title: Effect of Timing Error: A Case Study of Navigation Camera

abstract:
  We focus on the problem of timing errors in navigation camera as a case study
in a broader problem of the effect of a timing error in cyber-physical systems.
These systems rely on the requirement that certain things happen at the same
time or certain things happen periodically at some period $T$. However, as
these systems get more complex, timing errors can occur between the components
thereby violating the assumption about events being simultaneous (or periodic).
  We consider the problem of a surgical navigation system where optical markers
detected in the 2D pictures taken by two cameras are used to localize the
markers in 3D space. A predefined array of such markers, known as a reference
element, is used to navigate the corresponding CAD model of a surgical
instrument on patient's images. The cameras rely on the assumption that the
pictures from both cameras are taken exactly at the same time. If a timing
error occurs then the instrument may have moved between the pictures. We find
that, depending upon the location of the instrument, this can lead to a
substantial error in the localization of the instrument. Specifically, we find
that if the actual movement is $\delta$ then the observed movement may be as
high as $5\delta$ in the operating range of the camera. Furthermore, we also
identify potential issues that could affect the error in case there are changes
to the camera system or to the operating range.


title:
A 915-1220 TOPS/W Hybrid In-Memory Computing based Image Restoration and
  Region Proposal Integrated Circuit for Neuromorphic Vision Sensors in 65nm
  CMOS

abstract:
  In this work, we present a hybrid memory bit cell - collocated SRAM and DRAM
(CRAM) consisting of 11 transistors for in-memory computing (IMC) based image
restoration (IR) and region proposal (RP). A robust RP updated algorithm is
proposed to improve the performance. This work demonstrates IMC based global
parallel diffusion and column/row-wise projection to achieve a maximal energy
efficiency of 1220 TOPS/W for image restoration and 915 TOPS/W when combined
with region proposal.


title: ICARUS: A Lightweight Neural Plenoptic Rendering Architecture

abstract:
  The practical deployment of Neural Radiance Field (NeRF) in rendering
applications faces several challenges, with the most critical one being low
rendering speed on even high-end graphic processing units (GPUs). In this
paper, we present ICARUS, a novel lightweight graphics architecture tailored
for NeRF rendering. Unlike GPUs using general purpose computing and memory
architectures for NeRF, ICARUS executes the complete NeRF pipeline using
dedicated plenoptic cores (PLCore) consisting of a positional encoding unit
(PEU), a multi-layer perceptron (MLP) engine, and a volume rendering unit
(VRU). A PLCore takes in positions \& directions and renders the corresponding
pixel colors without any intermediate data going off-chip for temporary storage
and exchange, which can be time and power consuming. To implement the most
expensive component of NeRF, i.e., the MLP, we transform the fully connected
operations to approximated reconfigurable multiple constant multiplications
(MCMs), where common subexpressions are shared across different multiplications
to improve the computation efficiency. We build a prototype ICARUS using
Synopsys HAPS-80 S104, an FPGA-based prototyping system for large-scale
integrated circuits and systems. We evaluate the area and power consumption of
a PLCore using 40nm LP CMOS process. Working at 300 MHz, a single PLCore
occupies 7.59 $mm^2$ and consumes 309.8 mW, translating to 0.174 uJ/sample.
Evaluation results show that for NeRF rendering, the energy efficiency of
ICARUS is 146 times higher than GPUs. By scaling to a multi-core system, the
energy-efficient ICARUS can be deployed in practical edge applications for
NeRF-based rendering tasks.


title: "Keep the fight unfair": Military rhetoric in quantum technology

abstract:
  Doing quantum ethics properly will require detailed socio-political analysis
of the technologies and the organizations trying to build them. In this paper,
I contribute to this task by analysing the public rhetoric of American military
stakeholders in the quantum industry. I look at Air Force Research Laboratory
involvement in the 2020 Quantum 2 Business conference, where they were the main
sponsor. A critical thematic analysis shows a focus on enacting the violence of
war, maintaining narratives that the Air Force provides a secure future for
Americans, and marrying quantum technology with the aesthetics of war. I
contextualize this with anti-imperialist theory, arguing that this rhetoric and
the desire for quantum arms aligns with the reproduction of existing violent
power structures. Insights about this example of military involvement in
quantum spaces should help orient nascent critical quantum ethics
interventions.


title: A Fully Memristive Spiking Neural Network with Unsupervised Learning

abstract:
  We present a fully memristive spiking neural network (MSNN) consisting of
physically-realizable memristive neurons and memristive synapses to implement
an unsupervised Spiking Time Dependent Plasticity (STDP) learning rule. The
system is fully memristive in that both neuronal and synaptic dynamics can be
realized by using memristors. The neuron is implemented using the SPICE-level
memristive integrate-and-fire (MIF) model, which consists of a minimal number
of circuit elements necessary to achieve distinct depolarization,
hyperpolarization, and repolarization voltage waveforms. The proposed MSNN
uniquely implements STDP learning by using cumulative weight changes in
memristive synapses from the voltage waveform changes across the synapses,
which arise from the presynaptic and postsynaptic spiking voltage signals
during the training process. Two types of MSNN architectures are investigated:
1) a biologically plausible memory retrieval system, and 2) a multi-class
classification system. Our circuit simulation results verify the MSNN's
unsupervised learning efficacy by replicating biological memory retrieval
mechanisms, and achieving 97.5% accuracy in a 4-pattern recognition problem in
a large scale discriminative MSNN.


title:
Two-fold theoretical and experimental misinterpretation of luminosity in
  the origin of Hubble tension

abstract:
  The comparison of the intrinsic luminosity of extragalactic objects detected
at different redshifts requires the evaluation of the observed flux in a common
rest-frame emission band. It is usually performed by adding a term to the
observed magnitudes denominated K-correction. In this paper, the K-correction
transformation has been carefully scrutinized. As a result of this inspection,
it has been found that K-corrections systematically downgrades the measured
energy flux by a factor of (1+z). C-correction, a more appropriate rest-frame
magnitude transformation has been derived. C-correction has been applied to the
re-analysis of JLA (Joint Light-curve analysis) supernovae sample. The best
cosmological fit to JLA type Ia supernovae (SNIa) sample calibrated with CMB
Planck $h=0.675$ for the common luminosity-angular distances relation
$L2:D_L=D_A(1+z)^2$ corresponds to a closed Universe
$L2(h_0=0.48,\Omega_M=2.25$). Nevertheless, considering the new
luminosity-angular distances relation $L1:D_L=D_A(1+z)$ that accounts for
expansion lensing, the new SNIa analysis converges to
$L1(h_0=0.7558,\Omega_M=0.035,\Omega_\Lambda=0.965)$, providing a coherent
explanation of the Hubble tension between Planck and SH0ES ($h=0.7422 \pm
0.0182$) results.


title: Third-order Analysis of Channel Coding in the Moderate Deviations Regime

abstract:
  The channel coding problem in the moderate deviations regime is studied;
here, the error probability decays sub-exponentially to zero, and the rate
approaches the capacity slower than $O(1/\sqrt{n})$. Our result refines
Altu\u{g} and Wagner's moderate deviations result by deriving lower and upper
bounds on the third-order term in the asymptotic expansion of the maximum
achievable message set size. The third-order term of our expansion employs a
new quantity here called the channel skewness. For the binary symmetric channel
and most practically important $(n, \epsilon)$ pairs, including $n \in [100,
500]$ and $\epsilon \in [10^{-10}, 10^{-1}]$, an approximation up to the
channel skewness is the most accurate among several expansions in the
literature.


title:
Electrostatic partners and zeros of orthogonal and multiple orthogonal
  polynomials

abstract:
  For a given polynomial $P$ with simple zeros, and a given semiclassical
weight $w$, we present a construction that yields a linear second-order
differential equation (ODE), and in consequence, an electrostatic model for
zeros of $P$. The coefficients of this ODE are written in terms of a dual
polynomial that we call the electrostatic partner of $P$. This construction is
absolutely general and can be carried out for any polynomial with simple zeros
and any semiclassical weight on the complex plane. An additional assumption of
quasi-orthogonality of $P$ with respect to $w$ allows us to give more precise
bounds on the degree of the electrostatic partner. In the case of orthogonal
and quasi-orthogonal polynomials, we recover some of the known results and
generalize others. Additionally, for the Hermite--Pad\'e or multiple orthogonal
polynomials of type II, this approach yields a system of linear second-order
differential equations, from which we derive an electrostatic interpretation of
their zeros in terms of a vector equilibrium. More detailed results are
obtained in the special cases of Angelesco, Nikishin, and generalized Nikishin
systems. We also discuss the discrete-to-continuous transition of these models
in the asymptotic regime, as the number of zeros tends to infinity, into the
known vector equilibrium problems. Finally, we discuss how the system of
obtained second-order ODEs yields a third-order differential equation for these
polynomials, well described in the literature. We finish the paper by
presenting several illustrative examples.


title: Minimax decision rules for planning under uncertainty

abstract:
  It is common to use minimax rules to make decisions for planning when there
is great uncertainty on what will happen in the future. Minimax regret is one
popular version of this. We give an analysis of the behaviour of minimax rules
in the case with a finite set of possible future scenarios. The use of minimax
rules avoids the need to determine probabilities for each scenario, which is an
attractive feature in many public sector settings. However, minimax rules will
have sensitivity to the choice of scenarios. In many cases using a minimax
approach will mean the requirement for what may be regarded as arbitrary
probabilities on scenarios is replaced by a similarly arbitrary choice of a
very small number of specific scenarios. We investigate this phenomenon. When
regret-based rules are used there are also problems arising since the
independence of irrelevant alternatives property fails, which can lead to
opportunities to game the process. Our analysis of these phenomena considers
cases where the decision variables are chosen from a convex set in $R^n$, as
well as cases with a finite set of decision choices.


title:
Geometrically programmed self-limited assembly of tubules using DNA
  origami colloids

abstract:
  Self-assembly is one of the most promising strategies for making functional
materials at the nanoscale, yet new design principles for making self-limiting
architectures, rather than spatially unlimited periodic lattice structures, are
needed. To address this challenge, we explore the trade-offs between
addressable assembly and self-closing assembly of a specific class of
self-limiting structures: cylindrical tubules. We make triangular subunits
using DNA origami that have specific, valence-limited interactions and designed
binding angles, and study their assembly into tubules that have a self-limited
width that is much larger than the size of an individual subunit. In the
simplest case, the tubules are assembled from a single component by
geometrically programming the dihedral angles between neighboring subunits. We
show that the tubules can reach many micrometers in length and that their
average width can be prescribed through the dihedral angles. We find that there
is a distribution in the width and the chirality of the tubules, which we
rationalize by developing a model that considers the finite bending rigidity of
the assembled structure as well as the mechanism of self-closure. Finally, we
demonstrate that the distributions of tubules can be further sculpted by
increasing the number of subunit species, thereby increasing the assembly
complexity, and demonstrate that using two subunit species successfully reduces
the number of available end states by half. These results help to shed light on
the roles of assembly complexity and geometry in self-limited assembly and
could be extended to other self-limiting architectures, such as shells,
toroids, or triply-periodic frameworks.


title:
Estimating Conditional Average Treatment Effects with Missing Treatment
  Information

abstract:
  Estimating conditional average treatment effects (CATE) is challenging,
especially when treatment information is missing. Although this is a widespread
problem in practice, CATE estimation with missing treatments has received
little attention. In this paper, we analyze CATE estimation in the setting with
missing treatments where, thus, unique challenges arise in the form of
covariate shifts. We identify two covariate shifts in our setting: (i) a
covariate shift between the treated and control population; and (ii) a
covariate shift between the observed and missing treatment population. We first
theoretically show the effect of these covariate shifts by deriving a
generalization bound for estimating CATE in our setting with missing
treatments. Then, motivated by our bound, we develop the missing treatment
representation network (MTRNet), a novel CATE estimation algorithm that learns
a balanced representation of covariates using domain adaptation. By using
balanced representations, MTRNet provides more reliable CATE estimates in the
covariate domains where the data are not fully observed. In various experiments
with semi-synthetic and real-world data, we show that our algorithm improves
over the state-of-the-art by a substantial margin.


title: Minimal sets on continua with a dense free interval

abstract:
  We study minimal sets on continua $X$ with a dense free interval $J$ and a
locally connected remainder. This class of continua includes important spaces
such as the topologist's sine curve or the Warsaw circle. In the case when
minimal sets on the remainder are known and the remainder is connected, we
obtain a full characterization of the topological structure of minimal sets. In
particular, a full characterization of minimal sets on $X$ is given in the case
when $X\setminus J$ is a local dendrite.


title:
Ammonia in water-dominated ice mixtures explains the non-detection of
  the 2152 cm$^{-1}$ band

abstract:
  CO is one of the most abundant ice components on interstellar dust grains.
When it is mixed with amorphous solid water (ASW) or located on its surface, an
absorption band at 2152 cm$^{-1}$ is always present in laboratory measurements.
This spectral feature is attributed to the interaction of CO with dangling-OH
bonds (dOH) in ASW. However, this band is absent in observational spectra of
interstellar ices. This raises the question whether CO forms a relatively pure
layer on top of ASW or is in close contact with ASW, but not via dangling
bonds. We performed laboratory experiments to simulate the layered structure of
the ice mantle, i.e., we grew CO ice on top of: 1) Pure ASW; 2)
NH$_3$:H$_2$O=10:100 mixed ice; and 3) CO$_2$:H$_2$O=20:100 mixed ice. We found
that annealing the ice reduces the 2152 cm$^{-1}$ band, and that NH$_3$ blocks
the dOH on ASW surface and therefore reduces the 2152 cm$^{-1}$ band more
effectively than CO$_2$. We suggest that this difference between NH$_3$ and
CO$_2$ can be ascribed to the polarity of the guest molecule (NH$_3$ is a polar
species, whereas CO$_2$ is apolar). The polarity implies that the formation of
an H-bond between the N atom of ammonia and the dOH is a barrier-less reaction.
We also determined the pore surface area of the ice mixtures as a function of
the annealing temperature, and found that the non-detection of 2152 cm$^{-1}$
band does not necessarily exclude the possibility of a porous ice mantle.


title: A Modern Gauss-Markov Theorem? Really?

abstract:
  We show that the theorems in Hansen (2021a) (the version accepted by
Econometrica), except for one, are not new as they coincide with classical
theorems like the good old Gauss-Markov or Aitken Theorem, respectively; the
exceptional theorem is incorrect. Hansen (2021b) corrects this theorem. As a
result, all theorems in the latter version coincide with the above mentioned
classical theorems. Furthermore, we also show that the theorems in Hansen
(2022) (the version forthcoming in Econometrica) either coincide with the
classical theorems just mentioned, or contain extra assumptions that are alien
to the Gauss-Markov or Aitken Theorem.


title:
SPICEprop: Backpropagating Errors Through Memristive Spiking Neural
  Networks

abstract:
  We present a fully memristive spiking neural network (MSNN) consisting of
novel memristive neurons trained using the backpropagation through time (BPTT)
learning rule. Gradient descent is applied directly to the memristive
integrated-and-fire (MIF) neuron designed using analog SPICE circuit models,
which generates distinct depolarization, hyperpolarization, and repolarization
voltage waveforms. Synaptic weights are trained by BPTT using the membrane
potential of the MIF neuron model and can be processed on memristive crossbars.
The natural spiking dynamics of the MIF neuron model are fully differentiable,
eliminating the need for gradient approximations that are prevalent in the
spiking neural network literature. Despite the added complexity of training
directly on SPICE circuit models, we achieve 97.58% accuracy on the MNIST
testing dataset and 75.26% on the Fashion-MNIST testing dataset, the highest
accuracies among all fully MSNNs.


title: Chiral Phases on the Lattice

abstract:
  While chiral quantum field theories (QFTs) describe a wide range of physical
systems, from the standard model to topological quantum matter, the realization
of chiral QFTs on a lattice has proved to be difficult due to the
Nielsen-Ninomiya theorem and the possible presence of quantum anomalies. In
this thesis, we use the connection between chiral phases of matter and chiral
quantum field theories (QFTs) to define chiral QFTs on a lattice and allow a
huge class of exotic field theories to be simulated numerically. Our work
builds on the 'mirror fermion' approach to the problem of defining chiral
theories on a lattice, which defines chiral field theories as the edge modes of
chiral phases. We begin by reviewing the deep connections between chiral phases
of matter, chiral field theories, and anomalies. We then develop numerical
treatments of an $SU(2)$ chiral field theory, and provide a semiclassically
solvable definition of Abelian $2+1$ chiral topological orders. This leads to
an exactly solvable definition of chiral $U(1)$ SPT phases with zero
correlation length, which we use to extract the edge chiral field theories
exactly. These zero-correlation length models are vastly more simple than
previous approaches to defining chiral field theories on the lattice.


title: About the case of equality in the Reverse Brascamp-Lieb inequality

abstract:
  The case of equality in Barthe's Geometric Reverse Brascamp-Lieb inequality
is characterized, and some applications are provided.


title:
SMTNet: Hierarchical cavitation intensity recognition based on sub-main
  transfer network

abstract:
  With the rapid development of smart manufacturing, data-driven machinery
health management has been of growing attention. In situations where some
classes are more difficult to be distinguished compared to others and where
classes might be organised in a hierarchy of categories, current DL methods can
not work well. In this study, a novel hierarchical cavitation intensity
recognition framework using Sub-Main Transfer Network, termed SMTNet, is
proposed to classify acoustic signals of valve cavitation. SMTNet model outputs
multiple predictions ordered from coarse to fine along a network corresponding
to a hierarchy of target cavitation states. Firstly, a data augmentation method
based on Sliding Window with Fast Fourier Transform (Swin-FFT) is developed to
solve few-shot problem. Secondly, a 1-D double hierarchical residual block (1-D
DHRB) is presented to capture sensitive features of the frequency domain valve
acoustic signals. Thirdly, hierarchical multi-label tree is proposed to assist
the embedding of the semantic structure of target cavitation states into
SMTNet. Fourthly, experience filtering mechanism is proposed to fully learn a
prior knowledge of cavitation detection model. Finally, SMTNet has been
evaluated on two cavitation datasets without noise (Dataset 1 and Dataset 2),
and one cavitation dataset with real noise (Dataset 3) provided by SAMSON AG
(Frankfurt). The prediction accurcies of SMTNet for cavitation intensity
recognition are as high as 95.32%, 97.16% and 100%, respectively. At the same
time, the testing accuracies of SMTNet for cavitation detection are as high as
97.02%, 97.64% and 100%. In addition, SMTNet has also been tested for different
frequencies of samples and has achieved excellent results of the highest
frequency of samples of mobile phones.


title:
On-Demand Directional Photon Emission using Waveguide Quantum
  Electrodynamics

abstract:
  Routing quantum information between non-local computational nodes is a
foundation for extensible networks of quantum processors. Quantum information
can be transferred between arbitrary nodes by photons that propagate between
them, or by resonantly coupling nearby nodes. Notably, conventional approaches
involving propagating photons have limited fidelity due to photon loss and are
often unidirectional, whereas architectures that use direct resonant coupling
are bidirectional in principle, but can generally accommodate only a few local
nodes. Here, we demonstrate high-fidelity, on-demand, bidirectional photon
emission using an artificial molecule comprising two superconducting qubits
strongly coupled to a waveguide. Quantum interference between the photon
emission pathways from the molecule generate single photons that selectively
propagate in a chosen direction. This architecture is capable of both photon
emission and capture, and can be tiled in series to form an extensible network
of quantum processors with all-to-all connectivity.


title:
Electro-optic modulator based on Vanadium dioxide epsilon-near-zero
  vertical nanowire triple-cavity in Silicon Waveguide

abstract:
  We present an electro-optic modulator exploiting an array of Vanadium dioxide
nanorods operating in the epsilon near regime as the active switching material
integrated in a silicon waveguide. The modulator takes advantage of the
insulator-to-metal transition of Vanadium dioxide to achieve a robust
modulation depth of 19.7 dB/micron with a broad wavelength of operation. Using
simulations, we demonstrate how the effective permittivity of the metamaterial
can be tuned to a near-zero value by varying the nanorod geometry. The paper
also proposes a novel hexagonal array design which achieves low insertion
losses while retaining a strong modulation depth. The results provide insight
into the design of ultra-compact modulators with high operation frequencies and
low insertion losses.


title: Extinction of populations and a team of Lyapunov functions

abstract:
  We investigate the $d$-dimensional model, \begin{equation*}
  x_i' = \frac{d}{dt}{x_i} = x_i(c_i +\sum_{j=1}^{d'} s_{ij}z_j),\quad
\text{where } i=1,\ldots,d. \end{equation*} where each $z_j(t)$ is a time
fluctuating resource. This "nonautonomous" model is a generalization of an
autonomous Lotka-Volterra $d$-dimensional model. It is nonautonomous in the
sense that it is not specified how the $z_j(t)$ are determined. Write $S^T$ for
the transpose of $S=(s_{ij})$. When the rank of $S^T$ is equal to $d-k$ for $k
>0$, we show that at least $k$ species will die out and will do so
exponentially fast. For the proof, we invent a family of Lyapunov functions, a
"team" of Lyapunov functions that work together to show that $k$ species must
die, where $k$ is the dimension of the kernel of $S$. Each Lyapunov function
provides constraints as to which species must die out and together they provide
a picture of which are likely to die out. We present a "trophic" condition for
that Lotka-Volterra systems that guarantees that there is a trapping region
that is globally attracting.


title:
Queueing Systems with Preferred Service Delivery Times and Multiple
  Customer Classes

abstract:
  Motivated by the operational problems in click and collect systems, such as
curbside pickup programs, we study a joint admission control and capacity
allocation problem. We consider a system where arriving customers have
preferred service delivery times and gauge the service quality based on the
service provider's ability to complete the service as close as possible to the
preferred time. Customers can be of different priority classes, and their
priority may increase as they wait longer in the queue. The service provider
can reject customers upon their arrival if the system is overloaded or
outsource the service (alternatively work overtime) when the capacity is not
enough. The service provider's goal is to find the minimum-cost admission and
capacity allocation policy to dynamically decide when to serve and whom to
serve. We model this problem as a Markov Decision Process. Our structural
results partially characterize a set of suboptimal solutions, and we develop
solution methods using these results. We also develop a problem-specific
approximation method that is based on state aggregation to overcome the
computational challenges. We present extensive computational results and
discuss the impact of problem parameters on the optimal policy.


title: A mathematical study of the role of tBregs in breast cancer

abstract:
  A model for the mathematical study of immune response to breast cancer is
proposed and studied, both analytically and numerically. It is a simplification
of a complex one, recently introduced by two of the present authors. It serves
for a compact study of the dynamical role in cancer promotion of a relatively
recently described subgroup of regulatory B cells, which are evoked by the
tumour.


title:
Fast and accurate approximation to informed Bayes factors for focal
  parameters

abstract:
  We outline an approximation to informed Bayes factors for a focal parameter
$\theta$ that requires only the maximum likelihood estimate $\hat\theta$ and
its standard error. The approximation uses an estimated likelihood of $\theta$
and assumes that the posterior distribution for $\theta$ is unaffected by the
choice of prior distribution for the nuisance parameters. The resulting Bayes
factor for the null hypothesis $\mathcal{H}_0: \theta = \theta_0$ versus the
alternative hypothesis $\mathcal{H}_1: \theta \sim g(\theta)$ is then easily
obtained using the Savage--Dickey density ratio. Three real-data examples
highlight the speed and closeness of the approximation compared to bridge
sampling and Laplace's method. The proposed approximation facilitates Bayesian
reanalyses of standard frequentist results, encourages application of Bayesian
tests with informed priors, and alleviates the computational challenges that
often frustrate both Bayesian sensitivity analyses and Bayes factor design
analyses. The approximation is shown to fail under small sample sizes and when
the posterior distribution of the focal parameter is substantially influenced
by the prior distributions on the nuisance parameters. The methodology may also
be used to approximate the posterior distribution for $\theta$ under
$\mathcal{H}_1$.


title:
CAMERA: A Method for Cost-aware, Adaptive, Multifidelity, Efficient
  Reliability Analysis

abstract:
  Estimating probability of failure in aerospace systems is a critical
requirement for flight certification and qualification. Failure probability
estimation involves resolving tails of probability distribution, and Monte
Carlo sampling methods are intractable when expensive high-fidelity simulations
have to be queried. We propose a method to use models of multiple fidelities
that trade accuracy for computational efficiency. Specifically, we propose the
use of multifidelity Gaussian process models to efficiently fuse models at
multiple fidelity, thereby offering a cheap surrogate model that emulates the
original model at all fidelities. Furthermore, we propose a novel sequential
\emph{acquisition function}-based experiment design framework that can
automatically select samples from appropriate fidelity models to make
predictions about quantities of interest in the highest fidelity. We use our
proposed approach in an importance sampling setting and demonstrate our method
on the failure level set estimation and probability estimation on synthetic
test functions as well as two real-world applications, namely, the reliability
analysis of a gas turbine engine blade using a finite element method and a
transonic aerodynamic wing test case using Reynolds-averaged Navier--Stokes
equations. We demonstrate that our method predicts the failure boundary and
probability more accurately and computationally efficiently while using varying
fidelity models compared with using just a single expensive high-fidelity
model.


title:
MUAD: Multiple Uncertainties for Autonomous Driving benchmark for
  multiple uncertainty types and tasks

abstract:
  Predictive uncertainty estimation is essential for deploying Deep Neural
Networks in real-world autonomous systems. However, disentangling the different
types and sources of uncertainty is non trivial in most datasets, especially
since there is no ground truth for uncertainty. In addition, different degrees
of weather conditions can disrupt neural networks, resulting in inconsistent
training data quality. Thus, we introduce the MUAD dataset (Multiple
Uncertainties for Autonomous Driving), consisting of 8,500 realistic synthetic
images with diverse adverse weather conditions (night, fog, rain, snow),
out-of-distribution objects and annotations for semantic segmentation, depth
estimation, object and instance detection. MUAD allows to better assess the
impact of different sources of uncertainty on model performance. We propose a
study that shows the importance of having reliable Deep Neural Networks (DNNs)
in multiple experiments, and will release our dataset to allow researchers to
benchmark their algorithm methodically in ad-verse conditions. More information
and the download link for MUAD are available at https://muad-dataset.github.io/ .


title:
EnclaveTree: Privacy-preserving Data Stream Training and Inference Using
  TEE

abstract:
  The classification service over a stream of data is becoming an important
offering for cloud providers, but users may encounter obstacles in providing
sensitive data due to privacy concerns. While Trusted Execution Environments
(TEEs) are promising solutions for protecting private data, they remain
vulnerable to side-channel attacks induced by data-dependent access patterns.
We propose a Privacy-preserving Data Stream Training and Inference scheme,
called EnclaveTree, that provides confidentiality for user's data and the
target models against a compromised cloud service provider. We design a
matrix-based training and inference procedure to train the Hoeffding Tree (HT)
model and perform inference with the trained model inside the trusted area of
TEEs, which provably prevent the exploitation of access-pattern-based attacks.
The performance evaluation shows that EnclaveTree is practical for processing
the data streams with small or medium number of features. When there are less
than 63 binary features, EnclaveTree is up to ${\thicksim}10{\times}$ and
${\thicksim}9{\times}$ faster than na\"ive oblivious solution on training and
inference, respectively.


title: Enhancing Adversarial Robustness for Deep Metric Learning

abstract:
  Owing to security implications of adversarial vulnerability, adversarial
robustness of deep metric learning models has to be improved. In order to avoid
model collapse due to excessively hard examples, the existing defenses dismiss
the min-max adversarial training, but instead learn from a weak adversary
inefficiently. Conversely, we propose Hardness Manipulation to efficiently
perturb the training triplet till a specified level of hardness for adversarial
training, according to a harder benign triplet or a pseudo-hardness function.
It is flexible since regular training and min-max adversarial training are its
boundary cases. Besides, Gradual Adversary, a family of pseudo-hardness
functions is proposed to gradually increase the specified hardness level during
training for a better balance between performance and robustness. Additionally,
an Intra-Class Structure loss term among benign and adversarial examples
further improves model robustness and efficiency. Comprehensive experimental
results suggest that the proposed method, although simple in its form,
overwhelmingly outperforms the state-of-the-art defenses in terms of
robustness, training efficiency, as well as performance on benign examples.


title: Near-Optimal Correlation Clustering with Privacy

abstract:
  Correlation clustering is a central problem in unsupervised learning, with
applications spanning community detection, duplicate detection, automated
labelling and many more. In the correlation clustering problem one receives as
input a set of nodes and for each node a list of co-clustering preferences, and
the goal is to output a clustering that minimizes the disagreement with the
specified nodes' preferences. In this paper, we introduce a simple and
computationally efficient algorithm for the correlation clustering problem with
provable privacy guarantees. Our approximation guarantees are stronger than
those shown in prior work and are optimal up to logarithmic factors.


title: 3D Common Corruptions and Data Augmentation

abstract:
  We introduce a set of image transformations that can be used as corruptions
to evaluate the robustness of models as well as data augmentation mechanisms
for training neural networks. The primary distinction of the proposed
transformations is that, unlike existing approaches such as Common Corruptions,
the geometry of the scene is incorporated in the transformations -- thus
leading to corruptions that are more likely to occur in the real world. We also
introduce a set of semantic corruptions (e.g. natural object occlusions). We
show these transformations are `efficient' (can be computed on-the-fly),
`extendable' (can be applied on most image datasets), expose vulnerability of
existing models, and can effectively make models more robust when employed as
`3D data augmentation' mechanisms. The evaluations on several tasks and
datasets suggest incorporating 3D information into benchmarking and training
opens up a promising direction for robustness research.


title:
Deformable Radar Polygon: A Lightweight and Predictable Occupancy
  Representation for Short-range Collision Avoidance

abstract:
  Inferring the drivable area in a scene is a key capability for ensuring
vehicle avoids obstacles and enabling safe autonomous driving. However,
traditional occupancy grid map suffers from high memory consumption when
forming a fine-resolution grid for a large map. In this paper, we propose a
lightweight, accurate, and predictable occupancy representation for automotive
radars working for short-range applications that take interest in instantaneous
free space surrounding the sensor. This new occupancy format is a polygon
composed of a bunch of vertexes selected from radar measurements, which covers
free space inside and gives a Doppler moving velocity for each vertex. It not
only takes a very small memory for storage and update at every timeslot, but
also has the predictable shape-change property based on vertex Doppler
velocity. We name this kind of occupancy representation `deformable radar
polygon'. Two formation algorithms for radar polygon are introduced for both
single timeslot and continuous ISM update. To fit this new polygon
representation, a matrix-form collision detection method have been modeled as
well. The radar polygon algorithms and collision detection model have been
validated via extensive experiments with real collected data and simulations,
showing that the deformable radar polygon is very competitive in terms of its
completeness, smoothness, accuracy, lightweight as well as shape-predictable
property. Our codes will be made publicly available for ease of future works.


title: Continuous-Time Meta-Learning with Forward Mode Differentiation

abstract:
  Drawing inspiration from gradient-based meta-learning methods with infinitely
small gradient steps, we introduce Continuous-Time Meta-Learning (COMLN), a
meta-learning algorithm where adaptation follows the dynamics of a gradient
vector field. Specifically, representations of the inputs are meta-learned such
that a task-specific linear classifier is obtained as a solution of an ordinary
differential equation (ODE). Treating the learning process as an ODE offers the
notable advantage that the length of the trajectory is now continuous, as
opposed to a fixed and discrete number of gradient steps. As a consequence, we
can optimize the amount of adaptation necessary to solve a new task using
stochastic gradient descent, in addition to learning the initial conditions as
is standard practice in gradient-based meta-learning. Importantly, in order to
compute the exact meta-gradients required for the outer-loop updates, we devise
an efficient algorithm based on forward mode differentiation, whose memory
requirements do not scale with the length of the learning trajectory, thus
allowing longer adaptation in constant memory. We provide analytical guarantees
for the stability of COMLN, we show empirically its efficiency in terms of
runtime and memory usage, and we illustrate its effectiveness on a range of
few-shot image classification problems.


title:
Hierarchical and Modular Supervisory Control under Partial Observation:
  Normality

abstract:
  Conditions preserving observability of specifications between the plant and
its abstraction are fundamental for hierarchical supervisory control of
discrete-event systems under partial observation. Observation consistency and
local observation consistency were identified as such conditions. To preserve
normality, only observation consistency is required. Although observation
consistency preserves normality between the levels for normal specifications,
we show that for specifications that are not normal, observation consistency
does not guarantee that the supremal normal sublanguage computed on the low
level and on the high level coincide. Therefore, we define modified observation
consistency, under which the supremal normal sublanguages of different levels
coincide. Since we show that the verification of (modified) observation
consistency is PSPACE-hard for finite automata models and undecidable for
systems slightly more expressive than finite automata, we further discuss two
stronger conditions that are easy to verify, and hence of practical interest.
We apply modified observation consistency in modular systems to guarantee that
the global supervisor can be computed locally. In particular, we use the
coordination control framework, where the global and local computations of
supervisors coincide under the assumption that the coordinated events are all
observable.


title:
LILE: Look In-Depth before Looking Elsewhere -- A Dual Attention Network
  using Transformers for Cross-Modal Information Retrieval in Histopathology
  Archives

abstract:
  The volume of available data has grown dramatically in recent years in many
applications. Furthermore, the age of networks that used multiple modalities
separately has practically ended. Therefore, enabling bidirectional
cross-modality data retrieval capable of processing has become a requirement
for many domains and disciplines of research. This is especially true in the
medical field, as data comes in a multitude of types, including various types
of images and reports as well as molecular data. Most contemporary works apply
cross attention to highlight the essential elements of an image or text in
relation to the other modalities and try to match them together. However,
regardless of their importance in their own modality, these approaches usually
consider features of each modality equally. In this study, self-attention as an
additional loss term will be proposed to enrich the internal representation
provided into the cross attention module. This work suggests a novel
architecture with a new loss term to help represent images and texts in the
joint latent space. Experiment results on two benchmark datasets, i.e. MS-COCO
and ARCH, show the effectiveness of the proposed method.


title: RoLoMa: Robust Loco-Manipulation for Quadruped Robots with Arms

abstract:
  Deployment of robotic systems in the real world requires a certain level of
robustness in order to deal with uncertainty factors, such as mismatches in the
dynamics model, noise in sensor readings, and communication delays. Some
approaches tackle these issues reactively at the control stage. However,
regardless of the controller, online motion execution can only be as robust as
the system capabilities allow at any given state. This is why it is important
to have good motion plans to begin with, where robustness is considered
proactively. To this end, we propose a metric (derived from first principles)
for representing robustness against external disturbances. We then use this
metric within our trajectory optimization framework for solving complex
loco-manipulation tasks. Through our experiments, we show that trajectories
generated using our approach can resist a greater range of forces originating
from any possible direction. By using our method, we can compute trajectories
that solve tasks as effectively as before, with the added benefit of being able
to counteract stronger disturbances in worst-case scenarios.


title:
Learning Stochastic Parametric Differentiable Predictive Control
  Policies

abstract:
  The problem of synthesizing stochastic explicit model predictive control
policies is known to be quickly intractable even for systems of modest
complexity when using classical control-theoretic methods. To address this
challenge, we present a scalable alternative called stochastic parametric
differentiable predictive control (SP-DPC) for unsupervised learning of neural
control policies governing stochastic linear systems subject to nonlinear
chance constraints. SP-DPC is formulated as a deterministic approximation to
the stochastic parametric constrained optimal control problem. This formulation
allows us to directly compute the policy gradients via automatic
differentiation of the problem's value function, evaluated over sampled
parameters and uncertainties. In particular, the computed expectation of the
SP-DPC problem's value function is backpropagated through the closed-loop
system rollouts parametrized by a known nominal system dynamics model and
neural control policy which allows for direct model-based policy optimization.
We provide theoretical probabilistic guarantees for policies learned via the
SP-DPC method on closed-loop stability and chance constraints satisfaction.
Furthermore, we demonstrate the computational efficiency and scalability of the
proposed policy optimization algorithm in three numerical examples, including
systems with a large number of states or subject to nonlinear constraints.


title: Counting Points on Igusa Varieties of Hodge Type

abstract:
  Igusa varieties are algebraic varieties that arise in the study of special
fibers of Shimura varieties, and have demonstrated many applications in the
Langlands program via a Langlands-Kottwitz style point-counting formula due to
Shin in the case of PEL type.
  In this paper we formulate and prove an analogue of the Langlands-Rapoport
conjecture for Igusa varieties of Hodge type, building off the work of Kisin in
the case of Shimura varieties. We then use this description of the points to
derive a point-counting formula for Igusa varieties of Hodge type, generalizing
the fomula in PEL type of Shin, by drawing on the techniques of Kisin-Shin-Zhu.


title: Object Pose Estimation using Mid-level Visual Representations

abstract:
  This work proposes a novel pose estimation model for object categories that
can be effectively transferred to previously unseen environments. The deep
convolutional network models (CNN) for pose estimation are typically trained
and evaluated on datasets specifically curated for object detection, pose
estimation, or 3D reconstruction, which requires large amounts of training
data. In this work, we propose a model for pose estimation that can be trained
with small amount of data and is built on the top of generic mid-level
representations \cite{taskonomy2018} (e.g. surface normal estimation and
re-shading). These representations are trained on a large dataset without
requiring pose and object annotations. Later on, the predictions are refined
with a small CNN neural network that exploits object masks and silhouette
retrieval. The presented approach achieves superior performance on the Pix3D
dataset \cite{pix3d} and shows nearly 35\% improvement over the existing models
when only 25\% of the training data is available. We show that the approach is
favorable when it comes to generalization and transfer to novel environments.
Towards this end, we introduce a new pose estimation benchmark for commonly
encountered furniture categories on challenging Active Vision Dataset
\cite{Ammirato2017ADF} and evaluated the models trained on the Pix3D dataset.


title:
IGR J18249-3243: a new GeV-emitting FR II and the emerging population of
  high energy radio galaxies

abstract:
  The advent of new all-sky radio surveys such as the VLA Sky Survey (VLASS)
and the Rapid ASKAP Continuum Survey (RACS), performed with the latest
generation radio telescopes, is opening new possibilities on the classification
and study of extragalactic $\gamma$-ray sources, specially the underrepresented
ones like radio galaxies. In particular, the enhanced sensitivity (sub-mJy
level) and resolution (a few arcsec) provides a better morphological and
spectral classification. In this work, we present the reclassification of a
Fermi-LAT source as a new FRII radio galaxy from the INTEGRAL sample found to
emit at GeV energies. Through a broad-band spectral fitting from radio to
$\gamma$-ray, we find that the commonly invoked jet contribution is not
sufficient to account for the observed $\gamma$-ray flux. Our modeling suggests
that the observed emission could mainly originate in the lobes (rather than in
the radio core) by inverse Compton scattering of radio-emitting electrons off
the ambient photon fields. In addition, we cross-correlated the latest
generation radio surveys with a list of Fermi-LAT objects from the literature
considered to be candidate misaligned AGN, finding four new radio galaxies with
a double-lobed morphology. Additional four objects could be classified as such
thanks to previous studies in the literature, for a total of nine new radio
galaxies with GeV emission presented in this work. We foresee that further
objects of this class might be found in the near future with the advent of the
Square Kilometer Array (SKA), populating the GeV sky.


title:
Label Leakage and Protection from Forward Embedding in Vertical
  Federated Learning

abstract:
  Vertical federated learning (vFL) has gained much attention and been deployed
to solve machine learning problems with data privacy concerns in recent years.
However, some recent work demonstrated that vFL is vulnerable to privacy
leakage even though only the forward intermediate embedding (rather than raw
features) and backpropagated gradients (rather than raw labels) are
communicated between the involved participants. As the raw labels often contain
highly sensitive information, some recent work has been proposed to prevent the
label leakage from the backpropagated gradients effectively in vFL. However,
these work only identified and defended the threat of label leakage from the
backpropagated gradients. None of these work has paid attention to the problem
of label leakage from the intermediate embedding. In this paper, we propose a
practical label inference method which can steal private labels effectively
from the shared intermediate embedding even though some existing protection
methods such as label differential privacy and gradients perturbation are
applied. The effectiveness of the label attack is inseparable from the
correlation between the intermediate embedding and corresponding private
labels. To mitigate the issue of label leakage from the forward embedding, we
add an additional optimization goal at the label party to limit the label
stealing ability of the adversary by minimizing the distance correlation
between the intermediate embedding and corresponding private labels. We
conducted massive experiments to demonstrate the effectiveness of our proposed
protection methods.


title:
Bending Reality: Distortion-aware Transformers for Adapting to Panoramic
  Semantic Segmentation

abstract:
  Panoramic images with their 360-degree directional view encompass exhaustive
information about the surrounding space, providing a rich foundation for scene
understanding. To unfold this potential in the form of robust panoramic
segmentation models, large quantities of expensive, pixel-wise annotations are
crucial for success. Such annotations are available, but predominantly for
narrow-angle, pinhole-camera images which, off the shelf, serve as sub-optimal
resources for training panoramic models. Distortions and the distinct
image-feature distribution in 360-degree panoramas impede the transfer from the
annotation-rich pinhole domain and therefore come with a big dent in
performance. To get around this domain difference and bring together semantic
annotations from pinhole- and 360-degree surround-visuals, we propose to learn
object deformations and panoramic image distortions in the Deformable Patch
Embedding (DPE) and Deformable MLP (DMLP) components which blend into our
Transformer for PAnoramic Semantic Segmentation (Trans4PASS) model. Finally, we
tie together shared semantics in pinhole- and panoramic feature embeddings by
generating multi-scale prototype features and aligning them in our Mutual
Prototypical Adaptation (MPA) for unsupervised domain adaptation. On the indoor
Stanford2D3D dataset, our Trans4PASS with MPA maintains comparable performance
to fully-supervised state-of-the-arts, cutting the need for over 1,400 labeled
panoramas. On the outdoor DensePASS dataset, we break state-of-the-art by
14.39% mIoU and set the new bar at 56.38%. Code will be made publicly available
at https://github.com/jamycheung/Trans4PASS.


title:
Atomic-Scale Spin-Wave Polarizer Based on a Sharp Antiferromagnetic
  Domain Wall

abstract:
  We theoretically study the scattering of spin waves from a sharp domain wall
in an antiferromagnetic spin chain. While the continuum model for an
antiferromagnetic material yields the well-known result that spin waves can
pass through a wide domain wall with no reflection, here we show that, based on
the discrete spin Hamiltonian, spin waves are generally reflected by a domain
wall with a reflection coefficient that increases as the domain-wall width
decreases. Remarkably, we find that, in the interesting case of an atomically
sharp domain wall, the reflection of spin waves exhibits strong dependence on
the state of circular polarization of the spin waves, leading to mainly
reflection for one polarization while permitting partial transmission for the
other, thus realizing an atomic-scale spin-wave polarizer. The polarization of
the transmitted spin wave depends on the orientation of the spin in the sharp
domain wall, which can be controlled by an external field or spin torque. Our
utilization of a sharp antiferromagnetic domain wall as an atomic-scale
spin-wave polarizer leads us to envision that ultra-small magnetic solitons
such as domain walls and skyrmions may enable realizations of atomic-scale
spin-wave scatterers with useful functionalities.


title: Global Continuation of a Vlasov Model of Rotating Galaxies

abstract:
  A typical galaxy consists of a huge number of stars attracted to each other
by gravity. For instance, the Milky Way has about $10^{11}$ stars. Thus it is
typically modeled by the Vlasov-Poisson system. We prove an existence theorem
for axisymmetric steady states of galaxies that may rotate rapidly. Such states
are given in terms of a fairly general function $\phi$ of the particle energy
and angular momentum. The set $\mathcal K$ of such states form a connected set
in an appropriate function space. Along the set $\mathcal K$, we prove under
some conditions that either (a) the supports of the galaxies become unbounded
or (b) both the rotation speeds and the densities somewhere within the galaxy
become unbounded.


title:
A relationship and not a thing: A relational approach to algorithmic
  accountability and assessment documentation

abstract:
  Central to a number of scholarly, regulatory, and public conversations about
algorithmic accountability is the question of who should have access to
documentation that reveals the inner workings, intended function, and
anticipated consequences of algorithmic systems, potentially establishing new
routes for impacted publics to contest the operations of these systems.
Currently, developers largely have a monopoly on information about how their
systems actually work and are incentivized to maintain their own ignorance
about aspects of how their systems affect the world. Increasingly, legislators,
regulators and advocates have turned to assessment documentation in order to
address the gap between the public's experience of algorithmic harms and the
obligations of developers to document and justify their design decisions.
However, issues of standing and expertise currently prevent publics from
cohering around shared interests in preventing and redressing algorithmic
harms; as we demonstrate with multiple cases, courts often find computational
harms non-cognizable and rarely require developers to address material claims
of harm. Constructed with a triadic accountability relationship, algorithmic
impact assessment regimes could alter this situation by establishing procedural
rights around public access to reporting and documentation. Developing a
relational approach to accountability, we argue that robust accountability
regimes must establish opportunities for publics to cohere around shared
experiences and interests, and to contest the outcomes of algorithmic systems
that affect their lives. Furthermore, algorithmic accountability policies
currently under consideration in many jurisdictions must provide the public
with adequate standing and opportunities to access and contest the
documentation provided by the actors and the judgments passed by the forum.


title: A global bifurcation organizing rhythmic activity in a coupled network

abstract:
  We study a system of coupled phase oscillators near a saddle-node on an
invariant circle bifurcation and driven by random intrinsic frequencies. Under
the variation of control parameters, the system undergoes a phase transition
changing the qualitative properties of collective dynamics. Using the
Ott-Antonsen reduction and geometric techniques for ordinary differential
equations, we identify a heteroclinic bifurcation in a family of vector fields
on a cylinder, which explains the change in collective dynamics. Specifically,
we show that the heteroclinic bifurcation separates two topologically distinct
families of limit cycles: contractible limit cycles before the bifurcation from
noncontractible ones after the bifurcation. Both families are stable for the
model at hand.


title:
Analytical expressions for thermophysical properties of solid and liquid
  beryllium relevant for fusion applications

abstract:
  The status of the literature is reviewed for thermophysical properties of
pure polycrystalline solid and liquid beryllium which constitute input for the
modelling of intense plasma-surface interaction phenomena that are important
for fusion applications (thermal analysis, vapor shielding, melt motion,
arcing, dust generation, dust transport). Reliable experimental data are
analyzed for the latent heats, specific isobaric heat capacity, electrical
resistivity, thermal conductivity, mass density, vapor pressure, work function,
total hemispherical emissivity and absolute thermoelectric power from the room
temperature up to the normal boiling point of beryllium as well as for the
surface tension and the dynamic viscosity across the liquid state. Analytical
expressions are recommended for the temperature dependence of these
thermophysical properties, which involve high temperature extrapolations given
the absence of extended liquid beryllium measurements.


title:
Spectral function of chiral one-dimensional Fermi liquid in the regime
  of strong interactions

abstract:
  We study momentum-resolved tunneling into a system of spinless chiral
one-dimensional fermions, such as electrons at the edge of an integer quantum
Hall system. Interactions between particles give rise to broadening of the
spectral function of the system. We develop an approach that enables one to
obtain the shape of the peak in the spectral function in the regime of strong
interaction. We apply our technique to the special cases of short-range and
Coulomb interactions.


title:
Scalable Bayesian Optimization Using Vecchia Approximations of Gaussian
  Processes

abstract:
  Bayesian optimization is a technique for optimizing black-box target
functions. At the core of Bayesian optimization is a surrogate model that
predicts the output of the target function at previously unseen inputs to
facilitate the selection of promising input values. Gaussian processes (GPs)
are commonly used as surrogate models but are known to scale poorly with the
number of observations. We adapt the Vecchia approximation, a popular GP
approximation from spatial statistics, to enable scalable high-dimensional
Bayesian optimization. We develop several improvements and extensions,
including training warped GPs using mini-batch gradient descent, approximate
neighbor search, and selecting multiple input values in parallel. We focus on
the use of our warped Vecchia GP in trust-region Bayesian optimization via
Thompson sampling. On several test functions and on two reinforcement-learning
problems, our methods compared favorably to the state of the art.


title:
Propagating spatially-varying multiplicative shear bias to cosmological
  parameter estimation for stage-IV weak-lensing surveys

abstract:
  We consider the bias introduced by a spatially-varying multiplicative shear
bias (m-bias) on tomographic cosmic shear angular power spectra. To compute the
bias in the power spectra, we estimate the mode-coupling matrix associated with
an m-bias map using a computationally-efficient pseudo-Cl method. This allows
us to consider the effect of the m-bias to high l. We then conduct a Fisher
matrix analysis to forecast resulting biases in cosmological parameters. For a
Euclid-like survey with a spatially-varying m-bias, with zero mean and rms of
0.01, we find that parameter biases reach a maximum of ~10% of the expected
statistical error, if multipoles up to l_max = 5000 are included. We conclude
that the effect of the spatially-varying m-bias may be a sub-dominant but
potentially non-negligible contribution to the error budget in forthcoming weak
lensing surveys. We also investigate the dependence of parameter biases on the
amplitude and angular scale of spatial variations of the m-bias field, and
conclude that requirements should be placed on the rms of spatial variations of
the m-bias, in addition to any requirement on the mean value. We find that, for
a Euclid-like survey, biases generally exceed ~30% of the statistical error for
m-bias rms 0.02 - 0.03 and can exceed the statistical error for rms ~0.04 -
0.05. This allows requirements to be set on the permissible amplitude of
spatial variations of the m-bias that will arise due to systematics in
forthcoming weak lensing measurements.


title:
The Final Season Reimagined: 30 Tidal Disruption Events from the ZTF-I
  Survey

abstract:
  Tidal disruption events (TDEs) offer a unique way to study dormant black
holes. While the number of observed TDEs has grown thanks to the emergence of
wide-field surveys in the past few decades, questions regarding the nature of
the observed optical, UV, and X-ray emission remain. We present a uniformly
selected sample of 30 spectroscopically classified TDEs from the Zwicky
Transient Facility Phase I survey operations with follow-up \textit{Swift} UV
and X-ray observations. Through our investigation into correlations between
light curve properties, we recover a shallow positive correlation between the
peak bolometric luminosity and decay timescales. We introduce a new
spectroscopic class of TDE, TDE-featureless, which are characterized by
featureless optical spectra. We recover a difference between the light curve
timescales of the four TDE classes, with TDE-H+He having longer rise times. The
new TDE-featureless class shows larger peak bolometric luminosities, peak
blackbody temperatures, and peak blackbody radii. We examine the differences
between the X-ray bright and X-ray faint populations of TDEs in this sample,
finding that X-ray bright TDEs show longer decay timescales than the X-ray
faint sub-sample. We present a scheme for classifying the shapes of the optical
light curves, finding that TDEs with "structured" light curves have
significantly less massive host galaxies than other types of TDEs. This sample
of optically selected TDEs is the largest sample of TDEs from a single survey
yet, and the systematic discovery, classification, and follow-up of this sample
allows for robust characterization of TDE properties, an important stepping
stone looking forward toward the Rubin era.


title:
Topology optimization of dispersive plasmonic nanostructures in the
  time-domain

abstract:
  Topology optimization techniques have been applied in integrated optics and
nanophotonics for the inverse design of devices with shapes that cannot be
conceived by human intuition. At optical frequencies, these techniques have
only been utilized to optimize nondispersive materials using frequency-domain
methods. However, a time-domain formulation is more efficient to optimize
materials with dispersion. We introduce such a formulation for the Drude model,
which is widely used to simulate the dispersive properties of metals,
conductive oxides, and conductive polymers. Our topology optimization algorithm
is based on the finite-difference time-domain (FDTD) method, and we introduce a
time-domain sensitivity analysis that enables the evaluation of the gradient
information by using one additional FDTD simulation. The existence of
dielectric and metallic structures in the design space produces plasmonic field
enhancement that causes convergence issues. We employ an artificial damping
approach during the optimization iterations that, by reducing the plasmonic
effects, solves the convergence problem. We present several design examples of
2D and 3D plasmonic nanoantennas with optimized field localization and
enhancement in frequency bands of choice. Our method has the potential to speed
up the design of wideband optical nanostructures made of dispersive materials
for applications in nanoplasmonics, integrated optics, ultrafast photonics, and
nonlinear optics.


title:
Confirmation of Water Absorption in the Thermal Emission Spectrum of the
  Hot Jupiter WASP-77Ab with HST/WFC3

abstract:
  Secondary eclipse observations of hot Jupiters can reveal both their
compositions and thermal structures. Previous observations have shown a
diversity of hot Jupiter eclipse spectra, including absorption features,
emission features, and featureless blackbody-like spectra. We present a
secondary eclipse spectrum of the hot Jupiter WASP-77Ab observed between $1-5$
$\mu$m with the Hubble Space Telescope (HST) and the Spitzer Space Telescope.
The HST observations show signs of water absorption indicative of a
non-inverted thermal structure. We fit the data with both a one-dimensional
free retrieval and a grid of one-dimensional self-consistent forward models to
confirm this non-inverted structure. The free retrieval places a $3\sigma$
lower limit on the atmospheric water abundance of $\log(n_\mathrm{H_2O})>-4.78$
and can not constrain the CO abundance. The grid fit produces a slightly
super-stellar metallicity and constrains the carbon-to-oxygen ratio to less
than or equal to the solar value. We also compare our data to recent
high-resolution observations of WASP-77Ab taken with the Gemini-South/IGRINS
spectrograph and find that our observations are consistent with the best-fit
model to the high-resolution data. However, the metallicity derived from the
IGRINS data is significantly lower than that derived from our self-consistent
model fit. We find that this difference may be due to disequilibrium chemistry,
and the varying results between the models applied here demonstrate the
difficulty of constraining disequilibrium chemistry with low-resolution, low
wavelength coverage data alone. Future work to combine observations from
IGRINS, HST, and JWST will improve our estimate of the atmospheric composition
of WASP-77Ab.


title:
A Survey of Analysis Methods for Security and Safety verification in IoT
  Systems

abstract:
  Internet of Things (IoT) has been rapidly growing in the past few years in
all life disciplines. IoT provides automation and smart control to its users in
different domains such as home automation, healthcare systems, automotive, and
many more. Given the tremendous number of connected IoT devices, this growth
leads to enormous automatic interactions among sizeable IoT apps in their
environment, making IoT apps more intelligent and more enjoyable to their
users. But some unforeseen interactions of IoT apps and any potential malicious
behaviour can seriously cause insecure and unsafe consequences to its users,
primarily non-experts, who lack the required knowledge regarding the potential
impact of their IoT automation processes. In this paper, we study the problem
of security and safety verification of IoT systems. We survey techniques that
utilize program analysis to verify IoT applications' security and safety
properties. The study proposes a set of categorization and classification
attributes to enhance our understanding of the research landscape in this
domain. Moreover, we discuss the main challenges considered in the surveyed
work and potential solutions that could be adopted to ensure the security and
safety of IoT systems.


title: Deep Q-network using reservoir computing with multi-layered readout

abstract:
  Recurrent neural network (RNN) based reinforcement learning (RL) is used for
learning context-dependent tasks and has also attracted attention as a method
with remarkable learning performance in recent research. However, RNN-based RL
has some issues that the learning procedures tend to be more computationally
expensive, and training with backpropagation through time (BPTT) is unstable
because of vanishing/exploding gradients problem. An approach with replay
memory introducing reservoir computing has been proposed, which trains an agent
without BPTT and avoids these issues. The basic idea of this approach is that
observations from the environment are input to the reservoir network, and both
the observation and the reservoir output are stored in the memory. This paper
shows that the performance of this method improves by using a multi-layered
neural network for the readout layer, which regularly consists of a single
linear layer. The experimental results show that using multi-layered readout
improves the learning performance of four classical control tasks that require
time-series processing.


title:
Successful Recovery of an Observed Meteorite Fall Using Drones and
  Machine Learning

abstract:
  We report the first-time recovery of a fresh meteorite fall using a drone and
a machine learning algorithm. A fireball on the 1st April 2021 was observed
over Western Australia by the Desert Fireball Network, for which a fall area
was calculated for the predicted surviving mass. A search team arrived on site
and surveyed 5.1 km2 area over a 4-day period. A convolutional neural network,
trained on previously-recovered meteorites with fusion crusts, processed the
images on our field computer after each flight. meteorite candidates identified
by the algorithm were sorted by team members using two user interfaces to
eliminate false positives. Surviving candidates were revisited with a smaller
drone, and imaged in higher resolution, before being eliminated or finally
being visited in-person. The 70 g meteorite was recovered within 50 m of the
calculated fall line using, demonstrating the effectiveness of this methodology
which will facilitate the efficient collection of many more observed meteorite
falls.

categories: astro-ph.EP cs.LG physics.geo-ph physics.space-ph


title: Thomas-Yau conjecture and holomorphic curves

abstract:
  The main theme of this paper is the Thomas-Yau conjecture, primarily in the
setting of exact, (quantitatively) almost calibrated, unobstructed Lagrangian
branes inside Calabi-Yau Stein manifolds. In our interpretation, the conjecture
is that Thomas-Yau semistability is equivalent to the existence of special
Lagrangian representatives. We clarify how holomorphic curves enter this
conjectural picture, through the construction of bordism currents between
Lagrangians, and in the definition of the Solomon functional. Under some extra
hypothesis, we shall prove Floer theoretic obstructions to the existence of
special Lagrangians, using the technique of integration over moduli spaces. In
the converse direction, we set up a variational framework with the goal of
finding special Lagrangians under the Thomas-Yau semistability asumption, and
we shall make sufficient progress to pinpoint the outstanding technical
difficulties, both in Floer theory and in geometric measure theory.


title: Quasi time crystal

abstract:
  We discuss the possibility of making a quasi time crystal. A simple two-state
model is studied to clarify our definition. In a superposition of the ground
state and the excited state and the probability of observation varies
periodically in time during the lifetime of the excited state. The quasi time
crystal is also discussed around the first order quantum phase transition,
which is characterized by the degeneracy and crossing of the two lowest-energy
states in the infinite-volume limit. Our results have broad validity. As an
example, the one-dimensional transverse field Ising model with surface fields
is shown to have similar behavior. The oscillating magnetization profile is
solved exactly.


title: The $Higgs\to b\bar{b}, c\bar{c}, gg$ measurement at CEPC

abstract:
  Accurately measuring the properties of the Higgs boson is one of the core
physics objectives of the Circular Electron Positron Collider (CEPC). As a
Higgs factory, the CEPC is expected to operate at a centre-of-mass energy of
240 GeV, deliver an integrated luminosity of 5600 $fb^{-1}$, and produce one
million Higgs bosons. Combining the measurements of the $\ell^+\ell^-H$,
$\nu\bar{\nu} H$, and $q\bar{q}H$ channels, we conclude that the signal
strength of $H\to b\bar{b}/c\bar{c}/gg$ can be measured with a relative
accuracy of 0.27\%/3.82\%/1.52\%. We analyze the dependence of the expected
accuracies on the critical detector performances: Color Singlet Identification
(CSI) for the $q\bar{q}H$ channel and flavor tagging for both $\nu\bar{\nu} H$
and $q\bar{q}H$ channels. We observe that compared to the baseline CEPC
detector performance, the ideal flavor tagging increases the accuracy of the
$H\to b\bar{b}/c\bar{c}/gg$ signal strength by 2\%/52\%/10\% in the
$\nu\bar{\nu} H$ channel and 35\%/107\%/169\% in the $q\bar{q}H$ channel. A CSI
variable using the truth level information could improve the
signal-to-background ratio by almost an order of magnitude in the $q\bar{q}H$
channel, resulting in an improvement of 6\%/65\%/82\% for the accuracy of the
$q\bar{q}H(H\to b\bar{b}/c\bar{c}/gg)$ signal strength.


title: Doubly-polarized WZ hadronic cross sections at NLO QCD+EW accuracy

abstract:
  We present new results for next-to-leading order (NLO) electroweak (EW)
corrections to double polarization signals in the $WZ$ production channel at
the LHC using the $e^+\nu_e \mu^+ \mu^-$ final state. It is found that the EW
corrections are most sizable in the transverse momentum distributions of the
doubly longitudinal polarization, being around -10% compared to the NLO QCD
prediction at $p_{T,e}\approx 200$ GeV, which is in the accessible energy range
of the current LHC data.


title: A Correlation Thresholding Algorithm for Learning Factor Analysis Models

abstract:
  Factor analysis is a widely used method for modeling a set of observed
variables by a set of (typically fewer) unobserved latent factors. The
traditional method of Exploratory Factor Analysis (EFA), despite its widespread
application, is often criticized for three well-known weaknesses: (1) requiring
the number of factors to be known, (2) ad-hoc nature in learning the support of
coefficients, and (3) nonidentifiability of the parameters due to the rotation
invariance properties of the likelihood. While more recently developed
penalized EFA methods partially address these issues, they remain
computationally intense, and still require the number of latent factors to be
given. To address these issues, we propose a fast algorithm that simultaneously
learns the number of latent factors and a model structure that leads to
identifiable parameters. Our novel approach is motivated from the perspective
of graph theory, making use of thresholded correlation graphs from the observed
data to learn the latent factor structure. We establish theoretical guarantees
for our method with results for solution uniqueness and high-dimensional
consistency of the structure learning as well as the parameter estimation. We
also present a series of simulation studies and a real data example to test and
demonstrate its performance. Our results show that the correlation thresholding
algorithm is an accurate method for learning the structure of factor analysis
models and is robust to violations of our assumptions. Moreover, we show that
our algorithm scales well up to 1500 variables, while the competing methods are
not applicable in a reasonable amount of running time.


title:
Moments dynamics and stationary states for classical diffusion-type GKSL
  equations

abstract:
  The explicit dynamics of the moments for the GKSL equation and the approach
in finding stationary Gaussian states are obtained. In our case the GKSL
equation corresponds to Wiener stochastic processes. Such equations contain a
double commutator which can be understood as a quantum analog of the second
spatial derivative.


title: Powers of posinormal Hilbert-space operators

abstract:
  A bounded linear operator $A$ on a Hilbert space $\mathcal{H}$ is posinormal
if there exists a positive operator $P$ such that $AA^{*} = A^{*}PA$. We show
that if $A$ is posinormal with closed range, then $A^n$ is posinormal and has
closed range for all integers $n\ge 1$. Because the collection of posinormal
operators includes all hyponormal operators, we obtain as a corollary that
powers of closed-range hyponormal operators continue to have closed range. We
also present a simple example of a closed-range operator $T: \mathcal{H}\to
\mathcal{H}$ such that $T^2$ does not have closed range.


title: Spatial-Temporal Gating-Adjacency GCN for Human Motion Prediction

abstract:
  Predicting future motion based on historical motion sequence is a fundamental
problem in computer vision, and it has wide applications in autonomous driving
and robotics. Some recent works have shown that Graph Convolutional
Networks(GCN) are instrumental in modeling the relationship between different
joints. However, considering the variants and diverse action types in human
motion data, the cross-dependency of the spatial-temporal relationships will be
difficult to depict due to the decoupled modeling strategy, which may also
exacerbate the problem of insufficient generalization. Therefore, we propose
the Spatial-Temporal Gating-Adjacency GCN(GAGCN) to learn the complex
spatial-temporal dependencies over diverse action types. Specifically, we adopt
gating networks to enhance the generalization of GCN via the trainable adaptive
adjacency matrix obtained by blending the candidate spatial-temporal adjacency
matrices. Moreover, GAGCN addresses the cross-dependency of space and time by
balancing the weights of spatial-temporal modeling and fusing the decoupled
spatial-temporal features. Extensive experiments on Human 3.6M, AMASS, and 3DPW
demonstrate that GAGCN achieves state-of-the-art performance in both short-term
and long-term predictions. Our code will be released in the future.


title:
CycleMix: A Holistic Strategy for Medical Image Segmentation from
  Scribble Supervision

abstract:
  Curating a large set of fully annotated training data can be costly,
especially for the tasks of medical image segmentation. Scribble, a weaker form
of annotation, is more obtainable in practice, but training segmentation models
from limited supervision of scribbles is still challenging. To address the
difficulties, we propose a new framework for scribble learning-based medical
image segmentation, which is composed of mix augmentation and cycle consistency
and thus is referred to as CycleMix. For augmentation of supervision, CycleMix
adopts the mixup strategy with a dedicated design of random occlusion, to
perform increments and decrements of scribbles. For regularization of
supervision, CycleMix intensifies the training objective with consistency
losses to penalize inconsistent segmentation, which results in significant
improvement of segmentation performance. Results on two open datasets, i.e.,
ACDC and MSCMRseg, showed that the proposed method achieved exhilarating
performance, demonstrating comparable or even better accuracy than the
fully-supervised methods. The code and expert-made scribble annotations for
MSCMRseg are publicly available at https://github.com/BWGZK/CycleMix.


title:
Identification of active slip systems in polycrystals by Slip Trace --
  Modified Lattice Rotation Analysis (ST-MLRA)

abstract:
  A simple and versatile strategy, denominated Slip Trace - Modified Lattice
Rotation Analysis (ST-MLRA), is presented to enable the identification of the
active slip systems in polycrystalline alloys from surface information. The
slip plane trace orientation is used to define the potential active slip planes
while the actual slip direction within the active slip plane is obtained from
the grain rotation, as indicated by the stretching of the trace of the grain
orientation in the pole figure as a result of deformation. Examples of
application of the strategy in a HCP Mg alloy are presented for illustration.
They show that the strategy is simple to implement and allows to identify the
active slip system(s) in each grain.


title: A Matching Mechanism for Provision of Housing to the Marginalized

abstract:
  During this pandemic, there have been unprecedented community and local
government efforts to slow down the spread of the coronavirus, and also to
protect our local economies. One such effort is California's project Roomkey
that provided emergency housing to over 2000 vulnerable persons but fell short
of the set goal of 15,000. It is projected that the homelessness problem will
only get worse after the pandemic. With that in mind, we borrow from efforts
like project Roomkey and suggest a solution that looks to improve upon these
efforts to efficiently assign housing to the unhoused in our communities. The
pandemic, together with the project Roomkey, shed light on the underlying
supply demand mismatch that presents an opportunity for a matching mechanism
solution to assigning housing options to the unhoused in a way that maximizes
social welfare and minimizes susceptibility to strategic manipulation.
Additionally, we argue that this automated solution would cut down on the
amount of funding and personnel required for the assignment of housing to
unhoused persons. Our solution is not intended to replace current solutions to
homeless housing assignments but rather improve upon them. We can not postpone
a proper solution to homelessness anymore, the time is now as the need for an
efficient solution is most dire.


title: Simulating excited states of the Lipkin model on a quantum computer

abstract:
  We simulate the excited states of the Lipkin model using the recently
proposed Quantum Equation of Motion (qEOM) method in Ref
\cite{Ollitrault_2020}. The qEOM generalizes the EOM on classical computers and
gives access to collective excitations based on quasi-boson operators
$\hat{O}^\dagger_n(\alpha)$ of increasing configuration complexity $\alpha$.
The method is applied for increasing particle number. We show that the accuracy
strongly depend on the fermion to qubit encoding. Standard encoding leads to
large errors but the use of symmetries and the Gray code reduces the quantum
resources and improve significantly the results on current noisy quantum
devices. With this encoding scheme, we use IBM quantum machines to compute the
energy spectrum for a system of $N=2, 3$ and $4$ particles and compare the
accuracy against the exact solution. We show that the results for $\alpha = 2$,
the equivalent to second RPA, are notably more accurate than $\alpha = 1$ (RPA)
for large coupling strengths. Thus, we can use our scheme to implement an
algorithm with complexity $\alpha > 3$, which is an analogue for medium-mass
and heavy nuclei, to pave the way towards achieving nuclear spectroscopic
accuracy.


title: Weightless Neural Networks for Efficient Edge Inference

abstract:
  Weightless Neural Networks (WNNs) are a class of machine learning model which
use table lookups to perform inference. This is in contrast with Deep Neural
Networks (DNNs), which use multiply-accumulate operations. State-of-the-art WNN
architectures have a fraction of the implementation cost of DNNs, but still lag
behind them on accuracy for common image recognition tasks. Additionally, many
existing WNN architectures suffer from high memory requirements. In this paper,
we propose a novel WNN architecture, BTHOWeN, with key algorithmic and
architectural improvements over prior work, namely counting Bloom filters,
hardware-friendly hashing, and Gaussian-based nonlinear thermometer encodings
to improve model accuracy and reduce area and energy consumption. BTHOWeN
targets the large and growing edge computing sector by providing superior
latency and energy efficiency to comparable quantized DNNs. Compared to
state-of-the-art WNNs across nine classification datasets, BTHOWeN on average
reduces error by more than than 40% and model size by more than 50%. We then
demonstrate the viability of the BTHOWeN architecture by presenting an
FPGA-based accelerator, and compare its latency and resource usage against
similarly accurate quantized DNN accelerators, including Multi-Layer Perceptron
(MLP) and convolutional models. The proposed BTHOWeN models consume almost 80%
less energy than the MLP models, with nearly 85% reduction in latency. In our
quest for efficient ML on the edge, WNNs are clearly deserving of additional
attention.


title: Modularity of the ABCD Random Graph Model with Community Structure

abstract:
  The Artificial Benchmark for Community Detection (ABCD) graph is a random
graph model with community structure and power-law distribution for both
degrees and community sizes. The model generates graphs with similar properties
as the well-known LFR one, and its main parameter $\xi$ can be tuned to mimic
its counterpart in the LFR model, the mixing parameter $\mu$.
  In this paper, we investigate various theoretical asymptotic properties of
the ABCD model. In particular, we analyze the modularity function, arguably,
the most important graph property of networks in the context of community
detection. Indeed, the modularity function is often used to measure the
presence of community structure in networks. It is also used as a quality
function in many community detection algorithms, including the widely used
Louvain algorithm.


title:
Protection of quantum evolutions under parity-time symmetric
  non-Hermitian Hamiltonians by dynamical decoupling

abstract:
  Parity-time (PT) symmetric non-Hermitian Hamiltonians bring about many novel
features and interesting applications such as quantum gates faster than those
in Hermitian systems, and topological state transfer. The performance of
evolutions under $\mathcal{PT}$-symmetric Hamiltonians is degraded by the
inevitable noise and errors due to system-environment interaction and
experimental imperfections. In contrast to Hermitian Hamiltonians, the
fluctuations in dissipative beams that are utilized to generate non-Hermitian
contributions in the PT-symmetric Hamiltonians cause additional errors. Here we
achieve the protection of PT-symmetric Hamiltonians against noise acting along
the qubit's quantization axis by combining quantum evolutions with dynamical
decoupling sequences. We demonstrate the performance of our method by numerical
simulations. Realistic noise sources and parameters are chosen including:
constant detuning error, time-varying detuning noise and dissipative-beam
noise. The fidelities of the protected evolutions are well above the
unprotected ones under all the above situations. Our work paves the way for
further studies and applications of non-Hermitian $\mathcal{PT}$-symmetric
physics in noisy quantum systems.


title: MetaDT: Meta Decision Tree for Interpretable Few-Shot Learning

abstract:
  Few-Shot Learning (FSL) is a challenging task, which aims to recognize novel
classes with few examples. Recently, lots of methods have been proposed from
the perspective of meta-learning and representation learning for improving FSL
performance. However, few works focus on the interpretability of FSL decision
process. In this paper, we take a step towards the interpretable FSL by
proposing a novel decision tree-based meta-learning framework, namely, MetaDT.
Our insight is replacing the last black-box FSL classifier of the existing
representation learning methods by an interpretable decision tree with
meta-learning. The key challenge is how to effectively learn the decision tree
(i.e., the tree structure and the parameters of each node) in the FSL setting.
To address the challenge, we introduce a tree-like class hierarchy as our
prior: 1) the hierarchy is directly employed as the tree structure; 2) by
regarding the class hierarchy as an undirected graph, a graph convolution-based
decision tree inference network is designed as our meta-learner to learn to
infer the parameters of each node. At last, a two-loop optimization mechanism
is incorporated into our framework for a fast adaptation of the decision tree
with few examples. Extensive experiments on performance comparison and
interpretability analysis show the effectiveness and superiority of our MetaDT.
Our code will be publicly available upon acceptance.


title: Constitutional Implementation of Affirmative Action Policies in India

abstract:
  India is home to a comprehensive affirmative action program that reserves a
fraction of positions at governmental institutions for various disadvantaged
groups. While there is a Supreme Court-endorsed mechanism to implement these
reservation policies when all positions are identical, courts have refrained
from endorsing explicit mechanisms when positions are heterogeneous. This
lacunae has resulted in widespread adoption of unconstitutional mechanisms,
countless lawsuits, and inconsistent court rulings. Formulating mandates in the
landmark Supreme Court judgment Saurav Yadav (2020) as technical axioms, we
show that the 2SMH-DA mechanism is uniquely suited to overcome these
challenges.


title:
3D Radiative Hydrodynamic Modeling of the Near-Surface Shear Layer in
  the Solar Convection Zone

abstract:
  Understanding effects driven by rotation in the solar convection zone is
essential for many problems related to solar activity, such as the formation of
differential rotation, meridional circulation, and others. We present realistic
3D radiative hydrodynamics simulations of solar subsurface dynamics in the
presence of rotation in a local domain 80 Mm-wide and 25 Mm deep, located at 30
degrees latitude. The simulation results reveal the development of a shallow
10-Mm deep near-surface shear layer ("leptocline"), characterized by a strong
radial rotational gradient and self-organized meridional flows. This shear
layer is located in the hydrogen ionization zone associated with enhanced
anisotropic convective flows overshooting into a relatively stable zone between
the H and HeII ionization zones. The radial variations of the differential
rotation and meridional circulation profiles obtained from the simulations
agree with helioseismic observations, indicating that a major role in forming
the leptocline and subsurface meridional flows is played by the local Reynolds
stresses.


title:
Enhanced mechanical heterogeneity of cell collectives due to temporal
  fluctuations in cell elasticity

abstract:
  Cells are dynamic systems characterized by temporal variations in biophysical
properties such as stiffness and contractility. Recent studies show that the
recruitment and release of actin filaments into and out of the cell cortex - a
network of proteins underneath the cell membrane - leads to cell stiffening
prior to division and softening immediately afterward. In three-dimensional
(3D) cell collectives, it is unclear whether the stiffness change during
division at the single-cell scale controls the spatial structure and dynamics
at the multicellular scale. This is an important question to understand as cell
stiffness variations play an important role in tissue spatial organization and
cancer progression. Using a minimal 3D model incorporating cell birth, death,
and cell-to-cell elastic and adhesive interactions, we investigate the effect
of mechanical heterogeneity - variations in individual cell stiffnesses that
make up the tumor cell collective - on tumor spatial organization and cell
dynamics. We discover that spatial mechanical heterogeneity characterized by a
spheroid core composed of stiffer cells and softer cells in the periphery
emerge within dense 3D cell collectives, which may be a general feature of
multicellular tumor growth. We show that heightened spatial mechanical
heterogeneity enhances single-cell dynamics and volumetric tumor growth driven
by fluctuations in cell elasticity. Our results could have important
implications for understanding how spatiotemporal variations in single-cell
stiffness determine tumor growth and spread.


title:
Quantum simulation of a general anti-PT-symmetric Hamiltonian with a
  trapped ion qubit

abstract:
  Non-Hermitian systems satisfying parity-time (PT) symmetry have aroused
considerable interest owing to their exotic features. Anti-PT symmetry is an
important counterpart of the PT symmetry, and has been studied in various
classical systems. Although a Hamiltonian with anti-PT symmetry only differs
from its PT-symmetric counterpart in a global $\pm$ i phase, the ways they
change information and energy with the enivronment are completely different,
suggesting an essential different dynamics in anti-PT-symmetric systems from
their PT -symmetric variants. Moreover, theortical works have shown that qubits
with anti-PT-symmetric Hamiltonians (anti-PT-symmetric qubits) have superior
decoherence properties over Hermitian qubits, as well as PT-symmetric ones. So
far, the observation of anti-PT symmetry in individual quantum systems remains
elusive. Here, we implement an anti-PT -symmetric Hamiltonian of a single qubit
in a single trapped ion by a designed microwave and optical control-pulse
sequence. We characterize the anti-PT phase transition by mapping out the
eigenvalues at different dissipation rates. The full inforamtion of the quantum
state is also obtained by quantum state tomography. Our work allows quantum
simulation of genuine open-system feature of an anti-PT-symmetric system, which
paves the way for utilizing non-Hermitian properties for quantum information
processing.


title: Logarithmic Voronoi Cells for Gaussian Models

abstract:
  We extend the theory of logarithmic Voronoi cells to Gaussian statistical
models. In general, a logarithmic Voronoi cell at a point on a Gaussian model
is a convex set contained in its log-normal spectrahedron. We show that for
models of ML degree one and linear covariance models the two sets coincide. In
particular, they are equal for both directed and undirected graphical models.
We introduce decomposition theory of logarithmic Voronoi cells for the latter
family. We also study covariance models, for which logarithmic Voronoi cells
are, in general, strictly contained in log-normal spectrahedra. We give an
explicit description of logarithmic Voronoi cells for the bivariate correlation
model and show that they are semi-algebraic sets. Finally, we state a
conjecture that logarithmic Voronoi cells for unrestricted correlation models
are not semi-algebraic.


title: PetsGAN: Rethinking Priors for Single Image Generation

abstract:
  Single image generation (SIG), described as generating diverse samples that
have similar visual content with the given single image, is first introduced by
SinGAN which builds a pyramid of GANs to progressively learn the internal patch
distribution of the single image. It also shows great potentials in a wide
range of image manipulation tasks. However, the paradigm of SinGAN has
limitations in terms of generation quality and training time. Firstly, due to
the lack of high-level information, SinGAN cannot handle the object images well
as it does on the scene and texture images. Secondly, the separate progressive
training scheme is time-consuming and easy to cause artifact accumulation. To
tackle these problems, in this paper, we dig into the SIG problem and improve
SinGAN by fully-utilization of internal and external priors. The main
contributions of this paper include: 1) We introduce to SIG a regularized
latent variable model. To the best of our knowledge, it is the first time to
give a clear formulation and optimization goal of SIG, and all the existing
methods for SIG can be regarded as special cases of this model. 2) We design a
novel Prior-based end-to-end training GAN (PetsGAN) to overcome the problems of
SinGAN. Our method gets rid of the time-consuming progressive training scheme
and can be trained end-to-end. 3) We construct abundant qualitative and
quantitative experiments to show the superiority of our method on both
generated image quality, diversity, and the training speed. Moreover, we apply
our method to other image manipulation tasks (e.g., style transfer,
harmonization), and the results further prove the effectiveness and efficiency
of our method.


title:
The stabilizer bitorsors of the module and algebra harmonic coproducts
  are equal

abstract:
  In earlier work, we constructed a pair of "Betti" and "de Rham" Hopf algebras
and a pair of module-coalgebras over this pair, as well as the bitorsors
related to both structures (which will be called the "module" and "algebra"
stabilizer bitorsors). We showed that Racinet's torsor constructed out of the
double shuffle and regularization relations between multiple zeta values is
essentially equal to the "module" stabilizer bitorsor, and that the latter is
contained in the "algebra" stabilizer bitorsor. In this paper, we show the
equality of the "algebra" and "module" stabilizer bitorsors. We reduce the
proof to showing the equality of the associated "algebra" and "module" graded
Lie algebras. The argument for showing this equality involves the relation of
the "algebra" Lie algebra with the kernel of a linear map, the expression of
this linear map as a composition of three linear maps, the relation of one of
them with the "module" Lie algebra and the computation of the kernel of the
other one by discrete topology arguments.


title: Overview of Intermittency Analysis in Heavy-ion Collisions

abstract:
  A search for power-law fluctuations with fractality and intermittency
analysis to locate the critical point in the QCD phase diagram is summarized.
Experimental data on self-similar correlations and fluctuations with respect to
the size of the phase space volume in various high energy heavy-ion collisions
are presented, with special emphasis on background subtraction and efficiency
correction of the measurement. Phenomenological and theoretical work on the
subject is discussed. Finally, we highlight possible directions for future
research.


title:
The Best of Both Worlds: Reinforcement Learning with Logarithmic Regret
  and Policy Switches

abstract:
  In this paper, we study the problem of regret minimization for episodic
Reinforcement Learning (RL) both in the model-free and the model-based setting.
We focus on learning with general function classes and general model classes,
and we derive results that scale with the eluder dimension of these classes. In
contrast to the existing body of work that mainly establishes
instance-independent regret guarantees, we focus on the instance-dependent
setting and show that the regret scales logarithmically with the horizon $T$,
provided that there is a gap between the best and the second best action in
every state. In addition, we show that such a logarithmic regret bound is
realizable by algorithms with $O(\log T)$ switching cost (also known as
adaptivity complexity). In other words, these algorithms rarely switch their
policy during the course of their execution. Finally, we complement our results
with lower bounds which show that even in the tabular setting, we cannot hope
for regret guarantees lower than $o(\log T)$.