Abstract:
  Network appliances continue to offer novel opportunities to offload
processing from computing nodes directly into the data plane. One popular
concern of network operators and their customers is to move data increasingly
faster. A common technique to increase data throughput is to compress it before
its transmission. However, this requires compression of the data -- a time and
energy demanding pre-processing phase -- and decompression upon reception -- a
similarly resource consuming operation. Moreover, if multiple nodes transfer
similar data chunks across the network hop (e.g., a given pair of switches),
each node effectively wastes resources by executing similar steps. This paper
proposes ZipLine, an approach to design and implement (de)compression at line
speed leveraging the Tofino hardware platform which is programmable using the
P4_16 language. We report on lessons learned while building the system and show
throughput, latency and compression measurements on synthetic and real-world
traces, showcasing the benefits and trade-offs of our design.


Abstract:
  In this paper, we prove a maximum principle for the general multi-term
space-time-fractional transport equation and apply it for establishing
uniqueness of solution to an initial-boundary-value problem for this equation.
We also derive some comparison principles for solutions to the
initial-boundary-value problems with different problem data. Finally, we
present a maximum principle for the Cauchy problem for a time-fractional
transport equation on an unbounded domain.


Abstract:
  Kirigami, an ancient paper cutting art, offers a promising strategy for
2D-to-3D shape morphing through cut-guided deformation. Existing kirigami
designs for target 3D curved shapes rely on intricate cut patterns in thin
sheets, making the inverse design challenging. Motivated by the Gauss-Bonnet
theorem that correlates the geodesic curvature along the boundary with the
topological Gaussian curvature, here, we exploit programming the curvature of
cut boundaries rather than complex cut patterns in kirigami sheets for target
3D curved topologies through both forward and inverse designs. Such a new
strategy largely simplifies the inverse design. We demonstrate the achievement
of varieties of dynamic 3D shape shifting under both mechanical stretching and
remote magnetic actuation, and its potential application as an untethered
predator-like kirigami soft robot. This study opens a new avenue to encode
boundary curvatures for shape-programing materials with potential applications
in shape-morphing structures, soft robots, and multifunctional devices.


Abstract:
  Boltzmann transport equation (BTE) is an ideal tool to describe the
multiscale phonon transport phenomena, which are critical to applications like
microelectronics cooling. Numerically solving phonon BTE is extremely
computationally challenging due to the high dimensionality of such problems,
especially when mode-resolved properties are considered. In this work, we
demonstrate the use of physics-informed neural networks (PINNs) to efficiently
solve phonon BTE for multiscale thermal transport problems with the
consideration of phonon dispersion and polarization. In particular, a PINN
framework is devised to predict the phonon energy distribution by minimizing
the residuals of governing equations and boundary conditions, without the need
for any labeled training data. Moreover, geometric parameters, such as the
characteristic length scale, are included as a part of the input to PINN, which
enables learning BTE solutions in a parametric setting. The effectiveness of
the present scheme is demonstrated by solving a number of phonon transport
problems in different spatial dimensions (from 1D to 3D). Compared to existing
numerical BTE solvers, the proposed method exhibits superiority in efficiency
and accuracy, showing great promises for practical applications, such as the
thermal design of electronic devices.


Abstract:
  Infrared small target detection is an important fundamental task in the
infrared system. Therefore, many infrared small target detection methods have
been proposed, in which the low-rank model has been used as a powerful tool.
However, most low-rank-based methods assign the same weights for different
singular values, which will lead to inaccurate background estimation.
Considering that different singular values have different importance and should
be treated discriminatively, in this paper, we propose a non-convex tensor
low-rank approximation (NTLA) method for infrared small target detection. In
our method, NTLA regularization adaptively assigns different weights to
different singular values for accurate background estimation. Based on the
proposed NTLA, we propose asymmetric spatial-temporal total variation (ASTTV)
regularization to achieve more accurate background estimation in complex
scenes. Compared with the traditional total variation approach, ASTTV exploits
different smoothness intensities for spatial and temporal regularization. We
design an efficient algorithm to find the optimal solution of our method.
Compared with some state-of-the-art methods, the proposed method achieves an
improvement in terms of various evaluation metrics. Extensive experimental
results in various complex scenes demonstrate that our method has strong
robustness and low false-alarm rate. Code is available at
https://github.com/LiuTing20a/ASTTV-NTLA.


Abstract:
  We present a measurement of the combined $\nu_e$ + $\bar{\nu}_e$
flux-averaged charged-current inclusive cross section on argon using data from
the MicroBooNE liquid argon time projection chamber (LArTPC) at Fermilab. Using
the off-axis flux from the NuMI beam, MicroBooNE has reconstructed 214
candidate $\nu_e$ + $\bar{\nu}_e$ interactions with an estimated exposure of
2.4$\times10^{20}$ protons on target. Given the estimated purity of 38.6\%,
this implies the observation of 80 $\nu_e$ + $\bar{\nu}_e$ events in argon, the
largest such sample to date. The analysis includes the first demonstration of a
fully automated application of a dE/dx-based particle discrimination technique
of electron and photon induced showers in a LArTPC neutrino detector. We
measure the $\nu_e + \bar{\nu}_e$ flux-averaged charged-current total cross
section to be
${6.84\pm\!1.51~\textrm{(stat.)}\pm\!2.33~\textrm{(sys.)}\!\times\!10^{-39}~\textrm{cm}^{2}/~\textrm{nucleon}}$,
for neutrino energies above 250 MeV and an average neutrino flux energy of 905
MeV when this threshold is applied. The measurement is sensitive to neutrino
events where the final state electron momentum is above 48 MeV/c, includes the
entire angular phase space of the electron, and is in agreement with the
theoretical predictions from \texttt{GENIE} and \texttt{NuWro}. This
measurement is also the first demonstration of electron neutrino reconstruction
in a surface LArTPC in the presence of cosmic ray backgrounds, which will be a
crucial task for surface experiments like those that comprise the
Short-Baseline Neutrino (SBN) Program at Fermilab.


Abstract:
  Recognizing Activities of Daily Living (ADL) is a vital process for
intelligent assistive robots, but collecting large annotated datasets requires
time-consuming temporal labeling and raises privacy concerns, e.g., if the data
is collected in a real household. In this work, we explore the concept of
constructing training examples for ADL recognition by playing life simulation
video games and introduce the SIMS4ACTION dataset created with the popular
commercial game THE SIMS 4. We build Sims4Action by specifically executing
actions-of-interest in a "top-down" manner, while the gaming circumstances
allow us to freely switch between environments, camera angles and subject
appearances. While ADL recognition on gaming data is interesting from the
theoretical perspective, the key challenge arises from transferring it to the
real-world applications, such as smart-homes or assistive robotics. To meet
this requirement, Sims4Action is accompanied with a GamingToReal benchmark,
where the models are evaluated on real videos derived from an existing ADL
dataset. We integrate two modern algorithms for video-based activity
recognition in our framework, revealing the value of life simulation video
games as an inexpensive and far less intrusive source of training data.
However, our results also indicate that tasks involving a mixture of gaming and
real data are challenging, opening a new research direction. We will make our
dataset publicly available at https://github.com/aroitberg/sims4action.


Abstract:
  Since its advent in 2009, Bitcoin, a cryptography-enabled peer-to-peer
digital payment system, has been gaining increasing attention from both
academia and industry. An effort designed to overcome a cluster of bottlenecks
inherent in existing centralized financial systems, Bitcoin has always been
championed by the crypto community as an example of the spirit of
decentralization. While the decentralized nature of Bitcoin's Proof-of-Work
consensus algorithm has often been discussed in great detail, no systematic
study has so far been conducted to quantitatively measure the degree of
decentralization of Bitcoin from an asset perspective -- How decentralized is
Bitcoin as a financial asset? We present in this paper the first systematic
investigation of the degree of decentralization for Bitcoin based on its entire
transaction history. We proposed both static and dynamic analysis of Bitcoin
transaction network with quantifiable decentralization measures developed based
on network analysis and market efficiency study. Case studies are also
conducted to demonstrate the effectiveness of our proposed metrics.


Abstract:
  Dialogue state tracking is an essential part of goal-oriented dialogue
systems, while most of these state tracking models often fail to handle unseen
services. In this paper, we propose SGD-QA, a simple and extensible model for
schema-guided dialogue state tracking based on a question answering approach.
The proposed multi-pass model shares a single encoder between the domain
information and dialogue utterance. The domain's description represents the
query and the dialogue utterance serves as the context. The model improves
performance on unseen services by at least 1.6x compared to single-pass
baseline models on the SGD dataset. SGD-QA shows competitive performance
compared to state-of-the-art multi-pass models while being significantly more
efficient in terms of memory consumption and training performance. We provide a
thorough discussion on the model with ablation study and error analysis.


Abstract:
  Recent advances in Reinforcement Learning (RL) have surpassed human-level
performance in many simulated environments. However, existing reinforcement
learning techniques are incapable of explicitly incorporating already known
domain-specific knowledge into the learning process. Therefore, the agents have
to explore and learn the domain knowledge independently through a trial and
error approach, which consumes both time and resources to make valid responses.
Hence, we adapt the Deep Deterministic Policy Gradient (DDPG) algorithm to
incorporate an adviser, which allows integrating domain knowledge in the form
of pre-learned policies or pre-defined relationships to enhance the agent's
learning process. Our experiments on OpenAi Gym benchmark tasks show that
integrating domain knowledge through advisers expedites the learning and
improves the policy towards better optima.


Abstract:
  Surgical skills have a great influence on surgical safety and patients'
well-being. Traditional assessment of surgical skills involves strenuous manual
efforts, which lacks efficiency and repeatability. Therefore, we attempt to
automatically predict how well the surgery is performed using the surgical
video. In this paper, a unified multi-path framework for automatic surgical
skill assessment is proposed, which takes care of multiple composing aspects of
surgical skills, including surgical tool usage, intraoperative event pattern,
and other skill proxies. The dependency relationships among these different
aspects are specially modeled by a path dependency module in the framework. We
conduct extensive experiments on the JIGSAWS dataset of simulated surgical
tasks, and a new clinical dataset of real laparoscopic surgeries. The proposed
framework achieves promising results on both datasets, with the
state-of-the-art on the simulated dataset advanced from 0.71 Spearman's
correlation to 0.80. It is also shown that combining multiple skill aspects
yields better performance than relying on a single aspect.


Abstract:
  Long-range spatiotemporal correlations may play important roles in
nonequilibrium surface growth process. In order to investigate the effects of
long-range temporal correlation on dynamic scaling of growing surfaces, we
perform extensive numerical simulations of the (1+1)- and (2+1)-dimensional
Kardar-Parisi-Zhang (KPZ) growth system in the presence of temporally
correlated noise, and compare our results with previous theoretical predictions
and numerical simulations. We find that surface morphologies are obviously
affected with long-range temporal correlations, and as the temporal correlation
exponent increases, the KPZ surfaces develop gradually faceted patterns in the
saturated growth regimes. Our results show that the temporal correlated KPZ
system displays evidently nontrivial dynamic properties when $0<\theta<0.5$,
the characteristic roughness exponents satisfy $\alpha<\alpha_s$, and
$\alpha_{loc}$ exhibiting non-universal scaling within local window sizes,
which differs with the existing dynamic scaling classifications, both in the
(1+1)- and (2+1)-dimensions.


Abstract:
  A novel concept for dynamic focus shaping based on highly efficient coherent
beam combining with microlens arrays (MLA) as combining element is presented.
This concept allows to control the power weights of diffraction orders by
varying the absolute phases of an array of input beams. A proof-of-principle
experiment is supported by simulations. For this, an input beam matrix of 5 x 5
beams is combined proving both the ability for further power scaling and
dynamic focus shaping.


Abstract:
  We introduce a neural network architecture to solve inverse problems linked
to a one-dimensional integral operator. This architecture is built by unfolding
a forward-backward algorithm derived from the minimization of an objective
function which consists of the sum of a data-fidelity function and a
Tikhonov-type regularization function. The robustness of this inversion method
with respect to a perturbation of the input is theoretically analyzed. Ensuring
robustness is consistent with inverse problem theory since it guarantees both
the continuity of the inversion method and its insensitivity to small noise.
The latter is a critical property as deep neural networks have been shown to be
vulnerable to adversarial perturbations. One of the main novelties of our work
is to show that the proposed network is also robust to perturbations of its
bias. In our architecture, the bias accounts for the observed data in the
inverse problem. We apply our method to the inversion of Abel integral
operators, which define a fractional integration involved in wide range of
physical processes. The neural network is numerically implemented and tested to
illustrate the efficiency of the method. Lipschitz constants after training are
computed to measure the robustness of the neural networks.


Abstract:
  We describe the application of Semantic Segmentation by using the Self
Organizing Map technique to an high spatial and spectral resolution dataset
acquired along the H$\alpha$ line at 656.28 nm by the Interferometric
Bi-dimensional Spectrometer installed at the focus plane of the Dunn Solar
Telescope. This machine learning approach allowed us to identify several
features corresponding to the main structures of the solar photosphere and
chromosphere. The obtained results show the capability and flexibility of this
method to identifying and analyzing the fine structures which characterize the
solar activity in the low atmosphere. This is a first successful application of
the SOM technique to astrophysical data sets.


Abstract:
  This paper presents a novel intrinsic image transfer (IIT) algorithm for
illumination manipulation, which creates a local image translation between two
illumination surfaces. This model is built on an optimization-based framework
consisting of three photo-realistic losses defined on the sub-layers factorized
by an intrinsic image decomposition. We illustrate that all losses can be
reduced without the necessity of taking an intrinsic image decomposition under
the well-known spatial-varying illumination illumination-invariant reflectance
prior knowledge. Moreover, with a series of relaxations, all of them can be
directly defined on images, giving a closed-form solution for image
illumination manipulation. This new paradigm differs from the prevailing
Retinex-based algorithms, as it provides an implicit way to deal with the
per-pixel image illumination. We finally demonstrate its versatility and
benefits to the illumination-related tasks such as illumination compensation,
image enhancement, and high dynamic range (HDR) image compression, and show the
high-quality results on natural image datasets.


Abstract:
  In the era of Cyber Physical Systems, designers need to offer support for
run-time adaptivity considering different constraints, including the internal
status of the system. This work presents a run-time monitoring approach, based
on the Performance Application Programming Interface, that offers a unified
interface to transparently access both the standard Performance Monitoring
Counters (PMCs) in the CPUs and the custom ones integrated into hardware
accelerators. Automatic tools offer to Sw programmers the support to design and
implement Coarse-Grain Virtual Reconfigurable Circuits, instrumented with
custom PMCs. This approach has been validated on a heterogeneous application
for image/video processing with an overhead of 6% of the execution time.


Abstract:
  We demonstrate a cost-effective, fully software-based lock-in amplifier (LIA)
implemented on a commercial computer. The device is designed for application in
nonlinear spectroscopy, such as transient absorption and coherent
multidi-mensional spectroscopy, but may be also used in any other application.
The performance of our device is compared to a state-of-the-art commercial LIA
with nearly identical results for both devices. Advantages of our device over
commercial hardwired electronic lock-in amplifiers is the improved flexibility
in the data analysis and the possibility of arbitrary up-scaling of the number
of LIA channels.


Abstract:
  Let $(M,g)$ be a smooth compact Riemann surface with the multicomponent
boundary
$\Gamma=\Gamma_0\cup\Gamma_1\cup\dots\cup\Gamma_m=:\Gamma_0\cup\tilde\Gamma$.
Let $u=u^f$ obey $\Delta u=0$ in $M$, $u|_{\Gamma_0}=f,\,\,u|_{\tilde\Gamma}=0$
(the grounded holes) and $v=v^h$ obey $\Delta v=0$ in $M$,
$v|_{\Gamma_0}=h,\,\,\partial_\nu v|_{\tilde\Gamma}=0$ (the isolated holes).
Let $\Lambda_{g}^{\rm gr}: f\mapsto\partial_\nu u^f|_{\Gamma_{0}}$ and
$\Lambda_{g}^{\rm is}: h\mapsto\partial_\nu v^h|_{\Gamma_{0}}$ be the
corresponding DN-maps. The EIT problem is to determine $M$ from
$\Lambda_{g}^{\rm gr}$ or $\Lambda_{g}^{\rm is}$.
  To solve it, an algebraic version of the BC-method is applied. The main
instrument is the algebra of holomorphic functions on the ma\-ni\-fold
${\mathbb M}$, which is obtained by gluing two examples of $M$ along
$\tilde{\Gamma}$. We show that this algebra is determined by $\Lambda_{g}^{\rm
gr}$ (or $\Lambda_{g}^{\rm is}$) up to isometric isomorphism. Its Gelfand
spectrum (the set of characters) plays the role of the material for
constructing a relevant copy $(M',g',\Gamma_{0}')$ of $(M,g,\Gamma_{0})$. This
copy is conformally equivalent to the original, provides
$\Gamma_{0}'=\Gamma_{0},\,\,\Lambda_{g'}^{\rm gr}=\Lambda_{g}^{\rm
gr},\,\,\Lambda_{g'}^{\rm is}=\Lambda_{g}^{\rm is}$, and thus solves the
problem.


Abstract:
  Fusion energy is often regarded as a long-term solution to the world's energy
needs. However, even after solving the critical research challenges,
engineering and materials science will still impose significant constraints on
the characteristics of a fusion power plant. Meanwhile, the global energy grid
must transition to low-carbon sources by 2050 to prevent the worst effects of
climate change. We review three factors affecting fusion's future trajectory:
(1) the significant drop in the price of renewable energy, (2) the
intermittency of renewable sources and implications for future energy grids,
and (3) the recent proposition of intermediate-level nuclear waste as a product
of fusion. Within the scenario assumed by our premises, we find that while
there remains a clear motivation to develop fusion power plants, this
motivation is likely weakened by the time they become available. We also
conclude that most current fusion reactor designs do not take these factors
into account and, to increase market penetration, fusion research should
consider relaxed nuclear waste design criteria, raw material availability
constraints and load-following designs with pulsed operation.


Abstract:
  Chinese character decomposition has been used as a feature to enhance Machine
Translation (MT) models, combining radicals into character and word level
models. Recent work has investigated ideograph or stroke level embedding.
However, questions remain about different decomposition levels of Chinese
character representations, radical and strokes, best suited for MT. To
investigate the impact of Chinese decomposition embedding in detail, i.e.,
radical, stroke, and intermediate levels, and how well these decompositions
represent the meaning of the original character sequences, we carry out
analysis with both automated and human evaluation of MT. Furthermore, we
investigate if the combination of decomposed Multiword Expressions (MWEs) can
enhance the model learning. MWE integration into MT has seen more than a decade
of exploration. However, decomposed MWEs has not previously been explored.


Abstract:
  We present some results that complement our prequels
[arXiv:1809.08425,arXiv:1907.05770] on holomorphic vector bundles. We apply the
method of the Quot-scheme limit of Fubini-Study metrics developed therein to
provide a generalisation to the singular case of the result originally obtained
by X.W. Wang for the smooth case, which states that the existence of balanced
metrics is equivalent to the Gieseker stability of the vector bundle. We also
prove that the Bergman 1-parameter subgroups form subgeodesics in the space of
hermitian metrics. This paper also contains a review of techniques developed in
[arXiv:1809.08425,arXiv:1907.05770] and how they correspond to their
counterparts developed in the study of the Yau-Tian-Donaldson conjecture.


Abstract:
  Everything we know about the environment around us is thanks to light, a kind
of electromagnetic radiation. Astronomy takes advantage of it and all the
electromagnetic spectrum with the help of many devices to record them and
determine from which places in our Universe they come. These signals must be
processed to obtain the images that are will be then exposed to the public.
Astronomers know that these images will inspire and generate curiosity in each
person who sees them. This Science is inclusive and wants to transmit the
knowledge and the beautiful events that happen in the universe to all people.
We try to do that by developing and showing other forms of teaching, taking
advantage of new technologies that are available today to bring this knowledge
closer to the minorities in our country like visually impaired people.
  Because of this problem, the AstroBVI project arrived in Peru for the first
time, thanks to the distribution of tactile images of 3D galaxies that were
delivered to us from the Centro de Astronom\'ia de la Universidad de
Antofagasta of the and financed by the International Astronomical Union Office
of Astronomy for Development (IAU-OAD). This allowed the holding of seven
workshops during 2019, visiting various institutions and benefiting more than
160 participants with blindness and low vision, identifying in them a lot of
interest, which shows the enormous potential presented by these 3D tactile
materials.


Abstract:
  For a large liquid argon time projection chamber (LArTPC) operating on or
near the Earth's surface to detect neutrino interactions, the rejection of
cosmogenic background is a critical and challenging task because of the large
cosmic ray flux and the long drift time of the TPC. We introduce a superior
cosmic background rejection procedure based on the Wire-Cell three-dimensional
(3D) event reconstruction for LArTPCs. From an initial 1:20,000 neutrino to
cosmic-ray background ratio, we demonstrate these tools on data from the
MicroBooNE experiment and create a high performance generic neutrino event
selection with a cosmic contamination of 14.9\% (9.7\%) for a visible energy
region greater than O(200)~MeV. The neutrino interaction selection efficiency
is 80.4\% and 87.6\% for inclusive $\nu_\mu$ charged-current and $\nu_e$
charged-current interactions, respectively. This significantly improved
performance compared to existing reconstruction algorithms, marks a major
milestone toward reaching the scientific goals of LArTPC neutrino oscillation
experiments operating near the Earth's surface.


Abstract:
  We present optical follow-up observation results of three binary black hole
merger (BBH) events, GW190408_181802, GW190412, and GW190503_185404, which were
detected by the Advanced LIGO and Virgo gravitational wave (GW) detectors.
Electromagnetic (EM) counterparts are generally not expected for BBH merger
events. However, some theoretical models suggest that EM counterparts of BBH
can possibly arise in special environments, prompting motivation to search for
EM counterparts for such events. We observed high-credibility regions of the
sky for the three BBH merger events with telescopes of the Gravitational-wave
EM Counterpart Korean Observatory (GECKO), including the KMTNet. Our
observation started as soon as 100 minutes after the GW event alerts and
covered 29 - 63 deg$^2$ for each event with a depth of $\sim$ 22.5 mag in
$R$-band within hours of observation. No plausible EM counterparts were found
for these events, but from no detection in the GW190503_185404 event, for which
we covered 69% credibility region, we place the BBH merger EM counterpart
signal to be $M_{g}$ > -18.0 AB mag within about 1 day of the GW event. The
comparison of our detection limits with light curves of several kilonova models
suggests that a kilonova event could have been identified within hours from GW
alert with GECKO observations if the compact merger happened at < 400 Mpc and
the localization accuracy was of order of 50 deg$^2$. Our result gives a great
promise for the GECKO facilities to find EM counterparts within a few hours
from GW detection in future GW observation runs.


Abstract:
  In smoking cessation clinical research and practice, objective validation of
self-reported smoking status is crucial for ensuring the reliability of the
primary outcome, that is, smoking abstinence. Speech signals convey important
information about a speaker, such as age, gender, body size, emotional state,
and health state. We investigated (1) if smoking could measurably alter voice
features, (2) if smoking cessation could lead to changes in voice, and
therefore (3) if the voice-based smoking status assessment has the potential to
be used as an objective smoking cessation validation method.


Abstract:
  In recent times, deep learning-based steganalysis classifiers became popular
due to their state-of-the-art performance. Most deep steganalysis classifiers
usually extract noise residuals using high-pass filters as preprocessing steps
and feed them to their deep model for classification. It is observed that
recent steganographic embedding does not always restrict their embedding in the
high-frequency zone; instead, they distribute it as per embedding policy.
Therefore, besides noise residual, learning the embedding zone is another
challenging task. In this work, unlike the conventional approaches, the
proposed model first extracts the noise residual using learned denoising
kernels to boost the signal-to-noise ratio. After preprocessing, the sparse
noise residuals are fed to a novel Multi-Contextual Convolutional Neural
Network (M-CNET) that uses heterogeneous context size to learn the sparse and
low-amplitude representation of noise residuals. The model performance is
further improved by incorporating the Self-Attention module to focus on the
areas prone to steganalytic embedding. A set of comprehensive experiments is
performed to show the proposed scheme's efficacy over the prior arts. Besides,
an ablation study is given to justify the contribution of various modules of
the proposed architecture.


Abstract:
  Executing a Golog program on an actual robot typically requires additional
steps to account for hardware or software details of the robot platform, which
can be formulated as constraints on the program. Such constraints are often
temporal, refer to metric time, and require modifications to the abstract Golog
program. We describe how to formulate such constraints based on a modal variant
of the Situation Calculus. These constraints connect the abstract program with
the platform models, which we describe using timed automata. We show that for
programs over finite domains and with fully known initial state, the problem of
synthesizing a controller that satisfies the constraints while preserving the
effects of the original program can be reduced to MTL synthesis. We do this by
constructing a timed automaton from the abstract program and synthesizing an
MTL controller from this automaton, the platform models, and the constraints.
We prove that the synthesized controller results in execution traces which are
the same as those of the original program, possibly interleaved with
platform-dependent actions, that they satisfy all constraints, and that they
have the same effects as the traces of the original program. By doing so, we
obtain a decidable procedure to synthesize a controller that satisfies the
specification while preserving the original program.


Abstract:
  The $s$-colour size-Ramsey number of a hypergraph $H$ is the minimum number
of edges in a hypergraph $G$ whose every $s$-edge-colouring contains a
monochromatic copy of $H$. We show that the $s$-colour size-Ramsey number of
the $t$-power of the $r$-uniform tight path on $n$ vertices is linear in $n$,
for every fixed $r, s, t$, thus answering a question of Dudek, La Fleur,
Mubayi, and R\"odl (2017).
  In fact, we prove a stronger result that allows us to deduce that powers of
bounded degree hypergraph trees and powers of `long subdivisions' of bounded
degree hypergraphs have size-Ramsey numbers that are linear in the number of
vertices. This extends and strongly generalises recent results about the
linearity of size-Ramsey numbers of powers of bounded degree trees and of long
subdivisions of bounded degree graphs.


Abstract:
  The canonical forms associated to scattering amplitudes of planar Feynman
diagrams are interpreted in terms of masses of projectives, defined as the
modulus of their central charges, in the hearts of certain $t$-structures of
derived categories of quiver representations and, equivalently, in terms of
cluster tilting objects of the corresponding cluster categories.


Abstract:
  Learning meaningful representations of data that can address challenges such
as batch effect correction, data integration and counterfactual inference is a
central problem in many domains including computational biology. Adopting a
Conditional VAE framework, we identify the mathematical principle that unites
these challenges: learning a representation that is marginally independent of a
condition variable. We therefore propose the Contrastive Mixture of Posteriors
(CoMP) method that uses a novel misalignment penalty to enforce this
independence. This penalty is defined in terms of mixtures of the variational
posteriors themselves, unlike prior work which uses external discrepancy
measures such as MMD to ensure independence in latent space. We show that CoMP
has attractive theoretical properties compared to previous approaches,
especially when there is complex global structure in latent space. We further
demonstrate state of the art performance on a number of real-world problems,
including the challenging tasks of aligning human tumour samples with cancer
cell-lines and performing counterfactual inference on single-cell RNA
sequencing data. Incidentally, we find parallels with the fair representation
learning literature, and demonstrate CoMP has competitive performance in
learning fair yet expressive latent representations.


Abstract:
  In this paper, we propose a Zero-Touch, deep reinforcement learning
(DRL)-based Proactive Failure Recovery framework called ZT-PFR for stateful
network function virtualization (NFV)-enabled networks. To this end, we
formulate a resource-efficient optimization problem minimizing the network cost
function including resource cost and wrong decision penalty. As a solution, we
propose state-of-the-art DRL-based methods such as soft-actor-critic (SAC) and
proximal-policy-optimization (PPO). In addition, to train and test our DRL
agents, we propose a novel impending-failure model. Moreover, to keep network
status information at an acceptable freshness level for appropriate
decision-making, we apply the concept of age of information to strike a balance
between the event and scheduling based monitoring. Several key systems and DRL
algorithm design insights for ZT-PFR are drawn from our analysis and simulation
results. For example, we use a hybrid neural network, consisting long
short-term memory layers in the DRL agents structure, to capture
impending-failures time dependency.


Abstract:
  The contribution contains the preface to the Proceedings to the 23rd
International Workshop "What Comes Beyond the Standard Models", July 04 -- July
12, 2020, Bled, Slovenia, [Virtual Workshop -- July 6.--10. 2020], Volume 1:
Invited Talks and Volume 2: Further Talks And Scientific Debuts, published in
Bled workshops in physics, Vol.21, No. 1 and 2, DMFA-Zalo\v{z}nistvo,
Ljubljana, Dec. 2020, links to (most of) the published contributions, section
(by M.Yu. Khlopov) on VIA and virtual conference at Bled 2020, and two poems by
Astri Kleppe.


Abstract:
  Pre-trained models have achieved state-of-the-art results in various Natural
Language Processing (NLP) tasks. Recent works such as T5 and GPT-3 have shown
that scaling up pre-trained language models can improve their generalization
abilities. Particularly, the GPT-3 model with 175 billion parameters shows its
strong task-agnostic zero-shot/few-shot learning capabilities. Despite their
success, these large-scale models are trained on plain texts without
introducing knowledge such as linguistic knowledge and world knowledge. In
addition, most large-scale models are trained in an auto-regressive way. As a
result, this kind of traditional fine-tuning approach demonstrates relatively
weak performance when solving downstream language understanding tasks. In order
to solve the above problems, we propose a unified framework named ERNIE 3.0 for
pre-training large-scale knowledge enhanced models. It fuses auto-regressive
network and auto-encoding network, so that the trained model can be easily
tailored for both natural language understanding and generation tasks with
zero-shot learning, few-shot learning or fine-tuning. We trained the model with
10 billion parameters on a 4TB corpus consisting of plain texts and a
large-scale knowledge graph. Empirical results show that the model outperforms
the state-of-the-art models on 54 Chinese NLP tasks, and its English version
achieves the first place on the SuperGLUE benchmark (July 3, 2021), surpassing
the human performance by +0.8% (90.6% vs. 89.8%).


Abstract:
  Though pre-trained language models such as Bert and XLNet, have rapidly
advanced the state-of-the-art on many NLP tasks, they implicit semantics only
relying on surface information between words in corpus. Intuitively, background
knowledge influences the efficacy of understanding. Inspired by this common
sense, we focus on improving model pretraining by leveraging explicit
knowledge. Different from recent research that optimize pretraining model by
knowledge masking strategies, we propose a simple but general method to combine
explicit knowledge with pretraining. To be specific, we first match knowledge
facts from knowledge graph (KG) and then add a knowledge injunction layer to
transformer directly without changing its architecture. The present study seeks
to find the direct impact of explicit knowledge on transformer per-training. We
conduct experiments on various datasets for different downstream tasks. The
experimental results show that solely by adding external knowledge to
transformer can improve the learning performance on many NLP tasks.


Abstract:
  Given a stochastic dynamical system modelled via stochastic differential
equations (SDEs), we evaluate the safety of the system through
characterisations of its exit time moments. We lift the (possibly nonlinear)
dynamics into the space of the occupation and exit measures to obtain a set of
linear evolution equations which depend on the infinitesimal generator of the
SDE. Coupled with appropriate semidefinite positive matrix constraints, this
yields a moment-based approach for the computation of exit time moments of SDEs
with polynomial drift and diffusion dynamics. To extend the capability of the
moment approach, we propose a state augmentation method which allows us to
generate the evolution equations for a broader class of nonlinear stochastic
systems and apply the moment method to previously unsupported dynamics. In
particular, we show a general augmentation strategy for sinusoidal dynamics
which can be found in most physical systems. We employ the methodology on an
Ornstein-Uhlenbeck process and stochastic spring-mass-damper model to
characterise their safety via their expected exit times and show the additional
exit distribution insights that are afforded through higher order moments.


Abstract:
  The large imbalance in the neutron and proton densities in very neutron rich
systems increases the nuclear symmetry energy so that it governs many aspects
of neutron stars and their mergers. Extracting the density dependence of the
symmetry energy therefore constitutes an important scientific objective. Many
analyses have been limited to extracting values for the symmetry energy, $S_0$,
and its ``derivative'', $L$, at saturation density $\rho_0 \approx 2.6 \times
10^{14}~\mathrm{g/cm^3}$ $\approx 0.16~\mathrm{nucleons/fm^{3}}$, resulting in
constraints that appear contradictory. We show that most experimental
observables actually probe the symmetry energy at densities far from $\rho_0$,
making the extracted values of $S_0$ or $L$ imprecise. By focusing on the
densities these observables actually probe, we obtain a detailed picture of the
density dependence of the symmetry energy from $0.25\rho_0$ to $1.5\rho_0$.
From this experimentally derived density functional, we extract
$L_{01}=53.1\pm6.1 MeV$ at $\rho \approx 0.10~\mathrm{fm^{-3}}$, a neutron skin
thickness for $^{208}Pb$ of $R_{np} =$ $0.23\pm0.04$ fm, a symmetry pressure at
saturation density of $P_0=3.2\pm1.2 MeV/fm^3$ and suggests a radius for a 1.4
solar mass neutron star of $13.1\pm0.6$ km.


Abstract:
  Analyzing the financial benefit of marketing is still a critical topic for
both practitioners and researchers. Companies consider marketing costs as a
type of investment and expect this investment to be returned to the company in
the form of profit. On the other hand, companies adopt different innovative
strategies to increase their value. Therefore, this study aims to test the
impact of marketing investment on firm value and systematic risk. To do so,
data related to four Arabic emerging markets during the period 2010-2019 are
considered, and firm share price and beta share are considered to measure firm
value and systematic risk, respectively. Since a firm's ownership concentration
is a determinant factor in firm value and systematic risk, this variable is
considered a moderated variable in the relationship between marketing
investment and firm value and systematic risk. The findings of the study, using
panel data regression, indicate that increasing investment in marketing has a
positive effect on the firm value valuation model. It is also found that the
ownership concentration variable has a reinforcing role in the relationship
between marketing investment and firm value. It is also disclosed that it
moderates the systematic risk aligned with the monitoring impact of controlling
shareholders. This study provides a logical combination of governance-marketing
dimensions to interpret performance indicators in the capital market.


Abstract:
  Multi-component dark matter scenarios are studied in the model with $U(1)_X$
dark gauge symmetry that is broken into its product subgroup $Z_2 \times Z_3$
\'{a} la Krauss-Wilczek mechanism. In this setup, there exist two types of dark
matter fields, $X$ and $Y$, distinguished by different $Z_2 \times Z_3$
charges. The real and imaginary parts of the $Z_2$-charged field, $X_R$ and
$X_I$, get different masses from the $U(1)_X$ symmetry breaking. The field $Y$,
which is another dark matter candidate due to the unbroken $Z_3$ symmetry,
belongs to the Strongly Interacting Massive Particle (SIMP)-type dark matter.
Both $X_I$ and $X_R$ may contribute to $Y$'s $3\rightarrow 2$ annihilation
processes, opening a new class of SIMP models with a local dark gauge symmetry.
Depending on the mass difference between $X_I$ and $X_R$, we have either
two-component or three-component dark matter scenarios. In particular two- or
three-component SIMP scenarios can be realised not only for small mass
difference between $X$ and $Y$, but also for large mass hierarchy between them,
which is a new and unique feature of the present model. We consider both
theoretical and experimental constraints, and present four case studies of the
multi-component dark matter scenarios.


Abstract:
  Over the past 70 years, the number of international environmental agreements
(IEAs) has increased substantially, highlighting their prominent role in
environmental governance. This paper applies the toolkit of network analysis to
identify the network properties of international environmental cooperation
based on 546 IEAs signed between 1948 and 2015. We identify four stylised facts
that offer topological corroboration for some key themes in the IEA literature.
First, we find that a statistically significant cooperation network did not
emerge until early 1970, but since then the network has grown continuously in
strength, resulting in higher connectivity and intensity of cooperation between
signatory countries. Second, over time the network has become closer, denser
and more cohesive, allowing more effective policy coordination and knowledge
diffusion. Third, the network, while global, has a noticeable European imprint:
initially the United Kingdom and more recently France and Germany have been the
most strategic players to broker environmental cooperation. Fourth,
international environmental coordination started with the management of
fisheries and the sea, but is now most intense on waste and hazardous
substances. The network of air and atmosphere treaties is weaker on a number of
metrics and lacks the hierarchical structure found in other networks. It is the
only network whose topological properties are shaped significantly by
UN-sponsored treaties.


Abstract:
  With emerging thin film PIN based optoelectronics devices, a significant
research thrust is focused on the passivation of trap states for performance
enhancement. Among various methods, capacitance frequency technique (CFT) is
often employed to quantify trap state parameters, however, the trapped charge
induced electrostatic effect on the same is not yet established for such
devices. Herein, we present a theoretical methodology to incorporate such
effects in the CF characteristics of well-established carrier selective
perovskite-based PIN devices. We show that the electrostatic effect of trapped
charges leads to non-linear energy bands in perovskite layer which results in
the underestimation of trap density from existing models of CFT. Consequently,
a parabolic band approximation with effective length PBAEL model is developed
which accurately predicts the trap density for shallow or deep states from CFT
analysis. In addition, we demonstrate that the attempt to escape frequency,
crucial for trapped charge dynamics with continuum energy bands, can be well
extracted by eliminating non-linear effects at reduced perovskite thickness. We
believe that our work provides a unified theoretical platform for CFT to
extract trap state parameters for a broad class of organic and hybrid
materials-based thin film devices for energy conversion applications such as
solar cells, LEDs, etc.


Abstract:
  In educational applications, Knowledge Tracing (KT), the problem of
accurately predicting students' responses to future questions by summarizing
their knowledge states, has been widely studied for decades as it is considered
a fundamental task towards adaptive online learning. Among all the proposed KT
methods, Deep Knowledge Tracing (DKT) and its variants are by far the most
effective ones due to the high flexibility of the neural network. However, DKT
often ignores the inherent differences between students (e.g. memory skills,
reasoning skills, ...), averaging the performances of all students, leading to
the lack of personalization, and therefore was considered insufficient for
adaptive learning. To alleviate this problem, in this paper, we proposed
Leveled Attentive KNowledge TrAcing (LANA), which firstly uses a novel
student-related features extractor (SRFE) to distill students' unique inherent
properties from their respective interactive sequences. Secondly, the pivot
module was utilized to dynamically reconstruct the decoder of the neural
network on attention of the extracted features, successfully distinguishing the
performance between students over time. Moreover, inspired by Item Response
Theory (IRT), the interpretable Rasch model was used to cluster students by
their ability levels, and thereby utilizing leveled learning to assign
different encoders to different groups of students. With pivot module
reconstructed the decoder for individual students and leveled learning
specialized encoders for groups, personalized DKT was achieved. Extensive
experiments conducted on two real-world large-scale datasets demonstrated that
our proposed LANA improves the AUC score by at least 1.00% (i.e. EdNet 1.46%
and RAIEd2020 1.00%), substantially surpassing the other State-Of-The-Art KT
methods.


Abstract:
  Chest X-rays are the most commonly performed diagnostic examination to detect
cardiopulmonary abnormalities. However, the presence of bony structures such as
ribs and clavicles can obscure subtle abnormalities, resulting in diagnostic
errors. This study aims to build a deep learning-based bone suppression model
that identifies and removes these occluding bony structures in frontal CXRs to
assist in reducing errors in radiological interpretation, including DL
workflows, related to detecting manifestations consistent with tuberculosis
(TB). Several bone suppression models with various deep architectures are
trained and optimized using the proposed combined loss function and their
performances are evaluated in a cross-institutional test setting. The
best-performing model is used to suppress bones in the publicly available
Shenzhen and Montgomery TB CXR collections. A VGG-16 model is pretrained on a
large collection of publicly available CXRs. The CXR-pretrained model is then
fine-tuned individually on the non-bone-suppressed and bone-suppressed CXRs of
Shenzhen and Montgomery TB CXR collections to classify them as showing normal
lungs or TB manifestations. The performances of these models are compared using
several performance metrics, analyzed for statistical significance, and their
predictions are qualitatively interpreted through class-selective relevance
maps. It is observed that the models trained on bone-suppressed CXRs
significantly outperformed (p<0.05) the models trained on the
non-bone-suppressed CXRs. Models trained on bone-suppressed CXRs improved
detection of TB-consistent findings and resulted in compact clustering of the
data points in the feature space signifying that bone suppression improved the
model sensitivity toward TB classification.


Abstract:
  We present new numerical schemes for pricing perpetual Bermudan and American
options as well as $\alpha$-quantile options. This includes a new direct
calculation of the optimal exercise barrier for early-exercise options. Our
approach is based on the Spitzer identities for general L\'evy processes and on
the Wiener-Hopf method. Our direct calculation of the price of
$\alpha$-quantile options combines for the first time the Dassios-Port-Wendel
identity and the Spitzer identities for the extrema of processes. Our results
show that the new pricing methods provide excellent error convergence with
respect to computational time when implemented with a range of L\'evy
processes.


Abstract:
  The problem of repairing linear codes and, in particular, Reed Solomon (RS)
codes has attracted a lot of attention in recent years due to their extreme
importance to distributed storage systems. In this problem, a failed code
symbol (node) needs to be repaired by downloading as little information as
possible from a subset of the remaining nodes. By now, there are examples of RS
codes that have efficient repair schemes, and some even attain the cut-set
bound. However, these schemes fall short in several aspects; they require a
considerable field extension degree. They do not provide any nontrivial repair
scheme over prime fields. Lastly, they are all linear repairs, i.e., the
computed functions are linear over the base field. Motivated by these and by a
question raised in [GW17] on the power of nonlinear repair schemes, we study
the problem of nonlinear repair schemes of RS codes.
  Our main results are the first nonlinear repair scheme of RS codes with
asymptotically optimal repair bandwidth (asymptotically matching the cut-set
bound). This is the first example of a nonlinear repair scheme of any code and
also the first example that a nonlinear repair scheme can outperform all linear
ones. Lastly, we show that the cut-set bound for RS codes is not tight over
prime fields by proving a tighter bound, using additive combinatorics ideas.


Abstract:
  The fine-grained relationship between form and function with respect to deep
neural network architecture design and hardware-specific acceleration is one
area that is not well studied in the research literature, with form often
dictated by accuracy as opposed to hardware function. In this study, a
comprehensive empirical exploration is conducted to investigate the impact of
deep neural network architecture design on the degree of inference speedup that
can be achieved via hardware-specific acceleration. More specifically, we
empirically study the impact of a variety of commonly used macro-architecture
design patterns across different architectural depths through the lens of
OpenVINO microprocessor-specific and GPU-specific acceleration. Experimental
results showed that while leveraging hardware-specific acceleration achieved an
average inference speed-up of 380%, the degree of inference speed-up varied
drastically depending on the macro-architecture design pattern, with the
greatest speedup achieved on the depthwise bottleneck convolution design
pattern at 550%. Furthermore, we conduct an in-depth exploration of the
correlation between FLOPs requirement, level 3 cache efficacy, and network
latency with increasing architectural depth and width. Finally, we analyze the
inference time reductions using hardware-specific acceleration when compared to
native deep learning frameworks across a wide variety of hand-crafted deep
convolutional neural network architecture designs as well as ones found via
neural architecture search strategies. We found that the DARTS-derived
architecture to benefit from the greatest improvement from hardware-specific
software acceleration (1200%) while the depthwise bottleneck convolution-based
MobileNet-V2 to have the lowest overall inference time of around 2.4 ms.


Abstract:
  Semantic segmentation arises as the backbone of many vision systems, spanning
from self-driving cars and robot navigation to augmented reality and
teleconferencing. Frequently operating under stringent latency constraints
within a limited resource envelope, optimising for efficient execution becomes
important. To this end, we propose a framework for converting state-of-the-art
segmentation models to MESS networks; specially trained CNNs that employ
parametrised early exits along their depth to save computation during inference
on easier samples. Designing and training such networks naively can hurt
performance. Thus, we propose a two-staged training process that pushes
semantically important features early in the network. We co-optimise the
number, placement and architecture of the attached segmentation heads, along
with the exit policy, to adapt to the device capabilities and
application-specific requirements. Optimising for speed, MESS networks can
achieve latency gains of up to 2.83x over state-of-the-art methods with no
accuracy degradation. Accordingly, optimising for accuracy, we achieve an
improvement of up to 5.33 pp, under the same computational budget.


Abstract:
  Causal Learner is a toolbox for learning causal structure and Markov blanket
(MB) from data. It integrates functions for generating simulated Bayesian
network data, a set of state-of-the-art global causal structure learning
algorithms, a set of state-of-the-art local causal structure learning
algorithms, a set of state-of-the-art MB learning algorithms, and functions for
evaluating algorithms. The data generation part of Causal Learner is written in
R, and the rest of Causal Learner is written in MATLAB. Causal Learner aims to
provide researchers and practitioners with an open-source platform for causal
learning from data and for the development and evaluation of new causal
learning algorithms. The Causal Learner project is available at
http://bigdata.ahu.edu.cn/causal-learner.


Abstract:
  A \emph{domatic} (\emph{total domatic}) \emph{$k$-coloring} of a graph $G$ is
an assignment of $k$ colors to the vertices of $G$ such that each vertex
contains vertices of all $k$ colors in its closed neighborhood (neighborhood).
The \emph{domatic} (\emph{total domatic}) \emph{number} of $G$, denoted $d(G)$
($d_t (G)$), is the maximum $k$ for which $G$ has a domatic (total domatic)
$k$-coloring. In this paper, we show that for two non-trivial graphs $G$ and
$H$, the domatic and total domatic numbers of their Cartesian product $G \cart
H$ is bounded above by $\max\{|V(G)|, |V(H)|\}$ and below by $\max\{d(G),
d(H)\}$. Both these bounds are tight for an infinite family of graphs. Further,
we show that if $H$ is bipartite, then $d_t(G \cart H)$ is bounded below by
$2\min\{d_t(G),d_t(H)\}$ and $d(G \cart H)$ is bounded below by
$2\min\{d(G),d_t(H)\}$. These bounds give easy proofs for many of the known
bounds on the domatic and total domatic numbers of hypercubes \cite{chen,zel4}
and the domination and total domination numbers of hypercubes \cite{har,joh}
and also give new bounds for Hamming graphs. We also obtain the domatic (total
domatic) number and domination (total domination) number of $n$-dimensional
torus $\mathop{\cart}\limits_{i=1}^{n} C_{k_i}$ with some suitable conditions
to each $k_i$, which turns out to be a generalization of a result due to
Gravier \cite{grav2} %[\emph{Total domination number of grid graphs}, Discrete
Appl. Math. 121 (2002) 119-128] and give easy proof of a result due to
Klav\v{z}ar and Seifter \cite{sand}.


Abstract:
  High-harmonic generation (HHG) provides short-wavelength light that is useful
for precision spectroscopy and probing ultrafast dynamics. We report efficient,
phase-coherent harmonic generation up to 9th-order (333 nm) in chirped
periodically poled lithium niobate waveguides driven by phase-stable
$\leq$12-nJ, 100 fs pulses at 3 $\mu$m with 100 MHz repetition rate. A
mid-infrared to ultraviolet-visible conversion efficiency as high as 10% is
observed, amongst an overall 23% conversion of the fundamental to all
harmonics. We verify the coherence of the harmonic frequency combs despite the
complex highly nonlinear process. Numerical simulations based on a single
broadband envelope equation with quadratic nonlinearity give estimates for the
conversion efficiency within approximately 1 order of magnitude over a wide
range of experimental parameters. From this comparison we identify a
dimensionless parameter capturing the competition between three-wave mixing and
group-velocity walk-off of the harmonics that governs the cascaded HHG physics.
These results can inform cascaded HHG in a range of different platforms.


Abstract:
  Transformer model with multi-head attention requires caching intermediate
results for efficient inference in generation tasks. However, cache brings new
memory-related costs and prevents leveraging larger batch size for faster
speed. We propose memory-efficient lossless attention (called EL-attention) to
address this issue. It avoids heavy operations for building multi-head keys and
values, cache for them is not needed. EL-attention constructs an ensemble of
attention results by expanding query while keeping key and value shared. It
produces the same result as multi-head attention with less GPU memory and
faster inference speed. We conduct extensive experiments on Transformer, BART,
and GPT-2 for summarization and question generation tasks. The results show
EL-attention speeds up existing models by 1.6x to 5.3x without accuracy loss.


Abstract:
  Nowadays, as edge devices such as smartphones become prevalent, there are
increasing demands for personalized services. However, traditional
personalization methods are not suitable for edge devices because retraining or
finetuning is needed with limited personal data. Also, a full model might be
too heavy for edge devices with limited resources. Unfortunately, model
compression methods which can handle the model complexity issue also require
the retraining phase. These multiple training phases generally need huge
computational cost during on-device learning which can be a burden to edge
devices. In this work, we propose a dynamic personalization method called
prototype-based personalized pruning (PPP). PPP considers both ends of
personalization and model efficiency. After training a network, PPP can easily
prune the network with a prototype representing the characteristics of personal
data and it performs well without retraining or finetuning. We verify the
usefulness of PPP on a couple of tasks in computer vision and Keyword spotting.


Abstract:
  Randomized algorithms have propelled advances in artificial intelligence and
represent a foundational research area in advancing AI for Science. Future
advancements in DOE Office of Science priority areas such as climate science,
astrophysics, fusion, advanced materials, combustion, and quantum computing all
require randomized algorithms for surmounting challenges of complexity,
robustness, and scalability. This report summarizes the outcomes of that
workshop, "Randomized Algorithms for Scientific Computing (RASC)," held
virtually across four days in December 2020 and January 2021.


Abstract:
  Learning from visual data opens the potential to accrue a large range of
manipulation behaviors by leveraging human demonstrations without specifying
each of them mathematically, but rather through natural task specification. In
this paper, we present Learning by Watching (LbW), an algorithmic framework for
policy learning through imitation from a single video specifying the task. The
key insights of our method are two-fold. First, since the human arms may not
have the same morphology as robot arms, our framework learns unsupervised human
to robot translation to overcome the morphology mismatch issue. Second, to
capture the details in salient regions that are crucial for learning state
representations, our model performs unsupervised keypoint detection on the
translated robot videos. The detected keypoints form a structured
representation that contains semantically meaningful information and can be
used directly for computing reward and policy learning. We evaluate the
effectiveness of our LbW framework on five robot manipulation tasks, including
reaching, pushing, sliding, coffee making, and drawer closing. Extensive
experimental evaluations demonstrate that our method performs favorably against
the state-of-the-art approaches.


Abstract:
  The interface of Pt/TiO$_2$ plays an essential role in device engineering and
chemical reactions. Here, we report the electrostatic potential distribution of
a Pt/TiO$_2$ interface by electron holography. The decrease of the
electrostatic potential exists at TiO$_2$ in the vicinity of the interface,
indicating the presence of negative charge due to electron transfer from
TiO$_2$ and Pt. The decrease of the electrostatic potential can be understood
in the difference in work functions between Pt and TiO$_2$. This study reveals
the interplay between Pt and TiO$_2$ and the usefulness of electron holography
for probing the potential in nanoscale interfaces.


Abstract:
  Receding horizon control (RHC) is a popular procedure to deal with optimal
control problems. Due to the existence of state constraints, optimization-based
RHC often suffers the notorious issue of infeasibility, which strongly shrinks
the region of controllable state. This paper proposes a generalized control
barrier function (CBF) to enlarge the feasible region of constrained RHC with
only a one-step constraint on the prediction horizon. This design can reduce
the constrained steps by penalizing the tendency to move towards the constraint
boundary. Additionally, generalized CBF is able to handle high-order equality
or inequality constraints through extending the constrained step to nonadjacent
nodes. We apply this technique on an automated vehicle control task. The
results show that compared to multi-step pointwise constraints, generalized CBF
can effectively avoid the infeasibility issue in a larger partition of the
state space, and the computing efficiency is also improved by 14%-23%.


Abstract:
  The realization of a quantum network node of matter-based qubits compatible
with telecom-band operation and large-scale quantum information processing is
an outstanding challenge that has limited the potential of elementary quantum
networks. We propose a platform for interfacing quantum processors comprising
neutral atom arrays with telecom-band photons in a multiplexed network
architecture. The use of a large atom array instead of a single atom mitigates
the deleterious effects of two-way communication and improves the entanglement
rate between two nodes by nearly two orders of magnitude. Further, this system
simultaneously provides the ability to perform high-fidelity deterministic
gates and readout within each node, opening the door to quantum repeater and
purification protocols to enhance the length and fidelity of the network,
respectively. Using intermediate nodes as quantum repeaters, we demonstrate the
feasibility of entanglement distribution over approximately 1500 km based on
realistic assumptions, providing a blueprint for a transcontinental network.
Finally, we demonstrate that our platform can distribute approximately 25 Bell
pairs over metropolitan distances, which could serve as the backbone of a
distributed fault-tolerant quantum computer.


Abstract:
  We present new constrained and free-swimming experiments and simulations of a
pair of pitching hydrofoils interacting in a minimal school. The hydrofoils
have an out-of-phase synchronization and they are varied through in-line,
staggered, and side-by-side arrangements within the two-dimensional interaction
plane. It is discovered that there is a \textit{two-dimensionally} stable
equilibrium point for a side-by-side arrangement. In fact, this arrangement is
super-stable, meaning that hydrodynamic forces will passively maintain this
arrangement even under external perturbations and the school as a whole has no
net forces acting on it, causing it to drift to one side or the other.
Moreover, previously discovered \textit{one-dimensionally} stable equilibria
driven by wake vortex interactions are shown to be, in fact, two-dimensionally
\textit{unstable}, at least for an out-of-phase synchronization. Additionally,
it is discovered that a trailing-edge vortex mechanism provides the restorative
force to stabilize a side-by-side arrangement and the stable equilibrium is
further verified for freely-swimming foils where dynamic recoil motions are
present. When constrained, the swimmers experience a collective thrust and
efficiency increase up to 100\% and 40\%, respectively, in a side-by-side
arrangement, whereas the staggered arrangements output an even higher
efficiency improvement of 87\% with a 94\% increase in thrust. For
freely-swimming foils, the recoil motion attenuates the improvements at the
stable equilibrium, showing a more modest speed and efficiency enhancement of
up to 9\% and 6\%, respectively. These newfound schooling performance and
stability characteristics suggest that fluid-mediated equilibria may play a
role in the control strategies of schooling fish and fish-inspired robots.


Abstract:
  The fermionic and bosonic zero modes of the 1D interacting Kitaev chain at
the symmetric point are unveiled. The many-body structures of the Majorana zero
modes in the topological region are given explicitly by carrying out
perturbation expansion up to infinite order. We also give the analytic
expressions of the bosonic zero modes in the topologically trivial phase. Our
results are generalized to the hybrid fermion system comprised of the
interacting Kitaev model and the Su-Schrieffer-Heeger model, in which we show
that these two types of zero modes can coexist in certain region of its phase
diagram.


Abstract:
  Parker Solar Probe (PSP) routinely observes magnetic field deflections in the
solar wind at distances less than 0.3 au from the Sun. These deflections are
related to structures commonly called 'switchbacks' (SBs), whose origins and
characteristic properties are currently debated. Here, we use a database of
visually selected SB intervals - and regions of solar wind plasma measured just
before and after each SB - to examine plasma parameters, turbulent spectra from
inertial to dissipation scales, and intermittency effects in these intervals.
We find that many features, such as perpendicular stochastic heating rates and
turbulence spectral slopes are fairly similar inside and outside of SBs.
However, important kinetic properties, such as the characteristic break scale
between the inertial to dissipation ranges differ inside and outside these
intervals, as does the level of intermittency, which is notably enhanced inside
SBs and in their close proximity, most likely due to magnetic field and
velocity shears observed at the edges. We conclude that the plasma inside and
outside of a SB, in most of the observed cases, belongs to the same stream, and
that the evolution of these structures is most likely regulated by kinetic
processes, which dominate small scale structures at the SB edges.


Abstract:
  Recently, many cooperative distributed multi-agent reinforcement learning
(MARL) algorithms have been proposed in the literature. In this work, we study
the effect of adversarial attacks on a network that employs a consensus-based
MARL algorithm. We show that an adversarial agent can persuade all the other
agents in the network to implement policies that optimize an objective that it
desires. In this sense, the standard consensus-based MARL algorithms are
fragile to attacks.


Abstract:
  The LIBOR rate is currently scheduled for discontinuation, and the
replacement advocated by regulators in the US is the Secured Overnight
Financing Rate (SOFR). The change has the potential to disrupt the $200
trillion market of derivatives and debt tied to the LIBOR. The only SOFR linked
derivative with any significant liquidity and trading history is the SOFR
futures contract, traded at the CME since 2018. We use the historical record of
futures prices to construct dynamic arbitrage-free models for the SOFR term
structure. The models allow you to construct forward-looking SOFR term rates,
imply a SOFR discounting curve and price and risk and risk manage SOFR
derivatives, not yet liquidly traded in the market. We find that a standard
three-factor Gaussian arbitrage-free Nelson-Siegel model describes term rates
very well but a shadow-rate extension is needed to describe the behaviour near
the zero-boundary. We also find that the jumps and seasonal effects observed in
SOFR, do not need to be specifically accounted for in a model for the futures
prices. Finally we study the so-called convexity correction and find that it
becomes significant beyond the 2 year maturity. For validation purposes we
demonstrate that our model aligns closely with the methodology used by the
Federal Reserve to publish indicative SOFR term rates.


Abstract:
  The accuracy of theoretical mass, radius and effective temperature values for
M-dwarf stars is an active topic of debate. Differences between observed and
theoretical values have raised the possibility that current theoretical stellar
structure and evolution models are inaccurate towards the low-mass end of the
main sequence. To explore this issue we use the CHEOPS satellite to obtain
high-precision light curves of eclipsing binaries with low mass stellar
companions. We use these light curves combined with the spectroscopic orbit for
the solar-type companion to measure the mass, radius and effective temperature
of the M-dwarf star. Here we present the analysis of three eclipsing binaries.
We use the pycheops data analysis software to fit the observed transit and
eclipse events of each system. Two of our systems were also observed by the
TESS satellite -- we similarly analyse these light curves for comparison. We
find consistent results between CHEOPS and TESS, presenting three stellar radii
and two stellar effective temperature values of low-mass stellar objects. These
initial results from our on-going observing programme with CHEOPS show that we
can expect to have ~24 new mass, radius and effective temperature measurements
for very low mass stars within the next few years.


Abstract:
  The time frame in which hydrogen reionization occurred is highly uncertain,
but can be constrained by observations of Lyman-alpha (Ly$\alpha$) emission
from distant sources. Neutral hydrogen in the intergalactic medium (IGM)
attenuates Ly$\alpha$~photons emitted by galaxies. As reionization progressed
the IGM opacity decreased, increasing Ly$\alpha$~visibility. The galaxy
Ly$\alpha$~luminosity function (LF) is thus a useful tool to constrain the
timeline of reionization. In this work, we model the Ly$\alpha$~LF as a
function of redshift, $z=5-10$, and average IGM neutral hydrogen fraction,
$\overline{x}_\textsc{hi}$. We combine the Ly$\alpha$~luminosity probability
distribution obtained from inhomogeneous reionization simulations with a model
for the UV LF to model the Ly$\alpha$~LF. As the neutral fraction increases,
the average number density of Ly$\alpha$~emitting galaxies decreases, and are
less luminous, though for $\overline{x}_\textsc{hi} \lesssim 0.4$ there is only
a small decrease of the Ly$\alpha$~LF. We use our model to infer the IGM
neutral fraction at $z=6.6, 7.0, 7.3$ from observed Ly$\alpha$~LFs. We conclude
that there is a significant increase in the neutral fraction with increasing
redshift: $\overline{x}_\textsc{hi}(z=6.6)=0.08^{+ 0.08}_{- 0.05}, \,
\overline{x}_\textsc{hi}(z=7.0)=0.28 \pm 0.05$ and
$\overline{x}_\textsc{hi}(z=7.3)=0.83^{+ 0.06}_{- 0.07}$. We predict trends in
the Ly$\alpha$~luminosity density and Schechter parameters as a function of
redshift and the neutral fraction. We find that the Ly$\alpha$~luminosity
density decreases as the universe becomes more neutral. Furthermore, as the
neutral fraction increases, the faint-end slope of the Ly$\alpha$~LF steepens,
and the characteristic Ly$\alpha$~luminosity shifts to lower values, concluding
that the evolving shape of the Ly$\alpha$~LF -- not just its integral -- is an
important tool to study reionization.


Abstract:
  We develop an energy-based finite difference method for the wave equation in
second order form. The spatial discretization satisfies a summation-by-parts
(SBP) property. With boundary conditions and material interface conditions
imposed weakly by the simultaneous-approximation-term (SAT) method, we derive
energy estimates for the semi-discretization. In addition, error estimates are
derived by the normal mode analysis. The energy-based discretization does not
use any mesh-dependent parameter, even in the presence of Dirichlet boundary
conditions and material interfaces. Furthermore, similar to upwind
discontinuous Galerkin methods, numerical dissipation can be added to the
discretization through the boundary conditions. We present numerical
experiments that verify convergence and robustness of the proposed method.


Abstract:
  State-of-the-art topological devices require the use topological surface
states to drive electronic transport. In this study, we examine a tunable
topological system, $Ge(Bi_{x}Sb_{1-x})_{2}Te_{4}$, for a range of 'x' values
from 0 to 1, using a combination of Fourier Transform Scanning Tunneling
Spectroscopy (FT-STS) and Angle-Resolved Photoemission Spectroscopy (ARPES).
Our results show that the Dirac point shifts linearly with 'x', crossing the
Fermi energy near x = 0.7. This novel observation of a smoothly tunable,
isolated Dirac point crossing through the topological transport regime and
having strong linear dependence with substitution can be critical for future
topological spintronics applications.


Abstract:
  Code summarization is the task of generating readable summaries that are
semantically meaningful and can accurately describe the presumed task of a
software. Program comprehension has become one of the most tedious tasks for
knowledge transfer. As the codebase evolves over time, the description needs to
be manually updated each time with the changes made. An automatic approach is
proposed to infer such captions based on benchmarked and custom datasets with
comparison between the original and generated results.


Abstract:
  Specific developmental and operational characteristics of machine learning
(ML) components, as well as their inherent uncertainty, demand robust
engineering principles are used to ensure their quality. We aim to determine
how software systems can be (re-) architected to enable robust integration of
ML components. Towards this goal, we conducted a mixed-methods empirical study
consisting of (i) a systematic literature review to identify the challenges and
their solutions in software architecture forML, (ii) semi-structured interviews
with practitioners to qualitatively complement the initial findings, and (iii)
a survey to quantitatively validate the challenges and their solutions. In
total, we compiled and validated twenty challenges and solutions for (re-)
architecting systems with ML components. Our results indicate, for example,
that traditional software architecture challenges (e.g., component coupling)
also play an important role when using ML components; along new ML specific
challenges (e.g., the need for continuous retraining). Moreover, the results
indicate that ML heightened decision drivers, such as privacy, play a marginal
role compared to traditional decision drivers, such as scalability or
interoperability. Using the survey, we were able to establish a link between
architectural solutions and software quality attributes; which enabled us to
provide twenty architectural tactics used for satisfying individual quality
requirements of systems with ML components. Altogether, the results can be
interpreted as an empirical framework that supports the process of (re-)
architecting software systems with ML components.


Abstract:
  In this short note, we investigate the feedback control of relativistic
dynamics propelled by mass ejection, modeling, e.g., the relativistic rocket
control or the relativistic (space-travel) flight control. As an extreme case,
we also examine the control of relativistic photon rockets which are propelled
by ejecting photons.


Abstract:
  Using M-theory in physics, Cho, Gang, and Kim (JHEP 2020, 115 (2020) )
recently outlined a program that connects two parallel subjects of three
dimensional manifolds, namely, geometric topology and quantum topology. They
suggest that classical topological invariants such as Chern-Simons invariants
of $\text{SL}(2,\mathbb{C})$-flat connections and adjoint Reidemeister torsions
of a three manifold can be packaged together to produce a $(2+1)$-topological
quantum field theory, which is essentially equivalent to a modular tensor
category. It is further conjectured that every modular tensor category can be
obtained from a three manifold and a semi-simple Lie group. In this paper, we
study this program mathematically, and provide strong support for the
feasibility of such a program. The program produces an algorithm to generate
the potential modular $T$-matrix and the quantum dimensions of a candidate
modular data. The modular $S$-matrix follows from essentially a trial-and-error
procedure. We find modular tensor categories that realize candidate modular
data constructed from Seifert fibered spaces and torus bundles over the circle
that reveal many subtleties in the program. We make a number of improvements to
the program based on our computations. Our main result is a mathematical
construction of a premodular category from each Seifert fibered space with
three singular fibers and a family of torus bundles over the circle with
Thurston SOL geometry. The premodular categories from Seifert fibered spaces
are related to Temperley-Lieb-Jones categories and the ones from torus bundles
over the circle are related to metaplectic categories. We conjecture that a
resulting premodular category is modular if and only if the three manifold is a
$\mathbb{Z}_2$-homology sphere and condensation of bosons in premodular
categories leads to either modular or super-modular categories.


Abstract:
  Inferring 3D structure of a generic object from a 2D image is a long-standing
objective of computer vision. Conventional approaches either learn completely
from CAD-generated synthetic data, which have difficulty in inference from real
images, or generate 2.5D depth image via intrinsic decomposition, which is
limited compared to the full 3D reconstruction. One fundamental challenge lies
in how to leverage numerous real 2D images without any 3D ground truth. To
address this issue, we take an alternative approach with semi-supervised
learning. That is, for a 2D image of a generic object, we decompose it into
latent representations of category, shape and albedo, lighting and camera
projection matrix, decode the representations to segmented 3D shape and albedo
respectively, and fuse these components to render an image well approximating
the input image. Using a category-adaptive 3D joint occupancy field (JOF), we
show that the complete shape and albedo modeling enables us to leverage real 2D
images in both modeling and model fitting. The effectiveness of our approach is
demonstrated through superior 3D reconstruction from a single image, being
either synthetic or real, and shape segmentation.


Abstract:
  In this paper, we provide a generalized framework for Variational
Inference-Stochastic Optimal Control by using thenon-extensive Tsallis
divergence. By incorporating the deformed exponential function into the
optimality likelihood function, a novel Tsallis Variational Inference-Model
Predictive Control algorithm is derived, which includes prior works such as
Variational Inference-Model Predictive Control, Model Predictive PathIntegral
Control, Cross Entropy Method, and Stein VariationalInference Model Predictive
Control as special cases. The proposed algorithm allows for effective control
of the cost/reward transform and is characterized by superior performance in
terms of mean and variance reduction of the associated cost. The aforementioned
features are supported by a theoretical and numerical analysis on the level of
risk sensitivity of the proposed algorithm as well as simulation experiments on
5 different robotic systems with 3 different policy parameterizations.


Abstract:
  The Gleason scoring system is the primary diagnostic and prognostic tool for
prostate cancer. In recent years, with the development of digitisation devices,
the use of computer vision techniques for the analysis of biopsies has
increased. However, to the best of the authors' knowledge, the development of
algorithms to automatically detect individual cribriform patterns belonging to
Gleason grade 4 has not yet been studied in the literature. The objective of
the work presented in this paper is to develop a deep-learning-based system
able to support pathologists in the daily analysis of prostate biopsies. The
methodological core of this work is a patch-wise predictive model based on
convolutional neural networks able to determine the presence of cancerous
patterns. In particular, we train from scratch a simple self-design
architecture. The cribriform pattern is detected by retraining the set of
filters of the last convolutional layer in the network. From the reconstructed
prediction map, we compute the percentage of each Gleason grade in the tissue
to feed a multi-layer perceptron which provides a biopsy-level score.mIn our
SICAPv2 database, composed of 182 annotated whole slide images, we obtained a
Cohen's quadratic kappa of 0.77 in the test set for the patch-level Gleason
grading with the proposed architecture trained from scratch. Our results
outperform previous ones reported in the literature. Furthermore, this model
reaches the level of fine-tuned state-of-the-art architectures in a
patient-based four groups cross validation. In the cribriform pattern detection
task, we obtained an area under ROC curve of 0.82. Regarding the biopsy Gleason
scoring, we achieved a quadratic Cohen's Kappa of 0.81 in the test subset.
Shallow CNN architectures trained from scratch outperform current
state-of-the-art methods for Gleason grades classification.


Abstract:
  Reconfigurable intelligent surfaces (RISs), which enable tunable anomalous
reflection, have appeared as a promising method to enhance wireless systems. In
this paper, we propose to use an RIS as a spatial equalizer to address the
well-known multi-path fading phenomenon. By introducing some controllable paths
artificially against the multi-path fading through the RIS, we can perform
equalization during the transmission process instead of at the receiver, and
thus all the users can share the same equalizer. Unlike the beamforming
application of the RIS, which aims to maximize the received energy at
receivers, the objective of the equalization application is to reduce the
inter-symbol interference (ISI), which makes phase shifts at the RIS different.
To this end, we formulate the phase shift optimization problem and propose an
iterative algorithm to solve it. Simulation results show that the multi-path
fading effect can be eliminated effectively compared to benchmark schemes.


Abstract:
  The famous theorem of Conway and Coxeter on frieze patterns gave a geometric
interpretation to integral friezes via triangulations of polygons. In this
article, we review this result and show some of the development it has led to.
The last decade has seen a lot of activities on friezes. One reason behind this
is the connection to cluster combinatorics.


Abstract:
  Many AI-related tasks involve the interactions of data in multiple
modalities. It has been a new trend to merge multi-modal information into
knowledge graph(KG), resulting in multi-modal knowledge graphs (MMKG). However,
MMKGs usually suffer from low coverage and incompleteness. To mitigate this
problem, a viable approach is to integrate complementary knowledge from other
MMKGs. To this end, although existing entity alignment approaches could be
adopted, they operate in the Euclidean space, and the resulting Euclidean
entity representations can lead to large distortion of KG's hierarchical
structure. Besides, the visual information has yet not been well exploited. In
response to these issues, in this work, we propose a novel multi-modal entity
alignment approach, Hyperbolic multi-modal entity alignment(HMEA), which
extends the Euclidean representation to hyperboloid manifold. We first adopt
the Hyperbolic Graph Convolutional Networks (HGCNs) to learn structural
representations of entities. Regarding the visual information, we generate
image embeddings using the densenet model, which are also projected into the
hyperbolic space using HGCNs. Finally, we combine the structure and visual
representations in the hyperbolic space and use the aggregated embeddings to
predict potential alignment results. Extensive experiments and ablation studies
demonstrate the effectiveness of our proposed model and its components.


Abstract:
  Using the framework of operator or Calder\'on preconditioning, uniform
preconditioners are constructed for elliptic operators discretized with
continuous finite (or boundary) elements. The preconditioners are constructed
as the composition of an opposite order operator, discretized on the same
ansatz space, and two diagonal scaling operators.


Abstract:
  Adopting the pullback formalism, a new linear connection in Finsler geometry
has been introduced and investigated. Such connection unifies all formerly
known Finsler connections and some other connections not introduced so far.
Also, our connection is a Finslerian version of the Tripathi connection
introduced in Riemannian geometry. The existence and uniqueness of such
connection is proved intrinsically. An explicit intrinsic expression relating
this connection to Cartan connection is obtained. Some generalized Finsler
connections are constructed from Tripathi Finsler connection, by applying the
P1-process and C-process introduced by Matsumoto. Finally, under certain
conditions, many special Finsler connections are given.


Abstract:
  We look for ground state solutions to the Schr\"odinger-type system \[
\begin{cases} -\Delta u_j + \lambda_j u_j = \partial_jF(u)\\ \int_{\rn} u_j^2
\, dx = a_j^2\\ (\lambda_j,u_j) \in \mathbb{R} \times H^1(\mathbb{R}^N)
\end{cases} j \in \{1,\dots,M\} \] with $N,M\ge1$, where $a=(a_1,\dots,a_M) \in
]0,\infty[^M$ is prescribed and $(\lambda,u) =
(\lambda_1,\dots,\lambda_M,u_1,\dots u_M)$ is the unknown. We provide generic
assumptions about the nonlinearity $F$ which correspond to the
$L^2$-subcritical and $L^2$-critical cases, i.e., when the energy is bounded
from below for all or some values of $a$. Making use of a recent idea, we
minimize the energy over the constraint $\Set{\left|u_j\right|_{L^2}\le a_j
\text{ for all } j}$ and then provide further assumptions that ensure
$|u_j|_{L^2}=a_j$.


Abstract:
  Domain-specific dialogue systems generally determine user intents by relying
on sentence-level classifiers which mainly focus on single action sentences.
Such classifiers are not designed to effectively handle complex queries
composed of conditional and sequential clauses that represent multiple actions.
We attempt to decompose such queries into smaller single-action sub-queries
that are reasonable for intent classifiers to understand in a dialogue
pipeline. We release CANDLE (Conditional & AND type Expressions), a dataset
consisting of 3124 utterances manually tagged with conditional and sequential
labels and demonstrates this decomposition by training two baseline taggers.


Abstract:
  The mechanochemistry of ring-opening reactions of cyclopropane derivatives
turns out to be unexpectedly rich and puzzling. After showing that a rare
so-called uphill bifurcation in the case of trans-gem-difluorocyclopropane
turns into a downhill bifurcation upon substitution of fluorine by chlorine,
bromine and iodine in the thermal activation limit, the dichloro derivative is
studied systematically in the realm of mechanochemical activation. Detailed
exploration of the force-transformed potential energy surface of
trans-gem-dichlorocyclopropane in terms of Dijkstra path analysis unveils a
hitherto unknown topological catastrophe where the global shape of the energy
landscape is fundamentally changed. From thermal activation up to moderately
large forces, it is an uphill bifurcation that decides about dis-versus
conrotatory ring-opening followed by separate transition states along both
pathways. Above a critical force, the two distinct transition states merge to
yield a single transition state such that the decision about the dis-versus
conrotatory ring-opening process is taken at a newly established downhill
bifurcation. The discovery of a force-induced qualitative change of the
topology of a reaction network vastly transcends the previous understanding of
the ring-opening reaction of this species. It would be astonishing to not
discover a wealth of such catastrophes for mechanochemically activated
reactions which will greatly extend the known opportunities to manipulate
chemical reaction networks.


Abstract:
  Prototype learning is extensively used for few-shot segmentation. Typically,
a single prototype is obtained from the support feature by averaging the global
object information. However, using one prototype to represent all the
information may lead to ambiguities. In this paper, we propose two novel
modules, named superpixel-guided clustering (SGC) and guided prototype
allocation (GPA), for multiple prototype extraction and allocation.
Specifically, SGC is a parameter-free and training-free approach, which
extracts more representative prototypes by aggregating similar feature vectors,
while GPA is able to select matched prototypes to provide more accurate
guidance. By integrating the SGC and GPA together, we propose the Adaptive
Superpixel-guided Network (ASGNet), which is a lightweight model and adapts to
object scale and shape variation. In addition, our network can easily
generalize to k-shot segmentation with substantial improvement and no
additional computational cost. In particular, our evaluations on COCO
demonstrate that ASGNet surpasses the state-of-the-art method by 5% in 5-shot
segmentation.


Abstract:
  Mean-variance portfolio optimization problems often involve separable
nonconvex terms, including penalties on capital gains, integer share
constraints, and minimum position and trade sizes. We propose a heuristic
algorithm for this problem based on the alternating direction method of
multipliers (ADMM). This method allows for solve times in tens to hundreds of
milliseconds with around 1000 securities and 100 risk factors. We also obtain a
bound on the achievable performance. Our heuristic and bound are both derived
from similar results for other optimization problems with a separable objective
and affine equality constraints. We discuss a concrete implementation in the
case where the separable terms in the objective are piecewise-quadratic, and we
demonstrate their effectiveness empirically in realistic tax-aware portfolio
construction problems.


Abstract:
  The vast quantity of strong galaxy-galaxy gravitational lenses expected by
future large-scale surveys necessitates the development of automated methods to
efficiently model their mass profiles. For this purpose, we train an
approximate Bayesian convolutional neural network (CNN) to predict mass profile
parameters and associated uncertainties, and compare its accuracy to that of
conventional parametric modelling for a range of increasingly complex lensing
systems. These include standard smooth parametric density profiles,
hydrodynamical EAGLE galaxies and the inclusion of foreground mass structures,
combined with parametric sources and sources extracted from the Hubble Ultra
Deep Field. In addition, we also present a method for combining the CNN with
traditional parametric density profile fitting in an automated fashion, where
the CNN provides initial priors on the latter's parameters. On average, the CNN
achieved errors 19 $\pm$ 22 per cent lower than the traditional method's blind
modelling. The combination method instead achieved 27 $\pm$ 11 per cent lower
errors over the blind modelling, reduced further to 37 $\pm$ 11 per cent when
the priors also incorporated the CNN-predicted uncertainties, with errors also
17 $\pm$ 21 per cent lower than the CNN by itself. While the CNN is undoubtedly
the fastest modelling method, the combination of the two increases the speed of
conventional fitting alone by factors of 1.73 and 1.19 with and without
CNN-predicted uncertainties, respectively. This, combined with greatly improved
accuracy, highlights the benefits one can obtain through combining neural
networks with conventional techniques in order to achieve an efficient
automated modelling approach.


Abstract:
  In this paper, a 3D-RegNet-based neural network is proposed for diagnosing
the physical condition of patients with coronavirus (Covid-19) infection. In
the application of clinical medicine, lung CT images are utilized by
practitioners to determine whether a patient is infected with coronavirus.
However, there are some laybacks can be considered regarding to this diagnostic
method, such as time consuming and low accuracy. As a relatively large organ of
human body, important spatial features would be lost if the lungs were
diagnosed utilizing two dimensional slice image. Therefore, in this paper, a
deep learning model with 3D image was designed. The 3D image as input data was
comprised of two-dimensional pulmonary image sequence and from which relevant
coronavirus infection 3D features were extracted and classified. The results
show that the test set of the 3D model, the result: f1 score of 0.8379 and AUC
value of 0.8807 have been achieved.


Abstract:
  We study the motion of dispersed nanoprobes in entangled active-passive
polymer mixtures. By comparing the two architectures of linear vs.
unconcatenated and unknotted circular polymers, we demonstrate that novel, rich
physics emerge. For both polymer architectures, nanoprobes of size smaller than
the entanglement threshold of the solution move faster as activity is increased
and more energy is pumped in the system. For larger nanoprobes, a surprising
phenomenon occurs: while in linear solutions they move qualitatively as before,
in active-passive ring solutions nanoprobes {\it decelerate} with respect to
the purely passive conditions. We rationalize this effect in terms of the
non-equilibrium, topology-dependent association (clustering) of nanoprobes to
the cold component of the ring mixture reminiscent of the recently discovered
[Weber et al., Phys. Rev. Lett. 116, 058301 (2016)] phase separation in scalar
active-passive mixtures. We conclude with a potential connection to the
microrheology of the chromatin in the nuclei of the cells.


Abstract:
  In this paper, we propose a lightweight yet powerful dynamic epistemic logic
that captures not only the distinction between de dicto and de re knowledge but
also the distinction between de dicto and de re updates. The logic is based on
the dynamified version of an epistemic language extended with the assignment
operator borrowed from dynamic logic, following the work of Wang and Seligman
(Proc. AiML 2018). We obtain complete axiomatizations for the counterparts of
public announcement logic and event-model-based DEL based on new reduction
axioms taking care of the interactions between dynamics and assignments.


Abstract:
  Emotional voice conversion models adapt the emotion in speech without
changing the speaker identity or linguistic content. They are less data hungry
than text-to-speech models and allow to generate large amounts of emotional
data for downstream tasks. In this work we propose EmoCat, a language-agnostic
emotional voice conversion model. It achieves high-quality emotion conversion
in German with less than 45 minutes of German emotional recordings by
exploiting large amounts of emotional data in US English. EmoCat is an
encoder-decoder model based on CopyCat, a voice conversion system which
transfers prosody. We use adversarial training to remove emotion leakage from
the encoder to the decoder. The adversarial training is improved by a novel
contribution to gradient reversal to truly reverse gradients. This allows to
remove only the leaking information and to converge to better optima with
higher conversion performance. Evaluations show that Emocat can convert to
different emotions but misses on emotion intensity compared to the recordings,
especially for very expressive emotions. EmoCat is able to achieve audio
quality on par with the recordings for five out of six tested emotion
intensities.


Abstract:
  Dust grains play a central role in the physics and chemistry of cosmic
environments. They influence the optical and thermal properties of the medium
due to their interaction with stellar radiation; provide surfaces for the
chemical reactions that are responsible for the synthesis of a significant
fraction of key astronomical molecules; and they are building blocks of
pebbles, comets, asteroids, planetesimals, and planets. In this paper, we
review experimental studies of physical and chemical processes, such as
adsorption, desorption, diffusion, and reactions forming molecules, on the
surface of reliable cosmic dust grain analogues as related to processes in
diffuse, translucent, and dense interstellar clouds, protostellar envelopes,
planet-forming disks, and planetary atmospheres. The information that such
experiments reveal should be flexible enough to be used in many different
environments. In addition, we provide a forward look discussing new ideas,
experimental approaches, and research directions.