title: AutoDIME: Automatic Design of Interesting Multi-Agent Environments

abstract:
  Designing a distribution of environments in which RL agents can learn
interesting and useful skills is a challenging and poorly understood task, for
multi-agent environments the difficulties are only exacerbated. One approach is
to train a second RL agent, called a teacher, who samples environments that are
conducive for the learning of student agents. However, most previous proposals
for teacher rewards do not generalize straightforwardly to the multi-agent
setting. We examine a set of intrinsic teacher rewards derived from prediction
problems that can be applied in multi-agent settings and evaluate them in
Mujoco tasks such as multi-agent Hide and Seek as well as a diagnostic
single-agent maze task. Of the intrinsic rewards considered we found value
disagreement to be most consistent across tasks, leading to faster and more
reliable emergence of advanced skills in Hide and Seek and the maze task.
Another candidate intrinsic reward considered, value prediction error, also
worked well in Hide and Seek but was susceptible to noisy-TV style distractions
in stochastic environments. Policy disagreement performed well in the maze task
but did not speed up learning in Hide and Seek. Our results suggest that
intrinsic teacher rewards, and in particular value disagreement, are a
promising approach for automating both single and multi-agent environment
design.


title:
Structure of propagating high stress fronts in a shear thickening
  suspension

abstract:
  We report direct measurements of spatially resolved stress at the boundary of
a shear thickening cornstarch suspension revealing persistent regions of high
local stress propagating in the flow direction at the speed of the top
boundary. The persistence of these propagating fronts enables precise
measurements of their structure, including the profile of boundary stress
measured by Boundary Stress Microscopy (BSM) and the non-affine velocity of
particles at the bottom boundary of the suspension measured by particle image
velocimetry (PIV). In addition, we directly measure the relative flow between
the particle phase and the suspending fluid (fluid migration) and find the
migration is highly localized to the fronts and changes direction across the
front, indicating that the fronts are composed of a localized region of high
dilatant pressure and low particle concentration. The magnitude of the flow
indicates that the pore pressure difference driving the fluid migration is
comparable to the critical shear stress for the onset of shear thickening. The
propagating fronts fully account for the increase in viscosity with applied
stress reported by the rheometer and are consistent with the existence of a
stable jammed region in contact with one boundary of the system that generates
a propagating network of percolated frictional contacts spanning the gap
between the rheometer plates and producing strong localized dilatant pressure.


title: Ontological Learning from Weak Labels

abstract:
  Ontologies encompass a formal representation of knowledge through the
definition of concepts or properties of a domain, and the relationships between
those concepts. In this work, we seek to investigate whether using this
ontological information will improve learning from weakly labeled data, which
are easier to collect since it requires only the presence or absence of an
event to be known. We use the AudioSet ontology and dataset, which contains
audio clips weakly labeled with the ontology concepts and the ontology
providing the "Is A" relations between the concepts. We first re-implemented
the model proposed by soundevent_ontology with modification to fit the
multi-label scenario and then expand on that idea by using a Graph
Convolutional Network (GCN) to model the ontology information to learn the
concepts. We find that the baseline Siamese does not perform better by
incorporating ontology information in the weak and multi-label scenario, but
that the GCN does capture the ontology knowledge better for weak, multi-labeled
data. In our experiments, we also investigate how different modules can
tolerate noises introduced from weak labels and better incorporate ontology
information. Our best Siamese-GCN model achieves mAP=0.45 and AUC=0.87 for
lower-level concepts and mAP=0.72 and AUC=0.86 for higher-level concepts, which
is an improvement over the baseline Siamese but about the same as our models
that do not use ontology information.


title:
Continuation with Non-invasive Control Schemes: Revealing Unstable
  States in a Pedestrian Evacuation Scenario

abstract:
  This paper presents a framework to perform bifurcation analysis in laboratory
experiments or simulations. We employ control-based continuation to study the
dynamics of a macroscopic variable of a microscopically defined model,
exploring the potential viability of the underlying feedback control techniques
in an experiment. In contrast to previous experimental studies that used
iterative root-finding methods on the feedback control targets, we propose a
feedback control law that is inherently non-invasive. That is, the control
discovers the location of equilibria and stabilizes them simultaneously. We
call the proposed control zero-in-equilibrium feedback control and we prove
that it is able to stabilize branches of equilibria, except at singularities of
codimension n+1, where n is the number of state space dimensions the feedback
can depend on.
  We apply the method to a simulated evacuation scenario were pedestrians have
to reach an exit after maneuvering left or right around an obstacle. The
scenario shows a hysteresis phenomenon with bistability and tipping between two
possible steady pedestrian flows in microscopic simulations.
  We demonstrate for the evacuation scenario that the proposed control law is
able to uniformly discover and stabilize steady flows along the entire branch,
including points where other non-invasive approaches to feedback control become
singular.


title: Better Supervisory Signals by Observing Learning Paths

abstract:
  Better-supervised models might have better performance. In this paper, we
first clarify what makes for good supervision for a classification problem, and
then explain two existing label refining methods, label smoothing and knowledge
distillation, in terms of our proposed criterion. To further answer why and how
better supervision emerges, we observe the learning path, i.e., the trajectory
of the model's predictions during training, for each training sample. We find
that the model can spontaneously refine "bad" labels through a "zig-zag"
learning path, which occurs on both toy and real datasets. Observing the
learning path not only provides a new perspective for understanding knowledge
distillation, overfitting, and learning dynamics, but also reveals that the
supervisory signal of a teacher network can be very unstable near the best
points in training on real tasks. Inspired by this, we propose a new knowledge
distillation scheme, Filter-KD, which improves downstream classification
performance in various settings.


title:
The Familiarity Hypothesis: Explaining the Behavior of Deep Open Set
  Methods

abstract:
  In many object recognition applications, the set of possible categories is an
open set, and the deployed recognition system will encounter novel objects
belonging to categories unseen during training. Detecting such "novel category"
objects is usually formulated as an anomaly detection problem. Anomaly
detection algorithms for feature-vector data identify anomalies as outliers,
but outlier detection has not worked well in deep learning. Instead, methods
based on the computed logits of visual object classifiers give state-of-the-art
performance. This paper proposes the Familiarity Hypothesis that these methods
succeed because they are detecting the absence of familiar learned features
rather than the presence of novelty. The paper reviews evidence from the
literature and presents additional evidence from our own experiments that
provide strong support for this hypothesis. The paper concludes with a
discussion of whether familiarity detection is an inevitable consequence of
representation learning.


title:
Exploring symmetries in photoelectron holography with two-color linearly
  polarized fields

abstract:
  We investigate photoelectron holography in bichromatic linearly polarized
fields of commensurate frequencies $r\omega$ and $s\omega$, with emphasis on
the existing symmetries and for which values of the relative phases between the
two driving waves they are kept or broken. We show that there are three types
of relevant symmetries: the well-known half-cycle symmetry, which is broken for
$r+s$ odd, and reflections around the field crossings and maxima, which may or
may not be kept depending on how both waves are dephased. The three symmetries
are always present for monochromatic fields, while for bichromatic fields this
is not guaranteed, even if $r+s$ is even and the half-cycle symmetry is
retained. Breaking the half-cycle symmetry automatically breaks one of the
other two, while, if the half-cycle symmetry is retained, the other two
symmetries are either \textit{both} kept or broken. We analyze how these
features affect the ionization times and saddle-point equations for different
bichromatic fields. We also provide general expressions for the relative phases
$\phi$ which retain specific symmetries. As an application, we compute
photoelectron momentum distributions for $\omega-2\omega$ fields with the
Coulomb Quantum Orbit Strong-Field approximation and assess how holographic
structures such as the fan, the spider and interference carpets behave,
focusing on the reflection symmetries. The features encountered can be traced
back to the field gradient and amplitude affecting ionization probabilities and
quantum interference in different momentum regions.


title: Behavioural Curves Analysis Using Near-Infrared-Iris Image Sequences

abstract:
  This paper proposes a new method to estimate behavioural curves from a stream
of Near-Infra-Red (NIR) iris video frames. This method can be used in a Fitness
For Duty system (FFD). The research focuses on determining the effect of
external factors such as alcohol, drugs, and sleepiness on the Central Nervous
System (CNS). The aim is to analyse how this behaviour is represented on iris
and pupil movements and if it is possible to capture these changes with a
standard NIR camera. The behaviour analysis showed essential differences in
pupil and iris behaviour to classify the workers in "Fit" or "Unfit"
conditions. The best results can distinguish subjects robustly under alcohol,
drug consumption, and sleep conditions. The Multi-Layer-Perceptron and Gradient
Boosted Machine reached the best results in all groups with an overall accuracy
for Fit and Unfit classes of 74.0% and 75.5%, respectively. These results open
a new application for iris capture devices.


title: Pedestrian Stop and Go Forecasting with Hybrid Feature Fusion

abstract:
  Forecasting pedestrians' future motions is essential for autonomous driving
systems to safely navigate in urban areas. However, existing prediction
algorithms often overly rely on past observed trajectories and tend to fail
around abrupt dynamic changes, such as when pedestrians suddenly start or stop
walking. We suggest that predicting these highly non-linear transitions should
form a core component to improve the robustness of motion prediction
algorithms. In this paper, we introduce the new task of pedestrian stop and go
forecasting. Considering the lack of suitable existing datasets for it, we
release TRANS, a benchmark for explicitly studying the stop and go behaviors of
pedestrians in urban traffic. We build it from several existing datasets
annotated with pedestrians' walking motions, in order to have various scenarios
and behaviors. We also propose a novel hybrid model that leverages
pedestrian-specific and scene features from several modalities, both video
sequences and high-level attributes, and gradually fuses them to integrate
multiple levels of context. We evaluate our model and several baselines on
TRANS, and set a new benchmark for the community to work on pedestrian stop and
go forecasting.


title:
Comment on "Comparison of six simulation codes for positive streamers in
  air"(Plasma Sources Sci. Technol. 27 (2018) 095002)

abstract:
  Recently, a comparison of six codes for streamer discharge simulations were
performed in [1]. In this comment, we discuss about the big differences between
the results obtained by the different codes using the same deterministic model,
and raise questions on the convergence of the codes and the minimum spatial
resolution that are required for a converged results.


title: Performance of Large Aperture UCCA Arrays in a 5G User Dense Network

abstract:
  The transmitted signals in the fifth generation (5G) wireless networks suffer
from significant path loss due to the use of higher frequencies in Sub-6 GHz
and millimeter-wave (mmWave) bands. Inter-user interference in an ultra-dense
network offers additional challenges to provide a high data rate. Therefore, it
is desirable to generate narrow beams to extend the coverage of a 5G network by
increasing antenna gain and improve its capacity by reducing the inter-user
interference. This fact leads us to address the use of large aperture uniform
concentric circular antenna (UCCA) arrays for 5G beamforming in massive
multiple-input-multiple-output (MIMO) technology. Our analysis demonstrates
that a UCCA with a larger antenna element spacing is capable of generating a
significantly narrower beam with a moderate side-lobe level than a rectangular
planar antenna (RPA) array while operating with the same number of antenna
elements. This capability of the UCCA is analyzed to discover the performance
gain of a 5G network.


title: Gravitating spinning strings in AdS$_3$

abstract:
  In the AdS/CFT correspondence, single trace operators of large-$N$ gauge
theories at large spin $J$ can be described by classical spinning strings,
giving a geometric and classical description of their spectrum at strong
coupling. We observe that in AdS$_3$ these strings have significant
gravitational back-reaction at sufficiently large spin, since the gravitational
force does not decay at long distances. We construct solutions for folded
spinning strings coupled to gravity in AdS$_3$ and compute their spectrum,
corresponding to the leading Regge trajectory of Virasroro primary operators.
These solutions exist only below a maximal spin $J<J_\mathrm{max}$, and as
$J\to J_\mathrm{max}$ the solution approaches an extremal rotating BTZ black
hole.


title: Greedy Optimization of Electrode Arrangement for Epiretinal Prostheses

abstract:
  Visual neuroprostheses are the only FDA-approved technology for the treatment
of retinal degenerative blindness. Although recent work has demonstrated a
systematic relationship between electrode location and the shape of the
elicited visual percept, this knowledge has yet to be incorporated into retinal
prosthesis design, where electrodes are typically arranged on either a
rectangular or hexagonal grid. Here we optimize the intraocular placement of
epiretinal electrodes using dictionary learning. Importantly, the optimization
process is informed by a previously established and psychophysically validated
model of simulated prosthetic vision. We systematically evaluate three
different electrode placement strategies across a wide range of possible
phosphene shapes and recommend electrode arrangements that maximize visual
subfield coverage. In the near future, our work may guide the prototyping of
next-generation epiretinal prostheses.


title: Different Types of Topological Complexity on Higher Homotopic Distance

abstract:
  We first study the higher version of the relative topological complexity by
using the homotopic distance. We also introduced the generalized version of the
relative topological complexity of a topological pair on both the Schwarz genus
and the homotopic distance. With these concepts, we give some inequalities
including the topological complexity and the Lusternik-Schnirelmann category,
the most important parts of the study of robot motion planning in topology.
Finally, by defining the parametrised topological complexity via the homotopic
distance, we present some estimates on the higher setting of this concept.


title:
Improved upper limit on degree-scale CMB B-mode polarization power from
  the 670 square-degree POLARBEAR survey

abstract:
  We report an improved measurement of the degree-scale CMB $B$-mode angular
power spectrum over 670 square-degree sky area with POLARBEAR. In the original
analysis of the data, errors in the angle measurement of the continuously
rotating half-wave plate, a polarization modulator, caused significant data
loss. By introducing an angle-correction algorithm, the data volume is
increased by a factor of 1.8. We report a new analysis using the larger data
set. We find the measured $B$-mode spectrum is consistent with the $\Lambda$CDM
model with Galactic foregrounds. We place an upper limit on the
tensor-to-scalar ratio $r$ < 0.33 at 95% confidence level.


title: Learning from Label Proportions by Learning with Label Noise

abstract:
  Learning from label proportions (LLP) is a weakly supervised classification
problem where data points are grouped into bags, and the label proportions
within each bag are observed instead of the instance-level labels. The task is
to learn a classifier to predict the individual labels of future individual
instances. Prior work on LLP for multi-class data has yet to develop a
theoretically grounded algorithm. In this work, we provide a theoretically
grounded approach to LLP based on a reduction to learning with label noise,
using the forward correction (FC) loss of \citet{Patrini2017MakingDN}. We
establish an excess risk bound and generalization error analysis for our
approach, while also extending the theory of the FC loss which may be of
independent interest. Our approach demonstrates improved empirical performance
in deep learning scenarios across multiple datasets and architectures, compared
to the leading existing methods.


title:
Computationally efficient worst-case analysis of flow-controlled
  networks with Network Calculus

abstract:
  Networks with hop-by-hop flow control occur in several contexts, from data
centers to systems architectures (e.g., wormhole-routing networks on chip). A
worst-case end-to-end delay in such networks can be computed using Network
Calculus (NC), an algebraic theory where traffic and service guarantees are
represented as curves in a Cartesian plane. NC uses transformation operations,
e.g., the min-plus convolution, to model how the traffic profile changes with
the traversal of network nodes. NC allows one to model flow-controlled systems,
hence one can compute the end-to-end service curve describing the minimum
service guaranteed to a flow traversing a tandem of flow-controlled nodes.
However, while the algebraic expression of such an end-to-end service curve is
quite compact, its computation is often untractable from an algorithmic
standpoint: data structures tend to explode, making operations unfeasibly
complex, even with as few as three hops. In this paper, we propose
computational and algebraic techniques to mitigate the above problem. We show
that existing techniques (such as reduction to compact domains) cannot be used
in this case, and propose an arsenal of solutions, which include methods to
mitigate the data representation space explosion as well as computationally
efficient algorithms for the min-plus convolution operation. We show that our
solutions allow a significant speedup, enable analysis of previously unfeasible
case studies, and - since they do not rely on any approximation - still provide
exact results.


title:
Computational Fluid Dynamics and Machine Learning as tools for
  Optimization of Micromixers geometry

abstract:
  This work explores a new approach for optimization in the field of
microfluidics, using the combination of CFD (Computational Fluid Dynamics), and
Machine Learning techniques. The objective of this combination is to enable
global optimization with lower computational cost. The initial geometry is
inspired in a Y-type micromixer with cylindrical grooves on the surface of the
main channel and obstructions inside it. Simulations for circular obstructions
were carried out using the OpenFOAM software to observe the influences of
obstacles. The effects of obstruction diameter (OD), and offset (OF) in the
range of [20,140] mm and [10,160] mm, respectively, on percentage of mixing
($\varphi$), pressure drop ($\Delta P$) and energy cost ($\Delta P/\varphi$)
were investigated. Numerical experiments were analyzed using machine learning.
Firstly, a neural network was used to train the dataset composed by the inputs
OD and OF and outputs $\varphi$ and $\Delta P$. The objective functions (ObF)
chosen to numerically optimize the performance of micromixers with grooves and
obstructions were $\varphi$, $\Delta P$, $\Delta P/\varphi$. The genetic
algorithm obtained the geometry that offers the maximum value of $\varphi$ and
the minimum value of $\Delta P_s$. The results show that $\varphi$ increases
monotonically with increasing OD at all values of OF. The inverse is observed
with increasing offset. Furthermore, the results reveal that $\Delta P$ e
$\Delta P/\varphi$ also increase with OD. On the other hand, the pressure drop
and the cost of mixing energy present a maximum close to the lowest values of
OF. Finally, the optimal value obtained for the diameter was OD=131 mm and for
the offset OF=10 mm, which corresponds to obstruction of medium size close to
the channel wall.


title:
New calibrated models for the TRGB luminosity and a thorough analysis of
  theoretical uncertainties

abstract:
  The luminosity of the Tip of the Red Giant Branch (TRGB) is instrumental for
the construction of the distance ladder, and its accurate modelling is key for
determining the local Hubble parameter. In this work, we present an extensive
quantitative analysis of the TRGB luminosity, accounting for virtually all
input physics that affect it: chemical composition, opacity, diffusion, nuclear
reaction rates, electron screening, neutrinos, convection efficiency, boundary
conditions and mass loss, amongst others.
  Our analysis is based on a newly produced grid of $\sim 3\times 10^6$ TRGB
models, evolved from pre-main sequence up to the helium ignition at the TRGB,
and covering a wide range of metallicity (Z = 0.0001-0.02) and initial mass (M
= 0.8-1.4 $M_{\odot}$). Through a Monte-Carlo analysis, we study the systematic
variation of the TRGB luminosity due to the combined effect of all above input
physics, and show that a maximum theoretical uncertainty of about $1.6 \%$ is
still present on the current generation of models, dominated by systematics of
radiative opacity. Results are also provided in several photometric bands.
  As a by-product of our analysis, we demonstrate robust evidence for the
linear response of the Tip luminosity to individual changes of input physics,
which can significantly simplify future analyses. A comparison of our results
with other stellar evolution codes shows excellent agreement, while our grid of
models is available upon reasonable requests.


title: Efficient Analog CAM Design

abstract:
  Content Addressable Memories (CAMs) are considered a key-enabler for
in-memory computing (IMC). IMC shows order of magnitude improvement in energy
efficiency and throughput compared to traditional computing techniques.
Recently, analog CAMs (aCAMs) were proposed as a means to improve storage
density and energy efficiency. In this work, we propose two new aCAM cells to
improve data encoding and robustness as compared to existing aCAM cells. We
propose a methodology to choose the margin and interval width for data
encoding. In addition, we perform a comprehensive comparison against prior work
in terms of the number of intervals, noise sensitivity, dynamic range, energy,
latency, area, and probability of failure.


title: A combinatorial model for lane merging

abstract:
  A two lane road approaches a stoplight. The left lane merges into the right
just past the intersection. Vehicles approach the intersection one at a time,
with some drivers always choosing the right lane, while others always choose
the shorter lane, giving preference to the right lane to break ties. An arrival
sequence of vehicles can be represented as a binary string, where the zeros
represent drivers always choosing the right lane, and the ones represent
drivers choosing the shorter lane. From each arrival sequence we construct a
merging path, which is a lattice path determined by the lane chosen by each
car. We give closed formulas for the number of merging paths reaching the point
$(n,m)$ with exactly $k$ zeros in the arrival sequence, and the expected length
of the right lane for all arrival sequences with exactly $k$ zeros. Proofs
involve an adaptation of Andre's Reflection Principle. Other interesting
connections also emerge, including to: Ballot numbers, the expected maximum
number of heads or tails appearing in a sequence of $n$ coin flips, the largest
domino snake that can be made using pieces up to $[n:n]$, and the longest trail
on the complete graph $K_n$ with loops.


title: No More Than 6ft Apart: Robust K-Means via Radius Upper Bounds

abstract:
  Centroid based clustering methods such as k-means, k-medoids and k-centers
are heavily applied as a go-to tool in exploratory data analysis. In many
cases, those methods are used to obtain representative centroids of the data
manifold for visualization or summarization of a dataset. Real world datasets
often contain inherent abnormalities, e.g., repeated samples and sampling bias,
that manifest imbalanced clustering. We propose to remedy such a scenario by
introducing a maximal radius constraint $r$ on the clusters formed by the
centroids, i.e., samples from the same cluster should not be more than $2r$
apart in terms of $\ell_2$ distance. We achieve this constraint by solving a
semi-definite program, followed by a linear assignment problem with quadratic
constraints. Through qualitative results, we show that our proposed method is
robust towards dataset imbalances and sampling artifacts. To the best of our
knowledge, ours is the first constrained k-means clustering method with hard
radius constraints. Codes at https://bit.ly/kmeans-constrained


title:
HyperTransformer: A Textural and Spectral Feature Fusion Transformer for
  Pansharpening

abstract:
  Pansharpening aims to fuse a registered high-resolution panchromatic image
(PAN) with a low-resolution hyperspectral image (LR-HSI) to generate an
enhanced HSI with high spectral and spatial resolution. Existing pansharpening
approaches neglect using an attention mechanism to transfer HR texture features
from PAN to LR-HSI features, resulting in spatial and spectral distortions. In
this paper, we present a novel attention mechanism for pansharpening called
HyperTransformer, in which features of LR-HSI and PAN are formulated as queries
and keys in a transformer, respectively. HyperTransformer consists of three
main modules, namely two separate feature extractors for PAN and HSI, a
multi-head feature soft attention module, and a spatial-spectral feature fusion
module. Such a network improves both spatial and spectral quality measures of
the pansharpened HSI by learning cross-feature space dependencies and
long-range details of PAN and LR-HSI. Furthermore, HyperTransformer can be
utilized across multiple spatial scales at the backbone for obtaining improved
performance. Extensive experiments conducted on three widely used datasets
demonstrate that HyperTransformer achieves significant improvement over the
state-of-the-art methods on both spatial and spectral quality measures.
Implementation code and pre-trained weights can be accessed at
https://github.com/wgcban/HyperTransformer.


title:
The SEDIGISM survey: Molecular cloud morphology. I. Classification and
  star formation

abstract:
  We present one of the very first extensive classifications of a large sample
of molecular clouds based on their morphology. This is achieved using a
recently published catalogue of 10663 clouds obtained from the first data
release of the SEDIGISM survey. The clouds are classified into four different
morphologies by visual inspection and using an automated algorithm -- J plots.
The visual inspection also serves as a test for the J plots algorithm, as this
is the first time it has been used on molecular gas. Generally, it has been
found that the structure of molecular clouds is highly filamentary and our
observations indeed verify that most of our molecular clouds are elongated
structures. Based on our visual classification of the 10663 SEDIGISM clouds,
15% are ring-like, 57% are elongated, 15% are concentrated and 10% are clumpy
clouds. The remaining clouds do not belong to any of these morphology classes
and are termed unclassified. We compare the SEDIGISM molecular clouds with
structures identified through other surveys, i.e. ATLASGAL elongated structures
and the bubbles from Milky Way Project (MWP). We find that many of the ATLASGAL
and MWP structures are velocity coherent. ATLASGAL elongated structures overlap
with ~ 21% of the SEDIGISM elongated structures (elongated and clumpy clouds)
and MWP bubbles overlap with ~ 25% of the SEDIGISM ring-like clouds. We also
analyse the star-formation associated with different cloud morphologies using
two different techniques. The first technique examines star formation
efficiency (SFE) and the dense gas fraction (DGF), based on SEDIGISM clouds and
ATLASGAL clumps data. The second technique uses the high-mass star formation
(HMSF) threshold for molecular clouds. The results indicate that clouds with
ring-like and clumpy morphologies show a higher degree of star formation.


title:
ARM 4-BIT PQ: SIMD-based Acceleration for Approximate Nearest Neighbor
  Search on ARM

abstract:
  We accelerate the 4-bit product quantization (PQ) on the ARM architecture.
Notably, the drastic performance of the conventional 4-bit PQ strongly relies
on x64-specific SIMD register, such as AVX2; hence, we cannot yet achieve such
good performance on ARM. To fill this gap, we first bundle two 128-bit
registers as one 256-bit component. We then apply shuffle operations for each
using the ARM-specific NEON instruction. By making this simple but critical
modification, we achieve a dramatic speedup for the 4-bit PQ on an ARM
architecture. Experiments show that the proposed method consistently achieves a
10x improvement over the naive PQ with the same accuracy.


title: Non-linear predictive vector quantization of speech

abstract:
  In this paper we propose a Non-Linear Predictive Vector quantizer (PVQ) for
speech coding, based on Multi-Layer Perceptrons. We also propose a method to
evaluate if a quantizer is well designed, and if it exploits the correlation
between consecutive outputs. Although the results of the Non-linear PVQ do not
improve the results of the non-linear scalar predictor, we check that there is
some room for the PVQ improvement.


title: Parallel Fourier Ptychography reconstruction

abstract:
  Fourier ptychography has attracted a wide range of focus for its ability of
large space-bandwidth-produce, and quantative phase measurement. It is a
typical computational imaging technique which refers to optimizing both the
imaging hardware and reconstruction algorithms simultaneously. The data
redundancy and inverse problem algorithms are the sources of FPM's excellent
performance. But at the same time, this large amount of data processing and
complex algorithms also greatly reduce the imaging speed. In this article, we
propose a parallel Fourier ptychography reconstruction framework consisting of
three levels of parallel computing parts and implemented it with both central
processing unit (CPU) and compute unified device architecture (CUDA) platform.
In the conventional FPM reconstruction framework, the sample image is divided
into multiple sub-regions for separately processing because the illumination
angles for different subregions are varied for the same LED and different
subregions contain different defocus distances due to the non-planar
distribution or non-ideal posture of biological sample. We first build a
parallel computing sub-framework in spatial domain based on the above-mentioned
characteristics. And then, by utilizing the sequential characteristics of
different spectrum regions to update, a parallel computing sub-framework in the
spectrum domain is carried out in our scheme. The feasibility of the proposed
parallel FPM reconstruction framework is verified with different experimental
results acquired with the system we built.


title:
Optimization of Traffic Control in MMAP[c]/PH[c]/S Catastrophic Queueing
  Model with PH Retrial Times and Controllable Preemptive Repeat Priority
  Policy

abstract:
  The presented study elaborates a multi-server catastrophic retrial queueing
model considering preemptive repeat priority policy with phase-type (PH)
distributed retrial times. For the sake of comprehension, the scenario of model
operation prior and later to the occurrence of the disaster is referred to as
the normal scenario and as the catastrophic scenario, respectively. In the
normal scenario, the incoming heterogeneous calls are categorized as handoff
calls and new calls. Handoff calls are provided controllable preemptive
priority over new calls. In the catastrophic scenario, when a disaster causes
the shut down of the entire system and failure of all functioning channels, a
set of backup channels is quickly deployed to restore services. Due to the
emergency situation in the concerned area, the incoming heterogeneous calls are
divided into three categories: handoff, new call, and emergency calls.
Emergency calls are provided controllable preemptive priority over new/handoff
calls due to the pressing need to save lives in such situations. The Markov
chain's ergodicity criteria are established by demonstrating that it belongs to
the class of asymptotically quasi-Toeplitz Markov chains (AQTMC). Further, a
multi-objective optimization problem to obtain optimal number of backup
channels has been formulated and dealt by employing non-dominated sorting
genetic algorithm-II (NSGA-II) approach.


title:
Double Exponential method for Riemann Zeta, Lerch and Dirichlet
  L-functions

abstract:
  We provide efficient methods to evaluate the Riemann zeta, the Lerch zeta and
the Dirichlet $L$-functions. The method uses the Riemann-Siegel (RS) type
formulas and a modified double exponential (MDE) quadrature method near the
saddle point of appropriate integrands. We provide simplified derivations of
the RS formulas, containing finite series sums and residual integrals, for the
Lerch and the Dirichlet $L$-functions. The MDE method allows us to remove the
contribution of the singularities near the contour of integration for the
residual integrals. The method allows us to evaluate the residual integrals to
any prescribed accuracy. The numerical cost of evaluating them is minimal
compared to the main series sums. Thus even highly oscillatory integrands for
these functions can be evaluated with the same complexity as the RS formula. In
particular, the numerical complexity to evaluate the $\zeta(s)$ and the Lerch
zeta function with $s=\sigma+i t$ scales as $\sqrt{t}$, and for Dirichlet
$L$-function it scales as $\max(q, \sqrt{q t})$. The method allows the
automatic setting of integral cutoffs and finite discretization size to achieve
prescribed accuracy. Furthermore, it ensures that every halving of the
discretization interval almost leads to doubling the accuracy of the results.
Thus, it allows for a further check on the accuracy of the results obtained.


title:
Cellular Segmentation and Composition in Routine Histology Images using
  Deep Learning

abstract:
  Identification and quantification of nuclei in colorectal cancer haematoxylin
\& eosin (H\&E) stained histology images is crucial to prognosis and patient
management. In computational pathology these tasks are referred to as nuclear
segmentation, classification and composition and are used to extract meaningful
interpretable cytological and architectural features for downstream analysis.
The CoNIC challenge poses the task of automated nuclei segmentation,
classification and composition into six different types of nuclei from the
largest publicly known nuclei dataset - Lizard. In this regard, we have
developed pipelines for the prediction of nuclei segmentation using HoVer-Net
and ALBRT for cellular composition. On testing on the preliminary test set,
HoVer-Net achieved a PQ of 0.58, a PQ+ of 0.58 and finally a mPQ+ of 0.35. For
the prediction of cellular composition with ALBRT on the preliminary test set,
we achieved an overall $R^2$ score of 0.53, consisting of 0.84 for lymphocytes,
0.70 for epithelial cells, 0.70 for plasma and .060 for eosinophils.


title:
Self-Supervised Learning for Joint Pushing and Grasping Policies in
  Highly Cluttered Environments

abstract:
  Robots often face situations where grasping a goal object is desirable but
not feasible due to other present objects preventing the grasp action. We
present a deep Reinforcement Learning approach to learn grasping and pushing
policies for manipulating a goal object in highly cluttered environments to
address this problem. In particular, a dual reinforcement learning model
approach is proposed, which presents high resilience in handling complicated
scenes, reaching 98% task completion using primitive objects in a simulation
environment. To evaluate the performance of the proposed approach, we performed
two extensive sets of experiments in packed objects and a pile of objects
scenarios. Experimental results showed that the proposed method worked very
well in both scenarios and outperformed the recent state-of-the-art approaches


title:
The Pristine survey -- XVII. The C-19 stream is dynamically hot and more
  extended than previously thought

abstract:
  The C-19 stream is the most metal poor stellar system ever discovered, with a
mean metallicity $[Fe/H] = -3.38\pm0.06$. Its low metallicity dispersion
($\sigma_{\rm [Fe/H]}$ $<$ 0.18 at the 95\% confidence level) as well as
variations in sodium abundances strongly suggest a globular cluster origin. In
this work, we use VLT/UVES spectra of seven C-19 stars to derive more precise
velocity measurements for member stars, and to identify two new members with
radial velocities and metallicities consistent with the stream's properties.
One of these new member stars is located 30 $\deg$ away from the previously
identified body of C-19, implying that the stream is significantly more
extended than previously known and that more members likely await discovery. In
the main part of C-19, we measure a radial velocity dispersion $\sigma_v$ =
6.2$^{+2.0}_{-1.4}$ km s$^{-1}$ from nine members, and a stream width of
0.56$\deg\pm0.08\deg$, equivalent to $\sim$158 pc at a heliocentric distance of
18 kpc. These confirm that C-19 is comparatively hotter, dynamically, than
other known globular cluster streams and shares the properties of faint dwarf
galaxy streams. On the other hand, the variations in the Na abundances of the
three newly observed bright member stars, the variations in Mg and Al for two
of them, and the normal Ba abundance of the one star where it can be measured
provide further evidence for a globular cluster origin. The tension between the
dynamical and chemical properties of C-19 suggests that its progenitor
experienced a complex birth environment or disruption history.


title:
The Pristine survey XVIII: C-19: Tidal debris of a dark matter-dominated
  globular cluster?

abstract:
  The recently discovered C-19 stellar stream is a collection of kinematically
associated metal-poor stars in the halo of the Milky Way lacking an obvious
progenitor. The stream spans an arc of ~15 degrees on the sky, and
orbit-fitting suggests an apocentric distance of ~20 kpc and a pericentre of
~10 kpc. The narrow metallicity dispersion of stars with available spectra,
together with light element abundance variations, suggest a globular cluster
(GC) origin. The observed metallicity ([Fe/H] ~ -3.4), however, is much lower
than that of any known GC. In addition, the width and velocity dispersion of
the stream are similar to those expected from disrupting dwarf galaxies, and
substantially larger than the tidal debris of GCs able to disrupt on C-19's
orbit. We propose here an unconventional model where the C-19 progenitor is a
dark matter-dominated stellar system with GC-like abundance patterns. We use
N-body simulations to show that the tidal disruption of a ~100 pc King-model
stellar component embedded in a ~20 km/s cuspy cold dark matter halo yields
debris consistent with C-19's observed width and velocity dispersion. The
stellar component of the progenitor is fully disrupted, and is spread over two
distinct streams; one corresponding to C-19 and another possibly hiding behind
the Galactic plane. If such companion stream were found, it would suggest that
dark matter-dominated dwarfs may also develop GC-like enrichment patterns, a
finding that would inform our theoretical understanding of the formation of
multiple populations in GCs and dwarf galaxies alike.


title:
Preferential Accretion and Circumbinary Disk Precession in Eccentric
  Binary Systems

abstract:
  We present a suite of high resolution hydrodynamic simulations of binaries
immersed in circumbinary accretion disks (CBDs). For the first time, we
investigate the preferential accretion rate as a function of both eccentricity
$e_{\rm b}$ and mass ratio $q_{\rm b}$ in a densely sampled parameter space,
finding that when compared with circular binaries, mass ratios grow more
efficiently in binaries on moderately eccentric orbits ($0.0 \lesssim e_{\rm b}
\lesssim 0.4$), and high eccentricities ($e_{\rm b} \gtrsim 0.6 $) suppress
mass ratio growth. We suggest that this non-monotonic preferential accretion
behaviour may produce an observable shift in the mass ratio distributions of
stellar binaries and massive black hole binaries. We further find that the
response of a CBD can be divided into three regimes, depending on eccentricity
and mass ratio: (i) CBDs around circular binaries always precess freely,
whereas CBDs around eccentric binaries either (ii) undergo forced precession or
(iii) remain locked at an angle with respect to the binary periapsis. Forced
precession in eccentric binaries is associated with strong modulation of
individual accretion rates on the precession timescale, a potentially
observable signature in accreting binaries with short orbital periods.


title:
Suspicious Siblings: The Distribution of Mass and Spin Across Component
  Black Holes in Isolated Binary Evolution

abstract:
  The LIGO and Virgo gravitational-wave detectors have uncovered systems with
definitively non-zero spins, as well as systems with significant spin residing
in the heavier black hole of the pair. We investigate the ability of isolated
binary evolution in forming such highly spinning, asymmetric mass systems
through both accretion onto the first-born black hole and tidal spin-up of the
second-born black hole using a rapid population synthesis approach with
detailed considerations of spin-up through tidal interactions. Even with the
most optimistic assumptions regarding the efficiency at which an accreting star
receives material from a donor, we find that it is difficult to form systems
with significant mass asymmetry and moderate or high spins in the primary black
hole component. Assuming efficient angular momentum transport within massive
stars and Eddington-limited accretion onto black holes, we find that $< 1.5\%$
of systems in the underlying binary black hole population have a primary black
hole spin greater than 0.2 and a mass asymmetry of greater than 2:1 in our most
optimistic models, with most models finding that this criteria is only met in
$\sim 0.01\%$ of systems. The production of systems with significant mass
asymmetries and spin in the primary black hole component is thus an unlikely
byproduct of isolated evolution unless highly super-Eddington accretion is
invoked or angular momentum transport in massive stars is less efficient than
typically assumed.


title:
Purely Virtual Particles in Quantum Gravity, Inflationary Cosmology and
  Collider Physics

abstract:
  We review the concept of purely virtual particle and its uses in quantum
gravity, primordial cosmology and collider physics. The fake particle, or
"fakeon", which mediates interactions without appearing among the incoming and
outgoing states, can be introduced by means of a new diagrammatics. The
renormalization coincides with the one of the parent Euclidean diagrammatics,
while unitarity follows from spectral optical identities, which can be derived
by means of algebraic operations. The classical limit of a theory of physical
particles and fakeons is described by an ordinary Lagrangian plus Hermitian,
micro acausal and micro nonlocal self-interactions. Quantum gravity propagates
the graviton, a massive scalar field (the inflaton) and a massive spin-2
fakeon, and leads to a constrained primordial cosmology, which predicts the
tensor-to-scalar ratio $r$ in the window $0.4\lesssim 1000r\lesssim 3.5$. The
interpretation of inflation as a cosmic RG flow allows us to calculate the
perturbation spectra to high orders in the presence of the Weyl squared term.
In models of new physics beyond the standard model, fakeons evade various
phenomenological bounds, because they are less constrained than normal
particles. The resummation of self-energies reveals that it is impossible to
get too close to the fakeon peak. The related peak uncertainty, equal to the
fakeon width divided by 2, is expected to be observable.


title:
Massive black hole formation in dense stellar environments: Enhanced
  X-ray detection rates in high velocity dispersion nuclear star clusters

abstract:
  We analyze Chandra X-ray Observatory imaging of 108 galaxies hosting nuclear
star clusters (NSCs) to search for signatures of massive black holes (BHs).
NSCs are extremely dense stellar environments with conditions that can
theoretically facilitate massive BH formation. Recent work by Stone et al.
(2017) finds that sufficiently dense NSCs should be unstable to the runaway
growth of a stellar mass BH into a massive BH via tidal captures. Furthermore,
there is a velocity dispersion threshold ($40\;\rm{km\;s^{-1}}$) above which
NSCs should inevitably form a massive BH. To provide an observational test of
these theories, we measure X-ray emission from NSCs and compare to the measured
velocity dispersion and tidal capture runaway timescale. We find that NSCs
above the $40\;\rm{km\;s^{-1}}$ threshold are X-ray detected at roughly twice
the rate of those below (after accounting for contamination from X-ray
binaries). These results are consistent with a scenario in which dense,
high-velocity NSCs can form massive BHs, providing a formation pathway that
does not rely on conditions found only at high redshift.


title:
SU(2)-symmetric spin-boson model: Quantum criticality, fixed-point
  annihilation, and duality

abstract:
  Dissipative quantum impurity models represent conceptually simple yet
non-trivial settings for quantum criticality. Here we present results from
high-accuracy quantum Monte Carlo calculations for the SU(2)-symmetric
spin-boson (or Bose-Kondo) model, relevant to diverse problems such as cavity
quantum electrodynamics, magnetic moments in quantum critical magnets, and
Kondo-breakdown transitions in heavy-fermion metals. We study the model with a
power-law bath spectrum $\propto \omega^s$ where, in addition to a critical
phase predicted by perturbative renormalization group (RG), a stable
strong-coupling phase is present for all values of $0<s<1$. The critical phase
ceases to exist for $s<s^\ast = 0.6540(2)$, rendering the perturbative
prediction invalid. We provide direct numerical evidence for the collision and
annihilation of two intermediate-coupling RG fixed points at $s^\ast$
responsible for that. We uncover a surprising duality between the two fixed
points, corresponding to a reflection symmetry of the RG beta function. We then
utilize this duality to make analytical predictions for critical properties at
strong coupling which are in excellent agreement with the numerical results. We
comment on the consequences for impurity moments in critical magnets.

categories: cond-mat.str-el cond-mat.mes-hall cond-mat.stat-mech quant-ph


title:
Robustness versus sensitivity in non-Hermitian topological lattices
  probed by pseudospectra

abstract:
  Non-Hermitian topological systems simultaneously posses two antagonistic
features: ultra sensitivity due to exceptional points and robustness of
topological zero energy modes, and it is unclear which one prevails under
different perturbations. We study that question by applying the pseudospectra
theory on the prototypical non-Hermitian SSH (NHSSH) lattice. Topological modes
are robust with respect to chiral perturbations and sensitive to parity-time
(PT) symmetry preserving perturbations. In fact, the chiral symmetry exactly at
the exceptional point leads to the suppression of its sensitivity,
corresponding to a lower order exceptional point. However, counterintuitively,
they are most sensitive with respect to unstructured perturbations, leaving the
fingerprint of the pertinent higher order exceptional (HEP) point.


title: Constraining 3-3-1 Models at the LHC and Future Hadron Colliders

abstract:
  In this work, we derive lower mass bounds on the Z' gauge boson based on the
dilepton data from LHC with 13 TeV of center-of-mass energy, and forecast the
sensitivity of the High-Luminosity-LHC with $L=3000 fb^{-1}$, the High-Energy
LHC with $\sqrt{s}=27$~TeV, and also at the Future Circular Collider with
$\sqrt{s}=100$~TeV. We take into account the presence of exotic and invisible
decays of the Z' gauge boson to find a more conservative and robust limit,
different from previous studies. We investigate the impact of these new decays
channels for several benchmark models in the scope of two different 3-3-1
models. We found that in the most constraining cases, LHC with $139fb^{-1}$ can
impose $m_{Z^{\prime}}>4$~TeV. Moreover, we forecast HL-LHC, HE-LHC, and FCC
bounds that yield $m_{Z^{\prime}}>5.8$~ TeV, $m_{Z^{\prime}}>9.9$~TeV, and
$m_{Z^{\prime}}> 27$~TeV, respectively. Lastly, put our findings into
perspective with dark matter searches to show the region of parameter space
where a dark matter candidate with the right relic density is possible.


title: Quantum algorithms for grid-based variational time evolution

abstract:
  The simulation of quantum dynamics calls for quantum algorithms working in
first quantized grid encodings. Here, we propose a variational quantum
algorithm for performing quantum dynamics in first quantization. In addition to
the usual reduction in circuit depth conferred by variational approaches, this
algorithm also enjoys several advantages compared to previously proposed ones.
For instance, variational approaches suffer from the need for a large number of
measurements. However, the grid encoding of first quantized Hamiltonians only
requires measuring in position and momentum bases, irrespective of the system
size. Their combination with variational approaches is therefore particularly
attractive. Moreover, heuristic variational forms can be employed to overcome
the limitation of the hard decomposition of Trotterized first quantized
Hamiltonians into quantum gates. We apply this quantum algorithm to the
dynamics of several systems in one and two dimensions. Our simulations exhibit
the previously observed numerical instabilities of variational time propagation
approaches. We show how they can be significantly attenuated through subspace
diagonalization at a cost of an additional $\mathcal{O}(MN^2)$ 2-qubit gates
where $M$ is the number of dimensions and $N^M$ is the total number of grid
points.


title:
Metallicity, ionization parameter, and pressure variations of HII
  regions in the TYPHOON spiral galaxies

abstract:
  We present a spatially-resolved HII region study of the gas-phase
metallicity, ionization parameter, and ISM pressure maps of 6 local
star-forming and face-on spiral galaxies from the TYPHOON program.
Self-consistent metallicity, ionization parameter, and pressure maps are
calculated simultaneously through an iterative process to provide useful
measures of the local chemical abundance and its relation to localized ISM
properties. We constrain the presence of azimuthal variations in metallicity by
measuring the residual metallicity offset $\Delta$(O/H) after subtracting the
linear fits to the radial metallicity profiles. We however find weak evidence
of azimuthal variations in most of the galaxies, with small (mean 0.03 dex)
scatter. The galaxies instead reveal that HII regions with enhanced and reduced
abundances are found distributed throughout the disk. While the spiral pattern
plays a role in organizing the ISM, it alone does not establish the relatively
uniform azimuthal variations we observe. Differences in the metal abundances
are more likely driven by the strong correlations with the local physical
conditions. We find a strong and positive correlation between the ionization
parameter and the local abundances as measured by the relative metallicity
offset $\Delta$(O/H), indicating a tight relationship between local physical
conditions and their localized enrichment of the ISM. Additionally, we
demonstrate the impact of unresolved observations on the measured ISM
properties by rebinning the datacubes to simulate low-resolution (1 kpc)
observations, typical of large IFU surveys. We find that the ionization
parameter and ISM pressure diagnostics are impacted by the loss of resolution
such that their measured values are larger relative to the measured values on
sub-HII region scales.


title: Constraints on cosmologies inside black holes

abstract:
  We study the construction of holographic models with closed FRW cosmologies
on the worldvolume of a constant-tension brane inside a Schwarzschild-AdS black
hole. In dimensions d>2, having a smooth Euclidean solution where the brane
does not self-intersect limits the brane tension to T<T_*, preventing us from
realising a separation of scales between the brane and bulk curvature scales.
We show that adding interface branes to this model does not relax the condition
on the brane tension.


title: Non-Hermitian Quantum Quenches in Holography

abstract:
  The notion of non-Hermitian PT symmetric quantum theory has recently been
generalized to the gauge/gravity duality. We study the evolution of such
non-Hermitian holographic field theories when the couplings are varied with
time with particular emphasis on the question non-unitary time vs. unitary time
evolution. We show that a non-unitary time evolution in the dual quantum theory
corresponds to a violation of the Null Energy Condition (NEC) in the bulk of
the asymptotically AdS spacetime. We find that upon varying the non-Hermitian
coupling the horizon of a bulk AdS black hole shrinks. On the other hand
varying the Hermitian coupling in the presence of a constant non-Hermitian
coupling still violates the NEC but results in a growing horizon. We also show
that by introducing a non-Hermitian gauge field the time evolution can be made
unitary, e.g. the NEC in the bulk is obeyed, and an exactly equivalent purely
Hermitian description can be given.


title:
Rounding near-optimal quantum strategies for nonlocal games to
  strategies using maximally entangled states

abstract:
  For the classes of synchronous, binary constraint systems, and XOR nonlocal
games, we show that near-optimal finite-dimensional quantum strategies with
arbitrary states are approximate representations of their affiliated nonlocal
game algebra. We also show that finite-dimensional approximate representations
of these nonlocal game algebras are close to near-optimal strategies where the
players employ a maximally entangled state. As a corollary, we show that
near-optimal quantum strategies are close to a near-optimal quantum strategy
that uses a maximally entangled state.


title:
Fermi and eRosita bubbles as relics of the past activity of the Galactic
  black hole

abstract:
  The newly launched X-ray satellite, eRosita, has recently revealed two
gigantic bubbles extending to ~80 degrees above and below the Galactic center.
The morphology of these "eRosita bubbles" bears a remarkable resemblance to the
Fermi bubbles previously discovered by the Fermi Gamma-ray Space Telescope and
its counterpart, the microwave haze. The physical origin of these striking
structures has been intensely debated; however, because of their symmetry about
the Galactic center, they likely originate from some energetic outbursts from
the Galactic center in the past. Here we propose a theoretical model in which
the eRosita bubbles, Fermi bubbles, and the microwave haze could be
simultaneously explained by a single event of jet activity from the central
supermassive black hole a few million years ago. Using numerical simulations,
we show that this model could successfully reproduce the morphology and
multi-wavelength spectra of the observed bubbles and haze, which allows us to
derive critical constraints on the energetics and timescales of the outburst.
This study serves as an important step forward in our understanding of the past
Galactic center activity of our Milky Way Galaxy, and may bring valuable
insights into the broader picture of supermassive black hole-galaxy
co-evolution in the context of galaxy formation.


title:
Linear Run Time of Persistent Homology Computation with GPU
  Parallelization

abstract:
  Persistent homology is a crucial invariant that is used in many areas to
understand data. The $O(N^4)$ run time is a hindrance to its use on most large
datasets. We give a parallelization method to utilize multi-core machines and
clusters. We implement the computation of the $0^{th}$ persistent homology with
OpenMP parallelization and observe a 1.75 fold performance increase by using 2
threads on a dual core machine. We also benchmark the computation using larger
numbers of threads and show that the thread computational overhead decreases
performance. With GPU parallelization, we analytically and empirically decrease
the run time scaling from $O(N^4)$ to $O(N^3)$ and even $O(N^2)$ where $N$ is
the number of data points, for a large enough GPU. Next, we analytically show
run time scaling $O(N)$ for an even larger GPU.


title:
Curvature in the Balance: The Weyl Functional and Scalar Curvature of
  4-Manifolds

abstract:
  The infimum of the Weyl functional is shown to be surprisingly small on many
compact 4-manifolds that admit positive-scalar-curvature metrics. Results are
also proved that systematically compare the scalar and self-dual Weyl
curvatures of certain almost-Kaehler 4-manifolds.


title: The Renovated Thacher Observatory and First Science Results

abstract:
  Located on the campus of the Thacher School in Southern California, the
Thacher Observatory has a legacy of astronomy research and education that dates
back to the late 1950's. In 2016, the observatory was fully renovated with
upgrades including a new 0.7-m telescope, a research grade camera, and a slit
dome with full automation capabilities. The low-elevation site is bordered by
the Los Padres National Forest and therefore affords dark to very dark skies
allowing for accurate and precise photometric observations. We present a
characterization of the site including sky brightness, weather, and seeing, and
we demonstrate the on-sky performance of the facility. Our primary research
programs are based around our multi-band photometric capabilities and include
photometric monitoring of variable sources, a nearby supernova search and
followup program, a quick response transient followup effort, and exoplanet and
eclipsing binary light curves. Select results from these programs are included
in this work which highlight the broad range of science available to an
automated observatory with a moderately sized telescope.


title: Machine Learning for CUDA+MPI Design Rules

abstract:
  We present a new strategy for automatically exploring the design space of key
CUDA+MPI programs and providing design rules that discriminate slow from fast
implementations. In such programs, the order of operations (e.g., GPU kernels,
MPI communication) and assignment of operations to resources (e.g., GPU
streams) makes the space of possible designs enormous. Systems experts have the
task of redesigning and reoptimizing these programs to effectively utilize each
new platform. This work provides a prototype tool to reduce that burden.
  In our approach, a directed acyclic graph of CUDA and MPI operations defines
the design space for the program. Monte-Carlo tree search discovers regions of
the design space that have large impact on the program's performance. A
sequence-to-vector transformation defines features for each explored
implementation, and each implementation is assigned a class label according to
its relative performance. A decision tree is trained on the features and labels
to produce design rules for each class; these rules can be used by systems
experts to guide their implementations. We demonstrate our strategy using a key
kernel from scientific computing -- sparse-matrix vector multiplication -- on a
platform with multiple MPI ranks and GPU streams.


title: Global pointwise estimates of positive solutions to sublinear equations

abstract:
  We give bilateral pointwise estimates for positive solutions $u$ to the
sublinear integral equation
  \[ u = \mathbf{G}(\sigma u^q) + f \quad \textrm{in} \,\, \Omega,\] for $0 < q
< 1$, where $\sigma\ge 0$ is a measurable function, or a Radon measure, $f \ge
0$, and $\mathbf{G}$ is the integral operator associated with a positive kernel
$G$ on $\Omega\times\Omega$. Our main results, which include the existence
criteria and uniqueness of solutions, hold for quasi-metric, or
quasi-metrically modifiable kernels $G$.
  As a consequence, we obtain bilateral estimates, along with the existence and
uniqueness, for positive solutions $u$, possibly unbounded, to sublinear
elliptic equations involving the fractional Laplacian,
  \[ (-\Delta)^{\frac{\alpha}{2}} u = \sigma u^q + \mu \quad \textrm{in} \,\,
\Omega, \qquad u=0 \, \, \textrm{in} \,\, \Omega^c, \] where $0<q<1$, and $\mu,
\sigma \ge 0$ are measurable functions, or Radon measures, on a bounded uniform
domain $\Omega \subset \mathbf{R}^n$ for $0 < \alpha \le 2$, or on the entire
space $\mathbf{R}^n$, a ball or half-space, for $0 < \alpha <n$.


title: Gas inflows in the polar ring of NGC 4111: the birth of an AGN

abstract:
  We have used Hubble Space Telescope (HST) images, SAURON Integral Field
Spectroscopy (IFS) and adaptative optics assisted Gemini NIFS near-infrared
K-band IFS to map the stellar and gas distribution, excitation and kinematics
of the inner few kpc of the nearby edge-on S0 galaxy NGC 4111. The HST images
map its $\approx$ 450 pc diameter dusty polar ring, with an estimated gas mass
$\ge10^7$ M$_\odot$. The NIFS datacube maps the inner 110 pc radius at
$\approx$ 7 pc spatial resolution revealing a $\approx$ 220 pc diameter polar
ring in hot ($2267\pm166$ K) molecular H$_2$ 1-0 S(1) gas embedded in the polar
ring. The stellar velocity field shows disk-dominated kinematics along the
galaxy plane both in the SAURON large-scale and in the NIFS nuclear-scale data.
The large-scale [O III] $\lambda5007$ \AA velocity field shows a superposition
of two disk kinematics: one similar to that of the stars and another along the
polar ring, showing non-circular motions that seem to connect with the velocity
field of the nuclear H$_2$ ring, whose kinematics indicate accelerated inflow
to the nucleus. The estimated mass inflow rate is enough not only to feed an
Active Galactic Nucleus (AGN) but also to trigger circumnuclear star formation
in the near future. We propose a scenario in which gas from the polar ring,
which probably originated from the capture of a dwarf galaxy, is moving inwards
and triggering an AGN, as supported by the local X-ray emission, which seems to
be the source of the H$_2$ 1-0 S(1) excitation. The fact that we see neither
near-UV nor Br$\gamma$ emission suggests that the nascent AGN is still deeply
buried under the optically thick dust of the polar ring.


title:
BoostMIS: Boosting Medical Image Semi-supervised Learning with Adaptive
  Pseudo Labeling and Informative Active Annotation

abstract:
  In this paper, we propose a novel semi-supervised learning (SSL) framework
named BoostMIS that combines adaptive pseudo labeling and informative active
annotation to unleash the potential of medical image SSL models: (1) BoostMIS
can adaptively leverage the cluster assumption and consistency regularization
of the unlabeled data according to the current learning status. This strategy
can adaptively generate one-hot "hard" labels converted from task model
predictions for better task model training. (2) For the unselected unlabeled
images with low confidence, we introduce an Active learning (AL) algorithm to
find the informative samples as the annotation candidates by exploiting virtual
adversarial perturbation and model's density-aware entropy. These informative
candidates are subsequently fed into the next training cycle for better SSL
label propagation. Notably, the adaptive pseudo-labeling and informative active
annotation form a learning closed-loop that are mutually collaborative to boost
medical image SSL. To verify the effectiveness of the proposed method, we
collected a metastatic epidural spinal cord compression (MESCC) dataset that
aims to optimize MESCC diagnosis and classification for improved specialist
referral and treatment. We conducted an extensive experimental study of
BoostMIS on MESCC and another public dataset COVIDx. The experimental results
verify our framework's effectiveness and generalisability for different medical
image datasets with a significant improvement over various state-of-the-art
methods.


title: Discrete self-similar and ergodic Markov chains

abstract:
  The first aim of this paper is to introduce a class of Markov chains on
$\mathbb{Z}_+$ which are discrete self-similar in the sense that their
semigroups satisfy an invariance property expressed in terms of a discrete
random dilation operator. After showing that this latter property requires the
chains to be upward skip-free, we first establish a gateway relation, a concept
introduced in [26], between the semigroup of such chains and the one of
spectrally negative self-similar Markov processes on $\mathbb{R}_+$. As a
by-product, we prove that each of these Markov chains, after an appropriate
scaling, converge in the Skorohod metric, to the associated self-similar Markov
process. By a linear perturbation of the generator of these Markov chains, we
obtain a class of ergodic Markov chains, which are non-reversible. By means of
intertwining and interweaving relations, where the latter was recently
introduced in [27], we derive several deep analytical properties of such
ergodic chains including the description of the spectrum, the spectral
expansion of their semigroups, the study of their convergence to equilibrium in
the $\Phi$-entropy sense as well as their hypercontractivity property.


title:
A Semi-Monolithic Detector providing intrinsic DOI-encoding and sub-200
  ps CRT TOF-Capabilities for Clinical PET Applications

abstract:
  Current clinical PET systems utilize detectors where the scintillator
typically contains single elements of 3 - 6 mm width and about 20 mm height.
While providing good TOF performance, this design limits the spatial resolution
and causes radial astigmatism as the DOI remains unknown. We propose an
alternative, aiming to combine the advantages of current detectors with the DOI
capabilities shown for monolithic concepts, based on semi-monolithic
scintillators (slabs). An array of 8 monolithic LYSO slabs of dimensions 3.9 x
32 x 19 mm3 was read out by a 64-channel photosensor containing digital SiPMs.
The position estimation in the detector's monolithic and DOI direction was
based on a calibration with a fan beam collimator and the machine learning
technique gradient tree boosting (GTB). We achieved a positioning performance
in terms of mean absolute error (MAE) of 1.44 mm and 2.12 mm for DOI
considering a wide energy window of 300 - 700 keV. The energy resolution was
determined to be 11.3%. We established both an analytical and
machine-learning-based timing calibration approach and applied them for a
first-photon trigger. The analytical timing calibration corrects for electronic
and optical time skews leading to 240 ps coincidence resolving time (CRT) for a
pair of slab-detectors. The CRT was significantly improved by utilizing GTB to
predict the time difference based on specific training data and applied on top
of the analytical calibration. We achieved 209 ps for the wide energy window
and 198 ps for a narrow selection around the photopeak (411 - 561 keV). To
maintain the detector's sensitivity, no filters were applied to the data during
processing. Overall, the semi-monolithic detector provides attractive
performance characteristics. Especially, a good CRT can be achieved while
introducing DOI capabilities to the detector, making the concept suitable for
clinical PET scanners.


title:
Beyond Time Complexity: Data Movement Complexity Analysis for Matrix
  Multiplication

abstract:
  Data movement is becoming the dominant contributor to the time and energy
costs of computation across a wide range of application domains. However, time
complexity is inadequate to analyze data movement. This work expands upon Data
Movement Distance, a recently proposed framework for memory-aware algorithm
analysis, by 1) demonstrating that its assumptions conform with
microarchitectural trends, 2) applying it to six variants of matrix
multiplication, and 3) showing it to be capable of asymptotically
differentiating algorithms with the same time complexity but different memory
behavior, as well as locality optimized vs. non-optimized versions of the same
algorithm. In doing so, we attempt to bridge theory and practice by combining
the operation count analysis used by asymptotic time complexity with
per-operation data movement cost resulting from hierarchical memory structure.
Additionally, this paper derives the first fully precise, fully analytical form
of recursive matrix multiplication's miss ratio curve on LRU caching systems.
Our results indicate that the Data Movement Distance framework is a powerful
tool going forward for engineers and algorithm designers to understand the
algorithmic implications of hierarchical memory.


title: Groups having all elements off a normal subgroup with prime power order

abstract:
  We consider a finite group $G$ with a normal subgroup $N$ so that all
elements of $G \setminus N$ have prime power order. We prove that if there is a
prime $p$ so that all the elements in $G \setminus N$ have $p$-power order,
then either $G$ is a $p$-group or $G = PN$ where $P$ is a Sylow $p$-subgroup
and $(G,P,P \cap N)$ is a Frobenius-Wielandt triple. We also prove that if all
the elements of $G \setminus N$ have prime power orders and the orders are
divisible by two primes $p$ and $q$, then $G$ is a $\{ p, q \}$-group and $G/N$
is either a Frobenius group or a $2$-Frobenius group. If all the elements of $G
\setminus N$ have prime power orders and the orders are divisible by at least
three primes, then all elements of $G$ have prime power order and $G/N$ is
nonsolvable.


title: Application placement study of environment adaptive software

abstract:
  To use heterogeneous hardware, programmers must have sufficient technical
skills to utilize OpenMP, CUDA, and OpenCL. On the basis of this, I have
proposed environment-adaptive software that enables automatic conversion,
configuration, and high performance operation of once written code, in
accordance with the hardware. However, although it has been considered to
convert the code according to the offload devices, there has been no study
where to place the offloaded applications to satisfy users' requirements of
price and response time. In this paper, as a new element of environment-adapted
software, I examine a method to calculate appropriate locations using linear
programming method. I confirm that applications can be arranged appropriately
through simulations.


title:
Linear and nonlinear edge dynamics of trapped fractional quantum Hall
  droplets beyond the chiral Luttinger liquid paradigm

abstract:
  We report numerical studies of the linear and nonlinear edge dynamics of a
non-harmonically confined macroscopic fractional quantum Hall fluid. In the
long-wavelength and weak excitation limit, observable consequences of the
fractional transverse conductivity are recovered. The first non-universal
corrections to the chiral Luttinger liquid theory are then characterized: for a
weak excitation in the linear response regime, cubic corrections to the linear
wave dispersion and a broadening of the dynamical structure factor of the edge
excitations are identified; for stronger excitations, sizable nonlinear effects
are found in the dynamics. The numerically observed features are quantitatively
captured by a nonlinear chiral Luttinger liquid quantum Hamiltonian that
reduces to a driven Korteweg-de Vries equation in the semiclassical limit.


title: Evolving symbolic density functionals

abstract:
  Systematic development of accurate density functionals has been a
decades-long challenge for scientists. Despite the emerging application of
machine learning (ML) in approximating functionals, the resulting ML
functionals usually contain more than tens of thousands parameters, which makes
a huge gap in the formulation with the conventional human-designed symbolic
functionals. We propose a new framework, Symbolic Functional Evolutionary
Search (SyFES), that automatically constructs accurate functionals in the
symbolic form, which is more explainable to humans, cheaper to evaluate, and
easier to integrate to existing density functional theory codes than other ML
functionals. We first show that without prior knowledge, SyFES reconstructed a
known functional from scratch. We then demonstrate that evolving from an
existing functional $\omega$B97M-V, SyFES found a new functional, GAS22 (Google
Accelerated Science 22), that performs better on main-group chemistry. Our
framework opens a new direction in leveraging computing power for the
systematic development of symbolic density functionals.


title:
Proof the non-existence of causal classical electrodynamics of point
  charged particles

abstract:
  Up until now, a consistent causal theory of point charged particles (for
example electrons) interacting with electromagnetic field is not known. The
well-known problem is that the standard Lorentz force alone (in the case of
point particles) does not lead to a theory in which momentum and energy are
conserved. The need of radiation reaction force (or self-force) thus arises.
The well known candidate for such force, the Lorentz-Abraham-Dirac reaction
force, gives non-causal particle behavior, i.e., the particle starts to move
before the arrival of external electromagnetic fields. Alternative, causal
proposals provide non-physical behavior of the particle -- the particle moves
with non-zero acceleration long after any external forces acted on it. Below,
we question the existence of a causal theory. We show that for certain
electromagnetic pulse of radiation and point particle being initially at rest,
there {\it does not exists} a causal particle trajectory, such that the
particle ends up moving with constant velocity (when no external
electromagnetic fields act on it for very long time). This shows that the
proper, causal electrodynamics of point particles does not exist.


title: The resonance $X(4630)$

abstract:
  We investigate the structure $X(4630)$ discovered by the LHCb collaboration
in the process $B^{+}\rightarrow J/\psi \phi K^{+}$ as a resonance in the $
J/\psi \phi $ mass distribution. We explore this resonance as a
diquark-antidiquark state $X=[cs][\overline{c}\overline{s}]$ with spin-parities
$J^{\mathrm{PC}}=1^{-+}$. Its mass and current coupling are calculated using
the QCD two-point sum rule method by taking into account vacuum condensates up
to dimension $10$. We also study decays of this tetraquark to mesons
$J/\psi\phi $, $\eta _{c}\eta ^{(\prime )}$, and $\chi _{c1}\eta ^{(\prime )}$,
and compute partial widths of these channels. To this end, we employ the
light-cone sum rule approach and technical methods of soft-meson approximation
to extract strong coupling at relevant tetraquark-meson-meson vertices. Our
predictions for the mass $m=(4632\pm 60)~\mathrm{MeV}$ and width
$\Gamma=(159\pm 31)~\mathrm{MeV}$ of $X$ are in a very nice agreement with
recent measurements of the LHCb collaboration. These results allow us to
interpret the resonance $X(4630)$ as the tetraquark $X$ with spin-parities
$J^{\mathrm{PC}}=1^{-+}$.


title: Ridges, Neural Networks, and the Radon Transform

abstract:
  A ridge is a function that is characterized by a one-dimensional profile
(activation) and a multidimensional direction vector. Ridges appear in the
theory of neural networks as functional descriptors of the effect of a neuron,
with the direction vector being encoded in the linear weights. In this paper,
we investigate properties of the Radon transform in relation to ridges and to
the characterization of neural networks. We introduce a broad category of
hyper-spherical Banach subspaces (including the relevant subspace of measures)
over which the back-projection operator is invertible. We also give conditions
under which the back-projection operator is extendable to the full parent space
with its null space being identifiable as a Banach complement. Starting from
first principles, we then characterize the sampling functionals that are in the
range of the filtered Radon transform. Next, we extend the definition of ridges
for any distributional profile and determine their (filtered) Radon transform
in full generality. Finally, we apply our formalism to clarify and simplify
some of the results and proofs on the optimality of ReLU networks that have
appeared in the literature.


title: Reinventing High Performance Computing: Challenges and Opportunities

abstract:
  The world of computing is in rapid transition, now dominated by a world of
smartphones and cloud services, with profound implications for the future of
advanced scientific computing. Simply put, high-performance computing (HPC) is
at an important inflection point. For the last 60 years, the world's fastest
supercomputers were almost exclusively produced in the United States on behalf
of scientific research in the national laboratories. Change is now in the wind.
While costs now stretch the limits of U.S. government funding for advanced
computing, Japan and China are now leaders in the bespoke HPC systems funded by
government mandates. Meanwhile, the global semiconductor shortage and political
battles surrounding fabrication facilities affect everyone. However, another,
perhaps even deeper, fundamental change has occurred. The major cloud vendors
have invested in global networks of massive scale systems that dwarf today's
HPC systems. Driven by the computing demands of AI, these cloud systems are
increasingly built using custom semiconductors, reducing the financial leverage
of traditional computing vendors. These cloud systems are now breaking barriers
in game playing and computer vision, reshaping how we think about the nature of
scientific computation. Building the next generation of leading edge HPC
systems will require rethinking many fundamentals and historical approaches by
embracing end-to-end co-design; custom hardware configurations and packaging;
large-scale prototyping, as was common thirty years ago; and collaborative
partnerships with the dominant computing ecosystem companies, smartphone, and
cloud computing vendors.


title: Femtosecond Diffraction and Dynamic High Pressure Science

abstract:
  Solid state material at high pressure is prevalent throughout the Universe,
and an understanding of the structure of matter under such extreme conditions,
gleaned from x-ray diffraction, has been pursued for the best part of a
century. The highest pressures that can be reached to date (2 TPa) in
combination with x-ray diffraction diagnosis have been achieved by dynamic
compression via laser ablation . The past decade has witnessed remarkable
advances in x-ray technologies, with novel x-ray Free-Electron-Lasers (FELs)
affording the capacity to produce high quality single-shot diffraction data on
time scales below 100 fsec. We provide a brief history of the field of dynamic
compression, spanning from when the x-ray sources were almost always
laser-plasma based, to the current state-of-the art diffraction capabilities
provided by FELs. We give an overview of the physics of dynamic compression,
diagnostic techniques, and the importance of understanding how the rate of
compression influences the final temperatures reached. We provide illustrative
examples of experiments performed on FEL facilities that are starting to give
insight into how materials deform at ultra-high strain rates, their phase
diagrams, and the types of states that can be reached. We emphasize that there
often appear to be differences in the crystalline phases observed between use
of static and dynamic compression techniques. We give our perspective on both
the current state of this rapidly evolving field, and some glimpses of how we
see it developing in the near-to-medium term.


title:
Nodal Precession and Tidal Evolution of Two Hot-Jupiters: WASP-33 b and
  KELT-9 b

abstract:
  Hot Jupiters orbiting rapidly rotating stars on inclined orbits undergo
tidally induced nodal precession measurable over several years of observations.
The Hot Jupiters WASP-33 b and KELT-9 b are particularly interesting targets as
they are among the hottest planets found to date, orbiting relatively massive
stars. Here, we analyze archival and new data that span 11 and 5 years for
WASP-33 b and KELT-9 b, respectively, in order to to model and improve upon
their tidal precession parameters. Our work confirms the nodal precession for
WASP-33 b and presents the first clear detection of the precession of KELT-9 b.
We determine that WASP-33 and KELT-9 have gravitational quadrupole moments
$(6.3^{+1.2}_{-0.8})\times10^{-5}$ and $(3.26^{+0.93}_{-0.80})\times10^{-4}$,
respectively. We estimate the planets' precession periods to be
$1460^{+170}_{-130}$ years and $890^{+200}_{-140}$ years, respectively, and
that they will cease to transit their host stars around the years
$2090^{+17}_{-10}$~CE and $2074^{+12}_{-10}$~CE, respectively. Additionally, we
investigate both planets' tidal and orbital evolution, suggesting that a
high-eccentricity tidal migration scenario is possible to produce both system
architectures and that they will most likely not be engulfed by their hosts
before the end of their main sequence lifetimes.


title: Homomorphically Encrypted Computation using Stochastic Encodings

abstract:
  Homomorphic encryption (HE) is a privacy-preserving technique that enables
computation directly over ciphertext. Unfortunately, a key challenge for HE is
that implementations can be impractically slow and have limits on computation
that can be efficiently implemented. For instance, in Boolean constructions of
HE like TFHE, arithmetic operations need to be decomposed into constituent
elementary logic gates to implement so performance depends on logical circuit
depth. For even heavily quantized fixed-point arithmetic operations, these HE
circuit implementations can be slow.
  This paper explores the merit of using stochastic computing (SC) encodings to
reduce the logical depth required for HE computation to enable more efficient
implementations. Contrary to computation in the plaintext space where many
efficient hardware implementations are available, HE provides support for only
a limited number of primitive operators and their performance may not directly
correlate to their plaintext performance. Our results show that by layering SC
encodings on top of TFHE, we observe similar challenges and limitations that SC
faces in the plaintext space. Additional breakthroughs would require more
support from the HE libraries to make SC with HE a viable solution.


title:
Uncertainty Propagation for General Stochastic Hybrid Systems on Compact
  Lie Groups

abstract:
  This paper deals with uncertainty propagation of general stochastic hybrid
systems (GSHS) where the continuous state space is a compact Lie group. A
computational framework is proposed to solve the Fokker-Planck (FP) equation
that describes the time evolution of the probability density function for the
state of GSHS. The FP equation is split into two parts: the partial
differential operator corresponding to the continuous dynamics, and the
integral operator arising from the discrete dynamics. These two parts are
solved alternatively using the operator splitting technique. Specifically, the
partial differential equation is solved by the spectral method where the
density function is decomposed into a linear combination of a complete
orthonormal function basis brought forth by the Peter-Weyl theorem, thereby
resulting an ordinary differential equation. Next, the integral equation is
solved by approximating the integral by a finite summation using a quadrature
rule. The proposed method is then applied to a three-dimensional rigid body
pendulum colliding with a wall, evolving on the product of the
three-dimensional special orthogonal group and the Euclidean space. It is
illustrated that the proposed method exhibits numerical results consistent with
a Monte Carlo simulation, while explicitly generating the density function that
carries the complete stochastic information of the hybrid state.


title: Structured Pruning is All You Need for Pruning CNNs at Initialization

abstract:
  Pruning is a popular technique for reducing the model size and computational
cost of convolutional neural networks (CNNs). However, a slow retraining or
fine-tuning procedure is often required to recover the accuracy loss caused by
pruning. Recently, a new research direction on weight pruning,
pruning-at-initialization (PAI), is proposed to directly prune CNNs before
training so that fine-tuning or retraining can be avoided. While PAI has shown
promising results in reducing the model size, existing approaches rely on
fine-grained weight pruning which requires unstructured sparse matrix
computation, making it difficult to achieve real speedup in practice unless the
sparsity is very high.
  This work is the first to show that fine-grained weight pruning is in fact
not necessary for PAI. Instead, the layerwise compression ratio is the main
critical factor to determine the accuracy of a CNN model pruned at
initialization. Based on this key observation, we propose PreCropping, a
structured hardware-efficient model compression scheme. PreCropping directly
compresses the model at the channel level following the layerwise compression
ratio. Compared to weight pruning, the proposed scheme is regular and dense in
both storage and computation without sacrificing accuracy. In addition, since
PreCropping compresses CNNs at initialization, the computational and memory
costs of CNNs are reduced for both training and inference on commodity
hardware. We empirically demonstrate our approaches on several modern CNN
architectures, including ResNet, ShuffleNet, and MobileNet for both CIFAR-10
and ImageNet.


title:
AgileWatts: An Energy-Efficient CPU Core Idle-State Architecture for
  Latency-Sensitive Server Applications

abstract:
  User-facing applications running in modern datacenters exhibit irregular
request patterns and are implemented using a multitude of services with tight
latency requirements. These characteristics render ineffective existing energy
conserving techniques when processors are idle due to the long transition time
from a deep idle power state (C-state). While prior works propose management
techniques to mitigate this inefficiency, we tackle it at its root with
AgileWatts (AW): a new deep C-state architecture optimized for datacenter
server processors targeting latency-sensitive applications. AW is based on
three key ideas. First, AW eliminates the latency overhead of saving/restoring
the core context (i.e., micro-architectural state) when powering-off/-on the
core in a deep idle power state by i) implementing medium-grained power-gates,
carefully distributed across the CPU core, and ii) retaining context in the
power-ungated domain. Second, AW eliminates the flush latency overhead (several
tens of microseconds) of the L1/L2 caches when entering a deep idle power state
by keeping L1/L2 cache content power-ungated. A minimal control logic also
remains power-ungated to serve cache coherence traffic (i.e., snoops)
seamlessly. AW implements sleep-mode in caches to reduce caches leakage power
consumption and lowers a core voltage to the minimum operational voltage level
to minimize the leakage power of the power-ungated domain. Third, using a
state-of-the-art power efficient all-digital phase-locked loop (ADPLL) clock
generator, AW keeps the PLL active and locked during the idle state, further
cutting precious microseconds of wake-up latency at a negligible power cost.
Our evaluation with an accurate simulator calibrated against an Intel Skylake
server shows that AW reduces the energy consumption of Memcached by up to 71%
(35% on average) with up to 1% performance degradation.


title: Proof Methods in Random Matrix Theory

abstract:
  In this survey article, we give an introduction to two methods of proof in
random matrix theory: The method of moments and the Stieltjes transform method.
We thoroughly develop these methods and apply them to show both the semicircle
law and the Marchenko-Pastur law for random matrices with independent entries.
The material is presented in a pedagogical manner and is suitable for anyone
who has followed a course in measure-theoretic probability theory.


title: A streamable large-scale clinical EEG dataset for Deep Learning

abstract:
  Deep Learning has revolutionized various fields, including Computer Vision,
Natural Language Processing, as well as Biomedical research. Within the field
of neuroscience, specifically in electrophysiological neuroimaging, researchers
are starting to explore leveraging deep learning to make predictions on their
data without extensive feature engineering. The availability of large-scale
datasets is a crucial aspect of allowing the experimentation of Deep Learning
models. We are publishing the first large-scale clinical EEG dataset that
simplifies data access and management for Deep Learning. This dataset contains
eyes-closed EEG data prepared from a collection of 1,574 juvenile participants
from the Healthy Brain Network. We demonstrate a use case integrating this
framework, and discuss why providing such neuroinformatics infrastructure to
the community is critical for future scientific discoveries.


title: Optimal Clock Synchronization with Signatures

abstract:
  Cryptographic signatures can be used to increase the resilience of
distributed systems against adversarial attacks, by increasing the number of
faulty parties that can be tolerated. While this is well-studied for consensus,
it has been underexplored in the context of fault-tolerant clock
synchronization, even in fully connected systems. Here, the honest parties of
an $n$-node system are required to compute output clocks of small skew (i.e.,
maximum phase offset) despite local clock rates varying between $1$ and
$\vartheta>1$, end-to-end communication delays varying between $d-u$ and $d$,
and the interference from malicious parties. So far, it is only known that
clock pulses of skew $d$ can be generated with (trivially optimal) resilience
of $\lceil n/2\rceil-1$ (PODC `19), improving over the tight bound of $\lceil
n/3\rceil-1$ holding without signatures for \emph{any} skew bound (STOC `84,
PODC `85). Since typically $d\gg u$ and $\vartheta-1\ll 1$, this is far from
the lower bound of $u+(\vartheta-1)d$ that applies even in the fault-free case
(IPL `01).
  We prove matching upper and lower bounds of $\Theta(u+(\vartheta-1)d)$ on the
skew for the resilience range from $\lceil n/3\rceil$ to $\lceil n/2\rceil-1$.
The algorithm showing the upper bound is, under the assumption that the
adversary cannot forge signatures, deterministic. The lower bound holds even if
clocks are initially perfectly synchronized, message delays between honest
nodes are known, $\vartheta$ is arbitrarily close to one, and the
synchronization algorithm is randomized. This has crucial implications for
network designers that seek to leverage signatures for providing more robust
time. In contrast to the setting without signatures, they must ensure that an
attacker cannot easily bypass the lower bound on the delay on links with a
faulty endpoint.


title: Building 3D Generative Models from Minimal Data

abstract:
  We propose a method for constructing generative models of 3D objects from a
single 3D mesh and improving them through unsupervised low-shot learning from
2D images. Our method produces a 3D morphable model that represents shape and
albedo in terms of Gaussian processes. Whereas previous approaches have
typically built 3D morphable models from multiple high-quality 3D scans through
principal component analysis, we build 3D morphable models from a single scan
or template. As we demonstrate in the face domain, these models can be used to
infer 3D reconstructions from 2D data (inverse graphics) or 3D data
(registration). Specifically, we show that our approach can be used to perform
face recognition using only a single 3D template (one scan total, not one per
person). We extend our model to a preliminary unsupervised learning framework
that enables the learning of the distribution of 3D faces using one 3D template
and a small number of 2D images. This approach could also provide a model for
the origins of face perception in human infants, who appear to start with an
innate face template and subsequently develop a flexible system for perceiving
the 3D structure of any novel face from experience with only 2D images of a
relatively small number of familiar faces.


title: Physics and data informed neural networks for two-dimensional turbulence

abstract:
  Turbulence remains a problem that is yet to be fully understood, with
experimental and numerical studies aiming to fully characterise the statistical
properties of turbulent flows. Such studies require huge amount of resources to
capture, simulate, store and analyse the data. In this work, we present
physics-informed neural network (PINN) based methods to predict flow quantities
and features of two-dimensional turbulence with the help of sparse data in a
rectangular domain with periodic boundaries. While the PINN model can reproduce
all the statistics at large scales, the small scale properties are not captured
properly. We introduce a new PINN model that can effectively capture the energy
distribution at small scales performing better than the standard PINN based
approach. It relies on the training of the low and high wavenumber behaviour
separately leading to a better estimate for the full turbulent flow. With 0.1 %
training data, we observe that the new PINN model captures the turbulent field
at inertial scales leading to a general agreement of the kinetic energy spectra
upto eight to nine decades as compared with the solutions from direct numerical
simulation (DNS). We further apply these techniques to successfully capture the
statistical behaviour of large scale modes in the turbulent flow. We believe
such methods to have significant applications in enhancing the retrieval of
existing turbulent data sets at even shorter time intervals.


title: Improved Wavelets for Image Compression from Unitary Circuits

abstract:
  We benchmark the efficacy of several novel orthogonal, symmetric, dilation-3
wavelets, derived from a unitary circuit based construction, towards image
compression. The performance of these wavelets is compared across several photo
databases against the CDF-9/7 wavelets in terms of the minimum number of
non-zero wavelet coefficients needed to obtain a specified image quality, as
measured by the multi-scale structural similarity index (MS-SSIM). The new
wavelets are found to consistently offer better compression efficiency than the
CDF-9/7 wavelets across a broad range of image resolutions and quality
requirements, averaging 7-8% improved compression efficiency on high-resolution
photo images when high-quality (MS-SSIM = 0.99) is required.


title:
UVCGAN: UNet Vision Transformer cycle-consistent GAN for unpaired
  image-to-image translation

abstract:
  Image-to-image translation has broad applications in art, design, and
scientific simulations. The original CycleGAN model emphasizes one-to-one
mapping via a cycle-consistent loss, while more recent works promote
one-to-many mapping to boost the diversity of the translated images. With
scientific simulation and one-to-one needs in mind, this work examines if
equipping CycleGAN with a vision transformer (ViT) and employing advanced
generative adversarial network (GAN) training techniques can achieve better
performance. The resulting UNet ViT Cycle-consistent GAN (UVCGAN) model is
compared with previous best-performing models on open benchmark image-to-image
translation datasets, Selfie2Anime and CelebA. UVCGAN performs better and
retains a strong correlation between the original and translated images. An
accompanying ablation study shows that the gradient penalty and BERT-like
pre-training also contribute to the improvement.~To promote reproducibility and
open science, the source code, hyperparameter configurations, and pre-trained
model will be made available at: https://github.com/LS4GAN/uvcgan.


title: Thermal drag effect in quantum Hall circuits

abstract:
  We study the thermal drag between two mesoscopic quantum Hall (QH) circuits.
Each circuit consists of Ohmic contact perfectly coupled to quantum Hall edge
states. The drag is caused by strong capacitive coupling between Ohmic
contacts. The non-equilibirum conditions and the electron-electron interaction
are taken into account by using the non-equilibrium bosonization technique. The
thermal drag current in the passive circuit, the noise power of the
corresponding heat current, and the Fano factor are calculated and analyzed.