Abstract:
  It is becoming increasingly difficult for game developers to manage the cost
of developing a game, while meeting the high expectations of gamers. One way to
balance the increasing gamer expectation and development stress is to build an
active modding community around the game. There exist several examples of games
with an extremely active and successful modding community, with the Minecraft
game being one of the most notable ones.
  This paper reports on an empirical study of 1,114 popular and 1,114 unpopular
Minecraft mods from the CurseForge mod distribution platform, one of the
largest distribution platforms for Minecraft mods. We analyzed the relationship
between 33 features across 5 dimensions of mod characteristics and the
popularity of mods (i.e., mod category, mod documentation, environmental
context of the mod, remuneration for the mod, and community contribution for
the mod), to understand the characteristics of popular Minecraft mods. We
firstly verify that the studied dimensions have significant explanatory power
in distinguishing the popularity of the studied mods. Then we evaluated the
contribution of each of the 33 features across the 5 dimensions. We observed
that popular mods tend to have a high quality description and promote community
contribution.


Abstract:
  A graph is maximal knotless if it is edge maximal for the property of
knotless embedding in $R^3$. We show that such a graph has at least $\frac74
|V|$ edges, and construct an infinite family of maximal knotless graphs with
$|E| < \frac52|V|$. With the exception of $|E| = 22$, we show that for any $|E|
\geq 20$ there exists a maximal knotless graph of size $|E|$. We classify the
maximal knotless graphs through nine vertices and 20 edges. We determine which
of these maxnik graphs are the clique sum of smaller graphs and construct an
infinite family of maxnik graphs that are not clique sums.


Abstract:
  The abundance of clouds, located both spatially and temporally, often makes
remote sensing (RS) applications with optical images difficult or even
impossible to perform. Traditional cloud removing techniques have been studied
for years, and recently, Machine Learning (ML)-based approaches have also been
considered. In this manuscript, a novel method for the restoration of
clouds-corrupted optical images is presented, able to generate the whole
optical scene of interest, not only the cloudy pixels, and based on a Joint
Data Fusion paradigm, where three deep neural networks are hierarchically
combined. Spatio-temporal features are separately extracted by a conditional
Generative Adversarial Network (cGAN) and by a Convolutional Long Short-Term
Memory (ConvLSTM), from Synthetic Aperture Radar (SAR) data and optical
time-series of data respectively, and then combined with a U-shaped network.
The use of time-series of data has been rarely explored in the state of the art
for this peculiar objective, and moreover existing models do not combine both
spatio-temporal domains and SAR-optical imagery. Quantitative and qualitative
results have shown a good ability of the proposed method in producing
cloud-free images, by also preserving the details and outperforming the cGAN
and the ConvLSTM when individually used. Both the code and the dataset have
been implemented from scratch and made available to interested researchers for
further analysis and investigation.


Abstract:
  For the Su-Schrieffer-Heeger (SSH) model on the two-dimensional square
lattice, two third nearest neighbor hoppings which preserve chiral symmetry are
introduced. Like the case of one dimension, the longer-range hopping can drive
topological transitions and leads to the larger topological invariant (vectored
Berry phase for the two dimensional SSH model). We obtain the phase boundaries
and topological phase diagrams from the winding pattern of $\rho(k_l)$ of the
Bloch Hamiltonian. Effects of correlations on the extended two dimensional SSH
model are also investigated. The correlation shifts the phase boundaries and
leads to topological transitions. Several special points in non-interacting
phase diagrams are chosen to illustrate the different phase transitions and a
correlations-driven topologically non-trivial phase from the trivial one is
found. In this work, the slave-rotor mean field method is applied to the
interacting model and we focus on the case of physical electrons, i.e. the Mott
transition of charge degree of freedom is avoided in our investigations.


Abstract:
  This article introduces a multiple classifier method to improve the
performance of concatenate-designed neural networks, such as ResNet and
DenseNet, with the purpose to alleviate the pressure on the final classifier.
We give the design of the classifiers, which collects the features produced
between the network sets, and present the constituent layers and the activation
function for the classifiers, to calculate the classification score of each
classifier. We use the L2 normalization method to obtain the classifier score
instead of the Softmax normalization. We also determine the conditions that can
enhance convergence. As a result, the proposed classifiers are able to improve
the accuracy in the experimental cases significantly, and show that the method
not only has better performance than the original models, but also produces
faster convergence. Moreover, our classifiers are general and can be applied to
all classification related concatenate-designed network models.


Abstract:
  In this paper, we study the uniqueness of the difference of meromorphic
functions. We give a new proof of the following result: Let $f$ be a
transcendental meromorphic function of hyper-order less than $1$, let $\eta$ be
a non-zero complex number, $n\geq1$, an integer, and let $a,b,c$ be three
distinct periodic small functions with period $\eta$. If $f$ and
$\Delta_{\eta}^{n}f$ share $a,b,c$ CM, then $f\equiv\Delta_{\eta}^{n}f$, which
using a different method from \cite{gkzz}.


Abstract:
  The exponential increase in CPU time taken to deterministically solve NP-Hard
Combinatorial Optimisation Problems (COP), as the problem size scales, has
resulted in a search for non-deterministic optimisation solution techniques to
obtain solutions to COP efficiently. This paper juxtaposes classical and
quantum optimisation algorithms' performance to solve two common COP, the
Travelling Salesman Problem (TSP) and Quadratic Assignment Problem (QAP). The
two classical optimisation techniques applied are Branch and Bound (BNB) and
Simulated Annealing (SA), and the two quantum optimisation methods used are the
Variational Quantum Eigensolver (VQE) algorithm and Quantum Approximate
Optimisation Algorithm (QAOA). These algorithms are respectively executed on
classical and IBM's suite of Noisy Intermediate-Scale Quantum (NISQ) computers.
Our experimental results resemble and extend on previously reported results in
the literature. Extensions include critically analysing, comparing and
commenting on the performance of quantum optimisation computing techniques to
classical techniques, with respect to computational time and additional metrics
used to measure solution quality. Furthermore, a comparison of the impact of a
new set of basis gates on the quantum optimisation techniques was investigated;
the results did not reflect any consistent impact on results. The VQE algorithm
and QAOA executed on state of the art IBM quantum devices are outperformed by
classical optimisation techniques, highlighting the shortcomings of NISQ
devices.


Abstract:
  We present the Colorization Transformer, a novel approach for diverse high
fidelity image colorization based on self-attention. Given a grayscale image,
the colorization proceeds in three steps. We first use a conditional
autoregressive transformer to produce a low resolution coarse coloring of the
grayscale image. Our architecture adopts conditional transformer layers to
effectively condition grayscale input. Two subsequent fully parallel networks
upsample the coarse colored low resolution image into a finely colored high
resolution image. Sampling from the Colorization Transformer produces diverse
colorings whose fidelity outperforms the previous state-of-the-art on
colorising ImageNet based on FID results and based on a human evaluation in a
Mechanical Turk test. Remarkably, in more than 60% of cases human evaluators
prefer the highest rated among three generated colorings over the ground truth.
The code and pre-trained checkpoints for Colorization Transformer are publicly
available at
https://github.com/google-research/google-research/tree/master/coltran


Abstract:
  Automatically evaluating the quality of image captions can be very
challenging since human language is quite flexible that there can be various
expressions for the same meaning. Most of the current captioning metrics rely
on token level matching between candidate caption and the ground truth label
sentences. It usually neglects the sentence-level information. Motivated by the
auto-encoder mechanism and contrastive representation learning advances, we
propose a learning-based metric for image captioning, which we call Intrinsic
Image Captioning Evaluation($I^2CE$). We develop three progressive model
structures to learn the sentence level representations--single branch model,
dual branches model, and triple branches model. Our empirical tests show that
$I^2CE$ trained with dual branches structure achieves better consistency with
human judgments to contemporary image captioning evaluation metrics.
Furthermore, We select several state-of-the-art image captioning models and
test their performances on the MS COCO dataset concerning both contemporary
metrics and the proposed $I^2CE$. Experiment results show that our proposed
method can align well with the scores generated from other contemporary
metrics. On this concern, the proposed metric could serve as a novel indicator
of the intrinsic information between captions, which may be complementary to
the existing ones.


Abstract:
  The idea of intersectionality has become a frequent topic of discussion both
in academic sociology, as well as among popular movements for social justice
such as Black Lives Matter, intersectional feminism, and LGBT rights.
Intersectionality proposes that an individual's experience of society has
aspects that are irreducible to the sum of one's various identities considered
individually, but are "greater than the sum of their parts." In this work, we
show that the effects of intersectional identities can be statistically
observed in empirical data using information theory. We show that, when
considering the predictive relationship between various identities categories
such as race, sex, and income (as a proxy for class) on outcomes such as health
and wellness, robust statistical synergies appear. These synergies show that
there are joint-effects of identities on outcomes that are irreducible to any
identity considered individually and only appear when specific categories are
considered together (for example, there is a large, synergistic effect of race
and sex considered jointly on income irreducible to either race or sex). We
then show using synthetic data that the current gold-standard method of
assessing intersectionalities in data (linear regression with multiplicative
interaction coefficients) fails to disambiguate between truly synergistic,
greater-than-the-sum-of-their-parts interactions, and redundant interactions.
We explore the significance of these two distinct types of interactions in the
context of making inferences about intersectional relationships in data and the
importance of being able to reliably differentiate the two. Finally, we
conclude that information theory, as a model-free framework sensitive to
nonlinearities and synergies in data, is a natural method by which to explore
the space of higher-order social dynamics.


Abstract:
  Collecting annotated data for semantic segmentation is time-consuming and
hard to scale up. In this paper, we for the first time propose a unified
framework, termed as Multi-Dataset Pretraining, to take full advantage of the
fragmented annotations of different datasets. The highlight is that the
annotations from different domains can be efficiently reused and consistently
boost performance for each specific domain. This is achieved by first
pretraining the network via the proposed pixel-to-prototype contrastive loss
over multiple datasets regardless of their taxonomy labels, and followed by
fine-tuning the pretrained model over specific dataset as usual. In order to
better model the relationship among images and classes from different datasets,
we extend the pixel level embeddings via cross dataset mixing and propose a
pixel-to-class sparse coding strategy that explicitly models the pixel-class
similarity over the manifold embedding space. In this way, we are able to
increase intra-class compactness and inter-class separability, as well as
considering inter-class similarity across different datasets for better
transferability. Experiments conducted on several benchmarks demonstrate its
superior performance. Notably, MDP consistently outperforms the pretrained
models over ImageNet by a considerable margin, while only using less than 10%
samples for pretraining.


Abstract:
  We introduce a new Legendrian isotopy invariant for any closed orientable
Legendrian surface $L$ embedded in a closed contact $5$-manifold $(M, \xi)$
which admits an "admissable" open book $(B, f)$ (supporting $\xi$) for $L$. We
show that to any such $L$ and a fixed page $X$, one can assign an integer
$M\mathcal{P}_{X}(L)$, called "Relative Maximal Page Crossing Number of $L$
with respect to $X$", which is invariant under Legendrian isotopies of $L$. We
also show that one can extend this to a page-free invariant, i.e., one can
assign an integer $M\mathcal{P}_{(B,f)}(L)$, called "Absolute Maximal Page
Crossing Number of $L$ with respect to $(B, f)$", which is invariant under
Legendrian isotopies of $L$. In particular, this new invariant distinguishes
Legendrian surfaces in the standard five-sphere which can not be distinguished
by Thurston-Bennequin invariant.


Abstract:
  This paper investigates the transition from abrasive to adhesive wear in
gross-slip fretting assuming contact oxygenation concept which suggests that
adhesion appears in the inner part of the interface if the di-oxygen partial
pressure is below a threshold value. In the lateral sides, where di-oxygen
molecules are sufficient, oxidation and abrasion prevail. To assess this
phenomenon, 34NiCrMo16 flat-on-flat contacts are tested. Contact oxygenation is
quantified using the ''oxygen-distance, '' parameter defined as the averaged
width of the external abrasion corona. Confirming this concept, decreases with
contact pressure and frequency but remains constant versus sliding amplitude,
fretting cycles and contact area. evolution is formalized using a power law
formulation which allowed predicting wear transitions for plain and
macro-textured surfaces.


Abstract:
  The European Union Emission Trading Scheme (EU ETS) is a cornerstone of the
EU's strategy to fight climate change and an important device for plummeting
greenhouse gas (GHG) emissions in an economically efficient manner. The power
industry has switched to an auction-based allocation system at the onset of
Phase III of the EU ETS to bring economic efficiency by negating windfall
profits that have been resulted from grandfathered allocation of allowances in
the previous phases. In this work, we analyze and simulate the interaction of
oligopolistic generators in an electricity market with a game-theoretical
framework where the electricity and the emissions markets interact in a
two-stage electricity market. For analytical simplicity, we assume a single
futures market where the electricity is committed at the futures price, and the
emissions allowance is contracted in advance, prior to a spot market where the
energy and allowances delivery takes place. Moreover, a coherent risk measure
is applied (Conditional Value at Risk) to model both risk averse and risk
neutral generators and a two-stage stochastic optimization setting is
introduced to deal with the uncertainty of renewable capacity, demand,
generation, and emission costs. The performance of the proposed equilibrium
model and its main properties are examined through realistic numerical
simulations. Our results show that renewable generators are surging and
substituting conventional generators without compromising social welfare.
Hence, both renewable deployment and emission allowance auctioning are
effectively reducing GHG emissions and promoting low-carbon economic path.


Abstract:
  The self-adjoint angular flux and streamline-upwind Petrov-Galerkin transport
equations are discretized using reproducing kernels with the collocation method
to produce a discretization that is compatible with conservative reproducing
kernel smoothed particle hydrodynamics. A novel second derivative is derived
for the diffusion-like term in the self-adjoint angular flux equation. The
resulting equations involve only evaluations of kernels and physical data at
the nodal centers.


Abstract:
  VdW materials are a family of materials ranging from semimetal, semiconductor
to insulator, and their common characteristic is the layered structure. These
features make them widely applied in the fabrication of nano-photonic and
electronic devices, particularly, vdW heterojunctions. HBN is the only layered
material to date that is demonstrated to contain optically-detected electronic
spins, which can benefit the construction of solid qubit and quantum sensor,
etc., especially embedded in the nano-layered-devices. To realize this, Rabi
oscillation is a crucial step. Here, we demonstrate the Rabi oscillation of
V$_\text{B}^-$ spins in hBN. Interestingly, we find the behaviors of the spins
are completely different under the conditions of weak and relatively higher
magnetic field. The former behaves like a single wide peak, but the latter
behaves like multiple narrower peaks (e.g., a clear beat in Ramsey fringes). We
conjecture a strong coupling of the spin with the surrounding nuclear spins,
and the magnetic field can control the nuclear spin bath.


Abstract:
  This paper investigates the problem to determine whether a given stochastic
process generates a sampled Brownian filtration. A fairly general sufficient
condition is obtained by applying the Frank H. Clarke contraction criteria to a
functional whose construction relies on the quasi-invariance properties of a
sampled Wiener measure. The latter are investigated from the precise analytic
structure underlying this sampled Brownian motion. In particular, the
Cameron-Martin space is identified from usual Lebesgue integrals over a
Guseinov measure. As an application we obtain sufficient conditions for the
existence of a solution to a class of not necessarily Markovian stochastic
differential equations driven by a sampled Brownian motion. By taking a
sampling times set which coincides with the unit interval, the results apply to
continuous time models.


Abstract:
  The detection of nuclei and cells in histology images is of great value in
both clinical practice and pathological studies. However, multiple reasons such
as morphological variations of nuclei or cells make it a challenging task where
conventional object detection methods cannot obtain satisfactory performance in
many cases. A detection task consists of two sub-tasks, classification and
localization. Under the condition of dense object detection, classification is
a key to boost the detection performance. Considering this, we propose
similarity based region proposal networks (SRPN) for nuclei and cells detection
in histology images. In particular, a customized convolution layer termed as
embedding layer is designed for network building. The embedding layer is added
into the region proposal networks, enabling the networks to learn
discriminative features based on similarity learning. Features obtained by
similarity learning can significantly boost the classification performance
compared to conventional methods. SRPN can be easily integrated into standard
convolutional neural networks architectures such as the Faster R-CNN and
RetinaNet. We test the proposed approach on tasks of multi-organ nuclei
detection and signet ring cells detection in histological images. Experimental
results show that networks applying similarity learning achieved superior
performance on both tasks when compared to their counterparts. In particular,
the proposed SRPN achieve state-of-the-art performance on the MoNuSeg benchmark
for nuclei segmentation and detection while compared to previous methods, and
on the signet ring cell detection benchmark when compared with baselines. The
sourcecode is publicly available at:
https://github.com/sigma10010/nuclei_cells_det.


Abstract:
  Perhaps the largest debate in network Ecology, the emergence of structural
patterns stands out as a multifaceted problem. To the methodological challenges
-- pattern identification, statistical significance -- one has to add the
relationship between candidate architectures and dynamical performance. In the
case of mutualistic communities, the debate revolves mostly around two
structural arrangements (nestedness and modularity) and two requirements for
persistence, namely feasibility and stability. So far, it is clear that the
former is strongly related to nestedness, while the latter is enhanced in
modular systems. Adding to this, it has recently become clear that nestedness
and modularity are antagonistic patterns -- or, at the very least, their
coexistence in a single system is problematic. In this context, this work
addresses the role of the interaction architecture in the emergence and
maintenance of both properties, introducing the idea of hybrid architectural
configurations. Specifically, we examine in-block nestedness, compound by
disjoint subsets of species (modules) with internal nested organization, and
prove that it grants a balanced trade-off between stability and feasibility.
Remarkably, we analyze a large amount of empirical communities and find that a
relevant fraction of them exhibits a marked in-block nested structure. We
elaborate on the implications of these results, arguing that they provide new
insights about the key properties ruling community assembly.


Abstract:
  Event-triggered control (ETC) holds the potential to significantly improve
the efficiency of wireless networked control systems. Unfortunately, its
real-world impact has hitherto been hampered by the lack of a network stack
able to transfer its benefits from theory to practice specifically by
supporting the latency and reliability requirements of the aperiodic
communication ETC induces. This is precisely the contribution of this paper.
Our Wireless Control Bus (WCB) exploits carefully orchestrated network-wide
floods of concurrent transmissions to minimize overhead during quiescent,
steady-state periods, and ensures timely and reliable collection of sensor
readings and dissemination of actuation commands when an ETC triggering
condition is violated. Using a cyber-physical testbed emulating a water
distribution system controlled over a real-world multi-hop wireless network, we
show that ETC over WCB achieves the same quality of periodic control at a
fraction of the energy costs, therefore unleashing and concretely demonstrating
its full potential for the first time.


Abstract:
  In this paper, we investigate the feasibility and physical consequences of
cyber attacks against energy management systems (EMS). Within this framework,
we have designed a complete simulation platform to emulate realistic EMS
operations: it includes state estimation (SE), real-time contingency analysis
(RTCA), and security constrained economic dispatch (SCED). This software
platform allowed us to achieve two main objectives: 1) to study the cyber
vulnerabilities of an EMS and understand their consequences on the system, and
2) to formulate and implement countermeasures against cyber-attacks exploiting
these vulnerabilities. Our results show that the false data injection attacks
against state estimation described in the literature do not easily cause
base-case overflows because of the conservatism introduced by RTCA. For a
successful attack, a more sophisticated model that includes all of the EMS
blocks is needed; even in this scenario, only post-contingency violations can
be achieved. Nonetheless, we propose several countermeasures that can detect
changes due to cyber-attacks and limit their impact on the system.


Abstract:
  People navigating in unfamiliar buildings take advantage of myriad visual,
spatial and semantic cues to efficiently achieve their navigation goals.
Towards equipping computational agents with similar capabilities, we introduce
Pathdreamer, a visual world model for agents navigating in novel indoor
environments. Given one or more previous visual observations, Pathdreamer
generates plausible high-resolution 360 visual observations (RGB, semantic
segmentation and depth) for viewpoints that have not been visited, in buildings
not seen during training. In regions of high uncertainty (e.g. predicting
around corners, imagining the contents of an unseen room), Pathdreamer can
predict diverse scenes, allowing an agent to sample multiple realistic outcomes
for a given trajectory. We demonstrate that Pathdreamer encodes useful and
accessible visual, spatial and semantic knowledge about human environments by
using it in the downstream task of Vision-and-Language Navigation (VLN).
Specifically, we show that planning ahead with Pathdreamer brings about half
the benefit of looking ahead at actual observations from unobserved parts of
the environment. We hope that Pathdreamer will help unlock model-based
approaches to challenging embodied navigation tasks such as navigating to
specified objects and VLN.


Abstract:
  Using a 3D global solver of the linearized Euler equations, we model acoustic
oscillations over background velocity flow fields of single-cell meridional
circulation with deep and shallow return flows as well as a double-cell
meridional circulation profile. The velocities are generated using a mean-field
hydrodynamic and dynamo model -- moving through the regimes with minimal
parameter changes; counter-rotation near the base of the tachocline is induced
by sign inversion of the non-diffusive action of turbulent Reynolds stresses
($\Lambda$-effect) due to the radial inhomogeneity of the Coriolis number. By
mimicking the stochastic excitation of resonant modes in the convective
interior, we simulate realization noise present in solar observations. Using
deep-focusing to analyze differences in travel-time signatures between the
three regimes, as well as comparing to solar observations, we show that current
helioseismology techniques may offer important insights about the location of
the return flow, however, that it may not be possible to definitively
distinguish between profiles of single-cell or double-cell meridional
circulation.


Abstract:
  Inferring high-fidelity constraints on the spatial curvature parameter,
$\Omega_{\rm K}$, under as few assumptions as possible, is of fundamental
importance in cosmology. We propose a method to non-parametrically infer
$\Omega_{\rm K}$ from late-Universe probes alone. Using Gaussian Processes (GP)
to reconstruct the expansion history, we combine Cosmic Chronometers (CC) and
Type Ia Supernovae (SNe~Ia) data to infer constraints on curvature,
marginalized over the expansion history, calibration of the CC and SNe~Ia data,
and the GP hyper-parameters. The obtained constraints on $\Omega_{\rm K}$ are
free from parametric model assumptions for the expansion history, and are
insensitive to the overall calibration of both the CC and SNe~Ia data (being
sensitive only to relative distances and expansion rates). Applying this method
to \textit{Pantheon} SNe~Ia and the latest compilation of CCs, we find
$\Omega_{\rm K} = -0.03 \pm 0.26$, consistent with spatial flatness at the
$\mathcal{O}(10^{-1})$ level, and independent of any early-Universe probes.
Applying our methodology to future Baryon Acoustic Oscillations and SNe~Ia data
from upcoming Stage IV surveys, we forecast the ability to constrain
$\Omega_{\rm K}$ at the $\mathcal{O}(10^{-2})$ level.


Abstract:
  Light detection and ranging (LiDAR) has been widely used in autonomous
driving and large-scale manufacturing. Although state-of-the-art scanning LiDAR
can perform long-range three-dimensional imaging, the frame rate is limited by
both round-trip delay and the beam steering speed, hindering the development of
high-speed autonomous vehicles. For hundred-meter level ranging applications, a
several-time speedup is highly desirable. Here, we uniquely combine fiber-based
encoders with wavelength-division multiplexing devices to implement all-optical
time-encoding on the illumination light. Using this method, parallel detection
and fast inertia-free spectral scanning can be achieved simultaneously with
single-pixel detection. As a result, the frame rate of a scanning LiDAR can be
multiplied with scalability. We demonstrate a 4.4-fold speedup for a maximum
75-m detection range, compared with a time-of-flight-limited laser ranging
system. This approach has the potential to improve the velocity of LiDAR-based
autonomous vehicles to the regime of hundred kilometers per hour and open up a
new paradigm for ultrafast-frame-rate LiDAR imaging.


Abstract:
  Nonlocality in time is an important property of systems in which their
present state depends on the history of the whole evolution. Combined with the
nonlinearity of the process it poses serious difficulties in both analytical
and numerical treatment.
  We investigate a time-fractional porous medium equation that has proved to be
important in many applications, notably in hydrology and material sciences. We
show that the solution of both free boundary Dirichlet, Neumann, and Robin
problems on the half-line satisfies a Volterra integral equation with
non-Lipschitz nonlinearity. Based on this result we prove existence,
uniqueness, and construct a family of numerical methods that solve these
equations outperforming the usual na\"ive finite difference approach. Moreover,
we prove the convergence of these methods and illustrate the theory with
several numerical examples.


Abstract:
  Lat-Igusa-Todorov algebras are a natural generalization of Igusa-Todorov
algebras. They are defined using the generalized Igusa-Todorov functions given
in \cite{BLMV} and also verify the finitistic dimension conjecture. In this
article we give new ways to construct examples of Lat-Igusa-Todorov algebras.
On the other hand we show an example of a family of algebras that are not
Lat-Igusa-Todorov.


Abstract:
  The unexpectedly large radii of hot Jupiters are a longstanding mystery whose
solution will provide important insights into their interior physics. Many
potential solutions have been suggested, which make diverse predictions about
the details of inflation. In particular, although any valid model must allow
for maintaining large planetary radii, only some allow for radii to increase
with time. This reinflation process would potentially occur when the incident
flux on the planet is increased. In this work, we examine the observed
population of hot Jupiters to see if they grow as their parent stars brighten
along the main sequence. We consider the relation between radius and other
observables, including mass, incident flux, age, and fractional age (age over
main sequence lifetime), and show that main sequence brightening is often
sufficient to produce detectable reinflation. We further argue that these
provide strong evidence for the relatively rapid reinflation of giant planets,
and discuss the implications for proposed heating mechanisms. In our population
analysis we also find evidence for a "delayed-cooling effect", wherein planets
cool and contract far more slowly than expected. While not capable of
explaining the observed radii alone, it may represent an important component of
the effect. Finally, we identify a weak negative relationship between stellar
metallicity and planet radius which is presumably the result of enhanced
planetary bulk metallicity around metal-rich stars and has important
implications for planet formation theory.


Abstract:
  Searches for new resonances in the diphoton final state, with spin 0 as
predicted by theories with an extended Higgs sector and with spin 2 using a
warped extra-dimension benchmark model, are presented using 139 fb$^{-1}$ of
$\sqrt{s} = $ 13 TeV $pp$ collision data collected by the ATLAS experiment at
the LHC. No significant deviation from the Standard Model is observed and upper
limits are placed on the production cross-section times branching ratio to two
photons as a function of the resonance mass.


Abstract:
  We report on hollow-core fibers displaying rms core roughness under the
surface capillary waves thermodynamic limit and record loss in the
short-wavelength range (50.0 dB/km at 290 nm, 9.7 dB/km at 369 nm, 5.0 dB/km at
480 nm, 0.9 dB/km at 558 nm, 1.8 dB/km at 719 nm).


Abstract:
  The ferromagnetic (FM) nature of the metallic LaCo$_2$P$_2$ was investigated
with the positive muon spin rotation, relaxation and resonance ($\mu^+$SR)
technique. Transverse and zero field $\mu^+$SR measurements revealed that the
compound enters a long range FM ground state at $T_{\rm C}=130.91(65)$~K,
consistent with previous studies. Based on the reported FM structure, the
internal magnetic field was computed at the muon sites, which were predicted
with first principles calculations. The computed result agree well with the
experimental data. Moreover, although LaCo$_2$P$_2$ is a paramagnet at higher
temperatures $T>160$~K, it enters a short range ordered (SRO) magnetic phase
for $T_{\rm C}<T\leq160$~K. Measurements below the vicinity of $T_{\rm C}$
revealed that the SRO phase co-exists with the long range FM order at
temperatures $124\leq T\leq T_{\rm C}$. Such co-existence is an intrinsic
property and stems from competition between the 2D and 3D
interactions/fluctuations.


Abstract:
  Motivated by a famous question of Lehmer about the Mahler measure we study
and solve its analytic analogue.


Abstract:
  L0-regularization-based compressed sensing (L0-RBCS) has the potential to
outperform L1-regularization-based compressed sensing (L1-RBCS), but the
optimization in L0-RBCS is difficult because it is a combinatorial optimization
problem. To perform optimization in L0-RBCS, we propose a quantum-classical
hybrid system consisting of a quantum machine and a classical digital
processor. The coherent Ising machine (CIM) is a suitable quantum machine for
this system because this optimization problem can only be solved with a densely
connected network. To evaluate the performance of the CIM-classical hybrid
system theoretically, a truncated Wigner stochastic differential equation
(W-SDE) is obtained from the master equation for the density operator of the
network of degenerate optical parametric oscillators, and macroscopic equations
are derived by applying statistical mechanics to the W-SDE. We show that the
system performance in principle approaches the theoretical limit of compressed
sensing and this hybrid system may exceed the estimation accuracy of L1-RBCS in
actual situations, such as magnetic resonance imaging data analysis.


Abstract:
  This paper explores two topics at once: the use of denotational semantics to
bound the evaluation length of functional programs, and the semantics of strong
(that is, possibly under abstractions) call-by-value evaluation.
  About the first, we analyze de Carvalho's seminal use of relational semantics
for bounding the evaluation length of lambda-terms, starting from the
presentation of the semantics as an intersection types system. We focus on the
part of his work which is usually neglected in its many recent adaptations,
despite being probably the conceptually deeper one: how to transfer the
bounding power from the type system to the relational semantics itself. We
dissect this result and re-understand it via the isolation of a simpler size
representation property.
  About the second, we use relational semantics to develop a semantical study
of strong call-by-value evaluation, which is both a delicate and neglected
topic. We give a semantic characterization of terms normalizable with respect
to strong evaluation, providing in particular the first result of adequacy with
respect to strong call-by-value. Moreover, we extract bounds about strong
evaluation from both the type systems and the relational semantics.
  Essentially, we use strong call-by-value to revisit de Carvalho's semantic
bounds, and de Carvalho's technique to provide semantical foundations for
strong call-by-value.


Abstract:
  Conditional disclosure of secrets (CDS) is the problem of disclosing as
efficiently as possible, one secret from Alice and Bob to Carol if and only if
the inputs at Alice and Bob satisfy some function $f$. The information
theoretic capacity of CDS is the maximum number of bits of the secret that can
be securely disclosed per bit of total communication. All CDS instances, where
the capacity is the highest and is equal to $1/2$, are recently characterized
through a noise and signal alignment approach and are described using a graph
representation of the function $f$. In this work, we go beyond the best case
scenarios and further develop the alignment approach to characterize the linear
capacity of a class of CDS instances to be $(\rho-1)/(2\rho)$, where $\rho$ is
a covering parameter of the graph representation of $f$.


Abstract:
  While most strong-gravitational-lensing systems may be roughly modelled by a
single massive object between the source and the observer, in the details all
the structures near the light path contribute to the observed images. These
additional contributions, known as line-of-sight effects, are non-negligible in
practice. This article proposes a new theoretical framework to model the
line-of-sight effects, together with very promising applications at the
interface of weak and strong lensing. Our approach relies on the dominant-lens
approximation, where one deflector is treated as the main lens while the others
are treated as perturbations. The resulting framework is technically simpler to
handle than the multi-plane lensing formalism, while allowing one to
consistently model any sub-critical perturbation. In particular, it is not
limited to the usual external-convergence and external-shear parameterisation.
As a first application, we identify a specific notion of line-of-sight shear
that is not degenerate with the ellipticity of the main lens, and which could
thus be extracted from strong-lensing images. This result supports and improves
the recent proposal that Einstein rings might be powerful probes of cosmic
shear. As a second application, we investigate the distortions of
strong-lensing critical curves under line-of-sight effects, and more
particularly their correlations across the sky. We find that such correlations
may be used to probe, not only the large-scale structure of the Universe, but
also the dark-matter halo profiles of strong lenses. This last possibility
would be a key asset to improve the accuracy of the measurement of the
Hubble-Lema\^itre constant via time-delay cosmography.


Abstract:
  The inflow of cosmological gas onto haloes, while challenging to directly
observe and quantify, plays a fundamental role in the baryon cycle of galaxies.
Using the EAGLE suite of hydrodynamical simulations, we present a thorough
exploration of the physical properties of gas accreting onto haloes -- namely,
its spatial characteristics, density, temperature, and metallicity. Classifying
accretion as ``hot'' or `` cold'' based on a temperature cut of $10^{5.5}{\rm
K}$, we find that the covering fraction ($f_{\rm cov}$) of cold-mode accreting
gas is significantly lower than the hot-mode, with $z=0$ $f_{\rm cov}$ values
of $\approx 50\%$ and $\approx 80\%$ respectively. Active Galactic Nuclei (AGN)
feedback in EAGLE reduces inflow $f_{\rm cov}$ values by $\approx 10\%$, with
outflows decreasing the solid angle available for accretion flows. Classifying
inflow by particle history, we find that gas on first-infall onto a halo is
metal-depleted by $\approx 2$~dex compared to pre-processed gas, which we find
to mimic the circum-galactic medium (CGM) in terms of metal content. We also
show that high (low) halo-scale gas accretion rates are associated with
metal-poor (rich) CGM in haloes below $10^{12}M_{\odot}$, and that variation in
halo-scale gas accretion rates may offer a physical explanation for the
enhanced scatter in the star-forming main sequence at low
($\lesssim10^{9}M_{\odot}$) and high ($\gtrsim10^{10}M_{\odot}$) stellar
masses. Our results highlight how gas inflow influences several halo- and
galaxy-scale properties, and the need to combine kinematic and chemical data in
order to confidently break the degeneracy between accreting and outgoing gas in
CGM observations.


Abstract:
  Enriched versions of type A Schubert polynomials are constructed with
coefficients in a polynomial ring in variables $c_1, c_2, \ldots$. Specializing
these variables to $0$ recovers the double Schubert polynomials of Lascoux and
Sch\"utzenberger; specializing them to certain power series recovers the
back-stable double Schubert polynomials of Lam, Lee, and Shimozono;
specializing them to Schur Q-polynomials relates them to the type C double
Schubert polynomials of Ikeda, Mihalcea, and Naruse. Many formulas for
classical Schubert polynomials generalize to this setting. They give, and are
characterized by, formulas for degeneracy loci.


Abstract:
  Fine-tuning pre-trained language models (PLMs) has demonstrated its
effectiveness on various downstream NLP tasks recently. However, in many
low-resource scenarios, the conventional fine-tuning strategies cannot
sufficiently capture the important semantic features for downstream tasks. To
address this issue, we introduce a novel framework (named "CSS-LM") to improve
the fine-tuning phase of PLMs via contrastive semi-supervised learning.
Specifically, given a specific task, we retrieve positive and negative
instances from large-scale unlabeled corpora according to their domain-level
and class-level semantic relatedness to the task. We then perform contrastive
semi-supervised learning on both the retrieved unlabeled and original labeled
instances to help PLMs capture crucial task-related semantic features. The
experimental results show that CSS-LM achieves better results than the
conventional fine-tuning strategy on a series of downstream tasks with few-shot
settings, and outperforms the latest supervised contrastive fine-tuning
strategies. Our datasets and source code will be available to provide more
details.


Abstract:
  We push the boundaries of electronic structure-based \textit{ab-initio}
molecular dynamics (AIMD) beyond 100 million atoms. This scale is otherwise
barely reachable with classical force-field methods or novel neural network and
machine learning potentials. We achieve this breakthrough by combining
innovations in linear-scaling AIMD, efficient and approximate sparse linear
algebra, low and mixed-precision floating-point computation on GPUs, and a
compensation scheme for the errors introduced by numerical approximations.
  The core of our work is the non-orthogonalized local submatrix (NOLSM)
method, which scales very favorably to massively parallel computing systems and
translates large sparse matrix operations into highly parallel, dense matrix
operations that are ideally suited to hardware accelerators. We demonstrate
that the NOLSM method, which is at the center point of each AIMD step, is able
to achieve a sustained performance of 324 PFLOP/s in mixed FP16/FP32 precision
corresponding to an efficiency of 67.7\% when running on 1536 NVIDIA A100 GPUs.


Abstract:
  Koplienko \cite{Ko} found a trace formula for perturbations of self-adjoint
operators by operators of Hilbert-Schmidt class $\mathcal{B}_2(\mathcal{H})$.
In 2012, Potapov and Sukochev \cite{PoSu} obtained a similar formula for pairs
of contractions by answering an open question posed by Gesztesy, Pushnitski,
and Simon in \cite[Open Question 11.2]{GePu}. In this article, we give a still
another proof of the Koplienko trace formula in the case of unitary operators
via linear path by reducing the problem to a finite-dimensional one as in the
proof of Krein's trace formula by Voiculescu \cite{Voi}, Sinha and Mohapatra
\cite{MoSi94,MoSi96}. Consequently, we obtain the Koplienko trace formula for a
class of pairs of contractions using the Sch$\ddot{a}$ffer matrix unitary
dilation. Moreover, we also obtain the Koplienko trace formula for a pair of
self-adjoint operators and maximal dissipative operators using the Cayley
transform.


Abstract:
  Generating long and coherent text is an important but challenging task,
particularly for open-ended language generation tasks such as story generation.
Despite the success in modeling intra-sentence coherence, existing generation
models (e.g., BART) still struggle to maintain a coherent event sequence
throughout the generated text. We conjecture that this is because of the
difficulty for the decoder to capture the high-level semantics and discourse
structures in the context beyond token-level co-occurrence. In this paper, we
propose a long text generation model, which can represent the prefix sentences
at sentence level and discourse level in the decoding process. To this end, we
propose two pretraining objectives to learn the representations by predicting
inter-sentence semantic similarity and distinguishing between normal and
shuffled sentence orders. Extensive experiments show that our model can
generate more coherent texts than state-of-the-art baselines.


Abstract:
  Some recent papers have argued that frequency should have the dimensions of
angle/time, with the consequences that 1 Hz = $2\pi$ rad/s instead of 1
s$^{-1}$, and also that $\nu = \omega$ and $h = \hbar$. This letter puts the
case that this argument redefines the quantity frequency and then draws
conclusions from equations that rely on the standard definition being used. The
problems that this redefinition is designed to address arise from the
widespread unstated adoption of the Radian Convention, which treats the radian
as a dimensionless quantity equal to the number 1, in effect making the radian
a hidden unit. This convention is currently built-in to the SI, when it should
be separable from it. The unhelpful status of angles in the SI can be remedied
with minimal disruption by (1) changing the definition of the radian from a
radian equals 1 m/m to e.g. a right angle equals $\pi/2$ radians, and (2)
acknowledging that the adoption of the Radian Convention is acceptable, when
made explicit. The standard definitions of frequency and the hertz should
remain unchanged.


Abstract:
  Page 332 of Ramanujan's Lost Notebook contains a compelling identity for
$\zeta(1/2)$, which has been studied by many mathematicians over the years. On
the same page, Ramanujan also recorded the series, \begin{align*}
\frac{1^r}{\exp(1^s x) - 1} + \frac{2^r}{\exp(2^s x) - 1} + \frac{3^r}{\exp(3^s
x) - 1} + \cdots, \end{align*} where $s$ is a positive integer and $r-s$ is any
even integer. Unfortunately, Ramanujan doesn't give any formula for it. This
series was rediscovered by Kanemitsu, Tanigawa, and Yoshimoto, although they
studied it only when $r-s$ is a negative even integer. Recently, Dixit and the
second author generalized the work of Kanemitsu et al. and obtained a
transformation formula for the aforementioned series with $r-s$ is any even
integer. While extending the work of Kanemitsu et al., Dixit and the second
author obtained a beautiful generalization of Ramanujan's formula for odd zeta
values. In the current paper, we investigate transformation formulas for an
infinite series, and interestingly, we derive Ramanujan's formula for
$\zeta(1/2)$, Wigert's formula for $\zeta(1/k)$ as well as Ramanujan's formula
for $\zeta(2m+1)$. Furthermore, we obtain a new identity for $\zeta(-1/2)$ in
the spirit of Ramanujan.


Abstract:
  In this paper, we propose new learning algorithms for approximating
high-dimensional functions using tree tensor networks in a least-squares
setting. Given a dimension tree or architecture of the tensor network, we
provide an algorithm that generates a sequence of nested tensor subspaces based
on a generalization of principal component analysis for multivariate functions.
An optimal least-squares method is used for computing projections onto the
generated tensor subspaces, using samples generated from a distribution
depending on the previously generated subspaces. We provide an error bound in
expectation for the obtained approximation. Practical strategies are proposed
for adapting the feature spaces and ranks to achieve a prescribed error. Also,
we propose an algorithm that progressively constructs the dimension tree by
suitable pairings of variables, that allows to further reduce the number of
samples necessary to reach that error. Numerical examples illustrate the
performance of the proposed algorithms and show that stable approximations are
obtained with a number of samples close to the number of free parameters of the
estimated tensor networks.


Abstract:
  We consider online convex optimization (OCO) over a heterogeneous network
with communication delay, where multiple workers together with a master execute
a sequence of decisions to minimize the accumulation of time-varying global
costs. The local data may not be independent or identically distributed, and
the global cost functions may not be locally separable. Due to communication
delay, neither the master nor the workers have in-time information about the
current global cost function. We propose a new algorithm, termed Hierarchical
OCO (HiOCO), which takes full advantage of the network heterogeneity in
information timeliness and computation capacity to enable multi-step gradient
descent at both the workers and the master. We analyze the impacts of the
unique hierarchical architecture, multi-slot delay, and gradient estimation
error to derive upper bounds on the dynamic regret of HiOCO, which measures the
gap of costs between HiOCO and an offline globally optimal performance
benchmark.


Abstract:
  In this paper, we propose a new challenging task named as \textbf{partial
multi-view few-shot learning}, which unifies two tasks, i.e. few-shot learning
and partial multi-view learning, together. Different from the traditional
few-shot learning, this task aims to solve the few-shot learning problem given
the incomplete multi-view prior knowledge, which conforms more with the
real-world applications. However, this brings about two difficulties within
this task. First, the gaps among different views can be large and hard to
reduce, especially with sample scarcity. Second, due to the incomplete view
information, few-shot learning becomes more challenging than the traditional
one. To deal with the above issues, we propose a new \textbf{Meta-alignment and
Context Gated-aggregation Network} by equipping meta-alignment and context
gated-aggregation with partial multi-view GNNs. Specifically, the
meta-alignment effectively maps the features from different views into a more
compact latent space, thereby reducing the view gaps. Moreover, the context
gated-aggregation alleviates the view-missing influence by leveraging the
cross-view context. Extensive experiments are conducted on the PIE and ORL
dataset for evaluating our proposed method. By comparing with other few-shot
learning methods, our method obtains the state-of-the-art performance
especially with heavily-missing views.


Abstract:
  Optical frequency transfer provides the means for high-fidelity frequency
transfer across thousands of kilometers. A critical step in the further
developing optical frequency transfer is its capability to transfer a high
spectral purity feature from ultrastable lasers or optical clocks to any remote
locations and, at the same time, its adaptability to incorporate the optical
frequency transfer technique into any existing communication networks with
different topologies. Here we for the first time report a technique that
delivers optical-frequency signals to multiple independent remote hubs along a
ring optical-fiber network with passive phase stabilization. The technique
automatically corrects optical-fiber length fluctuations of arbitrary hubs
along the loop by mixing and shifting optical signals. Without the help of an
active phase tracker and a compensator, it could significantly mitigate some
technical problems such as the limited compensation speed and phase recovery
time, the phase jitter contamination caused by the servo bump in conventional
phase noise cancellation. Moreover, by transmitting optical signals along both
directions using the same optical source, it can improve the signal-to-noise
ratio at each hub. This technique maintains the same delay-limited phase noise
correction capability as in conventional techniques and, furthermore, improves
the phase jitter by a factor of 3, opening a way to a broad distribution of an
ultrastable frequency reference with high spectral purity and enabling a wide
range of applications beyond metrology over a ring fiber network with the
naturally impressive reliability and scalability.


Abstract:
  The controlled design of martensitic transformation (MT) to achieve specific
properties is crucial for the innovative application of shape memory alloys
(SMAs) in advanced technologies. Herein, we explore and design the MT behaviors
and the mechanical properties of Ti-Nb nanocomposites by combining
high-throughput phase-field simulations and machine learning (ML) approaches.
Based on the systematic phase-field simulations, we obtain data sets of the
mechanical properties for various nanocomposites constructed by four
macroscopic degrees of freedom, which can be employed to design and optimize
the microstructures for different applications. To accelerate the phase-field
screening of the desired metallic biomaterials, a ML assisted strategy is
adopted to perform multi-objective optimization of the mechanical properties,
through which promising nanocomposite configurations are pre-screened for the
next set of phase-field simulations. With the ML guided simulations, an
optimized candidate composed of Nb-rich matrix and Nb-lean nanofillers that
exhibits a combination of unprecedented mechanical properties, including
ultra-low modulus, linear super-elasticity, and near-hysteresis-free is
designed. The exceptional mechanical properties in the nanocomposite originate
from optimized continuous MT rather than a sharp first-order transition, which
is common in typical SMAs. This work provides a new computational approach and
design concept for developing novel functional materials with extraordinary
properties.


Abstract:
  Spintronic diodes are emerging as disruptive candidates for impacting several
technological applications ranging from the Internet of Things to Artificial
Intelligence. In this letter, an overview of the recent achievements on
spintronic diodes is briefly presented, underling the major breakthroughs that
have led these devices to have the largest sensitivity measured up to date for
a diode. For each class of spintronic diodes (passive, active, resonant,
non-resonant), we indicate the remaining developments to improve the
performances as well as the future directions. We also dedicate the last part
of this perspective to new ideas for developing spintronic diodes in
multiphysics systems by combining 2-dimensional materials and antiferromagnets.


Abstract:
  We propose a double bootstrap procedure for reducing coverage error in the
confidence intervals of descriptive statistics for independent and identically
distributed functional data. Through a series of Monte Carlo simulations, we
compare the finite sample performance of single and double bootstrap procedures
for estimating the distribution of descriptive statistics for independent and
identically distributed functional data. At the cost of longer computational
time, the double bootstrap with the same bootstrap method reduces confidence
level error and provides improved coverage accuracy than the single bootstrap.
Illustrated by a Canadian weather station data set, the double bootstrap
procedure presents a tool for visualising the distribution of the descriptive
statistics for the functional data.


Abstract:
  We report on the X-ray properties of the new transient Swift J0840.7$-$3516,
discovered with Swift/BAT in 2020 February, using extensive data of Swift,
MAXI, NICER, and NuSTAR. The source flux increased for $\sim 10^3$ s after the
discovery, decayed rapidly over $\sim$ 5 orders of magnitude in 5 days, and
then remained almost constant over 9 months. Large-amplitude short-term
variations on time scales of 1--$10^4$ s were observed throughout the decay. In
the initial flux rise, the source showed a hard power-law shaped spectrum with
a photon index of $\sim 1.0$ extending up to $\sim 30$ keV, above which an
exponential cutoff was present. The photon index increased in the following
rapid decay and became $\sim 2$ at the end of the decay. A spectral absorption
feature at 3--4 keV was detected in the decay. It is not straightforward to
explain all the observed properties by any known class of X-ray sources. We
discuss the possible nature of the source, including a Galactic low mass X-ray
binary with multiple extreme properties and a tidal disruption event by a
supermassive black hole or a Galactic neutron star.


Abstract:
  Currently available quantum hardware allows for small scale implementations
of quantum machine learning algorithms. Such experiments aid the search for
applications of quantum computers by benchmarking the near-term feasibility of
candidate algorithms. Here we demonstrate the quantum learning of a two-qubit
unitary by a sequence of three parameterized quantum circuits containing a
total of 21 variational parameters. Moreover, we variationally diagonalize the
unitary to learn its spectral decomposition, i.e., its eigenvalues and
eigenvectors. We illustrate how this can be used as a subroutine to compress
the depth of dynamical quantum simulations. One can view our implementation as
a demonstration of entanglement-enhanced machine learning, as only a single
(entangled) training data pair is required to learn a 4x4 unitary matrix.


Abstract:
  In the present paper, we first give a characterization for Bongartz
completion in $\tau$-tilting theory via $c$-vectors. Motivated by this
characterization, we give the definition of Bongartz completion in cluster
algebras using $c$-vectors. Then we prove the existence and uniqueness of
Bongartz completion in cluster algebras. We also prove that Bongartz completion
admits certain commutativity. We give two applications for Bongartz completion
in cluster algebras. As the first application, we prove the full subquiver of
the exchange quiver (or known as oriented exchange graph) of a cluster algebra
$\mathcal A$ whose vertices consist of seeds of $\mathcal A$ containing
particular cluster variables is isomorphic to the exchange quiver of another
cluster algebra. As the second application, we prove that in a cluster Poisson
algebra $\mathcal X_\bullet$, each cluster Poisson seed (up to seed
equivalence) of $\mathcal X_\bullet$ is uniquely determined by its negative
cluster Poisson variables.


Abstract:
  This paper studies the problem of autonomous exploration under localization
uncertainty for a mobile robot with 3D range sensing. We present a framework
for self-learning a high-performance exploration policy in a single simulation
environment, and transferring it to other environments, which may be physical
or virtual. Recent work in transfer learning achieves encouraging performance
by domain adaptation and domain randomization to expose an agent to scenarios
that fill the inherent gaps in sim2sim and sim2real approaches. However, it is
inefficient to train an agent in environments with randomized conditions to
learn the important features of its current state. An agent can use domain
knowledge provided by human experts to learn efficiently. We propose a novel
approach that uses graph neural networks in conjunction with deep reinforcement
learning, enabling decision-making over graphs containing relevant exploration
information provided by human experts to predict a robot's optimal sensing
action in belief space. The policy, which is trained only in a single
simulation environment, offers a real-time, scalable, and transferable
decision-making strategy, resulting in zero-shot transfer to other simulation
environments and even real-world environments.


Abstract:
  We present an approach for state and parameter estimation in retinal laser
treatment by a novel setup where both measurement and heating is performed by a
single laser. In this medical application, the temperature that is induced by
the laser in the patient's eye is critical for a successful and safe treatment.
To this end, we pursue a model-based approach using a model given by a heat
diffusion equation on a cylindrical domain, where the source term is given by
the absorbed laser power. The model is parametric in the sense that it involves
an absorption coefficient, which depends on the treatment spot and plays a
central role in the input-output behavior of the system. After discretization,
we apply a particularly suited parametric model order reduction to ensure
real-time tractability while retaining parameter dependence. We augment known
state estimation techniques, i.e., extended Kalman filtering and moving horizon
estimation, with parameter estimation to estimate the absorption coefficient
and the current state of the system. Eventually, we show first results for
simulated and experimental data from porcine eyes. We find that, regarding
convergence speed, the moving horizon estimation slightly outperforms the
extended Kalman filter on measurement data in terms of parameter and state
estimation, however, on simulated data the results are very similar.


Abstract:
  Smart environments powered by the Internet of Things aim at improving our
daily lives by automatically tuning ambient parameters (e.g. temperature,
interior light) and by achieving energy savings through self-managing
cyber-physical systems. Commercial solutions, however, only permit setting
simple target goals on those parameters and do not consider mediating
conflicting goals among different users and/or system administrators, and
feature limited compatibility across different IoT verticals. In this article,
we propose a declarative framework to represent smart environments, user-set
goals and customisable mediation policies to reconcile contrasting goals
encompassing multiple IoT systems. An open-source Prolog prototype of the
framework is showcased over two lifelike motivating examples.


Abstract:
  In this short note we observe that the boundary of a properly embedded
compact exact Lagrangian sub-manifolds in a subcritical Weinstein domain $X$
necessarily admits Reeb chords. The existence of this Reeb chords either
follows from an obstruction to the deformation of the boundary to a cylinder
over a Legendrian sub-manifold or from the fact that the wrapped Floer homology
of the Lagrangian vanishes once this boundary have been "collared".


Abstract:
  Honeypots are a well-studied defensive measure in network security. This work
proposes an effective low-cost honeypot that is easy to deploy and maintain.
The honeypot introduced in this work is able to handle commands in a
non-standard way by blocking them or replying with an insult to the attacker.
To determine the most efficient defense strategy, the interaction between
attacker and defender is modeled as a Bayesian two-player game. For the
empirical analysis, three honeypot instances were deployed, each with a slight
variation in its configuration. In total, over 200 distinct sessions were
captured, which allows for qualitative evaluation of post-exploitation
behavior. The findings show that attackers react to insults and blocked
commands in different ways, ranging from ignoring to sending insults
themselves. The main contribution of this work lies in the proposed framework,
which offers a low-cost alternative to more technically sophisticated and
resource-intensive approaches.


Abstract:
  For $0<\alpha<\frac{1}{3}$ we construct unique solutions to the fractal
Burgers equation $\partial_t u + u\partial_xu + (-\Delta)^\alpha u = 0$ which
develop a first shock in finite time, starting from smooth generic initial
data. This first singularity is an asymptotically self-similar, stable $H^6$
perturbation of a stable, self-similar Burgers shock profile. Furthermore, we
are able to compute the spatio-temporal location and H\"older regularity for
the first singularity. There are many results showing that gradient blowup
occurs in finite time for the supercritical range, but the present result is
the first example where singular solutions have been explicitly constructed and
so precisely characterized.


Abstract:
  We propose and demonstrate an experimental method to measure by absorption
imaging the size and local column density of a cloud of atoms, even when its
smallest dimension is smaller than the resolution of the imaging system. To do
this, we take advantage of the fact that, for a given total number of atoms, a
smaller and denser cloud scatters less photons when the gas is optically thick.
The method relies on making an ansatz on the cloud shape along the unresolved
dimension(s), and on providing an additional information such as the total
number of atoms. We demonstrate the method on \textit{in-situ} absorption
images of elongated 87Sr Fermi gases. We find significant non-linear
corrections to the estimated size and local density of the cloud compared to a
standard analysis. This allows us to recover an un-distorted longitudinal
density profile, and to measure transverse sizes as small as one fourth of our
imaging resolution. The ultimate limit of our method is the wavelength that is
used for imaging.


Abstract:
  In this paper, the interplay of the non-Herimiticity and the cascade of
delocalization transition in the quasi-periodic chain is studied. The study is
applied in a non-Hermitian interpolating Aubry-Andr{\'e}-Fibonacci (IAAF)
model, which combines the non-Hermitian Aubry-Andr{\'e} (AA) model and the
non-Hermitian Fibonacci model through a varying parameter, and the
non-Hermiticity in this model is introduced by the non-reciprocal hopping. In
the non-Hermitian AA limit, the system undergoes a delocalization transition by
tuning the potential strength. At the critical point, the spatial distribution
of the critical state shows a self-similar structure with the relative distance
between the peaks being the Fibonacci sequence, and the finite-size scaling of
the inverse participation ratios $({\rm IPRs})$ of the critical ground state
with lattice size $L$ shows that ${\rm IPR}_g\propto L^{-0.1189}$. In the
non-Hermitian Fibonacci limit, we find that the system is always in the
extended phase. Along the continuous deformation from the non-Hermitian AA
model into the non-Hermitian Fibonacci model in the IAAF model, the cascade of
the delocalization transition is found, but only a few plateaux appear.
Moreover, the self-similar structure of spatial distribution for the critical
modes along the cascade transition is also found. In addition, we find that the
delocalization transition and the real-complex transition for the excited
states happen at almost the same parameter. Our results show that the
non-Hermiticity provides an additional knob to control the cascade of the
delocalization transition besides the on-site potential.


Abstract:
  Whitney proved in 1931 that 4-connected planar triangulations are
Hamiltonian. Hakimi, Schmeichel, and Thomassen conjectured in 1979 that if $G$
is a 4-connected planar triangulation with $n$ vertices then $G$ contains at
least $2(n-2)(n-4)$ Hamiltonian cycles, with equality if and only if $G$ is a
double wheel. On the other hand, a recent result of Alahmadi, Aldred, and
Thomassen states that there are exponentially many Hamiltonian cycles in
5-connected planar triangulations. In this paper, we consider 4-connected
planar $n$-vertex triangulations $G$ that do not have too many separating
4-cycles or have minimum degree 5. We show that if $G$ has $O(n/{\log}_2 n)$
separating 4-cycles then $G$ has $\Omega(n^2)$ Hamiltonian cycles, and if
$\delta(G)\ge 5$ then $G$ has $2^{\Omega(n^{1/4})}$ Hamiltonian cycles. Both
results improve previous work. Moreover, the proofs involve a "double wheel"
structure, providing further evidence to the above conjecture.


Abstract:
  The astrobiology is an interdisciplinary science, combining the methods and
the means of physics, biology, chemistry and astronomy. Its main purpose is to
find out if the exoplanets are habitable and if so, to confirm life on them.
The basic conditions for habitability are the essential ones, like these on the
Earth. But additional, essential as well, exist, like the mass of the
exoplanet, the atmospheric composition and its location. To find the answer we
used basic molecular physics and classical mechanics knowledge. We also
proposed a method for exoplanet hunting and conformation and exploration of
their atmospheres. With the constantly improving techniques and apparatuses,
the answer converts from if to when are we going to find extraterrestrial life,
including microbial one.


Abstract:
  In a recent article M. Baggioli and A. Zaccone (Phys. Rev. Lett. {\bf 112},
145501 (2019)) claimed that an anharmonic damping, leading to a sound
attenuation proportional to $\omega^2$ (Akhiezer-type damping) would imply a
boson peak, i.e.\ a maximum in the vibrational density of states, divided by
the frequency squared (reduced density of states). This would apply both to
glasses and crystals.Here we show that this is not the case. In a
mathematically correct treatment of the model the reduced density of states
monotonously decreases, i.e.\ there is no boson peak. We further show that the
formula for the would-be boson peak, presented by the authors, corresponds to a
very short one-dimensional damped oscillator system. The peaks they show
correspond to resonances, which vanish in the thermodynamic limit.


Abstract:
  This work presents a novel version of recently developed Gauss-Newton method
for solving systems of nonlinear equations, based on upper bound of solution
residual and quadratic regularization ideas. We obtained for such method global
convergence bounds and under natural non-degeneracy assumptions we present
local quadratic convergence results. We developed stochastic optimization
algorithms for presented Gauss-Newton method and justified sub-linear and
linear convergence rates for these algorithms using weak growth condition (WGC)
and Polyak-Lojasiewicz (PL) inequality. We show that Gauss-Newton method in
stochastic setting can effectively find solution under WGC and PL condition
matching convergence rate of the deterministic optimization method. The
suggested method unifies most practically used Gauss-Newton method
modifications and can easily interpolate between them providing flexible and
convenient method easily implementable using standard techniques of convex
optimization.


Abstract:
  The element beryllium is detected for the first time in white dwarf stars.
This discovery in the spectra of two helium-atmosphere white dwarfs was made
possible only because of the remarkable overabundance of Be relative to all
other elements, heavier than He, observed in these stars. The measured Be
abundances, relative to chondritic, are by far the largest ever seen in any
astronomical object. We anticipate that the Be in these accreted planetary
bodies was produced by spallation of one or more of O, C, and N in a region of
high fluence of particles of MeV or greater energy.


Abstract:
  The addition of co-solutes to colloidal suspensions is often employed to
induce tunable depletion interactions. In this work we investigate effective
colloidal interactions arising from binary co-solute mixtures of hard spheres
and patchy particles. By changing the relative concentration of the two
species, we show that the resulting effective potential $V_\text{eff}$
continuously changes from the one obtained for a single-component hard sphere
co-solute to that mediated by the single-component patchy particle co-solute.
Interestingly, we find that, independently of the relative concentration of the
two components, the resulting $V_\text{eff}$ is additive, i.e., it is
well-described by the linear combination of the effective interactions mediated
by respective pure co-solutes. However, a breakdown of the additivity occurs
when the co-solute mixture is close to the onset of a demixing transition.
These results represent a step forward in understanding and predicting
colloidal behaviour in complex and crowded environments and for exploiting this
knowledge to design targeted colloidal superstructures.


Abstract:
  We investigate the $H_0$ tension in a range of extended model frameworks
beyond the standard $\Lambda$CDM without the data from cosmic microwave
background (CMB). Specifically, we adopt the data from baryon acoustic
oscillation, big bang nucleosynthesis and type Ia supernovae as indirect
measurements of $H_0$ to study the tension. We show that the estimated value of
$H_0$ from indirect measurements is overall lower than that from direct local
ones regardless of the data sets and a range of extended models to be analyzed,
which indicates that, although the significance of the tension varies depending
on models, the $H_0$ tension persists in a broad framework beyond the standard
$\Lambda$CDM model even without CMB data.


Abstract:
  Background: Trends in hospitalised case-fatality risk (HFR), risk of
intensive care unit (ICU) admission and lengths of stay for patients
hospitalised for COVID-19 in England over the pre-vaccination era are unknown.
  Methods: Data on hospital and ICU admissions with COVID-19 at 31 NHS trusts
in England were collected by Public Health England's Severe Acute Respiratory
Infections surveillance system and linked to death information. We applied
parametric multi-state mixture models, accounting for censored outcomes and
regressing risks and times between events on month of admission, geography, and
baseline characteristics.
  Findings: 20,785 adults were admitted with COVID-19 in 2020. Between March
and June/July/August estimated HFR reduced from 31.9% (95% confidence interval
30.3-33.5%) to 10.9% (9.4-12.7%), then rose steadily from 21.6% (18.4-25.5%) in
September to 25.7% (23.0-29.2%) in December, with steeper increases among older
patients, those with multi-morbidity and outside London/South of England. ICU
admission risk reduced from 13.9% (12.8-15.2%) in March to 6.2% (5.3-7.1%) in
May, rising to a high of 14.2% (11.1-17.2%) in September. Median length of stay
in non-critical care increased during 2020, from 6.6 to 12.3 days for those
dying, and from 6.1 to 9.3 days for those discharged.
  Interpretation: Initial improvements in patient outcomes, corresponding to
developments in clinical practice, were not sustained throughout 2020, with HFR
in December approaching the levels seen at the start of the pandemic, whilst
median hospital stays have lengthened. The role of increased transmission, new
variants, case-mix and hospital pressures in increasing COVID-19 severity
requires urgent further investigation.


Abstract:
  It is a classical result that configuration spaces of labelled particles in
$\mathbb{R}^d$ are free algebras over the little $d$-cubes operad
$\mathscr{C}_d$, and their $d$-fold bar construction is equivalent to the
$d$-fold suspension of the labelling space. The aim of this paper is to study a
variation of these spaces, namely the configuration space of labelled clusters
of points in $\mathbb{R}^d$. This configuration space is again an
$E_d$-algebra, but in general not a free one. We give geometric models for
their iterated bar construction in two different ways: one uses an additional
verticality constraint, and the other one uses a description of these clustered
configuration spaces as cellular $E_1$-algebras. In the last section, we show a
stable splitting result and present some applications.


Abstract:
  Business success in e-commerce depends on customer perceived value. A
customer with high perceived value buys, returns, and recommends items. The
perceived value is at risk whenever the information load harms users' shopping
experience. In e-grocery, shoppers face an overwhelming number of items, the
majority of which is irrelevant for the shopper. Recommender systems (RS)
enable businesses to master information overload (IO) by providing users with
an item ranking by relevance. Prior work proposes RS with short personalized
rankings (top-k). Given large order sizes and high user heterogeneity in
e-grocery, top-k RS are insufficient to diminish IO in this domain. To fill
this gap and raise business performance, this paper introduces an RS with a
personalized long ranking (top-N). Undertaking a randomized field experiment,
the paper establishes the merit of shifting from top-k to top-N rankings.
Specifically, the proposed RS reduces IO by 29.4% and lowers users' search time
by 3.3 seconds per item. The field experiment also reveals a 7% uplift in
revenue due to the top-N ranking. Substantial benefits for the customer and the
company highlight the business value of top-N rankings as a new design
requirement for recommender systems in e-grocery.


Abstract:
  COVID-19 has led to school closures in Japan to cope with the pandemic. Under
the state of emergency, in addition to school closure, after-school care has
not been sufficiently supplied. We independently collected individual level
data through internet surveys to construct short panel data from mid-March to
mid-June 2020, which covered before and after the state of emergency. We
analyze how the presence of school-aged children influences their parents views
about working from home. After controlling for various factors using a fixed
effects model, we find that in cases where parents were workers, and the
children are (1) in primary school, parents are willing to promote working from
home. If children are (2) in junior high school, the parents view is hardly
affected. (3) Surprisingly, workers whose children are primary school pupils
are most likely to support promotion of working from home after schools reopen.
Due to school closure and a lack of after-school care, parents need to work
from home, and this experience motivated workers with small children to
continue doing so to improve work-life balance even after schools reopen.


Abstract:
  This document discusses the curriculum of the business informatics program of
the Nijmegen Institute for Informatics and business informatics (NIII). The aim
is to provide a 'repository' with regard to the structure of the curriculum,
which will apply from 2003.
  In the past three years, the image of information science as a discipline has
been further concretised at both national and Nijmegen level. Curriculum 2003
is on the one hand the result of this concretization and on the other hand of
the three years of experience that has now been built up within the NIII with
the information science training. In this document, therefore, explicit
attention will also be paid to the 'migration' from the existing 'start-up'
curricula: 2000, 2001 and 2002. It should be noted here that the students of
cohort 2000 will in principle have completed the bachelor's phase of the
program this year (2003). Complete information science education. No specific
`migration 'is therefore necessary for this group of students.


Abstract:
  Learning to interact with the environment not only empowers the agent with
manipulation capability but also generates information to facilitate building
of action understanding and imitation capabilities. This seems to be a strategy
adopted by biological systems, in particular primates, as evidenced by the
existence of mirror neurons that seem to be involved in multi-modal action
understanding. How to benefit from the interaction experience of the robots to
enable understanding actions and goals of other agents is still a challenging
question. In this study, we propose a novel method, deep modality blending
networks (DMBN), that creates a common latent space from multi-modal experience
of a robot by blending multi-modal signals with a stochastic weighting
mechanism. We show for the first time that deep learning, when combined with a
novel modality blending scheme, can facilitate action recognition and produce
structures to sustain anatomical and effect-based imitation capabilities. Our
proposed system, can be conditioned on any desired sensory/motor value at any
time-step, and can generate a complete multi-modal trajectory consistent with
the desired conditioning in parallel avoiding accumulation of prediction
errors. We further showed that given desired images from different
perspectives, i.e. images generated by the observation of other robots placed
on different sides of the table, our system could generate image and joint
angle sequences that correspond to either anatomical or effect based imitation
behavior. Overall, the proposed DMBN architecture not only serves as a
computational model for sustaining mirror neuron-like capabilities, but also
stands as a powerful machine learning architecture for high-dimensional
multi-modal temporal data with robust retrieval capabilities operating with
partial information in one or multiple modalities.


Abstract:
  Light-in-flight (LIF) imaging is the measurement and reconstruction of
light's path as it moves and interacts with objects. It is well known that
relativistic effects can result in apparent velocities that differ
significantly from the speed of light. However, less well known is that
Rayleigh scattering and the effects of imaging optics can lead to observed
intensities changing by several orders of magnitude along light's path. We
develop a model that enables us to correct for all of these effects, thus we
can accurately invert the observed data and reconstruct the true
intensity-corrected optical path of a laser pulse as it travels in air. We
demonstrate the validity of our model by observing the photon arrival time and
intensity distribution obtained from single-photon avalanche detector (SPAD)
array data for a laser pulse propagating towards and away from the camera. We
can then reconstruct the true intensity-corrected path of the light in four
dimensions (three spatial dimensions and time).


Abstract:
  Neural networks (NNs) have been extremely successful across many tasks in
machine learning. Quantization of NN weights has become an important topic due
to its impact on their energy efficiency, inference time and deployment on
hardware. Although post-training quantization is well-studied, training optimal
quantized NNs involves combinatorial non-convex optimization problems which
appear intractable. In this work, we introduce a convex optimization strategy
to train quantized NNs with polynomial activations. Our method leverages hidden
convexity in two-layer neural networks from the recent literature, semidefinite
lifting, and Grothendieck's identity. Surprisingly, we show that certain
quantized NN problems can be solved to global optimality in polynomial-time in
all relevant parameters via semidefinite relaxations. We present numerical
examples to illustrate the effectiveness of our method.


Abstract:
  In this paper we continue the programme initiated in Part I, that is the
study of entanglement measures in the sine-Gordon model. In both parts, we have
focussed on one specific technique, that is the well-known connection between
branch point twist field correlators and measures of entanglement in 1+1D
integrable quantum field theory. Our papers apply this technique for the first
time to a non-diagonal theory with an involved particle spectrum, the
sine-Gordon model. In this Part II we focus on a different entanglement
measure, the symmetry resolved entanglement, and develop its associated twist
field description, exploiting the underlying U(1) symmetry of the theory. In
this context, conventional branch point twist fields are no longer the fields
required, but instead we must work with one of their composite generalisations,
which can be understood as the field resulting from the fusion of a standard
branch point twist field and the sine-Gordon exponential field associated with
U(1) symmetry. The resulting composite twist field has correlators which as
usual admit a form factor expansion. In this paper we write the associated form
factor equations and solve them for various examples in the breather sector by
using the method of angular quantisation. We show that, in the attractive
regime, this is the sector which provides the leading contribution to the
symmetry resolved entropies, both Renyi and von Neumann. We compute the latter
in the limit of a large region size and show that they satisfy the property of
equipartition, that is the leading contribution to the symmetry resolved
entanglement is independent of the symmetry sector.


Abstract:
  Given an undirected graph, the Densest-k-Subgraph problem (DkS) seeks to find
a subset of k vertices such that the sum of the edge weights in the
corresponding subgraph is maximized. The problem is known to be NP-hard, and is
also very difficult to approximate, in the worst-case. In this paper, we
present a new convex relaxation for the problem. Our key idea is to reformulate
DkS as minimizing a submodular function subject to a cardinality constraint.
Exploiting the fact that submodular functions possess a convex, continuous
extension (known as the Lov\'asz extension), we propose to minimize the
Lov\'asz extension over the convex hull of the cardinality constraints.
Although the Lov\'asz extension of a submodular function does not admit an
analytical form in general, for DkS we show that it does. We leverage this
result to develop a highly scalable algorithm based on the Alternating
Direction Method of Multipliers (ADMM) for solving the relaxed problem. Coupled
with a pair of fortuitously simple rounding schemes, we demonstrate that our
approach outperforms existing baselines on real-world graphs and can yield high
quality sub-optimal solutions which typically are a posteriori no worse than
65-80\% of the optimal density.


Abstract:
  Photo-realistic re-rendering of a human from a single image with explicit
control over body pose, shape and appearance enables a wide range of
applications, such as human appearance transfer, virtual try-on, motion
imitation, and novel view synthesis. While significant progress has been made
in this direction using learning-based image generation tools, such as GANs,
existing approaches yield noticeable artefacts such as blurring of fine
details, unrealistic distortions of the body parts and garments as well as
severe changes of the textures. We, therefore, propose a new method for
synthesising photo-realistic human images with explicit control over pose and
part-based appearance, i.e., StylePoseGAN, where we extend a non-controllable
generator to accept conditioning of pose and appearance separately. Our network
can be trained in a fully supervised way with human images to disentangle pose,
appearance and body parts, and it significantly outperforms existing single
image re-rendering methods. Our disentangled representation opens up further
applications such as garment transfer, motion transfer, virtual try-on, head
(identity) swap and appearance interpolation. StylePoseGAN achieves
state-of-the-art image generation fidelity on common perceptual metrics
compared to the current best-performing methods and convinces in a
comprehensive user study.


Abstract:
  Stifle joint issues are a major cause of lameness in dogs and it can be a
significant marker for various forms of diseases or injuries. A known Tibial
Plateau Angle (TPA) helps in the reduction of the diagnosis time of the cause.
With the state of the art object detection algorithm YOLO, and its variants,
this paper delves into identifying joints, their centroids and other regions of
interest to draw multiple line axes and finally calculating the TPA. The
methods investigated predicts successfully the TPA within the normal range for
80 percent of the images.


Abstract:
  In this paper we consider minimization of a difference-of-convex (DC)
function with and without linear constraints. We first study a smooth
approximation of a generic DC function, termed difference-of-Moreau-envelopes
(DME) smoothing, where both components of the DC function are replaced by their
respective Moreau envelopes. The resulting smooth approximation is shown to be
Lipschitz differentiable, capture stationary points, local, and global minima
of the original DC function, and enjoy some growth conditions, such as
level-boundedness and coercivity, for broad classes of DC functions. We then
develop four algorithms for solving DC programs with and without linear
constraints based on the DME smoothing. In particular, for a smoothed DC
program without linear constraints, we show that the classic gradient descent
method as well as an inexact variant can obtain a stationary solution in the
limit with a convergence rate of $\mathcal{O}(K^{-1/2})$, where $K$ is the
number of proximal evaluations of both components. Furthermore, when the DC
program is explicitly constrained in an affine subspace, we combine the
smoothing technique with the augmented Lagrangian function and derive two
variants of the augmented Lagrangian method (ALM), named LCDC-ALM and composite
LCDC-ALM, focusing on different structures of the DC objective function. We
show that both algorithms find an $\epsilon$-approximate stationary solution of
the original DC program in $\mathcal{O}(\epsilon^{-2})$ iterations. Comparing
to existing methods designed for linearly constrained weakly convex
minimization, the proposed ALM-based algorithms can be applied to a broader
class of problems, where the objective contains a nonsmooth concave component.
Finally, numerical experiments are presented to demonstrate the performance of
the proposed algorithms.


Abstract:
  Given a finite group $G$ acting on a ring $R$, Merling constructed an
equivariant algebraic $K$-theory $G$-spectrum, and work of Malkiewich and
Merling, as well as work of Barwick, provides an interpretation of this
construction as a spectral Mackey functor. This construction is powerful, but
highly categorical; as a result the Mackey functors comprising the homotopy are
not obvious from the construction and have therefore not yet been calculated.
In this work, we provide a computation of the homotopy Mackey functors of
equivariant algebraic $K$-theory in terms of a purely algebraic construction.
In particular, we construct Mackey functors out of the $n$th algebraic
$K$-groups of group rings whose multiplication is twisted by the group action.
Restrictions and transfers for these functors admit a tractable algebraic
description in that they arise from restriction and extension of scalars along
module categories of twisted group rings. In the case where the group action is
trivial, our construction recovers work of Dress and Kuku from the 1980's which
constructs Mackey functors out of the algebraic $K$-theory of group rings. We
develop many families of examples of Mackey functors, both new and old,
including $K$-theory of endomorphism rings, the $K$-theory of fixed subrings of
Galois extensions, and (topological) Hochschild homology of twisted group
rings.


Abstract:
  A significant challenge in seasonal climate prediction is whether a
prediction can beat climatology. We hereby present results from two data-driven
models - a convolutional (CNN) and a recurrent (RNN) neural network - that
predict 2 m temperature out to 52 weeks for six geographically-diverse
locations. The motivation for testing the two classes of ML models is to allow
the CNN to leverage information related to teleconnections and the RNN to
leverage long-term historical temporal signals. The ML models boast improved
accuracy of long-range temperature forecasts up to a lead time of 30 weeks for
PCC and up 52 weeks for RMSESS, however only for select locations. Further
iteration is required to ensure the ML models have value beyond regions where
the climatology has a noticeably reduced correlation skill, namely the tropics.


Abstract:
  We propose a fully differentiable architecture for simultaneous semantic and
instance segmentation (a.k.a. panoptic segmentation) consisting of a
convolutional neural network and an asymmetric multiway cut problem solver. The
latter solves a combinatorial optimization problem that elegantly incorporates
semantic and boundary predictions to produce a panoptic labeling. Our
formulation allows to directly maximize a smooth surrogate of the panoptic
quality metric by backpropagating the gradient through the optimization
problem. Experimental evaluation shows improvement by backpropagating through
the optimization problem w.r.t. comparable approaches on Cityscapes and COCO
datasets. Overall, our approach shows the utility of using combinatorial
optimization in tandem with deep learning in a challenging large scale
real-world problem and showcases benefits and insights into training such an
architecture.


Abstract:
  This article provides a link diagram to visualize relations between two
ordered sets representing precedences on decision-making options or solutions
to strategic form games. The diagram consists of floating loops whose any two
loops cross just twice each other. As problems formulated by relations between
two ordered sets, I give two examples: visualizing rankings from pairwise
comparisons on the diagram and finding Pareto optimal solutions to a game of
prisoners' dilemma. At visualizing rankings, we can see whether a ranking
satisfies Condorcet's principle or not by checking whether the top loop is
splittable or not. And at finding solutions to the game, when a solution of the
game of prisoners' dilemma is Pareto optimal, the loop corresponding to the
solution has no splittable loop above it. Throughout the article, I point out
that checking the splittability of loops is an essence. I also mention that the
diagram can visualize natural transformations between two functors on free
construction categories.


Abstract:
  Quantum noise limits the sensitivity of precision measurement devices, such
as laser interferometer gravitational-wave observatories and axion detectors.
In the shot-noise-limited regime, these resonant detectors are subject to a
trade-off between the peak sensitivity and bandwidth. One approach to
circumvent this limitation in gravitational-wave detectors is to embed an
anomalous-dispersion optomechanical filter to broaden the bandwidth. The
original filter cavity design, however, makes the entire system unstable.
Recently, we proposed the coherent feedback between the arm cavity and the
optomechanical filter to eliminate the instability via PT-symmetry. The
original analysis based upon the Hamiltonian formalism adopted the single-mode
and resolved-sideband approximations. In this paper, we go beyond these
approximations and consider realistic parameters. We show that the main
conclusion concerning stability remains intact, with both Nyquist analysis and
a detailed time-domain simulation.


Abstract:
  In Part 1, we introduced a stochastic model of an infectious disease, based
on the BDI (birth and death with immigration) process. We showed that random
processes defined by this model can capture the essence of the stochastic,
often erratic, behavior of the infection process. The most significant finding
was that it is negative binomial distributed with small r, hence it is
geometrically distributed with an exceedingly long tail. This leads to a much
larger disparity in the epidemic patterns than has been known to the modeling
community. In Part 2 we conduct simulation experiments by implementing an
event-driven simulator. Several independent runs are presented to verify the
findings reported in Part 1. The enormous variations among the sample paths
obtained from several consecutive and independent runs with statistically
identical conditions confirm our analysis. Note that the epidemic pattern that
we observe is merely one sample path taken out of infinitely many paths. Thus,
one such path cannot represent the ensemble of the underlying random process.
By plotting the simulation runs in the semi-log scale, we can see that the
probabilistic chances in the early phase of the infection process determine the
behavior of the process. Once the process has reached a considerable number,
the weak law of large numbers sets in, and the process behaves less erratically
than in the early phase. One important implication of our findings is that it
would be a futile effort to attempt to identify all plausible causes of the
epidemic patterns. Mere luck may play a more significant role than most people
believe. It would be worth remarking that the BDI process might be applicable
to explain the disparity in wealth among individuals of similar earning power,
expenditure, and investment portfolio.


Abstract:
  The emerging data-intensive applications are increasingly dependent on
data-intensive scalable computing (DISC) systems, such as Apache Spark, to
process large data. Despite their popularity, DISC applications are hard to
test. In recent years, fuzz testing has been remarkably successful; however, it
is nontrivial to apply such traditional fuzzing to big data analytics directly
because: (1) the long latency of DISC systems prohibits the applicability of
fuzzing, and (2) conventional branch coverage is unlikely to identify
application logic from the DISC framework implementation. We devise a novel
fuzz testing tool called BigFuzz that automatically generates concrete data for
an input Apache Spark program. The key essence of our approach is that we
abstract the dataflow behavior of the DISC framework with executable
specifications and we design schema-aware mutations based on common error types
in DISC applications. Our experiments show that compared to random fuzzing,
BigFuzz is able to speed up the fuzzing time by 1477X, improves application
code coverage by 271%, and achieves 157% improvement in detecting application
errors. The demonstration video of BigFuzz is available at
https://www.youtube.com/watch?v=YvYQISILQHs&feature=youtu.be.