title: Tight Lower Bounds for Approximate & Exact $k$-Center in $\mathbb{R}^d$

abstract:
  In the discrete $k$-center problem, we are given a metric space
$(P,\texttt{dist})$ where $|P|=n$ and the goal is to select a set $C\subseteq
P$ of $k$ centers which minimizes the maximum distance of a point in $P$ from
its nearest center. For any $\epsilon>0$, Agarwal and Procopiuc [SODA '98,
Algorithmica '02] designed an $(1+\epsilon)$-approximation algorithm for this
problem in $d$-dimensional Euclidean space which runs in $O(dn\log k) +
\left(\dfrac{k}{\epsilon}\right)^{O\left(k^{1-1/d}\right)}\cdot n^{O(1)}$ time.
In this paper we show that their algorithm is essentially optimal: if for some
$d\geq 2$ and some computable function $f$, there is an $f(k)\cdot
\left(\dfrac{1}{\epsilon}\right)^{o\left(k^{1-1/d}\right)} \cdot
n^{o\left(k^{1-1/d}\right)}$ time algorithm for $(1+\epsilon)$-approximating
the discrete $k$-center on $n$ points in $d$-dimensional Euclidean space then
the Exponential Time Hypothesis (ETH) fails.
  We obtain our lower bound by designing a gap reduction from a $d$-dimensional
constraint satisfaction problem (CSP) defined by Marx and Sidiropoulos [SoCG
'14] to discrete $d$-dimensional $k$-center. As a byproduct of our reduction,
we also obtain that the exact algorithm of Agarwal and Procopiuc [SODA '98,
Algorithmica '02] which runs in $n^{O\left(d\cdot k^{1-1/d}\right)}$ time for
discrete $k$-center on $n$ points in $d$-dimensional Euclidean space is
asymptotically optimal. Formally, we show that if for some $d\geq 2$ and some
computable function $f$, there is an $f(k)\cdot n^{o\left(k^{1-1/d}\right)}$
time exact algorithm for the discrete $k$-center problem on $n$ points in
$d$-dimensional Euclidean space then the Exponential Time Hypothesis (ETH)
fails. Previously, such a lower bound was only known for $d=2$ and was implicit
in the work of Marx [IWPEC '06].
  [see paper for full abstract]


title:
Decoherence predictions in a superconductive quantum device using the
  steepest-entropy-ascent quantum thermodynamics framework

abstract:
  The current stage of quantum computing technology, called noise
intermediate-scale quantum (NISQ) technology, is characterized by large errors
that prohibit it from being used for real applications. In these devices,
decoherence, one of the main sources of error, is generally modeled by
Markovian master equations such as the Lindblad master equation. In this work,
the decoherence phenomena are addressed from the perspective of the
steepest-entropy-ascent quantum thermodynamics (SEAQT) framework in which the
noise is in part seen as internal to the system. The framework is as well used
to describe changes in the energy associated with environmental interactions.
Three scenarios, an inversion recovery experiment, a Ramsey experiment, and a
two-qubit entanglement-disentanglement experiment are used to demonstrate the
applicability of this framework, which provides good results relative to the
experiments and the Lindblad equation, It does so, however, from a different
perspective as to the cause of the decoherence. These experiments are conducted
on the IBM superconductive quantum device ibmq_bogota.


title:
Expected Sensitivity to Invisible Higgs Boson Decays at the ILC with the
  SiD Detector (A Snowmass White Paper)

abstract:
  In the Standard Model (SM) of particle physics, the branching ratio for Higgs
boson decays to a final state which is invisible to collider detectors, $H
\rightarrow ZZ^{\star} \rightarrow \nu \bar{\nu} \nu \bar{\nu}$, is order
0.10%. In theories beyond the SM (BSM), this branching ratio can be enhanced by
decays to undiscovered particles like dark matter (DM). At the Large Hadron
Collider (LHC), the current best upper limit on the branching ratio of
invisible Higgs boson decays is 11% at 95% confidence level. We investigate the
expected sensitivity to invisible Higgs decays with the Silicon Detector (SiD)
at the International Linear Collider (ILC). We conclude that at $\sqrt{s}=250$
GeV with 900 fb$^{-1}$ integrated luminosity each for $e_{L}^-e_{R}^+$ and
$e_{R}^-e_{L}^+$ at nominal beam polarization fractions, the expected upper
limit is 0.16% at 95% confidence level.


title:
Searching for pulsars associated with polarised point sources using
  LOFAR: Initial discoveries from the TULIPP project

abstract:
  Discovering radio pulsars, particularly millisecond pulsars (MSPs), is
important for a range of astrophysical applications, such as testing theories
of gravity or probing the magneto-ionic interstellar medium. We aim to discover
pulsars that may have been missed in previous pulsar searches by leveraging
known pulsar observables (primarily polarisation) in the sensitive,
low-frequency radio images from the Low-Frequency Array (LOFAR) Two-metre Sky
Survey (LoTSS), and have commenced the Targeted search, using LoTSS images, for
polarised pulsars (TULIPP) survey. For this survey, we identified linearly and
circularly polarised point sources with flux densities brighter than 2 mJy in
LoTSS images at a centre frequency of 144 MHz with a 48 MHz bandwidth. Over 40
known pulsars, half of which are MSPs, were detected as polarised sources in
the LoTSS images and excluded from the survey. We have obtained beam-formed
LOFAR observations of 30 candidates, which were searched for pulsations using
coherent de-dispersion. Here, we present the results of the first year of the
TULIPP survey. We discovered two pulsars, PSRs J1049+5822 and J1602+3901, with
rotational periods of P=0.73 s and 3.7 ms, respectively. We also detected a
further five known pulsars (two slowly-rotating pulsars and three MSPs) for
which accurate sky positions were not available to allow a unique cross-match
with LoTSS sources. This targeted survey presents a relatively efficient method
by which pulsars, particularly MSPs, may be discovered using the flexible
observing modes of sensitive radio telescopes such as the Square Kilometre
Array and its pathfinders/precursors, particularly since wide-area all-sky
surveys using coherent de-dispersion are currently computationally infeasible.


title: WeakM3D: Towards Weakly Supervised Monocular 3D Object Detection

abstract:
  Monocular 3D object detection is one of the most challenging tasks in 3D
scene understanding. Due to the ill-posed nature of monocular imagery, existing
monocular 3D detection methods highly rely on training with the manually
annotated 3D box labels on the LiDAR point clouds. This annotation process is
very laborious and expensive. To dispense with the reliance on 3D box labels,
in this paper we explore the weakly supervised monocular 3D detection.
Specifically, we first detect 2D boxes on the image. Then, we adopt the
generated 2D boxes to select corresponding RoI LiDAR points as the weak
supervision. Eventually, we adopt a network to predict 3D boxes which can
tightly align with associated RoI LiDAR points. This network is learned by
minimizing our newly-proposed 3D alignment loss between the 3D box estimates
and the corresponding RoI LiDAR points. We will illustrate the potential
challenges of the above learning problem and resolve these challenges by
introducing several effective designs into our method. Codes will be available
at https://github.com/SPengLiang/WeakM3D.


title:
Helicity locking of square skyrmion crystal in a centrosymmetric lattice
  system without vertical mirror symmetry

abstract:
  We theoretically investigate the stability of a square skyrmion crystal (SkX)
in a centrosymmetric tetragonal lattice structure with the emphasis on the role
of the magnetic anisotropy arising from the absence of vertical mirror
symmetry. Our analysis is based on an effective bilinear and biquadratic model
in momentum space, which is a canonical model for itinerant magnets in a
weak-coupling regime. By performing the simulated annealing for the model on
the two-dimensional square lattice, we find that the off-diagonal spin
component in the interaction, which becomes nonzero when the vertical mirror
symmetry is broken, gives rise to the square SkX with a definite helicity in an
external magnetic field. We show that the helicity of the centrosymmetric SkXs
is determined by the competition between the off-diagonal and diagonal
anisotropic interactions, the latter of which appears in the discrete
fourfold-rotational lattice structure. Furthermore, we discuss
helicity-dependent physical phenomena by introducing odd-parity multipoles,
where electric (magnetic) and electric (magnetic) toroidal multipoles are
sources of an antisymmetric spin polarization and an Edelstein effect (a
magnetoelectric effect). We also discuss the stability of the SkXs with
different helicities in a magnetic field rotation. Our results provide a way of
engineering the helicity-locked SkXs by the symmetric anisotropic interaction
in centrosymmetric magnets, which is distinct from that by the antisymmetric
Dzyaloshinskii-Moriya interaction in noncentrosymmetric magnets.


title:
Arithmetic Averages of Viscosity Coefficient are Sufficient for
  Second-Order Finite-Volume Viscous Discretization on Unstructured Grids

abstract:
  In this short note, we discuss the use of arithmetic averages for the
evaluation of viscous coefficients such as temperature and velocity components
at a face as required in a cell-centered finite-volume viscous discretization
on unstructured grids, and show that second-order accuracy can be achieved even
when the arithmetic average is not linearly-exact second-order reconstruction
at a face center (e.g., the face center is not located exactly halfway between
two adjacent cell centroids) as typical in unstructured grids. Unlike inviscid
discretizations, where the solution has to be reconstructed in a linearly exact
manner to the face center for second-order accuracy, the viscous discretization
does not require the linear exactness for computing viscous coefficients at a
face. There are two requirements for second-order accuracy, and the arithmetic
average satisfies both of them. Second-order accuracy is numerically
demonstrated for a simple one-dimensional nonlinear diffusion problem and for a
three-dimensional viscous problem based on methods of manufactured solutions.


title: Snowmass21 Accelerator Modeling Community White Paper

abstract:
  After a summary of relevant comments and recommendations from various reports
over the last ten years, this paper examines the modeling needs in accelerator
physics, from the modeling of single beams and individual accelerator elements,
to the realization of virtual twins that replicate all the complexity to model
a particle accelerator complex as accurately as possible. We then discuss
cutting-edge and emerging computing opportunities, such as advanced algorithms,
AI/ML and quantum computing, computational needs in hardware, software
performance, portability and scalability, and needs for scalable I/O and
in-situ analysis. Considerations of reliability, long-term sustainability, user
support and training are considered next, before discussing the benefits of
ecosystems with integrated workflows based on standardized input and output,
and with integrated frameworks and data repositories developed as a community.
Last, we highlight how the community can work more collaboratively and
efficiently through the development of consortia and centers, and via
collaboration with industry.


title: Polynomial entropy of Morse-Smale diffeomorphisms on surfaces

abstract:
  A classical problem in dynamical systems is to measure the complexity of a
map in terms of their orbits. One of the main tools we have to achieve this
goal is entropy. However, many interesting families of dynamical systems have
every element with zero-entropy. One of said families are the Morse-Smale
diffeomorphisms. The first author and E. Pujals introduced the concept of
generalized entropy that extends the classical notion of entropy. In this work,
we compute the generalized entropy of Morse-Smale diffeomorphisms on surfaces.


title:
Next-to-leading order corrections to decays of the heavier CP-even Higgs
  boson in the two Higgs doublet model

abstract:
  We investigate the impact of electroweak (EW), scalar and QCD corrections to
the full set of decay branching ratios of an additional CP-even Higgs boson
($H$) in the two Higgs doublet model with a softly broken $Z_2$ symmetry. We
employ the improved gauge independent on-shell scheme in the renormalized
vertices. We particularly focus on the scenario near the alignment limit in
which couplings of the discovered 125 GeV Higgs boson ($h$) coincide with those
of the standard model while the $Hhh$ coupling vanishes at tree level. The
renormalized decay rate for $H\to hh$ can significantly be changed from the
prediction at tree level due to non-decoupling loop effects of additional Higgs
bosons, even in the near alignment case. We find that the radiative corrections
to the branching ratio of $H\to hh$ can be a few ten percent level in the case
with the masses of additional Higgs bosons being degenerate under the
constraints of perturbative unitarity, vacuum stability and the EW precision
data. Further sizable corrections can be obtained for the case with a mass
difference among the additional Higgs bosons.


title:
Dark-matter And Neutrino Computation Explored (DANCE) Community Input to
  Snowmass

abstract:
  This paper summarizes the needs of the dark matter and neutrino communities
as it relates to computation. The scope includes data acquisition, triggers,
data management and processing, data preservation, simulation, machine
learning, data analysis, software engineering, career development, and equity
and inclusion. Beyond identifying our community needs, we propose actions that
can be taken to strengthen this community and to work together to overcome
common challenges.


title:
NURD: Negative-Unlabeled Learning for Online Datacenter Straggler
  Prediction

abstract:
  Datacenters execute large computational jobs, which are composed of smaller
tasks. A job completes when all its tasks finish, so stragglers -- rare, yet
extremely slow tasks -- are a major impediment to datacenter performance.
Accurately predicting stragglers would enable proactive intervention, allowing
datacenter operators to mitigate stragglers before they delay a job. While much
prior work applies machine learning to predict computer system performance,
these approaches rely on complete labels -- i.e., sufficient examples of all
possible behaviors, including straggling and non-straggling -- or strong
assumptions about the underlying latency distributions -- e.g., whether
Gaussian or not. Within a running job, however, none of this information is
available until stragglers have revealed themselves when they have already
delayed the job. To predict stragglers accurately and early without labeled
positive examples or assumptions on latency distributions, this paper presents
NURD, a novel Negative-Unlabeled learning approach with Reweighting and
Distribution-compensation that only trains on negative and unlabeled streaming
data. The key idea is to train a predictor using finished tasks of
non-stragglers to predict latency for unlabeled running tasks, and then
reweight each unlabeled task's prediction based on a weighting function of its
feature space. We evaluate NURD on two production traces from Google and
Alibaba, and find that compared to the best baseline approach, NURD produces
2--11 percentage point increases in the F1 score in terms of prediction
accuracy, and 4.7--8.8 percentage point improvements in job completion time.


title: Adaptive Noisy Matrix Completion

abstract:
  Low-rank matrix completion has been studied extensively under various type of
categories. The problem could be categorized as noisy completion or exact
completion, also active or passive completion algorithms. In this paper we
focus on adaptive matrix completion with bounded type of noise. We assume that
the matrix $\mathbf{M}$ we target to recover is composed as low-rank matrix
with addition of bounded small noise. The problem has been previously studied
by \cite{nina}, in a fixed sampling model. Here, we study this problem in
adaptive setting that, we continuously estimate an upper bound for the angle
with the underlying low-rank subspace and noise-added subspace. Moreover, the
method suggested here, could be shown requires much smaller observation than
aforementioned method.


title: Exact mean-field models for spiking neural networks with adaptation

abstract:
  Networks of spiking neurons with adaption have been shown to be able to
reproduce a wide range of neural activities, including the emergent population
bursting and spike synchrony that underpin brain disorders and normal function.
Exact mean-field models derived from spiking neural networks are extremely
valuable, as such models can be used to determine how individual neuron and
network parameters interact to produce macroscopic network behaviour. In the
paper, we derive and analyze a set of exact mean-field equations for the neural
network with spike frequency adaptation. Specifically, our model is a network
of Izhikevich neurons, where each neuron is modeled by a two dimensional system
consisting of a quadratic integrate and fire equation plus an equation which
implements spike frequency adaptation. Previous work deriving a mean-field
model for this type of network, relied on the assumption of sufficiently slow
dynamics of the adaptation variable. However, this approximation did not
succeeded in establishing an exact correspondence between the macroscopic
description and the realistic neural network, especially when the adaptation
time constant was not large. The challenge lies in how to achieve a closed set
of mean-field equations with the inclusion of the mean-field expression of the
adaptation variable. We address this challenge by using a Lorentzian ansatz
combined with the moment closure approach to arrive at the mean-field system in
the thermodynamic limit. The resulting macroscopic description is capable of
qualitatively and quantitatively describing the collective dynamics of the
neural network, including transition between tonic firing and bursting.


title: On Euler systems for motives and Heegner points

abstract:
  We formulate an Iwasawa main conjecture for a higher rank Euler system for a
general motive. We prove "one half" of the main conjecture under mild
hypotheses. We also formulate a conjecture on "Darmon-type derivatives" of
Euler systems and give an application to the Tamagawa number conjecture.
Lastly, we specialize our general framework to the setting of Heegner points
and give a natural interpretation of the Heegner point main conjecture in terms
of rank two Euler systems.


title:
Design and Evaluation of an Augmented Reality Head-Mounted Display
  Interface for Human Robot Teams Collaborating in Physically Shared
  Manufacturing Tasks

abstract:
  We provide an experimental evaluation of a wearable augmented reality (AR)
system we have developed for human-robot teams working on tasks requiring
collaboration in shared physical workspace. Recent advances in AR technology
have facilitated the development of more intuitive user interfaces for many
human-robot interaction applications. While it has been anticipated that AR can
provided a more intuitive interface to robot assistants helping human workers
in various manufacturing scenarios, existing studies in robotics have been
largely limited to teleoperation and programming. Industry 5.0 envisions
cooperation between human and robot working in teams. Indeed, there exist many
industrial task that can benefit from human-robot collaboration. A prime
example is high-value composite manufacturing. Working with our industry
partner towards this example application, we evaluated our AR interface design
for shared physical workspace collaboration in human-robot teams. We conducted
a multi-dimensional analysis of our interface using establish metrics. Results
from our user study (n=26) show that subjectively, the AR interface feels more
novel and a standard joystick interface feels more dependable to users.
However, the AR interface was found to reduce physical demand and task
completion time, while increasing robot utilization. Furthermore, user's
freedom of choice to collaborate with the robot may also affect the perceived
usability of the system.


title: Domain Adaptive Hand Keypoint and Pixel Localization in the Wild

abstract:
  We aim to improve the performance of regressing hand keypoints and segmenting
pixel-level hand masks under new imaging conditions (e.g., outdoors) when we
only have labeled images taken under very different conditions (e.g., indoors).
In the real world, it is important that the model trained for both tasks works
under various imaging conditions. However, their variation covered by existing
labeled hand datasets is limited. Thus, it is necessary to adapt the model
trained on the labeled images (source) to unlabeled images (target) with unseen
imaging conditions. While self-training domain adaptation methods (i.e.,
learning from the unlabeled target images in a self-supervised manner) have
been developed for both tasks, their training may degrade performance when the
predictions on the target images are noisy. To avoid this, it is crucial to
assign a low importance (confidence) weight to the noisy predictions during
self-training. In this paper, we propose to utilize the divergence of two
predictions to estimate the confidence of the target image for both tasks.
These predictions are given from two separate networks, and their divergence
helps identify the noisy predictions. To integrate our proposed confidence
estimation into self-training, we propose a teacher-student framework where the
two networks (teachers) provide supervision to a network (student) for
self-training, and the teachers are learned from the student by knowledge
distillation. Our experiments show its superiority over state-of-the-art
methods in adaptation settings with different lighting, grasping objects,
backgrounds, and camera viewpoints. Our method improves by 4% the multi-task
score on HO3D compared to the latest adversarial adaptation method. We also
validate our method on Ego4D, egocentric videos with rapid changes in imaging
conditions outdoors.


title: Gradient Correction beyond Gradient Descent

abstract:
  The great success neural networks have achieved is inseparable from the
application of gradient-descent (GD) algorithms. Based on GD, many variant
algorithms have emerged to improve the GD optimization process. The gradient
for back-propagation is apparently the most crucial aspect for the training of
a neural network. The quality of the calculated gradient can be affected by
multiple aspects, e.g., noisy data, calculation error, algorithm limitation,
and so on. To reveal gradient information beyond gradient descent, we introduce
a framework (\textbf{GCGD}) to perform gradient correction. GCGD consists of
two plug-in modules: 1) inspired by the idea of gradient prediction, we propose
a \textbf{GC-W} module for weight gradient correction; 2) based on Neural ODE,
we propose a \textbf{GC-ODE} module for hidden states gradient correction.
Experiment results show that our gradient correction framework can effectively
improve the gradient quality to reduce training epochs by $\sim$ 20\% and also
improve the network performance.


title: On the drift wave eigenmode crossing zero frequency in Tokamak

abstract:
  The conventional ion temperature gradient or \eta_i mode is known to
propagate in the ion diamagnetic direction. Investigation of a generic drift
fluid model with warm ions and adiabatic electrons, reveals that as \eta_i
decreases, the propagation characteristics of the unstable mode may change
drastically, the mode frequency first decreases in magnitude, and reaches zero
for a critical \eta_i. But as \eta_i goes down further, the mode begins to
propagate in the electron diamagnetic direction. The lower toroidal mode number
perturbations are more prone to reversal in propagation direction. Even for
\eta_i=0, the mode remains unstable, drawing free energy form the density
gradients. Since finite ion temperature appears to be essential for propagation
in the electron direction, it is appropriate to introduce new terminology and
call this wave the warm ion electron drift (WIED) mode. The model drift wave
system is explored within the framework of the two dimensional (2D) weakly
asymmetric ballooning theory (WABT) for local eigenmode satisfying natural
boundary conditions. The physics behind the excitation of the eigenmode
crossing zero frequency is identified to be the reactive instability induced by
the curvature coupling between the positive energy wave and the negative energy
wave, a damped mode in electron direction coupled to a growing mode in the ion
direction in non-dissipative slab limit. Apart from its intrinsic scientific
value, this mechanism may shed some light onto the nature of tokamak edge
turbulence observed in frequencies moderately lower than the electron
diamagnetic frequency; understanding this phenomenon could be helpful in
conceptual design around the edge region of future tokamak.


title:
Sensitivity to decays of long-lived dark photons at the ILC (A Snowmass
  White Paper)

abstract:
  We investigate the sensitivity to long-lived dark photons produced in
Higgstrahlung events via the Higgs portal, $H \rightarrow \gamma_{D}
\gamma_{D}$, with the Silicon Detector (SiD) at the International Linear
Collider (ILC). These events provide a useful benchmark for tracking and vertex
detector performance with long-lived particle decays at displaced vertices. The
ILC is one of several Higgs factories proposed by the international community
to study the properties of the Higgs boson at high precision.


title:
Variability Selected Active Galactic Nuclei from ASAS-SN Survey:
  Constraining the Low Luminosity AGN Population

abstract:
  Low luminosity active galactic nuclei (LLAGN) probe accretion physics in the
low Eddington regime and can provide additional clues about galaxy evolution.
AGN variability is ubiquitous and thus provides a reliable tool for finding
AGN. We analyze the All-Sky Automated Survey for SuperNovae light curves of
1218 galaxies with $g<14$ mag and Sloan Digital Sky Survey spectra in search of
AGN. We find 37 objects that are both variable and have AGN-like structure
functions, which is about 3% of the sample. The majority of the variability
selected AGN are LLAGN with Eddington ratios ranging from $10^{-4}$ to
$10^{-2}$. We thus estimate the fraction of LLAGN in the population of galaxies
as 2% down to a median Eddington ratio of $2\times10^{-3}$. Combining the BPT
line ratio diagnostics and the broad-line AGN, up to $\sim$60% of the AGN
candidates are confirmed spectroscopically. The BPT diagnostics also classified
10-30% of the candidates as star forming galaxies rather than AGN.


title:
A Multi-parameter Updating Fourier Online Gradient Descent Algorithm for
  Large-scale Nonlinear Classification

abstract:
  Large scale nonlinear classification is a challenging task in the field of
support vector machine. Online random Fourier feature map algorithms are very
important methods for dealing with large scale nonlinear classification
problems. The main shortcomings of these methods are as follows: (1) Since only
the hyperplane vector is updated during learning while the random directions
are fixed, there is no guarantee that these online methods can adapt to the
change of data distribution when the data is coming one by one. (2) The
dimension of the random direction is often higher for obtaining better
classification accuracy, which results in longer test time. In order to
overcome these shortcomings, a multi-parameter updating Fourier online gradient
descent algorithm (MPU-FOGD) is proposed for large-scale nonlinear
classification problems based on a novel random feature map. In the proposed
method, the suggested random feature map has lower dimension while the
multi-parameter updating strategy can guarantee the learning model can better
adapt to the change of data distribution when the data is coming one by one.
Theoretically, it is proved that compared with the existing random Fourier
feature maps, the proposed random feature map can give a tighter error bound.
Empirical studies on several benchmark data sets demonstrate that compared with
the state-of-the-art online random Fourier feature map methods, the proposed
MPU-FOGD can obtain better test accuracy.


title:
A Squeeze-and-Excitation and Transformer based Cross-task System for
  Environmental Sound Recognition

abstract:
  Environmental sound recognition (ESR) is an emerging research topic in audio
pattern recognition. Many tasks are presented to resort to computational
systems for ESR in real-life applications. However, current systems are usually
designed for individual tasks, and are not robust and applicable to other
tasks. Cross-task systems, which promote unified knowledge modeling across
various tasks, have not been thoroughly investigated. In this paper, we propose
a cross-task system for three different tasks of ESR: acoustic scene
classification, urban sound tagging, and anomalous sound detection. An
architecture named SE-Trans is presented that uses attention mechanism-based
Squeeze-and-Excitation and Transformer encoder modules to learn channel-wise
relationship and temporal dependencies of the acoustic features. FMix is
employed as the data augmentation method that improves the performance of ESR.
Evaluations for the three tasks are conducted on the recent databases of DCASE
challenges. The experimental results show that the proposed cross-task system
achieves state-of-the-art performance on all tasks. Further analysis
demonstrates that the proposed cross-task system can effectively utilize
acoustic knowledge across different ESR tasks.


title:
Towards Afrocentric NLP for African Languages: Where We Are and Where We
  Can Go

abstract:
  Aligning with ACL 2022 special Theme on "Language Diversity: from Low
Resource to Endangered Languages", we discuss the major linguistic and
sociopolitical challenges facing development of NLP technologies for African
languages. Situating African languages in a typological framework, we discuss
how the particulars of these languages can be harnessed. To facilitate future
research, we also highlight current efforts, communities, venues, datasets, and
tools. Our main objective is to motivate and advocate for an Afrocentric
approach to technology development. With this in mind, we recommend
\textit{what} technologies to build and \textit{how} to build, evaluate, and
deploy them based on the needs of local African communities.


title:
Possible, Impossible, and Expected Diameters and Production Rates of
  Droplets in Aerosols and Sprays

abstract:
  Liquid atomization processes generating sprays and aerosols of droplets are
used in many delivery and coating systems involving pure solvents, solutions,
and suspensions. In our recent experimental work, we introduced a novel liquid
atomization process generating micro-sprays and aerosols of submicron-diameter
droplets for pure solvents, solutions, and suspensions: gas jets disintegrate
thin liquid films that are formed as bubbles approach a liquid surface. Here we
develop a theoretical description of droplet sizes and flow rates, using the
first principles of conservation of mass and energy, and accounting for the
ratios of specific energies and the ratios of specific energy rates provided by
the atomizing gas and dissipated by the atomized liquid. We introduce
atomization diagrams as a graphical tool to determine possible, impossible, and
expected droplet diameters and specific flow rates in aerosols and sprays
produced under various conditions. We find a reasonable agreement between the
theory and experiments for five different liquids converted into aerosols of
submicron-diameter droplets by an atomization process where gas jets
disintegrate thin liquid films that are formed as bubbles approach a liquid
surface, and also for five traditional pressure nozzles that produce sprays of
droplets of hundreds of microns in diameter. Our study explored the overall
range of Ohnesorge number between 0.01-100, and the analysis and atomization
diagrams contribute to understanding of liquid atomization and can serve as a
theoretical framework for comparing different liquid atomization techniques.


title: High Energy & High Luminosity $\gamma\gamma$ Colliders

abstract:
  With the best of modern standard lasers, high-energy $\gamma\gamma$ colliders
from electron beams of E larger than 250 GeV are possible at the expense of
photon luminosity, i.e. 10 times lower than for photon colliders at c.m.
energies below 0.5 TeV. This Snowmass21 Contributed Paper shows how Free
Electron Lasers (FEL) pave the way for High Energy & High Luminosity
$\gamma\gamma$ colliders. We present and assess a conceptual design study of
the Free Electron Laser that would be required for high luminosity
$\gamma\gamma$ colliders as second interaction region of 0.5 TeV to 10 TeV c.m.
$e^+e^-$ colliders.


title:
Represent, Compare, and Learn: A Similarity-Aware Framework for
  Class-Agnostic Counting

abstract:
  Class-agnostic counting (CAC) aims to count all instances in a query image
given few exemplars. A standard pipeline is to extract visual features from
exemplars and match them with query images to infer object counts. Two
essential components in this pipeline are feature representation and similarity
metric. Existing methods either adopt a pretrained network to represent
features or learn a new one, while applying a naive similarity metric with
fixed inner product. We find this paradigm leads to noisy similarity matching
and hence harms counting performance. In this work, we propose a
similarity-aware CAC framework that jointly learns representation and
similarity metric. We first instantiate our framework with a naive baseline
called Bilinear Matching Network (BMNet), whose key component is a learnable
bilinear similarity metric. To further embody the core of our framework, we
extend BMNet to BMNet+ that models similarity from three aspects: 1)
representing the instances via their self-similarity to enhance feature
robustness against intra-class variations; 2) comparing the similarity
dynamically to focus on the key patterns of each exemplar; 3) learning from a
supervision signal to impose explicit constraints on matching results.
Extensive experiments on a recent CAC dataset FSC147 show that our models
significantly outperform state-of-the-art CAC approaches. In addition, we also
validate the cross-dataset generality of BMNet and BMNet+ on a car counting
dataset CARPK. Code is at tiny.one/BMNet


title: Metaverse Native Communication: A Blockchain and Spectrum Prospective

abstract:
  Metaverse depicts a vista of constructing a virtual environment parallel to
the real world so people can communicate with others and objects through
digital entities. In the real world, communication relies on identities and
addresses that are recognized by authorities, no matter the link is established
via post, email, mobile phone, or landline. Metaverse, however, is different
from the real world, which requires a single identity belongs to the
individual. This identity can be an encrypted virtual address in the metaverse
but no one can trace or verify it. In order to achieve such addresses to hide
individuals in the metaverse, re-mapping the virtual address to the
individual's identity and a specific spectrum to support the address-based
communication for the metaverse are needed. Therefore, metaverse native or
meta-native communications based on blockchain could be a promising solution to
directly connect entities with their native encrypted addresses that gets rid
of the existing network services based on IP, cellular, HTTP, etc. This paper
proposes a vision of blockchain, encrypted address and address-based access
model for all users, devices, services, etc. to contribute to the metaverse.
Furthermore, the allocation architecture of a designated spectrum for the
metaverse is proposed to remove the barrier to access to the
metaverse/blockchain in response to the initiatives of metaverse and
decentralized Internet.


title:
Hardness for Triangle Problems under Even More Believable Hypotheses:
  Reductions from Real APSP, Real 3SUM, and OV

abstract:
  The $3$SUM hypothesis, the APSP hypothesis and SETH are the three main
hypotheses in fine-grained complexity. So far, within the area, the first two
hypotheses have mainly been about integer inputs in the Word RAM model of
computation. The "Real APSP" and "Real $3$SUM" hypotheses, which assert that
the APSP and $3$SUM hypotheses hold for real-valued inputs in a reasonable
version of the Real RAM model, are even more believable than their integer
counterparts.
  Under the very believable hypothesis that at least one of the Integer $3$SUM
hypothesis, Integer APSP hypothesis or SETH is true, Abboud, Vassilevska W. and
Yu [STOC 2015] showed that a problem called Triangle Collection requires
$n^{3-o(1)}$ time on an $n$-node graph.
  Our main result is a nontrivial lower bound for a slight generalization of
Triangle Collection, called All-Color-Pairs Triangle Collection, under the even
more believable hypothesis that at least one of the Real $3$SUM, the Real APSP,
and the OV hypotheses is true. Combined with slight modifications of prior
reductions, we obtain polynomial conditional lower bounds for problems such as
the (static) ST-Max Flow problem and dynamic Max Flow, now under the new weaker
hypothesis.
  Our main result is built on the following two lines of reductions.
  * Real APSP and Real $3$SUM hardness for the All-Edges Sparse Triangle
problem. Prior reductions only worked from the integer variants of these
problems.
  * Real APSP and OV hardness for a variant of the Boolean Matrix
Multiplication problem.
  Along the way we show that Triangle Collection is equivalent to a simpler
restricted version of the problem, simplifying prior work. Our techniques also
have other interesting implications, such as a super-linear lower bound of
Integer All-Numbers $3$SUM based on the Real $3$SUM hypothesis, and a tight
lower bound for a string matching problem based on the OV hypothesis.


title:
Novel Materials and Concepts for Next-Generation High Power Target
  Applications

abstract:
  Novel beam-intercepting materials and targetry concepts are essential to
improve the performance, reliability and operation lifetimes of next generation
multi-megawatt (multi-MW) accelerator target facilities. The beam-intercepting
materials and components must sustain an order-of-magnitude increase in
particle beam intensities and are beyond the current state-of-the-art. With
conventional materials already limiting the scope of experiments, it is crucial
to investigate novel target materials, technologies and concepts that will
satisfy the requirements and maximize the physics benefits of future energy and
intensity frontier experiments. This paper provides an overview of the related
targetry R&D required over the next 10 years to support and enable future
high-power accelerator target facilities.


title:
Decay of higher order derivatives for $L^p$ solutions to the
  compressible fluid model of Korteweg type

abstract:
  We present a new derivation for the optimal decay of \textit{arbitrary}
higher order derivatives for $L^p$ solutions to the compressible fluid model of
Korteweg type. This approach, based on Gevrey estimates, is to establish
uniform bounds on the growth of the radius of analyticity of the solution in
negative Besov norms. For that end, the maximal regularity property involving
Gevrey multiplier of heat kernel and non standard product Besov estimates are
well developed. Our approach is partly inspired by Oliver-Titi's work and is
applicable to a wide range of dissipative systems.


title: GOMP-ST: Grasp Optimized Motion Planning for Suction Transport

abstract:
  Suction cup grasping is very common in industry, but moving too quickly can
cause suction cups to detach, causing drops or damage. Maintaining a suction
grasp throughout a high-speed motion requires balancing suction forces against
inertial forces while the suction cups deform under strain. In this paper, we
consider Grasp Optimized Motion Planning for Suction Transport (GOMP-ST), an
algorithm that combines deep learning with optimization to decrease transport
time while avoiding suction cup failure. GOMP-ST first repeatedly moves a
physical robot, vacuum gripper, and a sample object, while measuring pressure
with a solid-state sensor to learn critical failure conditions. Then, these are
integrated as constraints on the accelerations at the end-effector into a
time-optimizing motion planner. The resulting plans incorporate real-world
effects such as suction cup deformation that are difficult to model
analytically. In GOMP-ST, the learned constraint, modeled with a neural
network, is linearized using Autograd and integrated into a sequential
quadratic program optimization. In 420 experiments with a physical UR5
transporting objects ranging from 1.3 to 1.7 kg, we compare GOMP-ST to baseline
optimizing motion planners. Results suggest that GOMP-ST can avoid suction cup
failure while decreasing transport times from 16% to 58%. For code, video, and
datasets, see https://sites.google.com/view/gomp-st.


title:
Synthesis of the Supremal Covert Attacker Against Unknown Supervisors by
  Using Observations

abstract:
  In this paper, we consider the problem of synthesizing the supremal covert
damage-reachable attacker, in the setup where the model of the supervisor is
unknown to the adversary but the adversary has recorded a (prefix-closed)
finite set of observations of the runs of the closed-loop system. The
synthesized attacker needs to ensure both the damage-reachability and the
covertness against all the supervisors which are consistent with the given set
of observations. There is a gap between the de facto supremality, assuming the
model of the supervisor is known, and the supremality that can be attained with
a limited knowledge of the model of the supervisor, from the adversary's point
of view. We consider the setup where the attacker can exercise sensor
replacement/deletion attacks and actuator enablement/disablement attacks. The
solution methodology proposed in this work is to reduce the synthesis of the
supremal covert damage-reachable attacker, given the model of the plant and the
finite set of observations, to the synthesis of the supremal safe supervisor
for certain transformed plant, which shows the decidability of the
observation-assisted covert attacker synthesis problem. The effectiveness of
our approach is illustrated on a water tank example adapted from the
literature.


title: Simulated bifurcation assisted by thermal fluctuation

abstract:
  Various kinds of Ising machines based on unconventional computing have
recently been developed for practically important combinatorial optimization.
Among them, the machines implementing a heuristic algorithm called simulated
bifurcation have achieved high performance, where Hamiltonian dynamics are
simulated by massively parallel processing. To further improve the performance
of simulated bifurcation, here we introduce thermal fluctuation to its dynamics
relying on the Nos\'e-Hoover method, which has been used to simulate
Hamiltonian dynamics at finite temperatures. We find that a heating process in
the Nos\'e-Hoover method can assist simulated bifurcation to escape from local
minima of the Ising problem, and hence lead to improved performance. We thus
propose heated simulated bifurcation and demonstrate its performance
improvement by numerically solving instances of the Ising problem with up to
2000 spin variables and all-to-all connectivity. Proposed heated simulated
bifurcation is expected to be accelerated by parallel processing.


title:
Spot the Difference: A Cooperative Object-Referring Game in
  Non-Perfectly Co-Observable Scene

abstract:
  Visual dialog has witnessed great progress after introducing various
vision-oriented goals into the conversation, especially such as GuessWhich and
GuessWhat, where the only image is visible by either and both of the questioner
and the answerer, respectively. Researchers explore more on visual dialog tasks
in such kind of single- or perfectly co-observable visual scene, while somewhat
neglect the exploration on tasks of non perfectly co-observable visual scene,
where the images accessed by two agents may not be exactly the same, often
occurred in practice. Although building common ground in non-perfectly
co-observable visual scene through conversation is significant for advanced
dialog agents, the lack of such dialog task and corresponding large-scale
dataset makes it impossible to carry out in-depth research. To break this
limitation, we propose an object-referring game in non-perfectly co-observable
visual scene, where the goal is to spot the difference between the similar
visual scenes through conversing in natural language. The task addresses
challenges of the dialog strategy in non-perfectly co-observable visual scene
and the ability of categorizing objects. Correspondingly, we construct a
large-scale multimodal dataset, named SpotDiff, which contains 87k Virtual
Reality images and 97k dialogs generated by self-play. Finally, we give
benchmark models for this task, and conduct extensive experiments to evaluate
its performance as well as analyze its main challenges.


title:
Manifestation of deformation and nonlocality in $\alpha$ and cluster
  decay

abstract:
  It is common to study the strong decay of a heavy nucleus as a tunneling
phenomenon where the $\alpha$ ($^4$He) or a light nuclear cluster tunnels
through the Coulomb barrier formed by its interaction with the heavier daughter
nucleus. The position and width of the Coulomb barrier are determined by the
total interaction potential between the two daughter nuclei. We examine the
effects of including nonlocality and deformation in the interaction potential
by calculating the half-lives, $t_{1/2}$, and thereby phenomenological
preformation factors of several nuclei which have the possibility of decaying
by emitting either an $\alpha$ or a light nuclear cluster. The effect of
deformation manifests itself by a decrease in $t_{1/2}$ for all the decays
studied. The effect of nonlocality is studied within two different models of
the nuclear potential: the energy-independent but angular momentum ($l$)
dependent Mumbai (M) potential and the energy-dependent Perey-Buck (PB)
potential. The nonlocal nuclear interaction leads to a decrease in all
half-lives studied. Though the decrease is larger due to the Perey-Buck
potential, half-lives evaluated using the Mumbai potential show a strong
sensitivity to the $l$ value in the decay. This feature of the $\alpha$ and
cluster decay half-lives can provide a complementary tool in addition to
scattering data which are more commonly used to fix the parameters of a
nonlocal potential in literature.


title:
Minimum Height Drawings of Ordered Trees in Polynomial Time: Homotopy
  Height of Tree Duals

abstract:
  We consider drawings of graphs in the plane in which vertices are assigned
distinct points in the plane and edges are drawn as simple curves connecting
the vertices and such that the edges intersect only at their common endpoints.
There is an intuitive quality measure for drawings of a graph that measures the
height of a drawing $\phi : G \rightarrow \mathbb{R}^2$ as follows. For a
vertical line $\ell$ in $\mathbb{R}^2$, let the height of $\ell$ be the
cardinality of the set $\ell \cap \phi(G)$. The height of a drawing of $G$ is
the maximum height over all vertical lines. In this paper, instead of abstract
graphs, we fix a drawing and consider plane graphs. In other words, we are
looking for a homeomorphism of the plane that minimizes the height of the
resulting drawing. This problem is equivalent to the homotopy height problem in
the plane, and the homotopic Fr\'echet distance problem. These problems were
recently shown to lie in NP, but no polynomial-time algorithm or NP-hardness
proof has been found since their formulation in 2009. We present the first
polynomial-time algorithm for drawing trees with optimal height. This
corresponds to a polynomial-time algorithm for the homotopy height where the
triangulation has only one vertex (that is, a set of loops incident to a single
vertex), so that its dual is a tree.


title: Global existence of strong solution to non-isothermal ideal gas system

abstract:
  This paper aims to establish the global existence of strong solutions to a
non-isothermal ideal gas model. We first show global well-posedness in the
Sobolev space $H^2(\mathbb{R}^3)$ by using energy estimates. We then prove the
global well-posedness for small-data solutions in the critical Besov space by
using Banach's fixed point theorem.


abstract:
  White paper to the Proceedings of the U.S. Particle Physics Community
Planning Exercise (Snowmass 2021): Linear collider based on laser-plasma
accelerators


title:
Measurements of protons and charged pions emitted from $\nu_{\mu}$
  charged-current interactions on iron at a mean neutrino energy of 1.49$\,$GeV
  using a nuclear emulsion detector

abstract:
  This study conducted an analysis of muons, protons, and charged pions emitted
from $\nu_{\mu}$ charged-current interactions on iron using a nuclear emulsion
detector. The emulsion detector with a 65$\,$kg iron target was exposed to a
neutrino beam corresponding to 4.0$\times$10$^{19}$ protons on target with a
mean neutrino energy of 1.49$\,$GeV. The measurements were performed at a
momentum threshold of 200 (50)$\,$MeV/$c$ for protons (pions), which are the
lowest momentum thresholds attempted up to now. The measured quantities are the
multiplicities, emission angles, and momenta of the muons, protons, and charged
pions. In addition to these inclusive measurements, exclusive measurements such
as the muon-proton emission-angle correlations of specific channels and the
opening angle between the protons of CC0$\pi$2$p$ events were performed. The
data were compared to Monte Carlo (MC) predictions and some significant
differences were observed. The results of the study demonstrate the capability
of detailed measurements of neutrino-nucleus interactions using a nuclear
emulsion detector to improve neutrino interaction models.


title:
Mixed-Precision Neural Network Quantization via Learned Layer-wise
  Importance

abstract:
  The exponentially large discrete search space in mixed-precision quantization
(MPQ) makes it hard to determine the optimal bit-width for each layer. Previous
works usually resort to iterative search methods on the training set, which
consume hundreds or even thousands of GPU-hours. In this study, we reveal that
some unique learnable parameters in quantization, namely the scale factors in
the quantizer, can serve as importance indicators of a layer, reflecting the
contribution of that layer to the final accuracy at certain bit-widths. These
importance indicators naturally perceive the numerical transformation during
quantization-aware training, which can precisely and correctly provide
quantization sensitivity metrics of layers. However, a deep network always
contains hundreds of such indicators, and training them one by one would lead
to an excessive time cost. To overcome this issue, we propose a joint training
scheme that can obtain all indicators at once. It considerably speeds up the
indicators training process by parallelizing the original sequential training
processes. With these learned importance indicators, we formulate the MPQ
search problem as a one-time integer linear programming (ILP) problem. That
avoids the iterative search and significantly reduces search time without
limiting the bit-width search space. For example, MPQ search on ResNet18 with
our indicators takes only 0.06 seconds. Also, extensive experiments show our
approach can achieve SOTA accuracy on ImageNet for far-ranging models with
various constraints (e.g., BitOps, compress rate).


title:
Wave propagation for a discrete diffusive vaccination epidemic model
  with bilinear incidence

abstract:
  The aim of the current paper is to study the existence of traveling wave
solutions (TWS) for a vaccination epidemic model with bilinear incidence. The
existence result is determined by the basic reproduction number $\Re_0$. More
specifically, the system admits a nontrivial TWS when $\Re_0>1$ and $c \geq
c^*$, where $c^*$ is the critical wave speed. We also found that the TWS is
connecting two different equilibria by constructing Lyapunov functional.
Lastly, we give some biological explanations from the perspective of
epidemiology.


title:
Physical Layer Security of RIS-Assisted Communications under
  Electromagnetic Interference

abstract:
  This work investigates the impact of the ever-present electromagnetic
interference (EMI) on the achievable secrecy performance of reconfigurable
intelligent surface (RIS)-aided communication systems. We characterize the
end-to-end RIS channel by considering key practical aspects such as spatial
correlation, transmit beamforming vector, phase-shift noise, the coexistence of
direct and indirect channels, and the presence of strong/mild EMI on the
receiver sides. We show that the effect of EMI on secrecy performance strongly
depends on the ability of the eavesdropper to cancel such interference; this
puts forth the potential of EMI-based attacks to degrade physical layer
security in RIS-aided communications.


title:
Real Robot Challenge 2021: Cartesian Position Control with Triangle
  Grasp and Trajectory Interpolation

abstract:
  We present our runner-up approach for the Real Robot Challenge 2021. We build
upon our previous approach used in Real Robot Challenge 2020. To solve the task
of sequential goal-reaching we focus on two aspects to achieving near-optimal
trajectory: Grasp stability and Controller performance. In the RRC 2021
simulated challenge, our method relied on a hand-designed Pinch grasp combined
with Trajectory Interpolation for better stability during the motion for fast
goal-reaching. In Stage 1, we observe reverting to a Triangular grasp to
provide a more stable grasp when combined with Trajectory Interpolation,
possibly due to the sim2real gap. The video demonstration for our approach is
available at https://youtu.be/dlOueoaRWrM. The code is publicly available at
https://github.com/madan96/benchmark-rrc.


title:
Multi-View Document Representation Learning for Open-Domain Dense
  Retrieval

abstract:
  Dense retrieval has achieved impressive advances in first-stage retrieval
from a large-scale document collection, which is built on bi-encoder
architecture to produce single vector representation of query and document.
However, a document can usually answer multiple potential queries from
different views. So the single vector representation of a document is hard to
match with multi-view queries, and faces a semantic mismatch problem. This
paper proposes a multi-view document representation learning framework, aiming
to produce multi-view embeddings to represent documents and enforce them to
align with different queries. First, we propose a simple yet effective method
of generating multiple embeddings through viewers. Second, to prevent
multi-view embeddings from collapsing to the same one, we further propose a
global-local loss with annealed temperature to encourage the multiple viewers
to better align with different potential queries. Experiments show our method
outperforms recent works and achieves state-of-the-art results.


title:
Are you aware of what you are watching? Role of machine heuristic in
  online content recommendations

abstract:
  Since recommender systems have been created and developed to automate the
recommendation process, users can easily consume their desired video content on
online platforms. In this line, several content recommendation algorithms are
introduced and employed to allow users to encounter content of their interests,
effectively. However, the recommendation systems sometimes regrettably
recommend inappropriate content, including misinformation or fake news. To make
it worse, people would unreservedly accept such content due to their cognitive
heuristic, machine heuristic, which is the rule of thumb that machines are more
accurate and trustworthy than humans. In this study, we designed and conducted
a web-based experiment where the participants are invoked machine heuristic by
experiencing the whole process of machine or human recommendation system. The
results demonstrated that participants (N = 89) showed a more positive attitude
toward a machine recommender than a human recommender, even the recommended
videos contain inappropriate content. While participants who have a high level
of trust in machines exhibited a negative attitude toward recommendations.
Based on these results, we suggest that a phenomenon known as algorithm
aversion might be simultaneously considered with machine heuristic in
investigating human interaction with a machine.


title:
Advanced RF Structures for Wakefield Acceleration and High-Gradient
  Research

abstract:
  Structure wakefield acceleration (SWFA) is one of the most promising AAC
schemes in several recent strategic reports, including DOE's 2016 AAC Roadmap,
report on the Advanced and Novel Accelerators for High Energy Physics Roadmap
(ANAR), and report on Accelerator and Beam Physics Research Goals and
Opportunities. SWFA aims to raise the gradient beyond the limits of
conventional radiofrequency (RF) accelerator technology, and thus the RF to
beam energy efficiency, by reducing RF breakdowns from confining the microwave
energy in a short (on the order of about 10 ns) and intense pulse excited by a
drive beam. We envision that the following research topics, within the scope of
AF7, are of great interest in the next decade: advanced wakefield structures,
terahertz and sub-terahertz (THz) structures, and RF breakdown physics.
Research on SWFA in the above directions would directly contribute to long-term
large-scale applications, including AAC-based linear colliders and compact
light sources. There is also potentially a strong synergy between SWFA and
other AAC concepts, when structures are combined with plasmas into hybrid AAC
schemes. Research on novel structures is at the core of advancing SWFA, and is
critical to future AAC-based linear colliders; at the same, it has a strong
synergy with other directions, such as cavity designs, high-power microwave
systems and sources, and compact light sources.


title: Steady solutions for the Euler system in an infinitely long nozzle

abstract:
  A recent prominent result asserts that steady incompressible Euler flows
strictly away from stagnation in a two-dimensional infinitely long strip must
be shear flows. On the other hand, flows with stagnation points, very
challenging in analysis, are interesting and important phenomenon in fluids. In
this paper, we study flows with stagnation points, such as Poiseuille flows,
and establish the uniqueness and existence of steady solutions for the Euler
system in an infinitely long nozzle.
  First, we prove a Liouville type theorem for steady Euler system with
Poiseuille flows as upstream far field state in an infinitely long strip. Due
to the appearance of stagnation points, the nonlinearity of the semilinear
equation for the stream function becomes non-Lipschitz. This creates a
challenging analysis problem since many classical analysis methods do not apply
directly. Second, the uniqueness of flows with positive horizontal velocity
inside the general nozzles was established. Finally, a class of steady
incompressible Euler flows, tending to Poiseuille flows in the upstream, are
proved to exist in an infinitely long nozzle via variational approach. One of
very interesting phenomena we observe is that the free boundary may appear for
flows with stagnation points.


title:
Wall stabilization of the rigid ballooning $m=1$ mode in a paraxial
  mirror trap

abstract:
  The prospect of stabilization of the $m=1$ "rigid" ballooning regime in an
open axially symmetric trap with the help of a conducting lateral wall
surrounding a column of isotropic plasma is studied. It is found that for
effective wall stabilization, the beta parameter must exceed $70\%$. The
dependence of the critical value of beta on the mirror ratio, the radial
pressure profile, and the axial profile of the vacuum magnet has been studied.
It is shown that when a conductive lateral wall is combined with conductive
ends simulating the attachment of end MHD stabilizers to the central cell of an
open trap, there are two critical beta values and two stability zones that can
merge, making stable the entire range of allowable beta values $0<\beta<1$.


title:
RIS Partitioning Based Scalable Beamforming Design for Large-Scale MIMO:
  Asymptotic Analysis and Optimization

abstract:
  In next-generation wireless networks, reconfigurable intelligent surface
(RIS)-assisted multiple-input multiple-output (MIMO) systems are foreseeable to
support a large number of antennas at the transceiver as well as a large number
of reflecting elements at the RIS. To fully unleash the potential of RIS, the
phase shifts of RIS elements should be carefully designed, resulting in a
high-dimensional non-convex optimization problem that is hard to solve with
affordable computational complexity. In this paper, we address this scalability
issue by partitioning RIS into sub-surfaces, so as to optimize the phase shifts
in sub-surface levels to reduce complexity. Specifically, each sub-surface
employs a linear phase variation structure to anomalously reflect the incident
signal to a desired direction, and the sizes of sub-surfaces can be adaptively
adjusted according to channel conditions. We formulate the achievable rate
maximization problem by jointly optimizing the transmit covariance matrix and
the RIS phase shifts. Then, we characterize the asymptotic behavior of the
system with an infinitely large number of transceiver antennas and RIS
elements. The asymptotic analysis provides useful insights on the understanding
of the fundamental performance-complexity tradeoff in RIS partitioning design.
We show that the achievable rate maximization problem has a rather simple form
in the asymptotic regime, and we develop an efficient algorithm to find the
optimal solution via one-dimensional (1D) search. Moreover, we discuss the
insights and impacts of the asymptotically optimal solution on finite-size
system design. By applying the asymptotic result to a finite-size system with
necessary modifications, we show by numerical results that the proposed design
achieves a favorable tradeoff between system performance and computational
complexity.


title: Transforming Sequence Tagging Into A Seq2Seq Task

abstract:
  Pretrained, large, generative language models (LMs) have had great success in
a wide range of sequence tagging and structured prediction tasks. Casting a
sequence tagging task as a Seq2Seq one requires deciding the formats of the
input and output sequences. However, we lack a principled understanding of the
trade-offs associated with these formats (such as the effect on model accuracy,
sequence length, multilingual generalization, hallucination). In this paper, we
rigorously study different formats one could use for casting input text
sentences and their output labels into the input and target (i.e., output) of a
Seq2Seq model. Along the way, we introduce a new format, which we show to not
only be simpler but also more effective. Additionally the new format
demonstrates significant gains in the multilingual settings -- both zero-shot
transfer learning and joint training. Lastly, we find that the new format is
more robust and almost completely devoid of hallucination -- an issue we find
common in existing formats. With well over a 1000 experiments studying 14
different formats, over 7 diverse public benchmarks -- including 3 multilingual
datasets spanning 7 languages -- we believe our findings provide a strong
empirical basis in understanding how we should tackle sequence tagging tasks.


title: Snowmass Whitepaper AF6: Plasma-Based Particle Sources

abstract:
  High-brightness beams generated by particle sources based on advanced
accelerator concepts have the potential to become an essential part of future
accelerator technology. High-gradient accelerators can generate and rapidly
accelerate particle beams to relativistic energies while minimizing
irreversible detrimental effects to the beam brightness that occur at low beam
energies. Due to the high accelerating gradients, these novel accelerators are
also significantly more compact than conventional technology. The beam
parameters of these particle sources are largely determined by the injection
and subsequent acceleration processes. While there has been significant
progress crucial parameters that are required for a future collider or more
near-term applications, including X-ray free-electron lasers (XFELs), such as a
sufficiently small energy spread and small emittance for bunches with a high
charge and at high pulse repetition rate. Major research and development
efforts are required to realize these approaches for a front-end injector for a
future collider in order to address these limitations. In particular, this
includes methods to control and manipulate the phase-space and spin
degrees-of-freedom of ultrashort LWFA electron bunches with high accuracy,
methods that increase the laser-to-electron beam efficiency and increased
repetition rate. This also includes the development of high-resolution
diagnostics, such as full 6D phase-space measurements, beam polarimetry and
high-fidelity simulation tools. A further increase in beam luminosity can be
achieve through emittance damping. For future colliders, the damping rings
might be replaced by a substantially more compact plasma-based approach. Here,
plasma wigglers are used to achieve similar damping performance but over a two
orders of magnitude reduced length.


title: Fast operator splitting methods for obstacle problems

abstract:
  The obstacle problem is a class of free boundary problems which finds
applications in many disciplines such as porous media, financial mathematics
and optimal control. In this paper, we propose two operator-splitting methods
to solve the linear and nonlinear obstacle problems. The proposed methods have
three ingredients: (i) Utilize an indicator function to formularize the
constrained problem as an unconstrained problem, and associate it to an initial
value problem. The obstacle problem is then converted to solving for the steady
state solution of an initial value problem. (ii) An operator-splitting strategy
to time discretize the initial value problem. After splitting, a heat equation
with obstacles is solved and other subproblems either have explicit solutions
or can be solved efficiently. (iii) A new constrained alternating direction
explicit method, a fully explicit method, to solve heat equations with
obstacles. The proposed methods are easy to implement, do not require to solve
any linear systems and are more efficient than existing numerical methods while
keeping similar accuracy. Extensions of the proposed methods to related free
boundary problems are also discussed.


title:
Optimization of ARQ Distribution for HARQ Strategies in Delay-Bounded
  Networks

abstract:
  Inspired by several delay-bounded mission-critical applications, optimizing
the end-to-end reliability of multi-hop networks is an important problem
subject to end-to-end delay constraints on the packets. Towards that direction,
Automatic Repeat Request (ARQ) based strategies have been recently proposed
wherein the problem statement is to distribute a certain total number of ARQs
(that capture end-to-end delay) across the nodes such that the end-to-end
reliability is optimized. Although such strategies provide a fine control to
trade end-to-end delay with end-to-end reliability, their performance degrades
in slowly-varying channel conditions. Pointing at this drawback, in this work,
we propose a Chase Combing Hybrid ARQ (CC-HARQ) based multi-hop network
addressing the problem statement of how to distribute a certain total number of
ARQs such that the end-to-end reliability is optimized. Towards solving the
problem, first we identify that the objective function of the optimization
problem is intractable due to the presence of Marcum-Q functions in it. As a
result, we propose an approximation on the objective function and then prove a
set of necessary and sufficient conditions on the near-optimal ARQ
distribution. Subsequently, we propose a low-complexity algorithm to solve the
problem for any network size. We show that CC-HARQ based strategies are
particularly appealing in slow-fading channels wherein the existing ARQ
strategies fail.


title: Dual Diffusion Implicit Bridges for Image-to-Image Translation

abstract:
  Common image-to-image translation methods rely on joint training over data
from both source and target domains. This excludes cases where domain data is
private (e.g., in a federated setting), and often means that a new model has to
be trained for a new pair of domains. We present Dual Diffusion Implicit
Bridges (DDIBs), an image translation method based on diffusion models, that
circumvents training on domain pairs. DDIBs allow translations between
arbitrary pairs of source-target domains, given independently trained diffusion
models on the respective domains. Image translation with DDIBs is a two-step
process: DDIBs first obtain latent encodings for source images with the source
diffusion model, and next decode such encodings using the target model to
construct target images. Moreover, DDIBs enable cycle-consistency by default
and is theoretically connected to optimal transport. Experimentally, we apply
DDIBs on a variety of synthetic and high-resolution image datasets,
demonstrating their utility in example-guided color transfer, image-to-image
translation as well as their connections to optimal transport methods.


title:
Shepherd Pre-trained Language Models to Develop a Train of Thought: An
  Iterative Prompting Approach

abstract:
  While Pre-trained Language Models (PLMs) internalize a great amount of world
knowledge, they have been shown incapable of recalling these knowledge to solve
tasks requiring complex & multi-step inference procedures. Similar to how
humans develop a "train of thought" for these tasks, how can we equip PLMs with
such abilities? In this work, we explore an iterative prompting framework, a
new prompting paradigm which progressively elicits relevant knowledge from PLMs
for multi-step inference tasks. We identify key limitations of existing
prompting methods, namely they are either restricted to queries with a single
identifiable relation/predicate, or being agnostic to input contexts, which
makes it difficult to capture variabilities across different inference steps.
We propose an iterative context-aware prompter, which addresses these
limitations by learning to dynamically synthesize prompts conditioned on the
current step's contexts. Experiments on three datasets involving multi-step
inference show the effectiveness of the iterative scheme and our proposed
prompter design.


title:
On zero-density estimates of zeta-functions associated with Fourier
  coefficients of cusp forms

abstract:
  Let $f(z)$ be a holomorphic cusp form for the full modular group
$SL_{2}(\mathbb{Z})$, and let $L(s,f)$ be the corresponding automorphic
$L$-function associated to $f$. In this paper, we show that \begin{align*}
N_{f}(\sigma,T)\ll T^{A(\sigma)(1-\sigma)+\varepsilon} \end{align*} with
\begin{align*} A(\sigma)= \begin{cases} \frac{4}{4\sigma-1} &\textup{for}\ \
\frac{21}{22}\leq\sigma \leq 1,\\ \frac{2}{13\sigma-11} &\textup{for}\ \
\frac{17}{18}\leq\sigma\leq \frac{21}{22}, \end{cases} \end{align*}
  where \begin{align*} N_{f}(\sigma,T):=\sum_{L(\beta+i\gamma,f)=0 \atop
\sigma\leq\beta\leq1 ,\ 10\leq|\gamma|\leq T}1. \end{align*} When $\sigma$ is
near to 1, this improves the result of Ivi\'{c} \cite{I2} and gives a best
possible estimate from the exhausted Weyl bounds. Moreover, such type results
cannot be obtained from the general results of Perelli \cite{P}.


title: Anomalous crystal shapes of topological crystalline insulators

abstract:
  Understanding crystal shapes is a fundamental subject in surface science. It
is now well studied how chemical bondings determine crystal shapes via
dependence of surface energies on surface orientations. Meanwhile, discoveries
of topological materials have led us to a new paradigm in surface science, and
one can expect that topological surface states may affect surface energies and
crystal facets in an unconventional way. Here we show that the surface energy
of glide-symmetric topological crystalline insulators (TCI) depends on the
surface orientation in a singular way via the parity of the Miller index. This
singular surface energy of the TCI affects equilibrium crystal shapes,
resulting in emergence of unique crystal facets of the TCI. This singular
dependence of the topological surface states is unique to the TCI protected by
the glide symmetry in contrast to a TCI protected by a mirror symmetry. In
addition, we show that such singular surface states of the TCI protected by the
glide symmetries can be realized in KHgSb with first-principles calculations.
Our results provide a basis for designs and manipulations of crystal facets by
utilizing symmetry and topology.


title: Toward CUPID-1T

abstract:
  Current experiments to search for broken lepton-number symmetry through the
observation of neutrinoless double-beta decay ($0\mathrm{\nu\beta\beta}$)
provide the most stringent limits on the Majorana nature of neutrinos and the
effective Majorana neutrino mass ($m_{\beta\beta}$). The next-generation
experiments will focus on the sensitivity to the $0\mathrm{\nu\beta\beta}$
half-life of $\mathcal{O}(10^{27}$--$10^{28}$~years$)$ and
$m_{\beta\beta}\lesssim15$~meV, which would provide complete coverage of the
so-called Inverted Ordering region of the neutrino mass parameter space. By
taking advantage of recent technological breakthroughs, new, future
calorimetric experiments at the 1-ton scale can increase the sensitivity by at
least another order of magnitude, exploring the large fraction of the parameter
space that corresponds to the Normal neutrino mass ordering. In case of a
discovery, such experiments could provide important insights toward a new
understanding of the mechanism of $0\mathrm{\nu\beta\beta}$.
  We present here a series of projects underway that will provide advancements
in background reduction, cryogenic readout, and physics searches beyond
$0\mathrm{\nu\beta\beta}$, all moving toward the next-to-next generation
CUPID-1T detector.


title: A new approach for extracting information from protein dynamics

abstract:
  Increased ability to predict protein structures is moving research focus
towards understanding protein dynamics. A promising approach is to represent
protein dynamics through networks and take advantage of well-developed methods
from network science. Most studies build protein dynamics networks from
correlation measures, an approach that only works under very specific
conditions, instead of the more robust inverse approach. Thus, we apply the
inverse approach to the dynamics of protein dihedral angles, a system of
internal coordinates, to avoid structural alignment. Using the
well-characterized adhesion protein, FimH, we show that our method identifies
networks that are physically interpretable, robust, and relevant to the
allosteric pathway sites. We further use our approach to detect dynamical
differences, despite structural similarity, for Siglec-8 in the immune system,
and the SARS-CoV-2 spike protein. Our study demonstrates that using the inverse
approach to extract a network from protein dynamics yields important
biophysical insights.


title: MCoNaLa: A Benchmark for Code Generation from Multiple Natural Languages

abstract:
  While there has been a recent burgeoning of applications at the intersection
of natural and programming languages, such as code generation and code
summarization, these applications are usually English-centric. This creates a
barrier for program developers who are not proficient in English. To mitigate
this gap in technology development across languages, we propose a multilingual
dataset, MCoNaLa, to benchmark code generation from natural language commands
extending beyond English. Modeled off of the methodology from the English
Code/Natural Language Challenge (CoNaLa) dataset, we annotated a total of 896
NL-code pairs in three languages: Spanish, Japanese, and Russian. We present a
quantitative evaluation of performance on the MCoNaLa dataset by testing with
state-of-the-art code generation systems. While the difficulties vary across
these three languages, all systems lag significantly behind their English
counterparts, revealing the challenges in adapting code generation to new
languages.


title: Scalable marginalization of latent variables for correlated data

abstract:
  Marginalization of latent variables or nuisance parameters is a fundamental
aspect of Bayesian inference and uncertainty quantification. In this work, we
focus on scalable marginalization of latent variables in modeling correlated
data, such as spatio-temporal or functional observations. We first introduce
Gaussian processes (GPs) for modeling correlated data and highlight the
computational challenge, where the computational complexity increases cubically
fast along with the number of observations. We then review the connection
between the state space model and GPs with Mat{\'e}rn covariance for temporal
inputs. The Kalman filter and Rauch-Tung-Striebel smoother were introduced as a
scalable marginalization technique of computing the likelihood and making
predictions of GPs without approximation. We then introduce recent efforts on
extending the scalable marginalization idea to linear model of
coregionalization for multivariate correlated output and spatio-temporal
observations. In the final part of this work, we introduce a novel
marginalization technique to estimate interaction kernels and to forecast
particle trajectories. The achievement lies in sparse representation of
covariance function, then applying conjugate gradient for solving the
computational challenges and improving predictive accuracy. The computational
advances achieved in this work outline a wide range of applications in
molecular dynamic simulation, cellular migration and agent based models.


title: Reducing Flipping Errors in Deep Neural Networks

abstract:
  Deep neural networks (DNNs) have been widely applied in various domains in
artificial intelligence including computer vision and natural language
processing. A DNN is typically trained for many epochs and then a validation
dataset is used to select the DNN in an epoch (we simply call this epoch "the
last epoch") as the final model for making predictions on unseen samples, while
it usually cannot achieve a perfect accuracy on unseen samples. An interesting
question is "how many test (unseen) samples that a DNN misclassifies in the
last epoch were ever correctly classified by the DNN before the last epoch?".
In this paper, we empirically study this question and find on several benchmark
datasets that the vast majority of the misclassified samples in the last epoch
were ever classified correctly before the last epoch, which means that the
predictions for these samples were flipped from "correct" to "wrong". Motivated
by this observation, we propose to restrict the behavior changes of a DNN on
the correctly-classified samples so that the correct local boundaries can be
maintained and the flipping error on unseen samples can be largely reduced.
Extensive experiments on different benchmark datasets with different modern
network architectures demonstrate that the proposed flipping error reduction
(FER) approach can substantially improve the generalization, the robustness,
and the transferability of DNNs without introducing any additional network
parameters or inference cost, only with a negligible training overhead.


title:
Detecting entanglement in quantum many-body systems via permutation
  moments

abstract:
  Multipartite entanglement plays an essential role in both quantum information
science and many-body physics. Due to the exponentially large dimension and
complex geometric structure of the state space, the detection of entanglement
in many-body systems is extremely challenging in reality. Conventional means,
like entanglement witness and entropy criterion, either highly depend on the
prior knowledge of the studied systems or the detection capability is
relatively weak. In this work, we propose a framework for designing
multipartite entanglement criteria based on permutation moments, which have an
effective implementation with either the generalized control-SWAP quantum
circuits or the random unitary techniques. These criteria show strong detection
capability in the multi-qubit Ising model with a long-range $XY$ Hamiltonian.
The quantities associated with these criteria have clear physical meaning and
can be used as entanglement quantifiers, with which we show the entanglement
scaling transition in a quantum dynamical phase transition. Furthermore, our
framework can also be generalized to detect the much more complicated
entanglement structure in quantum many-body systems.


title:
Patch-Fool: Are Vision Transformers Always Robust Against Adversarial
  Perturbations?

abstract:
  Vision transformers (ViTs) have recently set off a new wave in neural
architecture design thanks to their record-breaking performance in various
vision tasks. In parallel, to fulfill the goal of deploying ViTs into
real-world vision applications, their robustness against potential malicious
attacks has gained increasing attention. In particular, recent works show that
ViTs are more robust against adversarial attacks as compared with convolutional
neural networks (CNNs), and conjecture that this is because ViTs focus more on
capturing global interactions among different input/feature patches, leading to
their improved robustness to local perturbations imposed by adversarial
attacks. In this work, we ask an intriguing question: "Under what kinds of
perturbations do ViTs become more vulnerable learners compared to CNNs?" Driven
by this question, we first conduct a comprehensive experiment regarding the
robustness of both ViTs and CNNs under various existing adversarial attacks to
understand the underlying reason favoring their robustness. Based on the drawn
insights, we then propose a dedicated attack framework, dubbed Patch-Fool, that
fools the self-attention mechanism by attacking its basic component (i.e., a
single patch) with a series of attention-aware optimization techniques.
Interestingly, our Patch-Fool framework shows for the first time that ViTs are
not necessarily more robust than CNNs against adversarial perturbations. In
particular, we find that ViTs are more vulnerable learners compared with CNNs
against our Patch-Fool attack which is consistent across extensive experiments,
and the observations from Sparse/Mild Patch-Fool, two variants of Patch-Fool,
indicate an intriguing insight that the perturbation density and strength on
each patch seem to be the key factors that influence the robustness ranking
between ViTs and CNNs.


title:
Measurement of differential cross sections of the $\Sigma^+p$ elastic
  scattering in momentum range of 0.44-0.80 GeV/c

abstract:
  We performed a novel $\Sigma p$ scattering experiment at the J-PARC hadron
experimental facility. Approximately 2400 $\Sigma^+ p$ elastic scattering
events were identified by requesting the kinematical consistency of the elastic
scattering from $4.9 \times 10^7$ tagged $\Sigma^+$ beam particles in the
$\Sigma^+$ momentum range of 0.44 - 0.80 GeV/$c$. The differential cross
sections of the $\Sigma^+ p$ elastic scattering were derived with much better
precision compared to past experiments. The obtained values of differential
cross sections are around 2 mb/sr, which are not so large as most theoretical
predictions. Moreover, the phase shifts of $^3 S_1$ and $^1 P_1$ channels were
experimentally derived for the first time. The phase shift of the $^3 S_1$
channel wherein a large repulsive core was predicted owing to the Pauli effect
in the quark level was evaluated to be $20^\circ<|\delta_{^3S_1}|<35^\circ$. If
the sign is assumed to be negative, the strength of the interaction in this
channel was found to be moderately repulsive.


title:
Bridging the Data Gap between Training and Inference for Unsupervised
  Neural Machine Translation

abstract:
  Back-translation is a critical component of Unsupervised Neural Machine
Translation (UNMT), which generates pseudo parallel data from target
monolingual data. A UNMT model is trained on the pseudo parallel data with
translated source, and translates natural source sentences in inference. The
source discrepancy between training and inference hinders the translation
performance of UNMT models. By carefully designing experiments, we identify two
representative characteristics of the data gap in source: (1) style gap (i.e.,
translated vs. natural text style) that leads to poor generalization
capability; (2) content gap that induces the model to produce hallucination
content biased towards the target language. To narrow the data gap, we propose
an online self-training approach, which simultaneously uses the pseudo parallel
data {natural source, translated target} to mimic the inference scenario.
Experimental results on several widely-used language pairs show that our
approach outperforms two strong baselines (XLM and MASS) by remedying the style
and content gaps.


title: Concurrent CPU-GPU Task Programming using Modern C++

abstract:
  In this paper, we introduce Heteroflow, a new C++ library to help developers
quickly write parallel CPU-GPU programs using task dependency graphs.
Heteroflow leverages the power of modern C++ and task-based approaches to
enable efficient implementations of heterogeneous decomposition strategies. Our
new CPU-GPU programming model allows users to express a problem in a way that
adapts to effective separation of concerns and expertise encapsulation.
Compared with existing libraries, Heteroflow is more cost-efficient in
performance scaling, programming productivity, and solution generality. We have
evaluated Heteroflow on two real applications in VLSI design automation and
demonstrated the performance scalability across different CPU-GPU numbers and
problem sizes. At a particular example of VLSI timing analysis with
million-scale tasking, Heteroflow achieved 7.7x runtime speed-up (99 vs 13
minutes) over a baseline on a machine of 40 CPU cores and 4 GPUs.


title: Towards Formalizing HRI Data Collection Processes

abstract:
  Within the human-robot interaction (HRI) community, many researchers have
focused on the careful design of human-subjects studies. However, other parts
of the community, e.g., the technical advances community, also need to do
human-subjects studies to collect data to train their models, in ways that
require user studies but without a strict experimental design. The design of
such data collection is an underexplored area worthy of more attention. In this
work, we contribute a clearly defined process to collect data with three steps
for machine learning modeling purposes, grounded in recent literature, and
detail an use of this process to facilitate the collection of a corpus of
referring expressions. Specifically, we discuss our data collection goal and
how we worked to encourage well-covered and abundant participant responses,
through our design of the task environment, the task itself, and the study
procedure. We hope this work would lead to more data collection formalism
efforts in the HRI community and a fruitful discussion during the workshop.


title: Constructing unextendible product bases from multiqubit ones

abstract:
  The construction of multipartite unextendible product bases (UPBs) is a basic
problem in quantum information. We respectively construct two families of
$2\times2\times4$ and $2\times2\times2\times4$ UPBs of size eight by using the
existing four-qubit and five-qubit UPBs. As an application, we construct novel
families of multipartite positive-partial-transpose entangled states, as well
as their entanglement properties in terms of the geometric measure of
entanglement.


title:
COPA: Certifying Robust Policies for Offline Reinforcement Learning
  against Poisoning Attacks

abstract:
  As reinforcement learning (RL) has achieved near human-level performance in a
variety of tasks, its robustness has raised great attention. While a vast body
of research has explored test-time (evasion) attacks in RL and corresponding
defenses, its robustness against training-time (poisoning) attacks remains
largely unanswered. In this work, we focus on certifying the robustness of
offline RL in the presence of poisoning attacks, where a subset of training
trajectories could be arbitrarily manipulated. We propose the first
certification framework, COPA, to certify the number of poisoning trajectories
that can be tolerated regarding different certification criteria. Given the
complex structure of RL, we propose two certification criteria: per-state
action stability and cumulative reward bound. To further improve the
certification, we propose new partition and aggregation protocols to train
robust policies. We further prove that some of the proposed certification
methods are theoretically tight and some are NP-Complete problems. We leverage
COPA to certify three RL environments trained with different algorithms and
conclude: (1) The proposed robust aggregation protocols such as temporal
aggregation can significantly improve the certifications; (2) Our certification
for both per-state action stability and cumulative reward bound are efficient
and tight; (3) The certification for different training algorithms and
environments are different, implying their intrinsic robustness properties. All
experimental results are available at https://copa-leaderboard.github.io.


title: Privacy-preserving Online AutoML for Domain-Specific Face Detection

abstract:
  Despite the impressive progress of general face detection, the tuning of
hyper-parameters and architectures is still critical for the performance of a
domain-specific face detector. Though existing AutoML works can speedup such
process, they either require tuning from scratch for a new scenario or do not
consider data privacy. To scale up, we derive a new AutoML setting from a
platform perspective. In such setting, new datasets sequentially arrive at the
platform, where an architecture and hyper-parameter configuration is
recommended to train the optimal face detector for each dataset. This, however,
brings two major challenges: (1) how to predict the best configuration for any
given dataset without touching their raw images due to the privacy concern? and
(2) how to continuously improve the AutoML algorithm from previous tasks and
offer a better warm-up for future ones? We introduce "HyperFD", a new
privacy-preserving online AutoML framework for face detection. At its core
part, a novel meta-feature representation of a dataset as well as its learning
paradigm is proposed. Thanks to HyperFD, each local task (client) is able to
effectively leverage the learning "experience" of previous tasks without
uploading raw images to the platform; meanwhile, the meta-feature extractor is
continuously learned to better trade off the bias and variance. Extensive
experiments demonstrate the effectiveness and efficiency of our design.


title: A Holonomic Rattleback

abstract:
  The rattleback or celt top is a rigid body with the peculiar behaviour of
having a spontaneous spin reversion during its motion and this effect is
usually attributed purely to its nonholonomic nature. Actually, the rattleback
is the paradigmatic example in nonholonomic mechanics. We give an example of a
spinning rigid body having spontaneous spin reversion during its motion in the
context of holonomic mechanics.


title: Abelian Varieties with $p$-rank Zero

abstract:
  There is a well known theorem by Deuring which gives a criterion for when the
reduction of an elliptic curve with complex multiplication (CM) by the ring of
integers of an imaginary quadratic field has ordinary or supersingular
reduction. We generalise this and a similar theorem by Goren in dimension 2,
and classify the $p$-torsion group scheme of the reduction of 3-dimensional
abelian varieties with CM by the ring of integers of a cyclic sextic CM field.
We also prove a theorem in arbitrary dimension $g$ that distinguishes ordinary
and superspecial reduction for abelian varieties with CM by a cyclic CM field
of degree $2g$. As an application, we give algorithms to construct
supersingular non-superspecial, and superspecial abelian varieties of dimension
2 (surfaces) and dimension 3, and show that all such varieties have non-integer
endomorphisms of small degree.


title: Gradual Tensor Shape Checking

abstract:
  Tensor shape mismatch is a common source of bugs in deep learning programs.
Most of the studies conducted to solve this problem use the whole-program
analysis approach, which lacks compositionality. A type-based approach is
desirable in this respect, but since the problem of shape inference is
undecidable in general, fully automated shape inference is bound to be either
unsound or too conservative.
  We propose a type-based approach to detect tensor shape mismatches that works
practically under such limitations. One of the main features of our approach is
the best-effort shape inference. Because of the undecidability of shape
inference, our procedure performs it only in a best-effort manner. The
inference result may be too imprecise to statically guarantee the absence of
the shape inconsistencies, and in such cases, dynamic checks are inserted into
the program. Another main feature is gradual typing. Users can improve the
precision of the inference by adding appropriate type annotations to the
program.
  We formalize our approach and prove that it satisfies the criteria of gradual
typing proposed by Siek et al. in 2015. We implement a prototype shape checking
tool based on our approach and evaluate its effectiveness by applying it to
some deep neural network programs.


title: Precise Onboard Aircraft Cabin Localization using UWB and ML

abstract:
  Precise indoor positioning systems (IPSs) are key to perform a set of tasks
more efficiently during aircraft production, operation and maintenance. For
instance, IPSs can overcome the tedious task of configuring (wireless) sensor
nodes in an aircraft cabin. Although various solutions based on technologies of
established consumer goods, e.g., Bluetooth or WiFi, have been proposed and
tested, the published accuracy results fail to make these technologies relevant
for practical use cases. This stems from the challenging environments for
positioning, especially in aircraft cabins, which is mainly due to the
geometries, many obstacles, and highly reflective materials. To address these
issues, we propose to evaluate in this work an Ultra-Wideband (UWB)-based IPS
via a measurement campaign performed in a real aircraft cabin. We first
illustrate the difficulties that an IPS faces in an aircraft cabin, by studying
the signal propagation effects which were measured. We then investigate the
ranging and localization accuracies of our IPS. Finally, we also introduce
various methods based on machine learning (ML) for correcting the ranging
measurements and demonstrate that we are able to localize a node with respect
to an aircraft seat with a measured likelihood of 97%.


title: RBC: Rectifying the Biased Context in Continual Semantic Segmentation

abstract:
  Recent years have witnessed a great development of Convolutional Neural
Networks in semantic segmentation, where all classes of training images are
simultaneously available. In practice, new images are usually made available in
a consecutive manner, leading to a problem called Continual Semantic
Segmentation (CSS). Typically, CSS faces the forgetting problem since previous
training images are unavailable, and the semantic shift problem of the
background class. Considering the semantic segmentation as a context-dependent
pixel-level classification task, we explore CSS from a new perspective of
context analysis in this paper. We observe that the context of old-class pixels
in the new images is much more biased on new classes than that in the old
images, which can sharply aggravate the old-class forgetting and new-class
overfitting. To tackle the obstacle, we propose a biased-context-rectified CSS
framework with a context-rectified image-duplet learning scheme and a
biased-context-insensitive consistency loss. Furthermore, we propose an
adaptive re-weighting class-balanced learning strategy for the biased class
distribution. Our approach outperforms state-of-the-art methods by a large
margin in existing CSS scenarios.


title:
Neutrino oscillation phenomenology and impact of Professor Masatoshi
  Koshiba

abstract:
  Neutrino oscillation phenomenology is briefly reviewed, and impact of the
late Professor Masatoshi Koshiba on research on neutrino oscillation is
discussed from the viewpoint of phenomenology.


title:
Levenberg-Marquardt Method Based Cooperative Source Localization in SIMO
  Molecular Communication via Diffusion Systems

abstract:
  Molecular communication underpins nano-scale communications in
nanotechnology. The combination of multinanomachines to form nano-networks is
one of the main enabling methods. Due to the importance of source localization
in establishing nano-networks, this paper proposes a cooperative source
localization method for Molecular Communication via Diffusion (MCvD) systems
using multiple spherical absorption receivers. Since there is no exact
mathematical expression of the channel impulse response for multiple absorbing
receivers, we adopt an empirical expression and use Levenberg-Marquardt method
to estimate the distance of the transmitter to each receiver, based on which
the location of the transmitter is obtained using an iterative scheme where the
initial point is obtained using a multi-point localization method. Particle
based simulation is carried out to evaluate the performance of the proposed
method. Simulation results show that the proposed method can accurately
estimate the location of transmitter in short to medium communication ranges.