title: A DG Algebra resolution of trimmings of Gorenstein ideals

abstract:
  Let $(R,\mathfrak{m},\Bbbk)$ be a regular local ring of dimension 3. Let $I$
be a Gorenstein ideal of $R$ of grade 3. Buchsbaum and Eisenbud proved that
there is a skew-symmetric matrix of odd size such that $I$ is generated by the
sub-maximal pfaffians of this matrix. Let $J$ be the ideal obtained by
multiplying some of the pfaffian generators of $I$ by $\mathfrak{m}$; we say
that $J$ is a trimming of $I$. In this paper we construct an explicit free
resolution of $R/J$ with a DG algebra structure. Our work builds upon a recent
paper of Vandebogert. We use our DG algebra resolution to prove that recent
conjectures of Christensen, Veliche and Weyman on ideals of class $\mathbf{G}$
hold true in our context and to address the realizability question for ideals
of class $\mathbf{G}$.


title:
Mixture-of-experts VAEs can disregard variation in surjective multimodal
  data

abstract:
  Machine learning systems are often deployed in domains that entail data from
multiple modalities, for example, phenotypic and genotypic characteristics
describe patients in healthcare. Previous works have developed multimodal
variational autoencoders (VAEs) that generate several modalities. We consider
subjective data, where single datapoints from one modality (such as class
labels) describe multiple datapoints from another modality (such as images). We
theoretically and empirically demonstrate that multimodal VAEs with a mixture
of experts posterior can struggle to capture variability in such surjective
data.


title: GDC- Generalized Distribution Calibration for Few-Shot Learning

abstract:
  Few shot learning is an important problem in machine learning as large
labelled datasets take considerable time and effort to assemble. Most few-shot
learning algorithms suffer from one of two limitations- they either require the
design of sophisticated models and loss functions, thus hampering
interpretability; or employ statistical techniques but make assumptions that
may not hold across different datasets or features. Developing on recent work
in extrapolating distributions of small sample classes from the most similar
larger classes, we propose a Generalized sampling method that learns to
estimate few-shot distributions for classification as weighted random variables
of all large classes. We use a form of covariance shrinkage to provide
robustness against singular covariances due to overparameterized features or
small datasets. We show that our sampled points are close to few-shot classes
even in cases when there are no similar large classes in the training set. Our
method works with arbitrary off-the-shelf feature extractors and outperforms
existing state-of-the-art on miniImagenet, CUB and Stanford Dogs datasets by 3%
to 5% on 5way-1shot and 5way-5shot tasks and by 1% in challenging cross domain
tasks.


title:
Towards Generalizable Semantic Product Search by Text Similarity
  Pre-training on Search Click Logs

abstract:
  Recently, semantic search has been successfully applied to e-commerce product
search and the learned semantic space(s) for query and product encoding are
expected to generalize to unseen queries or products. Yet, whether
generalization can conveniently emerge has not been thoroughly studied in the
domain thus far. In this paper, we examine several general-domain and
domain-specific pre-trained Roberta variants and discover that general-domain
fine-tuning does not help generalization, which aligns with the discovery of
prior art. Proper domain-specific fine-tuning with clickstream data can lead to
better model generalization, based on a bucketed analysis of a publicly
available manual annotated query-product pair da


title: Survey of Aspect-based Sentiment Analysis Datasets

abstract:
  Aspect-based sentiment analysis (ABSA) is a natural language processing
problem that requires analyzing user-generated reviews in order to determine:
a) The target entity being reviewed, b) The high-level aspect to which it
belongs, and c) The sentiment expressed toward the targets and the aspects.
Numerous yet scattered corpora for ABSA make it difficult for researchers to
quickly identify corpora best suited for a specific ABSA subtask. This study
aims to present a database of corpora that can be used to train and assess
autonomous ABSA systems. Additionally, we provide an overview of the major
corpora concerning the various ABSA and its subtasks and highlight several
corpus features that researchers should consider when selecting a corpus. We
conclude that further large-scale ABSA corpora are required. Additionally,
because each corpus is constructed differently, it is time-consuming for
researchers to experiment with a novel ABSA algorithm on many corpora and often
employ just one or a few corpora. The field would benefit from an agreement on
a data standard for ABSA corpora. Finally, we discuss the advantages and
disadvantages of current collection approaches and make recommendations for
future ABSA dataset gathering.


title: Detector Challenges at the LUXE Experiment

abstract:
  The LUXE experiment, currently in design and planning, aims to perform
analyses of strong-field quantum electrodynamics interactions by colliding the
high-quality high-energy EU.XFEL electron beam with a powerful laser. With the
ability to collide laser pulses with bunches of $1.5 \times 10^9$ electrons /
$1 \times 10^8$ photons at 1Hz, this high-statistics environment presents an
opportunity to probe rare interactions in a new parameter space of a novel
regime. To do this requires a unique suite of detectors to measure three types
of particles, at highly varying fluxes dependent on laser interaction
parameters. The detectors measure electrons, positrons, or photons, and balance
sensitivity with high dynamic range and hardness to radiation damage. Presented
in brief in this note are the function, design, and reconstruction methods of
each of these detectors.


title:
Modeling of the nonlinear flame response of a Bunsen-type flame via
  multi-layer perceptron

abstract:
  This paper demonstrates the ability of neural networks to reliably learn the
nonlinear flame response of a laminar premixed flame, while carrying out only
one unsteady CFD simulation. The system is excited with a broadband, low-pass
filtered velocity signal that exhibits a uniform distribution of amplitudes
within a predetermined range. The obtained time series of flow velocity
upstream of the flame and heat release rate fluctuations are used to train the
nonlinear model using a multi-layer perceptron. Several models with varying
hyperparameters are trained and the dropout strategy is used as regularizer to
avoid overfitting. The best performing model is subsequently used to compute
the flame describing function (FDF) using mono-frequent excitations. In
addition to accurately predicting the FDF, the trained neural network model
also captures the presence of higher harmonics in the flame response. As a
result, when coupled with an acoustic solver, the obtained neural network model
is better suited than a classical FDF model to predict limit cycle oscillations
characterized by more than one frequency. The latter is demonstrated in the
final part of the present study. We show that the RMS value of the predicted
acoustic oscillations together with the associated dominant frequencies are in
excellent agreement with CFD reference data.


title:
Data Splits and Metrics for Method Benchmarking on Surgical Action
  Triplet Datasets

abstract:
  In addition to generating data and annotations, devising sensible data
splitting strategies and evaluation metrics is essential for the creation of a
benchmark dataset. This practice ensures consensus on the usage of the data,
homogeneous assessment, and uniform comparison of research methods on the
dataset. This study focuses on CholecT50, which is a 50 video surgical dataset
that formalizes surgical activities as triplets of <instrument, verb, target>.
In this paper, we introduce the standard splits for the CholecT50 and CholecT45
datasets and show how they compare with existing use of the dataset. CholecT45
is the first public release of 45 videos of CholecT50 dataset. We also develop
a metrics library, ivtmetrics, for model evaluation on surgical triplets.
Furthermore, we conduct a benchmark study by reproducing baseline methods in
the most predominantly used deep learning frameworks (PyTorch and TensorFlow)
to evaluate them using the proposed data splits and metrics and release them
publicly to support future research. The proposed data splits and evaluation
metrics will enable global tracking of research progress on the dataset and
facilitate optimal model selection for further deployment.


title:
On irreducibility of a certain class of homogeneous operators obtained
  from quotient modules

abstract:
  Let $ \Omega \subset \mathbb{C}^m $ be an open, connected and bounded set and
$\mathcal{A}(\Omega)$ be a function algebra of holomorphic functions on
$\Omega$. Suppose that $\mathcal{M}_q$ is the quotient Hilbert module obtained
from a submodule of functions in a Hilbert module $\mathcal{M}$ vanishing to
order $k$ along a smooth irreducible complex analytic set
$\mathcal{Z}\subset\Omega$ of codimension at least $2$. In this article, we
prove that the compression of the multiplication operators onto $\mathcal{M}_q$
is homogeneous with respect to a suitable subgroup of the automorphism group
Aut$(\Omega)$ of $\Omega$ depending upon a subgroup $G$ of Aut$(\Omega)$
whenever the tuple of multiplication operators on $\mathcal{M}$ is homogeneous
with respect to $G$ and both $\mathcal{M}$ as well as $\mathcal{M}_q$ are in
the Cowen-Douglas class. We show that these compression of multiplication
operators might be reducible even if the tuple of multiplication operators on
$\mathcal{M}$ is irreducible by exhibiting a concrete example. Moreover, the
irreducible components of these reducible operators are identified as
Generalized Wilkins' operators.


title:
Full one-loop radiative corrections to $e^+ e^-\to H^+H^-$ in the Inert
  Higgs Doublet Model

abstract:
  We compute the full one-loop radiative corrections for charged Higgs pair
production $e^{+}e^{-}\to H^{+}H^{-}$ in the Inert Higgs Doublet Model. The
on-shell renormalization scheme have been used. We include the full electroweak
contributions as well as the QED corrections (soft and hard). We use
FeynArts/FormCalc/LoopTools to compute the one-loop virtual corrections while
the real emission is evaluated by using Feynman Diagram Calculation. The
resummed cross section is introduced to cure the Coulomb singularity which
occurs in the QED corrections. We have analyzed the parameter space of the
Inert Higgs Doublet Model after taking into account theoretical constraints,
the collider experimental bounds and dark matter search bounds as well. The
resummed cross section is introduced to cure the Coulomb singularity which
occurs in the QED corrections. It is found that the weak interaction dominates
the radiative corrections, and its size is determined by the triple Higgs
coupling $\lambda_{h^0 H^+ H^-}$, which is further connected to the mass of the
charged Higgs boson. For $\sqrt{s}=250$ GeV and $\sqrt{s}=500$ GeV, the weak
corrections are around $-6\% \sim-5\%$ and $-10\% \sim -3\%$, respectively.
While for $\sqrt{s}=1000$ GeV, the weak corrections can reach $-15\% \sim
+25\%$. The new feature is that the weak corrections can be positive near the
threshold when charged Higgs boson is larger than 470 GeV. Six benchmarks for
future collider searches have been proposed.


title: Optimal Routing for Constant Function Market Makers

abstract:
  We consider the problem of optimally executing an order involving multiple
crypto-assets, sometimes called tokens, on a network of multiple constant
function market makers (CFMMs). When we ignore the fixed cost associated with
executing an order on a CFMM, this optimal routing problem can be cast as a
convex optimization problem, which is computationally tractable. When we
include the fixed costs, the optimal routing problem is a mixed-integer convex
problem, which can be solved using (sometimes slow) global optimization
methods, or approximately solved using various heuristics based on convex
optimization. The optimal routing problem includes as a special case the
problem of identifying an arbitrage present in a network of CFMMs, or
certifying that none exists.


title: Exploring the Universal Vulnerability of Prompt-based Learning Paradigm

abstract:
  Prompt-based learning paradigm bridges the gap between pre-training and
fine-tuning, and works effectively under the few-shot setting. However, we find
that this learning paradigm inherits the vulnerability from the pre-training
stage, where model predictions can be misled by inserting certain triggers into
the text. In this paper, we explore this universal vulnerability by either
injecting backdoor triggers or searching for adversarial triggers on
pre-trained language models using only plain text. In both scenarios, we
demonstrate that our triggers can totally control or severely decrease the
performance of prompt-based models fine-tuned on arbitrary downstream tasks,
reflecting the universal vulnerability of the prompt-based learning paradigm.
Further experiments show that adversarial triggers have good transferability
among language models. We also find conventional fine-tuning models are not
vulnerable to adversarial triggers constructed from pre-trained language
models. We conclude by proposing a potential solution to mitigate our attack
methods. Code and data are publicly available at
https://github.com/leix28/prompt-universal-vulnerability


title:
The Kolmogorov backward equation for stochastic Burgers equations and
  for stochastic 2D-Navier-Stokes equations

abstract:
  In this book we establish under suitable assumptions the uniqueness and
existence of viscosity solutions of Kolmogorov backward equations for
stochastic partial differential equations (SPDEs). In addition, we show that
this solution is the semigroup of the corresponding SPDE. This generalizes the
Feynman-Kac formula to SPDEs and establishes a link between solutions of
Kolmogorov equations and solutions of the corresponding SPDEs. In contrast to
the literature we only assume that the nonlinear part of the drift is Lipschitz
continuous on bounded sets (and not globally Lipschitz continuous) and we allow
the diffusion coefficient to be degenerate and non-constant. In the last part
of this book we apply our results to stochastic Burgers equations and to
stochastic 2-D Navier-Stokes equations.


title:
Hybrid photonic-plasmonic cavity design for very large Purcell factors
  at telecom wavelengths

abstract:
  Hybrid photonic-plasmonic cavities can be tailored to display high Q-factors
and extremely small mode volumes simultaneously, which results in large values
of the Purcell factor, FP. Amongst the different hybrid configurations, those
based on a nanoparticle-on-a-mirror (NPoM) plasmonic cavity provide one of the
lowest mode volumes, though so far their operation has been constrained to
wavelengths below 1 {\mu}m. Here, we propose a hybrid configuration consisting
of a silicon photonic crystal cavity with a slot at its center in which a gold
nanoparticle is introduced. This hybrid system operates at telecom wavelengths
and provides high Q-factor values (Q ${\approx} 10^{5}$) and small normalized
mode volumes (Vm ${\approx} 10^{-4}$), leading to extremely large Purcell
factor values, FP ${\approx} 10^{7}$ - ${10^{8}}$. The proposed cavity could be
used in different applications such as molecular optomechanics, bio- and
chemo-sensing, all-optical signal processing or enhanced Raman spectroscopy in
the relevant telecom wavelength regime.


title:
Demonstration of $\mathbb{C}P^2$ skyrmions in three-band superconductors
  by self-consistent solutions to a Bogoliubov-de Gennes model

abstract:
  Topological defects, such as magnetic-flux-carrying quantum vortices
determine the magnetic response of superconductors and hence are of fundamental
importance. Here, we show that stable $\mathbb{C}P^2$ skyrmions exist in
three-band $s+\rm{i} s$ superconductors as fully self-consistent solutions to a
microscopic Bogoluibov-de Gennes model. This allows us to calculate
microscopically the magnetic signatures of $\mathbb{C}P^2$ skyrmions and their
footprint in the local density of states.


title: Bounds on Efficiency Metrics in Photonics

abstract:
  In this paper, we present a method for computing bounds for a variety of
efficiency metrics in photonics. We focus on the special case where the
objective function can be written as the ratio of two quadratic functions of
the field and show that there exists a simple semidefinite programming
relaxation for this problem. We provide a numerical example of bounding the
maximal mode conversion purity for a device of given size. This paper is
accompanied by an open source implementation.


title:
Probing the Ellis-Bronnikov wormhole geometry with a scalar field:
  clouds, waves and Q-balls

abstract:
  The Ellis-Bronnikov solution provides a simple toy model for the study of
various aspects of wormhole physics. In this work we solve the Klein-Gordon
equation in this background and find an exact solution in terms of Heun's
function. This may describe 'scalar clouds' ($i.e.$ localized, particle-like
configuration) or scalar waves. However, in the former case, the radial
derivative of the scalar field is discontinuous at the wormhole's throat
(except for the spherical case). This pathology is absent for a suitable scalar
field self-interaction, and we provide evidence for the existence of
spherically symmetric and spinning Q-balls in a Ellis-Bronnikov wormhole
background.


title:
Approximate Top-$m$ Arm Identification with Heterogeneous Reward
  Variances

abstract:
  We study the effect of reward variance heterogeneity in the approximate
top-$m$ arm identification setting. In this setting, the reward for the $i$-th
arm follows a $\sigma^2_i$-sub-Gaussian distribution, and the agent needs to
incorporate this knowledge to minimize the expected number of arm pulls to
identify $m$ arms with the largest means within error $\epsilon$ out of the $n$
arms, with probability at least $1-\delta$. We show that the worst-case sample
complexity of this problem is $$\Theta\left( \sum_{i =1}^n
\frac{\sigma_i^2}{\epsilon^2} \ln\frac{1}{\delta} + \sum_{i \in G^{m}}
\frac{\sigma_i^2}{\epsilon^2} \ln(m) + \sum_{j \in G^{l}}
\frac{\sigma_j^2}{\epsilon^2} \text{Ent}(\sigma^2_{G^{r}}) \right),$$ where
$G^{m}, G^{l}, G^{r}$ are certain specific subsets of the overall arm set $\{1,
2, \ldots, n\}$, and $\text{Ent}(\cdot)$ is an entropy-like function which
measures the heterogeneity of the variance proxies. The upper bound of the
complexity is obtained using a divide-and-conquer style algorithm, while the
matching lower bound relies on the study of a dual formulation.


title: Position fixing with cold atom gravity gradiometers

abstract:
  This paper proposes a position fixing method for autonomous navigation using
partial gravity gradient solutions from cold atom interferometers. Cold atom
quantum sensors can provide ultra-precise measurements of inertial quantities,
such as acceleration and rotation rates. However, we investigate the use of
pairs of cold atom interferometers to measure the local gravity gradient and to
provide position information by referencing these measurements against a
suitable database. Simulating the motion of a vehicle, we use partial gravity
gradient measurements to reduce the positional drift associated with inertial
navigation systems. Using standard open source global gravity databases, we
show stable navigation solutions for trajectories of over 1000km.


title: The Navier-Stokes equations with body forces decaying coherently in time

abstract:
  The long-time behavior of solutions of the three-dimensional Navier--Stokes
equations in a periodic domain is studied. The time-dependent body force
decays, as time $t$ tends to infinity, in a coherent manner. In fact, it is
assumed to have a general and complicated asymptotic expansion which involves
complex powers of $e^t$, $t$, $\ln t$, or other iterated logarithmic functions
of $t$. We prove that all Leray-Hopf weak solutions admit an asymptotic
expansion which is independent of the solutions and is uniquely determined by
the asymptotic expansion of the body force. The proof makes use of the
complexifications of the Gevrey-Sobolev spaces together with those of the
Stokes operator and the bilinear form of the Navier-Stokes equations.


title:
Learning Downstream Task by Selectively Capturing Complementary
  Knowledge from Multiple Self-supervisedly Learning Pretexts

abstract:
  Self-supervised learning (SSL), as a newly emerging unsupervised
representation learning paradigm, generally follows a two-stage learning
pipeline: 1) learning invariant and discriminative representations with
auto-annotation pretext(s), then 2) transferring the representations to assist
downstream task(s). Such two stages are usually implemented separately, making
the learned representation learned agnostic to the downstream tasks. Currently,
most works are devoted to exploring the first stage. Whereas, it is less
studied on how to learn downstream tasks with limited labeled data using the
already learned representations. Especially, it is crucial and challenging to
selectively utilize the complementary representations from diverse pretexts for
a downstream task. In this paper, we technically propose a novel solution by
leveraging the attention mechanism to adaptively squeeze suitable
representations for the tasks. Meanwhile, resorting to information theory, we
theoretically prove that gathering representation from diverse pretexts is more
effective than a single one. Extensive experiments validate that our scheme
significantly exceeds current popular pretext-matching based methods in
gathering knowledge and relieving negative transfer in downstream tasks.


title:
Learning Local Equivariant Representations for Large-Scale Atomistic
  Dynamics

abstract:
  A simultaneously accurate and computationally efficient parametrization of
the energy and atomic forces of molecules and materials is a long-standing goal
in the natural sciences. In pursuit of this goal, neural message passing has
lead to a paradigm shift by describing many-body correlations of atoms through
iteratively passing messages along an atomistic graph. This propagation of
information, however, makes parallel computation difficult and limits the
length scales that can be studied. Strictly local descriptor-based methods, on
the other hand, can scale to large systems but do not currently match the high
accuracy observed with message passing approaches. This work introduces
Allegro, a strictly local equivariant deep learning interatomic potential that
simultaneously exhibits excellent accuracy and scalability of parallel
computation. Allegro learns many-body functions of atomic coordinates using a
series of tensor products of learned equivariant representations, but without
relying on message passing. Allegro obtains improvements over state-of-the-art
methods on the QM9 and revised MD-17 data sets. A single tensor product layer
is shown to outperform existing deep message passing neural networks and
transformers on the QM9 benchmark. Furthermore, Allegro displays remarkable
generalization to out-of-distribution data. Molecular dynamics simulations
based on Allegro recover structural and kinetic properties of an amorphous
phosphate electrolyte in excellent agreement with first principles
calculations. Finally, we demonstrate the parallel scaling of Allegro with a
dynamics simulation of 100 million atoms.

categories: physics.comp-ph cond-mat.mtrl-sci cs.LG physics.chem-ph


title: Revisiting and improving upper bounds for identifying codes

abstract:
  An identifying code $C$ of a graph $G$ is a dominating set of $G$ such that
any two distinct vertices of $G$ have distinct closed neighbourhoods within
$C$. These codes have been widely studied for over two decades. We give an
improvement over all the best known upper bounds, some of which have stood for
over 20 years, for identifying codes in trees, proving the upper bound of
$(n+\ell)/2$, where $n$ is the order and $\ell$ is the number of leaves
(pendant vertices) of the graph. In addition to being an improvement in size,
the new upper bound is also an improvement in generality, as it actually holds
for bipartite graphs having no twins (pairs of vertices with the same closed or
open neighbourhood) of degree 2 or greater. We also show that the bound is
tight for an infinite class of graphs and that there are several structurally
different families of trees attaining the bound. We then use our bound to
derive a tight upper bound of $2n/3$ for twin-free bipartite graphs of order
$n$, and characterize the extremal examples, as $2$-corona graphs of bipartite
graphs. This is best possible, as there exist twin-free graphs, and trees with
twins, that need $n-1$ vertices in any of their identifying codes. We also
generalize the existing upper bound of $5n/7$ for graphs of order $n$ and girth
at least 5 when there are no leaves, to the upper bound $\frac{5n+2\ell}{7}$
when leaves are allowed. This is tight for the $7$-cycle $C_7$ and for all
stars.


title: Bayes Point Rule Set Learning

abstract:
  Interpretability is having an increasingly important role in the design of
machine learning algorithms. However, interpretable methods tend to be less
accurate than their black-box counterparts. Among others, DNFs (Disjunctive
Normal Forms) are arguably the most interpretable way to express a set of
rules. In this paper, we propose an effective bottom-up extension of the
popular FIND-S algorithm to learn DNF-type rulesets. The algorithm greedily
finds a partition of the positive examples. The produced DNF is a set of
conjunctive rules, each corresponding to the most specific rule consistent with
a part of positive and all negative examples. We also propose two principled
extensions of this method, approximating the Bayes Optimal Classifier by
aggregating DNF decision rules. Finally, we provide a methodology to
significantly improve the explainability of the learned rules while retaining
their generalization capabilities. An extensive comparison with
state-of-the-art symbolic and statistical methods on several benchmark data
sets shows that our proposal provides an excellent balance between
explainability and accuracy.


title: Finite-size effects on the ferroelectricity in rhombohedral HfO$_2$

abstract:
  In this work we analyze the finite-size effects on the structural properties
and on the polarization of the rhombohedral phase of HfO$_2$ subjected to a
biaxial compressive strain. We show how the presence of surface charges affects
the polarization, leading to a strong reduction with respect to its bulk value.
This reduction can be ascribed to two mechanisms: the coupling between
compressive strain and the phase-transition order parameter; the changes in the
ferroelectric distortion. We give two alternative explanations of this
phenomenon: from an atomistic point of view, analyzing the evolution of the
bond lengths, and from a symmetry-analysis point of view, considering the
changes in the amplitude of the symmetry-allowed distortions, when a slab
configuration is considered. These results are independent on the
slab-thickness in the considered range, suggesting the absence of a critical
thickness for ferroelectricity in HfO$_2$, in agreement with the proposed
improper nature of hafnia ferroelectricity.


title: Nonconvex ancient solutions to Curve Shortening Flow

abstract:
  We construct an ancient solution to planar curve shortening. The solution is
at all times compact and embedded. For $t\ll0$ it is approximated by the
rotating Yin-Yang soliton, truncated at a finite angle $\alpha(t) = -t$, and
closed off by a small copy of the Grim Reaper translating soliton.


title:
Experimentally finding dense subgraphs using a time-bin encoded Gaussian
  boson sampling device

abstract:
  Gaussian Boson Sampling (GBS) is a quantum computing concept based on drawing
samples from a multimode nonclassical Gaussian state using photon-number
resolving detectors. It was initially posed as a near-term approach aiming to
achieve quantum advantage, but several applications have been proposed ever
since, such as the calculation of graph features or molecular vibronic spectra,
among others. For the first time, we use a time-bin encoded interferometer to
implement GBS experimentally and extract samples to enhance the search for
dense subgraphs in a graph. Our results indicate an improvement over classical
methods for subgraphs of sizes three and four in a graph containing ten nodes.
In addition, we numerically explore the role of imperfections in the optical
circuit and on the performance of the algorithm.


title:
Narcissus: A Practical Clean-Label Backdoor Attack with Limited
  Information

abstract:
  Backdoor attacks insert malicious data into a training set so that, during
inference time, it misclassifies inputs that have been patched with a backdoor
trigger as the malware specified label. For backdoor attacks to bypass human
inspection, it is essential that the injected data appear to be correctly
labeled. The attacks with such property are often referred to as "clean-label
attacks." Existing clean-label backdoor attacks require knowledge of the entire
training set to be effective. Obtaining such knowledge is difficult or
impossible because training data are often gathered from multiple sources
(e.g., face images from different users). It remains a question whether
backdoor attacks still present a real threat.
  This paper provides an affirmative answer to this question by designing an
algorithm to mount clean-label backdoor attacks based only on the knowledge of
representative examples from the target class. With poisoning equal to or less
than 0.5% of the target-class data and 0.05% of the training set, we can train
a model to classify test examples from arbitrary classes into the target class
when the examples are patched with a backdoor trigger. Our attack works well
across datasets and models, even when the trigger presents in the physical
world.
  We explore the space of defenses and find that, surprisingly, our attack can
evade the latest state-of-the-art defenses in their vanilla form, or after a
simple twist, we can adapt to the downstream defenses. We study the cause of
the intriguing effectiveness and find that because the trigger synthesized by
our attack contains features as persistent as the original semantic features of
the target class, any attempt to remove such triggers would inevitably hurt the
model accuracy first.


title: Invariant Smoothing with low process noise

abstract:
  In this paper we address smoothing-that is, optimisation-based-estimation
techniques for localisation problems in the case where motion sensors are very
accurate. Our mathematical analysis focuses on the difficult limit case where
motion sensors are infinitely precise, resulting in the absence of process
noise. Then the formulation degenerates, as the dynamical model that serves as
a soft constraint becomes an equality constraint, and conventional smoothing
methods are not able to fully respect it. By contrast, once an appropriate Lie
group embedding has been found, we prove theoretically that invariant smoothing
gracefully accommodates this limit case in that the estimates tend to be
consistent with the induced constraints when the noise tends to zero.
Simulations on the important problem of initial alignement in inertial
navigation show that, in a low noise setting, invariant smoothing may favorably
compare to state-of-the-art smoothers when using precise inertial measurements
units (IMU).


title: Moduli spaces of complex affine and dilation surfaces

abstract:
  We construct moduli spaces of complex affine and dilation surfaces. Using
ideas of Veech, we show that the the moduli space of affine surfaces with fixed
genus and with cone points of fixed complex order is a holomorphic affine
bundle over the moduli space of Riemann surfaces. Similarly, the moduli space
of dilation surfaces is a covering space of the moduli space of Riemann
surfaces. We classify the connected components of the moduli space of dilation
surfaces and show that any component is an orbifold K(G,1) where G is the
framed mapping class group of Calderon-Salter.


title:
Multi-view graph structure learning using subspace merging on Grassmann
  manifold

abstract:
  Many successful learning algorithms have been recently developed to represent
graph-structured data. For example, Graph Neural Networks (GNNs) have achieved
considerable successes in various tasks such as node classification, graph
classification, and link prediction. However, these methods are highly
dependent on the quality of the input graph structure. One used approach to
alleviate this problem is to learn the graph structure instead of relying on a
manually designed graph. In this paper, we introduce a new graph structure
learning approach using multi-view learning, named MV-GSL (Multi-View Graph
Structure Learning), in which we aggregate different graph structure learning
methods using subspace merging on Grassmann manifold to improve the quality of
the learned graph structures. Extensive experiments are performed to evaluate
the effectiveness of the proposed method on two benchmark datasets, Cora and
Citeseer. Our experiments show that the proposed method has promising
performance compared to single and other combined graph structure learning
methods.


title:
Strictly increasing and decreasing sequences in subintervals of words
  and a conjecture of Guo and Poznanovi\'c

abstract:
  It follows from well-known properties of the Robinson-Schensted algorithm,
Sch\"{u}tzenberger's Promotion operator, and Knuth equivalence that there is a
bijection on the set of permutations that "shifts left" the joint distribution
of lengths of strictly increasing and strictly decreasing sequences on all
index subintervals while fixing this distribution on all value subintervals.
Motivated by a conjecture of Guo and Poznanovi\'c, we establish an analogous
result in the context of words. (Earlier maps were known to exist in this
context but they failed to preserve strictly increasing and decreasing data.)
To define our bijection we make extensive use of Thomas and Yong's K-infusion
operator and the idea of K-Knuth equivalence. To construct our map we also need
a new variant of RSK which we introduce in this paper. Using our map we then
give a proof of the conjecture of Guo and Poznanovi\'c.


title: SMEFT Analysis of $m_{W}$

abstract:
  We use the Fitmaker tool to incorporate the recent CDF measurement of $m_W$
in a global fit to electroweak, Higgs, and diboson data in the Standard Model
Effective Field Theory (SMEFT) including dimension-6 operators at linear order.
We find that including any one of the SMEFT operators ${\cal O}_{HWB}$, ${\cal
O}_{HD}$, ${\cal O}_{\ell \ell}$ or ${\cal O}_{H \ell}^{(3)}$ with a non-zero
coefficient could provide a better fit than the Standard Model, with the
strongest pull for ${\cal O}_{HD}$ and no tension with other electroweak
precision data. We then analyse which tree-level single-field extensions of the
Standard Model could generate such operator coefficients with the appropriate
sign, and discuss the masses and couplings of these fields that best fit the
CDF measurement and other data. In particular, the global fit favours either a
singlet $Z^\prime$ vector boson, a scalar electroweak triplet with zero
hypercharge, or a vector electroweak triplet with unit hypercharge, followed by
a singlet heavy neutral lepton, all with masses in the multi-TeV range for unit
coupling.


title:
A-type antiferromagnetic order in semiconducting EuMg$_2$Sb$_2$ single
  crystals

abstract:
  Eu-based Zintl-phase materials EuA$_2$Pn$_2$ (A = Mg, In, Cd, Zn; Pn = Bi,
Sb, As, P) have generated significant recent interest owing to the complex
interplay of magnetism and band topology. Here, we investigated the electronic,
magnetic, and electronic properties of the layered Zintl-phase single crystals
of EuMg$_2$Sb$_2$ with the trigonal CaAl$_2$Si$_2$ crystal structure (space
group $P\bar{3}m1$). Electrical resistivity measurements complemented with
angle-resolved photoemission spectroscopy (ARPES) studies find an activated
behavior with the intrinsic conductivity at high temperatures indicating a
semiconducting electronic ground state with a narrow energy gap of 370 meV.
Magnetic susceptibility and zero-field heat-capacity measurements indicate that
the compound undergoes antiferromagnetic (AFM) ordering at the Neel temperature
$T_{\rm N}$ = 8.0(2) K. Zero-field neutron-diffraction measurements reveal that
the AFM ordering is A-type where the Eu ordered moments (Eu$^{2+}$, S= 7/2)
arranged in ab-plane layers are aligned ferromagnetically in the ab plane with
the Eu moments in adjacent layers aligned antiferromagnetically. We also find
that Eu-moment reorientation in the trigonal AFM domains within the ab planes
occurs below $T_{\rm N}$ at low fields < 0.05 T due to very small in-plane
anisotropy. Although isostructural semimetallic EuMg$_2$Bi$_2$ is reported to
host Dirac surface states, the observation of narrow-gap semiconducting
behavior in EuMg$_2$Sb$_2$ implies a strong role of spin-orbit coupling in
tuning the electronic states of these materials.


title:
Line Operators in Chern-Simons-Matter Theories and Bosonization in Three
  Dimensions

abstract:
  We study Chern-Simons theories at large $N$ with either bosonic or fermionic
matter in the fundamental representation. The most fundamental operators in
these theories are mesonic line operators, the simplest example being Wilson
lines ending on fundamentals. We classify the conformal line operators along an
arbitrary smooth path as well as the spectrum of conformal dimensions and
transverse spins of their boundary operators at finite 't Hooft coupling. These
line operators are shown to satisfy first-order chiral evolution equations, in
which a smooth variation of the path is given by a factorized product of two
line operators. We argue that this equation together with the spectrum of
boundary operators are sufficient to uniquely determine the expectation values
of these operators. We demonstrate this by bootstrapping the two-point function
of the displacement operator on a straight line. We show that the line
operators in the theory of bosons and the theory of fermions satisfy the same
evolution equation and have the same spectrum of boundary operators.


title:
Maximum entropy optimal density control of discrete-time linear systems
  and Schr\"odinger bridges

abstract:
  We consider an entropy-regularized version of optimal density control of
deterministic discrete-time linear systems. Entropy regularization, or a
maximum entropy (MaxEnt) method for optimal control has attracted much
attention especially in reinforcement learning due to its many advantages such
as a natural exploration strategy. Despite the merits, high-entropy control
policies introduce probabilistic uncertainty into systems, which severely
limits the applicability of MaxEnt optimal control to safety-critical systems.
To remedy this situation, we impose a Gaussian density constraint at a
specified time on the MaxEnt optimal control to directly control state
uncertainty. Specifically, we derive the explicit form of the MaxEnt optimal
density control. In addition, we also consider the case where a density
constraint is replaced by a fixed point constraint. Then, we characterize the
associated state process as a pinned process, which is a generalization of the
Brownian bridge to linear systems. Finally, we reveal that the MaxEnt optimal
density control induces the so-called Schr\"odinger bridge associated to a
discrete-time linear system.


title: A Julia Framework for Graph-Structured Nonlinear Optimization

abstract:
  Graph theory provides a convenient framework for modeling and solving
structured optimization problems. Under this framework, the modeler can
arrange/assemble the components of an optimization model (variables,
constraints, objective functions, and data) within nodes and edges of a graph,
and this representation can be used to visualize, manipulate, and solve the
problem. In this work, we present a ${\tt Julia}$ framework for modeling and
solving graph-structured nonlinear optimization problems. Our framework
integrates the modeling package ${\tt Plasmo.jl}$ (which facilitates the
construction and manipulation of graph models) and the nonlinear optimization
solver ${\tt MadNLP.jl}$ (which provides capabilities for exploiting graph
structures to accelerate solution). We illustrate with a simple example how
model construction and manipulation can be performed in an intuitive manner
using ${\tt Plasmo.jl}$ and how the model structure can be exploited by ${\tt
MadNLP.jl}$. We also demonstrate the scalability of the framework by targeting
a large-scale, stochastic gas network problem that contains over 1.7 million
variables.


title: The Importance of Future Information in Credit Card Fraud Detection

abstract:
  Fraud detection systems (FDS) mainly perform two tasks: (i) real-time
detection while the payment is being processed and (ii) posterior detection to
block the card retrospectively and avoid further frauds. Since human
verification is often necessary and the payment processing time is limited, the
second task manages the largest volume of transactions. In the literature,
fraud detection challenges and algorithms performance are widely studied but
the very formulation of the problem is never disrupted: it aims at predicting
if a transaction is fraudulent based on its characteristics and the past
transactions of the cardholder. Yet, in posterior detection, verification often
takes days, so new payments on the card become available before a decision is
taken. This is our motivation to propose a new paradigm: posterior fraud
detection with "future" information. We start by providing evidence of the
on-time availability of subsequent transactions, usable as extra context to
improve detection. We then design a Bidirectional LSTM to make use of these
transactions. On a real-world dataset with over 30 million transactions, it
achieves higher performance than a regular LSTM, which is the state-of-the-art
classifier for fraud detection that only uses the past context. We also
introduce new metrics to show that the proposal catches more frauds, more
compromised cards, and based on their earliest frauds. We believe that future
works on this new paradigm will have a significant impact on the detection of
compromised cards.


title:
Optimizing a low-dimensional convex function over a high-dimensional
  cube

abstract:
  For a matrix $W \in \mathbb{Z}^{m \times n}$, $m \leq n$, and a convex
function $g: \mathbb{R}^m \rightarrow \mathbb{R}$, we are interested in
minimizing $f(x) = g(Wx)$ over the set $\{0,1\}^n$. Since non-linearity only
stems from $g$, one might expect algorithms whose running time depends only
polynomially on $n$. Provided that $W$ is known explicitly, various algorithms
can be used for this problem. For instance, if $g$ is separable convex, we will
show how the framework of Hochbaum and Shanthikumar can be adapted. However,
knowledge of $W$ is a non-trivial assumption. We develop an algorithm that does
not require $W$ as an input, and achieves a running time of roughly $(m
\|W\|_{\infty})^{\mathcal{O}(m^3)} \cdot poly(n)$ for several classes of convex
functions $g$. When $W$ is known explicitly, the running time decreases to $(m
\|W\|_{\infty})^{\mathcal{O}(m^2)} \cdot poly(n)$. In this case, the running
time is on par with the running time of Hochbaum and Shanthikumar. However, in
contrast to Hochbaum and Shanthikumar, our algorithm also applies to sharp
convex functions, which is a generalization of strongly convex functions, a
class that reaches far beyond separable convex functions.


title: Violation of custodial symmetry from W-boson mass measurements

abstract:
  The new measurement of the W-boson mass from the CDF collaboration shows a
significant tension with the Standard Model prediction. We quantify this
discrepancy within a state-of-the-art analysis of electroweak precision data
and scrutinize the leading deformations of the Standard Model Effective Field
Theory arising at dimension six. We find evidence for a non-zero value of the
$T$ parameter, i.e. for a novel source of violation of custodial symmetry,
pointing to physics beyond the SM at the 4.5$\sigma$ level. We contextualize
the implications of our findings in light of other present anomalies in
Particle Physics.


title:
Constraining the reionization and thermal history of the Universe using
  a semi-numerical photon-conserving code SCRIPT

abstract:
  Given that the reionization history of cosmic hydrogen is yet to be
stringently constrained, it is worth checking the prospects of doing so using
physically motivated models and available observational data. For this purpose,
we use an extended version of the explicitly photon-conserving semi-numerical
model of reionization, $\texttt{SCRIPT}$, which also includes thermal evolution
of the intergalactic medium (IGM). The model incorporates the effects of
inhomogeneous recombination and radiative feedback self-consistently and is
characterized by five free parameters (two for the redshift-dependent
ionization efficiency, two for the ionizing escape fraction, and another for
reionization temperature increment). We constrain these free parameters by
simultaneously matching with various observational probes, e.g., estimates of
the ionized hydrogen fraction, the CMB scattering optical depth and the galaxy
UV luminosity function. In addition, we include the low-density IGM temperature
measurements obtained from Lyman-$\alpha$ absorption spectra at $z \sim 5.5$, a
probe not commonly used for Bayesian analysis of reionization parameters. We
find that the interplay of the various data sets, particularly inclusion of the
temperature data, leads to tightening of the parameter constraints. Our default
models prefer a late end of reionization (at $z \lesssim 6$), in agreement with
other recent studies. We can also derive constraints on the duration of
reionization, $\Delta z=1.81^{+0.51}_{-0.67}$ and the midpoint of reionization,
$z_{\mathrm{mid}}=7.0^{+0.30}_{-0.40}$. The constraints can be further
tightened by including other available and upcoming data sets.


title:
New physics effects on the $W$-boson mass from a doublet extension of
  the SM Higgs sector

abstract:
  Recently, the CDF collaboration has reported a new precision measurement of
the $W$-boson mass, $M_W$, showing a large deviation from the value predicted
by the Standard Model (SM). In this paper, we analyse possible new physics
contributions to $M_W$ from extended Higgs sectors. We focus on the
Two-Higgs-Doublet Model (2HDM) as a concrete example. Employing predictions for
the electroweak precision observables in the 2HDM at the two-loop level and
taking into account further theoretical and experimental constraints, we
identify parameter regions of the 2HDM in which the prediction for $M_W$ is
close to the new CDF value. We assess the compatibility of these regions with
precision measurements of the effective weak mixing angle and the total width
of the $Z$ boson.


title: On the ergodic behaviour of affine Volterra processes

abstract:
  We show the existence of a stationary measure for a class of multidimensional
stochastic Volterra systems of affine type. These processes are in general not
Markovian, a shortcoming which hinders their large-time analysis. We circumvent
this issue by lifting the system to a measure-valued stochastic PDE introduced
by Cuchiero and Teichmann, whence we retrieve the Markov property. Leveraging
on the associated generalised Feller property, we extend the Krylov-Bogoliubov
theorem to this infinite-dimensional setting and thus establish an approach to
the existence of invariant measures. We present concrete examples, including
the rough Heston model from Mathematical Finance.


title:
Perfect stimulated Raman adiabatic passage with imperfect finite-time
  pulses

abstract:
  We present a well-tailored sequence of two Gaussian-pulsed drives that
achieves perfect population transfer in STImulated Raman Adiabatic Passage
(STIRAP). We give a theoretical analysis of the optimal truncation and relative
placement of the Stokes and pump pulses. Further, we obtain the power and the
duration of the protocol for a given pulse width. Importantly, the duration of
the protocol required to attain a desired value of fidelity depends only
logarithmically on the infidelity. Subject to optimal truncation of the drives
and with reference to the point of fastest transfer, we obtain a new
adiabaticity criteria, which is remarkably simple and effective.


title: Braided Thompson groups with and without quasimorphisms

abstract:
  We study quasimorphisms and bounded cohomology of a variety of braided
versions of Thompson groups. Our first main result is that the Brin--Dehornoy
braided Thompson group $bV$ has an infinite-dimensional space of quasimorphisms
and thus infinite-dimensional second bounded cohomology. This implies that
despite being perfect, $bV$ is not uniformly perfect, in contrast to Thompson's
group $V$. We also prove that relatives of $bV$ like the ribbon braided
Thompson group $rV$ and the pure braided Thompson group $bF$ similarly have an
infinite-dimensional space of quasimorphisms. Our second main result is that,
in stark contrast, the close relative of $bV$ denoted $\hat{bV}$, which was
introduced concurrently by Brin, has trivial second bounded cohomology. This
makes $\hat{bV}$ the first example of a left-orderable group of type
$\operatorname{F}_\infty$ that is not locally indicable and has trivial second
bounded cohomology. This also makes $\hat{bV}$ an interesting example of a
subgroup of the mapping class group of the plane minus a Cantor set that is
non-amenable but has trivial second bounded cohomology, behaviour that cannot
happen for finite-type mapping class groups.


title:
The evolution of the high-frequency variability in the black hole
  candidate GRS 1915+105 as seen by RXTE

abstract:
  GRS 1915+105 can show type-C quasi-periodic oscillations (QPOs) in the power
density spectrum. A high-frequency QPO (HFQPO) at 67 Hz has been observed in
this source, albeit less often than the type-C QPOs. Besides these features,
GRS 1915+105 sometimes shows a broad bump in the power spectrum at around
30-150 Hz. We study the power spectra of GRS 1915+105 with the Rossi X-ray
Timing Explorer when the source was in the $\chi$ class. We find that the rms
amplitude of the bump depends strongly upon both the frequency of the type-C
QPO and the hardness ratio, and is correlated with the corona temperature and
anti-correlated with the radio flux at 15 GHz. The characteristic frequency of
the bump is better correlated with a combination of the frequency of the type-C
QPO and the hardness ratio than with the frequency of the type-C QPO alone. The
rms amplitude of the bump generally increases with energy from ~1-2% at ~3 keV
to ~10-15% at ~30 keV. We suggest that the bump and the high-frequency QPO may
be the same variability component but the properties of the corona affect the
coherence of this variability, leading either to a HFQPO when the spectrum is
in the relatively soft $\gamma$ class, or to a bump when the spectrum is in the
hard $\chi$ class. Finally, we discuss the anti-correlation between the rms
amplitude of the bump and the radio flux in the context of the relation between
the corona and the jet.


title:
MIME: Adapting a Single Neural Network for Multi-task Inference with
  Memory-efficient Dynamic Pruning

abstract:
  Recent years have seen a paradigm shift towards multi-task learning. This
calls for memory and energy-efficient solutions for inference in a multi-task
scenario. We propose an algorithm-hardware co-design approach called MIME. MIME
reuses the weight parameters of a trained parent task and learns task-specific
threshold parameters for inference on multiple child tasks. We find that MIME
results in highly memory-efficient DRAM storage of neural-network parameters
for multiple tasks compared to conventional multi-task inference. In addition,
MIME results in input-dependent dynamic neuronal pruning, thereby enabling
energy-efficient inference with higher throughput on a systolic-array hardware.
Our experiments with benchmark datasets (child tasks)- CIFAR10, CIFAR100, and
Fashion-MNIST, show that MIME achieves ~3.48x memory-efficiency and ~2.4-3.1x
energy-savings compared to conventional multi-task inference in Pipelined task
mode.


title:
Settling the Sample Complexity of Model-Based Offline Reinforcement
  Learning

abstract:
  This paper is concerned with offline reinforcement learning (RL), which
learns using pre-collected data without further exploration. Effective offline
RL would be able to accommodate distribution shift and limited data coverage.
However, prior algorithms or analyses either suffer from suboptimal sample
complexities or incur high burn-in cost to reach sample optimality, thus posing
an impediment to efficient offline RL in sample-starved applications.
  We demonstrate that the model-based (or "plug-in") approach achieves
minimax-optimal sample complexity without burn-in cost for tabular Markov
decision processes (MDPs). Concretely, consider a finite-horizon (resp.
$\gamma$-discounted infinite-horizon) MDP with $S$ states and horizon $H$
(resp. effective horizon $\frac{1}{1-\gamma}$), and suppose the distribution
shift of data is reflected by some single-policy clipped concentrability
coefficient $C^{\star}_{\text{clipped}}$. We prove that model-based offline RL
yields $\varepsilon$-accuracy with a sample complexity of \[ \begin{cases}
\frac{H^{4}SC_{\text{clipped}}^{\star}}{\varepsilon^{2}} &
(\text{finite-horizon MDPs})
\frac{SC_{\text{clipped}}^{\star}}{(1-\gamma)^{3}\varepsilon^{2}} &
(\text{infinite-horizon MDPs}) \end{cases} \] up to log factor, which is
minimax optimal for the entire $\varepsilon$-range. Our algorithms are
"pessimistic" variants of value iteration with Bernstein-style penalties, and
do not require sophisticated variance reduction.

categories: stat.ML cs.IT cs.LG cs.SY eess.SY math.IT math.ST stat.TH


title: Segmentation-Consistent Probabilistic Lesion Counting

abstract:
  Lesion counts are important indicators of disease severity, patient
prognosis, and treatment efficacy, yet counting as a task in medical imaging is
often overlooked in favor of segmentation. This work introduces a novel
continuously differentiable function that maps lesion segmentation predictions
to lesion count probability distributions in a consistent manner. The proposed
end-to-end approach--which consists of voxel clustering, lesion-level voxel
probability aggregation, and Poisson-binomial counting--is non-parametric and
thus offers a robust and consistent way to augment lesion segmentation models
with post hoc counting capabilities. Experiments on Gadolinium-enhancing lesion
counting demonstrate that our method outputs accurate and well-calibrated count
distributions that capture meaningful uncertainty information. They also reveal
that our model is suitable for multi-task learning of lesion segmentation, is
efficient in low data regimes, and is robust to adversarial attacks.


abstract:
  In the present note we study the interrelations between the sets of so-called
typical numbers and numbers that are normal in base two. Employing results by
Nakai and Shiokawa, we exhibit examples of numbers that belong to one set but
do not belong to the other and vice versa. Moreover, we demonstrate the set of
typical numbers is $\Pi_3^0$ in the Borel hierarchy, i.e., it can be expressed
as the union of countably many $F_{\sigma}\text{-sets.}$ Using the result by Ki
and Linton that asserts the same for normal numbers, we examine the Borel
complexity of the set of typical numbers that are not normal, proving that it
belongs to the $\Delta_4^0$ class.


title:
Neglectable effect of brain MRI data prepreprocessing for tumor
  segmentation

abstract:
  Magnetic resonance imaging (MRI) data is heterogeneous due to the differences
in device manufacturers, scanning protocols, and inter-subject variability. A
conventional way to mitigate MR image heterogeneity is to apply preprocessing
transformations, such as anatomy alignment, voxel resampling, signal intensity
equalization, image denoising, and localization of regions of interest (ROI).
Although preprocessing pipeline standardizes image appearance, its influence on
the quality of image segmentation and other downstream tasks on deep neural
networks (DNN) has never been rigorously studied.
  Here we report a comprehensive study of multimodal MRI brain cancer image
segmentation on TCIA-GBM open-source dataset. Our results demonstrate that most
popular standardization steps add no value to artificial neural network
performance; moreover, preprocessing can hamper model performance. We suggest
that image intensity normalization approaches do not contribute to model
accuracy because of the reduction of signal variance with image
standardization. Finally, we show the contribution of scull-stripping in data
preprocessing is almost negligible if measured in terms of clinically relevant
metrics.
  We show that the only essential transformation for accurate analysis is the
unification of voxel spacing across the dataset. In contrast, anatomy alignment
in form of non-rigid atlas registration is not necessary and most intensity
equalization steps do not improve model productiveness.


title:
The relation between the radio emission of the core and host galaxy
  properties in Fanaroff-Riley type II radio galaxies

abstract:
  We study the radio power of the core and its relation to the optical
properties of the host galaxy in samples of high excitation (HERG) and low
excitation (LERG) Fanaroff-Riley type II (FRII) radio galaxies. The radio
galaxy sample is divided into two groups of core/non-core FRII, based on the
existence of strong, weak or lack of single radio core component. We show that
FRII LERGs with radio emission of the core have significantly higher [O III]
line luminosities compared to the non-core LERG FRIIs. There is no significant
difference between the hosts of the core and non-core FRIIs of LERG type in
galaxy sizes, concentration indices, star formation rates, 4000-\AA\ break
strengths, colours, black hole masses and black hole to stellar masses. We show
that the results are not biased by the stellar masses, redshifts and angular
sizes of the radio galaxies. We argue that the detection of higher [O III]
luminosities in the core FRIIs may indicate the presence of higher amounts of
gas, very close to the AGN nucleus in the core FRIIs compared to the non-core
FRIIs or may result from the interaction of the radio jets with this gas. The
core and non-core FRIIs of the HERG type show no significant differences
perhaps due to our small sample size. The effect of relativistic beaming on the
radio luminosities and the contribution of restating AGN activity have also
been considered.


title:
MONCE Tracking Metrics: a comprehensive quantitative performance
  evaluation methodology for object tracking

abstract:
  Evaluating tracking model performance is a complicated task, particularly for
non-contiguous, multi-object trackers that are crucial in defense applications.
While there are various excellent tracking benchmarks available, this work
expands them to quantify the performance of long-term, non-contiguous,
multi-object and detection model assisted trackers. We propose a suite of MONCE
(Multi-Object Non-Contiguous Entities) image tracking metrics that provide both
objective tracking model performance benchmarks as well as diagnostic insight
for driving tracking model development in the form of Expected Average Overlap,
Short/Long Term Re-Identification, Tracking Recall, Tracking Precision,
Longevity, Localization and Absence Prediction.


title: Physically Disentangled Representations

abstract:
  State-of-the-art methods in generative representation learning yield semantic
disentanglement, but typically do not consider physical scene parameters, such
as geometry, albedo, lighting, or camera. We posit that inverse rendering, a
way to reverse the rendering process to recover scene parameters from an image,
can also be used to learn physically disentangled representations of scenes
without supervision. In this paper, we show the utility of inverse rendering in
learning representations that yield improved accuracy on downstream clustering,
linear classification, and segmentation tasks with the help of our novel
Leave-One-Out, Cycle Contrastive loss (LOOCC), which improves disentanglement
of scene parameters and robustness to out-of-distribution lighting and
viewpoints. We perform a comparison of our method with other generative
representation learning methods across a variety of downstream tasks, including
face attribute classification, emotion recognition, identification, face
segmentation, and car classification. Our physically disentangled
representations yield higher accuracy than semantically disentangled
alternatives across all tasks and by as much as 18%. We hope that this work
will motivate future research in applying advances in inverse rendering and 3D
understanding to representation learning.


title:
Bundle Structure of Massless Unitary Representations of the Poincar\'e
  Group

abstract:
  We exhibit the bundle structure of helicity representations of the Poincar\'e
group of four-dimensional spacetime. By Frobenius' reciprocity theorem it
prohibits one-particle states with total angular momentum less than the modulus
of the helicity. Partial derivatives of the momentum wave functions are no
operators which can be applied to states with non-vanishing helicity. They
allow only for covariant, noncommuting derivatives. Helicity is a topological
charge.


title: Oblique Lessons from the $W$ Mass Measurement at CDF II

abstract:
  The CDF collaboration recently reported a new precise measurement of the $W$
boson mass $M_W$ with a central value significantly larger than the SM
prediction. We explore the effects of including this new measurement on a fit
of the Standard Model (SM) to electroweak precision data. We characterize the
tension of this new measurement with the SM and explore potential beyond the SM
phenomena within the electroweak sector in terms of the oblique parameters $S$,
$T$ and $U$. We show that the large $M_W$ value can be accommodated in the fit
by a large, nonzero value of $U$, which is difficult to construct in explicit
models. Assuming $U = 0$, the electroweak fit strongly prefers large, positive
values of $T$. Finally, we study how the preferred values of the oblique
parameters may be generated in the context of models affecting the electroweak
sector at tree- and loop-level. In particular, we demonstrate that the
preferred values of $T$ and $S$ can be generated with a real SU(2)$_L$ triplet
scalar, the humble "swino," which can be heavy enough to evade current collider
constraints, or by (multiple) species of a singlet-doublet fermion pair. We
highlight challenges in constructing other simple models, such as a dark
photon, for explaining a large $M_W$ value, and several directions for further
study.


title: Higgs physics confronts the $M_W$ anomaly

abstract:
  The recent high-precision measurement of the $W$ mass by the CDF
collaboration is in sharp tension with the Standard Model prediction as
obtained by the electroweak fit. If confirmed, this finding can only be
explained in terms of new physics effects. In this work, we point out a
model-independent connection between the $M_W$ anomaly and Higgs physics
observables such as $h\to\gamma\gamma, Z\gamma$ and the ratio $h\to ZZ/WW$.
Moreover, we systematically classify new physics scenarios which can address
the $M_W$ anomaly via a tree-level contribution to the $\hat T$ parameter.
These include a real scalar triplet, a scalar quadruplet with the same
hypercharge of the Higgs doublet, a $Z'$ boson, a vector triplet with unit
hypercharge and a vector boson with the gauge quantum numbers of the Higgs
doublet. These solutions to the $M_W$ anomaly are characterized by new physics
states which are typically too heavy to be discovered in direct searches, but
which might leave their imprints in Higgs physics.


title:
Precise calculation of the W boson pole mass beyond the Standard Model
  with FlexibleSUSY

abstract:
  We present an updated calculation of the W boson pole mass in models beyond
the Standard Model with FlexibleSUSY. The calculation has a decoupling
behaviour and allows for a precise W pole mass prediction up to large new
physics scales. We apply the calculation to several Standard Model extensions,
including the MRSSM where we show that it can be compatible with large
corrections to the W boson mass that would be needed to fit the recent 2022 CDF
measurement.


title: Expressivity of Variational Quantum Machine Learning on the Boolean Cube

abstract:
  Categorical data plays an important part in machine learning research and
appears in a variety of applications. Models that can express large classes of
functions on the Boolean cube are useful for problems involving discrete-valued
data types, including those which are not Boolean. To this date, the commonly
used schemes for embedding classical data into variational quantum machine
learning models encode continuous values. Here we investigate quantum
embeddings for encoding Boolean-valued data into parameterized quantum circuits
used for machine learning tasks. We narrow down representability conditions for
functions on the $n$-dimensional Boolean cube with respect to previously known
results, using two quantum embeddings: a phase embedding and an embedding based
on quantum random access codes. We show that for any function on the Boolean
cube, there exists a variational linear quantum model based on a phase
embedding that can represent it. Additionally, we prove that an ensemble of
variational linear quantum models that use the quantum random access code
embedding can represent any function on the Boolean cube with degree $d\leq
\lceil\frac{n}{3}\rceil$ using $\lceil\frac{n}{3}\rceil$ qubits. Lastly, we
demonstrate the use of the embeddings presented by performing numerical
simulations and experiments on IBM quantum processors using the Qiskit machine
learning framework.


title:
On monochromatic arithmetic progressions in binary words associated with
  block-counting functions

abstract:
  Let $e_v(n)$ denote the number of occurrences of a fixed block $v$ of digits
in the binary expansion of $n \in \mathbb{N}$. In this paper we study
monochromatic arithmetic progressions in the class of binary words $(e_v(n)
\bmod{2})_{n \geq 0}$, which includes the famous Thue--Morse word $\mathbf{t}$
and Rudin--Shapiro word $\mathbf{r}$. We prove that the length of a
monochromatic arithmetic progression of difference $d \geq 3$ starting at $0$
in $\mathbf{r}$ is at most $(d+3)/2$, with equality for infinitely many $d$.
Moreover, we compute the maximal length of a monochromatic arithmetic
progression in $\mathbf{r}$ of difference $2^k-1$ and $2^k+1$. For a general
block $v$ we provide an upper bound on the length of a monochromatic arithmetic
progression of any difference $d$. We also prove other miscellaneous results
and offer a number of related problems and conjectures.


title: The slice-Bennequin inequality for the fractional Dehn twist coefficient

abstract:
  We characterize the fractional Dehn twist coefficient (FDTC) on the
$n$-stranded braid group as the unique homogeneous quasimorphism to the real
numbers of defect at most 1 that equals 1 on the positive full twist and
vanishes on the $(n-1)$-stranded braid subgroup. In a different direction, we
establish that the slice-Bennequin inequality holds with the FDTC in place of
the writhe. In other words, we establish an affine linear lower bound for the
smooth slice genus of the closure of a braid in terms of the braid's FDTC. We
also discuss connections between these two seemingly unrelated results. In the
appendix we provide a unifying framework for the slice-Bennequin inequality and
its counterpart for the FDTC.


title: Towards Online Domain Adaptive Object Detection

abstract:
  Existing object detection models assume both the training and test data are
sampled from the same source domain. This assumption does not hold true when
these detectors are deployed in real-world applications, where they encounter
new visual domain. Unsupervised Domain Adaptation (UDA) methods are generally
employed to mitigate the adverse effects caused by domain shift. Existing UDA
methods operate in an offline manner where the model is first adapted towards
the target domain and then deployed in real-world applications. However, this
offline adaptation strategy is not suitable for real-world applications as the
model frequently encounters new domain shifts. Hence, it becomes critical to
develop a feasible UDA method that generalizes to these domain shifts
encountered during deployment time in a continuous online manner. To this end,
we propose a novel unified adaptation framework that adapts and improves
generalization on the target domain in online settings. In particular, we
introduce MemXformer - a cross-attention transformer-based memory module where
items in the memory take advantage of domain shifts and record prototypical
patterns of the target distribution. Further, MemXformer produces strong
positive and negative pairs to guide a novel contrastive loss, which enhances
target specific representation learning. Experiments on diverse detection
benchmarks show that the proposed strategy can produce state-of-the-art
performance in both online and offline settings. To the best of our knowledge,
this is the first work to address online and offline adaptation settings for
object detection. Code at https://github.com/Vibashan/online-od


title:
Enhanced high-order harmonics through periodicity breaks: from
  backscattering to impurity states

abstract:
  Backscattering of delocalized electrons has been recently established [Phys.
Rev. A 105, L041101 (2022)] as a mechanism to enhance high-order harmonic
generation (HHG) in periodic systems with broken translational symmetry. Here
we study this effect for a variable spatial gap in an atomic chain. Propagating
the many-electron dynamics numerically, we find enhanced HHG and identify its
origin in two mechanisms, depending on the gap size, either backscattering or
enhanced tunneling from an impurity state. Since the gapped atomic chain
exhibits both impurities and vacancies in a unified setting, it provides
insight how periodicity breaks influence HHG in different scenarios.


title: Relativistic nonlinear axion magnetohydrodynamics

abstract:
  The new nonlinear axionically extended version of the general relativistic
magnetohydrodynamics is formulated. The self-consistent formalism of this
theory is based on the introduction into the Lagrangian of the new unified
scalar invariant, which is quadratic in the Maxwell tensor, and contains two
periodic functions of the pseudoscalar (axion) field. The constructed unified
invariant and the elaborated nonlinear theory as a whole, are invariant with
respect to two symmetries: first, the discrete symmetry associated with the
properties of the axion field; second, the Jackson's SO(2) type symmetry
intrinsic for the electromagnetism. The subsystem of the master equations,
which describes the velocity four-vector of the hydrodynamic flow, is
constructed in the framework of Eckart's theory of viscous heat-conducting
fluid. The axionically extended nonlinear Faraday, Gauss and Ampere equations
are supplemented by the ansatz about the large electric conductivity of the
medium, which is usually associated with vanishing of the electric field. We
have suggested two essentially new nonlinear models, in the framework of which
the anomalous electric conductivity is being compensated by the appropriate
behavior of the finite pseudoscalar (axion) field, providing the electric field
in the magnetohydrodynamic flow to be finite (either to be proportional to the
magnetic field, or to the angular velocity of the medium rotation)


title:
Linking Characteristics of the Polycyclic Aromatic Hydrocarbon
  Population with Galaxy Properties: A Quantitative Approach Using the NASA
  Ames PAH IR Spectroscopic Database

abstract:
  Utilizing the data and tools provided through the NASA Ames PAH IR
Spectroscopic Database (PAHdb), we study the PAH component of over 900
Spitzer-IRS galaxy spectra. Employing a database-fitting approach, the average
PAH size, the PAH size distribution, and PAH ionization fraction are deduced.
In turn, we examine their connection with the properties of the host galaxy. We
found that PAH population within galaxies consists of middle-sized PAHs with an
average number of carbon atoms of $\bar{N_{C}}$ = 55, and a charge state
distribution of $\sim$40% ionized - 60% neutral. We describe a correlation
between the 6.2/11.2 $\mu$m PAH ratio with the ionization parameter
($\gamma\equiv(G_{0}/n_{\rm e})(T_{\rm gas} / 1\ \mathrm{K})^{0.5}$), a
moderate correlation between the 8.6/11.2 $\mu$m PAH ratio and specific
star-formation rate, and a weak anti-correlation between $\gamma$ and M$_{*}$.
From the PAHdb decomposition we provide estimates for the 3.3 $\mu$m PAH band,
not covered by Spitzer observations, and establish a correlation between the
3.3/11.2 $\mu$m PAH ratio with N$_{\mathrm{C}}$. We further deliver a library
of mid-IR PAH template spectra parameterized on PAH size and ionization
fraction, which can be used in galaxy spectral energy distribution fitting
codes for the modeling of the mid-IR PAH emission component in galaxies.


title: Enriching the quantum toolbox of ultracold molecules with Rydberg atoms

abstract:
  We describe a quantum information architecture consisting of a hybrid array
of optically-trapped molecules and atoms. This design leverages the large
transition dipole moments of Rydberg atoms to mediate fast, high-fidelity gates
between qubits encoded in coherent molecular degrees of freedom. Error channels
of detuning, decay, pulse area noise, and leakage to other molecular states are
discussed. The molecule-Rydberg interaction can also be used to enable
nondestructive molecule detection and rotational state readout. We consider a
specific near-term implementation of this scheme using NaCs molecules and Cs
Rydberg atoms, showing that it is possible to implement 300~ns gates with a
potential fidelity of $> 99.9\%$.


title: Weyl's law for the Steklov problem on surfaces with rough boundary

abstract:
  The validity of Weyl's law for the Steklov problem on domains with Lipschitz
boundaries is a well-known open question in spectral geometry. We answer this
question in two dimensions and show that Weyl's law holds for an even larger
class of surfaces with rough boundaries. This class includes domains with
interior cusps as well as 'slow' exterior cusps. Moreover, the condition on the
speed of exterior cusps cannot be improved, which makes our result in a sense
optimal. The proof is based on the methods of Suslina and Agranovich combined
with some observations about the boundary behaviour of conformal mappings.


title: Damage spreading in the random cluster model

abstract:
  We investigate the damage spreading effect in the Fortuin-Kasteleyn random
cluster model for 2- and 3-dimensional grids with periodic boundary. For 2D the
damage function has a global maximum at $p=\sqrt{q}/(1+\sqrt{q})$ for all $q>0$
and also local maxima at $p=1/2$ and $p=q/(1+q)$ for $q\lesssim 0.75$. For 3D
we observe a local maximum at $p=q/(1+q)$ for $q\lesssim 0.46$ and a global
maximum at $p=1/2$ for $q\lesssim 4.5$. The chaotic phase of the model's
$(p,q)$-parameter space is where the coupling time is of exponential order and
we locate points on its boundary. For 3-dimensional grids the lower bound of
this phase may be equal to the corresponding critical point of the $q$-state
Potts model for $q\ge 3$.


title: Speculations on the W-Mass Measurement at CDF

abstract:
  The W Mass determination at the Tevatron CDF experiment reported a deviation
from the SM expectation at 7$\sigma$ level. We discuss a few possible
interpretations and their collider implications. We perform electroweak global
fits under various frameworks and assumptions. We consider three types of
electroweak global fits in the effective-field-theory framework: the $S$-$T$,
the $S$-$T$-$\delta G_F$, and the eight-parameter flavor-universal one. We
discuss the amounts of tensions between different $m_W$ measurements reflected
in these fits and the corresponding shifts in central values of these
parameters. With these electroweak fit pictures in hand, we present a few
different classes of models and discuss their compatibility with these results.
We find that while explaining the $m_W$ discrepancy, the single gauge boson
extensions face strong LHC direct search constraints and electroweak precision
constraints. Vector-like fermions can partially generate the needed shift to
the electroweak observables. The compatibility with top squark is also studied
in detail. We find non-degenerate top squark soft masses enhance the needed
operator coefficients, enabling an allowed explanation compatible with current
LHC measurements. Overall, more theory and experimental developments are highly
in demand to reveal the physics behind this discrepancy.


title:
Nonlinear Programming Solvers for Unconstrained and Constrained
  Optimization Problems: a Benchmark Analysis

abstract:
  In this paper we propose a set of guidelines to select a solver for the
solution of nonlinear programming problems. With this in mind, we present a
comparison of the convergence performances of commonly used solvers for both
unconstrained and constrained nonlinear programming problems. The comparison
involves accuracy, convergence rate, and convergence speed. Because of its
popularity among research teams in academia and industry, MATLAB is used as
common implementation platform for the solvers. Our study includes solvers
which are either freely available, or require a license, or are fully described
in literature. In addition, we differentiate solvers if they allow the
selection of different optimal search methods. As result, we examine the
performances of 23 algorithms to solve 60 benchmark problems. To enrich our
analysis, we will describe how, and to what extent, convergence speed and
accuracy can be improved by changing the inner settings of each solver.


title: Two-step estimation in linear regressions with adaptive learning

abstract:
  Weak consistency and asymptotic normality of the ordinary least-squares
estimator in a linear regression with adaptive learning is derived when the
crucial, so-called, `gain' parameter is estimated in a first step by nonlinear
least squares from an auxiliary model. The singular limiting distribution of
the two-step estimator is normal and in general affected by the sampling
uncertainty from the first step. However, this `generated-regressor' issue
disappears for certain parameter combinations.


title:
Doppler boosted dust emission and CIB-galaxy cross-correlations: a new
  probe of cosmology and astrophysics

abstract:
  We identify a new cosmological signal, the Doppler-boosted Cosmic Infrared
Background (DB-CIB), arising from the peculiar motion of the galaxies whose
thermal dust emission source the cosmic infrared background (CIB). This new
observable is an independent probe of the cosmic velocity field, highly
analogous to the well-known kinematic Sunyaev-Zel'dovich (kSZ) effect.
Interestingly, DB-CIB does not suffer from the 'kSZ optical depth degeneracy',
making it immune from the complex astrophysics of galaxy formation. We forecast
that the DB-CIB effect is detectable in the cross-correlation of CCAT-Prime and
DESI-like experiments. We show that it also acts as a new CMB foreground which
can bias future kSZ cross-correlations, if not properly accounted for.


title: Single-Photon Structured Light

abstract:
  We present a novel structured light technique that uses Single Photon
Avalanche Diode (SPAD) arrays to enable 3D scanning at high-frame rates and
low-light levels. This technique, called "Single-Photon Structured Light",
works by sensing binary images that indicates the presence or absence of photon
arrivals during each exposure; the SPAD array is used in conjunction with a
high-speed binary projector, with both devices operated at speeds as high as
20~kHz. The binary images that we acquire are heavily influenced by photon
noise and are easily corrupted by ambient sources of light. To address this, we
develop novel temporal sequences using error correction codes that are designed
to be robust to short-range effects like projector and camera defocus as well
as resolution mismatch between the two devices. Our lab prototype is capable of
3D imaging in challenging scenarios involving objects with extremely low albedo
or undergoing fast motion, as well as scenes under strong ambient illumination.


title: On the associativity of one-loop corrections to the celestial OPE

abstract:
  There has been recent interest in the question of whether QCD collinear
singularities can be viewed as the OPE of a two-dimensional CFT. We analyze a
version of this question for the self-dual limit of pure gauge theory
(incorporating states of both helicities). We show that the known one-loop
collinear singulaties do not form an associative chiral algebra. The failure of
associativity can be traced to a novel gauge anomaly on twistor space. We find
that associativity can be restored for certain gauge groups if we introduce an
unusual axion, which cancels the twistor space anomaly by a Green-Schwarz
mechanism. Alternatively, associativity can be restored for some gauge groups
with carefully chosen matter.


title: Extra $W$-Boson Mass from a D3-Brane

abstract:
  Motivated by the recent CDF measurement of the $W$-boson mass, we study
string-based particle physics models which can accommodate this deviation from
the Standard Model. We consider an F-theory GUT in which the visible sector is
realized on intersecting 7-branes, and extra sector states arise from a probe
D3-brane near an E-type Yukawa point. The D3-brane worldvolume naturally
realizes a strongly coupled sector which mixes with the Higgs. In the limit
where some extra sector states get their mass solely from Higgs vevs, this
leads to a contribution to the $\rho$ parameter which is too large, but as the
D3-brane is separated from the 7-brane stack, this effect is suppressed,
leading to O(1) - O(10) TeV scale extra sector states and a correction to
$\rho$ which would be in accord with the CDF result. We also estimate the
contribution to the oblique electroweak parameter $S$, and find that it is
compatible with existing constraints. This also leads to additional signatures,
including diphoton resonances (as well as other diboson final states) in the
O(1) - O(10) TeV range.


title: Correlating $W$-Boson Mass Shift with Muon ${g-2}$ in the 2HDM

abstract:
  We show an interesting correlation between the recent high precision
measurement of the $W$-boson mass by the CDF collaboration and the muon $(g-2)$
anomaly in the context of the two Higgs doublet model. One-loop diagrams
involving the exchange of neutral scalar bosons can explain the muon $(g-2)$,
which however requires significant mass splittings among members of the second
Higgs doublet. These splittings also generate a positive shift in the mass of
the $W$-boson, consistent with the recent CDF measurement. The charged and
neutral scalars of the model cannot be heavier than about 600 GeV for a
simultaneous explanation of the two anomalies. The entire parameter space of
the model can be tested at the LHC by a combination of same sign dimuon signals
in $pp \rightarrow (\mu^+ \mu^+ jj + {E\!\!\!\!/}_{T})$ and $pp \rightarrow
(\mu^+\mu^-\tau^+\tau^-+X)$ signals.


title: Hierarchical Bayesian Persuasion: Importance of Vice Presidents

abstract:
  We study strategic information transmission in a hierarchical setting where
information gets transmitted through a chain of agents up to a decision maker
whose action is of importance to every agent. This situation could arise
whenever an agent can communicate to the decision maker only through a chain of
intermediaries, for example, an entry-level worker and the CEO in a firm, or an
official in the bottom of the chain of command and the president in a
government. Each agent can decide to conceal part or all the information she
receives. Proving we can focus on simple equilibria, where the only player who
conceals information is the first one, we provide a tractable recursive
characterization of the equilibrium outcome, and show that it could be
inefficient. Interestingly, in the binary-action case, regardless of the number
of intermediaries, there are a few pivotal ones who determine the amount of
information communicated to the decision maker. In this case, our results
underscore the importance of choosing a pivotal vice president for maximizing
the payoff of the CEO or president.


title:
Galaxy and halo angular clustering in LCDM and Modified Gravity
  cosmologies

abstract:
  Using a suite of $N$-body simulations we study the angular clustering of
galaxies, halos, and dark matter in $\mathrm{\Lambda \text{CDM}}$ and Modified
Gravity (MG) scenarios. We consider two general categories of such MG models,
one is the $f(R)$ gravity, and the other is the normal branch of the
Dvali-Gabadadze-Porrati brane world (nDGP). To measure angular clustering we
construct a set of observer-frame lightcones and resulting mock sky catalogs.
We focus on the area-averaged angular correlation functions, $W_J$, and the
associated reduced cumulants, $S_J\equiv W_J/W_2^{(J-1)}$, and robustly measure
them up to the 9th order using counts-in-cells (CIC). We find that $0.15 < z <
0.3$ is the optimal redshift range to maximize the MG signal in our lightcones.
Analyzing various scales for the two types of statistics, we identify up to
20\% relative departures in MG measurements from general relativity (GR), with
varying signal significance. For the case of halos and galaxies, we find that
$3$rd order statistics offer the most sensitive probe of the different
structure formation scenarios, with both $W_3$ and the reduced skewness, $S_3$,
reaching from $2\sigma$ to $4\sigma$ significance at angular scales $\theta
\sim 0.13 ^\circ$. The MG clustering of the smooth dark matter field is
characterized by even stronger deviations ($\stackrel{>}{{}_\sim} 5\sigma$)
from GR, albeit at a bit smaller scales of $\theta\sim0.08^\circ$, where
baryonic physics is already important. Finally, we stress out that our mock
halo and galaxy catalogs are characterized by rather low surface number
densities when compared to existing and forthcoming state-of-the-art
photometric surveys. This opens up exciting potential for testing GR and MG
using angular clustering in future applications, with even higher precision and
significance than reported here.


title: Full-Spectrum Out-of-Distribution Detection

abstract:
  Existing out-of-distribution (OOD) detection literature clearly defines
semantic shift as a sign of OOD but does not have a consensus over covariate
shift. Samples experiencing covariate shift but not semantic shift are either
excluded from the test set or treated as OOD, which contradicts the primary
goal in machine learning -- being able to generalize beyond the training
distribution. In this paper, we take into account both shift types and
introduce full-spectrum OOD (FS-OOD) detection, a more realistic problem
setting that considers both detecting semantic shift and being tolerant to
covariate shift; and designs three benchmarks. These new benchmarks have a more
fine-grained categorization of distributions (i.e., training ID,
covariate-shifted ID, near-OOD, and far-OOD) for the purpose of more
comprehensively evaluating the pros and cons of algorithms. To address the
FS-OOD detection problem, we propose SEM, a simple feature-based semantics
score function. SEM is mainly composed of two probability measures: one is
based on high-level features containing both semantic and non-semantic
information, while the other is based on low-level feature statistics only
capturing non-semantic image styles. With a simple combination, the
non-semantic part is cancelled out, which leaves only semantic information in
SEM that can better handle FS-OOD detection. Extensive experiments on the three
new benchmarks show that SEM significantly outperforms current state-of-the-art
methods. Our code and benchmarks are released in
https://github.com/Jingkang50/OpenOOD.


title: Toward More Effective Human Evaluation for Machine Translation

abstract:
  Improvements in text generation technologies such as machine translation have
necessitated more costly and time-consuming human evaluation procedures to
ensure an accurate signal. We investigate a simple way to reduce cost by
reducing the number of text segments that must be annotated in order to
accurately predict a score for a complete test set. Using a sampling approach,
we demonstrate that information from document membership and automatic metrics
can help improve estimates compared to a pure random sampling baseline. We
achieve gains of up to 20% in average absolute error by leveraging stratified
sampling and control variates. Our techniques can improve estimates made from a
fixed annotation budget, are easy to implement, and can be applied to any
problem with structure similar to the one we study.


title: On the Generalization of BasicVSR++ to Video Deblurring and Denoising

abstract:
  The exploitation of long-term information has been a long-standing problem in
video restoration. The recent BasicVSR and BasicVSR++ have shown remarkable
performance in video super-resolution through long-term propagation and
effective alignment. Their success has led to a question of whether they can be
transferred to different video restoration tasks. In this work, we extend
BasicVSR++ to a generic framework for video restoration tasks. In tasks where
inputs and outputs possess identical spatial size, the input resolution is
reduced by strided convolutions to maintain efficiency. With only minimal
changes from BasicVSR++, the proposed framework achieves compelling performance
with great efficiency in various video restoration tasks including video
deblurring and denoising. Notably, BasicVSR++ achieves comparable performance
to Transformer-based approaches with up to 79% of parameter reduction and 44x
speedup. The promising results demonstrate the importance of propagation and
alignment in video restoration tasks beyond just video super-resolution. Code
and models are available at https://github.com/ckkelvinchan/BasicVSR_PlusPlus.


title:
A compendium of photon emission rates, absorption cross sections and
  scattering cross sections

abstract:
  We provide a compendium of the quantum mechanical equations for photon
emission rates, photon absorption cross sections, and photon scattering cross
sections. For each case, the different equations that apply for discrete or
continuous quantum states of the emitting, absorbing, or scattering material
are given.


title: Octions: An $E_8$ description of the Standard Model

abstract:
  We interpret the elements of the exceptional Lie algebra
$\mathfrak{e}_{8(-24)}$ as objects in the Standard Model, including lepton and
quark spinors with the usual properties, the Standard Model Lie algebra
$\mathfrak{su}(3)+\mathfrak{su}(2)+\mathfrak{u}(1)$, and the Lorentz Lie
algebra $\mathfrak{so}(3,1)$. The resulting model naturally contains GUTs based
on $SO(10)$ (Georgi--Glashow), $SU(5)$ (Georgi), and $SU(4)\times SU(2)\times
SU(2)$ (Pati--Salam). We then briefly speculate on the role of the remaining
elements of $\mathfrak{e}_8$, and propose a mechanism leading to exactly three
generations of particles.


title:
Causal Discovery and Causal Learning for Fire Resistance Evaluation:
  Incorporating Domain Knowledge

abstract:
  Experiments remain the gold standard to establish an understanding of
fire-related phenomena. A primary goal in designing tests is to uncover the
data generating process (i.e., the how and why the observations we see come to
be); or simply what causes such observations. Uncovering such a process not
only advances our knowledge but also provides us with the capability to be able
to predict phenomena accurately. This paper presents an approach that leverages
causal discovery and causal inference to evaluate the fire resistance of
structural members. In this approach, causal discovery algorithms are adopted
to uncover the causal structure between key variables pertaining to the fire
resistance of reinforced concrete (RC) columns. Then, companion inference
algorithms are applied to infer (estimate) the influence of each variable on
the fire resistance given a specific intervention. Finally, this study ends by
contrasting the algorithmic causal discovery with that obtained from domain
knowledge and traditional machine learning. Our findings clearly show the
potential and merit of adopting causality into our domain.


title: Position-wise optimizer: A nature-inspired optimization algorithm

abstract:
  The human nervous system utilizes synaptic plasticity to solve optimization
problems. Previous studies have tried to add the plasticity factor to the
training process of artificial neural networks, but most of those models
require complex external control over the network or complex novel rules. In
this manuscript, a novel nature-inspired optimization algorithm is introduced
that imitates biological neural plasticity. Furthermore, the model is tested on
three datasets and the results are compared with gradient descent optimization.


title: Six Statistical Senses

abstract:
  This article proposes a set of categories, each one representing a particular
distillation of important statistical ideas. Each category is labeled a "sense"
because we think of them as essential in helping every statistical mind connect
in constructive and insightful ways with statistical theory, methodologies and
computation. The illustration of each sense with statistical principles and
methods provides a sensical (but not random) tour of the conceptual landscape
of statistics, as a leading discipline in the data science ecosystem.


title:
Non-equilibrium phase transitions in competitive markets caused by
  network effects

abstract:
  Network effects are the added value derived solely from the popularity of a
product in an economic market. Using agent-based models inspired by statistical
physics, we propose a minimal theory of a competitive market for (nearly)
indistinguishable goods with demand-side network effects, sold by statistically
identical sellers. With weak network effects, the model reproduces conventional
microeconomics: there is a statistical steady state of (nearly) perfect
competition. Increasing network effects, we find a phase transition to a robust
non-equilibrium phase driven by the spontaneous formation and collapse of fads
in the market. When sellers update prices sufficiently quickly, an emergent
monopolist can capture the market and undercut competition, leading to a
symmetry- and ergodicity-breaking transition. The non-equilibrium phase
simultaneously exhibits three empirically established phenomena not contained
in the standard theory of competitive markets: spontaneous price fluctuations,
persistent seller profits, and broad distributions of firm market shares.


title:
Resonances, black hole mimickers and the greenhouse effect: consequences
  for gravitational-wave physics

abstract:
  Ultracompact objects with photonspheres are known to mimic many observational
features of black holes. It has been suggested that anomalous tidal heating or
the presence of resonances in gravitational wave signals would be a clear
imprint of a surface or absence of a horizon. Such claims and studies are all
based on a frequency-domain analysis, assuming stationarity. Here we show that
the object needs to first "fuel-up" until it reaches the stationary regime. The
presence of a stable light ring and large light-travel times inside the object
may in fact delay enormously the "charging-up" and effectively contribute to
the effacement of structure. In other words, black hole mimickers behave as
black holes more efficiently than previously thought. Our results have
implications for other resonant systems with sharp resonances, including
"floating orbits" around spinning black holes. A proper accounting of the
self-force for such systems seems to be both mandatory, and would have
important applications for tests of horizon physics.


title:
Modeling multi-phase gases in cosmological simulations using
  compressible multi-fluid hydrodynamics

abstract:
  The diffuse medium in and around galaxies can exist in a multi-phase state:
small, cold gas clouds contributing significantly to the total mass embedded in
pressure equilibrium with a hotter, more diffuse volume-filling component.
Modeling this multi-phase state in cosmological simulations poses a significant
challenge due to the requirements to spatially resolve the clouds and
consequently the interactions between the phases. In this paper, we present a
novel method to model this gas state in cosmological hydrodynamical
simulations. We solve the compressible two-fluid hydrodynamic equations using a
moving-mesh finite-volume method and define mass, momentum and energy exchange
terms between the phases as operator-split source terms. Using a stratified
flow model, our implementation is able to maintain volume fraction
discontinuities in pressure equilibrium to machine precision, allowing for the
treatment of both resolved and unresolved multi-phase fluids. The solver
remains second order accurate on smooth hydrodynamics problems. We use the
source and sink terms of an existing two-phase model for the interstellar
medium to demonstrate the value of this type of approach in simulations of
galaxy formation, compare it to its effective equation of state implementation,
and discuss its advantages in future large-scale simulations of galaxy
formation.