Abstract:
  We consider the (discrete) parabolic Anderson model $\partial u(t,x)/\partial
t=\Delta u(t,x) +\xi_t(x) u(t,x)$, $t\geq 0$, $x\in \mathbb{Z}^d$, where the
$\xi$-field is $\mathbb{R}$-valued and plays the role of a dynamic random
environment, and $\Delta$ is the discrete Laplacian. We focus on the case in
which $\xi$ is given by a properly rescaled symmetric simple exclusion process
under which it converges to an Ornstein--Uhlenbeck process. Scaling the
Laplacian diffusively and restricting ourselves to a torus, we show that in
dimension $d=3$ upon considering a suitably renormalised version of the above
equation, the sequence of solutions converges in law.
  As a by-product of our main result we obtain precise asymptotics for the
survival probability of a simple random walk that is killed at a scale
dependent rate when meeting an exclusion particle. Our proof relies on the
discrete theory of regularity structures of \cite{ErhardHairerRegularity} and
on novel sharp estimates of joint cumulants of arbitrary large order for the
exclusion process. We think that the latter is of independent interest and may
find applications elsewhere.


Abstract:
  We present an arithmetic circuit performing constant modular addition having
$\mathcal{O}(n)$ depth of Toffoli gates and using a total of $n+3$ qubits. This
is an improvement by a factor of two compared to the width of the
state-of-the-art Toffoli-based constant modular adder. The advantage of our
adder, compared to the ones operating in the Fourier-basis, is that it does not
require small angle rotations and their Clifford+T decomposition. Our circuit
uses a recursive adder combined with the modular addition scheme proposed by
Vedral et. al. The circuit is implemented and verified exhaustively with
QUANTIFY, an open-sourced framework. We also report on the Clifford+T cost of
the circuit.


Abstract:
  The recently proposed end-to-end transformer detectors, such as DETR and
Deformable DETR, have a cascade structure of stacking 6 decoder layers to
update object queries iteratively, without which their performance degrades
seriously. In this paper, we investigate that the random initialization of
object containers, which include object queries and reference points, is mainly
responsible for the requirement of multiple iterations. Based on our findings,
we propose Efficient DETR, a simple and efficient pipeline for end-to-end
object detection. By taking advantage of both dense detection and sparse set
detection, Efficient DETR leverages dense prior to initialize the object
containers and brings the gap of the 1-decoder structure and 6-decoder
structure. Experiments conducted on MS COCO show that our method, with only 3
encoder layers and 1 decoder layer, achieves competitive performance with
state-of-the-art object detection methods. Efficient DETR is also robust in
crowded scenes. It outperforms modern detectors on CrowdHuman dataset by a
large margin.


Abstract:
  We introduce a class of $n$-dimensional (possibly inhomogeneous) spin-like
lattice systems presenting modulated phases with possibly different textures.
Such systems can be parameterized according to the number of ground states, and
can be described by a phase-transition energy which we compute by means of
variational techniques. Degeneracies due to frustration are also discussed.


Abstract:
  We translate a closed text that is known in advance into a severely low
resource language by leveraging massive source parallelism. In other words,
given a text in 124 source languages, we translate it into a severely low
resource language using only ~1,000 lines of low resource data without any
external help. Firstly, we propose a systematic method to rank and choose
source languages that are close to the low resource language. We call the
linguistic definition of language family Family of Origin (FAMO), and we call
the empirical definition of higher-ranked languages using our metrics Family of
Choice (FAMC). Secondly, we build an Iteratively Pretrained Multilingual
Order-preserving Lexiconized Transformer (IPML) to train on ~1,000 lines
(~3.5%) of low resource data. To translate named entities correctly, we build a
massive lexicon table for 2,939 Bible named entities in 124 source languages,
and include many that occur once and covers more than 66 severely low resource
languages. Moreover, we also build a novel method of combining translations
from different source languages into one. Using English as a hypothetical low
resource language, we get a +23.9 BLEU increase over a multilingual baseline,
and a +10.3 BLEU increase over our asymmetric baseline in the Bible dataset. We
get a 42.8 BLEU score for Portuguese-English translation on the medical EMEA
dataset. We also have good results for a real severely low resource Mayan
language, Eastern Pokomchi.


Abstract:
  Recent advance in deep offline reinforcement learning (RL) has made it
possible to train strong robotic agents from offline datasets. However,
depending on the quality of the trained agents and the application being
considered, it is often desirable to fine-tune such agents via further online
interactions. In this paper, we observe that state-action distribution shift
may lead to severe bootstrap error during fine-tuning, which destroys the good
initial policy obtained via offline RL. To address this issue, we first propose
a balanced replay scheme that prioritizes samples encountered online while also
encouraging the use of near-on-policy samples from the offline dataset.
Furthermore, we leverage multiple Q-functions trained pessimistically offline,
thereby preventing overoptimism concerning unfamiliar actions at novel states
during the initial training phase. We show that the proposed method improves
sample-efficiency and final performance of the fine-tuned robotic agents on
various locomotion and manipulation tasks. Our code is available at:
https://github.com/shlee94/Off2OnRL.


Abstract:
  In this paper, we present a new approach based on dynamic factor models
(DFMs) to perform nowcasts for the percentage annual variation of the Mexican
Global Economic Activity Indicator (IGAE in Spanish). The procedure consists of
the following steps: i) build a timely and correlated database by using
economic and financial time series and real-time variables such as social
mobility and significant topics extracted by Google Trends; ii) estimate the
common factors using the two-step methodology of Doz et al. (2011); iii) use
the common factors in univariate time-series models for test data; and iv)
according to the best results obtained in the previous step, combine the
statistically equal better nowcasts (Diebold-Mariano test) to generate the
current nowcasts. We obtain timely and accurate nowcasts for the IGAE,
including those for the current phase of drastic drops in the economy related
to COVID-19 sanitary measures. Additionally, the approach allows us to
disentangle the key variables in the DFM by estimating the confidence interval
for both the factor loadings and the factor estimates. This approach can be
used in official statistics to obtain preliminary estimates for IGAE up to 50
days before the official results.


Abstract:
  Motivated by estimation of quantum noise models, we study the problem of
learning a Pauli channel, or more generally the Pauli error rates of an
arbitrary channel. By employing a novel reduction to the "Population Recovery"
problem, we give an extremely simple algorithm that learns the Pauli error
rates of an $n$-qubit channel to precision $\epsilon$ in $\ell_\infty$ using
just $O(1/\epsilon^2) \log(n/\epsilon)$ applications of the channel. This is
optimal up to the logarithmic factors. Our algorithm uses only unentangled
state preparation and measurements, and the post-measurement classical runtime
is just an $O(1/\epsilon)$ factor larger than the measurement data size. It is
also impervious to a limited model of measurement noise where heralded
measurement failures occur independently with probability $\le 1/4$.
  We then consider the case where the noise channel is close to the identity,
meaning that the no-error outcome occurs with probability $1-\eta$. In the
regime of small $\eta$ we extend our algorithm to achieve multiplicative
precision $1 \pm \epsilon$ (i.e., additive precision $\epsilon \eta$) using
just $O\bigl(\frac{1}{\epsilon^2 \eta}\bigr) \log(n/\epsilon)$ applications of
the channel.


Abstract:
  We find that the Casimir pressure in peptide films deposited on metallic
substrates is always repulsive which makes these films less stable. It is shown
that by adding a graphene sheet on top of peptide film one can change the sign
of the Casimir pressure by making it attractive. For this purpose, the
formalism of the Lifshitz theory is extended to the case when the film and
substrate materials are described by the frequency-dependent dielectric
permittivities, whereas the response of graphene to the electromagnetic field
is governed by the polarization tensor in (2+1)-dimensional space-time found in
the framework of the Dirac model. Both pristine and gapped and doped graphene
sheets are considered possessing some nonzero energy gap and chemical
potential. According to our results, in all cases the presence of graphene
sheet makes the Casimir pressure in peptide film deposited on a metallic
substrate attractive starting from some minimum film thickness. The value of
this minimum thickness becomes smaller with increasing chemical potential and
larger with increasing energy gap and the fraction of water in peptide film.
The physical explanation for these results is provided, and their possible
applications in organic electronics are discussed.


Abstract:
  Urban Air Mobility (UAM) has the potential to revolutionize transportation.
It will exploit the third dimension to help smooth ground traffic in densely
populated areas. To be successful, it will require an integrated approach able
to balance efficiency and safety while harnessing common resources and
information. In this work we focus on future urban air-taxi services, and
present the first methods and algorithms to efficiently operate air-taxi at
scale. Our approach is twofold. First, we use a passenger-centric perspective
which introduces traveling classes, and information sharing between transport
modes to differentiate quality of services. This helps smooth multimodal
journeys and increase passenger satisfaction. Second, we provide a flight
routing and recharging solution which minimizes direct operational costs while
preserving long term battery life through reduced energy-intense recharging.
Our methods, which surpass the performance of a general state-of-the-art
commercial solver, are also used to gain meaningful insights on the design
space of the air-taxi problem, including solutions to hidden fairness issues.


Abstract:
  In this paper, we consider user selection and downlink precoding for an
over-loaded single-cell massive multiple-input multiple-output (MIMO) system in
frequency division duplexing (FDD) mode, where the base station is equipped
with a dual-polarized uniform planar array (DP-UPA) and serves a large number
of single-antenna users. Due to the absence of uplink-downlink channel
reciprocity and the high-dimensionality of channel matrices, it is extremely
challenging to design downlink precoders using closed-loop channel probing and
feedback with limited spectrum resource. To address these issues, a novel
methodology -- active channel sparsification (ACS) -- has been proposed
recently in the literature for uniform linear array (ULA) to design sparsifying
precoders, which boosts spectral efficiency for multi-user downlink
transmission with substantially reduced channel feedback overhead. Pushing
forward this line of research, we aim to facilitate the potential deployment of
ACS in practical FDD massive MIMO systems, by extending it from ULA to DP-UPA
with explicit user selection and making the current ACS implementation
simplified. To this end, by leveraging Toeplitz structure of channel covariance
matrices, we extend the original ACS using scale-weight bipartite graph
representation to the matrix-weight counterpart. Building upon this, we propose
a multi-dimensional ACS (MD-ACS) method, which is a generalization of original
ACS formulation and is more suitable for DP-UPA antenna configurations. The
nonlinear integer program formulation of MD-ACS can be classified as a
generalized multi-assignment problem (GMAP), for which we propose a simple yet
efficient greedy algorithm to solve it. Simulation results demonstrate the
performance improvement of the proposed MD-ACS with greedy algorithm over the
state-of-the-art methods based on the QuaDRiGa channel models.


Abstract:
  The QCD$\times$QED factorization is studied for two-body non-leptonic and
semi-leptonic $B$ decays with heavy-light final states. These non-leptonic
decays, like $\bar{B}^0_{(s)}\to D^+_{(s)} \pi^-$ and $\bar{B}_d^0 \to D^+
K^-$, are among the theoretically cleanest non-leptonic decays as penguin loops
do not contribute and colour-suppressed tree amplitudes are suppressed in the
heavy-quark limit or even completely absent. Advancing the theoretical
calculations of such decays requires therefore also a careful analysis of QED
effects. Including QED effects does not alter the general structure of
factorization which is analogous for both semi-leptonic and non-leptonic
decays. For the latter, we express our result as a correction of the tree
amplitude coefficient $a_1$. At the amplitude level, we find QED effects at the
sub-percent level, which is of the same order as the QCD uncertainty. We
discuss the phenomenological implications of adding QED effects in light of
discrepancies observed between theory and experimental data, for ratios of
non-leptonic over semi-leptonic decay rates. At the level of the rate,
ultrasoft photon effects can produce a correction up to a few percent,
requiring a careful treatment of such effects in the experimental analyses.


Abstract:
  Accelerated multi-coil magnetic resonance imaging reconstruction has seen a
substantial recent improvement combining compressed sensing with deep learning.
However, most of these methods rely on estimates of the coil sensitivity
profiles, or on calibration data for estimating model parameters. Prior work
has shown that these methods degrade in performance when the quality of these
estimators are poor or when the scan parameters differ from the training
conditions. Here we introduce Deep J-Sense as a deep learning approach that
builds on unrolled alternating minimization and increases robustness: our
algorithm refines both the magnetization (image) kernel and the coil
sensitivity maps. Experimental results on a subset of the knee fastMRI dataset
show that this increases reconstruction performance and provides a significant
degree of robustness to varying acceleration factors and calibration region
sizes.


Abstract:
  Learning from examples with noisy labels has attracted increasing attention
recently. But, this paper will show that the commonly used CIFAR-based datasets
and the accuracy evaluation metric used in the literature are both
inappropriate in this context. An alternative valid evaluation metric and new
datasets are proposed in this paper to promote proper research and evaluation
in this area. Then, friends and foes are identified from existing methods as
technical components that are either beneficial or detrimental to deep learning
from noisy labeled examples, respectively, and this paper improves and combines
technical components from the friends category, including self-supervised
learning, new warmup strategy, instance filtering and label correction. The
resulting F&F method significantly outperforms existing methods on the proposed
nCIFAR datasets and the real-world Clothing1M dataset.


Abstract:
  Signatures of superconductivity at elevated temperatures above $T_c$ in high
temperature superconductors have been observed near 1/8 hole doping for
photoexcitation with infrared or optical light polarized either in the
CuO$_2$-plane or along the $c$-axis. While the use of in-plane polarization has
been effective for incident energies aligned to specific phonons, $c$-axis
laser excitation in a broad range between 5 $\mu$m and 400 nm was found to
affect the superconducting dynamics in striped La$_{1.885}$Ba$_{0.115}$CuO$_4$,
with a maximum enhancement in the $1/\omega$ dependence to the conductivity
observed at 800 nm. This broad energy range, and specifically 800 nm, is not
resonant with any phonon modes, yet induced electronic excitations appear to be
connected to superconductivity at energy scales well above the typical gap
energies in the cuprates. A critical question is what can be responsible for
such an effect at 800 nm? Using time-dependent exact diagonalization, we
demonstrate that the holes in the CuO$_2$ plane can be photoexcited into the
charge reservoir layers at resonant wavelengths within a multi-band Hubbard
model. This orbitally selective photoinduced charge transfer effectively
changes the in-plane doping level, which can lead to an enhancement of $T_c$
near the 1/8 anomaly.


Abstract:
  A dominating set of a graph $G$ is a set of vertices that contains at least
one endpoint of every edge on the graph. The domination number of $G$ is the
order of a minimum dominating set of $G$. The $(t,r)$ broadcast domination is a
generalization of domination in which a set of broadcasting vertices emits
signals of strength $t$ that decrease by 1 as they traverse each edge, and we
require that every vertex in the graph receives a cumulative signal of at least
$r$ from its set of broadcasting neighbors. In this paper, we extend the study
of $(t,r)$ broadcast domination to directed graphs. Our main result explores
the interval of values obtained by considering the directed $(t,r)$ broadcast
domination numbers of all orientations of a graph $G$. In particular, we prove
that in the cases $r=1$ and $(t,r) = (2,2)$, for every integer value in this
interval, there exists an orientation $\vec{G}$ of $G$ which has directed
$(t,r)$ broadcast domination number equal to that value. We also investigate
directed $(t,r)$ broadcast domination on the finite grid graph, the star graph,
the infinite grid graph, and the infinite triangular lattice graph. We conclude
with some directions for future study.


Abstract:
  Content feed, a type of product that recommends a sequence of items for users
to browse and engage with, has gained tremendous popularity among social media
platforms. In this paper, we propose to study the diversity problem in such a
scenario from an item sequence perspective using time series analysis
techniques. We derive a method called sliding spectrum decomposition (SSD) that
captures users' perception of diversity in browsing a long item sequence. We
also share our experiences in designing and implementing a suitable item
embedding method for accurate similarity measurement under long tail effect.
Combined together, they are now fully implemented and deployed in Xiaohongshu
App's production recommender system that serves the main Explore Feed product
for tens of millions of users every day. We demonstrate the effectiveness and
efficiency of the method through theoretical analysis, offline experiments and
online A/B tests.


Abstract:
  Test automation is common in software development; often one tests repeatedly
to identify regressions. If the amount of test cases is large, one may select a
subset and only use the most important test cases. The regression test
selection (RTS) could be automated and enhanced with Artificial Intelligence
(AI-RTS). This however could introduce ethical challenges. While such
challenges in AI are in general well studied, there is a gap with respect to
ethical AI-RTS. By exploring the literature and learning from our experiences
of developing an industry AI-RTS tool, we contribute to the literature by
identifying three challenges (assigning responsibility, bias in decision-making
and lack of participation) and three approaches (explicability, supervision and
diversity). Additionally, we provide a checklist for ethical AI-RTS to help
guide the decision-making of the stakeholders involved in the process.


Abstract:
  In this note, we show that the convolution of a discrete symmetric
log-concave distribution and a discrete symmetric bimodal distribution can have
any strictly positive number of modes. A similar result is proved for smooth
distributions.


Abstract:
  Blood glucose (BG) management is crucial for type-1 diabetes patients
resulting in the necessity of reliable artificial pancreas or insulin infusion
systems. In recent years, deep learning techniques have been utilized for a
more accurate BG level prediction system. However, continuous glucose
monitoring (CGM) readings are susceptible to sensor errors. As a result,
inaccurate CGM readings would affect BG prediction and make it unreliable, even
if the most optimal machine learning model is used. In this work, we propose a
novel approach to predicting blood glucose level with a stacked Long short-term
memory (LSTM) based deep recurrent neural network (RNN) model considering
sensor fault. We use the Kalman smoothing technique for the correction of the
inaccurate CGM readings due to sensor error. For the OhioT1DM dataset,
containing eight weeks' data from six different patients, we achieve an average
RMSE of 6.45 and 17.24 mg/dl for 30 minutes and 60 minutes of prediction
horizon (PH), respectively. To the best of our knowledge, this is the leading
average prediction accuracy for the ohioT1DM dataset. Different physiological
information, e.g., Kalman smoothed CGM data, carbohydrates from the meal, bolus
insulin, and cumulative step counts in a fixed time interval, are crafted to
represent meaningful features used as input to the model. The goal of our
approach is to lower the difference between the predicted CGM values and the
fingerstick blood glucose readings - the ground truth. Our results indicate
that the proposed approach is feasible for more reliable BG forecasting that
might improve the performance of the artificial pancreas and insulin infusion
system for T1D diabetes management.


Abstract:
  The key challenge in multiple-object tracking task is temporal modeling of
the object under track. Existing tracking-by-detection methods adopt simple
heuristics, such as spatial or appearance similarity. Such methods, in spite of
their commonality, are overly simple and lack the ability to learn temporal
variations from data in an end-to-end manner. In this paper, we present MOTR, a
fully end-to-end multiple-object tracking framework. It learns to model the
long-range temporal variation of the objects. It performs temporal association
implicitly and avoids previous explicit heuristics. Built upon DETR, MOTR
introduces the concept of "track query". Each track query models the entire
track of an object. It is transferred and updated frame-by-frame to perform
iterative predictions in a seamless manner. Tracklet-aware label assignment is
proposed for one-to-one assignment between track queries and object tracks.
Temporal aggregation network together with collective average loss is further
proposed to enhance the long-range temporal relation. Experimental results show
that MOTR achieves competitive performance and can serve as a strong
Transformer-based baseline for future research. Code is available at
\url{https://github.com/megvii-model/MOTR}.


Abstract:
  The stiffness of the Hodgkin-Huxley (HH) equations during an action potential
(spike) limits the use of large time steps. We observe that the neurons can be
evolved independently between spikes, $i.e.,$ different neurons can be evolved
with different methods and different time steps. This observation motivates us
to design fast algorithms to raise efficiency. We present an adaptive method,
an exponential time differencing (ETD) method and a library-based method to
deal with the stiff period. All the methods can use time steps one order of
magnitude larger than the regular Runge-Kutta methods to raise efficiency while
achieving precise statistical properties of the original HH neurons like the
largest Lyapunov exponent and mean firing rate. We point out that the ETD and
library methods can stably achieve maximum 8 and 10 times of speedup,
respectively.


Abstract:
  Singer voice classification is a meaningful task in the digital era. With a
huge number of songs today, identifying a singer is very helpful for music
information retrieval, music properties indexing, and so on. In this paper, we
propose a new method to identify the singer's name based on analysis of
Vietnamese popular music. We employ the use of vocal segment detection and
singing voice separation as the pre-processing steps. The purpose of these
steps is to extract the singer's voice from the mixture sound. In order to
build a singer classifier, we propose a neural network architecture working
with Mel Frequency Cepstral Coefficient as extracted input features from said
vocal. To verify the accuracy of our methods, we evaluate on a dataset of 300
Vietnamese songs from 18 famous singers. We achieve an accuracy of 92.84% with
5-fold stratified cross-validation, the best result compared to other methods
on the same data set.


Abstract:
  Payment channel networks, such as Bitcoin's Lightning Network, promise to
improve the scalability of blockchain systems by processing the majority of
transactions off-chain. Due to the design, the positioning of nodes in the
network topology is a highly influential factor regarding the experienced
performance, costs, and fee revenue of network participants. As a consequence,
today's Lightning Network is built around a small number of highly-connected
hubs. Recent literature shows the centralizing tendencies to be
incentive-compatible and at the same time detrimental to security and privacy.
The choice of attachment strategies therefore becomes a crucial factor for the
future of such systems. In this paper, we provide an empirical study on the
(local and global) impact of various attachment strategies for payment channel
networks. To this end, we introduce candidate strategies from the field of
graph theory and analyze them with respect to their computational complexity as
well as their repercussions for end users and service providers. Moreover, we
evaluate their long-term impact on the network topology.


Abstract:
  Spherically, plane, or hyperbolically symmetric spacetimes with an additional
hypersurface orthogonal Killing vector are often called ``static'' spacetimes
even if they contain regions where the Killing vector is non-timelike. It seems
to be widely believed that an energy-momentum tenor for a matter field
compatible with these spacetimes in general relativity is of the Hawking-Ellis
type I everywhere. We show in arbitrary $n(\ge 3)$ dimensions that, contrary to
popular belief, a matter field on a Killing horizon is not necessarily of type
I but can be of type II. Such a type-II matter field on a Killing horizon is
realized in the Gibbons-Maeda-Garfinkle-Horowitz-Strominger black hole in the
Einstein-Maxwell-dilaton system and may be interpreted as a mixture of a
particular anisotropic fluid and a null dust fluid.


Abstract:
  Principal component analysis (PCA) defines a reduced space described by PC
axes for a given multidimensional-data sequence to capture the variations of
the data. In practice, we need multiple data sequences that accurately obey
individual probability distributions and for a fair comparison of the sequences
we need PC axes that are common for the multiple sequences but properly capture
these multiple distributions. For these requirements, we present individual
ergodic samplings for these sequences and provide special reweighting for
recovering the target distributions.


Abstract:
  In many multiagent environments, a designer has some, but limited control
over the game being played. In this paper, we formalize this by considering
incompletely specified games, in which some entries of the payoff matrices can
be chosen from a specified set. We show that it is NP-hard for the designer to
make these choices optimally, even in zero-sum games. In fact, it is already
intractable to decide whether a given action is (potentially or necessarily)
played in equilibrium. We also consider incompletely specified symmetric games
in which all completions are required to be symmetric. Here, hardness holds
even in weak tournament games (symmetric zero-sum games whose entries are all
-1, 0, or 1) and in tournament games (symmetric zero-sum games whose
non-diagonal entries are all -1 or 1). The latter result settles the complexity
of the possible and necessary winner problems for a social-choice-theoretic
solution concept known as the bipartisan set. We finally give a mixed-integer
linear programming formulation for weak tournament games and evaluate it
experimentally.


Abstract:
  Introducing the notion of extended Schr\"odinger spaces, we define the
criticality and subcriticality of Schr\"odinger forms in the same manner as the
recurrence and transience of Dirichlet forms, and give a sufficient condition
for the subcriticality of Schr\"odinger forms in terms the bottom of spectrum.
We define a subclass of Hardy potentials and prove that Schr\"odinger forms
with potentials in this subclass are always critical, which leads us to optimal
Hardy type inequality. We show that this definition of criticality and
subcriticality is equivalent to that there exists an excessive function with
respect to Schr\"odinger semigroup and its generating Dirichlet form through
$h$-transform is recurrent and transient respectively. As an application, we
can show the recurrence and transience of a family of Dirichlet forms by
showing the criticality and subcriticaly of Schr\"odinger forms and show the
other way around through $h$-transform, We give a such example with fractional
Schr\"odinger operators with Hardy potential.


Abstract:
  A splitting BIBD is a type of combinatorial design that can be used to
construct splitting authentication codes with good properties. In this paper we
show that a design-theoretic approach is useful in the analysis of more general
splitting authentication codes. Motivated by the study of algebraic
manipulation detection (AMD) codes, we define the concept of a group generated
splitting authentication code. We show that all group-generated authentication
codes have perfect secrecy, which allows us to demonstrate that algebraic
manipulation detection codes can be considered to be a special case of an
authentication code with perfect secrecy.
  We also investigate splitting BIBDs that can be "equitably ordered". These
splitting BIBDs yield authentication codes with splitting that also have
perfect secrecy. We show that, while group generated BIBDs are inherently
equitably ordered, the concept is applicable to more general splitting BIBDs.
For various pairs $(k,c)$, we determine necessary and sufficient (or almost
sufficient) conditions for the existence of $(v, k \times c,1)$-splitting BIBDs
that can be equitably ordered. The pairs for which we can solve this problem
are $(k,c) = (3,2), (4,2), (3,3)$ and $(3,4)$, as well as all cases with $k =
2$.


Abstract:
  Introduction: Mobile apps, through artificial vision, are capable of
recognizing vegetable species in real time. However, the existing species
recognition apps do not take in consideration the wide variety of endemic and
native (Chilean) species, which leads to wrong species predictions. This study
introduces the development of a chilean species dataset and an optimized
classification model implemented to a mobile app. Method: the data set was
built by putting together pictures of several species captured on the field and
by selecting some pictures available from other datasets available online.
Convolutional neural networks were used in order to develop the images
prediction models. The networks were trained by performing a sensitivity
analysis, validating with k-fold cross validation and performing tests with
different hyper-parameters, optimizers, convolutional layers, and learning
rates in order to identify and choose the best models and then put them
together in one classification model. Results: The final data set was
compounded by 46 species, including native species, endemic and exotic from
Chile, with 6120 training pictures and 655 testing pictures. The best models
were implemented on a mobile app, obtaining a 95% correct prediction rate with
respect to the set of tests. Conclusion: The app developed in this study is
capable of classifying species with a high level of accuracy, depending on the
state of the art of the artificial vision and it can also show relevant
information related to the classified species.


Abstract:
  We study quantum effects of the vacuum light-matter interaction in materials
embedded in optical cavities. We focus on the electronic response of a
two-dimensional semiconductor placed inside a planar cavity. By using a
diagrammatic expansion of the electron-photon interaction, we describe
signatures of light-matter hybridization characterized by large asymmetric
shifts of the spectral weight at resonant frequencies. We follow the evolution
of the light-dressing from the cavity to the free-space limit. In the cavity
limit, light-matter hybridization results in a modification of the optical gap
with sizeable spectral weight appearing below the bare gap edge. In the limit
of large cavities, we find a residual redistribution of spectral weight which
becomes independent of the distance between the two mirrors. We show that the
photon dressing of the electronic response can be fully explained by using a
classical description of light. The classical description is found to hold up
to a strong coupling regime of the light-matter interaction highlighted by the
large modification of the photon spectra with respect to the empty cavity. We
show that, despite the strong coupling, quantum corrections are negligibly
small and weakly dependent on the cavity confinement. As a consequence, in
contrast to the optical gap, the single particle electronic band gap is not
sensibly modified by the strong-coupling. Our results show that quantum
corrections are dominated by off-resonant photon modes at high energy. As such,
cavity confinement can hardly be seen as a knob to control the quantum effects
of the light-matter interaction in vacuum.


Abstract:
  Recent discoveries of charge order and electronic nematic order in the
iron-based superconductors and cuprates have pointed towards the possibility of
nematic and charge fluctuations playing a role in the enhancement of
superconductivity. The Ba1-xSrxNi2As2 system, closely related in structure to
the BaFe2As2 system, has recently been shown to exhibit both types of ordering
without the presence of any magnetic order. We report single crystal X-ray
diffraction, resistance transport measurements, and magnetization of \BaSrLate,
providing evidence that the previously reported incommensurate charge order
with wavevector $(0,0.28,0)_{tet}$ in the tetragonal state of \BaNi~vanishes by
this concentration of Sr substitution together with nematic order. Our
measurements suggest that the nematic and incommensurate charge orders are
closely tied in the tetragonal state, and show that the $(0,0.33,0)_{tri}$
charge ordering in the triclinic phase of BaNi2As2 evolves to become
$(0,0.5,0)_{tri}$ charge ordering at $x$=0.65 before vanishing at $x$=0.71.


Abstract:
  We study the one-level density for families of L-functions associated with
cubic Dirichlet characters defined over the Eisenstein field. We show that the
family of $L$-functions associated with the cubic residue symbols $\chi_n$ with
$n$ square-free and congruent to 1 modulo 9 satisfies the Katz-Sarnak
conjecture for all test functions whose Fourier transforms are supported in
$(-13/11, 13/11)$, under GRH. This is the first result extending the support
outside the \emph{trivial range} $(-1, 1)$ for a family of cubic L-functions.
This implies that a positive density of the L-functions associated with these
characters do not vanish at the central point $s=1/2$. A key ingredient in our
proof is a bound on an average of generalized cubic Gauss sums at prime
arguments, whose proof is based on the work of Heath-Brown and Patterson.


Abstract:
  To inhibit the spread of rumorous information and its severe consequences,
traditional fact checking aims at retrieving relevant evidence to verify the
veracity of a given claim. Fact checking methods typically use knowledge graphs
(KGs) as external repositories and develop reasoning mechanism to retrieve
evidence for verifying the triple claim. However, existing methods only focus
on verifying a single claim. As real-world rumorous information is more complex
and a textual statement is often composed of multiple clauses (i.e. represented
as multiple claims instead of a single one), multiclaim fact checking is not
only necessary but more important for practical applications. Although previous
methods for verifying a single triple can be applied repeatedly to verify
multiple triples one by one, they ignore the contextual information implied in
a multi-claim statement and could not learn the rich semantic information in
the statement as a whole. In this paper, we propose an end-to-end knowledge
enhanced learning and verification method for multi-claim fact checking. Our
method consists of two modules, KG-based learning enhancement and multi-claim
semantic composition. To fully utilize the contextual information, the KG-based
learning enhancement module learns the dynamic context-specific representations
via selectively aggregating relevant attributes of entities. To capture the
compositional semantics of multiple triples, the multi-claim semantic
composition module constructs the graph structure to model claim-level
interactions, and integrates global and salient local semantics with multi-head
attention. Experimental results on a real-world dataset and two benchmark
datasets show the effectiveness of our method for multi-claim fact checking
over KG.


Abstract:
  Ferroelectric tunneling junctions (FTJ) are considered to be the
intrinsically most energy efficient memristors. In this work, specific
electrical features of ferroelectric hafnium-zirconium oxide based FTJ devices
are investigated. Moreover, the impact on the design of FTJ-based circuits for
edge computing applications is discussed by means of two example circuits.


Abstract:
  The number of non-isomorphic cubic fields L sharing a common discriminant
d(L) = d is called the multiplicity m = m(d) of d. For an assigned value of d,
these fields are collected in a multiplet M(d) = (L(1) ,..., L(m)). In this
paper, the information in all existing tables of totally real cubic number
fields L with positive discriminants d(L) < 10000000 is extended by computing
the differential principal factorization types tau(L) in (alpha1, alpha2,
alpha3, beta1, beta2, gamma, delta1, delta2, epsilon) of the members L of each
multiplet M(d) of non-cyclic fields, a new kind of arithmetical invariants
which provide succinct information about ambiguous principal ideals and
capitulation in the normal closures N of non-Galois cubic fields L. The
classification is arranged with respect to increasing 3-class rank of the
quadratic subfields K of the S3-fields N, and to ascending number of prime
divisors of the conductor f of N/K. The Scholz conjecture concerning the
distinguished index of subfield units (U(N) : U(0)) = 1 for ramified extensions
N/K with conductor f > 1 is refined and verified.


Abstract:
  One of the major challenges for low-rank multi-fidelity (MF) approaches is
the assumption that low-fidelity (LF) and high-fidelity (HF) models admit
"similar" low-rank kernel representations. Low-rank MF methods have
traditionally attempted to exploit low-rank representations of linear kernels,
which are kernel functions of the form $K(u,v) = v^T u$ for vectors $u$ and
$v$. However, such linear kernels may not be able to capture low-rank behavior,
and they may admit LF and HF kernels that are not similar. Such a situation
renders a naive approach to low-rank MF procedures ineffective. In this paper,
we propose a novel approach for the selection of a near-optimal kernel function
for use in low-rank MF methods. The proposed framework is a two-step strategy
wherein: (1) hyperparameters of a library of kernel functions are optimized,
and (2) a particular combination of the optimized kernels is selected, through
either a convex mixture (Additive Kernels) or through a data-driven
optimization (Adaptive Kernels). The two resulting methods for this generalized
framework both utilize only the available inexpensive low-fidelity data and
thus no evaluation of high-fidelity simulation model is needed until a kernel
is chosen. These proposed approaches are tested on five non-trivial problems
including multi-fidelity surrogate modeling for one- and two-species molecular
systems, gravitational many-body problem, associating polymer networks,
plasmonic nano-particle arrays, and an incompressible flow in channels with
stenosis. The results for these numerical experiments demonstrate the numerical
stability efficiency of both proposed kernel function selection procedures, as
well as high accuracy of their resultant predictive models for estimation of
quantities of interest. Comparisons against standard linear kernel procedures
also demonstrate increased accuracy of the optimized kernel approaches.


Abstract:
  The investigation of the energy frontier in physics requires novel concepts
for future colliders. The idea of a muon collider is very appealing since it
would allow to study particle collisions at up to tens of TeV energy, while
offering a cleaner experimental environment with respect to hadronic colliders.
One key element in the muon collider design is the low-emittance muon
production. Recently,the Low EMittance Muon Accelerator (LEMMA) collaboration
has explored the muon pair production close to its kinematic threshold by
annihilating 45 GeV positrons with electrons in a low Z material target. In
this configuration, muons are emerging from the target with a naturally
low-emittance. In this paper we describe the performance of a system, to study
this production mechanism, that consists in several segmented absorbers with
alternating active layers composed of fast Cherenkov detectors together with a
muon identification technique based on this detector. Passive layers were made
of tungsten. We collected data corresponding to muon and electron beams
produced at the H2 line in the North Area of the European Organization for
Nuclear Research (CERN) in September 2018.


Abstract:
  We explore equilibrium solutions of spherically symmetric boson stars in the
Palatini formulation of $f(\mathcal{R})$ gravity. We account for the
modifications introduced in the gravitational sector by using a recently
established correspondence between modified gravity with scalar matter and
general relativity with modified scalar matter. We focus on the quadratic
theory $f(\mathcal{R})=R+\xi R^2$ and compare its solutions with those found in
general relativity, exploring both positive and negative values of the coupling
parameter $\xi$. As matter source, a complex, massive scalar field with and
without self-interaction terms is considered. Our results show that the
existence curves of boson stars in Palatini $f(\mathcal{R})$ gravity are fairly
similar to those found in general relativity. Major differences are observed
for negative values of the coupling parameter which results in a repulsive
gravitational component for high enough scalar field density distributions.
Adding self-interactions makes the degeneracy between $f(\mathcal{R})$ and
general relativity even more pronounced, leaving very little room for
observational discrimination between the two theories.


Abstract:
  With the widespread use of toxic language online, platforms are increasingly
using automated systems that leverage advances in natural language processing
to automatically flag and remove toxic comments. However, most automated
systems -- when detecting and moderating toxic language -- do not provide
feedback to their users, let alone provide an avenue of recourse for these
users to make actionable changes. We present our work, RECAST, an interactive,
open-sourced web tool for visualizing these models' toxic predictions, while
providing alternative suggestions for flagged toxic language. Our work also
provides users with a new path of recourse when using these automated
moderation tools. RECAST highlights text responsible for classifying toxicity,
and allows users to interactively substitute potentially toxic phrases with
neutral alternatives. We examined the effect of RECAST via two large-scale user
evaluations, and found that RECAST was highly effective at helping users reduce
toxicity as detected through the model. Users also gained a stronger
understanding of the underlying toxicity criterion used by black-box models,
enabling transparency and recourse. In addition, we found that when users focus
on optimizing language for these models instead of their own judgement (which
is the implied incentive and goal of deploying automated models), these models
cease to be effective classifiers of toxicity compared to human annotations.
This opens a discussion for how toxicity detection models work and should work,
and their effect on the future of online discourse.


Abstract:
  The production of $\Lambda$ baryons and ${\rm K}^{0}_{\rm S}$ mesons (${\rm
V}^{0}$ particles) was measured in p-Pb collisions at $\sqrt{s_{\rm NN}} = 5$
TeV and pp collisions at $\sqrt{s} = 7$ TeV with ALICE at the LHC. The
production of these strange particles is studied separately for particles
associated with hard scatterings and the underlying event to shed light on the
baryon-to-meson ratio enhancement observed at intermediate transverse momentum
($p_{\rm T}$) in high multiplicity pp and p-Pb collisions. Hard scatterings are
selected on an event-by-event basis with jets reconstructed with the
anti-$k_{\rm T}$ algorithm using charged particles. The production of strange
particles associated with jets $p_{\rm T,\;jet}^{\rm ch}>10$ GeV/$c$ is
reported as a function of $p_{\rm T}$ in both systems; and its dependence on
$p_{\rm T}$ with jets $p_{\rm T,\;jet}^{\rm ch}>20$ GeV/$c$ and on angular
distance from the jet axis, $R({\rm V}^{0},\;{\rm jet})$, for jets with $p_{\rm
T,\;jet}^{\rm ch} > 10$ GeV/$c$ are reported in p-Pb collisions. The results
are compared with the strange particle production in the underlying event. The
$\Lambda/{\rm K}^{0}_{\rm S}$ ratio associated with jets in p-Pb collisions for
$R({\rm V}^{0},\;{\rm jet})<0.4$ is consistent with the ratio measured in pp
collisions and with the expectation of jets fragmenting in vacuum given by the
PYTHIA event generator.


Abstract:
  Recent results have demonstrated that samplers constructed with flow-based
generative models are a promising new approach for configuration generation in
lattice field theory. In this paper, we present a set of methods to construct
flow models for targets with multiple separated modes (i.e. theories with
multiple vacua). We demonstrate the application of these methods to modeling
two-dimensional real scalar field theory in its symmetry-broken phase. In this
context we investigate the performance of different flow-based sampling
algorithms, including a composite sampling algorithm where flow-based proposals
are occasionally augmented by applying updates using traditional algorithms
like HMC.


Abstract:
  We study the propagation of light under a strong electric field in
Born-Infeld electrrdynamics. The nonlinear effect can be described by the
effective indices of refraction. Because the effective indices of refraction
depend on the background electric field, the path of light can be bent when the
background field is non-uniform. We compute the bending angle of light by a
Born-Infeld-type Coulomb charge in the weak lensing limit using the trajectory
equation based on geometric optics. We also compute the deflection angle of
light by the Einstein-Born-Infeld black hole using the geodesic equation and
confirm that the contribution of the electric charge to the total bending angle
agree.


Abstract:
  In this paper, we investigate certain graded-commutative rings which are
related to the reciprocal plane compactification of the coordinate ring of a
complement of a hyperplane arrangement. We give a presentation of these rings
by generators and defining relations. This presentation was used by Holler and
I. Kriz to calculate the $\mathbb{Z}$-graded coefficients of localizations of
ordinary $RO((\mathbb{Z}/p)^n)$-graded equivariant cohomology at a given set of
representation spheres, and also more recently by the author in a
generalization to the case of an arbitrary finite group. We also give an
interpretation of these rings in terms of superschemes, which can be used to
further illuminate their structure.


Abstract:
  We present a framework for quantum process tomography of two-ion interactions
that leverages modulations of the trapping potential and composite pulses from
a global laser beam to achieve individual-ion addressing. Tomographic analysis
of identity and delay processes reveals dominant error contributions from laser
decoherence and slow qubit frequency drift during the tomography experiment. We
use this framework on two co-trapped $^{40}$Ca$^+$ ions to analyze both an
optimized and an overpowered M{\o}lmer-S{\o}rensen gate and to compare the
results of this analysis to a less informative Bell-state tomography
measurement and to predictions based on a simplified noise model. These results
show that the technique is effective for the characterization of two-ion
quantum processes and for the extraction of meaningful information about the
errors present in the system. The experimental convenience of this method will
allow for more widespread use of process tomography for characterizing
entangling gates in trapped-ion systems.


Abstract:
  Strain localization is responsible for mesh dependence in numerical analyses
concerning a vast variety of fields such as solid mechanics, dynamics,
biomechanics and geomechanics. Therefore, numerical methods that regularize
strain localization are paramount in the analysis and design of engineering
products and systems. In this paper we revisit the elasto-viscoplastic,
strain-softening, strain-rate hardening model as a means to avoid strain
localization on a mathematical plane in the case of a Cauchy continuum. Going
beyond previous works (de Borst and Duretz (2020); Needleman (1988); Sluys and
de Borst (1992); Wang et al. (1997)), we assume that both the frequency
{\omega} and the wave number k belong to the complex plane. Therefore, a
different expression for the dispersion relation is derived. We prove then that
under these conditions strain localization on a mathematical plane is possible.
The above theoretical results are corroborated by extensive numerical analyses,
where the total strain and plastic strain rate profiles exhibit mesh dependent
behavior.


Abstract:
  In this paper, we propose a new strategy for learning inertial robotic
navigation models. The proposed strategy enhances the generalisability of
end-to-end inertial modelling, and is aimed at wheeled robotic deployments.
Concretely, the paper describes the following. (1) Using precision robotics, we
empirically characterise the effect of changing the sensor position during
navigation on the distribution of raw inertial signals, as well as the
corresponding impact on learnt latent spaces. (2) We propose neural
architectures and algorithms to assimilate knowledge from an indexed set of
sensor positions in order to enhance the robustness and generalisability of
robotic inertial tracking in the field. Our scheme of choice uses continuous
domain adaptation (DA) and optimal transport (OT). (3) In our evaluation,
continuous OT DA outperforms a continuous adversarial DA baseline, while also
showing quantifiable learning benefits over simple data augmentation. We will
release our dataset to help foster future research.


Abstract:
  In this paper, for $1<p<\infty$, we obtain the $L^p$-boundedness of the
Hilbert transform $H^{\gamma}$ along a variable plane curve $(t,u(x_1,
x_2)\gamma(t))$, where $u$ is a Lipschitz function with small Lipschitz norm,
and $\gamma$ is a general curve satisfying some suitable smoothness and
curvature conditions.


Abstract:
  We compute the non-planar contribution to the universal anomalous dimension
of twist-two operators in N=4 supersymmetric Yang-Mills theory at four loops
through Lorentz spin eighteen. Exploiting the results of this and our previous
calculations along with recent analytic results for the cusp anomalous
dimension and some expected analytic properties, we reconstruct a general
expression valid for arbitrary Lorentz spin. We study various properties of
this general result, such as its large-spin limit, its small-x limit, and
others. In particular, we present a prediction for the non-planar contribution
to the anomalous dimension of the single-magnon operator in the beta-deformed
version of the theory.


Abstract:
  Deep neural networks have been well-known for their superb performance in
handling various machine learning and artificial intelligence tasks. However,
due to their over-parameterized black-box nature, it is often difficult to
understand the prediction results of deep models. In recent years, many
interpretation tools have been proposed to explain or reveal the ways that deep
models make decisions. In this paper, we review this line of research and try
to make a comprehensive survey. Specifically, we introduce and clarify two
basic concepts-interpretations and interpretability-that people usually get
confused. First of all, to address the research efforts in interpretations, we
elaborate the design of several recent interpretation algorithms, from
different perspectives, through proposing a new taxonomy. Then, to understand
the results of interpretation, we also survey the performance metrics for
evaluating interpretation algorithms. Further, we summarize the existing work
in evaluating models' interpretability using "trustworthy" interpretation
algorithms. Finally, we review and discuss the connections between deep models'
interpretations and other factors, such as adversarial robustness and data
augmentations, and we introduce several open-source libraries for
interpretation algorithms and evaluation approaches.


Abstract:
  We have designed honeycomb lattices for microwave photons with a frequency
imbalance between the two sites in the unit cell. This imbalance is the
equivalent of a mass term that breaks the lattice inversion symmetry. At the
interface between two lattices with opposite imbalance, we observe topological
valley edge states. By imaging the spatial dependence of the modes along the
interface, we obtain their dispersion relation that we compare to the
predictions of an ab initio tight-binding model describing our microwave
photonic lattices.


Abstract:
  The goal of this paper is to increase the membership list of the Chamaeleon
star forming region and the $\epsilon$ Cha moving group, in particular for
low-mass stars and substellar objects. We extended the search region
significantly beyond the dark clouds. Our sample has been selected based on
proper motions and colours obtained from Gaia and 2MASS. We present and discuss
the optical spectroscopic follow-up of 18 low-mass stellar objects in Cha I and
$\epsilon$ Cha. We characterize the properties of objects by deriving their
physical parameters, both from spectroscopy and photometry. We add three more
low-mass members to the list of Cha I, and increase the census of known
$\epsilon$ Cha members by more than 40%, confirming spectroscopically 13 new
members and relying on X-ray emission as youth indicator for 2 more. In most
cases the best-fitting spectral template is from objects in the TW Hya
association, indicating that $\epsilon$ Cha has a similar age. The first
estimate of the slope of the initial mass function in $\epsilon$ Cha down to
the sub-stellar regime is consistent with that of other young clusters. We
estimate our IMF to be complete down to $\approx 0.03$M$_{\odot}$. The IMF can
be represented by two power laws: for M $<$ 0.5 M$_{\odot}$ $\alpha = 0.42 \pm
0.11$ and for M $>$ 0.5 M$_{\odot}$ $\alpha = 1.44 \pm 0.12$. We find
similarities between $\epsilon$ Cha and the southernmost part of Lower
Centaurus Crux (LCC A0), both lying at similar distances and sharing the same
proper motions. This suggests that $\epsilon$ Cha and LCC A0 may have been born
during the same star formation event


Abstract:
  We observe the density wave angular pattern speed OMEGA-p to be near 12 to 17
km / s / kpc, by the separation between a typical optical HII region (from the
spiral arm dust lane) and using a HII evolution time model to yield its
relative speed, and independently by the separation between a typical radio
maser (from the spiral arm dust lane) with a maser model.


Abstract:
  An instance of the super-stable matching problem with incomplete lists and
ties is an undirected bipartite graph $G = (A \cup B, E)$, with an adjacency
list being a linearly ordered list of ties. Ties are subsets of vertices
equally good for a given vertex. An edge $(x,y) \in E \backslash M$ is a
blocking edge for a matching $M$ if by getting matched to each other neither of
the vertices $x$ and $y$ would become worse off. Thus, there is no disadvantage
if the two vertices would like to match up. A matching $M$ is super-stable if
there is no blocking edge with respect to $M$. It has previously been shown
that super-stable matchings form a distributive lattice and the number of
super-stable matchings can be exponential in the number of vertices. We give
two compact representations of size $O(m)$ that can be used to construct all
super-stable matchings, where $m$ denotes the number of edges in the graph. The
construction of the second representation takes $O(mn)$ time, where $n$ denotes
the number of vertices in the graph, and gives an explicit rotation poset
similar to the rotation poset in the classical stable marriage problem. We also
give a polyhedral characterisation of the set of all super-stable matchings and
prove that the super-stable matching polytope is integral, thus solving an open
problem stated in the book by Gusfield and Irving .


Abstract:
  The relaxation of field-line tension during magnetic reconnection gives rise
to a universal Fermi acceleration process involving the curvature drift of
particles. However, the efficiency of this mechanism is limited by the trapping
of energetic particles within flux-ropes. Using 3D fully kinetic simulations,
we demonstrate that the flux-rope kink instability leads to strong field-line
chaos in weak-guide-field regimes where the Fermi mechanism is most efficient,
thus allowing particles to transport out of flux-ropes and undergo further
acceleration. As a consequence, both ions and electrons develop clear power-law
energy spectra which contain a significant fraction of the released energy. The
low-energy bounds are determined by the injection physics, while the
high-energy cutoffs are limited only by the system size. These results have
strong relevance to observations of nonthermal particle acceleration in space
and astrophysics.


Abstract:
  Galactic Internet may already exist, if all stars are exploited as
gravitational lenses. In fact, the gravitational lens of the Sun is a
well-known astrophysical phenomenon predicted by Einstein's general theory of
relativity. It implies that, if we can send a probe along any radial direction
away from the Sun up to the minimal distance of 550 AU and beyond, the Sun's
mass will act as a huge magnifying lens, letting us "see" detailed radio maps
of whatever may lie on the other side of the Sun even at very large distances.
The 2009 book by this author, ref. [1], studies such future FOCAL space
missions to 550 AU and beyond. In this paper, however, we want to study another
possibility yet: how to create the future interstellar radio links between the
solar system and any future interstellar probe by utilizing the gravitational
lens of the Sun as a huge antenna. In particular, we study the Bit Error Rate
(BER) across interstellar distances with and without using the gravitational
lens effect of the Sun (ref. [2]). The conclusion is that only when we will
exploit the Sun as a gravitational lens we will be able to communicate with our
own probes (or with nearby Aliens) across the distances of even the nearest
stars to us in the Galaxy, and that at a reasonable Bit Error Rate. We also
study the radio bridge between the Sun and any other Star that is made up by
the two gravitational lenses of both the Sun and that Star. The alignment for
this radio bridge to work is very strict, but the power-saving is enormous, due
to the huge contributions of the two stars' lenses to the overall antenna gain
of the system. We study a few cases in detail. Finally, we find the information
channel capacity for each of those radio bridges, putting thus a physical
constraint to the amount of information transfer that will be possible even by
exploiting the stars as gravitational lenses


Abstract:
  Gaia DR2 published positions, parallaxes and proper motions for an
unprecedented 1,331,909,727 sources, revolutionising the field of Galactic
dynamics. We complement this data with the Astrometry Spread Function (ASF),
the expected uncertainty in the measured positions, proper motions and parallax
for a non-accelerating point source. The ASF is a Gaussian function for which
we construct the 5D astrometric covariance matrix as a function of position on
the sky and apparent magnitude using the Gaia DR2 scanning law and demonstrate
excellent agreement with the observed data. This can be used to answer the
question `What astrometric covariance would Gaia have published if my star was
a non-accelerating point source?'.
  The ASF will enable characterisation of binary systems, exoplanet orbits,
astrometric microlensing events and extended sources which add an excess
astrometric noise to the expected astrometry uncertainty. By using the ASF to
estimate the unit weight error (UWE) of Gaia DR2 sources, we demonstrate that
the ASF indeed provides a direct probe of the excess source noise.
  We use the ASF to estimate the contribution to the selection function of the
Gaia astrometric sample from a cut on astrometric_sigma5d_max showing high
completeness for $G<20$ dropping to $<1\%$ in underscanned regions of the sky
for $G=21$.
  We have added an ASF module to the Python package SCANNINGLAW
(https://github.com/gaiaverse/scanninglaw) through which users can access the
ASF.


Abstract:
  We compare the capabilities of two approaches to approximating graph
isomorphism using linear algebraic methods: the \emph{invertible map tests}
(introduced by Dawar and Holm) and proof systems with algebraic rules, namely
\emph{polynomial calculus}, \emph{monomial calculus} and \emph{Nullstellensatz
calculus}. In the case of fields of characteristic zero, these variants are all
essentially equivalent to the the Weisfeiler-Leman algorithms. In positive
characteristic we show that the invertible map method can simulate the monomial
calculus and identify a potential way to extend this to the monomial calculus.


Abstract:
  Relational semigroups with domain and range are a useful tool for modelling
nondeterministic programs. We prove that the representation class of
domain-range semigroups with demonic composition is not finitely axiomatisable.
We extend the result for ordered domain algebras and show that any relation
algebra reduct signature containing domain, range, converse, and composition,
but no negation, meet, nor join has the finite representation property. That is
any finite representable structure of such a signature is representable over a
finite base. We survey the results in the area of the finite representation
property.


Abstract:
  Single-species reaction-diffusion equations, such as the Fisher-KPP and
Porous-Fisher equations, support travelling wave solutions that are often
interpreted as simple mathematical models of biological invasion. Such
travelling wave solutions are thought to play a role in various applications
including development, wound healing and malignant invasion. One criticism of
these single-species equations is that they do not explicitly describe
interactions between the invading population and the surrounding environment.
In this work we study a reaction-diffusion equation that describes malignant
invasion which has been used to interpret experimental measurements describing
the invasion of malignant melanoma cells into surrounding human skin tissues.
This model explicitly describes how the population of cancer cells degrade the
surrounding tissues, thereby creating free space into which the cancer cells
migrate and proliferate to form an invasion wave of malignant tissue that is
coupled to a retreating wave of skin tissue. We analyse travelling wave
solutions of this model using a combination of numerical simulation, phase
plane analysis and perturbation techniques. Our analysis shows that the
travelling wave solutions involve a range of very interesting properties that
resemble certain well-established features of both the Fisher-KPP and
Porous-Fisher equations, as well as a range of novel properties that can be
thought of as extensions of these well-studied single-species equations. Of
particular interest is that travelling wave solutions of the invasion model are
very well approximated by trajectories in the Fisher-KPP phase plane that are
normally disregarded. This observation establishes a previously unnoticed link
between coupled multi-species reaction diffusion models of invasion and a
different class of models of invasion that involve moving boundary problems.


Abstract:
  Scientometric analysis of 146 and 59 research articles published in Indian
journal of Information Sources and Services (IJISS) and Pakistan Journal of
Library and Information Science has been carried out. Seven Volumes of the
IJISS containing 14 issues and Seven volumes of PJLIS containing 8 issues from
2011 - 2017 have been taken into consideration for the present study. The
number of contributions, authorship pattern & author productivity, average
citations, average length of articles, average keywords and collaborative
papers has been analyzed. Out of 146 of IJISS contributions, only 39 are single
authored and rest by multi authored with degree of collaboration 0.73 and week
collaboration among the authors and from 59 contributions of PJLIS only 18 are
single authored and rest by multi authored with degree of collaboration 0.69
and week collaboration among the authors. The study revealed that the author
productivity is 0.53 (IJISS) and 0.50 (PJLIS) and dominated by the Indian and
Pakistani authors.


Abstract:
  Load-generation balance and system inertia are essential for maintaining
frequency in power systems. Power grids are equipped with Rate-of-Change-of
Frequency (ROCOF) and Load Shedding (LS) relays in order to keep
load-generation balance. With the increasing penetration of renewables, the
inertia of the power grids is declining, which results in a faster drop in
system frequency in case of load-generation imbalance. In this context, we
analyze the feasibility of launching False Data Injection (FDI) in order to
create False Relay Operations (FRO), which we refer to as FRO attack, in the
power systems with high renewables. We model the frequency dynamics of the
power systems and corresponding FDI attacks, including the impact of
parameters, such as synchronous generators inertia, and governors time constant
and droop, on the success of FRO attacks. We formalize the FRO attack as a
Constraint Satisfaction Problem (CSP) and solve using Satisfiability Modulo
Theories (SMT). Our case studies show that power grids with renewables are more
susceptible to FRO attacks and the inertia of synchronous generators plays a
critical role in reducing the success of FRO attacks in the power grids.


Abstract:
  Motivated by the phenomenon of Coherent Perfect Absorption, we study the
shape of the deepest minima in the frequency-dependent single-channel
reflection of waves from a cavity with spatially uniform losses. We show that
it is largely determined by non-orthogonality factors $O_{nn}$ of the
eigenmodes associated with the non-selfadjoint effective Hamiltonian. For
cavities supporting chaotic ray dynamics we then use random matrix theory to
derive, fully non-perturbatively, the explicit probability density ${\cal
P}(O_{nn})$ of the non-orthogonality factors for systems with both broken and
preserved time reversal symmetry. The results imply that $O_{nn}$ are
heavy-tail distributed, with the universal tail ${\cal P}(O_{nn}\gg 1)\sim
O_{nn}^{-3}$.


Abstract:
  Expanding an idea of Raoul Bott, we propose a construction of canonical bases
for unitary representations that comes from big torus actions on families of
Bott-Samelson manifolds. The construction depends only on the choices of a
maximal torus, a Borel subgroup ,and a reduced expression for the longest
element of the Weyl group. It relies on a conjectural vanishing of higher
cohomology of sheaves of holomorphic sections of certain line bundles on the
total spaces of the families, hence the question mark in the title.


Abstract:
  We present an end-to-end model using streaming physiological time series to
accurately predict near-term risk for hypoxemia, a rare, but life-threatening
condition known to cause serious patient harm during surgery. Our proposed
model makes inference on both hypoxemia outcomes and future input sequences,
enabled by a joint sequence autoencoder that simultaneously optimizes a
discriminative decoder for label prediction, and two auxiliary decoders trained
for data reconstruction and forecast, which seamlessly learns future-indicative
latent representation. All decoders share a memory-based encoder that helps
capture the global dynamics of patient data. In a large surgical cohort of
73,536 surgeries at a major academic medical center, our model outperforms all
baselines and gives a large performance gain over the state-of-the-art
hypoxemia prediction system. With a high sensitivity cutoff at 80%, it presents
99.36% precision in predicting hypoxemia and 86.81% precision in predicting the
much more severe and rare hypoxemic condition, persistent hypoxemia. With
exceptionally low rate of false alarms, our proposed model is promising in
improving clinical decision making and easing burden on the health system.


Abstract:
  An effective approach in meta-learning is to utilize multiple "train tasks"
to learn a good initialization for model parameters that can help solve unseen
"test tasks" with very few samples by fine-tuning from this initialization.
Although successful in practice, theoretical understanding of such methods is
limited. This work studies an important aspect of these methods: splitting the
data from each task into train (support) and validation (query) sets during
meta-training. Inspired by recent work (Raghu et al., 2020), we view such
meta-learning methods through the lens of representation learning and argue
that the train-validation split encourages the learned representation to be
low-rank without compromising on expressivity, as opposed to the non-splitting
variant that encourages high-rank representations. Since sample efficiency
benefits from low-rankness, the splitting strategy will require very few
samples to solve unseen test tasks. We present theoretical results that
formalize this idea for linear representation learning on a subspace
meta-learning instance, and experimentally verify this practical benefit of
splitting in simulations and on standard meta-learning benchmarks.


Abstract:
  Characterizing multipartite quantum correlations beyond two parties is of
utmost importance for building cutting edge quantum technologies, although the
comprehensive picture is still missing. Here we investigate quantum
correlations (QCs) present in a multipartite system by exploring connections
between monogamy score (MS), localizable quantum correlations (LQC), and
genuine multipartite entanglement (GME) content of the state. We find that the
frequency distribution of GME for Dicke states with higher excitations
resembles that of random states. We show that there is a critical value of GME
beyond which all states become monogamous and it is investigated by considering
different powers of MS which provide various layers of monogamy relations.
Interestingly, such a relation between LQC and MS as well as GME does not hold.
States having a very low GME (low monogamy score, both positive and negative)
can localize a high amount of QCs in two parties. We also provide an upper
bound to the sum of bipartite QC measures including LQC for random states and
establish a gap between the actual upper bound and the algebraic maximum.


Abstract:
  We present a novel weighted average model based on the mixture of experts
(MoE) concept to provide robustness in Federated learning (FL) against the
poisoned/corrupted/outdated local models. These threats along with the non-IID
nature of data sets can considerably diminish the accuracy of the FL model. Our
proposed MoE-FL setup relies on the trust between users and the server where
the users share a portion of their public data sets with the server. The server
applies a robust aggregation method by solving the optimization problem or the
Softmax method to highlight the outlier cases and to reduce their adverse
effect on the FL process. Our experiments illustrate that MoE-FL outperforms
the performance of the traditional aggregation approach for high rate of
poisoned data from attackers.


Abstract:
  Primordial black holes (PBHs) as part of the Dark Matter (DM) would modify
the evolution of large-scale structures and the thermal history of the
universe. Future 21 cm forest observations, sensitive to small scales and the
thermal state of the Inter Galactic Medium (IGM), could probe the existence of
such PBHs. In this article, we show that the shot noise isocurvature mode on
small scales induced by the presence of PBHs can enhance the amount of low mass
halos, or minihalos, and thus, the number of 21 cm absorption lines. However,
if the mass of PBHs is as large as $M_{\rm PBH}\gtrsim 10 \, M_\odot$, with an
abundant enough fraction of PBHs as DM, $f_{\rm PBH}$, the IGM heating due to
accretion onto the PBHs counteracts the enhancement due to the isocurvature
mode, reducing the number of absorption lines instead. The concurrence of both
effects imprints distinctive signatures in the number of absorbers, allowing to
bound the abundance of PBHs. We compute the prospects for constraining PBHs
with future 21 cm forest observations, finding achievable competitive upper
limits on the abundance as low as $f_{\rm PBH} \sim 10^{-3}$ at $M_{\rm PBH}=
100 \, M_\odot$, or even lower at larger masses, in unexplored regions of the
parameter space by current probes. The impact of astrophysical X-ray sources on
the IGM temperature is also studied, which could potentially weaken the bounds.


Abstract:
  Self-adaptive systems continuously adapt to changes in their execution
environment. Capturing all possible changes to define suitable behaviour
beforehand is unfeasible, or even impossible in the case of unknown changes,
hence human intervention may be required. We argue that adapting to unknown
situations is the ultimate challenge for self-adaptive systems. Learning-based
approaches are used to learn the suitable behaviour to exhibit in the case of
unknown situations, to minimize or fully remove human intervention. While such
approaches can, to a certain extent, generalize existing adaptations to new
situations, there is a number of breakthroughs that need to be achieved before
systems can adapt to general unknown and unforeseen situations. We posit the
research directions that need to be explored to achieve unanticipated
adaptation from the perspective of learning-based self-adaptive systems. At
minimum, systems need to define internal representations of previously unseen
situations on-the-fly, extrapolate the relationship to the previously
encountered situations to evolve existing adaptations, and reason about the
feasibility of achieving their intrinsic goals in the new set of conditions. We
close discussing whether, even when we can, we should indeed build systems that
define their own behaviour and adapt their goals, without involving a human
supervisor.


Abstract:
  For a finite group $G$ and an inverse-closed generating set $C$ of $G$, let
$Aut(G;C)$ consist of those automorphisms of $G$ which leave $C$ invariant. We
define an $Aut(G;C)$-invariant normal subgroup $\Phi(G;C)$ of $G$ which has the
property that, for any $Aut(G;C)$-invariant normal set of generators for $G$,
if we remove from it all the elements of $\Phi(G;C)$, then the remaining set is
still an $Aut(G;C)$-invariant normal generating set for $G$. The subgroup
$\Phi(G;C)$ contains the Frattini subgroup $\Phi(G)$ but the inclusion may be
proper. The Cayley graph $Cay(G,C)$ is normal edge-transitive if $Aut(G;C)$
acts transitively on the pairs $\{c,c^{-1}\}$ from $C$. We show that, for a
normal edge-transitive Cayley graph $Cay(G,C)$, its quotient modulo $\Phi(G;C)$
is the unique largest normal quotient which is isomorphic to a subdirect
product of normal edge-transitive graphs of characteristically simple groups.
In particular, we may therefore view normal edge-transitive Cayley graphs of
characteristically simple groups as building blocks for normal edge-transitive
Cayley graphs whenever the subgroup $\Phi(G;C)$ is trivial. We explore several
questions which these results raise, some concerned with the set of all
inverse-closed generating sets for groups in a given family. In particular we
use this theory to classify all $4$-valent normal edge-transitive Cayley graphs
for dihedral groups; this involves a new construction of an infinite family of
examples, and disproves a conjecture of Talebi.


Abstract:
  With the rapid development of E-commerce and the increase in the quantity of
items, users are presented with more items hence their interests broaden. It is
increasingly difficult to model user intentions with traditional methods, which
model the user's preference for an item by combining a single user vector and
an item vector. Recently, some methods are proposed to generate multiple user
interest vectors and achieve better performance compared to traditional
methods. However, empirical studies demonstrate that vectors generated from
these multi-interests methods are sometimes homogeneous, which may lead to
sub-optimal performance. In this paper, we propose a novel method of Diversity
Regularized Interests Modeling (DRIM) for Recommender Systems. We apply a
capsule network in a multi-interest extractor to generate multiple user
interest vectors. Each interest of the user should have a certain degree of
distinction, thus we introduce three strategies as the diversity regularized
separator to separate multiple user interest vectors. Experimental results on
public and industrial data sets demonstrate the ability of the model to capture
different interests of a user and the superior performance of the proposed
approach.


Abstract:
  In the decade since 2010, successes in artificial intelligence have been at
the forefront of computer science and technology, and vector space models have
solidified a position at the forefront of artificial intelligence. At the same
time, quantum computers have become much more powerful, and announcements of
major advances are frequently in the news.
  The mathematical techniques underlying both these areas have more in common
than is sometimes realized. Vector spaces took a position at the axiomatic
heart of quantum mechanics in the 1930s, and this adoption was a key motivation
for the derivation of logic and probability from the linear geometry of vector
spaces. Quantum interactions between particles are modelled using the tensor
product, which is also used to express objects and operations in artificial
neural networks.
  This paper describes some of these common mathematical areas, including
examples of how they are used in artificial intelligence (AI), particularly in
automated reasoning and natural language processing (NLP). Techniques discussed
include vector spaces, scalar products, subspaces and implication, orthogonal
projection and negation, dual vectors, density matrices, positive operators,
and tensor products. Application areas include information retrieval,
categorization and implication, modelling word-senses and disambiguation,
inference in knowledge bases, and semantic composition.
  Some of these approaches can potentially be implemented on quantum hardware.
Many of the practical steps in this implementation are in early stages, and
some are already realized. Explaining some of the common mathematical tools can
help researchers in both AI and quantum computing further exploit these
overlaps, recognizing and exploring new directions along the way.


Abstract:
  Some implementations of variable neighborhood search based algorithms were
presented in \emph{C\'ecilia Daquin, Hamid Allaoui, Gilles Goncalves and
Tient\'e Hsu, Variable neighborhood search based algorithms for crossdock truck
assignment, RAIRO-Oper. Res., 55 (2021) 2291-2323}. This work is based on model
in \emph{Zhaowei Miao, Andrew Lim, Hong Ma, Truck dock assignment problem with
operational time constraint within crossdocks, European Journal of Operational
Research 192 (1), 2009, 105-115 }m which has been proven to be incorrect. We
reiterate and elaborate on the deficiencies in the latter and show that the
authors in the former were already aware of the deficiencies in the latter and
the proposed minor amendment does not overcome any of such deficiencies.


Abstract:
  Gamma-ray data from the Fermi-Large Area Telescope reveal an unexplained,
apparently diffuse, signal from the Galactic bulge. The origin of this
"Galactic Center Excess" (GCE) has been debated with proposed sources
prominently including self-annihilating dark matter and a hitherto undetected
population of millisecond pulsars (MSPs). We use a binary population synthesis
forward model to demonstrate that an MSP population arising from the accretion
induced collapse of O-Ne white dwarfs in Galactic bulge binaries can naturally
explain the GCE. Synchrotron emission from MSP-launched cosmic ray electrons
and positrons seems also to explain the mysterious "haze" of hard-spectrum,
non-thermal microwave emission from the inner Galaxy detected in WMAP and
Planck data.


Abstract:
  In HTTP Adaptive Streaming, video content is conventionally encoded by
adapting its spatial resolution and quantization level to best match the
prevailing network state and display characteristics. It is well known that the
traditional solution, of using a fixed bitrate ladder, does not result in the
highest quality of experience for the user. Hence, in this paper, we consider a
content-driven approach for estimating the bitrate ladder, based on
spatio-temporal features extracted from the uncompressed content. The method
implements a content-driven interpolation. It uses the extracted features to
train a machine learning model to infer the curvature points of the Rate-VMAF
curves in order to guide a set of initial encodings. We employ the VMAF quality
metric as a means of perceptually conditioning the estimation. When compared to
exhaustive encoding that produces the reference ladder, the estimated ladder is
composed by 74.3% of identical Rate-VMAF points with the reference ladder. The
proposed method offers a significant reduction of the number of encodes
required, 77.4%, at a small average Bj{\o}ntegaard Delta Rate cost, 1.12%.


Abstract:
  Recently, In the year 2020, Altun et al. \cite{AL} introduced the notion of
$p$-proximal contractions and discussed about best proximity point results for
this class of mappings. Then in the year 2021, Gabeleh and Markin \cite{GB}
showed that the best proximity point theorem proved by Altun et al. in
\cite{AL} follows from the fixed point theory. In this short note, we show that
if the $p$-proximal contraction constant $k<\frac{1}{3}$ then the existence of
best proximity point for $p$-proximal contractions follows from the celebrated
Banach contraction principle.


Abstract:
  Radiomic representations can quantify properties of regions of interest in
medical image data. Classically, they account for pre-defined statistics of
shape, texture, and other low-level image features. Alternatively, deep
learning-based representations are derived from supervised learning but require
expensive annotations from experts and often suffer from overfitting and data
imbalance issues. In this work, we address the challenge of learning
representations of 3D medical images for an effective quantification under data
imbalance. We propose a \emph{self-supervised} representation learning
framework to learn high-level features of 3D volumes as a complement to
existing radiomics features. Specifically, we demonstrate how to learn image
representations in a self-supervised fashion using a 3D Siamese network. More
importantly, we deal with data imbalance by exploiting two unsupervised
strategies: a) sample re-weighting, and b) balancing the composition of
training batches. When combining our learned self-supervised feature with
traditional radiomics, we show significant improvement in brain tumor
classification and lung cancer staging tasks covering MRI and CT imaging
modalities.


Abstract:
  We study decoupling theory for functions on $\mathbb{R}$ with Fourier
transform supported in a neighborhood of short Dirichlet sequences $\{\log
n\}_{n=N+1}^{N+N^{1/2}}$, as well as sequences with similar convexity
properties. We utilize the wave packet structure of functions with frequency
support near an arithmetic progression.


Abstract:
  This paper defines a methodology with in-depth data to identify the skills
needed by riders in the highest risk crash configurations to reduce casualty
rates. We present a case study using in-depth data of 803 powered-two-wheeler
crashes. Seven high-risk crash configuration based on the pre-crash
trajectories of the road-users involved were considered to investigate the
human errors as crash contributors. Primary crash contributing factor, evasive
manoeuvres performed, horizontal roadway alignment and speed-related factors
were identified, along with the most frequent configurations and those with the
greatest risk of severe injury. Straight Crossing Path/Lateral Direction was
the most frequent crash configuration and Turn Across Path/ Opposing Direction
that with the greatest risk of serious injury were identified. Multi-vehicle
crashes cannot be considered as a homogenous category of crashes to which the
same human failure is attributed, as different interactions between
motorcyclists and other road users are associated with both different types of
human error and different rider reactions. Human error in multiple-vehicle
crashes related to crossing paths configurations were different from errors
related to rear-end or head-on crashes. Multi-vehicle head-on crashes and
single-vehicle collisions frequently occur along curves. The involved collision
avoidance manoeuvres of the riders differed significantly among the highest
risk crash configurations. The most relevant lack of skills are identified and
linked to their most representative context. In most cases a combination of
different skills was required simultaneously to avoid the crash. The findings
underline the need to group accident cases, beyond the usual single-vehicle
versus multi-vehicle collision approach. Our methodology can also be applied to
support preventive actions based on riders training and eventually ADAS design.


Abstract:
  We continue to study the optical properties of the solar gravitational lens
(SGL). The aim is prospective applications of the SGL for imaging purposes. We
investigate the solution of Maxwell's equations for the electromagnetic (EM)
field, obtained on the background of a static gravitational field of the Sun.
We now treat the Sun as an extended body with a gravitational field that can be
described using an infinite series of gravitational multipole moments. Studying
the propagation of monochromatic EM waves in this extended solar gravitational
field, we develop a wave-optical treatment of the SGL that allows us to study
the caustics formed in an image plane in the SGL's strong interference region.
We investigate the EM field in several important regions, namely i) the area in
the inner part of the caustic and close to the optical axis, ii) the region
outside the caustic, and iii) the region in the immediate vicinity of the
caustic, especially around its cusps and folds. We show that in the first two
regions the physical behavior of the EM field may be understood using the
method of stationary phase. However, in the immediate vicinity of the caustic
the method of stationary phase is inadequate and a wave-optical treatment is
necessary. Relying on the angular eikonal method, we develop a new approach to
describe the EM field accurately in all regions, including the immediate
vicinity of the caustics and especially near the cusps and folds. The method
allows us to investigate the EM field in this important region, which is
characterized by rapidly oscillating behavior. Our results are new and can be
used to describe gravitational lensing by realistic astrophysical objects, such
as stars, spiral and elliptical galaxies.


Abstract:
  In two spatial dimensions, there are very few global existence results for
the Kuramoto-Sivashinsky equation. The majority of the few results in the
literature are strongly anisotropic, i.e. are results of thin-domain type. In
the spatially periodic case, the dynamics of the Kuramoto-Sivashinsky equation
are in part governed by the size of the domain, as this determines how many
linearly growing Fourier modes are present. The strongly anisotropic results
allow linearly growing Fourier modes in only one of the spatial directions. We
provide here the first proof of global solutions for the two-dimensional
Kuramoto-Sivashinsky equation with a linearly growing mode in both spatial
directions. We develop a new method to this end, categorizing wavenumbers as
low (linearly growing modes), intermediate (linearly decaying modes which serve
as energy sinks for the low modes), and high (strongly linearly decaying
modes). The low and intermediate modes are controlled by means of a Lyapunov
function, while the high modes are controlled with operator estimates in
function spaces based on the Wiener algebra.


Abstract:
  Improving the predictive capability of molecular properties in ab initio
simulations is essential for advanced material discovery. Despite recent
progress making use of machine learning, utilizing deep neural networks to
improve quantum chemistry modelling remains severely limited by the scarcity
and heterogeneity of appropriate experimental data. Here we show how training a
neural network to replace the exchange-correlation functional within a
fully-differentiable three-dimensional Kohn-Sham density functional theory
(DFT) framework can greatly improve simulation accuracy. Using only eight
experimental data points on diatomic molecules, our trained
exchange-correlation networks enable improved prediction accuracy of
atomization energies across a collection of 104 molecules containing new bonds
and atoms that are not present in the training dataset.


Abstract:
  Background and Objective:Computer-aided diagnosis (CAD) systems promote
diagnosis effectiveness and alleviate pressure of radiologists. A CAD system
for lung cancer diagnosis includes nodule candidate detection and nodule
malignancy evaluation. Recently, deep learning-based pulmonary nodule detection
has reached satisfactory performance ready for clinical application. However,
deep learning-based nodule malignancy evaluation depends on heuristic inference
from low-dose computed tomography volume to malignant probability, which lacks
clinical cognition. Methods:In this paper, we propose a joint radiology
analysis and malignancy evaluation network (R2MNet) to evaluate the pulmonary
nodule malignancy via radiology characteristics analysis. Radiological features
are extracted as channel descriptor to highlight specific regions of the input
volume that are critical for nodule malignancy evaluation. In addition, for
model explanations, we propose channel-dependent activation mapping to
visualize the features and shed light on the decision process of deep neural
network. Results:Experimental results on the LIDC-IDRI dataset demonstrate that
the proposed method achieved area under curve of 96.27% on nodule radiology
analysis and AUC of 97.52% on nodule malignancy evaluation. In addition,
explanations of CDAM features proved that the shape and density of nodule
regions were two critical factors that influence a nodule to be inferred as
malignant, which conforms with the diagnosis cognition of experienced
radiologists. Conclusion:Incorporating radiology analysis with nodule malignant
evaluation, the network inference process conforms to the diagnostic procedure
of radiologists and increases the confidence of evaluation results. Besides,
model interpretation with CDAM features shed light on the regions which DNNs
focus on when they estimate nodule malignancy probabilities.


Abstract:
  We demonstrate that crystal defects can act as a probe of intrinsic
non-Hermitian topology. In particular, in point-gapped systems with periodic
boundary conditions, a pair of dislocations may induce a non-Hermitian skin
effect, where an extensive number of Hamiltonian eigenstates localize at only
one of the two dislocations. An example of such a phase are two-dimensional
systems exhibiting weak non-Hermitian topology, which are adiabatically related
to a decoupled stack of Hatano-Nelson chains. Moreover, we show that strong
two-dimensional point-gap topology may also result in a dislocation response,
even when there is no skin effect present with open boundary conditions. For
both cases, we directly relate their bulk topology to a stable dislocation
non-Hermitian skin effect. Finally, and in stark contrast to the Hermitian
case, we find that gapless non-Hermitian systems hosting bulk exceptional
points also give rise to a well-localized dislocation response.


Abstract:
  After discussing the limitations inherent to all set-theoretic reflection
principles akin to those studied by A. L\'evy et. al. in the 1960's, we
introduce new principles of reflection based on the general notion of
\emph{Structural Reflection} and argue that they are in strong agreement with
the conception of reflection implicit in Cantor's original idea of the
unknowability of the \emph{Absolute}, which was subsequently developed in the
works of Ackermann, L\'evy, G\"odel, Reinhardt, and others. We then present a
comprehensive survey of results showing that different forms of the new
principles of Structural Reflection are equivalent to well-known large
cardinals axioms covering all regions of the large-cardinal hierarchy, thereby
justifying the naturalness of the latter.


Abstract:
  In this paper, we investigate the task of hallucinating an authentic
high-resolution (HR) human face from multiple low-resolution (LR) video
snapshots. We propose a pure transformer-based model, dubbed VidFace, to fully
exploit the full-range spatio-temporal information and facial structure cues
among multiple thumbnails. Specifically, VidFace handles multiple snapshots all
at once and harnesses the spatial and temporal information integrally to
explore face alignments across all the frames, thus avoiding accumulating
alignment errors. Moreover, we design a recurrent position embedding module to
equip our transformer with facial priors, which not only effectively
regularises the alignment mechanism but also supplants notorious pre-training.
Finally, we curate a new large-scale video face hallucination dataset from the
public Voxceleb2 benchmark, which challenges prior arts on tackling unaligned
and tiny face snapshots. To the best of our knowledge, we are the first attempt
to develop a unified transformer-based solver tailored for video-based face
hallucination. Extensive experiments on public video face benchmarks show that
the proposed method significantly outperforms the state of the arts.


Abstract:
  To support faster and more efficient networks, mobile operators and service
providers are bringing 5G millimeter wave (mmWave) networks indoors. However,
due to their high directionality, mmWave links are extremely vulnerable to
blockage by walls and human mobility. To address these challenges, we exploit
advances in artificially engineered metamaterials, introducing a wall-mounted
smart metasurface, called mmWall, that enables a fast mmWave beam relay through
the wall and redirects the beam power to another direction when a human body
blocks a line-of-sight path. Moreover, our mmWall supports multiple users and
fast beam alignment by generating multi-armed beams. We sketch the design of a
real-time system by considering (1) how to design a programmable,
metamaterial-based surface that refracts the incoming signal to one or more
arbitrary directions, and (2) how to split an incoming mmWave beam into
multiple outgoing beams and arbitrarily control the beam energy between these
beams. Preliminary results show the mmWall metasurface steers the outgoing beam
in a full 360-degrees, with an 89.8% single-beam efficiency and 74.5%
double-beam efficiency.


Abstract:
  Given a set P of n points in the plane, the unit-disk graph G_{r}(P) with
respect to a parameter r is an undirected graph whose vertex set is P such that
an edge connects two points p, q \in P if the Euclidean distance between p and
q is at most r (the weight of the edge is 1 in the unweighted case and is the
distance between p and q in the weighted case). Given a value \lambda>0 and two
points s and t of P, we consider the following reverse shortest path problem:
computing the smallest r such that the shortest path length between s and t in
G_r(P) is at most \lambda. In this paper, we present an algorithm of O(\lfloor
\lambda \rfloor \cdot n \log n) time and another algorithm of O(n^{5/4}
\log^{7/4} n) time for the unweighted case, as well as an O(n^{5/4} \log^{5/2}
n) time algorithm for the weighted case. We also consider the L_1 version of
the problem where the distance of two points is measured by the L_1 metric; we
solve the problem in O(n \log^3 n) time for both the unweighted and weighted
cases.