Abstract:
  Accurate estimates of network parameters are essential for modeling,
monitoring, and control in power distribution systems. In this paper, we
develop a physics-informed graphical learning algorithm to estimate network
parameters of three-phase power distribution systems. Our proposed algorithm
uses only readily available smart meter data to estimate the three-phase series
resistance and reactance of the primary distribution line segments. We first
develop a parametric physics-based model to replace the black-box deep neural
networks in the conventional graphical neural network (GNN). Then we derive the
gradient of the loss function with respect to the network parameters and use
stochastic gradient descent (SGD) to estimate the physical parameters. Prior
knowledge of network parameters is also considered to further improve the
accuracy of estimation. Comprehensive numerical study results show that our
proposed algorithm yields high accuracy and outperforms existing methods.


Abstract:
  The typical contrastive self-supervised algorithm uses a similarity measure
in latent space as the supervision signal by contrasting positive and negative
images directly or indirectly. Although the utility of self-supervised
algorithms has improved recently, there are still bottlenecks hindering their
widespread use, such as the compute needed. In this paper, we propose a module
that serves as an additional objective in the self-supervised contrastive
learning paradigm. We show how the inclusion of this module to regress the
parameters of an affine transformation or homography, in addition to the
original contrastive objective, improves both performance and learning speed.
Importantly, we ensure that this module does not enforce invariance to the
various components of the affine transform, as this is not always ideal. We
demonstrate the effectiveness of the additional objective on two recent,
popular self-supervised algorithms. We perform an extensive experimental
analysis of the proposed method and show an improvement in performance for all
considered datasets. Further, we find that although both the general homography
and affine transformation are sufficient to improve performance and
convergence, the affine transformation performs better in all cases.


Abstract:
  We treat the change point problem in ergodic diffusion processes from
discrete observations. Tonaki et al. (2020) proposed adaptive tests for
detecting changes in the diffusion and drift parameters in ergodic diffusion
models. When any changes are detected by this method, the next question to be
considered is where the change point is. Therefore, we propose the method to
estimate the change point of the parameter for two cases: the case where there
is a change in the diffusion parameter, and the case where there is no change
in the diffusion parameter but a change in the drift parameter. Furthermore, we
present rates of convergence and distributional results of the change point
estimators. Some examples and simulation results are also given.


Abstract:
  We consider Dirac-like operators with piecewise constant mass terms on spin
manifolds, and we study the behaviour of their spectra when the mass parameters
become large. In several asymptotic regimes, effective operators appear: the
extrinsic Dirac operator and a generalized MIT Bag Dirac operator. This extends
some results previously known for the Euclidean spaces to the case of general
spin geometry.


Abstract:
  We derive a model based on the structure of dependence between a Brownian
motion and its reflection according to a barrier. The structure of dependence
presents two states of correlation: one of comonotonicity with a positive
correlation and one of countermonotonicity with a negative correlation. This
model of dependence between two Brownian motions $B^1$ and $B^2$ allows for the
value of $\mathbb{P}\left(B^1_t - B^2_t \geq x\right)$ to be higher than
$\frac{1}{2}$ when $x$ is close to 0, which is not the case when the dependence
is modeled by a constant correlation. It can be used for risk management and
option pricing in commodity energy markets. In particular, it allows to capture
the asymmetry in the distribution of the difference between electricity prices
and its combustible prices.


Abstract:
  Galaxy clusters are an important tool for cosmology, and their detection and
characterization are key goals for current and future surveys. Using data from
the Wide-field Infrared Survey Explorer (WISE), the Massive and Distant
Clusters of WISE Survey (MaDCoWS) located 2,839 significant galaxy
overdensities at redshifts $0.7\lesssim z\lesssim 1.5$, which included
extensive follow-up imaging from the Spitzer Space Telescope to determine
cluster richnesses. Concurrently, the Atacama Cosmology Telescope (ACT) has
produced large area mm-wave maps in three frequency bands along with a large
catalog of Sunyaev-Zeldovich (SZ) selected clusters, as part of its Data
Release 5 (DR5). Using the maps and cluster catalog from DR5, we explore the
scaling between SZ mass and cluster richness. We use complementary radio survey
data from the Very Large Array, submillimeter data from Herschel, and ACT
224~GHz data to assess the impact of contaminating sources on the SZ signals.
We then use a hierarchical Bayesian model to fit the mass-richness scaling
relation. We find that MaDCoWS clusters have submillimeter contamination which
is consistent with a gray-body spectrum, while the ACT clusters are consistent
with no submillimeter emission on average. We find the best fit ACT SZ mass vs.
MaDCoWS richness scaling relation has a slope of $\kappa =
1.84^{+0.15}_{-0.14}$, where the slope is defined as $M\propto
\lambda_{15}^{\kappa}$ where $\lambda_{15}$ is the richness. Additionally, we
find that the approximate level of in-fill of the ACT and MaDCoWS cluster SZ
signals to be at the percent level


Abstract:
  Classical soft graviton theorem gives the gravitational wave-form at future
null infinity at late retarded time $u$ for a general classical scattering. The
large $u$ expansion has three known universal terms: the constant term, the
term proportional to $1/u$ and the term proportional to $\ln u/u^2$, whose
coefficients are determined solely in terms of the momenta of incoming and the
outgoing hard particles, including the momenta carried by outgoing
gravitational and electromagnetic radiation produced during scattering. For the
constant term, also known as the memory effect, the dependence on the momenta
carried away by the final state radiation / massless particles is known as
non-linear memory or null memory. It was shown earlier that for the coefficient
of the $1/u$ term the dependence on the momenta of the final state massless
particles / radiation cancels and the result can be written solely in terms of
the momenta of the incoming particles / radiation and the final state massive
particles. In this note we show that the same result holds for the coefficient
of the $\ln u/u^2$ term. Our result implies that for scattering of massless
particles the coefficients of the $1/u$ and $\ln u/u^2$ terms are determined
solely by the incoming momenta, even if the particles coalesce to form a black
hole and massless radiation. We use our result to compute the low frequency
flux of gravitational radiation from the collision of massless particles at
large impact parameter.


Abstract:
  This paper presents AppealNet, a novel edge/cloud collaborative architecture
that runs deep learning (DL) tasks more efficiently than state-of-the-art
solutions. For a given input, AppealNet accurately predicts on-the-fly whether
it can be successfully processed by the DL model deployed on the
resource-constrained edge device, and if not, appeals to the more powerful DL
model deployed at the cloud. This is achieved by employing a two-head neural
network architecture that explicitly takes inference difficulty into
consideration and optimizes the tradeoff between accuracy and
computation/communication cost of the edge/cloud collaborative architecture.
Experimental results on several image classification datasets show up to more
than 40% energy savings compared to existing techniques without sacrificing
accuracy.


Abstract:
  We theoretically investigate Majorana zero modes emerging in an anisotropic
XY spin chain with second neighbor interactions. The spin chain is
mathematically equivalent to the Kitaev chain composing of spinless fermions if
only nearest-neighbor interactions are considered. Recent studies on the Kitaev
chain with long-ranged interactions, have presented coexistence of several
Majorana zero modes. Investigating the topological phase diagram of the
anisotropic XY spin chain with second neighbor interactions, we find
coexistence of several Majorana zero modes similar to the Kitaev chain.
However, we confirm that one of zero modes is restricted into a Hilbert
subspace. The mode is regarded as a so-called weak zero mode that should
exhibit thermodynamical properties different from a (strong) zero mode
appearing in the whole Hilbert space. Since the quantum statistics of spin is
the same as a hard-core boson, we expect an experimental realization of the
weak mode in not only quantum spin materials but also optical lattices for cold
atom systems.


Abstract:
  For an algebraic K3 surface with complex multiplication (CM), algebraic
fibres of the associated twistor space away from the equator are again of CM
type. In this paper, we show that algebraic fibres corresponding to points at
the same altitude of the twistor base $S^2\simeq \mathbb{P}^1_\mathbb{C}$ share
the same CM endomorphism field. Moreover, we determine all the admissible
Picard numbers of the twistor fibres.


Abstract:
  This paper proposes a service restoration model for unbalanced distribution
systems and inverter-dominated microgrids (MGs), in which frequency dynamics
constraints are developed to optimize the amount of load restoration and
guarantee the dynamic performance of system frequency response during the
restoration process. After extreme events, the damaged distribution systems can
be sectionalized into several isolated MGs to restore critical loads and
tripped non-black start distributed generations (DGs) by black start DGs.
However, the high penetration of inverter-based DGs reduces the system inertia,
which results in low-inertia issues and large frequency fluctuation during the
restoration process. To address this challenge, we propose a two-level
simulation-assisted sequential service restoration model, which includes a
mixed integer linear programming (MILP)-based optimization model and a
transient simulation model. The proposed MILP model explicitly incorporates the
frequency response into constraints, by interfacing with transient simulation
of inverter-dominated MGs. Numerical results on a modified IEEE 123-bus system
have validated that the frequency dynamic performance of the proposed service
restoration model are indeed improved.


Abstract:
  Researchers and practitioners across many disciplines have recently adopted
computational notebooks to develop, document, and share their scientific
workflows - and the GIS community is no exception. This chapter introduces
computational notebooks in the geographical context. It begins by explaining
the computational paradigm and philosophy that underlie notebooks. Next it
unpacks their architecture to illustrate a notebook user's typical workflow.
Then it discusses the main benefits notebooks offer GIS researchers and
practitioners, including better integration with modern software, more natural
access to new forms of data, and better alignment with the principles and
benefits of open science. In this context, it identifies notebooks as the glue
that binds together a broader ecosystem of open source packages and
transferable platforms for computational geography. The chapter concludes with
a brief illustration of using notebooks for a set of basic GIS operations.
Compared to traditional desktop GIS, notebooks can make spatial analysis more
nimble, extensible, and reproducible and have thus evolved into an important
component of the geospatial science toolkit.


Abstract:
  Context: In order to preserve the value of Systematic Reviews (SRs), they
should be frequently updated considering new evidence that has been produced
since the completion of the previous version of the reviews. However, the
update of an SR is a time consuming, manual task. Thus, many SRs have not been
updated as they should be and, therefore, they are currently outdated.
Objective: The main contribution of this paper is to support the update of SRs.
Method: We propose USR-VTM, an approach based on Visual Text Mining (VTM)
techniques, to support selection of new evidence in the form of primary
studies. We then present a tool, named Revis, which supports our approach.
Finally, we evaluate our approach through a comparison of outcomes achieved
using USR-VTM versus the traditional (manual) approach. Results: Our results
show that USR-VTM increases the number of studies correctly included compared
to the traditional approach. Conclusions: USR-VTM effectively supports the
update of SRs.


Abstract:
  This paper is a theoretical and numerical study of the uniform growth of a
repeating sinusoidal imperfection in the line of a strut on a nonlinear elastic
Winkler type foundation. The imperfection is introduced by considering an
initially deformed shape which is a sine function with an half wavelength. The
restoring force is either a bi-linear or an exponential profile. Periodic
solutions of the equilibrium problem are found using three different
approaches: a semi-analytical method, an explicit solution of a Galerkin method
and a direct numerical resolution. These methods are found in very good
agreement and show the existence of a maximum imperfection size which leads to
a limit point in the equilibrium curve of the system. The existence of this
limit point is very important since it governs the appearance of localization
phenomena.
  Using the Galerkin method, we then establish an exact formula for this
maximum imperfection size and we show that it does not depend on the choice of
the restoring force. We also show that this method provides a better estimate
with respect to previous publications. The decrease of the maximum compressive
force supported by the beam as a function of the imperfection magnitude is also
determined. We show that the leading term of the development has a different
exponent than in subcritical buckling of elastic systems, and that the exponent
values depend on the choice of the restoring force.


Abstract:
  The determination of the ground state of quantum many-body systems via
digital quantum computers rests upon the initialization of a sufficiently
educated guess. This requirement becomes more stringent the greater the system.
Preparing physically-motivated ans\"{a}tze on quantum hardware is therefore
important to achieve quantum advantage in the simulation of correlated
electrons. In this spirit, we introduce the Gutzwiller Wave Function (GWF)
within the context of the digital quantum simulation of the Fermi-Hubbard
model. We present a quantum routine to initialize the GWF that comprises two
parts. In the first, the noninteracting state associated with the $U = 0$ limit
of the model is prepared. In the second, the non-unitary Gutzwiller projection
that selectively removes states with doubly-occupied sites from the wave
function is performed by adding to every lattice site an ancilla qubit, the
measurement of which in the $|0\rangle$ state confirms the projection was made.
Due to its non-deterministic nature, we estimate the success rate of the
algorithm in generating the GWF as a function of the lattice size and the
interaction strength $U/t$. The scaling of the quantum circuit metrics and its
integration in general quantum simulation algorithms are also discussed.


Abstract:
  In Twin Higgs models which contain the minimal particle content required to
address the little hierarchy problem (i.e. fraternal models), the twin tau has
been identified as a promising candidate for dark matter. In this class of
scenarios, however, the elastic scattering cross section of the twin tau with
nuclei exceeds the bounds from XENON1T and other recent direct detection
experiments. In this paper, we propose a modification to the Fraternal Twin
Higgs scenario that we call $\mathbb{Z}_2$FTH, incorporating visible and twin
hypercharged scalars (with $Y = 2$) which break twin electromagnetism. This
leads to new mass terms for the twin tau that are unrelated to its Yukawa
coupling, as well as additional annihilation channels via the massive twin
photon. We show that these features make it possible for the right-handed twin
tau to freeze out with an acceptable thermal relic abundance while scattering
with nuclei at a rate that is well below existing constraints. Nonetheless,
large portions of the currently viable parameter space in this model are within
the reach of planned direct detection experiments. The prospects for indirect
detection using gamma rays and cosmic-ray antiprotons are also promising in
this model. Furthermore, if the twin neutrino is light, the predicted deviation
of $\Delta N_\mathrm{eff} \approx 0.1$ would be within reach of Stage 4 CMB
experiments. Finally, the high luminosity LHC should be able to probe the
entire parameter space of the $\mathbb{Z}_2$FTH model through charged scalar
searches. We also discuss how searches for long-lived particles are starting to
constrain Fraternal Twin Higgs models.


Abstract:
  We define "robotic contact juggling" to be the purposeful control of the
motion of a three-dimensional smooth object as it rolls freely on a
motion-controlled robot manipulator, or "hand." While specific examples of
robotic contact juggling have been studied before, in this paper we provide the
first general formulation and solution method for the case of an arbitrary
smooth object in single-point rolling contact on an arbitrary smooth hand. Our
formulation splits the problem into four subproblems: (1) deriving the
second-order rolling kinematics; (2) deriving the three-dimensional rolling
dynamics; (3) planning rolling motions that satisfy the rolling dynamics; and
(4) feedback stabilization of planned rolling trajectories. The theoretical
results are demonstrated in simulation and experiment using feedback from a
high-speed vision system.


Abstract:
  Non-equilibrium defects often dictate macroscopic functional properties of
materials. In intercalation hosts, widely used in rechargeable batteries,
high-dimensional defects largely define reversibility and kinetics1,2,3,4.
However, transient defects briefly appearing during ionic transport have been
challenging to capture, limiting the understanding of their life cycle and
impact. Here, we overcome this challenge and track operando the interaction and
impact of metastable defects within NaxNi1-xMnyO2 intercalation hosts in a
charging sodium-ion battery. Three-dimensional coherent X-ray imaging3,4,5
reveals transformation and self-healing of a metastable domain boundary,
glissile dislocation loop, and stacking fault. A local strain gradient suggests
a quantifiable difference in ion diffusion, coincident with the macroscopic
change in diffusion coefficient. Analysis of the unexpected4,6 defect
anisotropy highlights the importance of mesostructure, suggesting a possible
control approach and disputing the rigidity of the framework layers. The shared
nature of oxygen framework layers makes our results applicable to a wide range
of intercalation materials.


Abstract:
  It is known that the Grothendieck group of the category of Schur functors is
the ring of symmetric functions. This ring has a rich structure, much of which
is encapsulated in the fact that it is a "plethory": a monoid in the category
of birings with its substitution monoidal structure. We show that similarly the
category of Schur functors is a "2-plethory", which descends to give the
plethory structure on symmetric functions. Thus, much of the structure of
symmetric functions exists at a higher level in the category of Schur functors.


Abstract:
  Voice activity detection is an essential pre-processing component for
speech-related tasks such as automatic speech recognition (ASR). Traditional
supervised VAD systems obtain frame-level labels from an ASR pipeline by using,
e.g., a Hidden Markov model. These ASR models are commonly trained on clean and
fully transcribed data, limiting VAD systems to be trained on clean or
synthetically noised datasets. Therefore, a major challenge for supervised VAD
systems is their generalization towards noisy, real-world data. This work
proposes a data-driven teacher-student approach for VAD, which utilizes vast
and unconstrained audio data for training. Unlike previous approaches, only
weak labels during teacher training are required, enabling the utilization of
any real-world, potentially noisy dataset. Our approach firstly trains a
teacher model on a source dataset (Audioset) using clip-level supervision.
After training, the teacher provides frame-level guidance to a student model on
an unlabeled, target dataset. A multitude of student models trained on mid- to
large-sized datasets are investigated (Audioset, Voxceleb, NIST SRE). Our
approach is then respectively evaluated on clean, artificially noised, and
real-world data. We observe significant performance gains in artificially
noised and real-world scenarios. Lastly, we compare our approach against other
unsupervised and supervised VAD methods, demonstrating our method's
superiority.


Abstract:
  Abell 3266 is one of the X-ray brightest galaxy clusters in the sky and is a
well-known merging system. Using the ability of the eROSITA telescope onboard
SRG (Spectrum R\"ontgen Gamma) to observe a wide field with a single pointing,
we analysed a new observation of the cluster out to a radius of R_200. The
X-ray images highlight sub-structures present in the cluster, including the
north-east--south-west merger seen in previous ASCA, Chandra, and XMM-Newton
data, a merging group towards the north-west, and filamentary structures
between the core and one or more groups towards the west. We compute spatially
resolved spectroscopic maps of the thermodynamic properties of the cluster,
including the metallicity. The merging subclusters are seen as low entropy
material within the cluster. The filamentary structures could be the rims of a
powerful outburst of an active galactic nucleus, or most likely material
stripped from the western group(s) as they passed through the cluster core.
Seen in two directions is a pressure jump at a radius of 1.1 Mpc, which is
consistent with a shock with a Mach number of ~1.5-1.7. The eROSITA data
confirm that the cluster is not a simple merging system, but it is made up of
several subclusters which are merging or will shortly merge. We computed a
hydrostatic mass from the eROSITA data, finding good agreement with a previous
XMM-Newton result. With this pointing we detect several extended sources, where
we find secure associations between z=0.36-1.0 for seven of them, that is
background galaxy groups and clusters, highlighting the power of eROSITA to
find such systems.


Abstract:
  In this paper we describe an algebraic/geometrical approach to quantum
scrambling. Generalized quantum subsystems are described by a $*$-closed unital
subalgebra $\cal A$ of operators evolving through a unitary channel. Quantum
scrambling is defined by how the associated physical degrees of freedom get
mixed up with others by the dynamics. This is accomplished by introducing a
quantity, the geometric algebra anti-correlator (GAAC), which measures the
degree of self-orthogonalization of the commutant of $\cal A$ induced by the
dynamics. This approach extends and unifies averaged bipartite OTOC, operator
entanglement, coherence generating power and Loschmidt echo. Each of these
concepts is indeed recovered by a special choice of $\cal A$. We compute
typical values of GAAC for random unitaries, we prove upper bounds and
characterize their saturation. For generic energy spectrum we find explicit
expressions for the infinite-time average of the GAAC which encode the relation
between $\cal A$ and the full system of Hamiltonian eigenstates. Finally, a
notion of ${\cal A}$-chaoticity is suggested.


Abstract:
  In this work we introduce a new concept, namely, $\tau_{s}$-extending modules
(rings) which is torsion-theoretic analogues of extending modules and then we
extend many results from extending modules to this new concept. For instance we
show that for any ring $R$ with unit, if $R$ is purely $\tau_{s}$-extending
then every cyclic $\tau$-nonsingular $R$-module is flat and we show that this
fact is true over a principal ideal domain as well. Also, we make a
classification for the direct sums of the rings to be purely
$\tau_{s}$-extending.


Abstract:
  Time series subject to change in regime have attracted much interest in
domains such as econometry, finance or meteorology. For discrete-valued
regimes, some models such as the popular Hidden Markov Chain (HMC) describe
time series whose state process is unknown at all time-steps. Sometimes, time
series are firstly labelled thanks to some annotation function. Thus, another
category of models handles the case with regimes observed at all time-steps. We
present a novel model which addresses the intermediate case: (i) state
processes associated to such time series are modelled by Partially Hidden
Markov Chains (PHMCs); (ii) a linear autoregressive (LAR) model drives the
dynamics of the time series, within each regime. We describe a variant of the
expection maximization (EM) algorithm devoted to PHMC-LAR model learning. We
propose a hidden state inference procedure and a forecasting function that take
into account the observed states when existing. We assess inference and
prediction performances, and analyze EM convergence times for the new model,
using simulated data. We show the benefits of using partially observed states
to decrease EM convergence times. A fully labelled scheme with unreliable
labels also speeds up EM. This offers promising prospects to enhance PHMC-LAR
model selection. We also point out the robustness of PHMC-LAR to labelling
errors in inference task, when large training datasets and moderate labelling
error rates are considered. Finally, we highlight the remarkable robustness to
error labelling in the prediction task, over the whole range of error rates.


Abstract:
  We study two classes of summary-based cardinality estimators that use
statistics about input relations and small-size joins in the context of graph
database management systems: (i) optimistic estimators that make uniformity and
conditional independence assumptions; and (ii) the recent pessimistic
estimators that use information theoretic linear programs. We begin by
addressing the problem of how to make accurate estimates for optimistic
estimators. We model these estimators as picking bottom-to-top paths in a
cardinality estimation graph (CEG), which contains sub-queries as nodes and
weighted edges between sub-queries that represent average degrees. We outline a
space of heuristics to make an optimistic estimate in this framework and show
that effective heuristics depend on the structure of the input queries. We
observe that on acyclic queries and queries with small-size cycles, using the
maximum-weight path is an effective technique to address the well known
underestimation problem for optimistic estimators. We show that on a large
suite of datasets and workloads, the accuracy of such estimates is up to three
orders of magnitude more accurate in mean q-error than some prior heuristics
that have been proposed in prior work. In contrast, we show that on queries
with larger cycles these estimators tend to overestimate, which can partially
be addressed by using minimum weight paths and more effectively by using an
alternative CEG. We then show that CEGs can also model the recent pessimistic
estimators. This surprising result allows us to connect two disparate lines of
work on optimistic and pessimistic estimators, adopt an optimization from
pessimistic estimators to optimistic ones, and provide insights into the
pessimistic estimators, such as showing that there are alternative
combinatorial solutions to the linear programs that define them.


Abstract:
  Epileptic seizures are a type of neurological disorder that affect many
people worldwide. Specialist physicians and neurologists take advantage of
structural and functional neuroimaging modalities to diagnose various types of
epileptic seizures. Neuroimaging modalities assist specialist physicians
considerably in analyzing brain tissue and the changes made in it. One method
to accelerate the accurate and fast diagnosis of epileptic seizures is to
employ computer aided diagnosis systems (CADS) based on artificial intelligence
(AI) and functional and structural neuroimaging modalities. AI encompasses a
variety of areas, and one of its branches is deep learning (DL). Not long ago,
and before the rise of DL algorithms, feature extraction was an essential part
of every conventional machine learning method, yet handcrafting features limit
these models' performances to the knowledge of system designers. DL methods
resolved this issue entirely by automating the feature extraction and
classification process; applications of these methods in many fields of
medicine, such as the diagnosis of epileptic seizures, have made notable
improvements. In this paper, a comprehensive overview of the types of DL
methods exploited to diagnose epileptic seizures from various neuroimaging
modalities has been studied. Additionally, rehabilitation systems and cloud
computing in epileptic seizures diagnosis applications have been exactly
investigated using various modalities.


Abstract:
  Brain Computer Interface (BCI) helps in processing and extraction of useful
information from the acquired brain signals having applications in diverse
fields such as military, medicine, neuroscience, and rehabilitation. BCI has
been used to support paralytic patients having speech impediments with severe
disabilities. To help paralytic patients communicate with ease, BCI based
systems convert silent speech (thoughts) to text. However, these systems have
an inconvenient graphical user interface, high latency, limited typing speed,
and low accuracy rate. Apart from these limitations, the existing systems do
not incorporate the inevitable factor of a patient's emotional states and
sentiment analysis. The proposed system EmoWrite implements a dynamic keyboard
with contextualized appearance of characters reducing the traversal time and
improving the utilization of the screen space. The proposed system has been
evaluated and compared with the existing systems for accuracy, convenience,
sentimental analysis, and typing speed. This system results in 6.58 Words Per
Minute (WPM) and 31.92 Characters Per Minute (CPM) with an accuracy of 90.36
percent. EmoWrite also gives remarkable results when it comes to the
integration of emotional states. Its Information Transfer Rate (ITR) is also
high as compared to other systems i.e., 87.55 bits per min with commands and
72.52 bits per min for letters. Furthermore, it provides easy to use interface
with a latency of 2.685 sec.


Abstract:
  In this paper, we design and simulate a terahertz (THz) controllable active
plasmonic waveguide structure based on a single graphene layer that is placed
on a random silicon grating substrate. Optical gain in the proposed THz active
plasmonic waveguide structure is provided by the stimulated emission process in
the photoexcited graphene monolayer that leads to the amplification of surface
plasmon polariton (SPP) waves. We use a random grating substrate to introduce
Anderson localization of the SPP waves propagating through the graphene
monolayer to enhance their optical amplification at resonant frequencies. It is
shown that the enhancement factor of the resonant peaks corresponding to the
graphene SPPs can be as high as 175. We also analyze their corresponding field
intensity distributions along the graphene monolayer and find out that their
intensities and localization positions are different from each other. By
investigating the pump dependent and temperature dependent variations of the
transmittance of the structure, it is shown that the resonant peak frequencies
are blue-shifted by increasing the temperature and the external pump intensity.
Also, we show that increasing the ambient temperature by 60 K can dramatically
reduce the output amplified intensity by a factor of 70. This property of the
proposed graphene-based THz plasmonic waveguide structure makes it useful in
temperature sensing applications and on/off switchable laser devices.


Abstract:
  We classify Einstein metrics on $\mathbb{R}^4$ invariant under a
four-dimensional group of isometries including a principal action of the
Heisenberg group. The metrics are either Ricci-flat or of negative Ricci
curvature. We show that all of the Ricci-flat metrics, including the simplest
ones which are hyper-K\"ahler, are incomplete. By contrast, those of negative
Ricci curvature contain precisely two complete examples: the complex hyperbolic
metric and a metric of cohomogeneity one known as the one-loop deformed
universal hypermultiplet.


Abstract:
  We provide a complete elaboration of the $L^2$-Hilbert space hypocoercivity
theorem for the degenerate Langevin dynamics with multiplicative noise,
studying the longtime behaviour of the strongly continuous contraction
semigroup solving the abstract Cauchy problem for the associated backward
Kolmogorov operator. Hypocoercivity for the Langevin dynamics with constant
diffusion matrix was proven previously by Dolbeault, Mouhot and Schmeiser in
the corresponding Fokker-Planck framework, and made rigorous in the Kolmogorov
backwards setting by Grothaus and Stilgenbauer. We extend these results to
weakly differentiable diffusion coefficient matrices, introducing
multiplicative noise for the corresponding stochastic differential equation.
The rate of convergence is explicitly computed depending on the choice of these
coefficients and the potential giving the outer force. In order to obtain a
solution to the abstract Cauchy problem, we first prove essential
self-adjointness of non-degenerate elliptic Dirichlet operators on Hilbert
spaces, using prior elliptic regularity results and techniques from Bogachev,
Krylov and R\"ockner. We apply operator perturbation theory to obtain essential
m-dissipativity of the Kolmogorov operator, extending the m-dissipativity
results from Conrad and Grothaus. We emphasize that the chosen Kolmogorov
approach is natural, as the theory of generalized Dirichlet forms implies a
stochastic representation of the Langevin semigroup as the transition kernel of
a diffusion process which provides a martingale solution to the Langevin
equation with multiplicative noise. Moreover, we show that even a weak solution
is obtained this way.


Abstract:
  High-quality dielectric-semiconductor interfaces are critical for reliable
high-performance transistors. We report the in-situ metalorganic chemical vapor
deposition (MOCVD) of Al$_2$O$_3$ on $\beta$-Ga$_2$O$_3$ as a potentially
better alternative to the most commonly used atomic layer deposition (ALD). The
growth of Al$_2$O$_3$ is performed in the same reactor as Ga$_2$O$_3$ using
trimethylaluminum and O$_2$ as precursors without breaking the vacuum at a
growth temperature of 600 $^0$C. The fast and slow near interface traps at the
Al$_2$O$_3$/ $\beta$-Ga$_2$O$_3$ interface are identified and quantified using
stressed capacitance-voltage (CV) measurements on metal oxide semiconductor
capacitor (MOSCAP) structures. The density of shallow and deep level initially
filled traps (D$_{it}$) are measured using ultra-violet (UV) assisted CV
technique. The average D$_{it}$ for the MOSCAP is determined to be 7.8 $\times$
10$^{11}$ cm$^{-2}$eV$^{-1}$. The conduction band offset of the Al$_2$O$_3$/
Ga$_2$O$_3$ interface is also determined from CV measurements and found out to
be 1.7 eV which is in close agreement with the existing literature reports of
ALD Al$_2$O$_3$/ Ga$_2$O$_3$ interface. The current-voltage characteristics are
also analyzed and the average breakdown field is extracted to be approximately
5.8 MV/cm. This in-situ Al$_2$O$_3$ dielectric on $\beta$-Ga$_2$O$_3$ with
improved dielectric properties can enable Ga$_2$O$_3$-based high performance
devices.


Abstract:
  Due to the distributed characteristics of Federated Learning (FL), the
vulnerability of global model and coordination of devices are the main
obstacle. As a promising solution of decentralization, scalability and
security, leveraging blockchain in FL has attracted much attention in recent
years. However, the traditional consensus mechanisms designed for blockchain
like Proof of Work (PoW) would cause extreme resource consumption, which
reduces the efficiency of FL greatly, especially when the participating devices
are wireless and resource-limited. In order to address device asynchrony and
anomaly detection in FL while avoiding the extra resource consumption caused by
blockchain, this paper introduces a framework for empowering FL using Direct
Acyclic Graph (DAG)-based blockchain systematically (DAG-FL). Accordingly,
DAG-FL is first introduced from a three-layer architecture in details, and then
two algorithms DAG-FL Controlling and DAG-FL Updating are designed running on
different nodes to elaborate the operation of DAG-FL consensus mechanism. After
that, a Poisson process model is formulated to discuss that how to set
deployment parameters to maintain DAG-FL stably in different federated learning
tasks. The extensive simulations and experiments show that DAG-FL can achieve
better performance in terms of training efficiency and model accuracy compared
with the typical existing on-device federated learning systems as the
benchmarks.


Abstract:
  Ki67 is a significant biomarker in the diagnosis and prognosis of cancer,
whose index can be evaluated by quantifying its expression in Ki67
immunohistochemistry (IHC) stained images. However, quantitative analysis on
multi-source Ki67 images is yet a challenging task in practice due to
cross-domain distribution differences, which result from imaging variation,
staining styles, and lesion types. Many recent studies have made some efforts
on domain generalization (DG), whereas there are still some noteworthy
limitations. Specifically in the case of Ki67 images, learning invariant
representation is at the mercy of the insufficient number of domains and the
cell categories mismatching in different domains. In this paper, we propose a
novel method to improve DG by searching the domain-agnostic subnetwork in a
domain merging scenario. Partial model parameters are iteratively pruned
according to the domain gap, which is caused by the data converting from a
single domain into merged domains during training. In addition, the model is
optimized by fine-tuning on merged domains to eliminate the interference of
class mismatching among various domains. Furthermore, an appropriate
implementation is attained by applying the pruning method to different parts of
the framework. Compared with known DG methods, our method yields excellent
performance in multiclass nucleus recognition of Ki67 IHC images, especially in
the lost category cases. Moreover, our competitive results are also evaluated
on the public dataset over the state-of-the-art DG methods.


Abstract:
  Convolutional Neural Networks(CNNs) has achieved remarkable performance
breakthrough in a variety of tasks. Recently, CNNs based methods that are fed
with hand-extracted EEG features gradually produce a powerful performance on
the EEG data based emotion recognition task. In this paper, we propose a novel
convolutional layer allowing to adaptively extract effective data-driven
spectrogram-like features from raw EEG signals, which we reference as scaling
layer. Further, it leverages convolutional kernels scaled from one data-driven
pattern to exposed a frequency-like dimension to address the shortcomings of
prior methods requiring hand-extracted features or their approximations. The
proposed neural network architecture based on the scaling layer, references as
ScalingNet, has achieved the state-of-the-art result across the established
DEAP benchmark dataset.


Abstract:
  This paper considers an information bottleneck problem with the objective of
obtaining a most informative representation of a hidden feature subject to a
R\'enyi entropy complexity constraint. The optimal bottleneck trade-off between
relevance (measured via Shannon's mutual information) and R\'enyi entropy cost
is defined and an iterative algorithm for finding approximate solutions is
provided. We also derive an operational characterization for the optimal
trade-off by demonstrating that the optimal R\'enyi entropy-relevance trade-off
is achievable by a simple time-sharing scalar coding scheme and that no coding
scheme can provide better performance. Two examples where the optimal Shannon
entropy-relevance trade-off can be exactly determined are further given.


Abstract:
  With the advance in user-friendly and powerful video editing tools, anyone
can easily manipulate videos without leaving prominent visual traces.
Frame-rate up-conversion (FRUC), a representative temporal-domain operation,
increases the motion continuity of videos with a lower frame-rate and is used
by malicious counterfeiters in video tampering such as generating fake
frame-rate video without improving the quality or mixing temporally spliced
videos. FRUC is based on frame interpolation schemes and subtle artifacts that
remain in interpolated frames are often difficult to distinguish. Hence,
detecting such forgery traces is a critical issue in video forensics. This
paper proposes a frame-rate conversion detection network (FCDNet) that learns
forensic features caused by FRUC in an end-to-end fashion. The proposed network
uses a stack of consecutive frames as the input and effectively learns
interpolation artifacts using network blocks to learn spatiotemporal features.
This study is the first attempt to apply a neural network to the detection of
FRUC. Moreover, it can cover the following three types of frame interpolation
schemes: nearest neighbor interpolation, bilinear interpolation, and
motion-compensated interpolation. In contrast to existing methods that exploit
all frames to verify integrity, the proposed approach achieves a high detection
speed because it observes only six frames to test its authenticity. Extensive
experiments were conducted with conventional forensic methods and neural
networks for video forensic tasks to validate our research. The proposed
network achieved state-of-the-art performance in terms of detecting the
interpolated artifacts of FRUC. The experimental results also demonstrate that
our trained model is robust for an unseen dataset, unlearned frame-rate, and
unlearned quality factor.


Abstract:
  The influence of magnetism on the properties of screw dislocations in
body-centered cubic chromium is investigated by means of ab initio
calculations. Screw dislocations having Burgers vectors 1/2 111 and 100 are
considered, following experimental observations showing activity for both slip
systems. At low temperature, chromium has a magnetic order close to
antiferromagnetism along 100 directions, for which 1/2 111 is not a periodicity
vector. Hence, dislocations with Burgers vectors 1/2 111 generate magnetic
faults when shearing the crystal, which constrain them to coexist and move
pairwise, leading to dissociated 111 super-dislocations. On the other side, 100
is a periodicity vector of the magnetic order of chromium, and no such magnetic
fault are generated when 100 dislocations glide. Dislocation properties are
computed in the magnetically ordered and non magnetic phases of chromium for
comparison purposes. We report a marginal impact of magnetism on the structural
properties and energies of dislocations for both slip systems. The Peierls
energy barrier opposing dislocation glide in {110} planes is comparable for
both 1/2 111 {110} and 100 {110} slip systems, with lower Peierls stresses in
the magnetically ordered phase of chromium.


Abstract:
  An approach is presented treating decision theory as a probabilistic theory
based on quantum techniques. Accurate definitions are given and thorough
analysis is accomplished for the quantum probabilities describing the choice
between separate alternatives, sequential alternatives characterizing
conditional quantum probabilities, and behavioral quantum probabilities taking
into account rational-irrational duality of decision making. The comparison
between quantum and classical probabilities is explained. The analysis
demonstrates that quantum probabilities serve as an essentially more powerful
tool of characterizing various decision-making situations including the
influence of psychological behavioral effects.


Abstract:
  Relic neutrinos from the Big Bang decoupled from the hot plasma predominantly
in helicity eigenstates. Their subsequent propagation through gravitational
inhomogeneities of the Universe alters the helicities of both Dirac and
Majorana neutrinos, thus providing an independent probe of the evolving
universe. We determine here the probability that relic neutrinos flip their
helicity, in terms of the spectrum of density inhomogeneities measured in the
Cosmic Microwave Background. As we find, for Dirac neutrinos the gravitational
helicity modifications are intermediate between the effects of magnetic fields
if the neutrino magnetic moment is of the magnitude predicted in the Standard
Model and the much larger effects if the magnetic moment is of the scale
consistent with the excess of low energy electron events seen by the XENON1T
experiment. We give succinct derivations, within general relativity, of the
semi-classical response of a spinning particle to a weak gravitational field in
an expanding universe, and estimate the helicity modifications of neutrinos
emitted by the Sun caused by the Sun's gravity.


Abstract:
  We apply the framework of optimal nonlinear control to steer the dynamics of
a whole-brain network of FitzHugh-Nagumo oscillators. Its nodes correspond to
the cortical areas of an atlas-based segmentation of the human cerebral cortex,
and the inter-node coupling strengths are derived from Diffusion Tensor Imaging
data of the connectome of the human brain. Nodes are coupled using an additive
scheme without delays and are driven by background inputs with fixed mean and
additive Gaussian noise. Optimal control inputs to nodes are determined by
minimizing a cost functional that penalizes the deviations from a desired
network dynamic, the control energy, and spatially non-sparse control inputs.
Using the strength of the background input and the overall coupling strength as
order parameters, the network's state-space decomposes into regions of low and
high activity fixed points separated by a high amplitude limit cycle all of
which qualitatively correspond to the states of an isolated network node. Along
the borders, however, additional limit cycles, asynchronous states and
multistability can be observed. Optimal control is applied to several
state-switching and network synchronization tasks, and the results are compared
to controllability measures from linear control theory for the same connectome.
We find that intuitions from the latter about the roles of nodes in steering
the network dynamics, which are solely based on connectome features, do not
generally carry over to nonlinear systems, as had been previously implied.
Instead, the role of nodes under optimal nonlinear control critically depends
on the specified task and the system's location in state space. Our results
shed new light on the controllability of brain network states and may serve as
an inspiration for the design of new paradigms for non-invasive brain
stimulation.


Abstract:
  The time resolution (TR) is one of the most important characteristic of a
detector. For the case of a scintillator, the collection of light also is
important, it depends of the effective area of the photo-sensor
(\textit{Scorer}). The Photomultipliers Tubes (PMTs), normally, have an
effective area greater than the Silicon Photomultipliers (SiPMs), however, due
to its voltage of operation, their size or cost, in some cases it can be
difficult to work with them. In which case, it is preferable to use SiPMs. The
value of TR also depends of the size and geometry of the scintillator, number
of photo-sensors and the electronic part.\\ In this work, we study the mean
optical photon arrival time distribution (AT) to a Scorer from a SiPM of
6$\times$6~mm$^2$. We define the variation of AT as \textit{the intrinsic time
resolution} (ITR). In Geant4, we simulated two different geometries: square and
hexagonal, for a BC-404 plastic scintillator coupled to one Scorer. The sources
simulated were Sr$^{90}$, Co$^{60}$, Cs$^{137}$, Na$^{22}$ and $\mu^-$ of
1~GeV. It is shown that AT and ITR depends of the geometry and size of the
plastic scintillator, the location of the Scorer, the incident particle and its
energy. Then, the ITR and therefore the TR is not a constant for a detector.
Finally, we show the relation between AT and the deposited energy by the
particle incident, which are related in the experiment to the response time
event of the detector and the deposited charge by the incident particle,
respectively.


Abstract:
  Real-world scenarios often require the anticipation of object interactions in
unknown future, which would assist the decision-making process of both humans
and agents. To meet this challenge, we present a new task named Visual
Relationship Forecasting (VRF) in videos to explore the prediction of visual
relationships in a reasoning manner. Specifically, given a subject-object pair
with H existing frames, VRF aims to predict their future interactions for the
next T frames without visual evidence. To evaluate the VRF task, we introduce
two video datasets named VRF-AG and VRF-VidOR, with a series of
spatio-temporally localized visual relation annotations in a video. These two
datasets densely annotate 13 and 35 visual relationships in 1923 and 13447
video clips, respectively. In addition, we present a novel Graph Convolutional
Transformer (GCT) framework, which captures both object-level and frame-level
dependencies by spatio-temporal Graph Convolution Network and Transformer.
Experimental results on both VRF-AG and VRF-VidOR datasets demonstrate that GCT
outperforms the state-of-the-art sequence modelling methods on visual
relationship forecasting.


Abstract:
  Multimedia related research and development has evolved rapidly in the last
few years with advancements in hardware, software and network infrastructures.
As a result, multimedia has been integrated into domains like Healthcare and
Medicine, Human facial feature extraction and tracking, pose recognition,
disparity estimation, etc. This survey gives an overview of the various
multimedia technologies and algorithms developed in the domains mentioned.


Abstract:
  In this paper, we deal with reversing and extended symmetries of shifts
generated by bijective substitutions. We provide equivalent conditions for a
permutation on the alphabet to generate a reversing/extended symmetry, and
algorithms how to check them. Moreover, we show that, for any finite group $G$
and any subgroup $P$ of the $d$-dimensional hyperoctahedral group, there is a
bijective substitution which generates an aperiodic hull with symmetry group
$\mathbb{Z}^{d}\times G$ and extended symmetry group $(\mathbb{Z}^{d} \rtimes
P)\times G$.


Abstract:
  While larger neural models are pushing the boundaries of what deep learning
can do, often more weights are needed to train models rather than to run
inference for tasks. This paper seeks to understand this behavior using search
spaces -- adding weights creates extra degrees of freedom that form new paths
for optimization (or wider search spaces) rendering neural model training more
effective. We then show how we can augment search spaces to train sparse models
attaining competitive scores across dozens of deep learning workloads. They are
also are tolerant of structures targeting current hardware, opening avenues for
training and inference acceleration. Our work encourages research to explore
beyond massive neural models being used today.


Abstract:
  Non-convex optimization problems can be approximately solved via relaxation
or local algorithms. For many practical problems such as optimal power flow
(OPF) problems, both approaches tend to succeed in the sense that relaxation is
usually exact and local algorithms usually converge to a global optimum. In
this paper, we study conditions which are sufficient or necessary for such
non-convex problems to simultaneously have exact relaxation and no spurious
local optima. Those conditions help us explain the widespread empirical
experience that local algorithms for OPF problems often work extremely well.


Abstract:
  In this paper, we consider the following non-local semi-linear parabolic
equation with advection: for $1 \le p<1+\frac{2}{N}$, \begin{equation*}
\begin{cases} u_t+v \cdot \nabla u-\Delta u=|u|^p-\int_{\mathbb T^N} |u|^p
\quad & \textrm{on} \quad \mathbb T^N, \\ \\ u \ \textrm{periodic} \quad &
\textrm{on} \quad \partial \mathbb T^N \end{cases} \end{equation*} with initial
data $u_0$ defined on $\mathbb T^N$. Here $v$ is an incompressible flow, and
$\mathbb T^N=[0, 1]^N$ is the $N$-torus with $N$ being the dimension.
  We first prove the local existence of mild solutions to the above equation
for arbitrary data in $L^2$. We then study the global existence of the
solutions under the following two scenarios: (1). when $v$ is a mixing flow;
(2). when $v$ is a shear flow. More precisely, we show that under these
assumptions, there exists a global solution to the above equation in the sense
of $L^2$.


Abstract:
  The free boundary problem for a two-dimensional fluid filtered in porous
media is studied. This is known as the one-phase Muskat problem and is
mathematically equivalent to the vertical Hele-Shaw problem driven by gravity
force. We prove that if the initial free boundary is the graph of a periodic
Lipschitz function, then there exists a global-in-time Lipschitz solution in
the strong $L^\infty_t L^2_x$ sense and it is the unique viscosity solution.
The proof requires quantitative estimates for layer potentials and pointwise
elliptic regularity in Lipschitz domains. This is the first construction of
unique global strong solutions for the Muskat problem with initial data of
arbitrary size.


Abstract:
  Following the recent progress in Terahertz (THz) signal generation and
radiation methods, joint THz communications and sensing applications are
shaping the future of wireless systems. Towards this end, THz spectroscopy is
expected to be carried over user equipment devices to identify material and
gaseous components of interest. THz-specific signal processing techniques
should complement this re-surged interest in THz sensing for efficient
utilization of the THz band. In this paper, we present an overview of these
techniques, with an emphasis on signal pre-processing (standard normal variate
normalization, min-max normalization, and Savitzky-Golay filtering), feature
extraction (principal component analysis, partial least squares, t-distributed
stochastic neighbor embedding, and nonnegative matrix factorization), and
classification techniques (support vector machines, k-nearest neighbor,
discriminant analysis, and naive Bayes). We also address the effectiveness of
deep learning techniques by exploring their promising sensing capabilities at
the THz band. Lastly, we investigate the performance and complexity trade-offs
of the studied methods in the context of joint communications and sensing; we
motivate the corresponding use-cases, and we present few future research
directions in the field.


Abstract:
  Since 2015 the gravitational-wave observations of LIGO and Virgo have
transformed our understanding of compact-object binaries. In the years to come,
ground-based gravitational-wave observatories such as LIGO, Virgo, and their
successors will increase in sensitivity, discovering thousands of stellar-mass
binaries. In the 2030s, the space-based LISA will provide gravitational-wave
observations of massive black holes binaries. Between the $\sim
10$-$10^3~\mathrm{Hz}$ band of ground-based observatories and the
$\sim10^{-4}$-$10^{-1}~\mathrm{Hz}$ band of LISA lies the uncharted decihertz
gravitational-wave band. We propose a Decihertz Observatory to study this
frequency range, and to complement observations made by other detectors.
Decihertz observatories are well suited to observation of intermediate-mass
($\sim10^2$-$10^4 M_\odot$) black holes; they will be able to detect
stellar-mass binaries days to years before they merge, providing early warning
of nearby binary neutron star mergers and measurements of the eccentricity of
binary black holes, and they will enable new tests of general relativity and
the Standard Model of particle physics. Here we summarise how a Decihertz
Observatory could provide unique insights into how black holes form and evolve
across cosmic time, improve prospects for both multimessenger astronomy and
multiband gravitational-wave astronomy, and enable new probes of gravity,
particle physics and cosmology.


Abstract:
  We study homogeneous Besov and Triebel--Lizorkin spaces defined on doubling
metric measure spaces in terms of a self-adjoint operator whose heat kernel
satisfies Gaussian estimates together with its derivatives. When the measure
space is a smooth manifold and such operator is a sum of squares of smooth
vector fields, we prove that their intersection with $L^\infty$ is an algebra
for pointwise multiplication. Our results apply to nilpotent Lie groups and
Grushin settings.


Abstract:
  Quantum fluctuations endow spacetime with a foamy texture. The degree of
foaminess is dictated by blackhole physics to be of the holographic type.
Applied to cosmology, the holographic foam model predicts the existence of dark
energy with critical energy density in the current (late) universe, the quanta
of which obey infinite statistics. Furthermore we use the deep similarities
between turbulence and the spacetime foam phase of strong quantum gravity to
argue that the early universe was in a turbulent regime when it underwent a
brief cosmic inflation with a "graceful" transition to a laminar regime. In
this scenario, both the late and the early cosmic accelerations have their
origins in spacetime foam.


Abstract:
  A graph is called Ricci-flat if its Ricci curvatures vanish on all edges,
here the definition of Ricci curvature on graphs was given by Lin-Lu-Yau. The
authors in arXiv:1301.0102 and arXiv:1802.02982 obtained a complete
characterization for all Ricci-flat graphs with girth at least five. In this
paper, we completely determined all Ricci-flat graphs with maximum degree at
most 4.


Abstract:
  In this paper, we present a new analysis on the $P$-wave charmonium
annihilation into two photons up to next-to-next-to-leading order (NNLO) QCD
corrections by using the principle of maximum conformality (PMC). The
conventional perturbative QCD prediction shows strong scale dependence and
deviates largely from the BESIII measurements. After applying the PMC, we
obtain a more precise scale-invariant pQCD prediction, which also agrees with
the BESIII measurements within errors, i.e.
$R={\Gamma_{\gamma\gamma}(\chi_{c2})}
/{\Gamma_{\gamma\gamma}(\chi_{c0})}=0.246\pm0.013$, where the error is for
$\Delta\alpha_s(M_\tau)=\pm0.016$. By further considering the color-octet
contributions, even the central value can be in agreement with the data. This
shows the importance of a correct scale-setting approach. We also give a
prediction for the ratio involving $\chi_{b0, b2} \to\gamma\gamma$, which could
be tested in future Belle II experiment.


Abstract:
  Time-reversal symmetry breaking and entropy production are universal features
of nonequilibrium phenomena. Despite its importance in the physics of active
and living systems, the entropy production of systems with many degrees of
freedom has remained of little practical significance because the
high-dimensionality of their state space makes it difficult to measure. We
introduce a local measure of entropy production and a numerical protocol to
estimate it. We establish a connection between the entropy production and
extractability of work in a given region of the system and show how this
quantity depends crucially on the degrees of freedom being tracked. We validate
our approach in theory, simulation, and experiments by considering systems of
active Brownian particles undergoing motility induced phase separation, as well
as active Brownian particles and E. Coli in a rectifying device in which the
time-reversal asymmetry of the particle dynamics couples to spatial asymmetry
to reveal its effects on a macroscopic scale.


Abstract:
  We introduce a partitioned coupling approach for iterative coupling of flow
processes in deformable fractures embedded in a poro-elastic medium that is
enhanced by interface quasi-Newton (IQN) methods. In this scope, a unique
computational decomposition into a fracture flow and a poro-elastic domain is
developed, where communication and numerical coupling of the individual solvers
are realized by consulting the open-source library preCICE. The underlying
physical problem is introduced by a brief derivation of the governing equations
and interface conditions of fracture flow and poro-elastic domain followed by a
detailed discussion of the partitioned coupling scheme. We evaluate the
proposed implementation and undertake a convergence study to compare a
classical interface quasi-Newton inverse least-squares (IQN-ILS) with the more
advanced interface quasi-Newton inverse multi-vector Jacobian (IQN-IMVJ)
method. These coupling approaches are verified for an academic test case before
the generality of the proposed strategy is demonstrated by simulations of two
complex fracture networks. In contrast to the development of specific solvers,
we promote the simplicity and computational efficiency of the proposed
partitioned coupling approach using preCICE and FEniCS for parallel
computations of hydro-mechanical processes in complex, three-dimensional
fracture networks.


Abstract:
  The need to recover high-dimensional signals from their noisy low-resolution
quantized measurements is widely encountered in communications and sensing. In
this paper, we focus on the extreme case of one-bit quantizers, and propose a
deep detector entitled LoRD-Net for recovering information symbols from one-bit
measurements. Our method is a model-aware data-driven architecture based on
deep unfolding of first-order optimization iterations. LoRD-Net has a
task-based architecture dedicated to recovering the underlying signal of
interest from the one-bit noisy measurements without requiring prior knowledge
of the channel matrix through which the one-bit measurements are obtained. The
proposed deep detector has much fewer parameters compared to black-box deep
networks due to the incorporation of domain-knowledge in the design of its
architecture, allowing it to operate in a data-driven fashion while benefiting
from the flexibility, versatility, and reliability of model-based optimization
methods. LoRD-Net operates in a blind fashion, which requires addressing both
the non-linear nature of the data-acquisition system as well as identifying a
proper optimization objective for signal recovery. Accordingly, we propose a
two-stage training method for LoRD-Net, in which the first stage is dedicated
to identifying the proper form of the optimization process to unfold, while the
latter trains the resulting model in an end-to-end manner. We numerically
evaluate the proposed receiver architecture for one-bit signal recovery in
wireless communications and demonstrate that the proposed hybrid methodology
outperforms both data-driven and model-based state-of-the-art methods, while
utilizing small datasets, on the order of merely $\sim 500$ samples, for
training.


Abstract:
  We show that the de Rham Hilbert complex with mixed boundary conditions on
bounded strong Lipschitz domains is closed and compact. The crucial results are
compact embeddings which follow by abstract arguments using functional analysis
together with particular regular decompositions. Higher Sobolev order results
are proved as well.


Abstract:
  Most neural vocoders employ band-limited mel-spectrograms to generate
waveforms. If full-band spectral features are used as the input, the vocoder
can be provided with as much acoustic information as possible. However, in some
models employing full-band mel-spectrograms, an over-smoothing problem occurs
as part of which non-sharp spectrograms are generated. To address this problem,
we propose UnivNet, a neural vocoder that synthesizes high-fidelity waveforms
in real time. Inspired by works in the field of voice activity detection, we
added a multi-resolution spectrogram discriminator that employs multiple linear
spectrogram magnitudes computed using various parameter sets. Using full-band
mel-spectrograms as input, we expect to generate high-resolution signals by
adding a discriminator that employs spectrograms of multiple resolutions as the
input. In an evaluation on a dataset containing information on hundreds of
speakers, UnivNet obtained the best objective and subjective results among
competing models for both seen and unseen speakers. These results, including
the best subjective score for text-to-speech, demonstrate the potential for
fast adaptation to new speakers without a need for training from scratch.


Abstract:
  Single-beam spectrally controlled (SBSC) two-dimensional (2D) Raman
spectroscopy is a unique 2D vibrational measurement technique utilizing trains
of short pulses that are generated from a single broadband pulse by pulse
shaping. This approach overcomes the difficulty of 2D Raman spectroscopy in
dealing with small-signal extraction and avoids complicated low-order cascading
effects, thus providing a new possibility for measuring the intramolecular and
intermolecular modes of molecular liquids using fifth-order 2D Raman
spectroscopy. Recently, for quantitatively investigating the mode-mode coupling
mechanism, Hurwitz et al. [Optics Express 28, 3803 (2020)] have developed a new
pulse design for this measurement to separate the contributions of the fifth-
and third-order polarizations, which are often overlapped in the original
single-beam measurements. Here, we describe a method for simulating these
original measurements and the new 2D Raman measurements on the basis of a
second-order response function approach. We carry out full molecular dynamics
simulations for carbon tetrachloride and liquid water using an
equilibrium-nonequilibrium hybrid algorithm, with the aim of explaining the key
features of the SBSC 2D Raman spectroscopic method from a theoretical point of
view. The predicted signal profiles and intensities provide valuable
information that can be applied to 2D spectroscopy experiments, allowing them
to be carried out more efficiently.


Abstract:
  In this paper, we study the problem of inferring time-varying Markov random
fields (MRF), where the underlying graphical model is both sparse and changes
sparsely over time. Most of the existing methods for the inference of
time-varying MRFs rely on the regularized maximum likelihood estimation (MLE),
that typically suffer from weak statistical guarantees and high computational
time. Instead, we introduce a new class of constrained optimization problems
for the inference of sparsely-changing MRFs. The proposed optimization problem
is formulated based on the exact $\ell_0$ regularization, and can be solved in
near-linear time and memory. Moreover, we show that the proposed estimator
enjoys a provably small estimation error. As a special case, we derive sharp
statistical guarantees for the inference of sparsely-changing Gaussian MRFs
(GMRF) in the high-dimensional regime, showing that such problems can be
learned with as few as one sample per time. Our proposed method is extremely
efficient in practice: it can accurately estimate sparsely-changing graphical
models with more than 500 million variables in less than one hour.


Abstract:
  Although deep neural networks have been widely employed and proven effective
in sentiment analysis tasks, it remains challenging for model developers to
assess their models for erroneous predictions that might exist prior to
deployment. Once deployed, emergent errors can be hard to identify in
prediction run-time and impossible to trace back to their sources. To address
such gaps, in this paper we propose an error detection framework for sentiment
analysis based on explainable features. We perform global-level feature
validation with human-in-the-loop assessment, followed by an integration of
global and local-level feature contribution analysis. Experimental results show
that, given limited human-in-the-loop intervention, our method is able to
identify erroneous model predictions on unseen data with high precision.


Abstract:
  This is the second year of the TREC Deep Learning Track, with the goal of
studying ad hoc ranking in the large training data regime. We again have a
document retrieval task and a passage retrieval task, each with hundreds of
thousands of human-labeled training queries. We evaluate using single-shot
TREC-style evaluation, to give us a picture of which ranking methods work best
when large data is available, with much more comprehensive relevance labeling
on the small number of test queries. This year we have further evidence that
rankers with BERT-style pretraining outperform other rankers in the large data
regime.


Abstract:
  Emotion detection is an important task that can be applied to social media
data to discover new knowledge. While the use of deep learning methods for this
task has been prevalent, they are black-box models, making their decisions hard
to interpret for a human operator. Therefore, in this paper, we propose an
approach using weighted $k$ Nearest Neighbours (kNN), a simple, easy to
implement, and explainable machine learning model. These qualities can help to
enhance results' reliability and guide error analysis. In particular, we apply
the weighted kNN model to the shared emotion detection task in tweets from
SemEval-2018. Tweets are represented using different text embedding methods and
emotion lexicon vocabulary scores, and classification is done by an ensemble of
weighted kNN models. Our best approaches obtain results competitive with
state-of-the-art solutions and open up a promising alternative path to neural
network methods.


Abstract:
  The burgeoning field of "algorithmic fairness" provides a novel set of
methods for reasoning about the fairness of algorithmic predictions and
decisions. Yet even as algorithmic fairness has become a prominent component of
efforts to enhance equality in domains such public policy, it also faces
significant limitations and critiques. The most fundamental issue is the
mathematical result known as the "impossibility of fairness" (an
incompatibility between mathematical definitions of fairness). Furthermore,
many algorithms that satisfy standards of fairness actually exacerbate
oppression. These two issues call into question whether algorithmic fairness
can play a productive role in the pursuit of equality. In this paper, I
diagnose these issues as the product of algorithmic fairness methodology and
propose an alternative path forward for the field. The dominant approach of
"formal algorithmic fairness" suffers from a fundamental limitation: it relies
on a narrow frame of analysis that is limited to specific decision-making
processes, in isolation from the context of those decisions. In light of this
shortcoming, I draw on theories of substantive equality from law and philosophy
to propose an alternative method: "substantive algorithmic fairness."
Substantive algorithmic fairness takes a more expansive scope to analyzing
fairness, looking beyond specific decision points to account for social
hierarchies and the impacts of decisions facilitated by algorithms. As a
result, substantive algorithmic fairness suggests reforms that combat
oppression and that provide an escape from the impossibility of fairness.
Moreover, substantive algorithmic fairness presents a new direction for the
field of algorithmic fairness: away from formal mathematical models of
"fairness" and towards substantive evaluations of how algorithms can (and
cannot) promote equality.


Abstract:
  Using the relationship between Siegel cusp forms of degree $2$ and cuspidal
automorphic representations of $\mathrm{GSp}(4,\mathbb{A}_{\mathbb{Q}})$, we
derive some congruences involving dimensions of spaces of Siegel cusp forms of
degree $2$ and the class number of $\mathbb{Q}(\sqrt{-p})$. We also obtain some
congruences between the $4$-core partition function $c_4(n)$ and dimensions of
spaces of Siegel cusp forms of degree $2$.


Abstract:
  This paper proposes a novel nature-inspired meta-heuristic algorithm called
the Golden Tortoise Beetle Optimizer (GTBO) to solve optimization problems. It
mimics golden tortoise beetle's behavior of changing colors to attract opposite
sex for mating and its protective strategy that uses a kind of anal fork to
deter predators. The algorithm is modeled based on the beetle's dual
attractiveness and survival strategy to generate new solutions for optimization
problems. To measure its performance, the proposed GTBO is compared with five
other nature-inspired evolutionary algorithms on 24 well-known benchmark
functions investigating the trade-off between exploration and exploitation,
local optima avoidance, and convergence towards the global optima is
statistically significant. We particularly applied GTBO to two well-known
engineering problems including the welded beam design problem and the gear
train design problem. The results demonstrate that the new algorithm is more
efficient than the five baseline algorithms for both problems. A sensitivity
analysis is also performed to reveal different impacts of the algorithm's key
control parameters and operators on GTBO's performance.


Abstract:
  Let $n > k > t \geq j \geq 1$ be integers. Let $X$ be an $n$-element set,
${X\choose k}$ the collection of its $k$-subsets. A family $\mathcal F \subset
{X\choose k}$ is called $t$-intersecting if $|F \cap F'| \geq t$ for all $F, F'
\in \mathcal F$. The $j$'th shadow $\partial^j \mathcal F$ is the collection of
all $(k - j)$-subsets that are contained in some member of~$\mathcal F$.
Estimating $|\partial^j \mathcal F|$ as a function of $|\mathcal F|$ is a
widely used tool in extremal set theory. A classical result of the second
author (Theorem \ref{th:1.3}) provides such a bound for $t$-intersecting
families. It is best possible for $|\mathcal F| = {2k - t\choose k}$.
  Our main result is Theorem \ref{th:1.4} which gives an asymptotically optimal
bound on $|\partial^j \mathcal F| / |\mathcal F|$ for $|\mathcal F|$ slightly
larger, e.g., $|\mathcal F| > \frac32 {2k - t\choose k}$. We provide further
improvements for $|\mathcal F|$ very large as well.


Abstract:
  We use coherence theory to explain why it is necessary to modify the
conventional setup of a Michelson interferometer to obtain Haidinger rings with
an extended source of white light. The modification consists of introducing a
glass slide into one of the two arms of the interferometer. This insertion
circumvents the drastic restriction imposed by the low temporal coherence of
white light, which prevents the observation of interference rings with the
conventional setup. In order to understand this restriction, we developed and
implemented a criterion for observing interference fringes. The modified setup
also makes it possible to perform a spectral interferometry experiment to
analyze the output of the interferometer and determine the refractive index of
the glass slide over the whole visible spectrum. The fit of measured data using
Sellmeier's law gives the extrapolated value of the refractive index to the IR
$n_\mathrm{IR}=1.5307\pm0.0003$ and the value of the characteristic wavelength
$\lambda_0=(164.5\pm0.4)\;\mathrm{nm}$ of the oscillator responsible for the
dispersion.


Abstract:
  Developing mechanisms that flexibly adapt dialog systems to unseen tasks and
domains is a major challenge in dialog research. Neural models implicitly
memorize task-specific dialog policies from the training data. We posit that
this implicit memorization has precluded zero-shot transfer learning. To this
end, we leverage the schema-guided paradigm, wherein the task-specific dialog
policy is explicitly provided to the model. We introduce the Schema Attention
Model (SAM) and improved schema representations for the STAR corpus. SAM
obtains significant improvement in zero-shot settings, with a +22 F1 score
improvement over prior work. These results validate the feasibility of
zero-shot generalizability in dialog. Ablation experiments are also presented
to demonstrate the efficacy of SAM.


Abstract:
  We introduce six quantities that generalize the equinoctial orbital elements
when some or all the perturbing forces that act on the propagated body are
derived from a disturbing potential. Three of the elements define a
non-osculating ellipse on the orbital plane, other two fix the orientation of
the equinoctial reference frame, and the last allows one to determine the true
longitude of the body. The Jacobian matrices of the transformations between the
new elements and the position and velocity are explicitly given. As a possible
application we investigate their use in the propagation of Earth's artificial
satellites showing a remarkable improvement compared to the equinoctial orbital
elements.


Abstract:
  We consider the Yang-Mills equations in $(1+d)$-dimensional Minkowski
spacetime. It is known that in the supercritical case, i.e., for $d \geq 5$,
these equations admit closed form equivariant self-similar blowup solutions
\cite{BieBiz15}. These solutions are furthermore conjectured to be the
universal attractors for generic large data equivariant evolutions. In this
paper we partially prove this conjecture. Namely, we show that for all odd $d
\geq 5$ the blowup mechanism exhibited by these solutions is stable.


Abstract:
  In this paper the equations of motion associated with a Lagrangian inspired
by relativistic optics in nonuniform moving media are considered. The model
describes optical effects in the nonuniform dispersionless moving medium. When
using the optical metric restricted to the Minkowski manifold, we have
established the Euler-Lagrange equations for geodesics. We have specified the
general model to the special case when the refractive index increases along the
direction $Z$. The exact analytical solutions of the corresponding
Euler-Lagrange equations have been constructed. Analysis of the solutions shows
that the light beams are bending to the axes $Z$ along which the refractive
index increases.


Abstract:
  Mainstream deep models for three-dimensional MRI synthesis are either
cross-sectional or volumetric depending on the input. Cross-sectional models
can decrease the model complexity, but they may lead to discontinuity
artifacts. On the other hand, volumetric models can alleviate the discontinuity
artifacts, but they might suffer from loss of spatial resolution due to
increased model complexity coupled with scarce training data. To mitigate the
limitations of both approaches, we propose a novel model that progressively
recovers the target volume via simpler synthesis tasks across individual
orientations.


Abstract:
  A dimension reduction method based on the "Nonlinear Level set Learning"
(NLL) approach is presented for the pointwise prediction of functions which
have been sparsely sampled. Leveraging geometric information provided by the
Implicit Function Theorem, the proposed algorithm effectively reduces the input
dimension to the theoretical lower bound with minor accuracy loss, providing a
one-dimensional representation of the function which can be used for regression
and sensitivity analysis. Experiments and applications are presented which
compare this modified NLL with the original NLL and the Active Subspaces (AS)
method. While accommodating sparse input data, the proposed algorithm is shown
to train quickly and provide a much more accurate and informative reduction
than either AS or the original NLL on two example functions with
high-dimensional domains, as well as two state-dependent quantities depending
on the solutions to parametric differential equations.


Abstract:
  The Insulator-Metal-Transition adjoined by a structural transition of VO2 is
induced above room temperature (340 K) by heating or self-heating. A steep
resistance-jump of up to five orders of magnitude occurs at this transition in
high quality, unstrained single crystals. Insulating domains sliding in the
sense of the electric current within the metallic background were found so far
exclusively in the current induced mixed metal-insulator phase of VO2 single
crystals; it is known for a long time that their uniformity and speed as
function of current density are very sensitive to crystal quality. The high
energetical cost of domain emission is the focus of our present investigations.
In this Communication we report on the surprising behavior of a needle-like VO2
single crystal. Several I-V closed loops traced at room temperature
concurrently with video recording of the crystal under the microscope, were
followed by R(T) measurements during three slow heating-cooling cycles between
room temperature and above 340 K, followed in their turn by an additional set
of I-V measurements and video recordings. The results show that the slow
cycling through the transition using external heat had a healing effect on the
reproducibility of R(T) while increasing the activation energy of conduction in
the insulating state and in reducing the damping term in the domains' sliding
velocity. The intriguing result of this set of measurements was that the energy
cost of the domain emission, was higher after healing than prior to it.


Abstract:
  In this work, we present to the NLP community, and to the wider research
community as a whole, an application for the diachronic analysis of research
corpora. We open source an easy-to-use tool coined: DRIFT, which allows
researchers to track research trends and development over the years. The
analysis methods are collated from well-cited research works, with a few of our
own methods added for good measure. Succinctly put, some of the analysis
methods are: keyword extraction, word clouds, predicting
declining/stagnant/growing trends using Productivity, tracking bi-grams using
Acceleration plots, finding the Semantic Drift of words, tracking trends using
similarity, etc. To demonstrate the utility and efficacy of our tool, we
perform a case study on the cs.CL corpus of the arXiv repository and draw
inferences from the analysis methods. The toolkit and the associated code are
available here: https://github.com/rajaswa/DRIFT.


Abstract:
  Recent studies stressed the fact that covariance matrices computed from
empirical financial time series appear to contain a high amount of noise. This
makes the classical Markowitz Mean-Variance Optimization model unable to
correctly evaluate the performance associated to selected portfolios. Since the
Markowitz model is still one of the most used practitioner-oriented tool,
several filtering methods have been proposed in the literature to fix the
problem. Among them, the two most promising ones refer to the Random Matrix
Theory or to the Power Mapping strategy. The basic idea of these methods is to
transform the correlation matrix maintaining the Mean-Variance Optimization
model. However, experimental analysis shows that these two strategies are not
adequately effective when applied to real financial datasets.
  In this paper we propose an alternative filtering method based on
Combinatorial Optimization. We advance a new Mixed Integer Quadratic
Programming model to filter those observations that may influence the
performance of a portfolio in the future. We discuss the properties of this new
model and we test it on some real financial datasets. We compare the
out-of-sample performance of our portfolios with the one of the portfolios
provided by the two above mentioned alternative strategies. We show that our
method outperforms them. Although our model can be solved efficiently with
standard optimization solvers the computational burden increases for large
datasets. To overcome this issue we also propose a heuristic procedure that
empirically showed to be both efficient and effective.


Abstract:
  Quantifying anisotropy in the chemical reactions of mesoscopic crystals has
mostly resorted on the combination of electron microscopy and diffraction. In
this work, we established crystal-facet-resolved kinetic measurements of
oxidation reactions in 2D transition metal dichalcogenides (TMDs) using optical
second-harmonic generation spectroscopy and scanning probe microscopy. We show
the in-plane anisotropy of their bond-breaking reactions is governed by their
structure and strongly material-dependent among four TMDs. The facet-resolved
analysis directly revealed that the reactions proceed fastest (slowest) for
chalcogen (metal)-terminated zigzag edges with armchair edges in the middle.
The degree of the anisotropy inducing trigonal oxidation patterns was much
higher in MoS2 and MoSe2 than WS2 and WSe2. Kinetic Wulff construction based on
edge-specific reaction rates verified the material-dependent mesoscopic
reaction patterns. We also show that the reactions are initiated at
substrate-mediated defects located on the bottom and top surfaces of 2D TMDs.


Abstract:
  The paper concerns the image, level and sojourn time sets associated with
sample paths of the Rosenblatt process. We obtain results regarding the
Hausdorff (both classical and macroscopic), packing and intermediate
dimensions, and the logarithmic and pixel densities. As a preliminary step we
also establish the time inversion property of the Rosenblatt process, as well
as some technical points regarding the distribution of $Z$.


Abstract:
  Nuclei instance segmentation plays an important role in the analysis of
Hematoxylin and Eosin (H&E)-stained images. While supervised deep learning
(DL)-based approaches represent the state-of-the-art in automatic nuclei
instance segmentation, annotated datasets are required to train these models.
There are two main types of tissue processing protocols, namely formalin-fixed
paraffin-embedded samples (FFPE) and frozen tissue samples (FS). Although
FFPE-derived H&E stained tissue sections are the most widely used samples, H&E
staining on frozen sections derived from FS samples is a relevant method in
intra-operative surgical sessions as it can be performed fast. Due to
differences in the protocols of these two types of samples, the derived images
and in particular the nuclei appearance may be different in the acquired whole
slide images. Analysis of FS-derived H&E stained images can be more challenging
as rapid preparation, staining, and scanning of FS sections may lead to
deterioration in image quality.
  In this paper, we introduce CryoNuSeg, the first fully annotated FS-derived
cryosectioned and H&E-stained nuclei instance segmentation dataset. The dataset
contains images from 10 human organs that were not exploited in other publicly
available datasets, and is provided with three manual mark-ups to allow
measuring intra-observer and inter-observer variability. Moreover, we
investigate the effects of tissue fixation/embedding protocol (i.e., FS or
FFPE) on the automatic nuclei instance segmentation performance of one of the
state-of-the-art DL approaches. We also create a baseline segmentation
benchmark for the dataset that can be used in future research.
  A step-by-step guide to generate the dataset as well as the full dataset and
other detailed information are made available to fellow researchers at
https://github.com/masih4/CryoNuSeg.


Abstract:
  Motivated by the mushy zones of sea ice, volcanoes, and icy moons of the
outer solar system, we perform a theoretical and numerical study of
boundary-layer convection along a vertical heated wall in a bounded ideal mushy
region. The mush is comprised of a porous and reactive binary alloy with a
mixture of saline liquid in a solid matrix, and is studied in the near-eutectic
approximation. Here we demonstrate the existence of four regions and study
their behavior asymptotically. Starting from the bottom of the wall, the four
regions are (i) an isotropic corner region; (ii) a buoyancy dominated vertical
boundary layer; (iii) an isotropic connection region; and (iv) a horizontal
boundary layer at the top boundary with strong gradients of pressure and
buoyancy. Scalings from numerical simulations are consistent with the
theoretical predictions. Close to the heated wall, the convection in the mushy
layer is similar to a rising buoyant plume abruptly stopped at the top, leading
to increased pressure and temperature in the upper region, whose impact is
discussed as an efficient melting mechanism.


Abstract:
  The multi analytical study of terrestrial analogues is a useful strategy to
deepen the knowledge about the geological and environmental evolution of Mars
and other extraterrestrial bodies. In spite of the increasing importance that
LIBS, NIR and Raman techniques are acquiring in the field of space exploration,
there is a lack web-based platform providing free access to a wide
multi-spectral database of terrestrial analogue materials. The Planetary
Terrestrial Analogue Library (PTAL) project aims at responding to this critical
need by developing and providing free web accessibility to LIBS, NIR and Raman
data from more than 94 terrestrial analogues selected according to their
congruence with Martian geological contexts. In this framework, the present
manuscript provides the scientific community with a complete overview of the
over 4500 Raman spectra collected to feed the PTAL database. Raman data,
obtained through the complementary use of laboratory and spacecraft-simulator
systems, confirmed the effectiveness of this spectroscopic technique for the
detection of major and minor mineralogical phases of the samples, the latter
being of critical importance for the recognition of geological processes that
could have occurred on Mars and other planets. In light of the forthcoming
missions to Mars, the results obtained through the RLS ExoMars Simulator offer
a valuable insight on the scientific outcome that could derive from the RLS
spectrometer that will soon land on Mars as part of the ExoMars rover payload.


Abstract:
  We study the differentially private multi group aggregation (PMGA) problem.
This setting involves a single server and $n$ users. Each user belongs to one
of $k$ distinct groups and holds a discrete value. The goal is to design
schemes that allow the server to find the aggregate (sum) of the values in each
group (with high accuracy) under communication and local differential privacy
constraints. The privacy constraint guarantees that the user's group remains
private. This is motivated by applications where a user's group can reveal
sensitive information, such as his religious and political beliefs, health
condition, or race. We propose a novel scheme, dubbed Query and Aggregate
(Q\&A) for PMGA. The novelty of Q\&A is that it is an interactive aggregation
scheme. In Q\&A, each user is assigned a random query matrix, to which he sends
the server an answer based on his group and value. We characterize the Q\&A
scheme's performance in terms of accuracy (MSE), privacy, and communication. We
compare Q\&A to the Randomized Group (RG) scheme, which is non-interactive and
adapts existing randomized response schemes to the PMGA setting. We observe
that typically Q\&A outperforms RG, in terms of privacy vs. utility, in the
high privacy regime.


Abstract:
  The event-by-event correlations between three flow amplitudes are measured
for the first time in Pb--Pb collisions, using higher-order Symmetric
Cumulants. We find that different three-harmonic correlations develop during
the collective evolution of the medium, when compared with correlations that
exist in the initial state. These new results cannot be interpreted in terms of
previous lower-order flow measurements, since contributions from two-harmonic
correlations are explicitly removed in the new observables. Comparison with
Monte Carlo simulations provides new and independent constraints for the
initial conditions and system properties of nuclear matter created in heavy-ion
collisions.


Abstract:
  We propose a robust framework for interpretable, few-shot analysis of
non-stationary sequential data based on flexible graphical models to express
the structured distribution of sequential events, using prototype radial basis
function (RBF) neural network emissions. A motivational link is demonstrated
between prototypical neural network architectures for few-shot learning and the
proposed RBF network infinite hidden Markov model (RBF-iHMM). We show that RBF
networks can be efficiently specified via prototypes allowing us to express
complex nonstationary patterns, while hidden Markov models are used to infer
principled high-level Markov dynamics. The utility of the framework is
demonstrated on biomedical signal processing applications such as automated
seizure detection from EEG data where RBF networks achieve state-of-the-art
performance using a fraction of the data needed to train long-short-term memory
variational autoencoders.


Abstract:
  EU needs an effective exit strategy from the crisis, with a special attention
to SMEs that represent the 99% of the enterprises active in the European
production system. To this end, innovation appears to be a key factor to
relaunch the EU industrial system. The BIVEE project proceeded for almost 4
years to develop a rich framework, i.e., a methodology and a cloud-based
software environment, that includes business principles, models, and best
practices, plus a number of advanced software services, to support and promote
production improvement and business innovation in virtual enterprise
environments (essentially, enterprise networks.)


Abstract:
  Knowledge Graph has been proven effective in modeling structured information
and conceptual knowledge, especially in the medical domain. However, the lack
of high-quality annotated corpora remains a crucial problem for advancing the
research and applications on this task. In order to accelerate the research for
domain-specific knowledge graphs in the medical domain, we introduce DiaKG, a
high-quality Chinese dataset for Diabetes knowledge graph, which contains
22,050 entities and 6,890 relations in total. We implement recent typical
methods for Named Entity Recognition and Relation Extraction as a benchmark to
evaluate the proposed dataset thoroughly. Empirical results show that the DiaKG
is challenging for most existing methods and further analysis is conducted to
discuss future research direction for improvements. We hope the release of this
dataset can assist the construction of diabetes knowledge graphs and facilitate
AI-based applications.


Abstract:
  The vorticity of world lines of observers associated to the rotation of a
massive body was reported by Lense and Thirring more than a century ago. In
their example the frame dragging effect induced by the vorticity, is directly
(explicitly) related to the rotation of the source. However in many other cases
it is not so, and the origin of vorticity remains obscure and difficult to
identify. Accordingly, in order to unravel this issue, and looking for the
ultimate origin of vorticity associated to frame dragging, we analyze in this
manuscript very different scenarios where frame dragging effect is present.
Specifically we consider general vacuum stationary spacetimes, general
electro-vacuum spacetimes, radiating electro-vacuum spacetimes and Bondi--Sachs
radiating spacetimes. We identify the physical quantities present in all these
cases, which determine the vorticity and may legitimately be considered as
responsible for the frame dragging. Doing so we provide a comprehensive
physical picture of frame dragging. Some observational consequences of our
results are discussed.


Abstract:
  eXtended Reality (XR) autism research, ranging from Augmented Reality to
Virtual Reality, focuses on socio-emotional abilities and high-functioning
autism. However common autism interventions address the entire spectrum over
social, sensory and mediation issues. To bridge the gap between autism research
and real interventions, we compared existing literature on XR and autism with
stakeholders' needs obtained by interviewing 34 skateholders, mainly
practitioners. It allow us first to suggest XR use cases that could better
support practitioners' interventions, and second to derive design guidelines
accordingly. Findings demonstrate that collaborative XR sensory-based and
mediation approaches would benefit the entire spectrum, and encourage to
consider the overall intervention context when designing XR protocols.


Abstract:
  Software Engineering researchers are increasingly using Natural Language
Processing (NLP) techniques to automate Software Vulnerabilities (SVs)
assessment using the descriptions in public repositories. However, the existing
NLP-based approaches suffer from concept drift. This problem is caused by a
lack of proper treatment of new (out-of-vocabulary) terms for the evaluation of
unseen SVs over time. To perform automated SVs assessment with concept drift
using SVs' descriptions, we propose a systematic approach that combines both
character and word features. The proposed approach is used to predict seven
Vulnerability Characteristics (VCs). The optimal model of each VC is selected
using our customized time-based cross-validation method from a list of eight
NLP representations and six well-known Machine Learning models. We have used
the proposed approach to conduct large-scale experiments on more than 100,000
SVs in the National Vulnerability Database (NVD). The results show that our
approach can effectively tackle the concept drift issue of the SVs'
descriptions reported from 2000 to 2018 in NVD even without retraining the
model. In addition, our approach performs competitively compared to the
existing word-only method. We also investigate how to build compact
concept-drift-aware models with much fewer features and give some
recommendations on the choice of classifiers and NLP representations for SVs
assessment.