title: Dark Matter In Extreme Astrophysical Environments

abstract:
  Exploring dark matter via observations of extreme astrophysical environments
-- defined here as heavy compact objects such as white dwarfs, neutron stars,
and black holes, as well as supernovae and compact object merger events -- has
been a major field of growth since the last Snowmass process. Theoretical work
has highlighted the utility of current and near-future observatories to
constrain novel dark matter parameter space across the full mass range. This
includes gravitational wave instruments and observatories spanning the
electromagnetic spectrum, from radio to gamma-rays. While recent searches
already provide leading sensitivity to various dark matter models, this work
also highlights the need for theoretical astrophysics research to better
constrain the properties of these extreme astrophysical systems. The unique
potential of these search signatures to probe dark matter adds motivation to
proposed next-generation astronomical and gravitational wave instruments.


title: Long-range connections, real-world networks and rates of diffusion

abstract:
  Long range connections play an essential role in dynamical processes on
networks, on the processing of information in biological networks, on the
structure of social and economical networks and in the propagation of opinions
and epidemics. Here we review the evidence for long range connections in real
world networks and discuss the nature of the nonlocal diffusion arising from
different distance-dependent laws. Particular attention is devoted to
exponential and power laws.


title: Distributed Pinning Set Stabilization of Large-Scale Boolean Networks

abstract:
  In this article, we design the distributed pinning controllers to globally
stabilize a Boolean network (BN), specially a sparsely connected large-scale
one, towards a preassigned subset of state space through the node-to-node
message exchange. Given an appointed state set, system nodes are partitioned
into two disjoint parts, which respectively gather the nodes whose states are
fixed or arbitrary with respect to the given state set. With such node
division, three parts of pinned nodes are selected and the state feedback
controllers are accordingly designed such that the resulting BN satisfies three
conditions: the states of the other nodes cannot affect the nodal dynamics of
fixed-state nodes, the subgraph of network structure induced by the fixed-state
nodes is acyclic, and the steady state of the subnetwork induced by the
fixed-state nodes lies in the state set given beforehand. If the BN after
control is acyclic, the stabilizing time is revealed to be no more than the
length of the longest path in the current network structure plus one. This
enables us to further design the pinning controllers with the constraint of
stabilizing time. Noting that the overall procedure runs in an exponentially
increasing time with respect to the largest number of functional variables in
the dynamics of pinned nodes, the sparsely-connected large-scale BNs can be
well addressed in a reasonable amount of time. Finally, we demonstrate the
applications of our theoretical results in a T-LGL survival signal network with
$29$ nodes and T-cell receptor signaling network with $90$ nodes.


title: Study Overview for Super Proton-Proton Collider

abstract:
  SPPC (Super Proton-Proton Collider) is a discovery machine that is designed
for energy frontier research in two decades from now, as the second stage of
the CEPC-SPPC project. The main objective is to carry out experiments at 125
TeV in center-of-mass energy in a two-ring collider of 100 km in circumference
and 20 T in dipole field. This white paper about SPPC describes the machine
related issues, including performance, design overview, design challenges, key
technologies and their maturity and required R&D, staged options and upgrades,
synergies with other facilities, and environmental impacts.


title:
Smoothing Matters: Momentum Transformer for Domain Adaptive Semantic
  Segmentation

abstract:
  After the great success of Vision Transformer variants (ViTs) in computer
vision, it has also demonstrated great potential in domain adaptive semantic
segmentation. Unfortunately, straightforwardly applying local ViTs in domain
adaptive semantic segmentation does not bring in expected improvement. We find
that the pitfall of local ViTs is due to the severe high-frequency components
generated during both the pseudo-label construction and features alignment for
target domains. These high-frequency components make the training of local ViTs
very unsmooth and hurt their transferability. In this paper, we introduce a
low-pass filtering mechanism, momentum network, to smooth the learning dynamics
of target domain features and pseudo labels. Furthermore, we propose a dynamic
of discrepancy measurement to align the distributions in the source and target
domains via dynamic weights to evaluate the importance of the samples. After
tackling the above issues, extensive experiments on sim2real benchmarks show
that the proposed method outperforms the state-of-the-art methods. Our codes
are available at https://github.com/alpc91/TransDA


title: Approximability and Generalisation

abstract:
  Approximate learning machines have become popular in the era of small
devices, including quantised, factorised, hashed, or otherwise compressed
predictors, and the quest to explain and guarantee good generalisation
abilities for such methods has just begun. In this paper we study the role of
approximability in learning, both in the full precision and the approximated
settings of the predictor that is learned from the data, through a notion of
sensitivity of predictors to the action of the approximation operator at hand.
We prove upper bounds on the generalisation of such predictors, yielding the
following main findings, for any PAC-learnable class and any given
approximation operator. 1) We show that under mild conditions, approximable
target concepts are learnable from a smaller labelled sample, provided
sufficient unlabelled data. 2) We give algorithms that guarantee a good
predictor whose approximation also enjoys the same generalisation guarantees.
3) We highlight natural examples of structure in the class of sensitivities,
which reduce, and possibly even eliminate the otherwise abundant requirement of
additional unlabelled data, and henceforth shed new light onto what makes one
problem instance easier to learn than another. These results embed the scope of
modern model compression approaches into the general goal of statistical
learning theory, which in return suggests appropriate algorithms through
minimising uniform bounds.


title:
UofA-Truth at Factify 2022 : Transformer And Transfer Learning Based
  Multi-Modal Fact-Checking

abstract:
  Identifying fake news is a very difficult task, especially when considering
the multiple modes of conveying information through text, image, video and/or
audio. We attempted to tackle the problem of automated
misinformation/disinformation detection in multi-modal news sources (including
text and images) through our simple, yet effective, approach in the FACTIFY
shared task at De-Factify@AAAI2022. Our model produced an F1-weighted score of
74.807%, which was the fourth best out of all the submissions. In this paper we
will explain our approach to undertake the shared task.


title:
Rotating waves in nonlinear media and critical degenerate Sobolev
  inequalities

abstract:
  We investigate the presence of rotating wave solutions of the nonlinear wave
equation $\partial_t^2 v - \Delta v +m v = |v|^{p-2} v$ in $\mathbb{R} \times
\mathbf{B}$, where $\mathbf{B} \subset \mathbb{R}^N$ is the unit ball,
complemented with Dirichlet boundary conditions on $\mathbb{R} \times
\partial\mathbf{B}$. Depending on the prescribed angular velocity $\alpha$ of
the rotation, this leads to a Dirichlet problem for a semilinear elliptic or
degenerate elliptic equation. We show that this problem is governed by an
associated critical degenerate Sobolev inequality in the half space. After
proving this inequality and the existence of associated extremal functions, we
then deduce necessary and sufficient conditions for the existence of ground
state solutions. Moreover, we analyze under which conditions on $\alpha$, $m$
and $p$ these ground states are nonradial and therefore give rise to truly
rotating waves. Our approach carries over to the corresponding Dirichlet
problems in an annulus and in more general Riemannian models with boundary,
including the hemisphere. We briefly discuss these problems and show that they
are related to a larger family of associated critical degenerate Sobolev
inequalities.


title: Carleson Perturbations for the Regularity Problem

abstract:
  We prove that the solvability of the regularity problem in $L^q(\partial
\Omega)$ is stable under Carleson perturbations. If the perturbation is small,
then the solvability is preserved in the same $L^q$, and if the perturbation is
large, the regularity problem is solvable in $L^{r}$ for some other $r\in
(1,\infty)$. We extend an earlier result from Kenig and Pipher to very general
unbounded domains, possibly with lower dimensional boundaries as in the theory
developed by Guy David and the last two authors. To be precise, we only need
the domain to have non-tangential access to its Ahlfors regular boundary,
together with a notion of gradient on the boundary.


title:
RotateQVS: Representing Temporal Information as Rotations in Quaternion
  Vector Space for Temporal Knowledge Graph Completion

abstract:
  Temporal factors are tied to the growth of facts in realistic applications,
such as the progress of diseases and the development of political situation,
therefore, research on Temporal Knowledge Graph (TKG) attracks much attention.
In TKG, relation patterns inherent with temporality are required to be studied
for representation learning and reasoning across temporal facts. However,
existing methods can hardly model temporal relation patterns, nor can capture
the intrinsic connections between relations when evolving over time, lacking of
interpretability. In this paper, we propose a novel temporal modeling method
which represents temporal entities as Rotations in Quaternion Vector Space
(RotateQVS) and relations as complex vectors in Hamilton's quaternion space. We
demonstrate our method can model key patterns of relations in TKG, such as
symmetry, asymmetry, inverse, and can further capture time-evolved relations by
theory. Empirically, we show that our method can boost the performance of link
prediction tasks over four temporal knowledge graph benchmarks.


title:
Sensitivity to longitudinal vector boson scattering in $\mathbf{W^\pm
  W^\pm jj}$ at future hadron colliders

abstract:
  We study the sensitivity to longitudinal vector boson scattering at a 100 TeV
$pp$ collider using events containing two leptonically-decaying
same-electric-charge $W$ bosons produced in association with two jets.


title: Societal impacts of particle physics projects

abstract:
  Large particle physics projects funded by the U.S. Government require an
evaluation and mitigation of each project's potential impacts on the local
communities. However, beyond meeting governmental requirements, particle
physics projects stand to play an essential role in local decision-making,
building relationships, and framing discussions about key projects by becoming
meaningfully engaged in their local communities. In this white paper for the
U.S. Particle Physics Community Planning Exercise (Snowmass), we examine
several local community engagement efforts made by three facilities: Lawrence
Berkeley National Laboratory (Berkeley Lab), Fermi National Accelerator
Laboratory (Fermilab), and the Sanford Underground Research Facility (SURF).
Although each facility focuses on a different endeavor in varying types of
communities, each study highlights the importance and benefits of employing
consistent outreach techniques, promoting diversity, establishing lasting
relationships, and creating environments for open and honest communication.


title:
Leveraging Unimodal Self-Supervised Learning for Multimodal Audio-Visual
  Speech Recognition

abstract:
  Training Transformer-based models demands a large amount of data, while
obtaining aligned and labelled data in multimodality is rather cost-demanding,
especially for audio-visual speech recognition (AVSR). Thus it makes a lot of
sense to make use of unlabelled unimodal data. On the other side, although the
effectiveness of large-scale self-supervised learning is well established in
both audio and visual modalities, how to integrate those pre-trained models
into a multimodal scenario remains underexplored. In this work, we successfully
leverage unimodal self-supervised learning to promote the multimodal AVSR. In
particular, audio and visual front-ends are trained on large-scale unimodal
datasets, then we integrate components of both front-ends into a larger
multimodal framework which learns to recognize parallel audio-visual data into
characters through a combination of CTC and seq2seq decoding. We show that both
components inherited from unimodal self-supervised learning cooperate well,
resulting in that the multimodal framework yields competitive results through
fine-tuning. Our model is experimentally validated on both word-level and
sentence-level tasks. Especially, even without an external language model, our
proposed model raises the state-of-the-art performances on the widely accepted
Lip Reading Sentences 2 (LRS2) dataset by a large margin, with a relative
improvement of 30%.


title: Inverted Pyramid Multi-task Transformer for Dense Scene Understanding

abstract:
  Multi-task dense scene understanding is a thriving research domain that
requires simultaneous perception and reasoning on a series of correlated tasks
with pixel-wise prediction. Most existing works encounter a severe limitation
of modeling in the locality due to heavy utilization of convolution operations,
while learning interactions and inference in a global spatial-position and
multi-task context is critical for this problem. In this paper, we propose a
novel end-to-end Inverted Pyramid multi-task (InvPT) Transformer to perform
simultaneous modeling of spatial positions and multiple tasks in a unified
framework. To the best of our knowledge, this is the first work that explores
designing a transformer structure for multi-task dense prediction for scene
understanding. Besides, it is widely demonstrated that a higher spatial
resolution is remarkably beneficial for dense predictions, while it is very
challenging for existing transformers to go deeper with higher resolutions due
to huge complexity to large spatial size. InvPT presents an efficient
UP-Transformer block to learn multi-task feature interaction at gradually
increased resolutions, which also incorporates effective self-attention message
passing and multi-scale feature aggregation to produce task-specific prediction
at a high resolution. Our method achieves superior multi-task performance on
NYUD-v2 and PASCAL-Context datasets respectively, and significantly outperforms
previous state-of-the-arts. Code and trained models will be publicly available.


title:
Reinforcement Learning Framework for Server Placement and Workload
  Allocation in Multi-Access Edge Computing

abstract:
  Cloud computing is a reliable solution to provide distributed computation
power. However, real-time response is still challenging regarding the enormous
amount of data generated by the IoT devices in 5G and 6G networks. Thus,
multi-access edge computing (MEC), which consists of distributing the edge
servers in the proximity of end-users to have low latency besides the higher
processing power, is increasingly becoming a vital factor for the success of
modern applications. This paper addresses the problem of minimizing both, the
network delay, which is the main objective of MEC, and the number of edge
servers to provide a MEC design with minimum cost. This MEC design consists of
edge servers placement and base stations allocation, which makes it a joint
combinatorial optimization problem (COP). Recently, reinforcement learning (RL)
has shown promising results for COPs. However, modeling real-world problems
using RL when the state and action spaces are large still needs investigation.
We propose a novel RL framework with an efficient representation and modeling
of the state space, action space and the penalty function in the design of the
underlying Markov Decision Process (MDP) for solving our problem.


title:
MSCET: A Multi-Scenario Offloading Schedule for Biomedical Data
  Processing and Analysis in Cloud-Edge-Terminal Collaborative Vehicular
  Networks

abstract:
  With the rapid development of Artificial Intelligence (AI) and Internet of
Things (IoTs), an increasing number of computation intensive or delay sensitive
biomedical data processing and analysis tasks are produced in vehicles,
bringing more and more challenges to the biometric monitoring of drivers. Edge
computing is a new paradigm to solve these challenges by offloading tasks from
the resource-limited vehicles to Edge Servers (ESs) in Road Side Units (RSUs).
However, most of the traditional offloading schedules for vehicular networks
concentrate on the edge, while some tasks may be too complex for ESs to
process. To this end, we consider a collaborative vehicular network in which
the cloud, edge and terminal can cooperate with each other to accomplish the
tasks. The vehicles can offload the computation intensive tasks to the cloud to
save the resource of edge. We further construct the virtual resource pool which
can integrate the resource of multiple ESs since some regions may be covered by
multiple RSUs. In this paper, we propose a Multi-Scenario offloading schedule
for biomedical data processing and analysis in Cloud-Edge-Terminal
collaborative vehicular networks called MSCET. The parameters of the proposed
MSCET are optimized to maximize the system utility. We also conduct extensive
simulations to evaluate the proposed MSCET and the results illustrate that
MSCET outperforms other existing schedules.


title: Enriques surfaces of non-degeneracy 3

abstract:
  We classify all non-extendable 3-sequences of half-fibers on Enriques
surfaces. If the characteristic is different from 2, we prove in particular
that every Enriques surface admits a 4-sequence, which implies that every
Enriques surface is the minimal desingularization of an Enriques sextic, and
that every Enriques surface is birational to a Castelnuovo quintic.


title:
Early-Time Ultraviolet Spectroscopy and Optical Follow-up Observations
  of the Type IIP Supernova 2021yja

abstract:
  We present three epochs of early-time ultraviolet (UV) and optical HST/STIS
spectroscopy of the young, nearby Type IIP supernova (SN) 2021yja. The HST and
two Swift UVOT spectra are consistent with those of other well-studied Type IIP
supernovae (SNe). The UV spectra exhibit rapid cooling at early times, while
less dramatic changes are seen in the optical. We also present Lick/KAIT
optical photometry up to the late-time-tail phase, showing a very long plateau
and shallow decline compared with other SNe IIP. Our modeling of the UV
spectrum with the TARDIS radiative-transfer code produces a good fit for a
high-velocity explosion, a low total extinction $E(B-V) = 0.07$ mag, and a
subsolar metallicity. We do not find a significant contribution to the UV flux
from an additional heating source, such as interaction with the circumstellar
medium, consistent with the observed flat plateau. Furthermore, the velocity
width of the Mg II $\lambda$2798 line is comparable to that of the hydrogen
Balmer lines, suggesting that the UV emission is confined to a region close to
the photosphere.


title: Quantum Parameterized Complexity

abstract:
  Parameterized complexity theory was developed in the 1990s to enrich the
complexity-theoretic analysis of problems that depend on a range of parameters.
In this paper we establish a quantum equivalent of classical parameterized
complexity theory, motivated by the need for new tools for the classifications
of the complexity of real-world problems. We introduce the quantum analogues of
a range of parameterized complexity classes and examine the relationship
between these classes, their classical counterparts, and well-studied problems.
This framework exposes a rich classification of the complexity of parameterized
versions of QMA-hard problems, demonstrating, for example, a clear separation
between the Quantum Circuit Satisfiability problem and the Local Hamiltonian
problem.


title: The Spectral Geometry of de Sitter Space in Asymptotic Safety

abstract:
  Within the functional renormalization group approach to Background
Independent quantum gravity, we explore the scale dependent effective geometry
of the de Sitter solution dS${}_4$. The investigation employs a novel approach
whose essential ingredient is a modified spectral flow of the metric dependent
d'Alembertian, or of similar hyperbolic kinetic operators. The corresponding
one-parameter family of spectra and eigenfunctions encodes information about
the nonperturbative backreaction of the dynamically gravitating vacuum
fluctuations on the mean field geometry of the quantum spacetime. Used as a
diagnostic tool, the power of the spectral flow method resides in its ability
to identify the scale dependent subsets of field modes that supply the degrees
of freedom which participate in the effective field theory description of the
respective scale. A central result is that the ultraviolet of Quantum Einstein
Gravity comprises far less effective degrees of freedom than predicted
(incorrectly) by background dependent reasoning. Exploring the quantum
spacetime's spatial geometry carried by physical fields, we find that
3-dimensional space disintegrates into a collection of coherent patches which
individually can, but in their entirety cannot be described by one of the
effective average actions occurring along the renormalization group trajectory.
A natural concept of an entropy is introduced in order to quantify this
fragmentation effect. Tentatively applied to the real Universe, surprising
analogies to properties of the observed cosmic microwave background are
uncovered. Furthermore, a set of distinguished field modes is found which, in
principle, has the ability to transport information about the asymptotic fixed
point regime from the ultraviolet, across almost the entire "scale history", to
cosmological distances in the observed Universe.


title:
Dynamical localization and many-body localization in periodically kicked
  quasiperiodic lattices

abstract:
  We study the combined effect of quasiperiodic disorder, driven and
interaction in the periodically kicked Aubry-Andr\'{e} model. In the
non-interacting limit, by analyzing the quasienergy spectrum statistics, we
verify the existence of a dynamical localization transition in the
high-frequency region, whereas we find the spectrum statistics becoming
intricate with no abrupt transition occurring in the low-frequency region due
to the emergence of the mobility edges in the quasienergy spectrum, which
separate the multifractal states from the localized (extended) states. When the
interaction is introduced, we find the periodically kicked incommensurate
potential can lead to a transition from ergodic to many-body-localization phase
in the high-frequency region. However, the many-body localization phase
vanishes in the low-frequency region even for strong quasiperiodic disorder.
Our studies demonstrate that the periodically kicked Aubry-Andr\'{e} model
displays rich dynamical phenomena and the driving frequency plays an important
role in the formation of many-body localization.


title: Coindex and rigidity of Einstein metrics on homogeneous Gray manifolds

abstract:
  Any $6$-dimensional strict nearly K\"ahler manifold is Einstein with positive
scalar curvature. We compute the coindex of the metric with respect to the
Einstein-Hilbert functional on each of the compact homogeneous examples.
Moreover, we show that the infinitesimal Einstein deformations on
$F_{1,2}=\mathrm{SU}(3)/T^2$ are not integrable into a curve of Einstein
metrics.


title: Estimating monotone densities by cellular binary trees

abstract:
  We propose a novel, simple density estimation algorithm for bounded monotone
densities with compact support under a cellular restriction. We show that its
expected error ($L_1$ distance) converges at a rate of $n^{-1/3}$, that its
expected runtime is sublinear and, in doing so, find a connection to the theory
of Galton--Watson processes.


title: Data Smells in Public Datasets

abstract:
  The adoption of Artificial Intelligence (AI) in high-stakes domains such as
healthcare, wildlife preservation, autonomous driving and criminal justice
system calls for a data-centric approach to AI. Data scientists spend the
majority of their time studying and wrangling the data, yet tools to aid them
with data analysis are lacking. This study identifies the recurrent data
quality issues in public datasets. Analogous to code smells, we introduce a
novel catalogue of data smells that can be used to indicate early signs of
problems or technical debt in machine learning systems. To understand the
prevalence of data quality issues in datasets, we analyse 25 public datasets
and identify 14 data smells.


title:
Beyond Explaining: Opportunities and Challenges of XAI-Based Model
  Improvement

abstract:
  Explainable Artificial Intelligence (XAI) is an emerging research field
bringing transparency to highly complex and opaque machine learning (ML)
models. Despite the development of a multitude of methods to explain the
decisions of black-box classifiers in recent years, these tools are seldomly
used beyond visualization purposes. Only recently, researchers have started to
employ explanations in practice to actually improve models. This paper offers a
comprehensive overview over techniques that apply XAI practically for improving
various properties of ML models, and systematically categorizes these
approaches, comparing their respective strengths and weaknesses. We provide a
theoretical perspective on these methods, and show empirically through
experiments on toy and realistic settings how explanations can help improve
properties such as model generalization ability or reasoning, among others. We
further discuss potential caveats and drawbacks of these methods. We conclude
that while model improvement based on XAI can have significant beneficial
effects even on complex and not easily quantifyable model properties, these
methods need to be applied carefully, since their success can vary depending on
a multitude of factors, such as the model and dataset used, or the employed
explanation method.


title:
Text-free non-parallel many-to-many voice conversion using normalising
  flows

abstract:
  Non-parallel voice conversion (VC) is typically achieved using lossy
representations of the source speech. However, ensuring only speaker identity
information is dropped whilst all other information from the source speech is
retained is a large challenge. This is particularly challenging in the scenario
where at inference-time we have no knowledge of the text being read, i.e.,
text-free VC. To mitigate this, we investigate information-preserving VC
approaches.
  Normalising flows have gained attention for text-to-speech synthesis, however
have been under-explored for VC. Flows utilize invertible functions to learn
the likelihood of the data, thus provide a lossless encoding of speech. We
investigate normalising flows for VC in both text-conditioned and text-free
scenarios. Furthermore, for text-free VC we compare pre-trained and
jointly-learnt priors. Flow-based VC evaluations show no degradation between
text-free and text-conditioned VC, resulting in improvements over the
state-of-the-art. Also, joint-training of the prior is found to negatively
impact text-free VC quality.


title: Analysis Facilities for HL-LHC

abstract:
  The HL-LHC presents significant challenges for the HEP analysis community.
The number of events in each analysis is expected to increase by an order of
magnitude and new techniques are expected to be required; both challenges
necessitate new services and approaches for analysis facilities. These services
are expected to provide new capabilities, a larger scale, and different access
modalities (complementing -- but distinct from -- traditional batch-oriented
approaches). To facilitate this transition, the US-LHC community is actively
investing in analysis facilities to provide a testbed for those developing new
analysis systems and to demonstrate new techniques for service delivery. This
whitepaper outlines the existing activities within the US LHC community in this
R&D area, the short- to medium-term goals, and the outline of common goals and
milestones.


title:
Approximate Decision Trees For Machine Learning Classification on Tiny
  Printed Circuits

abstract:
  Although Printed Electronics (PE) cannot compete with silicon-based systems
in conventional evaluation metrics, e.g., integration density, area and
performance, PE offers attractive properties such as on-demand ultra-low-cost
fabrication, flexibility and non-toxicity. As a result, it targets application
domains that are untouchable by lithography-based silicon electronics and thus
have not yet seen much proliferation of computing. However, despite the
attractive characteristics of PE, the large feature sizes in PE prohibit the
realization of complex printed circuits, such as Machine Learning (ML)
classifiers. In this work, we exploit the hardware-friendly nature of Decision
Trees for machine learning classification and leverage the hardware-efficiency
of the approximate design in order to generate approximate ML classifiers that
are suitable for tiny, ultra-resource constrained, and battery-powered printed
applications.


title: A refinement of ternary Boolean algebras

abstract:
  An algebraic structure with two constants and one ternary operation, which is
not completely commutative, is put forward to accommodate ternary Boolean
algebras. When the ternary operation is interpreted as Church's conditioned
disjunction, Boolean algebras are characterized as a subvariety. Different
interpretations for the ternary operation lead to distinct subvarieties. Rings
and near-rings of characteristic 2 are used to illustrate the procedure.


title:
End-to-End Modeling via Information Tree for One-Shot Natural Language
  Spatial Video Grounding

abstract:
  Natural language spatial video grounding aims to detect the relevant objects
in video frames with descriptive sentences as the query. In spite of the great
advances, most existing methods rely on dense video frame annotations, which
require a tremendous amount of human effort. To achieve effective grounding
under a limited annotation budget, we investigate one-shot video grounding, and
learn to ground natural language in all video frames with solely one frame
labeled, in an end-to-end manner. One major challenge of end-to-end one-shot
video grounding is the existence of videos frames that are either irrelevant to
the language query or the labeled frames. Another challenge relates to the
limited supervision, which might result in ineffective representation learning.
To address these challenges, we designed an end-to-end model via Information
Tree for One-Shot video grounding (IT-OS). Its key module, the information
tree, can eliminate the interference of irrelevant frames based on branch
search and branch cropping techniques. In addition, several self-supervised
tasks are proposed based on the information tree to improve the representation
learning under insufficient labeling. Experiments on the benchmark dataset
demonstrate the effectiveness of our model.


title: How Unequally Heavy Are the Tails of the Distributions of Income Growth?

abstract:
  Common moment-based measures of earnings risk, including the variance,
skewness, and kurtosis, may be undefined in population under heavy-tailed
distributions. In this light, we propose conditional Pareto exponents as novel
measures of earnings risk that are robust against non-existence of moments, and
develop estimation and inference methods for them. Using these measures with an
administrative data set for the UK, the New Earnings Survey Panel Dataset
(NESPD), and the US Panel Study of Income Dynamics (PSID), we quantify the tail
heaviness of the conditional distributions of earnings changes given age,
gender, and past earnings. Our main findings are that: 1) the population
kurtosis, skewness, and even variance may fail to exist for the conditional
distribution of earnings growth; 2) earnings risk is increasing over the life
cycle; 3) job stayers are more vulnerable to earnings risk, and 4) these
patterns appear in both the period 2007-2008 of great recession and the period
2015-2016 of a positive growth among others.


title: On-the-fly Strategy Adaptation for ad-hoc Agent Coordination

abstract:
  Training agents in cooperative settings offers the promise of AI agents able
to interact effectively with humans (and other agents) in the real world.
Multi-agent reinforcement learning (MARL) has the potential to achieve this
goal, demonstrating success in a series of challenging problems. However,
whilst these advances are significant, the vast majority of focus has been on
the self-play paradigm. This often results in a coordination problem, caused by
agents learning to make use of arbitrary conventions when playing with
themselves. This means that even the strongest self-play agents may have very
low cross-play with other agents, including other initializations of the same
algorithm. In this paper we propose to solve this problem by adapting agent
strategies on the fly, using a posterior belief over the other agents'
strategy. Concretely, we consider the problem of selecting a strategy from a
finite set of previously trained agents, to play with an unknown partner. We
propose an extension of the classic statistical technique, Gibbs sampling, to
update beliefs about other agents and obtain close to optimal ad-hoc
performance. Despite its simplicity, our method is able to achieve strong
cross-play with unseen partners in the challenging card game of Hanabi,
achieving successful ad-hoc coordination without knowledge of the partner's
strategy a priori.


title: Formalising Decentralised Exchanges in Coq

abstract:
  The number of attacks and accidents leading to significant losses of
crypto-assets is growing. According to Chainalysis, in 2021, approx. $14
billion has been lost due to various incidents, and this number is dominated by
Decentralized Finance (DeFi) applications. In order to address these issues,
one can use a collection of tools ranging from auditing to formal methods. We
use formal verification and provide the first formalisation of a DeFi contract
in a foundational proof assistant capturing contract interactions. We focus on
Dexter2, a decentralized, non-custodial exchange for the Tezos network similar
to Uniswap on Ethereum. The Dexter implementation consists of several smart
contracts. This poses unique challenges for formalisation due to the complex
contract interactions. Our formalisation includes proofs of functional
correctness with respect to an informal specification for the contracts
involved in Dexter's implementation. Moreover, our formalisation is the first
to feature proofs of safety properties of the interacting smart contracts of a
decentralized exchange. We have extracted our contract from Coq into CameLIGO
code, so it can be deployed on the Tezos blockchain. Uniswap and Dexter are
paradigmatic for a collection of similar contracts. Our methodology thus allows
us to implement and verify DeFi applications featuring similar interaction
patterns.


title:
Shallow Transits -- Deep Learning II: Identify Individual Exoplanetary
  Transits in Red Noise using Deep Learning

abstract:
  In a previous paper, we have introduced a deep learning neural network that
should be able to detect the existence of very shallow periodic planetary
transits in the presence of red noise. The network in that feasibility study
would not provide any further details about the detected transits. The current
paper completes this missing part. We present a neural network that tags
samples that were obtained during transits. This is essentially similar to the
task of identifying the semantic context of each pixel in an image -- an
important task in computer vision, called `semantic segmentation', which is
often performed by deep neural networks. The neural network we present makes
use of novel deep learning concepts such as U-Nets, Generative Adversarial
Networks (GAN), and adversarial loss. The resulting segmentation should allow
further studies of the light curves which are tagged as containing transits.
This approach towards the detection and study of very shallow transits is bound
to play a significant role in future space-based transit surveys such as PLATO,
which are specifically aimed to detect those extremely difficult cases of
long-period shallow transits. Our segmentation network also adds to the growing
toolbox of deep learning approaches which are being increasingly used in the
study of exoplanets, but so far mainly for vetting transits, rather than their
initial detection.


title: Algebraic $K$-theory and algebraic cobordism of almost mathematics

abstract:
  Faltings and Gabber--Ramero introduced almost mathematics. In another way,
Quillen's unpublished note "Modules theory over nonunital rings" characterized
that almost mathematics is derived from the bilocalization abelian category of
modules. Applying the concept of Quillen's bilocalization to Gabber and
Ramero's work, we establish the almost version of algebraic $K$-theory and
cobordism and prove that the $K$-theory of almost perfect complexes is
equivalent to the Thomason--Troubauth $K$-theory whose weak equivalences are
almost quasi-isomorphisms. Furthermore, we obtain that almost algebraic
cobordism holds tilting equivalence on the category of zero-section stable
integral perfectoid algebras with finite syntomic topology.


title:
Optimal Admission Control for Multiclass Queues with Time-Varying
  Arrival Rates via State Abstraction

abstract:
  We consider a novel queuing problem where the decision-maker must choose to
accept or reject randomly arriving tasks into a no buffer queue which are
processed by $N$ identical servers. Each task has a price, which is a positive
real number, and a class. Each class of task has a different price distribution
and service rate, and arrives according to an inhomogenous Poisson process. The
objective is to decide which tasks to accept so that the total price of tasks
processed is maximised over a finite horizon. We formulate the problem as a
discrete time Markov Decision Process (MDP) with a hybrid state space. We show
that the optimal value function has a specific structure, which enables us to
solve the hybrid MDP exactly. Moreover, we prove that as the time step is
reduced, the discrete time solution approaches the optimal solution to the
original continuous time problem. To improve the scalability of our approach to
a greater number of task classes, we present an approximation based on state
abstraction. We validate our approach on synthetic data, as well as a real
financial fraud data set, which is the motivating application for this work.


title: Geometric reconstructions of density based clusterings

abstract:
  DBSCAN* and HDBSCAN* are well established density based clustering
algorithms. However, obtaining the clusters of very large datasets is
infeasible, limiting their use in real world applications.
  By exploiting the geometry of Euclidean space, we prove that it is possible
to systematically construct the DBSCAN* and HDBSCAN* clusters of a finite
$X\subset \mathbb{R}^n$ from specific subsets of $X$. We are able to control
the size of these subsets and therefore our results make it possible to cluster
very large datasets.
  To illustrate our theory, we cluster the Microsoft Building Footprint
Database of the US, which is not possible using the standard implementations.


title: Interpretable machine learning in Physics

abstract:
  Adding interpretability to multivariate methods creates a powerful synergy
for exploring complex physical systems with higher order correlations while
bringing about a degree of clarity in the underlying dynamics of the system.


title:
Simultaneous measurement of proton and lepton kinematics in
  quasielastic-like $\nu_{\mu}$-hydrocarbon interactions from 2 to 20 GeV

abstract:
  Neutrino charged-current quasielastic-like scattering, a reaction category
extensively used in neutrino oscillation measurements, probes nuclear effects
that govern neutrino-nucleus interactions. This Letter reports the first
measurement of the triple-differential cross section for $\nu_{\mu}$
quasielastic-like reactions using the hydrocarbon medium of the MINERvA
detector exposed to a wide-band beam spanning 2 $\leq$ E$_\nu \leq$ 20 GeV. The
measurement maps the correlations among transverse and longitudinal muon
momenta and summed proton kinetic energies, and compares them to predictions
from a state-of-art simulation. Discrepancies are observed that likely reflect
shortfalls with modeling of pion and nucleon intranuclear scattering and/or
spectator nucleon ejection from struck nuclei. The separate determination of
leptonic and hadronic variables can inform experimental approaches to
neutrino-energy estimation.


title: Entanglement is more than transitive

abstract:
  One of the goals of science is to understand the relation between a whole and
its parts, as exemplified by the problem of certifying the entanglement of a
system from the knowledge of its reduced states. Here, we focus on a different
but related question: can a collection of marginal information reveal new
marginal information? We answer this affirmatively, and show that (non-)
entangled marginal states may exhibit (meta)transitivity of entanglement, i.e.,
imply that a different target marginal must be entangled. By showing that the
global $n$-qubit state compatible with certain two-qubit marginals in a tree
form is unique, we prove that transitivity exists for a system involving an
arbitrarily large number of qubits. We also completely characterize when
(meta)transitivity can occur in a tripartite scenario when the two-qudit
marginals given are either the Werner states or the isotropic states. Our
numerical results suggest that in the tripartite scenario, entanglement
transitivity is generic among the marginals derived from pure states.


title: Snowmass 2021 CMB-S4 White Paper

abstract:
  This Snowmass 2021 White Paper describes the Cosmic Microwave Background
Stage 4 project CMB-S4, which is designed to cross critical thresholds in our
understanding of the origin and evolution of the Universe, from the highest
energies at the dawn of time through the growth of structure to the present
day. We provide an overview of the science case, the technical design, and
project plan.

categories: astro-ph.CO astro-ph.IM gr-qc hep-ex hep-ph


title: Sketching 1-D stable manifolds of 2-D maps without the inverse

abstract:
  Saddle fixed points are the centerpieces of complicated dynamics in a system.
The one-dimensional stable and unstable manifolds of these saddle-points are
crucial to understanding the dynamics of such systems. While the problem of
sketching the unstable manifold is simple, plotting the stable manifold is not
as easy. Several algorithms exist to compute the stable manifold of
saddle-points, but they have their limitations, especially when the system is
not invertible. In this paper, we present a new algorithm to compute the stable
manifold of 2-dimensional systems which can also be used for non-invertible
systems. After outlining the logic of the algorithm, we demonstrate the output
of the algorithm on several examples.


title: Some open questions in axion theory

abstract:
  This white paper collects some open questions in several different aspects of
axion theories. The questions are related to quantization of axion couplings,
axion-magnetic monopole systems, axions in quantum gravity theory, axion
string/domain wall systems, and thermal friction from axion couplings. They
demonstrate many opportunities for axion theory, which call for more studies.


title:
Natural Hierarchical Cluster Analysis by Nearest Neighbors with
  Near-Linear Time Complexity

abstract:
  We propose a nearest neighbor based clustering algorithm that results in a
naturally defined hierarchy of clusters. In contrast to the agglomerative and
divisive hierarchical clustering algorithms, our approach is not dependent on
the iterative working of the algorithm, in the sense that the partitions of the
hierarchical clusters are purely defined in accordance with the input dataset.
Our method is a universal hierarchical clustering approach since it can be
implemented as bottom up or top down versions, both of which result in the same
clustering. We show that for certain types of datasets, our algorithm has
near-linear time and space complexity.


title:
Thermodynamic Driving Forces for Substrate Atom Extraction by Adsorption
  of Strong Electron Acceptor Molecules

abstract:
  A quantitative structural investigation is reported, aimed at resolving the
issue of whether substrate adatoms are incorporated into the monolayers formed
by strong molecular electron acceptors deposited onto metallic electrodes. A
combination of normal-incidence X-ray standing waves, low energy electron
diffraction, scanning tunnelling microscopy and X-ray photoelectron
spectroscopy measurements demonstrate that the systems TCNQ and F4TCNQ on
Ag(100) lie at the boundary between these two possibilities and thus represent
ideal model systems with which to study this effect. A room-temperature
commensurate phase of adsorbed TCNQ is found not to involve Ag adatoms, but to
adopt an inverted bowl configuration, long predicted but not previously
identified experimentally. By contrast, a similar phase of adsorbed F4TCNQ does
lead to Ag adatom incorporation in the overlayer, the cyano endgroups of the
molecule being twisted relative to the planar quinoid ring. Density functional
theory (DFT) calculations show that this behaviour is consistent with the
adsorption energetics. Annealing of the commensurate TCNQ overlayer phase leads
to an incommensurate phase that does appear to incorporate Ag adatoms. Our
results indicate that the inclusion (or exclusion) of metal atoms into the
organic monolayers is the result of both thermodynamic and kinetic factors.


title:
Optimal dispatch schedule for a fast EV charging station with account to
  supplementary battery health degradation

abstract:
  This paper investigates the usage of battery storage systems in a fast
charging station (FCS) for participation in energy markets and charging
electrical vehicles (EVs) simultaneously. In particular, we focus on optimizing
the scheduling strategies to reduce the overall operational cost of the system
over its lifetime by combining the model of battery degradation and energy
arbitrage. We implement the battery degradation as a penalty term within an
energy arbitrage model and show that the battery degradation plays an important
role in the optimal energy dispatch scheduling of the FCS system. In this case
study, with different penalty coefficients for the battery degradation penalty
term, it is found that including the penalty of battery usage in the scheduling
model will reduce the number of small charging/discharging cycles, thereby
prolonging the battery lifetime, while maintaining near optimal revenue from
grid services.


title: The Gaussian Part of a Compact Quantum Group

abstract:
  We introduce the Gaussian part of a compact quantum group $\mathbb{G}$,
namely the largest quantum subgroup of $\mathbb{G}$ supporting all the Gaussian
functionals of $\mathbb{G}$. We prove that the Gaussian part is always
contained in the Kac part, and characterise Gaussian parts of classical compact
groups, duals of classical discrete groups and $q$-deformations of compact Lie
groups. The notion turns out to be related to a new concept of "strong
connectedness" and we exhibit several examples of both strongly connected and
totally strongly disconnected compact quantum groups.


title: Data-Efficient Graph Grammar Learning for Molecular Generation

abstract:
  The problem of molecular generation has received significant attention
recently. Existing methods are typically based on deep neural networks and
require training on large datasets with tens of thousands of samples. In
practice, however, the size of class-specific chemical datasets is usually
limited (e.g., dozens of samples) due to labor-intensive experimentation and
data collection. This presents a considerable challenge for the deep learning
generative models to comprehensively describe the molecular design space.
Another major challenge is to generate only physically synthesizable molecules.
This is a non-trivial task for neural network-based generative models since the
relevant chemical knowledge can only be extracted and generalized from the
limited training data. In this work, we propose a data-efficient generative
model that can be learned from datasets with orders of magnitude smaller sizes
than common benchmarks. At the heart of this method is a learnable graph
grammar that generates molecules from a sequence of production rules. Without
any human assistance, these production rules are automatically constructed from
training data. Furthermore, additional chemical knowledge can be incorporated
in the model by further grammar optimization. Our learned graph grammar yields
state-of-the-art results on generating high-quality molecules for three monomer
datasets that contain only ${\sim}20$ samples each. Our approach also achieves
remarkable performance in a challenging polymer generation task with only $117$
training samples and is competitive against existing methods using $81$k data
points. Code is available at https://github.com/gmh14/data_efficient_grammar.


title: Thermal transport through magnetic ordering transitions of MnBi2Te4

abstract:
  MnBi2Te4 is an intriguing material that integrates the spin-orbit topology
and magnetism, leading to a rich magnetic phase diagram. Below the Neel
temperature TN = 25 K, MnBi2Te4 is antiferromagnetic (AFM); when an increasing
magnetic field is applied perpendicular to the layers, the magnetic ordering
undergoes a spin-flop transition followed by a canted-antiferromagnetic (CAFM)
ordering and finally into a ferromagnetic (FM) ordering. Here, we evaluate the
field dependence of thermal conductivity, k, in the in-plane direction under an
applied magnetic field along the cross-plane direction in MnBi2Te4 from 2K to
30K. k decreases with field in the AFM phase, saturates in the CAFM phase and
increases with field in the FM phase. The magnon dispersion for each magnetic
ordering was calculated and provided explanations for behaviors of thermal
conductivity, based on the calculated overlap between acoustic phonon
dispersions and the field dependence of the magnon dispersions. We also report
magneto-Seebeck coefficient, Nernst coefficient and thermal Hall data measured
in the same configuration.


title: A Muon Collider Facility for Physics Discovery

abstract:
  Muon colliders provide a unique route to deliver high energy collisions that
enable discovery searches and precision measurements to extend our
understanding of the fundamental laws of physics. The muon collider design aims
to deliver physics reach at the highest energies with costs, power consumption
and on a time scale that may prove favorable relative to other proposed
facilities. In this context, a new international collaboration has formed to
further extend the design concepts and performance studies of such a machine.
This effort is focused on delivering the elements of a $\sim$10 TeV center of
mass (CM) energy design to explore the physics energy frontier. The path to
such a machine may pass through lower energy options. Currently a 3 TeV CM
stage is considered. Other energy stages could also be explored, e.g. an
s-channel Higgs Factory operating at 125 GeV CM. We describe the status of the
R&D and design effort towards such a machine and lay out a plan to bring these
concepts to maturity as a tool for the high energy physics community.


title: A Noise-level-aware Framework for PET Image Denoising

abstract:
  In PET, the amount of relative (signal-dependent) noise present in different
body regions can be significantly different and is inherently related to the
number of counts present in that region. The number of counts in a region
depends, in principle and among other factors, on the total administered
activity, scanner sensitivity, image acquisition duration, radiopharmaceutical
tracer uptake in the region, and patient local body morphometry surrounding the
region. In theory, less amount of denoising operations is needed to denoise a
high-count (low relative noise) image than images a low-count (high relative
noise) image, and vice versa. The current deep-learning-based methods for PET
image denoising are predominantly trained on image appearance only and have no
special treatment for images of different noise levels. Our hypothesis is that
by explicitly providing the local relative noise level of the input image to a
deep convolutional neural network (DCNN), the DCNN can outperform itself
trained on image appearance only. To this end, we propose a noise-level-aware
framework denoising framework that allows embedding of local noise level into a
DCNN. The proposed is trained and tested on 30 and 15 patient PET images
acquired on a GE Discovery MI PET/CT system. Our experiments showed that the
increases in both PSNR and SSIM from our backbone network with relative noise
level embedding (NLE) versus the same network without NLE were statistically
significant with p<0.001, and the proposed method significantly outperformed a
strong baseline method by a large margin.


title:
High-Temperature Decomposition of Diisopropyl Methylphosphonate (DIMP)
  on Alumina: Mechanistic Predictions from Ab Initio Molecular Dynamics

abstract:
  The enhanced degradation of organophosphorous-based chemical warfare agents
(CWAs) on metal-oxide surfaces holds immense promise for neutralization
efforts; however, the underlying mechanisms in this process remain poorly
understood. We utilize large-scale quantum calculations for the first time to
probe the high-temperature degradation of diisopropyl methylphosphonate (DIMP),
a nerve agent simulant. Our Born-Oppenheimer molecular dynamics (BOMD)
calculations show that the $\gamma$-Al$_2$O$_3$ surface shows immense promise
for quickly adsorbing and destroying CWAs. We find that the alumina surface
quickly adsorbs DIMP at all temperatures, and subsequent decomposition of DIMP
proceeds via a propene elimination. Our BOMD calculations are complemented with
metadynamics simulations to produce free energy paths, which show that the
activation barrier decreases with temperature and DIMP readily decomposes on
$\gamma$-Al$_2$O$_3$. Our first-principle BOMD and metadynamics simulations
provide crucial diagnostics for sarin decomposition models and mechanistic
information for examining CWA decomposition reactions on other candidate metal
oxide surfaces.


title: Sensor Object Plausibilization with Boids Flocking Algorithm

abstract:
  Driver assistance systems are increasingly becoming part of the standard
equipment of vehicles and thus contribute to road safety. However, as they
become more widespread, the requirements for cost efficiency are also
increasing, and so few and inexpensive sensors are used in these systems.
Especially in challenging situations, this leads to the fact that target
discrimination cannot be ensured which in turn leads to a false reaction of the
driver assistance system. Typically, the interaction between moving traffic
participants is not modeled directly in the environmental model so that tracked
objects can split, merge or disappear. The Boids flocking algorithm is used to
model the interaction between road users on already tracked objects by applying
the movement rules (separation, cohesion, alignment) on the boids. This
facilitates the creation of semantic neighborhood information between road
users. We show in a comprehensive simulation that with only 7 boids per traffic
participant, the estimated median separatation between objects can improve from
2.4 m to 3 m for a ground truth of 3.7 m. The bottom percentile improves from
1.85 m to 2.8 m.


title: Interactive Robotic Grasping with Attribute-Guided Disambiguation

abstract:
  Interactive robotic grasping using natural language is one of the most
fundamental tasks in human-robot interaction. However, language can be a source
of ambiguity, particularly when there are ambiguous visual or linguistic
contents. This paper investigates the use of object attributes in
disambiguation and develops an interactive grasping system capable of
effectively resolving ambiguities via dialogues. Our approach first predicts
target scores and attribute scores through vision-and-language grounding. To
handle ambiguous objects and commands, we propose an attribute-guided
formulation of the partially observable Markov decision process (Attr-POMDP)
for disambiguation. The Attr-POMDP utilizes target and attribute scores as the
observation model to calculate the expected return of an attribute-based (e.g.,
"what is the color of the target, red or green?") or a pointing-based (e.g.,
"do you mean this one?") question. Our disambiguation module runs in real time
on a real robot, and the interactive grasping system achieves a 91.43\%
selection accuracy in the real-robot experiments, outperforming several
baselines by large margins.


title: Deep learning for radar data exploitation of autonomous vehicle

abstract:
  Autonomous driving requires a detailed understanding of complex driving
scenes. The redundancy and complementarity of the vehicle's sensors provide an
accurate and robust comprehension of the environment, thereby increasing the
level of performance and safety. This thesis focuses the on automotive RADAR,
which is a low-cost active sensor measuring properties of surrounding objects,
including their relative speed, and has the key advantage of not being impacted
by adverse weather conditions. With the rapid progress of deep learning and the
availability of public driving datasets, the perception ability of vision-based
driving systems has considerably improved. The RADAR sensor is seldom used for
scene understanding due to its poor angular resolution, the size, noise, and
complexity of RADAR raw data as well as the lack of available datasets. This
thesis proposes an extensive study of RADAR scene understanding, from the
construction of an annotated dataset to the conception of adapted deep learning
architectures. First, this thesis details approaches to tackle the current lack
of data. A simple simulation as well as generative methods for creating
annotated data will be presented. It will also describe the CARRADA dataset,
composed of synchronised camera and RADAR data with a semi-automatic annotation
method. This thesis then present a proposed set of deep learning architectures
with their associated loss functions for RADAR semantic segmentation. It also
introduces a method to open up research into the fusion of LiDAR and RADAR
sensors for scene understanding. Finally, this thesis exposes a collaborative
contribution, the RADIal dataset with synchronised High-Definition (HD) RADAR,
LiDAR and camera. A deep learning architecture is also proposed to estimate the
RADAR signal processing pipeline while performing multitask learning for object
detection and free driving space segmentation.


title: The Present and Future Status of Heavy Neutral Leptons

abstract:
  The existence of non-zero neutrino masses points to the likely existence of
multiple SM neutral fermions. When such states are heavy enough that they
cannot be produced in oscillations, they are referred to as Heavy Neutral
Leptons (HNLs). In this white paper we discuss the present experimental status
of HNLs including colliders, beta decay, accelerators, as well as astrophysical
and cosmological impacts. We discuss the importance of continuing to search for
HNLs, and its potential impact on our understanding on key fundamental
questions, and additionally we outline the future prospects for next-generation
future experiments or upcoming accelerator run scenarios.


title: Simultaneous Localisation and Mapping with Quadric Surfaces

abstract:
  There are many possibilities for how to represent the map in simultaneous
localisation and mapping (SLAM). While sparse, keypoint-based SLAM systems have
achieved impressive levels of accuracy and robustness, their maps may not be
suitable for many robotic tasks. Dense SLAM systems are capable of producing
dense reconstructions, but can be computationally expensive and, like sparse
systems, lack higher-level information about the structure of a scene.
Human-made environments contain a lot of structure, and we seek to take
advantage of this by enabling the use of quadric surfaces as features in SLAM
systems. We introduce a minimal representation for quadric surfaces and show
how this can be included in a least-squares formulation. We also show how our
representation can be easily extended to include additional constraints on
quadrics such as those found in quadrics of revolution. Finally, we introduce a
proof-of-concept SLAM system using our representation, and provide some
experimental results using an RGB-D dataset.


title:
A multi-organ point cloud registration algorithm for abdominal CT
  registration

abstract:
  Registering CT images of the chest is a crucial step for several tasks such
as disease progression tracking or surgical planning. It is also a challenging
step because of the heterogeneous content of the human abdomen which implies
complex deformations. In this work, we focus on accurately registering a subset
of organs of interest. We register organ surface point clouds, as may typically
be extracted from an automatic segmentation pipeline, by expanding the Bayesian
Coherent Point Drift algorithm (BCPD). We introduce MO-BCPD, a multi-organ
version of the BCPD algorithm which explicitly models three important aspects
of this task: organ individual elastic properties, inter-organ motion coherence
and segmentation inaccuracy. This model also provides an interpolation
framework to estimate the deformation of the entire volume. We demonstrate the
efficiency of our method by registering different patients from the LITS
challenge dataset. The target registration error on anatomical landmarks is
almost twice as small for MO-BCPD compared to standard BCPD while imposing the
same constraints on individual organs deformation.


title:
Higgs Self Couplings Measurements at Future proton-proton Colliders: a
  Snowmass White Paper

abstract:
  The Higgs boson trilinear and quartic self-couplings are directly related to
the shape of the Higgs potential; measuring them with precision is extremely
important, as they provide invaluable information on the electroweak symmetry
breaking and the electroweak phase transition. In this paper, we perform a
detailed analysis of double Higgs boson production, through the gluon gluon
fusion process, in the most promising decay channels di-bottom-quark
di-photons, di-bottom-quark di-tau, and four-bottom-quark for several future
colliders: the HL-LHC at 14 TeV and the FCC-hh at 100 TeV, assuming
respectively 3 inverse ab and 30 inverse ab of integrated luminosity. In the HL
LHC scenario, we expect an upper limit on the di Higgs cross section production
of 0.76 at 95% confidence level, corresponding to a significance of 2.8 sigma.
In the FCC-hh scenario, depending on the assumed detector performance and
systematic uncertainties, we expect that the Higgs self-coupling will be
measured with a precision in the range 4.8-8.5% at 95% confidence level.


title:
Interaction between near-wall streaks and large-scale motions in
  turbulent channel flows

abstract:
  The interactions between the near-wall streaks and the large-scale motions
(LSMs) of the outer region of wall-bounded turbulent flows are investigated.
The co-supporting hypothesis of Toh & Itano (2005) is checked in full-scale
channels at low to moderate Reynolds numbers, from two points of view. To study
the top-down influence of the outer structures on the spanwise motion of the
near-wall streaks, a method inspired by particle-image velocimetry is used to
track the spanwise position of the streaks. Their spanwise advection velocity
is found to be affected by the hierarchy of large-scale circulations in the
logarithmic layer, but their spanwise streak density is only weakly related to
the LSMs. The evidence suggests that a top-down influence exists and drives the
drift of the streaks in the spanwise direction, as suggested by Toh & Itano
(2005), but that the hypothesized streak accumulation rarely occurs. Numerical
experiments at Re_tau about 535 are then performed to clarify the role of the
near-wall streaks in the generation and preservation of the outer LSMs. The
results show that the merger of the near-wall streaks is only weakly correlated
with the generation of the LSMs, and that removing the near-wall roots of the
LSMs does not affect the evolution of their outer region. It is concluded that
the bottom-up influence from the near-wall streaks is not essential for the LSM
generation and preservation, also weakening the evidence for the co-supporting
hypothesis.


title:
From charge to spin: analogies and differences in quantum transport
  coefficients

abstract:
  We review some recent results from the mathematical theory of transport of
charge and spin in gapped crystalline quantum systems. The emphasis will be in
transport coefficients like conductivities and conductances. As for the former,
those are computed as appropriate expectations of current operators in a
non-equilibrium almost-stationary state (NEASS), which arises from the
perturbation of an equilibrium state by an external electric field. While for
charge transport the usual double-commutator Kubo formula is recovered (also
beyond linear response), we obtain formulas for appropriately-defined spin
conductivities which are still explicit but more involved. Certain "Kubo-like"
terms in these formulas are also shown to agree with corresponding
contributions to the spin conductance. In addition to that, we employ similar
techniques to show a new result, namely that even in systems with non-conserved
spin there is no generation of spin torque, namely the spin torque operator has
an expectation in the NEASS which vanishes faster than any power of the
intensity of the perturbing field.


title: Curvature Varifolds with Orthogonal Boundary

abstract:
  We consider the class $S^m_\perp(\Omega)$ of $m$-dimensional surfaces in
$\overline{\Omega} \subset {\mathbb R}^n$ which intersect $S = \partial \Omega$
orthogonally along the boundary. A piece of an affine $m$-plane in
$S^m_\perp(\Omega)$ is called an orthogonal slice. We prove estimates for the
area by the $L^p$-integral of the second fundamental form in three cases: first
when $\Omega$ admits no orthogonal slices, second for $m = p = 2$ if all
orthogonal slices are topological disks, and finally for all $\Omega$ if the
surfaces are confined to a neighborhood of $S$. The orthogonality constraint
has a weak formulation for curvature varifolds, we classify those varifolds of
vanishing curvature. As an application, we prove for any $\Omega$ the existence
of an orthogonal $2$-varifold which minimizes the $L^2$ curvature in the
integer rectifiable class.


title:
Intelligent Reconfigurable Surfaces vs. Decode-and-Forward: What is the
  Impact of Electromagnetic Interference?

abstract:
  This paper considers the use of an intelligent reconfigurable surface (IRS)
to aid wireless communication systems. The main goal is to compare this
emerging technology with conventional decode-and-forward (DF) relaying. Unlike
prior comparisons, we assume that electromagnetic interference (EMI),
consisting of incoming waves from external sources, is present at the location
where the IRS or DF relay are placed. The analysis, in terms of minimizing the
total transmit power, shows that EMI has a strong impact on DF relay-assisted
communications, even when the relaying protocol is optimized against EMI. It
turns out that IRS-aided communications is more resilient to EMI. To beat an
IRS, we show that the DF relay must use multiple antennas and actively suppress
the EMI by beamforming.


title:
Mobility, traffic and radio channel prediction: 5G and beyond
  applications

abstract:
  Machine learning (ML) is an important component for enabling automation in
Radio Access Networks (RANs). The work on applying ML for RAN has been under
development for many years and is now also drawing attention in 3GPP and
Open-RAN standardization fora. A key component of multiple features, also
highlighted in the recent 3GPP specification work, is the use of mobility,
traffic and radio channel prediction. These types of predictions form the
intelligence enablers to leverage the potentials for ML for RAN, both for
current and future wireless networks. This paper provides an overview with
evaluation results of current applications that utilize such intelligence
enablers, we then discuss how those enablers likely will be a cornerstone for
emerging 6G use cases such as wireless energy transmission.


title:
Routing Single Photons from a Trapped Ion Using a Photonic Integrated
  Circuit

abstract:
  Trapped ions are promising candidates for nodes of a scalable quantum network
due to their long-lived qubit coherence times and high-fidelity single and
two-qubit gates. Future quantum networks based on trapped ions will require a
scalable way to route photons between different nodes. Photonic integrated
circuits from fabrication foundries provide a compact solution to this problem.
However, these circuits typically operate at telecommunication wavelengths
which are incompatible with the strong dipole emissions of trapped ions. In
this work, we demonstrate the routing of single photons from a trapped ion
using a photonic integrated circuit. We employ quantum frequency conversion to
match the emission of the ion to the operating wavelength of a
foundry-fabricated silicon nitride photonic integrated circuit, achieving a
total transmission of 31$\pm$0.9% through the device. Using programmable phase
shifters, we switch the single photons between the output channels of the
circuit and also demonstrate a 50/50 beam splitting condition. These results
constitute an important step towards programmable routing and entanglement
distribution in large-scale quantum networks and distributed quantum computers.


title: On Hyperbolic Embeddings in 2D Object Detection

abstract:
  Object detection, for the most part, has been formulated in the euclidean
space, where euclidean or spherical geodesic distances measure the similarity
of an image region to an object class prototype. In this work, we study whether
a hyperbolic geometry better matches the underlying structure of the object
classification space. We incorporate a hyperbolic classifier in two-stage,
keypoint-based, and transformer-based object detection architectures and
evaluate them on large-scale, long-tailed, and zero-shot object detection
benchmarks. In our extensive experimental evaluations, we observe categorical
class hierarchies emerging in the structure of the classification space,
resulting in lower classification errors and boosting the overall object
detection performance.


title: Pairwise Valid Instruments

abstract:
  Finding valid instruments is difficult. We propose Validity Set Instrumental
Variable (VSIV) regression, a method for estimating treatment effects when the
instruments are partially invalid. VSIV regression exploits testable
implications for instrument validity to remove invalid variation in the
instruments. We show that the proposed VSIV estimators are asymptotically
normal under weak conditions and always remove or reduce the asymptotic bias
relative to standard IV estimators.


title:
Revealing the topological phase diagram of ZrTe$_5$ using the complex
  strain fields of microbubbles

abstract:
  Topological materials host robust properties, unaffected by microscopic
perturbations, owing to the global topological properties of the bulk electron
system. Materials in which the topological invariant can be changed by easily
tuning external parameters are especially sought after. Zirconium
pentatelluride (ZrTe$_5$) is one of a few experimentally available materials
that reside close to the boundary of a topological phase transition, allowing
the switching of its invariant by mechanical strain. Here, we unambiguously
identify a topological insulator - metal transition as a function of strain, by
a combination of ab initio calculations and direct measurements of the local
charge density. Our model quantitatively describes the response to complex
strain patterns found in bubbles of few layer ZrTe$_5$ without fitting
parameters, reproducing the mechanical deformation dependent closing of the
band gap observed using scanning tunneling microscopy. We calculate the
topological phase diagram of ZrTe$_5$ and identify the phase at equilibrium,
enabling the design of device architectures which exploit the unique
topological switching characteristics of the system.


title:
Localized Interlayer Excitons in MoSe2-WSe2 Heterostructures without a
  Moir\'e Potential

abstract:
  Trapped interlayer excitons (IXs) in MoSe2-WSe2 heterobilayers have generated
interest for use as single quantum emitter arrays and as an opportunity to
study moir\'e physics in transition metal dichalcogenide (TMD)
heterostructures. IXs are spatially indirectly excitons comprised of an
electron in the MoSe2 layer bound to a hole in the WSe2 layer. Previous reports
of spectrally narrow (<1 meV) photoluminescence (PL) emission lines at low
temperature have been attributed to IXs localized by the moir\'e potential
between the TMD layers. Here, we show that spectrally narrow IX PL lines are
present even when the moir\'e potential is suppressed by inserting a bilayer
hexagonal boron nitride (hBN) spacer between the TMD layers. We directly
compare the doping, electric field, magnetic field, and temperature dependence
of IXs in a directly contacted MoSe2-WSe2 region to those in a region separated
by bilayer hBN. Our results show that the localization potential resulting in
the narrow PL lines is independent of the moir\'e potential, and instead likely
due to extrinsic effects such as nanobubbles or defects. We show that while the
doping, electric field, and temperature dependence of the narrow IX lines is
similar for both regions, their excitonic g-factors have opposite signs,
indicating that the IXs in the directly contacted region are trapped by both
moir\'e and extrinsic localization potentials.


title: Snowmass White Paper: The Quest to Define QFT

abstract:
  This article provides a review of the literature on rigorous definitions and
constructions in Quantum Field Theory, spanning the period of seven decades.
Comparing with the ideas and constructions that exist in the modern physics
literature, we conclude that none of the existing systems of QFT axioms can
cover all the physical situations. Therefore, it is still an outstanding open
problem to formulate a complete definition of QFT. We argue that the question
is of relevance for both physicists and mathematicians.


title:
High Field Magnet Development for HEP in Europe: A Proposal from LDG HFM
  Expert Panel

abstract:
  The European Laboratory Directors Group (LDG) was mandated by CERN Council in
2021 to oversee the development of an Accelerator R&D Roadmap. To this end, a
set of expert panels was convened, covering the five broad areas of accelerator
R&D highlighted in the ESPPU. The High Field Magnet (HFM) Panel is proposing a
programme to demonstrate Nb3Sn magnet technology for large-scale deployment and
to investigate the suitability of high temperature superconductors (HTS) for
accelerator magnet applications. A summary of this programme is presented here.


title: Modular and Parameter-Efficient Multimodal Fusion with Prompting

abstract:
  Recent research has made impressive progress in large-scale multimodal
pre-training. In the context of the rapid growth of model size, it is necessary
to seek efficient and flexible methods other than finetuning. In this paper, we
propose to use prompt vectors to align the modalities. Our method achieves
comparable performance to several other multimodal fusion methods in
low-resource settings. We further show that our method is modular and
parameter-efficient for processing tasks involving two or more data modalities.


title: Machine Learning and Cosmology

abstract:
  Methods based on machine learning have recently made substantial inroads in
many corners of cosmology. Through this process, new computational tools, new
perspectives on data collection, model development, analysis, and discovery, as
well as new communities and educational pathways have emerged. Despite rapid
progress, substantial potential at the intersection of cosmology and machine
learning remains untapped. In this white paper, we summarize current and
ongoing developments relating to the application of machine learning within
cosmology and provide a set of recommendations aimed at maximizing the
scientific impact of these burgeoning tools over the coming decade through both
technical development as well as the fostering of emerging communities.

categories: hep-ph astro-ph.CO astro-ph.IM cs.LG stat.ML


title: POETREE: Interpretable Policy Learning with Adaptive Decision Trees

abstract:
  Building models of human decision-making from observed behaviour is critical
to better understand, diagnose and support real-world policies such as clinical
care. As established policy learning approaches remain focused on imitation
performance, they fall short of explaining the demonstrated decision-making
process. Policy Extraction through decision Trees (POETREE) is a novel
framework for interpretable policy learning, compatible with fully-offline and
partially-observable clinical decision environments -- and builds probabilistic
tree policies determining physician actions based on patients' observations and
medical history. Fully-differentiable tree architectures are grown
incrementally during optimization to adapt their complexity to the modelling
task, and learn a representation of patient history through recurrence,
resulting in decision tree policies that adapt over time with patient
information. This policy learning method outperforms the state-of-the-art on
real and synthetic medical datasets, both in terms of understanding,
quantifying and evaluating observed behaviour as well as in accurately
replicating it -- with potential to improve future decision support systems.


title: Graph filtering over expanding graphs

abstract:
  Our capacity to learn representations from data is related to our ability to
design filters that can leverage their coupling with the underlying domain.
Graph filters are one such tool for network data and have been used in a myriad
of applications. But graph filters work only with a fixed number of nodes
despite the expanding nature of practical networks. Learning filters in this
setting is challenging not only because of the increased dimensions but also
because the connectivity is known only up to an attachment model. We propose a
filter learning scheme for data over expanding graphs by relying only on such a
model. By characterizing the filter stochastically, we develop an empirical
risk minimization framework inspired by multi-kernel learning to balance the
information inflow and outflow at the incoming nodes. We particularize the
approach for denoising and semi-supervised learning (SSL) over expanding graphs
and show near-optimal performance compared with baselines relying on the exact
topology. For SSL, the proposed scheme uses the incoming node information to
improve the task on the existing ones. These findings lay the foundation for
learning representations over expanding graphs by relying only on the
stochastic connectivity model.


title: KATRIN: Status and Prospects for the Neutrino Mass and Beyond

abstract:
  The Karlsruhe Tritium Neutrino (KATRIN) experiment is designed to measure a
high-precision integral spectrum of the endpoint region of T2 beta decay, with
the primary goal of probing the absolute mass scale of the neutrino. After a
first tritium commissioning campaign in 2018, the experiment has been regularly
running since 2019, and in its first two measurement campaigns has already
achieved a sub-eV sensitivity. After 1000 days of data-taking, KATRIN's design
sensitivity is 0.2 eV at the 90% confidence level. In this white paper we
describe the current status of KATRIN; explore prospects for measuring the
neutrino mass and other physics observables, including sterile neutrinos and
other beyond-Standard-Model hypotheses; and discuss research-and-development
projects that may further improve the KATRIN sensitivity.

categories: nucl-ex astro-ph.CO hep-ex physics.ins-det


title:
Seeking Commonness and Inconsistencies: A Jointly Smoothed Approach to
  Multi-view Subspace Clustering

abstract:
  Multi-view subspace clustering aims to discover the hidden subspace
structures from multiple views for robust clustering, and has been attracting
considerable attention in recent years. Despite significant progress, most of
the previous multi-view subspace clustering algorithms are still faced with two
limitations. First, they usually focus on the consistency (or commonness) of
multiple views, yet often lack the ability to capture the cross-view
inconsistencies in subspace representations. Second, many of them overlook the
local structures of multiple views and cannot jointly leverage multiple local
structures to enhance the subspace representation learning. To address these
two limitations, in this paper, we propose a jointly smoothed multi-view
subspace clustering (JSMC) approach. Specifically, we simultaneously
incorporate the cross-view commonness and inconsistencies into the subspace
representation learning. The view-consensus grouping effect is presented to
jointly exploit the local structures of multiple views to regularize the
view-commonness representation, which is further associated with the low-rank
constraint via the nuclear norm to strengthen its cluster structure. Thus the
cross-view commonness and inconsistencies, the view-consensus grouping effect,
and the low-rank representation are seamlessly incorporated into a unified
objective function, upon which an alternating optimization algorithm is
performed to achieve a robust subspace representation for clustering.
Experimental results on a variety of real-world multi-view datasets have
confirmed the superiority of the proposed approach.


title:
A novel sampler for Gauss-Hermite determinantal point processes with
  application to Monte Carlo integration

abstract:
  Determinantal points processes are a promising but relatively under-developed
tool in machine learning and statistical modelling, being the canonical
statistical example of distributions with repulsion. While their mathematical
formulation is elegant and appealing, their practical use, such as simply
sampling from them, is far from straightforward.Recent work has shown how a
particular type of determinantal point process defined on the compact
multidimensional space $[-1, 1]^d$ can be practically sampled and further shown
how such samples can be used to improve Monte Carlo integration.This work
extends those results to a new determinantal point process on $\mathbb{R}^d$ by
constructing a novel sampling scheme. Samples from this new process are shown
to be useful in Monte Carlo integration against Gaussian measure, which is
particularly relevant in machine learning applications.


title:
Upper bound on the smuon mass from vacuum stability in the light of muon
  $g-2$ anomaly

abstract:
  We derive an upper bound on the smuon mass assuming that the muon $g-2$
anomaly is explained by the supersymmetric (SUSY) contribution. In the minimal
SUSY standard model, the SUSY contribution to the muon $g-2$ is enhanced when
the Higgsino mass parameter is large. Then, the smuon-smuon-Higgs trilinear
coupling is enhanced, which may destabilize the electroweak vacuum. We
calculate precisely the decay rate of the electroweak vacuum in such a case. We
include one-loop effects which are crucial to determine the overall
normalization of the decay rate. Requiring that the theoretical prediction of
the muon anomalous magnetic moment is consistent with the observed value at the
$1$ and $2\sigma$ levels (equal to the central value of the observed value), we
found that the lightest smuon mass should be smaller than $1.38$ and $1.68\
{\rm TeV}$ ($1.20\ {\rm TeV}$) for $\tan\beta=10$ (with $\tan\beta$ being the
ratio of the vacuum expectation values of the two Higgs bosons), respectively,
and the bound is insensitive to the value of $\tan\beta$.


title: MotionCLIP: Exposing Human Motion Generation to CLIP Space

abstract:
  We introduce MotionCLIP, a 3D human motion auto-encoder featuring a latent
embedding that is disentangled, well behaved, and supports highly semantic
textual descriptions. MotionCLIP gains its unique power by aligning its latent
space with that of the Contrastive Language-Image Pre-training (CLIP) model.
Aligning the human motion manifold to CLIP space implicitly infuses the
extremely rich semantic knowledge of CLIP into the manifold. In particular, it
helps continuity by placing semantically similar motions close to one another,
and disentanglement, which is inherited from the CLIP-space structure.
MotionCLIP comprises a transformer-based motion auto-encoder, trained to
reconstruct motion while being aligned to its text label's position in
CLIP-space. We further leverage CLIP's unique visual understanding and inject
an even stronger signal through aligning motion to rendered frames in a
self-supervised manner. We show that although CLIP has never seen the motion
domain, MotionCLIP offers unprecedented text-to-motion abilities, allowing
out-of-domain actions, disentangled editing, and abstract language
specification. For example, the text prompt "couch" is decoded into a sitting
down motion, due to lingual similarity, and the prompt "Spiderman" results in a
web-swinging-like solution that is far from seen during training. In addition,
we show how the introduced latent space can be leveraged for motion
interpolation, editing and recognition.


title:
Dependence of the top-quark mass measured in top-quark pair production
  on the parton distribution functions at the LHC and future colliders

abstract:
  The dependence of the top-quark mass measurement in top-quark production on
the parton distribution functions is explored through the differential
distribution of the invariant mass of the top-antitop system in top-quark pair
production at hadron colliders. Several different proton-proton collider
options are considered: 8 TeV, 13 TeV, 13.6 TeV, 14 TeV, and 100 TeV. The
top-quark mass is obtained from a chi-squared fit to the invariant mass of the
top-antitop-quark pair. The parton distribution function uncertainties are used
in the chi-square evaluation. The uncertainties are reduced through a fit to
the longitudinal momentum of the top-antitop system. The top-quark mass
uncertainty due to parton distribution functions is found to be reduced by 20%.


title: Surrogate Gap Minimization Improves Sharpness-Aware Training

abstract:
  The recently proposed Sharpness-Aware Minimization (SAM) improves
generalization by minimizing a \textit{perturbed loss} defined as the maximum
loss within a neighborhood in the parameter space. However, we show that both
sharp and flat minima can have a low perturbed loss, implying that SAM does not
always prefer flat minima. Instead, we define a \textit{surrogate gap}, a
measure equivalent to the dominant eigenvalue of Hessian at a local minimum
when the radius of the neighborhood (to derive the perturbed loss) is small.
The surrogate gap is easy to compute and feasible for direct minimization
during training. Based on the above observations, we propose Surrogate
\textbf{G}ap Guided \textbf{S}harpness-\textbf{A}ware \textbf{M}inimization
(GSAM), a novel improvement over SAM with negligible computation overhead.
Conceptually, GSAM consists of two steps: 1) a gradient descent like SAM to
minimize the perturbed loss, and 2) an \textit{ascent} step in the
\textit{orthogonal} direction (after gradient decomposition) to minimize the
surrogate gap and yet not affect the perturbed loss. GSAM seeks a region with
both small loss (by step 1) and low sharpness (by step 2), giving rise to a
model with high generalization capabilities. Theoretically, we show the
convergence of GSAM and provably better generalization than SAM. Empirically,
GSAM consistently improves generalization (e.g., +3.2\% over SAM and +5.4\%
over AdamW on ImageNet top-1 accuracy for ViT-B/32). Code is released at \url{
https://sites.google.com/view/gsam-iclr22/home}.


title:
First Experimental Evidence of a Beam-Beam Long-Range Compensation Using
  Wires in the Large Hadron Collider

abstract:
  In high intensity and high energy colliders such as the CERN Large Hadron
Collider and its future High Luminosity upgrade, interactions between the two
beams around the different Interaction Points impose machine performance
limitations. In fact, their effect reduces the beam lifetime and therefore the
collider's luminosity reach. Those interactions are called Beam-Beam Long-Range
interactions and a possible mitigation of their effect using DC wires was
proposed for the first time in the early 2000's. This solution is currently
being studied as an option for enhancing the HL-LHC performance. In 2017 and
2018, four demonstrators of wire compensators have been installed in the LHC. A
two-year long experimental campaign followed in order to validate the
possibility to mitigate the BBLR interactions in the LHC. During this campaign,
a proof-of-concept was completed and motivated an additional set of
experiments, successfully demonstrating the mitigation of BBLR interactions
effects in beam conditions compatible with the operational configuration. This
paper reports in detail the preparation of the experimental campaign, the
obtained results and draws some perspectives for the future.


title: Practical data monitoring in the internet-services domain

abstract:
  Large-scale monitoring, anomaly detection, and root cause analysis of metrics
are essential requirements of the internet-services industry. To address the
need to continuously monitor millions of metrics, many anomaly detection
approaches are being used on a daily basis by large internet-based companies.
However, in spite of the significant progress made to accurately and
efficiently detect anomalies in metrics, the sheer scale of the number of
metrics has meant there are still a large number of false alarms that need to
be investigated. This paper presents a framework for reliable large-scale
anomaly detection. It is significantly more accurate than existing approaches
and allows for easy interpretation of models, thus enabling practical data
monitoring in the internet-services domain.


title:
Ghost center and representations of the diagonal reduction algebra of
  $\mathfrak{osp}(1|2)$

abstract:
  Reduction algebras are known by many names in the literature, including step
algebras, Mickelsson algebras, Zhelobenko algebras, and transvector algebras,
to name a few. These algebras, realized by raising and lowering operators,
allow for the calculation of Clebsch-Gordan coefficients, branching rules, and
intertwining operators; and have connections to extremal equations and
dynamical R-matrices in integrable face models.
  In this paper we continue the study of the diagonal reduction superalgebra
$A$ of the orthosymplectic Lie superalgebra $\mathfrak{osp}(1|2)$. We construct
a Harish-Chandra homomorphism, Verma modules, and study the Shapovalov form on
each Verma module. Using these results, we prove that the ghost center (center
plus anti-center) of $A$ is generated by two central elements and one
anti-central element (analogous to the Scasimir due to Le\'{s}niewski for
$\mathfrak{osp}(1|2)$). As another application, we classify all
finite-dimensional irreducible representations of $A$. Lastly, we calculate an
infinite-dimensional tensor product decomposition explicitly.


title: DISTAL: The Distributed Tensor Algebra Compiler

abstract:
  We introduce DISTAL, a compiler for dense tensor algebra that targets modern
distributed and heterogeneous systems. DISTAL lets users independently describe
how tensors and computation map onto target machines through separate format
and scheduling languages. The combination of choices for data and computation
distribution creates a large design space that includes many algorithms from
both the past (e.g., Cannon's algorithm) and the present (e.g., COSMA). DISTAL
compiles a tensor algebra domain specific language to a distributed task-based
runtime system and supports nodes with multi-core CPUs and multiple GPUs. Code
generated by DISTAL is competitive with optimized codes for matrix multiply on
256 nodes of the Lassen supercomputer and outperforms existing systems by
between 1.8x to 3.7x (with a 45.7x outlier) on higher order tensor operations.


title: $k$-colouring $(m,n)$-mixed graphs with switching

abstract:
  A mixed graph is a set of vertices together with an edge set and an arc set.
An $(m,n)$-mixed graph $G$ is a mixed graph whose edges are each assigned one
of $m$ colours, and whose arcs are each assigned one of $n$ colours. A
\emph{switch} at a vertex $v$ of $G$ permutes the edge colours, the arc
colours, and the arc directions of edges and arcs incident with $v$. The group
of all allowed switches is $\Gamma$.
  Let $k \geq 1$ be a fixed integer and $\Gamma$ a fixed permutation group. We
consider the problem that takes as input an $(m,n)$-mixed graph $G$ and asks if
there a sequence of switches at vertices of $G$ with respect to $\Gamma$ so
that the resulting $(m,n)$-mixed graph admits a homomorphism to an
$(m,n)$-mixed graph on $k$ vertices. Our main result establishes this problem
can be solved in polynomial time for $k \leq 2$, and is NP-hard for $k \geq 3$.
This provides a step towards a general dichotomy theorem for the
$\Gamma$-switchable homomorphism decision problem.


title: yonder: A python package for data denoising and reconstruction

abstract:
  We present a standalone implementation of a data-deconvolution method based
on singular value decomposition. The tool is written in python and packaged in
the open-source yonder package. yonder receives as input two matrices, one for
the data and another for the errors, and outputs a denoised version of the
original dataset. In this Research Note, we briefly describe the methodology
and show a demonstration of the yonder on a simulated dataset.


title: Neural Solvers for Fast and Accurate Numerical Optimal Control

abstract:
  Synthesizing optimal controllers for dynamical systems often involves solving
optimization problems with hard real-time constraints. These constraints
determine the class of numerical methods that can be applied: computationally
expensive but accurate numerical routines are replaced by fast and inaccurate
methods, trading inference time for solution accuracy. This paper provides
techniques to improve the quality of optimized control policies given a fixed
computational budget. We achieve the above via a hypersolvers approach, which
hybridizes a differential equation solver and a neural network. The performance
is evaluated in direct and receding-horizon optimal control tasks in both low
and high dimensions, where the proposed approach shows consistent Pareto
improvements in solution accuracy and control performance.


title: Can A Neural Network Hear the Shape of A Drum?

abstract:
  We have developed a deep neural network that reconstructs the shape of a
polygonal domain given the first hundred of its Laplacian eigenvalues. Having
an encoder-decoder structure, the network maps input spectra to a latent space
and then predicts the discretized image of the domain on a square grid. We
tested this network on randomly generated pentagons. The prediction accuracy is
high and the predictions obey the Laplacian scaling rule. The network recovers
the continuous rotational degree of freedom beyond the symmetry of the grid.
The variation of the latent variables under the scaling transformation shows
they are strongly correlated with Weyl' s parameters (area, perimeter, and a
certain function of the angles) of the test polygons.


title: Combining AI/ML and PHY Layer Rule Based Inference -- Some First Results

abstract:
  In 3GPP New Radio (NR) Release 18 we see the first study item starting in May
2022, which will evaluate the potential of AI/ML methods for Radio Access
Network (RAN) 1, i.e., for mobile radio PHY and MAC layer applications. We use
the profiling method for accurate iterative estimation of multipath component
parameters for PHY layer reference, as it promises a large channel prediction
horizon. We investigate options to partly or fully replace some functionalities
of this rule based PHY layer method by AI/ML inferences, with the goal to
achieve either a higher performance, lower latency, or, reduced processing
complexity. We provide first results for noise reduction, then a combined
scheme for model order selection, compare options to infer multipath component
start parameters, and, provide an outlook on a possible channel prediction
framework.


title:
Things not Written in Text: Exploring Spatial Commonsense from Visual
  Signals

abstract:
  Spatial commonsense, the knowledge about spatial position and relationship
between objects (like the relative size of a lion and a girl, and the position
of a boy relative to a bicycle when cycling), is an important part of
commonsense knowledge. Although pretrained language models (PLMs) succeed in
many NLP tasks, they are shown to be ineffective in spatial commonsense
reasoning. Starting from the observation that images are more likely to exhibit
spatial commonsense than texts, we explore whether models with visual signals
learn more spatial commonsense than text-based PLMs. We propose a spatial
commonsense benchmark that focuses on the relative scales of objects, and the
positional relationship between people and objects under different actions. We
probe PLMs and models with visual signals, including vision-language pretrained
models and image synthesis models, on this benchmark, and find that image
synthesis models are more capable of learning accurate and consistent spatial
knowledge than other models. The spatial knowledge from image synthesis models
also helps in natural language understanding tasks that require spatial
commonsense.