Abstract:
  Sc3Mn3Al7Si5 is a rare example of a correlated metal in which the Mn moments
form a kagome lattice. The absence of magnetic ordering to the lowest
temperatures suggests that geometrical frustration of magnetic interactions may
lead to strong magnetic fluctuations. We have performed inelastic neutron
scattering measurements on Sc3Mn3Al7Si5, finding that phonon scattering
dominates for energies from ~20 - 50 meV. These results are in good agreement
with ab initio calculations of the phonon dispersions and densities of states,
and as well reproduce the measured specific heat. A weak magnetic signal was
detected at energies less than ~10 meV, present only at the lowest
temperatures. The magnetic signal is broad and quasielastic, as expected for
metallic paramagnets.


Abstract:
  In two dimensional digital geometry, two lattice points are 4-connected
(resp. 8-connected) if their Euclidean distance is at most one (resp.
$\sqrt{2}$). A set $S \subset Z^2$ is 4-connected (resp. 8-connected) if for
all pair of points $p_1, p_2$ in $S$ there is a path connecting $p_1$ to $p_2$
such that every edge consists of a 4-connected (resp. 8-connected) pair of
points. The original definition of digital convexity which states that a set $S
\subset Z^d$ is digital convex if $\conv(S) \cap Z^d= S$, where $\conv(S)$
denotes the convex hull of $S$ does not guarantee connectivity. However,
multiple algorithms assume connectivity. In this paper, we show that in two
dimensional space, any digital convex set $S$ of $n$ points is unimodularly
equivalent to a 8-connected digital convex set $C$. In fact, the resulting
digital convex set $C$ is 4-connected except for at most one point which is
8-connected to the rest of the set. The matrix of $SL_2(Z)$ defining the affine
isomorphism of $Z^2$ between the two unimodularly equivalent lattice polytopes
$S$ and $C$ can be computed in roughly $O(n)$ time. We also show that no
similar result is possible in higher dimension.


Abstract:
  We give a very simple derivation of the Atiyah-Patodi-Singer (APS) index
theorem and its small generalization by using the path integral of massless
Dirac fermions. It is based on the Fujikawa's argument for the relation between
the axial anomaly and the Atiyah-Singer index theorem, and only a minor
modification of that argument is sufficient to show the APS index theorem. The
key ingredient is the identification of the APS boundary condition and its
generalization as physical state vectors in the Hilbert space of the massless
fermion theory. The APS $\eta$-invariant appears as the axial charge of the
physical states.


Abstract:
  Micro-Expression Recognition has become challenging, as it is extremely
difficult to extract the subtle facial changes of micro-expressions. Recently,
several approaches proposed several expression-shared features algorithms for
micro-expression recognition. However, they do not reveal the specific
discriminative characteristics, which lead to sub-optimal performance. This
paper proposes a novel Feature Refinement ({FR}) with expression-specific
feature learning and fusion for micro-expression recognition. It aims to obtain
salient and discriminative features for specific expressions and also predict
expression by fusing the expression-specific features. FR consists of an
expression proposal module with attention mechanism and a classification
branch. First, an inception module is designed based on optical flow to obtain
expression-shared features. Second, in order to extract salient and
discriminative features for specific expression, expression-shared features are
fed into an expression proposal module with attention factors and proposal
loss. Last, in the classification branch, labels of categories are predicted by
a fusion of the expression-specific features. Experiments on three publicly
available databases validate the effectiveness of FR under different protocol.
Results on public benchmarks demonstrate that our FR provides salient and
discriminative information for micro-expression recognition. The results also
show our FR achieves better or competitive performance with the existing
state-of-the-art methods on micro-expression recognition.


Abstract:
  We present the first verified implementation of a decision procedure for the
quantifier-free theory of partial and linear orders. We formalise the procedure
in Isabelle/HOL and provide a specification that is made executable using
Isabelle's code generator. The procedure is already part of the development
version of Isabelle as a sub-procedure of the simplifier.


Abstract:
  Various applications of advanced air mobility (AAM) in urban environments
facilitate our daily life and public services. As one of the key issues of
realizing these applications autonomously, path planning problem has been
studied with main objectives on minimizing travel distance, flight time and
energy cost. However, AAM operations in metropolitan areas bring safety and
society issues. Because most of AAM aircraft are unmanned aerial vehicles
(UAVs) and they may fail to operate resulting in fatality risk, property damage
risk and societal impacts (noise and privacy) to the public. To quantitatively
assess these risks and mitigate them in planning phase, this paper proposes an
integrated risk assessment model and develops a hybrid algorithm to solve the
risk-based 3D path planning problem. The integrated risk assessment method
considers probability and severity models of UAV impact ground people and
vehicle. By introducing gravity model, the population density and traffic
density are estimated in a finer scale, which enables more accurate risk
assessment. The 3D risk-based path planning problem is first formulated as a
special minimum cost flow problem. Then, a hybrid estimation of distribution
algorithm (EDA) and risk-based A* (named as EDA-RA*) algorithm is proposed to
solve the problem. To improve computational efficiency, k-means clustering
method is incorporated into EDA-RA* to provide both global and local search
heuristic information, which formed the EDA and fast risk-based A* algorithm we
call EDA-FRA*. Case study results show that the risk assessment model can
capture high risk areas and the generated risk map enables safe UAV path
planning in urban complex environments.


Abstract:
  Designing efficient fault-tolerant circuits for converting between two
quantum codes is an effective way to realize universal fault-tolerant quantum
computing. In this work, we adopt the round-robin circuit construction to
design a conversion circuit between CSS 7-qubit error-correcting code and
Reed-Muller 15-qubit code. We take the pieceable fault-tolerant protocol to
guarantee the fault tolerance of our conversion circuit. A neural network
decoding scheme is proposed to suppress the propagation of quantum errors. Our
decoding scheme can effectively reduce the damaging effects of imperfect
syndrome extraction operations and predict possible logical errors that
occurred after the error correction process. We provide a detailed description
of our syndrome data model and analyze the depolarizing noise threshold of our
conversion circuit through numerical simulations of the stabilizer circuit.


Abstract:
  Robust estimation is an important problem in statistics which aims at
providing a reasonable estimator when the data-generating distribution lies
within an appropriately defined ball around an uncontaminated distribution.
Although minimax rates of estimation have been established in recent years,
many existing robust estimators with provably optimal convergence rates are
also computationally intractable. In this paper, we study several estimation
problems under a Wasserstein contamination model and present computationally
tractable estimators motivated by generative adversarial networks (GANs).
Specifically, we analyze properties of Wasserstein GAN-based estimators for
location estimation, covariance matrix estimation, and linear regression and
show that our proposed estimators are minimax optimal in many scenarios.
Finally, we present numerical results which demonstrate the effectiveness of
our estimators.


Abstract:
  Artificial neural networks will always make a prediction, even when
completely uncertain and regardless of the consequences. This obliviousness of
uncertainty is a major obstacle towards their adoption in practice. Techniques
exist, however, to estimate the two major types of uncertainty: model
uncertainty and observation noise in the data. Bayesian neural networks are
theoretically well-founded models that can learn the model uncertainty of their
predictions. Minor modifications to these models and their loss functions allow
learning the observation noise for individual samples as well. This paper is
the first to apply these techniques to predictive process monitoring. We found
that they contribute towards more accurate predictions and work quickly.
However, their main benefit resides with the uncertainty estimates themselves
that allow the separation of higher-quality from lower-quality predictions and
the building of confidence intervals. This leads to many interesting
applications, enables an earlier adoption of prediction systems with smaller
datasets and fosters a better cooperation with humans.


Abstract:
  We consider the problem of maximizing the minimum (weighted) value of all
components of a vector over a polymatroid. This is a special case of the
lexicographically optimal base problem introduced and solved by Fujishige. We
give an alternative formulation of the problem as a zero-sum game between a
maximizing player whose mixed strategy set is the base of the polymatroid and a
minimizing player whose mixed strategy set is a simplex. We show that this game
and three variations of it unify several problems in search, sequential testing
and queuing. We give a new, short derivation of optimal strategies for both
players and an expression for the value of the game. Furthermore, we give a
characterization of the set of optimal strategies for the minimizing player and
we consider special cases for which optimal strategies can be found
particularly easily.


Abstract:
  The strategy adopted in the original Morris-Thorne wormhole was to retain
complete control over the geometry at the expense of certain engineering
considerations. The purpose of this paper is to obtain several complete
wormhole solutions by assuming a noncommutative-geometry background with a
concomitant isotropic-pressure condition. This condition allows us to consider
a cosmological setting with a perfect-fluid equation of state. An extended form
of the equation generalizes the first solution and subsequently leads to the
generalized Chaplygin-gas model and hence to a third solution. The solutions
obtained extend several previous results. This paper also reiterates the need
for a noncommutative-geometry background to account for the enormous radial
tension that is characteristic of Morris-Thorne wormholes.


Abstract:
  We adapt the quasi-monotone method from [2] for composite convex minimization
in the stochastic setting. For the proposed numerical scheme we derive the
optimal convergence rate in terms of the last iterate, rather than on average
as it is standard for subgradient methods. The theoretical guarantee for
individual convergence of the regularized quasi-monotone method is confirmed by
numerical experiments on l1-regularized robust linear regression.


Abstract:
  Understanding the impact of environment on the formation and evolution of
dark matter halos and galaxies is a crucial open problem. Studying statistical
correlations in large simulated populations sheds some light on these impacts,
but the causal effect of an environment on individual objects is harder to
pinpoint. Addressing this, we present a new method for resimulating a single
dark matter halo in multiple large-scale environments. In the initial
conditions, we 'splice' (i.e. insert) the Lagrangian region of a halo into
different Gaussian random fields, while enforcing consistency with the
statistical properties of $\Lambda$CDM. Applying this technique, we demonstrate
that the mass of halos is primarily determined by the density structure inside
their Lagrangian patches, while the halos' concentration is more strongly
affected by environment. The splicing approach will also allow us to study, for
example, the impact of the cosmic web on accretion processes and galaxy
quenching.


Abstract:
  Integration is indispensable, not only in mathematics, but also in a wide
range of other fields. A deep learning method has recently been developed and
shown to be capable of integrating mathematical functions that could not
previously be integrated on a computer. However, that method treats integration
as equivalent to natural language translation and does not reflect mathematical
information. In this study, we adjusted the learning model to take mathematical
information into account and developed a wide range of learning models that
learn the order of numerical operations more robustly. In this way, we achieved
a 98.80% correct answer rate with symbolic integration, a higher rate than that
of any existing method. We judged the correctness of the integration based on
whether the derivative of the primitive function was consistent with the
integrand. By building an integrated model based on this strategy, we achieved
a 99.79% rate of correct answers with symbolic integration.


Abstract:
  In this paper we use the additive thermodynamic formalism to obtain new
bounds of the Hausdorff and box-counting dimension of certain non conformal
hyperbolic repellers defined by $C^r$, $r > 1$ piecewise expanding maps on a
$d$-dimensional smooth manifold $M$.


Abstract:
  The field of automatic music composition has seen great progress in the last
few years, much of which can be attributed to advances in deep neural networks.
There are numerous studies that present different strategies for generating
sheet music from scratch. The inclusion of high-level musical characteristics
(e.g., perceived emotional qualities), however, as conditions for controlling
the generation output remains a challenge. In this paper, we present a novel
approach for calculating the valence (the positivity or negativity of the
perceived emotion) of a chord progression within a lead sheet, using
pre-defined mood tags proposed by music experts. Based on this approach, we
propose a novel strategy for conditional lead sheet generation that allows us
to steer the music generation in terms of valence, phrasing, and time
signature. Our approach is similar to a Neural Machine Translation (NMT)
problem, as we include high-level conditions in the encoder part of the
sequence-to-sequence architectures used (i.e., long-short term memory networks,
and a Transformer network). We conducted experiments to thoroughly analyze
these two architectures. The results show that the proposed strategy is able to
generate lead sheets in a controllable manner, resulting in distributions of
musical attributes similar to those of the training dataset. We also verified
through a subjective listening test that our approach is effective in
controlling the valence of a generated chord progression.


Abstract:
  Communication in high frequencies such as millimeter wave and terahertz
suffer from high path-loss and intense shadowing which necessitates beamforming
for reliable data transmission. On the other hand, at high frequencies the
channels are sparse and consist of few spatial clusters. Therefore, beam
alignment (BA) strategies are used to find the direction of these channel
clusters and adjust the width of the beam used for data transmission. In this
work, a single-user uplink scenario where the channel has one dominant cluster
is considered. It is assumed that the user transmits a set of BA packets over a
fixed duration. Meanwhile, the base-station (BS) uses different probing beams
to scan different angular regions. Since the BS measurements are noisy, it is
not possible to find a narrow beam that includes the angle of arrival (AoA) of
the user with probability one. Therefore, the BS allocates a narrow beam to the
user which includes the AoA of the user with a predetermined error probability
while minimizing the expected beamwidth of the allocated beam. Due to
intractability of this noisy BA problem, here this problem is posed as an
end-to-end optimization of a deep neural network (DNN) and effects of different
loss functions are discussed and investigated. It is observed that the proposed
DNN based BA, at high SNRs, achieves a performance close to that of the optimal
BA when there is no-noise and for all SNRs, outperforms state-of-the-art.


Abstract:
  We prove potential automorphy results for a single Galois representation $G_F
\rightarrow GL_n(\overline{\mathbb{Q}}_l)$ where $F$ is a CM number field. The
strategy is to use the $p,q$ switch trick and modify the Dwork motives employed
in \cite{HSBT} to break self-duality of the motives, but not the Hodge-Tate
weights. Another key result to prove is the ordinarity of certain $p$-adic
representations, which follows from log geometry techniques. One input is the
automorphy lifting theorem in \cite{tap}.


Abstract:
  Histotripsy relies on cavitation to mechanically homogenize soft tissue.
There is strong evidence that the high stresses, strains, and strain rates
developed as bubbles grow and collapse contribute to this tissue
homogenization. While such stresses and strains have been examined
computationally in model systems with assumed constitutive models (e.g.,
finite-deformation Neo-Hookean model) and viscoelastic properties determined
under quasi-static conditions, recent studies proposed that the Quadratic Law
Kelvin-Voigt (QLKV) constitutive model, which additionally accounts for strain
stiffening, more accurately represents the viscoelastic response of soft
materials subjected to cavitation; this model has also been used to infer
viscoelastic properties at high rates. In this work, we use the QLKV model and
these properties to calculate the time-dependent stress, strain, and strain
rate fields produced during the growth and collapse of individual bubbles
subjected to a histotripsy-relevant pressure waveform in agarose gels of 0.3~\%
and 1.0~\% concentration and corresponding to actual (past) experiments. We
find that, as the gel concentration is increased, strain stiffening manifests
in larger elastic stresses and compressive stresses extending into the collapse
phase, particularly for the 1.0~\% concentration gel. As a result, the duration
of the collapse phase also increases. In comparison with the conventional
Neo-Hookean model, the compressive stress has a larger magnitude, extends
farther into the surrounding medium, and shows an increased departure from
growth/collapse symmetry close to the bubble; all of these effects are
magnified in the stiffer gel.


Abstract:
  Plasmas that are strongly magnetized in the sense that the gyrofrequency
exceeds the plasma frequency exhibit novel transport properties that are not
well understood. As a representative example, we compute the friction force
acting on a massive test charge moving through a strongly coupled and strongly
magnetized one-component plasma using a generalized Boltzmann kinetic theory.
Recent works studying the weakly coupled regime have shown that strong
magnetization leads to a transverse component of the friction force that is
perpendicular to both the Lorentz force and velocity of the test charge; in
addition to the stopping power component aligned antiparallel to the velocity.
Recent molecular dynamics simulations have also shown that strong Coulomb
coupling in addition to strong magnetization gives rise to a third component of
the friction force in the direction of the Lorentz force. Here, we show that
the generalized Boltzmann kinetic theory captures these effects, and generally
agrees well with the molecular dynamics simulations over a broad range of
Coulomb coupling and magnetization strength regimes. The theory is also used to
show that a "gyro" component of the friction in the direction of the Lorentz
force arises due to asymmetries associated with gyromotion during short-range
collisions. Computing the average motion of the test charge through the
background plasma, the transverse force is found to strongly influence the
trajectory by changing the gyroradius and the gyro friction force is found to
slightly change the gyrofrequency of the test charge resulting in a phase
shift.


Abstract:
  For the first time we search for the $\eta_{c2}(1D)$ in
$e^+e^-\to\gamma\eta_{c2}(1D)$ at $\sqrt{s}$ = 10.52, 10.58, and 10.867 GeV
with data samples of 89.5 fb$^{-1}$, 711 fb$^{-1}$, and 121.4 fb$^{-1}$,
respectively, accumulated with the Belle detector at the KEKB asymmetric energy
electron-positron collider. No significant $\eta_{c2}(1D)$ signal is observed
in the mass range between 3.8 and 3.88 GeV/$c^2$. The upper limit at 90\%
confidence level on the product of the Born cross section for $e^+e^- \to
\gamma\eta_{c2}(1D)$ and branching fraction for $\eta_{c2}(1D)\to \gamma
h_c(1P)$ is determined to be $\sigma(e^+e^- \to \gamma\eta_{c2}(1D)){\cal
B}(\eta_{c2}(1D)\to \gamma h_c(1P))$ $<$ 4.9 fb at $\sqrt{s}$ near 10.6 GeV.


Abstract:
  In this paper, we propose a novel Feature Decomposition and Reconstruction
Learning (FDRL) method for effective facial expression recognition. We view the
expression information as the combination of the shared information (expression
similarities) across different expressions and the unique information
(expression-specific variations) for each expression. More specifically, FDRL
mainly consists of two crucial networks: a Feature Decomposition Network (FDN)
and a Feature Reconstruction Network (FRN). In particular, FDN first decomposes
the basic features extracted from a backbone network into a set of facial
action-aware latent features to model expression similarities. Then, FRN
captures the intra-feature and inter-feature relationships for latent features
to characterize expression-specific variations, and reconstructs the expression
feature. To this end, two modules including an intra-feature relation modeling
module and an inter-feature relation modeling module are developed in FRN.
Experimental results on both the in-the-lab databases (including CK+, MMI, and
Oulu-CASIA) and the in-the-wild databases (including RAF-DB and SFEW) show that
the proposed FDRL method consistently achieves higher recognition accuracy than
several state-of-the-art methods. This clearly highlights the benefit of
feature decomposition and reconstruction for classifying expressions.


Abstract:
  We show how fitting sparse linear models over learned deep feature
representations can lead to more debuggable neural networks. These networks
remain highly accurate while also being more amenable to human interpretation,
as we demonstrate quantiatively via numerical and human experiments. We further
illustrate how the resulting sparse explanations can help to identify spurious
correlations, explain misclassifications, and diagnose model biases in vision
and language tasks. The code for our toolkit can be found at
https://github.com/madrylab/debuggabledeepnetworks.


Abstract:
  We present the analysis of multi-wavelength observations of seven
extragalactic radio sources, listed as unidentified in the Third Cambridge
Revised Catalog (3CR). X-ray observations, performed during Chandra Cycle 21,
were compared to VLA, WISE and Pan-STARRS observations in the radio, infrared
and optical bands, respectively. All sources in this sample lack a clear
optical counterpart, and are thus missing their redshift and optical
classification. In order to confirm the X-ray and infrared radio counterparts
of core and extended components, here we present for the first time radio maps
obtained manually reducing VLA archival data. As in previous papers on the
Chandra X-ray snapshot campaign, we report X-ray detections of radio cores and
two sources, out of the seven presented here, are found to be members of galaxy
clusters. For these two cluster sources (namely, 3CR 409 and 3CR 454.2), we
derived surface brightness profiles in four directions. For all seven sources,
we measured X-ray intensities of the radio sources and we also performed
standard X-ray spectral analysis for the four sources (namely, 3CR 91, 3CR 390,
3CR 409 and 3CR 428) with the brightest nuclei (more than 400 photons in the
2'' nuclear region). We also detected extended X-ray emission around 3CR 390
and extended X-ray emission associated with the northern jet of 3CR 158. This
paper represents the first attempt to give a multi-wavelength view of the
unidentified radio sources listed in the 3CR catalog.


Abstract:
  The lossless data compression algorithm based on Bayesian Attention Networks
is derived from first principles. Bayesian Attention Networks are defined by
introducing an attention factor per a training sample loss as a function of two
sample inputs, from training sample and prediction sample. By using a sharpened
Jensen's inequality we show that the attention factor is completely defined by
a correlation function of the two samples w.r.t. the model weights. Due to the
attention factor the solution for a prediction sample is mostly defined by a
few training samples that are correlated with the prediction sample. Finding a
specific solution per prediction sample couples together the training and the
prediction. To make the approach practical we introduce a latent space to map
each prediction sample to a latent space and learn all possible solutions as a
function of the latent space along with learning attention as a function of the
latent space and a training sample. The latent space plays a role of the
context representation with a prediction sample defining a context and a
learned context dependent solution used for the prediction.


Abstract:
  Collaborative Mobile crowdsourcing (CMCS) allows entities, e.g., local
authorities or individuals, to hire a team of workers from the crowd of
connected people, to execute complex tasks. In this paper, we investigate two
different CMCS recruitment strategies allowing task requesters to form teams of
socially connected and skilled workers: i) a platform-based strategy where the
platform exploits its own knowledge about the workers to form a team and ii) a
leader-based strategy where the platform designates a group leader that
recruits its own suitable team given its own knowledge about its Social Network
(SN) neighbors. We first formulate the recruitment as an Integer Linear Program
(ILP) that optimally forms teams according to four fuzzy-logic-based criteria:
level of expertise, social relationship strength, recruitment cost, and
recruiter's confidence level. To cope with NP-hardness, we design a novel
low-complexity CMCS recruitment approach relying on Graph Neural Networks
(GNNs), specifically graph embedding and clustering techniques, to shrink the
workers' search space and afterwards, exploiting a meta-heuristic genetic
algorithm to select appropriate workers. Simulation results applied on a
real-world dataset illustrate the performance of both proposed CMCS recruitment
approaches. It is shown that our proposed low-complexity GNN-based recruitment
algorithm achieves close performances to those of the baseline ILP with
significant computational time saving and ability to operate on large-scale
mobile crowdsourcing platforms. It is also shown that compared to the
leader-based strategy, the platform-based strategy recruits a more skilled team
but with lower SN relationships and higher cost.


Abstract:
  In this work, we perform Bayesian inference tasks for the chemical master
equation in the tensor-train format. The tensor-train approximation has been
proven to be very efficient in representing high dimensional data arising from
the explicit representation of the chemical master equation solution. An
additional advantage of representing the probability mass function in the
tensor train format is that parametric dependency can be easily incorporated by
introducing a tensor product basis expansion in the parameter space. Time is
treated as an additional dimension of the tensor and a linear system is derived
to solve the chemical master equation in time. We exemplify the tensor-train
method by performing inference tasks such as smoothing and parameter inference
using the tensor-train framework. A very high compression ratio is observed for
storing the probability mass function of the solution. Since all linear algebra
operations are performed in the tensor-train format, a significant reduction of
the computational time is observed as well.


Abstract:
  Single crystal inelastic neutron scattering is used to study spin wave
excitations in the fully polarized state of the frustrated quantum
ferro-antiferromagnet BaCdVO(PO$_4$)$_2$. The data analysis is based on a
Heisenberg spin Hamiltonian that includes as many distinct nearest-neighbor and
next-nearest neighbor interactions as allowed by crystal symmetry. All 8 such
exchange constants are obtained in a simultaneous fit to over 150 scans across
the dispersion manifold. This establishes a definitive quantitative model of
this material. It turns out to be substantially different from the one assumed
in numerous previous studies based on powder experiments.


Abstract:
  It is unclear if existing interpretations of deep neural network models
respond effectively to the needs of users. This paper summarizes the common
forms of explanations (such as feature attribution, decision rules, or probes)
used in over 200 recent papers about natural language processing (NLP), and
compares them against user questions collected in the XAI Question Bank. We
found that although users are interested in explanations for the road not taken
-- namely, why the model chose one result and not a well-defined, seemly
similar legitimate counterpart -- most model interpretations cannot answer
these questions.


Abstract:
  This study used the recent ATLAS jet substructure measurements to see if any
improvements can be made to the commonly used Pythia8 Monash and A14 tunes.


Abstract:
  In this paper we investigate real-time, dynamic traffic optimization in
railway systems. In order to enable practical solution times, we operate the
optimizer in a receding horizon fashion and with optimization horizons that are
shorter than the full path to destinations, using a model predictive control
(MPC) approach. We present new procedures to establish safe prediction
horizons, providing formal guarantees that the system is operated in a way that
satisfies hard safety constraints despite the fact that not all future train
interactions are taken into account, by characterizing the minimal required
optimization horizons. We also show that any feasible solution to our proposed
models is sufficient to maintain a safe, automated operation of the railway
system, providing an upper bound on the computations strictly required.
Additionally, we show that these minimal optimization horizons also
characterize an upper bound on computations required to construct a feasible
solution for any arbitrary optimization horizon, paving the way for anytime
algorithms. Finally, our results enable systematic solution reuse, when
previous schedules are available. We test our approach on a detailed simulation
environment of a real-world railway system used for freight transport.


Abstract:
  The problem of nonintegrability of the circular restricted three-body problem
is very classical and important in the theory of dynamical systems. It was
partially solved by Poincare in the nineteenth century: He showed that there
exists no real-analytic first integral which depends analytically on the mass
ratio of the second body to the first one and is functionally independent of
the Hamiltonian. When the mass of the second body becomes zero, the restricted
three-body problem reduces to the two-body Kepler problem. We prove the
nonintegrability of the restricted three-body problem both in the planar and
spatial cases for any nonzero mass of the second body. Our basic tool of the
proofs is a technique developed here for determining whether perturbations of
integrable systems which may be non-Hamiltonian are not meromorphically
integrable near resonant periodic orbits such that the first integrals and
commutative vector fields also depend meromorphically on the perturbation
parameter. The technique is based on generalized versions due to Ayoul and Zung
of the Morales-Ramis and Morales-Ramis-Simo theories.


Abstract:
  Protein fold classification is a classic problem in structural biology and
bioinformatics. We approach this problem using persistent homology. In
particular, we use alpha shape filtrations to compare a topological
representation of the data with a different representation that makes use of
knot-theoretic ideas. We use the statistical method of Angle-based Joint and
Individual Variation Explained (AJIVE) to understand similarities and
differences between these representations.


Abstract:
  3D point cloud (PC) -- a collection of discrete geometric samples of a
physical object's surface -- is typically large in size, which entails
expensive subsequent operations like viewpoint image rendering and object
recognition. Leveraging on recent advances in graph sampling, we propose a fast
PC sub-sampling algorithm that reduces its size while preserving the overall
object shape. Specifically, to articulate a sampling objective, we first assume
a super-resolution (SR) method based on feature graph Laplacian regularization
(FGLR) that reconstructs the original high-resolution PC, given 3D points
chosen by a sampling matrix $\H$. We prove that minimizing a worst-case SR
reconstruction error is equivalent to maximizing the smallest eigenvalue
$\lambda_{\min}$ of a matrix $\H^{\top} \H + \mu \cL$, where $\cL$ is a
symmetric, positive semi-definite matrix computed from the neighborhood graph
connecting the 3D points. Instead, for fast computation we maximize a lower
bound $\lambda^-_{\min}(\H^{\top} \H + \mu \cL)$ via selection of $\H$ in three
steps. Interpreting $\cL$ as a generalized graph Laplacian matrix corresponding
to an unbalanced signed graph $\cG$, we first approximate $\cG$ with a balanced
graph $\cG_B$ with the corresponding generalized graph Laplacian matrix
$\cL_B$. Second, leveraging on a recent theorem called Gershgorin disc perfect
alignment (GDPA), we perform a similarity transform $\cL_p = \S \cL_B \S^{-1}$
so that Gershgorin disc left-ends of $\cL_p$ are all aligned at the same value
$\lambda_{\min}(\cL_B)$. Finally, we perform PC sub-sampling on $\cG_B$ using a
graph sampling algorithm to maximize $\lambda^-_{\min}(\H^{\top} \H + \mu
\cL_p)$ in roughly linear time. Experimental results show that 3D points chosen
by our algorithm outperformed competing schemes both numerically and visually
in SR reconstruction quality.


Abstract:
  Theoretical issues: With the explosive growth in the research literature
production, the need for new approaches to structure knowledge emerged. Method:
Synthetic content analysis was used in our meta-study. Results and discussion:
Our meta-study showed that content analysis is frequently used in nursing
research in a very wide spectrum of applications. The trend of its use is
positive and it is used globally in a variety of research settings. The
synthetic content analysis used in our study showed to be a very helpful tool
in performing knowledge synthesis, replacing many of the routine activities of
conventional synthesis with automated activities this making such studies more
economically viable and easier to perform.


Abstract:
  People convey information extremely effectively through spoken interaction
using multiple channels of information transmission: the lexical channel of
what is said, and the non-lexical channel of how it is said. We propose
studying human perception of spoken communication as a means to better
understand how information is encoded across these channels, focusing on the
question 'What characteristics of communicative context affect listener's
expectations of speech?'. To investigate this, we present a novel behavioural
task testing whether listeners can discriminate between the true utterance in a
dialogue and utterances sampled from other contexts with the same lexical
content. We characterize how perception - and subsequent discriminative
capability - is affected by different degrees of additional contextual
information across both the lexical and non-lexical channel of speech. Results
demonstrate that people can effectively discriminate between different prosodic
realisations, that non-lexical context is informative, and that this channel
provides more salient information than the lexical channel, highlighting the
importance of the non-lexical channel in spoken interaction.


Abstract:
  Social and professional networks affect labor market dynamics, knowledge
diffusion and new business creation. To understand the determinants of how
these networks are formed in the first place, we analyze a unique dataset of
business cards exchanges among a sample of over 240,000 users of the
multi-platform contact management and professional social networking tool for
individuals Eight. We develop a structural model of network formation with
strategic interactions, and we estimate users' payoffs that depend on the
composition of business relationships, as well as indirect business
interactions. We allow heterogeneity of users in both observable and
unobservable characteristics to affect how relationships form and are
maintained. The model's stationary equilibrium delivers a likelihood that is a
mixture of exponential random graph models that we can characterize in
closed-form. We overcome several econometric and computational challenges in
estimation, by exploiting a two-step estimation procedure, variational
approximations and minorization-maximization methods. Our algorithm is
scalable, highly parallelizable and makes efficient use of computer memory to
allow estimation in massive networks. We show that users payoffs display
homophily in several dimensions, e.g. location; furthermore, users unobservable
characteristics also display homophily.


Abstract:
  Graph neural networks are emerging as continuation of deep learning success
w.r.t. graph data. Tens of different graph neural network variants have been
proposed, most following a neighborhood aggregation scheme, where the node
features are updated via aggregating features of its neighboring nodes from
layer to layer. Though related research surges, the power of GNNs are still not
on-par-with their counterpart CNNs in computer vision and RNNs in natural
language processing. We rethink this problem from the perspective of
information propagation, and propose to enhance information propagation among
GNN layers by combining heterogeneous aggregations. We argue that as richer
information are propagated from shallow to deep layers, the discriminative
capability of features formulated by GNN can benefit from it. As our first
attempt in this direction, a new generic GNN layer formulation and upon this a
new GNN variant referred as HAG-Net is proposed. We empirically validate the
effectiveness of HAG-Net on a number of graph classification benchmarks, and
elaborate all the design options and criterions along with.


Abstract:
  Galactic cosmic-rays (GCRs) are thought to be accelerated in strong shocks
induced by massive star winds and supernova explosions sweeping across the
interstellar medium. But the phase of the interstellar medium from which the
CRs are extracted has remained elusive until now. Here, we study in detail the
GCR source composition deduced from recent measurements by the AMS-02, Voyager
1 and SuperTIGER experiments to obtain information on the composition,
ionisation state and dust content of the GCR source reservoirs. We show that
the volatile elements of the CR material are mainly accelerated from a plasma
of temperature higher than $\sim 2$ MK, which is typical of the hot medium
found in galactic superbubbles energised by the activity of massive star winds
and supernova explosions. Another GCR component, which is responsible for the
overabundance of $^{22}$Ne, most likely arises from acceleration of massive
star winds in their termination shocks. From the CR-related $\gamma$-ray
luminosity of the Milky Way, we estimate that the ion acceleration efficiency
in both supernova shocks and wind termination shocks is of the order of
$10^{-5}$. The GCR source composition also shows evidence for a preferential
acceleration of refractory elements contained in interstellar dust. We suggest
that the GCR refractories are also produced in superbubbles, from shock
acceleration and subsequent sputtering of dust grains continuously incorporated
into the hot plasma through thermal evaporation of embedded molecular clouds.
Our model explains well the measured abundances of all primary and mostly
primary CRs from H to Zr, including the overabundance of $^{22}$Ne.


Abstract:
  Developing complex engineered systems (CES) poses significant challenges for
engineers, managers, designers, and businesspeople alike due to the inherent
complexity of the systems and contexts involved. Furthermore, experts have
expressed great interest in filling the gap in theory about how CES develop.
This article begins to address that gap in two ways. First, it reviews the
numerous definitions of CES along with existing theory and methods on CES
development processes. Then, it proposes the ComplEx System Integrated
Utilities Model (CESIUM), a novel framework for exploring how numerous system
and development process characteristics may affect the performance of CES.
CESIUM creates simulated representations of a system architecture, the
corresponding engineering organization, and the new product development process
through which the organization designs the system. It does so by representing
the system as a network of interdependent artifacts designed by agents. Agents
iteratively design their artifacts through optimization and share information
with other agents, thereby advancing the CES toward a solution. This paper
describes the model, conducts a sensitivity analysis, provides validation, and
suggests directions for future study.


Abstract:
  Entity alignment (EA) is the task of identifying the entities that refer to
the same real-world object but are located in different knowledge graphs (KGs).
For entities to be aligned, existing EA solutions treat them separately and
generate alignment results as ranked lists of entities on the other side.
Nevertheless, this decision-making paradigm fails to take into account the
interdependence among entities. Although some recent efforts mitigate this
issue by imposing the 1-to-1 constraint on the alignment process, they still
cannot adequately model the underlying interdependence and the results tend to
be sub-optimal. To fill in this gap, in this work, we delve into the dynamics
of the decision-making process, and offer a reinforcement learning (RL) based
model to align entities collectively. Under the RL framework, we devise the
coherence and exclusiveness constraints to characterize the interdependence and
restrict collective alignment. Additionally, to generate more precise inputs to
the RL framework, we employ representative features to capture different
aspects of the similarity between entities in heterogeneous KGs, which are
integrated by an adaptive feature fusion strategy. Our proposal is evaluated on
both cross-lingual and mono-lingual EA benchmarks and compared against
state-of-the-art solutions. The empirical results verify its effectiveness and
superiority.


Abstract:
  Emerging applications such as wireless sensing, position location, robotics,
and many more are driven by the ultra-wide bandwidths available at
millimeter-wave (mmWave) and Terahertz (THz) frequencies. The characterization
and efficient utilization of wireless channels at these extremely high
frequencies require detailed knowledge of the radio propagation characteristics
of the channels. Such knowledge is developed through empirical observations of
operating conditions using wireless transceivers that measure the impulse
response through channel sounding. Today, cutting-edge channel sounders rely on
several bulky RF hardware components with complicated interconnections, large
parasitics, and sub-GHz RF bandwidth. This paper presents a compact sliding
correlation-based channel sounder baseband built on a monolithic integrated
circuit (IC) using 65 nm CMOS, implemented as an evaluation board achieving a 2
GHz RF bandwidth. The IC is the worlds first gigabit-per-second channel sounder
baseband implemented in low-cost CMOS. The presented single-board system can be
employed at both the transmit and receive baseband to study multipath
characteristics and path loss. Thus, the singleboard implementation provides an
inexpensive and compact solution for sliding correlation-based channel sounding
with 1 ns multipath delay resolution.


Abstract:
  We compute asymptotic estimates for the Fourier coefficients of two mixed
mock modular forms, which come from Bailey pairs derived by Lovejoy and Osburn.
To do so, we employ the circle method due to Wright and a modified Tauberian
theorem. We encounter cancellation in our estimates for one of the mock theta
functions due to the auxiliary function $\theta_{n,p}$ arising from the
splitting of Hickerson and Mortenson. We deal with this by using higher order
asymptotic expansions for the Jacobi theta functions.


Abstract:
  Post-starburst (or "E+A") galaxies trace the fastest and most dramatic
processes in galaxy evolution. Recent work studying the evolution of galaxies
through this phase have revealed insights on how galaxies undergo structural
and stellar population changes as well as the role of various feedback
mechanisms. In this review, I summarize recent work on identifying
post-starburst galaxies; tracing the role of this phase through cosmic time;
measuring stellar populations, on-going star formation, morphologies,
kinematics, interstellar medium properties, and AGN activity; mechanisms to
cause the recent starburst and its end; and the future evolution to quiescence
(or not). The review concludes with a list of open questions and exciting
possibilities for future facilities.


Abstract:
  The axial and polar modes for the ring down of a Schwarzschild black hole are
calculated, by first deriving the Regge-Wheeler and Zerilli equations,
respectively, and finally applying the Asymptotic Iteration Method (AIM). We
were able to reach up to 500 iterations, obtaining for the first time
convergence for a wide range of large damping modes. The General Relativity
(GR) and a particular version of an extended model with an r-dependent
mass-function are compared. This mass-function allows an analytical solution
for the Tortoise coordinate. The example of the mass-function corresponds to
the leading correction for extended theories and serves as a starting point to
treat other r-dependent parameter mass-functions.


Abstract:
  We define and study positional marked patterns, permutations $\tau$ where one
of elements in $\tau$ is underlined. Given a permutation $\sigma$, we say that
$\sigma$ has a $\tau$-match at position $i$ if $\tau$ occurs in $\sigma$ in
such a way that $\sigma_i$ plays the role of the underlined element in the
occurrence. We let $pmp_\tau(\sigma)$ denote the number of positions $i$ which
$\sigma$ has a $\tau$-match. This defines a new class of statistics on
permutations, where we study such statistics and prove a number of results. In
particular, we prove that two positional marked patterns $1\underline{2}3$ and
$1\underline{3}2$ give rise to two statistics that have the same distribution.
The equidistibution phenomenon also occurs in other several collections of
patterns like $\left \{1\underline{2}3 , 1\underline{3}2 \right \}$, and $\left
\{ 1\underline234, 1\underline243, \underline2134, \underline2 1 4 3 \right
\}$, as well as two positional marked patterns of any length $n$: $\left \{
1\underline 2\tau , \underline 21\tau \right \}$.


Abstract:
  For the sparsity-rank-aware least squares estimations, the LiGME (Linearly
involved Generalized Moreau Enhanced) model was established recently in [Abe,
Yamagishi, Yamada, 2020] to use certain nonconvex enhancements of linearly
involved convex regularizers without losing their overall convexities. In this
paper, for further advancement of the LiGME model by incorporating multiple a
priori knowledge as hard convex constraints, we newly propose a convexly
constrained LiGME (cLiGME) model. The cLiGME model can utilize multiple convex
constraints while preserving benefits achieved by the LiGME model. We also
present a proximal splitting type algorithm for the proposed cLiGME model.
Numerical experiments demonstrate the efficacy of the proposed model and the
proposed optimization algorithm in a scenario of signal processing application.


Abstract:
  We develop a novel approach to conformal prediction when the target task has
limited data available for training. Conformal prediction identifies a small
set of promising output candidates in place of a single prediction, with
guarantees that the set contains the correct answer with high probability. When
training data is limited, however, the predicted set can easily become unusably
large. In this work, we obtain substantially tighter prediction sets while
maintaining desirable marginal guarantees by casting conformal prediction as a
meta-learning paradigm over exchangeable collections of auxiliary tasks. Our
conformalization algorithm is simple, fast, and agnostic to the choice of
underlying model, learning algorithm, or dataset. We demonstrate the
effectiveness of this approach across a number of few-shot classification and
regression tasks in natural language processing, computer vision, and
computational chemistry for drug discovery.


Abstract:
  The presence of extra dimensions generically modify the spacetime geometry of
a rotating black hole, by adding an additional hair, besides the mass $M$ and
the angular momentum $J$, known as the `tidal charge' parameter, $\beta$. In a
braneworld scenario with one extra spatial dimension, the extra dimension is
expected to manifest itself through -- (a) negative values of $\beta$, and (b)
modified gravitational perturbations. This in turn would affect the
quasi-normal modes of rotating black holes. We numerically solve the perturbed
gravitational field equations using the continued fractions method and
determine the quasi-normal mode spectra for the braneworld black hole. We find
that increasingly negative values of $\beta$ correspond to a diminishing
imaginary part of the quasi-normal mode, or equivalently, an increasing damping
time. Using the publicly available data of the properties of the remnant black
hole in the gravitational wave signal GW150914, we check for consistency
between the predicted values (for a given $\beta$) of the frequency and damping
time of the least-damped $\ell=2,m=2$ quasi-normal mode and measurements of
these quantities using other independent techniques. We find that it is highly
unlikely for the tidal charge, $\beta \lesssim -0.05$, providing a conservative
limit on the tidal charge parameter. Implications and future directions are
discussed.


Abstract:
  Magneto-electric multipoles, which are odd under both space-inversion $\cal
I$ and time-reversal $\cal T$ symmetries, are fundamental in understanding and
characterizing magneto-electric materials. However, the detection of these
magneto-electric multipoles is often not straightforward as they remain
"hidden" in conventional experiments in part since many magneto-electrics
exhibit combined $\cal IT$ symmetry. In the present work, we show that the
anti-symmetric Compton profile is a unique signature for all the
magneto-electric multipoles, since the asymmetric magnetization density of the
magneto-electric multipoles couples to space via spin-orbit coupling, resulting
in an anti-symmetric Compton profile. We develop the key physics of the
anti-symmetric Compton scattering using symmetry analysis and demonstrate it
using explicit first-principles calculations for two well-known representative
materials with magneto-electric multipoles, insulating LiNiPO$_4$ and metallic
Mn$_2$Au. Our work emphasizes the crucial roles of the orientation of the spin
moments, the spin-orbit coupling, and the band structure in generating the
anti-symmetric Compton profile in magneto-electric materials.


Abstract:
  We use the functional renormalization approach for quantum spin systems
developed by Krieg and Kopietz [Phys. Rev. B $\mathbf{99}$, 060403(R) (2019)]
to calculate the spin-spin correlation function $G (\boldsymbol{k}, \omega )$
of quantum Heisenberg magnets at infinite temperature. For small wavevectors
$\boldsymbol{k} $ and frequencies $\omega$ we find that $G ( \boldsymbol{k},
\omega )$ assumes in dimensions $d > 2$ the diffusive form predicted by
hydrodynamics. In three dimensions our result for the spin-diffusion
coefficient ${\cal{D}}$ is somewhat smaller than previous theoretical
predictions based on the extrapolation of the short-time expansion, but is
still about $30 \%$ larger than the measured high-temperature value of
${\cal{D}}$ in the Heisenberg ferromagnet Rb$_2$CuBr$_4\cdot$2H$_2$O. In
reduced dimensions $d \leq 2$ we find superdiffusion characterized by a
frequency-dependent complex spin-diffusion coefficient ${\cal{D}} ( \omega )$
which diverges logarithmically in $d=2$, and as a power-law ${\cal{D}} ( \omega
) \propto \omega^{-1/3}$ in $d=1$. Our result in one dimension implies scaling
with dynamical exponent $z =3/2$, in agreement with recent calculations for
integrable spin chains. Our approach is not restricted to the hydrodynamic
regime and allows us to calculate the dynamic structure factor $S (
\boldsymbol{k} , \omega )$ for all wavevectors. We show how the
short-wavelength behavior of $S ( \boldsymbol{k}, \omega )$ at high
temperatures reflects the relative sign and strength of competing exchange
interactions.


Abstract:
  We have improved our previous variational method based constituent quark
model by introducing a complete set of 3-dimensional harmonic oscillator bases
as the spatial part of the total wave function. To assess the validity of our
approach, we compared the binding energy, thus calculated with the exact value
for the hydrogen model. After fitting to the masses of the ground state
hadrons, we apply our new method to analyze the doubly-heavy tetraquark states
$qq'\bar{Q}\bar{Q'}$ and compared the result for the binding energies with that
from other works. We also calculated the ground state masses of $T_{sc}
(ud\bar{s}\bar{c})$ and $T_{sb} (ud\bar{s}\bar{b})$ with $(I,S) = (0,1),
(0,2)$. We found that $T_{bb} (ud\bar{b}\bar{b})$ and $us\bar{b}\bar{b}$, both
with $(I,S) = (0,1)$, are stable against the two lowest threshold meson states
with binding energies $-145$ MeV and $-42$ MeV, respectively. We further found
that $T_{cb} (ud\bar{c}\bar{b})$ is near the lowest threshold. The spatial
sizes for the tetraquarks are also discussed.


Abstract:
  While commodity GPUs provide a continuously growing range of features and
sophisticated methods for accelerating compute jobs, many state-of-the-art
solutions for point cloud rendering still rely on the provided point primitives
(GL_POINTS, POINTLIST, ...) of graphics APIs for image synthesis. In this
paper, we present several compute-based point cloud rendering approaches that
outperform the hardware pipeline by up to an order of magnitude and achieve
significantly better frame times than previous compute-based methods. Beyond
basic closest-point rendering, we also introduce a fast, high-quality variant
to reduce aliasing. We present and evaluate several variants of our proposed
methods with different flavors of optimization, in order to ensure their
applicability and achieve optimal performance on a range of platforms and
architectures with varying support for novel GPU hardware features. During our
experiments, the observed peak performance was reached rendering 796 million
points (12.7GB) at rates of 62 to 64 frames per second (50 billion points per
second, 802GB/s) on an RTX 3090 without the use of level-of-detail structures.
  We further introduce an optimized vertex order for point clouds to boost the
efficiency of GL_POINTS by a factor of 5x in cases where hardware rendering is
compulsory. We compare different orderings and show that Morton sorted buffers
are faster for some viewpoints, while shuffled vertex buffers are faster in
others. In contrast, combining both approaches by first sorting according to
Morton-code and shuffling the resulting sequence in batches of 128 points leads
to a vertex buffer layout with high rendering performance and low sensitivity
to viewpoint changes.


Abstract:
  The main result of this paper states that for a given countable system of
data, there exists a countable iterated function system consisting of Rakotch
contractions, such that its attractor is the graph of a fractal interpolation
function corresponding to the given countable system of data.


Abstract:
  We establish the existence and find some qualitative properties of open sets
that minimize functionals of the form $
F(\lambda_1(\Omega;\beta),\dots,\lambda_k(\Omega;\beta))$ under measure
constraint on $\Omega$, where $\lambda_i(\Omega;\beta)$ designates the $i$-th
eigenvalue of the Laplace operator on $\Omega$ with Robin boundary conditions
of parameter $\beta>0$. Moreover, we show that minimizers of
$\lambda_k(\Omega;\beta)$ for $k\geq 2$ verify the conjecture
$\lambda_k(\Omega;\beta)=\lambda_{k-1}(\Omega;\beta)$ in dimension three and
more.


Abstract:
  Let $R$ be a commutative ring with nonzero identity. Let $\mathcal{I}(R)$ be
the set of all ideals of $R$ and let $\delta : \mathcal{I}(R)\longrightarrow
\mathcal{I}(R)$ be a function. Then $\delta$ is called an expansion function of
ideals of $R$ if whenever $L, I, J$ are ideals of R with $J \subseteq I$, we
have $L \subseteq \delta( L)$ and $\delta(J)\subseteq \delta(I)$. Let $\delta$
be an expansion function of ideals of $R$. In this paper, we introduce and
investigate a new class of ideals that is closely related to the class of
$\delta$-primary ideals. A proper ideal $I$ of $R$ is said to be a
$1$-absorbing $\delta$-primary ideal if whenever nonunit elements $a,b,c \in R
$ and $abc\in I$, then $ab \in I$ or $c\in \delta(I).$ Moreover, we give some
basic properties of this class of ideals and we study the $1$-absorbing
$\delta$-primary ideals of the localization of rings, the direct product of
rings and the trivial ring extensions.


Abstract:
  The Hall-D Online Skim System (HOSS) was developed to simultaneously solve
two issues for the high intensity GlueX experiment. One was to parallelize the
writing of raw data files to disk in order to improve bandwidth. The other was
to distribute the raw data across multiple compute nodes in order to produce
calibration \textit{skims} of the data online. The highly configurable system
employs RDMA, RAM disks, and zeroMQ driven by Python to simultaneously store
and process the full high intensity GlueX data stream.


Abstract:
  We investigate second harmonic generation (SHG) from hexagonal periodic
arrays of triangular nano-holes of aluminum using a self-consistent methodology
based on the hydrodynamics-Maxwell-Bloch approach. It is shown that angular
polarization patterns of the far-field second harmonic response abide to
three-fold symmetry constraints on tensors. When a molecular layer is added to
the system and its parameters are adjusted to achieve the strong coupling
regime between a localized plasmon mode and molecular excitons, Rabi splitting
is observed from occurrence of both single- and two-photon transition peaks
within the SHG power spectrum. It is argued that the splitting observed for
both transitions results from direct transitions between lower and upper
polaritonic states of the strongly coupled system. This interpretation can be
accounted by a tailored three-level quantum model, with results in agreement
with the unbiased numerical approach. Our results suggest the hybrid states
formed in strongly coupled systems directly contribute to the nonlinear
dynamics. This opens new directions in designing THz sources and nonlinear
frequency converters.


Abstract:
  In this study I proposed a filtering beliefs method for improving performance
of Partially Observable Markov Decision Processes(POMDPs), which is a method
wildly used in autonomous robot and many other domains concerning control
policy. My method search and compare every similar belief pair. Because a
similar belief have insignificant influence on control policy, the belief is
filtered out for reducing training time. The empirical results show that the
proposed method outperforms the point-based approximate POMDPs in terms of the
quality of training results as well as the efficiency of the method.


Abstract:
  Collective strong coupling between a disordered ensemble of $N$ localized
molecular vibrations and a resonant optical cavity mode gives rise to 2
polariton and $N-1\gg2$ dark modes. Thus, experimental changes in
thermally-activated reaction kinetics due to polariton formation appear
entropically unlikely and remain a puzzle. Here we show that the overlooked
dark modes, while parked at the same energy as bare molecular vibrations, are
robustly delocalized across $\sim$2-3 molecules, yielding enhanced channels of
vibrational cooling, concomitantly catalyzing or suppressing a chemical
reaction. As an illustration, we theoretically show a 55% increase in an
electron transfer rate due to enhanced product stabilization.


Abstract:
  In this paper, we address the representation of coordinate constructions in
Enhanced Universal Dependencies (UD), where relevant dependency links are
propagated from conjunction heads to other conjuncts. English treebanks for
enhanced UD have been created from gold basic dependencies using a heuristic
rule-based converter, which propagates only core arguments. With the aim of
determining which set of links should be propagated from a semantic
perspective, we create a large-scale dataset of manually edited syntax graphs.
We identify several systematic errors in the original data, and propose to also
propagate adjuncts. We observe high inter-annotator agreement for this semantic
annotation task. Using our new manually verified dataset, we perform the first
principled comparison of rule-based and (partially novel) machine-learning
based methods for conjunction propagation for English. We show that learning
propagation rules is more effective than hand-designing heuristic rules. When
using automatic parses, our neural graph-parser based edge predictor
outperforms the currently predominant pipelinesusing a basic-layer tree parser
plus converters.


Abstract:
  One of the most common expectations of a quantum theory of gravity is that
space-time is uncertain or fluctuating at microscopic scales, making it a
stochastic medium for particle propagation. Particles traversing this
space-time may experience fluctuations in travel times or velocities, together
referred to as lightcone fluctuations, with even very small effects potentially
accumulating into observable signals over large distances. In this work we
present a heuristic model of lightcone fluctuations and study the resulting
modifications to neutrino propagation, including neutrino decoherence and
arrival time spread. We show the expected scale of such effects due to
`natural' Planck scale physics and consider how they may be observed in
neutrino detectors, and compare the potential of neutrinos to $\gamma$-ray
astronomy. Using simulations of neutrino mass states propagating in a
fluctuating environment, we determine an analytic decoherence operator in the
framework of open quantum systems to quantitatively evaluate neutrino
decoherence resulting from lightcone fluctuations, allowing experimental
constraints on neutrino decoherence to be connected to Planck scale
fluctuations in space-time and $\gamma$-ray results.


Abstract:
  Most of the common applications of Named Entity Recognition (NER) is on
English and other highly available languages. In this work, we present our
findings on Named Entity Recognition for 5 Nigerian Languages (Nigerian
English, Nigerian Pidgin English, Igbo, Yoruba and Hausa). These languages are
considered low-resourced, and very little openly available Natural Language
Processing work has been done in most of them. In this work, individual NER
models were trained and metrics recorded for each of the languages. We also
worked on a combined model that can handle Named Entity Recognition (NER) for
any of the five languages. The combined model works well for Named Entity
Recognition(NER) on each of the languages and with better performance compared
to individual NER models trained specifically on annotated data for the
specific language. The aim of this work is to share our learning on how
information extraction using Named Entity Recognition can be optimized for the
listed Nigerian Languages for inclusion, ease of deployment in production and
reusability of models. Models developed during this project are available on
GitHub https://git.io/JY0kk and an interactive web app
https://nigner.herokuapp.com/.


Abstract:
  We give relative error coresets for training linear classifiers with a broad
class of loss functions, including the logistic loss and hinge loss. Our
construction achieves $(1\pm \epsilon)$ relative error with $\tilde O(d \cdot
\mu_y(X)^2/\epsilon^2)$ points, where $\mu_y(X)$ is a natural complexity
measure of the data matrix $X \in \mathbb{R}^{n \times d}$ and label vector $y
\in \{-1,1\}^n$, introduced in by Munteanu et al. 2018. Our result is based on
subsampling data points with probabilities proportional to their $\ell_1$
$Lewis$ $weights$. It significantly improves on existing theoretical bounds and
performs well in practice, outperforming uniform subsampling along with other
importance sampling methods. Our sampling distribution does not depend on the
labels, so can be used for active learning. It also does not depend on the
specific loss function, so a single coreset can be used in multiple training
scenarios.


Abstract:
  Inspired by non-abelian vortex anyons in spinor Bose-Einstein condensates, we
consider the quantum double $\mathcal{D}(Q_8)$ anyon model as a platform to
carry out a particular instance of Shor's factorization algorithm. We provide
the excitation spectrum, the fusion rules, and the braid group representation
for this model, and design a circuit architecture that facilitates the
computation. All necessary quantum gates, less one, can be compiled exactly for
this hybrid topological quantum computer, and to achieve universality the last
operation can be implemented in a non-topological fashion. To analyse the
effect of decoherence on the computation, a noise model based on stochastic
unitary rotations is considered. The computational potential of this quantum
double anyon model is similar to that of the Majorana fermion based Ising anyon
model, offering a complementary future platform for topological quantum
computation.


Abstract:
  In this paper we prove a positive energy theorem related to fourth-order
gravitational theories, which is a higher-order analogue of the classical ADM
positive energy theorem of general relativity. We will also show that, in
parallel to the corresponding situation in general relativity, this result
intersects several important problems in geometric analysis. For instance, it
underlies positive mass theorems associated to the Paneitz operator, playing a
similar role in the positive $Q$-curvature conformal prescription problem as
the Schoen-Yau positive energy theorem does for the Yamabe problem. Several
other links to $Q$-curvature analysis and rigidity phenomena are established.


Abstract:
  We report on the possibility of studying the proprieties of cosmic diffuse
baryons by studying self-gravitating clumps and filaments connected to galaxy
clusters. While filaments are challenging to detect with X-ray observations,
the higher density of clumps makes them visible and a viable tracer to study
the thermodynamical proprieties of baryons undergoing accretion along cosmic
web filaments onto galaxy clusters. We developed new algorithms to identify
these structures and applied them to a set of non-radiative cosmological
simulations of galaxy clusters at high resolution. We find that in those
simulated clusters, the density and temperature of clumps are independent of
the mass of the cluster where they reside. We detected a positive correlation
between the filament temperature and the host cluster mass. The density and
temperature of clumps and filaments also tended to correlate. Both the
temperature and density decrease moving outward. We observed that clumps are
hotter, more massive, and more luminous if identified closer to the cluster
center. Especially in the outermost cluster regions (~3*R500,c or beyond),
X-ray observations might already have the potential to locate cosmic filaments
based on the distribution of clumps and to allow one to study the
thermodynamics of diffuse baryons before they are processed by the intracluster
medium.


Abstract:
  Common deployment models for Edge Computing are based on (composable)
microservices that are offloaded to cloudlets. Runtime adaptations-in response
to varying load, QoS fulfillment, mobility, etc.-are typically based on
coarse-grained and costly management operations such as resource re-allocation
or migration. The services themselves, however, remain non-adaptive, worsening
the already limited elasticity of Edge Computing compared to Cloud Computing.
Edge computing applications often have stringent requirements on the execution
time but are flexible regarding the quality of a computation. The potential
benefits of exploiting this trade-off remain untapped. This paper introduces
the concept of adaptable microservices that provide alternative variants of
specific functionalities. We define so-called service variants that differ
w.r.t. the internal functioning of the service, manifested in different
algorithms, parameters, and auxiliary data they use. Such variants allow
fine-grained trade-offs between the QoS (e.g., a maximum tolerable execution
time) and the quality of the computation. We integrate adaptable microservices
into an Edge Computing framework, show the practical impact of service
variants, and present a strategy for switching variants at runtime.


Abstract:
  In the past decade we have witnessed the failure of traditional polls in
predicting presidential election outcomes across the world. To understand the
reasons behind these failures we analyze the raw data of a trusted pollster
which failed to predict, along with the rest of the pollsters, the surprising
2019 presidential election in Argentina which has led to a major market
collapse in that country. Analysis of the raw and re-weighted data from
longitudinal surveys performed before and after the elections reveals clear
biases (beyond well-known low-response rates) related to mis-representation of
the population and, most importantly, to social-desirability biases, i.e., the
tendency of respondents to hide their intention to vote for controversial
candidates. We then propose a longitudinal opinion tracking method based on
big-data analytics from social media, machine learning, and network theory that
overcomes the limits of traditional polls. The model achieves accurate results
in the 2019 Argentina elections predicting the overwhelming victory of the
candidate Alberto Fern\'andez over the president Mauricio Macri; a result that
none of the traditional pollsters in the country was able to predict. Beyond
predicting political elections, the framework we propose is more general and
can be used to discover trends in society; for instance, what people think
about economics, education or climate change.


Abstract:
  We consider manifestly crossing symmetric dispersion relations for Mellin
amplitudes of scalar four point correlators in conformal field theories (CFTs).
This allows us to set up the non-perturbative Polyakov bootstrap for CFTs in
Mellin space on a firm foundation, thereby fixing the contact term ambiguities
in the crossing symmetric blocks. Our new approach employs certain "locality"
constraints replacing the requirement of crossing symmetry in the usual
fixed-$t$ dispersion relation. Using these constraints we show that the sum
rules based on the two channel dispersion relations and the present dispersion
relations are identical. Our framework allows us to connect with the
conceptually rich picture of the Polyakov blocks being Witten diagrams in
anti-de Sitter (AdS) space. We also give two sided bounds for Wilson
coefficients for effective field theories in AdS space.


Abstract:
  There is observational evidence of the presence of small-amplitude transverse
magnetohydrodynamic (MHD) waves with a wide range of frequencies in the threads
of solar prominences. It is believed that the waves are driven at the
photosphere and propagate along the magnetic field lines up to prominences
suspended in the corona. The dissipation of MHD wave energy in the partially
ionized prominence plasma is a heating mechanism whose relevance needs to be
explored. Here we consider a simple 1D model for a non-uniform thin thread and
investigate the heating associated with dissipation of Alfven waves. The model
assumes an ad hoc density profile and a uniform pressure, while the temperature
and ionization degree are self-consistently computed considering either LTE or
non-LTE approximations for the hydrogen ionization. A broadband driver for
Alfven waves is placed at one end of the magnetic field line, representing
photospheric excitation. The Alfvenic perturbations along the thread are
obtained by solving the linearized MHD equations for a partially ionized plasma
in the single-fluid approximation.We find that wave heating in the partially
ionized part of the thread is significant enough to compensate for energy
losses due to radiative cooling. A greater amount of heating is found in the
LTE case because the ionization degree for core prominence temperatures is
lower than that in the non-LTE approximation. This results in a greater level
of dissipation due to ambipolar diffusion in the LTE case. Conversely, in the
hot coronal part of the model, the plasma is fully ionized and wave heating is
negligible. The results of this simple model suggest that MHD wave heating can
be relevant for the energy balance in prominences. Further studies based on
more elaborate models are required.


Abstract:
  In video-assisted thoracoscopic surgeries, successful procedures of nodule
resection are highly dependent on the precise estimation of lung deformation
between the inflated lung in the computed tomography (CT) images during
preoperative planning and the deflated lung in the treatment views during
surgery. Lungs in the pneumothorax state during surgery have a large volume
change from normal lungs, making it difficult to build a mechanical model. The
purpose of this study is to develop a deformation estimation method of the 3D
surface of a deflated lung from a few partial observations. To estimate
deformations for a largely deformed lung, a kernel regression-based solution
was introduced. The proposed method used a few landmarks to capture the partial
deformation between the 3D surface mesh obtained from preoperative CT and the
intraoperative anatomical positions. The deformation for each vertex of the
entire mesh model was estimated per-vertex as a relative position from the
landmarks. The landmarks were placed in the anatomical position of the lung's
outer contour. The method was applied on nine datasets of the left lungs of
live Beagle dogs. Contrast-enhanced CT images of the lungs were acquired. The
proposed method achieved a local positional error of vertices of 2.74 mm,
Hausdorff distance of 6.11 mm, and Dice similarity coefficient of 0.94.
Moreover, the proposed method could estimate lung deformations from a small
number of training cases and a small observation area. This study contributes
to the data-driven modeling of pneumothorax deformation of the lung.


Abstract:
  A simplicial complex is a generalization of a graph: a collection of n-ary
relationships (instead of binary as the edges of a graph), named simplices. In
this paper, we develop a new tool to study the structure of simplicial
complexes: we generalize the graph notion of truss decomposition to complexes,
and show that this more powerful representation gives rise to different
properties compared to the graph-based one. This power, however, comes with
important computational challenges derived from the combinatorial explosion
caused by the downward closure property of complexes. Drawing upon ideas from
itemset mining and similarity search, we design a memory-aware algorithm,
dubbed STruD, which is able to efficiently compute the truss decomposition of a
simplicial complex. STruD adapts its behavior to the amount of available memory
by storing intermediate data in a compact way. We then devise a variant that
computes directly the n simplices of maximum trussness. By applying STruD to
several datasets, we prove its scalability, and provide an analysis of their
structure. Finally, we show that the truss decomposition can be seen as a
filtration, and as such it can be used to study the persistent homology of a
dataset, a method for computing topological features at different spatial
resolutions, prominent in Topological Data Analysis.


Abstract:
  It is commonly believed that a massive real scalar field $\phi$ only mediates
short-range interactions on the scale of its Compton wavelength via the Yukawa
potential. However, in the nonperturbative regime of nonlinear self coupling,
$\phi$ can also mediate larger scale interactions. Moreover, the classical
potential, namely, the static configuration of $\phi$ in the presence of an
external source, is not always unique for given boundary conditions. In this
paper, a complete set of finite-energy potentials (FEPs) induced by a Gaussian
source is identified in one, two, and three spatial dimensions when the
nonlinearity is of the Mexican-hat type, which is often prescribed to induce
spontaneous symmetry breaking. In the size-strength parameter space of the
source, phase boundaries are mapped out, across which the number of FEPs
differ. Additionally, softer phase transitions are delineated according to
whether the potential exhibits a light horizon at which $\phi$ vanishes. The
light horizon is of physical significance when $\phi$ couples with other
particles. For example, when $\phi$ is the Higgs field, all elementary
particles become massless at the light horizon. It is remarkable that white
dwarfs and neutron stars are potentially in a phase where light horizons exist,
whose outer radii are a few times the star sizes. Moreover, suppose elementary
fermions of mass greater than $\sim10^3$ GeV exist, then they may also be
surrounded by light horizons with radii comparable to the Higgs Compton
wavelength. Finally, nonperturbative states may also be realized in condensed
matter systems, wherein phase transitions are controllable using localized
sources.


Abstract:
  We show that the Rokhsar-Kivelson (RK) point in quantum dimer models (QDM)
can emerge in realistic quantum Ising spin systems. Specifically, we
investigate the $J_1$-$J_2$-$J_3$ transverse field Ising model on the
triangular lattice with large-scale quantum Monte Carlo simulations. We find
that the multicritical point in the phase diagram corresponds to the RK point
of the QDM on the honeycomb lattice. We further measure the spectral functions
and identify three branches of quadratic dispersions. In the phase diagram, we
also find a sequence of incommensurate states, which meet the microscopic
ingredients for the 'Cantor deconfinement' scenario. Our study provides a
promising direction to realise the RK deconfinement in experimental platforms
such as magnetic materials or programmable Rydberg arrays.


Abstract:
  While patents and standards have been identified as essential driving
components of innovation and market growth, the inclusion of a patent in a
standard poses many difficulties. These difficulties arise from the
contradicting natures of patents and standards, which makes their combination
really challenging, but, also, from the opposing business and market strategies
of different patent owners involved in the standardisation process. However, a
varying set of policies has been adopted to address the issues occurring from
the unavoidable inclusion of patents in standards concerning certain industry
sectors with a constant high degree of innovation, such as telecommunications.
As these policies have not always proven adequate enough, constant efforts are
being made to improve and expand them. The intriguing and complicated
relationship between patents and standards is finally examined through a review
of the use cases of well-known standards of the telecommunications sector which
include a growing set of essential patents.


Abstract:
  In this paper we study perturbations of rational Collet-Eckmann maps for
which the Julia set is the whole sphere, and for which the critical set is
allowed to be slowly recurrent. We show that any such map is a Lebesgue density
point of Collet-Eckmann maps in the space of rational maps of the same degree
at least $2$.


Abstract:
  Typically discussed in the context of optics, caustics are envelopes of
classical trajectories (rays) where the density of states diverges, resulting
in pronounced observable features such as bright points, curves, and extended
networks of patterns. Despite the apparent complexity of such patterns, they
are based on just a small number of fundamental forms described by the theory
of catastrophe optics. Here, we demonstrate caustics in the matter waves of an
atom laser, extending catastrophe optics to catastrophe atom optics. We
showcase the generic forms of fold and cusp caustics, and exploit internal
state manipulation to trace the flow of atoms. Atom optics affords new
perspectives for fundamental science, metrology, atom interferometry and
nano-fabrication techniques. Exploring the role of caustics in this context may
lead to innovation as the generation of caustics is inherently robust, and
leads to a variety of pronounced and sharply delineated features.


Abstract:
  A novel Lattice QCD (LQCD) method to determine the quark-diquark ($q$-$D$)
interaction potential together with the diquark mass ($m_D$) is proposed.
Similar to the HAL QCD method, $q$-$D$ potential is determined by demanding it
to reproduce the $q$-$D$ equal-time Nambu-Bethe-Salpeter (NBS) wave function.
To do this, it is necessary to use the masses of the quark and the diquark as
inputs, which however are not straightforwardly obtained because of the color
confinement of QCD. In this work, masses of quark and diquark are determined by
demanding that the p-wave spectrums from the two-point correlators be
reproduced by the potentials for $c$-$\bar{c}$ and $q$-$D$ sectors determined
from the NBS wave functions. Numerical calculations are performed by using 2+1
flavor QCD gauge configurations with the pion mass $m_\pi\simeq 700$ MeV
generated by PACS-CS collaboration. We apply our method to the $c$-$\bar c$
system and the charm-diquark system ($\Lambda_c$ baryon) to obtain the charm
quark mass, diquark mass and the $c$-$D$ potential. Our preliminary analysis
leads to the diquark mass $m_D \simeq 1.127$ GeV which is roughly consistent
with a naive estimate based on the constituent quark picture, i.e., $m_{D}
\simeq m_{\rho} \simeq 1.12$ GeV and $m_{D} \simeq 2m_N/3 \simeq 1.06$ GeV.


Abstract:
  Single-molecule magnets (SMMs) exhibiting open hysteresis loops may
potentially apply to molecule-based information processing and storage.
However, the capacity to retain magnetic memory is always limited by zero-field
quantum tunneling of magnetization (QTM). Herein, a well-designed dysprosium
metallacrown SMM, consisting of an endohedral approximate linear F-Dy-F strong
anisotropic moiety in a peripheral [15-MCNi-5] metallacrown (MC), is reported
with the largest reversal barrier of 1060 cm-1 among d-f SMMs. Rectangle-like
hysteresis loops are observed with the huge squareness (remanence/saturation
magnetization) up to 97% at 2 K. More importantly, zero-field QTM step is
phenomenologically removed by minimizing the dipole coupling and hyperfine
interactions. The results demonstrate for the first time that zero-field QTM
step can be eliminated via manipulating the ligand field and vanishing the
external magnetic perturbations, which illuminates a promising blueprint for
developing high-performance SMMs.


Abstract:
  This study compares the modularity performance of two artificial neural
network architectures: a Long Short-Term Memory (LSTM) recurrent network, and
Morphognosis, a neural network based on a hierarchy of spatial and temporal
contexts. Mazes are used to measure performance, defined as the ability to
utilize independently learned mazes to solve mazes composed of them. A maze is
a sequence of rooms connected by doors. The modular task is implemented as
follows: at the beginning of the maze, an initial door choice forms a context
that must be retained until the end of an intervening maze, where the same door
must be chosen again to reach the goal. For testing, the door-association mazes
and separately trained intervening mazes are presented together for the first
time. While both neural networks perform well during training, the testing
performance of Morphognosis is significantly better than LSTM on this modular
task.


Abstract:
  In this article, we study the dynamics of geodesic flows on Riemannian (not
necessarily compact) manifolds with no conjugate points. We prove the Anosov
Closing Lemma, the local product structure, and the transitivity of the
geodesic flows on $\Omega_1$ under the conditions of bounded asymptote and
uniform visibility. As an application, we further discuss about some generic
properties of the set of invariant probability measures


Abstract:
  In a graph $G$, a vertex dominates itself and its neighbours. A set
$D\subseteq V(G)$ is said to be a $k$-tuple dominating set of $G$ if $D$
dominates every vertex of $G$ at least $k$ times. The minimum cardinality among
all $k$-tuple dominating sets is the $k$-tuple domination number of $G$. In
this paper, we provide new bounds on this parameter. Some of these bounds
generalize other ones that have been given for the case $k=2$. In addition, we
improve two well-known lower bounds on the $k$-tuple domination number.


Abstract:
  In this paper, cooperative non-orthogonal multiple access (C-NOMA) is
considered in short packet communications with finite blocklength (FBL) codes.
The performance of a decode-and-forward (DF) relaying along with selection
combining (SC) and maximum ratio combining (MRC) strategies at the receiver
side is examined. We explore joint user pairing and resource allocation to
maximize fair throughput in a downlink (DL) scenario. In each pair, the user
with a stronger channel (strong user) acts as a relay for the other one (weak
user), and optimal power and blocklength are allocated to achieve max-min
throughput. To this end, first, only one pair is considered, and optimal
resource allocation is explored. Also, a suboptimal algorithm is suggested,
which converges to a near-optimal solution. Finally, the problem is extended to
a general scenario, and a suboptimal C-NOMA-based user pairing is proposed.
Numerical results show that the proposed C-NOMA scheme in both SC and MRC
strategies significantly improves the users' fair throughput compared to the
NOMA and OMA. It is also investigated that the proposed pairing scheme based on
C-NOMA outperforms the Hybrid NOMA/OMA scheme from the average throughput
perspective, while the fairness index degrades slightly.


Abstract:
  In 2020, the COVID-19 pandemic spreads all over the world. In order to
alleviate the spread of the epidemic, various blockade policies have been
implemented in many areas. In order to formulate a better epidemic prevention
policy for urban energy consumption of the commercial tourism cities, this
paper first analyses the energy characteristics of Macao during the epidemic
period from two aspects, based on the energy consumption data of Macao. On this
basis, the power consumption characteristics of commercial tourism cities
during the epidemic were analyzed. Then, this paper provides analysis of the
characteristics of the energy consumption in different fields of commercial
tourism cities from the aspects of hotel, transportation, tourism culture and
public utilities. Finally, a detailed analysis of the energy consumption
characteristics of commercial tourism cities represented by Macao during the
epidemic period is provided, by comparing with some typical countries.


Abstract:
  With the large data set delivered during the second run of the CERN LHC,
inclusive and differential measurements of the top quark-antiquark production
cross section at the ATLAS and CMS experiments often reach a precision that is
comparable or superior to that of the corresponding theoretical calculations.
The results of these measurements can be used to test the validity of
perturbative quantum chromodynamics (QCD), and to precisely extract the values
of parameters of the QCD Lagrangian such as the top quark mass
($m_\mathrm{t}$). The value of $m_\mathrm{t}$ can also be measured, with higher
precision, by fully or partially reconstructing the invariant mass of the top
quark decay products. However, the results of these measurements lack a clear
theoretical interpretation. In these proceedings, the most recent measurements
of the top quark mass and cross sections by ATLAS and CMS are presented.


Abstract:
  This work presents an analysis of the efficiency of image augmentations for
the face recognition problem from limited data. We considered basic
manipulations, generative methods, and their combinations for augmentations.
Our results show that augmentations, in general, can considerably improve the
quality of face recognition systems and the combination of generative and basic
approaches performs better than the other tested techniques.


Abstract:
  Accurately extracting structured content from PDFs is a critical first step
for NLP over scientific papers. Recent work has improved extraction accuracy by
incorporating elementary layout information, e.g., each token's 2D position on
the page, into language model pretraining. We introduce new methods that
explicitly model VIsual LAyout (VILA) groups, i.e., text lines or text blocks,
to further improve performance. In our I-VILA approach, we show that simply
inserting special tokens denoting layout group boundaries into model inputs can
lead to a 1.9% Macro F1 improvement in token classification. In the H-VILA
approach, we show that hierarchical encoding of layout-groups can result in
up-to 47% inference time reduction with less than 0.8% Macro F1 loss. Unlike
prior layout-aware approaches, our methods do not require expensive additional
pretraining, only fine-tuning, which we show can reduce training cost by up to
95%. Experiments are conducted on a newly curated evaluation suite, S2-VLUE,
that unifies existing automatically-labeled datasets and includes a new dataset
of manual annotations covering diverse papers from 19 scientific disciplines.
Pre-trained weights, benchmark datasets, and source code are available at
https://github.com/allenai/VILA.


Abstract:
  We consider the time interval ${\Delta}$ during which the market trade
time-series are averaged as the key factor of the consumption-based
asset-pricing model that causes modification of the basic pricing equation. The
duration of ${\Delta}$ determines Taylor series of investor's utility over
current and future values of consumption. We present consumption at current and
future moments as sums of their mean values and perturbations during ${\Delta}$
of the price at current moment t and perturbations of the payoff at day t+1.
For linear and quadratic Taylor series approximations of the basic equation we
obtain new relations on mean price, mean payoff, their volatilities, skewness
and amount of asset ${\xi}_{max}$ that delivers max to investor's utility. The
stochasticity of market trade time-series defines random properties of the
asset price time-series during ${\Delta}$. We introduce new market-based price
probability measure entirely determined by frequency-based probability measures
of the market trade value and volume. The conventional frequency-based price
probability is a very special case of the market-based price probability
measure when all trade volumes during ${\Delta}$ equal unit. Prediction of the
market-based price probability at horizon T equals forecast of the market trade
value and volume probabilities at same horizon. The similar Taylor series and
probability measures alike to market-based price probability can be used as
approximations of different versions of asset pricing, financial and economic
models that describe relations between economic and financial variables
averaged during some time interval ${\Delta}$.


Abstract:
  Human operators often diagnose industrial machinery via anomalous sounds.
Automated acoustic anomaly detection can lead to reliable maintenance of
machinery. However, deep learning-driven anomaly detection methods often
require an extensive amount of computational resources which prohibits their
deployment in factories. Here we explore a machine-driven design exploration
strategy to create OutlierNets, a family of highly compact deep convolutional
autoencoder network architectures featuring as few as 686 parameters, model
sizes as small as 2.7 KB, and as low as 2.8 million FLOPs, with a detection
accuracy matching or exceeding published architectures with as many as 4
million parameters. Furthermore, CPU-accelerated latency experiments show that
the OutlierNet architectures can achieve as much as 21x lower latency than
published networks.


Abstract:
  In this paper, we prove that the divisor class group of a double cover of the
complex projective space $\mathbb{P}^n$ is generated by divisorial sheaves
whose direct images split into direct sums of two invertible sheaves on
$\mathbb{P}^n$. This result shows that any locally free sheaf of rank two on
$\mathbb{P}^n$ is generated by direct sums of line bundles on $\mathbb{P}^n$
via some double cover. Moreover, we give a condition for an irreducible divisor
on $\mathbb{P}^n$ to be a splitting divisor for a double cover whose divisor
class group is finitely generated.


Abstract:
  We study the non-parametric estimation of an unknown density f with support
on R+^d based on an i.i.d. sample with multiplicative measurement errors. The
proposed fully-data driven procedure is based on the estimation of the Mellin
transform of the density f and a regularisation of the inverse of the Mellin
transform by a spectral cut-off. The upcoming bias-variance trade-off is dealt
with by a data-driven anisotropic choice of the cut-off parameter. In order to
discuss the bias term, we consider the Mellin-Sobolev spaces which characterize
the regularity of the unknown density f through the decay of its Mellin
transform. Additionally, we show minimax-optimality over Mellin-Sobolev spaces
of the spectral cut-off density estimator.


Abstract:
  In this monograph, we give an account of the relationship between the
algebraic structure of finitely generated and countable groups and the
regularity with which they act on manifolds. We concentrate on the case of
one--dimensional manifolds, culminating with a uniform construction of finitely
generated groups acting with prescribed regularity on the compact interval and
on the circle. We develop the theory of dynamical obstructions to smoothness,
beginning with classical results of Denjoy, to more recent results of Kopell,
and to modern results such as the $abt$--Lemma. We give a classification of the
right-angled Artin groups that have finite critical regularity and discuss
their exact critical regularities in many cases, and we compute the virtual
critical regularity of most mapping class groups of orientable surfaces.


Abstract:
  Conventional saliency prediction models typically learn a deterministic
mapping from images to the corresponding ground truth saliency maps. In this
paper, we study the saliency prediction problem from the perspective of
generative models by learning a conditional probability distribution over
saliency maps given an image, and treating the prediction as a sampling
process. Specifically, we propose a generative cooperative saliency prediction
framework based on the generative cooperative networks, where a conditional
latent variable model and a conditional energy-based model are jointly trained
to predict saliency in a cooperative manner. We call our model the SalCoopNets.
The latent variable model serves as a fast but coarse predictor to efficiently
produce an initial prediction, which is then refined by the iterative Langevin
revision of the energy-based model that serves as a fine predictor. Such a
coarse-to-fine cooperative saliency prediction strategy offers the best of both
worlds. Moreover, we generalize our framework to the scenario of weakly
supervised saliency prediction, where saliency annotation of training images is
partially observed, by proposing a cooperative learning while recovering
strategy. Lastly, we show that the learned energy function can serve as a
refinement module that can refine the results of other pre-trained saliency
prediction models. Experimental results show that our generative model can
achieve state-of-the-art performance. Our code is publicly available at:
\url{https://github.com/JingZhang617/SalCoopNets}.


Abstract:
  In this paper, we address the problem of training deep neural networks in the
presence of severe label noise. Our proposed training algorithm ScanMix,
combines semantic clustering with semi-supervised learning (SSL) to improve the
feature representations and enable an accurate identification of noisy samples,
even in severe label noise scenarios. To be specific, ScanMix is designed based
on the expectation maximisation (EM) framework, where the E-step estimates the
value of a latent variable to cluster the training images based on their
appearance representations and classification results, and the M-step optimises
the SSL classification and learns effective feature representations via
semantic clustering. In our evaluations, we show state-of-the-art results on
standard benchmarks for symmetric, asymmetric and semantic label noise on
CIFAR-10 and CIFAR-100, as well as large scale real label noise on WebVision.
Most notably, for the benchmarks contaminated with large noise rates (80% and
above), our results are up to 27% better than the related work. The code is
available at https://github.com/ragavsachdeva/ScanMix.