Abstract:
  Phosphine is now well established as a biosignature, which has risen to
prominence with its recent tentative detection on Venus. To follow up this
discovery and related future exoplanet biosignature detections, it is important
to spectroscopically detect the presence of phosphorus-bearing atmospheric
molecules that could be involved in the chemical networks producing, destroying
or reacting with phosphine. We start by enumerating phosphorus-bearing
molecules (P-molecules) that could potentially be detected spectroscopically in
planetary atmospheres and collecting all available spectral data. Gaseous
P-molecules are rare, with speciation information scarce. Very few molecules
have high accuracy spectral data from experiment or theory; instead, the best
available data is from the RASCALL approach and obtained using functional group
theory. Here, we present a high-throughput approach utilising established
computational quantum chemistry methods (CQC) to produce a database of
approximate infrared spectra for 958 P-molecules. These data are of interest
for astronomy and astrochemistry (importantly identifying potential ambiguities
in molecular assignments), improving RASCALL's underlying data, big data
spectral analysis and future machine learning applications. However, this data
will probably not be sufficiently accurate for secure experimental detections
of specific molecules within complex gaseous mixtures in laboratory or
astronomy settings.


Abstract:
  The success of software crowdsourcing depends on active and trustworthy pool
of worker supply. The uncertainty of crowd workers' behaviors makes it
challenging to predict workers' success and plan accordingly. In a competitive
crowdsourcing marketplace, competition for success over shared tasks adds
another layer of uncertainty in crowd workers' decision-making process.
Preliminary analysis on software worker behaviors reveals an alarming task
dropping rate of 82.9%. These factors lead to the need for an automated
recommendation system for CSD workers to improve the visibility and
predictability of their success in the competition. To that end, this paper
proposes a collaborative recommendation system for crowd workers. The proposed
recommendation system method uses five input metrics based on workers'
collaboration history in the pool, workers' preferences in taking tasks in
terms of monetary prize and duration, workers' specialty, and workers'
proficiency. The proposed method then recommends the most suitable tasks for a
worker to compete on based on workers' probability of success in the task.
Experimental results on 260 active crowd workers demonstrate that just
following the top three success probabilities of task recommendations, workers
can achieve success up to 86%


Abstract:
  The traveltime of compressional (P) and shear (S) waves have proven essential
in many applications of earthquake and exploration seismology. An accurate and
efficient traveltime computation for P and S waves is crucial for the success
of these applications. However, solutions to the Eikonal equation with a
complex phase velocity field in anisotropic media is challenging. The Eikonal
equation is a first-order, hyperbolic, nonlinear partial differential equation
(PDE) that represents the high-frequency asymptotic approximation of the wave
equation. The fast marching and sweeping methods are commonly used due to their
efficiency in numercally solving Eikonal equation. However, these methods
suffer from numerical inaccuracy in anisotropic media with sharp heterogeneity,
irregular surface topography and complex phase velocity fields. This study
presents a new method to solving the Eikonal equation by employing the
peridynamic differential operator (PDDO). The PDDO provides the nonlocal form
of the Eikonal equation by introducing an internal length parameter (horizon)
and a weight function with directional nonlocality. The operator is immune to
discontinuities in the form sharp changes in field or model variables and
invokes the direction of traveltime in a consistent manner. The weight function
controls the degree of association among points within the horizon. Solutions
are constructed in a consistent manner without upwind assumptions through
simple discretization. The capability of this approach is demonstrated by
considering different types of Eikonal equations on complex velocity models in
anisotropic media. The examples demonstrate its unconditional numerical
stability and results compare well with the reference solutions.


Abstract:
  In this paper we study affine hypersurfaces with non-degenerate second
fundamental form of arbitrary signature additionally equipped with an almost
symplectic structure $\omega$.
  We prove that if $R^p\omega=0$ or $\nabla^p\omega =0$ for some positive
integer $p$ then the rank of the shape operator is at most one. The results
provide complete classification of affine hypersurfaces with higher order
parallel almost symplectic forms and are generalization of recently obtained
results for Lorentzian affine hypersurfaces.


Abstract:
  Let K be a field of characteristic zero. We prove that images of a linear
K-derivation and a linear K-E-derivation of the ring K[x 1 ,x 2 ,x 3 ] of
polynomial in three variables over K are Mathieu-Zhao subspaces, which affirms
the LFED conjecture for linear K-derivations and linear K-E-derivations of K[x
1 ,x 2 ,x 3 ].


Abstract:
  Outflows driven by active galactic nuclei (AGN) are expected to have a
significant impact on the host galaxy evolution, but it is still debated how
they are accelerated and propagate on galaxy-wide scales. This work addresses
these questions by studying the link between X-ray, nuclear ultra-fast outflows
(UFOs) and extended ionised outflows, for the first time in two quasars close
to the peak of AGN activity ($z\sim2$), where AGN feedback is expected to be
more effective. As targets, we selected two multiple-lensed quasars at
$z\sim1.5$, HS 0810+2554 and SDSS J1353+1138, known to host UFOs and observed
with the near-IR integral field spectrometer SINFONI at the VLT. We performed a
kinematical analysis of the [O III]$\lambda$5007 optical emission line, in
order to trace the presence of ionised outflows. We detected spatially resolved
ionised outflows in both galaxies, extended more than 8 kpc and moving up to
$v>2000$ km/s. We derived mass outflow rates of $\sim$12 M$_{sun}$/yr and
$\sim$2 M$_{sun}$/yr for HS 0810+2554 and SDSS J1353+1138. Comparing with the
co-hosted UFO energetics, the ionised outflow energetics in HS 0810+2554 is
broadly consistent with a momentum-driven regime of wind propagation, while in
SDSS J1353+1138 it differs by a factor of $\sim$100 from theoretical
predictions, requiring either a massive molecular outflow or a high variability
of the AGN activity to account for such a discrepancy. By additionally
considering our results with those from the small sample of well-studied
objects (all local but one), with both UFO and extended
(ionised/atomic/molecular) outflow detections, we found that in 10 out of 12
galaxies the large-scale outflow energetics is consistent with the theoretical
predictions of either a momentum- or an energy-driven scenario. This suggests
that such models explain relatively well the acceleration mechanism of
AGN-driven winds on large scales.


Abstract:
  Topic modeling algorithms traditionally model topics as list of weighted
terms. These topic models can be used effectively to classify texts or to
support text mining tasks such as text summarization or fact extraction. The
general procedure relies on statistical analysis of term frequencies. The focus
of this work is on the implementation of the knowledge-based topic modelling
services in a KNIME workflow. A brief description and evaluation of the
DBPedia-based enrichment approach and the comparative evaluation of enriched
topic models will be outlined based on our previous work. DBpedia-Spotlight is
used to identify entities in the input text and information from DBpedia is
used to extend these entities. We provide a workflow developed in KNIME
implementing this approach and perform a result comparison of topic modeling
supported by knowledge base information to traditional LDA. This topic modeling
approach allows semantic interpretation both by algorithms and by humans.


Abstract:
  This paper introduces an improved image processing method usable in
capacitive imaging applications. Standard capacitive imaging tends to prefer
amplitude-based images over the use of phase due to better signal-to-noise
ratios. The new approach exploits the best features of both types of
information by combining them to form clearer images, hence improving both
defect detection and characterization in non-destructive evaluation. The
methodology is demonstrated and optimized using a benchmark sample. Additional
experiments on glass fibre composite sample illustrate the advantages of the
technique.


Abstract:
  Hybrid quantum systems involving cold atoms and microwave resonators can
enable cavity-mediated infinite-range interactions between atomic spin systems
and realize atomic quantum memories and transducers for microwave to optical
conversion. To achieve strong coupling of atoms to on-chip microwave
resonators, it was suggested to use atomic Rydberg states with strong electric
dipole transitions. Here we report on the realization of coherent coupling of a
Rydberg transition of ultracold atoms trapped on an integrated superconducting
atom chip to the microwave field of an on-chip coplanar waveguide resonator. We
observe and characterize the cavity driven Rabi oscillations between a pair of
Rydberg states of atoms in an inhomogeneous electric field near the chip
surface. Our studies demonstrate the feasibility, but also reveal the
challenges, of coherent state manipulation of Rydberg atoms interacting with
superconducting circuits.


Abstract:
  In this paper, a novel framework for anomaly estimation is proposed. The
basic idea behind our method is to reduce the data into a two-dimensional space
and then rank each data point in the reduced space. We attempt to estimate the
degree of anomaly in both spatial and density domains. Specifically, we
transform the data points into a density space and measure the distances in
density domain between each point and its k-Nearest Neighbors in spatial
domain. Then, an anomaly coordinate system is built by collecting two
unilateral anomalies from k-nearest neighbors of each point. Further more, we
introduce two schemes to model their correlation and combine them to get the
final anomaly score. Experiments performed on the synthetic and real world
datasets demonstrate that the proposed method performs well and achieve highest
average performance. We also show that the proposed method can provide
visualization and classification of the anomalies in a simple manner. Due to
the complexity of the anomaly, none of the existing methods can perform best on
all benchmark datasets. Our method takes into account both the spatial domain
and the density domain and can be adapted to different datasets by adjusting a
few parameters manually.


Abstract:
  Internet is one of the important inventions and a large number of persons are
its users. These persons use this for different purposes. There are different
social media platforms that are accessible to these users. Any user can make a
post or spread the news through the online platforms. These platforms do not
verify the users or their posts. So some of the users try to spread fake news
through these platforms. These news can be propaganda against an individual,
society, organization or political party. A human being is unable to detect all
these fake news. So there is a need for machine learning classifiers that can
detect these fake news automatically. Use of machine learning classifiers for
detecting fake news is described in this systematic literature review.


Abstract:
  In this paper, we propose a weakly supervised multilingual representation
learning framework, called cross-lingual self-training (XLST). XLST is able to
utilize a small amount of annotated data from high-resource languages to
improve the representation learning on multilingual un-annotated data.
Specifically, XLST uses a supervised trained model to produce initial
representations and another model to learn from them, by maximizing the
similarity between output embeddings of these two models. Furthermore, the
moving average mechanism and multi-view data augmentation are employed, which
are experimentally shown to be crucial to XLST. Comprehensive experiments have
been conducted on the CommonVoice corpus to evaluate the effectiveness of XLST.
Results on 5 downstream low-resource ASR tasks shows that our multilingual
pretrained model achieves relatively 18.6% PER reduction over the
state-of-the-art self-supervised method, with leveraging additional 100 hours
of annotated English data.


Abstract:
  We propose the first method to adaptively modify the duration of a given
speech signal. Our approach uses a Bayesian framework to define a latent
attention map that links frames of the input and target utterances. We train a
masked convolutional encoder-decoder network to produce this attention map via
a stochastic version of the mean absolute error loss function; our model also
predicts the length of the target speech signal using the encoder embeddings.
The predicted length determines the number of steps for the decoder operation.
During inference, we generate the attention map as a proxy for the similarity
matrix between the given input speech and an unknown target speech signal.
Using this similarity matrix, we compute a warping path of alignment between
the two signals. Our experiments demonstrate that this adaptive framework
produces similar results to dynamic time warping, which relies on a known
target signal, on both voice conversion and emotion conversion tasks. We also
show that our technique results in a high quality of generated speech that is
on par with state-of-the-art vocoders.


Abstract:
  We derive the 3D N=1 superpotential for the closed string sector of type IIB
supergravity on toroidal O5 orientifolds with co-calibrated G2 structure and RR
background flux. We find that such compactifications can provide full closed
string moduli stabilization on supersymmetric AdS$_3$ vacua, and once we
include brane-supersymmetry-breaking we also find indication for the existence
of classical 3D de Sitter solutions. The latter however are rather difficult to
reconcile with the shape moduli stabilization and flux quantization. We also
discuss the possibility of achieving scale separation in AdS$_3$ and dS$_3$
vacua, but such effects seems to be hindered by the geometric flux
quantization.


Abstract:
  In large-scale feature models, feature modeling and configuration processes
are highly expected to be done by a group of stakeholders. In this context,
recommendation techniques can increase the efficiency of feature-model design
and find optimal configurations for groups of stakeholders. Existing studies
show plenty of issues concerning feature model navigation support, group
members' satisfaction, and conflict resolution. This study proposes group
recommendation techniques for feature modeling and configuration on the basis
of addressing the mentioned issues.


Abstract:
  Early prediction of patients at risk of clinical deterioration can help
physicians intervene and alter their clinical course towards better outcomes.
In addition to the accuracy requirement, early warning systems must make the
predictions early enough to give physicians enough time to intervene.
Interpretability is also one of the challenges when building such systems since
being able to justify the reasoning behind model decisions is desirable in
clinical practice. In this work, we built an interpretable model for the early
prediction of various adverse clinical events indicative of clinical
deterioration. The model is evaluated on two datasets and four clinical events.
The first dataset is collected in a predominantly COVID-19 positive population
at Stony Brook Hospital. The second dataset is the MIMIC III dataset. The model
was trained to provide early warning scores for ventilation, ICU transfer, and
mortality prediction tasks on the Stony Brook Hospital dataset and to predict
mortality and the need for vasopressors on the MIMIC III dataset. Our model
first separates each feature into multiple ranges and then uses logistic
regression with lasso penalization to select the subset of ranges for each
feature. The model training is completely automated and doesn't require expert
knowledge like other early warning scores. We compare our model to the Modified
Early Warning Score (MEWS) and quick SOFA (qSOFA), commonly used in hospitals.
We show that our model outperforms these models in the area under the receiver
operating characteristic curve (AUROC) while having a similar or better median
detection time on all clinical events, even when using fewer features. Unlike
MEWS and qSOFA, our model can be entirely automated without requiring any
manually recorded features. We also show that discretization improves model
performance by comparing our model to a baseline logistic regression model.


Abstract:
  Online advertising fuels the (seemingly) free internet. However, although
users can access most of the web services free of charge, they pay a heavy
coston their privacy. They are forced to trust third parties and
intermediaries, who not only collect behavioral data but also absorb great
amounts of ad revenues. Consequently, more and more users opt out from
advertising by resorting to ad blockers, thus costing publishers millions of
dollars in lost ad revenues. Albeit there are various privacy-preserving
advertising proposals (e.g.,Adnostic, Privad, Brave Ads) from both academia and
industry, they all rely on centralized management that users have to blindly
trust without being able to audit, while they also fail to guarantee the
integrity of the per-formance analytics they provide to advertisers.
  In this paper, we design and deploy THEMIS, a novel, decentralized and
privacy-by-design ad platform that requires zero trust by users. THEMIS (i)
provides auditability to its participants, (ii) rewards users for viewing ads,
and (iii) allows advertisers to verify the performance and billing reports of
their ad campaigns. By leveraging smart contracts and zero-knowledge schemes,
we implement a prototype of THEMIS and early performance evaluation results
show that it can scale linearly on a multi sidechain setup while it supports
more than 51M users on a single-sidechain.


Abstract:
  The separability of the Hamilton-Jacobi equation has a well-known connection
to the existence of Killing vectors and rank-two Killing tensors. This paper
combines this connection with the detailed knowledge of the compactification
metrics of consistent truncations on spheres. The fact that both the inverse
metric of such compactifications, as well as the rank-two Killing tensors can
be written in terms of bilinears of Killing vectors on the underlying "round
metric," enables us to perform a detailed analyses of the separability of the
Hamilton-Jacobi equation for consistent truncations. We introduce the idea of a
separating isometry and show that when a consistent truncation, without
reduction gauge vectors, has such an isometry, then the Hamilton-Jacobi
equation is always separable. When gauge vectors are present, the gauge group
is required to be an abelian subgroup of the separating isometry to not impede
separability. We classify the separating isometries for consistent truncations
on spheres, $S^n$, for $n=2, \dots, 7$, and exhibit all the corresponding
Killing tensors. These results may be of practical use in both identifying when
supergravity solutions belong to consistent truncations and generating
separable solutions amenable to scalar probe calculations. Finally, while our
primary focus is the Hamilton-Jacobi equation, we also make some remarks about
separability of the wave equation.


Abstract:
  Li-ion batteries with nickel-manganese-cobalt (NMC) cathode and graphite
anode are popularly used in portable electronic devices and electric vehicles.
Calendar loss of the lithium ion battery is a dominating factor in battery
degradation. However, few modeling work was reported on studying the calendar
capacity loss of NMC-graphite Li-ion batteries. In this work, an
electrochemical model for NMC-graphite Li-ion battery was developed to
investigate its calendar loss behavior. Various factors affecting the calendar
loss of the NMC-graphite batteries were systematically investigated, with the
results validated with experimental data of a Sanyo 18,650 cylindrical cell. It
was found that at 25 o C working temperature and 100% SOC, the capacity drops
6.4% of its original capacity after 10 months. Also, when the anode particle
size decreases from 26.2 {\mu}m to 6.55 {\mu}m, the capacity drop ratio is over
22% after 10 months under the same operation condition. Our simulation results
demonstrate that a smaller SOC, a lower cell working temperature and a larger
particle size could prolong the battery life during the storage period. This
modeling work can help better understand the calendar loss behavior of
NMC-graphite Li-ion batteries, and serve as a robust reference for the battery
performance optimization in future.


Abstract:
  Clustering in high-dimensions poses many statistical challenges. While
traditional distance-based clustering methods are computationally feasible,
they lack probabilistic interpretation and rely on heuristics for estimation of
the number of clusters. On the other hand, probabilistic model-based clustering
techniques often fail to scale and devising algorithms that are able to
effectively explore the posterior space is an open problem. Based on recent
developments in Bayesian distance-based clustering, we propose a hybrid
solution that entails defining a likelihood on pairwise distances between
observations. The novelty of the approach consists in including both cohesion
and repulsion terms in the likelihood, which allows for cluster
identifiability. This implies that clusters are composed of objects which have
small "dissimilarities" among themselves (cohesion) and similar dissimilarities
to observations in other clusters (repulsion). We show how this modelling
strategy has interesting connection with existing proposals in the literature
as well as a decision-theoretic interpretation. The proposed method is
computationally efficient and applicable to a wide variety of scenarios. We
demonstrate the approach in a simulation study and an application in digital
numismatics.


Abstract:
  In this talk, we have reviewed the recent development on the time evolution
of lepton family number carried by Majorana neutrinos \cite{Adam:2021qiq}. This
article focuses on the subtle points of the derivation of the lepton family
numbers and their time evolution. We also show how the time evolution is
sensitive to $m_{ee}$ and $m_{e\mu}$ components of the effective Majorana mass
matrix by applying the formula to the two family case. The dependence on the
Majorana phase is clarified and the implication on CNB (cosmic neutrino
background) is also discussed.


Abstract:
  Calculating polarizabilities of large clusters with first-principles
techniques is challenging because of the unfavorable scaling of computational
cost with cluster size. To address this challenge, we demonstrate that
polarizabilities of large hydrogenated silicon clusters containing thousands of
atoms can be efficiently calculated with machine learning methods.
Specifically, we construct machine learning models based on the smooth overlap
of atomic positions (SOAP) descriptor and train the models using a database of
calculated random-phase approximation polarizabilities for clusters containing
up to 110 silicon atoms. We first demonstrate the ability of the machine
learning models to fit the data and then assess their ability to predict
cluster polarizabilities using k-fold cross validation. Finally, we study the
machine learning predictions for clusters that are too large for explicit
first-principles calculations and find that they accurately describe the
dependence of the polarizabilities on the ratio of hydrogen to silicon atoms
and also predict a bulk limit that is in good agreement with previous studies.


Abstract:
  In this paper, we introduce the notion of binomial edge ideals of a clutter
and obtain results similar to those obtained for graphs by Rauf \& Rinaldo in
\cite{raufrin}. We also answer a question posed in their paper.


Abstract:
  We examine the impact of baryonic physics on the halo distribution in
hydrodynamic simulations (Illustris, IllustrisTNG, and EAGLE), particularly
with regards to how it differs from that in dark matter only (DMO) simulations.
We find that, in general, DMO simulations produce halo mass functions (HMF)
that are shifted to higher halo masses than their hydrodynamic counterparts,
due to the lack of baryonic physics. However, the exact nature of this mass
shift is a complex function of mass, halo definition, redshift, and
larger-scale environment, and it also depends on the specifics of the baryonic
physics implemented in the simulation. We present fitting formulae for the
corrections one would need to apply to each DMO halo catalogue in order to
reproduce the HMF found in its hydrodynamic counterpart. We provide these
formulae for all three simulations, for five different halo definitions at
redshifts 0, 1, and 2. Additionally, we explore the dependence on environment
of this HMF discrepancy, and find that, in most cases, halos in low density
environments are slightly more impacted by baryonic physics than halos in high
density environments. We thus also provide environment-dependent mass
correction formulae that can reproduce the conditional, as well as global, HMF.
We show that our mass corrections also repair the large-scale clustering of
halos, though the environment-dependent corrections are required to achieve an
accuracy better than 2%. Finally, we examine the impact of baryonic physics on
the halo mass - concentration relation, and find that its slope in hydrodynamic
simulations is consistent with that in DMO simulations. Ultimately, we
recommend that any future work relying on DMO halo catalogues incorporate our
mass corrections to test the robustness of their results to baryonic effects.


Abstract:
  The mid-depth ocean stratification was fitted by Munk (1966) to an
exponential profile and shown to be consistent with a vertical
advective-diffusive balance. However, tracer release experiments show that
vertical diffusivity in the mid-depth ocean is an order of magnitude too small
to explain the observed 1 km exponential scale. Alternative mechanisms
suggested that the overturning is mostly adiabatic, that interior diapycnal
upwelling is negligible, and that nearly all mid-depth water upwells
adiabatically in the Southern Ocean (SO). In this picture, SO eddies and wind
set isopycnal slopes in the SO and therefore determine a non-vanishing
mid-depth interior stratification even in the adiabatic limit. The effect of SO
eddies on SO isopycnal slopes can be understood via either a marginal
criticality condition or via a near-vanishing residual overturning conditions
in the adiabatic limit. We examine the role of SO eddies vs interior mixing in
setting the mid-depth stratification by using eddy-permitting numerical
simulations, in which we artificially change the diapycnal mixing only away
from the SO. We find that SO isopycnal slopes change in response to changes of
the interior diapycnal mixing even when the wind forcing is constant,
consistent with previous studies. However, in the limit of small interior
mixing, the interior stratification is far from exponential, suggesting that SO
processes alone do not lead to the observed stratification. The results suggest
that while SO eddies contribute to the non-vanishing mid-depth interior
stratification, the exponential shape of the stratification must also involve
interior diapycnal mixing. Both SO eddies and interior diapycnal mixing are
therefore important in determining the interior mid-depth exponential
stratification.


Abstract:
  The convolutional neural networks (CNNs) trained on ILSVRC12 ImageNet were
the backbone of various applications as a generic classifier, a feature
extractor or a base model for transfer learning. This paper describes automated
heuristics based on model consensus, explainability and confident learning to
correct labeling mistakes and remove ambiguous images from this dataset. After
making these changes on the training and validation sets, the ImageNet-Clean
improves the model performance by 2-2.4 % for SqueezeNet and EfficientNet-B0
models. The results support the importance of larger image corpora and
semi-supervised learning, but the original datasets must be fixed to avoid
transmitting their mistakes and biases to the student learner. Further
contributions describe the training impacts of widescreen input resolutions in
portrait and landscape orientations. The trained models and scripts are
published on Github (https://github.com/kecsap/imagenet-clean) to clean up
ImageNet and ImageNetV2 datasets for reproducible research.


Abstract:
  All-sky monitors can measure the fluxes of astrophysical sources by measuring
the changes in observed counts as the source is occulted by the Earth. Such
measurements have typically been carried out by all-sky monitors like
$\textit{CGRO}$-BATSE and $\textit{Fermi}$-GBM. We demonstrate for the first
time the application of this technique to measure fluxes of sources using a
collimated instrument: the Cadmium Zinc Telluride detector on
$\textit{AstroSat}$. Reliable flux measurements are obtained for the Crab
nebula and pulsar, and for Cyg X-1 by carefully selecting the best occultation
data sets. We demonstrate that CZTI can obtain such measurements for hard
sources with intensities $\gtrsim1$Crab.


Abstract:
  Speaker extraction algorithm emulates human's ability of selective attention
to extract the target speaker's speech from a multi-talker scenario. It
requires an auxiliary stimulus to form the top-down attention towards the
target speaker. It has been well studied to use a reference speech as the
auxiliary stimulus. Visual cues also serve as an informative reference for
human listening. They are particularly useful in the presence of acoustic noise
and interference speakers. We believe that the temporal synchronization between
speech and its accompanying lip motion is a direct and dominant audio-visual
cue. In this work, we aim to emulate human's ability of visual attention for
speaker extraction based on speech-lip synchronization. We propose a
self-supervised pre-training strategy, to exploit the speech-lip
synchronization in a multi-talker scenario. We transfer the knowledge from the
pre-trained model to a speaker extraction network. We show that the proposed
speaker extraction network outperforms various competitive baselines in terms
of signal quality and perceptual evaluation, achieving state-of-the-art
performance.


Abstract:
  High gain parametric amplifier with a single-pass pulsed pump is known to
generate broadband twin photon fields that are entangled in amplitude and phase
but have complicated spectral correlation. Fortunately, they can be decomposed
into independent temporal modes. But the common treatment of parametric
interaction Hamiltonian does not consider the issue of time ordering problem of
interaction Hamiltonian and thus leads to incorrect conclusion that the mode
structure and the temporal mode functions do not change as the gain increases.
In this paper, we use an approach that is usually employed for treating
nonlinear interferometers and avoids the time ordering issue. This allows us to
derive an evolution equation in differential-integral form. Numerical solutions
for high gain situation indicate a gain-dependent mode structure that has its
mode distributions changed and mode functions broadened as the gain increases.


Abstract:
  This work addresses friction-induced modal interactions in jointed
structures, and their effects on the passive mitigation of vibrations by means
of friction damping. Under the condition of (nearly) commensurable natural
frequencies, the nonlinear character of friction can cause so-called nonlinear
modal interactions. If harmonic forcing near the natural frequency of a
specific mode is applied, for instance, another mode may be excited due to
nonlinear energy transfer and thus contribute considerably to the vibration
response. We investigate how this phenomenon affects the performance of
friction damping. To this end, we study the steady-state, periodic forced
vibrations of a system of two beams connected via a local mechanical friction
joint. The system can be tuned to continuously adjust the ratio between the
first two natural frequencies in the range around the $1:3$ internal resonance,
in order to trigger or suppress the emergence of modal interactions. Due to the
re-distribution of the vibration energy, the vibration level can in fact be
reduced in certain situations. However, in other situations, the multi-harmonic
character of the vibration has detrimental effects on the effective damping
provided by the friction joint. The resulting response level can be
significantly larger than in the absence of modal interactions. Moreover, it is
shown that the vibration behavior is highly sensitive in the neighborhood of
internal resonances. It is thus concluded that the condition of internal
resonance should be avoided in the design of friction-damped systems.


Abstract:
  Full insight into the dynamics of a coupled quantum system depends on the
ability to follow the effect of a local excitation in real-time. Here, we trace
the coherent evolution of a pair of coupled atomic spins by means of scanning
tunneling microscopy. We use a pump-probe scheme to detect the local
magnetization following a current-induced excitation performed on one of the
spins. Making use of magnetic interaction with the probe tip, we are able to
tune the relative precession of the spins. We show that only if their Larmor
frequencies match, the two spins can entangle, causing the excitation to be
swapped back and forth. These results provide insight into the locality of
electron-spin scattering, and set the stage for controlled migration of a
quantum state through an extended spin lattice.


Abstract:
  When observations must come from incompatible devices and cannot be produced
by compatible devices? This question motivates two integer valued
quantifications of incompatibility, called incompatibility dimension and
compatibility dimension. The first one quantifies how many states are minimally
needed to detect incompatibility if the test states are chosen carefully,
whereas the second one quantifies how many states one may have to use if they
are randomly chosen. With concrete examples we show that these quantities have
unexpected behaviour with respect to noise.


Abstract:
  Lifelong Learning (LL) refers to the ability to continually learn and solve
new problems with incremental available information over time while retaining
previous knowledge. Much attention has been given lately to Supervised Lifelong
Learning (SLL) with a stream of labelled data. In contrast, we focus on
resolving challenges in Unsupervised Lifelong Learning (ULL) with streaming
unlabelled data when the data distribution and the unknown class labels evolve
over time. Bayesian framework is natural to incorporate past knowledge and
sequentially update the belief with new data. We develop a fully Bayesian
inference framework for ULL with a novel end-to-end Deep Bayesian Unsupervised
Lifelong Learning (DBULL) algorithm, which can progressively discover new
clusters without forgetting the past with unlabelled data while learning latent
representations. To efficiently maintain past knowledge, we develop a novel
knowledge preservation mechanism via sufficient statistics of the latent
representation for raw data. To detect the potential new clusters on the fly,
we develop an automatic cluster discovery and redundancy removal strategy in
our inference inspired by Nonparametric Bayesian statistics techniques. We
demonstrate the effectiveness of our approach using image and text corpora
benchmark datasets in both LL and batch settings.


Abstract:
  For each of the simple Lie algebras $\mathfrak{g}=A_l$, $D_l$ or $E_6$, we
show that the all-genera one-point FJRW invariants of $\mathfrak{g}$-type,
after multiplication by suitable products of Pochhammer symbols, are the
coefficients of an algebraic generating function and hence are integral.
Moreover, we find that the all-genera invariants themselves coincide with the
coefficients of the unique calibration of the Frobenius manifold of
$\mathfrak{g}$-type evaluated at a special point. For the $A_4$ (5-spin) case
we also find two other normalizations of the sequence that are again integral
and of at most exponential growth, and hence conjecturally are the Taylor
coefficients of some period functions.


Abstract:
  Game theory deals with strategic interactions among players and evolutionary
game dynamics tracks the fate of the players' populations under selection. In
this paper, we consider the replicator equation for two-player-two-strategy
games involving cooperators and defectors. We modify the equation to include
the effect of mutation and also delay that corresponds either to the delayed
information about the population state or in realizing the effect of
interaction among players. By focusing on the four exhaustive classes of
symmetrical games -- the Stag Hunt game, the Snowdrift game, the Prisoners'
Dilemma game, and the Harmony game -- we analytically and numerically analyze
delayed replicator-mutator equation to find the explicit condition for the Hopf
bifurcation bringing forth stable limit cycle. The existence of the
asymptotically stable limit cycle imply the coexistence of the cooperators and
the defectors; and in the games, where defection is a stable Nash strategy, a
stable limit cycle does provide a mechanism for evolution of cooperation. We
find that while mutation alone can never lead to oscillatory cooperation state
in two-player-two-strategy games, the delay can change the scenario. On the
other hand, there are situations when delay alone cannot lead to the Hopf
bifurcation in the absence of mutation in the selection dynamics.


Abstract:
  Through an exact analysis, we show the existence of Mpemba effect in an
anisotropically driven inelastic Maxwell gas, a simplified model for granular
gases, in two dimensions. Mpemba effect refers to the couterintuitive
phenomenon of a hotter system relaxing to the steady state faster than a cooler
system, when both are quenched to the same lower temperature. The Mpemba effect
has been illustrated in earlier studies on isotropically driven granular gases,
but its existence requires non-stationary initial states, limiting experimental
realisation. In this paper, we demonstrate the existence of the Mpemba effect
in anisotropically driven granular gases even when the initial states are
non-equilibrium steady states. The precise conditions for the Mpemba effect,
its inverse, and the stronger version, where the hotter system cools
exponentially faster are derived.


Abstract:
  For a Weyl group $W$ of rank $r$, the $W$-Catalan number is the number of
antichains of the poset of positive roots, and the $W$-Narayana numbers refine
the $W$-Catalan number by keeping track of the cardinalities of these
antichains. The $W$-Narayana numbers are symmetric, i.e., the number of
antichains of cardinality $k$ is the same as the number of cardinality $r-k$.
However, this symmetry is far from obvious. Panyushev posed the problem of
defining an involution on root poset antichains that exhibits the symmetry of
the $W$-Narayana numbers.
  Rowmotion and rowvacuation are two related operators, defined as compositions
of "toggles," that give a dihedral action on the set of antichains of any
ranked poset. Rowmotion acting on root posets has been the subject of a
significant amount of research in the recent past. We prove that for the root
posets of classical types, rowvacuation is Panyushev's desired involution.


Abstract:
  In the research field of big data, one of important issues is how to recover
the sequentially changing sets of true features when the data sets arrive
sequentially. The paper presents a general framework for online updating
variable selection and parameter estimation in generalized linear models with
streaming datasets. This is a type of online updating penalty likelihoods with
differentiable or non-differentiable penalty function. The online updating
coordinate descent algorithm is proposed to solve the online updating
optimization problem. Moreover, a tuning parameter selection is suggested in an
online updating way. The selection and estimation consistencies, and the oracle
property are established, theoretically. Our methods are further examined and
illustrated by various numerical examples from both simulation experiments and
a real data analysis.


Abstract:
  This work deals with the problem of designing observers for the estimation of
a single function of the states for discrete-time nonlinear systems. Necessary
and sufficient conditions for the existence of lower order functional observers
with linear dynamics and linear output map are derived. The results provide a
direct generalization to Luenberger's linear theory of functional observers.
The design methodology is tested on a non-isothermal CSTR case study.


Abstract:
  We use machine learning methods for an automated classification of
experimental XPCS two-time correlation functions from an arrested liquid-liquid
phase separation of a protein solution. We couple simulations based on the
Cahn-Hilliard equation with a glass transition scenario and classify the
measured correlation maps automatically according to quench depth and critical
concentration at a glass/gel transition. We introduce routines and
methodologies using an autoencoder network and a differential evolution based
algorithm for classification of the measured correlation functions. The here
presented method is a first step towards handling large amounts of dynamic data
measured at high brilliance synchrotron and X-ray free-electron laser sources
facilitating fast comparison to phase field models of phase separation.


Abstract:
  We introduce a general theory on stationary approximations for locally
stationary continuous-time processes. Based on the stationary approximation, we
use $\theta$-weak dependence to establish laws of large numbers and central
limit type results under different observation schemes. Hereditary properties
for a large class of finite and infinite memory transformations show the
flexibility of the developed theory. Sufficient conditions for the existence of
stationary approximations for time-varying L\'evy-driven state space models are
derived and compared to existing results. We conclude with comprehensive
results on the asymptotic behavior of the first and second order localized
sample moments of time-varying L\'evy-driven state space models.


Abstract:
  We experimentally investigate the suitability of a multi-path waveguide
interferometer with mechanical shutters for performing a test for hypercomplex
quantum mechanics. Probing the interferometer with coherent light we
systematically analyse the influence of experimental imperfections that could
lead to a false-positive test result. In particular, we analyse the effects of
detector nonlinearity, input-power and phase fluctuations on different
timescales, closed-state transmissivity of shutters and crosstalk between
different interferometer paths. In our experiment, a seemingly small shutter
transmissivity in the order of about $2 \times 10^{-4}$ is the main source of
systematic error, which suggests that this is a key imperfection to monitor and
mitigate in future experiments.


Abstract:
  For a large class of transitive non-hyperbolic systems, we construct
nonhyperbolic ergodic measures with entropy arbitrarily close to its maximal
possible value. The systems we consider are partially hyperbolic with
one-dimension central direction for which there are positive entropy ergodic
measures whose central Lyapunov exponent is negative, zero, or positive. We
construct ergodic measures with zero central Lyapunov exponent whose entropy is
positive and arbitrarily close to the topological entropy of the set of points
with central Lyapunov exponent zero. This provides a restricted variational
principle for nonhyperbolic (zero exponent) ergodic measures.
  The result is applied to the setting of $\mathrm{SL}(2,\mathbb R)$ matrix
cocycles and provides a counterpart to Furstenberg's classical result: for an
open and dense subset of elliptic $\mathrm{SL}(2,\mathbb R)$ cocycles we
construct ergodic measures with upper Lyapunov exponent zero and with metric
entropy arbitrarily close to the topological entropy of the set of infinite
matrix products with subexponential growth of the norm.


Abstract:
  In this paper, we present the design of a new chaotic map circuit with a 65nm
CMOS process. This chaotic map circuit uses a dynamic parameter-control
topology and generates a wide chaotic range. We propose two designs of dynamic
parameter-controlled chaotic map (DPCCM)-based pseudo-random number generators
(PRNG). The randomness of the generated sequence is verified using three
different statistical tests, namely, NIST SP 800-22 test, FIPS PUB 140-2 test,
and Diehard test. Our first design offers a throughput of 200 MS/s with an
on-chip area of 0.024mm2 and a power consumption of 2.33mW. The throughput of
our second design is 300 MS/s with an area consumption of 0.132mm2 and power
consumption of 2.14mW. The wider chaotic range and lower-overhead, offered by
our designs, can be highly suitable for various applications such as, logic
obfuscation, chaos-based cryptography, re-configurable random number
generation,and hard-ware security for resource-constrained edge devices like
IoT.


Abstract:
  Recent advances in experiment and theory suggest that superfluid $^3$He under
planar confinement may form a pair-density wave (PDW) whereby superfluid and
crystalline orders coexist. While a natural candidate for this phase is a
unidirectional stripe phase predicted by Vorontsov and Sauls in 2007, recent
nuclear magnetic resonance measurements of the superfluid order parameter
rather suggest a two-dimensional PDW with noncollinear wavevectors, of possibly
square or hexagonal symmetry. In this work, we present a general mechanism by
which a PDW with the symmetry of a triangular lattice can be stabilized, based
on a superfluid generalization of Landau's theory of the liquid-solid
transition. A soft-mode instability at finite wavevector within the
translationally invariant planar-distorted B phase triggers a transition from
uniform superfluid to PDW that is first order due to a cubic term generally
present in the PDW free-energy functional. This cubic term also lifts the
degeneracy of possible PDW states in favor of those for which wavevectors add
to zero in triangles, which in two dimensions uniquely selects the triangular
lattice.


Abstract:
  Email threat is a serious issue for enterprise security, which consists of
various malicious scenarios, such as phishing, fraud, blackmail and
malvertisement. Traditional anti-spam gateway commonly requires to maintain a
greylist to filter out unexpected emails based on suspicious vocabularies
existed in the mail subject and content. However, the signature-based approach
cannot effectively discover novel and unknown suspicious emails that utilize
various hot topics at present, such as COVID-19 and US election. To address the
problem, in this paper, we present Holmes, an efficient and lightweight
semantic based engine for anomalous email detection. Holmes can convert each
event log of email to a sentence through word embedding then extract
interesting items among them by novelty detection. Based on our observations,
we claim that, in an enterprise environment, there is a stable relation between
senders and receivers, but suspicious emails are commonly from unusual sources,
which can be detected through the rareness selection. We evaluate the
performance of Holmes in a real-world enterprise environment, in which it sends
and receives around 5,000 emails each day. As a result, Holmes can achieve a
high detection rate (output around 200 suspicious emails per day) and maintain
a low false alarm rate for anomaly detection.


Abstract:
  The bottom quark forward-backward asymmetry ($A_{FB}^b$) data at LEP exhibits
a long-standing discrepancy with the standard model prediction. We propose a
novel method to probe the $Zb\bar{b}$ interactions through $gg\to Zh$
production at the LHC, which is sensitive to the axial-vector component of the
$Zb\bar{b}$ couplings. We demonstrate that the $Zh$ data collected at the 13
TeV LHC can already resolve the apparent degeneracy of the anomalous
$Zb\bar{b}$ couplings implied by the LEP precision electroweak measurements,
with a strong dependence on the observed distribution of the $Z$ boson
transverse momentum. We also show the potential of the HL-LHC to either verify
or exclude the anomalous $Zb\bar{b}$ couplings observed at LEP through
measuring the $Zh$ production rate at the HL-LHC, and this conclusion is not
sensitive to possible new physics contribution induced by top quark or Higgs
boson anomalous couplings in the loop.


Abstract:
  In this paper we study pricing of American put options on the Black and
Scholes market with a stochastic interest rate and finite-time maturity. We
prove that the option value is a $C^1$ function of the initial time, interest
rate and stock price. By means of Ito calculus we rigorously derive the option
value's early exercise premium formula and the associated hedging portfolio. We
prove the existence of an optimal exercise boundary splitting the state space
into continuation and stopping region. The boundary has a parametrisation as a
jointly continuous function of time and stock price, and it is the unique
solution to an integral equation which we compute numerically. Our results hold
for a large class of interest rate models including CIR and Vasicek models. We
show a numerical study of the option price and the optimal exercise boundary
for Vasicek model.


Abstract:
  In this paper, we propose a unified approach for solving structure-preserving
eigenvalue embedding problem (SEEP) for quadratic regular matrix polynomials
with symmetry structures. First, we determine perturbations of a quadratic
matrix polynomial, unstructured or structured, such that the perturbed
polynomials reproduce a desired invariant pair while maintaining the invariance
of another invariant pair of the unperturbed polynomial. If the latter is
unknown, it is referred to as no spillover perturbation. Then we use these
results for solving the SEEP for structured quadratic matrix polynomials that
include: symmetric, Hermitian, $\star$-even and $\star$-odd quadratic matrix
polynomials. Finally, we show that the obtained analytical expressions of
perturbations can realize existing results for structured polynomials that
arise in real-world applications, as special cases. The obtained results are
supported with numerical examples.


Abstract:
  This study aims to construct a theoretical formulation of a nonequilibrium
process for a system of Bose--Einstein condensate associated with a spontaneous
symmetry breakdown. For this, Thermo Field Dynamics is used. We then describe
the most general forms of a 4x4 transformation and an unperturbed Hamiltonian.
After calculating the $4\times 4$ self-energy and identifying its on-shell, we
impose the renormalization condition in which the entire on-shell self-energy
should vanish. This condition provides a sufficient number of independent
equations to determine all of the parameters in an unperturbed Hamiltonian,
among which the quantum transport equations for normal and anomalous number
distributions are included.


Abstract:
  IMSI catchers have been a long standing and serious privacy problem in pre-5G
mobile networks. To tackle this, 3GPP introduced the Subscription Concealed
Identifier (SUCI) and other countermeasures in 5G. In this paper, we analyze
the new SUCI mechanism and discover that it provides very poor anonymity when
used with the variable length Network Specific Identifiers (NSI), which are
part of the 5G standard. When applied to real-world name length data, we see
that SUCI only provides 1-anonymity, meaning that individual subscribers can
easily be identified and tracked. We strongly recommend 3GPP and GSMA to
standardize and recommend the use of a padding mechanism for SUCI before
variable length identifiers get more commonly used. We further show that the
padding schemes, commonly used for network traffic, are not optimal for padding
of identifiers based on real names. We propose a new improved padding scheme
that achieves much less message expansion for a given $k$-anonymity.


Abstract:
  Automatically identifying harmful content in video is an important task with
a wide range of applications. However, due to the difficulty of collecting
high-quality labels as well as demanding computational requirements, the task
has not yet had a fully general approach. Typically, only small subsets of the
problem are considered, such as identifying violent content. In cases where the
general problem is tackled, approximations and simplifications are made to deal
with the lack of labels and computational complexity. In this work, we identify
and tackle some of the main obstacles. First, we create an open dataset of 3589
video clips from film trailers and annotated by professionals in the field.
Second, we perform an analysis of our constructed dataset, investigating among
other things the relation between clip and trailer level annotations. Lastly,
we train audiovisual models on our dataset and conduct an in-depth study on our
modeling choices. We find that results greatly improve by combining the visual
and audio modality and that pre-training on large-scale video recognition
datasets as well as class balanced sampling further improves performance.
Further details of our dataset is available at this webpage:
https://vidharm.github.io/.


Abstract:
  In this paper, we study the trade-off between the transmission cost and the
control performance of the multi-loop networked control system subject to
network-induced delay. Within the linear-quadratic-Gaussian (LQG) framework,
the joint design of control policy and networking strategy is decomposed into
separation optimization problems. Based on the trade-off analysis, a scalable,
delay-dependent Value-of-Information (VoI) based scheduling policy is
constructed to quantify the value of transmitting the data packet, and enables
the decision-makers embedded in subsystems to determine the transmission
policy. The proposed scalable VoI inherits the task criticality of the previous
VoI metric meanwhile is sensitive to the system parameters such as information
freshness and network delays. The VoI-based scheduling policy is proved to
outperform the periodical triggering policy and existing Age-of-Information
(AoI) based policy for network control system under transmission delay. The
effectiveness of the constructed VoI with arbitrary network delay is validated
through numerical simulations.


Abstract:
  The electronic properties of $\pi$-conjugated two-dimensional (2D) polymers
near the Fermi level are determined by structural topology and chemical
composition. Thus, tight-binding (TB) calculations of the corresponding
fundamental network can be used to explore the parameter space to find
configurations with intriguing properties before designing the the atomistic 2D
polymer network. The vertex-transitive \textbf{fes} lattice, which is also
called square-octagon lattice, is rich in interesting topological features
including Dirac points and flat bands. Herein, we study its electronic and
topological properties within the TB framework using representative parameters
for chemical systems. Secondly, we demonstrate that the rational implementation
of band structure features obtained from TB calculations into 2D polymers is
feasible with a family of 2D polymers possessing \textbf{fes} structure. A
one-to-one band structure correspondence between fundamental network and 2D
polymers is found. Moreover, changing the relative length of linkers connecting
the triangulene units in the 2D polymers reflect tuning of hopping parameters
in the TB model. These perturbations allow to open sizeable local band gaps at
various positions in the Brillouin zone. From analysis of Berry curvature flux,
none of the polymers exhibits a large topologically non-trivial band gap.
However, we find a particular configuration of semimetallic characteristics
with separate electron and hole pockets, which possess very low effective
masses both for electrons (as small as $m^*_\mathrm{e} = 0.05$) and holes (as
small as $m^*_\mathrm{h} = 0.01$).


Abstract:
  We investigate the generation of entanglement through a quantum dot molecule
under the influence of vibrational phonon modes in a bias voltage junction. The
molecular quantum dot system is realized by coupled quantum dots inside a
suspended carbon nanotube. We consider the dynamical entanglement as a function
of bias voltage and temperature by taking into account the electron-phonon
interaction. In order to generate the robust entanglement between quantum dots
and preserve it to reach the maximal achievable amount steadily, we introduce
an asymmetric coupling protocol and apply the easy tunable bias voltage-driven
field. For an oscillating bias voltage, the time-varying entanglement can
periodically reach the maximum revival. In thermal entanglement dynamics, the
phenomena of thermal entanglement degradation and thermal entanglement revival
are observed which are intensively affected by the strength of phonon
decoherence. The revival of entanglement shows a larger value for a higher
phonon coupling.


Abstract:
  We consider the fully nonlinear equation with variable-exponent double phase
type degeneracies $$ \big[|Du|^{p(x)}+a(x)|Du|^{q(x)}\big]F(D^2u)=f(x). $$
Under some appropriate assumptions, by making use of geometric tangential
methods and combing a refined improvement-of-flatness approach with compactness
and scaling techniques we obtain the sharp local $C^{1,\alpha}$ regularity of
viscosity solutions to such equations.


Abstract:
  We construct an example of a smooth spatial quartic surface that contains 800
irreducible conics.


Abstract:
  Physics-informed neural networks (PINNs) impose known physical laws into the
learning of deep neural networks, making sure they respect the physics of the
process while decreasing the demand of labeled data. For systems represented by
Ordinary Differential Equations (ODEs), the conventional PINN has a continuous
time input variable and outputs the solution of the corresponding ODE. In their
original form, PINNs do not allow control inputs neither can they simulate for
long-range intervals without serious degradation in their predictions. In this
context, this work presents a new framework called Physics-Informed Neural Nets
for Control (PINC), which proposes a novel PINN-based architecture that is
amenable to \emph{control} problems and able to simulate for longer-range time
horizons that are not fixed beforehand. The framework has new inputs to account
for the initial state of the system and the control action. In PINC, the
response over the complete time horizon is split such that each smaller
interval constitutes a solution of the ODE conditioned on the fixed values of
initial state and control action for that interval. The whole response is
formed by feeding back the predictions of the terminal state as the initial
state for the next interval. This proposal enables the optimal control of
dynamic systems, integrating a priori knowledge from experts and data collected
from plants into control applications. We showcase our proposal in the control
of two nonlinear dynamic systems: the Van der Pol oscillator and the four-tank
system.


Abstract:
  Forecasting graph-based time-dependent data has many practical applications.
This task is challenging as models need not only to capture spatial dependency
and temporal dependency within the data, but also to leverage useful auxiliary
information for accurate predictions. In this paper, we analyze limitations of
state-of-the-art models on dealing with temporal dependency. To address this
limitation, we propose GSA-Forecaster, a new deep learning model for
forecasting graph-based time-dependent data. GSA-Forecaster leverages graph
sequence attention (GSA), a new attention mechanism proposed in this paper, for
effectively capturing temporal dependency. GSA-Forecaster embeds the graph
structure of the data into its architecture to address spatial dependency.
GSA-Forecaster also accounts for auxiliary information to further improve
predictions. We evaluate GSA-Forecaster with large-scale real-world graph-based
time-dependent data and demonstrate its effectiveness over state-of-the-art
models with 6.7% RMSE and 5.8% MAPE reduction.


Abstract:
  Random Forest (RFs) are among the most widely used Machine Learning (ML)
classifiers. Even though RFs are not interpretable, there are no dedicated
non-heuristic approaches for computing explanations of RFs. Moreover, there is
recent work on polynomial algorithms for explaining ML models, including naive
Bayes classifiers. Hence, one question is whether finding explanations of RFs
can be solved in polynomial time. This paper answers this question negatively,
by proving that computing one PI-explanation of an RF is D^P-complete.
Furthermore, the paper proposes a propositional encoding for computing
explanations of RFs, thus enabling finding PI-explanations with a SAT solver.
This contrasts with earlier work on explaining boosted trees (BTs) and neural
networks (NNs), which requires encodings based on SMT/MILP. Experimental
results, obtained on a wide range of publicly available datasets, demontrate
that the proposed SAT-based approach scales to RFs of sizes common in practical
applications. Perhaps more importantly, the experimental results demonstrate
that, for the vast majority of examples considered, the SAT-based approach
proposed in this paper significantly outperforms existing heuristic approaches.


Abstract:
  Substrates have strong effects on optoelectronic properties of
two-dimensional (2D) materials, which have emerged as promising platforms for
exotic physical phenomena and outstanding applications. To reliably interpret
experimental results and predict such effects at 2D interfaces, theoretical
methods accurately describing electron correlation and electron-hole
interaction such as first-principles many-body perturbation theory are
necessary. In our previous work [Phys. Rev. B 102, 205113(2020)], we developed
the reciprocal-space linear interpolation method that can take into account the
effects of substrate screening for arbitrarily lattice-mismatched interfaces at
the GW level of approximation. In this work, we apply this method to examine
the substrate effect on excitonic excitation and recombination of 2D materials
by solving the Bethe-Salpeter equation. We predict the nonrigid shift of 1s and
2s excitonic peaks due to substrate screening, in excellent agreements with
experiments. We then reveal its underlying physical mechanism through 2D
hydrogen model and the linear relation between quasiparticle gaps and exciton
binding energies when varying the substrate screening. At the end, we calculate
the exciton radiative lifetime of monolayer hexagonal boron nitride with
various substrates at zero and room temperature, as well as the one of WS2
where we obtain good agreement with experimental lifetime. Our work answers
important questions of substrate effects on excitonic properties of 2D
interfaces.


Abstract:
  AC transmission lines with lengths greater than 80km cannot be used to their
maximum capacity due to limits in voltage drops and transient stability. This
inefficient way of using conductors in a transmission line can be overcome if
the electrical frequency at which energy is transmitted is reduced. This is why
this work focuses on the comparison of the Modular Multilevel Matrix Converter
(MMMC) and the Back-to-Back Modular Multilevel Converter (BTB-MMC), topologies
that have shown qualities as frequency converters. For comparison, an
analytical model of each topology is used to relate design considerations to
their operational variables. Among the aspects to be compared are, the
requirements of the semiconductors, the required reactive components, operating
losses and fault tolerance. Detailed design equations, EMTP simulations, and
comparison table are presented.


Abstract:
  Among the Markov chains breaking detailed-balance that have been proposed in
the field of Monte-Carlo sampling in order to accelerate the convergence
towards the steady state with respect to the detailed-balance dynamics, the
idea of 'Lifting' consists in duplicating the configuration space into two
copies $\sigma=\pm$ and in imposing directed flows in each copy in order to
explore the configuration space more efficiently. The skew-detailed-balance
Lifted-Markov-chain introduced by K. S. Turitsyn, M. Chertkov and M. Vucelja
[Physica D Nonlinear Phenomena 240 , 410 (2011)] is revisited for the
Curie-Weiss mean-field ferromagnetic model, where the dynamics for the
magnetization is closed. The large deviations at various levels for empirical
time-averaged observables are analyzed and compared with their detailed-balance
counterparts, both for the discrete extensive magnetization $M$ and for the
continuous intensive magnetization $m=\frac{M}{N}$ for large system-size $N$.


Abstract:
  We propose a new fast fully unsupervised method to discover semantic
patterns. Our algorithm is able to hierarchically find visual categories and
produce a segmentation mask where previous methods fail. Through the modeling
of what is a visual pattern in an image, we introduce the notion of "semantic
levels" and devise a conceptual framework along with measures and a dedicated
benchmark dataset for future comparisons. Our algorithm is composed by two
phases. A filtering phase, which selects semantical hotsposts by means of an
accumulator space, then a clustering phase which propagates the semantic
properties of the hotspots on a superpixels basis. We provide both qualitative
and quantitative experimental validation, achieving optimal results in terms of
robustness to noise and semantic consistency. We also made code and dataset
publicly available.


Abstract:
  Discrete Ginzburg-Landau (DGL) equations with non-local nonlinearities have
been established as significant inherently discrete models in numerous physical
contexts, similar to their counterparts with local nonlinear terms. We study
two prototypical examples of non-local and local DGLs on the one-dimensional
infinite lattice. For the non-local DGL, we identify distinct scenarios for the
asymptotic behavior of the globally existing in time solutions depending on
certain parametric regimes. One of these scenarios is associated with a
restricted compact attractor according to J. K. Hale's definition. We also
prove the closeness of the solutions of the two models in the sense of a
"continuous dependence on their initial data" in the $l^2$ metric under general
conditions on the intrinsic linear gain or loss incorporated in the model. As a
consequence of the closeness results, in the dissipative regime we establish
the congruence of the attractors possessed by the semiflows of the non-local
and of the local model respectively, for initial conditions in a suitable
domain of attraction defined by the non-local system.


Abstract:
  Standard approaches to decision-making under uncertainty focus on sequential
exploration of the space of decisions. However, \textit{simultaneously}
proposing a batch of decisions, which leverages available resources for
parallel experimentation, has the potential to rapidly accelerate exploration.
We present a family of (parallel) contextual linear bandit algorithms, whose
regret is nearly identical to their perfectly sequential counterparts -- given
access to the same total number of oracle queries -- up to a lower-order
"burn-in" term that is dependent on the context-set geometry. We provide
matching information-theoretic lower bounds on parallel regret performance to
establish our algorithms are asymptotically optimal in the time horizon.
Finally, we also present an empirical evaluation of these parallel algorithms
in several domains, including materials discovery and biological sequence
design problems, to demonstrate the utility of parallelized bandits in
practical settings.


Abstract:
  Magnons enable transferring a magnetic moment or spin over macroscopic
distance. In quantum Hall ferromagnet, it has been predicted in the early 90s
that spin and charges are entangled, meaning that any change of the spin
texture modifies the charge distribution. As a direct consequence of this
entanglement, magnons carry an electric dipole moment. Here we report the first
evidence of the existence of this electric dipole moment in a graphene quantum
Hall ferromagnet using a Mach-Zehnder interferometer as a quantum sensor. By
propagating towards the interferometer through an insulating bulk, the magnon
electric dipole moment modifies the Aharonov-Bohm flux through the
interferometer, changing both its phase and its visibility. In particular, we
relate the phase shift to the sign of this electric dipole moment, and the
exponential loss of visibility to the flux of emitted magnons. Finally, we
probe the emission energy threshold of the magnons close to filling factor v=1.
Approaching v=0, we observe that the emission energy threshold diminishes
towards zero, which might be linked to the existence of gapless mode in the
canted-antiferromagnetic (CAF) phase at v=0. The detection and manipulation of
magnons based on their electric dipole open the field for a new type of
coherent magnon quantum circuits that will be electrostatically controlled.


Abstract:
  We propose a novel approach for modelling bat motion dynamics and use it to
predict roost locations using data from static acoustic detectors.
Specifically, radio tracking studies of Greater Horseshoe bats demonstrate that
bat movement can be split into two phases: dispersion and return. Dispersion is
easily understood and can be modelled as simple random motion. The return phase
is much more complex, as it requires intelligent directed motion and results in
all agents returning home in a stereotypical manner. Critically, combining
reaction-diffusion theory and domain shrinking we deterministically and
stochastically model a ``leap-frogging'' motion, which fits favourably with the
observed tracking data.


Abstract:
  We propose self-adaptive training -- a unified training algorithm that
dynamically calibrates and enhances training processes by model predictions
without incurring an extra computational cost -- to advance both supervised and
self-supervised learning of deep neural networks. We analyze the training
dynamics of deep networks on training data that are corrupted by, e.g., random
noise and adversarial examples. Our analysis shows that model predictions are
able to magnify useful underlying information in data and this phenomenon
occurs broadly even in the absence of any label information, highlighting that
model predictions could substantially benefit the training processes:
self-adaptive training improves the generalization of deep networks under noise
and enhances the self-supervised representation learning. The analysis also
sheds light on understanding deep learning, e.g., a potential explanation of
the recently-discovered double-descent phenomenon in empirical risk
minimization and the collapsing issue of the state-of-the-art self-supervised
learning algorithms. Experiments on the CIFAR, STL, and ImageNet datasets
verify the effectiveness of our approach in three applications: classification
with label noise, selective classification, and linear evaluation. To
facilitate future research, the code has been made publicly available at
https://github.com/LayneH/self-adaptive-training.


Abstract:
  In this paper, we study the task of source-free domain adaptation (SFDA),
where the source data are not available during target adaptation. Previous
works on SFDA mainly focus on aligning the cross-domain distributions. However,
they ignore the generalization ability of the pretrained source model, which
largely influences the initial target outputs that are vital to the target
adaptation stage. To address this, we make the interesting observation that the
model accuracy is highly correlated with whether or not attention is focused on
the objects in an image. To this end, we propose a generic and effective
framework based on Transformer, named TransDA, for learning a generalized model
for SFDA. Specifically, we apply the Transformer as the attention module and
inject it into a convolutional network. By doing so, the model is encouraged to
turn attention towards the object regions, which can effectively improve the
model's generalization ability on the target domains. Moreover, a novel
self-supervised knowledge distillation approach is proposed to adapt the
Transformer with target pseudo-labels, thus further encouraging the network to
focus on the object regions. Experiments on three domain adaptation tasks,
including closed-set, partial-set, and open-set adaption, demonstrate that
TransDA can greatly improve the adaptation accuracy and produce
state-of-the-art results. The source code and trained models are available at
https://github.com/ygjwd12345/TransDA.


Abstract:
  We study the effects of an attractive interaction between the boron (B) and
the nitrogen (N) atoms doped in a bilayer graphene (BLG), BC$_{14}$N, on the
electronic, the thermal and the optical properties for two different types of a
doping process: First, both the B and the N atoms are doped in the same layer
while the other layer is undoped. Second, the B and N atoms are doped in both
layers. An attractive interaction between the B and N atoms does not influence
the interlayer interaction in the first case, while it does in the second case.
We find that the strong B-N attractive interaction in one layer induces
metallic behavior due to the crossing of the valence band and the Fermi energy,
while the strong attractive interaction between both layers induces a
semiconductor property arising from the emergence a bandgap. We therefore
confirm that the metallic-like BLG is not a good material for thermal devices
because it has a low figure of merit, while we notice that the
semiconductor-like BLG has a high Seebeck coefficient and figure of merit as
well as a low thermal conductivity. The strong attractive interaction of the
B-N atoms between the layers gives rise to a prominent peak to appear in
dielectric function, the excitation and the absorption spectra in the low
energy, visible range, while a very weak peak is seen in the case of a strong
attractive interaction between the B and N doped in one layer. Controlling the
B and N atomic configurations in the BLG may help to improve the material for
use in both thermoelectric and optoelectronic devices.


Abstract:
  We consider a class of exponentials in the Weyl-Heisenberg algebra with
exponents of type at most linear in coordinates and arbitrary functions of
momenta. They are expressed in terms of normal ordering where coordinates stand
to the left from momenta. Exponents appearing in normal ordered form satisfy
differential equations with boundary conditions that could be solved
perturbatively order by order. Two propositions are presented for the
Weyl-Heisenberg algebra in 2 dimensions and their generalizations in higher
dimensions. These results can be applied to arbitrary noncommutative spaces for
construction of star products, coproducts of momenta and twist operators. They
can also be related to the BCH formula.


Abstract:
  Spin waves in magnetic microresonators are at the core of modern magnonics.
Here we demonstrate a new method of tunable excitation of different spin wave
modes in magnetic microdisks by using a train of laser pulses coming at a
repetition rate higher than the decay rate of spin precession. The microdisks
are etched in a transparent bismuth iron garnet film and the light pulses
influence the spins nonthermally through the inverse Faraday effect. The high
repetition rate of the laser stimulus of 10 GHz establishes an interplay
between the spin wave resonances in the frequency and momentum domains. As a
result, the excitation efficiency of different spin modes can be tuned by a
small variation of the external magnetic field. An additional degree of freedom
is provided by scanning the laser spot within the microdisk area. This makes
the proposed method for spin wave excitation advantageous for the forthcoming
application of magnonics for telecommunication and quantum technologies.


Abstract:
  Finding the best mathematical equation to deal with the different challenges
found in complex scenarios requires a thorough understanding of the scenario
and a trial and error process carried out by experts. In recent years, most
state-of-the-art equation discovery methods have been widely applied in
modeling and identification systems. However, equation discovery approaches can
be very useful in computer vision, particularly in the field of feature
extraction. In this paper, we focus on recent AI advances to present a novel
framework for automatically discovering equations from scratch with little
human intervention to deal with the different challenges encountered in
real-world scenarios. In addition, our proposal can reduce human bias by
proposing a search space design through generative network instead of
hand-designed. As a proof of concept, the equations discovered by our framework
are used to distinguish moving objects from the background in video sequences.
Experimental results show the potential of the proposed approach and its
effectiveness in discovering the best equation in video sequences. The code and
data are available at:
https://github.com/carolinepacheco/equation-discovery-scene-analysis


Abstract:
  This paper studies sequential search models that (1) incorporate unobserved
product quality, which can be correlated with endogenous observable
characteristics (such as price) and endogenous search cost variables (such as
product rankings in online search intermediaries); and (2) do not require
researchers to know the true distribution of the match value between consumers
and products. A likelihood approach to estimate such models gives biased
results. Therefore, I propose a new estimator -- pairwise maximum rank (PMR)
estimator -- for both preference and search cost parameters. I show that the
PMR estimator is consistent using only data on consumers' search order among
one pair of products rather than data on consumers' full consideration set or
final purchase. Additionally, we can use the PMR estimator to test for the true
match value distribution in the data. In the empirical application, I apply the
PMR estimator to quantify the effect of rankings in Expedia hotel search using
two samples of the data set, to which consumers are randomly assigned. I find
the position effect to be \$0.11-\$0.36, and the effect estimated using the
sample with randomly generated rankings is close to the effect estimated using
the sample with endogenous rankings. Moreover, I find that the true match value
distribution in the data is unlikely to be N(0,1). Likelihood estimation
ignoring endogeneity gives an upward bias of at least \$1.17; misspecification
of match value distribution as N(0,1) gives an upward bias of at least \$2.99.


Abstract:
  A left brace is a triple $(\mathcal{B},+,\cdot)$, where $(\mathcal{B},+)$ is
an abelian group, $(\mathcal{B},\cdot)$ is a group, and there is a
left-distributivity-like axiom that relates between the two operations in
$\mathcal{B}$. In analogy with a left brace, we define a left
$\mathscr{M}$-brace to be a triple $(\mathcal{B},+,\cdot)$, where
$(\mathcal{B},+)$ is a commutative monoid, $(\mathcal{B},\cdot)$ is a monoid,
and the axiom of left distributivity holds. A lcm-monoid $M$ is a
left-cancellative monoid such that $1$ is the unique invertible element in $M$,
and every pair of elements in $M$ admit a lcm with respect to
left-divisibility. The class of lcm-monoids contains the Gaussian,
quasi-Garside and Garside monoids. We show that every lcm-monoid induces a left
$\mathscr{M}$-brace. Furthermore, we show that every Gaussian group induces a
partial left brace.


Abstract:
  Modern software systems, such as Spark, are usually written in multiple
programming languages (PLs). Besides benefiting from code reuse, such systems
can also take advantages of specific PLs to implement certain features, to meet
various quality needs, and to improve development efficiency. In this context,
a change to such systems may need to modify source files written in different
PLs. We define a multi-programming-language commit (MPLC) in a version control
system (e.g., Git) as a commit that involves modified source files written in
two or more PLs. To our knowledge, the phenomenon of MPLCs in software
development has not been explored yet. In light of the potential impact of
MPLCs on development difficulty and software quality, we performed an empirical
study to understand the state of MPLCs, their change complexity, as well as
their impact on open time of issues and bug proneness of source files in
real-life software projects. By exploring the MPLCs in 20 non-trivial Apache
projects with 205,994 commits, we obtained the following findings: (1) 9% of
the commits from all the projects are MPLCs, and the proportion of MPLCs in 80%
of the projects goes to a relatively stable level; (2) more than 90% of the
MPLCs from all the projects involve source files written in two PLs; (3) the
change complexity of MPLCs is significantly higher than that of non-MPLCs in
all projects; (4) issues fixed in MPLCs take significantly longer to be
resolved than issues fixed in non-MPLCs in 80% of the projects; and (5) source
files that have been modified in MPLCs tend to be more bug-prone than source
files that have never been modified in MPLCs. These findings provide
practitioners with useful insights on the architecture design and quality
management of software systems written in multiple PLs.


Abstract:
  Deep anomaly detection methods learn representations that separate between
normal and anomalous samples. Very effective representations are obtained when
powerful externally trained feature extractors (e.g. ResNets pre-trained on
ImageNet) are fine-tuned on the training data which consists of normal samples
and no anomalies. However, this is a difficult task that can suffer from
catastrophic collapse, i.e. it is prone to learning trivial and non-specific
features. In this paper, we propose a new loss function which can overcome
failure modes of both center-loss and contrastive-loss methods. Furthermore, we
combine it with a confidence-invariant angular center loss, which replaces the
Euclidean distance used in previous work, that was sensitive to prediction
confidence. Our improvements yield a new anomaly detection approach, based on
$\textit{Mean-Shifted Contrastive Loss}$, which is both more accurate and less
sensitive to catastrophic collapse than previous methods. Our method achieves
state-of-the-art anomaly detection performance on multiple benchmarks including
$97.5\%$ ROC-AUC on the CIFAR-10 dataset.


Abstract:
  Bundles of filaments are subject to geometric frustration: certain
deformations (e.g. bending while twisted) require longitudinal variations in
spacing between filaments. While bundles are common -- from protein fibers to
yarns -- the mechanical consequences of longitudinal frustration are unknown.
We derive a geometrically-nonlinear formalism for bundle mechanics, using a
gauge-like symmetry under reptations along filament backbones. We relate force
balance to orientational geometry and assess the elastic cost of frustration in
twisted toroidal bundles.


Abstract:
  Reaching the 2030 targets for the EU primary energy use (PE) and CO2eq
emissions (CE) requires an accurate assessment of how different technologies
perform on these two fronts. In this regard, the focus in academia is
increasingly shifting from traditional technologies to electricity consuming
alternatives. Calculating and comparing their performance with respect to
traditional technologies requires conversion factors (CFs) like a primary
energy factor and a CO2eq intensity. These reflect the PE and CE associated
with each unit of electricity consumed. Previous work has shown that the
calculation and use of CFs is a contentious and multifaceted issue. However,
this has mostly remained a theoretical discussion. A stock-taking of how CFs
are actually calculated and used in academic literature has so far been
missing, impeding insight into what the contemporary trends and challenges are.
Therefore, we structurally review 65 publications across six methodological
aspects. We find that 72% of the publications consider only a single country,
86% apply a purely retrospective perspective, 54% apply a yearly temporal
resolution, 65% apply a purely operational (instead of a life-cycle)
perspective, 91% make use of average (rather than marginal) CFs, and 75% ignore
electricity imports from surrounding countries. We conclude that there is a
strong need in the literature for a publicly available, transparently
calculated dataset of CFs, which avoids the shortcomings found in the
literature. This would enable more accurate and transparent PE and CE
calculations, and support the development of new building energy performance
assessment methods and smart grid algorithms.


Abstract:
  In a typical Internet-of-Things setting that involves scientific
applications, a target computation can be evaluated in many different ways
depending on the split of computations among various devices. On the one hand,
different implementations (or algorithms)--equivalent from a mathematical
perspective--might exhibit significant difference in terms of performance. On
the other hand, some of the implementations are likely to show similar
performance characteristics. In this paper, we focus on analyzing the
performance of a given set of algorithms by clustering them into performance
classes. To this end, we use a measurement-based approach to evaluate and score
algorithms based on pair-wise comparisons; we refer to this approach
as"Relative performance analysis". Each comparison yields one of three
outcomes: one algorithm can be "better", "worse", or "equivalent" to another;
those algorithms evaluating to have equivalent performance are merged into the
same performance class. We show that our clustering methodology facilitates
algorithm selection with respect to more than one metric; for instance, from
the subset of equivalently fast algorithms, one could then select an algorithm
that consumes the least energy on a certain device.


Abstract:
  The planar problem of a viscous laminar flow around elliptical cylinders
under angle of attack is considered. From the solution of the laminar boundary
layer equations using the Loytsyansky local similarity method, the shear stress
at the ellipse boundary and the separation point is found. From the separation
points velocities equality, the circulation is found. A complete solution to
the problem of the velocity and pressure field outside the boundary layer is
also constructed. The coefficients of lift and resistance are found depending
on the angle of attack and the ellipse axes ratio. The theoretical results are
compared with the available experimental data and direct numerical solutions of
the Navier-Stokes equations.


Abstract:
  The dichromatic number $\vec{\chi}(D)$ of a digraph $D$ is the smallest $k$
for which it admits a $k$-coloring where every color class induces an acyclic
subgraph. Inspired by Hadwiger's conjecture for undirected graphs, several
groups of authors have recently studied the containment of directed graph
minors in digraphs with given dichromatic number. In this short note we improve
several of the existing bounds and prove almost linear bounds by reducing the
problem to a recent result of Postle on Hadwiger's conjecture.


Abstract:
  Modeling the time evolution of discrete sets of items (e.g., genetic
mutations) is a fundamental problem in many biomedical applications. We
approach this problem through the lens of continuous-time Markov chains, and
show that the resulting learning task is generally underspecified in the usual
setting of cross-sectional data. We explore a perhaps surprising remedy:
including a number of additional independent items can help determine time
order, and hence resolve underspecification. This is in sharp contrast to the
common practice of limiting the analysis to a small subset of relevant items,
which is followed largely due to poor scaling of existing methods. To put our
theoretical insight into practice, we develop an approximate likelihood
maximization method for learning continuous-time Markov chains, which can scale
to hundreds of items and is orders of magnitude faster than previous methods.
We demonstrate the effectiveness of our approach on synthetic and real cancer
data.


Abstract:
  Purpose: Computational Fluid Dynamics (CFD) simulations are performed to
investigate the impact of adding a grid to a two-inlet dry powder inhaler
(DPI). The purpose of the paper is to show the importance of the correct choice
of closure model and modeling approach, as well as to perform validation
against particle dispersion data obtained from in-vitro studies and flow
velocity data obtained from particle image velocimetry (PIV) experiments.
Methods: CFD simulations are performed using the Ansys Fluent 2020R1 software
package. Two RANS turbulence models (realisable $k - \epsilon$ and $k - \omega$
SST) and the Stress Blended Eddy Simulation (SBES) models are considered.
Lagrangian particle tracking for both carrier and fine particles is also
performed. Results: Excellent comparison with the PIV data is found for the
SBES approach and the particle tracking data are consistent with the dispersion
results, given the simplicity of the assumptions made. Conclusions: This work
shows the importance of selecting the correct turbulence modelling approach and
boundary conditions to obtain good agreement with PIV data for the flow-field
exiting the device. With this validated, the model can be used with much higher
confidence to explore the fluid and particle dynamics within the device.


Abstract:
  When primed with only a handful of training samples, very large pretrained
language models such as GPT-3, have shown competitive results when compared to
fully-supervised fine-tuned large pretrained language models. We demonstrate
that the order in which the samples are provided can be the difference between
near state-of-the-art and random guess performance: Essentially some
permutations are "fantastic" and some not. We analyse this phenomenon in
detail, establishing that: it is present across model sizes (even for the
largest current models), it is not related to a specific subset of samples, and
that a given good permutation for one model is not transferable to another.
While one could use a development set to determine which permutations are
performant, this would deviate from the few-shot setting as it requires
additional annotated data. Instead, we use the generative nature of the
language models to construct an artificial development set and based on entropy
statistics of the candidate permutations from this set we identify performant
prompts. Our method improves upon GPT-family models by on average 13% relative
across eleven different established text classification tasks.


Abstract:
  Sequentially obtained dataset usually exhibits different behavior at
different data resolutions/scales. Instead of inferring from data at each scale
individually, it is often more informative to interpret the data as an ensemble
of time series from different scales. This naturally motivated us to propose a
new concept referred to as the scale-based inference. The basic idea is that
more accurate prediction can be made by exploiting scale information of a time
series. We first propose a nonparametric predictor based on $k$-nearest
neighbors with an optimally chosen $k$ for a single time series. Based on that,
we focus on a specific but important type of scale information, the
resolution/sampling rate of time series data. We then propose an algorithm to
sequentially predict time series using past data at various resolutions. We
prove that asymptotically the algorithm produces the mean prediction error that
is no larger than the best possible algorithm at any single resolution, under
some optimally chosen parameters. Finally, we establish the general
formulations for scale inference, and provide further motivating examples.
Experiments on both synthetic and real data illustrate the potential
applicability of our approaches to a wide range of time series models.


Abstract:
  The $\text{t}\bar{\text{t}}\text{H}(\text{b}\bar{\text{b}})$ process is an
essential channel to reveal the Higgs properties but has an irreducible
background from the $\text{t}\bar{\text{t}}\text{b}\bar{\text{b}}$ process,
which produces a top quark pair in association with a b quark pair. Therefore,
understanding the $\text{t}\bar{\text{t}}\text{b}\bar{\text{b}}$ process is
crucial for improving the sensitivity of a search for the
$\text{t}\bar{\text{t}}\text{H}(\text{b}\bar{\text{b}})$ process. To this end,
when measuring the differential cross-section of the
$\text{t}\bar{\text{t}}\text{b}\bar{\text{b}}$ process, we need to distinguish
the b-jets originated from top quark decays, and additional b-jets originated
from gluon splitting. Since there are no simple identification rules, we adopt
deep learning methods to learn from data to identify the additional b-jets from
the $\text{t}\bar{\text{t}}\text{b}\bar{\text{b}}$ events. Specifically, by
exploiting the special structure of the
$\text{t}\bar{\text{t}}\text{b}\bar{\text{b}}$ event data, we propose several
loss functions that can be minimized to directly increase the matching
efficiency, the accuracy of identifying additional b-jets. We discuss the
difference between our method and another deep learning-based approach based on
binary classification arXiv:1910.14535 using synthetic data. We then verify
that additional b-jets can be identified more accurately by increasing matching
efficiency directly rather than the binary classification accuracy, using
simulated $\text{t}\bar{\text{t}}\text{b}\bar{\text{b}}$ event data in the
lepton+jets channel from pp collision at $\sqrt{s}$ = 13 TeV.


Abstract:
  Exposure mappings facilitate investigations of complex causal effects when
units interact in experiments. Current methods assume that the exposures are
correctly specified, but such an assumption cannot be verified, and its
validity is often questionable. This paper describes conditions under which one
can draw inferences about exposure effects when the exposures are misspecified.
The main result is a proof of consistency under mild conditions on the errors
introduced by the misspecification. The rate of convergence is determined by
the dependence between units' specification errors, and consistency is achieved
even if the errors are large as long as they are sufficiently weakly dependent.
In other words, exposure effects can be precisely estimated also under
misspecification as long as the units' exposures are not misspecified in the
same way. The limiting distribution of the estimator is discussed. Asymptotic
normality is achieved under stronger conditions than those needed for
consistency. Similar conditions also facilitate conservative variance
estimation.


Abstract:
  In this work, we determine the full expression of the local truncation error
of hyperbolic partial differential equations (PDEs) on a uniform mesh. If we
are employing a stable numerical scheme and the global solution error is of the
same order of accuracy as the global truncation error, we make the following
observations in the asymptotic regime, where the truncation error is dominated
by the powers of $\Delta x$ and $\Delta t$ rather than their coefficients.
Assuming that we reach the asymptotic regime before the machine precision error
takes over, (a) the order of convergence of stable numerical solutions of
hyperbolic PDEs at constant ratio of $\Delta t$ to $\Delta x$ is governed by
the minimum of the orders of the spatial and temporal discretizations, and (b)
convergence cannot even be guaranteed under only spatial or temporal
refinement. We have tested our theory against numerical methods employing
Method of Lines and not against ones that treat space and time together, and we
have not taken into consideration the reduction in the spatial and temporal
orders of accuracy resulting from slope-limiting monotonicity-preserving
strategies commonly applied to finite volume methods. Otherwise, our theory
applies to any hyperbolic PDE, be it linear or non-linear, and employing finite
difference, finite volume, or finite element discretization in space, and
advanced in time with a predictor-corrector, multistep, or a deferred
correction method. If the PDE is reduced to an ordinary differential equation
(ODE) by specifying the spatial gradients of the dependent variable and the
coefficients and the source terms to be zero, then the standard local
truncation error of the ODE is recovered. We perform the analysis with generic
and specific hyperbolic PDEs using the symbolic algebra package SymPy, and
conduct a number of numerical experiments to demonstrate our theoretical
findings.