title:
Detecting Distributional Differences in Labeled Sequence Data with
  Application to Tropical Cyclone Satellite Imagery

abstract:
  Our goal is to quantify whether, and if so how, spatio-temporal patterns in
tropical cyclone (TC) satellite imagery signal an upcoming rapid intensity
change event. To address this question, we propose a new nonparametric test of
association between a time series of images and a series of binary event
labels. We ask whether there is a difference in distribution between (dependent
but identically distributed) 24-h sequences of images preceding an event versus
a non-event. By rewriting the statistical test as a regression problem, we
leverage neural networks to infer modes of structural evolution of TC
convection that are representative of the lead-up to rapid intensity change
events. Dependencies between nearby sequences are handled by a bootstrap
procedure that estimates the marginal distribution of the label series. We
prove that type I error control is guaranteed as long as the distribution of
the label series is well-estimated, which is made easier by the extensive
historical data for binary TC event labels. We show empirical evidence that our
proposed method identifies archetypes of infrared imagery associated with
elevated rapid intensification risk, typically marked by deep or deepening core
convection over time. Such results provide a foundation for improved forecasts
of rapid intensification.


title: Derivatives Holdings and Systemic Risk in the U.S. Banking Sector

abstract:
  Foreign exchange and credit derivatives increase the bank's contributions to
systemic risk. Interest rate derivatives decrease it. The proportion of
non-performing loans over total loans and the leverage ratio have stronger
impact on systemic risk than derivatives holdings.


title: On the universality of fluctuations for the cover time

abstract:
  We consider random walks on vertex-transitive graphs of bounded degree. We
show that subject to a simple diameter condition (which guarantees in
particular that the walk is in some sense locally transient), the cover time
fluctuations are universal: after rescaling, they converge to a standard Gumbel
distribution. We further show by constructing an explicit counter-example that
our diameter condition is sharp in a very strong sense. Surprisingly, this
counter-example is also locally transient.
  We complement our result by showing that near the cover time, the
distribution of the uncovered set is close in total variation to a product
measure. The arguments rely on recent breakthroughs by Tessera and Tointon on
finitary versions of Gromov's theorem on groups of polynomial growth, which are
leveraged into strong heat kernel bounds that imply decorrelation of the
uncovered set. Another key aspect is an improvement on the exponential
approximation of hitting times due to Aldous and Brown, which is of independent
interest.


title: Size effects in adhesive contacts of viscoelastic media

abstract:
  Is the maximum force required to detach a rigid sphere from a viscoelastic
substrate dependent on the initial value of the contact radius? Experimental
and theoretical investigations reported in the literature have given opposite
responses. Here, we try to answer the above question by exploiting a fully
deterministic model in which adhesive interactions are described by
Lennard-Jones potential and the viscoelastic behaviour with the standard linear
solid model. When the approach and retraction phases are performed under
quasi-static conditions, the substrate behaves as an elastic medium and, as
expected, the pull-off force Fpo (i.e., the maximum tensile force) is found to
be independent of the maximum contact radius amax reached at the end of
loading. Size-dependent effects are instead observed (i.e., pull-off force Fpo
changes with amax) when transient effects occur as the larger the contact area,
the greater the size of the bulk volume involved in the dissipation. Results
are also discussed in the light of viscoelastic crack Persson's theory, which
is modified to capture size effects related to amax.


title:
Global renewable LCOE -- including socio-economic factors in assessments
  of resource potential

abstract:
  The global renewable resource assessments have been criticized for not
including country-specific economic and socio-political conditions. Here, we
take steps towards a more realistic assessment of renewable resource potential
by incorporating social considerations for land-use requirements, heterogenous
discount rate and electricity demand into the LCOE metric. We introduce three
metrics to assess the renewable resource potential: Renewable LCOE, Renewable
LCOE with country-dependent discount rate and Renewable LCOE available for
export. The latter measures a country's potential for renewable
self-sufficiency and export. We calculate and map these matrics for most
countries in the world. Our findings show that the economic and socio-political
factors greatly influence the assessment of cost and self-sufficiency.
Countries that stand out as having large potential for export of renewable
energy include the US, Australia and Saudi Arabia. The work that we present
here may be used for further analysis of future energy security of supply and
geopolitics.


title:
Seasonal prediction of renewable energy generation in Europe based on
  four teleconnection indices

abstract:
  With growing amounts of wind and solar power in the electricity mix of many
European countries, understanding and predicting variations of renewable energy
generation at multiple timescales is crucial to ensure reliable electricity
systems. At seasonal scale, the balance between supply and demand is mostly
determined by the large-scale atmospheric circulation, which is uncertain due
to climate change and natural variability. Here we employ four teleconnection
indices, which represent a linkage between atmospheric conditions at widely
separated regions, to describe the large-scale circulation at seasonal scale
over Europe. For the first time, we relate each of the teleconnections to the
wind and solar generation anomalies at country and regional level and we show
that dynamical forecasts of the teleconnection indices allow predicting
renewable generation at country level with positive skill levels. This model
unveils the co-variability of wind and solar generation in European countries
through its common dependence on the general circulation and the state of the
teleconnections.


title: Targeted Code Inspection based on Human Errors

abstract:
  As a direct cause of software defects, human error is the key to
understanding and identifying defects. We propose a new code inspection method:
targeted code inspection based on human error mechanisms of software engineers.
Based on the common erroneous mechanisms of human cognition, the method targets
error-prone codes with high efficiency and minimum effort. The proposed method
is supported by preliminary evidence in a pilot study.


title:
Theory of Disordered Superconductors with Applications to Nonlinear
  Current Response

abstract:
  I present a review of the theory and basic equations for charge transport in
superconducting alloys starting from the Keldysh formulation of the
quasiclassical transport equations developed by Eilenberger, Larkin and
Ovchinnikov and Eliashberg. This formulation is the natural extension of
Landau's theory of normal Fermi liquids to the superconducting state of
strongly correlated metals. For dirty metals the transport equations reduce to
equations for charge diffusion, with the current response given by the Drude
conductivity at low temperatures. The extension of the diffusion equation for
the charge and current response of a strongly disordered normal metal to the
superconducting state yields Usadel's equations for the non-equilibrium
quasiclassical Keldysh propagator. The conditions for the applicability of the
Usadel equations are discussed, the pair-breaking effect of disorder on the
current response, including the nonlinear current response to an EM field in
the dirty limit, $\tau \ll \hbar/\Delta$, are reported. The same nonlinearity
is shown to lead to source currents for photon generation and nonlinear Kerr
rotation driven by the nonlinear response to excitation of the superconductor
by a multi-mode EM field. The potential relevance of the nonlinear source
currents to SRF cavities as detectors of axion-like dark matter candidates is
briefly discussed.


title:
Infinite-horizon risk-sensitive performance criteria for translation
  invariant networks of linear quantum stochastic systems

abstract:
  This paper is concerned with networks of identical linear quantum stochastic
systems which interact with each other and external bosonic fields in a
translation invariant fashion. The systems are associated with sites of a
multidimensional lattice and are governed by coupled linear quantum stochastic
differential equations (QSDEs). The block Toeplitz coefficients of these QSDEs
are specified by the energy and coupling matrices which quantify the
Hamiltonian and coupling operators for the component systems. We discuss the
invariant Gaussian quantum state of the network when it satisfies a stability
condition and is driven by statistically independent vacuum fields. A
quadratic-exponential functional (QEF) is considered as a risk-sensitive
performance criterion for a finite fragment of the network over a bounded time
interval. This functional involves a quadratic function of dynamic variables of
the component systems with a block Toeplitz weighting matrix. Assuming the
invariant state, we study the spatio-temporal asymptotic rate of the QEF per
unit time and per lattice site in the thermodynamic limit of unboundedly
growing time horizons and fragments of the lattice. A spatio-temporal
frequency-domain formula is obtained for the QEF rate in terms of two spectral
functions associated with the real and imaginary parts of the invariant quantum
covariance kernel of the network variables. A homotopy method and asymptotic
expansions for evaluating the QEF rate are also discussed.


title: Decoupling Local and Global Representations of Time Series

abstract:
  Real-world time series data are often generated from several sources of
variation. Learning representations that capture the factors contributing to
this variability enables a better understanding of the data via its underlying
generative process and improves performance on downstream machine learning
tasks. This paper proposes a novel generative approach for learning
representations for the global and local factors of variation in time series.
The local representation of each sample models non-stationarity over time with
a stochastic process prior, and the global representation of the sample encodes
the time-independent characteristics. To encourage decoupling between the
representations, we introduce counterfactual regularization that minimizes the
mutual information between the two variables. In experiments, we demonstrate
successful recovery of the true local and global variability factors on
simulated data, and show that representations learned using our method yield
superior performance on downstream tasks on real-world datasets. We believe
that the proposed way of defining representations is beneficial for data
modelling and yields better insights into the complexity of real-world data.


title: Industry Characteristics and Financial Risk Spillovers

abstract:
  This paper proposes a new measure of tail risk spillover. The empirical
application provides evidence of significant volatility and tail risk
spillovers from the financial sector to many real economy sectors in the U.S.
economy in the period from 2001 to 2011. These spillovers increase in crisis
periods. The conditional coexceedance in a given sector is positively related
to its amount of debt financing, and negatively related to its relative
valuation and investment. Real economy sectors which require substantial
external financing, and whose value and investment activity are relatively
lower, are prime candidates for depreciation in the wake of crisis in the
financial sector.


title: De-Sequentialized Monte Carlo: a parallel-in-time particle smoother

abstract:
  Particle smoothers are SMC (Sequential Monte Carlo) algorithms designed to
approximate the joint distribution of the states given observations from a
state-space model. We propose dSMC (de-Sequentialized Monte Carlo), a new
particle smoother that is able to process $T$ observations in $\mathcal{O}(\log
T)$ time on parallel architecture. This compares favourably with standard
particle smoothers, the complexity of which is linear in $T$. We derive
$\mathcal{L}_p$ convergence results for dSMC, with an explicit upper bound,
polynomial in $T$. We then discuss how to reduce the variance of the smoothing
estimates computed by dSMC by (i) designing good proposal distributions for
sampling the particles at the initialization of the algorithm, as well as by
(ii) using lazy resampling to increase the number of particles used in dSMC.
Finally, we design a particle Gibbs sampler based on dSMC, which is able to
perform parameter inference in a state-space model at a $\mathcal{O}(\log(T))$
cost on parallel hardware.


title:
Iterative Self Knowledge Distillation -- From Pothole Classification to
  Fine-Grained and COVID Recognition

abstract:
  Pothole classification has become an important task for road inspection
vehicles to save drivers from potential car accidents and repair bills. Given
the limited computational power and fixed number of training epochs, we propose
iterative self knowledge distillation (ISKD) to train lightweight pothole
classifiers. Designed to improve both the teacher and student models over time
in knowledge distillation, ISKD outperforms the state-of-the-art self knowledge
distillation method on three pothole classification datasets across four
lightweight network architectures, which supports that self knowledge
distillation should be done iteratively instead of just once. The accuracy
relation between the teacher and student models shows that the student model
can still benefit from a moderately trained teacher model. Implying that better
teacher models generally produce better student models, our results justify the
design of ISKD. In addition to pothole classification, we also demonstrate the
efficacy of ISKD on six additional datasets associated with generic
classification, fine-grained classification, and medical imaging application,
which supports that ISKD can serve as a general-purpose performance booster
without the need of a given teacher model and extra trainable parameters.


title:
Polynomial convergence of iterations of certain random operators in
  Hilbert space

abstract:
  We study the convergence of random iterative sequence of a family of
operators on infinite dimensional Hilbert spaces, which are inspired by the
Stochastic Gradient Descent (SGD) algorithm in the case of the noiseless
regression, as studied in [1]. We demonstrate that its polynomial convergence
rate depends on the initial state, while the randomness plays a role only in
the choice of the best constant factor and we close the gap between the upper
and lower bounds.


title:
On the long and short-range adhesive interactions in viscoelastic
  contacts

abstract:
  Recently, tribologists have shown increasing interest in rate-dependent
phenomena occurring in viscoelastic fractures. However, in some cases,
conflicting results are obtained despite the use of similar theoretical models.
For this reason, we try to shed light on the effects that long and short-range
adhesion has on the pull-off force in the contact of viscoelastic media by
exploiting a recently developed numerical model. We find that, in the limit of
long-range adhesion, the unloading velocity has little effect on the pull-off
force, which is close to the value predicted by Bradley for rigid bodies. In
such case, the detachment process is characterized by a uniform bond-breaking
of the contact area, and viscous dissipation involves the bulk material. For
medium(short)-range adhesion, the pull-off force is instead a monotonic
increasing function of the pulling velocity and, at high speeds, reaches a
plateau that is a function of the adiabatic surface energy. In this case, the
detachment process is similar to the opening of a circular crack, and viscous
dissipation is localized at the contact edge.


title: StonkBERT: Can Language Models Predict Medium-Run Stock Price Movements?

abstract:
  To answer this question, we fine-tune transformer-based language models,
including BERT, on different sources of company-related text data for a
classification task to predict the one-year stock price performance. We use
three different types of text data: News articles, blogs, and annual reports.
This allows us to analyze to what extent the performance of language models is
dependent on the type of the underlying document. StonkBERT, our
transformer-based stock performance classifier, shows substantial improvement
in predictive accuracy compared to traditional language models. The highest
performance was achieved with news articles as text source. Performance
simulations indicate that these improvements in classification accuracy also
translate into above-average stock market returns.


title:
Effects of intercomponent couplings on the appearance of
  time-reversal-symmetry-breaking fermion quadrupling state in two-component
  London model

abstract:
  A detection of bosonic metallic state that breaks the $Z_2$ time-reversal
symmetry has been recently reported in Ba$_{1-x}$K$_x$Fe$_2$As$_2$ with a
doping level $x \approx 0.8$. This is a metallic state of fermionic quadruplets
that breaks time-reversal symmetry. As such, it has no condensate of Cooper
pairs but has a long-range order between fermionic quartets. In the present
manuscript, we investigate the emergence of this phase in a two-component
London model via Monte Carlo simulations as a function of various
intercomponent couplings.


title: Direct Telemetry Access

abstract:
  The emergence of programmable switches allows operators to collect a vast
amount of fine-grained telemetry data in real time. However, consolidating the
telemetry reports at centralized collectors to gain a network-wide view poses
an immense challenge. The received data has to be transported from the
switches, parsed, manipulated, and inserted in queryable data structures. As
the network scales, this requires excessive CPU processing. RDMA is a transport
protocol that bypasses the CPU and allows extremely high data transfer rates.
Yet, RDMA is not designed for telemetry collection: it requires a stateful
connection, supports only a small number of concurrent writers, and has limited
writing primitives, which restricts its data aggregation applicability.
  We introduce Direct Telemetry Access (DTA), a solution that allows fast and
efficient telemetry collection, aggregation, and indexing. Our system
establishes RDMA connections only from collectors' ToR switches, called
\emph{translators}, that process DTA reports from all other switches. DTA
features novel and expressive reporting primitives such as Key-Write, Append,
Sketch-Merge, and Key-Increment that allow integration of telemetry systems
such as INT and others. The translators then aggregate, batch, and write the
reports to collectors' memory in queryable form.


title: Quantum number towers for the Hubbard and Holstein models

abstract:
  In 1989, Elliott Lieb published a Physical Review Letter proving two theorems
about the Hubbard model. This paper used the concept of spin-reflection
positivity to prove that the ground state of the attractive Hubbard model was
always a nondegenerate spin singlet and to also prove that the ground state for
the repulsive model on a bipartite lattice had spin
||\Lambda_A|-|\Lambda_B||/2, corresponding to the difference in number of
lattice sites for the two sublattices. In addition, this work relates to
quantum number towers -- where the minimal energy state with a given quantum
number, such as spin, or pseudospin, is ordered, according to the spin or
pseudospin values. It was followed up in 1995 by a second paper that extended
some of these results to the Holstein model (and more general electron-phonon
models). These works prove results about the quantum numbers of these many-body
models in condensed matter physics and have been very influential. In this
chapter, I will discuss the context for these proofs, what they mean, and the
remaining open questions related to the original work. In addition, I will
briefly discuss some of the additional work that this methodology inspired.


title:
A multi-model ensemble Kalman filter for data assimilation and
  forecasting

abstract:
  Data assimilation (DA) aims to optimally combine model forecasts and noisy
observations. Multi-model DA generalizes the variational or Bayesian
formulation of the Kalman filter, and we prove here that it is also the minimum
variance linear unbiased estimator. However, previous implementations of this
approach have not estimated the model error, and have therewith not been able
to correctly weight the separate models and the observations. Here, we show how
multiple models can be combined for both forecasting and DA by using an
ensemble Kalman filter with adaptive model error estimation. This methodology
is applied to the Lorenz96 model, and it results in significant error
reductions compared to the best model and to an unweighted multi-model
ensemble.

categories: stat.ME cs.SY eess.SY math.OC physics.ao-ph


title: Are all Credit Default Swap Databases equal?

abstract:
  We compare the five major sources of corporate Credit Default Swap prices:
GFI, Fenics, Reuters, CMA, and Markit, using the most liquid single name 5-year
CDS in the iTraxx and CDX indexes from 2004 to 2010. Deviations from the common
trend among prices in the different databases are not random but are explained
by idiosyncratic factors, financing costs, global risk, and other trading
factors. The CMA quotes lead the price discovery process. Moreover, we find
that there is not a full agreement among databases in the results of the price
discovery analysis between stock and CDS returns.


title: Percolation on dense random graphs with given degrees

abstract:
  In this paper, we study the order of the largest connected component of a
random graph having two sources of randomness: first, the graph is chosen
randomly from all graphs with a given degree sequence, and then bond
percolation is applied. Far from being able to classify all such degree
sequences, we exhibit several new threshold phenomena for the order of the
largest component in terms of both sources of randomness. We also provide an
example of a degree sequence, for which the order of the largest component
undergoes an unbounded number of jumps in terms of the percolation parameter,
giving rise to a behavior that cannot be observed without percolation.


title: Inverse-Compton Scattering of the Cosmic Infrared Background

abstract:
  The thermal Sunyaev-Zel'dovich (tSZ) effect is the distortion generated in
the cosmic microwave background (CMB) spectrum by the inverse-Compton
scattering of CMB photons off free, energetic electrons, primarily located in
the intracluster medium (ICM). Cosmic infrared background (CIB) photons from
thermal dust emission in star-forming galaxies are expected to undergo the same
process. In this work, we perform the first calculation of the resulting
tSZ-like distortion in the CIB. Focusing on the CIB monopole, we use a halo
model approach to calculate both the CIB signal and the Compton-$y$ field that
generates the distortion. We self-consistently account for the redshift
co-evolution of the CIB and Compton-$y$ fields: they are (partially) sourced by
the same dark matter halos, which introduces new aspects to the calculation as
compared to the CMB case. We find that the inverse-Compton distortion to the
CIB monopole spectrum has a positive (negative) peak amplitude of $\approx 7.5$
Jy/sr ($\approx -9$ Jy/sr) at 2140 GHz (915 GHz). In contrast to the usual tSZ
effect, the distortion to the CIB spectrum has two null frequencies, at
approximately 190 GHz and 1440 GHz. We perform a Fisher matrix calculation to
forecast the detectability of this new distortion signal by future experiments.
$\textit{PIXIE}$ would have sufficient instrumental sensitivity to detect the
signal at $7\sigma$, but foreground contamination reduces the projected
signal-to-noise by a factor of $\approx 70$. A future ESA Voyage 2050
spectrometer would detect the CIB distortion at $\approx 2$-$12\sigma$
significance, even after marginalizing over foregrounds. A measurement of this
signal would provide new information on the star formation history of the
Universe, and the distortion anisotropies may be accessible by near-future
ground-based experiments.


title:
Measuring Systemic Risk: Common Factor Exposures and Tail Dependence
  Effects

abstract:
  We model systemic risk using a common factor that accounts for market-wide
shocks and a tail dependence factor that accounts for linkages among extreme
stock returns. Specifically, our theoretical model allows for firm-specific
impacts of infrequent and extreme events. Using data on the four sectors of the
U.S. financial industry from 1996 to 2011, we uncover two key empirical
findings. First, disregarding the effect of the tail dependence factor leads to
a downward bias in the measurement of systemic risk, especially during weak
economic times. Second, when these measures serve as leading indicators of the
St. Louis Fed Financial Stress Index, measures that include a tail dependence
factor offer better forecasting ability than measures based on a common factor
only.


title:
Quality Assessment of Low Light Restored Images: A Subjective Study and
  an Unsupervised Model

abstract:
  The quality assessment (QA) of restored low light images is an important tool
for benchmarking and improving low light restoration (LLR) algorithms. While
several LLR algorithms exist, the subjective perception of the restored images
has been much less studied. Challenges in capturing aligned low light and
well-lit image pairs and collecting a large number of human opinion scores of
quality for training, warrant the design of unsupervised (or opinion unaware)
no-reference (NR) QA methods. This work studies the subjective perception of
low light restored images and their unsupervised NR QA. Our contributions are
two-fold. We first create a dataset of restored low light images using various
LLR methods, conduct a subjective QA study and benchmark the performance of
existing QA methods. We then present a self-supervised contrastive learning
technique to extract distortion aware features from the restored low light
images. We show that these features can be effectively used to build an opinion
unaware image quality analyzer. Detailed experiments reveal that our
unsupervised NR QA model achieves state-of-the-art performance among all such
quality measures for low light restored images.


title: LTU Attacker for Membership Inference

abstract:
  We address the problem of defending predictive models, such as machine
learning classifiers (Defender models), against membership inference attacks,
in both the black-box and white-box setting, when the trainer and the trained
model are publicly released. The Defender aims at optimizing a dual objective:
utility and privacy. Both utility and privacy are evaluated with an external
apparatus including an Attacker and an Evaluator. On one hand, Reserved data,
distributed similarly to the Defender training data, is used to evaluate
Utility; on the other hand, Reserved data, mixed with Defender training data,
is used to evaluate membership inference attack robustness. In both cases
classification accuracy or error rate are used as the metric: Utility is
evaluated with the classification accuracy of the Defender model; Privacy is
evaluated with the membership prediction error of a so-called
"Leave-Two-Unlabeled" LTU Attacker, having access to all of the Defender and
Reserved data, except for the membership label of one sample from each. We
prove that, under certain conditions, even a "na\"ive" LTU Attacker can achieve
lower bounds on privacy loss with simple attack strategies, leading to concrete
necessary conditions to protect privacy, including: preventing over-fitting and
adding some amount of randomness. However, we also show that such a na\"ive LTU
Attacker can fail to attack the privacy of models known to be vulnerable in the
literature, demonstrating that knowledge must be complemented with strong
attack strategies to turn the LTU Attacker into a powerful means of evaluating
privacy. Our experiments on the QMNIST and CIFAR-10 datasets validate our
theoretical results and confirm the roles of over-fitting prevention and
randomness in the algorithms to protect against privacy attacks.


title: A $J$-Symmetric Quasi-Newton Method for Minimax Problems

abstract:
  Minimax problems have gained tremendous attentions across the optimization
and machine learning community recently. In this paper, we introduce a new
quasi-Newton method for minimax problems, which we call $J$-symmetric
quasi-Newton method. The method is obtained by exploiting the $J$-symmetric
structure of the second-order derivative of the objective function in minimax
problem. We show that the Hessian estimation (as well as its inverse) can be
updated by a rank-2 operation, and it turns out that the update rule is a
natural generalization of the classic Powell symmetric Broyden (PSB) method
from minimization problems to minimax problems. In theory, we show that our
proposed quasi-Newton algorithm enjoys local Q-superlinear convergence to a
desirable solution under standard regularity conditions. Furthermore, we
introduce a trust-region variant of the algorithm that enjoys global
R-superlinear convergence. Finally, we present numerical experiments that
verify our theory and show the effectiveness of our proposed algorithms
compared to Broyden's method and the extragradient method on three classes of
minimax problems.


title:
Portfolio Choice with Indivisible and Illiquid Housing Assets: The Case
  of Spain

abstract:
  This paper studies the investment decision of the Spanish households using a
unique data set, the Spanish Survey of Household Finance (EFF). We propose a
theoretical model in which households, given a fixed investment in housing,
allocate their net wealth across bank time deposits, stocks, and mortgage.
Besides considering housing as an indivisible and illiquid asset that restricts
the portfolio choice decision, we take into account the financial constraints
that households face when they apply for external funding. For every
representative household in the EFF we solve this theoretical problem and
obtain the theoretically optimal portfolio that is compared with households'
actual choices. We find that households significantly underinvest in stocks and
deposits while the optimal and actual mortgage investments are alike.
Considering the three types of financial assets at once, we find that the
households headed by highly financially sophisticated, older, retired, richer,
and unconstrained persons are the ones investing more efficiently.


title:
"I'm Just Overwhelmed": Investigating Physical Therapy Accessibility and
  Technology Interventions for People with Disabilities and/or Chronic
  Conditions

abstract:
  Many individuals with disabilities and/or chronic conditions (da/cc)
experience symptoms that may require intermittent or ongoing medical care.
However, healthcare is an often-overlooked domain for accessibility work, where
access needs associated with temporary and long-term disability must be
addressed to increase the utility of physical and digital interactions with
healthcare workers and spaces. Our work focuses on a specific domain of
healthcare often used by individuals with da/ccs: Physical Therapy (PT).
Through a twelve-person interview study, we examined how people's access to PT
for their da/cc is hampered by social (e.g., physically visiting a PT clinic)
and physiological (e.g., chronic pain) barriers, and how technology could
improve PT access. In-person PT is often inaccessible to our participants due
to lack of transportation and insufficient insurance coverage. As such, many of
our participants relied on at-home PT to manage their da/cc symptoms and worked
towards PT goals. Participants felt that PT barriers, such as having
particularly bad symptoms or feeling short on time, could be addressed with
well-designed technology that flexibly adapts to the person's dynamically
changing needs while supporting their PT goals. We introduce core design
principles (flexibility, movement tracking, community building) and tensions
(insurance) to consider when developing technology to support PT access.
Rethinking da/cc access to PT from a lens that includes social and
physiological barriers presents opportunities to integrate accessibility and
flexibility into PT technology.


title: Benefits of Rate-Sharing for Distributed Hypothesis Testing

abstract:
  We study distributed binary hypothesis testing with a single sensor and two
remote decision centers that are also equipped with local sensors. The
communication between the sensor and the two decision centers takes place over
three links: a shared link to both centers and an individual link to each of
the two centers. All communication links are subject to expected rate
constraints. This paper characterizes the optimal exponents region of the
type-II error for given type-I error thresholds at the two decision centers and
further simplifies the expressions in the special case of having only the
single shared link. The exponents region illustrates a gain under expected rate
constraints compared to equivalent maximum rate constraints. Moreover, it
exhibits a tradeoff between the exponents achieved at the two centers.


title:
Choosing an Appropriate Platform and Workflow for Processing Camera Trap
  Data using Artificial Intelligence

abstract:
  Camera traps have transformed how ecologists study wildlife species
distributions, activity patterns, and interspecific interactions. Although
camera traps provide a cost-effective method for monitoring species, the time
required for data processing can limit survey efficiency. Thus, the potential
of Artificial Intelligence (AI), specifically Deep Learning (DL), to process
camera-trap data has gained considerable attention. Using DL for these
applications involves training algorithms, such as Convolutional Neural
Networks (CNNs), to automatically detect objects and classify species. To
overcome technical challenges associated with training CNNs, several research
communities have recently developed platforms that incorporate DL in
easy-to-use interfaces. We review key characteristics of four AI-powered
platforms -- Wildlife Insights (WI), MegaDetector (MD), Machine Learning for
Wildlife Image Classification (MLWIC2), and Conservation AI -- including data
management tools and AI features. We also provide R code in an open-source
GitBook, to demonstrate how users can evaluate model performance, and
incorporate AI output in semi-automated workflows. We found that species
classifications from WI and MLWIC2 generally had low recall values (animals
that were present in the images often were not classified to the correct
species). Yet, the precision of WI and MLWIC2 classifications for some species
was high (i.e., when classifications were made, they were generally accurate).
MD, which classifies images using broader categories (e.g., "blank" or
"animal"), also performed well. Thus, we conclude that, although species
classifiers were not accurate enough to automate image processing, DL could be
used to improve efficiencies by accepting classifications with high confidence
values for certain species or by filtering images containing blanks.


title: Two-current transition amplitudes with two-body final states

abstract:
  We derive the on-shell form of amplitudes containing two external currents
with a single hadron in the initial state and two hadrons in the final state,
denoted as $1+\mathcal{J}\to 2+\mathcal{J}$. This class of amplitude is
relevant in precision tests of the Standard Model as well as for exploring the
structure of excited states in the QCD spectrum. We present a model-independent
description of the amplitudes where we sum to all orders in the strong
interaction. From this analytic form we are able to extract transition and
elastic resonance form factors consistent with previous work as well as a novel
Compton-like amplitude coupling a single particle state to a resonance. The
results also hold for reactions where the one-particle state is replaced with
the vacuum, namely $\mathcal{J}\to 2+\mathcal{J}$ amplitudes. We also
investigate constraints placed upon the formalism for the case of a conserved
vector current in the form of the Ward-Takahashi identity. The formalism
presented here is valid for currents of arbitrary Lorentz structure and quantum
numbers with spinless hadrons where any number of two-particle intermediate
channels may be open. When combined with the appropriate finite-volume
framework, this work facilitates the extraction of physical observables from
this class of amplitudes via lattice QCD calculations.


title:
Enhancing the Formation of Wigner Negativity in a Kerr Oscillator via
  Quadrature Squeezing

abstract:
  Motivated by quantum experiments with nanomechanical systems, the evolution
of a Kerr oscillator with focus on creation of states with a negative Wigner
function is investigated. Using the phase space formalism, results are
presented that demonstrate an asymptotic behavior in the large squeezing regime
for the negativity of a squeezed vacuum state under unitary evolution. The
analysis and model are extended to squeezed vacuum states of open systems,
adding the decoherence effects of damping and dephasing. To increase
experimental relevance, the regime of strong damping is considered. These
effects are investigated, yielding similar asymptotic results for the behavior
of these effects in the large squeezing regime. Combining these results, it is
shown that a weak nonlinearity as compared to damping may be improved by
increasing the squeezing of the initial state. It is also shown that this may
be done without exacerbating the effects of dephasing.


title:
The Discrete Gaussian model, I. Renormalisation group flow at high
  temperature

abstract:
  The Discrete Gaussian model is the lattice Gaussian free field conditioned to
be integer-valued. In two dimensions, at sufficiently high temperature, we show
that its macroscopic scaling limit on the torus is a multiple of the Gaussian
free field. Our proof starts from a single renormalisation group step after
which the integer-valued field becomes a smooth field which we then analyse
using the renormalisation group method.
  This paper also provides the foundation for the construction of the scaling
limit of the infinite-volume gradient Gibbs state of the Discrete Gaussian
model in the companion paper. Moreover, we develop all estimates for general
finite-range interaction with sharp dependence on the range. We expect these
estimates to prepare for a future analysis of the spread-out version of the
Discrete Gaussian model at its critical temperature.


title:
The Discrete Gaussian model, II. Infinite-volume scaling limit at high
  temperature

abstract:
  The Discrete Gaussian model is the lattice Gaussian free field conditioned to
be integer-valued. In two dimensions, at sufficiently high temperature, we show
that the scaling limit of the infinite-volume gradient Gibbs state with zero
mean is a multiple of the Gaussian free field.
  This article is the second in a series on the Discrete Gaussian model,
extending the methods of the first paper by the analysis of general external
fields (rather than macroscopic test functions on the torus). As a byproduct,
we also obtain a scaling limit for mesoscopic test functions on the torus.


title:
Tuning the rheology and microstructure of particle-laden fluid
  interfaces with Janus particles

abstract:
  Particle-laden fluid interfaces are the central component of many natural and
engineering systems. Extensive research efforts have been dedicated to
understand the mechanical properties and improve the stability of such
interfaces. Here, we explore the effect of Janus particle additives on the
interfacial rheology and microscopic structure of particle-laden fluid
interfaces. Using a custom-built interfacial stress rheometer, we show that the
addition of a small amount of platinum-polystyrene (Pt-PS) Janus particles
within a monolayer of PS colloids (1:40 number ratio) can lead to more than an
order-of-magnitude increase in surface moduli with enhanced elasticity, which
greatly improves the stability of the interface. This drastic change in
interfacial rheology is associated with the formation of local particle
clusters surrounding each Janus particle. We further explain the origin of
local particle clusters by considering the interparticle interactions at the
interface. Our experiments reveal the effect of local particle structures on
the macroscopic rheological behaviors of particle monolayers and demonstrate a
new way to tune the microstructure and mechanical properties of particle-laden
fluid interfaces.


title:
Bipolar oriented random planar maps with large faces and exotic
  SLE$_\kappa(\rho)$ processes

abstract:
  We consider bipolar oriented random planar maps with heavy-tailed face
degrees. We show for each $\alpha \in (1,2)$ that if the face degree is in the
domain of attraction of an $\alpha$-stable L\'evy process, the corresponding
random planar map has an infinite volume limit in the Benjamini-Schramm
topology. We also show in the limit that the properly rescaled contour
functions associated with the northwest and southeast trees converge in law to
a certain correlated pair of $\alpha$-stable L\'evy processes. Combined with
other work, this allows us to identify the scaling limit of the planar map with
an SLE$_\kappa(\rho)$ process with $\rho = \kappa-4 < -2$ on
$\sqrt{\kappa}$-Liouville quantum gravity for $\kappa \in (4/3,2)$ where
$\alpha, \kappa$ are related by $\alpha = 4/\kappa-1$.


title:
Stellar Mass-to-light Ratios: Composite Bulge+Disk Models and the
  Baryonic Tully-Fisher Relation

abstract:
  We present stellar population models to calculate the mass-to-light ratio
($\Upsilon_*$) based on galaxy's colors ranging from $GALEX$ FUV to Spitzer
IRAC1 at 3.6$\mu$m. We present a new composite bulge+disk $\Upsilon_*$ model
that considers the varying contribution from bulges and disks based on their
optical and near-IR colors. Using these colors, we build plausible star
formation histories and chemical enrichment scenarios based on the star
formation rate-stellar mass and mass-metallicity correlations for star-forming
galaxies. The most accurate prescription is to use the actual colors for the
bulge and disk components to constrain $\Upsilon_*$; however, a reasonable
bulge+disk model plus total color only introduces 5% more uncertainty. Full
bulge+disk $\Upsilon_*$ prescriptions applied to the baryonic TF relation
improves the linearity of the correlation, increases the slope and reduces the
total scatter by 4%.


title: A lattice study of $\pi\pi$ scattering at large $N_\text{c}$

abstract:
  We present the first lattice study of pion-pion scattering with varying
number of colors, $N_\text{c}$. We use lattice simulations with four degenerate
quark flavors, $N_\text{f}=4$, and $N_\text{c}=3-6$. We focus on two scattering
channels that do not involve vacuum diagrams. These correspond to two
irreducible representations of the SU(4) flavor group: the fully symmetric one,
$SS$, and the fully antisymmetric one, $AA$. The former is a repulsive channel
equivalent to the isospin-2 channel of SU(2). By contrast, the latter is
attractive and only exists for $N_\text{f} \geq 4$. A representative state is
$\left( |D_s^+ \pi^+\rangle - |D^+ K^+\rangle\right)/\sqrt{2}$. Using
L\"uscher's formalism, we extract the near-threshold scattering amplitude and
we match our results to Chiral Perturbation Theory (ChPT) at large
$N_\text{c}$. For this, we compute the analytical U$(N_\text{f})$ ChPT
prediction for two-pion scattering, and use the lattice results to constrain
the $N_\text{c}$ scaling of the relevant low-energy couplings.


title:
$Z^\prime$-mediated Majorana dark matter: suppressed direct-detection
  rate and complementarity of LHC searches

abstract:
  We study the direct-detection rate for axial-vectorial dark matter scattering
off nuclei in an $\mathrm{SU}(2)\times \mathrm{U}(1)$ invariant effective
theory and compare it against the LHC reach. Current constraints from direct
detection experiments are already bounding the mediator mass to be well into
the TeV range for WIMP-like scenarios. This motivates a consistent and
systematic exploration of the parameter space to map out possible regions where
the rates could be suppressed. We do indeed find such regions and proceed to
construct consistent UV models that generate the relevant effective theory. We
then discuss the corresponding constraints from both collider and
direct-detection experiments on the same parameter space. We find a benchmark
scenario, where even for future XENONnT experiment, LHC constraints will have a
greater sensitivity to the mediator mass.


title: A nonlinear PPH-type reconstruction based on equilateral triangles

abstract:
  In this paper we introduce a new nonlinear reconstruction operator over two
dimensional triangularized domains with equilateral triangles. We focus on the
local definition of the operator. The ideas behind this definition come from
some basic properties of the Harmonic mean of three positive values. We prove
some results regarding the approximation properties of the operator and we
carry out some numerical tests giving evidence of the avoidance of any Gibbs
effects.


title:
Pre-Trained Neural Language Models for Automatic Mobile App User
  Feedback Answer Generation

abstract:
  Studies show that developers' answers to the mobile app users' feedbacks on
app stores can increase the apps' star rating. To help app developers generate
answers that are related to the users' issues, recent studies develop models to
generate the answers automatically. Aims: The app response generation models
use deep neural networks and require training data. Pre-Trained neural language
Models (PTM) used in Natural Language Processing (NLP) take advantage of the
information they learned from a large corpora in an unsupervised manner, and
can reduce the amount of required training data. In this paper, we evaluate
PTMs to generate replies to the mobile app user feedbacks. Method: We train a
Transformer model from scratch and fine-tune two PTMs to evaluate the generated
responses, which are compared to RRGEN, a current app response model. We also
evaluate the models with different portions of the training data. Results: The
results on a large dataset evaluated by automatic metrics show that PTMs obtain
lower scores than the baselines. However, our human evaluation confirms that
PTMs can generate more relevant and meaningful responses to the posted
feedbacks. Moreover, the performance of PTMs has less drop compared to other
models when the amount of training data is reduced to 1/3. Conclusion: PTMs are
useful in generating responses to app reviews and are more robust models to the
amount of training data provided. However, the prediction time is 19X than
RRGEN. This study can provide new avenues for research in adapting the PTMs for
analyzing mobile app user feedbacks. Index Terms-mobile app user feedback
analysis, neural pre-trained language models, automatic answer generation


title: Log-Sobolev inequality for the $\varphi^4_2$ and $\varphi^4_3$ measures

abstract:
  The continuum $\varphi^4_2$ and $\varphi^4_3$ measures are shown to satisfy a
log-Sobolev inequality uniformly in the lattice regularisation under the
optimal assumption that their susceptibility is bounded. In particular, this
applies to all coupling constants in any finite volume, and uniformly in the
volume in the entire high temperature phases of the $\varphi^4_2$ and
$\varphi^4_3$ models.
  The proof uses a general criterion for the log-Sobolev inequality in terms of
the Polchinski (renormalisation group) equation, a recently proved remarkable
correlation inequality for Ising models with general external fields, the
Perron--Frobenius theorem, and bounds on the susceptibilities of the
$\varphi^4_2$ and $\varphi^4_3$ measures obtained using skeleton inequalities.


title: Graph-Coupled Oscillator Networks

abstract:
  We propose Graph-Coupled Oscillator Networks (GraphCON), a novel framework
for deep learning on graphs. It is based on discretizations of a second-order
system of ordinary differential equations (ODEs), which model a network of
nonlinear forced and damped oscillators, coupled via the adjacency structure of
the underlying graph. The flexibility of our framework permits any basic GNN
layer (e.g. convolutional or attentional) as the coupling function, from which
a multi-layer deep neural network is built up via the dynamics of the proposed
ODEs. We relate the oversmoothing problem, commonly encountered in GNNs, to the
stability of steady states of the underlying ODE and show that zero-Dirichlet
energy steady states are not stable for our proposed ODEs. This demonstrates
that the proposed framework mitigates the oversmoothing problem. Finally, we
show that our approach offers competitive performance with respect to the
state-of-the-art on a variety of graph-based learning tasks.


title:
Uniform tail estimates and $L^p(\mathbb{R}^N)$-convergence for
  finite-difference approximations of nonlinear diffusion equations

abstract:
  We obtain new equitightness and $C([0,T];L^p(\mathbb{R}^N))$-convergence
results for numerical approximations of generalized porous medium equations of
the form $$ \partial_tu-\mathfrak{L}[\varphi(u)]=g\qquad\text{in
$\mathbb{R}^N\times(0,T)$}, $$ where $\varphi:\mathbb{R}\to\mathbb{R}$ is
continuous and nondecreasing, and $\mathfrak{L}$ is a local or nonlocal
diffusion operator. Our results include slow diffusions, strongly degenerate
Stefan problems, and fast diffusions above a critical exponent. These results
improve the previous $C([0,T];L_{\text{loc}}^p(\mathbb{R}^N))$-convergence
obtained in a series of papers on the topic by the authors. To have
equitightness and global $L^p$-convergence, some additional restrictions on
$\mathfrak{L}$ and $\varphi$ are needed. Most commonly used symmetric operators
$\mathfrak{L}$ are still included: the Laplacian, fractional Laplacians, and
other generators of symmetric L\'evy processes with some fractional moment. We
also discuss extensions to nonlinear possibly strongly degenerate
convection-diffusion equations.


title: Towards a consistent interpretation of AIOps models

abstract:
  Artificial Intelligence for IT Operations (AIOps) has been adopted in
organizations in various tasks, including interpreting models to identify
indicators of service failures. To avoid misleading practitioners, AIOps model
interpretations should be consistent (i.e., different AIOps models on the same
task agree with one another on feature importance). However, many AIOps studies
violate established practices in the machine learning community when deriving
interpretations, such as interpreting models with suboptimal performance,
though the impact of such violations on the interpretation consistency has not
been studied. In this paper, we investigate the consistency of AIOps model
interpretation along three dimensions: internal consistency, external
consistency, and time consistency. We conduct a case study on two AIOps tasks:
predicting Google cluster job failures, and Backblaze hard drive failures. We
find that the randomness from learners, hyperparameter tuning, and data
sampling should be controlled to generate consistent interpretations. AIOps
models with AUCs greater than 0.75 yield more consistent interpretation
compared to low-performing models. Finally, AIOps models that are constructed
with the Sliding Window or Full History approaches have the most consistent
interpretation with the trends presented in the entire datasets. Our study
provides valuable guidelines for practitioners to derive consistent AIOps model
interpretation.


title: Multi-task head pose estimation in-the-wild

abstract:
  We present a deep learning-based multi-task approach for head pose estimation
in images. We contribute with a network architecture and training strategy that
harness the strong dependencies among face pose, alignment and visibility, to
produce a top performing model for all three tasks. Our architecture is an
encoder-decoder CNN with residual blocks and lateral skip connections. We show
that the combination of head pose estimation and landmark-based face alignment
significantly improve the performance of the former task. Further, the location
of the pose task at the bottleneck layer, at the end of the encoder, and that
of tasks depending on spatial information, such as visibility and alignment, in
the final decoder layer, also contribute to increase the final performance. In
the experiments conducted the proposed model outperforms the state-of-the-art
in the face pose and visibility tasks. By including a final landmark regression
step it also produces face alignment results on par with the state-of-the-art.


title: On Robust Optimal Linear Feedback Stock Trading

abstract:
  The take-off point for this paper is the Simultaneous Long-Short (SLS)
control class, which is known to guarantee the so-called robust positive
expectation (RPE) property. That is, the expected cumulative trading gain-loss
function is guaranteed to be positive for a broad class of stock price
processes. This fact attracts many new extensions and ramifications to the SLS
theory. However, it is arguable that "systematic" way to select an optimal
decision variable that is robust in the RPE sense is still unresolved. To this
end, we propose a modified SLS control structure, which we call the {double
linear feedback control scheme}, that allows us to solve the issue above for
stock price processes involving independent returns. In this paper, we go
beyond the existing literature by not only deriving explicit expressions for
the expected value and variance of cumulative gain-loss function but also
proving various theoretical results regarding {robust positive expected growth}
and {monotonicity}. Subsequently, we propose a new {robust optimal gain
selection problem} that seeks a solution maximizing the expected trading
gain-loss subject to the prespecified standard deviation {and} RPE constraints.
Under some mild conditions, we show that the optimal solution exists and is
unique. Moreover, a simple graphical approach that allows one to systematically
determine the optimal solution is also proposed. Finally, some numerical and
empirical studies using historical price data are also provided to support our
theory.


title: Log-Sobolev inequality for near critical Ising models

abstract:
  For general ferromagnetic Ising models whose coupling matrix has bounded
spectral radius, we show that the log-Sobolev constant satisfies a simple bound
expressed only in terms of the susceptibility of the model. This bound implies
very generally that the log-Sobolev constant is uniform in the system size up
to the critical point (including on lattices), without using any mixing
conditions. Moreover, if the susceptibility satisfies the mean-field bound as
the critical point is approached, our bound implies that the log-Sobolev
constant depends polynomially on the distance to the critical point and on the
volume. In particular, this applies to the Ising model on subsets of
$\mathbb{Z}^d$ when $d>4$.
  The proof uses a general criterion for the log-Sobolev inequality in terms of
the Polchinski (renormalisation group) equation, a recently proved remarkable
correlation inequality for Ising models with general external fields, the
Perron--Frobenius theorem, and the log-Sobolev inequality for product Bernoulli
measures.


title: CAT(0) spaces of higher rank

abstract:
  Ballmann's Rank Rigidity Conjecture predicts that a CAT(0) space of higher
rank with a geometric group action is rigid -- isometric to a Riemannian
symmetric space, a Euclidean building, or splits as a direct product. We
confirm this conjecture in rank 2. For CAT(0) spaces of higher rank $n\geq 2$
we prove rigidity if the space contains a periodic $n$-flat and every complete
geodesic lies in some $n$-flat. This does not require a geometric group action.


title:
Black-bounces as magnetically charged phantom regular black holes in
  Einstein-nonlinear electrodynamics gravity coupled to a self-interacting
  scalar field

abstract:
  The "black-bounce" spacetime geometries, were recently proposed in
\cite{Simpson_Visser} as regular spacetime configurations that neatly
interpolates between black holes and traversable wormholes of the Morris-Thorne
type. In this work we will present several black-bounce exact solutions of
General Relativity. Furthermore, we obtain a novel type of black-bounce
solutions, which in contrast of the Simpson-Visser type \cite{Simpson_Visser},
do not have the Ellis wormhole metric as a particular case. The source of these
solutions are linear superposition of phantom scalar fields and nonlinear
electromagnetic fields.


title:
Influence of the triangular Mn-O breathing mode on the magnetic ordering
  in multiferroic hexagonal manganites

abstract:
  We use a combination of symmetry analysis, phenomenological modelling and
first-principles density functional theory to explore the interplay between the
magnetic ground state and the detailed atomic structure in the hexagonal
rare-earth manganites. We find that the magnetic ordering is sensitive to a
breathing mode distortion of the Mn and O ions in the $ab$ plane, which is
described by the K\textsubscript{1} mode of the high-symmetry structure. Our
density functional calculations of the magnetic interactions indicate that this
mode particularly affects the single-ion anisotropy and the inter-planar
symmetric exchanges. By extracting the parameters of a magnetic model
Hamiltonian from our first-principles results, we develop a phase diagram to
describe the magnetic structure as a function of the anisotropy and exchange
interactions. This in turn allows us to explain the dependence of the magnetic
ground state on the identity of the rare-earth ion and on the
K\textsubscript{1} mode.


title: Faster exact solution of sparse MaxCut and QUBO problems

abstract:
  The maximum-cut problem is one of the fundamental problems in combinatorial
optimization. With the advent of quantum computers, both the maximum-cut and
the equivalent quadratic unconstrained binary optimization problem have
experienced much interest in recent years.
  This article aims to advance the state of the art in the exact solution of
both problems -- by using mathematical programming techniques on digital
computers. The main focus lies on sparse problem instances, although also dense
ones can be solved. We enhance several algorithmic components such as reduction
techniques and cutting-plane separation algorithms, and combine them in an
exact branch-and-cut solver. Furthermore, we provide a parallel implementation.
The new solver is shown to significantly outperform existing state-of-the-art
software for sparse MaxCut and QUBO instances. Furthermore, we improve the best
known bounds for several instances from the 7th DIMACS Challenge and the QPLIB,
and solve some of them (for the first time) to optimality.


title: Rediscovering orbital mechanics with machine learning

abstract:
  We present an approach for using machine learning to automatically discover
the governing equations and hidden properties of real physical systems from
observations. We train a "graph neural network" to simulate the dynamics of our
solar system's Sun, planets, and large moons from 30 years of trajectory data.
We then use symbolic regression to discover an analytical expression for the
force law implicitly learned by the neural network, which our results showed is
equivalent to Newton's law of gravitation. The key assumptions that were
required were translational and rotational equivariance, and Newton's second
and third laws of motion. Our approach correctly discovered the form of the
symbolic force law. Furthermore, our approach did not require any assumptions
about the masses of planets and moons or physical constants. They, too, were
accurately inferred through our methods. Though, of course, the classical law
of gravitation has been known since Isaac Newton, our result serves as a
validation that our method can discover unknown laws and hidden properties from
observed data. More broadly this work represents a key step toward realizing
the potential of machine learning for accelerating scientific discovery.


title:
Privacy-aware Distributed Hypothesis Testing in Gray-Wyner Network with
  Side Information

abstract:
  The problem of distributed binary hypothesis testing in the Gray-Wyner
network with side information is studied in this paper. An observer has access
to a discrete memoryless and stationary source and describes its observation to
two detectors via one common and two private channels. The channels are
considered error-free but rate-limited. Each detector also has access to its
own discrete memoryless and stationary source, i.e., the side information. The
goal is to perform two distinct binary hypothesis testings on the joint
distribution of observations at detectors. Additionally, the observer aims to
keep a correlated latent source private against the detectors. Equivocation is
used as the measure of the privacy preserved for the latent source. An
achievable inner bound is derived for the general case by introducing a
non-asymptotic account of the output statistics of the random binning.


title:
Microscopic theory on magnetic-field-tuned sweet spot of exchange
  interactions in multielectron quantum-dot systems

abstract:
  The exchange interaction in a singlet-triplet qubit defined by two-electron
states in the double-quantum-dot system ("two-electron singlet-triplet qubit")
typically varies monotonically with the exchange interaction and thus carries
no sweet spot. Here we study a singlet-triplet qubit defined by four-electron
states in the double-quantum-dot system ("four-electron singlet-triplet
qubit"). We demonstrate, using configuration-interaction calculations, that in
the four-electron singlet-triplet qubit the exchange energy as a function of
detuning can be non-monotonic, suggesting existence of sweet spots. We further
show that the tuning of the sweet spot and the corresponding exchange energy by
perpendicular magnetic field can be related to the variation of orbital
splitting. Our results suggest that a singlet-triplet qubit with more than two
electrons can have advantages in the realization of quantum computing.


title: Neural Collision Detection for Deformable Objects

abstract:
  We propose a neural network-based approach for collision detection with
deformable objects. Unlike previous approaches based on bounding volume
hierarchies, our neural approach does not require an update of the spatial data
structure when the object deforms. Our network is trained on the reduced
degrees of freedom of the object, so that we can use the same network to query
for collisions even when the object deforms. Our approach is simple to use and
implement, and it can readily be employed on the GPU. We demonstrate our
approach with two concrete examples: a haptics application with a finite
element mesh, and cloth simulation with a skinned character.


title:
EcoFlow: Efficient Convolutional Dataflows for Low-Power Neural Network
  Accelerators

abstract:
  Dilated and transposed convolutions are widely used in modern convolutional
neural networks (CNNs). These kernels are used extensively during CNN training
and inference of applications such as image segmentation and high-resolution
image generation. Although these kernels have grown in popularity, they stress
current compute systems due to their high memory intensity, exascale compute
demands, and large energy consumption.
  We find that commonly-used low-power CNN inference accelerators based on
spatial architectures are not optimized for both of these convolutional
kernels. Dilated and transposed convolutions introduce significant zero padding
when mapped to the underlying spatial architecture, significantly degrading
performance and energy efficiency. Existing approaches that address this issue
require significant design changes to the otherwise simple, efficient, and
well-adopted architectures used to compute direct convolutions.
  To address this challenge, we propose EcoFlow, a new set of dataflows and
mapping algorithms for dilated and transposed convolutions. These algorithms
are tailored to execute efficiently on existing low-cost, small-scale spatial
architectures and requires minimal changes to the network-on-chip of existing
accelerators. EcoFlow eliminates zero padding through careful dataflow
orchestration and data mapping tailored to the spatial architecture. EcoFlow
enables flexible and high-performance transpose and dilated convolutions on
architectures that are otherwise optimized for CNN inference.
  We evaluate the efficiency of EcoFlow on CNN training workloads and
Generative Adversarial Network (GAN) training workloads. Experiments in our new
cycle-accurate simulator show that EcoFlow 1) reduces end-to-end CNN training
time between 7-85%, and 2) improves end-to-end GAN training performance between
29-42%, compared to state-of-the-art CNN inference accelerators.


title:
Graphical criteria for the identification of marginal causal effects in
  continuous-time survival and event-history analyses

abstract:
  We consider continuous-time survival or more general event-history settings,
where the aim is to infer the causal effect of a time-dependent treatment
process. This is formalised as the effect on the outcome event of a (possibly
hypothetical) intervention on the intensity of the treatment process, i.e. a
stochastic intervention. To establish whether valid inference about the
interventional situation can be drawn from typical observational, i.e.
non-experimental, data we propose graphical rules indicating whether the
observed information is sufficient to identify the desired causal effect by
suitable re-weighting. In analogy to the well-known causal directed acyclic
graphs, the corresponding dynamic graphs combine causal semantics with local
independence models for multivariate counting processes. Importantly, we
highlight that causal inference from censored data requires structural
assumptions on the censoring process beyond the usual independent censoring
assumption, which can be represented and verified graphically. Our results
establish general non-parametric identifiability and do not rely on particular
survival models. We illustrate our proposal with a data example on HPV-testing
for cervical cancer screening, where the desired effect is estimated by
re-weighted cumulative incidence curves.


title:
Interactive Mobile App Navigation with Uncertain or Under-specified
  Natural Language Commands

abstract:
  We introduce Mobile app Tasks with Iterative Feedback (MoTIF), a new dataset
where the goal is to complete a natural language query in a mobile app. Current
datasets for related tasks in interactive question answering, visual common
sense reasoning, and question-answer plausibility prediction do not support
research in resolving ambiguous natural language requests or operating in
diverse digital domains. As a result, they fail to capture complexities of real
question answering or interactive tasks. In contrast, MoTIF contains natural
language requests that are not satisfiable, the first such work to investigate
this issue for interactive vision-language tasks. MoTIF also contains follow up
questions for ambiguous queries to enable research on task uncertainty
resolution. We introduce task feasibility prediction and propose an initial
model which obtains an F1 score of 61.1. We next benchmark task automation with
our dataset and find adaptations of prior work perform poorly due to our
realistic language requests, obtaining an accuracy of only 20.2% when mapping
commands to grounded actions. We analyze performance and gain insight for
future work that may bridge the gap between current model ability and what is
needed for successful use in application.


title:
Fabrication of Surface Ion Traps with Integrated Current Carrying Wires
  enabling High Magnetic Field Gradients

abstract:
  A major challenge for quantum computers is the scalable simultaneous
execution of quantum gates. One approach to address this in trapped ion quantum
computers is the implementation of quantum gates based on static magnetic field
gradients and global microwave fields. In this paper, we present the
fabrication of surface ion traps with integrated copper current carrying wires
embedded inside the substrate below the ion trap electrodes, capable of
generating high magnetic field gradients. The copper layer's measured sheet
resistance of 1.12 m$\Omega$/sq at room temperature is sufficiently low to
incorporate complex designs, without excessive power dissipation at high
currents causing a thermal runaway. At a temperature of 40 K the sheet
resistance drops to 20.9 $\mu\Omega$/sq giving a lower limit for the residual
resistance ratio of 100. Continuous currents of 13 A can be applied, resulting
in a simulated magnetic field gradient of 144 T/m at the ion position, which is
125 $\mu$m from the trap surface for the particular anti-parallel wire pair in
our design.


title:
Towards To-a-T Spatio-Temporal Focus for Skeleton-Based Action
  Recognition

abstract:
  Graph Convolutional Networks (GCNs) have been widely used to model the
high-order dynamic dependencies for skeleton-based action recognition. Most
existing approaches do not explicitly embed the high-order spatio-temporal
importance to joints' spatial connection topology and intensity, and they do
not have direct objectives on their attention module to jointly learn when and
where to focus on in the action sequence. To address these problems, we propose
the To-a-T Spatio-Temporal Focus (STF), a skeleton-based action recognition
framework that utilizes the spatio-temporal gradient to focus on relevant
spatio-temporal features. We first propose the STF modules with learnable
gradient-enforced and instance-dependent adjacency matrices to model the
high-order spatio-temporal dynamics. Second, we propose three loss terms
defined on the gradient-based spatio-temporal focus to explicitly guide the
classifier when and where to look at, distinguish confusing classes, and
optimize the stacked STF modules. STF outperforms the state-of-the-art methods
on the NTU RGB+D 60, NTU RGB+D 120, and Kinetics Skeleton 400 datasets in all
15 settings over different views, subjects, setups, and input modalities, and
STF also shows better accuracy on scarce data and dataset shifting settings.


title:
CWISE J014611.20-050850.0AB: The Widest Known Brown Dwarf Binary in the
  Field

abstract:
  While stars are often found in binary systems, brown dwarf binaries are much
rarer. Brown dwarf--brown dwarf pairs are typically difficult to resolve
because they often have very small separations. Using brown dwarfs discovered
with data from the Wide-field Infrared Survey Explorer (WISE) via the Backyard
Worlds: Planet 9 citizen science project, we inspected other, higher
resolution, sky surveys for overlooked cold companions. During this process we
discovered the brown dwarf binary system CWISE J0146$-$0508AB, which we find
has a very small chance alignment probability based on the similar proper
motions of the components of the system. Using follow-up near-infrared
spectroscopy with Keck/NIRES, we determined component spectral types of L4 and
L8 (blue), making CWISE J0146$-$0508AB one of only a few benchmark systems with
a blue L dwarf. At an estimated distance of $\sim$40 pc, CWISE J0146$-$0508AB
has a projected separation of $\sim$129 AU, making it the widest separation
brown dwarf pair found to date. We find that such a wide separation for a brown
dwarf binary may imply formation in a low-density star-forming region.


title:
Discovering true tauonium via two-photon fusion at $e^+e^-$ and hadron
  colliders

abstract:
  The feasibility of observing true tauonium, the bound state of two tau
leptons, $\mathcal{T}_0\equiv(\tau^+\tau^-)_0$, via photon-photon collisions at
$e^+e^-$ colliders and at the LHC, is studied. The production cross sections of
the process $\gamma\gamma\to\mathcal{T}_0\to\gamma\gamma$ -- as well as those
of all relevant backgrounds: spin-0 and 2 charmonium resonances decaying to
diphotons, and light-by-light scattering -- are computed in the equivalent
photon approximation for $e^+e^-$ collisions at BES III ($\sqrt{s} = 3.8$ GeV),
Belle II ($\sqrt{s} = 10.6$ GeV), and FCC-ee ($\sqrt{s} = 91.2$ GeV), as well
as for ultraperipheral p-p, p-Pb, and Pb-Pb collisions at the LHC. Despite
small $\mathcal{T}_0$ production cross sections and a final state swamped by
decays from overlapping pseudoscalar and tensor charmonium states -- the
$\mathrm{\chi_{c2}}$, $\mathrm{\eta_{c}(2S)}$, and $\mathrm{\chi_{c0}}$ states
have masses only 2.5, 84, and 139 MeV away, respectively, from the
$\mathcal{T}_0$ peak -- evidence and observation of the ground state of the
heaviest leptonium appears feasible at Belle II and FCC-ee, respectively, with
in-situ high-precision measurements of the irreducible backgrounds.


title: Webly Supervised Concept Expansion for General Purpose Vision Models

abstract:
  General purpose vision (GPV) systems are models that are designed to solve a
wide array of visual tasks without requiring architectural changes. Today, GPVs
primarily learn both skills and concepts from large fully supervised datasets.
Scaling GPVs to tens of thousands of concepts by acquiring data to learn each
concept for every skill quickly becomes prohibitive. This work presents an
effective and inexpensive alternative: learn skills from fully supervised
datasets, learn concepts from web image search results, and leverage a key
characteristic of GPVs -- the ability to transfer visual knowledge across
skills. We use a dataset of 1M+ images spanning 10k+ visual concepts to
demonstrate webly-supervised concept expansion for two existing GPVs (GPV-1 and
VL-T5) on 3 benchmarks - 5 COCO based datasets (80 primary concepts), a newly
curated series of 5 datasets based on the OpenImages and VisualGenome
repositories (~500 concepts) and the Web-derived dataset (10k+ concepts). We
also propose a new architecture, GPV-2 that supports a variety of tasks -- from
vision tasks like classification and localization to vision+language tasks like
QA and captioning to more niche ones like human-object interaction recognition.
GPV-2 benefits hugely from web data, outperforms GPV-1 and VL-T5 across these
benchmarks, and does well in a 0-shot setting at action and attribute
recognition.


title: Self-Organization In Stellar Evolution: Size-Complexity Rule

abstract:
  Complexity Theory is highly interdisciplinary, therefore any regularities
must hold on all levels of organization, independent on the nature of the
system. An open question in science is how complex systems self-organize to
produce emergent structures and properties, a branch of non-equilibrium
thermodynamics. It has long been known that there is a quantity-quality
transition in natural systems. This is to say that the properties of a system
depend on its size. More recently, this has been termed the size-complexity
rule, which means that to increase their size, systems must increase their
complexity, and that to increase their complexity they must grow in size. This
rule goes under different names in different disciplines and systems of
different nature, such as the area-speciation rule, economies of scale, scaling
relations (allometric) in biology and for cities, and many others. We apply the
size-complexity rule to stars to compare them with other complex systems in
order to find universal patterns of self-organization independent of the
substrate. Here, as a measure of complexity of a star, we are using the degree
of grouping of nucleons into atoms, which reduces nucleon entropy, increases
the variety of elements, and changes the structure of the star. As seen in our
previous work, complexity, using action efficiency, is in power law
proportionality of all other characteristics of a complex system, including its
size. Here we find that, as for the other systems studied, the complexity of
stars is in a power law proportionality with their size - the bigger a system
is, the higher its level of complexity is - despite differing explosion
energies and initial metallicities from simulations and data, which confirms
the size-complexity rule and our model.


title:
An integrated heterogeneous computing framework for ensemble simulations
  of laser-induced ignition

abstract:
  An integrated computational framework is introduced to study complex
engineering systems through physics-based ensemble simulations on heterogeneous
supercomputers. The framework is primarily designed for the quantitative
assessment of laser-induced ignition in rocket engines. We develop and combine
an implicit programming system, a compressible reacting flow solver, and a data
generation/management strategy on a robust and portable platform. We
systematically present this framework using test problems on a hybrid CPU/GPU
machine. Efficiency, scalability, and accuracy of the solver are
comprehensively assessed with canonical unit problems. Ensemble data management
and autoencoding are demonstrated using a canonical diffusion flame case.
Sensitivity analysis of the ignition of a turbulent, gaseous fuel jet is
performed using a simplified, three-dimensional model combustor. Our approach
unifies computer science, physics and engineering, and data science to realize
a cross-disciplinary workflow. The framework is exascale-oriented and can be
considered a benchmark for future computational science studies of real-world
systems.

categories: cs.CE cs.DC physics.data-an physics.flu-dyn


title:
A dynamic program to achieve capacity of multiple access channel with
  noiseless feedback

abstract:
  In this paper, we consider the problem of evaluating capacity expression of a
multiple access channel (MAC) with noiseless feedback. So far, the capacity
expression for this channel is known through a multi letter directed
information by Kramer [1]. Recently, it was shown in [2] that one can pose it
as a dynamic optimization problem, however, no dynamic program was provided as
the authors claimed there is no notion of state that is observed by both the
senders. In this paper, we build upon [2] to show that there indeed exists a
state and therefore a dynamic program (DP) that decomposes this dynamic
optimization problem, and equivalently a Bellman fixed-point equation to
evaluate capacity of this channel. We do so by defining a common belief on
private messages and private beliefs of the two senders, and using this common
belief as state of the system. We further show that this DP can be further
reduced to a DP with state as the common belief on just the messages. This
provides a single letter characterization of the capacity of this channel.


title:
Frequency comb and machine learning-based breath analysis for COVID-19
  classification

abstract:
  Human breath contains hundreds of volatile molecules that can provide
powerful, non-intrusive spectral diagnosis of a diverse set of diseases and
physiological/metabolic states. To unleash this tremendous potential for
medical science, we present a robust analytical method that simultaneously
measures tens of thousands of spectral features in each breath sample, followed
by efficient and detail-specific multivariate data analysis for unambiguous
binary medical response classification. We combine mid-infrared cavity-enhanced
direct frequency comb spectroscopy (CE-DFCS), capable of real-time collection
of tens of thousands of distinct molecular features at parts-per-trillion
sensitivity, with supervised machine learning, capable of analysis and
verification of extremely high-dimensional input data channels. Here, we
present the first application of this method to the breath detection of
Coronavirus Disease 2019 (COVID-19). Using 170 individual samples at the
University of Colorado, we report a cross-validated area under the
Receiver-Operating-Characteristics curve of 0.849(4), providing excellent
prediction performance. Further, this method detected a significant difference
between male and female breath as well as other variables such as smoking and
abdominal pain. Together, these highlight the utility of CE-DFCS for rapid,
non-invasive detection of diverse biological conditions and disease states. The
unique properties of frequency comb spectroscopy thus help establish precise
digital spectral fingerprints for building accurate databases and provide means
for simultaneous multi-response classifications. The predictive power can be
further enhanced with readily scalable comb spectral coverage.

categories: physics.med-ph cs.LG physics.bio-ph physics.chem-ph physics.optics


title:
Finite groups in which every maximal subgroup is nilpotent or normal or
  has $p'$-order

abstract:
  Let $G$ be a finite group and $p$ a fixed prime divisor of $|G|$. Combining
the nilpotence, the normality and the order of groups together, we prove that
if every maximal subgroup of $G$ is nilpotent or normal or has $p'$-order, then
(1) $G$ is solvable; (2) $G$ has a Sylow tower; (3) There exists at most one
prime divisor $q$ of $|G|$ such that $G$ is neither $q$-nilpotent nor
$q$-closed, where $q\neq p$.


title:
Finite groups in which every self-centralizing subgroup is a TI-subgroup
  or subnormal or has $p'$-order

abstract:
  We first give complete characterizations of the structure of finite group $G$
in which every subgroup (or non-nilpotent subgroup, or non-abelian subgroup) is
a TI-subgroup or subnormal or has $p'$-order for a fixed prime divisor $p$ of
$|G|$. Furthermore, we prove that every self-centralizing subgroup (or
non-nilpotent subgroup, or non-abelian subgroup) of $G$ is a TI-subgroup or
subnormal or has $p'$-order for a fixed prime divisor $p$ of $|G|$ if and only
if every subgroup (or non-nilpotent subgroup, or non-abelian subgroup) of $G$
is a TI-subgroup or subnormal or has $p'$-order. Based on these results, we
obtain the structure of finite group $G$ in which every self-centralizing
subgroup (or non-nilpotent subgroup, or non-abelian subgroup) is a TI-subgroup
or subnormal or has $p'$-order for a fixed prime divisor $p$ of $|G|$.


title: Morse homology for the Hamiltonian action in cotangent bundles

abstract:
  In this paper we use the gradient flow equation introduced in [10] to
construct a Morse complex for the Hamiltonian action $\mathbb A_H$ on a mixed
regularity space of loops in the cotangent bundle $T^*M$ of a closed manifold
$M$. Connections between pairs of critical points are realized as genuine
intersections between unstable and stable manifolds, which (despite being
infinite dimensional objects) turn out to have finite dimensional intersection
with good compactness properties. This follows from the existence of an
additional structure, namely a strongly integrable (0)-essential subbundle,
which behaves nicely under the negative gradient flow of the Hamiltonian action
and which is needed to make comparisons. Transversality is achieved by
generically perturbing the negative gradient vector field $-\nabla \mathbb A_H$
of the Hamiltonian action within a class of pseudo-gradient vector fields
preserving all good compactness properties of $-\nabla \mathbb A_H$. This
follows from an abstract transversality result of independent interest for
vector fields on a Hilbert manifold for which stable and unstable manifolds of
rest points are infinite dimensional. The resulting Morse homology is
independent of the choice of the Hamiltonian (and of all other choices but the
choice of the (0)-essential subbundle, which however only changes the
Morse-complex by a shift of the indices) and is isomorphic to the Floer
homology of $T^*M$ as well as to the singular homology of the free loop space
of $M$.


title:
Inequalities and equalities on the joint and generalized spectral and
  essential spectral radius of the Hadamard geometric mean of bounded sets of
  positive kernel operators

abstract:
  We prove new inequalities and equalities for the generalized and the joint
spectral radius (and their essential versions) of Hadamard (Schur) geometric
means of bounded sets of positive kernel operators on Banach function spaces.
In the case of nonnegative matrices that define operators on Banach sequences
we obtain additional results. Our results extend results of several authors
that appeared relatively recently.


title: Towards Training Reproducible Deep Learning Models

abstract:
  Reproducibility is an increasing concern in Artificial Intelligence (AI),
particularly in the area of Deep Learning (DL). Being able to reproduce DL
models is crucial for AI-based systems, as it is closely tied to various tasks
like training, testing, debugging, and auditing. However, DL models are
challenging to be reproduced due to issues like randomness in the software
(e.g., DL algorithms) and non-determinism in the hardware (e.g., GPU). There
are various practices to mitigate some of the aforementioned issues. However,
many of them are either too intrusive or can only work for a specific usage
context. In this paper, we propose a systematic approach to training
reproducible DL models. Our approach includes three main parts: (1) a set of
general criteria to thoroughly evaluate the reproducibility of DL models for
two different domains, (2) a unified framework which leverages a
record-and-replay technique to mitigate software-related randomness and a
profile-and-patch technique to control hardware-related non-determinism, and
(3) a reproducibility guideline which explains the rationales and the
mitigation strategies on conducting a reproducible training process for DL
models. Case study results show our approach can successfully reproduce six
open source and one commercial DL models.


title: Bare-Bones de Sitter

abstract:
  We compute the supersymmetry-breaking three-form fluxes generated by the
addition of anti-D3 branes at the tip of a Klebanov-Strassler throat. These
fluxes give rise to nontrivial terms in the superpotential when the throat is
embedded in a flux compactification. We describe these terms both from a
ten-dimensional and from a four-dimensional perspective and show that, upon
including K\"ahler-moduli stabilization, the resulting potential admits de
Sitter minima. Our proposed de Sitter construction does not require additional
supersymmetry-breaking (0,3) fluxes, and hence is more minimalist than the KKLT
proposal.


title: Dust Dynamics in Transitional Disks: Clumping and Disk Recession

abstract:
  The role of radiation pressure in dust migration and the opening of inner
cavities in transitional disks is revisited in this paper. Dust dynamics
including radiation pressure is often studied in axisymmetric models, but in
this work, we show that highly non-axisymmetric features can arise from an
instability at the inner disk edge. Dust grains clump into high density
features there, allowing radiation to leak around them and penetrate deeper
into the disk, changing the course of dust migration. Our proof-of-concept,
two-dimensional, vertically-averaged simulations show that the combination of
radiation pressure, shadowing, and gas drag can produce a net outward
migration, or recession, of the dust component of the disk. The recession speed
of the inner disk edge is on the order of $10^{-5}$ times Keplerian speed in
our parameter space, which is faster than the background viscous flow, assuming
a Shakura & Sunyaev viscosity alpha $\lesssim 10^{-3}$. This speed, if
sustained over the lifetime of the disk, can result in a dust cavity as large
as tens of au.


title:
Measuring the Total Ultraviolet Light from Galaxy Clusters at z=0.5-1.6:
  The Balance of Obscured and Unobscured Star-Formation

abstract:
  Combined observations from UV to IR wavelengths are necessary to fully
account for the star-formation in galaxy clusters. Low mass (log M/Msun<10)
galaxies are typically not individualy detected, particularly at higher
redshifts (z~1-2) where galaxy clusters are undergoing rapid transitions from
hosting mostly active, dust-obscured star-forming galaxies to quiescent,
passive galaxies. To account for these undetected galaxies, we measure the
total light emerging from GALEX/NUV stacks of galaxy clusters between
z=0.5-1.6. Combined with existing measurements from Spitzer, WISE, and
Herschel, we study the average UV through far-infrared (IR) spectral energy
distribution (SED) of clusters. From the SEDs, we measure the total stellar
mass and amount of dust-obscured and unobscured star-formation arising from all
cluster-member galaxies, including the low mass population. The relative
fraction of unobscured star-formation we observe in the UV is consistent with
what is observed in field galaxies. There is tentative evidence for lower than
expected unobscured star-formation at z~0.5, which may arise from rapid
redshift evolution in the low mass quenching efficiency in clusters reported by
other studies. Finally, the GALEX data places strong constraints on derived
stellar-to-halo mass ratios at z<1 which anti-correlate with the total halo
mass, consistent with trends found from local X-ray observations of clusters.
The data exhibit steeper slopes than implementations of the cluster
star-formation efficiency in semi-analytical models.


title:
Analytical covariance between voxel intensity distributions and
  line-intensity mapping power spectra

abstract:
  The power spectrum and the voxel intensity distribution (VID) are two of the
main proposed summary statistics to study line-intensity maps. We reformulate
the derivation of the VID in terms of the local overdensities and derive for
the first time an analytic covariance between the VID and the line-intensity
mapping power spectrum. We study the features of this covariance for different
experimental setups and show that we can recover similar results to
simulation-based covariances. With this formalism, we also compute the cosmic
variance contribution the VID uncertainty, which we find to be subdominant with
respect to the standard variance from Poisson sampling. Our results allow for
general joint analyses of the VID and the line-intensity mapping power
spectrum.


title:
Pinning down $|\Delta c|=|\Delta u|=1$ couplings with rare charm baryon
  decays

abstract:
  We study the full angular distribution of semileptonic rare charm baryon
decays in which the secondary baryon undergoes weak decay with sizable
polarization parameter, $\Xi_c^+\to\Sigma^+\,(\to p\pi^0)\ell^+\ell^-$,
$\Xi_c^0\to\Lambda^0\,(\to p \pi^-)\ell^+\ell^-$ and $\Omega_c^0\to\Xi^0\,(\to
\Lambda^{0} \pi^0)\ell^+\ell^-$. Such self-analyzing decay chains allow for
seven additional observables compared to three-body decays such as $\Lambda_c
\to p \ell^+ \ell^-$, with different sensitivities to the $|\Delta c|=|\Delta
u|=1$ weak couplings. Opportunities to test the standard model in $c \to u$
transitions with standard model null tests and other angular observables are
worked out. We show that a joint model-independent analysis of the leptonic
$A_{\text{FB}}^\ell$, hadronic $A_{\text{FB}}^{\text{H}}$, and combined
forward-backward asymmetry $A_{\text{FB}}^{\ell\text{H}}$ together with the
fraction of longitudinally produced leptons, $F_L$, is able to pin down the
dipole couplings $C_{7},C^\prime_{7}$ and the semileptonic (axial-) vector ones
$C_{10},C^\prime_{9},C^\prime_{10}$. $A_{\text{FB}}^{\text{H}}$ is also
accessible with dineutrino $c \to u \nu \bar \nu$ modes and probes right-handed
currents.


title:
From naked spheroids to disky galaxies: how do massive disk galaxies
  shape their morphology?

abstract:
  We investigate the assembly history of massive disk galaxies and describe how
they shape their morphology through cosmic time. Using SHARDS and HST data, we
modeled the surface brightness distribution of 91 massive galaxies at redshift
$0.14<z\leq 1$ in the wavelength range $0.5-1.6$ $\mu$m, deriving the
uncontaminated spectral energy distributions of their bulges and disks
separately. This spectrophotometric decomposition allows us to compare the
stellar populations properties of each component in individual galaxies. We
find that the majority of massive galaxies ($\sim85\%$) builds inside-out,
growing their extended stellar disk around the central spheroid. Some bulges
and disks could start forming at similar epochs, but these bulges grow more
rapidly than their disks, assembling $80\%$ of their mass in $\sim0.7$ Gyr and
$\sim3.5$ Gyr, respectively. Moreover, we infer that both older bulges and
older disks are more massive and compact than younger stellar structures. In
particular, we find that bulges display a bimodal distribution of mass-weighted
ages, i.e., they form in two waves. In contrast, our analysis of the disk
components indicates that they form at $z\sim1$ for both first and second-wave
bulges. This translates to first-wave bulges taking longer in acquiring a
stellar disk ($5.2$ Gyr) compared to second-wave less-compact spheroids ($0.7$
Gyr). We do not find distinct properties (e.g., mass, star formation timescale,
and mass surface density) for the disks in both types of galaxies. We conclude
that the bulge mass and compactness mainly regulate the timing of the stellar
disk growth, driving the morphological evolution of massive disk galaxies.


title:
Higgs probes of top quark contact interactions and their interplay with
  the Higgs self-coupling

abstract:
  We calculate the dominant contributions of third generation four-quark
operators to single-Higgs production and decay. They enter via loop corrections
to Higgs decays into gluons, photons and $b\bar{b}$, and in Higgs production
via gluon fusion and in association with top quark pairs. We show that these
loop effects can, in some cases, lead to better constraints than those from
fits to top quark data. Finally, we investigate whether these four-fermion
operators can spoil the determination of the trilinear Higgs self-coupling from
fits to single-Higgs data.


title:
The ALPINE-ALMA [CII] Survey: the population of [CII]-undetected
  galaxies and their role in the $\mathrm{L_{[CII]}}$-SFR relation

abstract:
  The [CII] 158$~\mu$m emission line represents so far one of the most
profitable tools for the investigation of the high-redshift galaxies in the
early Universe. Being one of the brightest cooling lines in the rest-frame
far-infrared regime of star-forming galaxies, it has been successfully
exploited as a tracer of star-formation rate (SFR) in local sources. The
picture is more complex at higher redshifts, where its usability in this
context is still under investigation. Recent results from the ALMA Large
Program to INvestigate [CII] at Early times (ALPINE) survey suggest that there
is no (or weak) evolution of the L$\mathrm{_{[CII]}}$-SFR relation up to
$z\sim6$ but their reliability is hampered by the presence of a large
population of [CII] non-detected galaxies. In this work, we characterize the
population of [CII] non-detections in ALPINE. By stacking their ALMA spectra,
we obtain a signal detected at $\sim5.1\sigma$, resulting in a [CII] luminosity
of $\mathrm{log(L_\mathrm{[CII]}}/\mathrm{L_{\odot}})$ $\sim7.8$. When
combining this value with those from the [CII] detections, we find a
$\mathrm{L_{[CII]}}$-SFR relation with a slope $b=1.14\pm0.11$, in agreement
within the uncertainties both with the linear relation found in the local
Universe, and with the previous findings from ALPINE at $z\sim5$. This suggests
that the [CII] line can be considered a good tracer of star formation up to the
distant Universe. Finally, we show that the galaxies of our sample that most
deviate from the observed L$_\mathrm{[CII]}$-SFR relation could suffer from a
less precise redshift estimation, perhaps artificially reducing their [CII]
luminosity. In this respect, we claim that there is no evidence in favour of a
deficit of [CII] content in high-z galaxies, in contrast with earlier studies.


title:
Spontaneous generation and active manipulation of real-space optical
  vortex

abstract:
  Optical vortices host the orbital nature of photons, which offers an extra
degree of freedom in photonic applications. Unlike vortices in other physical
entities, optical vortices require structural singularities, which restrict
their abilities in terms of dynamic and interactive characteristics. In this
study, we present the spontaneous generation and external magnetic
field-induced manipulation of an optical vortex and antivortex. A
gradient-thickness optical cavity (GTOC) consisting of an Al/SiO2/Ni/SiO2
multilayer structure realised the distinct transition between the trivial and
non-trivial topological phases, depending on the magneto-optic effects of the
Ni layer. In the non-trivial topological phase, the mathematical singularities
generating the optical vortex and antivortex pair in the reflected light
existed in the generalised parameter space of the thicknesses of the top and
bottom SiO2 layers, which is bijective to the real space of the GTOC. Coupled
with the magnetisation, the optical vortex and antivortex in the GTOC
experienced an effective spin-orbit interaction and showed topology-dependent
dynamics under external magnetic fields. We expect that field-induced
engineering of optical vortices will pave the way for the study of topological
photonic interactions and their applications.