title:
Thermodynamically Consistent Machine-Learned Internal State Variable
  Approach for Data-Driven Modeling of Path-Dependent Materials

abstract:
  Characterization and modeling of path-dependent behaviors of complex
materials by phenomenological models remains challenging due to difficulties in
formulating mathematical expressions and internal state variables (ISVs)
governing path-dependent behaviors. Data-driven machine learning models, such
as deep neural networks and recurrent neural networks (RNNs), have become
viable alternatives. However, pure black-box data-driven models mapping inputs
to outputs without considering the underlying physics suffer from unstable and
inaccurate generalization performance. This study proposes a machine-learned
physics-informed data-driven constitutive modeling approach for path-dependent
materials based on the measurable material states. The proposed data-driven
constitutive model is designed with the consideration of universal
thermodynamics principles, where the ISVs essential to the material
path-dependency are inferred automatically from the hidden state of RNNs. The
RNN describing the evolution of the data-driven machine-learned ISVs follows
the thermodynamics second law. To enhance the robustness and accuracy of RNN
models, stochasticity is introduced to model training. The effects of the
number of RNN history steps, the internal state dimension, the model
complexity, and the strain increment on model performances have been
investigated. The effectiveness of the proposed method is evaluated by modeling
soil material behaviors under cyclic shear loading using experimental
stress-strain data.


title:
Data-driven control of spatiotemporal chaos with reduced-order neural
  ODE-based models and reinforcement learning

abstract:
  Deep reinforcement learning (RL) is a data-driven method capable of
discovering complex control strategies for high-dimensional systems, making it
promising for flow control applications. In particular, the present work is
motivated by the goal of reducing energy dissipation in turbulent flows, and
the example considered is the spatiotemporally chaotic dynamics of the
Kuramoto-Sivashinsky equation (KSE). A major challenge associated with RL is
that substantial training data must be generated by repeatedly interacting with
the target system, making it costly when the system is computationally or
experimentally expensive. We mitigate this challenge in a data-driven manner by
combining dimensionality reduction via an autoencoder with a neural ODE
framework to obtain a low-dimensional dynamical model from just a limited data
set. We substitute this data-driven reduced-order model (ROM) in place of the
true system during RL training to efficiently estimate the optimal policy,
which can then be deployed on the true system. For the KSE actuated with
localized forcing ("jets") at four locations, we demonstrate that we are able
to learn a ROM that accurately captures the actuated dynamics as well as the
underlying natural dynamics just from snapshots of the KSE experiencing random
actuations. Using this ROM and a control objective of minimizing dissipation
and power cost, we extract a control policy from it using deep RL. We show that
the ROM-based control strategy translates well to the true KSE and highlight
that the RL agent discovers and stabilizes an underlying forced equilibrium
solution of the KSE system. We show that this forced equilibrium captured in
the ROM and discovered through RL is related to an existing known equilibrium
solution of the natural KSE.


title: The sequence of prime gaps is graphic

abstract:
  Let us call a simple graph on $n\geq 2$ vertices a prime gap graph if its
vertex degrees are $1$ and the first $n-1$ prime gaps. We show that such a
graph exists for every large $n$, and in fact for every $n\geq 2$ if we assume
the Riemann hypothesis. Moreover, an infinite sequence of prime gap graphs can
be generated by the so-called degree preserving growth process.


title: Using a novel fractional-order gradient method for CNN back-propagation

abstract:
  Computer-aided diagnosis tools have experienced rapid growth and development
in recent years. Among all, deep learning is the most sophisticated and popular
tool. In this paper, researchers propose a novel deep learning model and apply
it to COVID-19 diagnosis. Our model uses the tool of fractional calculus, which
has the potential to improve the performance of gradient methods. To this end,
the researcher proposes a fractional-order gradient method for the
back-propagation of convolutional neural networks based on the Caputo
definition. However, if only the first term of the infinite series of the
Caputo definition is used to approximate the fractional-order derivative, the
length of the memory is truncated. Therefore, the fractional-order gradient
(FGD) method with a fixed memory step and an adjustable number of terms is used
to update the weights of the layers. Experiments were performed on the COVIDx
dataset to demonstrate fast convergence, good accuracy, and the ability to
bypass the local optimal point. We also compared the performance of the
developed fractional-order neural networks and Integer-order neural networks.
The results confirmed the effectiveness of our proposed model in the diagnosis
of COVID-19.


title: A Transfer Principle for Branched Rough Paths

abstract:
  A branched rough path $X$ consists of a rough integral calculus for $X \colon
[0, T] \to \mathbb R^d$ which may fail to satisfy integration by parts. Using
Kelly's bracket extension [Kel12], we define a notion of pushforward of
branched rough paths through smooth maps, which leads naturally to a definition
of branched rough path on a smooth manifold. Once a covariant derivative is
fixed, we are able to give a canonical, coordinate-free definition of integral
against such rough paths. After characterising quasi-geometric rough paths in
terms of their bracket extension, we use the same framework to define
manifold-valued rough differential equations (RDEs) driven by quasi-geometric
rough paths. These results extend previous work on $3 > p$-rough paths
[ABCRF22], itself a generalisation of the Ito calculus on manifolds developed
by Meyer and Emery [Mey81, E89, E90], to the setting of non-geometric rough
calculus of arbitrarily low regularity.


title:
Accelerated Algorithms for a Class of Optimization Problems with
  Constraints

abstract:
  This paper presents a framework to solve constrained optimization problems in
an accelerated manner based on High-Order Tuners (HT). Our approach is based on
reformulating the original constrained problem as the unconstrained
optimization of a loss function. We start with convex optimization problems and
identify the conditions under which the loss function is convex. Building on
the insight that the loss function could be convex even if the original
optimization problem is not, we extend our approach to a class of nonconvex
optimization problems. The use of a HT together with this approach enables us
to achieve a convergence rate better than state-of-the-art gradient-based
methods. Moreover, for equality-constrained optimization problems, the proposed
method ensures that the state remains feasible throughout the evolution,
regardless of the convexity of the original problem.


title: Supporting Complex Information-Seeking Tasks with Implicit Constraints

abstract:
  Current interactive systems with natural language interface lack an ability
to understand a complex information-seeking request which expresses several
implicit constraints at once, and there is no prior information about user
preferences, e.g., "find hiking trails around San Francisco which are
accessible with toddlers and have beautiful scenery in summer", where output is
a list of possible suggestions for users to start their exploration. In such
scenarios, the user requests can be issued at once in the form of a complex and
long query, unlike conversational and exploratory search models that require
short utterances or queries where they often require to be fed into the system
step by step. This advancement provides the final user more flexibility and
precision in expressing their intent through the search process. Such systems
are inherently helpful for day-today user tasks requiring planning that are
usually time-consuming, sometimes tricky, and cognitively taxing. We have
designed and deployed a platform to collect the data from approaching such
complex interactive systems. In this paper, we propose an Interactive Agent
(IA) that allows intricately refined user requests by making it complete, which
should lead to better retrieval. To demonstrate the performance of the proposed
modeling paradigm, we have adopted various pre-retrieval metrics that capture
the extent to which guided interactions with our system yield better retrieval
results. Through extensive experimentation, we demonstrated that our method
significantly outperforms several robust baselines


title:
On the importance of scalability and resource estimation of quantum
  algorithms for domain sciences

abstract:
  The quantum information science community has seen a surge in new algorithmic
developments across scientific domains. These developments have demonstrated
polynomial or better improvements in computational and space complexity,
incentivizing further research in the field. However, despite recent progress,
many works fail to provide quantitative estimates on algorithmic scalability or
quantum resources required -- e.g., number of logical qubits, error thresholds,
etc. -- to realize the highly sought "quantum advantage." In this paper, we
discuss several quantum algorithms and motivate the importance of such
estimates. By example and under simple scaling assumptions, we approximate the
computational expectations of a future quantum device for a high energy physics
simulation algorithm and how it compares to its classical analog. We assert
that a standard candle is necessary for claims of quantum advantage.


title:
Fitting Generalized Tempered Stable distribution: Fractional Fourier
  Transform (FRFT) Approach

abstract:
  The paper investigates the rich class of Generalized Tempered Stable
distribution, an alternative to Normal distribution and the $\alpha$-Stable
distribution for modelling asset return and many physical and economic systems.
Firstly, we explore some important properties of the Generalized Tempered
Stable (GTS) distribution. The theoretical tools developed are used to perform
empirical analysis. The GTS distribution is fitted using three indexes: S\&P
500, SPY ETF and Bitcoin BTC. The Fractional Fourier Transform (FRFT) technique
evaluates the probability density function and its derivatives in the maximum
likelihood procedure. Based on the three sample data, The Kolmogorov-Smirnov
(KS) goodness-of-fit shows that the GTS distribution fits both sides of the
underlying distribution for SPY EFT index and Bitcoin BTC returns. Regarding
the S\&P 500 index, the Tempered Stable distribution fits the right side of the
underlying distribution, while the compound Poisson distribution fits the left
side.


title:
A Position-Free Path Integral for Homogeneous Slabs and Multiple
  Scattering on Smith Microfacets

abstract:
  We consider the problem of multiple scattering on Smith microfacets. This
problem is equivalent to computing volumetric light transport in a homogeneous
slab. Although the symmetry of the slab allows for significant simplification,
fully analytic solutions are scarce and not general enough for most
applications. Standard Monte Carlo simulation, although general, is expensive
and leads to variance that must be dealt with.
  We present the first unbiased, truly position-free path integral for
evaluating the BSDF of a homogeneous slab. We collapse the spatially-1D path
integral of previous works to a position-free form using an analytical
preintegration of collision distances. Evaluation of the resulting path
integral, which now contains only directions, reduces to simple recursive
manipulation of exponential distributions. Applying Monte Carlo to solve the
reduced integration problem leads to lower variance.
  Our new algorithm allows us to render multiple scattering on Smith
microfacets with less variance than prior work, and, in the case of conductors,
to do so without any bias. Additionally, our algorithm can also be used to
accelerate the rendering of BSDFs containing volumetrically scattering layers,
at reduced variance compared to standard Monte Carlo integration.


title:
Tightness without Counterexamples: A New Approach and New Results for
  Prophet Inequalities

abstract:
  Prophet inequalities consist of many beautiful statements that establish
tight performance ratios between online and offline allocation algorithms.
Typically, tightness is established by constructing an algorithmic guarantee
and a worst-case instance separately, whose bounds match as a result of some
"ingenuity". In this paper, we instead formulate the construction of the
worst-case instance as an optimization problem, which directly finds the tight
ratio without needing to construct two bounds separately.
  Our analysis of this complex optimization problem involves identifying
structure in a new "Type Coverage" dual problem. It can be seen as akin to the
celebrated Magician and OCRS (Online Contention Resolution Scheme) problems,
except more general in that it can also provide tight ratios relative to the
optimal offline allocation, whereas the earlier problems only establish tight
ratios relative to the ex-ante relaxation of the offline problem.
  Through this analysis, our paper provides a unified framework that derives
new results and recovers many existing ones. First, we show that the
"oblivious" method of setting a static threshold due to Chawla et al. (2020)
is, surprisingly, best-possible among all static threshold algorithms, for any
number $k$ of starting units. We emphasize that this result is derived without
needing to explicitly find any counterexample instances. We establish similar
"no separation" results for static thresholds in the IID setting, which
although previously known, required the construction of complicated
counterexamples. Finally, our framework and in particular our Type Coverage
problem yields a simplified derivation of the tight 0.745 ratio when $k=1$ in
the IID setting.


title: Electron spin coherence on a solid neon surface

abstract:
  A single electron floating on the surface of a condensed noble-gas liquid or
solid can act as a spin qubit with ultralong coherence time, thanks to the
extraordinary purity of such systems. Previous studies suggest that the
electron spin coherence time on a superfluid helium (He) surface can exceed 1
s. In this paper, we present theoretical studies of the electron spin coherence
on a solid neon (Ne) surface, motivated by our recent experimental realization
of single-electron charge qubit in this system. The major spin decoherence
mechanisms include the fluctuating Ne diamagnetic susceptibility due to thermal
phonons, the fluctuating thermal current in normal metal electrodes, and the
quasi-statically fluctuating nuclear spins of the $^{21}$Ne ensemble. We find
that at a typical experimental temperature about 10 mK in a fully
superconducting device, the electron spin decoherence is dominated by the third
mechanism of electron-nuclear spin-spin interaction. For natural Ne with 2700
ppm abundance of $^{21}$Ne, the inhomogeneous dephasing time $T_{2}^{*}$ is
around 0.16 ms, already better than most semiconductor quantum-dot spin qubits.
For isotopically purified Ne with 0.4 ppm of $^{21}$Ne, $T_2^*$ can be 1 s.
Moreover, under the application of Hahn echoes, the coherence time $T_{2}$ can
be improved to $30$ ms for natural Ne and around 200 s for purified Ne.
Therefore, the single-electron spin qubits on solid Ne surface can serve as
promising new spin qubits.


title: Milky Way thin and thick disk kinematics with GAIA EDR3 and RAVE DR5

abstract:
  We present a detailed analysis of kinematics of the Milky Way disk in solar
neighborhood using GAIA DR3 catalog. To determine the local kinematics of the
stellar disks of the Milky Way galaxy we use a complete sample of 278,228 red
giant branch (RGB) stars distributed in a cylinder, centered at the Sun with a
1 kpc radius and half-height of 0.5 kpc. We determine separately the
kinematical properties of RGB stars for each Galactic hemisphere in search for
possible asymmetries. The kinematical properties of the RGB stars reveal the
existence of two kinematically distinct components: the thin disk with mean
velocities ${V_R}, {V_{\phi}}, {V_Z}$ of about -1, -239, 0 km s$^{-1}$
correspondingly and velocity dispersions $\sigma_R, \sigma_{\phi}, \sigma_Z$ of
31, 20 and 11 km s$^{-1}$, and the Thick disk with mean velocities components
of about +1, -225, 0 km s$^{-1}$, and velocity dispersions of 49, 35, and 22 km
s$^{-1}$. We find that up to 500 pc height above/below the galactic plane,
Thick disk stars comprise about half the stars of the disk. Such high amount of
RGB stars with Thick disk kinematics points at the secular evolution scenario
origin for the Thick disk of the Milky Way galaxy.


title:
High-Power Mid-IR Few-Cycle Frequency Comb from Quadratic Solitons in an
  Optical Parametric Oscillator

abstract:
  Powerful and efficient optical frequency combs in the mid-infrared (MIR)
spectral region are highly desirable for a broad range of applications. Despite
extensive efforts utilizing various techniques, MIR frequency comb sources are
still lacking power, efficiency, or bandwidth for many applications. Here, we
report the generation of an intrinsically locked frequency comb source centered
at 4.18 {\mu}m from an optical parametric oscillator (OPO) operating in the
simulton regime, in which formation of purely quadratic solitons lead to
enhanced performance. We show advantages of operation in the simulton regime in
direct experimental comparisons to the conventional regime, which are also
supported by simulation and theory. We achieve 565 mW of average power, 900 nm
of instantaneous 3-dB bandwidth, 350% of slope efficiency, and 44% of
conversion efficiency; a performance that is superior to previous OPO
demonstrations and other sources in this wavelength range. This work opens a
new avenue toward MIR frequency comb generation with high power and efficiency
and suggests the great potential of soliton generation based on quadratic
nonlinearity in the MIR spectral region.


title:
Local well-posedness of the Cauchy problem for a p-adic Nagumo-type
  equation

abstract:
  We introduce a new family of p-adic non-linear evolution equations. We
establish the local well-posedness of the Cauchy problem for these equations in
Sobolev-type spaces. For a certain subfamily, we show that the blow-up
phenomenon occurs and provide numerical simulations showing this phenomenon.


title:
PFNN-2: A Domain Decomposed Penalty-Free Neural Network Method for
  Solving Partial Differential Equations

abstract:
  A new penalty-free neural network method, PFNN-2, is presented for solving
partial differential equations, which is a subsequent improvement of our
previously proposed PFNN method [1]. PFNN-2 inherits all advantages of PFNN in
handling the smoothness constraints and essential boundary conditions of
self-adjoint problems with complex geometries, and extends the application to a
broader range of non-self-adjoint time-dependent differential equations. In
addition, PFNN-2 introduces an overlapping domain decomposition strategy to
substantially improve the training efficiency without sacrificing accuracy.
Experiments results on a series of partial differential equations are reported,
which demonstrate that PFNN-2 can outperform state-of-the-art neural network
methods in various aspects such as numerical accuracy, convergence speed, and
parallel scalability.


title: Non-Markovian dynamics of black hole phase transition

abstract:
  We provide a comprehensive study on the non-Markovian dynamics of the black
hole phase transitions based on the underlying free energy landscape. By
assuming that the transition processes between different black hole states are
stochastic, the non-Markovian dynamics of the black hole phase transition is
governed by the generalized Langevin equation with the time-dependent friction
that represents the memory effect from the effective thermal bath when the
timescale of the system is comparable or shorter than the timescale of the
effective thermal bath. We consider the first passage problem associated with
the black hole phase transitions and derive the analytical expressions of the
mean first passage time in the weak, intermediate, and large friction regimes.
As the concrete examples, we study the effects of three types of time dependent
friction kernel (delta function friction, exponentially decayed friction, and
oscillatory friction) on the dynamics of Hawking-Page phase transition and the
small/large RNAdS black hole phase transition. we found that there is a kinetic
turnover point for each type of friction kernel when the friction strength
varies. For the exponentially decayed friction kernel, it is shown that the
non-Markovian effect slows down the phase transition dynamics in the weak
friction regime and speeds up the transition process in the strong friction
regime. For the oscillating decayed friction kernel, we found kinetic
resonances when the oscillating frequency of the effective thermal bath is
equal to the oscillating frequency of the black hole state in the initial
potential well on the free energy landscape.


title: Trisecting the 9-vertex complex projective plane

abstract:
  In this paper we will give a short and direct proof that Wolfgang Kuehnel's
9-vertex triangulation of the complex projective plane really is the complex
projective plane. The idea of our proof is to recall the trisection of the
complex projective plane into 3 bi-disks and then to see this trisection inside
a symmetry-breaking subdivision of the triangulation.


title: Generalization of the basis theorem for the B-type Coxeter groups

abstract:
  The OGS for non-abelian groups is an interesting generalization of the basis
of finite abelian groups. The definition of OGS states that every element of a
group has a unique presentation as a product of some powers of specific
generators of the group, in a specific given order. In case of the symmetric
groups Sn there is a paper of R. Shwartz, which demonstrates a strong
connection between the OGS and the standard Coxeter presentation of the
symmetric group, which is called the standard OGS of Sn. In this paper we
generalize the standard OGS of Sn to the finite classical Coxeter group Bn. We
describe the exchange laws for the generalized standard OGS of Bn, and we
connect it to the Coxeter length and the descent set of Bn.


title:
Generalized perfect difference families and their application to
  variable-weight geometric orthogonal codes

abstract:
  Motivated by the application in geometric orthogonal codes (GOCs), Wang et
al. introduced the concept of generalized perfect difference families (PDFs),
and established the equivalence between GOCs and a certain type of generalized
PDFs recently. Based on the relationship, we discuss the existence problem of
generalized $(n\times m,K,1)$-PDFs in this paper. By using some auxiliary
designs such as semi-perfect group divisible designs and several recursive
constructions, we prove that a generalized $(n\times m, \{3,4\}, 1)$-PDF exists
if and only if $nm\equiv1\pmod{6}$. The existence of a generalized $(n\times m,
\{3,4,5\}, 1)$-PDF is also completely solved possibly except for a few values.
As a consequence, some variable-weight perfect $(n\times m,K,1)$-GOCs are
obtained.\vspace{0.2cm}
  {\bf Keywords}: generalized perfect difference family, generalized perfect
difference packing, geometric orthogonal code, semi-perfect group divisible
design


title:
Variation-cognizant Probabilistic Power Flow Analysis via Multi-task
  Learning

abstract:
  With an increasing high penetration of solar photovoltaic generation in
electric power grids, voltage phasors and branch power flows experience more
severe fluctuations. In this context, probabilistic power flow (PPF) study aims
at characterizing the statistical properties of the state of the system with
respect to the random power injections. To avoid repeated power flow
calculations involved in PPF study, the present paper leverages regression
algorithms and neural networks to improve the estimation performance and speed
up the computation. Specifically, based on the variation level of the voltage
magnitude at each bus, we develop either a linear regression or a fully
connected neural network to approximate the inverse AC power flow mappings. The
proposed multi-task learning technique further improves the accuracy of branch
flow estimation by incorporating the errors of voltage angle differences into
the loss function design. Tested on IEEE-300 and IEEE-1354 bus systems with
real data, the proposed methods achieve better performance in estimating
voltage phasors and branch flows.


title:
Enhanced Seebeck coefficient through the magnetic fluctuations in
  Sr$_2$Ru$_{1-x}M_x$O$_4$ ($M = $ Co, Mn)

abstract:
  The layered perovskite Sr$_2$RuO$_4$ is a most intensively studied
superconductor, but its pairing mechanism, which is often coupled intimately
with magnetic fluctuations in correlated materials, is still an open question.
Here we present a systematic evolution of the Seebeck coefficient in Co- and
Mn-substituted Sr$_2$RuO$_4$ single crystals, in which ferromagnetic and
antiferromagnetic glassy states respectively emerge in proximity to the
superconducting phase of the parent compound. We find that the Seebeck
coefficient $S$ divided by temperature $T$, $S/T$, shows a maximum near
characteristic temperatures seen in the irreversible magnetization $M_{\rm ir}$
in both of the Co- and Mn-substituted crystals, demonstrating both of the
ferromagnetic and antiferromagnetic fluctuations to enhance the Seebeck
coefficient. Interestingly, $S/T$ increases with lowering temperature in the
parent compound, reminiscent of non-Fermi-liquid behavior, indicating an
essential role of coexisting ferromagnetic and antiferromagnetic fluctuations
for the itinerant electrons in Sr$_2$RuO$_4$.


title:
HatCUP: Hybrid Analysis and Attention based Just-In-Time Comment
  Updating

abstract:
  When changing code, developers sometimes neglect updating the related
comments, bringing inconsistent or outdated comments. These comments increase
the cost of program understanding and greatly reduce software maintainability.
Researchers have put forward some solutions, such as CUP and HEBCUP, which
update comments efficiently for simple code changes (i.e. modifying of a single
token), but not good enough for complex ones. In this paper, we propose an
approach, named HatCUP (Hybrid Analysis and Attention based Comment UPdater),
to provide a new mechanism for comment updating task. HatCUP pays attention to
hybrid analysis and information. First, HatCUP considers the code structure
change information and introduces a structure-guided attention mechanism
combined with code change graph analysis and optimistic data flow dependency
analysis. With a generally popular RNN-based encoder-decoder architecture,
HatCUP takes the action of the code edits, the syntax, semantics and structure
code changes, and old comments as inputs and generates a structural
representation of the changes in the current code snippet. Furthermore, instead
of directly generating new comments, HatCUP proposes a new edit or non-edit
mechanism to mimic human editing behavior, by generating a sequence of edit
actions and constructing a modified RNN model to integrate newly developed
components. Evaluation on a popular dataset demonstrates that HatCUP
outperforms the state-of-the-art deep learning-based approaches (CUP) by 53.8%
for accuracy, 31.3% for recall and 14.3% for METEOR of the original metrics.
Compared with the heuristic-based approach (HEBCUP), HatCUP also shows better
overall performance.


title:
On the value-distribution of the logarithms of symmetric square
  L-functions in the level aspect

abstract:
  We consider the value distribution of logarithms of symmetric square
L-functions associated with newforms of even weight and prime power level at
real s> 1/2. We prove that certain averages of those values can be written as
integrals involving a density function which is related with the Sato-Tate
measure. Moreover, we discuss the case of symmetric power L-functions.


title: Amplitude Amplification for Optimization via Subdivided Phase Oracle

abstract:
  We propose an algorithm using a modified variant of amplitude amplification
to solve combinatorial optimization problems via the use of a subdivided phase
oracle. Instead of dividing input states into two groups and shifting the phase
equally for all states within the same group, the subdivided phase oracle
changes the phase of each input state uniquely in proportion to their objective
value. We provide visualization of how amplitudes change after each iteration
of applying the subdivided phase oracle followed by conventional Grover
diffusion in the complex plane. We then show via numerical simulation that for
normal, skew normal, and exponential distribution of objective values, the
algorithm can be used to amplify the probability of measuring the optimal
solution to a significant degree independent of the search space size. In the
case of skew normal and exponential distributions, this probability can be
amplified to be close to unity, making our algorithm near deterministic. We
then modify our algorithm in order to demonstrate how it can be extended to a
broader set of objective value distributions. Finally, we discuss the speedup
compared to classical schemes using the query complexity model, and show that
our algorithm offers a significant advantage over these classical approaches.


title: Formation of primordial black holes after Starobinsky inflation

abstract:
  We adapted the Appleby-Battye-Starobinsky model of $F(R)$ gravity towards
describing double cosmological inflation and formation of primordial black
holes with masses up to $10^{19}$ g in the single-field model. We found that it
is possible to get an enhancement of the power spectrum of scalar curvature
perturbations to the level beyond the Hawking (black hole evaporation) limit of
$10^{15}$ g, so that the primordial black holes resulting from gravitational
collapse of those large primordial perturbations can survive in the present
universe and form part of cold dark matter. Our results agree with the current
measurements of cosmic microwave background radiation within $3\sigma$ but
require fine-tuning of the parameters.


title:
Singularities and full convergence of the M\"obius-invariant Willmore
  flow in $\mathbb{S}^3$

abstract:
  In this article we continue our investigation of the M\"obius-invariant
Willmore flow, starting to move in smooth and umbilic-free immersions $F_0$,
which map some fixed torus $\Sigma$ into $\mathbb{S}^n$. Here we investigate
the behaviour of flow lines $\{F_t\}$ of the MIWF in $\mathbb{S}^3$ starting to
move with Willmore energy below $8\pi$, as the time $t$ approaches the maximal
time of existence $T_{max}(F_0)$. We prove that any singularity of a flow line
$\{F_t\}$ of the MIWF can be interpreted as the support of a $2$-rectifiable,
integral varifold $\mu$ on $\mathbb{R}^4$, which is the weak limit of varifolds
$\{\mathcal{H}^2\lfloor_{F_{t_{j_l}}}\}$, for an appropriate sequence $t_{j_l}
\nearrow T_{max}(F_0)$, and that the support $spt(\mu)$ has either degenerated
to a point or is homeomorphic to either a $2$-sphere or a torus in
$\mathbb{S}^3$. The cases $spt(\mu)=\{point\} \vee spt(\mu) \cong \mathbb{S}^2$
can only occur, if the measures
$\mathcal{M}^0_l(\Omega):=\int_{F_{t_{j_l}}^{-1}(\Omega\cap \mathbb{S}^3)}
|A^0_{F_{t_{j_l}}}|^2\, d\mu_{F_{t_{j_l}}^*g_{euc}}$ on $\mathbb{R}^4$
concentrate in at least one point in $\mathbb{S}^3$, as $l \nearrow \infty$. If
the singularity $spt(\mu)$ is a torus, it can be parametrized by a bi-Lipschitz
homeomorphism, which is uniformly conformal w.r.t. some appropriate metric of
vanishing scalar curvature on $\Sigma$. If the initial immersion of a flow line
$\{F_t\}$ is assumed to parametrize a smooth Hopf-torus in $\mathbb{S}^3$ with
Willmore energy smaller than $8\pi$, then we obtain stronger types of
convergence of certain subsequences of $\{F_{t_{j_l}}\}$ to uniformly conformal
$W^{4,2}$-parametrizations of limit Hopf-tori, which finally yields a
surprisingly simple criterion for full $C^m$-convergence of such a flow line to
a smooth parametrization of a Clifford-torus, as $t\nearrow \infty$, for any
fixed integer $m$.


title: Forecasting Market Changes using Variational Inference

abstract:
  Though various approaches have been considered, forecasting near-term market
changes of equities and similar market data remains quite difficult. In this
paper we introduce an approach to forecast near-term market changes for equity
indices as well as portfolios using variational inference (VI). VI is a machine
learning approach which uses optimization techniques to estimate complex
probability densities. In the proposed approach, clusters of explanatory
variables are identified and market changes are forecast based on
cluster-specific linear regression. Apart from the expected value of changes,
the proposed approach can also be used to obtain the distribution of possible
outcomes, which can be used to estimate confidence levels of forecasts and risk
measures such as VaR (Value at Risk) for the portfolio. Another advantage of
the proposed approach is the clear model interpretation, as clusters of
explanatory variables (or market regimes) are identified for which the future
changes follow similar relationships. Knowledge about such clusters can provide
useful insights about portfolio performance and identify the relative
importance of variables in different market regimes. Illustrative examples of
equity and bond indices are considered to demonstrate forecasts of the proposed
approach during Covid-related volatility in early 2020 and subsequent benign
market conditions. For the portfolios considered, it is shown that the proposed
approach provides useful forecasts in both normal and volatile markets even
with only a few explanatory variables. Additionally the predicted estimate and
distribution adapt quickly to changing market conditions and thus may also be
useful in obtaining better real-time estimates of risk measures such as VaR
compared to traditional approaches.


title:
A Speed Bump: SN 2021aefx Shows that Doppler Shift Alone can Explain
  Early-Excess Blue Flux in Some Type Ia Supernovae

abstract:
  We present early-time photometric and spectroscopic observations of the Type
Ia Supernova (SN Ia) 2021aefx. The early time u-band light curve shows an
excess flux when compared to normal SNe Ia. We suggest that the early-excess
blue flux may be due to a rapid change in spectral velocity in the first few
days post explosion, produced by the emission of the Ca II H&K feature passing
from the u to the B bands on the time scale of a few days. This effect could be
dominant for all SNe Ia which have broad absorption features and early-time
velocities over 25,000 km/s. It is likely to be one of the main causes of
early-excess u-band flux in SNe Ia which have early-time high-velocities. This
effect may also be dominant in the UV filters, as well as in places where the
SN spectral energy distribution is quickly rising to longer wavelengths. The
rapid change in velocity can only produce a monotonic change (in flux-space) in
the u-band. For objects which explode at lower velocities, and have a more
structured shape in the early-excess emission, there must also be an additional
parameter producing the early-time diversity. More early time observations, in
particular early spectra, are required to determine how prominent this effect
is within SNe Ia.


title:
Finite abelian subgroups in the groups of birational and bimeromorphic
  selfmaps

abstract:
  Let $X$ be a complex projective variety. Suppose that the group of birational
automorphisms of $X$ contains finite subgroups isomorphic to
$(\mathbb{Z}/N_i\mathbb{Z})^r$ for $r$ fixed and $N_i$ arbitrarily large. We
show that $r$ does not exceed $2\dim(X)$. We also show that the same result
holds for groups of bimeromorphic automorphisms of compact K\"ahler threefolds.


title:
Physics-aware Reduced-order Modeling of Transonic Flow via
  $\beta$-Variational Autoencoder

abstract:
  Autoencoder-based reduced-order modeling has recently attracted significant
attention, owing to the ability to capture underlying nonlinear features.
However, its uninterpretable latent variables (LVs) severely undermine the
applicability to various physical problems. This study proposes physics-aware
reduced-order modeling using a $\beta$-variational autoencoder to address this
issue. The presented approach can quantify the rank and independence of LVs,
which is validated both quantitatively and qualitatively using various
techniques. Accordingly, LVs containing interpretable physical features were
successfully identified. It was also verified that these "physics-aware" LVs
correspond to the physical parameters that are the generating factors of the
dataset, i.e., the Mach number and angle of attack in this study. Moreover, the
effects of these physics-aware LVs on the accuracy of reduced-order modeling
were investigated, which verified the potential of this method to alleviate the
computational cost of the offline stage by excluding physics-unaware LVs.


title: D-instanton Effects in Type IIB String Theory

abstract:
  We study D-instanton contributions to supergraviton scattering amplitudes in
the ten-dimensional type IIB superstring theory beyond the leading
non-perturbative order. In particular, we determine the one-D-instanton
contribution to maximal R-symmetry violating (MRV) amplitudes with arbitrary
momenta at the first subleading order in string coupling, as well as the
effects of a D-/anti-D-instanton pair at leading nontrivial order in the
momentum expansion. These results confirm a number of predictions of S-duality,
and unveil some previously unknown pieces of type IIB string amplitudes.
  Our computation is based on the Neveu-Schwarz-Ramond formalism with picture
changing operators and vertical integration. The naive on-shell prescription
for D-instanton mediated amplitudes, based on integration over the moduli space
of worldsheet geometries as well as the moduli space of D-instanton boundary
conditions, suffers from potential open string divergences and regularization
ambiguities that are in principle resolved in the framework of open+closed
superstring field theory. In this paper, the "on-shell ambiguities" of
one-D-instanton MRV amplitudes are resolved by arguments involving
supersymmetry and soft limits, part of which is verified by a string field
theoretic computation in a highly nontrivial manner.


title: Systematically Missing Data in Causally Interpretable Meta-Analysis

abstract:
  Causally interpretable meta-analysis combines information from a collection
of randomized controlled trials to estimate treatment effects in a target
population in which experimentation may not be possible but covariate
information can be collected from a simple random sample. In such analyses, a
key practical challenge is systematically missing data when some baseline
covariates are not collected in all trials. Here, we provide identification
results for potential (counterfactual) outcome means and average treatment
effects in the target population when covariate data are systematically missing
from some of the trials in the meta-analysis. We propose three estimators for
the average treatment effect in the target population, examine their asymptotic
properties, and show that they have good finite-sample performance in
simulation studies. We use the estimators to analyze data from two large lung
cancer screening trials and target population data from the National Health and
Nutrition Examination Survey (NHANES). To accommodate the complex survey design
of the NHANES, we modify the methods to incorporate survey sampling weights and
allow for clustering.


title: Improved Low-Depth Set-Multilinear Circuit Lower Bounds

abstract:
  We prove strengthened lower bounds for constant-depth set-multilinear
formulas. More precisely, we show that over any field, there is an explicit
polynomial $f$ in VNP defined over $n^2$ variables, and of degree $n$, such
that any product-depth $\Delta$ set-multilinear formula computing $f$ has size
at least $n^{\Omega \left( n^{1/\Delta}/\Delta\right)}$. The hard polynomial
$f$ comes from the class of Nisan-Wigderson (NW) design-based polynomials.
  Our lower bounds improve upon the recent work of Limaye, Srinivasan and
Tavenas (STOC 2022), where a lower bound of the form $(\log n)^{\Omega (\Delta
n^{1/\Delta})}$ was shown for the size of product-depth $\Delta$
set-multilinear formulas computing the iterated matrix multiplication (IMM)
polynomial of the same degree and over the same number of variables as $f$.
Moreover, our lower bounds are novel for any $\Delta\geq 2$.
  For general set-multilinear formulas, a lower bound of the form $
n^{\Omega(\log n)}$ was already obtained by Raz (J. ACM 2009) for the more
general model of multilinear formulas. The techniques of LST give a different
route to set-multilinear formula lower bounds, and allow them to obtain a lower
bound of the form $(\log n)^{\Omega(\log n)}$ for the size of general
set-multilinear formulas computing the IMM polynomial. Our proof techniques are
another variation on those of LST, and enable us to show an improved lower
bound (matching that of Raz) of the form $n^{\Omega(\log n)}$, albeit for the
same polynomial $f$ in VNP (the NW polynomial). As observed by LST, if the same
$n^{\Omega(\log n)}$ size lower bounds for unbounded-depth set-multilinear
formulas could be obtained for the IMM polynomial, then using the
self-reducibility of IMM and using hardness escalation results, this would
imply super-polynomial lower bounds for general algebraic formulas.


title: The quest for a conifold conformal order

abstract:
  The holographic duality between cascading gauge theory and type IIB
supergravity on warped deformed conifold with fluxes reveals exotic thermal
phases with nonzero expectation values of certain operators, persistent to high
temperatures. These phases, in the limit of vanishing the strong coupling scale
of the cascading gauge theory, would realize thermal ordered conformal phases
in relativistic QFTs. We find that the dual Klebanov-Strassler and
Klebanov-Tseytlin black branes in this limit are outside the regime of the
supergravity approximation, rendering the construction of such conformal
ordered states unreliable. While we have been able to construct conformal order
in phenomenologically deformed effective theory of type IIB supergravity
reduced on warped deformed conifold with fluxes, the removal of the deformation
parameter causes the destruction of the thermal conformal ordered phases. Once
again, we find that the holographic models with the conformal ordered phases
are in the String Theory swampland.


title: MUTR3D: A Multi-camera Tracking Framework via 3D-to-2D Queries

abstract:
  Accurate and consistent 3D tracking from multiple cameras is a key component
in a vision-based autonomous driving system. It involves modeling 3D dynamic
objects in complex scenes across multiple cameras. This problem is inherently
challenging due to depth estimation, visual occlusions, appearance ambiguity,
etc. Moreover, objects are not consistently associated across time and cameras.
To address that, we propose an end-to-end \textbf{MU}lti-camera
\textbf{TR}acking framework called MUTR3D. In contrast to prior works, MUTR3D
does not explicitly rely on the spatial and appearance similarity of objects.
Instead, our method introduces \textit{3D track query} to model spatial and
appearance coherent track for each object that appears in multiple cameras and
multiple frames. We use camera transformations to link 3D trackers with their
observations in 2D images. Each tracker is further refined according to the
features that are obtained from camera images. MUTR3D uses a set-to-set loss to
measure the difference between the predicted tracking results and the ground
truths. Therefore, it does not require any post-processing such as non-maximum
suppression and/or bounding box association. MUTR3D outperforms
state-of-the-art methods by 5.3 AMOTA on the nuScenes dataset. Code is
available at: \url{https://github.com/a1600012888/MUTR3D}.


title:
Extracting Symbolic Models of Collective Behaviors with Graph Neural
  Networks and Macro-Micro Evolution

abstract:
  Collective behaviors are typically hard to model. The scale of the swarm, the
large number of interactions, and the richness and complexity of the behaviors
are factors that make it difficult to distill a collective behavior into simple
symbolic expressions. In this paper, we propose a novel approach to symbolic
regression designed to facilitate such modeling. Using raw and post-processed
data as an input, our approach produces viable symbolic expressions that
closely model the target behavior. Our approach is composed of two phases. In
the first, a graph neural network (GNN) is trained to extract an approximation
of the target behavior. In the second phase, the GNN is used to produce data
for a nested evolutionary algorithm called macro-micro evolution (MME). The
macro layer of this algorithm selects candidate symbolic expressions, while the
micro layer tunes its parameters. Experimental evaluation shows that our
approach outperforms competing solutions for symbolic regression, making it
possible to extract compact expressions for complex swarm behaviors.


title:
Quantum Key Infrastructure: A scalable quantum-proof key distribution
  system

abstract:
  We propose and implement a protocol for a scalable, cost-effective,
information-theoretically secure key distribution and management system. The
system, called Quantum Key Infrastructure (QKI), relies on pre-shared random
numbers between QKI clients and a group of Quantum Entropy Managers (QEMs). Any
group of QKI clients can use the QKI protocol to distill from the pre-shared
numbers a secret key. The clients are protected from QEM compromise via a
secret sharing scheme that allows the creation of the final key without the
need to trust individual QEMs. Precisely, if the number of compromised QEMs
does not exceed a certain threshold, confidentiality is guaranteed to QKI
clients and, at the same time, robustness against denial-of-service (DoS)
attacks. The QKI system can be used for quantum-secure communication, can be
easily integrated into existing network infrastructures, and can support
arbitrary groups of communication parties that have access to a key. We discuss
the high-level protocol, analyze its security, including its robustness against
disruption. A proof-of-principle demonstration of secure communication between
two distant clients with a QKI-based VPN using QEMs on Amazon Web Server (AWS)
nodes thousands of kilometres away from them was performed, demonstrating the
feasibility of QKI-enabled secret sharing one-time-pad encryption with a data
rate above 50 Mbit/s and a latency below 70 ms.


title: Semantically Informed Slang Interpretation

abstract:
  Slang is a predominant form of informal language making flexible and extended
use of words that is notoriously hard for natural language processing systems
to interpret. Existing approaches to slang interpretation tend to rely on
context but ignore semantic extensions common in slang word usage. We propose a
semantically informed slang interpretation (SSI) framework that considers
jointly the contextual and semantic appropriateness of a candidate
interpretation for a query slang. We perform rigorous evaluation on two
large-scale online slang dictionaries and show that our approach not only
achieves state-of-the-art accuracy for slang interpretation in English, but
also does so in zero-shot and few-shot scenarios where training data is sparse.
Furthermore, we show how the same framework can be applied to enhancing machine
translation of slang from English to other languages. Our work creates
opportunities for the automated interpretation and translation of informal
language.


title:
A high-order deferred correction method for the solution of free
  boundary problems using penalty iteration, with an application to American
  option pricing

abstract:
  This paper presents a high-order deferred correction algorithm combined with
penalty iteration for solving free and moving boundary problems, using a
fourth-order finite difference method. Typically, when free boundary problems
are solved on a fixed computational grid, the order of the solution is low due
to the discontinuity in the solution at the free boundary, even if a high-order
method is used. Using a detailed error analysis, we observe that the order of
convergence of the solution can be increased to fourth-order by solving
successively corrected finite difference systems, where the corrections are
derived from the previously computed lower order solutions. The penalty
iterations converge quickly given a good initial guess. We demonstrate the
accuracy and efficiency of our algorithm using several examples. Numerical
results show that our algorithm gives fourth-order convergence for both the
solution and the free boundary location. We also test our algorithm on the
challenging American put option pricing problem. Our algorithm gives the
expected high-order convergence.


title: LoopStack: a Lightweight Tensor Algebra Compiler Stack

abstract:
  We present LoopStack, a domain specific compiler stack for tensor operations,
composed of a frontend, LoopTool, and an efficient optimizing code generator,
LoopNest. This stack enables us to compile entire neural networks and generate
code targeting the AVX2, AVX512, NEON, and NEONfp16 instruction sets while
incorporating optimizations often missing from other machine learning compiler
backends. We evaluate our stack on a collection of full neural networks and
commonly used network blocks as well as individual operators, and show that
LoopStack generates machine code that matches and frequently exceeds the
performance of in state-of-the-art machine learning frameworks in both cases.
We also show that for a large collection of schedules LoopNest's compilation is
orders of magnitude faster than LLVM, while resulting in equal or improved run
time performance. Additionally, LoopStack has a very small memory footprint - a
binary size of 245KB, and under 30K lines of effective code makes it ideal for
use on mobile and embedded devices.


title:
POLITICS: Pretraining with Same-story Article Comparison for Ideology
  Prediction and Stance Detection

abstract:
  Ideology is at the core of political science research. Yet, there still does
not exist general-purpose tools to characterize and predict ideology across
different genres of text. To this end, we study Pretrained Language Models
using novel ideology-driven pretraining objectives that rely on the comparison
of articles on the same story written by media of different ideologies. We
further collect a large-scale dataset, consisting of more than 3.6M political
news articles, for pretraining. Our model POLITICS outperforms strong baselines
and the previous state-of-the-art models on ideology prediction and stance
detection tasks. Further analyses show that POLITICS is especially good at
understanding long or formally written texts, and is also robust in few-shot
learning scenarios.


title:
Teaching BERT to Wait: Balancing Accuracy and Latency for Streaming
  Disfluency Detection

abstract:
  In modern interactive speech-based systems, speech is consumed and
transcribed incrementally prior to having disfluencies removed. This
post-processing step is crucial for producing clean transcripts and high
performance on downstream tasks (e.g. machine translation). However, most
current state-of-the-art NLP models such as the Transformer operate
non-incrementally, potentially causing unacceptable delays. We propose a
streaming BERT-based sequence tagging model that, combined with a novel
training objective, is capable of detecting disfluencies in real-time while
balancing accuracy and latency. This is accomplished by training the model to
decide whether to immediately output a prediction for the current input or to
wait for further context. Essentially, the model learns to dynamically size its
lookahead window. Our results demonstrate that our model produces comparably
accurate predictions and does so sooner than our baselines, with lower flicker.
Furthermore, the model attains state-of-the-art latency and stability scores
when compared with recent work on incremental disfluency detection.


title:
A Unified Approach to Semantic Information and Communication based on
  Probabilistic Logic

abstract:
  Traditionally, studies on technical communication (TC) are based on
stochastic modeling and manipulation. This is not sufficient for semantic
communication (SC) where semantic elements are logically connected, rather than
stochastically correlated. To fill this void, by leveraging a logical
programming language called probabilistic logic (ProbLog), we propose a unified
approach to semantic information and communication through the interplay
between TC and SC. On top of the well-established existing TC layer, we
introduce a SC layer that utilizes knowledge bases of communicating parties for
the exchange of semantic information. These knowledge bases are logically
described, manipulated, and exploited using ProbLog. To make these SC and TC
layers interact, we propose various measures based on the entropy of a clause
in a knowledge base. These measures allow us to delineate various technical
issues on SC such as a message selection problem for improving the knowledge
base at a receiver. Extending this, we showcase selected examples in which SC
and TC layers interact with each other while taking into account constraints on
physical channels and efficiently utilizing channel resources.


title:
Discovery of PSR J0523-7125 as a Circularly Polarized Variable Radio
  Source in the Large Magellanic Cloud

abstract:
  We report the discovery of a highly circularly polarized, variable,
steep-spectrum pulsar in the Australian Square Kilometre Array Pathfinder
(ASKAP) Variables and Slow Transients (VAST) survey. The pulsar is located
about $1^\circ$ from the center of the Large Magellanic Cloud, and has a
significant fractional circular polarization of $\sim$20%. We discovered
pulsations with a period of 322.5 ms, dispersion measure (DM) of 157.5 pc
cm$^{-3}$, and rotation measure (RM) of $+456$ rad m$^{-2}$ using observations
from the MeerKAT and the Parkes telescopes. This DM firmly places the source,
PSR J0523$-$7125, in the Large Magellanic Cloud (LMC). This RM is extreme
compared to other pulsars in the LMC (more than twice that of the largest
previously reported one). The average flux density of $\sim$1 mJy at 1400 MHz
and $\sim$25 mJy at 400 MHz places it among the most luminous radio pulsars
known. It likely evaded previous discovery because of its very steep radio
spectrum (spectral index $\alpha \approx -3$, where $S_\nu \propto \nu^\alpha$)
and broad pulse profile (duty cycle $\gtrsim35$%). We discuss implications for
searches for unusual radio sources in continuum images, as well as
extragalactic pulsars in the Magellanic Clouds and beyond. Our result
highlighted the possibility of identifying pulsars, especially extreme pulsars,
from radio continuum images. Future large-scale radio surveys will give us an
unprecedented opportunity to discover more pulsars and potentially the most
distant pulsars beyond the Magellanic Clouds.


title: Nonergodic delocalized paramagnetic states in quantum neural networks

abstract:
  Typically, it is assumed that a high-energy eigenstate of a generic
interacting quantum many-body Hamiltonian is thermal and obeys the eigenstate
thermalization hypothesis. In this work, we show that the paramagnetic phase of
a quantum Hopfield neural network model is delocalized but nonergodic. The
combination of permutational symmetry and frustration in this model organize
its high-energy eigenstates into clusters, which can each be considered a large
quantum spin and has no correlation with others. This model provides another
ergodicity-breaking mechanism in quantum many-body systems.


title:
Constraints on Explosion Timescale of Core-Collapse Supernovae Based on
  Systematic Analysis of Light Curves

abstract:
  Explosion mechanism of core-collapse supernovae is not fully understood yet.
In this work, we give constraints on the explosion timescale based on $^{56}$Ni
synthesized by supernova explosions. First, we systematically analyze
multi-band light curves of 82 stripped-envelope supernovae (SESNe) to obtain
bolometric light curves, which is among the largest samples of the bolometric
light curves of SESNe derived from the multi-band spectral energy distribution.
We measure the decline timescale and the peak luminosity of the light curves
and estimate the ejecta mass ($M_{\rm ej}$) and $^{56}$Ni mass ($M_{\rm Ni}$)
to connect the observed properties with the explosion physics. We then carry
out one-dimensional hydrodynamics and nucleosynthesis calculations, varying the
progenitor mass and the explosion timescale. From the calculations, we show
that the maximum $^{56}$Ni mass that $^{56}$Ni-powered SNe can reach is
expressed as $M_{\rm Ni} \lesssim 0.2 \ M_{\rm ej}$. Comparing the results from
the observations and the calculations, we show that the explosion timescale
shorter than 0.3 sec explains the synthesized $^{56}$Ni mass of the majority of
the SESNe.


title:
Attack Detection for Networked Control Systems Using Event-Triggered
  Dynamic Watermarking

abstract:
  Dynamic watermarking schemes can enhance the cyber attack detection
capability of networked control systems (NCSs). This paper presents a linear
event-triggered solution to conventional dynamic watermarking (CDW) schemes.
Firstly, the limitations of CDW schemes for event-triggered state estimation
based NCSs are investigated. Secondly, a new event-triggered dynamic
watermarking (ETDW) scheme is designed by treating watermarking as symmetric
key encryption, based on the limit convergence theorem in probability. Its
security property against the generalized replay attacks (GRAs) is also
discussed in the form of bounded asymptotic attack power. Thirdly, finite
sample ETDW tests are designed with matrix concentration inequalities. Finally,
experimental results of a networked inverted pendulum system demonstrate the
validity of our proposed scheme.


title:
Community detection in multiplex networks based on orthogonal
  nonnegative matrix tri-factorization

abstract:
  Networks provide a powerful tool to model complex systems where the different
entities in the system are presented by nodes and their interactions by edges.
Recently, there has been a growing interest in multiplex networks as they can
represent the interactions between a pair of nodes through multiple types of
links, each reflecting a distinct type of interaction. One of the important
tools in understanding network topology is community detection. Although there
are numerous works on community detection in single layer networks, existing
work on multiplex community detection mostly focuses on learning a common
community structure across layers without taking the heterogeneity of the
different layers into account. In this paper, we introduce a new multiplex
community detection approach that can identify communities that are common
across layers as well as those that are unique to each layer. The proposed
algorithm employs Orthogonal Nonnegative Matrix Tri-Factorization to model each
layer's adjacency matrix as the sum of two low-rank matrix factorizations,
corresponding to the common and private communities, respectively. The proposed
algorithm is evaluated on both synthetic and real multiplex networks and
compared to state-of-the-art techniques.


title:
DFC: Anatomically Informed Fiber Clustering with Self-supervised Deep
  Learning for Fast and Effective Tractography Parcellation

abstract:
  White matter fiber clustering (WMFC) parcellates tractography data into
anatomically meaningful fiber bundles, usually in an unsupervised manner
without the need of labeled ground truth data. While widely used WMFC
approaches have shown good performance using classical machine learning
techniques, recent advances in deep learning reveal a promising direction
towards fast and effective WMFC. In this work, we propose a novel deep learning
framework for WMFC, Deep Fiber Clustering (DFC), which solves the unsupervised
clustering problem as a self-supervised learning task with a domain-specific
pretext task to predict pairwise fiber distances. This accelerates the fiber
representation learning to handle a known challenge in WMFC, i.e., the
sensitivity of clustering results to the point ordering along fibers. We design
a novel network architecture that represents input fibers as point clouds and
allows the incorporation of additional sources of input information from gray
matter parcellation. Thus DFC makes use of the combined white matter fiber
geometry and gray matter anatomical parcellation to improve anatomical
coherence of fiber clusters. In addition, DFC conducts outlier removal in a
natural way by rejecting fibers with low cluster assignment probabilities. We
evaluate DFC on three independently acquired cohorts (including data from 220
subjects) and compare it to several state-of-the-art WMFC algorithms.
Experimental results demonstrate superior performance of DFC in terms of
cluster compactness, generalization ability, anatomical coherence, and
computational efficiency. In addition, DFC parcellates whole brain tractography
with 50k fibers in about 1.5 minutes, providing a fast and efficient tool for
large data analysis.


title:
Chance-Constrained Stochastic Optimal Control via Path Integral and
  Finite Difference Methods

abstract:
  This paper addresses a continuous-time continuous-space chance-constrained
stochastic optimal control (SOC) problem via a Hamilton-Jacobi-Bellman (HJB)
partial differential equation (PDE). Through Lagrangian relaxation, we convert
the chance-constrained (risk-constrained) SOC problem to a risk-minimizing SOC
problem, the cost function of which possesses the time-additive Bellman
structure. We show that the risk-minimizing control synthesis is equivalent to
solving an HJB PDE whose boundary condition can be tuned appropriately to
achieve a desired level of safety. Furthermore, it is shown that the proposed
risk-minimizing control problem can be viewed as a generalization of the
problem of estimating the risk associated with a given control policy. Two
numerical techniques are explored, namely the path integral and the finite
difference method (FDM), to solve a class of risk-minimizing SOC problems whose
associated HJB equation is linearizable via the Cole-Hopf transformation. Using
a 2D robot navigation example, we validate the proposed control synthesis
framework and compare the solutions obtained using path integral and FDM.


title:
Re-defining Radiology Quality Assurance (QA) -- Artificial Intelligence
  (AI)-Based QA by Restricted Investigation of Unequal Scores (AQUARIUS)

abstract:
  There is an urgent need for streamlining radiology Quality Assurance (QA)
programs to make them better and faster. Here, we present a novel approach,
Artificial Intelligence (AI)-Based QUality Assurance by Restricted
Investigation of Unequal Scores (AQUARIUS), for re-defining radiology QA, which
reduces human effort by up to several orders of magnitude over existing
approaches. AQUARIUS typically includes automatic comparison of AI-based image
analysis with natural language processing (NLP) on radiology reports. Only the
usually small subset of cases with discordant reads is subsequently reviewed by
human experts. To demonstrate the clinical applicability of AQUARIUS, we
performed a clinical QA study on Intracranial Hemorrhage (ICH) detection in
1936 head CT scans from a large academic hospital. Immediately following image
acquisition, scans were automatically analyzed for ICH using a commercially
available software (Aidoc, Tel Aviv, Israel). Cases rated positive for ICH by
AI (ICH-AI+) were automatically flagged in radiologists' reading worklists,
where flagging was randomly switched off with probability 50%. Using AQUARIUS
with NLP on final radiology reports and targeted expert neuroradiology review
of only 29 discordantly classified cases reduced the human QA effort by 98.5%,
where we found a total of six non-reported true ICH+ cases, with radiologists'
missed ICH detection rates of 0.52% and 2.5% for flagged and non-flagged cases,
respectively. We conclude that AQUARIUS, by combining AI-based image analysis
with NLP-based pre-selection of cases for targeted human expert review, can
efficiently identify missed findings in radiology studies and significantly
expedite radiology QA programs in a hybrid human-machine interoperability
approach.


title: Design equivariant neural networks for 3D point cloud

abstract:
  This work seeks to improve the generalization and robustness of existing
neural networks for 3D point clouds by inducing group equivariance under
general group transformations. The main challenge when designing equivariant
models for point clouds is how to trade-off the performance of the model and
the complexity. Existing equivariant models are either too complicate to
implement or very high complexity. The main aim of this study is to build a
general procedure to introduce group equivariant property to SOTA models for 3D
point clouds. The group equivariant models built form our procedure are simple
to implement, less complexity in comparison with the existing ones, and they
preserve the strengths of the original SOTA backbone. From the results of the
experiments on object classification, it is shown that our methods are superior
to other group equivariant models in performance and complexity. Moreover, our
method also helps to improve the mIoU of semantic segmentation models. Overall,
by using a combination of only-finite-rotation equivariance and augmentation,
our models can outperform existing full $SO(3)$-equivariance models with much
cheaper complexity and GPU memory. The proposed procedure is general and forms
a fundamental approach to group equivariant neural networks. We believe that it
can be easily adapted to other SOTA models in the future.


title:
WIYN Open Cluster Study: The Old Open Cluster, NGC 188, and a
  Re-evaluation of Lithium-Richness Among Red Giants

abstract:
  We present WIYN/Hydra spectra of 34 red giant candidate members of NGC 188,
which, together with WOCS and Gaia data yield 23 single members, 6 binary
members, 4 single nonmembers, and 1 binary nonmember. We report [Fe/H] for 29
members and derive [Fe/H]$_{\rm{NGC188}}$ = +0.064 $\pm$ 0.018 dex
($\sigma_{\mu}$) (sky spectra yield A(Fe)$_{\odot}$ = 7.520 $\pm$ 0.015 dex
($\sigma_{\mu}$)). We discuss effects on the derived parameters of varying
Yale-Yonsei isochrones to fit the turnoff. We take advantage of the coolest,
lowest-gravity giants to refine the line list near Li 6707.8 \AA. Using
synthesis we derive detections of A(Li) = 1.17, 1.65, 2.04, and 0.60 dex for
stars 4346, 4705, 5027, and 6353, respectively, and 3$\sigma$ upper-limits for
the other members. Whereas only two of the detections meet the traditional
criterion for "Li-richness" of A(Li) > 1.5 dex, we argue that since the cluster
A(Li) vanish as subgiants evolve to the base of the RGB, all four stars are
Li-rich in this cluster's context. An incidence of even a few Li-rich stars in
a sample of 29 stars is far higher than what recent large surveys have found in
the field. All four stars lie either slightly or substantially away from the
cluster fiducial sequence, possibly providing clues about their Li-richness. We
discuss a number of possibilities for the origin for the Li in each star, and
suggest potentially discriminating future observations.


title: Negative superhumps in the eclipsing Z Cam + VY Scl star ES Dra

abstract:
  I analyzed All-Sky Automated Survey for Supernovae (ASAS-SN) Sky Patrol data
of ES Dra and classified it to be a Z Cam star with VY Scl-type fading
episodes. An analysis of Transiting Exoplanet Survey Satellite (TESS)
observations showed that this object shows shallow eclipses and that the
orbital period is 0.17749895(17) d. Negative superhumps with a period of
0.167830(2) d and the beat phenomenon between the period of negative superhumps
and the orbital period were detected in the TESS data between 2020 January and
March. The orbital profile systematically varied depending on the beat phase
and eclipses were missing in some phases. The eclipses in ES Dra were grazing
and the disk was probably not eclipsed in some phases depending on the
orientation of the tilted disk. These observations added a support to the
interpretation of the precessing, tilted disk as the origin of negative
superhumps. Negative superhumps disappeared 4 d before the VY Scl-type fading
started. It was likely that the mass-transfer rate quickly dropped when
negative superhumps disappeared and the decline of the total luminosity of the
disk took 4 d. This provides a measurement of the time-scale of the response of
the disk against a sudden decrease of the mass transfer in a VY Scl star.
Although one of standstills in ES Dra was terminated by brightening, the
identity of ES Dra as an IW And star would require further events.


title:
Robust Fine-tuning via Perturbation and Interpolation from In-batch
  Instances

abstract:
  Fine-tuning pretrained language models (PLMs) on downstream tasks has become
common practice in natural language processing. However, most of the PLMs are
vulnerable, e.g., they are brittle under adversarial attacks or imbalanced
data, which hinders the application of the PLMs on some downstream tasks,
especially in safe-critical scenarios. In this paper, we propose a simple yet
effective fine-tuning method called Match-Tuning to force the PLMs to be more
robust. For each instance in a batch, we involve other instances in the same
batch to interact with it. To be specific, regarding the instances with other
labels as a perturbation, Match-Tuning makes the model more robust to noise at
the beginning of training. While nearing the end, Match-Tuning focuses more on
performing an interpolation among the instances with the same label for better
generalization. Extensive experiments on various tasks in GLUE benchmark show
that Match-Tuning consistently outperforms the vanilla fine-tuning by $1.64$
scores. Moreover, Match-Tuning exhibits remarkable robustness to adversarial
attacks and data imbalance.


title:
Numerical Method for Highly Non-linear Mean-reverting Asset Price Model
  with CEV-type Process

abstract:
  It is well documented from various empirical studies that the volatility
process of an asset price dynamics is stochastic. This phenomenon called for a
new approach to describing the random evolution of volatility through time with
stochastic models. In this paper, we propose a mean-reverting theta-rho model
for asset price dynamics where the volatility diffusion factor of this model
follows a highly non-linear CEV-type process. Since this model lacks a
closed-form formula, we construct a new truncated EM method to study it
numerically under the Khasminskii-type condition. We justify that the truncated
EM solutions can be used to evaluate a path-dependent financial product.


title: Graphing, homotopy groups of spheres, and spaces of links and knots

abstract:
  We study homotopy groups of spaces of links, focusing on long links of
codimension at least three. In the case of multiple components, they admit
split injections from homotopy groups of spheres. We calculate them, up to
knotting, in a range depending on the dimensions of the source manifolds and
target manifold which roughly generalizes the triple-point-free range for
isotopy classes. At the edge of this range, joining components sends both a
parametrized long Borromean rings class and a Hopf fibration to a generator of
the first nontrivial homotopy group of the space of long knots. For spaces of
two-component long links, we give generators of the homotopy group in this
dimension in terms of this class from the Borromean rings and homotopy groups
of spheres. A key ingredient in most of our results is a graphing map that
increases source and target dimensions by one.


title: In-plane optical phonon modes of current-carrying graphene

abstract:
  In this work, we study the in-plane optical phonon modes of current-carrying
single-layer graphene whose coupling to the $\pi$ electron gas is strong. Such
modes are expected to undergo a frequency shift compared to the
non-current-carrying state due to the non-equilibrium occupation of the Dirac
cone electronic eigen-states with the flowing $\pi$ electron gas. Large
electron-phonon coupling (EPC) can be identified by an abrupt change in the
slope of the phonon mode dispersion known as the Kohn anomaly, which mainly
occurs for (i) the in-plane longitudinal/transverse optical (LO/TO) modes at
the Brillouin zone (BZ) center ($\Gamma$ point), and (ii) the TO modes at the
BZ corners ($K$ points). We show that the breaking of the rotational symmetry
by the DC current results in different frequency shifts to the $\Gamma$-TO and
$\Gamma$-LO modes. More specifically, the DC current breaks the TO-LO mode
degeneracy at the $\Gamma$ point which ideally would be manifested as the
splitting of the Raman G peak.


title: Enhancing Adversarial Training with Feature Separability

abstract:
  Deep Neural Network (DNN) are vulnerable to adversarial attacks. As a
countermeasure, adversarial training aims to achieve robustness based on the
min-max optimization problem and it has shown to be one of the most effective
defense strategies. However, in this work, we found that compared with natural
training, adversarial training fails to learn better feature representations
for either clean or adversarial samples, which can be one reason why
adversarial training tends to have severe overfitting issues and less satisfied
generalize performance. Specifically, we observe two major shortcomings of the
features learned by existing adversarial training methods:(1) low intra-class
feature similarity; and (2) conservative inter-classes feature variance. To
overcome these shortcomings, we introduce a new concept of adversarial training
graph (ATG) with which the proposed adversarial training with feature
separability (ATFS) enables to coherently boost the intra-class feature
similarity and increase inter-class feature variance. Through comprehensive
experiments, we demonstrate that the proposed ATFS framework significantly
improves both clean and robust performance.


title: A Two Parameters Equation for Word Rank-Frequency Relation

abstract:
  Let $f (\cdot)$ be the absolute frequency of words and $r$ be the rank of
words in decreasing order of frequency, then the following function can fit the
rank-frequency relation \[ f (r;s,t) = \left(\frac{r_{\tt max}}{r}\right)^{1-s}
\left(\frac{r_{\tt max}+t \cdot r_{\tt exp}}{r+t \cdot r_{\tt
exp}}\right)^{1+(1+t)s} \] where $r_{\tt max}$ and $r_{\tt exp}$ are the
maximum and the expectation of the rank, respectively; $s>0$ and $t>0$ are
parameters estimated from data. On well-behaved data, there should be $s<1$ and
$s \cdot t < 1$.


title:
The Multivariate Community Hawkes Model for Dependent Relational Events
  in Continuous-time Networks

abstract:
  The stochastic block model (SBM) is one of the most widely used generative
models for network data. Many continuous-time dynamic network models are built
upon the same assumption as the SBM: edges or events between all pairs of nodes
are conditionally independent given the block or community memberships, which
prevents them from reproducing higher-order motifs such as triangles that are
commonly observed in real networks. We propose the multivariate community
Hawkes (MULCH) model, an extremely flexible community-based model for
continuous-time networks that introduces dependence between node pairs using
structured multivariate Hawkes processes. We fit the model using a spectral
clustering and likelihood-based local refinement procedure. We find that our
proposed MULCH model is far more accurate than existing models both for
predictive and generative tasks.


title: Current reversal in polar flock at order-disorder interface

abstract:
  We studied a system of polar self-propelled particles (SPPs) on a thin
rectangular channel designed into three regions of order-disorder-order. The
division of the three region is made on the basis of the noise SPPs experiences
in that region. The noise in the two wide region is chosen lower than the
critical noise of order-disorder transition and noise in the middle region or
interface is higher than the critical noise. This make the arrangement of the
system analogous to the Josephson Junction (JJ) in solid state physics. Keeping
all other parameters fixed, we study the properties of the moving flock in the
bulk as well as along the interface for different width of the junction d. On
increasing junction system shows a order-to-disorder transition from coherent
moving SPPs in the whole system to the interrupted current for large interface
width. Surprisingly, inside the interface we observed the current reversal for
intermediate widths of the interface. Such current reversal is due to the large
randomness present inside the interface, which makes the wall of the interface
reflecting. The phenomena of current reversal in our present system is similar
to current reversal observed in JJ system. Hence Our study give a new
interesting collective properties of SPPs at the interface which can be useful
to design devices like switch using active agents.


title:
Twisted $2k$th moments of primitive Dirichlet $L$-functions: beyond the
  diagonal

abstract:
  We study the family of Dirichlet $L$-functions of all even primitive
characters of conductor at most $Q$, where $Q$ is a parameter tending to
$\infty$. For an arbitrary positive integer $k$, we approximate the twisted
$2k$th moment of this family by using Dirichlet polynomial approximations of
$L^k(s,\chi)$ of length $X$, with $Q<X<Q^2$. Assuming the Generalized
Lindel\"{o}f Hypothesis, we prove an asymptotic formula for these
approximations of the twisted moments. Our result agrees with the prediction of
Conrey, Farmer, Keating, Rubinstein, and Snaith for this family of
$L$-functions, and provides the first rigorous evidence beyond the diagonal
terms for their conjectured asymptotic formula for the general $2k$th moment of
this family.


title: Finding Sums of Four Squares via Complex Continued Fractions

abstract:
  The problem of representing a given positive integer as a sum of four squares
of integers has been widely concerned for a long time, and for a given positive
odd $n$ one can find a representation by doing arithmetic in a maximal order of
quaternion algebra once a pair of (positive) integers $x,y$ with
$x^2+y^2\equiv-1\mod n$ is given. In this paper, we introduce a new method to
find a representation of odd integer $w$ given $x,y$ satisfying the above
requirement. This method can avoid the complicated non-commutative structure in
quaternion algebra, which is similar to the one we use to obtain a
representation of a prime $p\equiv1\mod4$ as sum of two squares by doing
continued fraction expansions, except that here we will expand complex number
using Hurwitz algorithm.


title: Sequence Learning and Consolidation on Loihi using On-chip Plasticity

abstract:
  In this work we develop a model of predictive learning on neuromorphic
hardware. Our model uses the on-chip plasticity capabilities of the Loihi chip
to remember observed sequences of events and use this memory to generate
predictions of future events in real time. Given the locality constraints of
on-chip plasticity rules, generating predictions without interfering with the
ongoing learning process is nontrivial. We address this challenge with a memory
consolidation approach inspired by hippocampal replay. Sequence memory is
stored in an initial memory module using spike-timing dependent plasticity.
Later, during an offline period, memories are consolidated into a distinct
prediction module. This second module is then able to represent predicted
future events without interfering with the activity, and plasticity, in the
first module, enabling online comparison between predictions and ground-truth
observations. Our model serves as a proof-of-concept that online predictive
learning models can be deployed on neuromorphic hardware with on-chip
plasticity.


title: Eigenstripping, Spectral Decay, and Edge-Expansion on Posets

abstract:
  Fast mixing of random walks on hypergraphs has led to myriad breakthroughs in
theoretical computer science in the last five years. On the other hand, many
important applications (e.g. to locally testable codes, 2-2 games) rely on a
more general class of underlying structures called posets, and crucially take
advantage of non-simplicial structure. These works make it clear the global
expansion properties of posets depend strongly on their underlying architecture
(e.g. simplicial, cubical, linear algebraic), but the overall phenomenon
remains poorly understood. In this work, we quantify the advantage of different
poset architectures in both a spectral and combinatorial sense, highlighting
how regularity controls the spectral decay and edge-expansion of corresponding
random walks.
  We show that the spectra of walks on expanding posets (Dikstein, Dinur,
Filmus, Harsha APPROX-RANDOM 2018) concentrate in strips around a small number
of approximate eigenvalues controlled by the regularity of the underlying
poset. This gives a simple condition to identify poset architectures (e.g. the
Grassmann) that exhibit exponential decay of eigenvalues, versus architectures
like hypergraphs whose eigenvalues decay linearly -- a crucial distinction in
applications to hardness of approximation such as the recent proof of the 2-2
Games Conjecture (Khot, Minzer, Safra FOCS 2018). We show these results lead to
a tight characterization of edge-expansion on posets in the $\ell_2$-regime
(generalizing recent work of Bafna, Hopkins, Kaufman, and Lovett (SODA 2022)),
and pay special attention to the case of the Grassmann where we show our
results are tight for a natural set of sparsifications of the Grassmann graphs.
We note for clarity that our results do not recover the characterization of
expansion used in the proof of the 2-2 Games Conjecture which relies on
$\ell_\infty$ rather than $\ell_2$-structure.


title:
A near-term quantum algorithm for solving linear systems of equations
  based on the Woodbury identity

abstract:
  Quantum algorithms for solving linear systems of equations have generated
excitement because of the potential speed-ups involved and the importance of
solving linear equations in many applications. However, applying these
algorithms can be challenging. The Harrow-Hassidim-Lloyd algorithm and
improvements thereof require complex subroutines suitable for fault-tolerant
hardware such as Hamiltonian simulation, making it ill-suited to current
hardware. Variational algorithms, on the other hand, involve expensive
optimization loops, which can be prone to barren plateaus and local optima. We
describe a quantum algorithm for solving linear systems of equations that
avoids these problems. Our algorithm is based on the Woodbury identity, which
analytically describes the inverse of a matrix that is a low-rank modification
of another (easily-invertible) matrix. This approach only utilizes basic
quantum subroutines like the Hadamard test or the swap test, so it is
well-suited to current hardware. There is no optimization loop, so barren
plateaus and local optima do not present a problem. The low-rank aspect of the
identity enables us to efficiently transfer information to and from the quantum
computer. This approach can produce accurate results on current hardware. As
evidence of this, we estimate an inner product involving the solution of a
system of more than 16 million equations with 2% error using IBM's Auckland
quantum computer. To our knowledge, no system of equations this large has
previously been solved to this level of accuracy on a quantum computer.


title:
Localized elasticity governs the nonlinear rheology of colloidal
  supercooled liquids

abstract:
  We employ charge-stabilized colloidal suspensions to investigate the
nonlinear rheology of supercooled liquids with soft-repulsive potentials. With
the results from BD simulations, we find that the prominent shear thinning of
viscosity (eta) at large enough shear rates (sr), expressed as
eta~sr^(-lambda), can be understood by introducing the concept of localized
elastic region (LER). An LER is a transient region composed of the first
several coordination shells of a reference particle. In response to the
external shear, particles within LER undergo nearly affine displacement before
the yielding of LER. The elastic coherency of the particle motion decreases as
the distance from the reference particle (r) increases, and becomes negligible
if r is larger than a length scale (xi), which defines the spatial range of the
localized elastic response. The characteristic strain (gamma) and size (xi) of
LER respectively depend on the shear rate by gamma~sr^epsilon and xi~sr^(-nu).
Three exponents are related by lambda=1-epsilon=4*nu based on the "solidity"
picture of supercooled liquids. This relation connects the bulk nonlinear
rheology, the elastic properties, and the microscopic structural distortion of
the system. The relaxation of the LER is mainly promoted by the large-step
nonaffine particle displacement along the extensional direction of the shear
geometry. The step length is about 0.4 particle diameter. The elastic
deformation and the relaxation of the LER are ubiquitous in the flow, which can
be considered as the microscopic source of the bulk nonlinear viscoelasticity.
We also performed Rheo-Small Angle Neutron Scattering experiments on
charge-stabilized colloidal suspensions under steady shear. The experimental
result proves that the local elasticity plays a dominant role in the shear
thinning. Moreover, we find the clue of the shrinkage of the LER with shear
rate.


title: Maximal Dissent: a Fast way to Agree in Distributed Convex Optimization

abstract:
  Consider a set of agents collaboratively solving a distributed convex
optimization problem, asynchronously, under stringent communication
constraints. In such situations, when an agent is activated and is allowed to
communicate with only one of its neighbors, we would like to pick the one
holding the most informative local estimate. We propose new algorithms where
the agents with maximal dissent average their estimates, leading to an
information mixing mechanism that often displays faster convergence to an
optimal solution compared to randomized gossip. The core idea is that when two
neighboring agents whose distance between local estimates is the largest among
all neighboring agents in the network average their states, it leads to the
largest possible immediate reduction of the quadratic Lyapunov function used to
establish convergence to the set of optimal solutions. As a broader
contribution, we prove the convergence of max-dissent subgradient methods using
a unified framework that can be used for other state-dependent distributed
optimization algorithms. Our proof technique bypasses the need of establishing
the information flow between any two agents within a time interval of uniform
length by intelligently studying convergence properties of the Lyapunov
function used in our analysis.


title: Edge Resolvability of Crystal Cubic Carbon Structure

abstract:
  Chemical graph theory is commonly used to analyse and comprehend chemical
structures and networks, as well as their features. The resolvability
parameters for graph $G$= $(V,E)$ are a relatively new advanced field in which
the complete structure is built so that each vertex (atom) or edge (bond)
represents a distinct position. In this article, we study the resolvability
parameters i.e., edge resolvability of chemical graph of crystal structure of
cubic carbon $CCS(n)$


title:
On the period interpretation for some special values of Thakur
  hypergeometric functions

abstract:
  In 1995, Thakur invented and studied positive characteristic analogues of
hypergeometric functions. In this paper, we interpret the special values of
those functions as periods of a pre-$t$-motive. As a consequence, we show their
transcendence and linear independence results by using Chang's refined version
of the Anderson-Brownawell-Papanikolas criterion. Furthermore, as by-products,
we show some linear/algebraic independence results among the special values of
Kochubei multiple polylogarithms according to our period interpretation and the
corresponding refined criterion.


title:
Subnanometer Accuracy of Surface Characterization by Reflected-Light
  Differential Interference Microscopy

abstract:
  We theorize the surface step characterization by reflected incoherent-light
differential interference microscopy with consideration of the optical
diffraction effect. With the integration of localization analysis, we develop a
quantitative differential interference optical system, by which we demonstrate
that the axial resolution of measuring surface height variation is sensitive to
the shear distance between the two spatially differentiated beams. We fabricate
three nanometer-size steps by photolithography, and successfully characterize
their 1D height variations with 0.13 nm Hz^(-1/2) axial precision. Our result
suggests that the optical differential interference microscopy can be used for
real-time characterization of surface structure with a subnanometer accuracy
and a large field of view, which is greatly beneficial to the surface
characterization of micro/nano-electromechanical systems.


title:
Rate of moment convergence in the central limit theorem for elephant
  random walks

abstract:
  The one-dimensional elephant random walk is a typical model of discrete-time
random walk with step-reinforcement, and is introduced by Sch\"{u}tz and
Trimper (2004). It has a parameter $\alpha \in (-1,1)$: The case $\alpha=0$
corresponds to the simple symmetric random walk, and when $\alpha>0$ (resp.
$\alpha<0$), the mean displacement of the walker at time $n$ grows (resp.
vanishes) like $n^{\alpha}$. The walk admits a phase transition at $\alpha=1/2$
from the diffusive behavior to the superdiffusive behavior. In this paper, we
study the rate of the moment convergence in the central limit theorem for the
position of the walker when $-1 < \alpha \leq 1/2$. We find a crossover
phenomenon in the rate of convergence of the $2m$-th moments with
$m=2,3,\ldots$ inside the diffusive regime $-1<\alpha<1/2$.


title: Numerical sign problem and the tempered Lefschetz thimble method

abstract:
  The numerical sign problem is a major obstacle to the quantitative
understanding of many important physical systems with first-principles
calculations. Typical examples for such systems include finite-density QCD,
strongly-correlated electron systems and frustrated spin systems, as well as
the real-time dynamics of quantum systems. In this talk, we argue that the
"tempered Lefschetz thimble method" (TLTM) [M. Fukuma and N. Umeda,
arXiv:1703.00861] and its extension, the "worldvolume tempered Lefschetz
thimble method" (WV-TLTM) [M. Fukuma and N. Matsumoto, arXiv:2012.08468], may
be a reliable and versatile solution to the sign problem. We demonstrate the
effectiveness of the algorithm by exemplifying a successful application of
WV-TLTM to the Stephanov model, which is an important toy model of
finite-density QCD. We also discuss the computational scaling of WV-TLTM.


title:
The Asymmetric Valence-Bond-Solid States in Quantum Spin Chains: The
  Difference Between Odd and Even Spins

abstract:
  The qualitative difference in low-energy properties of spin $S$ quantum
antiferromagnetic chains with integer $S$ and half-odd-integer $S$ discovered
by Haldane can be intuitively understood in terms of the valence-bond picture
proposed by Affleck, Kennedy, Lieb, and Tasaki. Here we develop a similarly
intuitive diagrammatic explanation of the qualitative difference between chains
with odd $S$ and even $S$, which is at the heart of the theory of
symmetry-protected topological (SPT) phases.
  More precisely, we define one-parameter families of states, which we call the
asymmetric valence-bond solid (VBS) states, that continuously interpolate
between the Affleck-Kennedy-Lieb-Tasaki (AKLT) state and the trivial zero state
in quantum spin chains with $S=1$ and 2. The asymmetric VBS state is obtained
by systematically modifying the AKLT state. It always has exponentially
decaying truncated correlation functions and is a unique gapped ground state of
a short-ranged Hamiltonian. We also observe that the asymmetric VBS state
possesses the time-reversal, the $\mathbb{Z}_2\times\mathbb{Z}_2$, and the
bond-centered inversion symmetries for $S=2$, but not for $S=1$. This is
consistent with the known fact that the AKLT model belongs to the trivial SPT
phase if $S=2$ and to a nontrivial SPT phase if $S=1$. Although such
interpolating families of disordered states were already known, our
construction is unified and is based on a simple physical picture. It also
extends to spin chains with general integer $S$ and provides us with an
intuitive explanation of the essential difference between models with odd and
even spins.

categories: cond-mat.stat-mech math-ph math.MP quant-ph


title: Holographic heat engine efficiency of hyperbolic charged black holes

abstract:
  We consider a four-dimensional charged hyperbolic black hole as working
matter to establish a black hole holographic heat engine, and use the
rectangular cycle to obtain the heat engine efficiency. We find that when the
increasing of entropy is zero, the heat engine efficiency of the hyperbolic
black hole becomes the well-known Carnot efficiency. We also find that less
charge corresponds to higher efficiency in the case of q > 0. Furthermore, we
study the efficiency of the flat case and spherical case and compare the
efficiency with that of the hyperbolic charged black holes. Finally, we use
numerical simulation to study the efficiency in benchmark scheme.


title: Harmonic Center of an n-Simplex: Some Properties

abstract:
  A simplex in n dimensions is defined by the usual (n+1) linear inequality
constraints in n dimensions. Here we consider simplexes which are bounded sets.
The harmonic center has been defined earlier for polytopes in general. A
relationship between the rows of A is derived here, which must be satisfied for
the simplex to be bounded. Using this condition, an interesting relationship is
derived between the (n+1) residuals at the harmonic center of the simplex.
Finally, a linear expression is developed which is invariant everywhere in the
simplex.


title: Debiased Contrastive Learning of Unsupervised Sentence Representations

abstract:
  Recently, contrastive learning has been shown to be effective in improving
pre-trained language models (PLM) to derive high-quality sentence
representations. It aims to pull close positive examples to enhance the
alignment while push apart irrelevant negatives for the uniformity of the whole
representation space. However, previous works mostly adopt in-batch negatives
or sample from training data at random. Such a way may cause the sampling bias
that improper negatives (e.g. false negatives and anisotropy representations)
are used to learn sentence representations, which will hurt the uniformity of
the representation space. To address it, we present a new framework
\textbf{DCLR} (\underline{D}ebiased \underline{C}ontrastive
\underline{L}earning of unsupervised sentence \underline{R}epresentations) to
alleviate the influence of these improper negatives. In DCLR, we design an
instance weighting method to punish false negatives and generate noise-based
negatives to guarantee the uniformity of the representation space. Experiments
on seven semantic textual similarity tasks show that our approach is more
effective than competitive baselines. Our code and data are publicly available
at the link: \textcolor{blue}{\url{https://github.com/RUCAIBox/DCLR}}.


title:
Pseudo-PFLOW: Development of nationwide synthetic open dataset for
  people movement based on limited travel survey and open statistical data

abstract:
  People flow data are utilized in diverse fields such as urban and commercial
planning and disaster management. However, people flow data collected from
mobile phones, such as using global positioning system and call detail records
data, are difficult to obtain because of privacy issues. Even if the data were
obtained, they would be difficult to handle. This study developed
pseudo-people-flow data covering all of Japan by combining public statistical
and travel survey data from limited urban areas. This dataset is not a
representation of actual travel movements but of typical weekday movements of
people. Therefore it is expected to be useful for various purposes.
Additionally, the dataset represents the seamless movement of people throughout
Japan, with no restrictions on coverage, unlike the travel surveys. In this
paper, we propose a method for generating pseudo-people-flow and describe the
development of a "Pseudo-PFLOW" dataset covering the entire population of
approximately 130 million people. We then evaluated the accuracy of the dataset
using mobile phone and trip survey data from multiple metropolitan areas. The
results showed that a coefficient of determination of more than 0.5 was
confirmed for comparisons regarding population distribution and trip volume.