Abstract:
  Subcomponent millicharged dark matter that cools baryons via Coulomb
interactions has been invoked to explain the EDGES anomaly. However, this model
is in severe tension with constraints from cosmology and stellar emissions. In
this work, we consider the consequences of these millicharged particles
existing in composite blobs. The relevant degrees of freedom at high
temperature are minuscule elementary charges, which fuse at low temperatures to
make up blobs of larger charge. These blobs serve as the degrees of freedom
relevant in cooling the baryons sufficiently to account for the EDGES anomaly.
In such a model, cosmology and stellar constraints (which involve
high-temperature processes) apply only to the feebly-interacting elementary
charges and not to the blobs. This salvages a large range of parameter space
for millicharged blobs that can explain the EDGES anomaly. It also opens up new
parameter space for direct detection, albeit at low momentum transfers.


Abstract:
  We prove that semi-infinite Bruhat order on an affine Weyl group is
completely determined from those on the quotients by affine Weyl subgroups
associated with various maximal (standard) parabolic subgroups of finite type.
Furthermore, for an affine Weyl group of classical type, we give a complete
classification of all cover relations of semi-infinite Bruhat order (or
equivalently, all edges of the quantum Bruhat graphs) on the quotients in terms
of tableaux. Combining these we obtain a tableau criterion for semi-infinite
Bruhat order on an affine Weyl group of classical type. As an application, we
give new tableau models for the crystal bases of a level-zero fundamental
representation and a level-zero extremal weight module over a quantum affine
algebra of classical untwisted type, which we call quantum Kashiwara-Nakashima
columns and semi-infinite Kashiwara-Nakashima tableaux. We give an explicit
description of the crystal isomorphisms among three different realizations of
the crystal basis of a level-zero fundamental representation by quantum
Lakshmibai-Seshadri paths, quantum Kashiwara-Nakashima columns, and (ordinary)
Kashiwara-Nakashima columns.


Abstract:
  Aim: To study the multimessenger nature of the signal that can result from
the phase transition of a neutron star to a quark star and their corresponding
astrophysical observations. Methods: The phase transition process is initiated
by the abrupt pressure and density changes at the star center, giving rise to a
shock which deconfines matter followed by a weak front converting excess down
to strange quarks to attain absolute stability. This process's effects are
investigated by understanding how the energy escapes from the star in the form
of neutrino-antineutrino annihilation. For such annihilation process, the
corresponding energy deposition rate is calculated. Structural changes due to
the energy loss have been investigated in the likes of misalignment angle
evolution of the star and its astrophysical observation through gravitational
waves. Results: The energy and time signature for the neutrino-antineutrino
annihilation is compared with the observed isotropic energy for a short
gamma-ray burst. The misalignment angle evolves to align the star's tilt axis,
which can lead to the sudden increase or decrease of radio intensity from the
pulsar. The corresponding gravitational wave emission, both continuous and
burst, all lead towards multimessenger signals coming from the phase
transition.


Abstract:
  We study the convergence properties of decentralized fictitious play (DFP)
for the class of near-potential games where the incentives of agents are nearly
aligned with a potential function. In DFP, agents share information only with
their current neighbors in a sequence of time-varying networks, keep estimates
of other agents' empirical frequencies, and take actions to maximize their
expected utility functions computed with respect to the estimated empirical
frequencies. We show that empirical frequencies of actions converge to a set of
strategies with potential function values that are larger than the potential
function values obtained by approximate Nash equilibria of the closest
potential game. This result establishes that DFP has identical convergence
guarantees in near-potential games as the standard fictitious play in which
agents observe the past actions of all the other agents.


Abstract:
  Interactions among individuals in social networks lead to echo chambers where
the distribution of opinions follows a bimodal distribution with two peaks at
the opposite extremes. In issues with clear answers, such as global warming,
one of the echo chambers reflects an inaccurate judgment, potentially from
misinformation. However, in issues without clear answers such as elections, the
neutral consensus is preferable for promoting discourse. In this letter, we use
an opinion dynamics model to study the effect of a random dynamical nudge where
we present random input to each agent from the other individuals in the
network. We show that random dynamical nudge disallows the formation of echo
chambers and leads to a normal distribution of opinions centered around the
neutral consensus. The random dynamical nudge relies on the collective dynamics
and it does not require surveillance of every person's opinions. Social media
networks could implement a version of this self-feedback mechanism to prevent
the formation of segregated online communities on pressing issues such as
elections.


Abstract:
  Semantic segmentation from very fine resolution (VFR) urban scene images
plays a significant role in several application scenarios including autonomous
driving, land cover classification, and urban planning, etc. However, the
tremendous details contained in the VFR image severely limit the potential of
the existing deep learning approaches. More seriously, the considerable
variations in scale and appearance of objects further deteriorate the
representational capacity of those se-mantic segmentation methods, leading to
the confusion of adjacent objects. Addressing such is-sues represents a
promising research field in the remote sensing community, which paves the way
for scene-level landscape pattern analysis and decision making. In this
manuscript, we pro-pose a bilateral awareness network (BANet) which contains a
dependency path and a texture path to fully capture the long-range
relationships and fine-grained details in VFR images. Specif-ically, the
dependency path is conducted based on the ResT, a novel Transformer backbone
with memory-efficient multi-head self-attention, while the texture path is
built on the stacked convo-lution operation. Besides, using the linear
attention mechanism, a feature aggregation module (FAM) is designed to
effectively fuse the dependency features and texture features. Extensive
experiments conducted on the three large-scale urban scene image segmentation
datasets, i.e., ISPRS Vaihingen dataset, ISPRS Potsdam dataset, and UAVid
dataset, demonstrate the effective-ness of our BANet. Specifically, a 64.6%
mIoU is achieved on the UAVid dataset.


Abstract:
  We introduce an approach that accurately reconstructs 3D human poses and
detailed 3D full-body geometric models from single images in realtime. The key
idea of our approach is a novel end-to-end multi-task deep learning framework
that uses single images to predict five outputs simultaneously: foreground
segmentation mask, 2D joints positions, semantic body partitions, 3D part
orientations and uv coordinates (uv map). The multi-task network architecture
not only generates more visual cues for reconstruction, but also makes each
individual prediction more accurate. The CNN regressor is further combined with
an optimization based algorithm for accurate kinematic pose reconstruction and
full-body shape modeling. We show that the realtime reconstruction reaches
accurate fitting that has not been seen before, especially for wild images. We
demonstrate the results of our realtime 3D pose and human body reconstruction
system on various challenging in-the-wild videos. We show the system advances
the frontier of 3D human body and pose reconstruction from single images by
quantitative evaluations and comparisons with state-of-the-art methods.


Abstract:
  Salient object detection aims at detecting the most visually distinct objects
and producing the corresponding masks. As the cost of pixel-level annotations
is high, image tags are usually used as weak supervisions. However, an image
tag can only be used to annotate one class of objects. In this paper, we
introduce saliency subitizing as the weak supervision since it is
class-agnostic. This allows the supervision to be aligned with the property of
saliency detection, where the salient objects of an image could be from more
than one class. To this end, we propose a model with two modules, Saliency
Subitizing Module (SSM) and Saliency Updating Module (SUM). While SSM learns to
generate the initial saliency masks using the subitizing information, without
the need for any unsupervised methods or some random seeds, SUM helps
iteratively refine the generated saliency masks. We conduct extensive
experiments on five benchmark datasets. The experimental results show that our
method outperforms other weakly-supervised methods and even performs comparably
to some fully-supervised methods.


Abstract:
  In recent years, the unprecedented growth in digital payments fueled
consequential changes in fraud and financial crimes. In this new landscape,
traditional fraud detection approaches such as rule-based engines have largely
become ineffective. AI and machine learning solutions using graph computing
principles have gained significant interest. Graph neural networks and emerging
adaptive solutions provide compelling opportunities for the future of fraud and
financial crime detection. However, implementing the graph-based solutions in
financial transaction processing systems has brought numerous obstacles and
application considerations to light. In this paper, we overview the latest
trends in the financial crimes landscape and discuss the implementation
difficulties current and emerging graph solutions face. We argue that the
application demands and implementation challenges provide key insights in
developing effective solutions.


Abstract:
  One of the most challenging problems for the realization of a scalable
quantum computer is to design a physical device that keeps the error rate for
each quantum processing operation low. These errors can originate from the
accuracy of quantum manipulation, such as the sweeping of a gate voltage in
solid state qubits or the duration of a laser pulse in optical schemes. Errors
also result from decoherence, which is often regarded as more crucial in the
sense that it is inherent to the quantum system, being fundamentally a
consequence of the coupling to the external environment.
  Grouping small collections of qubits into clusters with symmetries can
protect parts of the calculation from decoherence. We use 4-level cores with a
straightforward generalization of discrete rotational symmetry, omega-rotation
invariance, to encode pairs of coupled qubits and universal 2-qubit logical
gates. We include quantum errors as a main source of decoherence, and show that
symmetry makes logical operations particularly resilient to untimely
anisotropic qubit rotations. We propose a scalable scheme for universal quantum
computation where cores play the role of quantum-computational transistors,
quansistors.
  Initialization and readout are achieved by coupling to leads. The external
leads are explicitly considered and are assumed to be the other main source of
decoherence. We show that quansistors can be dynamically decoupled from the
leads by tuning their internal parameters, giving them the versatility required
to act as controllable quantum memory units. With this dynamical decoupling,
logical operations within quansistors are also symmetry-protected from unbiased
noise in their parameters. We identify technologies that could implement
omega-rotation invariance. Many of our results can be generalized to
higher-level omega-rotation-invariant systems, or adapted to clusters with
other symmetries.


Abstract:
  Wireless power transfer (WPT) is an emerging paradigm that will enable using
wireless to its full potential in future networks, not only to convey
information but also to deliver energy. Such networks will enable trillions of
future low-power devices to sense, compute, connect, and energize anywhere,
anytime, and on the move. The design of such future networks brings new
challenges and opportunities for signal processing, machine learning, sensing,
and computing so as to make the best use of the RF radiations, spectrum, and
network infrastructure in providing cost-effective and real-time power supplies
to wireless devices and enable wireless-powered applications. In this paper, we
first review recent signal processing techniques to make WPT and wireless
information and power transfer as efficient as possible. Topics include power
amplifier and energy harvester nonlinearities, active and passive beamforming,
intelligent reflecting surfaces, receive combining with multi-antenna
harvester, modulation, coding, waveform, massive MIMO, channel acquisition,
transmit diversity, multi-user power region characterization, coordinated
multipoint, and distributed antenna systems. Then, we overview two different
design methodologies: the model and optimize approach relying on analytical
system models, modern convex optimization, and communication theory, and the
learning approach based on data-driven end-to-end learning and physics-based
learning. We discuss the pros and cons of each approach, especially when
accounting for various nonlinearities in wireless-powered networks, and
identify interesting emerging opportunities for the approaches to complement
each other. Finally, we identify new emerging wireless technologies where WPT
may play a key role -- wireless-powered mobile edge computing and
wireless-powered sensing -- arguing WPT, communication, computation, and
sensing must be jointly designed.


Abstract:
  In the image fusion field, the design of deep learning-based fusion methods
is far from routine. It is invariably fusion-task specific and requires a
careful consideration. The most difficult part of the design is to choose an
appropriate strategy to generate the fused image for a specific task in hand.
Thus, devising learnable fusion strategy is a very challenging problem in the
community of image fusion. To address this problem, a novel end-to-end fusion
network architecture (RFN-Nest) is developed for infrared and visible image
fusion. We propose a residual fusion network (RFN) which is based on a residual
architecture to replace the traditional fusion approach. A novel
detail-preserving loss function, and a feature enhancing loss function are
proposed to train RFN. The fusion model learning is accomplished by a novel
two-stage training strategy. In the first stage, we train an auto-encoder based
on an innovative nest connection (Nest) concept. Next, the RFN is trained using
the proposed loss functions. The experimental results on public domain data
sets show that, compared with the existing methods, our end-to-end fusion
network delivers a better performance than the state-of-the-art methods in both
subjective and objective evaluation. The code of our fusion method is available
at https://github.com/hli1221/imagefusion-rfn-nest


Abstract:
  We consider a cache-aided wireless device-to-device (D2D) network under the
constraint of one-shot delivery, where the placement phase is orchestrated by a
central server. We assume that the devices' caches are filled with uncoded
data, and the whole file database at the server is made available in the
collection of caches. Following this phase, the files requested by the users
are serviced by inter-device multicast communication. For such a system
setting, we provide the exact characterization of load-memory trade-off, by
deriving both the minimum average and the minimum peak sum-loads of links
between devices, for a given individual memory size at disposal of each user.


Abstract:
  The classical notion of retraction map used to approximate geodesics is
extended and rigorously defined to become a powerful tool to construct
geometric integrators. Using the geometry of the tangent and cotangent bundles,
we are able to tangently and cotangent lift such a map so that these lifts
inherit the same properties as the original one and they continue to be
extended retraction maps. In particular, the cotangent lift of this new notion
of retraction map is a natural symplectomorphism, what plays a key role for
constructing geometric integrators and symplectic methods. As a result, a wide
range of numerical methods are recovered and canonically constructed by using
different extended retraction maps, as well as some operations with Lagrangian
submanifolds.


Abstract:
  Learning with labels noise has gained significant traction recently due to
the sensitivity of deep neural networks under label noise under common loss
functions. Losses that are theoretically robust to label noise, however, often
makes training difficult. Consequently, several recently proposed methods, such
as Meta-Weight-Net (MW-Net), use a small number of unbiased, clean samples to
learn a weighting function that downweights samples that are likely to have
corrupted labels under the meta-learning framework. However, obtaining such a
set of clean samples is not always feasible in practice. In this paper, we
analytically show that one can easily train MW-Net without access to clean
samples simply by using a loss function that is robust to label noise, such as
mean absolute error, as the meta objective to train the weighting network. We
experimentally show that our method beats all existing methods that do not use
clean samples and performs on-par with methods that use gold samples on
benchmark datasets across various noise types and noise rates.


Abstract:
  Custom voice, a specific text to speech (TTS) service in commercial speech
platforms, aims to adapt a source TTS model to synthesize personal voice for a
target speaker using few speech data. Custom voice presents two unique
challenges for TTS adaptation: 1) to support diverse customers, the adaptation
model needs to handle diverse acoustic conditions that could be very different
from source speech data, and 2) to support a large number of customers, the
adaptation parameters need to be small enough for each target speaker to reduce
memory usage while maintaining high voice quality. In this work, we propose
AdaSpeech, an adaptive TTS system for high-quality and efficient customization
of new voices. We design several techniques in AdaSpeech to address the two
challenges in custom voice: 1) To handle different acoustic conditions, we use
two acoustic encoders to extract an utterance-level vector and a sequence of
phoneme-level vectors from the target speech during training; in inference, we
extract the utterance-level vector from a reference speech and use an acoustic
predictor to predict the phoneme-level vectors. 2) To better trade off the
adaptation parameters and voice quality, we introduce conditional layer
normalization in the mel-spectrogram decoder of AdaSpeech, and fine-tune this
part in addition to speaker embedding for adaptation. We pre-train the source
TTS model on LibriTTS datasets and fine-tune it on VCTK and LJSpeech datasets
(with different acoustic conditions from LibriTTS) with few adaptation data,
e.g., 20 sentences, about 1 minute speech. Experiment results show that
AdaSpeech achieves much better adaptation quality than baseline methods, with
only about 5K specific parameters for each speaker, which demonstrates its
effectiveness for custom voice. Audio samples are available at
https://speechresearch.github.io/adaspeech/.


Abstract:
  Autonomous CPSs are often required to handle uncertainties and self-manage
the system operation in response to problems and increasing risk in the
operating paradigm. This risk may arise due to distribution shifts,
environmental context, or failure of software or hardware components.
Traditional techniques for risk assessment focus on design-time techniques such
as hazard analysis, risk reduction, and assurance cases among others. However,
these static, design-time techniques do not consider the dynamic contexts and
failures the systems face at runtime. We hypothesize that this requires a
dynamic assurance approach that computes the likelihood of unsafe conditions or
system failures considering the safety requirements, assumptions made at design
time, past failures in a given operating context, and the likelihood of system
component failures. We introduce the ReSonAte dynamic risk estimation framework
for autonomous systems. ReSonAte reasons over Bow-Tie Diagrams (BTDs) which
capture information about hazard propagation paths and control strategies. Our
innovation is the extension of the BTD formalism with attributes for modeling
the conditional relationships with the state of the system and environment. We
also describe a technique for estimating these conditional relationships and
equations for estimating risk based on the state of the system and environment.
To help with this process, we provide a scenario modeling procedure that can
use the prior distributions of the scenes and threat conditions to generate the
data required for estimating the conditional relationships. To improve
scalability and reduce the amount of data required, this process considers each
control strategy in isolation and composes several single-variate distributions
into one complete multi-variate distribution for the control strategy in
question.


Abstract:
  Since the beginnings of the electronic age, a quest for ever faster and
smaller switches has been initiated, since this element is ubiquitous and
foundational in any electronic circuit to regulate the flow of current. Mott
insulators are promising candidates to meet this need as they undergo extremely
fast resistive switching under electric field. However the mechanism of this
transition is still under debate. Our spatially-resolved {\mu}-XRD imaging
experiments carried out on the prototypal Mott insulator (V0.95Cr0.05)2O3 show
that the resistive switching is associated with the creation of a conducting
filamentary path consisting in an isostructural compressed phase without any
chemical nor symmetry change. This clearly evidences that the resistive
switching mechanism is inherited from the bandwidth-controlled Mott transition.
This discovery might hence ease the development of a new branch of electronics
dubbed Mottronics.


Abstract:
  Self-training based unsupervised domain adaptation (UDA) has shown great
potential to address the problem of domain shift, when applying a trained deep
learning model in a source domain to unlabeled target domains. However, while
the self-training UDA has demonstrated its effectiveness on discriminative
tasks, such as classification and segmentation, via the reliable pseudo-label
selection based on the softmax discrete histogram, the self-training UDA for
generative tasks, such as image synthesis, is not fully investigated. In this
work, we propose a novel generative self-training (GST) UDA framework with
continuous value prediction and regression objective for cross-domain image
synthesis. Specifically, we propose to filter the pseudo-label with an
uncertainty mask, and quantify the predictive confidence of generated images
with practical variational Bayes learning. The fast test-time adaptation is
achieved by a round-based alternative optimization scheme. We validated our
framework on the tagged-to-cine magnetic resonance imaging (MRI) synthesis
problem, where datasets in the source and target domains were acquired from
different scanners or centers. Extensive validations were carried out to verify
our framework against popular adversarial training UDA methods. Results show
that our GST, with tagged MRI of test subjects in new target domains, improved
the synthesis quality by a large margin, compared with the adversarial training
UDA methods.


Abstract:
  In this early draft, we describe a decentralized, app-based approach to
COVID-19 vaccine distribution that facilitates zero knowledge verification,
dynamic vaccine scheduling, continuous symptoms reporting, access to aggregate
analytics based on population trends and more. To ensure equity, our solution
is developed to work with limited internet access as well. In addition, we
describe the six critical functions that we believe last mile vaccination
management platforms must perform, examine existing vaccine management systems,
and present a model for privacy-focused, individual-centric solutions.


Abstract:
  Possibility of the diverging gradient of the macroscopic quantity near the
boundary is investigated by a mono-speed Lorentz-gas model, with a special
attention to the regularizing effect of the grazing collision for the
infinite-range potential on the velocity distribution function (VDF) and its
influence on the macroscopic quantity. By careful numerical analyses of the
steady one-dimensional boundary-value problem, it is confirmed that the grazing
collision suppresses the occurrence of a jump discontinuity of the VDF on the
boundary. However, as the price for that regularization, the collision integral
becomes no longer finite in the direction of the molecular velocity parallel to
the boundary. Consequently, the gradient of the macroscopic quantity diverges,
even stronger than the case of the finite-range potential. A conjecture about
the diverging rate in approaching the boundary is made as well for a wide range
of the infinite-range potentials, accompanied by the numerical evidence.


Abstract:
  This paper considers a cooperative cognitive radio network with two primary
users (PUs) and two secondary users (SUs) that enables two-way communications
of primary and secondary systems in conjunction with non-linear energy
harvesting based simultaneous wireless information and power transfer (SWIPT).
With the considered network, SUs are able to realize their communications over
the licensed spectrum while extending relay assistance to the PUs. The overall
bidirectional end-to-end transmission takes place in four phases, which include
both energy harvesting (EH) and information transfer. A non-linear energy
harvester with a hybrid SWIPT scheme is adopted in which both power-splitting
and time-switching EH techniques are used. The SUs aid in relay cooperation by
performing an amplify-and-forward operation, whereas selection combining
technique is adopted at the PUs to extract the intended signal from multiple
received signals broadcasted by the SUs. Accurate outage probability
expressions for the primary and secondary links are derived under the
Nakagami-$m$ fading environment. Further, the system behavior is analyzed with
respect to achievable system throughput and energy efficiency. Since the
performance of the considered system is strongly affected by the spectrum
sharing factor and hybrid SWIPT parameters, particle swarm optimization is
implemented to optimize the system parameters so as to maximize the system
throughput and energy efficiency. Simulation results are provided to
corroborate the performance analysis and give useful insights into the system
behavior concerning various system/channel parameters.


Abstract:
  We introduce a model involving two adversaries Buster and Fixer taking turns
modifying a connected graph, where each round consists of Buster deleting a
subset of edges and Fixer responding by adding edges from a reserve set of
weighted edges to leave the graph connected. With the weights representing the
cost for Fixer to use specific reserve edges to reconnect the graph, we provide
a reasonable definition for what should constitute an optimal strategy for
Fixer to keep the graph connected for as long as possible as cheaply as
possible, and prove that a greedy strategy for Fixer satisfies our conditions
for optimality.


Abstract:
  In contexts in which the principle of dependent choice may not be available,
as toposes or Constructive Set Theory, standard locale theoretic results
related to complete regularity, as complete regularity of compact regular
locales, may fail to hold. For resolving this difficulty, B. Banaschewski and
A. Pultr introduced strongly regular locales. Unfortunately, Banaschewski and
Pultr's notion relies on non-constructive set existence principles that hinder
its use in Constructive Set Theory. In this article, a fully constructive
formulation of strong regularity for locales is introduced by replacing
non-constructive set existence with coinductive set definitions. As an
application, every strongly regular locale $L$ is proved to have a compact
regular compactification. Furthermore, every compactification is shown to enjoy
a natural extension property for an associated class of continuous maps, and to
be the minimal compactification with this property. Finally, an open problem
related to the existence of the compact regular reflection of a locale is
presented.


Abstract:
  For a finite-index $\mathrm{II}_1$ subfactor $N \subset M$, we prove the
existence of a universal Hopf $\ast$-algebra (or, a discrete quantum group in
the analytic language) acting on $M$ in a trace-preserving fashion and fixing
$N$ pointwise. We call this Hopf $\ast$-algebra the quantum Galois group for
the subfactor and compute it in some examples of interest, notably for
arbitrary irreducible finite-index depth-two subfactors. Along the way, we
prove the existence of universal acting Hopf algebras for more general
structures (tensors in enriched categories), in the spirit of recent work by
Agore, Gordienko and Vercruysse.


Abstract:
  We approach the Torelli problem of recostructing a curve from its Jacobian
from a computational point of view. Following Dubrovin, we design a machinery
to solve this problem effectively, which builds on methods in numerical
algebraic geometry. We verify this methods via numerical experiments with
curves up to genus 7.


Abstract:
  Two-dimensional (2D) platinum diselenide (PtSe$_2$) has received significant
attention for 2D transistor applications due to its high mobility. Here, using
molecular beam epitaxy, we investigate the growth of 2D PtSe$_2$ on highly
oriented pyrolytic graphite (HOPG) and unveil their electronic properties via
X-ray photoelectron spectroscopy, Raman spectra, and scanning tunnelling
microscopy/spectroscopy as well as density functional theory (DFT)
calculations. PtSe$_2$ adopts a layer-by-layer growth mode on HOPG and shows a
decreasing band gap with increasing layer number. For the layer numbers from
one to four, PtSe$_2$ has band gaps of $2.0 \pm 0.1$, $1.1 \pm 0.1$, $0.6 \pm
0.1$ and $0.20 \pm 0.1$ eV, respectively, and becomes semimetal from the fifth
layer. DFT calculations reproduce the layer-dependent evolution of both the
band gap and band edges, suggest an indirect band-gap structure, and elucidate
the underlying physics at the atomic level.


Abstract:
  We conduct the first 3D hydrodynamic simulations of oxygen-neon
white-dwarf-neutron star/black hole mergers (ONe WD-NS/BH mergers). Such
mergers constitute a significant fraction, and may even dominate, the inspiral
rates of all WD-NS binaries. We post-process our simulations to obtain the
nuclear evolution of these systems and couple the results to a supernova
spectral synthesis code to obtain the first light curves and spectra for these
transients. We find that the amount of $^{56}$Ni synthesised in these mergers
grows as a strong function of the WD mass, reaching typically $0.05$ and up to
$0.1\,{\rm M}_\odot$ per merger. Photodisintegration leads to similar amounts
of $^4$He and about a ten times smaller amount of $^1$H. The nuclear yields
from these mergers, in particular those of $^{55}$Mn, may contribute
significantly to Galactic chemical evolution. The transients expected from ONe
WD-NS mergers are dominantly red/infrared, evolve on month-long timescales and
reach bolometric magnitudes of up to -16.5. The current surveys must have
already detected these transients or are, alternatively, putting strong
constraints on merger scenarios. The properties of the expected transients from
WD-NS mergers best agree with faint type Iax supernovae. The Vera Rubin
Observatory (LSST) will be detecting up to hundreds of merging ONe WD-NS
systems per year. We simulate a subset of our models with 2D axisymmetric FLASH
code to investigate why they have been challenging for previous studies. We
find that the likely main challenge has been effectively modelling the nuclear
statistical equilibrium regime in such mergers.


Abstract:
  In online collaborative learning environments, students create content and
construct their own knowledge through complex interactions over time. To
facilitate effective social learning and inclusive participation in this
context, insights are needed into the correspondence between
student-contributed artifacts and their subsequent popularity among peers. In
this study, we represent student artifacts by their (a) contextual action logs
(b) textual content, and (c) set of instructor-specified features, and use
these representations to predict artifact popularity measures. Through a
mixture of predictive analysis and visual exploration, we find that the neural
embedding representation, learned from contextual action logs, has the
strongest predictions of popularity, ahead of instructor's knowledge, which
includes academic value and creativity ratings. Because this representation can
be learnt without extensive human labeling effort, it opens up possibilities
for shaping more inclusive student interactions on the fly in collaboration
with instructors and students alike.


Abstract:
  This paper presents a self-supervised learning method for pointer-generator
networks to improve spoken-text normalization. Spoken-text normalization that
converts spoken-style text into style normalized text is becoming an important
technology for improving subsequent processing such as machine translation and
summarization. The most successful spoken-text normalization method to date is
sequence-to-sequence (seq2seq) mapping using pointer-generator networks that
possess a copy mechanism from an input sequence. However, these models require
a large amount of paired data of spoken-style text and style normalized text,
and it is difficult to prepare such a volume of data. In order to construct
spoken-text normalization model from the limited paired data, we focus on
self-supervised learning which can utilize unpaired text data to improve
seq2seq models. Unfortunately, conventional self-supervised learning methods do
not assume that pointer-generator networks are utilized. Therefore, we propose
a novel self-supervised learning method, MAsked Pointer-Generator Network
(MAPGN). The proposed method can effectively pre-train the pointer-generator
network by learning to fill masked tokens using the copy mechanism. Our
experiments demonstrate that MAPGN is more effective for pointer-generator
networks than the conventional self-supervised learning methods in two
spoken-text normalization tasks.


Abstract:
  Many people are unaware of the digital dangers that lie around each
cyber-corner. Teaching people how to recognize dangerous situations is crucial,
especially for those who work on or with computers. We postulated that
interactive graphic vignettes could be a great way to expose professionals to
dangerous situations and demonstrate the effects of their choices in these
situations. In that way, we aimed to inoculate employees against cybersecurity
threats.
  We used the Comic-BEE platform to create interactive security awareness
vignettes and evaluated for how employees of a major industrial company
perceived them. For analysing the potential of these comics, we ran an
evaluation study as part of a capture-the-flag (CTF) event, an interactive
exercise for hacking vulnerable software. We evaluated whether the comics
fulfilled our requirements based on the responses of the participants. We
showed the comics, on various cybersecurity concepts, to 20 volunteers. In the
context of a CTF event, our requirements were not fulfilled. Most participants
considered the images distracting, stating a preference for text-only material.


Abstract:
  We introduce the notion of poly-stable pairs of formal schemes over the
valuation ring of a non-archimedean field. For such pairs we define and
investigate the dual intersection complex. We proceed to develop the so called
extended skeleton of a poly-stable pair via an approximation process using the
classical skeletons constructed by Berkovich. This is essentially a
generalization of a construction by Gubler, Rabinoff and Werner from the
strictly semi-stable case to the arbitrary poly-stable case and we extend their
results.


Abstract:
  This study leverages narrative from global newspapers to construct
theme-based knowledge graphs about world events, demonstrating that features
extracted from such graphs improve forecasts of industrial production in three
large economies compared to a number of benchmarks. Our analysis relies on a
filtering methodology that extracts "backbones" of statistically significant
edges from large graph data sets. We find that changes in the eigenvector
centrality of nodes in such backbones capture shifts in relative importance
between different themes significantly better than graph similarity measures.
We supplement our results with an interpretability analysis, showing that the
theme categories "disease" and "economic" have the strongest predictive power
during the time period that we consider. Our work serves as a blueprint for the
construction of parsimonious - yet informative - theme-based knowledge graphs
to monitor in real time the evolution of relevant phenomena in socio-economic
systems.


Abstract:
  We revisit the problem of bootstrapping CFT correlators of charged fields.
After discussing in detail how bounds for uncharged fields can be recycled to
the charged case, we introduce two sets of analytic functional bases for
correlators on the line. The first, which we call "simple", is essentially a
direct sum of analytic functionals for the uncharged case. We use it to
establish very general bounds on the OPE density appearing in charged
correlators. The second basis is dual to generalized free fields and we explain
how it is related to a charged version of the Polyakov bootstrap. We apply
these functionals to map out the space of correlators and obtain new improved
bounds on the 3d Ising twist defect.


Abstract:
  We examine the construction of the spin angular momentum in systems with
pseudoclassical Grassmann variables. In constrained systems there are many
different algebraic forms for the dynamical variables that will all agree on
the constraint surface. For the angular momentum, a particular form of the
generators is preferred, which yields superselection sectors of irreducible
spin^c(n) representations rather than reducible so(n) representations when
quantized in the Schr\"odinger realization.


Abstract:
  We developed a new class of mono- or few-layered two-dimensional polymers
based on dinuclear (arene)ruthenium nodes, obtained by combining the imine
condensation with an interfacial chemistry process, and use a modified
Langmuir-Schaefer method to transfer them onto solid surfaces. Robust
nano-sheets of 2D polymers including dinuclear complexes of heavy ruthenium
atoms as nodes were synthesised. These nano-sheets, whose thickness is of a few
tens of nanometers, were suspended onto solid porous membranes. Then, they were
thoroughly characterised with a combination of local probes, including Raman
scattering, Fourier transform infrared spectroscopy and transmission electron
microscopy in imaging and diffraction mode.


Abstract:
  Although speaker verification has conventionally been an audio-only task,
some practical applications provide both audio and visual streams of input. In
these cases, the visual stream provides complementary information and can often
be leveraged in conjunction with the acoustics of speech to improve
verification performance. In this study, we explore audio-visual approaches to
speaker verification, starting with standard fusion techniques to learn joint
audio-visual (AV) embeddings, and then propose a novel approach to handle
cross-modal verification at test time. Specifically, we investigate unimodal
and concatenation based AV fusion and report the lowest AV equal error rate
(EER) of 0.7% on the VoxCeleb1 dataset using our best system. As these methods
lack the ability to do cross-modal verification, we introduce a multi-view
model which uses a shared classifier to map audio and video into the same
space. This new approach achieves 28% EER on VoxCeleb1 in the challenging
testing condition of cross-modal verification.


Abstract:
  We present an English translation of Erwin Schr\"odinger's paper on "On the
Reversal of the Laws of Nature". In this paper Schr\"odinger analyses the idea
of time reversal of a diffusion process. Schr\"odinger's paper acted as a
prominent source of inspiration for the works of Bernstein on reciprocal
processes and of Kolmogorov on time reversal properties of Markov processes and
detailed balance. The ideas outlined by Schr\"odinger also inspired the
development of probabilistic interpretations of quantum mechanics by F\'enyes,
Nelson and others as well as the notion of "Euclidean Quantum Mechanics" as
probabilistic analogue of quantization. In the second part of the paper
Schr\"odinger discusses the relation between time reversal and statistical laws
of physics. We emphasize in our commentary the relevance of Schr\"odinger's
intuitions for contemporary developments in statistical nano-physics.


Abstract:
  Recent work demonstrated the benefits of studying continuous-time dynamics
governing the GAN training. However, this dynamics is analyzed in the model
parameter space, which results in finite-dimensional dynamical systems. We
propose a novel perspective where we study the local dynamics of adversarial
training in the general functional space and show how it can be represented as
a system of partial differential equations. Thus, the convergence properties
can be inferred from the eigenvalues of the resulting differential operator. We
show that these eigenvalues can be efficiently estimated from the target
dataset before training. Our perspective reveals several insights on the
practical tricks commonly used to stabilize GANs, such as gradient penalty,
data augmentation, and advanced integration schemes. As an immediate practical
benefit, we demonstrate how one can a priori select an optimal data
augmentation strategy for a particular generation task.


Abstract:
  Physics-Informed Neural Networks (PINNs) are a class of deep neural networks
that are trained, using automatic differentiation, to compute the response of
systems governed by partial differential equations (PDEs). The training of
PINNs is simulation-free, and does not require any training dataset to be
obtained from numerical PDE solvers. Instead, it only requires the physical
problem description, including the governing laws of physics, domain geometry,
initial/boundary conditions, and the material properties. This training usually
involves solving a non-convex optimization problem using variants of the
stochastic gradient descent method, with the gradient of the loss function
approximated on a batch of collocation points, selected randomly in each
iteration according to a uniform distribution. Despite the success of PINNs in
accurately solving a wide variety of PDEs, the method still requires
improvements in terms of computational efficiency. To this end, in this paper,
we study the performance of an importance sampling approach for efficient
training of PINNs. Using numerical examples together with theoretical
evidences, we show that in each training iteration, sampling the collocation
points according to a distribution proportional to the loss function will
improve the convergence behavior of the PINNs training. Additionally, we show
that providing a piecewise constant approximation to the loss function for
faster importance sampling can further improve the training efficiency. This
importance sampling approach is straightforward and easy to implement in the
existing PINN codes, and also does not introduce any new hyperparameter to
calibrate. The numerical examples include elasticity, diffusion and plane
stress problems, through which we numerically verify the accuracy and
efficiency of the importance sampling approach compared to the predominant
uniform sampling approach.


Abstract:
  A majority of microrobots are constructed using compliant materials that are
difficult to model analytically, limiting the utility of traditional
model-based controllers. Challenges in data collection on microrobots and large
errors between simulated models and real robots make current model-based
learning and sim-to-real transfer methods difficult to apply. We propose a
novel framework residual model learning (RML) that leverages approximate models
to substantially reduce the sample complexity associated with learning an
accurate robot model. We show that using RML, we can learn a model of the
Harvard Ambulatory MicroRobot (HAMR) using just 12 seconds of passively
collected interaction data. The learned model is accurate enough to be
leveraged as "proxy-simulator" for learning walking and turning behaviors using
model-free reinforcement learning algorithms. RML provides a general framework
for learning from extremely small amounts of interaction data, and our
experiments with HAMR clearly demonstrate that RML substantially outperforms
existing techniques.


Abstract:
  The Zwicky Transient Facility (ZTF) has been observing the entire northern
sky since the start of 2018 down to a magnitude of 20.5 ($5 \sigma$ for 30s
exposure) in $g$, $r$, and $i$ filters. Over the course of two years, ZTF has
obtained light curves of more than a billion sources, each with 50-1000 epochs
per light curve in $g$ and $r$, and fewer in $i$. To be able to use the
information contained in the light curves of variable sources for new
scientific discoveries, an efficient and flexible framework is needed to
classify them. In this paper, we introduce the methods and infrastructure which
will be used to classify all ZTF light curves. Our approach aims to be flexible
and modular and allows the use of a dynamical classification scheme and labels,
continuously evolving training sets, and the use of different machine learning
classifier types and architectures. With this setup, we are able to
continuously update and improve the classification of ZTF light curves as new
data becomes available, training samples are updated, and new classes need to
be incorporated.


Abstract:
  The technological exploitation of ferroelectricity in CMOS electron devices
offers new design opportunities, but also significant challenges from an
integration, optimization and modelling perspective. We here revisit the
working principle and the modelling of some novel ferroelectric based devices,
with an emphasis on energy efficiency and on applications to new computational
paradigms.


Abstract:
  Accurate prediction of physical properties is critical for discovering and
designing novel materials. Machine learning technologies have attracted
significant attention in the materials science community for their potential
for large-scale screening. Graph Convolution Neural Network (GCNN) is one of
the most successful machine learning methods because of its flexibility and
effectiveness in describing 3D structural data. Most existing GCNN models focus
on the topological structure but overly simplify the three-dimensional
geometric structure. However, in materials science, the 3D-spatial distribution
of atoms is crucial for determining the atomic states and interatomic forces.
This paper proposes an adaptive GCNN with a novel convolution mechanism that
simultaneously models atomic interactions among all neighbor atoms in
three-dimensional space. We apply the proposed model to two distinctly
challenging problems on predicting material properties. The first is Henry's
constant for gas adsorption in Metal-Organic Frameworks (MOFs), which is
notoriously difficult because of its high sensitivity to atomic configurations.
The second is the ion conductivity in solid-state crystal materials, which is
difficult because of few labeled data available for training. The new model
outperforms existing graph-based models on both data sets, suggesting that the
critical three-dimensional geometric information is indeed captured.


Abstract:
  A minimal perfect hash function $f$ for a set $S$ of $n$ keys is a bijective
function of the form $f : S \rightarrow \{0,\ldots,n-1\}$. These functions are
important for many practical applications in computing, such as search engines,
computer networks, and databases. Several algorithms have been proposed to
build minimal perfect hash functions that: scale well to large sets, retain
fast evaluation time, and take very little space, e.g., 2 - 3 bits/key. PTHash
is one such algorithm, achieving very fast evaluation in compressed space,
typically several times faster than other techniques. In this work, we propose
a new construction algorithm for PTHash enabling: (1) multi-threading, to
either build functions more quickly or more space-efficiently, and (2)
external-memory processing to scale to inputs much larger than the available
internal memory. Only few other algorithms in the literature share these
features, despite of their big practical impact. We conduct an extensive
experimental assessment on large real-world string collections and show that,
with respect to other techniques, PTHash is competitive in construction time
and space consumption, but retains 2 - 6$\times$ better lookup time.


Abstract:
  There is a growing need for data-driven research efforts on how the public
perceives the ethical, moral, and legal issues of autonomous AI systems. The
current debate on the responsibility gap posed by these systems is one such
example. This work proposes a mixed AI ethics model that allows normative and
descriptive research to complement each other, by aiding scholarly discussion
with data gathered from the public. We discuss its implications on bridging the
gap between optimistic and pessimistic views towards AI systems' deployment.


Abstract:
  Role of inhomogeneous perturbations of nematic aerogel on the form of the
order parameter emerging at the transition of liquid $^3$He in the superfluid
state is considered. It is shown that a region of stability of the polar
distorted ABM phase can begin right from the transition temperature. The
symmetry argument is given, which selects the most favorable aerogel for
stabilization of pure polar phase.


Abstract:
  The standard model characterizing the GRB spectrum invokes a
four-free-parameters empirical function, so-called the Band model. One
alternative model named cutoff power-law (CPL) implements a power-law plus an
exponential cutoff. These functions obtain almost equally good fits, and are
adopted in nearly all the GRB literature. Here, we reanalyze the sample defined
in Li. (39 bursts including 944 spectra). We classify the spectra by two
methods: (1) checking their corner-corner plots of the posteriors to determine
well-constrained $\beta$ (Band-better) and un-constrained $\beta$ (CPL-better)
categories; and (2) defining four groups by the different of deviance
information criterion (DIC). We found $\alpha=-0.64\pm0.28$ and $\rm
log_{10}(E_{\rm p})=\rm log_{10}(191)\pm0.41$ for the Band-better spectra and
$\alpha=-0.96\pm0.33$ and $\rm log_{10}(E_{\rm p})=\rm log_{10}(249)\pm0.40$
for the CPL-better spectra. With the "Correct" model (the "better" one between
Band and CPL) and v.v. the "Misused" model defined, we find significant
deviations in parameter distributions and parameter relations for the
Band-"Correct" and CPL-"Misused" cases. This means if a spectrum is
best-described as Band, applying CPL to derive the spectral parameters will
prominently deviate from their intrinsic shape, therefore affecting the
physical interpretation. Our analysis indicates that the "better" model should
be duly examined during GRB spectral analysis. In addition, the $\beta$
distribution exhibits a bimodal structure containing Band-better and CPL-better
spectra respectively, inferring Band and CPL both have physical origins.


Abstract:
  The design of optimal auctions is a problem of interest in economics, game
theory and computer science. Despite decades of effort, strategyproof,
revenue-maximizing auction designs are still not known outside of restricted
settings. However, recent methods using deep learning have shown some success
in approximating optimal auctions, recovering several known solutions and
outperforming strong baselines when optimal auctions are not known. In addition
to maximizing revenue, auction mechanisms may also seek to encourage socially
desirable constraints such as allocation fairness or diversity. However, these
philosophical notions neither have standardization nor do they have widely
accepted formal definitions. In this paper, we propose PreferenceNet, an
extension of existing neural-network-based auction mechanisms to encode
constraints using (potentially human-provided) exemplars of desirable
allocations. In addition, we introduce a new metric to evaluate an auction
allocations' adherence to such socially desirable constraints and demonstrate
that our proposed method is competitive with current state-of-the-art
neural-network based auction designs. We validate our approach through human
subject research and show that we are able to effectively capture real human
preferences. Our code is available at
https://github.com/neeharperi/PreferenceNet


Abstract:
  Ion transport in biological tissues is crucial in the study of many
biological and pathological problems. Some multi-cellular structures, like
smooth muscles on the vessel walls, could be treated as periodic bi-domain
structures, which consist of intracellular space and extracellular space with
semipermeable membranes in between. With the aid of two-scale homogenization
theory, macro-scale models are proposed based on an electro-neutral (EN)
microscale model with nonlinear interface conditions, where membranes are
treated as combinations of capacitors and resistors. The connectivity of
intracellular space is also taken into consideration. If the intracellular
space is fully connected and forms a syncytium, then the macroscale model is a
bidomain nonlinear coupled partial differential equations system. Otherwise,
when the intracellular cells are not connected, the macroscale model for
intracellular space is an ordinary differential system with source/sink terms
from the connected extracellular space.


Abstract:
  In a recent paper we have shown that data collected from linear systems
excited by persistently exciting inputs during low-complexity experiments, can
be used to design state- and output-feedback controllers, including optimal
Linear Quadratic Regulators (LQR), by solving linear matrix inequalities (LMI)
and semidefinite programs. We have also shown how to stabilize in the first
approximation unknown nonlinear systems using data. In contrast to the case of
linear systems, however, in the case of nonlinear systems the conditions for
learning a controller directly from data may not be fulfilled even when the
data are collected in experiments performed using persistently exciting inputs.
In this paper we show how to design experiments that lead to the fulfilment of
these conditions.


Abstract:
  Since the first signal in 2015, the gravitational-wave detections of merging
binary black holes (BBHs) by the LIGO and Virgo collaborations (LVC) have
completely transformed our understanding of the lives and deaths of compact
object binaries, and have motivated an enormous amount of theoretical work on
the astrophysical origin of these objects. We show that the phenomenological
fit to the redshift-dependent merger rate of BBHs from Abbott et al. (2020) is
consistent with a purely dynamical origin for these objects, and that the
current merger rate of BBHs from the LVC could be explained entirely with
globular clusters alone. While this does not prove that globular clusters are
the dominant formation channel, we emphasize that many formation scenarios
could contribute a significant fraction of the current LVC rate, and that any
analysis that assumes a single (or dominant) mechanism for producing BBH
mergers is implicitly using a specious astrophysical prior.


Abstract:
  The context of the present paper is stochastic thermodynamics - an approach
to nonequilibrium thermodynamics rooted within the broader framework of
stochastic control. In contrast to the classical paradigm of Carnot engines, we
herein propose to consider thermodynamic processes with periodic continuously
varying temperature of a heat bath and study questions of maximal power and
efficiency for two idealized cases, overdamped (first-order) and underdamped
(second-order) stochastic models. We highlight properties of optimal periodic
control, derive and numerically validate approximate formulae for the optimal
performance (power and efficiency).


Abstract:
  The use of cash bail as a mechanism for detaining defendants pre-trial is an
often-criticized system that many have argued violates the presumption of
"innocent until proven guilty." Many studies have sought to understand both the
long-term effects of cash bail's use and the disparate rate of cash bail
assignments along demographic lines (race, gender, etc). However, such work is
often susceptible to problems of infra-marginality -- that the data we observe
can only describe average outcomes, and not the outcomes associated with the
marginal decision.
  In this work, we address this problem by creating a hierarchical Bayesian
model of cash bail assignments. Specifically, our approach models cash bail
decisions as a probabilistic process whereby judges balance the relative costs
of assigning cash bail with the cost of defendants potentially skipping court
dates, and where these skip probabilities are estimated based upon features of
the individual case. We then use Monte Carlo inference to sample the
distribution over these costs for different magistrates and across different
races. We fit this model to a data set we have collected of over 50,000 court
cases in the Allegheny and Philadelphia counties in Pennsylvania. Our analysis
of 50 separate judges shows that they are uniformly more likely to assign cash
bail to black defendants than to white defendants, even given identical
likelihood of skipping a court appearance. This analysis raises further
questions about the equity of the practice of cash bail, irrespective of its
underlying legal justification.


Abstract:
  The cybersecurity breaches render surveillance systems vulnerable to video
forgery attacks, under which authentic live video streams are tampered to
conceal illegal human activities under surveillance cameras. Traditional video
forensics approaches can detect and localize forgery traces in each video frame
using computationally-expensive spatial-temporal analysis, while falling short
in real-time verification of live video feeds. The recent work correlates
time-series camera and wireless signals to recognize replayed surveillance
videos using event-level timing information but it cannot realize fine-grained
forgery detection and localization on each frame. To fill this gap, this paper
proposes Secure-Pose, a novel cross-modal forgery detection and localization
system for live surveillance videos using WiFi signals near the camera spot. We
observe that coexisting camera and WiFi signals convey common human semantic
information and the presence of forgery attacks on video frames will decouple
such information correspondence. Secure-Pose extracts effective human pose
features from synchronized multi-modal signals and detects and localizes
forgery traces under both inter-frame and intra-frame attacks in each frame. We
implement Secure-Pose using a commercial camera and two Intel 5300 NICs and
evaluate it in real-world environments. Secure-Pose achieves a high detection
accuracy of 95.1% and can effectively localize tampered objects under different
forgery attacks.


Abstract:
  Recently, deep learning based methods have demonstrated promising results on
the graph matching problem, by relying on the descriptive capability of deep
features extracted on graph nodes. However, one main limitation with existing
deep graph matching (DGM) methods lies in their ignorance of explicit
constraint of graph structures, which may lead the model to be trapped into
local minimum in training. In this paper, we propose to explicitly formulate
pairwise graph structures as a \textbf{quadratic constraint} incorporated into
the DGM framework. The quadratic constraint minimizes the pairwise structural
discrepancy between graphs, which can reduce the ambiguities brought by only
using the extracted CNN features.
  Moreover, we present a differentiable implementation to the quadratic
constrained-optimization such that it is compatible with the unconstrained deep
learning optimizer. To give more precise and proper supervision, a
well-designed false matching loss against class imbalance is proposed, which
can better penalize the false negatives and false positives with less
overfitting. Exhaustive experiments demonstrate that our method competitive
performance on real-world datasets.


Abstract:
  This paper focuses on the dynamics of the eight tridimensional principal
slices of the tricomplex Mandelbrot set: the Tetrabrot, the Arrowheadbrot, the
Mousebrot, the Turtlebrot, the Hourglassbrot, the Metabrot, the Airbrot
(octahedron) and the Firebrot (tetrahedron). In particular, we establish a
geometrical classification of these 3D slices using the properties of some
specific sets that correspond to projections of the bicomplex Mandelbrot set on
various two-dimensional vector subspaces, and we prove that the Firebrot is a
regular tetrahedron. Finally, we construct the so-called "Stella octangula" as
a tricomplex dynamical system composed of the union of the Firebrot and its
dual, and after defining the idempotent 3D slices of $\mathcal{M}_{3}$, we show
that one of them corresponds to a third Platonic solid: the cube.


Abstract:
  Being one of the most popular generative framework, variational
autoencoders(VAE) are known to suffer from a phenomenon termed posterior
collapse, i.e. the latent variational distributions collapse to the prior,
especially when a strong decoder network is used. In this work, we analyze the
latent representation of collapsed VAEs, and proposed a novel model, neighbor
embedding VAE(NE-VAE), which explicitly constraints the encoder to encode
inputs close in the input space to be close in the latent space. We observed
that for VAE variants that report similar ELBO, KL divergence or even mutual
information scores may still behave quite differently in the latent
organization. In our experiments, NE-VAE can produce qualitatively different
latent representations with majority of the latent dimensions remained active,
which may benefit downstream latent space optimization tasks. NE-VAE can
prevent posterior collapse to a much greater extent than it's predecessors, and
can be easily plugged into any autoencoder framework, without introducing
addition model components and complex training routines.


Abstract:
  In this paper a coalitional control and observation scheme is presented in
which the coalitions are changed online by enabling and disabling communication
links. Transitions between coalitions are made to best balance overall system
performance and communication costs. Linear Matrix Inequalities are used to
design the controller and observer, guaranteeing stability of the switching
system. Simulation results for vehicle platoon control are presented to
illustrate the proposed method.


Abstract:
  We consider online packing problems where we get a stream of axis-parallel
rectangles. The rectangles have to be placed in the plane without overlapping,
and each rectangle must be placed without knowing the subsequent rectangles.
The goal is to minimize the perimeter or the area of the axis-parallel bounding
box of the rectangles. We either allow rotations by 90 degrees or translations
only.
  For the perimeter version we give algorithms with an absolute competitive
ratio slightly less than 4 when only translations are allowed and when
rotations are also allowed.
  We then turn our attention to minimizing the area and show that the
asymptotic competitive ratio of any algorithm is at least $\Omega(\sqrt{n})$,
where $n$ is the number of rectangles in the stream, and this holds with and
without rotations. We then present algorithms that match this bound in both
cases. We also show that the competitive ratio cannot be bounded as a function
of OPT. We then consider two special cases.
  The first is when all the given rectangles have aspect ratios bounded by some
constant. The particular variant where all the rectangles are squares and we
want to minimize the area of the bounding square has been studied before and an
algorithm with a competitive ratio of 8 has been given [Fekete and Hoffmann,
Algorithmica, 2017]. We improve the analysis of the algorithm and show that the
ratio is at most 6, which is tight.
  The second special case is when all edges have length at least 1. Here, the
$\Omega(\sqrt n)$ lower bound still holds, and we turn our attention to lower
bounds depending on OPT. We show that any algorithm has an asymptotic
competitive ratio of at least $\Omega(\sqrt{OPT})$ for the translational case
and $\Omega(\sqrt[4]{OPT})$ when rotations are allowed. For both versions, we
give algorithms that match the respective lower bounds.


Abstract:
  Extreme multi-label classification (XML) is becoming increasingly relevant in
the era of big data. Yet, there is no method for effectively generating
stratified partitions of XML datasets. Instead, researchers typically rely on
provided test-train splits that, 1) aren't always representative of the entire
dataset, and 2) are missing many of the labels. This can lead to poor
generalization ability and unreliable performance estimates, as has been
established in the binary and multi-class settings. As such, this paper
presents a new and simple algorithm that can efficiently generate stratified
partitions of XML datasets with millions of unique labels. We also examine the
label distributions of prevailing benchmark splits, and investigate the issues
that arise from using unrepresentative subsets of data for model development.
The results highlight the difficulty of stratifying XML data, and demonstrate
the importance of using stratified partitions for training and evaluation.


Abstract:
  Inverse molecular design, i.e., designing molecules with specific target
properties, can be posed as an optimization problem. High-dimensional
optimization tasks in the natural sciences are commonly tackled via
population-based metaheuristic optimization algorithms such as evolutionary
algorithms. However, expensive property evaluation, which is often required,
can limit the widespread use of such approaches as the associated cost can
become prohibitive. Herein, we present JANUS, a genetic algorithm that is
inspired by parallel tempering. It propagates two populations, one for
exploration and another for exploitation, improving optimization by reducing
expensive property evaluations. Additionally, JANUS is augmented by a deep
neural network that approximates molecular properties via active learning for
enhanced sampling of the chemical space. Our method uses the SELFIES molecular
representation and the STONED algorithm for the efficient generation of
structures, and outperforms other generative models in common inverse molecular
design tasks achieving state-of-the-art performance.


Abstract:
  Mentorship in science is crucial for topic choice, career decisions, and the
success of mentees and mentors. Typically, researchers who study mentorship use
article co-authorship and doctoral dissertation datasets. However, available
datasets of this type focus on narrow selections of fields and miss out on
early career and non-publication-related interactions. Here, we describe
MENTORSHIP, a crowdsourced dataset of 743176 mentorship relationships among
738989 scientists across 112 fields that avoids these shortcomings. We enrich
the scientists' profiles with publication data from the Microsoft Academic
Graph and "semantic" representations of research using deep learning content
analysis. Because gender and race have become critical dimensions when
analyzing mentorship and disparities in science, we also provide estimations of
these factors. We perform extensive validations of the profile--publication
matching, semantic content, and demographic inferences. We anticipate this
dataset will spur the study of mentorship in science and deepen our
understanding of its role in scientists' career outcomes.


Abstract:
  This paper addresses the standard generalized likelihood ratio test (GLRT)
detection problem of weak signals in background noise. In so doing, we consider
a nonfluctuating target embedded in complex white Gaussian noise (CWGN), in
which the amplitude of the target echo and the noise power are assumed to be
unknown. Important works have analyzed the performance for the referred
scenario and proposed GLRT-based detectors. Such detectors are projected at an
early stage (i.e., prior to the formation of a post-beamforming scalar
waveform), thereby imposing high demands on hardware, processing, and data
storage. From a hardware perspective, most radar systems fail to meet these
strong requirements. In fact, due to hardware and computational constraints,
most radars use a combination of analog and digital beamformers (sums) before
any estimation or further pre-processing. The rationale behind this study is to
derive a GLRT detector that meets the hardware and system requirements. In this
work, we design and analyze a more practical and easy-to-implement GLRT
detector, which is projected after the analog beamforming. The performance of
the proposed detector is analyzed and the probabilities of detection (PD) and
false alarm (PFA) are derived in closed form. Moreover, we show that in the low
signal-to-noise ratio (SNR) regime, the post-beamforming GLRT detector performs
better than both the classic pre-beamforming GLRT detector and the square-law
detector. This finding suggests that if the signals are weak, instead of
processing the signals separately, we first must to reinforce the overall
signal and then assembling the system's detection statistic. At last, the SNR
losses are quantified, in which the superiority of the post-beamforming GLRT
detector was evidenced as the number of antennas and samples increase.


Abstract:
  We study multiplicities of jumping numbers of multiplier ideals in a smooth
variety of arbitrary dimension. We prove that the multiplicity function is a
quasi-polynomial, hence proving that the Poincar\'e series is a rational
function. We further study when the various components of the quasi-polynomial
have the highest possible degree and relate it to jumping numbers contributed
by Rees valuations. Finally, we study the special case of monomial ideals.


Abstract:
  In this work, we study the approximation of expected values of functional
quantities on the solution of a stochastic differential equation (SDE), where
we replace the Monte Carlo estimation with the evaluation of a deep neural
network. Once the neural network training is done, the evaluation of the
resulting approximating function is computationally highly efficient so that
using deep neural networks to replace costly Monte Carlo integration is
appealing, e.g., for near real-time computations in quantitative finance.
However, the drawback of these nowadays widespread ideas lies in the fact that
training a suitable neural network is likely to be prohibitive in terms of
computational cost. We address this drawback here by introducing a multilevel
approach to the training of deep neural networks. More precisely, we combine
the deep learning algorithm introduced by Beck et al. with the idea of
multilevel Monte Carlo path simulation of Giles. The idea is to train several
neural networks, each having a certain approximation quality and computational
complexity, with training data computed from so-called level estimators,
introduced by Giles. We show that under certain assumptions, the variance in
the training process can be reduced by shifting most of the computational
workload to training neural nets at coarse levels where producing the training
data sets is comparably cheap, whereas training the neural nets corresponding
to the fine levels requires only a limited number of training data sets. We
formulate a complexity theorem showing that the multilevel idea can indeed
reduce computational complexity.


Abstract:
  In this paper we consider Littlewood-Paley functions defined by the
semigroups associated with the operator $\mathcal{A}=-\frac{\Delta}{2}-x\nabla$
in the inverse Gaussian setting for Banach valued functions. We characterize
the uniformly convex and smooth Banach spaces by using
$L^p(\mathbb{R}^n,\gamma_{-1})$- properties of the
$\mathcal{A}$-Littlewood-Paley functions. We also use Littlewood-Paley
functions associated with $\mathcal{A}$ to characterize the K\"othe function
spaces with the UMD property.


Abstract:
  The isospin doublet scalar field with hypercharge 3/2 is introduced in some
new physics models such as tiny neutrino masses. Detecting the doubly charged
scalar bosons from the doublet field can be a good probe of such models.
However, their collider phenomenology has not been examined sufficiently. We
investigate collider signatures of the doubly and singly charged scalar bosons
at the LHC for the high-luminosity upgraded option (HL-LHC) by looking at
transverse mass distributions etc. With the appropriate kinematical cuts we
demonstrate the background reduction in the minimal model in the following two
cases depending on the mass of the scalar bosons. (1) The main decay mode of
the singly charged scalar bosons is the tau lepton and missing (as well as
charm and strange quarks). (2) That is into a top bottom pair. In the both
cases, we assume that the doubly charged scalar boson is heavier than the
singly charged ones. We conclude that the scalar doublet field with $Y = 3/2$
is expected to be detectable at the HL-LHC unless the mass is too large.


Abstract:
  $\alpha\mu$ is a search algorithm which repairs two defaults of Perfect
Information Monte Carlo search: strategy fusion and non locality. In this paper
we optimize $\alpha\mu$ for the game of Bridge, avoiding useless computations.
The proposed optimizations are general and apply to other imperfect information
turn-based games. We define multiple optimizations involving Pareto fronts, and
show that these optimizations speed up the search. Some of these optimizations
are cuts that stop the search at a node, while others keep track of which
possible worlds have become redundant, avoiding unnecessary, costly
evaluations. We also measure the benefits of parallelizing the double dummy
searches at the leaves of the $\alpha\mu$ search tree.


Abstract:
  We contribute to approximate algorithms for the quadratic assignment problem
also known as graph matching. Inspired by the success of the fusion moves
technique developed for multilabel discrete Markov random fields, we
investigate its applicability to graph matching. In particular, we show how
fusion moves can be efficiently combined with the dedicated state-of-the-art
dual methods that have recently shown superior results in computer vision and
bio-imaging applications. As our empirical evaluation on a wide variety of
graph matching datasets suggests, fusion moves significantly improve
performance of these methods in terms of speed and quality of the obtained
solutions. Our method sets a new state-of-the-art with a notable margin with
respect to its competitors.


Abstract:
  We consider tunneling of quasiparticles through a rectangular quantum well,
subject to periodic driving. The quasiparticles are the itinerant charges in
two-dimensional and three-dimensional semimetals having a quadratic
band-touching (QBT) point in the Brillouin zone. In order to analyze the
time-periodic Hamiltonian, we assume a non-adiabatic limit, where the Floquet
theorem is applicable. By deriving the Floquet scattering matrices, we chalk
out the transmission and shot noise spectra of the QBT semimetals. The spectra
show Fano resonances, which we identify with the (quasi)bound states of the
systems.


Abstract:
  We prove an inequality of H\"older type traducing the unique continuation
property at one time for the heat equation with a potential and Neumann
boundary condition. The main feature of the proof is to overcome the
propagation of smallness by a global approach using a refined parabolic
frequency function method. It relies with a Carleman commutator estimate to
obtain the logarithmic convexity property of the frequency function.


Abstract:
  In real-world settings involving consequential decision-making, the
deployment of machine learning systems generally requires both reliable
uncertainty quantification and protection of individuals' privacy. We present a
framework that treats these two desiderata jointly. Our framework is based on
conformal prediction, a methodology that augments predictive models to return
prediction sets that provide uncertainty quantification -- they provably cover
the true response with a user-specified probability, such as 90%. One might
hope that when used with privately-trained models, conformal prediction would
yield privacy guarantees for the resulting prediction sets; unfortunately this
is not the case. To remedy this key problem, we develop a method that takes any
pre-trained predictive model and outputs differentially private prediction
sets. Our method follows the general approach of split conformal prediction; we
use holdout data to calibrate the size of the prediction sets but preserve
privacy by using a privatized quantile subroutine. This subroutine compensates
for the noise introduced to preserve privacy in order to guarantee correct
coverage. We evaluate the method on large-scale computer vision datasets.


Abstract:
  In this paper, we consider a large-scale instance of the classical
Pickup-and-Delivery Vehicle Routing Problem (PDVRP) that must be solved by a
network of mobile cooperating robots. Robots must self-coordinate and
self-allocate a set of pickup/delivery tasks while minimizing a given cost
figure. This results in a large, challenging Mixed-Integer Linear Problem that
must be cooperatively solved without a central coordinator. We propose a
distributed algorithm based on a primal decomposition approach that provides a
feasible solution to the problem in finite time. An interesting feature of the
proposed scheme is that each robot computes only its portion of solution,
thereby preserving privacy of sensible information. The algorithm also exhibits
attractive scalability properties that guarantee solvability of the problem
even in large networks. To the best of our knowledge, this is the first attempt
to provide a scalable distributed solution to the problem. The algorithm is
first tested through Gazebo simulations on a ROS 2 platform, highlighting the
effectiveness of the proposed solution. Finally, experiments on a real testbed
with a team of ground and aerial robots are provided.


Abstract:
  Let $L$ be finite extension of $\mathbb{Q}_p$ with ring of integers
$\mathcal{O}_L$. I show that periodic topological cyclic homology of
$\mathcal{O}_L$, over the base $\mathbb{E}_{\infty}$-ring
$\mathbb{S}_{W(\mathbb{F}_q)}[z]$ carries a $p$-height one formal group law mod
$p$ that depends on the Eisenstein polynomial of $L$ over $\mathbb{Q}_p$ for a
choice of uniformizer $\varpi\in \mathcal{O}_L$.


Abstract:
  Magnetic resonance imaging systems rely on signal detection via
radiofrequency coil arrays which, ideally, need to provide both bendability and
form-fitting stretchability to conform to the imaging volume. However, most
commercial coils are rigid and of fixed size with a substantial mean offset
distance of the coil from the anatomy, which compromises the spatial resolution
and diagnostic image quality as well as patient comfort. Here, we propose a
soft and stretchable receive coil concept based on liquid metal and
ultra-stretchable polymer that conforms closely to a desired anatomy. Moreover,
its smart geometry provides a self-tuning mechanism to maintain a stable
resonance frequency over a wide range of elongation levels. Theoretical
analysis and numerical simulations were experimentally confirmed and
demonstrated that the proposed coil withstood the unwanted frequency detuning
typically observed with other stretchable coils (0.4% for the proposed coil as
compared to 4% for a comparable control coil). Moreover, the signal-to-noise
ratio of the proposed coil increased by up to 60% as compared to a typical,
rigid, commercial coil.


Abstract:
  Magnetic monopoles have been a subject of study for more than a century since
the first ideas by A. Vaschy and P. Curie, circa 1890. In 1974, Y. Nambu
proposed a model for magnetic monopoles exploring a parallelism between the
broken symmetry Higgs and the superconductivity Ginzburg-Landau theories in
order to describe the pions quark-antiquark confinement states. There, Nambu
describes an energetic string where its end points behave like two magnetic
monopoles with opposite magnetic charges -- quark and antiquark. Consequently,
not only the interaction among monopole and antimonopole, mediated by a massive
vector boson (Yukawa potential), but also the energetic string (linear
potential) contributes to the effective interaction potential. We propose here
a monopole-antimonopole non confining attractive interaction of the Nambu-type,
and then investigate the formation of bound states, the monopolium. Some
necessary conditions for the existence of bound states to be fulfilled by the
proposed Nambu-type potential, Kato weakness, Set\^o and Bargmann conditions,
are verified. In the following, ground state energies are estimated for a
variety of monopolium reduced mass, from $10^2$MeV to $10^2$TeV, and Compton
interaction lengths, from $10^{-2}$am to $10^{-1}$pm, where discussion about
non relativistic and relativistic limits validation is carried out.


Abstract:
  Precipitation nowcasting is an important task for weather forecasting. Many
recent works aim to predict the high rainfall events more accurately with the
help of deep learning techniques, but such events are relatively rare. The
rarity is often addressed by formulations that re-weight the rare events.
Somehow such a formulation carries a side effect of making "blurry" predictions
in low rainfall regions and cannot convince meteorologists to trust its
practical usability. We fix the trust issue by introducing a discriminator that
encourages the prediction model to generate realistic rain-maps without
sacrificing predictive accuracy. Furthermore, we extend the nowcasting time
frame from one hour to three hours to further address the needs from
meteorologists. The extension is based on consecutive attentions across
different hours. We propose a new deep learning model for precipitation
nowcasting that includes both the discrimination and attention techniques. The
model is examined on a newly-built benchmark dataset that contains both radar
data and actual rain data. The benchmark, which will be publicly released, not
only establishes the superiority of the proposed model, but also is expected to
encourage future research on precipitation nowcasting.


Abstract:
  Designing deep learning-based solutions is becoming a race for training
deeper models with a greater number of layers. While a large-size deeper model
could provide competitive accuracy, it creates a lot of logistical challenges
and unreasonable resource requirements during development and deployment. This
has been one of the key reasons for deep learning models not being excessively
used in various production environments, especially in edge devices. There is
an immediate requirement for optimizing and compressing these deep learning
models, to enable on-device intelligence. In this research, we introduce a
black-box framework, Deeplite Neutrino for production-ready optimization of
deep learning models. The framework provides an easy mechanism for the
end-users to provide constraints such as a tolerable drop in accuracy or target
size of the optimized models, to guide the whole optimization process. The
framework is easy to include in an existing production pipeline and is
available as a Python Package, supporting PyTorch and Tensorflow libraries. The
optimization performance of the framework is shown across multiple benchmark
datasets and popular deep learning models. Further, the framework is currently
used in production and the results and testimonials from several clients are
summarized.


Abstract:
  In this paper we study the problem of private information retrieval where a
user seeks to retrieve one of the $F$ files from a cluster of $N$ non-colluding
servers without revealing the identity of the requested file. In our setting
the servers are storage constrained in that they can only store a fraction
$\mu=t/N$ of each file. Furthermore, we assume that the files are stored in an
uncoded fashion. The rate of a PIR protocol is defined as the ratio of the file
size and the total number of bits downloaded. The maximum achievable rate is
referred to as capacity. It was previously shown that there are capacity
achieving PIR protocols when the file size is $N^F$ and complete files were
stored on all the servers. These results were further extended for the case
when servers store only a fraction of each file. However, the subpacketization
$v$ of the files required is exponential in the number of servers $N$. We
propose a novel uncoded PIR protocol based on combinatorial designs that are
also capacity achieving when the file size is $v \times t^F$. Our protocol has
linear subpacketization in the number of servers in contrast to previous work
in storage constrained uncoded PIR schemes. In the proposed PIR protocol, the
given system is projected to multiple instances of reduced systems with
replicated servers having full storage capacity. The subfiles stored in these
various instances are separately retrieved and lifted to solve the PIR problem
for the original system.


Abstract:
  High-pressure torsion (HPT), a technique of severe plastic deformation (SPD),
is shown as a promising processing method for exchange-spring magnetic
materials in bulk form. Powder mixtures of Fe and SmCo$_{5}$ are consolidated
and deformed by HPT exhibiting sample dimensions of several millimetres, being
essential for bulky magnetic applications. The structural evolution during HPT
deformation of Fe-SmCo$_{5}$ compounds at room- and elevated- temperatures of
chemical compositions consisting of 87, 47, 24 and 10 wt.% Fe is studied and
microstructurally analysed. Electron microscopy and synchrotron X-ray
diffraction reveal a dual-phase nanostructured composite for the as-deformed
samples with grain refinement after HPT deformation. SQUID magnetometry
measurements show hysteresis curves of an exchange coupled nanocomposite at
room temperature, while for low temperatures a decoupling of Fe and SmCo$_{5}$
is observed. Furthermore, exchange interactions between the hard- and
soft-magnetic phase can explain a shift of the hysteresis curve. Strong
emphasis is devoted to the correlation between the magnetic properties and the
evolving nano-structure during HPT deformation, which is conducted for a 1:1
composition ratio of Fe to SmCo$_{5}$. SQUID magnetometry measurements show an
increasing saturation magnetisation for increasing strain $\gamma$ and a
maximum of the coercive field strength at a shear strain of $\gamma$ = 75.


Abstract:
  Gravitational waves (GWs) have opened a new window on fundamental physics in
a number of important ways. The next generation of GW detectors may reveal more
information about the polarization structure of GWs. Additionally, there is
growing interest in theories of gravity beyond GR. One such theory which
remains viable within the context of recent measurements of the speed of
propagation of GWs is the teleparallel analogue of Horndeski gravity. In this
work, we explore the polarization structure of this newly proposed formulation
of Horndeski theory. In curvature-based gravity, Horndeski theory is almost
synonymous with extensions to GR since it spans a large portion of these
possible extensions. We perform this calculation by taking perturbations about
a Minkowski background and consider which mode propagates. The result is that
the polarization structure depends on the choice of model parameters in the
teleparallel Horndeski Lagrangian with a maximum of seven propagating degrees
of freedom. While the curvature-based Horndeski results follows as a particular
limit within this setup, we find a much richer structure of both massive and
massless cases which produce scalar--vector--tensor propagating degrees of
freedom. We also find that the GW polarization that emerges from the
teleparallel analogue of Horndeski gravity results in analogous massive and
massless modes which take on at most four polarizations in the massless sector
and two scalar ones in the massive sector. In none of the cases do we find
vector polarizations.


Abstract:
  Metamorphic testing seeks to verify software in the absence of test oracles.
Our application domain is ocean system modeling, where test oracles rarely
exist, but where symmetries of the simulated physical systems are known. The
input data set is large owing to the requirements of the application domain.
This paper presents work in progress for the automated generation of
metamorphic test scenarios using machine learning. We extended our previously
proposed method [1] to identify metamorphic relations with reduced
computational complexity. Initially, we represent metamorphic relations as
identity maps. We construct a cost function that minimizes for identifying a
metamorphic relation orthogonal to previously found metamorphic relations and
penalize for the identity map. A machine learning algorithm is used to identify
all possible metamorphic relations minimizing the defined cost function. We
propose applying dimensionality reduction techniques to identify attributes in
the input which have high variance among the identified metamorphic relations.
We apply mutation on these selected attributes to identify distinct metamorphic
relations with reduced computational complexity. For experimental evaluation,
we subject the two implementations of an ocean-modeling application to the
proposed method to present the use of metamorphic relations to test the two
implementations of this application.


Abstract:
  We consider light pulses in a circular array of $2N$ coupled nonlinear
optical waveguides. The waveguides are either hermitian or alternate gain and
loss in a $\mathcal{PT}$-symmetric fashion. Simple patterns in the array
include a ring of $2N$ pulses travelling abreast, and a breather -- a string of
pulses where all even and all odd waveguides flash in turn. In addition, the
structure displays solitons gyrating around the necklace by switching from one
waveguide to the next. Some of the gyrating solitons are stable while other
ones are weakly unstable and evolve into gyrating multiflash strings. By tuning
the gain-loss coefficient, the gyration of solitons in a nonhermitian array may
be reversed without changing the direction of their translational motion.


Abstract:
  Despite learning-based visual odometry (VO) has shown impressive results in
recent years, the pretrained networks may easily collapse in unseen
environments. The large domain gap between training and testing data makes them
difficult to generalize to new scenes. In this paper, we propose an online
adaptation framework for deep VO with the assistance of scene-agnostic
geometric computations and Bayesian inference. In contrast to learning-based
pose estimation, our method solves pose from optical flow and depth while the
single-view depth estimation is continuously improved with new observations by
online learned uncertainties. Meanwhile, an online learned photometric
uncertainty is used for further depth and pose optimization by a differentiable
Gauss-Newton layer. Our method enables fast adaptation of deep VO networks to
unseen environments in a self-supervised manner. Extensive experiments
including Cityscapes to KITTI and outdoor KITTI to indoor TUM demonstrate that
our method achieves state-of-the-art generalization ability among
self-supervised VO methods.


Abstract:
  In this paper, we explore the relation between the index of reducibility and
the Hilbert coefficients in local rings. Consequently, the main result of this
study provides a characterization of a sequentially Cohen-Macaulay ring in
terms of its Hilbert coefficients for non-parameter ideals. As corollaries to
the main theorem, we obtain characterizations of a Gorenstein/Cohen-Macaulay
ring in terms of its Chern coefficients for parameter ideals.


Abstract:
  The rapid production of data on the internet and the need to understand how
users are feeling from a business and research perspective has prompted the
creation of numerous automatic monolingual sentiment detection systems. More
recently however, due to the unstructured nature of data on social media, we
are observing more instances of multilingual and code-mixed texts. This
development in content type has created a new demand for code-mixed sentiment
analysis systems. In this study we collect, label and thus create a dataset of
Persian-English code-mixed tweets. We then proceed to introduce a model which
uses BERT pretrained embeddings as well as translation models to automatically
learn the polarity scores of these Tweets. Our model outperforms the baseline
models that use Na\"ive Bayes and Random Forest methods.


Abstract:
  We develop a hyperparameter optimisation algorithm, Automated Budget
Constrained Training (AutoBCT), which balances the quality of a model with the
computational cost required to tune it. The relationship between
hyperparameters, model quality and computational cost must be learnt and this
learning is incorporated directly into the optimisation problem. At each
training epoch, the algorithm decides whether to terminate or continue
training, and, in the latter case, what values of hyperparameters to use. This
decision weighs optimally potential improvements in the quality with the
additional training time and the uncertainty about the learnt quantities. The
performance of our algorithm is verified on a number of machine learning
problems encompassing random forests and neural networks. Our approach is
rooted in the theory of Markov decision processes with partial information and
we develop a numerical method to compute the value function and an optimal
strategy.


Abstract:
  The current paper is concerned with the spreading speeds of the following
parabolic-parabolic chemotaxis model with logistic source on $\mathbb{R}^{N}$,
\begin{equation} \begin{cases} u_t=\Delta u-\chi\nabla\cdot ( u\nabla v) +
u(a-bu),\quad x\in\mathbb{R}^{N},\cr {v_t}=\Delta v -\lambda v+\mu u,\quad x\in
\mathbb{R}^{N}. \end{cases}(1) \end{equation} where $\chi, \ a,\ b,\ \lambda,\
\mu$ are positive constants. Assume $b>\frac{N\mu\chi}{4}$. Among others, it is
proved that $2\sqrt{a}$ is the spreading speed of the global classical
solutions of (1) with nonempty compactly supported initial functions, that is,
$$ \lim_{t\to\infty}\sup_{|x|\geq ct}u(x,t;u_0,v_0)=0\quad \forall\,\,
c>2\sqrt{a} $$ and $$ \liminf_{t\to\infty}\inf_{|x|\leq ct}u(x,t;u_0,v_0)>0
\quad \forall\,\, 0<c<2\sqrt{a}. $$ where $(u(x,t;u_0,v_0), v(x,t;u_0,v_0))$ is
the unique global classical solution of (1) with $u(x,0;u_0,v_0)=u_0$,
$v(x,0;u_0,v_0)=v_0$, and ${\rm supp}(u_0)$, ${\rm supp}(v_0)$ are nonempty and
compact. It is well known that $2\sqrt{a}$ is the spreading speed of the
following Fisher-KPP equation, $$ u_t=\Delta u+u(a-bu),\quad \forall\,\
x\in\mathbb{R}^{N}. $$ Hence, if $b>\frac{N\mu\chi}{4}$, the chemotaxis neither
speeds up nor slows down the spatial spreading in the Fisher-KPP equation.


Abstract:
  The design of efficient and generic algorithms for solving combinatorial
optimization problems has been an active field of research for many years.
Standard exact solving approaches are based on a clever and complete
enumeration of the solution set. A critical and non-trivial design choice with
such methods is the branching strategy, directing how the search is performed.
The last decade has shown an increasing interest in the design of machine
learning-based heuristics to solve combinatorial optimization problems. The
goal is to leverage knowledge from historical data to solve similar new
instances of a problem. Used alone, such heuristics are only able to provide
approximate solutions efficiently, but cannot prove optimality nor bounds on
their solution. Recent works have shown that reinforcement learning can be
successfully used for driving the search phase of constraint programming (CP)
solvers. However, it has also been shown that this hybridization is challenging
to build, as standard CP frameworks do not natively include machine learning
mechanisms, leading to some sources of inefficiencies. This paper presents the
proof of concept for SeaPearl, a new CP solver implemented in Julia, that
supports machine learning routines in order to learn branching decisions using
reinforcement learning. Support for modeling the learning component is also
provided. We illustrate the modeling and solution performance of this new
solver on two problems. Although not yet competitive with industrial solvers,
SeaPearl aims to provide a flexible and open-source framework in order to
facilitate future research in the hybridization of constraint programming and
machine learning.