title: The decision problem for perfect matchings in dense hypergraphs

abstract:
  Given $1\le \ell <k$ and $\delta\ge0$, let $\textbf{PM}(k,\ell,\delta)$ be
the decision problem for the existence of perfect matchings in $n$-vertex
$k$-uniform hypergraphs with minimum $\ell$-degree at least
$\delta\binom{n-\ell}{k-\ell}$. For $k\ge 3$, the decision problem in general
$k$-uniform hypergraphs, equivalently $\textbf{PM}(k,\ell,0)$, is one of Karp's
21 NP-complete problems. Moreover, for $k\ge 3$, a reduction of Szyma\'{n}ska
showed that $\textbf{PM}(k, \ell, \delta)$ is NP-complete for $\delta <
1-(1-1/k)^{k-\ell}$. A breakthrough by Keevash, Knox and Mycroft [STOC '13]
resolved this problem for $\ell=k-1$ by showing that $\textbf{PM}(k, k-1,
\delta)$ is in P for $\delta > 1/k$. Based on their result for $\ell=k-1$,
Keevash, Knox and Mycroft conjectured that $\textbf{PM}(k, \ell, \delta)$ is in
P for every $\delta > 1-(1-1/k)^{k-\ell}$.
  In this paper it is shown that this decision problem for perfect matchings
can be reduced to the study of the minimum $\ell$-degree condition forcing the
existence of fractional perfect matchings. That is, we hopefully solve the
"computational complexity" aspect of the problem by reducing it to a well-known
extremal problem in hypergraph theory. In particular, together with existing
results on fractional perfect matchings, this solves the conjecture of Keevash,
Knox and Mycroft for $\ell\ge 0.4k$.


title: On geometric properties of ratio of two hypergeometric functions

abstract:
  R. K\"ustner proved in his 2002 paper that the function $w_{a,b,c}(z)=$
$F(a+1,b;c;z)/F(a,b;c;z)$ maps the unit disk $|z|<1$ onto a domain convex in
the direction of the imaginary axis under some condition on the real parameters
$a,b,c.$ Here $F(a,b;c;z)$ stands for the Gaussian hypergeometric function. In
this paper, we study the order of convexity of $w_{a,b,c}.$ In particular, we
partially solve the problem raised by the afore-mentioned paper by K\"ustner.


title: Field-Dependent Magnetic Domain Behavior in van der Waals Fe$_3$GeTe$_2$

abstract:
  Two-dimensional magnetic van der Waals (vdW) materials can show a variety of
topological nontrivial spin textures, such as Bloch- or N\'eel-type stripe,
skyrmion or bubble domains under certain external stimuli. It is critical to
understand the magnetic domain behavior in vdW materials in order to control
their size, and density in response to external stimuli such as electric and
magnetic fields. Here we examine the magnetic field dependence of topologically
non-trivial magnetization spin textures in vdW Fe$_3$GeTe$_2$. N\'eel-type
stripe domains and skyrmions are formed depending on the magnetic field-cooling
protocol used during in-situ Lorentz transmission electron microscopy (LTEM)
experiments. Use of quantitative reconstruction of magnetic induction maps, and
micromagnetic simulations, allow for understanding the LTEM results of
N\'eel-type stripe domains as well as skyrmions. In addition, the deformation
of skyrmion contrast is observed as a result of the introduction of an in-plane
magnetic field. We demonstrate the stability of the stripe domains and
skyrmions in response to externally applied magnetic field due to energy
barrier for domain wall annihilation. Our results establish an understanding of
the energy landscape that governs the behavior of the topologically non-trivial
spin textures in vdW materials which can be harnessed for spintronic
applications.


title:
Efficient search method of anomalous reflection by the central object in
  an EMRI system by future space gravitational wave detectors

abstract:
  In our previous work we investigated the effect of the hypothetical
reflecting boundary near the black hole event horizon on the waveform from
extreme mass-ratio inspirals (EMRIs). Even if the reflection efficiency is not
extremely high, we found that a significant modification of the waveform can be
expected. Then, the question is how to implement the search for this signature
in the actual data analysis of future space gravitational wave antennas, such
as LISA. In this paper we propose a simple but efficient method to detect the
signature of the reflecting boundary. The interesting feature of the effect of
the reflecting boundary on the orbital evolution of EMRIs is that the energy
and angular momentum loss rates periodically oscillate in the frequency domain.
The oscillation period is corresponding to the inverse time scale for the round
trip of gravitational waves between the hypothetical boundary and the angular
momentum barrier. We will show that this peculiar feature allows to detect the
signature of the reflecting boundary without much additional computational
cost.


title:
GenAD: General Representations of Multivariate Time Seriesfor Anomaly
  Detection

abstract:
  The reliability of wireless base stations in China Mobile is of vital
importance, because the cell phone users are connected to the stations and the
behaviors of the stations are directly related to user experience. Although the
monitoring of the station behaviors can be realized by anomaly detection on
multivariate time series, due to complex correlations and various temporal
patterns of multivariate series in large-scale stations, building a general
unsupervised anomaly detection model with a higher F1-score remains a
challenging task. In this paper, we propose a General representation of
multivariate time series for Anomaly Detection(GenAD). First, we pre-train a
general model on large-scale wireless base stations with self-supervision,
which can be easily transferred to a specific station anomaly detection with a
small amount of training data. Second, we employ Multi-Correlation Attention
and Time-Series Attention to represent the correlations and temporal patterns
of the stations. With the above innovations, GenAD increases F1-score by total
9% on real-world datasets in China Mobile, while the performance does not
significantly degrade on public datasets with only 10% of the training data.


title:
Improving greedy core-set configurations for active learning with
  uncertainty-scaled distances

abstract:
  We scale perceived distances of the core-set algorithm by a factor of
uncertainty and search for low-confidence configurations, finding significant
improvements in sample efficiency across CIFAR10/100 and SVHN image
classification, especially in larger acquisition sizes. We show the necessity
of our modifications and explain how the improvement is due to a probabilistic
quadratic speed-up in the convergence of core-set loss, under assumptions about
the relationship of model uncertainty and misclassification.


title:
Data-Dependent Early Completion of Dose Finding Trials for
  Drug-Combination

abstract:
  We propose a data-dependent early completion of dose finding trials for
drug-combination. The early completion is determined when a beta-binomial
probability for dose retainment with the trial data and the number of remaining
patients is high. This paper also proposes an early completion method that a
dose retainment probability is adjusted by a bivariate isotonic regression. We
demonstrate the early completion for a virtual trial. We evaluate the
performance of early completion method through simulation studies with 12
scenarios. We confirmed the superior performance for our proposed early
completion methods. We show the number of patients for determining early
completion before a trial starts and a program code for calculating dose
retainment probability in this paper.


title:
Optimization of Design Parameters for Gravitational Wave Detector DECIGO
  Including Fundamental Noises

abstract:
  The DECi hertz Interferometer Gravitational-Wave Observatory (DECIGO) is a
space gravitational wave (GW) detector. DECIGO was originally designed to be
sensitive enough to observe primordial GW background (PGW). However, due to the
lowered upper limit of the PGW by the Planck observation, further improvement
of the target sensitivity of DECIGO is required. In the previous studies,
DECIGO's parameters were optimized to maximize the signal-to-noise ratio (SNR)
of the PGW to quantum noise including the effect of diffraction loss. To
simulate the SNR more realistically, we optimize DECIGO's parameters
considering the GWs from double white dwarfs (DWDs) and the thermal noise of
test masses. We consider two cases of the cutoff frequency of GWs from DWDs. In
addition, we consider two kinds of thermal noise: thermal noise in a residual
gas and internal thermal noise. To investigate how the mirror geometry affects
the sensitivity, we calculate it by changing the mirror mass, keeping the
mirror thickness, and vice versa. As a result, we obtained the optimums for the
parameters that maximize the SNR that depends on the mirror radius. This result
shows that a thick mirror with a large radius gives a good SNR and enables us
to optimize the design of DECIGO based on the feasibility study of the mirror
size in the future.


title:
Understanding Hydrogen Rich Superconductors: Importance of Effective
  Mass and Dirty Limit

abstract:
  A class of hydrogen-rich (H-rich) systems, consistent with Type II
superconductivity, are known to have very high superconducting transition
temperatures Tc and upper critical magnetic field Bc2 values (up to 288 K and
222 T respectively). By looking at all the experimental H-rich superconductors
reported to date most are in the dirty limit, with only the highest Tc values
being in the crossover to the clean regime. In this framework, there is a clear
understanding of some previous unexplained behaviors: (1) A maximum in Bc2 as a
function of Tc; (2) a clear change in slope in Tc as a function of pressure in
CSH; (3) in zero magnetic field the width of the superconducting transition
decreasing with increasing Tc; and (4) in applied magnetic field the slope of
the superconducting width versus field decreasing with increasing Tc.
Ginzburg-Landau-Abrikosov-Gorkov (GLAG) theory is used to explain all four of
these effects within a framework where increasing Tc is related to increases in
both the electron effective mass and scattering length.


title:
Computational Rheometry of Yielding and Viscoplastic Flow in
  Vane-and-Cup Rheometer Fixtures

abstract:
  A computational analysis is presented to qualify traditional and fractal
vane-in-cup geometries for the rheometry of simple viscoplastic fluids with and
without slip. Numerical simulations based on an adaptive augmented Lagrangian
scheme are used to study the full two-dimensional flow field of yield-stress
fluids within and around vane tools with $N=3$ to $24$ arms for a wide range of
Bingham numbers, $\mathcal{B}$ (i.e.~the ratio of the yield stress over the
characteristic viscous stress). This allows for accurate calculations of the
velocity and stress fields around vanes with varying geometry, as well as
direct comparison to experimental observations of the output torque measured by
a rheometer, revealing sources of variation and error. We describe the impact
of the vane structure on the fluid velocity field, from few-arm cruciform vanes
($N\leq6$) that significantly perturb the flow away from ideal azimuthal
kinematics, to many-arm fractal vanes ($N\geq12$) in which the internal
structural features are successfully "cloaked" by a yield surface. This results
in the shearing of an almost-circular ring of viscoplastic fluid that is
indistinguishable from the annular ring of fluid deformed around a slip-free
rotating cylindrical bob and leads to more accurate rheometric measurements of
the material flow curve. Moreover, in direct comparison with data from previous
literature, we show that slip conditions on the vane surface do not impact the
velocity field or measured overall torque $\mathcal{T}$, while slip conditions
on a smooth outer wall have significant impact on data, even when using a vane
geometry. Finally, we describe the impact of vane topography and Bingham
number, $\mathcal{B}$, on the measured torque and rheometric accuracy of
vane-in-cup geometries for "simple" (inelastic) yield-stress fluids described
by either the Bingham plastic or Herschel-Bulkley constitutive model.


title: GiraffeDet: A Heavy-Neck Paradigm for Object Detection

abstract:
  In conventional object detection frameworks, a backbone body inherited from
image recognition models extracts deep latent features and then a neck module
fuses these latent features to capture information at different scales. As the
resolution in object detection is much larger than in image recognition, the
computational cost of the backbone often dominates the total inference cost.
This heavy-backbone design paradigm is mostly due to the historical legacy when
transferring image recognition models to object detection rather than an
end-to-end optimized design for object detection. In this work, we show that
such paradigm indeed leads to sub-optimal object detection models. To this end,
we propose a novel heavy-neck paradigm, GiraffeDet, a giraffe-like network for
efficient object detection. The GiraffeDet uses an extremely lightweight
backbone and a very deep and large neck module which encourages dense
information exchange among different spatial scales as well as different levels
of latent semantics simultaneously. This design paradigm allows detectors to
process the high-level semantic information and low-level spatial information
at the same priority even in the early stage of the network, making it more
effective in detection tasks. Numerical evaluations on multiple popular object
detection benchmarks show that GiraffeDet consistently outperforms previous
SOTA models across a wide spectrum of resource constraints.


title:
On the hyper-singular boundary integral equation methods for dynamic
  poroelasticity: three dimensional case

abstract:
  In our previous work [SIAM J. Sci. Comput. 43(3) (2021) B784-B810], an
accurate hyper-singular boundary integral equation method for dynamic
poroelasticity in two dimensions has been developed. This work is devoted to
studying the more complex and difficult three-dimensional problems with Neumann
boundary condition and both the direct and indirect methods are adopted to
construct combined boundary integral equations. The strongly-singular and
hyper-singular integral operators are reformulated into compositions of
weakly-singular integral operators and tangential-derivative operators, which
allow us to prove the jump relations associated with the poroelastic layer
potentials and boundary integral operators in a simple manner. Relying on both
the investigated spectral properties of the strongly-singular operators, which
indicate that the corresponding eigenvalues accumulate at three points whose
values are only dependent on two Lam\'e constants, and the spectral properties
of the Calder\'on relations of the poroelasticity, we propose
low-GMRES-iteration regularized integral equations. Numerical examples are
presented to demonstrate the accuracy and efficiency of the proposed
methodology by means of a Chebyshev-based rectangular-polar solver.


title:
A Data-Driven Approach to Robust Hypothesis Testing Using Sinkhorn
  Uncertainty Sets

abstract:
  Hypothesis testing for small-sample scenarios is a practically important
problem. In this paper, we investigate the robust hypothesis testing problem in
a data-driven manner, where we seek the worst-case detector over distributional
uncertainty sets centered around the empirical distribution from samples using
Sinkhorn distance. Compared with the Wasserstein robust test, the corresponding
least favorable distributions are supported beyond the training samples, which
provides a more flexible detector. Various numerical experiments are conducted
on both synthetic and real datasets to validate the competitive performances of
our proposed method.


title:
Colouring the sculpture through corresponding area from 2D to 3D with
  augmented reality

abstract:
  With the development of 3D modelling techniques and AR techniques, the
traditional methods of establishing 2D to 3D relation is no longer sufficient
to meet the demand for complex models and rapid relation building. This
dissertation presents a prototype development implemented that creating many-
to-many correspondences by marking image and 3D model regions that can be used
to colouring 3D model by colouring image for the end user. After comparing the
three methods in the conceptual design, I chose the creating render textures
relation to further development by changing to Zeus bust model and connecting
the AR environment. The results of testing each part of the prototype shows the
viability of the creating render textures relation method. The advantages of it
are easy to build many-to-many relations and adaptable for any model with
properly UV mapping. But there are still three main limitations and future work
will focus on solving them, building a database to store relation information
data and printing 3D coloured models in real world.


title: Detecting Golodness via Gr\"obner Degeneration

abstract:
  In this paper we study the extent to which Golodness may be transferred along
morphisms of DG-algebras. In particular, we show that if $I$ is a so-called
fiber invariant ideal, then Golodness of $I$ is equivalent to Golodness of the
initial ideal of $I$. We use this to transfer Golodness results for monomial
ideals to more general classes of ideals. We also prove that any so-called
rainbow monomial ideal with linear resolution defines a Golod ring; this result
encompasses and generalizes many known Golodness results for classes of
monomial ideals. We then combine the techniques developed to give a concise
proof that maximal minors of (sparse) generic matrices define Golod rings,
independent of characteristic.


title:
The Volcspeech system for the ICASSP 2022 multi-channel multi-party
  meeting transcription challenge

abstract:
  This paper describes our submission to ICASSP 2022 Multi-channel Multi-party
Meeting Transcription (M2MeT) Challenge. For Track 1, we propose several
approaches to empower the clustering-based speaker diarization system to handle
overlapped speech. Front-end dereverberation and the direction-of-arrival (DOA)
estimation are used to improve the accuracy of speaker diarization.
Multi-channel combination and overlap detection are applied to reduce the
missed speaker error. A modified DOVER-Lap is also proposed to fuse the results
of different systems. We achieve the final DER of 5.79% on the Eval set and
7.23% on the Test set. For Track 2, we develop our system using the Conformer
model in a joint CTC-attention architecture. Serialized output training is
adopted to multi-speaker overlapped speech recognition. We propose a neural
front-end module to model multi-channel audio and train the model end-to-end.
Various data augmentation methods are utilized to mitigate over-fitting in the
multi-channel multi-speaker E2E system. Transformer language model fusion is
developed to achieve better performance. The final CER is 19.2% on the Eval set
and 20.8% on the Test set.


title: Parsimonious Learning-Augmented Caching

abstract:
  Learning-augmented algorithms -- in which, traditional algorithms are
augmented with machine-learned predictions -- have emerged as a framework to go
beyond worst-case analysis. The overarching goal is to design algorithms that
perform near-optimally when the predictions are accurate yet retain certain
worst-case guarantees irrespective of the accuracy of the predictions. This
framework has been successfully applied to online problems such as caching
where the predictions can be used to alleviate uncertainties.
  In this paper we introduce and study the setting in which the
learning-augmented algorithm can utilize the predictions parsimoniously. We
consider the caching problem -- which has been extensively studied in the
learning-augmented setting -- and show that one can achieve quantitatively
similar results but only using a sublinear number of predictions.


title:
Probing the Early History of Cosmic Reionization by Future Cosmic
  Microwave Background Experiments

abstract:
  Cosmic Reionization imprints its signature on the temperature and
polarization anisotropies of the cosmic microwave background (CMB). Advances in
CMB telescopes have already placed a significant constraint on the history of
reionization. As near-future CMB telescopes target the maximum sensitivity, or
observations limited only by the cosmic variance (CV), we hereby forecast the
potential of future CMB observations in constraining the history of
reionization. In this study, we perform Markov Chain Monte Carlo analysis for
CV-limited E-mode polarization observations such as the LiteBIRD (Light
satellite for the studies of B-mode polarization and Inflation from cosmic
background Radiation Detection), based on a few different methods that vary in
the way of sampling reionization histories. We focus especially on estimating
the very early history of reionization that occurs at redshifts $z>15$, which
is quantified by the partial CMB optical depth due to free electrons at $z>15$,
$\tau_{z>15}$. We find that reionization with $\tau_{z>15} \sim 0.008$, which
are well below the current upper limit $\tau_{z>15} \sim 0.02$, are achievable
by reionization models with minihalo domination in the early phase and can be
distinguished from those with $\tau_{z>15} \lesssim 5\times 10^{-4}$ through
CV-limited CMB polarization observations. An accurate estimation of
$\tau_{z>15}$, however, remains somewhat elusive. We investigate whether
resampling the E-mode polarization data with limited spherical-harmonic modes
may resolve this shortcoming.


title:
Molecular Clouds Associated with {H \small{II}} regions and Candidates
  within l = 106.65$^\circ$ to 109.50$^\circ$ and b = ${-}$1.85$^\circ$ to
  0.95$^\circ$

abstract:
  We present a large-scale simultaneous survey of the CO isotopologues ($\rm
{}^{12}{CO}$, $\rm{}^{13}{CO}$, and $\rm{C}{}^{18}{O}$) J = 1 ${-}$ 0 line
emission toward the Galactic plane region of l = 106.65$^\circ$ to
109.50$^\circ$ and b = ${-}$1.85$^\circ$ to 0.95$^\circ$ using the Purple
Mountain Observatory 13.7 m millimeter-wavelength telescope. Except for the
molecular gas in the solar neighborhood, the emission from the molecular gas in
this region is concentrated in the velocity range of [${-}$60, ${-}$35] $\rm
km~s^{-1}$. The gas in the region can be divided into four clouds, with mass in
the range of $\sim$10$^{3}$ to 10$^{4}$\,${M_{\sun}}$. We have identified 25
filaments based on the $\rm {}^{13}{CO}$ data. The median excitation
temperature, length, line mass, line width, and virial parameter of the
filaments are 10.89 K, 8.49 pc, 146.11 ${M}_{\odot}~ \rm pc^{-1}$, 1.01 $\rm
km~s^{-1}$, and 3.14, respectively. Among these filaments, eight have virial
parameters of less than 2, suggesting that they are gravitationally bound and
can lead to star formation. Nineteen {H \small {II}} regions or candidates have
previously been found in the region and we investigate the relationships
between these {H \small {II}} regions/candidates and surrounding molecular
clouds in detail. Using morphology similarity and radial velocity consistency
between {H \small {II}} regions/candidates and molecular clouds as evidence for
association, and raised temperature and velocity broadening as signatures of
interaction, we propose that 12 {H \small {II}} regions/candidates are
associated with their surrounding molecular clouds. In the case of the {H
\small {II}} region of S142, the energy of the {H \small {II}} region is
sufficient to maintain the turbulence in the surrounding molecular gas.


title: Almost sure well-posedness for Hall MHD

abstract:
  We consider the magnetohydrodynamics system with Hall effect accompanied with
initial data in supercritical Sobolev space. Via an appropriate randomization
of the supercritical initial data, both local and small data global
well-posedness for the system are obtained almost surely in critical Sobolev
space.


title: MMLN: Leveraging Domain Knowledge for Multimodal Diagnosis

abstract:
  Recent studies show that deep learning models achieve good performance on
medical imaging tasks such as diagnosis prediction. Among the models,
multimodality has been an emerging trend, integrating different forms of data
such as chest X-ray (CXR) images and electronic medical records (EMRs).
However, most existing methods incorporate them in a model-free manner, which
lacks theoretical support and ignores the intrinsic relations between different
data sources. To address this problem, we propose a knowledge-driven and
data-driven framework for lung disease diagnosis. By incorporating domain
knowledge, machine learning models can reduce the dependence on labeled data
and improve interpretability. We formulate diagnosis rules according to
authoritative clinical medicine guidelines and learn the weights of rules from
text data. Finally, a multimodal fusion consisting of text and image data is
designed to infer the marginal probability of lung disease. We conduct
experiments on a real-world dataset collected from a hospital. The results show
that the proposed method outperforms the state-of-the-art multimodal baselines
in terms of accuracy and interpretability.


title: Efficiently Computable Converses for Finite-Blocklength Communication

abstract:
  This paper presents a method for computing a finite-blocklength converse for
the rate of fixed-length codes with feedback used on discrete memoryless
channels (DMCs). The new converse is expressed in terms of a stochastic control
problem whose solution can be efficiently computed using dynamic programming
and Fourier methods. For channels such as the binary symmetric channel (BSC)
and binary erasure channel (BEC), the accuracy of the proposed converse is
similar to that of existing special-purpose converse bounds, but the new
converse technique can be applied to arbitrary DMCs. We provide example
applications of the new converse technique to the binary asymmetric channel
(BAC) and the quantized amplitude-constrained AWGN channel.


title:
The double detonation of a double degenerate system, from Type Ia
  supernova explosion to its supernova remnant

abstract:
  Type Ia supernovae (SNe) are believed to be caused by the thermonuclear
explosion of a white dwarf (WD), but the nature of the progenitor system(s) is
still unclear. Recent theoretical and observational developments have led to
renewed interest in double degenerate models, in particular the "helium-ignited
violent merger" or "dynamically-driven double-degenerate double-detonation"
(D$^6$). In this paper we take the output of an existing D$^6$ SN model and
carry it into the supernova remnant (SNR) phase up to 4000 years after the
explosion, past the time when all the ejecta have been shocked. Assuming a
uniform ambient medium, we reveal specific signatures of the explosion
mechanism and spatial variations intrinsic to the ejecta. The first detonation
produces an ejecta tail visible at early times, while the second detonation
leaves a central density peak in the ejecta that is visible at late times. The
SNR shell is off-centre at all times, because of an initial velocity shift due
to binary motion. The companion WD produces a large conical shadow in the
ejecta, visible in projection as a dark patch surrounded by a bright ring. This
is a clear and long-lasting feature that is localized, and its impact on the
observed morphology is dependent on the viewing angle of the SNR. These results
offer a new way to diagnose the explosion mechanism and progenitor system using
observations of a Type Ia SNR.


title: Emergent scenario in mimetic gravity

abstract:
  The emergent scenario provides a possible way to avoid the big bang
singularity by assuming that the universe originates from an Einstein static
state. Therefore, an Einstein static universe stable under perturbations is
crucial to a successful implementation of the emergent mechanism. In this
paper, we analyze the stability of the Einstein static universe against the
scalar perturbations in the mimetic theory and find that stable Einstein static
solutions exist under certain conditions in this theory. In the original
mimetic gravity, the Einstein static universe is unstable. Then, we find that
the universe can naturally exit from the initial static state, evolve into an
inflationary era and then exit from the inflationary era. Thus, the emergent
scenario can be used to resolve the big bang singularity in the mimetic theory.


title:
Inflated Bendable Eversion Cantilever Mechanism with Inner Skeleton for
  Increased Payload Holding

abstract:
  Inflatable structures used in soft robotics applications exhibit unique
characteristics. In particular, the tip-extension structure, which grows from
the tip, can grow without friction against the environment. However, these
inflatable structures are inferior to rigid mechanisms in terms of their
load-bearing capacity. The stiffness of the tip-extension structure can be
increased by pressurization, but the structure cannot maintain its curved shape
and compliance. In this study, we proposed a mechanism that combines a skeleton
structure consisting of multi-joint links with functions to increase rigidity
while keeping low pressure and realizing the functions of bending and shape
fixation. We devised a design method for rigid articulated links and combined
it with a membrane structure that utilizes the advantages of the tip-extension
structure. The experimental results show that the payload of the designed
structure increases compared to that of the membrane-only structure. The
findings of this research can be applied to long robots that can be extended in
the air without drooping and to mechanisms that can wrap around the human body.


title: Adversarial Detection without Model Information

abstract:
  Prior state-of-the-art adversarial detection works are classifier model
dependent, i.e., they require classifier model outputs and parameters for
training the detector or during adversarial detection. This makes their
detection approach classifier model specific. Furthermore, classifier model
outputs and parameters might not always be accessible. To this end, we propose
a classifier model independent adversarial detection method using a simple
energy function to distinguish between adversarial and natural inputs. We train
a standalone detector independent of the classifier model, with a layer-wise
energy separation (LES) training to increase the separation between natural and
adversarial energies. With this, we perform energy distribution-based
adversarial detection. Our method achieves comparable performance with
state-of-the-art detection works (ROC-AUC > 0.9) across a wide range of
gradient, score and gaussian noise attacks on CIFAR10, CIFAR100 and
TinyImagenet datasets. Furthermore, compared to prior works, our detection
approach is light-weight, requires less amount of training data (40% of the
actual dataset) and is transferable across different datasets. For
reproducibility, we provide layer-wise energy separation training code at
https://github.com/Intelligent-Computing-Lab-Yale/Energy-Separation-Training


title:
Davis-Wielandt-Berezin radius inequalities of Reproducing kernel Hilbert
  space operators

abstract:
  Several upper and lower bounds of the Davis-Wielandt-Berezin radius of
bounded linear operators defined on a reproducing kernel Hilbert space are
given. Further, an inequality involving the Berezin number and the
Davis-Wielandt-Berezin radius for the sum of two bounded linear operators is
obtained, namely, if $A $ and $B$ are reproducing kernel Hilbert space
operators, then $$\eta(A+B) \leq \eta(A)+\eta(B)+\textbf{ber}(A^*B+B^*A),$$
where $\eta(\cdot)$ and $\textbf{ber}(\cdot)$ are the Davis-Wielandt-Berezin
radius and the Berezin number, respectively.


title:
Anomalous Flux in the Cosmic Optical Background Detected With New
  Horizons Observations

abstract:
  We used New Horizons LORRI images to measure the optical-band
($0.4\lesssim\lambda\lesssim0.9{\rm\mu m}$) sky brightness within a high
galactic-latitude field selected to have reduced diffuse scattered light from
the Milky Way galaxy (DGL), as inferred from the IRIS all-sky $100~\mu$m map.
We also selected the field to significantly reduce the scattered light from
bright stars (SSL) outside the LORRI field. Suppression of DGL and SSL reduced
the large uncertainties in the background flux levels present in our earlier
New Horizons COB results. The raw total sky level, measured when New Horizons
was 51.3 AU from the Sun, is $24.22\pm0.80{\rm ~nW ~m^{-2} ~sr^{-1}}.$
Isolating the COB contribution to the raw total required subtracting scattered
light from bright stars and galaxies, faint stars below the photometric
detection-limit within the field, and the hydrogen plus ionized-helium
two-photon continua. This yielded a highly significant detection of the COB at
${\rm 16.37\pm 1.47 ~nW ~m^{-2} ~sr^{-1}}$ at the LORRI pivot wavelength of
0.608 $\mu$m. This result is in strong tension with the hypothesis that the COB
only comprises the integrated light of external galaxies (IGL) presently known
from deep HST counts. Subtraction of the estimated IGL flux from the total COB
level leaves a flux component of unknown origin at ${\rm 8.06\pm1.92 ~nW
~m^{-2} ~sr^{-1}}.$ Its amplitude is equal to the IGL.


title: Abnormal improved dielectric in La doped relaxor ferroelectric PNN-PHT

abstract:
  Relaxor ferroelectrics are complex materials with distinct properties
different from classic ferroelectrics. Probing frequency depended dielectric
susceptibility is its trait. Other empirical laws build on classic
ferroelectrics are still applicable to relaxor ferroelectrics. Here we report
an abnormal improved dielectric in La doped relaxor ferroelectric
0.4PNN-0.6PHT, which deviated from the consistent variation relationship with
piezoelectric coefficient. A mesoscale mechanism is proposed to reveal the
origin of the improved dielectric response in 0.4PNN-0.6PHT, where the polar
nanoregions alignment can facilitate polarization response to external field.
This mechanism emphasizes the critical role of polarization fluctuation on the
macroscopic properties of 0.4PNN-0.6PHT.


title:
Rational design of piezoelectric metamaterials with tailored
  electro-momentum coupling

abstract:
  Piezoelectric materials have wide sensing and energy transduction
applications due to their inherent coupling of mechanical deformation and
electric field. Recent discoveries have revealed that asymmetric or
heterogeneous microstructures of piezoelectric composites can create an
additional coupling of macroscopic momentum to an electric field termed
electro-momentum coupling, introducing a new degree of design freedom. In this
work, by employing the homogenization scheme, the physical origin of
electro-momentum coupling is explored by a high throughput sweep over the
microstructure design space of a piezoelectric composite system. This study
shows how material constituent properties and geometrical arrangements can
affect electro-momentum coupling and be smartly tailored for applications of
interest.


title:
$\Xi$ hypernuclei $^{15}_{\Xi}$C, $^{12}_{\Xi}$Be and $\Xi N$ two-body
  interaction

abstract:
  We study the energy spectra of $\Xi$ hypernuclei $^{15}_{\Xi}$C and
$^{12}_{\Xi}$Be with a relativistic mean field (RMF) model with meson exchange
$\Xi N$ interactions. The RMF parameters are optimized to reproduce the average
energy of KINKA and IRRAWADDY events for the ground state and also the average
energy of KISO and IBUKI events for the excited state in $^{15}_{\Xi}$C. The
potential depth of average $\Xi N$ mean field potential is found to be about
$-12$ MeV in the nuclear matter limit. We further introduce the two-body $s$-
and $p$-wave interactions between valence nucleons and $\Xi$ particle. We found
that the $s$-wave interaction deduced from the HAL LQCD results is rather weak
to obtain the energy difference between IRRAWADDY and KINKA events. The
$p$-wave interaction is added and fitted to reproduce the energy difference.
The resulting interaction together with the $s$-wave one gives a reasonable
energy simultanously for the IBUKI event as an excited $\Xi_p$ state. The model
is further applied to predict the energy spectrum of $^{12}_{\Xi}$Be.


title:
A decision-tree framework to select optimal box-sizes for product
  shipments

abstract:
  In package-handling facilities, boxes of varying sizes are used to ship
products. Improperly sized boxes with box dimensions much larger than the
product dimensions create wastage and unduly increase the shipping costs. Since
it is infeasible to make unique, tailor-made boxes for each of the $N$
products, the fundamental question that confronts e-commerce companies is: How
many $K << N$ cuboidal boxes need to manufactured and what should be their
dimensions? In this paper, we propose a solution for the single-count shipment
containing one product per box in two steps: (i) reduce it to a clustering
problem in the $3$ dimensional space of length, width and height where each
cluster corresponds to the group of products that will be shipped in a
particular size variant, and (ii) present an efficient forward-backward
decision tree based clustering method with low computational complexity on $N$
and $K$ to obtain these $K$ clusters and corresponding box dimensions. Our
algorithm has multiple constituent parts, each specifically designed to achieve
a high-quality clustering solution. As our method generates clusters in an
incremental fashion without discarding the present solution, adding or deleting
a size variant is as simple as stopping the backward pass early or executing it
for one more iteration. We tested the efficacy of our approach by simulating
actual single-count shipments that were transported during a month by Amazon
using the proposed box dimensions. Even by just modifying the existing box
dimensions and not adding a new size variant, we achieved a reduction of
$4.4\%$ in the shipment volume, contributing to the decrease in non-utilized,
air volume space by $2.2\%$. The reduction in shipment volume and air volume
improved significantly to $10.3\%$ and $6.1\%$ when we introduced $4$
additional boxes.


title: An algebra of alignment for relational verification

abstract:
  Relational verification encompasses information flow security, regression
verification, translation validation for compilers, and more. Effective
alignment of the programs and computations to be related facilitates use of
simpler relational invariants which in turn enables automation and modular
reasoning. Alignment has been explored in terms of trace pairs, deductive rules
of relational Hoare logics (RHL), and several forms of product automata. This
article shows how a simple extension of Kleene Algebra with Tests (KAT), called
BiKAT, subsumes prior formulations, including alignment witnesses for
forall-exists properties, which brings to light new RHL rules for such
properties. Alignments can be discovered algorithmically or devised manually
but, in either case, their adequacy with respect to the original programs must
be proved; an explicit algebra enables constructive proof by equational
reasoning. Furthermore our approach inherits algorithmic benefits from existing
KAT-based techniques and tools, which are applicable to a range of semantic
models.


title: Removable edges in cubic matching covered graphs

abstract:
  An edge $e$ in a matching covered graph $G$ is removable if $G-e$ is matching
covered, which was introduced by Lov\'asz and Plummer in connection with ear
decompositions of matching covered graphs. A brick is a non-bipartite matching
covered graph without non-trivial tight cuts. The importance of bricks stems
from the fact that they are building blocks of matching covered graphs.
Carvalho et al. [Ear decompositions of matching covered graphs, Combinatorica,
19(2):151-174, 1999] showed that each brick other than $K_4$ and
$\overline{C_6}$ has $\Delta-2$ removable edges, where $\Delta$ is the maximum
degree of $G$. We show that every cubic brick $G$ other than $K_4$,
$\overline{C_6}$ and a particular graph of order 8 has a matching of size at
least $|V(G)|/7$, each edge of which is removable in $G$. Moreover, if $G$ is a
3-edge-connected near-bipartite cubic graph, then $G$ has a matching of size at
least $|V(G)|/2-3$, each edge of which is removable in $G$. The lower bounds
for the two sizes of matchings are sharp.


title:
Semidirect product rigidity of group von Neumann algebras arising from
  class $\mathscr{S}$, inductive limits and fundamental group

abstract:
  In this article we study property (T) groups arising from Rips construction
in geometric group theory in the spirit of \cite{CDK19} and certain inductive
limit groups from this class. Using interplay between Popa's
deformation/rigidity and methods in geometric group theory we are able to
extend the class of groups considered in \cite{CDK19} that remembers semidirect
product features while passing to the group von Neumann algebras. Combining
these results with the method developed in \cite{CDHK20} we are able to produce
more examples of property (T) group factors with trivial fundamental group. The
inductive limit groups do not have property (T) and provides examples of more
factors with trivial fundamental group. We are also able to show Cartan
rigidity for these groups.


title:
Devitalizing noise-driven instability of entangling logic in silicon
  devices with bias controls

abstract:
  The quality of quantum bits (qubits) in silicon is highly vulnerable to
charge noise that is omni-present in semiconductor devices and is in principle
hard to be suppressed. For a realistically sized quantum dot system based on a
silicon-germanium heterostructure whose confinement is manipulated with
electrical biases imposed on top electrodes, we computationally explore the
noise-robustness of 2-qubit entangling operations with a focus on the
controlled-X (CNOT) logic that is essential for designs of gate-based universal
quantum logic circuits. With device simulations based on the physics of bulk
semiconductors augmented with electronic structure calculations, we not only
quantify the degradation in fidelity of single-step CNOT operations with
respect to the strength of charge noise, but also discuss a strategy of device
engineering that can significantly enhance noise-robustness of CNOT operations
with almost no sacrifice of speed compared to the single-step case. Details of
device designs and controls that this work presents can establish a rare but
practical guideline for potential efforts to secure silicon-based quantum
processors using an electrode-driven quantum dot platform.


title: Euclidean algorithm for a class of linear orders

abstract:
  Borrowing inspiration from Marcone and Mont\'{a}lban's one-one correspondence
between the class of signed trees and the equimorphism classes of
indecomposable scattered linear orders, we find a subclass of signed trees
which has an analogous correspondence with equimorphism classes of
indecomposable finite rank discrete linear orders.
  We also introduce the class of \emph{finitely presented linear orders}-- the
smallest subclass of finite rank linear orders containing $\mathbf 1$, $\omega$
and $\omega^*$ and closed under finite sums and lexicographic products. For
this class we develop a generalization of the Euclidean algorithm where the
\emph{width} of a linear order plays the role of the Euclidean norm. Using this
as a tool we classify the isomorphism classes of finitely presented linear
orders in terms of an equivalence relation on their presentations using
\emph{3-signed trees}.


title:
Influence of the Halide Ion on the A Site Dynamics in FAPbX3 (X = Br and
  Cl)

abstract:
  The optoelectronic properties and ultimately photovoltaic performance of
hybrid lead halide perovskites, is inherently related to the dynamics of the
organic cations. Here we report on the dynamics of the formamidinium (FA)
cation in FAPbX3 perovskites for chloride and bromide varieties, as studied by
neutron spectroscopy. Elastic fixed window scan measurements showed the onset
of reorientational motion of FA cations in FAPbCl3 to occur at a considerably
higher temperature compared to that in FAPbBr3. In addition, we observed two
distinct dynamical transitions only in the chloride system, suggesting a
significant variation in the reorientational motions of the FA cation with
temperature. Quasielastic neutron scattering data analysis of FAPbCl3 showed
that in the low temperature orthorhombic phase, FA cations undergo 2-fold jump
reorientations about the C-H axis which evolve into an isotropic rotation in
the intermediate tetragonal and high temperature cubic phases. Comparing the
results with those from FAPbBr3, reveal that the time scale, barrier to
reorientation and the geometry of reorientational motion of the FA cation are
significantly different for the two halides. We note that this dependence of
the dynamic properties of the A-site cation on the halide, is unique to the FA
series; the geometry of methylammonium (MA) cation dynamics in MAPbX3 is known
to be insensitive to different halides.


title:
Convergence of random holomorphic functions with real zeros and
  extensions of the stochastic zeta function

abstract:
  In this article, we provide sufficient conditions under which the convergence
of random point processes on the real line implies the convergence in law, for
the topology of uniform convergence on compact sets, of suitable random
holomorphic functions whose zeros are given by the point processes which are
considered. We give explicit examples for which our result applies: some of
these examples are related to random matrix theory, and some others are
constructed from the Riemann zeta function.


title:
Component Prototypes towards a Low-Latency, Small-form-factor Optical
  Link for the ATLAS Liquid Argon Calorimeter Phase-I Trigger Upgrade

abstract:
  This paper presents several component prototypes towards a low-latency,
small-form-factor optical link designed for the ATLAS Liquid Argon Calorimeter
Phase-I trigger upgrade. A prototype of the custom-made dual-channel optical
transmitter module, the Miniature optical Transmitter (MTx), with separate
transmitter optical sub-assemblies (TOSAs) has been demonstrated at data rates
up to 8 Gbps per channel. A Vertical-Cavity Surface-Emitting Laser (VCSEL)
driver ASIC has been developed and is used in the current MTx prototypes. A
serializer ASIC prototype, operating at up to 8 Gbps per channel, has been
designed and tested. A low-latency, low-overhead encoder ASIC prototype has
been designed and tested. The latency of the whole link, including the
transmitter latency and the receiver latency but not the latency of the fiber,
is estimated to be less than 57.9 ns. The size of the MTx is 45 mm x 15 mm x 6
mm.


title:
Ferromagnetism induced by hybridization of Fe 3d orbitals with ligand
  InSb bands in n-type ferromagnetic semiconductor (In,Fe)Sb

abstract:
  Fe-doped III-V ferromagnetic semiconductor (FMS) (In,Fe)Sb is a promising
material for spintronic device applications because of the n-type carrier
conduction and the ferromagnetism with high Curie temperature (TC > 300 K). To
clarify the mechanism of the high-TC ferromagnetism, we have investigated the
electronic structure and magnetic properties of an (In,Fe)Sb thin film by
performing x-ray absorption spectroscopy (XAS) and x-ray magnetic circular
dichroism (XMCD) measurements at the Fe L2,3 edges. The magnetic-field
dependence of the XMCD spectra reveals that there are ferromagnetic-like Fe and
paramagnetic-like Fe components in the (In,Fe)Sb thin film. The XAS and XMCD
spectra of the ferromagnetic-like and paramagnetic-like Fe components resemble
those of other Fe-doped FMSs and extrinsic oxides, respectively. The finite
value of the ratio between the orbital and spin magnetic moments estimated by
applying the XMCD sum rules indicates that the valence state of the Fe ions
substituting for the In sites in (In,Fe)Sb is not purely ionic Fe3+, but
intermediate between Fe3+ and Fe2+. The qualitative correspondence between the
magnetic-field dependence of the visible-light magnetic circular dichroism
intensity and that of the XMCD intensity demonstrates that the Zeeman splitting
of the InSb band is proportional to the net magnetization of the doped Fe.
These results suggest that the ferromagnetism of (In,Fe)Sb originates from the
Fe 3d orbitals hybridized with the host InSb bands.


title:
Amplitude Spectrum Transformation for Open Compound Domain Adaptive
  Semantic Segmentation

abstract:
  Open compound domain adaptation (OCDA) has emerged as a practical adaptation
setting which considers a single labeled source domain against a compound of
multi-modal unlabeled target data in order to generalize better on novel unseen
domains. We hypothesize that an improved disentanglement of domain-related and
task-related factors of dense intermediate layer features can greatly aid OCDA.
Prior-arts attempt this indirectly by employing adversarial domain
discriminators on the spatial CNN output. However, we find that latent features
derived from the Fourier-based amplitude spectrum of deep CNN features hold a
more tractable mapping with domain discrimination. Motivated by this, we
propose a novel feature space Amplitude Spectrum Transformation (AST). During
adaptation, we employ the AST auto-encoder for two purposes. First, carefully
mined source-target instance pairs undergo a simulation of cross-domain feature
stylization (AST-Sim) at a particular layer by altering the AST-latent. Second,
AST operating at a later layer is tasked to normalize (AST-Norm) the domain
content by fixing its latent to a mean prototype. Our simplified adaptation
technique is not only clustering-free but also free from complex adversarial
alignment. We achieve leading performance against the prior arts on the OCDA
scene segmentation benchmarks.


title:
A line code with quick-resynchronization capability and low latency for
  the optical data links of LHC experiments

abstract:
  We propose a line code that has fast resynchronization capability and low
latency. Both the encoder and decoder have been implemented in FPGAs. The
encoder has also been implemented in an ASIC. The latency of the whole optical
link (not including the optical fiber) is estimated to be less than 73.9 ns. In
the case of radiation-induced link synchronization loss, the decoder can
recover the synchronization in 25 ns. The line code will be used in the ATLAS
liquid argon calorimeter Phase-I trigger upgrade and can also be potentially
used in other LHC experiments.


title:
The Miniature Optical Transmitter and Transceiver For the
  High-Luminosity LHC (HL-LHC) experiments

abstract:
  We present the design and test results of the Miniature optical Transmitter
(MTx) and Transceiver (MTRx) for the high luminosity LHC (HL-LHC) experiments.
MTx and MTRx are Transmitter Optical Subassembly (TOSA) and Receiver Optical
Subassembly (ROSA) based. There are two major developments: the Vertical Cavity
Surface Emitting Laser (VCSEL) driver ASIC LOCld and the mechanical latch that
provides the connection to fibers. In this paper, we concentrate on the
justification of this work, the design of the latch and the test results of
these two modules with a Commercial Off-The-Shelf (COTS) VCSEL driver.


title:
Potential Tribological and Antibacterial Benefits of Pulsed Laser
  Deposited Zirconia Thin Film on Ti6Al4V Bio-alloy

abstract:
  Demand for artificial body implants has been on the rise over the years.
However, wear and bacterial infection are identified as two major reasons that
can lead to inflammation and implant failure. In this communication, the
advantages of pulsed laser deposited zirconia thin film on Ti6Al4V bio-alloy at
room temperature and at an elevated substrate temperature are discussed wherein
a comparison of the change in surface roughness, wettability, surface free
energy, tribological and antibacterial properties of uncoated and zirconia
coated Ti6Al4V samples is presented. The results of tribological analysis
carried out using a standard ball-on-disc tribometer at different loads (2N, 5N
and 7N) exhibited advantageous effects of zirconia coating on Ti6Al4V.
Prominently, the sample coated at 200 C substrate temperature maintained very
low coefficient of friction up to hundreds of sliding cycles and showed a
notable reduction in the wear rate by 49% at 5N load. The in vitro bacterial
retention test showed a clear inhibition in growth of Staphylococcus aureus and
Klebsiella pneumonia bacteria on the surface of the coated samples indicating
the possibility of prevention of biofilm formation. More than 50% reduction in
density of Staphylococcus aureus was observed on coated sample in comparison to
pristine Ti6Al4V and this can be attributed to reduction in surface energy of
the sample after coating. Additionally, the observation of a larger number of
decimated bacteria on coated samples by fluorescence microscopy revealed
superior antibacterial properties of zirconia coating. The novelty of this work
is the use of pulsed laser deposition technique for zirconia coating which
dearly improves tribological and antibacterial properties of Ti6Al4V
simultaneously; this shows prospects of increasing durability of artificial
implant in the human body.


title: Learning to Bootstrap for Combating Label Noise

abstract:
  Deep neural networks are powerful tools for representation learning, but can
easily overfit to noisy labels which are prevalent in many real-world
scenarios. Generally, noisy supervision could stem from variation among
labelers, label corruption by adversaries, etc. To combat such label noises,
one popular line of approach is to apply customized weights to the training
instances, so that the corrupted examples contribute less to the model
learning. However, such learning mechanisms potentially erase important
information about the data distribution and therefore yield suboptimal results.
To leverage useful information from the corrupted instances, an alternative is
the bootstrapping loss, which reconstructs new training targets on-the-fly by
incorporating the network's own predictions (i.e., pseudo-labels).
  In this paper, we propose a more generic learnable loss objective which
enables a joint reweighting of instances and labels at once. Specifically, our
method dynamically adjusts the per-sample importance weight between the real
observed labels and pseudo-labels, where the weights are efficiently determined
in a meta process. Compared to the previous instance reweighting methods, our
approach concurrently conducts implicit relabeling, and thereby yield
substantial improvements with almost no extra cost. Extensive experimental
results demonstrated the strengths of our approach over existing methods on
multiple natural and medical image benchmark datasets, including CIFAR-10,
CIFAR-100, ISIC2019 and Clothing 1M. The code is publicly available at
https://github.com/yuyinzhou/L2B.


title: Restricted Chebyshev centers in $L_1$-predual spaces

abstract:
  In this paper, we provide a necessary and sufficient condition for the
existence of a restricted Chebyshev center of a compact subset of an
$L_{1}$-predual space in a closed convex subset of the $L_{1}$-predual space.
We also provide a geometrical characterization of an $L_{1}$-predual space in
terms of the restricted Chebyshev radius in the following manner. A real Banach
space $X$ is an $L_{1}$-predual space if and only if for each non-empty finite
subset $F$ of $X$ and closed convex subset $V$ of $X$, $rad_{V}(F) = rad_{X}(F)
+ d(V, cent_{X}(F))$, where we denote $rad_{X}(F)$, $rad_{V}(F)$, $cent_{X}(F)$
and $d(V, cent_{X}(F))$ to be the Chebyshev radius of $F$ in $X$, the
restricted Chebyshev radius of $F$ in $V$, the set of Chebyshev centers of $F$
in $X$ and the distance between the sets $V$ and $cent_{X}(F)$ respectively.
Furthermore, we explicitly describe the Chebyshev centers of closed bounded
subsets of an $M$-summand in the space of real-valued continuous functions on a
compact Hausdorff space.


title:
Giant transverse and longitudinal magneto-thermoelectric effect in
  polycrystalline nodal-line semimetal Mg3Bi2

abstract:
  Topological semimetals provide new opportunities for exploring new
thermoelectric phenomena, because of their exotic and nontrivial electronic
structure topology around the Fermi surface. In this study, we report on the
discovery of giant transverse and longitudinal magneto-thermoelectric (MTE)
effects in Mg3Bi2, which is predicted to be a type-II nodal-line semimetal in
the absence of spin-orbit coupling (SOC). The maximum transverse power factor
is 2182 {\mu}Wm^{-1}K^{-2} at 13.5 K and 6 Tesla. The longitudinal power factor
reaches up to 3043{\mu}Wm^{-1}K^{-2} at 15 K and 13 Tesla, which is 20 times
higher than in a zero-strength magnetic field and is also comparable to
state-of-the-art MTE materials. By compensating Mg loss in the Mg-rich
conditions for turning carrier concentration, the sample obtained in this work
shows a large linear non-saturating magnetoresistance of 940% under a field of
14 Tesla. This is a two-orders-of-magnitude increase with respect to the normal
Mg-deficiency Mg3Bi2 sample. Using density functional calculations, we
attribute the underlying mechanism to the parent nodal-line electronic
structure without SOC and the anisotropic Fermi surface shape with SOC,
highlighting the essential role of high carrier mobility and open electron
orbits in moment space. Our work offers a new avenue toward highly efficient
thermoelectric materials through the design of Fermi surfaces with special
topological electronic structures in novel quantum materials.


title: Optimal Clustering with Bandit Feedback

abstract:
  This paper considers the problem of online clustering with bandit feedback. A
set of arms (or items) can be partitioned into various groups that are unknown.
Within each group, the observations associated to each of the arms follow the
same distribution with the same mean vector. At each time step, the agent
queries or pulls an arm and obtains an independent observation from the
distribution it is associated to. Subsequent pulls depend on previous ones as
well as the previously obtained samples. The agent's task is to uncover the
underlying partition of the arms with the least number of arm pulls and with a
probability of error not exceeding a prescribed constant $\delta$. The problem
proposed finds numerous applications from clustering of variants of viruses to
online market segmentation. We present an instance-dependent
information-theoretic lower bound on the expected sample complexity for this
task, and design a computationally efficient and asymptotically optimal
algorithm, namely Bandit Online Clustering (BOC). The algorithm includes a
novel stopping rule for adaptive sequential testing that circumvents the need
to exactly solve any NP-hard weighted clustering problem as its subroutines. We
show through extensive simulations on synthetic and real-world datasets that
BOC's performance matches the lower bound asymptotically, and significantly
outperforms a non-adaptive baseline algorithm.


title: On Almost Sure Convergence Rates of Stochastic Gradient Methods

abstract:
  The vast majority of convergence rates analysis for stochastic gradient
methods in the literature focus on convergence in expectation, whereas
trajectory-wise almost sure convergence is clearly important to ensure that any
instantiation of the stochastic algorithms would converge with probability one.
Here we provide a unified almost sure convergence rates analysis for stochastic
gradient descent (SGD), stochastic heavy-ball (SHB), and stochastic Nesterov's
accelerated gradient (SNAG) methods. We show, for the first time, that the
almost sure convergence rates obtained for these stochastic gradient methods on
strongly convex functions, are arbitrarily close to their optimal convergence
rates possible. For non-convex objective functions, we not only show that a
weighted average of the squared gradient norms converges to zero almost surely,
but also the last iterates of the algorithms. We further provide last-iterate
almost sure convergence rates analysis for stochastic gradient methods on
weakly convex smooth functions, in contrast with most existing results in the
literature that only provide convergence in expectation for a weighted average
of the iterates.


title:
A Projection-free Algorithm for Constrained Stochastic Multi-level
  Composition Optimization

abstract:
  We propose a projection-free conditional gradient-type algorithm for smooth
stochastic multi-level composition optimization, where the objective function
is a nested composition of $T$ functions and the constraint set is a closed
convex set. Our algorithm assumes access to noisy evaluations of the functions
and their gradients, through a stochastic first-order oracle satisfying certain
standard unbiasedness and second moment assumptions. We show that the number of
calls to the stochastic first-order oracle and the linear-minimization oracle
required by the proposed algorithm, to obtain an $\epsilon$-stationary
solution, are of order $\mathcal{O}_T(\epsilon^{-2})$ and
$\mathcal{O}_T(\epsilon^{-3})$ respectively, where $\mathcal{O}_T$ hides
constants in $T$. Notably, the dependence of these complexity bounds on
$\epsilon$ and $T$ are separate in the sense that changing one does not impact
the dependence of the bounds on the other. Moreover, our algorithm is
parameter-free and does not require any (increasing) order of mini-batches to
converge unlike the common practice in the analysis of stochastic conditional
gradient-type algorithms.


title:
Autonomous inference of complex network dynamics from incomplete and
  noisy data

abstract:
  The availability of empirical data that capture the structure and behavior of
complex networked systems has been greatly increased in recent years, however a
versatile computational toolbox for unveiling a complex system's nodal and
interaction dynamics from data remains elusive. Here we develop a two-phase
approach for autonomous inference of complex network dynamics, and its
effectiveness is demonstrated by the tests of inferring neuronal, genetic,
social, and coupled oscillators dynamics on various synthetic and real
networks. Importantly, the approach is robust to incompleteness and noises,
including low resolution, observational and dynamical noises, missing and
spurious links, and dynamical heterogeneity. We apply the two-phase approach to
inferring the early spreading dynamics of H1N1 flu upon the worldwide airline
network, and the inferred dynamical equation can also capture the spread of
SARS and COVID-19 diseases. These findings together offer an avenue to discover
the hidden microscopic mechanisms of a broad array of real networked systems.


title: Image Difference Captioning with Pre-training and Contrastive Learning

abstract:
  The Image Difference Captioning (IDC) task aims to describe the visual
differences between two similar images with natural language. The major
challenges of this task lie in two aspects: 1) fine-grained visual differences
that require learning stronger vision and language association and 2) high-cost
of manual annotations that leads to limited supervised data. To address these
challenges, we propose a new modeling framework following the
pre-training-finetuning paradigm. Specifically, we design three self-supervised
tasks and contrastive learning strategies to align visual differences and text
descriptions at a fine-grained level. Moreover, we propose a data expansion
strategy to utilize extra cross-task supervision information, such as data for
fine-grained image classification, to alleviate the limitation of available
supervised IDC data. Extensive experiments on two IDC benchmark datasets,
CLEVR-Change and Birds-to-Words, demonstrate the effectiveness of the proposed
modeling framework. The codes and models will be released at
https://github.com/yaolinli/IDC.


title:
Convex inequalities and their applications to relative operator
  entropies

abstract:
  A considerable amount of literature in the theory of inequality is devoted to
the study of Jensen's and Young's inequality. This article presents a number of
new inequalities involving the log-convex functions and the geometrically
convex functions. As their consequences, we derive the refinements for Young's
inequality and Jensen's inequality. In addition, the operator Jensen's type
inequality is also developed for conditioned two functions. Utilizing these new
inequalities, we investigate the operator inequalities related to the relative
operator entropy.


title: STEC-IoT: A Security Tactic by Virtualizing Edge Computing on IoT

abstract:
  To a large extent, the deployment of edge computing (EC) can reduce the
burden of the explosive growth of the Internet of things. As a powerful hub
between the Internet of things and cloud servers, edge devices make the
transmission of cloud to things no longer complicated. However, edge nodes are
faced with a series of problems, such as large number, a wide range of
distribution, and complex environment, the security of edge computing should
not be underestimated. Based on this, we propose a tactic to improve the safety
of edge computing by virtualizing edge nodes. In detail, first of all, we
propose a strategy of edge node partition, virtualize the edge nodes dealing
with different types of things into various virtual networks, which are
deployed between the edge nodes and the cloud server. Second, considering that
different information transmission has different security requirement, we
propose a security tactic based on security level measurement. Finally, through
simulation experiments, we compare with the existing advanced algorithms which
are committed to virtual network security, and prove that the model proposed in
this paper has definite progressiveness in enhancing the security of edge
computing.


title: Log-based Anomaly Detection with Deep Learning: How Far Are We?

abstract:
  Software-intensive systems produce logs for troubleshooting purposes.
Recently, many deep learning models have been proposed to automatically detect
system anomalies based on log data. These models typically claim very high
detection accuracy. For example, most models report an F-measure greater than
0.9 on the commonly-used HDFS dataset. To achieve a profound understanding of
how far we are from solving the problem of log-based anomaly detection, in this
paper, we conduct an in-depth analysis of five state-of-the-art deep
learning-based models for detecting system anomalies on four public log
datasets. Our experiments focus on several aspects of model evaluation,
including training data selection, data grouping, class distribution, data
noise, and early detection ability. Our results point out that all these
aspects have significant impact on the evaluation, and that all the studied
models do not always work well. The problem of log-based anomaly detection has
not been solved yet. Based on our findings, we also suggest possible future
work.


title: On the Implicit Bias of Gradient Descent for Temporal Extrapolation

abstract:
  When using recurrent neural networks (RNNs) it is common practice to apply
trained models to sequences longer than those seen in training. This
"extrapolating" usage deviates from the traditional statistical learning setup
where guarantees are provided under the assumption that train and test
distributions are identical. Here we set out to understand when RNNs can
extrapolate, focusing on a simple case where the data generating distribution
is memoryless. We first show that even with infinite training data, there exist
RNN models that interpolate perfectly (i.e., they fit the training data) yet
extrapolate poorly to longer sequences. We then show that if gradient descent
is used for training, learning will converge to perfect extrapolation under
certain assumptions on initialization. Our results complement recent studies on
the implicit bias of gradient descent, showing that it plays a key role in
extrapolation when learning temporal prediction models.


title:
TinyM$^2$Net: A Flexible System Algorithm Co-designed Multimodal
  Learning Framework for Tiny Devices

abstract:
  With the emergence of Artificial Intelligence (AI), new attention has been
given to implement AI algorithms on resource constrained tiny devices to expand
the application domain of IoT. Multimodal Learning has recently become very
popular with the classification task due to its impressive performance for both
image and audio event classification. This paper presents TinyM$^2$Net -- a
flexible system algorithm co-designed multimodal learning framework for
resource constrained tiny devices. The framework was designed to be evaluated
on two different case-studies: COVID-19 detection from multimodal audio
recordings and battle field object detection from multimodal images and audios.
In order to compress the model to implement on tiny devices, substantial
network architecture optimization and mixed precision quantization were
performed (mixed 8-bit and 4-bit). TinyM$^2$Net shows that even a tiny
multimodal learning model can improve the classification performance than that
of any unimodal frameworks. The most compressed TinyM$^2$Net achieves 88.4%
COVID-19 detection accuracy (14.5% improvement from unimodal base model) and
96.8% battle field object detection accuracy (3.9% improvement from unimodal
base model). Finally, we test our TinyM$^2$Net models on a Raspberry Pi 4 to
see how they perform when deployed to a resource constrained tiny device.


title: The twisted baker map

abstract:
  As a model to provide a hands-on, elementary understanding of "vortex
dynamics", we introduce a piecewise linear non-invertible map called a twisted
baker map. We show that the set of hyperbolic repelling periodic points with
complex conjugate eigenvalues and that without complex conjugate eigenvalues
are simultaneously dense in the phase space. We also show that these two sets
equidistribute with respect to the normalized Lebesgue measure, in spite of a
non-uniformity in their Lyapunov exponents.


title: Compiler Support for Sparse Tensor Computations in MLIR

abstract:
  Sparse tensors arise in problems in science, engineering, machine learning,
and data analytics. Programs that operate on such tensors can exploit sparsity
to reduce storage requirements and computational time. Developing and
maintaining sparse software by hand, however, is a complex and error-prone
task. Therefore, we propose treating sparsity as a property of tensors, not a
tedious implementation task, and letting a sparse compiler generate sparse code
automatically from a sparsity-agnostic definition of the computation. This
paper discusses integrating this idea into MLIR.


title:
Can Open Domain Question Answering Systems Answer Visual Knowledge
  Questions?

abstract:
  The task of Outside Knowledge Visual Question Answering (OKVQA) requires an
automatic system to answer natural language questions about pictures and images
using external knowledge. We observe that many visual questions, which contain
deictic referential phrases referring to entities in the image, can be
rewritten as "non-grounded" questions and can be answered by existing
text-based question answering systems. This allows for the reuse of existing
text-based Open Domain Question Answering (QA) Systems for visual question
answering. In this work, we propose a potentially data-efficient approach that
reuses existing systems for (a) image analysis, (b) question rewriting, and (c)
text-based question answering to answer such visual questions. Given an image
and a question pertaining to that image (a visual question), we first extract
the entities present in the image using pre-trained object and scene
classifiers. Using these detected entities, the visual questions can be
rewritten so as to be answerable by open domain QA systems. We explore two
rewriting strategies: (1) an unsupervised method using BERT for masking and
rewriting, and (2) a weakly supervised approach that combines adaptive
rewriting and reinforcement learning techniques to use the implicit feedback
from the QA system. We test our strategies on the publicly available OKVQA
dataset and obtain a competitive performance with state-of-the-art models while
using only 10% of the training data.


title: Conditional Motion In-betweening

abstract:
  Motion in-betweening (MIB) is a process of generating intermediate skeletal
movement between the given start and target poses while preserving the
naturalness of the motion, such as periodic footstep motion while walking.
Although state-of-the-art MIB methods are capable of producing plausible
motions given sparse key-poses, they often lack the controllability to generate
motions satisfying the semantic contexts required in practical applications. We
focus on the method that can handle pose or semantic conditioned MIB tasks
using a unified model. We also present a motion augmentation method to improve
the quality of pose-conditioned motion generation via defining a distribution
over smooth trajectories. Our proposed method outperforms the existing
state-of-the-art MIB method in pose prediction errors while providing
additional controllability.


title: Transit timing variations of AU Microscopii b and c

abstract:
  Here we report large-amplitude transit timing variations (TTVs) for
AU\,Microcopii b and c as detected in combined TESS (2018, 2020) and CHEOPS
(2020, 2021) transit observations. AU Mic is a young planetary system with a
debris disk and two transiting warm Neptunes. A TTV on the order of several
minutes was previously reported for AU Mic b, which was suggested to be an
outcome of mutual perturbations between the planets in the system. In 2021, we
observed AU Mic b (five transits) and c (three transits) with the CHEOPS space
telescope to follow-up the TTV of AU Mic b and possibly detect a TTV for AU Mic
c. When analyzing TESS and CHEOPS 2020--2021 measurements together, we find
that a prominent TTV emerges with a full span of ~23 minutes between the two
TTV extrema. Assuming that the period change results from a periodic process -
such as mutual perturbations - we demonstrate that the times of transits in the
summer of 2022 are expected to be 30--85 minutes later than predicted by the
available linear ephemeris.


title: Vertical Federated Learning: Challenges, Methodologies and Experiments

abstract:
  Recently, federated learning (FL) has emerged as a promising distributed
machine learning (ML) technology, owing to the advancing computational and
sensing capacities of end-user devices, however with the increasing concerns on
users' privacy. As a special architecture in FL, vertical FL (VFL) is capable
of constructing a hyper ML model by embracing sub-models from different
clients. These sub-models are trained locally by vertically partitioned data
with distinct attributes. Therefore, the design of VFL is fundamentally
different from that of conventional FL, raising new and unique research issues.
In this paper, we aim to discuss key challenges in VFL with effective
solutions, and conduct experiments on real-life datasets to shed light on these
issues. Specifically, we first propose a general framework on VFL, and
highlight the key differences between VFL and conventional FL. Then, we discuss
research challenges rooted in VFL systems under four aspects, i.e., security
and privacy risks, expensive computation and communication costs, possible
structural damage caused by model splitting, and system heterogeneity.
Afterwards, we develop solutions to addressing the aforementioned challenges,
and conduct extensive experiments to showcase the effectiveness of our proposed
solutions.


title:
Steric effect of water molecule clusters on electrostatic interaction
  and electroosmotic transport in aqueous electrolytes: a mean-field approach

abstract:
  We theoretically study the size effect of water molecule clusters not only on
electrostatic interaction between two charged surfaces in an aqueous
electrolyte but also on electroosmotic transport in a nanofluidic channel.
Applying a free energy based mean-field approach accounting for different sizes
of ions and water molecule clusters, we derive a set of coupled equations to
compute electrostatic and electroosmotic properties between charged surfaces.
We verify that the smaller the size of a water cluster, the stronger the
electroosmotic transport in nanofluidic channels. In addition, we find that an
increase in size of a water cluster yields a decrease in electrostatic
interaction strength between similar or oppositely charged planar surfaces.


title:
ARIBA: Towards Accurate and Robust Identification of Backdoor Attacks in
  Federated Learning

abstract:
  The distributed nature and privacy-preserving characteristics of federated
learning make it prone to the threat of poisoning attacks, especially backdoor
attacks, where the adversary implants backdoors to misguide the model on
certain attacker-chosen sub-tasks. In this paper, we present a novel method
ARIBA to accurately and robustly identify backdoor attacks in federated
learning. By empirical study, we observe that backdoor attacks are discernible
by the filters of CNN layers. Based on this finding, we employ unsupervised
anomaly detection to evaluate the pre-processed filters and calculate an
anomaly score for each client. We then identify the most suspicious clients
according to their anomaly scores. Extensive experiments are conducted, which
show that our method ARIBA can effectively and robustly defend against multiple
state-of-the-art attacks without degrading model performance.


title: Using 5G in Smart Cities: A Systematic Mapping Study

abstract:
  5G is the fifth generation wireless network, with a set of characteristics,
e.g., high bandwidth and data rates. The scenarios of using 5G include enhanced
Mobile Broadband (eMBB), massive Machine Type Communications (mMTC), and
ultra-Reliable and Low-Latency Communications (uRLLC). 5G is expected to
support a wide variety of applications. We conducted a systematic mapping study
that covers the literature published between Jan 2012 and Dec 2019 regarding
using 5G in smart cities. The scenarios, architecture, technologies,
challenges, and lessons learned of using 5G in smart cities are summarized and
further analyzed based on 32 selected studies, and the results are that: (1)
The studies are distributed over 27 publication venues. 17 studies report
results based on academic studies and 13 studies use demonstration or toy
examples. Only 2 studies report using 5G in smart cities based on industrial
studies. 16 studies include assumptions of 5G network design or smart city
scenarios. (2) The most discussed smart city scenario is transportation,
followed by public safety, healthcare, city tourism, entertainment, and
education. (3) 28 studies propose and/or discuss the architecture of 5G-enabled
smart cities, containing smart city architecture (treating 5G as a component),
5G network architecture in smart cities, and business architecture of using 5G
in smart cities. (4) The most mentioned 5G-related technologies are radio
access technologies, network slicing, and edge computing. (5) Challenges are
mainly about complex context, challenging requirements, and network development
of using 5G in smart cities. (6) Most of the lessons learned identified are
benefits regarding 5G itself or the proposed 5G-related methods in smart
cities. This work provides a reflection of the past eight years of the state of
the art on using 5G in smart cities, which can benefit both researchers and
practitioners.


title:
Privacy Concerns Raised by Pervasive User Data Collection From
  Cyberspace and Their Countermeasures

abstract:
  The virtual dimension called `Cyberspace' built on internet technologies has
served people's daily lives for decades. Now it offers advanced services and
connected experiences with the developing pervasive computing technologies that
digitise, collect, and analyse users' activity data. This changes how user
information gets collected and impacts user privacy at traditional cyberspace
gateways, including the devices carried by users for daily use. This work
investigates the impacts and surveys privacy concerns caused by this data
collection, namely identity tracking from browsing activities, user input data
disclosure, data accessibility in mobile devices, security of delicate data
transmission, privacy in participating sensing, and identity privacy in
opportunistic networks. Each of the surveyed privacy concerns is discussed in a
well-defined scope according to the impacts mentioned above. Existing
countermeasures are also surveyed and discussed, which identifies corresponding
research gaps. To complete the perspectives, three complex open problems,
namely trajectory privacy, privacy in smart metering, and involuntary privacy
leakage with ambient intelligence, are briefly discussed for future research
directions before a succinct conclusion to our survey at the end.


title:
Entanglement of Nambu Spinors and Bell Inequality Test Without Beam
  Splitters

abstract:
  The identification of electronic entanglement in solids remains elusive so
far, which is owed to the difficulty of implementing spinor-selective beam
splitters with tunable polarization direction. Here, we propose to overcome
this obstacle by producing and detecting a particular type of entanglement
encoded in the Nambu spinor or electron-hole components of quasiparticles
excited in quantum Hall edge states. Due to the opposite charge of electrons
and holes, the detection of the Nambu spinor translates into a charge-current
measurement, which eliminates the need for beam splitters and assures a high
detection rate. Conveniently, the spinor correlation function at fixed
effective polarizations derives from a single current-noise measurement, with
the polarization directions of the detector easily adjusted by coupling the
edge states to a voltage gate and a superconductor, both having been realized
in experiments. We show that the violation of Bell inequality occurs in a large
parameter region. Our work opens a new route for probing quasiparticle
entanglement in solid-state physics exempt from traditional beam splitters.


title: Quenching of Massive Disk Galaxies in the IllustrisTNG Simulation

abstract:
  A rare population of massive disk galaxies have been found to invade the red
sequence dominated by early-type galaxies. These red/quenched massive disk
galaxies have recently gained great interest into their formation and origins.
The usually proposed quenching mechanisms, such as bar quenching and
environment quenching, seem not suitable for those bulge-less quenched disks in
low-density environment. In this paper, we use the IllustrisTNG-300 simulation
to investigate the formation of massive quenched central disk galaxies. It is
found that these galaxies contain less gas and harbor giant supermassive black
holes(SMBHs) (above $ 10^{8}M_{\odot}$) than their star forming counterparts.
By tracing their formation history, we found that quenched disk galaxies formed
early and preserved disk morphology for cosmological time scales. They have
experienced less than one major merger on average and it is mainly mini-mergers
(mass ratio $<$1/10) that contribute to the growth of their SMBHs. In the
Illustris-TNG simulation the black hole feedback mode switches from thermal to
kinetic feedback when the black hole mass is more massive than $\sim
10^{8}M_{\odot}$, which is more efficient to eject gas outside of the galaxy
and to suppress further cooling of hot gaseous halo. We conclude that kinetic
AGN feedback in massive red/quenched disk galaxy is the dominant quenching
mechanism.


title:
Near perfect two-photon interference out a down-converter on a silicon
  photonic chip

abstract:
  Integrated entangled photon-pair sources are key elements for enabling
large-scale quantum photonic solutions, and addresses the challenges of both
scaling-up and stability. Here we report the first demonstration of an
energy-time entangled photon-pair source based on spontaneous parametric
down-conversion in silicon-based platform through an optically induced
second-order ($\chi^{(2)}$) nonlinearity, ensuring type-0 quasi-phase-matching
of fundamental harmonic and its second-harmonic inside the waveguide. The
developed source shows a coincidence-to-accidental ratio of 1635 at 8 of $\mu$W
pump power. Remarkably, we report two-photon interference with near-perfect
visibility of 99.36$\pm1.94\%$, showing high-quality photonic entanglement
without excess background noise. This opens a new horizon for quantum
technologies requiring the integration of a large variety of building
functionalities on single chips.


title: On $\mathbb{F}_p$-roots of the Hilbert class polynomial modulo $p$

abstract:
  The Hilbert class polynomial $H_{\mathcal{O}}(x)\in \mathbb{Z}[x]$ attached
to an order $\mathcal{O}$ in an imaginary quadratic field $K$ is the monic
polynomial whose roots are precisely the distinct $j$-invariants of elliptic
curves over $\mathbb{C}$ with complex multiplication by $\mathcal{O}$. Let $p$
be a prime inert in $K$ and strictly greater than
$|\operatorname{disc}(\mathcal{O})|$. We show that the number of
$\mathbb{F}_p$-roots of $H_\mathcal{O}(x)\!\! \pmod{p}$ is either zero or
$|\operatorname{Pic}(\mathcal{O})[2]|$ by exhibiting a free and transitive
action of $\operatorname{Pic}(\mathcal{O})[2]$ on the set of
$\mathbb{F}_p$-roots of $H_\mathcal{O}(x)\!\! \pmod p$ whenever it is nonempty.
We also provide a concrete criterion for the nonemptiness of the set of
$\mathbb{F}_p$-roots. A similar result was first obtained by Xiao et al.~[Int.
J. Number Theory, DOI: 10.1142/S1793042122500555] and generalized much further
by Li et al.~[arXiv:2108.00168] (that covers the current result) with a
different approach.


title:
Ultrafast hard X-ray sources based on Relativistic Electrons at ELI
  Beamlines

abstract:
  We report the LWFA driven X-ray sources development at ELI beamlines. ELI
Gammatron beamline provides X-ray pulses of energies from 1-100 keV in betatron
scheme, and up to a MeV in Compton scheme. A state-of-the-art Ti:Sa
diode-pumped HAPLS laser (10-30 J, 30 fs @10 Hz) is used as a driving laser for
these X-ray sources. These sources are characterized with a low divergence (<
10 mrad), small source size (few {\mu}m), short pulse duration (a few fs) and
photon flux up to1010 photons per pulse. In addition, a separate Betatron X-ray
source is being developed in the ELI Plasma Physics Platform (P3)that will
serve as an active diagnostics of various plasma physics experiments. P3 is
dedicated to laboratory astrophysics, HED physics, multi-beam experiments, warm
dense matter and fundamental research. Besides the presentation of the two
X-ray sources, we will also introduce a novel optical probing method with
increased interferometric sensitivity for characterization of low-density gas
jets. The sensitivity is achieved together with using a novel optical
configuration employing multiple passes of the probe beam through the object
and relay-imaging of the object between the individual passes, and a short
probing beam (405 nm).


title: Double Hopf bifurcation analysis in the memory-based diffusion system

abstract:
  In this paper, we derive the algorithm for calculating the normal form of the
double Hopf bifurcation that appears in a memory-based diffusion system via
taking memory-based diffusion coefficient and the memory delay as the
perturbation parameters. Using the obtained theoretical results, we study the
dynamical classification near the double Hopf bifurcation point in a
predator-prey system with Holling type II functional response. We show the
existence of different kinds of stable spatially inhomogeneous periodic
solutions, the transition from one kind to the other as well as the coexistence
of two types of periodic solutions with different spatial profiles by varying
the memory-based diffusion coefficient and the memory delay.


title:
Ketamine-Medetomidine General Anesthesia Occurs With Alternation of
  Cortical Electrophysiological Activity Among High and Low Complex States

abstract:
  Anesthetic agents are known to induce a series of alterations in cortical
electrophysiological activity, such as the rise of signature patterns, changes
in statistical properties, and altered dynamic behavior of neural records.
Plenty of methods can be used to trace and monitor these changes, among them
complexity metrics demonstrated to have the power to discriminate states of
distinct levels of awareness. There is a consensus that anesthetic drugs are
capable of interfering with neural activities at different levels and time
scales, being able to induce alterations both locally as well as on the
spatiotemporal patterns throughout the whole cortex. However, it is still
unclear how such changes in the complexity of cortical activity are supposed to
occur, and experimental evidence is still needed. To this purpose, we have
analyzed an ECoG records database of a Ketamine-Medetomidine anesthetic
induction experiment in a non-human primate subject. The MDR-ECoG provided
records of cortical activity with both high temporal and spatial resolution
allied with extensive coverage of the cortical surface. The Permutation Entropy
and the Fractal Dimension were employed to evaluate the complexity of the
neural time series. The complexity of cortical activity was relatively constant
during awakened conditions. The transition to unconsciousness occurred in a
fast manner, requiring about 30 to 40 seconds for the first changes to take
place. During general anesthesia, the complexity assumed a considerable
variation at local levels and fluctuated without a defined period. On a global
scale, the cortex dynamically alternated among high and low complex states.
This study brings novel evidence of the effects of anesthetics on neural
activity and cortical dynamics, complementing the actual scenario for the
elucidation of anesthesia mechanisms in terms of circuits, pathways, and global
brain functioning.


title: Optimal Congestion Control for Time-varying Wireless Links

abstract:
  Modern networks exhibit a high degree of variability in link rates. Cellular
network bandwidth inherently varies with receiver motion and orientation, while
class-based packet scheduling in datacenter and service provider networks
induces high variability in available capacity for network tenants. Recent work
has proposed numerous congestion control protocols to cope with this
variability, offering different tradeoffs between link utilization and queuing
delay. In this paper, we develop a formal model of congestion control over
time-varying links, and we use this model to derive a bound on the performance
of any congestion control protocol running over a time-varying link with a
given distribution of rate variation. Using the insights from this analysis, we
derive an optimal control law that offers a smooth tradeoff between link
utilization and queuing delay. We compare the performance of this control law
to several existing control algorithms on cellular link traces to show that
there is significant room for optimization.


title:
Metallic and glassy states of the one-dimensional Mott insulator excited
  by electric pulses

abstract:
  The elucidation of nonequilibrium states in strongly correlated systems holds
the key to emerge novel quantum phases. The nonequilibrium-induced
insulator-to-metal transition is particularly interesting since it examines the
fundamental nature of the competition between itinerancy and localization of
the charge degrees of freedom. We investigate pulse-excited insulator-to-metal
transition of the half-filled one-dimensional extended Hubbard model.
Calculating the time-dependent optical conductivity with the time-dependent
density-matrix renormalization group, we find that strong mono- and half-cycle
pulses inducing quantum tunneling strongly suppress spectral weights
contributing to the Drude component $\sigma_\text{D}$, even if we introduce a
large number of carriers $\Delta n_\text{d}$. This is in contrast to the
metallic property with $\sigma_\text{D}\propto \Delta n_\text{d}$ induced by
photon absorption and chemical doping. The strong suppression of
$\sigma_\text{D}$ due to quantum tunneling is a result of the emergence of the
Hilbert-space fragmentation, which makes pulse-excited states glassy.


title:
A unified geometric description of the Universe: from inflation to
  late-time acceleration without an inflaton nor a cosmological constant

abstract:
  We present a cosmological model arising from a gravitational theory with an
infinite tower of higher-order curvature invariants that can reproduce the
entire evolution of the Universe: from inflation to late-time acceleration,
without invoking an inflaton nor a cosmological constant. The theory is
Einsteinian-like. The field equations for a
Friedmann-Lema\^{i}tre-Robertson-Walker metric are of second-order and can
reproduce a late-time evolution that is consistent with the acceleration
provided by the cosmological constant at low redshift. Our results force us to
reinterpret the nature of dark energy, becoming a mechanism that is inherited
solely from the geometry of spacetime.


title: Studying the neural representations of uncertainty

abstract:
  The study of the brain's representations of uncertainty is a central topic in
neuroscience. Unlike other cases of representation, uncertainty is a property
of an observer's representation of the world, posing specific methodological
challenges. We analyze how the literature on uncertainty addresses those
challenges and distinguish between "descriptive" and "process" approaches.
Descriptive approaches treat uncertainty reported by subjects or inferred from
stimuli as an independent variable used to test for a relationship to neural
responses. By contrast, process approaches treat uncertainty derived from
models of neural responses as a dependent variable used to test for a
relationship to subjects' reports or stimuli. To compare those two approaches,
we apply four criteria for neural representations: sensitivity, specificity,
invariance, functionality. Experiments can be cataloged by their approach and
whether they test for each criterion. Our analysis rigorously characterizes the
study of neural representations of uncertainty, shaping research questions and
guiding future experiments.


title: Atmospheric processes affecting methane on Mars

abstract:
  It is currently uncertain as to whether methane exists on Mars. Data from the
Curiosity Rover suggests a background methane concentration of a few tenths
parts per billion whereas data from the Trace Gas Orbiter suggest an upper
limit of twenty parts per trillion. If methane exists on Mars then we do not
understand fully the physical and chemical processes affecting its lifetime.
Atmospheric models suggest an over-estimate in the lifetime by a factor of
around six hundred compared with earlier observations. In the present work we
assume the Curiosity Rover background methane value and estimate the
uncertainty in atmospheric chemistry and mixing processes in our atmospheric
column model 1D TERRA. Results suggest that these processes can only explain a
factor of ~sixteen lowering in the methane lifetime. This implies that if
methane is present then additional, currently unknown processes are required to
explain the observed lifetime.


title: Essential norm of composition operators Harmonic Bloch spaces

abstract:
  In this paper we characterize essential norm of composition operators on the
spaces of Harmonic Bloch functions. These results extends the similar results
that were proven for composition operators on Bloch spaces.


title: Anchor Graph Structure Fusion Hashing for Cross-Modal Similarity Search

abstract:
  Cross-modal hashing still has some challenges needed to address: (1) most
existing CMH methods take graphs as input to model data distribution. These
methods omit to consider the correlation of graph structure among multiple
modalities; (2) most existing CMH methods ignores considering the fusion
affinity among multi-modalities data; (3) most existing CMH methods relax the
discrete constraints to solve the optimization objective, significantly
degrading the retrieval performance. To solve the above limitations, we propose
a novel Anchor Graph Structure Fusion Hashing (AGSFH). AGSFH constructs the
anchor graph structure fusion matrix from different anchor graphs of multiple
modalities with the Hadamard product, which can fully exploit the geometric
property of underlying data structure. Based on the anchor graph structure
fusion matrix, AGSFH attempts to directly learn an intrinsic anchor graph,
where the structure of the intrinsic anchor graph is adaptively tuned so that
the number of components of the intrinsic graph is exactly equal to the number
of clusters. Besides, AGSFH preserves the anchor fusion affinity into the
common binary Hamming space. Furthermore, a discrete optimization framework is
designed to learn the unified binary codes. Extensive experimental results on
three public social datasets demonstrate the superiority of AGSFH.


title:
CAU_KU team's submission to ADD 2022 Challenge task 1: Low-quality fake
  audio detection through frequency feature masking

abstract:
  This technical report describes Chung-Ang University and Korea University
(CAU_KU) team's model participating in the Audio Deep Synthesis Detection (ADD)
2022 Challenge, track 1: Low-quality fake audio detection. For track 1, we
propose a frequency feature masking (FFM) augmentation technique to deal with a
low-quality audio environment. %detection that spectrogram-based models can be
applied. We applied FFM and mixup augmentation on five spectrogram-based deep
neural network architectures that performed well for spoofing detection using
mel-spectrogram and constant Q transform (CQT) features. Our best submission
achieved 23.8% of EER ranked 3rd on track 1.


title:
Solar-like dynamos and rotational scaling of cycles from star-in-a-box
  simulations

abstract:
  Magnetohydrodynamic star-in-a-box simulations of convection and dynamos in a
solar-like star with different rotation rates are presented. These simulations
produce solar-like differential rotation with a fast equator and slow poles,
and magnetic activity that resembles that of the Sun with equatorward migrating
activity at the surface. Furthermore, the ratio of rotation to cycle period is
almost constant as the rotation period decreases in the limited sample
considered here. This is reminiscent of the suggested inactive branch of stars
from observations and differs from most earlier simulation results from
spherical shell models. While the exact excitation mechanism of the dynamos in
the current simulations is not yet clear, it is plausible that the greater
freedom that the magnetic field has due to the inclusion of the radiative core
and regions exterior to the star are important in shaping the dynamo.