual terms in the sum -- the period integrals. We are able
to give a dynamical condition such that $Q$ tends to a uniformly continuous
function in the limit $\lambda \to \infty$ and `$\sim$' can be replaced with
`$=$'. This implies improved bounds on period integrals, and this condition is
weaker than the recurrent directions having measure zero. Moreover, our result
implies improved bounds for period integrals if there is no $L^1$ measure on
$SN^*H$ that is invariant under the first return map. This generalizes a
theorem of Sogge and Zelditch.


title:
Cahn-Hilliard-Brinkman model for tumor growth with possibly singular
  potentials

abstract:
  We analyze a phase field model for tumor growth consisting of a
Cahn-Hilliard-Brinkman system, ruling the evolution of the tumor mass, coupled
with an advection-reaction-diffusion equation for a chemical species acting as
a nutrient. The main novelty of the paper concerns the discussion of the
existence of weak solutions to the system covering all the meaningful cases for
the nonlinear potentials; in particular,the typical choices given by the
regular, the logarithmic, and the double obstacle potentials are admitted in
our treatise. Compared to previous results related to similar models, we
suggest, instead of the classical no-flux condition, a Dirichlet boundary
condition for the chemical potential appearing in the Cahn-Hilliard-type
equation. Besides, abstract growth conditions for the source terms that may
depend on the solution variables are postulated.


title:
Low-frequency resolvent analysis of the laminar oblique shock wave /
  boundary layer interaction

abstract:
  Resolvent analysis is used to study the low-frequency behaviour of the
laminar oblique shock wave / boundary layer interaction (SWBLI). It is shown
that the computed optimal gain, which can be seen as a transfer function of the
system, follows a first-order low-pass filter equation, recovering the results
of Touber and Sandham (JFM, 2011). This behaviour is understood as proceeding
from the excitation of a single stable, steady global mode whose damping rate
sets the time scale of the filter. Different Mach and Reynolds numbers are
studied, covering different recirculation lengths $L$. This damping rate is
found to scale as $1/L$, leading to a constant Strouhal number $St_L$ as
observed in the literature. It is associated with a breathing motion of the
recirculation bubble. This analysis furthermore supports the idea that the
low-frequency dynamics of the SWBLI is a forced dynamics, in which background
perturbations continuously excite the flow. The investigation is then carried
out for 3D perturbations for which two regimes are identified. At low wave
numbers of the order of $L$, a modal mechanism similar to that of 2D
perturbations is found and exhibits larger values of the optimal gain. At
larger wave numbers of the order of the boundary layer thickness, the growth of
streaks, which results from a non-modal mechanism, is detected. No interaction
with the recirculation region is observed. Based on these results, the
potential prevalence of 3D effects in the low-frequency dynamics of the SWBLI
is discussed.


title: Quantum Fluctuations and Coherence of a Molecular Polariton Condensate

abstract:
  A full quantum theory beyond the mean-field regime is developed for an
exciton polariton condensate, to gain a complete understanding of quantum
fluctuations. We find analytical solution for the polariton density matrix,
showing the polariton nonlinearity causing fast relaxation correlated with the
pump so as to yield the condensation at threshold. Increasing the pump
intensity, a nonequilibrium phase transition towards the condensation of lower
polaritons emerges, with a statistics transiting from a thermal, through a
super-Poissonian and to a nonclassical distribution beyond the understanding at
the level of off-diagonal long-range order. The results signify the role of
dark states for polariton fluctuations, and lead to a nonclassical counting
statistics of emitted photons, which elaborates the role of the key parameters,
e.g., pump, detuning and temperature.


title:
Universal breakdown of Kibble-Zurek scaling in fast quenches across a
  phase transition

abstract:
  The crossing of a continuous phase transition in finite time gives rise to
the formation of topological defects described by the Kibble-Zurek mechanism
(KZM) in the limit of slow quenches. The KZM predicts a universal power-law
scaling of their density with the quench time in which the transition is
crossed. We focus on the deviations from KZM experimentally observed in rapid
quenches and establish their universality. While KZM scaling holds below a
critical quench rate, for faster quenches the defect density and the freeze-out
time become independent of the quench rate and exhibit a universal power-law
scaling with the final value of the control parameter. These predictions are
verified in paradigmatic scenarios using a $\phi^4$-theory and a model of a
strongly-coupled superconducting ring.

categories: cond-mat.stat-mech cond-mat.supr-con hep-th quant-ph


title:
Measurement of the nuclear modification factor of $b$-jets in 5.02 TeV
  Pb+Pb collisions with the ATLAS detector

abstract:
  This paper presents a measurement of $b$-jet production in Pb+Pb and $pp$
collisions at $\sqrt{s_{_\textrm{NN}}}=5.02$ TeV with the ATLAS detector at the
LHC. The measurement uses 260 $\textrm{pb}^{-1}$ of $pp$ collisions collected
in 2017 and 1.4 $\textrm{nb}^{-1}$ of Pb+Pb collisions collected in 2018. In
both collision systems, jets are reconstructed via the anti-$k_{t}$ algorithm.
The $b$-jets are identified from a sample of jets containing muons from the
semileptonic decay of $b$-quarks using template fits of the muon momentum
relative to the jet axis. In $pp$ collisions, $b$-jets are reconstructed for
radius parameters $R= 0.2$ and $R= 0.4$, and only $R= 0.2$ jets are used in
Pb+Pb collisions. For comparison, inclusive $R= 0.2$ jets are also measured
using 1.7 $\textrm{nb}^{-1}$ of Pb+Pb collisions collected in 2018 and the same
$pp$ collision data as the $b$-jet measurement. The nuclear modification
factor, $R_\textrm{AA}$, is calculated for both $b$-jets and inclusive jets
with $R= 0.2$ over the transverse momentum range of 80--290 GeV. The nuclear
modification factor for $b$-jets decreases from peripheral to central
collisions. The ratio of the $b$-jet $R_\textrm{AA}$ to inclusive jet
$R_\textrm{AA}$ is also presented and suggests that the $R_\textrm{AA}$ for
$b$-jets is larger than that for inclusive jets in central Pb+Pb collisions.
The measurements are compared with theoretical calculations and suggest a role
for mass and colour-charge effects in partonic energy loss in heavy-ion
collisions.


title:
Compressing multireference character of wave functions via fermionic
  mode optimization

abstract:
  In this work, we present a brief overview of the fermionic mode optimization
within the framework of tensor network state methods [PRL 117,210402(2016)],
and demonstrate that it has the potential to compress the multireference
character of the wave functions after finding optimal molecular orbitals
(modes), based on entanglement minimization. Numerical simulations have been
performed for the nitrogen dimer in the cc-pVDZ basis for the equilibrium and
for stretched geometries.


title:
Search for light Higgs bosons from supersymmetric cascade decays in pp
  collisions at $\sqrt{s}$ =13 TeV

abstract:
  A search is reported for pairs of light Higgs bosons (H$_1$) produced in
supersymmetric cascade decays in final states with small missing transverse
momentum. A data set of LHC pp collisions collected with the CMS detector at
$\sqrt{s}$ = 13 TeV and corresponding to an integrated luminosity of 138
fb$^{-1}$ is used. The search targets events where both H$_1$ bosons decay into
$\mathrm{b\bar{b}}$ pairs that are reconstructed as large-radius jets using
substructure techniques. No evidence is found for an excess of events beyond
the background expectations of the standard model (SM). Results from the search
are interpreted in the next-to-minimal supersymmetric extension of the SM,
where a "singlino" of small mass leads to squark and gluino cascade decays that
can predominantly end in a highly Lorentz-boosted singlet-like H$_1$ and a
singlino-like neutralino of small transverse momentum. Upper limits are set on
the product of the squark or gluino pair production cross section and the
square of the $\mathrm{b\bar{b}}$ branching fraction of the H$_1$ in a
benchmark model containing almost mass-degenerate gluinos and light-flavour
squarks. Under the assumption of an SM-like H$_1\to\mathrm{b\bar{b}}$ branching
fraction, H$_1$ bosons with masses in the range 40-120 GeV arising from the
decays of squarks or gluinos with a mass of 1200 to 2500 GeV are excluded at
95% confidence level.


title:
Comment on "On the relations between large-scale models of superfluid
  helium-4" [Phys. Fluids 33, 127124 (2021]"

abstract:
  We comment on the paper by M. S\'ykora, M. Pavelka, M. La Mantia, D. Jou, and
M. Grmela "On the relations between large-scale models of superfluid helium-4,"
Physics of Fluids, 33(12):127124(2021), where the authors have developed a
formalism for describing a coarse-grained flow of superfluid helium. This
formalism is greatly based on the Hall-Vinen-Bekarevich-Khalatnikov (HVBK)
model. We strongly disagree with the use of the HVBK equation approach for the
case of the three-dimensional quantum turbulence and expose our objections in
this comment. We discuss the HVBK method and also criticize the so-called
vortex bundles model, which serves as a basis for using the HVBK method in a
three-dimensional quantum turbulence.


title:
Singular value distribution of dense random matrices with block
  Markovian dependence

abstract:
  A block Markov chain is a Markov chain whose state space can be partitioned
into a finite number of clusters such that the transition probabilities only
depend on the clusters. Block Markov chains thus serve as a model for Markov
chains with communities. This paper establishes limiting laws for the singular
value distributions of the empirical transition matrix and empirical frequency
matrix associated to a sample path of the block Markov chain whenever the
length of the sample path is $\Theta(n^2)$ with $n$ the size of the state
space.
  The proof approach is split into two parts. First, we introduce a class of
symmetric random matrices with dependence called approximately uncorrelated
random matrices with variance profile. We establish their limiting eigenvalue
distributions by means of the moment method. Second, we develop a coupling
argument to show that this general-purpose result applies to block Markov
chains.


title: Machine Learning for Violence Risk Assessment Using Dutch Clinical Notes

abstract:
  Violence risk assessment in psychiatric institutions enables interventions to
avoid violence incidents. Clinical notes written by practitioners and available
in electronic health records are valuable resources capturing unique
information, but are seldom used to their full potential. We explore
conventional and deep machine learning methods to assess violence risk in
psychiatric patients using practitioner notes. The performance of our best
models is comparable to the currently used questionnaire-based method, with an
area under the Receiver Operating Characteristic curve of approximately 0.8. We
find that the deep-learning model BERTje performs worse than conventional
machine learning methods. We also evaluate our data and our classifiers to
understand the performance of our models better. This is particularly important
for the applicability of evaluated classifiers to new data, and is also of
great interest to practitioners, due to the increased availability of new data
in electronic format.


title:
Reconciling the Quality vs Popularity Dichotomy in Online Cultural
  Markets

abstract:
  We propose a simple model of an idealized online cultural market in which $N$
items, endowed with a hidden quality metric, are recommended to users by a
ranking algorithm possibly biased by the current items' popularity. Our goal is
to better understand the underlying mechanisms of the well-known fact that
popularity bias can prevent higher-quality items from becoming more popular
than lower-quality items, producing an undesirable misalignment between quality
and popularity rankings. We do so under the assumption that users, having
limited time/attention, are able to discriminate the best-quality only within a
random subset of the items. We discover the existence of a harmful regime in
which improper use of popularity can seriously compromise the emergence of
quality, and a benign regime in which wise use of popularity, coupled with a
small discrimination effort on behalf of users, guarantees the perfect
alignment of quality and popularity ranking. Our findings clarify the effects
of algorithmic popularity bias on quality outcomes, and may inform the design
of more principled mechanisms for techno-social cultural markets.


title: Holographic duals of M5-branes on an irregularly punctured sphere

abstract:
  We provide explicit holographic duals of M5-branes wrapped on a sphere with
one irregular puncture and one regular puncture of arbitrary type. The
solutions generalise the solutions corresponding to M5-branes wrapped on a disc
recently constructed by Bah-Bonetti-Minasian-Nardoni by allowing for a general
choice of regular puncture. We show that the central charges, flavour central
charges and conformal dimensions of BPS operators match with a class of
Argyres-Douglas theory.


title:
Asymptotically Optimal Quasi-Complementary Code Sets from Multivariate
  Functions

abstract:
  Owing to the more significant set size properties as compared to the set of
complete complementary codes (CCCs), quasi-complementary code sets (QCCSs) are
more convenient to support a large number of users in multicarrier
code-division multiple-access (MC-CDMA) system over CCCs. Besides set size, it
is also desirable to have a low maximum aperiodic correlation magnitude and
small alphabet size. This paper aims to construct asymptotically optimal and
near-optimal aperiodic QCCSs having a small alphabet size and low maximum
correlation magnitude. Using multivariate functions and its associated graph,
we propose a family of QCCSs consisting of multiple sets of CCCs and determine
the parameters of the proposed QCCSs. Unlike the existing constructions of
aperiodic QCCSs, the proposed construction can maintain a small alphabet size
irrespective of the increasing sequence length and large set size.


title: Algorithmic QUBO Formulations for k-SAT and Hamiltonian Cycles

abstract:
  Quadratic unconstrained binary optimization (QUBO) can be seen as a generic
language for optimization problems. QUBOs attract particular attention since
they can be solved with quantum hardware, like quantum annealers or quantum
gate computers running QAOA. In this paper, we present two novel QUBO
formulations for $k$-SAT and Hamiltonian Cycles that scale significantly better
than existing approaches. For $k$-SAT we reduce the growth of the QUBO matrix
from $O(k)$ to $O(log(k))$. For Hamiltonian Cycles the matrix no longer grows
quadratically in the number of nodes, as currently, but linearly in the number
of edges and logarithmically in the number of nodes.
  We present these two formulations not as mathematical expressions, as most
QUBO formulations are, but as meta-algorithms that facilitate the design of
more complex QUBO formulations and allow easy reuse in larger and more complex
QUBO formulations.


title:
Exploiting Null Space in Aerial Manipulation through Model-In-The-Loop
  Motion Planning

abstract:
  This paper presents a method for aerial manipulator end-effector trajectory
tracking by encompassing dynamics of the Unmanned Aerial Vehicle (UAV) and null
space of the manipulator attached to it in the motion planning procedure. The
proposed method runs in phases. Trajectory planning starts by not accounting
for roll and pitch angles of the underactuated UAV system. Next, we propose
simulating the dynamics on such a trajectory and obtaining UAV attitude through
the model. The full aerial manipulator state obtained in such a manner is
further utilized to account for discrepancies in planned and simulated
end-effector states. Finally, the end-effector pose is corrected through the
null space of the manipulator to match the desired end-effector pose obtained
in trajectory planning. Furthermore, we have applied the TOPP-RA approach on
the UAV by invoking the differential flatness principle. Finally, we conducted
experimental tests to verify effectiveness of the planning framework.


title: On the Balog-Ruzsa Theorem in short intervals

abstract:
  In this paper we give a short interval version of the Balog-Ruzsa theorem
concerning bounds for the $L_1$ norm of the exponential sum over $r$-free
numbers. As an application, we give a lower bound for the $L_1$ norm of the
exponential sum defined with the M\"obius function. Namely we show that
$$\int_{{\mathbb T}} \left|\sum_{|n-N|<H} \mu(n)e(n \alpha)\right| d \alpha \gg
H^{\frac{1}{6}}$$ when $H \gg N^{\frac{9}{17} + \varepsilon}$.


title: Frequently recurrence properties and block families

abstract:
  We study recurrence and the related (weaker form of recurrence) $\mathscr
P_\mathcal F$ property for linear operators on Banach spaces and for general
hereditarily upward families. We show that for special families, called block
families, hypercyclic operators with the $\mathscr P_\mathcal F$ are $\mathcal
F$-hypercyclic. Reciprocally, if $b\mathcal F$ is the block family of a given
family $\mathcal F$, adjoint operators that are $b\mathcal F$-hypercyclic have
the $\mathscr P_\mathcal F$ property. As a consequence, we are able to answer
an open question about the existence of reiteratively hypercyclic operators. We
also characterize backward shift operators with the $\mathscr P_\mathcal F$
property.


title:
Predicting batch queue job wait times for informed scheduling of urgent
  HPC workloads

abstract:
  There is increasing interest in the use of HPC machines for urgent workloads
to help tackle disasters as they unfold. Whilst batch queue systems are not
ideal in supporting such workloads, many disadvantages can be worked around by
accurately predicting when a waiting job will start to run. However there are
numerous challenges in achieving such a prediction with high accuracy, not
least because the queue's state can change rapidly and depend upon many
factors. In this work we explore a novel machine learning approach for
predicting queue wait times, hypothesising that such a model can capture the
complex behaviour resulting from the queue policy and other interactions to
generate accurate job start times.
  For ARCHER2 (HPE Cray EX), Cirrus (HPE 8600) and 4-cabinet (HPE Cray EX) we
explore how different machine learning approaches and techniques improve the
accuracy of our predictions, comparing against the estimation generated by
Slurm. We demonstrate that our techniques deliver the most accurate predictions
across our machines of interest, with the result of this work being the ability
to predict job start times within one minute of the actual start time for
around 65\% of jobs on ARCHER2 and 4-cabinet, and 76\% of jobs on Cirrus. When
compared against what Slurm can deliver, this represents around 3.8 times
better accuracy on ARCHER2 and 18 times better for Cirrus. Furthermore our
approach can accurately predicting the start time for three quarters of all job
within ten minutes of the actual start time on ARCHER2 and 4-cabinet, and for
90\% of jobs on Cirrus. Whilst the driver of this work has been to better
facilitate placement of urgent workloads across HPC machines, the insights
gained can be used to provide wider benefits to users and also enrich existing
batch queue systems and inform policy too.


title: Generalizing Hybrid Integrator-Gain Systems Using Fractional Calculus

abstract:
  The Hybrid Integral Gain Systems (HIGS) has recently gained a lot of
attention in the control of precision motion systems. HIGS is a nonlinear low
pass filter with a 52 degree phase advantage over its linear counterpart. This
property allows us to avoid the limitations typically associated with linear
controllers like the waterbed effect and bode's phase gain relation. In this
paper, we generalize HIGS via replacing the involved integer order integrator
with a fractional order one to adapt the phase lead from 0 degree (linear low
pass filter) to 52 degree (HIGS). To analyze this filter in the frequency
domain, the describing function of the proposed filter, i.e., the
fractional-order HIGS, is obtained using the Fourier expansion of the output
signal. In addition, this generalized HIGS is implemented in a PID structure
controlling a double integrator system to validate the performance of the
proposed filter in the time domain, in which changing the fractional variable
from zero to one, the output varies from the response of a nonlinear control
system to a linear one.


title: Predicting single-cell perturbation responses for unseen drugs

abstract:
  Single-cell transcriptomics enabled the study of cellular heterogeneity in
response to perturbations at the resolution of individual cells. However,
scaling high-throughput screens (HTSs) to measure cellular responses for many
drugs remains a challenge due to technical limitations and, more importantly,
the cost of such multiplexed experiments. Thus, transferring information from
routinely performed bulk RNA-seq HTS is required to enrich single-cell data
meaningfully. We introduce a new encoder-decoder architecture to study the
perturbational effects of unseen drugs. We combine the model with a transfer
learning scheme and demonstrate how training on existing bulk RNA-seq HTS
datasets can improve generalisation performance. Better generalisation reduces
the need for extensive and costly screens at single-cell resolution. We
envision that our proposed method will facilitate more efficient experiment
designs through its ability to generate in-silico hypotheses, ultimately
accelerating targeted drug discovery.


title:
DMINR: A Tool to Support Journalists Information Verification and
  Exploration

abstract:
  Journalists are key information workers who have specific requirements from
information systems to support the verification and exploration of information.
We overview the DMINR tool that has been designed and developed to meet the
needs of journalists through the examination of journalists information
behaviour in a newsroom. We outline our co-design process as well as the
design, implementation and deployment of the tool. We report a usability test
on the tool and conclude with details of how to develop the tool further


title: Generic Dijkstra: correctness and tractability

abstract:
  The recently-proposed generic Dijkstra algorithm finds shortest paths in
networks with continuous and contiguous resources. The algorithm was proposed
in the context of optical networks, but it is applicable to networks with
finite and discrete resources. The algorithm was published without a proof of
correctness, and with a minor shortcoming. We provide that missing proof, and
offer a correction to the shortcoming. To prove the algorithm correct, we
generalize the Bellman's principle of optimality. We also argue the stated
problem is tractable by analyzing the size of the search space in the
worst-case.


title:
Tragedy Plus Time: Capturing Unintended Human Activities from
  Weakly-labeled Videos

abstract:
  In videos that contain actions performed unintentionally, agents do not
achieve their desired goals. In such videos, it is challenging for computer
vision systems to understand high-level concepts such as goal-directed
behavior, an ability present in humans from a very early age. Inculcating this
ability in artificially intelligent agents would make them better social
learners by allowing them to evaluate human action under a teleological lens.
To validate the ability of deep learning models to perform this task, we curate
the W-Oops dataset, built upon the Oops dataset [15]. W-Oops consists of 2,100
unintentional human action videos, with 44 goal-directed and 30 unintentional
video-level activity labels collected through human annotations. Due to the
expensive segment annotation procedure, we propose a weakly supervised
algorithm for localizing the goal-directed as well as unintentional temporal
regions in the video leveraging solely video-level labels. In particular, we
employ an attention mechanism-based strategy that predicts the temporal regions
which contribute the most to a classification task. Meanwhile, our designed
overlap regularization allows the model to focus on distinct portions of the
video for inferring the goal-directed and unintentional activity while
guaranteeing their temporal ordering. Extensive quantitative experiments verify
the validity of our localization method. We further conduct a video captioning
experiment which demonstrates that the proposed localization module does indeed
assist teleological action understanding.


title: Spectral measures with arbitrary dimensions

abstract:
  It is known [Dai and Sun, J. Funct. Anal. 268 (2015), 2464--2477] that there
exist spectral measures with arbitrary Hausdorff dimensions, and it is natural
to pose the question of whether similar phenomena occur for other dimensions of
spectral measures. In this paper, we first obtain the formulae of Assouad
dimension and of lower dimension for a class of Moran measures in dimension one
that is introduced by An and He [J. Funct. Anal. 266 (2014), 343--354]. Based
on these results, we show the existence of spectral measures with arbitrary
Assound dimensions $\dim_A$ and lower dimensions $\dim_L$ ranging from $0$ to
$1$, including non-atomic zero-dimensional spectral measures and
one-dimensional singular spectral measures, and prove that the two values may
coincide. In fact, more is obtained that for any $0 \leq t \leq s \leq r \leq
u\leq 1$, there exists a spectral measure $\mu$ such that \[\dim_L \mu=t,
\dim_H \mu=s, \dim_P\mu=r~ \text{and} \dim_A\mu=u,\] where $\dim_H$ and
$\dim_P$ denote the Hausdorff dimension and packing dimension of the measure
$\mu$, respectively. This result improves and generalizes the result of Dai and
Sun more simply and flexibly.


title: Global second order Sobolev-regularity of $p$-harmonic functions

abstract:
  We prove a global version of the classical result that $p$-harmonic functions
belong to $W^{2,2}_{loc}$ for $1<p<3+\frac{2}{n-2}$. The proof relies on
Cordes' matrix inequalities [7] and techniques from the work of Cianchi and
Maz'ya [5,6].


title:
Binary transition metal nitride $M$N$_4$ ($M$ = V, Cr, Mn, Fe, Co) with
  a graphene-like structure and strong magnetic properties

abstract:
  Binary transition metal nitride with a graphene-like structure and strong
magnetic properties is rare. Based on the first-principles calculations, we
design two kinds of $M$N$_4$ ($M$ =transition metal) monolayers, which are
transition metal nitrides with a planar structure, made up of $M$N$_4$ units
aligned in the rhombic and squared patterns. The two structural lattices have
robust stability and good compatibility with different metal atoms, and the
underlying mechanism is the combination of $sp^2$ hybridization, coordinate
bond, and $\pi$ conjugation. With metal atom changing from V, Cr, Mn, Fe to Co,
the total charge of $M$N$_4$ system increases by one electron in turn, which
results in continuous adjustability of the electronic and magnetic properties.
The planar ligand field is another feature of the two $M$N$_4$ lattices, which
brings about the special splitting of five suborbitals of 3$d$ metal atom and
gives rise to large local moment and strong magnetism. Moreover, we uncover
$s$-CoN$_4$ monolayer to be high temperature ferromagnetic two-dimensional
material and the Curie temperatures of 613.9 K is determined by the tensor
renormalization group method.


title: On the Use of $L$-functionals in Regression Models

abstract:
  In this paper we survey and unify a large class or $L$-functionals of the
conditional distribution of the response variable in regression models. This
includes robust measures of location, scale, skewness, and heavytailedness of
the response, conditionally on covariates. We generalize the concepts of
$L$-moments (Sittinen, 1969), $L$-skewness, and $L$-kurtosis (Hosking, 1990)
and introduce order numbers for a large class of $L$-functionals through
orthogonal series expansions of quantile functions. In particular, we motivate
why location, scale, skewness, and heavytailedness have order numbers 1, 2,
(3,2), and (4,2) respectively and describe how a family of $L$-functionals,
with different order numbers, is constructed from Legendre, Hermite, Laguerre
or other types of polynomials. Our framework is applied to models where the
relationship between quantiles of the response and the covariates follow a
transformed linear model, with a link function that determines the appropriate
class of $L$-functionals. In this setting, the distribution of the response is
treated parametrically or nonparametrically, and the response variable is
either censored/truncated or not. We also provide a unified asymptotic theory
of estimates of $L$-functionals, and illustrate our approach by analyzing the
arrival time distribution of migrating birds. In this context a novel version
of the coefficient of determination is introduced, which makes use of the
abovementioned orthogonal series expansion.


title:
A Synthetic Roman Space Telescope High-Latitude Time-Domain Survey:
  Supernovae in the Deep Field

abstract:
  NASA will launch the Nancy Grace Roman Space Telescope (Roman) in the second
half of this decade, which will allow for a generation-defining measurement of
dark energy through multiple probes, including Type Ia supernovae (SNe Ia). To
improve decisions on survey strategy, we have created the first simulations of
realistic Roman images that include artificial SNe Ia injected as point sources
in the images. Our analysis combines work done on Roman simulations for weak
gravitational lensing studies as well as catalog-level simulations of SN
samples. We have created a time series of images over two years containing
$\sim$ 1,050 SNe Ia, covering a 1 square degree subarea of a planned 5 square
degree deep survey. We have released these images publicly for community use
along with input catalogs of all injected sources. We create secondary products
from these images by generating coadded images and demonstrating recovery of
transient sources using image subtraction. We perform first-use analyses on
these images in order to measure galaxy-detection efficiency, point
source-detection efficiency, and host-galaxy association biases. The simulated
images can be found here:
https://roman.ipac.caltech.edu/sims/SN_Survey_Image_sim.html.


title: Ultraviolet freeze-in baryogenesis

abstract:
  We study a mechanism through which the cosmic dark matter density can be
explained simultaneously with the observed baryon asymmetry of the Universe. At
the core of our proposal lie the out-of-equilibrium scattering processes of
bath particles which are responsible for the production of feebly-interacting
dark matter. The same processes violate $CP$, which further leads to an
asymmetry between matter and antimatter being generated in the visible sector.
We focus on the possibility that these interactions are described through
non-renormalizable operators, which leads to both dark matter and the baryon
asymmetry being produced at high temperatures. The mechanism is exemplified by
studying two concrete scenarios, one involving scalar and one involving fermion
dark matter. We find that in both cases it is, indeed, possible to achieve a
common explanation for the dark matter content and the matter-antimatter
asymmetry of the Universe, provided that dark matter is in the keV mass range.


title: First experience with He conditioning of an SRF photoinjector

abstract:
  The recent achievements in the performance of superconducting RF (SRF)
photoinjectors have opened a new era in the development of the reliable
high-brightness CW electron sources. While the SRF guns become one of the most
promising technologies, the compatibility of SRF environment with the complex
photocathodes remains on the forefront of the modern accelerator science. The
SRF cavities operate at cryogenic temperatures providing the ultra-high vacuum
(UHV) environment highly beneficial for the photocathode performance. However,
the necessity to keep the photocathodes at room temperature while being
surrounded by the cavity walls that are kept at cryogenic temperatures creates
an additional complexity for the SRF gun design and operation. The complex and
volatile chemical compounds used for photocathodes have a high chance of
contaminating the surfaces of an SRF cavity. When deposited, such compounds
could create centers for cold electron emission which degrade performance of
the SRF guns. Such a circumstance would require development of the in-situ
processing techniques for restoring the SRF cavity performance. This paper
presents the results of the successful implementation and application of the He
conditioning method for cavity restoration using the existing SRF photoinjector
at Brookhaven National Laboratory (BNL). The method has proven to be extremely
effective and resulted in a dramatic improvement of the BNL gun performance.


title:
Multi-Mask Least-Squares Deconvolution: Extracting RVs using tailored
  masks

abstract:
  To push the radial velocity (RV) exoplanet detection threshold, it is crucial
to find more reliable radial velocity extraction methods. The Least-Squares
Deconvolution (LSD) technique has been used to infer the stellar magnetic flux
from spectropolarimetric data for the past two decades. It relies on the
assumption that stellar absorption lines are similar in shape. Although this
assumption is simplistic, LSD provides a good model for intensity spectra and
likewise an estimate for their Doppler shift. We present the Multi-Mask
Least-Squares Deconvolution (MM-LSD) RV extraction pipeline which extracts the
radial velocity from two-dimensional echelle-order spectra using LSD with
multiple tailored masks after continuum normalisation and telluric absorption
line correction. The flexibility of LSD allows to exclude spectral lines or
pixels at will, providing a means to exclude variable lines or pixels affected
by instrumental problems. The MM-LSD pipeline was tested on HARPS-N data for
the Sun and selected well-observed stars with 5.7 < Vmag < 12.6. For FGK-type
stars with median signal-to-noise above 100, the pipeline delivered RV time
series with on average 12 per cent lower scatter as compared to the HARPS-N RV
extraction pipeline based on the Cross-Correlation Function technique. The
MM-LSD pipeline may be used as a standalone RV code, or modified and extended
to extract a proxy for the magnetic field strength.


title:
Une version polyatomique de l'algorithme Frank-Wolfe pour r\'esoudre le
  probl\`eme LASSO en grandes dimensions

abstract:
  Nous nous int\'eressons \`a la reconstruction parcimonieuse d'images \`a
l'aide du probl\`eme d'optimisation r\'egularis\'e LASSO. Dans de nombreuses
applications pratiques, les grandes dimensions des objets \`a reconstruire
limitent, voire emp\^echent, l'utilisation des m\'ethodes de r\'esolution
proximales classiques. C'est le cas par exemple en radioastronomie. Nous
d\'etaillons dans cet article le fonctionnement de l'algorithme
\textit{Frank-Wolfe Polyatomique}, sp\'ecialement d\'evelopp\'e pour r\'esoudre
le probl\`eme LASSO dans ces contextes exigeants. Nous d\'emontrons sa
sup\'eriorit\'e par rapport aux m\'ethodes proximales dans des situations en
grande dimension avec des mesures de Fourier, lors de la r\'esolution de
probl\`emes simul\'es inspir\'es de la radio-interf\'erom\'etrie.
  --
  We consider the problem of recovering sparse images by means of the penalised
optimisation problem LASSO. For various practical applications, it is
impossible to rely on the proximal solvers commonly used for that purpose due
to the size of the objects to recover, as it is the case for radio astronomy.
In this article we explain the mechanisms of the \textit{Polyatomic Frank-Wolfe
algorithm}, specifically designed to minimise the LASSO problem in such
challenging contexts. We demonstrate in simulated problems inspired from
radio-interferometry the preeminence of this algorithm over the proximal
methods for high dimensional images with Fourier measurements.


title:
Construction and classification of crystalline topological
  superconductor and insulators in three-dimensional interacting fermion
  systems

abstract:
  The natural existences of crystalline symmetry in real materials manifest the
importance of understanding crystalline symmetry protected topological(SPT)
phases, especially for interacting systems. In this paper, we systematically
construct and classify all the crystalline topological superconductors and
insulators in three-dimensional (3D) interacting fermion systems using the
novel concept of topological crystal. The corresponding higher-order
topological surface theory can also be systematically studied via higher-order
bulk-boundary correspondence. In particular, we discover an intriguing fact
that almost all topological crystals with nontrivial 2D block-states are
intrinsically interacting topological phases that cannot be realized in any
free-fermion systems. Moreover, the crystalline equivalence principle for 3D
interacting fermionic systems is also verified, with an additional subtle
"twist" on the spin of fermions.


title:
Wasserstein Convergence for Conditional Empirical Measures of
  Subordinated Dirichlet Diffusions on Riemannian Manifolds

abstract:
  The asymptotic behaviour of empirical measures has plenty of studies.
However, the research on conditional empirical measures is limited. Being the
development of Wang \cite{eW1}, under the quadratic Wasserstein distance, we
investigate the rate of convergence of conditional empirical measures
associated to subordinated Dirichlet diffusion processes on a connected compact
Riemannian manifold with absorbing boundary. We give the sharp rate of
convergence for any initial distribution and prove the precise limit for a
large class of initial distributions. We follow the basic idea of Wang, but
allow ourselves substantial deviations in the proof to overcome difficulties in
our non-local setting.


title: Local Dynamical Effects of Scale Invariance: the Lunar Recession

abstract:
  Scale invariance is expected in empty Universe models, while the presence of
matter tends to suppress it. As shown recently, scale invariance is certainly
absent in cosmological models with densities equal to or above the critical
value $\varrho_{\mathrm{c}} =3H^2_0/(8 \pi G)$. For models with densities below
$\varrho_{\mathrm{c}}$, the possibility of limited effects remains open. If
present, scale invariance would be a global cosmological property. Some traces
could be observable locally. For the Earth-Moon two-body system, the predicted
additional lunar recession would be increased by 0.92 cm/yr, while the tidal
interaction would also be slightly increased.
  The Earth-Moon distance is the most systematically measured distance in the
Solar System, thanks to the Lunar Laser Ranging (LLR) experiment active since
1970. The observed lunar recession from LLR amounts to 3.83 ($\pm 0.009$)
cm/yr; implying a tidal change of the length-of-the-day (LOD) by 2.395 ms/cy.
However, the observed change of the LOD since the Babylonian Antiquity is only
1.78 ms/cy, a result supported by paleontological data, and implying a lunar
recession of 2.85 cm/yr. The significant difference of (3.83-2.85) cm/yr = 0.98
cm/yr, already pointed out by several authors over the last two decades,
corresponds well to the predictions of the scale-invariant theory, which is
also supported by several other astrophysical tests.


title:
FuncPipe: A Pipelined Serverless Framework for Fast and Cost-efficient
  Training of Deep Learning Models

abstract:
  Training deep learning (DL) models has become a norm. With the emergence of
serverless computing and its benefits of true pay-as-you-go pricing and
scalability, systems researchers have recently started to provide support for
serverless-based training. However, the ability to train deep learning models
on serverless platforms is hindered by the inherent limitations of today's
serverless infrastructure and the explosive requirement on memory and
bandwidth. For example, existing AWS serverless functions have up to 10GB
memory and 70MB/s bandwidth while training an AmoebaNet-D model with batch size
64 can require 45GB memory and transfer 900MB data per iteration. The appalling
resource mismatch between serverless functions and DL models has stalled the
progress on serverless-based training.
  In this paper, we present FuncPipe, the first pipelined serverless framework
that enables fast and low-cost training of DL models. FuncPipe is designed with
the key insight that model partitioning can be leveraged to bridge both memory
and bandwidth gaps between the capacity of serverless functions and the
requirement of DL training. Conceptually simple, we have to answer a number of
design questions including how to partition the model, how to configure each
serverless function, and how to exploit each function's uplink/downlink
bandwidth. We co-optimize model partition and resource allocation with a
Mixed-Integer Quadratic Programming formulation and redesign storage-based
communication for efficient bandwidth usage. We implement FuncPipe on AWS and
AliCloud and show that it achieves up to 77% cost savings and 2.2X speedup
comparing to state-of-the-art serverless-based frameworks.


title: Pseudo-Particle Vertex Solver for Quantum Impurity Models

abstract:
  We present a quantum impurity solver based on a pseudo-particle framework,
which combines diagrammatic resummations for a three-point vertex with
diagrammatic Monte Carlo sampling of a four-point vertex. This recently
proposed approach [arXiv:2112.15549] is generalized here to fermionic impurity
problems and we discuss the technical details of the implementation, including
the time-stepping approach, the Monte Carlo updates, and the routines for
checking the two-particle irreducibility of the four-point vertex. We also
explain how the vertex information can be efficiently stored using a Dubiner
basis representation. The convergence properties of the algorithm are
demonstrated with applications to exactly solvable impurity models and
dynamical mean field theory simulations of the single-orbital Hubbard model. It
is furthermore shown that the algorithm can handle a two-orbital problem with
off-diagonal hybridizations, which would cause a severe sign problem in
standard hybridization-expansion Monte Carlo simulations. Since the
vertex-based algorithm successfully handles sign oscillating integrals in
equilibrium and samples only connected diagrams, it may be a promising approach
for real-time simulations.


title: An Analytic Boris Pusher for Plasma Simulation

abstract:
  This paper discusses how to improve the Boris pusher used to advance
relativistic charged particles in fixed electromagnetic fields. We first derive
a simpler solution to a flaw previously discovered by others. We then derive a
new analytic Boris pusher that is a minor modification to the original
split-time scheme, except for the calculation of {\gamma}. This analytic pusher
assumes that the change of {\gamma} during a particle advance is small. It is
less accurate than the pusher derived by Fei Li et. al., but is nearly twice as
fast. We will discuss when it is advantageous and when it is not.


title:
Multiparameter colored partition category and the product of reduced
  Kronecker coefficients

abstract:
  We introduce and study a multiparameter colored partition category
$\mathcal{CPar}(\textbf{x})$ by extending the construction of the partition
category, over an algebraically closed field $\mathbb{k}$ of characteristic
zero and for a multiparameter $\textbf{x}\in \mathbb{k}^{r}$. The morphism
spaces in $\mathcal{CPar}(\textbf{x})$ have bases in terms of partition
diagrams whose parts are colored by elements of the multiplicative cyclic group
$C_r$. The Deligne category for symmetric groups is a special case of the
additive Karoubi envelope of $\mathcal{CPar}(\textbf{x})$. We show that the
endomorphism spaces of $\mathcal{CPar}(\textbf{x})$ are generically semisimple.
The category $\mathcal{CPar}(\textbf{x})$ is rigid strict monoidal and we give
a presentation of $\mathcal{CPar}(\textbf{x})$ as a monoidal category. The path
algebra of $\mathcal{CPar}(\textbf{x})$ admits a triangular decomposition with
Cartan subalgebra being equal to the direct sum of the group algebras of
complex reflection groups $G(r,n)$. We compute the structure constants for the
classes of simple modules in the split Grothendieck ring of the category of
modules over the path algebra of the downward partition subcategory of
$\mathcal{CPar}(\textbf{x})$ in two ways. Among other things, this gives a
closed formula for the product of reduced Kronecker coefficients in terms of
Littlewood--Richardson coefficients for $G(r,n)$ and certain Kronecker
coefficients for the wreath product $(C_r \times C_r)\wr S_n$. For $r=1$, this
formula reduces to a formula for the reduced Kronecker coefficient given by
Littlewood. We also give two analogues of the Robinson--Schensted
correspondence for colored partition diagrams and, as an application, we
classify the equivalence classes of Green's left, right and two sided relations
for the colored partition monoid in terms of these correspondences.


title: Limit Theorems on the Mesoscopic Scale for the Anderson Model

abstract:
  In this paper, we study eigenvalue fluctuations of the finite volume Anderson
model in the mesoscopic scale. We carry out this study in a regime of
exponential localization and prove a central limit theorem for the eigenvalue
counting function in a shrinking interval.


title:
Data augmented turbulence modeling for three-dimensional separation
  flows

abstract:
  Field inversion and machine learning are implemented in this study to
describe three-dimensional separation flow around an axisymmetric hill and
augment the Spart-Allmaras model. The discrete adjoint method is used to solve
the field inversion problem, and an artificial neural network is used as the
machine learning model. A validation process for field inversion is proposed to
adjust the hyperparameters and obtain a physically acceptable solution. The
field inversion result shows that the non-equilibrium turbulence effects in the
boundary layer upstream of the mean separation line and in the separating shear
layer dominate the flow structure in the 3-D separating flow, which agrees with
prior physical knowledge. However, the effect of turbulence anisotropy on the
mean flow appears to be limited. Two approaches are proposed and implemented in
the machine learning stage to overcome the problem of sample imbalance while
reducing the computational cost during training. The results are all
satisfactory, which proves the effectiveness of the proposed approaches.


title:
Are there Einsteinian gravities involving covariant derivatives of the
  Riemann tensor?

abstract:
  We study the particle content of higher derivative theories of gravity built
with contractions of the Riemann tensor and its covariant derivatives. In the
absence of the latter, there is a family of theories exhibiting an Einsteinian
spectrum known as generalized quasi-topological gravities. In turn, we present
a no-go result for the construction of Einsteinian gravities involving
covariant derivatives of the Riemann tensor. We find that (truncated series)
finite order Lagrangians with covariant derivatives of the Riemann tensor
always present ghosts in the spectrum. This might be interpreted as a hint of
non-locality in any healthy UV completion of General Relativity.


title:
Justice in Misinformation Detection Systems: An Analysis of Algorithms,
  Stakeholders, and Potential Harms

abstract:
  Faced with the scale and surge of misinformation on social media, many
platforms and fact-checking organizations have turned to algorithms for
automating key parts of misinformation detection pipelines. While offering a
promising solution to the challenge of scale, the ethical and societal risks
associated with algorithmic misinformation detection are not well-understood.
In this paper, we employ and extend upon the notion of informational justice to
develop a framework for explicating issues of justice relating to
representation, participation, distribution of benefits and burdens, and
credibility in the misinformation detection pipeline. Drawing on the framework:
(1) we show how injustices materialize for stakeholders across three
algorithmic stages in the pipeline; (2) we suggest empirical measures for
assessing these injustices; and (3) we identify potential sources of these
harms. This framework should help researchers, policymakers, and practitioners
reason about potential harms or risks associated with these algorithms and
provide conceptual guidance for the design of algorithmic fairness audits in
this domain.


title:
Life is not Always Depressing: Exploring the Happy Moments of People
  Diagnosed with Depression

abstract:
  In this work, we explore the relationship between depression and
manifestations of happiness in social media. While the majority of works
surrounding depression focus on symptoms, psychological research shows that
there is a strong link between seeking happiness and being diagnosed with
depression. We make use of Positive-Unlabeled learning paradigm to
automatically extract happy moments from social media posts of both controls
and users diagnosed with depression, and qualitatively analyze them with
linguistic tools such as LIWC and keyness information. We show that the life of
depressed individuals is not always bleak, with positive events related to
friends and family being more noteworthy to their lives compared to the more
mundane happy events reported by control users.


title: Learning First-Order Rules with Differentiable Logic Program Semantics

abstract:
  Learning first-order logic programs (LPs) from relational facts which yields
intuitive insights into the data is a challenging topic in neuro-symbolic
research. We introduce a novel differentiable inductive logic programming (ILP)
model, called differentiable first-order rule learner (DFOL), which finds the
correct LPs from relational facts by searching for the interpretable matrix
representations of LPs. These interpretable matrices are deemed as trainable
tensors in neural networks (NNs). The NNs are devised according to the
differentiable semantics of LPs. Specifically, we first adopt a novel
propositionalization method that transfers facts to NN-readable vector pairs
representing interpretation pairs. We replace the immediate consequence
operator with NN constraint functions consisting of algebraic operations and a
sigmoid-like activation function. We map the symbolic forward-chained format of
LPs into NN constraint functions consisting of operations between subsymbolic
vector representations of atoms. By applying gradient descent, the trained well
parameters of NNs can be decoded into precise symbolic LPs in forward-chained
logic format. We demonstrate that DFOL can perform on several standard ILP
datasets, knowledge bases, and probabilistic relation facts and outperform
several well-known differentiable ILP models. Experimental results indicate
that DFOL is a precise, robust, scalable, and computationally cheap
differentiable ILP model.


title: ARChemist: Autonomous Robotic Chemistry System Architecture

abstract:
  Automated laboratory experiments have the potential to propel new
discoveries, while increasing reproducibility and improving scientists' safety
when handling dangerous materials. However, many automated laboratory workflows
have not fully leveraged the remarkable advancements in robotics and digital
lab equipment. As a result, most robotic systems used in the labs are
programmed specifically for a single experiment, often relying on proprietary
architectures or using unconventional hardware. In this work, we tackle this
problem by proposing a novel robotic system architecture specifically designed
with and for chemists, which allows the scientist to easily reconfigure their
setup for new experiments. Specifically, the system's strength is its ability
to combine together heterogeneous robotic platforms with standard laboratory
equipment to create different experimental setups. Finally, we show how the
architecture can be used for specific laboratory experiments through case
studies such as solubility screening and crystallisation.


title: Mixup-based Deep Metric Learning Approaches for Incomplete Supervision

abstract:
  Deep learning architectures have achieved promising results in different
areas (e.g., medicine, agriculture, and security). However, using those
powerful techniques in many real applications becomes challenging due to the
large labeled collections required during training. Several works have pursued
solutions to overcome it by proposing strategies that can learn more for less,
e.g., weakly and semi-supervised learning approaches. As these approaches do
not usually address memorization and sensitivity to adversarial examples, this
paper presents three deep metric learning approaches combined with Mixup for
incomplete-supervision scenarios. We show that some state-of-the-art approaches
in metric learning might not work well in such scenarios. Moreover, the
proposed approaches outperform most of them in different datasets.


title:
A low-eccentricity migration pathway for a 13-h-period Earth analogue in
  a four-planet system

abstract:
  It is commonly accepted that exoplanets with orbital periods shorter than 1
day, also known as ultra-short period (USP) planets, formed further out within
their natal protoplanetary disk, before migrating to their current-day orbits
via dynamical interactions. One of the most accepted theories suggests a
violent scenario involving high-eccentricity migration followed by tidal
circularization. Here, we present the discovery of a four planet system
orbiting the bright (V=10.5) K6 dwarf star TOI-500. The innermost planet is a
transiting, Earth-sized USP planet with an orbital period of $\sim$ 13 hours, a
mass of 1.42 $\pm$ 0.18 M$_{\oplus}$, a radius of $1.166^{0.061}_{-0.058}$
R$_{\oplus}$, and a mean density of 4.89$^{+1.03}_{-0.88}$ gcm$^{-3}$. Via
Doppler spectroscopy, we discovered that the system hosts three outer planets
on nearly circular orbits with periods of 6.6, 26.2, and 61.3d and minimum
masses of 5.03 $\pm$ 0.41 M$_{\oplus}$, 33.12 $\pm$ 0.88 M$_{\oplus}$ and
15.05$^{+1.12}_{-1.11}$ M$_{\oplus}$, respectively. The presence of both a USP
planet and a low-mass object on a 6.6-day orbit indicates that the architecture
of this system can be explained via a scenario in which the planets started on
low-eccentricity orbits, then moved inwards through a quasi-static secular
migration. Our numerical simulations show that this migration channel can bring
TOI-500 b to its current location in 2 Gyrs, starting from an initial orbit of
0.02au. TOI-500 is the first four planet system known to host a USP Earth
analog whose current architecture can be explained via a non-violent migration
scenario.


title:
An Explainable Regression Framework for Predicting Remaining Useful Life
  of Machines

abstract:
  Prediction of a machine's Remaining Useful Life (RUL) is one of the key tasks
in predictive maintenance. The task is treated as a regression problem where
Machine Learning (ML) algorithms are used to predict the RUL of machine
components. These ML algorithms are generally used as a black box with a total
focus on the performance without identifying the potential causes behind the
algorithms' decisions and their working mechanism. We believe, the performance
(in terms of Mean Squared Error (MSE), etc.,) alone is not enough to build the
trust of the stakeholders in ML prediction rather more insights on the causes
behind the predictions are needed. To this aim, in this paper, we explore the
potential of Explainable AI (XAI) techniques by proposing an explainable
regression framework for the prediction of machines' RUL. We also evaluate
several ML algorithms including classical and Neural Networks (NNs) based
solutions for the task. For the explanations, we rely on two model agnostic XAI
methods namely Local Interpretable Model-Agnostic Explanations (LIME) and
Shapley Additive Explanations (SHAP). We believe, this work will provide a
baseline for future research in the domain.


title: Symmetric Transformer-based Network for Unsupervised Image Registration

abstract:
  Medical image registration is a fundamental and critical task in medical
image analysis. With the rapid development of deep learning, convolutional
neural networks (CNN) have dominated the medical image registration field. Due
to the disadvantage of the local receptive field of CNN, some recent
registration methods have focused on using transformers for non-local
registration. However, the standard Transformer has a vast number of parameters
and high computational complexity, which causes Transformer can only be applied
at the bottom of the registration models. As a result, only coarse information
is available at the lowest resolution, limiting the contribution of Transformer
in their models. To address these challenges, we propose a convolution-based
efficient multi-head self-attention (CEMSA) block, which reduces the parameters
of the traditional Transformer and captures local spatial context information
for reducing semantic ambiguity in the attention mechanism. Based on the
proposed CEMSA, we present a novel Symmetric Transformer-based model
(SymTrans). SymTrans employs the Transformer blocks in the encoder and the
decoder respectively to model the long-range spatial cross-image relevance. We
apply SymTrans to the displacement field and diffeomorphic registration.
Experimental results show that our proposed method achieves state-of-the-art
performance in image registration. Our code is publicly available at
\url{https://github.com/MingR-Ma/SymTrans}.


title:
Some Minimal Notes on Notation and Minima: A Comment on "How Particular
  is the Physics of the Free Energy Principle?" by Aguilera, Millidge,
  Tschantz, and Buckley

abstract:
  We comment on a technical critique of the free energy principle in linear
systems by Aguilera, Millidge, Tschantz, and Buckley, entitled "How Particular
is the Physics of the Free Energy Principle?" Aguilera and colleagues identify
an ambiguity in the flow of the mode of a system, and we discuss the context
for this ambiguity in earlier papers, and their proposal of a more adequate
interpretation of these equations. Following that, we discuss a
misinterpretation in their treatment of surprisal and variational free energy,
especially with respect to their gradients and their minima. In sum, we argue
that the results in the target paper are accurate and stand up to rigorous
scrutiny; we also highlight that they, nonetheless, do not undermine the FEP.


title: On non-ideal chemical-reaction networks and phase separation

abstract:
  Much of the theory on chemical-reaction networks (CRNs) has been developed in
the ideal-solution limit, where interactions between the solutes are
negligible. However, there is a large variety of phenomena in biological cells
and soft-matter physics which appear to deviate from the ideal-solution
behaviour. Particularly striking is the case of liquid-liquid phase separation,
which is typically caused by inter-particle interactions. Here, we revisit a
number of known results in the domain of ideal CRNs, and we generalise and
adapt them to arbitrary interactions between the solutes which stem from a
given free energy. Among these is the form of the steady-state probability
distribution and Lyapunov functions for complex-balanced networks, where the
creation and annihilation rates are equal for all chemical complexes which
appear as reactants or products in the CRN. Finally, we draw a phase diagram
for complex-balanced reaction-diffusion solutions based on the minimisation of
such Lyapunov function with a rationale similar to that of equilibrium
thermodynamics, but for systems that may sustain non-equilibrium chemical
currents at steady state. Nevertheless, we find that complex-balanced networks
are not sufficient to create diffusion currents at steady state.


title:
Correlation of Dark Matter, Gas and Stellar Profiles in Dark Matter
  Halos

abstract:
  Halos of similar mass and redshift exhibit a large degree of variability in
their differential properties, such as dark matter, hot gas, and stellar mass
density profiles. This variability is an indicator of diversity in the
formation history of these dark matter halos that is reflected in the coupling
of scatters about the mean relations. In this work, we show that the strength
of this coupling depends on the scale at which halo profiles are measured. By
analyzing the outputs of the IllustrisTNG hydrodynamical cosmological
simulations we report the radial- and mass-dependent couplings between the dark
matter, hot gas, and stellar mass radial density profiles utilizing the
population diversity in dark matter halos. We find that for the same mass halos
the scatters in density of baryons and dark matter are strongly coupled at
large scales ($r>R_{200}$); but the coupling between gas and dark matter
density profiles fades near the core of halos ($r < 0.3 R_{200}$). We then show
that the correlation between halo profile and integrated quantities induces a
radius-dependent additive bias in the profile observables of halos when halos
are selected on properties other than their mass. We discuss the impact of this
effect on cluster abundance and cross-correlations cosmology with
multi-wavelength cosmological surveys.


title:
Superconducting properties of the spin Hall candidate Ta3Sb with
  eightfold degeneracy

abstract:
  We report the synthesis and characterization of phase pure Ta3Sb, a material
predicted to be topological with eightfold degenerate fermionic states [Science
353, aaf5037 (2016)] and to exhibit a large spin Hall effect [Sci. Adv. 5,
eaav8575 (2019]. We observe superconductivity in Ta3Sb with Tc~ 0.67 K in both
electrical resistivity \r{ho}(T) and specific heat C(T) measurements. Field
dependent measurements yield the superconducting phase diagram with an upper
critical field of Hc2(0) ~ 0.95 T, corresponding to a superconducting coherence
length of {\xi} ~18.6 nm. The gap ratio deduced from specific heat anomaly,
2{\Delta}0/kBTc is 3.46, a value close to the Bardeen-Cooper-Schrieffer (BCS)
value of 3.53. From a detailed analysis of both the transport and thermodynamic
data within the Ginsburg-Landau (GL) framework, a GL parameter of \k{appa} ~90
is obtained identifying Ta3Sb as an extreme type-II superconductor. The
observation of superconductivity in an eightfold degenerate fermionic compound
with topological surface states and predicted large spin Hall conductance
positions Ta3Sb as an appealing platform to further explore exotic quantum
states in multifold degenerate systems.


title:
Delocalization of dark and bright excitons in flat-band materials and
  the optical properties of V$_2$O$_5$

abstract:
  The simplest picture of excitons in materials with atomic-like localization
of electrons is that of Frenkel excitons, where electrons and holes stay close
together, which is associated with a large binding energy. Here, using the
example of the layered oxide V$_2$O$_5$ , we show how localized charge-transfer
excitations combine to form excitons that also have a huge binding energy but,
at the same time, a large electron-hole distance, and we explain this seemingly
contradictory finding. The anisotropy of the exciton delocalization is
determined by the local anisotropy of the structure, whereas the exciton
extends orthogonally to the chains formed by the crystal structure. Moreover,
we show that the bright exciton goes together with a dark exciton of even
larger binding energy and more pronounced anisotropy. These findings are
obtained by combining first principles many-body perturbation theory
calculations, ellipsometry experiments, and tight binding modelling, leading to
very good agreement and a consistent picture. Our explanation is general and
can be extended to other materials.


title: Generalized permutation tests

abstract:
  Permutation tests are an immensely popular statistical tool, used for testing
hypotheses of independence between variables and other common inferential
questions. When the number of observations is large, it is computationally
infeasible to consider every possible permutation of the data, and it is
typical to either take a random draw of permutations, or to restrict to a
subgroup or subset of permutations. In this work, we extend beyond these
possibilities to show how such tests can be run using any distribution over any
subset of permutations, with all the previous options as a special case.


title: Prescriptive and Descriptive Approaches to Machine-Learning Transparency

abstract:
  Specialized documentation techniques have been developed to communicate key
facts about machine-learning (ML) systems and the datasets and models they rely
on. Techniques such as Datasheets, FactSheets, and Model Cards have taken a
mainly descriptive approach, providing various details about the system
components. While the above information is essential for product developers and
external experts to assess whether the ML system meets their requirements,
other stakeholders might find it less actionable. In particular, ML engineers
need guidance on how to mitigate potential shortcomings in order to fix bugs or
improve the system's performance. We survey approaches that aim to provide such
guidance in a prescriptive way. We further propose a preliminary approach,
called Method Cards, which aims to increase the transparency and
reproducibility of ML systems by providing prescriptive documentation of
commonly-used ML methods and techniques. We showcase our proposal with an
example in small object detection, and demonstrate how Method Cards can
communicate key considerations for model developers. We further highlight
avenues for improving the user experience of ML engineers based on Method
Cards.


title: KL-Mat : Fair Recommender System via Information Geometry

abstract:
  Recommender system has intrinsic problems such as sparsity and fairness.
Although it has been widely adopted for the past decades, research on fairness
of recommendation algorithms has been largely neglected until recently. One
important paradigm for resolving the issue is regularization. However,
researchers have not been able to come up with a consensusly agreed
regularization term like regularization framework in other fields such as Lasso
or Ridge Regression. In this paper, we borrow concepts from information
geometry and propose a new regularization-based fair algorithm called KL-Mat.
The algorithmic technique leads to a more robust performance in accuracy
performance such as MAE. More importantly, the algorithm produces much fairer
results than vanilla matrix factorization approach. KL-Mat is fast,
easy-to-implement and explainable.


title: Predicting Sleeping Quality using Convolutional Neural Networks

abstract:
  Identifying sleep stages and patterns is an essential part of diagnosing and
treating sleep disorders. With the advancement of smart technologies, sensor
data related to sleeping patterns can be captured easily. In this paper, we
propose a Convolution Neural Network (CNN) architecture that improves the
classification performance. In particular, we benchmark the classification
performance from different methods, including traditional machine learning
methods such as Logistic Regression (LR), Decision Trees (DT), k-Nearest
Neighbour (k-NN), Naive Bayes (NB) and Support Vector Machine (SVM), on 3
publicly available sleep datasets. The accuracy, sensitivity, specificity,
precision, recall, and F-score are reported and will serve as a baseline to
simulate the research in this direction in the future.


title:
Plasma assisted CO$_2$ splitting to carbon and oxygen: A concept review
  analysis

abstract:
  This concept review paper analyses the possibility to develop a technical
solution for the challenging reaction of CO$_2$ splitting to carbon and O$_2$
(CO$_2$tC). This is a dream reaction in the area of addressing climate change
and greenhouse gas emissions. There are quite limited literature indications
that were reviewed, with an analysis also of the limits and perspectives.
Not-thermal plasma, in combination with catalysis, is one of the possibilities
to promote this reaction. Current studies on plasma-assisted CO$_2$ splitting
are limited mainly to the formation of CO. By combining data on the status of
current studies on plasma CO$_2$ splitting with those on CO$_2$tC it is
possible to propose a tentative novel approach. This is a conceptual idea
presented and discussed as vehicle to stimulate research in the area, including
proposing alternative possibilities. The challenges of this concept and
reaction are discussed.


title: Nonbacktracking spectral clustering of nonuniform hypergraphs

abstract:
  Spectral methods offer a tractable, global framework for clustering in graphs
via eigenvector computations on graph matrices. Hypergraph data, in which
entities interact on edges of arbitrary size, poses challenges for matrix
representations and therefore for spectral clustering. We study spectral
clustering for nonuniform hypergraphs based on the hypergraph nonbacktracking
operator. After reviewing the definition of this operator and its basic
properties, we prove a theorem of Ihara-Bass type to enable faster computation
of eigenpairs. We then propose an alternating algorithm for inference in a
hypergraph stochastic blockmodel via linearized belief-propagation, offering
proofs that both formalize and extend several previous results. We perform
experiments in real and synthetic data that underscore the benefits of
hypergraph methods over graph-based ones when interactions of different sizes
carry different information about cluster structure. Through an analysis of our
algorithm, we pose several conjectures about the limits of spectral methods and
detectability in hypergraph stochastic blockmodels writ large.

categories: cs.SI cs.LG math.PR physics.data-an physics.soc-ph


title:
Supervised machine learning classification for short straddles on the
  S&P500

abstract:
  In this working paper we present our current progress in the training of
machine learning models to execute short option strategies on the S&P500. As a
first step, this paper is breaking this problem down to a supervised
classification task to decide if a short straddle on the S&P500 should be
executed or not on a daily basis. We describe our used framework and present an
overview over our evaluation metrics on different classification models. In
this preliminary work, using standard machine learning techniques and without
hyperparameter search, we find no statistically significant outperformance to a
simple "trade always" strategy, but gain additional insights on how we could
proceed in further experiments.


title:
Vortex Lattice Melting Line in Superconductors with Paramagnetic
  Pair-Breaking

abstract:
  Recent experiments on the Iron-based superconductor FeSe in a high magnetic
field have suggested the presence of both the fluctuation-induced vortex liquid
regime and a Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) vortex lattice. To get a
general picture on the magnetic phase diagram in type II superconductors with
strong superconducting (SC) fluctuation and strong paramagnetic pair-breaking
(PPB) such as FeSe, the vortex lattice melting curve Hm(T) is theoretically
investigated in the situations where a FFLO state is expected to occur. In
general, the PPB tends to narrow the vortex liquid regime intervening between
Hc2(T) and Hm(T). In particular, the vortex liquid regime is found to rapidly
shrink upon entering, by cooling, the temperature range in which the FFLO state
with a periodic modulation parallel to the magnetic field is stable in the mean
field theory. Based on the present results, the high field SC phase diagrams of
FeSe in the parallel and perpendicular field configurations are discussed.


title: A Close Look into Human Activity Recognition Models using Deep Learning

abstract:
  Human activity recognition using deep learning techniques has become
increasing popular because of its high effectivity with recognizing complex
tasks, as well as being relatively low in costs compared to more traditional
machine learning techniques. This paper surveys some state-of-the-art human
activity recognition models that are based on deep learning architecture and
has layers containing Convolution Neural Networks (CNN), Long Short-Term Memory
(LSTM), or a mix of more than one type for a hybrid system. The analysis
outlines how the models are implemented to maximize its effectivity and some of
the potential limitations it faces.


title:
Computer Vision for Road Imaging and Pothole Detection: A
  State-of-the-Art Review of Systems and Algorithms

abstract:
  Computer vision algorithms have been prevalently utilized for 3-D road
imaging and pothole detection for over two decades. Nonetheless, there is a
lack of systematic survey articles on state-of-the-art (SoTA) computer vision
techniques, especially deep learning models, developed to tackle these
problems. This article first introduces the sensing systems employed for 2-D
and 3-D road data acquisition, including camera(s), laser scanners, and
Microsoft Kinect. Afterward, it thoroughly and comprehensively reviews the SoTA
computer vision algorithms, including (1) classical 2-D image processing, (2)
3-D point cloud modeling and segmentation, and (3) machine/deep learning,
developed for road pothole detection. This article also discusses the existing
challenges and future development trends of computer vision-based road pothole
detection approaches: classical 2-D image processing-based and 3-D point cloud
modeling and segmentation-based approaches have already become history; and
Convolutional neural networks (CNNs) have demonstrated compelling road pothole
detection results and are promising to break the bottleneck with the future
advances in self/un-supervised learning for multi-modal semantic segmentation.
We believe that this survey can serve as practical guidance for developing the
next-generation road condition assessment systems.


title:
Continual Learning for Peer-to-Peer Federated Learning: A Study on
  Automated Brain Metastasis Identification

abstract:
  Due to data privacy constraints, data sharing among multiple centers is
restricted. Continual learning, as one approach to peer-to-peer federated
learning, can promote multicenter collaboration on deep learning algorithm
development by sharing intermediate models instead of training data. This work
aims to investigate the feasibility of continual learning for multicenter
collaboration on an exemplary application of brain metastasis identification
using DeepMedic. 920 T1 MRI contrast enhanced volumes are split to simulate
multicenter collaboration scenarios. A continual learning algorithm, synaptic
intelligence (SI), is applied to preserve important model weights for training
one center after another. In a bilateral collaboration scenario, continual
learning with SI achieves a sensitivity of 0.917, and naive continual learning
without SI achieves a sensitivity of 0.906, while two models trained on
internal data solely without continual learning achieve sensitivity of 0.853
and 0.831 only. In a seven-center multilateral collaboration scenario, the
models trained on internal datasets (100 volumes each center) without continual
learning obtain a mean sensitivity value of 0.699. With single-visit continual
learning (i.e., the shared model visits each center only once during training),
the sensitivity is improved to 0.788 and 0.849 without SI and with SI,
respectively. With iterative continual learning (i.e., the shared model
revisits each center multiple times during training), the sensitivity is
further improved to 0.914, which is identical to the sensitivity using mixed
data for training. Our experiments demonstrate that continual learning can
improve brain metastasis identification performance for centers with limited
data. This study demonstrates the feasibility of applying continual learning
for peer-to-peer federated learning in multicenter collaboration.


title: Relaxation and dynamics of high-stress pre-displaced string resonators

abstract:
  Pre-displaced micromechanical resonators made from high-stress material give
rise to new rich static and dynamic behavior. Here, an analytical model is
presented to describe the mechanics of such pre-displaced resonators. The
bending and tension energies are derived and a modified Euler-Bernoulli
equation is obtained by applying the least action principle. By projecting the
model onto a cosine shape, the energy landscape is visualized, and the
pre-displacement dependence of stress and frequency is studied
semi-analytically. The analysis is extended with finite-element simulations,
including the mode shape, the role of overhang, and the stress distribution.


title:
Operational Optimization to Maximize Dynamic Range in EXCLAIM Microwave
  Kinetic Inductance Detectors

abstract:
  Microwave Kinetic Inductance Detectors (MKIDs) are highly scalable detectors
that have demonstrated nearly background-limited sensitivity in the
far-infrared from high-altitude balloon-borne telescopes and space-like
laboratory environments. In addition, the detectors have a rich design space
with many optimizable parameters, allowing highly sensitive measurements over a
wide dynamic range. For these reasons, MKIDs were chosen for the Experiment for
Cryogenic Large-Aperture Intensity Mapping (EXCLAIM), a balloon-borne telescope
targeting nearly background-limited performance in a high-altitude atmospheric
environment from 420-540 GHz. We describe MKID optimization in the specific
context of EXCLAIM and provide general results that apply to broader
applications. Extending the established approach of tone frequency tracking, we
show that readout power optimization enables significant, further improvement
in dynamic range.


title:
Poisoning Deep Learning based Recommender Model in Federated Learning
  Scenarios

abstract:
  Various attack methods against recommender systems have been proposed in the
past years, and the security issues of recommender systems have drawn
considerable attention. Traditional attacks attempt to make target items
recommended to as many users as possible by poisoning the training data.
Benifiting from the feature of protecting users' private data, federated
recommendation can effectively defend such attacks. Therefore, quite a few
works have devoted themselves to developing federated recommender systems. For
proving current federated recommendation is still vulnerable, in this work we
probe to design attack approaches targeting deep learning based recommender
models in federated learning scenarios. Specifically, our attacks generate
poisoned gradients for manipulated malicious users to upload based on two
strategies (i.e., random approximation and hard user mining). Extensive
experiments show that our well-designed attacks can effectively poison the
target models, and the attack effectiveness sets the state-of-the-art.


title: Improved random batch Ewald method in molecular dynamics simulations

abstract:
  The random batch Ewald (RBE) is an efficient and accurate method for
molecular dynamics (MD) simulations of physical systems at the nano-/micro-
scale. The method shows great potential to solve the computational bottleneck
of long-range interactions, motivating a necessity to accelerating short-range
components of the non-bonded interactions for a further speedup of MD
simulations. In this work, we present an improved RBE method for the
non-bonding interactions by introducing the random batch idea to constructing
neighbor lists for the treatment of both the short-range part of the Ewald
splitting and the Lennard-Jones potential. The efficiency of the novel neighbor
list algorithm owes to the stochastic minibatch strategy which can
significantly reduce the total number of neighbors. We implement the improved
RBE method in the LAMMPS package. The accuracy and stability of the algorithm
are demonstrated by both theoretical analysis and benchmark simulations.
Numerical tests on computer performance by conducting large-scaled MD
simulations for systems including up to 0.1 billion water molecules, run on
massive cluster with up to 50 thousand CPU cores, demonstrating the attractive
features such as the high parallel scalability and memory-saving of the method
in comparison to the existing methods.


title: Generative Retrieval for Long Sequences

abstract:
  Text retrieval is often formulated as mapping the query and the target items
(e.g., passages) to the same vector space and finding the item whose embedding
is closest to that of the query. In this paper, we explore a generative
approach as an alternative, where we use an encoder-decoder model to memorize
the target corpus in a generative manner and then finetune it on
query-to-passage generation. As GENRE(Cao et al., 2021) has shown that entities
can be retrieved in a generative way, our work can be considered as its
generalization to longer text. We show that it consistently achieves comparable
performance to traditional bi-encoder retrieval on diverse datasets and is
especially strong at retrieving highly structured items, such as reasoning
chains and graph relations, while demonstrating superior GPU memory and time
complexity. We also conjecture that generative retrieval is complementary to
traditional retrieval, as we find that an ensemble of both outperforms
homogeneous ensembles.


title:
PhysioGAN: Training High Fidelity Generative Model for Physiological
  Sensor Readings

abstract:
  Generative models such as the variational autoencoder (VAE) and the
generative adversarial networks (GAN) have proven to be incredibly powerful for
the generation of synthetic data that preserves statistical properties and
utility of real-world datasets, especially in the context of image and natural
language text. Nevertheless, until now, there has no successful demonstration
of how to apply either method for generating useful physiological sensory data.
The state-of-the-art techniques in this context have achieved only limited
success. We present PHYSIOGAN, a generative model to produce high fidelity
synthetic physiological sensor data readings. PHYSIOGAN consists of an encoder,
decoder, and a discriminator. We evaluate PHYSIOGAN against the
state-of-the-art techniques using two different real-world datasets: ECG
classification and activity recognition from motion sensors datasets. We
compare PHYSIOGAN to the baseline models not only the accuracy of class
conditional generation but also the sample diversity and sample novelty of the
synthetic datasets. We prove that PHYSIOGAN generates samples with higher
utility than other generative models by showing that classification models
trained on only synthetic data generated by PHYSIOGAN have only 10% and 20%
decrease in their classification accuracy relative to classification models
trained on the real data. Furthermore, we demonstrate the use of PHYSIOGAN for
sensor data imputation in creating plausible results.


title: A unified theory of information transfer and causal relation

abstract:
  Information transfer between coupled stochastic dynamics, measured by
transfer entropy and information flow, is suggested as a physical process
underlying the causal relation of systems. While information transfer analysis
has booming applications in both science and engineering fields, critical
mysteries about its foundations remain unsolved. Fundamental yet difficult
questions concern how information transfer and causal relation originate, what
they depend on, how they differ from each other, and if they are created by a
unified and general quantity. These questions essentially determine the
validity of causal relation measurement via information transfer. Here we
pursue to lay a complete theoretical basis of information transfer and causal
relation. Beyond the well-known relations between these concepts that
conditionally hold, we demonstrate that information transfer and causal
relation universally originate from specific information synergy and redundancy
phenomena characterized by high-order mutual information. More importantly, our
theory analytically explains the mechanisms for information transfer and causal
relation to originate, vanish, and differ from each other. Moreover, our theory
naturally defines the effect sizes of information transfer and causal relation
based on high-dimensional coupling events. These results may provide a unified
view of information, synergy, and causal relation to bridge Pearl's causal
inference theory in computer science and information transfer analysis in
physics.

categories: cond-mat.stat-mech cs.IT cs.LG math.IT physics.data-an


title: Signal Recovery with Non-Expansive Generative Network Priors

abstract:
  We study compressive sensing with a deep generative network prior. Initial
theoretical guarantees for efficient recovery from compressed linear
measurements have been developed for signals in the range of a ReLU network
with Gaussian weights and logarithmic expansivity: that is when each layer is
larger than the previous one by a logarithmic factor. It was later shown that
constant expansivity is sufficient for recovery. It has remained open whether
the expansivity can be relaxed allowing for networks with contractive layers,
as often the case of real generators. In this work we answer this question,
proving that a signal in the range of a Gaussian generative network can be
recovered from a few linear measurements provided that the width of the layers
is proportional to the input layer size (up to log factors). This condition
allows the generative network to have contractive layers. Our result is based
on showing that Gaussian matrices satisfy a matrix concentration inequality,
which we term Range Restricted Weight Distribution Condition (R2WDC), and
weakens the Weight Distribution Condition (WDC) upon which previous theoretical
guarantees were based on. The WDC has also been used to analyze other signal
recovery problems with generative network priors. By replacing the WDC with the
R2WDC, we are able to extend previous results for signal recovery with
expansive generative network priors to non-expansive ones. We discuss these
extensions for phase retrieval, denoising, and spiked matrix recovery.


title: On analytical formulas for the Virial coefficients

abstract:
  In many fields of statistical physics, for instance in the study of the
liquid-gas phase transition in finite nuclear matter, the Virial coefficients
of the Fermi gas play a major role. In this note, we provide relations, sum
rules, analytical formulas and numerical values for such coefficients.


title: Emotion Recognition In Persian Speech Using Deep Neural Networks

abstract:
  Speech Emotion Recognition (SER) is of great importance in Human-Computer
Interaction (HCI), as it provides a deeper understanding of the situation and
results in better interaction. In recent years, various machine learning and
deep learning algorithms have been developed to improve SER techniques.
Recognition of emotions depends on the type of expression that varies between
different languages. In this article, to further study this important factor in
Farsi, we examine various deep learning techniques on the SheEMO dataset. Using
signal features in low- and high-level descriptions and different deep networks
and machine learning techniques, Unweighted Average Recall (UAR) of 65.20 is
achieved with an accuracy of 78.29.


title: Localization of $(\infty, 1)$-categories and spectral sequences

abstract:
  We describe two types of localization for $(\infty, 1)$-categories which
determine the successive terms in the homotopy spectral sequence of a
(co)simplicial object.


title: Banach gradient flows for various families of knot energies

abstract:
  We establish long-time existence of Banach gradient flows for generalised
integral Menger curvatures and tangent-point energies, and for O'Hara's
self-repulsive potentials $E^{\alpha,p}$. In order to do so, we employ the
theory of curves of maximal slope in slightly smaller spaces compactly
embedding into the respective energy spaces associated to these functionals,
and add a term involving the logarithmic strain, which controls the
parametrisations of the flowing (knotted) loops. As a prerequisite, we prove in
addition that O'Hara's knot energies $E^{\alpha,p}$ are continuously
differentiable.


title: MeSHup: A Corpus for Full Text Biomedical Document Indexing

abstract:
  Medical Subject Heading (MeSH) indexing refers to the problem of assigning a
given biomedical document with the most relevant labels from an extremely large
set of MeSH terms. Currently, the vast number of biomedical articles in the
PubMed database are manually annotated by human curators, which is time
consuming and costly; therefore, a computational system that can assist the
indexing is highly valuable. When developing supervised MeSH indexing systems,
the availability of a large-scale annotated text corpus is desirable. A
publicly available, large corpus that permits robust evaluation and comparison
of various systems is important to the research community. We release a large
scale annotated MeSH indexing corpus, MeSHup, which contains 1,342,667 full
text articles in English, together with the associated MeSH labels and
metadata, authors, and publication venues that are collected from the MEDLINE
database. We train an end-to-end model that combines features from documents
and their associated labels on our corpus and report the new baseline.


title: Flavour and dark matter in a scoto/type-II seesaw model

abstract:
  The neutrino mass and dark matter (DM) problems are addressed in a Standard
Model extension where the type-II seesaw and scotogenic mechanisms coexist. The
model features a flavour $\mathcal{Z}_8$ discrete symmetry which is broken down
to a $\mathcal{Z}_2$, stabilising the (scalar or fermion) DM particle.
Spontaneous CP violation is implemented through the complex vacuum expectation
value of a singlet scalar field, inducing observable CP-violating effects in
the lepton sector. The structure of the effective neutrino mass matrix leads to
constraints on the low-energy neutrino observables, namely the atmospheric
neutrino mixing angle $\theta_{23}$, the Dirac CP-violating phase $\delta$ and
the absolute neutrino mass scale $m_{\rm lightest}$. In particular, in most
cases, the model selects one $\theta_{23}$ octant with $\delta \simeq 3\pi/2$.
Moreover, the obtained lower bounds on $m_{\rm lightest}$ are typically in the
range probed by cosmology. We also analyse the constraints imposed on the model
by current experimental limits on charged lepton flavour violating (cLFV)
processes, as well as future projected sensitivities. It is shown that the
Higgs triplet and scotogenic contributions to cLFV never overlap and that the
interplay among Yukawa couplings, dark charged scalar masses and mixing leads
to a wide parameter-space region compatible with current experimental bounds.
We investigate the scalar and fermion DM parameter space of our model by
considering relic density, direct-detection (DD) and collider constraints. For
scalar DM the mass interval $68 \ \text{GeV} \lesssim m_{\text{DM}} \lesssim 90
\ \text{GeV}$ is viable and will be probed by future DD searches. In the
fermion DM case, correct relic density is always obtained for $m_{\text{DM}}
\gtrsim 45$ GeV thanks to dark fermion-scalar coannihilation channels.


title: Bona fide Riesz projections for density estimation

abstract:
  The projection of sample measurements onto a reconstruction space represented
by a basis on a regular grid is a powerful and simple approach to estimate a
probability density function. In this paper, we focus on Riesz bases and
propose a projection operator that, in contrast to previous works, guarantees
the bona fide properties for the estimate, namely, non-negativity and total
probability mass $1$. Our bona fide projection is defined as a convex problem.
We propose solution techniques and evaluate them. Results suggest an improved
performance, specifically in circumstances prone to rippling effects.


title:
Process-BERT: A Framework for Representation Learning on Educational
  Process Data

abstract:
  Educational process data, i.e., logs of detailed student activities in
computerized or online learning platforms, has the potential to offer deep
insights into how students learn. One can use process data for many downstream
tasks such as learning outcome prediction and automatically delivering
personalized intervention. However, analyzing process data is challenging since
the specific format of process data varies a lot depending on different
learning/testing scenarios. In this paper, we propose a framework for learning
representations of educational process data that is applicable across many
different learning scenarios. Our framework consists of a pre-training step
that uses BERT-type objectives to learn representations from sequential process
data and a fine-tuning step that further adjusts these representations on
downstream prediction tasks. We apply our framework to the 2019 nation's report
card data mining competition dataset that consists of student problem-solving
process data and detail the specific models we use in this scenario. We conduct
both quantitative and qualitative experiments to show that our framework
results in process data representations that are both predictive and
informative.


title:
Representative period selection for power system planning using
  autoencoder-based dimensionality reduction

abstract:
  Power sector capacity expansion models (CEMs) that are used for studying
future low-carbon grid scenarios must incorporate detailed representation of
grid operations. Often CEMs are formulated to model grid operations over
representative periods that are sampled from the original input data using
clustering algorithms. However, such representative period selection (RPS)
methods are limited by the declining efficacy of the clustering algorithm with
increasing dimensionality of the input data and do not consider the relative
importance of input data variations on CEM outcomes. Here, we propose a RPS
method that addresses these limitations by incorporating dimensionality
reduction, accomplished via neural network based autoencoders, prior to
clustering. Such dimensionality reduction not only improves the performance of
the clustering algorithm, but also facilitates using additional features, such
as estimated outputs produced from parallel solutions of simplified versions of
the CEM for each disjoint period in the input data (e.g. 1 week). The impact of
incorporating dimensionality reduction as part of RPS methods is quantified
through the error in outcomes of the corresponding reduced-space CEM vs. the
full space CEM. Extensive numerical experimentation across various networks and
range of technology and policy scenarios establish the superiority of the
dimensionality-reduction based RPS methods.


title: Non-lorentzian spacetimes

abstract:
  I review some of my recent work on non-lorentzian geometry. I review the
classification of kinematical Lie algebras and their associated Klein
geometries. I then describe the Cartan geometries modelled on them and their
characterisation in terms of their intrinsic torsion. (To appear in the special
issue "Applications of transformation groups" to be published by the Journal of
Differential Geometry and Applications.)


title: How social influence affects the wisdom of crowds in influence networks

abstract:
  A long-standing debate is whether social influence improves the collective
wisdom of a crowd or undermines it. This paper addresses this question based on
a naive learning setting in influence systems theory: in our models individuals
evolve their estimates of an unknown truth according to the weighted-average
opinion dynamics. A formal mathematization is provided with rigorous
theoretical analysis. We obtain various conditions for improving, optimizing
and undermining the crowd accuracy, respectively. We prove that if the wisdom
of finite-size group is improved, then the collective estimate converges to the
truth as group size increases, provided individuals' variances are finite. We
show that whether social influence improves or undermines the wisdom is
determined by the social power allocation of the influence system: if the
influence system allocates relatively larger social power to relatively more
accurate individuals, it improves the wisdom; on the contrary, if the influence
system assigns less social power to more accurate individuals, it undermines
the wisdom. At a population level, individuals' susceptibilities to
interpersonal influence and network centralities are both crucial. To improve
the wisdom, more accurate individuals should be less susceptible and have
larger network centralities. Particularly, in democratic influence networks, if
relatively more accurate individuals are relatively less susceptible, the
wisdom is improved; if more accurate individuals are more susceptible, the
wisdom is undermined, which is consistent with the reported empirical evidence.
Our investigation provides a theoretical framework for understanding the role
social influence plays in the emergence of collective wisdom.