Abstract:
  We applied Deep Learning algorithm known as Generative Adversarial Networks
(GANs) to perform solar image-to-image translation. That is, from Solar
Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager(HMI) line of sight
magnetogram images to SDO/Atmospheric Imaging Assembly(AIA) 0304-{\AA} images.
The Ultraviolet(UV)/Extreme Ultraviolet(EUV) observations like the
SDO/AIA0304-{\AA} images were only made available to scientists in the late
1990s even though the magenetic field observations like the SDO/HMI have been
available since the 1970s. Therefore by leveraging Deep Learning algorithms
like GANs we can give scientists access to complete datasets for analysis. For
generating high resolution solar images we use the Pix2PixHD and Pix2Pix
algorithms. The Pix2PixHD algorithm was specifically designed for high
resolution image generation tasks, and the Pix2Pix algorithm is by far the most
widely used image to image translation algorithm. For training and testing we
used the data for the year 2012, 2013 and 2014. The results show that our deep
learning models are capable of generating high resolution(1024 x 1024 pixels)
AIA0304 images from HMI magnetograms. Specifically, the pixel-to-pixel Pearson
Correlation Coefficient of the images generated by Pix2PixHD and original
images is as high as 0.99. The number is 0.962 if Pix2Pix is used to generate
images. The results we get for our Pix2PixHD model is better than the results
obtained by previous works done by others to generate AIA0304 images. Thus, we
can use these models to generate AIA0304 images when the AIA0304 data is not
available which can be used for understanding space weather and giving
researchers the capability to predict solar events such as Solar Flares and
Coronal Mass Ejections. As far as we know, our work is the first attempt to
leverage Pix2PixHD algorithm for SDO/HMI to SDO/AIA0304 image-to-image
translation.


Abstract:
  Digital memcomputing machines (DMMs) are a novel, non-Turing class of
machines designed to solve combinatorial optimization problems. They can be
physically realized with continuous-time, non-quantum dynamical systems with
memory (time non-locality), whose ordinary differential equations (ODEs) can be
numerically integrated on modern computers. Solutions of many hard problems
have been reported by numerically integrating the ODEs of DMMs, showing
substantial advantages over state-of-the-art solvers. To investigate the
reasons behind the robustness and effectiveness of this method, we employ three
explicit integration schemes (forward Euler, trapezoid and Runge-Kutta 4th
order) with a constant time step, to solve 3-SAT instances with planted
solutions. We show that, (i) even if most of the trajectories in the phase
space are destroyed by numerical noise, the solution can still be achieved;
(ii) the forward Euler method, although having the largest numerical error,
solves the instances in the least amount of function evaluations; and (iii)
when increasing the integration time step, the system undergoes a
"solvable-unsolvable transition" at a critical threshold, which needs to decay
at most as a power law with the problem size, to control the numerical errors.
To explain these results, we model the dynamical behavior of DMMs as directed
percolation of the state trajectory in the phase space in the presence of
noise. This viewpoint clarifies the reasons behind their numerical robustness
and provides an analytical understanding of the unsolvable-solvable transition.
These results land further support to the usefulness of DMMs in the solution of
hard combinatorial optimization problems.


Abstract:
  The explosive growth of image data facilitates the fast development of image
processing and computer vision methods for emerging visual applications,
meanwhile introducing novel distortions to the processed images. This poses a
grand challenge to existing blind image quality assessment (BIQA) models,
failing to continually adapt to such subpopulation shift. Recent work suggests
training BIQA methods on the combination of all available human-rated IQA
datasets. However, this type of approach is not scalable to a large number of
datasets, and is cumbersome to incorporate a newly created dataset as well. In
this paper, we formulate continual learning for BIQA, where a model learns
continually from a stream of IQA datasets, building on what was learned from
previously seen data. We first identify five desiderata in the new setting with
a measure to quantify the plasticity-stability trade-off. We then propose a
simple yet effective method for learning BIQA models continually. Specifically,
based on a shared backbone network, we add a prediction head for a new dataset,
and enforce a regularizer to allow all prediction heads to evolve with new data
while being resistant to catastrophic forgetting of old data. We compute the
quality score by an adaptive weighted summation of estimates from all
prediction heads. Extensive experiments demonstrate the promise of the proposed
continual learning method in comparison to standard training techniques for
BIQA.


Abstract:
  Self-supervised learning (SSL), which can automatically generate ground-truth
samples from raw data, holds vast potential to improve recommender systems.
Most existing SSL-based methods perturb the raw data graph with uniform
node/edge dropout to generate new data views and then conduct the
self-discrimination based contrastive learning over different views to learn
generalizable representations. Under this scheme, only a bijective mapping is
built between nodes in two different views, which means that the
self-supervision signals from other nodes are being neglected. Due to the
widely observed homophily in recommender systems, we argue that the supervisory
signals from other nodes are also highly likely to benefit the representation
learning for recommendation. To capture these signals, a general socially-aware
SSL framework that integrates tri-training is proposed in this paper.
Technically, our framework first augments the user data views with the user
social information. And then under the regime of tri-training for multi-view
encoding, the framework builds three graph encoders (one for recommendation)
upon the augmented views and iteratively improves each encoder with
self-supervision signals from other users, generated by the other two encoders.
Since the tri-training operates on the augmented views of the same data sources
for self-supervision signals, we name it self-supervised tri-training.
Extensive experiments on multiple real-world datasets consistently validate the
effectiveness of the self-supervised tri-training framework for improving
recommendation. The code is released at https://github.com/Coder-Yu/QRec.


Abstract:
  Annotation is an effective reading strategy people often undertake while
interacting with digital text. It involves highlighting pieces of text and
making notes about them. Annotating while reading in a desktop environment is
considered trivial but, in a mobile setting where people read while
hand-holding devices, the task of highlighting and typing notes on a mobile
display is challenging. In this paper, we introduce GAVIN, a gaze-assisted
voice note-taking application, which enables readers to seamlessly take voice
notes on digital documents by implicitly anchoring them to text passages. We
first conducted a contextual enquiry focusing on participants' note-taking
practices on digital documents. Using these findings, we propose a method which
leverages eye-tracking and machine learning techniques to annotate voice notes
with reference text passages. To evaluate our approach, we recruited 32
participants performing voice note-taking. Following, we trained a classifier
on the data collected to predict text passage where participants made voice
notes. Lastly, we employed the classifier to built GAVIN and conducted a user
study to demonstrate the feasibility of the system. This research demonstrates
the feasibility of using gaze as a resource for implicit anchoring of voice
notes, enabling the design of systems that allow users to record voice notes
with minimal effort and high accuracy.


Abstract:
  This paper studies the end-to-end construction of an NLP Knowledge Graph (KG)
from scientific papers. We focus on extracting four types of relations:
evaluatedOn between tasks and datasets, evaluatedBy between tasks and
evaluation metrics, as well as coreferent and related relations between the
same type of entities. For instance, F1-score is coreferent with F-measure. We
introduce novel methods for each of these relation types and apply our final
framework (SciNLP-KG) to 30,000 NLP papers from ACL Anthology to build a
large-scale KG, which can facilitate automatically constructing scientific
leaderboards for the NLP community. The results of our experiments indicate
that the resulting KG contains high-quality information.


Abstract:
  3D deep learning has been increasingly more popular for a variety of tasks
including many safety-critical applications. However, recently several works
raise the security issues of 3D deep models. Although most of them consider
adversarial attacks, we identify that backdoor attack is indeed a more serious
threat to 3D deep learning systems but remains unexplored. We present the
backdoor attacks in 3D point cloud with a unified framework that exploits the
unique properties of 3D data and networks. In particular, we design two attack
approaches on point cloud: the poison-label backdoor attack (PointPBA) and the
clean-label backdoor attack (PointCBA). The first one is straightforward and
effective in practice, while the latter is more sophisticated assuming there
are certain data inspections. The attack algorithms are mainly motivated and
developed by 1) the recent discovery of 3D adversarial samples suggesting the
vulnerability of deep models under spatial transformation; 2) the proposed
feature disentanglement technique that manipulates the feature of the data
through optimization methods and its potential to embed a new task. Extensive
experiments show the efficacy of the PointPBA with over 95% success rate across
various 3D datasets and models, and the more stealthy PointCBA with around 50%
success rate. Our proposed backdoor attack in 3D point cloud is expected to
perform as a baseline for improving the robustness of 3D deep models.


Abstract:
  Equipping robots with the ability to infer human intent is a vital
precondition for effective collaboration. Most computational approaches towards
this objective employ probabilistic reasoning to recover a distribution of
"intent" conditioned on the robot's perceived sensory state. However, these
approaches typically assume task-specific notions of human intent (e.g.
labelled goals) are known a priori. To overcome this constraint, we propose the
Disentangled Sequence Clustering Variational Autoencoder (DiSCVAE), a
clustering framework that can be used to learn such a distribution of intent in
an unsupervised manner. The DiSCVAE leverages recent advances in unsupervised
learning to derive a disentangled latent representation of sequential data,
separating time-varying local features from time-invariant global aspects.
Though unlike previous frameworks for disentanglement, the proposed variant
also infers a discrete variable to form a latent mixture model and enable
clustering of global sequence concepts, e.g. intentions from observed human
behaviour. To evaluate the DiSCVAE, we first validate its capacity to discover
classes from unlabelled sequences using video datasets of bouncing digits and
2D animations. We then report results from a real-world human-robot interaction
experiment conducted on a robotic wheelchair. Our findings glean insights into
how the inferred discrete variable coincides with human intent and thus serves
to improve assistance in collaborative settings, such as shared control.


Abstract:
  Bell inequalities are relevant for many problems in quantum information
science, but finding them for many particles is computationally hard. Recently,
a computationally feasible method called cone-projection technique has been
developed to find all optimal Bell inequalities under some constraints, which
may be given by some symmetry or other linear conditions. In this paper we
extend this work in several directions. We use the method to generalize the
I4422 inequality to three particles and a so-called GYNI inequality to four
particles. Additionally, we find Bell inequalities for three particles that
generalize the I3322 inequality and the CHSH inequality at the same time. We
discuss the obtained inequalities in some detail and characterize their
violation in quantum mechanics.


Abstract:
  We outline two dishonest strategies that can be cheaply executed on the
Ethereum 2.0 beacon chain, even by validators holding less than one-third of
the total stake: malicious chain reorganizations ("reorgs") and finality
delays. In a malicious reorg, an attacker withholds their blocks and
attestations before releasing them at an opportune time in order to force a
chain reorganization, which they can take advantage of by double-spending or
front-running transactions. To execute a finality delay an attacker uses
delayed block releases and withholding of attestations to increase the mean and
variance of the time it takes blocks to become finalized. This impacts the
efficiency and predictability of the system. We provide a probabilistic and
cost analysis for each of these attacks, considering a validator with 30% of
the total stake.


Abstract:
  Mechanical instabilities can be exploited to design innovative structures,
able to change their shape in the presence of external stimuli. In this work,
we derive a mathematical model of an elastic beam subjected to an axial force
and constrained to smoothly slide along a rigid support, where the distance
between the rod midline and the constraint is fixed and finite. Using both
theoretical and computational techniques, we characterize the bifurcations of
such a mechanical system, in which the axial force and the natural curvature of
the beam are used as control parameters. We show that, in the presence of a
straight support, the rod can deform into shapes exhibiting helices and
perversions, namely transition zones connecting together two helices with
opposite chirality. The mathematical predictions of the proposed model are also
compared with some experiments, showing a good quantitative agreement. In
particular, we find that the buckled configurations may exhibit multiple
perversions and we propose a possible explanation for this phenomenon based on
the energy landscape of the mechanical system.


Abstract:
  In supervised learning, obtaining a large set of fully-labeled training data
is expensive. We show that we do not always need full label information on
every single training example to train a competent classifier. Specifically,
inspired by the principle of sufficiency in statistics, we present a statistic
(a summary) of the fully-labeled training set that captures almost all the
relevant information for classification but at the same time is easier to
obtain directly. We call this statistic "sufficiently-labeled data" and prove
its sufficiency and efficiency for finding the optimal hidden representations,
on which competent classifier heads can be trained using as few as a single
randomly-chosen fully-labeled example per class. Sufficiently-labeled data can
be obtained from annotators directly without collecting the fully-labeled data
first. And we prove that it is easier to directly obtain sufficiently-labeled
data than obtaining fully-labeled data. Furthermore, sufficiently-labeled data
naturally preserves user privacy by storing relative, instead of absolute,
information. Extensive experimental results are provided to support our theory.


Abstract:
  We prove that a random group, in Gromov's density model with $d<1/16$,
satisfies a universal sentence $\sigma$ (in the language of groups) if and only
if $\sigma$ is true in a nonabelian free group.


Abstract:
  PKS\,1351$-$018 at a redshift of $z=3.71$ is one of the most luminous, steady
synchrotron sources with a luminosity $> 10^{47}$\,erg~s$^{-1}$. The
synchrotron luminosity does not seem to vary by more than $\sim 25\%$ over 35
years. In order to appreciate this remarkable behavior, if it were at $z=0.5$,
it would have a flux density at 15 GHz in a range of $110 - 137$\,Jy over 11
yrs. In spite of this steady behavior, two strong $\gamma$-ray flares $\lesssim
10^{49}$\,erg~s$^{-1}$ were detected in 2011 and 2016. There is blazar-like
behavior coexisting with the steady behavior. This study is aimed at
elucidating the dual nature of this source. We find that the radio source is
extremely compact with a bright core and a steep spectrum secondary component,
12\,mas away, that appears to be constant in position and flux density in six
epochs from 1995 to 2018. We estimate that a jet with a time averaged power of
$(5.2 \pm 3.2) \times 10^{45}$\,erg~s$^{-1}$ terminates in this lobe that is
advancing $\gtrsim 0.9 c$ at a deprojected distance of $1-3$\,kpc from the
central engine. This is the rare case of a young ($\sim 6000$\,yr), very
powerful radio source that is viewed a few degrees from the jet axis. We find
evidence of a high velocity (4000\,km~s$^{-1}$), high ionization wind emanating
form a luminous quasar. The young radio jet appears to experience modest
bending as it navigates through the intense quasar environment.


Abstract:
  Inverse reinforcement learning attempts to reconstruct the reward function in
a Markov decision problem, using observations of agent actions. As already
observed in Russell [1998] the problem is ill-posed, and the reward function is
not identifiable, even under the presence of perfect information about optimal
behavior. We provide a resolution to this non-identifiability for problems with
entropy regularization. For a given environment, we fully characterize the
reward functions leading to a given policy and demonstrate that, given
demonstrations of actions for the same reward under two distinct discount
factors, or under sufficiently different environments, the unobserved reward
can be recovered up to a constant. We also give general necessary and
sufficient conditions for reconstruction of time-homogeneous rewards on finite
horizons, and for action-independent rewards, generalizing recent results of
Kim et al. [2021] and Fu et al. [2018].


Abstract:
  This work presents a microscale approach for simulating the dielectrophoresis
(DEP) assembly of polarizable particles under an external electric field. The
model is shown to capture interesting dynamical and topological features, such
as the formation of chains of particles and their incipient aggregation into
hierarchical structures. A quantitative characterization in terms of the number
and size of these structures is also discussed. This computational model could
represent a viable numerical tool to study the mechanical properties of
particle-based hierarchical materials and suggest new strategies for enhancing
their design and manufacture.


Abstract:
  While reinforcement learning algorithms provide automated acquisition of
optimal policies, practical application of such methods requires a number of
design decisions, such as manually designing reward functions that not only
define the task, but also provide sufficient shaping to accomplish it. In this
paper, we discuss a new perspective on reinforcement learning, recasting it as
the problem of inferring actions that achieve desired outcomes, rather than a
problem of maximizing rewards. To solve the resulting outcome-directed
inference problem, we establish a novel variational inference formulation that
allows us to derive a well-shaped reward function which can be learned directly
from environment interactions. From the corresponding variational objective, we
also derive a new probabilistic Bellman backup operator reminiscent of the
standard Bellman backup operator and use it to develop an off-policy algorithm
to solve goal-directed tasks. We empirically demonstrate that this method
eliminates the need to design reward functions and leads to effective
goal-directed behaviors.


Abstract:
  The recent star formation histories (SFHs) of post-starburst galaxies have
been determined almost exclusively from detailed modeling of their composite
star light. This has provided important but limited information on the number,
strength, and duration of bursts of star formation. In this work, we present a
direct and independent measure of the recent SFH of S12 (plate-mjd-fiber for
SDSS 623-52051-207; designated EAS12 in Smercina et al. 2018) from its star
cluster population. We detect clusters from high resolution, $UBR$ optical
observations from HST, and compare their luminosities and colors with stellar
population models to estimate the ages and masses of the clusters. No clusters
younger than $\sim$70 Myr are found, indicating star formation shut off at this
time. Clusters formed $\sim$120 Myr ago reach masses up to $\sim
\mbox{few}\times10^7~M_{\odot}$, several times higher than similar age
counterparts formed in actively merging galaxies like the Antennae and NGC
3256. We develop a new calibration based on known properties for 8 nearby
galaxies to estimate the star formation rate (SFR) of a galaxy from the mass of
the most massive cluster, $M_{\rm max}$. The cluster population indicates that
S12 experienced an extremely intense but short-lived burst $\sim$120 Myr ago,
with an estimated peak of $500^{+500}_{-250}~M_{\odot}~\mbox{yr}^{-1}$ and
duration of $50\pm25$ Myr, one of the highest SFRs estimated for any galaxy in
the nearby universe. Prior to the recent, intense burst, S12 was forming stars
at a moderate rate of $\sim 3{-}5~M_{\odot}~\mbox{yr}^{-1}$, typical of spiral
galaxies. However, the system also experienced an earlier burst approximately
$1{-}3$ Gyr ago. While fairly uncertain, we estimate that the SFR during this
earlier burst was $\sim20{-}30~M_{\odot}~\mbox{yr}^{-1}$, similar to the
current SFR in the Antennae and NGC 3256.


Abstract:
  We present an experimentally practical method to reveal
Einstein-Podolsky-Rosen steering in non-Gaussian spin states by exploiting a
connection to quantum metrology. Our criterion is based on the quantum Fisher
information, and uses bounds derived from generalized spin-squeezing parameters
that involve measurements of higher-order moments. This leads us to introduce
the concept of conditional spin-squeezing parameters, which quantify the
metrological advantage provided by conditional states, as well as detect the
presence of an EPR paradox.


Abstract:
  Abnormality detection is essential to the performance of safety-critical and
latency-constrained systems. However, as systems are becoming increasingly
complicated with a large quantity of heterogeneous data, conventional
statistical change point detection methods are becoming less effective and
efficient. Although Deep Learning (DL) and Deep Neural Networks (DNNs) are
increasingly employed to handle heterogeneous data, they still lack theoretic
assurable performance and explainability. This paper integrates zero-bias DNN
and Quickest Event Detection algorithms to provide a holistic framework for
quick and reliable detection of both abnormalities and time-dependent abnormal
events in the Internet of Things (IoT). We first use the zero-bias dense layer
to increase the explainability of DNN. We provide a solution to convert
zero-bias DNN classifiers into performance assured binary abnormality
detectors. Using the converted abnormality detector, we then present a
sequential quickest detection scheme that provides the theoretically assured
lowest abnormal event detection delay under false alarm constraints. Finally,
we demonstrate the effectiveness of the framework using both massive signal
records from real-world aviation communication systems and simulated data. Code
and data of our work is available at
\url{https://github.com/pcwhy/AbnormalityDetectionInZbDNN}


Abstract:
  We revisit the nonlinear stability of the critical invasion front in the
Ginzburg-Landau equation. Our main result shows that the amplitude of localized
perturbations decays with rate $t^{-3/2}$, while the phase decays diffusively.
We thereby refine earlier work of Bricmont and Kupiainen as well as Eckmann and
Wayne, who separately established nonlinear stability but with slower decay
rates. On a technical level, we rely on sharp linear estimates obtained through
analysis of the resolvent near the essential spectrum via a far-field/core
decomposition which is well suited to accurately describing the dynamics of
separate neutrally stable modes arising from far-field behavior on the left and
right.


Abstract:
  In this article, we present a semantics-level adaption of the Optional
Stopping Theorem, sketch an expected-cost analysis as its application, and
survey different variants of the Optional Stopping Theorem that have been used
in static analysis of probabilistic programs.


Abstract:
  Neural networks are very popular in many areas, but great computing
complexity makes it hard to run neural networks on devices with limited
resources. To address this problem, quantization methods are used to reduce
model size and computation cost, making it possible to use neural networks on
embedded platforms or mobile devices.
  In this paper, an integer-only-quantization scheme is introduced. This scheme
uses one layer that combines shift-based batch normalization and uniform
quantization to implement 4-bit integer-only inference. Without big integer
multiplication(which is used in previous integer-only-quantization methods),
this scheme can achieve good power and latency efficiency, and is especially
suitable to be deployed on co-designed hardware platforms. Tests have proved
that this scheme works very well for easy tasks. And for tough tasks,
performance loss can be tolerated for its inference efficiency. Our work is
available on github: https://github.com/hguq/IntegerNet.


Abstract:
  A search for the charged lepton flavor violating decay $J/\psi\to
e^{\pm}\tau^{\mp}$ with $\tau^{\mp} \to \pi^{\mp}\pi^0\nu_{\tau}$ is performed
with about $10$ billion $J/ \psi$ events collected with the BESIII detector at
the BEPCII. No significant signal is observed, and an upper limit is set on the
branching fraction $\mathcal{B}(J/\psi\to e^{\pm}\tau^{\mp})<7.5\times10^{-8}$
at the 90$\%$ confidence level. This improves the previously published limit by
two orders of magnitude.


Abstract:
  Batch normalization (BN) is a key facilitator and considered essential for
state-of-the-art binary neural networks (BNN). However, the BN layer is costly
to calculate and is typically implemented with non-binary parameters, leaving a
hurdle for the efficient implementation of BNN training. It also introduces
undesirable dependence between samples within each batch. Inspired by the
latest advance on Batch Normalization Free (BN-Free) training, we extend their
framework to training BNNs, and for the first time demonstrate that BNs can be
completed removed from BNN training and inference regimes. By plugging in and
customizing techniques including adaptive gradient clipping, scale weight
standardization, and specialized bottleneck block, a BN-free BNN is capable of
maintaining competitive accuracy compared to its BN-based counterpart.
Extensive experiments validate the effectiveness of our proposal across diverse
BNN backbones and datasets. For example, after removing BNs from the
state-of-the-art ReActNets, it can still be trained with our proposed
methodology to achieve 92.08%, 68.34%, and 68.0% accuracy on CIFAR-10,
CIFAR-100, and ImageNet respectively, with marginal performance drop
(0.23%~0.44% on CIFAR and 1.40% on ImageNet). Codes and pre-trained models are
available at: https://github.com/VITA-Group/BNN_NoBN.


Abstract:
  Electronic nematicity is often found in unconventional superconductors,
suggesting its relevance for electronic pairing. In the strongly hole-doped
iron-based superconductors, the symmetry channel and strength of the nematic
fluctuations, as well as the possible presence of long-range nematic order,
remain controversial. Here, we address these questions using transport
measurements under elastic strain. By decomposing the strain response into the
appropriate symmetry channels, we demonstrate the emergence of a giant in-plane
symmetric contribution, associated with the growth of both strong electronic
correlations and the sensitivity of these correlations to strain. We find
weakened remnants of the nematic fluctuations that are present at optimal
doping, but no change in the symmetry channel of nematic fluctuations with hole
doping. Furthermore, we find no evidence for a nematic-ordered state in the
AFe$_2$As$_2$(A = K, Rb, Cs) superconductors. These results revise the current
understanding of nematicity in hole-doped iron-based superconductors.


Abstract:
  The scientific and technological exploration of artificially designed
three-dimensional magnetic nanostructures opens the path to exciting novel
physical phenomena, originating from the increased complexity in spin textures,
topology, and frustration in three dimensions. Theory predicts that the
equilibrium magnetic ground state of two-dimensional systems which reflects the
competition between symmetric (Heisenberg) and antisymmetric
(Dzyaloshinskii-Moriya interaction (DMI)) exchange interaction is significantly
modified on curved surfaces when the radius of local curvature becomes
comparable to fundamental magnetic length scales. Here, we present an
experimental study of the spin texture in an 8 nm thin magnetic multilayer with
growth-induced in-plane anisotropy and DMI deposited onto the curved surface of
a 1.8 {\mu}m long non-magnetic carbon nanowire with a 67 nm radius. Using
magnetic soft x-ray tomography the three-dimensional spin configuration in this
nanotube was retrieved with about 30nm spatial resolution. The transition
between two vortex configurations on the two ends of the nanotube with opposite
circulation occurs through a domain wall that is aligned at an inclined angle
relative to the wire axis. Three-dimensional micromagnetic simulations support
the experimental observations and represent a visualization of the
curvature-mediated DMI. They also allow a quantitative estimate of the DMI
value for the magnetic multilayered nanotube.


Abstract:
  We present a model compound with a spin-1/2 spatially anisotropic frustrated
square lattice, in which three antiferromagnetic interactions and one
ferromagnetic interaction are competing. We observe an unconventional gradual
increase in the low-temperature magnetization curve reminiscent of the quantum
critical behavior between gapped and gapless phases. In addition, the specific
heat and electron spin resonance signals indicate one-dimensional
characteristics. These results demonstrate quantum critical behavior associated
with one dimensionalization caused by frustrated interactions in the spin-1/2
spatially anisotropic square lattice.


Abstract:
  We propose a fast algorithm for the probabilistic solution of boundary value
problems (BVPs), which are ordinary differential equations subject to boundary
conditions. In contrast to previous work, we introduce a Gauss--Markov prior
and tailor it specifically to BVPs, which allows computing a posterior
distribution over the solution in linear time, at a quality and cost comparable
to that of well-established, non-probabilistic methods. Our model further
delivers uncertainty quantification, mesh refinement, and hyperparameter
adaptation. We demonstrate how these practical considerations positively impact
the efficiency of the scheme. Altogether, this results in a practically usable
probabilistic BVP solver that is (in contrast to non-probabilistic algorithms)
natively compatible with other parts of the statistical modelling tool-chain.


Abstract:
  Hexagonal boron nitride (h-BN) has long been recognized as an ideal substrate
for electronic devices due to its dangling-bond-free surface, insulating nature
and thermal/chemical stability. Therefore, to analyse the lattice structure and
orientation of h-BN crystals becomes important. Here, the stacking order and
wrinkles of h-BN are investigated by transmission electron microscopy (TEM). It
is experimentally confirmed that the layers in the h-BN flakes are arranged in
the AA' stacking. The wrinkles in a form of threefold network throughout the
h-BN crystal are oriented along the armchair direction, and their formation
mechanism was further explored by molecular dynamics simulations. Our findings
provide a deep insight about the microstructure of h-BN and shed light on the
structural design/electronic modulations of two-dimensional crystals.


Abstract:
  The aim of this note is to prove that almost-minimizers of the perimeter are
Reifenberg flat, for a very weak notion of minimality. The main observation is
that smallness of the excess at some scale implies smallness of the excess at
all smaller scales.


Abstract:
  In this paper, we study a non-stationary stochastic bandit problem, which
generalizes the switching bandit problem. On top of the switching bandit
problem (\textbf{Case a}), we are interested in three concrete examples:
(\textbf{b}) the means of the arms are local polynomials, (\textbf{c}) the
means of the arms are locally smooth, and (\textbf{d}) the gaps of the arms
have a bounded number of inflexion points and where the highest arm mean cannot
vary too much in a short range. These three settings are very different, but
have in common the following: (i) the number of similarly-sized level sets of
the logarithm of the gaps can be controlled, and (ii) the highest mean has a
limited number of abrupt changes, and otherwise has limited variations. We
propose a single algorithm in this general setting, that in particular solves
in an efficient and unified way the four problems (a)-(d) mentioned.


Abstract:
  This paper deals with the first analysis of the neutron and gamma time series
measured with organic scintillators from plutonium samples by using information
measures. Fast neutron detection with organic scintillators has been widely
used for various nuclear applications and homeland security. One of the
significant attributes of special nuclear materials (SNM) is the high
multiplicity events in a short period of time. The time distributions of
neutron and gamma-rays events for the plutonium metal plates designed as fuel
plates for the Idaho National Laboratory (INL) Zero Power were measured with
the Fast Neutron Multiplicity Counter (FNMC) consisting of 8 EJ-309 liquid
scintillators and 8 stilbene detectors. Since the neutron correlated counts
within the coincidence window of 40 ns are related to 240Pu effective mass of
plutonium metal plates we investigated the randomness of the measured neutron
and gamma-rays events. To access such information, we resort to complexity
measures in the hope of being able to connect complexity values with the
reliability of detection. That was done through (i) application of Kolmogorov
complexity (KC) and its derivatives [Kolmogorov spectrum and its highest value
(KCM) and running complexity (RKC)] and (ii) establishing the breaking point
after which there exists a sharp drop in the running Kolmogorov complexity of
the neutron and gamma-rays time series. It was found that the complexity of all
the time series detected from the sample with 5, 9, 11, 13, and 15 plutonium
plates had the high almost identical values of KC while the sample with 3
plates had by one-third smaller KC values than all the others. These
calculations were supplemented by the Lypaunov exponents for a time series and
the NIST tests. The low KC values can be addressed to the different sources of
uncertainties in measuring procedure with the sample consisting of three
plates.


Abstract:
  Direct laser acceleration (DLA) of electrons in a plasma of near critical
electron density (NCD) and associated synchrotron-like radiation are discussed
for moderate relativistic laser intensity (the normalized laser amplitude $a_0$
$\leq$ 4.3) and ps-long pulse. This regime is typical for kJ PW-class laser
facilities designed for high energy density research. Currently, in experiments
at the PHELX laser it was demonstrated that interaction of 10$^{19}$ W/cm$^{2}$
sub-ps laser pulse with sub-mm long NCD plasma results in generation of
high-current well-directed super-ponderomotive electrons with effective
temperature that is 10$\times$ higher than the ponderomotive potential [O.
Rosmej et al., PPCF 62, 115024 (2020)]. Three-dimensional Particle-In-Cell
simulations provided a good agreement with the measured electron energy
distribution and were used in the current work to study synchrotron radiation
of the DLA accelerated electrons. The resulting x-ray spectrum with a critical
energy of 5 keV reveals an ultra-high photon number of 7$\times$10$^{11}$ in
the 1-30 keV photon energy range at the focused laser energy of 20 J. Numerical
simulations of a betatron x-ray phasecontrast imaging based on the DLA process
for the parameters of a PHELIX laser is presented. The results are of interest
for applications in high energy density (HED) experiments, which require a
picosecond x-ray pulse and a high photon flux.


Abstract:
  A fraction of the XMM-Newton/EPIC FOV is obscured by the dysfunctional (i.e.
bad) pixels. The fraction varies between different EPIC instruments in a given
observation. These complications affect the analysis of extended X-ray sources
observed with XMM-Newton/EPIC and the consequent scientific interpretation of
the results. For example, the accuracy of the widely used cosmological probe of
the gas mass of clusters of galaxies depends on the accuracy of the procedure
of removing the obscuration effect from the measured flux. The Science Analysis
Software (SAS) includes an option for recovering the lost fraction of the flux
measured by a primary instrument by utilising a supplementary image of the same
source. The correction may be accurate if the supplementary image is minimally
obscured at the locations of the bad pixels of the primary instrument. This can
be achieved e.g. by using the observation-based MOS2 image for correcting the
pn flux, or by using a synthetic model image. By utilising a sample of 27
galaxy cluster observations we evaluated the accuracy of the recovery method
based on observed images, as implemented in SAS 18.0.0. We found that the
accuracy of the recovered total flux in the 0.5-7.0 keV band in the full
geometric area within the central r = 6 arcmin is better than 0.1% on average
while in some individual cases the recovered flux may be uncertain by ~1%.


Abstract:
  Quantum ESPRESSO is an open-source distribution of computer codes for
quantum-mechanical materials modeling, based on density-functional theory,
pseudopotentials, and plane waves, and renowned for its performance on a wide
range of hardware architectures, from laptops to massively parallel computers,
as well as for the breadth of its applications. In this paper we present a
motivation and brief review of the ongoing effort to port Quantum ESPRESSO onto
heterogeneous architectures based on hardware accelerators, which will overcome
the energy constraints that are currently hindering the way towards exascale
computing.


Abstract:
  We present the general construction of the $U$-projector (the homomorphism of
the algebra into its field of $U$-invariants identical on the subalgebra of
$U$-invariants). It is shown how to apply $U$-projector to find the systems of
free generators of the fields of $U$-invariants for representations of
reductive groups.


Abstract:
  Social and information networks are gaining huge popularity recently due to
their various applications. Knowledge representation through graphs in the form
of nodes and edges should preserve as many characteristics of the original data
as possible. Some of the interesting and useful applications on these graphs
are graph classification, node classification, link prediction, etc. The Graph
Neural Networks have evolved over the last few years. Graph Neural Networks
(GNNs) are efficient ways to get insight into large and dynamic graph datasets
capturing relationships among billions of entities also known as knowledge
graphs.
  In this paper, we discuss the graph convolutional neural networks graph
autoencoders and spatio-temporal graph neural networks. The representations of
the graph in lower dimensions can be learned using these methods. The
representations in lower dimensions can be used further for downstream machine
learning tasks.


Abstract:
  We present a parameter estimation method for nonlinear mixed effect models
based on ordinary differential equations (NLME-ODEs). The method presented here
aims at regularizing the estimation problem in presence of model
misspecifications, practical identifiability issues and unknown initial
conditions. For doing so, we define our estimator as the minimizer of a cost
function which incorporates a possible gap between the assumed model at the
population level and the specific individual dynamic. The cost function
computation leads to formulate and solve optimal control problems at the
subject level. This control theory approach allows to bypass the need to know
or estimate initial conditions for each subject and it regularizes the
estimation problem in presence of poorly identifiable parameters. Comparing to
maximum likelihood, we show on simulation examples that our method improves
estimation accuracy in possibly partially observed systems with unknown initial
conditions or poorly identifiable parameters with or without model error. We
conclude this work with a real application on antibody concentration data after
vaccination against Ebola virus coming from phase 1 trials. We use the
estimated model discrepancy at the subject level to analyze the presence of
model misspecification.


Abstract:
  Natural language inference requires reasoning about contradictions,
negations, and their commonsense implications. Given a simple premise (e.g.,
"I'm mad at you"), humans can reason about the varying shades of contradictory
statements ranging from straightforward negations ("I'm not mad at you") to
commonsense contradictions ("I'm happy"). Moreover, these negated or
contradictory statements shift the commonsense implications of the original
premise in nontrivial ways. For example, while "I'm mad" implies "I'm unhappy
about something," negating the premise (i.e., "I'm not mad") does not
necessarily negate the corresponding commonsense implications.
  In this paper, we present the first comprehensive study focusing on
commonsense implications of negated statements and contradictions. We introduce
ANION1, a new commonsense knowledge graph with 624K if-then rules focusing on
negated and contradictory events. We then present joint generative and
discriminative inference models for this new resource, providing novel
empirical insights on how logical negations and commonsense contradictions
reshape the commonsense implications of their original premises.


Abstract:
  This paper investigates whether the framework of fractional quantum mechanics
can broaden our perspective of black hole thermodynamics. Concretely, we employ
a {\it space-fractional} derivative \cite{Rie} as our main tool. Moreover, we
restrict our analysis to the case of a Schwarzschild configuration. From a
subsequently modified Wheeler-DeWitt equation, we retrieve the corresponding
expressions for specific observables. Namely, the black hole mass spectrum,
$M$, its temperature $T$, and entropy, $S$. We find that these bear
consequential alterations conveyed through a fractional parameter, $\alpha$. In
particular, the standard results are recovered in the specific limit
$\alpha=2$. Furthermore, we elaborate how generalizations of the entropy-area
relation suggested by Tsallis and Cirto \cite{Tsallis} and Barrow \cite{Barrow}
acquire a complementary interpretation in terms of a fractional point of view.
A thorough discussion of our results is presented.


Abstract:
  Graph-based multi-view clustering aiming to obtain a partition of data across
multiple views, has received considerable attention in recent years. Although
great efforts have been made for graph-based multi-view clustering, it remains
a challenge to fuse characteristics from various views to learn a common
representation for clustering. In this paper, we propose a novel Consistent
Multiple Graph Embedding Clustering framework(CMGEC). Specifically, a multiple
graph auto-encoder(M-GAE) is designed to flexibly encode the complementary
information of multi-view data using a multi-graph attention fusion encoder. To
guide the learned common representation maintaining the similarity of the
neighboring characteristics in each view, a Multi-view Mutual Information
Maximization module(MMIM) is introduced. Furthermore, a graph fusion
network(GFN) is devised to explore the relationship among graphs from different
views and provide a common consensus graph needed in M-GAE. By jointly training
these models, the common latent representation can be obtained which encodes
more complementary information from multiple views and depicts data more
comprehensively. Experiments on three types of multi-view datasets demonstrate
CMGEC outperforms the state-of-the-art clustering methods.


Abstract:
  This paper describes an AI agent that plays the popular first-person-shooter
(FPS) video game `Counter-Strike; Global Offensive' (CSGO) from pixel input.
The agent, a deep neural network, matches the performance of the medium
difficulty built-in AI on the deathmatch game mode, whilst adopting a humanlike
play style. Unlike much prior work in games, no API is available for CSGO, so
algorithms must train and run in real-time. This limits the quantity of
on-policy data that can be generated, precluding many reinforcement learning
algorithms. Our solution uses behavioural cloning - training on a large noisy
dataset scraped from human play on online servers (4 million frames, comparable
in size to ImageNet), and a smaller dataset of high-quality expert
demonstrations. This scale is an order of magnitude larger than prior work on
imitation learning in FPS games.


Abstract:
  With superior optical properties, high flexibility in engineering its
material properties, and strong capability for large-scale on-chip integration,
graphene oxide (GO) is an attractive solution for on-chip integration of
two-dimensional (2D) materials to implement functional integrated photonic
devices capable of new features. Over the past decade, integrated GO photonics,
representing an innovative merging of integrated photonic devices and thin GO
films, has experienced significant development, leading to a surge in many
applications covering almost every field of optical sciences. This paper
reviews the recent advances in this emerging field, providing an overview of
the optical properties of GO as well as methods for the on-chip integration of
GO. The main achievements made in GO hybrid integrated photonic devices for
diverse applications are summarized. The open challenges as well as the
potential for future improvement are also discussed.


Abstract:
  Many self-propelled objects are large enough to exhibit inertial effects but
still suffer from environmental fluctuations. The corresponding basic equations
of motion are governed by active Langevin dynamics which involve inertia,
friction and stochastic noise for both the translational and orientational
degrees of freedom coupled via the self-propulsion along the particle
orientation. In this paper, we generalize the active Langevin model to
time-dependent parameters and explicitly discuss the effect of time-dependent
inertia for achiral and chiral particles. Realizations of this situation are
manifold ranging from minirockets which are self-propelled by burning their own
mass, dust particles in plasma which lose mass by evaporating material to
walkers with expiring activity. Here we present analytical solutions for
several dynamical correlation functions such as the mean-square displacement
and the orientational and velocity autocorrelation functions. If the parameters
exhibit a slow power-law in time, we obtain anomalous superdiffusion with a
non-trivial dynamical exponent. Finally we constitute the "Langevin rocket"
model by including orientational fluctuations in the traditional Tsiolkovsky
rocket equation. We calculate the mean reach of the Langevin rocket and discuss
different mass ejection strategies to maximize it. Our results can be tested in
experiments on macroscopic robotic or living particles or in self-propelled
mesoscopic objects moving in media of low viscosity such as complex plasma.


Abstract:
  Can a machine learn Machine Learning? This work trains a machine learning
model to solve machine learning problems from a University undergraduate level
course. We generate a new training set of questions and answers consisting of
course exercises, homework, and quiz questions from MIT's 6.036 Introduction to
Machine Learning course and train a machine learning model to answer these
questions. Our system demonstrates an overall accuracy of 96% for open-response
questions and 97% for multiple-choice questions, compared with MIT students'
average of 93%, achieving grade A performance in the course, all in real-time.
Questions cover all 12 topics taught in the course, excluding coding questions
or questions with images. Topics include: (i) basic machine learning
principles; (ii) perceptrons; (iii) feature extraction and selection; (iv)
logistic regression; (v) regression; (vi) neural networks; (vii) advanced
neural networks; (viii) convolutional neural networks; (ix) recurrent neural
networks; (x) state machines and MDPs; (xi) reinforcement learning; and (xii)
decision trees. Our system uses Transformer models within an encoder-decoder
architecture with graph and tree representations. An important aspect of our
approach is a data-augmentation scheme for generating new example problems. We
also train a machine learning model to generate problem hints. Thus, our system
automatically generates new questions across topics, answers both open-response
questions and multiple-choice questions, classifies problems, and generates
problem hints, pushing the envelope of AI for STEM education.


Abstract:
  The diffraction spectrum of an aperiodic solid is related to the group of
eigenvalues of the dynamical system associated with the solid. Those
eigenvalues with continuous eigenfunctions constitute the topological Bragg
spectrum. We relate the topological Bragg spectrum to the gap-labelling group,
which is the group of possible gap labels for the spectrum of a Schr\"odinger
operator describing the electronic motion in the solid.


Abstract:
  In this paper, we propose MixSpeech, a simple yet effective data augmentation
method based on mixup for automatic speech recognition (ASR). MixSpeech trains
an ASR model by taking a weighted combination of two different speech features
(e.g., mel-spectrograms or MFCC) as the input, and recognizing both text
sequences, where the two recognition losses use the same combination weight. We
apply MixSpeech on two popular end-to-end speech recognition models including
LAS (Listen, Attend and Spell) and Transformer, and conduct experiments on
several low-resource datasets including TIMIT, WSJ, and HKUST. Experimental
results show that MixSpeech achieves better accuracy than the baseline models
without data augmentation, and outperforms a strong data augmentation method
SpecAugment on these recognition tasks. Specifically, MixSpeech outperforms
SpecAugment with a relative PER improvement of 10.6$\%$ on TIMIT dataset, and
achieves a strong WER of 4.7$\%$ on WSJ dataset.


Abstract:
  This paper considers the balanced hypergraph partitioning problem, which asks
for partitioning the vertices into $k$ disjoint blocks of bounded size while
minimizing an objective function over the hyperedges. Here, we consider the
most commonly used connectivity metric. We describe our open source hypergraph
partitioner KaHyPar which is based on the successful multi-level approach --
driving it to the extreme of one level for (almost) every vertex. Using
carefully designed data structures and dynamic update techniques, this approach
offers a very good time-quality tradeoff. We present two preprocessing
techniques -- pin sparsification using locality sensitive hashing and community
detection based on the Louvain algorithm. The community structure is used to
guide the coarsening process that incrementally contracts vertices.
Portfolio-based partitioning of the contracted hypergraph already achieves good
initial solutions. While reversing the contractions, a combination of
highly-localized direct $k$-way local search and flow-based techniques that
take a more global view, refine the partition to achieve high quality.
Optionally, a memetic algorithm evolves a pool of solution candidates to obtain
even higher quality.
  We evaluate KaHyPar on a large set of instances from a wide range of
application domains. With respect to quality, KaHyPar outperforms all
previously considered systems that can handle large hypergraphs such as hMETIS,
PaToH, Mondriaan, or Zoltan. KaHyPar is also faster than most of these systems
except for PaToH which represents a different speed-quality tradeoff. The
results even extend to the special case of graph partitioning, where
specialized systems such as KaHIP should have an advantage.


Abstract:
  While classic control theory offers state of the art solutions in many
problem scenarios, it is often desired to improve beyond the structure of such
solutions and surpass their limitations. To this end, residual policy learning
(RPL) offers a formulation to improve existing controllers with reinforcement
learning (RL) by learning an additive "residual" to the output of a given
controller. However, the applicability of such an approach highly depends on
the structure of the controller. Often, internal feedback signals of the
controller limit an RL algorithm to adequately change the policy and, hence,
learn the task. We propose a new formulation that addresses these limitations
by also modifying the feedback signals to the controller with an RL policy and
show superior performance of our approach on a contact-rich peg-insertion task
under position and orientation uncertainty. In addition, we use a recent
Cartesian impedance control architecture as the control framework which can be
available to us as a black-box while assuming no knowledge about its
input/output structure, and show the difficulties of standard RPL. Furthermore,
we introduce an adaptive curriculum for the given task to gradually increase
the task difficulty in terms of position and orientation uncertainty. A video
showing the results can be found at https://youtu.be/SAZm_Krze7U .


Abstract:
  Video super-resolution (VSR) technology excels in reconstructing low-quality
video, avoiding unpleasant blur effect caused by interpolation-based
algorithms. However, vast computation complexity and memory occupation hampers
the edge of deplorability and the runtime inference in real-life applications,
especially for large-scale VSR task. This paper explores the possibility of
real-time VSR system and designs an efficient and generic VSR network, termed
EGVSR. The proposed EGVSR is based on spatio-temporal adversarial learning for
temporal coherence. In order to pursue faster VSR processing ability up to 4K
resolution, this paper tries to choose lightweight network structure and
efficient upsampling method to reduce the computation required by EGVSR network
under the guarantee of high visual quality. Besides, we implement the batch
normalization computation fusion, convolutional acceleration algorithm and
other neural network acceleration techniques on the actual hardware platform to
optimize the inference process of EGVSR network. Finally, our EGVSR achieves
the real-time processing capacity of 4K@29.61FPS. Compared with TecoGAN, the
most advanced VSR network at present, we achieve 85.04% reduction of
computation density and 7.92x performance speedups. In terms of visual quality,
the proposed EGVSR tops the list of most metrics (such as LPIPS, tOF, tLP,
etc.) on the public test dataset Vid4 and surpasses other state-of-the-art
methods in overall performance score. The source code of this project can be
found on https://github.com/Thmen/EGVSR.


Abstract:
  Visual Sensor Networks can be used in a variety of perception applications
such as infrastructure support for autonomous driving in complex road segments.
The pose of the sensors in such networks directly determines the coverage of
the environment and objects therein, which impacts the performance of
applications such as object detection and tracking. Existing sensor pose
optimisation methods in the literature either maximise the coverage of ground
surfaces, or consider the visibility of the target objects as binary variables,
which cannot represent various degrees of visibility. Such formulations cannot
guarantee the visibility of the target objects as they fail to consider
occlusions. This paper proposes two novel sensor pose optimisation methods,
based on gradient-ascent and Integer Programming techniques, which maximise the
visibility of multiple target objects in cluttered environments. Both methods
consider a realistic visibility model based on a rendering engine that provides
pixel-level visibility information about the target objects. The proposed
methods are evaluated in a complex environment and compared to existing methods
in the literature. The evaluation results indicate that explicitly modelling
the visibility of target objects is critical to avoid occlusions in cluttered
environments. Furthermore, both methods significantly outperform existing
methods in terms of object visibility.


Abstract:
  Interactions between users and videos are the major data source of performing
video recommendation. Despite lots of existing recommendation methods, user
behaviors on videos, which imply the complex relations between users and
videos, are still far from being fully explored. In the paper, we present a
model named Sagittarius. Sagittarius adopts a graph convolutional neural
network to capture the influence between users and videos. In particular,
Sagittarius differentiates between different user behaviors by weighting and
fuses the semantics of user behaviors into the embeddings of users and videos.
Moreover, Sagittarius combines multiple optimization objectives to learn user
and video embeddings and then achieves the video recommendation by the learned
user and video embeddings. The experimental results on multiple datasets show
that Sagittarius outperforms several state-of-the-art models in terms of
recall, unique recall and NDCG.


Abstract:
  We present {\tt RandomPoints}, a package in \emph{Macaulay2} designed mainly
to identify rational and geometric points in a variety over a finite field. We
provide tools to estimate the dimension of a variety. We also present methods
to obtain non-vanishing minors of a given size in a given matrix, by evaluating
the matrix at a point.


Abstract:
  This work presents a physics based compact model for SiC power MOSFETs that
accurately describes the I-V characteristics up to large voltages and currents.
Charge-based formulations accounting for the different physics of SiC power
MOSFETs are presented. The formulations account for the effect of the large
SiC/SiO2 interface traps density characteristic of SiC MOSFETs and its
dependence with temperature. The modeling of interface charge density is found
to be necessary to describe the electrostatics of SiC power MOSFETs when
operating at simultaneous high current and high voltage regions. The proposed
compact model accurately fits the measurement data extracted of a 160 milli
ohms, 1200V SiC power MOSFET in the complete IV plane from drain-voltage $V_d$
= 5mV up to 800 V and current ranges from few mA to 30 A.


Abstract:
  In this paper, we revisit the possibilities of Ba\~nados-Silk-West (BSW)
effect in Kerr-Newman-Taub-NUT (KNTN) spacetime for two neutral particles
moving over the equatorial plane and constant $\theta$ in Boyer-Lindquist
coordinate. Contrary to a previous study on this topic, we found that BSW
effect for two particles confined to move over the equatorial plane is not
possible. Numerical calculations shows that BSW effect in constant $\theta$
geodesics is possible under certain circumstances.


Abstract:
  The data distribution commonly evolves over time leading to problems such as
concept drift that often decrease classifier performance. We seek to predict
unseen data (and their labels) allowing us to tackle challenges due to a
non-constant data distribution in a \emph{proactive} manner rather than
detecting and reacting to already existing changes that might already have led
to errors. To this end, we learn a domain transformer in an unsupervised manner
that allows generating data of unseen domains. Our approach first matches
independently learned latent representations of two given domains obtained from
an auto-encoder using a Cycle-GAN. In turn, a transformation of the original
samples can be learned that can be applied iteratively to extrapolate to unseen
domains. Our evaluation on CNNs on image data confirms the usefulness of the
approach. It also achieves very good results on the well-known problem of
unsupervised domain adaption, where labels but not samples have to be
predicted.


Abstract:
  Using an extended Dicke model, we study the effect of the spin-orbit coupling
between the singlet and the triplet molecular excitons in organic
microcavities. We exactly solve the model in the strong coupling regime in the
single excitation space. We calculate the polaritonic states and the optical
absorption spectrum of the system and explore the spin-orbit induced splitting
of the lower polariton into two branches. The effect of this splitting in the
ultra strong coupling regime on the superradiant phase transition is explored
using a variational approach. We find that the spin-orbit coupling pushes the
system towards the superradiant phase and reduces the critical matter-light
coupling required for it.


Abstract:
  How far can we go with textual representations for understanding pictures? In
image understanding, it is essential to use concise but detailed image
representations. Deep visual features extracted by vision models, such as
Faster R-CNN, are prevailing used in multiple tasks, and especially in visual
question answering (VQA). However, conventional deep visual features may
struggle to convey all the details in an image as we humans do. Meanwhile, with
recent language models' progress, descriptive text may be an alternative to
this problem. This paper delves into the effectiveness of textual
representations for image understanding in the specific context of VQA. We
propose to take description-question pairs as input, instead of deep visual
features, and fed them into a language-only Transformer model, simplifying the
process and the computational cost. We also experiment with data augmentation
techniques to increase the diversity in the training set and avoid learning
statistical bias. Extensive evaluations have shown that textual representations
require only about a hundred words to compete with deep visual features on both
VQA 2.0 and VQA-CP v2.


Abstract:
  This paper investigates the well-posedness of a type of state constraint
ergodic Mean Field Game system in a bounded domain in which the
Hamilton-Jacobi-Bellman equation is paired with an infinite Dirichlet boundary
condition. In this setting, the infinite boundary condition prevents the
underlying stochastic trajectories from reaching the boundary, resulting in a
state constraint solution. We consider a class of superlinear power-like
Hamiltonians and establish existence and uniqueness results under appropriate
assumptions on the coupling. We treat both non-local and local couplings, and
establish the uniqueness of the system by exploiting the decay of the density
near the boundary.


Abstract:
  Minimax problems, such as generative adversarial network, adversarial
training, and fair training, are widely solved by a multi-step gradient descent
ascent (MGDA) method in practice. However, its convergence guarantee is
limited. In this paper, inspired by the primal-dual hybrid gradient method, we
propose a new semi-anchoring (SA) technique for the MGDA method. This makes the
MGDA method find a stationary point of a structured nonconvex-nonconcave
composite minimax problem; its saddle-subdifferential operator satisfies the
weak Minty variational inequality condition. The resulting method, named
SA-MGDA, is built upon a Bregman proximal point method. We further develop its
backtracking line-search version, and its non-Euclidean version for smooth
adaptable functions. Numerical experiments, including a fair classification
training, are provided.


Abstract:
  We present an IoT-based intelligent bed sensor system that collects and
analyses respiration-associated signals for unobtrusive monitoring in the home,
hospitals and care units. A contactless device is used, which contains four
load sensors mounted under the bed and one data processing unit (data logger).
Various machine learning methods are applied to the data streamed from the data
logger to detect the Respiratory Rate (RR). We have implemented Support Vector
Machine (SVM) and also Neural Network (NN)-based pattern recognition methods,
which are combined with either peak detection or Hilbert transform for robust
RR calculation. Experimental results show that our methods could effectively
extract RR using the data collected by contactless bed sensors. The proposed
methods are robust to outliers and noise, which are caused by body movements.
The monitoring system provides a flexible and scalable way for continuous and
remote monitoring of sleep, movement and weight using the embedded sensors.


Abstract:
  Recently proposed automatic pathological speech classification techniques use
unsupervised auto-encoders to obtain a high-level abstract representation of
speech. Since these representations are learned based on reconstructing the
input, there is no guarantee that they are robust to pathology-unrelated cues
such as speaker identity information. Further, these representations are not
necessarily discriminative for pathology detection. In this paper, we exploit
supervised auto-encoders to extract robust and discriminative speech
representations for Parkinson's disease classification. To reduce the influence
of speaker variabilities unrelated to pathology, we propose to obtain speaker
identity-invariant representations by adversarial training of an auto-encoder
and a speaker identification task. To obtain a discriminative representation,
we propose to jointly train an auto-encoder and a pathological speech
classifier. Experimental results on a Spanish database show that the proposed
supervised representation learning methods yield more robust and discriminative
representations for automatically classifying Parkinson's disease speech,
outperforming the baseline unsupervised representation learning system.


Abstract:
  In wireless communication, accurate channel state information (CSI) is of
pivotal importance. In practice, due to processing and feedback delays,
estimated CSI can be outdated, which can severely deteriorate the performance
of the communication system. Besides, to feedback estimated CSI, a strong
compression of the CSI, evaluated at the user equipment (UE), is performed to
reduce the over-the-air (OTA) overhead. Such compression strongly reduces the
precision of the estimated CSI, which ultimately impacts the performance of
multiple-input multiple-output (MIMO) precoding. Motivated by such issues, we
present a novel scalable idea of reporting CSI in wireless networks, which is
applicable to both time-division duplex (TDD) and frequency-division duplex
(FDD) systems. In particular, the novel approach introduces the use of a
channel predictor function, e.g., Kalman filter (KF), at both ends of the
communication system to predict CSI. Simulation-based results demonstrate that
the novel approach reduces not only the channel mean-squared-error (MSE) but
also the OTA overhead to feedback the estimated CSI when there is immense
variation in the mobile radio channel. Besides, in the immobile radio channel,
feedback can be eliminated, which brings the benefit of further reducing the
OTA overhead. Additionally, the proposed method provides a significant
signal-to-noise ratio (SNR) gain in both the channel conditions, i.e., highly
mobile and immobile.


Abstract:
  Each of the individual factors of the Drake Equation is considered. Each in
turn is either abandoned or redefined and finally reduced to a single new
factor, fd, the fraction of technological life that is detectable by any means.
However, neither the Drake Equation, nor its replacement, can actually solve
for N. Only a vibrant SETI program and, ultimately, contact with an alien
civilization might result in the determination of N.


Abstract:
  This paper studies the stability and $\mathcal{H}_{\infty}$ performance
analysis problem for linear networked and quantized control systems with both
communication delays random packet losses. To deal with the network-induced
uncertainties and random packet dropouts, a novel discrete-time stochastic
system model is developed for continuous-time networked control systems, and
further overapproximated via a polytopic system with norm-bounded uncertainty.
Based on the overapproximated system model, sufficient conditions are
established for linear networked and quantized control systems in different
cases to guarantee input-to-state stability and $\mathcal{H}_{\infty}$
performance with respect to the network-induced errors. Finally, a numerical
example is presented to illustrated the developed results.


Abstract:
  We provide out-of-sample certificates on the controlled invariance property
of a given set with respect to a class of black-box linear systems.
Specifically, we consider linear time-invariant models whose state space
matrices are known only to belong to a certain family due to a possibly inexact
quantification of some parameters. By exploiting a set of realizations of those
undetermined parameters, verifying the controlled invariance property of the
given set amounts to a linear program, whose feasibility allows us to establish
an a-posteriori probabilistic certificate on the controlled invariance property
of such a set with respect to the nominal linear time-invariant dynamics. The
proposed framework is applied to the control of a networked multi-agent system
with unknown weighted graph.


Abstract:
  We present TES, a new n-body integration code for the accurate and rapid
propagation of planetary systems in the presence of close encounters. TES
builds upon the classic Encke method and integrates only the perturbations to
Keplerian trajectories to reduce both the error and runtime of simulations.
Variable step size is used throughout to enable close encounters to be
precisely handled. A suite of numerical improvements are presented that
together makes TES optimal in terms of energy error. Lower runtimes are found
in all test problems considered when compared to direct integration using
ias15. TES is freely available.


Abstract:
  Semantic parsing maps natural language (NL) utterances into logical forms
(LFs), which underpins many advanced NLP problems. Semantic parsers gain
performance boosts with deep neural networks, but inherit vulnerabilities
against adversarial examples. In this paper, we provide the empirical study on
the robustness of semantic parsers in the presence of adversarial attacks.
Formally, adversaries of semantic parsing are considered to be the perturbed
utterance-LF pairs, whose utterances have exactly the same meanings as the
original ones. A scalable methodology is proposed to construct robustness test
sets based on existing benchmark corpora. Our results answered five research
questions in measuring the sate-of-the-art parsers' performance on robustness
test sets, and evaluating the effect of data augmentation.


Abstract:
  In this paper, we address the multi-robot collaborative perception problem,
specifically in the context of multi-view infilling for distributed semantic
segmentation. This setting entails several real-world challenges, especially
those relating to unregistered multi-agent image data. Solutions must
effectively leverage multiple, non-static, and intermittently-overlapping RGB
perspectives. To this end, we propose the Multi-Agent Infilling Network: an
extensible neural architecture that can be deployed (in a distributed manner)
to each agent in a robotic swarm. Specifically, each robot is in charge of
locally encoding and decoding visual information, and an extensible neural
mechanism allows for an uncertainty-aware and context-based exchange of
intermediate features. We demonstrate improved performance on a realistic
multi-robot AirSim dataset.


Abstract:
  In autonomous driving, goal-based multi-trajectory prediction methods are
proved to be effective recently, where they first score goal candidates, then
select a final set of goals, and finally complete trajectories based on the
selected goals. However, these methods usually involve goal predictions based
on sparse predefined anchors. In this work, we propose an anchor-free model,
named DenseTNT, which performs dense goal probability estimation for trajectory
prediction. Our model achieves state-of-the-art performance, and ranks 1st on
the Waymo Open Dataset Motion Prediction Challenge. Project page is at
https://github.com/Tsinghua-MARS-Lab/DenseTNT.


Abstract:
  This work provides new results for the analysis of random sequences in terms
of $\ell_p$-compressibility. The results characterize the degree in which a
random sequence can be approximated by its best $k$-sparse version under
different rates of significant coefficients (compressibility analysis). In
particular, the notion of strong $\ell_p$-characterization is introduced to
denote a random sequence that has a well-defined asymptotic limit (sample-wise)
of its best $k$-term approximation error when a fixed rate of significant
coefficients is considered (fixed-rate analysis). The main theorem of this work
shows that the rich family of asymptotically mean stationary (AMS) processes
has a strong $\ell_p$-characterization. Furthermore, we present results that
characterize and analyze the $\ell_p$-approximation error function for this
family of processes. Adding ergodicity in the analysis of AMS processes, we
introduce a theorem demonstrating that the approximation error function is
constant and determined in closed-form by the stationary mean of the process.
Our results and analyses contribute to the theory and understanding of
discrete-time sparse processes and, on the technical side, confirm how
instrumental the point-wise ergodic theorem is to determine the compressibility
expression of discrete-time processes even when stationarity and ergodicity
assumptions are relaxed.


Abstract:
  We develop a Van der Waerden type theorem in an axiomatic setting of graded
lattices and show that this axiomatic formulation can be applied to various
lattices, for instance the set partition and the Boolean lattices. We derive
the Hales-Jewett theorem as a corollary.


Abstract:
  Unmanned aerial vehicles (UAVs) are becoming largely ubiquitous with an
increasing demand for aerial data. Accurate navigation and localization,
required for precise data collection in many industrial applications, often
relies on RTK GNSS. These systems, able of centimeter-level accuracy, require a
setup and calibration process and are relatively expensive. This paper
addresses the problem of accurate positioning and navigation of UAVs through
cooperative localization. Inexpensive ultra-wideband (UWB) transceivers
installed on both the UAV and a support ground robot enable centimeter-level
relative positioning. With fast deployment and wide setup flexibility, the
proposed system is able to accommodate different environments and can also be
utilized in GNSS-denied environments. Through extensive simulations and test
fields, we evaluate the accuracy of the system and compare it to GNSS in urban
environments where multipath transmission degrades accuracy. For completeness,
we include visual-inertial odometry in the experiments and compare the
performance with the UWB-based cooperative localization.


Abstract:
  We propose a simple statistical-physics-inspired model for the effect of
intrinsic fluctuations on supply and demand in markets. The model consists of
agents that trade in two types of goods of which the total number is separately
conserved. The relative preference of an individual agent for the two types of
goods is determined by a utility that is identical for all agents. Market
supply and demand curves are computed and compared for various motivated
choices of the distribution of goods over the agents. In particular, we compare
the "mean-field" case, in which all agents have the same number of goods and
that is akin to the economics textbook case, to the case of Boltzmann-Gibbs
distributed goods, in which agents have a fluctuating number of goods. We find
that the resulting equilibrium prices are not equal for these two approaches,
especially when a large fraction of the agents can neither buy nor sell.


Abstract:
  We study the evolution of states of an infinite system of point particles
dwelling in a locally compact Polish space $X$. Each particle produces at
random a finite `cloud' of offsprings distributed over $X$ according to some
law, and disappears afterwards. The system's states are probability measures on
an appropriate space of locally finite counting measures on $X$. Their
evolution is obtained by solving the corresponding Fokker-Planck equation. We
prove that this equation has a unique solution and discuss some of its
properties. Our pivotal idea of dealing with infinite systems consists in
passing to tempered counting measures by imposing appropriate restrictions on
the branching. In this approach, we first solve a nonlinear evolution equation
in the space of bounded continuous functions on $X$ -- so called log-Laplace
equation. Next we solve the Kolmogorov equation which is then used to solve the
Fokker-Planck equation and thus describe the evolution in question.


Abstract:
  Document-level MT models are still far from satisfactory. Existing work
extend translation unit from single sentence to multiple sentences. However,
study shows that when we further enlarge the translation unit to a whole
document, supervised training of Transformer can fail. In this paper, we find
such failure is not caused by overfitting, but by sticking around local minima
during training. Our analysis shows that the increased complexity of
target-to-source attention is a reason for the failure. As a solution, we
propose G-Transformer, introducing locality assumption as an inductive bias
into Transformer, reducing the hypothesis space of the attention from target to
source. Experiments show that G-Transformer converges faster and more stably
than Transformer, achieving new state-of-the-art BLEU scores for both
non-pretraining and pre-training settings on three benchmark datasets.


Abstract:
  Given the abundance and ease of access of personal data today, individual
privacy has become of paramount importance, particularly in the healthcare
domain. In this work, we aim to utilise patient data extracted from multiple
hospital data centres to train a machine learning model without sacrificing
patient privacy. We develop a scheduling algorithm in conjunction with a
student-teacher algorithm that is deployed in a federated manner. This allows a
central model to learn from batches of data at each federal node. The teacher
acts between data centres to update the main task (student) algorithm using the
data that is stored in the various data centres. We show that the scheduler,
trained using meta-gradients, can effectively organise training and as a result
train a machine learning model on a diverse dataset without needing explicit
access to the patient data. We achieve state-of-the-art performance and show
how our method overcomes some of the problems faced in the federated learning
such as node poisoning. We further show how the scheduler can be used as a
mechanism for transfer learning, allowing different teachers to work together
in training a student for state-of-the-art performance.


Abstract:
  We consider mean field game systems in time-horizon $(0,T)$, where the
individual cost functional depends locally on the density distribution of the
agents, and the Hamiltonian is locally uniformly convex. We show that, even if
the coupling cost functions are mildly non-monotone, then the system is still
well posed due to the effect of individual noise. The rate of anti-monotonicity
(i.e. the aggregation rate of the cost functions) which can be afforded depends
on the intensity of the diffusion and on global bounds of solutions. We give
applications to either the case of globally Lipschitz Hamiltonians or the case
of quadratic Hamiltonians and couplings having mild growth.
  Under similar conditions, we investigate the long time behavior of solutions
and we give a complete description of the ergodic and long term properties of
the system. In particular we prove: (i) the turnpike property of solutions in
the finite (long) horizon $(0,T)$, (ii) the convergence of the system in
$(0,T)$ towards the system in $(0,\infty)$, (iii) the vanishing discount limit
of the infinite horizon problem and the long time convergence towards the
ergodic stationary solution.
  This way we extend previous results which were known only for the case of
monotone and smoothing couplings; our approach is self-contained and does not
need the use of the linearized system or of the master equation.


Abstract:
  We propose the Lie-algebraic interpretation of poly-analytic functions in
$L_2(\C,d\mu)$, with the Gaussian measure $d\mu$, based on a flag structure
formed by the representation spaces of the $\mathfrak{sl}(2)$-algebra realized
by differential operators in $z$ and $\bar z$. Following the pattern of the
one-dimensional situation, we define poly-Fock spaces in $d$ complex variables
in a Lie-algebraic way, as the invariant spaces for the action of generators of
a certain Lie algebra. In addition to the basic case of the algebra
$\mathfrak{sl}(d+1)$, we consider also the family of algebras
$\mathfrak{sl}(m_1+1) \otimes \ldots \otimes \mathfrak{sl}(m_n+1)$ for tuples
$\mathbf{m} = (m_1,m_2,\ldots,m_n)$ of positive integers whose sum is equal to
$d$.


Abstract:
  Deep reinforcement learning (DRL) is a promising way to achieve human-like
autonomous driving. However, the low sample efficiency and difficulty of
designing reward functions for DRL would hinder its applications in practice.
In light of this, this paper proposes a novel framework to incorporate human
prior knowledge in DRL, in order to improve the sample efficiency and save the
effort of designing sophisticated reward functions. Our framework consists of
three ingredients, namely expert demonstration, policy derivation, and
reinforcement learning. In the expert demonstration step, a human expert
demonstrates their execution of the task, and their behaviors are stored as
state-action pairs. In the policy derivation step, the imitative expert policy
is derived using behavioral cloning and uncertainty estimation relying on the
demonstration data. In the reinforcement learning step, the imitative expert
policy is utilized to guide the learning of the DRL agent by regularizing the
KL divergence between the DRL agent's policy and the imitative expert policy.
To validate the proposed method in autonomous driving applications, two
simulated urban driving scenarios (unprotected left turn and roundabout) are
designed. The strengths of our proposed method are manifested by the training
results as our method can not only achieve the best performance but also
significantly improve the sample efficiency in comparison with the baseline
algorithms (particularly 60\% improvement compared to soft actor-critic). In
testing conditions, the agent trained by our method obtains the highest success
rate and shows diverse and human-like driving behaviors as demonstrated by the
human expert.


Abstract:
  Each p-ring class field K(f) modulo a p-admissible conductor f over a
quadratic base field K with p-ring class rank r(f) mod f is classified
according to Galois cohomology and differential principal factorization type of
all members of its associated heterogeneous multiplet
M(K(f))=[(N(c,i))_{1<=i<=m(c)}]_{c|f} of dihedral fields N(c,i) with various
conductors c|f having p-multiplicities m(c) over K such that sum_{c|f}
m(c)=(p^r(f)-1)/(p-1). The advanced viewpoint of classifying the entire
collection M(K(f)), instead of its individual members separately, admits
considerably deeper insight into the class field theoretic structure of ring
class fields, and the actual construction of the multiplet M(K(f)) is enabled
by exploiting the routines for abelian extensions in the computational algebra
system Magma.


Abstract:
  We consider the problem of decentralized optimization over time-varying
directed networks. The network nodes can access only their local objectives,
and aim to collaboratively minimize a global function by exchanging messages
with their neighbors. Leveraging sparsification, gradient tracking and
variance-reduction, we propose a novel communication-efficient decentralized
optimization scheme that is suitable for resource-constrained time-varying
directed networks. We prove that in the case of smooth and strongly-convex
objective functions, the proposed scheme achieves an accelerated linear
convergence rate. To our knowledge, this is the first decentralized
optimization framework for time-varying directed networks that achieves such a
convergence rate and applies to settings requiring sparsified communication.
Experimental results on both synthetic and real datasets verify the theoretical
results and demonstrate efficacy of the proposed scheme.


Abstract:
  A set $R \subseteq V(G)$ is a resolving set of a graph $G$ if for all
distinct vertices $v,u \in V(G)$ there exists an element $r \in R$ such that
$d(r,v) \neq d(r,u)$. The metric dimension $\dim(G)$ of the graph $G$ is the
minimum cardinality of a resolving set of $G$. A resolving set with cardinality
$\dim(G)$ is called a metric basis of $G$. We consider vertices that are in all
metric bases, and we call them basis forced vertices. We give several
structural properties of sparse and dense graphs where basis forced vertices
are present. In particular, we give bounds for the maximum number of edges in a
graph containing basis forced vertices. Our bound is optimal whenever the
number of basis forced vertices is even. Moreover, we provide a method of
constructing fairly sparse graphs with basis forced vertices. We also study
vertices which are in no metric basis in connection to cut-vertices and
pendants. Furthermore, we show that deciding whether a vertex is in all metric
bases is co-NP-hard, and deciding whether a vertex is in no metric basis is
NP-hard.


Abstract:
  We consider an observational learning model with exogenous public payoff
shock. We show that confounded learning doesn't arise for almost all private
signals and almost all shocks, even if players have sufficiently divergent
preferences.


Abstract:
  Recent work in data-driven control has revived behavioral theory to perform a
variety of complex control tasks, by directly plugging libraries of past
input-output trajectories into optimal control problems. Despite recent
advances, two key aspects remain unclear when the data are corrupted by noise:
how can safety be guaranteed, and to what extent is the control performance
affected? In this work, we provide a quantitative answer to these questions. In
particular, we formulate a robustly safe version of the recently introduced
Behavioral Input-Output Parametrization (BIOP) for the optimal predictive
control of unknown constrained systems. The proposed framework has three main
advantages: 1) it allows one to safely operate the system while explicitly
quantifying, as a function of the noise level corrupting the data, how much the
performance degrades, 2) it can be used in combination with state-of-the-art
impulse response estimators, and finally, being a data-driven approach, 3) the
state-space parameters and the initial state need not be specified for
controller synthesis. We corroborate our results through numerical experiments.


Abstract:
  Cognitive diagnosis models (CDMs) are a popular tool for assessing students'
mastery of sets of skills. Given a set of $K$ skills tested on an assessment,
students are classified into one of $2^K$ latent skill set profiles that
represent whether they have mastered each skill or not. Traditional approaches
to estimating these profiles are computationally intensive and become
infeasible on large datasets. Instead, proxy skill estimates can be generated
from the observed responses and then clustered, and these clusters can be
assigned to different profiles. Building on previous work, we consider how to
optimally perform this clustering when not all $2^K$ profiles are possible,
e.g. because of hierarchical relationships among the skills, and when not all
possible profiles are present in the population. We compare hierarchical
clustering and several k-means variants, including semisupervised clustering
using simulated student responses. The empty k-means algorithm paired with a
novel method for generating starting centers yields the best overall
performance.


Abstract:
  Let $F$ be a totally real field and let $E/F$ be a CM quadratic extension. We
construct a $p$-adic $L$-function attached to Hida families for the group
$GL_{2/F} \times GL_{1/E}$. It interpolates critical Rankin--Selberg $L$-values
at all classical points corresponding to representations $\pi\boxtimes \chi$
with the weights of $\chi$ smaller than the weights of $\pi$. This confirms a
conjecture of the author, making related results on the $p$-adic Beilinson
conjecture and its Iwasawa-theoretic variants unconditional.
  Our $p$-adic $L$-function agrees with a previous result of Hida when $E/F$
splits above $p$, and it is new otherwise. We build it as a ratio of families
of global and local Waldspurger zeta integrals, the latter constructed using
the local Langlands correspondence in families.
  In an appendix of possibly independent recreational interest, we give a
reality-TV-inspired proof of an identity concerning double factorials.


Abstract:
  Local Group satellite galaxies show a wide diversity of star formation
histories (SFHs) whose origin is yet to be fully understood. Using
hydrodynamical simulations from the Constrained Local UniversE project, we
study the SFHs of satellites of Milky Way-like galaxies in a cosmological
context: while in the majority of the cases the accretion onto their host
galaxy causes the satellites to lose their gas, with a subsequent suppression
in SF, in about 25$\%$ of our sample we observe a clear enhancement of SF after
infall. Peaks in SF clearly correlate with the satellite pericentric passage
around its host and, in one case, with a satellite-satellite interaction. We
identify two key ingredients that result in enhanced SF after infall: galaxies
must enter the host's virial radius with a reservoir of cold gas $M_{\rm
gas,inf}/M_{\rm vir,inf}\gtrsim 10^{-2}$ and with a minimum pericentric
distance $\gtrsim$10 kpc (mean distance $\sim$50 kpc for the full sample), in
order to form new stars due to compression of cold gas at pericentric passage.
On the other hand, satellites that infall with little gas or whose pericentric
distance is too small, have their gas ram-pressure stripped and subsequent SF
quenched. The pericentric passage of satellites likewise correlates with SF
peaks in their hosts, suggesting that this mechanism induces bursts of SF in
satellites and central galaxies alike, in agreement with recent studies of our
Galaxy's SFH. Our findings can explain the recently reported multiple stellar
populations observed in dwarf galaxies such as Carina and Fornax, and should be
taken into account in semi-analytic models of galaxy formation and satellite
quenching.


Abstract:
  We use multiplicative calculus and obtain necessary and sufficient conditions
under which convergence of sequences in $\mathbb{R}^+$ follows from convergence
of weighted geometric means. Besides, we introduce some new types of
convergence for sequences of intuitionistic fuzzy numbers(IFNs) and apply the
obtained conditions to achieve the convergence of sequences of IFNs.


Abstract:
  The [OIII] 88 $\mu$m fine structure emission line has been detected into the
Epoch of Reionization (EoR) from star-forming galaxies at redshifts $6 < z
\lesssim 9$ with ALMA. These measurements provide valuable information
regarding the properties of the interstellar medium (ISM) in the highest
redshift galaxies discovered thus far. The [OIII] 88 $\mu$m line observations
leave, however, a degeneracy between the gas density and metallicity in these
systems. Here we quantify the prospects for breaking this degeneracy using
future ALMA observations of the [\oiii] 52 $\mu$m line. Among the current set
of ten [OIII] 88 $\mu$m emitters at $6 < z \lesssim 9$, we forecast 52 $\mu$m
detections (at 6-$\sigma$) in SXDF-NB1006-2, B14-6566, J0217-0208, and
J1211-0118 within on-source observing times of 2-10 hours, provided their gas
densities are larger than about $n_{\mathrm{H}} \gtrsim 10^2-10^3$ cm$^{-3}$.
Other targets generally require much longer integration times for a 6-$\sigma$
detection. Either successful detections of the 52 $\mu$m line, or reliable
upper limits, will lead to significantly tighter constraints on ISM parameters.
The forecasted improvements are as large as $\sim 3$ dex in gas density and
$\sim 1$ dex in metallicity for some regions of parameter space. We suggest
SXDF-NB1006-2 as a promising first target for 52 $\mu$m line measurements. We
discuss how such measurements will help in understanding the mass metallicity
relationship during the EoR.


Abstract:
  The dynamics of social relations and the possibility of reaching the state of
structural balance (Heider balance) are discussed for various topologies of the
networks of interacting actors under the influence of the temperature modeling
the social noise level. For that purpose, two main types of lattices are
considered. The first is created by removing some links from a regular
triangular lattice to produce a diluted triangular lattice or adding more links
to create an enhanced triangular lattice. The second is the classical random
graph. In both those cases, the full range of possible graph densities is
discussed, limited by the extreme cases of networks which consist of a small
number of separated triads and fully connected networks. The performed
simulations indicate that for random lattices a transition between the balanced
and imbalanced states is always possible and occurs at the critical temperature
which depends on the graph density. On the other hand, it is shown that this
transition and existence of the balanced state are not possible for networks
based on the diluted triangular lattices if the average node degree is too
close to the value characterizing the regular triangular lattice. The critical
temperatures are size-independent only for the diluted triangular lattices and
depend on the size of the system for the enhanced triangular lattices and
random graphs.


Abstract:
  Directing a jet of humid air to impinge on a surface that is cooled below the
dew point results in micro-sized water droplets. Lord Rayleigh discussed the
phenomenon called such behavior Breath Figures (BF). Historically, utilizing
dew as a water source was investigated by several scientists dating back to
Aristotle. However, due to the degrading effects of air as a non-condensable
gas (NCG) such efforts are limited to small scale water production systems.
Recently, the concept of BF has been utilized extensively in the generation of
micro-scale polymer patterns as a self-assembly process. However, the
generation of BF on surfaces while being impinged by a humid air jet has not
been quantified. In this work, we illustrate that a BF spot generated on a
cooled surface is a manifestation of a recovery concentration. The concept is
analogous to the concept of adiabatic-wall temperature defined for heat
transfer applications. Upon closer examination of the vapor concentration
distribution on a cooled impinged surface, we found that the distribution
exhibits distinct regimes depending on the radial location from the center of
the impingement region. The first regime is confined within the impingement
region, whereas the second regime lies beyond this radial location including
the wall jet region. Scaling analysis as well as numerical solution of the
former regime shows that the maximum concentration on the surface is equivalent
to its counterpart of a free unbounded jet with similar geometrical conditions.
Additionally, the scaling analysis of the latter regime reveals that the jet
speed and standoff distance are not important in determining the recovery
concentration. However, the recovery concentration is found to vary
monotonically with the radial location. Our conclusions are of great importance
in optimizing jet impingement where condensation phase change is prevalent.


Abstract:
  In this paper, we derive a general formula for the quantized fractional
corner charge in two-dimensional C_n-symmetric higher-order topological
insulators. We assume that the electronic states can be described by the
Wannier functions and that the edges are charge neutral, but we do not assume
vanishing bulk electric polarization. We expand the scope of the corner charge
formula obtained in previous works by considering more general surface
conditions, such as surfaces with higher Miller index and surfaces with surface
reconstruction. Our theory is applicable even when the electronic states are
largely modulated near system boundaries. It also applies to insulators with
non-vanishing bulk polarization, and we find that in such cases the value of
the corner charge depends on the surface termination even for the same bulk
crystal with C_3 or C_4 symmetry, via a difference in the Wyckoff position of
the center of the C_n-symmetric crystal.