title:
SSNbayes: An R package for Bayesian spatio-temporal modelling on stream
  networks

abstract:
  Spatio-temporal models are widely used in many research areas from ecology to
epidemiology. However, most covariance functions describe spatial relationships
based on Euclidean distance only. In this paper, we introduce the R package
SSNbayes for fitting Bayesian spatio-temporal models and making predictions on
branching stream networks. SSNbayes provides a linear regression framework with
multiple options for incorporating spatial and temporal autocorrelation.
Spatial dependence is captured using stream distance and flow connectivity
while temporal autocorrelation is modelled using vector autoregression
approaches. SSNbayes provides the functionality to make predictions across the
whole network, compute exceedance probabilities and other probabilistic
estimates such as the proportion of suitable habitat. We illustrate the
functionality of the package using a stream temperature dataset collected in
Idaho, USA.


title:
Efficient Distributed Computations in Anonymous Dynamic Congested
  Systems with Opportunistic Connectivity

abstract:
  In this work we address the question of efficiency of distributed computing
in anonymous, congested and highly dynamic and not-always-connected
networks/systems. More precisely, the system consists of an unknown number of
anonymous nodes with congestion on links and local computation. Links can
change arbitrarily from round to round, with only limitation that the union of
any T consecutive networks must form a temporarily connected (multi-)graph on
all nodes (knowledge of T is the only information the nodes require, otherwise
the communication would not be feasible). Nodes do not have any IDs, only some
number l of them have a bit distinguishing them from nodes without such a bit.
In each round a node can send and receive messages from its current neighbors.
Links and nodes are congested, in the sense that the length of messages and
local cache memory for local computation is (asymptotically) logarithmic.
  All-to-all communication is a fundamental principle in distributed computing
- it assumes that each node has an input message to be delivered to all other
nodes. Without loss of generality, the size of each input message is
logarithmic to fit in the link and node congestion assumption; otherwise, they
could be split in logarithmic batches and considered one-by-one. Because of
anonymity, each node needs to receive only a set of all input messages, each
accompanied by a number of initiating nodes (message multiplicity). We prove
that this task can be done in time polynomial in the (initially unknown) number
of nodes n and in the lower bound on the isoperimetric numbers of dynamically
evolving graphs. This allows to efficiently emulate a popular Congested Clique
model on top of Anonymous Dynamic Congested Systems (ADCS) with Opportunistic
Connectivity, even if the number of nodes may arbitrarily change in the
beginning of emulation.


title:
Validation and Improvement of the Pan-STARRS Photometric Calibration
  with the Stellar Color Regression Method

abstract:
  As one of the best ground-based photometric dataset, Pan-STARRS1 (PS1) has
been widely used as the reference to calibrate other surveys. In this work, we
present an independent validation and re-calibration of the PS1 photometry
using spectroscopic data from the LAMOST DR7 and photometric data from the
corrected Gaia EDR3 with the Stellar Color Regression (SCR) method. Using per
band typically a total of 1.5 million LAMOST-PS1-Gaia stars as standards, we
show that the PS1 photometric calibration precisions in the $grizy$ filters are
around $4\sim 5$ mmag when averaged over $20'$ regions. However, significant
large- and small-scale spatial variation of magnitude offset, up to over 1 per
cent, probably caused by the calibration errors in the PS1, are found for all
the $grizy$ filters. The calibration errors in different filters are
un-correlated, and are slightly larger for the $g$ and $y$ filters. We also
detect moderate magnitude-dependent errors (0.005, 0.005, 0.005, 0.004, 0.003
mag per magnitude in the 14 -- 17 magnitude range for the $grizy$ filters,
respectively) in the PS1 photometry by comparing with the Gaia EDR3 and other
catalogs. The errors are likely caused by the systematic uncertainties in the
PSF magnitudes. We provide two-dimensional maps to correct for such magnitude
offsets in the LAMOST footprint at different spatial resolutions from $20'$ to
$160'$. The results demonstrate the power of the SCR method in improving the
calibration precision of wide-field surveys when combined with the LAMOST
spectroscopy and Gaia photometry.


title: Documentation based Semantic-Aware Log Parsing

abstract:
  With the recent advances of deep learning techniques, there are rapidly
growing interests in applying machine learning to log data. As a fundamental
part of log analytics, accurate log parsing that transforms raw logs to
structured events is critical for subsequent machine learning and data mining
tasks. Previous approaches either analyze the source code for parsing or are
data-driven such as text clustering. They largely neglect to exploit another
widely available and valuable resource, software documentation that provides
detailed explanations for the messages, to improve accuracy. In this paper, we
propose an approach and system framework to use documentation knowledge for log
parsing. With parameter value identification, it not only can improve the
parsing accuracy for documented messages but also for undocumented messages. In
addition, it can discover the linkages between event templates that are
established by sharing parameters and indicate the correlation of the event
context.


title: Fairness Amidst Non-IID Graph Data: A Literature Review

abstract:
  Fairness in machine learning (ML), the process to understand and correct
algorithmic bias, has gained increasing attention with numerous literature
being carried out, commonly assume the underlying data is independent and
identically distributed (IID). On the other hand, graphs are a ubiquitous data
structure to capture connections among individual units and is non-IID by
nature. It is therefore of great importance to bridge the traditional fairness
literature designed on IID data and ubiquitous non-IID graph representations to
tackle bias in ML systems. In this survey, we review such recent advance in
fairness amidst non-IID graph data and identify datasets and evaluation metrics
available for future research. We also point out the limitations of existing
work as well as promising future directions.


title:
Investigating the genomic background of CRISPR-Cas genomes for
  CRISPR-based antimicrobials

abstract:
  CRISPR-Cas systems are an adaptive immunity that protects prokaryotes against
foreign genetic elements. Genetic templates acquired during past infection
events enable DNA-interacting enzymes to recognize foreign DNA for destruction.
Due to the programmability and specificity of these genetic templates,
CRISPR-Cas systems are potential alternative antibiotics that can be engineered
to self-target antimicrobial resistance genes on the chromosome or plasmid.
However, several fundamental questions remain to repurpose these tools against
drug-resistant bacteria. For endogenous CRISPR-Cas self-targeting,
antimicrobial resistance genes and functional CRISPR-Cas systems have to
co-occur in the target cell. Furthermore, these tools have to outplay DNA
repair pathways that respond to the nuclease activities of Cas proteins, even
for exogenous CRISPR-Cas delivery. Here, we conduct a comprehensive survey of
CRISPR-Cas genomes. First, we address the co-occurrence of CRISPR-Cas systems
and antimicrobial resistance genes in the CRISPR-Cas genomes. We show that the
average number of these genes varies greatly by the CRISPR-Cas type, and some
CRISPR-Cas types (IE and IIIA) have over 20 genes per genome. Next, we
investigate the DNA repair pathways of these CRISPR-Cas genomes, revealing that
the diversity and frequency of these pathways differ by the CRISPR-Cas type.
The interplay between CRISPR-Cas systems and DNA repair pathways is essential
for the acquisition of new spacers in CRISPR arrays. We conduct simulation
studies to demonstrate that the efficiency of these DNA repair pathways may be
inferred from the time-series patterns in the RNA structure of CRISPR repeats.
This bioinformatic survey of CRISPR-Cas genomes elucidates the necessity to
consider multifaceted interactions between different genes and systems to
design effective CRISPR-based antimicrobials.


title:
TURF: A Two-factor, Universal, Robust, Fast Distribution Learning
  Algorithm

abstract:
  Approximating distributions from their samples is a canonical
statistical-learning problem. One of its most powerful and successful
modalities approximates every distribution to an $\ell_1$ distance essentially
at most a constant times larger than its closest $t$-piece degree-$d$
polynomial, where $t\ge1$ and $d\ge0$. Letting $c_{t,d}$ denote the smallest
such factor, clearly $c_{1,0}=1$, and it can be shown that $c_{t,d}\ge 2$ for
all other $t$ and $d$. Yet current computationally efficient algorithms show
only $c_{t,1}\le 2.25$ and the bound rises quickly to $c_{t,d}\le 3$ for $d\ge
9$. We derive a near-linear-time and essentially sample-optimal estimator that
establishes $c_{t,d}=2$ for all $(t,d)\ne(1,0)$. Additionally, for many
practical distributions, the lowest approximation distance is achieved by
polynomials with vastly varying number of pieces. We provide a method that
estimates this number near-optimally, hence helps approach the best possible
approximation. Experiments combining the two techniques confirm improved
performance over existing methodologies.


title:
To what extent can Plug-and-Play methods outperform neural networks
  alone in low-dose CT reconstruction

abstract:
  The Plug-and-Play (PnP) framework was recently introduced for low-dose CT
reconstruction to leverage the interpretability and the flexibility of
model-based methods to incorporate various plugins, such as trained deep
learning (DL) neural networks. However, the benefits of PnP vs.
state-of-the-art DL methods have not been clearly demonstrated. In this work,
we proposed an improved PnP framework to address the previous limitations and
develop clinical-relevant segmentation metrics for quantitative result
assessment. Compared with the DL alone methods, our proposed PnP framework was
slightly inferior in MSE and PSNR. However, the power spectrum of the resulting
images better matched that of full-dose images than that of DL denoised images.
The resulting images supported higher accuracy in airway segmentation than DL
denoised images for all the ten patients in the test set, more substantially on
the airways with a cross-section smaller than 0.61cm$^2$, and outperformed the
DL denoised images for 45 out of 50 lung lobes in lobar segmentation. Our PnP
method proved to be significantly better at preserving the image texture, which
translated to task-specific benefits in automated structure segmentation and
detection.


title:
Forecasting Stock Options Prices via the Solution of an Ill-Posed
  Problem for the Black-Scholes Equation

abstract:
  In the previous paper (Inverse Problems, 32, 015010, 2016), a new heuristic
mathematical model was proposed for accurate forecasting of prices of stock
options for 1-2 trading days ahead of the present one. This new technique uses
the Black-Scholes equation supplied by new intervals for the underlying stock
and new initial and boundary conditions for option prices. The Black-Scholes
equation was solved in the positive direction of the time variable, This
ill-posed initial boundary value problem was solved by the so-called
Quasi-Reversibility Method (QRM). This approach with an added trading strategy
was tested on the market data for 368 stock options and good forecasting
results were demonstrated. In the current paper, we use the geometric Brownian
motion to provide an explanation of that effectivity using computationally
simulated data for European call options. We also provide a convergence
analysis for QRM. The key tool of that analysis is a Carleman estimate.


title: State transfers in vertex complemented coronas

abstract:
  In this paper, we study the existence of perfect state transfer and pretty
good state transfer in vertex complemented coronas. We prove that perfect state
transfer in vertex complemented coronas is extremely rare. In contrast, we give
sufficient conditions for vertex complemented coronas to have pretty good state
transfer.


title:
DeepONet-Grid-UQ: A Trustworthy Deep Operator Framework for Predicting
  the Power Grid's Post-Fault Trajectories

abstract:
  This paper proposes a new data-driven method for the reliable prediction of
power system post-fault trajectories. The proposed method is based on the
fundamentally new concept of Deep Operator Networks (DeepONets). Compared to
traditional neural networks that learn to approximate functions, DeepONets are
designed to approximate nonlinear operators. Under this operator framework, we
design a DeepONet to (1) take as inputs the fault-on trajectories collected,
for example, via simulation or phasor measurement units, and (2) provide as
outputs the predicted post-fault trajectories. In addition, we endow our method
with a much-needed ability to balance efficiency with reliable/trustworthy
predictions via uncertainty quantification. To this end, we propose and compare
two methods that enable quantifying the predictive uncertainty. First, we
propose a \textit{Bayesian DeepONet} (B-DeepONet) that uses stochastic gradient
Hamiltonian Monte-Carlo to sample from the posterior distribution of the
DeepONet parameters. Then, we propose a \textit{Probabilistic DeepONet}
(Prob-DeepONet) that uses a probabilistic training strategy to equip DeepONets
with a form of automated uncertainty quantification, at virtually no extra
computational cost. Finally, we validate the predictive power and uncertainty
quantification capability of the proposed B-DeepONet and Prob-DeepONet using
the IEEE 16-machine 68-bus system.


title:
Tombo Propeller: Bio-Inspired Deformable Structure toward
  Collision-Accommodated Control for Drones

abstract:
  There is a growing need for vertical take-off and landing vehicles, including
drones, which are safe to use and can adapt to collisions. The risks of damage
by collision, to humans, obstacles in the environment, and drones themselves,
are significant. This has prompted a search into nature for a highly resilient
structure that can inform a design of propellers to reduce those risks and
enhance safety. Inspired by the flexibility and resilience of dragonfly wings,
we propose a novel design for a biomimetic drone propeller called Tombo
propeller. Here, we report on the design and fabrication process of this
biomimetic propeller that can accommodate collisions and recover quickly, while
maintaining sufficient thrust force to hover and fly. We describe the
development of an aerodynamic model and experiments conducted to investigate
performance characteristics for various configurations of the propeller
morphology, and related properties, such as generated thrust force, thrust
force deviation, collision force, recovery time, lift-to-drag ratio, and noise.
Finally, we design and showcase a control strategy for a drone equipped with
Tombo propellers that collides in mid-air with an obstacle and recovers from
collision continuing flying. The results show that the maximum collision force
generated by the proposed Tombo propeller is less than two-thirds that of a
traditional rigid propeller, which suggests the concrete possibility to employ
deformable propellers for drones flying in a cluttered environment. This
research can contribute to morphological design of flying vehicles for agile
and resilient performance.


title:
Federated Learning with Sparsified Model Perturbation: Improving
  Accuracy under Client-Level Differential Privacy

abstract:
  Federated learning (FL) that enables distributed clients to collaboratively
learn a shared statistical model while keeping their training data locally has
received great attention recently and can improve privacy and communication
efficiency in comparison with traditional centralized machine learning
paradigm. However, sensitive information about the training data can still be
inferred from model updates shared in FL. Differential privacy (DP) is the
state-of-the-art technique to defend against those attacks. The key challenge
to achieve DP in FL lies in the adverse impact of DP noise on model accuracy,
particularly for deep learning models with large numbers of model parameters.
This paper develops a novel differentially-private FL scheme named Fed-SMP that
provides client-level DP guarantee while maintaining high model accuracy. To
mitigate the impact of privacy protection on model accuracy, Fed-SMP leverages
a new technique called Sparsified Model Perturbation (SMP), where local models
are sparsified first before being perturbed with additive Gaussian noise. Two
sparsification strategies are considered in Fed-SMP: random sparsification and
top-$k$ sparsification. We also apply R{\'e}nyi differential privacy to
providing a tight analysis for the end-to-end DP guarantee of Fed-SMP and prove
the convergence of Fed-SMP with general loss functions. Extensive experiments
on real-world datasets are conducted to demonstrate the effectiveness of
Fed-SMP in largely improving model accuracy with the same level of DP guarantee
and saving communication cost simultaneously.


title: G-Mixup: Graph Data Augmentation for Graph Classification

abstract:
  This work develops \emph{mixup for graph data}. Mixup has shown superiority
in improving the generalization and robustness of neural networks by
interpolating features and labels between two random samples. Traditionally,
Mixup can work on regular, grid-like, and Euclidean data such as image or
tabular data. However, it is challenging to directly adopt Mixup to augment
graph data because different graphs typically: 1) have different numbers of
nodes; 2) are not readily aligned; and 3) have unique typologies in
non-Euclidean space. To this end, we propose $\mathcal{G}$-Mixup to augment
graphs for graph classification by interpolating the generator (i.e., graphon)
of different classes of graphs. Specifically, we first use graphs within the
same class to estimate a graphon. Then, instead of directly manipulating
graphs, we interpolate graphons of different classes in the Euclidean space to
get mixed graphons, where the synthetic graphs are generated through sampling
based on the mixed graphons. Extensive experiments show that
$\mathcal{G}$-Mixup substantially improves the generalization and robustness of
GNNs.


title: Simulation of the RIBRAS Facility with GEANT4

abstract:
  A Geant4 simulation code was developed to perform realistic simulations of
the RIBRAS facility. A second order expansion of a finite solenoid field was
included to describe the beam optics with a good precision. A systematic study
of coil currents for several magnetic rigidities and focal points was
performed. Parameterizations of the coil currents for single and dual mode
operations were obtained. Dedicated routines were developed to simulate the
mechanism of direct reactions involving two and three particles in the final
state. The present simulations were employed to investigate the feasibility of
a Solenoidal Spectrometer with the RIBRAS facility. Our first results indicate
that the concept can be applied in the RIBRAS system under certain conditions.
Forthcoming studies both from simulations and experiment are already under
development.

categories: physics.acc-ph nucl-ex physics.comp-ph physics.ins-det


title: Structural and phase evolution in U$_3$Si$_2$ during steam corrosion

abstract:
  U$_3$Si$_2$ nuclear fuel is corroded in deuterated steam with in situ neutron
diffraction. Density functional theory is coupled with rigorous thermodynamic
description of the hydride including gas/solid entropy contributions. H absorbs
in the 2$b$ interstitial site of U$_3$Si$_2$H$_x$ and moves to 8$j$ for $x\ge
0.5$. Hydriding forces lattice expansion and change in $a/c$ ratio linked to
site preference. Rietveld refinement tracks the corrosion reactions at 350-500
\deg C and preference for the 8j site. Above 375 \deg C, formation of UO$_2$,
U$_3$Si$_5$ and USi$_3$ take place in the grain boundaries and bulk. Hydriding
occurs in bulk and precedes other reactions.


title:
Unconventional dual 1D-2D quantum spin liquid revealed by $ab$ $initio$
  studies on organic solids family

abstract:
  Organic solids host various electronic phases. Especially, a milestone
compound of organic solid, $\beta'$-$X$[Pd(dmit)$_2$]$_2$ with $X$=EtMe$_3$Sb
shows quantum spin-liquid (QSL) properties suggesting a novel state of matter.
However, nature of the QSL has been largely unknown. Here, we computationally
study five compounds comprehensively with different $X$ using 2D $ab$ $initio$
Hamiltonians and correctly reproduce experimental phase diagram with
antiferromagnetic order for $X$=Me$_4$P, Me$_4$As, Me$_4$Sb, Et$_2$Me$_2$As and
a QSL for $X$=EtMe$_3$Sb without adjustable parameters. We find that the QSL
for $X$=EtMe$_3$Sb exhibits 1D nature characterized by algebraic decay of spin
correlation along one direction, while exponential decay in the other
direction, indicating dimensional reduction from 2D to 1D. The 1D nature indeed
accounts for the experimental specific heat, thermal conductivity and magnetic
susceptibility. The identified QSL, however, preserves 2D nature as well
consistently with spin fractionalization into spinon with Dirac-like gapless
excitations and reveals duality bridging the 1D and 2D QSLs.


title: A Survey of Neural Trojan Attacks and Defenses in Deep Learning

abstract:
  Artificial Intelligence (AI) relies heavily on deep learning - a technology
that is becoming increasingly popular in real-life applications of AI, even in
the safety-critical and high-risk domains. However, it is recently discovered
that deep learning can be manipulated by embedding Trojans inside it.
Unfortunately, pragmatic solutions to circumvent the computational requirements
of deep learning, e.g. outsourcing model training or data annotation to third
parties, further add to model susceptibility to the Trojan attacks. Due to the
key importance of the topic in deep learning, recent literature has seen many
contributions in this direction. We conduct a comprehensive review of the
techniques that devise Trojan attacks for deep learning and explore their
defenses. Our informative survey systematically organizes the recent literature
and discusses the key concepts of the methods while assuming minimal knowledge
of the domain on the readers part. It provides a comprehensible gateway to the
broader community to understand the recent developments in Neural Trojans.


title:
On the Origins of the Block Structure Phenomenon in Neural Network
  Representations

abstract:
  Recent work has uncovered a striking phenomenon in large-capacity neural
networks: they contain blocks of contiguous hidden layers with highly similar
representations. This block structure has two seemingly contradictory
properties: on the one hand, its constituent layers exhibit highly similar
dominant first principal components (PCs), but on the other hand, their
representations, and their common first PC, are highly dissimilar across
different random seeds. Our work seeks to reconcile these discrepant properties
by investigating the origin of the block structure in relation to the data and
training methods. By analyzing properties of the dominant PCs, we find that the
block structure arises from dominant datapoints - a small group of examples
that share similar image statistics (e.g. background color). However, the set
of dominant datapoints, and the precise shared image statistic, can vary across
random seeds. Thus, the block structure reflects meaningful dataset statistics,
but is simultaneously unique to each model. Through studying hidden layer
activations and creating synthetic datapoints, we demonstrate that these simple
image statistics dominate the representational geometry of the layers inside
the block structure. We explore how the phenomenon evolves through training,
finding that the block structure takes shape early in training, but the
underlying representations and the corresponding dominant datapoints continue
to change substantially. Finally, we study the interplay between the block
structure and different training mechanisms, introducing a targeted
intervention to eliminate the block structure, as well as examining the effects
of pretraining and Shake-Shake regularization.


title: Arpu Kuilpu: An H5 from the Outer Main Belt

abstract:
  On 1 June 2019, just before 7:30 PM local time, the Desert Fireball Network
detected a -9.3 magnitude fireball over South Australia near the Western
Australia border. The event was observed by six fireball observatories, and
lasted for five seconds. One station was nearly directly underneath the
trajectory, greatly constraining the trajectory solution. This trajectory's
backward numerical integrations indicate that the object originated from the
outer main belt with a semi-major axis of 2.75 au. A light curve was also
extracted and showed that the body experienced very little fragmentation during
its atmospheric passage. A search campaign was conducted with several Desert
Fireball Network team members and other volunteers. One 42 g fragment was
recovered within the predicted fall area based on the dark flight model. Based
on measurements of short-lived radionuclides, the fragment was confirmed to be
a fresh fall. The meteorite, Arpu Kuilpu, has been classified as an H5 ordinary
chondrite. This marks the fifth fall recovered in Australia by the Desert
Fireball Network, and the smallest meteoroid ($\simeq 2$ kg) to ever survive
entry and be recovered as a meteorite.


title:
Interpolants and Explicit Definitions in Extensions of the Description
  Logic EL

abstract:
  We show that the vast majority of extensions of the description logic
$\mathcal{EL}$ do not enjoy the Craig interpolation nor the projective Beth
definability property. This is the case, for example, for $\mathcal{EL}$ with
nominals, $\mathcal{EL}$ with the universal role, $\mathcal{EL}$ with a role
inclusion of the form $r\circ s\sqsubseteq s$, and for $\mathcal{ELI}$. It
follows in particular that the existence of an explicit definition of a concept
or individual name cannot be reduced to subsumption checking via implicit
definability. We show that nevertheless the existence of interpolants and
explicit definitions can be decided in polynomial time for standard tractable
extensions of $\mathcal{EL}$ (such as $\mathcal{EL}^{++}$) and in ExpTime for
$\mathcal{ELI}$ and various extensions. It follows that these existence
problems are not harder than subsumption which is in sharp contrast to the
situation for expressive DLs. We also obtain tight bounds for the size of
interpolants and explicit definitions and the complexity of computing them:
single exponential for tractable standard extensions of $\mathcal{EL}$ and
double exponential for $\mathcal{ELI}$ and extensions. We close with a
discussion of Horn-DLs such as Horn-$\mathcal{ALCI}$.


title:
On the Sample Complexity of Stabilizing LTI Systems on a Single
  Trajectory

abstract:
  Stabilizing an unknown dynamical system is one of the central problems in
control theory. In this paper, we study the sample complexity of the
learn-to-stabilize problem in Linear Time-Invariant (LTI) systems on a single
trajectory. Current state-of-the-art approaches require a sample complexity
linear in $n$, the state dimension, which incurs a state norm that blows up
exponentially in $n$. We propose a novel algorithm based on spectral
decomposition that only needs to learn "a small part" of the dynamical matrix
acting on its unstable subspace. We show that, under proper assumptions, our
algorithm stabilizes an LTI system on a single trajectory with $\tilde{O}(k)$
samples, where $k$ is the instability index of the system. This represents the
first sub-linear sample complexity result for the stabilization of LTI systems
under the regime when $k = o(n)$.


title: Survivable Free Space Optical Mesh Network using High-Altitude Platforms

abstract:
  Free space optical (FSO) communication refers to the information transmission
technology based on the propagation of optical signals in space. FSO
communication requires that the transmitter and receiver directly see each
other. High-altitude platforms (HAPs) have been proposed for carrying FSO
transceivers in the stratosphere. A multihop HAP network with FSO links can
relay traffic between ground FSO nodes. In this study, we propose an end-to-end
switching model for forwarding traffic between massive pairs of ground FSO
nodes over a HAP network. A protection mechanism is employed for improving the
communication survivability in the presence of clouds, which may break the line
of sight (LoS) between HAPs and ground nodes. We propose an algorithm for
designing the topology of the survivable HAP network, given a set of ground FSO
nodes. The results demonstrate that, even though networks with survivable
capacity use more resources, they are not necessary much more expensive than
those without survivability in terms of equipment, i.e., HAPs and FSO devices,
and in terms of wavelength resource utilization.


title: Longest (Sub-)Periodic Subsequence

abstract:
  We present an algorithm computing the longest periodic subsequence of a
string of length $n$ in $O(n^7)$ time with $O(n^4)$ words of space. We obtain
improvements when restricting the exponents or extending the search allowing
the reported subsequence to be subperiodic down to $O(n^3)$ time and $O(n^2)$
words of space.


title: Pruning Networks with Cross-Layer Ranking & k-Reciprocal Nearest Filters

abstract:
  This paper focuses on filter-level network pruning. A novel pruning method,
termed CLR-RNF, is proposed. We first reveal a "long-tail" long-tail pruning
problem in magnitude-based weight pruning methods, and then propose a
computation-aware measurement for individual weight importance, followed by a
Cross-Layer Ranking (CLR) of weights to identify and remove the bottom-ranked
weights. Consequently, the per-layer sparsity makes up of the pruned network
structure in our filter pruning. Then, we introduce a recommendation-based
filter selection scheme where each filter recommends a group of its closest
filters. To pick the preserved filters from these recommended groups, we
further devise a k-Reciprocal Nearest Filter (RNF) selection scheme where the
selected filters fall into the intersection of these recommended groups. Both
our pruned network structure and the filter selection are non-learning
processes, which thus significantly reduce the pruning complexity, and
differentiate our method from existing works. We conduct image classification
on CIFAR-10 and ImageNet to demonstrate the superiority of our CLR-RNF over the
state-of-the-arts. For example, on CIFAR-10, CLR-RNF removes 74.1% FLOPs and
95.0% parameters from VGGNet-16 with even 0.3\% accuracy improvements. On
ImageNet, it removes 70.2% FLOPs and 64.8% parameters from ResNet-50 with only
1.7% top-5 accuracy drops. Our project is at https://github.com/lmbxmu/CLR-RNF.


title:
Improving Human Sperm Head Morphology Classification with Unsupervised
  Anatomical Feature Distillation

abstract:
  With rising male infertility, sperm head morphology classification becomes
critical for accurate and timely clinical diagnosis. Recent deep learning (DL)
morphology analysis methods achieve promising benchmark results, but leave
performance and robustness on the table by relying on limited and possibly
noisy class labels. To address this, we introduce a new DL training framework
that leverages anatomical and image priors from human sperm microscopy crops to
extract useful features without additional labeling cost. Our core idea is to
distill sperm head information with reliably-generated pseudo-masks and
unsupervised spatial prediction tasks. The predicted foreground masks from this
distillation step are then leveraged to regularize and reduce image and label
noise in the tuning stage. We evaluate our new approach on two public sperm
datasets and achieve state-of-the-art performances (e.g. 65.9% SCIAN accuracy
and 96.5% HuSHeM accuracy).


title:
Catalytic leverage of correlations and mitigation of dissipation in
  Information erasure

abstract:
  Correlations are a valuable resource for quantum information processing and
quantum thermodynamics. However, the preparation of some correlated states can
carry a substantial cost that should be compared against its value. We show
that classical correlations can be catalytically exploited, which enables to
mitigate heat and entropy dissipation in information erasure. These
correlations are naturally generated by the erasure process, and thus can be
considered free. Although we also show that maximum erasure with minimum
dissipation and no correlations is theoretically possible, catalysts are always
useful in practical erasure settings, where correlations are expected to take
place.


title: Lattice QCD Calculations of Parton Physics

abstract:
  In this document, we summarize the status and challenges of calculating
parton physics in lattice QCD for the US Particle Physics Community Planning
Exercise (a.k.a. "Snowmass"). While PDF-moments calculations have been very
successful and been continuously improved, new methods have been developed to
calculate distributions directly in $x$-space. Many recent lattice studies have
been focused on calculating isovector PDFs of the pion and nucleon, learning to
control systematics associated with excited-state contamination,
renormalization and continuum extrapolations, pion-mass and finite-volume
effects, etc. Although in some cases, the lattice results are already
competitive with experimental data, to reach the level of precision in a wide
range of $x$ for unpolarized nucleon PDFs impactful for future collider physics
remains a challenge, and may require exascale supercomputing power. The new
theoretical methods open the door for calculating other partonic observables
which will be the focus of the experimental program in nuclear physics,
including generalized parton distributions and transverse-momentum dependent
PDFs. A fruitful interplay between experimental data and lattice-QCD
calculations will usher in a new era for parton physics and hadron structure.


title:
One-bit Submission for Locally Private Quasi-MLE: Its Asymptotic
  Normality and Limitation

abstract:
  Local differential privacy~(LDP) is an information-theoretic privacy
definition suitable for statistical surveys that involve an untrusted data
curator. An LDP version of quasi-maximum likelihood estimator~(QMLE) has been
developed, but the existing method to build LDP QMLE is difficult to implement
for a large-scale survey system in the real world due to long waiting time,
expensive communication cost, and the boundedness assumption of derivative of a
log-likelihood function. We provided an alternative LDP protocol without those
issues, which is potentially much easily deployable to a large-scale survey. We
also provided sufficient conditions for the consistency and asymptotic
normality and limitations of our protocol. Our protocol is less burdensome for
the users, and the theoretical guarantees cover more realistic cases than those
for the existing method.


title: Exact Hopfions in a 3D Heisenberg Ferromagnet

abstract:
  We find exact static soliton solutions for the unit spin vector field of an
inhomogeneous, anisotropic three-dimensional (3D) Heisenberg ferromagnet. Each
soliton is labeled by two integers $n$ and $m$. It is a (modified) skyrmion in
the $z=0$ plane with winding number $n$, which twists out of the plane to
become a 3D soliton. Here $m$ arises due to the periodic boundary condition in
the $z$ direction. We use Whitehead's integral expression to find that the Hopf
invariant of the soliton is an integer $H =nm$, showing that it is a hopfion.
Plots of the preimages of a hopfion solution show that they are either unknots
or nontrivial knots, depending on $n$ and $m$. Any pair of preimage curves
links $H$ times, corroborating the interpretation of $H$ as a linking number.
We also calculate the exact hopfion energy and show that its topological lower
bound has a sublinear dependence on $H$.


title: Polar Coded Modulation via Hybrid Bit Labeling

abstract:
  Bit-interleaved coded modulation (BICM) and multilevel coded modulation (MLC)
are commonly used to combine polar codes with high order modulation. While BICM
benefits from simple design and the separation of coding and modulation, MLC
shows better performance under successive-cancellation decoding. In this paper
we propose a hybrid polar coded modulation scheme that lies between BICM and
MLC, wherein a fraction of bits are assigned to set-partition (SP) labeling and
the remaining bits are assigned for Gray labeling. The SP labeled bits undergo
sequential demodulation, using iterative demodulation and polar decoding
similar to MLC, whereas the Gray labeled bits are first demodulated in parallel
and then sent for decoding similar to BICM. Either polar codes or other channel
codes (such as LDPC codes) can be used for the Gray labeled bits. For length
2048 rate 1/2 polar code on 256-QAM, the performance gap between BICM (Gray
labeling only) and MLC (SP labeling only) can be almost fully closed by the
hybrid scheme. Notably, the hybrid scheme has a significant latency advantage
over MLC. These performance gains make the proposed scheme attractive for
future communication systems such as 6G.


title:
Landau quantization and spin polarization of cold magnetized quark
  matter

abstract:
  The magnetic field and density behaviors of various thermodynamic quantities
of strange quark matter under compact star conditions are investigated in the
framework of the thermodynamically self-consistent quasiparticle model. For
individual species, a larger number density $n_i$ leads to a larger magnetic
field strength threshold that align all particles parallel or antiparallel to
the magnetic field. Accordingly, in contrast to the finite baryon density
effect which reduces the spin polarization of magnetized strange quark matter,
the magnetic field effect leads to an enhancement of it. We also compute the
sound velocity as a function of the baryon density and find the sound velocity
shows an obvious oscillation with increasing density. Except for the
oscillation, similar to the zero-magnetic field case that the sound velocity
grows with increasing density and approaches the conformal limit $V_s^2=1/3$ at
high densities from below.


title: A short note on an inequality between KL and TV

abstract:
  The goal of this short note is to discuss the relation between
Kullback--Leibler divergence and total variation distance, starting with the
celebrated Pinsker's inequality relating the two, before switching to a simple,
yet (arguably) more useful inequality, apparently not as well known, due to
Bretagnolle and Huber. We also discuss applications of this bound for minimax
testing lower bounds.


title: The radio dichotomy of active galactic nuclei

abstract:
  The question of radio dichotomy in the active galactic nuclei (AGNs) is still
in debate even it has been proposed for more than forty years. In order to
solve the old riddle, we collect a sample of AGNs with optical $B$ band and
radio 6cm wavelength data to analyze the radio loudness ${\rm log}R$.
  Our results indicate a separation of ${\rm log}R = \langle 1.37 \pm 0.02
\rangle$ between radio-loud (RL) AGNs and radio-quiet (RQ) AGNs, suggest the
existence of an RL/RQ dichotomy. For the first time, we suggest combining radio
luminosity and radio loudness as a double-criterion to divide AGNs into RLs and
RQs to avoid misclassification problems that may happen in the single-criterion
scenario, we propose the double-criterion dividing line ${\rm log}L_{\rm 6cm} =
-2.7{\rm log}R +44.3$ by using a machine learning method.
  In addition, the key point of the RL/RQ dichotomy is the origin of radio
emission for the two classes, we suggest the radio emission from RLs and RQs
share the same origin, e.g. jets and mini-jets (aborted-jet or outflow),
through a correlation study between radio 6cm luminosity and optical $B$ band
luminosity.


title:
Unsupervised word-level prosody tagging for controllable speech
  synthesis

abstract:
  Although word-level prosody modeling in neural text-to-speech (TTS) has been
investigated in recent research for diverse speech synthesis, it is still
challenging to control speech synthesis manually without a specific reference.
This is largely due to lack of word-level prosody tags. In this work, we
propose a novel approach for unsupervised word-level prosody tagging with two
stages, where we first group the words into different types with a decision
tree according to their phonetic content and then cluster the prosodies using
GMM within each type of words separately. This design is based on the
assumption that the prosodies of different type of words, such as long or short
words, should be tagged with different label sets. Furthermore, a TTS system
with the derived word-level prosody tags is trained for controllable speech
synthesis. Experiments on LJSpeech show that the TTS model trained with
word-level prosody tags not only achieves better naturalness than a typical
FastSpeech2 model, but also gains the ability to manipulate word-level prosody.


title: Holistic Adversarial Robustness of Deep Learning Models

abstract:
  Adversarial robustness studies the worst-case performance of a machine
learning model to ensure safety and reliability. With the proliferation of
deep-learning based technology, the potential risks associated with model
development and deployment can be amplified and become dreadful
vulnerabilities. This paper provides a comprehensive overview of research
topics and foundational principles of research methods for adversarial
robustness of deep learning models, including attacks, defenses, verification,
and novel applications.


title: Laplacian state transfer in vertex complemented coronas

abstract:
  In this paper, we investigate the existence of Laplacian perfect state
transfer and Laplacian pretty good state transfer in vertex complemented
coronas. We prove that there is no Laplacian perfect state transfer in vertex
complemented coronas. In contrast, we give a sufficient condition for vertex
complemented coronas to have Laplacian pretty good state transfer.


title: Collision-free Path Planning in the Latent Space through cGANs

abstract:
  We show a new method for collision-free path planning by cGANs by mapping its
latent space to only the collision-free areas of the robot joint space. Our
method simply provides this collision-free latent space after which any
planner, using any optimization conditions, can be used to generate the most
suitable paths on the fly. We successfully verified this method with a
simulated two-link robot arm.


title:
Electron Transfer and Spin-Orbit Coupling: How Strong are Berry Force
  Effects In and Out of Equilibrium in the Presence of Nuclear Friction?

abstract:
  We investigate a spin-boson inspired model of electron transfer, where the
diabatic coupling is given by a position-dependent phase, exp(iWx). We consider
both equilibrium and nonequilibrium initial conditions. We show that, for this
model, all equilibrium results are completely invariant to the sign of W(to
infinite order). However, the nonequilibrium results do depend on the sign of
W, suggesting that photo-induced electron transfer dynamics are meaningfully
affected by Berry forces even in the presence of nuclear friction; furthermore,
whenever there is spin-orbit coupling, electronic spin polarization can emerge
(at least for some time).


title: Probabilistic Modeling Using Tree Linear Cascades

abstract:
  We introduce tree linear cascades, a class of linear structural equation
models for which the error variables are uncorrelated but need not be Gaussian
nor independent. We show that, in spite of this weak assumption, the tree
structure of this class of models is identifiable. In a similar vein, we
introduce a constrained regression problem for fitting a tree-structured linear
structural equation model and solve the problem analytically. We connect these
results to the classical Chow-Liu approach for Gaussian graphical models. We
conclude by giving an empirical-risk form of the regression and illustrating
the computationally attractive implications of our theoretical results on a
basic example involving stock prices.


title: Impact of Pretraining Term Frequencies on Few-Shot Reasoning

abstract:
  Pretrained Language Models (LMs) have demonstrated ability to perform
numerical reasoning by extrapolating from a few examples in few-shot settings.
However, the extent to which this extrapolation relies on robust reasoning is
unclear. In this paper, we investigate how well these models reason with terms
that are less frequent in the pretraining data. In particular, we examine the
correlations between the model performance on test instances and the frequency
of terms from those instances in the pretraining data. We measure the strength
of this correlation for a number of GPT-based language models (pretrained on
the Pile dataset) on various numerical deduction tasks (e.g., arithmetic and
unit conversion). Our results consistently demonstrate that models are more
accurate on instances whose terms are more prevalent, in some cases above
$70\%$ (absolute) more accurate on the top 10\% frequent terms in comparison to
the bottom 10\%. Overall, although LMs exhibit strong performance at few-shot
numerical reasoning tasks, our results raise the question of how much models
actually generalize beyond pretraining data, and we encourage researchers to
take the pretraining data into account when interpreting evaluation results.


title: Purity of thermal mixed quantum states

abstract:
  We develop a formula to evaluate the purity of a series of thermal
equilibrium states that can be calculated in numerical experiments without
knowing the exact form of the quantum state {\it a priori}. Canonical
typicality guarantees that there are numerous microscopically different
expressions of such states, which we call thermal mixed quantum (TMQ) states.
Suppose that we construct a TMQ state by a mixture of $N_\mathrm{samp}$
independent pure states. The weight of each pure state is given by its norm,
and the partition function is given by the average of the norms. To qualify how
efficiently the mixture is done, we introduce a quantum statistical quantity
called "normalized fluctuation of partition function (NFPF)". For smaller NFPF,
the TMQ state is closer to the equally weighted mixture of pure states, which
means higher efficiency, requiring a smaller $N_\mathrm{samp}$. The largest
NFPF is realized in the Gibbs state with purity-zero and exponentially large
$N_\mathrm{samp}$, while the smallest NFPF is given for thermal pure quantum
state with purity-1 and $N_\mathrm{samp}=1$. The purity is formulated using
solely the NFPF and roughly gives $N_\mathrm{samp}^{-1}$. Our analytical
results are numerically tested and confirmed by the two random sampling methods
built on matrix-product-state-based wave functions.


title:
Time Domain Simulation of DFIG-Based Wind Power System using
  Differential Transform Method

abstract:
  This paper proposes a new non-iterative time-domain simulation approach using
Differential Transform Method (DTM) to solve the set of non-linear
Differential-Algebraic Equations (DAEs) involved in a DFIG-based wind power
system. The DTM is an analytical as well as numerical approach applied to solve
high dimensional non-linear dynamical systems and the solution can be expressed
in the form of a series. In this approach, there is no need to compute
higher-order derivatives as DAEs are converted into a set of linear equations
after applying transformation rules so that the power series coefficients can
be computed directly. The transformation rules are used to transform power
system models of various devices, such as induction generator, wind turbine,
rotor and grid side converter, which includes trigonometric, square root,
exponential functions etc. Further, to increase the interval of convergence for
the series solutions, the multi-step DTM (MsDTM) approach is used. The
numerical performance of the proposed approach is compared with the traditional
numerical RK-4 method to demonstrate the potential of the proposed approach in
solving power system non-linear DAEs


title:
Case law retrieval: problems, methods, challenges and evaluations in the
  last 20 years

abstract:
  Case law retrieval is the retrieval of judicial decisions relevant to a legal
question. Case law retrieval comprises a significant amount of a lawyer's time,
and is important to ensure accurate advice and reduce workload. We survey
methods for case law retrieval from the past 20 years and outline the problems
and challenges facing evaluation of case law retrieval systems going forward.
Limited published work has focused on improving ranking in ad-hoc case law
retrieval. But there has been significant work in other areas of case law
retrieval, and legal information retrieval generally. This is likely due to
legal search providers being unwilling to give up the secrets of their success
to competitors. Most evaluations of case law retrieval have been undertaken on
small collections and focus on related tasks such as question-answer systems or
recommender systems. Work has not focused on Cranfield style evaluations and
baselines of methods for case law retrieval on publicly available test
collections are not present. This presents a major challenge going forward. But
there are reasons to question the extent of this problem, at least in a
commercial setting. Without test collections to baseline approaches it cannot
be known whether methods are promising. Works by commercial legal search
providers show the effectiveness of natural language systems as well as query
expansion for case law retrieval. Machine learning is being applied to more and
more legal search tasks, and undoubtedly this represents the future of case law
retrieval.


title:
Generation of multipartite entanglement between spin-1 particles with
  bifurcation-based quantum annealing

abstract:
  Quantum annealing is a way to solve a combinational optimization problem
where quantum fluctuation is induced by transverse fields. Recently, a
bifurcation-based quantum annealing with spin-1 particles was suggested as
another mechanism to implement the quantum annealing. In the bifurcation-based
quantum annealing, each spin is initially prepared in $|0\rangle$, let this
state evolve by a time-dependent Hamiltonian in an adiabatic way, and we find a
state spanned by $|\pm 1\rangle$ at the end of the evolution. Here, we propose
a scheme to generate multipartite entanglement, namely GHZ states, between
spin-1 particles by using the bifurcation-based quantum annealing. We gradually
decrease the detuning of the spin-1 particles while we adiabatically change the
amplitude of the external driving fields. Due to the dipole-dipole interactions
between the spin-1 particles, we can prepare the GHZ state after performing
this protocol. We discuss possible implementations of our scheme by using
nitrogen vacancy centers in diamond.


title:
Effect of expanding learning scope on the evolution of cooperation in
  scale-free networks

abstract:
  We study how expanding the scope for selecting game and learning (adaptation)
partners affects the evolution of cooperation in public goods games on
scale-free networks. We show the following three results. (i) Expanding the
scope for selecting game partners suppresses cooperation. (ii) Expanding the
scope for selecting learning partners promotes cooperation when cooperation
evolution is difficult. (iii) When cooperation is more likely to evolve,
slightly expanding the scope for selecting learning partners causes a
significant drop in the cooperation rate, but expanding the scope further
causes the cooperation rate to recover. Although (i) is explained by the
hub-centered mechanism, the well-known dynamic that promotes cooperation on
scale-free networks, (ii) and (iii) are caused by a completely different
mechanism that has heretofore been rarely mentioned.


title:
Generic thermodynamic model for solutions in various configurations:
  Part 1. Ternary system with single sublattice and unique ordered pairs

abstract:
  A generic thermodynamic model for ternary solutions has been derived
regardless of whether the solutions contain short-ranger ordering
configurations or how to select geometric interpolation methods. The model is
formulated by extensions of the Modified Quasichemical Model in the Pair
Approximation (MQMPA) into ternary solutions using an unbiased interpolation
scheme which might be ergodic over all reported geometric solution models, such
as Kohler, Toop, Muggianu and Chou models, with changeable interpolation
parameters. The interpolation is practically conducted to express the pair
interaction energies in ternary solutions by employing the counterparts in
their constituent binary solutions. The pair energies could be expanded in
terms of either the coordination-equivalent fractions (composition-dependent)
or the pair fractions (configuration-dependent). The generic thermodynamic
model is eventually validated by the real Li-Cu-Sn liquid with complicated
solution configurations over the entire composition zone to demonstrate its
effectiveness and accuracy.


title: On $q$-commuting co-extensions and $q$-commutant lifting

abstract:
  Consider a nonzero contraction $T$ and a bounded operator $X$ satisfying
$TX=qXT$ for a complex number $q$. There are some interesting results in the
literature on $q$-commuting dilation and $q$-commutant lifting of such pair
$(T,X)$ when $|q|=1$. Here we improve a few of them to the class of scalars $q$
satisfying $|q|\leq \dfrac{1}{\|T\|}$.


title:
Assessing the Influence of Input Magnetic Maps on Global Modeling of the
  Solar Wind and CME-driven Shock in the 2013 April 11 Event

abstract:
  In the past decade, significant efforts have been made in developing
physics-based solar wind and coronal mass ejection (CME) models, which have
been or are being transferred to national centers (e.g., SWPC, CCMC) to enable
space weather predictive capability. However, the input data coverage for space
weather forecasting is extremely limited. One major limitation is the solar
magnetic field measurements, which are used to specify the inner boundary
conditions of the global magnetohydrodynamic (MHD) models. In this study, using
the Alfven wave solar model (AWSoM), we quantitatively assess the influence of
the magnetic field map input (synoptic/diachronic vs. synchronic magnetic maps)
on the global modeling of the solar wind and the CME-driven shock in the 2013
April 11 solar energetic particle (SEP) event. Our study shows that due to the
inhomogeneous background solar wind and dynamical evolution of the CME, the
CME-driven shock parameters change significantly both spatially and temporally
as the CME propagates through the heliosphere. The input magnetic map has a
great impact on the shock connectivity and shock properties in the global MHD
simulation. Therefore this study illustrates the importance of taking into
account the model uncertainty due to the imperfect magnetic field measurements
when using the model to provide space weather predictions.


title: Balancing Domain Experts for Long-Tailed Camera-Trap Recognition

abstract:
  Label distributions in camera-trap images are highly imbalanced and
long-tailed, resulting in neural networks tending to be biased towards
head-classes that appear frequently. Although long-tail learning has been
extremely explored to address data imbalances, few studies have been conducted
to consider camera-trap characteristics, such as multi-domain and multi-frame
setup. Here, we propose a unified framework and introduce two datasets for
long-tailed camera-trap recognition. We first design domain experts, where each
expert learns to balance imperfect decision boundaries caused by data
imbalances and complement each other to generate domain-balanced decision
boundaries. Also, we propose a flow consistency loss to focus on moving
objects, expecting class activation maps of multi-frame matches the flow with
optical flow maps for input images. Moreover, two long-tailed camera-trap
datasets, WCS-LT and DMZ-LT, are introduced to validate our methods.
Experimental results show the effectiveness of our framework, and proposed
methods outperform previous methods on recessive domain samples.


title: Multiparameter Bernoulli Factories

abstract:
  We consider the problem of computing with many coins of unknown bias. We are
given samples access to $n$ coins with \emph{unknown} biases $p_1,\dots, p_n$
and are asked to sample from a coin with bias $f(p_1, \dots, p_n)$ for a given
function $f:[0,1]^n \rightarrow [0,1]$. We give a complete characterization of
the functions $f$ for which this is possible. As a consequence, we show how to
extend various combinatorial sampling procedures (most notably, the classic
Sampford Sampling for $k$-subsets) to the boundary of the hypercube.


title: High-frequency traveling-wave phononic cavity with sub-micron wavelength

abstract:
  Thin-film gallium nitride (GaN) as a proven piezoelectric material is a
promising platform for the phononic integrated circuits, which hold great
potential for scalable information processing processors. Here, an unsuspended
traveling phononic resonator based on high-acoustic-index-contrast mechanism is
realized in GaN-on-Sapphire with a frequency up to 5 GHz, which matches the
typical superconducting qubit frequency. A sixfold increment in quality factor
was found when temperature decreases from room temperature ($Q=5000$) to
$7\,\mathrm{K}$ ($Q=30000$) and thus a frequency-quality factor product of
$1.5\times10^{14}$ is obtained. Higher quality factors are available when the
fabrication process is further optimized. Our system shows great potential in
hybrid quantum devices via circuit quantum acoustodynamics.


title: Situation-based memory in spiking neuron-astrocyte network

abstract:
  Mammalian brains operate in a very special surrounding: to survive they have
to react quickly and effectively to the pool of stimuli patterns previously
recognized as danger. Many learning tasks often encountered by living organisms
involve a specific set-up centered around a relatively small set of patterns
presented in a particular environment. For example, at a party, people
recognize friends immediately, without deep analysis, just by seeing a fragment
of their clothes. This set-up with reduced "ontology" is referred to as a
"situation". Situations are usually local in space and time. In this work, we
propose that neuron-astrocyte networks provide a network topology that is
effectively adapted to accommodate situation-based memory. In order to
illustrate this, we numerically simulate and analyze a well-established model
of a neuron-astrocyte network, which is subjected to stimuli conforming to the
situation-driven environment. Three pools of stimuli patterns are considered:
external patterns, patterns from the situation associative pool regularly
presented to the network and learned by the network, and patterns already
learned and remembered by astrocytes. Patterns from the external world are
added to and removed from the associative pool. Then we show that astrocytes
are structurally necessary for an effective function in such a learning and
testing set-up. To demonstrate this we present a novel neuromorphic model for
short-term memory implemented by a two-net spiking neural-astrocytic network.
Our results show that such a system tested on synthesized data with selective
astrocyte-induced modulation of neuronal activity provides an enhancement of
retrieval quality in comparison to standard spiking neural networks trained via
Hebbian plasticity only. We argue that the proposed set-up may offer a new way
to analyze, model, and understand neuromorphic artificial intelligence systems.


title: Multi-style Training for South African Call Centre Audio

abstract:
  Mismatched data is a challenging problem for automatic speech recognition
(ASR) systems. One of the most common techniques used to address mismatched
data is multi-style training (MTR), a form of data augmentation that attempts
to transform the training data to be more representative of the testing data;
and to learn robust representations applicable to different conditions. This
task can be very challenging if the test conditions are unknown. We explore the
impact of different MTR styles on system performance when testing conditions
are different from training conditions in the context of deep neural network
hidden Markov model (DNN-HMM) ASR systems. A controlled environment is created
using the LibriSpeech corpus, where we isolate the effect of different MTR
styles on final system performance. We evaluate our findings on a South African
call centre dataset that contains noisy, WAV49-encoded audio.


title: A universal relation for operator complexity

abstract:
  We study Krylov complexity $C_K$ and operator entropy $S_K$ in operator
growth. We find that for a variety of systems, including chaotic ones and
integrable theories, the two quantities always enjoy a logarithmic relation
$S_K\sim \log{C_K}$ at long times, where dissipative behavior emerges in
unitary evolution. Otherwise, the relation does not hold any longer.
Universality of the relation is deeply connected to irreversibility of operator
growth.


title: Navigating Local Minima in Quantized Spiking Neural Networks

abstract:
  Spiking and Quantized Neural Networks (NNs) are becoming exceedingly
important for hyper-efficient implementations of Deep Learning (DL) algorithms.
However, these networks face challenges when trained using error
backpropagation, due to the absence of gradient signals when applying hard
thresholds. The broadly accepted trick to overcoming this is through the use of
biased gradient estimators: surrogate gradients which approximate thresholding
in Spiking Neural Networks (SNNs), and Straight-Through Estimators (STEs),
which completely bypass thresholding in Quantized Neural Networks (QNNs). While
noisy gradient feedback has enabled reasonable performance on simple supervised
learning tasks, it is thought that such noise increases the difficulty of
finding optima in loss landscapes, especially during the later stages of
optimization. By periodically boosting the Learning Rate (LR) during training,
we expect the network can navigate unexplored solution spaces that would
otherwise be difficult to reach due to local minima, barriers, or flat
surfaces. This paper presents a systematic evaluation of a cosine-annealed LR
schedule coupled with weight-independent adaptive moment estimation as applied
to Quantized SNNs (QSNNs). We provide a rigorous empirical evaluation of this
technique on high precision and 4-bit quantized SNNs across three datasets,
demonstrating (close to) state-of-the-art performance on the more complex
datasets. Our source code is available at this link:
https://github.com/jeshraghian/QSNNs.


title:
Quasi-monochromatic bright gamma-ray generation from synchronized
  Compton scattering via azimuthal spatial-temporal coupling

abstract:
  High energy photons can be generated via inverse Compton scattering (ICS) in
the collision between energetic electrons and intense laser pulse. The
development of laser plasma accelerators promises compact and all-optical
gamma-ray sources by colliding the electrons from laser wakefield accelerators
to its high-power driving pulse reflected by a plasma mirror. However, the law
of optical focusing hinders realization of both high photon yield and
monochromatic spectrum in this scenario. We propose an azimuthal
spatial-temporal convertor that decouples the focal field strength from laser
spot size using helical parabolic geometry. It decomposes the driving laser
beam into a pulse train of almost identical divergence angle and focal depth,
creating synchronized ICS in the optimized linear regime. The scheme resolves
the dilemma between high efficiency and narrow energy spread, facilitating the
generation of monochromatic gamma-ray using high power lasers beyond
relativistic field strengths.


title:
Selective measurement of longitudinal electron spin relaxation time
  $T_1$ of Ce$^{3+}$ ions in YAG lattice: Resonant spin inertia

abstract:
  Electron spin oriented along an external magnetic field is subject to
longitudinal spin relaxation with characteristic time $T_1$. The corresponding
decay is nonoscillating, so one cannot readily ascribe $T_1$ to a certain $g$
factor. This becomes a problem when several electronic states with different
$g$ factors are present in the system, e.g. electrons and holes. We solve this
problem by optically pumping spin polarization and then selectively
depolarizing it using a radio frequency (rf) field. By modulating the rf field
one can observe the retarded modulation of spin polarization which depends on
the relation between the modulation period and $T_1$. Using this selective spin
inertia method, we unveil the strong anisotropy of $T_1$ for rare-earth
Ce$^{3+}$ ions in a YAG crystal at low temperatures and low magnetic fields. We
also show that the large spread of Larmor frequencies within the electron
ensemble in this system is not static, but results from the fluctuations of
internal magnetic fields on a timescale much shorter than $T_1$.


title:
Evidence for a spinon Kondo effect in cobalt atoms on single-layer
  1T-TaSe$_2$

abstract:
  Quantum spin liquids (QSLs) are highly entangled, disordered magnetic states
that arise in frustrated Mott insulators and host exotic fractional excitations
such as spinons and chargons. Despite being charge insulators some QSLs are
predicted to exhibit gapless itinerant spinons that yield metallic behavior in
the spin channel. We have deposited isolated magnetic atoms onto single-layer
(SL) 1T-TaSe$_2$, a gapless QSL candidate, to experimentally probe how
itinerant spinons couple to impurity spin centers. Using scanning tunneling
spectroscopy we observe the emergence of new, impurity-induced resonance peaks
at the 1T-TaSe$_2$ Hubbard band edges when cobalt adatoms are positioned to
have maximal spatial overlap with the Hubbard band charge distribution. These
resonance peaks disappear when the spatial overlap is reduced or when the
magnetic impurities are replaced with non-magnetic impurities. Theoretical
simulations using a modified Anderson impurity model integrated with a gapless
quantum spin liquid show that these resonance peaks are consistent with a Kondo
resonance induced by spinons combined with spinon-chargon binding effects that
arise due to QSL gauge-field fluctuations.


title:
Improving the accuracy of hard photon emission by Sigmoid sampling of
  the QED-table in particle-in-cell-Monte-Carlo simulations

abstract:
  Research on laser-plasma interaction in the quantum-electrodynamic (QED)
regime has been greatly advanced by particle-in-cell & Monte-Carlo simulations
(PIC-MC). While these simulations are widely used, we find that noticeable
numerical error arises due to inappropriate implementation of the quantum
process accounting for hard photon emission and pair production in the PIC-MC
codes. The error stems from the low resolution of the QED table used to sample
photon energy, which is generated in the logarithmic scale and cannot resolve
high energy photons. We propose a new sampling method via Sigmoid function that
handles both the low energy and high energy end of the photon emission
spectrum. It guarantees the accuracy of PIC-MC algorithms for hard photon
radiation and other related processes in the strong-field QED regime.


title: Uses of Killing and Killing-Yano Tensors

abstract:
  In this contribution we have collected some facts about Killing and
Killing-Yano tensors that we feel are of general interest for researchers
working on problems that rely on differential geometry. We also include some of
our recent studies pertaining to currents, charges and (super)invariants for
particles and tensionless strings.


title: Coordinate rings of some $SL_2$-character varieties

abstract:
  We determine generators of the coordinate ring of $SL_2$-character varieties.
In the case of the free group $F_3$ we obtain an explicit equation of the
$SL_2$-character variety. For free groups $F_k$ we find transcendental
generators. Finally, for the case of the 2-torus, we get an explicit equation
of the $SL_2$-character variety and use the description to compute their
E-polynomials.


title:
MeshLeTemp: Leveraging the Learnable Vertex-Vertex Relationship to
  Generalize Human Pose and Mesh Reconstruction for In-the-Wild Scenes

abstract:
  We present MeshLeTemp, a powerful method for 3D human pose and mesh
reconstruction from a single image. In terms of human body priors encoding, we
propose using a learnable template human mesh instead of a constant template
utilized by previous state-of-the-art methods. The proposed learnable template
reflects not only vertex-vertex interactions but also the human pose and body
shape, being able to adapt to diverse images. We also introduce a strategy to
enrich the training data that contains both 2D and 3D annotations. We conduct
extensive experiments to show the generalizability of our method and the
effectiveness of our data strategy. As one of our ablation studies, we adapt
MeshLeTemp to another domain which is 3D hand reconstruction.


title: Saturable Purcell filter for circuit QED

abstract:
  We consider a typical circuit QED setup where a data artificial atom encodes
a qubit and is dispersively coupled to a measurement resonator that in turn is
coupled to a transmission line. We show theoretically that by placing a filter
artificial atom in this transmission line, the effective decay of the data
artificial atom into the transmission line (the Purcell decay) is suppressed.
When strong control fields are present in the transmission line, the filter
artificial atom is saturated and hence effectively switched off. This permits
both control and measurement of the data artificial atom using a single
transmission line, while maintaining the Purcell filtering capability and
therefore a long coherence time of the data artificial atom in the absence of
the control pulses. We also apply open systems optimal control techniques to
optimize the $\pi$-pulse on the data artificial atom, achieving high fidelity.
The considered setup is compatible with the frequency multiplexing, potentially
enabling both control and measurement of several qubits using a single
Purcell-filtered transmission line. Our work helps with the scalability of the
superconducting quantum computers by decreasing the ratio between the number of
qubits and the number of the required transmission lines. For the setups that
already use one transmission line both for measurement and control, our work
provides a way to filter out the qubit frequency completely without removing
the possibility of controlling the system.


title: Geometrically Equivariant Graph Neural Networks: A Survey

abstract:
  Many scientific problems require to process data in the form of geometric
graphs. Unlike generic graph data, geometric graphs exhibit symmetries of
translations, rotations, and/or reflections. Researchers have leveraged such
inductive bias and developed geometrically equivariant Graph Neural Networks
(GNNs) to better characterize the geometry and topology of geometric graphs.
Despite fruitful achievements, it still lacks a survey to depict how
equivariant GNNs are progressed, which in turn hinders the further development
of equivariant GNNs. To this end, based on the necessary but concise
mathematical preliminaries, we analyze and classify existing methods into three
groups regarding how the message passing and aggregation in GNNs are
represented. We also summarize the benchmarks as well as the related datasets
to facilitate later researches for methodology development and experimental
evaluation. The prospect for future potential directions is also provided.


title: Few-shot semantic segmentation via mask aggregation

abstract:
  Few-shot semantic segmentation aims to recognize novel classes with only very
few labelled data. This challenging task requires mining of the relevant
relationships between the query image and the support images. Previous works
have typically regarded it as a pixel-wise classification problem. Therefore,
various models have been designed to explore the correlation of pixels between
the query image and the support images. However, they focus only on pixel-wise
correspondence and ignore the overall correlation of objects. In this paper, we
introduce a mask-based classification method for addressing this problem. The
mask aggregation network (MANet), which is a simple mask classification model,
is proposed to simultaneously generate a fixed number of masks and their
probabilities of being targets. Then, the final segmentation result is obtained
by aggregating all the masks according to their locations. Experiments on both
the PASCAL-5^i and COCO-20^i datasets show that our method performs comparably
to the state-of-the-art pixel-based methods. This competitive performance
demonstrates the potential of mask classification as an alternative baseline
method in few-shot semantic segmentation. Our source code will be made
available at https://github.com/TinyAway/MANet.


title:
Electronic Excitations of Hematite Heteroepitaxial Films Measured by
  Resonant Inelastic X-Ray Scattering at the Fe L-edge

abstract:
  Resonant Inelastic X-Ray Scattering (RIXS) spectra of hematite were measured
at the Fe L3-edge for heteroepitaxial thin films which were undoped and doped
with 1% Ti, Sn or Zn, in the energy loss range in excess of 1 eV to study
electronic transitions. The spectra were measured for several momentum
transfers (q), conducted at both low temperature (T=14K) and room temperature.
While we can not rule out dispersive features possibly owing to propagating
excitations, the coarse envelopes of the general spectra did not appreciably
change shape with q, implying that the bulk of the observed L-edge RIXS
intensity originates from (mostly) non-dispersive ligand field (LF)
excitations. Summing the RIXS spectra over q and comparing the results at T=14
K to those at T=300 K, revealed pronounced temperature effects, including an
intensity change and energy shift of the 1.4 eV peak, a broadband intensity
increase of the 3-4 eV range, and higher energy features. The q-summed spectra
and their temperature dependences are virtually identical for nearly all of the
samples with different dopants, save for the temperature dependence of the
Ti-doped sample's spectrum, which we attribute to being affected by a large
number of free charge carriers. Comparing with magnetization measurements for
different temperatures and dopings likewise did not show a clear correlation
between the RIXS spectra and the magnetic ordering states. To clarify the
excited states, we performed spin multiplet calculations which were in
excellent agreement with the RIXS spectra over a wide energy range and provide
detailed electronic descriptions of the excited states. The implications of
these findings to the photoconversion efficiency of hematite photoanodes is
discussed.


title: Eliciting Best Practices for Collaboration with Computational Notebooks

abstract:
  Despite the widespread adoption of computational notebooks, little is known
about best practices for their usage in collaborative contexts. In this paper,
we fill this gap by eliciting a catalog of best practices for collaborative
data science with computational notebooks. With this aim, we first look for
best practices through a multivocal literature review. Then, we conduct
interviews with professional data scientists to assess their awareness of these
best practices. Finally, we assess the adoption of best practices through the
analysis of 1,380 Jupyter notebooks retrieved from the Kaggle platform.
Findings reveal that experts are mostly aware of the best practices and tend to
adopt them in their daily work. Nonetheless, they do not consistently follow
all the recommendations as, depending on specific contexts, some are deemed
unfeasible or counterproductive due to the lack of proper tool support. As
such, we envision the design of notebook solutions that allow data scientists
not to have to prioritize exploration and rapid prototyping over writing code
of quality.


title:
Long-term Causal Inference Under Persistent Confounding via Data
  Combination

abstract:
  We study the identification and estimation of long-term treatment effects
when both experimental and observational data are available. Since the
long-term outcome is observed only after a long delay, it is not measured in
the experimental data, but only recorded in the observational data. However,
both types of data include observations of some short-term outcomes. In this
paper, we uniquely tackle the challenge of persistent unmeasured confounders,
i.e., some unmeasured confounders that can simultaneously affect the treatment,
short-term outcomes and the long-term outcome, noting that they invalidate
identification strategies in previous literature. To address this challenge, we
exploit the sequential structure of multiple short-term outcomes, and develop
three novel identification strategies for the average long-term treatment
effect. We further propose three corresponding estimators and prove their
asymptotic consistency and asymptotic normality. We finally apply our methods
to estimate the effect of a job training program on long-term employment using
semi-synthetic data. We numerically show that our proposals outperform existing
methods that fail to handle persistent confounders.


title:
Radial-recombination for rigid rotational alignment of images and
  volumes

abstract:
  A common task in single particle electron cryomicroscopy (cryo-EM) is the
rigid alignment of images and/or volumes. In the context of images, a rigid
alignment involves estimating the inner-product between one image of $N\times
N$ pixels and another image that has been translated by some displacement and
rotated by some angle $\gamma$. In many situations the number of rotations
$\gamma$ considered is large (e.g., $\mathcal{O}(N)$), while the number of
translations considered is much smaller (e.g., $\mathcal{O}(1)$). In these
scenarios a naive algorithm requires $\mathcal{O}(N^{3})$ operations to
calculate the array of inner-products for each image-pair. This computation can
be accelerated by using a fourier-bessel basis and the fast-fourier-transform
(FFT), requiring only $\mathcal{O}(N^2)$ operations per image-pair. We propose
a simple data-driven compression algorithm to further accelerate this
computation, which we refer to as the `radial-SVD'. Our approach involves
linearly-recombining the different rings of the original images (expressed in
polar-coordinates), taking advantage of the singular-value-decomposition (SVD)
to choose a low-rank combination which both compresses the images and optimizes
a certain measure of angular discriminability. When aligning multiple images to
multiple targets, the complexity of our approach is $\mathcal{O}(N(\log(N)+H))$
per image-pair, where $H$ is the rank of the SVD used in the compression above.
The advantage gained by this approach depends on the ratio between $H$ and $N$;
the smaller $H$ is the better. In many applications $H$ can be quite a bit
smaller than $N$ while still maintaining accuracy. We present numerical results
in a cryo-EM application demonstrating that the radial- and degree-SVD can help
save a factor of $5$--$10$ for both image- and volume-alignment.


title:
Quantitative Oppenheim Conjecture for Quadratic Forms in 5 Variables
  over Function Fields

abstract:
  We translate Davenport's and Heilbronn's work on a quantitative version of
the Oppenheim conjecture for indefinite diagonal quadratic forms in 5 variables
into the setting of function fields.


title:
Ultrafast dynamics of electrons and phonons: from the two-temperature
  model to the time-dependent Boltzmann equation

abstract:
  The advent of pump-probe spectroscopy techniques paved the way to the
exploration of the ultrafast dynamics of electrons and phonons in crystalline
solids. Following photo-absorption of a pump pulse and the initial electronic
thermalization, the dynamics of electronic and vibrational degrees of freedom
is dominated by electron-phonon and phonon-phonon scattering processes.The
two-temperature model (TTM) and its generalizations -- as, e.g., the
non-thermal lattice model (NLM) -- provide valuable tools to describe these
phenomena and the ensuing coupled electron-phonon dynamics over timescales
ranging between few hundreds of femtoseconds and tens of picoseconds. While
more sophisticated theoretical approaches are nowadays available, the
conceptual and computational simplicity of the TTM makes it the method of
choice to model thermalization processes in pump-probe spectroscopy, and it
keeps being widely applied in both experimental and theoretical studies. In the
domain of ab-initio methods, the time-dependent Boltzmann equation (TDBE)
ameliorates many of the shortcomings of the TTM and it enables a realistic and
parameter-free description of ultrafast phenomena with full momentum
resolution. After a pedagogical introduction to the TTM and TDBE, in this
manuscript we review their application to the description of ultrafast process
in solid-state physics and materials science as well as their theoretical
foundation.


title: Boundedness of polarised Calabi-Yau fibrations

abstract:
  In this paper, we investigate the boundedness of good minimal models with
intermediate Kodaira dimensions, which has a natural Iitaka fibration to the
canonical models. We prove that good minimal models are bounded modulo crepant
birational when the base (canonical models) are bounded and the general fibers
of the Iitaka fibration are in a bounded family of polarized Calabi-Yau pairs.


title: Oscillation Dynamics in Short-Lived Facula Regions during Their Lifetime

abstract:
  We performed a multi-wave study of the oscillation dynamic in short-lived
facula regions during their lifetime. We studied oscillations in five regions,
three of which belonged to the beginning of the current solar activity cycle,
and two of them existed at the end of the previous cycle. We found that in the
facula regions of the current cycle, low-frequency 1-2 mHz oscillation
dominated at the early stages of the faculae formation, while in the regions of
the previous cycle, five-minute oscillations dominated at this stage. At the
maximal development phase of all the facula regions, the locations of observed
low frequencies are closely related to those of the coronal loops. These
results support the version that the sources of the low-frequency oscillations
in loops lie loops' foot points.


title:
Postulating dark matter particle mass and properties from two-thirds law
  for self-gravitating collisionless dark matter flow

abstract:
  Dark matter can be characterized by the mass and size of its smallest
constituents, which are challenging to be directly probed and detected. We
present a new approach to predict the mass and properties of dark matter
particles based on the nature of dark matter flow. The self-gravitating
collisionless dark matter flow exhibits an inverse mass and energy cascade from
small to large mass scales with a scale-independent constant energy flux (the
rate of energy transfer $\epsilon_u$). In this paper, we study the simplest
case with only gravitational interaction involved. In the absence of viscosity,
the energy cascade leads to a two-thirds law for pairwise velocity that extends
down to the smallest length scale, where quantum effects are dominant.
Combining the energy flux $\epsilon_u$, Planck constant $\hbar$, and
gravitational constant $G$ on that scale, the mass of dark matter particles is
found to be around $10^{12}$GeV and size is on the order of $10^{-13}$m. This
suggests a heavy dark matter scenario with a mass much greater than standard
thermal WIMPs.


title:
Learning to Solve Routing Problems via Distributionally Robust
  Optimization

abstract:
  Recent deep models for solving routing problems always assume a single
distribution of nodes for training, which severely impairs their
cross-distribution generalization ability. In this paper, we exploit group
distributionally robust optimization (group DRO) to tackle this issue, where we
jointly optimize the weights for different groups of distributions and the
parameters for the deep model in an interleaved manner during training. We also
design a module based on convolutional neural network, which allows the deep
model to learn more informative latent pattern among the nodes. We evaluate the
proposed approach on two types of well-known deep models including GCN and
POMO. The experimental results on the randomly synthesized instances and the
ones from two benchmark dataset (i.e., TSPLib and CVRPLib) demonstrate that our
approach could significantly improve the cross-distribution generalization
performance over the original models.


title: Neural Architecture Search for Dense Prediction Tasks in Computer Vision

abstract:
  The success of deep learning in recent years has lead to a rising demand for
neural network architecture engineering. As a consequence, neural architecture
search (NAS), which aims at automatically designing neural network
architectures in a data-driven manner rather than manually, has evolved as a
popular field of research. With the advent of weight sharing strategies across
architectures, NAS has become applicable to a much wider range of problems. In
particular, there are now many publications for dense prediction tasks in
computer vision that require pixel-level predictions, such as semantic
segmentation or object detection. These tasks come with novel challenges, such
as higher memory footprints due to high-resolution data, learning multi-scale
representations, longer training times, and more complex and larger neural
architectures. In this manuscript, we provide an overview of NAS for dense
prediction tasks by elaborating on these novel challenges and surveying ways to
address them to ease future research and application of existing methods to
novel problems.


title:
Projective representations of Heisenberg groups over the rings of order
  p^2

abstract:
  In this article we describe the 2-cocycles, Schur multiplier and
representation group of discrete Heisenberg groups over the unital rings of
order $p^2$. We describe all projective representations of Heisenberg groups
with entries from the rings $\mathbb Z/p^2\mathbb Z$ and
$\mathbb{F}_p[t]/(t^2)$ and obtain a classification of their degenerate and
non-degenerate 2-cocycles.


title: Explaining Reject Options of Learning Vector Quantization Classifiers

abstract:
  While machine learning models are usually assumed to always output a
prediction, there also exist extensions in the form of reject options which
allow the model to reject inputs where only a prediction with an unacceptably
low certainty would be possible. With the ongoing rise of eXplainable AI, a lot
of methods for explaining model predictions have been developed. However,
understanding why a given input was rejected, instead of being classified by
the model, is also of interest. Surprisingly, explanations of rejects have not
been considered so far.
  We propose to use counterfactual explanations for explaining rejects and
investigate how to efficiently compute counterfactual explanations of different
reject options for an important class of models, namely prototype-based
classifiers such as learning vector quantization models.


title:
MinT: A Fast Lightweight Envelope/Monte-Carlo Beam Optics Code for the
  Proton Beamlines of the Paul Scherrer Institute

abstract:
  We report about the methods used in, and the performance of, the new fast and
light-weight linear beam transport program MinT. MinT provides, beyond the
usual linear ion optics, methods to compute the effects of beam degradation,
multiple scattering and beam collimation. This is specifically important in
facilities where the ion beam passes matter, for instance in proton therapy
beamlines with an energy degrader as in the Proscan facility at PSI, but also
for modeling the beam traversing the Muon- and Pion-production targets of the
Paul Scherrer Institut's high intensity proton accelerator (HIPA). MinT is
intended to be useful as a support tool for the HIPA and Proscan control rooms.
This requires to have useful results within a few seconds. Hence simplicity and
speed of calculation is favored against higher accuracy. MinT has been designed
not only to replace the FORTRAN 77 codes TRANSPORT and TURTLE, but to combine
and extent their capabilities. MinT is a byte-code-compiler which translates an
input language, described by syntactic rules. This allows for control
structures like "if-then-else" or "while"-loops, thus providing a high
flexibility and readability.


title:
Pre-planetary nebulae: a context for principles, progress, and questions
  on how binaries and magnetic fields produce jets

abstract:
  Astrophysical outflows treated initially as spherically symmetric often show
evidence for asymmetry once seen at higher resolution. The preponderance of
aspherical and multipolar planetary nebulae (PN) and pre-planetary nebulae
(PPN) was evident after many observations from the Hubble Space Telescope.
Binary interactions have long been thought to be essential for shaping
asymmetric PN/PPN, but how? PPN are the more kinematically demanding of the
two, and warrant particular focus. I address how progress from observation and
theory suggests two broad classes of accretion driven PPN jets: one for wider
binaries (PPN-W) where the companion is outside the outer radius of the giant
and accretes via Roche lobe overflow, and the other which occurs in the later
stages of common envelope evolution (CEE) for close binaries (PPN-C). The
physics within these scenarios connects to progress and open questions about
the role and origin of magnetic fields in the engines and in astrophysical jets
more generally.


title:
CommerceMM: Large-Scale Commerce MultiModal Representation Learning with
  Omni Retrieval

abstract:
  We introduce CommerceMM - a multimodal model capable of providing a diverse
and granular understanding of commerce topics associated to the given piece of
content (image, text, image+text), and having the capability to generalize to a
wide range of tasks, including Multimodal Categorization, Image-Text Retrieval,
Query-to-Product Retrieval, Image-to-Product Retrieval, etc. We follow the
pre-training + fine-tuning training regime and present 5 effective pre-training
tasks on image-text pairs. To embrace more common and diverse commerce data
with text-to-multimodal, image-to-multimodal, and multimodal-to-multimodal
mapping, we propose another 9 novel cross-modal and cross-pair retrieval tasks,
called Omni-Retrieval pre-training. The pre-training is conducted in an
efficient manner with only two forward/backward updates for the combined 14
tasks. Extensive experiments and analysis show the effectiveness of each task.
When combining all pre-training tasks, our model achieves state-of-the-art
performance on 7 commerce-related downstream tasks after fine-tuning.
Additionally, we propose a novel approach of modality randomization to
dynamically adjust our model under different efficiency constraints.


title:
The quasi-fission phenomeonon of double charm $T_{cc}^+$ induced by
  nucleon

abstract:
  In this work, we study the reaction of a nucleon and a doubly charmed state
$T_{cc}$. Under the assumption of the $T_{cc}$ as a molecular state of
$D^{*}D$, the reaction of the nucleon and $T_{cc}$ is mediated by exchanges of
$\pi$, $\eta$, $\rho$, and $\omega$ meson, which results in split of $T_{cc}$
state with two $D$ mesons in final state. With the help of the effective
Lagrangians, the cross section of $p+T^+_{cc}\to p+D^++D^0$ process is
calculated, and a very large cross section can be obtained with very small
incoming momentum of proton. It decrease rapidly with the increase of the
momentum to about 10 mb at momenta of order of GeV. Such large cross section
suggests that induced by a proton the $T_{cc}^+$ state is very easy to decay
and transit to two $D$ mesons. In the rest frame of the $T_{cc}^+$ state, an
obvious accumulation of final $D$ meson at small momentum region can be
observed in predicted Dalitz plot, which is due to the molecular state
interpretation of $T_{cc}$ state. This novel quasi-fission phenomenon of double
charm molecular $T_{cc}^+$ state induced by a proton can be accessible at the
forthcoming PANDA experiment.


title:
Carrier-envelope offset stable, coherently combined ytterbium-doped
  fiber CPA delivering 1 kW of average power

abstract:
  We present a carrier-envelope offset (CEO) stable ytterbium-doped fiber
chirped-pulse amplification system employing the technology of coherent beam
combining and delivering more than 1 kW of average power at a pulse repetition
rate of 80 MHz. The CEO stability of the system is 220 mrad rms, characterized
out-of-loop with an f-to-2f interferometer in a frequency offset range of 10 Hz
to 20 MHz. The high-power amplification system boosts the average power of the
CEO stable oscillator by five orders of magnitude while increasing the phase
noise by only 100 mrad. No evidence of CEO noise deterioration due to coherent
beam combining is found. Low-frequency CEO fluctuations at the chirped-pulse
amplifier are suppressed by a slow loop feedback. To the best of our knowledge,
this is the first demonstration of a coherently combined laser system
delivering an outstanding average power and high CEO stability at the same
time.


title:
Tropical curves in abelian surfaces I: enumeration of curves passing
  through points

abstract:
  This paper is the first part in a series of three papers devoted to the study
of enumerative invariants of abelian surfaces through the tropical approach. In
this paper, we consider the enumeration of genus $g$ curves of fixed degree
passing through $g$ points. We compute the multiplicity provided by a
correspondence theorem due to T. Nishinou and show that it is possible to
refine this multiplicity in the style of Block-G\"ottsche to get tropical
refined invariants.


title: Disturbance Enhanced Uncertainty Relations

abstract:
  Uncertainty and disturbance are two most fundamental properties of a quantum
measurement and they are usually separately studied in terms of the preparation
and the measurement uncertainty relations. Here we shall establish an intimate
connection between them that goes beyond the above mentioned two kinds of
uncertainty relations. Our basic observation is that the disturbance of one
measurement to a subsequent measurement, which can be quantified based on
observed data, sets lower-bounds to uncertainty. This idea can be universally
applied to various measures of uncertainty and disturbance, with the help of
data processing inequality. The obtained relations, referred to as disturbance
enhanced uncertainty relations, immediately find various applications in the
field of quantum information. They ensure preparation uncertainty relation such
as novel entropic uncertainty relations independent of the Maassen and Uffink
relation. And they also result in a simple protocol to estimate coherence. We
anticipate that this new twist on uncertainty principle may shed new light on
quantum foundations and may also inspire further applications in the field of
quantum information.


title: Quantifying team chemistry in scientific collaboration

abstract:
  Team chemistry is the holy grail of understanding collaborative human
behavior, yet its quantitative understanding remains inconclusive. To reveal
the presence and mechanisms of team chemistry in scientific collaboration, we
reconstruct the publication histories of 560,689 individual scientists and
1,026,196 duos of scientists. We identify ability discrepancies between teams
and their members, enabling us to evaluate team chemistry in a way that is
robust against prior experience of collaboration and inherent randomness.
Furthermore, our network analysis uncovers a nontrivial modular structure that
allows us to predict team chemistry between scientists who have never
collaborated before. Research interest is the highest correlated ingredient of
team chemistry among six personal characteristics that have been commonly
attributed as the keys to successful collaboration, yet the diversity of the
characteristics cannot completely explain team chemistry. Our results may lead
to unlocking the hidden potential of collaboration by the matching of
well-paired scientists.


title: Exploiting Data Sparsity in Secure Cross-Platform Social Recommendation

abstract:
  Social recommendation has shown promising improvements over traditional
systems since it leverages social correlation data as an additional input. Most
existing work assumes that all data are available to the recommendation
platform. However, in practice, user-item interaction data (e.g.,rating) and
user-user social data are usually generated by different platforms, and both of
which contain sensitive information. Therefore, "How to perform secure and
efficient social recommendation across different platforms, where the data are
highly-sparse in nature" remains an important challenge. In this work, we bring
secure computation techniques into social recommendation, and propose S3Rec, a
sparsity-aware secure cross-platform social recommendation framework. As a
result, our model can not only improve the recommendation performance of the
rating platform by incorporating the sparse social data on the social platform,
but also protect data privacy of both platforms. Moreover, to further improve
model training efficiency, we propose two secure sparse matrix multiplication
protocols based on homomorphic encryption and private information retrieval.
Our experiments on two benchmark datasets demonstrate the effectiveness of
S3Rec.