Abstract:
  In the steady-state contingency analysis, the traditional Newton-Raphson
method suffers from non-convergence issues when solving post-outage power flow
problems, which hinders the integrity and accuracy of security assessment. In
this paper, we propose a novel robust contingency analysis approach based on
holomorphic embedding (HE). The HE-based simulator guarantees convergence if
the true power flow solution exists, which is desirable because it avoids the
influence of numerical issues and provides a credible security assessment
conclusion. In addition, based on the multi-area characteristics of real-world
power systems, a partitioned HE (PHE) method is proposed with an
interface-based partitioning of HE formulation. The PHE method does not
undermine the numerical robustness of HE and significantly reduces the
computation burden in large-scale contingency analysis. The PHE method is
further enhanced by parallel or distributed computation to become parallel PHE
(P${}^\mathrm{2}$HE). Tests on a 458-bus system, a synthetic 419-bus system and
a large-scale 21447-bus system demonstrate the advantages of the proposed
methods in robustness and efficiency.


Abstract:
  The $\alpha$-spectral radius of a connected graph $G$ is the spectral radius
of $A_\alpha$-matrix of $G$. In this paper, we discuss the methods for
comparing $\alpha$-spectral radius of graphs. As applications, we characterize
the graphs with the maximal $\alpha$-spectral radius among all unicyclic and
bicyclic graphs of order $n$ with diameter $d$, respectively. Finally, we
determine the unique graph with maximal signless Laplacian spectral radius
among bicyclic graphs of order $n$ with diameter $d$.


Abstract:
  Machine learning models are known to be vulnerable to adversarial attacks,
namely perturbations of the data that lead to wrong predictions despite being
imperceptible. However, the existence of "universal" attacks (i.e., unique
perturbations that transfer across different data points) has only been
demonstrated for images to date. Part of the reason lies in the lack of a
common domain, for geometric data such as graphs, meshes, and point clouds,
where a universal perturbation can be defined. In this paper, we offer a change
in perspective and demonstrate the existence of universal attacks for geometric
data (shapes). We introduce a computational procedure that operates entirely in
the spectral domain, where the attacks take the form of small perturbations to
short eigenvalue sequences; the resulting geometry is then synthesized via
shape-from-spectrum recovery. Our attacks are universal, in that they transfer
across different shapes, different representations (meshes and point clouds),
and generalize to previously unseen data.


Abstract:
  The large radii of many hot Jupiters can only be matched by models that have
hot interior adiabats, and recent theoretical work has shown that the interior
evolution of hot Jupiters has a significant impact on their atmospheric
structure. Due to its inflated radius, low gravity, and ultra-hot equilibrium
temperature, WASP-76b is an ideal case study for the impact of internal
evolution on observable properties. Hot interiors should most strongly affect
the non-irradiated side of the planet, and thus full phase curve observations
are critical to ascertain the effect of the interior on the atmospheres of hot
Jupiters. In this work, we present the first Spitzer phase curve observations
of WASP-76b. We find that WASP-76b has an ultra-hot day side and relatively
cold nightside with brightness temperatures of $2471 \pm 27~\mathrm{K}$/$1518
\pm 61~\mathrm{K}$ at $3.6~\micron$ and $2699 \pm 32~\mathrm{K}$/$1259 \pm
44~\mathrm{K}$ at $4.5~\micron$, respectively. These results provide evidence
for a dayside thermal inversion. Both channels exhibit small phase offsets of
$0.68 \pm 0.48^{\circ}$ at $3.6~\micron$ and $0.67 \pm 0.2^{\circ}$ at
$4.5~\mu\mathrm{m}$. We compare our observations to a suite of general
circulation models that consider two end-members of interior temperature along
with a broad range of frictional drag strengths. Strong frictional drag is
necessary to match the small phase offsets and cold nightside temperatures
observed. From our suite of cloud-free GCMs, we find that only cases with a
cold interior can reproduce the cold nightsides and large phase curve amplitude
at $4.5~\micron$, hinting that the hot interior adiabat of WASP-76b does not
significantly impact its atmospheric dynamics or that clouds blanket its
nightside.


Abstract:
  Many researchers have been concerned with whether social media has a negative
impact on the well-being of their audience. With the popularity of social
networking sites (SNS) steadily increasing, psychological and social sciences
have shown great interest in their effects and consequences on humans. In this
work, we investigate Facebook using the tools of HCI to find connections
between interface features and the concerns raised by these domains. Using an
empirical design analysis, we identify interface interferences impacting users'
online privacy. Through a subsequent survey (n=116), we find usage behaviour
changes due to increased privacy concerns and report individual cases of
addiction and mental health issues. These observations are the results of a
rapidly changing SNS creating a gap of understanding between users'
interactions with the platform and future consequences. We explore how HCI can
help close this gap and work towards more ethical user interfaces in the
future.


Abstract:
  The full physics potential of the next-generation Deep Underground Neutrino
Experiment (DUNE) is still being explored. In particular, there have been some
recent studies on the possibility of improving DUNE's neutrino energy
reconstruction. The main motivation is that a better determination of the
neutrino energy in an event-by-event basis will translate into an improved
measurement of the Dirac $CP$ phase and other neutrino oscillation parameters.
To further motivate studies and improvements on the neutrino energy
reconstruction, we evaluate the impact of energy resolution at DUNE on an
illustrative new physics scenario, viz. non-standard interactions (NSI) of
neutrinos with matter. We show that a better energy resolution in comparison to
the ones given in the DUNE conceptual and technical design reports may
significantly enhance the experimental sensitivity to NSI, particularly when
degeneracies are present. While a better reconstruction of the first
oscillation peak helps disentangling standard $CP$ effects from those coming
from NSIs, we find that the second oscillation peak also plays a nontrivial
role in improving DUNE's sensitivity.


Abstract:
  Transition metal dichalcogenide (TMD) moir\'e heterostructures provide an
ideal platform to explore the extended Hubbard model1 where long-range Coulomb
interactions play a critical role in determining strongly correlated electron
states. This has led to experimental observations of Mott insulator states at
half filling2-4 as well as a variety of extended Wigner crystal states at
different fractional fillings5-9. Microscopic understanding of these emerging
quantum phases, however, is still lacking. Here we describe a novel scanning
tunneling microscopy (STM) technique for local sensing and manipulation of
correlated electrons in a gated WS2/WSe2 moir\'e superlattice that enables
experimental extraction of fundamental extended Hubbard model parameters. We
demonstrate that the charge state of local moir\'e sites can be imaged by their
influence on STM tunneling current, analogous to the charge-sensing mechanism
in a single-electron transistor. In addition to imaging, we are also able to
manipulate the charge state of correlated electrons. Discharge cascades of
correlated electrons in the moir\'e superlattice are locally induced by ramping
the STM bias, thus enabling the nearest-neighbor Coulomb interaction (UNN) to
be estimated. 2D mapping of the moir\'e electron charge states also enables us
to determine onsite energy fluctuations at different moir\'e sites. Our
technique should be broadly applicable to many semiconductor moir\'e systems,
offering a powerful new tool for microscopic characterization and control of
strongly correlated states in moir\'e superlattices.


Abstract:
  The idea of coded caching for content distribution networks was introduced by
Maddah-Ali and Niesen, who considered the canonical $(N, K)$ cache network in
which a server with $N$ files satisfy the demands of $K$ users (equipped with
independent caches of size $M$ each). Among other results, their work provided
a characterization of the exact rate memory tradeoff for the problem when
$M\geq\frac{N}{K}(K-1)$. In this paper, we improve this result for large caches
with $M\geq \frac{N}{K}(K-2)$. For the case
$\big\lceil\frac{K+1}{2}\big\rceil\leq N \leq K$, we propose a new coded
caching scheme, and derive a matching lower bound to show that the proposed
scheme is optimal. This extends the characterization of the exact rate memory
tradeoff to the case $M\geq \frac{N}{K}\Big(K-2+\frac{(K-2+1/N)}{(K-1)}\Big)$.
For the case $1\leq N\leq \big\lceil\frac{K+1}{2}\big\rceil$, we derive a new
lower bound, which demonstrates that the scheme proposed by Yu et al. is
optimal and thus extend the characterization of the exact rate memory tradeoff
to the case $M\geq \frac{N}{K}(K-2)$.


Abstract:
  Segmentation-based methods are widely used for scene text detection due to
their superiority in describing arbitrary-shaped text instances. However, two
major problems still exist: 1) current label generation techniques are mostly
empirical and lack theoretical support, discouraging elaborate label design; 2)
as a result, most methods rely heavily on text kernel segmentation which is
unstable and requires deliberate tuning. To address these challenges, we
propose a human cognition-inspired framework, termed, Conceptual Text Region
Network (CTRNet). The framework utilizes Conceptual Text Regions (CTRs), which
is a class of cognition-based tools inheriting good mathematical properties,
allowing for sophisticated label design. Another component of CTRNet is an
inference pipeline that, with the help of CTRs, completely omits the need for
text kernel segmentation. Compared with previous segmentation-based methods,
our approach is not only more interpretable but also more accurate.
Experimental results show that CTRNet achieves state-of-the-art performance on
benchmark CTW1500, Total-Text, MSRA-TD500, and ICDAR 2015 datasets, yielding
performance gains of up to 2.0%. Notably, to the best of our knowledge, CTRNet
is among the first detection models to achieve F-measures higher than 85.0% on
all four of the benchmarks, with remarkable consistency and stability.


Abstract:
  Improving the image resolution and acquisition speed of magnetic resonance
imaging (MRI) is a challenging problem. There are mainly two strategies dealing
with the speed-resolution trade-off: (1) $k$-space undersampling with
high-resolution acquisition, and (2) a pipeline of lower resolution image
reconstruction and image super-resolution. However, these approaches either
have limited performance at certain high acceleration factor or suffer from the
error accumulation of two-step structure. In this paper, we combine the idea of
MR reconstruction and image super-resolution, and work on recovering HR images
from low-resolution under-sampled $k$-space data directly. Particularly, the
SR-involved reconstruction can be formulated as a variational problem, and a
learnable network unrolled from its solution algorithm is proposed. A
discriminator was introduced to enhance the detail refining performance.
Experiment results using in-vivo HR multi-coil brain data indicate that the
proposed SRR-Net is capable of recovering high-resolution brain images with
both good visual quality and perceptual quality.


Abstract:
  In the absence of an initial seed, the Biermann battery term of a non-ideal
induction equation acts as a source that generates weak magnetic fields. These
fields are then amplified via a dynamo mechanism. The Kelvin-Helmholtz
instability is a fluid phenomenon that takes place in many astrophysical
scenarios and can trigger the action of the Biermann battery and dynamo
processes. We aim to investigate the effect that the ionisation degree of the
plasma and the interaction between the charged and neutral species have on the
generation and amplification of magnetic fields during the different stages of
the instability. We use the two-fluid model implemented in the numerical code
Mancha-2F. We perform 2D simulations starting from a configuration with no
initial magnetic field and which is unstable due to a velocity shear. We vary
the ionisation degree of the plasma and we analyse the role that the different
collisional terms included in the equations of the model play on the evolution
of the instability and the generation of magnetic field. We find that when no
collisional coupling is considered between the two fluids, the effect of the
Biermann battery mechanism does not depend on the ionisation degree. However,
when elastic collisions are taken into account, the generation of magnetic
field is increased as the ionisation degree is reduced. This behaviour is
slightly enhanced if the process of charge-exchange is also considered. We also
find a dependence on the total density of the plasma related to the dependence
on the coupling degree between the two fluids. As the total density is
increased, the results from the two-fluid model converge to the predictions of
single-fluid models.


Abstract:
  High-angular-resolution cosmic microwave background experiments provide a
unique opportunity to conduct a survey of time-variable sources at millimeter
wavelengths, a population which has primarily been understood through follow-up
measurements of detections in other bands. Here we report the first results of
an astronomical transient survey with the South Pole Telescope (SPT) using the
SPT-3G camera to observe 1500 square degrees of the southern sky. The
observations took place from March to November 2020 in three bands centered at
95, 150, and 220 GHz. This survey yielded the detection of fifteen transient
events from sources not previously detected by the SPT. The majority are
associated with variable stars of different types, expanding the number of such
detected flares by more than a factor of two. The stellar flares are
unpolarized and bright, in some cases exceeding 1 Jy, and have durations from a
few minutes to several hours. Another population of detected events last for
2--3 weeks and appear to be extragalactic in origin. Though data availability
at other wavelengths is limited, we find evidence for concurrent optical
activity for two of the stellar flares. Future data from SPT-3G and forthcoming
instruments will provide real-time detection of millimeter-wave transients on
timescales of minutes to months.


Abstract:
  The Fermilab Muon $g-2$ collaboration recently announced the first result of
measurement of the muon anomalous magnetic moment ($g-2$), which confirmed the
previous result at the Brookhaven National Laboratory and thus the discrepancy
with its Standard Model prediction. We revisit low-scale supersymmetric models
that are naturally capable to solve the muon $g-2$ anomaly, focusing on two
distinct scenarios: chargino-contribution dominated and pure-bino-contribution
dominated scenarios. It is shown that the slepton pair-production searches have
excluded broad parameter spaces for both two scenarios, but they are not closed
yet. For the chargino-dominated scenario, the models with $m_{\tilde{\mu}_{\rm
L}}\gtrsim m_{\tilde{\chi}^{\pm}_1}$ are still widely allowed. For the
bino-dominated scenario, we find that, although slightly non-trivial, the
region with low $\tan \beta$ with heavy higgsinos is preferred. In the case of
universal slepton masses, the low mass regions with $m_{\tilde{\mu}}\lesssim
230$ GeV can explain the $g-2$ anomaly while satisfying the LHC constraints.
Furthermore, we checked that the stau-bino coannihilation works properly to
realize the bino thermal relic dark matter. We also investigate heavy staus
case for the bino-dominated scenario, where the parameter region that can
explain the muon $g-2$ anomaly is stretched to $m_{\tilde{\mu}}\lesssim 1.3$
TeV.


Abstract:
  Overparametrized neural networks, where the number of active parameters is
larger than the sample size, prove remarkably effective in modern deep learning
practice. From the classical perspective, however, much fewer parameters are
sufficient for optimal estimation and prediction, whereas overparametrization
can be harmful even in the presence of explicit regularization. To reconcile
this conflict, we present a generalization theory for overparametrized ReLU
networks by incorporating an explicit regularizer based on the scaled variation
norm. Interestingly, this regularizer is equivalent to the ridge from the angle
of gradient-based optimization, but is similar to the group lasso in terms of
controlling model complexity. By exploiting this ridge-lasso duality, we show
that overparametrization is generally harmless to two-layer ReLU networks. In
particular, the overparametrized estimators are minimax optimal up to a
logarithmic factor. By contrast, we show that overparametrized random feature
models suffer from the curse of dimensionality and thus are suboptimal.


Abstract:
  Since planet occurrence and primordial atmospheric retention probability
increase with period, the occurrence-weighted median planets discovered by
transit surveys may bear little resemblance to the low-occurrence, short-period
planets sculpted by atmospheric escape ordinarily used to calibrate
mass--radius relations and planet formation models. An occurrence-weighted
mass--radius relation for the low-mass planets discovered so far by transit
surveys orbiting solar-type stars requires both occurrence-weighted median
Earth-mass and Neptune-mass planets to have a few percent of their masses in
hydrogen/helium (H/He) atmospheres. Unlike the Earth that finished forming long
after the protosolar nebula was dissipated, these occurrence-weighted median
Earth-mass planets must have formed early in their systems' histories. The
existence of significant H/He atmospheres around Earth-mass planets confirms an
important prediction of the core-accretion model of planet formation. It also
implies core masses $M_{\text{c}}$ in the range $2~M_{\oplus}\lesssim
M_{\text{c}}\lesssim 8~M_{\oplus}$ that can retain their primordial
atmospheres. If atmospheric escape is driven by photoevaporation due to extreme
ultraviolet (EUV) flux, then our observation requires a reduction in the
fraction of incident EUV flux converted into work usually assumed in
photoevaporation models. If atmospheric escape is core driven, then the
occurrence-weighted median Earth-mass planets must have large Bond albedos. In
contrast to Uranus and Neptune that have at least 10% of their masses in H/He
atmospheres, these occurrence-weighted median Neptune-mass planets are H/He
poor. The implication is that they experienced collisions or formed in much
shorter-lived and/or hotter parts of their parent protoplanetary disks than
Uranus and Neptune's formation location in the protosolar nebula.


Abstract:
  Subgradient and Newton algorithms for nonsmooth optimization require
generalized derivatives to satisfy subtle approximation properties:
conservativity for the former and semismoothness for the latter. Though these
two properties originate in entirely different contexts, we show that in the
semi-algebraic setting they are equivalent. Both properties for a generalized
derivative simply require it to coincide with the standard directional
derivative on the tangent spaces of some partition of the domain into smooth
manifolds. An appealing byproduct is a new short proof that semi-algebraic maps
are semismooth relative to the Clarke Jacobian.


Abstract:
  Lung nodule detection from 3D Computed Tomography scans plays a vital role in
efficient lung cancer screening. Despite the SOTA performance obtained by
recent anchor-based detectors using CNNs for this task, they require
predetermined anchor parameters such as the size, number, and aspect ratio of
anchors, and have limited robustness when dealing with lung nodules with a
massive variety of sizes. To overcome these problems, we propose a 3D sphere
representation-based center-points matching detection network that is
anchor-free and automatically predicts the position, radius, and offset of
nodules without the manual design of nodule/anchor parameters. The SCPM-Net
consists of two novel components: sphere representation and center points
matching. First, to match the nodule annotation in clinical practice, we
replace the commonly used bounding box with our proposed bounding sphere to
represent nodules with the centroid, radius, and local offset in 3D space. A
compatible sphere-based intersection over-union loss function is introduced to
train the lung nodule detection network stably and efficiently. Second, we
empower the network anchor-free by designing a positive center-points selection
and matching process, which naturally discards pre-determined anchor boxes. An
online hard example mining and re-focal loss subsequently enable the CPM
process to be more robust, resulting in more accurate point assignment and
mitigation of class imbalance. In addition, to better capture spatial
information and 3D context for the detection, we propose to fuse multi-level
spatial coordinate maps with the feature extractor and combine them with 3D
squeeze-and-excitation attention modules. Experimental results on the LUNA16
dataset showed that our proposed framework achieves superior performance
compared with existing anchor-based and anchor-free methods for lung nodule
detection.


Abstract:
  The idea of possible modification to gravity theory, whether it is in the
Newtonian or general relativistic premises, is there for quite sometime. Based
on it, astrophysical and cosmological problems are targeted to solve. But none
of the Newtonian theories of modification has been performed from the first
principle. Here, we modify Poisson's equation and propose two possible ways to
modify the law gravitation which, however, reduces to Newton's law far away
from the center of source. Based on these modified Newton's laws, we attempt to
solve problems lying with white dwarfs. There are observational evidences for
possible violation of the Chandrasekhar mass-limit significantly: it could be
sub- as well as super-Chandrasekhar. We show that modified Newton's law, either
by modifying LHS or RHS of Poisson's equation, can explain them.


Abstract:
  Within the framework of canonical type-I seesaw, a feebly interacting massive
particle (FIMP) $\chi$ is introduced as a dark matter candidate. The
leptogenesis mechanism and dark matter relic density share a common origin via
decays of Majorana neutrinos $N$. Provided an additional species $\varphi$
whose energy density red-shifts as $\rho_{\varphi}\propto a^{-(4+n)}$, the
Hubble expansion rate is larger than the standard scenario, i.e., the Universe
expands faster. The consequences of such a fast expanding Universe on
leptogenesis as well as FIMP dark matter are investigated in detail. We
demonstrate a significant impact on the final baryon asymmetry and dark matter
abundance due to the existence of $\varphi$ for the strong washout scenario.
While for the weak washout scenario, the effects of FEU are relatively small.
We introduce scale factors $F_L$ and $F_\chi$ to describe the corresponding
effects of FEU. A semi-analytical approach to derive the efficiency factors
$\eta_L$ and $\eta_\chi$ in FEU is also discussed. The viable parameter space
for success thermal leptogenesis and correct FIMP DM relic density is obtained
for standard cosmology and FEU. Our results show that it is possible to
distinguish different cosmology scenarios for strong washout cases.


Abstract:
  In this paper, we study important Schr\"{o}dinger systems with linear and
nonlinear couplings \begin{equation}\label{eq:diricichlet} \begin{cases}
-\Delta u_1-\lambda_1 u_1=\mu_1 |u_1|^{p_1-2}u_1+r_1\beta
|u_1|^{r_1-2}u_1|u_2|^{r_2}+\kappa (x)u_2~\hbox{in}~\mathbb{R}^N,\\ -\Delta
u_2-\lambda_2 u_2=\mu_2 |u_2|^{p_2-2}u_2+r_2\beta
|u_1|^{r_1}|u_2|^{r_2-2}u_2+\kappa (x)u_1~ \hbox{in}~\mathbb{R}^N,\\ u_1\in
H^1(\mathbb{R}^N), u_2\in H^1(\mathbb{R}^N),\nonumber \end{cases}
\end{equation} with the condition $$\int_{\mathbb{R}^N} u_1^2=a_1^2,
\int_{\mathbb{R}^N} u_2^2=a_2^2,$$ where $N\geq 2$, $\mu_1,\mu_2,a_1,a_2>0$,
$\beta\in\mathbb{R}$, $2<p_1,p_2<2^*$, $2<r_1+r_2<2^*$, $\kappa(x)\in
L^{\infty}(\mathbb{R}^N)$ with fixed sign and $\lambda_1,\lambda_2$ are
Lagrangian multipliers. We use Ekland variational principle to prove this
system has a normalized radially symmetric solution for $L^2-$subcritical case
when $N\geq 2$, and use minimax method to prove this system has a normalized
radially symmetric positive solution for $L^2-$supercritical case when $N=3$,
$p_1=p_2=4,\ r_1=r_2=2$.


Abstract:
  We present radiative hydrodynamic simulations of solar flares generated by
the RADYN and RH codes to study the perturbations induced in photospheric Fe I
lines by electron beam heating. We investigate how variations in the beam
parameters result in discernible differences in the induced photospheric
velocities. Line synthesis revealed a significant chromospheric contribution to
the line profiles resulting in an apparent red asymmetry by as much as 40 m/s
close to the time of maximum beam heating which was not reflective of the
upflow velocities that arose from the radiative hydrodynamic simulations at
those times. The apparent redshift to the overall line profile was produced by
significant chromospheric emission that was blueshifted by as much as 400 m/s
and fills in the blue side of the near stationary photospheric absorption
profile. The velocity information that can be retrieved from photospheric line
profiles during flares must therefore be treated with care to mitigate the
effects of higher parts of the atmosphere providing an erroneous velocity
signal.


Abstract:
  We develop a theory of vector spaces spanned by orbit-finite sets. Using this
theory, we give a decision procedure for equivalence of weighted register
automata, which are the common generalization of weighted automata and register
automata for infinite alphabets. The algorithm runs in exponential time, and in
polynomial time for a fixed number of registers. As a special case, we can
decide, with the same complexity, language equivalence for unambiguous register
automata, which improves previous results in three ways: (a) we allow for order
comparisons on atoms, and not just equality; (b) the complexity is
exponentially better; and (c) we allow automata with guessing.


Abstract:
  In this paper, we present the submitted system for the third DIHARD Speech
Diarization Challenge from the DKU-Duke-Lenovo team. Our system consists of
several modules: voice activity detection (VAD), segmentation, speaker
embedding extraction, attentive similarity scoring, agglomerative hierarchical
clustering. In addition, the target speaker VAD (TSVAD) is used for the phone
call data to further improve the performance. Our final submitted system
achieves a DER of 15.43% for the core evaluation set and 13.39% for the full
evaluation set on task 1, and we also get a DER of 21.63% for core evaluation
set and 18.90% for full evaluation set on task 2.


Abstract:
  The local stability of ion-temperature gradient driven mode (ITG) in the
presence of a given radial electric field is investigated using gyrokinetic
theory and ballooning mode formalism with toroidal effect accounted for. It is
found that, zero frequency radial electric field induced poloidal rotation can
significantly stabilize ITG, while the associated density perturbation has
little effect on ITG stability due to the modification of finite-orbit-width
effect. However, the parallel mode structure is slightly affected due to the
evenly symmetric density modulation of ZFZF.


Abstract:
  Despite the prominence of neural abstractive summarization models, we know
little about how they actually form summaries and how to understand where their
decisions come from. We propose a two-step method to interpret summarization
model decisions. We first analyze the model's behavior by ablating the full
model to categorize each decoder decision into one of several generation modes:
roughly, is the model behaving like a language model, is it relying heavily on
the input, or is it somewhere in between? After isolating decisions that do
depend on the input, we explore interpreting these decisions using several
different attribution methods. We compare these techniques based on their
ability to select content and reconstruct the model's predicted token from
perturbations of the input, thus revealing whether highlighted attributions are
truly important for the generation of the next token. While this machinery can
be broadly useful even beyond summarization, we specifically demonstrate its
capability to identify phrases the summarization model has memorized and
determine where in the training pipeline this memorization happened, as well as
study complex generation phenomena like sentence fusion on a per-instance
basis.


Abstract:
  Over the past decade, many approaches have been introduced for traffic speed
prediction. However, providing fine-grained, accurate, time-efficient, and
adaptive traffic speed prediction for a growing transportation network where
the size of the network keeps increasing and new traffic detectors are
constantly deployed has not been well studied. To address this issue, this
paper presents DistTune based on Long Short-Term Memory (LSTM) and the
Nelder-Mead method. Whenever encountering an unprocessed detector, DistTune
decides if it should customize an LSTM model for this detector by comparing the
detector with other processed detectors in terms of the normalized traffic
speed patterns they have observed. If similarity is found, DistTune directly
shares an existing LSTM model with this detector to achieve time-efficient
processing. Otherwise, DistTune customizes an LSTM model for the detector to
achieve fine-grained prediction. To make DistTune even more time-efficient,
DistTune performs on a cluster of computing nodes in parallel. To achieve
adaptive traffic speed prediction, DistTune also provides LSTM re-customization
for detectors that suffer from unsatisfactory prediction accuracy due to for
instance traffic speed pattern change. Extensive experiments based on traffic
data collected from freeway I5-N in California are conducted to evaluate the
performance of DistTune. The results demonstrate that DistTune provides
fine-grained, accurate, time-efficient, and adaptive traffic speed prediction
for a growing transportation network.


Abstract:
  This paper develops a fractional stochastic partial differential equation
(SPDE) to model the evolution of a random tangent vector field on the unit
sphere. The SPDE is governed by a fractional diffusion operator to model the
L\'{e}vy-type behaviour of the spatial solution, a fractional derivative in
time to depict the intermittency of its temporal solution, and is driven by
vector-valued fractional Brownian motion on the unit sphere to characterize its
temporal long-range dependence. The solution to the SPDE is presented in the
form of the Karhunen-Lo\`{e}ve expansion in terms of vector spherical
harmonics. Its covariance matrix function is established as a tensor field on
the unit sphere that is an expansion of Legendre tensor kernels. The variance
of the increments and approximations to the solutions are studied and
convergence rates of the approximation errors are given. It is demonstrated how
these convergence rates depend on the decay of the power spectrum and variances
of the fractional Brownian motion.


Abstract:
  Since the advent of graphene ushered the era of two-dimensional materials,
many forms of hydrogenated graphene have been reported, exhibiting diverse
properties ranging from a tunable band gap to ferromagnetic ordering. Patterned
hydrogenated graphene with micron-scale patterns has been fabricated by
lithographic means. Here we report successful millimeter-scale synthesis of an
intrinsically honeycomb patterned form of hydrogenated graphene on Ru(0001) by
epitaxial growth followed by hydrogenation. Combining scanning tunneling
microscopy observations with density-functional-theory (DFT) calculations, we
reveal that an atomic-hydrogen layer intercalates between graphene and
Ru(0001). The result is a hydrogen honeycomb structure that serves as a
template for the final hydrogenation, which converts the graphene into graphane
only over the template, yielding honeycomb-patterned hydrogenated graphene
(HPHG). In effect, HPHG is a form of patterned graphane. DFT calculations find
that the unhydrogenated graphene regions embedded in the patterned graphane
exhibit spin-polarized edge states. This type of growth mechanism provides new
pathways for the fabrication of intrinsically patterned graphene-based
materials.


Abstract:
  The interlayer van der Waals interaction in twisted bilayer graphene (tBLG)
induces both in-plane and out-of-plane atomic displacements showing complex
patterns that depend on the twist angle. In particular, for small twist angles,
within each graphene layer, the relaxations give rise to a vortex-like
displacement pattern which is known to affect the dispersion of the flat bands.
Here, we focus on yet another structural property, the chirality of the twisted
bilayer. We perform first-principles calculations based on density functional
theory to investigate the properties induced by twist chirality in both real
and momentum space. In real space, we study the interplay between twist
chirality and atomic relaxation patterns. In momentum space, we investigate the
spin textures around the $K$ points of the Brillouin zone, showing that
alternating vortex-like textures are correlated with the chirality of tBLG.
Interestingly, the helicity of each vortex is inverted by changing the
chirality while the different twist angles also modify the spin textures. We
discuss the origin of the spin textures by calculating the layer weights and
using plot regression models.


Abstract:
  Super-resolution (SR) is a coveted image processing technique for mobile apps
ranging from the basic camera apps to mobile health. Existing SR algorithms
rely on deep learning models with significant memory requirements, so they have
yet to be deployed on mobile devices and instead operate in the cloud to
achieve feasible inference time. This shortcoming prevents existing SR methods
from being used in applications that require near real-time latency. In this
work, we demonstrate state-of-the-art latency and accuracy for on-device
super-resolution using a novel hybrid architecture called SplitSR and a novel
lightweight residual block called SplitSRBlock. The SplitSRBlock supports
channel-splitting, allowing the residual blocks to retain spatial information
while reducing the computation in the channel dimension. SplitSR has a hybrid
design consisting of standard convolutional blocks and lightweight residual
blocks, allowing people to tune SplitSR for their computational budget. We
evaluate our system on a low-end ARM CPU, demonstrating both higher accuracy
and up to 5 times faster inference than previous approaches. We then deploy our
model onto a smartphone in an app called ZoomSR to demonstrate the first-ever
instance of on-device, deep learning-based SR. We conducted a user study with
15 participants to have them assess the perceived quality of images that were
post-processed by SplitSR. Relative to bilinear interpolation -- the existing
standard for on-device SR -- participants showed a statistically significant
preference when looking at both images (Z=-9.270, p<0.01) and text (Z=-6.486,
p<0.01).


Abstract:
  We present GeoSP, a parallel method that creates a parcellation of the
cortical mesh based on a geodesic distance, in order to consider gyri and sulci
topology. The method represents the mesh with a graph and performs a K-means
clustering in parallel. It has two modes of use, by default, it performs the
geodesic cortical parcellation based on the boundaries of the anatomical
parcels provided by the Desikan-Killiany atlas. The other mode performs the
complete parcellation of the cortex. Results for both modes and with different
values for the total number of sub-parcels show homogeneous sub-parcels.
Furthermore, the execution time is 82 s for the whole cortex mode and 18 s for
the Desikan-Killiany atlas subdivision, for a parcellation into 350
sub-parcels. The proposed method will be available to the community to perform
the evaluation of data-driven cortical parcellations. As an example, we
compared GeoSP parcellation with Desikan-Killiany and Destrieux atlases in 50
subjects, obtaining more homogeneous parcels for GeoSP and minor differences in
structural connectivity reproducibility across subjects.


Abstract:
  Neural network classifiers are vulnerable to misclassification of adversarial
samples, for which the current best defense trains classifiers with adversarial
samples. However, adversarial samples are not optimal for steering attack
convergence, based on the minimization at the core of adversarial attacks. The
minimization perturbation term can be minimized towards $0$ by replacing
adversarial samples in training with duplicated original samples, labeled
differently only for training. Using only original samples, Target Training
eliminates the need to generate adversarial samples for training against all
attacks that minimize perturbation. In low-capacity classifiers and without
using adversarial samples, Target Training exceeds both default CIFAR10
accuracy ($84.3$%) and current best defense accuracy (below $25$%) with $84.8$%
against CW-L$_2$($\kappa=0$) attack, and $86.6$% against DeepFool. Using
adversarial samples against attacks that do not minimize perturbation, Target
Training exceeds current best defense ($69.1$%) with $76.4$% against
CW-L$_2$($\kappa=40$) in CIFAR10.


Abstract:
  Entity linking -- the task of identifying references in free text to relevant
knowledge base representations -- often focuses on single languages. We
consider multilingual entity linking, where a single model is trained to link
references to same-language knowledge bases in several languages. We propose a
neural ranker architecture, which leverages multilingual transformer
representations of text to be easily applied to a multilingual setting. We then
explore how a neural ranker trained in one language (e.g. English) transfers to
an unseen language (e.g. Chinese), and find that while there is a consistent
but not large drop in performance. How can this drop in performance be
alleviated? We explore adding an adversarial objective to force our model to
learn language-invariant representations. We find that using this approach
improves recall in several datasets, often matching the in-language
performance, thus alleviating some of the performance loss occurring from
zero-shot transfer.


Abstract:
  We analyze Hegselmann-Krause opinion formation models with leadership in
presence of time delay effects. In particular, we consider a model with
pointwise time variable time delay and a model with a distributed delay. In
both cases we show that, when the delays satisfy suitable smallness conditions,
then the leader can control the system, leading the group to any prefixed
state. Some numerical tests illustrate our analytical results.


Abstract:
  In this work we prove the uniqueness of solutions to the nonlocal linear
equation $L \varphi - c(x)\varphi = 0$ in $\mathbb{R}$, where $L$ is an
elliptic integro-differential operator, in the presence of a positive solution
or of an odd solution vanishing only at zero. As an application, we deduce the
nondegeneracy of layer solutions (bounded and monotone solutions) to the
semilinear problem $L u = f(u)$ in $\mathbb{R}$ when the nonlinearity is of
Allen-Cahn type. To our knowledge, this is the first work where such uniqueness
and nondegeneracy results are proven in the nonlocal framework when the
Caffarelli-Silvestre extension technique is not available. Our proofs are based
on a nonlocal Liouville-type method developed by Hamel, Ros-Oton, Sire, and
Valdinoci for nonlinear problems in dimension two.


Abstract:
  Predicting the final folded structure of protein molecules and simulating
their folding pathways is of crucial importance for designing viral drugs and
studying diseases such as Alzheimer's at the molecular level. To this end, this
paper investigates the problem of protein conformation prediction under the
constraint of avoiding high-entropy-loss routes during folding. Using the
well-established kinetostatic compliance (KCM)-based nonlinear dynamics of a
protein molecule, this paper formulates the protein conformation prediction as
a pointwise optimal control synthesis problem cast as a quadratic program (QP).
It is shown that the KCM torques in the protein folding literature can be
utilized for defining a reference vector field for the QP-based control
generation problem. The resulting kinetostatic control torque inputs will be
close to the KCM-based reference vector field and guaranteed to be constrained
by a predetermined bound; hence, high-entropy-loss routes during folding are
avoided while the energy of the molecule is decreased.


Abstract:
  In a recent Letter [T.~Dornheim \emph{et al.}, Phys.~Rev.~Lett.~\textbf{125},
085001 (2020)], we have presented the first \emph{ab initio} results for the
nonlinear density response of electrons in the warm dense matter regime. In the
present work, we extend these efforts by carrying out extensive new path
integral Monte Carlo (PIMC) simulations of a \emph{ferromagnetic} electron gas
that is subject to an external harmonic perturbation. This allows us to
unambiguously quantify the impact of spin-effects on the nonlinear density
response of the warm dense electron gas. In addition to their utility for the
description of warm dense matter in an external magnetic field, our results
further advance our current understanding of the uniform electron gas as a
fundamental model system, which is important in its own right.


Abstract:
  This article presents a method that uses turn-by-turn beam position data and
k-modulation data to measure the calibration factors of beam position monitors
in high energy accelerators. In this method, new algorithms have been developed
to reduce the effect of coupling and other sources of uncertainty, allowing
accurate estimates of the calibration factors. Simulations with known sources
of errors indicate that calibration factors can be recovered with an accuracy
of 0.7% rms for arc beam position monitors and an accuracy of 0.4% rms for
interaction region beam position monitors. The calibration factors are also
obtained from LHC experimental data and are used to evaluate the effect this
calibration has on a quadrupole correction estimated with the action and phase
jump method for a interaction region of the LHC.


Abstract:
  Point cloud registration is a common step in many 3D computer vision tasks
such as object pose estimation, where a 3D model is aligned to an observation.
Classical registration methods generalize well to novel domains but fail when
given a noisy observation or a bad initialization. Learning-based methods, in
contrast, are more robust but lack in generalization capacity. We propose to
consider iterative point cloud registration as a reinforcement learning task
and, to this end, present a novel registration agent (ReAgent). We employ
imitation learning to initialize its discrete registration policy based on a
steady expert policy. Integration with policy optimization, based on our
proposed alignment reward, further improves the agent's registration
performance. We compare our approach to classical and learning-based
registration methods on both ModelNet40 (synthetic) and ScanObjectNN (real
data) and show that our ReAgent achieves state-of-the-art accuracy. The
lightweight architecture of the agent, moreover, enables reduced inference time
as compared to related approaches. In addition, we apply our method to the
object pose estimation task on real data (LINEMOD), outperforming
state-of-the-art pose refinement approaches.


Abstract:
  We construct non-Abelian analogs for some KdV type equations, including the
(rational form of) exponential Calogero--Degasperis equation and
generalizations of the Schwarzian KdV equation. Equations and differential
substitutions under study contain arbitrary non-Abelian parameters.


Abstract:
  1I/'Oumuamua (or 1I) and 2I/Borisov (or 2I), the first InterStellar Objects
(ISOs) discovered passing through the solar system, have opened up entirely new
areas of exobody research. Finding additional ISOs and planning missions to
intercept or rendezvous with these bodies will greatly benefit from knowledge
of their likely orbits and arrival rates. Here, we use the local velocity
distribution of stars from the Gaia Early Data Release 3 Catalogue of Nearby
Stars and a standard gravitational focusing model to predict the velocity
dependent flux of ISOs entering the solar system. With an 1I-type ISO number
density of $\sim$0.1 AU$^{-3}$, we predict that a total of $\sim$6.9 such
objects per year should pass within 1 AU of the Sun. There will be a fairly
large high-velocity tail to this flux, with half of the incoming ISOs predicted
to have a velocity at infinity, v$_{\infty}$, $>$ 40 km s$^{-1}$. Our model
predicts that $\sim$92\% of incoming ISOs will be residents of the galactic
thin disk, $\sim$6\% ($\sim$4 per decade) will be from the thick disk, $\sim$1
per decade will be from the halo and at most $\sim$3 per century will be
unbound objects, ejected from our galaxy or entering the Milky Way from another
galaxy. The rate of ISOs with very low v$_{\infty}$ $\lesssim$ 1.5 km s$^{-1}$
is so low in our model that any incoming very low velocity ISOs are likely to
be previously lost solar system objects. Finally, we estimate a cometary ISO
number density of $\sim$7 $\times$ 10$^{-5}$ AU$^{-3}$ for 2I type ISOs,
leading to discovery rates for these objects possibly approaching once per
decade with future telescopic surveys.


Abstract:
  In game-theoretic learning, several agents are simultaneously following their
individual interests, so the environment is non-stationary from each player's
perspective. In this context, the performance of a learning algorithm is often
measured by its regret. However, no-regret algorithms are not created equal in
terms of game-theoretic guarantees: depending on how they are tuned, some of
them may drive the system to an equilibrium, while others could produce cyclic,
chaotic, or otherwise divergent trajectories. To account for this, we propose a
range of no-regret policies based on optimistic mirror descent, with the
following desirable properties: i) they do not require any prior tuning or
knowledge of the game; ii) they all achieve O(\sqrt{T}) regret against
arbitrary, adversarial opponents; and iii) they converge to the best response
against convergent opponents. Also, if employed by all players, then iv) they
guarantee O(1) social regret; while v) the induced sequence of play converges
to Nash equilibrium with O(1) individual regret in all variationally stable
games (a class of games that includes all monotone and convex-concave zero-sum
games).


Abstract:
  Intelligent robots provide a new insight into efficiency improvement in
industrial and service scenarios to replace human labor. However, these
scenarios include dense and dynamic obstacles that make motion planning of
robots challenging. Traditional algorithms like A* can plan collision-free
trajectories in static environment, but their performance degrades and
computational cost increases steeply in dense and dynamic scenarios.
Optimal-value reinforcement learning algorithms (RL) can address these problems
but suffer slow speed and instability in network convergence. Network of policy
gradient RL converge fast in Atari games where action is discrete and finite,
but few works have been done to address problems where continuous actions and
large action space are required. In this paper, we modify existing advantage
actor-critic algorithm and suit it to complex motion planning, therefore
optimal speeds and directions of robot are generated. Experimental results
demonstrate that our algorithm converges faster and stable than optimal-value
RL. It achieves higher success rate in motion planning with lesser processing
time for robot to reach its goal.


Abstract:
  Thermodynamic uncertainty relation (TUR) provides a stricter bound for
entropy production (EP) than that of the thermodynamic second law. This
stricter bound can be utilized to infer the EP and derive other trade-off
relations. Though the validity of the TUR has been verified in various
stochastic systems, its application to general Langevin dynamics has not been
successful in a unified way, especially for underdamped Langevin dynamics,
where odd parity variables in time-reversal operation such as velocity get
involved. Previous TURs for underdamped Langevin dynamics is neither
experimentally accessible nor reduced to the original form of the overdamped
Langevin dynamics in the zero-mass limit. Here, we find an operationally
accessible TUR for underdamped Langevin dynamics with an arbitrary
time-dependent protocol. We show that the original TUR is a consequence of our
underdamped TUR in the zero-mass limit. This indicates that the TUR formulation
presented here can be regarded as the universal form of the TUR for general
Langevin dynamics. The validity of our result is examined and confirmed for
three prototypical underdamped Langevin systems and their zero-mass limits;
free diffusion dynamics, charged Brownian particle in a magnetic field, and
molecular refrigerator.


Abstract:
  The use of Bayesian information criterion (BIC) in the model selection
procedure is under the assumption that the observations are independent and
identically distributed (i.i.d.). However, in practice, we do not always have
i.i.d. samples. For example, clustered observations tend to be more similar
within the same group, and longitudinal data is collected by measuring the same
subject repeatedly. In these scenarios, the assumption in BIC is not satisfied.
The concept of effective sample size is brought up and improved BIC is defined
by replacing the sample size in the original BIC expression with the effective
sample size, which will give us a better theoretical foundation in the
circumstance that mixed-effects models involve. Numerical experiment results
are also given by comparing the performance of our new BIC with other widely
used BICs.


Abstract:
  Based on relative energy estimates, we study the stability of solutions to
the Cahn-Hilliard equation with concentration dependent mobility with respect
to perturbations. As a by-product of our analysis, we obtain a weak-strong
uniqueness principle on the continuous level under realistic regularity
assumptions on strong solutions. We then show that the stability estimates can
be further inherited almost verbatim by appropriate Galerkin approximations in
space and time. This allows us to derive sharp bounds for the discretization
error in terms of certain projection errors and to establish order-optimal
a-priori error estimates for semi- and fully discrete approximation schemes.


Abstract:
  Performing imperfect or noisy measurements on a quantum system both impacts
the measurement outcome and the state of the system after the measurement. In
this paper we are concerned with imperfect calorimetric measurements. In
calorimetric measurements one typically measures the energy of a thermal
environment to extract information about the system. The measurement is
imperfect in the sense that we simultaneously measure the energy of the
calorimeter and an additional noise bath. Under weak coupling assumptions, we
find that the presence of the noise bath manifests itself by modifying the jump
rates of the reduced system dynamics. We study an example of a driven qubit
interacting with resonant bosons calorimeter and find increasing the noise
leads to a reduction in the power flowing from qubit to calorimeter and thus an
apparent heating up of the calorimeter.


Abstract:
  Let $\mathcal{G}$ be a connected reductive almost simple group over the Witt
ring $W(\mathbb{F})$ for $\mathbb{F}$ a finite field of characteristic $p$. Let
$R$ and $R'$ be complete noetherian local $W(\mathbb{F})$ -algebras with
residue field $\mathbb{F}$. Under a mild condition on $p$ in relation to
structural constants of $\mathcal{G}$, we show the following results: (1) Every
closed subgroup $H$ of $\mathcal{G}(R)$ with full residual image
$\mathcal{G}(\mathbb{F})$ is a conjugate of a group $\mathcal{G}(A)$ for
$A\subset R$ a closed subring that is local and has residue field $\mathbb{F}$
. (2) Every surjective homomorphism $\mathcal{G}(R)\to\mathcal{G}(R')$ is, up
to conjugation, induced from a ring homomorphism $R\to R'$. (3) The identity
map on $\mathcal{G}(R)$ represents the universal deformation of the
representation of the profinite group $\mathcal{G}(R)$ given by the reduction
map $\mathcal{G}(R)\to\mathcal{G}(\mathbb{F})$. This generalizes results of
Dorobisz and Eardley-Manoharmayum and of Manoharmayum, and in addition provides
an abstract classification result for closed subgroups of $\mathcal{G}(R)$ with
residually full image.
  We provide an axiomatic framework to study this type of question, also for
slightly more general $\mathcal{G}$, and we study in the case at hand in great
detail what conditions on $\mathbb{F}$ or on $p$ in relation to $\mathcal{G}$
are necessary for the above results to hold.


Abstract:
  Optical isolators are indispensible components in nearly all photonic systems
as they help ensure unidirectionality and provide crucial protection from
undesirable reflections. While commercial isolators are exclusively built on
magneto-optic (MO) principles they are not readily implemented within photonic
integrated circuits due to the need for specialized materials. Importantly, the
MO effect is generally weak, especially at shorter wavelengths. These
challenges as a whole have motivated extensive research on non-MO alternatives.
To date, however, no alternative technology has managed to simultaneously
combine linearity (i.e. no frequency shift), linear response (i.e. input-output
scaling), ultralow insertion loss, and large directional contrast on-chip. Here
we demonstrate an optical isolator design that leverages the unbeatable
transparency of a short, high quality dielectric waveguide, with the
near-perfect attenuation from a critically-coupled absorber. Our design concept
is implemented using a lithium niobate racetrack resonator in which phonon
mediated Autler-Townes splitting (ATS) breaks the chiral symmetry of the
resonant modes. We demonstrate on-chip optical isolators at wavelengths one
octave apart near 1550 nm and 780 nm, fabricated from the same lithium
niobate-on-insulator wafer. Linear optical isolation is demonstrated with
simultaneously <1 dB insertion loss, >39 dB contrast, and bandwidth as wide as
the optical mode that is used. Our results outperform the current best-in-class
MO isolator on-chip on both insertion loss and isolator figures-of-merit, and
demonstrate a lithographically defined wavelength adaptability that cannot yet
be achieved with any MO isolator.


Abstract:
  The High Energy Rapid Modular Ensemble of Satellites (HERMES) Technological
and Scientific pathfinder is a space borne mission based on a constellation of
LEO nanosatellites. The payloads of these CubeSats consist of miniaturized
detectors designed for bright high-energy transients such as Gamma-Ray Bursts
(GRBs). This platform aims to impact Gamma Ray Burst (GRB) science and enhance
the detection of Gravitational Wave (GW) electromagnetic counterparts. This
goal will be achieved with a field of view of several steradians, arcmin
precision and state of the art timing accuracy. The localization performance
for the whole constellation is proportional to the number of components and
inversely proportional to the average baseline between them, and therefore is
expected to increase as more. In this paper we describe the Payload Data
Handling Unit (PDHU) for the HERMES-TP and HERMES SP mission. The PDHU is the
main interface between the payload and the satellite bus. The PDHU is also in
charge of the on-board control and monitoring of the scintillating crystal
detectors. We will explain the TM/TC design and the distinct modes of
operation. We also discuss the on-board data processing carried out by the PDHU
and its impact on the output data of the detector.


Abstract:
  For an inverse temperature $\beta>0$, we define the $\beta$-circular Riesz
gas on $\mathbb{R}^d$ as any microscopic thermodynamic limit of Gibbs particle
systems on the torus interacting via the Riesz potential $g(x) = \Vert x
\Vert^{-s}$. We focus on the non integrable case $d-1<s<d$. Our main result
ensures, for any dimension $d\ge 1$ and inverse temperature $\beta>0$, the
existence of a $\beta$-circular Riesz gas which is not number-rigid. Recall
that a point process is said number rigid if the number of points in a bounded
Borel set $\Delta$ is a function of the point configuration outside $\Delta$.
It is the first time that the non number-rigidity is proved for a Gibbs point
process interacting via a non integrable potential. We follow a statistical
physics approach based on the canonical DLR equations. It is inspired by
Dereudre-Hardy-Lebl\'e and Ma\"ida (2021) where the authors prove the
number-rigidity of the $\text{Sine}_\beta$ process.


Abstract:
  We consider Morrey's open question whether rank-one convexity already implies
quasiconvexity in the planar case. For some specific families of energies,
there are precise conditions known under which rank-one convexity even implies
polyconvexity. We will extend some of these findings to the more general family
of energies $W:\operatorname{GL}^+(n)\rightarrow\mathbb{R}$ with an additive
volumetric-isochoric split, i.e. \[
  W(F)=W_{\rm iso}(F)+W_{\rm vol}(\det F)=\widetilde W_{\rm
iso}\bigg(\frac{F}{\sqrt{\det F}}\bigg)+W_{\rm vol}(\det F)\,, \] which is the
natural finite extension of isotropic linear elasticity. Our approach is based
on a condition for rank-one convexity which was recently derived from the
classical two-dimensional criterion by Knowles and Sternberg and consists of a
family of one-dimensional coupled differential inequalities. We identify a
number of \enquote{least} rank-one convex energies and, in particular, show
that for planar volumetric-isochorically split energies with a concave
volumetric part, the question of whether rank-one convexity implies
quasiconvexity can be reduced to the open question of whether the rank-one
convex energy function \[
  W_{\rm magic}^+(F)=\frac{\lambda_{\rm max}}{\lambda_{\rm
min}}-\log\frac{\lambda_{\rm max}}{\lambda_{\rm min}}+\log\det
F=\frac{\lambda_{\rm max}}{\lambda_{\rm min}}-2\log\lambda_{\rm min} \] is
quasiconvex. In addition, we demonstrate that under affine boundary conditions,
$W_{\rm magic}^+(F)$ allows for non-trivial inhomogeneous deformations with the
same energy level as the homogeneous solution, and show a surprising connection
to the work of Burkholder and Iwaniec in the field of complex analysis.


Abstract:
  The Uhlmann process is built on the density matrix of a mixed quantum state
and offers a way to characterize topological properties at finite temperatures.
We analyze an ideal spin-j quantum paramagnet in a magnetic field undergoing an
Uhlmann process and derive general formulae of the Uhlmann phase and Loschmidt
amplitude for arbitrary j as the system traverses a great circle in the
parameter space. A quantized jump of the Uhlmann phase signifies a topological
quantum phase transition (TQPT) of the underlying process, which is accompanied
by a zero of the Loschmidt amplitude. The exact results of j=1/2 and j=1
systems show topological regimes that only survive at finite temperatures but
not at zero temperature, and the number of TQPTs is associated with the winding
number in the parameter space. Our results pave the way for future studies on
finite-temperature topological properties, and possible experimental protocols
and implications for atomic simulators and digital simulations are discussed.


Abstract:
  Processing astronomical data often comes with huge challenges with regards to
data management as well as data processing. MeerKAT telescope is one of the
precursor telescopes of the World's largest observatory - Square Kilometre
Array. So far, MeerKAT data was processed using the South African computing
facility i.e. IDIA, and exploited to make ground-breaking discoveries. However,
to process MeerKAT data on UK's IRIS computing facility requires new
implementation of the MeerKAT pipeline. This paper focuses on how to transfer
MeerKAT data from the South African site to UK's IRIS systems for processing.
We discuss about our RapifXfer Data transfer framework for transferring the
MeerKAT data from South Africa to the UK, and the MeerKAT job processing
framework pertaining to the UK's IRIS resources.


Abstract:
  We study analytic properties of "$q$-deformed real numbers", a notion
recently introduced by two of us. A $q$-deformed positive real number is a
power series with integer coefficients in one formal variable~$q$. We study the
radius of convergence of these power series assuming that $q \in \C.$ Our main
conjecture, which can be viewed as a $q$-analogue of Hurwitz's Irrational
Number Theorem, provides a lower bound for these radii, given by the radius of
convergence of the $q$-deformed golden ratio. The conjecture is proved in
several particular cases and confirmed by a number of computer experiments. For
an interesting sequence of "Pell polynomials", we obtain stronger bounds.


Abstract:
  Abstractive summarization, the task of generating a concise summary of input
documents, requires: (1) reasoning over the source document to determine the
salient pieces of information scattered across the long document, and (2)
composing a cohesive text by reconstructing these salient facts into a shorter
summary that faithfully reflects the complex relations connecting these facts.
In this paper, we adapt TP-TRANSFORMER (Schlag et al., 2019), an architecture
that enriches the original Transformer (Vaswani et al., 2017) with the
explicitly compositional Tensor Product Representation (TPR), for the task of
abstractive summarization. The key feature of our model is a structural bias
that we introduce by encoding two separate representations for each token to
represent the syntactic structure (with role vectors) and semantic content
(with filler vectors) separately. The model then binds the role and filler
vectors into the TPR as the layer output. We argue that the structured
intermediate representations enable the model to take better control of the
contents (salient facts) and structures (the syntax that connects the facts)
when generating the summary. Empirically, we show that our TP-TRANSFORMER
outperforms the Transformer and the original TP-TRANSFORMER significantly on
several abstractive summarization datasets based on both automatic and human
evaluations. On several syntactic and semantic probing tasks, we demonstrate
the emergent structural information in the role vectors and improved syntactic
interpretability in the TPR layer outputs. Code and models are available at
https://github.com/jiangycTarheel/TPT-Summ.


Abstract:
  We generalize Mertens' product theorem to Chebotarev sets of prime ideals in
Galois extensions of number fields. Using work of Rosen, we extend an argument
of Williams from cyclotomic extensions to this more general case. Additionally,
we compute these products for Cheboratev sets in abelian extensions, $S_3$
sextic extensions, and sets of primes represented by some quadratic forms.


Abstract:
  The problem of quantifying uncertainty about the locations of multiple change
points by means of confidence intervals is addressed. The asymptotic
distribution of the change point estimators obtained as the local maximisers of
moving sum statistics is derived, where the limit distributions differ
depending on whether the corresponding size of changes is local, i.e. tends to
zero as the sample size increases, or fixed. A bootstrap procedure for
confidence interval generation is proposed which adapts to the unknown
magnitude of changes and guarantees asymptotic validity both for local and
fixed changes. Simulation studies show good performance of the proposed
bootstrap procedure, and some discussions about how it can be extended to
serially dependent errors is provided.


Abstract:
  We propose a nonlinear acoustic echo cancellation system, which aims to model
the echo path from the far-end signal to the near-end microphone in two parts.
Inspired by the physical behavior of modern hands-free devices, we first
introduce a novel neural network architecture that is specifically designed to
model the nonlinear distortions these devices induce between receiving and
playing the far-end signal. To account for variations between devices, we
construct this network with trainable memory length and nonlinear activation
functions that are not parameterized in advance, but are rather optimized
during the training stage using the training data. Second, the network is
succeeded by a standard adaptive linear filter that constantly tracks the echo
path between the loudspeaker output and the microphone. During training, the
network and filter are jointly optimized to learn the network parameters. This
system requires 17 thousand parameters that consume 500 Million floating-point
operations per second and 40 Kilo-bytes of memory. It also satisfies hands-free
communication timing requirements on a standard neural processor, which renders
it adequate for embedding on hands-free communication devices. Using 280 hours
of real and synthetic data, experiments show advantageous performance compared
to competing methods.


Abstract:
  We investigate a possibility to describe the non-Debye relaxation processes
using the Volterra-type equations with kernels given by the Prabhakar functions
with the upper parameter $\nu$ being negative. Proposed integro-differential
equations mimic the fading memory effects and are explicitly solved using the
umbral calculus and the Laplace transform methods. Both approaches lead to the
same results valid for admissible domain of the parameters $\alpha$, $\mu$ and
$\nu$ characterizing the Prabhakar function. For the special case $\alpha\in
(0,1]$, $\mu=0$ and $\nu=-1$ we recover the Cole-Cole model, in general having
a residual polarization. We also show that our scheme gives results equivalent
to those obtained using the stochastic approach to relaxation phenomena merged
with integral equations involving kernels given by the Prabhakar functions with
the positive upper parameter.


Abstract:
  Billions of X-ray images are taken worldwide each year. Machine learning, and
deep learning in particular, has shown potential to help radiologists triage
and diagnose images. However, deep learning requires large datasets with
reliable labels. The CheXpert dataset was created with the participation of
board-certified radiologists, resulting in the strong ground truth needed to
train deep learning networks. Following the structured format of Datasheets for
Datasets, this paper expands on the original CheXpert paper and other sources
to show the critical role played by radiologists in the creation of reliable
labels and to describe the different aspects of the dataset composition in
detail. Such structured documentation intends to increase the awareness in the
machine learning and medical communities of the strengths, applications, and
evolution of CheXpert, thereby advancing the field of medical image analysis.
Another objective of this paper is to put forward this dataset datasheet as an
example to the community of how to create detailed and structured descriptions
of datasets. We believe that clearly documenting the creation process, the
contents, and applications of datasets accelerates the creation of useful and
reliable models.


Abstract:
  Knowledge distillation aims to transfer representation ability from a teacher
model to a student model. Previous approaches focus on either individual
representation distillation or inter-sample similarity preservation. While we
argue that the inter-sample relation conveys abundant information and needs to
be distilled in a more effective way. In this paper, we propose a novel
knowledge distillation method, namely Complementary Relation Contrastive
Distillation (CRCD), to transfer the structural knowledge from the teacher to
the student. Specifically, we estimate the mutual relation in an anchor-based
way and distill the anchor-student relation under the supervision of its
corresponding anchor-teacher relation. To make it more robust, mutual relations
are modeled by two complementary elements: the feature and its gradient.
Furthermore, the low bound of mutual information between the anchor-teacher
relation distribution and the anchor-student relation distribution is maximized
via relation contrastive loss, which can distill both the sample representation
and the inter-sample relations. Experiments on different benchmarks demonstrate
the effectiveness of our proposed CRCD.


Abstract:
  Error-bounded lossy compression is a critical technique for significantly
reducing scientific data volumes. With ever-emerging heterogeneous
high-performance computing (HPC) architecture, GPU-accelerated error-bounded
compressors (such as cuSZ+ and cuZFP) have been developed. However, they suffer
from either low performance or low compression ratios. To this end, we propose
cuSZ+ to target both high compression ratios and throughputs. We identify that
data sparsity and data smoothness are key factors for high compression
throughputs. Our key contributions in this work are fourfold: (1) We propose an
efficient compression workflow to adaptively perform run-length encoding and/or
variable-length encoding. (2) We derive Lorenzo reconstruction in decompression
as multidimensional partial-sum computation and propose a fine-grained Lorenzo
reconstruction algorithm for GPU architectures. (3) We carefully optimize each
of cuSZ+ kernels by leveraging state-of-the-art CUDA parallel primitives. (4)
We evaluate cuSZ+ using seven real-world HPC application datasets on V100 and
A100 GPUs. Experiments show cuSZ+ improves the compression throughputs and
ratios by up to 18.4X and 5.3X, respectively, over cuSZ on the tested datasets.


Abstract:
  In the high energy limit of hadron collisions, the evolution of the gluon
density in the longitudinal momentum fraction can be deduced from the Balitsky
hierarchy of equations or, equivalently, from the nonlinear
Jalilian-Marian-Iancu-McLerran-Weigert-Leonidov-Kovner (JIMWLK) equation. The
solutions of the latter can be studied numerically by using its reformulation
in terms of a Langevin equation. In this paper, we present a comprehensive
study of systematic effects associated with the numerical framework, in
particular the ones related to the inclusion of the running coupling. We
consider three proposed ways in which the running of the coupling constant can
be included: "square root" and "noise" prescriptions and the recent proposal by
Hatta and Iancu. We implement them both in position and momentum spaces and we
investigate and quantify the differences in the resulting evolved gluon
distributions. We find that the systematic differences associated with the
implementation technicalities can be of a similar magnitude as differences in
running coupling prescriptions in some cases, or much smaller in other cases.


Abstract:
  We prove a formula for the polar degree of projective hypersurfaces in terms
of the Milnor data of the singularities, extending to 1-dimensional
singularities the Dimca-Papadima result for isolated singularities. We discuss
the semi-continuity of the polar degree in deformations, and we classify the
homaloidal cubic surfaces with 1-dimensional singular locus. Some open
questions are pointed out along the way.


Abstract:
  Postive semidefinite (PSD) cone is the cone of positive semidefinite
matrices, and is the object of interest in semidefinite programming (SDP). A
computational efficient approximation of the PSD cone is the $k$-PSD closure,
$1 \leq k < n$, cone of $n\times n$ real symmetric matrices such that all of
their $k\times k$ principal submatrices are positive semidefinite. For $k=1$,
one obtains a polyhedral approximation, while $k=2$ yields a second order conic
(SOC) approximation of the PSD cone. These approximations of the PSD cone have
been used extensively in real-world applications such as AC Optimal Power Flow
(ACOPF) to address computational inefficiencies where SDP relaxations are
utilized for convexification the non-convexities. In a recent series of
articles Blekharman et al. provided bounds on the quality of these
approximations. In this work, we revisit some of their results and also propose
a new dominant bound on quality of the $k$-PSD closure approximation of the PSD
cone. In addition, we characterize the extreme rays of the $2$-PSD closure.


Abstract:
  Despite many of the most common chaotic dynamical systems being continuous in
time, it is through discrete time mappings that much of the understanding of
chaos is formed. Henri Poincar\'e first made this connection by tracking
consecutive iterations of the continuous flow with a lower-dimensional,
transverse subspace. The mapping that iterates the dynamics through consecutive
intersections of the flow with the subspace is now referred to as a Poincar\'e
map, and it is the primary method available for interpreting and classifying
chaotic dynamics. Unfortunately, in all but the simplest systems, an explicit
form for such a mapping remains outstanding. This work proposes a method for
obtaining explicit Poincar\'e mappings by using deep learning to construct an
invertible coordinate transformation into a conjugate representation where the
dynamics are governed by a relatively simple chaotic mapping. The invertible
change of variable is based on an autoencoder, which allows for dimensionality
reduction, and has the advantage of classifying chaotic systems using the
equivalence relation of topological conjugacies. Indeed, the enforcement of
topological conjugacies is the critical neural network regularization for
learning the coordinate and dynamics pairing. We provide expository
applications of the method to low-dimensional systems such as the R\"ossler and
Lorenz systems, while also demonstrating the utility of the method on
infinite-dimensional systems, such as the Kuramoto--Sivashinsky equation.


Abstract:
  Multimodal generative models should be able to learn a meaningful latent
representation that enables a coherent joint generation of all modalities
(e.g., images and text). Many applications also require the ability to
accurately sample modalities conditioned on observations of a subset of the
modalities. Often not all modalities may be observed for all training data
points, so semi-supervised learning should be possible. In this study, we
propose a novel product-of-experts (PoE) based variational autoencoder that
have these desired properties. We benchmark it against a mixture-of-experts
(MoE) approach and an approach of combining the modalities with an additional
encoder network. An empirical evaluation shows that the PoE based models can
outperform the contrasted models. Our experiments support the intuition that
PoE models are more suited for a conjunctive combination of modalities.


Abstract:
  We explore the interplay of New Physics (NP) effects in $(g-2)_\ell$ and $h
\to \ell^+ \ell^-$ within the Standard Model Effective Field Theory (SMEFT)
framework, including one-loop Renormalization Group (RG) evolution of the
Wilson coefficients as well as matching to the observables below the
electroweak symmetry breaking scale. We include both the leading dimension six
chirality flipping operators including a Higgs and $SU(2)_L$ gauge bosons as
well as four-fermion scalar and tensor operators, forming a closed operator set
under the SMEFT RG equations. We compare present and future experimental
sensitivity to different representative benchmark scenarios. We also consider
two simple UV completions, a Two Higgs Doublet Model and a single scalar
LeptoQuark extension of the SM, and show how tree level matching to SMEFT
followed by the one-loop RG evolution down to the electroweak scale can
reproduce with high accuracy the $(g-2)_\ell$ and $h \to \ell^+ \ell^-$
contributions obtained by the complete one- and even two-loop calculations in
the full models.


Abstract:
  In this paper, it is established finite active-set identification of an
almost cyclic 2-coordinate descent method for problems with one linear coupling
constraint and simple bounds. First, general active-set identification results
are stated for non-convex objective functions. Then, under convexity and a
quadratic growth condition (satisfied by any strongly convex function),
complexity results on the number of iterations required to identify the active
set are given. In our analysis, a simple Armijo line search is used to compute
the stepsize, thus not requiring exact minimizations or additional information.


Abstract:
  A program is created to compute recursively the Moshinsky brackets. It is
very fast and provides highly accurate results. In the case of the
double-precision computations with a single-processor consumer notebook, the
computing time per bracket at any not small oscillator excitations is on the
scale of 10^{-8} s and the accuracy is very good for the total number of quanta
up to 80. The program is easy to handle.


Abstract:
  Device identification is one way to secure a network of IoT devices, whereby
devices identified as suspicious can subsequently be isolated from a network.
In this study, we present a machine learning-based method, IoTDevID, that
recognizes devices through characteristics of their network packets. As a
result of using a rigorous feature analysis and selection process, our study
offers a generalizable and realistic approach to modelling device behavior,
achieving high predictive accuracy across two public datasets. The model's
underlying feature set is shown to be more predictive than existing feature
sets used for device identification, and is shown to generalize to data unseen
during the feature selection process. Unlike most existing approaches to IoT
device identification, IoTDevID is able to detect devices using non-IP and
low-energy protocols.


Abstract:
  Weakly interacting Bose gas with an impurity is analyzed in one dimension.
The local spectral density is evaluated and an analytic expression valid for
all strengths of the impurity coupling to the background bosons, at all
distances and at all frequencies is obtained using the Bogoliubov-de Gennes
formalism.


Abstract:
  Intense transient electric ({\bf E}) and magnetic ({\bf B}) fields are
produced in the high energy heavy-ion collisions. The electromagnetic fields
produced in such high-energy heavy-ion collisions are proposed to give rise to
a multitude of exciting phenomenon including the Chiral Magnetic Effect. We use
a Monte Carlo (MC) Glauber model to calculate the electric and magnetic fields,
more specifically their scalar product $\bf{E}\cdot\bf{B}$, as a function of
space-time on an event-by-event basis for the Au+Au collisions at
$\sqrt{s_{NN}}=200$ GeV for different centrality classes. We also calculate the
same for the isobars Ruthenium and Zirconium at $\sqrt{s_{NN}}=200$ GeV. In the
QED sector $\bf{E}\cdot\bf{B}$ acts as a source of Chiral Separation Effect,
Chiral Magnetic Wave, etc., which are associated phenomena to the Chiral
Magnetic Effect. We also study the relationships between the electromagnetic
symmetry plane angle defined by $\bf{E}\cdot\bf{B}$ ($\psi_{E.B}$) and the
participant plane angle $\psi_{P}$ defined from the participating nucleons for
the second-fifth order harmonics.


Abstract:
  Results of an analysis of the BRITE-Constellation and SMEI photometry and
radial-velocity observations, archival and new, of two single-lined
spectroscopic binary systems $\nu$ Centauri and $\gamma$ Lupi are reported. In
the case of $\gamma$ Lup AB, a visual binary, an examination of the light-time
effect shows that component A is the spectroscopic binary. Both $\nu$ Cen and
$\gamma$ Lup exhibit light variations with the orbital period. The variations
are caused by the reflection effect, i.e. heating of the secondary's hemisphere
by the early-B main sequence (MS) primary component's light. The modelling of
the light curves augmented with the fundamental parameters of the primary
components obtained from the literature photometric data and Hipparcos
parallaxes, shows that the secondary components are pre-MS stars, in the
process of contracting onto the MS. $\nu$ Cen and $\gamma$ Lup A are thus found
to be non-eclipsing counterparts of the B2 IV eclipsing binary (and a $\beta$
Cephei variable) 16 (EN) Lac, the B5 IV eclipsing binary (and an SPB variable)
$\mu$ Eri, and the recently discovered LMC nascent eclipsing binaries.


Abstract:
  A composable infrastructure is defined as resources, such as compute,
storage, accelerators and networking, that are shared in a pool and that can be
grouped in various configurations to meet application requirements. This
freedom to 'mix and match' resources dynamically allows for experimentation
early in the design cycle, prior to the final architectural design or hardware
implementation of a system. This design provides flexibility to serve a variety
of workloads and provides a dynamic co-design platform that allows experiments
and measurements in a controlled manner. For instance, key performance
bottlenecks can be revealed early on in the experimentation phase thus avoiding
costly and time consuming mistakes. Additionally, various system-level
topologies can be evaluated when experimenting with new System on Chip (SoCs)
and new accelerator types. This paper details the design of an enterprise
composable infrastructure that we have implemented and made available to our
partners in the IBM Research AI Hardware Center (AIHC). Our experimental
evaluations on the composable system give insights into how the system works
and evaluates the impact of various resource aggregations and reconfigurations
on representative deep learning benchmarks.


Abstract:
  End-to-end (E2E) spoken language understanding (SLU) systems avoid an
intermediate textual representation by mapping speech directly into intents
with slot values. This approach requires considerable domain-specific training
data. In low-resource scenarios this is a major concern, e.g., in the present
study dealing with SLU for dysarthric speech. Pretraining part of the SLU model
for automatic speech recognition targets helps but no research has shown to
which extent SLU on dysarthric speech benefits from knowledge transferred from
other dysarthric speech tasks. This paper investigates the efficiency of
pre-training strategies for SLU tasks on dysarthric speech. The designed SLU
system consists of a TDNN acoustic model for feature encoding and a capsule
network for intent and slot decoding. The acoustic model is pre-trained in two
stages: initialization with a corpus of normal speech and finetuning on a
mixture of dysarthric and normal speech. By introducing the intelligibility
score as a metric of the impairment severity, this paper quantitatively
analyzes the relation between generalization and pathology severity for
dysarthric speech.


Abstract:
  Finite-element simulations of magnetostatic fields are performed in terms of
magnetic vector and total scalar potentials and compared for purpose of
modeling the accelerator magnets. The potentials represent the unknown
variables associated with the A and V formulations of magnetostatics to
describe the magnetic fields. The simulations are carried out with a single
software package, the COMSOL Multiphysics, where both formulations are
implemented. The numerical performance of these methods is illustrated with the
model example of a superconducting dipole magnet recently developed for
operation in the isochronous cyclotron SC200. The results of calculations are
analyzed and compared in terms of the relevant FEM parameters accounting for
performance of computation as well as the computational cost. We show in
particular that the use of scalar potential as compared to its vector
counterpart substantially reduces the number of degrees of freedom, the usage
of computer memory and the computational time for a similar relative error.


Abstract:
  We examine the magneto-optical response of chiral multifold fermions.
Specifically, we show that they are ideal candidates for observing the Helical
Magnetic Effect (HME) previously predicted for simple Weyl fermions. Unlike
Weyl fermions, the HME is present in multifold fermions even in the simplest
case where the low-energy dispersion is linear and spherically symmetric. In
this ideal case, we derive an analytical expression for the HME and find it is
proportional to the circular photogalvanic effect; for realistic parameters and
accounting for the geometry of the setup, the HME photocurrent could be roughly
the same order of magnitude as the circular photogalvanic effect observed in
multifold fermions. Additional non-linear and symmetry-breaking terms will ruin
the quantization but not hurt the observation of the HME.


Abstract:
  We propose a neutrino model based on a $\Delta(54)$ flavor symmetry suitable
for explaining the current neutrino oscillation data. Neutrino masses arise
from the type II seesaw mechanism where the $\Delta(54)$ field assignments has
led to a simple neutrino mass matrix with one texture zero and which satisfies
the magic symmetry consistent with the well-known trimaximal mixing matrix. We
found interesting predictions concerning neutrino masses and mixing. In
particular, only the normal neutrino mass hierarchy and the lower octant of the
atmospheric angles are allowed in this model. The model predicts as well that
the $CP$ conserving values for the Dirac $CP$ phase $\delta _{CP}$ are not
allowed and thus, $CP$ is always violated in the neutrino sector. We have also
investigated the sum of absolute neutrino masses from cosmological
observations, the effective Majorana mass $m_{\beta \beta }$\ from neutrinoless
double beta decay experiments, and the electron neutrino mass $m_{\beta }$\
from beta decays where we found that the obtained range of $m_{\beta \beta }$\
can be tested by several experiments in the near future.


Abstract:
  Pre-trained language models have been successful on text classification
tasks, but are prone to learning spurious correlations from biased datasets,
and are thus vulnerable when making inferences in a new domain. Prior work
reveals such spurious patterns via post-hoc explanation algorithms which
compute the importance of input features. Further, the model is regularized to
align the importance scores with human knowledge, so that the unintended model
behaviors are eliminated. However, such a regularization technique lacks
flexibility and coverage, since only importance scores towards a pre-defined
list of features are adjusted, while more complex human knowledge such as
feature interaction and pattern generalization can hardly be incorporated. In
this work, we propose to refine a learned language model for a target domain by
collecting human-provided compositional explanations regarding observed biases.
By parsing these explanations into executable logic rules, the human-specified
refinement advice from a small set of explanations can be generalized to more
training examples. We additionally introduce a regularization term allowing
adjustments for both importance and interaction of features to better rectify
model behavior. We demonstrate the effectiveness of the proposed approach on
two text classification tasks by showing improved performance in target domain
as well as improved model fairness after refinement.


Abstract:
  We utilize the Legendre-Fenchel transform and weak geodesics for
plurisubharmonic functions to construct a weight function that can be used in
the Berndtsson-Lempert method, to give an Ohsawa-Takegoshi extension type of
result. Theorem 4.1 and Theorem 0.1 in \cite{OT2017} (Theorem \ref{Theorem A}
and \ref{Theorem B} below) follow as two special cases of this result, thus
answering affirmatively a question posed by Ohsawa in remark 4.1 in
\cite{OT2017}, on the Berndtsson-Lempert method.


Abstract:
  Public earthquake early warning systems have the potential to reduce
individual risk by warning people of an incoming tremor but their development
has been hampered by costly infrastructure. Furthermore, users' understanding
of such a service and their reactions to warnings remains poorly studied. The
smartphone app of the Earthquake Network initiative turns users' smartphones
into motion detectors and provides the first example of purely smartphone-based
earthquake early warnings, without the need for dedicated seismic station
infrastructure and operating in multiple countries. We demonstrate here that
early warnings have been emitted in multiple countries even for damaging
shaking levels and so this offers an alternative in the many regions unlikely
to be covered by conventional early warning systems in the foreseeable future.
We also show that although warnings are understood and appreciated by users,
notably to get psychologically prepared, only a fraction take protective
actions such as "drop, cover and hold".


Abstract:
  Code-switching in automatic speech recognition (ASR) is an important
challenge due to globalization. Recent research in multilingual ASR shows
potential improvement over monolingual systems. We study key issues related to
multilingual modeling for ASR through a series of large-scale ASR experiments.
Our innovative framework deploys a multi-graph approach in the weighted finite
state transducers (WFST) framework. We compare our WFST decoding strategies
with a transformer sequence to sequence system trained on the same data. Given
a code-switching scenario between Arabic and English languages, our results
show that the WFST decoding approaches were more suitable for the
intersentential code-switching datasets. In addition, the transformer system
performed better for intrasentential code-switching task. With this study, we
release an artificially generated development and test sets, along with
ecological code-switching test set, to benchmark the ASR performance.


Abstract:
  Channel pruning is formulated as a neural architecture search (NAS) problem
recently. However, existing NAS-based methods are challenged by huge
computational cost and inflexibility of applications. How to deal with multiple
sparsity constraints simultaneously and speed up NAS-based channel pruning are
still open challenges. In this paper, we propose a novel Accurate and Automatic
Channel Pruning (AACP) method to address these problems. Firstly, AACP
represents the structure of a model as a structure vector and introduces a
pruning step vector to control the compressing granularity of each layer.
Secondly, AACP utilizes Pruned Structure Accuracy Estimator (PSAE) to speed up
the performance estimation process. Thirdly, AACP proposes Improved
Differential Evolution (IDE) algorithm to search the optimal structure vector
effectively. Because of IDE, AACP can deal with FLOPs constraint and model size
constraint simultaneously and efficiently. Our method can be easily applied to
various tasks and achieve state of the art performance. On CIFAR10, our method
reduces $65\%$ FLOPs of ResNet110 with an improvement of $0.26\%$ top-1
accuracy. On ImageNet, we reduce $42\%$ FLOPs of ResNet50 with a small loss of
$0.18\%$ top-1 accuracy and reduce $30\%$ FLOPs of MobileNetV2 with a small
loss of $0.7\%$ top-1 accuracy. The source code will be released after
publication.


Abstract:
  Direct numerical simulations are performed to contrast turbulent boundary
layers over a concave wall without and with free-stream turbulence. Adverse
pressure gradient near the onset of curvature leads to sharp decrease in skin
friction and intermittent separation. The presence of free-stream turbulence
reduces the probability of reverse flow, accelerates the recovery of the
boundary layer in the downstream zero-pressure gradient region, and leads to a
sustained and appreciable increase in the skin friction. The forcing also
promotes the amplification of coherent G\"ortler structures in the logarithmic
layer of the curved-wall boundary layer. Statistically, the spanwise and
wall-normal Reynolds stresses intensify and the radial distance between their
peaks increases downstream as the G\"ortler structures expand. The Reynolds
shear stress coefficient also increases in the logarithmic layer in contrast to
a decrease when a flat-plate boundary layer is exposed to free-stream
turbulence. In addition, the more coherent and energetic roll motions in the
forced flow promote mixing of free-stream and boundary-layer fluids, where the
former is seen more often deep within the buffer layer.


Abstract:
  Probabilistic programming languages allow programmers to write down
conditional probability distributions that represent statistical and machine
learning models as programs that use observe statements. These programs are run
by accumulating likelihood at each observe statement, and using the likelihood
to steer random choices and weigh results with inference algorithms such as
importance sampling or MCMC. We argue that naive likelihood accumulation does
not give desirable semantics and leads to paradoxes when an observe statement
is used to condition on a measure-zero event, particularly when the observe
statement is executed conditionally on random data. We show that the paradoxes
disappear if we explicitly model measure-zero events as a limit of positive
measure events, and that we can execute these type of probabilistic programs by
accumulating infinitesimal probabilities rather than probability densities. Our
extension improves probabilistic programming languages as an executable
notation for probability distributions by making it more well-behaved and more
expressive, by allowing the programmer to be explicit about which limit is
intended when conditioning on an event of measure zero.


Abstract:
  In parametric Bayesian learning, a prior is assumed on the parameter $W$
which determines the distribution of samples. In this setting, Minimum Excess
Risk (MER) is defined as the difference between the minimum expected loss
achievable when learning from data and the minimum expected loss that could be
achieved if $W$ was observed. In this paper, we build upon and extend the
recent results of (Xu & Raginsky, 2020) to analyze the MER in Bayesian learning
and derive information-theoretic bounds on it. We formulate the problem as a
(constrained) rate-distortion optimization and show how the solution can be
bounded above and below by two other rate-distortion functions that are easier
to study. The lower bound represents the minimum possible excess risk
achievable by any process using $R$ bits of information from the parameter $W$.
For the upper bound, the optimization is further constrained to use $R$ bits
from the training set, a setting which relates MER to information-theoretic
bounds on the generalization gap in frequentist learning. We derive
information-theoretic bounds on the difference between these upper and lower
bounds and show that they can provide order-wise tight rates for MER under
certain conditions. This analysis gives more insight into the
information-theoretic nature of Bayesian learning as well as providing novel
bounds.


Abstract:
  As the motions of nonconservative autonomous systems are typically not
periodic, the definition of nonlinear modes as periodic motions cannot be
applied in the classical sense. In this paper, it is proposed 'make the motions
periodic' by introducing an additional damping term of appropriate sign and
magnitude. It is shown that this generalized definition is particularly suited
to reflect the periodic vibration behavior induced by harmonic external forcing
or negative linear damping. In a large range, the energy dependence of modal
frequency, damping ratio and stability is reproduced well. The limitation to
isolated or weakly-damped modes is discussed.


Abstract:
  We consider regular variation for marked point processes with independent
heavy-tailed marks and prove a single large point heuristic: the limit measure
is concentrated on the cone of point measures with one single point. We then
investigate successive hidden regular variation removing the cone of point
measures with at most $k$ points, $k\geq 1$, and prove a multiple large point
phenomenon: the limit measure is concentrated on the cone of point measures
with $k+1$ points. We show how these results imply hidden regular variation in
Skorokhod space of the associated risk process. Finally, we provide an
application to risk theory in a reinsurance model where the $k$ largest claims
are covered and we study the asymptotic behavior of the residual risk.


Abstract:
  Enhancing interoperability and information exchange between domain-specific
software products for BIM is an important aspect in the Architecture,
Engineering, Construction and Operations industry. Recent research started
investigating methods from the areas of machine and deep learning for semantic
enrichment of BIM models. However, training and evaluation of these machine
learning algorithms requires sufficiently large and comprehensive datasets.
This work presents IFCNet, a dataset of single-entity IFC files spanning a
broad range of IFC classes containing both geometric and semantic information.
Using only the geometric information of objects, the experiments show that
three different deep learning models are able to achieve good classification
performance.