Abstract:
  We introduce a definition of the fractional Laplacian $(-\Delta)^{s(\cdot)}$
with spatially variable order $s:\Omega\to [0,1]$ and study the solvability of
the associated Poisson problem on a bounded domain $\Omega$. The initial
motivation arises from the extension results of Caffarelli and Silvestre, and
Stinga and Torrea; however the analytical tools and approaches developed here
are new. For instance, in some cases we allow the variable order $s(\cdot)$ to
attain the values $0$ and $1$ leading to a framework on weighted Sobolev spaces
with non-Muckenhoupt weights. Initially, and under minimal assumptions, the
operator $(-\Delta)^{s(\cdot)}$ is identified as the Lagrange multiplier
corresponding to an optimization problem; and its domain is determined as a
quotient space of weighted Sobolev spaces. The well-posedness of the associated
Poisson problem is then obtained for data in the dual of this quotient space.
Subsequently, two trace regularity results are established, allowing to
partially characterize functions in the aforementioned quotient space whenever
a Poincar\'e type inequality is available. Precise examples are provided where
such inequality holds, and in this case the domain of the operator
$(-\Delta)^{s(\cdot)}$ is identified with a subset of a weighted Sobolev space
with spatially variant smoothness $s(\cdot)$. The latter further allows to
prove the well-posedness of the Poisson problem assuming functional regularity
of the data.


Abstract:
  Following the collapse of their cores, some of the massive binary stars that
populate our Universe are expected to form merging binaries composed of black
holes and neutron stars. Gravitational-wave observations of the resulting
compact binaries can reveal precious details on the inner workings of the
supernova mechanism and the subsequent formation of compact objects. Within the
framework of the population-synthesis code MOBSE, we present the implementation
of a new supernova model that relies on the compactness of the collapsing star.
The model has two free parameters, namely the compactness threshold that
separates the formation of black holes and that of neutron stars, and the
fraction of the envelope that falls back onto the newly formed black holes. We
compare this model extensively against other prescriptions that are commonly
used in binary population synthesis. We find that the cleanest signatures of
the role of the pre-supernova stellar compactness are (i) the relative
formation rates of the different kinds of compact binaries, which mainly depend
on the compactness threshold parameter, and (ii) the location of the upper edge
of the mass gap between the lightest black holes and the heaviest neutron
stars, which mainly depends on the fallback fraction.


Abstract:
  We consider quantum stochastic processes and discuss a level 2.5 large
deviation formalism providing an explicit and complete characterisation of
fluctuations of time-averaged quantities, in the large-time limit. We analyse
two classes of quantum stochastic dynamics, within this framework. The first
class consists of the quantum jump trajectories related to photon detection;
the second is quantum state diffusion related to homodyne detection. For both
processes, we present the level 2.5 functional starting from the corresponding
quantum stochastic Schr\"odinger equation and we discuss connections of these
functionals to optimal control theory.


Abstract:
  Context: Prior research has established that a small proportion of
individuals dominate team communication during global software development. It
is not known, however, how these members' contributions affect their teams'
knowledge diffusion process, or whether their personality profiles are
responsible for their dominant presence. Objective: We set out to address this
gap through the study of repository artifacts. Method: Artifacts from ten teams
were mined from the IBM Rational Jazz repository. We employed social network
analysis (SNA) to group practitioners into two clusters, Top Members and
Others, based on the numbers of messages they communicated and their engagement
in task changes. SNA metrics (density, in-degree and closeness) were then used
to study practitioners' importance in knowledge diffusion. Thereafter, we
performed psycholinguistic analysis on practitioners' messages using linguistic
dimensions that had been previously correlated with the Big Five personality
profiles. Results: For our sample of 146 practitioners we found that Top
Members occupied critical roles in knowledge diffusion, and demonstrated more
openness to experience than the Others. Additionally, all personality profiles
were represented during teamwork, although openness to experience,
agreeableness and extroversion were particularly evident. However, no specific
personality predicted members' involvement in knowledge diffusion. Conclusion:
Task assignment that promotes highly connected team communication networks may
mitigate tacit knowledge loss in global software teams. Additionally, while
members expressing openness to experience are likely to be particularly driven
to perform, this is not entirely responsible for a global team's success.


Abstract:
  Since the first coronavirus case was identified in the U.S. on Jan. 21, more
than 1 million people in the U.S. have confirmed cases of COVID-19. This
infectious respiratory disease has spread rapidly across more than 3000
counties and 50 states in the U.S. and have exhibited evolutionary clustering
and complex triggering patterns. It is essential to understand the complex
spacetime intertwined propagation of this disease so that accurate prediction
or smart external intervention can be carried out. In this paper, we model the
propagation of the COVID-19 as spatio-temporal point processes and propose a
generative and intensity-free model to track the spread of the disease. We
further adopt a generative adversarial imitation learning framework to learn
the model parameters. In comparison with the traditional likelihood-based
learning methods, this imitation learning framework does not need to prespecify
an intensity function, which alleviates the model-misspecification. Moreover,
the adversarial learning procedure bypasses the difficult-to-evaluate integral
involved in the likelihood evaluation, which makes the model inference more
scalable with the data and variables. We showcase the dynamic learning
performance on the COVID-19 confirmed cases in the U.S. and evaluate the social
distancing policy based on the learned generative model.


Abstract:
  We report direct visualization of gigahertz-frequency Lamb waves propagation
in aluminum nitride phononic circuits by transmission-mode microwave impedance
microscopy (TMIM). Consistent with the finite-element modeling, the acoustic
eigenmodes in both a horn-shaped coupler and a sub-wavelength waveguide are
revealed in the TMIM images. Using fast Fourier transform filtering, we
quantitatively analyze the acoustic loss of individual Lamb modes along the
waveguide and the power coupling coefficient between the waveguide and the
parabolic couplers. Our work provides insightful information on the
propagation, mode conversion, and attenuation of acoustic waves in
piezoelectric nanostructures, which is highly desirable for designing and
optimizing phononic devices for microwave signal processing and quantum
information transduction.


Abstract:
  In two-dimensional random waves, phase singularities are point-like
dislocations with a behavior reminiscent of interacting particles. This --
qualitative -- consideration, stems from the spatial arrangement of these
entities, which finds its hallmark in a pair correlation reminiscent of
liquid-like system. Starting from their pair correlation function, we derive an
effective pair-interaction for phase singularities in random waves by using a
reverse Monte Carlo method. This study initiates a new approach for the
treatment of singularities in random waves and can be generalized to
topological defects in any system.


Abstract:
  The astrophysical sites where r-process elements are synthesized remain
mysterious: it is clear that neutron star mergers (kilonovae (KNe)) contribute,
and some classes of core-collapse supernovae (SNe) are also likely sources of
at least the lighter r-process species. The discovery of 60Fe on the Earth and
Moon implies that one or more astrophysical explosions have occurred near the
Earth within the last few million years, probably SNe. Intriguingly, 244Pu has
now been detected, mostly overlapping with 60Fe pulse. However, the 244Pu flux
may extend to before 12 Myr ago, pointing to a different origin. Motivated by
these observations and difficulties for r-process nucleosynthesis in SN models,
we propose that ejecta from a KN enriched the giant molecular cloud that gave
rise to the Local Bubble, where the Sun resides. Accelerator mass spectrometry
(AMS) measurements of 244Pu and searches for other live isotopes could probe
the origins of the r-process and the history of the solar neighborhood,
including triggers for mass extinctions, e.g., that at the end of the Devonian
epoch, motivating the calculations of the abundances of live r-process
radioisotopes produced in SNe and KNe that we present here. Given the presence
of 244Pu, other r-process species such as 93Zr, 107Pd, 129I, 135Cs, 182Hf,
236U, 237Np and 247Cm should be present. Their abundances and well-resolved
time histories could distinguish between the SN and KN scenarios, and we
discuss prospects for their detection in deep-ocean deposits and the lunar
regolith. We show that AMS 129I measurements in Fe-Mn crusts already constrain
a possible nearby KN scenario.


Abstract:
  An alias table is a data structure that allows for efficiently drawing
weighted random samples in constant time and can be constructed in linear time.
The PSA algorithm by H\"ubschle-Schneider and Sanders is able to construct
alias tables in parallel on the CPU. In this report, we transfer the PSA
algorithm to the GPU. Our construction algorithm achieves a speedup of 17 on a
consumer GPU in comparison to the PSA method on a 16-core high-end desktop CPU.
For sampling, we achieve an up to 24 times higher throughput. Both operations
also require several times less energy than on the CPU. Adaptations helping to
achieve this include changing memory access patterns to do coalesced access.
Where this is not possible, we first copy data to the faster shared memory
using coalesced access. We also enhance a generalization of binary search
enabling to search for a range of items in parallel. Besides naive sampling, we
also give improved batched sampling algorithms.


Abstract:
  The paper presents a discrete variation of the Frechet distance between
closed curves, which can be seen as an approximation of the continuous measure.
A rather straightforward approach to compute the discrete Frechet distance
between two closed sequences of m and n points using binary search takes O(mn
log mn) time. We present an algorithm that takes O(mn log* mn) time, where log*
is the iterated logarithm.


Abstract:
  In reinforcement learning, experience replay stores past samples for further
reuse. Prioritized sampling is a promising technique to better utilize these
samples. Previous criteria of prioritization include TD error, recentness and
corrective feedback, which are mostly heuristically designed. In this work, we
start from the regret minimization objective, and obtain an optimal
prioritization strategy for Bellman update that can directly maximize the
return of the policy. The theory suggests that data with higher hindsight TD
error, better on-policiness and more accurate Q value should be assigned with
higher weights during sampling. Thus most previous criteria only consider this
strategy partially. We not only provide theoretical justifications for previous
criteria, but also propose two new methods to compute the prioritization
weight, namely ReMERN and ReMERT. ReMERN learns an error network, while ReMERT
exploits the temporal ordering of states. Both methods outperform previous
prioritized sampling algorithms in challenging RL benchmarks, including MuJoCo,
Atari and Meta-World.


Abstract:
  Neural module networks (NMN) have achieved success in image-grounded tasks
such as Visual Question Answering (VQA) on synthetic images. However, very
limited work on NMN has been studied in the video-grounded language tasks.
These tasks extend the complexity of traditional visual tasks with the
additional visual temporal variance. Motivated by recent NMN approaches on
image-grounded tasks, we introduce Video-grounded Neural Module Network (VGNMN)
to model the information retrieval process in video-grounded language tasks as
a pipeline of neural modules. VGNMN first decomposes all language components to
explicitly resolve any entity references and detect corresponding action-based
inputs from the question. The detected entities and actions are used as
parameters to instantiate neural module networks and extract visual cues from
the video. Our experiments show that VGNMN can achieve promising performance on
two video-grounded language tasks: video QA and video-grounded dialogues.


Abstract:
  We study the following rainbow version of subgraph containment problems in a
family of (hyper)graphs, which generalizes the classical subgraph containment
problems in a single host graph. For a collection $\textbf{G}=\{G_1,
G_2,\ldots, G_{m}\}$ of not necessarily distinct graphs on the same vertex set
$[n]$, a (sub)graph $H$ on $[n]$ is rainbow if $E(H)\subseteq \bigcup_{i\in
[m]}E(G_i)$ and $|E(H)\cap E(G_i)|\le 1$ for $i\in[m]$. Note that if
$|E(H)|=m$, then a rainbow $H$ consists of exactly one edge from each $G_i$.
  Our main results are on rainbow clique-factors in (hyper)graph systems with
minimum $d$-degree conditions. In particular,
  (1) we obtain a rainbow analogue of an asymptotical version of the
Hajnal--Szemer\'{e}di theorem, namely, if $t\mid n$ and
$\delta(G_i)\geq(1-\frac{1}{t}+\varepsilon)n$ for each
$i\in[\frac{n}{t}\binom{t}{2}]$, then $\textbf{G}$ contains a rainbow
$K_t$-factor;
  (2) we prove that for $1\le d\le k-1$, essentially a minimum $d$-degree
condition forcing a perfect matching in a $k$-graph also forces rainbow perfect
matchings in $k$-graph systems.
  The degree assumptions in both results are asymptotically best possible
(although the minimum $d$-degree condition forcing a perfect matching in a
$k$-graph is in general unknown). For (1) we also discuss two directed versions
and a multipartite version. Finally, to establish these results, we in fact
provide a general framework to attack this type of problems, which reduces it
to subproblems with finitely many colors.


Abstract:
  It has been recently established by the first and third author that on
uniformly rectifiable sets the Green function is almost affine in the weak
sense, and moreover, in some scenarios such Green function estimates are
equivalent to the uniform rectifiability of a set. The present paper tackles a
strong analogue of these results, starting with the "flagship" degenerate
operators on sets with lower dimensional boundaries.
  We consider the elliptic operators $L_{\beta,\gamma} =- {\rm div}
D^{d+1+\gamma-n} \nabla$ associated to a domain $\Omega \subset \mathbb R^n$
with a uniformly rectifiable boundary $\Gamma$ of dimension $d < n-1$, the now
usual distance to the boundary $D = D_\beta$ given by $D_\beta(X)^{-\beta} =
\int_{\Gamma} |X-y|^{-d-\beta} d\sigma(y)$ for $X \in \Omega$, where $\beta >0$
and $\gamma \in (-1,1)$. In this paper we show that the Green function $G$ for
$L_{\beta,\gamma}$, with pole at infinity, is well approximated by multiples of
$D^{1-\gamma}$, in the sense that the function $\big| D\nabla\big(\ln\big(
\frac{G}{D^{1-\gamma}} \big)\big)\big|^2$ satisfies a Carleson measure estimate
on $\Omega$. We underline that the strong and the weak results are different in
nature and, of course, at the level of the proofs: the latter extensively used
compactness arguments, while the present paper relies on some intricate
integration by parts and the properties of the "magical" distance function from
a previous work from the first author, the third author, and Max Engelstein.


Abstract:
  We generalize the concept of a norm on a vector space to one of a norm on a
category. This provides a unified perspective on many specific matters in many
different areas of mathematics like set theory, functional analysis, measure
theory, topology, and metric space theory. We will especially address the two
last areas in which the monotone-light factorization and, respectively, the
Gromov-Hausdorff distance will naturally appear. In our formalization a
Schr\"oder-Bernstein property becomes an axiom of a norm which constitutes
interesting properties of the categories in question. The proposed concept
provides a convenient framework for metrizations.


Abstract:
  The quantum alcove model associated to a dominant weight plays an important
role in many branches of mathematics, such as combinatorial representation
theory, the theory of Macdonald polynomials, and Schubert calculus. For a
dominant weight, it is proved by Lenart-Lubovsky that the quantum alcove model
does not depend on the choice of a reduced alcove path, which is a shortest
path of alcoves from the fundamental one to its translation by the given
dominant weight. This is established through quantum Yang-Baxter moves, which
biject the objects of the model associated with two such alcove paths, and can
be viewed as a generalization of jeu de taquin slides to arbitrary root
systems. The purpose of this paper is to give a generalization of quantum
Yang-Baxter moves to the quantum alcove model corresponding to an arbitrary
weight, which was used to express a general Chevalley formula in the
equivariant $K$-group of semi-infinite flag manifolds. The generalized quantum
Yang-Baxter moves give rise to a "sijection" (bijection between signed sets),
and are shown to preserve certain important statistics, including weights and
heights. As an application, we prove that the generating function of these
statistics does not depend on the choice of a reduced alcove path. Also, we
obtain an identity for the graded characters of Demazure submodules of
level-zero extremal weight modules over a quantum affine algebra, which can be
thought of as a representation-theoretic analogue of the mentioned Chevalley
formula. Other applications and some open problems involving "signed crystals"
are discussed.


Abstract:
  The explosive growth of easily-accessible unlabeled data has lead to growing
interest in active learning, a paradigm in which data-hungry learning
algorithms adaptively select informative examples in order to lower
prohibitively expensive labeling costs. Unfortunately, in standard worst-case
models of learning, the active setting often provides no improvement over
non-adaptive algorithms. To combat this, a series of recent works have
considered a model in which the learner may ask enriched queries beyond labels.
While such models have seen success in drastically lowering label costs, they
tend to come at the expense of requiring large amounts of memory. In this work,
we study what families of classifiers can be learned in bounded memory. To this
end, we introduce a novel streaming-variant of enriched-query active learning
along with a natural combinatorial parameter called lossless sample compression
that is sufficient for learning not only with bounded memory, but in a
query-optimal and computationally efficient manner as well. Finally, we give
three fundamental examples of classifier families with small, easy to compute
lossless compression schemes when given access to basic enriched queries:
axis-aligned rectangles, decision trees, and halfspaces in two dimensions.


Abstract:
  The exact exchange-correlation (xc) kernel $f_\mathrm{xc}(x,x',\omega)$ of
linear response time-dependent density functional theory is computed over a
wide range of frequencies, for three canonical one-dimensional finite systems.
Methods used to ensure the numerical robustness of $f_\mathrm{xc}$ are set out.
The frequency dependence of $f_\text{xc}$ is found to be due largely to its
analytic structure, i.e. its singularities at certain frequencies, which are
required in order to capture particular transitions, including those of double
excitation character. However, within the frequency range of the first few
interacting excitations, $f_\mathrm{xc}$ is approximately frequency
independent, meaning the exact adiabatic approximation $f_\mathrm{xc}(\omega =
0)$ remedies the failings of the local density approximation and random phase
approximation for these lowest transitions. The key differences between the
exact $f_\mathrm{xc}$ and its common approximations are analyzed, and cannot be
eliminated by exploiting the limited gauge freedom in $f_\mathrm{xc}$. The
optical spectrum benefits from using as accurate as possible an $f_\mathrm{xc}$
and ground-state xc potential, while maintaining exact compatibility between
the two is of less importance.


Abstract:
  Recently, self-supervised learning methods have achieved remarkable success
in visual pre-training task. By simply pulling the different augmented views of
each image together or other novel mechanisms, they can learn much unsupervised
knowledge and significantly improve the transfer performance of pre-training
models. However, these works still cannot avoid the representation collapse
problem, i.e., they only focus on limited regions or the extracted features on
totally different regions inside each image are nearly the same. Generally,
this problem makes the pre-training models cannot sufficiently describe the
multi-grained information inside images, which further limits the upper bound
of their transfer performance. To alleviate this issue, this paper introduces a
simple but effective mechanism, called Exploring the Diversity and Invariance
in Yourself E-DIY. By simply pushing the most different regions inside each
augmented view away, E-DIY can preserve the diversity of extracted region-level
features. By pulling the most similar regions from different augmented views of
the same image together, E-DIY can ensure the robustness of region-level
features. Benefited from the above diversity and invariance exploring
mechanism, E-DIY maximally extracts the multi-grained visual information inside
each image. Extensive experiments on downstream tasks demonstrate the
superiority of our proposed approach, e.g., there are 2.1% improvements
compared with the strong baseline BYOL on COCO while fine-tuning Mask R-CNN
with the R50-C4 backbone and 1X learning schedule.


Abstract:
  Reinforcement learning control of an underground loader is investigated in
simulated environment, using a multi-agent deep neural network approach. At the
start of each loading cycle, one agent selects the dig position from a depth
camera image of the pile of fragmented rock. A second agent is responsible for
continuous control of the vehicle, with the goal of filling the bucket at the
selected loading point, while avoiding collisions, getting stuck, or losing
ground traction. It relies on motion and force sensors, as well as on camera
and lidar. Using a soft actor-critic algorithm the agents learn policies for
efficient bucket filling over many subsequent loading cycles, with clear
ability to adapt to the changing environment. The best results, on average 75%
of the max capacity, are obtained when including a penalty for energy usage in
the reward.


Abstract:
  The widespread adoption of Deep Neural Networks (DNNs) in important domains
raises questions about the trustworthiness of DNN outputs. Even a highly
accurate DNN will make mistakes some of the time, and in settings like
self-driving vehicles these mistakes must be quickly detected and properly
dealt with in deployment. Just as our community has developed effective
techniques and mechanisms to monitor and check programmed components, we
believe it is now necessary to do the same for DNNs. In this paper we present
DNN self-checking as a process by which internal DNN layer features are used to
check DNN predictions. We detail SelfChecker, a self-checking system that
monitors DNN outputs and triggers an alarm if the internal layer features of
the model are inconsistent with the final prediction. SelfChecker also provides
advice in the form of an alternative prediction. We evaluated SelfChecker on
four popular image datasets and three DNN models and found that SelfChecker
triggers correct alarms on 60.56% of wrong DNN predictions, and false alarms on
2.04% of correct DNN predictions. This is a substantial improvement over prior
work (SELFORACLE, DISSECTOR, and ConfidNet). In experiments with self-driving
car scenarios, SelfChecker triggers more correct alarms than SELFORACLE for two
DNN models (DAVE-2 and Chauffeur) with comparable false alarms. Our
implementation is available as open source.


Abstract:
  Artificial organisms are computer programs that self-replicate, mutate,
compete and evolve. How do these lifelike information-processing behaviours
could arise in diverse far-from-equilibrium physical systems remains an open
question. Here, I devise a toy model where the onset of self-replication of a
quantum artificial organism (a chain of lambda systems) is owing to
single-photon pulses added to a zero-temperature environment. The model results
in a replication probability that is proportional to the absorbed work from the
photon, in agreement with the theory of dissipative adaptation. Unexpectedly,
spontaneous mutations are unavoidable in this model, due to rare but finite
absorption of off-resonant photons. These results hint at self-replication as a
possible link between dissipative adaptation and open-ended evolution.


Abstract:
  ProtoDUNE-DP is a 6x6x6 m3 liquid argon time-projection-chamber operated at
the CERN Neutrino Platform in 2019-2020 as a prototype of the Dual Phase
concept for the DUNE Far Detector. The Photon Detection System (PDS) is based
on 36 8-inch photo-multiplier tubes (PMTs) and allows triggering on the
scintillation light signals produced by cosmic rays and other charged particles
traversing the detector. The acquisition and calibration software specifically
developed for the ProtoDUNE-DP PDS is described in this paper. This software
controls the high-voltage power supplies, the calibration system, and the PDS
DAQ. It has been developed with Qt Creator, and features different operation
modes, and a graphical user interface. This software has already been validated
and used during the ProtoDUNE-DP operation.


Abstract:
  There is sustained and widespread interest in understanding the limit, if
any, to the human lifespan. Apart from its intrinsic and biological interest,
changes in survival in old age have implications for the sustainability of
social security systems. A central question is whether the endpoint of the
underlying lifetime distribution is finite. Recent analyses of data on the
oldest human lifetimes have led to competing claims about survival and to some
controversy, due in part to incorrect statistical analysis. This paper
discusses the particularities of such data, outlines correct ways of handling
them and presents suitable models and methods for their analysis. We provide a
critical assessment of some earlier work and illustrate the ideas through
reanalysis of semi-supercentenarian lifetime data. Our analysis suggests that
remaining life-length after age 109 is exponentially distributed, and that any
upper limit lies well beyond the highest lifetime yet reliably recorded. Lower
limits to 95% confidence intervals for the human lifespan are around 130 years,
and point estimates typically indicate no upper limit at all.


Abstract:
  Data augmentation has been shown to effectively improve the performance of
multimodal machine learning models. This paper introduces a generative model
for data augmentation by leveraging the correlations among multiple modalities.
Different from conventional data augmentation approaches that apply low-level
operations with deterministic heuristics, our method learns a generator that
generates samples of the target modality conditioned on observed modalities in
the variational auto-encoder framework. Additionally, the proposed model is
able to quantify the confidence of augmented data by its generative
probability, and can be jointly optimised with a downstream task. Experiments
on Visual Question Answering as downstream task demonstrate the effectiveness
of the proposed generative model, which is able to improve strong UpDn-based
models to achieve state-of-the-art performance.


Abstract:
  We present an approach to measure the Milky Way (MW) potential using the
angular accelerations of stars in aggregate as measured by astrometric surveys
like Gaia. Accelerations directly probe the gradient of the MW potential, as
opposed to indirect methods using e.g. stellar velocities. We show that
end-of-mission Gaia stellar acceleration data may be used to measure the
potential of the MW disk at approximately 3$\sigma$ significance and, if recent
measurements of the solar acceleration are included, the local dark matter
density at ~2$\sigma$ significance. Since the significance of detection scales
steeply as $t^{5/2}$ for observing time $t$, future surveys that include
angular accelerations in the astrometric solutions may be combined with Gaia to
precisely measure the local dark matter density and shape of the density
profile.


Abstract:
  Recently it was shown that a certain class of phylogenetic networks, called
level-$2$ networks, cannot be reconstructed from their associated distance
matrices. In this paper, we show that they can be reconstructed from their
induced shortest and longest distance matrices. That is, if two level-$2$
networks induce the same shortest and longest distance matrices, then they must
be isomorphic. We further show that level-$2$ networks are reconstructible from
their shortest distance matrices if and only if they do not contain a subgraph
from a family of graphs. A generator of a network is the graph obtained by
deleting all pendant subtrees and suppressing degree-$2$ vertices. We also show
that networks with a leaf on every generator side is reconstructible from their
induced shortest distance matrix, regardless of level.


Abstract:
  AstroSat is India's first dedicated multi-wavelength space observatory
launched by the Indian Space Research Organisation (ISRO) on 28 September 2015.
After launch, the AstroSat Science Support Cell (ASSC) was set up as a joint
venture of ISRO and the Inter-University Centre for Astronomy and Astrophysics
(IUCAA) with the primary purpose of facilitating the use of AstroSat, both for
making observing proposals and for utilising archival data. The ASSC organises
meetings, workshops and webinars to train users in these activities, runs a
help desk to address user queries, provides utility tools and disseminates
analysis software through a consolidated web portal. It also maintains the
AstroSat Proposal Processing System (APPS) which is deployed at ISSDC, a
software platform central to the workflow management of AstroSat operations.
This paper illustrates the various aspects of ASSC functionality.


Abstract:
  As the data scale grows, deep recognition models often suffer from
long-tailed data distributions due to the heavy imbalanced sample number across
categories. Indeed, real-world data usually exhibit some similarity relation
among different categories (e.g., pigeons and sparrows), called category
similarity in this work. It is doubly difficult when the imbalance occurs
between such categories with similar appearances. However, existing solutions
mainly focus on the sample number to re-balance data distribution. In this
work, we systematically investigate the essence of the long-tailed problem from
a unified perspective. Specifically, we demonstrate that long-tailed
recognition suffers from both sample number and category similarity.
Intuitively, using a toy example, we first show that sample number is not the
unique influence factor for performance dropping of long-tailed recognition.
Theoretically, we demonstrate that (1) category similarity, as an inevitable
factor, would also influence the model learning under long-tailed distribution
via similar samples, (2) using more discriminative representation methods
(e.g., self-supervised learning) for similarity reduction, the classifier bias
can be further alleviated with greatly improved performance. Extensive
experiments on several long-tailed datasets verify the rationality of our
theoretical analysis, and show that based on existing state-of-the-arts
(SOTAs), the performance could be further improved by similarity reduction. Our
investigations highlight the essence behind the long-tailed problem, and claim
several feasible directions for future work.


Abstract:
  The existence of a constant density of two-level systems (TLS) was proposed
as the basis of some intriguing universal aspects of glasses at ultra-low
temperatures. Here we ask whether their existence is necessary for explaining
the universal density of states quasi-localized modes (QLM) in glasses at
ultra-low temperatures. A careful examination of the QLM that exist in a
generic atomistic model of a glass former reveals at least two types of them,
each exhibiting a different density of states, one depending on the frequency
as $\omega^3$ and the other as $\omega^4$. The properties of the glassy energy
landscape that is responsible for the two types of modes is examined here,
explaining the analytic feature responsible for the creations of (at least) two
families of QLM's. Although adjacent wells certainly exist in the complex
energy landscape of glasses, doubt is cast on the relevance of TLS for the
universal density of QLM's.


Abstract:
  The optical polarization plane of some blazars occasionally exhibits smooth
hundred degree long rotations. Multiple theoretical models have been proposed
to explain the nature of such events. A deterministic origin of these
rotations, however, remains uncertain. We aim to find repeating patterns of
flares in gamma-ray light curves of blazars, which accompany optical
polarization plane rotations. Such patterns have been predicted to occur by one
of the models explaining this phenomenon. For the blazar 3C 279, where multiple
polarization plane rotations have been reported in the literature, we obtain
the Fermi-LAT gamma-ray light curve and analyze its intervals adjacent to
polarization plane rotations. We find a complex characteristic pattern of
flares in the gamma-ray light curve that is repeated during periods adjacent to
three large amplitude EVPA rotation events in 3C 279. We discover a "hidden
EVPA rotation", which can only be seen in the relative Stokes parameters plane
and that occurred simultaneously with the fourth repetition of the pattern.
This finding strongly favors the hypothesis of emission features propagating in
the jet as the reason of optical polarization plane rotations. Furthermore, it
is compatible with the hypothesis of a sheath in the jet comprised of more
slowly propagating emission features.


Abstract:
  Low-scale baryogenesis could be discovered at $B$-factories and the LHC. In
the $B$-Mesogenesis paradigm [G. Elor, M. Escudero, and A. E. Nelson, PRD 99,
035031 (2019), arXiv:1810.00880], the CP violating oscillations and subsequent
decays of $B$ mesons in the early Universe simultaneously explain the origin of
the baryonic and the dark matter of the Universe. This mechanism for baryo- and
dark matter genesis from $B$ mesons gives rise to distinctive signals at
collider experiments, which we scrutinize in this paper. We study CP violating
observables in the $B^0_q-\bar{B}_q^0$ system, discuss current and expected
sensitivities for the exotic decays of $B$ mesons into a visible baryon and
missing energy, and explore the implications of direct searches for a TeV-scale
colored scalar at the LHC and in meson-mixing observables. Remarkably, we
conclude that a combination of measurements at BaBar, Belle, Belle II, LHCb,
ATLAS and CMS can fully test $B$-Mesogenesis.


Abstract:
  Resolvent analysis is performed to identify the origin of two-dimensional
transonic buffet over an airfoil. The base flow for the resolvent analysis is
the time-averaged flow over a NACA 0012 airfoil at a chord-based Reynolds
number of 2000 and a free-stream Mach number of 0.85. We reveal that the
mechanism of buffet is buried underneath the global low-Reynolds-number flow
physics. At this low Reynolds number, the dominant flow feature is the von
Karman shedding. However, we show that with the appropriate forcing input,
buffet can appear even at a Reynolds number that is much lower than what is
traditionally associated with transonic buffet. The source of buffet is
identified to be at the shock foot from the windowed resolvent analysis, which
is validated by companion simulations using sustained forcing inputs based on
resolvent modes. We also comment on the role of perturbations in the vicinity
of the trailing edge. The present study not only provides insights on the
origin of buffet but also serves a building block for low-Reynolds-number
compressible aerodynamics in light of the growing interests in Martian flights.


Abstract:
  In this paper, we consider the stabilization of wave equations with moving
boundary. First, we show the solution behaviour of wave equation with Neumann
boundary conditions, that is, the energy of wave equation with mixed boundary
conditions may decrease, increase or conserve depending on the different range
of parameter. Second, we prove the wellposedness and stabilization for the wave
equation with time delay and moving boundary.


Abstract:
  This paper establishes a central limit theorem under the assumption that
conditional variances can vary in a largely unstructured history-dependent way
across experiments subject only to the restriction that they lie in a fixed
interval. Limits take a novel and tractable form, and are expressed in terms of
oscillating Brownian motion. A second contribution is application of this
result to a class of multi-armed bandit problems where the decision-maker is
loss averse.


Abstract:
  We develop a flow renormalization approach for periodically-driven quantum
systems, which reveals prethermal dynamical regimes and associated timescales
via direct correspondence between real time and flow time behavior. In this
formalism, the dynamical problem is recast in terms of coupling constants of
the theory flowing towards an attractive fixed point that represents the
thermal Floquet Hamiltonian at long times, while narrowly avoiding a series of
unstable fixed points which determine distinct prethermal regimes at
intermediate times. We study a class of relevant perturbations that trigger the
onset of heating and thermalization, and demonstrate that the renormalization
flow has an elegant representation in terms of a flow of matrix product
operators. Our results permit microscopic calculations of the emergence of
distinct dynamical regimes directly in the thermodynamic limit in an efficient
manner, establishing a new computational tool for driven non-equilibrium
systems.


Abstract:
  We re-examine the XUV luminosity evolution of TRAPPIST-1 utilizing new
observational constraints (XUV and bolometric luminosity) from multi-epoch
X-ray/UV photometry. Following the formalism presented in Fleming et al.
(2020), we infer that TRAPPIST-1 maintained a saturated XUV luminosity,
relative to the bolometric luminosity, of $\log_{10}$(L$_{\rm XUV}$/L$_{\rm
bol}$) $= -3.03_{-0.23}^{+0.25}$ at early times for a period of $t_{sat} =
3.14_{-1.46}^{+2.22}$ Gyr. After the saturation phase, we find L$_{\rm XUV}$
decayed over time by an exponential rate of $\beta_{\rm XUV} =
-1.17_{-0.28}^{+0.27}$. Compared to our inferred age of the system, ${\rm age}
= 7.96_{-1.87}^{+1.78}$ Gyr, our result for $t_{sat}$ suggests that there is
only a $\sim4\%$~chance that TRAPPIST-1 still remains in the saturated phase
today, which is significantly lower than the previous estimate of 40\%. Despite
this reduction in $t_{sat}$, our results remain consistent in the conclusion
that the TRAPPIST-1 planets likely received an extreme amount XUV energy -- an
estimated integrated XUV energy of $\sim10^{30}-10^{32}$ erg over the star's
lifetime.


Abstract:
  This paper studies task adaptive pre-trained model selection, an
underexplored problem of assessing pre-trained models for the target task and
select best ones from the model zoo \emph{without fine-tuning}. A few pilot
works addressed the problem in transferring supervised pre-trained models to
classification tasks, but they cannot handle emerging unsupervised pre-trained
models or regression tasks. In pursuit of a practical assessment method, we
propose to estimate the maximum value of label evidence given features
extracted by pre-trained models. Unlike the maximum likelihood, the maximum
evidence is \emph{immune to over-fitting}, while its expensive computation can
be dramatically reduced by our carefully designed algorithm. The Logarithm of
Maximum Evidence (LogME) can be used to assess pre-trained models for transfer
learning: a pre-trained model with a high LogME value is likely to have good
transfer performance. LogME is \emph{fast, accurate, and general},
characterizing itself as the first practical method for assessing pre-trained
models. Compared with brute-force fine-tuning, LogME brings at most
$3000\times$ speedup in wall-clock time and requires only $1\%$ memory
footprint. It outperforms prior methods by a large margin in their setting and
is applicable to new settings. It is general enough for diverse pre-trained
models (supervised pre-trained and unsupervised pre-trained), downstream tasks
(classification and regression), and modalities (vision and language). Code is
available at this repository:
\href{https://github.com/thuml/LogME}{https://github.com/thuml/LogME}.


Abstract:
  Fe$_{1+x}$Te is a two dimensional van der Waals antiferromagnet that becomes
superconducting on anion substitution on the Te site. The parent phase of
Fe$_{1+x}$Te is sensitive to the amount of interstitial iron situated between
the iron-tellurium layers displaying collinear magnetic order coexisting with
low temperature metallic resistivity for small concentrations of interstitial
iron $x$ and helical magnetic order for large values of $x$. While this phase
diagram has been established through scattering [see for example E. E.
Rodriguez $\textit{et al.}$ Phys. Rev. B ${\bf{84}}$, 064403 (2011) and S.
R\"ossler $\textit{et al.}$ Phys. Rev. B ${\bf{84}}$, 174506 (2011)], recent
scanning tunnelling microscopy measurements [C. Trainer $\textit{et al.}$ Sci.
Adv. ${\bf{5}}$, eaav3478 (2019)] have observed a different magnetic structure
for small interstitial iron concentrations $x$ with a significant canting of
the magnetic moments along the crystallographic $c$ axis of $\theta$=28 $\pm$
3$^{\circ}$. In this paper, we revisit the magnetic structure of Fe$_{1.09}$Te
using spherical neutron polarimetry and scanning tunnelling microscopy to
search for this canting in the bulk phase and compare surface and bulk
magnetism. The results show that the bulk magnetic structure of Fe$_{1.09}$Te
is consistent with collinear in-plane order ($\theta=0$ with an error of $\sim$
5$^{\circ}$). Comparison with scanning tunnelling microscopy on a series of
Fe$_{1+x}$Te samples reveals that the surface exhibits a magnetic surface
reconstruction with a canting angle of the spins of $\theta=29.8^{\circ}$. We
suggest that this is a consequence of structural relaxation of the surface
layer resulting in an out-of-plane magnetocrystalline anisotropy. The magnetism
in Fe$_{1+x}$Te displays different properties at the surface when the symmetry
constraints of the bulk are removed.


Abstract:
  Inspired by the stringy quintessence F-term problem we highlight a generic
contribution to the effective moduli masses that arises due to kinetic mixings
between the moduli and the quintessence sector. We then proceed to discuss few
supergravity toy models that accommodate such effect, and point out possible
shortcomings. Interestingly, in the standard 2-derivative supergravity action
there is no term to mediate the supersymmetry breaking from the kinetic
quintessence sector to the gaugini and generate Majorana masses. Therefore we
also propose a 2-derivative supersymmetric invariant that plays exactly this
role.


Abstract:
  Extracting patterns and useful information from Natural Language datasets is
a challenging task, especially when dealing with data written in a language
different from English, like Italian. Machine and Deep Learning, together with
Natural Language Processing (NLP) techniques have widely spread and improved
lately, providing a plethora of useful methods to address both Supervised and
Unsupervised problems on textual information. We propose RECKONition, a
NLP-based system for Industrial Accidents at Work Prevention. RECKONition,
which is meant to provide Natural Language Understanding, Clustering and
Inference, is the result of a joint partnership with the Italian National
Institute for Insurance against Accidents at Work (INAIL). The obtained results
showed the ability to process textual data written in Italian describing
industrial accidents dynamics and consequences.


Abstract:
  We investigate the claim that the Utah congressional districts enacted in
2011 represent an unfair Republican gerrymander, and we evaluate the most
common measures of partisan fairness and gerrymandering in the context of
Utah's congressional districts. We do this by generating large ensembles of
alternative redistricting plans using Markov chain Monte Carlo methods. We also
propose a new metric of partisan fairness in Utah, namely, the Republican vote
share in the least-Republican district. This metric only makes sense in
settings with at most one competitive district, but it is very effective for
quantifying gerrymandering and for evaluating other metrics used in such
settings.


Abstract:
  In the spring of 2017 an ER-2 aircraft campaign was undertaken over
continental United States to observe energetic radiation from thunderstorms and
lightning. The payload consisted of a suite of instruments designed to detect
optical signals, electric fields, and gamma rays from lightning. Starting from
Georgia, USA, 16 flights were performed, for a total of about 70 flight hours
at a cruise altitude of 20 km. Of these, 45 flight hours were over thunderstorm
regions. An analysis of two gamma ray glow events that were observed over
Colorado at 21:47 UT on 8May 2017 is presented.We explore the charge structure
of the cloud system, as well as possible mechanisms that can produce the gamma
ray glows. The thundercloud system we passed during the gamma ray glow
observation had strong convection in the core of the cloud system. Electric
field measurements combined with radar and radio measurements suggest an
inverted charge structure, with an upper negative charge layer and a lower
positive charge layer. Based on modeling results, we were not able to
unambiguously determine the production mechanism. Possible mechanisms are
either an enhancement of cosmic background locally (above or below 20 km) by an
electric field below the local threshold or an enhancement of the cosmic
background inside the cloud but then with normal polarity and an electric field
well above the Relativistic Runaway Electron Avalanche threshold


Abstract:
  After providing a brief genealogy of our recently proposed model for High-Tc
cuprates, we investigate the details of the microscopic mechanism that produces
an attractive interaction between neighboring holes. We show that a peculiar
arrangement of the $p_x$ and $p_y$ oxygen orbitals makes the mutual magnetic
interaction of the holes with the localized copper ions to produce a net
attractive interaction between themselves, which is responsible for the
emergence of a superconducting phase. We also study the connection existing
between the proposed pseudogap order parameter and the spectral density. We
show that the occurrence of two sharp peaks in the latter, between which the
density of states suffers a depletion is a direct consequence of the d-wave
character of the pseudogap order parameter dependence on $\mathbf{k}$, which
breaks the 90$^\circ$-rotation symmetry of the oxygen lattices. The peak
separation in the spectral density works effectively as an overall pseudogap
order parameter for the cuprates. We explicitly calculate the spectral density
in the strange metal and pseudogap phases of Bi2212, at different temperatures,
and show that our results compare very well with the experimental data.


Abstract:
  We study the flat-band ferromagnetic phase of the Haldane-Hubbard model on a
honeycomb lattice within a bosonization scheme for flat-band Chern insulators,
focusing on the calculation of the spin-wave excitation spectrum. We consider
the Haldane-Hubbard model with the noninteracting lower bands in a nearly-flat
band limit, previously determined for the spinless model, and at 1/4-filling of
its corresponding noninteracting limit. Within the bosonization scheme, the
Haldane-Hubbard model is mapped into an effective interacting boson model,
whose quadratic term allows us to determine the spin-wave spectrum at the
harmonic approximation. We show that the excitation spectrum has two branches
with a Goldstone mode and Dirac points at center and at the K and K' points of
the first Brillouin zone, respectively. We also consider the effects on the
spin-wave spectrum due to an energy offset in the on-site Hubbard repulsion
energies and due to the presence of an staggered on-site energy term, both
quantities associated with the two triangular sublattices. In both cases, we
find that an energy gap opens at the K and K' points. Moreover, we also find
some evidences for an instability of the flat-band ferromagnetic phase in the
presence of the staggered on-site energy term. We provide some additional
results for the square lattice topological Hubbard model previous studied
within the bosonization formalism and comment on the differences between the
bosonization scheme implementation for the correlated Chern insulators on both
square and honeycomb lattices.


Abstract:
  The Sun is the primary source of energy for the Earth. The small changes in
total solar irradiance (TSI) can affect our climate in the longer timescale. In
the evolutionary timescale, the TSI varies by a large amount and hence its
influence on the Earth's mean surface temperature (T$_{s}$) also increases
significantly. We develop a mass-loss dependent analytical model of TSI in the
evolutionary timescale and evaluated its influence on the T$_{s}$. We
determined the numerical solution of TSI for the next 8.23 Gyrs to be used as
an input to evaluate the T$_{s}$ which formulated based on a zero-dimensional
energy balance model. We used the present-day albedo and bulk atmospheric
emissivity of the Earth and Mars as initial and final boundary conditions,
respectively. We found that the TSI increases by 10\% in 1.42 Gyr, by 40\% in
about 3.4 Gyrs, and by 120\% in about 5.229 Gyrs from now, while the T$_{s}$
shows an insignificant change in 1.644 Gyrs and increases to 298.86 K in about
3.4 Gyrs. The T$_{s}$ attains the peak value of 2319.2 K as the Sun evolves to
the red giant and emits the enormous TSI of 7.93$\times10^{6} Wm^{-2}$ in 7.676
Gys. At this temperature Earth likely evolves to be a liquid planet. In our
finding, the absorbed and emitted flux equally increases and approaches the
surface flux in the main sequence, and they are nearly equal beyond the main
sequence, while the flux absorbed by the cloud shows opposite trend.


Abstract:
  We introduce a new two-sided stable matching problem that describes the
summer internship matching practice of an Australian university. The model is a
case between two models of Kamada and Kojima on matchings with distributional
constraints. We study three solution concepts, the strong and weak stability
concepts proposed by Kamada and Kojima, and a new one in between the two,
called cutoff stability. Kamada and Kojima showed that a strongly stable
matching may not exist in their most restricted model with disjoint regional
quotas. Our first result is that checking its existence is NP-hard. We then
show that a cutoff stable matching exists not just for the summer internship
problem but also for the general matching model with arbitrary heredity
constraints. We present an algorithm to compute a cutoff stable matching and
show that it runs in polynomial time in our special case of summer internship
model. However, we also show that finding a maximum size cutoff stable matching
is NP-hard, but we provide a Mixed Integer Linear Program formulation for this
optimisation problem.


Abstract:
  Convolutional neural networks have allowed remarkable advances in single
image super-resolution (SISR) over the last decade. Among recent advances in
SISR, attention mechanisms are crucial for high-performance SR models. However,
the attention mechanism remains unclear on why and how it works in SISR. In
this work, we attempt to quantify and visualize attention mechanisms in SISR
and show that not all attention modules are equally beneficial. We then propose
attention in attention network (A$^2$N) for more efficient and accurate SISR.
Specifically, A$^2$N consists of a non-attention branch and a coupling
attention branch. A dynamic attention module is proposed to generate weights
for these two branches to suppress unwanted attention adjustments dynamically,
where the weights change adaptively according to the input features. This
allows attention modules to specialize to beneficial examples without otherwise
penalties and thus greatly improve the capacity of the attention network with
few parameters overhead. Experimental results demonstrate that our final model
A$^2$N could achieve superior trade-off performances comparing with
state-of-the-art networks of similar sizes. Codes are available at
https://github.com/haoyuc/A2N.


Abstract:
  Recent advances in self-attention and pure multi-layer perceptrons (MLP)
models for vision have shown great potential in achieving promising performance
with fewer inductive biases. These models are generally based on learning
interaction among spatial locations from raw data. The complexity of
self-attention and MLP grows quadratically as the image size increases, which
makes these models hard to scale up when high-resolution features are required.
In this paper, we present the Global Filter Network (GFNet), a conceptually
simple yet computationally efficient architecture, that learns long-term
spatial dependencies in the frequency domain with log-linear complexity. Our
architecture replaces the self-attention layer in vision transformers with
three key operations: a 2D discrete Fourier transform, an element-wise
multiplication between frequency-domain features and learnable global filters,
and a 2D inverse Fourier transform. We exhibit favorable accuracy/complexity
trade-offs of our models on both ImageNet and downstream tasks. Our results
demonstrate that GFNet can be a very competitive alternative to
transformer-style models and CNNs in efficiency, generalization ability and
robustness. Code is available at https://github.com/raoyongming/GFNet


Abstract:
  A uniformly accelerated detector in an inertial vacuum undergoes an
unavoidable dissipation, and the final steady-state becomes thermal. However,
to attain such a mixed state, there is no bound for the acceleration of the
single atomic detector. Here we show that the scenario is entirely different
for two atoms with the same energy levels. There exists a critical limit of the
acceleration for two atomic detectors, below which the purity of a particular
initial state can be preserved. We observe that the generator of the
dissipative dynamics (Lindbladian) is invariant under a weak symmetry
transformation at this limit. Hence one of the eigenstates of the symmetry
operator is unchanged during the evolution. This kind of state is called a
quantum dark state, which is essentially a decoherence-free subspace. As a
consequence, the system becomes localized, and it can skip the Unruh
thermalization. Beyond the critical limit, the symmetry is explicitly broken.
Therefore our results suggest that the system goes through a first-order
dissipative phase transition from a localized to a thermal phase.


Abstract:
  A method is proposed with which the locations of the roots of the monic
symbolic quintic polynomial $x^5 + a_4 x^4 + a_3 x^3 + a_2 x^2 + a_1 x + a_0$
can be determined using the roots of two resolvent quadratic polynomials:
$q_1(x) = x^2 + a_4 x + a_3$ and $q_2(x) = a_2 x^2 + a_1 x + a_0$, whose
coefficients are exactly those of the quintic polynomial. The different cases
depend on the coefficients of $q_1(x)$ and $q_2(x)$ and on some specific
relationships between them. The method is illustrated with the full analysis of
one of the possible cases. Some of the roots of the symbolic quintic equation
for this case have their isolation intervals determined and, as this cannot be
done for all roots with the help of quadratic equations only, finite intervals
containing 1 or 3 roots, or 0 or 2 roots, or, rarely, 0, or 2, or 4 roots of
the quintic are identified. Knowing the stationary points of the quintic
polynomial, lifts the latter indeterminacy and allows one to find the isolation
interval of each of the roots of the quintic. Separately, using the complete
root classification of the quintic, one can also lift this indeterminacy. The
method also allows to see how variation of the individual coefficients of the
quintic affect its roots. No root finding iterations or any numerical
approximations are used and no equations of degree higher than 2 are solved.


Abstract:
  A potential Mars Sample Return (MSR) architecture is being jointly studied by
NASA and ESA. As currently envisioned, the MSR campaign consists of a series of
3 missions: sample cache, fetch and return to Earth. In this paper, we focus on
the fetch part of the MSR, and more specifically the problem of autonomously
detecting and localizing sample tubes deposited on the Martian surface. Towards
this end, we study two machine-vision based approaches: First, a
geometry-driven approach based on template matching that uses hard-coded
filters and a 3D shape model of the tube; and second, a data-driven approach
based on convolutional neural networks (CNNs) and learned features.
Furthermore, we present a large benchmark dataset of sample-tube images,
collected in representative outdoor environments and annotated with ground
truth segmentation masks and locations. The dataset was acquired systematically
across different terrain, illumination conditions and dust-coverage; and
benchmarking was performed to study the feasibility of each approach, their
relative strengths and weaknesses, and robustness in the presence of adverse
environmental conditions.


Abstract:
  Existing methods for skeleton-based action recognition mainly focus on
improving the recognition accuracy, whereas the efficiency of the model is
rarely considered. Recently, there are some works trying to speed up the
skeleton modeling by designing light-weight modules. However, in addition to
the model size, the amount of the data involved in the calculation is also an
important factor for the running speed, especially for the skeleton data where
most of the joints are redundant or non-informative to identify a specific
skeleton. Besides, previous works usually employ one fix-sized model for all
the samples regardless of the difficulty of recognition, which wastes
computations for easy samples. To address these limitations, a novel approach,
called AdaSGN, is proposed in this paper, which can reduce the computational
cost of the inference process by adaptively controlling the input number of the
joints of the skeleton on-the-fly. Moreover, it can also adaptively select the
optimal model size for each sample to achieve a better trade-off between
accuracy and efficiency. We conduct extensive experiments on three challenging
datasets, namely, NTU-60, NTU-120 and SHREC, to verify the superiority of the
proposed approach, where AdaSGN achieves comparable or even higher performance
with much lower GFLOPs compared with the baseline method.


Abstract:
  Transition probability densities are fundamental to option pricing. Advancing
recent work in deep learning, we develop novel transition density function
generators through solving backward Kolmogorov equations in parametric space
for cumulative probability functions, using neural networks to obtain accurate
approximations of transition probability densities, creating ultra-fast
transition density function generators offline that can be trained for any
underlying. These are 'single solve' , so they do not require recalculation
when parameters are changed (e.g. recalibration of volatility) and are portable
to other option pricing setups as well as to less powerful computers, where
they can be accessed as quickly as closed-form solutions. We demonstrate the
range of application for one-dimensional cases, exemplified by the
Black-Scholes-Merton model, two-dimensional cases, exemplified by the Heston
process, and finally for a modified Heston model with time-dependent parameters
that has no closed-form solution.


Abstract:
  Voting rules based on evaluation inputs rather than preference orders have
been recently proposed, like majority judgement, range voting or approval
voting. Traditionally, probabilistic analysis of voting rules supposes the use
of simulation models to generate preferences data, like the Impartial Culture
(IC) or Impartial and Anonymous Culture (IAC) models. But these simulation
models are not suitable for the analysis of evaluation-based voting rules as
they generate preference orders instead of the needed evaluations. We propose
in this paper several simulation models for generating evaluation-based voting
inputs. These models, inspired by classical ones, are defined, tested and
compared for recommendation purpose.


Abstract:
  Vision transformers (ViTs) process input images as sequences of patches via
self-attention; a radically different architecture than convolutional neural
networks (CNNs). This makes it interesting to study the adversarial feature
space of ViT models and their transferability. In particular, we observe that
adversarial patterns found via conventional adversarial attacks show very low
black-box transferability even for large ViT models. However, we show that this
phenomenon is only due to the sub-optimal attack procedures that do not
leverage the true representation potential of ViTs. A deep ViT is composed of
multiple blocks, with a consistent architecture comprising of self-attention
and feed-forward layers, where each block is capable of independently producing
a class token. Formulating an attack using only the last class token
(conventional approach) does not directly leverage the discriminative
information stored in the earlier tokens, leading to poor adversarial
transferability of ViTs. Using the compositional nature of ViT models, we
enhance the transferability of existing attacks by introducing two novel
strategies specific to the architecture of ViT models. (i) Self-Ensemble: We
propose a method to find multiple discriminative pathways by dissecting a
single ViT model into an ensemble of networks. This allows explicitly utilizing
class-specific information at each ViT block. (ii) Token Refinement: We then
propose to refine the tokens to further enhance the discriminative capacity at
each block of ViT. Our token refinement systematically combines the class
tokens with structural information preserved within the patch tokens. An
adversarial attack, when applied to such refined tokens within the ensemble of
classifiers found in a single vision transformer, has significantly higher
transferability.


Abstract:
  In this paper we provide an expansion formula for Hawkes processes which
involves the addition of jumps at deterministic times to the Hawkes process in
the spirit of the well-known integration by parts formula (or more precisely
the Mecke formula) for Poisson functional. Our approach allows us to provide an
expansion of the premium of a class of cyber insurance derivatives (such as
reinsurance contracts including generalized Stop-Loss contracts) or risk
management instruments (like Expected Shortfall) in terms of so-called shifted
Hawkes processes. From the actuarial point of view, these processes can be seen
as "stressed" scenarios. Our expansion formula for Hawkes processes enables us
to provide lower and upper bounds on the premium (or the risk evaluation) of
such cyber contracts and to quantify the surplus of premium compared to the
standard modeling with a homogenous Poisson process.


Abstract:
  Models based on attention mechanisms have shown unprecedented speech
recognition performance. However, they are computationally expensive and
unnecessarily complex for keyword spotting, a task targeted to small-footprint
devices. This work explores the application of Lambda networks, an alternative
framework for capturing long-range interactions without attention, for the
keyword spotting task. We propose a novel \textit{ResNet}-based model by
swapping the residual blocks by temporal Lambda layers. Furthermore, the
proposed architecture is built upon uni-dimensional temporal convolutions that
further reduce its complexity. The presented model does not only reach
state-of-the-art accuracies on the Google Speech Commands dataset, but it is
85% and 65% lighter than its Transformer-based (KWT) and convolutional (Res15)
counterparts while being up to 100 times faster. To the best of our knowledge,
this is the first attempt to explore the Lambda framework within the speech
domain and therefore, we unravel further research of new interfaces based on
this architecture.


Abstract:
  In this letter, we consider the uplink of a cell-free Massive multiple-input
multiple-output (MIMO) network where each user is decoded by a subset of access
points (APs). An additional step is introduced in the cell-free Massive MIMO
processing: each AP in the uplink locally implements soft MIMO detection and
then shares the resulting bit log-likelihoods on the front-haul link. The
decoding of the data is performed at the central processing unit (CPU),
collecting the data from the APs. The non-linear processing at the APs consists
of the approximate computation of the posterior density for each received data
bit, exploiting only local channel state information. The proposed method
offers good performance in terms of frame-error-rate and considerably lower
complexity than the optimal maximum-likelihood demodulator.


Abstract:
  Paper presents a new view of seismic risk and tectonic stress reduction in
the earthquake focus


Abstract:
  The behaviors of deep neural networks (DNNs) are notoriously resistant to
human interpretations. In this paper, we propose Hypergradient Data Relevance
Analysis, or HYDRA, which interprets the predictions made by DNNs as effects of
their training data. Existing approaches generally estimate data contributions
around the final model parameters and ignore how the training data shape the
optimization trajectory. By unrolling the hypergradient of test loss w.r.t. the
weights of training data, HYDRA assesses the contribution of training data
toward test data points throughout the training trajectory. In order to
accelerate computation, we remove the Hessian from the calculation and prove
that, under moderate conditions, the approximation error is bounded.
Corroborating this theoretical claim, empirical results indicate the error is
indeed small. In addition, we quantitatively demonstrate that HYDRA outperforms
influence functions in accurately estimating data contribution and detecting
noisy data labels. The source code is available at
https://github.com/cyyever/aaai_hydra_8686.


Abstract:
  While commercial mid-air gesture recognition systems have existed for at
least a decade, they have not become a widespread method of interacting with
machines. This is primarily due to the fact that these systems require rigid,
dramatic gestures to be performed for accurate recognition that can be
fatiguing and unnatural. The global pandemic has seen a resurgence of interest
in touchless interfaces, so new methods that allow for natural mid-air gestural
interactions are even more important. To address the limitations of recognition
systems, we propose a neuromorphic gesture analysis system which naturally
declutters the background and analyzes gestures at high temporal resolution.
Our novel model consists of an event-based guided Variational Autoencoder (VAE)
which encodes event-based data sensed by a Dynamic Vision Sensor (DVS) into a
latent space representation suitable to analyze and compute the similarity of
mid-air gesture data. Our results show that the features learned by the VAE
provides a similarity measure capable of clustering and pseudo labeling of new
gestures. Furthermore, we argue that the resulting event-based encoder and
pseudo-labeling system are suitable for implementation in neuromorphic hardware
for online adaptation and learning of natural mid-air gestures.


Abstract:
  This paper is part of an ongoing endeavor to bring the theory of fair
division closer to practice by handling requirements from real-life
applications. We focus on two requirements originating from the division of
land estates: (1) each agent should receive a plot of a usable geometric shape,
and (2) plots of different agents must be physically separated. With these
requirements, the classic fairness notion of proportionality is impractical,
since it may be impossible to attain any multiplicative approximation of it. In
contrast, the ordinal maximin share approximation, introduced by Budish in
2011, provides meaningful fairness guarantees. We prove upper and lower bounds
on achievable maximin share guarantees when the usable shapes are squares, fat
rectangles, or arbitrary axes-aligned rectangles, and explore the algorithmic
and query complexity of finding fair partitions in this setting.


Abstract:
  We generalize Wentzel-Kramers-Brillouin (WKB) semi-classical equations for
pseudospin-1 $\alpha-\mathcal{T}_3$ materials with arbitrary hopping parameter
$0 < \alpha < 1$, which includes the dice lattice and graphene as two limiting
cases. In conjunction with a series-expansion method in powers of Planck
constant $\hbar$, we acquired and solved a system of recurrent differential
equations for semi-classical electron wave functions in $\alpha-\mathcal{T}_3$.
Making use of these obtained wave functions, we analyzed the physics-related
mechanism and quantified the transmission of pseudospin-1 Dirac electrons
across non-rectangular potential barriers in $\alpha-\mathcal{T}_3$ materials
with both zero and finite band gaps. Our studies reveal several unique
features, including the way in which the electron transmission depends on the
energy gap, the slope of the potential barrier profile and the transverse
momentum of incoming electrons. Specifically, we have found a strong dependence
of the obtained transmission amplitude on the geometry-phase $\phi = \tan^{-1}
\alpha$ of $\alpha-\mathcal{T}_3$ lattices. We believe our current findings can
be applied to Dirac cone-based tunneling transistors in ultrafast analog RF
devices, as well as to tunneling-current control by a potential barrier through
a one-dimensional array of scatters.


Abstract:
  The reconstruction of a smooth field onto a fixed grid is a necessary step
for direct comparisons to various real-world observations. Projecting SPH data
onto a fixed grid becomes challenging in adaptive environments, where some
particles may have smoothing lengths far below the grid size, whilst others are
resolved by thousands of pixels. In this paper we show how the common approach
of treating particles below the grid size as Monte Carlo tracers of the field
leads to significant reconstruction errors, and despite good convergence
properties is unacceptable for use in synthetic observations in astrophysics.
We propose a new method, where particles smaller than the grid size are
`blitted' onto the grid using a high-resolution pre-calculated kernel, and
those close to the grid size are subsampled, that allows for converged
predictions for projected quantities at all grid sizes.


Abstract:
  End-to-end neural network models achieve improved performance on various
automatic speech recognition (ASR) tasks. However, these models perform poorly
on edge hardware due to large memory and computation requirements. While
quantizing model weights and/or activations to low-precision can be a promising
solution, previous research on quantizing ASR models is limited. In particular,
the previous approaches use floating-point arithmetic during inference and thus
they cannot fully exploit efficient integer processing units. Moreover, they
require training/validation data during quantization, which may not be
available due to security/privacy concerns. To address these limitations, we
propose an integer-only, zero shot quantization scheme for ASR models. In
particular, we generate synthetic data whose runtime statistics resemble the
real data, and we use it to calibrate models during quantization. We apply our
method to quantize QuartzNet, Jasper, and Conformer and show negligible WER
change as compared to the full-precision baseline models, even without using
any training data. Moreover, we achieve up to 2.35x speedup on a T4 GPU and 4x
compression rate, with a modest WER degradation of <1% with INT8 quantization.


Abstract:
  A normal variety $X$ is called $H$-spherical for the action of the complex
reductive group $H$ if it contains a dense orbit of some Borel subgroup of $H$.
We resolve a conjecture of Hodges--Yong by showing that their spherical
permutations are characterized by permutation pattern avoidance. Together with
results of Gao--Hodges--Yong this implies that the sphericality of a Schubert
variety $X_w$ with respect to the largest possible Levi subgroup is
characterized by this same pattern avoidance condition.


Abstract:
  Graphs are topological spaces that include broader objects than discretized
manifolds, making them interesting playgrounds for the study of quantum phases
not realized by symmetry breaking. In particular they are known to support
anyons of an even richer variety than the two-dimensional space. We explore
this possibility by building a class of frustration-free and gapped
Hamiltonians based on discrete abelian gauge groups. The resulting models have
a ground state degeneracy that can be either a topological invariant, an
extensive quantity or a mixture of the two. For two basis of the degenerate
ground states which are complementary in quantum theory, the entanglement
entropy is exactly computed. The result for one basis has a constant global
term, known as the topological entanglement entropy, implying long-range
entanglement. On the other hand, the topological entanglement entropy vanishes
in the result for the other basis. Comparisons are made with similar
occurrences in the toric code. We analyze excitations and identify anyon-like
excitations that account for the topological entanglement entropy. An analogy
between the ground states of this system and the $\theta$-vacuum for a $U(1)$
gauge theory on a circle is also drawn.


Abstract:
  A novel space-time parallel index modulation scheme is proposed in this paper
for conveying extra digital information with the aid of space and time indices.
In this proposed scheme, time-indexing is cleverly performed in parallel
spatial modulation (PSM) schemes using transmit antenna grouping, and referred
to as time-indexed parallel spatial modulation (TI-PSM). In this scheme,
transmit antennas are divided into groups to adopt TI-SM scheme in large-scale
multiple input multiple output (MIMO) systems with inter-channel interference
(ICI) avoidance. This scheme is attractive due to both the high data rate and
good performance improvement that can be achieved. The bit error rate (BER)
performance of the TI-PSM scheme is evaluated and compared to that of the
conventional schemes. Simulation results demonstrated that a significant
improvement is achieved by the TI-PSM scheme as compared to the TI-SM and PSM
schemes for the same achieved rate. It is also shown that TI-SM and TI-PSM
schemes provide higher performance drop with channel estimation errors (CEEs)
than the conventional SM and PSM systems. Therefore, due to the high
performance improvements achieved in TI-PSM scheme, it can be effectively
regarded as a promising solution for various 5G and beyond wireless networks.


Abstract:
  With advancements in technology, personal computing devices are better
adapted for and further integrated into people's lives and homes. The
integration of technology into society also results in an increasing desire to
control who and what has access to sensitive information, especially for
vulnerable people including children and the elderly. With blockchain coming in
to the picture as a technology that can revolutionise the world, it is now
possible to have an immutable audit trail of locational data over time. By
controlling the process through inexpensive equipment in the home, it is
possible to control whom has access to such personal data. This paper presents
a blockchain based family security system for tracking the location of
consenting family members' smart phones. The locations of the family members'
smart phones are logged and stored in a private blockchain which can be
accessed through a node installed in the family home on a computer. The data
for the whereabouts of family members stays within the family unit and does not
go to any third party. The system is implemented in a small scale (one miner
and two other nodes) and the technical feasibility is discussed along with the
limitations of the system. Further research will cover the integration of the
system into a smart home environment, and ethical implementations of tracking,
especially of vulnerable people, using the immutability of blockchain.


Abstract:
  Over the past decades, numerous practical applications of machine learning
techniques have shown the potential of data-driven approaches in a large number
of computing fields. Machine learning is increasingly included in computing
curricula in higher education, and a quickly growing number of initiatives are
expanding it in K-12 computing education, too. As machine learning enters K-12
computing education, understanding how intuition and agency in the context of
such systems is developed becomes a key research area. But as schools and
teachers are already struggling with integrating traditional computational
thinking and traditional artificial intelligence into school curricula,
understanding the challenges behind teaching machine learning in K-12 is an
even more daunting challenge for computing education research. Despite the
central position of machine learning in the field of modern computing, the
computing education research body of literature contains remarkably few studies
of how people learn to train, test, improve, and deploy machine learning
systems. This is especially true of the K-12 curriculum space. This article
charts the emerging trajectories in educational practice, theory, and
technology related to teaching machine learning in K-12 education. The article
situates the existing work in the context of computing education in general,
and describes some differences that K-12 computing educators should take into
account when facing this challenge. The article focuses on key aspects of the
paradigm shift that will be required in order to successfully integrate machine
learning into the broader K-12 computing curricula. A crucial step is
abandoning the belief that rule-based "traditional" programming is a central
aspect and building block in developing next generation computational thinking.


Abstract:
  Cylindrical algebraic decomposition (CAD) plays an important role in the
field of real algebraic geometry and many other areas. As is well-known, the
choice of variable ordering while computing CAD has a great effect on the time
and memory use of the computation as well as the number of sample points
computed. In this paper, we indicate that typical CAD algorithms, if executed
with respect to a special kind of variable orderings (called "the perfect
elimination orderings"), naturally preserve chordality, which is an important
property on sparsity of variables. Experimentation suggests that if the
associated graph of the polynomial system in question is chordal (\emph{resp.},
is nearly chordal), then a perfect elimination ordering of the associated graph
(\emph{resp.}, of a minimal chordal completion of the associated graph) can be
a good variable ordering for the CAD computation. That is, by using the perfect
elimination orderings, the CAD computation may produce a much smaller full set
of projection polynomials than by using other naive variable orderings. More
importantly, for the complexity analysis of the CAD computation via a perfect
elimination ordering, a so-called $(m,d)$-property of the full set of
projection polynomials obtained via such an ordering is given, through which
the "size" of this set is characterized. This property indicates that when the
corresponding perfect elimination tree has a lower height, the full set of
projection polynomials also tends to have a smaller "size". This is well
consistent with the experimental results, hence the perfect elimination
orderings with lower elimination tree height are further recommended to be used
in the CAD projection.


Abstract:
  Traditional machine learning is centralized in the cloud (data centers).
Recently, the security concern and the availability of abundant data and
computation resources in wireless networks are pushing the deployment of
learning algorithms towards the network edge. This has led to the emergence of
a fast growing area, called federated learning (FL), which integrates two
originally decoupled areas: wireless communication and machine learning. In
this paper, we provide a comprehensive study on the applications of FL for
sixth generation (6G) wireless networks. First, we discuss the key requirements
in applying FL for wireless communications. Then, we focus on the motivating
application of FL for wireless communications. We identify the main problems,
challenges, and provide a comprehensive treatment of implementing FL techniques
for wireless communications.


Abstract:
  The use of local detectors and descriptors in typical computer vision
pipelines work well until variations in viewpoint and appearance change become
extreme. Past research in this area has typically focused on one of two
approaches to this challenge: the use of projections into spaces more suitable
for feature matching under extreme viewpoint changes, and attempting to learn
features that are inherently more robust to viewpoint change. In this paper, we
present a novel framework that combines learning of invariant descriptors
through data augmentation and orthographic viewpoint projection. We propose
rotation-robust local descriptors, learnt through training data augmentation
based on rotation homographies, and a correspondence ensemble technique that
combines vanilla feature correspondences with those obtained through
rotation-robust features. Using a range of benchmark datasets as well as
contributing a new bespoke dataset for this research domain, we evaluate the
effectiveness of the proposed approach on key tasks including pose estimation
and visual place recognition. Our system outperforms a range of baseline and
state-of-the-art techniques, including enabling higher levels of place
recognition precision across opposing place viewpoints and achieves
practically-useful performance levels even under extreme viewpoint changes.


Abstract:
  We study the widely used hierarchical agglomerative clustering (HAC)
algorithm on edge-weighted graphs. We define an algorithmic framework for
hierarchical agglomerative graph clustering that provides the first efficient
$\tilde{O}(m)$ time exact algorithms for classic linkage measures, such as
complete- and WPGMA-linkage, as well as other measures. Furthermore, for
average-linkage, arguably the most popular variant of HAC, we provide an
algorithm that runs in $\tilde{O}(n\sqrt{m})$ time. For this variant, this is
the first exact algorithm that runs in subquadratic time, as long as
$m=n^{2-\epsilon}$ for some constant $\epsilon > 0$. We complement this result
with a simple $\epsilon$-close approximation algorithm for average-linkage in
our framework that runs in $\tilde{O}(m)$ time. As an application of our
algorithms, we consider clustering points in a metric space by first using
$k$-NN to generate a graph from the point set, and then running our algorithms
on the resulting weighted graph. We validate the performance of our algorithms
on publicly available datasets, and show that our approach can speed up
clustering of point datasets by a factor of 20.7--76.5x.


Abstract:
  Machine learning practitioners invest significant manual and computational
resources in finding suitable learning rates for optimization algorithms. We
provide a probabilistic motivation, in terms of Gaussian inference, for popular
stochastic first-order methods. As an important special case, it recovers the
Polyak step with a general metric. The inference allows us to relate the
learning rate to a dimensionless quantity that can be automatically adapted
during training by a control algorithm. The resulting meta-algorithm is shown
to adapt learning rates in a robust manner across a large range of initial
values when applied to deep learning benchmark problems.


Abstract:
  With the advancement of the Internet of Things (IoT) and communication
platform, large scale sensor deployment can be easily implemented in an urban
city to collect various information. To date, there are only a handful of
research studies about understanding the usage of urban public spaces.
Leveraging IoT, various sensors have been deployed in an urban residential area
to monitor and study public space utilization patterns. In this paper, we
propose a data processing system to generate space-centric insights about the
utilization of an urban residential region of multiple points of interest
(PoIs) that consists of 190,000m$^2$ real estate. We identify the activeness of
each PoI based on the spectral clustering, and then study their corresponding
static features, which are composed of transportation, commercial facilities,
population density, along with other characteristics. Through the heuristic
features inferring, the residential density and commercial facilities are the
most significant factors affecting public place utilization.


Abstract:
  Information retrieval (IR) systems traditionally aim to maximize metrics
built on rankings, such as precision or NDCG. However, the
non-differentiability of the ranking operation prevents direct optimization of
such metrics in state-of-the-art neural IR models, which rely entirely on the
ability to compute meaningful gradients. To address this shortcoming, we
propose SmoothI, a smooth approximation of rank indicators that serves as a
basic building block to devise differentiable approximations of IR metrics. We
further provide theoretical guarantees on SmoothI and derived approximations,
showing in particular that the approximation errors decrease exponentially with
an inverse temperature-like hyperparameter that controls the quality of the
approximations. Extensive experiments conducted on four standard
learning-to-rank datasets validate the efficacy of the listwise losses based on
SmoothI, in comparison to previously proposed ones. Additional experiments with
a vanilla BERT ranking model on a text-based IR task also confirm the benefits
of our listwise approach.


Abstract:
  As an effective tool for two-dimensional data analysis, two-dimensional
canonical correlation analysis (2DCCA) is not only capable of preserving the
intrinsic structural information of original two-dimensional (2D) data, but
also reduces the computational complexity effectively. However, due to the
unsupervised nature, 2DCCA is incapable of extracting sufficient discriminatory
representations, resulting in an unsatisfying performance. In this letter, we
propose a complete discriminative tensor representation learning (CDTRL) method
based on linear correlation analysis for analyzing 2D signals (e.g. images).
This letter shows that the introduction of the complete discriminatory tensor
representation strategy provides an effective vehicle for revealing, and
extracting the discriminant representations across the 2D data sets, leading to
improved results. Experimental results show that the proposed CDTRL outperforms
state-of-the-art methods on the evaluated data sets.


Abstract:
  A stationary free boundary problem modeling a three-dimensional electrostatic
MEMS device is investigated. The device is made of a rigid ground plate and an
elastic top plate which is hinged at its boundary, the plates being held at
different voltages. The model couples a nonlocal fourth-order equation for the
deformation of the top plate to a Laplace equation for the electrostatic
potential in the free domain between the two plates. The strength of the
coupling is tuned by a parameter $\lambda$ which is proportional to the square
of the applied voltage difference. Existence of a stable stationary solution is
established for small values of $\lambda$. Nonexistence of stationary solutions
is obtained when $\lambda$ is large enough.


Abstract:
  Secret Sharing Schemes (SSS) are methods for distributing a secret among a
set of participants. One of the first Secret Sharing Schemes was proposed by M.
Mignotte, based on the Chinese remainder theorem over the ring of integers. In
this article we extend the Mignotte's scheme to the ring of Gaussian Integers
and study some of its properties. While doing this we aim to solve a gap in a
previous construction of such extension. In addition we show that any access
structure can be made through a SSS over $ \mathbb{Z}[i]$.


Abstract:
  We investigate fermions with Lifshitz scaling symmetry and study their
entanglement entropy in 1+1 dimensions as a function of the scaling exponent
$z$. Remarkably, in the ground state the entanglement entropy vanishes for even
values of $z$, whereas for odd values it is independent of $z$ and equal to the
relativistic case with $z=1$. We show this using the correlation method on the
lattice, and also using a holographic cMERA approach. The entanglement entropy
in a thermal state is a more detailed function of $z$ and $T$ which we plot
using the lattice correlation method. The dependence on the even- or oddness of
$z$ still shows for small temperatures, but is washed out for large
temperatures or large values of $z$.


Abstract:
  Quantum many-body systems are characterized by patterns of correlations that
define highly-non trivial manifolds when interpreted as data structures.
Physical properties of phases and phase transitions are typically retrieved via
simple correlation functions, that are related to observable response
functions. Recent experiments have demonstrated capabilities to fully
characterize quantum many-body systems via wave-function snapshots, opening new
possibilities to analyze quantum phenomena. Here, we introduce a method to data
mine the correlation structure of quantum partition functions via their path
integral (or equivalently, stochastic series expansion) manifold. We
characterize path-integral manifolds generated via state-of-the-art Quantum
Monte Carlo methods utilizing the intrinsic dimension (ID) and the variance of
distances from nearest neighbors (NN): the former is related to dataset
complexity, while the latter is able to diagnose connectivity features of
points in configuration space. We show how these properties feature universal
patterns in the vicinity of quantum criticality, that reveal how data
structures {\it simplify} systematically at quantum phase transitions. This is
further reflected by the fact that both ID and variance of NN-distances exhibit
universal scaling behavior in the vicinity of second-order and
Berezinskii-Kosterlitz-Thouless critical points. Finally, we show how
non-Abelian symmetries dramatically influence quantum data sets, due to the
nature of (non-commuting) conserved charges in the quantum case. Complementary
to neural network representations, our approach represents a first, elementary
step towards a systematic characterization of path integral manifolds before
any dimensional reduction is taken, that is informative about universal
behavior and complexity, and can find immediate application to both experiments
and Monte Carlo simulations.


Abstract:
  We prove a new computable version of Hall's Harem theorem. We apply it to
non-amenable computable coarse spaces.


Abstract:
  We introduce an automated tool for deploying ultra low-latency, low-power
deep neural networks with convolutional layers on FPGAs. By extending the
hls4ml library, we demonstrate an inference latency of $5\,\mu$s using
convolutional architectures, targeting microsecond latency applications like
those at the CERN Large Hadron Collider. Considering benchmark models trained
on the Street View House Numbers Dataset, we demonstrate various methods for
model compression in order to fit the computational constraints of a typical
FPGA device used in trigger and data acquisition systems of particle detectors.
In particular, we discuss pruning and quantization-aware training, and
demonstrate how resource utilization can be significantly reduced with little
to no loss in model accuracy. We show that the FPGA critical resource
consumption can be reduced by 97% with zero loss in model accuracy, and by 99%
when tolerating a 6% accuracy degradation.


Abstract:
  Depositing magnetic insulators on graphene has been a promising route to
introduce magnetism via exchange proximity interaction in graphene for future
spintronics applications. Molecule-based magnets may offer unique opportunities
because of their synthesis versatility. Here, we investigated the magnetic
proximity effect of epitaxial iron phthalocyanine (FePc) molecules on
high-quality monolayer and bilayer graphene devices on hexagonal boron nitride
substrate by probing the local and non-local transport. Although the FePc
molecules introduce large hole doping effects combined with mobility
degradation, the magnetic proximity gives rise to a canted antiferromagnetic
state under a magnetic field in the monolayer graphene. On bilayer graphene and
FePc heterostructure devices, the non-local transport reveals a pronounced
Zeeman spin-Hall effect. Further analysis of the scattering mechanism in the
bilayer shows a dominated long-range scattering. Our findings in
graphene/organic magnetic insulator heterostructure provide a new insight for
the use of molecule-based magnets in two-dimensional spintronic devices.


Abstract:
  We make precise the analogy between Goodwillie's calculus of functors in
homotopy theory and the differential calculus of smooth manifolds by
introducing a higher-categorical framework of which both theories are examples.
That framework is an extension to infinity-categories of the tangent categories
of Cockett and Cruttwell (introduced originally by Rosick\'y). A tangent
structure on an infinity-category X consists of an endofunctor on X, which
plays the role of the tangent bundle construction, together with various
natural transformations that mimic structure possessed by the ordinary tangent
bundles of smooth manifolds and that satisfy similar conditions.
  The tangent bundle functor in Goodwillie calculus is Lurie's tangent bundle
for infinity-categories, introduced to generalize the cotangent complexes of
Andr\'e, Quillen and Illusie. We show that Lurie's construction admits the
additional structure maps and satisfies the conditions needed to form a tangent
infinity-category, which we refer to as the Goodwillie tangent structure on the
infinity-category of infinity-categories.
  Cockett and Cruttwell (and others) have started to develop various aspects of
differential geometry in the abstract context of tangent categories, and we
begin to apply those ideas to Goodwillie calculus. For example, we show that
the role of Euclidean spaces in the calculus of manifolds is played in
Goodwillie calculus by the stable infinity-categories. We also show that
Goodwillie's n-excisive functors are the direct analogues of n-jets of smooth
maps between manifolds; to state that connection precisely, we develop a notion
of tangent (infinity, 2)-category and show that Goodwillie calculus is best
understood in that context.


Abstract:
  Preparation and measurement of physical systems are the operational building
blocks of any physical experiment, and to describe them is the first purpose of
any physical theory. It is remarkable that, in some situations, even when only
preparation and measurement devices of a single system are present and they are
uncharacterized, it is possible to distinguish between the behaviours of
quantum and classical systems relying only on observational data. Certifying
the physical origin of measurement statistics in the prepare and measure
scenario is of primal importance for developing quantum networks, distributing
quantum keys and certifying randomness, to mention a few applications, but,
surprisingly, no general methods to do so are known. We progress on this
problem by crafting a general, sufficient condition to certify that a given set
of preparations can only generate classical statistics, for any number of
generalized measurements. As an application, we employ the method to
demonstrate non-classicality activation in the prepare and measure scenario,
also considering its application in random access codes. Following that, we
adapt our method to certify, again through a sufficient condition, whether a
given set of measurements can never give rise to non-classical behaviors,
irrespective of what preparations they may act upon. This, in turn, allows us
to find a large set of incompatible measurements that cannot be used to
demonstrate non-classicality, thus showing incompatibility is not sufficient
for non-classicality in the prepare and measure scenario.