Abstract:
  We present the open-source pyratbay framework for exoplanet atmospheric
modeling, spectral synthesis, and Bayesian retrieval. The modular design of the
code allows the users to generate atmospheric 1D parametric models of the
temperature, abundances (in thermochemical equilibrium or
constant-with-altitude), and altitude profiles in hydrostatic equilibrium;
sample ExoMol and HITRAN line-by-line cross sections with custom resolving
power and line-wing cutoff values; compute emission or transmission spectra
considering cross sections from molecular line transitions, collision-induced
absorption, Rayleigh scattering, gray clouds, and alkali resonance lines; and
perform Markov chain Monte Carlo atmospheric retrievals for a given transit or
eclipse dataset. We benchmarked the pyratbay framework by reproducing
line-by-line cross-section sampling of ExoMol cross sections, producing
transmission and emission spectra consistent with petitRADTRANS models,
accurately retrieving the atmospheric properties of simulated transmission and
emission observations generated with TauREx models, and closely reproducing
Aura retrieval analyses of the space-based transmission spectrum of HD 209458b.
Finally, we present a retrieval analysis of a population of transiting
exoplanets, focusing on those observed in transmission with the HST WFC3/G141
grism. We found that this instrument alone can confidently identify when a
dataset shows H2O-absorption features; however, it cannot distinguish whether a
muted H2O feature is caused by clouds, high atmospheric metallicity, or low H2O
abundance. Our results are consistent with previous retrieval analyses. The
pyratbay code is available at PyPI (pip install pyratbay) and conda. The code
is heavily documented (https://pyratbay.readthedocs.io) and tested to provide
maximum accessibility to the community and long-term development stability.


Abstract:
  Volumetric imaging by fluorescence microscopy is often limited by anisotropic
spatial resolution from inferior axial resolution compared to the lateral
resolution. To address this problem, here we present a deep-learning-enabled
unsupervised super-resolution technique that enhances anisotropic images in
volumetric fluorescence microscopy. In contrast to the existing deep learning
approaches that require matched high-resolution target volume images, our
method greatly reduces the effort to put into practice as the training of a
network requires as little as a single 3D image stack, without a priori
knowledge of the image formation process, registration of training data, or
separate acquisition of target data. This is achieved based on the optimal
transport driven cycle-consistent generative adversarial network that learns
from an unpaired matching between high-resolution 2D images in lateral image
plane and low-resolution 2D images in the other planes. Using fluorescence
confocal microscopy and light-sheet microscopy, we demonstrate that the trained
network not only enhances axial resolution, but also restores suppressed visual
details between the imaging planes and removes imaging artifacts.


Abstract:
  A crucial question in galaxy formation is what role new accretion has in star
formation. Theoretical models have predicted a wide range of correlation
strengths between halo accretion and galaxy star formation. Previously, we
presented a technique to observationally constrain this correlation strength
for isolated Milky Way-mass galaxies at $z\sim 0.12$, based on the correlation
between halo accretion and the density profile of neighbouring galaxies. By
applying this technique to both observational data from the Sloan Digital Sky
Survey and simulation data from the UniverseMachine, where we can test
different correlation strengths, we ruled out positive correlations between
dark matter accretion and recent star formation activity. In this work, we
expand our analysis by (1) applying our technique separately to red and blue
neighbouring galaxies, which trace different infall populations, (2)
correlating dark matter accretion rates with $D_{n}4000$ measurements as a
longer-term quiescence indicator than instantaneous star-formation rates, and
(3) analyzing higher-mass isolated central galaxies with $10^{11.0} <
M_*/M_\odot < 10^{11.5}$ out to $z\sim 0.18$. In all cases, our results are
consistent with non-positive correlation strengths with $\gtrsim 85$ per cent
confidence, suggesting that processes such as gas recycling dominate star
formation in massive $z=0$ galaxies.


Abstract:
  Discretization of the uniform norm of functions from a given finite
dimensional subspace of continuous functions is studied. We pay special
attention to the case of trigonometric polynomials with frequencies from an
arbitrary finite set with fixed cardinality. We give two different proofs of
the fact that for any $N$-dimensional subspace of the space of continuous
functions it is sufficient to use $e^{CN}$ sample points for an accurate upper
bound for the uniform norm. Previous known results show that one cannot improve
on the exponential growth of the number of sampling points for a good
discretization theorem in the uniform norm.
  Also, we prove a general result, which connects the upper bound on the number
of sampling points in the discretization theorem for the uniform norm with the
best $m$-term bilinear approximation of the Dirichlet kernel associated with
the given subspace. We illustrate application of our technique on the example
of trigonometric polynomials.


Abstract:
  This paper presents a multi-lead fusion method for the accurate and automated
detection of the QRS complex location in 12 lead ECG (Electrocardiogram)
signals. The proposed multi-lead fusion method accurately delineates the QRS
complex by the fusion of detected QRS complexes of the individual 12 leads. The
proposed algorithm consists of two major stages. Firstly, the QRS complex
location of each lead is detected by the single lead QRS detection algorithm.
Secondly, the multi-lead fusion algorithm combines the information of the QRS
complex locations obtained in each of the 12 leads. The performance of the
proposed algorithm is improved in terms of Sensitivity and Positive
Predictivity by discarding the false positives. The proposed method is
validated on the ECG signals with various artifacts, inter and intra subject
variations. The performance of the proposed method is validated on the long
duration recorded ECG signals of St. Petersburg INCART database with
Sensitivity of 99.87% and Positive Predictivity of 99.96% and on the short
duration recorded ECG signals of CSE (Common Standards for Electrocardiography)
multi-lead database with 100% Sensitivity and 99.13% Positive Predictivity.


Abstract:
  This work presents a hybrid modeling approach to data-driven learning and
representation of unknown physical processes and closure parameterizations.
These hybrid models are suitable for situations where the mechanistic
description of dynamics of some variables is unknown, but reasonably accurate
observational data can be obtained for the evolution of the state of the
system. In this work, we propose machine learning to account for missing
physics and then data assimilation to correct the prediction. In particular, we
devise an effective methodology based on a recurrent neural network to model
the unknown dynamics. A long short-term memory (LSTM) based correction term is
added to the predictive model in order to take into account hidden physics.
Since LSTM introduces a black-box approach for the unknown part of the model,
we investigate whether the proposed hybrid neural-physical model can be further
corrected through a sequential data assimilation step. We apply this framework
to the weakly nonlinear Lorenz model that displays quasiperiodic oscillations,
the highly nonlinear Lorenz model, and two-scale Lorenz model. The hybrid
neural-physics model yields accurate results for the weakly nonlinear Lorenz
model with the predicted state close to the true Lorenz model trajectory. For
the highly nonlinear Lorenz model and the two-scale Lorenz model, the hybrid
neural-physics model deviates from the true state due to the accumulation of
prediction error from one time step to the next time step. The ensemble Kalman
filter approach takes into account the prediction error and updates the
diverged prediction using available observations in order to provide a more
accurate state estimate. The successful synergistic integration of neural
network and data assimilation for low-dimensional system shows the potential
benefits of the proposed hybrid-neural physics model for complex systems.


Abstract:
  The unique electronic and magnetic properties of Lanthanides molecular
complexes place them at the forefront of the race towards high-temperature
single-ion magnets and magnetic quantum bits. The design of compounds of this
class has so far been almost exclusively driven by static crystal field
considerations, with emphasis on increasing the magnetic anisotropy barrier.
This guideline has now reached its maximum potential and new progress can only
come from a deeper understanding of spin-phonon relaxation mechanisms. In this
work we compute relaxation times fully ab initio and unveil the nature of all
spin-phonon relaxation mechanisms, namely Orbach and Raman pathways, in a
prototypical Dy single-ion magnet. Computational predictions are in agreement
with the experimental determination of spin relaxation time and crystal field
anisotropy, and show that Raman relaxation, dominating at low temperature, is
triggered by low-energy phonons and little affected by further engineering of
crystal field axiality. A comprehensive analysis of spin-phonon coupling
mechanism reveals that molecular vibrations beyond the ion's first coordination
shell can also assume a prominent role in spin relaxation through an
electrostatic polarization effect. Therefore, this work shows the way forward
in the field by delivering a novel and complete set of chemically-sound design
rules tackling every aspect of spin relaxation at any temperature


Abstract:
  The evaluation of nucleation rates from molecular dynamics trajectories is
hampered by the slow nucleation time scale and impact of finite size effects.
Here, we show that accurate nucleation rates can be obtained in a very general
fashion relying only on the free energy barrier, transition state theory (TST),
and a simple dynamical correction for diffusive recrossing. In this setup, the
time scale problem is overcome by using enhanced sampling methods, in casu
metadynamics, whereas the impact of finite size effects can be naturally
circumvented by reconstructing the free energy surface from an appropriate
ensemble. Approximations from classical nucleation theory are avoided. We
demonstrate the accuracy of the approach by calculating macroscopic rates of
droplet nucleation from argon vapor, spanning sixteen orders of magnitude and
in excellent agreement with literature results, all from simulations of very
small (512 atom) systems.


Abstract:
  We report the largest broadband terahertz (THz) polarizer based on a flexible
ultra-transparent cyclic olefin copolymer (COC). The COC polarizers were
fabricated by nanoimprint soft lithography with the lowest reported pitch of 2
or 3 micrometers and depth of 3 micrometers and sub-wavelength Au bilayer wire
grid. Fourier Transform Infrared spectroscopy in a large range of 0.9 -20 THz
shows transmittance of bulk materials such as doped and undoped Si and
polymers. COC polarizers present more than doubled transmission intensity and
larger transmitting band when compared to Si. COC polarizers present superior
performance when compared to Si polarizers, with extinctions ratios of at least
4.4 dB higher and registered performance supported by numerical simulations.
Fabricated Si and COC polarizers' show larger operation gap when compared to a
commercial polarizer. Fabrication of these polarizers can be easily up-scaled
which certainly meets functional requirements for many THz devices and
applications, such as high transparency, lower cost fabrication and flexible
material.


Abstract:
  The Agda Universal Algebra Library (UALib) is a library of types and programs
(theorems and proofs) we developed to formalize the foundations of universal
algebra in dependent type theory using the Agda programming language and proof
assistant. The UALib includes a substantial collection of definitions,
theorems, and proofs from general algebra and equational logic, including many
examples that exhibit the power of inductive and dependent types for
representing and reasoning about relations, algebraic structures, and
equational theories. In this paper we discuss the logical foundations on which
the library is built, and describe the types defined in the first 13 modules of
the library. Special attention is given to aspects of the library that seem
most interesting or challenging from a type theory or mathematical foundations
perspective.


Abstract:
  Accurate and explainable health event predictions are becoming crucial for
healthcare providers to develop care plans for patients. The availability of
electronic health records (EHR) has enabled machine learning advances in
providing these predictions. However, many deep learning based methods are not
satisfactory in solving several key challenges: 1) effectively utilizing
disease domain knowledge; 2) collaboratively learning representations of
patients and diseases; and 3) incorporating unstructured text. To address these
issues, we propose a collaborative graph learning model to explore
patient-disease interactions and medical domain knowledge. Our solution is able
to capture structural features of both patients and diseases. The proposed
model also utilizes unstructured text data by employing an attention regulation
strategy and then integrates attentive text features into a sequential learning
process. We conduct extensive experiments on two important healthcare problems
to show the competitive prediction performance of the proposed method compared
with various state-of-the-art models. We also confirm the effectiveness of
learned representations and model interpretability by a set of ablation and
case studies.


Abstract:
  Trajectory planning is commonly used as part of a local planner in autonomous
driving. This paper considers the problem of planning a
continuous-curvature-rate trajectory between fixed start and goal states that
minimizes a tunable trade-off between passenger comfort and travel time. The
problem is an instance of infinite dimensional optimization over two continuous
functions: a path, and a velocity profile. We propose a simplification of this
problem that facilitates the discretization of both functions. This paper also
proposes a method to quickly generate minimal-length paths between start and
goal states based on a single tuning parameter: the second derivative of
curvature. Furthermore, we discretize the set of velocity profiles along a
given path into a selection of acceleration way-points along the path.
Gradient-descent is then employed to minimize cost over feasible choices of the
second derivative of curvature, and acceleration way-points, resulting in a
method that repeatedly solves the path and velocity profiles in an iterative
fashion. Numerical examples are provided to illustrate the benefits of the
proposed methods.


Abstract:
  Data sharing by researchers is a centerpiece of Open Science principles and
scientific progress. For a sample of 6019 researchers, we analyze the
extent/frequency of their data sharing. Specifically, the relationship with the
following four variables: how much they value data citations, the extent to
which their data-sharing activities are formally recognized, their perceptions
of whether sufficient credit is awarded for data sharing, and the reported
extent to which data citations motivate their data sharing. In addition, we
analyze the extent to which researchers have reused openly accessible data, as
well as how data sharing varies by professional age-cohort, and its
relationship to the value they place on data citations. Furthermore, we
consider most of the explanatory variables simultaneously by estimating a
multiple linear regression that predicts the extent/frequency of their data
sharing. We use the dataset of the State of Open Data Survey 2019 by Springer
Nature and Digital Science. Results do allow us to conclude that a desire for
recognition/credit is a major incentive for data sharing. Thus, the possibility
of receiving data citations is highly valued when sharing data, especially
among younger researchers, irrespective of the frequency with which it is
practiced. Finally, the practice of data sharing was found to be more prevalent
at late research career stages, despite this being when citations are less
valued and have a lower motivational impact. This could be due to the fact that
later-career researchers may benefit less from keeping their data private.


Abstract:
  Visual object tracking, which is representing a major interest in image
processing field, has facilitated numerous real world applications. Among them,
equipping unmanned aerial vehicle (UAV) with real time robust visual trackers
for all day aerial maneuver, is currently attracting incremental attention and
has remarkably broadened the scope of applications of object tracking. However,
prior tracking methods have merely focused on robust tracking in the
well-illuminated scenes, while ignoring trackers' capabilities to be deployed
in the dark. In darkness, the conditions can be more complex and harsh, easily
posing inferior robust tracking or even tracking failure. To this end, this
work proposed a novel discriminative correlation filter based tracker with
illumination adaptive and anti dark capability, namely ADTrack. ADTrack firstly
exploits image illuminance information to enable adaptability of the model to
the given light condition. Then, by virtue of an efficient and effective image
enhancer, ADTrack carries out image pretreatment, where a target aware mask is
generated. Benefiting from the mask, ADTrack aims to solve a dual regression
problem where dual filters, i.e., the context filter and target focused filter,
are trained with mutual constraint. Thus ADTrack is able to maintain
continuously favorable performance in all-day conditions. Besides, this work
also constructed one UAV nighttime tracking benchmark UAVDark135, comprising of
more than 125k manually annotated frames, which is also very first UAV
nighttime tracking benchmark. Exhaustive experiments are extended on
authoritative daytime benchmarks, i.e., UAV123 10fps, DTB70, and the newly
built dark benchmark UAVDark135, which have validated the superiority of
ADTrack in both bright and dark conditions on a single CPU.


Abstract:
  In this paper, we propose a novel framework to translate a portrait
photo-face into an anime appearance. Our aim is to synthesize anime-faces which
are style-consistent with a given reference anime-face. However, unlike typical
translation tasks, such anime-face translation is challenging due to complex
variations of appearances among anime-faces. Existing methods often fail to
transfer the styles of reference anime-faces, or introduce noticeable
artifacts/distortions in the local shapes of their generated faces. We propose
AniGAN, a novel GAN-based translator that synthesizes high-quality anime-faces.
Specifically, a new generator architecture is proposed to simultaneously
transfer color/texture styles and transform local facial shapes into anime-like
counterparts based on the style of a reference anime-face, while preserving the
global structure of the source photo-face. We propose a double-branch
discriminator to learn both domain-specific distributions and domain-shared
distributions, helping generate visually pleasing anime-faces and effectively
mitigate artifacts. Extensive experiments on selfie2anime and a new face2anime
dataset qualitatively and quantitatively demonstrate the superiority of our
method over state-of-the-art methods. The new dataset is available at
https://github.com/bing-li-ai/AniGAN .


Abstract:
  Double-parton scattering is investigated using events with a Z boson and
jets. The Z boson is reconstructed using only the dimuon channel. The
measurements are performed with proton-proton collision data recorded by the
CMS experiment at the LHC at $\sqrt{s} =$ 13 TeV, corresponding to an
integrated luminosity of 35.9 fb$^{-1}$ collected in the year 2016.
Differential cross sections of Z + $\geq$ 1 jet and Z + $\geq$ 2 jets are
measured with transverse momentum of the jets above 20 GeV and pseudorapidity
$|\eta|$ $\lt$ 2.4. Several distributions with sensitivity to double-parton
scattering effects are measured as functions of the angle and the transverse
momentum imbalance between the Z boson and the jets. The measured distributions
are compared with predictions from several event generators with different
hadronization models and different parameter settings for multiparton
interactions. The measured distributions show a dependence on the hadronization
and multiparton interaction simulation parameters, and are important input for
future improvements of the simulations.


Abstract:
  In this study, we investigate the metastable behavior of Metropolis-type
Glauber dynamics associated with the Blume-Capel model with zero chemical
potential and zero external field at very low temperatures. The corresponding
analyses for the same model with zero chemical potential and positive small
external field were performed in [Cirillo and Nardi, Journal of Statistical
Physics, 150: 1080-1114, 2013] and [Landim and Lemire, Journal of Statistical
Physics, 164: 346-376, 2016]. We obtain both large deviation-type and
potential-theoretic results on the metastable behavior in our setting. To this
end, we perform highly thorough investigation on the energy landscape, where it
is revealed that no critical configurations exist and alternatively a massive
flat plateau of saddle configurations resides therein.


Abstract:
  The Multi-Phase Transport model (AMPT) is used to investigate the
longitudinal broadening of the transverse momentum two-particle correlator
$C_{2}\left(\Delta\eta,\Delta\varphi\right)$, and its utility to extract the
specific shear viscosity, $\eta/s$, of the quark-gluon plasma formed in
ultra-relativistic heavy ion collisions. The results from these model studies
indicate that the longitudinal broadening of
$C_{2}\left(\Delta\eta,\Delta\varphi\right)$ is sensitive to the magnitude of
$\eta/s$. However, reliable extraction of the longitudinal broadening of the
correlator requires the suppression of possible self-correlations associated
with the definition of the collision centrality.


Abstract:
  We show that for $\Pi_2$-properties of second or third order arithmetic as
formalized in appropriate natural signatures the apparently weaker notion of
forcibility overlaps with the standard notion of consistency (assuming large
cardinal axioms).
  Among such $\Pi_2$-properties we mention: the negation of the Continuum
hypothesis, Souslin Hypothesis, the negation of Whitehead's conjecture on free
groups, the non-existence of outer automorphisms for the Calkin algebra, etc...
In particular this gives an a posteriori explanation of the success forcing
(and forcing axioms) met in producing models of such properties.
  Our main results relate generic absoluteness theorems for second order
arithmetic, Woodin's axiom $(*)$ and forcing axioms to Robinson's notion of
model companionship (as applied to set theory). We also briefly outline in
which ways these results provide an argument to refute the Continuum
hypothesis.


Abstract:
  In Part I of this study, we obtained the ray (group) velocity gradients and
Hessians with respect to the ray locations, directions and the anisotropic
model parameters, at nodal points along ray trajectories, considering general
anisotropic (triclinic) media and both, quasi-compressional and quasi-shear
waves. Ray velocity derivatives for anisotropic media with higher symmetries
were considered particular cases of general anisotropy. In this part, Part II,
we follow the computational workflow presented in Part I, formulating the ray
velocity derivatives directly for polar anisotropic (transverse isotropy with
tilted axis of symmetry, TTI) media for the coupled qP and qSV waves and for SH
waves. The acoustic approximation for qP waves is considered a special case.
The medium properties, normally specified at regular three-dimensional fine
grid points, are the five material parameters: the axial compressional and
shear velocities and the three Thomsen parameters, and two geometric
parameters: the polar angles defining the local direction of the medium
symmetry axis. All the parameters are assumed spatially (smoothly) varying,
where their gradients and Hessians can be reliably computed. Two case examples
are considered; the first represents compacted shale/sand rocks (with positive
anellipticity) and the second, unconsolidated sand rocks with strong negative
anellipticity (manifesting a qSV triplication). The ray velocity derivatives
obtained in this part are first tested by comparing them with the corresponding
numerical (finite difference) derivatives. Additionally, we show that exactly
the same results (ray velocity derivatives) can be obtained if we transform the
given polar anisotropic model parameters (five material and two geometric) into
the twenty-one stiffness tensor components of a general anisotropic (triclinic)
medium, and apply the theory derived in Part I.


Abstract:
  We provide a simple analysis of the big-bang nucleosynthesis (BBN)
sensitivity to the light dark matter (DM) generated by the thermal freeze-in
mechanism. It is shown that the ratio of the effective neutrino number shift
$\Delta N_{\nu}$ over the DM relic density $\omega\equiv \Omega h^2$, denoted
by $R_\chi\equiv\Delta N_\nu/\omega$, cancels the decaying particle mass and
the feeble coupling, rendering therefore a simple visualization of $\Delta
N_{\nu}$ at the BBN epoch in terms of the DM mass. This property drives one to
conclude that the shift with a sensitivity of $\Delta N_{\nu}\simeq
\mathcal{O}(0.1)$ cannot originate from a single warm DM under the
Lyman-$\alpha$ forest constraints. For the cold-plus-warm DM scenarios where
the Lyman-$\alpha$ constraints are diluted, the ratio $R_\chi$ can be
potentially used to test the thermal freeze-in mechanism in generating a small
warm component of DM and a possible excess at the level of $\Delta
N_{\nu}\simeq \mathcal{O}(0.01)$.


Abstract:
  We present a scheme for ground-state cooling of a mechanical resonator by
simultaneously coupling it to a superconducting qubit and a cavity field. The
Hamiltonian describing the hybrid system dynamics is systematically derived.
The cooling process is driven by a red-detuned ac drive on the qubit and a
laser drive on the optomechanical cavity. We have investigated cooling in the
weak and the strong coupling regimes for both the individual system, i.e.,
qubit assisted cooling and optomechanical cooling, and compared them with the
effective hybrid cooling. It is shown that hybrid cooling is more effective
compared to the individual cooling mechanisms, and could be applied in both the
resolved and the unresolved sideband regimes.


Abstract:
  Filtering packet traffic and rules of permit/denial of data packets into
network nodes are granted by facilitating Access Control Lists (ACL). This
paper proposes a procedure of adding a link load threshold value to the access
control list rules option, which acts on the basis of threshold value. The
ultimate goal of this enhanced ACL is to avoid congestion in targeted
subnetworks. The link load threshold value allows to decide that packet traffic
is rerouted by the router to avoid congestion, or packet drop happens on the
basis of packet priorities. The packet rerouting in case of high traffic loads,
based on new packet filtering procedure for congestion avoidance, will result
in the reduction of the overall packet drop ratio, and of over-subscription in
congested subnetworks.


Abstract:
  The prompt emission of GRBs has been investigated for more than 50 years but
remains poorly understood. Commonly, spectral and temporal profiles of
{\gamma}-ray emission are analysed. However, they are insufficient for a
complete picture on GRB-related physics. The addition of polarization
measurements provides invaluable information towards the understanding of these
astrophysical sources. In recent years, dedicated polarimeters, such as POLAR
and GAP, were built. The former of which observed low levels of polarization as
well as a temporal evolution of the polarization angle. It was understood that
a larger sample of GRB polarization measurements and time resolved studies are
necessary to constrain theoretical models. The POLAR-2 mission aims to address
this by increasing the effective area by an order of magnitude compared to
POLAR. POLAR-2 is manifested for launch on board the China Space Station in
2024 and will operate for at least 2 years. Insight from POLAR will aid in the
improvement of the overall POLAR-2 design. Major improvements (compared to
POLAR) will include the replacement of multi-anode PMTs (MAPMTs) with SiPMs,
increase in sensitive volume and further technological upgrades. POLAR-2 is
projected to measure about 50 GRBs per year with equal or better quality
compared to the best seen by POLAR. The instrument design, preliminary results
and anticipated scientific potential of this mission will be discussed.


Abstract:
  Dynamical magnets can pump spin currents into superconductors. To understand
such a phenomenon, we develop a method utilizing the generalized Usadel
equation to describe time-dependent situations in superconductors in contact
with dynamical ferromagnets. Our proof-of-concept theory is valid when there is
sufficient dephasing at finite temperatures, and when the ferromagnetic
insulators are weakly polarized. We derive the effective equation of motion for
the Keldysh Green's function focusing on a thin film superconductor sandwiched
between two noncollinear ferromagnetic insulators of which one is dynamical. In
turn, we compute the spin currents in the system as a function of the
temperature and the magnetizations' relative orientations. When the induced
Zeeman splitting is weak, we find that the spin accumulation in the
superconducting state is smaller than in the normal states due to the lack of
quasiparticle states inside the gap. This feature gives a lower backflow spin
current from the superconductor as compared to a normal metal. Furthermore, in
superconductors, we find that the ratio between the backflow spin current in
the parallel and anti-parallel magnetization configuration depends strongly on
temperature, in contrast to the constant ratio in normal metals.


Abstract:
  Data augmentation is an inexpensive way to increase training data diversity
and is commonly achieved via transformations of existing data. For tasks such
as classification, there is a good case for learning representations of the
data that are invariant to such transformations, yet this is not explicitly
enforced by classification losses such as the cross-entropy loss. This paper
investigates the use of training objectives that explicitly impose this
consistency constraint and how it can impact downstream audio classification
tasks. In the context of deep convolutional neural networks in the supervised
setting, we show empirically that certain measures of consistency are not
implicitly captured by the cross-entropy loss and that incorporating such
measures into the loss function can improve the performance of audio
classification systems. Put another way, we demonstrate how existing
augmentation methods can further improve learning by enforcing consistency.


Abstract:
  We use numerical simulations to demonstrate third-harmonic generation with
near-unity nonlinear circular dichroism (CD) and high conversion efficiency ($
10^{-2}\ \text{W}^{-2}$) in asymmetric Si-on-SiO$_2$ metasurfaces. The working
principle relies on the selective excitation of a quasi-bound state in the
continuum, characterized by a very high ($>10^5$) quality-factor. By tuning
multi-mode interference with the variation of the metasurface geometrical
parameters, we show the possibility of independent control of linear CD and
nonlinear CD. Our results pave the way for the development of all-dielectric
metasurfaces for nonlinear chiro-optical devices with high conversion
efficiency.


Abstract:
  Recent advances in representation learning have demonstrated an ability to
represent information from different modalities such as video, text, and audio
in a single high-level embedding vector. In this work we present a
self-supervised learning framework that is able to learn a representation that
captures finer levels of granularity across different modalities such as
concepts or events represented by visual objects or spoken words. Our framework
relies on a discretized embedding space created via vector quantization that is
shared across different modalities. Beyond the shared embedding space, we
propose a Cross-Modal Code Matching objective that forces the representations
from different views (modalities) to have a similar distribution over the
discrete embedding space such that cross-modal objects/actions localization can
be performed without direct supervision. In our experiments we show that the
proposed discretized multi-modal fine-grained representation (e.g.,
pixel/word/frame) can complement high-level summary representations (e.g.,
video/sentence/waveform) for improved performance on cross-modal retrieval
tasks. We also observe that the discretized representation uses individual
clusters to represent the same semantic concept across modalities.


Abstract:
  We obtained high-resolution spectra of the ultra-cool M-dwarf TRAPPIST-1
during the transit of its planet `b' using two high dispersion near-infrared
spectrographs, IRD instrument on the Subaru 8.2m telescope and HPF instrument
on the 10m Hobby-Eberly Telescope. These spectroscopic observations are
complemented by a photometric transit observation for planet `b' using the
APO/ARCTIC, which assisted us to capture the correct transit times for our
transit spectroscopy. Using the data obtained by the new IRD and HPF
observations, as well as the prior transit observations of planets `b', `e' and
`f' from IRD, we attempt to constrain the atmospheric escape of the planet
using the He I triplet 10830 {\AA} absorption line. We do not detect evidence
for any primordial extended H-He atmospheres in all three planets. To limit any
planet related absorption, we place an upper limit on the equivalent widths of
<7.754 m{\AA} for planet `b', <10.458 m{\AA} for planet `e', and <4.143 m{\AA}
for planet `f' at 95% confidence from the IRD data, and <3.467 m{\AA} for
planet `b' at 95% confidence from HPF data. Using these limits along with a
solar-like composition isothermal Parker wind model, we attempt to constrain
the mass-loss rates for the three planets. For TRAPPIST-1b, our models exclude
the highest possible energy-limited rate for a wind temperature <5000 K. This
non-detection of extended atmospheres having low mean-molecular weight in all
three planets aids in further constraining their atmospheric composition by
steering the focus towards the search of high molecular weight species in their
atmospheres.


Abstract:
  With the development of deep networks on various large-scale datasets, a
large zoo of pretrained models are available. When transferring from a model
zoo, applying classic single-model based transfer learning methods to each
source model suffers from high computational burden and cannot fully utilize
the rich knowledge in the zoo. We propose \emph{Zoo-Tuning} to address these
challenges, which learns to adaptively transfer the parameters of pretrained
models to the target task. With the learnable channel alignment layer and
adaptive aggregation layer, Zoo-Tuning \emph{adaptively aggregates channel
aligned pretrained parameters} to derive the target model, which promotes
knowledge transfer by simultaneously adapting multiple source models to
downstream tasks. The adaptive aggregation substantially reduces the
computation cost at both training and inference. We further propose lite
Zoo-Tuning with the temporal ensemble of batch average gating values to reduce
the storage cost at the inference time. We evaluate our approach on a variety
of tasks, including reinforcement learning, image classification, and facial
landmark detection. Experiment results demonstrate that the proposed adaptive
transfer learning approach can transfer knowledge from a zoo of models more
effectively and efficiently.


Abstract:
  In recent cross-disciplinary studies involving both optics and computing,
single-photon-based decision-making has been demonstrated by utilizing the
wave-particle duality of light to solve multi-armed bandit problems.
Furthermore, entangled-photon-based decision-making has managed to solve a
competitive multi-armed bandit problem in such a way that conflicts of
decisions among players are avoided while ensuring equality. However, as these
studies are based on the polarization of light, the number of available choices
is limited to two, corresponding to two orthogonal polarization states. Here we
propose a scalable principle to solve competitive decision-making situations by
using the orbital angular momentum of photons based on its high dimensionality,
which theoretically allows an unlimited number of arms. Moreover, by extending
the Hong-Ou-Mandel effect to more than two states, we theoretically establish
an experimental configuration able to generate multi-photon states with orbital
angular momentum and conditions that provide conflict-free selections at every
turn. We numerically examine total rewards regarding three-armed bandit
problems, for which the proposed strategy accomplishes almost the theoretical
maximum, which is greater than a conventional mixed strategy intending to
realize Nash equilibrium. This is thanks to the quantum interference effect
that achieves no-conflict selections, even in the exploring phase to find the
best arms.


Abstract:
  We show that the moduli stack of elliptic surfaces of Kodaira dimension one
and without multiple fiber satisfies a weak form of the topological
hyperbolicity.


Abstract:
  We present analysis of the spatial density structure for the outer disk from
8$-$14 \,kpc with the LAMOST DR5 13534 OB-type stars and observe similar
flaring on north and south sides of the disk implying that the flaring
structure is symmetrical about the Galactic plane, for which the scale height
at different Galactocentric distance is from 0.14 to 0.5 \,kpc. By using the
average slope to characterize the flaring strength we find that the thickness
of the OB stellar disk is similar but flaring is slightly stronger compared to
the thin disk as traced by red giant branch stars, possibly implying that
secular evolution is not the main contributor to the flaring but perturbation
scenarios such as interactions with passing dwarf galaxies should be more
possible. When comparing the scale height of OB stellar disk of the north and
south sides with the gas disk, the former one is slightly thicker than the
later one by $\approx$ 33 and 9 \,pc, meaning that one could tentatively use
young OB-type stars to trace the gas properties. Meanwhile, we unravel that the
radial scale length of the young OB stellar disk is 1.17 $\pm$ 0.05 \,kpc,
which is shorter than that of the gas disk, confirming that the gas disk is
more extended than stellar disk. What is more, by considering the mid-plane
displacements ($Z_{0}$) in our density model we find that almost all of $Z_{0}$
are within 100 \,pc with the increasing trend as Galactocentric distance
increases.


Abstract:
  Velocity-space anisotropy can significantly modify fusion reactivity. The
nature and magnitude of this modification depends on the plasma temperature, as
well as the details of how the anisotropy is introduced. For plasmas that are
sufficiently cold compared to the peak of the fusion cross-section, anisotropic
distributions tend to have higher yields than isotropic distributions with the
same thermal energy. At higher temperatures, it is instead isotropic
distributions that have the highest yields. However, the details of this
behavior depend on exactly how the distribution differs from an isotropic
Maxwellian. This paper describes the effects of anisotropy on fusion yield for
the class of anisotropic distribution functions with the same energy
distribution as a 3D isotropic Maxwellian, and compares those results with the
yields from bi-Maxwellian distributions. In many cases, especially for plasmas
somewhat below reactor-regime temperatures, the effects of anisotropy can be
substantial.


Abstract:
  The first generation of blockchain focused on digital currencies and secure
storage, management and transfer of tokenized values. Thereafter, the focus has
been shifting from currencies to a broader application space. In this paper, we
systematically explore marketplace types and properties, and consider the
mechanisms required to support those properties through blockchain. We propose
a generic and configurable framework for blockchain-based marketplaces, and
describe how popular marketplace types, price discovery policies, and other
configuration parameters are implemented within the framework by presenting
concrete event-based algorithms. Finally, we consider three use cases with
widely diverging properties and show how the proposed framework supports them.


Abstract:
  A tangle is a connected topological space constructed by gluing several
copies of the unit interval $[0, 1]$. We explore which tangles guarantee
envy-free allocations of connected shares for n agents, meaning that such
allocations exist no matter which monotonic and continuous functions represent
agents' valuations. Each single tangle $\mathcal{T}$ corresponds in a natural
way to an infinite topological class $\mathcal{G}(\mathcal{T})$ of multigraphs,
many of which are graphs. This correspondence links EF fair division of tangles
to EFk$_{outer}$ fair division of graphs. We know from Bil\`o et al that all
Hamiltonian graphs guarantee EF1$_{outer}$ allocations when the number of
agents is 2, 3, 4 and guarantee EF2$_{outer}$ allocations for arbitrarily many
agents. We show that exactly six tangles are stringable; these guarantee EF
connected allocations for any number of agents, and their associated
topological classes contain only Hamiltonian graphs. Any non-stringable tangle
has a finite upper bound r on the number of agents for which EF allocations of
connected shares are guaranteed. Most graphs in the associated non-stringable
topological class are not Hamiltonian, and a negative transfer theorem shows
that for each $k \geq 1$ most of these graphs fail to guarantee EFk$_{outer}$
allocations of vertices for r + 1 or more agents. This answers a question posed
in Bil\`o et al, and explains why a focus on Hamiltonian graphs was necessary.
With bounds on the number of agents, however, we obtain positive results for
some non-stringable classes. An elaboration of Stromquist's moving knife
procedure shows that the non-stringable lips tangle guarantees envy-free
allocations of connected shares for three agents. We then modify the discrete
version of Stromquist's procedure in Bil\`o et al to show that all graphs in
the topological class guarantee EF1$_{outer}$ allocations for three agents.


Abstract:
  This paper studies properties of binary runlength-limited sequences with
additional constraints on their Hamming weight and/or their number of runs of
identical symbols. An algebraic and a probabilistic (entropic) characterization
of the exponential growth rate of the number of such sequences, i.e., their
information capacity, are obtained by using the methods of multivariate
analytic combinatorics, and properties of the capacity as a function of its
parameters are stated. The second-order term in the asymptotic expansion of the
rate of these sequences is also given, and the typical values of the relevant
quantities are derived. Several applications of the results are illustrated,
including bounds on codes for weight-preserving and run-preserving channels
(e.g., the run-preserving insertion-deletion channel), a sphere-packing bound
for channels with sparse error patterns, and the asymptotics of constant-weight
sub-block constrained sequences. In addition, the asymptotics of a closely
related notion -- $ q $-ary sequences with fixed Manhattan weight -- is briefly
discussed, and an application in coding for molecular timing channels is
illustrated.


Abstract:
  We propose novel triple-${\bm q}$ multipole orders as possible candidates for
the two distinct low-temperature symmetry broken phases in quadrupolar system
PrV$_2$Al$_{20}$. An analysis of the experiment under [111] magnetic fields
indicates that the {\it ferro} octupole moments in the lower temperature phase
arise from the {\it antiferro} octupole interactions. We demonstrate that the
triple-${\bm q}$ multipole orders can solve this seemingly inconsistent issue.
Anisotropies of quadrupole moments stabilize a triple-${\bm q}$ order, which
further leads to the second transition to a coexisting phase with triple-${\bm
q}$ octupole moments. The cubic invariant of quadrupole moments formed by the
triple-${\bm q}$ components and the characteristic couplings with the octupole
moments in their free energy play important roles. We analyze a multipolar
exchange model by mean-field approximation and discuss the temperature and
magnetic field phase diagrams. Many of the microscopic results, such as the
number of phases and the magnitudes of critical fields in the phase diagrams,
are qualitatively consistent with the experiments in PrV$_2$Al$_{20}$.


Abstract:
  Rotational-vibrational transitions of the fundamental vibrational modes of
the $^{12}$C$^{14}$N$^+$ and $^{12}$C$^{15}$N$^+$ cations have been observed
for the first time using a cryogenic ion trap apparatus with an action
spectroscopy scheme. The lines P(3) to R(3) of $^{12}$C$^{14}$N$^+$ and R(1) to
R(3) of $^{12}$C$^{15}$N$^+$ have been measured, limited by the trap
temperature of approximately 4 K and the restricted tuning range of the
infrared laser. Spectroscopic parameters are presented for both isotopologues,
with band origins at 2000.7587(1) and 1970.321(1) cm$^{-1}$, respectively, as
well as an isotope independent fit combining the new and the literature data.


Abstract:
  WD 0145+234 is a white dwarf that is accreting metals from a circumstellar
disc of planetary material. It has exhibited a substantial and sustained
increase in 3-5 micron flux since 2018. Follow-up Spitzer photometry reveals
that emission from the disc had begun to decrease by late 2019. Stochastic
brightening events superimposed on the decline in brightness suggest the
liberation of dust during collisional evolution of the circumstellar solids. A
simple model is used to show that the observations are indeed consistent with
ongoing collisions. Rare emission lines from circumstellar gas have been
detected at this system, supporting the emerging picture of white dwarf debris
discs as sites of collisional gas and dust production.


Abstract:
  Magnetic fields lines are trapped in black hole event horizons by accreting
plasma. If the trapped field lines are lightly loaded with plasma, then their
motion is controlled by their footpoints on the horizon and thus by the spin of
the black hole. In this paper, we investigate the boundary layer between
lightly loaded polar field lines and a dense, equatorial accretion flow. We
present an analytic model for aligned prograde and retrograde accretion systems
and argue that there is significant shear across this "jet-disk boundary" at
most radii for all black hole spins. Specializing to retrograde aligned
accretion, where the model predicts the strongest shear, we show numerically
that the jet-disk boundary is unstable. The resulting mixing layer episodically
loads plasma onto trapped field lines where it is heated, forced to rotate with
the hole, and permitted to escape outward into the jet. In one case we follow
the mass loading in detail using Lagrangian tracer particles and find a
time-averaged mass-loading rate ~ 0.01 Mdot.


Abstract:
  Game publishers and anti-cheat companies have been unsuccessful in blocking
cheating in online gaming. We propose a novel, vision-based approach that
captures the final state of the frame buffer and detects illicit overlays. To
this aim, we train and evaluate a DNN detector on a new dataset, collected
using two first-person shooter games and three cheating software. We study the
advantages and disadvantages of different DNN architectures operating on a
local or global scale. We use output confidence analysis to avoid unreliable
detections and inform when network retraining is required. In an ablation
study, we show how to use Interval Bound Propagation to build a detector that
is also resistant to potential adversarial attacks and study its interaction
with confidence analysis. Our results show that robust and effective
anti-cheating through machine learning is practically feasible and can be used
to guarantee fair play in online gaming.


Abstract:
  In this paper, we prove that for the doubly symmetric binary distribution,
the lower increasing envelope and the upper envelope of the
minimum-relative-entropy region are respectively convex and concave. We also
prove that another function induced the minimum-relative-entropy region is
concave. These two envelopes and this function were previously used to
characterize the optimal exponents in strong small-set expansion problems and
strong Brascamp--Lieb inequalities. The results in this paper, combined with
the strong small-set expansion theorem derived by Yu, Anantharam, and Chen
(2021), and the strong Brascamp--Lieb inequality derived by Yu (2021), confirm
positively Ordentlich--Polyanskiy--Shayevitz's conjecture on the strong
small-set expansion (2019) and Polyanskiy's conjecture on the strong
Brascamp--Lieb inequality (2016). The proofs in this paper are based on the
equivalence between the convexity of a function and the convexity of the set of
minimizers of its Lagrangian dual.


Abstract:
  Deep learning techniques have the power to identify the degree of
modification of high energy jets traversing deconfined QCD matter on a
jet-by-jet basis. Such knowledge allows us to study jets based on their
initial, rather than final energy. We show how this new technique provides
unique access to the genuine configuration profile of jets over the transverse
plane of the nuclear collision, both with respect to their production point and
their orientation. Effectively removing the selection biases induced by
final-state interactions, one can in this way analyse the potential azimuthal
anisotropies of jet production associated to initial-state effects.
Additionally, we demonstrate the capability of our new method to locate with
remarkable precision the production point of a dijet pair in the nuclear
overlap region, in what constitutes an important step forward towards the long
term quest of using jets as tomographic probes of the quark-gluon plasma.


Abstract:
  In \cite{CMW19}, the authors introduced $k$-entanglement breaking linear maps
to understand the entanglement breaking property of completely positive maps on
taking composition. In this article, we do a systematic study of
$k$-entanglement breaking maps. We prove many equivalent conditions for a
$k$-positive linear map to be $k$-entanglement breaking, thereby study the
mapping cone structure of $k$-entanglement breaking maps. We discuss examples
of $k$-entanglement breaking maps and some of their significance. As an
application of our study, we characterize completely positive maps that reduce
Schmidt number on taking composition with another completely positive map.


Abstract:
  We consider a locally uniformly strictly elliptic second order partial
differential operator in $\mathbb{R}^d$, $d\ge 2$, with low regularity
assumptions on its coefficients, as well as an associated Hunt process and
semigroup. The Hunt process is known to solve a corresponding stochastic
differential equation that is pathwise unique. In this situation, we study the
relation of invariance, infinitesimal invariance, recurrence, transience,
conservativeness and $L^r$-uniqueness, and present sufficient conditions for
non-existence of finite infinitesimally invariant measures as well as finite
invariant measures. Our main result is that recurrence implies uniqueness of
infinitesimally invariant measures, as well as existence and uniqueness of
invariant measures, both in subclasses of locally finite measures. We can hence
make in particular use of various explicit analytic criteria for recurrence
that have been previously developed in the context of (generalized) Dirichlet
forms and present diverse examples and counterexamples for uniqueness of
infinitesimally invariant, as well as invariant measures and an example where
$L^1$-uniqueness fails for one infinitesimally invariant measure but holds for
another and pathwise uniqueness holds. Furthermore, we illustrate how our
results can be applied to related work and vice versa.


Abstract:
  Superconductors with kagome lattices have been identified for over 40 years,
with a superconducting transition temperature TC up to 7K. Recently, certain
kagome superconductors have been found to exhibit an exotic charge order, which
intertwines with superconductivity and persists to a temperature being one
order of magnitude higher than TC. In this work, we use scanning tunneling
microscopy (STM) to study the charge order in kagome superconductor RbV3Sb5. We
observe both a 2x2 chiral charge order and nematic surface superlattices
(predominantly 1x4). We find that the 2x2 charge order exhibits intrinsic
chirality with magnetic field tunability. Defects can scatter electrons to
introduce standing waves, which couple with the charge order to cause extrinsic
effects. While the chiral charge order resembles that discovered in KV3Sb5, it
further interacts with the nematic surface superlattices that are absent in
KV3Sb5 but exist in CsV3Sb5.


Abstract:
  Stochastic processes offer a fundamentally different paradigm of dynamics
than deterministic processes that students are most familiar with, the most
prominent example of the latter being Newtons laws of motion. Here, we discuss
in a pedagogical manner a simple and illustrative example of stochastic
processes in the form of a particle undergoing standard Brownian diffusion,
with the additional feature of the particle resetting repeatedly and at random
times to its initial condition. Over the years, many different variants of this
simple setting have been studied, including extensions to many-body interacting
systems, all of which serve as illustrations of peculiar static and dynamic
features that characterize stochastic dynamics at long times. We will provide
in this work a brief overview of this active and rapidly evolving field by
considering the arguably simplest example of Brownian diffusion in one
dimension. Along the way, we will learn about some of the general techniques
that a physicist employs to study stochastic processes.


Abstract:
  In this paper, we prove various radius results and obtain sufficient
conditions using the convolution for the Ma-Minda classes $\mathcal{S}^*(\psi)$
and $\mathcal{C}(\psi)$ of starlike and convex analytic functions. We also
obtain the Bohr radius for the class $ S_{f}(\psi):=
\{g(z)=\sum_{k=1}^{\infty}b_k z^k : g \prec f \}$ of subordinants, where $f\in
\mathcal{S}^*(\psi).$ The results are improvements and generalizations of
several well known results.


Abstract:
  Checkpointing large amounts of related data concurrently to stable storage is
a common I/O pattern of many HPC applications. However, such a pattern
frequently leads to I/O bottlenecks that lead to poor scalability and
performance. As modern HPC infrastructures continue to evolve, there is a
growing gap between compute capacity vs. I/O capabilities. Furthermore, the
storage hierarchy is becoming increasingly heterogeneous: in addition to
parallel file systems, it comprises burst buffers, key-value stores, deep
memory hierarchies at node level, etc. In this context, state of art is
insufficient to deal with the diversity of vendor APIs, performance and
persistency characteristics. This extended abstract presents an overview of
VeloC (Very Low Overhead Checkpointing System), a checkpointing runtime
specifically design to address these challenges for the next generation
Exascale HPC applications and systems. VeloC offers a simple API at user level,
while employing an advanced multi-level resilience strategy that transparently
optimizes the performance and scalability of checkpointing by leveraging
heterogeneous storage.


Abstract:
  We calculate the energy levels of a system of neutrinos undergoing collective
oscillations as functions of an effective coupling strength and radial distance
from the neutrino source using the quantum Lanczos (QLanczos) algorithm
implemented on IBM Q quantum computer hardware. Our calculations are based on
the many-body neutrino interaction Hamiltonian introduced in Ref.\
\cite{Patwardhan2019}. We show that the system Hamiltonian can be separated
into smaller blocks, which can be represented using fewer qubits than those
needed to represent the entire system as one unit, thus reducing the noise in
the implementation on quantum hardware. We also calculate transition
probabilities of collective neutrino oscillations using a Trotterization method
which is simplified before subsequent implementation on hardware. These
calculations demonstrate that energy eigenvalues of a collective neutrino
system and collective neutrino oscillations can both be computed on quantum
hardware with certain simplification to within good agreement with exact
results.


Abstract:
  We study the algorithmic content of Pontryagin - van Kampen duality. We prove
that the dualization is computable in the important cases of compact and
locally compact totally disconnected Polish abelian groups. The applications of
our main results include solutions to questions of Kihara and Ng about
presentations of connected Polish spaces, and an unexpected arithmetical
characterisation of direct products of solenoid groups among all Polish groups.


Abstract:
  Recent studies have demonstrated a perceivable improvement on the performance
of neural machine translation by applying cross-lingual language model
pretraining (Lample and Conneau, 2019), especially the Translation Language
Modeling (TLM). To alleviate the need for expensive parallel corpora by TLM, in
this work, we incorporate the translation information from dictionaries into
the pretraining process and propose a novel Bilingual Dictionary-based Language
Model (BDLM). We evaluate our BDLM in Chinese, English, and Romanian. For
Chinese-English, we obtained a 55.0 BLEU on WMT-News19 (Tiedemann, 2012) and a
24.3 BLEU on WMT20 news-commentary, outperforming the Vanilla Transformer
(Vaswani et al., 2017) by more than 8.4 BLEU and 2.3 BLEU, respectively.
According to our results, the BDLM also has advantages on convergence speed and
predicting rare words. The increase in BLEU for WMT16 Romanian-English also
shows its effectiveness in low-resources language translation.


Abstract:
  Near the surface of any neutron star there is a thin heat blanketing envelope
that produces substantial thermal insulation of warm neutron star interiors and
that relates the internal temperature of the star to its effective surface
temperature. Physical processes in the blanketing envelopes are reasonably
clear but the chemical composition is not. The latter circumstance complicates
inferring physical parameters of matter in the stellar interiors from
observations of the thermal surface radiation of the stars and urges one to
elaborate the models of blanketing envelopes. We outline physical properties of
these envelopes, particularly, the equation of state, thermal conduction, ion
diffusion and others. Various models of heat blankets are reviewed, such as
composed of separate layers of different elements, or containing diffusive
binary ion mixtures in or out of diffusion equilibrium. The effects of strong
magnetic fields in the envelopes are outlined as well as the effects of high
temperatures which induce strong neutrino emission in the envelopes themselves.
Finally, we discuss how the properties of the heat blankets affect thermal
evolution of neutron stars and the ability to infer important information on
internal structure of neutron stars from observations.


Abstract:
  We study the optical-pump induced ultrafast transient change of the X-ray
absorption at the $L_3$ absorption resonances of the transition metals Ni and
Fe in Fe$_{0.5}$Ni$_{0.5}$ alloy. We find the effect for both elements to occur
simultaneously on a femtosecond timescale. This effect may hence be used as a
handy cross-correlation scheme providing a time-zero reference for ultrafast
optical-pump soft X-ray-probe measurement. The method benefits from a
relatively simple experimental setup as the sample itself acts as
time-reference tool. In particular, this technique works with low flux
ultrafast soft X-ray sources. The measurements are compared to the
cross-correlation method introduced in an earlier publication.


Abstract:
  We propose a system of conservation laws with relaxation source terms (i.e.
balance laws) for non-isothermal viscoelastic flows of Maxwell fluids. The
system is an extension of the polyconvex elastodynamics of hyperelastic bodies
using additional structure variables. It is obtained by writing the Helmholtz
free energy as the sum of a volumetric energy density (function of the
determinant of the deformation gradient det F and the temperature $\theta$ like
the standard perfect-gas law or Noble-Abel stiffened-gas law) plus a polyconvex
strain energy density function of F, $\theta$ and of symmetric
positive-definite structure tensors that relax at a characteristic time scale.
One feature of our model is that it unifies various ideal materials ranging
from hyperelastic solids to perfect fluids, encompassing fluids with memory
like Maxwell fluids. We establish a strictly convex mathematical entropy to
show that the system is symmetric-hyperbolic. Another feature of the proposed
model is therefore the short-time existence and uniqueness of smooth solutions,
which define genuinely causal viscoelastic flows with waves propagating at
finite speed. In heat-conductors, we complement the system by a
Maxwell-Cattaneo equation for an energy-flux variable. The system is still
symmetric-hyperbolic, and smooth evolutions with finite-speed waves remain
well-defined.


Abstract:
  We introduce an optimisation method for variational quantum algorithms and
experimentally demonstrate a 100-fold improvement in efficiency compared to
naive implementations. The effectiveness of our approach is shown by obtaining
multi-dimensional energy surfaces for small molecules and a spin model. Our
method solves related variational problems in parallel by exploiting the global
nature of Bayesian optimisation and sharing information between different
optimisers. Parallelisation makes our method ideally suited to next generation
of variational problems with many physical degrees of freedom. This addresses a
key challenge in scaling-up quantum algorithms towards demonstrating quantum
advantage for problems of real-world interest.


Abstract:
  This work aims to interpolate parametrized Reduced Order Model (ROM) basis
constructed via the Proper Orthogonal Decomposition (POD) to derive a robust
ROM of the system's dynamics for an unseen target parameter value. A novel
non-intrusive Space-Time (ST) POD basis interpolation scheme is proposed, for
which we define ROM spatial and temporal basis \emph{curves on compact Stiefel
manifolds}. An interpolation is finally defined on a \emph{mixed part} encoded
in a square matrix directly deduced using the space part, the singular values
and the temporal part, to obtain an interpolated snapshot matrix, keeping track
of accurate space and temporal eigenvectors. Moreover, in order to establish a
well-defined curve on the compact Stiefel manifold, we introduce a new
procedure, the so-called oriented SVD. Such an oriented SVD produces unique
right and left eigenvectors for generic matrices, for which all singular values
are distinct. It is important to notice that the ST POD basis interpolation
does not require the construction and the subsequent solution of a
reduced-order FEM model as classically is done. Hence it is avoiding the
bottleneck of standard POD interpolation which is associated with the
evaluation of the nonlinear terms of the Galerkin projection on the governing
equations. As a proof of concept, the proposed method is demonstrated with the
adaptation of rigid-thermoviscoplastic finite element ROMs applied to a typical
nonlinear open forging metal forming process. Strong correlations of the ST POD
models with respect to their associated high-fidelity FEM counterpart
simulations are reported, highlighting its potential use for near real-time
parametric simulations using off-line computed ROM POD databases.


Abstract:
  Forecasting demand is one of the fundamental components of a successful
revenue management system in hospitality. The industry requires understandable
models that contribute to adaptability by a revenue management department to
make data-driven decisions. Data analysis and forecasts prove an essential role
for the time until the check-in date, which differs per day of week. This paper
aims to provide a new model, which is inspired by cubic smoothing splines,
resulting in smooth demand curves per rate class over time until the check-in
date. This model regulates the error between data points and a smooth curve,
and is therefore able to capture natural guest behavior. The forecast is
obtained by solving a linear programming model, which enables the incorporation
of industry knowledge in the form of constraints. Using data from a major hotel
chain, a lower error and 13.3% more revenue is obtained.


Abstract:
  We consider a standard federated learning (FL) architecture where a group of
clients periodically coordinate with a central server to train a statistical
model. We develop a general algorithmic framework called FedLin to tackle some
of the key challenges intrinsic to FL, namely objective heterogeneity, systems
heterogeneity, and infrequent and imprecise communication. Our framework is
motivated by the observation that under these challenges, various existing FL
algorithms suffer from a fundamental speed-accuracy conflict: they either
guarantee linear convergence but to an incorrect point, or convergence to the
global minimum but at a sub-linear rate, i.e., fast convergence comes at the
expense of accuracy. In contrast, when the clients' local loss functions are
smooth and strongly convex, we show that FedLin guarantees linear convergence
to the global minimum, despite arbitrary objective and systems heterogeneity.
We then establish matching upper and lower bounds on the convergence rate of
FedLin that highlight the effects of intermittent communication. Finally, we
show that FedLin preserves linear convergence rates under aggressive gradient
sparsification, and quantify the effect of the compression level on the
convergence rate. Our work is the first to provide tight linear convergence
rate guarantees, and constitutes the first comprehensive analysis of gradient
sparsification in FL.


Abstract:
  Catacondensed benzenoids (those benzenoids having no carbon atom belonging to
three hexagonal rings) form the simplest class of polycyclic aromatic
hydrocarbons (PAH). They have a long history of study and are of wide chemical
importance. In this paper, mathematical possibilities for natural extension of
the notion of a catacondensed benzenoid are discussed, leading under plausible
chemically and physically motivated restrictions to the notion of a
catacondensed chemical hexagonal complex (CCHC). A general polygonal complex is
a topological structure composed of polygons that are glued together along
certain edges. A polygonal complex is flat if none of its edges belong to more
than two polygons. A connected flat polygonal complex determines an orientable
or nonorientable surface, possibly with boundary. A CCHC is then a connected
flat polygonal complex all of whose polygons are hexagons and each of whose
vertices belongs to at most two hexagonal faces. We prove that all CCHC are
Kekulean and give formulas for counting the perfect matchings in a series of
examples based on expansion of cubic graphs in which the edges are replaced by
linear polyacenes of equal length. As a preliminary assessment of the likely
stability of molecules with CCHC structure, all-electron quantum chemical
calculations are applied to molecular structures based on several CCHC, using
either linear or kinked unbranched catafused polyacenes as the expansion motif.
The systems examined all have closed shells according to H\"uckel theory and
all correspond to minima on the potential surface, thus passing the most basic
test for plausibility as a chemical species.


Abstract:
  In this work GEM and single-hole Thick GEM structures, composed of different
coating materials, are studied. The used foils incorporate conductive layers
made of copper, aluminium, molybdenum, stainless steel, tungsten and tantalum.
The main focus of the study is the determination of the material dependence of
the formation of electrical discharges in GEM-based detectors. For this task,
discharge probability measurements are conducted with several Thick GEM samples
using a basic electronics readout chain. In addition to that, optical
spectroscopy methods are employed to study the light emitted during discharges
from the different foils. It is observed that the light spectra of GEMs include
emission lines from the conductive layer material. This indicates the presence
of the foil material in the discharge plasma after the initial spark. However,
no lines associated with the coating material are observed while studying spark
discharges induced in Thick GEMs. It is concluded that the conductive layer
material does not play a substantial role in terms of stability against primary
discharges. However, a strong material dependence is observed in the case of
secondary discharge formation, pointing to molybdenum coating as the one
providing increased stability.


Abstract:
  Is there a constant $r_0$ such that, in any invariant tree network linking
rate-$1$ Poisson points in the plane, the mean within-network distance between
points at Euclidean distance $r$ is infinite for $r > r_0$? We prove a slightly
weaker result. This is a continuum analog of a result of Benjamini et al (2001)
on invariant spanning trees of the integer lattice.


Abstract:
  Bose polarons, quasi-particles composed of mobile impurities surrounded by
cold Bose gas, can experience strong interactions mediated by the many-body
environment and form bipolaron bound states. Here we present a detailed study
of heavy polarons in a one-dimensional Bose gas by formulating a
non-perturbative theory and complementing it with exact numerical simulations.
We develop an analytic approach for weak boson-boson interactions and
arbitrarily strong impurity-boson couplings. Our approach is based on a
mean-field theory that accounts for deformations of the superfluid by the
impurities and in this way minimizes quantum fluctuations. The mean-field
equations are solved exactly in Born-Oppenheimer (BO) approximation leading to
an analytic expression for the interaction potential of heavy polarons which is
found to be in excellent agreement with quantum Monte-Carlo (QMC) results. In
the strong-coupling limit the potential substantially deviates from the
exponential form valid for weak coupling and has a linear shape at short
distances. Taking into account the leading-order Born-Huang corrections we
calculate bipolaron binding energies for impurity-boson mass ratios as low as 3
and find excellent agreement with QMC results.


Abstract:
  This work concerns video-language pre-training and representation learning.
In this now ubiquitous training scheme, a model first performs pre-training on
paired videos and text (e.g., video clips and accompanied subtitles) from a
large uncurated source corpus, before transferring to specific downstream
tasks. This two-stage training process inevitably raises questions about the
generalization ability of the pre-trained model, which is particularly
pronounced when a salient domain gap exists between source and target data
(e.g., instructional cooking videos vs. movies). In this paper, we first bring
to light the sensitivity of pre-training objectives (contrastive vs.
reconstructive) to domain discrepancy. Then, we propose a simple yet effective
framework, CUPID, to bridge this domain gap by filtering and adapting source
data to the target data, followed by domain-focused pre-training. Comprehensive
experiments demonstrate that pre-training on a considerably small subset of
domain-focused data can effectively close the source-target domain gap and
achieve significant performance gain, compared to random sampling or even
exploiting the full pre-training dataset. CUPID yields new state-of-the-art
performance across multiple video-language and video tasks, including
text-to-video retrieval [72, 37], video question answering [36], and video
captioning [72], with consistent performance lift over different pre-training
methods.


Abstract:
  Automatic image aesthetics assessment is a computer vision problem that deals
with the categorization of images into different aesthetic levels. The
categorization is usually done by analyzing an input image and computing some
measure of the degree to which the image adhere to the key principles of
photography (balance, rhythm, harmony, contrast, unity, look, feel, tone and
texture). Owing to its diverse applications in many areas, automatic image
aesthetic assessment has gained significant research attention in recent years.
This paper presents a literature review of the recent techniques of automatic
image aesthetics assessment. A large number of traditional hand crafted and
deep learning based approaches are reviewed. Key problem aspects are discussed
such as why some features or models perform better than others and what are the
limitations. A comparison of the quantitative results of different methods is
also provided at the end.


Abstract:
  We assume a generic real singlet scalar extension of the Standard Model
living in the vacuum $(v,w)$ at the electroweak scale with $v=246$ GeV and $w$
being respectively the Higgs and the singlet scalar vacuum expectation values.
By requiring {\it absolute} vacuum stability for the vacuum $(v,w)$, the
positivity condition and the perturbativity up to the Planck scale, we show
that the viable space of parameters in the model is strongly constrained for
various singlet scalar vacuum expectation values $w=0.1, 1, 10, 100$ TeV. Also,
it turns out that the singlet scalar mass can be from a few GeV up to less than
TeV.


Abstract:
  The constant increase in the complexity of data networks motivates the search
for strategies that make it possible to reduce current monitoring times. This
paper shows the way in which multilayer network representation and the
application of multiscale analysis techniques, as applied to software-defined
networks, allows for the visualization of anomalies from "coarse views of the
network topology". This implies the analysis of fewer data, and consequently
the reduction of the time that a process takes to monitor the network. The fact
that software-defined networks allow for the obtention of a global view of
network behavior facilitates detail recovery from affected zones detected in
monitoring processes. The method is evaluated by calculating the reduction
factor of nodes, checked during anomaly detection, with respect to the total
number of nodes in the network.


Abstract:
  This paper explores an efficient solution for Space-time Super-Resolution,
aiming to generate High-resolution Slow-motion videos from Low Resolution and
Low Frame rate videos. A simplistic solution is the sequential running of Video
Super Resolution and Video Frame interpolation models. However, this type of
solutions are memory inefficient, have high inference time, and could not make
the proper use of space-time relation property. To this extent, we first
interpolate in LR space using quadratic modeling. Input LR frames are
super-resolved using a state-of-the-art Video Super-Resolution method. Flowmaps
and blending mask which are used to synthesize LR interpolated frame is reused
in HR space using bilinear upsampling. This leads to a coarse estimate of HR
intermediate frame which often contains artifacts along motion boundaries. We
use a refinement network to improve the quality of HR intermediate frame via
residual learning. Our model is lightweight and performs better than current
state-of-the-art models in REDS STSR Validation set.


Abstract:
  How has the solar wind evolved to reach what it is today? In this review, I
discuss the long-term evolution of the solar wind, including the evolution of
observed properties that are intimately linked to the solar wind: rotation,
magnetism and activity. Given that we cannot access data from the solar wind 4
billion years ago, this review relies on stellar data, in an effort to better
place the Sun and the solar wind in a stellar context. I overview some clever
detection methods of winds of solar-like stars, and derive from these an
observed evolutionary sequence of solar wind mass-loss rates. I then link these
observational properties (including, rotation, magnetism and activity) with
stellar wind models. I conclude this review then by discussing implications of
the evolution of the solar wind on the evolving Earth and other solar system
planets. I argue that studying exoplanetary systems could open up new avenues
for progress to be made in our understanding of the evolution of the solar
wind.


Abstract:
  Despite continuous increasing of the number of ICRF sources, their sky
coverage is still not satisfactory. The goal of this study is to discuss some
new considerations for extending the ICRF source list. Statistical analysis of
the ICRF catalog allows us to identify less populated sky regions where new
ICRF sources or additional observations of the current ICRF sources are most
desirable to improve both the uniformity of the source distribution and the
uniformity of the distribution of the position errors. It is also desirable to
include more sources with high redshift in the ICRF list. These sources may be
of interest for astrophysics. To select prospective new ICRF sources, the OCARS
catalog is used. The number of sources in OCARS is about three times greater
than in the ICRF3, which gives us an opportunity to select new ICRF sources
that have already be tested and detected in astrometric and geodetic VLBI
experiments.


Abstract:
  We give a short and unified proof of the Brundan-Kleshchev isomorphism
between blocks of cyclotomic Hecke algebras and cyclotomic
KhovanovLauda-Rouquier algebras of type A.


Abstract:
  While averages and typical fluctuations often play a major role to understand
the behavior of a non-equilibrium system, this nonetheless is not always true.
Rare events and large fluctuations are also pivotal when a thorough analysis of
the system is being done. In this context, the statistics of extreme
fluctuations in contrast to the average plays an important role, as has been
discussed in fields ranging from statistical and mathematical physics to
climate, finance and ecology. Herein, we study Extreme Value Statistics (EVS)
of stochastic resetting systems which have recently gained lot of interests due
to its ubiquitous and enriching applications in physics, chemistry, queuing
theory, search processes and computer science. We present a detailed analysis
for the finite and large time statistics of extremals (maximum and arg-maximum
i.e., the time when the maximum is reached) of the spatial displacement in such
system. In particular, we derive an exact renewal formula that relates the
joint distribution of maximum and arg-maximum of the reset process to the
statistical measures of the underlying process. Benchmarking our results for
the maximum of a reset-trajectory that pertain to the Gumbel class for large
sample size, we show that the arg-maximum density attains to a uniform
distribution regardless of the underlying process at a large observation time.
This emerges as a manifestation of the renewal property of the resetting
mechanism. The results are augmented with a wide spectrum of Markov and
non-Markov stochastic processes under resetting namely simple diffusion,
diffusion with drift, Ornstein-Uhlenbeck process and random acceleration
process in one dimension. Rigorous results are presented for the first two
set-ups while the latter two are supported with heuristic and numerical
analysis.


Abstract:
  We develop the geometrical, analytical, and computational framework for
reactive island theory for three degrees-of-freedom time-independent
Hamiltonian systems. In this setting, the dynamics occurs in a 5-dimensional
energy surface in phase space and is governed by four-dimensional stable and
unstable manifolds of a three-dimensional normally hyperbolic invariant sphere.
The stable and unstable manifolds have the geometrical structure of spherinders
and we provide the means to investigate the ways in which these spherinders and
their intersections determine the dynamical evolution of trajectories. This
geometrical picture is realized through the computational technique of
Lagrangian descriptors. In a set of trajectories, Lagrangian descriptors allow
us to identify the ones closest to a stable or unstable manifold. Using an
approximation of the manifold on a surface of section we are able to calculate
the flux between two regions of the energy surface.


Abstract:
  Some data analysis applications comprise datasets, where explanatory
variables are expensive or tedious to acquire, but auxiliary data are readily
available and might help to construct an insightful training set. An example is
neuroimaging research on mental disorders, specifically learning a
diagnosis/prognosis model based on variables derived from expensive Magnetic
Resonance Imaging (MRI) scans, which often requires large sample sizes.
Auxiliary data, such as demographics, might help in selecting a smaller sample
that comprises the individuals with the most informative MRI scans. In active
learning literature, this problem has not yet been studied, despite promising
results in related problem settings that concern the selection of instances or
instance-feature pairs.
  Therefore, we formulate this complementary problem of Active Selection of
Classification Features (ASCF): Given a primary task, which requires to learn a
model f: x-> y to explain/predict the relationship between an
expensive-to-acquire set of variables x and a class label y. Then, the
ASCF-task is to use a set of readily available selection variables z to select
these instances, that will improve the primary task's performance most when
acquiring their expensive features z and including them to the primary training
set.
  We propose two utility-based approaches for this problem, and evaluate their
performance on three public real-world benchmark datasets. In addition, we
illustrate the use of these approaches to efficiently acquire MRI scans in the
context of neuroimaging research on mental disorders, based on a simulated
study design with real MRI data.


Abstract:
  Concept drift detection is a crucial task in data stream evolving
environments. Most of state of the art approaches designed to tackle this
problem monitor the loss of predictive models. However, this approach falls
short in many real-world scenarios, where the true labels are not readily
available to compute the loss. In this context, there is increasing attention
to approaches that perform concept drift detection in an unsupervised manner,
i.e., without access to the true labels. We propose a novel approach to
unsupervised concept drift detection based on a student-teacher learning
paradigm. Essentially, we create an auxiliary model (student) to mimic the
behaviour of the primary model (teacher). At run-time, our approach is to use
the teacher for predicting new instances and monitoring the mimicking loss of
the student for concept drift detection. In a set of experiments using 19 data
streams, we show that the proposed approach can detect concept drift and
present a competitive behaviour relative to the state of the art approaches.


Abstract:
  We describe in this paper an optimal control strategy for shaping a
large-scale swarm of particles using boundary global actuation. This problem
arises as a key challenge in many swarm robotics applications, especially when
the robots are passive particles that need to be guided by external control
fields. The system is large-scale and underactuated, making the control
strategy at the microscopic particle level infeasible. We consider the
Kolmogorov forward equation associated to the stochastic process of the single
particle to encode the macroscopic behaviour of the particles swarm. The
control inputs shape the velocity field of the density dynamics according to
the physical model of the actuators. We find the optimal actuation considering
an optimal control problem whose state dynamics is governed by a linear
parabolic advection-diffusion equation where the control induces a transport
field. From a theoretical standpoint, we show the existence of a solution to
the resulting nonlinear optimal control problem. From a numerical standpoint,
we employ the discrete adjoint method to accurately compute the reduced
gradient and we show how it commutes with the optimize-then-discretize
approach. Finally, numerical simulations show the effectiveness of the control
strategy in driving the density sufficiently close to the target.


Abstract:
  The aim of this work is to present an overview about the combination of the
Reduced Basis Method (RBM) with two different approaches for Fluid-Structure
Interaction (FSI) problems, namely a monolithic and a partitioned approach. We
provide the details of implementation of two reduction procedures, and we then
apply them to the same test case of interest. We first implement a reduction
technique that is based on a monolithic procedure where we solve the fluid and
the solid problems all at once. We then present another reduction technique
that is based on a partitioned (or segregated) procedure: the fluid and the
solid problems are solved separately and then coupled using a fixed point
strategy. The toy problem that we consider is based on the Turek-Hron benchmark
test case, with a fluid Reynolds number Re = 100.


Abstract:
  We establish that for any proper action of a Lie group on a manifold the
associated equivariant differentiable cohomology groups with coefficients in
modules of $\mathcal{C}^\infty$-functions vanish in all degrees except than
zero. Furthermore let $G$ be a Lie group of $CR$-automorphisms of a strictly
pseudo-convex $CR$-manifold $M$. We associate to $G$ a canonical class in the
first differential cohomology of $G$ with coefficients in the
$\mathcal{C}^\infty$-functions on $M$. This class is non-zero if and only if
$G$ is essential in the sense that there does not exist a $CR$-compatible
strictly pseudo-convex pseudo-Hermitian structure on $M$ which is preserved by
$G$. We prove that a closed Lie subgroup $G$ of $CR$-automorphisms acts
properly on $M$ if and only if its canonical class vanishes. As a consequence
of Schoen's theorem, it follows that for any strictly pseudo-convex
$CR$-manifold $M$, there exists a compatible strictly pseudo-convex
pseudo-Hermitian structure such that the CR-automorphism group for $M$ and the
group of pseudo-Hermitian transformations coincide, except for two kinds of
spherical $CR$-manifolds. Similar results hold for conformal Riemannian and
K\"ahler manifolds.


Abstract:
  Recent work has made significant progress in helping users to automate single
data preparation steps, such as string-transformations and table-manipulation
operators (e.g., Join, GroupBy, Pivot, etc.). We in this work propose to
automate multiple such steps end-to-end, by synthesizing complex data pipelines
with both string transformations and table-manipulation operators. We propose a
novel "by-target" paradigm that allows users to easily specify the desired
pipeline, which is a significant departure from the traditional by-example
paradigm. Using by-target, users would provide input tables (e.g., csv or json
files), and point us to a "target table" (e.g., an existing database table or
BI dashboard) to demonstrate how the output from the desired pipeline would
schematically "look like". While the problem is seemingly underspecified, our
unique insight is that implicit table constraints such as FDs and keys can be
exploited to significantly constrain the space to make the problem tractable.
We develop an Auto-Pipeline system that learns to synthesize pipelines using
reinforcement learning and search. Experiments on large numbers of real
pipelines crawled from GitHub suggest that Auto-Pipeline can successfully
synthesize 60-70% of these complex pipelines with up to 10 steps.


Abstract:
  We show that certain global anomalies can be detected in an elementary
fashion by analyzing the way the symmetry algebra is realized on the torus
Hilbert space of the anomalous theory. Distinct anomalous behaviours imprinted
in the Hilbert space are identified with the distinct cohomology "layers" that
appear in the classification of anomalies in terms of cobordism groups. We
illustrate the manifestation of the layers in the Hilbert for a variety of
anomalous symmetries and spacetime dimensions, including time-reversal
symmetry, and both in systems of fermions and in anomalous topological quantum
field theories (TQFTs) in 2+1d. We argue that anomalies can imply an exact
bose-fermi degeneracy in the Hilbert space, thus revealing a supersymmetric
spectrum of states; we provide a sharp characterization of when this phenomenon
occurs and give nontrivial examples in various dimensions, including in
strongly coupled QFTs. Unraveling the anomalies of TQFTs leads us to develop
the construction of the Hilbert spaces, the action of operators and the modular
data in spin TQFTs, material that can be read on its own.


Abstract:
  Detecting human-object interactions (HOI) is an important step toward a
comprehensive visual understanding of machines. While detecting non-temporal
HOIs (e.g., sitting on a chair) from static images is feasible, it is unlikely
even for humans to guess temporal-related HOIs (e.g., opening/closing a door)
from a single video frame, where the neighboring frames play an essential role.
However, conventional HOI methods operating on only static images have been
used to predict temporal-related interactions, which is essentially guessing
without temporal contexts and may lead to sub-optimal performance. In this
paper, we bridge this gap by detecting video-based HOIs with explicit temporal
information. We first show that a naive temporal-aware variant of a common
action detection baseline does not work on video-based HOIs due to a
feature-inconsistency issue. We then propose a simple yet effective
architecture named Spatial-Temporal HOI Detection (ST-HOI) utilizing temporal
information such as human and object trajectories, correctly-localized visual
features, and spatial-temporal masking pose features. We construct a new video
HOI benchmark dubbed VidHOI where our proposed approach serves as a solid
baseline.


Abstract:
  With the rise of digital currency systems that rely on blockchain to ensure
ledger security, the ability to perform cross-chain transactions is becoming a
crucial interoperability requirement. Such transactions allow not only funds to
be transferred from one blockchain to another (as done in atomic swaps), but
also a blockchain to verify the inclusion of any event on another blockchain.
Cross-chain bridges are protocols that allow on-chain exchange of
cryptocurrencies, on-chain transfer of assets to sidechains, and cross-shard
verification of events in sharded blockchains, many of which rely on Byzantine
fault tolerance (BFT) for scalability. Unfortunately, existing bridge protocols
that can transfer funds from a BFT blockchain incur significant computation
overhead on the destination blockchain, resulting in a high gas cost for smart
contract verification of events. In this paper, we propose Horizon, a
gas-efficient, cross-chain bridge protocol to transfer assets from a BFT
blockchain to another blockchain (e.g., Ethereum) that supports basic smart
contract execution.


Abstract:
  Teacher-student models provide a framework in which the typical-case
performance of high-dimensional supervised learning can be described in closed
form. The assumptions of Gaussian i.i.d. input data underlying the canonical
teacher-student model may, however, be perceived as too restrictive to capture
the behaviour of realistic data sets. In this paper, we introduce a Gaussian
covariate generalisation of the model where the teacher and student can act on
different spaces, generated with fixed, but generic feature maps. While still
solvable in a closed form, this generalization is able to capture the learning
curves for a broad range of realistic data sets, thus redeeming the potential
of the teacher-student framework. Our contribution is then two-fold: First, we
prove a rigorous formula for the asymptotic training loss and generalisation
error. Second, we present a number of situations where the learning curve of
the model captures the one of a realistic data set learned with kernel
regression and classification, with out-of-the-box feature maps such as random
projections or scattering transforms, or with pre-learned ones - such as the
features learned by training multi-layer neural networks. We discuss both the
power and the limitations of the framework.


Abstract:
  In this paper, we propose a formalization of the process of exploitation of
SQL injection vulnerabilities. We consider a simplification of the dynamics of
SQL injection attacks by casting this problem as a security capture-the-flag
challenge. We model it as a Markov decision process, and we implement it as a
reinforcement learning problem. We then deploy reinforcement learning agents
tasked with learning an effective policy to perform SQL injection; we design
our training in such a way that the agent learns not just a specific strategy
to solve an individual challenge but a more generic policy that may be applied
to perform SQL injection attacks against any system instantiated randomly by
our problem generator. We analyze the results in terms of the quality of the
learned policy and in terms of convergence time as a function of the complexity
of the challenge and the learning agent's complexity. Our work fits in the
wider research on the development of intelligent agents for autonomous
penetration testing and white-hat hacking, and our results aim to contribute to
understanding the potential and the limits of reinforcement learning in a
security environment.


Abstract:
  Most quantum information tasks based on Bell tests relie on the assumption of
measurement independence. However, it is difficult to ensure that the
assumption of measurement independence is always met in experimental
operations, so it is crucial to explore the effects of relaxing this assumption
on Bell tests. In this paper, we discuss the effects of relaxing the assumption
of measurement independence on 1-parameter family of Bell (1-PFB) tests. For
both general and factorizable input distributions, we establish the
relationship among measurement dependence, guessing probability, and the
maximum value of 1-PFB correlation function that Eve can fake. The
deterministic strategy when Eve fakes the maximum value is also given. We
compare the unknown information rate of Chain inequality and 1-PFB inequality,
and find the range of the parameter in which it is more difficult for Eve to
fake the maximum quantum violation in 1-PFB inequality than in Chain
inequality.


Abstract:
  For task-oriented dialog systems, training a Reinforcement Learning (RL)
based Dialog Management module suffers from low sample efficiency and slow
convergence speed due to the sparse rewards in RL.To solve this problem, many
strategies have been proposed to give proper rewards when training RL, but
their rewards lack interpretability and cannot accurately estimate the
distribution of state-action pairs in real dialogs. In this paper, we propose a
multi-level reward modeling approach that factorizes a reward into a
three-level hierarchy: domain, act, and slot. Based on inverse adversarial
reinforcement learning, our designed reward model can provide more accurate and
explainable reward signals for state-action pairs.Extensive evaluations show
that our approach can be applied to a wide range of reinforcement
learning-based dialog systems and significantly improves both the performance
and the speed of convergence.


Abstract:
  In this paper, we study the generalized gapped k-mer filters and derive a
closed form solution for their coefficients. We consider nonnegative integers
$\ell$ and $k$, with $k\leq \ell$, and an $\ell$-tuple
$B=(b_1,\ldots,b_{\ell})$ of integers $b_i\geq 2$, $i=1,\ldots,\ell$. We
introduce and study an incidence matrix $A=A_{\ell,k;B}$. We develop a
M\"obius-like function $\nu_B$ which helps us to obtain closed forms for a
complete set of mutually orthogonal eigenvectors of $A^{\top} A$ as well as a
complete set of mutually orthogonal eigenvectors of $AA^{\top}$ corresponding
to nonzero eigenvalues.
  The reduced singular value decomposition of $A$ and combinatorial
interpretations for the nullity and rank of $A$, are among the consequences of
this approach.
  We then combine the obtained formulas, some results from linear algebra, and
combinatorial identities of elementary symmetric functions and $\nu_B$, to
provide the entries of the Moore-Penrose pseudo-inverse matrix $A^{+}$ and the
Gapped k-mer filter matrix $A^{+} A$.