title:
Single-Purpose Algorithms vs. a Generic Graph Summarizer for Computing
  $k$-Bisimulations on Large Graphs

abstract:
  We investigate whether a generic graph summarization approach BRS can
outperform an existing single-purpose parallel algorithm for bisimulation.
Furthermore, we investigate whether an existing sequential bisimulation
algorithm can effectively be computed by the parallel BRS algorithm and how
such generic implementations compete against a parallel variant. To give a fair
comparison, we have reimplemented the original algorithms in the same framework
as was used for the generic BRS algorithm. We evaluate the performance of the
two native implementations against the implementations in the BRS algorithm for
$k$-bisimulation with $k=1, \dots, 10$, using five real-world and synthetic
graph datasets containing between $100$ million and two billion edges. Our
results show that our generic BRS algorithm outperforms the respective native
bisimulation algorithms for any value of~$k$. The generic algorithm has no
disadvantage over the native parallel algorithm. Furthermore, the execution
times of the generic BRS algorithm for the native parallel and native
sequential bisimulation variants are very similar. This shows that the
bisimulation variant computed by the native sequential algorithm can be
effectively computed in parallel by our BRS algorithm. These insights open a
new path for efficiently computing bisimulations on large graphs.


title:
Extracting resonance poles from numerical scattering data: type-II
  Pad\`e reconstruction

abstract:
  We present a FORTRAN 77 code for evaluation of resonance pole positions and
residues of a numerical scattering matrix element in the complex energy (CE) as
well as in the complex angular momentum (CAM) planes. Analytical continuation
of the S-matrix element is performed by constructing a type-II Pad\'e
approximant from given physical values [Bessis et al (1994); Vrinceanu et al
(2000); Sokolovski and Msezane (2004)] . The algorithm involves iterative
"preconditioning" of the numerical data by extracting its rapidly oscillating
potential phase component. The code has the capability of adding non-analytical
noise to the numerical data in order to select "true" physical poles,
investigate their stability and evaluate the accuracy of the reconstruction. It
has an option of employing multiple-precision (MPFUN) package [Bailey (1993)]
developed by D. H. Bailey wherever double precision calculations fail due to a
large number of input partial wave (energies) involved. The package has been
successfully tested on several models, as well as theF+H2->HF+H,F+HD->HF
+D,Cl+HCl->ClH+Cl and H+D2->HD+D reactions. Some detailed examples are given in
the text. PACS:34.50.Lf,34.50.Pi


title:
Adaptive Cross-Attention-Driven Spatial-Spectral Graph Convolutional
  Network for Hyperspectral Image Classification

abstract:
  Recently, graph convolutional networks (GCNs) have been developed to explore
spatial relationship between pixels, achieving better classification
performance of hyperspectral images (HSIs). However, these methods fail to
sufficiently leverage the relationship between spectral bands in HSI data. As
such, we propose an adaptive cross-attention-driven spatial-spectral graph
convolutional network (ACSS-GCN), which is composed of a spatial GCN (Sa-GCN)
subnetwork, a spectral GCN (Se-GCN) subnetwork, and a graph cross-attention
fusion module (GCAFM). Specifically, Sa-GCN and Se-GCN are proposed to extract
the spatial and spectral features by modeling correlations between spatial
pixels and between spectral bands, respectively. Then, by integrating attention
mechanism into information aggregation of graph, the GCAFM, including three
parts, i.e., spatial graph attention block, spectral graph attention block, and
fusion block, is designed to fuse the spatial and spectral features and
suppress noise interference in Sa-GCN and Se-GCN. Moreover, the idea of the
adaptive graph is introduced to explore an optimal graph through back
propagation during the training process. Experiments on two HSI data sets show
that the proposed method achieves better performance than other classification
methods.


title:
On the spectrum of a mixed-type operator with applications to rotating
  wave solutions

abstract:
  We study rotating wave solutions of the nonlinear wave equation $$ \left\{
\begin{aligned} \partial_{t}^2 v - \Delta v + m v & = |v|^{p-2} v \quad &&
\text{in $\mathbb{R} \times \mathbf{B}$} \\ v & = 0 && \text{on $\mathbb{R}
\times \partial \mathbf{B}$} \end{aligned} \right. $$ where $2<p<\infty$, $m
\in \mathbb{R}$ and $\mathbf{B} \subset \mathbb{R}^2$ denotes the unit disk. If
the angular velocity $\alpha$ of the rotation is larger than $1$, this leads to
a semilinear boundary value problem on $\mathbf{B}$ involving a mixed-type
operator, whose spectrum is related to the zeros of Bessel functions and could
generally be badly behaved. Based on new estimates for these zeros, we find
values of $\alpha$ such that the spectrum only consists of eigenvalues with
finite multiplicity and has no accumulation point. Combined with suitable
spectral estimates, this allows us to formulate an appropriate indefinite
variational setting and find ground state solutions of the reduced equation for
$p \in (2,4)$. Using a minimax characterization of the ground state energy, we
ultimately show that these ground states are nonradial and thus yield
nontrivial rotating waves, provided $m$ is sufficiently large.


title: Cognitive Radio Inspired Uplink Rate-Splitting Multiple Access

abstract:
  With the exponential increase of the number of devices in the communication
ecosystem toward the upcoming sixth generation (6G) of wireless networks, more
enabling technologies and potential wireless architectures are necessary to
fulfill the networking requirements of high throughput, massive connectivity,
ultra reliability, and heterogeneous Quality of Service (QoS). To this end,
schemes based on rate-splitting multiple access (RSMA) are expected to play a
pivotal role in next generation communication networks. In this work, we
investigate an uplink network consisting of a primary user (PU) and a secondary
user (SU) and, by introducing the concept of cognitive radio (CR) into the RSMA
framework, a protocol based on RSMA is proposed. This protocol aims to serve
the SU in a resource block which is originally allocated solely for the PU
without negatively affecting the QoS of the PU. Moreover, a similar but simpler
protocol based on successive interference cancellation is proposed. We derive
closed-form expressions for the outage probability of the SU for the two
proposed protocols, ensuring that there exists no negative impact for the PU.
To obtain further insights, asymptotic analysis is performed and the
corresponding diversity gains are presented. In the numerical results, we
validate the the theoretical analysis and illustrate the superiority of the
proposed protocols over two benchmark schemes.


title:
Delay Optimization of Conventional Non-Coherent Differential CPM
  Detection

abstract:
  The conventional non-coherent differential detection of continuous phase
modulation (CPM) is quite robust to channel impairments such as phase and
Doppler shifts. Its implementation is on top of that simple. It consists in
multiplying the received baseband signal by its conjugate and delayed version
of one symbol period. However it suffers from a signal-to-noise ratio gap
compared to the optimum coherent detection. In this paper, we improve the error
rate performance of the conventional differential detection by using a delay
higher than one symbol period. We derive the trellis description as well as the
branch and cumulative metric that take into account a delay of $K$ symbol
periods. We then derive an optimization criterion of $K$ based on the minimum
Euclidean distance between two differential signals. We finally determine
optimized delays for some popular CPM formats whose values are confirmed by
error rate simulations.


title: Correction of overfitting bias in regression models

abstract:
  Regression analysis based on many covariates is becoming increasingly common.
When the number $p$ of covariates is of the same order as the number $N$ of
observations, statistical inference like maximum likelihood estimation of
regression and nuisance parameters in the model becomes unreliable due to
overfitting. This overfitting most often leads to systematic biases in (some
of) the estimators. In the literature, several methods to overcome overfitting
bias or to adjust estimates have been proposed. The vast majority of these
focus on the regression parameters only, either via empirical regularization
methods or by expansion for small ratios $p/N$. This failure to correctly
estimate also the nuisance parameters may lead to significant errors in outcome
predictions. In this paper we study the overfitting bias of maximum likelihood
estimators for regression and the nuisance parameters in parametric regression
models in the overfitting regime ($p/N<1$). We compute the asymptotic
characteristic function of the maximum likelihood estimators, and show how it
can be used to estimate their overfitting biases by solving a small set of
non-linear equations. These estimated biases enable us to correct the
estimators and make them asymptotically unbiased. To illustrate the theory we
performed simulation studies for multiple parametric regression models. In all
cases we find excellent agreement between theory and simulations.


title:
A Study on the X-Ray Pulse Profile and Spectrum of the Crab Pulsar Using
  NICER and Insight-HXMT's Observations

abstract:
  We analyze the energy dependence of the X-ray pulse profile and the
phase-resolved spectra (PRS) of the Crab pulsar using observations from the
Neutron star Interior Composition Explorer (NICER) and the Hard X-ray
Modulation Telescope (Insight-HXMT). We parameterize the pulse profiles and
quantify the evolution of these parameters in the broad energy band of 0.4-250
keV. A log-parabola function is used to fit the PRS in 2-250 keV, and the
curvature of the spectrum, i.e., the evolution of the photon index with energy,
as represented by the parameter \{beta} of the log-parabola model, also changes
with phase. The relation of \{beta} and phase has two turning points slightly
later than those of the pulse intensity profile, where the values of \{beta}
are the lowest, suggesting that the energy-loss rate of the particles is the
lowest in the corresponding regions. A three-segment broken-power-law model is
also used to fit those PRS. The differences between the hard spectral index and
the soft ones have a distribution similar to that of \{beta}, confirming the
fitting results of the log-parabola model, while the broken energies are
generally higher in the region bridging the two pulses. We find
anticorrelations between the spectral indices and the curvature of the
log-parabola model fitting and a similar anticorrelation between the spectral
indices and broken energies of the broken-power-law model fitting, suggesting a
scenario where the highest-energy particles are produced in regions where
radiation energy loss is strongest.


title: Shi arrangements restricted to Weyl cones

abstract:
  We consider the restrictions of Shi arrangements to Weyl cones, their
relations to antichains in the root poset, and their intersection posets. For
any Weyl cone, we provide bijections between regions, flats intersecting the
cone, and antichains of a naturally-defined subposet of the root poset. This
gives a refinement of the parking function numbers via the Poincar\'e
polynomials of the intersection posets of all Weyl cones. Finally, we interpret
these Poincar\'e polynomials as the Hilbert series of two isomorphic graded
rings, one arising from the Varchenko-Gel'fand ring and another, which we call
the order ring since it turns out to be naturally associated to the order
polytope.


title: Effects of Haptic Feedback on the Wrist during Virtual Manipulation

abstract:
  We propose a haptic system for virtual manipulation to provide feedback on
the user's forearm instead of the fingertips. In addition to visual rendering
of the manipulation with virtual fingertips, we employ a device to deliver
normal or shear skin-stretch at multiple points near the wrist. To understand
how design parameters influence the experience, we investigated the effect of
the number and location of sensory feedback on stiffness perception.
Participants compared stiffness values of virtual objects, while the haptic
bracelet provided interaction feedback on the dorsal, ventral, or both sides of
the wrist. Stiffness discrimination judgments and duration, as well as
qualitative results collected verbally, indicate no significant difference in
stiffness perception with stimulation at different and multiple locations.


title: Very large set axioms over constructive set theories

abstract:
  We investigate large set axioms defined in terms of elementary embeddings
over constructive set theories, focusing on $\mathsf{IKP}$ and $\mathsf{CZF}$.
Most previously studied large set axioms, notably the constructive analogues of
large cardinals below $0^\sharp$, have proof-theoretic strength weaker than
full Second-order Arithmetic. On the other hand, the situation is dramatically
different for those defined via elementary embeddings. We show that by adding
to $\mathsf{IKP}$ the basic properties of an elementary embedding $j\colon V\to
M$ for $\Delta_0$-formulas, which we will denote by
$\mathsf{\Delta_0\text{-}BTEE}_M$, we obtain the consistency of $\mathsf{ZFC}$
and more. We will also see that the consistency strength of a Reinhardt set
exceeds that of $\mathsf{ZF+WA}$. Furthermore, we will define super Reinhardt
sets and $\mathsf{TR}$, which is a constructive analogue of $V$ being totally
Reinhardt, and prove that their proof-theoretic strength exceeds that of
$\mathsf{ZF}$ with choiceless large cardinals.


title:
What Language Model Architecture and Pretraining Objective Work Best for
  Zero-Shot Generalization?

abstract:
  Large pretrained Transformer language models have been shown to exhibit
zero-shot generalization, i.e. they can perform a wide variety of tasks that
they were not explicitly trained on. However, the architectures and pretraining
objectives used across state-of-the-art models differ significantly, and there
has been limited systematic comparison of these factors. In this work, we
present a large-scale evaluation of modeling choices and their impact on
zero-shot generalization. In particular, we focus on text-to-text models and
experiment with three model architectures (causal/non-causal decoder-only and
encoder-decoder), trained with two different pretraining objectives
(autoregressive and masked language modeling), and evaluated with and without
multitask prompted finetuning. We train models with over 5 billion parameters
for more than 170 billion tokens, thereby increasing the likelihood that our
conclusions will transfer to even larger scales. Our experiments show that
causal decoder-only models trained on an autoregressive language modeling
objective exhibit the strongest zero-shot generalization after purely
unsupervised pretraining. However, models with non-causal visibility on their
input trained with a masked language modeling objective followed by multitask
finetuning perform the best among our experiments. We therefore consider the
adaptation of pretrained models across architectures and objectives. We find
that pretrained non-causal decoder models can be adapted into performant
generative causal decoder models, using autoregressive language modeling as a
downstream task. Furthermore, we find that pretrained causal decoder models can
be efficiently adapted into non-causal decoder models, ultimately achieving
competitive performance after multitask finetuning. Code and checkpoints are
available at https://github.com/bigscience-workshop/architecture-objective.


title:
Faraday Waves in a Low-Viscosity Fluid Covered with a Floating Elastic
  Sheet

abstract:
  The standing surface waves in a rectangular vertically oscillating vessel
filled with water (Faraday waves) in the presence of a floating elastic sheet
are studied experimentally and theoretically. The threshold amplitude of the
instability and the wavelength of the patterns are measured as a function of
the frequency. A theoretical model based on Hamiltonian method is used to
describe the system. Using the experimental attenuation coefficients, we see a
very good agreement between the theory and the experiment for threshold
amplitude. Also, the dispersion relation obtained from the theory is consistent
with experiments.


title: Interactions between topological defects in (1+1) dimensions

abstract:
  In this thesis, we study interactions between topological defects in
two-dimensional spacetimes. These defects are called kinks. They are solutions
of scalar field theories with localized energy which propagate without losing
its shape. In order to understand the resonance phenomenon exhibited by those
models, we built a toy model where the kink's vibrational mode turns into a
quasinormal mode. This causes the suppression of resonance windows and,
consequently, its fractal structure is lost. Considering a higher order
polynomial as the scalar field potential, we find kinks with long-range tails,
which decay as a power law. We developed a numerical method to correctly
initialize this systems and applied it to a scalar field model containing kinks
with long-range tails in both sides. After the collision, the kink-antikink
pair is annihilated for velocities below an ultra-relativistic critical
velocity without bion formation. We also investigated a collision between
wobbling kinks of the double sine-Gordon model. When the kinks are already
wobbling before colliding, there appears resonance windows with separation
after a single bounce. On the second half of the thesis, we focused on
fermion-kink interactions. We studied what happens when a fermion binds to a
wobbling kink. The result is that the fermion escapes from the kink as
radiation and at a constant rate. This occurs if the energy gap between the
initial state and the continuum threshold is not too large. Lastly, we
investigated the interaction of a fermion with a background scalar field with
an impurity that preserves half of the Bogomol'nyi-Prasad-Sommerfield (BPS)
property. We found an adiabatic evolution near the BPS regime, which means that
the system is at a static BPS solution at every moment.


title:
Nonlinear dynamics of an epidemic compartment model with asymptomatic
  infections and mitigation

abstract:
  A significant proportion of the infections driving the current {SARS-CoV-2}
pandemic are transmitted asymptomatically. Here we introduce and study a simple
epidemic model with separate compartments comprising asymptomatic and
symptomatic infected individuals. The linear dynamics determining the outbreak
condition of the model is equivalent to a renewal theory approach with
exponential waiting time distributions. Exploiting a nontrivial conservation
law of the full nonlinear dynamics, we derive analytic bounds on the peak
number of infections in the absence and presence of mitigation through
isolation and testing. The bounds are compared to numerical solutions of the
differential equations.


title:
The Project Dialogism Novel Corpus: A Dataset for Quotation Attribution
  in Literary Texts

abstract:
  We present the Project Dialogism Novel Corpus, or PDNC, an annotated dataset
of quotations for English literary texts. PDNC contains annotations for 35,978
quotations across 22 full-length novels, and is by an order of magnitude the
largest corpus of its kind. Each quotation is annotated for the speaker,
addressees, type of quotation, referring expression, and character mentions
within the quotation text. The annotated attributes allow for a comprehensive
evaluation of models of quotation attribution and coreference for literary
texts.


title: Blowing-up solutions for a nonlocal Liouville type equation

abstract:
  We consider the nonlocal Liouville type equation $$ (-\Delta)^{\frac{1}{2}} u
= \varepsilon \kappa(x) e^u, \quad u > 0, \quad \mbox{in } I, \qquad u = 0,
\quad \mbox{in } \mathbb{R} \setminus I, $$ where $I$ is a union of $d \geq 2$
disjoint bounded intervals, $\kappa$ is a smooth bounded function with positive
infimum and $\varepsilon > 0$ is a small parameter. For any integer $1 \leq m
\leq d$, we construct a family of solutions $(u_\varepsilon)_{\varepsilon}$
which blow up at $m$ interior distinct points of $I$ and for which $\varepsilon
\int_I \kappa e^{u_\varepsilon} \, \rightarrow 2 m \pi$, as $\varepsilon \to
0$. Moreover, we show that, when $d = 2$ and $m$ is suitably large, no such
construction is possible.


title:
Benchmarking Active Learning Strategies for Materials Optimization and
  Discovery

abstract:
  Autonomous physical science is revolutionizing materials science. In these
systems, machine learning controls experiment design, execution, and analysis
in a closed loop. Active learning, the machine learning field of optimal
experiment design, selects each subsequent experiment to maximize knowledge
toward the user goal. Autonomous system performance can be further improved
with implementation of scientific machine learning, also known as inductive
bias-engineered artificial intelligence, which folds prior knowledge of
physical laws (e.g., Gibbs phase rule) into the algorithm. As the number,
diversity, and uses for active learning strategies grow, there is an associated
growing necessity for real-world reference datasets to benchmark strategies. We
present a reference dataset and demonstrate its use to benchmark active
learning strategies in the form of various acquisition functions. Active
learning strategies are used to rapidly identify materials with optimal
physical properties within a ternary materials system. The data is from an
actual Fe-Co-Ni thin-film library and includes previously acquired experimental
data for materials compositions, X-ray diffraction patterns, and two functional
properties of magnetic coercivity and the Kerr rotation. Popular active
learning methods along with a recent scientific active learning method are
benchmarked for their materials optimization performance. We discuss the
relationship between algorithm performance, materials search space complexity,
and the incorporation of prior knowledge.


title: The MIT Supercloud Workload Classification Challenge

abstract:
  High-Performance Computing (HPC) centers and cloud providers support an
increasingly diverse set of applications on heterogenous hardware. As
Artificial Intelligence (AI) and Machine Learning (ML) workloads have become an
increasingly larger share of the compute workloads, new approaches to optimized
resource usage, allocation, and deployment of new AI frameworks are needed. By
identifying compute workloads and their utilization characteristics, HPC
systems may be able to better match available resources with the application
demand. By leveraging datacenter instrumentation, it may be possible to develop
AI-based approaches that can identify workloads and provide feedback to
researchers and datacenter operators for improving operational efficiency. To
enable this research, we released the MIT Supercloud Dataset, which provides
detailed monitoring logs from the MIT Supercloud cluster. This dataset includes
CPU and GPU usage by jobs, memory usage, and file system logs. In this paper,
we present a workload classification challenge based on this dataset. We
introduce a labelled dataset that can be used to develop new approaches to
workload classification and present initial results based on existing
approaches. The goal of this challenge is to foster algorithmic innovations in
the analysis of compute workloads that can achieve higher accuracy than
existing methods. Data and code will be made publicly available via the
Datacenter Challenge website : https://dcc.mit.edu.


title:
Extragalactic HI survey with FAST : First look of the pilot survey
  results

abstract:
  As first data release of a pilot extragalactic HI survey with
Five-hundred-meter Aperture Spherical radio Telescope (FAST),we extracted 544
extragalaxies from three-dimensional(3D) spectral data to perform interactive
searching and computing, yielding global parameters for these detections,
extending redshift ranges of HI 21cm line up to z = 0.04 ,which covers part of
the sky region in right ascension(R.A. or $\alpha$) and declination(Dec or
$\delta$) range $00^{\rm h} 47^{\rm m}< \rm R.A.(J2000)<23^{\rm h}22^{\rm m}$
and $+24^{\circ}<\rm Dec.(J2000) <+43^{\circ}$ . The S/N of 544 HI detections
are greater than 5 flagged with code 1 to 4 based on baseline qualities or RFI
contamination. Besides, we find 16 of which without any counterparts in the
existing galaxy catalogs. The catalog can give a guidence for the future HI
observation with FAST.


title: VoiceFixer: A Unified Framework for High-Fidelity Speech Restoration

abstract:
  Speech restoration aims to remove distortions in speech signals. Prior
methods mainly focus on a single type of distortion, such as speech denoising
or dereverberation. However, speech signals can be degraded by several
different distortions simultaneously in the real world. It is thus important to
extend speech restoration models to deal with multiple distortions. In this
paper, we introduce VoiceFixer, a unified framework for high-fidelity speech
restoration. VoiceFixer restores speech from multiple distortions (e.g., noise,
reverberation, and clipping) and can expand degraded speech (e.g., noisy
speech) with a low bandwidth to 44.1 kHz full-bandwidth high-fidelity speech.
We design VoiceFixer based on (1) an analysis stage that predicts
intermediate-level features from the degraded speech, and (2) a synthesis stage
that generates waveform using a neural vocoder. Both objective and subjective
evaluations show that VoiceFixer is effective on severely degraded speech, such
as real-world historical speech recordings. Samples of VoiceFixer are available
at https://haoheliu.github.io/voicefixer.


title: Generative Negative Replay for Continual Learning

abstract:
  Learning continually is a key aspect of intelligence and a necessary ability
to solve many real-life problems. One of the most effective strategies to
control catastrophic forgetting, the Achilles' heel of continual learning, is
storing part of the old data and replaying them interleaved with new
experiences (also known as the replay approach). Generative replay, which is
using generative models to provide replay patterns on demand, is particularly
intriguing, however, it was shown to be effective mainly under simplified
assumptions, such as simple scenarios and low-dimensional data. In this paper,
we show that, while the generated data are usually not able to improve the
classification accuracy for the old classes, they can be effective as negative
examples (or antagonists) to better learn the new classes, especially when the
learning experiences are small and contain examples of just one or few classes.
The proposed approach is validated on complex class-incremental and
data-incremental continual learning scenarios (CORe50 and ImageNet-1000)
composed of high-dimensional data and a large number of training experiences: a
setup where existing generative replay approaches usually fail.


title: Ricci-Deturck flow from rough metrics and applications

abstract:
  Motivated by the recent work of Lamm and Simon, in this work we study the
short-time existence theory of Ricci-Deturck flow starting from rough metrics
which are bi-Lipschitz and have small local scaling invariant gradient
concentration. As applications, we use it to study several stability problems
related to the scalar curvature.


title: Gamma-ray Polarimetry of Transient Sources with POLAR

abstract:
  Polarization measurements of the gamma-ray component of transient sources are
of great scientific interest, they are however, also highly challenging. This
is due to the typical low signal to noise and the potential for significant
systematic errors. Both issues are made worse by the transient nature of the
events which prompt one to observe a large portion of the sky. The POLAR
instrument was designed as a dedicated transient gamma-ray polarimeter. It made
use of a large effective area and large field of view to maximize the signal to
noise as well as the number of observed transients. Additionally, it was
calibrated carefully on ground and in orbit to mitigate systematic errors. The
main scientific goal of POLAR was to measure the polarization of the prompt
emission of Gamma-Ray Bursts. During the 6 months operation in orbit POLAR
observed 55 Gamma-Ray Bursts of which 14 were bright enough to allow for
constraining polarization measurements. In this chapter we mainly discuss about
the POLAR instrument along with the calibration and analysis procedures. Two
analyses are described, the first is a straightforward method previously
implemented in polarization measurements, whilst the second was developed to
improve the sensitivity and to mitigate several of the issues with the former.
Both methods are described in detail along with information on how these can be
extended to perform time and energy resolved polarization measurements.


title: Probabilistic Compositional Embeddings for Multimodal Image Retrieval

abstract:
  Existing works in image retrieval often consider retrieving images with one
or two query inputs, which do not generalize to multiple queries. In this work,
we investigate a more challenging scenario for composing multiple multimodal
queries in image retrieval. Given an arbitrary number of query images and (or)
texts, our goal is to retrieve target images containing the semantic concepts
specified in multiple multimodal queries. To learn an informative embedding
that can flexibly encode the semantics of various queries, we propose a novel
multimodal probabilistic composer (MPC). Specifically, we model input images
and texts as probabilistic embeddings, which can be further composed by a
probabilistic composition rule to facilitate image retrieval with multiple
multimodal queries. We propose a new benchmark based on the MS-COCO dataset and
evaluate our model on various setups that compose multiple images and (or) text
queries for multimodal image retrieval. Without bells and whistles, we show
that our probabilistic model formulation significantly outperforms existing
related methods on multimodal image retrieval while generalizing well to query
with different amounts of inputs given in arbitrary visual and (or) textual
modalities. Code is available here: https://github.com/andreineculai/MPC.


title:
Comment on "Doubly periodic solutions of the focusing nonlinear
  Schr\"odinger equation: Recurrence, period doubling, and amplification
  outside the conventional modulation-instability band"

abstract:
  In their interesting article (Physical Review A, Vol. 101, 023843 (2020))
Conforti et al. present doubly periodic (elliptic) solutions of the nonlinear
Schr\"odinger equation, based on an earlier article by Akhmediev et.al.
(Theoretical and Mathematical Physics, Vol. 72, 809 (1987)). We present some
notes with respect to correctness, completeness, and representation of the
solutions obtained.


title: The Annual Cycle in Scintillation Timescale of PMN J1726+0639

abstract:
  We discovered rapid intra-day variability in radio source PMN J1726+0639 at
GHz frequencies, during a survey to search for such variability with the
Australia Telescope Compact Array. Follow-up observations were conducted over
two years and revealed a clear, repeating annual cycle in the rate, or
characteristic timescale, of variability, showing that the observed variations
can be attributed to scintillations from interstellar plasma inhomogeneities.
The strong annual cycle includes an apparent "standstill" in April and another
in September. We fit kinematic models to the data, allowing for finite
anisotropy in the scintillation pattern. The cycle implies a very high degree
of anisotropy, with an axial ratio of at least 13:1, and the fit is consistent
with a purely one-dimensional scintillation pattern. The position angle of the
anisotropy, and the transverse velocity component are tightly constrained. The
parameters are inconsistent with expectations from a previously proposed model
of scattering associated with plasma filaments radially oriented around hot
stars. We note that evidence for a foreground interstellar cloud causing
anomalous Ca II absorption towards the nearby star Rasalhague ($\alpha$ Oph)
has been previously reported, and we speculate that the interstellar
scintillation of PMN 1726+0639 might be associated with this nearby cloud.


title:
Improved data analysis on two-point correlation function with sequential
  Bayesian method

abstract:
  We report our progress in data analysis on two-point correlation functions of
the $B$ meson using sequential Bayesian method. The data set of measurement is
obtained using the Oktay-Kronfeld (OK) action for the bottom quarks (valence
quarks) and the HISQ action for the light quarks on the MILC HISQ lattices. We
find that the old initial guess for the $\chi^2$ minimizer in the fitting code
is poor enough to slow down the analysis somewhat. In order to find a better
initial guess, we adopt the Newton method. We find that the Newton method
provides a natural test to check whether the $\chi^2$ minimizer finds a local
minimum or the global minimum, and it also reduces the number of iterations
dramatically.


title:
Complex angular momentum theory of state-to state integral cross
  sections: resonance effects in the F+HD->HF(v'=3)+D reaction

abstract:
  State-to-state reactive integral cross sections (ICS) are often affected by
quantum mechanical resonances, especially at relatively low energies. An ICS is
usually obtained by summing partial waves at a given value of energy. For this
reason, the knowledge of pole positions and residues in the complex energy
plane is not sufficient for a quantitative description of the patterns produced
by a resonance. Such description is available in terms of the poles of an
S-matrix element in the complex plane of the total angular momentum. The
approach was recently implemented in a computer code ICS_Regge, available in
the public domain [Comp. Phys. Comm. 185 (2014) 2127]. In this paper, we employ
the ICS Regge package to analyse in detail, for the first time, the resonance
patterns predicted for the integral cross sections (ICS) of the benchmark
F+HD->HF(v'=3)+D reaction. The v = 0, j = 0, Omega = 0 -> v' = 3, j'= 0,1,2,
and Omega' = 0,1,2 transitions are studied for collision energies from 58.54 to
197.54 meV. For these energies, we find several resonances, whose contributions
to the ICS vary from symmetric and asymmetric Fano shapes to smooth sinusoidal
Regge oscillations. Complex energies of metastable states and Regge pole
positions and residues are found by Pad /'e reconstruction of the scattering
matrix elements. Accuracy of the ICS Regge code, relation between complex
energies and Regge poles, various types of Regge trajectories, and the origin
of the J-shifting approximation are also discussed.


title:
Wide-angle protostellar outflows driven by narrow jets in stratified
  cores

abstract:
  Most simulations of outflow feedback on star formation are based on the
assumption that outflows are driven by a wide angle "X-wind," rather than a
narrow jet. However, the arguments initially raised against pure jet-driven
flows were based on steady ejection in a uniform medium, a notion that is no
longer supported based on recent observations. We aim to determine whether a
pulsed narrow jet launched in a density-stratified, self-gravitating core could
reproduce typical molecular outflow properties, without the help of a
wide-angle wind component. We performed axisymmetric hydrodynamic simulations
using the MPI-AMRVAC code with optically thin radiative cooling on timescales
up to 10000 yrs. Then we computed and compared the predicted properties with
observational data. First, the jet-driven shell expands faster and wider
through a core with steeply decreasing density than through an uniform core.
Second, when blown into the same singular flattened core, a jet-driven shell
has a similar width as a wide-angle wind-driven shell in the first few hundred
years, but a decelerating expansion on long timescales. The flow adopts a
conical shape and a base opening angle reaching up to $90\unicode{xb0}$. Third,
after $\sim$ 10000 yrs, a pulsed jet-driven shell shows fitting features and a
qualitative resemblance with recent observations of protostellar outflows with
the Atacama Large Millimeter Array (ALMA), such as HH46-47 and CARMA-7. In
particular, similarities are seen in the shell widths, opening angles,
position-velocity diagrams, and mass-velocity distribution, with some showing a
closer resemblance than in simulations based on a wide-angle "X-wind" model.
Therefore, a realistic ambient density stratification in addition to
millenia-long integration times are equally essential to reliably predict the
properties of outflows driven by a pulsed jet and to confront them with the
observations.


title: $\Delta$-Tribrackets and Link Homotopy

abstract:
  We define a type of Niebrzydowski tribracket we call $\Delta$-tribrackets and
show that their counting invariants are invariants of link-homotopy. We further
identify several classes of tribrackets whose counting invariants for oriented
classical knots and links are trivial, including vertical tribrackets
satisfying the center-involutory condition and horizontal tribrackets
satisfying the late-commutativity condition. We provide examples and end with
questions for future research.


title: Characterizing Error Mitigation by Symmetry Verification in QAOA

abstract:
  Hardware errors are a major obstacle to demonstrating quantum advantage with
the quantum approximate optimization algorithm (QAOA). Recently, symmetry
verification has been proposed and empirically demonstrated to boost the
quantum state fidelity, the expected solution quality, and the success
probability of QAOA on a superconducting quantum processor. Symmetry
verification uses parity checks that leverage the symmetries of the objective
function to be optimized. We develop a theoretical framework for analyzing this
approach under local noise and derive explicit formulas for fidelity
improvements on problems with global $\mathbb{Z}_2$ symmetry. We numerically
investigate the symmetry verification on the MaxCut problem and identify the
error regimes in which this approach improves the QAOA objective. We observe
that these regimes correspond to the error rates present in near-term hardware.
We further demonstrate the efficacy of symmetry verification on an IonQ trapped
ion quantum processor where an improvement in the QAOA objective of up to
19.2\% is observed.


title:
Error Bounds for Discrete-Continuous Shortest Path Problems with
  Application to Free Flight Trajectory Optimization

abstract:
  Two-stage methods addressing continuous shortest path problems start local
minimization from discrete shortest paths in a spatial graph. The convergence
of such hybrid methods to global minimizers hinges on the discretization error
induced by restricting the discrete global optimization to the graph, with
corresponding implications on choosing an appropriate graph density. A prime
example is flight planning, i.e., the computation of optimal routes in view of
flight time and fuel consumption under given weather conditions. Highly
efficient discrete shortest path algorithms exist and can be used directly for
computing starting points for locally convergent optimal control methods. We
derive a priori and localized error bounds for the flight time of discrete
paths relative to the optimal continuous trajectory, in terms of the graph
density and the given wind field. These bounds allow designing graphs with an
optimal local connectivity structure. The properties of the bounds are
illustrated on a set of benchmark problems. It turns out that localization
improves the error bound by four orders of magnitude, but still leaves ample
opportunities for tighter error bounds by a posteriori estimators.


title: Generalization of Gamow states to multi-particle decay

abstract:
  For single-particle nonrelativistic quantum mechanics, a Gamow state is an
eigenfunction of the Hamiltonian with complex eigenvalue. Gamow states are not
normalizable; they depend on time via the usual multiplier exp(-iEt)
supplemented by a cutoff at an expanding wavefront. Gamow states have been used
to extend nuclear shell models; they are to metastable states as normalizable
eigenfunctions are to bound states. In this paper we generalize Gamow states to
decays with multiple outgoing particles. We derive the exact form of the
expanding wavefront, even for relativistic outgoing particles. In the
non-relativistic limit we derive the exact form of the multi-particle Gamow
state contribution to the system propagator (Green's function).


title: pysamoo: Surrogate-Assisted Multi-Objective Optimization in Python

abstract:
  Significant effort has been made to solve computationally expensive
optimization problems in the past two decades, and various optimization methods
incorporating surrogates into optimization have been proposed. However, most
optimization toolboxes do not consist of ready-to-run algorithms for
computationally expensive problems, especially in combination with other key
requirements, such as handling multiple conflicting objectives or constraints.
Thus, the lack of appropriate software packages has become a bottleneck for
solving real-world applications. The proposed framework, pysamoo, addresses
these shortcomings of existing optimization frameworks and provides multiple
optimization methods for handling problems involving time-consuming evaluation
functions. The framework extends the functionalities of pymoo, a popular and
comprehensive toolbox for multi-objective optimization, and incorporates
surrogates to support expensive function evaluations. The framework is
available under the GNU Affero General Public License (AGPL) and is primarily
designed for research purposes. For more information about pysamoo, readers are
encouraged to visit: anyoptimization.com/projects/pysamoo.


title:
Distributed learning optimisation of Cox models can leak patient data:
  Risks and solutions

abstract:
  Medical data are often highly sensitive, and frequently there are missing
data. Due to the data's sensitive nature, there is an interest in creating
modelling methods where the data are kept in each local centre to preserve
their privacy, but yet the model can be trained on and learn from data across
multiple centres. Such an approach might be distributed machine learning
(federated learning, collaborative learning) in which a model is iteratively
calculated based on aggregated local model information from each centre.
However, even though no specific data are leaving the centre, there is a
potential risk that the exchanged information is sufficient to reconstruct all
or part of the patient data, which would hamper the safety-protecting rationale
idea of distributed learning. This paper demonstrates that the optimisation of
a Cox survival model can lead to patient data leakage. Following this, we
suggest a way to optimise and validate a Cox model that avoids these problems
in a secure way. The feasibility of the suggested method is demonstrated in a
provided Matlab code that also includes methods for handling missing data.


title: Leveraging massively parallel reporter assays for evolutionary questions

abstract:
  A long-standing goal of evolutionary biology is to decode how gene regulatory
processes contribute to organismal diversity, both within and between species.
This question has remained challenging to answer, due both to the difficulties
of predicting function from non-coding sequence, and to the technological
constraints of laboratory research with non-model taxa. However, a recent
methodological development in functional genomics, the massively parallel
reporter assay (MPRA), makes it possible to test thousands to millions of
sequences for regulatory activity in a single in vitro experiment. It does so
by combining traditional, single-locus episomal reporter assays (e.g.,
luciferase reporter assays) with the scalability of high-throughput sequencing.
In this perspective, we discuss the execution, advantages, and limitations of
MPRAs for research in evolutionary biology. We review recent studies that have
made use of this approach to address explicitly evolutionary questions,
highlighting study designs that we believe are particularly well-positioned to
gain from MPRA approaches. Additionally, we propose solutions for extending
these powerful assays to rare taxa and those with limited genomic resources. In
doing so, we underscore the broad potential of MPRAs to drive genome-scale
functional evolutionary genetics studies in non-traditional model organisms.


title:
The drag exerted by weakly dissipative trapped lee waves on the
  atmosphere: application to Scorer's two-layer model

abstract:
  While it is known that trapped lee waves propagating at low levels in a
stratified atmosphere exert a drag on the mountains that generate them, the
distribution of the corresponding reaction force exerted on the atmospheric
mean circulation, defined by the wave momentum flux profiles, has not been
established, because for inviscid trapped lee waves these profiles oscillate
indefinitely downstream. A framework is developed here for the unambiguous
calculation of momentum flux profiles produced by trapped lee waves, which
circumvents the difficulties plaguing the inviscid trapped lee wave theory.
Using linear theory, and taking Scorer's two-layer atmosphere as an example,
the waves are assumed to be subject to a small dissipation, expressed as a
Rayleigh damping. The resulting wave pattern decays downstream, so the momentum
flux profile integrated over the area occupied by the waves converges to a
well-defined form. Remarkably, for weak dissipation, this form is independent
of the value of Rayleigh damping coefficient, and the inviscid drag, determined
in previous studies, is recovered as the momentum flux at the surface. The
divergence of this momentum flux profile accounts for the areally-integrated
drag exerted by the waves on the atmosphere. The application of this framework
to this and other types of trapped lee waves potentially enables the
development of physically-based parametrizations of the effects of trapped lee
waves on the atmosphere.


title:
DCMS: Motion Forecasting with Dual Consistency and Multi-Pseudo-Target
  Supervision

abstract:
  We present a novel framework for motion forecasting with Dual Consistency
Constraints and Multi-Pseudo-Target supervision. The motion forecasting task
predicts future trajectories of vehicles by incorporating spatial and temporal
information from the past. A key design of DCMS is the proposed Dual
Consistency Constraints that regularize the predicted trajectories under
spatial and temporal perturbation during the training stage. In addition, we
design a novel self-ensembling scheme to obtain accurate pseudo targets to
model the multi-modality in motion forecasting through supervision with
multiple targets explicitly, namely Multi-Pseudo-Target supervision. Our
experimental results on the Argoverse motion forecasting benchmark show that
DCMS significantly outperforms the state-of-the-art methods, achieving 1st
place on the leaderboard. We also demonstrate that our proposed strategies can
be incorporated into other motion forecasting approaches as general training
schemes.


title:
An Adaptive Time Stepping Scheme for Rate-Independent Systems with
  Non-Convex Energy

abstract:
  We investigate a local incremental stationary scheme for the numerical
solution of rate-independent systems. Such systems are characterized by a
(possibly) non-convex energy and a dissipation potential, which is positively
homogeneous of degree one. Due to the non-convexity of the energy, the system
does in general not admit a time-continuous solution. In order to resolve these
potential discontinuities, the algorithm produces a sequence of state variables
and physical time points as functions of a curve parameter. The main novelty of
our approach in comparison to existing methods is an adaptive choice of the
step size for the update of the curve parameter depending on a prescribed
tolerance for the residua in the energy-dissipation balance and in a
complementarity relation concerning the so-called local stability condition. It
is proven that, for tolerance tending to zero, the piecewise affine
approximations generated by the algorithm converge (weakly) to a so-called
$\mathbb{V}$-parametrized balanced viscosity solution. Numerical experiments
illustrate the theoretical findings and show that an adaptive choice of the
step size indeed pays off as they lead to a significant increase of the step
size during sticking and in viscous jumps.


title: The Effect of Aspect Changes on Near-Earth Asteroid Phase Curves

abstract:
  Phase curves of asteroids are typically considered to depend solely on the
scattering properties of airless particulate surfaces and the size of the
object being studied. In this study, we demonstrate the additional dependence
of phase curves on object shape, rotation pole orientation, and viewing
geometry over an apparition. Variations in the phase curve of near-Earth
asteroid (159402) 1999 AP10 over its apparition from July 2020 - January 2021
are verified to be due to aspect changes over the apparition. This is achieved
through shape modelling of the asteroid and simulation of the phase curve over
the apparition. We present simulations of asteroid phase curves over a range of
geometries to understand the potential magnitude of this aspect effect, and
under which circumstances it can begin to dominate in the phase curves. This
dependence on aspect may introduce significant additional uncertainty in the
properties derived from phase curve data. We provide and demonstrate software
code to estimate the aspect-related uncertainty in near-Earth asteroid phase
curves through simulation and model fitting of a randomly generated sample of
ellipsoidal asteroid models over the observed viewing geometry. We demonstrate
how ignoring this effect may lead to misleading interpretations of the data and
underestimation of uncertainties in further studies, such as those in the
infrared that use phase curve derived parameters when fitting physical
properties of an asteroid.


title:
Training a Helpful and Harmless Assistant with Reinforcement Learning
  from Human Feedback

abstract:
  We apply preference modeling and reinforcement learning from human feedback
(RLHF) to finetune language models to act as helpful and harmless assistants.
We find this alignment training improves performance on almost all NLP
evaluations, and is fully compatible with training for specialized skills such
as python coding and summarization. We explore an iterated online mode of
training, where preference models and RL policies are updated on a weekly
cadence with fresh human feedback data, efficiently improving our datasets and
models. Finally, we investigate the robustness of RLHF training, and identify a
roughly linear relation between the RL reward and the square root of the KL
divergence between the policy and its initialization. Alongside our main
results, we perform peripheral analyses on calibration, competing objectives,
and the use of OOD detection, compare our models with human writers, and
provide samples from our models using prompts appearing in recent related work.


title: Dementia in England: Quantifying and analysing modifiable risk

abstract:
  The prevalence of dementia is set to explode throughout the 21st century.
This trend has already started in developed countries and will continue to
place heavy pressures on both public health and social care services across the
world. No cure for dementia is likely within the foreseeable future, however,
medical research highlights the potential to diminish the risk of dementia
onset. Over one-third of dementia cases may be preventable if certain risk
factors are addressed at the individual, clinical, and population level. This
research further explores these modifiable risk factors and quantifies areal
risk through the use of a composite index. The index operates at National
Health Service Clinical Commission Group level to assess spatial differences
across England. Clear spatial patterns are observed between the north and south
of the country, and between London and the remainder of the country. The
framework adopted in this research provides a firm foundation upon which
similar indices could be produced, potentially at finer spatial resolutions,
incorporating more informed local knowledge and data on relevant dementia risk
factors.


title: Semantic keypoint-based pose estimation from single RGB frames

abstract:
  This paper presents an approach to estimating the continuous 6-DoF pose of an
object from a single RGB image. The approach combines semantic keypoints
predicted by a convolutional network (convnet) with a deformable shape model.
Unlike prior investigators, we are agnostic to whether the object is textured
or textureless, as the convnet learns the optimal representation from the
available training-image data. Furthermore, the approach can be applied to
instance- and class-based pose recovery. Additionally, we accompany our main
pipeline with a technique for semi-automatic data generation from unlabeled
videos. This procedure allows us to train the learnable components of our
method with minimal manual intervention in the labeling process. Empirically,
we show that our approach can accurately recover the 6-DoF object pose for both
instance- and class-based scenarios even against a cluttered background. We
apply our approach both to several, existing, large-scale datasets - including
PASCAL3D+, LineMOD-Occluded, YCB-Video, and TUD-Light - and, using our labeling
pipeline, to a new dataset with novel object classes that we introduce here.
Extensive empirical evaluations show that our approach is able to provide pose
estimation results comparable to the state of the art.


title: A note on shortest circuit cover of 3-edge colorable cubic signed graphs

abstract:
  A {sign-circuit cover} $\mathcal{F}$ of a signed graph $(G, \sigma)$ is a
family of sign-circuits which covers all edges of $(G, \sigma)$. The shortest
sign-circuit cover problem was initiated by M\'a$\check{\text{c}}$ajov\'a,
Raspaud, Rollov\'a, and \v{S}koviera (JGT 2016) and received many attentions in
recent years. In this paper, we show that every flow-admissible 3-edge
colorable cubic signed graph $(G, \sigma)$ has a sign-circuit cover with length
at most $\frac{20}{9} |E(G)|$.


title:
Generators for the cohomology ring of the moduli of one-dimensional
  sheaves on $\mathbb{P}^2$

abstract:
  We explore the structure of the cohomology ring of the moduli space of stable
1-dimensional sheaves on $\mathbb{P}^2$ of any degree. We obtain a minimal set
of tautological generators, which implies an optimal generation result for both
the cohomology and the Chow ring of the moduli space. Our approach is through a
geometric study of tautological relations.


title: The Stokes operator in two-dimensional bounded Lipschitz domains

abstract:
  We consider the Stokes resolvent problem in a two-dimensional bounded
Lipschitz domain $\Omega$ subject to homogeneous Dirichlet boundary conditions.
We prove $\mathrm{L}^p$-resolvent estimates for $p$ satisfying $\lvert 1 / p -
1 / 2 \rvert < 1 / 4 + \varepsilon$ for some $\varepsilon > 0$. We further show
that the Stokes operator admits the property of maximal regularity and that its
$\mathrm{H}^{\infty}$-calculus is bounded. This is then used to characterize
domains of fractional powers of the Stokes operator. Finally, we give an
application to the regularity theory of weak solutions to the Navier-Stokes
equations in bounded planar Lipschitz domains.


title:
Tracing Milky Way substructure with an RR Lyrae hierarchical clustering
  forest

abstract:
  RR Lyrae variable stars have long been reliable standard candles used to
discern structure in the Local Group. With this in mind, we present a routine
to identify groupings containing a statistically significant number of RR Lyrae
variables in the Milky Way environment. RR Lyrae variable groupings, or
substructures, with potential Galactic archaeology applications are found using
a forest of agglomerative, hierarchical clustering trees, whose leaves are
Milky Way RR Lyrae variables. Each grouping is validated by ensuring that the
internal RR Lyrae variable proper motions are sufficiently correlated.
Photometric information was collected from the Gaia second data release and
proper motions from the (early) third data release. After applying this routine
to the catalogue of 91234 variables, we are able to report sixteen unique RR
Lyrae substructures with physical sizes of less than 1 kpc. Five of these
substructures are in close proximity to Milky Way globular clusters with
previously known tidal tails and/or a potential connection to Galactic merger
events. One candidate substructure is in the neighbourhood of the Large
Magellanic Cloud but is more distant (and older) than known satellites of the
dwarf galaxy. Our study ends with a discussion of ways in which future surveys
could be applied to the discovery of Milky Way stellar streams.


title:
Assembly development for the Simons Observatory focal plane readout
  module

abstract:
  The Simons Observatory (SO) is a suite of instruments sensitive to
temperature and polarization of the cosmic microwave background (CMB) to be
located at Cerro Toco in the Atacama Desert in Chile. Five telescopes, one
large aperture telescope and four small aperture telescopes, will host roughly
70,000 highly multiplexed transition edge sensor (TES) detectors operated at
100 mK. Each SO focal plane module (UFM) couples 1,764 TESes to microwave
resonators in a microwave multiplexing (uMux) readout circuit. Before detector
integration, the 100 mK uMux components are packaged into multiplexing modules
(UMMs), which are independently validated to ensure they meet SO performance
specifications. Here we present the assembly developments of these UMM readout
packages for mid frequency (90/150 GHz) and ultra high frequency (220/280 GHz)
UFMs.


title:
A wavelet-mixed landmark survival model for the effect of short-term
  oscillations in longitudinal biomarker's profiles

abstract:
  Statistical methods to study the association between a longitudinal biomarker
and the risk of death are very relevant for the long-term monitoring of
biomarkers. In this context, sudden crises can cause the biomarker to undergo
very abrupt changes. Although these oscillations are typically short-term, they
can contain prognostic information. We propose a method that couples a linear
mixed-model with a wavelet smoothing to extract both the long-term component
and the short-term oscillations of the individual longitudinal biomarker
profiles. We then use them as predictors in a landmark model to study their
association with the risk of death. To illustrate the method, we use the
clinical application which motivated our work i.e. the monitoring of potassium
in Heart Failure patients. The dataset consists of real-world data coming from
the integration of Administrative Health Records and Outpatient and Inpatient
Clinic E-chart. Our method not only allows us to identify the short-term
oscillations but also reveals their prognostic role, according to their
duration, demonstrating the importance of including them in the modeling.
Compared to landmark analyses and joint models, the proposed method archives
higher predictive performances. In the context of the monitoring of potassium,
our analysis has important clinical implications because it allows us to derive
a dynamic score that can be used in clinical practice to assess the risk
related to an observed patient's potassium trajectory.


title:
Temperature Dependence of Thermodynamic, Dynamical and Dielectric
  Properties of Water Models

abstract:
  We investigate the temperature dependence of thermodynamic (density, isobaric
heat capacity), dynamical (self-diffusion coefficient, shear viscosity), and
dielectric properties of several water models, the commonly employed TIP3P
water model, well-established 4-point water model TIP4P-2005, and recently
developed 4-point water model TIP4P-D. We focus on the temperature range of
interest for the field of computational biophysics and soft matter (280-350 K).
The 4-point water models lead to a spectacularly improved agreement with
experimental data, strongly suggesting that the use of more modern
parametrizations should be favored compared to the more traditional TIP3P for
modeling temperature-dependent phenomena in biomolecular systems.


title:
Robust Quantification of Gender Disparity in Pre-Modern English
  Literature using Natural Language Processing

abstract:
  Research has continued to shed light on the extent and significance of gender
disparity in social, cultural and economic spheres. More recently,
computational tools from the Natural Language Processing (NLP) literature have
been proposed for measuring such disparity using relatively extensive datasets
and empirically rigorous methodologies. In this paper, we contribute to this
line of research by studying gender disparity, at scale, in copyright-expired
literary texts published in the pre-modern period (defined in this work as the
period ranging from the mid-nineteenth through the mid-twentieth century). One
of the challenges in using such tools is to ensure quality control, and by
extension, trustworthy statistical analysis. Another challenge is in using
materials and methods that are publicly available and have been established for
some time, both to ensure that they can be used and vetted in the future, and
also, to add confidence to the methodology itself. We present our solution to
addressing these challenges, and using multiple measures, demonstrate the
significant discrepancy between the prevalence of female characters and male
characters in pre-modern literature. The evidence suggests that the discrepancy
declines when the author is female. The discrepancy seems to be relatively
stable as we plot data over the decades in this century-long period. Finally,
we aim to carefully describe both the limitations and ethical caveats
associated with this study, and others like it.


title: What is the SKA-Low Sensitivity for Your Favourite Radio Source ?

abstract:
  The SKA will be the largest radio astronomy observatory ever built, providing
unprecedented sensitivity over a very broad frequency (50 MHz to 15.3 GHz). The
SKA-Low (50 - 350 MHz), will be built at the MRO in Western Australia. It will
consist of 512 stations each composed of 256 dual-polarised antennas, and the
sensitivity of an individual station is pivotal to the performance of the
entire SKA-Low telescope. The answer to the question in the title is, it
depends. The sensitivity of a low frequency array, such as an SKA-Low station,
depends strongly on the pointing direction of the digitally formed station beam
and the local sidereal time (LST), and is different for the two orthogonal
polarisations of the antennas. The accurate prediction of the SKA-Low
sensitivity in an arbitrary direction in the sky is crucial for future
observation planning. We present here a sensitivity calculator for the SKA-Low
radio telescope, using a database of pre-computed sensitivity values for two
realisations of an SKA-Low station architecture. One realisation uses the
log-periodic antennas selected for SKA-Low. The second uses a known benchmark,
in the form of the bowtie dipoles of the MWA. Data collected by both stations
(deployed at the MRO in 2019) were used to measure their sensitivity at
selected frequencies and over at least 24 h intervals, and were compared to the
predictions described in this paper. The sensitivity values stored in the
SQLite database were pre-computed for the X, Y and Stokes I polarisations in 10
MHz frequency steps, 0.5 hour LST intervals, and 5 degree resolution in
pointing directions. The database allows users to estimate the sensitivity of
SKA-Low for their favourite object using interactive web-based or command line
interface, which can also calculate the sensitivity for arbitrary pointing
directions, frequencies, and times without interpolations.


title:
Undirected $(1+\varepsilon)$-Shortest Paths via Minor-Aggregates:
  Near-Optimal Deterministic Parallel & Distributed Algorithms

abstract:
  This paper presents near-optimal deterministic parallel and distributed
algorithms for computing $(1+\varepsilon)$-approximate single-source shortest
paths in any undirected weighted graph.
  On a high level, we deterministically reduce this and other shortest-path
problems to $\tilde{O}(1)$ Minor-Aggregations. A Minor-Aggregation computes an
aggregate (e.g., max or sum) of node-values for every connected component of
some subgraph.
  Our reduction immediately implies:
  Optimal deterministic parallel (PRAM) algorithms with $\tilde{O}(1)$ depth
and near-linear work.
  Universally-optimal deterministic distributed (CONGEST) algorithms, whenever
deterministic Minor-Aggregate algorithms exist. For example, an optimal
$\tilde{O}(HopDiameter(G))$-round deterministic CONGEST algorithm for
excluded-minor networks.
  Several novel tools developed for the above results are interesting in their
own right:
  A local iterative approach for reducing shortest path computations "up to
distance $D$" to computing low-diameter decompositions "up to distance
$\frac{D}{2}$". Compared to the recursive vertex-reduction approach of [Li20],
our approach is simpler, suitable for distributed algorithms, and eliminates
many derandomization barriers.
  A simple graph-based $\tilde{O}(1)$-competitive $\ell_1$-oblivious routing
based on low-diameter decompositions that can be evaluated in near-linear work.
The previous such routing [ZGY+20] was $n^{o(1)}$-competitive and required
$n^{o(1)}$ more work.
  A deterministic algorithm to round any fractional single-source transshipment
flow into an integral tree solution.
  The first distributed algorithms for computing Eulerian orientations.


title:
Statistical Analysis on Random Quantum Sampling by Sycamore and
  Zuchongzhi Quantum Processors

abstract:
  Random quantum sampling, a task to sample bit-strings from a random quantum
circuit, is considered one of suitable benchmark tasks to demonstrate the
outperformance of quantum computers even with noisy qubits. Recently, random
quantum sampling was performed on the Sycamore quantum processor with 53 qubits
[Nature 574, 505 (2019)] and on the Zuchongzhi quantum processor with 56 qubits
[Phys. Rev. Lett. 127, 180501 (2021)]. Here, we analyze and compare statistical
properties of the outputs of random quantum sampling by Sycamore and
Zuchongzhi. Using the Marchenko-Pastur law and the Wasssertein distances, we
find that quantum random sampling of Zuchongzhi is more closer to classical
uniform random sampling than those of Sycamore. Some Zuchongzhi's bit-strings
pass the random number tests while both Sycamore and Zuchongzhi show similar
patterns in heatmaps of bit-strings. It is shown that statistical properties of
both random quantum samples change little as the depth of random quantum
circuits increases. Our findings raise a question about computational
reliability of noisy quantum processors that could produce statistically
different outputs for the same random quantum sampling task.


title:
Torsion phenomena for zero-cycles on a product of curves over a number
  field

abstract:
  For a smooth projective variety $X$ over a number field $k$ a conjecture of
Bloch and Beilinson predicts that the kernel of the Albanese map of $X$ is a
torsion group. In this article we consider a product $X=C_1\times\cdots\times
C_d$ of smooth projective curves and show that if the conjecture is true for
any subproduct of two curves, then it is true for $X$. Additionally, we produce
many new examples of non-isogenous elliptic curves $E_1, E_2$ with positive
rank over $\mathbb{Q}$ for which the image of the natural map
$E_1(\mathbb{Q})\otimes E_2(\mathbb{Q})\xrightarrow{\varepsilon}
\text{CH}_0(E_1\times E_2)$ is finite, including the first known examples of
rank greater than $1$. Combining the two results, we obtain infinitely many
nontrivial products $X=C_1\times\cdots\times C_d$ for which the analogous map
$\varepsilon$ has finite image.


title: Computational model for tumor response to adoptive cell transfer therapy

abstract:
  One of the barriers to the development of effective adoptive cell transfer
therapies (ACT), specifically for genetically engineered T-cell receptors
(TCRs), and chimeric antigen receptor (CAR) T-cells, is target antigen
heterogeneity. It is thought that intratumor heterogeneity is one of the
leading determinants of therapeutic resistance and treatment failure. While
understanding antigen heterogeneity is important for effective therapeutics, a
good therapy strategy could enhance the therapy efficiency. In this work we
introduce an agent-based model to rationalize the outcomes of two types of ACT
therapies over heterogeneous tumors: antigen specific ACT therapy and
multi-antigen recognition ACT therapy. We found that one dose of antigen
specific ACT therapy should be expected to reduce the tumor size as well as its
growth rate, however it may not be enough to completely eliminate it. A second
dose also reduced the tumor size as well as the tumor growth rate, but, due to
the intratumor heterogeneity, it turned out to be less effective than the
previous dose. Moreover, an interesting emergent phenomenon results from the
simulations, namely the formation of a shield-like structure of cells with low
oncoprotein expression. This shield turns out to protect cells with high
oncoprotein expression. On the other hand, our studies suggest that the earlier
the multi-antigen recognition ACT therapy is applied, the more efficient it
turns. In fact, it could completely eliminate the tumor. Based on our results,
it is clear that a proper therapeutic strategy could enhance the therapies
outcomes. In that direction, our computational approach provides a framework to
model treatment combinations in different scenarios and explore the
characteristics of successful and unsuccessful treatments.


title:
Geometric functionals of fractal percolation. II. Almost sure
  convergence and second moments

abstract:
  We determine almost sure limits of rescaled intrinsic volumes of the
construction steps of fractal percolation in $\mathbb{R}^d$ for any dimension
$d\geq 1$. We observe a factorization of these limit variables which allows, in
particular, to determine their expectations and covariance structure. We also
show convergence of rescaled expectations and variances of the intrinsic
volumes of the construction steps to expectations and variances of the limit
variables and give rates for this convergence in some cases.


title:
Generating Full Length Wikipedia Biographies: The Impact of Gender Bias
  on the Retrieval-Based Generation of Women Biographies

abstract:
  Generating factual, long-form text such as Wikipedia articles raises three
key challenges: how to gather relevant evidence, how to structure information
into well-formed text, and how to ensure that the generated text is factually
correct. We address these by developing a model for English text that uses a
retrieval mechanism to identify relevant supporting information on the web and
a cache-based pre-trained encoder-decoder to generate long-form biographies
section by section, including citation information. To assess the impact of
available web evidence on the output text, we compare the performance of our
approach when generating biographies about women (for which less information is
available on the web) vs. biographies generally. To this end, we curate a
dataset of 1,500 biographies about women. We analyze our generated text to
understand how differences in available web evidence data affect generation. We
evaluate the factuality, fluency, and quality of the generated texts using
automatic metrics and human evaluation. We hope that these techniques can be
used as a starting point for human writers, to aid in reducing the complexity
inherent in the creation of long-form, factual text.


title: Exploring Event Camera-based Odometry for Planetary Robots

abstract:
  Due to their resilience to motion blur and high robustness in low-light and
high dynamic range conditions, event cameras are poised to become enabling
sensors for vision-based exploration on future Mars helicopter missions.
However, existing event-based visual-inertial odometry (VIO) algorithms either
suffer from high tracking errors or are brittle, since they cannot cope with
significant depth uncertainties caused by an unforeseen loss of tracking or
other effects. In this work, we introduce EKLT-VIO, which addresses both
limitations by combining a state-of-the-art event-based frontend with a
filter-based backend. This makes it both accurate and robust to uncertainties,
outperforming event- and frame-based VIO algorithms on challenging benchmarks
by 32%. In addition, we demonstrate accurate performance in hover-like
conditions (outperforming existing event-based methods) as well as high
robustness in newly collected Mars-like and high-dynamic-range sequences, where
existing frame-based methods fail. In doing so, we show that event-based VIO is
the way forward for vision-based exploration on Mars.


title:
A note on the distribution of the extreme degrees of a random graph via
  the Stein-Chen method

abstract:
  We offer an alternative proof, using the Stein-Chen method, of Bollob\'{a}s'
theorem concerning the distribution of the extreme degrees of a random graph.
The same method also applies in a more general setting where the probability of
every pair of vertices being connected by edges depends on the number of
vertices.


title: NOEMA observations support a recoiling black hole in 3C186

abstract:
  3C186 is a powerful radio loud quasar (QSO) at the center of a cool-core
cluster at z=1.06. Previous studies reported evidence for a projected spatial
offset of ~1'' between the isophotal center of the galaxy and the point-source
QSO as well as a spectral shift of ~2000 km/s between the narrow and broad line
region of the system. In this work we report high-resolution molecular gas
CO(4-3) observations of the system taken with NOEMA interferometer. We clearly
detect a large reservoir of molecular gas, $M_{H_2}\sim8\times10^{10}~M_\odot$,
that is cospatial with the host galaxy and likely associated with a rotating
disk-like structure. We firmly confirm both the spatial offset of the galaxy's
gas reservoir with respect to the continuum emission of the QSO and the
spectral offset with respect to the redshift of the broad line region. Our
morphological and kinematical analysis confirms that the most likely scenario
to explain the 3C186 system is that the QSO is a kicked super-massive black
hole (SMBH), which we believe may have resulted from a strong gravitational
wave recoil as two SMBHs coalesced after the merger of their host galaxies.


title:
Analytical Uncertainty Propagation for Multi-Period Stochastic Optimal
  Power Flow

abstract:
  The increase in renewable energy sources (RESs), like wind or solar power,
results in growing uncertainty also in transmission grids. This affects grid
stability through fluctuating energy supply and an increased probability of
overloaded lines. One key strategy to cope with this uncertainty is the use of
distributed energy storage systems (ESSs). In order to securely operate power
systems containing renewables and use storage, optimization models are needed
that both handle uncertainty and apply ESSs. This paper introduces a compact
dynamic stochastic chance-constrained optimal power flow (CC-OPF) model, that
minimizes generation costs and includes distributed ESSs. Assuming Gaussian
uncertainty, we use affine policies to obtain a tractable, analytically exact
reformulation as a second-order cone problem (SOCP). We test the new model on
five different IEEE networks with varying sizes of 5, 39, 57, 118 and 300 nodes
and include complexity analysis. The results show that the model is
computationally efficient and robust with respect to constraint violation risk.
The distributed energy storage system leads to more stable operation with
flattened generation profiles. Storage absorbed RES uncertainty, and reduced
generation cost.


title:
A Decentralized Resource Management System Proposal For Disasters:
  NGO-RMSD (STK-AKYS)

abstract:
  Disaster and emergency management are under the responsibility of many
organizations and there are serious coordination problems in post-disaster
crisis management. This paper proposes a decentralized non-governmental
organization resource management system for disasters (NGO-RMSD / STK-AKYS).
This system is based on blockchain technology and it will enable the
non-governmental organizations (NGO) and public institutions to manage and
coordinate the resources in a trusted environment in the case of disasters. A
proof of concept implementation is developed by using the Quorum blockchain
framework which is more energy-efficient than crypto currency-based blockchain
solutions. Smart contracts are developed for the autonomous working of the
system. These smart contacts are used for the verification of the needs of the
one who is in need, delivering resources to the right people, and identifying
the urgent needs. The system aims to reach more disaster victims in a more
timely manner. NGO-RMSD is designed according to the needs of the NGOs in the
field. The application is shared with the free software license and further
development with the community is aimed.


title:
A Hierarchical Block Distance Model for Ultra Low-Dimensional Graph
  Representations

abstract:
  Graph Representation Learning (GRL) has become central for characterizing
structures of complex networks and performing tasks such as link prediction,
node classification, network reconstruction, and community detection. Whereas
numerous generative GRL models have been proposed, many approaches have
prohibitive computational requirements hampering large-scale network analysis,
fewer are able to explicitly account for structure emerging at multiple scales,
and only a few explicitly respect important network properties such as
homophily and transitivity. This paper proposes a novel scalable graph
representation learning method named the Hierarchical Block Distance Model
(HBDM). The HBDM imposes a multiscale block structure akin to stochastic block
modeling (SBM) and accounts for homophily and transitivity by accurately
approximating the latent distance model (LDM) throughout the inferred
hierarchy. The HBDM naturally accommodates unipartite, directed, and bipartite
networks whereas the hierarchy is designed to ensure linearithmic time and
space complexity enabling the analysis of very large-scale networks. We
evaluate the performance of the HBDM on massive networks consisting of millions
of nodes. Importantly, we find that the proposed HBDM framework significantly
outperforms recent scalable approaches in all considered downstream tasks.
Surprisingly, we observe superior performance even imposing ultra-low
two-dimensional embeddings facilitating accurate direct and hierarchical-aware
network visualization and interpretation.


title:
Uncertainty principles for the short-time Fourier transform on the
  lattice

abstract:
  In this paper, we study a few versions of the uncertainty principle for the
short-time Fourier transform on the lattice $\Z^n \times \T^n$. In particular,
we establish the uncertainty principle for orthonormal sequences,
Donoho--Stark's uncertainty principle, Benedicks-type uncertainty principle,
Heisenberg-type uncertainty principle and local uncertainty inequality for this
transform on $\Z^n \times \T^n$. Also, we obtain the Heisenberg-type
uncertainty inequality using the $k$-entropy of the short-time Fourier
transform on $\Z^n \times \T^n$.


title:
On the Existence of Photoluminescence and Room-Temperature Spin
  Polarization in Ambipolar V doped MoS$_2$ Monolayers

abstract:
  Opto-spintronics is an emerging field where ultra-thin
magnetic-semiconductors having high spin-valley coupling play an important
role. Here, we demonstrate substitutional vanadium (V) doping in MoS$_2$
lattice in different extent, leading to the coexistence of photoluminescence
(PL), valleypolarization (~32%), and valley splitting (~28 meV shift in PL with
helicity $\sigma^+$ and $\sigma^-$ of light excitation). A large V doping
causes semiconductor to metal transition in MoS$_2$ but with medium level
causing the existence of photoluminescence with high spin polarization. The
ambipolar nature of medium level V doped MoS$_2$ is shown here indicating its
potential as an opto-electronic material. The presence of V-dopants and their
different level of content are proven by both spectroscopic and microscopic
methods.A detailed temperature and power dependent photoluminescence studies
along with density functional theory-based calculations in support unravels the
emergence of the co-existence of spin-valley coupling and photoluminescence.
This study shows the potential of doping MoS$_2$ for deriving new materials for
next generation room temperature opto-spintronics.


title:
Effects of plasma on physical properties of water:
  nanocrystalline-to-amorphous phase transition and improving produce washing

abstract:
  Recently is was discovered in various applications that many physical and
chemical properties of water change their temperature dependence between about
35 and 60 degrees Celsius. In particular, heat conductance, light absorption,
and surface tension all change their temperature dependence. These drastic
changes were associated with water gradually changing its mesoscopic structure:
while at the higher temperatures water is a uniform media (amorphous state), at
the temperatures below transition it consists of many nano-to-micro-scale
clusters (crystalline state). This transition is similar to the second order
phase transition. In the present paper we show that treating water with
non-thermal plasma (adding plasma-created active compounds) can lower the
temperature of the transition and thus cause a significant change in such
physical quantities as surface tension, viscosity, freezing rate, and
wettability and washability. We present analytical estimates of the transition
temperature shift based on the Debya-Huckel theory. We discuss the
produce-washing experiments that illustrate the predicted effects.


title:
Stable non-singular cosmologies in beyond Horndeski theory and disformal
  transformations

abstract:
  In this note we collect, systemise and generalise the existing results for
relations between general Horndeski theories and beyond Horndeski theories via
disformal transformations of metric. We derive additional disformal
transformation rules relating Lagrangian functions of beyond Horndeski theory
and corresponding Horndeski theory and demonstrate that some of them become
singular at some moments(s) once one constructs a non-singular cosmological
solution in beyond Horndeski theory that is free from ghost, gradient
instabilities and strong gravity regime during the entire evolution of the
system. The key issue here is that such solutions are banned in Horndeski
theory due to existing no-go theorem. The proof of singular behaviour of
disformal relations in this case resolves the apparent contradiction between
the fact that Horndeski and beyond Horndeski theories appear related by field
redefinition but describe different physics in the context of non-singular
cosmologies.


title: Algebraic K-theory of elliptic cohomology

abstract:
  We calculate the mod (p, v_1, v_2) homotopy V(2)_* TC(BP<2>) of the
topological cyclic homology of the truncated Brown--Peterson spectrum BP<2>, at
all primes p\ge7, and show that it is a finitely generated and free
F_p[v_3]-module on 12p+4 generators in explicit degrees within the range -1 \le
* \le 2p^3+2p^2+2p-3. At these primes BP<2> is a form of elliptic cohomology,
and our result also determines the mod (p, v_1, v_2) homotopy of its algebraic
K-theory. Our computation is the first that exhibits chromatic redshift from
pure v_2-periodicity to pure v_3-periodicity in a precise quantitative manner.


title: A DNN Framework for Learning Lagrangian Drift With Uncertainty

abstract:
  Reconstructions of Lagrangian drift, for example for objects lost at sea, are
often uncertain due to unresolved physical phenomena within the data.
Uncertainty is usually overcome by introducing stochasticity into the drift,
but this approach requires specific assumptions for modelling uncertainty. We
remove this constraint by presenting a purely data-driven framework for
modelling probabilistic drift in flexible environments. We train a CNN to
predict the temporal evolution of probability density maps of particle
locations from $t$ to $t+1$ given an input velocity field. We generate
groundtruth density maps on the basis of ocean circulation model simulations by
simulating uncertainty in the initial position of particle trajectories.
Several loss functions for regressing the predicted density maps are tested.
Through evaluating our model on unseen velocities from a different year, we
find its outputs to be in good agreement with numerical simulations, suggesting
satisfactory generalisation to different dynamical situations.


title: NARX Identification using Derivative-Based Regularized Neural Networks

abstract:
  This work presents a novel regularization method for the identification of
Nonlinear Autoregressive eXogenous (NARX) models. The regularization method
promotes the exponential decay of the influence of past input samples on the
current model output. This is done by penalizing the sensitivity (i.e. partial
derivative) of the NARX model simulated output with respect to the past inputs.
The effectiveness of the approach is demonstrated through a simulation example,
where a neural network NARX model is identified with this novel method.
Moreover, it is shown that the proposed regularization approach improves the
model accuracy in terms of simulation error performance compared to that of
other regularization methods and model classes.


title:
Offline Distillation for Robot Lifelong Learning with Imbalanced
  Experience

abstract:
  Robots will experience non-stationary environment dynamics throughout their
lifetime: the robot dynamics can change due to wear and tear, or its
surroundings may change over time. Eventually, the robots should perform well
in all of the environment variations it has encountered. At the same time, it
should still be able to learn fast in a new environment. We investigate two
challenges in such a lifelong learning setting: first, existing off-policy
algorithms struggle with the trade-off between being conservative to maintain
good performance in the old environment and learning efficiently in the new
environment. We propose the Offline Distillation Pipeline to break this
trade-off by separating the training procedure into interleaved phases of
online interaction and offline distillation. Second, training with the combined
datasets from multiple environments across the lifetime might create a
significant performance drop compared to training on the datasets individually.
Our hypothesis is that both the imbalanced quality and size of the datasets
exacerbate the extrapolation error of the Q-function during offline training
over the "weaker" dataset. We propose a simple fix to the issue by keeping the
policy closer to the dataset during the distillation phase. In the experiments,
we demonstrate these challenges and the proposed solutions with a simulated
bipedal robot walking task across various environment changes. We show that the
Offline Distillation Pipeline achieves better performance across all the
encountered environments without affecting data collection. We also provide a
comprehensive empirical study to support our hypothesis on the data imbalance
issue.


title:
Composition operators on function spaces on the halfplane: spectra and
  semigroups

abstract:
  This paper considers composition operators on Zen spaces (a class of weighted
Bergman spaces of the right half-plane related to weighted function spaces on
the positive half-line by means of the Laplace transform). Generalizations are
given to work of Kucik on norms and essential norms, to work of Schroderus on
(essential) spectra, and to work by Arvanitidis and the authors on semigroups
of composition operators. The results are illustrated by consideration of the
Hardy--Bergman space; that is, the intersection of the Hardy and Bergman
Hilbert spaces on the half-plane.


title: XQA-DST: Multi-Domain and Multi-Lingual Dialogue State Tracking

abstract:
  In a task-oriented dialogue system, Dialogue State Tracking (DST) keeps track
of all important information by filling slots with values given through the
conversation. Existing methods generally rely on a predefined set of values and
struggle to generalise to previously unseen slots in new domains. In this
paper, we propose a multi-domain and multi-lingual dialogue state tracker in a
neural reading comprehension approach. Our approach fills the slot values using
span prediction, where the values are extracted from the dialogue itself. With
a novel training strategy and an independent domain classifier, empirical
results demonstrate that our model is a domain-scalable and open-vocabulary
model that achieves 53.2% Joint Goal Accuracy (JGA) on MultiWOZ 2.1. We show
its competitive transferability by zero-shot domain-adaptation experiments on
MultiWOZ 2.1 with an average JGA of 31.6% for five domains. In addition, it
achieves cross-lingual transfer with state-of-the-art zero-shot results, 64.9%
JGA from English to German and 68.6% JGA from English to Italian on WOZ 2.0.


title:
A Survey on Sustainable Software Ecosystems to Support Experimental and
  Observational Science at Oak Ridge National Laboratory

abstract:
  In the search for a sustainable approach for software ecosystems that
supports experimental and observational science (EOS) across Oak Ridge National
Laboratory (ORNL), we conducted a survey to understand the current and future
landscape of EOS software and data. This paper describes the survey design we
used to identify significant areas of interest, gaps, and potential
opportunities, followed by a discussion on the obtained responses. The survey
formulates questions about project demographics, technical approach, and skills
required for the present and the next five years. The study was conducted among
38 ORNL participants between June and July of 2021 and followed the required
guidelines for human subjects training. We plan to use the collected
information to help guide a vision for sustainable, community-based, and
reusable scientific software ecosystems that need to adapt effectively to: i)
the evolving landscape of heterogeneous hardware in the next generation of
instruments and computing (e.g. edge, distributed, accelerators), and ii) data
management requirements for data-driven science using artificial intelligence.


title: $T \overline{T}$ Deformations of Supersymmetric Quantum Mechanics

abstract:
  We define a manifestly supersymmetric version of the $T \overline{T}$
deformation appropriate for a class of $(0+1)$-dimensional theories with
$\mathcal{N} = 1$ or $\mathcal{N} = 2$ supersymmetry, including one
presentation of the super-Schwarzian theory which is dual to JT supergravity.
These deformations are written in terms of Noether currents associated with
translations in superspace, so we refer to them collectively as
$f(\mathcal{Q})$ deformations. We provide evidence that the $f(\mathcal{Q})$
deformations of $\mathcal{N} = 1$ and $\mathcal{N} = 2$ theories are on-shell
equivalent to the dimensionally reduced supercurrent-squared deformations of
$2d$ theories with $\mathcal{N} = (0,1)$ and $\mathcal{N} = (1,1)$
supersymmetry, respectively. In the $\mathcal{N} = 1$ case, we present two
forms of the $f(\mathcal{Q})$ deformation which drive the same flow, and
clarify their equivalence by studying the analogous equivalent deformations in
the non-supersymmetric setting.


title:
Structural reconstruction and anisotropic conductance in
  $4f$-ferromagnetic monolayer

abstract:
  Two-dimensional magnets are promising for nanoscale spintronic applications.
Currently, most available candidates are based on $3d$ transition metal
compounds, with hexagonal or honeycomb lattice geometry. Here, a GdCl$_3$
monolayer with $4f$ moments is theoretically studied, which can be exfoliated
from its existing bulk. Its orthorhombic structure and hendecahedral ion cages
are unique in two-dimensional. Furthermore, a significant structural
reconstruction is caused by the implantation of Li atoms into its interstitial
position, which also lead to ferromagnetism via a double-exchange-like process.
Its highly anisotropic conductance may be peculiarly useful for
nanoelectronics.


title: VisCUIT: Visual Auditor for Bias in CNN Image Classifier

abstract:
  CNN image classifiers are widely used, thanks to their efficiency and
accuracy. However, they can suffer from biases that impede their practical
applications. Most existing bias investigation techniques are either
inapplicable to general image classification tasks or require significant user
efforts in perusing all data subgroups to manually specify which data
attributes to inspect. We present VisCUIT, an interactive visualization system
that reveals how and why a CNN classifier is biased. VisCUIT visually
summarizes the subgroups on which the classifier underperforms and helps users
discover and characterize the cause of the underperformances by revealing image
concepts responsible for activating neurons that contribute to
misclassifications. VisCUIT runs in modern browsers and is open-source,
allowing people to easily access and extend the tool to other model
architectures and datasets. VisCUIT is available at the following public demo
link: https://poloclub.github.io/VisCUIT. A video demo is available at
https://youtu.be/eNDbSyM4R_4.


title: Extension of Lipschitz maps definable in Hensel minimal structures

abstract:
  In this paper, we establish a theorem on extension of Lipschitz maps
definable in Hensel minimal, non-trivially valued fields $K$ of
equicharacteristic zero. It may be regarded as a definable, non-Archimedean,
non-locally compact version of Kirszbraun's theorem.


title:
Non-Markovianity in photosynthetic reaction centers: A noise-induced
  quantum coherence perspective

abstract:
  The long-standing problem of nearly perfect photosynthetic yield in some
types of bacteria and nearly all kinds of plants despite the interaction with a
hot and noisy environment has witnessed quantum optical explanations in the
last decade or so. Typically in these explanations, photosynthetic reaction
centers are modeled as five-level quantum heat engines where the generation of
Fano-type interference due to the coupling of discrete state transitions with a
common Markovian reservoir is held responsible for the enhancement of the
photosynthetic efficiency. In this work, we go beyond the Born-Markov
approximation used in the earlier works and study the impact of non-Markovian
environments with Lorentzian spectral densities on the dynamics of
light-harvesting complexes.


title:
Scratch as Social Network: Topic Modeling and Sentiment Analysis in
  Scratch Projects

abstract:
  Societal matters like the Black Lives Matter (BLM) movement influence
software engineering, as the recent debate on replacing certain discriminatory
terms such as whitelist/blacklist has shown. Identifying relevant and trending
societal matters is important, and often done using social network analysis for
traditional social media channels such as twitter. In this paper we explore
whether this type of analysis can also be used for introspection of the
software world, by looking at the thriving scene of young Scratch programmers.
The educational programming language Scratch is not only used for teaching
programming concepts, but offers a platform for young programmers to express
and share their creativity on any topics of relevance. By analyzing titles and
project comments in a dataset of 106.032 Scratch projects, we explore which
topics are common in the Scratch community, whether socially relevant events
are reflected and how how the sentiment in the comments is. It turns out that
the diversity of topics within the Scratch projects make the analysis process
challenging. Our results nevertheless show that topics from pop and net culture
in particular are present, and even recent societal events such as the Covid-19
pandemic or BLM are to some extent reflected in Scratch. The tone in the
comments is mostly positive with catchy youth language. Hence, despite the
challenges, Scratch projects can be studied in the same way as social networks,
which opens up new possibilities to improve our understanding of the behavior
and motivation of novice programmers.


title:
Label Distribution Learning for Generalizable Multi-source Person
  Re-identification

abstract:
  Person re-identification (Re-ID) is a critical technique in the video
surveillance system, which has achieved significant success in the supervised
setting. However, it is difficult to directly apply the supervised model to
arbitrary unseen domains due to the domain gap between the available source
domains and unseen target domains. In this paper, we propose a novel label
distribution learning (LDL) method to address the generalizable multi-source
person Re-ID task (i.e., there are multiple available source domains, and the
testing domain is unseen during training), which aims to explore the relation
of different classes and mitigate the domain-shift across different domains so
as to improve the discrimination of the model and learn the domain-invariant
feature, simultaneously. Specifically, during the training process, we produce
the label distribution via the online manner to mine the relation information
of different classes, thus it is beneficial for extracting the discriminative
feature. Besides, for the label distribution of each class, we further revise
it to give more and equal attention to the other domains that the class does
not belong to, which can effectively reduce the domain gap across different
domains and obtain the domain-invariant feature. Furthermore, we also give the
theoretical analysis to demonstrate that the proposed method can effectively
deal with the domain-shift issue. Extensive experiments on multiple benchmark
datasets validate the effectiveness of the proposed method and show that the
proposed method can outperform the state-of-the-art methods. Besides, further
analysis also reveals the superiority of the proposed method.