title: Non-Hermitian Morphing of Topological Modes

abstract:
  Topological modes are usually localized at defects or boundaries of a much
larger topological lattice. Recent studies of non-Hermitian band theories
unveiled the non-Hermitian skin effect (NHSE), by which the bulk states
collapse to the boundary as skin modes. Here, we theoretically and
experimentally demonstrate that the NHSE can conversely affect wavefunctions of
topological modes. We show that topological modes can be delocalized by the
NHSE and even become in-gap extended modes occupying the entire bulk lattice.
Such a delocalization effect is experimentally realized using active mechanical
metamaterials in both one-dimensional (1D) and two-dimensional (2D) topological
lattices, as well as in a higher-order topological lattice. Furthermore, by the
judicious engineering of the non-Hermiticity distribution, the topological
modes can deform into a diversity of shapes. Our findings not only broaden and
deepen the current understanding of topological modes and the NHSE but also
open new grounds for topological applications.


title: On Uses of Van der Waerden Test: A Graphical Approach

abstract:
  Although several nonparametric tests are available for testing population
identical distributions or equal means in multiple groups problem, the Van der
Waerden test has asymptotically the same efficiency as the classical one-way
analysis of variance test under normality assumption where it depends on
inverse normal score. In this study the Waerden test statistic is used to
create a form that assists in deriving adjusted p values chart in terms of
Bonferroni and Benjamini-Hochberg methods. The sampling distribution of the
proposed form is derived as a gamma distribution that utilized to acquire
initial p values. The proposed method will provide well recognition and deep
knowledge where the changes occur. Simulation study is executed to study the
characteristic of the proposed method in terms of size and power of the test
and compared with original Waerden test and one-way analysis of variance test.
The results are of great practical values since the proposed method stretches
visible shape to decision maker to gives complete picture and deep insight
where the differences happen, keep size and power of the test almost similar to
original Waerden test and gives a glimpse of various information that completes
the pairwise comparisons. To show the benefits of the proposed method, two
applications are presented.


title:
HDNet: High-resolution Dual-domain Learning for Spectral Compressive
  Imaging

abstract:
  The rapid development of deep learning provides a better solution for the
end-to-end reconstruction of hyperspectral image (HSI). However, existing
learning-based methods have two major defects. Firstly, networks with
self-attention usually sacrifice internal resolution to balance model
performance against complexity, losing fine-grained high-resolution (HR)
features. Secondly, even if the optimization focusing on spatial-spectral
domain learning (SDL) converges to the ideal solution, there is still a
significant visual difference between the reconstructed HSI and the truth.
Therefore, we propose a high-resolution dual-domain learning network (HDNet)
for HSI reconstruction. On the one hand, the proposed HR spatial-spectral
attention module with its efficient feature fusion provides continuous and fine
pixel-level features. On the other hand, frequency domain learning (FDL) is
introduced for HSI reconstruction to narrow the frequency domain discrepancy.
Dynamic FDL supervision forces the model to reconstruct fine-grained
frequencies and compensate for excessive smoothing and distortion caused by
pixel-level losses. The HR pixel-level attention and frequency-level refinement
in our HDNet mutually promote HSI perceptual quality. Extensive quantitative
and qualitative evaluation experiments show that our method achieves SOTA
performance on simulated and real HSI datasets. Code and models will be
released.


title:
Time-aware Graph Neural Networks for Entity Alignment between Temporal
  Knowledge Graphs

abstract:
  Entity alignment aims to identify equivalent entity pairs between different
knowledge graphs (KGs). Recently, the availability of temporal KGs (TKGs) that
contain time information created the need for reasoning over time in such TKGs.
Existing embedding-based entity alignment approaches disregard time information
that commonly exists in many large-scale KGs, leaving much room for
improvement. In this paper, we focus on the task of aligning entity pairs
between TKGs and propose a novel Time-aware Entity Alignment approach based on
Graph Neural Networks (TEA-GNN). We embed entities, relations and timestamps of
different KGs into a vector space and use GNNs to learn entity representations.
To incorporate both relation and time information into the GNN structure of our
model, we use a time-aware attention mechanism which assigns different weights
to different nodes with orthogonal transformation matrices computed from
embeddings of the relevant relations and timestamps in a neighborhood.
Experimental results on multiple real-world TKG datasets show that our method
significantly outperforms the state-of-the-art methods due to the inclusion of
time information.


title:
Neural mechanisms underlying the temporal organization of naturalistic
  animal behavior

abstract:
  Naturalistic animal behavior exhibits a strikingly complex organization in
the temporal domain, whose variability stems from at least three sources:
hierarchical, contextual, and stochastic. What are the neural mechanisms and
computational principles generating such complex temporal features? In this
review, we provide a critical assessment of the existing behavioral and
neurophysiological evidence for these sources of temporal variability in
naturalistic behavior. We crystallize recent studies which converge on an
emergent mechanistic theory of temporal variability based on attractor neural
networks and metastable dynamics, arising from the coordinated interactions
between mesoscopic neural circuits. We highlight the crucial role played by
structural heterogeneities and by noise arising in mesoscopic circuits. We
assess the shortcomings and missing links in the current theoretical and
experimental literature and propose new directions of investigations to fill
these gaps.


title:
Simultaneous optical trapping and magnetic micromanipulation of
  ferromagnetic iron-doped upconversion microparticles in six degrees of
  freedom

abstract:
  Optical trapping of magnetic Fe-oxide particles is notoriously difficult due
to their high refractive indices, not to mention high absorptivity at the
trapping infra-red wavelengths. We synthesize Fe co-doped NaYF4:Yb,Er
ferromagnetic upconversion particles that not only have refractive indices
conducive for optical trapping, but also heat less than Haematite particles at
off-resonant wavelengths. These particles are hexagonal shaped with dimensions
of the order of 3 micrometer and also bear high coercivity of 20 mT and
saturation magnetisation of 1 Am^2/kg. This enables simultaneous use of optical
trapping and magnetic forces to generate micro-manipulation in all the six
degrees of freedom of a rigid body. We also show that these particles heat
significantly when illuminated on absorption resonance at 975 nm while emitting
visible light with possible implications for fluorescence microscopy and
photothermal therapy of cancer cells.


title: Greedy double subspaces coordinate descent method via orthogonalization

abstract:
  The coordinate descent method is an effective iterative method for solving
large linear least-squares problems. In this paper, for the highly coherent
columns case, we construct an effective coordinate descent method which
iteratively projects the estimate onto a solution space formed by two greedily
selected hyperplanes via Gram-Schmidt orthogonalization. Our methods may be
regarded as a simple block version of coordinate descent method which involves
two active columns. The convergence analysis of this method is provided and
numerical simulations also confirm the effectiveness for matrices with highly
coherent columns.


title:
Variational Inequalities For The Differences Of Averages Over Lacunary
  Sequences

abstract:
  Let $f$ be a locally integrable function defined on $\mathbb{R}$, and let
$(n_k)$ be a lacunary sequence. Define the operator $A_{n_k}$ by
$$A_{n_k}f(x)=\frac{1}{n_k}\int_0^{n_k}f(x-t)\, dt.$$ We prove various types of
new inequalities for the variation operator
$$\mathcal{V}_sf(x)=\left(\sum_{k=1}^\infty|A_{n_k}f(x)-A_{n_{k-1}}f(x)|^s\right)^{1/s}$$
when $2\leq s<\infty$.


title: Training language models to follow instructions with human feedback

abstract:
  Making language models bigger does not inherently make them better at
following a user's intent. For example, large language models can generate
outputs that are untruthful, toxic, or simply not helpful to the user. In other
words, these models are not aligned with their users. In this paper, we show an
avenue for aligning language models with user intent on a wide range of tasks
by fine-tuning with human feedback. Starting with a set of labeler-written
prompts and prompts submitted through the OpenAI API, we collect a dataset of
labeler demonstrations of the desired model behavior, which we use to fine-tune
GPT-3 using supervised learning. We then collect a dataset of rankings of model
outputs, which we use to further fine-tune this supervised model using
reinforcement learning from human feedback. We call the resulting models
InstructGPT. In human evaluations on our prompt distribution, outputs from the
1.3B parameter InstructGPT model are preferred to outputs from the 175B GPT-3,
despite having 100x fewer parameters. Moreover, InstructGPT models show
improvements in truthfulness and reductions in toxic output generation while
having minimal performance regressions on public NLP datasets. Even though
InstructGPT still makes simple mistakes, our results show that fine-tuning with
human feedback is a promising direction for aligning language models with human
intent.


title:
PatchMVSNet: Patch-wise Unsupervised Multi-View Stereo for
  Weakly-Textured Surface Reconstruction

abstract:
  Learning-based multi-view stereo (MVS) has gained fine reconstructions on
popular datasets. However, supervised learning methods require ground truth for
training, which is hard to be collected, especially for the large-scale
datasets. Though nowadays unsupervised learning methods have been proposed and
have gotten gratifying results, those methods still fail to reconstruct intact
results in challenging scenes, such as weakly-textured surfaces, as those
methods primarily depend on pixel-wise photometric consistency which is
subjected to various illuminations. To alleviate matching ambiguity in those
challenging scenes, this paper proposes robust loss functions leveraging
constraints beneath multi-view images: 1) Patch-wise photometric consistency
loss, which expands the receptive field of the features in multi-view
similarity measuring, 2) Robust twoview geometric consistency, which includes a
cross-view depth consistency checking with the minimum occlusion. Our
unsupervised strategy can be implemented with arbitrary depth estimation
frameworks and can be trained with arbitrary large-scale MVS datasets.
Experiments show that our method can decrease the matching ambiguity and
particularly improve the completeness of weakly-textured reconstruction.
Moreover, our method reaches the performance of the state-of-the-art methods on
popular benchmarks, like DTU, Tanks and Temples and ETH3D. The code will be
released soon.


title:
DetFlowTrack: 3D Multi-object Tracking based on Simultaneous
  Optimization of Object Detection and Scene Flow Estimation

abstract:
  3D Multi-Object Tracking (MOT) is an important part of the unmanned vehicle
perception module. Most methods optimize object detection and data association
independently. These methods make the network structure complicated and limit
the improvement of MOT accuracy. we proposed a 3D MOT framework based on
simultaneous optimization of object detection and scene flow estimation. In the
framework, a detection-guidance scene flow module is proposed to relieve the
problem of incorrect inter-frame assocation. For more accurate scene flow label
especially in the case of motion with rotation, a box-transformation-based
scene flow ground truth calculation method is proposed. Experimental results on
the KITTI MOT dataset show competitive results over the state-of-the-arts and
the robustness under extreme motion with rotation.


title:
Transformations in Learned Image Compression from a Modulation
  Perspective

abstract:
  In this paper, a unified transformation method in learned image
compression(LIC) is proposed from the perspective of modulation. Firstly, the
quantization in LIC is considered as a generalized channel with additive
uniform noise. Moreover, the LIC is interpreted as a particular communication
system according to the consistency in structures and optimization objectives.
Thus, the technology of communication systems can be applied to guide the
design of modules in LIC. Furthermore, a unified transform method based on
signal modulation (TSM) is defined. In the view of TSM, the existing
transformation methods are mathematically reduced to a linear modulation. A
series of transformation methods, e.g. TPM and TJM, are obtained by extending
to nonlinear modulation. The experimental results on various datasets and
backbone architectures verify that the effectiveness and robustness of the
proposed method. More importantly, it further confirms the feasibility of
guiding LIC design from a communication perspective. For example, when backbone
architecture is hyperprior combining context model, our method achieves
3.52$\%$ BD-rate reduction over GDN on Kodak dataset without increasing
complexity.


title:
Analysis of an alternative Navier-Stokes system: Weak entropy solutions
  and a convergent numerical scheme

abstract:
  We consider an alternative Navier-Stokes model for compressible viscous ideal
gases, originally proposed in \cite{Svard18}. We derive a priori estimates that
are sufficiently strong to support a weak entropy solution of the system.
Guided by these estimates, we propose a finite volume scheme, derive the
analogous estimates and demonstrate grid convergence towards a weak entropy
solution. Furthermore, this existence proof is valid for "large" initial data
and no a priori assumptions on the solution are needed.


title: Reservoir Computing with Spin Waves in Skyrmion Crystal

abstract:
  Magnetic skyrmions are nanometric spin textures characterized by a quantized
topological invariant in magnets and often emerge in a crystallized form called
skyrmion crystal in an external magnetic field. We propose that magnets hosting
a skyrmion crystal possess high potential for application to reservoir
computing, which is one of the most successful information processing
techniques inspired by functions of human brains. Our skyrmion-based reservoir
exploits precession dynamics of magnetizations, i.e., spin waves, propagating
in the skyrmion crystal. Because of complex interferences and slow relaxations
of the spin-wave dynamics, the skyrmion spin-wave reservoir attains several
important characteristics required for reservoir computing, e.g., the
generalization ability, the nonlinearity, and the short-term memory. We
investigate these properties by imposing three standard tasks to test the
performances of reservoir, i.e., the duration-estimation task, the short-term
memory task, and the parity-check task. Through these investigations, we
demonstrate that magnetic skyrmion crystals are promising materials for
spintronics reservoir devices. Because magnetic skyrmions emerge spontaneously
in magnets via self-organization process under application of a static magnetic
field, the proposed skyrmion reservoir requires neither advanced
nanofabrication nor complicated manufacturing for production in contrast to
other previously proposed magnetic reservoirs constructed with fabricated
spintronics devices. Our proposal is expected to realize a breakthrough in the
research of spintronics reservoirs of high performance.


title:
MF-Hovernet: An Extension of Hovernet for Colon Nuclei Identification
  and Counting (CoNiC) Challenge

abstract:
  Nuclei Identification and Counting is the most important morphological
feature of cancers, especially in the colon. Many deep learning-based methods
have been proposed to deal with this problem. In this work, we construct an
extension of Hovernet for nuclei identification and counting to address the
problem named MF-Hovernet. Our proposed model is the combination of multiple
filer block to Hovernet architecture. The current result shows the efficiency
of multiple filter block to improve the performance of the original Hovernet
model.


title: Tropical Lagrangian multi-sections and tropical locally free sheaves

abstract:
  This article is a continuation of the work "Tropical Lagrangian
multi-sections and smoothing of locally free sheaves over degenerated
Calabi-Yau surfaces". We generalize the notion of tropical Lagrangian
multi-sections to any dimensions. Together with some linear algebra data, we
construct a special class of locally free sheaves, called tropical locally free
sheaves. We will also provide the reverse construction and show that there is a
1-1 correspondence between isomorphism classes of tropical locally free sheaves
and tropical Lagrangian multi-sections modulo certain equivalence.


title:
A general alternating-direction implicit Newton method for solving
  complex continuous-time algebraic Riccati matrix equation

abstract:
  In this paper, applying the Newton method, we transform the complex
continuous-time algebraic Riccati matrix equation into a Lyapunov equation.
Then, we introduce an efficient general alternating-direction implicit (GADI)
method to solve the Lyapunov equation. The inexact Newton-GADI method is
presented to save computational amount effectively. Moreover, we analyze the
convergence of the Newton-GADI method. The convergence rate of the Newton-GADI
and Newton-ADI methods is compared by analyzing their spectral radii.
Furthermore, we give a way to select the quasi-optimal parameter. Corresponding
numerical tests are shown to illustrate the effectiveness of the proposed
algorithms.


title: Imaging Anisotropic Conductivities from Current Densities

abstract:
  In this paper, we propose and analyze a reconstruction algorithm for imaging
an anisotropic conductivity tensor in a second-order elliptic PDE with a
nonzero Dirichlet boundary condition from internal current densities. It is
based on a regularized output least-squares formulation with the standard
$L^2(\Omega)^{d,d}$ penalty, which is then discretized by the standard Galerkin
finite element method. We establish the continuity and differentiability of the
forward map with respect to the conductivity tensor in the
$L^p(\Omega)^{d,d}$-norms, the existence of minimizers and optimality systems
of the regularized formulation using the concept of H-convergence. Further, we
provide a detailed analysis of the discretized problem, especially the
convergence of the discrete approximations with respect to the mesh size, using
the discrete counterpart of H-convergence. In addition, we develop a projected
Newton algorithm for solving the first-order optimality system. We present
extensive two-dimensional numerical examples to show the efficiency of the
proposed method.


title:
A class of inverse curvature flows and $L^p$ dual Christoffel-Minkowski
  problem

abstract:
  In this paper, we consider a large class of expanding flows of closed,
smooth, star-shaped hypersurface in Euclidean space $\mathbb{R}^{n+1}$ with
speed $\psi u^\alpha\rho^\delta f^{-\beta}$, where $\psi$ is a smooth positive
function on unit sphere, $u$ is the support function of the hypersurface,
$\rho$ is the radial function, $f$ is a smooth, symmetric, homogenous of degree
one, positive function of the principal curvatures of the hypersurface on a
convex cone. When $\psi=1$, we prove that the flow exists for all time and
converges to infinity if $\alpha+\delta+\beta\le1, \beta>0$ and $\alpha\le0$,
while in case $\alpha+\delta+\beta>1,\alpha,\delta\le0$, the flow blows up in
finite time, and where we assume the initial hypersurface to be strictly
convex. In both cases the properly rescaled flows converge to a sphere centered
the origin. In particular, the results of Gerhardt \cite{GC,GC3} and Urbas
\cite{UJ2} can be recovered by putting $\alpha=\delta=0$. Our previous works
\cite{DL,DL2} can be recovered by putting $\delta=0$. By the convergence of
these flows, we can give a new proof of uniqueness theorems for solutions to
$L^p$-Minkowski problem and $L^p$-Christoffel-Minkowski problem with constant
prescribed data. Similarly, we pose the $L^p$ dual Christoffel-Minkowski
problem and prove a uniqueness theorem for solutions to $L^p$ dual Minkowski
problem and $L^p$ dual Christoffel-Minkowski problem with constant prescribed
data. At last, we focus on the longtime existence and convergence of a class of
anisotropic flows (i.e. for general function $\psi$). The final result not only
gives a new proof of many previously known solutions to $L^p$ dual Minkowski
problem, $L^p$-Christoffel-Minkowski problem, etc. by such anisotropic flows,
but also provides solutions to $L^p$ dual Christoffel-Minkowski problem with
some conditions.


title:
Convolutional Analysis Operator Learning by End-To-End Training of
  Iterative Neural Networks

abstract:
  The concept of sparsity has been extensively applied for regularization in
image reconstruction. Typically, sparsifying transforms are either pre-trained
on ground-truth images or adaptively trained during the reconstruction.
Thereby, learning algorithms are designed to minimize some target function
which encodes the desired properties of the transform. However, this procedure
ignores the subsequently employed reconstruction algorithm as well as the
physical model which is responsible for the image formation process. Iterative
neural networks - which contain the physical model - can overcome these issues.
In this work, we demonstrate how convolutional sparsifying filters can be
efficiently learned by end-to-end training of iterative neural networks. We
evaluated our approach on a non-Cartesian 2D cardiac cine MRI example and show
that the obtained filters are better suitable for the corresponding
reconstruction algorithm than the ones obtained by decoupled pre-training.


title:
SimKGC: Simple Contrastive Knowledge Graph Completion with Pre-trained
  Language Models

abstract:
  Knowledge graph completion (KGC) aims to reason over known facts and infer
the missing links. Text-based methods such as KGBERT (Yao et al., 2019) learn
entity representations from natural language descriptions, and have the
potential for inductive KGC. However, the performance of text-based methods
still largely lag behind graph embedding-based methods like TransE (Bordes et
al., 2013) and RotatE (Sun et al., 2019b). In this paper, we identify that the
key issue is efficient contrastive learning. To improve the learning
efficiency, we introduce three types of negatives: in-batch negatives,
pre-batch negatives, and self-negatives which act as a simple form of hard
negatives. Combined with InfoNCE loss, our proposed model SimKGC can
substantially outperform embedding-based methods on several benchmark datasets.
In terms of mean reciprocal rank (MRR), we advance the state-of-the-art by +19%
on WN18RR, +6.8% on the Wikidata5M transductive setting, and +22% on the
Wikidata5M inductive setting. Thorough analyses are conducted to gain insights
into each component. Our code is available at
https://github.com/intfloat/SimKGC .


title:
A Possible 3-Year Quasi-Periodic Oscillation in {\gamma}-Ray Emission
  from the FSRQ S5 1044+71

abstract:
  Variability is a typical observation feature of Fermi blazars, sometimes it
shows quasi-periodic oscillation (QPO). In this work, we obtained 5-day binned
light curves (with a time coverage of $\sim$ 12.9 yr) for S5 1044+71 based on
Fermi LAT data, adopted five different methods: Date-compensated Discrete
Fourier Transform (DCDFT), Jurkevich (JV), Lomb-Scargle Periodogram (LSP), a
Fortran 90 program (REDFIT) and the Weighted Wavelet Z-transform (WWZ) to the
$\gamma$-ray light curve, and found a possible QPO of 3.06 $\pm$ 0.43 yr at the
significance level of $\sim3.6\sigma$. A binary black hole model including
accretion model and dual-jets model is used to explain this quasi-periodic
variability. We also estimated the Doppler factors and the apparent velocity
for the two jet components. We speculate that this $\gamma$-ray quasi-periodic
modulation suggest the presence of a binary supermassive black hole in S5
1044+71.


title: Clique-factors in graphs with sublinear $\ell$-independence number

abstract:
  Given a graph $G$ and an integer $\ell\ge 2$, we denote by $\alpha_{\ell}(G)$
the maximum size of a $K_{\ell}$-free subset of vertices in $V(G)$. A recent
question of Nenadov and Pehova asks for determining the best possible minimum
degree conditions forcing clique-factors in $n$-vertex graphs $G$ with
$\alpha_{\ell}(G) = o(n)$, which can be seen as a Ramsey--Tur\'an variant of
the celebrated Hajnal--Szemer\'edi theorem. In this paper we find the
asymptotical sharp minimum degree threshold for $n$-vertex graphs $G$ with
$\alpha_\ell(G)\in (n^{1-\gamma}, n^{1-o(1)})$ for any positive constant
$\gamma<\frac{\ell-1}{\ell^2+2\ell}$.


title: Entanglement on nucleation barrier of polymer crystal

abstract:
  We propose a theoretical approach to quantitatively account for the role of
entanglement in the nucleation of polymer melts, which is the unique feature of
polymer differentiated from small molecules. By performing molecular dynamics
simulations, we obtain the nucleation barriers of polymer systems with
different entanglement densities, which exhibits an opposite trend compared to
the prediction of the classic nucleation theory (CNT). To amend the deficiency
of the CNT in polymer crystallization, we introduce the entanglement free
energy to reflect the role of entanglement in polymer nucleation. Specifically,
the polymer nucleation not only involves free energies of monomers inside and
on the surface of a nucleus as considered in the CNT, but also affects the
entanglement network around the nucleus. Our theoretical approach provides a
reasonable interpretation for the unsolved nucleation phenomena of polymers in
simulations and experiments.


title:
Bilayer-Skyrmion based design of Neuron and Synapse for Spiking Neural
  Network

abstract:
  Magnetic skyrmion technology is promising for the next-generation
spintronics-based memory and neuromorphic computing due to their small size,
non-volatility and low depinning current density. However, the Magnus force
originating from the skyrmion Hall effect causes the skyrmion to move along a
curved trajectory, which may lead to the annihilation of the skyrmion in a
nanotrack during current-induced skyrmion motion. Consequently, circuits
utilizing skyrmionic motion need to be designed to limit the impact of the
skyrmion Hall effect. In this work, we propose a design of an artificial
neuron, and a synapse using the bilayer device consisting of two
antiferromagnetically exchange-coupled ferromagnetic layers, which achieves
robustness against the skyrmion Hall effect by nullifying the Magnus force.
Using micromagnetic simulations, we show that the bilayer device can work as an
artificial neuron and also as a synapse by modifying its uniaxial anisotropy.
We also demonstrate that our proposed skyrmionic synapse has an intrinsic
property of highly linear and symmetric weight update, which is highly
desirable for the synapse operation. A spiking neural network implemented using
our proposed synapse and neuron was simulated and showed to achieve 96.23%
accuracy in performing classification on the MNIST handwritten digit dataset.


title:
Semantic-Aware Representation Blending for Multi-Label Image Recognition
  with Partial Labels

abstract:
  Training the multi-label image recognition models with partial labels, in
which merely some labels are known while others are unknown for each image, is
a considerably challenging and practical task. To address this task, current
algorithms mainly depend on pre-training classification or similarity models to
generate pseudo labels for the unknown labels. However, these algorithms depend
on sufficient multi-label annotations to train the models, leading to poor
performance especially with low known label proportion. In this work, we
propose to blend category-specific representation across different images to
transfer information of known labels to complement unknown labels, which can
get rid of pre-training models and thus does not depend on sufficient
annotations. To this end, we design a unified semantic-aware representation
blending (SARB) framework that exploits instance-level and prototype-level
semantic representation to complement unknown labels by two complementary
modules: 1) an instance-level representation blending (ILRB) module blends the
representations of the known labels in an image to the representations of the
unknown labels in another image to complement these unknown labels. 2) a
prototype-level representation blending (PLRB) module learns more stable
representation prototypes for each category and blends the representation of
unknown labels with the prototypes of corresponding labels to complement these
labels. Extensive experiments on the MS-COCO, Visual Genome, Pascal VOC 2007
datasets show that the proposed SARB framework obtains superior performance
over current leading competitors on all known label proportion settings, i.e.,
with the mAP improvement of 4.6%, 4.%, 2.2% on these three datasets when the
known label proportion is 10%. Codes are available at
https://github.com/HCPLab-SYSU/HCP-MLR-PL.


title:
Thermodynamic limit and boundary energy of the spin-1 Heisenberg chain
  with non-diagonal boundary fields

abstract:
  The thermodynamic limit and boundary energy of the isotropic spin-1
Heisenberg chain with non-diagonal boundary fields are studied. The finite size
scaling properties of the inhomogeneous term in the $ T-Q $ relation at the
ground state are calculated by the density matrix renormalization group. Based
on our findings, the boundary energy of the system in the thermodynamic limit
can be obtained from Bethe ansatz equations of a related model with parallel
boundary fields. These results can be generalized to the $SU(2)$ symmetric high
spin Heisenberg model directly.


title: Dissipation in spin chains using quantized nonequilibrium thermodynamics

abstract:
  We investigate the dynamics of an open chain of interacting spins using the
quantized version of the GENERIC equation from classical out-of-equilibrium
thermodynamics. We focus on both equilibrium and nonequilibrium scenarios for
chains consisting of few tens of spins. While in the equilibrium case we
demonstrate thermal equilibration to the correct many-body Gibbs density
matrix, in the nonequilibrium dynamics we show that the steady-state energy
transport behaves diffusively, consistently with Fourier's law of heat
transfer.


title: The Ultraviolet Upturn in field Luminous Red Galaxies at $0.3 < z < 0.7$

abstract:
  We derive the evolution of the ultraviolet upturn colour from a sample of
field luminous red galaxies at $0.3 < z < 0.7$ with $-24 < M_r < -21.5$. No
individual objects are securely detected, so we stack several hundred galaxies
within absolute magnitude and redshift intervals. We find that the colour of
the ultraviolet upturn (in observed $NUV-i$ which is approximately equivalent
to the classical $FUV-V$ at the redshifts of our targets) does not change
strongly with redshift to $z=0.7$. This behaviour is similar to that observed
in cluster ellipticals over this same mass range and at similar redshifts and
we speculate that the processes involved in the origin of the UV upturn are the
same. The observations are most consistent with spectral synthesis models
containing a fraction of a helium rich stellar population with abundances
between 37\% and 42\%, although we cannot formally exclude a contribution due
to residual star formation at the $\sim 0.5\%$ level (however, this appears
unlikely for cluster galaxies that are believed to be more quenched). This
suggests that the ultraviolet upturn is a primordial characteristic of early
type galaxies at all redshifts and that an unexpected nucleosynthesis channel
may lead to nearly complete chemical evolution at early times.


title:
ST-RRT*: Asymptotically-Optimal Bidirectional Motion Planning through
  Space-Time

abstract:
  We present a motion planner for planning through space-time with dynamic
obstacles, velocity constraints, and unknown arrival time. Our algorithm,
Space-Time RRT* (ST-RRT*), is a probabilistically complete, bidirectional
motion planning algorithm, which is asymptotically optimal with respect to the
shortest arrival time. We experimentally evaluate ST-RRT* in both abstract (2D
disk, 8D disk in cluttered spaces, and on a narrow passage problem), and
simulated robotic path planning problems (sequential planning of 8DoF mobile
robots, and 7DoF robotic arms). The proposed planner outperforms RRT-Connect
and RRT* on both initial solution time, and attained final solution cost. The
code for ST-RRT* is available in the Open Motion Planning Library (OMPL).


title:
GCNet: Graph Completion Network for Incomplete Multimodal Learning in
  Conversation

abstract:
  Conversations have become a critical data format on social media platforms.
Understanding conversation from emotion, content, and other aspects also
attracts increasing attention from researchers due to its widespread
application in human-computer interaction. In real-world environments, we often
encounter the problem of incomplete modalities, which has become a core issue
of conversation understanding. To address this problem, researchers propose
various methods. However, existing approaches are mainly designed for
individual utterances or medical images rather than conversational data, which
cannot exploit temporal and speaker information in conversations. To this end,
we propose a novel framework for incomplete multimodal learning in
conversations, called "Graph Complete Network (GCNet)", filling the gap of
existing works. Our GCNet contains two well-designed graph neural network-based
modules, "Speaker GNN" and "Temporal GNN", to capture temporal and speaker
information in conversations. To make full use of complete and incomplete data
in feature learning, we jointly optimize classification and reconstruction in
an end-to-end manner. To verify the effectiveness of our method, we conduct
experiments on three benchmark conversational datasets. Experimental results
demonstrate that our GCNet is superior to existing state-of-the-art approaches
in incomplete multimodal learning.


title: A hybrid RBF-FD and WLS mesh-free strong-form approximation method

abstract:
  Since the advent of mesh-free methods as a tool for the numerical analysis of
systems of Partial Differential Equations (PDEs), many variants of differential
operator approximation have been proposed. In this work, we propose a local
mesh-free strong-form method that combines the stability of Radial Basis
Function-Generated Finite Differences (RBF-FD) with the computational
effectiveness of Diffuse Approximation Method (DAM), forming a so-called hybrid
method. To demonstrate the advantages of a hybrid method, we evaluate its
computational complexity and accuracy of the obtained numerical solution by
solving a two-dimensional Poisson problem with an exponentially strong source
in the computational domain. Finally, we employ the hybrid method to solve a
three-dimensional Boussinesq's problem on an isotropic half-space and show that
the implementation overhead can be justified.


title: Passive and Active Learning of Driver Behavior from Electric Vehicles

abstract:
  Modeling driver behavior provides several advantages in the automotive
industry, including prediction of electric vehicle energy consumption. Studies
have shown that aggressive driving can consume up to 30% more energy than
moderate driving, in certain driving scenarios. Machine learning methods are
widely used for driver behavior classification, which, however, may yield some
challenges such as sequence modeling on long time windows and lack of labeled
data due to expensive annotation. To address the first challenge, passive
learning of driver behavior, we investigate non-recurrent architectures such as
self-attention models and convolutional neural networks with joint recurrence
plots (JRP), and compare them with recurrent models. We find that
self-attention models yield good performance, while JRP does not exhibit any
significant improvement. However, with the window lengths of 5 and 10 seconds
used in our study, none of the non-recurrent models outperform the recurrent
models. To address the second challenge, we investigate several active learning
methods with different informativeness measures. We evaluate uncertainty
sampling, as well as more advanced methods, such as query by committee and
active deep dropout. Our experiments demonstrate that some active sampling
techniques can outperform random sampling, and therefore decrease the effort
needed for annotation.


title:
EAG: Extract and Generate Multi-way Aligned Corpus for Complete
  Multi-lingual Neural Machine Translation

abstract:
  Complete Multi-lingual Neural Machine Translation (C-MNMT) achieves superior
performance against the conventional MNMT by constructing multi-way aligned
corpus, i.e., aligning bilingual training examples from different language
pairs when either their source or target sides are identical. However, since
exactly identical sentences from different language pairs are scarce, the power
of the multi-way aligned corpus is limited by its scale. To handle this
problem, this paper proposes "Extract and Generate" (EAG), a two-step approach
to construct large-scale and high-quality multi-way aligned corpus from
bilingual data. Specifically, we first extract candidate aligned examples by
pairing the bilingual examples from different language pairs with highly
similar source or target sentences; and then generate the final aligned
examples from the candidates with a well-trained generation model. With this
two-step pipeline, EAG can construct a large-scale and multi-way aligned corpus
whose diversity is almost identical to the original bilingual corpus.
Experiments on two publicly available datasets i.e., WMT-5 and OPUS-100, show
that the proposed method achieves significant improvements over strong
baselines, with +1.1 and +1.4 BLEU points improvements on the two datasets
respectively.


title: MANNER: Multi-view Attention Network for Noise Erasure

abstract:
  In the field of speech enhancement, time domain methods have difficulties in
achieving both high performance and efficiency. Recently, dual-path models have
been adopted to represent long sequential features, but they still have limited
representations and poor memory efficiency. In this study, we propose
Multi-view Attention Network for Noise ERasure (MANNER) consisting of a
convolutional encoder-decoder with a multi-view attention block, applied to the
time-domain signals. MANNER efficiently extracts three different
representations from noisy speech and estimates high-quality clean speech. We
evaluated MANNER on the VoiceBank-DEMAND dataset in terms of five objective
speech quality metrics. Experimental results show that MANNER achieves
state-of-the-art performance while efficiently processing noisy speech.


title:
The Role of Occlusion: Potential Extension of the ICH E9 (R1) Addendum
  on Estimands and Sensitivity Analysis for Time-to-Event Oncology Studies

abstract:
  The ICH E9 (R1) Estimands Guidance1 terminology does not completely address
the conceptual needs of time-to-event estimands in the complex oncology
context. We previously described how censoring and censoring mechanisms for
time-to-event endpoints can be embedded into the ICH E9 (R1) Estimands Guidance
terminology. This second paper by the Pharmaceutical Industry Working Group on
Estimands in Oncology Censoring Mechanisms Subteam discusses special issues in
the oncology clinical context that may require different approaches than some
other therapeutic areas as well as an extensions of the ICH E9 (R1) guidance.
The concept of censoring is discussed in the broader context of occluding
events, with occlusion representing any loss to further follow-up and/or
removal of further collected data from analysis. Occlusion constitutes a
broader concept than the estimand guidance's intercurrent event and terminal
event terminology and is appropriate to describe and handle situations like
withdrawal from assessments or situations where the requirements of different
estimands conflict. We characterize, provide additional details, practical
implications, and examples on the application of each estimands strategy for
handling occluding events.


title: The persistence principle over the logic $\mathbf{IL}^-$

abstract:
  We focus on the persistence principle $\mathbf{P}$: $A \rhd B \to \Box(A \rhd
B)$ over the weak sublogic $\mathbf{IL}^-$ of the basic interpretability logic
$\mathbf{IL}$. We study the logic $\mathbf{IL}^-(\mathbf{P})$ obtained by
adding $\mathbf{P}$ into $\mathbf{IL}^-$ from several perspectives. First, we
prove that $\mathbf{IL}^-(\mathbf{P})$ enjoys a weak version of the fixed point
property. Secondly, we introduce a system of sequent calculus for
$\mathbf{IL}^-(\mathbf{P})$, and prove the cut-elimination theorem for it. As a
consequence, we prove that $\mathbf{IL}^-(\mathbf{P})$ enjoys the Craig
interpolation property. Thirdly, we show that $\mathbf{IL}^-(\mathbf{P})$ is
the natural basis of a generalization of simplified Veltman semantics, and
prove that $\mathbf{IL}^-(\mathbf{P})$ has the finite frame property with
respect to that semantics. Finally, we prove that $\mathbf{IL}^-(\mathbf{P})$
is sound and complete with respect to some appropriate arithmetical semantics.


title:
Star sorts, Lelek fans, and the reconstruction of
  non-$\aleph\_0$-categorical theories in continuous logic

abstract:
  We prove a reconstruction theorem valid for arbitrary theories in continuous
(or classical) logic in a countable language, that is to say that we provide a
complete bi-interpretation invariant for such theories, taking the form of an
open Polish topological groupoid. More explicitly, for every such theory $T$ we
construct a groupoid $\mathbf{G}^*(T)$ that only depends on the
bi-interpretation class of $T$, and conversely, we reconstruct from
$\mathbf{G}^*(T)$ a theory that is bi-interpretable with $T$. The basis of
$\mathbf{G}^*(T)$ (namely, the set of objects, when viewed as a category) is
always homeomorphic to the Lelek fan. We break the construction of the
invariant into two steps. In the second step we construct a groupoid from any
\emph{reconstruction sort}, while in the first step such a sort is constructed.
This allows us to place our result in a common framework with previously
established ones, which only differ by their different choice of a
reconstruction sort.


title: HV-Net: Hypervolume Approximation based on DeepSets

abstract:
  In this letter, we propose HV-Net, a new method for hypervolume approximation
in evolutionary multi-objective optimization. The basic idea of HV-Net is to
use DeepSets, a deep neural network with permutation invariant property, to
approximate the hypervolume of a non-dominated solution set. The input of
HV-Net is a non-dominated solution set in the objective space, and the output
is an approximated hypervolume value of this solution set. The performance of
HV-Net is evaluated through computational experiments by comparing it with two
commonly-used hypervolume approximation methods (i.e., point-based method and
line-based method). Our experimental results show that HV-Net outperforms the
other two methods in terms of both the approximation error and the runtime,
which shows the potential of using deep learning technique for hypervolume
approximation.


title: Anatomy Studio II: A Cross-Reality Application for Teaching Anatomy

abstract:
  Virtual Reality has become an important educational tool, due to the pandemic
and increasing globalization of education. This paper presents a framework for
teaching Virtual Anatomy at the university level. Virtual classes have become a
staple of today's curricula because of the isolation and quarantine
requirements and the increased international collaboration. Our work builds on
the Visible Human Projects for Virtual Dissection material and provides a
medium for groups of students to do collaborative anatomical dissections in
real-time using sketching and 3D visualizations and audio coupled with
interactive 2D tablets for precise drawing. We describe the system
architecture, compare requirements with those of previous development, and
discuss the preliminary results. Discussions with Anatomists show that this is
an effective tool. We introduce avenues for further research and discuss
collaboration challenges posed by this context.


title:
Fault-Tolerant Backup Clustering Algorithm for Smart Connected
  Underwater Sensor Networks

abstract:
  This paper addresses poor cluster formation and frequent Cluster Head (CH)
failure issues of underwater sensor networks by proposing an energy-efficient
hierarchical topology-aware clustering routing (EEHTAC) protocol. In this
paper, fault-tolerant backup clustering (FTBC) algorithms and multi-parameter
cluster formation (MPCF) model were developed for the EEHTAC operation. The
MPCF model tackles the issue of poor cluster formation performance by
integrating multiple parameters to achieve effective clustering process. The
FTBC algorithms tackle the issue of frequent CH failures to avoid interruption
in data transmission. Performance of the MPCF model was evaluated using normal,
high-fault, and high routing overhead network scenarios. Performance metrics
employed for this analysis are temporal topology variation ratio (TTVR), CH
load distribution (CLD), and cluster stability (STB). Obtained results show
that operating with a CH retention period of 90s achieves better CH duty
cycling per round and improves the MPCF process with values of 25.69%, 55.56%,
and 60% for TTVR, CLD, and STB respectively. Performance of the FTBC-based
EEHTAC was evaluated relative to Energy-balanced Unequal Layering Clustering
(EULC) protocol. Performance indicators adopted for this evaluation are routing
overhead ({\Omega}), end to end delay ({\Delta}), CH failures recovered (CFR),
CH failures detected (CFD), received packets ({\theta}), and energy consumption
({\Sigma}). With reference to the best obtained values, EEHTAC demonstrated
performance improvement of 58.40%, 29.94%, 81.33%, 28.02%, 86.65%, and 54.35%
over EULC variants in terms of {\Omega}, {\Delta}, CFR, CFD, {\theta}, and
{\Sigma} respectively. Obtained results displayed that the MPCF model is
efficient for cluster formation performance and the FTBC-based EEHTAC protocol
can perform effectively well against an existing CBR protocol.


title: A Practical Guide to the Partition Function of Atoms and Ions

abstract:
  The partition function, $U$, the number of available states in an atom or
molecules, is crucial for understanding the physical state of any astrophysical
system in thermodynamic equilibrium. There are surprisingly few {\em useful}
discussions of the partition function's numerical value. Textbooks often define
$U$; some give tables of representative values, while others do a deep dive
into the theory of a dense plasma. Most say that it depends on temperature,
atomic structure, density, and that it diverges, that is, it goes to infinity,
at high temperatures, but few give practical examples. We aim to rectify this.
We show that there are two limits, one and two-electron (or closed-shell)
systems like H or He, and species with a complicated electronic structure like
C, N, O, and Fe. The high-temperature divergence does not occur for one and
two-electron systems in practical situations since, at high temperatures,
species are collisionally ionized to higher ionization stages and are not
abundant. The partition function is then close to the statistical weight of the
ground state. There is no such simplification for many-electron species. $U$ is
temperature-sensitive across the range of temperatures where an ion is abundant
but remains finite at even the highest practical temperatures. The actual value
depends on highly uncertain truncation theories in high-density plasmas. We
show that there are various theories for continuum lowering but that they are
not in good agreement. This remains a long-standing unsolved problem.


title:
Matrix Completion via Non-Convex Relaxation and Adaptive Correlation
  Learning

abstract:
  The existing matrix completion methods focus on optimizing the relaxation of
rank function such as nuclear norm, Schatten-p norm, etc. They usually need
many iterations to converge. Moreover, only the low-rank property of matrices
is utilized in most existing models and several methods that incorporate other
knowledge are quite time-consuming in practice. To address these issues, we
propose a novel non-convex surrogate that can be optimized by closed-form
solutions, such that it empirically converges within dozens of iterations.
Besides, the optimization is parameter-free and the convergence is proved.
Compared with the relaxation of rank, the surrogate is motivated by optimizing
an upper-bound of rank. We theoretically validate that it is equivalent to the
existing matrix completion models. Besides the low-rank assumption, we intend
to exploit the column-wise correlation for matrix completion, and thus an
adaptive correlation learning, which is scaling-invariant, is developed. More
importantly, after incorporating the correlation learning, the model can be
still solved by closed-form solutions such that it still converges fast.
Experiments show the effectiveness of the non-convex surrogate and adaptive
correlation learning.


title:
Upper large deviations for power-weighted edge lengths in spatial random
  networks

abstract:
  We study the large-volume asymptotics of the sum of power-weighted edge
lengths $\sum_{e \in E}|e|^\alpha$ in Poisson-based spatial random networks. In
the regime $\alpha > d$, we provide a set of sufficient conditions under which
the upper large deviations asymptotics are characterized by a condensation
phenomena, meaning that the excess is caused by a negligible portion of Poisson
points. Moreover, the rate function can be expressed through a concrete
optimization problem. This framework encompasses in particular directed,
bidirected and undirected variants of the $k$-nearest neighbor graph, as well
as suitable $\beta$-skeletons.


title:
Selective Pseudo-labeling and Class-wise Discriminative Fusion for Sound
  Event Detection

abstract:
  In recent years, exploring effective sound separation (SSep) techniques to
improve overlapping sound event detection (SED) attracts more and more
attention. Creating accurate separation signals to avoid the catastrophic error
accumulation during SED model training is very important and challenging. In
this study, we first propose a novel selective pseudo-labeling approach, termed
SPL, to produce high confidence separated target events from blind sound
separation outputs. These target events are then used to fine-tune the original
SED model that pre-trained on the sound mixtures in a multi-objective learning
style. Then, to further leverage the SSep outputs, a class-wise discriminative
fusion is proposed to improve the final SED performances, by combining multiple
frame-level event predictions of both sound mixtures and their separated
signals. All experiments are performed on the public DCASE 2021 Task 4 dataset,
and results show that our approaches significantly outperforms the official
baseline, the collar-based F 1, PSDS1 and PSDS2 performances are improved from
44.3%, 37.3% and 54.9% to 46.5%, 44.5% and 75.4%, respectively.


title:
Optimal Keywords Grouping in Sponsored Search Advertising under
  Uncertain Environments

abstract:
  In sponsored search advertising, advertisers need to make a series of keyword
decisions. Among them, how to group these keywords to form several adgroups
within a campaign is a challenging task, due to the highly uncertain
environment of search advertising. This paper proposes a stochastic programming
model for keywords grouping, taking click-through rate and conversion rate as
random variables, with consideration of budget constraints and advertisers'
risk-tolerance. A branch-and-bound algorithm is developed to solve our model.
Furthermore, we conduct computational experiments to evaluate the effectiveness
of our model and solution, with two real-world datasets collected from reports
and logs of search advertising campaigns. Experimental results illustrated that
our keywords grouping approach outperforms five baselines, and it can
approximately approach the optimum in a steady way. This research generates
several interesting findings that illuminate critical managerial insights for
advertisers in sponsored search advertising. First, keywords grouping does
matter for advertisers, especially in the situation with a large number of
keywords. Second, in keyword grouping decisions, the marginal profit does not
necessarily show the marginal diminishing phenomenon as the budget increases.
Such that, it's a worthy try for advertisers to increase their budget in
keywords grouping decisions, in order to obtain additional profit. Third, the
optimal keywords grouping solution is a result of multifaceted trade-off among
various advertising factors. In particular, assigning more keywords into
adgroups or having more budget won't certainly lead to higher profits. This
suggests a warning for advertisers that it's not wise to take the number of
keywords as the criterion for keywords grouping decisions.


title:
Time-to-Label: Temporal Consistency for Self-Supervised Monocular 3D
  Object Detection

abstract:
  Monocular 3D object detection continues to attract attention due to the cost
benefits and wider availability of RGB cameras. Despite the recent advances and
the ability to acquire data at scale, annotation cost and complexity still
limit the size of 3D object detection datasets in the supervised settings.
Self-supervised methods, on the other hand, aim at training deep networks
relying on pretext tasks or various consistency constraints. Moreover, other 3D
perception tasks (such as depth estimation) have shown the benefits of temporal
priors as a self-supervision signal. In this work, we argue that the temporal
consistency on the level of object poses, provides an important supervision
signal given the strong prior on physical motion. Specifically, we propose a
self-supervised loss which uses this consistency, in addition to
render-and-compare losses, to refine noisy pose predictions and derive
high-quality pseudo labels. To assess the effectiveness of the proposed method,
we finetune a synthetically trained monocular 3D object detection model using
the pseudo-labels that we generated on real data. Evaluation on the standard
KITTI3D benchmark demonstrates that our method reaches competitive performance
compared to other monocular self-supervised and supervised methods.


title:
Rethinking Reconstruction Autoencoder-Based Out-of-Distribution
  Detection

abstract:
  In some scenarios, classifier requires detecting out-of-distribution samples
far from its training data. With desirable characteristics, reconstruction
autoencoder-based methods deal with this problem by using input reconstruction
error as a metric of novelty vs. normality. We formulate the essence of such
approach as a quadruplet domain translation with an intrinsic bias to only
query for a proxy of conditional data uncertainty. Accordingly, an improvement
direction is formalized as maximumly compressing the autoencoder's latent space
while ensuring its reconstructive power for acting as a described domain
translator. From it, strategies are introduced including semantic
reconstruction, data certainty decomposition and normalized L2 distance to
substantially improve original methods, which together establish
state-of-the-art performance on various benchmarks, e.g., the FPR@95%TPR of
CIFAR-100 vs. TinyImagenet-crop on Wide-ResNet is 0.2%. Importantly, our method
works without any additional data, hard-to-implement structure, time-consuming
pipeline, and even harming the classification accuracy of known classes.


title: Voice-Face Homogeneity Tells Deepfake

abstract:
  Detecting forgery videos is highly desired due to the abuse of deepfake.
Existing detection approaches contribute to exploring the specific artifacts in
deepfake videos and fit well on certain data. However, the growing technique on
these artifacts keeps challenging the robustness of traditional deepfake
detectors. As a result, the development of generalizability of these approaches
has reached a blockage. To address this issue, given the empirical results that
the identities behind voices and faces are often mismatched in deepfake videos,
and the voices and faces have homogeneity to some extent, in this paper, we
propose to perform the deepfake detection from an unexplored voice-face
matching view. To this end, a voice-face matching detection model is devised to
measure the matching degree of these two on a generic audio-visual dataset.
Thereafter, this model can be smoothly transferred to deepfake datasets without
any fine-tuning, and the generalization across datasets is accordingly
enhanced. We conduct extensive experiments over two widely exploited datasets -
DFDC and FakeAVCeleb. Our model obtains significantly improved performance as
compared to other state-of-the-art competitors and maintains favorable
generalizability. The code has been released at
https://github.com/xaCheng1996/VFD.


title:
Data Augmentation Empowered Neural Precoding for Multiuser MIMO with
  MMSE Model

abstract:
  Precoding design exploiting deep learning methods has been widely studied for
multiuser multiple-input multiple-output (MU-MIMO) systems. However,
conventional neural precoding design applies black-box-based neural networks
which are less interpretable. In this paper, we propose a deep learning-based
precoding method based on an interpretable design of a neural precoding
network, namely iPNet. In particular, the iPNet mimics the classic minimum
mean-squared error (MMSE) precoding and approximates the matrix inversion in
the design of the neural network architecture. Specifically, the proposed iPNet
consists of a model-driven component network, responsible for augmenting the
input channel state information (CSI), and a data-driven sub-network,
responsible for precoding calculation from this augmented CSI. The latter
data-driven module is explicitly interpreted as an unsupervised learner of the
MMSE precoder. Simulation results show that by exploiting the augmented CSI,
the proposed iPNet achieves noticeable performance gain over existing black-box
designs and also exhibits enhanced generalizability against CSI mismatches.


title: The 2-adic valuation of the general degree 2 polynomial in 2 variables

abstract:
  The p-adic valuation of a polynomial can be given by its valuation tree. This
work describes the 2-adic valuation tree of the general degree 2 polynomial in
2 variables.


title: Gene-Environment Interplay in the Social Sciences

abstract:
  Nature (one's genes) and nurture (one's environment) jointly contribute to
the formation and evolution of health and human capital over the life cycle.
This complex interplay between genes and environment can be estimated and
quantified using genetic information readily available in a growing number of
social science data sets. To help the novice reader interested in understanding
individual decision making, public policy, and inequality using genetic data,
we introduce essential genetic terminology, review the literature in economics
and social-science genetics -- with a focus on the interplay between genes and
environment -- and discuss policy implications and future prospects of the use
of genetic data in the social sciences and economics.


title:
Diluting a triangular-lattice spin liquid: Synthesis and
  characterization of NaYb$_{1-x}$Lu$_{x}$S$_2$ single crystals

abstract:
  Yb-based magnets, with a perfect triangular lattice of pseudospin-$1/2$
Yb$^{3+}$ ions, have emerged as candidates for realizing a quantum spin-liquid
state, with NaYbS$_2$ being a prominent example. Here we present the
solid-solution series NaYb$_{1-x}$Lu$_{x}$S$_2$ with well-defined single
crystals over the entire substitution range $0\le x\le 1$. Chemical and
structural analysis indicate a statistically homogeneous replacement of
Yb$^{3+}$ by Lu$^{3+}$ ions. We magnetically characterize the relatively small
single crystals using electron spin resonance (ESR). Below $30$K the ESR
intensity can be well described by a Curie-Weiss function for all \textit{x},
with a decreasing Weiss temperature with increasing Lu content. This reduction
of the average magnetic interaction upon Lu substitution is also supported by
magnetization measurements. Importantly, no signs of magnetic or spin-glass
order are detected down to $2$K for any $x$. For $x>0.5$ the ESR linewidth
strongly increases, indicating the breakup of the magnetic system into
disconnected clusters as expected from percolation physics. The experimental
magnetization data are found to be in good agreement for all $x$ with results
of classical Monte-Carlo simulations for a triangular-lattice Heisenberg model,
amended with a small second-neighbor interaction. Taken together, our results
establish NaYb$_{1-x}$Lu$_{x}$S$_2$ as a family of diluted triangular-lattice
spin liquids.


title:
Aggregate effects of advertising decisions: a complex systems look at
  search engine advertising via an experimental study

abstract:
  Purpose: We model group advertising decisions, which are the collective
decisions of every single advertiser within the set of advertisers who are
competing in the same auction or vertical industry, and examine resulting
market outcomes, via a proposed simulation framework named EXP-SEA
(Experimental Platform for Search Engine Advertising) supporting experimental
studies of collective behaviors in the context of search engine advertising.
Design: We implement the EXP-SEA to validate the proposed simulation framework,
also conduct three experimental studies on the aggregate impact of electronic
word-of-mouth, the competition level, and strategic bidding behaviors. EXP-SEA
supports heterogeneous participants, various auction mechanisms, and also
ranking and pricing algorithms. Findings: Findings from our three experiments
show that (a) both the market profit and advertising indexes such as number of
impressions and number of clicks are larger when the eWOM effect presents,
meaning social media certainly has some effect on search engine advertising
outcomes, (b) the competition level has a monotonic increasing effect on the
market performance, thus search engines have an incentive to encourage both the
eWOM among search users and competition among advertisers, and (c) given the
market-level effect of the percentage of advertisers employing a dynamic greedy
bidding strategy, there is a cut-off point for strategic bidding behaviors.
Originality: This is one of the first research works to explore collective
group decisions and resulting phenomena in the complex context of search engine
advertising via developing and validating a simulation framework that supports
assessments of various advertising strategies and estimations of the impact of
mechanisms on the search market.


title: Neural Simulated Annealing

abstract:
  Simulated annealing (SA) is a stochastic global optimisation technique
applicable to a wide range of discrete and continuous variable problems.
Despite its simplicity, the development of an effective SA optimiser for a
given problem hinges on a handful of carefully handpicked components; namely,
neighbour proposal distribution and temperature annealing schedule. In this
work, we view SA from a reinforcement learning perspective and frame the
proposal distribution as a policy, which can be optimised for higher solution
quality given a fixed computational budget. We demonstrate that this Neural SA
with such a learnt proposal distribution, parametrised by small equivariant
neural networks, outperforms SA baselines on a number of problems: Rosenbrock's
function, the Knapsack problem, the Bin Packing problem, and the Travelling
Salesperson problem. We also show that Neural SA scales well to large problems
- generalising to significantly larger problems than the ones seen during
training - while achieving comparable performance to popular off-the-shelf
solvers and other machine learning methods in terms of solution quality and
wall-clock time.


title:
Carbon Footprint of Selecting and Training Deep Learning Models for
  Medical Image Analysis

abstract:
  The increasing energy consumption and carbon footprint of deep learning (DL)
due to growing compute requirements has become a cause of concern. In this
work, we focus on the carbon footprint of developing DL models for medical
image analysis (MIA), where volumetric images of high spatial resolution are
handled. In this study, we present and compare the features of four tools from
literature to quantify the carbon footprint of DL. Using one of these tools we
estimate the carbon footprint of medical image segmentation pipelines. We
choose nnU-net as the proxy for a medical image segmentation pipeline and
experiment on three common datasets. With our work we hope to inform on the
increasing energy costs incurred by MIA. We discuss simple strategies to
cut-down the environmental impact that can make model selection and training
processes more efficient.


title: Planetary Genealogy

abstract:
  The detection of exoplanets and accretion disks around newborn stars has
spawned new ideas and models of how our Solar System formed and evolved.
Meteorites as probes of geologic deep time can provide ground truth to these
models. In particular, stable isotope anomalies in meteorites have recently
emerged as key tracers of material flow in the early Solar System, allowing
cosmochemists to establish a "planetary isotopic genealogy". Although not
complete, this concept substantially advanced our understanding of Solar System
evolution, from the collapse of the Sun's parental molecular cloud to the
accretion of the planets.


title: Sharper Bounds for Proximal Gradient Algorithms with Errors

abstract:
  We analyse the convergence of the proximal gradient algorithm for convex
composite problems in the presence of gradient and proximal computational
inaccuracies. We derive new tighter deterministic and probabilistic bounds that
we use to verify a simulated (MPC) and a synthetic (LASSO) optimization
problems solved on a reduced-precision machine in combination with an
inaccurate proximal operator. We also show how the probabilistic bounds are
more robust for algorithm verification and more accurate for application
performance guarantees. Under some statistical assumptions, we also prove that
some cumulative error terms follow a martingale property. And conforming to
observations, e.g., in \cite{schmidt2011convergence}, we also show how the
acceleration of the algorithm amplifies the gradient and proximal computational
errors.

categories: math.OC cs.LG cs.NA math.NA stat.CO stat.ML


title: Safety-aware metrics for object detectors in autonomous driving

abstract:
  We argue that object detectors in the safety critical domain should
prioritize detection of objects that are most likely to interfere with the
actions of the autonomous actor. Especially, this applies to objects that can
impact the actor's safety and reliability. In the context of autonomous
driving, we propose new object detection metrics that reward the correct
identification of objects that are most likely to interact with the subject
vehicle (i.e., the actor), and that may affect its driving decision. To achieve
this, we build a criticality model to reward the detection of the objects based
on proximity, orientation, and relative velocity with respect to the subject
vehicle. Then, we apply our model on the recent autonomous driving dataset
nuScenes, and we compare eight different object detectors. Results show that,
in several settings, object detectors that perform best according to the
nuScenes ranking are not the preferable ones when the focus is shifted on
safety and reliability.


title: Non-local Lagrangian fields: Noether's theorem and Hamiltonian formalism

abstract:
  This article aims to study non-local Lagrangians with an infinite number of
degrees of freedom. We obtain an extension of Noether's theorem and Noether's
identities for such Lagrangians. We then set up a Hamiltonian formalism for
them. In addition, we show that $n$-order local Lagrangians can be treated as a
particular case and the standard results can be recovered. Finally, this
formalism is applied to the case of $p$-adic open string field.


title: Forms and Norms of Indecision in Argumentation Theory

abstract:
  One main goal of argumentation theory is to evaluate arguments and to
determine whether they should be accepted or rejected. When there is no clear
answer, a third option, being undecided, has to be taken into account.
Indecision is often not considered explicitly, but rather taken to be a
collection of all unclear or troubling cases. However, current philosophy makes
a strong point for taking indecision itself to be a proper object of
consideration. This paper aims at revealing parallels between the findings
concerning indecision in philosophy and the treatment of indecision in
argumentation theory. By investigating what philosophical forms and norms of
indecision are involved in argumentation theory, we can improve our
understanding of the different uncertain evidential situations in argumentation
theory.


title:
The 700 ks Chandra Spiderweb Field I: evidence for widespread nuclear
  activity in the Protocluster

abstract:
  (Abridged) We present an analysis of the 700 ks Chandra ACIS-S observation of
the field around the Spiderweb Galaxy at z=2.156, focusing on the nuclear
activity in the associated large-scale environment. We identify unresolved
X-ray sources down to flux limits of 1.3X10^{-16} and 3.9X10^{-16} erg/s/cm^2
in the soft and hard band, respectively. We search for counterparts in the
optical, NIR and submm bands to identify X-ray sources belonging to the
protocluster. We detect 107 X-ray unresolved sources within 5 arcmin
(corresponding to 2.5 Mpc) of J1140-2629, among which 13 have optical
counterparts with spectroscopic redshift 2.11<z<2.20, and 1 source with
photometric redshift consistent with this range. Our X-ray spectral analysis
shows that their intrinsic spectral slope is consistent with an average
<\Gamma>~1.84+-0.04. The best-fit intrinsic absorption for 5 protocluster X-ray
members is N_H>10^{23} cm^{-2}, while other 6 have upper limits of the order of
fewX10^{22} cm^{-2}. Two sources can only be fitted with very flat \Gamma<=1,
and are therefore considered Compton-thick candidates. Their 0.5-10 keV rest
frame luminosities are larger than 2X10^{43} erg/s, significantly greater than
X-ray luminosities expected from star formation activity. The X-ray luminosity
function of AGN in the volume associated to the Spiderweb protocluster in the
range 10^{43}<L_X<10^{44.5} erg/s, is at least 10 times higher than that in the
field at the same redshift and significantly flatter. The X-ray AGN fraction is
measured to be (25.5+-4.5)% in the stellar mass range log(M*/M_sun)>10.5,
corresponding to an enhancement of 6.0^{+9.0}_{-3.0} with respect to the COSMOS
field at comparable redshifts and stellar mass range. We conclude that the
galaxy population in the Spiderweb Protocluster is characterized by enhanced
X-ray nuclear activity triggered by environmental effects on Mpc scales.


title:
In-situ nitriding of Fe2VAl during laser surface remelting to manipulate
  microstructure and crystalline defects

abstract:
  Tailoring the physical properties of complex materials for targeted
applications requires optimizing the microstructure and crystalline defects
that influence electrical and thermal transport, and mechanical properties.
Laser surface remelting can be used to modify the sub-surface microstructure of
bulk materials and hence manipulate their properties locally. Here, we
introduce an approach to perform remelting in a reactive nitrogen atmosphere,
in order to form nitrides and induce segregation of nitrogen to structural
defects. These defects arise from the fast solidification of the full-Heusler
Fe2VAl compound that is a promising thermoelectric material. Advanced scanning
electron microscopy, including electron channelling contrast imaging and
three-dimensional electron backscatter diffraction, is complemented by atom
probe tomography to study the distribution of crystalline defects and their
local chemical composition. We reveal a high density of dislocations, which are
stable due to their character as geometrically necessary dislocations. At these
dislocations and low-angle grain boundaries, we observe segregation of nitrogen
and vanadium, which can be enhanced by repeated remelting in nitrogen
atmosphere. We propose that this approach can be generalized to other additive
manufacturing processes to promote local segregation and precipitation states,
thereby manipulating physical properties.


title: Triggered Gradient Tracking for Asynchronous Distributed Optimization

abstract:
  This paper proposes Asynchronous Triggered Gradient Tracking, i.e., a
distributed optimization algorithm to solve consensus optimization over
networks with asynchronous communication. As a building block, we start by
devising the continuous-time counterpart of the recently proposed
(discrete-time) distributed gradient tracking called Continuous Gradient
Tracking. By using a Lyapunov approach, we prove exponential stability of the
equilibrium corresponding to agents' estimates being consensual to the optimal
solution of the problem, with arbitrary initialization of the local estimates.
Then, we propose two triggered versions of the algorithm. In the first one, the
agents continuously integrate their local dynamics and exchange with neighbors
current local variables according to a synchronous communication protocol. In
Asynchronous Triggered Gradient Tracking, we propose a totally asynchronous
scheme in which each agent sends to neighbors its current local variables based
on a triggering condition that depends on a locally verifiable condition. The
triggering protocol preserves the linear convergence of the algorithm and
excludes the Zeno behavior. By using the stability analysis of Continuous
Gradient Tracking as a preparatory result, we show exponential stability of the
equilibrium point holds for both triggered algorithms and any estimates'
initialization. Finally, numerical simulations validate the effectiveness of
the proposed methods showing also the improved performance in terms of
inter-agent communication.


title: Gamma-Shadowed Two-Ray with Diffuse Power Composite Fading Model

abstract:
  In this paper, a novel gamma-shadowed two-ray with diffuse power (GS-TWDP)
composite fading model is proposed. The model is intended for modeling
propagation in the emerging wireless networks working at millimeter wave
(mmWave) frequencies, and is obtained as a combination of TWDP distribution for
description of multipath effects and gamma distribution for modeling variations
due to shadowing. After derivation of the exact probability density function
(PDF), cumulative distribution function (CDF) and moment generating functions
(MGF) expressions are obtained. Proposed model is verified by comparing the
analytically obtained results with those measured at 28 GHz and reported in
literature. Two upper bound average symbol error probability (ASEP) expressions
are then derived for M-ary rectangular quadrature amplitude modulation (RQAM)
by employing Chernoff and Chiani approximations of Gaussian Q-function, and are
used to investigate relationship between GS-TWDP parameters and system
performance. All the results are verified by Monte-Carlo simulation.


title:
An image-informed Cahn-Hilliard Keller-Segel multiphase field model for
  tumor growth with angiogenesis

abstract:
  We develop a new four-phase tumor growth model with angiogenesis, derived
from a diffuse-interface mixture model composed by a viable, a necrotic, a
liquid and an angiogenetic component, coupled with two massless chemicals
representing a perfectly diluted nutrient and an angiogenetic factor. This
model is derived from variational principles complying with the second law of
thermodynamics in isothermal situations, starting from biological constitutive
assumptions on the tumor cells adhesion properties and on the infiltrative
mechanics of tumor-induced vasculature in the tumor tissues, and takes the form
of a coupled degenerate Cahn-Hilliard Keller-Segel system for the mixture
components with reaction diffusion equations for the chemicals. The model is
informed by neuroimaging data, which give informations about the
patient-specific brain geometry and tissues microstructure, the distribution of
the different tumor components, the white matter fiber orientations and the
vasculature density. We describe specific and robust preprocessing steps to
extract quantitative informations from the neuroimaging data and to construct a
computational platform to solve the model on a patient-specific basis. We
introduce a finite element approximation of the model which preserve the
qualitative properties of the continuous solutions. Finally, we show simulation
results for the patient-specific tumor evolution of a patient affected by
GlioBlastoma Multiforme, considering two different test cases before surgery,
corresponding to situations with high or low nutrient supply inside the tumor,
and a test case after surgery. We show that our model correctly predicts the
overall extension of the tumor distribution and the intensity of the
angiogenetic process, paving the way for assisting the clinicians in properly
assessing the therapy outcomes and in designing optimal patient-specific
therapeutic schedules.


title: The Thue-Morse continued fractions in characteristic $2$ are algebraic

abstract:
  Let $a, b$ be distinct, non-constant polynomials in ${\mathbb F}_2 [z]$. Let
$\xi_{a, b}$ be the power series in ${\mathbb F}_2((z^{-1}))$ whose sequence of
partial quotients is the Thue-Morse sequence over $\{a, b\}$. We establish that
$\xi_{a,b}$ is algebraic of degree $4$.


title:
Plan Your Target and Learn Your Skills: Transferable State-Only
  Imitation Learning via Decoupled Policy Optimization

abstract:
  Recent progress in state-only imitation learning extends the scope of
applicability of imitation learning to real-world settings by relieving the
need for observing expert actions. However, existing solutions only learn to
extract a state-to-action mapping policy from the data, without considering how
the expert plans to the target. This hinders the ability to leverage
demonstrations and limits the flexibility of the policy. In this paper, we
introduce Decoupled Policy Optimization (DePO), which explicitly decouples the
policy as a high-level state planner and an inverse dynamics model. With
embedded decoupled policy gradient and generative adversarial training, DePO
enables knowledge transfer to different action spaces or state transition
dynamics, and can generalize the planner to out-of-demonstration state regions.
Our in-depth experimental analysis shows the effectiveness of DePO on learning
a generalized target state planner while achieving the best imitation
performance. We demonstrate the appealing usage of DePO for transferring across
different tasks by pre-training, and the potential for co-training agents with
various skills.


title: Dispersive analysis of the $\pi\pi$ and $\pi K$ scattering data

abstract:
  We present a data-driven analysis of the S-wave $\pi\pi \to \pi\pi\,(I=0,2)$
and $\pi K \to \pi K\,(I=1/2, 3/2)$ reactions using the partial-wave dispersion
relation. The contributions from the left-hand cuts are parametrized using the
expansion in a suitably constructed conformal variable, which accounts for its
analytical structure. The partial-wave dispersion relation is solved
numerically using the $N/D$ method. The fits to the experimental data
supplemented with the constraints from chiral perturbation theory at threshold
and Adler zero give the results consistent with Roy-like (Roy-Steiner)
analyses. For the $\pi\pi$ scattering we present the coupled-channel analysis
by including additionally the $K\bar{K}$ channel. By the analytic continuation
to the complex plane, we found poles associated with the lightest scalar
resonances $\sigma/f_0(500)$, $f_0(980)$, and $\kappa/K_0^*(700)$. For all the
channels we also performed the fits directly to the Roy-like (Roy-Steiner)
solutions in the physical region, in order to minimize the $N/D$ uncertainties
in the complex plane and to extract the most constrained Omn\`es functions.


title:
Look\&Listen: Multi-Modal Correlation Learning for Active Speaker
  Detection and Speech Enhancement

abstract:
  Active speaker detection and speech enhancement have become two increasingly
attractive topics in audio-visual scenario understanding. According to their
respective characteristics, the scheme of independently designed architecture
has been widely used in correspondence to each single task. This may lead to
the learned feature representation being task-specific, and inevitably result
in the lack of generalization ability of the feature based on multi-modal
modeling. More recent studies have shown that establishing cross-modal
relationship between auditory and visual stream is a promising solution for the
challenge of audio-visual multi-task learning. Therefore, as a motivation to
bridge the multi-modal cross-attention, in this work, a unified framework
ADENet is proposed to achieve target speaker detection and speech enhancement
with joint learning of audio-visual modeling.


title: Quantification of emotions in decision making

abstract:
  The problem of quantification of emotions in the choice between alternatives
is considered. The alternatives are evaluated in a dual manner. From one side,
they are characterized by rational features defining the utility of each
alternative. From the other side, the choice is affected by emotions labeling
the alternatives as attractive or repulsive, pleasant or unpleasant. A decision
maker needs to make a choice taking into account both these features, the
utility of alternatives and their attractiveness. The notion of utility is
based on rational grounds, while the notion of attractiveness is vague and
rather is based on irrational feelings. A general method, allowing for the
quantification of the choice combining rational and emotional features is
described. Despite that emotions seem to avoid precise quantification, their
quantitative evaluation is possible at the aggregate level. The analysis of a
series of empirical data demonstrates the efficiency of the approach, including
the realistic behavioral problems that cannot be treated by the standard
expected utility theory.


title:
First results of Low-latitude Ionospheric Irregularities measured by
  NavIC and GPS near the Anomaly Crest and the Magnetic Equator

abstract:
  Ionospheric irregularity studies are important aspects for understanding
ionospheric physics and related processes, especially near the low-latitude
regions. However, simultaneous measurements (utilizing the L-band signals of
NavIC and GPS) of irregularity scale sizes over the Indian longitude sector,
has not been addressed extensively. To address this problem, the paper presents
simultaneous characterization of low-latitude ionospheric irregularities over a
location near the northern EIA crest (Indore: 22.52$^\circ$N, 75.92$^\circ$E
geographic and magnetic dip of 32.23$^\circ$N) and a location (Hyderabad:
17.41$^\circ$N, 78.55$^\circ$E geographic and magnetic dip of 21.69$^\circ$N)
between the crest and the magnetic equator, utilizing the Indian navigation
system, NavIC and GPS L5 signal C/N$_o$ variations to determine the range of
the ionospheric irregularity scale sizes using Power Spectral Density (PSD)
analysis. The study period spans from September 2017- September 2019, covering
both disturbed and quiet-time conditions in the declining phase of solar cycle
24. Observations show that the irregularity scale size ranges from about 500 m
to 6 km. This study for the first time, shows the nature of the temporal PSD
for ionospheric scintillation during varying solar and geophysical conditions,
by measuring the irregularity scale sizes utilizing simultaneous observations
from NavIC and GPS from locations near the northern crest of the EIA and in
between crest and the magnetic equator, ensuring proper characterization of
ionosphere over the geosensitive Indian subcontinent.

categories: physics.space-ph astro-ph.EP astro-ph.SR physics.geo-ph physics.plasm-ph


title:
At least three xenon binding sites in the glycine binding domain of the
  N-methyl D-aspartate receptor

abstract:
  Xenon can produce general anesthesia. Its main protein target is the
N-methyl-D-aspartate receptor, a ionotropic channel playing a pivotal role in
the function of the central nervous system.
  The molecular mechanisms allowing this noble gas to have such a specific
effect remain obscure, probably as a consequence of the lack of structural data
at the atomic level of detail. Herein, as a result of five independent
molecular dynamics simulations, three different binding sites were found for
xenon in the glycine binding domain of the N-methyl-D-aspartate receptor. The
absolute binding free energy of xenon in these sites ranges between -8 and -14
kJ/mole. However, it depends significantly upon the protein conformer chosen
for performing the calculation, suggesting that larger values could probably be
obtained, if other conformers were considered.
  These three sites are next to each other, one of them being next to the
glycine site. This could explain why the F758W and F758Y mutations can prevent
competitive inhibition by xenon without affecting glycine binding.


title:
A Classifier-Lasso Approach for Estimating Production Functions with
  Latent Group Structures

abstract:
  I present a new estimation procedure for production functions with latent
group structures. I consider production functions that are heterogeneous across
groups but time-homogeneous within groups, and where the group membership of
the firms is unknown. My estimation procedure is fully data-driven and embeds
recent identification strategies from the production function literature into
the classifier-Lasso. Simulation experiments demonstrate that firms are
assigned to their correct latent group with probability close to one. I apply
my estimation procedure to a panel of Chilean firms and find sizable
differences in the estimates compared to the standard approach of
classification by industry.


title:
Whole-Body MPC and Dynamic Occlusion Avoidance: A Maximum Likelihood
  Visibility Approach

abstract:
  This paper introduces a novel approach for whole-body motion planning and
dynamic occlusion avoidance. The proposed approach reformulates the visibility
constraint as a likelihood maximization of visibility probability. In this
formulation, we augment the primary cost function of a whole-body model
predictive control scheme through a relaxed log barrier function yielding a
relaxed log-likelihood maximization formulation of visibility probability. The
visibility probability is computed through a probabilistic shadow field that
quantifies point light source occlusions. We provide the necessary algorithms
to obtain such a field for both 2D and 3D cases. We demonstrate 2D
implementations of this field in simulation and 3D implementations through
real-time hardware experiments. We show that due to the linear complexity of
our shadow field algorithm to the map size, we can achieve high update rates,
which facilitates onboard execution on mobile platforms with limited
computational power. Lastly, we evaluate the performance of the proposed MPC
reformulation in simulation for a quadrupedal mobile manipulator.


title:
Broadband Cross-Circular Polarization Carpet Cloaking based on a Phase
  Change Material Metasurface in the Mid-infrared Region

abstract:
  In view of the fact that most invisibility devices focus on linear
polarization cloaking and that the characteristics of mid infrared cloaking are
rarely studied, we propose a cross circularly polarized invisibility carpet
cloaking device in the mid infrared band. Based on the Pancharatnam Berry phase
principle, the unit cells with the cross circular polarization gradient phase
were carefully designed and constructed into a metasurface. In order to achieve
tunable cross circular polarization carpet cloaks, a phase change material is
introduced into the design of the unit structure. When the phase change
material is in amorphous and crystalline states, the proposed metasurface unit
cells can achieve high efficiency cross polarization conversion and reflection
intensity can be tuned. According to the phase compensation principle of carpet
cloaking, we construct a metasurface cloaking device with a phase gradient
using the designed unit structure. From the near and far field distributions,
the cross circular polarization cloaking property is confirmed in the broadband
wavelength range. The proposed cloaking device can effectively resist detection
of cross-circular polarization.


title:
Topological unidirectional guided resonances emerged from interband
  coupling

abstract:
  Unidirectional guided resonances (UGRs) are optical modes in photonic crystal
(PhC) slabs that radiate towards one side without the need for mirrors on the
other, represented from a topological perspective by the merged points of
paired, single-sided, half-integer topological charges. In this work, we report
a mechanism to realize UGRs by tuning the interband coupling effect originating
from up-down symmetry breaking. We theoretically demonstrate that a type of
polarization singularity, the circular-polarized states (CPs), emerge from
trivial polarization fields owing to the hybridization of two unperturbed
states. By tuning structural parameters, two half-charges carried by CPs evolve
in momentum space and merge to create UGRs. Our findings show that UGRs are
ubiquitous in PhC slabs, and can systematically be found from our method, thus
paving the way to new possibilities of light manipulation.


title: Pressure-robustness in the context of optimal control

abstract:
  This paper studies the benefits of pressure-robust discretizations in the
scope of optimal control of incompressible flows. Gradient forces that may
appear in the data can have a negative impact on the accuracy of state and
control and can only be correctly balanced if their $L^2$-orthogonality onto
discretely divergence-free test functions is restored. Perfectly orthogonal
divergence-free discretizations or divergence-free reconstructions of these
test functions do the trick and lead to much better analytic a priori estimates
that are also validated in numerical examples.


title:
ClarET: Pre-training a Correlation-Aware Context-To-Event Transformer
  for Event-Centric Generation and Classification

abstract:
  Generating new events given context with correlated ones plays a crucial role
in many event-centric reasoning tasks. Existing works either limit their scope
to specific scenarios or overlook event-level correlations. In this paper, we
propose to pre-train a general Correlation-aware context-to-Event Transformer
(ClarET) for event-centric reasoning. To achieve this, we propose three novel
event-centric objectives, i.e., whole event recovering, contrastive
event-correlation encoding and prompt-based event locating, which highlight
event-level correlations with effective training. The proposed ClarET is
applicable to a wide range of event-centric reasoning scenarios, considering
its versatility of (i) event-correlation types (e.g., causal, temporal,
contrast), (ii) application formulations (i.e., generation and classification),
and (iii) reasoning types (e.g., abductive, counterfactual and ending
reasoning). Empirical fine-tuning results, as well as zero- and few-shot
learning, on 9 benchmarks (5 generation and 4 classification tasks covering 4
reasoning types with diverse event correlations), verify its effectiveness and
generalization ability.


title:
eBaRe: An Efficient Backup and Restore Techniques in Hybrid L-1 Cache
  for Energy Harvesting Devices

abstract:
  Battery operated devices are rapidly increasing due to the bulk usage of IoT
enabled nodes in various fields. The alternative and promising solution to
replace battery-operated devices is energy harvesters, which helps to power up
the embedded devices. The energy harvester systematically stores sufficient
energy in a capacitor to power up the embedded device for computing the task.
This type of computation is defined as intermittent computing. Energy
harvesters cannot ensure a continuous power supply for embedded devices. All
registers and cache are volatile in conventional processors. We require a
Non-Volatile Memory (NVM) based Non-Volatile Processor (NVP), which store the
registers and cache contents during power failure.
  Introducing NVM at the cache level degrades the system performance and
consumes more energy than SRAM based caches. In this paper, an Efficient Backup
and Restore (eBaRe) hybrid cache architecture is proposed by integrating SRAM
and STT-RAM at a first-level cache. The eBaRe architecture proposes cache block
placement and migration policies to reduce the number of writes to STT-RAM.
During a power failure, the backup strategy finds the important blocks to
migrate from SRAM to STT-RAM. In comparison to baseline architecture, eBaRe
architecture reduces STT-RAM writes from 63.35\% to 35.93\%, resulting in a
32.85\% performance gain and 23.42\% reduction in energy consumption. Our
backup strategy decreases backup time by 34.46\% compared with baseline.


title:
Partial Wasserstein Adversarial Network for Non-rigid Point Set
  Registration

abstract:
  Given two point sets, the problem of registration is to recover a
transformation that matches one set to the other. This task is challenging due
to the presence of the large number of outliers, the unknown non-rigid
deformations and the large sizes of point sets. To obtain strong robustness
against outliers, we formulate the registration problem as a partial
distribution matching (PDM) problem, where the goal is to partially match the
distributions represented by point sets in a metric space. To handle large
point sets, we propose a scalable PDM algorithm by utilizing the efficient
partial Wasserstein-1 (PW) discrepancy. Specifically, we derive the
Kantorovich-Rubinstein duality for the PW discrepancy, and show its gradient
can be explicitly computed. Based on these results, we propose a partial
Wasserstein adversarial network (PWAN), which is able to approximate the PW
discrepancy by a neural network, and minimize it by gradient descent. In
addition, it also incorporates an efficient coherence regularizer for non-rigid
transformations to avoid unrealistic deformations. We evaluate PWAN on
practical point set registration tasks, and show that the proposed PWAN is
robust, scalable and performs more favorably than the state-of-the-art methods.


title:
Improving Ant Colony Optimization Efficiency for Solving Large TSP
  Instances

abstract:
  Ant Colony Optimization (ACO) is a family of nature-inspired metaheuristics
often applied to finding approximate solutions to difficult optimization
problems. Despite being significantly faster than exact methods, the ACOs can
still be prohibitively slow, especially if compared to basic problem-specific
heuristics. As recent research has shown, it is possible to significantly
improve the performance through algorithm refinements and careful parallel
implementation benefiting from multi-core CPUs and dedicated accelerators. In
this paper, we present a novel ACO variant, namely the Focused ACO (FACO). One
of the core elements of the FACO is a mechanism for controlling the number of
differences between a newly constructed and a selected previous solution. The
mechanism results in a more focused search process, allowing to find
improvements while preserving the quality of the existing solution. An
additional benefit is a more efficient integration with a problem-specific
local search.
  Computational study based on a range of the Traveling Salesman Problem
instances shows that the FACO outperforms the state-of-the-art ACOs when
solving large TSP instances. Specifically, the FACO required less than an hour
of an 8-core commodity CPU time to find high-quality solutions (within 1% from
the best-known results) for TSP Art Instances ranging from 100000 to 200000
nodes.


title: Factorizing Hidden Particle Production Rates

abstract:
  A method is proposed to streamline the computation of hidden particle
production rates by factorizing them into i) a model-independent SM
contribution, and ii) a observable-independent hidden sector contribution. The
Standard Model (SM) contribution can be computed once for each observable and
re-used for a wide array of hidden sector models, while the hidden sector
contribution can be computed once for each model, and re-used for a wide array
of observables. The SM contribution also facilitates extracting model
independent constraints on hidden particle production. The method is compatible
with effective field theory (EFT) and simplified model approaches. It is
illustrated by factorizing the rate of charged kaon decays into a charged
lepton and a number of hidden particles, and a single form factor $F_{\ell}$ is
found to parametrize the impact of general hidden sectors. We derive
model-independent constraints on the form factor $F_e$ that governs decays into
positrons and hidden particles.