title:
The relation between accretion rate and jet power in early-type galaxies
  with thermally unstable hot atmospheres

abstract:
  We use Chandra X-ray data and Very Large Array radio observations for a
sample of 20 nearby, massive, X-ray bright, early-type galaxies to investigate
the relation between the Bondi accretion rates and the mechanical jet powers.
We find a strong correlation ($\rho = 0.96$; BF$_{10} > 100$) between the Bondi
accretion power, $P_{\text{Bondi}}$, and mechanical jet power,
$P_{\text{jet}}$, for a subsample of 14 galaxies, which also host cool
H$\alpha$+[NII] line emitting gas and thus likely have thermally unstable
atmospheres. The results indicate that in all galaxies with thermally unstable
atmospheres the cooling atmospheric gas feeds the central black holes at a
similar jet-to-Bondi power ratio. For five out of six galaxies with no signs of
H$\alpha$+[NII] emission, however, the behaviour is different and the
jet-to-Bondi power ratio is lower by 1-2 orders of magnitude. We also
investigate the dependence of jet power on individual quantities in the Bondi
formula such as the supermassive black hole mass ($M_{\bullet}$) and the
specific entropy of the gas ($K$) at the Bondi radius. For the subsample of
H$\alpha$+[NII] emitting galaxies, we find a very tight correlation of
$P_{\text{jet}}$ with $M_{\bullet}$ ($\rho = 0.92$; BF$_{10} > 100$) and,
although poorly constrained, a hint of an anti-correlation for $P_{\text{jet}}$
and $K$ ($\rho = -0.46$; BF$_{10} = 1.2$).


title: Prospects for precise predictions of $a_\mu$ in the Standard Model

abstract:
  We discuss the prospects for improving the precision on the hadronic
corrections to the anomalous magnetic moment of the muon, and the plans of the
Muon $g-2$ Theory Initiative to update the Standard Model prediction.


title:
Kiloparsec-scale imaging of the CO(1-0)-traced cold molecular gas
  reservoir in a z~3.4 submillimeter galaxy

abstract:
  We present a high-resolution study of the cold molecular gas as traced by
CO(1-0) in the unlensed z$\sim$3.4 submillimeter galaxy SMM J13120+4242, using
multi-configuration observations with the Karl G. Jansky Very Large Array
(JVLA). The gas reservoir, imaged on 0.39" ($\sim$3 kpc) scales, is resolved
into two components separated by $\sim$11 kpc with a total extent of 16 $\sim$3
kpc. Despite the large spatial extent of the reservoir, the observations show a
CO(1-0) FWHM linewidth of only 267 $\pm$ 64 km s$^{-1}$. We derive a revised
line luminosity of L'$_\mathrm{CO(1-0)}$ = (10 $\pm$ 3) $\times$ 10$^{10}$ K km
s$^{-1}$ pc$^2$ and a molecular gas mass of M$_\mathrm{gas}$ = (13 $\pm$ 3)
$\times$ 10$^{10}$ ($\alpha_\mathrm{CO}$/1) M$_{\odot}$. Despite the presence
of a velocity gradient (consistent with previous resolved CO(6-5) imaging), the
CO(1-0) imaging shows evidence for significant turbulent motions which are
preventing the gas from fully settling into a disk. The system likely
represents a merger in an advanced stage. Although the dynamical mass is highly
uncertain, we use it to place an upper limit on the CO-to-H$_2$ mass conversion
factor $\alpha_\mathrm{CO}$ of 1.4. We revisit the SED fitting, finding that
this galaxy lies on the very massive end of the main sequence at z = 3.4. Based
on the low gas fraction, short gas depletion time and evidence for a central
AGN, we propose that SMM J13120 is in a rapid transitional phase between a
merger-driven starburst and an unobscured quasar. The case of SMM J13120
highlights the how mergers may drive important physical changes in galaxies
without pushing them off the main sequence.


title:
Axion signatures from supernova explosions through the nucleon
  electric-dipole portal

abstract:
  We consider axions coupled to nucleons and photons only through the nucleon
electric-dipole moment (EDM) portal. This coupling is a model-independent
feature of QCD axions, which solve the strong CP problem, and might arise as
well in more general axion-like particle setups. We revise the supernova (SN)
axion emission induced by the nucleon EDM coupling and refine accordingly the
SN 1987A bound. Furthermore, we calculate the axion flux from a future Galactic
SN and show that it might produce a peculiar and potentially detectable
gamma-ray signal in a large underground neutrino detector such as the proposed
Hyper-Kamiokande. The possibility to detect such a signal offers a way to
search for an oscillating nucleon EDM complementary to CASPERe, without relying
on the assumption that axions are a sizeable component of the dark matter.
Furthermore, if axions from SN produce an observable signal, they could also
lead to an amount of cosmological extra-radiation observable in future cosmic
surveys.


title: Glueballs in a Thermal Squeezeout Model

abstract:
  It has been shown that a first order confinement phase transition can
drastically change the relic dark matter abundance in confining dark sectors
with only heavy dark quarks. We study the phenomenology of one such model with
a Z' portal to Standard Model. We find that dark glueballs are long-lived in
this setup and dilute the dark matter abundance after their decay to Standard
Model. With this effect, the correct relic abundance is obtained with dark
matter masses up to $\mathcal{O}(10^6)$~TeV. We find that while a part of the
parameter space is already ruled out by direct detection and collider searches,
there is still a broad space of viable scenarios that can be probed by future
experiments.


title: Self-ionizing galactic winds

abstract:
  We present hydrodynamical simulations of galactic winds from star-forming
galaxies including non-equilibrium ionization and frequency-dependent radiative
transfer, processes that have remained largely unaccounted for in galactic wind
studies. We consider radiation from massive stars, the metagalactic UV/X-ray
background, and the self-radiation of the supernovae heated gas. We compare our
results to classical galactic wind solutions and show the importance of our
newly included physical processes toward observations of ions such as O III, O
VI, O VII and O VIII plus the observable soft X-ray spectra. Non-equilibrium
ionization is reflected in over-ionized gas compared to equilibrium solutions,
leading to much enhanced column densities of highly ionized species. The wind
produces excess soft X-ray ($E\gtrsim 100$ eV) radiation that is several orders
of magnitude higher compared to the metagalactic background. This radiation
ionizes the higher ions (such as O VII) somewhat, but affects the lower ions
(such as O III) significantly. We predict that the observable X-ray spectra
should contain the signatures of such non-equilibrium effects, especially in
X-ray lines such as O VII and O VIII. Simple estimates suggest that both the
temperature and density of the winds may be overestimated by factors of a few
to almost 2 orders of magnitude using simple equilibrium models. We conclude
that both the non-equilibrium ionization and the radiation from the wind itself
need to be considered for proper modeling of the optical/UV/X-ray emitting
plasma in galactic winds.


title:
Four-mode squeezed states in de Sitter space: A study with two field
  interacting quantum system

abstract:
  In this paper we study the application of four-mode squeezed states in the
cosmological context, studying two weakly coupled scalar fields in the planar
patch of the de Sitter space. We construct the four-mode squeezed state
formalism and connect this concept with the Hamiltonian of the two coupled
inverted harmonic oscillators having a time-dependent effective frequency in
the planar patch of the de Sitter space. Further, the corresponding evolution
operator for the quantum Euclidean vacuum state has been constructed, which
captures its dynamics. Using the Heisenberg picture coupled differential
equations describing the time evolution for all squeezing parameters
(amplitude, phase and angle) have been obtained, for the weakly coupled two
scalar field model. With the help of these evolutions for the coupled system,
we simulate the dynamics of the squeezing parameters in terms of conformal
time. From our analysis, we observe interesting dynamics, which helps us to
explore various underlying physical implications of the weakly coupled two
scalar field system in the planar patch of the de Sitter cosmological
background.


title: Fano 3-Folds, Reflexive Polytopes and Brane Brick Models

abstract:
  Reflexive polytopes in n dimensions have attracted much attention both in
mathematics and theoretical physics due to their connection to Fano n-folds and
mirror symmetry. This work focuses on the 18 regular reflexive polytopes
corresponding to smooth Fano 3-folds. For the first time, we show that all 18
regular reflexive polytopes have corresponding 2d (0,2) gauge theories realized
by brane brick models. These 2d gauge theories can be considered as the
worldvolume theories of D1-branes probing the toric Calabi-Yau 4-singularities
whose toric diagrams are given by the associated regular reflexive polytopes.
The generators of the mesonic moduli space of the brane brick models are shown
to form a lattice of generators due to the charges under the rank 3 mesonic
flavor symmetry. It is shown that the lattice of generators is the exact polar
dual reflexive polytope to the corresponding toric diagram of the brane brick
model. This duality not only highlights the close relationship between the
geometry and 2d gauge theory, but also opens up pathways towards new
discoveries in relation to reflexive polytopes and brane brick models.


title: Altered Axion Abundance from a Dynamical Peccei-Quinn Scale

abstract:
  We build a model in which the relic abundance of axions is altered from the
standard misalignment mechanism, either increased or decreased, due to the
presence of a new light scalar that couples to the radial part of the
Peccei-Quinn (PQ) field. The light scalar makes the effective PQ
symmetry-breaking scale dynamical, altering the early-time dynamics for the
axion and affecting its late-time dark matter abundance. We analyze this new
mechanism semi-analytically and numerically, showing that we can accommodate
both lighter or heavier axion dark matter, compared to the standard treatments.
We discuss implications of the model for axion searches and fundamental
physics.


title: Type IIB flux compactifications with $h^{1,1}=0$

abstract:
  We revisit flux compactifications of type IIB string theory on `spaces' dual
to rigid Calabi-Yau manifolds. This rather unexplored part of the string
landscapes harbors many interesting four-dimensional solutions, namely
supersymmetric $\mathcal{N}=1$ Minkowski vacua without flat direction and
infinite families of AdS vacua, some potentially with unrestricted rank for the
gauge group. We also comment on the existence of metastable dS solutions in
this setup. We discuss how these solutions fit into the web of swampland
conjectures.


title:
Coincidence between morphology and star-formation activity through
  cosmic time: the impact of the bulge growth

abstract:
  The origin of the quenching in galaxies is still highly debated. Different
scenarios and processes are proposed. We use multi-band (400-1600 nm)
bulge-disc decompositions of massive galaxies in the redshift range 0<z<2 to
explore the distribution and the evolution of galaxies in the log SFR-logM*
plane as a function of the stellar mass-weighted bulge-to-total ratio (BTM) and
also for internal galaxy components (bulge/disc) separately. We find evidence
of a clear link between the presence of a bulge and the flattening of the Main
Sequence in the high-mass end. All bulgeless galaxies (BTM<0.2) lie on the
main-sequence, and there is little evidence of a quenching channel without
bulge growth. Galaxies with a significant bulge component (BTM>0.2) are equally
distributed in number between star forming and passive regions. The vast
majority of bulges in the Main Sequence galaxies are quiescent, while star
formation is localized in the disc component. Our current findings underline a
strong correlation between the presence of the bulge and the star formation
state of the galaxy. A bulge, if present, is often quiescent, independently of
the morphology or the star formation activity of the host galaxy. Additionally,
if a galaxy is quiescent, with a large probability, is hosting a bulge.
Conversely, if the galaxy has a disky shape is highly probable to be star
forming.


title: BPS coherent states and localization

abstract:
  We introduce coherent states averaged over a gauge group action to study
correlators of half BPS states in ${\cal N}=4 $ SYM theory. The overlaps of
these averaged coherent states are a generating function of correlators and can
be written in terms of the Harish-Chandra-Itzykzon-Zuber (HCIZ) integral. We
show that this formula immediately leads to a computation of the normalization
of two point functions in terms of characters obtained originally in the work
of Corley, Jevicki and Ramgoolam. We also find various generalizations for
$A_{n-1}$ quivers that follow directly from other solvable integrals over
unitary groups. All of these can be computed using localization methods. When
we promote the parameters of the generating function to collective coordinates,
there is a dominant saddle that controls the effective action of these coherent
states in the regime where they describe single AdS giant gravitons. We also
discuss how to add open strings to this formulation. These will produce
calculations that rely on correlators of matrix components of unitaries in the
ensemble that is determined by the HCIZ integral to determine anomalous
dimensions. We also discuss how sphere giants arise from Grassman integrals,
how one gets a dominant saddle and how open strings are added in that case. The
fact that there is a dominant saddle helps to understand how a $1/N$ expansion
arises for open strings. We generalize the coherent state idea to study $1/4$
and $1/8$ BPS states as more general integrals over unitary groups.


title:
Comment on "Poynting vector controversy in axion modified
  electrodynamics"

abstract:
  In a recent publication, Tobar, McAllister, and Goryachev claim that using an
alternative definition of the Poynting vector can dramatically enhance the
sensitivity of their proposed axion dark matter detector. While the choice of
Poynting vector is indeed ambiguous, it cannot affect physical results, such as
the reading on a voltmeter, as long as the same definition is used
consistently. I explain this point in detail, and locate the specific errors in
their calculation.


title:
The Fornax3D project: discovery of ancient massive merger events in the
  Fornax cluster galaxies NGC 1380 and NGC 1427

abstract:
  We report the discovery of ancient massive merger events in the early-type
galaxies NGC 1380 and NGC 1427, members of the Fornax galaxy cluster. Both
galaxies have been observed by the MUSE IFU instrument on the VLT, as part of
the Fornax3D project. By fitting recently-developed population-orbital
superposition models to the observed surface brightness as well as stellar
kinematic, age, and metallicity maps, we obtain the stellar orbits, age and
metallicity distributions of each galaxy. We then decompose each galaxy into
multiple orbital-based components, including a dynamically hot inner stellar
halo component which is identified as the relic of past massive mergers. By
comparing to analogues from cosmological galaxy simulations, chiefly TNG50, we
find that the formation of such a hot inner stellar halo requires the merger
with a now-destroyed massive satellite galaxy of $3.7_{-1.5}^{+2.7} \times
10^{10}$ Msun (about $1/5$ of its current stellar mass) in the case of NGC 1380
and of $1.5_{-0.7}^{+1.6} \times10^{10}$ Msun (about $1/4$ of its current
stellar mass) in the case of NGC 1427. Moreover, we infer that the last massive
merger in NGC 1380 happened $\sim10$ Gyr ago based on the stellar age
distribution of the re-grown dynamically cold disk, whereas the merger in NGC
1427 ended $t\lesssim 8$ Gyr ago based on the stellar populations in its hot
inner stellar halo. The major merger event in NGC 1380 is the first one with
both merger mass and merger time quantitatively inferred in a galaxy beyond the
Local Volume. Moreover, it is the oldest and most massive merger uncovered in
nearby galaxies so far.


title: Semi-supervised Graph Neural Networks for Pileup Noise Removal

abstract:
  The high instantaneous luminosity of the CERN Large Hadron Collider leads to
multiple proton-proton interactions in the same or nearby bunch crossings
(pileup). Advanced pileup mitigation algorithms are designed to remove this
noise from pileup particles and improve the performance of crucial physics
observables. This study implements a semi-supervised graph neural network for
particle-level pileup noise removal, by identifying individual particles
produced from pileup. The graph neural network is firstly trained on charged
particles with known labels, which can be obtained from detector measurements
on data or simulation, and then inferred on neutral particles for which such
labels are missing. This semi-supervised approach does not depend on the ground
truth information from simulation and thus allows us to perform training
directly on experimental data. The performance of this approach is found to be
consistently better than widely-used domain algorithms and comparable to the
fully-supervised training using simulation truth information. The study serves
as the first attempt at applying semi-supervised learning techniques to pileup
mitigation, and opens up a new direction of fully data-driven machine learning
pileup mitigation studies.


title:
A Rapid and Large-Amplitude X-ray Dimming Event in a z ~ 2.6 Radio-Quiet
  Quasar

abstract:
  We report a dramatic fast X-ray dimming event in a z=2.627 radio-quiet type 1
quasar, which has an estimated supermassive black hole (SMBH) mass of
$6.3\times 10^{9} M_\odot$. In the high X-ray state, it showed a typical level
of X-ray emission relative to its UV/optical emission. Then its 0.5-2 keV
(rest-frame 1.8-7.3 keV) flux dropped by a factor of $\approx7.6$ within two
rest-frame days. The dimming is associated with spectral hardening, as the 2-7
keV (rest-frame 7.3-25.4 keV) flux dropped by only $17\%$ and the effective
power-law photon index of the X-ray spectrum changed from $\approx2.3$ to
$\approx0.9$. The quasar has an infrared (IR)-to-UV spectral energy
distribution and a rest-frame UV spectrum similar to those of typical quasars,
and it does not show any significant long-term variability in the IR and
UV/optical bands. Such an extremely fast and large-amplitude X-ray variability
event has not been reported before in luminous quasars with such massive SMBHs.
The X-ray dimming is best explained by a fast-moving absorber crossing the line
of sight and fully covering the X-ray emitting corona. Adopting a
conservatively small size of $5 {G} M_{\rm BH}/c^2$ for the X-ray corona, the
transverse velocity of the absorber is estimated to be $\approx 0.9c$. The
quasar is likely accreting with a high or even super-Eddington accretion rate,
and the high-velocity X-ray absorber is probably related to a powerful
accretion-disk wind. Such an energetic wind may eventually evolve into a
massive galactic-scale outflow, providing efficient feedback to the host
galaxy.


title: The molecular gas properties in local Seyfert 2 galaxies

abstract:
  We present a multi-wavelength study of the molecular gas properties of a
sample of local Seyfert 2 galaxies to assess if, and to what extent, the
presence of an active galactic nucleus (AGN) can affect the Interstellar Medium
(ISM) properties in a sample of 33 local Seyfert 2 galaxies. We compare the
molecular gas content (MH2), derived from new and archival low-J CO line
measurements of a sample of AGN and a control sample of star-forming galaxies
(SFGs). Both the AGN and the control sample are characterised in terms of
host-galaxy properties (e.g., stellar and dust masses, Mstar and Mdust,
respectively; and star formation rate, SFR). We also investigate the effect of
AGN activity onto the emission of polycyclic aromatic hydrocarbon (PAH)
molecules in the mid-infrared (MIR), a waveband where the dust-reprocessed
emission from the obscured AGN contributes the most. The AGN hosted in less
massive galaxies (i.e., log(Mstar/Msun)<10.5; log(Mdust/Msun)<7.5) show larger
molecular gas content with respect to SFGs matched in stellar and dust mass.
When comparing their depletion time(tdep~MH2/SFR), AGN show tdep~0.3-1.0 Gyr,
similar to those observed in the control sample of SFGs. Seyfert 2 galaxies
show fainter PAH luminosity at increasingly larger dominance of the nuclear
activity in the MIR. We find no clear evidence for a systematic reduction of
the molecular gas reservoir at galactic scale in Seyfert galaxies with respect
to SFGs. This is in agreement with recent studies showing that molecular gas
content only is reduced in regions of sub-kpc size, where the emission from the
accreting supermassive black hole dominates. Nonetheless, we show that the
impact of AGN activity on the ISM is clearly visible as suppression of the PAH
luminosity.


title: The TESS Triple-9 Catalog: 999 uniformly vetted candidate exoplanets

abstract:
  The Transiting Exoplanet Survey Satellite (TESS) has detected thousands of
exoplanet candidates since 2018, most of which have yet to be confirmed. A key
step in the confirmation process of these candidates is ruling out false
positives through vetting. Vetting also eases the burden on follow-up
observations, provides input for demographics studies, and facilitates training
machine learning algorithms. Here we present the TESS Triple-9 (TT9) catalog --
a uniformly-vetted catalog containing dispositions for 999 exoplanet candidates
listed on ExoFOP-TESS, known as TESS Objects of Interest (TOIs). The TT9 was
produced using the Discovery And Vetting of Exoplanets pipeline, DAVE, and
utilizing the power of citizen science as part of the Planet Patrol project.
More than 70% of the TOIs listed in the TT9 pass our diagnostic tests, and are
thus marked as true planetary candidates. We flagged 144 candidates as false
positives, and identified 146 as potential false positives. At the time of
writing, the TT9 catalog contains ~20% of the entire ExoFOP-TESS TOIs list,
demonstrates the synergy between automated tools and citizen science, and
represents the first stage of our efforts to vet all TOIs. The DAVE generated
results are publicly available on ExoFOP-TESS.


title: LinkBERT: Pretraining Language Models with Document Links

abstract:
  Language model (LM) pretraining can learn various knowledge from text
corpora, helping downstream tasks. However, existing methods such as BERT model
a single document, and do not capture dependencies or knowledge that span
across documents. In this work, we propose LinkBERT, an LM pretraining method
that leverages links between documents, e.g., hyperlinks. Given a text corpus,
we view it as a graph of documents and create LM inputs by placing linked
documents in the same context. We then pretrain the LM with two joint
self-supervised objectives: masked language modeling and our new proposal,
document relation prediction. We show that LinkBERT outperforms BERT on various
downstream tasks across two domains: the general domain (pretrained on
Wikipedia with hyperlinks) and biomedical domain (pretrained on PubMed with
citation links). LinkBERT is especially effective for multi-hop reasoning and
few-shot QA (+5% absolute improvement on HotpotQA and TriviaQA), and our
biomedical LinkBERT sets new states of the art on various BioNLP tasks (+7% on
BioASQ and USMLE). We release our pretrained models, LinkBERT and BioLinkBERT,
as well as code and data at https://github.com/michiyasunaga/LinkBERT.


title:
Adaptive Multi-User Clustering and Power Allocation for NOMA Systems
  with Imperfect SIC

abstract:
  Non-orthogonal multiple access (NOMA) is recognized as a promising radio
access technique for the next generation wireless systems. We consider a
practical downlink NOMA system with imperfect successive interference
cancellation and derive bounds on the power allocation factors for a given
number of users in each cluster. We propose a minimum
signal-to-interference-plus-noise ratio difference criterion between two
successive NOMA users in a cluster of users to achieve higher rates than an
equivalent orthogonal multiple access (OMA) system. We then propose multi-user
clustering and power allocation algorithms for downlink NOMA systems. Through
extensive simulations, we show that the proposed algorithms achieve higher
rates than the state-of-the-art algorithms.


title:
An EEG-Based Multi-Modal Emotion Database with Both Posed and Authentic
  Facial Actions for Emotion Analysis

abstract:
  Emotion is an experience associated with a particular pattern of
physiological activity along with different physiological, behavioral and
cognitive changes. One behavioral change is facial expression, which has been
studied extensively over the past few decades. Facial behavior varies with a
person's emotion according to differences in terms of culture, personality,
age, context, and environment. In recent years, physiological activities have
been used to study emotional responses. A typical signal is the
electroencephalogram (EEG), which measures brain activity. Most of existing
EEG-based emotion analysis has overlooked the role of facial expression
changes. There exits little research on the relationship between facial
behavior and brain signals due to the lack of dataset measuring both EEG and
facial action signals simultaneously. To address this problem, we propose to
develop a new database by collecting facial expressions, action units, and EEGs
simultaneously. We recorded the EEGs and face videos of both posed facial
actions and spontaneous expressions from 29 participants with different ages,
genders, ethnic backgrounds. Differing from existing approaches, we designed a
protocol to capture the EEG signals by evoking participants' individual action
units explicitly. We also investigated the relation between the EEG signals and
facial action units. As a baseline, the database has been evaluated through the
experiments on both posed and spontaneous emotion recognition with images
alone, EEG alone, and EEG fused with images, respectively. The database will be
released to the research community to advance the state of the art for
automatic emotion recognition.


title: Holographic reconstruction of asymptotically flat spacetimes

abstract:
  We present a "holographic" reconstruction of bulk spacetime geometry using
correlation functions of a massless field living at the "future boundary" of
the spacetime, namely future null infinity $\mathscr{I}^+$. It is holographic
in the sense that there exists a one-to-one correspondence between correlation
functions of a massless field in four-dimensional spacetime $\mathcal{M}$ and
those of another massless field living in three-dimensional null boundary
$\mathscr{I}^+$. The idea is to first reconstruct the bulk metric $g_{\mu\nu}$
by "inverting" the bulk correlation functions and re-express the latter in
terms of boundary correlators via the correspondence. This effectively allows
asymptotic observers close to $\mathscr{I}^+$ to reconstruct the deep interior
of the spacetime using only correlation functions localized near
$\mathscr{I}^+$.


title: The electronic structure of $\beta$-HgS via $GW$ calculations

abstract:
  The electronic structure of the zincblende $\beta$-HgS is not well
understood. Previous first-principles calculations using fully-relativistic
density functional theory and many-body perturbation theory in the
fully-relativistic $GW$ approach have predicted an inverted, topologically
non-trivial ordering of these states, with the $s$-like $\Gamma_6$ state
occupied. However, other calculations using the $GW$ approach in which
spin-orbit coupling is added perturbatively ("$GW$+SOC") predict the $p$-$d$
hybridized $\Gamma_7$ and $\Gamma_8$ states to be occupied and the $\Gamma_6$
state to be unoccupied, suggesting that $\beta$-HgS is a topologically trivial
small band gap semiconductor. In the present work, a plane-wave pseudopotential
fully-relativistic $GW$ calculation finds a band ordering in agreement with the
previous $GW$+SOC calculations. The calculated band gap is 0.10 eV and the
electron effective mass is 0.07 $m_e$, in good agreement with experiment.


title: Computational Complexity in Analogue Gravity

abstract:
  Analogue gravity helps to find some gravitational systems which are similar
to the evolution of perturbation in condensed matter systems. These analogies
provide a very good tool for either side. In other words, some aspects of
gravity could be simulated in condensed matter laboratories. In this study, we
are going to find an interpretation for computational complexity in condensed
matter systems and the analogue of the uncertainty principle as the Lloyd
bound. We show that this inequality roughly is equivalent to the KSS bound in
the fluid/gravity duality and provides some experimental criteria to test the
Lloyd bound in the laboratory.


title: Seq-2-Seq based Refinement of ASR Output for Spoken Name Capture

abstract:
  Person name capture from human speech is a difficult task in human-machine
conversations. In this paper, we propose a novel approach to capture the person
names from the caller utterances in response to the prompt "say and spell your
first/last name". Inspired from work on spell correction, disfluency removal
and text normalization, we propose a lightweight Seq-2-Seq system which
generates a name spell from a varying user input. Our proposed method
outperforms the strong baseline which is based on LM-driven rule-based
approach.


title: Collaborative Learning and Patterns of Practice

abstract:
  In this article, an overview of the background, the research approaches and
the patterns of practice in the field of collaborative learning are provided. A
definition of collaborative learning and an overview of fundamental aspects
that shape research and practice in this field are included. Pedagogies and
learning theories that are used as foundations of the field alongside goals and
objectives of collaborative learning approaches are discussed. Popular patterns
of practice, exploring their application in classrooms and elaborating on the
state of the art around those practices in research are outlined. A discussion
about important topics, open questions and future directions are provided in
conclusion.


title: ACR Loss: Adaptive Coordinate-based Regression Loss for Face Alignment

abstract:
  Although deep neural networks have achieved reasonable accuracy in solving
face alignment, it is still a challenging task, specifically when we deal with
facial images, under occlusion, or extreme head poses. Heatmap-based Regression
(HBR) and Coordinate-based Regression (CBR) are among the two mainly used
methods for face alignment. CBR methods require less computer memory, though
their performance is less than HBR methods. In this paper, we propose an
Adaptive Coordinate-based Regression (ACR) loss to improve the accuracy of CBR
for face alignment. Inspired by the Active Shape Model (ASM), we generate
Smooth-Face objects, a set of facial landmark points with less variations
compared to the ground truth landmark points. We then introduce a method to
estimate the level of difficulty in predicting each landmark point for the
network by comparing the distribution of the ground truth landmark points and
the corresponding Smooth-Face objects. Our proposed ACR Loss can adaptively
modify its curvature and the influence of the loss based on the difficulty
level of predicting each landmark point in a face. Accordingly, the ACR Loss
guides the network toward challenging points than easier points, which improves
the accuracy of the face alignment task. Our extensive evaluation shows the
capabilities of the proposed ACR Loss in predicting facial landmark points in
various facial images.


title: VPTR: Efficient Transformers for Video Prediction

abstract:
  In this paper, we propose a new Transformer block for video future frames
prediction based on an efficient local spatial-temporal separation attention
mechanism. Based on this new Transformer block, a fully autoregressive video
future frames prediction Transformer is proposed. In addition, a
non-autoregressive video prediction Transformer is also proposed to increase
the inference speed and reduce the accumulated inference errors of its
autoregressive counterpart. In order to avoid the prediction of very similar
future frames, a contrastive feature loss is applied to maximize the mutual
information between predicted and ground-truth future frame features. This work
is the first that makes a formal comparison of the two types of attention-based
video future frames prediction models over different scenarios. The proposed
models reach a performance competitive with more complex state-of-the-art
models. The source code is available at \emph{https://github.com/XiYe20/VPTR}.


title:
Learning to Collide: Recommendation System Model Compression with
  Learned Hash Functions

abstract:
  A key characteristic of deep recommendation models is the immense memory
requirements of their embedding tables. These embedding tables can often reach
hundreds of gigabytes which increases hardware requirements and training cost.
A common technique to reduce model size is to hash all of the categorical
variable identifiers (ids) into a smaller space. This hashing reduces the
number of unique representations that must be stored in the embedding table;
thus decreasing its size. However, this approach introduces collisions between
semantically dissimilar ids that degrade model quality. We introduce an
alternative approach, Learned Hash Functions, which instead learns a new
mapping function that encourages collisions between semantically similar ids.
We derive this learned mapping from historical data and embedding access
patterns. We experiment with this technique on a production model and find that
a mapping informed by the combination of access frequency and a learned low
dimension embedding is the most effective. We demonstrate a small improvement
relative to the hashing trick and other collision related compression
techniques. This is ongoing work that explores the impact of categorical id
collisions on recommendation model quality and how those collisions may be
controlled to improve model performance.


title:
Late-time asymptotics for geometric wave equations with inverse-square
  potentials

abstract:
  We introduce a new, physical-space-based method for deriving the precise
leading-order late-time behaviour of solutions to geometric wave equations on
asymptotically flat spacetime backgrounds and apply it to the setting of wave
equations with asymptotically inverse-square potentials on Schwarzschild black
holes. This provides a useful toy model setting for introducing methods that
are applicable to more general linear and nonlinear geometric wave equations,
such as wave equations for electromagnetically charged scalar fields, wave
equations on extremal Kerr black holes and geometric wave equations in even
space dimensions, where existing proofs for deriving precise late-time
asymptotics might not apply. The method we introduce relies on exploiting the
spatial decay properties of time integrals of solutions to derive the existence
and precise genericity properties of asymptotic late-time tails and obtain
sharp, uniform decay estimates in time.


title: Quantum Annealing and Computation

abstract:
  We introduce and review briefly the phenomenon of quantum annealing and
analog computation. The role of quantum fluctuation (tunneling) in random
systems with rugged (free) energy landscapes having macroscopic barriers are
discussed to demonstrate the quantum advantage in the search for the ground
state(s) through annealing. Quantum annealing as a physical (analog) process to
search for the optimal solutions of computationally hard problems are also
discussed.


title: Autoregressive Co-Training for Learning Discrete Speech Representations

abstract:
  While several self-supervised approaches for learning discrete speech
representation have been proposed, it is unclear how these seemingly similar
approaches relate to each other. In this paper, we consider a generative model
with discrete latent variables that learns a discrete representation for
speech. The objective of learning the generative model is formulated as
information-theoretic co-training. Besides the wide generality, the objective
can be optimized with several approaches, subsuming HuBERT-like training and
vector quantization for learning discrete representation. Empirically, we find
that the proposed approach learns discrete representation that is highly
correlated with phonetic units, more correlated than HuBERT-like training and
vector quantization.


title:
NNLander-VeriF: A Neural Network Formal Verification Framework for
  Vision-Based Autonomous Aircraft Landing

abstract:
  In this paper, we consider the problem of formally verifying a Neural Network
(NN) based autonomous landing system. In such a system, a NN controller
processes images from a camera to guide the aircraft while approaching the
runway. A central challenge for the safety and liveness verification of
vision-based closed-loop systems is the lack of mathematical models that
captures the relation between the system states (e.g., position of the
aircraft) and the images processed by the vision-based NN controller. Another
challenge is the limited abilities of state-of-the-art NN model checkers. Such
model checkers can reason only about simple input-output robustness properties
of neural networks. This limitation creates a gap between the NN model checker
abilities and the need to verify a closed-loop system while considering the
aircraft dynamics, the perception components, and the NN controller. To this
end, this paper presents NNLander-VeriF, a framework to verify vision-based NN
controllers used for autonomous landing. NNLander-VeriF addresses the
challenges above by exploiting geometric models of perspective cameras to
obtain a mathematical model that captures the relation between the aircraft
states and the inputs to the NN controller. By converting this model into a NN
(with manually assigned weights) and composing it with the NN controller, one
can capture the relation between aircraft states and control actions using one
augmented NN. Such an augmented NN model leads to a natural encoding of the
closed-loop verification into several NN robustness queries, which
state-of-the-art NN model checkers can handle. Finally, we evaluate our
framework to formally verify the properties of a trained NN and we show its
efficiency.


title:
On the ab initio description of electronic stopping of nuclei
  projectiles in matter

abstract:
  The process by which a nuclear projectile is decelerated by the electrons of
the condensed matter it traverses is currently being studied by following the
explicit dynamics of projectile and electrons from first principles in a
simulation box with a sample of the host matter in periodic boundary
conditions. The approach has been quite successful for diverse systems even in
the strong-coupling regime of maximal dissipation around the Bragg peak, in
spite of the nanometric finite sizes employed. This technique is here revisited
for periodic solids in the light of a recently proposed Floquet theory of
stopping, a time-periodic scattering framework characterising the stationary
dynamical solutions for a constant velocity projectile in an infinite solid.
The electronic energy uptake induced by constant-velocity protons in diamond
with a system size of a thousand atoms is studied under that light, using
time-dependent density-functional theory in real time. The three time regimes
arising are identified, namely, the initial transient, stationary scattering,
and the onset towards saturation. The stationary scattering regime is
characterised by time-periodic time derivatives of both energy and excitation
spectra, for which system-size convergence is needed, since the regime is
established while projectile replicas do not interfere with each other
significantly. Three different approaches to the calculation of the electronic
stopping power are compared and discussed in the context of the various
approximations involved, as well as how to understand the Floquet quasi-energy
conservation of the Kohn-Sham scattering problem in the projectile's reference
frame, in the context of known dissipative descriptions.


title: Uniqueness for the Nonlocal Liouville Equation in $\mathbb{R}$

abstract:
  We prove uniqueness of solutions for the nonlocal Liouville equation $$
(-\Delta)^{1/2} w = K e^w \quad \mbox{in $\mathbb{R}$} $$ with finite total
$Q$-curvature $\int_{\mathbb{R}} K e^w \, dx< +\infty$. Here the prescribed
$Q$-curvature function $K=K(|x|) > 0$ is assumed to be a positive,
symmetric-decreasing function satisfying suitable regularity and decay bounds.
In particular, we obtain uniqueness of solutions in the Gaussian case with
$K(x) = \exp(-x^2)$. Our uniqueness proof exploits a connection of the nonlocal
Liouville equation to ground state solitons for Calogero--Moser derivative NLS,
which is a completely integrable PDE recently studied by P. G\'erard and the
second author.


title:
The micromagnetic energy with Dzyaloshinskii-Moriya interaction in a
  thin-film regime relevant for boundary vortices

abstract:
  We consider the three-dimensional micromagnetic model with
Dzyaloshinskii-Moriya interaction in a thin-film regime. We prove the
Gamma-convergence of the micromagnetic energy in the considered regime, for
which the Gamma-limit energy is two-dimensional and relevant for boundary
vortices. We then study local minimizers of the Gamma-limit energy and prove a
uniqueness result in a certain setting.


title: Topological Experience Replay

abstract:
  State-of-the-art deep Q-learning methods update Q-values using state
transition tuples sampled from the experience replay buffer. This strategy
often uniformly and randomly samples or prioritizes data sampling based on
measures such as the temporal difference (TD) error. Such sampling strategies
can be inefficient at learning Q-function because a state's Q-value depends on
the Q-value of successor states. If the data sampling strategy ignores the
precision of the Q-value estimate of the next state, it can lead to useless and
often incorrect updates to the Q-values. To mitigate this issue, we organize
the agent's experience into a graph that explicitly tracks the dependency
between Q-values of states. Each edge in the graph represents a transition
between two states by executing a single action. We perform value backups via a
breadth-first search starting from that expands vertices in the graph starting
from the set of terminal states and successively moving backward. We
empirically show that our method is substantially more data-efficient than
several baselines on a diverse range of goal-reaching tasks. Notably, the
proposed method also outperforms baselines that consume more batches of
training experience and operates from high-dimensional observational data such
as images.


title:
Characterization and Verification of Trotterized Digital Quantum
  Simulation via Hamiltonian and Liouvillian Learning

abstract:
  The goal of digital quantum simulation is to approximate the dynamics of a
given target Hamiltonian via a sequence of quantum gates, a procedure known as
Trotterization. The quality of this approximation can be controlled by the so
called Trotter step, that governs the number of required quantum gates per unit
simulation time, and is intimately related to the existence of a
time-independent, quasilocal Hamiltonian that governs the stroboscopic
dynamics, refered to as the Floquet Hamiltonian of the Trotterization. In this
work, we propose a Hamiltonian learning scheme to reconstruct the implemented
Floquet Hamiltonian order-by-order in the Trotter step: this procedure is
efficient, i.e., it requires a number of measurements that scales polynomially
in the system size, and can be readily implemented in state-of-the-art
experiments. With numerical examples, we propose several applications of our
method in the context of verification of quantum devices, from the
characterization of the distinct sources of errors in digital quantum
simulators to the design of new types of quantum gates. Furthermore, we show
how our approach can be extended to the case of non-unitary dynamics and used
to learn Floquet Liouvillians, thereby offering a way of characterizing the
dissipative processes present in NISQ quantum devices.


title:
From ten-flavor tests of the $\beta$-function to $\alpha_s$ at the
  Z-pole

abstract:
  New tests are applied to two $\beta$-functions of the much-discussed BSM
model with ten massless fermion flavors in the fundamental representation of
the SU(3) color gauge group. The renormalization scheme of the two
$\beta$-functions is defined on the gauge field gradient flow in respective
finite or infinite physical volumes at zero lattice spacing. Recently published
results in the ten-flavor theory led to indicators of an infrared fixed point
(IRFP) in the finite-volume step $\beta$-function in the strong coupling regime
of the theory arXiv:2004.00754. We analyze our substantially extended set of
ten-flavor lattice ensembles at strong renormalized gauge couplings and find no
evidence or hint for IRFP in the finite-volume step $\beta$-function within
controlled lattice reach. We also discuss new ten-flavor tests of the recently
introduced lattice definition and algorithmic implementation of the
$\beta$-function defined on the gradient flow of the gauge field over infinite
Euclidean space-time in the continuum. Originally we introduced this new
algorithm to match finite-volume step $\beta$-functions in massless
near-conformal gauge theories with the infinite-volume $\beta$-function reached
in the chiral limit from small fermion mass deformations of spontaneous chiral
symmetry breaking.
  Results from the lattice analysis of the ten-flavor infinite-volume
$\beta$-function are consistent with the absence of IRFP from our step
$\beta$-function based analysis. We make important contact at weak coupling in
infinite volume with gradient flow based three-loop perturbation theory,
serving as a first pilot study toward the long-term goal of developing
alternate approach to the determination of the strong coupling $\alpha_s$ at
the Z-boson pole in QCD.


title:
An extended range of energy stable flux reconstruction methods on
  triangles

abstract:
  We present an extended range of stable flux reconstruction (FR) methods on
triangles through the development and application of the summation-by-parts
framework in two-dimensions. This extended range of stable schemes is then
shown to contain the single parameter schemes of \citet{Castonguay2011} on
triangles, and our definition enables wider stability bounds to be developed
for those single parameter families. Stable upwinded spectral difference (SD)
schemes on triangular elements have previously been found using Fourier
analysis. We used our extended range of FR schemes to investigate the linear
stability of SD methods on triangles, and it was found that a only first order
SD scheme could be recovered within this set of FR methods.


title: When Bousfield localizations and homotopy idempotent functors meet again

abstract:
  We adopt semimodel categories to extend fundamental results related to
Bousfield localizations of model categories. More specifically, we generalize
Bousfield-Friedlander Theorem and Hirschhorn Localization Theorem of cellular
model categories to settings where their classical formulation does not apply.
We use such results to answer, in the world of semimodel categories, an open
problem posed by May-Ponto about the existence of Bousfield localizations for
Hurewicz and mixed type model structures.


title:
Fault Detection and Isolation of Uncertain Nonlinear Parabolic PDE
  Systems

abstract:
  This paper proposes a novel fault detection and isolation (FDI) scheme for
distributed parameter systems modeled by a class of parabolic partial
differential equations (PDEs) with nonlinear uncertain dynamics. A key feature
of the proposed FDI scheme is its capability of dealing with the effects of
system uncertainties for accurate FDI. Specifically, an approximate ordinary
differential equation (ODE) system is first derived to capture the dominant
dynamics of the original PDE system. An adaptive dynamics identification
approach using radial basis function neural network is then proposed based on
this ODE system, so as to achieve locally-accurate identification of the
uncertain system dynamics under normal and faulty modes. A bank of FDI
estimators with associated adaptive thresholds are finally designed for
real-time FDI decision making. Rigorous analysis on the FDI performance in
terms of fault detectability and isolatability is provided. Simulation study on
a representative transport-reaction process is conducted to demonstrate the
effectiveness and advantage of the proposed approach.


title: Neural Inertial Localization

abstract:
  This paper proposes the inertial localization problem, the task of estimating
the absolute location from a sequence of inertial sensor measurements. This is
an exciting and unexplored area of indoor localization research, where we
present a rich dataset with 53 hours of inertial sensor data and the associated
ground truth locations. We developed a solution, dubbed neural inertial
localization (NILoc) which 1) uses a neural inertial navigation technique to
turn inertial sensor history to a sequence of velocity vectors; then 2) employs
a transformer-based neural architecture to find the device location from the
sequence of velocities. We only use an IMU sensor, which is energy efficient
and privacy preserving compared to WiFi, cameras, and other data sources. Our
approach is significantly faster and achieves competitive results even compared
with state-of-the-art methods that require a floorplan and run 20 to 30 times
slower. We share our code, model and data at https://sachini.github.io/niloc.


title: Direct Sampling with a Step Function

abstract:
  The direct sampling method proposed by Walker et al. (JCGS 2011) can generate
draws from weighted distributions possibly having intractable normalizing
constants. The method may be of interest as a useful tool in situations which
require drawing from an unfamiliar distribution. However, the original
algorithm can have difficulty producing draws in some situations. The present
work restricts attention to a univariate setting where the weight function and
base distribution of the weighted target density meet certain criteria. Here, a
variant of the direct sampler is proposed which uses a step function to
approximate the density of a particular augmented random variable on which the
method is based. Knots for the step function can be placed strategically to
ensure the approximation is close to the underlying density. Variates may then
be generated reliably while largely avoiding the need for manual tuning or
rejections. A rejection sampler based on the step function allows exact draws
to be generated from the target with lower rejection probability in exchange
for increased computation. Several applications of the proposed sampler
illustrate the method: generating draws from the Conway-Maxwell Poisson
distribution, a Gibbs sampler which draws the dependence parameter in a random
effects model with conditional autoregression structure, and a Gibbs sampler
which draws the degrees-of-freedom parameter in a regression with t-distributed
errors.


title: Near-optimality for infinite-horizon restless bandits with many arms

abstract:
  Restless bandits are an important class of problems with applications in
recommender systems, active learning, revenue management and other areas. We
consider infinite-horizon discounted restless bandits with many arms where a
fixed proportion of arms may be pulled in each period and where arms share a
finite state space. Although an average-case-optimal policy can be computed via
stochastic dynamic programming, the computation required grows exponentially
with the number of arms $N$. Thus, it is important to find scalable policies
that can be computed efficiently for large $N$ and that are near optimal in
this regime, in the sense that the optimality gap (i.e. the loss of expected
performance against an optimal policy) per arm vanishes for large $N$. However,
the most popular approach, the Whittle index, requires a hard-to-verify
indexability condition to be well-defined and another hard-to-verify condition
to guarantee a $o(N)$ optimality gap. We present a method resolving these
difficulties. By replacing a global Lagrange multiplier used by the Whittle
index with a sequence of Lagrangian multipliers, one per time period up to a
finite truncation point, we derive a class of policies, called fluid-balance
policies, that have a $O(\sqrt{N})$ optimality gap. Unlike the Whittle index,
fluid-balance policies do not require indexability to be well-defined and their
$O(\sqrt{N})$ optimality gap bound holds universally without sufficient
conditions. We also demonstrate empirically that fluid-balance policies provide
state-of-the-art performance on specific problems.


title:
Locomotion Policy Guided Traversability Learning using Volumetric
  Representations of Complex Environments

abstract:
  Despite the progress in legged robotic locomotion, autonomous navigation in
unknown environments remains an open problem. Ideally, the navigation system
utilizes the full potential of the robots' locomotion capabilities while
operating within safety limits under uncertainty. The robot must sense and
analyze the traversability of the surrounding terrain, which depends on the
hardware, locomotion control, and terrain properties. It may contain
information about the risk, energy, or time consumption needed to traverse the
terrain. To avoid hand-crafted traversability cost functions we propose to
collect traversability information about the robot and locomotion policy by
simulating the traversal over randomly generated terrains using a physics
simulator. Thousand of robots are simulated in parallel controlled by the same
locomotion policy used in reality to acquire 57 years of real-world locomotion
experience equivalent. For deployment on the real robot, a sparse convolutional
network is trained to predict the simulated traversability cost, which is
tailored to the deployed locomotion policy, from an entirely geometric
representation of the environment in the form of a 3D voxel-occupancy map. This
representation avoids the need for commonly used elevation maps, which are
error-prone in the presence of overhanging obstacles and multi-floor or
low-ceiling scenarios. The effectiveness of the proposed traversability
prediction network is demonstrated for path planning for the legged robot
ANYmal in various indoor and natural environments.


title: SUSY Nori Motives

abstract:
  We define stable supercurves and stable supermaps, and based on these notions
we develop a theory of Nori motives for the category of stable supermaps of
SUSY curves with punctures. This will require several preliminary
constructions, including the development of a basic theory of supercycles.


title:
OdontoAI: A human-in-the-loop labeled data set and an online platform to
  boost research on dental panoramic radiographs

abstract:
  Deep learning has remarkably advanced in the last few years, supported by
large labeled data sets. These data sets are precious yet scarce because of the
time-consuming labeling procedures, discouraging researchers from producing
them. This scarcity is especially true in dentistry, where deep learning
applications are still in an embryonic stage. Motivated by this background, we
address in this study the construction of a public data set of dental panoramic
radiographs. Our objects of interest are the teeth, which are segmented and
numbered, as they are the primary targets for dentists when screening a
panoramic radiograph. We benefited from the human-in-the-loop (HITL) concept to
expedite the labeling procedure, using predictions from deep neural networks as
provisional labels, later verified by human annotators. All the gathering and
labeling procedures of this novel data set is thoroughly analyzed. The results
were consistent and behaved as expected: At each HITL iteration, the model
predictions improved. Our results demonstrated a 51% labeling time reduction
using HITL, saving us more than 390 continuous working hours. In a novel online
platform, called OdontoAI, created to work as task central for this novel data
set, we released 4,000 images, from which 2,000 have their labels publicly
available for model fitting. The labels of the other 2,000 images are private
and used for model evaluation considering instance and semantic segmentation
and numbering. To the best of our knowledge, this is the largest-scale publicly
available data set for panoramic radiographs, and the OdontoAI is the first
platform of its kind in dentistry.


title:
Acquiring elastic properties of thin composite structure from
  vibrational testing data

abstract:
  The problem of acquiring elastic properties of a composite material from the
data of the vibrational testing is considered. The specimen is considered to
abide by the linear elasticity laws and subject to viscoelastic damping. The
BVP for transverse movement of such a specimen under harmonic load is
formulated and solved with finite-element method. The problem of acquiring the
elastic parameters is then formulated as a nonlinear least square optimization
problem. The usage of the automatic differentiation technique for stable and
efficient computation of the gradient and hessian allows to use well-studied
first and second order optimization methods, namely Newton and BFGS. The
results of the numerical experiments on simulated data are analyzed in order to
provide insights for the experiment planning.


title:
Investigating Data Variance in Evaluations of Automatic Machine
  Translation Metrics

abstract:
  Current practices in metric evaluation focus on one single dataset, e.g.,
Newstest dataset in each year's WMT Metrics Shared Task. However, in this
paper, we qualitatively and quantitatively show that the performances of
metrics are sensitive to data. The ranking of metrics varies when the
evaluation is conducted on different datasets. Then this paper further
investigates two potential hypotheses, i.e., insignificant data points and the
deviation of Independent and Identically Distributed (i.i.d) assumption, which
may take responsibility for the issue of data variance. In conclusion, our
findings suggest that when evaluating automatic translation metrics,
researchers should take data variance into account and be cautious to claim the
result on a single dataset, because it may leads to inconsistent results with
most of other datasets.


title:
NICGSlowDown: Evaluating the Efficiency Robustness of Neural Image
  Caption Generation Models

abstract:
  Neural image caption generation (NICG) models have received massive attention
from the research community due to their excellent performance in visual
understanding. Existing work focuses on improving NICG model accuracy while
efficiency is less explored. However, many real-world applications require
real-time feedback, which highly relies on the efficiency of NICG models.
Recent research observed that the efficiency of NICG models could vary for
different inputs. This observation brings in a new attack surface of NICG
models, i.e., An adversary might be able to slightly change inputs to cause the
NICG models to consume more computational resources. To further understand such
efficiency-oriented threats, we propose a new attack approach, NICGSlowDown, to
evaluate the efficiency robustness of NICG models. Our experimental results
show that NICGSlowDown can generate images with human-unnoticeable
perturbations that will increase the NICG model latency up to 483.86%. We hope
this research could raise the community's concern about the efficiency
robustness of NICG models.


title:
Visualizing the Relationship Between Encoded Linguistic Information and
  Task Performance

abstract:
  Probing is popular to analyze whether linguistic information can be captured
by a well-trained deep neural model, but it is hard to answer how the change of
the encoded linguistic information will affect task performance. To this end,
we study the dynamic relationship between the encoded linguistic information
and task performance from the viewpoint of Pareto Optimality. Its key idea is
to obtain a set of models which are Pareto-optimal in terms of both objectives.
From this viewpoint, we propose a method to optimize the Pareto-optimal models
by formalizing it as a multi-objective optimization problem. We conduct
experiments on two popular NLP tasks, i.e., machine translation and language
modeling, and investigate the relationship between several kinds of linguistic
information and task performances. Experimental results demonstrate that the
proposed method is better than a baseline method. Our empirical findings
suggest that some syntactic information is helpful for NLP tasks whereas
encoding more syntactic information does not necessarily lead to better
performance, because the model architecture is also an important factor.


title:
Non-Lie subgroups in Lie groups over local fields of positive
  characteristic

abstract:
  By Cartan's Theorem, every closed subgroup $H$ of a real (or $p$-adic) Lie
group $G$ is a Lie subgroup. For Lie groups over a local field ${\mathbb K}$ of
positive characteristic, the analogous conclusion is known to be wrong. We show
more: There exists a ${\mathbb K}$-analytic Lie group $G$ and a non-discrete,
compact subgroup $H$ such that, for every ${\mathbb K}$-analytic manifold $M$,
every ${\mathbb K}$-analytic map $f\colon M\to G$ with $f(M)\subseteq H$ is
locally constant. In particular, the set $H$ does not admit a non-discrete
${\mathbb K}$-analytic manifold structure which makes the inclusion of $H$ into
$G$ a ${\mathbb K}$-analytic map. We can achieve that, moreover, $H$ does not
admit a ${\mathbb K}$-analytic Lie group structure compatible with the
topological group structure induced by $G$ on $H$.


title: Restless Temporal Path Parameterized Above Lower Bounds

abstract:
  Reachability questions are one of the most fundamental algorithmic primitives
in temporal graphs -- graphs whose edge set changes over discrete time steps. A
core problem here is the NP-hard Short Restless Temporal Path: given a temporal
graph $\mathcal G$, two distinct vertices $s$ and $z$, and two numbers $\delta$
and $k$, is there a $\delta$-restless temporal $s$-$z$ path of length at most
$k$? A temporal path is a path whose edges appear in chronological order and a
temporal path is $\delta$-restless if two consecutive path edges appear at most
$\delta$ time steps apart from each other. Among others, this problem has
applications in neuroscience and epidemiology. While Short Restless Temporal
Path is known to be computationally hard, e.g., it is NP-hard for only three
time steps and W[1]-hard when parameterized by the feedback vertex number of
the underlying graph, it is fixed-parameter tractable when parameterized by the
path length $k$. We improve on this by showing that Short Restless Temporal
Path can be solved in (randomized) $4^{k-d}|\mathcal G|^{O(1)}$ time, where $d$
is the minimum length of a temporal $s$-$z$ path.


title:
WAVPROMPT: Towards Few-Shot Spoken Language Understanding with Frozen
  Language Models

abstract:
  Large-scale auto-regressive language models pretrained on massive text have
demonstrated their impressive ability to perform new natural language tasks
with only a few text examples, without the need for fine-tuning. Recent studies
further show that such a few-shot learning ability can be extended to the
text-image setting by training an encoder to encode the images into embeddings
functioning like the text embeddings of the language model. Interested in
exploring the possibility of transferring the few-shot learning ability to the
audio-text setting, we propose a novel speech understanding framework,
WavPrompt, where we finetune a wav2vec model to generate a sequence of audio
embeddings understood by the language model. We show that WavPrompt is a
few-shot learner that can perform speech understanding tasks better than a
naive text baseline. We conduct detailed ablation studies on different
components and hyperparameters to empirically identify the best model
configuration. In addition, we conduct a non-speech understanding experiment to
show WavPrompt can extract more information than just the transcriptions. Code
is available at https://github.com/Hertin/WavPrompt


title:
Measurement of software development effort estimation bias: Avoiding
  biased measures of estimation bias

abstract:
  In this paper, we propose improvements in how estimation bias, e.g., the
tendency towards under-estimating the effort, is measured. The proposed
approach emphasizes the need to know what the estimates are meant to represent,
i.e., the type of estimate we evaluate and the need for a match between the
type of estimate given and the bias measure used. We show that even perfect
estimates of the mean effort will not lead to an expectation of zero estimation
bias when applying the frequently used bias measure: (actual effort - estimated
effort)/actual effort. This measure will instead reward under-estimates of the
mean effort. We also provide examples of bias measures that match estimates of
the mean and the median effort, and argue that there are, in general, no
practical bias measures for estimates of the most likely effort. The paper
concludes with implications for the evaluation of bias of software development
effort estimates.


title:
On Triangulation as a Form of Self-Supervision for 3D Human Pose
  Estimation

abstract:
  Supervised approaches to 3D pose estimation from single images are remarkably
effective when labeled data is abundant. Therefore, much of the recent
attention has shifted towards semi and (or) weakly supervised learning.
Generating an effective form of supervision with little annotations still poses
major challenges in crowded scenes. However, since it is easy to observe a
scene from multiple cameras, we propose to impose multi-view geometrical
constraints by means of a differentiable triangulation and to use it as form of
self-supervision during training when no labels are available. We therefore
train a 2D pose estimator in such a way that its predictions correspond to the
re-projection of the triangulated 3D one and train an auxiliary network on them
to produce the final 3D poses. We complement the triangulation with a weighting
mechanism that nullify the impact of noisy predictions caused by self-occlusion
or occlusion from other subjects. Our experimental results on Human3.6M and
MPI-INF-3DHP substantiate the significance of our weighting strategy where we
obtain state-of-the-art results in the semi and weakly supervised learning
setup. We also contribute a new multi-player sports dataset that features
occlusion, and show the effectiveness of our algorithm over baseline
triangulation methods.


title: Indoor SLAM Using a Foot-mounted IMU and the local Magnetic Field

abstract:
  In this paper, a simultaneous localization and mapping (SLAM) algorithm for
tracking the motion of a pedestrian with a foot-mounted inertial measurement
unit (IMU) is proposed. The algorithm uses two maps, namely, a motion map and a
magnetic field map. The motion map captures typical motion patterns of
pedestrians in buildings that are constrained by e.g. corridors and doors. The
magnetic map models local magnetic field anomalies in the environment using a
Gaussian process (GP) model and uses them as position information. These maps
are used in a Rao-Blackwellized particle filter (RBPF) to correct the
pedestrian position and orientation estimates from the pedestrian
dead-reckoning (PDR). The PDR is computed using an extended Kalman filter with
zero-velocity updates (ZUPT-EKF). The algorithm is validated using real
experimental sequences and the results show the efficacy of the algorithm in
localizing pedestrians in indoor environments.


abstract:
  The ability to integrate context, including perceptual and temporal cues,
plays a pivotal role in grounding the meaning of a linguistic utterance. In
order to measure to what extent current vision-and-language models master this
ability, we devise a new multimodal challenge, Image Retrieval from Contextual
Descriptions (ImageCoDe). In particular, models are tasked with retrieving the
correct image from a set of 10 minimally contrastive candidates based on a
contextual description. As such, each description contains only the details
that help distinguish between images. Because of this, descriptions tend to be
complex in terms of syntax and discourse and require drawing pragmatic
inferences. Images are sourced from both static pictures and video frames. We
benchmark several state-of-the-art models, including both cross-encoders such
as ViLBERT and bi-encoders such as CLIP, on ImageCoDe. Our results reveal that
these models dramatically lag behind human performance: the best variant
achieves an accuracy of 20.9 on video frames and 59.4 on static pictures,
compared with 90.8 in humans. Furthermore, we experiment with new model
variants that are better equipped to incorporate visual and temporal context
into their representations, which achieve modest gains. Our hope is that
ImageCoDE will foster progress in grounded language understanding by
encouraging models to focus on fine-grained visual differences.


title:
Microstructural Characterization and Mechanical Property Evaluation of
  High Nitrogen Martensitic Stainless Steel Subjected to Heat Treatment

abstract:
  The High Nitrogen Martensitic Stainless Steel (HNMS) was subjected to three
different austenitizing cycles of 1050$^\circ$C, 1075$^\circ$C and
1100$^\circ$C followed by subzero treatment at -70$^\circ$C. The fraction of
retained austenite has been reduced after sub-zero treatment as revealed by
microstructural evolution. The material was subsequently tempered at different
temperatures ranging from 180$^\circ$C to 650$^\circ$C and the change in
micro-structure, hardness, tensile strength and toughness were investigated
after each heat treatment cycle. Optical microscopy, electron microscopy with
EDS and X-Ray diffraction techniques were used to characterize the material.
This has showed the constituents of microstructure were lath martensite,
precipitated metal carbides of type $M_{23}C_6$, $M_7C_6$ and carbo-nitrides.
Hardness, tensile testing and Charpy impact testing were carried to evaluate
mechanical properties after the heat treatment which has showed the better
mechanical properties for the samples solutionised at 1075$^\circ$C. Secondary
hardening has been observed on tempering above 450$^\circ$C which can be
attributed to the precipitation of secondary phase inter-metallic compounds.
Hardness attains a peak value at peculiar temperature range after which it
decreases on further tempering which is most likely because of the loss of
coherency of the precipitates with the metal matrix. This has been further
confirmed by the XRD of the specimens before and after tempering. The study
stablishes the structure-property correlation of HNMS for different heat
treatment cycles. The results indicate that a good combination of hardness and
strength can be achieved after solutionizing at 1075$^\circ$C followed by
double tempering at 525$^\circ$C.


title: Capacitive temperature sensing via displacement amplification

abstract:
  We propose the realization of capacitive temperature sensors based on the
concept of displacement amplification. Our design features two high coefficient
of thermal expansion (CTE) metallic layers separated by a low-CTE dielectric
layer; conductive and dielectric layers are then separated by a thin air gap
and glued together at a few locations. As the temperature increases, the
high-CTE layer tends to expand more than the low-CTE one. Owing to the
constraint to planar expansion imposed by the low-CTE layer, this results in
large out-of-plane displacements of the high-CTE layer -- hence the
displacement amplification term. In our case, the high-CTE layer buckles and
causes a reduction of the gap between conductive and dielectric layers; in
turn, this results in a large change of capacitance. First, we illustrate the
concept via numerical simulations. Then, we realize a low-cost prototype of
such sensor by using aluminum foil as conductor, paper as dielectric, and by
gluing the layers together with cyanoacrylate. Our results demonstrate the
potential of this simple design as a route towards efficient and low-cost
temperature sensors.


title:
Transmitting multiple high-frequency waves across length scales using
  the concurrent atomistic-continuum method

abstract:
  Coupled atomistic-continuum methods can describe large domains and model
dynamic material behavior for a much lower computational cost than traditional
atomistic techniques. However, these multiscale frameworks suffer from wave
reflections at the atomistic-continuum interfaces due to the numerical
discrepancy between the fine-scaled and coarse-scaled models. Such reflections
are non-physical and may lead to unfavorable outcomes such as artificial
heating in the atomistic region. In this work, we develop a technique to allow
the full spectrum of phonons to be incorporated into the coarse-scaled regions
of a periodic concurrent atomistic-continuum (CAC) framework. This scheme
tracks phonons generated at various time steps and thus allows multiple
high-frequency wave packets to travel between the atomistic and continuum
regions. Simulations performed with this method demonstrate the ability of the
technique to preserve the coherency of waves with a range of wavevectors as
they propagate through the domain. This work has applications for systems with
defined boundary conditions and may be extended to more complex problems
involving waves randomly nucleated from an impact within a multiscale
framework.


title: Remarks on special congruences

abstract:
  We study algebras and varieties where every non-trivial congruence has some
class being a non-trivial subuniverse of the algebra in question. Then we focus
on algebras where this non-trivial class is a unique non-singleton class of the
congruence. In particular, we investigate Rees algebras, quasi-Rees algebras
and algebras having the one-block-property. We also present results concerning
these properties on quotient algebras. Several examples of such algebras and
varieties are included.


title:
Protective Mission against a Highly Maneuverable Rogue Drone Using
  Defense Margin Strategy

abstract:
  The current paper studies a protective mission to defend a domain called the
safe zone from a rogue drone invasion. We consider a one attacker and one
defender drone scenario where only a noisy observation of the attacker at every
time step is accessible to the defender. Directly applying strategies used in
existing problems such as pursuit-evasion games are shown to be insufficient
for our mission. We introduce a new concept of defense margin to complement an
existing strategy and construct a control strategy that successfully solves our
problem. We provide analytical proofs to point out the limitations of the
existing strategy and how our defense margin strategy can be used to enhance
performance. Simulation results show that our suggested strategy outperforms
that of the existing strategy at least by 36.0%p in terms of mission success.


title:
An Overview & Analysis of Sequence-to-Sequence Emotional Voice
  Conversion

abstract:
  Emotional voice conversion (EVC) focuses on converting a speech utterance
from a source to a target emotion; it can thus be a key enabling technology for
human-computer interaction applications and beyond. However, EVC remains an
unsolved research problem with several challenges. In particular, as speech
rate and rhythm are two key factors of emotional conversion, models have to
generate output sequences of differing length. Sequence-to-sequence modelling
is recently emerging as a competitive paradigm for models that can overcome
those challenges. In an attempt to stimulate further research in this promising
new direction, recent sequence-to-sequence EVC papers were systematically
investigated and reviewed from six perspectives: their motivation, training
strategies, model architectures, datasets, model inputs, and evaluation
methods. This information is organised to provide the research community with
an easily digestible overview of the current state-of-the-art. Finally, we
discuss existing challenges of sequence-to-sequence EVC.


title:
Temperature-Aware Monolithic 3D DNN Accelerators for Biomedical
  Applications

abstract:
  In this paper, we focus on temperature-aware Monolithic 3D (Mono3D) deep
neural network (DNN) inference accelerators for biomedical applications. We
develop an optimizer that tunes aspect ratios and footprint of the accelerator
under user-defined performance and thermal constraints, and generates
near-optimal configurations. Using the proposed Mono3D optimizer, we
demonstrate up to 61% improvement in energy efficiency for biomedical
applications over a performance-optimized accelerator.


title:
Electrosorption-induced deformation of a porous electrode with
  non-convex pore geometry in electrolyte solutions: a theoretical study

abstract:
  Porous carbon is well known as a good candidate for the development of
electrochemical double-layer capacitors. Predominantly, many conventional
carbons are microporous and often well described by the assumption of slit pore
geometry. However, there is a class of carbons that is significantly different
from the others, namely templated mesoporous carbons. In this work, we study
electrosorption-induced deformation in CMK-3-like mesopores having nonconvex
geometry applying a mean-field approach. The model is based on the modified
Poisson-Boltzmann equation taking into account the excluded volume of the ions
within the hard-sphere model in the Percus-Yevick approximation. We assume that
the deformation is caused by two effects: ion osmotic pressure and
electrostatic interactions of the electric double layers on charged rods. The
latter were calculated via the Maxwell stress tensor agreeing with the modified
Poisson-Boltzmann equation. We estimated the pore-load modulus of the
CMK-3-like material and found an agreement with the previously obtained values
by small-angle neutron scattering (SANS). Additionally, we studied the
differential capacitance in the non-convex pore geometry and found that the
behavior of the differential capacitance profiles was similar to that of the
profiles obtained for flat electric double layers. Namely, we obtained the
crowding regime at rather high voltages and more pronounced profile asymmetry
with increasing differences in the ionic sizes.


title: Self-Supervised Learning for Recommender Systems: A Survey

abstract:
  Neural architecture-based recommender systems have achieved tremendous
success in recent years. However, when dealing with highly sparse data, they
still fall short of expectation. Self-supervised learning (SSL), as an emerging
technique to learn with unlabeled data, recently has drawn considerable
attention in many fields. There is also a growing body of research proceeding
towards applying SSL to recommendation for mitigating the data sparsity issue.
In this survey, a timely and systematical review of the research efforts on
self-supervised recommendation (SSR) is presented. Specifically, we propose an
exclusive definition of SSR, on top of which we build a comprehensive taxonomy
to divide existing SSR methods into four categories: contrastive, generative,
predictive, and hybrid. For each category, the narrative unfolds along its
concept and formulation, the involved methods, and its pros and cons.
Meanwhile, to facilitate the development and evaluation of SSR models, we
release an open-source library SELFRec, which incorporates multiple benchmark
datasets and evaluation metrics, and has implemented a number of
state-of-the-art SSR models for empirical comparison. Finally, we shed light on
the limitations in the current research and outline the future research
directions.


title: Quantum Advantage from Any Non-Local Game

abstract:
  We show a general method of compiling any $k$-prover non-local game into a
single-prover interactive game maintaining the same (quantum) completeness and
(classical) soundness guarantees (up to negligible additive factors in a
security parameter). Our compiler uses any quantum homomorphic encryption
scheme (Mahadev, FOCS 2018; Brakerski, CRYPTO 2018) satisfying a natural form
of correctness with respect to auxiliary (quantum) input. The homomorphic
encryption scheme is used as a cryptographic mechanism to simulate the effect
of spatial separation, and is required to evaluate $k-1$ prover strategies (out
of $k$) on encrypted queries.
  In conjunction with the rich literature on (entangled) multi-prover non-local
games starting from the celebrated CHSH game (Clauser, Horne, Shimonyi and
Holt, Physical Review Letters 1969), our compiler gives a broad framework for
constructing mechanisms to classically verify quantum advantage.


title: Characterizations of graph classes via convex geometries: A survey

abstract:
  Graph convexity has been used as an important tool to better understand the
structure of classes of graphs. Many studies are devoted to determine if a
graph equipped with a convexity is a {\em convex geometry}. In this work we
survey results on characterizations of well-known classes of graphs via convex
geometries. We also give some contributions to this subject.


title:
A deep learning model for burn depth classification using ultrasound
  imaging

abstract:
  Identification of burn depth with sufficient accuracy is a challenging
problem. This paper presents a deep convolutional neural network to classify
burn depth based on altered tissue morphology of burned skin manifested as
texture patterns in the ultrasound images. The network first learns a
low-dimensional manifold of the unburned skin images using an encoder-decoder
architecture that reconstructs it from ultrasound images of burned skin. The
encoder is then re-trained to classify burn depths. The encoder-decoder network
is trained using a dataset comprised of B-mode ultrasound images of unburned
and burned ex vivo porcine skin samples. The classifier is developed using
B-mode images of burned in situ skin samples obtained from freshly euthanized
postmortem pigs. The performance metrics obtained from 20-fold cross-validation
show that the model can identify deep-partial thickness burns, which is the
most difficult to diagnose clinically, with 99% accuracy, 98% sensitivity, and
100% specificity. The diagnostic accuracy of the classifier is further
illustrated by the high area under the curve values of 0.99 and 0.95,
respectively, for the receiver operating characteristic and precision-recall
curves. A post hoc explanation indicates that the classifier activates the
discriminative textural features in the B-mode images for burn classification.
The proposed model has the potential for clinical utility in assisting the
clinical assessment of burn depths using a widely available clinical imaging
device.


title: Proactive Image Manipulation Detection

abstract:
  Image manipulation detection algorithms are often trained to discriminate
between images manipulated with particular Generative Models (GMs) and
genuine/real images, yet generalize poorly to images manipulated with GMs
unseen in the training. Conventional detection algorithms receive an input
image passively. By contrast, we propose a proactive scheme to image
manipulation detection. Our key enabling technique is to estimate a set of
templates which when added onto the real image would lead to more accurate
manipulation detection. That is, a template protected real image, and its
manipulated version, is better discriminated compared to the original real
image vs. its manipulated one. These templates are estimated using certain
constraints based on the desired properties of templates. For image
manipulation detection, our proposed approach outperforms the prior work by an
average precision of 16% for CycleGAN and 32% for GauGAN. Our approach is
generalizable to a variety of GMs showing an improvement over prior work by an
average precision of 10% averaged across 12 GMs. Our code is available at
https://www.github.com/vishal3477/proactive_IMD.


title:
Wave-equation-based inversion with amortized variational Bayesian
  inference

abstract:
  Solving inverse problems involving measurement noise and modeling errors
requires regularization in order to avoid data overfit. Geophysical inverse
problems, in which the Earth's highly heterogeneous structure is unknown,
present a challenge in encoding prior knowledge through analytical expressions.
Our main contribution is a generative-model-based regularization approach,
robust to out-of-distribution data, which exploits the prior knowledge embedded
in existing data and model pairs. Utilizing an amortized variational inference
objective, a conditional normalizing flow (NF) is pretrained on pairs of low-
and high-fidelity migrated images in order to achieve a low-fidelity
approximation to the seismic imaging posterior distribution for previously
unseen data. The NF is used after pretraining to reparameterize the unknown
seismic image in an inversion scheme involving physics-guided data misfit and a
Gaussian prior on the NF latent variable. Solving this optimization problem
with respect to the latent variable enables us to leverage the benefits of
data-driven conditional priors whilst being informed by physics and data. The
numerical experiments demonstrate that the proposed inversion scheme produces
seismic images with limited artifacts when dealing with noisy and
out-of-distribution data.


title: Learning to Detect Mobile Objects from LiDAR Scans Without Labels

abstract:
  Current 3D object detectors for autonomous driving are almost entirely
trained on human-annotated data. Although of high quality, the generation of
such data is laborious and costly, restricting them to a few specific locations
and object types. This paper proposes an alternative approach entirely based on
unlabeled data, which can be collected cheaply and in abundance almost
everywhere on earth. Our approach leverages several simple common sense
heuristics to create an initial set of approximate seed labels. For example,
relevant traffic participants are generally not persistent across multiple
traversals of the same route, do not fly, and are never under ground. We
demonstrate that these seed labels are highly effective to bootstrap a
surprisingly accurate detector through repeated self-training without a single
human annotated label.


title: Epithelial Tissue Growth Dynamics: Universal or Not?

abstract:
  Universality of interfacial roughness in growing epithelial tissue has
remained a controversial issue. Kardar-Parisi-Zhang (KPZ) and Molecular Beam
Epitaxy (MBE) universality classes have been reported among other behaviors
including total lack of universality. Here, we utilize a kinetic division model
for deformable cells to investigate cell-colony scaling. With seemingly minor
model changes, it can reproduce both KPZ- and MBE-like scaling in
configurations that mimic the respective experiments. This result neutralizes
the apparent scaling controversy. It can be speculated that this diversity in
growth behavior is beneficial for efficient evolution and versatile growth
dynamics.


title: Radial Autoencoders for Enhanced Anomaly Detection

abstract:
  In classification problems, supervised machine-learning methods outperform
traditional algorithms, thanks to the ability of neural networks to learn
complex patterns. However, in two-class classification tasks like anomaly or
fraud detection, unsupervised methods could do even better, because their
prediction is not limited to previously learned types of anomalies. An
intuitive approach of anomaly detection can be based on the distances from the
centers of mass of the two respective classes. Autoencoders, although trained
without supervision, can also detect anomalies: considering the center of mass
of the normal points, reconstructions have now radii, with largest radii most
likely indicating anomalous points. Of course, radii-based classification were
already possible without interposing an autoencoder. In any space, radial
classification can be operated, to some extent. In order to outperform it, we
proceed to radial deformations of data (i.e. centric compression or expansions
of axes) and autoencoder training. Any autoencoder that makes use of a data
center is here baptized a centric autoencoder (cAE). A special type is the cAE
trained with a uniformly compressed dataset, named the centripetal autoencoder
(cpAE). The new concept is studied here in relation with a schematic artificial
dataset, and the derived methods show consistent score improvements. But tested
on real banking data, our radial deformation supervised algorithms alone still
perform better that cAEs, as expected from most supervised methods;
nonetheless, in hybrid approaches, cAEs can be combined with a radial
deformation of space, improving its classification score. We expect that
centric autoencoders will become irreplaceable objects in anomaly live
detection based on geometry, thanks to their ability to stem naturally on
geometrical algorithms and to their native capability of detecting unknown
anomaly types.


title: Split Conformal Prediction for Dependent Data

abstract:
  Split conformal prediction is a popular tool to obtain predictive intervals
from general statistical algorithms, with few assumptions beyond data
exchangeability. We show that coverage guarantees from split CP can be extended
to dependent processes, such as the class of stationary $\beta$-mixing
processes, by adding a small coverage penalty. In particular, we show that the
empirical coverage bounds for some $\beta$-mixing processes match the order of
the bounds under exchangeability. The framework introduced also extends to
non-stationary processes and to other CP methods, and experiments corroborate
our split CP's coverage guarantees under dependent data.


title: A Perspective on Solar Energetic Particles

abstract:
  The author has been fortunate to observe and participate in the rise of the
field of solar energetic particles (SEPs), from the early abundance studies, to
the contemporary paradigm of shock acceleration in large SEP events, and
element abundance enhancements that are power laws in mass-to-charge ratios
from H to Pb. Through painful evolution the "birdcage" model and the
"solar-flare myth" came and went, leaving us with shock waves and solar jets
that can interact as sources of SEPs.


title: Virtual Localization in equivariant Witt cohomology

abstract:
  We prove an analog of the virtual localization theorem of
Graber-Pandharipande, in the setting of an action by the normalizer of the
torus in $\text{SL}_2$, and with the Chow groups replaced by the cohomology of
a suitably twisted sheaf of Witt groups.


title: Cusp Bifurcation in Metastatic Breast Cancer Cells

abstract:
  Ordinary differential equations (ODEs) can model the transition of cell
states over time. Bifurcation theory is a branch of dynamical systems which
studies changes in the behavior of an ODE system while one or more parameters
are varied. We have found that concepts in bifurcation theory may be applied to
model metastatic cell behavior. Our results show how a specific phenomenon
called a cusp bifurcation describes metastatic cell state transitions,
separating two qualitatively different transition modalities. Moreover, we show
how the cusp bifurcation models other genetic networks, and we relate the
dynamics after the bifurcation to observed phenomena in commitment to enter the
cell cycle.


title:
Photo-induced cascaded harmonic and comb generation in silicon nitride
  microresonators

abstract:
  Silicon nitride (Si$_3$N$_4$) is an ever-maturing integrated platform for
nonlinear optics. Yet, due to the absence of second-order ($\chi^{(2)}$)
nonlinearity, Si$_3$N$_4$ is mostly considered for third-order ($\chi^{(3)}$)
nonlinear interactions. Recently, this limitation was overcome by optical
poling in both Si$_3$N$_4$ waveguides and microresonators via the photogalvanic
effect, resulting in the inscription of quasi-phase-matched $\chi^{(2)}$
gratings. Here, we report cascaded nonlinear effects in a normal dispersion
Si$_3$N$_4$ microresonator with combined $\chi^{(2)}$ and $\chi^{(3)}$
nonlinearities. We demonstrate that the photo-induced $\chi^{(2)}$ grating also
provides phase-matching for the sum-frequency generation process, enabling the
initiation and successive switching of primary combs at pump wavelength.
Additionally, the doubly resonant pump and second-harmonic fields allow for
cascaded third-harmonic generation, where a secondary optically written
$\chi^{(2)}$ grating is identified. Finally, we reach a low-noise, broadband
microcomb state evolved from the sum-frequency coupled primary comb. These
results expand the scope of cascaded effects in $\chi^{(2)}$ and $\chi^{(3)}$
microresonators.


title: Testing the identification of causal effects in data

abstract:
  This study demonstrates the existence of a testable condition for the
identification of the causal effect of a treatment on an outcome in
observational data, which relies on two sets of variables, namely observed
covariates to be controlled for and a suspected instrument. Under a causal
structure commonly found in empirical applications, the testable conditional
independence of the suspected instrument and the outcome given the treatment
and the covariates has two implications. First, the instrument is valid, i.e.\
it does not directly affect the outcome (other than through the treatment) and
is unconfounded conditional on the covariates. Second, the treatment is
unconfounded conditional on the covariates such that the treatment effect is
identified. We suggest tests of this conditional independence based on doubly
robust estimators and investigate their finite sample performance in a
simulation study. We also apply our testing approach to the evaluation of the
impact of fertility on female labor supply when using the sibling sex ratio of
the first two children as supposed instrument, which by and large points to a
violation of our testable implication, at least for the moderate set of
socio-economic covariates considered.


title: Thermal decomposition of hydrated graphite oxide: A computational study

abstract:
  We study the behavior of hydrated graphite oxide (GO) at high temperatures
using thermally accelerated molecular dynamics simulations based on ab initio
density functional theory. Our results suggest that GO, a viable candidate for
water treatment and desalination membranes, is more heat resilient than
currently used organic materials. The system we consider to represent important
aspects of thermal processes in highly disordered GO is a hydrated GO bilayer
in vacuum. Our study provides microscopic insight into reactions involving
water and functional epoxy-O and OH-groups bonded to graphene layers, and also
describes the swelling of the structure by water vapor pressure at elevated
temperatures. We find the system to withstand simulation temperatures up to
${\approx}$2,500 K before the graphitic layers start decomposing, implying the
possibility of cleaning biofouling residue from a GO-based membrane by heating
in an inert gas atmosphere.


title:
Safe Control for Nonlinear Systems with Stochastic Uncertainty via Risk
  Control Barrier Functions

abstract:
  Guaranteeing safety for robotic and autonomous systems in real-world
environments is a challenging task that requires the mitigation of stochastic
uncertainties. Control barrier functions have, in recent years, been widely
used for enforcing safety related set-theoretic properties, such as forward
invariance and reachability, of nonlinear dynamical systems. In this paper, we
extend this rich framework to nonlinear discrete-time systems subject to
stochastic uncertainty and propose a framework for assuring risk-sensitive
safety in terms of coherent risk measures. To this end, we introduce risk
control barrier functions (RCBFs), which are compositions of barrier functions
and dynamic, coherent risk measures. We show that the existence of such barrier
functions implies invariance in a coherent risk sense. Furthermore, we
formulate conditions based on finite-time RCBFs to guarantee finite-time
reachability to a desired set in the coherent risk. Conditions for
risk-sensitive safety and finite-time reachability of sets composed of Boolean
compositions of multiple RCBF are also formulated. We show the efficacy of the
proposed method through its application to a cart-pole system in a
safety-critical scenario.