title: A Scalable Approach to Modeling on Accelerated Neuromorphic Hardware

abstract:
  Neuromorphic systems open up opportunities to enlarge the explorative space
for computational research. However, it is often challenging to unite
efficiency and usability. This work presents the software aspects of this
endeavor for the BrainScaleS-2 system, a hybrid accelerated neuromorphic
hardware architecture based on physical modeling. We introduce key aspects of
the BrainScaleS-2 Operating System: experiment workflow, API layering, software
design, and platform operation. We present use cases to discuss and derive
requirements for the software and showcase the implementation. The focus lies
on novel system and software features such as multi-compartmental neurons, fast
re-configuration for hardware-in-the-loop training, applications for the
embedded processors, the non-spiking operation mode, interactive platform
access, and sustainable hardware/software co-development. Finally, we discuss
further developments in terms of hardware scale-up, system usability and
efficiency.


title: Diverse Counterfactual Explanations for Anomaly Detection in Time Series

abstract:
  Data-driven methods that detect anomalies in times series data are ubiquitous
in practice, but they are in general unable to provide helpful explanations for
the predictions they make. In this work we propose a model-agnostic algorithm
that generates counterfactual ensemble explanations for time series anomaly
detection models. Our method generates a set of diverse counterfactual
examples, i.e, multiple perturbed versions of the original time series that are
not considered anomalous by the detection model. Since the magnitude of the
perturbations is limited, these counterfactuals represent an ensemble of inputs
similar to the original time series that the model would deem normal. Our
algorithm is applicable to any differentiable anomaly detection model. We
investigate the value of our method on univariate and multivariate real-world
datasets and two deep-learning-based anomaly detection models, under several
explainability criteria previously proposed in other data domains such as
Validity, Plausibility, Closeness and Diversity. We show that our algorithm can
produce ensembles of counterfactual examples that satisfy these criteria and
thanks to a novel type of visualisation, can convey a richer interpretation of
a model's internal mechanism than existing methods. Moreover, we design a
sparse variant of our method to improve the interpretability of counterfactual
explanations for high-dimensional time series anomalies. In this setting, our
explanation is localised on only a few dimensions and can therefore be
communicated more efficiently to the model's user.


title: Collision Dynamics of Bose-Einstein Condensates

abstract:
  We study the collision dynamics of two Bose-Einstein condensates with their
dynamical wave functions modeled by a set of coupled, time-dependent
Gross-Pitaevskii equations. Beginning with an effective one-dimensional system,
we identify regimes characterized by the relationship between inter- and
intra-atomic interactions and the initial configuration of the system, akin to
the equilibrium phase diagram of two interacting Bose condensates. We consider
a dynamical setup in which two wave packets are initially at rest with a small
separation about the center of an asymmetric harmonic trap. Upon release, we
observe a rapid approach to dynamical equilibrium in the limits of very large
and very small inter-particle repulsion, characterized by periodic transmission
or reflection of the condensates as distinguishable units, whereas the
intermediate, critical regime is characterized by extended transient dynamics,
density fracturing, and dynamical mixing. We therefore have reason to believe
that non-trivial behavior may exist during the collisions of Bose-Einstein
condensates as a result of their non-linear interactions, and these effects may
be observable in a suitable laboratory environment.


title: High-fidelity GAN Inversion with Padding Space

abstract:
  Inverting a Generative Adversarial Network (GAN) facilitates a wide range of
image editing tasks using pre-trained generators. Existing methods typically
employ the latent space of GANs as the inversion space yet observe the
insufficient recovery of spatial details. In this work, we propose to involve
the padding space of the generator to complement the latent space with spatial
information. Concretely, we replace the constant padding (e.g., usually zeros)
used in convolution layers with some instance-aware coefficients. In this way,
the inductive bias assumed in the pre-trained model can be appropriately
adapted to fit each individual image. Through learning a carefully designed
encoder, we manage to improve the inversion quality both qualitatively and
quantitatively, outperforming existing alternatives. We then demonstrate that
such a space extension barely affects the native GAN manifold, hence we can
still reuse the prior knowledge learned by GANs for various downstream
applications. Beyond the editing tasks explored in prior arts, our approach
allows a more flexible image manipulation, such as the separate control of face
contour and facial details, and enables a novel editing manner where users can
customize their own manipulations highly efficiently.


title:
FGAN: Federated Generative Adversarial Networks for Anomaly Detection in
  Network Traffic

abstract:
  Over the last two decades, a lot of work has been done in improving network
security, particularly in intrusion detection systems (IDS) and anomaly
detection. Machine learning solutions have also been employed in IDSs to detect
known and plausible attacks in incoming traffic. Parameters such as packet
contents, sender IP and sender port, connection duration, etc. have been
previously used to train these machine learning models to learn to
differentiate genuine traffic from malicious ones. Generative Adversarial
Networks (GANs) have been significantly successful in detecting such anomalies,
mostly attributed to the adversarial training of the generator and
discriminator in an attempt to bypass each other and in turn increase their own
power and accuracy. However, in large networks having a wide variety of traffic
at possibly different regions of the network and susceptible to a large number
of potential attacks, training these GANs for a particular kind of anomaly may
make it oblivious to other anomalies and attacks. In addition, the dataset
required to train these models has to be made centrally available and publicly
accessible, posing the obvious question of privacy of the communications of the
respective participants of the network. The solution proposed in this work aims
at tackling the above two issues by using GANs in a federated architecture in
networks of such scale and capacity. In such a setting, different users of the
network will be able to train and customize a centrally available adversarial
model according to their own frequently faced conditions. Simultaneously, the
member users of the network will also able to gain from the experiences of the
other users in the network.


title: $F$-algebroids and deformation quantization via pre-Lie algebroids

abstract:
  In this paper, first we introduce a new approach to the notion of
$F$-algebroids, which is a generalization of $F$-manifold algebras and
$F$-manifolds, and show that $F$-algebroids are the corresponding
semi-classical limits of pre-Lie formal deformations of commutative associative
algebroids. Then we use the deformation cohomology of pre-Lie algebroids to
study pre-Lie infinitesimal deformations and extension of pre-Lie
$n$-deformations to pre-Lie $(n+1)$-deformations of a commutative associative
algebroid. Next we develop the theory of Dubrovin's dualities of $F$-algebroids
with eventual identities and use Nijenhuis operators on $F$-algebroids to
construct new $F$-algebroids. Finally we introduce the notion of
pre-$F$-algebroids, which is a generalization of $F$-manifolds with compatible
flat connections. Dubrovin's dualities of pre-$F$-algebroids with eventual
identities, Nijenhuis operators on pre-$F$-algebroids and their applications to
integral systems are discussed.


title:
db-A*: Discontinuity-bounded Search for Kinodynamic Mobile Robot Motion
  Planning

abstract:
  We consider time-optimal motion planning for dynamical systems that are
translation-invariant, a property that holds for many mobile robots, such as
differential-drives, cars, airplanes, and multirotors. Our key insight is that
we can extend graph-search algorithms to the continuous case when used
symbiotically with optimization. For the graph search, we introduce
discontinuity-bounded A* (db-A*), a generalization of the A* algorithm that
uses concepts and data structures from sampling-based planners. Db-A* reuses
short trajectories, so-called motion primitives, as edges and allows a maximum
user-specified discontinuity at the vertices. These trajectories are locally
repaired with trajectory optimization, which also provides new improved motion
primitives. Our novel kinodynamic motion planner, kMP-db-A*, has almost surely
asymptotic optimal behavior and computes near-optimal solutions quickly. For
our empirical validation, we provide the first benchmark that compares search-,
sampling-, and optimization-based time-optimal motion planning on multiple
dynamical systems in different settings. Compared to the baselines, kMP-db-A*
consistently solves more problem instances, finds lower-cost initial solutions,
and converges more quickly.


title: Operads with trivial $\mathbb{A}$-actions

abstract:
  We study operads with trivial $\mathbb{A}$-actions and prove an equivalence
between the category of $\mathbb{A}$-trivial operads and that of
pseudo-graded-Perm associative algebras. As a consequence, we show that
finitely generated $\mathbb{A}$-trivial operads are right noetherian of
integral Gelfand-Kirillov dimension and that every element in a prime
$\mathbb{A}$-trivial operad is central.


title: Event Generators for High-Energy Physics Experiments

abstract:
  We provide an overview of the status of Monte-Carlo event generators for
high-energy particle physics. Guided by the experimental needs and
requirements, we highlight areas of active development, and opportunities for
future improvements. Particular emphasis is given to physics models and
algorithms that are employed across a variety of experiments. These common
themes in event generator development lead to a more comprehensive
understanding of physics at the highest energies and intensities, and allow
models to be tested against a wealth of data that have been accumulated over
the past decades. A cohesive approach to event generator development will allow
these models to be further improved and systematic uncertainties to be reduced,
directly contributing to future experimental success. Event generators are part
of a much larger ecosystem of computational tools. They typically involve a
number of unknown model parameters that must be tuned to experimental data,
while maintaining the integrity of the underlying physics models. Making both
these data, and the analyses with which they have been obtained accessible to
future users is an essential aspect of open science and data preservation. It
ensures the consistency of physics models across a variety of experiments.


title:
Facial Expression Analysis Using Decomposed Multiscale Spatiotemporal
  Networks

abstract:
  Video-based analysis of facial expressions has been increasingly applied to
infer health states of individuals, such as depression and pain. Among the
existing approaches, deep learning models composed of structures for multiscale
spatiotemporal processing have shown strong potential for encoding facial
dynamics. However, such models have high computational complexity, making for a
difficult deployment of these solutions. To address this issue, we introduce a
new technique to decompose the extraction of multiscale spatiotemporal
features. Particularly, a building block structure called Decomposed Multiscale
Spatiotemporal Network (DMSN) is presented along with three variants: DMSN-A,
DMSN-B, and DMSN-C blocks. The DMSN-A block generates multiscale
representations by analyzing spatiotemporal features at multiple temporal
ranges, while the DMSN-B block analyzes spatiotemporal features at multiple
ranges, and the DMSN-C block analyzes spatiotemporal features at multiple
spatial sizes. Using these variants, we design our DMSN architecture which has
the ability to explore a variety of multiscale spatiotemporal features,
favoring the adaptation to different facial behaviors. Our extensive
experiments on challenging datasets show that the DMSN-C block is effective for
depression detection, whereas the DMSN-A block is efficient for pain
estimation. Results also indicate that our DMSN architecture provides a
cost-effective solution for expressions that range from fewer facial variations
over time, as in depression detection, to greater variations, as in pain
estimation.


title:
Efficient quantum imaginary time evolution by drifting real time
  evolution: an approach with low gate and measurement complexity

abstract:
  Quantum imaginary time evolution (QITE) is one of the promising candidates
for finding eigenvalues and eigenstates of a Hamiltonian. However, the original
QITE proposal [Nat. Phys. 16, 205-210 (2020)], which approximates the imaginary
time evolution by real time evolution, suffers from large circuit depth and
measurements due to the size of the Pauli operator pool and Trotterization. To
alleviate the requirement for deep circuits, we propose a time-dependent
drifting scheme inspired by the qDRIFT algorithm [Phys. Rev. Lett 123, 070503
(2019)], which randomly draws a Pauli term out of the approximated unitary
operation generators of QITE according to the strength and rescales that term
by the total strength of the Pauli terms. We show that this drifting scheme
removes the depth dependency on size of the operator pool and converges inverse
linearly to the number of steps. We further propose a deterministic algorithm
that selects the dominant Pauli term to reduce the fluctuation for the ground
state preparation. Meanwhile, we introduce an efficient measurement reduction
scheme across Trotter steps, which removes its cost dependence on the number of
iterations, and a measurement distribution protocol for different observables
within each time step. We also analyze the main error source of our scheme due
to the correlation length both theoretically and numerically. We numerically
test its validity of depth reduction, convergence performance, and faithfulness
of measurement reduction approximation on $\rm{LiH}$, $\rm{BeH_2}$, $\rm{H_6}$
and $\rm{NH_3}$ molecules. In particular, the results on $\rm{LiH}$ molecule
give the circuit depths comparable to that of the advanced adaptive variational
quantum eigensolver (VQE) methods.


title:
No Pain, Big Gain: Classify Dynamic Point Cloud Sequences with Static
  Models by Fitting Feature-level Space-time Surfaces

abstract:
  Scene flow is a powerful tool for capturing the motion field of 3D point
clouds. However, it is difficult to directly apply flow-based models to dynamic
point cloud classification since the unstructured points make it hard or even
impossible to efficiently and effectively trace point-wise correspondences. To
capture 3D motions without explicitly tracking correspondences, we propose a
kinematics-inspired neural network (Kinet) by generalizing the kinematic
concept of ST-surfaces to the feature space. By unrolling the normal solver of
ST-surfaces in the feature space, Kinet implicitly encodes feature-level
dynamics and gains advantages from the use of mature backbones for static point
cloud processing. With only minor changes in network structures and low
computing overhead, it is painless to jointly train and deploy our framework
with a given static model. Experiments on NvGesture, SHREC'17, MSRAction-3D,
and NTU-RGBD demonstrate its efficacy in performance, efficiency in both the
number of parameters and computational complexity, as well as its versatility
to various static backbones. Noticeably, Kinet achieves the accuracy of 93.27%
on MSRAction-3D with only 3.20M parameters and 10.35G FLOPS.


title: On the Parameterized Complexity of the Maximum Exposure Problem

abstract:
  We investigate the parameterized complexity of Maximum Exposure Problem
(MEP). Given a range space (R, P) where R is the set of ranges containing a set
P of points, and an integer k, MEP asks for k ranges which on removal results
in the maximum number of exposed points. A point p is said to be exposed when p
is not contained in any of the ranges in R. The problem is known to be NP-hard.
In this letter, we give fixed-parameter tractable results of MEP with respect
to different parameterizations.


title:
To Type or Not to Type? A Systematic Comparison of the Software Quality
  of JavaScript and TypeScript Applications on GitHub

abstract:
  JavaScript (JS) is one of the most popular programming languages, and widely
used for web apps and even backend development. Due to its dynamic nature,
however, JS applications often have a reputation for poor software quality. As
a type-safe superset of JavaScript, TypeScript (TS) offers features to address
this. However, there is currently insufficient empirical evidence to broadly
support the claim that TS apps exhibit better software quality than JS apps.
  We therefore conducted a repository mining study based on 604 GitHub projects
(299 for JS, 305 for TS) with over 16M LoC and collected four facets of
software quality: a) code quality (# of code smells per LoC), b) code
understandability (cognitive complexity per LoC), c) bug proneness (bug fix
commit ratio), and d) bug resolution time (mean time a bug issue is open). For
TS, we also collected how frequently the type-safety ignoring `any` type was
used.
  The analysis indicates that TS apps exhibit significantly better code quality
and understandability than JS apps. Contrary to expectations, however, bug
proneness and bug resolution time of our TS sample were not significantly lower
than for JS: mean bug fix commit ratio was more than 60% larger (0.126 vs.
0.206), and TS projects needed on average more than an additional day to fix
bugs (31.86 vs. 33.04 days). Furthermore, reducing the usage of the `any` type
in TS apps was significantly correlated with all metrics except bug proneness
(Spearman's rho between 0.17 and 0.26).
  Our results indicate that the perceived positive influence of TypeScript for
avoiding bugs in comparison to JavaScript may be more complicated than assumed.
While using TS seems to have benefits, it does not automatically lead to less
and easier to fix bugs. However, more research is needed in this area,
especially concerning the potential influence of project complexity and
developer experience.


title:
Collective Decision Making using Attractive and Repulsive Forces in
  Markovian Opinion Dynamics

abstract:
  In this paper, we model a decision making process in a network of interacting
agents using Markovian opinion dynamics, where each agent switches between
decisions according to a continuous time Markov chain. We are inspired by the
problem of modeling how interaction among road users in a traffic intersection
affects the choice that each agent makes concerning where to exit and whether
or not to give way. We introduce attractive/repulsive forces that act within
and between groups of agents, with the objective of resembling the behaviors
emerging in networks where agents make decisions that depend both on their own
preferences and the decisions of the surrounding agents. Our model extends the
possibility of using Markovian opinion dynamics to describe a wider scope of
practical scenarios, in which groups of stochastic agents make collective
decisions. In traffic applications, our method could be used in autonomous
vehicles to predict the decisions of human road users.


title: L-MAC: Location-aware MAC Protocol for Wireless Sensor Networks

abstract:
  This paper presents the design, implementation and performance evaluation of
a location MAC protocol, called L-MAC, for wireless sensor networks. L-MAC is a
combination of TDMA and CSMA while offsetting the high overhead of time slot
assignment by allocating the time slots to sensor nodes based on their location
information. This design avoids high computation complexity of time slot
assignment incurred by node mobility and node failure. The area which the
wireless sensor network occupies is divided into blocks and each block is
associated with an inter-block time slot and an intra-block time slot. In the
inter-block time slot, the sensor nodes stay active and receive the packets
from nodes outside of the block. In the intra-block time slot, the sensor nodes
communicate with peer nodes in the same block under CSMA. Sensor nodes stay
sleep in all other time slots unless they have traffic to send. L-MAC is
implemented and evaluated in NS-2.


title:
Performance Comparisons of Self-stabilizing Algorithms for Maximal
  Independent Sets

abstract:
  Sensor networks, such as ultra-wideband sensors for the smart warehouse, may
need to run distributed algorithms for automatically determining a topological
layout. In this paper, we present 5 different self-stabilizing algorithms
(their central and distributed counterparts) for determining maximal
independent sets. The performance of the algorithms, in terms of time
complexity, simulation analysis, and size of maximal independent sets found are
then compared.


title: Domain supersolids in binary dipolar condensates

abstract:
  Two-component dipolar condensates are now experimentally producible, and we
theoretically investigate the nature of supersolidity in this system. We
predict the existence of a binary supersolid state in which the two components
form a series of alternating domains, producing an immiscible double
supersolid. Remarkably, we find that a dipolar component can even induce
supersolidity in a nondipolar component. In stark contrast to single-component
supersolids, the number of crystal sites is not strictly limited by the
condensate populations, and the density is hence substantially lower. Our
results are applicable to a wide range of dipole moment combinations, marking
an important step towards long-lived bulk-supersolidity.


title:
Enabling matter power spectrum emulation in beyond-$\Lambda$CDM
  cosmologies with COLA

abstract:
  We compare and validate COLA (COmoving Lagrangian Acceleration) simulations
against existing emulators in the literature, namely Bacco and Euclid Emulator
2. Our analysis focuses on the non-linear response function, i.e., the ratio
between the non-linear dark matter power spectrum in a given cosmology with
respect to a pre-defined reference cosmology, which is chosen to be the Euclid
Emulator 2 reference cosmology in this paper. We vary three cosmological
parameters, the total matter density, the amplitude of the primordial scalar
perturbations and the spectral index. By comparing the COLA non-linear response
function with those computed from each emulator in the redshift range $0 \leq z
\leq 3$, we find that the COLA method is in excellent agreement with the two
emulators for scales up to $k \sim 1 \ h$/Mpc as long as the deviations of the
matter power spectrum from the reference cosmology are not too large. We
validate the implementation of massive neutrinos in our COLA simulations by
varying the sum of neutrino masses to three different values, $0.0$ eV, $0.058$
eV and $0.15$ eV. We show that all three non-linear prescriptions used in this
work agree at the $1\%$ level at $k \leq 1 \ h$/Mpc. We then introduce the
Effective Field Theory of Dark Energy in our COLA simulations using the
$N$-body gauge method. We consider two different modified gravity models in
which the growth of structure is enhanced or suppressed at small scales, and
show that the response function with respect to the change of modified gravity
parameters depends weakly on cosmological parameters in these models.


title:
Multispectral time-resolved energy-momentum microscopy using
  high-harmonic extreme ultraviolet radiation

abstract:
  A 790-nm-driven high-harmonic generation source with a repetition rate of 6
kHz is combined with a plane-grating monochromator and a
high-detection-efficiency photoelectron time-of-flight momentum microscope to
enable time- and momentum-resolved photoemission spectroscopy over a spectral
range of 23.6$-$45.5 eV with sub-100-fs time resolution. Three-dimensional (3D)
Fermi surface mapping is demonstrated on graphene-covered Ir(111) with energy
and momentum resolutions of $\lesssim$100 meV and
$\lesssim$0.1$\mathring{A}$$^{-1}$, respectively. The table-top experiment sets
the stage for measuring the $k_z$-dependent ultrafast dynamics of 3D electronic
structure, including band structure, Fermi surface, and carrier dynamics in 3D
materials as well as 3D orbital dynamics in molecular layers.


title: Black-holes and neutron stars in entangled relativity

abstract:
  I present two results that show that, despite its unusual non-linear form,
the phenomenology of entangled relativity remains close to the one of general
relativity -- without having any free parameter that can be fine tuned in order
to facilitate this. In particular, I present the analytical solutions for
spherically charged black-holes, and both the analytical and numerical
solutions for neutron stars.


title:
Gene expression noise accelerates the evolution of a biological
  oscillator

abstract:
  Gene expression is a biochemical process, where stochastic binding and
un-binding events naturally generate fluctuations and cell-to-cell variability
in gene dynamics. These fluctuations typically have destructive consequences
for proper biological dynamics and function (e.g., loss of timing and synchrony
in biological oscillators). Here, we show that gene expression noise
counter-intuitively accelerates the evolution of a biological oscillator and,
thus, can impart a benefit to living organisms. We used computer simulations to
evolve two mechanistic models of a biological oscillator at different levels of
gene expression noise. We first show that gene expression noise induces
oscillatory-like dynamics in regions of parameter space that cannot oscillate
in the absence of noise. We then demonstrate that these noise-induced
oscillations generate a fitness landscape whose gradient robustly and quickly
guides evolution by mutation towards robust and self-sustaining oscillation.
These results suggest that noise can help dynamical systems evolve or learn new
behavior by revealing cryptic dynamic phenotypes outside the bifurcation point.


title: Cosmological sudden singularities in $f(R,T)$ gravity

abstract:
  In this work, we study the possibility of finite-time future cosmological
singularities appearing in $f(R,T)$ gravity, where $R$ is the Ricci scalar and
$T$ is the trace of the stress-energy tensor. We present the theory in both the
geometrical and the dynamically equivalent scalar-tensor representation and
obtain the respective equations of motion. In a background
Friedmann-Lema\^{i}tre-Robertson-Walker (FLRW) universe with an arbitrary
curvature and for a generic $C^\infty$ function $f(R,T)$, we prove that the
conservation of the stress-energy tensor prevents the appearance of sudden
singularities in the cosmological context at any order in the time-derivatives
of the scale factor. However, if this assumption is dropped, the theory allows
for sudden singularities to appear at the level of the third time-derivative of
the scale factor $a(t)$, which are compensated by divergences in either the
first time-derivatives of the energy density $\rho(t)$ or the isotropic
pressure $p(t)$. For these cases, we introduce a cosmological model featuring a
sudden singularity that is consistent with the current measurements for the
cosmological parameters, namely, the Hubble constant, deceleration parameter,
and age of the universe, and provide predictions for the still unmeasured jerk
and snap parameters. Finally, we analyse the constraints on a particular model
of the function $f(R,T)$ that guarantees that the system evolves in a direction
favorable to the energy conditions at the divergence time.


title:
A Comprehensive Survey of Spectrum Sharing Schemes from a
  Standardization and Implementation Perspective

abstract:
  As the services and requirements of next-generation wireless networks become
increasingly diversified, it is estimated that the current frequency bands of
mobile network operators (MNOs) will be unable to cope with the immensity of
anticipated demands. Due to spectrum scarcity, there has been a growing trend
among stakeholders toward identifying practical solutions to make the most
productive use of the exclusively allocated bands on a shared basis through
spectrum sharing mechanisms. However, due to the technical complexities of
these mechanisms, their design presents challenges, as it requires coordination
among multiple entities. To address this challenge, in this paper, we begin
with a detailed review of the recent literature on spectrum sharing methods,
classifying them on the basis of their operational frequency regime that is,
whether they are implemented to operate in licensed bands (e.g., licensed
shared access (LSA), spectrum access system (SAS), and dynamic spectrum sharing
(DSS)) or unlicensed bands (e.g., LTE-unlicensed (LTE-U), licensed assisted
access (LAA), MulteFire, and new radio-unlicensed (NR-U)). Then, in order to
narrow the gap between the standardization and vendor-specific implementations,
we provide a detailed review of the potential implementation scenarios and
necessary amendments to legacy cellular networks from the perspective of
telecom vendors and regulatory bodies. Next, we analyze applications of
artificial intelligence (AI) and machine learning (ML) techniques for
facilitating spectrum sharing mechanisms and leveraging the full potential of
autonomous sharing scenarios. Finally, we conclude the paper by presenting open
research challenges, which aim to provide insights into prospective research
endeavors.


title: Finiteness theorems for complements of large divisors

abstract:
  We prove finiteness results on integral points on complements of large
divisors in projective varieties over finitely generated fields of
characteristic zero. To do so, we prove a function field analogue of arithmetic
finiteness results of Corvaja-Zannier and Levin using Wang's function field
Subspace Theorem. We then use a method of Evertse-Gy\H{o}ry for concluding
finiteness of integral points over finitely generated fields from known
finiteness results over number fields.


title:
Infinitesimal Torelli for weighted complete intersections and certain
  Fano threefolds

abstract:
  We generalize the classical approach of describing the infinitesimal Torelli
map in terms of multiplication in a Jacobi ring to the case of quasi-smooth
complete intersections in weighted projective space. As an application, we
prove the infinitesimal Torelli theorem for hyperelliptic Fano threefolds of
Picard rank 1, index 1, degree 4 and study the action of the automorphism group
on cohomology. The results of this paper are used to prove Lang-Vojta's
conjecture for the moduli of such Fano threefolds in a follow-up paper.


title: Hyperbolicity of the moduli of certain Fano threefolds

abstract:
  We prove the Shafarevich conjecture for Fano threefolds of Picard rank 1,
index 1 and degree 4.


title: Electroweak fragmentation at high energies: A Snowmass White Paper

abstract:
  In particle collisions at energies higher than the mass scale involved, the
collinear splitting becomes the dominant phenomena. Suitable descriptions of
the physics in this regime include the parton distribution functions, the
initial state radiations, the final state radiations and the fragmentation
functions. It is of fundamental importance to formulate those functions with
consistent theoretical treatments, and to provide the adequate formalism for
applications, as motivated by the recent discussions of partonic scatterings at
the multi-TeV energy regime. In this report, we briefly present the resummation
of the final-state logarithms as fragmentation functions for the electroweak
processes. As an explicit example, we study an electroweak gauge boson
splitting in the process $pp\to WZj$ and demonstrate the important effects from
the fragmentation.


title: PACS: A Dataset for Physical Audiovisual CommonSense Reasoning

abstract:
  In order for AI to be safely deployed in real-world scenarios such as
hospitals, schools, and the workplace, they should be able to reason about the
physical world by understanding the physical properties and affordances of
available objects, how they can be manipulated, and how they interact with
other physical objects. This research field of physical commonsense reasoning
is fundamentally a multi-sensory task since physical properties are manifested
through multiple modalities, two of them being vision and acoustics. Our paper
takes a step towards real-world physical commonsense reasoning by contributing
PACS: the first audiovisual benchmark annotated for physical commonsense
attributes. PACS contains a total of 13,400 question-answer pairs, involving
1,377 unique physical commonsense questions and 1,526 videos. Our dataset
provides new opportunities to advance the research field of physical reasoning
by bringing audio as a core component of this multimodal problem. Using PACS,
we evaluate multiple state-of-the-art models on this new challenging task.
While some models show promising results (70% accuracy), they all fall short of
human performance (95% accuracy). We conclude the paper by demonstrating the
importance of multimodal reasoning and providing possible avenues for future
research.


title: Towards Explainable Evaluation Metrics for Natural Language Generation

abstract:
  Unlike classical lexical overlap metrics such as BLEU, most current
evaluation metrics (such as BERTScore or MoverScore) are based on black-box
language models such as BERT or XLM-R. They often achieve strong correlations
with human judgments, but recent research indicates that the lower-quality
classical metrics remain dominant, one of the potential reasons being that
their decision processes are transparent. To foster more widespread acceptance
of the novel high-quality metrics, explainability thus becomes crucial. In this
concept paper, we identify key properties and propose key goals of explainable
machine translation evaluation metrics. We also provide a synthesizing overview
over recent approaches for explainable machine translation metrics and discuss
how they relate to those goals and properties. Further, we conduct own novel
experiments, which (among others) find that current adversarial NLP techniques
are unsuitable for automatically identifying limitations of high-quality
black-box evaluation metrics, as they are not meaning-preserving. Finally, we
provide a vision of future approaches to explainable evaluation metrics and
their evaluation. We hope that our work can help catalyze and guide future
research on explainable evaluation metrics and, mediately, also contribute to
better and more transparent text generation systems.


title:
Review of Disentanglement Approaches for Medical Applications -- Towards
  Solving the Gordian Knot of Generative Models in Healthcare

abstract:
  Deep neural networks are commonly used for medical purposes such as image
generation, segmentation, or classification. Besides this, they are often
criticized as black boxes as their decision process is often not human
interpretable. Encouraging the latent representation of a generative model to
be disentangled offers new perspectives of control and interpretability.
Understanding the data generation process could help to create artificial
medical data sets without violating patient privacy, synthesizing different
data modalities, or discovering data generating characteristics. These
characteristics might unravel novel relationships that can be related to
genetic traits or patient outcomes. In this paper, we give a comprehensive
overview of popular generative models, like Generative Adversarial Networks
(GANs), Variational Autoencoders (VAEs), and Flow-based Models. Furthermore, we
summarize the different notions of disentanglement, review approaches to
disentangle latent space representations and metrics to evaluate the degree of
disentanglement. After introducing the theoretical frameworks, we give an
overview of recent medical applications and discuss the impact and importance
of disentanglement approaches for medical applications.


title:
Revisiting flat band superconductivity: dependence on minimal quantum
  metric and band touchings

abstract:
  A critical result in superconductivity is that flat bands, though
dispersionless, can still host nonzero superfluid weight due to quantum
geometry. We show that the derivation of the mean field superfluid weight in
previous literature is incomplete, which can lead to severe quantitative and
even qualitative errors. We derive the complete equations and demonstrate that
the minimal quantum metric -- the metric with minimum trace -- is related to
the superfluid weight in isolated flat bands. We complement this result with an
exact calculation of the Cooper pair mass in attractive Hubbard models with the
uniform pairing condition. When the orbitals are located at high symmetry
positions, the Cooper pair mass is exactly given by the quantum metric, which
is guaranteed to be minimal. Moreover, we study the effect of closing the band
gap between the flat and dispersive bands, and develop a mean-field theory of
pairing for different band-touching points via the $S$-matrix construction. In
mean field, we show that a non-isolated flat band can actually be beneficial
for superconductivity. This is a promising result in the search for high
temperature superconductivity as the material does not need to have flat bands
that are isolated from other bands by the thermal energy. Our work resolves a
fundamental caveat in understanding the relation of multiband superconductivity
to quantum geometry, and the results on band touchings widen the class of
systems advantageous for the search of high temperature flat band
superconductivity.


title: Visualization Onboarding Grounded in Educational Theories

abstract:
  The aim of visualization is to support people in dealing with large and
complex information structures, to make these structures more comprehensible,
facilitate exploration, and enable knowledge discovery. However, users often
have problems reading and interpreting data from visualizations, in particular
when they experience them for the first time. A lack of visualization literacy,
i.e., knowledge in terms of domain, data, visual encoding, interaction, and
also analytical methods can be observed. To support users in learning how to
use new digital technologies, the concept of onboarding has been successfully
applied in other domains. However, it has not received much attention from the
visualization community so far. This chapter aims to fill this gap by defining
the concept and systematically laying out the design space of onboarding in the
context of visualization as a descriptive design space. On this basis, we
present a survey of approaches from the academic community as well as from
commercial products, especially surveying educational theories that inform the
onboarding strategies. Additionally, we derived design considerations based on
previous publications and present some guidelines for the design of
visualization onboarding concepts.


title:
Photons guided by axons may enable backpropagation-based learning in the
  brain

abstract:
  Despite great advances in explaining synaptic plasticity and neuron function,
a complete understanding of the brain's learning algorithms is still missing.
Artificial neural networks provide a powerful learning paradigm through the
backpropagation algorithm which modifies synaptic weights by using feedback
connections. Backpropagation requires extensive communication of information
back through the layers of a network. This has been argued to be biologically
implausible and it is not clear whether backpropagation can be realized in the
brain. Here we suggest that biophotons guided by axons provide a potential
channel for backward transmission of information in the brain. Biophotons have
been experimentally shown to be produced in the brain, yet their purpose is not
understood. We propose that biophotons can propagate from each post-synaptic
neuron to its pre-synaptic one to carry the required information backward. To
reflect the stochastic character of biophoton emissions, our model includes the
stochastic backward transmission of teaching signals. We demonstrate that a
three-layered network of neurons can learn the MNIST handwritten digit
classification task using our proposed backpropagation-like algorithm with
stochastic photonic feedback. We model realistic restrictions and show that our
system still learns the task for low rates of biophoton emission,
information-limited (one bit per photon) backward transmission, and in the
presence of noise photons. Our results suggest a new functionality for
biophotons and provide an alternate mechanism for backward transmission in the
brain.


title:
Work-From-Home is Here to Stay: Call for Flexibility in Post-Pandemic
  Work Policies

abstract:
  In early 2020, the Covid-19 pandemic forced employees in tech companies
worldwide to abruptly transition from working in offices to working from their
homes. During two years of predominantly working from home, employees and
managers alike formed expectations about what post-pandemic working life should
look like. Many companies are currently experimenting with new work policies
that balance both employee- and manager expectations to where, when and how
work should be done in the future. In this article, we gather experiences from
17 companies and their sites, covering 12 countries. We share the results of
corporate surveys of employee preferences for working from home and analyse new
work policies. Our results are threefold. First, through the new work policies
all companies are formally giving more flexibility to the employees with
regards to working time and work location. Second, there is a great variation
in how much flexibility the companies are willing to yield to the employees.
The variation is related both to industry type, size of the companies, and
company culture. Third, we document a change in the psychological contract
between employees and managers, where the option of working from home is
converted from an exclusive perk that managers could choose to give to the few,
to a core privilege that all employees feel they are entitled to. Finally,
there are indications that as the companies learn and solicit feedback
regarding the efficiency of the chosen strategies, we might see further
developments and changes of the work policies with respect to how much
flexibility to work whenever and from anywhere they grant. Through these
findings, the paper contributes to a growing literature about the new trends
emerging from the pandemic in tech companies and spells out practical
implications onwards.


title:
Adiabatic paths of Hamiltonians, symmetries of topological order, and
  automorphism codes

abstract:
  The recent "honeycomb code" is a fault-tolerant quantum memory defined by a
sequence of checks which implements a nontrivial automorphism of the toric
code. We argue that a general framework to understand this code is to consider
continuous adiabatic paths of gapped Hamiltonians and we give a conjectured
description of the fundamental group and second and third homotopy groups of
this space in two spatial dimensions. A single cycle of such a path can
implement some automorphism of the topological order of that Hamiltonian. We
construct such paths for arbitrary automorphisms of two-dimensional doubled
topological order. Then, realizing this in the case of the toric code, we turn
this path back into a sequence of checks, constructing an automorphism code
closely related to the honeycomb code.

categories: quant-ph cond-mat.str-el hep-th math-ph math.MP


title:
Individualizing Head-Related Transfer Functions for Binaural Acoustic
  Applications

abstract:
  A Head Related Transfer Function (HRTF) characterizes how a human ear
receives sounds from a point in space, and depends on the shapes of one's head,
pinna, and torso. Accurate estimations of HRTFs for human subjects are crucial
in enabling binaural acoustic applications such as sound localization and 3D
sound spatialization. Unfortunately, conventional approaches for HRTF
estimation rely on specialized devices or lengthy measurement processes. This
work proposes a novel lightweight method for HRTF individualization that can be
implemented using commercial-off-the-shelf components and performed by average
users in home settings. The proposed method has two key components: a
generative neural network model that can be individualized to predict HRTFs of
new subjects from sparse measurements, and a lightweight measurement procedure
that collects HRTF data from spatial locations. Extensive experiments using a
public dataset and in house measurement data from 10 subjects of different ages
and genders, show that the individualized models significantly outperform a
baseline model in the accuracy of predicted HRTFs. To further demonstrate the
advantages of individualized HRTFs, we implement two prototype applications for
binaural localization and acoustic spatialization. We find that the performance
of a localization model is improved by 15 degree after trained with
individualized HRTFs. Furthermore, in hearing tests, the success rate of
correctly identifying the azimuth direction of incoming sounds increases by
183% after individualization.


title:
Not All Points Are Equal: Learning Highly Efficient Point-based
  Detectors for 3D LiDAR Point Clouds

abstract:
  We study the problem of efficient object detection of 3D LiDAR point clouds.
To reduce the memory and computational cost, existing point-based pipelines
usually adopt task-agnostic random sampling or farthest point sampling to
progressively downsample input point clouds, despite the fact that not all
points are equally important to the task of object detection. In particular,
the foreground points are inherently more important than background points for
object detectors. Motivated by this, we propose a highly-efficient single-stage
point-based 3D detector in this paper, termed IA-SSD. The key of our approach
is to exploit two learnable, task-oriented, instance-aware downsampling
strategies to hierarchically select the foreground points belonging to objects
of interest. Additionally, we also introduce a contextual centroid perception
module to further estimate precise instance centers. Finally, we build our
IA-SSD following the encoder-only architecture for efficiency. Extensive
experiments conducted on several large-scale detection benchmarks demonstrate
the competitive performance of our IA-SSD. Thanks to the low memory footprint
and a high degree of parallelism, it achieves a superior speed of 80+
frames-per-second on the KITTI dataset with a single RTX2080Ti GPU. The code is
available at \url{https://github.com/yifanzhang713/IA-SSD}.


title:
Scattering of spin waves by a Bloch domain wall: effect of the dipolar
  interaction

abstract:
  It is known that a Bloch domain wall in an anisotropic ferromagnet is
transparent to spin waves. This result is derived by approximating the dipolar
interaction between magnetic moments by an effective anisotropy interaction. In
this paper we study the the scattering of spin waves by a domain wall taking
into account the full complexity of the dipolar interaction, treating it
perturbatively in the distorted wave Born approximation. Due to the
peculiarities of the dipolar interaction, the implementation of this
approximation is not straightforward. The difficulties are circumvented here by
realizing that the contribution of the dipolar interaction to the spin wave
operator can be split into two terms: i) an operator that commutes with the
spin wave operator in absence of dipolar interaction, and ii) a local operator
suitable to be treated as a perturbation in the distorted wave Born
approximaton. We analyze the scattering parameters obtained within this
approach. It turns out that the reflection coefficient does not vanish in
general, and that the transmitted waves suffer a lateral shift even at normal
incidence. This lateral shift can be greatlty enhanced by making the spin wave
go through an array of well separated domain walls. The outgoing spin wave will
no be attenuated by the scattering at the domain walls since the reflection
coefficient vanishes at normal incidence. This effect may be very useful to
control the spin waves in magnonic devices.


title:
Can we integrate spatial verification methods into neural-network loss
  functions for atmospheric science?

abstract:
  In the last decade, much work in atmospheric science has focused on spatial
verification (SV) methods for gridded prediction, which overcome serious
disadvantages of pixelwise verification. However, neural networks (NN) in
atmospheric science are almost always trained to optimize pixelwise loss
functions, even when ultimately assessed with SV methods. This establishes a
disconnect between model verification during vs. after training. To address
this issue, we develop spatially enhanced loss functions (SELF) and demonstrate
their use for a real-world problem: predicting the occurrence of thunderstorms
(henceforth, "convection") with NNs. In each SELF we use either a neighbourhood
filter, which highlights convection at scales larger than a threshold, or a
spectral filter (employing Fourier or wavelet decomposition), which is more
flexible and highlights convection at scales between two thresholds. We use
these filters to spatially enhance common verification scores, such as the
Brier score. We train each NN with a different SELF and compare their
performance at many scales of convection, from discrete storm cells to tropical
cyclones. Among our many findings are that (a) for a low (high) risk threshold,
the ideal SELF focuses on small (large) scales; (b) models trained with a
pixelwise loss function perform surprisingly well; (c) however, models trained
with a spectral filter produce better-calibrated probabilities than a pixelwise
model. We provide a general guide to using SELFs, including technical
challenges and the final Python code, as well as demonstrating their use for
the convection problem. To our knowledge this is the most in-depth guide to
SELFs in the geosciences.

categories: cs.LG cs.AI physics.ao-ph stat.AP stat.ME


title: Associator dependent algebras and Koszul duality

abstract:
  We resolve a ten year old open question of Loday of describing Koszul operads
that act on the algebra of octonions. In fact, we obtain the answer by solving
a more general classification problem: we find all Koszul operads among those
encoding associator dependent algebras.


title: Light Deflection Angle by Superentropic Black Holes

abstract:
  Motivated by recent works on light deflection and shadow behaviors on AdS
geometries, we investigate the deflection angle of light rays by superentropic
black holes. Taking appropriate approximations, we first obtain the involved
expression. For large values of the impact parameter, we get a specific value
being zero for ordinary black holes without AdS backgrounds.
  Then, we examine and analyze such an optical quantity by providing graphical
discussions in terms of a bounded region of the moduli space required by
superentropic black hole conditions. Concretely, we study the deflection angle
aspects by varying the black hole mass being fixed in the previous findings.


title: Pointers for Quantum Measurement Theory

abstract:
  In the iconic measurements of atomic spin-1/2 or photon polarization, one
employs two spatially separated and noninteracting detectors. Each detector is
binary, registering the presence or absence of the atom or the photon. For
measurements on a $d$-state particle we recast the standard von Neumann
measurement formalism by replacing the familiar pointer variable with an array
of such detectors, one for each of the $d$ possible outcomes. We show that the
unitary dynamics of the premeasurement process restricts the detector outputs
to the subspace of single outcomes, so that the pointer emerges from the
apparatus. We propose a physical extension of this apparatus which replaces
each detector with an ancilla qubit coupled to a readout device. This
explicitly separates the pointer into distinct quantum and (effectively)
classical parts, and delays the quantum to classical transition. As a result,
one not only recovers the collapse scenario of an ordinary apparatus, but one
can also observe a superposition of the quantum pointer states.


title:
Some general fixed-point theorems for nonlinear mappings connected with
  one Cauchy theorem

abstract:
  In this work, using a new geometrical approach we study to the existence of
the fixed-point of mappings that independence of the smoothness, and also of
their single-values or multi-values. This work proved the theorems that
generalize in some sense the Brouwer and Schauder fixed-point theorems, and
also such type results in multi-valued cases. One can reckon this approach is
based on the generalization of the one theorem Cauchy and on the convexity
properties of sets. As the used approach is based on the geometry of the image
of the examined mappings that are independent of the topological properties of
the space we could to prove the general results for almost every vector space.
The general results we applied to the study of the nonlinear equations and
inclusions in VTS, and also by applying these results are investigated
different concrete nonlinear problems. Here provided also sufficient conditions
under which the conditions of the theorems will fulfill.


title:
Multispectral Satellite Data Classification using Soft Computing
  Approach

abstract:
  A satellite image is a remotely sensed image data, where each pixel
represents a specific location on earth. The pixel value recorded is the
reflection radiation from the earth's surface at that location. Multispectral
images are those that capture image data at specific frequencies across the
electromagnetic spectrum as compared to Panchromatic images which are sensitive
to all wavelength of visible light. Because of the high resolution and high
dimensions of these images, they create difficulties for clustering techniques
to efficiently detect clusters of different sizes, shapes and densities as a
trade off for fast processing time. In this paper we propose a grid-density
based clustering technique for identification of objects. We also introduce an
approach to classify a satellite image data using a rule induction based
machine learning algorithm. The object identification and classification
methods have been validated using several synthetic and benchmark datasets.


title: Teaching language models to support answers with verified quotes

abstract:
  Recent large language models often answer factual questions correctly. But
users can't trust any given claim a model makes without fact-checking, because
language models can hallucinate convincing nonsense. In this work we use
reinforcement learning from human preferences (RLHP) to train "open-book" QA
models that generate answers whilst also citing specific evidence for their
claims, which aids in the appraisal of correctness. Supporting evidence is
drawn from multiple documents found via a search engine, or from a single
user-provided document. Our 280 billion parameter model, GopherCite, is able to
produce answers with high quality supporting evidence and abstain from
answering when unsure. We measure the performance of GopherCite by conducting
human evaluation of answers to questions in a subset of the NaturalQuestions
and ELI5 datasets. The model's response is found to be high-quality 80\% of the
time on this Natural Questions subset, and 67\% of the time on the ELI5 subset.
Abstaining from the third of questions for which it is most unsure improves
performance to 90\% and 80\% respectively, approaching human baselines.
However, analysis on the adversarial TruthfulQA dataset shows why citation is
only one part of an overall strategy for safety and trustworthiness: not all
claims supported by evidence are true.


title: The Todd-Coxeter Algorithm for Semigroups and Monoids

abstract:
  In this paper we provide an account of the Todd-Coxeter algorithm for
computing congruences on semigroups and monoids. We also give a novel
description of an analogue for semigroups of the so-called Felsch strategy from
the Todd-Coxeter algorithm for groups.


title:
On the Theoretical Foundation of Overset Grid Methods for Hyperbolic
  Problems II: Entropy Bounded Formulations for Nonlinear Conservation Laws

abstract:
  We derive entropy conserving and entropy dissipative overlapping domain
formulations for systems of nonlinear hyperbolic equations in conservation
form, such as would be approximated by overset mesh methods. The entropy
conserving formulation imposes two-way coupling at the artificial interface
boundaries through nonlinear penalty functions that vanish when the solutions
coincide. The penalty functions are expressed in terms of entropy conserving
fluxes originally introduced for finite volume schemes. Entropy dissipation and
additional coupling in the overlap region are added through the use of linear
penalties.


title: On the growth rates for a three-layer flow in porous media

abstract:
  We study the displacement of three immiscible Stokes fluids with constant
viscosities in a porous medium. The middle-layer fluid is contained in a
bounded region. We give an analysis of the linear stability of this process.
This stability problem has no solution (in general).


title:
Prediction of chaotic attractors in quasiperiodically forced logistic
  map using deep learning

abstract:
  We forecast two different chaotic dynamics of the quasiperiodically forced
logistic map using the well-known deep learning framework Long Short-Term
Memory. We generate two data sets and use one in the training process and the
other in the testing process. The predicted values are evaluated using the
metric called Root Mean Square Error and visualized using the scatter plots.
The robustness of the Long Short-Term Memory model is evaluated using the
number of units in the layers of the model. We also make multi-step forecasting
of the considered system. We show that the considered Long Short-Term Memory
model performs well in predicting chaotic attractors upto three steps.


title: Short Text Topic Modeling: Application to tweets about Bitcoin

abstract:
  Understanding the semantic of a collection of texts is a challenging task.
Topic models are probabilistic models that aims at extracting "topics" from a
corpus of documents. This task is particularly difficult when the corpus is
composed of short texts, such as posts on social networks. Following several
previous research papers, we explore in this paper a set of collected tweets
about bitcoin. In this work, we train three topic models and evaluate their
output with several scores. We also propose a concrete application of the
extracted topics.


title:
Lower-Critical Dimension of the Random-Field XY Model and the
  Zero-Temperature Critical Line

abstract:
  The random-field XY model is studied in spatial dimensions d=3 and 4, and
in-between, as the limit q --> \infty of the q-state clock models, by the exact
renormalization-group solution of the hierarchical lattice or, equivalently,
the Migdal-Kadanoff approximation to the hypercubic lattices. The
lower-critical dimension is determined between 3.81 < d_c <4. When the
random-field is scaled with q, a line segment of zero-temperature criticality
is found in d=3. When the random-field is scaled with q^2, a universal phase
diagram is found at intermediate temperatures in d=3.


title:
A Vanka-type multigrid solver for complex-shifted Laplacian systems from
  diagonalization-based parallel-in-time algorithms

abstract:
  We propose and analyze a Vanka-type multigrid solver for solving a sequence
of complex-shifted Laplacian systems arising in diagonalization-based
parallel-in-time algorithms for evolutionary equations. Under suitable
assumption, local Fourier analysis shows the proposed Vanka-type smoother
achieves a uniform smoothing factor, which is verified by several numerical
examples.


title:
Application of Quantum Density Matrix in Classical Question Answering
  and Classical Image Classification

abstract:
  Quantum density matrix represents all the information of the entire quantum
system, and novel models of meaning employing density matrices naturally model
linguistic phenomena such as hyponymy and linguistic ambiguity, among others in
quantum question answering tasks. Naturally, we argue that applying the quantum
density matrix into classical Question Answering (QA) tasks can show more
effective performance. Specifically, we (i) design a new mechanism based on
Long Short-Term Memory (LSTM) to accommodate the case when the inputs are
matrixes; (ii) apply the new mechanism to QA problems with Convolutional Neural
Network (CNN) and gain the LSTM-based QA model with the quantum density matrix.
Experiments of our new model on TREC-QA and WIKI-QA data sets show encouraging
results. Similarly, we argue that the quantum density matrix can also enhance
the image feature information and the relationship between the features for the
classical image classification. Thus, we (i) combine density matrices and CNN
to design a new mechanism; (ii) apply the new mechanism to some representative
classical image classification tasks. A series of experiments show that the
application of quantum density matrix in image classification has the
generalization and high efficiency on different datasets. The application of
quantum density matrix both in classical question answering tasks and classical
image classification tasks show more effective performance.


title: Operator Sketching for Deep Unrolling Networks

abstract:
  In this work we propose a new paradigm for designing efficient deep unrolling
networks using operator sketching. The deep unrolling networks are currently
the state-of-the-art solutions for imaging inverse problems. However, for
high-dimensional imaging tasks, especially the 3D cone-beam X-ray CT and 4D MRI
imaging, the deep unrolling schemes typically become inefficient both in terms
of memory and computation, due to the need of computing multiple times the
high-dimensional forward and adjoint operators. Recently researchers have found
that such limitations can be partially addressed by stochastic unrolling with
subsets of operators, inspired by the success of stochastic first-order
optimization. In this work, we propose a further acceleration upon stochastic
unrolling, using sketching techniques to approximate products in the
high-dimensional image space. The operator sketching can be jointly applied
with stochastic unrolling for the best acceleration and compression
performance. Our numerical experiments on X-ray CT image reconstruction
demonstrate the remarkable effectiveness of our sketched unrolling schemes.


title:
AI Annotated Recommendations in an Efficient Visual Learning Environment
  with Emphasis on YouTube (AI-EVL)

abstract:
  In this article, we create a system called AI-EVL. This is an annotated-based
learning system. We extend AI to learning experience. If a user from the main
YouTube page browses YouTube videos and a user from the AI-EVL system does the
same, the amount of traffic used will be much less. It is due to ignoring
unwanted contents which indicates a reduction in bandwidth usage too. This
system is designed to be embedded with online learning tools and platforms to
enrich their curriculum. In evaluating the system using Google 2020 trend data,
we were able to extract rich ontological information for each data. Of the data
collected, 34.86% belong to wolfram, 30.41% to DBpedia, and 34.73% to
Wikipedia. The video subtitle information is displayed interactively and
functionally to the user over time as the video is played. This effective
visual learning system, due to the unique features, prevents the user's
distraction and makes learning more focused. The information about the subtitle
text is displayed in multiple layers including AI-annotated topics,
Wikipedia/DBpedia, and Wolfram enriched texts via interactive and visual
widgets.


title: A Measure Theoretic Paradox from a continuous colouring rule

abstract:
  Given a probability space $(X, {\cal B}, m)$, measure preserving
transformations $g_1, \dots , g_k$ of $X$, and a colour set $C$, a colouring
rule is a way to colour the space with $C$ such that the colours allowed for
apoint $x$ are determined by that point's location and the colours of the
finitely $g_1 (x), \dots , g_k(x)$ with $g_i(x) \not= x$ for all $i$ and almost
all $x$. We represent a colouring rule as a correspondence $F$ defined on
$X\times C^k$ with values in $C$. A function $f: X\rightarrow C$ satisfies the
rule at $x$ if $f(x) \in F( x, f(g_1 x), \dots , f(g_k x))$. A colouring rule
is paradoxical if it can be satisfied in some way almost everywhere with
respect to $m$, but not in {\bf any} way that is measurable with respect to a
finitely additive measure that extends the probability measure $m$ defined on
${\cal B}$ and for which the finitely many transformations $g_1, \dots , g_k$
remain measure preserving. Can a colouring rule be paradoxical if both $X$ and
the colour set $C$ are convex and compact sets and the colouring rule says if
$c: X\rightarrow C$ is the colouring function then the colour $c(x)$ must lie
($m$ a.e.) in $F(x, c(g_1(x) ), \dots , c(g_k(x)))$ for a non-empty
upper-semi-continuous convex-valued correspondence $F$ defined on $X\times
C^k$? The answer is yes, and we present such an example. We show that this
result is robust, including that any colouring that approximates the
correspondence by $\epsilon$ for small enough positive $\epsilon$ also cannot
be measurable in the same finitely additive way. Because non-empty
upper-semi-continuous convex-valued correspondences on Euclidean space can be
approximated by continuous functions, there are paradoxical colouring rules
that are defined by continuous functions.


title:
Which is a better cosmological probe: Number counts or cosmic
  magnification?

abstract:
  The next generation of cosmological surveys will have unprecedented
measurement precision, hence they hold the power to put theoretical ideas to
the most stringent tests yet. However, in order to realise the full potential
of these measurements, we need to ensure that we apply the most effective
analytical tools. We need to identify which cosmological observables are the
best cosmological probes. Two commonly used cosmological observables are the
galaxy number counts, and the cosmic magnification. The galaxy number counts
are a diagnostic of the growth of structure, hence are able to probe the nature
of dark energy (DE). On the other hand, the cosmic magnification is a
diagnostic of cosmic distances and sizes -- consequently, a diagnostic of the
geometry of the large scale structure -- hence is also able to probe the nature
of DE. Both of these observables have been investigated extensively in
cosmological analyses, but only separately. In this paper, by using the angular
power spectrum, we investigate both the galaxy number counts and the cosmic
magnification, on ultra-large scales. Our results suggest that measuring
relativistic effects in the cosmic magnification angular power spectrum will be
relatively better than in the number-count angular power spectrum, at all
redshifts z. On the other hand, we found that without relativistic effects, the
number-count angular power spectrum will be relatively better in probing the
imprint of interacting DE (IDE), at all z. At low z (up to around z = 0.1),
relativistic effects enables the cosmic magnification angular power spectrum to
be a relatively better probe of the IDE imprint; while at higher z (up to z <
3), the number-count angular power spectrum becomes the better probe of IDE
imprint. However, at z = 3 and higher, our results suggest that either the
number-count or the magnification angular power spectrum, will suffice to probe
IDE.


title:
Drive&Segment: Unsupervised Semantic Segmentation of Urban Scenes via
  Cross-modal Distillation

abstract:
  This work investigates learning pixel-wise semantic image segmentation in
urban scenes without any manual annotation, just from the raw non-curated data
collected by cars which, equipped with cameras and LiDAR sensors, drive around
a city. Our contributions are threefold. First, we propose a novel method for
cross-modal unsupervised learning of semantic image segmentation by leveraging
synchronized LiDAR and image data. The key ingredient of our method is the use
of an object proposal module that analyzes the LiDAR point cloud to obtain
proposals for spatially consistent objects. Second, we show that these 3D
object proposals can be aligned with the input images and reliably clustered
into semantically meaningful pseudo-classes. Finally, we develop a cross-modal
distillation approach that leverages image data partially annotated with the
resulting pseudo-classes to train a transformer-based model for image semantic
segmentation. We show the generalization capabilities of our method by testing
on four different testing datasets (Cityscapes, Dark Zurich, Nighttime Driving
and ACDC) without any finetuning, and demonstrate significant improvements
compared to the current state of the art on this problem. See project webpage
https://vobecant.github.io/DriveAndSegment/ for the code and more.


title: Power-law scaling of correlations in statistically polarised nano-NMR

abstract:
  Nano-scale nuclear magnetic resonance with NV centres heralds a paradigm
shift in biosensing, but its success has been so far prevented by diffusion
broadening of spectral features, which hinders the ability to resolve
frequencies in spectral measurements. The problem is a direct consequence of
the widely accepted assumption that nuclear spin signal correlations decay
exponentially in nano-NMR. However, a more accurate analysis of diffusion shows
that correlations survive for a longer time due to a power-law scaling,
yielding the possibility for improved resolution. Nevertheless, such behaviour
remains to be demonstrated in experiments. Using three different experimental
setups and disparate measurement techniques, we present overwhelming evidence
of power-law decay of correlations. These result in sharp-peaked spectral
lines, potentially allowing for improved resolution even in the presence of
diffusion broadening.


title:
Enantioselective optical forces in gain-functionalized single core-shell
  chiral nanoparticles

abstract:
  We propose a gain-assisted enantioselective scheme in dye-doped chiral
core-shell particles using a single plane wave. We demonstrate that optical
pulling and pushing forces on the gain-functionalized chiral particles can be
achieved and tuned using externally controllable parameters using dye molecules
concentration and pumping rate. The results demonstrate that controlling the
pump rate allows one to efficiently exert pulling forces on a given chiral
enantiomer, while pushing the enantiomer of opposite handedness, demonstrating
an all-optical chiral resolution of racemic mixtures. Since this scheme depends
on the gain of single chiral particles, which also compensate for optical
losses, this method may be applied not only to lossless chiral particles but
also to lossy plasmonic chiral particles.


title:
Evaluating Token-Level and Passage-Level Dense Retrieval Models for Math
  Information Retrieval

abstract:
  With the recent success of dense retrieval methods based on bi-encoders, a
number of studies have applied this approach to various interesting downstream
retrieval tasks with good efficiency and in-domain effectiveness. Recently, we
have also seen the presence of dense retrieval models in Math Information
Retrieval (MIR) tasks, but the most effective systems remain "classic"
retrieval methods that consider rich structure features. In this work, we try
to combine the best of both worlds: a well-defined structure search method for
effective formula search and bi-encoder dense retrieval models to capture
contextual similarities in mathematical documents. Specifically, we have
evaluated two representative bi-encoder models (ColBERT and DPR) for
token-level and passage-level dense retrieval on recent MIR tasks. To our best
knowledge, this is the first time a DPR model has been evaluated in the MIR
domain. Our result shows that bi-encoder models are complementary to existing
structure search methods, and we are able to advance the state of the art on a
recent MIR dataset. We have made our model checkpoints and source code publicly
available for the reproduction of our results.


title:
Improving clinical trial interpretation with Acceptability Curve
  Estimation (ACE)

abstract:
  Effective decision making from randomised controlled clinical trials relies
on robust interpretation of the numerical results. However, the language we use
to describe clinical trials can cause confusion in both trial design and in
comparing results across trials. Acceptability curve estimation (ACE) aids
comparison between trials (even where of different designs) by harmonising
reporting of results, acknowledging different interpretations of the results
may be valid in different situations, and moving the focus from comparison to a
pre-specified value to interpretation of the trial data. ACE can be applied to
historical trials or incorporated into statistical analysis plans for future
analyses. An online tool enables ACE on up to three trials simultaneously.


title:
Constraints of anisotropy on warm power-law inflation in light of Planck
  results

abstract:
  In this article, we examine the effects of anisotropy on a model of canonical
warm inflation with the power-law potential. The results of this model are
compared to Planck satellite observational data. Using a conventional local
scalar fi?eld in the Bianchi type I metric, the slow-roll conditions and the
suitable regions, where the free parameters of the model show a good agreement
with Planck results, are investigated in detail. Following the usual
calculations for warm inflationary approaches, the early universe in two
different dissipative regimes, namely the weak dissipative and the strong
dissipative ones, is investigated. In this regard, the slow-roll parameters and
their observational indices in both regimes are obtained and finally, it is
shown that this model is in good agreement with observatio


title: Identification of maximal $C^*$-covers of some operator algebras

abstract:
  We use results on inclusions of free products and extensions of completely
positive maps to determine the maximal $C^*$-envelope for upper triangular $3
\times 3$ matrices. We consider these same results in the context of larger
upper triangular matrices and graph algebras associated to cycle graphs.


title: Force-matching Coarse-Graining without Forces

abstract:
  Coarse-grained (CG) molecular simulations have become a standard tool to
study molecular processes on time-~and length-scales inaccessible to all-atom
simulations. Learning CG force fields from all-atom data has mainly relied on
force-matching and relative entropy minimization. Force-matching is
straightforward to implement but requires the forces on the CG particles to be
saved during all-atom simulation, and because these instantaneous forces depend
on all degrees of freedom, they provide a very noisy signal that makes training
the CG force field data inefficient. Relative entropy minimization does not
require forces to be saved and is more data-efficient, but requires the CG
model to be re-simulated during the iterative training procedure, which can
make the training procedure extremely costly or lead to failure to converge.
Here we present \emph{flow-matching}, a new training method for CG force fields
that combines the advantages of force-matching and relative entropy
minimization by leveraging normalizing flows, a generative deep learning
method. Flow-matching first trains a normalizing flow to represent the CG
probability density by using relative entropy minimization without suffering
from the re-simulation problem because flows can directly sample from the
equilibrium distribution they represent. Subsequently, the forces of the flow
are used to train a CG force field by matching the coarse-grained forces
directly, which is a much easier problem than traditional force-matching as it
does not suffer from the noise problem. Besides not requiring forces,
flow-matching also outperforms classical force-matching by an order of
magnitude in terms of data efficiency and produces CG models that can capture
the folding and unfolding of small proteins.

categories: physics.comp-ph cs.LG physics.bio-ph physics.chem-ph


title: Feature Selection for Vertex Discriminant Analysis

abstract:
  We revisit vertex discriminant analysis (VDA) from the perspective of
proximal distance algorithms. By specifying sparsity sets as constraints that
directly control the number of active features, VDA is able to fit multiclass
classifiers with no more than $k$ active features. We combine our sparse VDA
approach with repeated cross validation to fit classifiers across the full
range of model sizes on a given dataset. Our numerical examples demonstrate
that grappling with sparsity directly is an attractive approach to model
building in high-dimensional settings. Applications to kernel-based VDA are
also considered.


title: Short-Lived Radionuclides in Meteorites and the Sun's Birth Environment

abstract:
  The solar nebula contained a number of short-lived radionuclides (SLRs) with
half-lives of tens of Myr or less, comparable to the timescales for formation
of protostars and protoplanetary disks. Therefore, determining the origins of
SLRs would provide insights into star formation and the Sun's astrophysical
birth environment. In this chapter, we review how isotopic studies of
meteorites reveal the existence and abundances of these now-extinct
radionuclides; and the evidence that the SLR ${}^{10}{\rm Be}$, which uniquely
among the SLRs is not produced during typical stellar nucleosynthesis, was
distributed homogeneously in the solar nebula. We review the evidence that the
SLRs ${}^{26}{\rm Al}$, ${}^{53}{\rm Mn}$, and ${}^{182}{\rm Hf}$, and other
radionuclides, were also homogeneously distributed and can be used to date
events during the Solar System's planet-forming epoch. The homogeneity of the
SLRs, especially ${}^{10}{\rm Be}$, strongly suggests they were all inherited
from the Sun's molecular cloud, and that production by irradiation within the
solar nebula was very limited, except for ${}^{36}{\rm Cl}$. We review
astrophysical models for the origin of ${}^{10}{\rm Be}$, showing that it
requires that the Sun formed in a spiral arm of the Galaxy with higher star
formation rate than the Galaxy-wide average. Likewise, we review the
astrophysical models for the origins of the other SLRs and show that they
likely arose from contamination of the Sun's molecular cloud by massive stars
over tens of Myr, most likely dominated by ejecta from Wolf-Rayet stars. The
other SLRs also demand formation of the Sun in a spiral arm of the Galaxy with
a star formation rate as high as demanded by the Solar System initial
${}^{10}{\rm Be}$ abundance. We discuss the astrophysical implications, and
suggest further tests of these models and future directions for the field.

categories: astro-ph.EP astro-ph.GA astro-ph.SR physics.geo-ph


title: Dispersive Sum Rules in AdS$_2$

abstract:
  Dispersion relations for S-matrices and CFT correlators translate UV
consistency into bounds on IR observables. In this note, we construct
dispersive sum rules for 1D CFTs. We use them to prove bounds on
higher-derivative couplings in weakly-coupled non-gravitational EFTs in
AdS$_2$. At the leading order in the bulk-point limit, the bounds agree with
the flat-space result. We compute the leading universal effect of finite AdS
radius on the bounds. Along the way, we give an explicit formula for anomalous
dimensions in general higher-derivative contact Witten diagrams in AdS$_2$.


title: Self-Consistency Improves Chain of Thought Reasoning in Language Models

abstract:
  We explore a simple ensemble strategy, self-consistency, that significantly
improves the reasoning accuracy of large language models. The idea is to sample
a diverse set of reasoning paths from a language model via chain of thought
prompting then return the most consistent final answer in the set. We evaluate
self-consistency on a range of arithmetic and commonsense reasoning benchmarks,
and find that it robustly improves accuracy across a variety of language models
and model scales without the need for additional training or auxiliary models.
When combined with a recent large language model, PaLM-540B, self-consistency
increases performance to state-of-the-art levels across several benchmark
reasoning tasks, including GSM8K (56.5% -> 74.4%), SVAMP (79.0% -> 86.6%), AQuA
(35.8% -> 48.3%), StrategyQA (75.3% -> 81.6%) and ARC-challenge (85.2% ->
88.7%).


title: SETI in 2021

abstract:
  In this second installment of SETI in 20xx, we very briefly and subjectively
review developments in SETI in 2021. Our primary focus is 93 papers and books
published or made public in 2021, which we sort into six broad categories:
results from actual searches, new search methods and instrumentation, target
and frequency selection, the development of technosignatures, theory of ETIs,
and social aspects of SETI.


title: Interpreting Class Conditional GANs with Channel Awareness

abstract:
  Understanding the mechanism of generative adversarial networks (GANs) helps
us better use GANs for downstream applications. Existing efforts mainly target
interpreting unconditional models, leaving it less explored how a conditional
GAN learns to render images regarding various categories. This work fills in
this gap by investigating how a class conditional generator unifies the
synthesis of multiple classes. For this purpose, we dive into the widely used
class-conditional batch normalization (CCBN), and observe that each feature
channel is activated at varying degrees given different categorical embeddings.
To describe such a phenomenon, we propose channel awareness, which
quantitatively characterizes how a single channel contributes to the final
synthesis. Extensive evaluations and analyses on the BigGAN model pre-trained
on ImageNet reveal that only a subset of channels is primarily responsible for
the generation of a particular category, similar categories (e.g., cat and dog)
usually get related to some same channels, and some channels turn out to share
information across all classes. For good measure, our algorithm enables several
novel applications with conditional GANs. Concretely, we achieve (1) versatile
image editing via simply altering a single channel and manage to (2)
harmoniously hybridize two different classes. We further verify that the
proposed channel awareness shows promising potential in (3) segmenting the
synthesized image and (4) evaluating the category-wise synthesis performance.


title: DiffPoseNet: Direct Differentiable Camera Pose Estimation

abstract:
  Current deep neural network approaches for camera pose estimation rely on
scene structure for 3D motion estimation, but this decreases the robustness and
thereby makes cross-dataset generalization difficult. In contrast, classical
approaches to structure from motion estimate 3D motion utilizing optical flow
and then compute depth. Their accuracy, however, depends strongly on the
quality of the optical flow. To avoid this issue, direct methods have been
proposed, which separate 3D motion from depth estimation but compute 3D motion
using only image gradients in the form of normal flow. In this paper, we
introduce a network NFlowNet, for normal flow estimation which is used to
enforce robust and direct constraints. In particular, normal flow is used to
estimate relative camera pose based on the cheirality (depth positivity)
constraint. We achieve this by formulating the optimization problem as a
differentiable cheirality layer, which allows for end-to-end learning of camera
pose. We perform extensive qualitative and quantitative evaluation of the
proposed DiffPoseNet's sensitivity to noise and its generalization across
datasets. We compare our approach to existing state-of-the-art methods on
KITTI, TartanAir, and TUM-RGBD datasets.


title:
Quasilinear-time Computation of Generic Modal Witnesses for Behavioural
  Inequivalence

abstract:
  We provide a generic algorithm for constructing formulae that distinguish
behaviourally inequivalent states in systems of various transition types such
as nondeterministic, probabilistic or weighted; genericity over the transition
type is achieved by working with coalgebras for a set functor in the paradigm
of universal coalgebra. For every behavioural equivalence class in a given
system, we construct a formula which holds precisely at the states in that
class. The algorithm instantiates to deterministic finite automata, transition
systems, labelled Markov chains, and systems of many other types. The ambient
logic is a modal logic featuring modalities that are generically extracted from
the functor; these modalities can be systematically translated into custom sets
of modalities in a postprocessing step. The new algorithm builds on an existing
coalgebraic partition refinement algorithm. It runs in time $\mathcal{O}((m+n)
\log n)$ on systems with $n$ states and $m$ transitions, and the same
asymptotic bound applies to the dag size of the formulae it constructs. This
improves the bounds on run time and formula size compared to previous
algorithms even for previously known specific instances, viz. transition
systems and Markov chains; in particular, the best previous bound for
transition systems was $\mathcal{O}(m n)$.


title: One After Another: Learning Incremental Skills for a Changing World

abstract:
  Reward-free, unsupervised discovery of skills is an attractive alternative to
the bottleneck of hand-designing rewards in environments where task supervision
is scarce or expensive. However, current skill pre-training methods, like many
RL techniques, make a fundamental assumption - stationary environments during
training. Traditional methods learn all their skills simultaneously, which
makes it difficult for them to both quickly adapt to changes in the
environment, and to not forget earlier skills after such adaptation. On the
other hand, in an evolving or expanding environment, skill learning must be
able to adapt fast to new environment situations while not forgetting
previously learned skills. These two conditions make it difficult for classic
skill discovery to do well in an evolving environment. In this work, we propose
a new framework for skill discovery, where skills are learned one after another
in an incremental fashion. This framework allows newly learned skills to adapt
to new environment or agent dynamics, while the fixed old skills ensure the
agent doesn't forget a learned skill. We demonstrate experimentally that in
both evolving and static environments, incremental skills significantly
outperform current state-of-the-art skill discovery methods on both skill
quality and the ability to solve downstream tasks. Videos for learned skills
and code are made public on https://notmahi.github.io/disk


title:
Connectivity of the Feasible and Sublevel Sets of Dynamic Output
  Feedback Control with Robustness Constraints

abstract:
  This paper considers the optimization landscape of linear dynamic output
feedback control with $\mathcal{H}_\infty$ robustness constraints. We consider
the feasible set of all the stabilizing full-order dynamical controllers that
satisfy an additional $\mathcal{H}_\infty$ robustness constraint. We show that
this $\mathcal{H}_\infty$-constrained set has at most two path-connected
components that are diffeomorphic under a mapping defined by a similarity
transformation. Our proof technique utilizes a classical change of variables in
$\mathcal{H}_\infty$ control to establish a subjective mapping from a set with
a convex projection to the $\mathcal{H}_\infty$-constrained set. This proof
idea can also be used to establish the same topological properties of strict
sublevel sets of linear quadratic Gaussian (LQG) control and optimal
$\mathcal{H}_\infty$ control. Our results bring positive news for
gradient-based policy search on robust control problems.


title: Physics-driven Synthetic Data Learning for Biomedical Magnetic Resonance

abstract:
  Deep learning has innovated the field of computational imaging. One of its
bottlenecks is unavailable or insufficient training data. This article reviews
an emerging paradigm, imaging physics-based data synthesis (IPADS), that can
provide huge training data in biomedical magnetic resonance without or with few
real data. Following the physical law of magnetic resonance, IPADS generates
signals from differential equations or analytical solution models, making the
learning more scalable, explainable, and better protecting privacy. Key
components of IPADS learning, including signal generation models, basic deep
learning network structures, enhanced data generation, and learning methods are
discussed. Great potentials of IPADS have been demonstrated by representative
applications in fast imaging, ultrafast signal reconstruction and accurate
parameter quantification. Finally, open questions and future work have been
discussed.


title: Magnetic fields in star formation: from clouds to cores

abstract:
  In this chapter we review recent advances in understanding the roles that
magnetic fields play throughout the star formation process, gained through
observations and simulations of molecular clouds, the dense, star-forming phase
of the magnetised, turbulent interstellar medium (ISM). Recent results broadly
support a picture in which the magnetic fields of molecular clouds transition
from being gravitationally sub-critical and near equipartition with turbulence
in low-density cloud envelopes, to being energetically sub-dominant in dense,
gravitationally unstable star-forming cores. Magnetic fields appear to play an
important role in the formation of cloud substructure by setting preferred
directions for large-scale gas flows in molecular clouds, and can direct the
accretion of material onto star-forming filaments and hubs. Low-mass star
formation may proceed in environments close to magnetic criticality; high-mass
star formation remains less well-understood, but may proceed in more
supercritical environments. The interaction between magnetic fields and
(proto)stellar feedback may be particularly important in setting star formation
efficiency. We also review a range of widely-used techniques for quantifying
the dynamic importance of magnetic fields, concluding that better-calibrated
diagnostics are required in order to use the spectacular range of forthcoming
observations and simulations to quantify our emerging understanding of how
magnetic fields influence the outcome of the star formation process.


title:
A Critical String Theory in (3+1)+4 Dimensions and an extension of the
  Standard Model

abstract:
  We consider open strings moving in a (3+1)+4 dimensional space, assuming that
there are two local $N=1$ world-sheet supersymmetries. One is associated with
the dynamics of the oriented string in (3+1) dimensional Minkowski spacetime
and the other with a 4-dimensional internal space adding $SU(3)_C$ and
$SU(2)_L$ quantum numbers to it. Together, the longitudinal and time components
of the world-sheet fermions add the weak hypercharge to a state. At zero
temperature, we consider the Fermi Sea of the world-sheet fermions as the
ground state of non-interacting strings. The theory is regular in (3+1)+4
dimensions. Even though the full spectrum contains both bosons and fermions,
there is no space-time supersymmetry for the lack of triality. There are three
types of left-handed neutrinos. The right-handed neutrino is sterile. The
vacuum energy after the tachyon induced spontaneous breaking of the $SU(2)_L$
symmetry makes the string be point objects with spin $\leq1$ in the matter
sector.


title: Renormalization of gluonic leading-twist Operators in covariant Gauges

abstract:
  We provide the all-loop structure of gauge-variant operators required for the
renormalisation of Green's functions with insertions of twist-two operators in
Yang-Mills theory. Using this structure we work out an explicit basis valid up
to 4-loop order for an arbitrary compact simple gauge group. To achieve this we
employ a generalised gauge symmetry, originally proposed by Dixon and Taylor,
which arises after adding to the Yang-Mills Lagrangian also operators
proportional to its equation of motion. Promoting this symmetry to a
generalised BRST symmetry allows to generate the ghost operator from a single
exact operator in the BRST-generalised sense. We show that our construction
complies with the theorems by Joglekar and Lee. We further establish the
existence of a generalised anti-BRST symmetry which we employ to derive
non-trivial relations among the anomalous dimension matrices of ghost and
equation-of-motion operators. For the purpose of demonstration we employ the
formalism to compute the N=2,4 Mellin moments of the gluonic splitting function
up to 4 loops and its N=6 Mellin moment up to 3 loops, where we also take
advantage of additional simplifications of the background field formalism.


title:
Efficient simulation of Gottesman-Kitaev-Preskill states with Gaussian
  circuits

abstract:
  We study the classical simulatability of Gottesman-Kitaev-Preskill (GKP)
states in combination with arbitrary displacements, a large set of symplectic
operations and homodyne measurements. For these types of circuits, neither
continuous-variable theorems based on the non-negativity of quasi-probability
distributions nor discrete-variable theorems such as the Gottesman-Knill
theorem can be employed to assess the simulatability. We first develop a method
to evaluate the probability density function corresponding to measuring a
single GKP state in the position basis following arbitrary squeezing and a
large set of rotations. This method involves evaluating a transformed Jacobi
theta function using techniques from analytic number theory. We then use this
result to identify two large classes of multimode circuits which are
classically efficiently simulatable and are not contained by the GKP encoded
Clifford group. Our results extend the set of circuits previously known to be
classically efficiently simulatable.


title: Masked Discrimination for Self-Supervised Learning on Point Clouds

abstract:
  Masked autoencoding has achieved great success for self-supervised learning
in the image and language domains. However, mask based pretraining has yet to
show benefits for point cloud understanding, likely due to standard backbones
like PointNet being unable to properly handle the training versus testing
distribution mismatch introduced by masking during training. In this paper, we
bridge this gap by proposing a discriminative mask pretraining Transformer
framework, MaskPoint}, for point clouds. Our key idea is to represent the point
cloud as discrete occupancy values (1 if part of the point cloud; 0 if not),
and perform simple binary classification between masked object points and
sampled noise points as the proxy task. In this way, our approach is robust to
the point sampling variance in point clouds, and facilitates learning rich
representations. We evaluate our pretrained models across several downstream
tasks, including 3D shape classification, segmentation, and real-word object
detection, and demonstrate state-of-the-art results while achieving a
significant pretraining speedup (e.g., 4.1x on ScanNet) compared to the prior
state-of-the-art Transformer baseline. Code will be publicly available at
https://github.com/haotian-liu/MaskPoint.


title:
A discontinuous Galerkin spectral element method for a nonconservative
  compressible multicomponent flow model

abstract:
  In this work, we propose an accurate, robust, and stable discretization of
the gamma-based compressible multicomponent model by Shyue [J. Comput. Phys.,
142 (1998), 208-242] where each component follows a stiffened gas equation of
state (EOS). We here extend the framework proposed in Renac [J. Comput. Phys.
382 (2019), 1-26] and Coquel et al. [J. Comput. Phys. 431 (2021) 110135] for
the discretization of hyperbolic systems, with both fluxes and nonconservative
products, to unstructured meshes with curved elements in multiple space
dimensions. The framework relies on the discontinuous Galerkin spectral element
method (DGSEM) using collocation of quadrature and interpolation points. We
modify the integrals over discretization elements where we replace the physical
fluxes and nonconservative products by two-point numerical fluctuations. The
contributions of this work are threefold. First, we analyze the semi-discrete
DGSEM discretization and prove that the scheme is high-order accurate,
free-stream preserving, and entropy stable when excluding material interfaces.
Second, we design a three-point scheme with a HLLC solver that does not require
a root-finding algorithm for approximating the nonconservative products. The
scheme is proved to be robust and entropy stable for convex entropies,
preserves uniform states across material interfaces, satisfies a discrete
minimum principle on the specific entropy and maximum principles on the EOS
parameters. Third, the HLLC solver is applied at interfaces in the DGSEM
scheme, while we consider two kinds of fluctuations in the integrals over
discretization elements: material interface preserving and entropy
conservative. Time integration is performed using SSP Runge-Kutta schemes. The
high-order accuracy, nonlinear stability, and robustness of the present scheme
are assessed through several numerical experiments in one and two space
dimensions.