title: Threshold Rates of Codes Ensembles: Linear is Best

abstract:
  In this work, we prove new results concerning the combinatorial properties of
random linear codes.
  Firstly, we prove a lower bound on the list-size required for random linear
codes over $\mathbb F_q$ $\varepsilon$-close to capacity to list-recover with
error radius $\rho$ and input lists of size $\ell$. We show that the list-size
$L$ must be at least $\frac{\log_q\binom{q}{\ell}-R}{\varepsilon}$, where $R$
is the rate of the random linear code. As a comparison, we also pin down the
list size of random codes which is $\frac{\log_q\binom{q}{\ell}}{\varepsilon}$.
This leaves open the possibility (that we consider likely) that random linear
codes perform better than random codes for list-recoverability, which is in
contrast to a recent gap shown for the case of list-recovery from erasures
(Guruswami et al., IEEE TIT 2021B).
  Next, we consider list-decoding with constant list-sizes. Specifically, we
obtain new lower bounds on the rate required for list-of-$3$ decodability of
random linear codes over $\mathbb F_2$; and list-of-$2$ decodability of random
linear codes over $\mathbb F_q$ (for any $q$). This expands upon Guruswami et
al. (IEEE TIT 2021A) which only studied list-of-$2$ decodability of random
linear codes over $\mathbb F_2$. Further, in both cases we are able to show
that the rate is larger than that which is possible for uniformly random codes.


title: Tunable Quantum Neural Networks in the QPAC-Learning Framework

abstract:
  In this paper, we investigate the performances of tunable quantum neural
networks in the Quantum Probably Approximately Correct (QPAC) learning
framework. Tunable neural networks are quantum circuits made of
multi-controlled X gates. By tuning the set of controls these circuits are able
to approximate any Boolean functions. This architecture is particularly suited
to be used in the QPAC-learning framework as it can handle the superposition
produced by the oracle. In order to tune the network so that it can approximate
a target concept, we have devised and implemented an algorithm based on
amplitude amplification. The numerical results show that this approach can
efficiently learn concepts from a simple class.


title:
Multitask Network for Joint Object Detection, Semantic Segmentation and
  Human Pose Estimation in Vehicle Occupancy Monitoring

abstract:
  In order to ensure safe autonomous driving, precise information about the
conditions in and around the vehicle must be available. Accordingly, the
monitoring of occupants and objects inside the vehicle is crucial. In the
state-of-the-art, single or multiple deep neural networks are used for either
object recognition, semantic segmentation, or human pose estimation. In
contrast, we propose our Multitask Detection, Segmentation and Pose Estimation
Network (MDSP) -- the first multitask network solving all these three tasks
jointly in the area of occupancy monitoring. Due to the shared architecture,
memory and computing costs can be saved while achieving higher accuracy.
Furthermore, our architecture allows a flexible combination of the three
mentioned tasks during a simple end-to-end training. We perform comprehensive
evaluations on the public datasets SVIRO and TiCaM in order to demonstrate the
superior performance.


title:
Single Photon Production at Hadron Colliders at NNLO QCD with Realistic
  Photon Isolation

abstract:
  Isolated photons at hadron colliders are defined by permitting only a limited
amount of hadronic energy inside a fixed-size cone around the candidate photon
direction. This isolation criterion admits contributions from collinear photon
radiation off QCD partons and from parton-to-photon fragmentation processes. We
compute the NNLO QCD corrections to isolated photon and photon-plus-jet
production, including these two contributions. Our newly derived results allow
us to reproduce the isolation prescription used in the experimental
measurements, performing detailed comparisons with data from the LHC
experiments. We quantify the impact of different photon isolation
prescriptions, including no isolation at all, on photon-plus-jet cross sections
and discuss possible measurements of the photon fragmentation functions at
hadron colliders.


title:
A Re-defined and Generalized Percent-Overlap-of-Activation Measure for
  Studies of fMRI Reproducibility and its Use in Identifying Outlier Activation
  Maps

abstract:
  Functional Magnetic Resonance Imaging~(fMRI) is a popular non-invasive
modality to investigate activation in the human brain. The end result of most
fMRI experiments is an activation map corresponding to the given paradigm.
These maps can vary greatly from one study to the next, so quantifying the
reliability of identified activation over several fMRI studies is important.
The percent overlap of activation~\citep{romboutsetal98,machielsenetal00} is a
global reliability measure between activation maps drawn from any two fMRI
studies. A slightly modified but more intuitive measure is provided by the
~\citet{jaccard1901} coefficient of similarity, whose use we study in this
paper. A generalization of these measures is also proposed to comprehensively
summarize the reliability of multiple fMRI studies. Finally, a testing
mechanism to flag potentially anomalous studies is developed. The methodology
is illustrated on studies involving left- and right-hand motor task paradigms
performed by a right-hand dominant male subject several times over a period of
two months, with excellent results.


title:
Nanoscale inhomogeneity of charge density waves dynamics in
  La$_{2-x}$Sr$_x$NiO$_4$

abstract:
  While stripe phases with broken rotational symmetry of charge density appear
in many complex correlated systems, the heterogeneity of spatial ordering and
dynamics remains elusive. This missing info is at the heart of understanding
the structure and function relation in quantum complex materials. We focus here
on the spatial heterogeneity of the motion of charge density wave (CDW) at
nanoscale in the archetypal case of La$_{2-x}$Sr$_x$NiO$_{4+y}$ perovskite at
low temperature. We report compelling evidence that the unconventional
increasing motion of CDW at T < 50K is related with the decreasing of its
correlation length using resonant soft X-ray photon correlation spectroscopy
(XPCS). The key result of this work is the direct visualization of nanoscale
spatial inhomogeneity of CDW relaxation dynamics by scanning micro X-ray
diffraction (SmXRD) showing a nanoscale landscape of percolating short range
dynamic CDW puddles competing with large quasi-static CDW puddles giving rise
to a novel form of nanoscale phase separation of the incommensurate stripes
order landscape.


title:
The robustness of slow contraction and the shape of the scalar field
  potential

abstract:
  We use numerical relativity simulations to explore the conditions for a
canonical scalar field $\phi$ minimally coupled to Einstein gravity to generate
an extended phase of slow contraction that robustly smooths the universe for a
wide range of initial conditions and then sets the conditions for a graceful
exit stage. We show that to achieve robustness it suffices that the potential
$V(\phi)$ is negative and $M_{\rm Pl}|V_{,\phi}/V|\gtrsim5$ during the
smoothing phase. We also show that, to exit slow contraction, the potential
must have a minimum. Beyond the minimum, we find no constraint on the uphill
slope including the possibility of ending on a positive potential plateau or a
local minimum with $V_{\rm min}>0$. Our study establishes ultralocality for a
wide range of potentials as a key both to robust smoothing and to graceful
exit.


title: Mapping Thermoelectric Transport in a Multicomponent Alloy Space

abstract:
  Interest in high entropy alloy thermoelectric materials is predicated on
achieving ultralow lattice thermal conductivity $\kappa\sub{L}$ through large
compositional disorder. However, here we show that for a given mechanism, such
as mass contrast phonon scattering, $\kappa\sub{L}$ will be minimized along the
binary alloy with the highest mass contrast, such that adding an
intermediate-mass atom to increase atomic disorder can increase thermal
conductivity. Only when each component adds an independent scattering mechanism
(such as adding strain fluctuation to an existing mass fluctuation) is there a
benefit. In addition, both charge carriers and heat-carrying phonons are known
to experience scattering due to alloying effects, leading to a trade-off in
thermoelectric performance. We apply analytic transport models, based on
perturbation and effective medium theories, to predict how alloy scattering
will affect the thermal and electronic transport across the full compositional
range of several pseudo-ternary and pseudo-quaternary alloy systems. To do so,
we demonstrate a multicomponent extension to both thermal and electronic binary
alloy scattering models based on the virtual crystal approximation. Finally, we
show that common functional forms used in computational thermodynamics can be
applied to this problem to further generalize the scattering behavior that is
modeled.


title:
Fourier spectrum and related characteristics of the fundamental bright
  soliton solution

abstract:
  We derive exact analytical expressions for the spatial Fourier spectrum of
the fundamental bright soliton solution for the $(1 + 1)$-dimensional nonlinear
Schr\"odinger equation. Similar to a Gaussian profile, the Fourier transform
for the hyperbolic secant shape is also shape-preserving. We further confirm
that the fundamental soliton indeed satisfies essential characteristics such as
Parseval's relation and the stretch-bandwidth reciprocity relationship. The
fundamental bright solitons find rich applications in nonlinear fiber optics
and optical telecommunication systems.


title:
Upper and Lower Bounds for the Correlation Length of the Two-Dimensional
  Random-Field Ising Model

abstract:
  We study the rate of correlation decay in the two-dimensional random-field
Ising model at weak field strength $\varepsilon$. We combine elements of the
recent proof of exponential decay of correlations with a quantitative
refinement of a result of Aizenman--Burchard on the tortuosity of random curves
to obtain an upper bound of the form $\exp(\exp(O(1/\varepsilon^{2})))$ on the
correlation length of the model at all temperatures. Conversely, we show, by
adapting methods of Fisher--Fr\"{o}hlich--Spencer, that on square domains of
side length as large as $\exp(O(1/\varepsilon^{2/3}))$ the model continues to
exhibit strong dependence on boundary conditions at low temperature.


title:
ElitePLM: An Empirical Study on General Language Ability Evaluation of
  Pretrained Language Models

abstract:
  Nowadays, pretrained language models (PLMs) have dominated the majority of
NLP tasks. While, little research has been conducted on systematically
evaluating the language abilities of PLMs. In this paper, we present a
large-scale empirical study on general language ability evaluation of PLMs
(ElitePLM). In our study, we design four evaluation dimensions, i.e. memory,
comprehension, reasoning, and composition, to measure ten widely-used PLMs
within five categories. Our empirical results demonstrate that: (1) PLMs with
varying training objectives and strategies are good at different ability tests;
(2) fine-tuning PLMs in downstream tasks is usually sensitive to the data size
and distribution; (3) PLMs have excellent transferability between similar
tasks. Moreover, the prediction results of PLMs in our experiments are released
as an open resource for more deep and detailed analysis on the language
abilities of PLMs. This paper can guide the future work to select, apply, and
design PLMs for specific tasks. We have made all the details of experiments
publicly available at https://github.com/RUCAIBox/ElitePLM.


title: Machine learning techniques in joint default assessment

abstract:
  This paper studies the consequences of capturing non-linear dependence among
the covariates that drive the default of different obligors and the overall
riskiness of their credit portfolio. Joint default modeling is, without loss of
generality, the classical Bernoulli mixture model. Using an application to a
credit card dataset we show that, even when Machine Learning techniques perform
only slightly better than Logistic Regression in classifying individual
defaults as a function of the covariates, they do outperform it at the
portfolio level. This happens because they capture linear and non-linear
dependence among the covariates, whereas Logistic Regression only captures
linear dependence. The ability of Machine Learning methods to capture
non-linear dependence among the covariates produces higher default correlation
compared with Logistic Regression. As a consequence, on our data, Logistic
Regression underestimates the riskiness of the credit portfolio.


title: Multiplicity theorems involving functions with non-convex range

abstract:
  Here is a sample of the results proved in this paper: Let $f:{\bf R}\to {\bf
R}$ be a continuous function, let $\rho>0$ and let $\omega:[0,\rho[\to
[0,+\infty[$ be a continuous increasing function such that $\lim_{t\to
\rho^-}\omega(t)=+\infty$. Consider $C^0([0,1])\times C^0([0,1])$ endowed with
the norm $$\|(\alpha,\beta)\|=\int_0^1|\alpha(t)|dt+\int_0^1|\beta(t)|dt\ .$$
Then, the following assertions are equivalent: $(a)$ the restriction of $f$ to
$\left [-{{\sqrt{\rho}}\over {2}},{{\sqrt{\rho}}\over {2}}\right ]$ is not
constant; $(b)$ for every convex set $S\subseteq C^0([0,1])\times C^0([0,1])$
dense in $C^0([0,1])\times C^0([0,1])$, there exists $(\alpha,\beta)\in S$ such
that the problem
$$\cases{-\omega\left(\int_0^1|u'(t)|^2dt\right)u''=\beta(t)f(u)+\alpha(t) & in
$[0,1]$\cr & \cr u(0)=u(1)=0\cr & \cr \int_0^1|u'(t)|^2dt<\rho\cr}$$ has at
least two classical solutions.


title: Vortex-Stretching based Large Eddy Simulation Framework for Wind Farms

abstract:
  In large wind farms, wake distribution behind a wind turbine causes a
considerable reduction of wind velocity for downstream wind turbines, resulting
in a significant amount of power loss. Therefore, it is very crucial to predict
wind turbine wakes efficiently. Thus, we propose a large-eddy simulation (LES)
methodology, which takes the vorticity stretching to model transients in wind
turbine wakes. In addition, we present an improved actuator disk model, which
accounts for two-way feedback between the atmosphere and the wakes. First, we
show that the vertical profile of the mean wind predicted with the new model
has an excellent agreement with experimental measurements. Next, we validate
the predicted Reynolds stresses against wind tunnel data and show that the
dispersive stresses account for about 40% of Reynolds stresses. Finally, we
show that the proposed LES method accurately predicts the characteristics of
wind turbine wakes. Comparing the LES results with previously reported data, we
have found that the new LES framework accurately predicts the flow statistics
in both the near-wake and the far-wake regions.


title: Extended Abstract: Productive Parallel Programming with Parsl

abstract:
  Parsl is a parallel programming library for Python that aims to make it easy
to specify parallelism in programs and to realize that parallelism on arbitrary
parallel and distributed computing systems. Parsl relies on developers
annotating Python functions-wrapping either Python or external applications-to
indicate that these functions may be executed concurrently. Developers can then
link together functions via the exchange of data. Parsl establishes a dynamic
dependency graph and sends tasks for execution on connected resources when
dependencies are resolved. Parsl's runtime system enables different compute
resources to be used, from laptops to supercomputers, without modification to
the Parsl program.


title:
Attentive activation function for improving end-to-end spoofing
  countermeasure systems

abstract:
  The main objective of the spoofing countermeasure system is to detect the
artifacts within the input speech caused by the speech synthesis or voice
conversion process. In order to achieve this, we propose to adopt an attentive
activation function, more specifically attention rectified linear unit (AReLU)
to the end-to-end spoofing countermeasure system. Since the AReLU employs the
attention mechanism to boost the contribution of relevant input features while
suppressing the irrelevant ones, introducing AReLU can help the countermeasure
system to focus on the features related to the artifacts. The proposed
framework was experimented on the logical access (LA) task of ASVSpoof2019
dataset, and outperformed the systems using the standard non-learnable
activation functions.


title: Masked Generative Distillation

abstract:
  Knowledge distillation has been applied to various tasks successfully. The
current distillation algorithm usually improves students' performance by
imitating the output of the teacher. This paper shows that teachers can also
improve students' representation power by guiding students' feature recovery.
From this point of view, we propose Masked Generative Distillation (MGD), which
is simple: we mask random pixels of the student's feature and force it to
generate the teacher's full feature through a simple block. MGD is a truly
general feature-based distillation method, which can be utilized on various
tasks, including image classification, object detection, semantic segmentation
and instance segmentation. We experiment on different models with extensive
datasets and the results show that all the students achieve excellent
improvements. Notably, we boost ResNet-18 from 69.90% to 71.69% ImageNet top-1
accuracy, RetinaNet with ResNet-50 backbone from 37.4 to 41.0 Boundingbox mAP,
SOLO based on ResNet-50 from 33.1 to 36.2 Mask mAP and DeepLabV3 based on
ResNet-18 from 73.20 to 76.02 mIoU. Our codes are available at
https://github.com/yzd-v/MGD.


title:
Probing the Effect of Molecular Structure Saddling on Ultrafast Charge
  Migration via Time-Resolved X-ray Diffraction

abstract:
  Metal-corroles are macro-cycle organic molecules with numerous practical
applications. In particular, copper corroles exhibit an interesting saddled
geometry, which has attracted significant attention from theoreticians and
experimentalists over the years. The present work is dedicated to understand
the effect of structural saddling in copper corrole on potential probe signals
via imaging ultrafast coherent electron dynamics. A linearly polarised pulse is
used to trigger the electron dynamics and time-resolved x-ray diffraction is
employed to image the triggered dynamics. It is found that the symmetry
reduction in the time-resolved diffraction signals and electronic flux
densities is a signature of the saddling in copper corrole during ultrafast
charge migration. Moreover, analysis of the electronic flux density reveals
that the diagonal nitrogen atoms mediate coherent charge migration between them
via central copper atom. Correlation of the flux densities and the diffraction
signals indicates that the signature of the charge migration is encoded in
time-resolved diffraction signals.


title:
Three-dimensional imaging and analysis of annual layers in tree trunk
  and tooth cementum

abstract:
  The growth of plants, animals, and humans can give rise to layered structures
associated with annual periodicity. Thickness variations are often correlated
to nutrition supply and stress factors. The annual layers in a tree trunk with
millimeter thickness can be directly counted, whereas the layers in tooth
cementum with micrometer thickness are made visible using optical microscopy.
These optical techniques rely on the surface evaluation or thin, optically
transparent slices. Hard X-ray tomography with micrometer resolution, however,
provides a three-dimensional view without physical slicing. We have developed a
procedure to enhance the tomography data of annual layers in human and bovid
tooth cementum. The analysis of a substantial part of an archeological human
tooth demonstrated that the detected number of layers depended on the selected
region and could vary between 13 and 27. The related average thickness of the
annual layers was found to be (5.4 $\pm$ 1.9) $\mu$m for the human tooth,
whereas the buffalo tooth exhibited a layer periodicity of 46 $\mu$m. The
present study elucidates the potential of combining computational tools with
high-quality micro computed tomography data to quantify the annual layers in
tooth cementum for a variety of purposes including age-at-death determination.


title:
Using Ontologies for the Formalization and Recognition of Criticality
  for Automated Driving

abstract:
  Knowledge representation and reasoning has a long history of examining how
knowledge can be formalized, interpreted, and semantically analyzed by
machines. In the area of automated vehicles, recent advances suggest the
ability to formalize and leverage relevant knowledge as a key enabler in
handling the inherently open and complex context of the traffic world. This
paper demonstrates ontologies to be a powerful tool for a) modeling and
formalization of and b) reasoning about factors associated with criticality in
the environment of automated vehicles. For this, we leverage the well-known
6-Layer Model to create a formal representation of the environmental context.
Within this representation, an ontology models domain knowledge as logical
axioms, enabling deduction on the presence of critical factors within traffic
scenes and scenarios. For executing automated analyses, a joint description
logic and rule reasoner is used in combination with an a-priori predicate
augmentation. We elaborate on the modular approach, present a publicly
available implementation, and evaluate the method by means of a large-scale
drone data set of urban traffic scenarios.


title:
Average Age of Information Minimization in Reliable Covert Communication
  on Time-Varying Channels

abstract:
  In this letter, we propose reliable covert communications with the aim of
minimizing age of information (AoI) in the time-varying channels. We named the
time duration that channel state information (CSI) is valid as a new metric, as
age of channel variation (AoC). To find reliable covert communication in a
fresh manner in dynamic environments, this work considers a new constraint that
shows a relation between AoI and AoC. With the aid of the proposed constraint,
this paper addresses two main challenges of reliable covert communication with
the aim of minimizing AoI: 1) users packets with desirable size; 2)
guaranteeing the negligible probability of Willies detection, in time-varying
networks. In the simulation results, we compare the performance of the proposed
constraint in reliable covert communication with the aim of minimizing AoI with
conventional optimization of the requirement of information freshness in covert
communications.


title:
In situ transport characterization of magnetic states in Nb/Co
  superconductor/ferromagnet heterostructures

abstract:
  Employment of the non-trivial proximity effect in superconductor/ferromagnet
(S/F) heterostructures for the creation of novel superconducting devices
requires accurate control of magnetic states in complex thin-film multilayers.
In this work, we study experimentally in-plane transport properties of
microstructured Nb/Co multilayers. We apply various transport characterization
techniques, including magnetoresistance, Hall effect, and the
first-order-reversal-curves (FORC) analysis. We demonstrate how FORC can be
used for detailed in situ characterization of magnetic states. It reveals that
upon reduction of the external field, the magnetization in ferromagnetic layers
first rotates in a coherent scissor-like manner, then switches abruptly into
the antiparallel state and after that splits into the polydomain state, which
gradually turns into the opposite parallel state.


title:
A review of the Separation Theorem of Chebyshev-Markov-Stieltjes for
  polynomial and some rational Krylov subspaces

abstract:
  The accumulated quadrature weights of Gaussian quadrature formulae constitute
bounds on the integral over the intervals between the quadrature nodes.
Classical results in this concern date back to works of Chebyshev, Markov and
Stieltjes and are referred to as Separation Theorem of
Chebyshev-Markov-Stieltjes (CMS Theorem). Similar separation theorems hold true
for some classes of rational Gaussian quadrature. The Krylov subspace for a
given matrix and initial vector is closely related to orthogonal polynomials
associated with the spectral distribution of the initial vector in the
eigenbasis of the given matrix, and Gaussian quadrature for the
Riemann-Stielthes integral associated with this spectral distribution. Similar
relations hold true for rational Krylov subspaces. In the present work,
separation theorems are reviewed in the context of Krylov subspaces including
rational Krylov subspaces with a single complex pole of higher multiplicity and
some extended Krylov subspaces. For rational Gaussian quadrature related to
some classes of rational Krylov subspaces with a single pole, the underlying
separation theorems are newly introduced here.


title: BiOcularGAN: Bimodal Synthesis and Annotation of Ocular Images

abstract:
  Current state-of-the-art segmentation techniques for ocular images are
critically dependent on large-scale annotated datasets, which are
labor-intensive to gather and often raise privacy concerns. In this paper, we
present a novel framework, called BiOcularGAN, capable of generating synthetic
large-scale datasets of photorealistic (visible light and near infrared) ocular
images, together with corresponding segmentation labels to address these
issues. At its core, the framework relies on a novel Dual-Branch StyleGAN2
(DB-StyleGAN2) model that facilitates bimodal image generation, and a Semantic
Mask Generator (SMG) that produces semantic annotations by exploiting
DB-StyleGAN2's feature space. We evaluate BiOcularGAN through extensive
experiments across five diverse ocular datasets and analyze the effects of
bimodal data generation on image quality and the produced annotations. Our
experimental results show that BiOcularGAN is able to produce high-quality
matching bimodal images and annotations (with minimal manual intervention) that
can be used to train highly competitive (deep) segmentation models that perform
well across multiple real-world datasets. The source code will be made publicly
available.


title: Bratteli diagrams, translation flows and their $C^*$-algebras

abstract:
  In [LT16], Kathryn Lindsey and the second author constructed a translation
surface from a bi-infinite Bratteli diagram. We continue an investigation into
these surfaces. The construction given in [LT16] was essentially combinatorial.
Here, we provide explicit links between the path space of the Bratteli diagram
and the surface, including various intermediate topological spaces. This allows
us to relate the $C^{*}$-algebras associated with left and right tail
equivalence on the Bratteli diagram and the vertical and horizontal foliations
of the surface, under some mild hypotheses. This also allows us to relate the
K-theory of the $C^{*}$-algebras involved. We also treat the case of finite
genus surfaces in some detail, where the process of Rauzy-Veech induction (and
its inverse) provide an explicit construction of the Bratteli diagrams
involved.


title: SUBS: Subtree Substitution for Compositional Semantic Parsing

abstract:
  Although sequence-to-sequence models often achieve good performance in
semantic parsing for i.i.d. data, their performance is still inferior in
compositional generalization. Several data augmentation methods have been
proposed to alleviate this problem. However, prior work only leveraged
superficial grammar or rules for data augmentation, which resulted in limited
improvement. We propose to use subtree substitution for compositional data
augmentation, where we consider subtrees with similar semantic functions as
exchangeable. Our experiments showed that such augmented data led to
significantly better performance on SCAN and GeoQuery, and reached new SOTA on
compositional split of GeoQuery.


title: Parameterized Vietoris-Rips Filtrations via Covers

abstract:
  A challenge in computational topology is to deal with large filtered
geometric complexes built from point cloud data such as Vietoris-Rips
filtrations. This has led to the development of schemes for parallel
computation and compression which restrict simplices to lie in open sets in a
cover of the data. We extend the method of acyclic carriers to the setting of
persistent homology to give detailed bounds on the relationship between
Vietoris-Rips filtrations restricted to covers and the full construction. We
show how these complexes can be used to study data over a base space and use
our results to guide the selection of covers of data. We demonstrate these
techniques on a variety of covers, and show the utility of this construction in
investigating higher-order homology of a model of high-dimensional image
patches.


title:
Critical Balance and Scaling of Stably Stratified Turbulence at Low
  Prandtl Number

abstract:
  We extend the scaling relations of stably stratified turbulence from the
geophysical regime of unity Prandtl number to the astrophysical regime of
extremely small Prandtl number applicable to stably stratified regions of stars
and gas giants. A transition to a new turbulent regime is found to occur when
the Prandtl number drops below the inverse of the buoyancy Reynolds number,
i.e. $PrRb<1$, which signals a shift of the dominant balance in the buoyancy
equation. Application of critical balance arguments then derives new
predictions for the anisotropic energy spectrum and dominant balance of the
Boussinesq equations in the $PrRb\ll1$ regime. We find that all the standard
scaling relations from the unity $Pr$ limit of stably stratified turbulence
simply carry over if the Froude number, $Fr$, is replaced by a modified Froude
number, $Fr_M\equiv Fr/(PrRb)^{1/4}$. The geophysical and astrophysical regimes
are thus smoothly connected across the $PrRb=1$ transition. Applications to
vertical transport in stellar radiative zones and modification to the
instability criterion for the small-scale dynamo are discussed.


title: Efficient Fine-Tuning of BERT Models on the Edge

abstract:
  Resource-constrained devices are increasingly the deployment targets of
machine learning applications. Static models, however, do not always suffice
for dynamic environments. On-device training of models allows for quick
adaptability to new scenarios. With the increasing size of deep neural
networks, as noted with the likes of BERT and other natural language processing
models, comes increased resource requirements, namely memory, computation,
energy, and time. Furthermore, training is far more resource intensive than
inference. Resource-constrained on-device learning is thus doubly difficult,
especially with large BERT-like models. By reducing the memory usage of
fine-tuning, pre-trained BERT models can become efficient enough to fine-tune
on resource-constrained devices. We propose Freeze And Reconfigure (FAR), a
memory-efficient training regime for BERT-like models that reduces the memory
usage of activation maps during fine-tuning by avoiding unnecessary parameter
updates. FAR reduces fine-tuning time on the DistilBERT model and CoLA dataset
by 30%, and time spent on memory operations by 47%. More broadly, reductions in
metric performance on the GLUE and SQuAD datasets are around 1% on average.


title: Classical ground states of spin lattices

abstract:
  We present a generalization of the Luttinger-Tisza-Lyons-Kaplan (LTLK) theory
of classical ground states of Bravais lattices with Heisenberg coupling to
non-Bravais lattices. It consists of adding certain Lagrange parameters to the
diagonal of the Fourier transformed coupling matrix analogous to the theory of
the general ground state problem already published. This approach is
illustrated by an application to a modified honeycomb lattice, which has
exclusive three-dimensional ground states as well as a classical spin-liquid
ground state for different values of the two coupling constants. Another
example, the modified square lattice, shows that we can also obtain so-called
incommensurable ground states by our method.


title: Learning to Transfer Prompts for Text Generation

abstract:
  Pretrained language models (PLMs) have made remarkable progress in text
generation tasks via fine-tuning. While, it is challenging to fine-tune PLMs in
a data-scarce situation. Therefore, it is non-trivial to develop a general and
lightweight model that can adapt to various text generation tasks based on
PLMs. To fulfill this purpose, the recent prompt-based learning offers a
potential solution. In this paper, we improve this technique and propose a
novel prompt-based method (PTG) for text generation in a transferable setting.
First, PTG learns a set of source prompts for various source generation tasks
and then transfers these prompts as target prompts to perform target generation
tasks. To consider both task- and instance-level information, we design an
adaptive attention mechanism to derive the target prompts. For each data
instance, PTG learns a specific target prompt by attending to highly relevant
source prompts. In extensive experiments, PTG yields competitive or better
results than fine-tuning methods. We release our source prompts as an open
resource, where users can add or reuse them to improve new text generation
tasks for future research. Code and data can be available at
https://github.com/RUCAIBox/Transfer-Prompts-for-Text-Generation.


title:
Large gauge transformations, local coordinates and cosmological
  observables

abstract:
  In recent years new types of coordinate transformations have appeared in
cosmology on top of the standard gauge transformations, such as the dilatations
and special conformal transformations, or the ones leading to (conformal) Fermi
coordinates. Some of these can remove effects that are invariant under the
standard gauge transformations and also affect asymptotic boundary conditions,
thus introducing a non-trivial ambiguity in our cosmological modeling. In this
short note we point out that this ambiguity is irrelevant for the quantities we
use to compare our model with observations -- the cosmological observable
relations -- as they are invariant under all of these transformations.
Importantly, this invariance holds only if one takes into account all the
relativistic contributions to an observable, which is not the case in the
literature in general. We finally also show that the practically-relevant
property of conformal Fermi coordinates (a FLRW metric up to second order in
distance) can be achieved through a globally-defined standard gauge
transformation.


title:
A theory for the stabilization of polar crystal surfaces by a liquid
  environment

abstract:
  Polar crystal surfaces play an important role in the functionality of many
materials, and have been studied extensively over many decades. In this
article, a theoretical framework is presented that extends existing theories by
placing the surrounding solution environment on an equal footing with the
crystal itself. By considering the polar crystal as a stack of parallel plate
capacitors, the equilibrium adsorbed surface charge density is derived by
minimizing the free energy of the system. In analogy to the well-known
diverging surface energy of a polar crystal surface at zero temperature, for a
crystal in solution it is shown that the "polar catastrophe" manifests as a
diverging free energy cost to perturb the system from equilibrium. We also show
how, in the slab geometry often employed in both theoretical and computational
studies of interfaces, an electric displacement field emerges as an
electrostatic boundary boundary condition, and its implications for molecular
simulations are discussed. Importantly, we show that the origins of this
electric displacement field are rooted in the slab geometry itself, rather than
the use of periodic boundary conditions.


title:
Learn To Remember: Transformer with Recurrent Memory for Document-Level
  Machine Translation

abstract:
  The Transformer architecture has led to significant gains in machine
translation. However, most studies focus on only sentence-level translation
without considering the context dependency within documents, leading to the
inadequacy of document-level coherence. Some recent research tried to mitigate
this issue by introducing an additional context encoder or translating with
multiple sentences or even the entire document. Such methods may lose the
information on the target side or have an increasing computational complexity
as documents get longer. To address such problems, we introduce a recurrent
memory unit to the vanilla Transformer, which supports the information exchange
between the sentence and previous context. The memory unit is recurrently
updated by acquiring information from sentences, and passing the aggregated
knowledge back to subsequent sentence states. We follow a two-stage training
strategy, in which the model is first trained at the sentence level and then
finetuned for document-level translation. We conduct experiments on three
popular datasets for document-level machine translation and our model has an
average improvement of 0.91 s-BLEU over the sentence-level baseline. We also
achieve state-of-the-art results on TED and News, outperforming the previous
work by 0.36 s-BLEU and 1.49 d-BLEU on average.


title: Effective Security by Obscurity

abstract:
  "Security by obscurity" is a bromide which is frequently applied to undermine
the perceived value of a certain class of techniques in security. This usage
initially stemmed from applications and experience in the areas of
cryptographic theory, and the open vs. closed source debate. Through the
perceived absence of true security, the field of security by obscurity has not
coalesced into a viable or recognizable approach for security practitioners.
The ramifications of this has resulted in these techniques going underused and
underappreciated by defenders, while they continue to provide value to
attackers, which creates an unfortunate information asymmetry. Exploring
effective methods for employing security by obscurity, it can be seen that
examples are already embedded unrecognized in other viable security
disciplines, such as information hiding, obfuscation, diversity, and moving
target defense. In showing that obscurity measures are an achievable and
desirable supplement to other security measures, it is apparent that the
in-depth defense of an organization's assets can be enhanced by intentional and
effective use of security by obscurity.


title:
Bright and dark Autler-Townes states in the atomic Rydberg multilevel
  spectroscopy

abstract:
  We investigated the Autler-Townes splitting produced by microwave transitions
between atomic Rydberg states explored by optical spectroscopy from the ground
electronic state. The laser-atom Hamiltonian describing the double irradiation
of such a multilevel system is analysed on the basis of the Morris-Shore
transformation. The application of this transformation to the microwave-dressed
atomic system allows the identification of bright, dark, and spectator states
associated with different configurations of atomic states and microwave
polarisation. We derived synthetic spectra that show the main features of
Rydberg spectroscopy. Complex Autler-Townes spectra are obtained in a regime of
strong microwave dressing, where a hybridisation of the Rydberg fine structure
states is produced by the driving.


title: Adaptable Adapters

abstract:
  State-of-the-art pretrained NLP models contain a hundred million to trillion
parameters. Adapters provide a parameter-efficient alternative for the full
finetuning in which we can only finetune lightweight neural network layers on
top of pretrained weights. Adapter layers are initialized randomly. However,
existing work uses the same adapter architecture -- i.e., the same adapter
layer on top of each layer of the pretrained model -- for every dataset,
regardless of the properties of the dataset or the amount of available training
data. In this work, we introduce adaptable adapters that contain (1) learning
different activation functions for different layers and different input data,
and (2) a learnable switch to select and only use the beneficial adapter
layers. We show that adaptable adapters achieve on-par performances with the
standard adapter architecture while using a considerably smaller number of
adapter layers. In addition, we show that the selected adapter architecture by
adaptable adapters transfers well across different data settings and similar
tasks. We propose to use adaptable adapters for designing efficient and
effective adapter architectures. The resulting adapters (a) contain about 50%
of the learning parameters of the standard adapter and are therefore more
efficient at training and inference, and require less storage space, and (b)
achieve considerably higher performances in low-data settings.


title:
Point Cloud Semantic Segmentation using Multi Scale Sparse Convolution
  Neural Network

abstract:
  Point clouds have the characteristics of disorder, unstructured and
sparseness.Aiming at the problem of the non-structural nature of point clouds,
thanks to the excellent performance of convolutional neural networks in image
processing, one of the solutions is to extract features from point clouds based
on two-dimensional convolutional neural networks. The three-dimensional
information carried in the point cloud can be converted to two-dimensional, and
then processed by a two-dimensional convolutional neural network, and finally
back-projected to three-dimensional.In the process of projecting 3D information
to 2D and back-projection, certain information loss will inevitably be caused
to the point cloud and category inconsistency will be introduced in the
back-projection stage;Another solution is the voxel-based point cloud
segmentation method, which divides the point cloud into small grids one by
one.However, the point cloud is sparse, and the direct use of 3D convolutional
neural network inevitably wastes computing resources. In this paper, we propose
a feature extraction module based on multi-scale ultra-sparse convolution and a
feature selection module based on channel attention, and build a point cloud
segmentation network framework based on this.By introducing multi-scale sparse
convolution, network could capture richer feature information based on
convolution kernels of different sizes, improving the segmentation result of
point cloud segmentation.


title: Cross-View Cross-Scene Multi-View Crowd Counting

abstract:
  Multi-view crowd counting has been previously proposed to utilize
multi-cameras to extend the field-of-view of a single camera, capturing more
people in the scene, and improve counting performance for occluded people or
those in low resolution. However, the current multi-view paradigm trains and
tests on the same single scene and camera-views, which limits its practical
application. In this paper, we propose a cross-view cross-scene (CVCS)
multi-view crowd counting paradigm, where the training and testing occur on
different scenes with arbitrary camera layouts. To dynamically handle the
challenge of optimal view fusion under scene and camera layout change and
non-correspondence noise due to camera calibration errors or erroneous
features, we propose a CVCS model that attentively selects and fuses multiple
views together using camera layout geometry, and a noise view regularization
method to train the model to handle non-correspondence errors. We also generate
a large synthetic multi-camera crowd counting dataset with a large number of
scenes and camera views to capture many possible variations, which avoids the
difficulty of collecting and annotating such a large real dataset. We then test
our trained CVCS model on real multi-view counting datasets, by using
unsupervised domain transfer. The proposed CVCS model trained on synthetic data
outperforms the same model trained only on real data, and achieves promising
performance compared to fully supervised methods that train and test on the
same single scene.


title:
Physical Layer Security for 6G Systems why it is needed and how to make
  it happen

abstract:
  Sixth generations (6G) systems will be required to meet diverse constraints
in an integrated ground-air-space global network. In particular, meeting overly
aggressive latency constraints, operating in massive connectivity regimes, with
low energy footprint and low computational effort, while providing explicit
security guarantees, can be challenging. In this setting, quality of security
(QoSec) is envisioned as a flexible security framework for future networks with
highly diverse non-functional requirements. Mirroring the differentiated
services (DiffServ) networking paradigm, different security levels could be
conceptualized, moving away from static security controls, captured currently
in zero-trust security architectures. In parallel, the integration of
communications and sensing, along with embedded (on-device) AI, can provide the
foundations for building autonomous and adaptive security controls,
orchestrated by a vertical security plane in coordination with a vertical
semantic plane. It is in this framework, that we envision the incorporation of
physical layer security (PLS) schemes in 6G security protocols, introducing
security controls at all layers, for the first time.


title: Why The Trans Programmer?

abstract:
  Through online anecdotal evidence and online communities, there is an
in-group idea of trans people (specifically trans-feminine individuals)
disproportionately entering computer science education & fields. Existing data
suggests this is a plausible trend, yet no research has been done into exactly
why. As computer science education (traditional schooling or self-taught
methods) is integral to working in computer science fields, a simple research
survey was conducted to gather data on 138 trans people's experiences with
computer science & computer science education. This article's purpose is to
shed insight on the motivations for trans individuals choosing computer science
paths, while acting as a basis and call to action for further research.


title: Exploring Proxying QUIC and HTTP/3 for Satellite Communication

abstract:
  Low-Earth Orbit satellites have gained momentum to provide Internet
connectivity, augmenting those in the long-established geostationary orbits. At
the same time, QUIC has been developed as the new transport protocol for the
web. While QUIC traffic is fully encrypted, intermediaries such as performance
enhancing proxies (PEPs) - in the past essential for Internet over satellite
performance - can no longer tamper with and optimize transport connections. In
this paper, we present a satellite emulation testbed and use it to compare QUIC
and TCP as well as HTTP/3 and HTTP/1.1 with and without minimal PEP
functionality. Evaluating goodput over time, we find that the slow start
threshold is reached up to 2s faster for QUIC PEP in comparison to QUIC
Non-PEP. Moreover, we find that HTTP/3 and HTTP/3-PEP outperform HTTP/1.1 and
HTTP/1.1-PEP in multiple web performance scenarios, where HTTP/3-PEP improves
over HTTP/3 for Page Load Time by over 7s in edge cases. Hence, our findings
hint that these performance gains may warrant exploring PEPs for QUIC.


title: Virgo Detector Characterization and Data Quality during the O3 run

abstract:
  The Advanced Virgo detector has contributed with its data to the rapid growth
of the number of detected gravitational-wave signals in the past few years,
alongside the two LIGO instruments. First, during the last month of the
Observation Run 2 (O2) in August 2017 (with, most notably, the compact binary
mergers GW170814 and GW170817) and then during the full Observation Run 3 (O3):
an 11 months data taking period, between April 2019 and March 2020, that led to
the addition of about 80 events to the catalog of transient gravitational-wave
sources maintained by LIGO, Virgo and KAGRA. These discoveries and the manifold
exploitation of the detected waveforms require an accurate characterization of
the quality of the data, such as continuous study and monitoring of the
detector noise. These activities, collectively named {\em detector
characterization} or {\em DetChar}, span the whole workflow of the Virgo data,
from the instrument front-end to the final analysis. They are described in
details in the following article, with a focus on the associated tools, the
results achieved by the Virgo DetChar group during the O3 run and the main
prospects for future data-taking periods with an improved detector.


title: Privacy Amplification via Random Participation in Federated Learning

abstract:
  Running a randomized algorithm on a subsampled dataset instead of the entire
dataset amplifies differential privacy guarantees. In this work, in a federated
setting, we consider random participation of the clients in addition to
subsampling their local datasets. Since such random participation of the
clients creates correlation among the samples of the same client in their
subsampling, we analyze the corresponding privacy amplification via non-uniform
subsampling. We show that when the size of the local datasets is small, the
privacy guarantees via random participation is close to those of the
centralized setting, in which the entire dataset is located in a single host
and subsampled. On the other hand, when the local datasets are large, observing
the output of the algorithm may disclose the identities of the sampled clients
with high confidence. Our analysis reveals that, even in this case, privacy
guarantees via random participation outperform those via only local
subsampling.


title: Training Mixed-Domain Translation Models via Federated Learning

abstract:
  Training mixed-domain translation models is a complex task that demands
tailored architectures and costly data preparation techniques. In this work, we
leverage federated learning (FL) in order to tackle the problem. Our
investigation demonstrates that with slight modifications in the training
process, neural machine translation (NMT) engines can be easily adapted when an
FL-based aggregation is applied to fuse different domains. Experimental results
also show that engines built via FL are able to perform on par with
state-of-the-art baselines that rely on centralized training techniques. We
evaluate our hypothesis in the presence of five datasets with different sizes,
from different domains, to translate from German into English and discuss how
FL and NMT can mutually benefit from each other. In addition to providing
benchmarking results on the union of FL and NMT, we also propose a novel
technique to dynamically control the communication bandwidth by selecting
impactful parameters during FL updates. This is a significant achievement
considering the large size of NMT engines that need to be exchanged between FL
parties.


title:
[Mg/Fe] ratios in the solar neighbourhood: stellar yields and chemical
  evolution scenarios

abstract:
  Context. The [Mg/Fe] abundance ratios are a fundamental fossil signature to
trace the chemical evolution of the disc. Despite of the huge observational and
theoretical effort, discrepancies between models and data are still present and
several explanations have been put forward to explain the [$\alpha$/Fe]
bimodality. Aims. In this work, we take advantage of a new AMBRE:HARPS dataset,
which provides new and more precise [Mg/Fe] estimations, as well as reliable
stellar ages for a subsample of stars, to study the evolution of the solar
neighbourhood. Methods. The above data are compared with detailed chemical
evolution models for the Milky Way, exploring the most used prescriptions for
stellar yields and different formation scenarios for the Galactic disc, i.e.
the delayed two-infall and the parallel model, also including prescriptions for
stellar radial migration. Results. We see that most of the stellar yields
struggle to reproduce the observed trend of the data and that semi-empirical
yields are still the best to describe the [Mg/Fe] evolution in the thick and
thin discs. In particular, most of the yields still predict a steeper decrease
of the [Mg/Fe] ratio at high metallicity than what is shown by the data. The
bulk of the data are well reproduced by the parallel and two-infall scenarios,
but both scenarios have problems in explaining the most metal-rich and
metal-poor tails of the low-$\alpha$ data. These tails can be explained in
light of radial migration from inner and outer disc regions, respectively.
Conclusions. Despite of the evidence of stellar migration, it is difficult to
estimate the actual contribution of stars from other parts of the disc to the
solar vicinity. However, the comparison between data and models suggests that
peculiar histories of star formation, such as that of the two-infall model, are
still needed to reproduce the observed distribution of stars.


title:
A study on rare-earth Laves phases for magnetocaloric liquefaction of
  hydrogen

abstract:
  We are witnessing a great transition towards a society powered by renewable
energies to meet the ever-stringent climate target. Hydrogen, as an energy
carrier, will play a key role in building a climate-neutral society. Although
liquid hydrogen is essential for hydrogen storage and transportation,
liquefying hydrogen is costly with the conventional methods based on
Joule-Thomas effect. As an emerging technology which is potentially more
efficient, magnetocaloric hydrogen liquefaction is a "game-changer". In this
work, we have investigated the rare-earth-based Laves phases RAl2 and RNi2 for
magnetocaloric hydrogen liquefaction. We have noticed an unaddressed feature
that the magnetocaloric effect of second-order magnetocaloric materials can
become "giant" near the hydrogen boiling point. This feature indicates strong
correlations, down to the boiling point of hydrogen, among the three important
quantities of the magnetocaloric effect: the maximum magnetic entropy change,
the maximum adiabatic temperature change, and the Curie temperature. Via a
comprehensive literature review, we interpret the correlations for a rare-earth
intermetallic series as two rules: (1) maximum magnetic entropy change
increases with decreasing Curie temperature; (2) maximum adiabatic temperature
change decreases near room temperature with decreasing Curie temperature but
increases at cryogenic temperatures. Moreover, we have developed a mean-field
approach to describe these two rules theoretically. The dependence of maximum
magnetocaloric effect on Curie temperature revealed in this work helps us
quickly anticipate the magnetocaloric performance of rare-earth-based
compounds, guiding material design and accelerating the discoveries of
magnetocaloric materials for hydrogen liquefaction. More importantly, our work
demonstrates a phenomenon in cryogenic physics that "cool" favors magnetic
cooling.


title:
Optimal Thermal Management, Charging, and Eco-driving of Battery
  Electric Vehicles

abstract:
  This paper addresses optimal battery thermal management (BTM), charging, and
eco-driving of a battery electric vehicle (BEV) with the goal of improving its
grid-to-meter energy efficiency. Thus, an optimisation problem is formulated,
aiming at finding the optimal trade-off between trip time and charging cost.
The formulated problem is then transformed into a hybrid dynamical system,
where the dynamics in driving and charging modes are modeled with different
functions and with different state and control vectors. Moreover, to improve
computational efficiency, we propose modelling the driving dynamics in a
spatial domain, where decisions are made along the traveled distance. Charging
dynamics are modeled in a temporal domain, where decisions are made along a
normalized charging time. The actual charging time is modeled as a scalar
variable that is optimized simultaneously with the optimal state and control
trajectories, for both charging and driving modes. The performance of the
proposed algorithm is assessed over a road with a hilly terrain, where two
charging possibilities are considered along the driving route. According to the
results, trip time including driving and charging times, is reduced by 44 %,
compared to a case without battery active heating/cooling.


title: Beyond 6: the role of dimension-8 operators in an EFT for the 2HDM

abstract:
  The Standard Model effective field theory (SMEFT) is the tool of choice for
studying deviations of Higgs couplings from the Standard Model predictions. The
SMEFT is an expansion in an infinite tower of higher dimension operators, which
is typically truncated at dimension-6. We consider the effective theory
including dimension-8 operators and examine the matching to the 2 Higgs Doublet
Model (2HDM) that is assumed to be valid at some high scale. Both the limits
from the direct production of single Higgs bosons and the indirect limits on
the Higgs tri-linear coupling are considered in the context of the SMEFT
matched to the 2HDM, and the importance of the assumptions about the expansions
in powers of the high scale are examined numerically.


title: Nested Dissection Meets IPMs: Planar Min-Cost Flow in Nearly-Linear Time

abstract:
  We present a nearly-linear time algorithm for finding a minimum-cost flow in
planar graphs with polynomially bounded integer costs and capacities. The
previous fastest algorithm for this problem is based on interior point methods
(IPMs) and works for general sparse graphs in $O(n^{1.5}\text{poly}(\log n))$
time [Daitch-Spielman, STOC'08]. Intuitively, $\Omega(n^{1.5})$ is a natural
runtime barrier for IPM-based methods, since they require $\sqrt{n}$
iterations, each routing a possibly-dense electrical flow.
  To break this barrier, we develop a new implicit representation for flows
based on generalized nested-dissection [Lipton-Rose-Tarjan, JSTOR'79] and
approximate Schur complements [Kyng-Sachdeva, FOCS'16]. This implicit
representation permits us to design a data structure to route an electrical
flow with sparse demands in roughly $\sqrt{n}$ update time, resulting in a
total running time of $O(n\cdot\text{poly}(\log n))$.
  Our results immediately extend to all families of separable graphs.


title: Simulation of reversible molecular mechanical logic gates and circuits

abstract:
  Landauer's principle places a fundamental lower limit on the work required to
perform a logically irreversible operation. Logically reversible gates provide
a way to avoid these work costs, and also simplify the task of making the
computation as a whole thermodynamically reversible. The inherent reversibility
of mechanical logic gates would make them good candidates for the design of
practical logically reversible computing systems if not for the relatively
large size and mass of such systems. In this paper, we outline the design and
simulation of reversible molecular mechanical logic gates that come close to
the limits of thermodynamic reversibility even under the effects of thermal
noise, and outline associated circuit components from which arbitrary
combinatorial reversible circuits can be constructed and simulated. We
demonstrate that isolated components can be operated in a thermodynamically
reversible manner, and explore the complexities of combining components to
implement more complex computations. Finally, we demonstrate a method to
construct arbitrarily large reversible combinatorial circuits using multiple
external controls and signal boosters with a working half-adder circuit.


title:
On the problem of projectile motion in a medium with quadratic
  resistance

abstract:
  A classic problem of the motion of a projectile thrown at an angle to the
horizon in a medium with a quadratic resistance law is studied. An approximate
analytical solution of the equations of projectile motion is presented, which
has a high accuracy. The proposed formulas are universal, that is, they can be
used for any initial conditions of throwing over a wide range of the change of
medium resistance coefficient. The motion of a shuttlecock of badminton,
bullet, table tennis ball and volleyball are presented as examples.


title:
Efficient Score Computation and Expectation-Maximization Algorithm in
  Regime-Switching Models

abstract:
  This study proposes an efficient algorithm for score computation for
regime-switching models, and derived from which, an efficient
expectation-maximization (EM) algorithm. Different from existing algorithms,
this algorithm does not rely on the forward-backward filtering for smoothed
regime probabilities, and only involves forward computation. Moreover, the
algorithm to compute score is readily extended to compute the Hessian matrix.


title: The exact order of discrepancy for Levin's normal number in base 2

abstract:
  Mordechay Levin has constructed a number $\alpha$ which is normal in base 2,
and such that the sequence $\left\{2^n \alpha\right\}_{n=0,1,2,\ldots}$ has
very small discrepancy $D_N$. Indeed we have $N\cdot D_N = \mathcal{O}
\left(\left(\log N\right)^2\right)$. That means, that $\alpha$ is normal of
extremely high quality. In this paper we show that this estimate is best
possible, i.e., $N\cdot D_N \geq c \cdot \left(\log N\right)^2$ for infinitely
many $N$.


title: Snowmass White Paper: Higher Spin Gravity and Higher Spin symmetry

abstract:
  Higher Spin Gravity refers to extensions of gravity including at least one
field of spin greater than two. These extensions are expected to provide
manageable models of quantum gravity thanks to the infinite-dimensional (higher
spin) gauge symmetry constraining them. One of the key aspects of Higher Spin
Gravity/Symmetry is the range and diversity of topics it embraces: (a) higher
spin fields play a role in quantum gravity, AdS/CFT, string theory and are
expected to have important consequences in cosmology and black hole physics;
(b) higher spin symmetry finds applications in Conformal Field Theories,
condensed matter systems and dualities therein; (c) these models often rely on
tools developed in the study of the mathematical foundations of QFT or in pure
mathematics: from deformation quantization and non-commutative geometry to
conformal geometry, graded geometry (including BV-BRST quantization), and
geometry of PDEs. Recent exciting applications also involve (d) modelling the
coalescence of black hole binaries as scattering of massive higher spin
particles.


title: RAFT-MSF: Self-Supervised Monocular Scene Flow using Recurrent Optimizer

abstract:
  Learning scene flow from a monocular camera still remains a challenging task
due to its ill-posedness as well as lack of annotated data. Self-supervised
methods demonstrate learning scene flow estimation from unlabeled data, yet
their accuracy lags behind (semi-)supervised methods. In this paper, we
introduce a self-supervised monocular scene flow method that substantially
improves the accuracy over the previous approaches. Based on RAFT, a
state-of-the-art optical flow model, we design a new decoder to iteratively
update 3D motion fields and disparity maps simultaneously. Furthermore, we
propose an enhanced upsampling layer and a disparity initialization technique,
which overall further improves accuracy up to 7.2%. Our method achieves
state-of-the-art accuracy among all self-supervised monocular scene flow
methods, improving accuracy by 34.2%. Our fine-tuned model outperforms the best
previous semi-supervised method with 228 times faster runtime. Code will be
publicly available.


title:
PSCNN: A 885.86 TOPS/W Programmable SRAM-based Computing-In-Memory
  Processor for Keyword Spotting

abstract:
  Computing-in-memory (CIM) has attracted significant attentions in recent
years due to its massive parallelism and low power consumption. However,
current CIM designs suffer from large area overhead of small CIM macros and bad
programmablity for model execution. This paper proposes a programmable CIM
processor with a single large sized CIM macro instead of multiple smaller ones
for power efficient computation and a flexible instruction set to support
various binary 1-D convolution Neural Network (CNN) models in an easy way.
Furthermore, the proposed architecture adopts the pooling write-back method to
support fused or independent convolution/pooling operations to reduce 35.9\% of
latency, and the flexible ping-pong feature SRAM to fit different feature map
sizes during layer-by-layer execution.The design fabricated in TSMC 28nm
technology achieves 150.8 GOPS throughput and 885.86 TOPS/W power efficiency at
10 MHz when executing our binary keyword spotting model, which has higher power
efficiency and flexibility than previous designs.


title: RangeSeg: Range-Aware Real Time Segmentation of 3D LiDAR Point Clouds

abstract:
  Semantic outdoor scene understanding based on 3D LiDAR point clouds is a
challenging task for autonomous driving due to the sparse and irregular data
structure. This paper takes advantages of the uneven range distribution of
different LiDAR laser beams to propose a range aware instance segmentation
network, RangeSeg. RangeSeg uses a shared encoder backbone with two range
dependent decoders. A heavy decoder only computes top of a range image where
the far and small objects locate to improve small object detection accuracy,
and a light decoder computes whole range image for low computational cost. The
results are further clustered by the DBSCAN method with a resolution weighted
distance function to get instance-level segmentation results. Experiments on
the KITTI dataset show that RangeSeg outperforms the state-of-the-art semantic
segmentation methods with enormous speedup and improves the instance-level
segmentation performance on small and far objects. The whole RangeSeg pipeline
meets the real time requirement on NVIDIA\textsuperscript{\textregistered}
JETSON AGX Xavier with 19 frames per second in average.


title: A Real Time 1280x720 Object Detection Chip With 585MB/s Memory Traffic

abstract:
  Memory bandwidth has become the real-time bottleneck of current deep learning
accelerators (DLA), particularly for high definition (HD) object detection.
Under resource constraints, this paper proposes a low memory traffic DLA chip
with joint hardware and software optimization. To maximize hardware utilization
under memory bandwidth, we morph and fuse the object detection model into a
group fusion-ready model to reduce intermediate data access. This reduces the
YOLOv2's feature memory traffic from 2.9 GB/s to 0.15 GB/s. To support group
fusion, our previous DLA based hardware employes a unified buffer with
write-masking for simple layer-by-layer processing in a fusion group. When
compared to our previous DLA with the same PE numbers, the chip implemented in
a TSMC 40nm process supports 1280x720@30FPS object detection and consumes 7.9X
less external DRAM access energy, from 2607 mJ to 327.6 mJ.


title:
Nilpotency of skew braces and multipermutation solutions of the
  Yang-Baxter equation

abstract:
  We study relations between different notions of nilpotency in the context of
skew braces and applications to the structure of solutions to the Yang-Baxter
equation. In particular, we consider annihilator nilpotent skew braces, an
important class that turns out to be a brace-theoretic analog to the class of
nilpotent groups. In this vein, several well-known theorems in group theory are
proved in the more general setting of skew braces.


title: StreamingHub: Interactive Stream Analysis Workflows

abstract:
  Reusable data/code and reproducible analyses are foundational to quality
research. This aspect, however, is often overlooked when designing interactive
stream analysis workflows for time-series data (e.g., eye-tracking data). A
mechanism to transmit informative metadata alongside data may allow such
workflows to intelligently consume data, propagate metadata to downstream
tasks, and thereby auto-generate reusable, reproducible analytic outputs with
zero supervision. Moreover, a visual programming interface to design, develop,
and execute such workflows may allow rapid prototyping for interdisciplinary
research. Capitalizing on these ideas, we propose StreamingHub, a framework to
build metadata propagating, interactive stream analysis workflows using visual
programming. We conduct two case studies to evaluate the generalizability of
our framework. Simultaneously, we use two heuristics to evaluate their
computational fluidity and data growth. Results show that our framework
generalizes to multiple tasks with a minimal performance overhead.


title: Summary of the parallel session HR3

abstract:
  This is the summary of the parallel session entitled "Time and Philosophy in
Physics", chaired by Shokoufe Faraji at the sixteenth Marcel Grossmann Meeting.
This parallel session aimed to discuss open issues related to Time and
fundamental laws from different perspectives in a complementary point of view.


title: The dusty heart of Circinus: I. Imaging the circumnuclear dust in N-band

abstract:
  Active galactic nuclei play a key role in the evolution of galaxies, but
their inner workings and physical connection to the host are poorly understood
due to a lack of angular resolution. Infrared interferometry makes it possible
to resolve the circumnuclear dust in the nearby Seyfert 2 galaxy, Circinus.
Previous observations have revealed complex structures and polar dust emission
but interpretation was limited to simple models. MATISSE makes it possible to
image these structures for the first time. We observed the Circinus Galaxy with
VLTI/MATISSE, producing 150 correlated flux spectra and 100 closure phase
spectra. We reconstructed images in the N-band at ~10 mas resolution. We fit
blackbody functions with dust extinction to several aperture-extracted fluxes
from the images to produce a temperature distribution of central dusty
structures. We find significant substructure in the circumnuclear dust: central
unresolved flux of ~0.5 Jy, a thin disk 1.9 pc in diameter oriented along ~45
deg,and a ~4x1.5 pc polar emission extending orthogonal to the disk. The polar
emission exhibits patchiness, which we attribute to clumpy dust. Flux
enhancements to the east and west of the disk are seen for the first time. We
distinguish the temperature profiles of the disk and of the polar emission: the
disk shows a steep temperature gradient indicative of denser material; the
polar profile is flatter, indicating clumpiness and/or lower dust density. The
unresolved flux is fitted with a high temperature, ~370 K. The polar dust
remains warm (~200 K) out to 1.5 pc from the disk. The recovered morphology and
temperature distribution resembles modeling of accretion disks with
radiation-driven winds at large scales, but we placed new constraints on the
subparsec dust. The subparsec features imaged here place new constraints on the
physical modeling of circumnuclear dust in active galaxies.


title: Computing Maximal Unique Matches with the r-index

abstract:
  In recent years, pangenomes received increasing attention from the scientific
community for their ability to incorporate population variation information and
alleviate reference genome bias. Maximal Exact Matches (MEMs) and Maximal
Unique Matches (MUMs) have proven themselves to be useful in multiple
bioinformatic contexts, for example short-read alignment and multiple-genome
alignment. However, standard techniques using suffix trees and FM-indexes do
not scale to a pangenomic level. Recently, Gagie et al. [JACM 20] introduced
the $r$-index that is a Burrows-Wheeler Transform (BWT)-based index able to
handle hundreds of human genomes. Later, Rossi et al. [JCB 22] enabled the
computation of MEMs using the $r$-index, and Boucher et al. [DCC 21] showed how
to compute them in a streaming fashion. In this paper, we show how to augment
Boucher et al.'s approach to enable the computation of MUMs on the $r$-index,
while preserving the space and time bounds. We add additional $O(r)$ samples of
the longest common prefix (LCP) array, where $r$ is the number of equal-letter
runs of the BWT, that permits the computation of the second longest match of
the pattern suffix with respect to the input text, which in turn allows the
computation of candidate MUMs. We implemented a proof-of-concept of our
approach, that we call mum-phinder, and tested on real-world datasets. We
compared our approach with competing methods that are able to compute MUMs. We
observe that our method is up to 8 times smaller, while up to 19 times slower
when the dataset is not highly repetitive, while on highly repetitive data, our
method is up to 6.5 times slower and uses up to 25 times less memory.


title: Shells in CO$_2$ Clusters

abstract:
  Abundance spectra of (CO$_2$)$_N$ clusters (up to $N \approx$ 500) acquired
under a wide range of adiabatic expansion conditions are analyzed within the
evaporative ensemble framework. Analysis reveals that cluster stability
functions display strikingly universal patterns for all expansion conditions.
These patterns reflect the inherent properties of individual clusters. From
this analysis the size-dependent cluster binding energies are determined, shell
and subshell closing sizes identified, and cuboctahedral packing ordering for
sizes above $N \approx 130$ is confirmed. It is demonstrated that a few percent
variation in the dissociation energies translates into significant abundance
variations, especially for the larger clusters.

categories: physics.atm-clus cond-mat.mes-hall cond-mat.stat-mech physics.chem-ph


title:
A Model of Fluid-Structure and Biochemical Interactions with
  Applications to Subclinical Leaflet Thrombosis

abstract:
  Subclinical leaflet thrombosis (SLT) is a potentially serious complication of
aortic valve replacement with a bioprosthetic valve in which blood clots form
on the replacement valve. SLT is associated with increased risk of transient
ischemic attacks and strokes and can progress to clinical leaflet thrombosis.
SLT following aortic valve replacement also may be related to subsequent
structural valve deterioration, which can impair the durability of the valve
replacement. Because of the difficulty in clinical imaging of SLT, models are
needed to determine the mechanisms of SLT and could eventually predict which
patients will develop SLT. To this end, we develop methods to simulate leaflet
thrombosis that combine fluid-structure interaction and a simplified thrombosis
model that allows for deposition along the moving leaflets. Additionally, this
model can be adapted to model deposition or absorption along other moving
boundaries. We present both convergence results and quantify the model's
ability to realize changes in stroke volume and pressures. These new approaches
are an important advancement in thrombosis modeling in that it incorporates
both adhesion to the surface of the leaflets and feedback to the
fluid-structure interaction.


title: Quantum transfer of interacting qubits

abstract:
  The transfer of quantum information between different locations is key to
many quantum information processing tasks. Whereas, the transfer of a single
qubit state has been extensively investigated, the transfer of a many-body
system configuration has insofar remained elusive. We address the problem of
transferring the state of n interacting qubits. Both the exponentially
increasing Hilbert space dimension, and the presence of interactions
significantly scale-up the complexity of achieving high-fidelity transfer. By
employing tools from random matrix theory and using the formalism of quantum
dynamical maps, we derive a general expression for the average and the variance
of the fidelity of an arbitrary quantum state transfer protocol for n
interacting qubits. Finally, by adopting a weak-coupling scheme in a spin
chain, we obtain the explicit conditions for high-fidelity transfer of 3 and 4
interacting qubits.


title: Better plain ViT baselines for ImageNet-1k

abstract:
  It is commonly accepted that the Vision Transformer model requires
sophisticated regularization techniques to excel at ImageNet-1k scale data.
Surprisingly, we find this is not the case and standard data augmentation is
sufficient. This note presents a few minor modifications to the original Vision
Transformer (ViT) vanilla training setting that dramatically improve the
performance of plain ViT models. Notably, 90 epochs of training surpass 76%
top-1 accuracy in under seven hours on a TPUv3-8, similar to the classic
ResNet50 baseline, and 300 epochs of training reach 80% in less than one day.


title:
Electrical Switching of the Edge Current Chirality in Quantum Anomalous
  Hall Insulators

abstract:
  A quantum anomalous Hall (QAH) insulator is a topological state of matter, in
which the interior is insulating but electrical current flows along the edges
of the sample, in either clockwise (right-handed) or counter-clockwise
(left-handed) direction dictated by the spontaneous magnetization orientation.
Such chiral edge current (CEC) eliminates any backscattering, giving rise to
quantized Hall resistance and zero longitudinal resistance. In this work, we
fabricate mesoscopic QAH sandwich (i.e. magnetic topological insulator
(TI)/TI/magnetic TI) Hall bar devices and succeed in switching the CEC
chirality in QAH insulators through spin-orbit torque (SOT) by applying a
current pulse and suitably controlled gate voltage. The well-quantized QAH
states with opposite CEC chiralities are demonstrated through four- and
three-terminal measurements before and after SOT switching. Our theoretical
calculations show that the SOT that enables the magnetization switching can be
generated by both bulk and surface carriers in QAH insulators, in good
agreement with experimental observations. Current pulse-induced switching of
the CEC chirality in QAH insulators will not only advance our knowledge in the
interplay between magnetism and topological states but also expedite easy and
instantaneous manipulation of the QAH state in proof-of-concept
energy-efficient electronic and spintronic devices as well as quantum
information applications.


title: Robust low-rank tensor regression via truncation and adaptive Huber loss

abstract:
  This paper investigates robust low-rank tensor regression with only finite
$(1+\epsilon)$-th moment noise based on the generalized tensor estimation
framework proposed by Han et al. (2022). The theoretical result shows that when
$\epsilon \geq 1$, the robust estimator possesses the minimax optimal rate.
While $1> \epsilon>0$, the rate is slower than the deviation bound of
sub-Gaussian tails.


title:
An Explore of Virtual Reality for Awareness of the Climate Change
  Crisis: A Simulation of Sea Level Rise

abstract:
  Virtual Reality (VR) technology has been shown to achieve remarkable results
in multiple fields. Due to the nature of the immersive medium of Virtual
Reality it logically follows that it can be used as a high-quality educational
tool as it offers potentially a higher bandwidth than other mediums such as
text, pictures and videos. This short paper illustrates the development of a
climate change educational awareness application for virtual reality to
simulate virtual scenes of local scenery and sea level rising until 2100 using
prediction data. The paper also reports on the current in progress work of
porting the system to Augmented Reality (AR) and future work to evaluate the
system.


title: On Natural Measures of SLE- and CLE-Related Random Fractals

abstract:
  In this paper, we construct and then prove the up-to constants uniqueness of
the natural measure on several random fractals, namely the SLE cut points, SLE
boundary touching points, CLE pivotal points and the CLE carpet/gasket. As an
application, we also show the equivalence between our natural measures defined
in this paper (i.e. CLE pivotal and gasket measures) and their discrete analogs
of counting measures in critical continuum planar Bernoulli percolation in
[Garban-Pete-Schramm, J. Amer. Math. Soc.,2013]. Although the existence and
uniqueness for the natural measure for CLE carpet/gasket have already been
proved in [Miller-Schoug, arXiv:2201.01748], in this paper we provide with a
different argument via the coupling of CLE and LQG.


title: Water wave problem with inclined walls

abstract:
  We study free surface water waves in a 2-D symmetric triangular channel with
sides that have a 45o slope. We develop models for small amplitude nonlinear
waves, extending earlier studies that have considered the linearized problem.
We see that a combination of heuristic small amplitude expansions lead to a
relatively simple system that we then use to study interactions of low
frequency modes. The formalism relies on an explicit construction of the normal
modes of the linear problem and a new way to represent the free surface. We
argue that the construction can be applied to more general geometries. We also
examine the structure and some dynamical features of spectral truncations for
the lowest even modes.


title:
Simpler is Better: off-the-shelf Continual Learning Through Pretrained
  Backbones

abstract:
  In this short paper, we propose a baseline (off-the-shelf) for Continual
Learning of Computer Vision problems, by leveraging the power of pretrained
models. By doing so, we devise a simple approach achieving strong performance
for most of the common benchmarks. Our approach is fast since requires no
parameters updates and has minimal memory requirements (order of KBytes). In
particular, the "training" phase reorders data and exploit the power of
pretrained models to compute a class prototype and fill a memory bank. At
inference time we match the closest prototype through a knn-like approach,
providing us the prediction. We will see how this naive solution can act as an
off-the-shelf continual learning system. In order to better consolidate our
results, we compare the devised pipeline with common CNN models and show the
superiority of Vision Transformers, suggesting that such architectures have the
ability to produce features of higher quality. Moreover, this simple pipeline,
raises the same questions raised by previous works \cite{gdumb} on the
effective progresses made by the CL community especially in the dataset
considered and the usage of pretrained models. Code is live at
https://github.com/francesco-p/off-the-shelf-cl


title:
Hydrodynamic Limits of non-Markovian Interacting Particle Systems on
  Sparse Graphs

abstract:
  Consider an interacting particle system indexed by the vertices of a
(possibly random) locally finite graph whose vertices and edges are equipped
with marks representing parameters of the model such as the environment and
initial conditions. Each particle takes values in a countable state space and
evolves according to a pure jump process whose jump intensities depend on only
the states (or histories) and marks of itself and particles and edges in its
neighborhood. Under mild conditions, it is shown that if the sequence of
(marked) interaction graphs converges locally in probability to a limit
(marked) graph that satisfies a certain finite dissociability property, then
the corresponding sequence of empirical measures of the particle trajectories
converges weakly to the law of the marginal dynamics at the root vertex of the
limit graph. The proof of this limit relies on several results of independent
interest. First, such interacting particle systems are shown to be well posed
on almost surely finitely dissociable graphs, which include graphs of maximal
bounded degree and any Galton-Watson tree whose offspring distribution has a
finite first moment. A counterexample is provided to show that well-posedness
can fail for dynamics on graphs outside this class. Second, the dynamics on a
locally convergent sequence of graphs are shown to converge in the local weak
sense to the dynamics on the limit graph when the latter is finitely
dissociable. Finally, the dynamics are also shown to exhibit an (annealed)
asymptotic correlation decay property. These results complement recent work
that establishes hydrodynamic limits of locally interacting probabilistic
cellular automata and diffusions on sparse random graphs. However, the analysis
of jump processes requires very different techniques, including percolation
arguments and notions such as (consistent) spatial localization and causal
chains.


title:
SparCAssist: A Model Risk Assessment Assistant Based on Sparse Generated
  Counterfactuals

abstract:
  We introduce SparcAssist, a general-purpose risk assessment tool for the
machine learning models trained for language tasks. It evaluates models' risk
by inspecting their behavior on counterfactuals, namely out-of-distribution
instances generated based on the given data instance. The counterfactuals are
generated by replacing tokens in rational subsequences identified by ExPred,
while the replacements are retrieved using HotFlip or
Masked-Language-Model-based algorithms. The main purpose of our system is to
help the human annotators to assess the model's risk on deployment. The
counterfactual instances generated during the assessment are the by-product and
can be used to train more robust NLP models in the future.


title: A dynamic mass transport method for Poisson-Nernst-Planck equations

abstract:
  A dynamic mass-transport method is proposed for approximately solving the
Poisson-Nernst-Planck(PNP) equations. The semi-discrete scheme based on the JKO
type variational formulation naturally enforces solution positivity and the
energy law as for the continuous PNP system. The fully discrete scheme is
further formulated as a constrained minimization problem, shown to be solvable,
and satisfy all three solution properties (mass conservation, positivity and
energy dissipation) independent of time step size or the spatial mesh size.
Numerical experiments are conducted to validate convergence of the computed
solutions and verify the structure preserving property of the proposed scheme.


title:
An Empirical Study on Internet Traffic Prediction Using Statistical
  Rolling Model

abstract:
  Real-world IP network traffic is susceptible to external and internal factors
such as new internet service integration, traffic migration, internet
application, etc. Due to these factors, the actual internet traffic is
non-linear and challenging to analyze using a statistical model for future
prediction. In this paper, we investigated and evaluated the performance of
different statistical prediction models for real IP network traffic; and showed
a significant improvement in prediction using the rolling prediction technique.
Initially, a set of best hyper-parameters for the corresponding prediction
model is identified by analyzing the traffic characteristics and implementing a
grid search algorithm based on the minimum Akaike Information Criterion (AIC).
Then, we performed a comparative performance analysis among AutoRegressive
Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA), SARIMA with
eXogenous factors (SARIMAX), and Holt-Winter for single-step prediction. The
seasonality of our traffic has been explicitly modeled using SARIMA, which
reduces the rolling prediction Mean Average Percentage Error (MAPE) by more
than 4% compared to ARIMA (incapable of handling the seasonality). We further
improved traffic prediction using SARIMAX to learn different exogenous factors
extracted from the original traffic, which yielded the best rolling prediction
results with a MAPE of 6.83%. Finally, we applied the exponential smoothing
technique to handle the variability in traffic following the Holt-Winter model,
which exhibited a better prediction than ARIMA (around 1.5% less MAPE). The
rolling prediction technique reduced prediction error using real Internet
Service Provider (ISP) traffic data by more than 50\% compared to the standard
prediction method.


title: Machine learning and density functional theory

abstract:
  Over the past decade machine learning has made significant advances in
approximating density functionals, but whether this signals the end of
human-designed functionals remains to be seen. Ryan Pederson, Bhupalee Kalita
and Kieron Burke discuss the rise of machine learning for functional design.


title: Conditional $\beta$-VAE for De Novo Molecular Generation

abstract:
  Deep learning has significantly advanced and accelerated de novo molecular
generation. Generative networks, namely Variational Autoencoders (VAEs) can not
only randomly generate new molecules, but also alter molecular structures to
optimize specific chemical properties which are pivotal for drug-discovery.
While VAEs have been proposed and researched in the past for pharmaceutical
applications, they possess deficiencies which limit their ability to both
optimize properties and decode syntactically valid molecules. We present a
recurrent, conditional $\beta$-VAE which disentangles the latent space to
enhance post hoc molecule optimization. We create a mutual information driven
training protocol and data augmentations to both increase molecular validity
and promote longer sequence generation. We demonstrate the efficacy of our
framework on the ZINC-250k dataset, achieving SOTA unconstrained optimization
results on the penalized LogP (pLogP) and QED scores, while also matching
current SOTA results for validity, novelty and uniqueness scores for random
generation. We match the current SOTA on QED for top-3 molecules at 0.948,
while setting a new SOTA for pLogP optimization at 104.29, 90.12, 69.68 and
demonstrating improved results on the constrained optimization task.


title:
Causal Regularization: On the trade-off between in-sample risk and
  out-of-sample risk guarantees

abstract:
  In recent decades, a number of ways of dealing with causality in practice,
such as propensity score matching, the PC algorithm and invariant causal
prediction, have been introduced. Besides its interpretational appeal, the
causal model provides the best out-of-sample prediction guarantees. In this
paper, we study the identification of causal-like models from in-sample data
that provide out-of-sample risk guarantees when predicting a target variable
from a set of covariates.
  Whereas ordinary least squares provides the best in-sample risk with limited
out-of-sample guarantees, causal models have the best out-of-sample guarantees
but achieve an inferior in-sample risk. By defining a trade-off of these
properties, we introduce $\textit{causal regularization}$. As the
regularization is increased, it provides estimators whose risk is more stable
across sub-samples at the cost of increasing their overall in-sample risk. The
increased risk stability is shown to lead to out-of-sample risk guarantees. We
provide finite sample risk bounds for all models and prove the adequacy of
cross-validation for attaining these bounds.


title: Optimal Projection Filters

abstract:
  We present the two new notions of projection of a stochastic differential
equation (SDE) onto a submanifold developed in Armstrong, Brigo e Rossi
Ferrucci (2019, 2018): the Ito-vector and Ito-jet projections. This allows one
to systematically and optimally develop low dimensional approximations to high
dimensional SDEs using differential geometric techniques. Our new projections
are based on optimality arguments and yield a well-defined "optimal"
approximations to the original SDE in the mean-square sense. We also show that
the earlier Stratonovich projection satisfies an optimality criterion that is
more ad hoc and less natural than the criteria satisfied by the new
projections. As an application, we consider approximating the solution of the
non-linear filtering problem within a given manifold of densities, using either
the Hellinger or L2 direct metrics and related Information Geometry structures
on the space of densities. The Stratonovich projection had yielded the
projection filters studied in Brigo, Hanzon and Le Gland (1998, 1999), while
the new projections lead to the optimal projection filters. The optimal
projection filters have been introduced in Armstrong, Brigo and Rossi Ferrucci
(2019), where numerical examples for the Gaussian case are given and where they
are compared to more traditional nonlinear filters.


title: A Bidirectional Conversion Network for Cross-Spectral Face Recognition

abstract:
  Face recognition in the infrared (IR) band has become an important supplement
to visible light face recognition due to its advantages of independent
background light, strong penetration, ability of imaging under harsh
environments such as nighttime, rain and fog. However, cross-spectral face
recognition (i.e., VIS to IR) is very challenging due to the dramatic
difference between the visible light and IR imageries as well as the lack of
paired training data. This paper proposes a framework of bidirectional
cross-spectral conversion (BCSC-GAN) between the heterogeneous face images, and
designs an adaptive weighted fusion mechanism based on information fusion
theory. The network reduces the cross-spectral recognition problem into an
intra-spectral problem, and improves performance by fusing bidirectional
information. Specifically, a face identity retaining module (IRM) is introduced
with the ability to preserve identity features, and a new composite loss
function is designed to overcome the modal differences caused by different
spectral characteristics. Two datasets of TINDERS and CASIA were tested, where
performance metrics of FID, recognition rate, equal error rate and normalized
distance were compared. Results show that our proposed network is superior than
other state-of-the-art methods. Additionally, the proposed rule of Self
Adaptive Weighted Fusion (SAWF) is better than the recognition results of the
unfused case and other traditional fusion rules that are commonly used, which
further justifies the effectiveness and superiority of the proposed
bidirectional conversion approach.


title: Exotic Quantum Difference Equations and Integral Solutions

abstract:
  One of the fundamental objects in the $K$-theoretic enumerative geometry of
Nakajima quiver varieties is known as the the capping operator. It is uniquely
determined as the fundamental solution to a system of $q$-difference equations.
Such difference equations involve shifts of two sets of variables, the
variables arising as equivariant parameters for a torus that acts on the
variety and an additional set of variables known as K\"ahler parameters. The
difference equations in the former variables were identified with the qKZ
equations in [28]. The difference equations in the latter variables were
identified representation theoretically in [30] using an analog of the quantum
dynamical Weyl group.
  Once this representation theoretic description is known, there is an obvious
generalization of these equations, which we refer to as exotic quantum
difference equations. They depend on a choice of alcove in a certain hyperplane
arrangement in $\mathrm{Pic}(X)\otimes \mathbb{R}$, with the usual difference
equations corresponding to the alcove containing small anti-ample line bundles.
As our main result, we relate the fundamental solution of these equations back
to quasimap counts using the so-called vertex with descendants, with
descendants given in terms of $K$-theoretic stable envelopes.
  In the case of the Hilbert scheme of points in $\mathbb{C}^2$, we write our
exotic quantum difference equations using the quantum toroidal algebra. We use
the results of [6] to obtain formulas for the $K$-theoretic stable envelopes of
arbitrary slope. Using this, we are able to write explicit formulas for the
solutions of the exotic difference equations. These formulas can be written as
contour integrals. As a partially conjectural application of our results, we
apply the saddlepoint approximation to these integrals to diagonalize the Bethe
subalgebras of the quantum toroidal algebra for arbitrary slope.


title: New Horizons for Fundamental Physics with LISA

abstract:
  The Laser Interferometer Space Antenna (LISA) has the potential to reveal
wonders about the fundamental theory of nature at play in the extreme gravity
regime, where the gravitational interaction is both strong and dynamical. In
this white paper, the Fundamental Physics Working Group of the LISA Consortium
summarizes the current topics in fundamental physics where LISA observations of
GWs can be expected to provide key input. We provide the briefest of reviews to
then delineate avenues for future research directions and to discuss
connections between this working group, other working groups and the consortium
work package teams. These connections must be developed for LISA to live up to
its science potential in these areas.


title:
Level-based Blocking for Sparse Matrices: Sparse Matrix-Power-Vector
  Multiplication

abstract:
  The multiplication of a sparse matrix with a dense vector (SpMV) is a key
component in many numerical schemes and its performance is known to be severely
limited by main memory access. Several numerical schemes require the
multiplication of a sparse matrix polynomial with a dense vector, which is
typically implemented as a sequence of SpMVs. This results in low performance
and ignores the potential to increase the arithmetic intensity by reusing the
matrix data from cache. In this work we use the recursive algebraic coloring
engine (RACE) to enable blocking of sparse matrix data across the polynomial
computations. In the graph representing the sparse matrix we form levels using
a breadth-first search. Locality relations of these levels are then used to
improve spatial and temporal locality when accessing the matrix data and to
implement an efficient multithreaded parallelization. Our approach is
independent of the matrix structure and avoids shortcomings of existing
"blocking" strategies in terms of hardware efficiency and parallelization
overhead. We quantify the quality of our implementation using performance
modelling and demonstrate speedups of up to 3$\times$ and 5$\times$ compared to
an optimal SpMV-based baseline on a single multicore chip of recent Intel and
AMD architectures. As a potential application, we demonstrate the benefit of
our implementation for a Chebyshev time propagation scheme, representing the
class of polynomial approximations to exponential integrators. Further
numerical schemes which may benefit from our developments include $s$-step
Krylov solvers and power clustering algorithms.


title: A general scheme for separably reducible properties

abstract:
  We propose a general scheme for studying separably reducible properties in
metric spaces and then apply it to obtain separable determinacy of Lipschitz
property and the separable determinacy of slopes