title:
Dynamical simulation via quantum machine learning with provable
  generalization

abstract:
  Much attention has been paid to dynamical simulation and quantum machine
learning (QML) independently as applications for quantum advantage, while the
possibility of using QML to enhance dynamical simulations has not been
thoroughly investigated. Here we develop a framework for using QML methods to
simulate quantum dynamics on near-term quantum hardware. We use generalization
bounds, which bound the error a machine learning model makes on unseen data, to
rigorously analyze the training data requirements of an algorithm within this
framework. This provides a guarantee that our algorithm is resource-efficient,
both in terms of qubit and data requirements. Our numerics exhibit efficient
scaling with problem size, and we simulate 20 times longer than Trotterization
on IBMQ-Bogota.


title:
Measurement of inclusive and leading subjet fragmentation in pp and
  Pb-Pb collisions at $\sqrt{s_{\rm NN}}$ = 5.02 TeV

abstract:
  This article presents new measurements of the fragmentation properties of
jets in both proton-proton (pp) and heavy-ion collisions with the ALICE
experiment at the Large Hadron Collider (LHC). We report distributions of the
fraction $z_r$ of transverse momentum carried by subjets of radius $r$ within
jets of radius $R$. Charged-particle jets are reconstructed at midrapidity
using the anti-$k_{\rm{T}}$ algorithm with jet radius $R=0.4$, and subjets are
reconstructed by reclustering the jet constituents using the anti-$k_{\rm{T}}$
algorithm with radii $r=0.1$ and $r=0.2$. In proton-proton collisions, we
measure both the inclusive and leading subjet distributions. We compare these
measurements to perturbative calculations at next-to-leading logarithmic
accuracy, which suggest a large impact of threshold resummation and
hadronization effects on the $z_r$ distribution. In heavy-ion collisions, we
measure the leading subjet distributions, which allow access to a region of
harder jet fragmentation than has been probed by previous measurements of jet
quenching via hadron fragmentation distributions. The $z_r$ distributions
enable extraction of the parton-to-subjet fragmentation function and allow for
tests of the universality of jet fragmentation functions in the quark-gluon
plasma (QGP). We find indications that there is a turnover in the ratio between
the distributions in Pb-Pb and pp collisions as $z_r \rightarrow 1$, exposing
qualitatively new possibilities to disentangle competing jet quenching
mechanisms. By comparing our results to theoretical calculations based on an
independent extraction of the parton-to-jet fragmentation function, we find
consistency with the universality of jet fragmentation and no indication of
factorization breaking in the QGP.


title:
A co-kurtosis based dimensionality reduction method for combustion
  datasets

abstract:
  Principal Component Analysis (PCA) is a dimensionality reduction technique
widely used to reduce the computational cost associated with numerical
simulations of combustion phenomena. However, PCA, which transforms the
thermo-chemical state space based on eigenvectors of co-variance of the data,
could fail to capture information regarding important localized chemical
dynamics, such as the formation of ignition kernels, appearing as outlier
samples in a dataset. In this paper we propose an alternate dimensionality
reduction procedure, co-kurtosis PCA (CoK-PCA), wherein the required principal
vectors are computed from a high-order joint statistical moment, namely the
co-kurtosis tensor, which may better identify directions in the state space
that represent stiff dynamics. We first demonstrate the potential of the
proposed CoK-PCA method using a synthetically generated dataset that is
representative of typical combustion simulations. Thereafter, we characterize
and contrast the accuracy of CoK-PCA against PCA for datasets representing
spontaneous ignition of premixed ethylene in a simple homogeneous reactor and
ethanol-fueled homogeneous charged compression ignition (HCCI) engine.
Specifically, we compare the low-dimensional manifolds in terms of
reconstruction errors of the original thermo-chemical state, and species
production and heat release rates computed from the reconstructed state. We
find that, even using a simplistic linear reconstruction, the co-kurtosis based
reduced manifold represents the original thermo-chemical state more accurately
than PCA, especially in the regions where chemical reactions are important. We
observe that the accuracy of the CoK-PCA can be further improved by adopting
many of the refinements (e.g., non-linear reconstruction, localized manifolds)
already widely in use with PCA.


title: Broadcast of a restricted set of qubit and qutrit states

abstract:
  The no-cloning theorem forbids the distribution of an unknown state to more
than one receiver. However, if the sender knows the state, and the state is
chosen from a restricted set of possibilities, a procedure known as remote
state preparation can be used to broadcast a state. Here we examine a remote
state preparation protocol that can be used to send the state of a qubit,
confined to the equator of the Bloch sphere, to an arbitrary number of
receivers. The entanglement cost is less than that of using teleportation to
accomplish the same task. We present a number of variations on this task,
probabilistically sending an unknown qubit state to two receivers, sending
different qubit states to two receivers, and sending qutrit states to two
receivers. Finally, we discuss some applications of these protocols.


title:
Causal structure in the presence of sectorial constraints, with
  application to the quantum switch

abstract:
  Existing work on quantum causal structure assumes that one can perform
arbitrary operations on the systems of interest. But this condition is often
not met. Here, we extend the framework for quantum causal modelling to cases
where a system can suffer \textit{sectorial contraints}, that is, restrictions
on the orthogonal subspaces of its Hilbert space that may be mapped to one
another. Our framework (a) proves that a number of different intuitions about
causal relations turn out to be equivalent; (b) shows that quantum causal
structures in the presence of sectorial constraints can be represented with a
directed graph; and (c) defines a fine-graining of the causal structure in
which the individual sectors of a system bear causal relations, which provides
a more detailed analysis than its coarse-grained counterpart. As an example, we
apply our framework to purported photonic implementations of the quantum switch
to show that while their coarse-grained causal structure is cyclic, their
fine-grained causal structure is acyclic. We therefore conclude that these
experiments realize indefinite causal order only in a weak sense. Notably, this
is the first argument to this effect that is not rooted in the assumption that
the causal relata must be localized in spacetime.


title: W-boson mass in the triplet seesaw model

abstract:
  The CDF collaboration has recently published a precision measurement of the
$W$-boson mass that differs from the Standard Model prediction by seven
standard deviations. This result can be explained with additional electroweak
multiplets that either break the custodial symmetry or contribute to oblique
parameters at loop level. Here, we study one of the best-motivated scenarios
involving new multiplets: the type-II seesaw model, which involves a scalar
triplet that generates Majorana neutrino masses and can furthermore resolve the
$W$-boson mass discrepancy. This requires a doubly-charged scalar with mass
between 100 and 200 GeV as well as other scalars with a fixed mass splitting.
The entire preferred parameter space is testable at the LHC.


title: Do t-Statistic Hurdles Need to be Raised

abstract:
  Many scholars have called for raising statistical hurdles to guard against
false discoveries in academic publications. I show these calls are unlikely to
be justified empirically. Published data exhibit bias: results that fail to
meet existing hurdles are often unobserved. These unobserved results must be
extrapolated, leading to weak identification of revised hurdles. In contrast,
statistics that can target only published findings (e.g. empirical Bayes
shrinkage and the local FDR) can be strongly identified, as data on published
findings is plentiful. I demonstrate these results in a general theory and in
an empirical analysis of the cross-sectional return predictability literature.


title:
Efficient Representations of Radiality Constraints in Optimization of
  Islanding and De-Energization in Distribution Grids

abstract:
  Optimization of power distribution system topology is complicated by the
requirement that the system be operated in a radial configuration. In this
paper, we discuss existing methods for enforcing radiality constraints and
introduce two new formulations that enable optimization over partially
energized or islanded network topologies. The first builds on methods that use
so-called parent-child constraints, but enforces those constraints on an
abstracted network which enables an equivalent formulation with significantly
less variables and constraints. The second formulation builds on existing
approaches which directly generate constraints disallowing loops, and through
an iterative approach seeks to limit the number of these constraints which must
be enforced.


title: Deep learning techniques for energy clustering in the CMS ECAL

abstract:
  The reconstruction of electrons and photons in CMS depends on topological
clustering of the energy deposited by an incident particle in different
crystals of the electromagnetic calorimeter (ECAL). These clusters are formed
by aggregating neighbouring crystals according to the expected topology of an
electromagnetic shower in the ECAL. The presence of upstream material
(beampipe, tracker and support structures) causes electrons and photons to
start showering before reaching the calorimeter. This effect, combined with the
3.8T CMS magnetic field, leads to energy being spread in several clusters
around the primary one. It is essential to recover the energy contained in
these satellite clusters in order to achieve the best possible energy
resolution for physics analyses. Historically satellite clusters have been
associated to the primary cluster using a purely topological algorithm which
does not attempt to remove spurious energy deposits from additional pileup
interactions (PU). The performance of this algorithm is expected to degrade
during LHC Run 3 (2022+) because of the larger average PU levels and the
increasing levels of noise due to the ageing of the ECAL detector. New methods
are being investigated that exploit state-of-the-art deep learning
architectures like Graph Neural Networks (GNN) and self-attention algorithms.
These more sophisticated models improve the energy collection and are more
resilient to PU and noise, helping to preserve the electron and photon energy
resolution achieved during LHC Runs 1 and 2. This work will cover the
challenges of training the models as well the opportunity that this new
approach offers to unify the ECAL energy measurement with the particle
identification steps used in the global CMS photon and electron reconstruction.

categories: hep-ex cs.LG physics.data-an physics.ins-det


title: Building planar polygon spaces from the projective braid arrangement

abstract:
  The moduli space of planar polygons with generic side lengths is a smooth,
closed manifold. It is known that these manifolds contain the real points of
the moduli space of distinct points on the projective line as an open dense
subset. Hence, such a polygon space is a compactification of this real moduli
space. Kapranov showed that the real points of the Deligne-Mumford-Knudson
compactification can be obtained from the projective Coxeter complex of type
$A$ (equivalently, the projective braid arrangement) by iteratively blowing up
along the minimal building set. In this paper we show that these planar polygon
spaces can also be obtained from the projective Coxeter complex of type $A$ by
performing an iterative cellular surgery along a sub-collection of the minimal
building set. Interestingly, this sub-collection is determined by the
combinatorial data associated with the length vector called the genetic code.


title: Generic properties of nonexpansive mappings on unbounded domains

abstract:
  We investigate typical properties of nonexpansive mappings on unbounded,
closed and convex subsets of hyperbolic metric spaces. For a metric of uniform
convergence on bounded sets, we show that the typical nonexpansive mapping is a
contraction in the sense of Rakotch on every bounded subset and there is a
bounded set which is mapped into itself by this mapping. In particular, we
obtain that the typical nonexpansive mapping in this setting has a unique fixed
point. Nevertheless, it turns out that the typical mapping is not a Rakotch
contraction on the whole space and that it has the maximal possible Lipschitz
constant of one on a residual subset of its domain. By typical we mean that the
complement of the set of mappings with this property is $\sigma$-porous, that
is, small in a topological sense. For a metric of pointwise convergence, we
show that the set of Rakotch contractions is meagre.


title:
Stochastic state-transition-change process and three polarizers
  experiment

abstract:
  The well known experiment with three linear polarizers clearly demonstrates
interesting properties of light. The beam intensity of photon beam transmitted
through one, two or three polarizers can be measured in dependence on the
rotations of the polarizers. The experiment has attracted attention of many
researchers because the measured intensities cannot be explained by photons
being only absorbed by the polarizers; the polarizers must also change a
property (called polarization) of the transmitted photons. However,
contemporary theories of light have not allowed to determine the probability
(density) functions characterizing the two effects. It has strongly limited
understanding of the physical phenomenon. In this paper example data of beam
intensities corresponding to 3 ideal identical linear polarizers are analyzed
with the help of probabilistic model based on state-transition-change (STC)
stochastic process. Using the process, it has been possible to determine
numerical values of the probability (density) functions for the first time.
Measured beam intensities corresponding to sequences of different polarization
sensitive devices and other optical elements may be analyzed in similar way,
too. The results provide new perspective on description of polarization of
light and particle-matter interaction in general.


title:
How Conservative are Language Models? Adapting to the Introduction of
  Gender-Neutral Pronouns

abstract:
  Gender-neutral pronouns have recently been introduced in many languages to a)
include non-binary people and b) as a generic singular. Recent results from
psycholinguistics suggest that gender-neutral pronouns (in Swedish) are not
associated with human processing difficulties. This, we show, is in sharp
contrast with automated processing. We show that gender-neutral pronouns in
Danish, English, and Swedish are associated with higher perplexity, more
dispersed attention patterns, and worse downstream performance. We argue that
such conservativity in language models may limit widespread adoption of
gender-neutral pronouns and must therefore be resolved.


title: AI-Empowered Data Offloading in MEC-Enabled IoV Networks

abstract:
  Advancements in smart vehicle design have enabled the creation of Internet of
Vehicle (IoV) technologies that can utilize the information provided by various
sensors and wireless communication to perform complex functionality. Many of
these functionalities rely on high computational power and low latency. Mobile
Edge Computing (MEC) technologies have been proposed as a way to meet these
requirements, as their proximity and decentralization provide unique benefits
for networks like real-time communication, higher throughput, and flexibility.
Diverse challenges to the process of offloading data in a MEC-enabled IoV
network have emerged, such as offloading reliability in highly mobile
environments, security for users within the same network, and energy management
to keep users from being disincentivized to participate in the network. This
article surveys research studies that use AI as part of the data offloading
process, categorized based on four main issues: reliability, security, energy
management, and service seller profit. Afterward, this article discusses
challenges and future perspectives for IoV technologies.


title: Einstein-Cartan cosmology and the CMB anisotropies

abstract:
  We derive linear scalar perturbation equations for Einstein-Cartan field
equations of Weyssenhoff fluid, as well as for the corresponding perturbations
of Bianchi identity and geodesic equations. The equations are given in both
conformal Newtonian and synchronous gauges. They are suitable for numerical
implementation when precise evolution of torsion and its perturbation will be
extracted from N-body cosmic simulations of the large scale structures in the
Universe. A rising number of problems of the concordance cosmological model
forces us to include the rotational degrees of freedom realized through torsion
in the Einstein-Cartan gravity.


title: Optical conductivity of cuprates in a new light

abstract:
  Understanding the physical properties of unconventional superconductors as
well as of other correlated materials presents a formidable challenge. Their
unusual evolution with doping, frequency, and temperature, has frequently led
to non-Fermi-liquid (non-FL) interpretations. Optical conductivity is a major
challenge in this context. Here, the optical spectra of two archetypal
cuprates, underdoped HgBa$_2$CuO$_{4+\delta}$ and optimally-doped
Bi$_2$Sr$_2$CaCu$_2$O$_{8+\delta}$, are interpreted based on the standard Fermi
liquid (FL) paradigm. At both dopings, perfect frequency-temperature FL scaling
is found to be modified by the presence of a second, gapped electronic
subsystem. This non-FL component emerges as a well-defined mid-infrared
spectral feature after the FL contribution -- determined independently by
transport -- is subtracted. Temperature, frequency and doping evolution of the
MIR feature identifies a gapped rather than dissipative response. In contrast,
the dissipative response is found to be relevant for pnictides and ruthenates.
Such an unbiased FL/non-FL separation is extended across the cuprate phase
diagram, providing a natural explanation why the superfluid density is
attenuated on the overdoped side. Thus, we obtain a unified interpretation of
optical responses and transport measurements in all analyzed physical regimes
and all analyzed compounds.


title: Minimal models of field theories: Chiral Higher Spin Gravity

abstract:
  There exists a unique class of local Higher Spin Gravities with propagating
massless fields in $4d$ -- Chiral Higher Spin Gravity. Originally, it was
formulated in the light-cone gauge. We construct a covariant form of this
theory as a Free Differential Algebra up to NLO, i.e. at the level of equations
of motion. It also contains the recently discovered covariant forms of the
higher spin extensions of SDYM and SDGR, as well as SDYM and SDGR themselves.
From the mathematical viewpoint the result is equivalent to taking the minimal
model (in the sense of $L_\infty$-algebras) of the jet-space extension of the
BV-BRST formulation of Chiral Higher Spin Gravity, thereby, containing also
information about (presymplectic AKSZ) action, counterterms, anomalies, etc.


title:
ProTranslator: zero-shot protein function prediction using textual
  description

abstract:
  Accurately finding proteins and genes that have a certain function is the
prerequisite for a broad range of biomedical applications. Despite the
encouraging progress of existing computational approaches in protein function
prediction, it remains challenging to annotate proteins to a novel function
that is not collected in the Gene Ontology and does not have any annotated
proteins. This limitation, a side effect from the widely-used multi-label
classification problem setting of protein function prediction, hampers the
progress of studying new pathways and biological processes, and further slows
down research in various biomedical areas. Here, we tackle this problem by
annotating proteins to a function only based on its textual description so that
we do not need to know any associated proteins for this function. The key idea
of our method ProTranslator is to redefine protein function prediction as a
machine translation problem, which translates the description word sequence of
a function to the amino acid sequence of a protein. We can then transfer
annotations from functions that have similar textual description to annotate a
novel function. We observed substantial improvement in annotating novel
functions and sparsely annotated functions on CAFA3, SwissProt and GOA
datasets. We further demonstrated how our method accurately predicted gene
members for a given pathway in Reactome, KEGG and MSigDB only based on the
pathway description. Finally, we showed how ProTranslator enabled us to
generate the textual description instead of the function label for a set of
proteins, providing a new scheme for protein function prediction. We envision
ProTranslator will give rise to a protein function "search engine" that returns
a list of proteins based on the free text queried by the user.


title:
Quasi-stationary Distribution for the Invasion Model on a Complete
  Bipartite Graph

abstract:
  In the discrete-time Invasion Model on a finite connected graph, every vertex
is initially assigned a "yes" or "no" opinion. Each unit of time, independently
of the past, a vertex is uniformly sampled, along with a uniformly sampled
neighbor. The opinion of the former is assigned to the latter with no change to
the opinion of any other vertex. This model is closely related to the Voter
Model, with roles swapped: there the sampled vertex accepts the opinion of the
neighbor. Both models eventually reach a consensus, an absorbing state for the
dynamics corresponding to all vertices having the same opinion. We study the
Quasistationary Distribution (QSD) for the Invasion Model on the complete
bipartite graph. Specifically, we obtain the limit of the QSD for the model as
the size of one partition, the "large partition", tends to infinity while the
size of the other, the "small" partition, remains fixed. Our results show that
at the limit, the density of "yes" opinions in the large partition tends to a
uniform distribution on the unit interval and, conditioned on this density, the
opinions on the small partition are independent and identically distributed
with probability of a "yes" at a given vertex equal to the density. This result
is in stark contrast to the analogous result for the Voter Model where the
limit QSD is a heavy-tailed perturbation to consensus. A distinctive feature of
the dynamics we study is the co-existence of two scales in the eigenvector
equation for the QSD with non-trivial interaction which determines the
structure of the limit QSD.


title:
Symmetric Derivation of the Singlet Correlations within a Quaternionic
  3-sphere

abstract:
  We present a symmetric derivation of the strong correlations predicted by the
entangled singlet state in a local-realistic model in which the physical space
is assumed to be a quaternionic 3-sphere, or $S^3$, available as the spatial
part of a solution of Einstein's field equations of general relativity, and
compare it with Bell's local model for the singlet correlations set within a
flat Euclidean space ${\mathrm{I\!R}^3}$. Since the probabilities of observing
measurement outcomes for Bell's local model have never been fully worked out
before, our analysis may also provide useful comparisons for other less
compelling models, such as those relying on retrocausality and
superdeterminism. Apart from the conservation of spin angular momentum, the key
attribute underlying the strong singlet correlations within $S^3$ turns out to
be the spinorial sign changes intrinsic to the quaternions that constitute the
3-sphere. We also discuss anew a macroscopic experiment that can, in principle,
test the 3-sphere hypothesis.


title: Wave-like behaviour in (0,1) binary sequences

abstract:
  This work presents a comprehensive study of the properties of finite (0,1)
binary systems from the mathematical viewpoint of quantum theory where a
complex wavefunction $\psi_{n}$ is considered as an analogous probability
measure. This is a quantum-inspired extension of the GenomeBits model to
characterise observed genome sequences, where $\psi_{n}$ is related to an
alternating (0,1) binary series having independent distributed terms. The real
and imaginary spectrum of $\psi_{n}$ {\emph vs.} the nucleotide base positions
display characteristic features of sound waves.


title: Learning to Revise References for Faithful Summarization

abstract:
  In many real-world scenarios with naturally occurring datasets, reference
summaries are noisy and contain information that cannot be inferred from the
source text. On large news corpora, removing low quality samples has been shown
to reduce model hallucinations. Yet, this method is largely untested for
smaller, noisier corpora. To improve reference quality while retaining all
data, we propose a new approach: to revise--not remove--unsupported reference
content. Without ground-truth supervision, we construct synthetic unsupported
alternatives to supported sentences and use contrastive learning to
discourage/encourage (un)faithful revisions. At inference, we vary style codes
to over-generate revisions of unsupported reference sentences and select a
final revision which balances faithfulness and abstraction. We extract a small
corpus from a noisy source--the Electronic Health Record (EHR)--for the task of
summarizing a hospital admission from multiple notes. Training models on
original, filtered, and revised references, we find (1) learning from revised
references reduces the hallucination rate substantially more than filtering
(18.4\% vs 3.8\%), (2) learning from abstractive (vs extractive) revisions
improves coherence, relevance, and faithfulness, (3) beyond redress of noisy
data, the revision task has standalone value for the task: as a pre-training
objective and as a post-hoc editor.


title: Structural Nested Mean Models Under Parallel Trends Assumptions

abstract:
  In this paper, we generalize methods in the Difference in Differences (DiD)
literature by showing that both additive and multiplicative standard and coarse
Structural Nested Mean Models (Robins, 1994, 1997, 1998, 2000, 2004; Lok and
Degruttola, 2012; Vansteelandt and Joffe, 2014) are identified under parallel
trends assumptions. Our methodology enables adjustment for time-varying
covariates, identification of effect heterogeneity as a function of
time-varying covariates, and estimation of treatment effects under a general
class of treatment patterns (e.g. we do not restrict to the `staggered
adoption' setting). We stress that these extensions come essentially for free,
as our parallel trends assumption is not stronger than other parallel trends
assumptions in the DiD literature. However, in contrast to much of the DiD
literature, we only consider panel data, not repeated cross sectional data.


title:
New classification of graphs in view of the domination number of central
  graphs

abstract:
  For a graph $G$, the central graph $C(G)$ is the graph constructed from $G$
by subdividing each edge of $G$ with one vertex and also by adding an edge to
every pair of non-adjacent vertices in $G$. Also for a graph $G$, let
$\gamma(G)$ and $\tau(G)$ be the domination number of $G$ and the minimum
cardinarity of a vertex cover of $G$, respectively. In this paper, we give a
new classification of graphs concerning the domination number of central graphs
and minimum vertex covers of graphs. Namely, we show that any graph $G$ with at
least three vertices can be classified into one of the two classes of graphs
with $\gamma(C(G))=\tau(G)$ and $\gamma(C(G))=\tau(G)+1$, respectively,
together with some special properties concerning a vertex cover of $G$. We also
give some new results on the domination number of central graphs.


title: A Hierarchical N-Gram Framework for Zero-Shot Link Prediction

abstract:
  Due to the incompleteness of knowledge graphs (KGs), zero-shot link
prediction (ZSLP) which aims to predict unobserved relations in KGs has
attracted recent interest from researchers. A common solution is to use textual
features of relations (e.g., surface name or textual descriptions) as auxiliary
information to bridge the gap between seen and unseen relations. Current
approaches learn an embedding for each word token in the text. These methods
lack robustness as they suffer from the out-of-vocabulary (OOV) problem.
Meanwhile, models built on character n-grams have the capability of generating
expressive representations for OOV words. Thus, in this paper, we propose a
Hierarchical N-Gram framework for Zero-Shot Link Prediction (HNZSLP), which
considers the dependencies among character n-grams of the relation surface name
for ZSLP. Our approach works by first constructing a hierarchical n-gram graph
on the surface name to model the organizational structure of n-grams that leads
to the surface name. A GramTransformer, based on the Transformer is then
presented to model the hierarchical n-gram graph to construct the relation
embedding for ZSLP. Experimental results show the proposed HNZSLP achieved
state-of-the-art performance on two ZSLP datasets.


title: Plasma-like Description for Elementary and Composite Quantum Particles

abstract:
  Schr{\"o}dinger noticed in 1952 that a scalar complex wave function can be
made real by a gauge transformation. The author showed recently that one real
function is also enough to describe matter in the Dirac equation in an
arbitrary electromagnetic or Yang--Mills field. This suggests some "symmetry"
between positive and negative frequencies and, therefore, particles and
antiparticles, so the author previously considered a description of
one-particle wave functions as plasma-like collections of a large number of
particles and antiparticles. The description has some similarities with Bohmian
mechanics. This work offers a criterion for approximation of continuous charge
density distributions by discrete ones with quantized charge based on the
equality of partial Fourier sums, and an example of such approximation is
computed using the homotopy continuation method. An example mathematical model
of the description is proposed. The description is also extended to composite
particles, such as nucleons or large molecules, regarded as collections
including a composite particle and a large number of pairs of elementary
particles and antiparticles. While it is not clear if this is a correct
description of the reality, it can become a basis of an interesting model or
useful picture of quantum mechanics.


title: Zeros of the electric field around a charged knot

abstract:
  Consider a knotted loop of wire with charge distributed uniformly on it. The
electric field around such a loop was recently proven to have at least $2t + 1$
equilibrium points, where $t$ is a topological invariant known as the knot's
tunnel number. Here we show numerically that this lower bound is not sharp, and
we offer conjectures (but not proofs) for the minimum number of equilibrium
points around each type of knot with five or fewer crossings. We also visualize
the equipotential surfaces of charged knots. These are the first steps in the
study of "electrostatic knot theory."


title:
Tunable fractional quantum Hall point contacts in graphene via local
  anodic oxidation of graphite gates

abstract:
  In an all-van der Waals heterostructure, the active layer, gate dielectrics,
and gate electrodes are all assembled from two-dimensional crystals. Owing to
the absence of dangling bonds within their two-dimensional bulk, these devices
host ultraclean electron systems. Here, we describe methods for implementing
nanoscale electrostatic control without contaminating critical interfaces.
Using a resist-free local anodic oxidation process, we pattern sub-100nm
features in graphite gates which are then integrated into the heterostructure.
Using a quantum point contact as a benchmark device, we demonstrate selective
partitioning of both integer and fractional quantum Hall edge modes at high
magnetic fields. Furthermore, we find evidence for fragile interaction-driven
edge reconstruction effects, including the formation of a quantum dot at an
electrostatic potential saddle point. Our results pave the way for precision
nanoscale experiments on correlated states in these materials, including single
anyon control in the fractional quantum Hall regime.


title:
Learning to Fold Real Garments with One Arm: A Case Study in Cloud-Based
  Robotics Research

abstract:
  Autonomous fabric manipulation is a longstanding challenge in robotics, but
evaluating progress is difficult due to the cost and diversity of robot
hardware. Using Reach, a cloud robotics platform that enables low-latency
remote execution of control policies on physical robots, we present the first
systematic benchmarking of fabric manipulation algorithms on physical hardware.
We develop 4 novel learning-based algorithms that model expert actions,
keypoints, reward functions, and dynamic motions, and we compare these against
4 learning-free and inverse dynamics algorithms on the task of folding a
crumpled T-shirt with a single robot arm. The entire lifecycle of data
collection, model training, and policy evaluation is performed remotely without
physical access to the robot workcell. Results suggest a new algorithm
combining imitation learning with analytic methods achieves 84% of human-level
performance on the folding task. See
https://sites.google.com/berkeley.edu/cloudfolding for all data, code, models,
and supplemental material.


title: DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings

abstract:
  We propose DiffCSE, an unsupervised contrastive learning framework for
learning sentence embeddings. DiffCSE learns sentence embeddings that are
sensitive to the difference between the original sentence and an edited
sentence, where the edited sentence is obtained by stochastically masking out
the original sentence and then sampling from a masked language model. We show
that DiffSCE is an instance of equivariant contrastive learning (Dangovski et
al., 2021), which generalizes contrastive learning and learns representations
that are insensitive to certain types of augmentations and sensitive to other
"harmful" types of augmentations. Our experiments show that DiffCSE achieves
state-of-the-art results among unsupervised sentence representation learning
methods, outperforming unsupervised SimCSE by 2.3 absolute points on semantic
textual similarity tasks.


title:
Strong-field scattering of two spinning black holes: Numerics versus
  Analytics

abstract:
  We present new Numerical Relativity calculations of the scattering angle
$\chi$ between two, equal-mass, black holes on hyperbolic-like orbits. We build
upon previous work considering, for the first time, spinning black holes, with
equal spins either aligned or antialigned with the orbital angular momentum. We
detail the numerical techniques used in the computation of $\chi$. Special care
is taken in estimating error uncertainties on the quantities computed. The
numerical values are compared with analytical predictions obtained using a new,
state-of-the-art, effective one body model valid on generic orbits that
incorporates post-Newtonian analytic information up to 5PN in the nonspinning,
conservative sector and that has been additionally informed by Numerical
Relativity simulations of quasi-circular coalescing black hole binaries. Our
results indicate that the spin sector of the analytic model should be improved
further in order to achieve satisfactory consistency with the most relativistic
spinning configurations.


title: The McKay Conjecture and central isomorphic character triples

abstract:
  We refine the reduction theorem of the McKay Conjecture proved by Isaacs,
Malle and Navarro. Assuming the inductive McKay condition, we obtain a strong
version of the McKay Conjecture that gives central isomorphic character
triples.


title: Clifford theory, automorphisms and generalized Harish-Chandra theory

abstract:
  We work towards a version of generalized Harish-Chandra theory compatible
with Clifford theory and with the action of automorphisms on irreducible
characters. This provides a fundamental tool to verify the inductive conditions
for the so-called Local-Global conjectures in representation theory of finite
groups in the crucial case of groups of Lie type in non-defining
characteristic. In particular, as shown by the author in a previous paper, this
as a strong impact on the verification of the inductive condition for Dade's
Conjecture. As a by-product, we also show how to extend the parametrization of
generalized Harish-Chandra series given by Brou\'e, Malle and Michel to the
non-unipotent case by assuming maximal extendibility.


title: A full degree-of-freedom photonic crystal spatial light modulator

abstract:
  Harnessing the full complexity of optical fields requires complete control of
all degrees-of-freedom within a region of space and time -- an open goal for
present-day spatial light modulators (SLMs), active metasurfaces, and optical
phased arrays. Here, we solve this challenge with a programmable photonic
crystal cavity array enabled by four key advances: (i) near-unity vertical
coupling to high-finesse microcavities through inverse design, (ii) scalable
fabrication by optimized, 300 mm full-wafer processing, (iii)
picometer-precision resonance alignment using automated, closed-loop
"holographic trimming", and (iv) out-of-plane cavity control via a high-speed
micro-LED array. Combining each, we demonstrate near-complete spatiotemporal
control of a 64-resonator, two-dimensional SLM with nanosecond- and
femtojoule-order switching. Simultaneously operating wavelength-scale modes
near the space- and time-bandwidth limits, this work opens a new regime of
programmability at the fundamental limits of multimode optical control.


title: Modular Control Plane Verification via Temporal Invariants

abstract:
  Satisfiability Modulo Theory (SMT)-based tools for network control plane
analysis make it possible to reason exhaustively about interactions with peer
networks and to detect vulnerabilities such as accidental use of a network as
transit or prefix hijacking. SMT-based reasoning also facilitates synthesis and
repair. To scale SMT-based verification to large networks, we introduce
Timepiece, a new modular control plane verification system. While past
verifiers like Minesweeper were based on analysis of stable paths, we show that
such models, when deployed naively in service of modular verification, are
unsound. To rectify the situation, we adopt a routing model based around a
logical notion of time and develop a sound, expressive, and scalable
verification engine. Our system requires that a user specifies interfaces
between module components. We develop methods for defining these interfaces
using predicates inspired by temporal logic, and show how to use those
interfaces to verify a range of network-wide properties such as reachability,
"no transit," and "no hijacking." Verifying a prefix-filtering policy using a
non-modular verification engine times out on a 320-node fattree network after 4
hours. However, Timepiece verifies a 4,500-node fattree in 6.5 minutes on a
96-core virtual machine. Modular verification of individual routers is
embarrassingly parallel and completes in seconds, which allows verification to
scale beyond non-modular engines, while still allowing the full power of
SMT-based symbolic reasoning.


title:
Exploring Artificial Intelligence as a General Purpose Technology with
  Patent Data -- A Systematic Comparison of Four Classification Approaches

abstract:
  Artificial Intelligence (AI) is often defined as the next general purpose
technology (GPT) with profound economic and societal consequences. We examine
how strongly four patent AI classification methods reproduce the GPT-like
features of (1) intrinsic growth, (2) generality, and (3) innovation
complementarities. Studying US patents from 1990-2019, we find that the four
methods (keywords, scientific citations, WIPO, and USPTO approach) vary in
classifying between 3-17% of all patents as AI. The keyword-based approach
demonstrates the strongest intrinsic growth and generality despite identifying
the smallest set of AI patents. The WIPO and science approaches generate each
GPT characteristic less strikingly, whilst the USPTO set with the largest
number of patents produces the weakest features. The lack of overlap and
heterogeneity between all four approaches emphasises that the evaluation of AI
innovation policies may be sensitive to the choice of classification method.


title:
People are not coins. Morally distinct types of predictions necessitate
  different fairness constraints

abstract:
  A recent paper (Hedden 2021) has argued that most of the group fairness
constraints discussed in the machine learning literature are not necessary
conditions for the fairness of predictions, and hence that there are no genuine
fairness metrics. This is proven by discussing a special case of a fair
prediction. In our paper, we show that Hedden 's argument does not hold for the
most common kind of predictions used in data science, which are about people
and based on data from similar people; we call these human-group-based
practices. We argue that there is a morally salient distinction between
human-group-based practices and those that are based on data of only one
person, which we call human-individual-based practices. Thus, what may be a
necessary condition for the fairness of human-group-based practices may not be
a necessary condition for the fairness of human-individual-based practices, on
which Hedden 's argument is based. Accordingly, the group fairness metrics
discussed in the machine learning literature may still be relevant for most
applications of prediction-based decision making.


title:
Performance and limitations of the QAOA at constant levels on large
  sparse hypergraphs and spin glass models

abstract:
  The Quantum Approximate Optimization Algorithm (QAOA) is a general purpose
quantum algorithm designed for combinatorial optimization. We analyze its
expected performance and prove concentration properties at any constant level
(number of layers) on ensembles of random combinatorial optimization problems
in the infinite size limit. These ensembles include mixed spin models and
Max-$q$-XORSAT on sparse random hypergraphs. To enable our analysis, we prove a
generalization of the multinomial theorem which is a technical result of
independent interest. We then show that the performance of the QAOA at constant
levels for the pure $q$-spin model matches asymptotically the ones for
Max-$q$-XORSAT on random sparse Erd\H{o}s-R\'{e}nyi hypergraphs and every
large-girth regular hypergraph. Through this correspondence, we establish that
the average-case value produced by the QAOA at constant levels is bounded away
from optimality for pure $q$-spin models when $q\ge 4$ is even. This limitation
gives a hardness of approximation result for quantum algorithms in a new regime
where the whole graph is seen.

categories: quant-ph cond-mat.stat-mech cs.CC cs.DS math-ph math.MP


title: Distributive Invariant Centrally Essential Rings

abstract:
  In recent years, centrally essential rings have been intensively studied in
ring theory. In particular, they find applications in homological algebra,
group rings, and the structural theory of rings. The class of essentially
central rings strongly extends the class of commutative rings. For such rings,
a number of recent papers contain positive answers to some important questions
from ring theory that previously had positive answers for commutative rings and
negative answers in the general case. This work is devoted to a similar topic.
A familiar description of right Noetherian, right distributive centrally
essential rings is generalized on a larger class of rings. Let $A$ be a ring
with prime radical $P(A)$. It is proved that $A$ is a right distributive, right
invariant centrally essential ring and $P(A)$ is a finitely generated right
ideal such that the factor-ring $A/P(A)$ does non contain an infinite direct
sum of non-zero ideals if and only if $A=A_1\times\cdots\times A_n$, where
every ring $A_k$ is either a commutative Pr\"ufer domain or an Artinian
uniserial ring.


title:
Addressing Tactic Volatility in Self-Adaptive Systems Using Evolved
  Recurrent Neural Networks and Uncertainty Reduction Tactics

abstract:
  Self-adaptive systems frequently use tactics to perform adaptations. Tactic
examples include the implementation of additional security measures when an
intrusion is detected, or activating a cooling mechanism when temperature
thresholds are surpassed. Tactic volatility occurs in real-world systems and is
defined as variable behavior in the attributes of a tactic, such as its latency
or cost. A system's inability to effectively account for tactic volatility
adversely impacts its efficiency and resiliency against the dynamics of
real-world environments. To enable systems' efficiency against tactic
volatility, we propose a Tactic Volatility Aware (TVA-E) process utilizing
evolved Recurrent Neural Networks (eRNN) to provide accurate tactic
predictions. TVA-E is also the first known process to take advantage of
uncertainty reduction tactics to provide additional information to the
decision-making process and reduce uncertainty. TVA-E easily integrates into
popular adaptation processes enabling it to immediately benefit a large number
of existing self-adaptive systems. Simulations using 52,106 tactic records
demonstrate that: I) eRNN is an effective prediction mechanism, II) TVA-E
represents an improvement over existing state-of-the-art processes in
accounting for tactic volatility, and III) Uncertainty reduction tactics are
beneficial in accounting for tactic volatility. The developed dataset and tool
can be found at https://tacticvolatility.github.io/


title:
On a conjecture of Talagrand on selector processes and a consequence on
  positive empirical processes

abstract:
  For appropriate Gaussian processes, Michel Talagrand (1987) proved that the
event that the supremum is significantly larger than its expectation can be
covered by a set of half-spaces whose sum of measures is small. We prove a
conjecture of Talagrand that is the analog of this result in the Bernoulli-$p$
setting, as well as a version of Talagrand's result for general positive
empirical processes.


title:
Share With Thy Neighbors: Single-View Reconstruction by Cross-Instance
  Consistency

abstract:
  Approaches to single-view reconstruction typically rely on viewpoint
annotations, silhouettes, the absence of background, multiple views of the same
instance, a template shape, or symmetry. We avoid all of these supervisions and
hypotheses by leveraging explicitly the consistency between images of different
object instances. As a result, our method can learn from large collections of
unlabelled images depicting the same object category. Our main contributions
are two approaches to leverage cross-instance consistency: (i) progressive
conditioning, a training strategy to gradually specialize the model from
category to instances in a curriculum learning fashion; (ii) swap
reconstruction, a loss enforcing consistency between instances having similar
shape or texture. Critical to the success of our method are also: our
structured autoencoding architecture decomposing an image into explicit shape,
texture, pose, and background; an adapted formulation of differential
rendering, and; a new optimization scheme alternating between 3D and pose
learning. We compare our approach, UNICORN, both on the diverse synthetic
ShapeNet dataset - the classical benchmark for methods requiring multiple views
as supervision - and on standard real-image benchmarks (Pascal3D+ Car, CUB-200)
for which most methods require known templates and silhouette annotations. We
also showcase applicability to more challenging real-world collections
(CompCars, LSUN), where silhouettes are not available and images are not
cropped around the object.


title: Certain transformations and values of p-adic hypergeometric functions

abstract:
  We prove two transformations for the $p$-adic hypergeometric functions which
can be described as $p$-adic analogues of a Euler's transformation and a
transformation of Clausen. We first evaluate certain character sums, and then
relate them to the $p$-adic hypergeometric functions to deduce the
transformations. We use a character sum identity proved by Ahlgren, Ono, and
Penniston to deduce the $p$-adic Clausen's transformation. We also deduce
special values of certain $p$-adic hypergeometric functions.


title:
Unsupervised Human Action Recognition with Skeletal Graph Laplacian and
  Self-Supervised Viewpoints Invariance

abstract:
  This paper presents a novel end-to-end method for the problem of
skeleton-based unsupervised human action recognition. We propose a new
architecture with a convolutional autoencoder that uses graph Laplacian
regularization to model the skeletal geometry across the temporal dynamics of
actions. Our approach is robust towards viewpoint variations by including a
self-supervised gradient reverse layer that ensures generalization across
camera views. The proposed method is validated on NTU-60 and NTU-120
large-scale datasets in which it outperforms all prior unsupervised
skeleton-based approaches on the cross-subject, cross-view, and cross-setup
protocols. Although unsupervised, our learnable representation allows our
method even to surpass a few supervised skeleton-based action recognition
methods. The code is available in:
www.github.com/IIT-PAVIS/UHAR_Skeletal_Laplacian


title: Cellular Topology Optimization on Differentiable Voronoi Diagrams

abstract:
  Cellular structures manifest their outstanding mechanical properties in many
biological systems. One key challenge for designing and optimizing these
geometrically complicated structures lies in devising an effective geometric
representation to characterize the system's spatially varying cellular
evolution driven by objective sensitivities. A conventional discrete cellular
structure, e.g., a Voronoi diagram, whose representation relies on discrete
Voronoi cells and faces, lacks its differentiability to facilitate large-scale,
gradient-based topology optimizations. We propose a topology optimization
algorithm based on a differentiable and generalized Voronoi representation that
can evolve the cellular structure as a continuous field. The central piece of
our method is a hybrid particle-grid representation to encode the previously
discrete Voronoi diagram into a continuous density field defined in a Euclidean
space. Based on this differentiable representation, we further extend it to
tackle anisotropic cells, free boundaries, and functionally-graded cellular
structures. Our differentiable Voronoi diagram enables the integration of an
effective cellular representation into the state-of-the-art topology
optimization pipelines, which defines a novel design space for cellular
structures to explore design options effectively that were impractical for
previous approaches. We showcase the efficacy of our approach by optimizing
cellular structures with up to thousands of anisotropic cells, including femur
bone and Odonata wing.


title: Adversarial Contrastive Learning by Permuting Cluster Assignments

abstract:
  Contrastive learning has gained popularity as an effective self-supervised
representation learning technique. Several research directions improve
traditional contrastive approaches, e.g., prototypical contrastive methods
better capture the semantic similarity among instances and reduce the
computational burden by considering cluster prototypes or cluster assignments,
while adversarial instance-wise contrastive methods improve robustness against
a variety of attacks. To the best of our knowledge, no prior work jointly
considers robustness, cluster-wise semantic similarity and computational
efficiency. In this work, we propose SwARo, an adversarial contrastive
framework that incorporates cluster assignment permutations to generate
representative adversarial samples. We evaluate SwARo on multiple benchmark
datasets and against various white-box and black-box attacks, obtaining
consistent improvements over state-of-the-art baselines.


title: CDF II $W$-mass anomaly faces first-order electroweak phase transition

abstract:
  We suggest an appealing strategy to probe a large class of scenarios beyond
the Standard Model simultaneously explaining the recent CDF II measurement of
the $W$ boson mass and predicting first-order phase transitions (FOPT) testable
in future gravitational-wave (GW) experiments. Our analysis deploys
measurements from the GW channels and high energy particle colliders. We
discuss this methodology focusing on the specific example provided by an
extension of the Standard Model of particle physics that incorporates an
additional scalar $\mathrm{SU}(2)_{\rm L}$ triplet coupled to the Higgs boson.
We show that within this scenario a strong electroweak FOPT is naturally
realised consistently with the measured $W$ boson mass-shift. Potentially
observable GW signatures imply the triplet mass scale to be TeV-ish,
consistently with the value preferred by the $W$ mass anomaly. This model can
be tested in future space-based interferometers such as LISA, DECIGO, BBO,
TianQin, TAIJI projects and in future colliders such as FCC, ILC, CEPC.


title: A Collision Mechanism for the Removal of Earth's Trojan Asteroids

abstract:
  Due to their strong resonances with their host planet, Trojan asteroids can
remain in stable orbits for billions of years. As a result, they are powerful
probes for constraining the dynamical and chemical history of the solar system.
Although we have detected thousands of Jupiter Trojans and dozens of Neptune
Trojans, there are currently no known long-term stable Earth Trojans. Dynamical
simulations show that the parameter space for stable Earth Trojans in
substantial, so their apparent absence poses a mystery. This work uses a large
ensemble of N-body simulations to explore how the Trojan population dynamically
responds if Earth suffers large collisions, such as those thought to have
occurred to form the Moon and/or to have given Earth its Late Veneer. We show
that such collisions can be highly disruptive to the primordial Trojan
population, and could have eliminated it altogether. More specifically, if
Earth acquired the final 1\% of its mass through ${\cal O}(10)$ collisions,
then only $\sim1\%$ of the previously bound Trojan population would remain.


title: The $v$-number and Castelnuovo-Mumford regularity of graphs

abstract:
  We prove that for every integer $k\geq 1$, there exists a connected graph
$H_k$ such that $v(H_k)=reg(H_k)+k$, where $v(G)$ and $reg(G)$ denote the
$v$-number and the (Castelnuovo-Mumford) regularity of a graph $G$
respectively.


title: Feature anomaly detection system (FADS) for intelligent manufacturing

abstract:
  Anomaly detection is important for industrial automation and part quality
assurance, and while humans can easily detect anomalies in components given a
few examples, designing a generic automated system that can perform at human or
above human capabilities remains a challenge. In this work, we present a simple
new anomaly detection algorithm called FADS (feature-based anomaly detection
system) which leverages pretrained convolutional neural networks (CNN) to
generate a statistical model of nominal inputs by observing the activation of
the convolutional filters. During inference the system compares the
convolutional filter activation of the new input to the statistical model and
flags activations that are outside the expected range of values and therefore
likely an anomaly. By using a pretrained network, FADS demonstrates excellent
performance similar to or better than other machine learning approaches to
anomaly detection while at the same time FADS requires no tuning of the CNN
weights. We demonstrate FADS ability by detecting process parameter changes on
a custom dataset of additively manufactured lattices. The FADS localization
algorithm shows that textural differences that are visible on the surface can
be used to detect process parameter changes. In addition, we test FADS on
benchmark datasets, such as the MVTec Anomaly Detection dataset, and report
good results.


title: TorchSparse: Efficient Point Cloud Inference Engine

abstract:
  Deep learning on point clouds has received increased attention thanks to its
wide applications in AR/VR and autonomous driving. These applications require
low latency and high accuracy to provide real-time user experience and ensure
user safety. Unlike conventional dense workloads, the sparse and irregular
nature of point clouds poses severe challenges to running sparse CNNs
efficiently on the general-purpose hardware. Furthermore, existing sparse
acceleration techniques for 2D images do not translate to 3D point clouds. In
this paper, we introduce TorchSparse, a high-performance point cloud inference
engine that accelerates the sparse convolution computation on GPUs. TorchSparse
directly optimizes the two bottlenecks of sparse convolution: irregular
computation and data movement. It applies adaptive matrix multiplication
grouping to trade computation for better regularity, achieving 1.4-1.5x speedup
for matrix multiplication. It also optimizes the data movement by adopting
vectorized, quantized and fused locality-aware memory access, reducing the
memory movement cost by 2.7x. Evaluated on seven representative models across
three benchmark datasets, TorchSparse achieves 1.6x and 1.5x measured
end-to-end speedup over the state-of-the-art MinkowskiEngine and SpConv,
respectively.


title: SelfD: Self-Learning Large-Scale Driving Policies From the Web

abstract:
  Effectively utilizing the vast amounts of ego-centric navigation data that is
freely available on the internet can advance generalized intelligent systems,
i.e., to robustly scale across perspectives, platforms, environmental
conditions, scenarios, and geographical locations. However, it is difficult to
directly leverage such large amounts of unlabeled and highly diverse data for
complex 3D reasoning and planning tasks. Consequently, researchers have
primarily focused on its use for various auxiliary pixel- and image-level
computer vision tasks that do not consider an ultimate navigational objective.
In this work, we introduce SelfD, a framework for learning scalable driving by
utilizing large amounts of online monocular images. Our key idea is to leverage
iterative semi-supervised training when learning imitative agents from
unlabeled data. To handle unconstrained viewpoints, scenes, and camera
parameters, we train an image-based model that directly learns to plan in the
Bird's Eye View (BEV) space. Next, we use unlabeled data to augment the
decision-making knowledge and robustness of an initially trained model via
self-training. In particular, we propose a pseudo-labeling step which enables
making full use of highly diverse demonstration data through "hypothetical"
planning-based data augmentation. We employ a large dataset of publicly
available YouTube videos to train SelfD and comprehensively analyze its
generalization benefits across challenging navigation scenarios. Without
requiring any additional data collection or annotation efforts, SelfD
demonstrates consistent improvements (by up to 24%) in driving performance
evaluation on nuScenes, Argoverse, Waymo, and CARLA.


title:
Learning Future Object Prediction with a Spatiotemporal Detection
  Transformer

abstract:
  We explore future object prediction -- a challenging problem where all
objects visible in a future video frame are to be predicted. We propose to
tackle this problem end-to-end by training a detection transformer to directly
output future objects. In order to make accurate predictions about the future,
it is necessary to capture the dynamics in the scene, both of other objects and
of the ego-camera. We extend existing detection transformers in two ways to
capture the scene dynamics. First, we experiment with three different
mechanisms that enable the model to spatiotemporally process multiple frames.
Second, we feed ego-motion information to the model via cross-attention. We
show that both of these cues substantially improve future object prediction
performance. Our final approach learns to capture the dynamics and make
predictions on par with an oracle for 100 ms prediction horizons, and
outperform baselines for longer prediction horizons.


title: Online Two-Dimensional Vector Packing with Advice

abstract:
  We consider the online two-dimensional vector packing problem, showing a
lower bound of $11/5$ on the competitive ratio of any {\sc AnyFit} strategy for
the problem. We provide strategies with competitive ratio
$\max\!\left\{2,6\big/\big(1+3\tan(\pi/4-\gamma/2)\big)+\epsilon\right\}$ and
logarithmic advice, for any instance where all the input vectors are restricted
to have angles in the range $[\pi/4-\gamma/2,\pi/4+\gamma/2]$, for
$0\leq\gamma<\pi/3$ and
$\max\left\{5/2,4\big/\big(1+2\tan(\pi/4-\gamma/2)\big)+\epsilon\right\}$ and
logarithmic advice, for any instance where all the input vectors are restricted
to have angles in the range $[\pi/4-\gamma/2,\pi/4+\gamma/2]$, for
$0\leq\gamma\leq\pi/3$. In addition, we give a $5/2$-competitive strategy also
using logarithmic advice for the unrestricted vectors case. These results
should be contrasted to the currently best competitive strategy, FirstFit,
having competitive ratio~$27/10$.


title:
Accelerating Physics Simulations with TPUs: An Inundation Modeling
  Example

abstract:
  Recent advancements in hardware accelerators such as Tensor Processing Units
(TPUs) speed up computation time relative to Central Processing Units (CPUs)
not only for machine learning but, as demonstrated here, also for scientific
modeling and computer simulations. To study TPU hardware for distributed
scientific computing, we solve partial differential equations (PDEs) for the
physics simulation of fluids to model riverine floods. We demonstrate that TPUs
achieve a two orders of magnitude speedup over CPUs. Running physics
simulations on TPUs is publicly accessible via the Google Cloud Platform, and
we release a Python interactive notebook version of the simulation.


title:
Grover Search Inspired Alternating Operator Ansatz of Quantum
  Approximate Optimization Algorithm for Search Problems

abstract:
  We use the mapping between two computation frameworks , Adiabatic Grover
Search (AGS) and Adiabatic Quantum Computing (AQC), to translate the Grover
search algorithm into the AQC regime. We then apply Trotterization on the
schedule-dependent Hamiltonian of AGS to obtain the values of variational
parameters in the Quantum Approximate Optimization Algorithm (QAOA) framework.
The goal is to carry the optimal behavior of Grover search algorithm into the
QAOA framework without the iterative machine learning processes.


title:
AI-Based Automated Speech Therapy Tools for persons with Speech Sound
  Disorders: A Systematic Literature Review

abstract:
  This paper presents a systematic literature review of published studies on
AI-based automated speech therapy tools for persons with speech sound disorders
(SSD). The COVID-19 pandemic has initiated the requirement for automated speech
therapy tools for persons with SSD making speech therapy accessible and
affordable. However, there are no guidelines for designing such automated tools
and their required degree of automation compared to human experts. In this
systematic review, we followed the PRISMA framework to address four research
questions: 1) what types of SSD do AI-based automated speech therapy tools
address, 2) what is the level of autonomy achieved by such tools, 3) what are
the different modes of intervention, and 4) how effective are such tools in
comparison with human experts. An extensive search was conducted on digital
libraries to find research papers relevant to our study from 2007 to 2022. The
results show that AI-based automated speech therapy tools for persons with SSD
are increasingly gaining attention among researchers. Articulation disorders
were the most frequently addressed SSD based on the reviewed papers. Further,
our analysis shows that most researchers proposed fully automated tools without
considering the role of other stakeholders. Our review indicates that
mobile-based and gamified applications were the most frequent mode of
intervention. The results further show that only a few studies compared the
effectiveness of such tools compared to expert Speech-Language Pathologists
(SLP). Our paper presents the state-of-the-art in the field, contributes
significant insights based on the research questions, and provides suggestions
for future research directions.


title: galstreams: A Library of Milky Way Stellar Stream Footprints and Tracks

abstract:
  Nearly a hundred stellar streams have been found to date around the Milky Way
and the number keeps growing at an ever faster pace. Here we present the
\galstreams\ library, a compendium of angular position, distance, proper motion
and radial velocity tracks for nearly a hundred (\Nunique) Galactic stellar
streams. The information published in the literature has been collated and
homogenised in a consistent format and used to provide a set of features
uniformly computed throughout the library: e.g. stream length, end points, mean
pole, stream's coordinate frame, polygon footprint, and pole and angular
momentum tracks. We also use the information compiled to analyse the
distribution of several observables across the library and to assess where the
main deficiencies are found in the characterisation of individual stellar
streams, as a resource for future follow-up efforts. The library is intended to
facilitate keeping track of new discoveries and to encourage the use of
automated methods to characterise and study the ensemble of known stellar
streams by serving as a starting point. The library is publicly available as a
Python package and served at the galstreams GitHub repository.


title:
The Milky Way tomography with APOGEE: intrinsic density distribution and
  structure of mono-abundance populations

abstract:
  The spatial distribution of mono-abundance populations (MAPs, selected in
[Fe/H] and [Mg/Fe]) reflect the chemical and structural evolution in a galaxy
and impose strong constraints on galaxy formation models. In this paper, we use
APOGEE data to derive the intrinsic density distribution of MAPs in the Milky
Way, after carefully considering the survey selection function. We find that a
single exponential profile is not a sufficient description of the Milky Way's
disc. Both the individual MAPs and the integrated disc exhibit a broken radial
density distribution; densities are relatively constant with radius in the
inner Galaxy and rapidly decrease beyond the break radius. We fit the intrinsic
density distribution as a function of radius and vertical height with a 2D
density model that considers both a broken radial profile and radial variation
of scale height (i.e., flaring). There is a large variety of structural
parameters between different MAPs, indicative of strong structure evolution of
the Milky Way. One surprising result is that high-$\alpha$ MAPs show the
strongest flaring. The young, solar-abundance MAPs present the shortest scale
height and least flaring, suggesting recent and ongoing star formation confined
to the disc plane. Finally we derive the intrinsic density distribution and
corresponding structural parameters of the chemically defined thin and thick
discs. The chemical thick and thin discs have local surface mass densities of
5.62$\pm$0.08 and 15.69$\pm$0.32 ${\rm M_{\odot} pc^{-2}}$, respectively,
suggesting a massive thick disc with a local surface mass density ratio between
thick to thin disc of 36%.


title:
Charge-density waves in kagome-lattice extended Hubbard models at the
  van Hove filling

abstract:
  The Hubbard model on the kagome lattice is presently often considered as a
minimal model to describe the rich low-temperature behavior of AV$_{3}$Sb$_{5}$
compounds (with A=K, Rb, Cs), including charge-density waves (CDWs),
superconductivity, and possibly broken time-reversal symmetry. Here, we
investigate, via variational Jastrow-Slater wave functions, the properties of
its ground state when both onsite $U$ and nearest-neighbor $V$ Coulomb
repulsions are considered at the van Hove filling. Our calculations reveal the
presence of different interaction-driven CDWs and, contrary to previous
renormalization-group studies, the absence of ferromagnetism and charge- or
spin-bond order. No signatures of chiral phases are detected. Remarkably, the
CDWs triggered by the nearest-neighbor repulsion possess charge
disproportionations that are not compatible with the ones observed in
AV$_{3}$Sb$_{5}$. As an alternative mechanism to stabilize charge-bond order,
we consider the electron-phonon interaction, modeled by coupling the hopping
amplitudes to quantum phonons, as in the Su-Schrieffer-Heeger model. Our
results show the instability towards a tri-hexagonal distortion with $2\times
2$ periodicity, in a closer agreement with experimental findings.


title:
Chiral Broken Symmetry Descendants of the Kagom\'e Lattice Chiral Spin
  Liquid

abstract:
  The breaking of chiral and time-reversal symmetries provides a pathway to
exotic quantum phenomena and topological phases. In particular, the breaking of
chiral (mirror) symmetry in quantum materials has been shown to have important
technological applications. Recent work has extensively explored the resulting
emergence of chiral charge orders and chiral spin liquids on the kagom\'e
lattice. Such chiral spin liquids are closely tied to bosonic fractional
quantum Hall states and host anyonic quasiparticles; however, their connection
to nearby magnetically ordered states has remained a mystery. Here, we show
that two distinct non-coplanar magnetic orders with uniform spin chirality, the
XYZ umbrella state and the Octahedral spin crystal, emerge as competing orders
in close proximity to the kagom\'e chiral spin liquid. Our results highlight
the intimate link between a many-body topologically ordered liquid and broken
symmetry states with nontrivial real-space topology.


title:
Super-critical accretion of medium-weight seed black holes in gaseous
  proto-galactic nuclei

abstract:
  Accretion at sustained or episodic super-Eddington (SE) rates has been
proposed as a pathway to grow efficiently light seeds produced by Pop-III
stars. We investigate if SE accretion can be sustained onto a black hole (BH)
with $M_{\rm BH} \sim 10^3M_{\odot}$ in the centre of a gas-rich proto-galaxy
at $z=15$. We perform high-resolution smoothed-particle hydrodynamical
simulations, including two different sub-grid models for SE accretion, one
based on the slim disc paradigm, and one inspired by recent
radiation-magnetohydrodynamical simulations by Jiang and collaborators.
Radiative feedback has the form of a thermal dump to surrounding gas particles,
with the radiative efficiency being set according to the different SE accretion
models. We find that, in all simulations, supernova feedback rapidly quenches
accretion after $\sim1Myr$, irrespective of the sub-grid model used for
accretion, because it drives winds that evacuate the gas in the inner disc.
Quenching is stronger in the model based on the simulations of Jiang and
collaborators relative to the slim disc model because of its higher radiative
efficiency. The SE growth phase is always very brief, lasting a few $10^5$yr.
In the most optimistic case, the BH reaches $\sim$10$^4M_{\odot}$. We
extrapolate the final BH masses from $z=15$ to $z\sim6$, assuming subsequent
galaxy mergers will replenish the gas reservoir and trigger new cycles of SE
accretion. We find that at most BH seeds would grow to $\sim$10$^6M_{\odot}$,
comparable to the mass of massive BHs in spiral galaxies such as the Milky Way,
but falling short of the mass of the high-redshift quasars.


title: Pion and kaon fragmentation functions at next-to-next-to-leading order

abstract:
  We present a new determination of unpolarised charged pion and kaon
fragmentation functions from a set of single-inclusive electron-positron
annihilation and lepton-nucleon semi-inclusive deep-inelastic scattering data.
The determination includes next-to-next-to-leading order QCD corrections to
both processes, and is carried out in a framework that combines a
neural-network parametrisation of fragmentation functions with a Monte Carlo
representation of their uncertainties. We discuss the quality of the
determination, in particular its dependence on higher order corrections.


title: Revealing the Galaxy-Halo Connection Through Machine Learning

abstract:
  Understanding the connections between galaxy stellar mass, star formation
rate, and dark matter halo mass represents a key goal of the theory of galaxy
formation. Cosmological simulations that include hydrodynamics, physical
treatments of star formation, feedback from supernovae, and the radiative
transfer of ionizing photons can capture the processes relevant for
establishing these connections. The complexity of these physics can prove
difficult to disentangle and obfuscate how mass-dependent trends in the galaxy
population originate. Here, we train a machine learning method called
Explainable Boosting Machines (EBMs) to infer how the stellar mass and star
formation rate of nearly 6 million galaxies simulated by the Cosmic
Reionization on Computers (CROC) project depend on the physical properties of
halo mass, the peak circular velocity of the galaxy during its formation
history $v_\mathrm{peak}$, cosmic environment, and redshift. The resulting EBM
models reveal the relative importance of these properties in setting galaxy
stellar mass and star formation rate, with $v_\mathrm{peak}$ providing the most
dominant contribution. Environmental properties provide substantial
improvements for modeling the stellar mass and star formation rate in only
$\lesssim10\%$ of the simulated galaxies. We also provide alternative
formulations of EBM models that enable low-resolution simulations, which cannot
track the interior structure of dark matter halos, to predict the stellar mass
and star formation rate of galaxies computed by high-resolution simulations
with detailed baryonic physics.


title:
Elementary derivation of the stacking rules of fermionic invertible
  topological phases in one dimension

abstract:
  Invertible fermionic topological (IFT) phases are gapped phases of matter
with nondegenerate ground states on any closed spatial manifold. When open
boundary conditions are imposed, nontrivial IFT phases support gapless boundary
degrees of freedom. Distinct IFT phases in one-dimensional space with an
internal symmetry group $G^{\,}_{f}$ have been characterized by a triplet of
indices $([(\nu,\rho)],[\mu])$. Our main result is an elementary derivation of
the fermionic stacking rules of one-dimensional IFT phases for any given
internal symmetry group $G^{\,}_{f}$ from the perspective of the boundary,
i.e., we give an explicit operational definition for the boundary
representation $([(\nu^{\,}_{\wedge},\rho^{\,}_{\wedge})],[\mu^{\,}_{\wedge}])$
obtained from stacking two IFT phases characterized by the triplets of boundary
indices $([(\nu^{\,}_{1},\rho^{\,}_{1})],[\mu^{\,}_{1}])$ and
$([(\nu^{\,}_{2},\rho^{\,}_{2})],[\mu^{\,}_{2}])$, respectively.


title: Machine Learning Algebraic Geometry for Physics

abstract:
  We review some recent applications of machine learning to algebraic geometry
and physics. Since problems in algebraic geometry can typically be reformulated
as mappings between tensors, this makes them particularly amenable to
supervised learning. Additionally, unsupervised methods can provide insight
into the structure of such geometrical data. At the heart of this programme is
the question of how geometry can be machine learned, and indeed how AI helps
one to do mathematics. This is a chapter contribution to the book Machine
learning and Algebraic Geometry, edited by A. Kasprzyk et al.


title: Pauli crystal melting in shaken optical traps

abstract:
  Pauli crystals are ordered geometric structures that emerge in trapped
noninteracting fermionic systems due to their underlying Pauli repulsion. The
deformation of Pauli crystals - often called melting - has been recently
observed in experiments, but the mechanism that leads to it remains unclear. We
address this question by studying the melting dynamics of N=6 fermions as a
function of periodic driving and experimental imperfections in the trap
(anisotropy and anharmonicity) by employing a combination of numerical
simulations and Floquet theory. Surprisingly, we reveal that the melting of
Pauli crystals is not simply a direct consequence of an increase in system
energy, but is instead related to the trap geometry and the population of the
Floquet modes. We show that the melting is absent in traps without
imperfections and triggered only by a sufficiently large shaking amplitude in
traps with imperfections.


title: Parallel QND measurement tomography of multi-qubit quantum devices

abstract:
  An efficient characterization of QND measurements is an important ingredient
towards certifying and improving the performance and scalability of quantum
processors. In this work, we introduce a protocol for the parallelized QND
measurement tomography (QND-MT) of a multiqubit quantum processor that
addresses single- and two-qubit measurements. We demonstrate the excellent
scaling of this protocol experimentally, fully characterizing a 7-qubit IBM-Q
quantum processor for direct measurements and measurement-and-reset schemes.
Our protocol reconstructs the Choi matrices of the single- and two-qubit
measurement processes, extracts relevant quantifiers -- fidelity, QND-ness,
destructiveness -- and identifies sources of errors that limit the performance
of the device for repeated QND measurements. We also introduce quantification
schemes that reveal the influence of cross-talk on the measurement outcomes.


title:
On the Superiority of the $|V_{cb}|-\gamma$ Plots over the Unitarity
  Triangle Plots in the 2020s

abstract:
  The UT plots played already for three decades an important role in the tests
of the SM and the determination of the CKM parameters. As of 2022, among the
four CKM parameters, $V_{us}$ and $\beta$ are already measured with respectable
precision, while this is not the case of $|V_{cb}|$ and $\gamma$. In the case
of $|V_{cb}|$ the main obstacle are the significant tensions between its
inclusive and exclusive determinations from tree-level decays. The present
uncertainty in $\gamma$ of $4^\circ$ from tree-level decays will be reduced to
$1^\circ$ by the LHCb and Belle II collaborations in the coming years.
Unfortunately in the UT plots $|V_{cb}|$ is not seen and the experimental
improvements in the determination of $\gamma$ from tree-level decays at the
level of a few degrees are difficult to appreciate. In view of these
deficiencies of the UT plots with respect to $|V_{cb}|$ and $\gamma$ and the
central role these two CKM parameters will play in this decade, the recently
proposed plots of $|V_{cb}|$ versus $\gamma$ extracted from various processes
appear to be superior to the UT plots in the flavour phenomenology. We
illustrate this idea with $\Delta M_s$, $\Delta M_d$, $\epsilon_K$ and with
rare decays $B_s\to\mu^+\mu^-$, $B_d\to\mu^+\mu^-$, $K^+\to \pi^+\nu\bar\nu$
and $K_L\to\pi^0\nu\bar\nu$. The power of $\epsilon_K$,
$K^+\to\pi^+\nu\bar\nu)$ and $K_{L}\to\pi^0\nu\bar\nu)$ in the determination of
$|V_{cb}|$, due to their strong dependence on $|V_{cb}|$, is transparently
exhibited in this manner. Combined with future reduced errors on $\gamma$ and
$|V_{cb}|$ from tree-level decays such plots can better exhibit possible
inconsistenices between various determinations of these two parameters, caused
by new physics, than it is possible with the UT plots. This can already be
illustrated on the example of the $2.7\sigma$ anomaly in $B_s\to\mu^+\mu^-$.


title:
Status of the two-Higgs-doublet model in light of the CDF $m_W$
  measurement

abstract:
  The most recent the $W$-boson mass measurement by the CDF Collaboration with
a substantially reduced uncertainty indicated a large deviation from the
standard model prediction, as large as $7\sigma$ if taken literally. In terms
of the Peskin-Takeuchi parameters of $S$ and $T$, the central values would
shift to larger values. It has profound consequences in searching for physics
beyond the SM. We show in the framework of two-Higgs-doublet Models the effect
of the new $m^{\rm CDF}_W$ measurement on the parameter space. Combined this
$m^{\rm CDF}_W$ measurement with other constraints including theoretical
requirements, flavor-changing neutral currents in $B$ physics, maintaining the
perturbativity of all couplings until the cutoff scale $\Lambda_c =1$ TeV,
Higgs precision data, and direct collider search limits from the LEP, Tevatron,
and LHC experiments, we find upper bounds on the masses of the heavy Higgs
bosons $M_{H, A, H^\pm} \lesssim 1000-1100 $ GeV in type-I, II, X, and Y for
the normal scenario and $M_{H^\pm, A} \lesssim 410 - 440 $ GeV in type I and X
for the inverted scenario. Another important finding is that type-II and type-Y
in the inverted scenario are completely excluded. Such unprecedented findings
imply that the upcoming LHC run can readily close out the still-available
parameter space.


title: Cogenesis of Baryon Asymmetry and Gravitational Dark Matter from PBH

abstract:
  We propose a scenario where dark matter (DM) with a wide mass range from a
few keV to PeV can be produced solely from evaporating primordial black holes
(PBH), while being consistent with the required free streaming length for
structure formation. If DM does not have any other interactions apart from
gravity and the universe has a PBH dominated phase at early epoch, then PBH
evaporation typically leads to overproduction of DM in this mass range. By
incorporating this gravitational DM within a Type-I seesaw scenario with three
right handed neutrinos (RHN), we bring the abundance of PBH generated DM within
observed limits by late entropy injection due to decay of one of the RHNs,
acting as the diluter. The diluter, due to its feeble coupling with the bath
particles, gets produced primarily from the PBH evaporation thereby leading to
the second stage of early matter domination after the end of PBH dominated era.
The other two RHNs contribute to the origin of light neutrino mass and also
lead to the observed baryon asymmetry via leptogenesis with contributions from
both thermally and PBH generated RHNs. The criteria of DM relic and baryon
asymmetry can be satisfied simultaneously if DM mass gets restricted to a
ballpark in the MeV-GeV regime with the requirement of resonant leptogenesis
for heavier DM mass in order to survive the large entropy dilution at late
epochs.


title:
Results on photon-mediated dark matter-nucleus interactions from the
  PICO-60 C$_{3}$F$_{8}$ bubble chamber

abstract:
  Many compelling models predict dark matter coupling to the electromagnetic
current through higher multipole interactions, while remaining electrically
neutral. Different multipole couplings have been studied, among them anapole
moment, electric and magnetic dipole moments, and millicharge. This study sets
limits on the couplings for these photon-mediated interactions using
non-relativistic contact operators in an effective field theory framework.
Using data from the PICO-60 bubble chamber leading limits for dark matter
masses between 2.7 GeV/c$^2$ and 24 GeV/c$^2$ are reported for the coupling of
these photon-mediated dark matter-nucleus interactions. The detector was filled
with 52 kg of C$_3$F$_8$ operating at thermodynamic thresholds of 2.45 keV and
3.29 keV, reaching exposures of 1404 kg-day and 1167 kg-day, respectively.


title: Maximal entanglement velocity implies dual unitarity

abstract:
  A global quantum quench can be modeled by a quantum circuit with local
unitary gates. In general, entanglement grows linearly at a rate given by
entanglement velocity, which is upper bounded by the growth of the light cone.
  We show that the unitary interactions achieving the maximal rate must remain
unitary if we exchange the space and time directions -- a property known as
dual unitarity. Our results are robust: approximate maximal entanglement
velocity also implies approximate dual unitarity.
  We further show that maximal entanglement velocity is always accompanied by a
specific dynamical pattern of entanglement, which yields simpler analyses of
several known exactly solvable models.


title:
Three-point intrinsic alignments of dark matter halos in the
  IllustrisTNG simulation

abstract:
  We use the IllustrisTNG suite of cosmological simulations to measure
intrinsic alignment (IA) bispectra of dark matter subhalos between redshifts 0
and 1. We decompose the intrinsic shear field into E- and B-modes and find that
the bispectra $B_{\delta\delta\mathrm{E}}$ and $B_{\delta\mathrm{EE}}$, between
the matter overdensity field, $\delta$, and the E-mode field, are detected with
high significance. We also model the IA bispectra analytically using a method
consistent with the two-point non-linear alignment model. We use this model and
the simulation measurements to infer the intrinsic alignment amplitude
$A_\mathrm{IA}$ and find that values of $A_\mathrm{IA}$ obtained from IA power
spectra and bispectra agree well at scales up to $k_\mathrm{max}= 2 \, h
\mathrm{Mpc}^{-1}$, for example at $z=1$ $A_\mathrm{IA} = 2.13 \pm$ 0.02 from
the cross power spectrum between the matter overdensity and E-mode fields and
$A_\mathrm{IA} =2.11 \pm$ 0.03 from $B_{\delta\delta \mathrm{E}}$. This
demonstrates that a single physically motivated model can jointly model
two-point and three-point statistics of intrinsic alignments, thus enabling a
cleaner separation between intrinsic alignments and cosmological weak lensing
signals.


title: The distribution of Manin's iterated integrals of modular forms

abstract:
  We determine the asymptotic distribution of Manin's iterated integrals of
length at most 2. For length at least 3 we compute all the asymptotic moments
and show that these in general do not determine a unique distribution.


title: In Private, Secure, Conversational FinBots We Trust

abstract:
  In the past decade, the financial industry has experienced a technology
revolution. While we witness a rapid introduction of conversational bots for
financial services, there is a lack of understanding of conversational user
interfaces (CUI) features in this domain. The finance industry also deals with
highly sensitive information and monetary transactions, presenting a challenge
for developers and financial providers. Through a study on how to design
text-based conversational financial interfaces with N=410 participants, we
outline user requirements of trustworthy CUI design for financial bots. We
posit that, in the context of Finance, bot privacy and security assurances
outweigh conversational capability and postulate implications of these
findings. This work acts as a resource on how to design trustworthy FinBots and
demonstrates how automated financial advisors can be transformed into trusted
everyday devices, capable of supporting users' daily financial activities.


title: Metastates and Replica Symmetry Breaking

abstract:
  In this review we define and discuss metastates, mathematical tools with
general applicability to thermodynamic systems which are particularly useful
when working with disordered or inhomogeneous short-range systems. In an
infinite such system there may be many competing thermodynamic states, which
can lead to the absence of a straightforward thermodynamic limit of local
correlation functions. A metastate is a probability measure on the
infinite-volume thermodynamic states that restores the connection between those
states and the Gibbs states observed in finite volumes. After introducing the
basic metastates and discussing their properties, we present possible scenarios
for the spin-glass phase and discuss what the metastate approach reveals about
how replica symmetry breaking would manifest itself in finite-dimensional
short-range spin glasses.

categories: cond-mat.dis-nn cond-mat.stat-mech math-ph math.MP


title: Constraining Energy-dependent emissivity profiles of AGN inflows

abstract:
  The emissivity of the accretion flow is a key parameter affecting the shape
of both the energy and variability power spectrum of AGN. We explore the
energy-dependence of the power spectrum for five AGN, across the XMM-Newton
bandpass, and across the 0.01-1 mHz frequency range, finding a ubiquitous
flattening of the power spectrum towards higher energies. We develop a
framework to explore this behaviour and thereby extract the energy dependence
of the emissivity assuming a simple disc-like geometry for the inflow. We find
that the emissivity ranges from R^-2 at energies around the soft excess and
increases to R^-4 or steeper above ~4-6 keV. We describe the changing
emissivity index with a linear function in energy, finding the best-fitting
slopes to vary between AGN. We attempt to correlate the slope of the linear
function against key AGN parameters but, as yet, the sample size is too small
to confirm hints of a correlation with Eddington ratio.


title:
Probing Heavy Sterile Neutrinos at Ultrahigh Energy Neutrino Telescopes
  via the Dipole Portal

abstract:
  The question of how heavy a sterile neutrino can be probed in experiments
leads us to investigate the Primakoff production of heavy sterile neutrinos up
to PeV masses from ultrahigh-energy neutrinos via the magnetic dipole portal.
Despite the suppression from the small magnetic moment, the transition is
significantly enhanced by tiny $t$-channel momentum transfers, similar to the
resonant production of pions and axions in an external electromagnetic field.
Based on the current IceCube measurement of astrophysical neutrinos up to PeV
energies, strong constraints can already be derived on the transition magnetic
moments of sterile neutrinos up to TeV masses. Moreover, we investigate the
sensitivity of future tau neutrino telescopes, which are designed for EeV
cosmogenic neutrino detection. We find that sterile neutrino masses as large as
$30~{\rm TeV}$ can be probed at tau neutrino telescopes such as GRAND, POEMMA,
and Trinity.


title:
Simulate Time-integrated Coarse-grained Molecular Dynamics with
  Geometric Machine Learning

abstract:
  Molecular dynamics (MD) simulation is the workhorse of various scientific
domains but is limited by high computational cost. Learning-based force fields
have made major progress in accelerating ab-initio MD simulation but are still
not fast enough for many real-world applications that require long-time MD
simulation. In this paper, we adopt a different machine learning approach where
we coarse-grain a physical system using graph clustering, and model the system
evolution with a very large time-integration step using graph neural networks.
A novel score-based GNN refinement module resolves the long-standing challenge
of long-time simulation instability. Despite only trained with short MD
trajectory data, our learned simulator can generalize to unseen novel systems
and simulate for much longer than the training trajectories. Properties
requiring 10-100 ns level long-time dynamics can be accurately recovered at
several-orders-of-magnitude higher speed than classical force fields. We
demonstrate the effectiveness of our method on two realistic complex systems:
(1) single-chain coarse-grained polymers in implicit solvent; (2)
multi-component Li-ion polymer electrolyte systems.


title: Provably Efficient Kernelized Q-Learning

abstract:
  We propose and analyze a kernelized version of Q-learning. Although a kernel
space is typically infinite-dimensional, extensive study has shown that
generalization is only affected by the effective dimension of the data. We
incorporate such ideas into the Q-learning framework and derive regret bounds
for arbitrary kernels. In particular, we provide concrete bounds for linear
kernels and Gaussian RBF kernels; notably, the latter bound looks almost
identical to the former, only that the actual dimension is replaced by a
different notion of dimensionality. Finally, we test our algorithm on a suite
of classic control tasks; remarkably, under the Gaussian RBF kernel, it
achieves reasonably good performance after only 1000 environmental steps, while
its neural network counterpart, deep Q-learning, still struggles.


title: Exclusive $Z^0$ production in $ep$ and $eA$ collisions at high energies

abstract:
  In this work the $k_{\perp}$-factorization formalism is applied to compute
the exclusive $Z^0$ boson photoproduction in $ep$ and $eA$ collisions. The
study is also extended to $pp$ and $AA$ processes. The nuclear effects are
investigated considering heavy and light ions. Analytical models for the
unintegrated gluon distribution are taken into account and the corresponding
theoretical uncertainty is quantified. The analysis is done for electron-ion
collisions at the Large Hadron-Electron Collider (LHeC), its high-energy
upgrade (HE-LHeC) and at the Future Circular Collider (FCC) in lepton-hadron
mode. Additionally, ultra-peripheral heavy ion collisions at future runs of the
Large Hadron Collider (LHC) and at the FCC (hadron-hadron mode) are also
considered.


title: A uniform bound for solutions to a thermo-diffusive system

abstract:
  We obtain uniform in time bounds for the solutions to a class of
thermo-diffusive systems with classical and fractional diffusions. In the
classical diffusion case, the nonlinearities are assumed to be at most
exponentially growing, while for the fractional diffusions they should grow
slightly sub-exponentially. The proofs are based on the strategy of Herrero,
Lacey, and Velazquez.


title:
Ramification and Discriminants of Vector Bundles and a Quick Proof of
  Bogomolov's Theorem

abstract:
  By analyzing degeneracy loci over projectivized vector bundles, we recompute
the degree of the discriminant locus of a vector bundle and provide a new proof
of the Bogomolov instability theorem.


title:
A note on the invariants of the Volterra gyrostat and each of its known
  special cases, and implications for low order models

abstract:
  The model of the Volterra gyrostat (VG) has not only played an important role
in rigid body dynamics but also served as the foundation of low-order models of
many naturally occurring systems, including atmospheric dynamics. It is well
known that VG possesses two invariants, or constants of motion, corresponding
to kinetic energy and squared angular momentum, which leads to oscillatory
solutions to its equations of motion. Nine distinct subclasses of the VG have
been identified, two of which the Euler gyroscope and Lorenz gyrostat are each
known to have two constants. This paper provides a complete characterization of
the constants of motion of the VG and its subclasses, showing how these enjoy
two constants of motion even when rendered in terms of a non-invertible
transformation of parameters, leading to a transformed Volterra gyrostat (tVG).
If the quadratic coefficients of the tVG sum to zero, as they do for the VG,
the system conserves energy. In all of these cases, the flows preserve volume;
however, physical models where the quadratic coefficients do not sum to zero
are ubiquitous, and characterization of constants of motion and the resulting
dynamics for this more general class of models with volume conservation but
without energy conservation is lacking. We provide such a characterization for
each of the subclasses. Those with three linear feedback terms have no
constants of motion, and thereby admit rich transient dynamics. The asymptotic
dynamics of these more general models nonetheless appears simple, with
attractors being either fixed points or periodic orbits.


title:
A Constructive Proof that Many Groups with Non-Torsion 2-Cohomology are
  Not Matricially Stable

abstract:
  A discrete group is matricially stable if every function from the group to a
complex unitary group that is "almost multiplicative" in the point-operator
norm topology is "close" to a genuine unitary representation. It follows from a
recent result due to Dadarlat that all amenable, groups with non-torsion
integral 2-cohomology are not matricially stable, but the proof does not lead
to explicit examples of asymptotic representations that are not perturbable to
genuine representations. The purpose of this paper is to give an explicit
formula, in terms of cohomological data, for asymptotic representations that
are not perturbable to genuine representations for a class of groups that
contains all finitely generated groups with a non-torsion 2-cohomology class
that corresponds to a central extension where the middle group is residually
finite. This class includes polycyclic groups with non-torsion 2-cohomology.