Abstract:
  Post-click conversion, as a strong signal indicating the user preference, is
salutary for building recommender systems. However, accurately estimating the
post-click conversion rate (CVR) is challenging due to the selection bias,
i.e., the observed clicked events usually happen on users' preferred items.
Currently, most existing methods utilize counterfactual learning to debias
recommender systems. Among them, the doubly robust (DR) estimator has achieved
competitive performance by combining the error imputation based (EIB) estimator
and the inverse propensity score (IPS) estimator in a doubly robust way.
However, inaccurate error imputation may result in its higher variance than the
IPS estimator. Worse still, existing methods typically use simple
model-agnostic methods to estimate the imputation error, which are not
sufficient to approximate the dynamically changing model-correlated target
(i.e., the gradient direction of the prediction model). To solve these
problems, we first derive the bias and variance of the DR estimator. Based on
it, a more robust doubly robust (MRDR) estimator has been proposed to further
reduce its variance while retaining its double robustness. Moreover, we propose
a novel double learning approach for the MRDR estimator, which can convert the
error imputation into the general CVR estimation. Besides, we empirically
verify that the proposed learning scheme can further eliminate the high
variance problem of the imputation learning. To evaluate its effectiveness,
extensive experiments are conducted on a semi-synthetic dataset and two
real-world datasets. The results demonstrate the superiority of the proposed
approach over the state-of-the-art methods. The code is available at
https://github.com/guosyjlu/MRDR-DL.


Abstract:
  Reconfigurable intelligent surfaces (RISs) are set to be a revolutionary
technology in the 6th generation of wireless systems. In this work, we study
the application of RIS in a multi-user passive localization scenario, where we
have one transmitter (Tx) and multiple asynchronous receivers (Rxs) with known
locations. We aim to estimate the locations of multiple users equipped with
RISs. The RISs only reflect the signal from the Tx to the Rxs and are not used
as active transceivers themselves. Each Rx receives the signal from the Tx (LOS
path) and the reflected signal from the RISs (NLOS path). We show that users'
3D position can be estimated with submeter accuracy in a large area around the
transmitter, using the LOS and NLOS time-of-arrival measurements at the Rxs. We
do so, by developing the signal model, deriving the Cramer-Rao bounds, and
devising an estimator that attains these bounds. Furthermore, by
orthogonalizing the RIS phase profiles across different users, we circumvent
inter-path interference.


Abstract:
  In this paper we prove the existence of isoperimetric regions of any volume
in Riemannian manifolds with Ricci bounded below and with a mild assumption at
infinity, that is Gromov-Hausdorff asymptoticity to simply connected models of
constant sectional curvature. The previous result is a consequence of a general
structure theorem for perimeter-minimizing sequences of sets of fixed volume on
noncollapsed Riemannian manifolds with a lower bound on the Ricci curvature. We
show that, without assuming any further hypotheses on the asymptotic geometry,
all the mass and the perimeter lost at infinity, if any, are recovered by at
most countably many isoperimetric regions sitting in some Gromov-Hausdorff
limits at infinity. The Gromov-Hausdorff asymptotic analysis conducted allows
us to provide, in low dimensions, a result of nonexistence of isoperimetric
regions in Cartan-Hadamard manifolds that are Gromov-Hausdorff asymptotic to
the Euclidean space. While studying the isoperimetric problem in the smooth
setting, the nonsmooth geometry naturally emerges, and thus our treatment
combines techniques from both the theories.


Abstract:
  Practical quantum networking architectures are crucial for scaling the
connection of quantum resources. Yet quantum network testbeds have thus far
underutilized the full capabilities of modern lightwave communications, such as
flexible-grid bandwidth allocation. In this work, we implement flex-grid
entanglement distribution in a deployed network for the first time, connecting
nodes in three distinct campus buildings time-synchronized via the Global
Positioning System (GPS). We quantify the quality of the distributed
polarization entanglement via log-negativity, which offers a generic metric of
link performance in entangled bits per second. After demonstrating successful
entanglement distribution for two allocations of our eight dynamically
reconfigurable channels, we demonstrate remote state preparation -- the first
realization on deployed fiber -- showcasing one possible quantum protocol
enabled by the distributed entanglement network. Our results realize an
advanced paradigm for managing entanglement resources in quantum networks of
ever-increasing complexity and service demands.


Abstract:
  This paper addresses black-box optimization over multiple information sources
whose both fidelity and query cost change over the search space, that is they
are location dependent. The approach uses: (i) an Augmented Gaussian Process,
recently proposed in multi-information source optimization as a single model of
the objective function over search space and sources, and (ii) a Gaussian
Process to model the location-dependent cost of each source. The former is used
into a Confidence Bound based acquisition function to select the next source
and location to query, while the latter is used to penalize the value of the
acquisition depending on the expected query cost for any source-location pair.
The proposed approach is evaluated on a set of Hyperparameters Optimization
tasks, consisting of two Machine Learning classifiers and three datasets of
different sizes.


Abstract:
  Adaptive networks have the capability to pursue solutions of global
stochastic optimization problems by relying only on local interactions within
neighborhoods. The diffusion of information through repeated interactions
allows for globally optimal behavior, without the need for central
coordination. Most existing strategies are developed for cooperative learning
settings, where the objective of the network is common to all agents. We
consider in this work a team setting, where a subset of the agents form a team
with a common goal while competing with the remainder of the network. We
develop an algorithm for decentralized competition among teams of adaptive
agents, analyze its dynamics and present an application in the decentralized
training of generative adversarial neural networks.


Abstract:
  We study the condition for the dilaton stabilization in Type IIB flux
compactifications consistent with the KKLT scenario. Since the
Gukov-Vafa-Witten superpotential depends linearly on the dilaton, the dilaton
mass squared is given by a sum of the gravitino mass squared and additional
terms determined by the complex structure moduli stabilization. If the dilaton
mass is not much enhanced from the gravitino mass, the mass mixing with the
K\"ahler modulus in the presence of the non-perturbative effect generates the
saddle point at the supersymmetric field values, hence the potential becomes
unstable. When the complex structure moduli other than the conifold modulus are
neglected, the saddle point problem arises over the controllable parameter
space. We also point out that the dilaton stabilization condition is equivalent
to the condition on the NS 3-form fluxes, $|H_{(1,2)}| > | H_{(0,3)}|$.


Abstract:
  This paper studies the differential lattice, defined to be a lattice $L$
equipped with a map $d:L\to L$ that satisfies a lattice analog of the Leibniz
rule for a derivation. Isomorphic differential lattices are studied and
classifications of differential lattices are obtained for some basic lattices.
Several families of derivations on a lattice are explicitly constructed, giving
realizations of the lattice as lattices of derivations. Derivations on a finite
distributive lattice are shown to have a natural structure of lattice.
Moreover, derivations on a complete infinitely distributive lattice form a
complete lattice. For a general lattice, it is conjectured that its poset of
derivations is a lattice that uniquely determines the given lattice.


Abstract:
  Recent advancements in computer vision promise to automate medical image
analysis. Rheumatoid arthritis is an autoimmune disease that would profit from
computer-based diagnosis, as there are no direct markers known, and doctors
have to rely on manual inspection of X-ray images. In this work, we present a
multi-task deep learning model that simultaneously learns to localize joints on
X-ray images and diagnose two kinds of joint damage: narrowing and erosion.
Additionally, we propose a modification of label smoothing, which combines
classification and regression cues into a single loss and achieves 5% relative
error reduction compared to standard loss functions. Our final model obtained
4th place in joint space narrowing and 5th place in joint erosion in the global
RA2 DREAM challenge.


Abstract:
  We propose a new Arithmetic Distribution Neural Network (ADNN) for learning
the distributions of temporal pixels during background subtraction. In our
ADNN, the arithmetic distribution operations are utilized to propose the
arithmetic distribution layers, including the product distribution layer and
the sum distribution layer. Furthermore, in order to improve the accuracy of
the proposed approach, an improved Bayesian refinement model based on
neighboring information, with a GPU implementation, is introduced. In the
forward pass and backpropagation of the proposed arithmetic distribution
layers, histograms are considered as probability density functions rather than
matrices. Thus, the proposed approach is able to utilize the probability
information of the histogram and achieve promising results with a very simple
architecture compared to traditional convolutional neural networks. Evaluations
using standard benchmarks demonstrate the superiority of the proposed approach
compared to state-of-the-art traditional and deep learning methods. To the best
of our knowledge, this is the first method to propose network layers based on
arithmetic distribution operations for learning distributions during background
subtraction.


Abstract:
  Owing to the development of research on local aggregation operators, dramatic
breakthrough has been made in point cloud analysis models. However, existing
local aggregation operators in the current literature fail to attach decent
importance to the local information of the point cloud, which limits the power
of the models. To fit this gap, we propose an efficient Vector Attention
Convolution module (VAConv), which utilizes K-Nearest Neighbor (KNN) to extract
the neighbor points of each input point, and then uses the elevation and
azimuth relationship of the vectors between the center point and its neighbors
to construct an attention weight matrix for edge features. Afterwards, the
VAConv adopts a dual-channel structure to fuse weighted edge features and
global features. To verify the efficiency of the VAConv, we connect the VAConvs
with different receptive fields in parallel to obtain a Multi-scale graph
convolutional network, VA-GCN. The proposed VA-GCN achieves state-of-the-art
performance on standard benchmarks including ModelNet40, S3DIS and ShapeNet.
Remarkably, on the ModelNet40 dataset for 3D classification, VA-GCN increased
by 2.4% compared to the baseline.


Abstract:
  The Neolithic Revolution began c. 10000 years ago and is characterised by the
ultimate, near complete transition from hunting and gathering to agricultural
food production on land. The Neolithic Revolution is thought to have been
catalysed by a combination of local population pressure, cultural diffusion,
property rights and climate change. We undertake a thought experiment that
examines trends in these key hypothesised catalysts and patters of today to
explore whether society could be on a path towards another paradigm shift in
food production: away from hunting of wild fish towards a transition to mostly
fish farming. We find similar environmental and cultural pressures have driven
the rapid rise of aquaculture, during a period that has now been coined the
Blue Revolution, providing impetus for such a transition in coming decades to
centuries. We also highlight the interacting and often mutually reinforcing
impacts of 1)technological and scientific advancement, 2)environmental
awareness and collective action and 3)globalisation and trade influencing the
trajectory and momentum of the Blue Revolution. We present two qualitative
narratives that broadly fall within two future trajectories: 1)a ubiquitous
aquaculture transition and 20commercial aquaculture and fisheries coexistence.
This scenarios approach aims to encourage logical, forward thinking, and
innovative solutions to complex systems dynamics. Scenario-based thought
experiments are useful to explore large scale questions, increase the
accessibility to a wider readership and ideally catalyse discussion around
proactive governance mechanisms. We argue the future is not fixed and society
now has greater foresight and capacity to choose the workable balance between
fisheries sand aquaculture that supports economic, environmental, cultural and
social objectives through combined planning, policies and management.


Abstract:
  Exclusive rare Higgs decays into lepton pair plus one light hadron, such as
$h\to \rho^0(\omega)\ell\bar{\ell}$, $h\to \pi^0\ell\bar{\ell}$, $h\to \pi^+
(K^+)\ell^-\bar{\nu}_\ell$, and $h\to \rho^+(K^{*+})\ell^-\bar{\nu}_\ell$, have
been explored in the standard model. Decay amplitudes are dominantly from the
Higgs couplings to gauge bosons and to charged leptons, and their branching
ratios are predicted in the range of $10^{-8}\sim 10^{-5}$. We have also
analyzed the differential dilepton invariant mass and angular distributions of
$h\to \rho^0 \ell^+\ell^-$ decays. It will be challenging to search for these
rare processes. Nevertheless, experimental studies of them, in particular,
$h\to \rho^0\ell^+\ell^-$ with $\ell=e,\mu$, might be interesting both to help
deepen our understanding of the standard model and to probe new physics beyond
the standard model in the future high-precision experiments.


Abstract:
  A desirable goal for autonomous agents is to be able to coordinate on the fly
with previously unknown teammates. Known as "ad hoc teamwork", enabling such a
capability has been receiving increasing attention in the research community.
One of the central challenges in ad hoc teamwork is quickly recognizing the
current plans of other agents and planning accordingly. In this paper, we focus
on the scenario in which teammates can communicate with one another, but only
at a cost. Thus, they must carefully balance plan recognition based on
observations vs. that based on communication. This paper proposes a new metric
for evaluating how similar are two policies that a teammate may be following -
the Expected Divergence Point (EDP). We then present a novel planning algorithm
for ad hoc teamwork, determining which query to ask and planning accordingly.
We demonstrate the effectiveness of this algorithm in a range of increasingly
general communication in ad hoc teamwork problems.


Abstract:
  One commonly used clinical approach towards detecting melanomas recognises
the existence of Ugly Duckling nevi, or skin lesions which look different from
the other lesions on the same patient. An automatic method of detecting and
analysing these lesions would help to standardize studies, compared with manual
screening methods. However, it is difficult to obtain expertly-labelled images
for ugly duckling lesions. We therefore propose to use self-supervised machine
learning to automatically detect outlier lesions. We first automatically detect
and extract all the lesions from a wide-field skin image, and calculate an
embedding for each detected lesion in a patient image, based on automatically
identified features. These embeddings are then used to calculate the L2
distances as a way to measure dissimilarity. Using this deep learning method,
Ugly Ducklings are identified as outliers which should deserve more attention
from the examining physician. We evaluate through comparison with
dermatologists, and achieve a sensitivity rate of 72.1% and diagnostic accuracy
of 94.2% on the held-out test set.


Abstract:
  Let $A$ be an abelian variety over a number field $\mathrm E\subset \mathbb
C$ and let $\mathbf G$ denote the Mumford--Tate group of $A$. After replacing
$\mathrm E$ by a finite extension, the action of the absolute Galois group
$\mathrm{Gal}(\overline{\mathrm E}/\mathrm E)$ on the $\ell$-adic cohomology
$\mathrm{H}^1_{\mathrm{\acute{e}t}}(A_{\overline{\mathrm E}},\mathbb Q_\ell)$
factors through $\mathbf G(\mathbb Q_\ell).$ We show that for $v$ an odd prime
of $\mathrm E$ where $A$ has good reduction, the conjugacy class of Frobenius
$\mathrm{Frob}_v$ in $\mathbf G(\mathbb Q_\ell)$ is independent of $\ell$.
Along the way we prove that every point in the $\mu$-ordinary locus of the
special fiber of Shimura varieties has a special point lifting it.


Abstract:
  A stable map of a closed orientable $3$-manifold into the real plane is
called a stable map of a link in the manifold if the link is contained in the
set of definite fold points. We give a complete characterization of the
hyperbolic links in the $3$-sphere that admit stable maps into the real plane
with exactly one (connected component of a) fiber having two singular points.


Abstract:
  In this paper, we present a neural model for joint dropped pronoun recovery
(DPR) and conversational discourse parsing (CDP) in Chinese conversational
speech. We show that DPR and CDP are closely related, and a joint model
benefits both tasks. We refer to our model as DiscProReco, and it first encodes
the tokens in each utterance in a conversation with a directed Graph
Convolutional Network (GCN). The token states for an utterance are then
aggregated to produce a single state for each utterance. The utterance states
are then fed into a biaffine classifier to construct a conversational discourse
graph. A second (multi-relational) GCN is then applied to the utterance states
to produce a discourse relation-augmented representation for the utterances,
which are then fused together with token states in each utterance as input to a
dropped pronoun recovery layer. The joint model is trained and evaluated on a
new Structure Parsing-enhanced Dropped Pronoun Recovery (SPDPR) dataset that we
annotated with both two types of information. Experimental results on the SPDPR
dataset and other benchmarks show that DiscProReco significantly outperforms
the state-of-the-art baselines of both tasks.


Abstract:
  Given high-dimensional time series data (e.g., sensor data), how can we
detect anomalous events, such as system faults and attacks? More challengingly,
how can we do this in a way that captures complex inter-sensor relationships,
and detects and explains anomalies which deviate from these relationships?
Recently, deep learning approaches have enabled improvements in anomaly
detection in high-dimensional datasets; however, existing methods do not
explicitly learn the structure of existing relationships between variables, or
use them to predict the expected behavior of time series. Our approach combines
a structure learning approach with graph neural networks, additionally using
attention weights to provide explainability for the detected anomalies.
Experiments on two real-world sensor datasets with ground truth anomalies show
that our method detects anomalies more accurately than baseline approaches,
accurately captures correlations between sensors, and allows users to deduce
the root cause of a detected anomaly.


Abstract:
  We carry out a study in the approach of a dimension-8 effective field theory
on the anomalous quartic $W^+W^-\gamma\gamma$ couplings that arise from the
process $pp \to W\gamma\gamma$ at the Future Circular Collider-hadron hadron
(FCC-hh). The process is sensitive to the $W^+W^-\gamma\gamma$ vertex and
limits may be placed on the anomalous $f_ {M,i}/\Lambda^4$ and $ f_
{T,j}/\Lambda^4$ couplings by measurements of $W\gamma\gamma$ production. We
illustrate the physics opportunities and reach of the FCC-hh operating for a
high integrated luminosity scenario of ${\cal L}=1, 5, 10, 30$ ${\rm ab}^{-1}$
of proton-proton collisions at a center-of-mass energy of $\sqrt{s}=100$ TeV.
Production cross-sections of the signal and the sensitivity constraints on the
anomalous $f_ {M,i}/\Lambda^4$ and $ f_ {T,j}/\Lambda^4$ couplings at $95\%$
C.L. are identified through the leptonic and hadronic decay modes of the
$W$-boson. The best FCC-hh sensitivity limits on these anomalous couplings
might reach up to the order of magnitude ${\cal O}(10^{-4}-10^1)$. These
results show that the $pp \to W\gamma\gamma$ channel at the FCC-hh has a
greater potential to research for the anomalous quartic $W^+W^-\gamma\gamma$
couplings and to research for new physics.


Abstract:
  In this work, a robust and efficient text-to-speech (TTS) synthesis system
named Triple M is proposed for large-scale online application. The key
components of Triple M are: 1) A sequence-to-sequence model adopts a novel
multi-guidance attention to transfer complementary advantages from guiding
attention mechanisms to the basic attention mechanism without in-domain
performance loss and online service modification. Compared with single
attention mechanism, multi-guidance attention not only brings better
naturalness to long sentence synthesis, but also reduces the word error rate by
26.8%. 2) A new efficient multi-band multi-time vocoder framework, which
reduces the computational complexity from 2.8 to 1.0 GFLOP and speeds up LPCNet
by 2.75x on a single CPU.


Abstract:
  Statistical models of real world data typically involve continuous
probability distributions such as normal, Laplace, or exponential
distributions. Such distributions are supported by many probabilistic modelling
formalisms, including probabilistic database systems. Yet, the traditional
theoretical framework of probabilistic databases focusses entirely on finite
probabilistic databases.
  Only recently, we set out to develop the mathematical theory of infinite
probabilistic databases. The present paper is an exposition of two recent
papers which are cornerstones of this theory. In (Grohe, Lindner; ICDT 2020) we
propose a very general framework for probabilistic databases, possibly
involving continuous probability distributions, and show that queries have a
well-defined semantics in this framework. In (Grohe, Kaminski, Katoen, Lindner;
PODS 2020) we extend the declarative probabilistic programming language
Generative Datalog, proposed by (B\'ar\'any et al.~2017) for discrete
probability distributions, to continuous probability distributions and show
that such programs yield generative models of continuous probabilistic
databases.


Abstract:
  We are concerned with the dependence of the lowest positive eigenvalue of the
Dirac operator on the geometry of rectangles, subject to infinite-mass boundary
conditions. We conjecture that the square is a global minimiser both under the
area or perimeter constraints. Contrary to well-known non-relativistic
analogues, we show that the present spectral problem does not admit explicit
solutions. We prove partial optimisation results based on a variational
reformulation and newly established lower and upper bounds to the Dirac
eigenvalue. We also propose an alternative approach based on symmetries of
rectangles and a non-convex minimisation problem; this implies a sufficient
condition formulated in terms of a symmetry of the minimiser which guarantees
the conjectured results.


Abstract:
  We give an $O(k^3 n \log n \min(k,\log^2 n) \log^2(nC))$-time algorithm for
computing maximum integer flows in planar graphs with integer arc {\em and
vertex} capacities bounded by $C$, and $k$ sources and sinks. This improves by
a factor of $\max(k^2,k\log^2 n)$ over the fastest algorithm previously known
for this problem [Wang, SODA 2019].
  The speedup is obtained by two independent ideas. First we replace an
iterative procedure of Wang that uses $O(k)$ invocations of an $O(k^3 n \log^3
n)$-time algorithm for maximum flow algorithm in a planar graph with $k$ apices
[Borradaile et al., FOCS 2012, SICOMP 2017], by an alternative procedure that
only makes one invocation of the algorithm of Borradaile et al. Second, we show
two alternatives for computing flows in the $k$-apex graphs that arise in our
modification of Wang's procedure faster than the algorithm of Borradaile et al.
In doing so, we introduce and analyze a sequential implementation of the
parallel highest-distance push-relabel algorithm of Goldberg and Tarjan~[JACM
1988].


Abstract:
  TxGraffiti is an automated conjecturing program that produces graph theoretic
conjectures in the form of conjectured inequalities. This program written and
maintained by the second author since 2017 was inspired by the successes of
previous automated conjecturing programs including Fajtlowicz's GRAFFITI and
DeLaVi\~{n}a's GRAFFITI.pc. In this paper we prove and generalize several
conjectures generated by TxGraffiti when it was prompted to conjecture on the
\emph{independence number}, the \emph{domination number}, and the
\emph{matching number} (and generalizations of each of these graph invariants).
Moreover, in several instances we also show the proposed inequalities relating
these graph invariants are sharp.


Abstract:
  The PC and FCI algorithms are popular constraint-based methods for learning
the structure of directed acyclic graphs (DAGs) in the absence and presence of
latent and selection variables, respectively. These algorithms (and their
order-independent variants, PC-stable and FCI-stable) have been shown to be
consistent for learning sparse high-dimensional DAGs based on partial
correlations. However, inferring conditional independences from partial
correlations is valid if the data are jointly Gaussian or generated from a
linear structural equation model -- an assumption that may be violated in many
applications. To broaden the scope of high-dimensional causal structure
learning, we propose nonparametric variants of the PC-stable and FCI-stable
algorithms that employ the conditional distance covariance (CdCov) to test for
conditional independence relationships. As the key theoretical contribution, we
prove that the high-dimensional consistency of the PC-stable and FCI-stable
algorithms carry over to general distributions over DAGs when we implement
CdCov-based nonparametric tests for conditional independence. Numerical studies
demonstrate that our proposed algorithms perform nearly as good as the
PC-stable and FCI-stable for Gaussian distributions, and offer advantages in
non-Gaussian graphical models.


Abstract:
  We present elemental abundance results for HE 2148-2039 and HE 2155-2043
based on a detailed high-resolution spectroscopic analysis. The high-resolution
SUBARU/HDS spectra used for our analysis have a resolution of R~60 000.
Although limited information based on photometry and low-resolution
spectroscopy are available, we present for the first time an abundance analysis
based on high-resolution spectra for both the objects. Our analysis shows that
the two objects are extremely metal-poor with [Fe/H]< -3. Among the
neutron-capture elements, abundances of only Sr and Ba could be determined in
our programme stars. For both the objects [Ba/Fe] is found to be < 0. While
strontium is under abundant in HE 2148-2039 with [Sr/Fe]~ -2.02, Sr is near
solar in HE 2155-2043. The locations of the programme stars in the absolute
carbon abundance, A(C) vs. [Fe/H] diagram show that HE 2148-2039 is a CEMP-no
Group II object and HE 2155-2043 is a CEMP-no Group III object. Observed
[Sr/Ba] ratios are characteristics of a fast rotating massive star progenitor
for HE 2155-2043 and a metal-poor Asymptotic giant branch (AGB) star for HE
2148-2039. The estimated [Sc/Mn] as well as [C/Cr] ratios in HE 2155-2043 show
that the surface chemical composition of this object is mono-enriched. The
surface chemical composition of HE 2148-2039 is also found to be mono-enriched
based on [Mg/C] ratio. With respect to their locations in the [C/N] vs. T eff
diagram, HE 2148-2039 shows signatures of mixing, and HE 2155-2043 falls in the
unmixed region of [C/N] vs. Teff plot. Kinematic analysis shows that both the
objects belong to Galactic halo population.


Abstract:
  Estimators for mutual information are typically biased. However, in the case
of the Kozachenko-Leonenko estimator for metric spaces, a type of nearest
neighbour estimator, it is possible to calculate the bias explicitly.


Abstract:
  This work reports the general design and characterization of two exotic,
anomalous nonequilibrium topological phases. In equilibrium systems, the Weyl
nodes or the crossing points of nodal lines may become the transition points
between higher-order and first-order topological phases defined on
two-dimensional slices, thus featuring both hinge Fermi arc and surface Fermi
arc. We advance this concept by presenting a strategy to obtain, using
time-sequenced normal insulator phases only, Floquet higher-order Weyl
semimetals and Floquet higher-order nexus semimetals, where the concerned
topological singularities in the three-dimensional Brillouin zone border
anomalous two-dimensional higher-order Floquet phases. The fascinating
topological phases we obtain are previously unknown and can be experimentally
studied using, for example, a three-dimensional lattice of coupled ring
resonators.


Abstract:
  It is showed that complex scalar fields with a self-interaction potential may
propagate along null geodesics on flat Friedmann--Lema\^itre--Robertson--Walker
universes with different time-dependent scale factors. This occurs provided
they self interact adequately, for different forms of potentials, and
  even for the massive case.


Abstract:
  While many real-world data streams imply that they change frequently in a
nonstationary way, most of deep learning methods optimize neural networks on
training data, and this leads to severe performance degradation when dataset
shift happens. However, it is less possible to annotate or inspect newly
streamed data by humans, and thus it is desired to measure model drift at
inference time in an unsupervised manner. In this paper, we propose a novel
method of model drift estimation by exploiting statistics of batch
normalization layer on unlabeled test data. To remedy possible sampling error
of streamed input data, we adopt low-rank approximation to each
representational layer. We show the effectiveness of our method not only on
dataset shift detection but also on model selection when there are multiple
candidate models among model zoo or training trajectories in an unsupervised
way. We further demonstrate the consistency of our method by comparing model
drift scores between different network architectures.


Abstract:
  Measurement plays a quintessential role in the control of quantum systems.
Beyond initialization and readout which pertain to projective measurements,
weak measurements in particular, through their back-action on the system, may
enable various levels of coherent control. The latter ranges from observing
quantum trajectories to state dragging and steering. Furthermore, just like the
adiabatic evolution of quantum states that is known to induce the Berry phase,
sequential weak measurements may lead to path-dependent geometric phases. Here
we measure the geometric phases induced by sequences of weak measurements and
demonstrate a topological transition in the geometric phase controlled by
measurement strength. This connection between weak measurement induced quantum
dynamics and topological transitions reveals subtle topological features in
measurement-based manipulation of quantum systems. Our protocol could be
implemented for classes of operations (e.g. braiding) which are topological in
nature. Furthermore, our results open new horizons for measurement-enabled
quantum control of many-body topological states.


Abstract:
  Evolution algebras are non-associative algebras that describe non-Mendelian
hereditary processes and have connections with many other areas. In this paper
we obtain necessary and sufficient conditions for a given algebra $A$ to be an
evolution algebra. We prove that the problem is equivalent to the so-called
$SDC$ $problem$, that is, the $simultaneous$ $diagonalisation$ $via$
$congruence$ of a given set of matrices. More precisely we show that an
$n$-dimensional algebra $A$ is an evolution algebra if, and only if, a certain
set of $n$ symmetric $n\times n$ matrices $\{M_{1}, \ldots, M_{n}\}$ describing
the product of $A$ are $SDC$. We apply this characterisation to show that while
certain classical genetic algebras (representing Mendelian and auto-tetraploid
inheritance) are not themselves evolution algebras, arbitrarily small
perturbations of these are evolution algebras. This is intriguing as evolution
algebras model asexual reproduction unlike the classical ones.


Abstract:
  In this work we investigate symmetry breaking in the presence of a turbulent
environment. The transition from a symmetric state to a symmetry-breaking state
is demonstrated using two examples: (i) the transition of a two-dimensional
flow to a three dimensional flow as the fluid layer thickness is varied and
(ii) the dynamo instability in a thin layer flow as the magnetic Reynolds
number is varied. We show that these examples have similar critical exponents
that differ from the mean-field predictions. The critical behavior can be
related to the multiplicative nature of the fluctuations and can be predicted
in certain limits using results from the statistical properties of random
interfaces. Our results indicate the possibility of existence of a new class of
out-of-equilibrium phase transition controlled by the multiplicative noise.


Abstract:
  To characterize the "average" of a sample of graphs, one can compute the
sample Frechet mean (or median) graph, which provides an interpretable summary
of the graph sample. In this paper, we address the following foundational
question: does the mean or median graph inherit the structural properties of
the graphs in the sample? An important graph property is the edge density.
Because sparse graphs provide prototypical models for real networks, one would
like to guarantee that the edge density be preserved when computing the sample
mean (or median). In this paper, we prove that the edge density is an
hereditary property, which can be transmitted from a graph sample to its sample
Frechet mean (or median), irrespective of the method used to estimate the mean
or the median. Specifically, we prove the following result: the number of edges
of the Frechet mean (or median) graph of a set of graphs is bounded by the
maximal number of edges amongst all the graphs in the sample. We prove the
result for the graph Hamming distance, and the spectral adjacency pseudometric,
using very different arguments.


Abstract:
  The combination of the Internet of Things and the Edge Computing gives many
opportunities to support innovative applications close to end users. Numerous
devices present in both infrastructures can collect data upon which various
processing activities can be performed. However, the quality of the outcomes
may be jeopardized by the presence of outliers. In this paper, we argue on a
novel model for outliers detection by elaborating on a `soft' approach. Our
mechanism is built upon the concepts of candidate and confirmed outliers. Any
data object that deviates from the population is confirmed as an outlier only
after the study of its sequence of magnitude values as new data are
incorporated into our decision making model. We adopt the combination of a
sliding with a landmark window model when a candidate outlier is detected to
expand the sequence of data objects taken into consideration. The proposed
model is fast and efficient as exposed by our experimental evaluation while a
comparative assessment reveals its pros and cons.


Abstract:
  The newly established Heisenberg limit in arbitrary waveform estimation is
quite different with parameter estimation and shows a unique characteristic of
a future quantum version of oscilloscope. However, it is still a non-trivial
challenge to generate a large number of exotic quantum entangled states to
achieve this quantum limit. Here, by employing the time-domain quantum
difference detection method, we demonstrate Heisenberg-limited waveform quantum
estimation with diamond spins under ambient condition in the experiment.
Periodic dynamical decoupling is applied to enhance both the dynamic range and
sensitivity by one order of magnitude. Using this quantum-enhanced estimation
scheme, the estimation error of an unknown waveform is reduced by more than $5$
dB below the standard quantum limit with $N\sim{\text{2}} \times
{\text{1}}{{\text{0}}^3}$ resources, where more than ${1 \times
{\text{1}}{{\text{0}}^5}}$ resources would be required to achieve a similar
error level using classical detection. This work provides an essential step
towards realizing quantum-enhanced structure recognition in a continuous space
and time.


Abstract:
  Content moderation is often performed by a collaboration between humans and
machine learning models. However, it is not well understood how to design the
collaborative process so as to maximize the combined moderator-model system
performance. This work presents a rigorous study of this problem, focusing on
an approach that incorporates model uncertainty into the collaborative process.
First, we introduce principled metrics to describe the performance of the
collaborative system under capacity constraints on the human moderator,
quantifying how efficiently the combined system utilizes human decisions. Using
these metrics, we conduct a large benchmark study evaluating the performance of
state-of-the-art uncertainty models under different collaborative review
strategies. We find that an uncertainty-based strategy consistently outperforms
the widely used strategy based on toxicity scores, and moreover that the choice
of review strategy drastically changes the overall system performance. Our
results demonstrate the importance of rigorous metrics for understanding and
developing effective moderator-model systems for content moderation, as well as
the utility of uncertainty estimation in this domain.


Abstract:
  There is a recent interest in investigating few-shot NER, where the
low-resource target domain has different label sets compared with a
resource-rich source domain. Existing methods use a similarity-based metric.
However, they cannot make full use of knowledge transfer in NER model
parameters. To address the issue, we propose a template-based method for NER,
treating NER as a language model ranking problem in a sequence-to-sequence
framework, where original sentences and statement templates filled by candidate
named entity span are regarded as the source sequence and the target sequence,
respectively. For inference, the model is required to classify each candidate
span based on the corresponding template scores. Our experiments demonstrate
that the proposed method achieves 92.55% F1 score on the CoNLL03 (rich-resource
task), and significantly better than fine-tuning BERT 10.88%, 15.34%, and
11.73% F1 score on the MIT Movie, the MIT Restaurant, and the ATIS
(low-resource task), respectively.


Abstract:
  A new method for calculating the symmetry-projected energy of coupled-cluster
singles and doubles (CCSD) wave function through the Monte Carlo method is
proposed. We present benchmark calculations in considering the three-level
Lipkin model which is a simple and minimal model with two phases: spherical and
deformed ones. It is demonstrated that this new method gives good ground state
energy and low-lying spectra.


Abstract:
  Internet of Things (IoT)-based smart home applications are rising in
popularity. However, this trend attracts malicious activity, which causes
cost-efficient security to be in high demand. This paper proposes a low-end
design that reinforces the security of a home network. It uses private
blockchain technology and localization via RSSI-based trilateration. We
investigated the benefits of private blockchains over their public counterpart,
and we improve the precision of the localization algorithm by testing it
against different wireless technologies. The results conclude that using a
private blockchain with a WiFi-based communication system produces the most
efficient iteration of the proposed design.


Abstract:
  The beam aperture of a particle accelerator defines the clearance available
for the circulating beams and is a parameter of paramount importance for the
accelerator performance. At the CERN Large Hadron Collider (LHC), the knowledge
and control of the available aperture is crucial because the nominal proton
beams carry an energy of 362 MJ stored in a superconducting environment. Even a
tiny fraction of beam losses could quench the superconducting magnets or cause
severe material damage. Furthermore, in a circular collider, the performance in
terms of peak luminosity depends to a large extent on the aperture of the inner
triplet quadrupoles, which are used to focus the beams at the interaction
points. In the LHC, this aperture represents the smallest aperture at
top-energy with squeezed beams and determines the maximum potential reach of
the peak luminosity. Beam-based aperture measurements in these conditions are
difficult and challenging. In this paper, we present different methods that
have been developed over the years for precise beam-based aperture measurements
in the LHC, highlighting applications and results that contributed to boost the
operational LHC performance in Run 1 (2010-2013) and Run 2 (2015-2018).


Abstract:
  Propylene oxide is one of the simplest organic chiral molecules and has
attracted considerable interest from the scientific community a few years ago,
when it was discovered in the interstellar medium. Here, we report a
preliminary study on the interaction between propylene oxide and rare-gas
atoms, specifically He, Ne, and Ar. The interaction potentials as a function of
the distance between the center-of-mass of propylene oxide and the
rare-gas-atom are calculated for fourteen leading configurations at
CCSD(T)/aug-cc-pVDZ level of theory. Symmetry Adapted Perturbation Theory has
been employed for the analysis of the intermolecular potential, revealing that
most of the contribution is given by dispersion and exchange forces.


Abstract:
  This work studies a two-component Fornberg-Whitham (FW) system, which can be
considered as a model for the propagation of shallow water waves. It's known
that its solutions depend continuously on their initial data from the local
well-posedness result. In this paper, we further show that such dependence is
not uniformly continuous in $H^{s}(R)\times H^{s-1}(R)$ for $s>\frac{3}{2}$,
but H\"{o}ler continuous in a weaker topology. Besides, we also establish that
the FW system is ill-posed in the critical Sobolev space
$H^{\frac{3}{2}}(R)\times H^{\frac{1}{2}}(R)$ by proving the norm inflation.


Abstract:
  Versatile nanoscale sensors that are susceptible to changes in a variety of
physical quantities often exhibit limited selectivity. This paper reports a
novel scheme based on microwave-dressed spin states for optically probed
nanoscale temperature detection using diamond quantum sensors, which provides
selective sensitivity to temperature changes. By combining this scheme with a
continuous pump-probe scheme using ensemble nitrogen-vacancy centers in
nanodiamonds, a sub-microsecond temporal resolution with thermal sensitivity of
3.7 K$\cdot$Hz$^{-1/2}$ that is insensitive to variations in external magnetic
fields on the order of 2 G is demonstrated. The presented results are favorable
for the practical application of time-resolved nanoscale quantum sensing, where
temperature imaging is required under fluctuating magnetic fields.


Abstract:
  First, we construct some families of nonsolvable anticommutative algebras,
solvable Lie algebras and even nilpotent Lie algebras, that can be endowed with
the structure of simple Hom-Lie algebras. This situation shows that a
classification of simple Hom-Lie algebras would be unrealistic without any
further restrictions. Therefore, we introduce the class of \emph{strongly
simple Hom-Lie algebras}, which is the class of anticommutative algebras that
are simple Hom-Lie with respect to all their twisting maps. We show some of its
properties, provide a characterisation and explore some of its subclasses.
Then, we classify completely regular simple Hom-Lie algebras over any arbitrary
field. Furthermore, we establish that every simple anticommutative algebra of
dimension $3$ turns out to be a simple Lie algebra where its Lie bracket is
deformed by a bijective linear map, and also we determine all the simple
Hom-Lie algebras in dimension $2$, that were wrongly claimed to be nonexistent
in \cite{CH}.


Abstract:
  We propose OmniLytics, a blockchain-based secure data trading marketplace for
machine learning applications. Utilizing OmniLytics, many distributed data
owners can contribute their private data to collectively train an ML model
requested by some model owners, and receive compensation for data contribution.
OmniLytics enables such model training while simultaneously providing 1) model
security against curious data owners; 2) data security against the curious
model and data owners; 3) resilience to malicious data owners who provide
faulty results to poison model training; and 4) resilience to malicious model
owners who intend to evade payment. OmniLytics is implemented as a blockchain
smart contract to guarantee the atomicity of payment. In OmniLytics, a model
owner splits its model into the private and public parts and publishes the
public part on the contract. Through the execution of the contract, the
participating data owners securely aggregate their locally trained models to
update the model owner's public model and receive reimbursement through the
contract. We implement a working prototype of OmniLytics on Ethereum blockchain
and perform extensive experiments to measure its gas cost, execution time, and
model quality under various parameter combinations. For training a CNN on the
MNIST dataset, the MO is able to boost its model accuracy from 62% to 83%
within 500ms in blockchain processing time.This demonstrates the effectiveness
of OmniLytics for practical deployment.


Abstract:
  We introduce a general approach for modeling the dynamic of multivariate time
series when the data are of mixed type (binary/count/continuous). Our method is
quite flexible and conditionally on past values, each coordinate at time $t$
can have a distribution compatible with a standard univariate time series model
such as GARCH, ARMA, INGARCH or logistic models whereas past values of the
other coordinates play the role of exogenous covariates in the dynamic. The
simultaneous dependence in the multivariate time series can be modeled with a
copula. Additional exogenous covariates are also allowed in the dynamic. We
first study usual stability properties of these models and then show that
autoregressive parameters can be consistently estimated equation-by-equation
using a pseudo-maximum likelihood method, leading to a fast implementation even
when the number of time series is large. Moreover, we prove consistency results
when a parametric copula model is fitted to the time series and in the case of
Gaussian copulas, we show that the likelihood estimator of the correlation
matrix is strongly consistent. We carefully check all our assumptions for two
prototypical examples: a GARCH/INGARCH model and logistic/log-linear INGARCH
model. Our results are illustrated with numerical experiments as well as two
real data sets.


Abstract:
  Inferring the probability distribution of sentences or word sequences is a
key process in natural language processing. While word-level language models
(LMs) have been widely adopted for computing the joint probabilities of word
sequences, they have difficulty in capturing a context long enough for sentence
probability estimation (SPE). To overcome this, recent studies introduced
training methods using sentence-level noise-contrastive estimation (NCE) with
recurrent neural networks (RNNs). In this work, we attempt to extend it for
contextual SPE, which aims to estimate a conditional sentence probability given
a previous text. The proposed NCE samples negative sentences independently of a
previous text so that the trained model gives higher probabilities to the
sentences that are more consistent with \textcolor{blue}{the} context. We apply
our method to a simple word-level RNN LM to focus on the effect of the
sentence-level NCE training rather than on the network architecture. The
quality of estimation was evaluated against multiple-choice cloze-style
questions including both human and automatically generated questions. The
experimental results show that the proposed method improved the SPE quality for
the word-level RNN LM.


Abstract:
  The way molecules absorb, transfer, and emit light can be modified by
coupling them to optical cavities. The extent of the modification is often
defined by the cavity-molecule coupling strength, which depends on the number
of coupled molecules. We experimentally and numerically study the evolution of
photoemission from a thin layered J-aggregated molecular material strongly
coupled to a Fabry-Perot microcavity as a function of the number of coupled
layers. We unveil an important difference between the strong coupling
signatures obtained from reflection spectroscopy and from polariton assisted
photoluminescence. We also study the effect of the vibrational modes supported
by the molecular material on the polariton assisted emission both for a focused
laser beam and for normally incident excitation, for two different excitation
wavelengths: a laser in resonance with the lower polariton branch, and a laser
not in resonance. We found that the Raman scattered photons play an important
role in populating the lower polariton branch, especially when the system was
excited with a laser in resonance with the lower polariton branch. We also
found that the polariton assisted photoemission depends on the extent of
modification of the molecular absorption induced by the molecule-cavity
coupling.


Abstract:
  Motivated by recent experiments on the phonon contribution to the thermal
Hall effect in the cuprates, we present an analysis of chiral phonon transport.
We assume the chiral behavior arises from a non-zero phonon Hall vicosity,
which is likely induced by the coupling to electrons. Phonons with a non-zero
phonon Hall viscosity have an intrinsic thermal Hall conductivity, but Chen et
al. (Phys. Rev. Lett. 124, 167601 (2020)) have argued that a significantly
larger thermal Hall conductivity can arise from an extrinsic contribution which
is inversely proportional to the density of impurities. We solve the Boltzmann
equation for phonon transport and compute the temperature ($T$) dependence of
the thermal Hall conductivity originating from skew scattering off point-like
impurities. We find that the dominant source for thermal Hall transport is an
interference between impurity skew scattering channels with opposite parity.
The thermal Hall conductivity $\sim T^{d+2}$ at low $T$ in $d$ dimensions, and
has a window of $T$-independent behavior for $T > T_{\rm imp}$, where $T_{\rm
imp}$ is determined by the ratio of scattering potentials with opposite parity.
We also consider the role of non-specular scattering off the sample boundary,
and find that it leads to negligible corrections to thermal Hall transport at
low $T$.


Abstract:
  The nonanalyticity and the sign problem in the Z3-symmetric heavy quark model
at low temperature are studied phenomenologically. For the free heavy quarks,
the nonanalyticity is analyzed in the relation to the zeros of the grand
canonical partition function. The Z3-symmetric effective Polyakov-line model
(EPLM) in strong coupling limit is also considered as an phenomenological model
of Z3-symmetric QCD with large quark mass at low temperature. We examine how
the Z3-symmetric EPLM approaches to the original one in the zero-temperature
limit. The effects of the Z3-symmetry affect the structure of zeros of the
microscopic probability density function at the nonanalytic point. The average
value of the Polyakov line can detect the structure, while the other
thermodynamic quantities are not sensible to the structure in the
zero-temperature limit. The effect of the imaginary quark chemical potential is
also discussed. The imaginary part of the quark number density is very
sensitive to the symmetry structure at the nonanalytical point. For a
particular value of the imaginary quark number chemical potential, large quark
number may be induced in the vicinity of the nonanalytical point.


Abstract:
  In the residential sector, electric water heaters are appliances with a
relatively high power consumption and a significant thermal inertia, which is
particularly suitable for Demand Response schemes. The success of efficient DR
schemes via the control of water heaters presupposes an accurate estimate of
their power demand at each instant. Although the load of water heaters is
rarely directly measured, a large penetration of Smart Meters (SMs) in
distribution grids enables to indirectly infer this information on a large
scale via load disaggregation. For that purpose, a considerable number of
Non-Intrusive Load Monitoring (NILM) approaches are suggested in the
literature. However, they require data streams at a time resolution in the
range of one second or higher, which is not realistic for standard SMs. Hence,
this paper proposes an unsupervised approach to detect and disaggregate the
load profile of water heaters from standard SM data with a time resolution in
the minute range. Evaluated on multiple real loads with sub-metering, the
proposed approach achieves a Normalized Mean Absolute Error (NMAE) lower than
2% and a precision generally higher than 92% with time resolutions between 5
and 15 minutes.


Abstract:
  Field measurement campaigns have grown exponentially in recent years,
stemming from the need for reliable data to validate urban climate models and
obtain a better understanding of urban climate features. Also contributing to
this growth is the Local Climate Zone (LCZ) scheme, firstly developed to
enhance the accuracy in the contextualisation of urban measurements, and lately
used for characterising urban areas. Due to its relative novelty, researchers
are still investigating the potential of LCZs and its indicators for urban
temperature variability detection. In this respect, the present study
introduces the results of an extensive monitoring campaign carried out in the
city of Madrid over a two-year period (2016-2018). The aim of this work is to
further examine the relationships between LCZs and air temperature differences,
with emphasis on their hourly and seasonal evolution. A graphical and
statistical analysis to identify temperature variability trends for each LCZ is
performed. Results support the existing evidence suggesting a high level of
effectiveness in capturing the heat island (UHI) profile of different urban
areas, while underperforming when it comes to capturing diurnal temperature
variability. The incorporation of indicators that explain the daytime
temperature variation phenomenon into the LCZ scheme is therefore recommended,
warranting further research.


Abstract:
  The polyhedral model allows a structured way of defining semantics-preserving
transformations to improve the performance of a large class of loops. Finding
profitable points in this space is a hard problem which is usually approached
by heuristics that generalize from domain-expert knowledge. Existing problem
formulations in state-of-the-art heuristics depend on the shape of particular
loops, making it hard to leverage generic and more powerful optimization
techniques from the machine learning domain. In this paper, we propose PolyGym,
a shape-agnostic formulation for the space of legal transformations in the
polyhedral model as a Markov Decision Process (MDP). Instead of using
transformations, the formulation is based on an abstract space of possible
schedules. In this formulation, states model partial schedules, which are
constructed by actions that are reusable across different loops. With a simple
heuristic to traverse the space, we demonstrate that our formulation is
powerful enough to match and outperform state-of-the-art heuristics. On the
Polybench benchmark suite, we found transformations that led to a speedup of
3.39x over LLVM O3, which is 1.83x better than the speedup achieved by ISL. Our
generic MDP formulation enables using reinforcement learning to learn
optimization policies over a wide range of loops. This also contributes to the
emerging field of machine learning in compilers, as it exposes a novel problem
formulation that can push the limits of existing methods.


Abstract:
  Prediction models often fail if train and test data do not stem from the same
distribution. Out-of-distribution (OOD) generalization to unseen, perturbed
test data is a desirable but difficult-to-achieve property for prediction
models and in general requires strong assumptions on the data generating
process (DGP). In a causally inspired perspective on OOD generalization, the
test data arise from a specific class of interventions on exogenous random
variables of the DGP, called anchors. Anchor regression models, introduced by
Rothenhaeusler et al. (2021), protect against distributional shifts in the test
data by employing causal regularization. However, so far anchor regression has
only been used with a squared-error loss which is inapplicable to common
responses such as censored continuous or ordinal data. Here, we propose a
distributional version of anchor regression which generalizes the method to
potentially censored responses with at least an ordered sample space. To this
end, we combine a flexible class of parametric transformation models for
distributional regression with an appropriate causal regularizer under a more
general notion of residuals. In an exemplary application and several simulation
scenarios we demonstrate the extent to which OOD generalization is possible.


Abstract:
  Shannon entropy in position ($S_{\rvec}$) and momentum ($S_{\pvec}$) spaces,
along with their sum ($S_t$) are presented for unit-normalized densities of He,
Li$^+$ and Be$^{2+}$ ions, spatially confined at the center of an impenetrable
spherical enclosure defined by a radius $r_c$. Both ground as well as some
selected low-lying singly excited states, \emph{viz.,} 1sns (n $=$ 2-4) $^3$S,
1snp (n $=$ 2-3) $^3$P, 1s3d $^3$D are considered within a density functional
methodology that makes use of a work-function-based exchange potential along
with two correlation potentials (local Wigner-type parametrized functional as
well as the more involved non-linear gradient- and Laplacian-dependent
Lee-Yang-Parr functional). The radial Kohn-Sham (KS) equation is solved using
an optimal spatial discretization scheme via the generalized pseudospectral
(GPS) method. A detailed systematic analysis of the confined system (relative
to corresponding free system) has been performed for these quantities with
respect to $r_c$ in tabular and graphical forms, \emph{with and without}
electron correlation. Due to compression, the pattern of entropy in
aforementioned states gets characterized by various crossovers at intermediate
and lower $r_c$ regions. The impact of electron correlation is more pronounced
in weaker confinement limit, and appears to decay with rise in confinement
strength. The exchange-only results are quite good to provide a decent
qualitative discussion. The lower-bounds provided by entropic uncertainty
relation holds good in all cases. Several other new interesting features are
observed.


Abstract:
  Many non-linear scalar field theories possess a screening mechanism that can
suppress any associated fifth force in dense environments. As a result, these
theories can evade local experimental tests of new forces. Chameleon-like
screening, which occurs because of non-linearities in the scalar potential or
the coupling to matter, is well understood around extended objects. However,
many experimental tests of these theories involve objects with spatial extent
much smaller than the scalar field's Compton wavelength, and which could
therefore be considered point-like. In this work, we determine how the fifth
forces are screened in the limit that the source objects become extremely
compact.


Abstract:
  Controllable Image Captioning (CIC) -- generating image descriptions
following designated control signals -- has received unprecedented attention
over the last few years. To emulate the human ability in controlling caption
generation, current CIC studies focus exclusively on control signals concerning
objective properties, such as contents of interest or descriptive patterns.
However, we argue that almost all existing objective control signals have
overlooked two indispensable characteristics of an ideal control signal: 1)
Event-compatible: all visual contents referred to in a single sentence should
be compatible with the described activity. 2) Sample-suitable: the control
signals should be suitable for a specific image sample. To this end, we propose
a new control signal for CIC: Verb-specific Semantic Roles (VSR). VSR consists
of a verb and some semantic roles, which represents a targeted activity and the
roles of entities involved in this activity. Given a designated VSR, we first
train a grounded semantic role labeling (GSRL) model to identify and ground all
entities for each role. Then, we propose a semantic structure planner (SSP) to
learn human-like descriptive semantic structures. Lastly, we use a role-shift
captioning model to generate the captions. Extensive experiments and ablations
demonstrate that our framework can achieve better controllability than several
strong baselines on two challenging CIC benchmarks. Besides, we can generate
multi-level diverse captions easily. The code is available at:
https://github.com/mad-red/VSR-guided-CIC.


Abstract:
  More than 20 years after Fickett attempted to prove the Hyers-Ulam stability
of isometries defined on bounded subsets of $\mathbb{R}^n$ in 1981,
V\"{a}is\"{a}l\"{a} improved Fickett's result significantly. In this paper, we
will improve Fickett's theorem by proving the Hyers-Ulam stability of
isometries defined on bounded subsets of $\mathbb{R}^n$ using a more intuitive
method different from that used by V\"{a}is\"{a}l\"{a}.


Abstract:
  We extend Bourke and Garner's idempotent adjunction between monads and
pretheories to the framework of $\infty$-categories and we use this to prove
many classical results about monads in the $\infty$-categorical framework.
Amongst other things, we show that the category of algebras for an accessible
monads on a locally presentable $\infty$-category $\mathcal{E}$ is again
locally presentable, and that a diagram of accessible monads on a locally
presentable $\infty$-category admits a colimit. Our results also provide a new
and simpler way to construct and describe monads in terms of theories.


Abstract:
  Generalization of machine learning models trained on a set of source domains
on unseen target domains with different statistics, is a challenging problem.
While many approaches have been proposed to solve this problem, they only
utilize source data during training but do not take advantage of the fact that
a single target example is available at the time of inference. Motivated by
this, we propose a method that effectively uses the target sample during
inference beyond mere classification. Our method has three components - (i) A
label-preserving feature or metric transformation on source data such that the
source samples are clustered in accordance with their class irrespective of
their domain (ii) A generative model trained on the these features (iii) A
label-preserving projection of the target point on the source-feature manifold
during inference via solving an optimization problem on the input space of the
generative model using the learned metric. Finally, the projected target is
used in the classifier. Since the projected target feature comes from the
source manifold and has the same label as the real target by design, the
classifier is expected to perform better on it than the true target. We
demonstrate that our method outperforms the state-of-the-art Domain
Generalization methods on multiple datasets and tasks.


Abstract:
  Optical spectroscopy and density-functional calculations reveal electronic
properties of the nonmagnetickagome metal KV$_3$Sb$_5$. Temperature and
frequency-dependent optical measurements down to 10K and up to 2 eV energy
range confirm bulk nature of the charge-density-wave (CDW) state below 78 K and
gauge the charge gap of $\Delta_{CDW} \approx$ 60 meV at 10 K. We further
detect strong phonon anomalies and the prominent low-energy localization peak
indicative of the unconventional charge transport caused by electron-phonon or
electron-electron interactions. Possible CDW structures of KV$_3$Sb$_5$, the
star and hexagon (inverse star), are strongly reminiscent of $p$-wave states
expected in the Hubbard model on the kagome lattice at the filling level of the
van Hove singularity. The proximity to this regime may have intriguing and
far-reaching implications for the physics of KV$_3$Sb$_5$ and related
materials.


Abstract:
  In this paper, we investigate the evolution of spacelike curves in
Lorentz-Minkowski plane $\mathbb{R}^{2}_{1}$ along prescribed geometric flows
(including the classical curve shortening flow or mean curvature flow as a
special case), which correspond to a class of quasilinear parabolic initial
boundary value problems, and can prove that this flow exists for all time.
Moreover, we can also show that the evolving spacelike curves converge to a
spacelike straight line or a spacelike Grim Reaper curve as time tends to
infinity.


Abstract:
  Higher-order networks have so far been considered primarily in the context of
studying the structure of complex systems, i.e., the higher-order or multi-way
relations connecting the constituent entities. More recently, a number of
studies have considered dynamical processes that explicitly account for such
higher-order dependencies, e.g., in the context of epidemic spreading processes
or opinion formation. In this chapter, we focus on a closely related, but
distinct third perspective: how can we use higher-order relationships to
process signals and data supported on higher-order network structures. In
particular, we survey how ideas from signal processing of data supported on
regular domains, such as time series or images, can be extended to graphs and
simplicial complexes. We discuss Fourier analysis, signal denoising, signal
interpolation, and nonlinear processing through neural networks based on
simplicial complexes. Key to our developments is the Hodge Laplacian matrix, a
multi-relational operator that leverages the special structure of simplicial
complexes and generalizes desirable properties of the Laplacian matrix in graph
signal processing.


Abstract:
  It has been known that even though two elemental metals, $X$ and $Y$, are
immiscible, they can form alloys on surfaces of other metal $Z$. In order to
understand such surface alloying of immiscible metals, we study the energetic
stability of binary alloys, $XZ$ and $YZ$, in several structures with various
coordination numbers (CNs). By analyzing the formation energy modified to
enhance the subtle energy difference between metastable structures, we find
that $XZ$ and $YZ$ with B2-type structure (CN$=$8) become energetically stable
when the $X$ and $Y$ metals form an alloy on the $Z$ metal surface. This is
consistent with the experimental results for Pb-Sn alloys on metal surfaces
such as Rh(111) and Ru(0001). Some suitable metal substrates are also predicted
to form Pb-Sn alloys.


Abstract:
  The moduli space of $n$ chains in the plane with generic side lengths that
terminate on a fixed line is a smooth, closed manifold of dimension $n-1$. This
manifold is also equipped with a locally standard action of
$\mathbb{Z}_2^{n-1}$. The orbit space of this action is a simple polytope
called the moment polytope. Interestingly, this manifold is also the fixed
point set of an involution on a toric manifold known as the abelian polygon
space. In this article we show that the moment polytope of the moduli space of
chains is completely characterized by the combinatorial data, called the
\emph{short code} of the length vector. We also classify aspherical chain
spaces using a result of Davis, Januszkiewicz and Scott.


Abstract:
  Open Cloud Robot Table Organization Challenge (OCRTOC) is one of the most
comprehensive cloud-based robotic manipulation competitions. It focuses on
rearranging tabletop objects using vision as its primary sensing modality. In
this extended abstract, we present our entry to the OCRTOC2020 and the key
challenges the team has experienced.


Abstract:
  Let $G=(V,E)$ be a finite simple graph. The Sombor index $SO(G)$ of $G$ is
defined as $\sum_{uv\in E(G)}\sqrt{d_u^2+d_v^2}$, where $d_u$ is the degree of
vertex $u$ in $G$. Let $G$ be a connected graph constructed from pairwise
disjoint connected graphs $G_1,\ldots ,G_k$ by selecting a vertex of $G_1$, a
vertex of $G_2$, and identifying these two vertices. Then continue in this
manner inductively. We say that $G$ is a polymer graph, obtained by
point-attaching from monomer units $G_1,...,G_k$. In this paper, we consider
some particular cases of these graphs that are of importance in chemistry and
study their Sombor index.


Abstract:
  We revisit Wagoner et al. (1967), a classic contribution in the development
of Big Bang Nucleosynthesis. We demonstrate that it presents an incorrect
expression for the temperature of the early universe as a function of time in
the high temperature limit, $T \gtrsim 10^{10}$K. As this incorrect expression
has been reproduced elsewhere, we present a corrected form for the initial
conditions required for calculating the formation of the primordial elements in
the Big Bang.


Abstract:
  Stable organic radicals serve as model systems for investigating metal-free
magnetic phenomena at (sub)nanometer length scales. We have investigated at the
single-molecule level the stable $\pi$-radical 2,2-diphenyl-1-picrylhydrazyl
(DPPH) adsorbed at the elbow sites of the $22\times\sqrt{3}$ reconstructed
Au(111) surface. Low-temperature scanning tunneling microscopy (STM) and
-spectroscopy under ultrahigh vacuum conditions at 8~K reveal structural as
well as frontier-orbital related electronic details of DPPH/Au(111). A
Kondo-like spectroscopic signature indicates preservation of the unpaired
electron spin state.


Abstract:
  Language coverage bias, which indicates the content-dependent differences
between sentence pairs originating from the source and target languages, is
important for neural machine translation (NMT) because the target-original
training data is not well exploited in current practice. By carefully designing
experiments, we provide comprehensive analyses of the language coverage bias in
the training data, and find that using only the source-original data achieves
comparable performance with using full training data. Based on these
observations, we further propose two simple and effective approaches to
alleviate the language coverage bias problem through explicitly distinguishing
between the source- and target-original training data, which consistently
improve the performance over strong baselines on six WMT20 translation tasks.
Complementary to the translationese effect, language coverage bias provides
another explanation for the performance drop caused by back-translation. We
also apply our approach to both back- and forward-translation and find that
mitigating the language coverage bias can improve the performance of both the
two representative data augmentation methods and their tagged variants.


Abstract:
  In this paper, a third-order compact gas-kinetic scheme (GKS) on unstructured
tetrahedral mesh is constructed for the compressible Euler and Navier-Stokes
solutions. The time-dependent gas distribution function at a cell interface is
used to calculate the fluxes for the updating the cell-averaged flow variables
and to evaluate the time accurate cell-averaged flow variables as well for
evolving the cell-averaged gradients of flow variables. With the accurate
evolution model for both flow variables and their slopes, the quality of the
scheme depends closely on the accuracy and reliability of the initial
reconstruction of flow variables. The reconstruction scheme becomes more
challenge on tetrahedral mesh, where the conventional second-order unlimited
least-square reconstruction can make the scheme be linearly unstable when using
cell-averaged conservative variables alone with von Neumann neighbors.
Benefiting from the evolved cell-averaged slopes, on tetrahedral mesh the GKS
is linearly stable from a compact third-order smooth reconstruction with a
large CFL number. In order to further increase the robustness of the high-order
compact GKS for capturing discontinuous solution, a new two-step
multi-resolution weighted essentially non-oscillatory (WENO) reconstruction
will be proposed. The novelty of the reconstruction includes the following.
Firstly, it releases the stability issue from a second-order compact
reconstruction through the introduction of a pre-reconstruction step. Secondly,
in the third-order non-linear reconstruction, only one more large stencil is
added beside those in the second-order one, which significantly simplifies the
high-order reconstruction. The proposed third-order scheme shows good
robustness in high speed flow computation and favorable mesh adaptability in
cases with complex geometry.


Abstract:
  We propose a new prediction method for multivariate linear regression
problems where the number of features is less than the sample size but the
number of outcomes is extremely large. Many popular procedures, such as
penalized regression procedures, require parameter tuning that is
computationally untenable in such large-scale problems. We take a different
approach, motivated by ideas from simultaneous estimation problems, that
performs linear shrinkage on ordinary least squares parameter estimates. Our
approach is extremely computationally efficient and tuning-free. We show that
it can asymptotically outperform ordinary least squares without any structural
assumptions on the true regression coefficients and illustrate its good
performance in simulations and an analysis of single-cell RNA-seq data.


Abstract:
  We study the performance of low-density parity-check (LDPC) codes over finite
integer rings, over two channels that arise from the Lee metric. The first
channel is a discrete memory-less channel (DMC) matched to the Lee metric. The
second channel adds to each codeword an error vector of constant Lee weight,
where the error vector is picked uniformly at random from the set of vectors of
constant Lee weight. It is shown that the marginal conditional distribution of
the two channels coincides, in the limit of large blocklengths. The performance
of selected LDPC code ensembles is analyzed by means of density evolution and
finite-length simulations, with belief propagation decoding and with a
low-complexity symbol message passing algorithm.


Abstract:
  We present a new linearly implicit exponential integrator that preserves the
polynomial first integrals or Lyapunov functions for the conservative and
dissipative stiff equations, respectively. The method is tested by both
oscillated ordinary differential equations and partial differential equations,
e.g., an averaged system in wind-induced oscillation, the Fermi-Pasta-Ulam
systems, and the polynomial pendulum oscillators. The numerical simulations
confirm the conservative properties of the proposed method and demonstrate its
good behavior in superior running speed when compared with fully implicit
schemes for long-time simulations.


Abstract:
  Annotation is a major hurdle in the semantic segmentation of microscopy
images and volumes due to its prerequisite expertise and effort. This work
enables the training of semantic segmentation networks on images with only a
single point for training per instance, an extreme case of weak supervision
which drastically reduces the burden of annotation. Our approach has two key
aspects: (1) we construct a graph-theoretic soft-segmentation using individual
seeds to be used within a regularizer during training and (2) we use an
objective function that enables learning from the constructed soft-labels. We
achieve competitive results against the state-of-the-art in point-supervised
semantic segmentation on challenging datasets in digital pathology. Finally, we
scale our methodology to point-supervised segmentation in 3D fluorescence
microscopy volumes, obviating the need for arduous manual volumetric
delineation. Our code is freely available.


Abstract:
  The Landauer principle states that any logically irreversible information
processing must be accompanied by dissipation into the environment. In this
study, we investigate the heat dissipation associated with finite-time
information erasure and the effect of quantum coherence in such processes. By
considering a scenario wherein information is encoded in an open quantum system
whose dynamics are described by the Markovian Lindblad equation, we show that
the dissipated heat is lower-bounded by the conventional Landauer cost, as well
as a correction term inversely proportional to the operational time. To clarify
the relation between quantum coherence and dissipation, we derive a lower bound
for heat dissipation in terms of quantum coherence. This bound quantitatively
implies that the creation of quantum coherence in the energy eigenbasis during
the erasure process inevitably leads to additional heat costs. The obtained
bounds hold for arbitrary operational time and control protocol. By following
an optimal control theory, we numerically present an optimal protocol and
illustrate our findings by using a single-qubit system.


Abstract:
  The charged and neutral pion mass difference can be attributed to both the
QED and QCD contributions. The current quark mass difference ($\Delta m$) is
the source of the QCD contribution. Here, in a two flavour non-local NJL model,
we try to estimate the QCD contribution. Interestingly, we find that the
strength of the $U(1)_A$ symmetry breaking parameter $c$ plays a crucial role
for obtaining the pion mass difference while intertwined with the current quark
mass difference. To obtain the QCD contribution for the pion mass difference we
can scan the parameter space in $\{\Delta m,\; c\}$ and by comparing this with
the existing results this parameter space is constrained. Further, using a
fitted value of $c$ we can determine the allowed range for the current quark
mass difference in the model.


Abstract:
  We obtain lower norm bounds for multivariate functions in weighted Lebesgue
spaces on unbounded domains in finite Euclidean space. These are identified by
the solution of a system of nonlinear integral equations, with linear
constrains on one-dimensional marginals. We recognize the importance of the
Smirnov condition for the corresponding weights, which guarantees uniqueness of
solutions of the system. Sufficient conditions on weights are formulated so to
satisfy the Smirnov property, and counterexamples are constructed, when the
property is violated.


Abstract:
  We prove the two-scale transformation method which allows rigorous
homogenisation of problems defined on locally periodic domains by
transformation on periodic domains. The idea to consider periodic substitute
problems was originally proposed by M. A. Peter for the homogenisation on
evolving microstructure and is applied in several works. However, only the
homogenisation of the periodic substitute problems was proven, whereas the
method itself was just postulated (i.e. the equivalence to the homogenisation
of the actual problem had to be assumed). In this work, we develop this idea
further and formulate a rigorous two-scale convergence concept for microscopic
transformation to prove this method. Moreover, we show a new two-scale
transformation rule for gradients which allows to derive new limit problems
that are now transformationally independent.


Abstract:
  Long gamma ray bursts (LGRBs) are associated to the collapse of a massive
star and the formation of a relativistic jet. As the jet propagates through the
star, it forms an extended, hot cocoon. The dynamical evolution of the
jet/cocoon system and its interaction with the environment has been studied
extensively both analytically and numerically. On the other hand, the role
played by the supernova (SN) explosion associated with LGRBs in determining the
outcome of the system has been barely considered. In this paper, we discuss the
large landscape of outcomes resulting from the interaction of the SN, jet and
cocoon. We show that the outcome depends mainly on three timescales: the times
for the cocoon and supernova shock wave to break through the surface of the
progenitor star, and the time needed for the cocoon to engulf completely the
progenitor star. The delay between the launch of the SN shock moving through
the progenitor star and the jet can be related to these three timescales.
Depending on the ordering of these time scales, the jet-cocoon might propagate
inside the SN ejecta or the other way around, and the outcome for the
properties of the explosion would be different. We discuss the imprint of the
complex interaction between the jet-cocoon and the supernova shock on the
emergent thermal and non-thermal radiation.


Abstract:
  We study some aspects of dynamical compactification scenario where
stabilisation of extra dimensions occurs due to presence the Gauss-Bonnet term
and non-zero spatial curvature. In the framework of the model under
consideration there exists two-stages scenario of evolution of a Universe: on
the first stage, the space evolves from a totally anisotropic state to the
state with 3-dimensional (corresponding to our "real", world) expanding and
$D$-dimensional contracting isotropic subspaces; on the second stage, constant
curvature of extra dimensions begins to play role and provide compactification
of extra dimensions. It is already known that such a scenario is realizable
when constant curvature of extra dimensions is negative. Here we show that a
range of coupling constants for which exponential solutions with 3-dimensional
expanding and $D$-dimensional contracting isotropic subspaces are stable is
located in a zone where compactification solutions with positively curved extra
space are unstable, so that two-stage scenario analogous to the one described
above is \emph{not} realizable. Also we study "nearly-Friedmann", regime for
the case of arbitrary constant curvature of extra dimensions and describe new
parametrization of the general solution for the model under consideration which
provide elegant way of describing areas of existence over parameters space.


Abstract:
  Reinforcement learning synthesizes controllers without prior knowledge of the
system. At each timestep, a reward is given. The controllers optimize the
discounted sum of these rewards. Applying this class of algorithms requires
designing a reward scheme, which is typically done manually. The designer must
ensure that their intent is accurately captured. This may not be trivial, and
is prone to error. An alternative to this manual programming, akin to
programming directly in assembly, is to specify the objective in a formal
language and have it "compiled" to a reward scheme. Mungojerrie
(https://plv.colorado.edu/mungojerrie/) is a tool for testing reward schemes
for $\omega$-regular objectives on finite models. The tool contains
reinforcement learning algorithms and a probabilistic model checker.
Mungojerrie supports models specified in PRISM and $\omega$-automata specified
in HOA.


Abstract:
  The ever-growing popularity and rapid improving of artificial intelligence
(AI) have raised rethinking on the evolution of wireless networks. Mobile edge
computing (MEC) provides a natural platform for AI applications since it
provides rich computation resources to train AI models, as well as low-latency
access to the data generated by mobile and Internet of Things devices. In this
paper, we present an infrastructure to perform machine learning tasks at an MEC
server with the assistance of a reconfigurable intelligent surface (RIS). In
contrast to conventional communication systems where the principal criteria are
to maximize the throughput, we aim at optimizing the learning performance.
Specifically, we minimize the maximum learning error of all users by jointly
optimizing the beamforming vectors of the base station and the phase-shift
matrix of the RIS. An alternating optimization-based framework is proposed to
optimize the two terms iteratively, where closed-form expressions of the
beamforming vectors are derived, and an alternating direction method of
multipliers (ADMM)-based algorithm is designed together with an error level
searching framework to effectively solve the nonconvex optimization problem of
the phase-shift matrix. Simulation results demonstrate significant gains of
deploying an RIS and validate the advantages of our proposed algorithms over
various benchmarks.


Abstract:
  Queueing-network models are developed for enhanced and next-generation 911
(E911 and NG911) systems, which capture both their cyber-components
(communications, data-processing) and physical-world-elements (call takers,
vehicle dispatch). The models encompass both the call-processing and dispatch
functions of 911, and can represent the interdependencies between multiple
PSAPs enabled by NG911. An instantiation of the model for a future NG911 system
for Charlotte, North Carolina, is developed and used to assess performance
metrics. Representation of cyber-threats (e.g. Distributed Denial-of-Service
attacks) within the queueing-network model is undertaken. Based on these
representations, the model is used for analysis of holistic threat impacts, as
a step toward risk and vulnerability assessment for future emergency response
systems.


Abstract:
  We give an upper bound for the dimension of the bounded derived categories of
$(m,n)$-Igusa-Todorov algebras which is a generalization of $n$-Igusa-Todorov
algebras, where $m,n$ are two nonnegative integers. As an applications, we get
a new upper bound for the dimension of bounded derived categories in terms of
the projective dimensions of certain of simple modules as well as radical layer
length of artin algebra $\Lambda$.


Abstract:
  In this paper we study approximations of functions of Sobolev spaces
$W^2_{p,\loc}(\Omega)$, $\Omega\subset\mathbb R^n$, by Lipschitz continuous
functions. We prove that if $f\in W^2_{p,\loc}(\Omega)$, $1\leq p<\infty$, then
there exists a sequence of closed sets $\{A_k\}_{k=1}^{\infty},A_k\subset
A_{k+1}\subset \Omega$, such that the restrictions $f \vert_{A_k}$ are
Lipschitz continuous functions and $\cp_p\left(S\right)=0$,
$S=\Omega\setminus\bigcup_{k=1}^{\infty}A_k$. Using these approximations we
prove the change of variables formula in the Lebesgue integral for mappings of
Sobolev spaces $W^2_{p,\loc}(\Omega;\mathbb R^n)$ with the Luzin
capacity-measure $N$-property.


Abstract:
  We present a new conceptual model of the Earth's glacial-interglacial cycles,
one leading to governing equations for which the vector field has a hyperplane
of discontinuities. This work extends the classic Budyko- and Sellers-type
conceptual energy balance models of temperature-albedo feedback by removing the
standard assumption of planetary symmetry about the equator. The dynamics of
separate Northern and Southern Hemisphere ice caps are coupled to an equation
representing the annual global mean surface temperature. The system has a
discontinuous switching mechanism based on mass balance principles for the
Northern Hemisphere ice sheet. We show the associated Filippov system admits a
unique nonsmooth and attracting limit cycle that represents the cycling between
glacial and interglacial states. Due to the vastly different time scales
involved, the model presents a nonsmooth geometric perturbation problem, for
which we use ad hoc mathematical techniques to produce the periodic orbit. We
find climatic changes in the Northern Hemisphere drive synchronous changes in
the Southern Hemisphere, as is observed for the Earth on orbital time scales.


Abstract:
  Marginal-likelihood based model-selection, even though promising, is rarely
used in deep learning due to estimation difficulties. Instead, most approaches
rely on validation data, which may not be readily available. In this work, we
present a scalable marginal-likelihood estimation method to select both
hyperparameters and network architectures, based on the training data alone.
Some hyperparameters can be estimated online during training, simplifying the
procedure. Our marginal-likelihood estimate is based on Laplace's method and
Gauss-Newton approximations to the Hessian, and it outperforms cross-validation
and manual-tuning on standard regression and image classification datasets,
especially in terms of calibration and out-of-distribution detection. Our work
shows that marginal likelihoods can improve generalization and be useful when
validation data is unavailable (e.g., in nonstationary settings).


Abstract:
  We prove that there exist Delone sets in $\mathbb{R}^d$, $d \geq 2$, which
cannot be mapped onto the standard lattice $\mathbb{Z}^d$ by Lipschitz
bijections. The impossibility of the Lipschitz-rectifiability crucially uses
ideas of Lipschitz regular maps recently introduced by M. Dymond, V. Kalu\v{z}a
and E. Kopeck\'a.


Abstract:
  In this work, we explore the dynamics of entanglement of an isolated quantum
system consisting of two time-dependent, coupled harmonic oscillators. Through
the use of a numerical method that relies on the estimation of the system's
Wigner representation by a specific Gaussian function, we investigate the time
evolution of the entanglement entropy after an instant quench in the inherent
parameters of the system. Besides, from the comparison of the results obtained
from the analytical expression for the time-dependent von Neumann entropy with
the numerically computed entropy data, the effectiveness of the numerical
method is tested for a variety of angular frequency combinations. Also, we
analyze how the entropy of entanglement change as a function of time.


Abstract:
  Energy providers are moving to the smart meter era, encouraging consumers to
install, free of charge, these devices in their homes, automating consumption
readings submission and making consumers life easier. However, the increased
deployment of such smart devices brings a lot of security and privacy risks. In
order to overcome such risks, Intrusion Detection Systems are presented as
pertinent tools that can provide network-level protection for smart devices
deployed in home environments. In this context, this paper is exploring the
problems of Advanced Metering Infrastructures (AMI) and proposing a novel
Machine Learning (ML) Intrusion Prevention System (IPS) to get optimal
decisions based on a variety of factors and graphical security models able to
tackle zero-day attacks.


Abstract:
  The development of neural networks has greatly improved the performance in
various computer vision tasks. In the filed of image denoising, convolutional
neural network based methods such as DnCNN break through the limits of
classical methods, achieving better quantitative results. However, the
epistemic uncertainty existing in neural networks limits further improvements
in their performance over denoising tasks. Therefore, we develop and study
different solutions to minimize uncertainty and further improve the removal of
noise. From the perspective of ensemble learning, we implement manipulations to
noisy images from the point of view of spatial and frequency domains and then
denoise them using pre-trained denoising networks. We propose a fusion model
consisting of two attention modules, which focus on assigning the proper
weights to pixels and channels. The experimental results show that our model
achieves better performance on top of the baseline of regular pre-trained
denoising networks.


Abstract:
  Auger-like exciton-exciton annihilation (EEA) is considered the key
fundamental limitation to quantum yield in devices based on excitons in
two-dimensional (2d) materials. Since it is challenging to experimentally
disentangle EEA from competing processes, guidance of a quantitative theory is
highly desirable. The very nature of EEA requires a material-realistic
description that is not available to date. We present a many-body theory of EEA
based on first-principle band structures and Coulomb interaction matrix
elements that goes beyond an effective bosonic picture. Applying our theory to
monolayer MoS$_2$ encapsulated in hexagonal BN, we obtain an EEA coefficient in
the order of $10^{-3}$ cm$^{2}$s$^{-1}$ at room temperature, suggesting that
carrier losses are often dominated by other processes, such as defect-assisted
scattering. Our studies open a perspective to quantify the efficiency of
intrinsic EEA processes in various 2d materials in the focus of modern
materials research.


Abstract:
  We present the results of computer simulations on a class of percolative
systems, called protected percolation, that violates the Harris criterion. The
Harris criterion states whether the critical behavior at a phase transition
from a disordered state to an ordered state will be altered by impurities. We
have incorporated impurities into our simulations to test whether the critical
exponents for protected percolation are altered by impurities. We find that the
critical exponents for three-dimensional protected percolation simulations
indeed change with impurities in the form of missing sites and immortal sites.
On the other hand, the critical exponents for both standard percolation and
protected percolation in two dimensions are stable against impurities.


Abstract:
  Creating presentation materials requires complex multimodal reasoning skills
to summarize key concepts and arrange them in a logical and visually pleasing
manner. Can machines learn to emulate this laborious process? We present a
novel task and approach for document-to-slide generation. Solving this involves
document summarization, image and text retrieval, slide structure and layout
prediction to arrange key elements in a form suitable for presentation. We
propose a hierarchical sequence-to-sequence approach to tackle our task in an
end-to-end manner. Our approach exploits the inherent structures within
documents and slides and incorporates paraphrasing and layout prediction
modules to generate slides. To help accelerate research in this domain, we
release a dataset about 6K paired documents and slide decks used in our
experiments. We show that our approach outperforms strong baselines and
produces slides with rich content and aligned imagery.


Abstract:
  The interplay between activity and elasticity often found in active and
living systems triggers a plethora of autonomous behaviors ranging from
self-assembly and collective motion to actuation. Amongst these, spontaneous
self-oscillations of mechanical structures is perhaps the simplest and most
wide-spread type of non-equilibrium phenomenon. Yet, we lack experimental model
systems to investigate the various dynamical phenomena that may appear. Here,
we report self-oscillation and synchronization transitions in a
centimeter-sized model system for one-dimensional elasto-active structures. By
combining precision-desktop experiments of elastically coupled self-propelled
particles with numerical simulations and analytical perturbative theory, we
demonstrate that the dynamics of single chain follows a Hopf bifurcation. We
show that this instability is controlled by a single non-dimensional
elasto-active number that quantifies the interplay between activity and
elasticity. Finally, we demonstrate that pairs of coupled elasto-active chains
can undergo a synchronization transition: the oscillations phases of both
chains lock when the coupling link is sufficiently stiff. Beyond the canonical
case considered here, we anticipate our work to open avenues for the
understanding and design of the self-organisation and response of active
artificial and biological solids, e.g. in higher dimensions and for more
intricate geometries.


Abstract:
  Recent studies on Question Answering (QA) and Conversational QA (ConvQA)
emphasize the role of retrieval: a system first retrieves evidence from a large
collection and then extracts answers. This open-retrieval ConvQA setting
typically assumes that each question is answerable by a single span of text
within a particular passage (a span answer). The supervision signal is thus
derived from whether or not the system can recover an exact match of this
ground-truth answer span from the retrieved passages. This method is referred
to as span-match weak supervision. However, information-seeking conversations
are challenging for this span-match method since long answers, especially
freeform answers, are not necessarily strict spans of any passage. Therefore,
we introduce a learned weak supervision approach that can identify a
paraphrased span of the known answer in a passage. Our experiments on QuAC and
CoQA datasets show that the span-match weak supervisor can only handle
conversations with span answers, and has less satisfactory results for freeform
answers generated by people. Our method is more flexible as it can handle both
span answers and freeform answers. Moreover, our method can be more powerful
when combined with the span-match method which shows it is complementary to the
span-match method. We also conduct in-depth analyses to show more insights on
open-retrieval ConvQA under a weak supervision setting.


Abstract:
  Browder (1960) proved that for every continuous function $F : X \times Y \to
Y$, where $X$ is the unit interval and $Y$ is a nonempty, convex, and compact
subset of $\dR^n$, the set of fixed points of $F$, defined by $C_F := \{ (x,y)
\in X \times Y \colon F(x,y)=y\}$ has a connected component whose projection to
the first coordinate is $X$. We extend this result to the case where $X$ is a
connected and compact Hausdorff space.