Abstract:
  This paper investigates the transmission power control in over-the-air
federated edge learning (Air-FEEL) system. Different from conventional power
control designs (e.g., to minimize the individual mean squared error (MSE) of
the over-the-air aggregation at each round), we consider a new power control
design aiming at directly maximizing the convergence speed. Towards this end,
we first analyze the convergence behavior of Air-FEEL (in terms of the
optimality gap) subject to aggregation errors at different communication
rounds. It is revealed that if the aggregation estimates are unbiased, then the
training algorithm would converge exactly to the optimal point with mild
conditions; while if they are biased, then the algorithm would converge with an
error floor determined by the accumulated estimate bias over communication
rounds. Next, building upon the convergence results, we optimize the power
control to directly minimize the derived optimality gaps under both biased and
unbiased aggregations, subject to a set of average and maximum power
constraints at individual edge devices. We transform both problems into convex
forms, and obtain their structured optimal solutions, both appearing in a form
of regularized channel inversion, by using the Lagrangian duality method.
Finally, numerical results show that the proposed power control policies
achieve significantly faster convergence for Air-FEEL, as compared with
benchmark policies with fixed power transmission or conventional MSE
minimization.


Abstract:
  The social media platform is a convenient medium to express personal thoughts
and share useful information. It is fast, concise, and has the ability to reach
millions. It is an effective place to archive thoughts, share artistic content,
receive feedback, promote products, etc. Despite having numerous advantages
these platforms have given a boost to hostile posts. Hate speech and derogatory
remarks are being posted for personal satisfaction or political gain. The
hostile posts can have a bullying effect rendering the entire platform
experience hostile. Therefore detection of hostile posts is important to
maintain social media hygiene. The problem is more pronounced languages like
Hindi which are low in resources. In this work, we present approaches for
hostile text detection in the Hindi language. The proposed approaches are
evaluated on the Constraint@AAAI 2021 Hindi hostility detection dataset. The
dataset consists of hostile and non-hostile texts collected from social media
platforms. The hostile posts are further segregated into overlapping classes of
fake, offensive, hate, and defamation. We evaluate a host of deep learning
approaches based on CNN, LSTM, and BERT for this multi-label classification
problem. The pre-trained Hindi fast text word embeddings by IndicNLP and
Facebook are used in conjunction with CNN and LSTM models. Two variations of
pre-trained multilingual transformer language models mBERT and IndicBERT are
used. We show that the performance of BERT based models is best. Moreover, CNN
and LSTM models also perform competitively with BERT based models.


Abstract:
  In this paper, we address zero-shot learning (ZSL), the problem of
recognizing categories for which no labeled visual data are available during
training. We focus on the transductive setting, in which unlabelled visual data
from unseen classes is available. State-of-the-art paradigms in ZSL typically
exploit generative adversarial networks to synthesize visual features from
semantic attributes. We posit that the main limitation of these approaches is
to adopt a single model to face two problems: 1) generating realistic visual
features, and 2) translating semantic attributes into visual cues. Differently,
we propose to decouple such tasks, solving them separately. In particular, we
train an unconditional generator to solely capture the complexity of the
distribution of visual data and we subsequently pair it with a conditional
generator devoted to enrich the prior knowledge of the data distribution with
the semantic content of the class embeddings. We present a detailed ablation
study to dissect the effect of our proposed decoupling approach, while
demonstrating its superiority over the related state-of-the-art.


Abstract:
  We construct closed immersions from initial degenerations of the spinor
variety $\mathbb{S}_n$ to inverse limits of strata associated to even
$\Delta$-matroids. As an application, we prove that these initial degenerations
are smooth and irreducible for $n\leq 5$ and identify the log canonical model
of the Chow quotient of $\mathbb{S}_5$ by the action of the diagonal torus of
$\operatorname{GL}(5)$.


Abstract:
  We provide an abstract characterization for the Cuntz semigroup of unital
commutative AI-algebras, as well as a characterization for abstract Cuntz
semigroups of the form $\text{Lsc} (X,\overline{\mathbb{N}})$ for some
$T_1$-space $X$. In our investigations, we also uncover new properties that the
Cuntz semigroup of all AI-algebras satisfies.


Abstract:
  Understanding the effects of interventions, such as restrictions on community
and large group gatherings, is critical to controlling the spread of COVID-19.
Susceptible-Infectious-Recovered (SIR) models are traditionally used to
forecast the infection rates but do not provide insights into the causal
effects of interventions. We propose a spatiotemporal model that estimates the
causal effect of changes in community mobility (intervention) on infection
rates. Using an approximation to the SIR model and incorporating spatiotemporal
dependence, the proposed model estimates a direct and indirect (spillover)
effect of intervention. Under an interference and treatment ignorability
assumption, this model is able to estimate causal intervention effects, and
additionally allows for spatial interference between locations. Reductions in
community mobility were measured by cell phone movement data. The results
suggest that the reductions in mobility decrease Coronavirus cases 4 to 7 weeks
after the intervention.


Abstract:
  Governments, Healthcare, and Private Organizations in the global scale have
been using digital tracking to keep COVID-19 outbreaks under control. Although
this method could limit pandemic contagion, it raises significant concerns
about user privacy. Known as ~"Contact Tracing Apps", these mobile applications
are facilitated by Cellphone Service Providers (CSPs), who enable the spatial
and temporal real-time user tracking. Accordingly, it might be speculated that
CSPs collect information violating the privacy policies such as GDPR, CCPA, and
others. To further clarify, we conducted an in-depth analysis comparing privacy
legislations with the real-world practices adapted by CSPs. We found that three
of the regulations (GDPR, COPPA, and CCPA) analyzed defined mobile location
data as private information, and two (T-Mobile US, Boost Mobile) of the five
CSPs that were analyzed did not comply with the COPPA regulation. Our results
are crucial in view of the threat these violations represent, especially when
it comes to children's data. As such proper security and privacy auditing is
necessary to curtail such violations. We conclude by providing actionable
recommendations to address concerns and provide privacy-preserving monitoring
of the COVID-19 spread through the contact tracing applications.


Abstract:
  Nonlinear surface-plasmon polaritons~(NSPPs) in nanophotonic waveguides
excite with dissimilar temporal properties due to input field modifications and
material characteristics, but they possess similar nonlinear spectral
evolution. In this work, we uncover the origin of this similarity and establish
that the spectral dynamics is an inherent property of the system that depends
on the synthetic dimension and is beyond waveguide geometrical dimensionality.
To this aim, we design an ultra-low loss nonlinear plasmonic waveguide, to
establish the invariance of the surface plasmonic frequency combs~(FCs) and
phase singularities for plasmonic peregrine waves and Akhmediev breather. By
finely tuning the nonlinear coefficient of the interaction interface, we
uncover the conservation conditions through this plasmonic system and employ
the mean-value evolution of the quantum NSPP field commensurate with the
Schr\"odinger equation to evaluate spectral dynamics of the plasmonic
FCs~(PFCs). Through providing suppressed interface losses and modified
nonlinearity as dual requirements for conservative conditions, we propose
exciting PFCs as equally spaced invariant quantities of this plasmonic scheme
and prove that the spectral dynamics of the NSPPs within the interaction
interface yields the formation of plasmonic analog of the synthetic photonic
lattice, which we termed \textit{synthetic plasmonic lattice}~(SPL).


Abstract:
  We continue our previous study of cylindrically symmetric, static
electrovacuum spacetimes generated by a magnetic field, involving optionally
the cosmological constant, and investigate several classes of exact solutions.
These spacetimes are due to magnetic fields that are perpendicular to the axis
of symmetry.


Abstract:
  Factorial designs are widely used due to their ability to accommodate
multiple factors simultaneously. The factor-based regression with main effects
and some interactions is the dominant strategy for downstream data analysis,
delivering point estimators and standard errors via one single regression.
Justification of these convenient estimators from the design-based perspective
requires quantifying their sampling properties under the assignment mechanism
conditioning on the potential outcomes. To this end, we derive the sampling
properties of the factor-based regression estimators from both saturated and
unsaturated models, and demonstrate the appropriateness of the robust standard
errors for the Wald-type inference. We then quantify the bias-variance
trade-off between the saturated and unsaturated models from the design-based
perspective, and establish a novel design-based Gauss--Markov theorem that
ensures the latter's gain in efficiency when the nuisance effects omitted
indeed do not exist. As a byproduct of the process, we unify the definitions of
factorial effects in various literatures and propose a location-shift strategy
for their direct estimation from factor-based regressions. Our theory and
simulation suggest using factor-based inference for general factorial effects,
preferably with parsimonious specifications in accordance with the prior
knowledge of zero nuisance effects.


Abstract:
  One of the most ubiquitous and technologically important phenomena in nature
is the nucleation of homogeneous flowing systems. The microscopic effects of
shear on a nucleating system are still imperfectly understood, although in
recent years a consistent picture has emerged. The opposing effects of shear
can be split into two major contributions for simple liquids: increase of the
energetic cost of nucleation, and enhancement of the kinetics. In this
perspective, we describe the latest computational and theoretical techniques
which have been developed over the past two decades. We collate and unify the
overarching influences of shear, temperature, and supersaturation on the
process of homogeneous nucleation. Experimental techniques and capabilities are
discussed, against the backdrop of results from simulations and theory.
Although we primarily focus on simple liquids, we also touch upon the sheared
nucleation of more complex systems, including glasses and polymer melts. We
speculate on the promising directions and possible advances that could come to
fruition in the future.


Abstract:
  In this paper, we develop general techniques for computing the G-index of a
closed, spin, hyperbolic 2- or 4-manifold, and apply these techniques to
compute the G-index of the fully symmetric spin structure of the Davis
hyperbolic 4-manifold.


Abstract:
  In binary classification, kernel-free linear or quadratic support vector
machines are proposed to avoid dealing with difficulties such as finding
appropriate kernel functions or tuning their hyper-parameters. Furthermore,
Universum data points, which do not belong to any class, can be exploited to
embed prior knowledge into the corresponding models so that the generalization
performance is improved. In this paper, we design novel kernel-free Universum
quadratic surface support vector machine models. Further, we propose the L1
norm regularized version that is beneficial for detecting potential sparsity
patterns in the Hessian of the quadratic surface and reducing to the standard
linear models if the data points are (almost) linearly separable. The proposed
models are convex such that standard numerical solvers can be utilized for
solving them. Nonetheless, we formulate a least squares version of the L1 norm
regularized model and next, design an effective tailored algorithm that only
requires solving one linear system. Several theoretical properties of these
models are then reported/proved as well. We finally conduct numerical
experiments on both artificial and public benchmark data sets to demonstrate
the feasibility and effectiveness of the proposed models.


Abstract:
  To operate efficiently across a wide range of workloads with varying power
requirements, a modern processor applies different current management
mechanisms, which briefly throttle instruction execution while they adjust
voltage and frequency to accommodate for power-hungry instructions (PHIs) in
the instruction stream. Doing so 1) reduces the power consumption of non-PHI
instructions in typical workloads and 2) optimizes system voltage regulators'
cost and area for the common use case while limiting current consumption when
executing PHIs.
  However, these mechanisms may compromise a system's confidentiality
guarantees. In particular, we observe that multilevel side-effects of
throttling mechanisms, due to PHI-related current management mechanisms, can be
detected by two different software contexts (i.e., sender and receiver) running
on 1) the same hardware thread, 2) co-located Simultaneous Multi-Threading
(SMT) threads, and 3) different physical cores.
  Based on these new observations on current management mechanisms, we develop
a new set of covert channels, IChannels, and demonstrate them in real modern
Intel processors (which span more than 70% of the entire client and server
processor market). Our analysis shows that IChannels provides more than 24x the
channel capacity of state-of-the-art power management covert channels. We
propose practical and effective mitigations to each covert channel in IChannels
by leveraging the insights we gain through a rigorous characterization of real
systems.


Abstract:
  We develop the integration theory of two-parameter controlled paths $Y$
allowing us to define integrals of the form \begin{equation}
  \int_{[s,t] \times [u,v]}
  Y_{r,r'}
  \;d(X_{r}, X_{r'}) \end{equation} where $X$ is the geometric $p$-rough path
that controls $Y$. This extends to arbitrary regularity the definition
presented for $2\leq p<3$ in the recent paper of Hairer and Gerasimovi\v{c}s
where it is used in the proof of a version of H\"{o}rmander's theorem for a
class of SPDEs. We extend the Fubini type theorem of the same paper by showing
that this two-parameter integral coincides with the two iterated one-parameter
integrals \[
  \int_{[s,t] \times [u,v]}
  Y_{r,r'}
  \;d(X_{r}, X_{r'})
  =
  \int_{s}^{t}
  \int_{u}^{v}
  Y_{r,r'}
  \;dX_{r'}
  \;dX_{r'}
  =
  \int_{u}^{v}
  \int_{s}^{t}
  Y_{r,r'}
  \;dX_{r}
  \;dX_{r'}. \] A priori these three integrals have distinct definitions, and
so this parallels the classical Fubini's theorem for product measures. By
extending the two-parameter Young-Towghi inequality in this context, we derive
a maximal inequality for the discrete integrals approximating the two-parameter
integral. We also extend the analysis to consider integrals of the form
\begin{equation*}
  \int_{[s,t] \times [u,v]}
  Y_{r,r'}
  \;
  d(X_{r}, \tilde{X}_{r'}) \end{equation*} for possibly different rough paths
$X$ and $\tilde{X}$, and obtain the corresponding Fubini type theorem. We prove
continuity estimates for these integrals in the appropriate rough path
topologies. As an application we consider the signature kernel, which has
recently emerged as a useful tool in data science, as an example of a
two-parameter controlled rough path which also solves a two-parameter rough
integral equation.


Abstract:
  We revisit the calculation of vacuum energy density in compact space times.
By explicitly computing the effective action through the heat kernel method, we
compute vacuum energy density for the general case of $k$ compact spatial
dimensions in $p+k$ dimensional Minkowski space time. Additionally, we use this
formalism to calculate the Casimir force on a piston placed in such space
times, and note the deviations from previously reported results in the
literature.


Abstract:
  We study the problem of simulating a two-user multiple access channel over a
multiple access network of noiseless links. Two encoders observe independent
and identically distributed (i.i.d.) copies of a source random variable each,
while a decoder observes i.i.d. copies of a side-information random variable.
There are rate-limited noiseless communication links and independent pairwise
shared randomness resources between each encoder and the decoder. The decoder
has to output approximately i.i.d. copies of another random variable jointly
distributed with the two sources and the side information. We are interested in
the rate tuples which permit this simulation. This setting can be thought of as
a multi-terminal generalization of the point-to-point channel simulation
problem studied by Bennett et al. (2002) and Cuff (2013). General inner and
outer bounds on the rate region are derived. For the specific case where the
sources at the encoders are conditionally independent given the
side-information at the decoder, we completely characterize the rate region.
Our bounds recover the existing results on function computation over such
multi-terminal networks. We then show through an example that an additional
independent source of shared randomness between the encoders strictly improves
the communication rate requirements, even if the additional randomness is not
available to the decoder. Furthermore, we provide inner and outer bounds for
this more general setting with independent pairwise shared randomness resources
between all the three possible node pairs.


Abstract:
  A cluster algebra is a commutative algebra whose structure is decided by a
skew-symmetrizable matrix or a quiver. When a skew-symmetrizable matrix is
invariant under an action of a finite group and this action is admissible, the
folded cluster algebra is obtained from the original one. Any cluster algebra
of non-simply-laced affine type can be obtained by folding a cluster algebra of
simply-laced affine type with a specific $G$-action. In this paper, we study
the combinatorial properties of quivers in the cluster algebra of affine type.
We prove that for any quiver of simply-laced affine type, $G$-invariance and
$G$-admissibility are equivalent. This leads us to prove that the set of
$G$-invariant seeds forms the folded cluster pattern.


Abstract:
  We consider the decay $B\to\ell\ell\ell^{\prime}\nu$, taking into account the
leading $1/m_b$ and $q^2$ corrections calculated in the QCD factorization
framework as well as the soft corrections calculated employing dispersion
relations and quark-hadron duality. We extend the existing results for the
radiative decay $B\to\gamma\ell\nu$ to the case of non-zero (but small) $q^2$,
the invariant mass squared of the dilepton pair $\ell^+\ell^-$. This restricts
us to the case $\ell\neq\ell'$ as otherwise the same sign $\ell$ and $\ell'$
cannot be distinguished. We further study the sensitivity of the results to the
leading moment of the $B$-meson distribution amplitude and discuss the
potential to extract this quantity at LHCb and the Belle II experiment.


Abstract:
  We develop a resource theory of symmetric distinguishability, the fundamental
objects of which are elementary quantum information sources, i.e., sources that
emit one of two possible quantum states with given prior probabilities. Such a
source can be represented by a classical-quantum state of a composite system
$XA$, corresponding to an ensemble of two quantum states, with $X$ being
classical and $A$ being quantum. We study the resource theory for two different
classes of free operations: $(i)$ ${\rm{CPTP}}_A$, which consists of quantum
channels acting only on $A$, and $(ii)$ conditional doubly stochastic (CDS)
maps acting on $XA$. We introduce the notion of symmetric distinguishability of
an elementary source and prove that it is a monotone under both these classes
of free operations. We study the tasks of distillation and dilution of
symmetric distinguishability, both in the one-shot and asymptotic regimes. We
prove that in the asymptotic regime, the optimal rate of converting one
elementary source to another is equal to the ratio of their quantum Chernoff
divergences, under both these classes of free operations. This imparts a new
operational interpretation to the quantum Chernoff divergence. We also obtain
interesting operational interpretations of the Thompson metric, in the context
of the dilution of symmetric distinguishability.


Abstract:
  In this study, we investigated the employment status of recent University of
Ottawa physics MSc and PhD graduates, finding that 94% of graduates are either
employed or pursuing further physics education one year post-graduation. Our
database was populated from the public online repository of MSc and PhD theses
submitted between the academic years of 2011 to 2019, with employment
information collected in 2020 from the professional social media platform
LinkedIn. Our results highlight that graduates primarily find employment
quickly and in their field of study, with most graduates employed in either
academia or physics-related industries. We also found that a significant
portion of employed graduates, 20%, find employment in non-traditional physics
careers, such as business management and healthcare. Graduates with careers in
academia tend to have lower online connectivity compared to graduates with
careers in industry or non-traditional fields, suggesting a greater importance
for online networking for students interested in non-academic careers.


Abstract:
  Bound-systems of $\Xi^-$--$^{14}_{}{\rm N}$ are studied via $\Xi^-$ capture
at rest followed by emission of a twin single-$\Lambda$ hypernucleus in the
emulsion detectors. Two events forming extremely deep $\Xi^-$ bound states were
obtained by analysis of a hybrid method in the E07 experiment at J-PARC and
reanalysis of the E373 experiment at KEK-PS. The decay mode of one event was
assigned as $\Xi^-+^{14}_{}{\rm N}\to^{5}_{\Lambda}{\rm
He}$+$^{5}_{\Lambda}{\rm He}$+$^{4}_{}{\rm He}$+n. Since there are no excited
states for daughter particles, the binding energy of the $\Xi^-$ hyperon,
$B_{\Xi^-}$, in $^{14}_{}{\rm N}$ nucleus was uniquely determined to be 6.27
$\pm$ 0.27 MeV. Another $\Xi^-$--$^{14}_{}{\rm N}$ system via the decay
$^{9}_{\Lambda}{\rm Be}$ + $^{5}_{\Lambda}{\rm He}$ + n brings a $B_{\Xi^-}$
value, 8.00 $\pm$ 0.77 MeV or 4.96 $\pm$ 0.77 MeV, where the two possible
values of $B_{\Xi^-}$ correspond to the ground and the excited states of the
daughter $^{9}_{\Lambda}{\rm Be}$ nucleus, respectively. Because the
$B_{\Xi^-}$ values are larger than those of the previously reported events
(KISO and IBUKI), which are both interpreted as the nuclear $1p$ state of the
$\Xi^-$--$^{14}_{}{\rm N}$ system, these new events give the first indication
of the nuclear $1s$ state of the $\Xi$ hypernucleus, $^{15}_{\Xi}{\rm C}$.


Abstract:
  Recent research on disorder effects in topological phases in quasicrystalline
systems has received much attention. In this work, by numerically computing the
(spin) Bott index and the thermal conductance, we reveal the effects of
disorder on a class D chiral topological superconductor and a class DIII
time-reversal-invariant topological superconductor in a two-dimensional
Ammann-Beenker tiling quasicrystalline lattice. We demonstrate that both the
topologically protected chiral and helical Majorana edge modes are robust
against weak disorder in the quasicrystalline lattice. More fascinating is the
discovery of disorder-induced topologically nontrivial phases exhibiting chiral
and helical Majorana edge modes in class D and DIII topological superconductor
systems, respectively. Our findings open the door for the research on
disorder-induced Majorana edge modes in quasicrystalline systems.


Abstract:
  Inverse patchy colloids are nano- to micro-scale particles with a surface
divided into differently charged regions. This class of colloids combines
directional, selective bonding with a relatively simple particle design: owing
to the competitive interplay between the orientation-dependent attraction and
repulsion -- induced by the interactions between like/oppositely charged areas
-- experimentally accessible surface patterns are complex enough to favor the
stabilization of specific structures of interest. Most important, the behavior
of heterogeneously charged units can be ideally controlled by means of external
parameters, such as the pH and the salt concentration. We present a concise
review about this class of systems, spanning the range from the synthesis of
model inverse patchy particles to their self-assembly, covering their
coarse-grained modeling and the related numerical/analytical treatments.


Abstract:
  With the development of earth observation technology, massive amounts of
remote sensing (RS) images are acquired. To find useful information from these
images, cross-modal RS image-voice retrieval provides a new insight. This paper
aims to study the task of RS image-voice retrieval so as to search effective
information from massive amounts of RS data. Existing methods for RS
image-voice retrieval rely primarily on the pairwise relationship to narrow the
heterogeneous semantic gap between images and voices. However, apart from the
pairwise relationship included in the datasets, the intra-modality and
non-paired inter-modality relationships should also be taken into account
simultaneously, since the semantic consistency among non-paired representations
plays an important role in the RS image-voice retrieval task. Inspired by this,
a semantics-consistent representation learning (SCRL) method is proposed for RS
image-voice retrieval. The main novelty is that the proposed method takes the
pairwise, intra-modality, and non-paired inter-modality relationships into
account simultaneously, thereby improving the semantic consistency of the
learned representations for the RS image-voice retrieval. The proposed SCRL
method consists of two main steps: 1) semantics encoding and 2)
semantics-consistent representation learning. Firstly, an image encoding
network is adopted to extract high-level image features with a transfer
learning strategy, and a voice encoding network with dilated convolution is
devised to obtain high-level voice features. Secondly, a consistent
representation space is conducted by modeling the three kinds of relationships
to narrow the heterogeneous semantic gap and learn semantics-consistent
representations across two modalities. Extensive experimental results on three
challenging RS image-voice datasets show the effectiveness of the proposed
method.


Abstract:
  We have analysed the Ca-K images obtained at Kodaikanal Observatory as a
function of latitude and time for the period of 1913 - 2004 covering the Solar
Cycle 15 to 23. We have classified the chromospheric activity into plage,
Enhanced Network (EN), Active Network (AN), and Quiet Network (QN) areas to
differentiate between large strong active and small weak active regions. The
strong active regions represent toroidal and weak active regions poloidal
component of the magnetic field. We find that plages areas mostly up to 50 deg
latitude belt vary with about 11-year Solar Cycle. We also find that weak
activity represented by EN, AN and QN varies with about 11-year with
significant amplitude up to about 50 deg latitude in both the hemispheres. The
amplitude of variation is minimum around 50 deg latitude and again increases by
small amount in the polar region. In addition, the plots of plages, EN, AN and
QN as a function of time indicate the maximum of activity at different latitude
occur at different epoch. To determine the phase difference for the different
latitude belts, we have computed the cross-correlation coefficients of other
latitude belts with 35 deg latitude belt. We find that activity shifts from
mid-latitude belts towards equatorial belts at fast speed at the beginning of
Solar Cycle and at slower speed as the cycle progresses. The speed of shift
varies between approximately 19 and 3 m/s considering all the data for the
observed period. This speed can be linked with speed of meridional flows those
believed to occur between convection zone and the surface of the Sun.


Abstract:
  This study discusses the Review Bomb, a phenomenon consisting of a massive
attack by groups of Internet users on a website that displays users' review on
products. It gained attention, especially on websites that aggregate numerical
ratings. Although this phenomenon can be considered an example of online
misinformation, it differs from conventional spam review, which happens within
larger time spans. In particular, the Bomb occurs suddenly and for a short
time, because in this way it leverages the notorious problem of cold-start: if
reviews are submitted by a lot of fresh new accounts, it makes hard to justify
preventative measures. The present research work is focused on the case of The
Last of Us Part II, a video game published by Sony, that was the target of the
widest phenomenon of Review Bomb, occurred in June 2020. By performing an
observational analysis of a linguistic corpus of English reviews and the
features of its users, this study confirms that the Bomb was an ideological
attack aimed at breaking down the rating system of the platform Metacritic.
Evidence supports that the bombing had the unintended consequence to induce a
reaction from users, ending into a consistent polarisation of ratings towards
extreme values. The results not only display the theory of polarity in online
reviews, but them also provide insights for the research on the problem of
cold-start detection of spam review. In particular, it illustrates the
relevance of detecting users discussing contextual elements instead of the
product and users with anomalous features.


Abstract:
  Arrays of nanoparticles exploited in light scattering applications commonly
only feature either a periodic or a rather random arrangement of its
constituents. For the periodic case, light scattering is mostly governed by the
strong spatial correlations of the arrangement, expressed by the structure
factor. For the random case, structural correlations cancel each other out and
light scattering is mostly governed by the scattering properties of the
individual scatterer, expressed by the form factor. In contrast to these
extreme cases, we show here, for the first time, that hyperuniform disorder in
self-organized large-area arrays of high refractive index nanodisks enables
both structure and form factor to impact the resulting scattering pattern,
offering novel means to tailor light scattering. The scattering response from
our nearly hyperuniform interfaces can be exploited in a large variety of
applications and constitutes a novel class of advanced optical materials.


Abstract:
  We study the clustering task under anisotropic Gaussian Mixture Models where
the covariance matrices from different clusters are unknown and are not
necessarily the identical matrix. We characterize the dependence of
signal-to-noise ratios on the cluster centers and covariance matrices and
obtain the minimax lower bound for the clustering problem. In addition, we
propose a computationally feasible procedure and prove it achieves the optimal
rate within a few iterations. The proposed procedure is a hard EM type
algorithm, and it can also be seen as a variant of the Lloyd's algorithm that
is adjusted to the anisotropic covariance matrices.


Abstract:
  Deep learning based generative adversarial networks (GAN) can effectively
perform image reconstruction with under-sampled MR data. In general, a large
number of training samples are required to improve the reconstruction
performance of a certain model. However, in real clinical applications, it is
difficult to obtain tens of thousands of raw patient data to train the model
since saving k-space data is not in the routine clinical flow. Therefore,
enhancing the generalizability of a network based on small samples is urgently
needed. In this study, three novel applications were explored based on parallel
imaging combined with the GAN model (PI-GAN) and transfer learning. The model
was pre-trained with public Calgary brain images and then fine-tuned for use in
(1) patients with tumors in our center; (2) different anatomies, including knee
and liver; (3) different k-space sampling masks with acceleration factors (AFs)
of 2 and 6. As for the brain tumor dataset, the transfer learning results could
remove the artifacts found in PI-GAN and yield smoother brain edges. The
transfer learning results for the knee and liver were superior to those of the
PI-GAN model trained with its own dataset using a smaller number of training
cases. However, the learning procedure converged more slowly in the knee
datasets compared to the learning in the brain tumor datasets. The
reconstruction performance was improved by transfer learning both in the models
with AFs of 2 and 6. Of these two models, the one with AF=2 showed better
results. The results also showed that transfer learning with the pre-trained
model could solve the problem of inconsistency between the training and test
datasets and facilitate generalization to unseen data.


Abstract:
  Deep learning algorithms are a key component of many state-of-the-art vision
systems, especially as Convolutional Neural Networks (CNN) outperform most
solutions in the sense of accuracy. To apply such algorithms in real-time
applications, one has to address the challenges of memory and computational
complexity. To deal with the first issue, we use networks with reduced
precision, specifically a binary neural network (also known as XNOR). To
satisfy the computational requirements, we propose to use highly parallel and
low-power FPGA devices. In this work, we explore the possibility of
accelerating XNOR networks for traffic sign classification. The trained binary
networks are implemented on the ZCU 104 development board, equipped with a Zynq
UltraScale+ MPSoC device using two different approaches. Firstly, we propose a
custom HDL accelerator for XNOR networks, which enables the inference with
almost 450 fps. Even better results are obtained with the second method - the
Xilinx FINN accelerator - enabling to process input images with around 550
frame rate. Both approaches provide over 96% accuracy on the test set.


Abstract:
  This paper presents a novel Sliding Mode Control (SMC) algorithm to handle
mismatched uncertainties in systems via a novel Self-Learning Disturbance
Observer (SLDO). A computationally efficient SLDO is developed within a
framework of feedback-error learning scheme in which a conventional estimation
law and a Neuro-Fuzzy Structure (NFS) work in parallel. In this framework, the
NFS estimates the mismatched disturbances and becomes the leading disturbance
estimator while the former feeds the learning error to the NFS to learn system
behavior. The simulation results demonstrate that the proposed SMC based on
SLDO (SMC-SLDO) ensures the robust control performance in the presence of
mismatched time-varying uncertainties when compared to SMC, integral SMC (ISMC)
and SMC based on a Basic Nonlinear Disturbance Observer (SMC-BNDO), and also
remains the nominal control performance in the absence of mismatched
uncertainties. Additionally, the SMC-SLDO not only counteracts mismatched
time-varying uncertainties but also improve the transient response performance
in the presence of mismatched time-invariant uncertainties. Moreover, the
controller gain of the SMC-SLDO is required to be selected larger than the
upper bound of the disturbance estimation error rather than the upper bound of
the actual disturbance to guarantee the system stability which results in
eliminating the chattering effects on the control signal.


Abstract:
  Graph Neural Networks (GNNs) require a relatively large number of labeled
nodes and a reliable/uncorrupted graph connectivity structure in order to
obtain good performance on the semi-supervised node classification task. The
performance of GNNs can degrade significantly as the number of labeled nodes
decreases or the graph connectivity structure is corrupted by adversarial
attacks or due to noises in data measurement /collection. Therefore, it is
important to develop GNN models that are able to achieve good performance when
there is limited supervision knowledge -- a few labeled nodes and noisy graph
structures. In this paper, we propose a novel Dual GNN learning framework to
address this challenge task. The proposed framework has two GNN based node
prediction modules. The primary module uses the input graph structure to induce
regular node embeddings and predictions with a regular GNN baseline, while the
auxiliary module constructs a new graph structure through fine-grained spectral
clusterings and learns new node embeddings and predictions. By integrating the
two modules in a dual GNN learning framework, we perform joint learning in an
end-to-end fashion. This general framework can be applied on many GNN baseline
models. The experimental results validate that the proposed dual GNN framework
can greatly outperform the GNN baseline methods when the labeled nodes are
scarce and the graph connectivity structure is noisy.


Abstract:
  This study examines habits and perceptions related to pay to publish and open
access practices in fields that have attracted little research to date:
philosophy and ethics. The study is undertaken in the Spanish context, where
the culture of publication and the book and journal publishing industry has
some specific characteristics with regard to paying to publish, such as not
offering open access distribution of books published for a fee. The study draws
on data from a survey of 201 researchers, a public debate with 26 researchers,
and 14 in-depth interviews. The results reveal some interesting insights on the
criteria researchers apply when selecting publishers and journals for their
work, the extent of paying to publish (widespread in the case of books and
modest for journals) and the debates that arise over the effects it has on
manuscript review and unequal access to resources to cover publication fees.
Data on the extent of open access and the researchers views on dissemination of
publicly funded research are also presented.


Abstract:
  Despite evidence for the existence of dark matter (DM) from very high and low
redshifts, a moderate amount of DM particle decay remains a valid possibility.
This includes both models with very long-lived yet unstable particles or mixed
scenarios where only a small fraction of dark matter is allowed to decay. In
this paper, we investigate how DM particles decaying into radiation affect
non-linear structure formation. We look at the power spectrum and its redshift
evolution, varying both the decay lifetime ($\tau$) and the fraction of
decaying-to-total dark matter ($f$), and we propose a fitting function that
reaches sub-percent precision below $k\sim10$ h/Mpc. Based on this fit, we
perform a forecast analysis for a Euclid-like weak lensing (WL) survey,
including both massive neutrino and baryonic feedback parameters. We find that
with WL observations alone, it is possible to rule out decay lifetimes smaller
than $\tau=75$ Gyr (at 95 percent CL) for the case that all DM is unstable.
This constraint improves to $\tau=182$ Gyr if the WL data is combined with CMB
priors from the Planck satellite and to $\tau=275$ Gyr if we further assume
baryonic feedback to be fully constrained by upcoming Sunyaev-Zeldovich or
X-ray data. The latter shows a factor of 3.2 improvement compared to
constraints from CMB data alone. Regarding the scenario of a strongly decaying
sub-component of dark matter with $\tau\sim 30$ Gyr or lower, it will be
possible to rule out a decaying-to-total fraction of $f>0.49$, $f>0.21$, and
$f>0.13$ (at the 95 percent CL) for the same three scenarios. We conclude that
the upcoming stage-IV WL surveys will allow us to significantly improve current
constraints on the stability of the dark matter sector.


Abstract:
  The inscribed angle theorem, a famous result about the angle subtended by a
chord within a circle, is well known and commonly taught in school curricula.
In this paper, we present a generalisation of this result (and other related
circle theorems) to the rectangular hyperbola. The notion of angle is replaced
by pseudo-angle, defined via the Minkowski inner product. Indeed, in Minkowski
space, the unit hyperbola is the set of points a unit metric distance from the
origin, analogous to the Euclidean unit circle. While this is a result of pure
geometrical interest, the connection to Minkowski space allows an
interpretation in terms of special relativity where, in the limit $c\to\infty$,
it leads to a familiar result from non-relativistic dynamics. This
non-relativistic result can be interpreted as an inscribed angle theorem for
the parabola, which we show can also be obtained from the Euclidean inscribed
angle theorem by taking the limit of a family of ellipses ananlogous to the
non-relativistic limit $c\to\infty$. This simple result could be used as a
pedagogical example to consolidate understanding of pseudo-angles in
non-Euclidean spaces or to demonstrate the power of analytic continuation.


Abstract:
  Human pose estimation is a major computer vision problem with applications
ranging from augmented reality and video capture to surveillance and movement
tracking. In the medical context, the latter may be an important biomarker for
neurological impairments in infants. Whilst many methods exist, their
application has been limited by the need for well annotated large datasets and
the inability to generalize to humans of different shapes and body
compositions, e.g. children and infants. In this paper we present a novel
method for learning pose estimators for human adults and infants in an
unsupervised fashion. We approach this as a learnable template matching problem
facilitated by deep feature extractors. Human-interpretable landmarks are
estimated by transforming a template consisting of predefined body parts that
are characterized by 2D Gaussian distributions. Enforcing a connectivity prior
guides our model to meaningful human shape representations. We demonstrate the
effectiveness of our approach on two different datasets including adults and
infants.


Abstract:
  The Rindler spacetime describing a series of accelerating observers is Ricci
flat, but it still has novel optical effects. In the case of WKB approximation,
we derive the light geodesics in the Rindler frame based on the covariant wave
equation and geodesic equations. Then, we use ABCD matrix optics method to
explore the propagation characteristics of Rindler frame, thus link three
different optical transformation scenes (geometry, gravity and vacuum
refractive index) together. Moreover, the propagation characteristics of hollow
beam in Rindler spacetime are described analytically. Those characteristics are
quite different from the ones in the flat spacetime. Based on these
calculations, we simply demonstrate the position uncertain relationship between
the transverse beam size and the momentum, which surprisingly coincides with
the derivation of quantization. We hope that we can provide one simple method
to analyze the beam propagation in the accelerated frame.


Abstract:
  We present a novel approach for disentangling the content of a text image
from all aspects of its appearance. The appearance representation we derive can
then be applied to new content, for one-shot transfer of the source style to
new content. We learn this disentanglement in a self-supervised manner. Our
method processes entire word boxes, without requiring segmentation of text from
background, per-character processing, or making assumptions on string lengths.
We show results in different text domains which were previously handled by
specialized methods, e.g., scene text, handwritten text. To these ends, we make
a number of technical contributions: (1) We disentangle the style and content
of a textual image into a non-parametric, fixed-dimensional vector. (2) We
propose a novel approach inspired by StyleGAN but conditioned over the example
style at different resolution and content. (3) We present novel self-supervised
training criteria which preserve both source style and target content using a
pre-trained font classifier and text recognizer. Finally, (4) we also introduce
Imgur5K, a new challenging dataset for handwritten word images. We offer
numerous qualitative photo-realistic results of our method. We further show
that our method surpasses previous work in quantitative tests on scene text and
handwriting datasets, as well as in a user study.


Abstract:
  In this paper we present our system for the detection and classification of
acoustic scenes and events (DCASE) 2020 Challenge Task 4: Sound event detection
and separation in domestic environments. We introduce two new models: the
forward-backward convolutional recurrent neural network (FBCRNN) and the
tag-conditioned convolutional neural network (CNN). The FBCRNN employs two
recurrent neural network (RNN) classifiers sharing the same CNN for
preprocessing. With one RNN processing a recording in forward direction and the
other in backward direction, the two networks are trained to jointly predict
audio tags, i.e., weak labels, at each time step within a recording, given that
at each time step they have jointly processed the whole recording. The proposed
training encourages the classifiers to tag events as soon as possible.
Therefore, after training, the networks can be applied to shorter audio
segments of, e.g., 200 ms, allowing sound event detection (SED). Further, we
propose a tag-conditioned CNN to complement SED. It is trained to predict
strong labels while using (predicted) tags, i.e., weak labels, as additional
input. For training pseudo strong labels from a FBCRNN ensemble are used. The
presented system scored the fourth and third place in the systems and teams
rankings, respectively. Subsequent improvements allow our system to even
outperform the challenge baseline and winner systems in average by,
respectively, 18.0% and 2.2% event-based F1-score on the validation set. Source
code is publicly available at https://github.com/fgnt/pb_sed.


Abstract:
  Block-sparse signal recovery without knowledge of block sizes and boundaries,
such as those encountered in multi-antenna mmWave channel models, is a hard
problem for compressed sensing (CS) algorithms. We propose a novel Sparse
Bayesian Learning (SBL) method for block-sparse recovery based on popular CS
based regularizers with the function input variable related to total variation
(TV). Contrary to conventional approaches that impose the regularization on the
signal components, we regularize the SBL hyperparameters. This iterative
TV-regularized SBL algorithm employs a majorization-minimization approach and
reduces each iteration to a convex optimization problem, enabling a flexible
choice of numerical solvers. The numerical results illustrate that the
TV-regularized SBL algorithm is robust to the nature of the block structure and
able to recover signals with both block-patterned and isolated components,
proving useful for various signal recovery systems.


Abstract:
  Population synthesis studies of binary black-hole mergers often lack robust
black-hole spin estimates as they cannot accurately follow tidal spin-up during
the late black-hole-Wolf-Rayet evolutionary phase. We provide an analytical
approximation of the dimensionless second-born black-hole spin given the binary
orbital period and Wolf-Rayet stellar mass at helium depletion or carbon
depletion. These approximations are obtained from fitting a sample of around
$10^5$ detailed MESA simulations that follow the evolution and spin up of close
black-hole--Wolf-Rayet systems with metallicities in the range
$[10^{-4},1.5Z_\odot]$. Following the potential spin up of the Wolf-Rayet
progenitor, the second-born black-hole spin is calculated using up-to-date core
collapse prescriptions that account for any potential disk formation in the
collapsing Wolf-Rayet star. The fits for second-born black hole spin provided
in this work can be readily applied to any astrophysical modeling that relies
on rapid population synthesis, and will be useful for the interpretation of
gravitational-wave sources using such models.


Abstract:
  Vector representations have become a central element in semantic language
modelling, leading to mathematical overlaps with many fields including quantum
theory. Compositionality is a core goal for such representations: given
representations for 'wet' and 'fish', how should the concept 'wet fish' be
represented?
  This position paper surveys this question from two points of view. The first
considers the question of whether an explicit mathematical representation can
be successful using only tools from within linear algebra, or whether other
mathematical tools are needed. The second considers whether semantic vector
composition should be explicitly described mathematically, or whether it can be
a model-internal side-effect of training a neural network.
  A third and newer question is whether a compositional model can be
implemented on a quantum computer. Given the fundamentally linear nature of
quantum mechanics, we propose that these questions are related, and that this
survey may help to highlight candidate operations for future quantum
implementation.


Abstract:
  Combination and aggregation techniques can significantly improve forecast
accuracy. This also holds for probabilistic forecasting methods where
predictive distributions are combined. There are several time-varying and
adaptive weighting schemes such as Bayesian model averaging (BMA). However, the
quality of different forecasts may vary not only over time but also within the
distribution. For example, some distribution forecasts may be more accurate in
the center of the distributions, while others are better at predicting the
tails. Therefore, we introduce a new weighting method that considers the
differences in performance over time and within the distribution. We discuss
pointwise combination algorithms based on aggregation across quantiles that
optimize with respect to the continuous ranked probability score (CRPS). After
analyzing the theoretical properties of pointwise CRPS learning, we discuss B-
and P-Spline-based estimation techniques for batch and online learning, based
on quantile regression and prediction with expert advice. We prove that the
proposed fully adaptive Bernstein online aggregation (BOA) method for pointwise
CRPS online learning has optimal convergence properties. They are confirmed in
simulations and a probabilistic forecasting study for European emission
allowance (EUA) prices.


Abstract:
  This paper presents the definition and implementation of a quantum computer
architecture to enable creating a new computational device - a quantum computer
as an accelerator In this paper, we present explicitly the idea of a quantum
accelerator which contains the full stack of the layers of an accelerator. Such
a stack starts at the highest level describing the target application of the
accelerator. Important to realise is that qubits are defined as perfect qubits,
implying they do not decohere and perform good quantum gate operations. The
next layer abstracts the quantum logic outlining the algorithm that is to be
executed on the quantum accelerator. In our case, the logic is expressed in the
universal quantum-classical hybrid computation language developed in the group,
called OpenQL. We also have to start thinking about how to verify, validate and
test the quantum software such that the compiler generates a correct version of
the quantum circuit. The OpenQL compiler translates the program to a common
assembly language, called cQASM. We need to develop a quantum operating system
that manages all the hardware of the micro-architecture. The layer below the
micro-architecture is responsible of the mapping and routing of the qubits on
the topology such that the nearest-neighbour-constraint can be be respected. At
any moment in the future when we are capable of generating multiple good
qubits, the compiler can convert the cQASM to generate the eQASM, which is
executable on a particular experimental device incorporating the
platform-specific parameters. This way, we are able to distinguish clearly the
experimental research towards better qubits, and the industrial and societal
applications that need to be developed and executed on a quantum device.


Abstract:
  Correspondence-based rotation search and point cloud registration are two
fundamental problems in robotics and computer vision. However, the presence of
outliers, sometimes even occupying the great majority of the putative
correspondences, can make many existing algorithms either fail or have very
high computational cost. In this paper, we present RANSIC (RANdom Sampling with
Invariant Compatibility), a fast and highly robust method applicable to both
problems based on a new paradigm combining random sampling with invariance and
compatibility. Generally, RANSIC starts with randomly selecting small subsets
from the correspondence set, then seeks potential inliers as graph vertices
from the random subsets through the compatibility tests of invariants
established in each problem, and eventually returns the eligible inliers when
there exists at least one K-degree vertex (K is automatically updated depending
on the problem) and the residual errors satisfy a certain termination condition
at the same time. In multiple synthetic and real experiments, we demonstrate
that RANSIC is fast for use, robust against over 95% outliers, and also able to
recall approximately 100% inliers, outperforming other state-of-the-art solvers
for both the rotation search and the point cloud registration problems.


Abstract:
  Computational models of biological processes provide one of the most powerful
methods for a detailed analysis of the mechanisms that drive the behavior of
complex systems. Logic-based modeling has enhanced our understanding and
interpretation of those systems. Defining rules that determine how the output
activity of biological entities is regulated by their respective inputs has
proven to be challenging, due to increasingly larger models and the presence of
noise in data, allowing multiple model parameterizations to fit the
experimental observations.
  We present several Boolean function metrics that provide modelers with the
appropriate framework to analyze the impact of a particular model
parameterization. We demonstrate the link between a semantic characterization
of a Boolean function and its consistency with the model's underlying
regulatory structure. We further define the properties that outline such
consistency and show that several of the Boolean functions under study violate
them, questioning their biological plausibility and subsequent use. We also
illustrate that regulatory functions can have major differences with regard to
their asymptotic output behavior, with some of them being biased towards
specific Boolean outcomes when others are dependent on the ratio between
activating and inhibitory regulators.
  Application results show that in a specific signaling cancer network, the
function bias can be used to guide the choice of logical operators for a model
that matches data observations. Moreover, graph analysis indicates that the
standardized Boolean function bias becomes more prominent with increasing
numbers of regulators, confirming the fact that rule specification can
effectively determine regulatory outcome despite the complex dynamics of
biological networks.


Abstract:
  The application of machine learning(ML) and genetic programming(GP) to the
image compression domain has produced promising results in many cases. The need
for compression arises due to the exorbitant size of data shared on the
internet. Compression is required for text, videos, or images, which are used
almost everywhere on web be it news articles, social media posts, blogs,
educational platforms, medical domain, government services, and many other
websites, need packets for transmission and hence compression is necessary to
avoid overwhelming the network. This paper discusses some of the
implementations of image compression algorithms that use techniques such as
Artificial Neural Networks, Residual Learning, Fuzzy Neural Networks,
Convolutional Neural Nets, Deep Learning, Genetic Algorithms. The paper also
describes an implementation of Vector Quantization using GA to generate
codebook which is used for Lossy image compression. All these approaches prove
to be very contrasting to the standard approaches to processing images due to
the highly parallel and computationally extensive nature of machine learning
algorithms. Such non-linear abilities of ML and GP make it widely popular for
use in multiple domains. Traditional approaches are also combined with
artificially intelligent systems, leading to hybrid systems, to achieve better
results.


Abstract:
  The thermodynamic properties of Bi-Sn were studied at 600 and 900K using a
quasi-lattice theory. After successful fitting of Gibbs free energies of mixing
and thermodynamic activities, the fitting parameters were used to investigate
the enthalpy of mixing, the entropy of mixing, concentration fluctuations,
Warren-Cowley short range order parameter, surface concentrations and surface
tensions of the binary systems. Positive and symmetrically shaped enthalpies of
mixing were observed in all composition range, while negative excess entropies
of mixing were observed. Bi-Sn showed a slight preference for like-atoms as
nearest neighbours in all composition range. The nature of atomic order in
Bi-Sn at 600 and 900K appeared similar. The highest tendency for
homocoordination exists at composition where mole fraction of Bi is about 40%.
It was also observed that Bi (whose surface tension is lower than that of Sn)
has the highest surface enrichment in the Bi-Sn systems. Unlike many previous
applications of the quasi-lattice theory where constant values were used to
approximate coordination numbers, temperature and composition-dependent
coordination numbers were applied in this work.


Abstract:
  We have performed ab-initio molecular dynamics simulations to elucidate the
mechanism of the phase transition at high pressure from hexagonal graphite (HG)
to hexagonal diamond (HD) or to cubic diamond (CD). The transition from HG to
HD is found to occur swiftly in very small time of 0.2 ps, with large
cooperative displacements of all the atoms. We observe that alternate layers of
atoms in HG slide in opposite directions by (1/3, 1/6, 0) and (-1/3, -1/6, 0),
respectively, which is about 0.7 {\AA} along the pm[2, 1, 0] direction, while
simultaneously puckering by about pm0.25 {\AA} perpendicular to the a-b plane.
The transition from HG to CD occurred with more complex cooperative
displacements. In this case, six successive HG layers slide in pairs by 1/3
along [0, 1, 0], [-1, -1, 0] and [1, 0, 0], respectively along with the
puckering as above. We have also performed calculations of the phonon spectrum
in HG at high pressure, which reveal soft phonon modes that may facilitate the
phase transition involving the sliding and puckering of the HG layers. The
zero-point vibrational energy and the vibrational entropy are found to have
important role in stabilizing HG up to higher pressures (>10 GPa) and
temperatures than that estimated (<6 GPa) from previous enthalpy calculations.


Abstract:
  Recent medical imaging studies have given rise to distinct but inter-related
datasets corresponding to multiple experimental tasks or longitudinal visits.
Standard scalar-on-image regression models that fit each dataset separately are
not equipped to leverage information across inter-related images, and existing
multi-task learning approaches are compromised by the inability to account for
the noise that is often observed in images. We propose a novel joint
scalar-on-image regression framework involving wavelet-based image
representations with grouped penalties that are designed to pool information
across inter-related images for joint learning, and which explicitly accounts
for noise in high-dimensional images via a projection-based approach. In the
presence of non-convexity arising due to noisy images, we derive non-asymptotic
error bounds under non-convex as well as convex grouped penalties, even when
the number of voxels increases exponentially with sample size. A projected
gradient descent algorithm is used for computation, which is shown to
approximate the optimal solution via well-defined non-asymptotic optimization
error bounds under noisy images. Extensive simulations and application to a
motivating longitudinal Alzheimer's disease study illustrate significantly
improved predictive ability and greater power to detect true signals, that are
simply missed by existing methods without noise correction due to the
attenuation to null phenomenon.


Abstract:
  The availability of multi-omics data has revolutionized the life sciences by
creating avenues for integrated system-level approaches. Data integration links
the information across datasets to better understand the underlying biological
processes. However, high-dimensionality, correlations and heterogeneity pose
statistical and computational challenges. We propose a general framework,
probabilistic two-way partial least squares (PO2PLS), which addresses these
challenges. PO2PLS models the relationship between two datasets using joint and
data-specific latent variables. For maximum likelihood estimation of the
parameters, we implement a fast EM algorithm and show that the estimator is
asymptotically normally distributed. A global test for testing the relationship
between two datasets is proposed, and its asymptotic distribution is derived.
Notably, several existing omics integration methods are special cases of
PO2PLS. Via extensive simulations, we show that PO2PLS performs better than
alternatives in feature selection and prediction performance. In addition, the
asymptotic distribution appears to hold when the sample size is sufficiently
large. We illustrate PO2PLS with two examples from commonly used study designs:
a large population cohort and a small case-control study. Besides recovering
known relationships, PO2PLS also identified novel findings. The methods are
implemented in our R-package PO2PLS. Supplementary materials for this article
are available online.


Abstract:
  Hard sphere systems are often used to model simple fluids. The configuration
spaces of hard spheres in a three-dimensional torus modulo various symmetry
groups are comparatively simple, and could provide valuable information about
the nature of phase transitions. Specifically, the topological changes in the
configuration space as a function of packing fraction have been conjectured to
be related to the onset of first-order phase transitions. The critical
configurations for one to twelve spheres are sampled using a Morse-theoretic
approach, and are available in an online, interactive database. Explicit
triangulations are constructed for the configuration spaces of the two sphere
system, and their topological and geometric properties are studied. The
critical configurations are found to be associated with geometric changes to
the configuration space that connect previously distant regions and reduce the
configuration space diameter as measured by the commute time and diffusion
distances. The number of such critical configurations around the packing
fraction of the solid-liquid phase transition increases exponentially with the
number of spheres, suggesting that the onset of the first-order phase
transition in the thermodynamic limit is associated with a discontinuity in the
configuration space diameter.


Abstract:
  Answering a long standing question, we give an example of a Hilbert module
and a nonzero bounded right linear map having a kernel with trivial orthogonal
complement. In particular, this kernel is different from its own double
orthogonal complement.


Abstract:
  Neural networks often require large amounts of data to generalize and can be
ill-suited for modeling small and noisy experimental datasets. Standard network
architectures trained on scarce and noisy data will return predictions that
violate the underlying physics. In this paper, we present methods for embedding
even--odd symmetries and conservation laws in neural networks and propose novel
extensions and use cases for physical constraint embedded neural networks. We
design an even--odd decomposition architecture for disentangling a neural
network parameterized function into its even and odd components and demonstrate
that it can accurately infer symmetries without prior knowledge. We highlight
the noise resilient properties of physical constraint embedded neural networks
and demonstrate their utility as physics-informed noise regulators. Here we
employed a conservation of energy constraint embedded network as a
physics-informed noise regulator for a symbolic regression task. We showed that
our approach returns a symbolic representation of the neural network
parameterized function that aligns well with the underlying physics while
outperforming a baseline symbolic regression approach.


Abstract:
  Small form-factor, narrowband, and highly directive antennas are of critical
importance in a variety of applications spanning wireless communications,
remote sensing, Raman spectroscopy, and single photon emission enhancement.
Surprisingly, we show that the classical directivity limit can be appreciably
surpassed for electrically small multilayer spherical antennas excited by a
point electric dipole even if limiting ourselves to purely dielectric
materials. Experimentally feasible designs of superdirective antennas are
established by using a stochastic optimization algorithm combined with a
rigorous analytic solution.


Abstract:
  Recently, [JHEP 20 131 (2020)] obtained (a similar, scaled version of) the
($a,b$)-phase diagram derived from the Kazakov--Zinn-Justin solution of the
Hermitian two-matrix model with interactions \[\mathrm{Tr\,}\Big\{\frac{a}{4}
(A^4+B^4)+\frac{b}{2} ABAB\Big\}\,,\] starting from Functional Renormalization.
We comment on something unexpected: the phase diagram of [JHEP 20 131 (2020)]
is based on a $\beta_b$-function that does not have the one-loop structure of
the Wetterich-Morris Equation. This raises the question of how to reproduce the
phase diagram from a set of $\beta$-functions that is, in its totality,
consistent with Functional Renormalization. A non-minimalist, yet simple
truncation that could lead to the phase diagram is provided. Additionally, we
identify the ensemble for which the result of op. cit. would be entirely
correct.


Abstract:
  Climate change, which is now considered one of the biggest threats to
humanity, is also the reason behind various other environmental concerns.
Continued negligence might lead us to an irreparably damaged environment. After
the partial failure of the Paris Agreement, it is quite evident that we as
individuals need to come together to bring about a change on a large scale to
have a significant impact. This paper discusses our approach towards obtaining
a realistic measure of the carbon footprint index being consumed by a user
through day-to-day activities performed via a smart phone app and offering
incentives in weekly and monthly leader board rankings along with a reward
system. The app helps ease out decision makings on tasks like travel, shopping,
electricity consumption, and gain a different and rather numerical perspective
over the daily choices.


Abstract:
  In this article we present recent advances on interval methods for rigorous
computation of Poincar\'e maps. We also discuss the impact of choice of
Poincar\'e section and coordinate system on obtained bounds for computing
Poincar\'e map nearby fixed points.


Abstract:
  Foraminifera are single-celled marine organisms that construct shells that
remain as fossils in the marine sediments. Classifying and counting these
fossils are important in e.g. paleo-oceanographic and -climatological research.
However, the identification and counting process has been performed manually
since the 1800s and is laborious and time-consuming. In this work, we present a
deep learning-based instance segmentation model for classifying, detecting, and
segmenting microscopic foraminifera. Our model is based on the Mask R-CNN
architecture, using model weight parameters that have learned on the COCO
detection dataset. We use a fine-tuning approach to adapt the parameters on a
novel object detection dataset of more than 7000 microscopic foraminifera and
sediment grains. The model achieves a (COCO-style) average precision of $0.78
\pm 0.00$ on the classification and detection task, and $0.80 \pm 0.00$ on the
segmentation task. When the model is evaluated without challenging sediment
grain images, the average precision for both tasks increases to $0.84 \pm 0.00$
and $0.86 \pm 0.00$, respectively. Prediction results are analyzed both
quantitatively and qualitatively and discussed. Based on our findings we
propose several directions for future work, and conclude that our proposed
model is an important step towards automating the identification and counting
of microscopic foraminifera.


Abstract:
  For the observational modeling of horizontal abundance distributions and of
magnetic geometries in chemically peculiar (CP) stars, Zeeman Doppler mapping
(ZDM) has become the method of choice. Comparisons between abundance maps
obtained for CP stars and predictions from numerical simulations of atomic
diffusion have always proved unsatisfactory, with the blame routinely put on
theory. Expanding a previous study aimed at clarifying the question of the
uniqueness of ZDM maps, this paper inverts the roles between observational
modeling and time-dependent diffusion results, casting a cold eye on essential
assumptions and algorithms underlying ZDM, in particular the Tikhonov-style
regularization functionals, from 1D to 3D. We show that these have been
established solely for mathematical convenience, but that they in no way
reflect the physical reality in the atmospheres of magnetic CP stars.
Recognizing that the observed strong magnetic fields in most well-mapped stars
require the field geometry to be force-free, we demonstrate that many published
maps do not meet this condition. There follows a discussion of the frequent
changes in magnetic and abundance maps of well observed stars and a caveat
concerning the use of least squares deconvolution in ZDM analyses. It emerges
that because of the complexity and non-linearity of the field-dependent
chemical stratifications, Tikhonov based ZDM inversions cannot recover the true
abundance and magnetic geometries. As our findings additionally show, there is
no way to define a physically meaningful 3D regularization functional instead.
ZDM remains dysfunctional and does not provide any observational constraints
for the modeling of atomic diffusion.


Abstract:
  In this paper, we propose different algorithms for the solution of a tensor
linear discrete ill-posed problem arising in the application of the meshless
method for solving PDEs in three-dimensional space using multiquadric radial
basis functions. It is well known that the truncated singular value
decomposition (TSVD) is the most common effective solver for ill-conditioned
systems, but unfortunately the operation count for solving a linear system with
the TSVD is computationally expensive for large-scale matrices. In the present
work, we propose algorithms based on the use of the well known Einstein product
for two tensors to define the tensor global Arnoldi and the tensor Gloub Kahan
bidiagonalization algorithms. Using the so-called Tikhonov regularization
technique, we will be able to provide computable approximate regularized
solutions in a few iterations.


Abstract:
  Fairness is an important property in data-mining applications, including
recommender systems. In this work, we investigate a case where users of a
recommender system need (or want) to be fair to a protected group of items. For
example, in a job market, the user is the recruiter, an item is the job seeker,
and the protected attribute is gender or race. Even if recruiters want to use a
fair talent recommender system, the platform may not provide a fair recommender
system, or recruiters may not be able to ascertain whether the recommender
system's algorithm is fair. In this case, recruiters cannot utilize the
recommender system, or they may become unfair to job seekers. In this work, we
propose methods to enable the users to build their own fair recommender
systems. Our methods can generate fair recommendations even when the platform
does not (or cannot) provide fair recommender systems. The key challenge is
that a user does not have access to the log data of other users or the latent
representations of items. This restriction prohibits us from adopting existing
methods, which are designed for platforms. The main idea is that a user has
access to unfair recommendations provided by the platform. Our methods leverage
the outputs of an unfair recommender system to construct a new fair recommender
system. We empirically validate that our proposed method improves fairness
substantially without harming much performance of the original unfair system.


Abstract:
  We provide a categorical interpretation for _escrows_, i.e. trading protocols
in trustless environment, where the exchange between two agents is mediated by
a third party where the buyer locks the money until they receive the goods they
want from the seller. A simplified escrow system can be modeled as a certain
kind of _optic_ in a monoidal category $\mathcal M$ (e.g., the category of sets
with cartesian product); escrows can be regarded as morphisms of a category
$\mathcal E(\mathcal M)$, with the same objects of $\mathcal M$, and where the
hom-objects are $\langle X , Y \rangle = \mathsf{Opt}_{\mathcal M}(\left[
\begin{smallmatrix} Y \\ X \end{smallmatrix} \right], \left[
\begin{smallmatrix} X \\ Y \end{smallmatrix} \right])$. When $X$ is a comonoid
and $Y$ is a monoid in $\mathcal M$, $\mathcal E(\mathcal M)(X,Y)$ is a monoid
in $\mathsf{Set}$ (or in the base of enrichment chosen to model one's specific
problem), acting on the set of optics $\left[ \begin{smallmatrix} B \\ B
\end{smallmatrix} \right] \to \left[ \begin{smallmatrix} X \\ Y
\end{smallmatrix} \right]$. Moreover, we define a map $$\lhd : \langle Y , X
\rangle \times \mathsf{Opt}(\left[ \begin{smallmatrix} Y \\ X \end{smallmatrix}
\right], \left[ \begin{smallmatrix} B \\ B \end{smallmatrix} \right]) \to
\mathsf{Opt}(\left[ \begin{smallmatrix} Y \\ X \end{smallmatrix} \right],
\left[ \begin{smallmatrix}{X\otimes B}\\ {Y\otimes B} \end{smallmatrix}
\right])$$ having action-like properties. This has the following
interpretation: the object $B$ acts as an intermediary in a transaction between
$X$ and $Y$, modeled by an escrow in $\langle Y , X \rangle$.


Abstract:
  We study the mean properties of a large representative sample of 217 galaxies
showing CIII] emission at $2<z<4$, selected from a parent sample of $\sim$750
main-sequence star-forming galaxies in the VANDELS survey. These CIII] emitters
have a broad range of UV luminosities, thus allowing a detailed stacking
analysis to characterize their stellar mass, star formation rate (SFR) and
stellar metallicity, as a function of the UV emission line ratios, EWs, and the
carbon-to-oxygen (C/O) abundance ratio. Reliable CIII] detections represent
$\sim$30% of the parent sample. Extreme CIII] emitters
(EW(CIII])$\gtrsim$8\r{A}) are exceedingly rare ($\sim$3%) in VANDELS. The UV
line ratios of the sample suggest no ionization source other than massive
stars. Stacks with larger EW(CIII]) show larger EW(Ly$\alpha$) and lower
metallicity, but not all CIII] emitters are Ly$\alpha$ emitters. The stellar
metallicities of CIII] emitters are not significantly different from that of
the parent sample, increasing from $\sim$10% to $\sim$40% solar for stellar
masses $\log$(M$_{\star}$/M$_{\odot})\sim$9-10.5. The stellar mass-metallicity
relation of the CIII] emitters is consistent with previous works showing strong
evolution from $z=0$ to $z\sim3$. The C/O abundances of the sample range
35%-150% solar, with a noticeable increase with FUV luminosity and a smooth
decrease with the CIII] EW. We discuss the CIII] emitters in the C/O-Fe/H and
the C/O-O/H planes and find they follow stellar and nebular abundance trends
consistent with those of Milky Way halo and thick disc stars and local HII
galaxies, respectively. A qualitative agreement is also found with chemical
evolution models, which suggests that CIII] emitters at $z\sim$3 are
experiencing an active phase of chemical enrichment.


Abstract:
  We describe models focused at the understudied problem of translating between
monolingual and code-mixed language pairs. More specifically, we offer a wide
range of models that convert monolingual English text into Hinglish (code-mixed
Hindi and English). Given the recent success of pretrained language models, we
also test the utility of two recent Transformer-based encoder-decoder models
(i.e., mT5 and mBART) on the task finding both to work well. Given the paucity
of training data for code-mixing, we also propose a dependency-free method for
generating code-mixed texts from bilingual distributed representations that we
exploit for improving language model performance. In particular, armed with
this additional data, we adopt a curriculum learning approach where we first
finetune the language models on synthetic data then on gold code-mixed data. We
find that, although simple, our synthetic code-mixing method is competitive
with (and in some cases is even superior to) several standard methods
(backtranslation, method based on equivalence constraint theory) under a
diverse set of conditions. Our work shows that the mT5 model, finetuned
following the curriculum learning procedure, achieves best translation
performance (12.67 BLEU). Our models place first in the overall ranking of the
English-Hinglish official shared task.


Abstract:
  Open conjectures state that, for every $x\in[0,1]$, the orbit
$\left(x_n\right)_{n=1}^\infty$ of the mean-median recursion
$$x_{n+1}=(n+1)\cdot\mathrm{median}\left(x_1,\ldots,x_{n}\right)-\left(x_1+\cdots+x_n\right),\quad
n\geqslant 3,$$ with initial data $\left(x_1,x_2,x_3\right)=(0,x,1)$, is
eventually constant, and that its transit time and limit functions (of $x$) are
unbounded and continuous, respectively. In this paper we prove that, for the
slightly modified recursion
$$x_{n+1}=n\cdot\mathrm{median}\left(x_1,\ldots,x_{n}\right)-\left(x_1+\cdots+x_n\right),\quad
n\geqslant 3,$$ first suggested by Akiyama, the transit time function is
unbounded but the limit function is discontinuous.


Abstract:
  The research in Environmental Sound Classification (ESC) has been
progressively growing with the emergence of deep learning algorithms. However,
data scarcity poses a major hurdle for any huge advance in this domain. Data
augmentation offers an excellent solution to this problem. While Generative
Adversarial Networks (GANs) have been successful in generating synthetic speech
and sounds of musical instruments, they have hardly been applied to the
generation of environmental sounds. This paper presents EnvGAN, the first ever
application of GANs for the adversarial generation of environmental sounds. Our
experiments on three standard ESC datasets illustrate that the EnvGAN can
synthesize audio similar to the ones in the datasets. The suggested method of
augmentation outshines most of the futuristic techniques for audio
augmentation.


Abstract:
  We study a weakly-interacting one-dimensional Bose gas with two impurities
coupled locally to the boson density. We derive analytical results for the
induced interaction between the impurities at arbitrary coupling and separation
$r$. At $r\lesssim \xi$, where $\xi$ denotes the healing length of the Bose
gas, the interaction is well described by the mean-field contribution. Its form
changes as the coupling is increased, approaching a linear function of $r$ at
short distances in the regime of strong coupling. The mean-field contribution
decays exponentially at arbitrary coupling for $r\gg\xi$. At such long
distances, however, the effect of quantum fluctuations becomes important,
giving rise to a long-ranged quantum contribution to the induced interaction.
At longest distances it behaves as $1/r^3$, while at strong coupling we find an
intermediate distance regime with a slower decay, $1/r$. The quantum
contribution in the crossover regime is also calculated. The induced
interaction between impurities (i.e., polarons) is attractive and leads to the
formation of their bound state, known as bipolaron. We discuss its binding
energy.


Abstract:
  We derive symmetric and antisymmetric kernels by symmetrizing and
antisymmetrizing conventional kernels and analyze their properties. In
particular, we compute the feature space dimensions of the resulting polynomial
kernels, prove that the reproducing kernel Hilbert spaces induced by symmetric
and antisymmetric Gaussian kernels are dense in the space of symmetric and
antisymmetric functions, and propose a Slater determinant representation of the
antisymmetric Gaussian kernel, which allows for an efficient evaluation even if
the state space is high-dimensional. Furthermore, we show that by exploiting
symmetries or antisymmetries the size of the training data set can be
significantly reduced. The results are illustrated with guiding examples and
simple quantum physics and chemistry applications.


Abstract:
  Combinatorial optimization problem (COP) over graphs is a fundamental
challenge in optimization. Reinforcement learning (RL) has recently emerged as
a new framework to tackle these problems and has demonstrated promising
results. However, most RL solutions employ a greedy manner to construct the
solution incrementally, thus inevitably pose unnecessary dependency on action
sequences and need a lot of problem-specific designs. We propose a general RL
framework that not only exhibits state-of-the-art empirical performance but
also generalizes to a variety class of COPs. Specifically, we define state as a
solution to a problem instance and action as a perturbation to this solution.
We utilize graph neural networks (GNN) to extract latent representations for
given problem instances for state-action encoding, and then apply deep
Q-learning to obtain a policy that gradually refines the solution by flipping
or swapping vertex labels. Experiments are conducted on Maximum $k$-Cut and
Traveling Salesman Problem and performance improvement is achieved against a
set of learning-based and heuristic baselines.


Abstract:
  A new connection between structure and dynamics in glass-forming liquids is
presented. We show how the origin of spatially localized excitations, as
defined by dynamical facilitation (DF) theory, can be understood from a
structure-based framework. This framework is constructed by associating
excitation events in DF theory to hopping events between energy minima in the
potential energy landscape (PEL). By reducing the PEL to an equal energy well
picture and applying a harmonic approximation, we develop a field theory to
describe elastic fluctuations about inherent states, which are energy
minimizing configurations of the PEL. We model an excitation as a shear
transformation zone (STZ) inducing a localized pure shear deformation onto an
inherent state. We connect STZs to T1 transition events that break the elastic
bonds holding the local structure of an inherent state. A formula for the
excitation energy barrier, denoted as $J_\sigma$, is obtained as a function of
inherent-state elastic moduli and radial distribution function. The energy
barrier from the current theory is compared to one predicted by the DF theory
where good agreement is found in various two-dimensional continuous
poly-disperse atomistic models of glass formers. These results strengthen the
role of structure and elasticity in driving glassy dynamics through the
creation and relaxation of localized excitations.


Abstract:
  Interfaces impede heat flow in micro/nanostructured systems. Conventional
theories for interfacial thermal transport were derived based on bulk phonon
properties of the materials making up the interface without explicitly
considering the atomistic interfacial details, which are found critical to
correctly describing thermal boundary conductance (TBC). Recent theoretical
studies predicted the existence of localized phonon modes at the interface
which can play an important role in understanding interfacial thermal
transport. However, experimental validation is still lacking. Through a
combination of Raman spectroscopy and high-energy resolution electron
energy-loss spectroscopy (EELS) in a scanning transmission electron microscope,
we report the first experimental observation of localized interfacial phonon
modes at ~12 THz at a high-quality epitaxial Si-Ge interface. These modes are
further confirmed using molecular dynamics simulations with a high-fidelity
neural network interatomic potential, which also yield TBC agreeing well with
that measured from time-domain thermoreflectance (TDTR) experiments.
Simulations find that the interfacial phonon modes have obvious contribution to
the total TBC. Our findings may significantly contribute to the understanding
of interfacial thermal transport physics and have impact on engineering TBC at
interfaces in applications such as electronics thermal management and
thermoelectric energy conversion.


Abstract:
  The diffusion, "explosion" and "evaporation" of dimers and the subsequent
coalescence are treated in a formal way by identifying and solving the
differential equations deduced from the respective behaviors of dimers in the
different cases. This study leads to analytic formulas allowing to calculate,
in a simple and fast way, the size statistics obtained after the coalescence of
the dimers or their constituents once the dimers have completely disappeared.
These formulas are of capital interest to characterize systems in which the
dimers initially present disappear. 4 Study assumptions. Six assumptions will
be made for this study: 1. Initial probabilities are known.


Abstract:
  Quantum synchronizable codes are kinds of quantum error-correcting codes that
can not only correct the effects of quantum noise on qubits but also the
misalignment in block synchronization. This paper contributes to constructing
two classes of quantum synchronizable codes by the cyclotomic classes of order
two over $\mathbb{Z}_{2q}$, whose synchronization capabilities can reach the
upper bound under certain conditions. Moreover, the quantum synchronizable
codes possess good error-correcting capability towards bit errors and phase
errors.


Abstract:
  In this work, we mainly concern the limiting behavior of the electromagnetic
field of two species Vlasov-Maxwell-Botlzmann system in diffusive limits. As
knudsen numbers goes to zero, the electric magnetic and magnetic field may
perserve or vanish. We verify rigorously Navier-Stokes, Navier-Stokes-Poisson
and Navier-Stokes Maxwell limit of the two species Vlasov-Maxwell-Boltzmann
system on the torus in three dimension. The justification is based on the
unified and uniform estimates of solutions to the dimensionless
Vlasov-Maxwell-Boltzmann. The uniform estimates of solutions are obtained by
employing the hypocoercivity of the linear Boltzmann operator and constructing
a equation containing damping term of electric field.


Abstract:
  We consider repair tasks: given a critic (e.g., compiler) that assesses the
quality of an input, the goal is to train a fixer that converts a bad example
(e.g., code with syntax errors) into a good one (e.g., code with no syntax
errors). Existing works create training data consisting of (bad, good) pairs by
corrupting good examples using heuristics (e.g., dropping tokens). However,
fixers trained on this synthetically-generated data do not extrapolate well to
the real distribution of bad inputs. To bridge this gap, we propose a new
training approach, Break-It-Fix-It (BIFI), which has two key ideas: (i) we use
the critic to check a fixer's output on real bad inputs and add good (fixed)
outputs to the training data, and (ii) we train a breaker to generate realistic
bad code from good code. Based on these ideas, we iteratively update the
breaker and the fixer while using them in conjunction to generate more paired
data. We evaluate BIFI on two code repair datasets: GitHub-Python, a new
dataset we introduce where the goal is to repair Python code with AST parse
errors; and DeepFix, where the goal is to repair C code with compiler errors.
BIFI outperforms existing methods, obtaining 90.5% repair accuracy on
GitHub-Python (+28.5%) and 71.7% on DeepFix (+5.6%). Notably, BIFI does not
require any labeled data; we hope it will be a strong starting point for
unsupervised learning of various repair tasks.


Abstract:
  The main memory access latency has not much improved for more than two
decades while the CPU performance had been exponentially increasing until
recently. Approximate memory is a technique to reduce the DRAM access latency
in return of losing data integrity. It is beneficial for applications that are
robust to noisy input and intermediate data such as artificial intelligence,
multimedia processing, and graph processing. To obtain reasonable outputs from
applications on approximate memory, it is crucial to protect critical data
while accelerating accesses to non-critical data. We refer the minimum size of
a continuous memory region that the same error rate is applied in approximate
memory to as the approximation granularity. A fundamental limitation of
approximate memory is that the approximation granularity is as large as a few
kilo bytes. However, applications may have critical and non-critical data
interleaved with smaller granularity. For example, a data structure for graph
nodes can have pointers (critical) to neighboring nodes and its score
(non-critical, depending on the use-case). This data structure cannot be
directly mapped to approximate memory due to the gap between the approximation
granularity and the granularity of data criticality. We refer to this issue as
the granularity gap problem. In this paper, we first show that many
applications potentially suffer from this problem. Then we propose a framework
to quantitatively evaluate the performance overhead of a possible method to
avoid this problem using known techniques. The evaluation results show that the
performance overhead is non-negligible compared to expected benefit from
approximate memory, suggesting that the granularity gap problem is a
significant concern.


Abstract:
  While data sharing is crucial for knowledge development, privacy concerns and
strict regulation (e.g., European General Data Protection Regulation (GDPR))
unfortunately limit its full effectiveness. Synthetic tabular data emerges as
an alternative to enable data sharing while fulfilling regulatory and privacy
constraints. The state-of-the-art tabular data synthesizers draw methodologies
from generative Adversarial Networks (GAN) and address two main data types in
the industry, i.e., continuous and categorical. In this paper, we develop
CTAB-GAN, a novel conditional table GAN architecture that can effectively model
diverse data types, including a mix of continuous and categorical variables.
Moreover, we address data imbalance and long-tail issues, i.e., certain
variables have drastic frequency differences across large values. To achieve
those aims, we first introduce the information loss and classification loss to
the conditional GAN. Secondly, we design a novel conditional vector, which
efficiently encodes the mixed data type and skewed distribution of data
variable. We extensively evaluate CTAB-GAN with the state of the art GANs that
generate synthetic tables, in terms of data similarity and analysis utility.
The results on five datasets show that the synthetic data of CTAB-GAN
remarkably resembles the real data for all three types of variables and results
into higher accuracy for five machine learning algorithms, by up to 17%.


Abstract:
  This work deals with the topological classification of singular foliation
germs on $(\mathbb C^{2},0)$. Working in a suitable class of foliations we fix
the topological invariants given by the separatrix set, the Camacho-Sad indices
and the projective holonomy representations and we construct a minimal family
of foliation germs containing all the topological classes and which is
topologically complete. We prove factorization properties of equisingular
families through this family up to topological conjugacy.


Abstract:
  For monolayers of chemically active particles at a fluid interface,
collective dynamics are predicted to arise owing to activity-induced Marangoni
flow even if the particles are not self-propelled. Here we test this prediction
by employing a monolayer of spherically symmetric active TiO_2 particles
located at an oil-water interface with or without addition of a non-ionic
surfactant. Due to the spherical symmetry, an individual particle does not
self-propel. However, the gradients produced by the photochemical fuel
degradation give rise to long-ranged Marangoni flows. For the case in which
surfactant is added to the system, we indeed observe the emergence of
collective motion, with dynamics dependent on the particle coverage of the
monolayer. The experimental observations are discussed within the framework of
a simple theoretical mean field model.


Abstract:
  We consider the problem of making expressive static analyzers interactive.
Formal static analysis is seeing increasingly widespread adoption as a tool for
verification and bug-finding, but even with powerful cloud infrastructure it
can take minutes or hours to get batch analysis results after a code change.
While existing techniques offer some demand-driven or incremental aspects for
certain classes of analysis, the fundamental challenge we tackle is doing both
for arbitrary abstract interpreters.
  Our technique, demanded abstract interpretation, lifts program syntax and
analysis state to a dynamically evolving graph structure, in which program
edits, client-issued queries, and evaluation of abstract semantics are all
treated uniformly. The key difficulty addressed by our approach is the
application of general incremental computation techniques to the complex,
cyclic dependency structure induced by abstract interpretation of loops with
widening operators. We prove that desirable abstract interpretation
meta-properties, including soundness and termination, are preserved in our
approach, and that demanded analysis results are equal to those computed by a
batch abstract interpretation. Experimental results suggest promise for a
prototype demanded abstract interpretation framework: by combining incremental
and demand-driven techniques, our framework consistently delivers analysis
results at interactive speeds, answering 95% of queries within 1.2 seconds.


Abstract:
  In this paper, we introduce the notion of completely non-trivial module of a
Lie conformal algebra. By this notion, we classify all finite irreducible
modules of a class of $\mathbb{Z}^+$-graded Lie conformal algebras
$\mathcal{L}=\bigoplus_{i=0}^{\infty} \mathbb{C}[\partial]L_i$ satisfying $
[{L_0}_\lambda L_0]=(\partial+2\lambda)L_0,$ and $[{L_1}_\lambda L_i]\neq 0$
for any $i\in \mathbb{Z}^+$. These Lie conformal algebras include Block type
Lie conformal algebra $\mathcal{B}(p)$ and map Virasoro Lie conformal algebra
$\mathcal{V}(\mathbb{C}[T])=Vir\otimes \mathbb{C}[T]$. As a result, we show
that all non-trivial finite irreducible modules of these algebras are free of
rank one as a $\mathbb{C}[\partial]$-module.


Abstract:
  We view disentanglement learning as discovering an underlying structure that
equivariantly reflects the factorized variations shown in data. Traditionally,
such a structure is fixed to be a vector space with data variations represented
by translations along individual latent dimensions. We argue this simple
structure is suboptimal since it requires the model to learn to discard the
properties (e.g. different scales of changes, different levels of abstractness)
of data variations, which is an extra work than equivariance learning. Instead,
we propose to encode the data variations with groups, a structure not only can
equivariantly represent variations, but can also be adaptively optimized to
preserve the properties of data variations. Considering it is hard to conduct
training on group structures, we focus on Lie groups and adopt a
parameterization using Lie algebra. Based on the parameterization, some
disentanglement learning constraints are naturally derived. A simple model
named Commutative Lie Group VAE is introduced to realize the group-based
disentanglement learning. Experiments show that our model can effectively learn
disentangled representations without supervision, and can achieve
state-of-the-art performance without extra constraints.