Abstract:
  The \textit{KArlsruhe TRItium Neutrino} (KATRIN) experiment aims to measure
the neutrino mass with a sensitivity of \SI{0.2}{\electronvolt}
(\SI{90}{\percent} CL). This will be achieved by a precision measurement of the
endpoint region of the $\upbeta$-electron spectrum of tritium decay. The
$\upbeta$-electrons are produced in the \textit{Windowless Gaseous Tritium
Source} (WGTS) and guided magnetically through the beamline. In order to
accurately extract the neutrino mass the source activity is required to be
stable and known to a high precision. The WGTS therefore undergoes constant
extensive monitoring from several measurement systems. The \textit{Forward Beam
Monitor} (FBM) is one such monitoring system. The FBM system comprises a
complex mechanical setup capable of inserting a detector board into the KATRIN
beamline with a positioning precision of better than \SI{0.3}{\milli\metre}.
The electron flux density at that position is on the order of
\SI{e6}{\per\second\per\milli\metre\squared}. The detector board contains two
silicon detector chips of \pin diode type which can measure the
$\upbeta$-electron flux from the source with a precision of \SI{0.1}{\percent}
within \SI{60}{\second} with an energy resolution of FWHM $=$
\SI{2}{\kilo\electronvolt}. The unique challenge in developing the FBM arise
from its designated operating environment inside the Cryogenic Pumping Section
which is a potentially tritium contaminated ultra-high vacuum chamber at
cryogenic temperatures in the presence of a \SI{1}{\tesla} strong magnetic
field. Each of theses parameters do strongly limit the choice of possible
materials which e.g. caused difficulties in detector noise reduction, heat
dissipation and lubrication. In order to completely remove the FBM from the
beam tube a \SI{2}{\meter} long traveling distance into the beamline is needed
demanding a robust as well as highly precise moving mechanism.


Abstract:
  We propose a fully automated system that simultaneously estimates the camera
intrinsics, the ground plane, and physical distances between people from a
single RGB image or video captured by a camera viewing a 3-D scene from a fixed
vantage point. To automate camera calibration and distance estimation, we
leverage priors about human pose and develop a novel direct formulation for
pose-based auto-calibration and distance estimation, which shows
state-of-the-art performance on publicly available datasets. The proposed
approach enables existing camera systems to measure physical distances without
needing a dedicated calibration process or range sensors, and is applicable to
a broad range of use cases such as social distancing and workplace safety.
Furthermore, to enable evaluation and drive research in this area, we
contribute to the publicly available MEVA dataset with additional distance
annotations, resulting in MEVADA -- the first evaluation benchmark in the world
for the pose-based auto-calibration and distance estimation problem.


Abstract:
  For a prime number $p$, we study the asymptotic distribution of CM points on
the moduli space of elliptic curves over $\mathbb{C}_p$. In stark contrast to
the complex case, in the $p$-adic setting there are infinitely many different
measures describing the asymptotic distribution of CM points. In this paper we
identify all of these measures. A key insight is to translate this problem into
a $p$-adic version of Linnik's classical problem on the asymptotic distribution
of integer points on spheres. To do this translation, we use the close
relationship between the deformation theories of elliptic curves and formal
modules and then apply results of Gross and Hopkins. We solve this $p$-adic
Linnik problem using a deviation estimate extracted from the bounds for the
Fourier coefficients of cuspidal modular forms of Deligne, Iwaniec and Duke. We
also identify all accumulation measures of an arbitrary Hecke orbit.


Abstract:
  Terrain adaptation is an essential capability for a ground robot to
effectively traverse unstructured off-road terrain in real-world field
environments such as forests. However, the expected robot behaviors generated
by terrain adaptation methods cannot always be executed accurately due to
setbacks such as wheel slip and reduced tire pressure. To address this problem,
we propose a novel approach for consistent behavior generation that enables the
ground robot's actual behaviors to more accurately match expected behaviors
while adapting to a variety of unstructured off-road terrain. Our approach
learns offset behaviors that are used to compensate for the inconsistency
between the actual and expected behaviors without requiring the explicit
modeling of various setbacks. Our approach is also able to estimate the
importance of the multi-modal features to improve terrain representations for
better adaptation. In addition, we develop an algorithmic solver for our
formulated regularized optimization problem, which is guaranteed to converge to
the global optimal solution. To evaluate the method, we perform extensive
experiments using various unstructured off-road terrain in real-world field
environments. Experimental results have validated that our approach enables
robots to traverse complex unstructured off-road terrain with more navigational
behavior consistency, and it outperforms previous methods, particularly so on
challenging terrain.


Abstract:
  Smart contract risk can be defined as a financial risk of loss due to cyber
attacks on or contagious failures of smart contracts. Its quantification is of
paramount importance to technology platform providers as well as companies and
individuals when considering the deployment of this new technology. That is
why, as our primary contribution, we propose a structural framework of
aggregate loss distribution for smart contract risk under the assumption of a
tree-stars graph topology representing the network of interactions among smart
contracts and their users. Up to our knowledge, there exist no theoretical
frameworks or models of an aggregate loss distribution for smart contracts in
this setting. To achieve our goal, we contextualize the problem in the
probabilistic graph-theoretical framework using bond percolation models. We
assume that the smart contract network topology is represented by a random tree
graph of finite size, and that each smart contract is the center of a {random}
star graph whose leaves represent the users of the smart contract. We allow for
heterogeneous loss topology superimposed on this smart contract and user
topology and provide analytical results and instructive numerical examples.


Abstract:
  A novel confidence interval estimator is proposed for the risk difference in
noninferiority binomial trials. The confidence interval is consistent with an
exact unconditional test that preserves the type-I error, and has improved
power, particularly for smaller sample sizes, compared to the confidence
interval by Chan & Zhang (1999). The improved performance of the proposed
confidence interval is theoretically justified and demonstrated with
simulations and examples. An R package is also distributed that implements the
proposed methods along with other confidence interval estimators.


Abstract:
  Interaction of electromagnetic, acoustic and even gravitational waves with
accelerating bodies forms a class of nonstationary time-variant processes.
Scattered waves contain intrinsic signatures of motion, which manifest in a
broad range of phenomena, including Sagnac interference, Doppler and
micro-Doppler frequency shifts. While general relativity is often required to
account for motion, instantaneous rest frame approaches are frequently used to
describe interactions with slowly accelerating objects. Here we investigate
theoretically and experimentally an interaction regime, which is neither
relativistic nor adiabatic. The test model considers an accelerating scatterer
with a long-lasting relaxation memory. The slow decay rates violate the
instantaneous reaction assumption of quasi-stationarity, introducing
non-Markovian contributions to the scattering process. Memory signatures in
scattering from a rotating dipole are studied theoretically, showing symmetry
breaking of micro-Doppler combs. A quasi-stationary numeric analysis of
scattering in the short memory limit is proposed and validated experimentally
with an example of electromagnetic pulses interacting with a rotating wire.


Abstract:
  Based on results of Digne-Michel-Lehrer (2003) we give two formulae for
two-variable Green functions attached to Lusztig induction in a finite
reductive group. We present applications to explicit computation of these Green
functions, to conjectures of Malle and Rotilio, and to scalar products between
Lusztig inductions of Gelfand-Graev characters.


Abstract:
  We study how a general configuration of finitely-many point vortices, in a
state of uniform rotation or translation with Newtonian interaction or
generalized surface quasi-geostrophic interactions, can be desingularized into
a steady configuration of vortex patches. Using a technique first introduced by
Hmidi and Mateu for vortex pairs, we reformulate the problem for the patch
boundaries so that it no longer appears singular, at which point classical
techniques such as Lyapunov--Schmidt reduction can be used. Provided the point
vortex equilibrium is non-degenerate in a natural sense, solutions can be
constructed directly using the implicit function theorem, yielding asymptotics
for the shape of the patch boundaries. As an application, we prove the
existence of various families of solutions with patches arranged in asymmetric
pairs, regular polygons, body-centered polygons, and nested regular polygons.
These configurations are degenerate due to their additional symmetries, but
this difficulty can be overcome by integrating the appropriate symmetries into
the formulation of the problem.


Abstract:
  We investigate the mass spectra of exotic hadrons known as hexaquarks in the
form of dibaryons. We use a phenomenological model based on an extended version
of the G\"ursey-Radicati mass formula for hadrons to include non-charmed
baryons, charmed baryons, and non-strange dibaryons to be able to predict
masses of potential dibaryon states. We perform six numerical fits of this
model to input data for three different sets of masses of baryons and
dibaryons. We find that the model can fit some of the data sets well,
especially the sets including charmed baryons and non-strange dibaryons, and
observe that the predicted mass of one of the dibaryons is close to the
measured mass of the observed hexaquark candidate $d^*(2380)$ reported by the
WASA-at-COSY experiment. The predicted mass of the deuteron is slightly larger
than its measured mass. Finally, for the data sets including charmed baryon and
non-strange dibaryon masses, we find that the predicted masses of potential
dibaryon states are all in the range from 1900 MeV to 3700 MeV.


Abstract:
  Despite impressive results, deep learning-based technologies also raise
severe privacy and environmental concerns induced by the training procedure
often conducted in datacenters. In response, alternatives to centralized
training such as Federated Learning (FL) have emerged. Perhaps unexpectedly,
FL, in particular, is starting to be deployed at a global scale by companies
that must adhere to new legal demands and policies originating from governments
and civil society for privacy protection. However, the potential environmental
impact related to FL remains unclear and unexplored. This paper offers the
first-ever systematic study of the carbon footprint of FL. First, we propose a
rigorous model to quantify the carbon footprint, hence facilitating the
investigation of the relationship between FL design and carbon emissions. Then,
we compare the carbon footprint of FL to traditional centralized learning. Our
findings show that FL, despite being slower to converge in some cases, may
result in a comparatively greener impact than a centralized equivalent setup.
We performed extensive experiments across different types of datasets,
settings, and various deep learning models with FL. Finally, we highlight and
connect the reported results to the future challenges and trends in FL to
reduce its environmental impact, including algorithms efficiency, hardware
capabilities, and stronger industry transparency.


Abstract:
  By employing the ${\rm AdS}_3/{\rm CFT}_2$ correspondence in this note we
observe an analogy between the structures found in connection with the
Arkani-Hamed-Bai-He-Yan (ABHY) associahedron used for understanding scattering
amplitudes, and the one used for understanding space-time emerging from
patterns of entanglement. The analogy suggests the natural interpretation for
the associahedron as a holographic entanglement polytope associated to the
${\rm CFT}_2$ vacuum. Our observations hint at the possibility that the
factorization properties of scattering amplitudes are connected to the notion
of separability of space-time as used in the theory of holographic quantum
entanglement.


Abstract:
  This paper uses linear magnetohydrodynamics to model resonant absorption in
coronal plasma with a Cartesian coordinate system. We impose line-tied boundary
conditions and tilt the background magnetic field to be oblique to the
transition region. Halberstadt & Goedbloed (1993, 1995); Goedbloed& Halberstadt
(1994); Arregui et al. (2003) show that line-tied boundary conditions cause
their resonant absorption models to produce steep boundary layers/evanescent
fast waves. We aim to study the importance of the boundary layers and assess
their significance in a solar context. We calculate the solutions in a model
where we impose line-tied boundary conditions and compare this with a model
where we include the chromosphere instead. Results are calculated analytically
and then verified numerically. We show that line-tied boundary conditions can
cause the model to overestimate the boundary layers' amplitude significantly.
If the fast waves can propagate in the chromosphere, then the line-tied model
accurately predicts the boundary layers' amplitude. However, if the fast waves
are evanescent, then the boundary layers' size is reduced significantly, and
the line-tied model overestimates their amplitude. This leads to the
counter-intuitive result that the length scales tangential to the transition
region can play an essential role in determining line-tied boundary conditions'
validity. The results suggest that line-tied boundary conditions can cause the
model to generate unphysically large boundary layers. However, researchers may
wish to continue to use them in their models for their simplicity and ability
to significantly reduce computation time if they understand and are aware of
their flaws.


Abstract:
  In this article, Joseph-Louis Lagrange analyzed those numbers which may be
represented by the quadratic form $Bt^2 + Ctu + Du^2$. After proving a few
theorems on the divisors of such numbers (and their possible forms), Lagrange
developed a method for minimizing $C$ in order to determine systematically the
possible forms for the divisors of numbers represented by the forms $t^2 \pm
au^2$.
  This article was originally published as "Recherches d'Arithm\'etique" in the
1773 volume of the Nouveaux M\'emoires de l'Acad\'emie royale des Sciences et
Belles-Lettres de Berlin (printed 1775), pp. 265-312. It is the first of two
articles on this subject, the latter of which appeared in the 1775 volume of
the Nouveaux M\'emoires.


Abstract:
  As the COVID-19 pandemic continues to devastate globally, the use of chest
X-ray (CXR) imaging as a complimentary screening strategy to RT-PCR testing
continues to grow given its routine clinical use for respiratory complaint. As
part of the COVID-Net open source initiative, we introduce COVID-Net CXR-2, an
enhanced deep convolutional neural network design for COVID-19 detection from
CXR images built using a greater quantity and diversity of patients than the
original COVID-Net. To facilitate this, we also introduce a new benchmark
dataset composed of 19,203 CXR images from a multinational cohort of 16,656
patients from at least 51 countries, making it the largest, most diverse
COVID-19 CXR dataset in open access form. The COVID-Net CXR-2 network achieves
sensitivity and positive predictive value of 95.5%/97.0%, respectively, and was
audited in a transparent and responsible manner. Explainability-driven
performance validation was used during auditing to gain deeper insights in its
decision-making behaviour and to ensure clinically relevant factors are
leveraged for improving trust in its usage. Radiologist validation was also
conducted, where select cases were reviewed and reported on by two
board-certified radiologists with over 10 and 19 years of experience,
respectively, and showed that the critical factors leveraged by COVID-Net CXR-2
are consistent with radiologist interpretations. While not a production-ready
solution, we hope the open-source, open-access release of COVID-Net CXR-2 and
the respective CXR benchmark dataset will encourage researchers, clinical
scientists, and citizen scientists to accelerate advancements and innovations
in the fight against the pandemic.


Abstract:
  In this paper, we present Uformer, an effective and efficient
Transformer-based architecture for image restoration, in which we build a
hierarchical encoder-decoder network using the Transformer block. In Uformer,
there are two core designs. First, we introduce a novel locally-enhanced window
(LeWin) Transformer block, which performs nonoverlapping window-based
self-attention instead of global self-attention. It significantly reduces the
computational complexity on high resolution feature map while capturing local
context. Second, we propose a learnable multi-scale restoration modulator in
the form of a multi-scale spatial bias to adjust features in multiple layers of
the Uformer decoder. Our modulator demonstrates superior capability for
restoring details for various image restoration tasks while introducing
marginal extra parameters and computational cost. Powered by these two designs,
Uformer enjoys a high capability for capturing both local and global
dependencies for image restoration. To evaluate our approach, extensive
experiments are conducted on several image restoration tasks, including image
denoising, motion deblurring, defocus deblurring and deraining. Without bells
and whistles, our Uformer achieves superior or comparable performance compared
with the state-of-the-art algorithms. The code and models are available at
https://github.com/ZhendongWang6/Uformer.


Abstract:
  Brain rhythms emerge as a result of synchronization among interconnected
spiking neurons. Key properties of such rhythms can be gleaned from the
phase-resetting curve (PRC). Inferring the macroscopic PRC and developing a
systematic phase reduction theory for emerging rhythms remains an outstanding
theoretical challenge. Here we present a practical theoretical framework to
compute the PRC of generic spiking networks with emergent collective
oscillations. To do so, we adopt a refractory density approach where neurons
are described by the time since their last action potential. In the
thermodynamic limit, the network dynamics are captured by a continuity equation
known as the refractory density equation. We develop an appropriate adjoint
method for this equation which in turn gives a semi-analytical expression of
the infinitesimal PRC. We confirm the validity of our framework for specific
examples of neural networks. Our theoretical findings highlight the
relationship between key biological properties at the individual neuron scale
and the macroscopic oscillatory properties assessed by the PRC.


Abstract:
  Quality diversity (QD) is a growing branch of stochastic optimization
research that studies the problem of generating an archive of solutions that
maximize a given objective function but are also diverse with respect to a set
of specified measure functions. However, even when these functions are
differentiable, QD algorithms treat them as "black boxes", ignoring gradient
information. We present the differentiable quality diversity (DQD) problem, a
special case of QD, where both the objective and measure functions are first
order differentiable. We then present MAP-Elites via a Gradient Arborescence
(MEGA), a DQD algorithm that leverages gradient information to efficiently
explore the joint range of the objective and measure functions. Results in two
QD benchmark domains and in searching the latent space of a StyleGAN show that
MEGA significantly outperforms state-of-the-art QD algorithms, highlighting
DQD's promise for efficient quality diversity optimization when gradient
information is available. Source code is available at
https://github.com/icaros-usc/dqd.


Abstract:
  We show that there is an action of the symmetric group on the Hochschild
cochain complex of a twisted group algebra with coefficients in a bimodule.
This allows us to define the symmetric Hochschild cohomology of twisted group
algebras, similarly to th construction of symmetric group cohomology due to
Staic. We give explicit embeddings and connecting homomorphisms between the
symmetric cohomology spaces and symmetric Hochschild cohomology of twisted
group algebras.


Abstract:
  Most online handwriting recognition systems require the use of specific
writing surfaces to extract positional data. In this paper we present a online
handwriting recognition system for word recognition which is based on inertial
measurement units (IMUs) for digitizing text written on paper. This is obtained
by means of a sensor-equipped pen that provides acceleration, angular velocity,
and magnetic forces streamed via Bluetooth. Our model combines convolutional
and bidirectional LSTM networks, and is trained with the Connectionist Temporal
Classification loss that allows the interpretation of raw sensor data into
words without the need of sequence segmentation. We use a dataset of words
collected using multiple sensor-enhanced pens and evaluate our model on
distinct test sets of seen and unseen words achieving a character error rate of
17.97% and 17.08%, respectively, without the use of a dictionary or language
model


Abstract:
  In this paper, the author studies the boundedness for a large class of
sublinear operator $T_\alpha, \alpha\in[0,n)$ generated by Calder{\'o}n-Zygmund
operators ($\alpha=0$) and generated by fractional integral operator
($\alpha>0$) on generalized mixed Morrey spaces $M^\varphi_{\vec{q}}(\Bbb
R^n)$. Moreover, the boundeness for the commutators of $T_\alpha,
\alpha\in[0,n)$ on generalized mixed Morrey spaces $M^\varphi_{\vec{q}}(\Bbb
R^n)$ is also studied. As applications, we obtain the boundedness for
Hardy-Littlewood maximal operator, Calder\'on-Zygmund singular integral
operators, fractional integral operator, fractional maximal operator and their
commutators on generalzied mixed Morrey spaces.


Abstract:
  The purpose of this paper is to show three general formulas of three global
characteristic coefficients of Collatz function. The Collatz function is
defined by the following operation on an arbitrary positive integer if N is odd
multiply it by 3 and add 1 then the sum obtained is divided by 2, if N is even
divide it by 2. Based on the principle, we define the n-order function denoted
by T^n such as the different expressions of that function are results of
applying Collatz function n times to a natural numbers which are expressed in
well determined forms. Based on these expressions, we can characterize that
n-order function by three global characteristic coefficients. In the first, we
define these three global coefficients. Secondly, we show that each global
characteristic coefficient has a general expression as a function of n.


Abstract:
  The spin structure function of the neutron is traditionally determined by
measuring the spin asymmetry of inclusive electron deep inelastic scattering
(DIS) off polarized3He nuclei. In such experiments, nuclear effects can lead to
large model dependencies in the interpretation of experimental data. Here we
study the feasibility of suppressing such model dependencies by tagging both
spectator protons in the process of DIS off neutrons in3He at the forthcoming
Electron-Ion Collider (EIC). This allows reconstructing the momentum of the
struck neutron to ensure it was nearly at rest in the initial state, thereby
reducing sensitivity to nuclear corrections, and suppress contributions from
electron DIS off protonsin3He. Using realistic accelerator and detector
configurations, we find that the EIC can probe the neutron spin structure from
xB of 0.003 to 0.651. We further find that the double spectator tagging method
results in reduced uncertainties bya factor of 4 on the extracted neutron spin
asymmetries over all kinematics, and by a factor of 10 in the low-xB
region,thereby providing valuable insight to the spin and flavor structure of
nucleons


Abstract:
  Order dependencies (ODs) capture relationships between ordered domains of
attributes. Approximate ODs (AODs) capture such relationships even when there
exist exceptions in the data. During automated discovery of ODs, validation is
the process of verifying whether an OD holds. We present an algorithm for
validating approximate ODs with significantly improved runtime performance over
existing methods for AODs, and prove that it is correct and has optimal
runtime. By replacing the validation step in a leading algorithm for
approximate OD discovery with ours, we achieve orders-of-magnitude improvements
in performance.


Abstract:
  We consider the discrete defocusing nonlinear Schr\"odinger equation in its
integrable version, which is called defocusing Ablowitz-Ladik lattice. We
consider periodic boundary conditions with period $N$ and initial data sample
according to the Generalized Gibbs ensemble. In this setting, the Lax matrix of
the Ablowitz-Ladik lattice is a random CMV-periodic matrix and it is related to
the Killip-Nenciu Circular $\beta$-ensemble at high-temperature. We obtain the
generalized free energy of the Ablowitz-Ladik lattice and the density of states
of the random Lax matrix by establishing a mapping to the one-dimensional
log-gas. For the Gibbs measure related to the Hamiltonian of the Ablowitz-Ladik
flow, we obtain the density of states via a particular solution of the
double-confluent Heun equation.


Abstract:
  We characterize absorption-time distributions for birth-death Markov chains
with an absorbing boundary state. Based on generic features of the transition
rates, the asymptotic distribution for such "extinction-prone" chains is either
Gaussian, Gumbel, or a convolution of Gumbel distributions. We show that
several birth-death models of evolutionary dynamics, epidemiology, and chemical
reactions fall into these classes, establishing new results for the
absorption-time distribution in each case. Possible connections to African
sleeping sickness are discussed.


Abstract:
  The emergent universe scenario is a proposal for resolving the Big Bang
singularity problem in the standard Friedmann-Lemaitre-Robertson-Walker
cosmology. In the context of this scenario, the Universe originates from a
nonsingular static state. In the present work, considering the realization of
the emergent universe scenario, we address the possibility of having a
nonsingular Kantowski-Sachs type static state. Considering four and five
dimensional models (with and without brane), it is shown that both the
existence and stability of a nonsingular state depend on the dimensions of the
spacetime and the nature of the fluid supporting the geometry.


Abstract:
  In this article, we summarize two agnostic approaches in the framework of
spatially curved Friedmann-Robertson-Walker (FRW) cosmologies discussed in
detail in (Kerachian et al., 2020, 2019). The first case concerns the dynamics
of a fluid with an unspecified barotropic equation of state (EoS), for which
the only assumption made is the non-negativity of the fluid's energy density.
The second case concerns the dynamics of a non-minimally coupled real scalar
field with unspecified positive potential. For each of these models, we define
a new set of dimensionless variables and a new evolution parameter. In the
framework of these agnostic setups, we are able to identify several general
features, like symmetries, invariant subsets and critical points, and provide
their cosmological interpretation.


Abstract:
  Background: The Pauli exclusion principle plays a crucial role as a building
block of many-body quantal systems comprised of fermions. It also induces a
"Pauli repulsion" in the interaction between di-nuclear systems. It has been
shown in [Phys. Rev. C 95, 031601 (2017)] that the Pauli repulsion widens the
nucleus-nucleus potential barrier, thus hindering sub-barrier fusion. Purpose:
To investigate the proton and neutron contributions to the Pauli repulsion,
both in the bare potential neglecting shape polarization and transfer between
the reactants, as well as in the dynamical potential obtained by accounting for
such dynamical rearrangements. Methods: As the basis of our study we utilize
the Pauli kinetic energy (PKE) obtained by studying the nuclear localization
function (NLF). Recently this approach has been generalized to incorporate all
of the dynamical and time-odd terms present in the nuclear energy density
functional. This approach is employed in the DCFHF and DC-TDHF methods.
Results: The PKE spatial distribution shows that a repulsion occurs in the neck
between the nuclei when they first touch. Inside the barrier, neutrons can
contribute significantly more to the Pauli repulsion in neutron-rich systems.
Dynamical effects tend to lower the Pauli repulsion near the barrier. Proton
and neutron dynamical contributions to the PKE significantly differ inside the
barrier for asymmetric collisions, which is interpreted as an effect of
multinucleon transfer. Conclusions: The PKE is shown to make a significant
contribution to nuclear interaction potentials. Protons and neutrons can play
very different roles in both the bare potential and in the dynamical
rearrangement. Further microscopic studies are required to better understand
the role of transfer and to investigate the effect of pairing and deformation.


Abstract:
  We study the early kinetic decoupling effect in a pseudo-Nambu-Goldstone
(pNG) dark matter (DM) model. The pNG DM scattering processes with particles in
the thermal bath in the early Universe are suppressed by the small momentum
transfer. As a result, kinetic equilibrium is not maintained, and the
temperature of DM is different from the temperature of the thermal bath at the
freeze-out era. This temperature difference affects the thermal relic abundance
of DM. We investigate the early kinetic decoupling in the Higgs resonance
region, 50 GeV $\lesssim m_\chi \lesssim m_h/2$, where $m_\chi$ is the mass of
the DM, and $m_h/2 \simeq$ 62.5 GeV. We find that the DM-Higgs coupling
determined to obtain the measured value of the DM energy density is
underestimated in the literature. The enhancement in the coupling leads larger
value of the Higgs invisible decay rate. It enlarges the capability to discover
the DM signals from the decay of the Higgs bosons at collider experiments.


Abstract:
  Semantic segmentation aims to robustly predict coherent class labels for
entire regions of an image. It is a scene understanding task that powers
real-world applications (e.g., autonomous navigation). One important
application, the use of imagery for automated semantic understanding of
pedestrian environments, provides remote mapping of accessibility features in
street environments. This application (and others like it) require detailed
geometric information of geographical objects. Semantic segmentation is a
prerequisite for this task since it maps contiguous regions of the same class
as single entities. Importantly, semantic segmentation uses like ours are not
pixel-wise outcomes; however, most of their quantitative evaluation metrics
(e.g., mean Intersection Over Union) are based on pixel-wise similarities to a
ground-truth, which fails to emphasize over- and under-segmentation properties
of a segmentation model. Here, we introduce a new metric to assess region-based
over- and under-segmentation. We analyze and compare it to other metrics,
demonstrating that the use of our metric lends greater explainability to
semantic segmentation model performance in real-world applications.


Abstract:
  Generative Adversarial Networks (GANs) have swiftly evolved to imitate
increasingly complex image distributions. However, majority of the developments
focus on performance of GANs on balanced datasets. We find that the existing
GANs and their training regimes which work well on balanced datasets fail to be
effective in case of imbalanced (i.e. long-tailed) datasets. In this work we
introduce a novel theoretically motivated Class Balancing regularizer for
training GANs. Our regularizer makes use of the knowledge from a pre-trained
classifier to ensure balanced learning of all the classes in the dataset. This
is achieved via modelling the effective class frequency based on the
exponential forgetting observed in neural networks and encouraging the GAN to
focus on underrepresented classes. We demonstrate the utility of our
regularizer in learning representations for long-tailed distributions via
achieving better performance than existing approaches over multiple datasets.
Specifically, when applied to an unconditional GAN, it improves the FID from
$13.03$ to $9.01$ on the long-tailed iNaturalist-$2019$ dataset.


Abstract:
  Building on H. Tran's study of Loewner hulls generated by complex-valued
driving functions, which showed the existence of a phase transition, we answer
the question of whether the phase transition for complex-driven hulls matches
the phase transition for real-driven hulls. This is accomplished through a
detailed study of the Loewner hulls generated by driving functions
$c\sqrt{1-t}$ and $c\sqrt{\tau + t}$ for $c \in \mathbb{C}$ and $\tau \geq 0$.
This family also provides examples of new geometric behavior that is possible
for complex-driven hulls but prohibited for real-driven hulls.


Abstract:
  This paper presents NEUROSPF, a tool for the symbolic analysis of neural
networks. Given a trained neural network model, the tool extracts the
architecture and model parameters and translates them into a Java
representation that is amenable for analysis using the Symbolic PathFinder
symbolic execution tool. Notably, NEUROSPF encodes specialized peer classes for
parsing the model's parameters, thereby enabling efficient analysis. With
NEUROSPF the user has the flexibility to specify either the inputs or the
network internal parameters as symbolic, promoting the application of program
analysis and testing approaches from software engineering to the field of
machine learning. For instance, NEUROSPF can be used for coverage-based testing
and test generation, finding adversarial examples and also constraint-based
repair of neural networks, thus improving the reliability of neural networks
and of the applications that use them. Video URL: https://youtu.be/seal8fG78LI


Abstract:
  Processing and analyzing time series data\-sets have become a central issue
in many domains requiring data management systems to support time series as a
native data type. A crucial prerequisite of these systems is time series
matching, which still is a challenging problem. A time series is a
high-dimensional data type, its representation is storage-, and its comparison
is time-consuming. Among the representation techniques that tackle these
challenges, the symbolic aggregate approximation (SAX) is the current state of
the art. This technique reduces a time series to a low-dimensional space by
segmenting it and discretizing each segment into a small symbolic alphabet.
However, SAX ignores the deterministic behavior of time series such as cyclical
repeating patterns or trend component affecting all segments and leading to a
distortion of the symbolic distribution. In this paper, we present a season-
and a trend-aware symbolic approximation. We show that this improves the
symbolic distribution and increase the representation accuracy without
increasing its memory footprint. Most importantly, this enables a more
efficient time series matching by providing a match up to three orders of
magnitude faster than SAX.


Abstract:
  The achievement of all-fibre functional nano-modules for subcellular
label-free measurement has long been pursued due to the limitations of
manufacturing techniques. In this paper, a compact all-fibre label-free
nano-sensor composed of a fibre taper and zinc oxide nano-gratings is designed
and applied for the early monitoring of apoptosis in single living cells.
Because of its nanoscale dimensions, mechanical flexibility and minimal
cytotoxicity to cells, the sensing module can be loaded in cells for long-term
in situ tracking with high sensitivity. A gradual increase in the nuclear
refractive index during the apoptosis process is observed, revealing the
increase in molecular density and the decrease in cell volume. The strategy
used in this study not only contributes to the understanding of internal
environmental variations during cellular apoptosis but also provides a new
platform for non-fluorescent all-fibre devices to investigate cellular events
and to promote new progress in fundamental cell biochemical engineering.


Abstract:
  A radial velocity study by Donati et al. (2016) reported the detection of a
close-in giant planet in a 4.93 d orbit around the ~2 Myr old weak-lined T
Tauri star V830 Tau. Because of the stringent timescale constraints that a very
young host star like V830 Tau would place on hot Jupiter formation models and
inward migration mechanisms, independent confirmation of the planet's existence
is needed but so far has not been obtained. We present new Chandra X-ray
observations of V830 Tau. The Chandra observations in combination with previous
XMM-Newton observations reveal strong variable X-ray emission with an X-ray
luminosity spanning the range log Lx = 30.10 - 30.87 ergs/s. Chandra High
Energy Transmission Grating (HETG) spectra show emission lines formed over a
range of plasma temperatures from ~4 MK (Ne IX) to ~16 MK (S XV). At the
separation of the reported planet (0.057 au) the X-ray flux is ~10$^{6}$ -
10$^{7}$ times greater than the Sun's X-ray flux at Jupiter. We provide
estimates of the X-ray ionization and atmospheric heating rates at the planet's
separation and identify areas of uncertainty that will need to be addressed in
any future atmospheric models.


Abstract:
  We study the Gluino-Glue operator in the context of Supersymmetric ${\cal
N}{=}1$ Yang-Mills (SYM) theory. This composite operator is gauge invariant,
and it is directly connected to light bound states of the theory; its
renormalization is very important as a necessary step for the study of
low-lying bound states via numerical simulations. We make use of a
Gauge-Invariant Renormalization Scheme (GIRS). This requires the calculation of
the Green's function of a product of two Gluino-Glue operators, situated at
distinct space-time points. Within this scheme, the mixing with non-gauge
invariant operators which have the same quantum numbers is inconsequential. We
compute the one-loop conversion factor relating the GIRS scheme to
$\overline{\rm MS}$. This conversion factor can be used in order to convert to
$\overline{\rm MS}$ Green's functions which are obtained via lattice
simulations and are renormalized nonperturbatively in GIRS.


Abstract:
  We provide a lowerbound on the sample complexity of distribution-free parity
learning in the realizable case in the shuffle model of differential privacy.
Namely, we show that the sample complexity of learning $d$-bit parity functions
is $\Omega(2^{d/2})$. Our result extends a recent similar lowerbound on the
sample complexity of private agnostic learning of parity functions in the
shuffle model by Cheu and Ullman. We also sketch a simple shuffle model
protocol demonstrating that our results are tight up to $poly(d)$ factors.


Abstract:
  Although the security benefits of domain name encryption technologies such as
DNS over TLS (DoT), DNS over HTTPS (DoH), and Encrypted Client Hello (ECH) are
clear, their positive impact on user privacy is weakened by--the still
exposed--IP address information. However, content delivery networks, DNS-based
load balancing, co-hosting of different websites on the same server, and IP
address churn, all contribute towards making domain-IP mappings unstable, and
prevent straightforward IP-based browsing tracking.
  In this paper, we show that this instability is not a roadblock (assuming a
universal DoT/DoH and ECH deployment), by introducing an IP-based website
fingerprinting technique that allows a network-level observer to identify at
scale the website a user visits. Our technique exploits the complex structure
of most websites, which load resources from several domains besides their
primary one. Using the generated fingerprints of more than 200K websites
studied, we could successfully identify 84% of them when observing solely
destination IP addresses. The accuracy rate increases to 92% for popular
websites, and 95% for popular and sensitive websites. We also evaluated the
robustness of the generated fingerprints over time, and demonstrate that they
are still effective at successfully identifying about 70% of the tested
websites after two months. We conclude by discussing strategies for website
owners and hosting providers towards hindering IP-based website fingerprinting
and maximizing the privacy benefits offered by DoT/DoH and ECH.


Abstract:
  Online learning methods, like the online gradient algorithm (OGA) and
exponentially weighted aggregation (EWA), often depend on tuning parameters
that are difficult to set in practice. We consider an online meta-learning
scenario, and we propose a meta-strategy to learn these parameters from past
tasks. Our strategy is based on the minimization of a regret bound. It allows
to learn the initialization and the step size in OGA with guarantees. It also
allows to learn the prior or the learning rate in EWA. We provide a regret
analysis of the strategy. It allows to identify settings where meta-learning
indeed improves on learning each task in isolation.


Abstract:
  We examine a real electroweak triplet scalar field as dark matter, abandoning
the requirement that its relic abundance is determined through freeze out in a
standard cosmological history (a situation which we refer to as a 'miracle-less
WIMP'). We extract the bounds on such a particle from collider searches,
searches for direct scattering with terrestrial targets, and searches for the
indirect products of annihilation. Each type of search provides complementary
information, and each is most effective in a different region of parameter
space. LHC searches tend to be highly dependent on the mass of the SU(2)
charged partner state, and are effective for very large or very tiny mass
splitting between it and the neutral dark matter component. Direct searches are
very effective at bounding the Higgs portal coupling, but ineffective once it
falls below $\lambda_{\text{eff}} \lesssim 10^{-3}$. Indirect searches suffer
from large astrophysical uncertainties due to the backgrounds and $J$-factors,
but do provide key information for $\sim$ 100 GeV to TeV masses. Synthesizing
the allowed parameter space, this example of WIMP dark matter remains viable,
but only in miracle-less regimes.


Abstract:
  With technological advancements and the exponential growth of data, we have
been unfolding different capabilities of neural networks in different sectors.
In this paper, I have tried to use a specific type of Neural Network known as
Convolutional Neural Network(CNN/ConvNet) in the stock market. In other words,
I have tried to construct and train a convolutional neural network on past
stock prices data and then tried to predict the movement of stock price i.e.
whether the stock price would rise or fall, in the coming time.


Abstract:
  In the present paper, we investigate the regularity and symmetry properties
of weak solutions to semilinear elliptic equations which are locally stable.


Abstract:
  It is common to assume that all narrow absorption lines (NALs) at extreme
high-velocity shifts form in cosmologically intervening gas or galaxies
unrelated to quasars. However, previous detailed studies of individual quasars
have shown that some NALs at these large velocity shifts do form in high-speed
quasar ejecta. We search for extreme high-velocity NAL outflows (with speeds
$\sim$0.1-0.2c) based on relationships with associated absorption lines (AALs)
and broad absorption-line (BAL) outflows. We find that high-velocity NALs are
strongly correlated with AALs, BALs, and radio loudness, indicating that a
significant fraction of high-velocity systems are either ejected from the
quasars or form in material swept up by the radio jets (and are not unrelated
intervening gas). We also consider line-locked C IV doublets as another
indicator of high-velocity NALs formed in outflows. The fact that line-locked
NALs are highly ionized and correlated with BAL outflows and radio-loud quasars
implies that physical line locking due to radiative forces is both common and
real, which provides indirect evidence that a significant fraction of
high-velocity NALs are intrinsic to quasars.


Abstract:
  The uncanny ability of multiple particles to interfere with one another is
one of the core principles of quantum mechanics, and serves as foundation for
quantum information processing. In particular, the interplay of constructive
and destructive interference with the characteristic exchange statistics of
indistinguishable particles give rise to the Hong-Ou-Mandel (HOM) effect, where
the bunching of bosons can lead to a perfect suppression of two-particle
coincidences between the output ports of a balanced beam splitter. Conversely,
in the case of two fermions, anti-bunching can systematically enhance these
coincidences up to twice the baseline value of distinguishable particles. As
such, the respective emergence of dips or peaks in the HOM experiment may at
first glance appear to be indicative of the bosonic/fermionic nature of the
incident particles. In this work, we demonstrate experimentally that the
two-particle coincidence statistics of two bosons can instead be seamlessly
tuned from the expected case of suppression to substantial enhancement by an
appropriate choice of the observation basis. To this end, our photonic setting
leverages birefringent polarization couplers to selectively introduce
dissipation in the photons polarization degree of freedom. Notably, the
mechanism underpinning this this highly unusual behaviour does not act on the
individual phases accumulated by pairs of particles along specific paths, but
instead allows them to jointly evade losses, while indistinguishable photons
are prevented from being simultaneously detected in orthogonal modes. Our
findings reveal a new approach to harnessing non-Hermitian settings for the
manipulation of multi-particle quantum states and as functional elements in
quantum simulation and information processing.


Abstract:
  We prove localization with high probability on sets of size of order $N/\log
N$ for the eigenvectors of non-Hermitian finitely banded $N\times N$ Toeplitz
matrices $P_N$ subject to small random perturbations, in a very general
setting. As perturbation we consider $N\times N$ random matrices with
independent entries of zero mean, finite moments, and which satisfy an
appropriate anti-concentration bound. We show via a Grushin problem that an
eigenvector for a given eigenvalue $z$ is well approximated by a random linear
combination of the singular vectors of $P_N-z$ corresponding to its small
singular values. We prove precise probabilistic bounds on the local
distribution of the eigenvalues of the perturbed matrix and provide a detailed
analysis of the singular vectors to conclude the localization result.


Abstract:
  Let $X$ be a smooth compact connected manifold. Let $G=\mbox{Diff}\, X$ be
the group of diffeomorphisms of $X$, equipped with the $C^\infty$-topology, and
let $H$ be the stabilizer of some point in $X$. Then the inclusion $H\to G$,
which is a morphism of two regular Fr\'echet--Lie groups, is an epimorphism in
the category of smooth Lie groups modelled on complete locally convex spaces.
At the same time, in the latter category, epimorphisms between finite
dimensional Lie groups have dense range. We also prove that if $G$ is a
Banach--Lie group and $H$ is a proper closed subgroup, the inclusion $H\to G$
is not an epimorphism in the category of Hausdorff groups.


Abstract:
  The theory of constructing instantaneous equilibrium (ieq) transition under
arbitrary time-dependent temperature and potential variation for a Brownian
particle is developed. It is shown that it is essential to consider the
underdamped dynamics for temperature-changing transitions. The ieq is
maintained by a time-dependent auxiliary position and momentum potential, which
can be calculated for given time-dependent transition protocols. Explicit
analytic results are derived for the work and heat statistics, energy, and
entropy changes for harmonic and non-harmonic trapping potential with arbitrary
time-dependent potential parameters and temperature protocols. Numerical
solutions of the corresponding Langevin dynamics are computed to confirm the
theoretical results. Although ieq transition of the reverse process is not the
time-reversal of the ieq transition of the forward process due to the
odd-parity of controlling parameters, their phase-space distribution functions
restore the time-reversal symmetry, and hence the energy and entropy changes of
the ieq of the reverse process are simply the negative of that of the forward
process. Furthermore, it is shown that it is possible to construct an ieq
transition that has zero entropy production at a finite transition rate, i.e.,
a fast ieq isentropic process, and is further demonstrated by explicit Langevin
dynamics simulations. Our theory provides fundamental building blocks for
designing controlled microscopic heat engine cycles. Implications for
constructing an efficient Brownian heat engine are also discussed.


Abstract:
  We prove that the G\"{a}rtner--Ellis generating function of probability
distributions associated with KMS states of weakly interacting fermions on the
lattice can be written as the limit of logarithms of Gaussian Berezin
integrals. The covariances of the Gaussian integrals are shown to have a
uniform Pfaffian bound and to be summable in general cases of interest,
including systems that are not translation invariant. The Berezin integral
representation can thus be used to obtain convergent expansions of the
generating function in terms of powers of its parameter. The derivation and
analysis of the expansions of logarithms of Berezin integrals are the subject
of the second part of the present work. Such technical results are also useful,
for instance, in the context of quantum information theory, in the computation
of relative entropy densities associated with fermionic Gibbs states, and in
the theory of quantum normal fluctuations for weakly interacting fermion
systems.


Abstract:
  This study examined the problems of personal income tax on revenue generation
in Gombe state. The methodology used in data collection is survey, which
utilized both primary and secondary types of data. Purposive sampling technique
was adopted in selecting a sample of 150 respondents from both employees of
state board of internal revenue service and tax payers in the state. The chi
square statistics test was used in testing the hypotheses. The study found that
tax avoidance/evasion and complete absences of information technology are
serious problems affecting revenue generation in the state. It recommends that
government should device strict measures in dealing and punishing individuals
engage in tax avoidance and evasion. It should also employ the use of
information technology as it is the only way problems experience in personal
income tax collection can be reduced drastically.


Abstract:
  This paper introduces a novel geometric multigrid solver for unstructured
curved surfaces. Multigrid methods are highly efficient iterative methods for
solving systems of linear equations. Despite the success in solving problems
defined on structured domains, generalizing multigrid to unstructured curved
domains remains a challenging problem. The critical missing ingredient is a
prolongation operator to transfer functions across different multigrid levels.
We propose a novel method for computing the prolongation for triangulated
surfaces based on intrinsic geometry, enabling an efficient geometric multigrid
solver for curved surfaces. Our surface multigrid solver achieves better
convergence than existing multigrid methods. Compared to direct solvers, our
solver is orders of magnitude faster. We evaluate our method on many geometry
processing applications and a wide variety of complex shapes with and without
boundaries. By simply replacing the direct solver, we upgrade existing
algorithms to interactive frame rates, and shift the computational bottleneck
away from solving linear systems.


Abstract:
  We prove an analogue of Selberg's zero density estimate for $\zeta(s)$ that
holds for any $\mathrm{GL}_2$ $L$-function. We use this estimate to study the
distribution of the vector of fractional parts of $\gamma\mathbf{\alpha}$,
where $\mathbf{\alpha}\in\mathbb{R}^n$ is fixed and $\gamma$ varies over the
imaginary parts of the nontrivial zeros of a $\mathrm{GL}_2$ $L$-function.


Abstract:
  This article investigates the Mahler measure of a family of 2-variate
polynomials, denoted by $P_d, d\geq 1$, unbounded in both degree and genus. By
using a closed formula for the Mahler measure introduced in "Volume function
and Mahler measure of exact polynomials" (by Guilloux and March\'e), we are
able to compute $m(P_d)$, for arbitrary $d$, as a sum of the values of
dilogarithm at special roots of unity. We prove that $m(P_d)$ converges and the
limit is proportional to $\zeta(3)$, where $\zeta$ is the Riemann zeta
function.


Abstract:
  Lattice-Boltzmann methods are known for their simplicity, efficiency and ease
of parallelization, usually relying on uniform Cartesian meshes with a strong
bond between spatial and temporal discretization. This fact complicates the
crucial issue of reducing the computational cost and the memory impact by
automatically coarsening the grid where a fine mesh is unnecessary, still
ensuring the overall quality of the numerical solution through error control.
This work provides a possible answer to this interesting question, by
connecting, for the first time, the field of lattice-Boltzmann Methods (LBM) to
the adaptive multiresolution (MR) approach based on wavelets. To this end, we
employ a MR multi-scale transform to adapt the mesh as the solution evolves in
time according to its local regularity. The collision phase is not affected due
to its inherent local nature and because we do not modify the speed of the
sound, contrarily to most of the LBM/Adaptive Mesh Refinement (AMR) strategies
proposed in literature, thus preserving the original structure of any LBM
scheme. Besides, an original use of the MR allows the scheme to resolve the
proper physics by efficiently controlling the accuracy of the transport phase.
We carefully test our method to conclude on its adaptability to a wide family
of existing lattice Boltzmann schemes, treating both hyperbolic and parabolic
systems of equation, thus being less problem-dependent than the AMR approaches,
which have a hard time granting an effective control on the error. The ability
of the method to yield a very efficient compression rate and thus a
computational cost reduction for solutions involving localized structures with
loss of regularity is also shown, while guaranteeing a precise control on the
approximation error introduced by the spatial adaptation of the mesh. The
numerical strategy is implemented on a specific open-source platform called
SAMURAI with a dedicated data-structure relying on set algebra.


Abstract:
  The neuromorphic event cameras, which capture the optical changes of a scene,
have drawn increasing attention due to their high speed and low power
consumption. However, the event data are noisy, sparse, and nonuniform in the
spatial-temporal domain with an extremely high temporal resolution, making it
challenging to design backend algorithms for event-based vision. Existing
methods encode events into point-cloud-based or voxel-based representations,
but suffer from noise and/or information loss. Additionally, there is little
research that systematically studies how to handle static and dynamic scenes
with one universal design for event-based vision. This work proposes the
Aligned Event Tensor (AET) as a novel event data representation, and a neat
framework called Event Frame Net (EFN), which enables our model for event-based
vision under static and dynamic scenes. The proposed AET and EFN are evaluated
on various datasets, and proved to surpass existing state-of-the-art methods by
large margins. Our method is also efficient and achieves the fastest inference
speed among others.


Abstract:
  A major shift is happening within neurophysiology: a population doctrine is
drawing level with the single-neuron doctrine that has long dominated the
field. Population-level ideas have so far had their greatest impact in motor
neuroscience, but they hold great promise for resolving open questions in
cognition as well. Here, we codify the population doctrine and survey recent
work that leverages this view to specifically probe cognition. Our discussion
is organized around five core concepts that provide a foundation for
population-level thinking: (1) state spaces, (2) manifolds, (3) coding
dimensions, (4) subspaces, and (5) dynamics. The work we review illustrates the
progress and promise that population neurophysiology holds for cognitive
neuroscience$-$for delivering new insight into attention, working memory,
decision-making, executive function, learning, and reward processing.


Abstract:
  Dependency networks (Heckerman et al., 2000) are potential probabilistic
graphical models for systems comprising a large number of variables. Like
Bayesian networks, the structure of a dependency network is represented by a
directed graph, and each node has a conditional probability table. Learning and
inference are realized locally on individual nodes; therefore, computation
remains tractable even with a large number of variables. However, the
dependency network's learned distribution is the stationary distribution of a
Markov chain called pseudo-Gibbs sampling and has no closed-form expressions.
This technical disadvantage has impeded the development of dependency networks.
In this paper, we consider a certain manifold for each node. Then, we can
interpret pseudo-Gibbs sampling as iterative m-projections onto these
manifolds. This interpretation provides a theoretical bound for the location
where the stationary distribution of pseudo-Gibbs sampling exists in
distribution space. Furthermore, this interpretation involves structure and
parameter learning algorithms as optimization problems. In addition, we compare
dependency and Bayesian networks experimentally. The results demonstrate that
the dependency network and the Bayesian network have roughly the same
performance in terms of the accuracy of their learned distributions. The
results also show that the dependency network can learn much faster than the
Bayesian network.


Abstract:
  We study the fair division problem of allocating multiple resources among a
set of agents with Leontief preferences that are each required to complete a
finite amount of work, which we term "limited demands". We examine the behavior
of the classic Dominant Resource Fairness (DRF) mechanism in this setting and
show it is fair but only weakly Pareto optimal and inefficient in many natural
examples. We propose as an alternative the Least Cost Product (LCP) mechanism,
a natural adaptation of Maximum Nash Welfare to this setting. We characterize
the structure of allocations of the LCP mechanism in this setting, show that it
is Pareto efficient, and that it satisfies the relatively weak fairness
property of sharing incentives. While we prove it satisfies the stronger
fairness property of (expected) envy freeness in some special cases, we provide
a counterexample showing it does not do so in general, a striking contrast to
the "unreasonable fairness" of Maximum Nash Welfare in other settings.
Simulations suggest, however, that these violations of envy freeness are rare
in randomly generated examples.


Abstract:
  The Multi-speaker Multi-style Voice Cloning Challenge (M2VoC) aims to provide
a common sizable dataset as well as a fair testbed for the benchmarking of the
popular voice cloning task. Specifically, we formulate the challenge to adapt
an average TTS model to the stylistic target voice with limited data from
target speaker, evaluated by speaker identity and style similarity. The
challenge consists of two tracks, namely few-shot track and one-shot track,
where the participants are required to clone multiple target voices with 100
and 5 samples respectively. There are also two sub-tracks in each track. For
sub-track a, to fairly compare different strategies, the participants are
allowed to use only the training data provided by the organizer strictly. For
sub-track b, the participants are allowed to use any data publicly available.
In this paper, we present a detailed explanation on the tasks and data used in
the challenge, followed by a summary of submitted systems and evaluation
results.


Abstract:
  With the recent advances of open-domain story generation, the lack of
reliable automatic evaluation metrics becomes an increasingly imperative issue
that hinders the fast development of story generation. According to conducted
researches in this regard, learnable evaluation metrics have promised more
accurate assessments by having higher correlations with human judgments. A
critical bottleneck of obtaining a reliable learnable evaluation metric is the
lack of high-quality training data for classifiers to efficiently distinguish
plausible and implausible machine-generated stories. Previous works relied on
\textit{heuristically manipulated} plausible examples to mimic possible system
drawbacks such as repetition, contradiction, or irrelevant content in the text
level, which can be \textit{unnatural} and \textit{oversimplify} the
characteristics of implausible machine-generated stories. We propose to tackle
these issues by generating a more comprehensive set of implausible stories
using {\em plots}, which are structured representations of controllable factors
used to generate stories. Since these plots are compact and structured, it is
easier to manipulate them to generate text with targeted undesirable
properties, while at the same time maintain the grammatical correctness and
naturalness of the generated sentences. To improve the quality of generated
implausible stories, we further apply the adversarial filtering procedure
presented by \citet{zellers2018swag} to select a more nuanced set of
implausible texts. Experiments show that the evaluation metrics trained on our
generated data result in more reliable automatic assessments that correlate
remarkably better with human judgments compared to the baselines.


Abstract:
  Alzheimer's Disease (AD) is a severe brain disorder, destroying memories and
brain functions. AD causes chronically, progressively, and irreversibly
cognitive declination and brain damages. The reliable and effective evaluation
of early dementia has become essential research with medical imaging
technologies and computer-aided algorithms. This trend has moved to modern
Artificial Intelligence (AI) technologies motivated by deeplearning success in
image classification and natural language processing. The purpose of this
review is to provide an overview of the latest research involving deep-learning
algorithms in evaluating the process of dementia, diagnosing the early stage of
AD, and discussing an outlook for this research. This review introduces various
applications of modern AI algorithms in AD diagnosis, including Convolutional
Neural Network (CNN), Recurrent Neural Network (RNN), Automatic Image
Segmentation, Autoencoder, Graph CNN (GCN), Ensemble Learning, and Transfer
Learning. The advantages and disadvantages of the proposed methods and their
performance are discussed. The conclusion section summarizes the primary
contributions and medical imaging preprocessing techniques applied in the
reviewed research. Finally, we discuss the limitations and future outlooks.


Abstract:
  We present results of time-series data simulation. We aimed at estimating the
threshold used for detecting signals in amplitude spectra, calculated from
simulating TESS photometry of up to one year duration. We selected the
threshold at a false alarm probability FAP=0.1% and derived S/N ratios between
4.6 and 5.7 depending on the data cadence and coverage. We also provide a
formula to estimate the threshold for any FAP adopted and a given number of
data points. Our result confirms that, to avoid spurious detection, space-based
photometry may require substantially higher S/N than that typically being
employed for ground-based data.


Abstract:
  In this paper we introduce a new method simulating stent graft deployment for
assisting endovascular repair of abdominal aortic aneurysms. The method relies
on intraoperative images coupled with mechanical models. A multi-step algorithm
has been developed to increase the reliability of simulations. The first step
predicts the position of the stent graft within the aorta. The second step is
an axisymmetric geometric reconstruction of each individual stent. The third
step minimizes the rotation of each stent around its main axis. Finally, the
last step virtually deploys each stent within a deployment box extracted from
the preoperative CT scan. A proof of concept is performed on a patient. The
accuracy is compatible with the clinical threshold of 3 mm: the average
distance between target and simulated stents is 1.73 $\pm$ 0.37 mm.
Fenestrations of the stent-graft are reconstructed with a maximum error of less
than 2.5 mm, which is enables a secure catheterization of secondary arteries.
In summary, the method is able to assist EVAR practitioners by providing all
necessary information for a fast and accurate stent graft positioning,
combining intraoperative data and a mechanical model in a very low cost
framework.


Abstract:
  We study the cross-stitch flat band lattice with a $\mathcal{PT}$-symmetric
on-site potential and uncover mobility edges with exact solutions. Furthermore,
we study the relationship between the $\mathcal{PT}$ symmetry broken point and
the localization-delocalization transition point, and verify that mobility
edges in this non-Hermitian model is available to signal the $\mathcal{PT}$
symmetry breaking.


Abstract:
  In cerebrovascular networks, some vertices are more connected to each other
than with the rest of the vasculature, defining a community structure. Here, we
introduce a class of model networks built by rewiring Random Regular Graphs,
which enables to reproduce this community structure and other topological
properties of cerebrovascular networks. We use these model networks to study
the global flow reduction induced by the removal of a single edge. We
analytically show that this global flow reduction can be expressed as a
function of the initial flow rate in the removed edge and of a topological
quantity, both of which display probability distributions following Cauchy
laws, i.e. with large tails. As a result, we show that the distribution of
blood flow reductions is strongly influenced by the community structure. In
particular, the probability of large flow reductions increases substantially
when the community structure is stronger, weakening the network resilience to
single capillary occlusions. We discuss the implications of these findings in
the context of Alzheimer's Disease, in which the importance of vascular
mechanisms, including capillary occlusions, is beginning to be uncovered.


Abstract:
  This note concerns Loomis-Whitney inequalities in Heisenberg groups
$\mathbb{H}^n$: $$|K| \lesssim
\prod_{j=1}^{2n}|\pi_j(K)|^{\frac{n+1}{n(2n+1)}}, \qquad K \subset
\mathbb{H}^n.$$ Here $\pi_{j}$, $j=1,\ldots,2n$, are the vertical Heisenberg
projections to the hyperplanes $\{x_j=0\}$, respectively, and $|\cdot|$ refers
to a natural Haar measure on either $\mathbb{H}^n$, or one of the hyperplanes.
The Loomis-Whitney inequality in the first Heisenberg group $\mathbb{H}^1$ is a
direct consequence of known $L^p$ improving properties of the standard Radon
transform in $\mathbb{R}^2$. In this note, we show how the Loomis-Whitney
inequalities in higher dimensional Heisenberg groups can be deduced by an
elementary inductive argument from the inequality in $\mathbb{H}^1$. The same
approach, combined with multilinear interpolation, also yields the following
strong type bound: $$\int_{\mathbb{H}^n} \prod_{j=1}^{2n}
f_j(\pi_j(p))\;dp\lesssim \prod_{j=1}^{2n} \|f_j\|_{\frac{n(2n+1)}{n+1}}$$ for
all nonnegative measurable functions $f_1,\ldots,f_{2n}$ on $\mathbb{R}^{2n}$.
These inequalities and their geometric corollaries are thus ultimately based on
planar geometry. Among the applications of Loomis-Whitney inequalities in
$\mathbb{H}^n$, we mention the following sharper version of the classical
geometric Sobolev inequality in $\mathbb{H}^n$: $$\|u\|_{\frac{2n+2}{2n+1}}
\lesssim \prod_{j=1}^{2n}\|X_ju\|^{\frac{1}{2n}}, \qquad u \in
BV(\mathbb{H}^n),$$ where $X_j$, $j=1,\ldots,2n$, are the standard horizontal
vector fields in $\mathbb{H}^n$.


Abstract:
  We study the classical weighted perfect matchings problem for bipartite
graphs or sometimes referred to as the assignment problem, i.e., given a
weighted bipartite graph $G = (U\cup V,E)$ with weights $w : E \rightarrow
\mathcal{R}$ we are interested to find the maximum matching in $G$ with the
minimum/maximum weight. In this work we present a new and arguably simpler
analysis of one of the earliest techniques developed for solving the assignment
problem, namely the auction algorithm. Using our analysis technique we present
tighter and improved bounds on the runtime complexity for finding an
approximate minumum weight perfect matching in $k$-left regular sparse
bipartite graphs.


Abstract:
  There has been a growing interest in model-agnostic methods that can make
deep learning models more transparent and explainable to a user. Some
researchers recently argued that for a machine to achieve a certain degree of
human-level explainability, this machine needs to provide human causally
understandable explanations, also known as causability. A specific class of
algorithms that have the potential to provide causability are counterfactuals.
This paper presents an in-depth systematic review of the diverse existing body
of literature on counterfactuals and causability for explainable artificial
intelligence. We performed an LDA topic modelling analysis under a PRISMA
framework to find the most relevant literature articles. This analysis resulted
in a novel taxonomy that considers the grounding theories of the surveyed
algorithms, together with their underlying properties and applications in
real-world data. This research suggests that current model-agnostic
counterfactual algorithms for explainable AI are not grounded on a causal
theoretical formalism and, consequently, cannot promote causability to a human
decision-maker. Our findings suggest that the explanations derived from major
algorithms in the literature provide spurious correlations rather than
cause/effects relationships, leading to sub-optimal, erroneous or even biased
explanations. This paper also advances the literature with new directions and
challenges on promoting causability in model-agnostic approaches for
explainable artificial intelligence.


Abstract:
  We revisit large spectroscopic data sets for field stars from the literature
to derive the upper Li envelope in the high metallicity regime in our Galaxy.
We take advantage of Gaia EDR3 data and state-of-the-art stellar models to
precisely determine the position of the sample dwarf stars in the
Hertzsprung-Russell diagram. The highest Li abundances are found in field
metal-rich warm dwarfs from the GALAH survey, located on the hot side of the
Li-dip. Their mean Li value agrees with what was recently derived for warm
dwarfs in metal-rich clusters, pointing towards a continuous increase of Li up
to super-solar metallicity. However, if only cool dwarfs are considered in
GALAH, as done in the other literature surveys, it is found that the upper Li
envelope decreases at super-solar metallicities, blurring the actual Li
evolution picture. We confirm the suggestion that field and open cluster
surveys that found opposite Li behaviour in the high metallicity regime do not
sample the same types of stars: The first ones, with the exception of GALAH,
miss warm dwarfs that can potentially preserve their original Li content.
Although we can discard the bending of the Li upper envelope at high
metallicity derived from the analysis of cool star samples, we still need to
evaluate the effects of atomic diffusion on warm, metal-rich early-F and late-A
type dwarfs before deriving the actual Li abundance at high metallicity.


Abstract:
  Multi-modal Probabilistic Active Sensing (MMPAS) uses sensor fusion and
probabilistic models to control the perception process of robotic sensing
platforms. MMPAS is successfully employed in environmental exploration,
collaborative mobile robotics, and target tracking, being fostered by the high
performance guarantees on autonomous perception. In this context, we propose a
bi-Radio-Visual PAS scheme to solve the transmitter discovery problem.
Specifically, we firstly exploit the correlation between radio and visual
measurements to learn a target detection model in a self-supervised manner.
Then, the model is combined with antenna radiation anisotropies into a Bayesian
Optimization framework that controls the platform. We show that the proposed
algorithm attains an accuracy of 92%, overcoming two other probabilistic active
sensing baselines.


Abstract:
  Query optimizer is at the heart of the database systems. Cost-based optimizer
studied in this paper is adopted in almost all current database systems. A
cost-based optimizer introduces a plan enumeration algorithm to find a
(sub)plan, and then uses a cost model to obtain the cost of that plan, and
selects the plan with the lowest cost. In the cost model, cardinality, the
number of tuples through an operator, plays a crucial role. Due to the
inaccuracy in cardinality estimation, errors in cost model, and the huge plan
space, the optimizer cannot find the optimal execution plan for a complex query
in a reasonable time. In this paper, we first deeply study the causes behind
the limitations above. Next, we review the techniques used to improve the
quality of the three key components in the cost-based optimizer, cardinality
estimation, cost model, and plan enumeration. We also provide our insights on
the future directions for each of the above aspects.


Abstract:
  In this paper we study the boundary controllability for a system of two
coupled degenerate/singular parabolic equations with a control acting on only
one equation. We analyze both approximate and null boundary controllability
properties. Besides, we provide an estimate on the null-control cost. The
proofs are based on the use of the moment method by Fattorini and Russell
together with some results on biorthogonal families.


Abstract:
  In the non-negative matrix factorization (NMF) problem, the input is an
$m\times n$ matrix $M$ with non-negative entries and the goal is to factorize
it as $M\approx AW$. The $m\times k$ matrix $A$ and the $k\times n$ matrix $W$
are both constrained to have non-negative entries. This is in contrast to
singular value decomposition, where the matrices $A$ and $W$ can have negative
entries but must satisfy the orthogonality constraint: the columns of $A$ are
orthogonal and the rows of $W$ are also orthogonal. The orthogonal non-negative
matrix factorization (ONMF) problem imposes both the non-negativity and the
orthogonality constraints, and previous work showed that it leads to better
performances than NMF on many clustering tasks. We give the first
constant-factor approximation algorithm for ONMF when one or both of $A$ and
$W$ are subject to the orthogonality constraint. We also show an interesting
connection to the correlation clustering problem on bipartite graphs. Our
experiments on synthetic and real-world data show that our algorithm achieves
similar or smaller errors compared to previous ONMF algorithms while ensuring
perfect orthogonality (many previous algorithms do not satisfy the hard
orthogonality constraint).


Abstract:
  NLP Ghana is an open-source non-profit organization aiming to advance the
development and adoption of state-of-the-art NLP techniques and digital
language tools to Ghanaian languages and problems. In this paper, we first
present the motivation and necessity for the efforts of the organization; by
introducing some popular Ghanaian languages while presenting the state of NLP
in Ghana. We then present the NLP Ghana organization and outline its aims,
scope of work, some of the methods employed and contributions made thus far in
the NLP community in Ghana.


Abstract:
  Value-at-risk (VaR) is an established measure to assess risks in critical
real-world applications with random environmental factors. This paper presents
a novel VaR upper confidence bound (V-UCB) algorithm for maximizing the VaR of
a black-box objective function with the first no-regret guarantee. To realize
this, we first derive a confidence bound of VaR and then prove the existence of
values of the environmental random variable (to be selected to achieve no
regret) such that the confidence bound of VaR lies within that of the objective
function evaluated at such values. Our V-UCB algorithm empirically demonstrates
state-of-the-art performance in optimizing synthetic benchmark functions, a
portfolio optimization problem, and a simulated robot task.


Abstract:
  In the old spirit of Kaluza-Klein, we consider a spacetime of the form $P =
M_4 \times K$, where $K$ is the Lie group $\mathrm{SU}(3)$ equipped with a
left-invariant metric that is not fully right-invariant. We observe that a
complete generation of fermionic fields can be encoded in the 64 components of
a single spinor over the 12-dimensional spacetime. The behaviour of the
spinorial function along the internal space $K$ can be chosen so that, after
pairing and fibre-integration over $K$, the resulting Dirac kinetic terms in
four dimensions couple to the $\mathfrak{u}(1) \oplus \mathfrak{su}(2) \oplus
\mathfrak{su}(3)$ gauge fields in the exact chiral representations present in
the Standard Model. Although we describe the action of the internal Dirac
operator on the 12-dimensional spinor, the full calculation of the fermionic
mass terms produced by the model is longer and is not carried out here. We
calculate instead the action of the internal Laplace operator on the spinor
components.


Abstract:
  Let (X j , d j , $\mu$ j), j = 0, 1,. .. , m be metric measure spaces. Given
0 < p $\kappa$ $\le$ $\infty$ for $\kappa$ = 1,. .. , m and an analytic family
of multilinear operators T z : L p 1 (X 1) x $\bullet$ $\bullet$ $\bullet$ L p
m (X m) $\rightarrow$ L 1 loc (X 0), for z in the complex unit strip, we prove
a theorem in the spirit of Stein's complex interpolation for analytic families.
Analyticity and our admissibility condition are defined in the weak (integral)
sense and relax the pointwise definitions given in [9]. Continuous functions
with compact support are natural dense subspaces of Lebesgue spaces over metric
measure spaces and we assume the operators T z are initially defined on them.
Our main lemma concerns the approximation of continuous functions with compact
support by similar functions that depend analytically in an auxiliary parameter
z. An application of the main theorem concerning bilinear estimates for
Schr{\"o}dinger operators on L p is included.


Abstract:
  We propose a new framework for understanding and representing related salient
events in a video using visual semantic role labeling. We represent videos as a
set of related events, wherein each event consists of a verb and multiple
entities that fulfill various roles relevant to that event. To study the
challenging task of semantic role labeling in videos or VidSRL, we introduce
the VidSitu benchmark, a large-scale video understanding data source with $29K$
$10$-second movie clips richly annotated with a verb and semantic-roles every
$2$ seconds. Entities are co-referenced across events within a movie clip and
events are connected to each other via event-event relations. Clips in VidSitu
are drawn from a large collection of movies (${\sim}3K$) and have been chosen
to be both complex (${\sim}4.2$ unique verbs within a video) as well as diverse
(${\sim}200$ verbs have more than $100$ annotations each). We provide a
comprehensive analysis of the dataset in comparison to other publicly available
video understanding benchmarks, several illustrative baselines and evaluate a
range of standard video recognition models. Our code and dataset is available
at vidsitu.org.


Abstract:
  We compare the contribution of Active Galactic Nuclei (AGN) and
star-formation towards dust heating in sub-mm galaxies (SMGs). We have used
ALMA at $0."1$ resolution to image a complete flux-limited sample of seven
sub-mm sources previously shown to have spectral energy distributions (SEDs)
that were as well-fitted by obscured AGN as star-forming galaxy templates.
Indeed, two sub-mm sources were known to be quasars from their absorbed X-ray
emission. We find the sub-mm sizes of all SMGs to be small ($\approx1-2$kpc)
and generally $\sim3$ times smaller than any host detected in the
Near-Infra-Red (NIR). In all cases, the five SMGs are comparable in sub-mm size
to the two known quasars and four $z\approx6$ quasars, also observed with ALMA.
We detect no evidence of diffuse spiral arms in this complete sample. We then
convert the Far-Infra-Red (FIR) luminosities to star-formation rate (SFR)
surface densities and find that the SMGs occupy the same range as the known
quasars in our sample. We conclude that in terms of sub-mm size, extent
relative to host and SFR density as well as luminosity and Mid-IR (MIR) colour,
there is little distinction between the SMGs and sub-mm bright quasars.
Finally, we present preliminary evidence that SMGs with higher MIR luminosities
and sub-mm loud quasars tend to have dust components that range to hotter
temperatures than their less luminous SMG counterparts. In light of these
results, we continue to suggest that luminous SMGs may host dust-absorbed
quasars that may simultaneously dominate the FIR and hard X-ray backgrounds.


Abstract:
  The Proper Forcing Axiom implies that compact Hausdorff spaces are either
first-countable or contain a converging $\omega_1$-sequence.


Abstract:
  In recent years, the task of video prediction-forecasting future video given
past video frames-has attracted attention in the research community. In this
paper we propose a novel approach to this problem with Vector Quantized
Variational AutoEncoders (VQ-VAE). With VQ-VAE we compress high-resolution
videos into a hierarchical set of multi-scale discrete latent variables.
Compared to pixels, this compressed latent space has dramatically reduced
dimensionality, allowing us to apply scalable autoregressive generative models
to predict video. In contrast to previous work that has largely emphasized
highly constrained datasets, we focus on very diverse, large-scale datasets
such as Kinetics-600. We predict video at a higher resolution on unconstrained
videos, 256x256, than any other previous method to our knowledge. We further
validate our approach against prior work via a crowdsourced human evaluation.


Abstract:
  Recently pre-trained multimodal models, such as CLIP, have received a surge
of attention for their exceptional capabilities towards connecting images and
natural language. The textual representations in English can be desirably
transferred to multilingualism and support promising downstream multimodal
tasks for different languages. Nevertheless, previous fairness discourse in
vision-and-language learning mainly focuses on monolingual representational
biases, and rarely scrutinizes the principles of multilingual fairness in this
multimodal setting, where one language is equated to a group of individuals and
images provide the universal grounding for bridging different languages.
  In this paper, we provide a nuanced understanding of individual fairness and
group fairness by viewing language as the recipient of fairness notions. We
define new fairness notions within multilingual context and analytically
articulate that, pre-trained vision-and-language representations are
individually fair across languages but not guaranteed to group fairness.
Furthermore, we conduct extensive experiments to explore the prevalent group
disparity across languages and protected groups including race, gender and age.


Abstract:
  Knowledge graph embedding techniques are key to making knowledge graphs
amenable to the plethora of machine learning approaches based on vector
representations. Link prediction is often used as a proxy to evaluate the
quality of these embeddings. Given that the creation of benchmarks for link
prediction is a time-consuming endeavor, most work on the subject matter uses
only a few benchmarks. As benchmarks are crucial for the fair comparison of
algorithms, ensuring their quality is tantamount to providing a solid ground
for developing better solutions to link prediction and ipso facto embedding
knowledge graphs. First studies of benchmarks pointed to limitations pertaining
to information leaking from the development to the test fragments of some
benchmark datasets. We spotted a further common limitation of three of the
benchmarks commonly used for evaluating link prediction approaches:
out-of-vocabulary entities in the test and validation sets. We provide an
implementation of an approach for spotting and removing such entities and
provide corrected versions of the datasets WN18RR, FB15K-237, and YAGO3-10. Our
experiments on the corrected versions of WN18RR, FB15K-237, and YAGO3-10
suggest that the measured performance of state-of-the-art approaches is altered
significantly with p-values <1%, <1.4%, and <1%, respectively. Overall,
state-of-the-art approaches gain on average absolute $3.29 \pm 0.24\%$ in all
metrics on WN18RR. This means that some of the conclusions achieved in previous
works might need to be revisited. We provide an open-source implementation of
our experiments and corrected datasets at at
https://github.com/dice-group/OOV-In-Link-Prediction.


Abstract:
  Our paper explores the game theoretic value of the 7-in-a-row game. We reduce
the problem to solving a finite board game, which we target using Proof Number
Search. We present a number of heuristic improvements to Proof Number Search
and examine their effect within the context of this particular game. Although
our paper does not solve the 7-in-a-row game, our experiments indicate that we
have made significant progress towards it.


Abstract:
  Memory bandwidth is strongly correlated to the complexity of the memory
access pattern of a running application. To improve memory performance of
applications with irregular and/or unpredictable memory patterns, we need tools
to analyze these patterns during application development. In this work, we
present a software tool for the analysis and visualization of memory access
patterns. We perform memory tracing and profiling, we do data processing and
filtering, and we use visualization algorithms to produce three dimensional
graphs that describe the patterns both in space and in time. Finally, we
evaluate our toolflow on a variety of applications.


Abstract:
  We present MeerKAT neutral hydrogen (HI) observations of the Fornax A group,
that is likely falling into the Fornax cluster for the first time. Our HI image
is sensitive to 1.4 x 10$^{19}$ cm$^{-2}$ over 44.1 km s$^{-1}$, where we
detect HI in 10 galaxies and a total of 1.12 x 10$^{9}$ Msol of HI in the
intra-group medium (IGM). We search for signs of pre-processing in the 12 group
galaxies with confirmed optical redshifts that reside within our HI image.
There are 9 galaxies that show evidence of pre-processing and we classify the
pre-processing status of each galaxy, according to their HI morphology and gas
(atomic and molecular) scaling relations. Galaxies yet to experience
pre-processing have extended HI disks, a high HI content with a H$_2$-to-HI
ratio an order of magnitude lower than the median for their stellar mass.
Galaxies currently being pre-processed display HI tails, truncated HI disks
with typical gas ratios. Galaxies in the advanced stages of pre-processing are
HI deficient. If there is any HI, they have lost their outer HI disk and
efficiently converted their HI to H$_2$, resulting in H$_2$-to-HI ratios an
order of magnitude higher than the median for their stellar mass. The central,
massive galaxy in our group underwent a 10:1 merger 2 Gyr ago, and ejected 6.6
- 11.2 x 10$^{8}$ Msol of HI that we detect as clouds and streams in the IGM,
some forming coherent structures up to 220 kpc in length. We also detect giant
(100 kpc) ionised hydrogen (H$\alpha$) filaments in the IGM, likely from cool
gas being removed (and ionised) from an infalling satellite. The H$\alpha$
filaments are situated within the hot halo of NGC 1316 and some regions contain
HI. We speculate that the H$\alpha$ and multiphase gas is supported by magnetic
pressure (possibly assisted by the AGN), such that the hot gas can condense and
form HI that survives in the hot halo for cosmological timescales.


Abstract:
  Motivated by the possibility of observing the co-existence between magnetism
and unconventional superconductivity in heavy-fermion Ce$_{1-x}$Sm$_x$CoIn$_5$
alloys, we studied how the samarium substitution on the cerium site affects the
magnetic field-tuned-quantum criticality of stoicheometric CeCoIn$_5$ by
performing specific heat and resistivity measurements. By applying an external
magnetic field, we have observed Fermi-liquid to non-Fermi-liquid crossovers in
the temperature dependence of the electronic specific heat normalized by
temperature and of the resistivity. We obtained the magnetic-field-induced
quantum critical point (QCP) by extrapolating to zero temperature the
temperature - magnetic field dependence at which the crossovers take place.
Furthermore, a scaling analysis of the electronic specific heat is used to
confirm the existence of the QCP. We have found that the magnitude of the
magnetic-field-induced QCP decreases with increasing samarium concentration.
Our analysis of heat capacity and resistivity data reveals a zero-field QCP for
$x_\textrm{cr} \approx 0.15$, which falls inside the region where Sm ions
antiferromagnetism and superconductivity co-exist.


Abstract:
  Demixing of multicomponent biomolecular systems via liquid-liquid phase
separation (LLPS) has emerged as a potentially unifying mechanism governing the
formation of several membrane-less intracellular organelles ("condensates"),
both in the cytoplasm (e.g., stress granules) and in the nucleoplasm (e.g.,
nucleoli). While both in vivo experiments and studies of synthetic systems
demonstrate that LLPS is strongly affected by the presence of a macromolecular
elastic network, a fundamental understanding of the role of such networks on
LLPS is still lacking. Here we show that, upon accounting for capillary forces
responsible for network expulsion, small-scale heterogeneity of the network,
and its nonlinear mechanical properties, an intriguing picture of LLPS emerges.
Specifically, we predict that, in addition to the experimentally observed
cavitated droplets which fully exclude the network, two other phases are
thermodynamically possible: elastically arrested, size-limited droplets at the
network pore scale, and network-including macroscopic droplets. In particular,
pore size-limited droplets may emerge in chromatin networks, with implications
for structure and function of nucleoplasmic condensates.


Abstract:
  Label noise in multiclass classification is a major obstacle to the
deployment of learning systems. However, unlike the widely used
class-conditional noise (CCN) assumption that the noisy label is independent of
the input feature given the true label, label noise in real-world datasets can
be aleatory and heavily dependent on individual instances. In this work, we
investigate the instance-dependent noise (IDN) model and propose an efficient
approximation of IDN to capture the instance-specific label corruption.
Concretely, noting the fact that most columns of the IDN transition matrix have
only limited influence on the class-posterior estimation, we propose a
variational approximation that uses a single-scalar confidence parameter. To
cope with the situation where the mapping from the instance to its confidence
value could vary significantly for two adjacent instances, we suggest using
instance embedding that assigns a trainable parameter to each instance. The
resulting instance-confidence embedding (ICE) method not only performs well
under label noise but also can effectively detect ambiguous or mislabeled
instances. We validate its utility on various image and text classification
tasks.


Abstract:
  Since language processing systems generally allocate/discard memory with
complex reference relationships, including circular and indirect references,
their implementation is often not trivial. Here, the allocated memory and the
references can be abstracted to the labeled vertices and edges of a graph. And
there exists a graph rewriting language, a programming language or a
calculation model that can handle graph intuitively, safely and efficiently.
Therefore, the implementation of a language processing system can be highly
expected as an application field of graph rewriting language. To show this, in
this research, we implemented G-machine, the virtual machine for lazy
evaluation, in hypergraph rewriting language, HyperLMNtal.


Abstract:
  Multiple molecular lines are useful for studying the physical properties of
multiphase star-forming gas in different types of galaxies. We probe the
molecular gas throughout the disc of the spiral galaxy NGC~5033, hosting an
active galactic nucleus (AGN), using multiple low-$J$ CO lines [$^{12}$CO(1-0,
2-1, 3-2 and $^{13}$CO(1-0, 2-1)] and dense gas tracers [HCN(1-0) and
HCO$^{+}$(1-0)]. Firstly, we determine the ratios of the integrated intensity
maps and the ratio of intensities in position velocity diagrams. Secondly, we
obtain the ratios of CO lines and high-density tracers at the centre; and
thirdly, we model these line ratios using a radiative transfer code. Line ratio
diagnostics reveal that the south of the gaseous disc contains cooler gas than
the northern part, and the centre hosts warmer and less tenuous gas with a
similar dense gas fraction compared to most galaxies of similar type. Our model
results mostly agree with the empirical ones in the sense that the central
region of NGC~5033 harbours warmer gas than that in the centres of normal
spirals and lenticulars without showing AGN activity. Finally, the
beam-averaged total molecular gas mass and gas surface density along the
galaxy's major axis show a radial gradient, i.e. increasing from the outskirts
up to the central region of size $1$~kpc where there is a depression in both
gas mass and surface density.


Abstract:
  Electron spins are amongst the most coherent solid-state systems known,
however, to be used in devices for quantum sensing and information processing
applications, they must be typically placed near interfaces. Understanding and
mitigating the impacts of such interfaces on the coherence and spectral
properties of electron spins is critical to realize such applications, but is
also challenging: inferring such data from single-spin studies requires many
measurements to obtain meaningful results, while ensemble measurements
typically give averaged results that hide critical information. Here, we report
a comprehensive study of the coherence of near-surface bismuth donor spins in
28-silicon at millikelvin temperatures. In particular, we use strain-induced
frequency shifts caused by a metallic electrode to make spatial maps of spin
coherence as a function of depth and position relative to the electrode. By
measuring magnetic-field-insensitive clock transitions we separate magnetic
noise caused by surface spins from charge noise. Our results include
quantitative models of the strain-split spin resonance spectra and extraction
of paramagnetic impurity concentrations at the silicon surface. The interplay
of these decoherence mechanisms for such near-surface electron spins is
critical for their application in quantum technologies, while the combination
of the strain splitting and clock transition extends the coherence lifetimes by
up to two orders of magnitude, reaching up to 300 ms at a mean depth of only
100nm. The technique we introduce here to spatially map coherence in
near-surface ensembles is directly applicable to other spin systems of active
interest, such as defects in diamond, silicon carbide, and rare earth ions in
optical crystals.