title:
Emergent Phase, Thermodynamic Geometry and Criticality of Charged Black
  Holes from R\'enyi Statistics

abstract:
  Recently, a novel emergent phase can occur from thermodynamic consideration
of the asymptotically flat Reissner-Nordstr\"om black hole (RN-AF) using
R\'enyi statistics. We present an analysis of the thermodynamical and
mechanical stabilities of the RN-AF in both the Gibbs-Boltzmann (GB) and the
alternative R\'enyi statistics when charge $q$ and electrostatic potential
$\phi$ are treated as pressure and volume, respectively. Interestingly, the
emergent phase of the RN-AF can be both thermodynamically and mechanically
stable in some range of parameters in the framework of R\'enyi thermodynamics.
With the construction of the Maxwell equal area law in $q-\phi$ plane, the
coexistence line between the near-extremal black hole phase and the emergent
phase can be found in some values of charge which can be associated as the
vapor pressure at which the liquid and gas phases coexist. In the aspect of
thermodynamic geometry, the microscopic interaction between the black hole
microstructures can be repulsive in the R\'enyi description. This implies that
a novel correlation between the microstates of a self-gravitating system could
be emerged via the nonextensive nature of long-range interaction systems.
Finally, we also investigate the critical phenomena of the RN-AF in R\'enyi
statistics compared to that of the van der Waals (vdW) fluid and find that the
critical exponents of the relevant physical quantities of both systems are
identical. This implies that both systems are in the same universality class of
the phase transition.


title: How cooperatively folding are homopolymer molecular knots?

abstract:
  Detailed thermodynamic analysis of complex systems with multiple stable
configurational states allows for insight into the cooperativity of each
individual transition. In this work we derive a heat capacity decomposition
comprising contributions from each individual configurational state, which
together sum to a baseline heat capacity, and contributions from each
state-to-state transition. We apply this analysis framework to a series of
replica exchange molecular dynamics simulations of linear and 1-1
coarse-grained homo-oligomer models which fold into stable, configurationally
well-defined molecular knots, in order to better understand the parameters
leading to stable and cooperative folding of these knots. We find that a stiff
harmonic backbone bending angle potential is key to achieving knots with
specific 3D structures. Tuning the backbone equilibrium angle in small
increments yields a variety of knot topologies, including $3_1$, $5_1$, $7_1$,
and $8_{19}$ types. Populations of different knotted states as functions of
temperature can also be manipulated by tuning backbone torsion stiffness or by
adding side chain beads. We find that sharp total heat capacity peaks for the
homo-oligomer knots are largely due to a coil-to-globule transition, rather
than a cooperative knotting step. However, in some cases the cooperativity of
globule-to-knot and coil-to-globule transitions are comparable, suggesting that
highly cooperative folding to knotted structures can be achieved by refining
the model parameters or adding sequence specificity.


title: The Effect of Supernova Convection On Neutron Star and Black Hole Masses

abstract:
  Our understanding of the convective-engine paradigm driving core-collapse
supernovae has been used for 2 decades to predict the remnant mass distribution
from stellar collapse. These predictions improve as our understanding of this
engine increases. In this paper, we review our current understanding of
convection (in particular, the growth rate of convection) in stellar collapse
and study its effect on the remnant mass distribution. We show how the depth of
the mass gap between neutron stars and black holes can help probe this
convective growth. We include a study of the effects of stochasticity in both
the stellar structure and the convective seeds caused by stellar burning. We
study the role of rotation and its effect on the pair-instability mass gap.
Under the paradigm limiting stellar rotation to those stars in tight binaries,
we determine the effect of rotation on the remnant mass distribution.


title:
Highly fabrication tolerant InP based polarization beam splitter based
  on p-i-n structure

abstract:
  In this work, a novel highly fabrication tolerant polarization beam splitter
(PBS) is presented on an InP platform. To achieve the splitting, we combine the
Pockels effect and the plasma dispersion effect in a symmetric 1x2 Mach-Zehnder
interferometer (MZI). One p-i-n phase shifter of the MZI is driven in forward
bias to exploit the plasma dispersion effect and modify the phase of both the
TE and TM mode. The other arm of the MZI is driven in reverse bias to exploit
the Pockels effect which affects only the TE mode. By adjusting the voltages of
the two phase shifters, a different interference condition can be set for the
TE and the TM modes thereby splitting them at the output of the MZI. By
adjusting the voltages, the very tight fabrication tolerances known for fully
passive PBS are eased. The experimental results show that an extinction ratio
better than 15 dB and an on-chip loss of 3.5 dB over the full C-band
(1530-1565nm) are achieved.


title:
The CDF W-mass, muon g-2, and dark matter in a $U(1)_{L_\mu-L_\tau}$
  model with vector-like leptons

abstract:
  We study the CDF $W$-mass, muon $g-2$, and dark matter observables in a local
$U(1)_{L_\mu-L_\tau}$ model in which the new particles include three
vector-like leptons ($E_1,~ E_2,~ N$), a new gauge boson $Z'$, a scalar $S$
(breaking $U(1)_{L_\mu-L_\tau}$), a scalar dark matter $X_I$ and its partner
$X_R$. We find that the CDF $W$-mass disfavors $m_{E_1}= m_{E_2}={m_N}$ or
$s_L=s_R=0$ where $s_{L(R)}$ is mixing parameter of left (right)-handed fields
of vector-like leptons. A large mass splitting between $E_1$ and $E_2$ is
favored when the differences between $s_L$ and $s_R$ becomes small. The muon
$g-2$ anomaly can be simultaneously explained for appropriate difference
between $s_L$ $(m_{E_1})$ and $s_R$ $(m_{E_2})$, and some regions are excluded
by the diphoton signal data of the 125 GeV Higgs. Combined with the CDF
$W$-mass, muon $g-2$ anomaly and other relevant constraints, the correct dark
matter relic density is mainly obtained in two different scenarios: (i)
$X_IX_I\to Z'Z',~ SS$ for $m_{Z'}(m_S)<m_{X_I}$ and (ii) the co-annihilation
processes for $min(m_{E_1},m_{E_2},m_N,m_{X_R})$ closed to $m_{X_I}$. Finally,
we use the direct searches for $2\ell+E_T^{miss}$ event at the LHC to constrain
the model, and show the allowed mass ranges of the vector-like leptons and dark
matter.


title: Finite-temperature surface elasticity of crystalline solids

abstract:
  Surface energies and surface elasticity largely affect the mechanical
response of nanostructures as well as the physical phenomenon associated with
surfaces such as evaporation and adsorption. Studying surface energies at
finite temperatures is therefore of immense interest for nanoscale
applications. However, calculating surface energies and derived quantities from
atomistic ensembles is usually limited to zero temperature or involve
cumbersome thermodynamic integration techniques at finite temperature. Here, we
illustrate a technique to identify the energy and elastic properties of
surfaces of solids at non-zero temperature based on a Gaussian phase packets
(GPP) approach (which in the isothermal limit coincides with a maximum-entropy
formulation). Using this setup, we investigate the effect of temperature on the
surface properties of different crystal faces for six pure metals -- copper,
nickel, alumimum, iron, tungsten and vanadium -- thus covering both FCC and BCC
lattice structures. While the obtained surface energies and stresses usually
show a decreasing trend with increasing temperature, the elastic constants do
not show such a consistent trend across the different materials and are quite
sensitive to temperature changes. Validation is performed by comparing the
obtained surface energy densities of selected BCC and FCC materials to those
calculated via molecular dynamics.


title:
Differential Data-Aided Beam Training for RIS-Empowered Multi-Antenna
  Communications

abstract:
  The Reconfigurable Intelligent Surface (RIS) constitutes one of the prominent
technologies for the next generation of wireless communications. It is
envisioned to enhance the signal coverage in cases when the direct link of the
communication is weak. Recently, beam training based on codebook selection is
proposed to obtain the optimized phase configuration of the RIS, and then, the
data is transmitted and received by using the classical coherent demodulation
scheme (CDS). This training approach is able to avoid the large overhead
required by the channel sounding process, and it also circumvents complex
optimization problems. However, the beam training still requires the
transmission of some reference signals to test the different phase
configurations of the codebook, which reduces the spectral efficiency. The best
codeword is chosen according to the received energy of the reference signals.
In this paper, the data transmission and reception based on non-CDS (NCDS) is
proposed during the beam training process in order to increase the efficiency
of the system, and at the same time, enable the energy measurement for the
determination of the best beam for the RIS. After choosing the best codebook,
NCDS is still more suitable to transmit information for high mobility scenarios
as compared to the classical CDS. Analytical expressions for the
Signal-to-Interference and Noise Ratio (SINR) for the non-coherent
RIS-empowered system are presented. Moreover, a detailed comparison between the
NCDS and CDS in terms of efficiency and complexity is also given. The extensive
computer simulation results verify the accuracy of the presented analysis and
showcase that the proposed system outperforms the existing solutions.


title: CMOS compatible multi-band plasmonic TE-pass polarizer

abstract:
  A CMOS-compatible plasmonic TE-pass polarizer capable of working in the O, E,
S, C, L, and U bands is numerically analyzed. The device is based on an
integrated hybrid plasmonic waveguide (HPW) with a segmented metal design. The
segmented metal will avoid the propagation of the TM mode, confined in the slot
of the HPW, while the TE fundamental mode will pass. The TE mode is not
affected by the metal segmentation since it is confined in the core of the HPW.
The concept of the segmented metal can be exploited in a plasmonic circuit with
HPWs as the connecting waveguides between parts of the circuit and in a silicon
photonics circuit with strip or slab waveguides connecting the different parts
of the circuit. Using 3D FDTD simulations, it is shown that for a length of 5.5
{\mu}m the polarization extinction ratios are better than 20 dB and the
insertion losses are less than 1.7 dB over all the optical communication bands.

categories: physics.optics cond-mat.other physics.app-ph physics.class-ph physics.comp-ph


title:
DialogVED: A Pre-trained Latent Variable Encoder-Decoder Model for
  Dialog Response Generation

abstract:
  Dialog response generation in open domain is an important research topic
where the main challenge is to generate relevant and diverse responses. In this
paper, we propose a new dialog pre-training framework called DialogVED, which
introduces continuous latent variables into the enhanced encoder-decoder
pre-training framework to increase the relevance and diversity of responses.
With the help of a large dialog corpus (Reddit), we pre-train the model using
the following 4 tasks, used in training language models (LMs) and Variational
Autoencoders (VAEs) literature: 1) masked language model; 2) response
generation; 3) bag-of-words prediction; and 4) KL divergence reduction. We also
add additional parameters to model the turn structure in dialogs to improve the
performance of the pre-trained model. We conduct experiments on PersonaChat,
DailyDialog, and DSTC7-AVSD benchmarks for response generation. Experimental
results show that our model achieves the new state-of-the-art results on all
these datasets.


title:
TimeBERT: Enhancing Pre-Trained Language Representations with Temporal
  Information

abstract:
  Time is an important aspect of text documents, which has been widely
exploited in natural language processing and has strong influence, for example,
in temporal information retrieval, where the temporal information of queries or
documents need to be identified for relevance estimation. Event-related tasks
like event ordering, which aims to order events by their occurrence time, also
need to determine the temporal information of events. In this work, we
investigate methods for incorporating temporal information during pre-training,
to further improve the performance on time-related tasks. Compared with BERT
which utilizes synchronic document collections (BooksCorpus and English
Wikipedia) as the training corpora, we use long-span temporal news collection
for building word representations, since temporal information constitutes one
of the most significant features of news articles. We then introduce TimeBERT,
a novel language representation model trained on a temporal collection of news
articles via two new pre-training tasks, which harness two distinct temporal
signals to construct time-aware language representation. The experimental
results show that TimeBERT consistently outperforms BERT and other existing
pre-trained models, with substantial gains on different downstream NLP tasks or
applications for which time is of importance.


title:
Hypocoercivity and hypocontractivity concepts for linear dynamical
  systems

abstract:
  For linear dynamical systems (in continuous-time and discrete-time) we
revisit and extend the concepts of hypocoercivity and hypocontractivity and
give a detailed analysis of the relations of these concepts to (asymptotic)
stability and (semi-)dissipativity respectively (semi-)contractivity. On the
basis of these results, the short-time decay behavior of the solutions of
linear continuous-time and discrete-time systems is characterized.


title:
Minimax Robust Quickest Change Detection using Wasserstein Ambiguity
  Sets

abstract:
  We study the robust quickest change detection under unknown pre- and
post-change distributions. To deal with uncertainties in the data-generating
distributions, we formulate two data-driven ambiguity sets based on the
Wasserstein distance, without any parametric assumptions. The minimax robust
test is constructed as the CUSUM test under least favorable distributions, a
representative pair of distributions in the ambiguity sets. We show that the
minimax robust test can be obtained in a tractable way and is asymptotically
optimal. We investigate the effectiveness of the proposed robust test over
existing methods, including the generalized likelihood ratio test and the
robust test under KL divergence based ambiguity sets.


title:
Quantum Compressive Sensing: Mathematical Machinery, Quantum Algorithms,
  and Quantum Circuitry

abstract:
  Compressive sensing is a sensing protocol that facilitates reconstruction of
large signals from relatively few measurements by exploiting known structures
of signals of interest, typically manifested as signal sparsity. Compressive
sensing's vast repertoire of applications in areas such as communications and
image reconstruction stems from the traditional approach of utilizing
non-linear optimization to exploit the sparsity assumption by selecting the
lowest-weight (i.e. maximum sparsity) signal consistent with all acquired
measurements. Recent efforts in the literature consider instead a data-driven
approach, training tensor networks to learn the structure of signals of
interest. The trained tensor network is updated to "project" its state onto one
consistent with the measurements taken, and is then sampled site by site to
"guess" the original signal. In this paper, we take advantage of this computing
protocol by formulating an alternative "quantum" protocol, in which the state
of the tensor network is a quantum state over a set of entangled qubits.
Accordingly, we present the associated algorithms and quantum circuits required
to implement the training, projection, and sampling steps on a quantum
computer. We supplement our theoretical results by simulating the proposed
circuits with a small, qualitative model of LIDAR imaging of earth forests. Our
results indicate that a quantum, data-driven approach to compressive sensing,
may have significant promise as quantum technology continues to make new leaps.


title: Lattice zonotopes of degree 2

abstract:
  The Ehrhart polynomial $ehr_P (n)$ of a lattice polytope $P$ gives the number
of integer lattice points in the $n$-th dilate of $P$ for all integers $n\geq
0$. The degree of $P$ is defined as the degree of its $h^\ast$-polynomial, a
particular transformation of the Ehrhart polynomial with many useful properties
which serves as an important tool for classification questions in Ehrhart
theory. A zonotope is the Minkowski (pointwise) sum of line segments. We
classify all Ehrhart polynomials of lattice zonotopes of degree $2$ thereby
complementing results of Scott (1976), Treutlein (2010), and Henk-Tagami
(2009). Our proof is constructive: by considering solid-angles and the lattice
width, we provide a characterization of all $3$-dimensional zonotopes of degree
$2$.


title: Elastic Spin and Orbital Angular Momenta

abstract:
  Motivated by recent theoretical and experimental interest in the spin and
orbital angular momenta of elastic waves, we revisit canonical wave momentum,
spin, and orbital angular momentum in isotropic elastic media. We show that
these quantities are described by simple universal expressions, which differ
from the results of [G. J. Chaplain {\it et al}., Phys. Rev. Lett. {\bf 128},
064301 (2022)] and do not require separation of the longitudinal and transverse
parts of the wavefield. For cylindrical elastic modes, the normalized
$z$-component of the total (spin+orbital) angular momentum is quantized and
equals the azimuthal quantum number of the mode, while the orbital and spin
parts are not quantized due to the spin-orbit geometric-phase effects. In
contrast to the claims of the above article, longitudinal, transverse, and
`hybrid' contributions to the angular momenta are equally important and cannot
be neglected. As another application of the general formalism, we calculate the
transverse spin angular momentum of a surface Rayleigh wave.


title: Two-component self-gravitating isothermal slab models

abstract:
  We revisit the problem of the isothermal slab (in standard Cartesian
coordinates, density distributions and mean gravitational potential are
considered to be independent of $x$ and $y$ and to be a function of $z$,
symmetric with respect to the $z = 0$ plane) in the context of the general
issues related to the role of weak collisionality in inhomogeneous
self-gravitating stellar systems. We thus consider the two-component case, that
is a system of heavy and light stars with assigned mass ratio ($\mu$) and
assigned global relative abundance ($\alpha$; the ratio of the total mass of
the heavy and light stars). The system is imagined to start from an initial
condition in which the two species are well mixed and have identical spatial
and velocity distributions and to evolve into a final configuration in which
collisions have generated equipartition and mass segregation. Initial and final
distribution functions are assumed to be Maxwellian. Application of mass and
energy conservation allows us to derive the properties of the final state from
the assumed initial conditions. In general, the derivation of these properties
requires a simple numerical integration of the Poisson equation. Curiously, the
case in which the heavy stars are exactly twice as massive as the light stars
($\mu = 2$) turns out to admit a relatively simple analytic solution. Although
the general framework of this investigation is relatively straightforward, some
non-trivial issues related to energy conservation and the possible use of a
virial constraint are noted and clarified. The formulation and the results of
this paper prepare the way to future studies in which the evolution induced by
weak collisionality will be followed either by considering the action of
standard collision operators or by means of dedicated numerical simulations.


title:
A biset-enriched categorical model for Proto-Quipper with dynamic
  lifting

abstract:
  Quipper and Proto-Quipper are a family of quantum programming languages that,
by their nature as circuit description languages, involve two runtimes: one at
which the program generates a circuit and one at which the circuit is executed,
normally with probabilistic results due to measurements. Accordingly, the
language distinguishes two kinds of data: parameters, which are known at
circuit generation time, and states, which are known at circuit execution time.
Sometimes, it is desirable for the results of measurements to control the
generation of the next part of the circuit. Therefore, the language needs to
turn states, such as measurement outcomes, into parameters, an operation we
call dynamic lifting. The goal of this paper is to model this interaction
between the runtimes by providing a general categorical structure enriched in
what we call "bisets". We demonstrate that the biset-enriched structure
achieves a proper semantics of the two runtimes and their interaction, by
showing that it models a variant of Proto-Quipper with dynamic lifting. The
present paper deals with the concrete categorical semantics of this language,
whereas a companion paper [FKRS2022a] deals with the syntax, type system,
operational semantics, and abstract categorical semantics.


title: LAST: Latent Space Assisted Adaptive Sampling for Protein Trajectories

abstract:
  Molecular dynamics (MD) simulation is widely used to study protein
conformations and dynamics. However, conventional simulation suffers from being
trapped in some local energy minima that are hard to escape. Thus, most
computational time is spent sampling in the already visited regions. This leads
to an inefficient sampling process and further hinders the exploration of
protein movements in affordable simulation time. The advancement of deep
learning provides new opportunities for protein sampling. Variational
autoencoders are a class of deep learning models to learn a low-dimensional
representation (referred to as the latent space) that can capture the key
features of the input data. Based on this characteristic, we proposed a new
adaptive sampling method, latent space assisted adaptive sampling for protein
trajectories (LAST), to accelerate the exploration of protein conformational
space. This method comprises cycles of (i) variational autoencoders training,
(ii) seed structure selection on the latent space and (iii) conformational
sampling through additional MD simulations. The proposed approach is validated
through the sampling of four structures of two protein systems: two metastable
states of E. Coli adenosine kinase (ADK) and two native states of Vivid (VVD).
In all four conformations, seed structures were shown to lie on the boundary of
conformation distributions. Moreover, large conformational changes were
observed in a shorter simulation time when compared with conventional MD (cMD)
simulations in both systems. In metastable ADK simulations, LAST explored two
transition paths toward two stable states while cMD became trapped in an energy
basin. In VVD light state simulations, LAST was three times faster than cMD
simulation with a similar conformational space.


title: Proto-Quipper with dynamic lifting

abstract:
  Quipper is a functional programming language for quantum computing.
Proto-Quipper is a family of languages aiming to provide a formal foundation
for Quipper. In this paper, we extend Proto-Quipper-M with a construct called
dynamic lifting, which is present in Quipper. By virtue of being a circuit
description language, Proto-Quipper has two separate runtimes: circuit
generation time and circuit execution time. Values that are known at circuit
generation time are called parameters, and values that are known at circuit
execution time are called states. Dynamic lifting is an operation that enables
a state, such as the result of a measurement, to be lifted to a parameter,
where it can influence the generation of the next portion of the circuit. As a
result, dynamic lifting enables Proto-Quipper programs to interleave classical
and quantum computation. We describe the syntax of a language we call
Proto-Quipper-Dyn. Its type system uses a system of modalities to keep track of
the use of dynamic lifting. We also provide an operational semantics, as well
as an abstract categorical semantics for dynamic lifting based on enriched
category theory. We prove that both the type system and the operational
semantics are sound with respect to our categorical semantics. Finally, we give
some examples of Proto-Quipper-Dyn programs that make essential use of dynamic
lifting. A concrete categorical model is given in a companion paper
[FKRS2022b].


title:
Evidence for modification of $b$ quark hadronization in
  high-multiplicity $pp$ collisions at $\sqrt{s} = 13$ TeV

abstract:
  The production rate of $B^{0}_{s}$ mesons relative to $B^{0}$ mesons is
measured by the LHCb experiment in $pp$ collisions at a center-of-mass energy
$\sqrt{s} = 13$ TeV over the forward rapidity interval $2<y<4.5$ as a function
of the charged particle multiplicity measured in the event. Evidence at the
3.4$\sigma$ level is found for an increase of the ratio of $B^{0}_{s}$ to
$B^{0}$ cross-sections with multiplicity at transverse momenta below 6 GeV/$c$,
with no significant multiplicity dependence at higher transverse momentum.
Comparison with data from $e^{+}e^{-}$ collisions implies that the density of
the hadronic medium may affect the production rates of $B$ mesons. This is
qualitatively consistent with the emergence of quark coalescence as an
additional hadronization mechanism in high-multiplicity collisions.


title: High-quality Conversational Systems

abstract:
  Conversational systems or chatbots are an example of AI-Infused Applications
(AIIA). Chatbots are especially important as they are often the first
interaction of clients with a business and are the entry point of a business
into the AI (Artificial Intelligence) world. The quality of the chatbot is,
therefore, key. However, as is the case in general with AIIAs, it is especially
challenging to assess and control the quality of chatbot systems. Beyond the
inherent statistical nature of these systems, where occasional failure is
acceptable, we identify two major challenges. The first is to release an
initial system that is of sufficient quality such that humans will interact
with it. The second is to maintain the quality, enhance its capabilities,
improve it and make necessary adjustments based on changing user requests or
drift. These challenges exist because it is impossible to predict the real
distribution of user requests and the natural language they will use to express
these requests. Moreover, any empirical distribution of requests is likely to
change over time. This may be due to periodicity, changing usage, and drift of
topics.
  We provide a methodology and set of technologies to address these challenges
and to provide automated assistance through a human-in-the-loop approach. We
notice that it is crucial to connect between the different phases in the
lifecycle of the chatbot development and to make sure it provides its expected
business value. For example, that it frees human agents to deal with tasks
other than answering human users. Our methodology and technologies apply during
chatbot training in the pre-production phase, through to chatbot usage in the
field in the post-production phase. They implement the `test first' paradigm by
assisting in agile design, and support continuous integration through
actionable insights.


title: Microstructure of Charged AdS Black Hole with Minimal Length Effects

abstract:
  In this work, the microstructure of charged AdS black holes under minimal
length effects is investigated. We study the thermodynamics of black holes in
the extended phase space, where the cosmological constant is regarded as the
thermodynamic pressure. The modified Hawking temperature and phase transition
are obtained based on the generalized uncertainty principle. Then, we use
thermodynamic geometry to explore the microstructure of black holes, and our
research shows that the microstructure of black holes can be determined by the
ratio of GUP parameter to charge. For a small ratio, the black hole exhibits
the typical RN-AdS microstructure with van der Waals phase transition and
repulsive/attractive interactions. As the ratio increases, the reentrant phase
transition takes place, both repulsion-attraction coexisted black hole and the
attraction dominated black hole are found in this case. For a large ratio, the
black hole behaves like a Schwarzchild-AdS black hole in which neither phase
transition nor repulsive interaction exists in the system. These results
suggest that the GUP effect will reduce the repulsive interaction presented by
the charged AdS black hole. Moreover, some interesting effects of GUP on the
microstructure of black hole are also discussed from the perspective of black
hole molecules and holographic theory.


title:
SMEFT predictions for $gg\to hh$ at full NLO QCD and truncation
  uncertainties

abstract:
  We present a calculation of the NLO QCD corrections for Higgs-boson pair
production in gluon fusion including effects of anomalous couplings within
Standard Model Effective Field Theory (SMEFT). We study effects of different
truncation options of the EFT expansion in $1/\Lambda$ and of double operator
insertions, both at total cross-section level as well as for the distribution
of the invariant mass of the Higgs-boson pair, at $\sqrt{s}=13$ TeV. The NLO
corrections are implemented in the generator $\texttt{ggHH_SMEFT}$ in the
$\texttt{Powheg-Box-V2}$ framework.


title: Retrieving black hole information from the main Lorentzian saddle point

abstract:
  One of the most striking evidences of the information loss paradox is that,
according to the Hawking's calculation, the correlation functions of a test
scalar field exponentially decay in time. In this paper, I argue that a
judicious use of the steepest descent expansion on the classical saddle point
(the Black Hole background), is enough to change this early time decay into a
late time growing, in agreement with information retrieval. I will explicitly
show this in the Jackiw-Teitelboim gravity. There, the so-called "ramp" is
analytically obtained without the need of any other subdominant configurations
of the gravity path integral.


title: Dropout Inference with Non-Uniform Weight Scaling

abstract:
  Dropout as regularization has been used extensively to prevent overfitting
for training neural networks. During training, units and their connections are
randomly dropped, which could be considered as sampling many different
submodels from the original model. At test time, weight scaling and Monte Carlo
approximation are two widely applied approaches to approximate the outputs.
Both approaches work well practically when all submodels are low-bias complex
learners. However, in this work, we demonstrate scenarios where some submodels
behave closer to high-bias models and a non-uniform weight scaling is a better
approximation for inference.


title:
TERMinator: A Neural Framework for Structure-Based Protein Design using
  Tertiary Repeating Motifs

abstract:
  Computational protein design has the potential to deliver novel molecular
structures, binders, and catalysts for myriad applications. Recent neural
graph-based models that use backbone coordinate-derived features show
exceptional performance on native sequence recovery tasks and are promising
frameworks for design. A statistical framework for modeling protein sequence
landscapes using Tertiary Motifs (TERMs), compact units of recurring structure
in proteins, has also demonstrated good performance on protein design tasks. In
this work, we investigate the use of TERM-derived data as features in neural
protein design frameworks. Our graph-based architecture, TERMinator,
incorporates TERM-based and coordinate-based information and outputs a Potts
model over sequence space. TERMinator outperforms state-of-the-art models on
native sequence recovery tasks, suggesting that utilizing TERM-based and
coordinate-based features together is beneficial for protein design.


title: On local entropy, stochastic control and deep neural networks

abstract:
  In this paper, we connect some recent papers on smoothing of energy
landscapes and scored-based generative models of machine learning to classical
work in stochastic control. We clarify these connections providing rigorous
statements and representations which may serve as guidelines for further
learning models.


title: Image of Lie polynomial of degree $2$ evaluated on Nilpotent Lie algebra

abstract:
  We delineate the image of multilinear Lie polynomial of degree $2$ evaluated
on $L$ where $L$ is a finite-dimensional nilpotent Lie algebra over field $k$
with $\dim L' \leq 4$.


title:
Quasi-periodic oscillations in the $\gamma$-ray light curves of bright
  active galactic nuclei

abstract:
  The detection of quasi-periodic oscillations (QPOs) in the light curves of
active galactic nuclei (AGNs) can provide insights on the physics of the
super-massive black-holes (SMBHs) powering these systems, and could represent a
signature of the existence of SMBH binaries, setting fundamental constraints on
SMBH evolution in the Universe. Identification of long term QPOs, with periods
of the order of months to years, is particularly challenging and can only be
achieved via all-sky monitoring instruments that can provide unbiased,
continuous light-curves of astrophysical objects. The Fermi-LAT satellite,
thanks to its monitoring observing strategy, is an ideal instrument to reach
such a goal, and we aim to identify QPOs in the $\gamma$-ray light-curves of
the brightest AGNs within the Fermi-LAT catalog. We analyze the light curves of
the thirty-five brightest Fermi-LAT AGNs, including data from the beginning of
the Fermi mission (August 2008) to April 2020, and energies from 100 MeV to 300
GeV. Two time binnings are investigated, 7 and 30 days. The search for
quasi-periodic features is then performed using the continuous wavelet
transform. The significance of the result is tested via Monte Carlo simulations
of artificial light curves with the same power spectral density and probability
distribution function as the original light curves. We identify thirty quasars
with candidate QPOs. We confirm several QPO candidates discussed in the
literature: PKS 2247-131, B2 1520+31, PKS 0426-380, PKS 0537-441, S5 0716+714,
Mrk 421, PKS 1424-418, PG 1553+113, Mrk 501 and PKS 2155-304. The most
significant QPO (> 4$\sigma$ in the global wavelet spectrum, with a period of
about 1100 days) is observed in the quasar S5 1044+71, and is reported here for
the first time.


title:
Hidden interplay of current-induced spin and orbital torques in bulk
  Fe$_3$GeTe$_2$

abstract:
  Low crystal symmetry of magnetic van der Waals materials naturally promotes
spin-orbital complexity unachievable in common magnetic materials used for
spin-orbit torque switching. Here, using first-principles methods, we
demonstrate that an interplay of spin and orbital degrees of freedom has a
profound impact on spin-orbit torques in a prototype van der Waals ferromagnet:
Fe$_3$GeTe$_2$ (FGT). While we show that bulk FGT hosts strong "hidden"
current-induced torques harvested by each of its layers, we uncover that their
origin alternates between the conventional spin flux torque and the so-called
orbital torque as the magnetization direction is varied. A drastic difference
in the behavior of the two types of torques results in a non-trivial evolution
of switching properties with doping. Our findings promote the design of
non-equilibrium orbital properties as the guiding mechanism for crafting the
properties of spin-orbit torques in layered van der Waals materials.


title:
Genuine pro-$p$ Iwahori--Hecke algebras, Gelfand--Graev representations,
  and some applications

abstract:
  We study the Iwahori-component of the Gelfand-Graev representation of a
central cover of a split linear reductive group and utilize our results for
three applications. In fact, it is advantageous to begin at the pro-$p$ level.
Thus to begin we study the structure of a genuine pro-$p$ Iwahori-Hecke
algebra, establishing Iwahori-Matsumoto and Bernstein presentations. With this
structure theory we first describe the pro-$p$ part of the Gelfand-Graev
representation and then the more subtle Iwahori part.
  For the first application we relate the Gelfand-Graev representation to the
metaplectic representation of Sahi-Stokman-Venkateswaran, which conceptually
realizes the Chinta-Gunnells action from the theory of Weyl group multiple
Dirichlet series. For the second we compute the Whittaker dimension of the
constituents of regular unramified principal series; for the third we do the
same for unitary unramified principal series.


title:
Systematic Literature Review: Anti-Phishing Defences and Their
  Application to Before-the-click Phishing Email Detection

abstract:
  Most research into anti-phishing defence assumes that the mal-actor is
attempting to harvest end-users' personally identifiable information or login
credentials and, hence, focuses on detecting phishing websites. The defences
for this type of attack are usually activated after the end-user clicks on a
link, at which point the link is checked. This is known as after-the-click
detection. However, more sophisticated phishing attacks (such as spear-phishing
and whaling) are rarely designed to get the end-user to visit a website.
Instead, they attempt to get the end-user to perform some other action, for
example, transferring money from their bank account to the mal-actors account.
These attacks are rarer, and before-the-click defence has been investigated
less than after-the-click defence. To better integrate and contextualize these
studies in the overall anti-phishing research, this paper presents a systematic
literature review of proposed anti-phishing defences. From a total of 6330
papers, 21 primary studies and 335 secondary studies were identified and
examined. The current research was grouped into six primary categories,
blocklist/allowlist, heuristics, content, visual, artificial
intelligence/machine learning and proactive, with an additional category of
"other" for detection techniques that do not fit into any of the primary
categories. It then discusses the performance and suitability of using these
techniques for detecting phishing emails before the end-user even reads the
email. Finally, it suggests some promising areas for further research.


title: Some results on Quillen's Conjecture via equivalent-poset techniques

abstract:
  We extend the Main Theorem of Aschbacher and Smith on Quillen's Conjecture
from $p>5$ to the remaining odd primes $p = 3,5$. In the process, we develop
further combinatorial and homotopical methods for studying the poset of
nontrivial elementary abelian $p$-subgroups of a finite group. The techniques
lead to a number of further results on the Conjecture, often reducing
dependence on the CFSG; in particular, we also provide some partial results
toward the case of $p=2$.


title:
Core excitations: how far can we push correlated single-reference
  formalism for Delta-based methods?

abstract:
  We investigate the use of orbital-optimized references in conjunction with
single-reference coupled-cluster theory with single and double substitutions
(CCSD) for the study of core excitations and ionizations of 18 small organic
molecules, without any use of response theory or equation-of-motion formalisms.
Three schemes are employed to successfully address the convergence difficulties
associated with the coupled-cluster equations, and the spin contamination
resulting from the use of a spin symmetry-broken reference, in the case of
excitations. In order to gauge the inherent potential of the methods studied,
an effort is made to provide reasonable basis set limit estimates for the
transition energies. Overall, we find that the two best-performing schemes
studied here for Delta-CCSD are capable of predicting excitation and ionization
energies with errors comparable to experimental accuracies. The proposed
Delta-CCSD schemes seem to fare better than the widely used equation-of-motion
CCSD (EOM-CCSD) with core-valence separation protocol, with statistical errors
being reduced by more than a factor of two when compared to FC-CVS-EOM-CCSD.


title:
A Uniform Framework for Diagnosis of Discrete-Event Systems with
  Unreliable Sensors using Linear Temporal Logic

abstract:
  In this paper, we investigate the diagnosability verification problem of
partially-observed discrete-event systems (DES) subject to unreliable sensors.
In this setting, upon the occurrence of each event, the sensor reading may be
non-deterministic due to measurement noises or possible sensor failures.
Existing works on this topic mainly consider specific types of unreliable
sensors such as the cases of intermittent sensors failures, permanent sensor
failures or their combinations. In this work, we propose a novel \emph{uniform
framework} for diagnosability of DES subject to, not only sensor failures, but
also a very general class of unreliable sensors. Our approach is to use linear
temporal logic (LTL) with semantics on infinite traces to describe the possible
behaviors of the sensors. A new notion of $\varphi$-diagnosability is proposed
as the necessary and sufficient condition for the existence of a diagnoser when
the behaviors of sensors satisfy the LTL formula $\varphi$. Effective approach
is provided to verify this notion. We show that, our new notion of
$\varphi$-diagnosability subsumes all existing notions of robust diagnosability
of DES subject to sensor failures. Furthermore, the proposed framework is
user-friendly and flexible since it supports an arbitrary user-defined
unreliable sensor type based on the specific scenario of the application. As
examples, we provide two new notions of diagnosability, which have never been
investigated in the literature, using our uniform framework.


title:
Structure and fluctuations of a slow ICME sheath observed at 0.5 au by
  the Parker Solar Probe

abstract:
  Sheaths ahead of interplanetary coronal mass ejections (ICMEs) are turbulent
heliospheric structures. Knowledge of their structure and fluctuations is
important for understanding their geoeffectiveness, their role in accelerating
particles, and the interaction of ICMEs with the solar wind. We studied
observations from the Parker Solar Probe of a sheath observed at 0.5 au in
March 2019, ahead of a slow streamer blowout CME. To examine the MHD-scale
turbulent properties, we calculated fluctuation amplitudes, magnetic
compressibility, partial variance of increments (PVI), cross helicity
($\sigma_c$), residual energy ($\sigma_r$), and the Jensen-Shannon permutation
entropy and complexity. The sheath consisted of slow and fast flows separated
by a 15-min change in magnetic sector that coincided with current sheet
crossings and a velocity shear zone. Fluctuation amplitudes and PVI were
greater through the sheath than upstream. Fluctuations had mostly negative
$\sigma_r$ and positive $\sigma_c$ in the sheath, the latter indicating an
anti-sunward sense of propagation. The velocity shear region marked an increase
in temperature and specific entropy, and the faster flow behind had local
patches of positive $\sigma_r$ as well as higher fluctuation amplitudes and
PVI. Fluctuations in the preceding wind and sheath were stochastic, with the
sheath fluctuations showing lower entropy and higher complexity than upstream.
The two-part sheath structure likely resulted from a warp in the heliospheric
current sheet (HCS) being swept up and compressed. The ejecta accelerated and
heated the wind at the sheath rear, which then interacted with the slower wind
ahead of the HCS warp. This caused differences in fluctuation properties across
the sheath. Sheaths of slow ICMEs can thus have complex structure where
fluctuation properties are not just downstream shock properties, but are
generated within the sheath.


title: The Global Glimm Property

abstract:
  It is known that a C*-algebra with the Global Glimm Property is nowhere
scattered (it has no elementary ideal-quotients), and the Global Glimm Problem
asks if the converse holds. We provide a new approach to this long-standing
problem by showing that a C*-algebra has the Global Glimm Property if and only
if it is nowhere scattered and its Cuntz semigroup is ideal-filtered (the Cuntz
classes generating a given ideal are downward directed) and has property (V) (a
weak form of being sup-semilattice ordered).
  We show that ideal-filteredness and property (V) are automatic for
C*-algebras that have stable rank one or real rank zero, thereby recovering the
solutions to the Global Glimm Problem in these cases. We also use our approach
to solve the Global Glimm Problem for new classes of C*-algebras.


title: Bisimulation Makes Analogies in Goal-Conditioned Reinforcement Learning

abstract:
  Building generalizable goal-conditioned agents from rich observations is a
key to reinforcement learning (RL) solving real world problems. Traditionally
in goal-conditioned RL, an agent is provided with the exact goal they intend to
reach. However, it is often not realistic to know the configuration of the goal
before performing a task. A more scalable framework would allow us to provide
the agent with an example of an analogous task, and have the agent then infer
what the goal should be for its current state. We propose a new form of state
abstraction called goal-conditioned bisimulation that captures functional
equivariance, allowing for the reuse of skills to achieve new goals. We learn
this representation using a metric form of this abstraction, and show its
ability to generalize to new goals in simulation manipulation tasks. Further,
we prove that this learned representation is sufficient not only for goal
conditioned tasks, but is amenable to any downstream task described by a
state-only reward function. Videos can be found at
https://sites.google.com/view/gc-bisimulation.


title:
Can deep learning match the efficiency of human visual long-term memory
  in storing object details?

abstract:
  Humans have a remarkably large capacity to store detailed visual information
in long-term memory even after a single exposure, as demonstrated by classic
experiments in psychology. For example, Standing (1973) showed that humans
could recognize with high accuracy thousands of pictures that they had seen
only once a few days prior to a recognition test. In deep learning, the primary
mode of incorporating new information into a model is through gradient descent
in the model's parameter space. This paper asks whether deep learning via
gradient descent can match the efficiency of human visual long-term memory to
incorporate new information in a rigorous, head-to-head, quantitative
comparison. We answer this in the negative: even in the best case, models
learning via gradient descent appear to require approximately 10 exposures to
the same visual materials in order to reach a recognition memory performance
humans achieve after only a single exposure. Prior knowledge induced via
pretraining and bigger model sizes improve performance, but these improvements
are not very visible after a single exposure (it takes a few exposures for the
improvements to become apparent), suggesting that simply scaling up the
pretraining data size or model size might not be enough for the model to reach
human-level memory efficiency.


title:
Collaborative Learning for Hand and Object Reconstruction with
  Attention-guided Graph Convolution

abstract:
  Estimating the pose and shape of hands and objects under interaction finds
numerous applications including augmented and virtual reality. Existing
approaches for hand and object reconstruction require explicitly defined
physical constraints and known objects, which limits its application domains.
Our algorithm is agnostic to object models, and it learns the physical rules
governing hand-object interaction. This requires automatically inferring the
shapes and physical interaction of hands and (potentially unknown) objects. We
seek to approach this challenging problem by proposing a collaborative learning
strategy where two-branches of deep networks are learning from each other.
Specifically, we transfer hand mesh information to the object branch and vice
versa for the hand branch. The resulting optimisation (training) problem can be
unstable, and we address this via two strategies: (i) attention-guided graph
convolution which helps identify and focus on mutual occlusion and (ii)
unsupervised associative loss which facilitates the transfer of information
between the branches. Experiments using four widely-used benchmarks show that
our framework achieves beyond state-of-the-art accuracy in 3D pose estimation,
as well as recovers dense 3D hand and object shapes. Each technical component
above contributes meaningfully in the ablation study.


title: Semi-discrete Lagrangian 2-forms and the Toda hierarchy

abstract:
  We present a variational theory of continuous-time integrable lattice systems
(semi-discrete equations). This is a natural extension of the ideas known by
the names "Lagrangian multiforms" and "Pluri-Lagrangian systems", which have
previously been established in both the fully discrete and fully continuous
cases. The main feature of these ideas is to capture a hierarchy of commuting
equations in a single variational principle. Our main example to illustrate the
new semi-discrete theory of Lagrangian multiforms is the Toda lattice. This ODE
describes the evolution in continuous time of a 1-dimensional lattice of
particles with nearest-neighbour interaction. It is part of an integrable
hierarchy of ODEs, each of which involves a derivative with respect to a
continuous variable and a number of lattice shifts. We will use the Lagrangian
multiform theory to derive PDEs in the continuous variables of the Toda
hierarchy, which do not involve any lattice shifts.


title:
Vertical distribution of cyclopropenylidene and propadiene in the
  atmosphere of Titan

abstract:
  Titan's atmosphere is a natural laboratory for exploring the photochemical
synthesis of organic molecules. Significant recent advances in the study of the
atmosphere of Titan include: (a) detection of C$_3$ molecules: C$_3$H$_6$,
CH$_2$CCH$_2$, c-C$_3$H$_2$, and (b) retrieval of C$_6$H$_6$, which is formed
primarily via C$_3$ chemistry, from Cassini-UVIS data. The detection of
$c$-C$_3$H$_2$ is of particular significance since ring molecules are of great
astrobiological importance. Using the Caltech/JPL KINETICS code, along with the
best available photochemical rate coefficients and parameterized vertical
transport, we are able to account for the recent observations. It is
significant that ion chemistry, reminiscent of that in the interstellar medium,
plays a major role in the production of c-C$_3$H$_2$ above 1000 km.


title:
Treating Crowdsourcing as Examination: How to Score Tasks and Online
  Workers?

abstract:
  Crowdsourcing is an online outsourcing mode which can solve the current
machine learning algorithm's urge need for massive labeled data. Requester
posts tasks on crowdsourcing platforms, which employ online workers over the
Internet to complete tasks, then aggregate and return results to requester. How
to model the interaction between different types of workers and tasks is a hot
spot. In this paper, we try to model workers as four types based on their
ability: expert, normal worker, sloppy worker and spammer, and divide tasks
into hard, medium and easy task according to their difficulty. We believe that
even experts struggle with difficult tasks while sloppy workers can get easy
tasks right, and spammers always give out wrong answers deliberately. So, good
examination tasks should have moderate degree of difficulty and
discriminability to score workers more objectively. Thus, we first score
workers' ability mainly on the medium difficult tasks, then reducing the weight
of answers from sloppy workers and modifying the answers from spammers when
inferring the tasks' ground truth. A probability graph model is adopted to
simulate the task execution process, and an iterative method is adopted to
calculate and update the ground truth, the ability of workers and the
difficulty of the task successively. We verify the rightness and effectiveness
of our algorithm both in simulated and real crowdsourcing scenes.


title: Cupolets in a Chaotic Neuron Model

abstract:
  This paper reports the first finding of cupolets in a chaotic Hindmarsh-Rose
neural model. Cupolets (chaotic, unstable, periodic, orbit-lets) are unstable
periodic orbits that have been stabilized through a particular control scheme
applying a binary control sequence. We demonstrate different neural dynamics
(periodic or chaotic) of the Hindmarsh-Rose model through a bifurcation diagram
where the external input current, $I$, is the bifurcation parameter. We select
a region in the chaotic parameter space and provide the results of numerical
simulations. In this chosen parameter space, a control scheme is applied when
the trajectory intersects with either of the two control planes. The size of
the control is determined by a bit in a binary control sequence. The control is
either a small microcontrol (0) or a large macrocontrol (1) that adjusts the
future dynamics of the trajectory . We report the discovery of many cupolets
with corresponding control sequences and comment on the differences with
previously reported cupolets in the double scroll system. We provide some
examples of the generated cupolets and conclude by discussing potential
implications for biological neurons.


title:
Triadic interactions induce blinking and chaos in the connectivity of
  higher-order networks

abstract:
  Ecological, biochemical and neuronal networks are characterized by triadic
interactions formed by one node regulating the interaction between two other
nodes. However, little is known about the effect of triadic interactions on
macroscopic network properties. Here, we show that the combination of positive
and negative regulatory interactions turns percolation into a fully-fledged
dynamical system where the giant connected component intermittently involves a
different set of nodes, and the order parameter undergoes period doubling and a
route to chaos. This phenomenon is captured by a theory validated by numerical
simulations on both synthetic and real networks.

categories: nlin.AO cond-mat.dis-nn cond-mat.stat-mech nlin.CD physics.soc-ph


title: Measuring vesicle loading with holographic microscopy

abstract:
  We report a label-free method for quantifying the loading of lipid vesicles.
Using an inline holographic microscopy setup, we capture holograms of vesicles
in a single shot, and use a Lorenz-Mie scattering model to extract the vesicle
refractive index and thus loading. In contrast to existing methods, no
fluorescent reporters, fluorescent tracers, or radioactive tracers are
necessary.


title: On subspace designs

abstract:
  The aim of this paper is to investigate the theory of subspace designs, which
have been originally introduced by Guruswami and Xing in 2013 to give the first
construction of positive rate rank-metric codes list decodable beyond half the
distance. However, sets of subspaces with special pattern of intersections with
other subspaces have been already studied, such as spreads, generalised arcs
and caps, and Cameron-Liebler sets. In this paper we provide bounds involving
the dimension of the subspaces forming a subspace design and the parameters of
the ambient space, showing constructions satisfying the equality in such
bounds. Then we also introduce two dualities relations among them. Among
subspace designs, those that we call s-designs are central in this paper, as
they generalize the notion of s-scattered subspace to subspace design and for
their special properties. Indeed, we prove that, for certain values of s, they
correspond to the optimal subspace designs, that is those subspace designs that
are associated with linear maximum sum-rank metric codes. Special attention has
been paid for the case s=1 for which we provide several examples, yielding
surprising to families of two-intersection sets with respect to hyperplanes
(and hence two-weight linear codes). Moreover, s-designs can be used to
construct explicit lossless dimension expanders (a linear-algebraic analogue of
expander graphs), without any restriction on the order of the field. Another
class of subspace designs we study is those of cutting designs, since they
extend the notion of cutting blocking set recently introduced by Bonini and
Borello. These designs turn out to be very interesting as they in one-to-one
correspondence with minimal sum-rank metric codes. The latter codes have been
introduced in this paper and they naturally extend the notions of minimal codes
in both Hamming and rank metrics.


title:
Hierarchical Control for Cooperative Teams in Competitive Autonomous
  Racing

abstract:
  We study the problem of autonomous racing amongst teams composed of
cooperative agents subject to realistic safety and fairness rules. We develop a
hierarchical controller to solve this problem consisting of two levels,
extending prior work where bi-level hierarchical control is applied to
head-to-head autonomous racing. A high-level planner constructs a discrete game
that encodes the complex rules with simplified dynamics to produce a sequence
of target waypoints. The low-level controller uses the resulting waypoints as a
reference trajectory and computes high-resolution control inputs by solving a
simplified racing game with a reduced set of rules. We consider two approaches
for the low-level planner: training a multi-agent reinforcement learning (MARL)
policy and solving a linear-quadratic Nash game (LQNG) approximation. We test
our controllers against three baselines on a simple oval track and a complex
track: an end-to-end MARL controller, a MARL controller tracking a fixed racing
line, and an LQNG controller tracking a fixed racing line. Quantitative results
show that our hierarchical methods outperform their respective baseline methods
in terms of race wins, overall team performance, and abiding by the rules.
Qualitatively, we observe the hierarchical controllers mimicking actions
performed by expert human drivers such as coordinated overtaking moves,
defending against multiple opponents, and long-term planning for delayed
advantages. We show that hierarchical planning for game-theoretic reasoning
produces both cooperative and competitive behavior even when challenged with
complex rules and constraints.


title:
Robust interpolation for dispersed gas-droplet flows using statistical
  learning with the Fully Lagrangian Approach

abstract:
  A novel methodology is presented for reconstructing the Eulerian number
density field of dispersed gas-droplet flows modelled using the Fully
Lagrangian Approach (FLA). In this work, the nonparametric framework of kernel
regression is used to accumulate the FLA number density contributions of
individual droplets in accordance with the spatial structure of the dispersed
phase. The high variation which is observed in the droplet number density field
for unsteady flows is accounted for by using the Eulerian-Lagrangian
transformation tensor, which is central to the FLA, to specify the size and
shape of the kernel associated with each droplet. This procedure enables a high
level of structural detail to be retained, and it is demonstrated that far
fewer droplets have to be tracked in order to reconstruct a faithful Eulerian
representation of the dispersed phase. Furthermore, the kernel regression
procedure is easily extended to higher dimensions, and inclusion of the droplet
radius within the phase space description additionally enables the droplet size
distribution and its statistics, such as average and variance, to be determined
for polydisperse flows. The developed methodology is applied to a range of 1D
and 2D steady-state and transient flows, for both monodisperse and polydisperse
droplets, and it is shown that kernel regression performs well across this
variety of cases. A comparison is made against conventional direct trajectory
methods to determine the saving in computational expense which can be gained,
and it is found that $10^3$ times fewer droplet realisations are needed to
reconstruct a qualitatively similar representation of the number density field
in which the absolute error is generally below $10^{-1}$.


title:
Searches for new phenomena in events with two leptons, jets, and missing
  transverse momentum in $139~\text{fb}^{-1}$ of $\sqrt{s}=13~$TeV $pp$
  collisions with the ATLAS detector

abstract:
  Searches for new phenomena inspired by supersymmetry in final states
containing an $e^+e^-$ or $\mu^+\mu^-$ pair, jets, and missing transverse
momentum are presented. These searches make use of proton-proton collision data
with an integrated luminosity of $139~\text{fb}^{-1}$, collected during
2015-2018 at a centre-of-mass energy $\sqrt{s}=13~$TeV by the ATLAS detector at
the Large Hadron Collider. Two searches target the pair production of charginos
and neutralinos. One uses the recursive-jigsaw reconstruction technique to
follow up on excesses observed in $36.1~\text{fb}^{-1}$ of data, and the other
uses conventional event variables. The third search targets pair production of
coloured supersymmetric particles (squarks or gluinos) decaying through the
next-to-lightest neutralino $(\tilde\chi_2^0)$ via a slepton $(\tilde\ell)$ or
$Z$ boson into $\ell^+\ell^-\tilde\chi_1^0$, resulting in a kinematic endpoint
or peak in the dilepton invariant mass spectrum. The data are found to be
consistent with the Standard Model expectations. Results are interpreted using
simplified models and exclude masses up to 900 GeV for electroweakinos, 1550
GeV for squarks, and 2250 GeV for gluinos.


title: A note on monotonicity and Bochner formulas in Carnot groups

abstract:
  In this note we prove two monotonicity formulas for solutions of $\Delta_H f
= c$ and $\Delta_H f - \p_t f = c$ in Carnot groups. Such formulas involve the
right-invariant \emph{carr\'e du champ} of a function and they are false for
the left-invariant one. The main results, Theorems 1.1 and 1.2, display a
resemblance with two deep monotonicity formulas respectively due to
Alt-Caffarelli-Friedman for the standard Laplacian, and to Caffarelli for the
heat equation. In connection with this aspect we ask the question whether an
"almost monotoniccity" formula be possible. In the last section we discuss the
failure of the nondecreasing monotonicity of an Almgren type functional.


title: Towards Teachable Reasoning Systems

abstract:
  Our goal is a teachable reasoning system for question-answering (QA), where a
user can interact with faithful answer explanations, and correct errors so that
the system improves over time. Our approach is three-fold: First, generated
chains of reasoning show how answers are implied by the system's own internal
beliefs. Second, users can interact with the explanations to identify erroneous
model beliefs and provide corrections. Third, we augment the model with a
dynamic memory of such corrections. Retrievals from memory are used as
additional context for QA, to help avoid previous mistakes in similar new
situations - a novel type of memory-based continuous learning. To our
knowledge, this is the first system to generate chains that are both faithful
(the answer follows from the reasoning) and truthful (the chain reflects the
system's own beliefs, as ascertained by self-querying). In evaluation, users
judge that a majority (65%+) of generated chains clearly show how an answer
follows from a set of facts - substantially better than a high-performance
baseline. We also find that using simulated feedback, our system (called
EntailmentWriter) continually improves with time, requiring feedback on only
25% of training examples to reach within 1% of the upper-bound (feedback on all
examples). We observe a similar trend with real users. This suggests new
opportunities for using language models in an interactive setting where users
can inspect, debug, correct, and improve a system's performance over time.


title:
The outer orbit of the high-mass stellar triple system Herschel 36
  determined with the VLTI

abstract:
  Multiplicity is a ubiquitous characteristic of massive stars. Multiple
systems offer us a unique observational constraint on the formation of
high-mass systems. Herschel 36 A is a massive triple system composed of a close
binary (Ab1-Ab2) and an outer component (Aa). We measured the orbital motion of
the outer component of Herschel 36 A using infrared interferometry with the
AMBER and PIONIER instruments of ESO's Very Large Telescope Interferometer. Our
immediate aims are to constrain the masses of all components of this system and
to determine if the outer orbit is co-planar with the inner one. Reported
spectroscopic data for all three components of this system and our
interferometric data allow us to derive full orbital solutions for the outer
orbit Aa-Ab and the inner orbit Ab1-Ab2. For the first time, we derive the
absolute masses of mAa = 22.3 +/- 1.7 M_sun, mAb1 = 20.5 +/- 1.5 M_sun and mAb2
= 12.5 +/- 0.9 M_sun. Despite not being able to resolve the close binary
components, we infer the inclination of their orbit by imposing the same
parallax as the outer orbit. Inclinations derived from the inner and outer
orbits imply a modest difference of about 22 deg. between the orbital planes.
We discuss this result and the formation of Herschel 36 A in the context of
Core Accretion and Competitive Accretion models, which make different
predictions regarding the statistic of the relative orbital inclinations.


title: Strain driven conducting domain walls in a Mott insulator

abstract:
  Rewritable nanoelectronics offers new perspectives and potential to both
fundamental research and technological applications. Such interest has driven
the research focus into conducting domain walls: pseudo 2D conducting channels
that can be created, positioned, and deleted in situ. However, the study of
conductive domain walls is largely limited to wide-gap ferroelectrics, where
the conductivity typically arises from changes in charge carrier density, due
to screening charge accumulation at polar discontinuities. This work shows
that, in narrow-gap correlated insulators with strong charge lattice coupling,
local strain gradients can drive enhanced conductivity at the domain walls,
removing polar discontinuities as a criteria for conductivity. By combining
different scanning probe microscopy techniques, we demonstrate that the domain
wall conductivity in GaV4S8 does not follow the established screening charge
model but rather arises from the large surface reconstruction across the
Jahn-Teller transition and the associated strain gradients across the domain
walls. This mechanism can turn any structural, or even magnetic, domain wall
conducting, if the electronic structure of the host is susceptible to local
strain gradients, drastically expanding the range of materials and phenomena
that may be applicable to domain wall based nanoelectronics.


title: A Bubble Model for the Gating of K$_\mathrm{v}$ Channels

abstract:
  Voltage-gated K$_\mathrm{v}$ channels play fundamental roles in many
biological processes, such as the generation of the action potential. The
gating mechanism of K$_\mathrm{v}$ channels is characterized experimentally by
single-channel recordings and ensemble properties of the channel currents. In
this work, we propose a bubble model coupled with a Poisson-Nernst-Planck (PNP)
system to capture the key characteristics, particularly the delay in the
opening of channels. The coupled PNP system is solved numerically by a
finite-difference method and the solution is compared with an analytical
approximation. We hypothesize that the stochastic behaviour of the gating
phenomenon is due to randomness of the bubble and channel sizes. The predicted
ensemble average of the currents under various applied voltages across the
channels is consistent with experimental observations, and the Cole-Moore delay
is captured by varying the holding potential.


title:
The Rise of Buoyant Magnetic Structures through Convection with a
  Background Magnetic Field

abstract:
  Inspired by observations of sunspots embedded in active regions, it is often
assumed that large-scale, strong magnetic flux emerges from the Sun's deep
interior in the form of arched, cylindrical structures, colloquially known as
flux tubes. Here, we continue to examine the different dynamics encountered
when these structures are considered as concentrations in a volume-filling
magnetic field rather than as isolated entities in a field-free background. Via
2.5D numerical simulations, we consider the buoyant rise of magnetic flux
concentrations from a radiative zone through an overshooting convection zone
that self-consistently (via magnetic pumping) arranges a volume-filling
large-scale background field. This work extends earlier papers that considered
the evolution of such structures in a purely adiabatic stratification with an
assumed form of the background field. This earlier work established the
existence of a bias that created an increased likelihood of successful rise for
magnetic structures with one (relative) orientation of twist and a decreased
likelihood for the other. When applied to the solar context, this bias is
commensurate with the solar hemispherical helicity rules (SHHR). This paper
establishes the robustness of this selection mechanism in a model incorporating
a more realistic background state, consisting of overshooting convection and a
turbulently-pumped mean magnetic field. Ultimately, convection only weakly
influences the selection mechanism, since it is enacted at the initiation of
the rise, at the edge of the overshoot zone. Convection does however add
another layer of statistical fluctuations to the bias, which we investigate in
order to explain variations in the SHHR.


title: False vacuum Skyrmions revisited

abstract:
  We consider the classical static soliton solutions of the Skyrme model with
false vacuum potential. We make use of fully three-dimensional relaxation
calculations to construct global energy minimizers in the sectors of
topological degrees from $Q=1$ to $Q=6$. These solutions may be metastable,
they contain a domain of true vacuum inside the core. Further, we explore small
regions of negative topological charge density which appear for the Skyrmions
of degrees $Q=3,5,6$.


title:
Global existence versus blow-up for multi-d hyperbolized compressible
  Navier-Stokes equations

abstract:
  We consider the non-isentropic compressible Navier-Stokes equations in two or
three space dimensions for which the heat conduction of Fourier's law is
replaced by Cattaneo's law and the classical Newtonian flow is replaced by a
revised Maxwell flow. We show that a physical entropy exists for this new
model. For two special cases, we show the global well-posedness of solutions
with small initial data and the blow-up of solutions in finite time for a class
of large initial data. Moreover, for vanishing relaxation parameters, the
solutions (if it exists) are shown to converge to solutions of the classical
system.


title: Diffraction of an Off-axis Vector-Beam by a Tilted Aperture

abstract:
  Manifestations of orbital angular momentum induced effects in the diffraction
of a radially polarized vector beam by an off-axis tilted aperture are studied
both experimentally and theoretically. Experiments were carried out to extract
the degree of circular polarization, which was shown to be proportional to the
on-axis component of the spin angular momentum density. We report a clear
separation of the regions of dominance of the right and left circular
polarizations associated with positive and negative topological charges
respectively, which is reminiscent of the standard vortex-induced transverse
shift, albeit in the diffraction scenario. The experimental results are
supported by model simulations and the agreement is quite satisfactory. The
results are useful to appreciate the orbit-orbit related effects due to
unavoidable misalignment problems (especially for vortex beams).


title:
Electrified Autonomous Freight Benefit analysis on Fleet, Infrastructure
  and Grid Leveraging Grid-Electrified Mobility (GEM) Model

abstract:
  This paper analyzes the potential benefit of heavy-duty vehicle (HDV)
electrification and automation on fleet cost, infrastructure cost, grid, and
environmental impact. In this work, we extended the vehicle electrification
benefit analysis tool: Grid-Electrified Mobility (GEM) model, which had
primarily been used to study light-duty passenger vehicles (LDVs), to analyze
the heavy-duty vehicle electrification. The extended model is derived for
freight transportation and key results and findings on the impact of freight
electrification and automation are presented and discussed.


title:
Mean-square stability of linear systems over channels with random
  transmission delays

abstract:
  This work studies the mean-square stability and stabilization problem for
netwroked feedback systems. Data transmission delays in the network channels of
the systems are considered. It is assumed that these delays are i.i.d.
processes with given PMFs. A necessary and sufficient condition of mean-square
(input-output) stability is studied for the networked feedback systems in both
time-domain and frequency-domain. Furthermore, according to this condition,
mean-square stabization via output feedback is studied for the networked
feedback systems in terms of the state-space models.


title:
Dark matter halo mass density profiles around an accreting supermassive
  blackhole in the Schr\"{o}dinger-Newton approach

abstract:
  We study how supermassive blackhole accretion affects the mass density
profile of a fuzzy dark matter soliton core at the centre of a dark matter
halo. The supermassive blackhole at the center of a galaxy was regarded as a
point mass. The Schr\"{o}dinger-Newton equation for the scalar field was solved
numerically. We find that the time-dependent perturbation has a significant
squeezing effect on the soliton density profile, which both reduces the size of
the core and increases the central density.


title: Beryllium isotopic composition and Galactic cosmic ray propagation

abstract:
  The isotopic composition of beryllium nuclei and its energy dependence encode
information of fundamental importance about the propagation of cosmic rays in
the Galaxy. The effects of decay on the spectrum of the unstable beryllium--10
isotope can be described introducing the average survival probability $P_{\rm
surv} (E_0)$ that can inferred from measurements of the isotopic ratio Be10/Be9
if one has sufficiently good knowledge of the nuclear fragmentation cross
sections that determine the isotopic composition of beryllium nuclei at
injection. The average survival probability can then be interpreted in terms of
propagation parameters, such as the cosmic ray average age, adopting a
theoretical framework for Galactic propagation. Recently the AMS02
Collaboration has presented preliminary measurements of the beryllium isotopic
composition that extend the observations to a broad energy range ($E_0 \simeq
0.7$-12 GeV/n) with small errors. In this work we discuss the average survival
probability that can be inferred from the preliminary AMS02 data, adopting
publically available models of the nuclear fragmentation cross sections, and
interpret the results in the framework of a simple diffusion model, This study
shows that the effects of decay decrease more slowly than the predictions,
resulting in an average cosmic ray age that increases with energy. An
alternative possibility is that the cosmic ray age distribution is broader than
in the models that are now commonly accepted, suggesting that the Galactic
confinement volume has a non trivial structure and is formed by an inner halo
contained in an extended one.


title:
A note on the rational homological dimension of lattices in positive
  characteristic

abstract:
  We show via $\ell^2$-homology that the rational homological dimension of a
lattice in a product of simple simply connected Chevalley groups over global
function fields is equal to the rational cohomological dimension and to the
dimension of the associated Bruhat--Tits building.


title:
Faster online calibration without randomization: interval forecasts and
  the power of two choices

abstract:
  We study the problem of making calibrated probabilistic forecasts for a
binary sequence generated by an adversarial nature. Following the seminal paper
of Foster and Vohra (1998), nature is often modeled as an adaptive adversary
who sees all activity of the forecaster except the randomization that the
forecaster may deploy. A number of papers have proposed randomized forecasting
strategies that achieve an $\epsilon$-calibration error rate of
$O(1/\sqrt{T})$, which we prove is tight in general. On the other hand, it is
well known that it is not possible to be calibrated without randomization, or
if nature also sees the forecaster's randomization; in both cases the
calibration error could be $\Omega(1)$. Inspired by the equally seminal works
on the "power of two choices" and imprecise probability theory, we study a
small variant of the standard online calibration problem. The adversary gives
the forecaster the option of making two nearby probabilistic forecasts, or
equivalently an interval forecast of small width, and the endpoint closest to
the revealed outcome is used to judge calibration. This power of two choices,
or imprecise forecast, accords the forecaster with significant power -- we show
that a faster $\epsilon$-calibration rate of $O(1/T)$ can be achieved even
without deploying any randomization.


title: Local Order Metrics for Two-Phase Media Across Length Scales

abstract:
  The capacity to devise order metrics for microstructures of multiphase
heterogeneous media is a highly challenging task, given the richness of the
possible geometries and topologies of the phases that can arise. This
investigation initiates a program to formulate order metrics to characterize
the degree of order/disorder of the microstructures of two-phase media in
$d$-dimensional Euclidean space $\mathbb{R}^d$ across length scales. In
particular, we propose the use of the local volume-fraction variance
$\sigma^2_{_V}(R)$ associated with a spherical window of radius $R$ as an order
metric. We determine $\sigma^2_{_V}(R)$ as a function of $R$ for 22 different
models across the first three space dimensions, including both hyperuniform and
nonhyperuniform systems with varying degrees of short- and long-range order. We
find that the local volume-fraction variance as well as asymptotic coefficients
and integral measures derived from it provide reasonably robust and sensitive
order metrics to categorize disordered and ordered two-phase media across all
length scales.


title:
Variational Kalman Filtering with Hinf-Based Correction for Robust
  Bayesian Learning in High Dimensions

abstract:
  In this paper, we address the problem of convergence of sequential
variational inference filter (VIF) through the application of a robust
variational objective and Hinf-norm based correction for a linear Gaussian
system. As the dimension of state or parameter space grows, performing the full
Kalman update with the dense covariance matrix for a large scale system
requires increased storage and computational complexity, making it impractical.
The VIF approach, based on mean-field Gaussian variational inference, reduces
this burden through the variational approximation to the covariance usually in
the form of a diagonal covariance approximation. The challenge is to retain
convergence and correct for biases introduced by the sequential VIF steps. We
desire a framework that improves feasibility while still maintaining reasonable
proximity to the optimal Kalman filter as data is assimilated. To accomplish
this goal, a Hinf-norm based optimization perturbs the VIF covariance matrix to
improve robustness. This yields a novel VIF- Hinf recursion that employs
consecutive variational inference and Hinf based optimization steps. We explore
the development of this method and investigate a numerical example to
illustrate the effectiveness of the proposed filter.


title:
Bosonic pair production and squeezing for optical phase measurements in
  long-lived dipoles coupled to a cavity

abstract:
  We propose to simulate bosonic pair creation using large arrays of long-lived
dipoles with multilevel internal structure coupled to an undriven optical
cavity. Entanglement between the atoms, generated by the exchange of virtual
photons through a common cavity mode, grows exponentially fast and is described
by two-mode squeezing (TMS) of effective bosonic quadratures. The mapping
between an effective bosonic model and the natural spin description of the
dipoles allows us to realize the analog of optical homodyne measurements via
straightforward global rotations and population measurements of the electronic
states, and we propose to exploit this for quantum-enhanced sensing of an
optical phase (common and differential between two ensembles). We discuss a
specific implementation based on Sr atoms and show that our sensing protocol is
robust to sources of decoherence intrinsic to cavity platforms. Our proposal
can open unique opportunities for the observation of continuous variable
entanglement in atomic systems and associated applications in next-generation
optical atomic clocks.


title:
Attention Consistency on Visual Corruptions for Single-Source Domain
  Generalization

abstract:
  Generalizing visual recognition models trained on a single distribution to
unseen input distributions (i.e. domains) requires making them robust to
superfluous correlations in the training set. In this work, we achieve this
goal by altering the training images to simulate new domains and imposing
consistent visual attention across the different views of the same sample. We
discover that the first objective can be simply and effectively met through
visual corruptions. Specifically, we alter the content of the training images
using the nineteen corruptions of the ImageNet-C benchmark and three additional
transformations based on Fourier transform. Since these corruptions preserve
object locations, we propose an attention consistency loss to ensure that class
activation maps across original and corrupted versions of the same training
sample are aligned. We name our model Attention Consistency on Visual
Corruptions (ACVC). We show that ACVC consistently achieves the state of the
art on three single-source domain generalization benchmarks, PACS, COCO, and
the large-scale DomainNet.


title:
Newly observed $a_0(1817)$ as the scaling point of constructing the
  scalar meson spectroscopy

abstract:
  Stimulated by the newly observed $a_0(1817)$ by the BESIII Collaboration, we
find a perfect Regge trajectory composed of the $a_0(980)$, $a_0(1450)$, and
$a_0(1817)$, which leads us to categorize the $a_0(980)$, $a_0(1450)$, and
$a_0(1817)$ into the isovector scalar meson family. This scenario is supported
by their two-body Okubo-Zweig-Iizuka allowed strong decay behaviors. In this
scheme, we also predict the third radial excitation of the $a_0(980)$, which is
denoted as the $a_0(2115)$, accessible at future experiment as a direct test of
this assignment. We find another Regge trajectory which contains three
isoscalar scalar states $f_0(980)$, $X(1812)$, and $f_0(2100)$. We investigate
their two-body Okubo-Zweig-Iizuka allowed strong decay patterns, which are
roughly consistent with the experimental data. The $f_0(980)$, $X(1812)$, and
$f_0(2100)$ can be well grouped into the isoscalar scalar meson family. We want
to emphasize that these two Regge trajectories have a similar slope. In
summary, the present work provides a scheme of constructing the scalar meson
family based on these reported scalar states. The possibility of the
$f_0(1710)$ as the candidate of the scalar glueball cannot be excluded by the
observation of the $a_0(1817)$ since the $a_0(1817)$ is more suitable as the
isovector partner of the $X(1812)$.


title: Symmetric doubly periodic gravity-capillary waves with small vorticity

abstract:
  We construct small amplitude gravity-capillary water waves with small nonzero
vorticity, in three spatial dimensions, bifurcating from uniform flows. The
waves are symmetric, and periodic in both horizontal coordinates. The proof is
inspired by Lortz' construction of magnetohydrostatic equilibria in
reflection-symmetric toroidal domains. It relies on a representation of the
vorticity as the cross product of two gradients, and on prescribing a
functional relationship between the Bernoulli function and the orbital period
of the water particles. The presence of the free surface introduces significant
new challenges. In particular, the resulting free boundary problem is not
elliptic, and the involved maps incur a loss of regularity under Fr\'echet
differentiation. Nevertheless, we show that a version of the
Crandall--Rabinowitz local bifurcation method applies, by carefully tracking
the loss of regularity.


title: Unsupervised Word Segmentation using K Nearest Neighbors

abstract:
  In this paper, we propose an unsupervised kNN-based approach for word
segmentation in speech utterances. Our method relies on self-supervised
pre-trained speech representations, and compares each audio segment of a given
utterance to its K nearest neighbors within the training set. Our main
assumption is that a segment containing more than one word would occur less
often than a segment containing a single word. Our method does not require
phoneme discovery and is able to operate directly on pre-trained audio
representations. This is in contrast to current methods that use a two-stage
approach; first detecting the phonemes in the utterance and then detecting
word-boundaries according to statistics calculated on phoneme patterns.
Experiments on two datasets demonstrate improved results over previous
single-stage methods and competitive results on state-of-the-art two-stage
methods.


title:
Requirements on Quantum Superpositions of Macro-Objects for Sensing
  Neutrinos

abstract:
  We examine a macroscopic system in a quantum superposition of two spatially
separated localized states as a detector for a stream of weakly interacting
relativistic particles. We do this using the explicit example of neutrinos with
MeV-scale energy scattering from a solid object via neutral-current
neutrino-nucleus scattering. Presuming the (anti-)neutrino source to be a
nuclear fission reactor, we utilize the estimated flux and coherent elastic
neutrino-nucleus cross section to constrain the spatial separation ${\Delta}$x
and describe the temporal evolution of the sensing system. Particularly, we
find that a potentially measurable relative phase between quantum superposed
components is obtained for a single gram scale mass placed in a superposition
of spatial components separated by $10^{-14}$m under sufficient cooling and
background suppression.


title:
3D Magic Mirror: Clothing Reconstruction from a Single Image via a
  Causal Perspective

abstract:
  This research aims to study a self-supervised 3D clothing reconstruction
method, which recovers the geometry shape, and texture of human clothing from a
single 2D image. Compared with existing methods, we observe that three primary
challenges remain: (1) the conventional template-based methods are limited to
modeling non-rigid clothing objects, e.g., handbags and dresses, which are
common in fashion images; (2) 3D ground-truth meshes of clothing are usually
inaccessible due to annotation difficulties and time costs. (3) It remains
challenging to simultaneously optimize four reconstruction factors, i.e.,
camera viewpoint, shape, texture, and illumination. The inherent ambiguity
compromises the model training, such as the dilemma between a large shape with
a remote camera or a small shape with a close camera.
  In an attempt to address the above limitations, we propose a causality-aware
self-supervised learning method to adaptively reconstruct 3D non-rigid objects
from 2D images without 3D annotations. In particular, to solve the inherent
ambiguity among four implicit variables, i.e., camera position, shape, texture,
and illumination, we study existing works and introduce an explainable
structural causal map (SCM) to build our model. The proposed model structure
follows the spirit of the causal map, which explicitly considers the prior
template in the camera estimation and shape prediction. When optimization, the
causality intervention tool, i.e., two expectation-maximization loops, is
deeply embedded in our algorithm to (1) disentangle four encoders and (2) help
the prior template update. Extensive experiments on two 2D fashion benchmarks,
e.g., ATR, and Market-HQ, show that the proposed method could yield
high-fidelity 3D reconstruction. Furthermore, we also verify the scalability of
the proposed method on a fine-grained bird dataset, i.e., CUB.


title:
Learning to Borrow -- Relation Representation for Without-Mention
  Entity-Pairs for Knowledge Graph Completion

abstract:
  Prior work on integrating text corpora with knowledge graphs (KGs) to improve
Knowledge Graph Embedding (KGE) have obtained good performance for entities
that co-occur in sentences in text corpora. Such sentences (textual mentions of
entity-pairs) are represented as Lexicalised Dependency Paths (LDPs) between
two entities. However, it is not possible to represent relations between
entities that do not co-occur in a single sentence using LDPs. In this paper,
we propose and evaluate several methods to address this problem, where we
borrow LDPs from the entity pairs that co-occur in sentences in the corpus
(i.e. with mention entity pairs) to represent entity pairs that do not co-occur
in any sentence in the corpus (i.e. without mention entity pairs). We propose a
supervised borrowing method, SuperBorrow, that learns to score the suitability
of an LDP to represent a without-mention entity pair using pre-trained entity
embeddings and contextualised LDP representations. Experimental results show
that SuperBorrow improves the link prediction performance of multiple
widely-used prior KGE methods such as TransE, DistMult, ComplEx and RotatE.


title: A new instability in clustering dark energy?

abstract:
  In this paper, we study the effective field theory (EFT) of dark energy for
the $k$-essence model beyond linear order. Using particle-mesh $N$-body
simulations that consistently solve the dark energy evolution on a grid, we
find that the next-to-leading order in the EFT expansion, which comprises the
terms of the equations of motion that are quadratic in the field variables,
gives rise to a new instability in the regime of low speed of sound (high Mach
number). We rule out the possibility of a numerical artefact by considering
simplified cases in spherically and plane symmetric situations analytically. If
the speed of sound vanishes exactly, the non-linear instability makes the
evolution singular in finite time, signalling a breakdown of the EFT framework.
The case of finite (but small) speed of sound is subtle, and the local
singularity could be replaced by some other type of behaviour with strong
non-linearities. While an ultraviolet completion may cure the problem in
principle, there is no reason why this should be the case in general. As a
result, for a large range of the effective speed of sound $c_s$, a linear
treatment is not adequate.


title: DEPFET Active Pixel Sensors

abstract:
  An array of DEPFET pixels is one of several concepts to implement an active
pixel sensor. Similar to PNCCD and SDD detectors, the typically
$450~\mu\text{m}$ thick silicon sensor is fully depleted by the principle of
sideward depletion. They have furthermore in common to be back-illuminated
detectors, which allows for ultra-thin and homogeneous photon entrance windows.
This enables relatively high quantum efficiencies at low energies and close to
$100\%$ for photon energies between $1~\text{keV}$ and $10~\text{keV}$.
Steering of the DEPFET sensor is enabled by a so-called Switcher ASIC and
readout is performed by e.g. a VERITAS ASIC. The configuration enables a
readout time of a few microseconds per row. This results in full frame readout
times of a few milliseconds for a $512 \times 512$ pixel array in a rolling
shutter mode. The read noise is then typically three electrons equivalent noise
charge RMS. DEPFET detectors can be applied in particular for spectroscopy in
the energy band from $0.2~\text{keV}$ to $20~\text{keV}$. For example, an
energy resolution of about $130~\text{eV}~\text{FWHM}$ is achieved at an energy
of $6~\text{keV}$ which is close to the theoretical limit given by Fano noise.
Pixel sizes of a few tens of microns up to a centimetre are feasible by the
DEPFET concept.


title: Few-Shot Head Swapping in the Wild

abstract:
  The head swapping task aims at flawlessly placing a source head onto a target
body, which is of great importance to various entertainment scenarios. While
face swapping has drawn much attention, the task of head swapping has rarely
been explored, particularly under the few-shot setting. It is inherently
challenging due to its unique needs in head modeling and background blending.
In this paper, we present the Head Swapper (HeSer), which achieves few-shot
head swapping in the wild through two delicately designed modules. Firstly, a
Head2Head Aligner is devised to holistically migrate pose and expression
information from the target to the source head by examining multi-scale
information. Secondly, to tackle the challenges of skin color variations and
head-background mismatches in the swapping procedure, a Head2Scene Blender is
introduced to simultaneously modify facial skin color and fill mismatched gaps
in the background around the head. Particularly, seamless blending is achieved
with the help of a Semantic-Guided Color Reference Creation procedure and a
Blending UNet. Extensive experiments demonstrate that the proposed method
produces superior head swapping results in a variety of scenes.


title: Self-Supervised Learning of Object Parts for Semantic Segmentation

abstract:
  Progress in self-supervised learning has brought strong general image
representation learning methods. Yet so far, it has mostly focused on
image-level learning. In turn, tasks such as unsupervised image segmentation
have not benefited from this trend as they require spatially-diverse
representations. However, learning dense representations is challenging, as in
the unsupervised context it is not clear how to guide the model to learn
representations that correspond to various potential object categories. In this
paper, we argue that self-supervised learning of object parts is a solution to
this issue. Object parts are generalizable: they are a priori independent of an
object definition, but can be grouped to form objects a posteriori. To this
end, we leverage the recently proposed Vision Transformer's capability of
attending to objects and combine it with a spatially dense clustering task for
fine-tuning the spatial tokens. Our method surpasses the state-of-the-art on
three semantic segmentation benchmarks by 17%-3%, showing that our
representations are versatile under various object definitions. Finally, we
extend this to fully unsupervised segmentation - which refrains completely from
using label information even at test-time - and demonstrate that a simple
method for automatically merging discovered object parts based on community
detection yields substantial gains.


title: The Price and Cost of Bitcoin

abstract:
  Explaining changes in bitcoin's price and predicting its future have been the
foci of many research studies. In contrast, far less attention has been paid to
the relationship between bitcoin's mining costs and its price. One popular
notion is the cost of bitcoin creation provides a support level below which
this cryptocurrency's price should never fall because if it did, mining would
become unprofitable and threaten the maintenance of bitcoin's public ledger.
Other research has used mining costs to explain or forecast bitcoin's price
movements. Competing econometric analyses have debunked this idea, showing that
changes in mining costs follow changes in bitcoin's price rather than preceding
them, but the reason for this behavior remains unexplained in these analyses.
This research aims to employ economic theory to explain why econometric studies
have failed to predict bitcoin prices and why mining costs follow movements in
bitcoin prices rather than precede them. We do so by explaining the chain of
causality connecting a bitcoin's price to its mining costs.