Abstract:
  King 2, one of the oldest clusters in the Milky Way, with an age of $\sim$ 6
Gyr and distance of $\sim 5700$ pc, has been observed with UVIT payload on the
\textit{ASTROSAT}. With membership information derived from {\it Gaia} EDR3,
the cluster is found to have 39 blue straggler stars (BSSs). We created
multi-wavelength spectra-energy distributions (SED) of all the BSSs. Out of 10
UV detected BSSs, 6 bright ones fitted with double component SEDs and were
found to have hotter companions with properties similar to extreme horizontal
branch (EHB)/subdwarf B (sdB) stars, with a range in luminosity and
temperature, suggesting a diversity among the hot companions. We suggest that
at least 15\% of BSSs in this cluster are formed via mass-transfer pathway.
When we compared their properties to EHBs and hotter companions to BSS in open
and globular clusters, we suggest that EHB/sdBs like companions can form in
binaries of open clusters as young as 6 Gyr.


Abstract:
  The ability to quickly learn from a small quantity oftraining data widens the
range of machine learning applications. In this paper, we propose a
data-efficient image captioning model, VisualGPT, which leverages the
linguistic knowledge from a large pretrained language model(LM). A crucial
challenge is to balance between the use of visual information in the image and
prior linguistic knowledge acquired from pretraining. We designed a novel
self-resurrecting encoder-decoder attention mechanism to quickly adapt the
pretrained LM as the language decoder ona small amount of in-domain training
data. The proposed self-resurrecting activation unit produces sparse
activations but has reduced susceptibility to zero gradients. We train the
proposed model, VisualGPT, on 0.1%, 0.5% and 1% of MSCOCO and Conceptual
Captions training data. Under these conditions, we outperform the best baseline
model by up to 10.8% CIDEr on MS COCO and upto 5.4% CIDEr on Conceptual
Captions. Further, Visual-GPT achieves the state-of-the-art result on IU X-ray,
a medical report generation dataset. To the best of our knowledge, this is the
first work that improves data efficiency of image captioning by utilizing LM
pretrained on unimodal data. Our code is available at:
https://github.com/Vision-CAIR/VisualGPT.


Abstract:
  This paper proposes a novel integrated dynamic method based on Behavior Trees
for planning and allocating tasks in mixed human robot teams, suitable for
manufacturing environments. The Behavior Tree formulation allows encoding a
single job as a compound of different tasks with temporal and logic
constraints. In this way, instead of the well-studied offline centralized
optimization problem, the role allocation problem is solved with multiple
simplified online optimization sub-problem, without complex and cross-schedule
task dependencies. These sub-problems are defined as Mixed-Integer Linear
Programs, that, according to the worker-actions related costs and the workers'
availability, allocate the yet-to-execute tasks among the available workers. To
characterize the behavior of the developed method, we opted to perform
different simulation experiments in which the results of the action-worker
allocation and computational complexity are evaluated. The obtained results,
due to the nature of the algorithm and to the possibility of simulating the
agents' behavior, should describe well also how the algorithm performs in real
experiments.


Abstract:
  Previous works on neural text-to-speech (TTS) have been addressed on limited
speed in training and inference time, robustness for difficult synthesis
conditions, expressiveness, and controllability. Although several approaches
resolve some limitations, there has been no attempt to solve all weaknesses at
once. In this paper, we propose STYLER, an expressive and controllable TTS
framework with high-speed and robust synthesis. Our novel audio-text aligning
method called Mel Calibrator and excluding autoregressive decoding enable rapid
training and inference and robust synthesis on unseen data. Also, disentangled
style factor modeling under supervision enlarges the controllability in
synthesizing process leading to expressive TTS. On top of it, a novel noise
modeling pipeline using domain adversarial training and Residual Decoding
empowers noise-robust style transfer, decomposing the noise without any
additional label. Various experiments demonstrate that STYLER is more effective
in speed and robustness than expressive TTS with autoregressive decoding and
more expressive and controllable than reading style non-autoregressive TTS.
Synthesis samples and experiment results are provided via our demo page, and
code is available publicly.


Abstract:
  Faces play a central role in the combinatorial and computational aspects of
polyhedra. In this paper, we present the first formalization of faces of
polyhedra in the proof assistant Coq. This builds on the formalization of a
library providing the basic constructions and operations over polyhedra,
including projections, convex hulls and images under linear maps. Moreover, we
design a special mechanism which automatically introduces an appropriate
representation of a polyhedron or a face, depending on the context of the
proof. We demonstrate the usability of this approach by establishing some of
the most important combinatorial properties of faces, namely that they
constitute a family of graded atomistic and coatomistic lattices closed under
interval sublattices. We also prove a theorem due to Balinski on the
$d$-connectedness of the adjacency graph of polytopes of dimension $d$.


Abstract:
  The success of Neural Machine Translation (NMT) largely depends on the
availability of large bitext training corpora. Due to the lack of such large
corpora in low-resource language pairs, NMT systems often exhibit poor
performance. Extra relevant monolingual data often helps, but acquiring it
could be quite expensive, especially for low-resource languages. Moreover,
domain mismatch between bitext (train/test) and monolingual data might degrade
the performance. To alleviate such issues, we propose AUGVIC, a novel data
augmentation framework for low-resource NMT which exploits the vicinal samples
of the given bitext without using any extra monolingual data explicitly. It can
diversify the in-domain bitext data with finer level control. Through extensive
experiments on four low-resource language pairs comprising data from different
domains, we have shown that our method is comparable to the traditional
back-translation that uses extra in-domain monolingual data. When we combine
the synthetic parallel data generated from AUGVIC with the ones from the extra
monolingual data, we achieve further improvements. We show that AUGVIC helps to
attenuate the discrepancies between relevant and distant-domain monolingual
data in traditional back-translation. To understand the contributions of
different components of AUGVIC, we perform an in-depth framework analysis.


Abstract:
  A recent development in the theory of fractional differential equations with
variable coefficients has been a method for obtaining an exact solution in the
form of an infinite series involving nested fractional integral operators. This
solution representation is constructive but difficult to calculate in practice.
Here we show a new representation of the solution function, as a convergent
series of single fractional integrals, which will be easier to use for
computational work and applications. In the particular case of constant
coefficients, the solution is given in terms of the Mittag-Leffler function. We
also show some applications in Cauchy problems for partial differential
equations involving both time-fractional and space-fractional operators and
with time-dependent coefficients.


Abstract:
  The purpose of this paper is to study the incompressible non-resistive MHD
equations in $\mathbb{R}^3$. We establish the global well-posedness of
classical solutions if the initial data is axially symmetric and the swirl
components of the velocity and magnetic vorticity vanish. In particular, the
special axially symmetric initial data can be arbitrarily large and satisfy low
regularity assumptions.


Abstract:
  In the presence of interactions the frequency of a simple harmonic oscillator
deviates from the noninteracting one. Various methods can be used to compute
the changes to the frequency perturbatively. Some of them resemble the methods
used in quantum field theory where the bare values of the parameters of the
theory run when an interaction is added. In this paper, we review some of these
techniques and introduce some new ones in line with quantum field theory
methods. Moreover, we investigate the case of more than one oscillator to see
how the frequencies of small oscillations change when non-linear terms are
added to a linear system and observe an interesting beat phenomenon in
degenerate coupled oscillatory systems.


Abstract:
  Scene understanding requires the extraction and representation of scene
components together with their properties and inter-relations. We describe a
model in which meaningful scene structures are extracted from the image by an
iterative process, combining bottom-up (BU) and top-down (TD) networks,
interacting through a symmetric bi-directional communication between them
(counter-streams structure). The model constructs a scene representation by the
iterative use of three components. The first model component is a BU stream
that extracts selected scene elements, properties and relations. The second
component (cognitive augmentation) augments the extracted visual representation
based on relevant non-visual stored representations. It also provides input to
the third component, the TD stream, in the form of a TD instruction,
instructing the model what task to perform next. The TD stream then guides the
BU visual stream to perform the selected task in the next cycle. During this
process, the visual representations extracted from the image can be combined
with relevant non-visual representations, so that the final scene
representation is based on both visual information extracted from the scene and
relevant stored knowledge of the world. We describe how a sequence of
TD-instructions is used to extract from the scene structures of interest,
including an algorithm to automatically select the next TD-instruction in the
sequence. The extraction process is shown to have favorable properties in terms
of combinatorial generalization, generalizing well to novel scene structures
and new combinations of objects, properties and relations not seen during
training. Finally, we compare the model with relevant aspects of the human
vision, and suggest directions for using the BU-TD scheme for integrating
visual and cognitive components in the process of scene understanding.


Abstract:
  High dynamic range (HDR) video reconstruction from sequences captured with
alternating exposures is a very challenging problem. Existing methods often
align low dynamic range (LDR) input sequence in the image space using optical
flow, and then merge the aligned images to produce HDR output. However,
accurate alignment and fusion in the image space are difficult due to the
missing details in the over-exposed regions and noise in the under-exposed
regions, resulting in unpleasing ghosting artifacts. To enable more accurate
alignment and HDR fusion, we introduce a coarse-to-fine deep learning framework
for HDR video reconstruction. Firstly, we perform coarse alignment and pixel
blending in the image space to estimate the coarse HDR video. Secondly, we
conduct more sophisticated alignment and temporal fusion in the feature space
of the coarse HDR video to produce better reconstruction. Considering the fact
that there is no publicly available dataset for quantitative and comprehensive
evaluation of HDR video reconstruction methods, we collect such a benchmark
dataset, which contains $97$ sequences of static scenes and 184 testing pairs
of dynamic scenes. Extensive experiments show that our method outperforms
previous state-of-the-art methods. Our dataset, code and model will be made
publicly available.


Abstract:
  Motivated by sequential budgeted allocation problems, we investigate online
matching problems where connections between vertices are not i.i.d., but they
have fixed degree distributions -- the so-called configuration model. We
estimate the competitive ratio of the simplest algorithm, GREEDY, by
approximating some relevant stochastic discrete processes by their continuous
counterparts, that are solutions of an explicit system of partial differential
equations. This technique gives precise bounds on the estimation errors, with
arbitrarily high probability as the problem size increases. In particular, it
allows the formal comparison between different configuration models. We also
prove that, quite surprisingly, GREEDY can have better performance guarantees
than RANKING, another celebrated algorithm for online matching that usually
outperforms the former.


Abstract:
  For a compact Hausdorff space $K$, we give descriptions of the dual of
$C(K)^\delta$, the Dedekind completion of the Banach lattice $C(K)$ of
continuous, real-valued functions on $K$. We characterize those functionals
which are $\sigma$-order continuous and order continuous, respectively, in
terms of Oxtoby's category measures. This leads to a purely topological
characterization of hyper-Stonean spaces.


Abstract:
  In gravitational-wave data analysis, we regularly work with a host of
non-trivial prior probabilities on compact binary masses, redshifts, and spins.
We must regularly manipulate these priors, computing the implied priors on a
transformed basis of parameters or reweighting posterior samples from one prior
to another. Here, I detail some common manipulations, presenting a table of
Jacobians with which to transform priors between mass parametrizations,
describing the conversion between source- and detector-frame priors, and
deriving analytic expressions for priors on the "effective spin" parameters
regularly invoked in gravitational-wave astronomy.


Abstract:
  In recent years, the need for neutral benchmark studies that focus on the
comparison of methods from computational sciences has been increasingly
recognised by the scientific community. While general advice on the design and
analysis of neutral benchmark studies can be found in recent literature,
certain amounts of flexibility always exist. This includes the choice of data
sets and performance measures, the handling of missing performance values and
the way the performance values are aggregated over the data sets. As a
consequence of this flexibility, researchers may be concerned about how their
choices affect the results or, in the worst case, may be tempted to engage in
questionable research practices (e.g. the selective reporting of results or the
post-hoc modification of design or analysis components) to fit their
expectations or hopes. To raise awareness for this issue, we use an example
benchmark study to illustrate how variable benchmark results can be when all
possible combinations of a range of design and analysis options are considered.
We then demonstrate how the impact of each choice on the results can be
assessed using multidimensional unfolding. In conclusion, based on previous
literature and on our illustrative example, we claim that the multiplicity of
design and analysis options combined with questionable research practices lead
to biased interpretations of benchmark results and to over-optimistic
conclusions. This issue should be considered by computational researchers when
designing and analysing their benchmark studies and by the scientific community
in general in an effort towards more reliable benchmark results.


Abstract:
  Knowledge about entities and their interrelations is a crucial factor of
success for tasks like question answering or text summarization. Publicly
available knowledge graphs like Wikidata or DBpedia are, however, far from
being complete. In this paper, we explore how information extracted from
similar entities that co-occur in structures like tables or lists can help to
increase the coverage of such knowledge graphs. In contrast to existing
approaches, we do not focus on relationships within a listing (e.g., between
two entities in a table row) but on the relationship between a listing's
subject entities and the context of the listing. To that end, we propose a
descriptive rule mining approach that uses distant supervision to derive rules
for these relationships based on a listing's context. Extracted from a suitable
data corpus, the rules can be used to extend a knowledge graph with novel
entities and assertions. In our experiments we demonstrate that the approach is
able to extract up to 3M novel entities and 30M additional assertions from
listings in Wikipedia. We find that the extracted information is of high
quality and thus suitable to extend Wikipedia-based knowledge graphs like
DBpedia, YAGO, and CaLiGraph. For the case of DBpedia, this would result in an
increase of covered entities by roughly 50%.


Abstract:
  In this paper, we study how to fairly allocate m indivisible chores to n
(asymmetric) agents. We consider (weighted) proportionality up to any item
(PROPX) and show that a (weighted) PROPX allocation always exists and can be
computed efficiently. For chores, we argue that PROPX might be a more reliable
relaxation for proportionality by the facts that any PROPX allocation ensures
2-approximation of maximin share (MMS) fairness [Budish, 2011] for symmetric
agents and of anyprice share (APS) fairness [Babaioff et al, 2021] for
asymmetric agents. APS allocations for chores have not been studied before the
current work, and our result implies a 2-approximation algorithm. Another
by-product result is that an EFX and a weighted EF1 allocation for indivisible
chores exist if all agents have the same ordinal preference, which might be of
independent interest. We then consider the partial information setting and
design algorithms that only use agents' ordinal preferences to compute
approximately PROPX allocations. Our algorithm achieves a 2-approximation for
both symmetric and asymmetric agents, and the approximation ratio is optimal.
Finally, we study the price of fairness (PoF), i.e., the loss in social welfare
by enforcing allocations to be (weighted) PROPX. We prove that the tight ratio
for PoF is Theta(n) for symmetric agents and unbounded for asymmetric agents.


Abstract:
  In order to define graph transformations by the simultaneous application of
concurrent rules, we have adopted in previous work a structure of attributed
graphs stable by unions. We analyze the consequences on parallel independence,
a property that characterizes the possibility to resort to sequential
rewriting. This property turns out to depend not only on the left-hand side of
rules, as in algebraic approaches to graph rewriting, but also on their
right-hand side. It is then shown that, of three possible definitions of
parallel rewriting, only one is convenient in the light of parallel
independence.


Abstract:
  We present a linearly arrayed, 16-element, superconducting nanowire
single-photon detector with 83.4$\%$ system detection efficiency at 1550 nm and
a mean per-element dead-time of 9.6-ns, enabling counting at 1 giga-count per
second with $>50\%$ System Detection Efficiency. This device was designed and
fabricated in an existing scalable commercial process.


Abstract:
  Widespread uptake of COVID-19 vaccines is necessary to achieve herd immunity.
However, surveys have found concerning numbers of U.S. adults hesitant or
unwilling to be vaccinated. Online misinformation may play an important role in
vaccine hesitancy, but we lack a clear picture of the extent to which it will
impact vaccination uptake. Here, we study how vaccination rates and vaccine
hesitancy are associated with levels of online misinformation about vaccines
shared by 1.6 million Twitter users geolocated at the U.S. state and county
levels. We find a negative relationship between misinformation and vaccination
uptake rates. Online misinformation is also correlated with vaccine hesitancy
rates taken from survey data. Associations between vaccine outcomes and
misinformation remain significant when accounting for political as well as
demographic and socioeconomic factors. While vaccine hesitancy is strongly
associated with Republican vote share, we observe that the effect of online
misinformation on hesitancy is strongest across Democratic rather than
Republican counties. These results suggest that addressing online
misinformation must be a key component of interventions aimed to maximize the
effectiveness of vaccination campaigns.


Abstract:
  For a graph $G$ the imbalance of an edge $uv$ of $G$ is
$|deg_G(u)-deg_G(v)|$. Irregularity of a graph $G$ is defined as the sum of
imbalances over all edges of $G$. In this paper we consider expansions and Pell
graphs. If $H$ is an expansion of $G$ with respect to the sets $V_1$ and $V_2$,
we express the irregularity of $H$ using $G, V_1$ and $V_2$. For Pell graphs
the imbalance of their edges is studied. The number of edges of a Pell graph
with a fixed imbalance $k$ is expressed. Using these results the irregularity
and the $\sigma$-index of Pell graphs are given.


Abstract:
  Gravitational waves from the distant sources are gravitationally lensed
during their propagation through the intervening matter inhomogeneities before
arriving at detectors. It has been proposed in the literature that the variance
of the lensed waveform can be used to extract information of the matter power
spectrum at very small scales and of low-mass dark halos. In this paper, we
show that the variance of the amplitude fluctuation and that of the phase
fluctuation of the lensed waveform obey a simple relation irrespective of the
shape of the matter power spectrum. We study conditions under which this
relation can be violated and discuss some potential applications of the
relation. This relation may be used to confirm the robustness of claimed
observations of gravitational lensing of gravitational waves and the subsequent
reconstruction of the matter power spectrum.


Abstract:
  Strain induced by a magnetic field is a common phenomenon for ferromagnets,
but few antiferromagnets show large strain induced by a magnetic field. On the
basis of linear strain measurements of sintered samples of triangular
antiferromagnet ACrS2 (A = Cu, Ag, and Au) in magnetic fields up to 9 T, the
AgCrS2 sample was found to show a large strain, yielding a large volume change
over 700 ppm, which is one of the largest volume changes measured to date for
an antiferromagnet. This large strain appeared only at the N\'eel temperature
of 42 K and was not restored to its initial state when the applied magnetic
field was decreased to zero; however, it was initialized by cooling the sample
to far below the N\'eel temperature. These results suggest that the coexistence
of magnetically ordered and paramagnetic phases at the first-order phase
transition plays an important role. AuCrS2 showed a magnetic-field-induced
strain with similar features, although it was smaller than that in AgCrS2.


Abstract:
  Given a theory containing both heavy and light fields (the UV theory), a
standard procedure is to integrate out the heavy field to obtain an effective
field theory (EFT) for the light fields. Typically the EFT equations of motion
consist of an expansion involving higher and higher derivatives of the fields,
whose truncation at any finite order may not be well-posed. In this paper we
address the question of how to make sense of the EFT equations of motion, and
whether they provide a good approximation to the classical UV theory. We
propose an approach to solving EFTs which leads to a well-posedness statement.
For a particular choice of UV theory we rigorously derive the corresponding EFT
and show that a large class of classical solutions to the UV theory are well
approximated by EFT solutions. We also consider solutions of the UV theory
which are not well approximated by EFT solutions and demonstrate that these are
close, in an averaged sense, to solutions of a modified EFT.


Abstract:
  Computing the gradient of a function provides fundamental information about
its behavior. This information is essential for several applications and
algorithms across various fields. One common application that require gradients
are optimization techniques such as stochastic gradient descent, Newton's
method and trust region methods. However, these methods usually requires a
numerical computation of the gradient at every iteration of the method which is
prone to numerical errors. We propose a simple limited-memory technique for
improving the accuracy of a numerically computed gradient in this
gradient-based optimization framework by exploiting (1) a coordinate
transformation of the gradient and (2) the history of previously taken descent
directions. The method is verified empirically by extensive experimentation on
both test functions and on real data applications. The proposed method is
implemented in the R package smartGrad and in C++.


Abstract:
  We analyze the appearance of the trilinear squark-Higgs couplings $x_q$ in
Green functions in the Higgs sector of the MSSM and in threshold corrections to
the SM. The results are constraints on maximal powers of $x_q$ in QCD-related
loop corrections. In practice these often imply all-order resummations of
leading or subleading $x_q$ contributions in SM-parametrized expressions. We
present a variety of all-order resummation relations for $\Delta \lambda$ which
include such $x_q$-enhanced terms and different orders in Yukawa and gauge
couplings. We contrast which terms cannot be resummed.


Abstract:
  The emergence of glassy dynamics and the glass transition in dense disordered
systems is still not fully understood theoretically. Mode-coupling theory (MCT)
has shown to be effective in describing some of the non-trivial features of
glass formation, but it cannot explain the full glassy phenomenology due to the
strong approximations on which it is based. Generalized mode-coupling theory
(GMCT) is a hierarchical extension of the theory, which is able to outclass MCT
by carefully describing the dynamics of higher order correlations in its
generalized framework. Unfortunately, the theory has so far only been developed
for single component systems and as a result works poorly for highly
polydisperse materials. In this paper, we solve this problem by developing GMCT
for multi-component systems. We use it to predict the glassy dynamics of the
binary Kob-Andersen Lennard-Jones mixture, as well as its purely repulsive
Weeks-Chandler-Andersen analogue. Our results show that each additional level
of the GMCT hierarchy gradually improves the predictive power of GMCT beyond
its previous limit. This implies that our theory is able to harvest more
information from the static correlations, thus being able to better understand
the role of attraction in supercooled liquids from a first-principles
perspective.


Abstract:
  Vast amount of data generated from networks of sensors, wearables, and the
Internet of Things (IoT) devices underscores the need for advanced modeling
techniques that leverage the spatio-temporal structure of decentralized data
due to the need for edge computation and licensing (data access) issues. While
federated learning (FL) has emerged as a framework for model training without
requiring direct data sharing and exchange, effectively modeling the complex
spatio-temporal dependencies to improve forecasting capabilities still remains
an open problem. On the other hand, state-of-the-art spatio-temporal
forecasting models assume unfettered access to the data, neglecting constraints
on data sharing. To bridge this gap, we propose a federated spatio-temporal
model -- Cross-Node Federated Graph Neural Network (CNFGNN) -- which explicitly
encodes the underlying graph structure using graph neural network (GNN)-based
architecture under the constraint of cross-node federated learning, which
requires that data in a network of nodes is generated locally on each node and
remains decentralized. CNFGNN operates by disentangling the temporal dynamics
modeling on devices and spatial dynamics on the server, utilizing alternating
optimization to reduce the communication cost, facilitating computations on the
edge devices. Experiments on the traffic flow forecasting task show that CNFGNN
achieves the best forecasting performance in both transductive and inductive
learning settings with no extra computation cost on edge devices, while
incurring modest communication cost.


Abstract:
  The use of ultrasonic guided waves to probe the materials/structures for
damage continues to increase in popularity for non-destructive evaluation (NDE)
and structural health monitoring (SHM). The use of high-frequency waves such as
these offers an advantage over low-frequency methods from their ability to
detect damage on a smaller scale. However, in order to assess damage in a
structure, and implement any NDE or SHM tool, knowledge of the behaviour of a
guided wave throughout the material/structure is important (especially when
designing sensor placement for SHM systems). Determining this behaviour is
extremely diffcult in complex materials, such as fibre-matrix composites, where
unique phenomena such as continuous mode conversion takes place. This paper
introduces a novel method for modelling the feature-space of guided waves in a
composite material. This technique is based on a data-driven model, where prior
physical knowledge can be used to create structured machine learning tools;
where constraints are applied to provide said structure. The method shown makes
use of Gaussian processes, a full Bayesian analysis tool, and in this paper it
is shown how physical knowledge of the guided waves can be utilised in
modelling using an ML tool. This paper shows that through careful consideration
when applying machine learning techniques, more robust models can be generated
which offer advantages such as extrapolation ability and physical
interpretation.


Abstract:
  Fock-Goncharov's moduli spaces $\mathscr{X}_{{\rm PGL}_3,\frak{S}}$ of framed
${\rm PGL}_3$-local systems on punctured surfaces $\frak{S}$ provide prominent
examples of cluster $\mathscr{X}$-varieties and higher Teichm\"uller spaces. In
a previous paper of the author (arXiv:2011.14765), the so-called ${\rm SL}_3$
quantum trace map is constructed for each triangulable punctured surface
$\frak{S}$ and its ideal triangulation $\Delta$, as a homomorphism from the
stated ${\rm SL}_3$-skein algebra of the surface to a quantum torus algebra
that deforms the ring of Laurent polynomials in the cube-roots of the cluster
coordinate variables for the cluster $\mathscr{X}$-chart for $\mathscr{X}_{{\rm
PGL}_3,\frak{S}}$ associated to $\Delta$. We develop quantum mutation maps
between special subalgebras of the cube-root quantum torus algebras for
different triangulations, and show that the ${\rm SL}_3$ quantum trace maps are
compatible under these quantum mutation maps. As a result, the quantum ${\rm
SL}_3$-${\rm PGL}_3$ duality map constructed in the previous paper is shown to
be independent of the choice of an ideal triangulation.


Abstract:
  A typical crowdsourcing software development(CSD) marketplace consists of a
list of software tasks as service demands and a pool of freelancer developers
as service suppliers. Highly dynamic and competitive CSD market places may
result in task failure due to unforeseen risks, such as increased competition
over shared worker supply, or uncertainty associated with workers' experience
and skills, and so on. To improve CSD effectiveness, it is essential to better
understand and plan with respect to dynamic worker characteristics and risks
associated with CSD processes. In this paper, we present a hybrid simulation
model, CrowdSim, to forecast crowdsourcing task failure risk in competitive CSD
platforms. CrowdSim is composed of three layered components: the macro-level
reflects the overall crowdsourcing platform based on system dynamics,the
meso-level represents the task life cycle based on discrete event simulation,
and the micro-level models the crowd workers' decision-making processes based
on agent-based simulation. CrowdSim is evaluated through three CSD decision
scenarios to demonstrate its effectiveness, using a real-world historical
dataset and the results demonstrate CrowdSim's potential in empowering
crowdsourcing managers to explore crowdsourcing outcomes with respect to
different task scheduling options.


Abstract:
  Federated Learning marks a turning point in the implementation of
decentralized machine learning (especially deep learning) for wireless devices
by protecting users' privacy and safeguarding raw data from third-party access.
It assigns the learning process independently to each client. First, clients
locally train a machine learning model based on local data. Next, clients
transfer local updates of model weights and biases (training data) to a server.
Then, the server aggregates updates (received from clients) to create a global
learning model. However, the continuous transfer between clients and the server
increases communication costs and is inefficient from a resource utilization
perspective due to the large number of parameters (weights and biases) used by
deep learning models. The cost of communication becomes a greater concern when
the number of contributing clients and communication rounds increases. In this
work, we propose a novel framework, FedZip, that significantly decreases the
size of updates while transferring weights from the deep learning model between
clients and their servers. FedZip implements Top-z sparsification, uses
quantization with clustering, and implements compression with three different
encoding methods. FedZip outperforms state-of-the-art compression frameworks
and reaches compression rates up to 1085x, and preserves up to 99% of bandwidth
and 99% of energy for clients during communication.


Abstract:
  We study differential rotation in late-stage shell convection in a 3D
hydrodynamic simulation of a rapidly rotating $16M_\odot$ helium star with a
particular focus on the convective oxygen shell. We find that the oxygen shell
develops a quasi-stationary pattern of differential rotation that is neither
described by uniform angular velocity as assumed in current stellar evolution
models of supernova progenitors, nor by uniform specific angular momentum.
Instead, the oxygen shell develops a positive angular velocity gradient with
faster rotation at the equator than at the pole by tens of percent. We show
that the angular momentum transport inside the convection zone is not
adequately captured by a diffusive mixing-length flux proportional to the
angular velocity or angular momentum gradient. Zonal flow averages reveal
stable large-scale meridional flow and an entropy deficit near the equator that
mirrors the patterns in the angular velocity. The structure of the flow is
reminiscent of simulations of stellar surface convection zones and the
differential rotation of the Sun, suggesting that similar effects are involved;
future simulations will need to address in more detail how the interplay of
buoyancy, inertial forces, and turbulent stresses shapes differential rotation
during late-stage convection in massive stars. Our findings may have
implications for neutron star birth spin periods and supernova explosion
scenarios that involve rapid core rotation. If convective regions develop
positive angular velocity gradients, angular momentum could be shuffled out of
the core region more efficiently, potentially making the formation of
millisecond magnetars more difficult.


Abstract:
  In the decremental single-source shortest paths problem, the goal is to
maintain distances from a fixed source $s$ to every vertex $v$ in an $m$-edge
graph undergoing edge deletions. In this paper, we conclude a long line of
research on this problem by showing a near-optimal deterministic data structure
that maintains $(1+\epsilon)$-approximate distance estimates and runs in
$m^{1+o(1)}$ total update time.
  Our result, in particular, removes the oblivious adversary assumption
required by the previous breakthrough result by Henzinger et al. [FOCS'14],
which leads to our second result: the first almost-linear time algorithm for
$(1-\epsilon)$-approximate min-cost flow in undirected graphs where capacities
and costs can be taken over edges and vertices. Previously, algorithms for max
flow with vertex capacities, or min-cost flow with any capacities required
super-linear time. Our result essentially completes the picture for approximate
flow in undirected graphs.
  The key technique of the first result is a novel framework that allows us to
treat low-diameter graphs like expanders. This allows us to harness expander
properties while bypassing shortcomings of expander decomposition, which almost
all previous expander-based algorithms needed to deal with. For the second
result, we break the notorious flow-decomposition barrier from the
multiplicative-weight-update framework using randomization.


Abstract:
  We present algorithms for the Max-Cover and Max-Unique-Cover problems in the
data stream model. The input to both problems are $m$ subsets of a universe of
size $n$ and a value $k\in [m]$. In Max-Cover, the problem is to find a
collection of at most $k$ sets such that the number of elements covered by at
least one set is maximized. In Max-Unique-Cover, the problem is to find a
collection of at most $k$ sets such that the number of elements covered by
exactly one set is maximized. Our goal is to design single-pass algorithms that
use space that is sublinear in the input size. Our main algorithmic results
are:
  If the sets have size at most $d$, there exist single-pass algorithms using
$\tilde{O}(d^{d+1} k^d)$ space that solve both problems exactly. This is
optimal up to polylogarithmic factors for constant $d$.
  If each element appears in at most $r$ sets, we present single pass
algorithms using $\tilde{O}(k^2 r/\epsilon^3)$ space that return a $1+\epsilon$
approximation in the case of Max-Cover. We also present a single-pass algorithm
using slightly more memory, i.e., $\tilde{O}(k^3 r/\epsilon^{4})$ space, that
$1+\epsilon$ approximates Max-Unique-Cover.
  In contrast to the above results, when $d$ and $r$ are arbitrary, any
constant pass $1+\epsilon$ approximation algorithm for either problem requires
$\Omega(\epsilon^{-2}m)$ space but a single pass $O(\epsilon^{-2}mk)$ space
algorithm exists. In fact any constant-pass algorithm with an approximation
better than $e/(e-1)$ and $e^{1-1/k}$ for Max-Cover and Max-Unique-Cover
respectively requires $\Omega(m/k^2)$ space when $d$ and $r$ are unrestricted.
En route, we also obtain an algorithm for a parameterized version of the
streaming Set-Cover problem.


Abstract:
  We derive in the closed and unimprovable form the bilateral non-asymptotic
relations between growth of entire functions and decay rate at infinity of its
Taylor coefficients. We investigate the functions of one as well as of several
complex variables.
  We will apply the convex analysis: Young-Fenchel (Legendre) transform, Young
inequality, saddle-point method etc.


Abstract:
  The production of K$^{*}(892)^{0}$ and $\phi(1020)$ mesons in proton-proton
(pp) and lead-lead (Pb-Pb) collisions at $\sqrt{s_\mathrm{NN}} = 5.02$ TeV has
been measured using the ALICE detector at the Large Hadron Collider (LHC). The
transverse momentum ($p_{\mathrm{T}}$) distributions of K$^{*}(892)^{0}$ and
$\phi(1020)$ mesons have been measured at midrapidity $(|y|<0.5)$ up to
$p_{\mathrm{T}} = 20$ GeV$/c$ in inelastic pp collisions and for several Pb-Pb
collision centralities. The collision centrality and collision energy
dependence of the average transverse momenta agree with the radial flow
scenario observed with stable hadrons, showing that the effect is stronger for
more central collisions and higher collision energies. The $\mathrm{K^{*0}/K}$
ratio is found to be suppressed in Pb-Pb collisions relative to pp collisions:
this indicates a loss of the measured K$^{*}(892)^{0}$ signal due to
rescattering of its decay products in the hadronic phase. In contrast, for the
longer-lived $\phi(1020)$ mesons, no such suppression is observed. The nuclear
modification factors ($R_{\rm AA}$) of K$^{*}(892)^{0}$ and $\phi(1020)$ mesons
are calculated using pp reference spectra at the same collision energy. In
central Pb-Pb collisions for $p_{\rm T} > 8$ GeV$/c$, the $R_{\rm AA}$ values
of K$^{*}(892)^{0}$ and $\phi(1020)$ are below unity and observed to be similar
to those of pions, kaons, and (anti)protons. The $R_{\rm AA}$ values at high
$p_{\mathrm T}$ for K$^{*}(892)^{0}$ and $\phi(1020)$ mesons are in agreement
within uncertainties for $\sqrt{s_\mathrm{NN}} = 5.02$ and 2.76 TeV.


Abstract:
  We analyse Sector 20 TESS photometry of the ultra-hot Jupiter WASP-12b, and
extract its phase curve to study the planet's atmospheric properties. We
successfully recover the phase curve with an amplitude of 549 $\pm$ 62 ppm, and
a secondary eclipse depth of 609$^{+74}_{-73}$ ppm. The peak of the phase curve
is shifted by 0.049 $\pm$ 0.015 in phase, implying that the brightest spot in
the atmosphere is shifted from the substellar point towards the planet's
evening terminator. Assuming zero albedo, the eclipse depth infers a day-side
brightness temperature of 3128$^{+64}_{-68}$ K. No significant detection of
flux from the night-side is found at 60 $\pm$ 97 ppm, implying a night-side
brightness temperature of $<$2529 K (1-$\sigma$). We do not detect any
significant variability in the light from the planet over the $\sim$27 days of
the TESS observations. Finally, we note that an ephemeris model taking orbital
decay into account provides a significantly better fit than a constant-period
model.


Abstract:
  In this paper, we will study the large scale structure formation using the
gravitational partition function. We will assertively argue that the system of
gravitating galaxies can be analyzed using the Tsallis statistical mechanics.
The divergences in the Tsallis gravitational partition function can be removed
using the mathematical riches of the generalization of the dimensional
regularization (GDR). The finite gravitational partition function thus obtained
will be used to evaluate the thermodynamics of the system of galaxies and thus,
to understand the clustering of galaxies in the universe. The correlation
function which is believed to contain the information of clustering of galaxies
will also be discussed in this formalism.


Abstract:
  For second-order elliptic or parabolic equations with subcritical or critical
drifts, it is well-known that the Harnack inequality holds and their bounded
weak solutions are H\"older continuous. We construct time-independent
supercritical drifts in $L^{n-\lambda}(\mathbb{R}^n)$ with arbitrarily small
$\lambda>0$ such that the Harnack inequality and the H\"older continuity fail
in both the elliptic and the parabolic cases, thus confirming a conjecture by
Seregin, Silvestre, Sverak and Zlatos. These results are sharp, and they also
apply to a toy model of the axi-symmetric Navier-Stokes equations in space
dimension $3$.


Abstract:
  Active particles often swim in confined environments. The transport
mechanisms, especially the global one as reflected by the Taylor dispersion
model, are of great practical interest to various applications. For active
dispersion process in confined flows, previous analytical studies focused on
the long-time asymptotic values of dispersion characteristics. Only several
numerical studies preliminarily investigated the temporal evolution. Extending
recent studies of Jiang & Chen (J. Fluid Mech., vol. 877, 2019, pp. 1--34; J.
Fluid Mech., vol. 899, 2020, A18), this work makes the first analytical attempt
to investigate the transient process. The temporal evolution of the local
distribution in the confined-section--orientation space, drift, dispersivity
and skewness, is explored based on moments of distributions. We introduce the
biorthogonal expansion method for solutions because the classic integral
transform method for passive transport problems is not applicable due to the
self-propulsion effect. Two types of boundary condition, the reflective
condition and the Robin condition for wall accumulation, are imposed
respectively. A detailed study on spherical and ellipsoidal swimmers dispersing
in a plane Poiseuille flow demonstrates the influences of the swimming, shear
flow, wall accumulation and particle shape on the transient dispersion process
after a point-source release. The swimming-induced diffusion makes the local
distribution reach its equilibrium state faster than that of passive particles.
Though the wall accumulation significantly affects the evolution of the local
distribution and the drift, the time scale to reach the Taylor regime is not
obviously changed. The shear-induced alignment of ellipsoidal particles can
enlarge the dispersivity but has less influence on the drift and the skewness.


Abstract:
  The main result of the present paper is the proof of the Strange Duality for
elliptic surfaces -- a duality between global sections of determinantal line
bundles on moduli spaces of stable sheaves on a fixed elliptic surface. For
this, we employ the "Marian-Oprea trick": using Bridgeland's birational
isomorphisms, we reduce the problem from a pair of general moduli spaces to a
pair of Hilbert schemes. The latter case is a theorem by Marian-Oprea.


Abstract:
  Arising from modulation instability, Turing rolls in optical Kerr
microresonators have been used in the generation of optical frequency combs and
the synthesis of microwave and terahertz frequencies. In this work, by applying
electro-optic modulation on terahertz-frequency Turing rolls, we implement
electro-optic frequency division with a microcomb to synthesize variable
low-noise microwave signals. We also actively stabilize the terahertz
oscillations to a microwave reference via intracavity power modulation,
obtaining fractional frequency instabilities that are better than those of the
free-running situation by up to six orders of magnitude. This study not only
highlights the extraordinary spectral purity of Turing roll oscillations but
also opens the way for bidirectional terahertz-to-microwave links with hybrid
optical frequency comb techniques.


Abstract:
  Axion-like particles (ALPs) are pseudo-scalar particles that are candidates
for ultralight dark matter. ALPs interact with photons slightly and cause the
rotational oscillation of linear polarization. DANCE searches for ALP dark
matter by enhancing the rotational oscillation in a bow-tie ring cavity. The
signal to noise ratio of DANCE can be improved by long-term observation, and we
are planning a year-long observation for the final DANCE. In this document, I
will report on the control systems of the ring cavity we developed for the
future long-term observation.


Abstract:
  Kink-like flapping motions of current sheets are commonly observed in the
magnetotail. Such oscillations have periods of a few minutes down to a few
seconds and they propagate toward the flanks of the plasma sheet. Here, we
report a short-period ($T\approx25$ s) flapping event of a thin current sheet
observed by the Magnetospheric Multiscale (MMS) spacecraft in the dusk-side
plasma sheet following a fast Earthward plasma flow. We characterize the
flapping structure using the multi-spacecraft spatiotemporal derivative and
timing methods, and we find that the wave-like structure is propagating along
the average current direction with a phase velocity comparable to the ion
velocity. We show that the wavelength of the oscillating current sheet scales
with its thickness as expected for a drift-kink mode. The decoupling of the ion
bulk motion from the electron bulk motion suggests that the current sheet is
thin. We discuss the presence of the lower hybrid waves associated with
gradients of density as a broadening process of the thin current sheet.


Abstract:
  This paper proposes a novel multiple-input multiple-output (MIMO) symbol
detector that incorporates a deep reinforcement learning (DRL) agent into the
Monte Carlo tree search (MCTS) detection algorithm. We first describe how the
MCTS algorithm, used in many decision-making problems, is applied to the MIMO
detection problem. Then, we introduce a self-designed deep reinforcement
learning agent, consisting of a policy value network and a state value network,
which is trained to detect MIMO symbols. The outputs of the trained networks
are adopted into a modified MCTS detection algorithm to provide useful node
statistics and facilitate enhanced tree search process. The resulted scheme,
termed the DRL-MCTS detector, demonstrates significant improvements over the
original MCTS detection algorithm and exhibits favorable performance compared
to other existing linear and DNN-based detection methods under varying channel
conditions.


Abstract:
  In this short note we develop a constitutive relation that is linear in both
the Cauchy stress and the linearized strain, by linearizing implicit
constitutive relations between the stress and the deformation gradient that
have been put into place to describe the response of elastic bodies (see
\cite{rajagopal2003implicit}), by assuming that the displacement gradient is
small. These implicit equations include the classical linearized elastic
constitutive approximation as well as constitutive relations that imply
limiting strain, as special subclasses.


Abstract:
  In this paper, a decomposition theorem for (covariant) unitary group
representations on Kaplansky-Hilbert modules over Stone algebras is
established, which generalizes the well-known Hilbert space case (where it
coincides with the decomposition of Jacobs, de Leeuw and Glicksberg). The proof
rests heavily on the operator theory on Kaplansky-Hilbert modules, in
particular the spectral theorem for Hilbert-Schmidt homomorphisms on such
modules. As an application, a generalization of the celebrated
Furstenberg-Zimmer structure theorem to the case of measure-preserving actions
of arbitrary groups on arbitrary probability spaces is established.


Abstract:
  How the brain processes information from external stimuli in order to
perceive the world and act on it is one of the greatest questions in
neuroscience. To address this question different time series analyzes
techniques have been employed to characterize the statistical properties of
brain signals during cognitive tasks. Typically response-specific processes are
addressed by comparing the time course of average event-related potentials in
different trials type. Here we analyze monkey Local Field Potentials data
during visual pattern discrimination called Go/No-Go task in the light of
information theory quantifiers. We show that the Bandt-Pompe symbolization
methodology to calculate entropy and complexity of data is a useful tool to
distinguish response-related differences between Go and No-Go trials. We
propose to use an asymmetry index to statistically validate trial type
differences. Moreover, by using the multi-scale approach and embedding time
delays to downsample the data we can estimate the important time scales in
which the relevant information is been processed.


Abstract:
  Developments in computer science in recent years are moving into hospitals.
Surgeons are faced with ever new technical challenges. Visual perception plays
a key role in most of these. Diagnostic and training models are needed to
optimize the training of young surgeons. In this study, we present a model for
classifying experts, 4th-year residents and 3rd-year residents, using only eye
movements. We show a model that uses a minimal set of features and still
achieve a robust accuracy of 76.46 % to classify eye movements into the correct
class. Likewise, in this study, we address the evolutionary steps of visual
perception between three expertise classes, forming a first step towards a
diagnostic model for expertise.


Abstract:
  We discuss the integer sequence transform $a \mapsto b$ where $b_n$ is the
number of real roots of the polynomial $a_0 + a_1x + a_2x^2 + \cdots + a_nx^n$.
It is shown that several sequences $a$ give the trivial sequence $b = (0,1,0,1,
0,1,\ldots)$, i.e., ${b_n = n \bmod 2}$, among them the Catalan numbers,
central binomial coefficients, $n!$ and $\binom{n+k}{n}$ for a fixed $k$. We
also look at some sequences $a$ for which $b$ is more interesting such as $a_n
= (n+1)^k$ for $k \geq 3$. Further, general procedures are given for
constructing real sequences $a_n$ for which $b_n$ is either always maximal or
minimal.


Abstract:
  We study the stability and instability of ALE Ricci-flat metrics around which
a Lojasiewicz inequality is satisfied for a variation of Perelman's
$\lambda$-functional adapted to the ALE situation and denoted
$\lambda_{\operatorname{ALE}}$. This functional was introduced by the authors
in a recent work and it has been proven that it satisfies a good enough
Lojasiewicz inequality at least in neighborhoods of integrable ALE Ricci-flat
metrics in dimension larger than or equal to 5.


Abstract:
  When trained on large, unfiltered crawls from the internet, language models
pick up and reproduce all kinds of undesirable biases that can be found in the
data: they often generate racist, sexist, violent or otherwise toxic language.
As large models require millions of training examples to achieve good
performance, it is difficult to completely prevent them from being exposed to
such content. In this paper, we first demonstrate a surprising finding:
pretrained language models recognize, to a considerable degree, their
undesirable biases and the toxicity of the content they produce. We refer to
this capability as self-diagnosis. Based on this finding, we then propose a
decoding algorithm that, given only a textual description of the undesired
behavior, reduces the probability of a language model producing problematic
text. We refer to this approach as self-debiasing. Self-debiasing does not rely
on manually curated word lists, nor does it require any training data or
changes to the model's parameters. While we by no means eliminate the issue of
language models generating biased text, we believe our approach to be an
important step in this direction.


Abstract:
  A joint determination of the reactor antineutrino spectra resulting from the
fission of $^{235}$U and $^{239}$Pu has been carried out by the Daya Bay and
PROSPECT collaborations. This Letter reports the level of consistency of
$^{235}$U spectrum measurements from the two experiments and presents new
results from a joint analysis of both data sets. The measurements are found to
be consistent. The combined analysis reduces the degeneracy between the
dominant $^{235}$U and $^{239}$Pu isotopes and improves the uncertainty of the
$^{235}$U spectral shape to about 3\%. The ${}^{235}$U and $^{239}$Pu
antineutrino energy spectra are unfolded from the jointly deconvolved reactor
spectra using the Wiener-SVD unfolding method, providing a data-based reference
for other reactor antineutrino experiments and other applications. This is the
first measurement of the $^{235}$U and $^{239}$Pu spectra based on the
combination of experiments at low- and highly enriched uranium reactors.


Abstract:
  It has been a century since the species-area relationship (SAR) was first
proposed as a power law to explain how species richness scales with area. There
have been many attempts to explain the origin of this predominant form. Apart
from the power law, numerous empirical studies also report a semi-log form of
the SAR, but very few have addressed its incidence. In this work, we test
whether these relationships could emerge from the assembly of large random
communities on island-like systems. We reformulate the generalized
Lotka-Volterra model by introducing an area parameter that determines the
species richness of the assembled communities. Our analysis demonstrates that
the two most widely reported relationship forms can emerge due to differences
in immigration rates and skewness towards weak interactions. We particularly
highlight the incidence of the semi-log SAR for low immigration rates from a
source pool, which is consistent with several previous empirical studies. The
two SAR forms might show good fits to data over a large span of areas but a
power-law overestimates species richness on smaller islands in remote
archipelagoes.


Abstract:
  We propose the first general PAC-Bayesian generalization bounds for
adversarial robustness, that estimate, at test time, how much a model will be
invariant to imperceptible perturbations in the input. Instead of deriving a
worst-case analysis of the risk of a hypothesis over all the possible
perturbations, we leverage the PAC-Bayesian framework to bound the averaged
risk on the perturbations for majority votes (over the whole class of
hypotheses). Our theoretically founded analysis has the advantage to provide
general bounds (i) that are valid for any kind of attacks (i.e., the
adversarial attacks), (ii) that are tight thanks to the PAC-Bayesian framework,
(iii) that can be directly minimized during the learning phase to obtain a
robust model on different attacks at test time.


Abstract:
  This review describes an emerging field of waveguide quantum electrodynamics
(WQED) studying interaction of photons propagating in a waveguide with
localized quantum emitters. In such systems, atoms and guided photons are
hybridized with each other and form polaritons that can propagate along the
waveguide, contrary to the cavity quantum optics setup. Emerging in such a
system collective light-atom interactions result in super- and sub-radiant
quantum states, that are promising for quantum information processing, and give
rise to peculiar quantum correlations between photons. The review is aimed at
both experimentalists and theoreticians from various fields of physics
interested in the rapidly developing subject of WQED. We highlight recent
groundbreaking experiments performed for different quantum platforms, including
cold atoms, superconducting qubits, semiconductor quantum dots, quantum
solid-state defects and at the same time provide a comprehensive introduction
into various theoretical techniques to study atom-photon interactions in the
waveguide.


Abstract:
  Mapping the thermal transport properties of materials at the nanoscale is of
critical importance for optimizing heat conduction in nanoscale devices.
Several methods to determine the thermal conductivity of materials have been
developed, most of them yielding an average value across the sample, thereby
disregarding the role of local variations. Here, we present a method for the
spatially-resolved assessment of the thermal conductivity of suspended graphene
by using a combination of confocal Raman thermometry and a finite-element
calculations-based fitting procedure. We demonstrate the working principle of
our method by extracting the two-dimensional thermal conductivity map of one
pristine suspended single-layer graphene sheet and one irradiated using helium
ions. Our method paves the way for spatially resolving the thermal conductivity
of other types of layered materials. This is particularly relevant for the
design and engineering of nanoscale thermal circuits (e.g. thermal diodes).


Abstract:
  One of the most popular methods of the machine learning prediction
explanation is the SHapley Additive exPlanations method (SHAP). An imprecise
SHAP as a modification of the original SHAP is proposed for cases when the
class probability distributions are imprecise and represented by sets of
distributions. The first idea behind the imprecise SHAP is a new approach for
computing the marginal contribution of a feature, which fulfils the important
efficiency property of Shapley values. The second idea is an attempt to
consider a general approach to calculating and reducing interval-valued Shapley
values, which is similar to the idea of reachable probability intervals in the
imprecise probability theory. A simple special implementation of the general
approach in the form of linear optimization problems is proposed, which is
based on using the Kolmogorov-Smirnov distance and imprecise contamination
models. Numerical examples with synthetic and real data illustrate the
imprecise SHAP.


Abstract:
  We consider a family of tensor network states defined on regular lattices
that can be efficiently prepared with a quantum computer by adiabatic
evolution. This family comprises relevant classes of states, such as injective
Matrix Product and Projected Entangled-Pair States, and some corresponding to
classical spin models. We show how uniform lower bounds to the gap of the
parent Hamiltonian along the adiabatic trajectory can be efficiently computed
using semi-definite programming. We also derive a set of observables whose
expectation values can be easily determined and that form a complete set, in
the sense that they uniquely characterize the state. We identify a subset of
those observables which can be efficiently computed if one has access to the
quantum state and local measurements, and analyze how they can be used in
verification procedures.


Abstract:
  In this paper we give an example of a closed, strongly one-sided dense set
which is not of uniform density type. We also show that there is a set of
uniform density type which is not of strong uniform density type.


Abstract:
  We calculate thermodynamic potentials and their derivatives for the
three-dimensional $O(2)$ model using tensor-network methods to investigate the
well-known second-order phase transition. We also consider the model at
non-zero chemical potential to study the Silver Blaze phenomenon, which is
related to the particle number density at zero temperature. Furthermore, the
temperature dependence of the number density is explored using asymmetric
lattices. Our results for both zero and non-zero magnetic field, temperature,
and chemical potential are consistent with those obtained using other methods.


Abstract:
  We show that viscoelastic plane Poiseuille flow becomes linearly unstable in
the absence of inertia, in the limit of high elasticities, for ultra-dilute
polymer solutions. While inertialess elastic instabilities have been predicted
for curvilinear shear flows, this is the first ever report of a purely elastic
linear instability in a rectilinear shear flow. The novel instability continues
upto a Reynolds number ($Re$) of $O(1000)$, corresponding to the recently
identified elasto-inertial turbulent state believed to underlie the
maximum-drag-reduced regime. Thus, for highly elastic ultra-dilute polymer
solutions, a single linearly unstable modal branch may underlie transition to
elastic turbulence at zero $Re$, and to elasto-inertial turbulence at moderate
$Re$, implying the existence of continuous pathways connecting the turbulent
states to each other, and to the laminar base state.


Abstract:
  Null shells are a useful geometric construction to study the propagation of
infinitesimally thin concentrations of massless particles or impulsive waves.
In this paper, we determine and study the necessary and sufficient conditions
for the matching of two spacetimes with respective null embedded hypersurfaces
as boundaries. Whenever the matching is possible, it is shown to depend on a
diffeomorphism between the set of null generators in each boundary and a scalar
function, called step function, that determines a shift of points along the
null generators. Generically there exists at most one possible matching but in
some circumstances this is not so. When the null boundaries are totally
geodesic, the point-to-point identification between them introduces a freedom
whose nature and consequences are analyzed in detail. The expression for the
energy-momentum tensor of a general null shell is also derived. Finally, we
find the most general shell (with non-zero energy, energy flux and pressure)
that can be generated by matching two Minkowski regions across a null
hyperplane. This allows us to show how the original Penrose's cut-and-paste
construction fits and connects with the standard matching formalism.


Abstract:
  Anytime a robot manipulator is controlled via visual feedback, the
transformation between the robot and camera frame must be known. However, in
the case where cameras can only capture a portion of the robot manipulator in
order to better perceive the environment being interacted with, there is
greater sensitivity to errors in calibration of the base-to-camera transform. A
secondary source of uncertainty during robotic control are inaccuracies in
joint angle measurements which can be caused by biases in positioning and
complex transmission effects such as backlash and cable stretch. In this work,
we bring together these two sets of unknown parameters into a unified problem
formulation when the kinematic chain is partially visible in the camera view.
We prove that these parameters are non-identifiable implying that explicit
estimation of them is infeasible. To overcome this, we derive a smaller set of
parameters we call Lumped Error since it lumps together the errors of
calibration and joint angle measurements. A particle filter method is presented
and tested in simulation and on two real world robots to estimate the Lumped
Error and show the efficiency of this parameter reduction.


Abstract:
  Integrating external knowledge into commonsense reasoning tasks has shown
progress in resolving some, but not all, knowledge gaps in these tasks. For
knowledge integration to yield peak performance, it is critical to select a
knowledge graph (KG) that is well-aligned with the given task's objective. We
present an approach to assess how well a candidate KG can correctly identify
and accurately fill in gaps of reasoning for a task, which we call KG-to-task
match. We show this KG-to-task match in 3 phases: knowledge-task
identification, knowledge-task alignment, and knowledge-task integration. We
also analyze our transformer-based KG-to-task models via commonsense probes to
measure how much knowledge is captured in these models before and after KG
integration. Empirically, we investigate KG matches for the SocialIQA (SIQA)
(Sap et al., 2019b), Physical IQA (PIQA) (Bisk et al., 2020), and MCScript2.0
(Ostermann et al., 2019) datasets with 3 diverse KGs: ATOMIC (Sap et al.,
2019a), ConceptNet (Speer et al., 2017), and an automatically constructed
instructional KG based on WikiHow (Koupaee and Wang, 2018). With our methods we
are able to demonstrate that ATOMIC, an event-inference focused KG, is the best
match for SIQA and MCScript2.0, and that the taxonomic ConceptNet and
WikiHow-based KGs are the best matches for PIQA across all 3 analysis phases.
We verify our methods and findings with human evaluation.


Abstract:
  Over the years, with the advancement of technology, Web technology has many
improvements. In the early days, the web was one-way communication, and only
the customer was able to see the content of the site and could not enter
information. However, day by day, the web made significant progress, and
technologies such as HTTP, ajax, WebSocket introduced that make pages dynamic
and Give us both sides. In short, it is a new type of communications protocol,
which was faster and more efficient than previous communication protocols.
After the web socket's unveiling, like any other technology, Its security has
been discussed, and technology's security has always been a challenge for us.
Therefore, in this article, we examine the structure and security problems that
can occur in a web socket to choose an excellent alternative to HTTP and use
it.


Abstract:
  The Winograd Schema (WS) has been proposed as a test for measuring
commonsense capabilities of models. Recently, pre-trained language model-based
approaches have boosted performance on some WS benchmarks but the source of
improvement is still not clear. This paper suggests that the apparent progress
on WS may not necessarily reflect progress in commonsense reasoning. To support
this claim, we first show that the current evaluation method of WS is
sub-optimal and propose a modification that uses twin sentences for evaluation.
We also propose two new baselines that indicate the existence of artifacts in
WS benchmarks. We then develop a method for evaluating WS-like sentences in a
zero-shot setting to account for the commonsense reasoning abilities acquired
during the pretraining and observe that popular language models perform
randomly in this setting when using our more strict evaluation. We conclude
that the observed progress is mostly due to the use of supervision in training
WS models, which is not likely to successfully support all the required
commonsense reasoning skills and knowledge.


Abstract:
  We develop a workflow to use current quantum computing hardware for solving
quantum many-body problems, using the example of the fermionic Hubbard model.
Concretely, we study a four-site Hubbard ring that exhibits a transition from a
product state to an intrinsically interacting ground state as hopping
amplitudes are changed. We locate this transition and solve for the ground
state energy with high quantitative accuracy using a variational quantum
algorithm executed on an IBM quantum computer. Our results are enabled by a
variational ansatz that takes full advantage of the maximal set of commuting
$\mathbb{Z}_2$ symmetries of the problem and a Lanczos-inspired error
mitigation algorithm. They are a benchmark on the way to exploiting near term
quantum simulators for quantum many-body problems.


Abstract:
  An important issue in heterogeneous wireless networks is how to optimally
utilize various radio resources. While many methods have been proposed for
managing radio resources in each network, these methods are not suitable for
heterogeneous wireless networks. In this study, a new management method is
proposed which provides acceptable service quality and roaming rate, reduces
the cost of the service, and utilizes big data technology for its operation. In
our proposed scheme, by considering various parameters such as the type of the
service, the information related to the location of the user, the movement
direction of the user, the cost of the service, and a number of other
statistical measures, the most suitable technology for radio access will be
selected. It is expected that besides improving the decision making accuracy in
selecting the radio access technology and the balanced distribution of network
resources, the proposed method provides lower roaming and lower probability of
stopping roaming requests entering the network. By considering the various
service classes and various quality of service requirements regarding delay,
vibration and so on, this can be useful in optimal implementation of
heterogeneous wireless networks.


Abstract:
  Deep learning has received extensive research interest in developing new
medical image processing algorithms, and deep learning based models have been
remarkably successful in a variety of medical imaging tasks to support disease
detection and diagnosis. Despite the success, the further improvement of deep
learning models in medical image analysis is majorly bottlenecked by the lack
of large-sized and well-annotated datasets. In the past five years, many
studies have focused on addressing this challenge. In this paper, we reviewed
and summarized these recent studies to provide a comprehensive overview of
applying deep learning methods in various medical image analysis tasks.
Especially, we emphasize the latest progress and contributions of
state-of-the-art unsupervised and semi-supervised deep learning in medical
image analysis, which are summarized based on different application scenarios,
including classification, segmentation, detection, and image registration. We
also discuss the major technical challenges and suggest the possible solutions
in future research efforts.


Abstract:
  We study the inflationary consequences of Degenerate Higher Order Scalar
Tensor (DHOST) theories in a de Sitter background. We perturb the de Sitter
background by operators breaking either the degeneracy condition, i.e
scordatura DHOST, or the shift symmetry in the scalar field. We first consider
derivative scordatura and find that in all cases the power spectra of curvature
perturbations are scale-invariant. We then investigate small perturbations by
an axion-like potential, and show that in this scenario the power spectrum
becomes scale-dependent. The modifications to the spectral index and its first
two derivatives are compatible with the latest inflationary constraints.
Moreover the tensor to scalar ratio and the non-Gaussianities of these models
could be within reach of future experiments.


Abstract:
  The way developers implement their algorithms and how these implementations
behave on modern CPUs are governed by the design and organization of these. The
vectorization units (SIMD) are among the few CPUs' parts that can and must be
explicitly controlled. In the HPC community, the x86 CPUs and their
vectorization instruction sets were de-facto the standard for decades. Each new
release of an instruction set was usually a doubling of the vector length
coupled with new operations. Each generation was pushing for adapting and
improving previous implementations. The release of the ARM scalable vector
extension (SVE) changed things radically for several reasons. First, we expect
ARM processors to equip many supercomputers in the next years. Second, SVE's
interface is different in several aspects from the x86 extensions as it
provides different instructions, uses a predicate to control most operations,
and has a vector size that is only known at execution time. Therefore, using
SVE opens new challenges on how to adapt algorithms including the ones that are
already well-optimized on x86. In this paper, we port a hybrid sort based on
the well-known Quicksort and Bitonic-sort algorithms. We use a Bitonic sort to
process small partitions/arrays and a vectorized partitioning implementation to
divide the partitions. We explain how we use the predicates and how we manage
the non-static vector size. We explain how we efficiently implement the sorting
kernels. Our approach only needs an array of O(log N) for the recursive calls
in the partitioning phase, both in the sequential and in the parallel case. We
test the performance of our approach on a modern ARMv8.2 and assess the
different layers of our implementation by sorting/partitioning integers, double
floating-point numbers, and key/value pairs of integers. Our approach is faster
than the GNU C++ sort algorithm by a speedup factor of 4 on average.


Abstract:
  Road traffic accidents, especially vehicle pedestrian collisions in
crosswalk, globally pose a severe threat to human lives and have become a
leading cause of premature deaths. In order to protect such vulnerable road
users from collisions, it is necessary to recognize possible conflict in
advance and warn to road users, not post facto. A breakthrough for proactively
preventing pedestrian collisions is to recognize pedestrian's potential risks
based on vision sensors such as CCTVs. In this study, we propose a predictive
collision risk area estimation system at unsignalized crosswalks. The proposed
system applied trajectories of vehicles and pedestrians from video footage
after preprocessing, and then predicted their trajectories by using deep LSTM
networks. With use of predicted trajectories, this system can infer collision
risk areas statistically, further severity of levels is divided as danger,
warning, and relative safe. In order to validate the feasibility and
applicability of the proposed system, we applied it and assess the severity of
potential risks in two unsignalized spots in Osan city, Korea.


Abstract:
  Order diagrams allow human analysts to understand and analyze structural
properties of ordered data. While an experienced expert can create easily
readable order diagrams, the automatic generation of those remains a hard task.
In this work, we adapt force-directed approaches, which are known to generate
aesthetically-pleasing drawings of graphs, to the realm of order diagrams. Our
algorithm ReDraw thereby embeds the order in a high dimension and then
iteratively reduces the dimension until a two-dimensional drawing is achieved.
To improve aesthetics, this reduction is equipped with two force-directed steps
where one optimizes on distances of nodes and the other on distances of lines
in order to satisfy a set of a priori fixed conditions. By respecting an
invariant about the vertical position of the elements in each step of our
algorithm we ensure that the resulting drawings satisfy all necessary
properties of order diagrams. Finally, we present the results of a user study
to demonstrate that our algorithm outperforms comparable approaches on drawings
of lattices with a high degree of distributivity.


Abstract:
  It is required to find an optimal order of constructing the edges of a
network so as to minimize the sum of the weighted connection times of relevant
pairs of vertices. Construction can be performed anytime anywhere in the
network, with a fixed overall construction speed. The problem is strongly
NP-hard even on stars. We present polynomial algorithms for the problem on
trees with a fixed number of leaves, and on general networks with a fixed
number of relevant pairs.


Abstract:
  A multiple server setting is considered, where each server has tunable speed,
and increasing the speed incurs an energy cost. Jobs arrive to a single queue,
and each job has two types of sub-tasks, map and reduce, and a {\bf precedence}
constraint among them: any reduce task of a job can only be processed once all
the map tasks of the job have been completed. In addition to the scheduling
problem, i.e., which task to execute on which server, with tunable speed, an
additional decision variable is the choice of speed for each server, so as to
minimize a linear combination of the sum of the flow times of jobs/tasks and
the total energy cost. The precedence constraints present new challenges for
the speed scaling problem with multiple servers, namely that the number of
tasks that can be executed at any time may be small but the total number of
outstanding tasks might be quite large. We present simple speed scaling
algorithms that are shown to have competitive ratios, that depend on the power
cost function, and/or the ratio of the size of the largest task and the
shortest reduce task, but not on the number of jobs, or the number of servers.


Abstract:
  One of the key challenges when developing a predictive model is the
capability to describe the domain knowledge and the cause-effect relationships
in a simple way. Decision rules are a useful and important methodology in this
context, justifying their application in several areas, particularly in
clinical practice. Several machine-learning classifiers have exploited the
advantageous properties of decision rules to build intelligent prediction
models, namely decision trees and ensembles of trees (ETs). However, such
methodologies usually suffer from a trade-off between interpretability and
predictive performance. Some procedures consider a simplification of ETs, using
heuristic approaches to select an optimal reduced set of decision rules. In
this paper, we introduce a novel step to those methodologies. We create a new
component to predict if a given rule will be correct or not for a particular
patient, which introduces personalization into the procedure. Furthermore, the
validation results using three public clinical datasets suggest that it also
allows to increase the predictive performance of the selected set of rules,
improving the mentioned trade-off.


Abstract:
  Denoising and demosaicking are essential yet correlated steps to reconstruct
a full color image from the raw color filter array (CFA) data. By learning a
deep convolutional neural network (CNN), significant progress has been achieved
to perform denoising and demosaicking jointly. However, most existing CNN-based
joint denoising and demosaicking (JDD) methods work on a single image while
assuming additive white Gaussian noise, which limits their performance on
real-world applications. In this work, we study the JDD problem for real-world
burst images, namely JDD-B. Considering the fact that the green channel has
twice the sampling rate and better quality than the red and blue channels in
CFA raw data, we propose to use this green channel prior (GCP) to build a
GCP-Net for the JDD-B task. In GCP-Net, the GCP features extracted from green
channels are utilized to guide the feature extraction and feature upsampling of
the whole image. To compensate for the shift between frames, the offset is also
estimated from GCP features to reduce the impact of noise. Our GCP-Net can
preserve more image structures and details than other JDD methods while
removing noise. Experiments on synthetic and real-world noisy images
demonstrate the effectiveness of GCP-Net quantitatively and qualitatively.


Abstract:
  Network functions virtualization (NFV) is a new concept that has received the
attention of both researchers and network providers. NFV decouples network
functions from specialized hardware devices and virtualizes these network
functions as software instances called virtualized network functions (VNFs).
NFV leads to various benefits, including more flexibility, high resource
utilization, and easy upgrades and maintenances. Despite recent works in this
field, placement and chaining of VNFs need more attention. More specifically,
some of the existing works have considered only the placement of VNFs and
ignored the chaining part. So, they have not provided an integrated view of
host or bandwidth resources and propagation delay of paths. In this paper, we
solve the VNF placement and chaining problem as an optimization problem based
on the particle swarm optimization (PSO) algorithm. Our goal is to minimize the
required number of used servers, the average propagation delay of paths, and
the average utilization of links while meeting network demands and constraints.
Based on the obtained results, the algorithm proposed in this study can find
feasible and high-quality solutions.


Abstract:
  We show that the (3+1)-dimensional gauged non-linear sigma model minimally
coupled to a U(1) gauge field possesses analytic solutions representing gauged
solitons at finite Baryon density whose electromagnetic field is a Force Free
Plasma. These gauged solitons manifest a crystalline structure and generate in
a very natural way persistent currents able to support Force Free Plasma
electromagnetic fields. The trajectories of charged test particles moving
within these configurations can be characterized. Quite surprisingly, despite
the non-integrable nature of the theory, some of the perturbations of these
gauged solitons allow to identify a proper resurgent parameter. In particular,
the perturbations of the solitons profile are related to the Lam\'e operator.
On the other hand, the electromagnetic perturbations on the configurations
satisfy a two-dimensional effective Schrodinger equation, where the soliton
background interacts with the electromagnetic perturbations through an
effective two-dimensional periodic potential. We studied numerically the band
energy spectrum for different values of the free parameters of the theory and
we found that bands-gaps are modulated by the potential strength. Finally we
compare our crystal solutions with those of the (1+1)- dimesional Gross-Neveu
model.


Abstract:
  Magnetic skyrmions are stable topological solitons with complex non-coplanar
spin structures. Their nanoscopic size and the low electric currents required
to initiate and control their motion has opened a new field of research,
skyrmionics, that aims at using skyrmions as information carriers for data
storage and manipulation. Recent advances in skyrmionics prompt for a thorough
understanding of the detailed three-dimensional (3D) spin texture of a skyrmion
including skyrmion-skyrmion interactions and their coupling to surfaces and
interfaces. These properties crucially affect application-related aspects such
as the stability and mobility of skyrmions in confined structures. To date,
however, experimental techniques to measure the three-dimensional spin texture
with nanometer resolution are largely missing. We therefore adapt holographic
vector field electron tomography to the problem and report on the first
quantitative reconstruction of the 3D spin texture of skyrmions with sub-10
nanometer resolution. The reconstructed textures reveal a variety of previously
unseen local deviations from a homogeneous Bloch character within the skyrmion
tubes (SkTs), details of the collapse of the skyrmion texture at surfaces, and
a correlated modulation of the SkT in FeGe along their tube axes. The
quantitative 3D data of the magnetic induction also allow to experimentally
confirm some principles of skyrmion formation by deriving spatially resolved
maps of the magnetic energy density across these magnetic solitons.


Abstract:
  We report on experimental results obtained from collisions of slow highly
charged Ar9+ ions with a carbon monoxide dimer (CO)2 target. A COLTRIMS setup
and a Coulomb explosion imaging approach are used to reconstruct the structure
of the CO dimers. The three dimensional structure is deduced from the 2-body
and 3-body dissociation channels from which both the intermolecular bond length
and the relative orientation of the two molecules are determined. For the
3-body channels, the experimental data are interpreted with the help of a
classical model in which the trajectories of the three emitted fragments are
numerically integrated. We measured the equilibrium intermolecular distance to
be Re = 4.2 A. The orientation of both CO molecules with respect to the dimer
axis is found to be quasi-isotropic due to the large vibrational temperature of
the gas jet.


Abstract:
  A standard approach to quantum computing is based on the idea of promoting a
classically simulable and fault-tolerant set of operations to a universal set
by the addition of magic quantum states. In this context, we develop a general
framework to discuss the value of the available, non-ideal magic resources,
relative to those ideally required. We single out a quantity, the
Quantum-assisted Robustness of Magic (QRoM), which measures the overhead of
simulating the ideal resource with the non-ideal ones through
quasiprobability-based methods. This extends error mitigation techniques,
originally developed for Noisy Intermediate Scale Quantum (NISQ) devices, to
the case where qubits are logically encoded. The QRoM shows how the addition of
noisy magic resources allows to boost classical quasiprobability simulations of
a quantum circuit and allows the construction of explicit protocols,
interpolating between classical simulation and an ideal quantum computer.


Abstract:
  We have conducted a systematic survey for z $<$ 0.04 active Galactic nuclei
(AGNs) that may have changed spectral class over the past decade. We use
SkyMapper, Pan-STARRS and the V\'eron-Cetty & V\'eron (2010) catalogue to
search the entire sky for these ``changing-look'' AGNs using a variety of
selection methods, where Pan-STARRS has a coverage of 3$\pi$ steradians (sky
north of Declination $-30^\circ$) and SkyMapper has coverage of $\sim$
21,000$~\rm{deg^2}$ (sky south of Declination $0^\circ$). We use small aperture
photometry to measure how colour and flux have changed over time, where a
change may indicate a change in spectral type. Optical colour and flux are used
as a proxy for changing H$\alpha$ equivalent width, while WISE 3.4 $\mu$m flux
is used to look for changes in the hot dust component. We have identified four
AGNs with varying spectra selected using our optical colour selection method.
Three AGNs were confirmed from recent observations with WiFeS on the 2.3 m
telescope at Siding Spring and the other was identified from archival spectra
alone. From this, we identify two new changing look AGNs; NGC 1346 and 2MASX
J20075129-1108346. We also recover Mrk 915 and Mrk 609, which are known to have
varying spectra in the literature, but they do not meet our specific criteria
for changing look AGNs.


Abstract:
  Over the last two years, The Alan Turing Institute and the Information
Commissioner's Office (ICO) have been working together to discover ways to
tackle the difficult issues surrounding explainable AI. The ultimate product of
this joint endeavour, Explaining decisions made with AI, published in May 2020,
is the most comprehensive practical guidance on AI explanation produced
anywhere to date. We have put together this workbook to help support the uptake
of that guidance. The goal of the workbook is to summarise some of main themes
from Explaining decisions made with AI and then to provide the materials for a
workshop exercise that has been built around a use case created to help you
gain a flavour of how to put the guidance into practice. In the first three
sections, we run through the basics of Explaining decisions made with AI. We
provide a precis of the four principles of AI explainability, the typology of
AI explanations, and the tasks involved in the explanation-aware design,
development, and use of AI/ML systems. We then provide some reflection
questions, which are intended to be a launching pad for group discussion, and a
starting point for the case-study-based exercise that we have included as
Appendix B. In Appendix A, we go into more detailed suggestions about how to
organise the workshop. These recommendations are based on two workshops we had
the privilege of co-hosting with our colleagues from the ICO and Manchester
Metropolitan University in January 2021. The participants of these workshops
came from both the private and public sectors, and we are extremely grateful to
them for their energy, enthusiasm, and tremendous insight. This workbook would
simply not exist without the commitment and keenness of all our collaborators
and workshop participants.


Abstract:
  Over the last few years of the heyday of hybrid halide perovskites, so many
metal cations additives have been tested to improve their optoelectronic
properties that it is already difficult to find an element that has not yet
been tried. In general, the variety of these approaches is united under the
name "doping", however, there is currently no clear understanding of the
mechanisms of the influence of the metal ion additives on the properties of the
lead halide perovskite materials. For many ions there is even no consensus on
the most fundamental questions: what lattice position does a given ion occupy
and is it incorporated in the structure at all? Here, we derived a system of
effective radii of different metal ions in the iodine environment for the set
of iodide compounds and reveal their crystal chemical role in the APbI3
perovskites. We analysed the possible lattice positions for 40 most common
monovalent, divalent, and trivalent metals to reveal whether they could
successfully enter into the perovskite structures. We show that, at most, three
parameters - effective size, electronegativity and the softness of metal ions
are the main ones for crystal chemical analysis of the possibility of metal
doping of hybrid halide perovskites. Our results provide a useful theoretical
guidance to rationalize and improve current doping strategies of hybrid halide
perovskites with metal ions.


Abstract:
  In this paper, we examine the Hyers-Ulam and Hyers-Ulam-Rassias stability of
solutions of a general class of nonlinear Volterra integral equations. By using
a fixed point alternative and improving a technique commonly used in similar
problems, we extend and improve some well-known results on this problem. We
also provide some examples visualizing the improvement of the results
mentioned.


Abstract:
  We show that there is an equivalence of $\infty$-categories between Lie
algebroids and certain kinds of curved Lie algebras. For this we develop a
method to study the $\infty$-category of curved Lie algebras using the homotopy
theory of algebras over a complete operad.


Abstract:
  Robots and autonomous systems need to know where they are within a map to
navigate effectively. Thus, simultaneous localization and mapping or SLAM is a
common building block of robot navigation systems. When building a map via a
SLAM system, robots need to re-recognize places to find loop closure and reduce
the odometry drift. Image-based place recognition received a lot of attention
in computer vision, and in this work, we investigate how such approaches can be
used for 3D LiDAR data. Recent LiDAR sensors produce high-resolution 3D scans
in combination with comparably stable intensity measurements. Through a
cylindrical projection, we can turn this information into a panoramic image. As
a result, we can apply techniques from visual place recognition to LiDAR
intensity data. The question of how well this approach works in practice has
not been answered so far. This paper provides an analysis of how such visual
techniques can be with LiDAR data, and we provide an evaluation on different
datasets. Our results suggest that this form of place recognition is possible
and an effective means for determining loop closures.


Abstract:
  In this paper we explore whatever combining two chaotic dynamical systems
using the fuzzy logic operator XOR can maintain or not the chaotic properties
of the resulting dynamical system. This study is motivated by techniques used
in applications to secure communications ,images encryption and cryptography.


Abstract:
  Multi-person pose estimation and tracking serve as crucial steps for video
understanding. Most state-of-the-art approaches rely on first estimating poses
in each frame and only then implementing data association and refinement.
Despite the promising results achieved, such a strategy is inevitably prone to
missed detections especially in heavily-cluttered scenes, since this
tracking-by-detection paradigm is, by nature, largely dependent on visual
evidences that are absent in the case of occlusion. In this paper, we propose a
novel online approach to learning the pose dynamics, which are independent of
pose detections in current fame, and hence may serve as a robust estimation
even in challenging scenarios including occlusion. Specifically, we derive this
prediction of dynamics through a graph neural network~(GNN) that explicitly
accounts for both spatial-temporal and visual information. It takes as input
the historical pose tracklets and directly predicts the corresponding poses in
the following frame for each tracklet. The predicted poses will then be
aggregated with the detected poses, if any, at the same frame so as to produce
the final pose, potentially recovering the occluded joints missed by the
estimator. Experiments on PoseTrack 2017 and PoseTrack 2018 datasets
demonstrate that the proposed method achieves results superior to the state of
the art on both human pose estimation and tracking tasks.


Abstract:
  This article proves a weak Harnack inequality with a tail term for sign
changing supersolutions of a mixed local and nonlocal parabolic equation. Our
argument is purely analytic. It is based on energy estimates and the Moser
iteration technique. Instead of the parabolic John-Nirenberg lemma, we adopt a
lemma of Bombieri to the mixed local and nonlocal parabolic case. To this end,
we prove an appropriate reverse H\"older inequality and a logarithmic estimate
for weak supersolutions.


Abstract:
  Transportation services play a crucial part in the development of modern
smart cities. In particular, on-demand ridesharing services, which group
together passengers with similar itineraries, are already operating in several
metropolitan areas. These services can be of significant social and
environmental benefit, by reducing travel costs, road congestion and CO2
emissions.
  Unfortunately, despite their advantages, not many people opt to use these
ridesharing services. We believe that increasing the user satisfaction from the
service will cause more people to utilize it, which, in turn, will improve the
quality of the service, such as the waiting time, cost, travel time, and
service availability. One possible way for increasing user satisfaction is by
providing appropriate explanations comparing the alternative modes of
transportation, such as a private taxi ride and public transportation. For
example, a passenger may be more satisfied from a shared-ride if she is told
that a private taxi ride would have cost her 50% more. Therefore, the problem
is to develop an agent that provides explanations that will increase the user
satisfaction.
  We model our environment as a signaling game and show that a rational agent,
which follows the perfect Bayesian equilibrium, must reveal all of the
information regarding the possible alternatives to the passenger. In addition,
we develop a machine learning based agent that, when given a shared-ride along
with its possible alternatives, selects the explanations that are most likely
to increase user satisfaction. Using feedback from humans we show that our
machine learning based agent outperforms the rational agent and an agent that
randomly chooses explanations, in terms of user satisfaction.


Abstract:
  We present a versatile automated theorem proving framework which is capable
of automated proofs of outer bounds in network information theory, discovery of
inner bounds in network information theory (in conjunction with the method by
Lee and Chung), simplification of capacity regions involving auxiliary random
variables, deduction of properties of information-theoretic quantities (e.g.
Wyner and G\'acs-K\"orner common information), and discovery of
non-Shannon-type inequalities, under a unified framework. Our method is based
on the linear programming approach for proving Shannon-type information
inequalities studied by Yeung and Zhang, together with a novel pruning method
for searching auxiliary random variables. We introduce the concept of
existential information inequalities, which provides an axiomatic framework for
a wide range of problems in information theory. To demonstrate the use of the
framework, we present a new outer bound for the broadcast channel discovered by
the framework.