title: On Survivorship Bias in MS MARCO

abstract:
  Survivorship bias is the tendency to concentrate on the positive outcomes of
a selection process and overlook the results that generate negative outcomes.
We observe that this bias could be present in the popular MS MARCO dataset,
given that annotators could not find answers to 38--45% of the queries, leading
to these queries being discarded in training and evaluation processes. Although
we find that some discarded queries in MS MARCO are ill-defined or otherwise
unanswerable, many are valid questions that could be answered had the
collection been annotated more completely (around two thirds using modern
ranking techniques). This survivability problem distorts the MS MARCO
collection in several ways. We find that it affects the natural distribution of
queries in terms of the type of information needed. When used for evaluation,
we find that the bias likely yields a significant distortion of the absolute
performance scores observed. Finally, given that MS MARCO is frequently used
for model training, we train models based on subsets of MS MARCO that simulates
more survivorship bias. We find that models trained in this setting are up to
9.9% worse when evaluated on versions of the dataset with more complete
annotations, and up to 3.5% worse at zero-shot transfer. Our findings are
complementary to other recent suggestions for further annotation of MS MARCO,
but with a focus on discarded queries. Code and data for reproducing the
results of this paper are available in an online appendix.


title:
EXFOR-based simultaneous evaluation of neutron-induced uranium and
  plutonium fission cross sections for JENDL-5

abstract:
  The neutron-induced fission cross sections were simultaneously evaluated for
the JENDL-5 library for $^{233,235}$U and $^{239,241}$Pu from 10 keV to 200 MeV
and for $^{238}$U and $^{240}$Pu from 100 keV to 200 MeV. Evaluation was
performed by least-squares fitting of Schmittroth's roof function to the
logarithms of the experimental cross sections and cross section ratios in the
EXFOR library. A simultaneous evaluation code SOK was used with its extension
to data in arbitrary unit. The outputs of the code were adopted as the
evaluated cross sections without any further corrections. The newly obtained
evaluated cross sections were compared with the evaluated cross sections in the
JENDL-4.0 library and the IAEA Neutron Data Standards 2017. The evaluated cross
sections were also validated against the californium-252 spontaneous fission
neutron spectrum averaged cross sections, $\Sigma\Sigma$ (coupled thermal/fast
uranium and boron carbide spherical assembly) neutron spectrum averaged cross
sections, and small-sized LANL fast system criticalities. The changes in the
obtained evaluated cross sections from those in the JENDL-4.0 library are
within 4% ($^{241}$Pu), 3% ($^{233}$U, $^{240}$Pu), or 2% ($^{235}$U,
$^{239}$Pu). The newly evaluated $^{235}$U, $^{238}$U and $^{239}$Pu cross
sections agree with the IAEA Neutron Data Standards 2017 within 2% with some
exceptions.


title: Quasiconformal, Lipschitz, and BV mappings in metric spaces

abstract:
  Consider a mapping $f\colon X\to Y$ between two metric measure spaces. We
study generalized versions of the local Lipschitz number $\mathrm{Lip} f$, as
well as of the distortion number $H_f$ that is used to define quasiconformal
mappings. Using these, we give sufficient conditions for $f$ being a BV mapping
$f\in BV_{\mathrm{loc}}(X;Y)$ or a Newton-Sobolev mapping $f\in
N_{\mathrm{loc}}^{1,p}(X;Y)$, with $1\le p<\infty$.


title:
Prediction Approach against DDoS Attack based on Machine Learning
  Multiclassfier

abstract:
  DDoS attacks, also known as distributed denial of service (DDoS) attacks,
have emerged as one of the most serious and fastest-growing threats on the
Internet. Denial-of-service (DDoS) attacks are an example of cyber attacks that
target a specific system or network in an attempt to render it inaccessible or
unusable for a period of time. As a result, improving the detection of diverse
types of DDoS cyber threats with better algorithms and higher accuracy while
keeping the computational cost under control has become the most significant
component of detecting DDoS cyber threats. In order to properly defend the
targeted network or system, it is critical to first determine the sort of DDoS
assault that has been launched against it. A number of ensemble classification
techniques are presented in this paper, which combines the performance of
various algorithms. They are then compared to existing Machine Learning
Algorithms in terms of their effectiveness in detecting different types of DDoS
attacks using accuracy, F1 scores, and ROC curves. The results show high
accuracy and good performance.


title: Insight into Voting Problem Complexity Using Randomized Classes

abstract:
  The first step in classifying the complexity of an NP problem is typically
showing the problem in P or NP-complete. This has been a successful first step
for many problems, including voting problems. However, in this paper we show
that this may not always be the best first step. We consider the problem of
constructive control by replacing voters (CCRV) introduced by Loreggia et al.
(2015) for the scoring rule First-Last, which is defined by $\langle 1, 0,
\dots, 0, -1\rangle$. We show that this problem is equivalent to Exact Perfect
Bipartite Matching (EPBM), and so CCRV for First-Last can be determined in
random polynomial time. So on the one hand, if CCRV for First-Last is
NP-complete then RP = NP, which is extremely unlikely. On the other hand,
showing that CCRV for First-Last is in P would also show that EPBM is in P,
which would solve a well-studied 40-year-old open problem.
  Considering RP as an option for classifying problems can also help classify
problems that until now had escaped classification. For example, the sole open
problem in the comprehensive table from Erd\'{e}lyi et al. (2021) is CCRV for
2-Approval. We show that this problem is in RP, and thus easy since it is
widely assumed that P = RP.


title: Topological energy braiding of the non-Bloch bands

abstract:
  Recent works discovered that the complex energies of non-Hermitian periodic
systems braid topologically on Brillouin zone. However, the non-Hermitian skin
effect, a unique feature of non-Hermitian systems, will break the topological
energy braiding in open systems. Here, we introduce a non-Hermitian
tight-binding model to reveal the interplay between the topological energy
braiding and the non-Hermitian skin effect of the open systems. Surprisingly,
the non-Hermitian skin effect can induces the topological energy braiding of
the non-Bloch bands in open systems such as Hopf link, despite the
topologically trivial Bloch bands in periodic system. Furthermore, we uncover
that the topology of the non-Bloch bands can be characterized by non-Bloch
winding number and vorticity defined on the generalized Brillouin zone. The
topological phase transition between distinct non-Bloch bands occurs with the
separation closing at exceptional points. We hope that our results can shed the
light on the topological energy braiding of the non-Bloch bands for
non-Hermitian systems.


title:
Reducing number of gates in quantum random walk search algorithm via
  modification of coin operators

abstract:
  This paper examines a way to simplify the circuit of quantum random walk
search algorithm, when the traversing coin is constructed by both generalized
Householder reflection and an additional phase multiplier. If an appropriate
relation between corresponding parameters is realized, our algorithm becomes
more robust to deviations in the phases. In this modification marking coin is
not needed, and all advantages from above mentioned optimization to the
stability, are preserved. It is shown explicitly how to construct such walk
coin in order to obtain more robust quantum algorithm.


title:
Negative sign free formulations of generalized Kitaev models with higher
  symmetries

abstract:
  We provide a negative-sign-free formulation of the auxiliary field quantum
Monte Carlo algorithm for generalized Kitaev models with higher symmetries. Our
formulation is based on the Abrikosov fermion representation of the spin-1/2
degree of freedom and the phase pinning approach [Phys. Rev. B 104, L081106
(2021)]. Enhancing the number of fermion flavors or orbitals from one to $N$
allows one to generalize the inherent $Z_2$ global symmetry to
Z$_2$$\times$SU($N$)$_o$. Using this general approach, we study the
Z$_2$$\times$SU(2)$_o$ Kitaev-Heisenberg model reflecting the competition
between the isotropic Heisenberg exchange and Kitaev-type bond-directional
exchange interactions. We show that the symmetry enhancement provides a path to
escape frustration and that the spin liquid phases in the original Z$_2$
symmetric model are not present in this model. Nevertheless, the ground-state
phase diagram is extremely rich and has points with higher global and local
continuous symmetries as well as de-confined quantum critical points.


title: Visualization Psychology for Eye Tracking Evaluation

abstract:
  Technical progress in hardware and software enables us to record gaze data in
everyday situations and over long time spans. Among a multitude of research
opportunities, this technology enables visualization researchers to catch a
glimpse behind performance measures and into the perceptual and cognitive
processes of people using visualization techniques. The majority of eye
tracking studies performed for visualization research is limited to the
analysis of gaze distributions and aggregated statistics, thus only covering a
small portion of insights that can be derived from gaze data. We argue that
incorporating theories and methodology from psychology and cognitive science
will benefit the design and evaluation of eye tracking experiments for
visualization. This book chapter provides an overview of how eye tracking can
be used in a variety of study designs. Further, we discuss the potential merits
of cognitive models for the evaluation of visualizations. We exemplify these
concepts on two scenarios, each focusing on a different eye tracking study.
Lastly, we identify several call for actions.


title:
Spectral dynamics of guided edge removals and identifying transient
  amplifiers for death-Birth updating

abstract:
  The paper deals with two interrelated topics, identifying transient
amplifiers in an iterative process and analyzing the process by its spectral
dynamics, which is the change in the graph spectra by edge manipulations.
Transient amplifiers are networks representing population structures which
shift the balance between natural selection and random drift. Thus, amplifiers
are highly relevant for understanding the relationships between spatial
structures and evolutionary dynamics. We study an iterative procedure to
identify transient amplifiers for death-Birth updating. The algorithm starts
with a regular input graph and iteratively removes edges until desired
structures are achieved. Thus, a sequence of candidate graphs is obtained. The
edge removals are guided by quantities derived from the sequence of candidate
graphs. Moreover, we are interested in the Laplacian spectra of the candidate
graphs and analyze the iterative process by its spectral dynamics. The results
show that although transient amplifiers for death-Birth updating are rare, a
substantial number of them can be obtained by the proposed procedure. The
graphs identified share structural properties and have some similarity to
dumbbell and barbell graphs. Also, the spectral dynamics possesses
characteristic features useful for deducing links between structural and
spectral properties and for distinguishing transient amplifiers among
evolutionary graphs in general.


title:
Statistics of Extreme Turbulent Circulation Events from Multifractality
  Breaking

abstract:
  Recent numerical explorations of extremely intense circulation fluctuations
at high Reynolds number flows have brought to light novel aspects of turbulent
intermittency. Vortex gas modeling ideas, introduced alongside such
developments, have led to accurate descriptions of the core and the
intermediate tails of circulation probability distribution functions (cPDFs),
as well as the scaling exponents associated to statistical moments of
circulation. We extend the predictive reach of the vortex gas picture of
turbulence, by emphasizing that multifractality breaking, one of its salient
phenomenological ingredients, is the key concept to disclose the asymptotic
form of cPDF tails. A remarkable analytical agreement is found with previous
results derived within the framework of the instanton approach to circulation
intermittency.


title: The closed string field theory action vanishes

abstract:
  Using the dilaton theorem, we show that the classical action of closed string
field theory vanishes on-shell.


title:
Polarization, Polarizing Efficiency, and Grain alignment towards the
  direction of the cluster NGC 2345

abstract:
  We have investigated the grain alignment and dust properties towards the
direction of the cluster NGC 2345 using the multi-band optical polarimetric
observations. For the majority of the stars, the observed polarization is found
to be due to the interstellar medium with average values of maximum
polarization and wavelength corresponding to it as 1.55% and 0.58 $\mu m$,
respectively. This reveals a similar size distribution of dust grains to that
in the general interstellar medium in the direction of NGC 2345. Alteration of
dust properties near the distance of 1.2 kpc towards the direction of NGC 2345
has been noticed. The dust grains located beyond this distance are found to be
aligned with the Galactic magnetic field, whereas a dispersion in orientation
of the dust grains lying in the foreground of this distance is found.
Polarizing efficiency of grains in this direction is found to be close to the
average efficiency for our Galaxy. The decreased grain size along with the
increased polarizing efficiency towards the core region of the cluster
indicates the local radiation field is higher within the cluster which is
responsible for the increased alignment efficiency of small grains. The
wavelength of maximum polarization (associated with the average size of aligned
grains) is also found to increase with extinction and reduces with the increase
in polarizing efficiency, which can be explained by the radiative torque
alignment mechanism.


title: The universality in urban commuting across and within cities

abstract:
  Commuting is a key mechanism that governs the dynamics of cities. Despite its
importance, very little is known of the properties and mechanisms underlying
this crucial urban process. Here, we capitalize on $\sim$ 50 million
individuals' smartphone data from 234 Chinese cities to show that urban
commuting obeys remarkable regularities. These regularities can be generalized
as two laws: (i) the scale-invariance of the average commuting distance across
cities, which is a long-awaited validation of Marchetti's constant conjecture,
and (ii) a universal inverted U-shape of the commuting distance as a function
of the distance from the city centre within cities, indicating that the city
centre's attraction is bounded. Motivated by such empirical findings, we
develop a simple urban growth model that connects individual-level mobility
choices with macroscopic urban spatial structure and faithfully explains both
commuting laws. Our results further show that the scale-invariants of human
mobility will ultimately lead to the polycentric transition in cities, which
could be used to better inform urban development strategies.


title:
Fractional one-sided measure theoretic second-order elliptic operators
  and applications to stochastic partial differential equations

abstract:
  In this work we introduce and study fractional measure theoretic elliptic
operators on the torus and a new stochastic process named W-Brownian motion. We
establish some regularity and spectral results related to the operators cited
above, more precisely, we were able to provide sharp bounds for the growth rate
of eigenvalues to an associated eigenvalue problem. Moreover, we show how the
Cameron-Martin space associated to the W-Brownian motion relates to Sobolev
spaces connected with the elliptic operators mentioned above. Finally
applications of the theory developed on stochastic partial differential
equations are given.


title:
Increasing Imaging Resolution by Non-Regular Sampling and Joint Sparse
  Deconvolution and Extrapolation

abstract:
  Increasing the resolution of image sensors has been a never ending struggle
since many years. In this paper, we propose a novel image sensor layout which
allows for the acquisition of images at a higher resolution and improved
quality. For this, the image sensor makes use of non-regular sampling which
reduces the impact of aliasing. Therewith, it allows for capturing details
which would not be possible with state-of-the-art sensors of the same number of
pixels. The non-regular sampling is achieved by rotating prototype pixel cells
in a non-regular fashion. As not the whole area of the pixel cell is sensitive
to light, a non-regular spatial integration of the incident light is obtained.
Based on the sensor output data, a high-resolution image can be reconstructed
by performing a deconvolution with respect to the integration area and an
extrapolation of the information to the insensitive regions of the pixels. To
solve this challenging task, we introduce a novel joint sparse deconvolution
and extrapolation algorithm. The union of non-regular sampling and the proposed
reconstruction allows for achieving a higher resolution and therewith an
improved imaging quality.


title:
Performance and Interpretability Comparisons of Supervised Machine
  Learning Algorithms: An Empirical Study

abstract:
  This paper compares the performances of three supervised machine learning
algorithms in terms of predictive ability and model interpretation on
structured or tabular data. The algorithms considered were scikit-learn
implementations of extreme gradient boosting machines (XGB) and random forests
(RFs), and feedforward neural networks (FFNNs) from TensorFlow. The paper is
organized in a findings-based manner, with each section providing general
conclusions supported by empirical results from simulation studies that cover a
wide range of model complexity and correlation structures among predictors. We
considered both continuous and binary responses of different sample sizes.
  Overall, XGB and FFNNs were competitive, with FFNNs showing better
performance in smooth models and tree-based boosting algorithms performing
better in non-smooth models. This conclusion held generally for predictive
performance, identification of important variables, and determining correct
input-output relationships as measured by partial dependence plots (PDPs).
FFNNs generally had less over-fitting, as measured by the difference in
performance between training and testing datasets. However, the difference with
XGB was often small. RFs did not perform well in general, confirming the
findings in the literature. All models exhibited different degrees of bias seen
in PDPs, but the bias was especially problematic for RFs. The extent of the
biases varied with correlation among predictors, response type, and data set
sample size. In general, tree-based models tended to over-regularize the fitted
model in the tails of predictor distributions. Finally, as to be expected,
performances were better for continuous responses compared to binary data and
with larger samples.


title:
Gravitational waves from small spin-up and spin-down events of neutron
  stars

abstract:
  Recently, Espinoza et al. (2014, 2021) reported the existence of "glitch
candidates" and "anti-glitch candidates" which are effectively small spin-ups
and spin-downs of a neutron star with magnitudes smaller than those seen in
typical glitches. The physical origin of these small events is not yet
understood. In this paper, we outline a model that can account for the changes
in spin, and crucially, is independently testable with gravitational wave
observations. In brief, the model posits that small spin-up/spin-down events
are caused by the excitation and decay of non-axisymmetric $f$-modes which
radiate angular momentum away in a burst-like way as gravitational waves. The
model takes the change in spin frequency as an input and outputs the initial
mode amplitude and the signal-to-noise ratio achievable from gravitational wave
detectors. We find that the model presented here will become falsifiable once
3rd generation gravitational wave detectors, like the Einstein Telescope, begin
taking data.


title:
Nontrivial global solutions to some quasilinear wave equations in three
  space dimensions

abstract:
  We construct nontrivial global solutions to a certain system of quasilinear
wave equations in three space dimensions. Starting from a global solution to
the geometric reduced system satisfying several pointwise estimates, we find a
matching exact global solution to the original quasilinear wave equations. Our
construction applies to two types of equations which are of great interest. One
is John's counterexamples $\Box u=u_t^2$ or $\Box u=u_t\Delta u$, and the other
is the 3D compressible Euler equations with no vorticity.


title: Sharp Weak Type Estimates for Maximal Operators associated to Rare Bases

abstract:
  Let $\mathcal{B}$ denote a nonempty translation invariant collection of
intervals in $\mathbb{R}^n$ (which we regard as a rare basis), and define the
associated geometric maximal operator $M_\mathcal{B}$ by $$M_\mathcal{B}f(x) =
\sup_{x \in R \in \mathcal{B}} \frac{1}{|R|}\int_R |f|.$$ We provide a
sufficient condition on $\mathcal{B}$ so that the estimate $$ |\{x \in
\mathbb{R}^n : M_{\mathcal{B}}f(x) > \alpha\}|\leq C_n \int_{\mathbb{R}^{n}}
\frac{|f|}{\alpha}\left(1+\log^+\frac{|f|}{\alpha}\right)^{n-1} $$ is sharp. As
a corollary we obtain sharp weak type estimates for maximal operators
associated to several classes of rare bases including C\'ordoba, Soria and
Zygmund bases.


title: Rationality in current era -- A recent survey

abstract:
  Rationality has been an intriguing topic for several decades. Even the scope
of definition of rationality across different subjects varies. Several theories
(e.g., game theory) initially evolved on the basis that agents (e.g., humans)
are perfectly rational. One interpretation of perfect rationality is that
agents always make the optimal decision which maximizes their expected
utilities. However, subsequently this assumption was relaxed to include bounded
rationality where agents have limitations in terms of computing resources and
biases which prevents them to take the optimal decision. However, with recent
advances in (quantum) computing, artificial intelligence (AI), science and
technology etc., has led to the thought that perhaps the concept of rationality
would be augmented with machine intelligence which will enable agents to take
decision optimally with higher regularity. However, there are divergent views
on this topic. The paper attempts to put forward a recent survey (last five
years) of research on these divergent views. These viewsmay be grouped into
three schools of thoughts. The first school is the one which is sceptical of
progress of AI and believes that human intelligencewill always supersede
machine intelligence. The second school of thought thinks that advent of AI and
advances in computing will help in better understanding of bounded rationality.
Third school of thought believes that bounds of bounded rationality will be
extended by advances in AI and various other fields. This survey hopes to
provide a starting point for further research.


title:
Resampling Images to a Regular Grid from a Non-Regular Subset of Pixel
  Positions Using Frequency Selective Reconstruction

abstract:
  Even though image signals are typically defined on a regular two-dimensional
grid, there also exist many scenarios where this is not the case and the
amplitude of the image signal only is available for a non-regular subset of
pixel positions. In such a case, a resampling of the image to a regular grid
has to be carried out. This is necessary since almost all algorithms and
technologies for processing, transmitting or displaying image signals rely on
the samples being available on a regular grid. Thus, it is of great importance
to reconstruct the image on this regular grid so that the reconstruction comes
closest to the case that the signal has been originally acquired on the regular
grid. In this paper, Frequency Selective Reconstruction is introduced for
solving this challenging task. This algorithm reconstructs image signals by
exploiting the property that small areas of images can be represented sparsely
in the Fourier domain. By further taking into account the basic properties of
the Optical Transfer Function of imaging systems, a sparse model of the signal
is iteratively generated. In doing so, the proposed algorithm is able to
achieve a very high reconstruction quality, in terms of PSNR and SSIM as well
as in terms of visual quality. Simulation results show that the proposed
algorithm is able to outperform state-of-the-art reconstruction algorithms and
gains of more than 1 dB PSNR are possible.


title: Lattice Simulations of Axion-U(1) Inflation

abstract:
  If gauge fields are coupled to an axion field during inflation, they lead to
unique observational signatures within the reach of next generation
experiments. However, this system often shows strong backreaction effects,
invalidating the standard perturbation theory approach. In this paper, we
present the first nonlinear lattice simulation of inflation driven by an axion
field coupled to an abelian U(1) field. We use it to fully characterize the
statistics of the comoving curvature perturbation $\zeta$. We find that
non-Gaussianity of $\zeta$ is large in the linear regime, whereas it is
strongly suppressed when the dynamics becomes nonlinear. This relaxes bounds
from overproduction of primordial black holes.


title: Forecasting Urban Development from Satellite Images

abstract:
  Forecasting where and when new buildings will emerge is a rather unexplored
niche topic, but relevant in disciplines such as urban planning, agriculture,
resource management, and even autonomous flight. In this work, we present a
method that accomplishes this task using satellite images and a custom neural
network training procedure. In stage A, a DeepLapv3+ backbone is pretrained
through a Siamese network architecture aimed at solving a building change
detection task. In stage B, we transfer the backbone into a change forecasting
model that relies solely on the initial input image. We also transfer the
backbone into a forecasting model predicting the correct time range of the
future change. For our experiments, we use the SpaceNet7 dataset with 960 km2
spatial extension and 24 monthly frames. We found that our training strategy
consistently outperforms the traditional pretraining on the ImageNet dataset.
Especially with longer forecasting ranges of 24 months, we observe F1 scores of
24% instead of 16%. Furthermore, we found that our method performed well in
forecasting the times of future building constructions. Hereby, the strengths
of our custom pretraining become especially apparent when we increase the
difficulty of the task by predicting finer time windows.


title: Elevation Mapping for Locomotion and Navigation using GPU

abstract:
  Perceiving the surrounding environment is crucial for autonomous mobile
robots. An elevation map provides a memory-efficient and simple yet powerful
geometric representation for ground robots. The robots can use this information
for navigation in an unknown environment or perceptive locomotion control over
rough terrain. Depending on the application, various post processing steps may
be incorporated, such as smoothing, inpainting or plane segmentation. In this
work, we present an elevation mapping pipeline leveraging GPU for fast and
efficient processing with additional features both for navigation and
locomotion. We demonstrated our mapping framework through extensive hardware
experiments. Our mapping software was successfully deployed for underground
exploration during DARPA Subterranean Challenge and for various experiments of
quadrupedal locomotion.


title:
Non-reciprocal emissivity, partial coherence, and amplification of
  internal energy from photon recycling when thermal radiation is sourced
  within matter

abstract:
  Photons excited into ground state modes at finite temperature display
partitioning among photon phases, lifetimes and distances travelled since
creation. These distributions set the distance from an interface a created
photon has some chance of emission. Excited photons at each frequency have a
phase velocity set by each mode propagation index which determines mode density
and internal energy contribution. All photons emitted after striking an
interface obliquely are refracted. Their exit intensities are then irreversible
except when weak internal attenuation occurs. At low temperature attenuation
index is small so reversibility is approximate. As temperature rises refraction
direction varies. Total emission remains reversible after transitioning through
a non equilibrium state with no other heat inputs. In equilibrium the densities
of excitations that create and annihilate photons are in balance with photon
densities while emissivity depends on both indices and internal incident
direction. Modelled exit intensities from pure water and crystalline silica
contain strong resonant intensities and match data accurately. Intrinsic
resonances formed within liquids and compounds are due to photon modes
hybridising with localized excitations, including molecular oscillations and
the anharmonic component of lattice distortions. They explain the many resonant
spectral intensities seen in remote sensing. Each hybrid oscillator is a
photonic virtual bound state whose energy fluctuates between levels separated
by mode energy. Refraction induces solid angle changes and often anomalous
refraction while thermal recycling of internally reflected photons modifies
intensities and internal energy. Enhanced internal heat flux from phonon drag
by photon density gradients under an external temperature gradient is also
predicted.


title: Discrete hyperbolic curvature flow in the plane

abstract:
  Hyperbolic curvature flow is a geometric evolution equation that in the plane
can be viewed as the natural hyperbolic analogue of curve shortening flow. It
was proposed by Gurtin and Podio-Guidugli (1991) to model certain wave
phenomena in solid-liquid interfaces. We introduce a semidiscrete finite
difference method for the approximation of hyperbolic curvature flow and prove
error bounds for natural discrete norms. We also present numerical simulations,
including the onset of singularities starting from smooth strictly convex
initial data.


title:
Low-rank Meets Sparseness: An Integrated Spatial-Spectral Total
  Variation Approach to Hyperspectral Denoising

abstract:
  Spatial-Spectral Total Variation (SSTV) can quantify local smoothness of
image structures, so it is widely used in hyperspectral image (HSI) processing
tasks. Essentially, SSTV assumes a sparse structure of gradient maps calculated
along the spatial and spectral directions. In fact, these gradient tensors are
not only sparse, but also (approximately) low-rank under FFT, which we have
verified by numerical tests and theoretical analysis. Based on this fact, we
propose a novel TV regularization to simultaneously characterize the sparsity
and low-rank priors of the gradient map (LRSTV). The new regularization not
only imposes sparsity on the gradient map itself, but also penalize the rank on
the gradient map after Fourier transform along the spectral dimension. It
naturally encodes the sparsity and lowrank priors of the gradient map, and thus
is expected to reflect the inherent structure of the original image more
faithfully. Further, we use LRSTV to replace conventional SSTV and embed it in
the HSI processing model to improve its performance. Experimental results on
multiple public data-sets with heavy mixed noise show that the proposed model
can get 1.5dB improvement of PSNR.


title: Designer magnetic topological graphene nanoribbons

abstract:
  The interplay of magnetism and topology lies at the heart of condensed matter
physics, which offers great opportunities to design intrinsic magnetic
topological materials hosting a variety of exotic topological quantum states
including the quantum anomalous Hall effect (QAHE), axion insulator state, and
Majorana bound states. Extending this concept to one-dimension (1D) systems
offers additional rich quantum spin physics with great promise for
molecular-scale spintronics. Despite recent progress in the discovery of
symmetry-protected topological quantum phases in 1D graphene nanoribbons
(GNRs), the rational design and realization of magnetic topological GNRs
(MT-GNRs) represents a grand challenge, as one must tackle multiple dimensions
of complexity including time-reversal symmetry (TRS), spatial symmetry (width,
edge, end geometry) and many-electron correlations. Here, we devised a new
route involving the real- and reciprocal-space descriptions by unifying the
chemists and physicists perspectives, for the design of such MT-GNRs with
non-trivial electronic topology and robust magnetic terminal. Classic Clar's
rule offers a conceptually qualitative real-space picture to predict the
transition from closed-shell to open-shell with terminal magnetism, and band
gap reopening with possible non-trivial electronic topology in a series of
wave-like GNRs, which are further verified by first principle calculations of
band-structure topology in a momentum-space. With the advance of on-surface
synthesis and careful design of molecular precursors, we have fabricated these
MT-GNRs with observation of topological edge bands, whose terminal pi-magnetism
can be directly captured using a single-nickelocene spin sensor. Moreover, the
transition from strong anti-ferromagnetic to weak coupling (paramagnetism-like)
between terminal spins can be controlled by tuning the length of MT-GNRs.


title:
LiftPool: Lifting-based Graph Pooling for Hierarchical Graph
  Representation Learning

abstract:
  Graph pooling has been increasingly considered for graph neural networks
(GNNs) to facilitate hierarchical graph representation learning. Existing graph
pooling methods commonly consist of two stages, i.e., selecting the top-ranked
nodes and removing the rest nodes to construct a coarsened graph
representation. However, local structural information of the removed nodes
would be inevitably dropped in these methods, due to the inherent coupling of
nodes (location) and their features (signals). In this paper, we propose an
enhanced three-stage method via lifting, named LiftPool, to improve
hierarchical graph representation by maximally preserving the local structural
information in graph pooling. LiftPool introduces an additional stage of graph
lifting before graph coarsening to preserve the local information of the
removed nodes and decouple the processes of node removing and feature
reduction. Specifically, for each node to be removed, its local information is
obtained by subtracting the global information aggregated from its neighboring
preserved nodes. Subsequently, this local information is aligned and propagated
to the preserved nodes to alleviate information loss in graph coarsening.
Furthermore, we demonstrate that the proposed LiftPool is localized and
permutation-invariant. The proposed graph lifting structure is general to be
integrated with existing downsampling-based graph pooling methods. Evaluations
on benchmark graph datasets show that LiftPool substantially outperforms the
state-of-the-art graph pooling methods in the task of graph classification.


title:
Motion Compensated Three-Dimensional Frequency Selective Extrapolation
  for Improved Error Concealment in Video Communication

abstract:
  During transmission of video data over error-prone channels the risk of
getting severe image distortions due to transmission errors is ubiquitous. To
deal with image distortions at decoder side, error concealment is applied. This
article presents Motion Compensated Three-Dimensional Frequency Selective
Extrapolation, a novel spatio-temporal error concealment algorithm. The
algorithm uses fractional-pel motion estimation and compensation as initial
step, being followed by the generation of a model of the distorted signal. The
model generation is conducted by an enhanced version of Three-Dimensional
Frequency Selective Extrapolation, an existing error concealment algorithm.
Compared to this existent algorithm, the proposed one yields an improvement in
concealment quality of up to 1.64 dB PSNR. Altogether, the incorporation of
motion compensation and the improved model generation extends the already high
extrapolation quality of the underlying Frequency Selective Extrapolation,
resulting in a gain of more than 3 dB compared to other well-known error
concealment algorithms.


title: 20 years of ordinal patterns: Perspectives and challenges

abstract:
  In 2002, in a seminal article, Christoph Bandt and Bernd Pompe proposed a new
methodology for the analysis of complex time series, now known as Ordinal
Analysis. The ordinal methodology is based on the computation of symbols (known
as ordinal patterns) which are defined in terms of the temporal ordering of
data points in a time series, and whose probabilities are known as ordinal
probabilities. With the ordinal probabilities, the Shannon entropy can be
calculated, which is the permutation entropy. Since it was proposed, the
ordinal method has found applications in fields as diverse as biomedicine and
climatology. However, some properties of ordinal probabilities are still not
fully understood, and how to combine the ordinal approach of feature extraction
with machine learning techniques for model identification, time series
classification or forecasting remains a challenge. The objective of this
perspective article is to present some recent advances and to discuss some open
problems.


title:
Gleo-Det: Deep Convolution Feature-Guided Detector with Local Entropy
  Optimization for Salient Points

abstract:
  Feature detection is an important procedure for image matching, where
unsupervised feature detection methods are the detection approaches that have
been mostly studied recently, including the ones that are based on
repeatability requirement to define loss functions, and the ones that attempt
to use descriptor matching to drive the optimization of the pipelines. For the
former type, mean square error (MSE) is usually used which cannot provide
strong constraint for training and can make the model easy to be stuck into the
collapsed solution. For the later one, due to the down sampling operation and
the expansion of receptive fields, the details can be lost for local
descriptors can be lost, making the constraint not fine enough. Considering the
issues above, we propose to combine both ideas, which including three aspects.
1) We propose to achieve fine constraint based on the requirement of
repeatability while coarse constraint with guidance of deep convolution
features. 2) To address the issue that optimization with MSE is limited,
entropy-based cost function is utilized, both soft cross-entropy and
self-information. 3) With the guidance of convolution features, we define the
cost function from both positive and negative sides. Finally, we study the
effect of each modification proposed and experiments demonstrate that our
method achieves competitive results over the state-of-the-art approaches.


title:
Narrowing the Gap between Combinatorial and Hyperbolic Knot Invariants
  via Deep Learning

abstract:
  We present a statistical approach for the discovery of relationships between
mathematical entities that is based on linear regression and deep learning with
fully connected artificial neural networks. The strategy is applied to
computational knot data and empirical connections between combinatorial and
hyperbolic knot invariants are revealed.


title:
Globular Clusters UVIT Legacy Survey (GlobULeS) I. FUV-optical
  Color-Magnitude Diagrams for Eight Globular Clusters

abstract:
  We present the first results of eight Globular Clusters (GCs) from the
AstroSat/UVIT Legacy Survey program GlobULeS based on the observations carried
out in two FUV filters (F148W and F169M). The FUV-optical and FUV-FUV
color-magnitude diagrams (CMDs) of GCs with the proper motion membership were
constructed by combining the UVIT data with HST UV Globular Cluster Survey
(HUGS) data for inner regions and Gaia Early Data Release (EDR3) for regions
outside the HST's field. We detect sources as faint as F148W $\sim$ 23.5~mag
which are classified based on their locations in CMDs by overlaying stellar
evolutionary models. The CMDs of 8 GCs are combined with the previous UVIT
studies of 3 GCs to create stacked FUV-optical CMDs to highlight the
features/peculiarities found in the different evolutionary sequences. The FUV
(F148W) detected stellar populations of 11 GCs comprises 2,816 Horizontal
Branch (HB) stars (190 Extreme HB candidates), 46 post-HB (pHB), 221 Blue
Straggler Stars (BSS), and 107 White Dwarf (WD) candidates. We note that the
blue HB color extension obtained from F148W$-$G color and the number of FUV
detected EHB candidates are strongly correlated with the maximum internal
Helium (He) variation within each GC, suggesting that the FUV-optical plane is
the most sensitive to He abundance variations in the HB. We discuss the
potential science cases that will be addressed using these catalogues including
HB morphologies, BSSs, pHB, and, WD stars.


title:
Quasiparticle spectrum in mesoscopic superconducting junctions with weak
  magnetization

abstract:
  We theoretically investigate the effects of the weak magnetization on the
local density of states of mesoscopic proximity structures, where two
superconducting terminals are attached to a side surface of the diffusive
ferromagnet wire with a phase difference. When there is no phase difference,
the local density of states is significantly modified by the magnetization in
both spin-singlet $s$-wave and spin-triplet $p$-wave cases. When the phase
difference is $\pi$, the local density of stets is less modified by the
magnetization compared with the in-phase case because of the destructive
interference of Cooper pairs.


title: On discrete spectra of Bergman--Toeplitz operators with harmonic symbols

abstract:
  In the present article, we study the discrete spectrum of certain bounded
Toeplitz operators with harmonic symbol on a Bergman space. Using the methods
of classical perturbaton theory and recent results by Borichev-Golinskii-Kupin
and Favorov-Golinskii, we obtain a quantitative result on the distribution of
the discrete spectrum of the operator in the unbounded (outer) component of its
Fredholm set.


title:
Memory-Disaggregated In-Memory Object Store Framework for Big Data
  Applications

abstract:
  The concept of memory disaggregation has recently been gaining traction in
research. With memory disaggregation, data center compute nodes can directly
access memory on adjacent nodes and are therefore able to overcome local memory
restrictions, introducing a new data management paradigm for distributed
computing. This paper proposes and demonstrates a memory disaggregated
in-memory object store framework for big data applications by leveraging the
newly introduced ThymesisFlow memory disaggregation system. The framework
extends the functionality of the pre-existing Apache Arrow Plasma object store
framework to distributed systems by enabling clients to easily and efficiently
produce and consume data objects across multiple compute nodes. This allows big
data applications to increasingly leverage parallel processing at reduced
development costs. In addition, the paper includes latency and throughput
measurements that indicate only a modest performance penalty is incurred for
remote disaggregated memory access as opposed to local (~6.5 vs ~5.75 GiB/s).
The results can be used to guide the design of future systems that leverage
memory disaggregation as well as the newly presented framework. This work is
open-source and publicly accessible at https://doi.org/10.5281/zenodo.6368998.


title:
Universal approach to sending-or-not-sending twin field quantum key
  distribution

abstract:
  We present the method of decoy-state analysis after bit-flip error correction
and using confidential observed numbers. Taking this tool we then construct a
universal approach to sending-or-not-sending (SNS) protocol of twin-field
quantum key distribution. In this improved protocol, the code bits are not
limited to heralded events in time windows participated by pulses of intensity
$\mu_z$ and vacuum. All kinds of heralded events can be used for code bits to
distill the final keys. The number of intensities (3 or 4) and the kinds of
heralded events for code bits are automatically chosen by the key rate
optimization itself. Numerical simulation shows that the key rate rises
drastically in typical settings, up to 80\% improvement compared with the prior
results. Also, larger intensity value can be used for decoy pulses. This makes
the protocol more robust in practical experiments.


title: Pre-explosion helium shell flash in type Ia supernovae

abstract:
  I study the possibility that within the frame of the core degenerate (CD)
scenario for type Ia supernovae (SNe Ia) the merger process of the core of the
asymptotic giant branch (AGB) star and the white dwarf (WD) maintains an
envelope mass of ~0.03Mo that causes a later helium shell flash. I estimate the
number of pre-explosion helium shell flash events to be less than few per cents
of all CD scenario SNe Ia. A helium shell flash while the star moves to the
left on the HR diagram as a post-AGB star (late thermal pulse -- LTP) or along
the WD cooling track (very-LTP) causes the star to expand and become a born
again AGB star. Merger remnants exploding while still on the AGB form peculiar
SNe Ia or peculiar SNe II (if hydrogen is detected), while an explosion inside
an inflated born-again star results in an early flux excess in the light curve
of the SN Ia. The fraction of systems that might show an early flux excess due
to LTP/VLTP is <0.0003 of all SNe Ia, much below the observed fraction. In the
frame of the CD scenario SNe Ia with early flux excess result from SN ejecta
collision with planetary nebula fallback gas, or from mixing of 56Ni to the
outer regions of the SN ejecta. Ongoing sky surveys might find about one case
per year where LTP/VLTP influences the SN light curve.


title:
Emergence of Wulff-Crystals from atomistic systems on the FCC and HCP
  lattices

abstract:
  We consider a system of $N$ hard spheres sitting on the nodes of either the
$\mathrm{FCC}$ or $\mathrm{HCP}$ lattice and interacting via a sticky-disk
potential. As $N$ tends to infinity (continuum limit), assuming the interaction
energy does not exceed that of the ground-state by more than $N^{2/3}$ (surface
scaling), we obtain the variational coarse grained model by
$\Gamma$-convergence. More precisely, we prove that the continuum limit
energies are of perimeter type and we compute explicitly their Wulff shapes.
Our analysis shows that crystallization on $\mathrm{FCC}$ is preferred to that
on $\mathrm{HCP}$ for $N$ large enough. The method is based on integral
representation and concentration-compactness results that we prove for general
periodic lattices in any dimension.


title:
Beyond Duplicates: Towards Understanding and Predicting Link Types in
  Issue Tracking Systems

abstract:
  Software projects use Issue Tracking Systems (ITS) like JIRA to track issues
and organize the workflows around them. Issues are often inter-connected via
different links such as the default JIRA link types Duplicate, Relate, Block,
or Subtask. While previous research has mostly focused on analyzing and
predicting duplication links, this work aims at understanding the various other
link types, their prevalence, and characteristics towards a more reliable link
type prediction. For this, we studied 607,208 links connecting 698,790 issues
in 15 public JIRA repositories. Besides the default types, the custom types
Depend, Incorporate, Split, and Cause were also common. We manually grouped all
75 link types used in the repositories into five general categories: General
Relation, Duplication, Composition, Temporal / Causal, and Workflow. Comparing
the structures of the corresponding graphs, we observed several trends. For
instance, Duplication links tend to represent simpler issue graphs often with
two components and Composition links present the highest amount of hierarchical
tree structures (97.7%). Surprisingly, General Relation links have a
significantly higher transitivity score than Duplication and Temporal / Causal
links. Motivated by the differences between the link types and by their
popularity, we evaluated the robustness of two state-of-the-art duplicate
detection approaches from the literature on the JIRA dataset. We found that
current deep-learning approaches confuse between Duplication and other links in
almost all repositories. On average, the classification accuracy dropped by 6%
for one approach and 12% for the other. Extending the training sets with other
link types seems to partly solve this issue. We discuss our findings and their
implications for research and practice.


title:
Large-scale DFT methods for calculations of materials with complex
  structures

abstract:
  Large-scale density functional theory (DFT) calculations provide a powerful
tool to investigate the atomic and electronic structure of materials with
complex structures. This article reviews a large-scale DFT calculation method,
the multi-site support function (MSSF) method, in the CONQUEST code. MSSFs are
linear combinations of the basis functions which belong to a group of atoms in
a local region. The method can reduce the computational time while preserving
accuracy. The accuracy of MSSFs has been assessed for bulk Si, Al, Fe and NiO
and hydrated DNA, which demonstrate the applicability of the MSSFs for varied
materials. The applications of MSSFs on large systems with several thousand
atoms, which have complex interfaces and non-periodic structures, indicate that
the MSSF method is promising for precise investigations of materials with
complex structures.


title: Large-scale geometry obstructs localization

abstract:
  We explain the coarse geometric origin of the fact that certain spectral
subspaces of topological insulator Hamiltonians are delocalized, in the sense
that they cannot admit an orthonormal basis of localized wavefunctions, with
respect to any uniformly discrete set of localization centers. This is a robust
result requiring neither spatial homogeneity nor symmetries, and applies to
Landau levels of disordered quantum Hall systems on general Riemannian
manifolds.

categories: math-ph cond-mat.mes-hall math.KT math.MP math.OA


title: Reflection positivity and infrared bounds for quantum spin systems

abstract:
  The method of reflection positivity and infrared bounds allows to prove the
occurrence of phase transitions in systems with continuous symmetries. We
review the method in the context of quantum spin systems. The novel aspect is a
proof for long-range interactions that involve the Euclidean distance between
sites.


title:
Characterizing Visualization Insights through Entity-Based Interaction:
  An Exploratory Study

abstract:
  One of the primary purposes of visualization is to assist users in
discovering insights. While there has been much research in information
visualization aiming at complex data transformation and novel presentation
techniques, relatively little has been done to understand how users derive
insights through interactive visualization of data. This paper presents a
crowdsourced study with 158 participants investigating the relation between
entity-based interaction (an action + its target entity) and the resulting
insight. To this end, we generalized the interaction with an existing CO2
Explorer as entity-based interaction and enabled users to input notes and refer
to relevant entities to assist their narratives. We logged interactions of
users freely exploring the visualization and characterized their externalized
insights about the data. Using entity-based interactions and references to
infer insight characteristics (category, overview versus detail, and prior
knowledge), we found evidence that compared with interactions, entity
references improved insight characterization from slight/fair to fair/moderate
agreements. To interpret prediction outcomes, feature importance and
correlation analysis indicated that, e.g., detailed insights tended to have
more mouse-overs in the chart area and cite the vertical reference lines in the
line chart as evidence. We discuss study limitations and implications on
knowledge-assisted visualization, e.g., insight recommendations based on user
exploration.


title: Explanation of the $W$ mass shift at CDF II in the Georgi-Machacek Model

abstract:
  The CDF II experiment has recently determined the mass of the $W$ boson to be
$m_W(\text{CDF II}) = 80.4335 \pm 0.0094~$GeV, which deviates from the standard
model prediction at $7\sigma$ level. Although this new result is in tension
with other experiments such as those at LHC and LEP, it is worth discussing
possible implications on new physics by this anomaly. We show that this large
discrepancy can be explained by non-aligned vacuum expectation values of
isospin triplet scalar fields in the Georgi-Machacek model extended with
custodial symmetry breaking terms in the potential. The latter is required to
avoid an undesirable Nambu-Goldstone boson as well as to be consistent
treatment of radiative corrections. With $m_W(\text{CDF II})$ as one of the
renormalization inputs at the 1-loop level, we derive the required difference
in the triplet vacuum expectation values, followed by a discussion of
phenomenological consequences in the scenario.


title:
Improving constraints on gluon spin-momentum correlations in
  transversely polarized protons via midrapidity open-heavy-flavor electrons in
  $p^{\uparrow}+p$ collisions at $\sqrt{s}=200$ GeV

abstract:
  Polarized proton-proton collisions provide leading-order access to gluons,
presenting an opportunity to constrain gluon spin-momentum correlations within
transversely polarized protons and enhance our understanding of the
three-dimensional structure of the proton. Midrapidity open-heavy-flavor
production at $\sqrt{s}=200$ GeV is dominated by gluon-gluon fusion, providing
heightened sensitivity to gluon dynamics relative to other production channels.
Transverse single-spin asymmetries of electrons and positrons from heavy-flavor
hadron decays are measured at midrapidity using the PHENIX detector at the
Relativistic Heavy Ion Collider. These charge-separated measurements are
sensitive to gluon correlators that can in principle be related to gluon
orbital angular momentum via model calculations. Explicit constraints on gluon
correlators are extracted for two separate models, one of which had not been
constrained previously.


title: Global Trajectory Helps Person Retrieval in a Camera Network

abstract:
  We are concerned about retrieving a query person from the videos taken by a
non-overlapping camera network. Existing methods often rely on pure visual
matching or consider temporal constraint, but ignore the spatial information of
the camera network. To address this problem, we propose a framework of person
retrieval based on cross-camera trajectory generation which integrates both
temporal and spatial information. To obtain the pedestrian trajectories, we
propose a new cross-camera spatio-temporal model that integrates the walking
habits of pedestrians and the path layout between cameras, forming a joint
probability distribution. Such a spatio-temporal model among a camera network
can be specified using sparsely sampled pedestrian data. Based on the
spatio-temporal model, the cross-camera trajectories of a specific pedestrian
can be extracted by the conditional random field model, and further optimized
by the restricted nonnegative matrix factorization. Finally, a trajectory
re-ranking technology is proposed to improve the person retrieval results. To
verify the effectiveness of our approach, we build the first dataset of
cross-camera pedestrian trajectories over an actual monitoring scenario, namely
the Person Trajectory Dataset. Extensive experiments have verified the
effectiveness and robustness of the proposed method.


title:
Thermodynamic perfect fluid spheres admitting an orthogonal flat
  synchronization

abstract:
  We analyze the interpretation as a thermodynamic perfect fluid in local
thermal equilibrium of the spherically symmetric perfect fluid solutions that
admit a flat synchronization orthogonal to the fluid flow. The ideal gas sonic
condition is examined for this family of metrics, and the macroscopic
conditions for physical reality are accurately tested for some specifc
solutions.


title:
A Graph Diffusion Scheme for Decentralized Content Search based on
  Personalized PageRank

abstract:
  Decentralization is emerging as a key feature of the future Internet.
However, effective algorithms for search are missing from state-of-the-art
decentralized technologies, such as distributed hash tables and blockchain.
This is surprising, since decentralized search has been studied extensively in
earlier peer-to-peer (P2P) literature. In this work, we adopt a fresh outlook
for decentralized search in P2P networks that is inspired by advancements in
dense information retrieval and graph signal processing. In particular, we
generate latent representations of P2P nodes based on their stored documents
and diffuse them to the rest of the network with graph filters, such as
personalized PageRank. We then use the diffused representations to guide search
queries towards relevant content. Our preliminary approach is successful in
locating relevant documents in nearby nodes but the accuracy declines sharply
with the number of stored documents, highlighting the need for more
sophisticated techniques.


title: Spending Privacy Budget Fairly and Wisely

abstract:
  Differentially private (DP) synthetic data generation is a practical method
for improving access to data as a means to encourage productive partnerships.
One issue inherent to DP is that the "privacy budget" is generally "spent"
evenly across features in the data set. This leads to good statistical parity
with the real data, but can undervalue the conditional probabilities and
marginals that are critical for predictive quality of synthetic data. Further,
loss of predictive quality may be non-uniform across the data set, with subsets
that correspond to minority groups potentially suffering a higher loss.
  In this paper, we develop ensemble methods that distribute the privacy budget
"wisely" to maximize predictive accuracy of models trained on DP data, and
"fairly" to bound potential disparities in accuracy across groups and reduce
inequality. Our methods are based on the insights that feature importance can
inform how privacy budget is allocated, and, further, that per-group feature
importance and fairness-related performance objectives can be incorporated in
the allocation. These insights make our methods tunable to social contexts,
allowing data owners to produce balanced synthetic data for predictive
analysis.


title:
Epicardial Adipose Tissue Segmentation from CT Images with A Semi-3D
  Neural Network

abstract:
  Epicardial adipose tissue is a type of adipose tissue located between the
heart wall and a protective layer around the heart called the pericardium. The
volume and thickness of epicardial adipose tissue are linked to various
cardiovascular diseases. It is shown to be an independent cardiovascular
disease risk factor. Fully automatic and reliable measurements of epicardial
adipose tissue from CT scans could provide better disease risk assessment and
enable the processing of large CT image data sets for a systemic epicardial
adipose tissue study. This paper proposes a method for fully automatic semantic
segmentation of epicardial adipose tissue from CT images using a deep neural
network. The proposed network uses a U-Net-based architecture with slice depth
information embedded in the input image to segment a pericardium region of
interest, which is used to obtain an epicardial adipose tissue segmentation.
Image augmentation is used to increase model robustness. Cross-validation of
the proposed method yields a Dice score of 0.86 on the CT scans of 20 patients.


title:
Temperature induced giant shift of phonon energy in epitaxial boron
  nitride layers

abstract:
  The recent progress in the growth of large-area boron nitride epilayers opens
up new possibilities for future applications. However, it remains largely
unclear how weakly attached two-dimensional BN layers interact with their
substrate and how their properties are influenced by defects. In this work, we
investigate hBN layers grown by Metal Organic Vapor Phase Epitaxy (MOVPE) using
Fourier-transform Infrared (FTIR) spectroscopy in the temperature range of
160-540 K. Our measurements reveal strong differences in the character of
layer-substrate interaction for as-grown and delaminated epitaxial layers. A
much weaker interaction of as-grown layers is explained by wrinkles formation
that reduces strain at the layer-substrate interface, which for layers
transferred to other substrates occurs only in a limited temperature range. The
most striking result is the observation of a giant increase in the $E_{1u}$
phonon energy of up to $\sim6$ cm$^{-1}$ in a narrow temperature range. We show
that the amplitude and temperature range of the anomaly is strongly modified by
UV light illumination. The observed giant effect is explained in terms of
strain generation resulting from charge redistribution between shallow traps
and different defects, which can be interpreted as a result of strong
electron-phonon coupling in hBN. The observed narrow temperature range of the
anomaly indicates that the effect may be further enhanced for example by
electrostrictive effects, expected for sp$^2$ boron nitride.


title:
Towards A COLREGs Compliant Autonomous Surface Vessel in a Constrained
  Channel

abstract:
  In this paper, we look at the role of autonomous navigation in the maritime
domain. Specifically, we examine how an Autonomous Surface Vessel(ASV) can
achieve obstacle avoidance based on the Convention on the International
Regulations for Preventing Collisions at Sea (1972), or COLREGs, in real-world
environments. Our ASV is equipped with a broadband marine radar, an Inertial
Navigation System (INS), and uses official Electronic Navigational Charts
(ENC). These sensors are used to provide situational awareness and, in series
of well-defined steps, we can exclude land objects from the radar data, extract
tracks associated with moving vessels within range of the radar, and then use a
Kalman Filter to track and predict the motion of other moving vessels in the
vicinity. A Constant Velocity model for the Kalman Filter allows us to solve
the data association to build a consistent model between successive radar
scans. We account for multiple COLREGs situations based on the predicted
relative motion. Finally, an efficient path planning algorithm is presented to
find a path and publish waypoints to perform real-time COLREGs compliant
autonomous navigation within highly constrained environments. We demonstrate
the results of our framework with operational results collected over the course
of a 3.4 nautical mile mission on the Charles River in Boston in which the ASV
encountered and successfully navigated multiple scenarios and encounters with
other moving vessels at close quarters.


title:
A sharp interface Lagrangian-Eulerian method for flexible-body
  fluid-structure interaction

abstract:
  This paper introduces a sharp-interface approach to simulating
fluid-structure interaction involving flexible bodies described by general
nonlinear material models and across a broad range of mass density ratios. This
new flexible-body immersed Lagrangian-Eulerian (ILE) approach incorporates the
geometrical and domain solution flexibility of the immersed boundary (IB)
method with an accuracy comparable to body-fitted approaches that sharply
resolve flows and stresses up to the fluid-structure interface. Unlike many IB
methods, our ILE formulation uses distinct momentum equations for the fluid and
solid subregions with a Dirichlet-Neumann coupling strategy that connects fluid
and solid subproblems through simple interface conditions. We use a penalty
method involving two representations of the fluid-structure interface. These
two representations are connected by approximate Lagrange multiplier forces
that impose kinematic interface conditions. This approach also enables the use
of multi-rate time stepping, which allows us to take different time step sizes
for the fluid and structure subproblems. Our fluid solver relies on an immersed
interface method for discrete surfaces to impose stress jump conditions along
complex interfaces. The dynamics of the volumetric structural mesh are
determined using a standard finite element approach to large-deformation
nonlinear elasticity via a nearly incompressible solid mechanics formulation.
This formulation also readily accommodates compressible structures for cases in
which part of the solid boundary does not contact the incompressible fluid.
Comparisons are made with computational and experimental benchmarks. We also
demonstrate the capabilities of this methodology by applying it to model the
transport and capture of a cylindrical blood clot in an inferior vena cava
filter.

categories: math.NA cs.NA math-ph math.MP physics.flu-dyn


title:
Capabilities and Skills in Manufacturing: A Survey Over the Last Decade
  of ETFA

abstract:
  Industry 4.0 envisions Cyber-Physical Production Systems (CPPSs) to foster
adaptive production of mass-customizable products. Manufacturing approaches
based on capabilities and skills aim to support this adaptability by
encapsulating machine functions and decoupling them from specific production
processes. At the 2022 IEEE conference on Emerging Technologies and Factory
Automation (ETFA), a special session on capability- and skill-based
manufacturing is hosted for the fourth time. However, an overview on
capability- and skill based systems in factory automation and manufacturing
systems is missing. This paper aims to provide such an overview and give
insights to this particular field of research. We conducted a concise
literature survey of papers covering the topics of capabilities and skills in
manufacturing from the last ten years of the ETFA conference. We found 247
papers with a notion on capabilities and skills and identified and analyzed 34
relevant papers which met this survey's inclusion criteria. In this paper, we
provide (i) an overview of the research field, (ii) an analysis of the
characteristics of capabilities and skills, and (iii) a discussion on gaps and
opportunities.


title: (P)reheating and gravitational waves in $\alpha$-attractor models

abstract:
  We study post-inflationary evolution in $\alpha$-attractor T-models of
inflation. We consider the dynamics of both scalar fields present in these
models: the inflaton and the spectator, as a negative field-space curvature may
lead to geometrical destabilization of the spectator. We perform
state-of-the-art lattice simulations with a dedicated numerical code optimized
for those models. We corroborate earlier findings that the perturbations of the
spectator field are much more unstable than the perturbations of the inflaton
field, so the dynamics of early stages of preheating is dominated by the
evolution of spectator perturbations. We also calculate the spectrum of
gravitational waves originating from scalar fluctuations and find that the
$\alpha$-attractor T-models can be constrained or even ruled out by present
cosmological observations, but not by direct searches of gravitational waves.


title:
Episodic ejection from a low-mass young stellar object traced by H$_2$O
  masers

abstract:
  We present the project of a VLBI study of the 22 GHz H$_2$O maser in a
prototypical low-mass protostellar system IRAS 16293-2422. The observation was
conducted to characterise the cause of the newly discovered enhanced maser
activity in the source and to study the source's ejection behaviour as traced
by maser emission. Single-dish monitoring and analysis of archival data
indicate that the activity of the H$_2$O maser in IRAS 16293-2422 has a cyclic
character and traces episodic ejection events in the source. A new maser flare
was recently discovered in a spectral feature that has never shown such a
significant increase in flux density before. The flare of this feature seems to
indicate the beginning of a new cycle of activity.


title:
Using Glowscript to Teach Numerical Modeling in Undergraduate Biology
  Education

abstract:
  Mathematical and numerical modeling is an increasingly important, yet often
neglected, topic for biology students. We have found Glowscript to facilitate
teaching and introducing computer simulations to students. In particular, the
built-in the graphics and graphing capabilities can provide students with
immediate feedback. Glowscript is a web-based form of visual Python that runs
in a standard browser, and students can easily embed their simulations in
external websites. Here we show various examples of how Glowscript is
implemented in an undergraduate computational biology course. We hope these
examples inspire others to adopt Glowscript in their science classrooms.


title: One-dimensional model of freely decaying two-dimensional turbulence

abstract:
  We construct a discrete shell-model for two-dimensional turbulence that takes
into account local and nonlocal interactions between velocity modes in Fourier
space. In real space, its continuous limit is described by the one-dimensional
Burgers equation. We find a novel approximate scaling solution of such an
equation and show that it well describes the main characteristics of the energy
spectrum in fully developed, freely decaying two-dimensional turbulence.


title: A Survey on Formal Verification Approaches for Dependable Systems

abstract:
  The complexity of digital embedded systems has been increasing in different
safety-critical applications such as industrial automation, process control,
transportation, and medical digital devices. The correct operation of these
systems relies too heavily on the behavior of the embedded digital device. As a
result, any mistake or error made during the design stage of the embedded
device can change the overall functionality of the critical system and cause
catastrophic consequences. To detect these errors and eliminate their effects
on the system, new error detection approaches must be innovated and used in the
design of the digital system. However, these methods require enormous costs and
time. One of these methods being employed to solve this issue is called
Verification and Validation (V&V) which confirms that the system behavior meets
the requirements early in the development process, before moving on to the
implementation phase. Because of their benefits and importance in the building
of complex digital systems, the employment of formal V&V methods has recently
attracted a lot of attention. This paper focuses on presenting various studies
on formal verification approaches and how the V&V can be achieved for
developing high dependable digital embedded systems


title:
Forecasting Foreign Exchange Rates With Parameter-Free Regression
  Networks Tuned By Bayesian Optimization

abstract:
  The article is concerned with the problem of multi-step financial time series
forecasting of Foreign Exchange (FX) rates. To address this problem, we
introduce a parameter-free regression network termed RegPred Net. The exchange
rate to forecast is treated as a stochastic process. It is assumed to follow a
generalization of Brownian motion and the mean-reverting process referred to as
the generalized Ornstein-Uhlenbeck (OU) process, with time-dependent
coefficients. Using past observed values of the input time series, these
coefficients can be regressed online by the cells of the first half of the
network (Reg). The regressed coefficients depend only on - but are very
sensitive to - a small number of hyperparameters required to be set by a global
optimization procedure for which, Bayesian optimization is an adequate
heuristic. Thanks to its multi-layered architecture, the second half of the
regression network (Pred) can project time-dependent values for the OU process
coefficients and generate realistic trajectories of the time series.
Predictions can be easily derived in the form of expected values estimated by
averaging values obtained by Monte Carlo simulation. The forecasting accuracy
on a 100 days horizon is evaluated for several of the most important FX rates
such as EUR/USD, EUR/CNY, and EUR/GBP. Our experimental results show that the
RegPred Net significantly outperforms ARMA, ARIMA, LSTMs, and Autoencoder-LSTM
models in this task.


title:
Improving Feature Generalizability with Multitask Learning in Class
  Incremental Learning

abstract:
  Many deep learning applications, like keyword spotting, require the
incorporation of new concepts (classes) over time, referred to as Class
Incremental Learning (CIL). The major challenge in CIL is catastrophic
forgetting, i.e., preserving as much of the old knowledge as possible while
learning new tasks. Various techniques, such as regularization, knowledge
distillation, and the use of exemplars, have been proposed to resolve this
issue. However, prior works primarily focus on the incremental learning step,
while ignoring the optimization during the base model training. We hypothesize
that a more transferable and generalizable feature representation from the base
model would be beneficial to incremental learning.
  In this work, we adopt multitask learning during base model training to
improve the feature generalizability. Specifically, instead of training a
single model with all the base classes, we decompose the base classes into
multiple subsets and regard each of them as a task. These tasks are trained
concurrently and a shared feature extractor is obtained for incremental
learning. We evaluate our approach on two datasets under various
configurations. The results show that our approach enhances the average
incremental learning accuracy by up to 5.5%, which enables more reliable and
accurate keyword spotting over time. Moreover, the proposed approach can be
combined with many existing techniques and provides additional performance
gain.


title: GypSum: Learning Hybrid Representations for Code Summarization

abstract:
  Code summarization with deep learning has been widely studied in recent
years. Current deep learning models for code summarization generally follow the
principle in neural machine translation and adopt the encoder-decoder
framework, where the encoder learns the semantic representations from source
code and the decoder transforms the learnt representations into human-readable
text that describes the functionality of code snippets. Despite they achieve
the new state-of-the-art performance, we notice that current models often
either generate less fluent summaries, or fail to capture the core
functionality, since they usually focus on a single type of code
representations. As such we propose GypSum, a new deep learning model that
learns hybrid representations using graph attention neural networks and a
pre-trained programming and natural language model. We introduce particular
edges related to the control flow of a code snippet into the abstract syntax
tree for graph construction, and design two encoders to learn from the graph
and the token sequence of source code, respectively. We modify the
encoder-decoder sublayer in the Transformer's decoder to fuse the
representations and propose a dual-copy mechanism to facilitate summary
generation. Experimental results demonstrate the superior performance of GypSum
over existing code summarization models.


title:
LINA -- A social augmented reality game around mental health, supporting
  real-world connection and sense of belonging for early adolescents

abstract:
  Early adolescence is a time of major social change; a strong sense of
belonging and peer connectedness is an essential protective factor in mental
health during that period. In this paper we introduce LINA, an augmented
reality (AR) smartphone-based serious game played in school by an entire class
(age 10+) together with their teacher, which aims to facilitate and improve
peer interaction, sense of belonging and class climate, while creating a safe
space to reflect on mental health and external stressors related to family
circumstance. LINA was developed through an interdisciplinary collaboration
involving a playwright, software developers, psychologists, and artists, via an
iterative co-development process with young people. A prototype has been
evaluated quantitatively for usability and qualitatively for efficacy in a
study with 91 early adolescents (agemean=11.41). Results from the Game User
Experience Satisfaction Scale (GUESS-18) and data from qualitative focus groups
showed high acceptability and preliminary efficacy of the game. Using AR, a
shared immersive narrative and collaborative gameplay in a shared physical
space offers an opportunity to harness adolescent affinity for digital
technology towards improving real-world social connection and sense of
belonging.


title:
We're Not Gonna Break It! Consistency-Preserving Operators for Efficient
  Product Line Configuration

abstract:
  When configuring a software product line, finding a good trade-off between
multiple orthogonal quality concerns is a challenging multi-objective
optimisation problem. State-of-the-art solutions based on search-based
techniques create invalid configurations in intermediate steps, requiring
additional repair actions that reduce the efficiency of the search. In this
work, we introduce consistency-preserving configuration operators
(CPCOs)--genetic operators that maintain valid configurations throughout the
entire search. CPCOs bundle coherent sets of changes: the activation or
deactivation of a particular feature together with other (de)activations that
are needed to preserve validity. In our evaluation, our instantiation of the
IBEA algorithm with CPCOs outperforms two state-of-the-art tools for optimal
product line configuration in terms of both speed and solution quality. The
improvements are especially pronounced in large product lines with thousands of
features.


title: Topological Data Analysis for Anomaly Detection in Host-Based Logs

abstract:
  Topological Data Analysis (TDA) gives practioners the ability to analyse the
global structure of cybersecurity data. We use TDA for anomaly detection in
host-based logs collected with the open-source Logging Made Easy (LME) project.
We present an approach that builds a filtration of simplicial complexes
directly from Windows logs, enabling analysis of their intrinsic structure
using topological tools. We compare the efficacy of persistent homology and the
spectrum of graph and hypergraph Laplacians as feature vectors against a
standard log embedding that counts events, and find that topological and
spectral embeddings of computer logs contain discriminative information for
classifying anomalous logs that is complementary to standard embeddings. We end
by discussing the potential for our methods to be used as part of an
explainable framework for anomaly detection.


title: Trainable Compound Activation Functions for Machine Learning

abstract:
  Activation functions (AF) are necessary components of neural networks that
allow approximation of functions, but AFs in current use are usually simple
monotonically increasing functions. In this paper, we propose trainable
compound AF (TCA) composed of a sum of shifted and scaled simple AFs. TCAs
increase the effectiveness of networks with fewer parameters compared to added
layers. TCAs have a special interpretation in generative networks because they
effectively estimate the marginal distributions of each dimension of the data
using a mixture distribution, reducing modality and making linear dimension
reduction more effective. When used in restricted Boltzmann machines (RBMs),
they result in a novel type of RBM with mixture-based stochastic units.
Improved performance is demonstrated in experiments using RBMs, deep belief
networks (DBN), projected belief networks (PBN), and variational auto-encoders
(VAE).


title:
Electron heating in 2-D: combining Fermi-Ulam acceleration and
  magnetic-moment non-adiabaticity in a mirror-configuration plasma

abstract:
  We analyze a new mechanism for the creation and confinement of energetic
electrons in a mirror-configuration plasma. A Fermi-Ulam-type process, driven
by end-localized coherent electrostatic oscillations, provides axial
acceleration while a natural non-adiabaticity of {\mu} provides phase
decorrelation and energy isotropization. This novel 2-D combination causes the
electron energy distribution function, calculated with a diffusive-loss model,
to assume a Maxwellian shape with the {\mu} non-adiabaticity reducing loss-cone
escape and annulling the absolute-barrier energy-limiting Chirikov criterion of
lower dimensional models. The theoretical predictions are compared with data
from an experiment.


title: Using the Projected Belief Network at High Dimensions

abstract:
  The projected belief network (PBN) is a layered generative network (LGN) with
tractable likelihood function, and is based on a feed-forward neural network
(FFNN). There are two versions of the PBN: stochastic and deterministic
(D-PBN), and each has theoretical advantages over other LGNs. However,
implementation of the PBN requires an iterative algorithm that includes the
inversion of a symmetric matrix of size M X M in each layer, where M is the
layer output dimension. This, and the fact that the network must be always
dimension-reducing in each layer, can limit the types of problems where the PBN
can be applied. In this paper, we describe techniques to avoid or mitigate
these restrictions and use the PBN effectively at high dimension. We apply the
discriminatively aligned PBN (PBN-DA) to classifying and auto-encoding
high-dimensional spectrograms of acoustic events. We also present the
discriminatively aligned D-PBN for the first time.


title: On spherical barycentric coordinates

abstract:
  This paper describes a novel construction of generalized barycentric
coordinates of points on a sphere with respect to the vertices of a given
spherical polygon that is contained in a common hemisphere. While in the
standard approach such coordinates are derived from their classical planar
counterparts (e.g. Wachspress, or mean value), we instead derive them from 3D
barycentric coordinates of the origin and show that they are endowed with some
useful properties such as edge linearity and Lagrange property. In addition, we
show that spherical mean value coordinates of both approaches coincide while
their corresponding spherical wachspress coordinates are in general different.


title: An open-source simulation package for power electronics education

abstract:
  Extension of the open-source simulation package GSEIM for power electronics
applications is presented. Recent developments in GSEIM, including those
oriented specifically towards power electronic circuits, are described. Some
examples of electrical element templates, which form a part of the GSEIM
library, are discussed. Representative simulation examples in power electronics
are presented to bring out important features of the simulator. Advantages of
GSEIM for educational purposes are discussed. Finally, plans regarding future
developments in GSEIM are presented.


title:
On the use of the generalized Littlewood theorem concerning integrals of
  the logarithm of analytical functions for calculations of infinite sums and
  analysis of zeroes of analytical functions

abstract:
  Recently, we have established and used the generalized Littlewood theorem
concerning contour integrals of the logarithm of analytical function to obtain
a few new criteria equivalent to the Riemann hypothesis. Here, the same theorem
is applied to calculate certain infinite sums and study the properties of
zeroes of a few analytical functions.


title:
Strong rate of convergence of the Euler scheme for SDEs with irregular
  drift driven by Levy noise

abstract:
  We study the strong rate of convergence of the Euler--Maruyama scheme for a
multidimensional stochastic differential equation (SDE) $$ dX_t=b(X_t)dt+dL_t,
$$ with irregular $\beta$-H\"older drift, $\beta>0$, driven by a L\'evy process
with exponent $\alpha\in(0,2]$. For $\alpha\in[2/3,2]$ we obtain strong $L_p$
and almost sure convergence rates in the whole range $\beta>1-\alpha/2$, where
the SDE is known to be strongly well--posed. This significantly improves the
current state of the art both in terms of convergence rate and the range
of~$\alpha$. In particular, the obtained convergence rate does not deteriorate
for large $p$ and is always at least $n^{-1/2}$; this allowed us to show for
the first time that the the Euler--Maruyama scheme for such SDEs converges
almost surely and obtain explicit convergence rate. Furthermore, our results
are new even in the case of smooth drifts. Our technique is based on a new
extension of the stochastic sewing arguments.


title: A Bayesian Approach To Graph Partitioning

abstract:
  A new algorithm based on bayesian inference for learning local graph
conductance based on Gaussian Process(GP) is given that uses advanced MCMC
convergence ideas to create a scalable and fast algorithm for convergence to
stationary distribution which is provided to learn the bahavior of conductance
when traversing the indirected weighted graph. First metric embedding is used
to represent the vertices of the graph. Then, uniform induced conductance is
calculated for training points. Finally, in the learning step, a gaussian
process is used to approximate the uniform induced conductance. MCMC is used to
measure uncertainty of estimated hyper-parameters.


title:
Causal Analysis of Generic Time Series Data Applied for Market
  Prediction

abstract:
  We explore the applicability of the causal analysis based on temporally
shifted (lagged) Pearson correlation applied to diverse time series of
different natures in context of the problem of financial market prediction.
Theoretical discussion is followed by description of the practical approach for
specific environment of time series data with diverse nature and sparsity, as
applied for environments of financial markets. The data involves various
financial metrics computable from raw market data such as real-time trades and
snapshots of the limit order book as well as metrics determined upon social
media news streams such as sentiment and different cognitive distortions. The
approach is backed up with presentation of algorithmic framework for data
acquisition and analysis, concluded with experimental results, and summary
pointing out at the possibility to discriminate causal connections between
different sorts of real field market data with further discussion on present
issues and possible directions of the following work.


title: Sequence-Based Target Coin Prediction for Cryptocurrency Pump-and-Dump

abstract:
  As the pump-and-dump schemes (P&Ds) proliferate in the cryptocurrency market,
it becomes imperative to detect such fraudulent activities in advance, to
inform potentially susceptible investors before they become victims. In this
paper, we focus on the target coin prediction task, i.e., to predict the pump
probability of all coins listed in the target exchange before a pump. We
conduct a comprehensive study of the latest P&Ds, investigate 709 events
organized in Telegram channels from Jan. 2019 to Jan. 2022, and unearth some
abnormal yet interesting patterns of P&Ds. Empirical analysis demonstrates that
pumped coins exhibit intra-channel homogeneity and inter-channel heterogeneity,
which inspires us to develop a novel sequence-based neural network named SNN.
Specifically, SNN encodes each channel's pump history as a sequence
representation via a positional attention mechanism, which filters useful
information and alleviates the noise introduced when the sequence length is
long. We also identify and address the coin-side cold-start problem in a
practical setting. Extensive experiments show a lift of 1.6% AUC and 41.0% Hit
Ratio@3 brought by our method, making it well-suited for real-world
application. As a side contribution, we release the source code of our entire
data science pipeline on GitHub, along with the dataset tailored for studying
the latest P&Ds.


title: Scalar propagator for planar gravitational waves

abstract:
  We construct the massive scalar propagator for planar gravitational wave
backgrounds propagating on Minkowski space. We represent the propagator in
terms of the Bessel's function of suitably deformed nonlocal distance
functions, the deformation being caused by gravitational waves. We calculate
the propagator both for nonpolarized, for the plus ($+$) and cross ($\times$)
polarized plane waves, as well as for more general planar waves both in $D$
space-time dimensions. The propagator is useful for studying interactions of
scalar fields on planar gravitational wave backgrounds in the context of
interacting quantum fields, in which renormalization plays a crucial role. As
simple applications of the propagator we calculate the one-loop effective
action, the scalar mass generated by the scalar self-interaction term and the
one-loop energy-momentum tensor, all renormalized by using dimensional
regularization and renormalization. While the effective action and scalar mass
remain unaffected by gravitational waves, the energy-momentum tensor exhibits
flow of energy density in the direction of gravitational waves which grows
quadratically with the scalar field mass, gravitational wave amplitude and its
frequency.


title:
Bunkbed conjecture for complete bipartite graphs and related classes of
  graphs

abstract:
  Let $G = (V,E)$ be a simple finite graph. The corresponding bunkbed graph
$G^\pm$ consists of two copies $G^+ = (V^+,E^+),G^- = (V^-,E^-)$ of $G$ and
additional edges connecting any two vertices $v_+ \in V_+,v_- \in V_-$ that are
the copies of a vertex $v \in V$. The bunkbed conjecture states that for
independent bond percolation on $G^\pm$, for all $v,w \in V$, it is more likely
for $v_-,w_-$ to be connected than for $v_-,w_+$ to be connected. While this
seems very plausible, so far surprisingly little is known rigorously. Recently
the conjecture has been proved for complete graphs. Here we give a proof for
complete bipartite graphs, complete graphs minus the edges of a complete
subgraph, and symmetric complete $k$-partite graphs.


title:
NFT Appraisal Prediction: Utilizing Search Trends, Public Market Data,
  Linear Regression and Recurrent Neural Networks

abstract:
  In this paper we investigate the correlation between NFT valuations and
various features from three primary categories: public market data, NFT
metadata, and social trends data.


title: High-Frequency-Based Volatility Model with Network Structure

abstract:
  This paper introduces one new multivariate volatility model that can
accommodate an appropriately defined network structure based on low-frequency
and high-frequency data. The model reduces the number of unknown parameters and
the computational complexity substantially. The model parameterization and
iterative multistep-ahead forecasts are discussed and the targeting
reparameterization is also presented. Quasi-likelihood functions for parameter
estimation are proposed and their asymptotic properties are established. A
series of simulation experiments are carried out to assess the performance of
the estimation in finite samples. An empirical example is demonstrated that the
proposed model outperforms the network GARCH model, with the gains being
particularly significant at short forecast horizons.


title: An Iterative Labeling Method for Annotating Fisheries Imagery

abstract:
  In this paper, we present a methodology for fisheries-related data that
allows us to converge on a labeled image dataset by iterating over the dataset
with multiple training and production loops that can exploit crowdsourcing
interfaces. We present our algorithm and its results on two separate sets of
image data collected using the Seabed autonomous underwater vehicle. The first
dataset comprises of 2,026 completely unlabeled images, while the second
consists of 21,968 images that were point annotated by experts. Our results
indicate that training with a small subset and iterating on that to build a
larger set of labeled data allows us to converge to a fully annotated dataset
with a small number of iterations. Even in the case of a dataset labeled by
experts, a single iteration of the methodology improves the labels by
discovering additional complicated examples of labels associated with fish that
overlap, are very small, or obscured by the contrast limitations associated
with underwater imagery.


abstract:
  With the development of online business, customer service agents gradually
play a crucial role as an interface between the companies and their customers.
Most companies spend a lot of time and effort on hiring and training customer
service agents. To this end, we propose AdaCoach: A Virtual Coach for Training
Customer Service Agents, to promote the ability of newly hired service agents
before they get to work. AdaCoach is designed to simulate real customers who
seek help and actively initiate the dialogue with the customer service agents.
Besides, AdaCoach uses an automated dialogue evaluation model to score the
performance of the customer agent in the training process, which can provide
necessary assistance when the newly hired customer service agent encounters
problems. We apply recent NLP technologies to ensure efficient run-time
performance in the deployed system. To the best of our knowledge, this is the
first system that trains the customer service agent through human-computer
interaction. Until now, the system has already supported more than 500,000
simulation training and cultivated over 1000 qualified customer service agents.


title: Preimages under the bubblesort operator

abstract:
  We study preimages of permutations under the bubblesort operator
$\mathbf{B}$. We achieve a description of these preimages much more complete
than what is known for the more complicated sorting operators $\mathbf{S}$
(stacksort) and $\mathbf{Q}$ (queuesort). We describe explicitly the set of
preimages under $\mathbf{B}$ of any permutation $\pi$ from the left-to-right
maxima of $\pi$, showing that there are $2^{k-1}$ such preimages if $k$ is the
number of these left-to-right maxima. We further consider, for each $n$, the
tree $T_n$ recording all permutations of size $n$ in its nodes, in which an
edge from child to parent corresponds to an application of $\mathbf{B}$ (the
root being the identity permutation), and we present several properties of
these trees. In particular, for each permutation $\pi$, we show how the subtree
of $T_n$ rooted at $\pi$ is determined by the number of left-to-right maxima of
$\pi$ and the length of the longest suffix of left-to-right maxima of $\pi$.
Building on this result, we determine the number of nodes and leaves at every
height in such trees, and we recover (resp. obtain) the average height of nodes
(resp. leaves) in $T_n$.


title: Learning to Transfer Role Assignment Across Team Sizes

abstract:
  Multi-agent reinforcement learning holds the key for solving complex tasks
that demand the coordination of learning agents. However, strong coordination
often leads to expensive exploration over the exponentially large state-action
space. A powerful approach is to decompose team works into roles, which are
ideally assigned to agents with the relevant skills. Training agents to
adaptively choose and play emerging roles in a team thus allows the team to
scale to complex tasks and quickly adapt to changing environments. These
promises, however, have not been fully realised by current role-based
multi-agent reinforcement learning methods as they assume either a pre-defined
role structure or a fixed team size. We propose a framework to learn role
assignment and transfer across team sizes. In particular, we train a role
assignment network for small teams by demonstration and transfer the network to
larger teams, which continue to learn through interaction with the environment.
We demonstrate that re-using the role-based credit assignment structure can
foster the learning process of larger reinforcement learning teams to achieve
tasks requiring different roles. Our proposal outperforms competing techniques
in enriched role-enforcing Prey-Predator games and in new scenarios in the
StarCraft II Micro-Management benchmark.


title:
Computationally efficient neural network classifiers for next generation
  closed loop neuromodulation therapy, a case study in epilepsy

abstract:
  This work explores the potential utility of neural network classifiers for
real-time classification of field-potential based biomarkers in next-generation
responsive neuromodulation systems. Compared to classical filter-based
classifiers, neural networks offer an ease of patient-specific parameter
tuning, promising to reduce the burden of programming on clinicians. The paper
explores a compact, feed-forward neural network architecture of only dozens of
units for seizure-state classification in refractory epilepsy. The proposed
classifier offers comparable accuracy to filter classifiers on
clinician-labelled data, while reducing detection latency. As a trade-off to
classical methods, the paper focuses on keeping the complexity of the
architecture minimal, to accommodate the on-board computational constraints of
implantable pulse generator systems.