title: WikiOmnia: generative QA corpus on the whole Russian Wikipedia

abstract:
  The General QA field has been developing the methodology referencing the
Stanford Question answering dataset (SQuAD) as the significant benchmark.
However, compiling factual questions is accompanied by time- and
labour-consuming annotation, limiting the training data's potential size. We
present the WikiOmnia dataset, a new publicly available set of QA-pairs and
corresponding Russian Wikipedia article summary sections, composed with a fully
automated generative pipeline. The dataset includes every available article
from Wikipedia for the Russian language. The WikiOmnia pipeline is available
open-source and is also tested for creating SQuAD-formatted QA on other
domains, like news texts, fiction, and social media. The resulting dataset
includes two parts: raw data on the whole Russian Wikipedia (7,930,873 QA pairs
with paragraphs for ruGPT-3 XL and 7,991,040 QA pairs with paragraphs for
ruT5-large) and cleaned data with strict automatic verification (over 160,000
QA pairs with paragraphs for ruGPT-3 XL and over 3,400,000 QA pairs with
paragraphs for ruT5-large).


title: Partial-duals for planar ribbon graphs

abstract:
  In 2009, Chmutov introduced the partial-duality for a ribbon graph $G$.
Recently, Gross, Mansour and Tucker enumerated all possible partial-duals of
$G$ by genus and introduced the partial-dual genus polynomial of a ribbon graph
$G.$ This paper mainly enumerates partial-duals for planar ribbon graphs.
First, we obtain a formula for the maximum partial-dual genus for any planar
ribbon graph and give a negative answer to the interpolating conjecture of
Gross, Mansour and Tucker. Then we show that there is a recurrence relation
between the partial-dual genus polynomials of planar ribbon graphs $G-e$ and
$G$. Furthermore, two related results are also given. These recurrence
relations give new approaches to calculate the partial-genus dual polynomials
for some planar ribbon graphs. In addition, we prove the asymptotic normality
for some partial-dual genus distributions.


title:
Dynamic attenuation scheme in measurement-device-independent quantum key
  distribution over turbulent channels

abstract:
  Measurement-device-independent quantum key distribution (MDI QKD) offers
great security in practice because it removes all detector side channels.
However, conducting MDI QKD over free-space channels is challenging. One of the
largest culprits is the mismatched transmittance of the two independent
turbulent channels causing a reduced Hong-Ou-Mandel visibility and thus a lower
secret key rate. Here we introduce a dynamic attenuation scheme, where the
transmittance of each of the two channels is monitored in real time by
transmitting bright light pulses from each users to the measurement device.
Based on the measured channel transmittance, a suitable amount of attenuation
is introduced to the low-loss channel at the measurement device. Our simulation
results show a significant improvement of QKD performance, especially when
using short raw keys.


title: Mixed state information theoretic measures in boosted black brane

abstract:
  In this paper, we study various mixed state information theoretic quantities
for a boosted black brane geometry. We have considered two set ups, namely, the
subsystem taken parallel and perpendicular to the direction of the boost. The
quantities that we calculate are the entanglement wedge cross-section, mutual
information, entanglement negativity and mutual complexity. In particular, we
study the dependence of these quantities on the boost parameter. We then
proceed to calculate the asymmetry ratios of these quantities, and observe that
they are independent of the subsystem size. Finally, we proceed to study an
interesting limit of the boosted black brane geometry, which is the so called
AdS wave geometry. We once again compute all the mixed state information
theoretic quantities for this geometry.


title:
One-step Method for Material Quantitation using In-line Tomography with
  Single Scanning

abstract:
  Objective: Quantitative technique based on In-line phase-contrast computed
tomography with single scanning attracts more attention in application due to
the flexibility of the implementation. However, the quantitative results
usually suffer from artifacts and noise, since the phase retrieval and
reconstruction are independent ("two-steps") without feedback from the original
data. Our goal is to develop a method for material quantitative imaging based
on a priori information specifically for the single-scanning data. Method: An
iterative method that directly reconstructs the refractive index decrement
delta and imaginary beta of the object from observed data ("one-step") within
single object-to-detector distance (ODD) scanning. Simultaneously, high-quality
quantitative reconstruction results are obtained by using a linear
approximation that achieves material decomposition in the iterative process.
Results: By comparing the equivalent atomic number of the material
decomposition results in experiments, the accuracy of the proposed method is
greater than 97.2%. Conclusion: The quantitative reconstruction and
decomposition results are effectively improved, and there are feedback and
corrections during the iteration, which effectively reduce the impact of noise
and errors. Significance: This algorithm has the potential for quantitative
imaging research, especially for imaging live samples and human breast
preclinical studies.


title: Finite volume based film flow and ice accretion models on aircraft wings

abstract:
  The thin runback water films driven by the gas flow, the pressure gradient
and the gravity on the iced aircraft surface are investigated in this paper. A
three-dimensional film flow model based on Finite Volume Method (FVM) and the
lubrication theory is proposed to describe the flow. The depth-averaged
velocity of the film is stored in Cartesian coordinates to avoid the appearance
of the metric tensors. The governing equations are discretized in the first
layer structured grid cell which is selected as the grids for film flow. In
order to verify this method, comparisons between numerical results and
experimental results of ice shapes on NACA 0012 airfoil and GLC-305 swept wing
are presented, both showing a good agreement for rime and glaze ice condition.
Overall, this model shows great potential to model ice accretion reasonably
under different icing conditions. Besides, the present method doesn't require
analytic metric terms, and can be easily coupled to existing finite volume
solvers for logically Cartesian meshes.


title: Optimizing shortcut Brownian heat engine

abstract:
  Shortcut to isothermality provides a powerful method to realize quasistatic
thermodynamic processes with finite-time manipulation. We implement the
shortcut strategy to design and optimize Brownian heat engines, and formulate a
rigorous geometric description of the energetics with the thermodynamic length.
Along a given closed path in the control parameter space, the optimal control
protocol is to manipulate the control parameters with a proper constant
velocity to maximize the output power. We also propose the principle to find
out the closed path with large maximum power. Our results generalize the
previous optimization in the highly underdamped and the overdamped regimes to
the general-damped situation, and are applicable for arbitrary finite-time
cycles.


title:
Observational constraints on nonlinear matter extensions of general
  relativity: Separable trace power models

abstract:
  The search for the physical mechanism underlying the observational evidence
for the acceleration of the recent universe is a compelling goal of modern
fundamental cosmology. Here we quantitatively study a class of homogeneous and
isotropic cosmological models in which the matter side of Einstein's equations
includes, in addition to the canonical term, a term proportional to the trace
of the energy-momentum tensor, $T=\rho-3p$, and constrain these models using
low redshift background cosmology data. One may think of these models as
extensions of general relativity with a nonlinear matter Lagrangian, and they
can be studied either as phenomenological extensions of the standard
$\Lambda$CDM model, containing both matter and a cosmological constant, or as
direct alternatives to it, where there is no cosmological constant but the
additional terms would have to be responsible for accelerating the universe.
Overall, our main finding is that parametric extensions of $\Lambda$CDM are
tightly constrained, with additional model parameters being constrained to
their canonical behaviours to within one standard deviation, while alternative
models in this class (which do not have a $\Lambda$CDM limit) are ruled out.
This provides some insight on the level of robustness of the $\Lambda$CDM model
and on the parameter space still available for phenomenological alternatives
and extensions.


title:
PiouCrypt: Decentralized Lattice-based Method for Visual Symmetric
  Cryptography

abstract:
  In recent years, establishing secure visual communications has turned into
one of the essential problems for security engineers and researchers. However,
only limited novel solutions are provided for image encryption, and limiting
the visual cryptography to only limited schemes can bring up negative
consequences, especially with emerging quantum computational systems. This
paper presents a novel algorithm for establishing secure private visual
communication. The proposed method has a layered architecture with several
cohesive components, and corresponded with an NP-hard problem, despite its
symmetric structure. This two-step technique is not limited to gray-scale
pictures, and furthermore, utilizing a lattice structure causes to proposed
method has optimal resistance for the post-quantum era, and is relatively
secure from the theoretical dimension.


title: A classification of regular diagonal quadratic forms

abstract:
  A positive-definite integral quadratic form is called regular if it
represents every positive integer which is locally represented. In this
article, we classify all regular diagonal quadratic forms of rank greater than
3.


title: Elliptic Loops

abstract:
  Given a local ring $(R,\mathfrak{m})$ and an elliptic curve
$E(R/\mathfrak{m})$, elliptic loops are defined as the points of
$\mathbb{P}^2(R)$ projecting to $E$ under the canonical modulo-$\mathfrak{m}$
reduction, endowed with an operation that extends the curve's addition. These
objects are proved to be power associative abelian algebraic loops, which are
almost never groups. When they have no points of order $3$, their affine parts
are obtained as a stratification of elliptic curves defined over $R$, called
layers. Special families of associative triples are identified when
$\mathfrak{m}^e = \{0\}$ for small values of $e \in \mathbb{Z}$. In the special
case $R = \mathbb{Z}/p^e\mathbb{Z}$, the infinity part of an elliptic loop may
be generated by two elements, the group structure of layers may be established
and the points with the same projection and the same order are identified.


title: On two remarkable groups of area-preserving homeomorphisms

abstract:
  We prove that on a symplectic sphere, the group of Hamiltonian homeomorphisms
in the sense of Oh and M\"uller is a proper normal subgroup of the group of
finite energy Hamiltonian homeomorphisms. Moreover we detect
infinite-dimensional flats inside the quotient of these groups endowed with the
natural Hofer pseudo-metric.


title: Sharper Bounds on Four Lattice Constants

abstract:
  The Korkine--Zolotareff (KZ) reduction, and its generalisations, are widely
used lattice reduction strategies in communications and cryptography. The KZ
constant and Schnorr's constant were defined by Schnorr in 1987. The KZ
constant can be used to quantify some useful properties of KZ reduced matrices.
Schnorr's constant can be used to characterize the output quality of his block
$2k$-reduction and is used to define his semi block $2k$-reduction, which was
also developed in 1987. Hermite's constant, which is a fundamental constant
lattices, has many applications, such as bounding the length of the shortest
nonzero lattice vector and the orthogonality defect of lattices. Rankin's
constant was introduced by Rankin in 1953 as a generalization of Hermite's
constant. It plays an important role in characterizing the output quality of
block-Rankin reduction, proposed by Gama et al. in 2006. In this paper, we
first develop a linear upper bound on Hermite's constant and then use it to
develop an upper bound on the KZ constant. These upper bounds are sharper than
those obtained recently by the authors, and the ratio of the new linear upper
bound to the nonlinear upper bound, developed by Blichfeldt in 1929, on
Hermite's constant is asymptotically 1.0047. Furthermore, we develop lower and
upper bounds on Schnorr's constant. The improvement to the lower bound over the
sharpest existing one developed by Gama et al. is around 1.7 times
asymptotically, and the improvement to the upper bound over the sharpest
existing one which was also developed by Gama et al. is around 4 times
asymptotically. Finally, we develop lower and upper bounds on Rankin's
constant. The improvements of the bounds over the sharpest existing ones, also
developed by Gama et al., are exponential in the parameter defining the
constant.


title: Randomized Maximum Likelihood via High-Dimensional Bayesian Optimization

abstract:
  Randomized Maximum Likelihood (RML) is an approximate posterior sampling
methodology, widely used in Bayesian inverse problems with complex forward
models, particularly in petroleum engineering applications. The procedure
involves solving a multi-objective optimization problem, which can be
challenging in high-dimensions and when there are constraints on computational
costs. We propose a new methodology for tackling the RML optimization problem
based on the high-dimensional Bayesian optimization literature. By sharing data
between the different objective functions, we are able to implement RML at a
greatly reduced computational cost. We demonstrate the benefits of our
methodology in comparison with the solutions obtained by alternative
optimization methods on a variety of synthetic and real-world problems,
including medical and fluid dynamics applications. Furthermore, we show that
the samples produced by our method cover well the high-posterior density
regions in all of the experiments.


title: VDTR: Video Deblurring with Transformer

abstract:
  Video deblurring is still an unsolved problem due to the challenging
spatio-temporal modeling process. While existing convolutional neural
network-based methods show a limited capacity for effective spatial and
temporal modeling for video deblurring. This paper presents VDTR, an effective
Transformer-based model that makes the first attempt to adapt Transformer for
video deblurring. VDTR exploits the superior long-range and relation modeling
capabilities of Transformer for both spatial and temporal modeling. However, it
is challenging to design an appropriate Transformer-based model for video
deblurring due to the complicated non-uniform blurs, misalignment across
multiple frames and the high computational costs for high-resolution spatial
modeling. To address these problems, VDTR advocates performing attention within
non-overlapping windows and exploiting the hierarchical structure for
long-range dependencies modeling. For frame-level spatial modeling, we propose
an encoder-decoder Transformer that utilizes multi-scale features for
deblurring. For multi-frame temporal modeling, we adapt Transformer to fuse
multiple spatial features efficiently. Compared with CNN-based methods, the
proposed method achieves highly competitive results on both synthetic and
real-world video deblurring benchmarks, including DVD, GOPRO, REDS and BSD. We
hope such a Transformer-based architecture can serve as a powerful alternative
baseline for video deblurring and other video restoration tasks. The source
code will be available at \url{https://github.com/ljzycmd/VDTR}.


title:
The Z-axis, X-axis, Weight and Disambiguation Methods for Constructing
  Local Reference Frame in 3D Registration: An Evaluation

abstract:
  The local reference frame (LRF), as an independent coordinate system
generated on a local 3D surface, is widely used in 3D local feature descriptor
construction and 3D transformation estimation which are two key steps in the
local method-based surface matching. There are numerous LRF methods have been
proposed in literatures. In these methods, the x- and z-axis are commonly
generated by different methods or strategies, and some x-axis methods are
implemented on the basis of a z-axis being given. In addition, the weight and
disambiguation methods are commonly used in these LRF methods. In existing
evaluations of LRF, each LRF method is evaluated with a complete form. However,
the merits and demerits of the z-axis, x-axis, weight and disambiguation
methods in LRF construction are unclear. In this paper, we comprehensively
analyze the z-axis, x-axis, weight and disambiguation methods in existing LRFs,
and obtain six z-axis and eight x-axis, five weight and two disambiguation
methods. The performance of these methods are comprehensively evaluated on six
standard datasets with different application scenarios and nuisances.
Considering the evaluation outcomes, the merits and demerits of different
weight, disambiguation, z- and x-axis methods are analyzed and summarized. The
experimental result also shows that some new designed LRF axes present superior
performance compared with the state-of-the-art ones.


title:
Hadronic supercriticality in spherically expanding sources: application
  to GRB prompt emission

abstract:
  Relativistic hadronic plasmas can become under certain conditions
supercritical, abruptly and efficiently releasing the energy stored in protons
through photon outbursts. Past studies have tried to relate the features of
such hadronic supercriticalities (HSC) to the phenomenology of Gamma-Ray Burst
(GRB) prompt emission. In this work we investigate, for the first time, HSC in
adiabatically expanding sources. We examine the conditions required to trigger
HSC, study the role of expansion velocity, and discuss our results in relation
to GRB prompt emission. We find multi-pulse light curves from slowly expanding
regions ($u_{\rm exp}\lesssim 0.01 c)$ that are a manifestation of the natural
HSC quasi-periodicity, while single-pulse light curves with a fast rise and
slow decay are found for higher velocities. The formation of the photon
spectrum is governed by an in-source electromagnetic cascade. The peak photon
energy is $\sim 1$ MeV ($\sim 1$ GeV) for maximum proton energies $\sim 1-10$
PeV ($1-10$ EeV) assuming a jet Lorentz factor 100. Peak $\gamma$-ray
luminosities are in the range $10^{49}-10^{52}$ erg s$^{-1}$, with the
MeV-peaked spectra being $\sim 100-300$ times more luminous than their
GeV-peaked analogues. HSC bursts peaking in the MeV are also copious $\sim 10$
TeV neutrino emitters, with an all-flavour fluence $\sim 10\%$ of the
$\gamma$-ray one. The hypothesis that typical long-duration GRBs are powered by
HSC could be tested in the near future with more sensitive neutrino telescopes
like IceCube-Gen2.


title: Advances in Thunder Sound Synthesis

abstract:
  A recent comparative study evaluated all known thunder synthesis techniques
in terms of their perceptual realness. The findings concluded that none of the
synthesised audio extracts seemed as realistic as the genuine phenomenon. The
work presented herein is motivated by those findings, and attempts to create a
synthesised sound effect of thunder indistinguishable from a real recording.
The technique supplements an existing implementation with physics-inspired,
signal-based design elements intended to simulate environmental occurrences. In
a listening test conducted with over 50 participants, this new implementation
was perceived as the most realistic synthesised sound, though still
distinguishable from a real recording. Further improvements to the model, based
on insights from the listening test, were also implemented and described
herein.


title: Attention Mechanism based Cognition-level Scene Understanding

abstract:
  Given a question-image input, the Visual Commonsense Reasoning (VCR) model
can predict an answer with the corresponding rationale, which requires
inference ability from the real world. The VCR task, which calls for exploiting
the multi-source information as well as learning different levels of
understanding and extensive commonsense knowledge, is a cognition-level scene
understanding task. The VCR task has aroused researchers' interest due to its
wide range of applications, including visual question answering, automated
vehicle systems, and clinical decision support. Previous approaches to solving
the VCR task generally rely on pre-training or exploiting memory with long
dependency relationship encoded models. However, these approaches suffer from a
lack of generalizability and losing information in long sequences. In this
paper, we propose a parallel attention-based cognitive VCR network PAVCR, which
fuses visual-textual information efficiently and encodes semantic information
in parallel to enable the model to capture rich information for cognition-level
inference. Extensive experiments show that the proposed model yields
significant improvements over existing methods on the benchmark VCR dataset.
Moreover, the proposed model provides intuitive interpretation into visual
commonsense reasoning.


title:
Lie symmetries reduction and spectral methods on the fractional
  two-dimensional heat equation

abstract:
  In this paper, the Lie symmetry analysis is proposed for a space-time
convection-diffusion fractional differential equations with the
Riemann-Liouville derivative by (2+1) independent variables and one dependent
variable. We find a reduction form of our governed fractional differential
equation using the similarity solution of our Lie symmetry. One-dimensional
optimal system of Lie symmetry algebras is found. We present a computational
method via the spectral method based on Bernstein's operational matrices to
solve the two-dimensional fractional heat equation with some initial
conditions.


title: Deep Learning based Automatic Detection of Dicentric Chromosome

abstract:
  Automatic detection of dicentric chromosomes is an essential step to estimate
radiation exposure and development of end to end emergency bio dosimetry
systems. During accidents, a large amount of data is required to be processed
for extensive testing to formulate a medical treatment plan for the masses,
which requires this process to be automated. Current approaches require human
adjustments according to the data and therefore need a human expert to
calibrate the system. This paper proposes a completely data driven framework
which requires minimum intervention of field experts and can be deployed in
emergency cases with relative ease. Our approach involves YOLOv4 to detect the
chromosomes and remove the debris in each image, followed by a classifier that
differentiates between an analysable chromosome and a non-analysable one.
Images are extracted from YOLOv4 based on the protocols described by
WHO-BIODOSNET. The analysable chromosome is classified as Monocentric or
Dicentric and an image is accepted for consideration of dose estimation based
on the analysable chromosome count. We report an accuracy in dicentric
identification of 94.33% on a 1:1 split of Dicentric and Monocentric
Chromosomes.


title: An Adaptive Task-Related Component Analysis Method for SSVEP recognition

abstract:
  Steady-state visual evoked potential (SSVEP) recognition methods are equipped
with learning from the subject's calibration data, and they can achieve extra
high performance in the SSVEP-based brain-computer interfaces (BCIs), however
their performance deteriorate drastically if the calibration trials are
insufficient. This study develops a new method to learn from limited
calibration data and it proposes and evaluates a novel adaptive data-driven
spatial filtering approach for enhancing SSVEPs detection. The spatial filter
learned from each stimulus utilizes temporal information from the corresponding
EEG trials. To introduce the temporal information into the overall procedure,
an multitask learning approach, based on the bayesian framework, is adopted.
The performance of the proposed method was evaluated into two publicly
available benchmark datasets, and the results demonstrated that our method
outperform competing methods by a significant margin.


title: Limit theorems of Chatterjee's rank correlation

abstract:
  Establishing the limiting distribution of Chatterjee's rank correlation for a
general, possibly non-independent, pair of random variables has been eagerly
awaited to many. This paper shows that (a) Chatterjee's rank correlation is
asymptotically normal as long as one variable is not a measurable function of
the other, and (b) the corresponding asymptotic variance is uniformly bounded
by 36. Similar results also hold for Azadkia-Chatterjee's graph-based
correlation coefficient, a multivariate analogue of Chatterjee's original
proposal. The proof is given by appealing to H\'ajek representation and
Chatterjee's nearest-neighbor CLT.


title: A Survey of Layer-Two Blockchain Protocols

abstract:
  After the success of the Bitcoin blockchain, came several cryptocurrencies
and blockchain solutions in the last decade. Nonetheless, Blockchain-based
systems still suffer from low transaction rates and high transaction processing
latencies, which hinder blockchains' scalability. An entire class of solutions,
called Layer-1 scalability solutions, have attempted to incrementally improve
such limitations by adding/modifying fundamental blockchain attributes.
Recently, a completely different class of works, called Layer-2 protocols, have
emerged to tackle the blockchain scalability issues using unconventional
approaches. Layer-2 protocols improve transaction processing rates, periods,
and fees by minimizing the use of underlying slow and costly blockchains. In
fact, the main chain acts just as an instrument for trust establishment and
dispute resolution among Layer-2 participants, where only a few transactions
are dispatched to the main chain. Thus, Layer-2 blockchain protocols have the
potential to transform the domain. However, rapid and discrete developments
have resulted in diverse branches of Layer-2 protocols. In this work, we
systematically create a broad taxonomy of such protocols and implementations.
We discuss each Layer-2 protocol class in detail and also elucidate their
respective approaches, salient features, requirements, etc. Moreover, we
outline the issues related to these protocols along with a comparative
discussion. Our thorough study will help further systematize the knowledge
dispersed in the domain and help the readers to better understand the field of
Layer-2 protocols.


title:
RLens: A Computer-aided Visualization System for Supporting Reflection
  on Language Learning under Distributed Tutorship

abstract:
  With the rise of the gig economy, online language tutoring platforms are
becoming increasingly popular. These platforms provide temporary and flexible
jobs for native speakers as tutors and allow language learners to have
one-on-one speaking practices on demand, on which learners occasionally
practice the language with different tutors. With such distributed tutorship,
learners can hold flexible schedules and receive diverse feedback. However,
learners face challenges in consistently tracking their learning progress
because different tutors provide feedback from diverse standards and
perspectives, and hardly refer to learners' previous experiences with other
tutors. We present RLens, a visualization system for facilitating learners'
learning progress reflection by grouping different tutors' feedback, tracking
how each feedback type has been addressed across learning sessions, and
visualizing the learning progress. We validate our design through a
between-subjects study with 40 real-world learners. Results show that learners
can successfully analyze their progress and common language issues under
distributed tutorship with RLens, while most learners using the baseline
interface had difficulty achieving reflection tasks. We further discuss design
considerations of computer-aided systems for supporting learning under
distributed tutorship.


title:
Optimal reinforcement learning near the edge of synchronization
  transition

abstract:
  Recent experimental and theoretical studies have indicated that the putative
criticality of cortical dynamics may corresponds to a synchronization phase
transition. The critical dynamics near such a critical point needs further
investigation specifically when compared to the critical behavior near the
standard absorbing state phase transition. Since the phenomena of learning and
self-organized criticality (SOC) at the edge of synchronization transition can
emerge jointly in spiking neural networks due to the presence of spike-timing
dependent plasticity (STDP), it is tempting to ask: What is the relationship
between synchronization and learning in neural networks? Further, does learning
benefit from SOC at the edge of synchronization transition? In this paper, we
intend to address these important issues. Accordingly, we construct a
biologically inspired model of an autonomous cognitive system which learns to
perform stimulus-response tasks. We train this system using a reinforcement
learning rule implemented through dopamine-modulated STDP. We find that the
system exhibits a continuous transition from synchronous to asynchronous neural
oscillations upon increasing the average axonal time delay. We characterize the
learning performance of the system and observe that it is optimized near the
synchronization transition. We also study neuronal avalanches in the system and
provide evidence that optimized learning is achieved in a slightly
supercritical state.


title:
Beta Residuals: Improving Fault-Tolerant Control for Sensory Faults via
  Bayesian Inference and Precision Learning

abstract:
  Model-based fault-tolerant control (FTC) often consists of two distinct
steps: fault detection & isolation (FDI), and fault accommodation. In this work
we investigate posing fault-tolerant control as a single Bayesian inference
problem. Previous work showed that precision learning allows for stochastic FTC
without an explicit fault detection step. While this leads to implicit fault
recovery, information on sensor faults is not provided, which may be essential
for triggering other impact-mitigation actions. In this paper, we introduce a
precision-learning based Bayesian FTC approach and a novel beta residual for
fault detection. Simulation results are presented, supporting the use of beta
residual against competing approaches.


title: Federated Learning Cost Disparity for IoT Devices

abstract:
  Federated learning (FL) promotes predictive model training at the Internet of
things (IoT) devices by evading data collection cost in terms of energy, time,
and privacy. We model the learning gain achieved by an IoT device against its
participation cost as its utility. Due to the device-heterogeneity, the local
model learning cost and its quality, which can be time-varying, differs from
device to device. We show that this variation results in utility unfairness
because the same global model is shared among the devices. By default, the
master is unaware of the local model computation and transmission costs of the
devices, thus it is unable to address the utility unfairness problem. Also, a
device may exploit this lack of knowledge at the master to intentionally reduce
its expenditure and thereby enhance its utility. We propose to control the
quality of the global model shared with the devices, in each round, based on
their contribution and expenditure. This is achieved by employing differential
privacy to curtail global model divulgence based on the learning contribution.
In addition, we devise adaptive computation and transmission policies for each
device to control its expenditure in order to mitigate utility unfairness. Our
results show that the proposed scheme reduces the standard deviation of the
energy cost of devices by 99% in comparison to the benchmark scheme, while the
standard deviation of the training loss of devices varies around 0.103.


title: Comparison communication protocols

abstract:
  We introduce a restriction of the classical 2-party deterministic
communication protocol where Alice and Bob are restricted to using only
comparison functions. We show that the complexity of a function in the model
is, up to a constant factor, determined by a complexity measure analogous to
Yao's tiling number, which we call the geometric tiling number which can be
computed in polynomial time. As a warm-up, we consider an analogous restricted
decision tree model and observe a 1-dimensional analog of the above results.


title:
Investigating the sources of low-energy events in a SuperCDMS-HVeV
  detector

abstract:
  Recent experiments searching for sub-GeV/$c^2$ dark matter have observed
event excesses close to their respective energy thresholds. Although specific
to the individual technologies, the measured excess event rates have been
consistently reported at or below event energies of a few-hundred eV, or with
charges of a few electron-hole pairs. In the present work, we operated a 1-gram
silicon SuperCDMS-HVeV detector at three voltages across the crystal (0 V, 60 V
and 100 V). The 0 V data show an excess of events in the tens of eV region.
Despite this event excess, we demonstrate the ability to set a competitive
exclusion limit on the spin-independent dark matter--nucleon elastic scattering
cross section for dark matter masses of $\mathcal{O}(100)$ MeV/$c^2$, enabled
by operation of the detector at 0 V potential and achievement of a very low
$\mathcal{O}(10)$ eV threshold for nuclear recoils. Comparing the data acquired
at 0 V, 60 V and 100 V potentials across the crystal, we investigated possible
sources of the unexpected events observed at low energy. The data indicate that
the dominant contribution to the excess is consistent with a hypothesized
luminescence from the printed circuit boards used in the detector holder.


title: Pathologies of Pre-trained Language Models in Few-shot Fine-tuning

abstract:
  Although adapting pre-trained language models with few examples has shown
promising performance on text classification, there is a lack of understanding
of where the performance gain comes from. In this work, we propose to answer
this question by interpreting the adaptation behavior using post-hoc
explanations from model predictions. By modeling feature statistics of
explanations, we discover that (1) without fine-tuning, pre-trained models
(e.g. BERT and RoBERTa) show strong prediction bias across labels; (2) although
few-shot fine-tuning can mitigate the prediction bias and demonstrate promising
prediction performance, our analysis shows models gain performance improvement
by capturing non-task-related features (e.g. stop words) or shallow data
patterns (e.g. lexical overlaps). These observations alert that pursuing model
performance with fewer examples may incur pathological prediction behavior,
which requires further sanity check on model predictions and careful design in
model evaluations in few-shot fine-tuning.


title: NICO++: Towards Better Benchmarking for Domain Generalization

abstract:
  Despite the remarkable performance that modern deep neural networks have
achieved on independent and identically distributed (I.I.D.) data, they can
crash under distribution shifts. Most current evaluation methods for domain
generalization (DG) adopt the leave-one-out strategy as a compromise on the
limited number of domains. We propose a large-scale benchmark with extensive
labeled domains named NICO++ along with more rational evaluation methods for
comprehensively evaluating DG algorithms. To evaluate DG datasets, we propose
two metrics to quantify covariate shift and concept shift, respectively. Two
novel generalization bounds from the perspective of data construction are
proposed to prove that limited concept shift and significant covariate shift
favor the evaluation capability for generalization. Through extensive
experiments, NICO++ shows its superior evaluation capability compared with
current DG datasets and its contribution in alleviating unfairness caused by
the leak of oracle knowledge in model selection.


title: Kernels of splitting homomorphisms

abstract:
  Lei and Wu have given a description of the second homotopy group of a closed
orientable 3-manifold in terms of the kernels of the epimorphisms from the
fundamental group of a Heegaard splitting surface onto the fundamental groups
of the two handlebody sides. In this note, we give a geometric derivation of
this result and collect some observations about the relation between the
various groups and the topology of the 3-manifold and the Heegaard splitting.


title: BLEWhisperer: Exploiting BLE Advertisements for Data Exfiltration

abstract:
  Bluetooth technology has enabled short-range wireless communication for
billions of devices. Bluetooth Low-Energy (BLE) variant aims at improving power
consumption on battery-constrained devices. BLE-enabled devices broadcast
information (e.g., as beacons) to nearby devices via advertisements.
Unfortunately, such functionality can become a double-edged sword at the hands
of attackers. In this paper, we primarily show how an attacker can exploit BLE
advertisements to exfiltrate information from BLE-enable devices. In
particular, our attack establishes a communication medium between two devices
without requiring any prior authentication or pairing. We develop a
proof-of-concept attack framework on the Android ecosystem and assess its
performance via a thorough set of experiments. Our results indicate that such
an exfiltration attack is indeed possible though with a low data rate.
Nevertheless, we also demonstrate potential use cases and enhancements to our
attack that can further its severeness. Finally, we discuss possible
countermeasures to prevent such an attack.


title: Continual Hippocampus Segmentation with Transformers

abstract:
  In clinical settings, where acquisition conditions and patient populations
change over time, continual learning is key for ensuring the safe use of deep
neural networks. Yet most existing work focuses on convolutional architectures
and image classification. Instead, radiologists prefer to work with
segmentation models that outline specific regions-of-interest, for which
Transformer-based architectures are gaining traction. The self-attention
mechanism of Transformers could potentially mitigate catastrophic forgetting,
opening the way for more robust medical image segmentation. In this work, we
explore how recently-proposed Transformer mechanisms for semantic segmentation
behave in sequential learning scenarios, and analyse how best to adapt
continual learning strategies for this setting. Our evaluation on hippocampus
segmentation shows that Transformer mechanisms mitigate catastrophic forgetting
for medical image segmentation compared to purely convolutional architectures,
and demonstrates that regularising ViT modules should be done with caution.


title: Interdependent Public Projects

abstract:
  In the interdependent values (IDV) model introduced by Milgrom and Weber
[1982], agents have private signals that capture their information about
different social alternatives, and the valuation of every agent is a function
of all agent signals. While interdependence has been mainly studied for
auctions, it is extremely relevant for a large variety of social choice
settings, including the canonical setting of public projects. The IDV model is
very challenging relative to standard independent private values, and welfare
guarantees have been achieved through two alternative conditions known as {\em
single-crossing} and {\em submodularity over signals (SOS)}. In either case,
the existing theory falls short of solving the public projects setting.
  Our contribution is twofold: (i) We give a workable characterization of
truthfulness for IDV public projects for the largest class of valuations for
which such a characterization exists, and term this class \emph{decomposable
valuations}; (ii) We provide possibility and impossibility results for welfare
approximation in public projects with SOS valuations. Our main impossibility
result is that, in contrast to auctions, no universally truthful mechanism
performs better for public projects with SOS valuations than choosing a project
at random. Our main positive result applies to {\em excludable} public projects
with SOS, for which we establish a constant factor approximation similar to
auctions. Our results suggest that exclusion may be a key tool for achieving
welfare guarantees in the IDV model.


title: Counting divisorial contractions with centre a $cA_n$-singularity

abstract:
  First, we simplify the existing classification due to Kawakita and Yamamoto
of 3-dimensional divisorial contractions with centre a $cA_n$-singularity.
Next, we consider divisorial contractions of discrepancy at least 2 to a fixed
variety with centre a $cA_n$-singularity. We show that if there exists one such
divisorial contraction, then there exist uncountably many such divisorial
contractions.


title:
Evaluating Mixed-initiative Conversational Search Systems via User
  Simulation

abstract:
  Clarifying the underlying user information need by asking clarifying
questions is an important feature of modern conversational search system.
However, evaluation of such systems through answering prompted clarifying
questions requires significant human effort, which can be time-consuming and
expensive. In this paper, we propose a conversational User Simulator, called
USi, for automatic evaluation of such conversational search systems. Given a
description of an information need, USi is capable of automatically answering
clarifying questions about the topic throughout the search session. Through a
set of experiments, including automated natural language generation metrics and
crowdsourcing studies, we show that responses generated by USi are both inline
with the underlying information need and comparable to human-generated answers.
Moreover, we make the first steps towards multi-turn interactions, where
conversational search systems asks multiple questions to the (simulated) user
with a goal of clarifying the user need. To this end, we expand on currently
available datasets for studying clarifying questions, i.e., Qulac and ClariQ,
by performing a crowdsourcing-based multi-turn data acquisition. We show that
our generative, GPT2-based model, is capable of providing accurate and natural
answers to unseen clarifying questions in the single-turn setting and discuss
capabilities of our model in the multi-turn setting. We provide the code, data,
and the pre-trained model to be used for further research on the topic.


title: Weak ergodicity breaking in Josephson-junctions arrays

abstract:
  We study the quantum dynamics of Josephson junction arrays. We find isolated
groups of low-entanglement eigenstates, that persist even when the Josephson
interaction is strong enough to destroy the organization of the spectrum in
multiplets, and a perturbative description is no more possible. These
eigenstates provide a weak ergodicity breaking, and are reminiscent of the
quantum scars. Due to the presence of these eigenstates, initializing with a
charge-density-wave state, the system does not thermalize and the
charge-density-wave order persists for long times. Considering global
ergodicity probes, we find that the system tends towards more ergodicity for
increasing system size: The parameter range where the bulk of the eigenstates
look nonergodic shrinks for increasing system size. We study two geometries, a
one-dimensional chain and a two-leg ladder. In the latter case, adding a
magnetic flux makes the system more ergodic.


title:
Gravitational Solitons and Complete Ricci Flat Riemannian Manifolds of
  Infinite Topological Type

abstract:
  We present several new space-periodic solutions of the static vacuum Einstein
equations in higher dimensions, both with and without black holes, having
Kasner asymptotics. These latter solutions are referred to as gravitational
solitons. Further partially compactified solutions are also obtained by taking
appropriate quotients, and the topologies are computed explicitly in terms of
connected sums of products of spheres. In addition, it is shown that there is a
correspondence, via Wick rotation, between the spacelike slices of the solitons
and black hole solutions in one dimension less. As a corollary, the solitons
give rise to complete Ricci flat Riemannian manifolds of infinite topological
type and generic holonomy, in dimensions 4 and higher.


title: Energetically Consistent Model Reduction for Metriplectic Systems

abstract:
  The metriplectic formalism is useful for describing complete dynamical
systems which conserve energy and produce entropy. This creates challenges for
model reduction, as the elimination of high-frequency information will
generally not preserve the metriplectic structure which governs long-term
stability of the system. Based on proper orthogonal decomposition, a provably
convergent metriplectic reduced-order model is formulated which is guaranteed
to maintain the algebraic structure necessary for energy conservation and
entropy formation. Numerical results on benchmark problems show that the
proposed method is remarkably stable, leading to improved accuracy over long
time scales at a moderate increase in cost over naive methods.


title:
Synthesis and Properties of La$_{1-x}$Sr$_x$NiO$_3$ and
  La$_{1-x}$Sr$_x$NiO$_2$

abstract:
  Superconductivity has been realized in films of La$_{1-x}$Sr$_x$NiO$_2$. Here
we report synthesis and characterization of polycrystalline samples of
La$_{1-x}$Sr$_x$NiO$_3$ and La$_{1-x}$Sr$_x$NiO$_2$ ($0\le x\le 0.2$).
Magnetization and resistivity measurements reveal that La$_{1-x}$Sr$_x$NiO$_3$
are paramagnetic metals and La$_{1-x}$Sr$_x$NiO$_2$ exhibit insulating
behavior. Superconductivity is not detected in bulk samples of
La$_{1-x}$Sr$_x$NiO$_2$. The absence of superconductivity in bulk
La$_{1-x}$Sr$_x$NiO$_2$ may be due to the generation of hydroxide during
reduction or a small amount of nickel impurities. The effect of interface in
films of La$_{1-x}$Sr$_x$NiO$_2$ may also play a role for superconductivity.


title: The weak-type Carleson theorem via wave packet estimates

abstract:
  We prove that the weak-$L^{p}$ norms, and in fact the sparse $(p,1)$-norms,
of the Carleson maximal partial Fourier sum operator are $\lesssim (p-1)^{-1}$
as $p\to 1^+$. This is an improvement on the Carleson-Hunt theorem, where the
same upper bound on the growth order is obtained for the restricted weak-$L^p$
type norm, and which was the strongest quantitative bound prior to our result.
Furthermore, our sparse $(p,1)$-norms bound imply new and stronger results at
the endpoint $p=1$. In particular, we obtain that the Fourier series of
functions from the weighted Arias de Reyna space $ QA_{\infty}(w) $, which
contains the weighted Antonov space $L\log L\log\log\log L(\mathbb T; w)$,
converge almost everywhere whenever $w\in A_1$. This is an extension of the
results of Antonov and Arias De Reyna, where $w$ must be Lebesgue measure.
  The backbone of our treatment is a new, sharply quantified near-$L^1$
Carleson embedding theorem for the modulation-invariant wave packet transform.
The proof of the Carleson embedding relies on a newly developed smooth
multi-frequency decomposition which, near the endpoint $p=1$, outperforms the
abstract Hilbert space approach of past works, including the seminal one by
Nazarov, Oberlin and Thiele. As a further example of application, we obtain a
quantified version of the family of sparse bounds for the bilinear Hilbert
transforms due to Culiuc, Ou and the first author.


title:
Symmetry-protected topological exceptional chains in non-Hermitian
  crystals

abstract:
  In non-Hermitian systems, the defective band degeneracies, so-called
exceptional points (EPs), can form robust exceptional lines (ELs) in 3D
momentum space in the absence of any symmetries, and a topological invariant,
termed discriminant number (DN), can characterize the braiding topology around
an EL. Here, we show that a natural orientation can be assigned to every EL
according to the DN around it, and prove the source-free principle of ELs as a
corollary of the generalized Fermion doubling theorem for EPs on an arbitrary
closed oriented surface, which indicates that if several ELs flow into a
junction, the same number of outflow ELs from the junction must exist. Based on
this principle, we discover three different mechanisms that can stabilize the
junction of ELs and therefore guarantee the formation of various types of
exceptional chains (ECs) under the protection of mirror, mirror-adjoint, or
$\mathcal{C}_2\mathcal{T}$ symmetries. Furthermore, we analyze the
thresholdless perturbations to a Hermitian nodal line and map out all possible
EC configurations that can be evolved. By strategically designing the structure
and materials, we further exhibit that these exotic ECs can be readily observed
in non-Hermitian photonic crystals. Our results directly manifest the combined
effect of spatial symmetry and topology on the non-Hermitian singularities and
pave the way for manipulating the morphology of ELs in non-Hermitian
crystalline systems.


title: Automorphic Forms on Unitary Groups

abstract:
  This manuscript provides a more detailed treatment of the material from my
lecture series at the 2022 Arizona Winter School on Automorphic Forms Beyond
$GL_2$. The main focus of this manuscript is automorphic forms on unitary
groups, with a view toward algebraicity results. I also discuss related aspects
of automorphic $L$-functions in the setting of unitary groups.


title: Effective numerical computation of $p(x)-$Laplace equations in 2D

abstract:
  In this article we implement a method for the computation of a nonlinear
elliptic problem with nonstandard growth driven by the $p(x)-$Laplacian
operator. Our implementation is based in the {\em decomposition--coordination}
method that allows us, via an iterative process, to solve in each step a linear
differential equation and a nonlinear algebraic equation. Our code is
implemented in {\sc MatLab} in 2 dimensions and turns out to be extremely
efficient from the computational point of view.


title: 3D Jackiw-Pi Model: (Anti-)Chiral Superfield Approach to BRST Formalism

abstract:
  We discuss and derive the continuous Becchi-Rouet-Stora-Tyutin (BRST)and
anti-BRST symmetry transformations for the Jackiw-Pi (JP) model of three (2 +
1)-dimensional (3D) massive non-Abelian 1-form gauge theory by exploiting the
standard technique of (anti-)chiral superfield approach (ACSA) to BRST
formalism where a few appropriate and specific sets of (anti-)BRST invariant
quantities (i. e. physical quantities at quantum level) play a very important
role. We provide the explicit derivation of the nilpotency and absolute
anticommutativity properties of (anti-)BRST conserved charges and the existence
of the Curci-Ferrari (CF) condition within the realm of ACSA to BRST formalism
where we take only a single Grassmannian variable into account. We also provide
clear proof of (anti-) BRST invariances of the coupled (but equivalent)
Lagrangian densities within the framework of ACSA to BRST approach where the
emergence of the CF-condition is observed.


title: Homogeneous algebraic varieties and transitivity degree

abstract:
  Let $X$ be an algebraic variety such that the group $\text{Aut}(X)$ acts on
$X$ transitively. We define the transitivity degree of $X$ as a maximal number
$m$ such that the action of $\text{Aut}(X)$ on $X$ is $m$-transitive. If the
action of $\text{Aut}(X)$ is $m$-transitive for all $m$, the transitivity
degree is infinite. We compute the transitivity degree for all quasi-affine
toric varieties and for many homogeneous spaces of algebraic groups. Also we
discuss a conjecture and open questions related to this invariant.


title:
Fast solution of Sylvester-structured systems for spatial source
  separation of the Cosmic Microwave Background

abstract:
  Implementation of many statistical methods for large, multivariate data sets
requires one to solve a linear system that, depending on the method, is of the
dimension of the number of observations or each individual data vector. This is
often the limiting factor in scaling the method with data size and complexity.
In this paper we illustrate the use of Krylov subspace methods to address this
issue in a statistical solution to a source separation problem in cosmology
where the data size is prohibitively large for direct solution of the required
system. Two distinct approaches are described: one that uses the method of
conjugate gradients directly to the Kronecker-structured problem and another
that reformulates the system as a Sylvester matrix equation. We show that both
approaches produce an accurate solution within an acceptable computation time
and with practical memory requirements for the data size that is currently
available.


title:
MUGEN: A Playground for Video-Audio-Text Multimodal Understanding and
  GENeration

abstract:
  Multimodal video-audio-text understanding and generation can benefit from
datasets that are narrow but rich. The narrowness allows bite-sized challenges
that the research community can make progress on. The richness ensures we are
making progress along the core challenges. To this end, we present a
large-scale video-audio-text dataset MUGEN, collected using the open-sourced
platform game CoinRun [11]. We made substantial modifications to make the game
richer by introducing audio and enabling new interactions. We trained RL agents
with different objectives to navigate the game and interact with 13 objects and
characters. This allows us to automatically extract a large collection of
diverse videos and associated audio. We sample 375K video clips (3.2s each) and
collect text descriptions from human annotators. Each video has additional
annotations that are extracted automatically from the game engine, such as
accurate semantic maps for each frame and templated textual descriptions.
Altogether, MUGEN can help progress research in many tasks in multimodal
understanding and generation. We benchmark representative approaches on tasks
involving video-audio-text retrieval and generation. Our dataset and code are
released at: https://mugen-org.github.io/.


title:
Large deviations for quadratic functionals of stable Gauss-Markov chains
  and entropy production

abstract:
  In this paper we establish a large deviation principle for the entropy
production rate of possible non-stationary, centered stable Gauss-Markov
chains, verifying the Gallavotti-Cohen symmetry. We reach this goal by
developing a large deviation theory for quasi-Toeplitz quadratic functionals of
multivariate centered stable Gauss-Markov chains, which differ from a perfect
Toeplitz form by the addition of quadratic boundary terms.

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


title:
Giant Shear Displacement by Light-Induced Raman Force in Bilayer
  Graphene

abstract:
  Coherent excitation of shear phonons in van der Waals layered materials is a
non-destructive mechanism to fine-tune the electronic state of the system. We
develop a diagrammatic theory for the displacive Raman force and apply it to
the shear phonon's dynamics. We obtain a rectified Raman force density in
bilayer graphene of the order of ${\cal F}\sim 10{\rm nN/nm^2}$ leading to a
giant shear displacement $Q_0 \sim 50$pm for an intense infrared laser. We
discuss both circular and linear displacive Raman forces. We show that the
laser frequency and polarization can effectively tune $Q_0$ in different
electronic doping, temperature, and scattering rates. We reveal that the finite
$Q_0$ induces a Dirac crossing pair in the low-energy dispersion that
photoemission spectroscopy can probe. Our finding provides a systematic pathway
to simulate and analyze the coherent manipulation of staking order in the
heterostructures of layered materials by laser irradiation.


title:
Positive solutions of quasilinear elliptic equations with Fuchsian
  potentials in Wolff class

abstract:
  Using Harnack's inequality and a scaling argument we study Liouville-type
theorems and the asymptotic behaviour of positive solutions near an isolated
singular point $\zeta \in \partial\Omega\cup\{\infty\}$ for the quasilinear
elliptic equation $$-\text{div}(|\nabla u|_A^{p-2}A\nabla u)+V|u|^{p-2}u
=0\quad\text{ in } \Omega,$$ where $\Omega$ is a domain in $\mathbb{R}^d$,
$d\geq 2$, $1<p<d$, and $A=(a_{ij})\in L_{\rm loc}^{\infty}(\Omega;
\mathbb{R}^{d\times d})$ is a symmetric and locally uniformly positive definite
matrix. It is assumed that the potential $V$ belongs to a certain Wolff class
and has a generalized Fuchsian-type singularity at an isolated point $\zeta\in
\partial \Omega \cup \{\infty\}$.


title: Understanding Modified Two-Slit Experiments using Path Markers

abstract:
  Some modified two-slit interference experiments were carried out showing an
apparent paradox in wave-particle duality. In a typical such experiment, the
screen, where the interference pattern is supposed to be formed, is replaced by
a converging lens. The converging lens forms the images of the two slits at two
spatially separated detectors. It was claimed that each of these two detectors
give information about which slit a photon came from, even though they come
from the region of interference. These experiments generated a lot of debate.
Their various refutations were largely philosophical in nature, pointing out
that the controversial claims involved some questionable assumptions. Here such
an experiment is theoretically analyzed by introducing path markers which are
two orthogonal polarization states of the photon. Analyzing the polarization at
the two detectors shows that the photons which give rise to interference, and
reach a particular detector, always come from both the slits. This provides
clarity in understanding such experiments, without resorting to any
philosophical arguments.


title: Geo-Enabled Business Process Modeling

abstract:
  Recent advancements in location-aware analytics have created novel
opportunities in different domains. In the area of process mining, enriching
process models with geolocation helps to gain a better understanding of how the
process activities are executed in practice. In this paper, we introduce our
idea of geo-enabled process modeling and report on our industrial experience.
To this end, we present a real-world case study to describe the importance of
considering the location in process mining. Then we discuss the shortcomings of
currently available process mining tools and propose our novel approach for
modeling geo-enabled processes focusing on 1) increasing process
interpretability through geo-visualization, 2) incorporating location-related
metadata into process analysis, and 3) using location-based measures for the
assessment of process performance. Finally, we conclude the paper by future
research directions.


title:
Tabletop Experiments for Quantum Gravity Are Also Tests of the
  Interpretation of Quantum Mechanics

abstract:
  Recently there has been a great deal of interest in tabletop experiments
intended to exhibit the quantum nature of gravity by demonstrating that it can
induce entanglement. We argue that these experiments also provide new
information about the interpretation of quantum mechanics: under appropriate
assumptions, $\psi$-complete interpretations will generally predict that these
experiments will have a positive result, $\psi$-nonphysical interpretations
predict that these experiments will not have a positive result, and for
$\psi$-supplemented models there may be arguments for either outcome. We
suggest that a positive outcome to these experimenst would rule out a class of
quantum gravity models that we refer to as $\psi$-incomplete quantum gravity
(PIQG) - i.e. models of the interaction between quantum mechanics and gravity
in which gravity is coupled to non-quantum beables rather than quantum beables.
We review some existing PIQG models and consider what more needs to be done to
make these sorts of approaches more appealing, and finally we discuss a
cosmological phenomenon which could be regarded as providing evidence for PIQG
models.


title: Shock-Boundary Layer Interactions in Supersonic Turbine Cascades

abstract:
  The physics of shock-boundary layer interactions in a supersonic turbine
cascade is investigated through a wall-resolved large eddy simulation. Special
attention is given to the characterization of the low-frequency dynamics of the
separation bubbles using flow visualization, spectral analysis, space-time
cross correlations, and flow modal decomposition. The mean flowfield shows
different shock structures formed on both sides of the airfoil. On the suction
side, an oblique shock impinges on the turbulent boundary layer, whereas a Mach
reflection interacts with the pressure side boundary layer. Instantaneous flow
visualizations illustrate elongated streamwise structures on the incoming
boundary layers and their interactions with the shocks and separation bubbles.
The passage of high-speed (low-speed) streaks through the recirculation bubbles
leads to the downstream (upstream) motion of the separation point on both
suction and pressure sides, resulting in spanwise modulation of the bubbles.
Space-time cross-correlations reveal that the near-wall streaks drive the
suction side separation bubble motion, which in turn promotes the oscillations
of the reattachment shock and shear layer flapping. Space-time correlations
also indicate the existence of a $\pi$ phase jump in the pressure fluctuations
along the separation bubble on the suction side. After this phase jump, a
downstream propagating pressure disturbance is observed, while prior to this
point, the pressure disturbances dominantly propagate in the upstream
direction. Finally, the organized motions in the shock-boundary layer
interactions and their corresponding characteristic frequencies are identified
using proper orthogonal decomposition.


title:
Topological Electronic Structure Evolution with Symmetry Breaking Spin
  Reorientation in (Fe$_{1-x}$Co$_{x}$)Sn

abstract:
  Topological materials hosting kagome lattices have drawn considerable
attention due to the interplay between topology, magnetism, and electronic
correlations. The (Fe$_{1-x}$Co$_x$)Sn system not only hosts a kagome lattice
but has a tunable symmetry breaking magnetic moment with temperature and
doping. In this study, angle resolved photoemission spectroscopy and first
principles calculations are used to investigate the interplay between the
topological electronic structure and varying magnetic moment from the planar to
axial antiferromagnetic phases. A theoretically predicted gap at the Dirac
point is revealed in the low temperature axial phase but no gap opening is
observed across a temperature dependent magnetic phase transition. However,
topological surface bands are observed to shift in energy as the surface
magnetic moment is reduced or becomes disordered over time during experimental
measurements. The shifting surface bands may preclude the determination of a
temperature dependent bulk gap but highlights the intricate connections between
magnetism and topology with a surface/bulk dichotomy that can affect material
properties and their interrogation.


title:
Explaining $W$ boson mass anomaly and dark matter with a $U(1)$ dark
  sector

abstract:
  The $W$ boson mass recently reported by the CDF collaboration shows a
deviation from the standard model prediction with an excess at $7\sigma$ level.
We investigate two simple extensions of the standard model with an extra $U(1)$
dark sector. One is the $U(1)_x$ extension, where the $U(1)_x$ gauge field
mixes with the standard model through gauge kinetic terms. The other is a
general $U(1)_{\alpha Y+\beta q}$ extension of the standard model. Fitting
various experimental constraints we find the $U(1)_x$ extension with only
kinetic mixing can enhance the $W$ boson mass for at most 10 MeV. While the
$U(1)_{\alpha Y+\beta q}$ extension can easily generate 77 MeV enhancement of
the $W$ boson mass and also offer a viable dark matter candidate with mass
ranging from several hundred GeV to TeV, which may be detected by future dark
matter direct detection experiments with improved sensitivities.


title:
Melting of heavy vector mesons and quasinormal modes in a finite density
  plasma from holography

abstract:
  In this work, we investigate the melting of charmonium states within a
holographic QCD model in the context of Einstein-Maxwell-Dilaton (EMD) theory.
In the dual field theory, the model describes the heavy mesons inside a finite
temperature and density medium. First, we calculate the spectrum at zero
temperature. Then, at finite temperature, we obtain the spectral functions,
where the heavy vector meson are represented by peaks. We show that the
charmonium melts down at temperatures above the confinement/deconfinement
temperature of the quark-gluon plasma. We also observe that the chemical
potential speeds up the melting process. This finding is in agreement with
results previously reported in the literature. In the gravitational side of the
theory, we solve the perturbation equations in the hydrodynamics limit. From
this result, we read off the diffusion coefficient by comparing the dispersion
relation against the corresponding result obtained in the dual field theory. We
also investigate the behavior of the diffusion coefficient as a function of the
temperature. The perturbation equations are solved numerically, in order to get
the quasinormal frequencies. We report the emergence of a new mode whose real
part increases rapidly at a certain value of the chemical potential while its
imaginary part decreases with the increasing of the chemical potential.
Finally, by comparing against results obtained in the conformal plasma, we
observe that the real part of the frequency increases, while the imaginary part
decreases when we consider the non-conformal plasma.


title: Self-Aware Personalized Federated Learning

abstract:
  In the context of personalized federated learning (FL), the critical
challenge is to balance local model improvement and global model tuning when
the personal and global objectives may not be exactly aligned. Inspired by
Bayesian hierarchical models, we develop a self-aware personalized FL method
where each client can automatically balance the training of its local personal
model and the global model that implicitly contributes to other clients'
training. Such a balance is derived from the inter-client and intra-client
uncertainty quantification. A larger inter-client variation implies more
personalization is needed. Correspondingly, our method uses uncertainty-driven
local training steps and aggregation rule instead of conventional local
fine-tuning and sample size-based aggregation. With experimental studies on
synthetic data, Amazon Alexa audio data, and public datasets such as MNIST,
FEMNIST, CIFAR10, and Sent140, we show that our proposed method can achieve
significantly improved personalization performance compared with the existing
counterparts.


title:
A Novel ASIC Design Flow using Weight-Tunable Binary Neurons as Standard
  Cells

abstract:
  In this paper, we describe a design of a mixed signal circuit for a binary
neuron (a.k.a perceptron, threshold logic gate) and a methodology for
automatically embedding such cells in ASICs. The binary neuron, referred to as
an FTL (flash threshold logic) uses floating gate or flash transistors whose
threshold voltages serve as a proxy for the weights of the neuron. Algorithms
for mapping the weights to the flash transistor threshold voltages are
presented. The threshold voltages are determined to maximize both the
robustness of the cell and its speed. The performance, power, and area of a
single FTL cell are shown to be significantly smaller (79.4%), consume less
power (61.6%), and operate faster (40.3%) compared to conventional CMOS logic
equivalents. Also included are the architecture and the algorithms to program
the flash devices of an FTL. The FTL cells are implemented as standard cells,
and are designed to allow commercial synthesis and P&R tools to automatically
use them in synthesis of ASICs. Substantial reductions in area and power
without sacrificing performance are demonstrated on several ASIC benchmarks by
the automatic embedding of FTL cells. The paper also demonstrates how FTL cells
can be used for fixing timing errors after fabrication.


title: Eigen mode selection in human subject game experiment

abstract:
  Eigen mode selection ought to be a practical issue in some real game systems,
as it is a practical issue in the dynamics behaviour of a building, bridge, or
molecular, because of the mathematical similarity in theory. However, its
reality and accuracy have not been known in real games. We design a 5-strategy
game which, in the replicator dynamics theory, is predicted to exist two eigen
modes. Further, in behaviour game theory, the game is predicted that the mode
selection should depends on the game parameter. We conduct human subject game
experiments by controlling the parameter. The data confirm that, the
predictions on the mode existence as well as the mode selection are
significantly supported. This finding suggests that, like the equilibrium
selection concept in classical game theory, eigen mode selection is an issue in
game dynamics theory.


title:
Dynamic Programming of Stochastic 2-D Navier-Stokes Equations Forced by
  Levy Noise

abstract:
  In this article, we study optimal feedback control synthesis of stochastic 2D
Navier-Stokes equations perturbed Levy type noise with distributed stochastic
control process acting on the state equation. We use the dynamic programming
approach to solve this control problem which involves the study of second order
infinite dimensional Hamilton- Jacobi-Bellman (HJB) equation consisting of an
integro-differential operator with Levy measure associated with the stochastic
control problem. Using the regularizing properties of the transition semigroup
corresponding to the stochastic 2D Navier-Stokes equation, we obtain a smooth
solution in weighted function space for the HJB equation and solve the
resultant feedback control problem.


title: Intelligent Explorations of the String Theory Landscape

abstract:
  The goal of identifying the Standard Model of particle physics and its
extensions within string theory has been one of the principal driving forces in
string phenomenology. Recently, the incorporation of artificial intelligence in
string theory and certain theoretical advancements have brought to light
unexpected solutions to mathematical hurdles that have so far hindered progress
in this direction. In this review we focus on model building efforts in the
context of the $E_8\times E_8$ heterotic string compactified on smooth
Calabi-Yau threefolds and discuss several areas in which machine learning is
expected to make a difference.


title:
Dynamics of co-orbital exoplanets in a first order resonance chain with
  tidal dissipation

abstract:
  Co-orbital planets (in a $1:1$ mean motion resonance) can be formed within a
Laplace resonance chain. Here, we develop a secular model to study the dynamics
of the resonance chain $p:p:p+1$, where the co-orbital pair is in a first-order
mean motion resonance with the outermost third planet. Our model takes into
account tidal dissipation through the use of a Hamiltonian version of the
constant time-lag model, which extends the Hamiltonian formalism of the
point-mass case. We show the existence of several families of equilibria, and
how these equilibria extend to the complete system. In one family, which we
call the main branch, a secular resonance between the libration frequency of
the co-orbitals and the precession frequency of the pericentres has unexpected
dynamical consequences when tidal dissipation is added. We report the existence
of two distinct mechanisms that make co-orbital planets much more stable within
the $p:p:p+1$ resonance chain rather than outside it. The first one is due to
negative real parts of the eigenvalues of the linearised system with tides, in
the region of the secular resonance mentioned above. The second one comes from
non-linear contributions of the vector field and it is due to eccentricity
damping. These two stabilising mechanisms increase the chances of a
still-to-come detection of exoplanets in the co-orbital configuration.


title: Descent theory of simple sheaves on $C_1$-fields

abstract:
  Let $K$ be a $C_1$-field of any characteristic and $X$ a projective variety
over $K$. In this article we prove that for a finite Galois extension $L$ of
$K$, a simple sheaf with covering datum on $X \times_K L$ descends to a simple
sheaf on $X$. As a consequence, we show that there is a $1-1$ correspondence
between the set of geometrically stable sheaves on $X$ with fixed Hibert
polynomial $P$ and the set of $K$-rational points of the corresponding moduli
space.


title: The combinatorics of Farey words and their traces

abstract:
  The set of Kleinian groups which are free on two parabolic generators is
parameterised by the closed Riley slice of Schottky space. A Farey word is a
word in such a group which represents a non-boundary-parallel geodesic that can
be pinched down to a puncture; in the interior of the Riley slice such a word
is loxodromic, and the pinching process corresponds to deforming the word to be
parabolic. Keen and Series showed that the geometry of the Riley slice is
detected by the real loci of the trace polynomials of these words. We study
these trace polynomials from a combinatorial viewpoint, and give a recursion
formula for them which enables efficient calculation of the polynomials without
performing matrix multiplication; we also present some intriguing examples to
show that there is much still to be learned about them.


title:
Multipolar invariants and the eccentricity enhancement function
  parametrization of gravitational radiation

abstract:
  Gravitational radiation can be decomposed as an infinite sum of radiative
multipole moments, which parametrize the waveform at infinity. The
multipolar-post-Minkowskian formalism provides a connection between these
multipoles and the source multipole moments, known as explicit integrals over
the matter source. The gravitational wave energy, angular momentum and linear
momentum fluxes are then expressed as multipolar expansions containing certain
combinations of the source moments. We compute several gauge-invariant
quantities as "building blocks" entering the multipolar expansion of both
radiated energy and angular momentum at the 2.5 post-Newtonian (PN) level of
accuracy in the case of hyperboliclike motion, by completing previous studies
through the calculation of tail effects up to the fractional 1PN order. We
express such multipolar invariants in terms of certain eccentricity enhancement
factor functions, which are the counterpart of the well known enhancement
functions already introduced in the literature for ellipticlike motion.
Finally, we use the complete 2.5PN-accurate averaged energy and angular
momentum fluxes to study the associated adiabatic evolution of orbital elements
under gravitational radiation reaction.


title: A Psycho-linguistic Analysis of BitChute

abstract:
  In order to better support researchers, journalist, and practitioners in
their use of the MeLa-BitChute dataset for exploration and investigative
reporting, we provide new psycho-linguistic metadata for the videos, comments,
and channels in the dataset using LIWC22. This paper describes that metadata
and methods to filter the data using the metadata. In addition, we provide
basic analysis and comparison of the language on BitChute to other social media
platforms. The MeLa-BitChute dataset and LIWC metadata described in this paper
can be found at:
https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/KRD1VS.


title: On a conjecture of Harris

abstract:
  For $d \ge 4$, the Noether-Lefschetz locus $\mathrm{NL}_d$ parametrizes
smooth, degree $d$ surfaces in $\mathbb{P}^3$ with Picard number at least $2$.
A conjecture of Harris states that there are only finitely many irreducible
components of the Noether-Lefschetz locus of non-maximal codimension. Voisin
showed that the conjecture is false for sufficiently large $d$, but is true for
$d \le 5$. She also showed that for $d=6, 7$, there are finitely many
\emph{reduced}, irreducible components of $\mathrm{NL}_d$ of non-maximal
codimension. In this article, we prove that for any $d \ge 6$, there are
infinitely many \emph{non-reduced} irreducible components of $\mathrm{NL}_d$ of
non-maximal codimension.


title: Examples of non-flat bundles of rank one

abstract:
  In this article we give several examples of line bundles on certain
non-compact surfaces that cannot be equipped with a flat connection.


title: Initial state reconstruction on graphs

abstract:
  The presence of noise is an intrinsic problem in acquisition processes for
digital images. One way to enhance images is to combine the forward and
backward diffusion equations. However, the latter problem is well known to be
exponentially unstable with respect to any small perturbations on the final
data. In this scenario, the final data can be regarded as a blurred image
obtained from the forward process, and that image can be pixelated as a
network. Therefore, we study in this work a regularization framework for the
backward diffusion equation on graphs. Our aim is to construct a spectral
graph-based solution based upon a cut-off projection. Stability and convergence
results are provided together with some numerical experiments.


title:
Identifying Topological Superconductivity in 2D Transition-Metal
  Dichalcogenides

abstract:
  We study the superconducting paring instabilities and gap functions for
prototypical two-dimensional (2D) transition-metal dichalcogenides (TMDCs)
WS$_{2}$, MoTe$_{2}$, and MoS$_{2}$ in the 2H phase under both hole and
electron doping at $10$ K. Our first-principles quantum many-body Green's
function approach allows us to treat the full $d$ and $p$ manifold of orbitals
with strong spin-orbit coupling, yielding pairing predictions with material
specific detail. The resulting gap functions exhibit a variety of mixed-parity
superconducting states, including $s$, $p$, $d$, $f$, $d \pm id$, and $p \pm
ip$ pairing modes. In particular, we predict 3% and 4% hole doped WS$_{2}$ to
be a chiral $p \pm ip$ topological superconductor. For 1% hole doped MoS$_{2}$,
we find a competition between three doubly degenerate chiral and non-chiral
instabilities. Overall, the relative pairing strengths are found to follow the
Fermi surface topology, due to nesting between the Fermi surface sheets.
Finally, we discuss our predictions in relation to available experimental data
and classify the topology of the predicted superconducting pairing symmetries.


title:
\`It\`ak\'ur\`oso: Exploiting Cross-Lingual Transferability for Natural
  Language Generation of Dialogues in Low-Resource, African Languages

abstract:
  We investigate the possibility of cross-lingual transfer from a
state-of-the-art (SoTA) deep monolingual model (DialoGPT) to 6 African
languages and compare with 2 baselines (BlenderBot 90M, another SoTA, and a
simple Seq2Seq). The languages are Swahili, Wolof, Hausa, Nigerian Pidgin
English, Kinyarwanda & Yor\`ub\'a. Generation of dialogues is known to be a
challenging task for many reasons. It becomes more challenging for African
languages which are low-resource in terms of data. Therefore, we translate a
small portion of the English multi-domain MultiWOZ dataset for each target
language. Besides intrinsic evaluation (i.e. perplexity), we conduct human
evaluation of single-turn conversations by using majority votes and measure
inter-annotator agreement (IAA). The results show that the hypothesis that deep
monolingual models learn some abstractions that generalise across languages
holds. We observe human-like conversations in 5 out of the 6 languages. It,
however, applies to different degrees in different languages, which is
expected. The language with the most transferable properties is the Nigerian
Pidgin English, with a human-likeness score of 78.1%, of which 34.4% are
unanimous. The main contributions of this paper include the representation
(through the provision of high-quality dialogue data) of under-represented
African languages and demonstrating the cross-lingual transferability
hypothesis for dialogue systems. We also provide the datasets and host the
model checkpoints/demos on the HuggingFace hub for public access.


title:
Learning 3D Semantics from Pose-Noisy 2D Images with Hierarchical Full
  Attention Network

abstract:
  We propose a novel framework to learn 3D point cloud semantics from 2D
multi-view image observations containing pose error. On the one hand, directly
learning from the massive, unstructured and unordered 3D point cloud is
computationally and algorithmically more difficult than learning from
compactly-organized and context-rich 2D RGB images. On the other hand, both
LiDAR point cloud and RGB images are captured in standard automated-driving
datasets. This motivates us to conduct a "task transfer" paradigm so that 3D
semantic segmentation benefits from aggregating 2D semantic cues, albeit pose
noises are contained in 2D image observations. Among all difficulties, pose
noise and erroneous prediction from 2D semantic segmentation approaches are the
main challenges for the task transfer. To alleviate the influence of those
factor, we perceive each 3D point using multi-view images and for each single
image a patch observation is associated. Moreover, the semantic labels of a
block of neighboring 3D points are predicted simultaneously, enabling us to
exploit the point structure prior to further improve the performance. A
hierarchical full attention network~(HiFANet) is designed to sequentially
aggregates patch, bag-of-frames and inter-point semantic cues, with
hierarchical attention mechanism tailored for different level of semantic cues.
Also, each preceding attention block largely reduces the feature size before
feeding to the next attention block, making our framework slim. Experiment
results on Semantic-KITTI show that the proposed framework outperforms existing
3D point cloud based methods significantly, it requires much less training data
and exhibits tolerance to pose noise. The code is available at
https://github.com/yuhanghe01/HiFANet.


title:
CPFair: Personalized Consumer and Producer Fairness Re-ranking for
  Recommender Systems

abstract:
  Recently, there has been a rising awareness that when machine learning (ML)
algorithms are used to automate choices, they may treat/affect individuals
unfairly, with legal, ethical, or economic consequences. Recommender systems
are prominent examples of such ML systems that assist users in making
high-stakes judgments. A common trend in the previous literature research on
fairness in recommender systems is that the majority of works treat user and
item fairness concerns separately, ignoring the fact that recommender systems
operate in a two-sided marketplace. In this work, we present an
optimization-based re-ranking approach that seamlessly integrates fairness
constraints from both the consumer and producer-side in a joint objective
framework. We demonstrate through large-scale experiments on 8 datasets that
our proposed method is capable of improving both consumer and producer fairness
without reducing overall recommendation quality, demonstrating the role
algorithms may play in minimizing data biases.


title:
Intracranial EEG structure-function coupling predicts surgical outcomes
  in focal epilepsy

abstract:
  Alterations to structural and functional brain networks have been reported
across many neurological conditions. However, the relationship between
structure and function -- their coupling -- is relatively unexplored,
particularly in the context of an intervention. Epilepsy surgery alters the
brain structure and networks to control the functional abnormality of seizures.
Given that surgery is a structural modification aiming to alter the function,
we hypothesized that stronger structure-function coupling preoperatively is
associated with a greater chance of post-operative seizure control. We
constructed structural and functional brain networks in 39 subjects with
medication-resistant focal epilepsy using data from intracranial EEG
(pre-surgery), structural MRI (pre-and post-surgery), and diffusion MRI
(pre-surgery). We investigated pre-operative structure-function coupling at two
spatial scales a) at the global iEEG network level and b) at the resolution of
individual iEEG electrode contacts using virtual surgeries. At global network
level, seizure-free individuals had stronger structure-function coupling
pre-operatively than those that were not seizure-free regardless of the choice
of interictal segment or frequency band. At the resolution of individual iEEG
contacts, the virtual surgery approach provided complementary information to
localize epileptogenic tissues. In predicting seizure outcomes,
structure-function coupling measures were more important than clinical
attributes, and together they predicted seizure outcomes with an accuracy of
85% and sensitivity of 87%. The underlying assumption that the structural
changes induced by surgery translate to the functional level to control
seizures is valid when the structure-functional coupling is strong. Mapping the
regions that contribute to structure-functional coupling using virtual
surgeries may help aid surgical planning.


title:
Replica Higher-Order Topology of Hofstadter Butterflies in Twisted
  Bilayer Graphene

abstract:
  The Hofstadter energy spectrum of twisted bilayer graphene is found to have
recursive higher-order topological properties. We demonstrate that higher-order
topological insulator (HOTI) phases, characterized by localized corner states,
occur as replicas of the original HOTIs to fulfill the self-similarity of the
Hofstadter spectrum. We show the existence of the exact flux translational
symmetry of twisted bilayer graphene at all commensurate angles. Based on this
result, we carefully identify that the original HOTI phase at zero flux is
re-entrant at a half-flux periodicity, where the effective twofold rotation is
preserved. In addition, numerous replicas of the original HOTIs are found for
fluxes without protecting symmetries. Similar to the original HOTIs, replica
HOTIs feature both localized corner states and edge-localized real-space
topological markers. The replica HOTIs originate from the different interaction
scales, namely, intralayer and interlayer couplings, in twisted bilayer
graphene. The topological aspect of Hofstadter butterflies revealed in our
results highlights symmetry-protected topology in quantum fractals.


title:
Thermal SZ effect in a magnetized IGM dominated by interacting DM
  decay/annihilation during dark ages

abstract:
  During cosmic dawn, the thermal history of the universe is well studied, and
a study of this era can give us some of the most useful insight into the
universe before the recombination epoch. Its precise modeling and future
high-precision measurements will be a valuable tool for determining the thermal
history of the universe. In the present work, we study the thermal and
ionization history of IGM in the presence of decaying magnetic fields via
ambipolar and turbulent decay, Baryon-Dark matter (BDM) interaction, including
the DM decay/annihilation. The BDM interaction cross-sections considered are of
the form $\sigma=\sigma_0 v^{n}$, where $n=-2$ and $n=-4$. In this work, we
show that in the current scenario, the decay/annihilation of the DM particles
have a considerable impact on the temperature and ionization histories at low
redshift. With the addition of the concept of fractional interaction, which
states that if a fraction of the DM particles interacts with the baryons, the
temperature and ionization fraction of the baryons show a strong dependence on
the percentage of DM particles interacting with the baryons. We have also
studied the interesting consequences of the present scenario on the thermal
Sunyaev-Zeldovich (tSZ) effect. We show that the highest value of the absolute
value of the mean $y-$parameter in the current DM decay/annihilation scenario
is well within the values derived from experimental data such as PLANCK, FIRAS,
and PIXIE. Later we calculate the bound on the ordinary magnetic fields
originating from the Dark photons.


title:
An Extension of Heron's Formula to Tetrahedra, and the Projective Nature
  of Its Zeros

abstract:
  A natural extension of Heron's 2000 year old formula for the area of a
triangle to the volume of a tetrahedron is presented. This extension gives the
fourth power of the volume as a polynomial in six simple rational functions of
the areas of its four faces and of its three medial parallelograms, which will
be referred to herein as interior faces. Geometrically, these rational
functions are the areas of the triangles into which the exterior faces are
divided by the points at which the tetrahedron's in-sphere touches those faces.
This leads to a conjecture as to how the formula likely extends to
$n$-dimensional simplices for all $n > 3$. Remarkably, for $n = 3$ the zeros of
the overall polynomial constitute a five-dimensional real semi-algebraic
variety consisting almost entirely of collinear tetrahedra with vertices at
infinite distances from one another. These unconventional Euclidean
configurations can be identified with a quotient of the Klein quadric by an
action of a group of reflections isomorphic to $\mathbb Z_2^4$, wherein
four-point configurations in the finite affine plane constitute a distinguished
three-dimensional subset. The paper closes by noting that the algebraic
structure of the zeros in the finite affine plane naturally defines the
associated $4$-element, rank-$3$ chirotope, aka affine oriented matroid.


title:
Learning Compositional Representations for Effective Low-Shot
  Generalization

abstract:
  We propose Recognition as Part Composition (RPC), an image encoding approach
inspired by human cognition. It is based on the cognitive theory that humans
recognize complex objects by components, and that they build a small compact
vocabulary of concepts to represent each instance with. RPC encodes images by
first decomposing them into salient parts, and then encoding each part as a
mixture of a small number of prototypes, each representing a certain concept.
We find that this type of learning inspired by human cognition can overcome
hurdles faced by deep convolutional networks in low-shot generalization tasks,
like zero-shot learning, few-shot learning and unsupervised domain adaptation.
Furthermore, we find a classifier using an RPC image encoder is fairly robust
to adversarial attacks, that deep neural networks are known to be prone to.
Given that our image encoding principle is based on human cognition, one would
expect the encodings to be interpretable by humans, which we find to be the
case via crowd-sourcing experiments. Finally, we propose an application of
these interpretable encodings in the form of generating synthetic attribute
annotations for evaluating zero-shot learning methods on new datasets.


title: Dynamical mass generation in Minkowski space at QCD scale

abstract:
  We undertake the challenging task to reveal the properties of dressed
light-quarks in the space- and time-like regions. For that aim, we solved the
Dyson-Schwinger equation (DSE) in Minkowski space for the quark propagator in a
QCD inspired model, focusing on the realization of dynamical chiral symmetry
breaking (DCSB) in the large coupling regime. The DSE is considered in the
quenched approximation within the rainbow-ladder truncation with a massive
gluon and a Pauli-Villars term, which is used to tune the infrared (IR) physics
of the model. The solution of the DSE in Minkowski space is performed by
resorting to the integral representation of the quark self-energy and
propagator, which leads to a coupled set of closed self-consistent equations
for the spectral densities, taking into account finite on-mass-shell
renormalization. The parameters of the model are chosen such that the gluon
mass scale is consistent with recent lattice QCD (LQCD) calculations, the
Pauli-Villars mass is lowered down to about 1 GeV to concentrate strength in
the infrared momentum region, and the coupling constant and renormalized mass
are tuned to reproduce LQCD results for the quark mass function in the Landau
gauge. Future application to study the pion consistently with DCSB within the
Bethe-Salpeter framework with self-energies in Minkowski space is also
delineated.


title:
The Existence and Uniqueness of Solutions for Kernel-Based System
  Identification

abstract:
  The notion of reproducing kernel Hilbert space (RKHS) has emerged in system
identification during the past decade. In the resulting framework, the impulse
response estimation problem is formulated as a regularized optimization defined
on an infinite-dimensional RKHS consisting of stable impulse responses. The
consequent estimation problem is well-defined under the central assumption that
the convolution operators restricted to the RKHS are continuous linear
functionals. Moreover, according to this assumption, the representer theorem
hold, and therefore, the impulse response can be estimated by solving a
finite-dimensional program. Thus, the continuity feature plays a significant
role in kernel-based system identification. This paper shows that this central
assumption is guaranteed to be satisfied in considerably general situations,
namely when the kernel is an integrable function and the input signal is
bounded. Furthermore, the strong convexity of the optimization problem and the
continuity property of the convolution operators imply that the kernel-based
system identification admits a unique solution. Consequently, it follows that
kernel-based system identification is a well-defined approach.


title: 3-D Acoustic Trapping With Standing Waves

abstract:
  In this work, we will describe an experimental setup for a standing--wave
ultrasound trap for air microbubbles in oil. We develop a model for the finite
acoustic beam using the angular spectrum technique, and reconstruct the
pressure field using the General Lorenz-Mie Theory framework, which was
validated using a finite elements method (FEM) simulation. Using Stokes' drag
law, we were able to obtain the radius of the trapped bubbles and estimate the
minimum acoustic force necessary to trap them, which ranged from 3 nN to 780
nN. We also present the force profile as a function of distance for different
bubble that were trapped experimentally, and show that a standing wave formed
by interfering infinite plane waves cannot explain the observed acoustic
trapping of bubbles in 3-D.


title:
A Data-Driven Methodology for Considering Feasibility and Pairwise
  Likelihood in Deep Learning Based Guitar Tablature Transcription Systems

abstract:
  Guitar tablature transcription is an important but understudied problem
within the field of music information retrieval. Traditional signal processing
approaches offer only limited performance on the task, and there is little
acoustic data with transcription labels for training machine learning models.
However, guitar transcription labels alone are more widely available in the
form of tablature, which is commonly shared among guitarists online. In this
work, a collection of symbolic tablature is leveraged to estimate the pairwise
likelihood of notes on the guitar. The output layer of a baseline tablature
transcription model is reformulated, such that an inhibition loss can be
incorporated to discourage the co-activation of unlikely note pairs. This
naturally enforces playability constraints for guitar, and yields tablature
which is more consistent with the symbolic data used to estimate pairwise
likelihoods. With this methodology, we show that symbolic tablature can be used
to shape the distribution of a tablature transcription model's predictions,
even when little acoustic data is available.


title: Mixed Isogeometric Discretizations for Planar Linear Elasticity

abstract:
  In this article we suggest two discretization methods based on isogeometric
analysis (IGA) for planar linear elasticity. On the one hand, we apply the
well-known ansatz of weakly imposed symmetry for the stress tensor and obtain a
well-posed mixed formulation. Such modified mixed problems have been already
studied by different authors. But we concentrate on the exploitation of IGA
results to handle also curved boundary geometries. On the other hand, we
consider the more complicated situation of strong symmetry, i.e. we discretize
the mixed weak form determined by the so-called Hellinger-Reissner variational
principle. We show the existence of suitable approximate fields leading to an
inf-sup stable saddle-point problem. For both discretization approaches we
prove convergence statements and in case of weak symmetry we illustrate the
approximation behavior by means of several numerical experiments.


title:
Synthetic Distracted Driving (SynDD1) dataset for analyzing distracted
  behaviors and various gaze zones of a driver

abstract:
  This article presents a synthetic distracted driving (SynDD1) dataset for
machine learning models to detect and analyze drivers' various distracted
behavior and different gaze zones. We collected the data in a stationary
vehicle using three in-vehicle cameras positioned at locations: on the
dashboard, near the rearview mirror, and on the top right-side window corner.
The dataset contains two activity types: distracted activities, and gaze zones
for each participant and each activity type has two sets: without appearance
blocks and with appearance blocks such as wearing a hat or sunglasses. The
order and duration of each activity for each participant are random. In
addition, the dataset contains manual annotations for each activity, having its
start and end time annotated. Researchers could use this dataset to evaluate
the performance of machine learning algorithms for the classification of
various distracting activities and gaze zones of drivers.